United States      Office of         EPA/600/3-90/060
            Environmental Protection  Research and Development  September 1990
            Agency        Washington DC 20460
v>EPA      Environmental
            Monitoring and
I       I    Assessment Program

|     |1   Ecological Indicators
                                y^v Printed on Recycled Paper

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                                                         EPA/600/3-90/060
                                                         September 1990

     ENVIRONMENTAL MONITORING AND ASSESSMENT PROGRAM
                         ECOLOGICAL INDICATORS
                                     Edited by

                                Carolyn T. Hunsaker1
                           Oak Ridge National Laboratory7
                           Environmental Sciences Division
                               Oak Ridge, Tennessee

                                 Dean E. Carpenter
              NSI Technology Services Corporation - Environmental Sciences
                        Research Triangle Park, North Carolina
                                Contract 68-02-4444
                                   Project Officer

                                   Jay J. Messer
               Atmospheric Research and Exposure Assessment Laboratory
                         U.S. Environmental  Protection Agency
                        Research Triangle Park, North Carolina
Chicago,  iV'^n^r01'  R°™  ±6
                                               60604>   °°m I6?0
         ATMOSPHERIC RESEARCH AND EXPOSURE ASSESSMENT LABORATORY
                     OFFICE OF RESEARCH AND DEVELOPMENT
                    U.S. ENVIRONMENTAL PROTECTION AGENCY
                    RESEARCH  TRIANGLE PARK, NORTH CAROLINA
 Research sponsored by U.S. Environmental Protection Agency under EPA Interagency Agreement DW89934074-2 with the U.S.
 Department of Energy.

2 Operated by Martin Marietta Energy Systems, Inc., under Contract DE-AC05-840R21400 with the U.S. Department of Energy.

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                                             NOTICE
The information in this document has been funded wholly or in part by the  United States  Environmental
Protection Agency. It has been subjected to the Agency's review, and it has been approved for publication
as an  EPA document  Mention of trade names or commercial products does not constitute endorsement or
recommendation for use.

This report should be cited as follows.

        Hunsaker, C.T., and D.E. Carpenter, eds.  1990. Ecological Indicators for the Environmental
        Monitoring and Assessment Program.  EPA 600/3-90/060.  U.S. Environmental  Protection
        Agency, Office of Research and Development, Research Triangle Park, NC.

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                                     CONTENTS
        Figures  	         x
        Tables	        xi
        Executive Summary	        xii
        Acronyms	      xviii
        Glossary	       xxi
        Acknowledgements	      xxvi

1       Background  	       1-1

        1.1    EMAP and the Need for Ecological Monitoring  	       1-1
        1.2   The  Environmental Monitoring and Assessment Program	       1-2
        1.3   Purpose of Indicator Conceptual Plan	       1-2
        1.4   Report Organization  and Content	       1-3
        1.5   References	       1-3

2       EMAP Indicator Concepts	       2-1

        2.1    EMAP Indicators  	       2-1
              2.1.1   Rationale  	       2-3
              2.1.2  EMAP  Indicators and Risk Assessment Endpoints	       2-4

        2.2   EMAP Design  Objectives and Sampling Approach	       2-5
              2.2.1   Definition and Classification of Ecological Resources	       2-5
              2.2.2  Design Objectives for Resource Classes	       2-6
              2.2.3  Making Unbiased Estimates with Known Confidence   	       2-7
              2.2.4  Extent  of Ecological Resources	       2-8
              2.2.5  Current Status of Ecological Resources	       2-8
              2.2.6  Identifying Possible Causes  for Subnominal Conditions	     2-10
              2.2.7  Identification of Regional Trends	     2-11

        2.3   Issues Regarding the  Application of the EMAP Indicator Strategy  	     2-12
              2.3.1   Refinement of the EMAP Sampling Design	     2-14
              2.3.2  Importance of Scale to Indicators   	     2-14
              2.3.3  Definition of a Subnominal Resource	     2-15
              2.3.4  Monitoring of Structure and Function	     2-16
              2.3.5  Implication of Indicators for Classification  	     2-17
              2.3.6  Use of Non-Frame Data	     2-17
              2.3.7  Use of Stressor  Indicators	     2-18
              2.3.8  Interpretation and Summarization  of Indicators	     2-18

        2.4   Future  Indicator Research	     2-21

        2.5    References	     2-24

3       Indicator Strategy for Near-Coastal  Waters  	       3-1

        3.1    Introduction	       3-1
        3.2    Identification of Indicators for Near-Coastal Waters	       3-2
              3.2.1   Perceptions of Near-Coastal Resource  Condition	       3-2

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              3.2.2   Environmental  Values for Near-Coastal Waters	      3-3
              3.2.3   Estuarine Indicators Appropriate for EMAP	      3-4
              3.2.4   Estuarine Indicators Not Appropriate for EMAP	     3-10

        3.3    Application of Indicators for  Near-Coastal  Waters	     3-12
              3.3.1   Definition of the Subnominal Threshold	     3-12
              3.3.2   Dissolved Oxygen	     3-12
              3.3.3   Benthic Abundance, Biomass, and  Species Composition   	     3-13
              3.3.4   Fish Abundance, Species Composition, and Gross Pathology  ...     3-13

        3.4    Research Needs	     3-14

        3.5    References	     3-16

4       Indicator Strategy for Inland  Surface Waters  	      4-1

        4.1    Introduction	      4-1
              4.1.1   Legislative Mandate for Inland Surface Water Monitoring  	      4-1
              4.1.2   Limitations of Current Inland Surface Water Monitoring  Programs       4-2
              4.1.3   Inland  Surface Water Resource Classes	      4-3

        4.2    Identification  of  Indicators for Inland Surface Waters	      4-4
              4.2.1   Perceptions of Inland Surface Water Condition	      4-4
              4.2.2   Environmental  Values for Inland Surface Waters	     .4-5
              4.2.3   Hazards to  Inland Surface Waters	      4-5
              4.2.4   Inland  Surface Water Indicators Appropriate for EMAP	      4-7
              4.2.5   Inland  Surface Water Indicators Not Appropriate for EMAP  ....     4-12

        4.3    Application of Inland  Surface Water Indicators	     4-13

        4.4    Research Needs for EMAP-lnland Surface  Waters	     4-14
              4.4.1   Ecological Guilds  	     4-14
              4.4.2   Exposure and  Habitat Indicators	     4-17
              4.4.3   Monitoring  as  Research   	     4-17

        4.5    References	     4-17

5       Indicator Strategy for Wetlands  	      5-1

        5.1    Introduction	      5-1
              5.1.1   Legislative Mandate for Wetlands Monitoring	      5-1
              5.1.2   Wetland  Resource Classification  	      5-1

        5.2    Identification  of  Wetland Indicators	      5-4
              5.2.1   Perceptions of Wetland  Condition	      5-4
              5.2.2   Environmental Values for Wetlands	      5-4
              5.2.3   Hazards  to Wetlands	      5-5
              5.2.4   Wetland  Indicators Appropriate for EMAP	      5-5
              5.2.5   Wetland  Indicators Not Appropriate for EMAP	     5-10

        5.3    Application of Wetland Indicators	     5-12
                                          IV

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        5.4   Research Needs	      5-12
              5.4.1   Research Priorities	      5-12
              5.4.2   Interaction with EMAP Resource Croups and Other Agencies ...      5-13

        5.5   References	      5-13

6       Indicator Strategy for Forests	       6-1

        6.1   Introduction	       6-1
              6.1.1   Legislative Mandate for Forest Monitoring	       6-1
              6.1.2   Forest Resource Classes  	       6-2

        6.2   Identification of Indicators   	       6-2
              6.2.1   Perceptions of Forest Condition  	       6-3
              6.2.2   Environmental Values for Forests	       6-3
              6.2.3   Hazards to  Forest Ecosystems	       6-4
              6.2.4   Forest Indicators Appropriate for EMAP  	       6-4
              6.2.5   Forest Indicators Not Appropriate for EMAP	       6-9

        6.3   Application of  Forest Indicators	      6-10

        6.4   Research Needs for EMAP Forests  	      6-11

        6.5   References	      6-11

7       Indicator Strategy for Arid  Lands   	       7-1

        7.1   Introduction  	       7-1

        7.2   Identification of Arid Land Indicators	       7-2
              7.2.1   Arid Land Indicators Appropriate for  EMAP	       7-5
              7.2.2   Arid Land Indicators Not Selected for EMAP	       7-8

        7.3   Application of Arid Land Indicators	       7-9

        7.4   Research Needs for EMAP Forests  	      7-10

        7.5   References	      7-10

8       Indicator Strategy for Agroecosystems  	       8-1

        8.1   Introduction	       8-1

        8.2   Identification of Agroecosystem  Indicators	       8-3
              8.2.1   Perceptions of Agroecosystem Condition	       8-3
              8.2.2   Environmental Values for Agroecosystems  	       8-3
              8.2.3   Agroecosystem Indicators Appropriate for EMAP	       8-4
              8.2.4   Agroecosystem Indicators Not Appropriate  for EMAP  	       8-8

        8.3   Application of Agroecosystem Indicators	       8-9

        8.4   Research Priorities for Agroecosystems	      8-10

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        8.5    References	     8-10

9       Indicators Relevant to Multiple Resource Categories   	       9-1

        9.1    Introduction	       9-1

        9.2    Animal Indicators	       9-1
              9.2.1   Identification and Application of Animal Indicators	       9-3
              9.2.2   Animal Indicators Not Appropriate for  EMAP	       9-8
              9.2.3   Research Needs for Animals  	       9-9

        9.3    Biomarkers	     9-10
              9.3.1   Identification of Biomarkers for EMAP   	     9-11
              9.3.2   The Application of Biomarkers  in EMAP	     9-14
              9.3.3   Sampling Considerations for Animals  	     9-15
              9.3.4   Research Needs for Biomarkers  	     9-16

        9.4    Landscape and  Habitat Indicators	     9-16
              9.4.1   Identification of Landscape and Habitat Indicators	     9-17
              9.4.2   Landscape Indicators  Not Appropriate for  EMAP	     9-18
              9.4.3   Research Needs for Landscape  and Habitat Indicators  	     9-18

        9.5    Stressor Indicators  	     9-19

        9.6    References	     9-19
              9.6.1   References for Animal Life  	     9-19
              9.6.2   Biomarker References  	     9-23
              9.6.3   Landscape and Habitat References   	     9-24

10      Indicator Strategy  for Atmospheric Stressors	     10-1

        10.1   Introduction	     10-1

        10.2   Atmospheric Indicators Appropriate for EMAP	     10-2
              10.2.1  High-Priority Research Indicators	     10-2
              10.2.2  Other Research Indicators	     10-3

        10.3   Atmospheric Monitoring Strategy  	     10-3

        10.4   Research Needs	     10-4

11      Conclusions and Future Directions   	     11-1

Appendices

    A   Indicator Fact Sheets for Near-Coastal Waters

        A.1    Dissolved Oxygen  	       A-1
        A.2    Benthic Abundance, Biomass, and Species Composition	       A-2
        A.3    Biological Sediment Mixing Depth  	       A-4
        A.4    Extent  and  Density of Submerged  Aquatic Vegetation  	       A-6
        A.5    Fish Abundance and  Species Composition   	       A-7

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    A.6   Presence of Large Indigenous Bivalves	      A-9
    A.7   Gross  Pathology:  Fish	     A-10
    A.8   Acute  Sediment Toxicity  	     A-12
    A,9   Chemical Contaminants in  Sediments   	     A-14
    A.10  Water Clarity  	     A-16
    A.11  Water Column Toxicity	,	     A-17
    A.12  Chemical Contaminants in  Fish and Shellfish	     A-19
    A.13  Dissolved Oxygen  	     A-21

B   Indicator Fact Sheets for Inland Surface Waters

    B.1    Lake Trophic  Status   	      B-1
    B.2    Fish Index of  Biotic Integrity	      B-4
    B.3    Macroinvertebrate Assemblage	      B-6
    B.4   Relative Abundance of Semiaquatic Vertebrates	      B-9
    B.5    Diatom Assemblages in Lake Sediments	     B-11
    B.6    Top Carnivore Index:  Fish	     B-14
    B.7    External Pathology:  Fish	     B-15
    B.8   Water Column and Sediment Toxicity	     B-16
    B.9    Chemical Contaminants in  Fish  	     B-19
    B.10  Routine Water Chemistry	     B-22
    B.11  Physical Habitat Quality	     B-25
    B.12  Water Column Bacteria	     B-28
    B.13  Heavy Metals and Man-Made  Organics (Toxics)	     B-30

C   Indicator Fact Sheets for Wetlands

    C.1    Organic Matter and Sediment Accretion	      C-1
    C.2    Wetland Extent and Type Diversity	      C-3
    C.3    Abundance and Species Composition of Vegetation   	      C-5
    C.4   Leaf Area,  Solar Transmittance, and Greenness  	      C-7
    C.5    Macroinvertebrate Abundance, Biomass, and Species Composition  ....      C-9
    C.6   Soil  and Aquatic Microbial  Community Structure  	     C-11
    C.7    Nutrients in Water and Sediments  	     C-12
    C.8   Chemical Contaminants in  Water and Sediments	  .     C-14
    C.9   Hydroperiod	     C-16
    C10  Bioassays	     C-19
    C.11  Chemical Contaminants in  Tissues  	     C-20

D   Indicator Fact Sheets for Forests

    D.1    Tree Growth Efficiency  	      D-1
    D.2   Visual  Symptoms of Foliar Damage:  Trees	      D-5
    D.3   Nitrogen Export	      D-8
    D.4   Litter Dynamics	     D-10
    D.5   Microbial Biomass and Respiration in Soils	     D-11
    D.6   Nutrients in Tree Foliage	     D-14
    D.7   Chemical Contaminants in  Tree Foliage	     D-16
    D.8   Soil  Productivity Index	     D-17
    D.9   Stable  Isotopes  	     D-21
    D.10  Carbohydrates and Secondary Chemicals in Plants  	     D-23
    D.11  Bioassay: Mosses and Lichens   	     D-25

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E   Indicator Fact Sheets for Arid Lands

    E.1    Vegetation Biomass	       E-1
    E.2    Riparian Extent	       E-4
    E.3    Energy Balance	       E-6
    E.4    Water Balance   	      E-10
    E.5    Soil Erosion   	      E-13
    E.6    Charcoal Record  	      E-15
    E.7    Species Composition  and Ecotone Location of Vegetation	      E-17
    E.8    Dendrochronology:  Trees and Shrubs	      E-19
    E.9    Pollen Record	      E-21
    E.10  Woodrat Midden Record	      E-22
    E.11   Abundance and  Species Composition of Lichens and Cryptogamic Crusts      E-23
    E.12  Foliar Chemistry  	      E-25
    E.13  Physiochemical  Soil Factors	      E-27
    E.14  Exotic Plants	      E-30
    E.15  Livestock Grazing	      E-31
    E.16  Fire Regime  	      E-33
    E.17  Mechanical Disturbance of Soils and Vegetation	      E-35
    E.18  Chemical Contaminants in Wood	      E-36

F   Indicator Fact Sheets for Agroecosysterns

    F.1    Nutrient Budgets	       F-1
    F.2    Soil Erosion	       F-3
    F.3    Microbial Biomass in Soils  	       F-5
    F.4    Land Use/Extent of Noncrop Vegetation	       F-8
    F.5    Crop Yield	       F-9
    F.6    Livestock Production	      F-11
    F.7    Visual Symptoms of Foliar Damage:  Crops   	      F-13
    F.8    Agricultural Pest Density 	      F-15
    F.9    Lichens  and Mosses,  Clover, Earthworm Bioassays  	      F-17
    F.10  Quantity and Quality of Irrigation Waters	      F-19
    F.11   Soil Productivity Index	      F-21

G   Indicators of Relevance to Multiple Resource Categories

    G.1    Indicator Fact Sheets for Animals
          G.1.1   Relative Abundance:  Animals 	       G-1
          G.1.2  Demographics:  Animals	       G-4
          G.1.3  Morphological Asymmetry:  Animals   	       G-5

    G.2   Indicator Fact Sheets for Biomarkers
          G.2.1   DNA Alteration: Adducts	       G-8
          G.2.2  DNA Alteration: Secondary Modification  	      G-10
          G.2.3  DNA Alteration: Irreversible  Event	      G-13
          G.2.4  Cholinesterase Levels	      G-15
          G.2.5  Metabolites of Xenobiotic Chemicals   	      G-16
          G.2.6  Porphyrin Accumulation 	      G-18
          G.2.7  Histopathologic Alterations   	      G-20
          G.2.8  Macrophage Phagocytic Activity  	      G-23

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          C.2,9  Blood Chemistry	      G-25
          G.2.10 Cytochrome P-450 Monooxygenase System	      G-27
          G.2.11 Enzyme-Altered Foci	      G-29

    G.3   Indicator Fact Sheets for Landscape and Habitat Indicators
          G.3.1   Abundance or Density of Key Physical Features and
                 Structural Elements	      G-32
          G.3.2  Linear Classification and Physical Structure of Habitat	      G-33
          G.3.3  Habitat Proportions	      G-35
          G.3.4  Patch Size and  Perimeter-to-Area Ratio	      G-38
          G.3.5  Fractal Dimension	      G-40
          G.3.6  Contagion or Habitat  Patchiness	      G-42
          G.3.7  Gamma  Index of Network Connectivity   	      G-44
          C.3.8  Patton's  Diversity Index	      G-45

H   Indicator Fact Sheets for Atmospheric  Stressors

    H.1   Ozone  	       H-1
    H.2   Sulfur Dioxide	       H-4
    H.3   Nitric Acid	,	       H-6
    H.4   Ionic Constituents in Precipitation   	       H-7
    H.5   Metals and Organics (Toxins)   	       H-9
    H.6   Free  Radicals	      H-11
    H.7   Carbon Dioxide	      H-12
    H.8   Other Greenhouse Gases	      H-13
    H.9   Ultraviolet Type  B  Radiation	      H-14
    H.10  Airborne  Particles	      H-15

I    Workshop Participants, Contributors, and Technical Reviewers

    1.1     Contributors to  the  Identification of Research  Indicators  Relevant to
          Near-Coastal Waters	        1-1
    I.2     Contributors to  the  Identification of Research  Indicators  Relevant to
          Inland Surface Waters	        I-3
    I.3     Contributors to  the  Identification of Research  Indicators  Relevant to
          Wetlands	        I-5
    1.4     Contributors to  the  Identification of Research  Indicators  Relevant to
          Forests  	,	        1-7
    1.5     Contributors to  the  Identification of Research  Indicators  Relevant to
          Arid Lands	        1-9
    1.6     Contributors to  the  Identification of Research  Indicators  Relevant to
          Agroecosystems	       1-12
    I.7     Contributors to the  Identification of Research  Indicators  Relevant to
          Multiple Resource Categories	       1-15
    I.8     Technical Reviewers	       1-16
                                     IX

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                                              FIGURES
1       EMAP conceptual indicator strategy	        xiv
2       Indicator selection, prioritization, and evaluation approach for EMAP	        xvi
2-1     EMAP conceptual strategy	       2-2
2-2     Cumulative frequency distribution for the Index of Biotic Integrity
        in a region	      2-10
2-3     Correlative approach to initial partitioning of subnominal systems among
        possible causes  	,	      2-11
2-4     Regional trends in the extent and condition of a  resource over time	      2-12
2-5     Hypothetical comparison of an ecological  resource class among four regions	      2-13
2-6     Classification of an ecological resource category into four resource classes	      2-13
2-7     Indicator selection, prioritization, and evaluation approach for EMAP	      2-22
2-8     Criteria  matrix for EMAP research indicator selection	      2-23
3-1     Diagram of the proposed EMAP-Near-Coastal Indicator Strategy for estuaries	       3-7
4-1     Diagram of the proposed EMAP-lnland Surface Waters Indicator Strategy  	       4-8
5-1     Diagram of the proposed EMAP-Wetlands Indicator Strategy  	       5-6
6-1     Diagram of the proposed EMAP-Forests Indicator  Strategy	       6-6
7-1     Diagram of the proposed EMAP-Arid Lands  Indicator  Strategy 	       7-5
8-1     Conceptual model of agroecosystem 	       8-2
8-2     Diagram of the proposed EMAP-Agroecosystems Indicator Strategy  	       8-6
9-1     Conceptual view  of how biomarkers may  be useful to EMAP  	      9-12
10-1    Diagram of the proposed EMAP-Air and Deposition Indicator Strategy	      10-2
F-1     An example of the potential  use of soil productivity index and information on
        erosion  rates for three soil  series   	      F-21

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                                              TABLES
3-1     Evaluation of Some Candidate Indicators for Near-Coastal Waters by
        EMAP Selection Criteria	       3-5
4-1     Chronology of EMAP Indicator Development for Inland Surface Waters  	       4-9
4-2     Evaluation of Some Candidate Indicators for Inland Surface Waters by
        EMAP Selection Criteria	      4-10
4-3     Linkages Between Potential Environmental  Values, Measurements, Metrics, and
        Response Indicators for EMAP-lnland Surface Waters	      4-15
4-4     Linkages Between Potential Environmental  Values, Measurements, Metrics, and
        Exposure Indicators for EMAP-lnland Surface Waters	      4-16
5-1     Major Federal Laws, Directives, and Regulations for the Management and
        Protection of Wetlands  	       5-2
5-2     Proposed EMAP-Wetland Resource Classes	       5-2
5-3     Traditional Cowardin System for Defining Wetland Classes 	       5-3
5-4     Chronology of EMAP Indicator Development for Wetlands 	       5-5
5-5     Evaluation of Some Candidate Indicators for Wetlands by
        EMAP Selection Criteria	       5-7
6-1     Evaluation of Some Candidate Indicators for Forests by
        EMAP Selection Criteria	       6-5
7-1     Evaluation of Candidate Indicators for Arid Lands by
        EMAP Selection Criteria	       7-3
8-1     Evaluation of Some Candidate Indicators for Agroecosystems by
        EMAP Selection Criteria	       8-5
8-2     Environmental Values Addressed by High-Priority Research Indicators for an  Agroecosystem    8-6
9-1     Research  Indicators  Applying to Multiple Resource Categories  	       9-2
9-2     Capability of Various Animal Types to Satisfy EMAP Indicator Selection Criteria  	       9-4
9-3     Relative Usefulness of Animal Types as  Indicators Within Ecological Resource Categories       9-5
11-1    Research  Indicators  Listed  by  Resource  Category and  Indicator Type	      11-3
H-1     Ozone Monitoring  Season  by State	       H-2
                                                 XI

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                                       EXECUTIVE SUMMARY


In 1988 the Science Advisory Board of the  U.S.  Environmental  Protection Agency (EPA)  recommended
implementing a program within EPA to monitor the status and trends of ecological condition and to develop
innovative  methods for anticipating emerging  problems before they become  crises.  More  recently, EPA
established a program  aimed at monitoring for results; that is, confirming that the nation's environmental
protection efforts are truly  maintaining or improving environmental quality.  EPA's Office of  Research and
Development (ORD) began planning the Environmental Monitoring and Assessment Program (EMAP) in
response to these needs for better assessments of the condition of the nation's ecological resources.  Planning
is  being conducted  in cooperation with  other  agencies and organizations that share  responsibilities for
renewable  natural resources or environmental  quality.

When  fully  implemented, EMAP will answer several critical questions:   What is the current  extent of our
ecological resources, and how are they distributed geographically? What proportions of the  resources are
currently in good or acceptable condition?   What proportions are degrading or improving, in  what regions,
and at what rate?  Are these  changes correlated with patterns and trends in  environmental  stresses?  Are
adversely affected resources improving overall  in response to control  and mitigation programs?

EMAP scientists will answer these questions by designing and implementing over the next five years integrated
monitoring networks with the following  objectives.

    •  Estimate current status, extent, changes,  and trends in indicators of the condition of the nation's
       ecological resources on a regional basis with known confidence

    •  Monitor indicators of pollutant exposure and habitat condition and seek associations between
       human-induced stresses and ecological  condition

    •  Provide  periodic statistical summaries and interpretive reports on status and trends to the EPA
       Administrator and the public

EMAP networks will provide statistically unbiased estimates of status, trends, and associations with quantifiable
confidence limits over regional and national scales for periods of years to decades.  EMAP will also provide
a framework for cooperative planning and implementation with other agencies and  organizations that have
active monitoring programs in the ecological and natural resource areas.  This framework will provide for
direct integration of these data, where appropriate,  and will enable EMAP to supplement existing networks
to fill data gaps,  if necessary.


PURPOSE OF THIS DOCUMENT

The  purpose of  this document is threefold:  (1) to inform potential  EMAP  data  users of  the  approach
proposed to describe ecological condition; (2) to define a strategy for evaluating, prioritizing, and selecting
indicators that will facilitate coordination and integration among each of six EMAP resource categories; and
(3) to seek expert advice and environmental data sets from the scientific community that are needed to better
characterize the spatial and temporal  variability of the proposed indicators on a regional scale.


EMAP  BACKGROUND

Six broad  ecological resource categories have been defined within  EMAP:   near-coastal waters,  inland
surface waters, wetlands, forests,  arid lands, and  agroecosystems.  Within each of these categories, several

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ecological resource  classes  have been identified  (e.g.,  large estuaries, small lakes, emergent estuarine
wetlands, oak-hickory forests, sagebrush-dominated shrubland, and  orchard  cropland).   In  addition  to
measures of extent  (numbers,  length,  area),  EMAP  will make  routine  measurements  of environmental
indicators on  resource sampling units  selected from each  of  these resource classes. The measurements
will be used to provide regional- and national-scale assessments of the condition of the  nation's  resources.

The EMAP sampling design will lead to unbiased estimates with  known confidence of the  extent of resource
classes and their current ecological condition.  The design must also be sufficiently flexible to accommodate
sampling of many distinct resource classes and  identification of emerging environmental issues,  as  well as to
associate  resource condition with  pollutant  exposure,  habitat condition, and natural and human-induced
stresses.

The proposed sampling design uses a systematic triangular sampling grid of points randomly placed over the
United States.  Grid density can be increased or decreased to meet specific needs, but the baseline density
is approximately one point per 640 km2, which results in about 12,600 points across the  contiguous United
States and approximately 2,400 and 56 points in Alaska and Hawaii, respectively.  Next, a two-stage process
is used to select points from the grid for landscape description and sampling site selection. In the  first stage,
a set of points is selected by  using probability  methods; the landscape within a  hexagonal area (landscape
sampling unit) centered  on each of these  grid  points then will be  characterized (by  using maps, aerial
photography, and satellite imagery) to estimate the  extent of each resource class and to  facilitate selection
of resource  sampling units upon  which indicators will be measured.  In the second stage of the  process, a
subset of resource sampling units  is selected for each resource class from which regional population estimates
are to be made.  Because the points  represent a probability sample, the measurements  of resource extent
and environmental indicators  can be extrapolated to yield estimates for resource classes  for regions or the
entire  nation.

Placement of the grid determines the ultimate sampling location, and many of the sampling sites will not be
available for intensive or continuous monitoring.  Also, indicators must be measured on an adequate number
of resource sampling units to make regional population estimates with sufficiently narrow confidence bounds.
EMAP  will therefore operate as a series of annual surveys, measuring indicators during a particular  season or
other time period that is likely to be specific to each resource category and possibly to each resource class.
For  example,  late summer  may  be  selected  as the  index   period for  making  measurements on  fish
populations, when stream flows and dissolved  oxygen levels  may be lowest and effects of stressors may be
highest

EMAP's objectives include describing current  condition as well as documenting trends in condition. Optimal
achievement of either objective  would  require different designs; the EMAP  design resolves this issue by
selecting distinct subsets of resource sampling units each year in  a four-year cycle, returning to the  first year's
subset in  the  fifth year;  a particular site therefore will  be sampled only every fourth year, and  condition
estimates  will be  based  on four-year  running  averages.   Regional or national  trends are expected to be
discernible within 10-15 years.  Because individual  sites are sampled  only once every four years, and 10-
15 site visits are required to detect a trend, EMAP will provide  little information  about the conditions at any
particular site  for a  period  of 40-60 years.   This probability-based survey  approach   places  important
constraints on indicator selection.
INDICATOR STRATEGY

In EMAP, we  have defined several  categories of indicators - response, exposure, habitat, and stressor,
examples of which are  given  in  Figure 1.   Response indicators are  characteristics of the environment
measured to provide  evidence of the  biological  condition  of  a resource at the  organism,  population,
community,  or  ecosystem level of organization.  Exposure and habitat indicators are diagnostic  indicators

                                                 xiii

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that are  measured  in conjunction with response indicators.   Exposure indicators are characteristics of the
environment measured to provide evidence of the occurrence or magnitude of a response indicator's contact
with a  physical,  chemical, or biological stress.   Habitat indicators  are  physical  attributes measured  to
characterize conditions necessary to support an organism,  population, or community in the absence of
pollutants.   Stressor indicators are characteristics  measured to  quantify a natural process, environmental
hazard indicators, or a management activity that effect changes in exposure and  habitat.  Response, exposure,
and habitat indicators will be measured during annual surveys at sampling sites  associated with points on the
EMAP grid.  Stressor indicators normally will not be  measured at the sampling sites;  instead, stressor data will
usually be  obtained from other monitoring programs.

EMAP will  use response  indicators to describe ecological condition.  Exposure and  habitat indicators will be
used to  provide  plausible explanations for observed differences and changes  in  response  indicators, and
stressor indicators will  be  used to identify possible causes for changes in  exposure and habitat indicators.
Analyses will rely on correlative empirical approaches and thus cannot prove causality. The use of correlative
approaches will,  however, identify hazards that are geographically widespread, most intense, or associated
Response
indicators  :



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Exposure-Habitat
Indicators (E)

Bio markers
Pathogens
Btoassays
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Ambient Concentrations
-Water, Air. Soil, Sediment
Exotics /GenefcaJry-
Englneered Organisms
Habitat Structure

Landscape Pattern
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Hazard Indicators Natural Process Indicators
Atmospheric Deposition / Emissions
Demographic* Climatic Fluctuations
Discharge Estimate* Pest-Disease Relations
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Permits
Successtonal Stage

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Management Indicators
Dredging / Filling
Fire Management
Harvest Rate
Hydrologic Modification
Landscape Pattern
Pest Control
                 \
f
                                        STRESSOR INDICATORS (S)
                                           (OFF-FRAME DATA)
Figure 1.   EMAP conceptual indicator strategy.  Indicators are objective, quantifiable surrogates for
            assessment end points and stressors  (environmental  hazards, management actions, and
            natural phenomena).  The circle indicates that  analysis is by statistical association, rather
            than  by explicit (causal)  mathematical relationships.
                                                  XIV

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with most rapid deterioration. Once EMAP indentifies a geographic area with a high proportion of a resource
in unacceptable  condition,  other  projects  must  be designed  and  implemented to pinpoint  causes  and
solutions.

EMAP also must  present  its  results in such  a way that they are readily interpretable by the public  and by
decision-makers.  Assigning a threshold value to response indicators that will enable decision-makers and the
public to distinguish  "good"  ecological condition  from "bad" ecological condition  is not possible a priori,
because  public perceptions of ecological "health" can differ from region to  region and can change with time.
Also,  ecological condition may reflect exposure to a multitude of environmental stresses  including those  that
are related to  human activities and those that are natural.  Which indicators to implement in a  long-term
monitoring program that will  provide the most cost-effective information on  the status and trends of ecological
condition must be carefully evaluated, and the effective use of relevant data  collected by other operational
monitoring programs  clearly  requires careful  consideration.

Several other issues related to interpretation and summary presentations of indicator data require resolution.
These include  addressing situations when resource  sampling units in poor  condition  are  associated with
multiple  exposure or habitat indicators.  The use of indices, which are mathematical combinations of indicator
values, provides a means to  rank resources on an acceptable-to-unacceptable continuum based on multiple
measurements.  Research is  needed to select the  most appropriate  mathematical schemes to combine data
that will  summarize results without losing critical  information.

Geographic information systems provide the opportunity to overlay patterns in exposure, habitat, or  stressor
indicators  onto  patterns in  response indicators  across  broad geographic  regions to provide plausible
explanations why ecological condition in some regions is  unacceptable  (or appears  to be degrading) or
acceptable (or appears to  be improving).   EMAP also will investigate the use of multivariate statistical
techniques in  analyzing regional data sets.  Finally, retrospective analysis techniques will be investigated for
EMAP as a possible means of evaluating current  ecological condition  at single sites and over  larger areas,
where archived remote sensing or  other data are  available.


INDICATOR SELECTION

To compile the sets of research indicators presented in this document, each Resource Group began evaluating
candidate  indicators (Figure 2) that had been proposed for their  resource categories  over the past three
decades.  Draft criteria for research indicator selection  were  formulated, and a final matrix  was developed.
Each group judged its candidate indicators against this matrix to identify a set of research  indicators believed
to be most promising for  further evaluation.  Comments on this document from 22 external peer reviewers
and the EPA Science Advisory Board were used to  refine these indicator sets and the EMAP indicator strategy.
High-priority research  indicators must have well-established,  readily  standardized methods and a reasonable
amount of available environmental data; they must  pass a  second round  of testing (existing data analysis,
simulation, and local  field tests) before being considered developmental indicators that  are  suitable for
regional  demonstration   projects.    Those  developmental  indicators that   are  selected   for   long-term
implementation are termed core indicators (Figure 2).

The application of the EMAP indicator strategy to develop a list of research indicators is described separately
for each  resource category.   Each description begins with a discussion of past and current monitoring efforts
and a premliminary  list of EMAP  resource  classes specific to the category.   Introductory comments are
followed by a  description of the indicator selection process, including public  perceptions  of the resource
category,  environmental  hazards affecting the  resource,  research  indicators appropriate   for  EMAP,  and
candidate indicators  not appropriate for EMAP. Next is a discussion of how the research indicators would
be applied as a comprehensive set to assess ecological condition and how that condition would be correlated
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                                         CANDIDATE INDICATORS
I                                                            Expert Knowledge
                                                            Literature Review
                                                            Peer Review
                                 1
                                         RESEARCH INDICATORS
I                                                            Analysis of Existing Data

                                                            UMM**. Field Tests
                                                            Peer Review
                                  I
                                      DEVELOPMENTAL INDICATORS
                                                      I
EVALUATE ACTUAL PERFORMANCE


                       CORE INDICATORS
         IMPLEMENT REGIONAL AND NATIONAL MONITORING     I     PERIODIC REEVALUATION
           Figure 2.  Indicator selection, prioritization, and evaluation approach for EMAP.


with patterns and trends in environmental stresses.  Each discussion concludes with a summary of research
needs that are crucial for conducting regional survey monitoring in the respective resource categories.

Another set of indicators that are likely appropriate for determining condition in several  ecological resources
was compiled by scientists with expertise in  animal ecology, biomarkers,  and landscape ecology.  These
indicators will be included as part of the indicator sets, as  appropriate, for each ecological  resource when
routine, operational  monitoring is implemented.

Finally,  indicators of atmospheric stressors were also  evaluated.   Atmospheric deposition  and gaseous
compounds typically require continuous monitoring, at least over some index periods, to produce data that
are useful for a long-term monitoring program. Access to randomly chosen sites, the large numbers of EMAP
sites, and the start-up  costs of a  new network prohibit EMAP from being capable of routinely  monitoring
atmospheric stressors as  part of the EMAP grid.  Therefore, it is suggested that the most efficient way for
EMAP to acquire data on atmospheric stressor indicators is by using existing networks within the context of
the EMAP  probability-based  sampling design.   Regional estimates of deposition and  exposure could be
obtained by fitting surfaces to the atmospheric monitoring data.

The document concludes by  presenting an indicator matrix  (Table 1) that lists all EMAP research indicators
by indicator type and resource category.  The matrix includes those indicators that are undergoing further
evaluation  as well as those that the resource groups chose  not to evaluate further  at this time.
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FUTURE RESEARCH

The next step in the EMAP indicator development  strategy is twofold:   (1) evaluation of the potential
effectiveness  of each  research indicator for use in quantitative  regional assessments of ecological  condition
by analyzing existing  regional data sets and (2) evaluation of additional candidate indicators in order to fill
gaps in the indicator  strategy for each resource category.  Unfortunately (as is evident in the indicator fact
sheets), few regional data sets exist that are derived from probability sampling.  Therefore, we currently are
unable to determine the effectiveness of indicator measurements for long-term, regional  monitoring.  If such
data sets  are inadequate or nonexistent, local field tests  will be  used to obtain the statistical parameters
necessary to  evaluate each  research indicator.

This document  serves as  the  initial basis for the  development of  an indicator  research  plan to  be
implemented for long-term  monitoring in EMAP.  The concepts presented here are intended to facilitate
consistency among the resource groups in  selecting,  evaluating, and developing indicators for monitoring.
Over the long term, EMAP also will undertake a research program on  indicators. An international workshop
will be held in October 1990 to begin this  process. Long-term research will focus, in part, on fundamental
understanding of what constitutes desirable ecological  condition and  what factors are indicative of stability
and  resilience that confer long-term sustainability.
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                                          ACRONYMS

AFA - American Forestery Association
ANC - acid-neutralizing capacity
ARNEWS - Acid Rain National Early Warning System
ASTM - American Society for Testing and Materials
AVHRR - Advanced Very High Resolution Radiometer
BBS - Breeding Bird Survey
BCI - Biotic Condition Index
BLM - Bureau of Land Management
BOD  - biochemical oxygen demand
CBC - Christmas Bird Count
CHC - chlorinated hydrocarbon
CPUE - cost per unit effort
CV - coefficient of variation
DBH  - diameter at breast height
DIC - dissolved inorganic carbon
DO - dissolved oxygen
DOC - dissolved organic carbon
ELISA - enzyme-linked immunosorbent assays
EMAP - Environmental Monitoring and Assessment Program
EPA - U.S. Environmental Protection Agency
FDA - Food and Drug Administration
FIA -  Forest Inventoiy and Analysis (USDA-FS  program)
FIFRA - Federal Insecticide,  Fungicide and Rodenticide Act
FLPMA  - Federal Lands Policy and Management Act
FPM-MAG - Forest Pest Management, Methods Application  Group (USFS)
FWS - Fish and Wildlife Service
GAO - General Accounting Office
GC -  gas  chiomatography
GEM  - global environmental  monitoring
CIS - Geographic Information System
HCN -  Historic Climatic Network
HLI - Habitat Layers  Index
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HLCS - Habitat Linear Classification System
HQA - habitat quality index
IBI - index of biotic integrity
ICP - inductively coupled plasma
LAI - leaf area index
LDI - Landscape Development Intensity (index)
LHQI - Lake  Habitat Quality Index
LTER - long-term ecological research
MCI -  macroinvertebrate community index
MIS - management indicator species
MPDI - Microwave polarization difference index
MS - mass spectrometry
MSS -  multispectral scanner
NADP - National Acid Deposition  Program
NAPAP - National Acid Precipitation Assessment Program
MAS -  National Academy of Scienc
NASA - National Aeronautics and Space Administration
NASQAN  - National Stream Quality Accounting Network
NBS -  National Bioaccumulation Study
NDDN - National Dry Deposition  Network
NDVI - normalized difference vegetation index
NEPA - National Environmental Policy Act
NOAA - National Oceanic  and Atmospheric Administration
NPDES - National Pollution Discharge Elimination System
NPP -  net primary productivity
NPS -  National Park Service
NRC - National Research Council
NS&T - National Status and Trends (program of NOAA)
NSWS - National Surface Water Survey
NTN - National Trends Network
NWI - National Wetland Inventory
ORD - Office of Research  and Development
PAH -  polycyclic aromatic  hydrocarbon
PCB - polychlorinated biphenyl
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pH - inverse log concentration of H+ ion
RCRA -  Resource Conservation and Recovery Act
RPA - Renewable Resources Planning Act
SAV - submerged aquatic vegetation
SHQI -  Stream Habitat Quality Index
IDS - total dissolved solids
TKN - total Kjeldahl nitrogen
TM - Thematic Mapping
TOC - total organic carbon
TSI - trophic state index
TSS - total suspended solids
UNEP -  United  Nations Environment Programme
USDA - U.S. Department of Agriculture
USDA-FS - U.S.  Department of Agriculture, Forest Service
USDOI  - U.S. Department of the Interior
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                                               GLOSSARY3
Assessment endpoint - A quantitative or quantifiable expression of the environmental value being considered
in the environmental analysis; examples include a 25% reduction in gamefish biomass or  local  extinction of
an avian species (Suter 1990).

Best management practices - Management practices targeted at  minimizing specific watershed disturbances,
such as soil erosion, pollutant transport, stormwater runoff, or similar land-use-related disturbances.

Biodiversity  - A conceptual  term referring to the variety and variability among  living organisms and  the
ecological complexes in which they occur; diversity can be defined as the number of different items and their
relative frequencies.  For biological diversity, these items are organized at many levels, ranging from complete
ecosystems to the chemical structures that are the molecular basis of heredity.  Thus, the term encompasses
different ecosystems, species, genes,  and their relative abundance (OTA 1987).

Biomarker - An indicator of cellular or physiological  processes  that signal events in biological systems or
samples.  A biological marker of effect may be an indicator of an endogenous component of the  biological
system, a  measure of the functional  capacity  of the  system, or an  altered state of the system that is
recognized as impairment or disease.  A  biological  marker of  exposure may be the  identification of an
exogenous substance within  the  system,  the  interactive  product between  a xenobiotic compound  and
endogenous components, or  other event in the biological  system related to the exposure (NRC 1987).

Bottom-up approach -  Assessing  ecological condition  based  on first  principles,  i.e., pollutant effects  are
related causally to pollutant sources  by transport and fate  models.

Candidate indicator - Indicator identified  for each resource category by  using a  combination of literature
review, expert workshops, and interviews with scientists and environmental  managers, which was then judged
against specific EMAP criteria to determine its feasibility as a research indicator.

Characterization - The documentation of essential traits.

Classification - A hierarchical partitioning of ecological  resource  categories based on increasing  similarity of
specifically defined attributes  (e.g.,  lakes classes can be distinguished by size - large lakes versus  small lakes;
the small lake class can be further partitioned by hydrologic lake type -  small seepage lakes  versus small
drainage lakes); a procedure by which  land areas are identified and assigned to particular  categories, on the
basis of specific guidelines related  to observed  characteristics or  attributes.

Core indicator - EMAP indicator that is selected for long-term,  routine monitoring based on its performance
as demonstrated in  a  regional demonstration project.

Cumulative frequency distribution - A distribution generated by a function (F(x)) such that at any value for
the variable x, F(x) represents the proportion of the resource sampling units in the target population having
a value for the variable  that  is less than or equal to x.  In EMAP, x is usually a  measurement of physical
extent or an indicator measurement
3 The definitions of these terms are operational at the time of this writing; some terms may be used within EMAP in slightly different
  ways depending on the specific  resource category to which they are applied.  It is anticipated that the definitions for some terms
  will be refined as EMAP progresses.

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Developmental indicator - An EMAP indicator that has passed evaluation for expected performance (existing
data analyses, simulation, and small-scale field tests) and, with the concurrence of scientific peer reviewers,
is deemed suitable for actual  performance testing in a  regional  demonstration project

Diagnostic indicator - Characteristics of the environment measured  for the purpose of correlative analysis
to determine plausible explanations for subnominal conditions; a collective  term for EMAP exposure, habitat,
and  stressor indicators.

Ecological indicator - Response indicator.

Ecological resource category (resource category) - The aggregations of ecological resource classes that are
conveniently dealt with by ecologists with specific disciplinary expertise; six categories currently are identified:
near-coastal waters, inland surface waters, wetlands, forests, arid lands, and agroecosystems. ecosystems.

Ecological resource class (resource class) - A subdivision of an ecological  resource category; examples
include small lakes, oak-hickory forests, emergent estuarine wetlands, field cropland, mesohaline estuaries, and
sagebrush dominated desert scrub.

Ecological risk assessment - The application of a formal framework to estimate the effects of human action
on a natural resource and to interpret the significance of those  effects in light of the uncertainties identified
in each component of the assessment process.  Steps  in the framework include initial hazard identification,
exposure  assessment, dose-response assessment, and risk characterization.

Ecosystem - A local complex of interacting plants, animals, and their physical surroundings which is generally
isolated from adjacent systems by some boundary, across which energy and matter move; examples include
a watershed, an ecoregion,  or a biome.

Ecosystem function - Attributes of the rate of change of structural components of an ecosystem; examples
include primary  productivity, denitrif(cation rates, and  species fecundity rates.

Ecosystem structure -  Attributes  of the  instantaneous state of  an  ecosystem; examples include species
population density, species richness  or evenness, and  standing crop biomass.

Environmental indicators - A collective term for response, exposure and  habitat,  and stressor  indicators.

Explicit sampling frame  - The representation of a target  population (resource category,  class,  or subclass),
each unit of which has  a unique identification code,  used to  implement a sampling strategy;  an example
includes a list of all lakes greater than 4  ha in  the Northeast.

Exposure  indicator - A characteristic of the environment  measured to provide evidence of the occurrence
or magnitude of a response indicator's contact  with a physical,  chemical,  or  biological stress.

Habitat indicator  - A physical attribute measured to characterize  conditions necessary to support an organism,
population, or community in the absence of  pollutants.

Hazard -  A state  that may result  in an  undesired event;  the cause  of risk.  In EMAP, any human-related
event or  activity  that unintentionally or inadvertently  can  affect  ecological  condition; examples are  acidic
deposition that may decrease the acid-neutralizing capacity of surface water, or application of fertilizer to a
forested watershed that may increase nutrient levels in adjacent streams.
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Hazard indicator - Measures that reflect human activities that unintentionally affect ecological resources (e.g.,
measures of pollutant release, number of permits issued for construction activity, and rates of application of
fertilizers to forests and crops that influence nutrient concentrations  in adjacent streams).

Implicit sampling frame - A set of rules or criteria used to select resource sampling  units  that cannot be
listed a priori by  a unique  identification code (upon  which indicators will be measured); the  rules  are
developed as part of the landscape characterization activities performed on the landscape sampling units.

Index  (indices)  - Mathematical aggregation(s)  of indicators or metrics; one example is the  Index of  Biotic
Integrity (IBI), which combines several metrics describing fish community structure,  incidence of pathology,
population sizes, and other characteristics.

Index  period  -  Sampling period that yields the  maximum amount  of information  during the  year, which
may vary from one indicator or resource class to another.

Indicator - A characteristic of the environment that, when  measured, quantifies the  magnitude  of stress,
habitat characteristics, degree of exposure to the stressor, or degree of ecological response to the exposure.

Interpenetrating design - The  monitoring survey design  used in EMAP,  in which a  new set of resource
sampling units (RSUs) is selected each year during four successive years. The four-year cycle is  repeated by
using the  same set of RSUs as in the first cycle; therefore, the same set of RSUs sampled in year 1  would
be  resampled  in year 5.

Kriging - A weighted,  moving-average estimation  technique based on geostatistics  that uses the  spatial
correlation of point measurements to estimate values  at adjacent, unmeasured  points.

Landscape - The fundamental traits of a specific geographic area, including its biological composition, physical
environment, and  anthropogenic or social patterns.

Landscape characterization  - The  documentation of principal components  and  patterns of  landscape
structure,  including attributes of the physical environment, biological  composition, and  cultural  patterns.  In
EMAP, a term referring to the  process of describing land  use or  land cover within  the landscape sampling
units.

Landscape ecology - The study of the distribution  patterns of communities and ecosystems, the  ecological
processes that affect those patterns, and changes in  pattern and process over time (Forman  and Godron
1986).

Landscape indicator - A characteristic of the  environment, calculated  from remotely sensed data, used to
describe spatial distribution of physical, biological, and cultural features across a geographic  area.

Landscape sampling unit - The selected units (e.g., 40-km2 hexagons) upon which landscape characterization
will be performed.

Management indicator - Measures that reflect human  activities that intentionally alter an ecological resource
to meet some management objective; for example, the dredging or  filling of a wetland for the purpose of
housing development.

Maximum/minimum  operators approach  -  A  mathematical aggregation  scheme used to   produce  an
ecological condition index based on several response  indicator values; the index assumes the value of the
most subnominal indicator.
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Measurement endpoint - A quantitative summary of the results of a biological monitoring study, a toxicity
test, or other activity intended to reveal the effects of a stressor or hazard.

Natural process indicator - Measures that reflect cyclic or acyclic phenomena that affect ecological condition,
regardless of the presence of management actions or environmental hazards; examples include natural climatic
fluctuations, predator-prey  cycles, and insect and  disease epidemics.

Nominal - The state of having desirable or acceptable ecological condition.

Population  estimate - A statistical estimate  of some  characteristic (or  distribution of characteristics) that
applies to an  explicitly defined  target population  (category,  class,  or  subclass),  e.g., the  median  acid-
neutralizing capacity (or the cumulative frequency  distribution of acid-neutralizing  capacity) for  all small lakes
in the Northeast

Probability  sample/sampling - A  sample  chosen  in such  a manner that the  probability of  each selected
unit is known; for EMAP, each resource sampling unit (e.g.,  a lake, a forest, an estuary)  upon which  indicator
measurements are to be made will have a known probability  of being selected.

Region - Any extensive geographic area that generally corresponds  in size to EPA administrative  Regions III
through X (e.g.,  physiographic regions, ecoregions, major river basins).

Regional ecological resource class (regional resource class) - An ecological resource class that is  distributed
over some natural spatial range,  e.g., southeastern oak-hickory forests or small  lakes in the Northeast

Regional  reference site -  One of a  population  of benchmark or control sites  that, taken collectively,
represent an ecoregion  or other broad biogeographic  area; the  sites,  as a whole, represent the best ecological
conditions that can be reasonably attained,  given the prevailing  topography, soil, geology, potential vegetation,
and general land use of the region.

Research indicator - A candidate indicator identified for an  EMAP resource  category which  has  been
prioritized on the basis  of several criteria (e.g., regionally applicable, integrates effects, monotonic, conducive
to synoptic monitoring)  and, following peer review, has been selected for further evaluation for use in EMAP,
as possible developmental  indicators;  evaluation of expected  performance includes  analyzing existing data,
performing simulation studies with realistic scenarios  and  expected spatial and temporal variability, and
conducting limited  field tests.

Resource sampling unit - A particular ecological resource (e.g., a stream  segment, a  forest stand,  a wetland,
an  estuary)  upon which indicator measurements will be  made; more than one resource sampling unit can
occur  in a landscape sampling unit

Response indicator - A characteristic of the environment  measured to  provide  evidence  of  the  biological
condition of a resource at the organism, population,  community, or ecosystem process  level of organization.

Stressor indicator - A characteristic measured to  quantify a natural process, an  environmental hazard, or a
management action that effects changes in exposure and habitat.

Stressor - Measurements used  to  provide information on  human activities or externalities that can cause
stress in ecological entities; three types of stressor indicators  are considered in  EMAP:  hazard indicators,
management indicators, and natural process indicators.  Examples are the incidence of fertilizer application,
which can  increase nutrient concentrations in  lakes;  incidence of dredging/filling,  which  can  diminish
availability of wetland habitat; and climatic fluctuations, which can promote damage by pathogens.
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Subnominal - The state of having undesirable or unacceptable ecological condition.

Target population - The set of ecological resources from which a sample is drawn.

Threshold  -  The value for a particular  response indicator  used to distinguish  nominal from subnominal
ecological condition.

Tier 1 resource sample - All resource sampling units of each resource class within all landscape sampling
units.

Tier 2 resource sample - A  subsample of the Tier 1 resource sample used for field sampling of indicators.

Top-down approach - Assessing ecological condition based on correlative analyses; i.e., pollutant effects are
associated temporally or spatially with pollutant sources by statistically correlational analysis.


REFERENCES

Forman, R.T.T., and M. Godron.  1986.  Landscape Ecology.  John Wiley & Sons, New York.

NRC.  1987.  Biological markers in environmental health research.  (Committee on Biological Markers of the
National Research Council).  Environ. Health Perspect.  74:3-9.

OTA.  1987.  Technologies to maintain biological diversity.  OTA-F-331.  Office of Technology Assessment,
Washington, DC. 47 pp.

Suter, G.W.,  II. 1990. Endpoints for regional ecological risk assessments.  Environ.  Manage. 14(1):9-23.
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                                     ACKNOWLEDGEMENTS
The authors sincerely thank the many scientists who have contributed to this document through workshops,
writing, and reviews (see Appendix I).

We appreciate  the editorial and technical assistance  provided  by Penny Keilar of Kilkelly  Environmental
Associates and Margaret Lyday of Automated Sciences Croup, Inc.
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                                             SECTION  1

                                           BACKGROUND



1.1   EMAP AND THE NEED FOR ECOLOGICAL MONITORING

Increasingly, reports  appear on symptoms of current or  potential  ecological  problems:  declining fish and
shellfish harvests  and toxic algal  blooms in near-coastal  waters, dying high-elevation forests,  diseased and
cancerous fish in  lakes and rivers, and decreasing biodiversity.  We presently lack an integrated approach to
monitoring indicators  of  ecological  condition  and  indicators  of  pollutant exposure  and  habitat loss  or
degradation in our ecological resources.  We therefore cannot determine whether the frequency and extent
of the problems are increasing on a regional scale, whether such patterns are warning indicators of significant
long-term  changes in  ecological  condition, or whether  such patterns are natural or are associated  with
changes in ambient pollutant levels or other human  activities.  The lack of a framework for  efficiently using
data collected by the U.S. Environmental Protection  Agency (EPA) and other organizations or a  monitoring
scheme to fill the critical gaps in existing data means  that ecological assessments of most new environmental
issues take four  to five years  to produce useful results.  The need to establish  baseline conditions against
which  future changes can be documented  with  confidence has grown more   acute with  the  increasing
complexity, scale, and social importance of environmental issues such as acidic deposition, global atmospheric
change, and declining biodiversity.

EPA, the  U.S. Congress, and  private organizations with environmental and natural resource interests  have
long recognized  the  need to  fill  this critical  data gap.  Congressional hearings  in 1983  and  1984 on the
National Environmental Monitoring Improvement Act led to the conclusion that despite hundreds of millions
of dollars spent annually on environmental monitoring, federal agencies could assess neither the current status
of ecological resources nor the overall progress toward legally  mandated goals (U.S. House of Representatives
1984).  More recently, Portney (1988) called  for a Bureau  of Environmental Statistics,  and  Roughgarden
(1989) suggested a U.S. ecological survey to remedy these shortcomings.  In 1988, the EPA Science Advisory
Board recommended that a program  be implemented within EPA to monitor ecological status and trends, as
well as to  develop innovative methods for anticipating emerging problems  before they reach crisis proportions
(U.S.  EPA 1988).  EPA's  Office of Research and  Development (ORD) began planning the Environmental
Monitoring and Assessment Program  (EMAP) in  response to this strongly and widely felt need.  EMAP is fully
consistent with recent directives within EPA aimed at  managing for results,  that is, confirming that the nation's
environmental protection  efforts are  truly maintaining or  improving environmental quality.

The need  for better environmental monitoring systems is not restricted to emerging problems.  ORD provides
much of  the scientific  basis  for  regulatory programs estimated to cost  billions  of  dollars  annually.   The
Conservation  Foundation (1987) estimated 1985 annual expenditures at $65 billion in 1982 dollars, and the
National Association  of Manufacturing put the current estimate at $70  billion a year (ES&T 1989).  The
most common  approach  couples single-species laboratory tests;  computer  models that predict  pollutant
transport,  fate,  and  exposure in  the  environment;  and dose-response  models that relate  the resulting
exposures to the corresponding effects on biota. Years of  peer review have left little doubt that this approach
forms a rational scientific basis for the regulation of individual pollutants. The potential for differential toxicity
of pollutants  to  sensitive species, different life stages of the same  species,  ecological compensation  and
magnification, cumulative  effects, and cascading effects on ecosystem trophic structure,  however, all point to
the  need  for validating our  current approach through  ongoing surveillance of  indicators  of  ecological
condition.

We are obviously not without environmental monitoring  data.  Approximately $350 million  is spent  each
year within EPA on  environmental monitoring, about half of which is associated with ambient monitoring.

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This amount  is at least  equaled in relevant monitoring activities by  other federal, state, and private
organizations.  Lack of comparability of data among programs, the absence of a framework within which to
integrate data across  networks, and  failure to measure indicators that adequately reflect public concerns,
however, have thus far prevented these efforts from meeting the critical needs for reliable national assessments
of ecological condition.


1.2  THE ENVIRONMENTAL MONITORING AND ASSESSMENT PROGRAM

When  fully  implemented, EMAP  will answer  critical questions for  policymakers,  decision-makers, and  the
public:  What is the current extent of our ecological resources, and  how are they distributed geographically?
What proportions  of  the  resources  are currently  in good or acceptable  condition?  What proportions  are
degrading or improving, in what regions,  and at what rate? Are  these changes correlated with patterns and
trends  in environmental stresses?  Are adversely affected  resources improving overall in response to control
and mitigation programs?

To answer these questions,  EMAP scientists will design and  implement a number of long-term, integrated
monitoring networks over the next five years with the  following objectives.

     •  Estimate current status, extent, changes, and trends in indicators of the condition of the nation's
       ecological resources on a  regional basis with  known confidence

     •  Monitor indicators  of pollutant exposure and habitat condition and seek associations between
       human-induced stresses and ecological condition

     •  Provide periodic statistical  summaries and  interpretive reports on ecological status and trends
       to the  EPA Administrator and the public

These  EMAP  networks will  provide statistically unbiased estimates  of status,  trends, and relationships with
quantifiable confidence limits over regional and national  scales for periods of years to  decades.  EMAP  will
also provide a framework for cooperating with  other  agencies  that have  active  monitoring programs that
address some of EMAP's  needs for  certain resources.  This framework will provide for direct integration of
these data,  where appropriate, and will  enable EMAP to supplement the coverage of existing  networks to
fill critical data gaps,  if necessary.

EMAP  consists of  five basic activities:   (1)  strategic evaluation, development, and testing of indicators of
ecological condition,  pollutant exposure, and habitat condition; (2) design and  evaluation of integrated
statistical  monitoring  frameworks  and   protocols  for   collecting   data  on  indicators;  (3)   nationwide
characterization of  the  extent  and  location of  ecological  resources;  (4) demonstration studies and
implementation  of integrated sampling designs;  and (5) development  of data handling, quality assurance,
and statistical analytical  procedures for  efficient analysis and reporting  of status  and trends  data.   This
document addresses  only the first activity:  It presents several concepts regarding the use  of indicators in
EMAP,  as described in the  next  section.  The remaining activities  are  addressed  in other  EMAP planning
documents  currently  in preparation or in review.


1.3 PURPOSE  OF INDICATOR  CONCEPTUAL PLAN

This document is not an implementation plan.  Its purpose is to serve as an interim conceptual plan for the
indicator  component of  EMAP  as  more detailed plans are prepared for each ecological  resource.  The
purpose of  this  document is threefold:   (1) to inform potential EMAP data users of the approach  proposed
to describe  ecological condition;  (2) to define a common strategy within EMAP for selecting and evaluating

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indicators for  further testing that will facilitate coordination  and  integration among six  EMAP resource
categories (Section 1.4); and (3) to seek expert advice and ecological data sets from the scientific community
that are needed to better characterize the spatial and temporal variability of the proposed indicators on a
regional scale.

EMAP indicators include indicators related to ecological condition; pollutant exposure and habitat condition;
and hazards, natural processes, and management activities.  The conceptual  basis for these categories and
their definitions are presented in  Section 2.1.

The indicator approaches in this document grew out of a series of scoping workshops held during summer
1989. The goal of these workshops was not to develop a comprehensive implementation  plan for monitoring
ecological condition, or even to gain  consensus on a  list of indicators to be  used in EMAP.  Instead, their
goal  was to bring together  a variety  of  disciplinary experts who could establish a point of departure for
developing,  over  a  two-year time frame, an implementation plan for monitoring each ecological resource.
This strategy will  provide EPA decision-makers, other  potential data  users, and scientific reviewers with an
idea about what is meant by "condition of ecological resources/' what techniques might be used to measure
it, and how these measurements could be  used to identify  plausible  explanations for poor condition  where
it is discovered.
1.4  REPORT ORGANIZATION AND CONTENT

Section 2 describes a conceptual framework for evaluation and application of indicators in EMAP.  Sections 3
through 8 present a  rationale for indicator selection  for each of six ecological resource categories:   inland
surface waters, wetlands, forests, near-coastal waters, agroecosystems, and arid  lands. Rationales for selecting
several types of indicators that could be applicable to some or all of the resource categories are described
in Section 9.  A rationale for  monitoring atmospheric stressors in EMAP is outlined in Section 10. Section
11 summarizes this document and discusses the  next steps  in the implementation strategy for the indicators
component of EMAP.  Appendices A through H  provide detailed  information  on the indicators discussed in
Sections 3 through 10, respectively, including estimates of spatial and temporal variability, past performance,
and levels of cost or effort.  Lists of workshop participants and technical reviewers who have contributed their
expertise during  refinement of the indicator strategy are provided in Appendix I.


1.5  REFERENCES

Conservation  Foundation.    1987.   State  of the  Environment:  A View  Toward  the  Nineties.    The
Conservation Foundation,  Washington, DC.

ES&T.  1989. Environmental index.  Environ. Sci. Technol. 23:1067.

Portney,  P.   1988.   Reforming environmental regulation:  Three modest  proposals.   Issues Sci. Technol.
4:72-81.

Roughgarden, J.  1989. The United States needs an ecological survey.  Bioscience 39:5.

Schaeffer, D.J., E.E. Herricks,  and H.W. Kerster.  1988.  Ecosystem health:  I.  Measuring ecosystem health.
Environ. Manage. 12:445-455.

U.S. EPA.  1988.  Future  Risk:   Research Strategies  of the 1990s.  SAB-EC-88-040.  U.S. Environmental
Protection Agency, Science Advisory Board, Washington, DC.
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U.S. House of Representatives.  1984.  Environmental Monitoring and Improvement Act.  Hearings before
the Subcommittee on Natural Resources, Agricultural Research, and the Environment of the Committee on
Science and Technology.  March 28, 1984.  U.S. Government Printing Office, Washington,  DC.
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                                              SECTION 2

                                     EMAP INDICATOR CONCEPTS

                                              J.J. Messer1


To meet  EMAP's first objective (Section 1.2),  we  must  identify indicators of the  condition  of ecological
resources and make unbiased estimates of the regional distribution of values for these indicators  for each class
of ecological resource of interest.  To  meet the second objective, we must also define and identify indicators
of pollutant exposure and habitat loss or degradation, as well as both human and natural  sources of stress
that might be associated with poor or changing ecological condition.  To meet the third objective, we must
establish procedures for analyzing and displaying monitoring data that will be meaningful to decision-makers
and the public.

This section  proposes a conceptual approach to defining environmental indicators and  its relation to the
evolving practice of ecological risk assessment.  It also addresses some of the problems that must be resolved
in implementing the proposed approach and discusses data analysis and presentation of results.  The section
concludes by defining the indicator selection  strategy that was used by EMAP scientists to identify potential
indicators and that ultimately will be used  to select the most cost-effective indicators for  implementation in
a long-term monitoring program.


2.1  EMAP INDICATORS

EMAP recognizes three broad categories of indicators (see Figure 2-1):

    1.  Response Indicator:  A characteristic of the environment measured to provide evidence of the
       biological condition of a resource at the organism, population, community, or ecosystem process
       level of organization.

    2.  Exposure and Habitat Indicators:  Diagnostic indicators that are measured in conjunction with
       response indicators.

         Exposure  indicator —  A characteristic  of the environment measured to  provide  evidence
         of the occurrence or magnitude of a response indicator's contact with a physical, chemical,
         or biological stress.

         Habitat Indicator - A physical attribute measured to characterize conditions necessary to
         support an organism, population,  or community in the absence of pollutants.

    3.  Stressor Indicator:  A characteristic  measured  to quantify a natural  process, an environmental
       hazard,  or a management action that effects  changes in exposure and habitat.

Response,  exposure, and habitat  indicators  will  be  measured  during annual  surveys  at sampling sites
associated with points on the EMAP grid (Section 2.2).  Stressor indicators,  by convention in EMAP, are not
normally measured at the  sampling sites; instead they will be measured  in various other  ways (e.g., fixed-
      Atmospheric Research and Exposure Assessment Laboratory, U.S. Environmental Protection Agency, Research Triangle Park, North
      Carolina  27711


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            Response
          Indicators 
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EMAP will rely on response indicators to describe ecological condition, for reasons described in the following
section.  Statistical associations among the values of response indicators and those of exposure and habitat
and stressor indicators, coupled with knowledge about plausible processes  and effects mechanisms, will be
used to  identify  possible  reasons for poor  or changing ecological  condition, where they  correspond to
plausible causal pathways.  The selection criteria for all indicators are described in Section 2.4, and examples
of indicators  for each  ecological resource  are described  in Sections 3-10  and the  appendices  to  this
document.

2.1.1   Rationale

EMAP  serves two purposes:   (1) identifying classes of ecological resources  whose condition is deteriorating
widely or  rapidly  (thus  deserving  of  priority attention)  and  (2)  ensuring  that  the  sum  total  of  our
environmental protection  efforts are achieving the desired results.  Traditionally, many monitoring programs
have assumed the presence or  absence of pollutants is equivalent to  poor or good  condition.   Ecological
resources are potentially  affected by  multiple  pollutants, however,  arriving in multiple media,  and these
pollutants interact with management actions and natural processes to exert both population  and higher order
(e.g., community) effects.  The complexity of  environmental  processes and subsequent  effects at regional  and
national  scales makes it unwise to  determine ecological condition solely based on indicators of pollutant
releases to the environment or  compliance  with regulations.   Nor should  we  rely  on adherence to air or
water quality standards or prescriptive  targets  meant to protect physical  habitat  extent  or quality.   For
example, lack of toxicity and adequate concentrations of dissolved oxygen  are necessary but insufficient to
support a healthy fish community if siltation  destroys spawning habitat.  Conversely, safety  factors built  into
ambient  standards or differences in sensitivity  among species  in situ and laboratory test organisms  may
sometimes lead to conclusions that healthy biotic communities are present  in locations where water quality
standards or bioassay LC50's  are  exceeded.   In fact, response  indicators  serve as a check, not only on
adherence to regulations,  but also on the underlying scientific basis of such regulations.

EMAP  response indicators must  meet one of several requirements.   Some indicators chosen to track the
success of monitoring programs should be clearly related to  aspects of the environment valued by the public
(e.g., productivity, biodiversity, recreational value, or  "services"  performed  by  ecological resources such as
water storage and flood protection by wetlands  or cleansing of atmospheric pollutants  by forests).   Response
indicators chosen to identify emerging problems may be less easily perceived as valuable  by the public (e.g.,
stability or resilience), but should be  unequivocally linked  to the probability of future  damage  to publicly
valued aspects of the environment.  Examples of the latter  may include shifts in nutrient processing rates in
forest soils which presage damage to trees (Likens and Bormann  1979), or changes in zooplankton species
that lead to  shifts in the composition of the fish  community  (Brooks and Dodson 1965).  Response indicators
may be  more or less specific to particular  stresses (e.g.,  toxicants, nutrients, acidity,  or physical habitat
alteration).

While  it  is critical that response indicators be related to  aspects of the environment valued by the public, it
is important that they not  include metrics that are based so/e/y on public expectations.  One example of such
a metric  is the degree to which water bodies meet "designated use" criteria, which differ from state to state
and may change  over time.  Questionnaires  provide another  example:  Changing mixes of summer homes
and farming activity in rural lake districts  may cause a shift  in  public perception of water quality  as lake use
changes from fishing and  body contact recreation to water  storage, even when  there is no  actual change in
the ecological condition of lakes in  the region.  Trends in  public education  and  degree of expectations
regarding environmental quality may mask actual trends in ecological condition  or make trends apparent
where  none exist. Such changes are not a matter amenable to research or risk assessment, but fall into the
category  of risk management  and public  policy.
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2.1,2  EMAP Indicators and Risk Assessment Endpoints

Risk assessment is the formal framework adopted by EPA to estimate the  risks to human health and welfare
arising from various hazards and associated mitigation strategies (Yosie 1987, Deisler 1988).  Although initially
applied by EPA in the human  carcinogen  area,  the  risk assessment paradigm  is now  being  applied to
ecological  risks at various spatial  scales (Bascietto et al. 1990; Hunsaker et al.  1990).  Ecological risk
assessment estimates the effects of human action on a natural resource and interprets the significance of those
effects in light of the uncertainties identified in each component of the assessment process.  The ideal output
of the  risk assessment  process is an estimated probability that an event of a certain magnitude will occur (e.g.,
a  90% probability of loss of fish  populations in 20% of the  lakes in a region;  Suter 1990a).   The key
components of risk assessment include (1) selection of endpoints, (2) qualitative and quantitative description
of the  sources of the  hazard (e.g.,  locations and  emission  levels for  pollutant sources), (3)  identification and
description of the reference  environment within which effects occur  or are expected to occur, (4) estimation
or measurement of spatiotemporal patterns of exposure, and (5) quantification  of the relationship  between
exposure in the modified environment and effects on biota.  The adaptation of  the standard  risk  assessment
paradigm to accommodate ecological risk at different time and space scales has been proposed (Suter 1990a;
Hunsaker et  al. 1990; Warren-Hicks  et al. 1989), and this is currently under active discussion within EPA
(Bascietto et  al. 1990)

Although the  sequence of activities in traditional risk assessment moves from source(s) to exposure  assessment
and  then to effects assessment, an epidemiological  approach to ecological risk assessment is also appropriate
(Suter  1990b; Fava et al. 1987).  It is the latter  step in the  risk paradigm - effects-driven  assessment - that
is under development for EMAP. In effects-driven risk assessments, an effect is observed, and one then works
toward identification  of the  hazard or stressors by  using exposure information.  The degree to which EMAP
indicators can be used in  a  diagnostic manner has yet to  be determined.

It  is paramount to the success of  a monitoring  program that the characteristics of the environment  being
monitored  be appropriate for the purpose of the program's assessment goals  (National  Research  Council
1990). The indicators of EMAP share  some common characteristics with "endpoints," components of the risk
assessment process.   Suter  (1990a) recognizes two types of endpoints that  are used in  risk assessments.
Assessment endpoints are  "formal  expressions of the  actual environmental value that is  to  be  protected."
They should  have unambiguous operational definitions,  have social or biological  relevance,  and be amenable
to prediction  or measurement (e.g., a  decrease in biodiversity, probability  of a >10% reduction in game fish
production, or the proportion of raptors  killed within a region of pesticide  use). The quantitative results of
the  measurements taken to characterize assessment endpoints   are termed  measurement  endpoints.
Measurement endpoints must correspond to or  be  predictive of assessment  endpoints.    Examples of
measurement endpoints include the percentage  of trees that exhibit visual symptoms of foliar damage due
to oxidants, or the fraction of streams that exceed  the  LC50 for a toxicant to largemouth bass.  Many of the
criteria for good measurement endpoints for regional  assessments  (Suter 1990a) were  used as  criteria for
EMAP indicators.   In  EMAP,  the proportion of sites subnominal with respect to particular response indicators
within a  region are the proposed assessment endpoints.

Assessment endpoints by  definition must relate to the environmental values identified for  each  resource
category.  Therefore,  the measurement endpoints  for EMAP are ideally response indicators.   Exposure and
habitat indicators are not  our first choice as  measurement endpoints because they do not integrate effects
of multiple pollutants or effects of unknown hazards.  Occasionally, as in the case of wetland extent,  a
habitat indicator can  serve as both  a  measurement endpoint and an assessment endpoint, for example, "no
net  loss  of wetlands."  Exposure or  habitat  indicators also could  be used to  identify possible threats to
ecosystems.

The following example describes how EMAP could use a  risk assessment framework. The hazard  is  acidic
deposition  and its  effect on  fish in lakes of the northeastern United  States.  The assessment endpoint in this

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example is the fraction  of lakes that could support brook trout but do  not.  The measurement endpoint is
the distribution of the response indicator, presence or absence of brook trout,  in a sample of lakes.  Exposure
information  is provided by both exposure indicators and stressor indicators in EMAP.  Exposure indicators
could include water pH and calcium and aluminum concentrations.  A related habitat indicator could be lake
bottom  substrate.  Stressor indicators  could include data on acidic  deposition; state fish and game records
on fishing pressure, stocking practices, and the like; and meteorological records for the region.  If sampling
indicates poor  condition for fish in northeastern lakes, then the exposure, habitat, and stressor indicators
would be used to determine  if that  condition  is associated more  strongly with low pH, high aluminum,
climate  variations,  physical habitat disturbance, or acidic deposition.

It is important to keep the purpose of EMAP in mind when considering a risk assessment framework.  EMAP
response indicators (measurement endpoints) are monitoring tools that need  to be  primarily  anticipatory of
environmental  effects rather than  predictive.  EMAP  indicators  must meet specific criteria associated with
annual  surveys  conducted on probability samples of sites during some  index period (see Section 2.2).
Because the development of  indicators for EMAP is just beginning, the indicators are  usually  described in
terms of a category of measurement endpoints, rather than a specific measurement endpoint. This document
identifies important environmental values for each specific resource category (Sections 3-8) rather than specific
assessment endpoints.   Specific  measurement  and assessment  endpoints will  be determined  as EMAP core
indicators are identified.  The indicator strategy for inland surface waters illustrates  the development and
linkage  of measurement endpoints and assessment endpoints (see Tables 4-3  and 4-4).

The type of risk assessment that EMAP could accomplish is likely to be an initial or "screening" assessment,
i.e., EMAP indicators could eliminate some possible reasons for poor ecological condition. Additional research
and more focused risk assessment would  then be required to  determine cause-and-effect relationships.


2.2  EMAP  DESIGN OBJECTIVES AND SAMPLING APPROACH

To better explain the application of the types of indicators  to be used in the  proposed indicator strategy for
EMAP, the design  objectives of the program and the  sampling approach required to meet these objectives
are summarized in this  section.

2.2.1  Definition and Classification of Ecological Resources

EMAP seeks to  define the condition  of the nation's ecological resources.  What are these resources, and
how do they differ from "ecosystems"?  The word ecosystem usually is  used  to  refer  to a  local complex of
interacting plants, animals, and their physical surroundings,  which is generally isolated  from  adjacent systems
by some boundary or "ecotone."  An ecosystem could be a watershed or, on  a larger  scale, an  ecoregion or
a biome (Shelford 1963; Lotspeich 1980;  Bailey  1983).  Ecosystems thus may contain  forests,  lakes, streams,
surrounding wetlands, and  interspersed agriculture. It  is extremely difficult to identify clearly defined target
populations  of ecosystems, particularly because ecosystems "nest" into poorly defined categories  of increasing
spatial scale. For this reason, EMAP has classified ecosystems into ecological resources, which will form the
basis for the reporting units of the program.

Ecological resources  are hierarchically organized within  EMAP as categories, classes, and subclasses.  An
ecological resource  category  is the grossest level of aggregation  used in  EMAP to  describe major types of
ecological resources:   near-coastal  waters,  inland  surface  waters,  wetlands, forests,  arid   lands, and
agroecosystems. Subdivisions  of these categories, termed ecological resource classes, represent the largest
groups of resources for  which  strictly comparable response and exposure or habitat indicator  measurements
can be made.  Examples of resource classes include large estuaries, small lakes, emergent estuarine wetlands,
oak-hickory  forests, sagebrush-dominated shrubland, and orchard cropland. Ecological resource subclasses
represent a  further subdivision within  classes,  distinguished from other  entities  in the same  class by some

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additional attribute(s),  such as small seepage lakes versus  small drainage lakes.   Measurements  of  extent
(numbers, length, area) and indicators will be made on resource sampling units (RSUs), which represent the
entities  that comprise the  resource classes.  These  measurements  will provide regional- and  national-scale
assessments.  Reporting on subclasses likely will occur only for special circumstances.

Many of the resource classes are found throughout the  nation.  In different  regions,  however (depending
on levels of stress, factors that affect susceptibility to a given level of stress, and the success of environmental
protection  efforts),  ecological  resource classes may exhibit differences  in current conditions  and different
trends.  In  many cases, such information will be more useful to decision-makers than data aggregated at the
national level.  Thus, regional resource classes are the smallest groups for which EMAP data are anticipated
to be routinely  reported.  A region within EMAP is qualitatively defined as any extensive geographic area.
Regions will generally correspond in size to EPA  administrative Regions  III through X.   Occasionally,  subsets
of regional resource classes (such as small, c/earwater seepage lakes with low acid-neutralizing  capacity) may
be identified  by post-stratification during data analysis, or before actual  sampling for special studies.   While
special  assessments of condition  in these subsets may be performed, depending on the  urgency of the
environmental issue and the degree to which it constitutes a  regional or national concern, these data are
unlikely to be routinely reported as part of EMAP's annual reports or periodic integrated assessments.

2.2.2  Design Objectives  for Resource Classes

EMAP's objectives (Section  1) require that several types of questions for all classes of ecological  resources
be answered.

     •  What is the current extent (numbers, length, area) and geographic distribution of each  resource
       class of interest?

     •  What proportion  of each resource class is currently  in good or acceptable  condition?

     •  What proportions are degrading or improving,  in what regions, and at what rate?

     •  Are these changes correlated with patterns and trends in environmental stresses?

     •  Are adversely  affected resource classes improving overall in response to control  and mitigation
       programs?

Answers to these questions require a statistically  based sampling design  that will provide  unbiased estimates
with known  confidence  limits for well-defined  ecological  resource classes (populations).   Such estimates,
which  include  estimates of extent and  current status  or condition,  are  termed  population estimates.
Development of the sampling design, or monitoring survey design, requires a strategy that  allows sampling
of any  spatially distributed and  identifiable ecological  resource without having an explicit  sampling frame
available (e.g., an explicit list of lakes or all wetlands present in a region targeted for sampling). The design
should  lead  to  explicit probability samples  (samples selected by using probability  methods) of resource
classes, and also be flexible and adaptive to accommodate the sampling of many distinct resource classes and
emerging environmental  stresses. The monitoring program objective for each resource class is to describe the
current status, and trends  in status, of response  indicators  for that class over regions  of the  United  States.
An important additional objective is to determine associations between response indicators and indicators of
environmental  stress  and  exposure.   These objectives  require   a  probability-based  design  that enables
monitoring for detection of spatial and temporal  patterns for each ecological  resource, as well as affording
some degree of coincidence in sampling  across ecological  resources.
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2.2.3  Making Unbiased Estimates with Known  Confidence

The  proposed  EMAP design strategy uses a systematic triangular sampling grid of points  randomly placed
over the United  States.  This grid will enable sampling of ecological resources  to  produce regional  and
national population estimates of resource extent, current status (condition), and change over time in extent
and  status.  Landscape  descriptions (described  below) will  be completed  for hexagonal areas centered on
each grid  point; these hexagons are termed landscape sampling units (LSUs). LSUs are  used to estimate
the extent of each resource class and to define implicit sampling frames  (e.g,  a set of criteria or rules  that
govern which  RSUs are to be  included  in  a  target population)  for selecting RSUs on  which  response,
exposure,  and habitat indicators will be measured. The first sample (Tier 1 resource  sample) is all RSUs of
each resource class within each LSU;  it is used to estimate the extent of  resource classes (e.g., the number
of RSUs or their  surface area or total length).  The second, or "double," sample (Tier 2 resource sample)
is a subsample of the first and is used for field  sampling of indicators. A sufficient number of RSUs will be
sampled annually for each resource class from which regional population  estimates are to be  made.  EMAP
currently uses 50 RSUs as a planning target, but this sample size will be adjusted depending on the variance
of the  indicator measurements within a particular resource class.  Specific sample sizes will depend on the
precision and accuracy  requirements for  each class.  Tier 1  resource  sample measurements are  completed
each time a  new landscape description  is acquired, approximately every 10  years.   The  Tier 2  resource
sample forms the basis for annual  field measurement of indicators.

The  point grid established for  the United States  is  triangular and systematic, with  approximately 27  km
between  points in each direction.  A fixed position that  represents a  permanent location for the base  grid
is established, and the sampling points are generated by a random shift  of the entire grid from this base
location.   This  randomization establishes the systematic grid sample as a probability sample. The proposed
base grid density  results in approximately one point  per  640 km2, yielding approximately  12,600 points in
the contiguous 48 states and  approximately 2,400 points and 26 points  in Alaska and Hawaii, respectively.

Following  placement of the grid, the landscape will be characterized within the  LSUs by using a combination
of maps, aerial photography, and  satellite imagery to define  the extent of each  ecological resource class.
These  LSU descriptions  constitute  a probability area  sample of the United States.  The probability sample
enables regional and national estimates of areal  and linear extent of ecological resource classes and  numbers
of all discrete ecological  resources  of  interest (e.g., lakes  or stream reaches).

Indicator measurements for a resource class are taken on each Tier 2 RSU during an  index period specific
to each resource.  The index period is the sampling window selected  to yield the  maximum amount of
information during the year; it could vary from one system (and one  indicator) to  another.  Measurements
during  the index  period  provide  a  "snapshot1 of conditions during  some  meaningful  time period.   For
example, a late summer index period may be selected for making measurements on fish populations, when
stream  flows  and dissolved oxygen levels  may  be lowest and effects  of stressors  may be  highest  Several
reasons exist for measuring indicators only during index periods.   Because the systematic grid determines the
sampling location, RSUs may occur almost anywhere, often on private property or in places where  access is
difficult.   Many  of  the sites will therefore  not be  available for  intensive  or  continuous monitoring.
Additionally,  indicator  measurements  must be  made on  a sufficient number of  RSUs to make regional
population estimates with adequate confidence  bounds.   With as many as 50  RSUs per resource class  and
approximately 10-20 resource classes per  resource category, cost is a critical factor.   Thus, EMAP will operate
as a  series of annual surveys during index periods.

EMAP's objectives include describing current and ongoing status and detecting trends of ecological condition
through the measurement of indicators. These objectives conflict in how samples are allocated in  space  and
time; assessment of status is best done by  making measurements on  as many RSUs as possible, whereas trend
detection is best  done by repeatedly  making measurements on the same units over  time.  This conflict is
resolved by using a new set of RSUs for each year in four successive years and then repeating the four-year

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cycle and  using exactly the same  RSUs  as  in the  first four-year cycle.   This interpenetrating design is
completed so that the same, systematic triangular grid pattern (at one-fourth the density) is retained in each
year and  any  particular RSU will be sampled only every fourth year.  Based  on initial simulation studies,
detection  of regional  and  national trends in response indicators  on the order of 1%  per year is expected to
be possible within approximately 10-15 years.  Status of ecological condition is estimated annually  by using
four-year  running averages over the four interpenetrating subsets.

EMAP thus will  provide statistically unbiased estimates  of status, trends, and associations among indicators
with  quantifiable  confidence limits over  regional  and  national scales  for  periods of years to decades.
However, because individual RSUs  are sampled only once every four years and 10-15 samples are  required
to detect a significant trend, EMAP  will provide little  trend information about the conditions at any particular
site for at least 40-60 years.  Furthermore, the probability-based survey approach in  EMAP places important
constraints on indicator selection and local interpretation, which are discussed  in the following sections.

2.2.4  Extent of Ecological Resources

Quantifying the extent of ecological resources (including numbers, length, area)  is important for two reasons.
First,  we  are often concerned with the outright loss of resources,  as  in  the  current case with wetlands.
Second, we may be  more willing to accept  a large  area in  poor condition for an  abundant  resource than
for a  limited resource. All  resources are potentially at risk, so EMAP  will strive to estimate the current extent
of specific classes  within all six  resource categories in the United States.  Rare or local  resources (e.g.,  the
Okefenokee Swamp or ponds in the Great Basin that support  relict populations of pupfish) are best dealt with
as special cases.

2.2.5  Current Status of  Ecological Resources

EMAP must estimate, for  each resource class,  what proportion is  in  good or  acceptable condition.  As
explained in Section  2.1,  such estimates should rely on response indicators.  Many current indicators have
evolved in a bottom-up, or regulatory, environment, and it is  likely that others (appropriate in earlier settings)
should now be reevaluated. Considerable thought has been given  in EMAP to the types of indicators that
are appropriate to a  "top-down", environmental  monitoring approach that seeks to  quantify the proportions
of resource classes that are in  acceptable condition.

2.2.5.1 Ecosystem Health or  Integrity

Condition  has  been  expressed as "health"  or  "integrity" in  both  environmental literature  and  legislative
language, but what is meant by these terms is not clear. Schaeffer et al. (1988) define ecological health, by
human analogy, as the absence of disease.  Although neither ecosystems nor  ecological resources function
as "super-organisms,"  many aspects of a  human-ecosystem analogy are useful for illustrating concepts about
indicators. Appearances of overt diseased states in humans are familiar:  gross pathology, contagious diseases,
and trauma, for example, are immediately identifiable and undesirable. In such  cases the damage has already
occurred.  Analogous appearances  of ecological  "diseases" include changes  in the relative numbers of native
species, delays in  succession, accumulation or loss of biomass,  changes in primary production and nutrient
cycling rates,  loss of nutrients,  and changes in stability and resilience.   In  both cases,  causal factors  can
include pathogens, toxics,  genetic damage, and  physical injury.

In medical practice,  it is common to  look  for symptoms of diseases  that may be in  the early stages of
development.   Human analogs  include elevated oral  temperature, high  or low blood pressure, enzyme
imbalances, and  indicators  of low-level exposure to  toxics and carcinogens  in body tissues.   Symptoms in
ecological resources also might be sought that are analogous to symptoms of advanced harm in humans such
as drastic weight loss, tumors, trauma, or malaise.  Such analogs might include accumulation of toxics  in soils
and   sediments  and  the  presence of toxics, carcinogens,  or biomarkers  in  plant  and animal tissues.

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Measurements of temperature, heart function, body fluids, and the like must be compared with statistics from
populations of normal  individuals  in  order to assess  abnormal  conditions.   Typically, we compare the
measurements of symptoms  in individuals with expected norms, which may vary among subpopulations (e.g.,
race, sex).  Normal values for ecological indicators also will vary by species and  resource category or class,
for which baseline data describing reference states must be gathered.

A concept  related to health is that of "wellness" in  humans, defined as the ability to resist disease or injury.
Karr et al. (1986) defined a  system as healthy "when its inherent potential is realized, its condition is stable,
its capacity for self-repair ... is preserved, and  minimal external support for management is needed."  The
most appropriate term for wellness in  ecological resources appears to be integrity.  Lack of integrity might
be exhibited  by symptoms such as low population densities that could threaten mating success, imbalances
in trophic levels, or declining ability to  buffer changes in  nutrient status or pH.   Declining integrity also may
result from some naturally occurring pathogen  or  climatic event.

Poor or unacceptable ecological condition thus might be thought of as the presence of advanced  damage,
symptoms of  impending damage, or declining ability to resist damage. We are most interested in conditions
that seriously threaten the benefits of our ecological  resources (including genetic diversity, recreation, research,
flood control, atmospheric buffering) and the capacity of the resources to continue to provide these  benefits
to society.  It is more desirable and cost-effective to strive for maintaining ecological integrity, or at least  to
detect early signs of damage, than  it is  to  implement controls or mitigation  after the damage has reached an
advanced state.  For many types of damage, however, even though action  may come too late to restore an
individual resource, signs of current damage can  act as a trigger to target efforts toward  preventing more
widespread damage in the future.  This concept is similar to the use of "mortality statistics"  in the decision-
making arena for public health and safety:  Elevated human mortality  rates associated with particular  regions,
diseases,  occupations, or activities prompt efforts to be directed  more actively  toward avoiding future excess
mortalities. From this perspective, therefore, even estimating the extent of damage after  the fact is a critical
concern in EMAP.

There are many  challenges to establishing  measures  of  ecological condition that are  analogous to human
health and wellness.  Ecological resources are  at a higher hierarchical level than individual  organisms;  thus
evidence of  damage and  early warning  signs at this higher  level  will inherently  be  more variable.
Furthermore,  we  must be prepared to judge the health of a resource  with respect to certain decisions about
management, for example, managing certain landscapes for crop production or commercial timber or certain
lakes and streams for stocked fisheries.   What is probably most critical for managed  resources is that  they be
as resilient  as possible to  stressors  other than management actions, and that their prior condition would be
restored if the management action were terminated.

2.2.5.2  Distinguishing Nominal from  Subnominal Condition

One of the major tasks  in EMAP is determining what value (threshold) for a particular indicator is  required
for a  resource  to be considered in good or acceptable condition.  Subjective  adjectives  such  as "good,"
"acceptable,"  "desirable," "undamaged," and "not at risk" can all be appropriate descriptors, depending on the
context in which  they are used.  For EMAP, the more general terms nominal and subnominal are  used  to
refer to desirable and undesirable conditions, respectively.

If it were known a priori exactly what value for an indicator  reflected the threshold between nominal and
subnominal condition, the percentages of a regional resource class  in each category could easily be displayed
as a  bar  graph  or pie chart.  In reality, however, opinions on the value  of the  threshold will differ at the
outset  and will change  as  more data  become  available.   Consequently, it  is important to  know the
distribution of values for each response indicator for a resource  class.  Figure 2-2 illustrates  how this would
be accomplished  in EMAP by using a  hypothetical cumulative frequency distribution of values for the Index
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 100%
   75%  -
   50%  -
   25%  -
     0%
                             44,800 km
                           20     30     40
                           IBI  Score
50     60
                                                Threshold
Figure  2-2. Cumulative frequency distribution for the Index of Biotic Integrity in a region, and how it
           might be  used to define the proportion of subnominal streams within a  region.

of Biotic Integrity (IBI) developed by Karr et  al. (1986),  an index that describes the community composition
of fish  in a regional resource class of streams.

The curve in  Figure 2-2  is produced by measuring the IBI for a probability  sample of RSUs (e.g., stream
segments). If we assume that an IBI score of 45 (see Figure 2-2) is the threshold that separates nominal from
subnominal systems, then 38% of the stream kilometers in the region would be subnominal.  The height of
the bar represents the total  number and the total extent (e.g., the number of  kilometers) of the stream
resource in the  region.  Separate  curves can be constructed for other indicators (e.g., invertebrate species
richness or the bottom area covered by filamentous algae). A subnominal score for any individual response
indicator  measured on a resource sampling  unit could  make that unit subnominal, or a scheme could be
used that would combine  indicator values to produce a total score or index.  Perhaps most important, the
proportion of  the resource that lay above or below any previous threshold can be reestimated as thresholds
are modified,  or the proportion can be reestimated to examine how changes in threshold values affect the
estimated proportion of subnominal  resources.

2.2.6   Identifying Possible Causes for Subnominal  Conditions

In addition to knowing what  proportion of an ecological resource class is in subnominal condition, it also is
desirable to know what exposure, habitat, and stressor  indicators are correlated with subnominal condition.
In a freshwater example of this concept (Figure 2-3),  indicators of physical habitat (e.g., hydrologic, bottom,
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          Extent
            of
        Resource

        (number,
      length, area)
                                                                  Nominal
                                                                  Unknown
                                                                  pH
                                                                  Toxicity
                                                                  Oxygen/Eutrophication
                                                                  Habitat
>  Nominal (%)
                                                     Subnominal (%)
Figure 2-3.  Correlative approach to initial partitioning of subnominal systems among possible causes.

and riparian conditions) and exposure (e.g., acidity, sediment toxicity, or hypoxia) were measured.  Additional
correlations with stressor indicator data on conventional point sources,  industrial  or  mine wastes, nonpoint
sources, acid rain, or hydrologic mitigation (e.g.,  dam operations, consumptive  uses) are  used  to identify
possible reasons for poor condition.  Statistical expansion, through the EMAP design,  enables the  number or
proportions of subnominal sites in the region to be estimated for each factor that  might influence condition,
although relative  error of the estimate may be  high if the  number  of sampling  units representing the
subnominal category is small.

Correlations, of course, do not prove causality.  If a depauperate fish community is accompanied by poor
physical habitat, hypoxia, acidity, or toxic sediments, however, prudence suggests that we begin  by seeking
the simplest or most obvious explanation.  While  EMAP's diagnostic capability undoubtedly will  be limited
early in the program, it will provide an opportunity  to conduct correlative analysis of environmental indicators
on a regional basis that, in general, does not exist  in other monitoring programs.  Nonetheless, as shown in
Figure  2-3,  the condition of some of  the  subnominal systems is likely  to  result from  one  or several
unidentified stressors, including resource management, local climatic fluctuations, and natural phenomena; this
issue is discussed  further in Section  2.3.7.   In other cases, subnominal  condition might be associated with
exposure to more than one pollutant  and/or poor physical  habitat.   Figure 2-3  does not  illustrate the
possibility of multiple possible causes.

2.2.7   Identification of Regional Trends

By conducting periodic remeasurements of indicators on a sample of approximately  800  RSUs per year (or
about  50-60  RSUs for each resource class within a defined region) over  a four-year cycle,  it  should  be
possible to determine whether statistically significant changes are occurring  from year to  year and  whether
these changes represent long-term directional trends, as illustrated in Figure 2-4.  The amount of the resource
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                                           Trends
  Extent
    of
Resource
                                                                              Loss of
                                                                              Resource
                                                                                Nominal
                                                                                Unknown
                                                                                PH
                                                                                Toxicity
                                                                                Oxygen/Eutrophication
                                                                                Habitat
              1990     1995     2000     2005     2010    2015

 Figure 2-4.  Regional trends in the extent and condition  of a resource over time.

 may change (e.g., stream kilometers may be lost because of channelization or regional drought), the fractionof
 the resource in  subnominal condition may change, and the possible cause or causes  (as assessed through
 correlative analyses) for subnominal conditions may change over time.  In  this illustration, the reduction  in
 total  kilometers  of eutrophic streams (as  indicated by low dissolved  oxygen) is more than offset by the
 increase in total  kilometers that have  become subnominal  because of  pesticides  (toxicity)  or habitat
 degradation.

 As illustrated for forests in Figures 2-5 and 2-6, the proportions of  resource classes in subnominal condition,
 and  the possible explanations for this condition,  may vary from one region to another (Figure 2-5) or from
 one resource class to another  within the same region  (Figure 2-6).  Differences could be due either  to
 differences in stressors or susceptibility to disturbance. Such  information can be important in determining the
 most efficient and effective monitoring efforts.  For example, despite the national  importance of acid rain  as
 it affects  lakes and streams, low acidic deposition  rates or alkaline soils and geology make the problem
 inconsequential  in many regions of the country.


 2.3  ISSUES REGARDING THE APPLICATION OF THE  EMAP INDICATOR STRATEGY

 Many issues will need to be  studied and resolved regarding the application of the proposed indicator strategy.
 These issues are discussed briefly in this section.  The final  selection of indicators will  depend on  resolving
 these issues during the design  and implementation phases of EMAP.
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                                      Regionalization
           Extent
             of
          Resource
            Class
                     Resource Class:  Spruce Fir
                                                                               _L
                               Nominal
                               Unknown
                               Ozone
                               Toxic'rty
                               Nutrient Deficiency
                               Alteration
                         Region A
Region B
Region C
Region D
Figure 2-5.  Hypothetical  comparison  of an ecological resource class  among four regions.   Results of
            associations of response indicators with exposure and habitat indicators (developed through
            correlative  analyses) are also shown and, in this example, clearly differ among regions.
                                 Resource Classification
            Extent
             of
          Resource
            Class
                      Region A
                      Resource Category: Forests
                                Nominal
                                Unknown
                                Ozone
                                Toxicity
                                Nutrient Deficiency
                                Alteration
                         Class A        Class B         Class C         Class D
                        (White Pine)    (Oak-Hickory)    (Spruce Fir)  (Maple-Beech-Birch)

Figure 2-6. Classification  of an ecological  resource category into four resource classes.  Results of
           associations of response indicators with exposure and habitat indicators (developed through
           correlative  analyses)  are  also  shown  and,  in  this  example,  clearly  differ  among
           subclasses.
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2.3.1   Refinement of the EMAP Sampling Design

The  EMAP sampling design, as currently conceived,  is subject to  refinement  as  more data are gathered.
Aspects of the current approach  for  landscape  characterization,  the number of  resource sampling  units
selected for measurement, and the possibility of revisiting sites within a given year are discussed below.

2.3.1.1  Landscape Characterization

Characterizing all LSUs may prove to be unnecessary in areas of homogeneous topography.  Conversely, it
may be necessary to increase the LSU density in  complex terrain (e.g., in the Sierra Nevada) or in resource
classes that have  narrow,  linear geographic distributions (e.g., the  redwoods in the Pacific  Northwest).
Currently available data are insufficient to determine the density required for adequate characterization at this
time.   Imagery acquisition is inexpensive relative to interpretation,  however, and thus the proposed strategy
is to acquire imagery for all 12,600 LSUs, and then to increase the density  of LSU interpretation until the
resulting precision  is adequate.

The current scheme  proposed for collecting imagery is to  acquire complete national Thematic Mapper (TM)
satellite coverage and 1:40,000-scale color  infrared photographic coverage for the 12,600  LSUs.  Because
TM frames contain multiple  LSUs,  acquiring complete TM  coverage  of the  United States is  possible,  and
interpretation can subsequently be done for any LSU density. Acquiring photography at less than the  12,600
density would result in little cost savings because of the fixed expense associated with flight lines; nationwide
interpretation of more LSUs, while possible, is prohibitively expensive at this time.  One fourth of the  12,600
LSUs (3,200 nationwide) will be characterized initially.  Higher density photography (at an LSU density greater
than 12,600) will be acquired  only where needed.

2.3.1.2 Number of Sampling Units and Multiple Sampling

A target of 50 RSUs per regional resource class is used in EMAP as a planning tool.  As both existing and
new data  are acquired and  more reliable variance estimates can be made, this number will be adjusted to
meet the design objectives for the various indicators.  In practice, setting the  design objectives is an iterative
procedure that depends on  findings from the initial surveys.

To what extent multiple visits to a site can be accommodated or prove to be necessary for  some indicators
is  unknown  at this  time.   While  intensive monitoring is  precluded and  routine seasonal  monitoring is
extremely unlikely, more than one visit during the index period likely will be necessary in some cases (e.g.,
to place and retrieve traps or other sampling gear).  For some resource classes,  more than one  index period
(e.g.,  winter and  summer  for large  lakes) may be necessary.   For  these  reasons,  providing  detailed
specifications for the ultimate Tier 2 sampling design, which undoubtedly will vary from one resource category
(and class) to another, currently is not possible.

2.3.2  Importance of Scale to Indicators

EMAP  is being designed to provide information  to decision-makers concerned with national  and regional
environmental quality. These individuals do not base their  decisions on the actions of individual  polluters,
but,  rather, strive  to target environmental  protection efforts  in the  most effective way  to ensure overall
environmental quality across regions or the entire nation.   For example,  they determine  funding allocation
among air pollution research or control, point or nonpoint discharges to waterways, or habitat preservation.

Hierarchy theorists suggest that geographic scale  is related to time scale (O'Neill et al. 1986).  Regional
geographic trends  thus might best be monitored at seasonal,  annual,  or  longer time intervals.  The EMAP
sampling design focuses on annual or less frequent surveys conducted during an index period (Section 2.2.3).
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One example of using the index approach is to measure fish populations in late summer, when stream flows
and dissolved oxygen levels  may be lowest and stress may be greatest  These synoptic survey data, collected
on a probability sample of RSUs (in this case, stream reaches), will provide an  unbiased estimate of how the
entire target  resource changes over time during the index period only.  Nevertheless, if the effects of short-
term events on aquatic populations (e.g., periods of hypoxia or toxic episodes that co-occur with fish kills or
increased drift of invertebrates) are localized and are not detected during the following index period, then
the population of subnominal reaches (as indicated by decreased numbers of fish or invertebrates) will remain
constant, even if this particular set of reaches (and other reaches, selected for sampling as part of the four-
year interpenetrating design) experience such events from  one year to the  next  Even though the specific
reaches at long-term  risk from episodic,  localized impacts cannot be identified, comparisons among regions
of the  proportions of resource classes experiencing such risks  is possible. If there is  a long-term effect (>1
year) from episodic events or if several episodic events result in a cumulative impact that is detectable during
the index period, the  percentage of subnominal systems will increase  over time, assuming other conditions
remain constant.  In other cases, short-lived but regional phenomena, such as  fire or  hurricanes, might have
widespread ecological effects, but such  events  usually can  be identified  from existing data.

O'Neill (1988) also  has pointed out that indicators  (or state variables) should be of the same hierarchical
level if one is to detect or predict associations among them.  This means that exposure or  habitat indicators
that are highly variable in time are probably inappropriate if they are to be compared to response indicators
that change slowly, or change over broad geographic areas, or both. O'Neill notes that an  exception occurs
when the reference-level state variable  (endpoint or indicator) is very  unstable.   In this case, a variable on
a lower hierarchical  level can cause a "catastrophic" response in the reference level (e.g., a  forest stressed by
a hard winter may  be catastrophically affected  by a disease outbreak that normally would only injure a few
trees).  Although the results of such events  may be detected by  EMAP,  the associated changes in exposure
or habitat indicators likely will not be detected.
Evaluation  of indicators must account for the variability within  RSUs during  a  given year.   In some types of
resource classes, within-RSU variability for some indicators may exceed  among-RSU variability.  In such cases,
population estimates will have relatively  broad confidence bounds, and the cumulative frequency distribution
curves will tend to be flattened.   This  flattening effect can be reduced by making multiple measurements
within  an RSU.  As discussed in Section  2.3.1.2, whether measurements for  any one  indicator will be taken
more than once  per year has not yet been decided, but multiple measurements generally are undesirable
from a cost standpoint. The EPA National Surface Water Survey did demonstrate that within-system variability
in the  chemistry of lakes and streams was much smaller than among-system variability (Eilers 1989; Messer
et al. 1988).   The variability shown in  this  survey resulted in  cumulative frequency  distributions that were
quite different among regional classes of lakes and streams, but showed little  change between sampling
periods.

2.3.3  Definition of a Subnominal Resource

Response indicators will be  used  to distinguish nominal from subnominal resources.  Several approaches to
setting threshold values for response indicators may be taken to enable distinguishing nominal and subnominal
resources:  numeric criteria, reference sites, and classification.

Numeric criteria are based on data not collected as part of the EMAP  sampling  frame.  Examples  include
a maximum percentage of flounder with tumors collected  from a defined near-coastal area or a minimum
number of waterfowl in a flyway.  Subnominal  resources would  be those that do not meet such criteria.

A second approach is to measure indicators on regional  reference sites, populations of benchmark or control
sites that, taken collectively, represent  an  ecoregion or other broad  biogeographic  area.   The  sites, as a
whole,  represent the best  ecological conditions  that  can  be  reasonably attained, given the prevailing
topography, soil, geology, potential vegetation, and general  land use of the region.  An example of potential
regional reference sites is the long-term ecological  research sites (LTERs) designated as resources  for the

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National Science Foundation's Man and the Biosphere program.  Indicator values at these sites could provide
initial definitions of thresholds for subnominal condition.   The threshold could be based on  some statistic
(e.g., the estimated upper or lower quintile, 80% or 20%, of the population exhibiting a particular indicator
value) for a population of regional  reference sites in  each  region.   Estimates could  be made of pristine,
nominal, and subnominal percentages, if desired.

Another approach  is to use multivariate classification techniques on data from the EMAP surveys themselves.
In the EPA National Stream Survey (Messer et al. 1988), for example, classification based on multiple chemical
indicators revealed groups of streams that were impacted by acid drainage from mines  or had unusually low
acid-neutralizing capacity (ANC)  because  of natural  geological formations or  successional   processes of
vegetation.  In this approach, somewhat simplified by  way  of this example, classification is used to  set a
threshold  for an index that can  be used to compare present and future  percentages of subnominal systems
on the basis of common criteria.  This approach becomes increasingly complicated as the number of stressors
acting on  a regional resource class increases.

The  numeric criteria approach relies on information totally external to EMAP.  The reference-site approach
would use EMAP data, but might require sampling of a "special-interest" group of sites.  Most EMAP scientists
currently  favor using reference  sites,  but  recognize  that  the  selection  of reference  sites  reflects current
professional judgement and thus is subjective.  The classification approach  would be  based solely on data
from the EMAP probability sample of the  resource classes.  There are currently insufficient data to evaluate
the general utility  of the multivariate classification approach. Despite the current uncertainty, an important
consideration is that the definition of a subnominal resource can be revised during subsequent years, and
percentages of subnominal resources can  be recalculated,  provided no  new response  indicators have  been
incorporated.  For example,  in 1995, after revising a threshold based on five years of  monitoring data, one
might recalculate the percentage of subnominal resources from that originally published for a  1990 survey.

How should multiple indicators  be integrated?  It is arguable whether any  single response  indicator would
put an RSU into a  subnominal category. For example, if diatom assemblages  and benthic stream communities
respond differently to toxics  and nutrient enrichment, is the stream nominal or subnominal if only one of
these communities  exhibits  subnominal  conditions?   Is it more  subnominal if both communities are
subnominal? Such questions will be addressed in EMAP through evaluations of the use of indices to describe
ecological condition.

It also has not been determined  how apparent conflicts between response indicators and exposure or habitat
indicators should be interpreted.  Situations undoubtedly will arise in which  all measured response indicators
are deemed to  be nominal,  but values for exposure or  habitat  indicators (based on regulatory or  other
criteria) would lead one  to expect the resource  to be subnominal.  In such situations, the resource may be
insensitive (i.e., the criteria for the exposure and  habitat indicators may be too stringent), or the resource may
be threatened (i.e.,  the  response time  of the system  is delayed).  This issue  also will be  explored within
EMAP as  data become available.

2.3.4  Monitoring of Structure and Function

The  relationships  between structure and  function  are somewhat elusive.   Cairns and Pratt (1986)  offered
several  hypotheses to relate processes  (function) and composition (structure):  (1) structure and  function are
so intimately linked that  changing one necessarily changes the other, (2)  changes in function do not lead to
changes in  structure, or  (3) structure and function seem  to be unlinked because analytical techniques are
inadequate to identify their connections.  Depending on how structure and function  are defined, they may
not always  be inextricably linked.

An evaluation of  ecological  condition  can be  based either on current structural measurements or  on a
projection of future structural changes  that result from current activities.  In the former situation, monitoring

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and evaluation of structural  characteristics (e.g., macroinvertebrate assemblages, species diversity, population
sizes)  may be the  most advantageous  avenue for determining past impacts that  have affected  ecological
condition.   For  determination of current status and as a  means  of anticipating problems, monitoring and
evaluation  of ecological processes (e.g., growth  rate, death rate, respiration, primary  productivity) warrants
further evaluation  in EMAP.   Schindler  (1987)  suggested,  however, that monitoring ecosystem function
(processes), rather  than structure, is  less likely to provide early  warnings of changing condition because
feedback mechanisms in natural systems can moderate the effects of perturbations. When possible, EMAP
will focus on indicators of structure and will pursue identifying additional indicators of function as appropriate.

2.3.5   Implication  of Indicators for Classification

As noted  in Section 2.2.6,  classification  enables  estimates to be made for particular resource  classes  or
subclasses  (Figure 2-6).  In  general, we want to compare  the  condition of resource classes among regions
or to  identify subclasses that display relatively high proportions of subnominal condition.   Such comparison
and identification require that indicators be comparable among regions or classes and subclasses.

Certain types of indicators cannot be measured  on all resource classes because the organisms are different.
Other  types of indicators (e.g., zooplankton species  richness, nutrient  levels) can be measured on any
resource, but the data are not comparable among classes (e.g., species richness of zooplankton  in lakes versus
streams).   In other  cases,  collection or  measurement techniques vary  (e.g., collecting  fish  by seining versus
shocking).  Stratification of sampling (by forest type,  stream  order, etc.) to compare  many kinds of indicator
measurements will  likely be required.

2.3.6   Use of Non-Frame Data

In some cases, monitoring data collected by other programs may prove to be extremely  valuable in describing
ecological condition or  assessing possible causes through correlative approaches.  Some of these data may
be collected by using a probability frame and, given that  they meet other criteria, they  meet EMAP's first
objective of providing unbiased estimates of ecological condition with known confidence.  In most cases, data
collected with a probability frame can  be associated with the EMAP sampling frame to provide such estimates.
A major effort is ongoing to  cooperate and coordinate monitoring efforts with agencies  that have probability-
based monitoring programs.   Examples include the Forest Inventory and Analysis Program (U.S. Department
of Agriculture [USDA] Forest Service),  the  National Wetland Inventory trends  program (U.S. Fish and Wildlife
Service),  the USDA National Agricultural Statistical  Survey, and  the National  Resource Inventory Program
(USDA Soil Conservation Service). In many cases, EMAP will likely carry out cooperative sampling by using
the EMAP  grid to identify sampling sites  associated  with these programs.  To the  extent that data are not
collected on the same plots, only the population statistical approach described in Section 2.2.3 can be  used
for assessment.

Data collected on  nonprobability frames present  a  more  difficult problem,  which  has nothing to do with
quality  or  relevance.    Examples  include  acidic deposition  data  from  the National  Acid  Deposition
Program/National Trends Network (NADP/NTN) and water quality data from  the  National Stream Quality
Accounting Network (NASQAN).  Both  of these networks collect data highly relevant  to the  EMAP mission
(e.g., acidic deposition rates  and nutrient export rates from watersheds, respectively),  and data quality in  both
networks is excellent. Neither network,  however, provides unbiased estimates of the measured variables over
well-defined target resources with known confidence, because the monitoring sites have been located to meet
specific needs;  this  subjectivity  could  result in unknown biases in  the data.  At the same time, it would  be
very expensive (and probably impossible) to reconstruct such networks with a probability design, and replacing
them would result  in the  loss of invaluable time series.

Two approaches might  enable  effective use of data  collected on nonprobability frames.  For networks that
sample media that  are  relatively homogeneous on  a  spatial basis (such  as  the atmosphere  over sufficient

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averaging times),  kriging and some other spatial interpolation techniques can be used to estimate the value
of a variable between two monitoring points or an average value for the entire area covered by the network.
EMAP proposes  such an  approach  for  monitoring  exposure to  atmospheric  pollutants  (Section  10)  by
modifying and supplementing the current NADP/NTN to  include additional variables and by either building
new stations or relocating existing ones.  Networks such  as NASQAN that sample discrete  entities (streams)
whose characteristics do not vary systematically across regions must be dealt with differently. In these cases,
models (hypotheses) must  be constructed and tested to  determine whether such sampling networks yield
population estimates  that do not differ significantly  from those produced by unbiased sampling.   EMAP
scientists are currently testing these assumptions by  using historical data available from NASQAN  and the
National Surface  Water Survey (Messer et al.  1988).

Data  such as that from the  NADP/NTN can be used  in the site-by-site approach described previously if the
variance about the interpolated values is not too high.   High variance  may be particularly problematic in
high-elevation areas, around air pollution sources, and along coastlines; the extent of this problem is currently
under evaluation.  The NASQAN-type data are unlikely to be amenable to an interpolation  approach, but
they may be quite  useful  in examining trends  in certain  water quality indicators.

2.3.7  Use of Stressor Indicators

Stressor indicators,  as defined in EMAP, are measures of human activities and  natural processes that effect
changes in exposure  and  habitat indicators, which ultimately are related to changes in response indicators.
Most Stressor data are not likely to be  collected as part of the EMAP sampling frame for several reasons.

    1.  Only a small  portion of the land area  in the United States is expected to be systematically
        characterized by higher resolution imagery.   Although some types of Stressor indicators  will
        be identified  and  quantified in  proportion to their occurrence on  the sampling frame, large
        and sparsely distributed  point sources of air and water pollutants that affect regional airsheds
        or  major watersheds are not likely to fall within  the LSUs.

    2.  While habitat data that represent local stressors will be characterized from the LSUs, larger
        or more distant landscape features that extend outside the LSUs must be obtained from other
        remote sensing sources, possibly involving imagery of coarser  resolution.

    3.  The  spatial  resolution  of  data  for   many  stressor indicators  (e.g.,  fertilizer sales  and
        demographics) is counties or other "regional" units, and data of  higher  resolution are  not
        available.  Although it is technically possible, EMAP likely  will not be able to duplicate on
        the sampling frame such  efforts that  are already occurring in other programs.  Therefore,
        EMAP will  make use of existing data on stressor  indicators wherever possible.

The  use of stressor indicators is partly determined by scale, as noted  in  item 2 above, but few are likely to
lend themselves to site-by-site analyses.  Stressor data will be most useful  in comparisons among regions or
fairly large geographical areas.  The quality of stressor indicator data  must meet sampling design objectives.

2.3.8  Interpretation and  Summarization of Indicators

In addition to the above issues, which  deal principally with the selection of indicators and their application
to long-term monitoring of ecological  condition, several other issues must be resolved regarding  how to
interpret and  present these data once  they  have been  compiled.   Resolving the issues  described  in the
following section will help  EMAP meet  its third objective, which is  to  report results to the EPA Administrator
and  the public.
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2.3.8.1  Monitoring and Interpretation of Exposure and Habitat Indicators

Exposure and habitat indicators should enable the actual threats to the condition of each resource category
to be quantified and should  enable the major  potential  threats  to be quantitatively  projected.  Routine
exploratory monitoring of exposure indicators (e.g., surveys of all  known toxic organics or heavy metals) is
probably inefficient,  given  constantly changing analytical techniques and detection  limits and  the resultant
uncertainty  in  interpreting  data.   Routine  screening for certain  indicators,  such as metals  or organics
commonly associated with other indicators,  is desirable, however.  Archiving media or tissue  samples would
allow a more thorough suite  of analyses to be performed  at a later time and would  avoid costly routine
monitoring of chemical  species that  may have little informational content at the time of their collection.

A methodology must be developed to address situations when RSUs in  subnominal condition are associated
with multiple exposure or habitat indicators (e.g., toxic sediments and habitat structure). Assessing cumulative
impacts is becoming increasingly critical if overall  environmental conditions are  to be improved  (Preston and
Bedford 1988; CEARC  and USNRC 1986).  EMAP is to provide  information  that will help guide funding
allocations that might best  increase the fraction of resources that  are in nominal condition,  for example, a
decision to target reductions in toxics or air pollutants versus a decision to control  some land-use practices.
To aid in such decisions, it may  be necessary to identify resource classes or subclasses whose condition may
not be improved by a single control  or mitigation activity  (e.g.,  those  impacted by both toxic  organics and
habitat damage).

In some cases, the suite of  exposure  and habitat indicators  may not be sufficiently comprehensive to provide
possible explanations as to why a certain fraction of  resource is subnominal.   If the size  of  this "unknown"
fraction is very small, not  knowing  its causes may be acceptable.  If, however, the size of the unknown
fraction is large or increasing rapidly it may be urgent to increase  the effort toward distinguishing the cause
or causes.

In the latter case, determining whether the  unknown fraction is primarily a result  of natural  processes such
as disease  has certain implications for EMAP  in  identifying those ecological  resources  most  at risk from
human-induced  stresses.  If the resources exhibit  natural cyclic behavior and at times experience periods of
intense natural stress, these resources could be particularly susceptible to human-induced  stresses during select
periods of time.  Thus, long-term trends in this "naturally" subnominal fraction  may provide an important clue
to unanticipated effects from human  stresses. The unknown fraction overall is expected to be relatively large
early  in the program, but should diminish as additional indicators are developed and implemented.

2.3.8.2  Use and Development of Indices

The  application  of  indices  in  EMAP, although not yet explicitly  defined, represents a  long-term research
activity that warrants a  brief summary here.  Indices are single values  based on mathematical aggregations
or combinations of  individual indicator values.   In  most cases, the  indicators that are combined are  not
actually commensurable, i.e., they  do not have a common unit of measure.  Environmental indices and their
properties were  thoroughly  reviewed by Ott (1978).  Washington  (1984) presented a specialized review of
indices used in  water quality  studies, and Walworth  and  Sumner (1986, 1987) reviewed  indices used to
describe the  nutritional status  of plants and  soils.   The IBI devised  by Karr et al. (1986)  combines a number
of metrics  describing fish  community  structure,  incidence of  pathology, population  sizes,  and  other
characteristics to provide an overall index of community  integrity.  This index differs  from earlier indices
based on information theory, for example, the Shannon-Weaver (1963) diversity index, in that it reflects what
experts  believe fish  communities should look like according to knowledge of  ecologically healthy systems.
The  index can be customized to different biogeographic areas (Miller  et al. 1988).

Indices have  particular strengths and weaknesses for assessment  Indices can provide an approximate ranking
of RSUs  on  a "good-to-bad"  continuum  based  on  multiple response  indicators.   A common criticism of

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indices has been that their use results in a loss of information.  If properly constructed, however,  the user
can examine the original components of the index.  Proper construction hinges particularly on the aggregation
scheme (Ott 1978). Summing individual  response indicator values, each of which is barely nominal, can yield
an index value that  is more subnominal than it would be in the case  when only  one indicator is clearly
subnominal.  Multiplicative aggregation schemes tend to have  the opposite effect.  Although the future use
of EMAP data in developing and testing  indices may lead  to other aggregation schemes, at this time, the use
of the maximum/minimum operators approach (in which the index assumes the value of the worst indicator)
appears to be the  most appropriate aggregation scheme for EMAP response indicators.

Indices  in  EMAP could be  applied  in  two stages.  First,  indicators  could  be combined into meaningful
subindices, but these subindices still  may not adequately  characterize an RSU. For  example, a subindex of
visible foliar damage may  adequately describe several aspects  of leaf pathology, but may reveal little about
the vegetative diversity or growth efficiency at the site.  In the  second stage, indicators and subindices could
be combined into  indices, and cumulative frequency distributions for the indices could  be estimated for the
target resource.  Unfortunately,  multivariable indices have  wider confidence bounds about estimates, because
each variable in the  index contributes to the overall error of the  estimate (Cochran  1978).  Determining the
subnominal threshold for a condition index would not be  difficult with  maximum or  minimum operators (the
threshold  is determined independently for  each indicator or subindex),  but it would be quite difficult with
indices derived from other aggregation schemes.  The maximum/minimum operators option would show not
only the subnominal proportion of the  population, but also the  proportion that is  close to  subnominal or
threatened.

2.3.8.3  Use  of Maps  for Data Analysis and Interpretation

Maps often provide an informative way  to examine and explain data.  Geographic  Information System (CIS)
technology facilitates the analysis process  by overlaying patterns of exposure, habitat, or stressor indicators
onto patterns of response indicator values  to reveal important spatial  coincidences.  Such maps, generated
periodically over sufficiently long time periods, are also useful  in identifying and displaying changes through
time in these indicators.  EMAP data could  be  displayed  in  several ways, including (1) as  individual data
points  (color-coded,  with  colors designating particular  ranges  of indicator values),  (2) as  bars (with heights
proportional to indicator values), or (3) as isopleths (lines connecting discrete points for which indicator values
or ranges are the  same; for this type of display, the areas  between the lines can be shaded).   An area on
such maps that is  dominated  by a  certain color of points, or the incidence  of high or low bars,  or  some
particular shading  may signify an area of concern or improvement.

Maps produced from data derived by using a probability frame present some problems.  Such problems may
not be  unique to  probability sampling,  but may be revealed only when results of  probability sampling are
available  as  a comparison.   For  example,  when  sampling  weights  (the expansion  factor applied to a
probability sample value to obtain  a population estimate)  are significantly different, a  relatively rare entity
appears to have the  same importance on a map as a common one.  As a second example, displaying values
from streams of different order or lakes  of different size may hide (or create)  underlying geographic patterns
that can be mistakenly attributed to a stressor rather than  a regional pattern in the types of systems sampled.
An example is provided by Jager and Sale  (1989), who used stream chemistry  data from a probability sample
of streams in the Southern Blue Ridge.  The relationship between ANC of the streams and  stream altitude
resulted in their predicting low-ANC streams whose relative geographic area was greater than the estimated
proportion of the total length of these streams.  Both results may be appropriate, according to whether the
more appropriate  issue is  the  probability of finding a low-ANC stream or  the probable ANC of  any stream
that is found.  Careful analysis of each  situation  will avoid (or at least identify) such problems in displaying
mapped data.
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2.3.8.4  Application of Exploratory Statistics

Many exploratory statistical techniques can  be used with  regional data sets.  Many  examples related to
ecological assessments are  presented by Green (1979) and  Cauch  (1982).  Cluster analysis can be used to
identify  classes of sites with  similar characteristics that may cause them to respond (or that  already have
responded) similarly to particular stressors.  Canonical correlation analysis can be used to identify multivariate
factors that  reflect some common characteristic (e.g., a pollutant source) and  that can  be related to other
multivariate  factors (e.g.,  groups of tolerant species).  Discriminant analysis can  be  used  to  identify indicators
that are most closely linked to other indicators in complex sets of  data.

Converting data to ratios and other derived forms based on conceptual models also may reveal patterns that
do  not appear in  simple analyses.  By analyzing ion ratios related to  thermodynamic models in Southern Blue
Ridge streams  (Velbel 1985), Messer et  al.  (1988)  were  able to  identify  a class of streams representing
approximately 75% of the target resource whose chemistry is predominantly controlled  by hydrology (dilution).

The range of possible analyses is too great  to discuss  in  detail, but it  is critical to note that multivariate
statistical techniques also are capable of producing  spurious or misleading results.   As with mapped data,
great care must be taken to  avoid  incorrect  conclusions.

2.3.8.5  Opportunities for Retrospective Analyses

EMAP is being designed to  establish  regional  estimates of current conditions  against which to  compare future
trends.  Opportunities also exist  to  compare current conditions against  past trends at single sites  and over
larger areas,  where archived  remote sensing or other data  are available.  For example, sediment and tree
cores  may serve  as  a record of  past changes for comparison with a current regional probability sample.
Regional surveys  have been  conducted that  may offer an  opportunity for retrospective analysis of regional
trends.  Such opportunities  will be exploited wherever the original data meet minimal criteria for documented
and demonstrably comparable methods.


2.4  EMAP  INDICATOR STRATEGY

Section  2.1   outlined  a  conceptual approach  to   identifying indicators for  EMAP.   As  a  first step in
implementing this approach, an early version  of that section was circulated to the six EMAP  Resource Groups.
These groups then began identifying and categorizing candidate indicators that had been proposed for their
respective resource categories over the past  three decades,  using a combination of literature review, expert
workshops, and interviews  with managers and scientists knowledgeable about these issues (Figure 2-7). Draft
criteria were proposed for  identifying the most promising indicators.  Although the criteria varied  over time
and among  groups, the groups reached consensus on a final matrix (Figure 2-8).  Some of the criteria  are
critical;  others are desirable and  even mutually exclusive.

Each Resource Group, aided by more than  200 ecologists and resource managers, judged the  candidate
indicators against the criteria  (Figure 2-8).  The  indicator  identification  and prioritization  process  provided
critical feedback to the conceptual plan as presented in this section. This entire document was reviewed by
22  external  peer reviewers  with particular expertise in the various resource categories and by a subcommittee
of the EPA Science Advisory Board.  Peer review comments resulted in additional  refinement of the concept
and individual  resource strategies.  The results of these deliberations,  which are  summarized  in Sections 3
through 9,  were used to  identify a  set of research indicators  believed to be the  most promising for
additional evaluation.  Research  indicators must  have well-established,  readily standardized  methodologies,
and a reasonable amount  of environmental data should  be available.  Fact sheets were compiled  for these
indicators, and they  appear in the  appendices of this document.
                                                 2-21

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                                        CANDIDATE INDICATORS

                            IDENTIFY AND PRIORITIZE
                   EVALUATE EXPECTED PERFORMANCE     |     f,imu'!lti?ns, „
                                                       1     Limited-Scale Reid Tests
                                  I
                                 UN
                                  I
                      Peer Review


   RESEARCH INDICATORS

                      Analysis of Existing Data
                      Simulations
                      Umited-Scale
                      Peer Review

DEVELOPMENTAL INDICATORS
EVALUATE ACTUAL PERFORMANCE    1    |^j^|^Oretration Pr°iects


                       CORE INDICATORS   ~*	
                                                      I
       IMPLEMENT REGIONAL AND NATIONAL MONITORING     I     PERIODIC REEVALUATION
Figure 2-7.  Indicator selection, prioritization, and evaluation approach for EMAP.


The  next step in the indicator selection  process (Figure 2-7) is to evaluate  the  research indicators; this
evaluation is based on  analysis of existing data sets,  simulation studies representing realistic scenarios and
expected spatial  and temporal  variability, and limited-scale  field  tests.  Those indicators  that pass this
evaluation are considered developmental indicators that are suitable for regional demonstration projects.
This  process has been completed by EMAP-Near Coastal for  estuaries in the  Virginian Province, a marine
biogeographic province  that extends from Cape Cod  south to the southern extent of the Chesapeake Bay.
Following  a peer review,  which  recommended additional refinements in the  strategy,  the Near-Coastal
Demonstration  Project  is  being  conducted  in this  area  in summer 1990.   Following  evaluation  of
developmental indicators, based on data from a  regional demonstration project, a  final set of core indicators
will be selected for long-term implementation.

Each step in this  process requires  passing external peer review. Criteria for selecting developmental and core
indicators are currently being developed. The number of indicators carried through each step in the process
depends on currently available funding.  Periodic evaluation of the  data will result  in careful  and  deliberate
adjustments in the set of core indicators  measured.   Reprioritization of research indicators  is expected  to
occur periodically as new  indicators  become  available or  as  new  information on  previously  examined
indicators warrants.

We emphasize that the next eight sections present only the first step in the indicator selection process.  In
addition to explaining the indicator component of EMAP, this document also serves as an important internal
integration  role in the program and  as a mechanism  to seek comment, relevant  data,  and advice from the
ecological research community.  The latter two objectives are discussed further in  Section  11.
                                               2-22

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2.5  REFERENCES

Bailey, R.C.  1983.  Delineation of ecosystem regions.  Environ. Manage.  7:365-373.

Bascietto, J., D. Hinckley, J. Plafkin, and M. Slimak.  1990.  Ecotoxicity and ecological risk assessment:
Regulatory applications at EPA.  Environ.  Sci.  Technol. 24(1):10-15.

Brooks, J.L., and S.I. Dodson. 1965. Predation, body size, and composition of plankton. Science 150:28-
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Cairns, J., Jr., and J.R. Pratt.  1986.  Developing a sampling strategy.  Pages 168-186.  In:  B.C. Isom, ed.
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CEARC and USNRC, eds.  1986.  Cumulative Environmental  Effects:  A Binational Perspective.  Canadian
Environmental Assessment Research Council and the  U.S. National Research Council, Hull, Quebec.

Cochran, W.  1978.  Sampling Statistics.   Wiley-lnterscience,  New York.

Deisler, P.P., Jr.  1988.  The risk management-risk assessment interface. Environ. Sci.  Technol. 220:15-19.

Eilers, J.  1989.  Personal  communication.  Telephone discussion with D.  E. Carpenter, October 16.  U.S.
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Fava, J.A.,  W.J. Adams,  R.J. Larson, G.W. Dickson, K.L Dickson,  and  W.E. Bishop.  1987.  Research
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Gauch, H.G.  1982.  Multivariate Analysis in Community Ecology. Cambridge University Press.

Green, R.H.  1979. Sampling Design and  Statistical Methods for Environmental Biologists.  Wiley-lnterscience,
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Hughes, R.,  D. Larsen, and J. Omernik. 1986.  Regional reference sites:  A  method for assessing stream
potentials.  Environ. Manage. 10:629-635.

Hunsaker, C.T., R.L. Graham, G.W. Suter II, R.V. O'Neill,  L.W. Barnthouse, and  R.H. Gardner.  1990.
Assessing ecological risk on a regional scale.  Environ. Manage. 14(3):325-332.

Jager,  Y.I., and J.M. Sale.  1989.  Prediction of stream acid neutralizing capacity in the Southern Blue Ridge
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Karr, J.R., K.D. Fausch, P.L. Angermeier, P.R. Yant, and I.J. Schlosser. 1986.  Assessing Biological  Integrity
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Lotspeich, F.B.  1980.   Watersheds as the basic ecosystem:   This conceptual framework provides a  basis
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Messer, J.J., C.W. Ariss, J.R. Baker, S.K. Drouse, K.N. Eshleman, A.J. Kinney, W.S.  Overton,  M.J.  Sale,
and R.D. Schonbrod. 1988. Stream chemistry  in the Southern Blue Ridge: Feasibility  of a regional synoptic
sampling approach.  Water Resour.  Bull.  24(4):821-829.

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Miller, D.L, P.M. Leonard, R.M.  Hughes, J.R. Karr,  P.B. Moyle,  L.H.  Schrader,  B.A. Thompson, R.A.
Daniels,  K.D. Fausch, GA. Fitzhugh, J.R. Gammon,  D.B. Halliwell, P.L.  Angermeier, and D.J.  Orth.
1988.  Regional applications of an index of biotic integrity for use in water resource management.  Fisheries
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National Research Council. 1990.  Managing troubled waters, the role of marine environmental monitoring.
National Academy Press, Washington,  DC.

O'Neill,  R.V.   1988.   Hierarchy  theory and global  change.   Pages 29-45.   In:   T. Rosswall,  R.G.
Woodmansee, and P.G.  Risser,  eds.   Scales  and Global Change:    Spatial  and Temporal  Variability in
Biospheric and Geospheric  Processes.  John Wiley &  Sons, New York.  355 pp.

O'Neill, R.V., D.L DeAngelis, J.B. Waide, and T.F.H. Allen.  1986.  A Hierarchical Concept of Ecosystems.
Princeton University Press,  Princeton, NJ.

Ott, W.R.  1978.  Environmental Indices:  Theory and  Practice.  Ann Arbor Science Publ., Ann Arbor, Ml.
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Preston, E.M., and B.L. Bedford.  1988.  Evaluating cumulative effects  on  wetland functions:  A conceptual
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Schaeffer, D.J., E.E. Herricks, and H.W. Kerster. 1988.  Ecosystem health: I.  Measuring ecosystem health.
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Schindler, D.W.  1987.  Detecting ecosystem  responses to anthropogenic stress.  Can. J. Fish. Aquat. Sci.
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Shannon, C.E, and W.  Weaver.  1963.  The Mathematical Theory of Communication.  University of Illinois
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Suter, G.W., 11.   1990a.  Endpoints for  regional ecological risk assessments.  Environ. Manage. 14(1):9-23.

Suter, G.W., II.   1990b.   Use of  biomarkers in ecological risk assessment.  In:   McCarthy,  J.F., and  L.R.
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Velbel, M.A. 1985.  Hydrogeocyclical constraints on  mass balances in forested watersheds of the southern
Appalachians.  Pages 231-247.  In: J.  Drever, Ed.  The Chemistry of Weathering.  D. Reidel.

Walworth, J.L., and M.E. Sumner.  1986.  Foliar diagnosis:  A review.  Pages 193-241.  In:   B.P. Tinker,
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Walworth, J.L, and M.E. Sumner.  1987.  The Diagnosis and Recommendation Integrated System  (DRIS).
Pages 149-188.   In:  Advances in Soil Science,  Vol. VI.  Springer-Verlag, New York.

Warren-Hicks, W., B.R. Parkhurst, and S.S.  Baker, Jr., eds.  1989.   Ecological assessment  of hazardous
waste sites:  A field and laboratory  reference document.  EPA 600/3-89/013.  U.S. Environmental Protection
Agency, Office of Research and Development,  Corvallis, OR.  251 pp. plus Appendix.
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Washington, H.C.  1984.  Diversity, biotic, and similarity indices:  A review special  relevance to aquatic
ecosystems. Water Res. 18:653-694.

Yosie, T.F.  1987.  EPA's risk assessment culture.  Environ. Sci. Technol.  21(6):526-531.
                                                 2-26

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                                              SECTION 3

                         INDICATOR STRATEGY FOR NEAR-COASTAL WATERS

                                             K. John Scott1
3.1  INTRODUCTION

The nation's  near-coastal  waters comprise  a diverse EMAP  resource category that includes tidal rivers,
estuaries,  and near-shore waters.  EMAP will monitor and assess the condition of ecological resources from
the inland boundary of tidal waters out to the continental shelf break.  Near-coastal waters are particularly
sensitive to anthropogenic input because they serve as critical spawning and nursery habitats for many marine
organisms. Estuaries are especially complex and variable and are highly valued by the public. They are also
the areas  where many anthropogenic inputs are deposited  and ultimately  sequestered.  Most of the U.S.
population resides in the coastal zone. It is currently estimated that by the year 2000, 75% of the population
will live within 80 km (50 miles) of the coast (U.S. EPA 1988). The continued high-population pressure along
the coast  poses  an increasing threat to these resources.

A  series of indicators is proposed in this section for use  in assessing the status and trends in the condition
of the nation's estuaries.  Ultimately, EMAP will also  address the condition of continental shelf waters and
the Great Lakes.  Although part of the near-coastal environment, coastal wetlands and the specific indicators
to be measured for them are discussed  in Section 5.

A  classification scheme was used  to subdivide the estuaries into resource classes that have  similar physical
features and are likely to respond  to stressors in a similar manner.  Potential classification variables  identified
for reducing  within-class variance in indicator  values  (obtained from a review  of the  literature)  included
salinity, sediment type,  pollutant loadings  and  variables  used to  infer  pollutant loadings  (e.g., human
population density), and physical dimensions. Salinity and pollutant loadings were subsequently ruled out as
classification variables because areal extent of estuarine classes based on these variables could vary from year
to year.

A classification scheme based on physical dimension (surface area, length/width) was chosen because physical
dimensions:

        •  change minimally over the time scale  of decades and do not adversely influence the
           value of resulting data to address alternative or "new" objectives;

        •  allow aggregation or segregation of the data into geographic units that are meaningful
           from a  regulatory and general  interest perspective; and

        •  define groups of systems that can be sampled with a common design  and for which
           data can be aggregated to make  meaningful  regional and national statements about
           ecological condition over time.

The three near-coastal resource  classes that are currently  defined  for EMAP are (1)  large, continuously
distributed estuaries (e.g., Chesapeake Bay, Long Island Sound); (2) large, continuously distributed tidal rivers
(e.g.,  Potomac, Delaware, Hudson Rivers);  and (3) small, discretely distributed estuaries, bays, inlets and tidal
creeks, and rivers (e.g., Barnegat Bay, Indian River  Bay, Lynnhaven Bay, Elizabeth River).  The purposes of
    'Science Applications International Corporation, EPA Environmental Research Laboratory, Narragansett, Rhode Island

                                                  3-1

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partitioning estuaries into classes with similar attributes (e.g., size, shape, resource distributions) include (1) a
common sampling design can be applied to each class;  (2) the within-class variability of indicator values
should be  less than the between-class variability,  thereby reducing  the  number of samples necessary to
characterize a class distribution accurately; and (3) the confidence with which inferences can be made about
estuaries that are not sampled is increased.

What follows is an  overview of  the  rationale  for  selecting estuarine indicators and  a  strategy for their
application,  as well as  an approach for refining the indicators proposed for long-term implementation.  The
topics specifically addressed are  as follows.

        •  Current perceptions  of estuarine  condition  and our identification of environmental
           values associated with  estuaries

        •  A set of  high-priority research  indicators and a discussion of why  some candidate
           indicators were not proposed for  further evaluation in  EMAP

        •  Additional research indicators that may be incorporated into the set of high-priority
           research  indicators in the near future

        •  The process proposed for defining the subnominal condition of estuarine resources and
           how the  various indicator responses would be used to  identify plausible explanations
           for subnominal  condition

        •  The  role of the 1990 EMAP-Near Coastal  demonstration project in evaluating  the
           research  indicators and the resultant research priorities for their development and
           evaluation
3.2  IDENTIFICATION OF  INDICATORS FOR NEAR-COASTAL WATERS

3.2.1   Perceptions of Near-Coastal Resource Condition

Some of the most important  and productive of the  nation's  biological systems exist in near-coastal areas,
where the majority of commercial and recreational harvests of fish and shellfish occur.  Near-coastal areas
also provide spawning and  nursery habitats for many of these harvested species. Public use of near-coastal
systems for  recreation generates annual revenues exceeding several billion dollars (U.S. OTA 1987).

Increasing public concern has been expressed over the condition of the near-coastal environment and  its
ability to support healthy marine organisms.  The U.S. Office of Technology Assessment (U.S. OTA 1987) has
described the  extent  of degradation  of  the marine environment   The pollution of  beaches along the
northeastearn  coast in summer 1988 stimulated extensive coverage of near-coastal environmental problems
in the popular  press (Toufexis  1988; Morganthau 1988).  While the occurrence of medical waste on beaches
may not be representative  of the  seriousness of  our coastal problems  (Waldichuk 1989),  these incidents
provide visible reminders that something  is wrong.  The types of problems in  the coastal zone include the
failure of about half of the assessed waters to meet designated uses;  closure  of fisheries due to toxic and
pathogenic contamination;  low dissolved oxygen (DO) levels in bays and estuaries; and loss of critical  habitat
for fish, shellfish, and water birds (Hamner 1988).  Closure of shellfish  beds and declines in  commercial and
sport  fisheries are resulting  in significant revenue losses for these industries (U.S. OTA 1987).
                                                 3-2

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3.2.2   Environmental Values for Near-Coastal Waters

The concerns noted above must be related to indicators that can be consistently measured across regions and
over time in order to assess status and trends in the ecological condition of near-coastal resources, particularly
as that ecological condition relates to public values and  expectations.  It  is, therefore, important to translate
public and scientific values and concerns into "assessment endpoints" that can  be more  directly assessed
through the measurement of one or several indicators.

Environmental values associated with near-coastal systems include productivity, biodiversity, and sustainability.
Water-column and macrophytic production support the detritus-based benthic food  chains in  most estuaries.
This  production also supports  most filter-feeding invertebrates,  fish, and  larval  forms of organisms that are
abundant in near-coastal waters. Disruption of natural production rates, at a minimum,  will result  in altered
food  webs and species composition of higher trophic  levels.   High  biotic  diversity  is a function of the
complex habitat structure and the extreme variability in temperature and  salinity regimes typical of estuaries.
Maintaining this diversity  is critical  to the ability of these systems  to resist natural and anthropogenic stress.

The  public highly values the use of near-coastal waters for commercial or  recreational fishing, activities which
can  be related to the health  of fish and shellfish  populations.  Are these populations  present in  densities
sufficient  to  make commercial and  recreational  harvesting feasible?   Also,  if the fish  and  shellfish are
abundant, are they free of disease and other manifestations of stress, and  finally, are they safe to eat? More
than  half of the population resides or works in  counties along  the coast.  A significant segment of this
population  is drawn  to  the   coastal  areas for  recreational  purposes,  such   as  boating, swimming,  and
sightseeing.   Floating debris, odor, excessive plant growth, and discoloration  alter public  attitudes toward
water quality.  These visible forms  of pollution have a strong influence on public perception of near-coastal
ecosystem quality (West  1989).   In addition to the public value of commercial and  recreational fishing,
regulatory  mandates  also exist to maintain  and  protect other naturally  reproducing  populations  and
communities and  their habitats (Federal Water Pollution Control Act  1972 and subsequent amendments;
Marine Mammal  Protection Act 1988; Marine Protection, Research and Sanctuaries Act of 1972).

The  environmental values discussed above may  be affected by any number of anthropogenic and natural
factors.  For example,  wetland loss and  subsequent declines  in  nursery areas for fishery populations are
severely affected  by shoreline development.   Hurricanes  and sea-level  rise can  also affect shoreline erosion
and  habitat extent A challenge for EMAP is to identify  plausible explanations  of unhealthy conditions.   In
formulating a strategy to meet this challenge, we considered exposure and habitat indicators that, when used
in concert,  would broadly identify the possible  environmental  hazard  or hazards, or at  least  rule out
improbable explanations.  The  major environmental hazards addressed by  the near-coastal research  indicators
are as follows.

        •  Presence and  extent of hypoxic or anoxic waters

        •  Cultural eutrophication, including both primary and secondary  production in the water
           column and benthos

        •  Toxic contamination of biological tissue, the  water column, and sediments

        •  Habitat modification, primarily modifications to submerged aquatic vegetation (SAV) and
           benthos

        •  Cumulative  impacts  resulting  from  interactive effects  of  various categories  of
           environmental stress
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        •  Emerging problems such as sea-level rise, declines in biodiversity, and introduction of
           exotic species

3.2.3  Estuarine Indicators Appropriate for EMAP

The  goal of EMAP-Near Coastal is to identify a set of indicators  that could  be used to assess  the  major
hazards or impacts that estuaries and other near-coastal resources are, or could  be, experiencing. While it
is impossible to anticipate all future hazards, we have  tried  to select a set of indicators that are  related to
fundamental  estuarine processes.   Because measures that address critical  public  concerns and  integrated
ecological processes are of particular  concern, the  focus of EMAP-Near Coastal is on the ecological and
biological effects of anthropogenic  input, rather than on the documentation of  the presence of  the  inputs
themselves (Segar et al. 1987).

The  criteria used to select  and prioritize near-coastal  research indicators (Table 3-1)  generally agree with
those suggested by others for  identifying indicators of environmental quality (Pearson and  Rosenberg  1978;
Wolfe and O'Conner 1986; Boesch and Rosenberg 1981; Karr et al.  1986; Kelly  and Harwell 1989; NRC
1990).  A set of research indicators is presented here to stimulate discussion and  comment  The set of core
indicators to be implemented in the  long-term monitoring of near-coastal waters will evolve over the next
several years (as described in  Section 11).

Work on near-coastal indicator selection  began with  discussions among EPA-ORD staff in spring and summer
1989.  Following preliminary selection and categorization of  candidate indicators  by the EMAP-Near Coastal
team, a series of workshops to identify, evaluate, and discuss  candidate indicators  of ecological condition and
environmental quality was  held  in December  1989.  Workshop participants (see Appendix I) were selected
on the basis of  recommendations from the Estuarine  Research Federation, National Oceanic and Atmospheric
Administration (NOAA), and EPA Program Offices and Regions.  They included a  combination of researchers
from private consulting firms, governmental agencies (e.g., U.S. Geological Survey,  NOAA's National Marine
Fisheries Service,  EPA, and state regulatory  and resource  management  agencies),  universities  and  other
nonprofit organizations.  Participants had a broad range of monitoring experience on all coasts (i.e., Atlantic,
Pacific, the Gulf of Mexico)  and  in a wide variety of  marine/estuarine environments (e.g., tidal flats, large and
small estuaries, and tidal rivers).   Participants were requested to  recommend  measurement and analysis
methods for candidate indicators.  Conclusions and  findings  of the workshops have been  incorporated into
the following discussions of EMAP research  indicators for estuaries.  A written  external peer review of these
indicators was  performed in April 1990  (Appendix I.8), followed by a review by the  EPA Science Advisory
Board in May 1990.  A report detailing the near-coastal indicator selection  process, including the  findings of
the associated workshops, will be prepared in summer 1990.

A summary of  how some candidate estuarine indicators were judged against  the  EMAP indicator selection
criteria is given in  Table 3-1.  The identification of certain estuarine indicators as  higher priority  (Figure 3-1),
although necessarily subjective, was based  on  our review of the literature and experience with near-coastal
monitoring programs.  High-priority research indicators  are those for which sufficient data presently exist to
define the sensitivity and reliability of responses to stress with a  known degree of confidence. The temporal
variability of these  indicators over the proposed index  period is known,  and  in  general, their responses to
major  types of stress are understood.  A limited-scale  field  test is needed to further develop standardized
protocols for some high-priority research indicators.  Those  indicators not designated as high-priority either
need to have sampling methods developed  or  have  only limited data to evaluate reliability and  sensitivity of
responses to stress. The temporal variability of responses during the index period  is often unknown and must
be established as relatively stable before  the indicator achieves high-priority status.  Comparison of responses
among a number of response  indicators is necessary to  reveal whether they are appropriate and sensitive for
determining status and  trends of ecological resource condition.
                                                  3-4

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           EMAP Near-Coastal  Indicator Strategy:  Estuaries
   Response Indicators (R)

  Dissolved Oxygen*

  Benthic Abundance, Biomass,
   and Species Composition

  Biological Sediment Mixing
   Depth

  Extent and Density of Submerged
   Aquatic Vegetation*

  Fish Abundance and Species
   Composition

  Presence of Large Indigenous
   Bivalves

  Gross Pathology:  Fish
   SPATIAL
ASSOCIATIONS
  TEMPORAL
ASSOCIATIONS
Exposure-Habitat Indicators (E)

Acute Sediment Toxicity

Chemical Contaminants in
 Sediments

Water Clarity

Biomarkers

Water Column Toxicity

Chemical Contaminants in Fish and
 Shellfish
Figure 3-1.  Diagram of the proposed EMAP-Near Coastal Indicator Strategy for estuaries.  Indicators in
            bold  lettering are high-priority research indicators.  Response indicators with an asterisk
            (*) also can serve as exposure and habitat  indicators.

The  strategy  for  indicator  selection  is based on  the  premise that indicators must relate  to  assessment
endpoints.  With the exceptions  of oil and natural gas drilling, dredging, and disposal activities, and the
limited amount of currently allowed ocean  dumping, the  major stressors to  the near-coastal environment
originate on land.  Estuarine systems  are  the  repositories  of  point- and nonpoint-source inputs extending
inland from hundreds to thousands of  kilometers.  Stressor  indicators (e.g., land use and  discharge estimates)
provide information on the magnitude  of such sources.  Anthropogenic activities represented by these stressor
indicators result in a series  of hazards, such  as contaminant and nutrient loadings and habitat modification,
which ultimately produce  the major environmental  impacts identified  earlier.   The  selected exposure
indicators are directly related  to types of hazard or impact.  The response indicators "respond" to the hazards
in a  cumulative fashion and thus serve to  indicate the overall  integrated condition of a  marine water body.

All indicators are proposed for measurement during a  specific index period,  when  biological responses to
environmental perturbations  are expected to  be most pronounced.  In  temperate climates, the warmer,
summer  months have been selected as the  index  period  for  estuaries.  Temperature-dependent biological
responses to stressors tend to  increase dose rates to organisms through higher feeding, filtration, or respiration
rates.  Metabolic and reproductive  rates are also enhanced  at higher temperatures,  increasing the  natural
physiological stress on  the  estuarine organisms.  Exposures to anoxic or  hypoxic conditions and chemical
contaminants will also be higher during summer index periods as a result of high phytoplankton growth and
ultimate decomposition and low stream flow or river flow, respectively.

Those research indicators not designated  as  high priority could eventually  provide valuable information on
ecological condition, but require  additional  controlled  testing across known gradients  before they can be
applied  in a survey mode.  Lower priority  research indicators  are proposed for additional testing during the
                                                3-7

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1990 EMAP-Near  Coastal demonstration project  (Section  3.4).  Testing will  likely occur at predetermined
stations with known presence of contaminants or eutrophic conditions to determine indicator sensitivity, and
to compare responses  to those of high-priority research indicators also measured at these stations.  Some
indicators, such as those proposed for fish, will be measured at all  stations.

A  brief description of  each estuarine research indicator is given here. More  detailed descriptions of the
indicators, their application, suitable index period, variability, and research needs are listed in Appendices A
and  C as referenced by the code  in parentheses at the end of each description  below.

3.2.3.1   Response Indicators for Estuaries

Response indicators are used by EMAP to quantify and classify the condition of ecological resource classes.
Seven response indicators have been selected  as research indicators for estuaries,  two of which are recognized
as having high-priority  status.

High-Priority Research Indicators

Dissolved  Oxygen.  Hypoxic  or anoxic  conditions are  a  functional response of  the ecosystem to primary
production imbalances which  can  result from excessive nutrient loadings.   The  measurement of  DO  as  a
response indicator would be based on multiple (>6) water-column profiles at each station during the index
period.   Hypoxic  waters would be defined  as those having DO concentrations  lower than 2 ppm.  This
indicator would be expressed as the proportion of waters experiencing hypoxia.  Associated stressor indicators
are flushing rate,  nutrient discharge,  and loadings data (see A.13, also). (A.1)

Benthic  Abundance,  Biomass, and  Species Composition.   This  multimetric indicator,   proposed for
measurement at each  sampling station,  reflects the  ability of the benthos  to  support bottom-feeding fish
populations and to maintain the natural sediment processing features important  to nutrient and contaminant
flux.  The benthic  community is  also an integrator of overall water quality and may respond to contaminants
or to eutrophic conditions  as a cumulative response  indicator  (Pearson and  Rosenberg 1978;  Rhoads et al.
1978;  Sanders  et  al. 1980; Holland  et al. 1987).  (A.2)

Other Research Indicators

Biological  Sediment Mixing Depth.  This response indicator is a  measurement  of the depth of the oxidized
sediment column,  as characterized  by discrete color changes. An integrative measure, this indicator describes
the functional activity of the benthos as  it relates to sediment mixing  processes;  a mature, healthy benthic
community is generally inhabited by  longer lived, larger infauna.  These organisms process, mix, and oxidize
the sediment column;  and when they are absent, the mixing depth is shallow or absent  The absence of
these fauna will restrict the ability  of the benthic system to process  excess organic matter and contaminants
(Rhoads  and Germano 1986).  Mixing depth  measurements would be made with a benthic  interface camera
(Rhoads  and Germano 1982).   (A.3)

Extent and Density of Submerged Aquatic Vegetation.  This indicator  is a measure of SAV acreage and bed
density.  The root systems of SAV beds stabilize the sediments, and above-ground  growth reduces waves and
creates a depositional  environment.   SAV is  particularly sensitive to high levels of turbidity  and herbicides.
Bed outlines and  density measurements would be made from visible-color aerial  photography.  (A.4)

Fish Abundance  and  Species Composition.  This indicator, also a  measure of cumulative impacts, would
reflect a  host of responses to anthropogenic and natural factors;  it is expected to  integrate the sum of water
quality conditions (Weinstein  et al.  1980) and would  require  the  extensive  use of  ancillary variables to
interpret changes.   Timed bottom  trawls  would be made at each station on  three separate occasions during
                                                 3-8

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the index period.  After the taxa have been counted and sorted by species, length and weight measurements
would be made for selected  target species.  (A.5)

Presence of Large Indigenous Bivalves.   As part  of the routine sampling at each station,  a rocking chair
dredge would  be deployed to determine the  presence of large infaunal, filter-feeding bivalves. The presence
of large filter-feeding genera,  such as Mercenaria, Mya, Tagelus, or Rangia, indicates the ability of the habitat
to support shellfish.   This information  will be  used to design  a sampling program to determine shellfish
contaminants based on species-specific  distributions.  The presence, relative abundance, and distribution of
these species  would  be recorded.  Selected species  would  be  measured, and tissue samples  would  be
archived for chemical analyses (see indicator A.12). (A.6)

Gross Pathology:   Fish.   Gross  pathological  abnormalities  in  fish  are  believed  to be  a  response  to
contaminant  exposures.   Such  abnormalities can also  influence  the marketability of  the affected fish
populations.  A specified  number of each target species is  proposed for examination for gross pathological
abnormalities (external lesions, fin erosion, cataracts, scoliosis, lordosis, and others). Affected individuals and
their  flesh tissue would be archived for  more  detailed histological  examination and  analysis  of chemical
residue (see indicator A.12).   (A.7)

3.2.3.2  Exposure and Habitat Indicators for Estuaries

Exposure and  habitat  indicators are used by EMAP to identify and  quantify changes in exposure and physical
habitat that are associated  with changes in response indicators.   Eight exposure and  habitat indicators have
been selected  as research  indicators for  estuaries, three of which are recognized as having high-priority status.

High-Priority  Research Indicators

Acute Sediment Toxicity.   Acute  sediment toxicity, like the benthic response indicator (A.2),  is also  an
integrated measure which, in  this case, specifically indicates contaminant exposure and potential effects  on
the benthos (Swartz 1987). This  indicator is measured by exposing amphipod crustaceans to sediments from
each  station for 10 days.  Significant mortality  in  this bioassay  indicates a risk to the benthic community,
because amphipods are typically  the first  group of organisms to experience a decline or to disappear from
perturbed habitats (Sanders et al. 1980).  (A.8)

Chemical Contaminants in Sediments. The selected suite of chemical contaminants  in sediments is a direct
measure of exposure, which  can  be related to responses in the benthic community  and  to  acute sediment
toxicity.   This  indicator provides a  link to several existing data  bases and ongoing monitoring programs,
including NOAA's National Status  and Trends  (NS&T)  program.  The contaminants  on NOAA's  list  are
proposed for measurement in  the surficial sediments (top 2 cm) as an indication  of recent contaminant input
from water-column sources and as an exposure indicator for benthic fauna.  The NS&T suite of contaminant
measures includes chlorinated pesticides, polychlorinated biphenyls (PCBs),  polycyclic  aromatic hydrocarbons
(PAHs), major elements, and  tCxic metals (NOAA  1987).  (A.9)

Water Clarity.  In addition to affecting public perceptions of aesthetic value, algal blooms and  high suspended
particulate loads, and resultant poor water clarity, can have significant biological effects.  Poor water clarity
impairs the photosynthetic ability of rooted vegetation, algal blooms  can  result in  high  dissolved  oxygen
demands and  hypoxia, and  suspended solids can lead to siltation and the  smothering of  benthic fauna.
Water-column  profiles of light transmission and fluorescence are proposed to be made several times (>6) at
each  station.   Fluorescence  measurements would indicate the  relative contribution of  phytoplankton  to
reductions in light transmission measurements.  (A.10)
                                                 3-9

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Other  Research Indicators

Biomarkers.  A desirable feature of a monitoring program would  be to detect  an organism's  response to
human-induced  stresses  at  the biochemical and cellular  level before  the  stresses  produce a detectable
response at the organism and population  levels.  Although the use  of biomarkers as early-warning response
indicators requires  more basic research,  their  present value  for regional survey monitoring is to provide
information to support or refute hypotheses on why ecological condition of near-coastal waters is subnominal.
(C2.1-G2.11)

Water-Column Toxicity.   The  proposed chronic toxicity tests are  integrated  measures  of water-column
exposure to contaminants and would be related to the responses of the benthic  indicators.  Three tests are
proposed for evaluation:  the sperm-cell  fertilization test,  the red-algae reproductive test, and the bivalve
larval toxicity test  The sensitivity of these three  metrics would be  assessed in selected high-stress and  low-
stress conditions.   (A. 11)

Chemical Contaminants in  Fish and Shellfish.   This proposed set of contaminants is similar to that for
NOAA's NS&T program (NOAA 1989).  This suite serves as a direct measure of contaminant exposure in the
large infaunal bivalves and fish.  Whole shellfish  tissue and fish flesh would be  archived for analysis.  The
ultimate applicability of this  measure is depends entirely on the occurrence and distribution of target fish
and shellfish populations as sampled  by EMAP.   (A.12)

Dissolved  Oxygen.  Because DO may explain changes in the distribution of benthic or fish  communities, it
can serve as an  exposure indicator as well as a high-priority response indicator.   In  this case, near-bottom
DO  measurements would be collected continuously for a  60- to 75-day period.  The deployment stations
would be selected to represent areas where DO  concentrations are expected to  be the most variable.  This
information would  be used to  define worst-case  exposure conditions relative to  variability and to establish
the minimum deployment duration necessary to  characterize the index  period.   (A.13)

Extent and Density of Submerged  Aquatic Vegetation. In addition  to serving as a response indicator, this
indicator is also a direct measure of habitat modification and loss and thus also serves as a habitat indicator.
SAV beds provide spawning  and  nursery  habitats for fish and crabs. (A.4)

3.2.3.3 Stressor Indicators  and Ancillary Variables for Estuaries

Several types of stressor indicators (those  not actively measured on the EMAP sampling frame) will  be  used
to evaluate asssociations among response indicators and exposure  and  habitat indicators.   These  measures
include natural,  economic, social, and engineering factors  that can be used to identify possible sources of
regional environmental problems.  They may include pollutant source measures (e.g.,  point-source loadings)
or land-use and  demographic patterns.   Other  potential  stressor  indicators include freshwater  discharge,
atmospheric  deposition,  precipitation, wind speed  and   direction,  fishery landings,  and shellfish   bed
classification.  More detailed discussions of EMAP stressor  indicators are presented in Sections 9.5 and 10.

A set of additional, ancillary variables will  be collected at each sampling  site to provide basic information on
the environmental setting. They will be used to normalize values for exposure and response indicators across
environmental gradients and to define subpopulations of interest. The ancillary variables include temperature,
salinity, water depth, pH, and grain size,  organic content,  and percent water of  sediments.

3.2.4  Estuarine  Indicators  Not Appropriate for EMAP

Several authors have recommended indicators of ecological health that, while appropriate for other monitoring
designs, are not included in the EMAP-Near Coastal strategy (Livingston 1984; O'Conner and Dewling 1986;
Chapman et al.  1987; Segar et a!.  1987;  Taub 1987; also  see White 1984; IEEE 1986; Boyle  1987).   The

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constraints placed on indicator selection by the EMAP design include the goal of assessments at national and
regional spatial scales, the restriction of sampling to a well-defined index period, and the focus on response
in ecological structure and function.

Some indicators were excluded because their short-term or local variability was determined to be too large
relative to longer term and regional variability.  Measurements of phytoplankton or zooplankton abundance
and diversity fall into this category because of the potential for large spatial and temporal variability.  Although
the planktonic community is  a good integrator of water-column  phenomena, the spatial variability, temporal
variability,  and episodic  nature of planktonic events are incompatible with  sampling once or twice a year
during an  index period.  To address the concern  over phytoplankton abundance as  related to primary
production imbalances,  the use of remotely sensed chlorophyll pigment measurements will  be evaluated.
Chlorophyll concentration  as measured by  fluorometry could  be  used as a  surrogate for phytoplankton
abundance.

Shellfish growth and tissue contaminants, to be measured by using deployed oysters, were seriously considered
as near-coastal indicators.   It was  determined that  changes in  growth  could  not be related to a specific
environmental problem because of the unknown effects of temperature, salinity, food supply, and food quality
variations expected to occur throughout a region.  The infection of certain shellfish populations by MSX and
other shellfish pathogens was also a concern because of the potential for disease transmittal to uninfected
locations.  Because of these  problems, focus was directed  to evaluating  indigenous bivalve distribution as a
research  indicator.   Information on contamination  in these bivalves will  be directly compared with  that
collected by NOAA's NS&T program.   NS&T  will provide trends  in bivalve contamination for EMAP, and the
two approaches will be evaluated  during the first  year of sampling.

Measurements  of  water-column contaminants and  nutrients were also rejected  as appropriate indicators
because of the potential  for high background (relative to a signal) temporal and spatial variability that results
from point sampling.  We are not aware  of  sampling instrumentation that could  be efficiently deployed to
collect time-integrated or space-integrated  water samples for such  parameters.   In  addition, the  high cost
associated with the logistics of point sampling for water-column contaminants was a prohibitive factor.  There
is a similar concern over the collection of samples for the water-column toxicity tests, but if some  measure
related to waterborne contaminants is necessary, the toxicity tests involve much lower costs than chemical
analyses  and are not chemical specific.

Indicator species, such as those particularly tolerant or sensitive  to pollution,  are presently not proposed for
near-coastal waters.   The geographic  scope  of  EMAP appears to preclude their  use, even within regions.
However, certain functional types of organisms,  in  the sense of species guilds, may be useful indicators.  For
example,  certain types of benthic species tend to  dominate disturbed sites,  while  an entirely different suite
of species tends to dominate unperturbed sites.  Some of the characteristics of benthic species that may be
useful  in differentiating function  are short-lived versus  long-lived, deposit-feeders versus suspension-feeders,
and sediment surface-feeders  versus subsurface deposit-feeders. The use of functional types to describe varied
benthic impacts within and across regions will be evaluated as data are collected over the next several years.

Another group of common measures that are not  included as research indicators is waterfowl and mammal
abundance and demographics.  The near-term emphasis on estuarine classes prevented a detailed evaluation
of waterfowl as a response indicator for near-coastal  waters. Monitoring  of waterfowl is discussed in Section
9.2.  Candidate indicators of marine  mammal condition will be evaluated before EMAP is implemented in
other regions where these organisms are important,  for example, the Californian and Columbian provinces.
Indicators of marine mammal and waterfowl health will be examined when the  monitoring network expands
into off-shore waters of the continental shelf.
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3.3  APPLICATION OF INDICATORS FOR NEAR-COASTAL WATERS

3.3.1   Definition of the Subnominal Threshold

Response indicators will be used to distinguish nominal and subnominal near-coastal resources.  As described
in Section 2, the designation of nominal status  may require all  response indicators to fall within acceptable
ranges.   For estuaries,  these indicators  are  DO; benthic abundance,  biomass and species  composition;
biological sediment mixing depth; extent and density of SAV; fish abundance, species  composition, and gross
pathology; and presence of large bivalves. A quantitative definition of the  subnominal threshold for each of
the research indicators will be determined during  the demonstration project  This definition will account for
spatial variability, temporal variability, and  measurement error,  as well as provide the means for  informed
judgement  as to  ecological  significance.   The  numerical  thresholds will  also be supported by strong
correlations with associated exposure, habitat, and stressor indicators.  In many cases, the established value
may be specific to a particular salinity zone, sediment grain size, or water depth;  therefore, parameters may
also be used to normalize the  numerical thresholds.

Subnominal  condition for any single response indicator can be defined by using either external criteria, such
as a state regulation for DO,  or standards established by comparison with a regional reference site.   External
criteria  that  can be used to define  subnominal thresholds are available,  however,  only for their extreme
values.  For  example, a benthic community devoid of animals is clearly subnominal, as is a 100% incidence
of pathologies in target fish species.  For nonextreme indicator values, which  are expected to be the norm,
the identification of regional reference sites that are known to be relatively  pristine may be necessary. These
reference sites may be  used to define nominal  condition for a defined  region.   Regardless, subnominal
thresholds for any of the response indicators will not be designated until the retrospective analyses of historic
data and the analysis of demonstration  project  data have been completed.

Examples of how the research  indicators were  assembled to provide some diagnostic capability follow.

3.3.2  Dissolved Oxygen

A site will be defined as subnominal if  any of the water-column  profiles have observed DO concentrations
<2 ppm. Concentrations >5 ppm will be considered nominal, and the intermediate range will be defined
as marginal, or threatened.  These thresholds are  based on available information  on DO concentrations that
adversely affect estuarine biota (Reish  and Barnard 1960; Coutant 1985; Renaud 1986),  and most states
employ the  5-ppm value as a regulatory standard.  These data  will  be correlated first with values of the
benthic and fish  indicator responses; under acute oxygen stress, the  benthic infauna  and  fish abundances
should  be depressed  relative to expected nominal values.

Water clarity and chlorophyll pigment data would be indicative of conditions conducive to hypoxia. These
data, however, are not expected to be exactly compatible with the DO data since algal blooms and  observed
hypoxia are not likely to occur simultaneously.  Data on  nutrient and  biochemical oxygen demand loadings
(stressors) during the preceding one to  two months would provide the most useful  diagnostic information.
Subnominal areas or classes can also  be selected for  retrospective analyses by using satellite imagery for
estimates of chlorophyll  during the preceding spring and summer months.  Natural DO depressions, which
can result from a combination of high temperature,  low flow, shallow depth, and highly stratified conditions,
also may occur.   Although naturally occurring, such conditions  would also  be expected to  have adverse
effects on the benthic and fish communities.
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3.3.3  Benthic Abundance, Biomass, and  Species Composition

The  individual components of this  indicator will be expressed as abundance, biomass, or number of species
per unit area.  Because the subnominal threshold for these metrics will vary with salinity zone, depth, and
sediment grain size, the data will be evaluated to determine whether these factors can be used to normalize
community parameters across habitat types.  Additional data on species composition will be explored to relate
species presence and abundance with  habitat type and pollutant stressor.  These data, however, will be most
useful in defining community  functional types (e.g., deposit feeding versus suspension feeding, head down
versus surface feeders).

This  benthic  indicator would first be assessed relative to water  depth, grain size, and salinity.  Subnominal
conditions would  be reflected by abnormally low or abnormally high abundance or biomass; both factors will
usually fluctuate in the same direction.  Low  numbers of species might also indicate subnominal conditions;
alternatively,  low species  number could be  associated  with  either  low biomass and abundance or high
biomass and  abundance.

Subnominal conditions may result from cultural eutrophication, which may cause shifts in species composition
and  abundance due to organic  loading or direct toxicity due to  hypoxic stress.  In  the case of low DO,
organic carbon levels  in the sediment (measured as part of the contaminant suite) should  be elevated, and
the biologically mixed layer (sediment mixing depth) should be depressed.  If the subnominal condition is due
to contaminant stress,  however,  acute sediment toxicity should be evident or sediment contaminant levels
should be elevated.   In either case, certain infaunal feeding  types would be associated with stress due to
organic loading or eutrophy, and certain  sensitive species, such as amphipods, would be absent Additionally,
stressor data  would be evaluated as an explanation for subnominal condition - physical stress  (e.g.,  storm or
trawl-induced scouring)  or climatic conditions.

3.3.4 Fish Abundance, Species Composition, and Gross Pathology

Factors controlling the species composition and abundance of estuarine fish communities are complex and
not well understood. However, estuaries with depauperate fish communities or those dominated by pollution-
tolerant species will be considered subnominal (Haedrich and Haedrich 1974; Jeffries and Terceiro  1985).
While the habitat conditions in a  region strongly control  the composition of the fish assemblage,  polluted
areas are thought to exhibit less diverse and  less stable  fish communities.

Two approaches will be evaluated to  develop metrics for estuarine fish communities.  The first will involve
establishing the species composition expected at each RSU,  based on a set of habitat characteristics (e.g.,
depth,  salinity,  bottom  type) from reference  areas.   The observed  species composition will  be compared
with  that  expected to  define subnominal  conditions.  The  second approach will  employ  an integrative
measure similar to the Index of Biotic  Integrity (Karr et al. 1986). This method, which defines a single value
describing the condition of a freshwater community, will require considerable development and evaluation
before  application  in estuarine environments.

The  incidence of  gross pathologies is expected to be correlated with sediment contaminant concentrations,
especially in severely polluted sites  (Sinderman 1979; Malins et al. 1988).  Various methods will be evaluated
to quantify and interpret the  incidence of these pathologies  in order  to  establish background or  nominal
conditions.
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3.4  RESEARCH NEEDS

A major research effort is currently underway to evaluate the set of research indicators for estuarine resources.
The initial application of this set of indicators will occur during the EMAP-Near Coastal demonstration project
to be conducted  in the Virginian biogeographic province during summer 1990.  One  of the primary goals
of this project is to apply  and evaluate the research indicators presented here.    This evaluation includes
determining the specificity, sensitivity,  reliability, and repeatability of the indicator responses over a broad
range of environmental conditions.   The  application and refinement  of  sampling  methods for research
indicators will also occur at this time.  A final objective is  to demonstrate the ability of the EMAP sampling
design and indicator suite to estimate the extent of subnominal estuaries  in the Virginian Province.  Indicator
data will be collected from approximately 160 stations ranging from Cape Cod southward to the mouth of
Chesapeake Bay.  All high-priority research  indicators will be sampled  at these stations  and at an additional
24-36 "indicator validation" stations. The 24  stations were chosen to represent known conditions of chemical
contamination and DO, such that they could be divided into 12 polluted and 12 unpolluted locations.  They
are also equally divided among three salinity  zones (oligohaline, mesohaline, and polyhaline) and the northern
and southern  portions of the  province.  Thus, four stations are apportioned to each salinity  zone for each
biogeographic region.   The  indicators will be evaluated  as to their ability to  distinguish  nominal from
subnominal condition, and to do so consistently across salinity and latitudinal gradients.  The  other research
indicators, with the exception of SAV,  will, for the most part,  be sampled only at these additional validation
stations.

Retrospective analyses of historical data on research indicators to define their respective  ranges of values and
variability characteristics are ongoing. Analyses that have been completed include (1) Chesapeake Bay benthic
data for a  number of species  and biomass for three salinity zones,  (2) Monte Carlo simulations of available
fish trawl data for the Virginian biogeographic province to estimate catch  probabilities and establish the target
species list, (3) regression analyses of contaminant residues  in fish from Maryland, and (4) time-series analysis
of DO data from long-term deployments in the Chesapeake Bay and  Gulf of Mexico.  Work will continue
on these and other data sets so that these  results can be compared and integrated with the Demonstration
Project data.   These comparisons will  focus on components of data variability, precision and  accuracy.  An
evaluation  of the  effectiveness  of the indicator  suite is extremely important for   the  assessment and
interpretation scheme discussed in  Section  3.3.

Two candidate indicators that have potential as research indicators are chlorophyll pigment and suspended
solids. Subnominal DO concentrations would indicate the need to identify algal  bloom conditions that may
caused the observed hypoxia.  An a posteriori  examination of satellite images  for selected RSUs would
provide this  diagnostic capability.   It would  also be  useful  to examine  regional-scale  suspended  solids
distributions,  particularly as related to riverine  flow,  storm and runoff events, and  the condition of SAV beds.
Data  for both  indicators would  be obtained from  Advanced Very High Resolution  Radiometer (AVHRR)
imagery.

In addition to the  research issues  already  discussed, specific research  needs associated  with the research
indicators are as follows.

Dissolved  Oxygen

    • Biological requirements for DO.  The definition of subnominal condition requires that critical
      tolerances of key species be  defined.

    • Instrumentation.  Instrumentation needs to be developed that can be deployed for extended time
      periods (e.g.,  >7 days between  servicing) without sacrificing accuracy or precision.
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     In situ  methods.  Development of instrumentation and methods for continuous measurements of
     chemical species such as ammonia that are typically excluded from monitoring programs because
     of sampling problems would be advantageous. Instrumentation for continuous, time-integrated water
     sampling is also a research  need.

     Remote sensing.  As  indicated  previously, the  development  and validation of algorithms for
     chlorophyll pigment and  suspended  solids estimates would greatly enhance  EMAP's  diagnostic
     capabilities. Chlorophyll pigment estimates would indicate the occurrence of bloom conditions that
     might lead to eutrophication and  low-DO events.  Unusual runoff and attendant high suspended
     solids loadings could also be detected in  relation  to changes in  benthic communities and SAV.
Benthos
    • Contaminant bioavailability. The effects of sediment properties (grain size, organic carbon content,
     and sulfide concentration) on contaminant availability under natural conditions is largely unknown.
     Normalization factors that account for the influence of geochemical parameters on availability need
     to  be  developed  in  order to  understand  potential  contaminant effects on benthic infaunal
     communities.

    • Community  function. Much work needs to be done to establish functional attributes of estuarine
     benthic species.  This information is necessary  in order  to understand how changes in species
     composition  and abundance affect basic community characteristics such as productivity and habitat
     structure.  Functional attributes can  also be used  to establish more relevant indices that describe
     benthic communities.

    • Sediment toxicity tests.  Tests using low-salinity species need to be developed for routine use, and
     the effects of grain size on the acute test  response would need to be quantified.  Chronic sediment
     toxicity test methods should be developed as a  more sensitive exposure indicator.

    • Threshold concentrations.  As is recommended for DO, it would  be  useful to establish threshold
     levels of contaminants and  organic loading that  cause adverse biological effects.

    • Deposition rates.  Current data on sediment contamination has no temporal benchmark to identify
     whether contaminant inputs were recent or  historical.  The value of sediment contaminant data
     would be greatly enhanced if sediment deposition rates were known.
Fish
      Fish species composition. The relationship between the areal distribution of target species during
      the  summer index  period  and  basic habitat  characteristics is necessary to define subnominal
      condition.  The stability of this distribution during the index period will also need to be established.

      Indices. The development of community indices similar  to those used for describing freshwater
      resources (e.g., Index of Biotic Integrity) would be useful.

      Biomarkers.  These indicators  appear to have  near-term potential  as exposure indicators and
      longer term potential as response indicators.  Their use in  EMAP would require significant research
      and  testing to distinguish  factors that affect response indicators and to establish  suborganismal  to
      whole-organism  links in biological effects.
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    • Sediment toxicity tests.  Good candidates for chronic sediment tests are those life stages of fish
     that are  known to be sensitive and that come in contact with sediments.  New  methods would
     need to  be developed.


3.5 REFERENCES

Boesch, D.F.,  and  R.  Rosenberg.  1981.   Responses to stress in marine benthic communities.   Pages
179-200.  In:   G.W. Barret and R. Rosenberg, eds.  Stress Effects  on Natural Ecosystems.  John Wiley &
Sons, New York.

Boyle, T.P.  1987.  New Approaches to Monitoring Aquatic Ecosystems.  ASTM STP 940.  American Society
for Testing and Materials, Philadelphia, PA.

Chapman, P.W., R.N. Dexter, and L Goldstein.  1987.  Development of monitoring programmes to  assess
the long-term  health of aquatic  systems,  a model from Puget Sound, USA. Mar. PolluL Bull. 18:521-527.

Coutant,  C.C.    1985.   Striped  bass,  temperature,  dissolved  oxygen:   A  speculative hypothesis for
environmental  risk.  Trans. Am.  Fish. Soc. 114:31-61.

Haedrich, R.L., and S.O. Haedrich.  1974.   A seasonal survey of the fishes in the Mystic  River, a polluted
estuary in downtown Boston,  Massachusetts. Estuarine Coastal Mar. Sci. 2:59-73.

Hamner, R.W. 1988.   Water  quality managers meet the coastal  zone.   Environ. Prof.  10:95-97.

Holland, A.F.,  A.T. Shaughnessy, and M.H. Hiegel. 1987.  Long-term variation in mesohaline Chesapeake
Bay macrobenthos:  Spatial and  temporal patterns.  Estuaries 10:227-245.

IEEE Ocean Engineering Society.   1986.   Proceedings of Oceans 86, Volume #3, Monitoring Strategies
Symposium. Institute of Electrical and Electronics  Engineers, New York.

Jeffries, H.P., and M. Terceiro.   1985.  Cycle of changing abundances  in the fishes of the  Narragansett Bay
area.  Mar.  Ecol. Prog. Ser. 25:239-244.

Karr, J.R., K.D. Fausch, P.L. Angermeier, P.R. Yant, and I.J. Schlosser.  1986.  Assessing biolotical integrity
in running  waters:   A method and  rationale.   Special Publication 5.   Illinois  Natural  History Survey,
Champaign, IL.

Kelly, J.R., and MA. Harwell.  1989.  Indicators of ecosystem response and recovery. Pages 9-35. In: S.A.
Levin, M.A.  Harwell, J.R. Kelly, and K.D.  Kimball, eds.  Ecotoxicology:  Problems and Approaches.  Springer-
Verlag, New York.

Livingston,  R.J.  1984.  Aquatic field  monitoring and  meaningful measures of stress.  Pages 681-722.  In:
H.H. White, ed.  Concepts in Marine Pollution Measurements.  University of Maryland  Sea Grant, College
Park, MD.

Malins, D.C.,  B.B.  McCain, J.T.  Landahl, M.S. Myers, M.M. Krahn,  D.W. Brown, S.L Chan,  and W.T.
Roubal.   1988.   Neoplastic and other diseases in  fish  in relation to toxic chemicals:  An  overview.  Pages
43-67.   In:   D.C.  Malins and  A. Jensen,  eds.   Aquatic Toxicology,  Toxic Chemicals, and Aquatic Life:
Research and  Management  Elsevier Science, Amsterdam.
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Morganthau, T.  1988.  Don't go near the water:   Is it too late to save our dying coasts?   Newsweek,
August 1:42-47.

NOAA.   1987.  A summary of selected data on chemical contaminants in tissues collected during 1984,
1985,  and  1986.   Technical  Memorandum  NOS  OMA  38.    National  Oceanic  and  Atmospheric
Administration, National Ocean Service, Office of Oceanography and Marine Assessment, Washington, DC.

NOAA.  1989.  A summary of data on tissue contamination from the first three years (1986-1988) of Mussel
Watch project Technical Memorandum NOS OMA 49. National Oceanic and  Atmospheric Administration,
National Ocean Service, Office of Oceanography and Marine Assessment, Washington, DC.

NRC. 1990.  Managing Troubled Waters:  The Role of Marine Environmental Monitoring.  National Academy
of Sciences, National Research Council, National Academy Press, Washington, DC.

O'Conner, J.S., and R.T. Dewling. 1986. Indices of marine degradation:  Their utility.  Environ. Manage.
10:335-343.

Pearson, T.H., and R. Rosenberg. 1978. Macrobenthic succession  in relation to organic enrichment and
pollution of the marine environment  Oceanogr. Mar. Biol. Ann. Rev. 16:229-311.

Reish, D.J. and J.L Barnard.  1960.   Field toxicity tests in marine water utilizing the polychaetous annelid
Capitella cap/tata  (Fabricius).  Pacif. Nat. 1:1-8.

Renaud, M.L  1986. Detecting and avoiding oxygen deficient sea water  by Brown Shrimp, Penaeus aztecus
(Ives) and White Shrimp Penaeus setiferus (Linnaeus).  J. Exp.  Mar. Biol.  98:283-292.

Rhoads, D.C, P.L McCall, and J.Y. Yingst.  1978.  Disturbance and  production on the estuarine sea floor.
Am. Sci. 66:577-586.

Rhoads, D.C, and J.D.  Cermano. 1982. Characterization  of organism-sediment relations using sediment
profile imaging: An efficient method of remote ecological monitoring of the seafloor (REMOTS System). Mar.
Ecol. Progr. Ser. 8:115-128.

Rhoads, D.C., and J.D. Germano. 1986. Interpreting long-term changes in benthic community structure:
A  new protocol.  Hydrobiologia 142:291-308.

Sanders, H.L., J.F. Grassle, G.R. Hampson, L.S.  Morse, S. Garner-Price, and C.C. Jones.  1980. Anatomy
of an oil spill:   Long term effects from the grounding of the barge Florida off West Falmouth,  Massachusetts.
J. Mar. Res. 38:265-380.

Segar, DA., D.J.H. Phillips, and E. Stamman.  1987.  Strategies for  long-term pollution  monitoring of the
coastal oceans.  Pages  12-27.   In:  T.P.  Boyle,  ed.   New Approaches to Monitoring Aquatic Ecosystems.
ASTM STP 940. American Society for Testing and Materials,  Philadelphia, PA.

Sinderman, C.J. 1979.   Pollution-associated diseases and abnormalities of fish and shellfish: A review. Fish.
Bull. 76:717-741.

Swartz, R.C.  1987. Toxicological methods for determining the effects of contaminated sediment on  marine
organisms.   Pages  183-198.  In:  K.L. Dickson, A.W. Maki,  and W.A.  Brungs, eds.   Fate  and Effects  of
Sediment Bound Chemicals in Aquatic Systems.  Pergamon Press, New York.
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Taub, F.B.  1987.  Indicators of change in natural and human-impacted ecosystems:  Status. Pages 115-144.
In:  S. Draggan, J.J. Cohrssen, and R.E. Morrison,  eds.  Preserving Ecological Systems, the  Agenda for Long-
Term  Research and Development  Praeger Publishers, New York.

Toufexis, T.  1988.  Our filthy seas:  The world's oceans face a growing threat from man-made pollution.
Time, August 1:44-50.

U.S. OTA.  1987.  Wastes in the  Marine Environment  OTA-O-334.  U.S. Office of Technology Assessment,
Government Printing Office,  Washington, DC.

U.S. EPA.  1988.   Environmental  Progress and  Challenges:  EPA's Update.  EPA 230/07-88/033.   U.S.
Environmental Protection Agency, Washington,  DC.

Waldichuk, M.  1989.  The state of  pollution  in  the marine environment  Mar. Pollut Bull. 20:598-602.

Weinstein, M.P., S.L. Weiss, and M.F.  Walters.  1980.  Multiple determination of community structure in
shallow marsh habitats, Cape Fear River estuary, North Carolina, U.S.A. Mar. Biol.  48:227-243.

West,  N.   1989.  A preliminary review of water quality parameters and  recreational  user  perceptions  of
nearshore water  quality.  J. Coast. Res.  5:563-572.

White,  H.H.   1984.   Concepts in Marine Pollution Measurements.  University of Maryland  Sea  Grant,
College Park, MD.

Wolfe,  D.A.  and  J.S.  O'Conner.  1986.  Some limitations of indicators and their place  in  monitoring
schemes.  Pages  878-884.  In:  Oceans  '86 Proceedings, Vol. 3, Monitoring Strategies Symposium.  Marine
Technology Society, Institute of Electrical and Electronics Engineers,  New York.
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                                             SECTION 4

                       INDICATOR STRATEGY FOR INLAND SURFACE WATERS

                              Robert M. Hughes1 and Steven C. Paulsen2
4.1  INTRODUCTION
Surface waters are often  in  a  more natural  state than the  terrestrial or  wetland  resources they  drain.
Although  lakes  and  streams can  certainly  be  impaired by  land use, they  are less  subject  to  drastic
environmental perturbations, such as logging and farming of uplands or conversion of entire wetlands.   Of
course, channel and flow modifications,  fishery exploitation,  and pollution seriously impair the condition of
many surface waters.  Nevertheless, streams draining the corn  belt of the Midwest, overgrazed rangelands
of the West, and clearcut forests of the Pacific  Northwest  may still  retain  some species  and abundances
comparable to those expected  of  natural habitats,  where the channel and riparian  zone have not been
seriously disturbed.   Similarly, lakes in  urbanized and  farmed landscapes  of  the Great Lakes states and
northeastern  United States may maintain trophic states and  species similar to those of presettlement  times,
if runoff and riparian development are controlled.

Although 75% of the earth's surface is covered by water, only 0.02% of the earth's total volume of water
is contained in rivers and  lakes (Ehrlich  et al.  1977).   Inland surface  waters comprise less than 2% of the
conterminous United  States by area (Geraghty  et al. 1973) -  a small proportion that belies their importance
for  our society.   Lakes and rivers provide habitat for aquatic life, sources of drinking and  irrigation waters,
and locations for recreation, aesthetic appreciation, and navigation.   As reflected in the following statements,
the integrity of surface water  is  important to our way of life.

        •    Inland surface waters provide  79%  of the water  consumed  daily in the United
            States  for drinking, irrigation, and other uses (Ehrlich et al. 1977).

        •    Water use in  seven southwestern  states exceeds runoff 9 out of 10 years (Ehrlich
            et al. 1977).

        •    Swimming and  fishing rank first and  second,  respectively,  among  all  outdoor
            participatory sports (USDOI  1989).

        •    The 31.5 million fishermen who fished inland waters in 1988 spent $329.8 million
            on their sport (USDOI  1989).


4.1.1   Legislative Mandate for  Inland Surface Water Monitoring

Congress passed  the  Federal  Water Pollution  Control Act (P.L. 92-500) in  1972, thereby establishing the
protection of surface waters as a national priority. The primary objective of  P.L. 92-500 was to restore and
maintain the physical, chemical, and  biological  integrity of  the nation's waters.   An interim  goal was to
provide for the  protection and propagation of fish,  shellfish, and wildlife and  for  recreation  in and on the
water  (Section  101 (a)).   Several  other sections of the  Act  relate to the protection of aquatic ecosystems.
    'NSI - Environmental Sciences, U.S. EPA Environmental Research Laboratory, Corvallis, Oregon

    University of Nevada, Environmental  Research Center, Las Vegas, Nevada

                                                 4-1

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Section  105(d)(3)  requires EPA to accelerate, on a  priority basis,  the  development and  application of
improved  methods for measuring the effects of pollutants on the chemical, physical, and biological integrity
of water.  Section 304(a)(1) states that EPA shall develop and publish criteria on the effects of pollutants on
community diversity, productivity, biological stability, and eutrophication.  Section 305(b) mandates biennial
reports that assess the extent to which all waters provide for the protection and propagation of a balanced
community of aquatic  life.

The  Act has been  further strengthened  since  1972.  The  Water Quality Standards Regulation (U.S.  EPA
1983) requires states to designate uses for  aquatic life  that are consistent with the goals of the Act, provide
criteria sufficient to protect those uses, and establish an antidegradation policy that will protect high-quality
waters from being degraded to criteria levels.

The  Water Quality Act of 1987 amends  P.L. 92-500 and  emphasizes ambient standards and assessments as
the driving forces  behind further pollution  abatement  Section  303(c)(2)(B) allows states  to adopt criteria
based on  biomonitoring (a fundamental aspect of EMAP).  Section 304(I)(A) requires  a list of those waters not
expected  to attain  protection and  propagation of balanced biological communities.   Section  304(m)(2)(g)
requires  EPA  to  study the effectiveness of applying  the best  available pollution  controls for protecting
balanced  communities.  Section  314(a)  requires trophic  classification of all publicly owned lakes and an
assessment of the status  and trends of water quality in those lakes.  Section 319 mandates identification of
waters that cannot  protect balanced aquatic communities without nonpoint-source pollution controls.

Other legislation requires assessing environmental risk to aquatic communities.  Of particular importance are
stressors with  potential  regional  impacts that  fall  under the Federal Lands Policy and Management  Act
(FLPMA), the National  Environmental Policy Act (NEPA), the Resource Conservation and  Recovery Act (RCRA),
and  the Federal Insecticide,  Fungicide and Rodenticide Act (FIFRA).  In addition, the Endangered Species Act
mandates assessments  and protection of rare and  threatened species, and the  National Forest Management
Act of 1976  requires conservation of vertebrate diversity.

4.1.2  Limitations of  Current  Inland  Surface  Water Monitoring Programs

Despite the legislative  mandate to protect aquatic life, the responsible federal agencies  have  not adequately
assessed either the status of ecological resources  or the overall  progress toward legally mandated goals of
mitigating or preventing  adverse  ecological effects.  The  Study  Croup on Environmental Monitoring (NRC
1977)  stated  that EPA  monitoring  programs  (1)  did  not provide data  needed to  predict  the effects of
management decisions, (2) did not  assess long-term  environmental  changes, and  (3)  focused on pollution
sources rather than discovery and prediction of environmental problems.  This group recommended that EPA
conduct long-term monitoring  of natural and impacted ecosystems to evaluate environmental effects.  The
EPA Science Advisory Board's Ecology Committee (SAB 1980) further recommended that the agency increase
its biomonitoring activities.

In 1981,  the  U.S.  General  Accounting  Office (U.S. CAO)  concluded that EPA's water quality monitoring
program was inadequate (U.S. GAO 1981).  This conclusion was  based on the small number of samples and
infrequent sampling periods, lack of statistical and ecological representativeness,  and  inability to  associate
problems with causes. Seven years later, GAO (1988) concluded that EPA still needed  to improve  its ability
to measure the environmental results of its  regulatory  programs.   GAO  observed that  EPA rarely  used
biological community  measurements of quality and had reduced  its monitoring activities in recent years.  In
addition,  data  collected  by  EPA were not easily  accessible  and made analysis of status and trends difficult
because of limited  monitoring  coordination, unsuitable network design, and data incompatibility.

The Agency itself has also recognized deficiencies in  its surface  water monitoring  programs.   A monitoring
strategy produced by  EPA's  Office of Water (U.S. EPA 1984) stated that EPA's fixed-station  network  had
insufficient statistical reliability  to support national assessments.   The strategy also concluded that biological

                                                  4-2

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monitoring needed more emphasis and that waters not  known to have  problems should  be evaluated.
Nonpoint-source impacts and controls were essentially unknown, the environmental effects of source controls
were  not assessed, available data were not  properly integrated, and national studies were needed.

Three years later,  the same concerns were expressed (U.S. EPA 1987a):   Increased biological  monitoring
was  needed  to characterize ecosystems and identify problems and trends,  better data  were  needed  to
determine  if  pollutant controls were  achieving the desired results, data on the effects of  nonpoint-sources
were  lacking,  less  expensive  problem-screening and  trend-monitoring  methods  incorporating biological
techniques were  needed, greater  consistency in monitoring and  reporting  were  required to create sound
national evaluations, and too little ambient monitoring had been conducted.  The Agency clearly needed a
national  monitoring framework with well-defined objectives and guidance.

Finally, the Agency concluded that biological criteria and biological and physical habitat monitoring were
needed to assess  the impacts of nonpoint-source pollution  and controls.  This  conclusion was reached
because of the typically nontoxic, episodic nature of nonpoint-source loading and the extent of the problem
(76%  of  impaired  lakes and  65% of impaired rivers result from  nonpoint-source  pollution, predominantly
agricultural; U.S. EPA 1989a).

Given the unambiguous legal mandate and the periodic internal and external reviews and recommendations,
it is obvious  that our  current approach to  surface water monitoring must  be changed.  A comprehensive
framework that supports regional and national assessments, incorporates sound  statistical survey designs, and
focuses on biological indicators of resource condition is needed.  Such a framework would also be useful to
other federal agencies that monitor freshwater resources.

4.1.3   Inland Surface Water  Resource Classification

A number  of different classification schemes could  be used for lakes and streams based on formation (e.g.,
lake type),  fisheries use (warm water vs. cold  water), dominant terrestrial vegetation (streams  in  forested  or
agricultural regions),  and extent.   For  lakes, this classification  will have little influence  on  the discussion  of
appropriate indicators and  methodologies;  however, size distinction  can  be important  when  discussing
indicators for streams and rivers.  While the taxa which are sampled in streams and rivers may be the same,
the appropriate indicator derived from the field metrics and the sampling methodologies are likely  to be quite
different  for  small versus large systems.   For  inland surface waters,  the  issue  of resource extent  is  an
important consideration at the class level because  the standard rules for selecting a representative lake  or
stream segment within a landscape sampling unit tend to favor smaller  systems because they are  more
numerous.  There are, for example, twice as many lakes in the 1- to 4-ha size range as there are in the  4-
to 2000-ha range; similar statistics occur for stream size.  To  balance the inclusion of different size systems
in the final sample, it  is necessary to use size as the initial classification criterion.

The  initial  size boundaries of  lake and stream  classes will be somewhat arbitrary, but will  eventually  be
related to their respective size distributions.   Reservoirs will not  be distinguished  from lakes because doing
so on the basis of characterization  information is difficult; also, in many cases, lakes and reservoirs function
similarly.

Because  large lakes (>2000 ha) are relatively  scarce (200-250 in the continental United States), they will  be
censused.  Smaller lakes can  be sampled with the grid design. A division of lakes into three size classes, 1
to 10 ha, 10 to 2000 ha,  and greater than 2000 ha, appears to be sufficient.  Streams also will be classified
by size and initially partitioned into two classes:  (1) small streams sampled from a grid frame and (2) large
streams that will be  censused.
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4.2  IDENTIFICATION OF INDICATORS FOR INLAND SURFACE WATERS

4.2.1   Perceptions of Inland Surface Water Condition

Although freshwater  resources may support rich biotic communities compared to the intensively  managed
terrestrial  resources  they  often  drain,  many  of these  resources  have been  significantly  affected  by
anthropogenic activities, as demonstrated by the following information.

        • Twenty-five percent of the threatened and endangered species of Oregon are aquatic
          (U.S. Fish  and Wildlife Service 1986).

        • Twenty percent of the native fishes of the western  United States have become extinct
          or are  endangered (Miller 1961).

        • Thirty-two  percent of the  native  fishes  of  the  Colorado  River  basin  are extinct,
          endangered, or threatened (Carlson  and Muth 1989).

        • Since 1910,  annual Columbia River  salmon  and  steelhead  runs  have declined  by
          75-85%, which represents 7-14 million fish (Ebel et al. 1989).

        • The Missouri  River commercial fish harvest has been reduced by more than 80% since
          1945 (Hesse  et al. 1989).

        • Thirty-four percent of the native species of  Illinois fishes have been extirpated  or
          decimated (Smith 1971).

        • Since 1850, 67% of the fish species from the Illinois River and 44% from the Maumee
          River have declined or disappeared; in small or medium-sized streams, the percentage
          of species lost ranged from 70 to 84% (Karr et al.  1985).

        • Since 1933,  the  Tennessee River system has lost 20% of its mollusk species (Isom
          1969),  and  46%  of  the remaining species are  endangered or  seriously  depleted
          throughout their ranges (Jenkinson 1981).

        • One hundred ninety-nine fish species (27% of the  fish fauna)  in the United States are
          endangered, threatened,  or of special concern (Williams et al. 1989).

        • In the  past 100 years,  40 North American  fish taxa  have become extinct,  19 since
          1964 (almost one per year).  The most common contributing  factors were habitat loss
          (73%) and introduced species (68%) (Miller et al. 1989).

        • Thirty-eight states reported closures, restrictions, or advisories relating to  fisheries in
          1985 (Moyer 1986).

        • The  ecological risks of physical alteration of aquatic habitats and point- and nonpoint-
          source discharges were ranked as the second and third most serious environmental
          threats in  a survey of EPA scientists (U.S.  EPA 1987b).

Similar  information on other aquatic biota are virtually  unknown at this time.  Although this  information
implies serious and widespread biological impairment, EPA estimates that criteria for designated beneficial uses
were met in  74% of the river miles and 75% of the lake acres evaluated (U.S. EPA 1987b).  This apparent
contradiction may result from inappropriate or inconsistent use designations, unsuitable  ecological  indicators

                                                 4-4

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and assessment criteria, or inadequate monitoring designs. Thus we require a national monitoring framework
with appropriate biological indicators for inland surface waters to  resolve these  apparent contradictions and
to provide baseline data for future assessment and management decisions.

4.2.2  Environmental  Values  for  Inland Surface Waters

To be effective, monitoring programs must provide data that can  be related to  an assessment endpoint,  an
example being the degree to which the designated beneficial use of an aquatic resource is achieved.  Criteria
for  meeting a designated use are attained  if the system is  being used for its intended  purpose,  such  as
maintaining habitat  for  aquatic life, fishing,  water sports, aesthetics, navigation, or  water supply.   Use
designation has been ineffective for protecting biological integrity because  many designated uses,  as well  as
the criteria used to assess use  impairment, have little relationship to ecological condition.  Often the uses are
imprecisely defined.  For example, maintenance of "warmwater fish" or of "fishable" water may be considered
attained whether the water supports a few carp or a large number  of smallmouth bass, and "aquatic life" may
likewise mean blue-green algae or arctic char.  It is also quite possible to have excellent fishing and impaired
ecological condition  or vice versa.

An alternative to "designated use" as an environmental value is the protection of biological integrity, mandated
in P.L. 92-500.  Biological integrity, although not  defined by the legislation, means "a balanced, integrated,
adaptive community of organisms  having  a  species composition, diversity,  and  functional  organization
comparable to that of  natural  habitat in the region" (Karr and Dudley  1981).  Two states, Maine and Ohio,
have developed quantitative biological criteria based on this concept of biological integrity. Maine's biological
standards for  Class A (highest quality) waters consist of specific values for six macroinvertebrate indices, based
on  conditions  in  naturally  occurring  reference communities  and assessed by using  a dichotomous key
(Courtemanch and Davies 1988).  Ohio EPA (1988) used reference sites to set numerical criteria,  specific to
ecoregions and stream size, for  two fish assemblage indices and one macroinvertebrate index.

It is generally  recognized that  an understanding of what constitutes biological integrity or impairment cannot
be determined a priori.  Rather, this understanding depends on knowing the acceptable range of values for
measured components  of the system.  In practice, therefore,  integrity or  impairment  is determined by  an
evaluation  of  indicator scores.  Defining integrity and  impairment thus depends on  choosing appropriate
indicators and determining  their  expected  or  acceptable values.   The selection  of  indicators should  be
influenced  by our  knowledge  and  perception  of the critical components and processes  within an  ecosystem
that  best define the  ecosystem's attainable structure and function.

At least two  philosophically  distinct approaches  exist  regarding assessment endpoints and  indicators  of
ecosystem integrity.  The first is to define biological integrity based on the presence of specific characteristics
of interest, implying that one can identify and quantify such a set of measurements. The second is to define
biological integrity  based on the  absence of known problems. This second approach recognizes that in many
instances one  does not know exactly what the system should look like but can readily identify what it should
not look like.  These two approaches, which present slightly different perspectives,  should not  be viewed as
conflicting, but complementary.

4.2.3  Hazards to Inland Surface Waters
                                      b
Current hazards to inland surface waters fall into nine  categories, some of which are related to land use
and  pollutants, and some of which are related  directly to resource management.

        1.  Cultural  eutrophication (anthropogenically induced nutrient enrichment)

        2.  Contamination (both point  and  nonpoint, as well as toxic and  nontoxic)
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        3.  Atmospheric stressors (e.g.,  acidic deposition and toxics  such as herbicides and
           polychorinated biphenyls)

        4.  Habitat alteration (physical structure and substrate)

        5.  Flow modification

        6.  Thermal alteration

        7.  Species introduction

        8.  Harvest imbalance (overstocking or overharvesting)

        9.  Global climate change

Perturbations within each  of these categories potentially threaten surface water condition,  either in the
context of  traditional concepts of beneficial uses or of ecological  condition.

Cultural eutrophication can result in  aesthetic degradation of  streams and  lakes by inducing the formation
of surface blooms of noxious blue-green  algae, which also contribute to taste and  odor problems in drinking
water. Additional problems can result if aquatic macrophytes reach such magnitude that they interfere with
boating and swimming. A very serious ecological consequence of accelerated eutrophication is the alteration
of fish populations via changes in the plankton community or as  a result of oxygen  depletion.

Contamination  of fish  and shellfish by heavy metals and synthetic organic  chemicals  is of obvious concern
to fishermen and consumers,  as  is the presence  of  fish lesions  and  tumors.   Less apparent but equally
important are the toxic effects on nongame components of  aquatic and  semiaquatic animals.   Although
acute  effects such  as  fish  kills are  obvious,  chronic  effects  can  result in loss of  desirable species  and
domination of communities by tolerant,  undesirable, and nuisance species.

Substances deposited  as  a result of anthropogenic emissions may act  as nutrients (e.g., nitrogen) or as
contaminants (acid rain, toxics). Atmospheric stressors are listed as a separate hazard  category because they
are regional in  extent  and  are not related  to  the permitting  process used by states to protect water quality.

Physical habitat alteration is one of the most serious hazards to inland surface waters, particularly rivers.  It
includes direct  channel modification  (dams, channelization) and indirect effects of land use (loss of natural
riparian vegetation, sedimentation).  Channel modifications may  improve some designated  uses, such as
navigation  and water  supply, but barriers and loss of habitat diversity usually result in fewer or different
native species and increased populations of exotics.  For example,  loss of riparian trees and shrubs decreases
cover, increases sedimentation and turbidity,  and changes the food base from  leaves to algae.   The fauna
subsequently can change, for  example, from one characterized by smallmouth bass and mayflies to one that
is characterized  by carp and worms, and losses  in  fishing quality and changes in  natural diversity  and
composition occur.

Through drainage and  diversion, flow modification  may result not  only in the direct loss of  aquatic resources
but also the loss of all  uses and species dependent on them.  Removal of vegetation, urbanization, and water
projects may also disrupt ground water  recharge,  lake water levels, and  the timing and magnitude of flood
and  low flows.   Streams become less suitable for  boating and aquatic life, and lakes develop unappealing
"bathtub rings."

Cooling water discharges or removal of riparian vegetation may result in thermal pollution.  The former  may
result in fish kills (depending on the  ratio  of  effluent to receiving water)  or, more often, changes  in species

                                                  4-6

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composition and production.   Removal of riparian vegetation typically has a chronic impact by altering the
food base and physical habitat  In cold-water systems where food is not limiting, elevated temperatures may
increase fish production.   However, if  food is  limiting  or if  species  are  near their  thermal  limits,  fish
production decreases and species are eliminated.

Introduced species,  whether  desirable game fish or  nuisance  plants  and animals, often  create  problems.
For example,  the Asiatic clam and zebra  mussel often clog water intake structures; water hyacinth and
Eurasian milfoil  interfere with boating.   Introduced  species, which may outcompete or prey upon  native
species, are usually extremely difficult to eliminate.

Overharvest of fish and shellfish is responsible for decreased sizes of  individuals and populations  and thus
decreased reproductive rates.  Overharvest of larger game fish individuals and species is implicated as a cause
of the  stunting  of sunfish  in  lakes.   Fishing  pressure  on  large carnivorous game fish is associated with
increases in their less  desirable competitors  and  prey.

The potential impacts of global climate change on aquatic conditions are virtually unknown, but the potential
change as a result of this perturbation  is of an unparalleled magnitude.

4.2.4  Inland Surface Water Indicators Appropriate for  EMAP

The  process of  selecting ecological indicators for inland surface  waters  was based on ongoing research in
EPA's programs  on aquatic toxicology, acid rain, and  ecoregion/biocriteria programs, all of which share some
goals with EMAP.  Important  activities in developing a suite of research  indicators for inland surface waters
are listed in Table 4-1. A written external peer review  of these indicators was performed  in April 1990
(Appendix I.8), followed by a review by the EPA Science Advisory Board in May 1990.  EMAP  workshop
participants are  listed  in Appendix I.

Four general criteria were  used for selecting response  indicators for inland surface waters.  Although listed
individually, it is important  not to think of each indicator in isolation.  Rather, they should be considered
as a suite of tests used to estimate the health of surface waters with acceptable levels of uncertainty.

        •  The  indicators must be biological and incorporate elements  of ecosystem structure and
           function.   An indicator should correlate  with  changes  in other unmonitored biological
           components and  should  incorporate  changes in predation,  competition,  life  histories,
           natality, mortality, and  migration without  directly measuring these rates. Also, indicators
           applied to lakes should be interpretable at several trophic levels.

        •  The  indicators should be  socially relevant.  There must be  clear connections  with
           environmental values, and they must be responsive to the individual  or cumulative effects
           of a  broad array of potential stressors. An ideal  indicator is applicable in  a broad  range
           of surface  water types across the  nation.   Finally, it should provide early  warning of
           detrimental ecological change or indicate the early  stages of recovery.

        •  The  indicators must be sensitive to varying levels of stressors, but not to the degree that
           they produce false  alarms  or excessive  noise.  In fact, they  should be insensitive  to
           acceptable, natural variations or at  least useful for distinguishing  unacceptable and
           acceptable situations.  Another useful feature is sensitivity to  important episodes that do
           not coincide with the sampling period.

        •  Useful indicators are cost-effective, providing considerable information in a limited amount
           of sampling time.   They should be  implementable  by persons with  basic ecological
           training,  providing reproducible results with  low sampling variability.  Also,    tey

                                                 4-7

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           have  been  used successfully  in  long-term  monitoring  programs  by several  different
           investigators or agencies.

A  summary of how some candidate  indicators for inland  surface waters were judged against the EMAP
indicator selection criteria is listed in  Table  4-2.  Developing a strategy for  measuring indicators of inland
surface water condition is greatly aided by (1) knowledge of historical distributions of fish species in many
regions (Lee et al. 1980), (2) information from more than 20 years of benthic macroinvertebrate and trophic
state monitoring by  state agencies, and (3)  the presence of minimally  impacted reference areas in  most
regions.  The  EMAP-lnland Surface Waters strategy focuses on response indicators that are assemblages of
organisms,  as explained by  Courtemanch  et al. (1989), Karr (1981), Karr  et al. (1986), Ohio EPA (1988),
Plafkin et al. (1989), Schaeffer et al. (1988), and Schindler  (1987).  The data will be analyzed through use
of multiple structural and functional guilds and  integrating indices. Multimetric indices can be broken into
separate  metrics (and  scores) to  diagnose possible reasons for subnominal  condition and better interpret
biological responses.

Candidate indicators for inland surface waters that have been evaluated and proposed as research indicators
are shown  in  Figure 4-1.  The  identification of high-priority research indicators for inland surface waters
(Figure 4-1), although necessarily  subjective, was based on our review of the literature and experience with
inland  surface water monitoring  programs.  Uncertainty remains about some  research  indicators because
measurement techniques have not been thoroughly standardized or tested,  or some question exists about
whether  they are sufficiently sensitive or applicable to all inland  surface water classes and conditions.  In
some instances we have selected an analytical tool (e.g., Index of Biotic Integrity for streams of the Mississippi
basin), whereas in others we only list  an assemblage (diatoms and fish in lakes)  because the metrics are not
            EMAP-lnland  Surface Waters Indicator Strategy
   Response Indicators (R)

 Lake Trophic Status

 Fish Index of Biotic Integrity

 Macroinvertebrate
   Assemblage

 Diatom Assemblage in
   Lake Sediments

 Relative Abundance of
   Semiaquatic Vertebrates

 Top Carnivore Index: Fish

 External Pathology: Fish
   SPATIAL
ASSOCIATIONS
  TEMPORAL
ASSOCIATIONS
Exposure-Habitat Indicators (E)

 Water-Column and Sediment
  Toxicity

 Chemical Contaminants in Fish

 Routine Water Chemistry

 Physical Habitat Quality

 Biomarkers

 Water-Column Bacteria

 Heavy Metals and Man-Made
  Organics (Toxics)
 Figure 4-1.  Diagram of the proposed EMAP-lnland Surface Waters Indicator Strategy.  Indicators in bold
             are high-priority indicators.
                                                 4-8

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Table 4-1.  Chronology of EMAP Indicator Development for Inland Surface Waters

Biological Monitoring for Acidification  Effects Workshop (Burlington,  ON), March 1988.

Indicator Workshop  (Chicago, IL), October 1988

Workshop on Recovery of Lotic Communities and Ecosystems Following Disturbance:  Theory and Application
(Duluth, MN), October  1988

EPA Ecological Assessment Workshop  (Annapolis, MD), November-December 1988

Meetings with USCS and USFWS, (Washington, DC), December 1988, March 1989, August 1989, December
1989, April 1990

Monthly Aquatic Indicator Task Croup Conference Calls, January-June 1989

Biological Criteria Workshop (Annapolis, MD), February 1989

Water Quality Standards for the 21st Century (Dallas, TX), March 1989

Biological Monitoring Workshop (Athens, GA), March  1989

Rapid Bioassessment Protocols for Use in Streams and Rivers (published  May 1989)

International Symposium on the Design  of Water Quality Information Systems (Ft Collins, CO), June  1989

Indicator Workshop  (Corvallis, OR),  July 1989

Workshop on  Use of Biological Surveys for Diagnosing Aquatic Ecosystem Stressors (Minneapolis,  MN),
September 1989

National Symposium on Water  Quality Assessment (Ft. Collins, CO), October 1989

SETAC (Society  of Environmental Toxicology and Chemistry), Special Symposium on  Community Metrics
(Toronto, ON), October 1989

EMAP-lnland Surface Waters Workshop  (Las Vegas, NV), January 1990

U.S.D.A. Forest  Service  Biodiversity  Workshop (Corvallis, OR), February 1990

Biological Criteria Workshop (Corvallis, OR),  March 1990

EPA Science Advisory Board Ecoregion/Biocriteria Research  Review (Corvallis, OR), April 1990

Third Annual  Ecological Quality Assurance Workshop  (Burlington, ON), April 1990

ASTM (American Society for Testing and Materials) Aquatic Toxicology Symposium and  Sediment Toxicity
Taskgroup Meeting (San Francisco, CA),  April 1990.

Biomonitoring/Biocriteria Workshop  (Montgomery, AL), May  1990

Lake Indicators Workshop (New Orleans, LA), May 1990         	                    	
                                               4-9

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yet integrated into an index.  Comparison of responses from  a number of response indicators to known
stressors is necessary to reveal whether these research indicators are appropriate and sensitive for determining
status and trends in ecological condition.

A  brief description of each research indicator is given here.  More detailed descriptions of the indicators,
their application, suitable index period, variability, and research needs are listed in Appendices  B and G,
as referenced by the code in parentheses at the end of each description below.

4.2.4.1  Response  Indicators for Inland Surface Waters

Response  indicators are used by EMAP to quantify and classify the  condition of ecological resource classes.
Eight response indicators have been selected as research indicators for inland surface waters, all of which are
recognized as having high-priority status.

    Lake trophic status.  Is sensitive to chemical stressors, particularly nutrients.  It relates to lake primary
    production and clarity and is  of great concern to fishermen, boaters, and swimmers.  (B.1)

    Fish  Index of  Biotic  Integrity.  Is  sensitive to physical, chemical, and biological stressors; assesses
    taxonomic and trophic  groups, sensitive and  tolerant  species,  and  community abundance and
    condition; integrates species composition data into an  index understandable by the general public
    and meaningful to ecologists.   (B.2)

    Macro!nvertebrate assemblage. Is used  in  the  same way as the fish  IBI and  complements it; this
    indicator  is necessary in  small  streams containing few fish species or where fish are absent  (B.3)

    Diatom  assemblage  in  lake  sediments.   Diatoms are  sensitive to  water quality and  substrate
    changes; this indicator integrates water and bottom conditions and can be  used to assess  food  base
    and aesthetic appearance of lakes.  Existing data  bases make it useful for  assessing historical change.
    (B.4)

    Relative abundance of semiaquatic  vertebrates.  Is sensitive to physical,  chemical, and biological
    stressors; along with top carnivores, often the first species to disappear; of great interest to the public.
    It could  include amphibians, reptiles, birds, and  mammals.  (B.5)

    Top  carnivore index:   Fish.   Top carnivores are sensitive to  harvest pressure and  physical and
    chemical  habitat deterioration;  the index is most useful for salmonid streams containing only one
    or two fish species; it can be  used to assess condition of species of greatest interest to the public.
    The index includes information on size classes, growth, abundance, anomalies, and  management
    actions such  as stocking  and catch  restrictions.  (B.6)

    External pathology:  Fish. Is sensitive to  general stress and toxic chemicals; it can be used to assess
    presence of disease and  condition of populations and is of  considerable interest to persons who eat
    fish.   (B.7)

4.2.4.2 Exposure  and Habitat Indicators  for Inland  Surface  Wafers

Exposure  and habitat indicators are used by EMAP to identify and quantify changes  in exposure and physical
habitat that  are  associated with  changes in  response response indicators and  to address the traditional
concerns of management agencies and the public. Seven exposure and habitat indicators have been selected
as research  indicators for  inland surface waters, four of which are recognized as having high-priority status.
                                                 4-11

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High-Priority Research Indicators

    Water-column and sediment toxicity.  Provides a snapshot evaluation of toxicity using bioassays;
    can be used to estimate the  severity of toxic conditions  nationwide  and identify "hot spots."  (B.8)

    Chemical contaminants  in fish.  Assesses bioconcentration and  potential ecological hazard to long-
    lived and large species; direct measure of potential health problems  by human consumption.  (B.9)

    Routine water chemistry.  Evaluates a number of conventional  chemicals of importance to aquatic
    life; some chemicals are used to calculate trophic state; assesses  eutrophication, acidification, and
    salinity.  (B.10)

    Physical habitat quality.  Is sensitive to hydrological and physical changes; integrates morphological
    condition of stream or lake bed and banks; assesses suitability for spawning, rearing, and feeding by
    biota; offers a measure of the aesthetic appearance of the water body.  (B.11)

Other Research Indicators

    Biomarkers.  A desirable feature of a monitoring program would  be to detect an organism's  response
    to human-induced stresses  at the  biochemical and  cellular level  before the  stresses  produce  a
    detectable response at the organism and population levels.  Although the use of biomarkers as early-
    warning response  indicators  requires  more  basic  research,  their present value  for regional survey
    monitoring is to  provide  information to support or refute hypotheses  on why ecological condition of
    inland surface waters is subnominal.  (G2.1-G2.11)

    Water-column  bacteria.  Provides  a  snapshot evaluation of bacterial contamination;  can  be used
    to gauge the swimmability of waters  and the risk of illness from consumption  of fish or  shellfish.
    (B.12)

    Heavy metals and man-made organics (toxics).  Concentrations of toxics in waters and sediments
    are useful for evaluating possible acute and chronic exposures to aquatic biota.  Application of these
    indicators is recommended for sites with known or suspected toxicity.  (B.13)

4.2.4.3 Stressor Indicators for Inland Surface Waters

Stressor indicators of  importance to  inland surface waters differ from  those  important to other resource
categories. Assessments of proportions of terrestrial vegetation and diversity, patch size and connectivity,  key
physical features, and criteria air pollutants are markedly less important for inland surface waters than are the
landscape loading stressors (proportions of land use, pollution sources, drainage modifications).   Further,  our
concern is at the watershed  and  drainage level, not at the landscape  sampling unit  (LSD)  level.  Additional
research is required to determine  if regional correlations between  landscape features and inland surface  water
condition  can result from  randomly located LSUs as opposed  to the  more traditional watershed inventory
approach.  More detailed  discussions of EMAP stressor indicators are  presented in Sections 9.5 and  10.

4.2.5  Inland Surface Water Indicators Not Appropriate for EMAP

Community process and rate  measures (primary  production,  respiration,  nutrient  cycling) are  important
measurements of ecological condition in surface waters.  Long-term  data indicate, however, that they show
little change before,  or upon the occurrence of, important structural changes,  presumably because there are
too many compensating mechanisms (Schaeffer et  al. 1988, Schindler 1987). Important process changes are
often difficult to detect because the background variability is high (i.e., the signal-to-noise ratio  is low).  Use
                                                 4-12

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of these measures also requires  multiple measurements and site visits, which are incompatible with EMAP's
index period concept and spatial design (see Section 2.2).

Our focus on community structure, rather than on population rates,  precludes estimating  population birth,
growth, or mortality rates.  These rates tend to be more variable than community  measurements and to
require frequent  sampling for meaningful  assessments.  We do, however, propose  to include population
structure measurements  (density, size classes,  condition, and  scale  annuli  for growth  estimation) of top
carnivores or key species in species-depauperate systems.

Aside from sediment core diatoms  and  pigment concentration (in  the trophic state  index), we would not
recommend assessing microorganisms or plankton as response indicators.  Relative to macroorganisms, such
groups have  poorly  developed  taxonomies,  and taxon abundances fluctuate dramatically over short time
periods.  Pathogenic bacterial density is  included as an exposure indicator.

Internal gross pathology  of  fish is  too time-consuming  for  regional surveys.   External  pathology is  an
appropriate  substitute, however, and is associated  with  desirability of game fish.   Exposure and  habitat
indicators should  be designed to evaluate "presence of" and "absence of" conditions in a reasonably efficient
manner.   We recommend focusing on  physical habitat quality and biological  measurements of toxicity or
stress,  instead of  measurements  of toxic chemical concentrations. The latter are extremely expensive, often
relate poorly to toxicity, and involve a potentially enormous number  of chemicals, many of which lack toxicity
criteria.  Limited sampling for toxics would be possible in special cases (e.g., a repeat of the national dioxin
survey).


4.3  APPLICATION OF INLAND SURFACE WATER  INDICATORS

All inland surface water  resources are impacted to some degree and  thus  could be considered subnominal
in the strictest sense,  or, if  we consider  human impacts natural, all the resources  are  nominal.   Some
compromise between these two extremes is needed  to set criteria, one that is useful and protective without
being arbitrary.  Several  methods (involving the use of regional reference sites, historical  data, pristine sites,
and models) for determining  numerical criteria  can be used  to  decide if ecological condition, as  determined
by values of  response  indicators, is  nominal or subnominal.  Each  has advantages and disadvantages.

Our preferred option for interpreting  regional or national condition of inland surface waters uses a series of
regional reference sites (Hughes  et al.  1986; Hughes 1989).  The use of reference sites integrates professional
judgement, an understanding of historical or pristine conditions,  and  knowledge of current ecological research
in selecting the least disturbed but typical sites within a region.   The  condition (response indicator values) and
statistical variability found in  the reference sites become the model for the region.  This approach is being
considered by the EPA in its bioassessment, biocriteria, and nonpoint-source programs (Plafkin et  al.  1989;
U.S. EPA  1988; 1989a,b). The regional reference  site approach,  however,  may require sampling as many
as 800 benchmark sites (50  sites each for 16 surface water classes)  in the conterminous United States. Site
selection would require careful analyses  of mapped data and conscientious reconnaissance of candidate sites.
Although  large rivers or  large lakes  are  not  planned for  inclusion  in the  initial phases of implementation,
reference  sites for these resource classes can  be selected simply by  determining minimally impacted reaches
or shorelines in various sections of the river or lake (Hughes and  Gammon 1987; Ohio EPA 1988; these
references also describe sampling methods for  such waters).

Under ideal conditions, we could compare EMAP sample data with  historical  data (the second option) or
with data from existing pristine  sites (the third  option).  These  approaches are apparently what the authors
of P.L. 92-500 had in mind for the phrase "physical, chemical, and  biological integrity" (National Commission
on Water Quality 1976).  Unfortunately, historical data of appropriate quality are rare, except for sedimentary
diatoms.   Existing pristine sites could  be used in place of historical  data,  but these  sites are often  protected

                                                4-13

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because they  are unusual  and thus are rarely regionally representative.  Additionally, given the extent of
atmospheric deposition, few sites can be considered truly pristine.

A fourth  option for determining inland  surface water condition  is through the use of one or more models.
Models may be based on field data for a key variable or set of variables, complex ecosystem studies, trophic
state models,  or laboratory toxicity tests;  however, key exposure variables  differ considerably across the
nation.   Current research  on stream  ecosystems,  for example,  stresses  physical habitat in the Pacific
Northwest, flow in the Intermountain West, and sediment in the Midwest  Adequate  research to represent
this diversity of exposures requires many models and much data collection.   Ecosystem studies are  an
extremely expensive way  to generate models, and the sites studied  are likely to be  representative of few
resource classes. Trophic state models are inappropriate for streams and incorporate little insight into stressors
other than the oversupply of nutrients.  Toxicity tests have little relevance for nontoxic stressors (e.g., physical
habitat, flow,  food base, biotic management,  nontoxic contaminants).

Given the proposed set of research indicators, how are they linked, what measurements are to be taken,
and  how  are  the  data  to be analyzed and integrated?  These linkages and potential scoring criteria are
outlined for response indicators in  Table 4-3  and for exposure  and habitat indicators  in  Table 4-4.


4.4  RESEARCH NEEDS FOR  EMAP-INIAND SURFACE WATERS

We  have outlined a series of options  for determining subnominal thresholds for the response indicators.
While  we are likely to adopt  the  use  of  regional reference  sites  for setting  these thresholds, this subject
would  benefit from  substantial research.  Reference site selection and sampling would be required before
indicators are  monitored within a region.  Because the concept of reference sites is also central to biological
criteria for surface waters  (U.S. EPA 1990),  EMAP may further serve EPA and the states  by selecting and
monitoring a set of regional reference sites.

The  suite of research indicators for  inland surface waters requires diverse plans for their future development.
In the  near term, the selected fish and macroinvertebrate indices require development and testing outside
the regions and water bodies in which they initially have been developed (e.g., Miller et al. 1988 for the IBI).
For  example,  how  applicable  is  the  IBI  to the reservoirs of the  southeastern  United States?   What
macroinvertebrate  guilds dominate sand-bottomed desert and plains streams?  We also  must develop and
assess a diatom assemblage index or expand the environment  reconstruction approach  outlined by Battarbee
(1986).  Three general areas for long-term indicator development are ecological guilds, exposure indicators,
and  monitoring as research.

4.4.1 Ecological Guilds

The  fish and macroinvertebrate indices  are based  on a limited number of metrics. Development of several
other metrics  would offer  further insight  into ecological assessments.   For example, Margalef (1963)  listed
several characteristics of mature and immature ecosystems from which additional indicator guilds  could  be
developed.  How might other  guilds and the guilds we have already proposed as research indicators respond
to different types and intensities of disturbance?

We  need answers  to questions  regarding "natural" and acceptable  variability versus  "anthropogenic" and
unacceptable  variability.  There is evidence that some perturbation is  stimulatory.  Are communities/species
that experience considerable natural variability more resilient  (pre-adapted) to disturbance?  Does variability
consistently increase with  disturbance?  If the answer to both of these questions  is yes, then perhaps  some
statistical measurement of species variability itself would be a  useful indicator.  How is variability that results
from natural changes distinguished from  variability resulting from anthropogenic changes, especially in a setting
where historical anthropogenic changes now appear "natural."  Examples include  absence of snags in  rivers,

                                                 4-14

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4-16

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loss of headwater streams and wetlands, and altered trophic states.  Might highly stressed systems be  more
structurally stable than natural or slightly stressed systems if only the most tolerant organisms persist there?
Perhaps variability as a function of stress is  described by a hyperbolic function.

To what degree are species that are tolerant of naturally occurring stressors also tolerant of anthropogenic
contaminants?  Species differ in their  life cycles  (simple-complex),  reproduction  (r-K), survivorship (type  1,
2, or 3), feeding and  habitat requirements (specialized-generalized),  life span (short-long), and individual size
at maturity (small-large).  Are any of these species characteristics effective measures of perturbation in surface
waters?  Thoughtful  examination of the behavior of various guilds is certain to increase our understanding
of ecological guilds and their response to different stressors.

4.4.2   Exposure and Habitat Indicators

Several aspects of exposure and  habitat indicators require development  Biomarkers appear to present  some
promising possibilities for application in EMAP; however, additional research is needed to improve our ability
to interpret the results from these measurements.  Given the increasing range of chemicals being introduced
into aquatic systems, there is a growing need for measurement techniques that provide a chemical screen for
various classes of compounds.   These screens  would improve our ability to identify potential exposure  to
classes of chemical compounds and improve the diagnostic capability of EMAP  and other programs that use
bioassessmenL

4.4.3   Monitoring as Research

Careful study of the data  base generated  by  EMAP is likely to produce long-term  improvements in our
understanding and application of ecological indicators.  A consistently collected set of  species composition
and abundance data from across the nation  can be used to produce unequivocal answers about the various
components  of indicator  variability  (measurement,  index  period,  interannual, among-site, within-site).
Presently, our variability estimates are based on a few sites in  a small number of places over relatively  short
time frames.

It is useful to consider our current environmental management practices as manipulations or experiments  in
progress; however, few scientists are proposing hypotheses or collecting sufficient data to test them. EMAP
offers scientists that  opportunity because the data will be freely available.   Comparisons of disturbed and
relatively undisturbed sites would reveal the ecological impairments, on a national and regional scale, of our
current land  use  practices  and  water resource regulations.   These observations can serve to generate
additional hypotheses and bona fide experimental manipulations, especially ecosystem restorations.  Research
on restoration ecology in streams and  lakes would not only improve our  knowledge of indicators, it would
also demonstrate the ecological  benefits of restoration on a national  scale.


4.5  REFERENCES

Battarbee, R.W.  1986.  Diatom analysis.  Pages 527-570.  In:  B.E. Berglund,  ed.  Handbook of Holocene
Paleoecology and Paleohydrology.  John  Wiley & Sons, New York.

Carlson, CA., and R.T.  Muth.  1989.  The Colorado  River:  Lifeline of the  American Southwest.  Pages
220-239.  In: D.P. Dodge, ed.  Proceedings of the International Large Rivers Symposium. Can. Spec.  Publ.
Fish. Aquat. Sci. 106.

Courtemanch, D.L.,  and S.P. Davies.  1988. Implementation of biological standards and criteria in Maine's
water classification law.  Pages  4-9. In:  T.P. Simon, L.L. Hoist, and L.j. Shepard, eds.   Proceedings of the
                                                4-17

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First National Workshop on Biological Criteria.  EPA 905/9-89/003.  U.S. Environmental Protection Agency,
Chicago, IL.

Courtemanch, D.L, S.P. Davies, and E.B. Laverty.  1989.  Incorporation of biological  information in water
quality planning.  Environ. Manage. 13:35-41.

Ebel, W.J., CD. Becker, J.W. Mullan, and  H.L Raymond.  1989.  The Columbia River - toward a holistic
understanding.   Pages 205-219.   In:   D.P. Dodge, ed.   Proceedings of  the  International  Large  River
Symposium.  Can. Spec. Publ. Fish. Aquat  Sci.  106.

Ehrlich, P.R., A.H. Ehrlich, and J.P. Holdren.  1977.  Ecoscience:  Population,  Resources, Environment
Freeman, San Francisco, CA.  1051 pp.

Geraghty, J.J., D.W. Miller, F. Van Der Leeden, and F.L. Troise.  1973.  Water atlas of the United States.
Water  Information Center, Inc., Port Washington,  NY.  122 plates.

Hesse, L.W., J.C. Schmulbach, J.M Carr, K.D. Keenlyne, D.C. Unkenholz, J.W. Robinson, and G.E. Mestl.
1989.  Missouri River  fishery resources in relation  to past, present, and future stresses.  Pages 352-371.  In:
D.P. Dodge, ed.  Proceedings of the International Large River Symposium.  Can. Spec. Publ. Fish. Aquat. Sci.
106.

Hughes, R.M.  1989.  Ecoregional  biological criteria. Pages 147-151.  In:  Water Quality Standards for the
21st Century.  U.S. Environmental  Protection Agency,  Office of Water, Washington, DC.

Hughes, R.M.,  and J.R. Gammon.  1987.   Longitudinal  changes in fish assemblages and water  quality  in
the Willamette  River,  Oregon. Trans. Am.  Fish. Soc. 116:196-209.

Hughes, R.M.,  D.P. Larsen, and J.M. Omernik.  1986.   Regional reference  sites:  A method for assessing
stream potentials.  Environ. Manage. 10:629-635.

Isom, B.C.  1969.  The mussel resource of the Tennessee  River.  Malacologia 7:397-425.

Jenkinson, J.J.   1981.  Endangered  or threatened aquatic  mollusks of the Tennessee River system.  Bull.
Am. Malacolog. Union 1981:43-45.

Karr, J.R.  1981.  Assessment of biotic integrity using fish communities.  Fisheries  6:21-27.

Karr,  J.R.,  and D.R.  Dudley.  1981.   Ecological perspective on water quality goals.   Environ. Manage.
5:55-68.

Karr,  J.R.,  L.A. Toth, and D.R. Dudley.   1985.  Fish  communities of  midwestern  rivers: A  history  of
degradation.  Bioscience 35:90-95.

Karr, J.R., D.D. Fausch, P.L Angermeier, P.R. Yant, and I.J. Schlosser.  1986.  Assessing biological integrity
in running waters:  A method and  its rationale.    Special  Publication  5.  Illinois  Natural  History Survey,
Champaign.  28 pp.

Lee, D.S., CR. Gilbert, C.H. Hocutt, R.E. Jenkins, D.E. McAllister, and J.R. Stauffer, Jr.  1980. Atlas  of
North American Freshwater Fishes. North Carolina State Museum of Natural History,  Raleigh.  854 pp.

Margalef, R. 1963.   On certain unifying principles in ecology.  Am. Natural. 97:357-374.
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Miller, D.L, P.M. Leonard, R.M.  Hughes, J.R.  Karr, P.B. Moyle, LH. Schrader, BA, Thompson,  RA
Daniels,  K.D. Fausch, CA. Fitzhugh, J.R. Gammon,  D.B. Halliwell, P.L Angermeier, and  D.J. Orth.
1988.  Regional applications of an index of biotic integrity for use in water resource management  Fisheries
13:12-20.

Miller, R.R.  1961.  Man and the changing fish fauna of the American Southwest   Pap. Mich. Acad. Sci.
Arts Lett 46:365-405.

Miller, R.R., J.D. Williams, and J. E. Williams.   1989.  Extinctions of North American fishes during the
past century.  Fisheries 14:22-38.

Moyer, S.  1986.  Aquatic contaminants:  A threat to the sport fishing industry.  Sport Fishing Institute,
Washington, DC. 165 pp.

National Commission on Water Quality. 1976.  Water quality analysis and environmental impact of Public
Law 92-500.  Washington,  DC.  27 pp.

NRC.   1977.  Environmental monitoring.  National Academy of Sciences, National Research Council Study
Group on Environmental  Monitoring,  Washington, DC.  173  pp.

Ohio  EPA.  1988.  Biological criteria for the protection of aquatic life.  Division of Water Quality Monitoring
and Assessment,  Columbus, OH.  384 pp.

Plafkin, J.L M.T.  Barbour, K.D. Porter, S.K. Gross, and R.M.  Hughes.   1989.   Rapid bioassessment
protocols for use in  streams and rivers:  Benthic macroinvertebrates  and fish.   EPA  444/4-89/001.   U.S.
Environmental Protection  Agency, Washington, DC.  162 pp.

SAB.  1980.  Goals and criteria for design of a biological monitoring system.  U.S.  Environmental Protection
Agency, Science Advisory Board, Ecology Committee, Washington, DC.  53 pp.

Schaeffer, D.J., E.E. Herricks, and H.W. Kerster.  1988.  Ecosystem health:  I. Measuring ecosystem health.
Environ. Manage. 12:445-455.

Schindler,  D.W.  1987.  Detecting ecosystem responses to anthropogenic stress.  Can. J. Fish.  Aquat Sci.
44:6-25.

Smith, P.W.   1971.   Illinois streams:   A classification  based on their fishes  and an analysis  of factors
responsible for disappearance of native species.  Biology Notes  76.  Illinois Natural History Survey, Urbana.
14 pp.

USDOI.  1989.  News release.   July  17.  U.S.  Department of the Interior, Washington, DC.

U.S.  EPA.  1983.  Water quality standards regulation.  U.S. Environmental Protection Agency.   Fed. Regis.
48 (217):51400-51413.

U.S. EPA.  1984.  Monitoring strategy. U.S.  Environmental Protection Agency, Office of Water, Washington,
DC.   132 pp.

U.S.  EPA.  1987a.  Surface water monitoring:  A framework for  change.   U.S.  Environmental Protection
Agency, Office of Water and Office of Policy, Planning, and Evaluation, Washington, DC.  57 pp.
                                               4-19

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U.S. EPA. 1987b.   Unfinished  business:  A comparative assessment  of  environmental  problems.   U.S.
Environmental Protection Agency,  Office of Policy Analysis and Office of Policy,  Planning, and Evaluation,
Washington, DC.  100 pp.

U.S. EPA. 1988.  Report of the national workshop on instream  biological monitoring and criteria.   U.S.
Environmental Protection Agency,  Office of Water Regulations and Standards, Washington, DC.  34 pp.

U.S. EPA. 1989a.  Nonpoint-sources agenda  for the future.  U.S.  Environmental  Protection Agency, Office
of Water, Washington, DC.  31  pp.

U.S. EPA. 1989b.  Region IV workshop on biomonitoring and biocriteria.  U.S.  Environmental  Protection
Agency, Water Quality Management Branch and Ecological Support Branch, Atlanta, GA.  26 pp.

U.S EPA.  1990.   Biological criteria:   National program guidance for surface waters.  EPA 440/5-90/004.
U.S. Environmental Protection Agency, Office of Water Regulations and Standards, Washington, DC.

U.S. Fish and Wildlife Service.  1986.  Endangered and threatened wildlife and  plants.   Washington, DC.
30pp.

U.S. GAO.  1981.  Better  monitoring techniques are  needed to  assess the quality of rivers and  streams.
Volume I.  CED-81-30.  U.S. General  Accounting Office, Washington, DC.

U.S. GAO.   1988.    Protecting  human health  and  the environment through improved  management
GAO/RCED-88-101.  U.S. General Accounting Office, Washington, DC.  246 pp.

Williams, J.E., J.E. Johnson, D.A. Hendrickson, S. Contreras-Bolderas, J.D. Williams, M. Navarro-Mendoza,
D.E. McAllister,  and J.E. Deacon. 1989.  Fishes of North America endangered, threatened, or of special
concern:  1989.  Fisheries 14:2-20.
                                               4-20

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                                             SECTION 5

                               INDICATOR STRATEGY FOR WETIANDS

                               Nancy C. Leibowitz1 and Mark T. Brown2
5.1  INTRODUCTION
Wetlands are "areas that are inundated or saturated by surface or ground water at a frequency or duration
to support, and that under normal circumstances do support, a prevalence of vegetation typically adapted for
life in saturated  soil conditions" (33 CFR Section  328.3).  Wetlands are  productive, diverse ecosystems that
are important to both the environmental  and economic health of the nation, and  wetlands  provide habitat
for wildlife and endangered species, nurture commercial and  recreational fisheries, help reduce flood damages,
and abate water pollution (Conservation Foundation 1988).   Although the EMAP wetland resource category
includes all wetlands as defined above, this section focuses on inland wetlands.

5.1.1  Legislative Mandate for Wetlands  Monitoring

Historically, wetlands have been the object of efforts to convert land to "more productive" use.  Such efforts
have resulted in the loss  of more than 50%  of the nation's contiguous wetlands since presettlement times.
Wetlands are currently affected by both habitat modification (including both hydrologic and physical alteration)
and contamination from  point and nonpoint sources  of pollution.

As perceptions of the role of wetlands in the  landscape have changed,  pressure has  mounted to conserve
these resources  for future generations.  Since the early 1970s, interest in wetland  protection has increased
significantly as scientists  begin to identify and quantify the many  values of these ecosystems.  Interest in
wetland protection  has been translated at both  the  federal and state level  into  laws  and  public  policies
(Mitsch and Gosselink 1986).  Table 5-1 lists the most  pertinent  legislation  protecting  wetland function and
extent.  The  most recent additions to this list  include the proposed EPA-Army  Corps of Engineers wetland
mitigation policy and  the National Wetland  Forum's  recommendations  of  "no net  loss  of the nation's
remaining wetland base,  as defined by acreage and function" (Conservation Foundation 1988).

Despite current  progress  in wetlands conservation, there is concern that the successes  in preserving wetland
acreage fall far short of what is needed to effectively maintain wetland  functions (Zelazny and  Feierabend
1988).  Implementation of EMAP will  allow progress  in protecting  the wetland  resource to be evaluated at
both regional and national scales.

5.1.2  Wetland  Resource Classification

The proposed resource classes for inland wetlands  are listed in Table 5-2.  These classes were modeled on
the Cowardin wetland classification system (Table 5-3, Cowardin  et al. 1979),  but minor modifications were
made to meet EMAP monitoring objectives and its design constraints.  The EMAP sampling design constraints
are to (1) ensure that the wetland classification  includes classes  that are functionally distinct; (2) limit  the
number of wetland classes per region to enable an adequate number of samples  per class, given logistics and
costs; (3) include only those wetland classes that  would be  detectable on 1:40,000 aerial imagery (resource
sampling units greater than 1.0 ha on average); and  (4) define distinct and logical  boundaries between
    'NSI - Environmental Sciences, EPA Environmental Research Laboratory, Corvallis, Oregon

    University of Florida, Center for Wetlands, Gainesville.

                                                 5-1

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Table 5-1.  Major  Federal  Laws,  Directives, and Regulations  for the Management and Protection of
           Wetlands
          Directive
                               Date
                                Responsible Agency
Executive Order 11990
  Protection of Wetlands

Executive Order 11988
  Floodplain Management

Federal Water Pollution
  Control Act (PL 92-500)
  as Amended

  Section 404-Dredge and
   Fill Permit Program

  Section 401--Water
   Quality Certification

National Environmental
  Policy Act

Coastal Zone Management
  Act
                            May 1977


                            May 1977


                            1972, 1977
                               1975


                               1972
                                All agencies


                                All agencies
                                All agencies

                                All agencies




                                All agencies

                                Office of
                                Coastal Zone Management
Table 5-2. Proposed EMAP-Wetland Resource Classes
System
       Class
    Colloquial Name
Lacustrine


Palustrine
 Riverine
Shallows
Emergent (flooded)

Shallows

Emergent
  Flooded
  Saturated

Scrub/Shrub
  Saturated
  Flooded

Forested
  Saturated
  Flooded

Shallows
Emergent (flooded)
Aquatic beds, mudflats
Marsh
Aquatic beds, mudflats, open water, playa
lakes or basins,  farm ponds, prairie pothole
                                     Marsh, prairie pothole
                                     Fen, bog, wet meadow
                                     Carolina  bays, pocosins,  bog
                                     Carr
Blue spruce bogs, white cedar swamps
Bottomland hardwoods, cypress swamps

Aquatic beds, mudflats
Marsh
                                               5-2

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Table 5-3.  Traditional Cowardin System for Defining Wetland  Classes1
System
Subsystem
   Class
Lacustrine
Palustrine
Littoral
Riverine
Lower Perennial
Aquatic Bed
Unconsolidated Shore
Non-Persistent Emergent

Aquatic Bed
Unconsolidated Shore
Moss-Lichen
Emergent
Scrub-Shrub
Forests

Aquatic Bed
Unconsolidated Shore
Non-Persistent Emergent
1 From Cowardin et al. (1979).
wetland  resource classes and the classes selected  by the EMAP Inland Surface Waters and Near-Coastal
groups.

We adopted the general framework of the Cowardin wetland classification scheme (including the Lacustrine,
Palustrine, and Riverine systems), but modified the  classes according to the following rules:

        •  Lumping of many subclasses of the full  Cowardin et al.  (1979) wetland classification
           was deemed necessary to allow for an  adequate number of samples for each  class
           that was refined, given the current  EMAP sampling design (Section 2.2).  For example,
           aquatic  beds, mudflats, and open  water areas are lumped into a new class termed
           Shallows.

        •  A vernal pools class was not included  because the pools would  not be detectable  by
           the highest resolution imagery currently  planned to  be provided by EMAP landscape
           characterization activities (1:40,000 scale with 1.0-ha minimum area).

The colloquial names of the wetlands in each class were included for clarification. These were derived from
(a) the list of  colloquial wetland  classes  provided by  the EPA  Office  of Wetlands Protection  and (b) both
regional  and  local  sources of wetland  classifications  (often provided  by regional wetland experts).   This
wetland class list will be refined in cooperation with  the EPA Office of Wetlands Protection and the U.S. Fish
and Wildlife Service's National Wetland Inventory (NWI).

Given  the legislative mandate for wetland monitoring and a classification system to emphasize ecologically
important regional wetland resources, the following four  topics  of the EMAP indicator strategy for wetlands
are discussed in  this section.
                                                 5-3

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        1.  Wetland qualities and health as perceived by the public and their relevance to the
           selection of EMAP indicators

        2.  High-priority research indicators that have  been  proposed for further testing  by
           EMAP,  utilizing both small-scale field testing and analysis of existing data sets

        3.  Other research indicators and  research priorities that will  be addressed during the
           next five years

        4.  Determination  of subnominal thresholds for response indicators  and the importance
           of understanding causal  pathways of potential stressors in  wetlands


5.2  IDENTIFICATION OF WETLAND INDICATORS

5.2.1   Perceptions  of Wetland Condition

Depending on the  U.S. region, wetlands  represent 3-36% of the landscape. They offer  opportunities for
aesthetics and recreation (hiking, canoeing, birding, hunting, and fishing), for preservation and  protection of
native  animal species, and for research and  education.   Recent national, state, and  private acquisitions of
wetland areas underscore the increasing public awareness of the importance of preserving wetlands.

Loss of wetland  acreage has  been highlighted in the minds of the public in  recent years so that acreage by
itself has become virtually synonymous with wetland condition.  Loss of wetland acreage and declines in the
quality of wetland habitat have been suggested as causes of decline in some animal species.  Many species
of threatened  birds and  mammals have been shown to be "wetland-dependent," a term  that has become
increasingly important to the public.  However, the focus on loss of wetland acreage and its  direct effects
on  native animal species has overshadowed  a second extremely important change that may be occurring:
the loss of wetland functional integrity or health.  This change, which is demonstrated  by rapid changes in
species composition due to the invasion of exotic species and species considered noxious, is often  attributed
by the public to human  impacts.

5.2.2  Environmental Values for Wetlands

Environmental values for  wetlands are related to three groups  of ecological functions:   (1) water  quality
functions, (2) water quantity (hydrologic) functions, and (3) ecological  support  In addition,  "no net loss of
wetlands" is now a goal for federal agencies  because of the  importance of  these  functions.

Wetlands serve  two primary functions relative to water quality (Preston and Bedford  1988).  They improve
water quality through sedimentation, pollutant immobilization,  and limited uptake of various pollutants and
nutrients (Kuenzler  1989).   Secondly, their organic substrate can act as a filter to immobilize substances as
they pass from surface waters through wetland soils to ground waters.

The hydrologic functions associated with wetlands include water storage, flood abatement, and ground water
recharge and discharge.  Wetlands can act  as buffers against flooding by storing large influxes of storm water
and releasing  it slowly, minimizing flood  peaks and  maintaining base flow.  Wetlands  can  serve as either
ground water discharge  or infiltration areas, depending on  local hydrologic and climatologic regimes.  In
individual wetlands, bidirectional flow is a seasonal characteristic of some wetland classes.

Wetlands are also important for supporting aquatic and terrestrial organisms. Wetland productivity, which
often exceeds that of surrounding ecological  communities, can sustain  not only internal trophic relationships
                                                 5-4

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but also external trophic relationships, that is, those that depend on the export of biomass from wetlands.
Wetlands  also offer habitat features not found in other vegetation types.

5.2.3  Hazards to Wetlands

Four major hazards are associated with wetland  loss or damage:  (1) hydrologic source alteration, (2) direct
physical alteration, (3) toxic contaminant influx, and  (4) nutrient input and sediment imbalance.  Vegetation
removal, invasion by exotic  or  nuisance species, and global atmospheric change are  also hazards.   The
relative importance of these  hazards varies by region and according to the specific type and configuration of
the hazard.  Although  global climate change is not presently  a known stressor,  it  has the  potential for
overshadowing the effects of all other stressors  in the future.  Excellent overviews of hazards to wetlands
are provided in Adamus (1988)  and  Mitsch and Gosselink (1986).

5.2.4  Wetland  Indicators Appropriate for  EMAP

To date, no widely accepted set of measures for  determining wetland health exists,  although many candidate
indicators  have been suggested.  The approach  to selecting  a set of  research indicators  for inland wetlands
is discussed  below. A chronology of  indicator development activities by EMAP-Wetlands  scientists is outlined
in Table 5-4.  A written external peer review of these indicators was performed in April 1990 (Appendix I.8),
followed by a review by the EPA Science Advisory Board in May 1990.  EMAP workshop participants and
external reviewers are  listed in Appendix I.

The  designation of wetland research indicators  as having high-priority  status,  while  necessarily subjective,
was  based on a  review  of the literature and  experience with wetland monitoring programs.  For some high-
priority research indicators, either measurement techniques have not been thoroughly standardized or tested
or some question exists about whether  they are  sufficiently sensitive or  applicable to  all wetland classes.
Small-scale field tests and regional demonstration projects will further aid the development of standardized
protocols  for  high-priority research  indicators.   Comparison  of  how response indicators react to known
stressors  is  necessary   to reveal  whether  research  indicators  are  appropriate  and  correspond  to  the
environmental values for wetlands (see 5.2.2).

A goal of  EMAP is to monitor a set of response, exposure, and habitat indicators that address  all the major
hazards or impacts that wetlands are (or could  be) experiencing. Although it is impossible to  anticipate all


Table 5-4.  Chronology of EMAP Indicator Development for Wetlands


1989-1990      Refined a list of candidate  wetland indicators through informal  interactions with wetland
                scientists.

August 1989    Presented an initial  list of proposed EMAP indicators for wetlands.

October 1989   Presented a description of EMAP wetland indicators at the EPA Water Quality  Symposium.

March 1990    Comprehensively reviewed the  literature on  wetland  indicators and mapping found  sites
                during the compilation of a draft report for the EPA Office of Policy and  Program Evaluation.

April 1990      Review of EMAP wetland indicators by external reviewers.

May 1990      Review of EMAP wetland indicators by the  EPA Science Advisory  Board.
                                                 5-5

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future hazards (emerging problems), the set of indicators is linked to fundamental ecological processes andthus
could indicate current or  impending widespread impacts.  Ultimately, we need to develop the knowledge
base that will allow us to distinguish those impacts that are detrimental to long-term wetland  sustainability
from effects of natural perturbation (e.g.,  climate or beaver activity) as well as resource management (e.g.,
waterfowl management).

In evaluating a list of  candidate indicators for wetlands,  research indicators were proposed that are believed
to be both widely applicable and sensitive to wetland hazards (Table 5-5).  The research indicators assigned
high-priority status have been extensively used for characterizing or describing site-specific wetlands.  Criteria
used for evaluating candidate wetland  indicators include those listed  in Table 5-5.

The response, exposure, and habitat indicators for wetlands  proposed for further testing are  illustrated  in
Figure 5-1.  A brief description of each research indicator is provided below.  More detailed descriptions  of
these indicators, their  application,  suitable  index  period, variability, and research  needs  are  listed  in
Appendices C and G, as referenced by the code in parentheses that follows each  description below. More
detailed  discussions of EMAP stressor indicators are presented in  Sections 9.5 and 10.
                          EMAP-Wetlands Indicator Strategy
      Response Indicators (R)
    Organic Matter and Sediment
      Accretion

    Wetland Extent and Type
     Diversity*

    Abundance and Species
     Composition of Vegetation*

    Relative Abundance: Animals

    Leaf Area, Solar Transmittance,
     and Greenness

    Macro! nvertebrate Abundance,
      Biomass, and Species
      Composition

    Soil and Aquatic Microbial
     Community Structure

    Demographics: Animals

    Morphological Asymmetry:
     Animals
   SPATIAL
ASSOCIATIONS
  TEMPORAL
ASSOCIATIONS
Exposure-Habitat Indicators (E)

 Nutrients In Water and Sediments

 Chemical Contaminants in Water
  and Sediments

 Hydroperiod

 Linear Classification and Physical
  Structure of Habitat

 Landscape Pattern

 Biomarkers

 Bioassays

 Chemical Contaminants in Tissues
 Figure 5-1.  Diagram of the proposed EMAP-Wetlands Indicator Strategy. Indicators in bold lettering are
             high-priority research indicators. Response indicators with an asterisk (*) also function as
             exposure and habitat indicators.
                                                  5-6

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                                     5-7

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5.2.4.1  Response Indicators for Wetlands

Response  indicators are used  by  EMAP to quantify and classify the condition  of ecological resource classes.
Nine response indicators have been selected as research indicators for wetlands, four of which are recognized
as having  high-priority status.

High-Priority Research Indicators

Organic Matter and Sediment Accretion.  Sediment accretion refers to the  accumulation of both mineral
and organic matter in wetlands.  The mineral portion  of sediments, in particular, enters the system through
external pathways (such as overbank flow) and is thus a surrogate for wetland hydrology.  Sediment  accretion
rates also provide a good indication of trends in  trophic status and  long-term  sustainability of ecological
values.  Changes in environmental processes on surrounding landscape, such as accelerated rates  of drainage
or erosion, are often  reflected in altered wetland hydrology and subsequent sediment accretion  rates.  The
rates of organic matter and sediment accretion integrate  both the  (1)  hydrologic history and  (2) vegetation
response and primary productivity of a wetland.  The rates of organic matter and sediment  accretion may
indicate water  purification  capacity, habitat  quality for  particular groups of species,  and  the long-term
sustainability of a wetland.  Significant change in these rates often is  an early warning of deteriorating wetland
condition. (C.1)

Wetland Extent and Type Diversity. This measurement  indicates the geographic extent and  distribution of
wetlands.  Changes in areal extent and diversity of vegetation types indicate regional  or national "hot spots"
of detrimental impacts.  (C.2)

Abundance, Diversity,  and Species Composition of Vegetation.   Wetland plants  are  reliable indicators of
certain stressors,  hydrologic conditions,  and habitat values.  Plants  are immobile  and sampling methods are
well  developed.   (C.3)

Relative Abundance:  Animals.   Presence of certain  water bird species  is indicative  of landscape health.
Water  birds also serve  as  bioaccumulators  and are  highly noticeable  to the public.   Contamination  or
population measures  may reflect problems with other resource categories that  serve  as part-time habitat
(C.4)  The usefulness of other classes of animals as indicators of wetland health  is being evaluated.  (G1.1)

Other  Research  Indicators

Leaf Area, Solar Transmittance, and Greenness.   Changes in canopy  characteristics  (e.g., premature leaf
drop and  yellowing of leaves) occur  in response to environmental stress and are highly visible to the public.
Solar transmittance has potential  as a surrogate for  biomass estimates in that it is a nondestructive technique
and  is  less time-consuming.  (C.5)

Macroinvertebrate Abundance,  Biomass, and Species Composition.   Macroinvertebrates are  sensitive to
biological, chemical, and physical stressors.  They are critical components of the food chain and support
animal species  of public concern.  (C.6)

Soil  and Aquatic Microbial Community  Structure.    Microbial communities are  sensitive  to  some
contaminants  and  are  linked to fundamental  ecological  processes  such  as nutrient cycling and  litter
decomposition.   (C.7)

Demographics:  Animals.  Population vigor is reflected in the recruitment of individuals into the breeding
population and  their subsequent  survivorship.  Parameters include age structure, sex ratio, fertility,  mortality,
survivorship, and dispersal; and such measurements are only appropriate for definite keystone species. (C1.2)
                                                  5-8

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Morphological Asymmetry:  Animals.  The  morphological variability in structures such as teeth and bones
of bilaterally symmetrical  organisms has been found to increase with exposure to  chemical  contaminants,
hybridization,  and  inbreeding.  This parameter  would  be an  early-warning indicator of  population-level
responses to human-induced stresses.  (G1.3)

5.2.4.2  Exposure and Habitat Indicators for Wetlands

Exposure and  habitat indicators are used by EMAP to identify and quantify changes in exposure and physical
habitat that are associated with changes in response indicators.  Ten exposure and response indicators have
been  selected as research indicators, seven of which are recognized as having high-priority status.

High-Priority  Research Indicators

Wetland Extent and Type Diversity. In addition to being a response indicator,  changes in areal extent and
diversity of vegetation  types determine the quantity of habitat accessible to animal populations  in a region.

Abundance, Diversity, and  Species Composition of Vegetation.  In addition to being a response indicator,
these measures reflect the quality of animal habitat as opposed to pattern and extent measures, which reflect
the quantity of habitat.  (C.3)

Nutrients in Water and  Sediments.  A primary stress to wetlands is high nutrient loadings  from urban and
agricultural  runoff and from sewage inflows.   Unnaturally high nutrient loadings deleteriously affect plant
species composition and,  subsequently, animal populations.  (C.8)

Chemical Contaminants in  Water  and Sediments.  Measurement of contaminants in wetlands  will  provide
a diagnostic tool for interpreting bioaccumulation data.  Wetlands are important  sinks for metals  and organic
compounds because most wetlands are recipients of urban and agricultural runoff, sewage, and other aquatic
pollutants.   (C.9)

Hydroperiod.  Hydroperiod is  defined  as total  days of inundation  per year,  and  is  the  most significant
controlling factor shaping wetland structure and  function.  It is also the factor  most often  manipulated by
humans.  Changes in hydroperiod have significant effects on species composition, nutrient pathways and rates
of cycling,  habitat quality, and gross production.   (C.10)

Linear Classification and Physical Structure of Habitat  The distribution and disappearance of certain
species  is  associated with  critical  features of their habitat   Many studies  have  also  demonstrated the
relationship between animal diversity and vertical vegetation  profile.  (G3.2)

Landscape Pattern.   Landscape indicators, calculated from data derived from remote sensing, describe the
spatial distribution  of  physical,  biological, and  cultural  features across a geographic area.  These  spatial
patterns directly reflect the available animal habitat as well as predict other functional attributes of wetlands,
including ground water recharge, contaminant interception potential,  and  storm water detention.   (G3.3-
G3.8)

Other Research Indicators

Biomarkers.   A desirable feature of a monitoring program would be to detect an  organism's  response to
human-induced  stresses  at  the biochemical  and cellular  level before  the stresses  produce a detectable
response at the organism  and population  levels.  Although the use of biomarkers as early-warning response
indicators requires  more  basic research,  their present value  for regional  survey monitoring  is  to provide
information  to support or refute hypotheses on  why the ecological  condition  of wetlands is subnominal.
(G2.1-G2.11)

                                                  5-9

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Bioassays.  One of the recognized functions of wetlands is their ability to filter pollutants from point and
nonpoint sources.  Excessive pollutant loading,  however, can overwhelm the assimilative capacity of wetlands
and result in degradation of their biological  condition.  The ability of wetlands to sustain healthy organisms
can indicate the degree of wetland contamination.  Bioassays  involve placing an organism or population in
the field or into "microcosms" constructed with materials from the field to be tested in the laboratory. (C.10)

Chemical Contaminants in Tissues.   Measurements of contaminant bioaccumulation in  plant and animal
tissues would  be an indicator of exposure to contaminants and should help  explain why wetlands are in
subnominal  condition.  (C.11)

5.2.5   Wetland  Indicators Not Appropriate for EMAP

Response indicators of wetland condition for a regional and national program employing an  index concept
must  include the following general  characteristics:

    •   The suite  of indicators must be limited in number.  A national program for assessment of
       wetland condition cannot sample every constituent or structural  component

    •   Measurements  must be integrative to detect condition.   The chosen suite of indicators must
       integrate the broad  spectrum of wetland resources and their  responses to stressors.

    •   Measurements that require frequent sampling intervals or spatially intensive sampling techniques
       are inappropriate.

    •   Indicators must respond quickly to stressors.  Long-lived components or  those with exceptional
       capabilities of adapting to  stressors may not be appropriate.

When evaluated  by these criteria, many indicators proposed in the literature (Schaeffer  et al. 1988; Brooks
and Hughes 1988; Adamus et al.  1987; Chapman et al. 1987) are not suitable for EMAP. Others are clearly
not appropriate  because they are not ecologically significant  in wetlands or are too complex  to interpret
Similarly, some community-level indicators that are good measures of wetland condition also are inappropriate
for EMAP.  Community-level indicators have been  proposed as the simplest and possibly most integrative of
ecological indicators.  Generally accepted community-level effects of changes in  ecological condition can be
grouped  into five categories (after Schaeffer et al. [1988], and Rapport et al. [1985]):  (1) decline in numbers
of native species, (2) change in standing biomass, (3) change in net  or gross primary production  (4) change
in pathways of nutrient cycling, and (5) retrogression.

The first  community-level  indicator  listed above, decline in  numbers  of native species, is actually one metric
of the high-priority research indicator "Abundance,  Diversity, and Species Composition of Vegetation." Ratios
of native vegetation species  to exotic species are expected to be sensitive  indicators  of ecological stress.
Within each of the other categories listed, however, measurements exist that could detect changes in wetland
health, but  because of measurement  constraints, are  inappropriate  for a national program.   For example,
effects of stress may be exhibited as reduction in the size  distribution of species (Rapport et al.  1985)  and
changes  in the mechanisms  of,  and capacity  for,  damping undesirable  oscillations (Schaeffer et al. 1988).
Both  of these phenomena are functional responses to stress, but development of  indicators to assess their use
in regional surveys would  be difficult

Primary production is an  ambiguous measure of wetland condition, especially when quantified on a once-
per-year  basis.   Net production  is often   measured  by sampling vegetation  at two  different  times  and
calculating the difference in  standing biomass;  a positive difference  is interpreted as net production during
the time interval.  Annual growth rates as evidenced in tree-ring analysis may also be used as estimates of

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net production.   Many ecosystems  exhibit  no net  production,  whereas  others  may  exhibit  significant
accumulation of biomass in early stages of succession followed by declines to no net growth  during  later
climax phases.   As a result,  net production is a poor indicator of ecological condition.  In addition,  net
production measurements exhibit tremendous intrasite and intersite variability.

Gross primary  production, while  the most valuable indication of productivity, is  difficult  to  measure in
emergent vegetation.  The most common method uses an infrared gas analyzer to measure the concentrations
of carbon dioxide  in air before and after it has passed through an  ecosystem.  A portion  of the community
(a twig with leaves, a whole tree, or a part of the entire community)  is enclosed in a clear plastic tent, and
air inflows and outflows are sampled.  Many variables affect accuracy, so many, in fact, that it is impossible
to detect changes in gross  primary  production that could  be attributed to  stressors.  The  complexity of
measurement techniques, potential for conflicting results, and large  intrasite and intersite variability eliminate
gross  production as a response indicator.

Direct measurement of nutrient cycling is exceptionally difficult in ecosystems that lack clearly defined input
and output points  necessary for  mass balance equations.  Nutrient  export from a wetland watershed, either
via forest clearcutting or because of altered  hydrologic regimes,  leads to significantly  modified nutrient
pathways. Similarly, declines in the abundance of consumers and decomposers can significantly alter nutrient
cycling and may impair overall ecological function.  Organic matter accretion, decomposition, and measures
of microbial community structure  are surrogates of  nutrient cycling, which may indicate  sustainability  of a
wetland.

Retrogression is a large-scale ecosystem change in the direction of earlier stages of succession.  Retrogression
is determined through measures of community organization and species composition.  Although retrogression
is not included as a wetlands research indicator, insofar as retrogression refers to changes in the successional
stages of vegetation, it is indirectly included as an indicator of ecological condition.

Although many classes of animals (mammals, fish, reptiles, and amphibians) are an important  component of
wetlands, may  serve as excellent bioaccumulators of contaminants,  and are highly visible  to the public, the
EMAP sampling scheme may not permit their measurement in wetlands.  Animal mobility,  high degrees of
spatial and  temporal animal  variability, and  naturally variable wetland forcing functions  compound  the
difficulties of quantifying animal community  structure, especially with the  EMAP survey approach.   The
authors  suggest monitoring water birds and possibly small  mammals such as muskraL  The suitability of
monitoring other  animal  classes in wetlands  is discussed  in Section 9.2.  The  point  count method is
considered appropriate  for  monitoring birds  in EMAP.  This  method is a  means  of obtaining indices of
abundance for  comparing bird populations of different habitats (or of the same habitat in different locations)
during the index period (See Section 9.2).  Monitoring water birds might require a specialized technique such
as playback of vocalization  because  of low detectability; thus the inclusion of water birds as a wetland
response indicator will be determined by the  feasibility of such sampling under the  design of EMAP.  Even
more difficult than determining changes in ecological condition is providing plausible explanations for changes
in animal population sizes.  For example, some mammal species are affected by hunting pressure or, because
of their  mobility, are subject to  accidental death on roadways.  These anthropogenic pressures result in
fluctuations in animal populations that would  have to be accounted for.

An alternative to measuring animal communities in wetlands would be for EMAP to monitor habitat (primarily
for wetland-dependent birds and  mammals) by using aerial imagery  or  wetland vegetation structure.  Habitat
and wetland vegetation structure could serve as indicators of herbivore productivity or habitat suitability.  For
example, aerial photographs  could be examined for  the presence  of  beaver or  muskrat  houses.  Wetland
habitat suitability could be modeled or approximated by using vegetation or landscape indicators (see Section
9.3), as  suggested by the literature (Asherin et al. 1979; Klopatek et  al. 1981; Christensen 1986).  Landscape
pattern  is considered very important for monitoring the status  of wetlands, and landscape indices are useful
for indirectly monitoring animal habitat  Unfortunately, community effects due to toxicants and some other

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stressors would not be reflected  in habitat measures.  In addition, lag times are often noted between altered
habitat and  subsequent animal response.


5.3  APPLICATION OF WETLAND INDICATORS

If the ecological condition of regional wetland classes is  to be determined, a threshold value that defines
subnominal  condition  must  be  developed  for each  response indicator.  The most  promising short-term
approach for setting subnominal thresholds (Section 2.3.2) for  regional wetland classes  (considering the wide
variation among wetland classes), and that may provide needed  baseline data for statistical classification, is
the use of reference wetlands.  Wetlands selected in  the  Tier 1 resource sample will be partitioned on the
basis of surrounding landscape attributes into a continuum of landscape development intensities that  range
from relatively pristine to  highly developed.  Measurements  of  response indicators, along with landscape
development intensities, will  be  used to define the subnominal threshold.

To determine the  relative condition of wetlands,  the characteristics of components (structure) and processes
(function)  in healthy wetlands must  be known,  but  in many wetland  classes, these have  not been well
defined.  In  addition,  it  is becoming increasingly difficult to find wetlands  not obviously exposed  to  some
hazard. Most wetlands are located in topographic low points, places that collect surface water runoff, and
thus they are subjected to the various constituents carried by runoff. As a result, wetlands are affected by
activities in  the surrounding  landscape so that few, if any, wetlands can be considered  pristine (Schindler
1987).  Thus, reference wetlands must be carefully selected to account for human alteration of landscapes
that may have adversely affected the entire regional population of wetlands.

A  basic challenge  in interpreting  data  from  wetland  monitoring  programs  is  distinguishing  natural
environmental fluctuations from  signs  of human-induced stress. What may be considered a normal variation
in  one wetland ecosystem or at one time interval  may be interpreted  as a response  to  low-level  stress  in
another.  Very little  is known  about these  normal  variations, termed "stability envelopes"  (Duinker and
Beanlands 1986).  Of critical  importance is  establishing, with sufficient sampling and an adequate period  of
record, the  stability envelope for different wetland  classes.


5.4  RESEARCH NEEDS  FOR EMAP-WETLANDS

5.4.1  Research Priorities

Two critical areas  of research are  needed to both meet the objectives of EMAP and increase the efficiency
and reliability of the set of research indicators for inland wetlands.  First, in  the near term, small-scale field
studies  and  regional demonstration  projects will  test whether  the set of  research indicators can  yield
unambiguous information.  This  project will involve testing measurement techniques, field protocols, and data
analysis techniques. The second critical area of research is to increase the efficiency and  reliability of the
set of  indicators for wetlands.  Metrics most sensitive to  changes in wetland condition  must be identified.
In particular, efforts should concentrate on developing sensitive vegetation and macroinvertebrate indices for
detecting responses to stress.

Analysis of  landscape  indicators measured from  remotely sensed data  also  need development and testing
(see Section 9.4).   A proposed  index called the Landscape Development Intensity  (LDI) index,  which is  a
measure of developmental impact and therefore a possible indicator of potential  ecological impacts, may
provide a quick and cost-effective method for identifying  populations of wetlands that are most likely  to be
subnominal.  Further, the LDI  may allow for landscape-scale classification.  This  in  turn will quickly and
economically enable the targeting of  monitoring  efforts within  regions.   The LDI has been applied in  some
preliminary  work in Florida and has shown  some promise in identifying threatened populations of wetlands

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in urbanizing landscapes (Brown and Kentula 1990).  Further testing is needed to both expand its applicability
by incorporating additional variables  in the index and tailor  its  use and weighting system for  identifying
threatened wetlands over a region.

In the longer term,  EMAP-Wetlands must support research on  wetland structure to better  understand  stress
pathways and their diagnosis.  As this research is accomplished, significant advances can be made to develop
response indicators, refine subnominal thresholds, and  improve efficiency for detecting subnominal wetland
populations.

5.4.2 Interaction with EMAP Resource Groups and Other Agencies

EMAP-Wetlands will coordinate  efforts and assessments  with  several other  EMAP  resource groups whose
resource categories are closely linked.  For example, because wetlands both transform and filter contaminants
carried in  flowing waters, wetland presence and condition directly influence both inland surface water and
near-coastal resources.  Similarly,  wetlands  are  important in arid landscapes because of their water storage
and  animal support functions. The EMAP  resource category that currently represents  the  greatest potential
hazard to  wetlands  is agroecosystems  because of the threat of conversion of wetlands to agricultural lands.

In addition to coordinating with other EMAP resource groups, it has been essential for the success of EMAP-
Wetlands to initiate coordination with  the U.S. Fish and Wildlife Service's National Wetlands Inventory (NWI)
on issues  of  statistical  design and wetland  classification.  The NWI is currently mandated by Congress to
monitor status and  trends in  wetland acreage.  To date, only NWI  publishes  status and trends reports on
wetlands;  the "Status and Trends  Survey of Wetlands and Deepwater Habitats in the Conterminous United
States, 1950's to 1970's" (Frayer et al.  1983) represents the first comprehensive inventory of wetland acreage
and  wetland loss.    The objective of the  study was to develop  national  statistical estimates of wetland
distribution and abundance for the lower 48 states during the 1950s and  1970s and to calculate the change
over this period.  The NWI survey was designed  to  develop national statistics that would, on the average,
estimate with a  probability of 90% the total acreage and change  within 10% of the actual value of each
wetland type. A network was also established for future  monitoring.  A current study will summarize status
and  trends in wetland acreage from the  1970s to the 1980s,  and future studies will  be conducted at 10-
year intervals.

In addition to the discussions with NWI, we have communicated frequently over the past year with managers
of the  Breeding Bird Survey  data base and several  bird data  bases housed at the Cornell  Laboratory of
Ornithology.  We have also contacted  more than 400 wetland scientists from government agencies, academic
institutions, and the private sector and asked them to  plot the locations of wetlands they have monitored.
Contacts have been made to obtain  wetlands-related  data  from the USDA  Forest Service (wetland  types
covered by  the Forest Inventory and  Analysis program),  Soil Conservation  Service  (National  Resource
Inventory), and  Federal Emergency Management Agency (floodplain acreage estimates).


5.5  REFERENCES

Adamus, P.R.  1988.  Inland wetlands research and  monitoring plan, 1990-2000:   An ecosystems strategic
planning  initiative.    Internal  Report.  U.S.  Environmental  Protection  Agency,  Environmental  Research
Laboratory, Corvallis, OR.

Adamus, P.R., E.S. Clairain, jr., D.R.  Smith, and R.E. Young.  1987.  Wetland evaluation technique (WET).
Volume II.  Technical Report Y-87. U.S. Army Corps  of Engineers, Waterways Experiment Station, Vicksburg,
MS.
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Asherin, D. A.,  H.L Short, and J.E. Roelle.   1979.  Evaluation of wildlife habitat inventories:  Regional
evaluation of habitat quality using  rapid assessment methodologies.  Pages 404-424.  In:  Transactions of
the 44th  North  American  Wildlife and  Natural Resources Conference.   Wildlife Management Institute,
Washington, DC.

Brooks, R.P., and R.M.  Hughes.   1988.  Guidelines for assessing the biotic communities of  freshwater
wetlands.   Pages 276-282.   In:  J.A. Kusler, M.L.  Quammen,  and G. Brooks, eds.   Proceedings of the
National Wetlands Symposium:  Mitigation of Impacts and Losses.  ASWM Technical Report 3.  Association
of State Wetland Managers,  Berne, NY.

Brown, M.T., and M.E. Kentula.   1990.  A method for  selecting wetland comparison  sites in landscapes
dominated by humanity.  In preparation.  Center for Wetlands,  University of Florida, Gainesville.

Chapman, P.M., R.N. Dexter, and  L Goldstein.  1987.  Development of monitoring programmes to assess
the long-term health of aquatic ecosystems: A model from Puget Sound, USA.  Mar. Pollut Bull. 18(1):521-
527.

Christensen, A.G.  1986.   Cumulative effects analysis:  Origins,  acceptance, and  value  to  grizzly  bear
management  Pages 213-216.  In:  G.P. Contaras  and  K.E. Evans,  eds.  Proceedings of the Grizzly  Bear
Habitat Symposium, Missoula, Montana.  General Technical Report INT-207.  U.S. Department of Agriculture,
Forest  Service Intermountain Research Station, Ogden, UT.

Conservation Foundation.   1988.   Protecting America's wetlands:  An  action agenda.   The final report of
the National Wetland Policy Forum.  The Conservation Foundation,  Washington, DC.  69 pp.

Cowardin, LM., V. Carter, F.C. Golet, and E.T. La Roe.  1979. Classification of Wetlands  and Deepwater
Habitats of the  United States.  FWS/OBS-79/31.   U.S. Department of Interior, Fish and Wildlife Service,
Washington, DC.

Duinker, P.N., and G.E.  Beanlands. 1986.  The significance of environmental impacts:  An exploration of
the concept  Environ.  Manage. 10(1):1-10.

Frayer, W.E., T.J. Monahan, D.C. Bowden, and F.A. Graybill.   1983.   Status  and  Trends of Wetlands and
Deepwater Habitats in  the Conterminous United States, 1950's to 1970's.  U.S. Department of Interior, Fish
and Wildlife Service, St Petersburg, FL.

Klopatek, J.M., J.T. Kitchings, R.J. Olson, K.D. Kumar, and L.K. Mann.  1981.  A hierarchical  system for
evaluating regional ecological resources.   Biol. Conserv. 20:271-290.

Kuenzler, E.J.  1989.  Value of forested wetlands as filters for  sediments and  nutrients.  Pages  85-96. In:
D.D. Hook and R. Les, eds.  Proceedings of the Symposium:  The Forested Wetlands of the Southern United
States,  July 12-14, 1988, Orlando, FL.   Gen.  Tech. Rep. SE-50.   U.S. Department of Agriculture, Forest
Service, Southeastern Forest Experiment  Station, Asheville, NC.   163 pp.

Mitsch, W.J., and J.G. Gosselink.  1986.  Wetlands.  Van Nostrand Reinhold Company,  New York.  539pp.

Preston, E.M., and B.L Bedford.  1988.  Evaluating cumulative effects on wetland  functions:  A conceptual
overview and generic framework.   Environ. Manage.  12(5):565-583.

Rapport, D.J., H.A. Regier, and T.C. Hutchinson.  1985.  Ecosystem  behavior under  stress.  Am. Natur.
125(5):617-640.
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Schaeffer, D.J., E.E. Herricks, and H.W. Kerster.  1988. Ecosystem health:  I.  Measuring ecosystem health.
Environ. Manage. 12(4):445-455.

Schindler, D.W.  1987.   Detecting ecosystem responses to anthropogenic  stress.  Can. J. Fish. AquaL Sci.
44(Suppl. 1):6-25.

Zelazny, }., and J.S.  Feierabend,  eds.   1988.   Increasing our wetland resources.   National Wildlife
Federation, Washington, DC.
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                                              SECTION 6

                                 INDICATOR  STRATEGY FOR FORESTS

                    Kurt H. Riitters,1  Beverly Law,2 Robert Kucera,1 Alisa Gallant,3
                                    Rob DeVelice,3-4 Craig Palmer3


6.1  INTRODUCTION

A forested ecosystem includes the living organisms of the forest and extends from the top of the tree canopy
to the lowest soil layers affected by biotic processes (Waring and Schlesinger 1985).  This ecological definition
determines the scope of the forest indicator strategy.  Implementation of indicator measurements will require
operational definitions of forests and resource categories.

Forests  provide many amenities important to the social, economic,  and cultural aspects of life.   Statistics
from  a  recent national assessment  of  forests (USDA-FS  1982) provide some  indication  of the  extent and
value of forests and why improved  monitoring programs are supported by  the public and  Congress.


    •  Forests covered approximately 298 million hectares (737 million acres), or about one-third of
       the total land area in the United States.

    •  Employment for more than 3  million  workers originated from forests,  and  in  many rural
       locations, the primary employment sector was forest-related.

    •  The  annual  harvest of timber  material  was worth more than $6 billion, and timber-based
       economic activities were valued at more than $48 billion (4.1% of  the gross national product).

    •  The social and cultural values of water, wetlands, and animals are often closely associated with,
       and are sometimes contingent upon, forested ecosystems.

    •  Although difficult to quantify, the social and cultural values of forests are tremendous.

6.1.1   Legislative Mandate for Forest Monitoring

Public and Congressional support for forest monitoring has a very long history relative to other ecological
resources.  Almost 100 years ago, the Organic Act of 1891 established the National Forests and included
provisions for the  inventory of these lands.  Later, the Forestry Research (McSweeney-McNary) Act  of  1928
required a current and comprehensive inventory and  analysis  of  all  of the nation's forest resources.   This
early  legislation focused on timber inventory, and it  was not until 1974 and the  passage of the Forest and
Rangeland  Renewable  Resources Planning  Act (RPA) that much consideration  was  given  to  monitoring
    JNSI - Environmental Sciences, U.S. EPA Atmospheric Research and Exposure Assessment Laboratory, Research Triangle Park, North
    Carolina

    National Council of the Pulp and Paper Industry for Air and Stream Improvement, U.S. EPA Environmental Research Laboratory,
    Cotvallis, Oregon

    3NSI - Environmental Sciences, U.S. EPA Environmental Research Laboratory, Corvallis, Oregon

    ^Current affiliation: The Nature Conservancy, Montana Natural Heritage Program, Helena, Montana

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nontimber resources.  Since passage of the RPA, Congress has passed the National Forest Management Act
(1976), the Federal Land Policy and Management Act (1976), the Soil and Water Conservation Act (1977),
and the Forest Ecosystems and Atmospheric Pollution  Research Act (1988). These legislative acts share the
following assignments (Lund 1986).

    •  Preparation and  maintenance of continuous  natural resource inventories

    •  Coordination and cooperation among resource agencies and organizations to avoid duplication
       of inventory and planning efforts

    •  Determination of both current and potential changes in  renewable natural  resources

    •  Determination of resource interactions and  management alternatives

    •  Submission to the nation of periodic assessment reports  of the natural resources

Forest  monitoring is also relevant for assessments  made pursuant to  other federal legislation, such as the
Federal Insecticide, Fungicide, and  Rodenticide Act, the National Environmental Policy Act,  the  Resource
Conservation and Recovery Act, and the Endangered Species Act.  An important trend in  this legislation has
been the increasing emphasis given to resources other than timber,  such as animals, rangeland, water, and
recreation, as valued components of the forest.

6.1.2  Forest Resource Classes

The  EMAP resource classes defined for forests are  based on major forest types.   This classification system
was proposed because forest condition and change appear to be  directly associated with major forest type.
The EMAP sampling design (Section 2.2), however,  reserves the capability to  poststratify by other criteria if
more convenient for the analysis and  interpretation of indicator data.

Twenty-two forest resource classes are currently  identified:

             1.   Oak/Hickory                          12.  Loblolly/Shortleaf Pine
             2.   Oak/Gum/Cypress                      13.  Douglas Fir
             3.   Elm/Ash/Cottonwood                   14.  Hemlock/Sitka  Spruce
             4.   Maple/Beech/Birch                    15.  Ponderosa Pine
             5.   Aspen/Birch                           16.  White Pine-Western
             6.   Larch                                 17.  Lodgepole Pine
             7.   Western  Hardwood                    18.  Fir/Spruce-Western
             8.   Redwood                             19.  Pinyon/Juniper
             9.   White/Red/Jack Pine                   20.  Oak/Pine
            10.   Spruce/Fir-Eastern                      21.  Ohia
            11.   Longleaf/Slash Pine                    22.  Spruce/Hardwood


6.2  IDENTIFICATION  OF INDICATORS

Forests are open  systems that exchange energy and materials with  other resources, including adjacent forests,
upstream  and downstream  nonforested ecosystems, and the atmosphere (Waring and Schlesinger 1985).
Forests also  host a diversity of inhabitants that take part in complex and interacting processes. These features
complicate the task of identifying  indicators of forest  response, exposure, and  habitat  Nevertheless, a
National Research Council (NRC) committee reviewing biomarkers of air pollution stress and damage  in forests
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concluded  that the  "knowledge of the structure and physiology of forests  and trees is now sufficient to
develop a basis for detecting disruption or disturbance from a variety of causes" (NRC 1989).

Surveys of forest stress and damage based on sets of indicators (such as the EMAP approach) are one element
of an overall strategy recommended  by the NRC committee to elucidate cause-and-effect relationships in
forests.  Many  measurements could be considered for these surveys, but from a  practical standpoint,  not
everything can be measured. Thus, it is essential that a strategy to identify indicators be stated clearly in  the
context of  EMAP's goals and objectives (see Section 1).

The sequential strategy outlined in the four steps below is suggested to develop candidate indicators of forest
condition and associated stresses.

    1.  Identify perceptions (including those of resource managers, scientists, private industry, legislators,
       and the general public) of forest condition.

    2.  Identify  environmental values related to these public perceptions.

    3.  Identify  environmental hazards, management actions, and natural phenomena  that affect the
       environmental values.

    4.  Identify  indicators that represent environmental values, address issues of concern,  and satisfy
       the  criteria of the  EMAP design.

6.2.1   Perceptions  of Forest Condition

The ecological  condition of forests is perceived in  a variety of ways.  Common perceptions are based on
the extent  and distribution of forest lands for  recreational  use; the economic value of timber production;
the maintenance of viable game populations; the yield of high-quality water; the ability of forests to recover
from natural stresses such as insects, diseases, and fire; and the maintenance of diversity within and among
species, ecosystems,  and regional  landscapes.

Within each of these general categories of concern, there are many more specific  concerns.  For example,
some are concerned about the loss of old-growth forests  and the reduction of habitat for certain species of
birds, mammals, and amphibians that depend on old-growth forests (Franklin 1988).  Others are concerned
about the impact of decreasing  forest cover in watersheds  on the quality of water for public  consumption
and fisheries  production.

Listing public perceptions of forest status  can provide only a glimpse of what types of questions might be
asked of a  forest monitoring program.  There are innumerable perceptions  of specific values, and many of
them cannot be anticipated. The identification  of public perceptions can, however, suggest broad areas of
concern to guide the indicator strategy.  In the EMAP framework, such areas are expressed as environmental
values.

6.2.2   Environmental Values for  Forests

In the EMAP framework, environmental values relate the  public perceptions  of forest condition to forest
resource  attributes that can be measured.  For example, the public perception of a  healthy forest as one
that  can recover from insect  infestation,  disease,  and  other natural factors is  one component of  the
"sustainability" value.   But a term  such as  sustainability  can  have different meanings in the scientific
community, and therefore it is difficult to determine what to measure. The choice of indicators, as surrogates
for environmental values,  defines  the attributes  of forest  resources that will  be measured  (see also Section
2.1).

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In addition  to sustainability,  other  important  values  of forest condition include productivity,  aesthetics,
biodiversity, and extent.  "Extent" refers to the distribution of particular species, populations, or communities
such as forest types in a  region.  "Productivity" reflects  the potential  of the forest to utilize energy, the
accumulation rate of stored energy, and the consumptive rate of stored energy to support life in the forest
"Aesthetics" is the enjoyment of scenery and recreation.  "Biodiversity" encompasses genetic diversity, diversity
of species within an ecosystem, and diversity of ecosystems and regional  landscapes.

More effort is needed to identify environmental values.  A  thorough review of the scientific literature on
topics such as hierarchy theory, disturbance, succession, and  stability and  steady-state concepts may allow a
less ambiguous  concept of ecological condition to  be developed.   Individuals  will always have  different
perceptions  of forests,  and thus of how to describe their condition.  For now, analysts  must be sensitive to
these differences; ultimately, we may be able to identify a set of values that relates to everyone's perceptions
of forests.

6.2.3  Hazards  to Forest Ecosystems

Forests everywhere are under continual and variable stress from a  variety  of natural and human-related
sources.  The major human-induced  hazards are global climate change,  chemical pollution (particularly in
the atmosphere), and land-use change.  Relatively rapid changes in global climate may occur as a  result of
increases in the concentrations of trace gases  in the atmosphere (MAS 1983), which ultimately may have
severe  ecological consequences  (Abrahamson  1989).   Pollution stresses of  concern include ozone,  acid
deposition, and airborne toxins (Mclaughlin 1985; AFA 1987; NAPAP  1988).  Land uses such as urbanization
and  agricultural  development may reduce forest acreage or alter the biotic  integrity of forests.   Resource
management practices may alter biodiversity, species distributions, and  other  forest attributes.  The relative
importance  of these hazards will vary by forest class and geographic  region.

6.2.4  Forest Indicators Appropriate for  EMAP

No widely accepted definition  of forest health  or integrity exists. An almost unlimited  number of  potential
measurements of forests could be considered for monitoring.  Many  candidate indicators can be identified
from existing monitoring systems (e.g., NSEPB 1985; USDA-FS 1985; UNEP and UN-ECE 1987; Magasi 1988),
from symposia (e.g., Agren  1984; Schmid-Haas 1985), from technical publications  (e.g., Materna 1984; Smith
1984; Waring 1984; Schaeffer et al. 1988), and from workshops and committee reports  (e.g., Alexander and
Carlson 1988; NRC 1989).

The  EMAP  indicator  selection  process for forests began  in  July 1989  with  a preliminary  identification of
about 150 candidate indicators from  the literature.   The list was augmented,  and indicators were screened
according to feasibility and appropriateness in the EMAP context, at a workshop in  late July 1989.  A draft
report  that identified  17 (mainly response) indicators was prepared from the  results of  the  workshop.  The
draft report was circulated for  comments  and discussed at a second workshop in late August 1989.

The objectives of the second workshop were to reach a consensus on  the recommended indicators and to
add technical data for the  indicator fact sheets  (Appendix D).  The emphasis was to  identify a consensus set
of highly feasible and interpretable response  indicators. A reduced list  of eight indicators resulted from the
workshop.   Indicators were added later in response  to both information on indicators that are applicable to
multiple  resource categories (Section 9)  and the suggestions  of  reviewers who participated in a written
external  peer review  (Appendix I.8) in April  1990.  A  review was also conducted by the EPA  Science
Advisory Board  in May 1990.

During this  selection  process, candidate indicators were ranked according to the EMAP indicator  selection
criteria (Table 6-1).  These rankings are partly subjective,  and different individuals could develop other
rankings  based on differences in background, experience, and perception.  It is  important to note that not

                                                 6-4

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even  the high-priority research indicators are  ready for  implementation; an  important lesson  from the
indicator selection process is that much remains to be done to improve the feasibility and interpretability of
regional forest monitoring.

The designation of indicators as either response, exposure, or habitat is necessary to develop an integrated
and concise summary of recommendations in the EMAP framework.   We expect that,  in the future, the
designation of some indicators may be changed, perhaps to more than one  indicator  type.  For example,
while so/7 productivity index  is currently  listed as an exposure and habitat indicator, soil  productivity could
conceivably be considered a response indicator (see also Section 7). Better definition of environmental values
will enable a better designation of indicator types.

A  brief description of each  research indicator (Figure 6-1) is given here.  More detailed descriptions of the
indicators, their application,  suitable index period, variability, and research needs are  listed in Appendices
C, D, and G,  as referenced by the code in parentheses that follows each description  below.

6.2.4.1   Response Indicators for  Forests

Response indicators are  used by EMAP to  quantify and classify the condition  of ecological resource classes.
Nine  response indicators have been selected as research indicators for forests,  three of which are recognized
as having high-priority status.
                          EMAP-Forests Indicator Strategy
     Response Indicators (R)

   Relative Abundance: Animals

   Tree Growth Efficiency

   Visual Symptoms of Foliar
    Damage: Trees*

   Nitrogen Export

   Litter Dynamics

   Microbial Biomass and
     Respiration in Soils

   Abundance and Species
    Composition: Understory
    Vegetation

   Demographics: Animals

   Morphological Asymmetry:
    Animals
   SPATIAL
ASSOCIATIONS
  TEMPORAL
ASSOCIATIONS
 Exposure-Habitat Indicators (E)

Nutrients in Tree Foliage

Chemical Contaminants in Tree
 Foliage

Soil Productivity Index

Abundance and Density of Key
 Physical Features

Linear Classification and Physical
  Structure of Habitat

Landscape Pattern

Stable Isotopes

Bioassays: Mosses and Lichens

Carbohydrates and Secondary
 Chemicals: Trees

Animal Biomarkers
 Figure 6-1. Diagram  of the proposed EMAP-Forests Indicator Strategy.   Indicators in bold are high-
            priority research indicators.  Response indicators with an asterisk (*) also could function as
            exposure and habitat indicators.
                                                  6-6

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High-Priority Research Indicators

Relative Abundance:  Animals.  The status of a community of organisms can  sometimes  be assessed  by
the status of a few species or types of species that play critical roles.  Some types of birds could be used as
indicators that integrate across  resource classes, whereas small mammals, reptiles, and amphibians generally
would serve to monitor condition within  a  resource class.  (G1.1)

Tree  Growth Efficiency.   Tree growth efficiency is  a measure of the  overall ability of trees to maintain
themselves in an ecosystem,  which is an  obvious but  sometimes overlooked condition for the perpetuation
of forests.   Measurements of periodic tree dimensional or biomass  growth,  and an index  of capacity for
growth, are used to construct an integrative index.  (D.1)

Visual Symptoms of Foliar  Damage:  Trees.  Visual symptoms  are measures of the health of individual
trees  and  populations  in  terms  of pathological conditions, and they  are  measures of aesthetic  quality.
Defoliation and discoloration of foliage can  be  used to construct an overall index of forest condition.  (D.2)

Other Research Indicators

Nitrogen Export  The export  of nitrogen, which  is usually conserved in terrestrial ecosystems, is generally
considered  to be an  indicator of  damage  or  change  in forest  structure,  function, or  composition.
Unfortunately, nitrogen export  from forests cannot be  effectively measured in a survey.  An  indicator needs
to be developed so that nitrogen exports  that are measured during an index  period truly reflect the overall
forest response to stresses.  (D.3)

Litter Dynamics. The rates and pathways of forest litter decomposition and recycling are important indicators
of the continued health and  productivity  of forests.  Unfortunately, constructing an index of litter dynamics
is complex.  It may be  possible to  develop indices based on litter chemistry  or decomposition rate.

Microbial Biomass and Respiration  in  Soils.  Soil microorganisms  play a vital  role in the retention and
release  of nutrients in forest soils and can  be sensitive to changes in forest  condition.   Microbial  biomass
and mycorrhizal density, for  example, may  help to link vegetative responses to soil productivity.  (D.10)

Abundance and Species Composition:  Understory Vegetation.  Understory vegetation can be a sensitive
indicator of forest  responses  to environmental stresses.  Measures of the amount and distribution  of various
life-forms can be  summarized  into useful  indices.   For  this  research  indicator,  reference  is made to an
analogous indicator that would be  used to  monitor wetlands vegetation.  (C.3)

Demographics:  Animals.  Population vigor is reflected in  the recruitment of individuals into the breeding
population and their subsequent survivorship.   Parameters include age structure, sex ratio, fertility, mortality,
survivorship, and dispersal; and such measurements are only appropriate for definite keystone  species. (G1.2)

Morphological Asymmetry:  Animals.  The morphological variability in  structures such  as teeth and bones
of bilaterally symmetrical  organisms has  been  found to increase with exposure  to chemical contaminants,
hybridization, and inbreeding.   This  parameter would  be an  early-warning  indicator  of population-level
responses to human-induced stresses.  (G1.3)

6.2.4.2   Exposure and Habitat Indicators for Forests

Exposure and habitat  indicators are  used by EMAP to identify and quantify changes in exposure and  physical
habitat that are associated with changes  in  response indicators.   Ten exposure and habitat  indicators have
been  selected as research indicators for forests, five  of which are  recognized  as having high-priority status.
                                                 6-7

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High-Priority  Research Indicators

Visual Symptoms of Foliar Damage:   Trees.   Visible symptoms may indicate  a  plausible mechanism  of
subnominal forest condition  on tree foliage.  (D.2)

Nutrients in Tree Foliage.  Nominal forest condition depends on a sufficient supply and the proper balance
of critical nutrients in foliage.  Imbalances may signal imbalances in forest processes or indicate exposure to
specific types of stresses.  It may be possible to construct a composite index of foliar nutrient concentrations
to measure imbalance,  and to disaggregate the composite index into individual nutrient concentrations for
more detailed analyses.  (D.5)

Chemical Contaminants in Tree  Foliage. Measurements of chemical contaminants can  identify toxic levels
of elements and  the occurrence of toxic anthropogenic compounds.  Detection  of certain  contaminants in
foliage can provide information on possible mechanisms by which changes  in forest condition  might occur.
(D.6)

Soil Productivity Index.   Chemical imbalances  in  the soil can signal changes in  ecosystem function and
indicate  exposure to specific types of  stresses.   Roots  may be damaged directly,  and  indirect effects can
occur through decreases in nutrient availability or stresses on beneficial  microbial populations.  Erosion and
moisture characteristics are examples of physical characteristics that affect soil productivity.  It may be possible
to construct an index of physiochemical  soil factors which may be disaggregated into individual metrics for
more detailed analyses.  (D.7)

Abundance and Density of Key Physical Features.  Certain physical features of habitats (e.g., cliffs, outcrops,
sinks, seeps, talus slopes) are critical  to animal  diversity and abundance.  Land-use practices can  alter  the
density and distribution of many  key  physical  features.   Many habitat features are specific  to  particular
resource classes, but determining what to measure in a given class can be based on existing  literature.
(G3.1).

Linear Classification and Physical Structure of Habitat  The distribution  and disappearance of certain
species is  associated with  critical features of their  habitat   For example, overgrazing can result in  the
elimination  of palatable understory species, compaction of forest soil, and conversion  to open shrublands
and grasslands.   Species that depend  on  forest habitat  may  not be able to survive  in  a habitat that is
predominately grassland.   Many studies  have also demonstrated the relationship between animal  diversity
and vertical vegetation  profile.  (C3.2)

Landscape Pattern.   Landscape  indicators, calculated from data derived from remote sensing, describe  the
spatial distribution of physical,  biological, and  cultural features  across a  geographic   area.   Thus, these
indicators are  used to generally quantify resource structure, including habitat proportions, patch size, diversity,
and contagion. Landscape pattern has been correlated  to disturbance and animal  habitat or  presence. (C3.3-
G3.8)

Other Research  Indicators

Stable Isotopes.   Certain ratios of stable isotopes in plant tissue can  be used to distinguish possible effects
of climate from those of pollutants. Reliable quantitative analyses and interpretations are not possible without
further study of natural variation and  specific responses to particular stresses.  In addition, standard sampling
and measurement techniques need to  be developed.  (D.8)

Bioassays:  Mosses and  Lichens.  Mosses and  lichens, which derive nutrition directly from the atmosphere
and act  as filters for and accumulators of contaminants, have been utilized in several European monitoring
programs.  Although mosses and lichens can serve as a binary (yes or no) indicator of contaminant exposure,

                                                  6-8

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their use  has been  criticized because  the contaminant  uptake and  loss rates are  not known, and  total
accumulation is  thus difficult to estimate.  (D.9)

Carbohydrates and Secondary Chemicals:   Trees.   Starch  reserves have low  priority  in carbohydrate
allocation, and reductions indicate either a lower total assimilation or less  efficient  assimilation of carbon
which, in  turn, is indicative of or related to nutrient imbalance or other stressors such as soil contaminants.
Secondary chemicals (e.g., chemicals produced for defense against herbivores) require relatively large amounts
of energy to produce, and certain types are produced in larger quantities under  certain types  of stresses.
Development of standardized testing and interpretive methods is needed.  (D.11)

Animal Biomarkers. A desirable feature of a monitoring program would be to detect an organism's response
to human-induced  stresses at the biochemical and  cellular level  before the stresses produce a detectable
response at  the organism  and population  levels.  Although the use of  biomarkers as early-warning response
indicators  requires  more  basic  research, their  present value  for  regional  survey  monitoring is  to  provide
information  to support or  refute hypotheses on why ecological condition of forests is subnominal.   (G2.1-
C2.11)

6.2.5   Forest Indicators Not Appropriate  for  EMAP

During the initial evaluation of candidate forest  indicators with criteria similar to the  EMAP indicator selection
criteria (Table 6-1),  it was easy to endorse some  indicators and to eliminate  many others from further
consideration.  But many candidate indicators  need to be evaluated more  completely.   It  is expected that
the failure to  propose certain candidate indicators as research  indicators will  cause  scientists from diverse
backgrounds to promote and defend them. This feedback  will be a constructive part of EMAP's development
of indicators.

To help guide further evaluation of indicators of forest condition, the following criteria could  be  considered
(after Schaeffer et al. 1988).

            • Is not dependent upon the presence,  absence, or condition of a single  species

            • Is not dependent upon a census or inventory of many species

            • Reflects knowledge of "normal" changes, for example,  due to succession or
              other sequential  changes

            • Is one of several indicators that collectively represent a set of environmental
              values, but is not redundant

            • Is dimensionless, single-valued, and monotonic in relation to a defined range
              of condition

            •  Has  known statistical properties

            • Responds to stresses  but not to poor sampling design

            • Is practical  and  feasible

            •  Is comparable among classes of forests, for example,  among forest type, size,
              and density classes
                                                 6-9

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            • Integrates responses and has a stable value for several months each year and
              over a geographic area as large as a forest sampling unit


6.3  APPLICATION OF FOREST INDICATORS

Response indicators are used to identify forests in subnominal condition. Numeric criteria, regional reference
sites, and classification have been  suggested (Section 2.3.3) as approaches to defining subnominal threshold
values for response indicators. Numeric criteria based on experience and expert judgement are available for
use as  subnominal thresholds for the high-priority response  indicators, but  these  are difficult to justify
objectively.  This approach seems appropriate as a starting point when no better information exists.  The
development of threshold values  based on regional reference sites can be  recommended  for the other
response indicators and for comparison to thresholds based on  numeric criteria. The  classification approach
can always be applied retrospectively once  a  complete data  set is available.

As starting points, the subnominal thresholds for the suggested  high-priority  indicators  can  be  developed
based on the accepted definitions  and experiences of existing monitoring systems.   For example, the United
Nations' Environment Program and its Economic Commission  for Europe (UNEP and UN-ECE 1987) provides
guidelines for the indicator of visual  symptoms of foliar damage in trees, and the literature  indicates  that
certain levels of tree growth efficiency are associated with the probability of insect attack (e.g., Mitchell et
al. 1983) and mortality (Matson and Boone  1984).  These  starting points  can be refined with additional
experience or research.

An approach to defining  subnominal thresholds  that requires  further  development is based  on  simulation
models  of forested ecosystems.  Although this option can  potentially consider the full range  of  impacts to
forest ecosystems, current understanding of forest processes as they relate to nominal  structure, function,
and composition  does not permit  construction or verification of models for all forest types in all regions.

Analysis and interpretation of indicators is tied intimately to the classification scheme.  Even when conditions
are "normal" everywhere, the same indicator may take on a different value in different forest classes.  While
many indicators meet the  criteria of comparability among forest classes, those that do  not could be analyzed
separately for each class. An alternative approach that does permit direct comparisons among dissimilar forest
classes is the  normalization of indicators to class-specific  baselines, which could  be estimated from experience
or from simulation  models.

We did not investigate multivariate applications of indicators. As a  starting point for application of response
indicators, the maximum/minimum operator approach (Section 2.3.3) can be suggested.  With this approach,
each response indicator is evaluated  separately,  and a subnominal score for  any one indicator effectively
classifies the  sampled location as subnominal.  Refinements to this technique  are  possible, and the specific
approach depends on  the degree of independence among  response indicators  and the relative  weights
assigned to each  environmental  value.

Indices  of forest  condition  and  stress were considered as part of  the indicator evaluation process.  Two
approaches are possible to identify integrative measures of forest condition and stress.  The first involves  the
measurement of biologically integrative indicators, for example, growth (D.1) and visual symptoms (D.2).  The
second  approach involves constructing a mathematically integrative index from several  measurements,  for
example, as was suggested for soil (D.8)  and foliar chemicals (D.7).  Each step  in any aggregation process is
expected to result in some loss of specific  information. Indices should  be developed so  that they can be
broken  into  separate metrics (and  scores) to  diagnose  possible  causes and better  interpret  biological
responses.
                                                 6-10

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6.4  RESEARCH NEEDS FOR EMAP-FORESTS

Many topics for research were suggested during the  indicator evaluation process.  The topics ranged from
investigations of specific indicators to the development of an overall strategy to guide  further identification
and testing of indicators.  Much needs to be done to improve our capabilities for monitoring forest condition
and forest stresses, and to make monitoring data more interpretable for  a wider variety of policy questions.
A few possibilities are mentioned here.

Considering the earlier  definition of "forested  ecosystem" (Section 6.1), the scope  of indicators needs to be
expanded.   The lower  priority research  indicators (Figure  6-1) are a start in this direction.   Research  also
needs to be focused on  the analysis of indicators, alone  and  in combination, to detect and interpret the
signals of change in  forest condition.

Many measurements that could  be used to indicate forest condition and stresses are made "off-frame," that
is, at locations other than those sampled  by  EMAP.   Examples include ongoing  monitoring  programs that
measure air pollutants,  insect and disease  infestations, timber inventory, and satellite imagery.  By building
linkages to these auxiliary data bases, it may be possible to improve the interpretations of regional status and
trends of forest condition.

Forests are just one category of terrestrial resources.  Where  appropriate, the indicators  suggested for forests
should be  comparable  to indicators  suggested for  other  categories of terrestrial  resources.   For example,
similar types of measurements of nontree vegetation,  soil  chemistry, productivity, and pathology have  been
recommended  for forests, arid lands (Section  7), and agroecosystems (Section 8).  The measurements are
associated with indicators that differ  not so much  in  substance as in name and  method  of  measurement
An  important part of EMAP forest indicator strategy will be  the evaluation  of research indicators for other
EMAP resource categories with a view toward integration and refinement of forest indicators.

Finally, since forest field measurements are expensive, it is important to improve the efficiency of monitoring.
A high  priority should  be placed  on the  identification and  use  of emerging technologies such as remote
sensing that could markedly improve measurement efficiency and the ability to interpret  measured indicators.

6.5  REFERENCES

Abrahamson, D.E., ed.  1989.  The  Challenge of Global Warming.  Island  Press, Washington,  DC.

AFA.  1987. White Paper on the Forest Effects of Air  Pollution. American Forestry Association, Washington,
DC.

Agren, G.I., ed.  1984.  State and change  of forest ecosystems:   Indicators in current research.  Report 13.
Swedish University of Agricultural Sciences, Department of Ecology and  Environmental  Research, Uppsala.

Alexander, S.A.,  and J.A. Carlson.   1988.   Visual  Damage Survey:   Project Manual.   Forest Pathology
Laboratory, Department of Plant Pathology, Physiology, and Weed Sciences, Virginia  Polytechnic Institute
and State University, Blacksburg, VA.

Franklin, J.F.   1988.  Structural and functional diversity in temperate forests.  Pages 166-175.  In:   E.O.
Wilson, ed.  Biodiversity.  National Academy  Press, Washington,  DC.

Lund, H.C.  1986.   A primer on integrating resource inventories.  General Technical Report WO-49.  U.S.
Department of  Agriculture, Forest Service.  64 pp.
                                                6-11

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Magasi, LP.  1988.   Acid Rain  National Early  Warning System.   Manual  on plot establishment and
monitoring.  Information Report DPC-X-25.  Canadian Forest Service, Ottawa.

Materna, J.   1984.   Impact  of Atmospheric Pollution on  Natural  Ecosystems.   Pages 397-416.   In:
M. Treshow, ed.  Air Pollution and Plant Life.  John Wiley, New York.

Matson, P.A., and R. Boone.  1984.  Natural disturbance and nitrogen mineralization:  Wave form dieback
of mountain hemlock in the Oregon Cascades. Ecology 65:1511-1516.

Mclaughlin, S.B.  1985.  Effects of air pollution on forests:  A critical  review.  J. Air PolluL Control Assoc.
35:512-534.

Mitchell, R.C., Waring, R.H., and G.B. Pitman.  1983.  Thinning  lodgepole pine increases tree vigor and
resistance to mountain  pine beetle.  For. Sci. 29:204-211.

NAPAP.  1988.  Effects on forests.  Chapter 7. In:  Interim Assessment:  The Causes and  Effects of Acidic
Deposition.  Volume 4.  National Acid Precipitation Assessment Program, Washington,  DC.

NAS.  1983.  Changing Climate Report of the Carbon Dioxide Assessment Committee.  National Academy
of Sciences, National Academy Press, Washington,  DC.

NRC.  1989.  The Committee's Report   Pages 1-26.   In:   Biologic Markers of Air-Pollution  Stress and
Damage in Forests.   National Academy of Sciences, National  Research  Council, National  Academy  Press,
Washington, DC.

NSEPB.   1985.   Monitor  1985:   The National  Swedish Environmental Monitoring Programme (PMK).
National Environmental Protection Board, Research and Development Department, Environmental Monitoring
Section, Solna, Sweden.

Schaeffer, D.J., E.E. Herricks, and H.W. Kerster. 1988.  Ecosystem health: I.  Measuring ecosystem health.
Environ. Manage.  12:445-455.

Schmid-Haas, P.  1985.  Inventorying and monitoring endangered  forests.  Proceedings of  the IUFRO/FAO
Conference, August 19-24, Zurich, Switzerland.

Smith, W.H.  1984.  Ecosystem pathology:   A new perspective for phytopathology.  For. Ecol. Manage.
9:193-219.

UNEP and  UN-ECE. 1987.  Manual on Methodologies and Criteria for Harmonized Sampling, Assessment,
Monitoring, and Analysis of the  Effects of Air Pollution on Forests. Convention on Long-Range Transboundary
Air Pollution, International Cooperative Programme  on Assessment and Monitoring of Air Pollution Effects on
Forests.  Sponsored  by the United Nations Environment Program and United Nations  Economic Commission
for Europe.

USDA-FS.  1982.  An analysis of the timber situation in the United States, 1952-2030.   For. Res. Report
23.  U.S. Department of Agriculture, Forest Service, Washington, DC.

USDA-FS.  1985.  Forest Service Resource Inventory: An Overview. U.S. Department of Agriculture,  Forest
Service, Forest Resource Economics  Research Staff, Washington, DC.

Waring, R.H.  1984.  Imbalanced ecosystems: Assessments  and consequences.  Pages 49-78.  In:
                                               6-12

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C.I. Agren, ed.  State and Change  of  Forest Ecosystems:  Indicators in  Current  Research.  Report 13.
Swedish University of Agricultural Sciences, Department of Ecology and  Environmental Research, Uppsala.

Waring, R.H., and W.H.  Schlesinger.   1985.  Forest Ecosystems:  Concepts and Management  Academic
Press, Orlando, FL.
                                              6-13

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                                             SECTION 7

                              INDICATOR STRATEGY FOR ARID IANDS

                     Carl A. Fox,1 Christopher D.  Elvidge,1 and Dale W. Johnson1


7.1  INTRODUCTION

The most diverse EMAP resource category consists  of  the grasslands, shrublands, woodlands, and  tundra,
collectively called arid lands.  The resource classes identified for this category were  intended  to be both
hierarchical and comprehensive and were designed to include all terrestrial  resources not part of the EMAP
wetland, forest, or agroecosystem categories.

Nineteen  resource classes  have  been identified, ranging from barren to  those  covered  extensively  by
vegetation:


1.  Unvegetated                                       11. Shrubland-Blackbrush(Co/eogyne)-Dominated
2.  Steppe Grassland - Spring Green-up                12. Shrubland - Creosote bush (Larrea)-Dominated
3.  Steppe Grassland - Summer Green-up              13. Shrubland - Saltbush (Atr/p/ex)-Dominated
4.  Prairie Grassland - Spring Green-up                 14, Shrubland - Microphyllous Scrub-Dominated
5.  Prairie Grassland - Summer Green-up               15. Shrubland - Whitethorn (Acac/a)-Dominated
6.  Savanna - Spring Green-up                        16. Woodland - Evergreen
7.  Savanna - Summer Green-up                       17. Woodland - Deciduous
8.  Chaparral/Shrub-Dominated                        18. Tundra
9.  Chaparral/Shrub-Tree                              19. Riparian
10. Shrubland - Sagebrush (Artemes/a)-Dominated

The classification scheme for this category is a modification of several previously developed  classification
systems (e.g., Brown et al. 1979; Mouat and Johnson 1978;  Mouat et al. 1981).

In the conterminous United  States, these resource classes occupy most of the  land  surface area (excluding
forests at  higher  elevations)  west of  latitude 95°W.  This western region contains  important commercial,
agricultural (e.g.,  grazing),  mineral,  and energy resources.  Species (including  man) occupying  this  region,
however,  depend upon (and are adapted  to) the  limited,  but reliable,  surface and  ground water supplies.
Because of  dramatic population increases over  the  past 20  years, especially  in  western sunbelt states
(particularly  California, Nevada, and  Arizona), the  ecological  resources  in  arid regions are experiencing
increasing environmental pressures, including competition for water.

Some of the  most unique  plant and  animal  species  inhabit these arid and  semiarid regions.  Of the eight
states with the highest number of rare, threatened,  or endangered plant and vertebrate species, six and three
respectively, are  western states.  Increasing  utilization  (urbanization), resource development (mining), and
careless management (overgrazing) of arid ecosystems have contributed  to desertification and destruction of
natural  habitats.  Declines of species such as the  desert tortoise, bighorn sheep, desert pupfish, California
condor, saguaro cactus, and Joshua tree may indicate that these resources are being affected by changes in
land use, air quality (ozone, air toxics, sulfur dioxide), and water quality (increased salinity, pesticides,  ground
water withdrawal, hazardous waste contamination).
    'Desert Research Institute, University of Nevada System, Reno, Nevada

                                                 7-1

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Because arid ecosystems are so closely linked  to water resource availability, they may be severely impacted
by global  climate change.  A  recent study found that arid ecosystems are perhaps the most susceptible of
resource categories to global climate change  because of attendant potential changes in water cycles  (EOS
1989).  Vegetation  within  these  ecosystems is  also susceptible to  potential  effects  of  climate change,
particularly temperature.  Temperature effects on plant growth are well established (Wang 1960).   Most
metabolic activities are restricted to a range of about 5°C of the optimum temperature for each plant species
(Sosebee and Wan 1988).  Many  plant species, particularly those in the Great Basin, have been  shown to
be unable to acclimate to temperature changes (Caldwell 1985). Small shifts in temperature, combined with
decreased moisture availability or other stress, thus can exacerbate effects or ecological condition.  In addition,
there is clearly the potential for adjacent forests to become "desertified" with global warming and changes in
precipitation regimes.

Gradual changes  in climate  coupled with extensive human  utilization  may result in  substantial degradation
of arid land and  water resources.   Projected  temperature increases, changes  in  precipitation  patterns, and
increasing demands  for  high-quality water  will place heavy  political, social, and  economic  pressures on
government and industry nationally and worldwide.  An active  and effective regional monitoring program can
identify and document environmental changes and establish  ecologically sound guidelines for air, water, and
natural resource management in arid and semiarid  lands.

As discussed above, critical environmental issues facing arid  and semiarid ecosystems include global change,
desertification, air quality, and water resource management.  Although global change and air quality concerns
are relatively new issues, desertification and water resource management have been important issues  since
man first occupied arid and semiarid regions.  The hunting of large mammals, gathering and transporting of
seeds, and development of extensive irrigation systems by the native people in these regions had a profound
effect on  the landscape and the  associated  biological populations (Bender  1982).   The introduction of
livestock,  further  development of irrigation systems, and the increase  in human  populations has continued
to place pressure on  arid resources on a global scale.

Important environmental values associated with these  issues include aesthetics, biodiversity,  productivity, and
sustainability.  The unique nature of arid species and  habitats,  the concern over the importance of arid
ecosystems to global  climate,  and  the apparent increasing rate and extent of desertification in arid regions
have  led to an increased awareness on the value of  these arid resources.  Specific  resources and habitats,
such as riparian vegetation communities, are continually being threatened and decreasing to the extent that
they are being lost entirely from the landscape.  Development of indicators to monitor and assess  the status
and  trends  in the condition  of these important resources will  be critical in maintaining arid  ecosystem
function.
7.2  IDENTIFICATION  OF ARID LAND INDICATORS

The research indicators for arid lands were developed through a series of workshops and interactive meetings
between EMAP-Arid Lands members and scientific representatives of natural resource agencies who manage
arid ecosystems.  Two workshops were held in September 1989 and January 1990 which assembled more
than  50 recognized  experts  (Appendix I)  with  backgrounds  in  plant and animal  biology, ecology,  soil
biogeochemistry, animal life management, statistics, remote sensing, geobotany, microbiology, air and water
quality,  soil science, and  other related disciplines.  These experts represented  governmental,  private,  and
international institutions.  The candidate indicators were compiled by several working groups at the workshops
and presented  for review to all participants.  Facts sheets for indicators  that satisfied  the  EMAP indicator
selection criteria (Table 7-1) were subsequently written by selected authors and sent to participants for review.
Comments were incorporated into  the final indicator report   A written external  peer review of these
indicators was performed  in April 1990 (Appendix I.8), followed by a review by the EPA Science Advisory
Board in May 1990. The  second workshop also confirmed four key issues for arid resources that will be

                                                 7-2

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addressed by the indicators. These were desertification, global climate change, biodiversity, and degradation
or loss of riparian communities.
7.2.1  Arid  Land Indicators Appropriate for EMAP
This section  summarizes the provisional set of response indicators and exposure and habitat indicators for arid
lands  in  the conterminous United States (Figure 7-1).   Indicators for the tundra  resource  class, located
exclusively in Alaska, were not evaluated in  this initial phase of EMAP indicator development  The research
indicators will be used to detect and quantify status and trends in ecological condition of arid resource classes
and to provide  plausible explanations of changing condition.   Many of the indicators will  be employed
spatially within  a probabilistic  framework.  Others,  however, will census entire regions, and still others will
be  retrospective in assessing  past  trends  in time and space.  The retrospective  indicators will  be used to
examine  natural phenomena  and help quantify natural variation.
The selection of some  research indicators for  arid lands as  high-priority (Figure 7-1), although necessarily
subjective, was based on our  review of the literature and experience with desert and grassland monitoring
programs. Their measurement techniques often have not been thoroughly standardized or tested, or some
question  exists about whether they are sufficiently sensitive or applicable to all arid resource classes. Small-
scale field tests and regional demonstration  projects will  further develop standardized  protocols.
                      EMAP-Arid Lands Indicator Strategy
   Response Indicators (R)
  Vegetation Biomass
  Riparian Ecosystem Condition
  Energy Balance
  Water Balance
  Soil Erosion
  Retrospective Indicators
  Species Composition and
   Ecotone Location of
   Vegetation
  Relative Abundance of Animal
   Species
  Abundance and Species
  Composition
   of Lichens and Cryptogamic
  Crusts
  Demographics: Animals
  Morphological Asymmetry:
  Animals
   SPATIAL
ASSOCIATIONS
  TEMPORAL
ASSOCIATIONS
Exposure-Habitat Indicators (E)
Foliar Chemistry
Physiochemical Soil Factors
Exotic Plants
Livestock Grazing
Fire Regime
Abundance and Density of Key
 Physical Features
Mechanical Disturbance of Soils and
 Vegetation
Linear Classification and Physical
 Structure of Habitat
Landscape Pattern
Biomarkers
Chemical Contaminants in Wood
Figure 7-1.  Diagram of the proposed EMAP-Arid Lands Indicator Strategy.  Indicators in bold lettering
             are core indicators.
                                                 7-5

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A  brief description of each research indicator is given here.   More detailed descriptions of the indicators,
their application, suitable index period, variability, and research needs are listed in Appendices E and G, as
referenced by the code  in parentheses at the end of each description  below.

7.2.1.1  Response Indicators for Arid Lands

Response  indicators are  used  by EMAP to quantify and classify the condition of ecological resource classes.
Eleven  response indicators have  been selected  as research  indicators  for  arid lands, eight  of  which are
recognized as having high-priority status.

High-Priority Research Indicators

Vegetation Biomass.   Productivity and amount of green vegetation will be used as an indicator of condition
of arid and semiarid vegetation. Biomass  indices to be developed include measures of productivity, leaf area
index (LAI),  and greenness (calculated by  using red and near infrared data from satellite images).  The utility
of remote sensing for measuring major vegetation responses to stressors on  a regional scale is documented
by Asrar et al. (1985), Tucker  et al. (1985), and Becker and Choudhury (1988).  Recent work by Elvidge and
Mouat (1989) indicates that with data of high spectral resolution (10-nm-wide bands or smaller) it is possible
to detect  and monitor trace quantities of green vegetation at a site with less  than  5% green plant cover,
based on the chlorophyll red edge at 0.7  pm.  Data from satellite sensors having high spectral resolution will
be available in about  10  years. Integration of remote-sensing data with productivity measurements in the field
will allow the determination of extent and  distribution of productivity changes.  (E.1)

Riparian Extent. The special aesthetic and functional value of riparian resources in arid and semiarid regions
calls for an  indicator to  measure  and track their  integrity (Johnson and Simpson 1988).  Riparian zones  in
the western  United  States have  been widely  degraded  by improper  grazing  (U.S.  GAO  1988b), water
diversion, and watershed disturbance.  Measurements of areal  extent of the riparian zone, in combination
with other response indicators such as vegetation structure (G3.2) and species composition (E.7), will indicate
the condition of this  important resource class.  (E.2)

Energy Balance.  Processes associated with desertification  result in increased albedo  or  brightness of land
surfaces when they are observed with satellite sensors. By coupling ground-based meteorological data with
remotely sensed albedo  and temperature measurements, it is possible to monitor the energy  balance of a
region (e.g.,  Vukovich et al. 1987).   (E.3)

Water Balance.  This indicator includes  measurements of water inputs  (precipitation,  ground- and surface-
water flows), outputs (including transpiration  and evaporation), and withdrawal  (wells, diversions) of water.
Recent work by Carlson and Buffum (1989)  indicates  that  remotely  sensed data can be successfully integrated
with ground-based meteorological data for  estimating evapotranspiration  on a regional basis.  (E.4)

Soil  Erosion.  The form and  rate of soil erosion are significant measures  of  changes in arid  lands.  Soil
erosion will  be  measured  by  monitoring  the  form and density  of  drainage systems and  the areal extent  of
bare soil with aerial and satellite  imagery.  A satellite-based laser  altimeter for measuring surface profiles is
currently under  development by the National  Aeronautics and Space Administration (NASA) and may provide
the means to detect  loss of material by erosion.  (E.5)

Retrospective Indicators.  The western  United  States contains a  rich  record of tree  rings and  fossil plant
remains (pollen, charcoal, woodrat middens) documenting the  response of plant communities  to climatic
change (e.g., Graybill 1985).   By measuring both  modern  and ancient materials, a long-term record for these
indices can  be developed.  With these retrospective data  the  rate of magnitude of future vegetation changes
can be compared with the changes  observable in a fossil  record extending into the ice ages.  (E.6, E.8-E.10)
                                                  7-6

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Species Composition  and Ecotone Location of Vegetation.  This indicator will be used to detect changes
in species composition, spatial distribution  of various species, and species diversity.  The  location of plant
assemblages  in arid  and semiarid regions responds  dynamically and  differentially to factors such as grazing
and  climate  change.  Both  synoptic and reference site measurements  will be  used,  in  addition to data
obtained from the retrospective indicator analysis.  Variation in space and time will be measurable with this
approach.  (E.7)

Relative Abundance:  Animals.  The  status of a community of organisms can sometimes be assessed by
the status of a few species or categories of species that play critical roles.  Birds could be used as indicators
that  integrate across  resource classes; whereas small mammals, reptiles, and amphibians generally would serve
to monitor conditions  within a class.  (G1.1)

Other Research Indicators

Abundance and Species  Composition of Lichens and  Cryptogamic Crusts.   The abundance and species
composition  of lichens and  cryptogamic crusts are sensitive to air  pollution  and  mechanical disturbance.
Cryptogamic crusts on  arid soils control soil stability, nutrient status, and soil moisture dynamics (Harper and
Marble 1988).  (E.11)

Demographics: Animals.  Population vigor is  reflected in the recruitment of individuals  into the breeding
population and their subsequent survivorship.  Parameters  include age structure, sex ratio,  fertility,  mortality,
survivorship,  and dispersal; and such measurements are only appropriate for definite keystone species.  (G1.2)

Morphological Asymmetry:  Animals.  The morphological variability in structures such as fins, teeth, and
bones of  bilaterally symmetrical  organisms has  been  found  to  increase  with exposure to  chemical
contaminants, hybridization, and  inbreeding.    This  parameter would  be an  early-warning  indicator of
population-level responses to human-induced stressors.  (C1.3)

7.2.1.2  Exposure and Habitat Indicators for Arid Lands

High-Priority Research Indicators

Exposure and habitat indicators are used by EMAP to identify and quantify changes in exposure and physical
habitat that are associated with changes in response indicators.  Eleven  exposure and habitat indicators have
been selected as research  indicators for  arid  lands, nine of which are recognized as having high-priority status.

Foliar Chemistry.   Nominal woodland, shrubland, or grassland  condition  depends on a  sufficient supply
and  the proper balance  of  critical nutrients in foliage.   Imbalances may signal  imbalances  in vegetation
processes or  indicate exposure to specific types of stresses.   Also, the  detection of  certain foliar contaminants
can provide information on possible mechanisms by which changes in  vegetation condition might occur. (E.12)

Physiochemical Soil Factors. The chemical and physical properties  of soil (pH and salinity profiles, carbon
and  nitrogen content,  nutrient availability, structure and bulk density) have a direct impact on vegetation
(Leonard et al. 1988).  Hydrologic  properties of soils (principally infiltration  rate and hydrologic conductivity)
can  be altered  by vegetation changes and mechanical disturbances (improper  grazing, vehicular traffic, and
fire).   Decreased  water-holding  capacity and infiltration rates of soil can  increase runoff,  which frequently
accelerates erosion.  (E.13)

Exotic Plants.  Introduced species  are  common  in disturbed ecosystems.  Although, in  general, introduced
species degrade the  functioning  and diversity of an area, their relative impacts  among ecosystems are highly
variable.  (E.14)
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Livestock Crazing.  Grazing by cattle and  sheep has  had major  impacts  on as much  as  40-80 million
hectares (100-200 million acres) of land  in the western United States (U.S. GAO 1988a).  Grazing can alter
species  composition, impact riparian systems, and accelerate erosion (U.S. BLM 1978).  The effects of grazing
by domestic livestock, as well as wild and feral ungulates, must be accounted for when environmental impacts
of other factors are assessed.  This indicator will  be measured by counting grazing animals on resource
sampling units.  (E.15)

Fire Regime.  Fire is a  natural phenomenon in  most desert and grassland ecosystems, playing a crucial role
in the release of nutrients and the creation of animal habitat  Environmental damage can occur, however,
if fires occur too frequently  or too infrequently.  The extent and frequency of fires can be readily measured
with remotely sensed data.   (E.16)

Abundance and Density of Key Physical Features:  Certain physical features of habitats (e.g., cliffs, outcrops,
sinks, seeps, talus slopes) are critical to  animal  diversity and abundance.  Land-use  practices can alter the
density  and  distribution of  many key physical  features.  Many habitat features are specific to particular
resource classes, but determining what  to measure  in  a  given  class can be  based on existing literature.
(G3.1).

Mechanical Disturbance of Soils  and Vegetation.  Mechanical disturbances such as roads, trails,  mines,
disposal sites, and gravel extraction areas will fragment habitat, increase erosion, and  promote the growth of
exotic plant species (Webb and  Wilshire 1983).   Mechanical  disturbance is  readily measured  by using
remotely sensed  data.   (E.17)

Linear  Classification and  Physical Structure of Habitat  The distribution and disappearance  of certain
species  is  associated with  critical  features of their habitat   For  example, overgrazing can  result  in the
elimination of palatable  plant species, soil compaction, and desertification. Species that depend on woodland,
shrubland, or grassland  habitat may not be able to survive in a habitat that is  predominately unvegetated.
Many studies have also demonstrated the relationship between animal diversity and vertical vegetation profile.
(G3.2)

Landscape Pattern.  Landscape indicators, calculated  from data  derived from remote sensing, describe the
spatial  distribution  of physical,  biological, and  cultural features across a geographic area.  These spatial
patterns directly  reflect  the  available habitat for animals, and they can be used to predict other functional
attributes of arid ecosystems. (G3.3-G3.8)

Other Research  Indicators

Biomarkers.  A  desirable feature of a monitoring  program would be  to detect an organism's response to
human-induced stresses at the biochemical  and cellular  level  before the stresses  produce a detectable
response at the organism and population levels.  Although the use of biomarkers as  early-warning response
indicators  requires more basic  research, their present value for regional survey monitoring is to provide
information to support or refute hypotheses on why ecological condition of arid  lands is subnominal.  (G2.1-
G2.11)

Chemical  Contaminants in Wood. Compounds of anthropogenic origin will be measured in woody  plant
tissue.  By archiving samples we will have materials for analysis  of contaminants  in time series.   (E.18)

7.2.2  Arid Land Indicators Not Selected for EMAP

Of the  candidate indicators  that were evaluated for arid  lands, many did not rank high enough when judged
against  the EMAP indicator  selection criteria (Table  7-1) to  be proposed as research indicators. Some of the
candidate  indicators not selected, however,  would be quite valuable  at more intensive ecological research

                                                  7-8

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sites.  Examples of candidate indicators and the main reason for their not being proposed as EMAP research
indicators are listed  below.

    Microbial biomass in soils.  Considered too variable, given the anticipated field sampling cycle of
    EMAP.

    Historic photo  analysis.   Of high value as a retrospective indicator, but rejected  because of cost
    considerations.

    Plant litter  decay rates.  Discounted because it is not readily measured.

    Seed  production.  Annual variability in  response to temperature and precipitation  variations  make
    this indicator of questionable utility.

    Plant pigments.  This  requires laboratory analysis for determination and is time-consuming.  Remote
    sensing approaches for detecting changes in  plant pigment concentrations (chlorophyll red edge shifts)
    are currently being tested and may provide valuable results in  areas where green vegetation is dense
    enough to produce an adequate signal.

    C, H, O isotopic ratios. This indicator may provide valuable information about the  sources of plant
    carbon  and plant water, but is  not readily  measured.   Isotopic ratios  would  be an appropriate
    method for  intensive studies.

    Visual symptoms of foliar damage. Not considered sufficiently objective  and  quantifiable in arid
    resource classes.

    Lignin/cellulose/chlorophyll/nitrogen ratios.  Not considered cost-effective or readily interpretable.

    Chlorophyll fluorescence.  Not considered cost-effective or readily measured.

    Cation  distribution  above water table:    Not  considered  cost-effective because of  arduous
    measurement  procedures.


7.3  APPLICATION  OF ARID LAND INDICATORS

One approach for determining subnominal condition in arid resource classes begins  with estimating pristine
conditions at each site, given the physical setting.  Values of response indicators would likely be nominal  for
conditions that  existed prior to the spread  of  introduced species or prior  to  prolonged grazing or water
withdrawal.  Obtaining such  pristine values  will  not be widely possible,  however,  because  of the lack of
site-specific  historical information regarding ecological condition in arid  regions prior  to human  intervention.
Where  available,  historic  photo analysis can  and  has been successfully used  in  some arid  systems;  for
example, the study of arid and semiarid regions by Hastings and Turner (1965) quantified  species composition
and mortality in a specific region  by  using this  method.

A  more  realistic approach to determining  subnominal conditions  will  be  to  use (1)  reference  sites and
(2) response indicator values ascribed as subnominal.  Response indicators values in  regional  reference sites
with protected or (relatively) undisturbed settings can be used to provide examples of  nominal conditions.
Conversely,  a series of indicator  findings  corresponding to degraded  conditions  will be  used to  identify
subnominal  threshold  values.  For example,  sites with mechanical disturbances or  infestations of noxious
introduced species would be considered subnominal.  All of this leads to the future development of integrated
                                                  7-9

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indices of ecological condition, with the scores or values in a number of indicators being used to calculate
the indices.
7.4  RESEARCH NEEDS FOR EMAP-ARID LANDS

The  highest priority for research indicators for arid lands  is to develop a  means for distinguishing whether
subnominal conditions are a result of human-induced stress or a result of stress related to short-term climatic
influences, such as annual fluctuations in temperature and precipitation.  Some of the research indicators have
been well tested, are likely to be applicable for EMAP, and can be readily implemented;  others, however,
will require additional development before they become routinely operational for EMAP.  The development
and  use of retrospective indicators will be a key element for assessing near-term trends.

It is  believed that many of these research indicators can be assessed by using airborne and satellite imagery.
The cost-effectiveness of these remote sensing techniques can be enhanced by the development of automated
procedures for data handling and analysis.  Resource-class-specific  expert systems  should  be investigated as
a tool for assessing  possible reasons for observed changes based on spectral signatures.


7.5  REFERENCES

Asrar, G.,  E.T. Kanemasu,  R.D. Jackson, and P.J. Pinter, Jr.  1985.  Estimation  of total above-ground
phytomass production using remotely sensed data.  Remote Sens.  Environ. 17:211-220.

Becker,  F., and B.J. Choudhury. 1988.  Relative sensitivity of normalized difference vegetation index (NDVI)
and  microwave polarization difference index (MPDI) for vegetation and desertification monitoring.  Remote
Sens. Environ.  24:297-312.

Bender, C.L  1982. Reference Handbook on the Deserts of North America.  Greenwood  Press, Westport,
CT.

Brown,  D.E., C.H. Lowe, and C.P. Pase.  1979. A digitized classification system for the biotic communities
of North America with community (series) and association examples for the Southwest.  J.  Arizona-Nevada
Acad. Sci. (Suppl) 14:1-16.

Caldwell, M.M.   1985.   Cold desert.  In:  B.F. Chabot and H.A. Mooney,  eds.   Physiological  Ecology of
North American Plant Communities.  Chapman and Hall, New York.

Carlson, T.N., and  M.J. Buffum.  1989.  On estimating total daily evapotranspiration from remote surface
temperature measurements.  Remote Sens.  Environ. 29:197-208.

Elvidge,  C.D.,  and DA  Mouat   1989.   Detection limits of green vegetation in 1988 AVIRIS  data.
Proceedings of the 7th Thematic Mapper Conference,  Calgary, Alberta.

EOS. 1989.   Warming will alter water resources.   EOS (Trans. Am. Ceophys. Union) January  31, p. 67.

Graybill, D.A.  1985.  Western U.S. tree-ring index chronology data  for detection of arboreal  response to
increasing carbon dioxide.  University of Arizona,  Laboratory of Tree-Ring Research, Tucson.

Harper, K.T., and J.R. Marble.  1988.  A role for nonvascular plants in  management of arid and semiarid
rangelands.  In:  P.T. Tueller, ed. Vegetation Science Applications for  Rangeland Analysis and Management
Kluwer Academic Publishers, Dordrecht,  The Netherlands.

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Hastings, J.R., and  R.M. Turner.  1965.  The Changing Mile:  An  Ecological Study of Vegetation Change
with Time in the Lower Mile of an Arid and Semiarid Region.  University of Arizona  Press, Tucson, AZ.

Johnson, R.R., and J.M. Simpson.  1988.  Desertification of wet riparian ecosystems in arid regions of the
North American Southwest  Pages 1373-1382.  In:  Proceedings of Arid Lands:  Today and  Tomorrow.
Westview Press, Boulder, CO.

Leonard, S.C., R.L Miles,  and P.T.  Tueiler.  1988.  Vegetation-soil relationships on arid and  semi-arid
rangelands.  In: P.T. Tueiler, ed. Vegetation Science Applications for Rangeland Analysis and Management
Kluwer Academic  Publishers, Dordrecht, The Netherlands.

Mouat, DA, and R.R. Johnson.  1978. An inventory and assessment of wildlife habitat in Grand Canyon
National  Park using remote sensing techniques.  Pages 105-113.  In:  The Application of Remote Sensing
Data to Wildlife Management  Proceedings of the Fourth Pecora Symposium, Sioux Falls, SD.

Mouat, DA, J.B. Bale, ICE.  Foster, and B.D. Treadwell.  1981.  The use  of remote sensing for an integrated
inventory of a semi-arid area.  J.  Arid  Environ. 4:169-179.

Sosebee, R.E., and C. Wan.  1988.  Ecophysiology of range plants. In:  P.T. Tueiler, ed. Vegetation Science
Applications for Rangeland Analysis and Management.  Kluwer Academic Publishers, Dordrecht

Tucker, C.J., C.L.  Vanpraet, M.J. Sharman, and G. Van Ittersum.  1985.  Satellite remote sensing of total
herbaceous  biomass production in the Senegalese Sahel:  1980-1984.  Remote Sens. Environ. 17:233-250.

U.S. BLM.  1978.  The effects  of surface disturbance (primarily livestock  use)  on the salinity of public lands
in the upper Colorado  River Basin:   1977  status  report.   BLM/YA/TR-78/01.  U.S. Bureau of Land
Management, Denver, CO.

U.S. GAO.   1988a.  Management of public rangelands by the  Bureau of Land Management. GAO/T-RCED-
88-58.  U.S. General Accounting Office, Washington,  DC.

U.S. GAO.   1988b.   Restoring  degraded  riparian  areas on western   rangelands.   GAO/T-RCED-88-20.
U.S. General Accounting Office, Washington, DC.

Vukovich, F.M.,  D.L. Toll,  and  R.E. Murphy.  1987.   Surface temperature and  albedo  relationships in
Senegal derived from NOAA-7 satellite data.   Remote Sens. Environ. 22:413-422.

Wang, I.Y.   1960.  A critique  of the  heat unit approach  to plant response studies.  Ecology 41:785-789.

Webb, R.H., and H.G. Wilshire, eds.  1983.  Environmental Effects of Off-Road  Vehicles:  Impacts and
Management in Arid Lands.  Springer-Verlag, New York.
                                               7-11

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                                              SECTION  8

                           INDICATOR STRATEGY FOR AGROECOSYSTEMS

                            C. lee Campbell,1 Walter Heck,2 Tom  Moser3
8.1  INTRODUCTION
Agroecosystems provide the vast majority of the food consumed in the United States and continue to provide
surplus food for export to other countries.  Agroecosystems,  the most intensively managed EMAP  resource
category,  are typically located on some of the most productive land and  cover  nearly 30% of the earth's
surface (Coleman  and Hendrix 1988).  Yield from U.S. agroecosystems  is higher than from those in most
other countries (Anonymous 1989).  The extensive acreage devoted to crop and livestock  production  results
from the  single largest alteration of  native habitat in North America in recent geological  time (Jackson and
Piper 1989).  By  design,  agroecosystems are among the most intensively managed  resources in the world
(Pimentel and  Dazhong 1990); because  of the periodic and chronic disturbances  created  by agriculture,
agroecosystems are also among the  fastest changing of landscapes (Elliott and Cole 1989).

Agroecosystems are intensively monitored on a local, regional, and national level.   Growers, agricultural
extension agents, individuals in agribusiness, and  economists regularly assess and report on the current status
of crops and livestock production.   Conditions that affect crop or livestock yields (e.g., poor soil quality or
diseases)  are usually corrected by management practices, such as applications of fertilizers  or pesticides.
Although  agricultural systems  may  appear to be  simple  ecosystems, even  annual monocultures can  be
ecologically complex (Coleman and Hendrix 1988; Paul and Robertson 1989).   More effort  needs to be
devoted to monitoring their long-term sustainability and the condition of resources not directly associated with
crop and  livestock production.

Agroecosystems in EMAP  are defined as lands managed  for crop, pasture, and livestock production and the
biotic and abiotic  components of the underlying soils, surrounding transition  zones (Carroll 1990),  drainage
networks, and adjacent areas that support natural vegetation and native animals.  These areas form a unified
whole  consisting  of food  and fiber production,  natural  vegetation, and  native  animals,  as presented
conceptually in  Figure 8-1.

Four EMAP-Agroecosystem  resource classes  were identified  as agricultural production units  with common
management practices and common  environmental  problems:  (1) field, vegetable, and  forage crops; (2) fruit
and nut crops;  (3) managed pasture and nonconfined animal operations; and (4) confined animal feeding
operations.  Idle or natural areas within an agroecosystem will  be assigned to  their associated  resource class;
those areas remaining idle for a designated period and are of sufficient size will be reclassified according to
their nonintensive resource categories (e.g., forests, arid  lands).

The following sections provide a strategy that could be used to select and apply a proposed set of indicators
for agroecosystems.  We will specifically address the following items.
    'North Carolina State University, Department of Plant Pathology, Raleigh, North Carolina

    2U.S. Department of Agriculture, Agricultural Research Service, Air Quality Program, Raleigh, North Carolina

    3NSI - Environmental Sciences, U.S. EPA Environmental Research Laboratory, Corvallis, Oregon

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             Inputs
A. Management Practices
  Chemicals
     • Pesticides
     • Other organics
     • Fertilizers
     • Atmospheric

  Water
     • Irrigation

  Soil Manipulation
     • Tillage

B.  Natural
     • Pests
     • Precipitation
     • Radiation sunlight (UV-B)
     • Soil development
     • Temperature
     • Relative humidity
     • ET
Agroecosystem


1) Food & Fiber Production
• Soil
• Vegetation
• Animals

2) Natural Vegetation
• Soil
• Vegetation
• Animals

3) Animal Life
• Soil
• Vegetation
• Animals


                         \
Harvests
  •  Crops
  •  Animals
  •  Wildlife

Surface Runoff
  •  Salts
  •  Nutrients
  •  Pesticides
  •  Sediment

Leaching to Groundwater
  •  Nutrients
  •  Pesticides
  •  Fertilizers
                              Erosion
                                • Sediments
                                • Nutrients/pesticides

                              Atmosphere
                                • Methane, nitrous
                                  oxide
                                • Pesticides
                                • Dust
                        Figure 8-1.  Conceptual model of agroecosystem.
      •  The public perception of the ecological condition in  agroecosystems and associated
         environmental values
      •  A set of research  indicators that define the ecological condition of agroecosystems
      •  How we might define subnominal condition in  agroecosystems

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8.2  IDENTIFICATION OF AGROECOSYSTEM INDICATORS

8.2.1   Perceptions of Agroecosystem Condition

Agroecosystems are essential for human welfare, cover vast acreage,  provide  habitat for natural vegetation
and native animals in  both managed and adjacent areas, and transport substantial amounts of residual exports
to aquatic and terrestrial  ecosystems.  Income generated by agricultural products makes agriculture a leading
sector  of the U.S. economy.  Agroecosystems also provide habitat and  food for diverse biota and serve as
important pathways for biogeochemical  cycles.  They are designed and managed to sustain  high productivity
through intensive development of improved crop plants and animals and  through the application of chemicals
and mechanical  energy (Lowrance et al. 1984).  Because of the  importance of agriculture to societal well-
being,  public concern   regarding  status of  and  trends  in   ecological  condition  in  agroecosystems  is
unquestioned.  The time is  ideal for initiating a long-term monitoring program of agroecosystems, because
the emphasis in  agricultural  production  is shifting from strict maximization to regeneration and optimization
of resource utilization, while maintaining sustainability with minimum environmental damage (Elliott and Cole
1989).

The public is becoming increasingly concerned about the quality of food  and  water  available for consumption
as well as the health  of the  overall biosphere (Buttel 1990; Soule et al.  1990).  Use of pesticides and other
agricultural chemicals has  become  routine.   This  dependence,  however,  makes agriculture the  largest
contributor of nonpoint-source pollutant loadings for streams and  lakes in  the United States (Anonymous
1989;  Levins and Vandermeer 1990;  Soule  et al. 1990).  The public  perception is thus one of concern for
food and  water  quality  and  environmental  health, confounded with  a  strong desire to have an abundant
supply of  inexpensive food and fiber  products (Buttel 1990).

8.2.2   Environmental Values for Agroecosystems

What constitutes a "healthy"  agroecosystem cannot be determined  a priori. Instead, the perception of health
is  based  on knowledge of an acceptable range of values for  measures related to specific components of
agroecosystems.   In screening indicators for  EMAP, both public and scientific concerns were translated into
environmental values  that could  be assessed directly through the measurements of one or several indicators
(Figure 2-1).

Several pertinent scientific, social,  economic, and environmental issues are associated with  agricultural crop
and animal production  systems  and their surroundings.  These  issues can be essentially  summarized  by
environmental values: productivity and  sustainability, biodiversity, and  freedom from contamination.

Productivity and  sustainability refer to the capacity of a  particular agroecosystem to  maintain an economically
acceptable level  of crop or  livestock productivity over time. A primary objective of agroecosystem managers
is  to  obtain  maximum  productivity with a  minimum  of external  inputs.  Potential decreases in  system
sustainability  can be  masked  in  the short term  by management practices and increased inputs.  The time
scale of public concern for sustainability,  however, is decades or centuries, not a single crop-growing season.

Although linked  to sustainability, biodiversity refers to the ability of a  highly  managed ecosystem to support
species that do  not contribute directly  to crop  and livestock  production.  Such species include important
game  and unmanaged animal life,  controllers of pollination and insect pests, songbirds, and  wildflowers.

Contamination of food  and  resources  is an issue of  growing public  concern, especially  as it affects the
productivity, structure, or function  of an  agroecosystem.  Human-induced stressors  of contamination include
atmospheric deposition, point sources into potential irrigation water, agricultural  chemicals, agricultural wastes,
and some biological technologies.  Specific types of contamination include pesticides, antibiotics or residues
in  food, and  other agricultural wastes;  salinization of soils; and the presence of exotic pests.

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8.2.3 Agroecosystem Indicators Appropriate for EMAP

Two EMAP workshops were held in Raleigh, North Carolina, in August 1989 to discuss candidate indicators
of agroecosystem health, structure,  and function. Each participant (see Appendix I) was asked to develop a
list of possible indicators, related to his or her area of expertise.  From these workshops, 130 candidate
indicators were  proposed.  After candidate indicators were summarized and examined for redundancy,  90
specific  indicators were considered, from which a refined candidate indicator list for further examination was
developed.

An extended fact sheet was  prepared for  each indicator for incorporation  into this report  Further critical
examination of each  indicator was performed  by one or more members of EMAP-Agroecosystems  and the
results were presented at an  indicator strategy workshop in  Idaho Falls,  Idaho,  in May  1990.  A written
external  review  of these indicators  also was performed in April 1990 (Appendix  I.8),  followed by a review
by the EPA Science Advisory Board  in May 1990.  Candidate indicators were again considered in extensive,
critical discussions, which included  an evaluation based on the EMAP indicator selection criteria (Table 8-1).
The final list of research indicators was prioritized,  and needed measurements were identified.  Specific
information  needed  from demonstration studies in  order to  further evaluate research  indicators was also
identified.

Figure 8-2 provides an overview of the proposed EMAP-Agroecosystems indicators strategy, and Table 8-2
identifies the specific environmental values addressed by each  indicator. A brief description of each  research
indicator is given here. More detailed descriptions of the indicators, their application,  suitable  index period,
variability, and research needs are  listed in Appendices F and  G,  as referenced by the code in parentheses
at the end of  each description below.

8.2.3.1   Response Indicators for Agroecosystems

Response indicators are used  by EMAP to quantify and classify the condition of ecological resource classes.
Ten response  indicators  have been selected as research indicators  for agroecosystems, eight  of  which are
recognized as  having high-priority status.

High-Priority  Research Indicators

Nutrient Budgets.  The  cyclic processing  of chemical elements within an ecosystem  is fundamental to the
maintenance of the system.  Nutrient concentrations in soil and organisms and nutrient leaching from soil will
be measured.   Major changes in  these  concentrations  can  provide  an early warning  of agroecosystem
response to stress.  (F.1)

Soil Erosion.   Loss of soil due to  erosion depletes the productivity of the land.  As the more  productive
surface  layer is  lost, less fertile soil  layers  are  incorporated into the plow layer.   Erosion on  farmland also
results in the entry of sediments and chemicals into surface waters. Nominal or subnominal status and  long-
term trends in soil erosion can be evaluated in terms of site tolerance (effect on productivity) to soil loss, as
classified by the Soil  Conservation  Service. (F.2)

Microbial Biomass in Soils.  Soil microorganisms play a vital  role in  the retention and release of  nutrients
and energy  in agricultural soils,  and they are sensitive to environmental hazards. (F.3)

Land Use/Extent of Noncrop Vegetation.  Information on land use (cropland, pasture,  set aside, abandoned,
woodlot) will  indicate trends in crop preferences and grower  perceptions concerning market  opportunities,
profitability, and sustainability of production.  The  relative areal extent of crop and noncrop  vegetation  on
arable lands can reflect agroecosystem condition.   Land use can  be related to other  indicators such as the
index of soil productivity, agricultural  pest density,  and quality of irrigation waters. (F.4)

                                                 8-4

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                EMAP-Agroecosystems Indicator Strategy
  Response Indicators (R)

 Nutrient Budgets

 Soil Erosion

 Mlcrobial Biomass In Soils

 Land Use/ Extent of Noncrop
  Vegetation*

 Relative Abundance: Animals

 Crop Yield

 Livestock Production

 Visual Symptoms of Foliar
  Damage: Crops*

 Demographics: Animals

 Morphological Asymmetry:
  Animals
       SPATIAL
    ASSOCIATIONS
      TEMPORAL
    ASSOCIATIONS
    Exposure-Habitat Indicators (E)

   Agricultural Pest Density

   Lichens and Mosses, Clover,
    Earthworm Bioassays

   Quantity/ Quality of Irrigation Waters

   Soil Productivity Index

   Abundance and Density of Key
    Physical Features

   Linear Classification and Physical
    Structure of Habitat

   Landscape Pattern

   Biomarkers
Figure 8-2.  Diagram of the proposed EMAP-Agroecosystems Indicator Strategy.  Indicators in  bold
            lettering are high-priority research indicators. Response indicators with an asterisk (*)
            function as exposure and habitat indicators.
                                                             also
Table 8-2.  Environmental Values Addressed by High-Priority Research Indicators for an Agroecosystem

                                                            Environmental Value
 High-Priority Research Indicator
Sustainability of
crop resources
                                                            Contamination
             Integrity of
             noncrop resources
 Nutrient budgets
 Soil erosion
 Microbial biomass in soils
 Land use/Extent of noncrop veg.
 Relative abundance: animals
 Crop yield
 Livestock production
 Foliar damage
 Pest density
 Bioassays
 Irrigation water quantity
 Irrigation water quality
 Soil productivity
 Habitat and landscape descriptors
        X
        X
        X
        X
        X
        X
        X
X
X

X

X
                                                  8-6

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Relative Abundance:  Animals.  The status of a community of organisms can sometimes be assessed by the
status of a few species or categories of species that play critical  roles.  Some types of birds could be  used
as indicators that integrate across resource classes, whereas small mammals, reptiles, and amphibians generally
would be used to monitor condition within a resource class.   (G1.1)

Crop Yield.  Crop yield integrates the action of a large  array of biotic and abiotic factors that affect  crop
growth.  Yield for a given crop, determined at crop  maturity,  can be compared with established standards
in a specific area.  Crop yield related to given inputs, such as amount of fertilizer, also provides an index
of production efficiency.  (F.5)

Livestock production.  The production of beef, pork, poultry, and fish for human consumption; horses; and
other nonfood animals  is a vital function of agroecosystems. Responses of livestock to a  large array of biotic
and abiotic factors can be synthesized into measures of production efficiency and overall productivity.  Waste
output from confined feeding operations  also provides the potential for resource contamination and  should
be monitored.  (F.6)

Visual Symptoms of  Foliar Damage:  Crops.  Crop condition is often determined by the  presence  or
absence of visible symptoms  caused  by plant pests (such  as  pathogens  or insects),  pollutants, nutrient
imbalance, pesticide toxicity, or weather  extremes.  Environmental data (weather, types and concentrations
of pollutants), pest density, soil and chemical inputs can  be related to  specific foliar symptoms.  (F.7)

Other Research Indicators

Demographics:  Animals.  Population vigor is reflected in the recruitment of individuals into the breeding
population and their subsequent survivorship.  Parameters include age structure, sex ratio, fertility, mortality,
survivorship, and dispersal; and such measurements are appropriate only for definite keystone species.  (G1.2)

Morphological Asymmetry:  Animals.  The  morphological variability in structures such as teeth and bones
of bilaterally symmetrical organisms has been found  to increase with  exposure to  chemical  contaminants,
hybridization,  and inbreeding.  This  parameter  would  be an early-warning  indicator  of  population-level
responses to man-made stressors.   (G1.3)

8.2.3.2   Exposure and Habitat Indicators for Agroecosystems

Exposure and habitat indicators are used by EMAP to  identify and quantify changes in exposure and physical
habitat  that are  associated with changes  in response  indicators.  Ten exposure and habitat indicators  have
been  selected as research indicators for agroecosystems, nine of which are recognized as having high-priority
status.

High-Priority Research Indicators

Visual Symptoms  of Foliar Damage:  Crops.  Environmental data (weather, types and concentrations of
pollutants), pest  density, soil and chemical inputs can  be related to specific foliar  symptoms.   (F.7)

Agricultural Pest Density.  Density of pests,  such  as  insects,  pathogens, and weeds,  is  an  indicator of the
degree  of biological stress to which  crop plants are  exposed.  Species diversity and frequency of plant-
parasitic nematodes and weeds are relatively easy to measure (i.e.,  extent of weeds, presence of nematodes
in soil samples).   (F.8)

Lichens and Mosses, Clover,  Earthworm Bioassays.  Lichens  or  mosses, higher plants such  as white  clover,
and animals such as earthworms can serve as biomonitors for a variety of hazards. Known responses of these
species  to specific stressors  such  as air pollutants and a  knowledge of the  relationships between bioassay

                                                 8-7

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species and crop or noncrop plant responses would enable a quantification of such stresses as a  plausible
explanation for subnominal agroecosystem condition.   (F.9)

Quantity and  Quality of  Irrigation  Waters.   The  quality of surface water  and ground water  impacts
productivity, sustainability, and condition (health) of plants and animals in the agroecosystem.  Quantity of
water available for use in agroecosystems may also serve as an indication of sustainability of farming practices.
The use of surveys would require that indices be used to represent annual or seasonal averages.  (F.10)

Soil Productivity Index. Each soil  has a productivity capacity for a specific crop or sequence of crops under
a defined set of  management practices.  Maintaining soil productivity depends on using best management
practices, the characteristics of the  site, and the biotic and abiotic stresses applied to the system.  A change
in soil productivity over time indicates a shift in sustainability of the system.  Important metrics that would
be considered in  this index include  soil rooting depth, topsoil thickness, available water capacity, texture, bulk
density, permeability, clay fraction, pH,  and soil organic matter content.  (F.11)

Land Use and Extent of Noncrop  Vegetation:  In addition to serving as a response indicator,  land use and
extent of noncrop vegetation also can influence the abundance and species composition of native animals.
(F.4)

Abundance and Density of Key Physical Features:  Certain physical features of habitats (e.g., cliffs, outcrops,
sinks, seeps, talus slopes) are critical to animal diversity and abundance.  Land-use  practices  can  alter the
density and  distribution of many  key physical features.  Many habitat features are specific to particular
resource classes,  but determining what to measure in a given class can  be based on existing  literature. (G3.1)

Linear Classification and  Physical Structure of Habitat A large proportion of the managed landscape in
the United States is composed of patches of natural vegetation within a matrix of different agroecosystems.
The habitat layer index, derived from a  combination of aerial photograph interpretation and field validation,
appears  to  be an  appropriate  indicator for evaluating  habitat  (vegetative  community) structure.   Habitat
structure  can be  used  to interpret potential suitability of an area for occupancy, foraging, and  breeding
activities of animal life.  (G3.2)

Landscape Pattern.  Landscape indicators, calculated from data derived from  remote sensing, describe the
spatial distribution of physical,  biological,  and cultural  features  across  a  geographic  area.   These spatial
patterns directly  reflect the available  habitat for animals as well as  predict other functional attributes of
agroecosystems. (G3.3-G3.8)

Other Research  Indicators

Biomarkers.  A  desirable  feature of a monitoring program  would  be to detect an organism's response to
human-induced stresses at the biochemical  and  cellular  level before the stresses produce  a detectable
response  at the organism and population levels. Although the  use  of  biomarkers as early-warning response
indicators  requires more  basic  research, their  present  value for regional  survey monitoring  is  to provide
information to support or  refute hypotheses on  why ecological condition of agroecosystems is subnominal.
(G2.1-G2.11)

8.2.4  Agroecosystem  Indicators not Appropriate for EMAP

Among the initial list of candidate  indicators, many were excluded  because they are in very early stages of
development  For example, there is evidence that assays of leaf surface microflora can indicate the presence
of adverse biochemical substances  or an excess of specific types of  radiation; analysis of animal hair or bird
feathers shows promise for detecting the presence of pesticides.  Although  many of these  indicators would
be  appropriate for the  examination of specific cause-effect relationships within  agroecosystems, they are not

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considered appropriate at this time for examining agroecosystem condition in a regional or national monitoring
system.

Several candidate indicators were not selected  for further  consideration  because continuous monitoring,
which is inconsistent with  EMAP's survey monitoring design,  is needed.  For example,  chlorophyll content
in leaves changes in response to phenologic, physiologic, and  environmental conditions (Tucker et al. 1981);
therefore, meaningful interpretations of chlorophyll data gained from measurement techniques, such as use
of satellite  remote  sensing,  would require  weekly or  monthly observations during the  growing period.
Indicators such as water content in foliage and populations of soil bacteria were excluded because their large
spatial and temporal variabilities would preclude meaningful interpretations within the EMAP context


8.3  APPLICATION  OF AGROECOSYSTEM INDICATORS

Assessing  the "health" of  any ecological resource  is challenging.    The fact that biotic  populations and
ecosystems are dynamic creates problems in defining threshold values for response indicators to determine
subnominal condition.  Such difficulties are compounded for agroecosystems  because these ecosystems are
created and  maintained by  man  and are intentionally  and continuously perturbed for the production of
market commodities.

The  response indicators described in  Section 8.2.3 (e.g., nutrient budgets, crop yields, livestock production,
soil  microbial biomass,  and visual  symptoms of foliage to crops) would  be used initially to  determine the
proportion of agroecosystems that are subnominal. The exposure and habitat indicators (e.g., agricultural pest
density, bioassays, quantity and quality of irrigation waters, soil productivity  index, and land  use or  extent of
noncrop vegetation) would be used to  look for plausible associations between exposure or physical habitat
and  ecological  condition.  Nominal means that "the values of indicators measured on the resource indicate
that  it is exhibiting structural and  functional attributes typical  of resources that are experiencing little or no
stress."  For agroecosystems, this definition is expanded to include the concept that a nominal  agroecosystem
has the structural  and functional attributes of resources that experience little or no stress  except  for those
stresses intentionally and continuously placed on it by man for efficient crop and livestock production.  The
potential  for continuous  change  in  agroecosystems or for growers to  actively respond  to situations  or
conditions that would cause  a resource sampling unit to be classified as subnominal (e.g., native vegetation
decline due to fungicide applications) complicates the definition of what  constitutes subnominal condition.

Three basic approaches, as detailed in Section  2.3.3, are available for setting subnominal threshold values -
the use of existing numeric criteria, reference sites, and classification.  In agroecosystems, the use of numeric
criteria, which are  based  on  external conditions such as  management targets or yield  projections,  are
appropriate for an agronomic view. If production or a yield target is the primary criterion  of interest,  then
from an agronomic view, the  agroecosystem is nominal if production targets (which may be long-term average
yields) are met. Such a view, however, is not appropriate for EMAP, because its  major goal is to monitor
ecological condition, not short-term productivity or economic return.

The  second approach would  be to survey regional  reference sites  and develop a reference  population for
each  response indicator.  Using this approach, the reference population  for agroecosystems could be sites or
grower operations that employ best management practices for the given crops, livestock, and types of adjacent
areas being considered.  This approach  has several  advantages for agroecosystems.  Reference populations
could be established regionally and, with proper documentation, future  determinations of resource  sampling
units  as nominal or  subnominal would  be possible on the basis of new or changing criteria  obtained from
the reference sites.  While  it would be quite difficult to establish regional reference sites that  are not exposed
to regional air pollutants, the use of exposure indicators such as the white clover bioassay (see indicator F.9)
could, however, enable calibration or standardization of pollutant effects among regional reference sites.
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The  third approach, classification  of sites using a multivariate index,  would  rely totally  on data from the
EMAP  probability sample of  the  target resource population.   No such "index of agroecosystem health"
currently exists to facilitate  the identification of a subnominal  threshold.  However, this  approach might
eventually represent the best blend of the agronomic and ecological approaches to determining agroecosystem
condition.

The detailed approach for establishing the subnominal thresholds for specific response indicators and specific
agroecosystem  resource  classes remains to  be formulated.   A combination of the  possible approaches,
particularly regional reference sites and classification,  may be  the  best solution to the problem.


8.4  RESEARCH PRIORITIES FOR AGROECOSYSTEMS

A three-stage indicator evaluation project is proposed, which follows the EMAP indicator evaluation framework
(Figure 2-7) and  would  be  implemented in  cooperation with the U.S.  Department  of Agriculture (USDA)
National  Agricultural  Statistical Service  and  the  USDA Agricultural  Research Service.  The project would
evaluate  the proposed  research  indicators,  establish and  evaluate  specific criteria and  decision  rules for
determining ecological condition,  specify the logistics of data collection to support chosen indicators, and
develop the most effective ways to report data on individual  indicators and on  indices.

The first  stage of the evaluation project would establish for each research indicator its (1) range of possible,
likely,  and  desirable values;  (2)  spatial  and  temporal  variability  of values in  resource  sampling units;
(3) reliability and information  content; (4) usefulness  or sensitivity in determining ecological condition; and
(5) appropriate critical index period or periods.   Further,  in stage 1 the three  approaches for establishing
the subnominal threshold for response indicators would be  critically examined, and  one or  more specific
strategies for threshold establishment would  be developed.

In the second stage of the proposed evaluation project, a limited geographical area would be sampled, and
specific indicator data would be collected.  Based on the  results of stage 2, field-implementation protocols
for each  indicator would be finalized, and an analysis of cost-effectiveness for  each  indicator that appears
suitable and appropriate would be conducted.   At  the same  time, the actual values  of  the subnominal
thresholds would be established and evaluated for regional and  national appropriateness for the purposes of
EMAP. The specific strategies for establishing the subnominal threshold also would  be compared. From the
overall analysis of indicators and  threshold  establishment  strategies, a provisional index of agroecosystem
condition would be developed and evaluated for sensitivity, efficiency, reliability,  and appropriateness. Those
research  indicators that pass the first two stages  of testing would  be identified  as developmental indicators
(see  Figure 2-7).

In the third and  final  stage  of the  evaluation  project, the  developmental indicators would be sampled
throughout a region.  Final evaluation of developmental indicators and criteria for establishing the subnominal
threshold would be performed.  Based on results of stage 3, multiregional or national plans for implementing
and  reporting the core  indicators for the EMAP agroecosystem resource category would be developed.


8.5 REFERENCES

Anonymous.  1989.  Alternative  Agriculture.  Committee on  the Role  of Alternative Farming Methods in
Modern  Production Agriculture.  National Academy Press,  Washington, DC.   464 pp.

Buttel,  F.H.   1990.   Social  relations and the growth  of modern  agriculture.   Pages 113-146.   In:
C.R. Carroll, J.H. Vandermeer, and P. Rosset, eds. Agroecology.  McGraw-Hill, New York.
                                                 8-10

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Carroll, C.R.  1990.   The  interface between natural  areas and  agroecosystems.  Pages 341-362.  In:
C.R. Carroll, J.H. Vandermeer, and P. Rosset, eds. Agroecology. McGraw-Hill, New York.

Coleman, D.C., and P.P. Hendrix.  1988.  Agroecosystem  processes.  Pages 149-170.  In:  L.R. Pomeroy
and J.J. Alberts, eds.  Concepts of Ecosystem Ecology.  Springer-Verlag, New York.

Elliott, E.T., and C.V. Cole.  1989.  A perspective on agroecosystem science.   Ecology 70:1597-1602.

Jackson,  W., and  J. Piper.   1989.  The necessary marriage  between ecology and  agriculture.  Ecology
70:1591-1593.

Levins, R., and j.H. Vandermeer.  1990.  The agroecosystem embedded in a complex ecological community.
Pages 341-362.  In:  C.R. Carroll, J.H. Vandermeer,  and P. Rosset, eds.  Agroecology. McGraw-Hill, New
York.

Lowrance,  R., C.R. Stinner, and  G.J. House,  eds.  1984.  Agricultural  Ecosystems:  Unifying Concepts.
John Wiley & Sons, New York.

Paul, EA., and G.P. Robertson.  1989.  Ecology and the agricultural sciences:  A false dichotomy?  Ecology
70:1594-1597.

Pimentel, D., and W. Dazhong.  1990.  Technological changes in energy use in  U.S. agricultural production.
Pages 147-164.  In:  C.R. Carroll, J.H. Vandermeer,  and P. Rosset, eds.  Agroecology. McGraw-Hill, New
York.

Soule, J., D.Carre, and W. Jackson.  1990.  Ecological impact of modern agriculture.  Pages 165-188.  In:
C.R. Carroll, J.H. Vandermeer, and P. Rosset, eds. Agroecology. McGraw-Hill, New York.

Tucker, C.J., B.N. Holben, J.H.  Elgin, Jr., and J.E. McMurtrey.  1981.  Remote sensing of total  dry-matter
accumulation in winter  wheat.   Remote Sens. Environ.  11:171-190.
                                              8-11

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                                             SECTION 9

                   INDICATORS RELEVANT TO MULTIPLE RESOURCE CATEGORIES

                                    C.T. Hunsaker1, J.F. McCarthy1,
                                    LR. Shugart', and R.V. O'Neill1


9.1  INTRODUCTION

For practical purposes EMAP has focused initially on six  ecological resource categories;  however, in reality
these are components of the "whole" ecosystem,  or biosphere.   Indicators that apply to several or all EMAP
resource categories will allow comparison of ecological condition both within and among categories.  Animal
indicators, biomarkers, and landscape indices are examples of such  indicators and are discussed in this section
and Appendix C.

Animal indicators,  biomarkers, and landscape indicators are addressed in this section because they are EMAP
resource categories.  Eventually, the  indicator  strategy for  each  resource category (Sections 3 through 8) will
incorporate  these  indicator types.  Some animal  indicators are  already proposed for the individual  resource
categories.  Most,  however, are discussed as a group in this section for ease of presentation and because the
strategy for  monitoring these indicators in EMAP is not fully developed at this time.

Stressor indicators  are also relevant to all resource categories and, like the three types of indicators already
mention, the strategy for their  inclusion  in  EMAP currently is not well  defined.  For  these reasons, this
section also addresses stressor  indicators.  The use of  stressor  indicators  will depend  on the availability  of
consistent external (off-frame) data sets for large geographic regions and their data quality.  The same stressor
indicators will be  useful for several EMAP resource categories and are discussed only  briefly  in this report

Landscape  indicators  should  be useful as habitat indicators for most EMAP terrestrial resource categories,
and their application to aquatic systems resource categories will  be as stressor indicators.  Landscape indices
have  been used to model the spread of disturbances  such as fire and the  movement of wildlife (Turner  et
al. 1989; O'Neill  et  al. 1988b).   In addition, indicators of  landscape pattern and  habitat structure will
probably be used  to indicate the potential for certain  animals to be present in an area, as well as to identify
areas for further evaluation of animal condition.  The extent to  which EMAP can monitor terrestrial  animals
has not yet been  determined.  From an efficiency standpoint,  it would be ideal to gather data on  animals
that  are  also used for biomarker measurements.  Although biomarkers  may  eventually serve as  valuable
indicators for defining animal health at the regional scale,  they are  collectively considered research indicators
at this time.

The identification  of EMAP indicators relevant to many resource  categories was done by a small group  of
experts within  input  from various EMAP  workshops and  resource  groups.   Contributors  and workshop
participants  are listed in Appendix I.


9.2  ANIMAL INDICATORS

Monitoring animal indicators is necessary for determining ecological condition of EMAP resource classes.
Animals are often  endpoints in ecological assessments, because the public is very interested in  the well being
of animal populations. In addition, animals can be monitored at various spatial scales, and they are part of
    JOak Ridge National Laboratory, Environmental Sciences Division, Oak Ridge, Tennessee

                                                 9-1

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all ecosystems.  Appropriate EMAP indicators for animals (Table 9-1) focus on attributes of ecosystem structure
(habitat indicators such as landscape and habitat vertical structure) and composition (response indicators such
as relative abundance of select species).  For  simplicity, "animals" is used  in this discussion to include birds,
reptiles, amphibians, mammals, fish  and  other  aquatic organisms,  and  selected invertebrates  (e.g., bees,
butterflies, and  snails).  Section 9.2 focuses on terrestrial animals and amphibians (organisms that require both
terrestrial and aquatic environments).  Aquatic organisms are addressed in Sections 3 and 4.

Indicators that  quantify the  health of animal  populations  and  communities are  needed for EMAP.  It has
sometimes  been assumed  that monitoring habitat variables obviates  the  need to monitor animals directly;
however,  the presence of suitable habitat does not guarantee  that the species of interest are present
Population density can vary tremendously because of biotic factors while habitat-carrying capacity remains
roughly constant (Schamberger 1988).  Conversely, inferences based solely on biotic variables such as animal
population density can be misleading.  Among vertebrates, for example, socially subordinate individuals may


Table 9-1.  Research Indicators Applying to Multiple Resource  Categories*

RESPONSE INDICATORS

Animal Life
Relative Abundance:  Animals
Demographics: Animals
Morphological Asymmetry:   Animals

EXPOSURE AND  HABITAT INDICATORS

Biomarkers
DNA Alteration:  Adduct,  Secondary Modification, Irreversible  Event
Cholinesterase  Levels
Metabolites of  Xenobiotic  Chemicals
Porphyrin Accumulation
Histopathologic Alterations
Macrophage  Phagocytotic ActivityO
Blood Chemistry Assays
Cytochrome  P-450 Monooxygenase System
Enzyme-Altered Foci

Habitat
Abundance or Density of Key Physical Features
Linear Classification and Physical Structure of Habitat

Landscape
Habitat Proportions
Patch Size and Perimeter-to-Area Ratio
Fractal  Dimension
Contagion or Habitat Patch iness
Gamma Index  of Network Connectivity
Patton's Diversity Index


"Indicators in bold lettering are high-priority research indicators.
                                                 9-2

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occur in areas of marginal habitat (Van Home 1983). Also, exotic species may displace residents and, in turn,
affect ecological processes.  Thus, monitoring both  habitat and animal variables is necessary in most  cases.

Although states and federal resource agencies collect large amounts of wildlife data, no standardized national
or regional inventory exists that permits a consistent summarization  of wildlife and fish resources, even  for
big game populations which have received the most attention.  Few data are available for assessing the status
of nongame and small game populations over large  regions (Flather and  Hoekstra 1989).  Breeding birds are
the only animals for which comparative data are available on a continental scale (Robbins et al. 1986).  Even
if EMAP monitors only a few animal indicators, the program will begin to help fill a void  in our knowledge
of regional ecological condition.  Of course, for migratory species, diagnosing trends in animal indicators will
always be complicated.   The challenge for  EMAP is to select measurable  indicators of animals at multiple
levels of  biological organization - regional/landscape, community/ecosystem, and  population/species.

9.2.1   Identification  and Application of Animal Indicators for EMAP

The  monitoring survey  design  of EMAP poses  a challenge for monitoring animals.  An  EMAP-sponsored
workshop of experts on monitoring animals (see Appendix I) was held in March 1990 to identify appropriate
animal indicators for  EMAP.  The only animal response  indicator suggested for  high-priority research status
was the relative abundance, when feasible, or presence/absence of selected species.  The simplest metric,
presence/absence, is the most  likely for EMAP,  even though relative abundance measurements yield  more
information  on community composition and  are more desirable. Habitat structure (vertical and horizontal)
and several  landscape indices are high-priority habitat indicators for animals.  Animal types were rated  on
how well they satisfied the EMAP indicator selection criteria  (Table 9-2).  These animal types  were also
evaluated  with regard to their value as indicators for inland surface waters,  wetlands, forests, arid  lands, and
agroecosystems (Table 9-3).  Eventually, one  or more animal types will be selected for monitoring within an
EMAP  resource category.  Use of the same animal indicators across resource classes or even categories, while
not necessary, would aid  integrated assessment  efforts.

If animals are trapped during monitoring, serious consideration should be given to sampling certain biomarkers
(Section 9.3) from captured animals. A suite of biomarkers could be  measured from ubiquitous animals such
as deer mice and leopard frogs to  provide a  baseline for these indicators  across resource  classes and even
categories.   Biomarkers of sensitive  species or species specific to certain conditions could also be monitored.
Establishing  one or more  intensive  animal monitoring sites (reference sites) that are  representative for each
EMAP  resource class will be important for the development of animal indicators.  Ideally, these sites would
be located at existing  long-term  research sites such as the National Science Foundation's Long-Term Ecological
Research Sites (LTERs), Biosphere Reserves, or the 20-year sites of the U.S. Fish  and Wildlife Service (FWS).
Data from  intensive  monitoring sites can  help define indicator variability  from  natural  causes, be used to
improve  monitoring designs, and  serve as  important  checks on  the  temporal  and  spatial  scales  being
monitored by the extensive EMAP  sampling design.

9.2.1.1  Birds

Because field sampling methods for animals are often resource intensive, EMAP must make maximum  use of
existing regional and  national  programs that monitor animals, as well as long-term intensive sampling sites
such as LTERs and Biosphere Reserves. Birds are one of the few animals for which consistent, historical data
sets exist on regional and national scales:  the North American Breeding Bird Survey (BBS), the Christmas Bird
Count  (CBC), and the Breeding Bird Census  (Eagles 1981; Verner 1985).  The Manoment  Bird Observatory
coordinates  the International Shorebird Survey during autumn  migration, and the FWS surveys waterfowl by
airplane during the breeding season and winter.  Birds  may also be the  one animal type that is a  useful
response  indicator  in most resource categories  (Table 9-3).   The point  count or  Indexes  Ponctuels
d'Abondance  method using a  fixed 10-min  time period for  counts at  a  field sampling unit  is considered
appropriate  for monitoring birds in EMAP.   The method is a means of  obtaining indices of abundance for

                                                 9-3

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Table 9-3.  Relative Usefulness of Animal Types as Indicators Within Ecological Resource Categories'

VERTEBRATES
Small Mammals
Reptiles
Lizards
Turtles and
Tortoises
Amphibians
Frogs and
Toads
Salamanders
Birds
INVERTEBRATES
Grasshoppers
Ground-Dwelling
Beetles
Butterflies
Bees
Ants
Termites
Snails and Slugs
Inland
Waters Wetlands

no M
no no

H H
H H
H H
H M

no M
L M
no L
no ?
no no
no no
H H
Forests
Coniferous

M
L

-
L
H
H

L
H
L
L
M
L
Md
Deciduous

M
L"

-
M
H
H

L
H
L
M
H
M
H
Tundra

H
no

-
no
no
H

M
M
L
H
L
no
L
Arid Lands
Deserts

H
H

H
L
L
L

H
Mc
M
M
H
H
Le
Scrub

M
M

-
L
L
M

M
M
H
M
H
M
M
Grasslands

H
H

-
L
L
H

H
H
M
M
M
H
M
Agroeco-
systems

H
L

-
L
L
M

H
L
L
H
L
no
L
aH = high;  M = medium; L = low.  Bold letters indicate the most useful animal groups within a resource category; animal indicators
for Near Coastal  were not ranked.
bHigh in Southwest
cHigh in Great Basin.
dHigh in Northwest.
eMedium in Chihuahuan Desert
 comparing  bird  populations of different habitats  (or of the same habitat  in different  locations) during the
 breeding season (Ryder 1986).  Because of low detectability, the monitoring of some species such as marsh
 birds (Gibbs and Melvin 1989) or woodland  raptors (Fuller and Mosher  1981) may  require a specialized
 technique such as playback of vocalizations.

 The BBS is an ongoing program sponsored jointly by the FWS and the Canadian Wildlife Service.  Its main
 purpose is to estimate population trends of the many bird species that nest in North America north of Mexico
 and that migrate across international boundaries.   The survey provides information, both locally (by ecological
 or political  regions) and on a  continental scale. Information includes (1) short-term population changes that
 can be  correlated  with specific weather  incidents, (2) recovery  periods  following  catastrophic declines,
 (3) normal year-to-year variations, (4) long-term population trends, and (5) invasions of exotic species (Robbins
                                                   9-5

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et al. 1986).  The BBS surveys 500 bird species by using 2000 random roadside routes that are stratified by
physiographic region (Bystrak 1981).

The CBC  is the best known and probably most used source of information on geographical  distribution of
nongame  birds  in winter.  Counts are made in more than 1500 circles of 24-km (15-mi) diameter.  The most
meaningful data are expressed in number of birds observed per party-hour.  Scientific studies have worked
with CBC data  (Butcher 1990; Bock and Root 1981), and popular evaluations of the CBC have been written
by Bock (1979), Robbins  (1966), and Wing and Jenks (1939).   Both the  BBS and  CBC are  biased toward
birds that occur near roads and rely on volunteers (Faanes and Bystrak 1981),  so  data quality may  be an
issue for EMAP. However, such  monitoring programs are excellent examples of how EMAP could use animal
indicator data from  external sources.  Birds will be important animal indicators because of their applicability
to several  resource  categories,  extensive  historical  data  sets, knowledge  of ecological  requirements,
standardized monitoring techniques, high public  interest, and  response to  both immediate  and  long-term
stressors.

Cooperrider et  al. (1986) discuss the monitoring of songbirds, raptors, marsh birds and shorebirds, waterfowl,
colonial waterbirds, and upland game birds.  They recommended  that efforts  to attribute changes in  bird
populations to changes in  habitat quality be tempered by the fact that most birds are highly vagile.   Because
they spend less than one third of the year on summer breeding grounds north of Mexico, migratory species
could be more  affected  by habitat destruction  and  pesticide  use in their wintering grounds in the Neotropics
than by hazards in  the United States.   Songbirds in general  are the  easiest group of birds to monitor  across
a variety of habitats.

Although  some raptors are difficult to detect and count because they occur at low densities  in a variety of
habitats (Kochert 1986), migratory  raptors can be efficiently monitored (Bednarz et al. 1990).  In  western
flyways  raptors  are monitored at five sites,  and in eastern  North America, at 35 sites  (Hawk Migration Studies
1989).  Marsh birds and shorebirds can easily be surveyed because they are often readily observable in open
areas, but may require  monitoring techniques quite different from  those used for songbirds.   Most species
of waterfowl (ducks, geese, and  swans) are migratory  to some extent, and thus habitat features for  different
species  will vary considerably throughout the year.  Of the two major requirements for successful waterfowl
production, availability of  high-quality water areas and secure upland nesting habitat, the former is currently
at a premium in the northern Great Plains and the  latter in the prairie pothole area (Connors 1986).  Upland
game birds include partridge, grouse,  turkey, quail, pigeons, and doves.   These birds (at least the  males)
engage  in vocalization that has  a  measurable seasonal and daily peak; monitoring during these peaks  can
reduce variability in the distribution of indicator values.  In  most instances a version of the auditory census
for  upland game birds will probably be the most efficient monitoring approach for EMAP (Eng 1986).

Any bird that predominantly feeds in aquatic systems  and tends to nest in groups of closely associated nests
is considered a colonial waterbird.  Species of colonial waterbirds  range  from small  storm petrels  to large
pelicans.  And  some, such as the  petrel, lead a truly pelagic existence except when they come to  offshore
rocks  and islands to nest.   Most colonial waterbirds are near or at the highest trophic level of an ecosystem
and are thus sensitive to changes in the health of other freshwater and marine  organisms.  Changes or
reductions in  biomass or  species  composition at  lower trophic levels often  cause  stress  in  these  birds,
expressed as failure to  breed, abandonment of eggs and young, late  nesting,  depressed  growth  rates,   or
reduced fledgling success (Speich 1986).   Point counts should be useful for monitoring colonial water birds;
estimates  of some species can also be made from aerial photography,

9.2.1.2  Amphibians and Reptiles

Although  amphibians and reptiles have received relatively little attention with regard  to impact assessment,
they are valuable indicators of ecological condition (Gibbons 1989; Hall 1980; Beiswenger 1989). Abundance
and diversity fluctuate directly with changes in the composition and amount of microhabitats, and microhabitat

                                                 9-6

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changes often result from land management practices (Jones 1981; Ortega et al. 1982;  Tinkle 1982;  and
Luckenbach and Bury 1983).  Amphibians and reptiles are also important ecosystem components, and they
make up large proportions of vertebrates in certain ecosystems (Bury and Raphael 1983).  Measurements can
be made on individual animals (Hall and Swineford 1981), entire communities (Scott 1982), or species' guilds.
An indicator known as fluctuating (morphological) asymmetry  provides a means of determining environmental
exposure to  certain  contaminants  (see Appendix C-1) and  has been used for certain lizards (Leary  and
Allendorf 1989).  True frogs (Ranidae) and toads (Bufonidae)  ate reported to be excellent indicators of both
atmospheric  and aquatic conditions.   Long-term  data (at  least  10 years) are needed  for  reptiles  and
amphibians to separate subnominal condition due to anthropogenic stressors from that due to natural stressors
such as climate variability.

The  FWS National  Ecological  Research Center is currently  investigating the disappearance of amphibians
throughout the western United States (Corn et al. 1989; Corn and Fogleman 1984; Corn and Bury 1987;
Freda and Dunson 1985; Pierce et al. 1984); this change appears to be occurring on a global scale. Because
native  fish  and  benthic  organisms are  still  present  at  many  of the western  sites  from which frogs  and
salamanders have disappeared, the amphibians appear to be more sensitive to some contaminants than other
aquatic organisms.   Amphibians  respire cutaneously and thus may be exposed directly  to  atmospheric
contaminants such as pesticides.  Airborne contaminants become supersaturated in fog, and amphibians often
respire out of the water during foggy conditions. The decline of these animals may  be an early warning of
change in ecological resource  condition.   Comparison of past versus  present amphibian distributions (either
abundance or presence/absence data), combined with tissue  samples and atmospheric stressor data,  can
provide a useful tool in determining one aspect of ecological change.

Many lizards  and certain toads are  also excellent indicators of functional aspects of ecosystems (Pianka 1980;
Short 1983).  For example, Jones and Clinski (1985) and Jones (1989a) found that certain  reptiles that dwell
in leaf litter were lost from riparian ecosystems that were altered by stream  impoundment  Species such as
skinks (Eumeces) that are sensitive to changes in the processing of litter and  organic material can be used as
indicators  of changes  in  ecosystem  function.    Measurements   of  species composition  or  simple
presence/absence of guilds (e.g., species that increase with increased shrub cover) also have been used to
detect changes in ecosystem function (e.g., Pianka 1980; Jones 1986; Jones 1989b).

Both amphibians and reptiles rank relatively high when judged against the EMAP indicator selection  criteria
(Table 9-2).   Frogs, toads, and  salamanders are top  candidates for  inland  waters  and  wetlands,  and
salamanders are also favorable candidates for forests (Table  9-3).   Lizards are top candidates in desert and
grassland ecosystems, while turtles are favored in inland waters.  When pitfall traps are arranged systematically
and data are standardized by  unit effort  (e.g., season, arrangement, size) the results can be quantified  and
compared - for example, relative abundance of certain  species between two or more habitat types (Jones
1986;  Bury and Corn 1987).  Pitfall traps are usually arranged along transects  and will have a bias  toward
not sampling sedentary,  microhabitat-specific animals or animals too large for the  traps.   Auditory, time-
constant transects can be used to  sample frogs and  toads.  This method requires training for sounds but is
relatively inexpensive and nondestructive.  Useful ancillary data for reptiles and amphibians include soils, litter,
water availability, and horizontal extent of vegetation.

9.2.1.3  Mammals

Small mammals satisfy many of the criteria for indicator selection in Table 9-2.  They respond  to long-term
stressors, have standard sampling methodology and  existing  data bases, and have a short generation time.
Small mammals can  be excellent indicators for arid  lands and agroecosystems and good  indicators for forests
and  wetlands (Table 9-3).  Kapustka  et  al. (1989) discuss the problems with  using small  mammals as
indicators, but they nevertheless recommend their use in assessments.  Small mammals are  easily collected,
and their population parameters can be quantified reasonably well.  For these  reasons, it would be useful
                                                9-7

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for  EMAP to  monitor changes  in species composition and,  where  feasible, relative abundance of small
mammals over time and in  relation to landscape features.

9.2.1.4  Invertebrates

Many invertebrates  rank high with regard to satisfying the selection criteria for indicators.  Factors that support
their application include their  rapid responses to disturbances and their role as food sources for many other
animals.  Snails and slugs would be  useful animal types for aquatic ecosystems or habitats near water;  and
grasshoppers, beetles, butterflies, bees, ants, and termites are most useful for monitoring terrestrial resource
categories (Table  9-3).

The honey bee colony presents  an opportunity to conduct testing at several levels of biological organization,
from biochemical to population level. In addition, extrapolations to the community and ecosystem levels can
be  made by observing the  pollination  syndrome.  Bees can  be used  for multimedia sampling, and body
burdens of contaminants have been  shown to correlate well with  levels in environmental media.  Bees can
be used to monitor fairly large areas, as their  flight range is 1.6 to 3 km  (Kapustka et al. 1989).  Bee colonies
are inexpensive and practically self-sustaining test systems.   Technical support is readily available from state
and federal agencies, bee research laboratories,  and  bee keepers (Bromenshenk and Preston 1986).  Sampling
time varies from 5 to 20 min  per hive, and samples should  be taken from at least two to three hives at any
terrestrial RSU  (Kapustka et al. 1989).

Grasshoppers  are numerous,  occur  in  many habitats,  respond sensitively to their environments, and are
sufficiently  diverse that particular sites may support a dozen  or more  species.  Thus grasshoppers provide
an  animal assemblage whose  shifting composition can be used to monitor change  (Joern 1987).

Taxonomically  and  ecologically  well known, butterflies are easy to sample  and represent the ecologically
important insect herbivore guild. They can be  monitored by netting all  species encountered at one or more
transects within an RSU during an  empirically determined, fixed-time period (e.g., 20  min per transect).
Because  of differences in species'  phenologies, each site must be sampled at  least  twice  a year  (late
spring/early summer and mid- to late summer).

9.2.1.5   Summary

The monitoring of ecological resources at regional scales requires serial collections of field measurements  of
animal species or guilds.   EMAP should  use existing animal  monitoring programs when appropriate (e.g.,
Breeding Bird Survey), augmenting these activities when necessary.  The  U.S. Fish and Wildlife Service should
be  encouraged to  help with  the  monitoring of certain animals.   The high-priority research  indicator for
animals is relative abundance, when  feasible,  or presence/absence of select species.  Animal types that would
be  useful to monitor in EMAP  are presented  in Table 9-3.  For certain situations, it may be  desirable  to
include particular sensitive or endangered species,  or species  that pose a direct threat to them  (e.g., exotic
or  feral species).   Morphological asymmetry and species demographics are  considered  additional research
indicators for  EMAP that are  probably  useful when applied to a specific hypothesis or when a problem is
suspected.   Animal indicators determined not  to be appropriate for  EMAP are discussed in Section 9.2.2.
Habitat and landscape indicators are relevant  to monitoring animals  and are discussed  in Section 9.4 and
Appendix G-3.   Climate  data will  be  especially important for interpreting the data for animal response
indicators.

9.2.2  Animal Indicators Not Appropriate for EMAP

Many types of animals will not be  useful indicators  of ecological condition for EMAP.   In general, snakes
are poor indicators  of ecological condition because  they are difficult to sample (Jones 1986). Large terrestrial
mammals are heavily managed,  and  they are difficult to monitor.  Three categories of indicators have proven

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to be poor indicators of ecological condition or to have limited value for examining possible causes of poor
condition.    These  generally  unsatisfactory  indicators  are (1) species  richness,  irrespective of  species
composition; (2) information-theoretic diversity  indices; and (3) ecological indicator species as traditionally
applied in land management decisions. While the concept of focusing on keystone species (species that play
pivotal roles in ecosystems and upon which the diversity of a large part of the community depends) is initially
appealing, in most situations we do not know what these species are.

Most ecologists will agree that biodiversity is not simply the number of species  in a defined area.  Richness
is an important aspect of biodiversity, but knowing that one community  contains 500 species,  and another
community 50 species,  does not tell  us much about the potential biodiversity within the community or the
relative importance for conservation purposes. In fact, species richness can be a  misleading indicator of biotic
value,  if many species in the sample are "weedy" or highly tolerant of human disturbance or pollution (Noss
1983).  Thus, it is necessary to consider species composition (identity) in addition to species richness.  Karr's
Index of Biotic Integrity (IBI) does this to a great extent for aquatic systems (Karr et al. 1986).

Ecologists usually define "diversity" in a way that takes into consideration the relative frequency or abundance
of each species or other entity, in addition to the number of entities in the  collection.  Several  indices,
primarily derived  from  information  theory,  combine  richness  with  a  measure of  evenness of  relative
abundances (e.g.,  Shannon and Weaver 1949). Unfortunately, the number of indices and  interpretations
proliferated to the point where species diversity was in  danger  of becoming a "nonconcept" (Hurlbert 1971).
Diversity indices result in considerable loss of information (such as species identity), heavily depend on sample
size, and generally  have fallen out of favor in  the  scientific community.   As  Pielou  (1975) noted, "a
community's diversity index is merely  a single descriptive statistic, only one of the many needed to summarize
its characteristics,  and  by  itself, not very informative."   Nevertheless,  diversity  indices still  are  used in
misleading ways in some  environmental  assessments (Noss and Harris 1986).

Landres et al. (1988), in a critique of the uses of vertebrates  as ecological indicators, defined  an indicator
species as  "an organism whose characteristics  (e.g.,  presence or absence,  population density, dispersion,
reproductive success) are  used as an  index of attributes too difficult, inconvenient, or expensive to measure
for other species or environmental conditions of interest."  The use of indicator  species to monitor or assess
environmental conditions is a firmly  established tradition  in  ecology, environmental  toxicology,  pollution
control, agriculture, forestry, and wildlife and  range management  This tradition, however,  has  encountered
many  conceptual  and  procedural problems.   In  toxicity  testing, for  example, the usual  assumption that
responses at higher  levels of  biological organization can  be predicted  by single-species toxicity tests is not
scientifically supportable (Cairns 1983).  Hierarchy  theory  suggests that indication of effects across levels of
ecological organization  will  be problematic.  Of course, indicators at different levels of organization  can be
used to identify unhealthy resources, which  then might require further monitoring to determine the extent
of the problem.  The magnitude of an ecological hazard  becomes more evident as the number of animal
indicators at  different levels of organization indicate an unhealthy condition.

Landres et  al. (1988) pointed out several difficulties with using indicator  species to assess population trends
of other species and  to evaluate overall  animal habitat quality.  They also noted  that the ecological criteria
used to select indicators are often ambiguous and fallible.  Norse et al. (1986) and Wilcove  (1988) described
how the "management  indicator species" concept of the U.S.D.A. Forest  Service, adopted as  a  result of the
National Forest Management Act of 1976, is subject to bias when applied to forest planning.  Conceding that
traditions and regulations strongly support the continued use of indicator species,  Landres et al. (1988) provide
recommendations to  make the use of indicators more scientifically rigorous and effective.

9.2.3  Research Needs for Animals

Animals could be considered the ultimate response indicators for EMAP in that animals are at the  highest
tropic  levels  within  ecosystems,  and thus are most  responsive to changes in  any ecosystem  component

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However, animals are traditionally monitored  in an intensive manner at a few sites.  Once animal indicators
are selected for various resource classes, field  monitoring designs can be developed and tested to determine
the required sampling densities and frequencies.

Knowing how sensitive animal  community composition  is to various  levels of environmental stress is also
required.  Through the indicator evaluation process, data will be collected on the  response of the animal
community composition to known gradients of environmental factors.   Although  this is a "space replacing
time"  analysis and  has the weaknesses of such research, EMAP needs such data in order to help explain
observed trends in  animal indicators.  Also, whether the concept of keystone species is useful for  EMAP
needs to be determined, as well as whether demographic data are practical for the program.

EMAP should emphasize developing nondestructive and cost-effective sampling techniques for animals. The
impact of and attitudes toward destructive sampling of animals for a national program must be considered
carefully.   Within  a  two-year  time  period,  definitive studies could  be accomplished that  compare the
information acquired by using visual  sampling methods to that using sampling with transects of pitfall traps.
Of course, visual sampling might be much more useful for some EMAP resource categories than others, such
as arid lands versus forests.  The use of automated sampling aids,  such as tape recorders for birds and bats,
should be investigated (Fenton  et at. 1987; Thomas and West 1984).  The application of biomarkers and
animal indicators as diagnostic  indicators in EMAP must  be  investigated further.   For example, the analysis
of mammal  hair for contaminants  might  be an acceptable and powerful tool  for determining contaminant
exposure.  Similarly, morphological asymmetry in  individuals should  be explored as a useful indicator of
contaminant exposure.

A good  index  of  terrestrial animal  integrity  would  be  useful to EMAP.   Karr's IBI  is used to  evaluate
compositional  and functional changes  in aquatic  communities that may signal biotic  deterioration (Karr et
al.  1986).   Water quality,  habitat  structure,  energy source, flow  regime,  and  biotic interactions are
incorporated in  the IBI.   The IBI combines several sets  of  metrics  on  species richness and abundance in
different trophic and functional categories, with measures of the health  of individuals.  Use of the  IBI requires
a reference site for comparison to determine degree of disturbance or  perturbation.  No terrestrial IBIs have
yet been perfected; however, these problems probably are resolvable through further work.


9.3  BIOMARKERS

Biomarkers have recently  received considerable attention among  environmental toxicologists as  a new and
potentially very  powerful  and  informative tool for detecting and  documenting exposure to, and effects  of,
environmental contamination (McCarthy and Shugart 1990).  Biomarkers are measurements that indicate, in
biochemical or cellular  terms,  exposure  of an organism to a chemical.  Because  of the commonality of
biochemical and cellular structure and function among organisms, biomarkers are applicable to most ecological
resource categories.   Many animal  biomarkers are applicable to both terrestrial  and aquatic resources.
Biomarkers  have been developed  for  small mammals such as mice  that are routinely used in laboratory
research.  The  technique  has also  been applied to aquatic organisms,  most often fish.

Most  of the research  on  field  evaluation  of biomarkers has focused on marine and freshwater animals.  In
the terrestrial environment, biomarker measurements primarily have focused on birds.  Extensive biomedical
laboratory research with rodents and  rabbits, however, suggests that biomarker approaches would be equally
successful for other terrestrial animals.  Nevertheless,  field evaluation  of biomarkers in terrestrial animals is
limited.  The  lack of experience and  data on  biomarker responses  suggests that the  initial application of
biomarker techniques in a regional monitoring survey such as EMAP should be focused on aquatic animals
and that the monitoring of biomarkers for terrestrial  animals should be limited to a few trial locations.
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More information on biomarkers  is available for terrestrial plants than  for aquatic macrophytes  or algae.
For terrestrial  plants, however, most research has centered on the effects of gaseous pollutants (NOX, SO2,
O3).  Only a few plant  biomarkers have been identified that respond to toxic environmental pollutants.

Biomarkers are often divided into  those that indicate exposure to hazards and those that demonstrate toxic
effects resulting from the exposure.  Biomarkers of exposure are defined as (1) an exogenous substance within
a system, (2)  the interactive product between a xenobiotic compound and  endogenous components, or
(3)  some other  event  in  the biological  system related to  exposure.    Exposure  biomarkers may  include
measures of internal dose or the biologically effective dose, that is, the amount of material interacting with
critical subcellular, cellular,  and tissue targets or with  an established surrogate (National Research Council
1987).

An  effects biomarker is  defined  as (1) an  indicator of an endogenous component  of the biological system,
(2)  a measure of the functional capacity of the system, or (3) an altered state of the system that is recognized
as impairment or disease. These conditions include an actual health impairment or a  recognizable disease,
an  early precursor of a disease process that indicates a potential for  impairment of health,  or  an event
peripheral to any disease process, but correlated with it and  thus  predictive of development of impaired
health (National Research Council 1987).

9.3.1  Identification of Biomarkers For EMAP

The primary application of  biomarkers within  EMAP  would  be  as exposure  indicators;  however, some
biomarkers can  be used as  response indicators.  A conceptual view of how biomarkers may  be useful  is
shown in Figure 9-1.   Biomarkers provide information that complements and  extends  other  exposure
indicators, such  as analyses of ambient chemicals, toxicity tests, and  measurement  of body burdens of toxic
chemicals.   In general,  biomarkers measured in organisms collected from (or confined to)  monitoring sites
would be used to determine whether the subnominal status of an ecological resource is correlated with toxic
chemicals   in   the  environment     Some   categories   of  biomarkers   (e.g.,   immunological   and
physiological/bioenergetic  biomarkers) also are sensitive indicators of organismal response  to nonchemical
stress, such as pathogens.

Biomarkers identified  as appropriate for EMAP are listed in Table 9-2 and discussed  in  detail  in Appendix
G-2.  A  few additional  biomarkers specific to an ecological resource category (e.g., forests) are included in
the indicator strategies (Sections 3-8).  Although there  are many more  biomarkers than those  discussed in
this section, these are proposed as appropriate research biomarkers after considering the  EMAP indicator
selection criteria (Table 2-1).  The  selection and descriptions of biomarkers reflect the cumulative knowledge
and insights of approximately 50 experts who participated in a biomarker workshop in Keystone, Colorado,
in July 1989 sponsored  by the Society of Environmental Toxicology and Chemistry.  The complete report of
this workshop contains a comprehensive review of the state of science on biomarkers (Hugget 1990).  There
are several  reasons  for  not considering  any biomarker as  a high-priority  research  indicator for  EMAP.
Although biomarkers show great promise as response indicators, more research  is needed to understand the
significance of a change or trend in  measurements and  to allow the  interpretation  of exactly what factor or
agent induced the biomarker response. The feasibility of monitoring terrestrial animals within EMAP for both
population (Section 9.2) and biomarker indicators also needs further evaluation.  In  addition, most biomarker
monitoring requires destructive sampling for small animals, birds, and fish; and the impacts and applicability
of such  sampling at regional  and national scales need to be  seriously evaluated.

Animals  in the  natural environment are exposed  to  a  variety of  stresses,  including sublethal  levels of
contaminants,  unfavorable or fluctuating temperatures,  elevated sediment loads, hypoxia, and  limited food
availability.  These factors, singly or in combination, can impose considerable stress on  physiological systems
(Weidemeyer et al. 1984).  Stress that exceeds the tolerance limits of  organisms  is obvious because  it  is
normally lethal.  Sublethal stress is more insidious because adverse effects are generally manifested subtly at

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                                HIGH
                           TOXICOLOGICAL
                             RELEVANCE
                                 I
                                 I
 MOLECULAR
PHYSIOLOGICAL
                                    DETOX SYSTEMS
          IMMUNOLOGICAL
                                         HISTOPATHOLOGY
  SHORT-TERM
   RESPONSE
                    LONG-TERM
                     RESPONSE
                                                BIOENERGETICS
          REPRODUCTIVE
           COMPETENCE
                                                   POPULATION
                                                      AND
                                                   COMMUNITY
     I
     I
   HIGH
ECOLOGICAL
RELEVANCE
  Figure 9-1.  Conceptual view of how biomarkers may be useful to EMAP (Source: Adams et al. 1990).
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the biochemical or cellular level.  However, depending on its severity,  sublethal stress, can reduce growth,
impair reproduction, predispose organisms to infectious diseases, and reduce the capacity of the organism to
tolerate other types of stress, including exposure to toxic chemicals. At the population level, effects of stress
may be manifested as reduced recruitment and depletion of compensatory reserves.  Many effects biomarkers
(histological, immunological, and physiological/bioenergetic biomarkers) are sensitive and informative measures
of the organism's response to cumulative stress introduced  through chemical, physical, or pathogenic sources.

Measurements of chemical concentrations  in environmental  media are specific, quantitative,  sensitive,  and
precise.  The biological significance of the chemical concentrations measured in air, water, soil, or food is,
however,  not at all clear.   We currently  understand the toxic action  of only  a few of the thousands of
chemicals in  the environment   Almost no information is  available on the interactions of complex mixtures
of chemicals, or on the role of environmental  stresses on an  organism's susceptibility to toxic exposure.
Furthermore, a  chemical survey is a snapshot in time  and  space.  Surveys will  not capture variations in
concentrations over time that result from intermittent exposures such as storm events or releases of effluents
by industries.  Spatial patchiness of contaminant  patterns also requires extensive and expensive sampling and
chemical analyses.  Biomarkers offer an alternative way to monitor and  interpret environmental conditions.

Evidence of exposure provides  a  temporally integrated measure of bioavailable contaminant levels  and  is
therefore much more relevant to the risk posed to the organism by the environment than is the concentration
of contaminants in  soil, water,  or air.  Furthermore, mobile  organisms integrate exposure  over their  spatial
range, and measurements on such organisms can  help overcome problems of patchiness of ambient chemicals.
For analyses  of exposure to complex mixtures of chemicals,  btomarkers provide an opportunity to examine
the pharmacokinetic and toxicological  interactions within  exposed organisms as  well the cumulative impact
of toxic exposure.   These attributes  make biomarkers  especially appropriate for application  in  a regional
monitoring survey such  as  EMAP.

Toxicity tests (laboratory bioassays) have proven very useful  in detecting  and quantifying adverse effects of
individual chemicals,  mixtures,  effluents, and sediments.  Toxicity tests have limitations, however, because
they do not necessarily  account for the effect of (1) chemical speciation  in the environment, (2) kinetics and
hysteresis  in sorption of chemicals to sediment, (3) accumulation through food chains, and (4) modes of toxic
action  which are  not  readily  measured  as short-term  (7- to  21-day) effects  on  survival, growth, or
reproduction.  In situ  monitoring of organisms collected or confined  near discharges  is a  more realistic
approach  for determining the integrated exposure and effect  of environmental pollutants.  The combination
of laboratory toxicity test data with demonstration of in situ effects in receiving bodies provides a compelling
logical link between toxicity test results and effects observed in  the environment.

Biomarkers may also prove to  be a useful addition  to conventional survival  and toxicity tests for detecting
other mechanisms of toxic action.  For example, a growth assay using  larvae could be useful for detecting
genotoxic effects if the larvae also were examined for DNA alterations at the end of the regular test period.

Measurement of tissue  concentrations  is  highly  recommended as an  indicator of exposure to  persistent
compounds such as metals and  certain classes of organic chemicals such as many polychlorinated compounds.
In EMAP,  tissue concentrations are considered to be an exposure indicator that is distinct  from biomarkers.
However, when measuring tissue residues is not feasible, such  as with compounds  that do  not readily
bioaccumulate  (because of rapid  metabolism, for example)  or with  complex mixtures that  require time-
intensive and costly  analyses  that may not identify all  toxic  chemicals, indirect  measures of  exposure
(biomarkers) may be required or preferred.  In addition,  since the relationship between tissue concentrations
and toxic effects is complex and not fully understood, biomarker measurements may indicate a response that
is of toxicological significance.
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9.3.2  The Application of Biomarkers in  EMAP

The results of population monitoring are the ultimate indicators of ecological effects.  However, population
responses such as occurrence, abundance, and reproduction do  not provide  an indication of their cause.
Correlation  of  population  parameters with body  burdens of chemicals and with sensitive  and responsive
molecular and biochemical biomarkers of exposure is supporting evidence for  a  causal linkage between
exposure and effects.  Correlations  of population parameters with ambient concentrations of chemicals or
indices of chemical loading provide  additional  support for a causal link between  chemical releases  into the
environment and ecological effects.   Nevertheless, although data from population  monitoring can  provide
valuable  information on ecological condition,  such  data  tend to be  rather insensitive indicators of effects
because  of  the variability of  animal populations  and the  imprecision of estimates from field monitoring.
Therefore, it may be useful to monitor biomarkers to provide a more sensitive and precise indicator of the
nature and magnitude of effects, and to gain insights  into what may have caused the effects.  The correlations
between the effects biomarkers and  exposure indicators are expected to  be better than those between the
population monitoring parameters and the exposure indicators.

Conceptually, the use of both  exposure  and  habitat indicators and response  indicators within EMAP will
permit preliminary identification of  possible  reasons  for ecologically relevant effects.   Responses at the
population and community level are highly relevant to ecological conditions,  but such responses are slow
and are difficult to attribute unequivocally  to toxicants.  In contrast, responses at lower levels of organization
are  more rapid and can be more clearly linked  to toxic exposure; however,  it is difficult to relate these
responses to effects at the community level. Our approach is to measure  responses at several different levels
of biological organization,  including  metrics of both  exposure to toxicants (generally responses in the upper
left quadrant of Figure 9-1, but also including tissue burdens of chemicals) and effects (generally the lower
right quadrant).

The division between biomarkers of exposure and biomarkers of  effects is arbitrary.  This lack of a discrete
separation is a  natural consequence of the  interdependence inherent in the organization of biological  systems.
The goal in examining responses at  these  different levels  of organization  is to answer two critical questions.

        1.  Are organisms  exposed  to levels  of toxicants that exceed the  capacity of  normal
            detoxication and repair  systems?

        2.  If there  is evidence of  exposure,  i.e., is the chemical stress consistent with observed
            impacts on the condition of the populations  or communities?

Evidence of exposure from analyses  of ambient chemicals, toxicity tests,  or tissue concentrations and from
biomarker responses at lower  levels  of biological  organization can provide an  answer  to the first question.
In particular, biomarkers of exposure indicate the  biological significance of chemicals that may have entered
the organism; that is, did the chemical reach molecular and biochemical targets and cause detectable damage
or  induce a protective  response?   The  second  question can  be addressed  by determining whether the
responses to  the toxicants are propagated through  successively higher levels  of biological  organization
(biomarkers of effects and population parameters).   If  chemical exposure is  responsible for a high-level
ecological  effect,  responses  should   be  apparent at intermediate levels of organization.   Alternatively, if
evidence  of chemical exposure is not revealed, or if biomarker  responses  indicate  effects only in the most
sensitive and responsive exposure parameters  (e.g.,  genetic  damage), and not  at higher  levels of biological
organization (e.g., histopathological evidence of neoplasia or tumors,  or  reduced  growth or other measures
offish health),  community- and population-level effects could not be reasonably attributed to chemical agents.

Some  biomarkers are general  indicators  of  exposure or  effects, and  others are indicative  of particular
chemicals or classes of  chemicals.   For example, inhibition  of the  enzyme aminolevulinic dehydrase is a
specific indicator of exposure to lead, induction of the cytochrome P450 monooxygenase system is a specific

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response to organic contaminants such as polycyclic aromatic hydrocarbons, and detection of DNA or protein
adducts demonstrate  exposure to specific  chemicals.  Conversely, other biomarkers provide evidence  of
responses to chemicals but are not specific to one or a group of toxic agents.  For example, DNA integrity
can be adversely affected  by chemical modification of DNA, physical  damage from ionizing radiation  or
ultraviolet light, or inhibition of DNA repair systems.  Similarly, induction of heat-shock stress proteins is a
general indicator of response to a wide range of chemical or physical  insults. Both types of biomarkers are
useful, but may be most appropriately used  at different tiers of a monitoring program.  General indicators can
be sensitive tools for  screening studies to determine in a cost-effective  manner  if  there is any evidence  of
stress.  If these general biomarkers suggest a problem, a second tier of testing with  more specific biomarkers
may be warranted.

9.3.3   Sampling Considerations For Animal Biomarkers

The pathways of exposure and  spatial range of animals  are among the criteria that should be considered
when selecting species for monitoring.  The habitat and food preferences of monitored species are  important
factors that may aid in identifying  the  sources and routes of exposure.  Fish and other aquatic species are
exposed through surface  waters and  sediment, and comparison  of  water-column-associated species and
sediment-associated species can distinguish  the contribution of sediment to exposure.  Similarly, herbivorous
rodents, such as voles and some mice,  provide information on  different pathways of exposure than those for
muskrats and some shrews that dig in  the  soil (e.g.,  Loar et al. 1989).

In general, for chemicals  such as  metals that are not biomagnified, physical position of organisms  in the
environment  may  be more important than trophic  position in  determining exposure.   Typically, soil-  or
sediment-associated organisms display the highest tissue concentrations of contaminating metals and may be
most  useful for measuring  biomarkers of  exposure to  metals  (e.g., Martin and  Coughtrey  1982).  For
compounds such  as  persistent lipophilic  chemicals (polychlorinated  biphenyls  and polycyclic aromatic
hydrocarbons),  accumulation through trophic levels  may be  the  most important  exposure pathway (e.g.,
Thomann 1981).

Confined animals can be  used to test  hypotheses about different pathways of exposure.  Confinement can
limit access to defined exposure pathways.  For example,  animals can be provided with clean water and
denied access to surface water, or  vegetation can  be removed from an enclosure and the animals can be
provided with uncontaminated food.  Agricultural  livestock  that drink well water  can serve as sentinels  of
ground water quality.

The home range area of  an animal must  match the size of the study site and the  degree of geographic
resolution required for a particular study.  For example, voles,  which range within an area of approximately
400 m2,  were useful for studying local sites such as Love Canal (Christian 1983).  Larger sites might  permit
the use of rabbits or groundhogs, which range over hectares and therefore integrate exposure over a wider
geographic area.  Bluegill sunfish have been useful  in studies of contaminated streams because tag-recapture
studies have demonstrated they are confined to a 100-m reach in the streams  (Loar et al.  1989).   Sessile
animals,  such  as clam or  mussel colonies,  provide accurate spatial resolution; however, other animals that
are somewhat mobile  can  avoid very isolated "hot spots" of contamination. Animals  can be confined in order
to increase the degree of geographic resolution  in observations, to confirm and test hypotheses about the
location of contaminants,  and to test for the  presence  of localized "hot spots" of pollutants.

Index  periods for sampling  are  important because responses  of many candidate biomarkers  can vary,
depending on  physiological and environmental factors such as sex,  reproductive  condition, temperature, and
food availability. As discussed for individual biomarkers in Appendix G-2, certain times of the year are likely
to be  inappropriate for biomarker  surveys.   More  importantly,  if responses of organisms from different
geographic locations  are  to  be compared, organisms  should be  collected so  that seasonal and internal
physiological influences are similar among sites. For example, responses of poikilothermic organisms collected

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in winter cannot be compared with those from the same species collected during other seasons; likewise, for
some indicators, responses of reprodactively active females should not be compared to those from males or
immature females.

9.3.4   Research Needs for Biomarkers

The development, application, validation, and interpretation of biomarkers is a relatively new field of research.
With few exceptions, even those biomarkers considered to be well understood and validated  lack a historical
data base comparable to more traditional  methods for indicating  exposure,  such as analysts of ambient
chemical concentrations or standard  toxicity tests.

Many biomarkers are still research tools. Methods have not been standardized, and many biomarker assays
require  fairly sophisticated  equipment.  Standardizing techniques and developing quality assurance/quality
control procedures are presently being considered by the American Society for Testing and Materials (ASTM),
but it is likely to be several  years before definitive standards are developed and accepted by ASTM.  Many
biomarker methods could be easily simplified and measurement costs could be decreased  by economy of
scale  if  large numbers of samples were processed and  automated  clinical equipment  were available.   For
example, research-grade spectrophotometers and fluorometers are not required for enzyme biomarkers; these
assays can be easily adapted to highly automated centrifugal analyzers that  are routinely used for human and
veterinary blood chemistry  profiles.   Likewise,  development  of  monoclonal  antibodies could replace
sophisticated and time-consuming quantification of metabolites or  proteins with simple, quick (even field-
portable) enzyme-linked immunosorbent assay kits.  Until very recently, there has been little or no incentive
to implement these improvements,  but the  increased  interest in  applying  biomarkers suggests that  this
situation will change in the  near future.

The  biomarkers recommended  for EMAP  are qualitative indicators  of exposure, and the significance of
biomarker  responses  must  be  interpreted within the  context  of how they correlate with  the  better
documented exposure indicators.  Nevertheless, biomarkers  can provide valuable and  informative data that
will  corroborate  and  extend other diagnostic  indicators, and the  significant long-term  advantages that
biomarkers offer for EMAP  counterbalance  the limitations imposed by their currently  limited  data base in
the short term.
9.4  LANDSCAPE AND HABITAT INDICATORS

Indicators of habitat structure and landscape pattern will be very important for characterization of potential
habitat for animals; animal monitoring will be limited  because of the large geographic focus and  limited
sampling frequencies of EMAP.  EMAP needs indicators of habitat structure and landscape pattern that can
be measured efficiently through  remote sensing, entered  in digital format into  a Geographic Information
System, and  related directly to aspects of terrestrial and aquatic  ecosystems. Several of the indices described
in Section 9.4 represent progress in this direction.  However, no  good indicators of connectivity, juxtaposition,
and many other aspects of landscape ecology exist that are relevant to animal biodiversity.  In addition, the
relationship between changes in  landscape  indicators and their effect on animal  species or guilds needs to
be quantified.  Such relationships will  most likely be developed first for birds at  the regional scale because
of the extensive data available in the BBS.

EMAP needs some indicators for monitoring ecological  structure across large geographic areas, both within
and across  EMAP resource categories.  Landscape  and habitat indicators are appropriate for this and can
be developed  from data  bases  compiled  as a  result  of  the  EMAP  landscape  characterization  activities.
Landscape indicators describe  the spatial  pattern  of the  landscape in a horizontal  plane,  while  habitat
indicators usually emphasize the vertical plane and  focus on a relatively  smaller scale.  Both can be used
to quantify the general structure  of ecological resources.   Some  landscape  indicators are well established

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(e.g.,  proportions of land use or land  cover, drainage density, patch size), and others have been proposed
only recently (e.g.,  fractal dimension, contagion).  Similarly, research on the Habitat Layers  Index (HLI) has
been  ongoing for several years at local areas, but its  application to large geographic areas has only  recently
been  proposed.

9.4.1    Identification  of Landscape and Habitat Indicators

9.4.1.1  Landscape Indicators

Landscape indicators that could  be useful for EMAP are listed  in Table 9-2.  Landscape indicators (or indices
as in  O'Neill et al. [1988a])  are calculated  by using  algorithms applied to land-use/land  cover data for the
purpose of quantifying significant landscape  pattern in a single number.   It is likely that several  landscape
indicators may be necessary for adequate characterization of pattern, depending on the complexity of the
landscape and the assessment endpoint.  Two criteria were used to evaluate landscape indicators.  First, the
landscape parameter must be easy to measure, for example, by using remotely sensed imagery.  Second, the
measure must be readily interpretable in  ecological terms; that is, it must be possible to  interpret a change
in the indicator in  terms that have immediate  meaning for  the environment.  While  landscape indicators
obviously describe the horizontal landscape pattern, they can  also be linked to ecological processes such as
nutrient cycling (Osborne and Wiley 1988; Peterjohn and Correll 1984) to wildlife migrations (Freemark and
Merriam 1986; Noss 1983; O'Neill et al. 1988b; van Dorp  and Opdam 1987), and to  susceptibility of an
ecological resource to  a  disturbance such as fire or insect attack (Turner (1987;  Sharpe et  al. 1987; Hayes
et al.  1987).

Six landscape indicators  were identified as  appropriate for EMAP and are  discussed in detail  in Appendix
G-3;  four  of these,  habitat proportions,  patch size and perimeter-to-area ratio,  fractal  dimension, and
contagion  were designated  high-priority research indicators  because they have  been  applied  to  large
geographic areas.

9.4.1.2  Habitat Indicators

Two  high-priority  habitat indicators are proposed  for  EMAP:  the abundance  or  density  of  key  physical
features and structural elements and  the  linear classification and physical  structure  of habitat (LCPSH).
Research in many different resource categories has demonstrated that certain physical features of  habitats
(e.g.,  cliffs, outcrops, sinks,  seeps, tallus, slopes) and structural  elements (e.g., snags, downed  logs) are critical
to animal diversity  and abundance.  The abundance or density of such features or structural elements is
therefore  an  important indicator of potential  habitat for  animals.  This indicator  contains more  detailed
information than the surface cover features included  in the LCPSH.

The three-dimensional structure of terrestrial habitats  can influence the diversity of animal  communities.
Short and Williamson (1986)  developed  methods  to (1)  measure  habitat  structure  for  inventory and
assessment work,  (2)  describe  the relative structural complexity  of different landscapes,  (3) describe the
direction and rate of change in habitat structure over time,  and (4)  describe the  potential distribution of
animal species having particular dependencies on  the specialized  structure of habitats. The HLI describes
the relative structural complexity of habitat for an area by comparing the  number of habitat layers present
and the total area of those habitat layers with the most complex  habitat structure that could occur in that
area.  Habitat layers provide the framework of the species-habitat matrix used in the formulation of habitat
guilds (Short  1983).  This HLI emphasizes the vertical structure of habitats,  because birds (e.g., Karr 1971;
Rabenold  1978;  Ceibert 1979),  mammals (Maser et  al.  1981),  and  herpetofauna (Heatwole 1982) are
dependent on the vertical  dimension of vegetation communities.  The HLI is predictive of species  richness
for birds and vertebrates.  It is an objective, quantitive measure because habitat layers have finite definitions
and can be counted and  measured from aerial photography with  ground verification or  from field visits.
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Other habitat indicators and relative abundance and location of plant food sources for selected animals can
be monitored at the same time.

The LCPSH  is a simplification of the HLI (three or four layers only) and  is an extension of the HLI from the
local to the regional scale.  The  LCPSH is proposed as a high-priority research indicator that can be used
to monitor  important habitat  variables for  terrestrial animals over long time  periods  and  across  large
geographic areas.  It uses a standard sampling format and standard sampling procedures and can be applied
to all terrestrial resources and to certain wetland resource classes.  The LCPSH thus can be used to monitor
important animal  habitat variables both  within and across resource categories, can use both map-based and
field survey  data,  and provides  individual  numeric values for a  variety of different habitat features including
habitat layers, surface cover features (e.g., sand,  asphalt, rock), and vegetation variables within the understory,
midstory, and overstory layers.  The values for different metrics from an RSU provide a  "signature" for that
unit  The  signature  then can be compared among  RSUs  and  over time  and can  be used  to develop
summaries that describe the condition of potential  animal  habitats for a region or the impacts of different
land-use changes on important animal habitat metrics.

The LCPSH  is proposed as an  EMAP indicator  that is especially relevant for comparison  among regions.  It
can describe potential habitat and vegetation structure  and, when applied over  time, can describe rates and
magnitude of changes in land  use,  plant succession, desertification, etc.  Indicators for horizontal landscape
pattern can  also describe potential animal habitat, as well as general ecological condition, pollutant loadings,
and susceptibility of a resource to disturbance such as fire or insect attack.

9.4.2   Landscape Indicators Not Appropriate  for EMAP

Numerous other  metrics have  been proposed  to describe the pattern or  texture of landscapes.  Many of
these have been developed for  analysis with  remotely sensed imagery (Haralick and Anderson 1971; Haralick
and Shanmugam  1974).  As many as 16 texture indices have been proposed (Haralick et al. 1973), but
analyses  reveal that they  are highly correlated and  thus are simply different ways of presenting the  same
information.  The few indices that are statistically independent do  not appear to correspond in any obvious
way to ecological processes on the landscape.  While these texture indices may be useful for classification
of satellite imagery, we conclude that alone  they would not make useful landscape indicators.

9.4.3   Research  Needs for Landscape  and  Habitat Indicators

While  many studies have  shown  the importance  of relationships between  landscape pattern and ecological
processes, we have only begun to investigate the usefulness of landscape  pattern in understanding ecosystem
function.  Additional research is needed to quantify the influence of data  resolution and spatial extent on the
characterization of landscape  pattern.   Additional work  is  also  needed  on quantifying  the  relationships
between landscape  indices and  ecological  processes in  order to incorporate  spatial  pattern into regional
ecological risk assessments.  A  landscape ecology research program is being developed as part of the EMAP
Landscape Characterization activity.

The position of ecotone boundaries on the landscape may eventually become the most sensitive and practical
method  for large-scale, long-term  monitoring  (di Castri  et al. 1988).   Predicted climate changes  due to
increasing CO2 concentrations would result in temperature changes of a few degrees and changes in seasonal
precipitation patterns, both of which occur in time frames of decades to centuries. Thus unidirectional trends
will be difficult to detect in population, community,  or  ecosystem parameters.  During the  latest  glacial
periods,  climate change showed its most dramatic ecological  effects in the  geographic repositioning of those
ecotones between major vegetation types. This effect  was far more dramatic than, for example,  the  loss of
species or alterations in energy  flow and nutrient cycling.  It is likely that future long-term  changes in climate,
and  similar slow but pervading  changes in  the  environment, will  also be  reflected  in  the  movement of
ecotones.  Ecotone movement  should be  measurable in field  surveys where the geographic limit of a  species

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range occurs and especially where  this coincides with  steep  topography which  amplifies  the latitudinal
gradient  Such changes could  be detected on  transects across current ecotones.   Monitoring of these
transects could  probably  be accomplished by the analysis of remotely sensed data.  The ability to monitor
large ecotones will be investigated by using  the EMAP Characterization data base.


9.5  STRESSOR INDICATORS

Stressor indicators are physical, chemical, biological,  social, and economic data  or information on  human
activities that can cause environmental disturbance. All indicators that are not response, exposure, or habitat
indicators are stressor indicators.  Generally,  stressors  will  not be measured by EMAP.   Much  of the data on
Stressor indicators will be provided by EMAP-Characterization,  and additional  data will  be compiled from
other local,  state, and federal data  bases.  Examples of stressors include land  use,  contaminant use, air
pollution, number of hazardous waste sites or wastewater discharges, and  road density.  While resource
categories may  have many stressors in common, some will be unique to a category.  Examples of stressor
indicators for inland surface waters include pollutant loadings as  measured or estimated at point sources and
as calculated from land uses, flow and channel modification, and species introductions.  These  represent
chemical, physical,  and  biological stressors,  respectively.  Stressor indicators for  agroecosystems include
chemical usage in and export from agroecosystems and  number and  size of farms.  Climate and weather
represent stressors important to all resource  categories, and resource management  practices are stressors to
many ecological resources.  A discussion of atmospheric stressors  that EMAP  may monitor  is  the topic of
Section 10.
9.6  REFERENCES

9.6.1   References for Animal Life

Bednarz, J.C., D. Klem, Jr., LJ. Goodrich, and  S.E. Senner.  1990.  Migration counts of raptors at Hawk
Mountain Pennsylvania as indicators of population trends (1934-1986).  Auk 107(1):96-109.

Beiswenger,  R.E.  1989.   Integrating anuran amphibian species into environmental assessment programs.
Pages 159-165.  In:  R.C. Szaro, K.E. Severson, and D.R. Patton, eds. Management of Amphibians, Reptiles,
and Small Mammals in North America.  General Technical Report RM-166.  U.S. Department of Agriculture,
Forest  Service, Fort Collins, CO.

Bock, C.E. 1979. Christmas bird count.  Nat. Hist. 88(10):7-11.

Bock, C.E., and  T.L.  Root  1981. The Christmas Bird Count and  avian ecology.   In:  C.J. Ralph and J.M.
Scott, eds. Estimating Numbers of Terrestrial Birds.  Stud. Avian Biol. 6:17-23.

Bromenshenk, J.J., and E.M. Preston.  1986.  Public participation in environmental monitoring:  A means
of attaining network capability.   Environ.  Monitor. Assess. 6:35-47.

Bury, R.B., and P.S. Corn.  1987.  Evaluation  of pitfall trapping  in  Northwest forests:  Trap arrays with
differences.  J. Wildlife Manage. 51(1):112-119.

Bury, R.B., and M.G.  Raphael.  1983.  Inventory methods for amphibians and reptiles.  Proceedings of the
International Conference on Renewable Resources. Inventories for Monitoring Changes and Trends.  Oregon
State University,  Corvallis.
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Butcher, G.S.  1990. An evaluation of the Christmas Bird Count for monitoring population trends of selected
species. Wildlife Soc. Bull. 18(2):  in press.

Bystrak, D.  1981.  The North American breeding bird survey. Stud. Avian Biol. 6:34-41.

Cairns, J.  1983.  Are single species  toxicity tests alone adequate  for estimating environmental hazard?
Hydrobiologia  100:47-57.

Connors, P.G.  1986.  Marsh and  shorebirds.  Pages 351- 369. In:  A.Y. Cooperrider, R.J. Boyd, and H.R.
Stuart, eds.  Inventory and Monitoring of Wildlife Habitat.  U.S. Department of the Interior, Bureau of Land
Management, Service Center, Denver, CO.

Cooperrider, A.Y., R.J. Boyd, and  H.R. Stuart, eds.  1986.  Inventory and Monitoring of Wildlife Habitat
U.S. Department of the Interior, Bureau of Land Management, Service Center, Denver,  CO.

Corn,  P.S., and J.C. Fogleman.  1984.  Extinction of montane  populations of the northern leopard frog
(Rana pipiens)  in Colorado.  J. Herpetol. 18:147-152.

Corn,  P.S.,  and  R.B.  Bury.   1987.   The  potential  role of  acidic  precipitation in declining amphibian
populations  in the Colorado  Front Range.  Aquatic Effects Peer Review of the  National Acid Precipitation
Assessment Program, New Orleans, LA.

Corn,  P.S., W. Stolzenburg, and R.B.  Bury.  1989.  Acid precipitation studies in Colorado and Wyoming:
Interim report  of surveys  of montane amphibians  and water  chemistry.   Biological  Report 80(40.26).
U.S. Fish and Wildlife Service, Washington, DC.

Eagles, P.F.J.  1981. Breeding bird censuses using spot-mapping techniques upon samples of homogeneous
habitats.   In:  C.J. Ralph and J.M. Scott, eds.  Estimating Numbers of Terrestrial  Birds.   Stud. Avian Biol.
6:455-460.

Eng, R.L.  1986. Upland game birds.  Pages  407-425.   In:  A.Y. Cooperrider,  R.J. Boyd, and H.R.  Stuart,
eds. Inventory  and Monitoring  of Wildlife  Habitat.  U.S.  Department  of  the Interior, Bureau  of Land
Management, Service Center, Denver, CO.

Faanes, CA.,  and D.  Bystrak.  1981.  The  role of observer bias in the North American breeding bird
survey. Stud.  Avian Biol. 6:353-359.

Fenton, M.B., D.C. Tennant, and  J. Wyszecki.   1987.   Using echolocation calls to  measure the distance
of bats:  The case of Euderma maculatum.  J. Mammal.  68:142-144.

Flather, C.H.,  and T.W. Hoekstra.  1989. An analysis of the wildlife  and  fish situation in the United  States:
1989-2040.   General Technical Report RM-178.   U.S.  Department  of Agriculture,  Forest Service,  Rocky
Mountain Forest and Range Experiment Station, Fort Collins, CO.

Franklin, J.F.,  K. Cromack, W. Denison, A. McKee, C. Maser,  J. Sedell, F. Swanson,  and G. Juday.  1981.
Ecological characteristics of old-growth Douglas-fir forests.   General  Technical Report  PNW-118.   U.S.
Department of Agriculture,  Forest Service, Pacific Northwest Forest and Range Experiment Station, Portland,
OR.

Freda, J., and W.A. Dunson.  1985. The effect of acidic precipitation on amphibian breeding in temporary
ponds in Pennsylvania.   Biology Report  80(40.22).  U.S. Department of the Interior, Fish and Wildlife Service,
Washington, DC.  85 pp.

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Fuller, M.R., and JA. Mosher.  1981.  Methods of detecting and counting raptors:  A  review.  In:  C.J.
Ralph and J.M. Scott, eds.  Estimating Numbers of Terrestrial Birds.  Stud. Avian Biol. 6:235-246.

Gibbons, J.W.  1989.   The management of amphibians, reptiles and  small  mammals in North America:
The  need for an environmental attitude adjustment.  Pages 4-10.  In:  R.C. Szaro, K.E. Severson, and  D.R.
Patton, eds.  Management of Amphibians, Reptiles, and Small Mammals in North America.  General Technical
Report RM-166.  U.S. Department of Agriculture, Forest Service, Fort Collins, CO.

Gibbs, J.P., and S.M. Melvin.  1989. An assessment of wading birds and other  wetlands avifauna and  their
habitats in Maine.  Maine Department of Inland  Fisheries and Wildlife, Bangor.

Hall, R.J.   1980.  Effects of environmental contaminants on reptiles:  A review.  Special  Science  Report
228.  U.S. Department of the  Interior, Fish  and Wildlife Service, Washington, DC.

Hall, R.J., and D.M. Swineford.  1981. Acute  toxicities of toxaphene and endrin to larvae of seven species
of amphibians.  Toxicol.  Lett. 8:331-336.

Hawk Mitigation  Studies.  1989.  Hawk Migration of North America Newsletter (Hilton,  NY) 15(1).

Hurlbert, S.H.  1971.   The nonconcept of species diversity:  A critique and some alternative parameters.
Ecology 52:577-586.

Joern, A.  1987.    Behavioral  responses  underlying  ecological  patterns of  resource  use in  rangeland
grasshoppers.   Pages 137-161,  In:   J.L.  Capinera, ed.   Integrated Pest Management on Rangeland,  A
Shortgrass  Prairie  Perspective.  Westview Press, Boulder, CO.

Jones, K.B.   1981.  Effects of grazing on  lizard  abundance  and diversity in  western Arizona.  Southwest
Nat.  26(2):107-115.

Jones, K.B.  1986.  Amphibians and  reptiles.   Pages 267-290.  In:  A.Y. Cooperrider, R.J. Boyd, and  H.R.
Stuart, eds.  Inventory and Monitoring of Wildlife Habitat. U.S. Department of the Interior, Bureau of Land
Management, Service Center,  Denver, CO.

Jones, K.B.  1989a.  Comparison of herpetofaunas of a natural and altered riparian ecosystem.  Pages 222-
227.  In: R.C. Szaro, K.E. Severson, and D.R. Patton, eds. Management of Amphibians, Reptiles, and Small
Mammals in  North America.   General Technical Report RM-166.   U.S. Department of Agriculture, Forest
Service, Fort Collins,  Colorado.

Jones, K.B.   1989b.  Distribution  and habitat associations  of herpetofauna in  Arizona:  Comparisons by
habitat type.   Pages 109-128.   In:  R.C. Szaro, K.E.  Severson, and  D.R. Patton, eds.   Management of
Amphibians, Reptiles, and Small Mammals  in  North America.   General  Technical Report  RM-166.   U.S.
Department  of Agriculture, Forest Service, Fort  Collins, CO.

Jones, K.B., and P.C. Glinski.  1985. Microhabitats of lizards in a southwestern riparian community.  Pages
342-346.  In:  R. Roy Johnson et al.,  eds.   Riparian Ecosystems and  Their  Management:  Reconciling
Conflicting Uses.  First  North American Riparian  Conference.  General Technical Report Number RM-120.
U.S.  Department of Agriculture, Forest Service, Rocky Mountain Forest and Range Experimental Station,  Fort
Collins, CO.

Kapustka,  L, T. LaPoint, J. Fairchild, K. McBee, and  J. Bromenshenk.  1989.   Pages 8-1 to 8-88.   In:
W. Warren-Hicks,  B.R.  Parkhurst, and S.S. Baker, eds.   Ecological Assessments of Hazardous Waste Sites:
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A Field  and Laboratory  Reference  Document.  EPA 600/3-89/013.  U.S.  Environmental  Protection Agency,
Corvallis OR.

Karr, J.R., K.D. Fausch,  P.L Angermeier, P.R. Yant, and I.J. Schlosser. 1986.  Assessing biological integrity
in  running waters:  A method and its rationale.  Special Publication No. 5.  Illinois Natural History Survey,
Champaign.

Kochert, M.N.   1986.  Raptors.  Pages 313-349.   In:  A.Y. Cooperrider, R.J.  Boyd, and  H.R. Stuart, eds.
Inventory and Monitoring of Wildlife Habitat  U.S. Department of the Interior, Bureau of  Land Management,
Service  Center, Denver, CO.

Landres, P.B., J. Verner,  and J.W. Thomas.   1988.  Ecological uses of vertebrate indicator species:  A
critique. Conserv. Biol. 2:316-328.

Leary,  R.F., and F.W. Allendorf.   1989.  Fluctuating asymmetry as an indicator of stress:   Implications for
conservation biology.  Trends Ecol. EvoluL  4:214-217.

Luckenbach, R.A., and  R.B. Bury.  1983.   Effects of offroad vehicles on the biota of the Algodones Dunes,
Imperial County, California.   J. Appl. Ecol.  20:265-286.

Norse,  E.A., K.L. Rosenbaum, D.S. Wilcove, B.A. Wilcox, W.H. Rom me, D.W. Johnston, and M.L Stout
1986.   Conserving Biological Diversity in our National Forests.  The Wilderness Society, Washington, DC.

Noss, R.F.   1983.  A regional landscape approach to maintain diversity.   BioScience 33:700-706.

Noss,  R.F., and  LD. Harris.  1986.   Nodes,  networks,  and MUMs:   Preserving  diversity at all scales.
Environ. Manage. 10:299-309.

O'Neill, R.V., B.T. Milne,  M.G.  Turner,  and  R.H. Gardner.   1988b.   Resource utilization  scale and
landscape pattern.  Landscape Ecol. 2:63-69.

Ortega, A.,  M.E. Maury, and R. Barbault.   1982.  Spatial organization and  habitat partitioning in a mountain
lizard community of Mexico.  Ecol. Gen. 3(3):323-330.

Pianka, E.R. 1980.   Guild  structure in  desert lizards.  Oikos 35:194-201.

Pielou, E.C. 1975.   Ecological Diversity.  Wiley-lnterscience, New York.

Pierce,  B.A., J.B. Hoskins, and E. Epstein.  1984. Acid tolerance in Connecticut wood frogs (Rana sy/vat/ca).
J. Herpetol. 18(2):159-167.

Robbins, C.S.  1966.  The Christmas count.  Pages 154-163.   In:   Stefferud, A., ed.   Birds in  Our Lives.
U.S. Department of the Interior, Fish and Wildlife Service,  Washington, DC.

Robbins, C.S., D. Bystrak,  and P.H.  Geissler.  1986.  The  Breeding Bird Survey:   Its First Fifteen Years,
1965-1979.   Resource Publication  157.   U.S.  Department of the  Interior, Fish  and  Wildlife Service,
Washington, DC.

Ryder,  RA.  1986.   Songbirds.   Pages 291-312.   In:  A.Y. Cooperrider, R.J.  Boyd, and  H.R. Stuart, eds.
Inventory and Monitoring of Wildlife Habitat. U.S. Department of the Interior, Bureau of Land Management,
Service Center, Denver, CO.
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Schamberger, M. 1988.  Monitoring wildlife habitat:  A critique of approaches. Statistical J. United Nations
ECE  5:303-313.

Scott, N.S., Jr., ed.   1982.  Herpetological communities.   Wildlife  Research  Report 13.  U.S. Department
of the Interior, Fish  and Wildlife Service.

Shannon, C.E., and  W. Weaver. 1949.  The Mathematical Theory of Communication.  University of Illinois
Press, Urbana.

Short, H.L   1983.   Wildlife guilds  in Arizona desert habitats.   Technical Note 362.   U.S. Department of
the Interior, Bureau of Land Management, Denver, CO.  257 pp.

Speich,  S.M.  1986.  Colonial waterbirds.  Pages 387-405.  In:   A.Y.  Cooperrider,  R.J. Boyd, and  H.R.
Stuart, eds.  Inventory and Monitoring of  Wildlife Habitat. U.S. Department of the Interior, Bureau of  Land
Management, Service Center, Denver, CO.

Thomas, D.W.,  and S.D. West.  1984.  On the use of  ultrasonic detectors for bat  species identification
and  calibration of QMC mini bat detectors.  Can. J. Zool. 62:2677-2679.

Tinkle, D.W. 1982. Results of experimental density manipulation in an Arizona lizard  community.  Ecology
63(1):57-65.

Turner,  M.C., R. H. Gardner, V.H. Dale, and R.V. O'Neill.  1989.   Predicting the spread of disturbance
in heterogeneous landscapes.  Oikos 55:121-129.

Van  Home, B.   1983.  Density as  a  misleading  indicator  of habitat quality.   J.  Wildlife Manage.  47:893-
901.

Verner,  J.  1985. Assessment of counting techniques.  Current Ornithol. 2:247-302.

Wilcove, D.S. 1988. National Forests:  Policies for the Future.  Volume 2.   Protecting Biological Diversity.
The  Wilderness Society, Washington, DC.

Wing, L, and M. Jenks.  1939.  Christmas censuses:  The amateur's contribution to science.  Bird  Lore
41:343-350.

9.6.2 Biomarker References

Adams,  S.M., K.L. Shepherd, M.S. Greeley, Jr., M.G. Ryan, 8.D. Jimenez, LR. Shugart, and J.F. McCarthy.
1990.  The  use  of bioindicators for assessing the effects of  pollutant stress  on fish. Mar. Environ. Res. (in
press).

Christian, J.J. 1983. Love Canal's unhealthy voles.  Nat.  Hist. 10:8-16.

Hugget, ed.  1990.  The existing and potential value of biomarkers in evaluating exposure and Environmental
effects of toxic chemicals.  Society of Environmental Toxicology and Chemistry.  In press.

Loar, J.M., S.M. Adams, M.C. Black, H.L.  Boston, A.J. Gatz, Jr., M.A. Huston, B.D. Jimenez, J.F. McCarthy,
S.D.  Reagan, J.G. Smith,  G.R.  Southworth, and A.J. Stewart.  1989. First annual report on the Y-12 plant
biological  monitoring and  abatement program.  ORNL/TM-10265.   Oak Ridge  National Laboratory,  Oak
Ridge, TN.
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Martin, M.H., and P.J. Coughtrey.  1982.  Biological Monitoring of Heavy Metal Pollution.  Applied Science
Publishers, London.

McCarthy, J.F., and L R. Shugart, eds.  1990.   Biomarkers of environmental  contamination.  Proceedings
of an American Chemical Society Symposium.  Lewis Publishers, Chelsea, Ml.

National Research Council.  1987.  Biological markers in environmental health research.  Environ. Health
PerspecL 74:3-9.

Thomann, R.V.   1981.   Equilibrium model  of fate of microcontaminants in diverse  aquatic food chains.
Can. J. Fish. AquaL Sci.  38:280-296.

Weidemeyer, G.A., D.J. McLeoy,  and C.P. Goodyear.  1984.  Assessing the tolerance of fish populations
to environmental  stress:  The problem and methods of monitoring.  Pages 163-195. In: V.W. Cairns, P.V.
Hodson,  and J.O. Nriagu, eds.  Contaminant Effects on Fisheries.   Wiley, New York.

9.6.3  Landscape and Habitat References

di Castri, F., A.J.  Hansen, and M.M. Holland, eds.  1988.  A new look at ecotones.  Biology International
Special Issue 17.

Freemark,  K.E.,  and  H.G.  Merriam.   1986.   Importance  of  area  and  habitat heterogeneity  to bird
assemblages in temperate forest fragments.  Biol.  Conserv. 36:115-141.

Geibert,  E.H.   1979.   Songbird diversity along a  power line  right-of-way  in  an  urbanizing Rhode  Island
environment  Trans. Northeast Sect. Wildl. Soc.  36:32-44.

Haralick, R.M.,  and D. Anderson.   1971.  Texture-tone study with applications to digitized imagery.
Technical Report  182-2.  University of Kansas Center for Research, Lawrence.

Haralick, R.M., and K.S.  Shanmugam.   1974.  Combined spectral and spatial  processing ERTS  imagery
data.  Remote Sens. Environ. 3:3-13.

Haralick, R.M., K. Shanmugam, and I.  Dinstein.   1973.  Textural features for image classification.   IEEE
Trans. Systems, Man, Cybernetics SMC-3:610-621.

Hayes,  T.D.,  D.H. Riskind, and W.L. Pace.   1987.   Patch-within-patch  restoration  of man-modified
landscapes within Texas state parks.  Pages 173-198.  In:  M.G. Turner, ed.  Landscape Heterogeneity and
Disturbance. Springer-Verlag, New York.

Heatwole, H.   1982.  A review of structuring in herpetofaunal assemblages.   Pages 1-19.  In:   N.J.  Scott,
Jr., ed.  Wildlife Res. Rep. 13. U.S. Department  of the Interior, Fish and Wildlife  Service, Washington, DC.

Karr, J.R. 1971.   Structure of avian communities in selected  Panama  and  Illinois habitats.  Ecol. Monogr.
41:207-233.

Maser, C, B.R. Mate, J.F. Franklin, and C.T. Dryness.  1981. Natural history of Oregon coast mammals.
General Technical Report PNW0133.  U.S. Department of Agriculture, Forest Service, Pacific  Northwest  Forest
and  Range Experiment Station, Portland, OR.

Noss, R.F.   1983.  A  regional landscape approach  to  maintain diversity.  BioScience 33:700-706.
                                               9-24

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O'Neill, R.V., J.K. Krummel, R.H. Gardner, G.  Sugihara, B. Jackson, D.L DeAngelis, B.T. Milne, M.G.
Turner, B. Zygmunt, S.W. Christensen, V.H. Dale, and R.L. Graham. 1988a.  Indices of landscape pattern.
Landscape Ecol.  1:153-162.

O'Neill, R.V., B.T. Milne, M.G. Turner, and R.H.  Gardner.  1988b.   Resource  utilization  scale  and
landscape  pattern.  Landscape Ecol. 2:63-69.

Osborne,  LL, and M.J. Wiley.  1988.   Empirical  relationships between land use/cover and stream water
quality in an agricultural watershed.  J. Environ. Manage.  26:9-27.

Peterjohn, W.T., and D.L. Correll.   1984.  Nutrient dynamics in an agricultural watershed:  Observations
on the role  of a riparian forest  Ecology 65(5):1466-1475.

Rabenold, K.N.   1978.  Foraging strategies,  diversity,  and seasonably in bird  communities of Appalachian
spruce-fir forests.  Ecol. Monogr. 48:397-424.

Sharpe, D.M., G.R. Guntenspergen,  D.P. Dunn, LA.  Leitner, and  F. Sterns.  1987.  Vegetation dynamics
in a southern Wisconsin  agricultural landscape.   Pages 137-155.   In:   M.G.  Turner, ed.   Landscape
Heterogeneity and Disturbance.  Springer-Verlag,  New York.

Short,  H.L  1983.  Wildlife guilds in Arizona desert  habitats.   Technical Note 362.   U.S. Department of
the Interior,  Bureau of Land Management, Denver, CO.  257 pp.

Short,  H.L,  and  S.C. Williamson.  1986.  Evaluating the structure  of habitat for  wildlife.  Pages 97-104.
In:  J. Verner, M.L. Morrison, and C.J. Ralph, eds.  Modeling Habitat Relationships of Terrestrial Vertebrates.
University  of Wisconsin Press, Madison.

Turner M.G., ed.  1987.  Landscape  Heterogeneity and Disturbance.  Springer-Verlag, New York.

van Dorp, D., and P.F.M.  Opdam.  1987. Effects of patch size, isolation and  regional abundance on forest
bird communities.  Landscape Ecol. 1:59-73.
                                               9-25

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                                            SECTION 10

                       INDICATOR STRATEGY FOR ATMOSPHERIC STRESSORS

                                          Steven Bromberg1


10.1  INTRODUCTION

Sections  3  through 9 describe response, exposure, and  habitat (on-frame) indicators that are proposed as
research  indicators for the respective EMAP resource categories. This section addresses atmospheric stressors
(e.g., gases, particles,  UV-B), the  only indicators to date that will be field-monitored  by EMAP outside the
resource  sampling units (off-frame).

As  a result of  requirements  of the Clean Air Act and  subsequent amendments,  most  of the atmospheric
exposure monitoring projects being performed in this country are concentrated in urban areas.  This focus
has resulted in a notable lack of data that describe exposure regimes in nonurban locations.  Only during
the past several years  have attempts  been made  to characterize atmospheric exposure  in relatively
unpopulated areas.

Several  networks in nonurban areas have  been deployed  in support of the National Acid Precipitation
Assessment Program (NAPAP), established in 1980.  The National Acid Deposition Program/National  Trends
Network (NADP/NTN) is collecting weekly samples of wet precipitation measurements from approximately 200
sites distributed nationwide.  The National Dry  Deposition Network  (NDDN) has been  in operation since
1986  and  is collecting air-related samples and data on  meteorology from approximately 50 sites,  located
primarily in the East.  The National Park Service (NPS) has deployed air concentration samplers for  ozone,
sulfur dioxide, sulfate, and nitrate throughout the federal park system.  The long-term continuation of these
networks upon completion of NAPAP remains questionable, however.

If a nonurban  monitoring program is implemented by EMAP-Air and Deposition, presently operating sites
that can  contribute to the EMAP mission must be maintained.  Loss of existing expertise, interruption of long-
term data  sequences, and closure of current sites as a result of discontinuing these networks would be
inefficient because of  the prohibitively high costs to initiate entirely new networks.  Thus, EMAP must take
advantage  of all appropriate existing collecting  systems to  avoid  duplication, to achieve  an active data-
gathering system as quickly as possible,  and to gather data in the most economical manner.

The primary goal of EMAP - Air  and Deposition is  to be able to provide, on a seasonal and annual basis,
regional  estimates of exposure  with known  accuracy  and  precision.  A secondary  goal  is to  provide
interpolated estimates  of exposure at a particular location, also with known precision and accuracy.  Exposure
will initially be expressed in  concentration units; as technology allows and ecosystem researchers become
adept at  using flux  information, exposure will  be expressed in deposition units.

The suite of candidate variables to be  sampled was extensive; each EMAP resource group had to be very
restrictive in selecting  atmospheric contaminants as research indicators for their resource category.  In some
cases, contaminants of importance to all categories would be collected throughout the  network; however,
pollutants of special importance to a particular  resource class  and/or region would be  monitored  only at
selected  locations.
    U.S. Environmental Protection Agency, Atmospheric Research and Exposure Assessment Laboratory, Research Triangle Park, North
    Carolina

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10.2  ATMOSPHERIC INDICATORS APPROPRIATE FOR EMAP
Figure 10-1  lists the proposed research indicators of atmospheric stressors.  A written external peer review
of these indicators was performed in April 1990 (Appendix I.8), followed by a review by the EPA Science
Advisory Board in May 1990.  Enclosed in parentheses following each research indicator description below
is the indicator identification code for easy reference to the corresponding fact sheets in Appendix H.
10.2.1 High-Priority  Research  Indicators
The classification of atmospheric indicators as having high-priority research status, while necessarily subjective,
was based on our review of the literature and experience with atmospheric deposition monitoring programs.
Ozone:  Ozone is a  transformation  product of atmospheric emissions that is regulated by EPA National
Ambient Air Quality Standards and is considered a  stress to ecological resources as well as to human health.
The deposition of and exposure to ozone affect vegetation through disruption of  physiological processes.
(H.1)
Sulfur Dioxide:  Sulfur dioxide is a product  of atmospheric emissions that is regulated by EPA National
Ambient Air Quality Standards and is considered a stress  to ecological resources as well as human health.
The  deposition of and exposure to  sulfur dioxide affect vegetation through disruption of physiological
processes.  (H.2)
                EMAP-Air and Deposition Indicator Strategy
    Response Indicators (R)
   From EMAP-:
     Near-Coastal
     Inland Surface Waters
     Wetlands
     Forests
     Arid Ecosystems
     Agroecosytems
   SPATIAL
ASSOCIATIONS
  TEMPORAL
ASSOCIATIONS
   Stressor Indicators (S)
Ozone
Sulfur Dioxide
Nitric Acid
Ionic Constituents in Precipitation
Metals and Organics (Toxics)
Free Radicals
Carbon Dioxide
Other Greenhouse Gases
Ultraviolet Type B Radiation
Airborne Particles
 Figure 10-1.    Diagram of the proposed EMAP-Air and Deposition Indicator Strategy.  Indicators in bold
                lettering are high-priority research indicators.
                                               10-2

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Nitric Acid:  Nitric acid is a transformation  product of atmospheric emissions.  Nitric acid is not presently
regulated  by EPA; however, it  is considered  a stress to ecological resources as well as human health.  The
deposition of and exposure to  nitric acid are thought to affect vegetation through disruption of physiological
processes.  (H.3)

Ionic Constituents in Precipitation:  Nine major ions in wetfall are identified that may have direct impacts
on  terrestrial vegetation and aquatic biota through such processes as acidification,  or indirect  impacts  by
disruptions in nutrient cycling.   (H.4)

10.2.2  Other  Research Indicators

Several  research indicators that could have significant impacts on  ecological  resources are insufficiently
developed for  immediate evaluation and  are being considered for development and  refinement by EMAP-
Air and Deposition.  Research  indicators currently  under consideration include those described  below.

Metals and Organics (Toxics):  The effects from chronic deposition of airborne toxic  chemicals on terrestrial
and aquatic organisms and their potential  interaction with other stressors to  induce antagonistic or synergistic
effects are  unknown.   The  persistence of  these  compounds can result  in  adverse biological effects  by
incorporation and accumulation into food chains and disruption  of ecological processes.  (H.5)

Free Radicals:  Products of oxidation  reactions in  the atmosphere, these short-lived  compounds are highly
energetic  and,  upon exposure  to vegetation, could be disruptive of its normal biochemistry.  (H.6)

Carbon Dioxide:  Although not the greatest absorber of infrared radiation per  molecule, the huge flux of
carbon  into the troposphere by fossil  fuel sources has made carbon  dioxide a  leading factor in long-term
global climate trends and its consequential effects on ecological resources.   This indirect climate effect and
the direct fertilization effect of increasing carbon dioxide concentrations on all vegetation types makes it a
potentially important atmospheric stressor. (H.7)

Other Greenhouse Gases: The chemistry of the  free troposphere is characterized  by gases which absorb
infrared radiation yet have little direct effect on vegetation, and their increase  has  led to the  greenhouse
theory of  global warming.  (H.8)

Ultraviolet Type B Radiation:  Increased UV-B exposure to terrestrial organisms may have a significant effect
on  reproduction.  Increased intensity  may also stress aquatic microorganisms, which in turn would affect
organisms of higher trophic levels.   (H.9)

Airborne  Particles: Extremes of particle loading in the atmosphere from sources  such as severe dust storms,
volcanic activity, and power plants  could have short-term effects on ecological resources. Particle loading is
also related to  atmospheric extinction  and the environmental value of visibility.  (H.10)


10.3  ATMOSPHERIC  MONITORING  STRATEGY

Where  possible,  it  is imperative to build on  current monitoring systems.    EMAP,  however, will  be very
selective in choosing existing sites for incorporation  into the monitoring system. Only if sites fulfill the EMAP
design criteria will they be incorporated.  If  located at an  existing ecosystem research site or near a Tier 2
resource  sampling  unit, existing wet  and  dry deposition  sites would  be  incorporated  into  the EMAP
atmospheric exposure monitoring network.  Sites from NADP/NTN, NDDN, NPS, and selected states would
be  used as both wet and dry deposition  monitoring locations. The network would  be supplemented with
new sites  as  needed, or with additional equipment as appropriate.
                                                 10-3

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The first task would be to determine how well existing sites describe exposure on a regional basis or when
interpolating between monitoring sites.   An  evaluation of the ability to detect directional trends  is also
necessary.   Results  of these analyses would indicate where additional sites are needed and which sites are
duplicative.  These  analyses also would reveal where improvements in accuracy  and precision are required
in order to  ensure  directional trends can  be detected.

Using a combination of existing sites, relocated sites,  and new sites, an optimum network design would
enable EMAP researchers to obtain seasonal and annual estimates of regional exposures to selected pollutants
with known precision and accuracy.  Estimates of precision and accuracy for interpolated values at selected
points by  techniques such as  kriging would also be available.

Several  options are available for deploying the atmospheric monitoring network.  Intensive monitoring sites
can  be operated  that  provide data of  relatively high temporal  resolution  (e.g.,  continuous ozone and
meteorological data, weekly particulate data).  These sites are sophisticated, require highly trained operators,
and are costly to install and maintain.  The resulting data, however, are of high accuracy and precision. The
site density  of such a network would probably  be relatively low because of capital and operating costs.

A second option  is  to install  passive monitoring sites.   Samplers  installed  at these sites could  collect
contaminants by diffusing gases onto an adsorbent.  Data collected by this method, however, are not very
accurate or  precise, and monitors are not available for  many of the constituents  of interest  The advantage
is that such  monitors are relatively inexpensive to install  and operate, enabling many monitors to be deployed
for the same cost as a few of  the intensive sites.  More monitors would provide better spatial coverage than
would  the intensive sites, but with  much reduced accuracy.

The  third option is to use a combination of  active  monitors at intensive sites and passive  monitors at
extensive sites. A small number of intensive sites would be retained to provide accurate data.  The  passive
samplers would  be deployed  in  a relatively dense network to yield required spatial  resolution.   Data from
the passive  monitors would be compared with  that from  the active monitors to ensure that  a consistent
relationship  exists between the techniques.  During the network evaluation and design phase of the project,
these deployment options would be examined.

Regardless of the option selected, regional estimates of  exposure with  known accuracy and  precision  will  be
provided.   The  degree of accuracy and  precision available would depend on the selected approach; the
option selected would  depend  on  resources available  and on identified  assessment needs.  If interpolated
estimates are inadequate for a particular location, additional equipment will be installed to satisfy the existing
need.
10.4  RESEARCH  NEEDS

Research needs fall into two categories.  Development and improvement of sampling and analytical methods
are needed for most atmospheric stressors, particularly for toxic pollutants.  These improvements are required
to enhance methods sensitivity, simplify sampling and analytical procedures, and  reduce costs.

The second area of required research is the development of data analysis and display techniques. Combining
data from various networks of differing precision and accuracy for use in network analysis is a particular need.
New and improved techniques for displaying temporal changes and regional patterns are required for better
presentation  of research results.
                                                 10-4

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                                            SECTION 11

                                SUMMARY AND FUTURE DIRECTIONS
Section  1  emphasized several purposes of this document  including:   (1) information  transfer, (2)  program
integration, and (3) solicitation of input from the scientific community.  Sections 2 through 10 explained the
rationale and framework for identifying and prioritizing indicators for further evaluation,  for both the program
in general and for each ecological resource category.  The appendices provide details on  each indicator
discussed  in  Sections  3 through  10.   This section represents  the  information  transfer purpose of this
document.

A critical element of EMAP is program integration,  one aspect of which is to adopt a common philosophy
and nomenclature for indicators among ecological resource categories.   Section 2 establishes  the framework
for the EMAP indicatory strategy and provides general guidance to scientists who are identifying and evaluating
indicators  for the resource  categories.   The  results of  applying a   preliminary  strategy  resulted  in  its
modification to be more broadly applicable.  This process  was and will continue to be recursive.

Adopting a program  strategy for identifying and evaluating  indicators has helped reveal shortcomings in our
understanding of the resource categories and the measurements needed to understand their condition.  Each
EMAP Resource Croup has its own perspective - resulting from the awareness of the  environmental issues
associated with each resource category as well as the way  in which each resource has been  monitored and
studied  in the past. Delineating  shortcomings in our list is meant to stimulate an active  effort to fill the  gaps,
with assistance from those ecologists who understand how  a problem in their  resource category of expertise
was dealt with previously.  The unfilled  cells in the matrix presented in  Table 11-1, which presents the
currently identified research indicators, reflect the current gaps as of the writing of this document

The final purpose of this report is to set the stage  for the next  step in furthering the EMAP indicator strategy.
As was shown in Figure 2-7 (Section 2.4), the EMAP Resource Croups have  reviewed literature on potentially
appropriate  indicators,  and through workshops  and an  initial peer-review process,  they have  identified
indicators  that warrant in-depth  data analysis and subsequent demonstration in  limited-scale field  projects.

The next step in  the strategy is  evaluation of existing data.  It is  unlikely that many regional data sets exist
that are derived from probability sampling of the proposed indicators. In many cases, however, data collected
from nonprobability-based networks that use standardized protocols may be  useful in establishing components
of sampling variance,  both among sites  and within and  among years.   EMAP  scientists are particularly
interested  in  being made aware of such data sets,  because anything  that can  be done prior to  initiating
limited-scale field evaluation will greatly increase  the cost-effectiveness  of such efforts.  Anyone having data
or research results that might assist EMAP scientists in  such analyses are urged to contact:

    EMAP Indicator Coordinator
    EPA Environmental Research Laboratory
    200 Southwest 35th Street
    Corvallis, OR  97333

Following  completion and peer  review of these exploratory evaluations, EMAP will test the most promising
indicators  in regional demonstration projects.  For these demonstration  projects, measurements will be made
on a full set of developmental indicators  for a resource class by using a probability sample.  The probability
sample will be based on either the unmodified EMAP Tier  2 design (Section 2.2.3) or special Tier  2 designs
needed  to better evaluate alternatives for long-term implementation.   Each demonstration project will  be
subject to peer review following completion.
                                                11-1

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A  principal purpose of the  demonstration  projects is to identify core indicators, that  is, those indicators to
which  EMAP  is willing to make a monitoring commitment for at  least 20 years.   Designation as a core
indicator depends upon both the information content of the indicator and its  associated logistical feasibility
or costs.  Because most indicators have not been tested for regional monitoring, it is  unlikely that sufficient
data exist to carefully evaluate either component   Regional demonstration  projects will, however, provide
such data.

In addition to the demonstration projects, EMAP will coordinate a research program to identify new indicators
for potential field testing.  This program will include both intramural  and extramural expertise, and will focus
on filling  the  gaps  in the current understanding of what measurements are needed  to quantify ecological
condition  in the nation's resources and to improve the ability  to correlate that condition with patterns and
trends  in  environmental stresses.  Early-warning indicators are  especially needed, as are better indicators of
community structure, indicators  of ecosystem processes that can be measured in surveys, animal indicators
suitable for regional monitoring,  and  indices for summarizing information on indicators.

Finally, EMAP must continuously reevaluate its indicator strategy and indicator choices over  time, as was
shown in  Figure 2-7 (Section 2.4). To  ensure its long-term survival, EMAP must maintain sufficient stability
to continue identifying long-term  trends, but at the same time it must avoid succumbing to routine consistency
that erodes the program's utility.  Iterative evaluation will ensure that EMAP remains a challenging program
that is both stable and flexible through the coming decades.
                                                 11-2

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Table  11-1.    Research indicators listed by resource category and indicator type.  (Codes in parentheses
               are cross-references to the appendices, where the indicator is discussed in detail.)  Indicators
               in bold have high-priority research status.
RESPONSE
Ecosystem
Process Rates
and Storage

Community
Structure







Population
Structure



Pathology

NEAR-COASTAL
• Dissolved Oxygen (A.1)
• Biological Sediment
Mixing Depth (A.3)
• Extent/Density: Submerged
Aquatic Vegetation (A.4)
• Benthlc Abundance/
Biomass/ Species
Composition (A.2)
• Fish Abundance/Species
Composition (A.5)







• Presence of Large
Indigenous Bivalves (A.6)



• Gross Pathology: Fish (A.7)

INLAND
SURFACE WATERS
• Lake Trophic Status (B.1)



• Fish Index of Biotlc
Integrity (B.2)
• Macroinvertebrate
Assemblage (B.3)
- Diatom Assemblage In
Lake Sediments (B.4)
• Relative Abundance of
Semlaquatlc Vertebrates
(B.5)





• Top Carnivore Index:
Fish (B.6)



• External Pathology: Fish
(B.7)
WETLANDS
• Organic Matter/ Sediment
Accretion (C.1)
• Wetland Extent/ Type
Diversity (C.2)

• Abundance/ Diversity/
Species Composition:
Vegetation (C.3)
• Relative Abundance:
Animals (G1.1)
• Leaf Area/ Solar
Transmittance/ Greenness
(C.4)
• Macroinvertebrate
Abundance/ Biomass/
Species Composition
(C.5)
• Soil/Aquatic Microbial
Community Structure
(C.6)
• Demographics: Animals
(G1.2)
• Morphological
Asymmetry: Animals
(G1.3)


                                                  11-3

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Table 11-1.    (Continued)
RESPONSE
Ecosystem
Process Rates
and Storage



Community
Structure






Population
Structure







Pathology


FORESTS
• Nitrogen Export (D.3)
• Litter Dynamics (D.4)
• Microbial Biomass/
Respiration in Soils (D.5)



• Relative Abundance:
Animals (G1.1)
• Abundance/Species
Composition of Understory
Vegetation (C.3)



• Tree Growth Efficiency
(D.1)
• Demographics: Animals
(G1.2)
• Morphological Asymmetry:
Animals (G1.3)



• Visual Symptoms of
Foliar Damage: Trees (0.2)

ARID LANDS
• Vegetation Biomass (E.1)
• Riparian Extent (E.2)
• Energy Balance (E.3)
• Water Balance (E.4)
• Soil Erosion (E.5)
• Charcoal Record (E.6)

• Species Composition/
Ecotone Location of
Vegetation (E.7)
• Relative Abundance:
Animals (G1.1)
• Abundance/Species
Composition of Lichens/
Cryptogam ic Crusts (E.8)
• Dendrochronology: Trees
and Shrubs (E.8)
• Pollen Record (E.9)
• Woodrat Midden Record
(E.10)
• Demographics: Animals
(G1.2)
• Morphological Asymmetry:
Animals (G1. 3)



AGROECOSYSTEMS
• Nutrient Budgets (F.1)
• Soil Erosion (F.2)
• Microbial Biomass
in Soils (F.3)
• Land Use/ Extent of
Noncrop Vegetation
(F.4)
• Relative Abundance:
Animals (G1.1)






• Crop Yield (F.5)
• Livestock Production
(F.6)
• Demographics: Animals
(G1.2)
• Morphological Asymmetry:
Animals (G1.3)


• visual Symptoms of
Foliar Damage: Crops
(F.7)
                                              11-4

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Table 11-1.    (Continued)
EXPOSURE/
HABITAT
Biomarkers
Pathogens
Bioassays
Tissue
Concentrations
Ambient
Concentrations
Exotics-GEOs
Habitat
Landscape
NEAR-COASTAL
• DNA Alteration (G2.1 - G2.3)
• Cholinesterase Levels (G2.4)
• Metabolites of Xenobiotic
Chemicals (G2.5)
• Porphyrin Accumulation
(G2.6)
• Histopathologic Alterations
(G2.7)
• Macrophage Phagocytotic
Activity (G2.8)
• Blood Chemistry (G2.9)
• Cytochrome P-450
Monooxygenase System
(G2.10)
• Enzyme-Altered Foci (G2.11)

• Acute Sediment Toxiclty
(A.8)
• Water Column Toxicity (A.1 1)
• Chemical Contaminants in
Fish/ Shellfish (A.1 2)
• Chemical Contaminants
In Sediments (A.9)
• Water Clarity (A.1 0)
• Dissolved Oxygen (A.1 3)

• Extent/ Density of
Submerged Aquatic
Vegetation (A.4)

INLAND
SURFACE WATERS
• DNA Alteration: (G2.1 - G2.3)
• Cholinesterase Levels (G2.4)
• Metabolites of Xenobiotic
Chemicals (G2.5)
• Porphyrin Accumulation
(G2.6)
• Histopathologic Alterations
(G2.7)
• Macrophage Phagocytotic
Activity (G2.8)
• Blood Chemistry (G2.9)
• Cytochrome P-450
Monooxygenase System
(G2.10)
• Enzyme-Altered Foci (G2.1 1)
• Water-Column Bacteria (B.12)
• Water-Column/
Sediment Toxiclty (B.8)
• Chemical Contaminants
In Fish (B.9)
• Routine Water Chemistry
(B.10)
• Heavy Metals/Man-made
Organics (Toxics) (B.13)

• Physical Habitat Quality
(B.11)

WETLANDS
• DNA Alteration: (G2.1 - G2.3)
• Cholinesterase Levels (G2.4)
• Metabolites of Xenobiotic
Chemicals (G2.5)
• Porphyrin Accumulation
(G2.6)
• Histopathologic Alterations
(G2.7)
• Macrophage Phagocytotic
Activity (G2.8)
• Blood Chemistry (G2.9)
• Cytochrome P-450
Monooxygenase System
(G2.10)
• Enzyme-Altered Foci (G2.11)

•Bioassays (C.10)
• Chemical Contaminants
in Tissues (C.11)
• Nutrients In Water/
Sediments (C.7)
• Chemical Contaminants
in Water/ Sediments
(C.8)

• Hydroperiod (C.9)
• Abundance/ Density of Key
Physical Features (G3.1)
• Linear Classification/
Physical Structure of Habitat
(G3.2)
• Habitat Proportions (Cover
Types) (G3.3)
• Patch Size/Perimeter to
Area Ratio (G3.4)
• Fractal Dimension (G3.5)
• Contagion/Habitat
Patchiness (G3.6)
• Gamma Index of Network
Connectivity (G3.7)
• Patton's Diversity Index (G3.8)
                                              11-5

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Table 11-1.    (Continued)
    EXPOSURE/
     HABITAT
    Biomarkers
       FORESTS
• Stable Isotopes (D.9)
• Carbohydrates/Secondary
 Chemicals: Trees (D.10)
• DNA Alteration (G2 1 - G2.3)
• Cholinesterase Levels (G2.4)
' Metabolites of Xenobiotic
 Chemicals (G2.5)
• Porphyrin Accum. (G2.6)
• Histopathologic Alter. (G2.7)
1 Macrophage Phagocytotic
 Activity (G2.8)
1 Blood Chemistry (G2.9)
1 Cytochrome P-450
 Monooxygenase System
 (G2.10)
• Enzyme-Altered Foci (G2 11)
      ARID LANDS
 • DNA Alie.'otion (b? 1  G23)
 • Cholinesterase Levels (G2.4)
 • Metabolites of Xenotjiotic
  Chernicc.is (G2 5)
 • Porphyrin Accumulation
  (G2.6)
 • Histopathoiogic Alt°iations
  (G2.7)
 • Macrophage Phagocytotic
  Activity (G2 8}
 • Blood Chemistry (G2.9)
 • Cytochrome P-450
  Monooxygenase System
  (G2 10)
 • Enzyme-Altered Foci (G2.11)
  AGROECOSYSTEMS
 • DNA Alteration (G2.1 - G2.3)
 • Cholinesterase Levels (G2.4)
 • Metabolites of Xenobiotic
  Chemicals (G2.5)
 • Porphyrin Accumulation
  (G2 6)
 • Histopathologic Alterations
  (G2.7)
 • Macrophage Phagocytotic
  Activity (G2.8)
 • Blood Chemistry (G2 9)
 • Cytochrome P-450
  Monooxygenase System
  (G2.10)
 • Enzyme-Altered Foci (G2.11)
    Pathogens
• Visual Symptoms of Foliar
 Damage: Trees (D.2)
                             • Visual Symptoms of Foliar
                              Damage: Crops (F.7)
                             • Agricultural Pest Density
                              (F.8)
   Bioassays
• Bioassays: Mosses and
 Lichens (D.11)
                             • Lichens/ Mosses/ Clover/
                              Earthworm Bioassays (F.9)
   Tissue
   Concentrations
• Nutrients In Tree Foliage
 (D.6)
• Chemical Contaminants
 In Tree Foliage (D.7)
•Foliar Chemistry (E.12)
   Ambient
   Concentrations
• Soil Productivity Index
 (D.8)
• Physlochemical Soil Factors
 (E.13)
* Chemical Contaminants in
 Wood (E.18)
• Quantity/Quality of
 Irrigation Waters (F.10)
• Soil Productivity Index (F.11)
   Exotics-GEOs


     Habitat
                            • Exotic Plants (E.14)
                            • Livestock Grazing (E.15)
• Abundance/Density of Key
 Physical Features (G3.1)
• Linear Classification and
 Physical Structure of Habitat
 (G3.2)
• Riparian Extent (E.2)
»Fire Regime (E.16)
• Mechanical Disturbance of
 Soils and Vegetation (E.17)
• Abundance/Density of Key
 Physical Features (G3.1)
• Linear Classification and
 Physical Structure of Habitat
 (G3.2)
• Land Use/Extent of Noncrop
 Vegetation (F.4)
> Abundance/Density of Key
 Physical Features (G3.1)
> Linear Classification and
 Physical Structure of Habitat
 (G3.2)
    Landscape
• Habitat Proportions (Cover
 Types) (G3.3)
• Patch Size/Perimeter to
 Area Ratio (G3.4)
> Fractal Dimension (G3.5)
' Contagion/Habitat
 Patchiness (G3.6)
• Gamma Index of Network
 Connectivity (G3.7)
• Patton's Diversity Index (G3.8)
1 Habitat Proportions (Cover
 Types) (G3.3)
• Patch Size/Perimeter to
 Area Ratio (G3.4)
• Fractal Dlmsnston (G3.5)
> Contagion/Habitat
 Patchiness (G3.6)
• Gamma Index of Network
 Connectivity (G3.7)
• Patton's Diversity Index (G3.8)
• Habitat Proportions (Cover
 Types) (G3.3)
• Patch Size/Perimeter to
 Area Ratio (G3.4)
• Fractal Dimension (G3.5)
• Contagion/Habitat
 Patchiness (G3.6)
• Gamma Index of Network
 Connectivity (G3.7)
• Patton's Diversity Index (G3.8)
                                                     ! i  6

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Table 11-1.    (Continued)
ATMOSPHERIC
STBESSOBS

Chemical









Physical

Biological
ALL RESOURCE
CATEGORIES
• Ozone (H.1)
• Sulfur Dioxide (H.2)
• Nitric Acid (H.3)
• ionic Constituents
In Precipitation (H.4)
• Metals and Organics
(Toxins) (H.5)
• Free Radicals (H.6)
• Carbon Dioxide (H.7)
• Other Greenhouse
Gases (H.8)
« Ultraviolet Type B
Radiation (H.9)
• Airborne Particles (H.10)

                                              11-7

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APPENDIX A:  INDICATOR FACT SHEETS FOR

           NEAR-COASTAL WATERS
                   Authors
                 John Scott
  Science Applications  International Corporation
     U.S. Environmental  Protection Agency
       Environmental Research Laboratory
          Narragansett,  Rhode Island
                Fred Holland
              Steven Weisberg
              Versar Corporation
             Columbia, Maryland
              Steven Schimmel
     U.S. Environmental Protection Agency
      Environmental Research Laboratory
          Narragansett, Rhode Island

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A.1  INDICATOR:  Dissolved Oxygen

CATEGORY:  Response/ Ecosystem  Process Rates and Storage

STATUS:  High-Prioriry Research

APPLICATION:  The  dissolved  oxygen (DO)  indicator is designed  to provide data on estuarine ecosystem
processes  as they affect water quality and on the habitability of estuarine waters for marine life, including
economically important resources.   Because these data needs are different, this fact sheet addresses DO as
an indicator of near-coastal ecosystem processes, whereas fact sheet A.13 addresses DO as an exposure
indicator of anoxic conditions to marine life.  The assessment endpoint is extent of eutrophication because
wide fluctuations in DO often result from increased primary productivity due to nutrient enrichment and can
often  lead to hypoxia.  In a technical sense, DO  is a  prime  physical  indicator of estuarine ecosystem
processes  because (1)  DO  concentrations may reflect prior nutrient loading and (2) anaerobic conditions and
may lead  to  the  formation of  anaerobic sediments and/or  H2S (a highly toxic compound), which affects
biogeochemical cycling of essential elements (e.g., remineralization of toxicants).  Although causes of changes
in the concentration of DO are complex, the ability to make meaningful  measurements  is readily available.

INDEX PERIOD:   Water column profiles of  DO would  be measured from mid-June to September.  This
period was chosen because of the  elevated temperatures, low  riverine flow conditions,  and low  DO
conditions known to exist at this time.

MEASUREMENTS:   Water  column  profiles of DO would  be measured at least six times at  each location.
The method of choice is a  system that employs a polarographic oxygen probe (Gnaiger 1983) manufactured
by Sea Bird Electronics, Inc., that utilizes a  plastic membrane  on the sensor and is  equipped  with a data
logger.  Calibration is completed by Winkler titration,  and air  calibration checks  are  used as a backup
method.   The capital  cost for the conductivity, temperature, and DO unit is approximately $30,000.  Two
mid-level  field technicians  can calibrate, deploy, and  download  data in  about  1  h.   For six deployments,
about 12 person-hours are required.  The recommended interannual sampling frequency would be every year.

It is difficult to assess all aspects of uncertainty that can influence measurement error; for example, all relevant
sensors (pressure transducer, conductivity cell, and thermistor) must provide usable data.  A field calibration
for accuracy,  precision, and reliability would be required.  The computed  resolution of the Sea  Bird system
is 0.01 ppm,  and accuracy  is stated  by the manufacturer to be ±0.2 ppm.  DO saturation estimates include
uncertainties in the thermistor and conductivity sensors.

VARIABILITY:   The spatial  variability within  an estuarine sampling unit is expected to yield coefficients of
variation of approximately 100%.  The expected temporal variability of DO during the index period would
produce a range from 0 to 7-10 ppm.

PRIMARY  PROBLEMS:  The question of representativeness of the data is of concern.  The index period  is
subject to  several  processes that may cause the low oxygen "event" to be  missed in one or more estuaries.
It  is expected that the method of  single point measurements that are spread  out over the index period
would identify sites consistently experiencing low DO.  The EMAP Near-Coastal demonstration project during
summer 1990 will  provide  critical information regarding the suitability of  the sampling design.

REFERENCES:

Gnaiger, E.   1983. In situ measurement of  oxygen  profiles in  lakes:  Microstratifications, oscillations,  and
the limits  of comparison with  chemical  methods.  Pages 245-264.  In:   E. Gnaiger and H. Forstner, eds.
Polarographic Oxygen Sensors, Springer-Verlag, New York.
                                                A-1

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A.2  INDICATOR:  Benthic Abundance, Biomass, and Species Composition

CATEGORY:   Response/ Community Structure

STATUS:  High-Priority Research

APPLICATION:  Estuarine benthic organisms are preferred prey of many fish  and crabs, and as such, form
key intermediate linkages between higher  and lower trophic levels.  Their burrowing and feeding activities
have important effects on oxygen,  nutrient,  carbon, and mineral cycling, including the sequestering and cycling
of contaminants. Most benthic organisms are immobile and cannot avoid changes in environmental conditions
that occur at a site.  Immobility makes benthic assemblages relatively easy to sample and the data that are
collected straightforward to interpret. The  responses of benthic organisms to anthropogenic stress and natural
environmental changes are relatively well  understood and include  not only changes in  population size and
diversity but also changes in growth rate, age-class composition, and recruitment success. As a result, benthic
population and community characteristics including abundance, biomass, and species composition are sensitive
indicators of contaminant and dissolved oxygen stress and serve as good integrators of estuarine environmental
quality.

INDEX PERIOD:   Benthic  abundance and species composition data would be collected  during summer
(mid-June to September), the period when  benthic exposure to stressful, low dissolved oxygen concentrations
associated with eutrophication is  most likely to occur.  In addition, the effects of contaminant exposure are
likely to be most severe at the high temperatures that occur during this time period.  Major recruitment and
mortality for most dominant species should occur prior to the designated sampling period, and most benthic
populations  should also be  relatively stable during this  period.

MEASUREMENTS: Three 200-cm2 box cores  of bottom  sediments would be collected at each station and
sieved through a 0.5-mm screen.  All taxa collected would be identified and counted to the lowest taxonomic
level  practical. The  biomass of regionally  dominant species would be estimated as wet biomass.  Methods
would generally follow those of Holme and Mclntyre (1971) and Holland et al. (1987).  The estimated  capital
equipment cost for the grab sampler and the sieves is $2000.  Three mid-level field technicians can deploy
the grab three times and sieve and package the samples in about 1  h.  The  cost of processing all samples
from each station is $400, and the measurement error is estimated at 10-20%  The recommended interannual
sampling frequency would  be every year.

VARIABILITY: The major sources of variation in benthic population  and community characteristics  include
fluctuations associated with variation in sediment characteristics and salinity. Samples would be collected  for
all  major salinity strata.  The influence of  variation in sediment  characteristics on benthic  abundance would
be accounted for by making appropriate  adjustments to the  benthic data based on the  data  collected  on
sediment characteristics.  The spatial variability of benthic diversity and biomass estimates within estuarine
sampling units are expected to have a coefficient of variation (CV) <50%, after  normalization  for sediment
characteristics. Temporal  variability  during the index period is  expected  to be  minimal (CV <  30%).

PRIMARY PROBLEMS:  The identification of all taxa  to the species level would be  time-consuming and
expensive.  The  required  level  of taxonomic identification would  be  evaluated as part of the Virginian
Province Demonstration Project in summer 1990.  The sample  design assumes that the seasonal periodicity
for dominant  populations  would  be similar throughout the study region and  that variation in benthic stock
size and community  characteristics due to variation in sediment characteristics and salinity can be quantified
by using conventional  parametric  statistical  methods  (e.g., regression  analysis).  The validity of these
assumptions would also be  tested in the demonstration project.  Results of evaluating available data suggest
they are reasonable  assumptions.
                                                 A-2

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REFERENCES:

Holland, A.F., A.T. Shaughnessy, and M.H. Hiegel.  1987.  Long-term variation in mesohaline Chesapeake
Bay:  Spatial and temporal patterns.  Estuaries 10:227-245.

Holme, N.A., and A.D. Mclntyre.  1971.   Methods for Study of  Marine Benthos.   Blackwell Scientific
Publications., Oxford, England.
                                               A-3

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A.3  INDICATOR:  Biological Sediment Mixing Depth

CATEGORY:   Response/ Ecosystem Process Rates and Storage

STATUS:  Research

APPLICATION:   The proposed benthic  indicators  were selected  to  allow  the assessment of the benthic
condition.  The condition  of the benthic  environment is  important  because the benthos are (1) a food
resource for  important  finfisheries,  (2)  consumers  of  a  significant  proportion  of  primary production,
(3) sediment processors capable of controlling nutrient and contaminant sediment fluxes, (4) sensitive indicators
of contaminant- and eutrophication-induced hypoxic acute stresses, (5) determinants of habitat structure, and
(6) indicators of cumulative anthropogenic stress.  Biological mixing depth directly relates to  the ability of the
benthos to distribute and oxidize sediments  and is generally negatively correlated with both physical and
anthropogenically induced disturbance.  The  biological mixing depth in  fine-grained sediments indicates the
types, sizes, and  activities of the resident fauna.  Chemical  contamination  of  the sediments  often inhibits
burrowing activities of "conveyer-belt" forms, thereby inhibiting natural sediment processing.  More resistant,
short-lived  species are able to  maintain a shallow mixed zone near the sediment-water interface.

Under enrichment conditions, resulting from proximity to sewage outfalls or detrital fallout from algal blooms,
increased demands of the sediment for oxygen can exclude  both  long-lived and short-lived species, which
will cause a depression of the mixing depth, migration of the oxic-anoxic sediment boundary (redox potential
discontinuity) toward  the sediment-water interface,  and the scavenging of oxygen from near-bottom waters.

INDEX  PERIOD:   The  sampling period  should  occur when  biological  activity is at  a maximum, which is
usually at peak summer temperatures.  Recruitment to  the benthic community tends  to occur before and
during this  index  period, which is also the most likely time frame for hypoxic events.  Stress effects occurring
at this time would likely have significant  effects on  the structure of the benthos through adult mortality and
by altering normal recruitment patterns.

MEASUREMENTS:  Biological mixing depth data are collected with a  benthic interface camera  (Rhoads and
Germane 1982, 1986),  which photographs the sediment-water interface in the vertical.  The photograph is
then  processed by image analysis to quantify mixing depth  and  grain  size.  Presence and  abundance of
surface tube structures, boundary roughness, penetration depth, and presence of feeding voids and methane
gas can  also be detected by this analysis.  The ease  of sampling allows this method to be used for large-scale
characterization and mapping.  Measurement error  in the mixing depth estimation is typically 0.1 to 0.3 cm,
and changes  of 0.5 cm  have  been shown to be statistically  significant.  The capital cost for  the  initial design
and lease of the  lightweight  camera is $30,000.  Sampling requires two mid-level technicians for 0.5 h at
each station within an estuarine sampling unit, and image processing can  be accomplished for $50 per image.

A natural summer-winter cycle occurs in the vertical extent of  the mixing depth that is related to temperature
and driven by biological activity in the benthos.  Therefore, these measurements should be made with the
same  interannual frequency as those for benthic  communities.

VARIABILITY:  Because  the  mixing depth is related to the  composition,  abundance, and  activity of the
infauna, the same factors controlling regional variability of these indicators are operable.  The spatial variability
of mixing depths within  an estuarine sampling  unit is expected  to produce  coefficients of  variation (CV) in
the range of 50  to 100%.  The expected  temporal variability of  mixing depth during the index  period is
also expected to  be of the same magnitude,  that is, CV < 30%.

PRIMARY PROBLEMS:  The strengths of this indicator include ease of  sampling, rapid data turnaround (<30
days), and  a  functional approach to benthic processes.  Its primary weakness is that the sediment-organism
process paradigm  has  not  been  fully  tested  in  low-saline  and  freshwater habitats.    For immediate

                                                 A-4

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implementation in EMAP, a lightweight version (<80 kg,  or  200  Ib)  of the  camera would have to  be
designed, fabricated, and  tested; the design, materials, and vendors  have  been  identified.

REFERENCES:

Rhoads, D.C., and J.D. Germano.  1982.   Characterization of organism-sediment relations using sediment
profile imaging: An efficient method of remote ecological monitoring of the seafloor (REMOTS system).  Mar.
Ecol. Prog. Ser. 8:115-128.

Rhoads, D.C., and J.D. Germano.  1986.  Interpreting long-term  changes in benthic community structure:
A new protocol.  Hydrobiologia 142:291-308
                                               A-5

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A.4  INDICATOR:  Extent and  Density of Submerged Aquatic Vegetation

CATEGORY:    Response/ Ecosystem Process Rates and Storage
               Exposure and Habitat/  Habitat

STATUS:  Research

APPLICATION: Submerged aquatic vegetation (SAV) beds provide important spawning and  nursery habitats
for fish and crabs by providing cover, protection, and a food source.  SAV also reduces currents and baffles
waves, which  creates a depositional environment and improves  water quality.  The root  systems of SAV
stabilize sediments.  Changes in environmental conditions associated with  eutrophication and increases in
suspended sediment concentrations adversely affect SAV by reducing the amount of light that reaches
submerged stems and leaves.  SAV are also adversely affected by excessive use of herbicides.   A positive
correlation generally exists  between SAV acreage and the abundance of  commercially and recreationally
important fish  and crabs. SAV acreage  is  therefore a good indicator of estuarine water quality and "health."
Furthermore, the  recent increased understanding of environmental requirements for SAV has assisted in the
interpretation of SAV data. Scientists can better separate changes due to natural processes from changes due
to pollution stress.

INDEX PERIOD:  Sampling would be conducted in the August-September period of the water column when
peak  biomass  of SAV occurs.

MEASUREMENTS:  SAV bed outlines and density (areal cover classes) would be digitized from normal-color
aerial photography (conventional mapping camera).   Factors to be considered when scheduling overflights
include tidal stage, time of day, sun angle, wind, turbidity,  and atmospheric conditions. Digitized data would
be used to estimate SAV acreage and bed density within  sampling strata and within the region as a whole.
This indicator could be monitored in the Virginian Province (Cape  Hatteras to Cape Cod) for  $250K per year.
The recommended interannual sampling frequency would be five years.

VARIABILITY:  The expected spatial and temporal variability of SAV areal extent and bed density within an
estuarine sampling unit and during an index period,  respectively, were not estimated because sufficient data
are unavailable.

PRIMARY PROBLEMS:   Because  image  acquisition is  limited  by  season,  tidal  conditions,  atmospheric
conditions, and other requirements, acquiring the needed data would be difficult.


BIBLIOGRAPHY:

Orth, R., J. Simmons, J. Capelli,  V. Carter, L.  Hindman, S. Hodges, K.  Moore, and  N.  Ribicki.   1985.
Distribution of submerged aquatic vegetation in the Chesapeake Bay and  tributaries.  U.S. Environmental
Protection Agency/Virginia Institute of Marine Sciences Joint Publication, Annapolis, MD.

Stevenson, J.C.   1988.  Comparative ecology of submersed grass beds in freshwater, estuarine,  and marine
environments.  Limnol.  Oceanogr.  33:867-893.
                                                A-6

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A.5  INDICATOR:  Fish Abundance and Species Composition

CATEGORY:   Response/ Community Structure

STATUS:  Research

APPLICATION:  One of the principal means by which the public assesses  the  environmental  quality of
estuaries is by the quality and  abundance of the fish assemblage.  Although community  measures of the
estuarine fish  have  not yet  been well established, the concept is  based  on a simple pretext  There is  a
societal concern on the diversity of the fish community outright, as well as on  how that diversity contributes
to ecosystem sustainability.   Species assemblages reflect the cumulative impact of water quality parameters,
contaminant inputs, and  habitat conditions.  While abundance and species composition  may not be very
sensitive to small stresses, assemblages dominated by tolerant species such as  mummichogs  and carp,  or
locations where little or  no fish  are captured,  can be identified as subnominal.  Species assemblage
measurements have the advantage of adding only a trivial incremental cost for its measurement,  since trawling
is  already being accomplished  to gather fish for  contaminant and pathogen  analyses, and all community
measures can  be made in the field.

INDEX PERIOD:  Fish trawls will be conducted from June to September.  This is the time of year that many
marine  species of fish utilize the estuaries for nursery and spawning habitat; catch probabilities, as a result,
will be  enhanced.  This  also is the  time of year that the potential for stress effects due to  low  dissolved
oxygen  is greatest.

MEASUREMENTS:   Fish would  be collected with a 12-m  (40-ft) otter trawl deployed for 10 min against the
tide.  Trawling operations would be standardized to allow the comparison of catch data.  Three quantitative
collections would be conducted at each estuarine sampling unit at approximately one-month intervals during
the index period.  From  these  collections, all taxa would be enumerated, and lengths would  be  measured
for the  first 30 individuals  of each target taxa.   Capital cost  for the  otter trawl  is $1600.  The trawling
operation will  take three  mid-level field technicians 1.5 h each to deploy, sort, and enumerate the samples.
Data entry adds another 2 h. Trawling three times requires a total of 20 person-hours at each  station within
an estuarine sampling unit.  The recommended interannual sampling frequency  would be  every year.

VARIABILITY:   The expected temporal variability in species abundance of estuarine  fishes during  the index
period would  produce ranges that deviate  >100% from the mean value.  Many,  though not all,  estuarine
fish inhabit different parts of the estuary at different  periods during the summer.  Having information  to
composite from three different summer  sampling  periods should  reduce the annual temporal variability;
however, considerable small-scale temporal variability and spatial variability  would still remain. Because of
microhabitat differences that are not apparent from  the water surface, the expected spatial variability of
species assemblages within an estuarine sampling unit would produce a range that deviates >100% from the
mean value during each  period at a subset of the sampling stations.

PRIMARY PROBLEMS:  The analytical methods for this  indicator  still require some development.  Some
simple measurements, such as average biomass of fish caught in the three quantitative  trawling operations and
the proportion of tolerant  species,  are likely candidates;   however,  more refined methods are  being
developed.  An approach is being developed that allows definition of desirable  species expected at  a station,
based on a variety of physical characteristics such as salinity, temperature, bottom type, and  latitude. Catch
probabilities for a number of species have been calculated from existing trawling data.  The EMAP Near-
Coastal  Demonstration  Project  will  provide the data  necessary to  determine  expected species.  With this
approach, a nominal condition  would be defined  by  the  fraction of these expected  taxa that  are caught at
a station.   Another alternative  is an  integrative approach, such as the Index of  Biotic  Integrity  (IBI), that
combines numerous measures,  such  as species composition,  abundance, and  gross pathology, into a single
value.   The IBI has been successfully applied  in the freshwater  environment and  would  be used as an

                                                A-7

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indicator for inland  surface waters  (see indicator  B.2) but still needs to be adapted for application in the
estuarine environment.

BIBLIOGRAPHY:

Wedemyer,  G.A., D.J. McLeay, and  C.P.  Goodyear.   1984.  Assessing  the  tolerance of fish and fish
populations to environmental stress:  The problems and methods of monitoring.  Pages 163-195.  In:  V.W.
Cairns, P.V.  Hodson, and J.O.  Nriagu, eds.   Contaminant Effects on  Fisheries, John Wiley and Sons,  New
York.

Goodyear, C.P.  1983.  Measuring effects of contaminant stress on fish populations.  Pages 414-424.  In:
W.E. Bishop, R.D. Cardwell, and B.B.  Heidolph, eds.  Aquatic Toxicology and Hazard Assessment,  Sixth
Symposium.  ASTM  STP 802.  American Society for Testing  and Materials, Philadelphia.
                                                A-8

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A.6  INDICATOR:  Presence of Large Indigenous Bivalves

CATEGORY:  Response/ Populations

STATUS:  Research

APPLICATION: Estuarine waters serve as nursery grounds to many economically important species.  Observed
reductions in commercial and recreational catches of numerous taxa have been attributed to anthropogenic
inputs (U.S.  OTA 1987).  Shellfish productivity is a meaningful indicator because shellfish are economically
important as  a commercial  and recreational resource, and the  presence of shellfish suggests that water and
sediment quality conditions are acceptable for other activities such as swimming or fishing (Franz 1982).  The
use of indigenous filter-feeding  shellfish for contaminant measurements (see indicator A.12)  also provides a
good indication of site-specific water column exposures to chemical pollutants because these organisms are
relatively immobile.

INDEX PERIOD:  This indicator can be measured at any time  of the year because the abundance of these
organisms over a certain size range does  not fluctuate greatly.

MEASUREMENTS:   Infaunal bivalves would be collected  using a rocking chair dredge, then enumerated and
identified to the species level. The data would be presented as the fraction of estuarine habitat that supports
infaunal bivalve populations.  Up to 10 selected target species would be measured  to provide an  indication
of the relative age structure  of the population.  Selected species would also be archived for chemical analyses.
The rocking chair  dredges  cost $1500 apiece.  The dredge can be deployed  and the sample sorted and
enumerated by three mid-level  field technicians in approximately  1.5 h.   The recommended interannual
sampling frequency would  be three to five years.

VARIABILITY:  The temporal variability of a bivalve species' presence during the  index  period is expected
to be low (<30%) because these  are long-lived organisms.   The  spatial variability of a  bivalve  species'
presence within  an estuarine sampling unit was not estimated  because data of this nature is unavailable.

PRIMARY PROBLEMS:  Relatively few problems are associated with  data collection; use of the  sampling
apparatus is straightforward, and most data will  be generated in the field.

REFERENCES:

U.S. OTA.  1987.  Wastes in marine environments.  U.S. Office  of Technology  Assessment, Government
Printing Office, Washington, DC.

Franz,  D.R.  1982.  An  historical perspective on mollusks in  lower New York Harbor, with emphasis on
oysters.   Pages 187-197.   In:   C.F. Mayer,  ed.   Ecological Stress and the  New York Bight:  Science and
Management.  Estuarine Research Federation, Columbia,  SC.
                                                A-9

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A.7  INDICATOR:  Cross Pathology:  Fish

CATEGORY:   Response/ Pathology

STATUS:  Research

APPLICATION:  Gross  pathology of finfish from estuarine waters  provides an evaluation of finfish health,
which  reflects estuarine condition.  In contaminated aquatic environments, finfish are known to develop a
number of readily  observed pathologic  changes.   Some are easily noted through  external examination,
including  the occurrence of lesions such as fin  rot, skin ulcerations,  some skeletal  abnormalities,  and
epidermal growths.  Gross examination of internal organs can provide additional (and possibly more valuable)
information because many pathologic conditions, including various tumor types and certain parasitic infections,
may be easily observed through gross  internal examination  but provide no  external indications of their
presence.   In addition,  pathological condition in finfish  may provide an  indication of health risks to larger
population groups  (e.g., avian,  mammalian,  human)  that  utilize the  same systems  for  food sources  and
recreation. Therefore, this indicator should be of interest to all resource and regulatory agencies, as well as
the general public.

INDEX PERIOD:  An optimal sampling  period for  measuring  gross pathology  is during the warmer  months
of the year, because biological systems respond directly to temperature.  The disease conditions that are easily
detectable by gross examination, such as  fin  rot and  lymphocystis, are known to occur more often  during
the spring and summer months.

MEASUREMENTS:  Fish would  be collected three times  during the index period by using an otter trawl.  A
gross examination would be conducted  on at least three  target finfish species collected at each station by
using the  methods  of Amlacher (1970)  and  Couch (1985).  The examination  would consist of a thorough
external inspection  of the fins,  eyes, body surfaces, branchial  chamber, and buccal cavity.  Additionally, a
midline incision would  be made in the ventral abdomen  to observe  the visceral organs and to perform a
thorough gross internal examination.  Various  pathologic changes would be noted, and tissue would be taken
from any  suspect or unidentifiable internal or external lesions and fixed in 10% neutral buffered formalin.
The pathology indicator can be  measured from the  same  trawling collections used to measure fish abundance
and composition (see indicator A.5).  An  additional 1 to 2 h  of a mid-level field technician is required to
document the pathologies.  Verification of these observations by expert pathologists costs about $200 at each
station within an  estuarine sampling unit.

One potential source of measurement error is associated with  the individuals conducting gross examinations.
Because multiple teams will  be collecting data and assessing  fish pathology simultaneously,  it is imperative
that adequate training be provided to all teams prior to sampling in  order to ensure uniform characterization.
Because the  natural interannual variability is  unknown, annual  sampling would be required.

VARIABILITY:  The expected spatial and  temporal variabilities  in the  incidence of gross pathologies within
an estuarine  sampling unit and  during the index period,  respectively, are unknown and were not estimated.

PRIMARY PROBLEMS:   In  addition to  the  experience  factor  noted  above,  the representativeness  of the
collections is a concern.  The stability in the  response of this indicator would be evaluated  by comparing the
three separate collections to the combined data.  If large enough numbers of finfish are carefully examined
from each sampling unit,  some information concerning their health status can  be obtained.

REFERENCES:

Amlacher, E.  1970. Textbook of Fish  Diseases.   T.H.F.  Publications, New Jersey.
                                                A-10

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Sinderman, C.J.   1979.  Pollution-associated  diseases and  abnormalities of fish and shellfish.  Fish. Bull.
76:717-749.

Couch, J.A. 1985.  Prospective study of infectious and non-infectious diseases in oysters and fishes in three
Gulf of Mexico estuaries.  Dis. AquaL Org. 1:59-82.
                                                 A-11

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A.8  INDICATOR:  Acute Sediment Toxicity

CATEGORY:   Exposure-Habitat/ Bioassays

STATUS:  High-Priority Research

APPLICATION:  The response  indicators affected by  sediment toxicity  are  benthic  community structure,
community function, and population dynamics.  Benthic assemblages represent ecologically and economically
important resources  whose protection  is mandated by a variety of EPA regulations (e.g., Ocean  Dumping,
301 [h],  404[c]).  Also, degradation of  benthic  productivity adversely affects other segments of the marine
ecosystem, especially demersal fishes.   Benthic toxicity  results from the  bioaccumulation  of chemicals  in
sediments (e.g., metals, polychlorinated biphenyls, polycyclic aromatic hydrocarbons, chlorinated hydrocarbons).
Toxic sediments promote the  degradation or  complete elimination of sensitive benthic  species.  As the
sediments become increasingly toxic, the natural benthic community is replaced by a depauperate assemblage
of stress-tolerant species. The replacement of species often results in a reduction in diversity, loss of preferred
demersal fish prey (e.g., phoxocephalid and ampeliscid  amphipods), and appearance of opportunistic species
(e.g.,  Capitella capitata).

INDEX  PERIOD:  No critical sampling period for this indicator is known; therefore, sampling may occur  at
any time of the year.

MEASUREMENTS: The sediment toxicity indicator is based on laboratory bioassays of field-collected sediment.
Acute response criteria based typically  on 10-day bioassays for benthos are mortality, emergence from toxic
sediment, and behavioral effects (e.g.,  inability to rebury in clean sediments).  Several standard methods exist
for acute sediment toxicity tests (Swartz et al.  1985).   The American Society  for Testing  and  Materials is
currently developing standard guidelines for both marine and freshwater amphipod sediment bioassays. For
a typical experimental or survey design, these procedures specify the collection of 200 ml  of sediment from
each  of three to five replicate grab samples at each station.  The bioassays are conducted for 10 days under
static conditions at constant temperature and salinity. The amphipods Ampelisca abdita, Rhepoxynius abronius,
Rhepoxynius hudsoni, Eohaustorius  estuaries, Hyalella azteca, and Grandidierella japonica have been used  in
acute sediment  bioassays.  With five  replicates, the amphipod procedure can  detect statistically significant
increases  in mortality of about 20% in comparison with a control group.  Sediments for this indicator can
be collected  by using the same gear and crew as for  the benthic community measurements (see indicator
A.2).  An additional four grabs are required, which take two mid-level  field  technicians  45 to 60 min  to
collect,  composite, and  package for shipment.  The  cost of the toxicity  tests is $500-$750 per  sample,
depending on the species.  The expected spatial variability of sediment toxicity within  an estuarine sampling
unit would produce a range that deviates  >100% of the mean value (Long and Buchman 1989).

Because long-term transport and geochemical processes generally control sediment contamination, temporal
changes in sediment toxicity can be monitored at infrequent intervals.  The interannual sampling frequency
therefore would be  three to five years.

VARIABILITY:  Detection of temporal or spatial differences in toxicity will be a function of  the heterogeneity
of contaminant concentrations  in the collected  sediments and system-specific particle deposition  rates.  The
expected temporal variability of sediment toxicity throughout the  year would produce a range that  deviates
<10% of the mean  value.

PRIMARY PROBLEMS:   Acute sediment toxicity is not an  early-warning indicator of benthic degradation.
Toxicity usually  occurs only after substantial  chemical contamination of the seabed.  The development and
application of chronic and sublethal test methods with whole organisms are critical to  the identification  of
habitats experiencing sublethal impacts.
                                                A-12

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REFERENCES:

Long, E.R., and M.F. Buchman.  1989.  An evaluation of candidate measures of biological  effects for the
National Status and Trends Program.  Technical Memorandum NOS OMA 45. U.S. National Oceanographic
and Atmospheric Administration, Rockville, MD.

Swartz, R.C., W.A.  DeBen, J.K.P. Jones, J.O. Lamberson, and FA Cole.  1985. Phoxocephalid amphipod
bioassay for marine sediment.  Pages 152-175.  In:  R.D. Cardwell, R. Purdy, and R.C. Banner, eds.  ASTM
STP 865.  American Society for Testing and Materials,  Philadelphia,  PA.
                                              A-13

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A.9  INDICATOR:  Chemical Contaminants in Sediments

CATEGORY:  Exposure-Habitat/ Ambient Concentrations

STATUS:  High-Priority Research

APPLICATION: The endpoint of concern is the chemical contamination of the marine ecosystem, particularly
the potential ecological effects of those contaminants sequestered  in sediments.  Bottom sediments in some
industrialized and urban harbors have  become so contaminated that they represent a threat to both human
and ecological health.  Contamination, however, is not limited to these urban/industrial centers; pollutant
runoff from agricultural areas is also an important source of contaminant input to estuaries.  The geographic
extent  of contaminated  sediments and the ecological exposure to them  is  largely  unknown,  even  in
contaminated industrial areas.  This information is necessary in order to  assess the effectiveness of pollution
abatement programs.

INDEX PERIOD:  Sampling for chemical contaminants in sediments would occur in the summer index period
to be concurrent  with other  indicator  measurements.  There is no reason, however, that they could  not be
sampled  at any other time of the year.

MEASUREMENTS: Surface sediments (top 2 cm) would be collected and composited from several box cores
from each station within  an estuarine  sampling unit. The constituents to be measured would be the same
as those  measured  by the National Oceanic and Atmospheric Administration's National Status and  Trends
(NS&T) program.  These compounds include chlorinated pesticides,  polychlorinated biphenyls, polyaromatic
hydrocarbons,  major elements, and toxic  metals. Coprostanol and  Clostridium spores would be measured
as indicators of sewage loading.  Total organic carbon and grain  size would also be quantified.  Sediment
samples for this indicator can be collected by using the same gear and crew as for the benthic community
measurements (see  indicator A.2).  An additional two grabs are  required, which take two  mid-level field
technicians approximately 30 min to  collect, composite and package  for shipment.  The chemical analyses
can be conducted for about $1000 per sample.  As noted for sediment toxicity (see indicator A.8), long-term
transport and geochemical processes control sediment contamination, and a three- to five-year  interannual
sampling  frequency would be adequate to detect  significant  contaminant trends.  NS&T data would be
evaluated to confirm the appropriate sampling frequency.

The major source  of  measurement  variability  for  this indicator would  occur  in sample collection and
processing.  With adequate  standardization of methods, however, this  error should be  reduced such that
coefficients of variation (CV) are <10%.

VARIABILITY:  The signal-to-noise ratio, variance, and precision of these data can  best be determined through
analysis of data collected during the 1990  EMAP -  Near-Coastal demonstration  project.   As noted  above,
temporal variability during the index period is expected to be small (CV < 10%).  Spatial variability has been
examined at the  NS&T sites for four years of data  (U.S. NOAA 1988).  The average within-site coefficient
of variation  for organic contaminants  was between  57  and 75%, and for metals the CV  was  21 to 69%.
These data are normalized for grain size effects.

PRIMARY PROBLEMS: The  proposed analytes for the sediment chemistry indicator are well established and
described by  standard methods or documented programs (e.g., NS&T).  As an exposure indicator,  the
interpretation of these data will suffer from a lack of information  on the bioavailability of the contaminants
and subsequent ecological effects.
                                               A-14

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REFERENCE:

U.S. NOAA.  1988.  National Status and Trends Program for marine environmental quality.  A summary of
selected data on  chemical contaminants in  sediments collected during 1984, 1985,  1986,  and  1987.
Technical Memorandum NOS OMA 44.  National Oceanographic and Atmospheric Administration, Rockville,
MD.
                                             A-15

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A.10  INDICATOR:  Water Clarity

CATEGORY:  Exposure and Habitat/ Ambient Concentrations

STATUS:  High-Priority Research

APPLICATION:  Water clarity may be the one indicator that is perceived as most indicative of water quality
to the public because of the ease in making an instantaneous assessment of this parameter.  In the context
of EMAP, water clarity is used as an indicator of water quality conditions because decreased clarity (1)  may
indicate increased nutrient enrichment with consequent decreases in dissolved oxygen (DO) concentrations,
(2)  impairs light penetration in the water, which may affect the growth of submerged aquatic vegetation,
(3)  may signal nuisance algal blooms including those indirectly toxic to marine life and humans because  they
ingest fish products  tainted by  the algal  toxins, (4) may be correlated with fish  and shellfish kills, (5) is
accompanied by the loss of aesthetic appeal due to reduced visibility, which may affect recreational activities
in shallow waters (e.g., recreational crabbing by dip net, scuba diving).  Changes in water clarity may  also
be  due to industrial  effluent color and turbidity.

INDEX PERIOD:  Water clarity would be measured during the June-September sampling period for the same
reasons as those for  measuring DO (i.e., major algal  blooms such as dinoflagellates typically occur during this
period).

MEASUREMENTS:   Water clarity  would  be  quantified as  light  transmission  by  using  a  Sea  Tech
transmissometer with a  10-cm  path length.  This  instrument measures  the attenuation  of  light due to
absorption or scattering by particles in the water; it would be  lowered through  the water column to  obtain
a vertical profile  of water clarity.   The amount  of attenuation is a function of the concentration  and
characteristics of  suspended material in the water column; however, absolute estimates of suspended solids
concentrations would not be generated.  These data would be used only to provide  information on relative
light transmission.

In addition to transmissometry, a fluorometry profile would be generated at each station within an estuarine
sampling  unit to estimate  the concentration  of chlorophyll a.  The fluorometer emits light (425  nm)  that
excites chlorophyll, which  results in an emission at 685 nm.  The instrument measures this light emission.
Fluorometry  and  transmissometry together  provide  a picture of  water  clarity  and  the contribution of
phytoplankton  to turbidity.  Capital costs for the transmissometer and fluorometer are $1200, and  they can
be  deployed along with  the rest of the conductivity, temperature, and DO package  at no extra cost.

VARIABILITY:  The major source of variability associated with these measurements is the spatial and  temporal
variability of suspended solids, including phytoplankton. The expected spatial and temporal variabilities, within
an  estuarine sampling unit and during the index period respectively,  were not estimated because data at this
scale was unavailable.

PRIMARY PROBLEMS:  As noted above, the primary problem with these measurements would be the effects
of  changes in suspended paniculate type throughout the water column.  Different particle types and  their
resultant  light transmission would  not be measured consistently using the transmissometer, and  thus  only
relative transmission values would be reported.

BIBLIOGRAPHY:

Champ, MA., G.A. Gould, III, W.E.  Bozzo, S.G. Achleson, and K.C. Vierra.  1980.  Characterization of light
extinction  and attenuation in Chesapeake Bay, August 1977.  Pages  263-277.   In:  V.S.  Kennedy, ed.
Estuarine Perspectives.  Academic Press,  New York.
                                                A-16

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A.11  INDICATOR:  Water Column Toxicity

CATEGORY:  Exposure and Habitat/ Bioassays

STATUS:  Research

APPLICATION:  The water column toxicity indicator is designed to provide estimates of the acute and chronic
toxicity  of ambient near-coastal waters.  Water column toxicity in near-coastal waters is considered important
because (1) it is widely perceived  as  one of the most serious  near-coastal  impacts, (2) most marine and
estuarine  plants and animals  spend at least some portion  of their life  cycle as water  column organisms,
especially commercially important larval forms of vertebrates and invertebrates, (3) many of the commercial
marine  and estuarine species  and the  organisms that they eat spend most or all of their  life cycles in the
water column, (4) the public is very concerned about pollutant-induced mortalities and sublethal effects, and
(5) there is evidence that such  toxicity is widespread in industrialized estuaries. Anthropogenic inputs to many
rivers, coastal urban environments, and  the atmosphere are likely to eventually reach  coastal marine and
estuarine waters. In addition,  considerable quantities of toxic pollutants are discharged directly to the water
column through industrial and municipal effluent, urban runoff, etc.; therefore,  water column toxicity from
these and other sources may  be a  significant factor affecting ecological  condition.

INDEX  PERIOD:  The June to September  sampling period  is appropriate because water quality conditions
are expected to be most stable as a result of low runoff and riverine flow, which tend to reduce the pulsed
inputs of contaminants.  Because of the elevated summer temperature, biological responses  to adverse water
quality conditions should be more easily detectable.

MEASUREMENTS: The following species and sublethal response parameters have been selected for  the near-
coastal  toxicity indicator: red  alga, Champ/a parvu/a -  reproduction (U.S.  EPA  1988);  sea urchin, Arbacia
punctulata - reproductive success as percent fertilization (U.S. EPA  1988); bivalve  larval test, Mytilus edulis,
Crassostrea v/rg/n/ca - survival,  growth, fertilization (Chapman and Morgan 1983).  Standard methods for these
tests exist and have been routinely applied in water quality assessments.  The data generated from the toxicity
tests would consist of absolute values of survivors (all species), number of fertile eggs (sea  urchin), and normal
shell growth (bivalve  larval test).  Each test method has  a minimum criterion for response under control
conditions.  A standard reference seawater for the indicator should be  established and  compared with the
monitoring data.  The values generated in the  monitoring study would be compared with the  reference
responses, with  other values from other sample  locations, and with past values from the  same station for
trend analysis.   The water samples could be collected and packaged  for shipment by a mid-level field
technician in about 1 h.  The water grab sampler costs  $1000.  Samples can be tested  for about $900 per
station.

An estimate of intertest variability, in the form of coefficients of variation, has recently been completed after
a series of precision tests  for two  of the proposed tests:   C parvu/a (31%) and A.  punctulata (41%).  A
recommended interannual sampling frequency was not  suggested.

VARIABILITY:  The  variability associated with  these tests  on a regional  scale will be determined  after
completion of the demonstration project in 1990.  Temporal and spatial variability  is  related to  pollutant
discharges, runoff, and riverine discharges.  The  expected spatial variability of water column toxicity within
an estuarine sampling unit would produce  a range that deviates up to 100% of the mean  value.

PRIMARY PROBLEMS: The most important technical concern with the toxicity indicator is the determination
of the most appropriate method  for sample compositing which would provide a sample that is representative
of the surrounding ambient waters.  The main logistical  concern associated  with  the toxicity indicator  is the
timely delivery of the water samples to  one or more central testing facilities. The samples must be collected,

                                                A-17

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shipped at 40°C, and received for testing within a 24-h period.  Commercial overnight delivery is a logical
option that likely would be used.

REFERENCES:

U.S. EPA. 1988.  Short-term methods for estimating the chronic toxicity of effluent and receiving waters to
marine and estuarine organisms.  EPA 600/4-87/028.  U.S. Environmental  Protection Agency, Cincinnati, OH.

Chapman, P.M., and J.D. Morgan.  1983.  Sediment bioassays with oyster  larvae.  Bull. Environ. Contam.
Toxicol. 31:438-444.
                                                A-18

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A.12  INDICATOR:  Chemical Contaminants in Fish and Shellfish

CATEGORY:  Exposure and Habitat/ Tissue Concentrations

STATUS:  Research

APPLICATION:  Thousands of chemical  compounds,  which are either produced  or mobilized  by human
activities, are released either directly or indirectly to the  marine environment;  however, most  of these
compounds are not accumulated by biota. Therefore,  selected chemical measurements made on the tissues
of fish and shellfish would be used  to provide information on  the  extent of chemical contamination.  In
addition, detailed analysis of the data for certain less mobile species  may provide important information on
the sources  of  contamination.  Because  the analytical techniques available  for measurement of chemical
compounds are very sensitive, the results may provide an early warning for  emerging problems that could
affect the viability of  ecological systems.   Finally, the  presence  of chemical  contaminants  in commercially
important marine resources is of concern from a human health  perspective.

INDEX  PERIOD:  The summer sampling period is appropriate because the fish and shellfish would be
metabolically active during this period  and bioaccumulation rates should be at a maxima.  Fish  must be
collected for sampling during a  specific time of the year to  minimize seasonal differences.

MEASUREMENTS: Several  classes of compounds including polychlorinated biphenyls, chlorinated pesticides,
polycyclic aromatic hydrocarbons, and metals have  been shown  to accumulate  in  aquatic  organisms.
Representative compounds from each of these  chemical classes should be measured.   Those now measured
by the National Status and Trends (NS&T) Program of the National Oceanic and Atmospheric Administration
(NOAA) would  constitute the initial suite of selected compounds (U.S. NOAA 1987).  The  selection of this
chemical suite  allows for  comparisons with historical  data.  The results should  be reported  in  units of
nanogram- or microgram-per-gram wet weight,  dry weight, and lipid weight.  The fish and shellfish analyzed
for chemical contaminants could be collected by using  the same gear and personnel previously described for
fish and shellfish  indicators (see  indicators A.5, A.6) at no additional cost.   Chemical analyses  would  cost
$1000 per sample.  The recommended interannual sampling frequency would be  every year.

The  levels of uncertainty  associated with  the measurement of contaminants in tissues would be specific to
the compounds being  measured and the methods that are employed.  In general, however, the measurement
error is likely to be between  10 and 20%.

VARIABILITY:   Spatial variability  in  fish contaminant  concentrations within  an estuarine sampling unit is
expected to  be large (>100%) because of the subject's mobility; however, this should not be the case with
bivalves.  Temporal variability of indigenous bivalves  during the index period should yield coefficients of
variation that range from 0 to 50%; thus, a synoptic sample would provide reliable estimates of contamination
trends.  The variability associated with the regional sampling design would be quantified after the 1990 EMAP
Near-Coastal Demonstration Project.

PRIMARY PROBLEMS: The overriding technical problem is that  many different methods exist that could be
used for the measurement of chemical residues in tissues. Differences in analytical  methods affect detection
limits, information generated,  costs, and most importantly, the comparability  of data  generated  by  different
laboratories.  Even if the same method or similar methods are used by different laboratories, large differences
can occur in the data that are produced. A strong quality assurance program must be instituted to maximize
the comparability of results and should include intercalibration exercises and  the use  of standard reference
materials.  The NS&T program has developed an appropriate quality assurance  program, in which EMAP plans
to participate.
                                               A-19

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REFERENCES:
U.S. NOAA.  1987.  National status and trends program for marine environmental quality, progress report.
Technical  Memorandum  NOS  OMA 38.   National  Oceanographic and  Atmospheric Administration,
Washington,  DC.
                                             A-20

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A.13  INDICATOR:  Dissolved Oxygen

CATEGORY:  Exposure and Habitat/ Ambient Concentrations

STATUS:  Research

APPLICATION:  The dissolved oxygen (DO)  indicator is designed  to provide data on estuarine  ecosystem
processes  and  on the  habitability of estuarine  waters  for  marine life, including economically  important
resources.  Because these data needs are different, this fact sheet addresses DO as an exposure indicator of
anoxic condition as it affects estuarine biota, whereas fact sheet A.1 addresses DO as an indicator of estuarine
ecosystem processes. DO is a prime physical exposure indicator of water quality because (1) it is an essential
requirement for all aerobic  marine  biota, (2) anaerobic  conditions produce  H2S and  NH3 (highly toxic
compounds), and  (3)  insufficient DO concentrations may increase the  stress  from other  contaminants to
marine  organisms.  Although causes of  changes in  DO concentration  are  complex, the ability to make
meaningful measurements is readily available. As an exposure indicator that potentially impacts the benthic
and  fish response indicators,  information on the temporal  variability of DO .is necessary.  This research
indicator is  proposed  so that the optimum  deployment period for characterizing DO  exposure may be
established.  These data would also be used to assess the representativeness of the DO measurements that
indicate the result of ecosystem  processes.

INDEX  PERIOD:  DO would  be  measured continuously  over  a 60-75-day  period  between  June and
September at  up to 30 locations; this  is the sampling window when hypoxia  is  most  likely to occur.   In
addition, single point measurements would be completed at each station of  an estuarine sampling unit during
the time of  mooring deployment and retrievals, as well as at the weekly meter servicing.

MEASUREMENTS: The method of choice is a system that employs a polarographic oxygen probe utilizing
a plastic membrane on the sensor and is equipped with a data logger (Gnaiger 1983).  These systems can
be deployed in situ for periods of several days to one week.  They will be serviced at approximately one-
week intervals.  Calibration  is completed by Winkler titration with air calibration checks as a backup method.
The  advantage of the  multiday  in situ  measurement  is that it provides a more  time-integrated  exposure
assessment than discrete samples.  The capital costs for the deployed Hydrolab DO units are about  $5600
each.  The meters can be retrieved, serviced, calibrated, and redeployed by four mid-level field technicians
in 1  h.  During the index period, the meters will be serviced in this manner seven times.

It is difficult  to assess all aspects of uncertainty (e.g., all relevant sensors [pressure transducer, conductivity cell,
and thermistor] must provide  usable data).   A field calibration of accuracy, precision, and reliability is in
order regardless of the in situ system deployed.   The computed resolution  of the Hydrolab system is in  the
range of 0.01  ppm, and accuracy is stated by the manufacturer to  be ±0.2 ppm.   DO saturation  estimates
include uncertainties in the thermistor and conductivity sensors.

VARIABILITY:   The expected temporal  variability among DO records during the index  period  would yield
coefficients of variation >100%.  The expected spatial variability of DO records within an estuarine sampling
unit would produce a  range that deviates >100% from the  mean value.

PRIMARY PROBLEMS:   Instrument error (e.g., drift) can be a problem for continuous  in situ measurements,
but methods exist for reasonable quality checks on the measurement system.  The long deployment  period
would also make these units prone to vandalism and theft
                                               A-21

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REFERENCE:

Cnaiger, E.  1983.  In situ  measurement of oxygen  profiles in lakes:  Microstratifications, oscillations, and
the limits of  comparison  with chemical  methods.  Pages 245-264.   In:  E. Gnaiger  and H. Forstner, eds.
Polarographic Oxygen Sensors, Springer-Verlag, New  York.
                                                A-22

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      APPENDIX B:  INDICATOR FACT SHEETS FOR

               INLAND SURFACE WATERS
                         /Authors
                   Robert M. Hughes
                    Sandra A. Thiele
NSI Technology Services Corporation - Environmental  Sciences
        U.S. EPA Environmental Research Laboratory
                    Corvallis, Oregon

                    Dennis McMullen
      Technology Applications International Corporation
 U.S. EPA Environmental Monitoring and Support Laboratory
                    Cincinnati,  Ohio

                     Jim Lazorchak
 U.S. EPA Environmental Monitoring and Support Laboratory
                    Cincinnati,  Ohio

                     Steven  Paulsen
                   University of  Nevada
              Environmental Research Center
                   Las Vegas, Nevada

                     Sushil  S. Dixit
                   Queen's  University
        U.S. EPA Environmental Research Laboratory
                    Corvallis, Oregon

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                       Contributors

                       John Baker
                       Dave Peck
                       Jim Pollard
        Lockheed Environmental Systems Corporation
   U.S. EPA Environmental Monitoring Systems  Laboratory
                    Las Vegas, Nevada

                     David P. Larsen
        U.S.  EPA Environmental Research Laboratory
                     Corvallis,  Oregon

                       Steve Hedtke
        U.S.  EPA Environmental Research Laboratory
                    Duluth, Minnesota

                      Thorn Whittier
NSI Technology Services Corporation -  Environmental Sciences
        U.S.  EPA Environmental Research Laboratory
                     Corvallis,  Oregon

                      Phil Kaufmann
                   Utah State  University
                    Corvallis, Oregon

                      James R.  Karr
      Virginia Polytechnic Institute and State University
                    Blacksburg, Virginia

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B.1  INDICATOR:  Lake Trophic Status

CATEGORY:  Response/ Ecosystem Process Rates

STATUS:  High-Priority Research

APPLICATION:  Excessive enrichment of lakes with nutrients has been a significant problem for some time.
It is the most often cited problem that occurs in the Nation's lakes (U.S. EPA 1987). Early concerns centered
around the release of organic material from point sources.  The microbial degradation of these compounds
resulted in decreased oxygen concentrations in receiving waters.  Interest then shifted toward the role of point
and nonpoint sources of inorganic nutrients, particularly P and N (Bachmann 1980; Dillon and Rigler 1974;
Heiskary et al. 1987; Jones  and Bachmann 1976; Larsen et al. 1988; Omernik et al.  1988; Smith 1982).
The endpoints that are most often of public concern are (1) abundance of bloom-forming algae, which may
lead to taste, odor, and toxicity  problems, (2) formation of surface algal scums, (3) reduction of water clarity,
which  reduces aesthetic value,  and  (4) excessive growth of macrophytes, which reduces  swimmable and
beatable area. Several indices of lake condition based on trophic state have been developed  (Brezonik 1984;
Canfield  and Jones  1984; Carlson 1977; Porcella et al. 1980; Shapiro  1979;  Uttormark and Wall  1975;
Walker  1984; Walmsley 1984).  These trophic state indices (TSIs)  generally  utilize some combination of
measures of chlorophyll-a (indication  of  phytoplankton biomass),  total P or total N, and Secchi disk
transparency  (measure of water transparency).

INDEX PERIOD:  The appropriate sampling window for lake trophic status is  mid  to  late growing season
(generally July-August), when transparency is lowest, chlorophyll concentration  highest, and  recreational use
most intense.

MEASUREMENTS:   The  measurements  needed  for constructing  TSIs vary.    In  general,  TSIs  require
measurement of chlorophyll  a (phycocyanin, a blue-green algal  pigment can  also be measured on the same
sample to assess  nuisance algal abundance),  total P and total N (see indicator B.9, Routine Water Chemistry),
and  Secchi  disk  transparency or extinction coefficient  (as measures of water clarity).   It would  take a
technician <0.5  h to conduct the above measurements.  The expected  measurement error is 10%.

VARIABILITY: The expected spatial  variability of  trophic status measures within a lake sample unit  ranges
<10%  from  the  mean  value;  however,  the temporal  variability within the  index period  is likely have
coefficient of variation of 20-40% (Knowlton et al. 1984; Marshall and Peters  1989; Smeltzer et al. 1989).

PRIMARY PROBLEMS:  Measurement of a TSI  is relatively straightforward;  however, interpretation  of the
results  requires some care.  As with other indicators, the  expectation  of the index value or attainable quality
is  expected  to vary with region and lake  type.   Trophic condition  spans a continuum from  low-nutrient,
low-biomass oligotrophic waters to high-nutrient,  high-biomass  eutrophic waters; all  of these states  can  be
considered natural.    However,  enrichment  beyond what is regionally expected can become a problem.
Dystrophic lakes, lakes containing large amounts of humic substances, do not  fit well into most of the trophic
state indices.  Lakes  impacted by excessive macrophyte growth are also  not addressed well by this  trophic
index concept.  The major problem or limitation  of lake trophic indices as response indicators is that they
provide only an index of condition relative to this specific problem and are relatively insensitive to the wider
range of potential problems  that can occur.  Recently, attempts are  being made to develop indices  of lake
condition which  address a wider array of condition  issues (Davic and DeShon 1989).

REFERENCES:

Bachmann, R.W. 1980.  The role of agricultural sediments and chemicals in eutrophication. J. WaL  PolluL
Control Fed.  52:2425-2600.
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Brezonik, P.L  1984.  Trophic state indices:  Rationale for multivariate approaches.   Pages  441-445.  In:
Lake and Reservoir Management:  Proceedings of the Third Annual Conference of the North American Lake
Management Society, Knoxville, TN.  EPA 440/5-84/001.  U.S. Environmental Protection Agency, Washington,
DC.

Canfield, D.E. Jr., and J.R. Jones.   1984.   Assessing the trophic status of lakes with aquatic macrophytes.
Pages 446-451.  In:  Lake and Reservoir Management:   Proceedings of the Third Annual Conference of the
North American Lake Management Society, Knoxville, TN. EPA 440/5-84/001. U.S.  Environmental Protection
Agency, Washington, DC.

Carlson, R.E.  1977.  A trophic state index for lakes.  Limnol. Oceangr. 22:361-369.

Davic, R.D., and J.E. DeShon.  1989.   The Ohio lake condition index: A new multiparameter approach to
lake  classification.   Lake Reservoir Manage. 5:1-8.

Dillon, P.J., and F.H. Rigler.  1974.  The phosphorus-chlorophyll relationship in lakes.   Limnol. Oceanogr.
19:767-773.

Heiskary, S.A., C.B. Wilson, and  D.P.  Larsen.  1987.  Analysis of regional patterns in lake  water  quality:
Using ecoregions for lake management  in Minnesota.  Lake Reservoir  Manage. 3:337-344.

Jones, J.R., and R.W. Bachmann.   1976.  Prediction of  phosphorus and chlorophyll  levels in  lakes.  J. Wat.
Pollut. Control Fed. 48:2176-2182.

Knowlton, M.F., M.V. Hoyer, and J.R. Jones.  1984.  Sources of variability in phosphorus and chlorophyll
and  their effects on use of lake survey  data. Wat.  Resour. Bull.  20:397-407.

Larsen, D.P., D.R.  Dudley, and R.M. Hughes.   1988.   A regional  approach for assessing attainable surface
water quality:  An  Ohio case study. J.  Soil Wat. Conserv. 43:171-176.

Marshall, C.T., and R.H. Peters.  1989. General patterns in the seasonal development of chlorophyll a  for
temperate lakes. Limnol. Oceanogr. 34:856-867.

Omernik, J.M., D.P. Larsen, C.M.  Rohm, and  S.E.  Clarke.  1988.  Summer total phosphorus in lakes:   A
map of Minnesota, Wisconsin, and Michigan, USA.  Environ. Manage. 12:815-825.

Porcella, D.B., S.A. Peterson, and  D.P. Larsen. 1980.   Index to  evaluate lake restoration. J. Environ. Eng.
106:1151-1169.

Shapiro, J.  1979.   The current status of lake trophic indices:  A review.  Pages 53-99.  In:  T.E. Maloney,
ed.  Lake and reservoir classification systems.  EPA 600/3-79/074.  U.S.  Environmental Protection Agency,
Washington, DC.

Smeltzer, E., V. Garrison, and W.W. Walker, Jr. 1989.  Eleven years of lake eutrophication monitoring in
Vermont:   A critical evaluation.   Pages 53-62.  In: J. Taggart, ed.  Enhancing States' Lake Management
Programs.  North American Lake Management  Society.  Washington, DC.

Smith, V.H. 1982.  The nitrogen  and phosphorus dependence of algal biomass in lakes:  An empirical and
theoretical  analysis.   Limnol. Oceanogr. 27:1101-1112.

U.S. EPA.  1987.  National water  quality inventory:  1986 report to  Congress.  EPA 440/4-87/008. U.S.
Environmental Protection Agency,  Washington,  DC.

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Uttormark, P.O., and J.P. Wall.  1975.  Lake classification: A trophic characterization of Wisconsin  lakes.
EPA 600/3-75/003.  U.S. Environmental  Protection Agency, Washington, DC.

Walker, W.W., Jr.  1984.  Trophic state indices in reservoirs.  Pages 435-440.  In:  Lake and Reservoir
Management:  Proceedings of the Third Annual Conference of the North American Lake Management Society,
Knoxville,  Tennessee.  EPA 440/5-84/001.  U.S. Environmental Protection  Agency, Washington,  DC.

Walmsley, R.D.  1984.  A chlorophyll a  trophic states classification system  for South African impoundments.
J. Environ. Qual. 13:97-104.
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B.2  INDICATOR:  Fish  Index of Biotic Integrity

CATEGORY:   Response/  Community Structure

STATUS:  High-Priority Research

APPLICATION:  The Index of Biotic  Integrity (IBI)  is used  to  measure  health and complexity of a fish
assemblage relative to those of assemblages at a series of minimally  impacted sites of similar size from the
same ecological region (Fausch et al.  1984; Karr  et  al.  1986).   Values  range from 12  (very poor) to 60
(excellent).   It is  used  to  measure  the  sensitivity  of fish  to  a wide  range of anthropogenic  stressors
(eutrophication, acidification, contaminants, physical habitat degradation, flow modification, introduced species,
thermal pollution,  overharvest). The IBI metrics are used to  assess taxonomic richness, habitat and trophic
guilds, sensitive (canary) species, generally tolerant species, individual  condition, and abundance.  When the
fish assemblage is  assessed, results are  less variable and more rapidly obtained than those determined from
measurements of ecosystem and population processes. The 12 metrics that make  up the IBI differ in their
sensitivities to  stressors.    For example,  eutrophication  results  in  shifts from  insectivory  to  omnivory,
contaminants reduce species richness, and thermal pollution  increases the abundance of tolerant species.

The IBI was recommended in the EPA's bioassessment protocol  for inland waters (Plafkin et al.  1989) and
by  participants in  an EPA environmental  indicators workshop (U.S.  EPA  1989).   The IBI is applicable to
streams and  rivers throughout the Midwest and in  Oregon, California,  Colorado,  Appalachia, and New
England (Miller et al. 1988).  It  has been  used  in Canada  (Steedman 1988) and France (Oberdorff and
Hughes 1989); its acceptance is  further  supported by applications documented  in  more than  30 journal
publications.  Modifications for use in the  Southwest and Southeast and in lakes have not been completed.

INDEX  PERIOD:   Collections should occur in  summer, during periods  of relatively stable  flows  and
temperatures.  Storm flows and major migratory events should be avoided.  The actual calendar dates would
vary with  latitude  and  altitude.

MEASUREMENTS:   Field  measurements include number and age class of individuals for each fish species and
the incidence of disease  or anomalies.  All measurements are  made  by  experienced taxonomists or fish
biologists.  Sampling gear  (electrofishers, seines, toxicants, gill nets, traps) varies regionally and with water body
size and type. Sampling is most effective in nearshore areas, in a variety of habitats (pools, riffles,  bars, runs),
and in  areas of concealment  Collection effort varies from  1 to 5  workhours per  person  and  requires a
minimum  of two  persons (a competent taxonomist  and one or more technicians).  A small  number of
laboratory identifications  may be necessary for some species and for voucher (quality assurance)  specimens;
0.5-1.0 h  per resource sampling unit is needed to calculate the  index.  IBI metrics include (1) the number
of native fish species, darter/benthic species, sunfish/water column  species, sucker/long-lived species, intolerant
species, and  individuals and  (2) the percent of top  carnivore individuals,  tolerant  individuals, omnivorous
individuals, insectivorous  individuals, exotic/hybrid  individuals, and diseased individuals.

Field  data for each metric are scored  as 5, 3, or  1,  the score depending on whether they  are similar to,
deviate slightly from, or  deviate  greatly from reference  sites.   Hughes and Gammon (1987) used ±  for
marginal values, and Ohio EPA (1988) recommends scoring proportional metrics as 1 when the catch is much
lower than expected.  Some redundancy  is built into the IBI.  This  reduces measurement error (estimated
at 5%), but  may disguise  important changes, unless the staff  examine each metric for those changes.

VARIABILITY:  Previous research  (Karr et al.  1986; Hughes and  Gammon 1987; Ohio EPA 1988)  indicates
a difference of 4  IBI points,  or 10%,  is considered significant change  at  a site.  As long as the habitat is
stable, there should be very little difference in spatial or temporal replicates.  In a study of 105 regional
reference  sites  in Ohio (sampled two to three times  over two summers), 95% of the IBI values  were ±5.6
from  the true overall means of the sites; 75% of the  IBI values were ±3.3 from the site means. Standard

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errors were  <4 (Ohio EPA 1988).  Highly perturbed habitats demonstrate greater variability than minimally
impacted systems (Ohio EPA 1988; Karr et al. 1987), and the use of fewer metrics also increases variance
(Miller et al. 1988).

PRIMARY PROBLEMS:  For nationwide implementation, the IBI must be evaluated for use in the Southwest,
the Southeast,  and in lakes and reservoirs. Temporal and spatial variability should be further assessed through
examination of several statewide data sets.  Relationships between individual metrics and particular stressors
need  greater clarification before  the index is  used  as a diagnostic tool. A series of reference water bodies
(Hughes et al.  1986) in each ecoregion  of the United States must be sampled to determine what IBI values
are nominal (acceptable,  attainable, unimpaired,  indicative of health).  The IBI has been used  rarely in cold
water systems  (Miller et al. 1988; Steedman 1988) and is inappropriate in waters containing few fish species.

REFERENCES:

Fausch, K.D.,  J.R.  Karr, and P.R. Yant.  1984. Regional application of an index of biotic integrity based on
stream fish  communities.  Trans. Am. Fish. Soc.  113:39-55.

Hughes,  R.M., and J.R. Gammon.  1987. Longitudinal changes in fish assemblages and water quality  in the
Willamette  River, Oregon.  Trans. Am. Fish. Soc. 116:196-209.

Hughes,  R.M., D.P. Larsen, and J.M. Omernik.  1986.  Regional reference sites:  A method for assessing
stream potentials.  Environ. Manage. 10:629-635.

Karr, J.R., K.D. Fausch,  P.L. Angermeier, P.R. Yant, and I.J. Schlosser. 1986.  Assessing biological  integrity
in running  waters: A method  and its  rationale.   Special Publication 5.   Illinois Natural History Survey,
Urbana.

Karr,  J.R., P.R. Yant, K.D. Fausch, and  I.J. Schlosser.  1987.  Spatial and temporal variability of the Index
of Biotic  Integrity in three Midwestern streams.  Trans. Am.  Fish. Soc. 116:1-11.

Miller, D.L., and 13 others.  1988.  Regional applications of an Index of Biotic Integrity  for use in  water
resource  management.   Fisheries 13:12-20.

Oberdorff,  T., and R.M. Hughes.  1990. Modification and application of the Index of Biotic Integrity to the
Seine River, France.  Submitted  to Hydrobiologica.

Ohio EPA.   1988.  Biological criteria for the protection  of aquatic life.   Ohio  Environmental  Protection
Agency, Columbus.

Plafkin, J.L, M.T. Barbour, K.D. Porter, S.K.  Gross, and  R.M.  Hughes.   1989.   Rapid bioassessment
protocols for use in streams  and  rivers:  Benthic  macroinvertebrates and fish. EPA 444/4-89/001.  Office of
Water Regulations and Standards, U.S. Environmental Protection Agency, Washington, DC.

Steedman,  R.J. 1988.  Modification and assessment of an  index of biotic integrity to quantify stream quality
in southern Ontario.  Can. J. Fish. Aquat. Sci. 45:492-501.

U.S. EPA.  1989.  Results.   Workshop  on environmental indicators for the surface water program.  U.S.
Environmental  Protection Agency, Office of Water  Regulations and  Standards and  Office of Management
Systems and Evaluation,  Washington, DC. 25 pp.
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B.3  INDICATOR:  Macroinvertebrate Assemblage

CATEGORY:  Response/ Community Structure

STATUS:  High-Priority Research

APPLICATION:  The  macroinvertebrate community index can provide a measure of health and complexity
of variously impacted sites with similar habitat in the  same ecological region (Ohio EPA 1988; Plafkin et al.
1989).  The various indices available incorporate several structural and functional  metrics that differ in their
sensitivities  to physical  and  chemical  stressors.    Taken  together  these metrics can be sensitive to
eutrophication, acidification, contaminants,  physical  habitat  degradation, introduced species,  and  thermal
pollution.   Introduced  predators  typically impact  the  larger taxa,  acidification reduces  Ephemerop-
tera-Plecoptera-Trichoptera (EPT)  taxa, and eutrophication  increases  the  abundance of tolerant taxa.

Although macroinvertebrate assemblages are widely sampled and analyzed by state water pollution  biologists,
there is no widely accepted multimetric index for these assemblages  like  the Index of Biotic Integrity for fish
at this time; however, several have been  proposed recently,  and these are  being  evaluated.   Participants at
an EPA indicators workshop highly recommended the  benthic community as an ecological indicator (U.S. EPA
1989).

INDEX  PERIOD:   Nationwide, the most  appropriate  collection  period is summer.  Mid  to late summer is
preferred in temperate zones; however, in the southern states, winter can be an  excellent time.  Sampling
is most effective when food supplies,  flows, and temperatures are relatively stable and when later instars of
insects are  most abundant.  Because of the  high development rates of many macroinvertebrates, it is crucial
that collections within an ecoregion occur over a relatively short period and during stable periods.

MEASUREMENTS:  The raw data recorded are the number of individuals of each species. An experienced
taxonomist is  needed for making accurate identifications.  Standard taxonomic references would be  used to
identify taxa to the lowest practical  level.   Although  keys  are available that allow identification of most
macroinvertebrates to the generic level (e.g., Edmunds et al. 1976; Merrit and Cummins 1984;  Pennak 1989;
Wiggins 1977), species-level keys may need  to be  identified  regionally for some groups.

Different semiquantitative sampling devices are effective for different habitats.  Kick netting with a standard
level  of effort is  appropriate in gravel   riffles and  runs,  whereas  aquatic sweep nets can  be used  for
macrophytes and  snags.  Sampling of the  more stable  substrates in a  variety  of erosional habitats (e.g., riffles,
bars)  is recommended.  Collection effort is estimated at 1-2  h  for two  persons; laboratory sorting and
identifications  are expected to require 3-5  h per sample.  At $25/h, the laboratory work could  cost $75-
$125, or an average  of $100 per sample (Plafkin et  al. 1989).  Candidate  metrics to be evaluated include
counts  of total taxa,  mayfly  taxa, caddis fly  taxa, true  fly taxa,  and  EPT taxa;  percentages of  tolerant
individuals,  intolerant individuals, true fly/noninsect  individuals; percent contribution of dominant taxon
individuals,  scraper individuals, shredder  individuals,  collector individuals;  modified Hilsenhoff biotic index
(Hilsenoff 1982);  and chironomid abundances.

VARIABILITY:   It  is essential to limit natural variability by careful choice of  index  period, sampling method,
and habitat;  by  using  a consistent sampling  effort;  and  by analyzing multiple structural and  functional
characteristics  of  the  assemblage.  Despite such precautions, high variability is likely because of the high
spatial variability of the benthos, high development rates of the animals, and their varied microhabitat  require-
ments.  In a  study conducted in one run of a high-quality  creek, Ohio EPA (1988) found its invertebrate
community  index varied by only 4 points (6%) between the 25th and 75th percentiles; however, sampling
stations were standardized for physical habitat.  Fiske (1988), also standardizing for substrate, reports that only
metric values  10-50% different from the controls are considered significant  Combining metrics would reduce
this measurement variability, but sampling natural habitats rather than standard  substrate would increase it

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Natural spatial variability within the resource sampling unit or temporal variability during the index period
are expected to have a range that deviates 10-30% from the mean values of different  metrics (Bode and
Novak 1988; Courtemanch and Davies 1988).

PRIMARY PROBLEMS:  Before nationwide implementation, the series of candidate metrics must be rigorously
evaluated (singly and in combination) through use of available data sets from across the country.  Both spatial
variability and temporal variability need greater evaluation. Metric and index sensitivities  to various stressors
must be examined, and a series of ecoregional reference sites (Hughes et al. 1986) should be selected and
sampled.

REFERENCES:

Bode,  R.W., and MA. Novak. 1988. Proposed biological criteria for New York streams.  Pages 42-48.  In:
T.P. Simon, L.L. Hoist, and L.J. Shepard, eds.  Proceedings of the  First National Workshop on Biological
Criteria.  EPA 905/9-89/003.   U.S. Environmental  Protection Agency, Chicago,  IL.

Courtemanch, D.L, and  S.P. Davies.  1988.  Implementation of biological standards and criteria in  Maine's
water classification law.  Pages 4-9.  In:  T.P. Simon,  L.L. Hoist,  and L.J. Shepard, eds.   Proceedings of the
First National Workshop on Biological Criteria.  EPA 905/9-89/003.  U.S. Environmental  Protection Agency,
Chicago, IL.

Edmunds,  G.F.  Jr.,  S.L  Jensen,  and  L Berner.   1976.  The Mayflies of  North and Central America.
University of Minnesota Press, Minneapolis-St. Paul.  330 pp.

Fiske,  S.  1988. The use of  biosurvey data  in the regulation of permitted nonpoint dischargers in Vermont
streams.  Pages 67-74.  In: T.P. Simon, L.L. Hoist, and L.J. Shepard, eds. Proceedings of the First National
Workshop  on Biological Criteria.  EPA 905/9-89/003.   U.S. Environmental  Protection Agency, Chicago, IL.

Hilsenhoff, W.L. 1982.  Using a biotic index to evaluate water quality in streams.  Technical Bulletin 132.
Wisconsin  Department of Natural Resources,  Madison.

Hughes, R.M., D.P.  Larsen, and J.M. Omernik.   1986.  Regional reference sites:  A method for assessing
stream potentials.  Environ. Manage. 10:629-635.

Merrit, R.W., and  K.W. Cummins, eds.  1984.  An Introduction to the Aquatic Insects  of North America.
2nd edition.  Kendall/Hunt Publishing, Dubuque,  IA.

Ohio  EPA.   1988.   Biological criteria  for the protection  of  aquatic life.  Ohio Environmental  Protection
Agency, Columbus,  OH.

Pennak, R.W.  1989.  Fresh-Water  Invertebrates of the United States:  Protozoa to Mollusca.  3rd  edition.
John Wiley and  Sons, New York.   628 pp.

Plafkin, J.L, M.T. Barbour,  K.D.  Porter, S.K. Gross, and R.M. Hughes.   1989.  Rapid  bioassessment
protocols  for use in streams  and  rivers:  Benthic  macroinvertebrates  and fish.  EPA 444/4-89/001.   U.S.
Environmental Protection Agency,  Office  of Water Regulations and Standards, Washington, DC.

U.S. EPA.  1989.   Results.   Workshop on  environmental indicators for the surface water program.   U.S.
Environmental Protection Agency, Office  of Water  Regulations  and  Standards and Office of Management
Systems and  Evaluation, Washington, DC. 25 pp.
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Wiggins, G.B. 1977.  Larvae of the North American Caddisfly Genera. University of Toronto Press, Toronto.
401  pp.
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B.4  INDICATOR:  Relative Abundance of Semiaquatic Vertebrates

CATEGORY:  Response/ Community Structure

STATUS:  High-Priority Research

APPLICATION:  The relative abundance of semiaquatic vertebrates (amphibians, reptiles, birds, mammals) is
an important measure of environmental health to the  public.  Semiaquatic vertebrates are among the first
organisms to  suffer from physical  habitat modifications  and overharvesL  Birds appear to be the taxonomic
group which  can be  sampled most efficiently.  They  are  easily identified during the breeding season by
observation or  song.  Amphibians and mammals, on the other  hand, are much more difficult to sample,
requiring a labor-intensive trapping or search method.  Amphibians are still under consideration, however,
because of their sensitivity to environmental disturbance, as well as the concern that their populations are
declining.

INDEX  PERIOD:   The optimum sampling period  for  birds is during the June  breeding season, with
observations earlier or later in the month, depending upon latitude.  Because birdsong is an important  means
of identification, sampling any later than mid-July would be ineffective.  Field visits later in the season  would
also tend to record transient birds in addition  to residents.

MEASUREMENTS: A random, single visit sampling frame already exists in the U.S. Fish and Wildlife Service
Breeding Bird Survey (BBS).  Regional trends  in individual bird species have been traced  over  25  years.
Although there is no bird community index as  advanced as Karr's Index of Biotic  Integrity for fish  (see
indicator B.2),  much  work has been done with  bird communities and guilds (Short and Burnham  1982;
Verner 1984; Brooks  1989).  Brooks' response guilds use metrics based  on a  species' wetland dependency,
habitat specificity, and trophic level.  Elements of both the BBS methodology and  Brooks' response guilds
could be adapted to the EMAP framework.  Mammal presence, especially beaver, otter, muskrat, and  nutria,
can be assessed from sign (burrows, lodges, slides, tracks, feeding remains, scat).

The average time to complete a  25-mile BBS  auto transect is 4 h.  The time to walk or canoe a transect
along an inland waters sampling unit may require 2 to 4  h, depending  upon the size of the sampling unit
and length of transect Accurate surveys require a field  crew familiar with bird habits and song and mammal
sign.

Sampling error depends upon the differing skills of field  staff. For example, Faanes and Bystrak (1981)  report
a 20% sampling error for birds recorded in the BBS.  As  many as one third of the species detected by one
observer may be missed by another.   Second-year  participants reported 3-11%  more species than new
participants.  Birds may be misidentified by observers of any experience level (Robbins et al. 1986).  Robbins
et al.  stress  that this variability  of detection  does  not detract  from  the  validity of  the  BBS.    The
misidentifications and  overlooked  species balance out over the years.  The  survey is a random sample which
provides a regional picture of bird population trends.  Measurement error is reduced by having a well-trained
field  crew follow standardized  field procedures.

VARIABILITY:  The expected spatial variability of bird  populations within  resource sampling units  results from
their high mobility. Suitable habitat may not be used consistently from year to year, depending upon changes
in recruitment and survival.  Species also vary in their  site tenacity in locating nesting sites.

The expected temporal variability of local breeding bird  populations during the index  period is due to climate
fluctuations and conditions on the wintering grounds.  Poor weather conditions and wind or water noise may
also interfere with field observation.
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PRIMARY PROBLEMS:  Avian  response guilds  must be  constructed for various  regions of the country.
Response guild metrics should be tested in both the aggregated and disaggregated state.  The optimum index
period differs from the late summer sampling window of other response indicators (fish, trophic state).  The
nominal-subnominal boundary must be defined by selection and sampling of regional reference sites of each
ecosystem type.

REFERENCES:

Brooks, R.P.  1989. A methodology for biological monitoring of cumulative impacts on wetland, stream and
riparian components of watersheds.  In:  Proceedings of the International Symposium:  Wetlands and River
Corridor Management 5-9 July 1989, Charleston, SC.

Faanes, CA., and D. Bystrak.  1981.  The role of observer bias in the North American breeding bird survey.
Stud.  Avian Biol. 6:353-359.

Robbins, C.S., D. Bystrak, and P.M. Geissler.  1986.  The Breeding Bird Survey: Its first fifteen years, 1965-
 1979.  Resource Publication 157. U.S. Department of the  Interior,  Fish and Wildlife Service, Washington,
DC   196 pp.

Short, H.L, and  K.P. Burnham.  1982.  Technique for  structuring  wildlife guilds to evaluate impacts on
wildlife communities.  Special Scientific Report - Wildlife No. 244.  U.S. Department of the Interior, Fish and
Wildlife Service,  Washington, DC.  34 pp.

Verner, J.  1984. The guild  concept applied to management of bird populations.  Environ. Manage. 8:1-14.
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B.5  INDICATOR:  Diatom Assemblages in Lake Sediments

CATEGORY:   Response/ Community Structure (Retrospective)

STATUS:  High-Priority Research

APPLICATION:   Diatom assemblages  are  important  components of aquatic  ecosystems and  are  proven
indicators for evaluating present surface water quality and for making comparison with historical or reference
conditions. A number of taxa correlate strongly (positively and negatively) with various water quality variables
and can be used to reconstruct specific lakewater characteristics (Lowe 1974).  Species vary in their response
to eutrophication (Smol  et al. 1983; Agbeti  and Dickman 1989), acidification (Charles  et al.  1989),  and
recovery (Dixit et al.  1990),  metal contamination  (Dixit et al.  1988, 1990; Kingston  and  Birks 1990),
salinification (Fritz and  Battarbee 1986), thermal alterations (Smol 1988), and land  use changes (Tuchman et
al. 1984; Bradbury 1986). The use of diatoms in environmental assessment is not an experimental technique;
it has already been effectively used in  answering a  diverse set of environmental questions in  many regions
of North America.  The application of this  indicator is appropriate nationwide.

INDEX PERIOD:  Because surface sediments integrate diatoms temporally the samples can be collected at
any given time.  A surface sediment  assemblage  can  represent one  to several years of accumulation,
depending on the resolution attained in sectioning the cores.

MEASUREMENTS:  Sediment  cores should be generally taken from  the deep basin  for  surface sample or
stratigraphic analysis.   Sediment cores  offer  a unique  historical  perspective  of the  nominal or  reference
condition  of lakes. Sophisticated coring and sectioning techniques are available (e.g., Clew 1988) to provide
fine temporal  resolution and biological detail  of changes occurring between years  (Dixit et al. 1989, 1990).

Diatom  identification and enumeration are made by following standardized taxonomic procedures such as
those developed during the Paleoecological Investigation of Recent Lake Acidification (PIRLA) project (Camburn
et al. 1984-1986). Presence and absence of indicator  species, early warning indicators, species  richness and
diversity, relative composition  of species, and  relative  proportions of indicator assemblages are  determined
from the  data set.  Multivariate assessment of indicator response to  individual stressors  is made by using
Canonical Correspondence Analysis (ter Braak 1986).   A variable score can be easily developed, compared
with scores for reference sites, and synthesized into  an index that represents overall ecosystem  condition or
health.  The  weighted  averaging regression  and calibration technique (Birks et  al.  1990)  is used to develop
predictive or inference models for multiple environmental variables.  Sophisticated error estimation techniques
are also available for diatom inferences (Birks et al. 1990).  For a number of environmental variables, the
error associated with diatom models is known.  For example, in the PIRLA II research,  estimations of pH
(mean standard deviation [SD; ±0.28]), acid  neutralizing capacity (±12 yjequiv/L), monomeric aluminum (±1.3
/jmol/L), and dissolved organic  carbon (±55 /7mol/L)  are made with high accuracy (Sullivan et al. 1990). This
means that the diatom  assemblage can be used to check long-term  levels of exposure  variables that will
otherwise be measured only once  in 4  years.

A sediment  core  can be easily taken  and sectioned  by two  technicians in  2-3  h.   Sample  preparation,
taxonomic identification, and counting (500 valves or frustules) requires one technician or frustules and eight
expert hours per  sample.  At $8/h for a  laboratory technician and  $20/h for a  taxonomist, the cost per
sample analysis is approximately $170.  Although the number of samples required for analysis from each core
will depend  on the  type of information  sought, it would be necessary to analyze at least  three samples per
core to obtain historic  information.  The analysis of a  single  surface sediment sample from a lake sampling
unit would be sufficient for subsequent sampling runs.
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Core collection and sectioning (SD for pH =  <0.16; n = 3) and the sample preparation (SD for pH = 0.04;
n = 8) for microscopic study is relatively straightforward, and variation from this source is minimal and can
be quantified  (Charles et al. 1990).

VARIABILITY:  Because surface sediments integrate over time, the temporal variability of diatom assemblages
throughout the year is inconsequential.  The expected spatial variability of  an  assemblage within a lake
sampling unit is low. The standard deviation of pH predictions based on assemblages was only 0.21 (n=16).
A deep water core provides a reliable environmental assessment of an entire lake (Charles et al. 1990). The
protocol  produced in the PIRLA project provides  a good model for sampling design (Charles and Whitehead
1986).

PRIMARY PROBLEMS:  Although large ecological data sets  are available for lake environments in the eastern
United States and the northern Great Lakes states, such sets for other ecoregions of the United States would
be  necessary to further develop taxonomic capability for relatively unstudied regions  of the United States,
including preparation of photographic plates  and  reference slides. Taxonomic  research is important because
the biggest source of inaccuracy in diatom analysis comes from inconsistent or inaccurate identifications. The
approach used to promote correct and consistent taxonomy in PIRLA research (Camburn et al. 1984-1986;
Charles  and Whitehead  1986) can be easily adapted for a nationwide monitoring program.

REFERENCES:

Agbeti, M., and M. Dickman. 1989. Use of lake fossil diatom assemblages to determine historical changes
in trophic status. Can. J. Fish. AquaL Sci. 46:1013-1021.

Birks, H.J.B.,  J.M.  Line,  S. Juggins, A.C. Stevenson, and  C.J.F. ter Braak.   1990.  Diatoms  and pH
reconstructions.  Phil. Trans. R. Soc.  (London) B  327:263-278.

Bradbury,  J.P.  1986.   Effects  of forest fire  and other disturbances of wilderness lakes  in  northeastern
Minnesota II:  Paleolimnology.  Arch. Hydrobiol. 106:203- 217.

Camburn, K.E., J.C. Kingston, and D.F.  Charles.   1984-1986.   PIRLA  Diatom  Iconograph.   PIRLA
Unpublished Report Series No. 3.  Indiana University,  Bloomington.

Charles, D.F., and D.R.  Whitehead.   1986.   Paleoecological Investigation  of Recent Lake Acidification.
EA-4906.  Electric Power Research institute, Palo Alto, CA.

Charles, D.F., R.W. Battarbee, I. Ren berg,  H. van Dam, and J.P. Smol.  1989.  Paleoecological analysis of
lake acidification trends in North America and  Europe using diatoms and chrysophytes.  Pages 207-276. In:
S.A. Norton, S.E. Lindberg,  and A.L.  Page, eds.  Soils, Aquatic Processes, and Lake Acidification.  Springer-
Verlag, New York.

Charles, D.F., S.S. Dixit, J.P. Smol, and B.F. Cumming.  1990.  Variability in diatom  and chrysophyte
assemblages and inferred  pH: Paleolimnological studies of Big Moose Lake, New York. (USA). J. Paleolimnol.
In press.

Dixit, S.S., A.S. Dixit, and R.D. Evans.  1988.  Sedimentary diatom assemblages and their utility in computing
diatom-inferred  pH in Sudbury Ontario lakes.  Hydrobiologia 169:135-148.

Dixit, S.S., A.S. Dixit,  and J.P.  Smol.   1989.   Lake  acidification  recovery  can  be  monitored  using
chrysophycean microfossils.  Can. J.  Fish. AquaL Sci. 46:1309-1312.
                                                B-12

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Dixit, A.S.,  S.S. Dixit,  and  J.P. Smol.   1990.   Algal microfossils  provide high temporal  resolution  of
environmental change.  Environ. Sci. Tech.  Submitted.

Fritz, S.C., and R.W.  Battarbee.  19S6.  Sedimentary diatom assemblages in freshwater and saline  lakes of
the Northern Great Plains, North America:  Preliminary results. Pages 265-271.  In:  Proceedings of the 9th
International  Diatom Symposium, Otto Koeltz, Koenigstein.

Clew, J.R.  1988.  A  portable extruding device for close interval sectioning of unconsolidated core samples.
J. Paleolimnol. 1:235-239.

Kingston, J.C., and H.J.B. Birks.  1990.  Dissolved organic carbon reconstructions  from diatom assemblages
in PIRLA project lakes, North America.  Phil. Trans. R. Soc. Lond. B 327:279-288.

Lowe,  R.L.   1974.   Environmental  requirements and  pollution  tolerances of freshwater  diatoms.   EPA
670/4-74/005.  U.S. Environmental Protection Agency, Cincinnati, OH.

Smol, J.P.  1988.  Paleoclimate proxy data for freshwater arctic diatoms.  Verh. Int. Verein. Limnol. 23:837-
 844.

Smol, J.P., S.R.  Brown, and  R.N. McNeely.  1983.  Cultural disturbances and trophic history of a small
meromictic lake  from central  Canada.  Hydrobiologia  103:125-130

Sullivan, T.J., D.F. Charles, J.P. Smol, B.F. dimming, A.R. Selle, D.R. Thomas, J.A. Bernert, and S.S. Dixit.
1990.  Quantification  of changes in lakewater chemistry in  response to acidic deposition.   Nature.  In press.

ter Braak, C.J.F.  1986.  Canonical correspondence analysis:  A new eigenvector technique for multivariate
direct gradient analysis.  Ecology 1966:1667-1179.

Tuchman, M.L, E.F. Stoermer, and H.J.  Carney.   1984.  Effects of increased salinity on the diatom
assemblages in Fonda Lake, Michigan.  Hydrobiologia 109:179-188.
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B.6  INDICATOR:  Top  Carnivore Index:  Fish

CATEGORY:   Response

STATUS:  High-Priority Research

APPLICATION:  This indicator relates directly to the "fishable" endpoint because the top carnivores in most
inland aquatic ecosystems are sport fish, and  they provide  an important assessment of species commonly
valued by the public.  The relative abundance of juveniles,  adults, and large adults indicates the adequacy
of the site for rearing and reproduction and the potential impact of bioaccumulation.  These vertebrates are
sensitive to overharvest, acid deposition, contaminants, and physical habitat/flow modification. Partial diagnosis
is  possible through association  with  known or suspected stressors.   The application of this indicator is
appropriate nationwide.

INDEX  PERIOD:   Collections  should occur  in summer,  during periods  of  relatively  stable flows  and
temperatures. Storm flows and major migratory events should be avoided. The actual calendar dates would
vary with  latitude and  altitude.

MEASUREMENTS:  This  is essentially the top carnivore metric of the Index of Biotic Integrity (IBI) indicator
(B.2) and only requires indicating the size/age class of those  fishes. No additional time, equipment, staff, or
analyses are needed.   In  trout streams that support only one to three fish species, this may be the only fish
assemblage indicator measured.   For lakes, Casselman et al. (1985) estimated measurement error as having
coefficients of variation (CV) from 44 to 84%,  the  CV depending on species and gear type.

VARIABILITY: The variability of  this single metric is greater than that of  the IBI values because top carnivores
tend  toward  rareness  naturally  and because  the multimetric  IBI offers no redundancy.  For  lakes,  the
estimated  CVs for natural variability range from 24 to 27% (Casselman  et al. 1985).  Most of this variability
is  a result of temporal variability within  the  index period,  rather  than  spatial variability within the  lake
sampling unit

PRIMARY PROBLEMS: In fish assemblages composed of only one or two species (trout streams), population
estimates  from two  to three  catch or  removal sampling passes are  needed for accurate evaluation.  A
duplicate catch or removal pass is a standard fishery technique for streams, but lake estimates from gill netting
and electrofishing are  likely  to  be biased toward  larger  and littoral  individuals, respectively  (Hocutt and
Stauffer 1980).

REFERENCES:

Casselman,  J.M., MA  Henderson, and  T.  Schoner.    1985.   Fish  sampling techniques-natural  and
observational variability.   Draft report.  Ontario Ministry of Natural  Resources, Maple, Ontario.

Hocutt, C.H., and J.R. Stauffer, Jr.  1980.  Biological  Monitoring of Fish. D.C. Heath, Lexington, MA.
                                                 B-14

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B.7  INDICATOR:  External Pathology:  Fish

CATEGORY:  Response/ Pathology

STATUS:  High-Priority Research

APPLICATION: Stressors in general produce functional change in particular cells, tissues, and organs. If the
timing, duration, or intensity of these stressors is sufficient, structural changes occur.  Eventually, changes in
fish populations and the assemblage occur.  Typically, pathology increases as Index  of Biotic Integrity (IBI;
see indicator B.2)  scores decline and as the level of contaminants and pathogens rises.  In addition, the public
is sensitive to this concern because of the fear of cancer and birth defects.  Eutrophication affects gill and
fin structure, and the skin and skeleton are affected by contaminants.  This indicator is  appropriate worldwide
and is also an IBI metric.

INDEX PERIOD:  The optimal sampling window is summer or when species are most stressed.

MEASUREMENTS: The eyes, fins, scales, operculum, and gills of all fish are analyzed in the field. A checklist
is used by a trained technician; the time required for the examination is <0.5 h, depending on the number
of fish examined.   The expected measurement error is  <5%.

VARIABILITY:  According to previous use in the western and midwestern United States (Hughes and Gammon
1987; Ohio EPA 1988), the expected spatial variability of gross fish pathology within a  resource sampling unit
and its temporal variability during the index  period each produce ranges that deviate <10% from their mean
values, although  individual metrics have 0-65% variability among  observers (Coede 1988).   Measures of
external pathology have the lowest variability.

PRIMARY PROBLEMS: Regional reference sites are used to define normal or acceptable conditions;  therefore,
the reference sites must be selected and sampled  before  measurements are analyzed.

REFERENCES:

Goede,  R.  1988. Personal  communication.  Telephone conversation  with R.M. Hughes.   Utah Game and
Fish, Logan.

Hughes, R.M., and J.R. Gammon.  1987.  Longitudinal changes in fish assemblages and water quality in the
Willamette River,  Oregon.  Trans. Am.  Fish. Soc. 116:196-209.

Ohio EPA.  1988.   Biological  criteria  for the protection  of aquatic life.  Ohio Environmental  Protection
Agency,  Columbus.
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B.8  INDICATOR:  Water Column and Sediment Toxicity

CATEGORY:   Exposure and Habitat/ Bioassays

STATUS:  High-Priority Research

APPLICATION:  Toxicity indicators are designed to estimate regional incidence of acute or chronic toxicity
of water and  sediment in streams,  rivers, lakes, and  reservoirs.  Acute  toxicity is  used to determine  if
short-term exposure to ambient samples results in mortality.  Chronic toxicity is  a measure of longer term
exposures that may  result in reduced growth or  reproduction of test species. Water column toxicity is  a
critical indicator of water quality and has been given considerable emphasis  in the Water Quality Act of 1987
(Mount et al. 1984, 1986; Lazorchak  and Love 1985;  Burton et al. 1987a,b; Braidech et al. 1988; Lazorchak
et al. 1989; Willingham  et  al. 1989).   The Congress,  the  EPA, the States,  and  the  public have placed  a
significant emphasis  on achieving a nontoxic environment   Sediment toxicity is an important indicator of
sediment quality.  Both  short- and long-term accumulations of toxics have been  detected by this indicator
(Nebeker et al. 1984; Burton et al. 1987a, b).  When used with results of water column toxicity, sediment
toxicity will give an estimate of total  community exposure to  toxic substances.

The  results from  acute and chronic  invertebrate, fish,  and plant toxicity  tests determine if pollutants are
present in toxic amounts  in water bodies.  The proportion of waters exposed to toxics as compared to other
stressors can be estimated.

INDEX PERIOD:  Acute and chronic effects from all  sources can be measured during  late summer or early
autumn low-flow periods, when contaminant concentrations are likely to be at maxima.

MEASUREMENTS:   Standard tests for  water column toxicity, available  from EPA, are  being used   in the
National  Pollution Discharge Elimination System  (NPDES) permitting program to  measure effluent toxicity
(Peltier and Weber 1985; Weber  et al.  1989).  Also, standard tests available from the American Society for
Testing and Materials (ASTM) and from EPA are being used to evaluate sediment quality in the Great Lakes
and  large to  medium size rivers  (U.S.  EPA/Corps of Engineers  1977; Nebeker et al. 1984;  Nelson et al.
1989). These tests are useful  diagnostic tools to assess the success of nonpoint and point source strategies
for controlling exposure to toxic substances.

Samples are collected from streams or lakes and shipped to the  laboratory  or brought to a staging area such
as a mobile laboratory within  24  to  48 h of collection.  At the fixed or mobile  laboratory, ambient water
samples are allocated such  that fish  and invertebrate acute and chronic tests, as well as a 4-day algal or
duckweed growth test, can be performed.  Whole sediments and water extracts from sediments can be used
to test for chronic toxicity to fish,  planktonic invertebrates, algae, and duckweed. An acute sediment toxicity
test is performed on  a burrowing macroinvertebrate.  Testing both extracts and whole sediments provides the
advantage of distinguishing toxics contained in  the interstitial water from  those attached to the sediments
themselves.

Acute tests of fish and invertebrate planktonic and benthic species, performed with ambient water or sediment
extract, measure lethal effects  over a 2-day period (static renewed  daily) that are significantly  different from
effects measured in a composite reference sediment and water obtained from a series of minimally impacted
reference sites for each region. Performance controls (standard  laboratory  water and  sediment) are used as
a  quality  assurance  check against reference site water and  sediment in  case such  water is toxic to or
incompatible  with the test organism.  The estimated cost of  1  h per day plus test organisms is $100 per
sample.

Chronic tests of fish with ambient water and sediment extract are used to measure growth differences  during
a  7-day  period (static renewed daily)  in comparison to data  for a standard laboratory water  extract or

                                                B-16

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reference site water or sediment extract  The estimated cost of 0.5 h per day plus test organisms is $125
per sample.

Chronic tests of invertebrate species,  conducted with planktonic species and ambient water or sediment
extract, measure a reduction in reproduction over a 7-day period (static renewed daily) in comparison to data
for a standard water extract or reference site water or sediment extract The estimated cost of 0.5 h per day
and organisms is $125 per sample.

Chronic tests of invertebrate benthic species with whole sediment measure growth differences during a 7-
to 10-day exposure period in comparison to a standard water and reference sediment The estimated  cost
of 1  h per day and test organisms is $200-$275 per sample.

Algal chronic tests, conducted with ambient water or  sediment extract, measure the difference in algal growth
over a 4-day period in comparison to a standard water extract or reference site water or sediment extract
Chronic tests of rooted aquatic plant (duckweed) measure the differences in  chlorophyll a, biomass,  and
number of fronds over a 4-day period (static renewed daily) in comparison to  a standard water extract or
reference site water or sediment extract  The estimated cost of 0.5 h per day and tests organisms is $150
per sample.

Sampling time per site should be <1 h at a cost of  $25.  This could be significantly lower when integrated
with time used for  other  chemical or biological sampling.  The coefficient of variation for  these methods
ranges between 30  and 35%.

VARIABILITY:  It is expected that spatial variation in  contaminant concentrations  within a resource sampling
unit and temporal variation during the index period will far exceed the individual indicator test variation  (see
indicator B.10, Chemical Contaminants in Water Column).

PRIMARY PROBLEMS:  No major problems are anticipated.  Some  modification of existing ASTM and  EPA
methods for sediment testing may be  necessary to fit EMAP needs.  Only logistic and monetary  constraints
can be expected.

REFERENCES:

Braidech, T., D. Munro,  and J.M. Lazorchak.  1988.   Red River toxic profile study.  Prepared for the
International Red River  Pollution Board.  U.S.  Environmental Protection Agency and Environment  Canada.
U.S.  Environmental  Protection Agency, Region VIII Water  Division, Denver, CO.

Burton, G.A., A. Drotar, J.M. Lazorchak, and  L.L  Bahls.  1987a.  Relationship of microbial activity  and
Ceriodaphnia responses to mining impacts  on  the Clark Fork River, Montana. Arch. Environ. Contam. Toxicol.
16:523-530.

Burton, GA, Jr., J.M. Lazorchak, W.T. Waller, and C.R. Lanza.  1987b. Arsenic toxicity  changes in the
presence of sediment.  Bull. Environ. Contam.  Toxicol. 38:491-499.

Lazorchak, J.M., and J. Love.  1985. South Platte River, Colorado stream toxicity profile. U.S. Environmental
Protection Agency, Region VIII, Denver, CO.

Lazorchak, J.M., L.  Parrish, J. Wagner, and  W. Wuerthele.  1989.  Andover  (Dos Lomos/Ferris Haggerity)
Copper Mine and Haggerity Creek, Wyoming, toxicity evaluation (draft report). U.S. Environmental Protection
Agency, Region VIM, Denver, CO.
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Mount,  D.I.,  N.A. Thomas, T.J. Norberg, M.T. Barbour, T.H. Roush, and W.F. Brandes.  1984.  Effluent
and ambient toxicity testing and instream community response on the Ottawa River, Lima, Ohio.  EPA 600/3-
84/080.  U.S. Environmental  Protection  Agency,  Office of Water,  Permits Division, Washington, DC,  and
Environmental Research Laboratory, Duluth, MN.

Mount,  D.I.,  T.J. Norberg-King, and A.E. Steen.  1986.  Validity of effluent and ambient toxicity tests for
predicting  biological  impact,  Naugatuck  River,  Waterbury,  Connecticut   EPA  600/8-86/005.    U.S.
Environmental Protection Agency, Environmental Research Laboratory, Duluth, MN, and Office of Water,
Permits  Division, Washington, DC.

Nebeker, A.V., MA.  Cairns, J.W. Gakstatter, K.W. Malueg, C.S. Schuytema, and D.F. Krawczyk.  1984.
Biological methods for determining toxicity of contaminated freshwater sediments to invertebrates.  Environ.
Toxicol. Chem. 3:617-630.

Nelson, M.K., C.G. Ingersoll, and  F.J. Dwyer.  1989.  Proposed guide for conducting solid-phase sediment
toxicity  tests  with freshwater  invertebrates.   Draft 3 dated January  19.   American  Society  for Testing  and
Materials, Philadelphia, PA.

Peltier,  W.H.,  and  C.I. Weber, eds.   1985.   Methods for  measuring  the acute toxicity of  effluents to
freshwater and marine organisms.   3rd edition.  EPA 600/4-85/013.   U.S. Environmental Protection Agency,
Environmental Monitoring  Systems  Laboratory, Cincinnati, OH.

U.S. EPA/Corps of Engineers.  1977.   Ecological  evaluation  of proposed  discharge of dredge material in
waters.  U.S. Army Corps  of Engineers Waterways  Experiment Station, Vicksburg, MS.

Weber, C.I., W.H. Peltier, T.J.  Norberg-King,  W.B.  Horning, II, F.A.  Kessler, J.R. Menkedick, T.W.
Neiheisel, PA. Lewis, D.J. Klemm, Q.H. Pickering, E.L. Robinson, J.M.  Lazorchak, L.J. Wymer, and R.W.
Freyberg.  1989.  Short-term  methods for estimating the chronic toxicity of effluents and receiving waters to
freshwater organisms. 2nd edition.  EPA 600/4-89/001.  U.S. Environmental Protection Agency, Environmental
Monitoring Systems  Laboratory, Cincinnati, OH.

Willingham,  W.T., L. Parrish, J.M.  Lazorchak, G.J. Rodriguez, and J. Love.  1989. Toxicity profile of the
Upper Arkansas River, Colorado using Ceriodaphnia and fathead minnows. Draft report.  U.S. Environmental
Protection Agency, Region VIII, Denver,  CO.
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B.9  INDICATOR:  Chemical Contaminants in Fish

CATEGORY:  Exposure and Habitat/ Tissue Concentrations

STATUS:  High-Priority Research

APPLICATION:  Thousands of chemicals that are  produced or mobilized by  humans are released  into the
aquatic environment  Most of these compounds, however, are not accumulated by biota.  Only compounds
that are  relatively  stable  and  have  certain physical  or  chemical  properties  bioaccumulate  to  high
concentrations in aquatic organisms.  Examples of chemicals  known to bioaccumulate in organisms include
polychlorinated biphenyls (PCBs), chlorinated pesticides, poiycydic aromatic hydrocarbons, and metals. Many
of these occur in nearly nondetectable concentrations in water or  sediments,  yet when they accumulate in
top consumers, they may cause reproductive, carcinogenic, or teratogenic problems.  Therefore, measurement
of selected chemicals in fish  tissues is proposed to deteimine the extent of  contamination in the  nation's
inland  waters.

Examination of tissue concentrations for these compounds should allow for detection of contamination, even
when  concentrations  in  ambient waters are below detection  limits, and a comparison  between tissue
concentrations and existing toxicological data should provide  information regarding the potential risk to the
exposed organisms.   Examination of fish tissue concentrations can provide an  early warning for exposure to
animals higher in the food chain such  as aquatic  birds  and mammais.  Also, detection of contaminants in
important commercial and sport species would provide information regarding risks to human health  through
consumption  of contaminated organisms.  Finally, because the method involves the measurement of specific
chemicals, this indicator has high diagnostic value, allowing for  inferences to be  made regarding potential
sources of contamination.

INDEX PERIOD:  The selection of an index period for measuring fish tissue contamination would probably
be  based on  ease of sampling more  than any other factor.   Low-flow periods in  the summer months may
prove to be the  best time to collect  fish for this purpose.

MEASUREMENTS: The U.S.  Fish and Wildlife Service (FWS)  routinely analyzes 114 organic chemicals and
23  metals in  its national contaminant monitoring program (U.S. FWS 1989).  Methods for analysis of the 66
analytes in the National Bioaccumulation Study (NBS) have  been developed for  whole-body samples by the
EPA's Environmental  Research Laboratory in Duluth. Existing analytical sample collection, preservation, and
transport methods would be examined for their applicability to EMAP.  Costs for conducting chemical  analyses
for  the NBS have been estimated at $2000-52500 per fish sample.  In addition to these methods, methods
developed in  the U.S. National Oceanic and Atmospheric Administration (NOAA) Status and Trends Program
for  measuring tissue  contamination  in  estuarine  and   marine  organisms  (see indicator  A.13, Chemical
Contaminants in Fish and Shellfish) may prove useful in  the development of this indicator  for EMAP (Tetra
Tech 1986;  U.S. NOAA  1985,  1988).  For  more sensitive  analyses, EMAP investigators  may choose  to
examine specific types of  tissues, such as the liver, which is known to show higher contaminant levels than
whole-body samples for a number  of compounds.  The liver  is also often a target organ for many of these
compounds.

Where numeric criteria do not exist,  selection of values  of concern for each compound could be based on
U.S. Food and Drug Administration action levels or state "levels of concern."   An alternative method would
be to base this delineation  on data collected at NBS background sites or EMAP reference  sites.

One parameter that is important for interpreting this indicator is  the lipid content of the fish.  Some of the
compounds known to bioaccumulate are highly lipophilic, and their concentrations in fish  tissue would be
directly related to the lipid  content of the organism; therefore, data need to  be expressed as nanogram or
microgram per gram  lipid weight, as well as nanogram or microgram per gram wet weight and dry weight.

                                               B-19

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Information  may also be needed to normalize the data on the sex and reproductive state of the fish, both
of which affect lipid content.

The error associated with measuring contaminant concentrations in tissues would depend on the contaminants
chosen, the  specific tissue preparation and analytical methods selected, and the interlaboratory variability that
is encountered when more than one laboratory is  used to analyze the samples.  Data from the NBS would
help identify some of these sources of error; however, all  NBS  data were analyzed at ERL-Duluth.  FWS and
NOAA  data are available, and  scientists from  the  former  recommend a single laboratory  to reduce
interlaboratory variability (Kefner 1990).  Acceptable precision  and accuracy are 10-32% (Smith 1989).

One option  that would  significantly reduce monitoring  costs is  to analyze tissues from a subset of the EMAP
sampling units and  to  coordinate sampling with  other state  and  federal  agencies  that  monitor tissue
contamination.  The danger with this approach is that the EMAP grid design may be violated or that the
sampling density would  be inadequate.

The  Federal Water Pollution Control Act establishes a  process for developing information about the quality
of the nation's water resources and reporting this information  to the EPA and the U.S. Congress;  therefore,
national data  bases  of  contaminants  in  fish  tissues already exist, but their  inability to  produce  unbiased
regional and national estimates with known confidence has reduced the value of this data.   In the National
Water Quality Inventory - 1988 report to Congress (U.S. EPA  1989),  46  states and  U.S. territories reported
on fishing bans or  advisories due  to contaminated fish tissues;  585 fishing advisories  and 134 bans were
identified for the 1986-1988 period. PCBs, mercury, dioxin, and DDT were the most commonly cited causes;
industrial discharges and land disposal were the most common  sources of contamination.  The report did not
attempt to correlate contaminant tissue levels in fish with adverse effects on the aquatic ecosystem or any
component  thereof (e.g., organism, population, community). Also, states tend to focus their efforts on those
water bodies  most likely to be impacted by anthropogenic activities.  As a result, the representativeness  of
the data contained in this  report to Congress is unknown, and therefore the information has little value  in
making regional-scale assessments of the overall extent of inland water contamination.

In addition  to the activities of  the states in monitoring tissue  contamination, the EPA  initiated the NBS  in
1986.  The  study is a one-time screening activity designed to determine the extent to which water pollutants
are bioaccumulating in  fish and to identify correlations with  sources of  contamination.  In the study, fish
sampled from approximately  400 sites  throughout the  United  States are being  analyzed  for  66 highly
bioaccumulative pollutants, including dioxins, PCBs, and  several pesticides.  Fish have  been sampled at (1)
potential problem sites  with significant industrial,  urban, or  agricultural  activity; (2) relatively undisturbed
background  areas; (3) locations with  important sport or commercial  fisheries;  and (4) a number of locations
randomly selected for nationwide coverage.  The NBS results  have been evaluated  (Tetra Tech, Inc. 1990).

VARIABILITY:   An examination of data from the randomly selected sites in  the NBS would provide some
estimate of the spatial variability and temporal variability within  a resource sampling unit and during the index
period, respectively.

PRIMARY PROBLEMS:   As stated above, only certain  types of chemicals bioaccumulate  in fish tissue;
therefore, the selection  of chemicals to measure would be an  important consideration.   For many classes of
compounds  that do not bioaccumulate (e.g., ammonia,  chlorine), this indicator would provide no information
regarding their ability to impact aquatic  ecosystems.  As more and more chemicals are manufactured and
introduced  into the aquatic environment and  the determination must be made of which compounds  to
monitor in  fish tissue, EMAP strategists  might   want  to  rely on  models  capable  of  predicting the
bioaccumulative nature  of each compound  based on its physical or chemical  properties.   Random site
selection would probably miss  most "hot spots" associated with point source  discharges, but may provide a
good indication of the extent  of diffuse,  nonpoint source loadings.
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REFERENCES:

Kefner, W. 1990.  Personal communication. Telephone conversation with  R.M. Hughes.  U.S. Department
of the  Interior, Fish and Wildlife Service, Tuscon, AZ.

Smith, C.J.  1989.  Patuxent Analytical Control Facility Reference Manual.  U.S. Fish and Wildlife Service,
Laurel, MD.

Tetra Tech, Inc.  1986.  Recommended protocols for measuring metals in Puget Sound water, sediment, and
tissue samples.  Final Report TC-3090-04.   Prepared for Resource Planning Associates for the Puget Sound
Dredged  Disposal Analysis (PSDDA) component of the Puget Sound Estuary Program, as monitored by the
U.S. Army Corps of Engineers, Seattle District  Tetra Tech,  Inc., Bellevue,  WA.

Tetra Tech, Inc.  1990.  Bioaccumulation of selected pollutants  in fish - a national study.  Vol.  1:   Draft
report  and Appendices  A and B, submitted to U.S. Environmental Protection  Agency, Office  of Water
Regulations and Standards, Washington, DC.  Tetra Tech, Inc.

U.S. EPA.   1989.  National Water Quality Inventory - 1988 report  to  Congress.   U.S.  Environmental
Protection Agency, Washington, DC.

U.S. FWS.  1989.  Field operations  manual for resource contaminant assessment.  U.S. Fish and Wildlife
Service, Laurel, MD.

U.S. NOAA.  1985.  Standard analytical procedures of the  NOAA  National Analytical Facility, 1985-1986:
Extractable toxic organic compounds. 2nd  edition.  NOAA Technical Memorandum NMFS F/NWC-92.  U.S.
National  Oceanic and Atmospheric Administration, National Marine Fisheries Service, Rockville,  MD.

U.S. NOAA.  1988.  Standard analytical procedures  of the NOAA National Analytical Facility, 1988:   New
HPLC  cleanup and revised extraction procedures for organic  contaminants.   U.S. National  Oceanic and
Atmospheric Administration, National Marine Fisheries Service, Rockville, MD.
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B.10  INDICATOR:  Routine Water Chemistry

CATEGORY:    Exposure and Habitat/ Ambient Concentrations

STATUS:  High-Priority Research

APPLICATION: This indicator is composed of water quality parameters that measure the potential exposure
of aquatic biota to common chemical and physical stressors.   Several different groupings of these variables
would be used to identify different categories of stressors.

Eutrophication: Excessive enrichment of lakes and streams with  nutrients has long been considered a major
problem threatening surface waters throughout the world.  Blooms of nuisance algae, reduced water clarity,
and fish mortality  resulting from low dissolved oxygen (DO) are results of cultural eutrophication that concern
the public. The loading of N and P in excessive amounts from nonpoint sources  such as agricultural  runoff
or point sources like wastewater treatment facilities has been repeatedly linked to increases in phytoplankton
biomass (Bachmann 1980; Dillon and  Rigler 1974; Heiskary et al. 1987; Jones and Bachmann 1976; Larsen
et al.  1988; Omernik et al. 1988) and reduction in water clarity.  Because control of phytoplankton biomass
appears to vary with respect  to N  and P concentrations (Smith  1979), both total N and  total P should be
measured.  Summer and winter fish kills resulting from depletion of DO as a result of high algal biomass have
also caused concerns in a number of  areas (Barica and Mathias 1979; Casselman and Harvey 1975).

Acidic Deposition:  A second anthropogenic stress of concern in surface  waters is acidification of lakes and
streams and its detrimental impact on  the biota, particularly fish.  Low pH, acid-neutralizing capacity (ANQ,
dissolved inorganic carbon (DIC),  and  dissolved organic carbon  (DOC) can suggest acid  deposition.   The
impact of acidic inputs of anthropogenic origin  on the  pH and ANC of surface waters has been evaluated
(National Academy of Sciences 1981; National Research Council  of Canada  1981), and the current chemical
status of lakes and some streams within the United States has recently been established (Linthurst et al.  1986;
Landers et al. 1987;  Kaufmann et al. 1988).  EMAP would track condition  of biological components  which
might be impacted by acidic  deposition, whereas  previous  spatially extensive studies have tracked primarily
chemistry.  ANC,  pH, and SO42" would be used  in this program as diagnostic indicators to  identify when
acidic deposition  is the most  probable cause for classifying a fraction of the resource  population.

Contamination, Thermal Alteration:   High levels of residual Cl, total  suspended solids  (TSS), and total
dissolved solids (IDS) are often associated with nontoxic contaminants and/or salinization problems. Unusually
high or low water temperatures may indicate thermal pollution.

INDEX PERIOD:   In general,  the  appropriate index  period would be when  the biological  sampling is
conducted. The peak growing season is the most appropriate time to measure components which are specific
to eutrophication.  Generally, a late July to early September index period  would be suitable.  This period
would be near the time of maximum algal biomass.  The  times of fall mixing for lakes and of  spring base
flow for streams are the index periods which were used in the EPA National Surface Water Survey (NSWS)
to measure acidic deposition;  however, water chemistry data from spring,  summer, and autumn are available
from NSWS - Phase I which  can be used to  assess the comparability of a late summer measurement period
to previously used spring and autumn index petiods.

MEASUREMENTS: Parameters that would be measured are N, P, Cl, NH4,  Mg, K,  Na, Ca, Al, SO/, NO3,
Mn,  Fe, DO, Secchi depth, pH, ANC, DOC, DiC, conductivity, TDS,  TSS, and temperature.  Standard field
and laboratory methods would be  used; laboratory costs are estimated to average $300-$500 per  resource
sampling unit  In lakes, a mid-epilirnnetic water sample for total N and  total P would be taken.  A  profile
of DO saturation  would be taken  in lakes and  streams.   In streams, however, DO saturation must be taken
at a consistent time in early morning to avoid confusion resulting from diel shifts  in  DO.  Total  N and total
P samples would  be taken at the same time.  An  extensive literature base was developed during the  NSWS

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for the collection, handling, analysis for and quality assurance of the measures specific to acidic deposition
studies.  The NSWS procedures would be followed to ensure comparability with the previous studies of the
NSWS.  It should be possible to keep measurement error to <10%.

VARIABILITY:  The expected  spatial variability of  these physical and chemical measures within a resource
sampling unit should produce  ranges that deviate <10% from the mean values; however, temporal variability
for many of the measures during the index period is likely to produce ranges that deviate 20-30% from the
mean values.

PRIMARY PROBLEMS:  In order for these exposure indicators to help determine probable stressors,  expected
baseline values from regional  reference sites are needed.  Considerable  logistic and quality assurance effort
is needed to select contract  laboratories.  The use of the total N and total P  data would  be  relatively
straightforward; however, the most appropriate way to summarize the DO data for  lakes would require some
development  Traditional  methods for calculating and  presenting hypolimnetic oxygen  deficits require
relatively extensive information about the morphometry of the lake. Morphometry data would be available
for few EMAP lakes, and therefore we would have to present  DO data in a different fashion.  This method
could be developed over the  next year, evaluated with existing data, and field tested  early in the  program.
For the acidic deposition issue, a major problem would be determining if it is necessary to sample during the
fall and  spring as in NSWS; if data  from late summer are comparable enough to the NSWS data, the index
period could be  changed.  This can be evaluated  somewhat for lakes with the summer data from the EPA
Eastern Lake Survey -  Phase II and the EPA Long-Term Monitoring  Network.

REFERENCES:

Bach man n,  R.W.  1980.  The role of agricultural sediments and chemicals in eutrophication.  J. WaL Pollut.
Control  Fed. 52:2425-2600.

Barica, J., and JA. Mathias.   1979.   Oxygen depletion  and winterkill risk in  small prairie lakes under
extended ice cover. J. Fish. Res. Bd. Can. 36:980-986.

Casselman,  J.M., and  H.H. Harvey. 1975.  Selective fish mortality resulting from low winter oxygen.  Verh.
Int. Verein.  Limnol. 19:2418-2429.

Dillon, P.J., and F.H.  Rigler.   1974. The phosphorus-chlorophyll relationship in  lakes.  Limnol. Oceanogr.
19:767-773.

Heiskary, SA, C.B. Wilson, and D.P. Larsen.  1987.  Analysis of regional patterns in lake water quality:
Using ecoregions for lake management in Minnesota.  Lake Reservoir Manage. 3:337-344.

Jones, J.R., and R.W.  Bachmann. 1976.  Prediction of phosphorus and chlorophyll levels in lakes. J. Wat.
Pollut. Control Fed. 48:2176-2182.

Kaufmann, P.R., P. Herligy, J. Elwood, M. Mitsch, W. Overton, M. Sale, J. Messer,  K. Cougan,  D. Peck,
K. Reckhow, A. Kinney, S. Christie, D. Brown, C. Hagley, and H. Jaeger.   1988. Chemical characteristics
of streams  in  the  mid-Atlantic  and  southeastern  United States.  Volume  I.  Population descriptions and
physico-chemical relationships.  EPA 600/3-88/021 a. U.S. Environmental Protection Agency, Washington, DC.
397  pp.

Larsen, D.P., D.R.  Dudley, and  R.M. Hughes.  1988. A regional  approach for assessing attainable surface
water quality:  An  Ohio case study.  J. Soil Wat. Conserv. 43: 171-176.
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Landers, D.H., J.M Eilers, D.F. Brakke, P.E.  Kellar, M.E. Silverstein,  R.D.  Schonbrod, R.E.  Crowe, RA
Linthurst, J.M. Omernik, S.A. Teague, and E.P. Meier.  1987. Characteristics of lakes in the western United
States.   Volume I.  Population  descriptions and physico-chemical relationships.  EPA 600/3-86/054a.  U.S.
Environmental Protection Agency, Washington, DC.  176 pp.

Linthurst, RA, D.H. Landers, J.M. Eilers, D.F. Brakke, W.S. Overton, E.P. Meier, and R.E. Crowe.  1986.
Characteristics of lakes in the eastern United States. Volume I. Population descriptions and physico-chemical
relationships.   EPA 600/4-86/007a.   U.S. Environmental Protection Agency, Washington, DC.   136 pp.

National Academy of Sciences.  1981. Atmospheric-Biospheric Interaction: Toward a Better Understanding
of the Ecological Consequences of Fossil Fuel Combustion.  Academy Press, Washington, DC.   263 pp.

National Research Council of  Canada.  1981. Acidification in the Canadian aquatic environment NRCC
Publication  No. 18475.  National Research Council of Canada, Ottawa. 369 pp.

Omernik, J.M., D.P. Larsen, C.M. Rohm, and S.E.  Clarke.  1988.  Summer total phosphorus in lakes:  A
map of  Minnesota, Wisconsin,  and Michigan, USA.  Environ. Manage. 12:815-825.

Smith, V.H.  1979.  Nutrient  dependence of primary productivity in  lakes.   Limnol. Oceanogr. 24:1051-
1064.
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B.11  INDICATOR:  Physical  Habitat Quality

CATEGORY:  Exposure and Habitat/ Habitat

STATUS:  High-Priority Research

APPLICATION:   This  indicator estimates instream  and  lake  deterioration  of physical  habitat quality, a
commonly recognized stressor of aquatic ecosystems that is measurable nationwide.  Assessing habitat quality
is an  integral component of assessing the ecological status of lakes and streams. The authors of the 1972
Water Pollution Control Act  recognized  this by establishing the  restoration and maintenance of physical
integrity of the nation's waters as an objective.

Physical habitat integrity is a  critical aspect of the abiotic component of any  ecosystem and  needs to be
evaluated  quantitatively by EMAP.  Establishing physical habitat quality is important in meeting the EMAP goal
of evaluating the effectiveness of control  strategies and EMAP's objective of attributing probable cause of
subnominal  ecological condition.  When ecological status of a lake or stream is determined to be subnominal
and habitat quality has been  found to  be adequate, such damage can be  attributed to other factors such
as water quality. This is especially important in a monitoring  system such  as  EMAP, where chemistry  and
toxicity are sampled too infrequently to detect spills, intentional dumping, or other episodic events.  Without
assessing habitat quality, establishing probable cause of ecological  condition would be difficult

National nonpoint-source assessments demonstrate that habitat alteration is a major cause of poor ecological
condition  in streams,  rivers, and  lakes (Judy et  al. 1984;  ASIWPCA 1985;  U.S.  EPA 1987).  Guidance for
evaluating use attainment in EPA's National Water Quality Standards program includes habitat assessment (U.S.
EPA 1983).   More recent policy development and  bioassessment guidance for  streams emphasize habitat
assessment (U.S. EPA 1988; Plafkin et al.  1989).  A statistically designed field assessment of physical habitat
quality is needed to determine quantitatively the status and extent of habitat alteration in the streams, rivers,
lakes, and reservoirs of the United  States.  Both  instream  and surrounding topographical features affect the
quality and  quantity of  physical  habitat,  which  in turn affects the structure and  composition of resident
biological  communities.   Where habitat  condition differs substantially from reference  conditions, habitat
alteration  is suspected.

INDEX PERIOD:  A habitat quality assessment (HQA) would be performed quantitatively during the same
index period as the other surface water assessments.

MEASUREMENTS:  Habitat quality evaluation is accomplished by characterizing selected physical parameters
and by  systematic habitat assessment  Key parameters are identified to provide a  consistent assessment of
habitat  quality.   The  necessary information is  collected   during  biological  surveys and during landscape
characterization.

Physical characterization parameters to be measured  are general  land use and physical stream,  lake, or
reservoir characteristics.   Physical characterization starts  with  the riparian  zone and  proceeds inlake or
instream to  sediment/substrate descriptions.  Such information  would provide  insight as to what organisms
should be present or are expected to  be present  Also,  data  on  water odors, surface oil, and recent  and
current weather conditions would  be collected.

The predominate use  of  land  surrounding  the  resource sampling unit would  be characterized  by remote
sensing  because of its  potential effect  on water quality.  Local  erosion, existing or potential, would  be
recorded in  the field because  movement of soil into a  stream or lake  alters the physical habitat and reduces
biological  integrity.  Point and nonpoint sources, such as  landfills, feedlots,  wetlands, septic systems, dams,
impoundments, and mines would also be identified from remote sensing and field data.  The physical habitat
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parameters included in an HQA would be weighted so that habitat quality can be rated as excellent, good,
fair, or poor.

In streams, the primary parameters measured would be bottom substrate, instream cover, embeddedness, silt
cover,  and  flow  velocity.   Secondary characteristics, such as channel  alteration,  bottom  scouring  and
deposition, and  pool/riffle  and run/bend  ratios, would  also  be evaluated.   Finally,  bank stability,  bank
vegetation, and streamside cover would be measured.

In lakes, the primary variables are surface area, maximum depth, lake level fluctuation,  hydrologic residence
time, proportion of littoral surface area, coefficient of variation in lake depth, macrophyte cover, and substrate.
Secondary characteristics include shoreline development or complexity, shoreline vegetation, and proportion
of artificial shoreline.

A total score would be obtained for each resource sampling unit and compared to a series of ecoregional
reference  sites.  The comparison provides a comparability measure by classifying each unit as  unimpaired,
slightly impaired,  moderately impaired, or severely  impaired, depending  on the degree  that the physical
habitat deviates from reference conditions.  Field sheets with the above information would be and filled out
and scored by field crews.   On the assumption that the  sheets could be  completed in 2 h,  the estimated
data collection costs are  $25-$50 per resource sampling unit,  the cost depending on  the expertise  of the
sampling crew.

VARIABILITY:  The expected spatial variability of an  HQA within a resource sample unit was not estimated;
the variability of individual  measurements, however, produces ranges that deviate 10-20% from their mean
values  (Platts et al.  1983).   The expected temporal variability  of an  HQA during the  index  period  would
produce a range that deviates  >10% of the mean value  only if periods of intense floods or droughts occur
during the period.

PRIMARY PROBLEMS:  Habitat assessment of  small and medium-size streams and rivers is expected to be
straightforward and present few problems, if staff are thoroughly trained and tested.  Research is needed to
test similar assessment  methods for  large water bodies.

REFERENCES:

ASIWPCA. 1985. America's Clean Water: The States Nonpoint Source Assessment, 1985.  American Society
for Inland Water  Pollution Control Administrators, Washington, DC.

Judy, R.D., Jr., P.N. Seeley, T.M. Murray, S.C. Svirsky, M.R. Whitworth,  and L.S. Ischinger.  1984.  1982
national fisheries survey:  Volume 1.  Technical Report FWS/OBS-84/06. U.S. Fish and  Wildlife Service, Fort
Collins, CO.

Platts, W.S., W.F. Megahan, and G.W. Minshall.  1983.   Methods for evaluating stream, riparian, and biotic
conditions.  General Technical Report INT-138. U.S. Department of Agriculture, Forest Service, Ogden, UT.

Plafkin, J.L, M.T. Barbour, K.D. Porter, S.K. Gross, and R.M.  Hughes.   1989.    Rapid  bioassessment
protocols  for use in streams and rivers:   Benthic macroinvertebrates and fish.  EPA  444/4-89/001.   U.S.
Environmental Protection Agency, Office of Water, Washington, DC.  162 pp.

U.S. EPA.   1983.  Technical  support  manual:  Water  body surveys and assessments for conducting use
attainability analyses.   U.S Environmental Protection Agency, Office of Water  Regulations and Standards,
Washington,  DC.  214 pp.
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U.S. EPA.  1987.  National Water Quality Inventory: 1986 report to Congress. U.S Environmental Protection
Agency, Office of Water Regulations and Standards, Washington,  DC.  184 pp.

U.S. EPA.  1988.  Report of the national workshop on  instream biological  monitoring and  criteria.   U.S
Environmental Protection Agency, Office of Water Regulations and Standards, Washington, DC.  34 pp.
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B.12    INDICATOR:  Water Column Bacteria

CATEGORY:   Exposure and Habitat/ Pathogens

STATUS:  Research

APPLICATION:  This indicator can be used  in environmental monitoring to determine the extent to which
water bodies are contaminated by pathogenic microorganisms.  Two types of pathogen  contamination can
exist: (1) pathogenic organisms introduced into surface waters and (2) indigenous organisms that grow to high
densities because of increased nutrient loading.

Contamination of the first type has been routinely assessed in the past by using fecal coliforms; however, in
1986 the EPA recommended two new indicators of fecal contamination, Escherichia coli and enterococci, for
assessing surface water quality.  Their density in bathing beach water samples has been shown to be directly
related to gastrointestinal illness in swimmers  (U.S. EPA  1986).  Thus,  monitoring for either one of these
measures can be used to determine the risk of illness associated with exposure to human pathogens.  The
data from  monitoring  £. coli  or enterococci  helps characterize the  quality  not only  of waters used for
recreation,  but also of  source waters for drinking.  The sources of fecal contamination may be inadequately
treated sewage (point sources) or runoff from pastures, feed lots, and urban areas (nonpoint sources).  In the
National Water Quality Inventory - 1988 report to Congress (U.S. EPA 1989), the states reported  that of the
30% of impaired river  and  stream lengths, 19% were  affected by fecal contamination. Also, of the 25% of
impaired lake and reservoir areas, 12% were affected by fecal contamination.

The second type of potential pathogenic contamination (i.e.,  increased levels of indigenous bacteria resulting
from high organic nutrient loading) is not currently included in routine monitoring programs, although  it has
been examined in some special studies. EMAP strategists may choose to include certain species of indigenous
bacteria in the  program's inland surface water  monitoring, because the density of  potentially  pathogenic
bacteria is a  possible indicator of exposure of humans, fish, and other animals to infectious doses of these
organisms.  Densities of these potential pathogens can then  be compared with incidences of fish  disease and
kills and with reported incidences of human infection.   Organisms such as Aeromonas hydrophila and A.
salmonidda may cause fish  diseases and subsequent kills.  Also, A. hydrophila can cause wound infections
in humans and  other  animals, and it has been shown  to be  pathogenic  to  reptiles (Shotts et at.  1972).
Finally, the density  of A. hydrophila in natural waters correlates closely with a number of parameters used
to assess trophic condition  (Rippy and Cabelli  1979).

INDEX  PERIOD:   Fluctuations  in bacterial populations  are frequent and depend  on  season  as  well as
pollutants.  In general,  population densities will  be highest in late  summer and early autumn.

MEASUREMENTS:  Routine  methods for identifying and enumerating bacteria in environmental samples have
been developed by  the U.S. EPA (1985).  Grab samples  are collected and stored on ice before processing
by  membrane filtration, followed by growth of viable  bacteria on  selective media.  Confirmation of species
is made by biochemical tests or biotechnology methods.  The analytical cost per assay is approximately $30.

In a study  of the EPA membrane filter method  for enterococci that involved 11  laboratories, the standard
deviation between means of duplicates from analysts  in the same  laboratory  was 0.150 (count/100 mL) +
5.16 (dilution factor), where the  dilution factor = 100 divided by the volume of original sample filtered (U.S.
EPA 1985).  A similar study for E. coli showed  the standard deviation  between means of duplicates from
analysts in the same laboratory to be 0.233 (count/100 mL)  + 0.82  (dilution factor), where again the dilution
factor  = 100 divided  by the volume of original sample filtered.   Analysis of reference samples produced
coefficients of variation of 24% and 29%,  respectively (U.S.  EPA 1985).
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VARIABILITY:  Bacterial population densities fluctuate frequently during the index period.  Populations would
also exhibit high spatial variability within a resource sampling unit  This variability can be  reduced somewhat
by collecting samples at consistent depths or water temperatures.  Two multiyear studies were evaluated to
generate an estimate of natural variability.  The average log standard deviation for enterococci and E. coli in
these studies (conducted in summertime) was 0.4 (U.S. EPA 1986).

PRIMARY PROBLEMS:  Some baseline monitoring is needed to determine the sampling frequency necessary
to meet data quality objectives.  Also, the  appropriateness of the  selected index  period must be evaluated.

REFERENCES:

Rippy, S.R., and V.J. Cabelli. 1979.  Membrane filter procedure  for enumeration of Aeromonas hydrophila
in fresh water.  Appl.  Environ. Microbiol. 38(1 ):108-113.

Shotts, E.B., Jr., J.L Caines, L Martin, and A.K. Prestwood.  1972. Aeromonas-induced deaths among fish
and  reptiles in an eutrophic inland lake.  J. Am. VeL Med. Assoc. 161:603-607.

U.S. EPA.  1985.   Test  methods for Escherichia  coli and  enterococci in water by the  membrane  filter
procedure.  EPA 600/4-85/076.  U.S. Environmental  Protection Agency, Environmental Monitoring Systems
Laboratory, Cincinnati, OH.

U.S. EPA.  1986. Ambient water quality criteria for bacteria - 1986.  EPA 440/5-84/002. U.S. Environmental
Protection Agency, Office of Water Regulations and Standards, Washington,  DC.

U.S. EPA.  1989.   National Water Quality  Inventory - 1988 report  to Congress.   U.S.  Environmental
Protection Agency, Washington, DC.
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B.13    INDICATOR:   Heavy Metals and Man-Made Organics (Toxics)

CATEGORY:  Exposure and  Habitat/ Ambient Concentrations

STATUS:  Research

APPLICATION: Thousands of chemicals are released either directly or indirectly into the aquatic environment
One component of a  comprehensive program for monitoring these ecosystems is an analysis of water and
sediment samples for the presence of the most toxic, stable, or  prevalent of these compounds:  the heavy
metals and man-made organics.  Information  on the concentration of these environmental contaminants can
then be correlated with observed impacts on biological communities to improve  the diagnostic capabilities
of the program.

INDEX PERIOD:  Sampling should take  place during low flow, when contaminant concentrations  are likely
to be at their highest because of low volume. For lakes, this index period is not as critical, especially if water
levels are maintained fairly constant throughout the year. Sediment  contamination would probably be more
stable than water-column contamination, although sedimentation following peak flow may introduce toxics
in high concentrations to the sediment on a seasonal basis.

MEASUREMENTS:   Standard analytical methods exist for the analysis of most chemicals  currently perceived
to be hazardous to aquatic ecosystems.  These methods have been published  by the U.S. EPA (1978, 1982,
1983,  1986, 1988a,b, 1989), the U.S. Geological  Survey (1972),  the American Society for Testing and
Materials (1988), and published jointly in a manual by the American  Public Health  Association, the American
Water Works Association, and the Water Pollution Control Federation (1989). A  number of metals can  be
evaluated in a single sample by inductively coupled plasma (ICP) emission spectroscopy.  Recent developments
with ICP/mass spectrometry (MS) may improve the resolution of this type of analysis and should be evaluated.
For organic contaminants,  gas  chromatography (GQ/MS allows for the identification and quantification of a
wide  range of volatile  and semivolatile  organic compounds.  Research  is currently under way to develop
liquid chromatography/MS methods for the identification and quantification of a number of nonvolatile organic
compounds.

Current protocols for sample collection, storage,  transport, and  preparation need to be evaluated for their
applicability to EMAP.   Sample  handling would,  to  a  great extent, depend on  the analytes selected  for
monitoring and the constraints imposed by the analytical methods employed.  The cost of the analyses would
also depend on the methods and analytes selected, but some estimates are available.  Routine GC/MS screens
for the  165 priority pollutants currently average  $350-$400 per sample,  and ICP analysis averages $250-
$300 per sample.

The error  associated  with  measuring ambient  concentrations  of  contaminants would  depend on  the
contaminants chosen,  the specific sample handling and  analytical methods selected, and the interlaboratory
variability that is encountered when more than one laboratory is  used to analyze the samples.  Based on the
results of  several interlaboratory  studies conducted  at  EPA's  EMSL-Cincinnati laboratory, single-laboratory
precision for most of the  analytical  methods applicable to EMAP  is approximately 90% (10% error), and
multiple-laboratory precision is approximately 80% (20% error).

One approach that would  significantly reduce the cost for assessing this indicator would  be to archive water
and sediment samples (or extracts of these samples) and perform  the chemical analyses only if data from the
response indicators or the bioassays suggest high  levels  of  toxic  exposure.

National data bases of water quality indicators already exist, but  their inability to support unbiased estimates
with  known  confidence has  hindered  the value of  the data.  The Federal Water Pollution Control Act
establishes  a process  for  developing information about the quality of the  nation's water resources and

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reporting this information to the EPA and the U.S. Congress.  In the National Water Quality Inventory - 1988
report to Congress (U.S.  EPA 1989), the states reported elevated levels of toxics in  one third of monitored
river distance and about one seventh  of monitored lake area.  Also, sediment contamination by toxics was
reported by 33 states.  The states reported 533 incidents, primarily caused by heavy metals, polychlorinated
biphenyls, and pesticides.  In addition, more than 1000 pollution-caused fish kills were reported by 35 states,
representing roughly 36 million fish killed.  Among the leading causes cited by the states were oil and gas,
pesticides, ammonia, and chlorine.  Commonly cited  sources include agriculture, spills, and municipal and
industrial discharges. The report did not attempt to compare contaminant levels with adverse effects on the
aquatic  ecosystem or any component thereof (e.g., organism, population, community).  Also, states tend to
focus their efforts on those water bodies most likely to be impacted  by anthropogenic activities.  As a result,
the representativeness of the data contained in the report to Congress is unknown,  and therefore, the data
has little value  in making regional-scale assessments of the  extent of inland water contamination.

VARIABILITY:   Few data currently exist on nonsampling variability,  such  as that due to  spatial patchiness or
temporal variability  associated  with seasonal  loadings.  The expected  spatial  variability  of contaminant
measures within a  resource sampling  unit, based  on hazardous waste investigations, would produce  ranges
that deviate >100% from their mean  values.  The expected temporal variability of contaminant measures
during the  index period, based on  hazardous  waste investigations, also would produce ranges that deviate
>100% from their mean values.

PRIMARY PROBLEMS:   Random site selection  would probably miss  a number of "hot spots" associated with
point-source discharges, but may provide a good indication of the extent of diffuse, nonpoint-source loadings.
Agricultural activities may affect the  data if sampling occurs shortly after application of agricultural chemicals.
Also,  selection of sampling sites near  point-source discharges may bias the data set

A protocol  must be developed for selection of chemicals to be monitored in this program that is based on
toxicological data, information on loadings and chemical stability, and available data on fate and transport
For many chemicals, detection in the environment does not correlate strongly with biological effects because
of limited bioavailability, rapid degradation, and a number of other factors.  In a program such as EMAP, only
those chemicals expected to have  significant adverse biological effects should be monitored.  Where numeric
criteria do  not  exist, selection of  levels of concern for each compound  could  be based on EPA  numerical
standards or state "levels of concern,"  but some evaluation  of the concentration  is needed to assess  risk or
potential problem,  especially for naturally occurring elements.

REFERENCES:

American Society for Testing and Materials.  1988.  Annual Book of ASTM Standards,  Volume 11:  Water.
American Society for Testing and  Materials, Philadelphia, PA.

American Public Health Association, American Water Works Association, and Water Pollution Control
Federation.  1989.   Standard Methods for the Examination  of Water and Wastewater,  17th Ed.   American
Public Health Association, Washington, DC.

U.S. EPA. 1978. Methods for benzidine, chlorinated organic compounds, pentachlorophenol and pesticides
in  water and wastewater.  U.S. Environmental Protection Agency, Washington, DC.

U.S. EPA.  1982. Methods for organic chemical analysis of municipal and industrial wastewater.  EPA 600/4-
82/057. U.S. Environmental Protection Agency, Environmental Monitoring Systems Laboratory, Cincinnati, OH.

U.S. EPA.  1983. Methods for chemical analysis of water and wastewater, 2nd edition EPA 600/4-79/020.
U.S. Environmental  Protection  Agency, Environmental  Monitoring and Support  Laboratory, Cincinnati, OH.
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U.S.  EPA.  1986.  Test methods for evaluating solid wastes.  EPA Publication SW846.   U.S. Environmental
Protection Agency, Office of Solid Waste and Emergency Response, Washington,  DC.

U.S.  EPA.   1988a.  Availability,  adequacy,  and comparability of testing procedures  for the analysis of
pollutants established under Section 304(h) of the Federal Water Pollution Control Act - report to Congress.
EPA  600/9-87/030.  U.S. Environmental Protection Agency,  Environmental Monitoring Systems Laboratory,
Cincinnati, OH.

U.S.  EPA.  1988b.  Methods for the determination of organic compounds in  drinking water.   EPA 600/4-
88/039. U.S. Environmental Protection Agency, Environmental Monitoring Systems Laboratory, Cincinnati, OH.

U.S.  EPA.   1989.  National Water  Quality Inventory -  1988  report to Congress.   U.S. Environmental
Protection Agency, Washington, DC.

U.S.  Geological Survey.  1972. Methods for organic substances in water.  U.S. Geological Survey Techniques
of Water Resources Inventory,  Book 5.   U.S. Department of the Interior,  Geological Survey, Denver, CO.
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 APPENDIX C:  INDICATOR FACT SHEETS FOR WETLANDS
                         Authors
                     Mark T. Brown
                      Karla Brandt
                   Center for Wetlands
                   University of Florida
                    Gainesville, Florida
                      Paul Adamus
NSI Technology Services Corporation -  Environmental Sciences
        U.S. EPA Environmental Research  Laboratory
                    Corvallis, Oregon

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C.1  INDICATOR:  Organic Matter and Sediment Accretion

CATEGORY:   Response/  Ecosystem  Process Rates and Storage

STATUS:  High-Priority Research

APPLICATION: One function of wetlands is as filters for suspended solids carried into wetlands from eroded
uplands.   As such, wetlands are depositional  sites  for sediments.   The  rate of sedimentation,  along with
sediment source and sediment distribution within a wetland, can indicate hydrologic history (Brinson 1988),
as well as changes in  the surrounding landscape that result from accelerated  rates of drainage or erosion.

In some cases, public awareness of the consequences of altered accretion rates on water quality is very high
(e.g., in Louisiana, where wetlands are being lost, and around certain eutrophic lakes, where natural flushing
has been cut off and  lake bottoms are silting in at a  high rate).    On  the whole,  however,  public
understanding of the importance of this indicator is low.

Combined with information on change in wetland area, hydroperiod, and nutrient concentrations of incoming
waters, accretion  rates can be  used to indicate trends in trophic status.  Thus, the rate  of organic matter
accretion may indicate the long-term sustainability of the supporting environment Significant changes in this
rate may be an early warning of stress.

INDEX PERIOD:  Annual accretion of sediment is  usually measured during base flow conditions  or during
the driest time of year, but  in general it  is not highly variable  throughout the year.

MEASUREMENTS:   Measurement techniques for accretion consist of placing surfaces in the system so that
materials can settle in a  "natural" manner (Frazier 1967, Kolb  and Van  Lopik 1958).  Samples of accreted
material are collected on an annual or semiannual basis, and the volume and composition  (e.g., organic
matter, clay, sand, silt) are determined.  Labor costs would be about 1  person-day per site.

The rate  of subsidence due  to geologic activity must be determined in  conjunction with sedimentation and
accretion and can be measured  against permanent monuments established for that purpose.  Loss of elevation
in soil surface  is measured against the monument whose elevation is fixed. Establishment  of the  monument
will require  2 person-days per site, but monitoring costs are  negligible.  The recommended interannual
sampling frequency for accretion and  sedimentation is every year.

VARIABILITY:   Locations for  measuring  accretion   must be carefully selected  to  account for  the  spatial
variability in microtopographical differences within a wetland.  The expected spatial variability of sedimentation
rates within a resource sampling unit would produce a range that deviates as  much as 100% from the mean
value.  The temporal variability of accretion during the index period would depend directly on the  hydrologic
system and frequency of flooding and in general would be greater in  a tidal  wetland than  in  a riverine
wetland.

PRIMARY PROBLEMS:   The role of wetlands as sediment  depositories  in the landscape  has often been
quantified, but seldom on a regional or comparative basis.   The high  spatial variation in  rates of accretion
and sedimentation is problematic.

REFERENCES:

Brinson, M.M.  1988.  Strategies for assessing the cumulative effects of wetland alteration on water quality.
Environ. Manage.  12(5):655-662.
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Frazier, D.E.  1967.   Recent deltaic deposits of the Mississippi  River.  Trans.  Gulf Coast Assoc. Geol. Soc.
17:287-315.

Kolb, C.R., and J.R.  Van  Lopik.  1958.  Geology of the Mississippi  deltaic  plain-Southeastern Louisiana.
Technical Report, Second Edition.  U.S. Army Corps of Engineers, Waterways Experiment Station, Vicksburg,
MS. 482 pp.


BIBLIOGRAPHY:

Barko, J.W., and R.M. Smart.  1983.  Effects of organic matter additions to sediment on the growth of
aquatic plants.  J. Ecol. 71:161-175.

Bertness, M.D.   1988.  Peat accumulation and the success of marsh plants.  Ecology 69(3):703-713.

Faulkner, S.P.,  W.H. Patrick, Jr., and R.P. Gambrell.  1989.  Field techniques for measuring wetland soil
parameters.  Soil  Sci. Soc. Am. J. 53:883-890.

Furness, H.D.  1983. Wetlands as accreting systems—inorganic sediments.  J. Limnolog.  Soc. S. Africa 9:90-
95.

Hakanson, L 1984.  Sediment sampling in different aquatic environments: Statistical aspects. Water Resour.
Res.  20(1):41-44.

Handov, J.K.   1986.  The nature of sediments in some wetlands.  Acta Hydrochem.  Hydrobiol. 14:485-
493.

Hatton, R.S., R.D. DeLaune, and W.H. Patrick, Jr.  1983.  Sedimentation,  accretion, and  subsidence in
marshes of Barataria  Basin, Louisiana.  Limnol. Oceanogr. 28(3):494-502.

Kadlec, R.H., and J.A. Robins.  1984. Sedimentation and sediment accretion  in Michigan coastal wetlands.
Chem. Ecol. 44:119-150.

Martin, D.B., and WA  Hartman.  1987.  The effect of cultivation on sediment composition and deposition
in prairie pothole wetlands.  Wat. Air. Soil Pollut 34:45-53.

Morris, J.T., and W.B. Bowden.  1986.  A mechanistic,  numerical model  of sedimentation, mineralization,
and  decomposition for marsh sediments.  Soil Sci. Soc. Am. J. 50:96-105.

Phillips, J.D.  1989.   Fluvial sediment storage  in wetlands.  Wat. Resour.  Bull. 25:867-873.

Rostan, J.C., C. Amoros, and J. Juget. 1987.  The organic content of the surficial sediment:  A method for
the study of ecosystems development in abandoned  river channels.  Hydrobiologia 148:45-62.
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C.2  INDICATOR:  Wetland Extent and Type Diversity

CATEGORY:   Response/ Ecosystem Process Rates and Storage
              Exposure and  Habitat/  Landscape

STATUS:  High-Priority Research

APPLICATION:  Functional attributes of wetlands,  including processes  such as water recharge and water
treatment, are most importantly related  to the areal extent and landscape context of wetlands.  Landscape
indicators also function  as exposure and habitat indicators because the spatial patterns of wetlands provide
a quantitative measure of the available habitat for many waterfowl, wading birds, and other animals whose
life histories are linked  to  wet areas.   Wetland size,  patchiness,  and contiguity are also  important habitat
parameters for many wetland  animals  (see Appendix G-3).  Remote  sensing  offers  a  powerful tool  for
monitoring the condition of wetland  resources.  Remotely sensed data provide information on  response
indicators such as  spatial extent, size, and  distribution of wetland community types throughout the United
States.  Depending upon the classification schemes used, remote sensing can detect most community-level
changes and  some species-level vegetation  changes.

INDEX PERIOD:  The most common period to  sample forested wetlands is during the growing season.  This
holds for herbaceous wetlands as well, except when aerial photography  is used  to  determine extent of
inundation, in which case  a  "wet season" index period is warranted.  Where changes in  dominant species
or types  between evergreen and deciduous wetlands  are of concern or where canopy otherwise precludes
detection of surface water, winter  photography is warranted.

MEASUREMENTS:   The first  step in  mapping wetland systems is  to develop classification systems whereby
mapped  units can  be categorized.  Once an appropriate classification scheme has  been adopted, wetland
types and boundaries within  regions of  interest are mapped.  To achieve an accurate and repeatable map
of wetland spatial  extent, type diversity, and spatial pattern, the  limits and  types of wetlands indicated  by
aerial photography are interpreted on a clear overlay, from which a rectified and georeferenced map is made.
Photographs from different years can  be used to determine changes  in  areal extent and type diversity.

Periodic  aerial photographic reconnaissance and mapping are necessary to detect changes in spatial extent
Optimally, biannual overflights may be necessary in  some areas of the country, whereas in others, it may  be
necessary to  acquire photography  only  every 5 or  10 years.  Initial frequency could be  based on human
population growth rates within a region; higher frequency (1  to  2  years) is  warranted in high-growth-rate
districts and lower frequency (3 to 5 years)  in slower growing areas.

To measure  changes  in  type diversity and  within-wetland  heterogeneity, annual  aerial  photographic
reconnaissance and mapping  may be sufficient.  Interpretation may lead to misidentification of wetland type,
but usually, such events occur  <1% of  the time. Of  greater concern is the variability in coverage of some
wetland types that results from seasonal or  yearly variation  in hydrology.  There are  potential  problems in
determining differences  in wetland size  from year to  year as a result  of variation in spatial extent that can
be attributed to natural variation or to seasonal variation in  rainfall patterns.  In forested  wetlands, changes
in dominant canopy species are not easily detected  by remote sensing unless the wetland  has been logged;
thus, a high measurement frequency is not crucial.  On the other hand, herbaceous wetlands and those with
open water are more likely to exhibit changes in shorter periods of time and may require  annual or biennial
photography and mapping.  Historical photographs  can be  used to compare  past status in areal extent and
pattern with status as revealed  by  EMAP.

VARIABILITY:  The areas of many wetlands change  significantly as the result of variation in seasonal rainfall,
at times covering larger expanses with a typical wetland signature and at other times showing the signature
of drought-stressed vegetation  or  bare ground.   Additional corroborating information such  as direct

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measurement of hydroperiods and rainfall is required; otherwise,  short-term changes in spatial coverage in
some wetlands may be difficult to ascertain.  Extreme values could deviate as much as 100% from the mean
value during the  index  period;  however, because this  variability  can  easily  be  accounted  for  with
corroborating climatic information,  it is not expected to pose serious limitations.  Identification of new roads,
structures, fill, beaver diversions,  and similar features can also suggest relatively abrupt changes in the spatial
extent of a resource sampling  unit   Because  the  sampling unit will be  censused via remote  sensing,
considerations of the spatial variability of this indicator within a resource sampling unit is unwarranted.

PRIMARY PROBLEMS: The determination of regional changes in acreage, type diversity, and spatial patterns
using remotely sensed data is a relatively commonplace task.  No problems are foreseen that would limit the
use of this indicator  in EMAP to determine the status of wetland  resources.

BIBLIOGRAPHY:

Adams, M.S., et al.   1977. Assessment of aquatic environments  by remote sensing.  IES Report 84.  Institute
for Environmental Studies, University of Wisconsin, Madison.

Brown, M., and  J.J. Dinsmore.  1986.  Implications of marsh size  and  isolation for marsh bird management
J. Wildl. Manage. 50:392-397.

Carter,  V.  1975.  The use of remote sensing data in the  management of inland wetlands.  Pages 31-50.
In: M.W. Lefors, H.H. Ridgeway, and T.B. Helfgott, eds.  Proceedings of the Second Wetlands  Conference:
Delineation of Wetlands.  Report 24.  Storrs Institute of Water Resources, University of Connecticut 118 pp.

Cuthery, F.S., and  F.C.  Bryant.   1982.  Status of playas in  the  southern  Great  Plains.  Wild!. Soc.  Bull.
10:309-317.

Kantrud, HA.,  and  R.E. Stewart.  1977.  Use of natural basin wetlands by breeding waterfowl in North
Dakota.  J. Wildl. Manage. 41:243-253.

Patterson,  J.H.  1976.  The role  of environmental  heterogeneity in  the regulation of duck  populations.
J. Wildl. Manage. 40:22-32.

Sullivan, M.F.   1986.  Organization of low-relief landscapes  in  North  and Central  Florida.   MS  Thesis,
University of Florida, Gainesville.

"finer, R.W., Jr.  1984.   Wetlands of the United States:    Current  Status and Recent  Trends.   U.S.
Department of Interior, Fish and Wildlife Service, Newton Corner, MA.
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C.3  INDICATOR:  Abundance, Diversity, and Species Composition of Vegetation

CATEGORY:  Response/ Community Structure
              Exposure and Habitat/ Habitat

STATUS:  High-Priority Research

APPLICATION:  Vegetation  is probably the most common wetland component that is used to detect change
in ecological condition. Vegetative characteristics such as major species,  life form, and density can indicate
ecological productivity, water chemistry,  landscape  aesthetics, and animal habitat suitability  (Brooks and
Hughes 1988).  Sampling methods are well  developed.   The community plant diversity and related process
functions (e.g., water quality improvement) are not the only aspects of wetland vegetation that are valued  by
the general  public.  The importance of habitat - a major  component of which is vegetation  - in maintaining
populations  of endangered animal  species is also recognized,  although not always widely appreciated by the
public (Lefebvre 1988). Wetland  plants, because they are immobile, are reliable indicators of certain types
of stressors.

Changes in vegetation patterns and species composition are community-level response indicators that integrate
the effects of a wide  variety of potential stressors.   As  a result,  impacts  to vegetative species cannot  be
attributed to certain types of stressors without simultaneous data on other factors (such as hydroperiod) and
direct  measurements  of  nutrient levels and  pollutants.   The measurement of community patterns  relates
directly to habitat condition. Vegetation may be used to assess ecological status in two  basic ways:

(1) Sensitive species:  Indicator plants for specific water  quality parameters can be identified by comparing
plant communities in wetlands where such parameters are known.  Changes in the ratio  of exotic to native
species can  be interpreted as a community-level  response to anthropogenic impacts.

(2) Community structure: Plant community structure in wetlands reflects many biotic and abiotic interactions.
The effects of inundation, soil composition and chemistry,  nutrient availability, salinity, previous anthropogenic
activities, fire, etc.,  all  contribute to changing community structure and species composition.   Probably the
single most  important contributing variable is hydrology (see  indicator C.9, "Hydroperiod").

INDEX  PERIOD: Most frequently, wetland vegetation measurements are conducted in mid-growing season.
The  dormant season should be avoided  because at that  time (1) many species have not yet emerged from
the topsoil,  (2) the  species composition and diversity are not at maximum  potential, and (3) the vegetation
is in juvenile form,  seed heads are not yet formed, and it is extremely difficult to identify many of the plant
species. Early and late growing seasons are not considered suitable periods  because there is greater variation
in vegetation during these growth  phases.

MEASUREMENTS:  Two general parameters  of vegetation, species composition and  abundance, should  be
measured to develop  a quantitative assessment of ecological  status. The exact methods will depend on the
community  type that  is to be sampled.   Methods for sampling wetland vegetation  are described  by
Frederickson and Reid (1988) and Britton and Greeson (1988), and  include sampling by quadrats or transects.
By measuring these two parameters, the  following metrics can be developed:  areal cover,  species richness,
relative abundance, relative dominance, importance values, diversity, presence/absence of indicator species,
and  spatial  patterning.  Vegetation analysis  is labor-intensive, both  in  the  field and  in the  laboratory
(identifying and cataloging species).  The expected cost to characterize community structure is approximately
3 person-days per resource sampling unit for field work and 3 person-days for laboratory work in sorting and
identifying species and cataloging data.

VARIABILITY: The  expected spatial variability of vegetation measures within resource sampling  units can  be
relatively high (with ranges that deviate >100% from  the mean value), depending on the patchiness of the

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community.   If the quadrat sampling method is employed, a sufficient number of replicates is required to
adequately characterize within-community variability.  If line strip transects are employed, within-community
variability  is less problematic (ranges that deviate <10%  from the mean value).  The expected  temporal
variability  of vegetation measures during the index period  is low (ranges that deviate <10% from the mean
value).

PRIMARY  PROBLEMS:  The biggest problems associated with vegetation as an indicator are the labor-intensive
sampling regime and the insensitivity of some species to some stressor types.


REFERENCES:

Britton, L.J., and P.E. Greeson, eds.  1988.   Methods for collection and analysis of aquatic biological and
microbiological samples.  Book 5, Chapter 4A.  In:  Techniques of Water-Resources investigations of the
United States  Geological Survey.  Open-File Report 88-190.  U.S. Department of the Interior, Geological
Survey, Lakewood, CO.

Brooks, R.P.,  and R.M.  Hughes.   1988.   Guidelines  for assessing the biotic communities  of freshwater
wetlands.  Pages 276-282. In: J.A. Kusler, M.L. Quammen, and G.  Brooks, eds.  Proceedings of the National
Wetlands  Symposium:  Mitigation of Impacts  and Losses.  ASWM Technical Report 3.   Association of State
Wetland Managers,  Berne, NY.

Frederickson, L.H.,  and F.A. Reid.   1988.   Considerations of  community  characteristics for  sampling
vegetation.  Section  13.4.1.   In:  Waterfowl Management Handbook.  Fish and Wildlife Leaflet  13.  U.S.
Department of the Interior, Fish and Wildlife  Service, Washington, DC.

Lefebvre,  L   1988.   Manatee teamwork:  Glamour species  promoting habitat protection.  The Monitor
8(5):1,4.   Florida Defenders  of the Environment, Gainesville.
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C.4  INDICATOR:  Leaf Area, Solar Transmittance, and Greenness

CATEGORY:  Response/ Ecosystem Process Rates and Storage

STATUS:  Research

APPLICATION:  Measurements of canopy closure (sunlight transmittance) and photosynthetic potential (leaf
area and greenness) are indicators of the vegetative response to stressors that  cause decreases in primary
production or increases in respiration (see also indicators D.1, "Tree Growth Efficiency," and E.1, "Vegetation
Biomass").  Long-term stress usually results in changes in community composition.  In herbaceous wetlands,
changes in community structure occur rapidly in response to stress, whereas in forested wetlands, changes
in community structure take much longer to appear.  The  use of  leaf area,  sunlight  transmittance, and
greenness in these long-turnover-time systems may make the detection of stress easier. Ericaceous wetlands,
in particular, will reflect changes  in light absorption caused by stress.

Death of large numbers of plants in any one area will generate concern among some members of the public.
Autumnal leaf drop in cypress forests brings telephone calls to county foresters and  extension offices from
alarmed citizens who believe the trees are dying.  The concepts of leaf area  and greenness in themselves,
however, seem unlikely to  have much meaning to the public.

An increased drop rate of green and yellow leaves  can indicate the onset of  stress.  Declines  in greenness
may indicate stress; temporary surges in greenness may indicate nutrient enrichment during which the wetland
is being invaded by upland species. Greenness can be used as an index of leaf area in forested wetlands
and  of areal cover in herbaceous wetlands.

INDEX PERIOD:  Most frequently, wetland vegetation measurements  are conducted in mid-growing season.
Early and late growing seasons  are not considered suitable  periods  because  there  is greater variation in
vegetation during these growth phases.

MEASUREMENTS:  Both aerial reconnaissance and field-derived data can be  used to measure greenness.
Greenness can be measured via remote sensing of reflectance in the visible bands and, for some applications,
in the infrared bands.  Canopy  reflectance values  measured with a  hand-held Lycor reflectometer show
differences that indicate definite signatures for  differing community types (Odum et al. 1989).  Further work
may yield a quick method  of determining condition  by  comparing reflectance of stressed and unstressed
wetlands.

Greenness and its relationship to ecological stress, although used widely in agricultural applications, have not
been extensively used  in ecological monitoring.  No greenness value is absolute; greenness as an indicator
of stress or condition must  always be evaluated in reference to greenness measured in a reference wetland.

Standard techniques for determination of leaf area index are relatively time-consuming, requiring the harvest
and  measurement of a known area of leaf biomass (requiring 2 to 3 person-days per resource sampling unit).
Sunlight transmittance is measured by using a hand-held solarimeter at ground or water surface. Numerous
replicate measurements are necessary within each wetland community. Simple measures require <1 person-
hour per resource sampling unit

The  sampling frequency is determined by the desired level of  temporal resolution in change. Synoptic one-
time measurements should  be  sufficient to establish status of wetland  condition.   An  annual or biennial
sampling frequency is sufficient to determine overall trends in wetland condition; however, where more detail
is desired to show effects of special events, monthly measurements on subsamples of the sample population
would be necessary.
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VARIABILITY:  The expected spatial variability of these canopy measurements within a resource sampling unit
would produce ranges that deviate >10% from the mean values.  The expected temporal variability of canopy
measurements during the index period was not estimated.

PRIMARY PROBLEMS:   The greatest impediment to implementation is the lack of widespread use of  the
measures as indicators of ecological stress.

REFERENCE:

Odum,  H.T,  B.T. Rushton, M. Paulic,  S. Everett, T. McClanahan,  M. Munroe, and R.W. Wolfe.  1989.
Evaluation of Alternatives for  Restoration of Soil and Vegetation on Phosphate Clay Settling  Ponds.  Final
Report to Florida Institute of Phosphate Research, Bartow, FL. Contract #86-03-076R.  University of Florida,
Center for Wetlands, Gainesville.  193 pp.

BIBLIOGRAPHY:

Curran, P.J.  1983. Multispectral remote sensing for the estimation of green leaf area index.  Philos. Trans.
Royal Soc.  London A 309:257-270.
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C.5  INDICATOR:  Macro!nvertebrate Abundance, Biomass, and Species Composition

CATEGORY:  Response/ Community Structure

STATUS:  Research

APPLICATION:   Aquatic  insects  (such as  dragonflies  and  midges)  are  found  in  all  wetland  types,
bioaccumulate to some extent, and are responsive to all four  major wetland stresses (altered hydroperiod,
excess sediment, changes in nutrient cycling,  and contaminants).  Benthic/epiphytic macrocrustaceans (such
as amphipods, crayfish, oligochaetes, and isopods) have similar advantages as response indicators, but they
are not as easily dispersed as  aquatic insects and thus may be better indicators of conditions at specific sites.
Mollusks,  in  addition  to their  immobility,  are  highly  bioaccumulative; their deformities may  indicate
contamination.

Macroinvertebrates  have  been posited  for  a  number of  years as one of the most important indicators of
ecological condition in aquatic environments.  Several different measures have been used and/or proposed
as effective means of determining wetland  condition.   They include  measures of (1) sensitive  species,
(2) populations, and (3) community structure.

INDEX PERIOD:  An optimal  sampling window is mid-growing  season. Sampling should not be done during
or immediately after extreme events  (e.g., drought, first flood,  leafing, leaf-drop).

MEASUREMENTS:  Sampling  techniques (e.g., mesh size,  gear  type) must be the same in order to compare
index values (Averett 1981).  Methods of sampling invertebrates are discussed by Weber  (1973), Merritt  and
Cummins (1984), and Frederickson and Reid (1988).  In general, the  methodology requires the collection of
organisms living within a  standard sized area or volumetric unit  and the  use of sweep nets or specially
designed enclosing equipment  In some cases artificial substrate (e.g., plastic  plants) having a known surface
area can be  placed in a wetland  and allowed  to be colonized; colonizing organisms are subsequently
counted.

There is significant expenditure of time in the  preparation, identification, and analysis of invertebrate samples
in the laboratory. Collection,  processing, and  identification may take up to 40 person-days for each resource
sampling unit.

VARIABILITY:  Macroinvertebrate populations are extremely patchy. Their high variation is both spatial  and
temporal, resulting from events (e.g., emergence, sudden floods, predation) that may cause entire populations
to appear or  disappear almost overnight

PRIMARY PROBLEMS:  The major drawbacks of using these organisms as indicators are their extremely patchy
spatial and temporal distribution and the labor-intensive preparation, identification, and analysis of invertebrate
samples in  the  laboratory.

REFERENCES:

Averett, R.C.   1981.  Species diversity and its measurement  Pages B3-B6.   In:  P.E. Greeson, ed.  Biota
and  biological parameters as  ecological indicators. Geological  Survey Circular  848-B.   U.S. Department of
the Interior, Geological Survey, Alexandria,  VA.

Frederickson, L.H., and FA. Reid.   1988.  Initial considerations for sampling wetland invertebrates.  Section
13.3.2.  In:  Waterfowl Management Handbook.  Fish and Wildlife Leaflet 13.  U.S.  Department of the
Interior, Fish and Wildlife Service, Washington,  DC.
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Merritt, R.W., and  K.W. Cummins.   1984.  An Introduction to the Aquatic  Insects of North  America.
Kendall/Hunt Publishing Company, Dubuque, IA.

Weber, C.I., ed.  1973.  Biological field and laboratory methods for measuring the quality of surface waters
and  effluents.   EPA  670/4-73/001.   Program Element  1BA027.   U.S. Environmental Protection  Agency,
National Environmental Research Center, Cincinnati, OH.

BIBLIOGRAPHY:

Downing, J.A., and  H. Cyr.  1985.  Quantitative estimation of epiphytic invertebrate populations.   Can. j.
Fish. AquaL Sci. 42:1570-1579.

Driver, EA  1977.  Chironomid communities in small prairie ponds: Some characteristics and controls.
FreshwaL  Biol.  7:121-133.

Erman, D.C., and N.A. Erman.  1975. Macroinvertebrate composition and production in some Sierra Nevada
minerotrophic peatlands.  Ecology 56(3):591-603.

Murkin, H.R., and  D.A.  Wrubleski.   1987.  Aquatic invertebrates of freshwater wetlands: function and
ecology.   Pages 239-249.  In:  D.D. Hook, W.H. McKee, Jr., H.K. Smith, J. Gregory, V.G. Burrell, Jr., M.R.
DeVoe, R.E. Sojka, S. Gilbert, R. Banks, L.H. Stolzy, D.  Brooks, T.D. Matthews,  and T.H. Shear, eds.  The
Ecology and Management of Wetlands.  Vol. 1:   Ecology of Wetlands. Croom Helm, London &  Sydney.

Reid, F.   1985.  Wetland invertebrates in relation  to hydrology and  water chemistry.  Pages 51-60.  In:
M.D. Knighton,  ed., Water  Impoundments for Wildlife:   A Habitat Management Workshop.   General
Technical  Report NC-100.  U.S.  Department of Agriculture, Forest Service, St. Paul, MN.

Resh, H.V., and D.M. Rosenberg, eds. 1984.  Ecology of Aquatic  Insects:  Secondary Production of Aquatic
Insects. Praeger, New York.

Resh, V.H, and D.G. Price.  1984.  Sequential sampling:  A cost effective approach for monitoring  benthic
macroinvertebrates in environmental impact assessment  Environ.  Manage. 8:75-80.

Schmal, R.N., and D.F.  Sanders. 1978.  Effects of Stream Channelization on Aquatic Macroinvertebrates,
Buena Vista Marsh,  Portage County,  Wisconsin.  FWS/OBS-78/92.  U.S. Department of the Interior, Fish &
Wildlife Service.

Walker, I.R., C.H. Fernando, and C.G. Paterson.  1985.  Associations of Chironomidae (Diptera) of shallow,
acid, humic lakes and bog  pools in Atlantic  Canada, and  a comparison with an earlier paleoecological
investigation. Hydrobiologia 120:11-22.

Wiggins,  G.B.,  R.J.  Mackay, and I.M. Smith.  1980.  Evolutionary and ecological strategies of animals in
annual temporary pools.  Arch. Hydrobiol. Suppl. 58:97-206.

Witter, ]A., and S. Croson.  1976.   Insects and  wetlands.  Pages 271-295.  In:  D.L. Tilton,  R.H.  Kadlec,
and  C.J. Richardson, eds.  Proceedings of a National Symposium on Freshwater Wetlands and Sewage Effluent
Disposal.  University of Michigan, Ann Arbor.
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C.6  INDICATOR:  Soil and Aquatic Microbial Community Structure

CATEGORY:  Response/ Community Structure

STATUS:  Research

APPLICATION:   Microbial  communities are  tightly linked to fundamental  ecological  processes such  as
decomposition,  denitrification, and  respiration; they have been used  to indicate human influence and are
sensitive to the  presence of some contaminants (e.g., Ames and Microtox tests); and they can be measured
in wetlands where there is  no standing water.

INDEX PERIOD:  Maximum numbers  may be present late in the growing season  or  as detrital biomass is
maximized.

MEASUREMENTS:   The most common  methods used to  identify aquatic microbial  communities involve
processing water samples through a  membrane filter and growing the filtered bacteria on a selective medium.
Sediment communities  have been examined  in extracts prepared  from sediment cores and  on agar  plates
prepared with soil  suspensions.

VARIABILITY:  Temporal variability  of  microbial community structure  during the index period may be high
according to normal hydrologic  variation; spatial variability  should be  moderate in most resource sampling
units.

PRIMARY PROBLEMS:  Microbiological methods for isolation, characterization,  and identification of organisms
in nature are still not well defined.   Microorganisms can develop tolerances to concentrations of pollutants
that were once  toxic.  Microbiological  methods are known but are time-consuming and costly.

BIBLIOGRAPHY:

Gambrell, R.P., and W.J. Patrick, jr.  1978.  Chemical and  microbiological properties of anaerobic soils and
sediments.   Pages  375-423.   In:   D.D.  Hook and R.M.M.  Crawford,  eds.   Plant  Life  in Anaerobic
Environments.  Ann Arbor Science,  Ml.

Godshalk, G.L., and R.G. Wetzel.  1978. Decomposition in the littoral zone  of lakes. Pages 131-143. In:
R.E. Good, D.F.  Whigham, and R.C.  Simpson,  eds.  Freshwater Wetlands, Ecology Processes and Management
Potential.  Academic Press,  New York.

Henebry, M.S., and J. Cairns, Jr. 1984.  Protozoan colonization rates and trophic status of some freshwater
wetland lakes. J. Protozool. 31(3):456-467.

Hodson, R.E. 1980. Microbial  degradation of industrial wastes applied to freshwater swamps and marshes.
Rept.  No. A-082-GA.  Georgia Institute of Technology, Atlanta.

Murray, R.E., and R.E. Hodson.   1985.  Annual cycle of bacterial  secondary  production  in five aquatic
habitats of the Okefenokee Swamp ecosystem. Appl. Environ. Microbiol.  49(3):650-655.

Polumin, N.V.C.  1984. The decomposition of emergent macrophytes in freshwater. Adv. Ecol. Res. 14:115-
166.

Pratt, J.R., and  J. Cairns, Jr. 1985.   Determining  microbial  community equilibrium  in disturbed wetland
ecosystems.  Pages 201-209.  In:  F.J. Webb, ed.  Proceedings of the Twelfth Annual Conference on Wetland
Restoration.  Hillsborough Community  College, Tampa, FL.

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C7  INDICATOR:  Nutrients in Water and Sediments

CATEGORY:  Exposure and  Habitat/ Ambient Concentrations

STATUS:  High-Priority Research

APPLICATION: Despite widespread concern when cultural eutrophication renders lakes and ponds unsuitable
for  recreational use, the  public generally does not recognize this potential problem  in wetlands,  nor the
relationship between the  symptoms of eutrophication and  the ability of wetlands to remove nutrients before
they reach lakes and  ponds.  Many studies throughout the  United States have documented  nutrient
concentrations in "reference"  wetlands and in wetlands exposed to urban and agricultural runoff and  sewage,
but these data have not been compiled or synthesized.  If this were to be accomplished, it could strengthen
the potential  to link nutrient concentrations to response indicators.

INDFX PERIOD:  An optimal period to sample nutrients in the sediment and aquatic environment would be
in mid-growing season, when metabolic and assimilation rates of aquatic biota are at their peak and  ambient
concentrations are at minima. For  purposes of EMAP, sampling immediately following an extreme rainfall
event or drought should be avoided in order to minimize temporal variation that may suggest abnormally high
or low concentrations.

MEASUREMENTS: Water and soil samples should be analyzed for total Kjeldahl N (TKN), total P, K,  Ca,  Mg,
Na, and pH,  as well as Cl where wastewater contamination is suspected.  In situations where the need for
additional information on  N  and P species warrants the additional costs for analysis, NOX, NH4, organic N,
TKN, POA, organic P, and total P should be measured.  The resources required to acquire samples are minor,
approximately 1  person-hour a site.  The laboratory analysis cost for  each  resource sampling unit, assuming
three samples each  of surface waters, interstitial water, and soil,  will  be about $900.  Samples of the water
column and interstitial waters should be collected on a 2- to 4-year frequency;  where significant temporal
variation  in  nutrient concentrations results  from  climatic  pulsing or anthropogenic events,  more frequent
collection  may be needed.

VARIABILITY: The expected spatial variability of nutrient concentrations within a resource sampling  unit
would  produce a range that deviates 100% from the mean value for an element  The  variability in soil
and water samples taken  from different locations  might be overcome  through the use of composite samples.
The expected temporal variability  of nutrient concentrations during the  index period is associated with
hydrologic and climatic fluctuations  in  surface and rain inputs that can dilute concentrations  or introduce
significant concentrations  with surface inflows, and can also have a range that deviates  100%  from the mean
value for an  element

PRIMARY PROBLEMS:  No problems stand in the way of implementation, except for possible high temporal
and spatial variability within  a resource sampling  unit;  however,  these can be overcome through composite
sampling.  There is sufficient experience with methods and interpretation of results that implementation should
provide significant results early in  the program.   The  added diagnostic ability that  results from the  nutrient
data should help in  interpreting other indicators.

BIBLIOGRAPHY:

Bowden, W.B.  1987.  The  biochemistry of nitrogen  in freshwater wetlands. Biochemistry 4:313-348.

Dickerman, J.A., A.J. Stewart, and J.C. Lance.  1985.  The Impact of Wetlands on the Movement of Water
and Nonpoint Pollutants  from Agricultural  Watersheds.  U.S. Department of Agriculture, Soil Conservation
Service, Agricultural Research Service,  Water Quality & Watershed Research Laboratory, Durant, OK.
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Howard-Williams, C.  1985.  Cycling and retention of nitrogen and phosphorous in wetlands: A theoretical
and applied perspective.  FreshwaL Biol. 15:391-431.

Nixon, S.W., and V. Lee.  1985.  Wetlands and Water Quality:  A Regional Review of Recent Research in
the United States on the Role of Freshwater and Saltwater Wetlands as Sources, Sinks, and Transformers of
Nitrogen,  Phosphorus, and Various Heavy Metals.  U.S. Army Corps of Engineers,  Waterways  Experiment
Station, Vicksburg, MS.

Richardson, C.J., D.L Tilton, J. Kadlec, J.P.M.  Chamie, and  WA Wentz.  1978.   Nutrient dynamics in
northern  wetland ecosystems.   Pages 217-241.   In:  R.E.  Good, D.F. Whigham, and R.L. Simpson, eds.
Freshwater Wetlands:  Ecological Processes and Management Potential. Academic Press, New York.

Richardson, C.J.   1985.  Mechanism  controlling phosphorus retention  capacity in freshwater wetlands.
Science 228:1424-1427.

Whigham, D.F., C. Chitterling, and B. Palmer.  1988.  Impacts of freshwater wetlands on water quality:
A landscape perspective.  Environ. Manage. 12(5):663-671.

Whigham, D.F., and S.E. Bayley. 1978.   Nutrient dynamics in freshwater wetlands. Pages 468-478.  In:
P.E.  Greeson,  J.R. Clark,  and  J.E.  Clark,  eds.   Wetland Functions and Values:   The State  of Our
Understanding.  American Water Resources Association. Minneapolis, MN.
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C.8  INDICATOR:  Chemical Contaminants in Water and Sediments

CATEGORY:  Exposure and  Habitat/ Ambient Concentrations

STATUS:  Research

APPLICATION: Public awareness of pollutants, especially of carcinogens and airborne contaminants, appears
to be rising. Most of the public's concern is concentrated on the effects of pollutants on human health, but
there is also considerable acknowledgement of their ecological effects, such  as the effects of pesticides on
avian reproduction and the  effects of oil on  birds and mammals.  The relatively large amounts of organic
matter and organic compounds within many wetland ecosystems increase the likelihood of sorption of man-
made organic compounds, thereby improving water quality downstream.

Sediments can be a reservoir for some organic compounds (Smith et al. 1988). Through sediment accretion,
wetlands can be significant "sinks" for metals and organic compounds.  Limiting measurements of contaminant
concentrations to those in the water column may result in serious errors of omission, especially for wetlands,
where sediments hold the record  of past contamination events.

Few studies have documented contaminant concentrations in  wetlands.  Because wetlands are potentially
important  sinks for metals and  organic  compounds,  and  because most wetlands (being located  at
topographically low points)  are hydrologically exposed to urban and agricultural runoff, sewage, and other
pollutant sources, a data  base is  needed to assess contaminant exposure to wetland response indicators.

INDEX PERIOD:  An optimal period to sample chemical  contaminants in  a wetland environment would be
mid-growing season, when metabolic and assimilation rates of  wetland biota  are at their peak and ambient
concentrations of contaminants are expected to be minimal.  For purposes of EMAP,  samples should  not be
collected immediately  following an extreme rainfall event, because sampling during extreme events may bias
data and show temporal variation that may suggest abnormally high  or low concentrations.  Where significant
temporal variation  in contaminant concentrations results from  climatic pulsing or anthropogenic events, the
index period  may need to be restricted.

MEASUREMENTS:   Measurements of soil contaminants may be conducted during routine sampling for other
indicators with little regard for temporal variation,  unless there has been some recent major event that would
bias the sample.  Soil  samples would  be a composite of numerous samples to reduce the cost of analyzing
replicate samples.  At a minimum, the surface  (top 10 cm)  would be sampled.  Where suspected historic
activities warrant, a second composite sample would be taken at a depth of 10-20 cm.  Composite sampling
would minimize the need for duplicate samples.

Water and soil  samples should be screened for several of the most  common metals and organic compounds
and "positives" analyzed further in greater detail.  Analyzed in  this manner, the laboratory  analysis expenses
would be about  $400 for each resource  sampling unit   The sample  acquisition  costs are  minor  -
approximately 1 person-hour for  each  sampling unit.  Contaminants in the water column may be  measured
during routine sampling for other indicators on an annual  basis  or less frequently. Soil samples are important
components in ecosystem cycling and  must accompany any water  analysis for contaminants.

VARIABILITY:   The expected spatial variability of chemical contaminants within a resource sampling unit is
high, but a specific estimate was not available.  The temporal variation of contaminant concentrations is
associated with hydrologic and climatic fluctuations in surface and rain inputs that can dilute concentrations
or introduce significant concentrations  with surface inflows; dissolved oxygen status, plant  uptake rates, and
microbial  process  rates also influence temporal variations in ambient concentrations. The expected coefficient
of variation (CV) of chemical contaminants during the index period  is high, at least 50%.  An example is
provided by Oberts and Osgood (1988), who monitored Pb concentrations in a wastewater treatment wetland

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for three months during autumn; they found total Pb in base water flow had a CV of 35%, whereas the Pb
distribution for total flow from the same wetland had a CV of 133%. Another example by Nixon and Lee
(1986) revealed a freshwater tidal wetland that exhibited a three-month CV (based on  import/export values)
for several elements, as follows:  Cu = 73%, Cd = 79%, Ni = 50%, Pb - 106%, and Zn  = 446%.

PRIMARY PROBLEMS:  No major problems prevent regional implementation, except the high temporal and
spatial variability of chemical contamination.   Implementation should be  preceded by a discussion of the
trade-off between the expense  of replicate samples and the potential for erroneous results through the use
of composite samples. There is sufficient experience with methods and interpretation of results that significant
results could occur soon after implementation.

REFERENCES:

Nixon, S.W., and V. Lee.   1986. Wetlands and water quality:  A regional review of recent research in the
United States on the role of freshwater and saltwater wetlands as sources, sinks, and transformers of nitrogen,
phosphorus, and various heavy metals. Technical Report Y-86-2.  Prepared for U.S. Army Corps of Engineers,
Waterways Experiment  Station,  Vicksburg,  MS.  University of Rhode Island, Providence.

Oberts, G.L, and R.A.  Osgood.  1988.  Final report on the function of the wetland treatment system to the
impacts of Lake  McCarrons.  Publication No. 590-88-095.  Metropolitan  Counsel  of the Twin Cities Area,
SL Paul, MN.

Smith, JA., P.J. Witkowski, and T.V. Fusillo.   1988.  Manmade organic  compounds  in the  surface waters
of the United  States -  A review of current understanding.  Circular 1007.  U.S. Department  of Interior.
Geological Survey, Government  Printing Office, Washington, DC.

BIBLIOGRAPHY:

Johnson, B.T.  1986.  Potential  impact of selected agricultural chemical contaminants on a northern prairie
wetland~A microcosm evaluation.  Environ. Toxicol. Chem.  5:473-485.

Oliver, J.D.  1985.  A  system for examining the response of aquatic ecosystems to gradual chemical inputs
and field applications in Okefenokee Swamp, Georgia.  Arch. Hydrobiol.  103:415-423.

Taylor, G.J., and A.A. Crowder.  1983. Accumulation of atmospherically  deposited metals in wetland soils
of Sudbury, Ontario. WaL Air Soil PolluL 19:29-42.
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C9  INDICATOR:  Hydroperiod

CATEGORY:  Exposure  and Habitat/ Habitat

STATUS:  High-Priority  Research

APPLICATION:  "Hydrology is  probably  the single  most important determinant for the establishment and
maintenance  of specific types  of wetlands  and wetland  processes" (Mitsch  and Cosselink 1986,  p.  55).
Change in wetland  hydrology or hydroperiod is probably the most common impact associated with human
alteration of.the landscape.  Ecosystem-level responses to an altered  hydroperiod  are  changes in  species
composition,  habitat  quality, and water  storage capacity,  among others (Zimmerman 1988).   Because
hydrologic conditions affect nutrient availability, soil oxygen and salinity,  sediment properties, pH, and species
composition (Mitsch and Gosselink 1986), changes in hydroperiod can bring about changes in nearly all other
components of wetlands, both  abiotic and biotic.

Hydroperiod  is interpolated from water-level records and  can be  reported as total  number of days of
inundation a year.  Also of value,  where water levels exhibit greater fluctuation, is the number of continuous
days of inundation.  Temporal variation of high- and  low-water events may also  indicate  hydrological
alteration. Hydroperiod  is determined by factors outside the wetland, both natural (e.g., precipitation patterns)
and anthropogenic (e.g., urban  and agricultural development).  Changes in hydroperiod  may cause,  but are
not necessarily symptomatic of, changes within  the wetland.   Thus, monitoring hydroperiod  can indicate
changes originating  in the landscape surrounding the  wetland,  and to  a lesser  degree,  changes originating
within  the wetland.

Changes in hydroperiod can indicate changes in habitat  Habitat quality is related to vegetative community
composition and distribution, which  in turn  are  profoundly influenced  by hydroperiod and by the quantity
and quality of incoming water.  The  measurement of hydroperiod is also essential for interpretation of most
of the other  indicators.  Movement of nutrients in wetlands is primarily via water,  so an assessment of
hydrology is important for testing relationships of nutrient budgets (Gosselink and Turner 1978, cited in Kadlec
1984).

INDEX PERIOD:  Because hydroperiod varies from year to year with changes in rainfall,  surface inputs from
runoff  and streams, and groundwater levels and because  water levels  fluctuate  seasonally within  most
wetlands, the index period  required to identify subnominal hydroperiods is extremely variable.  Some classes
of wetlands exhibit  very little variation  in  water  levels throughout the year and may exhibit yearly changes
that result only from yearly variation in  precipitation.  Water level data in these wetlands may need to be
gathered on a semiannual or bimonthly frequency.

MEASUREMENTS:   Hydroperiod can be  monitored by measuring surface water level, either with continuous
water-level recorders or with staff gauges  read at regular intervals.   Measurement of rainfall at the wetland
site, essential in early phases of implementation, may  be eliminated during later phases as background  data
are accumulated and analyzed.  In addition to water levels, precipitation measured at the resource sampling
unit in most circumstances will provide additional diagnostic information. Precipitation can also be monitored
by using rain gauges or data from a nearby weather station.

Water  level data should be monitored  by  using staff  gauges.  For wetlands  that exhibit a much wider
fluctuation  in water levels  on  a daily, weekly, or monthly basis, continuous water  level recording devices
should be used to obtain data of much finer resolution to ensure that fluctuations are  adequately represented.
Instrumentation will cost approximately $2000 per resource sampling unit for continuous monitoring systems
and about 12 person-days  a year for each resource sampling unit for data recovery, entry, and analysis.  If
staff gauges are used monthly,  instrumentation will cost approximately $200 a  resource sampling unit and
require about 12 person-days a year for each site for data recovery, entry, and analysis.

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VARIABILITY:  The spatial variability in hydroperiod within a resource sampling unit depends on the variability
of ground surface elevations. Resource sampling units with significant variation in microtopographic relief will
have numerous locations that have hydroperiods that are longer (holes) and shorter  (hummocks) than that
of the  sampling location.   Basin-type wetlands often have deep central  portions  and shallow edges that
require  careful placement  of the water-level measurement apparatus and  careful interpretation of data.
Within-wetland variability can  produce ranges that deviate  100% from the  mean value,  according  to
placement  of  apparatus.   However, variability can easily be overcome with  experienced  placement  of
monitoring  equipment and  should not pose serious limitations.

The temporal variability of hydroperiod during the index period can be an  important consideration, especially
with basin-type wetlands, whose hydrology is determined more by direct rainfall than by runoff or ground
water conditions;  ranges  may  deviate  50% from  the  mean value.   With  proper  corroborating data,
climate-driven variation can be accounted for, and temporal variation should not pose serious limitations.

PRIMARY PROBLEMS:  There are no significant problems with implementation of hydroperiod as a  habitat
indicator.   Hydroperiods are more accurately known for  some wetland types than  for others.

REFERENCES:

Gosselink,  J.C., and R.E. Turner.  1978.  The role  of hydrology in freshwater wetland ecosystems.   Pages
63-78.   In:  R.E. Good,  D.F. Whigham, and R.L. Simpson, eds. Freshwater Wetlands:  Ecological Processes
and Management Potential.  Academic  Press, New York.
Kadlec, J A. 1984.  Hydrology.  Pages 19-26.  In:  J.H. Murkin, ed.  Marsh Ecology Research Program Long-
Term Monitoring Procedures Manual.   Delta  Waterfowl Research Station, Portage la Prairie, Manitoba,
Canada.

Mitsch, W.J., and J.G. Cosselink.  1986.  Wetlands.  Van Nostrand Reinhold Company, New York.

Zimmerman, J.H.  1988.  A multi-purpose wetland characterization procedure, featuring the hydroperiod.
Pages 31-48.   In:   J.A. Kusler and  G.  Brooks, eds.  Wetland Hydrology.  Proceedings of the  National
Wetlands Symposium.  Association  of Wetland  Managers, Berne, NY.

BIBLIOGRAPHY:

Allison, G.B., and J.R. Forth.  1982.  Estimation of historical groundwater recharge rate. Australian J. Soil
Res.   20:255-259.

Carter, V.,  M.S. Bedinger, R.P. Novitski, and W.O. Wilen.  1 978.  Water Resources and Wetlands.  Pages
344-376.   In:   P.E.  Greeson, J.R. Clark, and J.E. Clark, eds.  Wetland Functions and Values:  The State of
Our  Understanding.  American Water Resources Association.   Minneapolis, MN.

Fredrickson, L.H., and T.S.  Taylor.  1982. Management of  seasonally-flooded impoundments for wildlife.
Resource Publication 1 48.  U.S. Department of Interior, Fish and Wildlife Service, Washington, DC. 28  pp.

Heilman, J.L.   1982.   Evaluating  depth to shallow groundwater  using Heat Capacity Mapping Mission
(HCMM) data.  Photogrammetric. Eng. Remote Sens. 48(1 2):1 903-1 906.

Kadlec, R.  1988. Monitoring wetland responses.  Pages 114-120.  In:  J. Zelazny and J.S. Feierabend, eds.
Increasing Our Wetland Resources.  National Wildlife Federation, Washington, DC.
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LeBaugh, J.W.  1986.  Wetland ecosystems studies from a hydrologic perspective.  Wat Resour. Bull. 22:1-
10.

Paterson,  N.J., and T.H. Whillans.  1984.   Human interference with natural water  level  regimes in the
context of other cultural stresses on Great Lakes wetlands. Pages 209-251.  In:  H.H. Prince and F.M. D'ltri,
ed.  Coastal Wetlands.  Lewis Pub., Inc.

Sloan, C.E.  1970.  Biotic and hydrologic variables in prairie potholes  in North Dakota,  j. Range Manage.
23:260-263.

Stewart, R.E.,  and HA Kantrud.  1971.  Classification  of natural ponds and lakes in the glaciated prairie
region.  Resource Publication 92.  U.S. Department of  Interior, Fish and Wildlife Service,  Washington,  DC.
57pp.

Theriot, R.F.,  and D.R. Sanders. 1986.  A concept and procedure for developing and utilizing vegetation
flood tolerance indices in wetland delineation.  Technical Report Y-86-1.   U.S. Army Corps of Engineers,
Waterways Experiment Station, Vicksburg, MS.  25 pp.

Whitlow, T.H. and R.W. Harris.  1979.   Flood Tolerance in Plants:  A State-of-the-Art Review.  Tech. RepL
E-79-2. U.S. Army Corps of Engineers, Waterways Experiment Station, Vicksburg, MS.  257 pp.

Winter, T.C.   1988.  A conceptual framework for  assessing cumulative  impacts on the hydrology of nontidal
wetlands.   Environ. Manage. 12(5):605-620.

Zimmerman, J.H.  1988.  A multi-purpose  wetland characterization procedure, featuring the  hydroperiod.
Department of Landscape  Architecture  and Institute  of Environmental Studies,  University of Wisconsin,
Madison.
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CIO  INDICATOR:  Bioassays

CATEGORY:  Exposure and Habitat/ Bioassays

STATUS:  Research

APPLICATION: One of the recognized functions of wetlands is their ability to filter pollutants from point and
nonpoint sources.  Excessive pollutant loading, however, can overwhelm the assimilative capacity of wetlands
and result in degradation of their biological integrity.  The ability of wetlands to sustain healthy organisms can
indicate the degree of wetland  contamination.  Bioassays involve placing an organism or population in the
field or into "microcosms" constructed with materials from the field to be tested in the laboratory.  Responses
of the organism are observed and recorded.

INDEX  PERIOD:   An  optimal period  to observe  organisms  in  a  wetland environment would be  in
mid-growing season, when  metabolic and  assimilation rates of aquatic biota are at their peak.

MEASUREMENTS:  Methods entail  either collection  of soil and water samples in  the field for controlled
bioassays in the laboratory  or the introduction of organisms into some type of constraining  environment  in
the field for later retrieval.

VARIABILITY:  The expected spatial and temporal variability of bioassay measures within a resource sampling
unit and during the index period,  respectively, were not estimated because specific bioassays  were not
recommended at  this time.

PRIMARY  PROBLEMS:  Much  work is needed on  developing protocol  and target species for wetland
communities. Bioassays would not be useful for detecting changes in wetland community structure, because
they commonly test a single species  at a time, without regard  to effects on  biotic competition. They would
not be useful for testing effects of stressors such as hydroperiod,  whose ecological effects are mainly  indirect
and time-lagged.

BIBLIOGRAPHY:

Cairns, J., Jr. 1974. Indicator species vs the concept of community structure as an  index of pollution. WaL
Resour.  Bull.  10:338-347.

Fremling,  C.R., and W.L Mauck. 1980.  Methods  for using nymphs of burrowing mayflies (Ephemeroptera,
Hexagenia) as toxicity  test organisms.  Pages 81-97.   In:   Aquatic Invertebrate Bioassays.  Spec. Tech. Pub.
715.   American Society for Testing and Materials, Philadelphia.

Helawell, J.M. 1977.  Change in natural and managed ecosystems: Detection, measurement and assessment.
Proc. R. Soc. London, Ser.  B 197:31-57.

Herricks, E.E., and J. Cairns, Jr. 1982.  Biological monitoring. III. Receiving system methodology based on
community structure.  WaL Res. 16:141-153.
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C.11  INDICATOR:  Chemical Contaminants in Tissues

CATEGORY:  Exposure-Habitat/ Tissue Concentrations

STATUS:  Research

APPLICATION:  Bioaccumulation  integrates the level of contaminant exposure within the recent past  The
longer lived the organisms chosen as  indicators, the greater the period of time over which the exposure is
integrated (all other  things being  equal).  Measurements of bioaccumulation would  be as an indicator of
contaminant  dose to an organism to help explain  why  wetland response indicators are in subnominal
condition.

The bioaccumulation of contaminants (especially metals and pesticides) in aquatic organisms has been studied
for many years (see Cairns and Dickson 1980; Hellawell  1986; review by Phillips 1980).

INDEX PERIOD: An optimal period to measure chemical contaminants in tissues would be in mid-growing
season, when metabolic and assimilation rates of wetland  biota are at their peak.

MEASUREMENTS:  Methods for analysis of tissue samples are well developed.  The choice  of organism is
greatly dependent on the  pollutant.  There are many species-specific differences regarding bioaccumulation
of metals and organics. All higher organisms are potential  candidates for bioaccumulation sampling, although
for consistency and the assurance of an adequate sample size, macrophytes, macroinvertebrates,  fish, and
herpetofauna are the most likely candidates.  Birds and mammals, however, should not be entirely ruled out
A sufficient sample size is required to overcome limitations imposed by within-population variability.  This
suggests that species with large populations be chosen as indicators. In summary, organisms that are selected
should be  ubiquitous,  relatively immobile,  and able to bioaccumulate a large variety of anthropomorphic
substances.  Determination of bioaccumulation in floral and faunal tissues may be the most costly of indicators
if sample collection and processing are included.

VARIABILITY:  The expected spatial variability in measuring tissue residues within the same sampling unit is
high.  Many factors can affect tissue concentrations, not the least of which is the age and diet of an individual
organism compared with others  in the sample.  Also of  potential significance  is mobility of some species,
which may have high or low tissue concentrations resulting from travels or life stages spent in  other parts of
the landscape.  The expected temporal variability of measurements during the index period can be significant

PRIMARY PROBLEMS: The biggest problems associated with the use of bioaccumulation as an indicator are
determination of which species to use and interpreting the ecological implications of the data.  Mollusks,
certain insects, and macroinvertebrates have been suggested by various authors.

REFERENCES:

Cairns, J. Jr., and K.L. Dickson.  1980.  The ABCs of biological monitoring.  Pages 1-31.  In: C.H.  Hocutt
and J. R.  Stauffer, Jr.,  eds.   Biological Monitoring of Fish.   Lexington  Books, D.  C.  Heath and Company,
Lexington, MA.

Hellawell, J.M.  1986.  Biological  Indicators of Freshwater Pollution and Environmental Management Elsevier
Applied Science Publishers,  London.

Phillips,  D.J.H. 1980.  Quantitative Aquatic Biological  Indicators.  Applied Science Publishers Ltd., London.
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BIBLIOGRAPHY:

Antonovics, J., A.D. Bradshaw, and R.G. Turner.  1971.  Heavy metal toxicity in plants.  Adv. Ecol. Res.
7:1-85.

Biddinger, G.R., and S.P. Gloss.  1984.   The importance of trophic transfer  in the bioaccumulation of
chemical contaminants in aquatic ecosystem.  Residue Rev. 91:103-145.

Carriker,  N.E.   1977.  Heavy metal  interactions with natural  organics in aquatic  environments.   Ph.D.
Dissertation, Department of Environmental Engineering Sciences, University of Florida, Gainesville.  155 pp.

Grue, C.E., L.R. Deweese, P. Mineau,  G.A. Swanson, J.R. Foster, P.M. Arnold, J.N. Huckins, P.J. Sheehan,
and  W.K. Marshall.  1986.   Potential impacts of agricultural chemicals  on waterfowl and other wildlife
inhabiting prairie wetlands: An  evaluation of research needs and approaches. Transactions of the 51st North
American  Wildlife and Natural Resources Conference.  Wildlife Management Institute, Washington, DC.

Keith, J.O.  1966.  Insecticide contamination  in wetland habitats and their effects on fish-eating birds.  Pages
71-85.  In:  N.W. Moore, ed.  Pesticides in the Environment and Their Effects on Wildlife.  Blackwell Science
Publishers, Oxford.

Larsen, V.J. and  H.H. Shierup.  1981.  Macrophyte cycling of zinc, copper,  lead and cadmium in the littoral
zone of a polluted and non-polluted lake:   II.  Seasonal changes in heavy metal content in above-ground
biomass and decomposing leaves of Phragmites australis (Cav.) Trin.  Aquat. Bot. 11:211-230.

Lewin,  S.A., MA.  Harwell,  J.R.  Kelly,  and  K.D.  Kimball, eds.   1988.    Ecotoxicology:   Problems  and
Approaches.  Springer-Verlag, New York, NY.

Mclntosh, A.W., B.K. Shephard, R.A.  Mayes, G.J. Atchison, and D.W. Nelson.  1978.   Some aspects of
sediment distribution  and  macrophyte cycling of heavy metals in a  contaminated lake.  J. Environ.  Qual.
7:301-305.

Mouvet, C.  1985.   The  use  of aquatic bryophytes to  monitor heavy metals pollution  of freshwaters as
illustrated  by case studies. Verh. Internal. Verein. Limnol.  22(4):2420-2425.

Rand,  G.M., and S.R. Petroulli.   1985.  Fundamentals  of Aquatic  Toxicology.  Hemisphere Publishers,
Washington, DC. 666 pp.

Robinson-Wilson, E.  1981.  The function of rooted aquatic macrophytes with respect to contaminant areas.
Ecol. Soc. Am. Bull. 62(2):73-74.

Scheuhammer, A.M.   1987.   Reproductive  effects of chronic, low-level dietary metal exposure  in  birds.
Transactions of the 52nd North American Wildlife and Natural  Resources Conference.  Wildlife Management
Institute, Washington,  DC.

Stephenson, M., and G.L. Mackie.   1988.   Multivariate analysis of correlations between environmental
parameters and cadmium concentrations in Hyalella azteca (Crustacea: Amphipoda) from central Ontario lakes.
Can. J. Fish. Aquat.  Sci. 45:1705-1710.
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          APPENDIX D:  INDICATOR FACT SHEETS FOR FORESTS
                                 Authors
                              Kurt H. Riitters
         NSI Technology Services Corporation - Environmental Sciences
      U.S. EPA Atmospheric Research and Exposure Assessment Laboratory
                   Research Triangle Park, North Carolina

                               Beverly Law
National Council of the Pulp and Paper Industry for Air and Stream Improvement
                 U.S. EPA Environmental Research Laboratory
                             Corvallis, Oregon

                              Robert Kucera
         NSI Technology Services Corporation - Environmental Sciences
      U.S. EPA Atmospheric Research and Exposure Assessment Laboratory
                   Research Triangle Park, North Carolina

                              Alisa Gallant
                               Craig Palmer
         NSI Technology Services Corporation - Environmental Sciences
                 U.S. EPA Environmental Research Laboratory
                             Corvallis, Oregon

                              Rob DeVelice
            Nature Conservancy, Montana Natural Heritage Program
                             Helena, Montana

                            Rick Van Remortel
                 Lockheed Engineering and Science Company
            U.S. EPA Environmental Monitoring Systems Laboratory
                            Las Vegas, Nevada

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                 Contributors

              Samuel Alexander
Virginia Polytechnic Institute and State University
    Department of Pathology and Physiology
              Blacksburg, Virginia

                 Les Magasi
       Forestry Canada, Maritime Region
       Hugh John Fleming Forestry Center
          Fredericton, New Brunswick

                Richard Waring
            Oregon  State University
              College of Forestry
               Corvallis, Oregon

                Carol Wessman
            University of Colorado
                 CIRES/CSES
               Boulder, Colorado

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D.I  INDICATOR:  Tree Growth Efficiency

CATEGORY:   Response/ Population Structure

STATUS:  High-Priority Research

APPLICATION: The  productivity and sustainability of trees are endpoints of concern. Tree growth efficiency
is a  response  indicator that is related  to those two environmental values; it reflects the ability of trees to
maintain a healthy and  productive presence in an ecosystem.  It is related to productivity in  that higher
growth  efficiency is  associated with higher gross growth per unit  of capacity for growth (e.g., Waring and
Schlesinger 1985).   It  is related to sustainability in that higher growth efficiency  is associated with higher
resistance to insect attack (Mitchell  et  al. 1983).

The  rationale for incorporating a growth efficiency indicator is based on carbon allocation patterns in trees.
Carbon allocation to stem-wood and  protective chemicals has lower priority, physiologically, in comparison
to allocation to storage reserves, new foliage, and roots (Waring and Pitman 1985).  This means that effects
of environmental stresses that alter carbon  allocation are manifested first in reduced stem-wood growth and
in reduced production  of protective chemicals (See also E.I, Vegetation  Biomass). Stem-wood growth is used
in the  indicator,  rather than  amount of protective chemicals,  because  it  is easier to measure wood growth
(See also E.7,  Dendrochronology:  Trees and Shrubs).

Growth efficiency can  be estimated by several functions because  growth, and  capacity for  growth, can be
measured in several ways.  We consider the family of functions that normalize the amount of stem-wood
produced to the  amount of light intercepted by (or to the leaf area of) the canopy trees.  Several forms of
the ratio can be  recommended for  testing.  The numerator  can be stem-wood  volume growth (m3 ha"1 yr1)
or biomass growth (kg  ha1 yr1); these values  are derived from repeated measurements of tree dimensions at
specific locations.  The denominator can be  one of several  indices of the amount  of light absorbed by the
overstory trees at that same  location;  candidate indices include leaf area index (LAI),  sapwood basal  area,
fraction of photosynthetically active radiation absorbed by the canopy (%APAR) and the normalized difference
vegetation index  (NDVI).

Conversions among  the various expressions of growth efficiency  require  estimates of stem-wood specific
gravity,  the canopy light extinction coefficient, canopy  reflectance  in specific wavelengths, and/or  leaf  area-
to-sapwood area  ratio.  The  various expressions differ  in relative accuracy, precision, cost, and applicability
to the  EMAP design.

For  application as a response indicator, it  is  possible to derive  initial estimates  of  threshold  values for
subnominal growth efficiency based on values from the literature.  However, these  are reported as a variety
of units, and conversions to  standard  units are not always possible.  Reports are also usually made on an
individual-tree basis  rather than on an  areal basis.  Experience and testing will improve these estimates over
time.

While growth  efficiency changes indicate changing productivity and sustainability, there are situations where
productivity and sustainability may change without a change in growth efficiency.  For example, a  forest may
lose  foliage (denominator decreases) and grow slower (numerator decreases) at the same time.  Clearly, these
changes would be of interest; it  has been suggested that we explore the application of the numerator alone,
and  denominator alone, as independent response  indicators.  No  action has been  taken  on this  suggestion
for this  document because early reviewers  felt that because  foliage loss precedes growth  reductions, growth
efficiency alone would  suffice. The suggestion will be reevaluated because it cannot  be guaranteed that these
subtle differences in  timing would be detectable in the sampling design envisioned by EMAP.  Thus, both the
numerator and denominator  will be considered as separate indicators in the future.
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Changes  in growth efficiency over  regional  populations may occur for a variety of reasons,  and not all of
them  are of  regulatory  concern.   For  example, if the regional  population is  aging, the regional growth
efficiency would decline over time.  Although  these changes would suggest increased probability of, for
example,  regional  insect outbreaks, or decreased regional growth  rates,  they  would  not normally  be of
concern  at a  specific location.  To interpret the growth efficiency indicator at a specific location, auxiliary
information about stand species composition, age, stand density, and other features is required.

INDEX  PERIOD:  Stem-wood  growth  and  foliage  dynamics   follow  well-known  annual  cycles, and
measurements during the period of  most rapid changes from spring through mid-  summer should be avoided.
Ideally, stem-wood growth would be measured during the dormant season, but foliage measurements would
be unreliable  at that time.  A iogistically efficient compromise is to make both measurements during a mid-
to late-summer  index period, when new foliage is fully expanded but old foliage is not yet senescing.

MEASUREMENTS: The  measurements would vary, depending on  the form of the function used to estimate
growth efficiency. To utilize volume growth  in the numerator, tree heights and stem diameters are measured
periodically on permanent plots by using standard mensurational techniques (e.g., Husch  et al.  1972).  These
measurements are converted to volume estimates, again by standard methods (Husch et al. 1972). To  utilize
biomass  growth  in the  numerator,  it is necessary  to convert volume to weight  The  most  straightforward
method is to  use species-specific constants of wood specific gravity, but more accurate methods are possible
if additional measurements are made on the resource sampling unit.  Another technique utilizes allometric
equations based  on tree stem diameter and  (sometimes) height to estimate stem-wood weight directly. The
estimated resource required is two technician-days.  The  measurement error of 5- to  10-year  gross stem-
wood volume growth is estimated to have a coefficient of variation (CV) of about 10% when typical forest
inventory procedures are used. The recommended interannual sampling frequency is 10 years.

The denominator may  be estimated from  field (ground-based) or remotely sensed data. One difference
between field and remote measurements is that the remote  measurements can include vegetation other than
overstory trees,  whereas the field measurements consider only the vegetation above the instrument.   After
several alternatives were  reviewed,  one nondestructive method for each general approach was selected for
discussion.

(1) Remotely  sensed data.  NDVI is an index of LAI in coniferous forests that is obtained by ratioing channel
1  (infrared)  wavelengths  and channel  2   (red)  wavelengths  from the Advanced  Very  High Resolution
Radiometer (AVHRR) sensor on the NOAA  meteorological satellite (e.g., Running and Neman! 1988).  As a
measure of light  absorption,  NDVI is correlated with net primary productivity (Tucker and Sellers 1986). The
AVHRR provides daily coverage at 1.1-km  nominal resolution, an appropriate scale for regional vegetation
analysis.  The actual resolution at the edge of the field of vision is less, but better resolution may be possible
with future developments in remote sensing technology. Daily coverage provides an opportunity to monitor
canopy phenology over the growing season, and this could improve estimates of growth capacity.   Plot designs
for obtaining  the numerator of the growth efficiency indicator should consider the relatively  low resolution
of AVHRR data.  Unprocessed AVHRR data  for the contiguous United States costs  about  $100.  The
measurement error of satellite-based methods was not estimated.

(2)  Field data.   Hand-held devices are available  (e.g., Decagon Devices,  Inc., Li-Cor, Inc.)  to measure
instantaneous fluxes of photosynthetically active radiation (PAR, ca. 400-700 nm) quickly and easily.  The ratio
of PAR under a forest canopy to ambient PAR (e.g., in a clearing) is the percentage of PAR absorbed  by the
canopy (%APAR). %APAR has been tested as a measure of leaf area index over  ranges of LAI, stand density,
and solar illumination angle, and the results suggest that %APAR may  be a  widely applicable  index  of LAI
(e.g.,  Pierce and Running 1988). It seems less  likely that canopy  phenology can be conveniently monitored
by APAR methods.  The measurement error  of  %APAR can be estimated with  a CV of  <5%  in  good
conditions, but the CV can be much higher in poor conditions (see below). The measurements may be made
                                                D-2

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directly on a forest plot in  about two man-hours.  The estimated cost of the APAR measurement devices
ranges from $1500 to $6500; field measurements would take two technician-hours per resource  sampling
unit.  The recommended interannual sampling frequency is 1 to 5 years.

VARIABILITY:  The expected spatial variability of growth efficiency within a homogeneous resource  sampling
unit should be less than 15%.  The expected  temporal variability within the index period would produce a
range that deviates  <5% of the mean value.

PRIMARY PROBLEMS:  (1) Volume and biomass growth estimates: The accuracy of standard procedures may
be less than is required for accurate monitoring, and although more accurate methods exist, they are far more
expensive.  (2) %APAR: Using current techniques, good estimates  require clear  or uniformly overcast weather
and the availability of open clearings near the sampled forest stand.  (3) Light absorption, in general:  Remote
sensing methods are the most promising, but they have not  been calibrated  across the country,  and  they
require further development. The available imagery from AVHRR works well on flat terrain, but has  not been
proven to work well on  hilly or mountainous terrain, or where the forest  stands are  intermittent because of
rock outcrops and disturbances.  Clear-cut and young regeneration areas in coniferous forest areas are usually
dominated  by broadleaf species that have spectral properties  different from conifers.

REFERENCES:

Husch, B., C.I. Miller, and T.W. Beers.  1972.   Forest Mensuration.  Second Edition.  Ronald Press, New
York.

Mitchell, R.G.,  R.H. Waring, and G.B. Pitman.  1983.  Thinning lodgepole  pine increases tree vigor and
resistance to mountain pine  beetle.  For.  Sci.  29:204-211.

Pierce, L.L., and  S.W. Running.  1988.  Rapid estimation of coniferous  forest leaf area using a  portable
integrating radiometer.   Ecology 69(6):1762-1767.

Running, S.W., and R.R. Neman!.  1988.   Relating seasonal patterns of the AVHRR vegetation  index to
simulated  photosynthesis and transpiration of forests in different climates.   Remote Sens. Environ.  24:347-
367.

Tucker, C.J., and P.J. Sellers.  1986.  Satellite remote sensing of primary production.  Int.  J. Remote Sens.
7(11):1395-1416.

Waring,  R.H., and G.B. Pitman.   1985.   Modifying  lodgepole pine stands to change  susceptibility to
mountain pine beetle attack.  Ecology 66:889-897.

Waring, R.H., and  W.H. Schlesinger.  1985.  Forest Ecosystems: Concepts and Management  Academic
Press, Orlando.  340 pp.

Waring,  R.H.   1989.   Personal communication.  Statement at  the  EMAP-Forest Indicator Workshop in
Corvallis,  OR, August 29.

BIBLIOGRAPHY:

Botkin, D.B., M. Dacis,  J. Estes, A. Knoll, R.V. O'Neill, L. Orgel, L.B. Slobotkin, j.C.G. Walker, J. Walsh,
and  D.C. White.   1986. Remote Sensing of the Biosphere.  National  Academy Press, Washington,  DC.
135 pp.
                                               D-3

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Coward, S.N., CJ. Tucker, and D.C. Dye.  1985.   North American vegetation patterns observed with the
NOAA-7 Advanced Very High Resolution Radiometer.  Vegetation 64:3-14.

Running, S.W., D.L. Peterson, M.A. Spanner, and K.B. Teuber. 1986.  Remote sensing of coniferous forest
leaf area.  Ecology 67:273-276.

Waring, R.H., W.G. Thies, and D. Muscato.  1980.  Stem  growth  per  unit leaf area:  A measure of tree
vigor.  For. Sci. 26:112-117.

Waring, R.H.  1983.  Estimating forest growth and efficiency  in relation to canopy leaf area. Adv. Ecol. Res.
13:327-354.

Woodwell, C.M., J.E. Hobbie,  R.A. Houghton, J.M. Melillo, B. Moore, A.B. Park, B.J. Peterson, and G.R.
Shaver.  1984. Measurement  of changes in the vegetation of the earth by satellite  imagery.   Pages 221-
240.   In:   G.M.  Woodwell,  ed.   The Role  of Terrestrial  Vegetation  in  the  Global Carbon Cycle:
Measurement by Remote Sensing, SCOPE  23.  Wiley,  New York.
                                               D-4

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D.2  INDICATOR:  Visual Symptoms of Foliar Damage:  Trees

CATEGORY:  Response/ Cross Pathology
             Exposure and  Habitat/ Pathogens

STATUS:  High-Priority Research

APPLICATION:  Observations and measurements of visual symptoms and tree mortality identify conditions
and agents directly related to forest health.  Measures of visual symptoms  provide a response indicator for
the environmental values of  productivity and aesthetics, and they provide exposure and habitat variables for
analyses to suggest possible  causes of forest condition changes.  Visual symptom measurements should be
developed  on  a  regional basis  to be  consistent with  regional  tree species,  pests  and pathogens, and
environmental features.  The measurements may be adapted to different sampling frequencies and densities
as required by  the heterogeneity of regional forests or by the degree of resolution needed.

The  response  indicator described in Measurements  enables  interspecific  comparisons  and international
communications concerning  forest health.   This  "European  method" (UNEP and UN-ECE 1987), based on
apparent defoliation,  has been used in investigations of visual symptoms in Europe and  in Canada, but visual
symptom monitoring can be conducted differently by  other methods.  For example  "crown density" and
"transparency" are measurement methods which have been developed  to measure the  amount of foliage on
loblolly pines and  shortleaf  pines  (Anderson  and  Belanger 1986) and sugar  maple and other  hardwoods
(Millers and Lachance 1989), respectively.

"Indicator plant" observations are suggested for many visual  damage surveys.   Tree species can be  ranked
according to  relative pollutant sensitivity by looking at visual symptoms of damage, and indicator  plants in
the understory can be used to signal exposure of the forest to specific pollutants. An example of this process
is the ranking of sensitivity of woody plants to sulfur dioxide and photochemical oxidants (Davis and Wilhour
1976). A positive damage measurement could indicate presence of a pollutant above a threshold exposure,
as in the case of the  National Park Service  milkweed survey (Bennett and Stolte 1985).  Indicator plants may
be useful in  general identification of forest damage related to exposure to ethylene,  ozone, peroxyacetyl
nitrate, fluorides, sulfur dioxide, chlorides, and  nutrient deficiencies. Further diagnostic evaluations of pollutant
damage would  require more intensive investigations. Because the indicator plants are actual components of
the forest ecosystem, as well as indicators of possible damage to other species, they could be viewed as both
exposure indicators and response indicators.

INDEX PERIOD:  Most damage  assessments occur in mid to late growing season (July through September),
but some pest surveys are conducted earlier or later to more easily locate and  identify a particular pest.  For
a synoptic survey such as EMAP, mid to late growing  season is appropriate (Alexander and Carlson 1989),
because the measurements should be made within the active growing  season after the first flush of needles
or leaves have  fully expanded and prior to fall discoloration.

MEASUREMENTS:  Plot measurements made at each resource sampling unit include elevation, slope,  aspect,
stand disturbance, and air pollution indicator  plants (e.g., Skelly et al.  1989).  The measurements made on
dominant and  codominant  trees at the plot are species, diameter, crown ratio  (estimated), crown class,
discoloration, defoliation (and/or transparency, and/or crown density), crown dieback, and identified insects
and pathogens.  A smaller sample of trees are selected off-plot for measurements which require destructive
sampling. The measurements on these  trees are height,  height to live  crown, defoliation, crown density or
transparency, diameter,  annual  increment from  cores, main-stem injury type  and location, crown-needle
retention, crown dieback, branch-needle retention, branch-needle length, branch-twig symptoms,  branch-
discoloration  type,  root signs, and root symptoms.  Root samples are cultured  to screen for particular fungi.
These  measurements are described in detail in the National Vegetation Survey Project Manual for the Visual
Damage Survey (Alexander and Carlson 1989), which was developed from a United Nations initiative (UNEP

                                                D-5

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and UN-ECE 1987) and the National Acid Precipitation Assessment Program Forest Response Program (Millers
and Miller-Weeks 1986; Zedaker and Nicholas 1986), and other sources.

The European method involves ocular estimation of the proportion of foliage which  is present  on a tree
relative  to  a  standard which  is an ideal, fully foliated tree of the  same species.   Training, experience,
photographs of fully foliated  trees, and the sample tree itself with imagined full foliage are the basis for
defining and  maintaining  the standard.  The transparency  and crown  density  methods, in contrast,  are
objective methods  of determining  the area  of  the  tree crown  which  is without  branches or  leaves.
Transparency and crown density give a measure of the amount of foliage that is actually on the tree, without
reference to the foliage condition expected of an ideal,  healthy tree.

The European method requires considerable training, skill, and  field measurement time to enable crews to
determine the subjective estimate of defoliation repeatedly within a 10% range.  The transparency and crown
density methods are considered to be easier to accomplish in the field and would be expected to be more
repeatable  in determining a more objective value.

Interspecific comparability is another important issue to consider.  The European method results  in a value
which could be called "fraction of foliage absent relative to that of a healthy tree."  With acceptance of the
UN-ECE convention for assessment (UNEP and UN-ECE 1987) this value is theoretically comparable with the
values obtained from measurements  of other  tree species.   The transparency  and crown density methods
result in an absolute value which is  not comparable with  other tree species.  For example, the normal
transparency or crown density of white ash or loblolly pine is much different than the normal transparency
or density of  sugar maple or western hemlock.  Comparison  between species would require that  the values
of each would have to be normalized with respect to "normal transparency" for  each  species.

The estimated measurement error for all visual measures associated with this indicator is 10%.

In addition to field measurements, further sources of information within the U.S. Department of Agriculture-
Forest Service (USDA-FS) include Forest Pest Management and Forest Inventory and Analysis programs. These
programs obtain mortality data during routine inventories and conduct special detection and evaluation surveys
of forests which  could be statistically linked to the EMAP monitoring design.

The measurement error for each of the  different metrics may be determined  by accessing Visual  Damage
Survey data from the 1988 and 1989 tests conducted by the USDA-FS National  Vegetation Survey and the
North American  Sugar Maple Decline Project.  Many of these data  are the "presence-absence" type of data.
Training, experience, and  effective quality assurance and quality control programs are essential and  effective
in obtaining high-quality  data  (Alexander 1990; Burkman 1990).

VARIABILITY: A summary index of response is based on percentages of defoliation and discoloration.  The
expected spatial variability of  the mean summary index within  a resource sampling unit would  produce a
range that deviates <50% from the mean value.  The expected temporal variability of the visual index during
the index period  would produce a range that deviates <20% from the mean value.

PRIMARY PROBLEMS:  Standardization of measurement and assessment methods to allow comparability is
difficult.  The  UN-ECE has obtained agreement on protocols from its member countries, but in practice there
may be deviation from the standardized techniques.  This could be  due to different  interpretations of the
ideal, fully foliated tree which is used  as a standard.  The assessment convention for  health  classification is
expected to change as information is  acquired.  Since it is a subjective measurement, consideration should
be given to establishing agreement on an objective standard.
                                                D-6

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Destructive sampling is required for increment core,  branch,  and root symptom evaluation.  Destructive
sampling conflicts with  permanent plot protocols and should be done outside the field plots within a resource
sampling unit

REFERENCES:

Alexander, SA  1990.  Personal communication.   Telephone discussion with R.  Kucera, January 12.
Department of Plant Pathology,  Physiology and  Weed  Science, Virginia  Polytechnic Institute and State
University,  Blacksburg,  VA.

Alexander, S.A.,  and  J.A. Carlson.   1989.   Visual  damage  survey-project  manual.   Forest  Pathology
Laboratory.  Department of Plant Pathology, Physiology and Weed Science, Virginia Polytechnic Institute and
State  University, Blacksburg, VA.  53 pp.

Anderson,  R.L., and R.P.  Belanger.  1986.  A crown  rating method for assessing tree vigor of loblolly and
shortleaf pines.  Pages 538-543.  In: Proceedings of the  Fourth Biennial Silvicultural  Research Conference,
November  4-6, Atlanta, GA.   General  Technical  Report SE32.   Southeastern Forest Experiment Station,
Asheville, NC.

Bennett, J.P,, and KAV. Stolte.  1985. Using vegetation biomonitors to assess air pollution injury in national
parks.   Milkweed survey.   Natural Resources Report  Series No.  85-1.   National Park Service, Air Quality
Division, Research Branch, Denver, CO.

Burkman, W.A.  1990.  Personal communication.  Correspondence with R. Kucera, January 30.

Davis, D.D.,  and R.G. Wilhour. 1976.  Susceptibility of woody plants to sulfur dioxide and photochemical
oxidants.  A  literature  review.  U.S. Environmental Protection Agency, Environmental Research Laboratory,
Corvallis, OR.

Millers, I., and D. Lachance.  1989.   North American  Sugar Maple  Decline Project.   Cooperative field
manual.  U.S. National Acid Precipitation Assessment Program, Terrestrial Effects Task Group, Forest Response
Program - Eastern Hardwoods Research Cooperative; Forestry Canada; U.S. Department of Agriculture, Forest
Service. 16 pp. (Note: this document is being revised in  1990 as a USDA Forest Service General Technical
Publication.)

Millers, I.,  and M. Miller-Weeks.  1986.  Quality Assurance Supplement for Symptoms and Trends - 1986.
U.S. Department  of Agriculture, Forest Service, Forest  Pest Management, Durham, NH.  77 pp.

Skelly, J.M., D.D. Davis,  W. Merrill, E.A. Cameron,  H.D. Brown, D.B. Drummond, and LS. Dochinger.
1989.   Diagnosing injury  to Eastern forest trees.   Agricultural  Information  Service, College of Agriculture,
Department of  Plant Pathology, the Pennsylvania State University, University Park, PA.

UNEP and  UN-ECE.  1987. Manual on Methodologies and Criteria for Harmonized Sampling, Assessment,
Monitoring, and Analysis of the Effects of  Air Pollution on Forests.  Convention on Long-Range Transboundary
Air Pollution,  International Cooperative Programme on  Assessment and Monitoring of Air Pollution  Effects on
Forests,  United Nations Environment Programme  and United  Nations  Economic Commission for Europe,
Hamburg,  FRG.

Zedaker, S.M.,  and N.S. Nicholas.  1986.  Quality assurance methods manual for site classification and field
measurements.  U.S. Department of Agriculture, Forest Service,  Forest Response Program.   89  pp.  (Note:
this document is being revised in 1990 as an EPA external document)
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D.3  INDICATOR:  Nitrogen Export

CATEGORY:  Response/ Ecosystem  Process Rates and Storage

STATUS:  Research

APPLICATION:  In most forest communities, disturbances may change the normal patterns of accumulation
and cycling of nutrients through vegetation and other forest components (e.g., Bormann et al. 1974; Bormann
and Likens 1979; Swank and Waide 1980).  Such changes can alter downstream water chemistry (Likens and
Bormann 1974; Sollins et al. 1981).  Studies of nutrient cycling and loss have emphasized nitrogen for several
reasons (Vitousek et al. 1982):

        •   Nitrogen  is the element most often limiting to forest growth.

        •   Losses of nitrogen often increase more  than do  losses of other nutrients following
            disturbance.

        •   Increased  production and loss  of nitrate can cause  increased solution losses of
            cation nutrients.

        •   Increased  nitrification can increase rates of nitrous oxide production and volatiliza-
            tion.

Ecosystem-level stability may be  gauged by monitoring  downstream  water quality (O'Neill et al. 1977),
particularly nitrate  concentrations (Vitousek  et al. 1979).  Nitrate concentrations  in surface  water  are  the
products of many  site-specific processes (Vitousek et al.  1979, 1982) and  of chance  occurrences  such as
wildfire (Crier 1975),  insect  defoliation (Swank et al.  1981),  and animal foraging (Singer et al. 1984).
Although disturbances do  not necessarily result in altered  nitrate concentrations (Vitousek et al. 1982),
changes in nitrate concentrations  are a "sure sign" of some type of disturbance within  the ecosystem.

INDEX PERIOD: An optimal sampling window for measuring nitrogen export rates has not been determined,
because nitrogen export tends to be an episodic event.

MEASUREMENTS:   To implement this indicator, samples of water (either  surface water runoff or ground
water)  are obtained,  and  NO3" concentration (and possibly other  chemicals) is determined by  standard
laboratory procedures.

VARIABILITY: Because nitrogen export is an integrative measure, the spatial variability of nitrogen export
within  the resource sampling unit is inconsequential. The expected temporal variability of nitrogen exports
throughout the year was  not estimated.

PRIMARY PROBLEMS: (1)  It has not been  possible to identify a suitable index period for the EMAP sampling
design,  because  concentrations can fluctuate rapidly and unpredictably over the season.  (2) Continuous
monitoring (to characterize total export and  to alleviate the problem  of index period sampling) is  not
envisioned by EMAP.

REFERENCES:

Bormann, F.H.,  C.E.  Likens, T.G. Siccama, R.S.  Pierce, and J.S. Eaton.   1974.  The export of nutrients
and  recovery of  stable conditions following deforestation at Hubbard Brook.  Ecol. Monogr.  44:255-277.
                                                D-8

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Bormann, F.H., and G.E. Likens.  1979.  Patterns and Processes in a Forested  Ecosystem.  Springer-Verlag,
New York.

Crier,  CC.  1975.   Wildfire effects on nutrient distribution and leaching in a coniferous ecosystem.  Can.
J. For.  Res. 5:599-607.

Likens, G.E., and F.H. Bormann.  1974.  Linkages between terrestrial and aquatic ecosystems.  BioScience
24:447-456.

O'Neill, R.V.,  B.S. Ausmus, D.R. Jackson, R.I. Van Hook, P. Van Voris, C. Washburn, and A.P. Watson.
1977.  Monitoring terrestrial ecosystems by analysis of nutrient export  WaL Air Soil Pollut  8:271-277.

Singer, F.J., W.T.  Swank, and E.E.C.  Clebsch.   1984.  Effects of wild  pig rooting  in a deciduous forest
J. Wildlife Manage.  48:464-473.

Sollins, P., K. Cromack  Jr., F.M. McCorison, R.H. Waring, and R.D. Harr.  1981.  Changes in nitrogen
cycling at an old-growth  Douglas fir site after disturbance.  J. Environ. Qual. 10:37-42.

Swank, W.T., and J.B. Waide.  1980.  Interpretation of nutrient cycling  research in a management context:
Evaluating potential effects of alternative management strategies on site  productivity.  Pages 137- 158.  In:
R.H. Waring,  ed.   Forests:   Fresh  Perspectives from Ecosystem  Analysis.  Oregon  State University Press,
Corvallis.

Swank, W.T.,  J.B.  Waide, DA Crossley, Jr., and R.L. Todd.   1981.  Insect defoliation enhances nitrate
export from forest ecosystems.  Oecologia 51:297-299.

Vitousek, P.M., J.R.  Gosz, CC. Crier, J.M. Melillo, WA  Reiners, and R.L. Todd.  1979.   Nitrate losses
from disturbed ecosystems.  Science 204:469-474.

Vitousek, P.M., J.R.  Gosz, C.C. Crier, J.M. Melillo, and WA.  Reiners.  1982.   A comparative analysis of
potential nitrification and  nitrate mobility in forest ecosystems.  Ecol. Monogr.  52:155-177.
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D.4  INDICATOR:  Litter  Dynamics

CATEGORY:  Response/ Ecosystem Process Rates and Storage

STATUS:  Research

APPLICATION: Large quantities of nutrients circulate within a forest ecosystem.  Although part of the annual
requirements of plants can be met by reabsorption before the loss of tissues, the remaining nutrients must
be obtained by uptake from the soil.  The majority of  soil nutrients is derived from the decomposition of
organic  litter, including woody material such as limbs, insect frass, and fallen leaves.  Thus, litter dynamics
such as  the  rate and pathways of decomposition are important determinants of ecosystem productivity and
condition (Waring and Schlesinger 1985).

A potential indicator is the chemical composition of litter and  its changes over time  as it decomposes. This
process  can  be affected by the chemistry of litter before it falls, by the dynamics of populations  of microbes
which feed on litter after it falls, and by the abiotic environment. A common index of litter chemistry during
decomposition is the ratio of carbon to some other chemical  such as lignin, nitrogen, or phosphorous.

INDEX  PERIOD:   Measurement of litter dynamics during an EMAP-defined index period does  not seem
possible, because although litter dynamics are useful response indicators over long periods, in the short term
they may be too greatly influenced by  current weather patterns to  allow accurate measurements.  It may be
possible to place artificial "litter" samples at field locations and return parts of them to the laboratory during
an index period each succeeding year.  Probably the best sampling period would then  be late summer or
early autumn.

MEASUREMENTS:   Numerous measurements  would  be considered, for example, the concentrations of
nutrients (e.g., N, P, K) and other chemicals (lignin), the moisture content, and microbial activity  rates.  With
artificial samples, loss of dry weight would also be determined.

VARIABILITY:  The expected spatial variability of litter  dynamics  within the resource  sampling  unit and its
temporal variability throughout the year  were not estimated.

PRIMARY  PROBLEMS:  The  outstanding problem is the  inability  to define a  practical measurement for a
one-time sample during an index period.

REFERENCE:

Waring, R.H., and  W.H.  Schlesinger.   1985.   Forest Ecosystems, Concepts and Management.   Academic
Press, Orlando.
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D.5   INDICATOR:  Microbial Biomass and Respiration in Soils

CATEGORY:  Response/ Ecosystem Process Rates and Storage

STATUS:  Research

APPLICATION:  Soil organisms have an important role in the retention and  release of nutrients and energy
transfer in forest ecosystems through processes such as nitrogen fixation; production of hormones, antibiotics,
and  metal chelator; nutrient cycling;  material  transfer  between  plants through  mycorrhizal  hyphae; and
creation and maintenance of soil structure through the production of humic  compounds and polysaccharide
glues (Perry et al. 1989). When these  key biological processes are disrupted, ecosystems become fragile and
subject to threshold  changes (DeAngelis et al. 1986).

During the initial development of this indicator,  measurements of key soil biological variables would  be used
to establish  baseline status in terms of the presence and distribution of soil  flora and fauna with  respect to
other soil  variables that are being measured (see indicator D.8, Soil Productivity Index).  All such constituents
would be evaluated  in relation to forest condition.

The  component variables of interest may vary widely across different forested regions of the United States,
but initially are defined to include the measurement of variables relating to (1) soil  microbial biomass, (2)  soil
respiration, and  (3) mycorrhizal fungi.  Soil microbial  biomass is defined as the living part of the soil organic
matter, excluding plant roots and soil fauna larger than 5000 pm3 (Jenkinson and  Ladd 1981).  Soil respira-
tion  is defined as an energy-consuming process including the uptake of oxygen and/or the release of carbon
dioxide by  living,  metabolizing  entities in  the  soil (Anderson  1982).   Soil mycorrhizal  fungi  can   be
characterized by observing the functional types of mycorrhizae (e.g.,  obligatory or facultative) on the tips of
roots collected as part of the microbial biomass sampling.  If there is a shift in type, there is likely to be a
resulting change in forest response.

Soil  biological data can also contribute  diagnostic  information by indicating possible mechanisms and causes
of subnominal forest condition.  The most promising interpretations of ecosystem biological processes would
consider both soil and vegetative productivity as inputs.

INDEX PERIOD: The optimal index period for sampling is late June through early September for the mid-
latitude forests of the  United States.  Soil biological sampling for a  given  plot should be performed at  the
same interval of the index period during each repeat visit.  Also, soil biological sampling should be performed
concurrently with the  soil and foliar productivity sampling at a given plot.

MEASUREMENTS:  Soil  biological data can be  obtained  by field excavation of soil core samples followed
by laboratory  characterization of the types and amounts  of biota present   For  the determination of  soil
microbial  biomass by chloroform fumigation, the  core samples will be processed  according to procedures
defined by Vance et  al. (1987), which are based on methodology outlined  in  a series of papers by Jenkinson
and  Powlson (1976a,b) and summarized  by Parkinson and Paul (1982).   (See also indicator F.3, Microbial
Biomass in Soils.)  For the determination of soil respiration rates, the samples would be  handled  according
to procedures similar to those described by Anderson (1982). For the determination of soil mycorrhizal fungi,
a number of methods  exist, but none  have been  positively identified for their appropriateness in EMAP.

Estimates  of measurement error  for each analytical  parameter may be derived  by accessing  existing  soil
biological  data that satisfied  stringent quality assurance criteria.  For microbial biomass,  an average CV of
<15% is typical for replicate samples in agricultural assay work (Vance et  al. 1987). The laboratory bias is
expected  to be <10% of the reference value.  For the  sample measurement  system  as a whole (e.g.,
sampling,  preparation,  and  analysis), an average  CV of 30% or less is likely.   Collection of soil  for two
composite samples per resource sampling unit would require 0.5-0.7 h.

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VARIABILITY:   The expected spatial variability of soil biological  measures within a  resource sampling unit
would produce a range that deviates <50% of the mean value. A significant amount of soil spatial variability
also could be  present at any given plot within the sampling unit Uncertainty in the soil biological data at
a plot can  be greatly reduced,  however, by the use of a  "composite" or "transect" sample design that can
accommodate  within-plot differences in soil characteristics.

It is anticipated that a design  could be adopted whereby the samples  that are collected will control the
within-plot uncertainty to a level that is (1) less than the measurement system uncertainty and (2) negligible
with respect to the regional soil aggregation variability (Taylor 1987).  The resulting high  level of data quality
would allow the data users to focus on discerning "real" temporal changes in soil biological processes within
a highly variable population framework.

PRIMARY PROBLEMS: This indicator has tremendous potential for establishing linkages between the various
exposure and  response core indicators.  However, most of the work that has been undertaken to date has
been for research purposes and may not be amenable to  implementation  in a large-scale program such  as
EMAP.  Relationships among the individual variables are less than well defined. Because of the potential for
large spatial variability of soils within and among forest plots and the unavoidable necessity of performing
destructive sampling to collect soil biological samples, a rigorous sample design must be developed.  Because
of the richness of species that compose the soil flora and fauna,  it would probably be necessary to identify
species diversity in the samples that are collected to help  discern and interpret the categorical quantitative
changes reflected in  microbial  biomass measurements.  Temporal changes in the  balance of soil flora and
fauna can provide an early indication of microbial population shifts or other changes in  ecosystem function.

REFERENCES:

Anderson, J.P.E.  1982.  Soil  respiration.  Pages 831-871.  In:  A.L Page,  ed.  Methods of Soil Analysis,
Part 2.  Agronomy Monograph  No. 9.  American Society of Agronomy,  Madison, Wl.  1159 pp.

DeAngelis, D.L., W.M.  Post, and CC Travis.   1986.  Positive Feedback  in Natural  Systems.   Springer-
Verlag, Berlin, FRG.

Jenkinson, D.S., and J.N.  Ladd.  1981.  Microbial  biomass in  soil:  Measurement and turnover.  Pages
415-471.  In:   E.A. Paul and J.N. Ladd, eds.  Soil  Biochemistry.  Marcel Dekker, Inc., New York.   480 pp.

Jenkinson, D.S., and D.S. Powlson.   1976a.   The effects of biocidal  treatments on metabolism in soil-l:
Fumigation with chloroform.  Soil Biol. Biochem. 8:167-177.

Jenkinson, D.S., and D.S.  Powlson.  1976b.  The effects of biocidal treatments on metabolisms  in soil-V:
A method for  measuring soil biomass.  Soil Biol. Biochem. 8:209-213.

Parkinson, Dv and  E.A.  Paul.  1982.  Microbial  biomass.  Pages 821-830.   In:  A.L.  Page, ed.  Methods
of Soil Analysis, Part  2.  Agronomy Monograph No. 9.  American Society of Agronomy, Madison, Wl.  1159
pp.

Perry, D.A., M.P. Amaranthus, J.G. Borchers, S.L Borchers, and R.E. Brainerd.   1989.  Bootstrapping in
ecosystems.  BioScience  39(4):230-237.

Taylor, J.K. 1987.   Quality assurance  of chemical measurements.  Lewis Publishers, Chelsea, Ml.  328 pp.

Vance, E.D., P.C. Brookes, and D.S. Jenkinson.  1987. An extraction method for  measuring soil  microbial
biomass.  Comm. Soil Sci.  Plant Anal.  18
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BIBLIOGRAPHY:

Vogt,  K., and H.  Persson.   1990.   Root Methods.   In: Techniques  and Approaches  in  Forest Tree
Ecophysiology. J.P. Lassou and T.M. Hinckley, eds.  CRC Press,  Boca Raton, FL.
                                              D-13

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D.6  INDICATOR:  Nutrients in Tree Foliage

CATEGORY:  Exposure and  Habitat/ Tissue Concentrations

STATUS:  High-Priority Research

APPLICATION:    The relative  proportions  and  concentrations  of foliar  nutrients may indicate  possible
mechanisms and  causes of abnormal forest  condition.   Individual nutrient concentrations and their ratios
could be applied  to development of threshold or critical levels.  Foliar nutrient measurements would enable
establishment of baselines, comparisons of element ratios, and correlations with other  measured indicators
such as radial growth, height growth, visual symptoms, and  atmospheric deposition gradients (Ke 1989;
Landolt et al. 1989).

INDEX  PERIOD:    The  optimal  index  period depends  on  the  selected  measurement  option  (see
Measurements).   Fresh foliage (Option 1 below) would be sampled in  late summer.   Ideally, hardwood
samples would  be taken late in the growing season but at least  two weeks  before the onset of autumn
coloration,  and conifer samples  would be taken during the dormant season.   Litter (Option 2) would  be
sampled in early  autumn; litter traps must be deployed in the summer and revisited after  leaf senescence
but before  winter snow.

MEASUREMENTS:  There are two possible approaches for sampling foliar nutrients which differ in time of
year and cost.  The less expensive option  involves sampling litter rather than fresh foliage.

Option 1:  The branch samples taken for needle retention measurements (see indicator D.2, Visual Symptoms
of Foliar Damage) would also be used for foliar  analysis. Hardwood leaf samples must  be taken specifically
for this purpose.  Where possible, at least two samples each will be taken of healthy and symptomatic leaves.
Procedures described in the Acid  Rain National Early Warning System Manual  on Plot Establishment and
Monitoring (Morrison 1988) would  be followed to obtain foliar concentrations of N, P, K, Ca, Mg, S, Fe, Mn,
Zn, Cu, Na, B, and Al.   The collection, handling, and laboratory  work involved in foliar analysis will cost
approximately $65 per sample,  not including travel and per diem  costs.

Option 2:  Samples of the current  year's needles and leaves would be collected prior to snowfall in autumn
by placing  an appropriate litter  trap in the field.  The  samples would be processed in the laboratory in the
same as fashion described in Option  1.  In this case, the cost of sample collection and analysis  is about $35
per sample.

Samples should  be taken from trees very close to the  permanent field  plots.   Fresh foliage should  be
composited by species for analysis.  Litter samples  may be obtained directly on the permanent plot,  but
separation  of foliage by species may  not be practical.  Under both options, a  subsample could  be oven-
dried, ground, and stored frozen under a vacuum for  later analysis.  The  cost of archiving samples has not
been determined. The  estimated laboratory measurement error is 5% for all elements.

VARIABILITY:   The expected spatial variability  of nutrient concentrations within a resource sampling unit
would produce a range  that deviates <50%  of the mean value.  The expected temporal variability of these
indicators during  the index period would produce a range that deviates <80% of the  mean value.

PRIMARY PROBLEMS:  Existing laboratories would probably require special instruction to perform analyses
with lower measurement error  than  is required for routine crop and  horticultural analyses.
                                                D-14

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REFERENCES:

Ke, J.K.  1989.  Evaluation  of foliar symptoms on Norway spruce [P/cea abies  (L.) Karst.] and relationship
to nutrient status. M.S. thesis, Graduate School Ecology Program, Pennsylvania State University, State College,
PA.  139 pp.

Landolt, W., M. Guecheva,  and J.B. Bucher.   1989.  The spatial distribution of different elements  in and
on the foliage of Norway spruce growing in Switzerland.  Environ. PolluL  56:155-167.

Morrison, I.K.  1988.  Foliage sampling and analysis.  Pages 11B/1-11B/4.  In:  L.P. Magasi, ed.  Acid rain
national early warning  system:  Manual  on plot establishment and monitoring.   Information Report DPC-X-
25.  Canadian Forestry Service, Ottawa.
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D.7  INDICATOR:  Chemical Contaminants in Tree Foliage

CATEGORY:  Exposure and Habitat/ Tissue Concentrations

STATUS:  High-Priority Research

APPLICATION:   Like nutrients (D.5), the amount and concentrations of foliar contaminants may indicate
possible mechanisms and  causes  of  abnormal forest condition.   Two  applications  of this indicator  are
anticipated.  First, at approximately 10% of the field plots within a resource sampling unit, a comprehensive
list of contaminants would  be  measured in order to assay and survey the  presence of toxins.  Second, upon
identification of suspect pollutants, specific archived foliage samples would be analyzed for concentrations of
particular chemicals from specific plots.

INDEX PERIOD:  Because toxin measurements would  be conducted on a subset of the foliage samples that
are collected for foliar nutrients analyses, the sampling period is the same as the Nutrients in Tree Foliage
indicator (D.5).

MEASUREMENTS:  Some example compounds can be  inferred from a review of similar protocols from other
monitoring programs.    Measurements of potential exposure  indicators have been incorporated in  the
European monitoring systems.  The United Nations (UNEP and UN-ECE 1987) recommends analysis of F, Cl,
Cd, and Pb concentrations.  The Swedish monitoring system  measures concentrations of S, As, V, Cr, Ni,  Cu,
Zn, Cd, Hg, and Pb in  moss  on intensive representative plots (SNV  1985).  Northern European  countries
recommend monitoring  Pb, Cr,  Cu, and  Cd in the organic  debris and humus (Nordic Council of Ministers
1988).

See the Nutrients in Tree Foliage indicator (D.5) for field collection costs;  laboratory analysis costs for various
toxins have not been estimated.  The estimated  laboratory measurement error is 5% for all  elements.

VARIABILITY:  The expected spatial variability of chemical contaminants in foliage within a resource sample
unit would produce a range that deviates <50% from the mean  values.   The  expected temporal variability
of these indicators during the  index period would produce a range that deviates <80% of the mean values.

PRIMARY PROBLEMS:  The possible decay or transformation of chemicals in archived samples is a concern.

REFERENCES:

Nordic  Council of Ministers.  1988.  Guidelines for integrated  monitoring in the  Nordic  countries.  The
Steering Body for Environmental Monitoring,  Nordic Council of Ministers, Copenhagen, Denmark.  61 pp.

SNV.  1985.  Monitor 1985:  The National Swedish Environmental Monitoring Programme  (PMK),  National
Environmental  Protection Board (SNV), Research and  Development Department, Environmental Monitoring
Section, Solna, Sweden.  207 pp.

UNEP and UN-ECE.  1987.  Manual on methodologies and criteria  for  harmonized  sampling, assessment,
monitoring, and analysis  of the effects of air pollution on forests.  Convention on Long-Range Transboundary
Air Pollution, International  Cooperative Programme on  Assessment and Monitoring of Air Pollution Effects on
Forests, United Nations  Environment Programme  and United  Nations Economic Commission for Europe.
96  pp.
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D.8  INDICATOR:  Soil Productivity Index

CATEGORY:  Exposure and Habitat/ Ambient Concentrations

STATUS:  High-Priority Research

APPLICATION:  Soil productivity is generally defined  as the capacity of a given volume of soil  to  produce
a vegetative response under a specified system of management (SEA-AR 1981).  Initial measurements of key
soil productivity variables will  be used to establish baseline levels and  ratios among physical, chemical, and
biological soil  constituents  in  relation to nominal and  subnominal condition as estimated by response
indicators.  Periodic  remeasurement of these variables will be used to assess trends that might show increases
or decreases in value over time.

The specific parameters of interest will vary among forested regions of the  United States.  Generally speaking,
they will include specific soil  nutrients (see  indicator  D.5), toxic substances  (see indicator  D.6), credibility
factors,  soil  structural characteristics,  parent materials,  and ancillary data  such as  soil moisture supply (e.g.,
Palmer  Drought Index).  Soil productivity data  provide interpretive information that is not available through
foliar chemical analysis  because plants may compensate for limited  soil nutrients and moisture.

Forest productivity can  be affected by chronic or acute deficiencies of essential soil nutrients  needed for
plant growth.   Productivity can also be disrupted  by changes  in  the populations of microorganisms that are
essential to  biological cycling processes within  the forest floor.   These effects may be caused by long-term
natural  perturbations or short-term  changes due to human intervention.  For  example, whole-tree harvesting
in commercial forests can cause changes in macronutrient cycling (e.g., potassium).  A low ambient level of
magnesium  in some forest soils is  an example of a naturally occurring stress that could be aggravated by
certain management practices (Ballard and Carter 1985). Forest floor disturbances can interfere with  nitrogen
cycling (Peterson et at. 1984), and  the effects of burning (Debano and Klopatek 1988) and disruption  of the
soil mycorrhizal fungi on tree roots (Vogt and  Persson 1990)  are other examples  of stresses in forest soils.

Soil productivity can be adversely affected  by the  presence of toxic substances and contaminants in the soil.
This presence  indicates exposure  to  potentially  detrimental  chemical compounds  and  elements  possibly
resulting from land use practices (e.g., application of pesticides,  mineral extraction), atmospheric deposition
(e.g. sulfur in  acid precipitation), or naturally occurring phenomena  (e.g., overabundance of magnesium in
serpentine parent  materials).

Plant metabolic processes are disrupted by toxicity or contamination in at least two ways; the  plants may
be (1) directly  affected  through uptake of the  substances, and (2) indirectly affected through  an associated
decrease in  soil nutrient availability.  In the first case,  substances taken up by plants can affect physiological
processes and internal physical structure (Mclaughlin 1985).  This lowers the  rate of photosynthesis, growth,
and resistance to secondary stresses (Mclaughlin 1985; Miller  1983).  In the second case, mobile substances
bind with soil nutrients and migrate to lower soil horizons, decreasing plant  nutrient availability.

Chemical  toxicity  can also reduce the  number and  variety  of soil decomposer  microorganisms,  thereby
decreasing the rate  at  which  nutrients become  available  for  plant  uptake  (Verein Deutscher  Ingenieure
Commission for Air  Pollution 1987) and effectively lowering the site productivity.  This  has implications for
management of mineral  extraction, pesticide applications, and atmospheric emissions.   Because  the degree
of toxic effects on plant tissues and growth is related  to the duration of exposure, concentration, exposure
regime,  and chemical dynamics of  the ecosystem, initial discovery of such substances in the soil can  signal
the need for closer  monitoring of exposed ecosystems.

INDEX PERIOD:  No optimum index period exists because of the relatively low (<10%) temporal variability
of most soil nutrient concentrations during the late spring to early  autumn  sampling window for the mid-

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latitude forests of the United States.   However, any given field plot should be sampled at about the same
time during the index period.  Also, soil characterization and sampling should be done concurrently with the
foliar sampling and  tree measurements at a given plot.

MEASUREMENTS:  Soil classification data  can be obtained from existing soil survey information or by on-
site soil excavation and characterization. Soils on  unmapped plots should be mapped to the soil series level
according to accepted  National  Cooperative Soil  Survey  standards.    Each plot should  be thoroughly
characterized for  physical  soil  and landform  features while  at the field  site.

Composite soil samples from O, A, E, B, and C master horizons present within the resource sampling units
would  be obtained  once every four years.  The soil  physical and chemical variables of interest for a given
region  would be  analyzed  in these samples, and  a portion of each sample would be archived for possible
analyses  in the future.  The soils  would be  sampled according to  procedures similar to those described  in
the Direct-Delayed  Research Program field methods manual (Kern et al. 1988)  and the ARNEWS manual
(Morrison 1988).

Soil  samples would be  prepared  and analyzed according to procedures similar to those described in the
Aquatics Effects Research Program soil preparation and analysis methods handbook (Blume et al.  1990) and
the Forest Response Program soil analytical methods manual (Robarge and Fernandez 1987).  The anticipated
suite of laboratory analytical measurements for regions of the  eastern  United States includes the following.

    Soil organic biomass (organic  horizons only)
    Bulk density
    Rock fragment estimation
    Particle size analysis (mineral  horizons only)
    pH in water and  in 0.01 M calcium chloride
        Exchangeable Ca, Mg,  K, Na, Fe,  and Al in 1  M ammonium chloride
        Cation  exchange capacity in 1 M  ammonium chloride
        Exchangeable acidity in barium chloride triethanolamine
        Mineralizable N by anaerobic incubation
        Extractable  P by Bray #1
        Extractable  sulfate in water
        Total C,  N, and S
        Total Fe, Mn, Cu, Zn, B, and Mo (organic horizons only)
        Total Pb, Cd, Ni, Cr,  and V (organic horizons only)

The suite  of  laboratory  analytical  measurements  for  regions of the  western  United States   is  under
development.

Resources required  to characterize and collect field samples include one day per plot  for a soil  scientist at
an estimated cost of $40  per sample.  Laboratory preparation expenditures are about $75 per sample, and
laboratory analysis costs are approximately $250  per sample.

A coefficient of variation (CV) estimate for each soil analytical parameter may be derived by accessing existing
soil survey data that satisfied stringent quality assurance criteria (Byers et al. 1990). For most of the analytical
laboratory measurements,  an  average CV of 10% or  less  is  typical for  replicate samples.   The  expected
laboratory bias is 5% or less of the reference value.  For the sample measurement system as a whole (e.g.,
sampling, preparation, and analysis),  an average CV of 20% or less is typical.

VARIABILITY:   The expected  spatial  variability of soil productivity within a  resource sampling unit would
have a range that deviates 80% from the mean values.  The expected temporal variability of soil productivity
throughout the year would produce a range that deviates <10% from the mean values.   It is recognized that

                                                D-18

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a significant amount of soil spatial variability can be present not  only within a resource sampling  unit, but
even within a single plot in that unit  Uncertainty in the soil productivity values at a plot can be greatly
reduced, however, by the use of a "composite" sample design that recognizes and accommodates the within-
plot differences  in soil characteristics.  It is  anticipated that a design would be adopted whereby  the field
samples would control the within-plot uncertainty to a level that is (1) less than the measurement system
uncertainty and  (2) negligible with respect to the regional soil aggregation variability (Taylor 1987).

PRIMARY PROBLEMS:  Because of relatively low analyte  concentrations  in forest soils, existing laboratories
will be required to  perform the analyses with a higher level of precision and accuracy  than is  normally
required for the more traditional crop and horticultural soil analysis. Because of the potential for  large soil
spatial variability across a forest plot and the unavoidable necessity of performing destructive sampling to
collect soil samples,  a rigorous sample design must be developed.

REFERENCES:

Ballard, T.M., and R.E. Carter.  1985.  Evaluating forest stand nutrient  status.  Land Management Report
ISSN 0702-9861: no. 20.  Information Services Branch, Ministry of Forests, Victoria, BC.

Blume, L.J., B.A. Schumacher, P.W.  Shaffer, K.A. Cappo,  M.L. Papp, R.D. Van Remortel,  D.S. Coffey, and
D.J. Chaloud.   1990.  Handbook of Methods for  Acid  Deposition Studies, Laboratory  Analyses for  Soil
Chemistry.  U.S.  Environmental Protection Agency, Washington, DC.  In  press.

Byers, G.E., R.D. Van Remortel, M.J. Miah, J.E. Teberg,  M.L. Papp, B.A. Schumacher, B.L. Conkling, D.L
Cassell, and P.W. Shaffer.  1990. Direct/Delayed Response Project: Quality Assurance Report for Physical
and Chemical Analyses of Soils from  the Mid-Appalachian Region  of the United States.  EPA 600/4-90/001.
U.S. Environmental Protection Agency, Environmental Monitoring Systems Laboratory, Las Vegas,  NV.  337 pp.

Debano, L.F., and J.M. Klopatek.  1988.  Phosphorus dynamics of Pinyon-Juniper soils following simulated
burning.   Soil Sci. Soc. Am. J. 52:271-277.

Kern, J.S., M.L.  Papp,  J.J.  Lee, and L.J. Blume.   1988.  Soil  Sampling  Manual for the Direct/Delayed
Response Project Mid-Appalachian Soil Survey.  Internal report.   U.S.  Environmental Protection Agency,
Corvallis, OR.

Mclaughlin, S.B.  1985.  Effects of air pollution on forests:   A critical  review.  J. Air Pollut Control Assoc.
35:512-534.

Miller, P.R.  1983.   Ozone  effects  in the San  Bernardino  National  Forest.   In:  Air Pollution  and the
Productivity of the Forest.  Proceedings of the Symposium, October 4-5,  Washington, DC.

Morrison, I.K.   1988.   Soil description, sampling, and analysis.   Pages 11A/1-11A/4. In:   L.P. Magasi, ed.
Acid Rain National  Early  Warning System:   Manual on  plot establishment and  monitoring.  Information
Report DPC-X-25. Canadian Forestry Service, Ottawa, ON.

Peterson, C.E.,  P.J.  Ryan, and S.P.  Gessel.  1984.   Response  of Northwest Douglas-fir stands  to urea:
Correlations with forest  soil  properties.  Soil Sci. Soc. Am. J. 48:162-169.

Robarge, W.P.,  and I.  Fernandez.  1987.  Quality Assurance Methods Manual for Laboratory Analytical
Techniques in the Forest Response Program.  Internal Report, Rev.  1. U.S. Environmental Protection Agency,
Corvallis, OR.
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Science &  Education Administration  -  Agricultural  Research.   1981.   Soil  erosion effects on soil
productivity:  A research perspective.  J. Soil Wat. Conserv. 36:82-90.

Taylor, J.K.  1987.  Quality assurance of chemical measurements.  Lewis Publishers, Chelsea, Ml.  328 pp.

Verein Deutscher Ingenieure Commission for Air Pollution.  1987.  Acidic precipitation - formation and
impact on terrestrial ecosystems.  Kommission Reinhaltung der Luft, Dusseldorf, West Germany.

Vogt, K.,  and H.  Persson.  1990.  Root  methods.  In:   Techniques  and  Approaches  in  Forest Tree
Ecophysiofogy.  J.P.  Lassou and T.M. Hinckley, eds.   CRC Press,  Boca Raton, FL.
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D.9  INDICATOR:  Stable  Isotopes

CATEGORY:  Exposure and Habitat/ Biomarkers

STATUS:  Research

APPLICATION:  Isotopic composition is an exposure indicator that  is useful for gross separation of effects
due  to  climate and pollution.  Departure from a normal ratio of 13C to 12C in  plant tissue is a  general
integrative index of stress.  Once stress is identified, speculation of the probable cause(s) can be made by
observing climatic and deposition patterns over a known forest disturbance gradient.  If climatic change is
involved, alterations in the stable isotopic composition  of oxygen  and hydrogen in  leaves and other plant
tissues should  be evident in shifts in the 18O/16O and the 2H/1H ratios.

Air pollutant exposure may be diagnosed in some cases by observing shifts in the isotopic composition of
S or  N along spatial and temporal gradients (Waring 1990). For example, if the H and O isotopic signatures
of weather and  tree  rings  leave  no record  of major weather changes over  the  last few decades, but  C
isotopes indicate closure of leaf pores is constraining photosynthesis, then we would expect to find changing
S  and/or N isotope  ratios  in  wood cellulose of more recent tree rings.   This  signal  should increase
dramatically in trees close to a major source of pollution, as  it is known to do in sensitive plants  such as
epiphytic mosses and  lichens (Waring 1988).

In another example,  epiphytic lichens and  some  mosses provide  a  cumulative record of the  isotopic
composition of atmospheric S.  Protected understory plants tend to take  up S almost exclusively from the
soil.  The use of S isotope data with biological parameters such as areal extent of species  helps in assessing
the extent to which the system is perturbed  by the anthropogenic additions of sulfur (Krouse  1989).

INDEX  PERIOD:  If  the analyses are made from archived  samples of foliage and increment cores (see
indicator D.2, Visual Symptoms of Foliar Damage:  Trees), or other sources, an index period is not applicable.


MEASUREMENTS:  There are known  natural  abundance isotopic compositions of C, H,  O,  N, and S in
ecosystem components.  The object is  to  measure the degree  of fractionation  (the  altering  of  isotope
abundances) of these  isotopes in  wood cellulose and other plant tissue.  A mass  spectrometer  is required
for accurate detection of the small  differences in the  ratios  of  heavy to light isotopes  (13C/12C, 18O/16O,
2H/1H, 15N/14N,  and  34S/32S), and gaseous samples are required  for the  isotopic determinations.   Many
combustion schemes have been developed to  quantitatively break down diverse molecules into the simple
gases most suitable for mass spectrometry. For measurement of 13C/12C ratio, see the report by the National
Council for Air and  Stream  Improvement (1989). Current analysis costs range from  $30 to $100  per sample
through commercial firms (Peterson and Fry  1987).  The estimated laboratory measurement error is 5%.

VARIABILITY:  The sulfur isotope composition of all components  of  an ecosystem may be consistent over
a large area, yet there could  be significant variations in  one specimen (Krouse 1989). The expected spatial
variability of isotopic ratios  within a resource sampling unit would  produce a range that deviates <5% from
the mean values. The expected temporal variability of  the ratios during the index  period  would produce a
range that also deviates  <5% from the mean values.

PRIMARY PROBLEMS:  This is a promising  technique  that is perhaps a  way to cleanly  separate climate-
from pollution-caused  changes in  forest condition, but the technique requires  development.
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REFERENCES:

Krouse, H.R.   1989.  Sulfur isotope studies of the pedosphere and biosphere.   In:  P.W. Rundel, J.R.
Ehleringer,  K.A. Nagy, eds.  Stable Isotopes in Ecological Research.  Springer-Verlag, New  York.

Peterson, B.J., and B. Fry.  1987.  Stable isotopes in ecosystem studies.  Ann. Rev. Ecol. SysL 18:293-320.

National Council for Air and Stream Improvement.  1989.  Biological  indicators of air pollution stress in
conifers: progress report.  Tech.  Bull.  No. 561.  National Council for Air and Stream Improvement, New
York.

Waring, R.H.  1988.  Trees as  environmental historians.  For. World, Winter:10-12.

Waring, R.H.   1990.  Ecosystem  stress and disturbance.   In:  Third Annual Conference of Comparative
Analysis of  Ecosystems:  Patterns,  Mechanisms, and Theories.  Springer-Verlag, New York.  In press.
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D.10  INDICATOR:  Carbohydrates and Secondary Chemicals in Plants

CATEGORY:   Exposure and Habitat/ Biomarkers

STATUS:  Research

APPLICATION:  Carbohydrates and secondary chemicals could be utilized to discover changes in allocation
of photosynthate or decreasing photosynthetic activity and point to specific stresses that cause certain types
of changes in  allocation. The rationale for this exposure indicator is that although stress-induced change in
one  dimension of  carbon allocation  may be  masked by changes  in other dimensions, the general result of
stress is that resources may be allocated differently.  Some of these changes would be detected by other
types of gross  indicators, for example,  "growth." The intention  here is to suggest a biochemical analog for
carbon allocation,  and we would be looking for characteristic changes in the type and amount of chemicals
produced  under different stresses.

Plants produce  a  wide  variety of secondary  metabolites for  a  variety  of  purposes,  including  healants,
repellents,  and  attractants.   The term "secondary  metabolites"  here includes cinnamic  acids, alkaloids,
flavonoids, phenylpropanoid  compounds,  phenolic compounds, terpenoid  compounds,  steroids,  and
carotenoids. They are produced by five main biosynthetic routes:  (1) sugar metabolism, (2) acetate-malonate
pathway, (3) acetate-mevalonate  pathway,  (4) shikimic acid pathway, and  (5) as metabolites derived from
amino acids.

Although the function of most secondary metabolites is unknown, their complexity of structure is perhaps
indicative  of a specificity of function that has not yet been discovered.  Turnover is often quite  rapid for
these metabolites,  and they are not,  in general, storage compounds.  Given the complexity of structure and
the associated high energy cost associated with  biosynthesis of these compounds, it has been suggested that
their  concentrations could  be monitored as  a  biochemical indicator of stress exposure  or  response.  The
rationale is that smaller concentrations may indicate less overall energy is  available  for  producing these
metabolites. On the other hand, higher concentrations may indicate that more are "needed" in response to
particular  stresses.   Thus, both high and low concentrations  can  indicate a stress response.  The array of
changes in different secondary chemicals may have diagnostic value. Changes in secondary metabolites have
been noted in response to most possible stresses, including nutrients, phytohormones, light, temperature, pH,
aeration, antibiotics, and microorganisms.

Carbohydrates can be utilized  to discover the occurrence of buffering or compensation in response to stress;
that  is, stresses  may  drain down ecosystem reserves prior to a  response when  responses are threshold
phenomena.   Photosynthesis is the process of converting carbon dioxide  and water into carbohydrates and
oxygen.  Carbohydrates are the precursors of all  plant products and are usually the major components of
plant cells. Some of the carbohydrates may not be utilized immediately and are stored in various forms until
needed.  The  major forms of  storage are sucrose (simple  sugar), sucrose-based oligosaccharide (small-chain
polymer),  starch  (polysaccharide  of  glucose),  fructan  (polymer  of  fructose),  and  mannose-containing
polysaccharide.  An index of reserves could be constructed by measuring the total energy stored in the major
reserve carbohydrates.

INDEX PERIOD:  An optimal  sampling period  is unknown for secondary chemicals,  but would depend on
metabolite and organism.  The period of maximum potential for  carbohydrates is usually just prior to spring
bud-burst.

MEASUREMENTS:   (1) Concentrations  of secondary chemicals from various plant parts.  Specific plant parts
or chemicals have  not been determined.
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(2) Total  concentrations of carbohydrates in  various forms in old roots  and twigs.  Measurement requires
laboratory capabilities:  solubilization in  aqueous solvents or  aprotic dipolar organic solvents, freeze-drying
or precipitation, and  measurement with gas-liquid  chromatography or other techniques.   Protocols  are
available  for extracting and  measuring the concentrations of major storage carbohydrates in  plant tissues.

The measurement error is unknown for secondary chemicals, but would depend on metabolite and organism.

VARIABILITY:  The expected spatial variability of secondary chemicals within  a resource sampling unit and
temporal  variability during the  index  period were not estimated.

PRIMARY PROBLEMS:  Secondary chemicals are highly variable in space and time and are possibly  too
responsive to stresses.  Some concentrations depend, for example, on whether  the sun is  shining or not.
The relative uniqueness of secondary metabolites confounds comparisons; different metabolites are found
in different species of plants, and the function of most secondary chemicals is unknown.

The index  period for carbohydrate measurement is subjective  (just before a  phenological event), and  the
sampling  window is narrow.  The reference concentrations can be different  for different parts  of a  plant,
and for different species. Carbohydrates are probably highly variable from tree to tree.

BIBLIOGRAPHY:

Carbohydrates

Dey,  P.M., and R.A.  Dixon.   1985.  Biochemistry  of Storage  Carbohydrates in Green  Plants.   Academic
Press, London.  378  pp.

Duffus, CM.,  and J.H. Duffus.  1984.  Carbohydrate Metabolism in Plants.  Longman,  London.  183  pp.

Kramer,  P.J., and T.T. Kozlowski.  1979.  Physiology of Woody Plants.  Academic Press, New  York.

National  Research Council.  1990.  Biologic Markers of Air Pollution Stress and Damage in Forests.  National
Academy Press, Washington, DC.  363 pp.

Waring,  R.A.,  and W.H. Schlesinger.  1985.  Forest Ecosystems:   Concepts  and Management.   Academic
Press, Orlando.

Secondary Chemicals

DiCosmo,  F.,  and G.H.N. Towers.  1983.   Stress and secondary metabolism in cultured plant cells.  In:
Phytochemical Adaptation to Stress.  Recent  Advances in  Phytochemistry 18:97-175

Geissman, T.A., and D.H.G. Crout.  1969.  Organic Chemistry of Secondary Plant Metabolism.  Freeman,
Cooper and Company, San Francisco.  592 pp.

Vickery, M.L., and B. Vickery.  1981.  Plant Metabolism.  University Park Press, Baltimore.  335  pp.
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D.11   INDICATOR:   Bioassay:  Mosses and Lichens

CATEGORY:   Exposure and Habitat/ Bioassay

STATUS:  Research

APPLICATION:  Mosses and  lichens can indicate exposure of forest resources to some airborne pollutants.
Exposure of forests to airborne pollutants can disrupt ecological processes (e.g., nutrient cycling) and decrease
productivity.   The degree of  ecological impact  depends on  the toxicity of the  compounds,  their rate of
deposition, and subsequent chemical interactions within the ecosystem.  Determining the exposure of forests
to particular atmospheric pollutants can identify gradients along which to evaluate effects on forest condition.

Mosses and lichens absorb many elements and compounds from  air and precipitation. Thus, they have been
used in a number of  studies as biomonitors for  indicating presence of airborne pollutants (Manning and Feder
1980;  Ferry  et  al.  1973).   Whereas known locations  of  pollutant emitters, coupled  with  atmospheric
circulation models, can suggest areas of exposure to atmospheric pollutants, bioindicators placed systematically
in forests can actually demonstrate exposure.

INDEX PERIOD:  The  uptake of toxic chemicals by forest  vegetation is likely to be greatest during the
growing season.   Thus, exposure of moss or lichen samples to potential pollutants could coincide with this
period, mainly spring through  summer.  If certain atmospheric contaminants are emitted in the winter (e.g.,
SO3 from  heating), this would not be appropriate.

MEASUREMENTS: To sample forests for exposure to atmospheric deposition of pollutants, moss bags and/or
lichen  transplants are positioned  on  forest plots.  After 30-day  (or other suitable length) exposure periods,
accumulated  heavy metals  are measured, and concentrations are compared  with control samples.  These
measurements yield relative concentrations (mg cm2 day1) of heavy metals and allow estimation of exposure
gradients.

VARIABILITY:  The  expected spatial variability  of accumulation within a  resource  sampling  unit and its
temporal variability during the index period  were not estimated.

PRIMARY PROBLEMS:  (1) Problems center around determining which areas of the United States should
be monitored for heavy metals.   Major emitters are required  to report emissions to the  EPA;  however,
numerous  smaller, but coilectiveiy very important,  emitters  must be  considered also.  Without a  priori
information, a systematic grid  of sample points is appropriate.

(2) Data  from existing atmospheric sampling networks  (e.g., EPA Toxic Air Monitoring Study)  may  help to
establish  baselines;  however,  sample stations are usually associated  with  urban centers.   Few data are
available with which  to determine changes in pollutant  concentration over distances away from urban areas.
A useful exception is data available from many of the  national  parks.

(3) The actual relationship between concentration of toxic compounds in the atmosphere and concentration
collected by  moss and lichens is not clear.  Thus, only assessment  of  relative exposure over time  may be
possible.  Parallel measurements of air toxics (see indicator H.5,  Metals  and Organics) using sophisticated air
sampling apparatus and organic  indicators would help calibrate  the  organic indicators.

(4) Although the effects of airborne toxins on forest vegetation  have not been well  substantiated,  this indicator
seems  important from the perspective of EMAP's goal to monitor exposure to airborne pollution.
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REFERENCES:
Ferry, B.W., M.S. Baddeley, and D.L. Hawks worth.  1973. Air Pollution and Lichens.  The Athlone Press
of the University of London, London. 389 pp.

Manning,  W.J.,  and  W.A.  Feder.   1980.   Biomonitoring Air Pollutants  with  Plants.   Applied Science
Publishers  Ltd.,  London.  142  pp.
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APPENDIX E:  INDICATOR FACT SHEETS FOR ARID IANDS
                        Authors
                       Carl A. Fox
                    J. Timothy Ball
                 Christopher D. Elvidge
                    Dale W. Johnson
                    David A.  Mouat
                 Desert Research Institute
                  University of Nevada
                     Reno, Nevada

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E.1  INDICATOR:  Vegetation Biomass

CATEGORY:  Response/ Ecosystem Process Rates and Storage

STATUS:  High-Priority Research

APPLICATION:  An important environmental value for arid and semiarid resource classes is the continued
productivity of grassland,  shrubland, and  woodland  vegetation.  This value includes secondary and higher
levels of  productivity  (animal  habitat) as  well as primary  productivity  (vegetative  growth).   Net primary
productivity (NPP), the chemical energy stored or accumulated by vegetation per  unit time,  is critical to
continued ecosystem function.  NPP is perhaps the best integrator of ecological response to environmental
stresses and perturbations and is among other things dependent upon the lack of major disturbances and
resource availability.

When disturbance or levels of change are moderate (i.e., not at the intensity of fire or mechanical disruption),
changes  in annual  NPP will generally precede changes  in vegetation  structure.  When large  or moderate
disturbances occur, their impact can be judged in  part by the recovery of vegetation toward predisturbance
levels of NPP.  Advances in  remote sensing technology and an improved understanding of spectral signatures
related to functional properties of plants offer exciting prospects for large-scale assessments of current and
potential NPP.

(1) Vegetation Greenness:  Total plant chlorophyll (green leaf material) is an excellent integrator of NPP in
ecosystems, and  it can be  easily  monitored  with  airborne  and  satellite  remote  sensing.   Because the
acquisition of carbon  is among the highest priorities for  plants,  resource  investment  in  leaves and  their
constituents is probably pushed to the point where the return on that investment (sunlight capture) is  marginal
in its effect   Thermodynamic complexities of capturing sunlight and carbon  require  that  the  ratio of
chlorophyll to other resource-expensive leaf constituents remain within a narrow range. Thus any limitation
or impediment to plant resource acquisition and growth is likely to be reflected  by a decrease in the total
green leaf material  (i.e., chlorophyll) deployed by  plants.

(2) Annual Wood Increment:  Because of its ability to provide a historic (i.e., retrospective) record of annual
biomass accumulations, this  indicator was addressed separately (see Indicator E.8, "Dendrochronology:  Trees
and  Shrubs").   When woody vegetation  is present within a sampling  unit,  one measurement  which  could
give  important insight into vegetation response is annual wood increment. Allocation of C to wood is a lower
priority than allocation to  either leaves and stems for energy capture or roots for mineral nutrient and water
acquisition.  The earliest and most sensitive reduction in C allocation will occur in wood when environmental
stress reduces NPP (see also Indicator D.1, "Tree  Growth Efficiency").   Incorporation of trace  material  into
wood and other plant materials can  also provide evidence of exposure to growth-inhibiting toxins.  In
addition, C, H, and O isotope abundances in  wood  may provide evidence of the state of the stomatal and
photosynthetic systems at the time wood is being formed.  Measurement of relative woody growth increment
requires  approximately 0.5  h of technician time  per  plant.  There is a spectral signature for lignin  and
cellulose (the constituents of wood); thus it may be  possible to  monitor annual changes in  woody  biomass
remotely.

(3) Ratio of Root Biomass to Shoot Biomass:   Investment of C to roots versus leaves and stems  involves
some degree of trade-off.   Conditions which  tend to restrict a plant's  ability to obtain water or mineral
nutrients may cause increases in the root/shoot ratio.  If above-ground resources,  particularly light, are limiting,
the root/shoot  ratio may decline.   Either  investment is  probably made so  that it is  efficient at the margin.
Although the literature contains numerous  studies documenting shifts in C allocation pattern in response to
environmental changes, the  data often have not  been obtained in a manner which allows an economic
analysis of investment patterns.  This is an area for future research.  Nonetheless, comparative  measures of
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C allocation in exposed versus controlled areas would be an excellent indicator of ecosystem function and
health.

MEASUREMENTS:  Three separate measurements can be used as an indicator of NPP.

(1) Vegetation Greenness: The resources required to monitor chlorophyll content or greenness involve image
acquisition and processing.  Satellite data extends back to 1972  with the LANDSAT thematic mapper and
LANDSAT multispectral scanner data sets.  Satellite data having coarse spatial resolution  (1.1 km) from the
Advanced Very High Resolution Radiometer (AVHRR) of the National Oceanic and Atmospheric Administration
(NOAA) has been used to conduct long-term regional analyses of  vegetative response to stressors (e.g., Asrar
et al. 1985; Tucker et al. 1985; Becker and Choudhury 1988).  Extensive ground truthing of remotely sensed
measurements of vegetation  are  under way in  the First International Satellite-Land  Surface  Climatology
Program  Field Experiment Program of the National Aeronautic and Space Administration (NASA).   NASA
plans to  undertake a second  such  experimental program within the  next several years.

(2) Annual Wood Increment:  Measurement of relative woody growth increment requires approximately 0.5 h
of technician time per plant.  There is a spectral signature for lignin and cellulose (the constituents of wood);
thus it may be possible to monitor annual changes in woody biomass remotely.

(3) Ratio of Root Biomass to Shoot Biomass:   Examining root/shoot ratios in nature is labor intensive and
depends  greatly  on the size  of the plant and the  precision  desired; estimated  labor costs are 1  day of
technician time for each sample shrub.

INDEX PERIOD:  To  assess the  potential  NPP  in  seasonally active grassland, shrubland, and woodland
systems,  the sampling window for  remote  sensing should include the period of peak vegetation growth to
facilitate  repeatability  among  years.  Growth increments in woody plants and  annual standing  biomass in
ephemeral vegetation (e.g., grasslands) should  be measured at the  end of annual or seasonal periods of
productivity.

VARIABILITY: The expected spatial variability of biomass measures within a resource sampling unit varies
with habitat quality; the range can  deviate  >100% from the mean value.  Under some conditions vegetative
cover and productivity are quite uniform, for example, across flat valley bottoms.  Because the entire resource
sampling unit is being monitored, the expected spatial variability of remotely sensed data  is inconsequential.
The expected temporal variability of the biomass measures during the index  period were not estimated.

PRIMARY PROBLEMS:  The most  significant problem is that the  field measurements are  all labor intensive.
Additional  research is needed to determine if spectral signatures other than that of chlorophyll can be used
to determine specific details about  the physiological or functional status of plants.  Detectable changes in the
quantity  of other plant pigments should  be useful in this regard.

In assessing the  impact of some environmental change, it would  be extremely  useful to  quantify a species'
potential NPP by the availability of its limiting resources.  This  is  a simplistic assumption, however, because
in most cases plant growth is limited by multiple resources.  In arid zones, for example, water is an important
resource, but the growth  rate of most plants  inhabiting these areas  also  increases  in  response  to  N
applications.  Research to develop an improved understanding of the effect of  resource limitations on NPP
is therefore needed.

REFERENCES:

Asrar,  G., E.T.  Kanemasu,  R.D.  Jackson, and P.J.  Pinter,  Jr.  1985.  Estimation of total above-ground
phytomass production  using remotely sensed data.  Remote Sens. Environ. 17:211-220.
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Becker, F., and B.J. Choudhury.  1988.  Relative sensitivity of normalized difference vegetation index (NDVI)
and microwave polarization difference index (MPDI) for vegetation and desertification monitoring.  Remote
Sens.  Environ. 24:297-312.

Tucker, C.J.,  C.L Vanpraet, M.J. Sharman, and C. Van Ittersum. 1985.  Satellite remote sensing of total
herbaceous biomass production in the Senegalese Sahel: 1980-1984.  Remote Sens. Environ. 17:233-250.

BIBLIOGRAPHY:

Bloom, A.J.,  F.S. Chapin, HA. Mooney.   1985.   Resource limitation  in plants - an economic analogy.
Annual  Rev. Ecol. SysL 16: 363-392.

Botkin, D.B., et al.  1986.  Remote Sensing of the Biosphere.  National Academy Press, Washington,  DC.
135 pp.

Ellison et al.  1951.  Indicators of condition and trend on high range watersheds of the Intermountain region.
USDA Forest Service handbook no. 19.  U.S. Department of Agriculture, Forest Service, Fort Collins, CO.

Field, C.B., and  HA. Mooney.  1986. The photosynthesis - nitrogen relationship in wild plants. Pages 25-
55.  In:   T.A. Givnish, ed.  On the Economy of Plant Form and Function.   Cambridge University Press,
London.

Graetz, R.D.  1987.  Satellite remote sensing of Australian rangelands.  Remote Sens. Environ. 23:313-331.

Pickup, G., and V.H. Chewings.  1988.  Forecasting patterns of soil erosion in arid  lands from Landsat MSS
data.  InL J. Remote Sens. 9:69-84.

Reppert and  Francis. 1973.   Interpretation of trend in range condition from 3-step data.  Research Paper
RM-103.  U.S. Department of  Agriculture, Forest Service, Fort Collins, CO.

Waring, R.H.  1983. Estimating forest growth and efficiency  in relation to canopy leaf area. Adv. Ecol.  Res.
13:327-354.
                                                E-3

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E.2  INDICATOR:  Riparian Extent

CATEGORY:   Response/ Ecosystem  Process Rates and Storage
              Exposure and Habitat/ Landscape

STATUS:  High-Priority Research

APPLICATION:  This indicator is designed to monitor the areal extent of riparian  habitat in arid lands both
as a threatened resource class that directly relates to environmental values such as water quantity and quality,
soil erosion, and aesthetics and as extent affects animal and plant populations.  Riparian habitats in the West
have been widely depleted and degraded (U.S. GAO 1988; Johnson and Simpson 1985).  Because of their
unique dependence on surface waters and their intense human utilization or manipulation, riparian systems
are  capable  of  reflecting  the  overall  condition  of  surrounding  ecological  resources (Groeneveld  and
Griepentrog 1985).

INDEX PERIOD:  Satellite data of late spring to early summer is best for monitoring the status and  extent
of riparian systems, because  the  riparian vegetation  is in  a full leaf-out condition,  and a high sun angle
reduces shadow effects in steep terrain.

MEASUREMENTS:  Riparian zones associated with rivers, streams, and springs stand out  clearly on  remotely
sensed data of grasslands, shrublands, and woodlands.  The areal extent of riparian vegetation can be readily
tracked  by  using Thematic Mapper  (TM)  satellite data  (e.g.,  Groeneveld  et  al.  1985).   This can  be
accomplished with aerial photography or airborne video data, in conjunction with a field survey program.

The  estimated costs of remote-sensing-based measurements performed on a landscape sampling unit are the
following:  (1) TM or airborne  data  purchase  (-$500), (2) computer time (-$500), and (3)  analyst time
(-$500).   Thus the total cost per landscape  sampling unit would be about  $2000.   The recommended
interannual sampling frequency would  be approximately five years.

VARIABILITY:   TM data would  provide full spatial coverage of  each landscape sampling unit; therefore,
considerations of spatial variability for  parameters within a  resource sampling unit are inconsequential.  The
expected temporal variability of riparian extent during the  index period would produce a range that deviates
<10% from the mean value.

PRIMARY PROBLEMS: The tracking of species composition changes cannot be performed in detail by using
satellite data.  Monitoring for these changes must be done by low-altitude remote sensing and  field surveys.

REFERENCES:

Groeneveld, D.P., and T.E. Griepentrog.  1985.  Interdependence of groundwater,  riparian vegetation and
streambank stability:   A case study.   Pages 44-48.   In:   Riparian Ecosystems and Their  Management-
Reconciling Conflicting  Uses.   Proceedings  of  the First  North American  Riparian  Conference.   General
Technical Report RM-120.  U.S. Department  of Agriculture, Forest Service, Fort Collins, CO.

Groeneveld, D.P., C.D. Elvidge, and  D.A. Mouat.  1985.  Hydrologic alteration  and associated vegetation
changes  in the Owens Valley, California.  Pages 1373-1382. In:   Proceedings of Arid  Lands:  Today and
Tomorrow.  Westview Press,  Boulder,  CO.

Johnson, R.R., and J.M.  Simpson.  1985.  Desertification  of wet riparian ecosystems in arid regions of the
North American  Southwest.  Pages 1383-1393.  In:   Proceedings of Arid Lands:  Today and Tomorrow.
Westview Press, Boulder, CO.
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U.S. CAO.  1988.  Restoring degraded riparian areas on western rangelands.  GAO/T/RCED-88-20.  U.S.
General Accounting Office, Washington, DC.
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E.3  INDICATOR:  Energy Balance

CATEGORY:   Response/ Ecosystem Process  Rates and  Storage

STATUS:  High-Priority Research

APPLICATION:  The input of solar energy drives the interrelated biogeochemical cycles of carbon, oxygen,
nitrogen, water, etc., in virtually all ecosystems.  Solar energy impinging upon a site is dissipated through a
mix of five primary flux processes  or pathways:  reflection, reradiation, conduction of sensible heat into the
ground, sensible heating of the air,  and evaporating water as  latent heat in the air.  The  rates of several
important biogeochemical cycles (e.g., H2O,  C, and O)  are directly dependent upon or closely related to the
particular mix of these dissipation processes occurring at any given time. Because these biogeochemical cycles
are linked to energy dissipation processes  in well-characterized ways, the rates  of some  biogeochemical
processes, an  indicator  of ecosystem function, can be  inferred from the relative rates of energy dissipation
processes.

Sensible and  Latent Heating of Air:  A measure of particular importance is the Bowen ratio,  the  ratio of
sensible  heat  flux  to latent  heat flux in air.  This ratio in  essence indicates the relative importance of the
hydrologic cycle as an energy dissipater at the site of  measurement.  When vegetation is present, stomatal
conductance  and the resultant rate of plant  transpiration  are  usually the factors controlling the Bowen  ratio.
Nowhere is this more  true than in arid and semiarid environments (Jarvis and McNaughton 1986).   Other
factors which  influence the  Bowen ratio are leaf area  and surface aerodynamic roughness.

It has been demonstrated that stomatal conductance is primarily and linearly related to the leaf photosynthetic
rate,  given  constant  relative  humidity  and   CO2 concentration  (Wong  et  al.  1979,  1985).   The
conductance/photosynthesis  relationship increases linearly with  relative humidity and as an  inverse function
of the CO2 concentration (Ball et al. 1986;  Ball 1988). The photosynthetic rate, relative humidity, and the
CO2 concentration thus form  a multiplicative index  to which stomatal conductance  responds linearly. The
slope of  the conductance response varies between species and  particularly between C3 (cool climate) and C4
(warm climate) species.

The Bowen ratio, then, directly reflects the photosynthetic capacity of the area's vegetation and would change
if the site's vegetation changed. Impacts on the vegetation by  factors such as air toxics, which enter leaves
and affect photosynthesis, would be expected to induce a decline  in stomatal conductance and  a decrease
in water vapor and latent heat flux from vegetation.  Such a change could be taken as an early warning sign
to long-term  vegetation change.   Also, because the Bowen  ratio reflects stomatal conductance and  many
pollutants must enter the stomata before they affect  plant metabolism, this measure of stomatal conductance
may indicate susceptibility of ecosystems to airborne  pollutants. The plant-mediated  flux of water vapor and
accompanying latent heat into the atmosphere is one of the primary feedbacks that the  vegetation has upon
climate.

Reflection:   Reflectance of  the total solar spectrum  (measured as surface albedo) can  be a major route of
solar  energy dissipation. The albedo can range from  near zero above heavy vegetation  cover or above soils
rich in organic matter to values as high as 0.7 in areas where a salt crust covers the  soil. Particularly in arid
and  semiarid  regions,  loss of vegetation generally results in increased  albedo.  Thus with  vegetation loss,
incoming energy which might have  been dissipated as latent or sensible  heat in the lower  atmosphere is
reflected  back through  the atmosphere.

Soil Heating:  Soil heat flux can be a major energy dissipation pathway, especially where vegetation is sparse
and soil  is dark or moist  On both a diurnal and annual basis,  the  net flux of energy to or from  the  soil will
be near  zero  unless the climate is changing. Thus soil temperature measurements made at opposite points
in either the diurna! cycle or the annual cycle give  a good indication of the  importance of  soil  heat flux to

                                                  E-6

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the energy  balance  of  a site for  the  respective time scales, especially if soil  heat  capacity and thermal
conductivity are determined.  For example, Schmidlin et al. (1983) found that mean annual soil temperature
of well-drained soils in Nevada can be  estimated from  as little  as two readings taken on equally spaced
months (e.g., January and July, February  and August).  They  then correlated  mean  annual soil temperature
with elevation and geographical parameters.   These parameters would, of course,  change and  have to  be
recalibrated  if the climate warmed or cooled.  Soil temperature records would  be valuable in tracking climatic
change and  can be used in conjunction with soil water measurements.  These parameters provide a baseline
against which future  changes in soils may occur in response  to climate change (e.g., soil C and N content;
Post et al. 1982,  1985;  Parton et al. 1987).

Solar Radiation:  There is a paucity of high-quality  measurements of solar radiation across North America.
In part, this lack of data stems from problems in interpreting remote measurements of solar radiation, such
as the deposition of dust, etc., on  instrument lenses and the inability  to differentiate types and altitude of
clouds (which influence downwelling long-wave radiation). A well-executed, larger,  long-term solar radiation
sensor network would prove particularly valuable in testing the hypothesis that increased cloudiness tends
to mitigate the  influence of increasing  "greenhouse gases" in  the atmosphere.

Reradiation:  Remote measurement of reradiation  (i.e.,  terrestrial radiative flux) is probably  not practical.
Although durable, semiconductor-based thermopile sensors are available, their field of view is probably too
small to  capture the heterogeneity of  radiative  surfaces  at a site.  There are a number  of approaches to
estimating terrestrial  radiative flux, including direct temperature measurement and application of the Stefan-
Boltzman equation (Sellers et al. 1988).

In summary, regular  and continuous monitoring of the surface energy  balance parameters, particularly the
Bowen ratio, constitutes  an excellent means of assessing the functional  state of the primary producers within
an ecosystem.  Linked with satellite measurements  of plant canopy characteristics,  the  energy balance can
be  used to  calibrate and validate  inferences of the functional  state of the  vegetation.   Continuity of
measurements from  simple automated  stations can give critical temporal information  at a frequency which
is  not practical for  satellite-based  measurements.   Continuous  solar  radiation records  could be  a very
significant data  base, especially in  addressing questions regarding climate change.

INDEX PERIOD:  In  general, data on energy balance  in arid and semiarid ecosystems would be most valuable
for the period when  plants are metabolically active.   Monitoring stations can be easily automated and should
operate continuously; year-round solar radiation measurements might be especially valuable.

MEASUREMENTS:  Measurements  should  emphasize an  understanding of regional energy balance  and its
connection to remotely sensed data (Kittel and Schimel 1987; Running et  al. 1989).  Advanced Very High
Resolution Radiometer satellite data would be used to measure surface albedo and temperature on an annual
or seasonal  basis. Thematic mapper and SPOT satellite data  would provide more detailed spatial resolution
of albedo and temperature changes on a less frequent basis.

Field  measurements  provide  information  about process-level  ecosystem function which  can not be  directly
measured with  remote sensing techniques (e.g., Vukovich  et al. 1987).   Measurement of the Bowen ratio, for
example, involves measurement of wind vectors, air  temperature, and humidity at the base of the planetary
boundary layer. A small station with appropriate pyranometers, thermocouples, humidity sensors, soil heat
flux  plates, and satellite-linked  data retrieval would  cost  approximately  $8000.

VARIABILITY: Spatial variation in energy balance parameters  is largely a function  of vegetation type and land
use.   Temporal variation is a function of available  soil  water within  and  among seasons.   Assessment of
regional variation in  surface energy balance is currently under way in  tallgrass prairies by the  International
Satellite Land Surface Climatology Program of the National Aeronautics and Space Administration, First Field
Experiment  (Sellers et al. 1988)

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PRIMARY PROBLEMS:  Research is needed to determine what remotely sensed canopy parameters should
be used  to drive regional level models of energy balance.  Data on stomatal responses to  photosynthesis,
humidity, and CO2 need to be extended to a wider variety of species than is currently available. These data
would  need to be placed in a framework which links particular stomatal characteristics to plants typical of
particular ecosystem  conditions  (e.g.,  disturbance).   Maintenance  and  calibration of  sensors,  especially
pyranometers,  would  be  an important concern.

REFERENCES:

Ball, J.T.  1988. An analysis of stomatal conductance.  Ph.D. Dissertation, Stanford University, Palo Alto, CA.

Ball, J.T.,  I.E.  Woodrow,  and  J.A. Berry.   1986.   A model  predicting stomatal  conductance  and  its
contribution to the control  of photosynthesis under different environmental conditions.  Pages 549-552.  In:
J. Biggins, ed.   Progress in  Photosynthesis  Research Vol.  II.  Martinus Nijhoff, Dordrecht, The Netherlands.

Jarvis,  P.)., and K- C. McNaughton.  1986.   Stomatal control of transpiration:  Scaling up from the leaf to
region.  Adv. Ecol. Res. 15:1-49.

Kittel,  T.G.F.,  and D.S. Schimel.  1987.  Monitoring the ecological impact of global  change:  A coupled
ecosystem modelling-remote sensing approach. In: Global Climate Ecosystems Monitoring Workshop. U.S.
Environmental  Protection Agency and  Institute  of Science and Public Affairs, Florida State University,
Tallahassee.

Parton, W.J., D.S. Schimel, C.V. Cole, and D,S. Ojima. 1987.  Analysis of factors controlling soil organic
matter  levels in Great Plains Grasslands.  Soil Sci. Soc. Am. J. 51:1173-9.

Post, W.M., W.R. Emanuel, P.J. Zinke, and A.G. Stangenberger. 1982.  Soil carbon pools and world  life
zones.   Nature 298:156-159

Post, W.M., J. Pastor, P.J. Zinke, and A.G. Stangenbergrer.  1985.  Global patterns of soil nitrogen storage.
Nature 317:613-616.

Running, S.W., R.R. Neman!,  D.L. Peterson, L.E. Bane, D.F. Potts,  L.L. Pierce, and M.A. Spanner.   1989.
Mapping regional  forest  evapotranspiration and  photosynthesis by  coupling satellite data with  ecosystem
simulation.  Ecology 70:1090-1101.

Schmidlin, T.W., F.F. Peterson, and R.O. Gifford.  1983.  Soil  temperature regimes in Nevada. Soil  Sci.
Soc. Am. J.  47:977-982.

Sellers, P.J., F.G. Hall, G. Asrar, D.E. Strebel, and R.E. Murphy. 1988. The First ISLSCP Field Experiment
(FIFE).   Bull. Am. Meteorol. Soc.  69:22-27.

Vukovich, F.M., D.L Toll, and R.E. Murphy.  1987.  Surface temperature and albedo relationships  in Senegal
derived from NOAA-7 satellite data.  Remote Sens. Environ. 22:413-422.

Wong, S.C., I.R. Cowan, and G.D. Farquhar.  1979.  Stomatal conductance correlates with photosynthetic
capacity.  Nature 282:424-426.

Wong, S.C., I.R. Cowan, and G.D. Farquhar.  1985.  Leaf conductance in relation to CO2 assimilation rate.
I.  Influence of  nitrogen nutrition,  phosphorus  nutrition,  photon flux density,  and  ambient  CO2 during
ontogeny.  Plant Physiol. 78:821-825.

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BIBLIOGRAPHY:

Sellers, P.J., Y. Mintz, Y.C. Sud, and A.  Dalcher.  1986.  A simple  biosphere (SiB) model for use within
general circulation models.  J. Atmos.  Sci. 43:505-531.
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E.4  INDICATOR:  Water Balance

CATEGORY:  Response/ Ecosystem Process  Rates

STATUS:  High-Priority Research

APPLICATION:   Water  is a resource critical to arid ecosystems.   As precipitation  amounts decline, the
variability of  precipitation generally  increases markedly; this  variability  has important consequences  for
evolution and adaptation of arid zone organisms and the functioning of arid ecosystems.  Some organisms
have evolved  structural, functional, and life  history attributes specialized for dealing with variability in water
supply, while  others have evolved  traits which allow them  to exploit relatively  stable water sources.  Two
examples of organisms that require relatively stable water supplies are mesquite (Prosopis spp.), which uses
primarily ground water, and riparian zone species, which can use a combination of surface water and ground
water.   Despite  adaptation of  their component  species to restricted  and variable water  supplies,  both
decreases and increases  in water supply can significantly affect the productivity  and species composition of
arid  lands.  Water  balance affects  productivity of animals ranging from soil microorganisms to vertebrates.
Plants have a major impact on water supplies, not simply because they consume the resource, but equally
importantly, they intercept precipitation and contribute to soil conditions favorable for percolation of water
into  the  soil.  Monitoring of water balance in arid  regions can thus be used  as a predictor of ecosystem
productivity and as an indicator of disturbance.  Also, transpiration is a  significant portion of the hydrologic
cycle because it represents one of the important feedbacks of the biosphere to  the atmosphere and climatic
system.   Any water balance  monitoring program  must be closely linked to  synoptic  and  local weather
monitoring  efforts.   Particularly important as an indicator  of changes  in  ecosystem function is  a change
(increasing or  decreasing) in the  variability of values for the three water-balance parameters discussed below.

Water Consumption by Vegetation:  As was  briefly discussed under "Energy Balance" (indicator E.3), water
consumption by plants is closely tied to photosynthetic activity.  This is true not only because stomata must
be open for photosynthesis to occur, but also because stomatal conductance to water vapor and  to CO2 is
apparently linearly  related to the photosynthetic rate of a leaf (Wong et al. 1979, 1985; Ball et  al. 1986).
In monitoring the  Bowen ratio (see  indicator  E.3), the latent  and sensible  heat fluxes  are determined
separately so  that a value for water flux can be obtained  from  vegetation. Recent work by Carlson and
Buffum  (1989) suggests that it may be possible to track evapotranspiration on  a regional scale by using a
combination of satellite remote sensing data and data from the meteorological network. Atmospheric stressors
which  enter leaves  and affect photosynthesis (e.g.,  air toxics)  would be expected to induce a decline  in
stomatal  conductance and a decrease  in water vapor and latent  heat flux  from vegetation.  Such a change
could be taken as an  early-warning sign of  long-term damage  to vegetation.   Any decrease in stomatal
conductance (reflected in a lower rate of water use) should render a plant less  susceptible to damage from
airborne toxics, which require direct access to the  leaf mesophyll cells  before damage can occur.

Measurement of the ratio of stable C isotopes (13C/12C) accumulated in leaves provides an integrated measure
of the ratio of stomatal  conductance to photosynthesis (Farquhar et al. 1982).   Coupling stable  C isotope
abundance  measurements with measurement of the H and O isotopes accumulated in leaf material appears
promising as a direct integrator of the amount of water expended for each  unit C gained and of the relative
humidity of the air at  the leaf surface.  The interpretation of the H and O isotope abundances in leaves is
less well established than that for C isotope abundances (White 1988).  Both the H and O in ground water
tend to be enriched in the respective heavier isotopes (2H and 18O) relative to surface water and the meteoric
water line.  By sampling relative abundance of these isotopes  in water within  a plant and in the alternate
water sources, it is possible to determine the portion of water coming  from the two sources.

Ground  Water Depth and  Use:   As mentioned above,  some highly productive arid ecosystems utilize
relatively stable ground water sources.  Fluctuations may either force plants into drought stress or flood  the
root zone.  Plants  which  normally use stable water supplies  are less likely to  withstand water stress than

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plants which normally encounter fluctuations in water supply.   Fluctuations may also affect salinity or trace
element accumulation.  Lowered ground water depth due to  increased pumping, upstream impoundment,
diversion,  channelization,  etc.,  could  adversely affect such communities.  In extreme cases, withdrawal  of
ground water has caused the ground  to collapse or sink.  In arid regions, ground water levels  can stabilize,
once near-equilibrium between use and inflow  is achieved.   Salts  accumulate in soil  above such a water
table, which becomes an  "inverted  leaching"  profile. Shifts in  a water table could result in changes  in this
salt accumulation profile, thereby providing a record of past shifts, against which ongoing shifts can be gauged.

Monitoring of Stream Flows:   Runoff  patterns  vary markedly with differences  in vegetation  type, species
composition, and areal cover, as well as with  soil physical properties that influence infiltration.  The southern
shrubland and grassland ecosystems in North America are prone to  flash flooding, in part because of sparse
vegetation,  but more because  they are near the  source  of energetic subtropical storms.  In these areas,
monitoring of flooding events would be important for documenting erosion and possibly shifts in vegetation.
In areas where flow is less episodic, stream flow data properly coupled to synoptic and local weather data
is a  good  integrator of vegetation and soil conditions.  Accumulation  of records  of stream flow data  would
provide a good baseline against which purported hydrologic change can be gauged.

INDEX PERIOD:  Most  measurement  systems would record in  place continuously; for example, flow gauges
in streams and floats in wells.  Data might exist or be obtainable only during specific seasons, such as Bowen
ratio and stream flow data collected during growing seasons or  well records at time of peak and least ground
water withdrawal.

MEASUREMENTS:

(1) Ground  Water Level Monitoring:  Depth  records to  ground water and water  table behavior are needed.
Some ground water data bases do exist (e.g., U.S.  Geological Survey), but their extent and usefulness is not
known at  present. To reduce regional uncertainty, new  wells may need to be installed  in existing networks.
Well installation cost  varies but is approximately $80/m ($25/ft).

(2) Vegetation Use of Ground Water Versus Surface Water:   Both H and  O in ground water  tend to be
enriched in  the respective  heavier isotopes (2H and  18O) relative to surface water and the meteoric water line.
By sampling the relative abundance of these isotopes in  water  within a plant and in the two alternate water
sources, the contribution of water from each source can be determined.

(3) Historical Fluctuations:  As wells  are installed, exchangeable Ca,  Mg, Na, and K are measured in soils
above the water table.  Graphic plots of exchangeable  cation profiles as a function of height above water
table would indicate  previous  positions of  the water table.   Based upon an additional  $20 a  sample  for
analyses, 15 samples  a  core  (25-50-cm intervals),  and  10 cores per unit, the total cost is  $30OO  for each
resource sampling unit.

VARIABILITY: Ground water depths will vary within a resource sampling unit.  Water use by vegetation is
likely to  be  rather  consistent  (with ranges  that  deviate <10% from  mean values)  if the cover and slope
exposure are uniform; however, this kind of uniformity in land surface is rarely found.  Stream flow will be
highly variable (with  ranges  that deviate  <100% from  mean values)  across  a  resource  sampling unit,
depending largely on topography, precipitation event size, and  localization, but also on vegetation cover type
and  density.

PRIMARY PROBLEMS: Establishing a network of wells for ground water measurements on remote sites  would
be expensive. H and O isotope methodology for plant-ground water  and plant-atmosphere interactions needs
more research.  The use of these methods may be restricted to  sites  with stable ground  water.  They
probably work less well in  riparian zones, washes, or playas.  Subsoil may be highly heterogeneous in texture
and  mineralogy, thereby increasing variability in analyses.

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REFERENCES:

Ball, J.T.,  I.E.  Woodrow,  and  J.A. Berry.   1986.  A model predicting stomatal conductance and  its
contribution to  the control of photosynthesis under different environmental conditions. Pages 549-552.  In:
J. Biggins, ed.  Progress in Photosynthesis Research, Vol. II.  Martinus Nijhoff, Dordrecht, The Netherlands.

Carlson, T.N., and  M.J. Buffum.  1989. On  estimating total daily evapotranspiration from remote surface
temperature measurements.  Remote Sens.  Environ. 29:197-208.

Farquhar,  C.D.,  M.H.  O'Leary,  and J.A.  Berry.   1982.   On the  relationship  between  carbon isotope
discrimination and the intercellular carbon dioxide concentration  in leaves.  Australian J. Plant Physiol. 9: 121-
137.

White, J.VV.C  1988.   Stable  hydrogen isotope ratios  in plants:  A review of current theory and some
potential applications. Pages 142-162.  In:  P.W. Rundel, J.R. Ehleringer, and K.A. Nagy, eds. Stable Isotopes
in Ecological  Research.  Ecological Studies Vol. 68.  Springer-Verlag,  New York.

Wong, S.C., I.R. Cowan, and G.D. Farquhar.   1979.  Stomatal conductance correlates with photosynthetic
capacity.  Nature 282:424-426.

Wong, S.C., I.R. Cowan, and G.D. Farquhar.  1985.  Leaf conductance in relation to CO2 assimilation rate.
I.   Influence of nitrogen  nutrition, phosphorus nutrition, photon flux density, and ambient  CO2  during
ontogeny.  Plant Physiol. 78:821-825.

BIBLIOGRAPHY:

Ball, J.T.  1988. An analysis of stomatal conductance.  Ph.D. Dissertation,  Stanford University, Palo  Alto, CA.

Freeze, R.A., and J.A.  Cherry.   1979.   Groundwater  Hydrology.  Prentice-Hall, Inc., Englewood  Cliffs, NJ.
604 pp.

Gat, J.R.   1971.  Comments on  the stable isotope method in  regional ground-water investigations.  Wat.
Resour. Res.  7:980-993.

Ingraham, N.I., and B.E. Taylor.  1989. The  Effects of snow melt on the hydrogen  isotope ratios of creek
discharge in Suprise Valley, California.  J. Hydrol. 106:233-244.

Ingraham, N.L.  1988.  Light stable isotope systematics of large-scale hydrologic regimes in California and
Nevada.   Ph.D. Dissertation, University of California, Davis.  169 pp.

Sabins, F.F., Jr.  1986.  Remote Sensing: Principles  and  Interpretation. W.H. Freeman, New York.  449pp.
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E.5  INDICATOR:  Soil  Erosion

CATEGORY:  Response/ Ecosystem  Process Rates and Storage

STATUS:   High-Priority Research

APPLICATION:   Accelerated erosion is  one of the primary indicators of desertification  (FAO 1979) and is
closely  linked  to vegetation  loss and  surface  soil disturbances  (Webb and Wilshire 1983).  Structural
characteristics of natural shrubland  and grassland soils are extremely sensitive to disturbance.   Structural
degradation initially manifests itself by destruction of soil crusts (cf. indicator E.11, "Abundance and Species
Composition of Lichens and Crytogamic  Crusts") and changes (generally a decrease) in infiltration  rates.  The
destruction of soil crusts and  loss of vegetation  in disturbed areas can  result in  increased wind erosion.
Decreases  in soil infiltration rates are associated with increased runoff, which accelerates sheet, rill, and gully
erosion.

Water erosion consists of particle detachment (interrill or sheet erosion) followed by particle transport (rill and
gully erosion). A landscape in nominal condition would be characterized by a naturally established drainage
density, a standard ratio of interrill to rill area, and a stable erosion rate.  Disturbance of native soil structural
characteristics would  result in an alteration of  the  interrill/rill  ratio and  an accelerated erosion rate.  If
continued, the drainage  pattern for  an impacted area becomes  more dense and more linear in outline.

The rate of wind erosion is controlled by wind speed and physical  properties of the soil.  Shrubland soils are
commonly protected  from wind  erosion by vegetation,  which breaks the wind speed at ground level and
holds soil in place with its roots.  Soil crusts (including cryptogamic crusts)  also serve to resist wind erosion.
Areas which have lost vegetation and the protective soil crusts are subjected to wind erosion during episodes
of strong winds.  This indicator would track a series of parameters linked to or resulting from accelerated
erosion, and would be applicable to all arid resource classes.

SNDEX  PERIOD:  There is  no optimal sampling  window for this  indicator.   However, because  of  high
seasonal variation, it  must be sampled during the same season on repeat field visits.

MEASUREMENTS:  (1) Integration of Factors Linked to Water Erosion Susceptibility:  These factors include
information on soil characteristics, slopes,  rate and  timing of annual precipitation, vegetation cover, and
mechanical disturbance.  These factors would  be obtained from EMAP-Characterization (soil  information and
slopes),  meteorological data, and the vegetation biomass and mechanical disturbance indicators (E.1 and E.17).
The Food  and  Agriculture Organization (FAO  1979)  provided an example  of  the  development  of  the
integration of factors to estimate water erosion  susceptibility on a regional basis. The estimated cost is $500
a resource sampling unit.

(2)  indicators of Accelerated Water Erosion:  The interrill/rili ratio, gully density, and  alterations in drainage
density  and drainage pattern would  be  measured  for appropriate soil/landform units  by using low-altitude
aerial photography or videography.   The total cost of  aerial data  acquisition  (SI 00) and analysis ($500) is
$600 for each resource  sampling  unit

(3)  Integration of Factors Linked  to Wind  Erosion:  Measures of vegetation cover, mechanical disturbance,
and high wind events would be obtained by monitoring other indicators (E.1  and E.17)  and meteorological
data.  These data would be integrated to identify areas at risk of accelerated erosion by wind.  FAO (1979)
provides an example of this style of integrated index for wind erosion.  The estimated cost is $500 a resource
sampling unit.

VARIABILITY: The expected temporal variability of erosion-related  measurements derived from airborne data
during the index period  would produce a  range that deviates 5-50% from the mean value.  This variability

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is  induced primarily by  seasonal and  diurnal alterations  in  illumination conditions and can be  largely
eliminated by acquiring data with standardized illumination conditions.  Because the remote sensing data will
census the entire resource sampling unit, the spatial variability of measurements is  inconsequential.

PRIMARY PROBLEMS:  Measurement procedures must be standardized.  The integration of factors to obtain
indices for wind and water erosion would  require some effort but is achievable.

REFERENCES:

FAO.   1979.   A Provisional  Methodology for Soil Degradation  Assessment  United  Nations, Food and
Agriculture Organization,  Rome, Italy.

Webb, R.H., and H.G. Wilshire, eds.  1983.  Environmental  Effects of Off-Road Vehicles:   Impacts and
Management in Arid Regions.  Springer-Verlag, New York.
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E.6  INDICATOR:  Charcoal Record

CATEGORY:   Response/  Ecosystem  Process Rate and Storage (Retrospective)

STATUS:  High-Priority Research

APPLICATION:  The purpose of charcoal analysis in a monitoring program is to identify areas where plant
communities  are or were  undergoing stress.   When  plant communities  are  under stress,  fire frequency
increases.  Healthy communities are characterized by lower fire frequency.  Actually, the relationship is much
more complex than this description, because although a community may be unhealthy, the fire starting agent
(e.g., lightning or man) must also be  present  Fires reflect stress factors such as beetle kill, drought succeeding
periods of wet climate when fuels were accumulating  but not being burned off, or the impact of fires on
accumulated  fuels after fire suppression policies.  In addition, periods of forest clearance by humans can be
identified by  periods in pollen records when charcoal abundance increases.

By constructing  a charcoal record,  a natural or baseline fire  frequency can  be determined  for any plant
community from which  to judge  current trends in  fire frequency.   Charcoal  frequency is used more as  a
regional indicator than is  pollen production.  Because we can identify the origin of certain pollen types (e.g.,
aquatic and littoral  plant pollen) as  local  rather than regional,  we can separate  between local and regional
signals. This  cannot be done with charcoal.  Therefore, charcoal becomes by default an indicator of regional
fire rather than local fire.  Although a locally occurring fire  may temporarily mask the regional charcoal fire
record, with  great quantities  of charcoal, the use of several  collection localities within  a resource sampling
unit  would net a good regional record, because local fires can be factored out of  the  record.

INDEX PERIOD: Collection  in early winter  after the fire season  is best,  because the  fire activity during the
previous fire  season can  be assessed.

MEASUREMENTS:  Two  types of measurements must be taken:  (1) Charcoal abundance:  As with pollen
abundance the use  of tracers in the sample,  given a standard sample volume or collection area size, can be
used to derive charcoal abundance.  (2) Charcoal size:  Changing size is monitored by direct measurement
with an ocular  micrometer.  Both  changing charcoal  abundance and size can reflect both changing fire
frequency and fuel  type.   Larger charcoal and more abundant charcoal is produced by  pine forests than by
sagebrush steppe.  But when the environment  remains the same, changing charcoal size and  abundance
reflect changing fire frequency and indirectly  changing plant community health. Cost of charcoal analysis can
be covered under the pollen analyses costs.   As with  pollen analysis, an interannual  sampling frequency of
the same frequency as is used for sampling fossil pollen cores would be  adequate for revealing evidence of
plant community stress or change.

VARIABILITY: The expected spatial variability of charcoal  records within a resource  sampling unit was not
estimated.  Because the charcoal records are temporally integrative measures, the variability during the index
period is inconsequential.

PRIMARY PROBLEMS:  As mentioned above, the  primary problem  would be a temporary masking of the
regional charcoal record  by local  fires.

BIBLIOGRAPHY:

Mehringer, P.J., Jr., and P.E. Wigand.  1990.  Comparison of Late Holocene environments from  woodrat
middens and  pollen, Diamond Craters, Oregon.  In:  P.S. Martin, J. Betancourt, and  T.R. Van Devender, eds.
Fossil Packrat Middens:  The Last 40,000 Years of Biotic Changes.  University of Arizona Press, Tucson.  In
press.
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Mehringer, P.J. Jr., and P.E. Wigand.  1987.  Western juniper in  the Holocene.  In:  Proceedings of the
Pinyon-Juniper Conference, January 13-16, 1986,  Reno, Nevada.  General Technical Report !NT-215.

Clark, J.S.  1988.  Stratigraphic charcoal  analysis  on  petrographic thin sections:  Application to fire  history
in northwestern Minnesota.  Quaternary Res. 30(1): 81-91.

Heinselman, M.L  1981.  Fire intensity and frequency as factors in the distribution and structure of northern
ecosystems.   In:  H.A. Mooney, T.M. Bonnicksen, N.L. Christensen, J.E. Lotan, and  W.A. Reiners, eds.  Fire
regimes and ecosystems pioperties. General Technical  Report GTR-WO-26.  U.S. Department of Agriculture,
Forest Service, Washington,  DC.

Inversen, J.  1941.  Land occupation: Denmark's  Stone Age.  Denmarks Geologiske Forenhandlungen !!:66.
                                                E-16

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E.7  INDICATOR: Species Composition and  Ecotone Location of Vegetation

CATEGORY:  Response/ Community Structure

STATUS:  High-Priority Research

APPLICATION: Species composition and ecotone location are important integrators of environmental change
in arid and semiarid regions.  These parameters could (1) determine the effectiveness of land  management
practices, (2) detect the response of vegetation to man-made stressors, and (3) detect vegetation response to
fluctuations or alterations in  long-term climate patterns.

Vegetation patterns in the arid zones frequently have sharp boundaries between competing plant assemblages.
These  boundaries are  known as  ecotones.  A prime example of a prominent  ecotone is the  boundary
between sagebrush and  pinyon-juniper  woodlands  in the Great Basin  area covering  Nevada and Utah.
Another example  is the distinct boundary separating annual grasslands from chaparral on the central  California
coast  The position of these ecotones is known to respond to changes in climatic regime.  These ecotones
can be readily located on Thematic Mapper (TM) satellite data.  This capability offers a tool  for detecting
vegetation shifts in response to  climatic change or changes in  land-use practices or other agents.

Species composition responds  to  a  large number  of factors, including grazing  pattern,  changes in  soil
properties, fire, mechanical disturbances,  erosion, water availability, and climatic  fluctuations/alterations.  The
spatial  and spectral  resolutions of current satellite  data  are too coarse to adequately  determine species
composition in most sparsely vegetated arid  landscapes.   The measurement of species  composition would
require low-altitude aerial photography and field measurements.  This indicator would be applicable to all
arid  resource classes.

INDEX PERIOD:  The optimal sampling window would be during the growing season from late spring to early
autumn, when most species are flowering.

MEASUREMENTS: Species composition and ecotone  locations would be measured by using low-altitude aerial
photography (<1  m resolution) and field measurements. Trained interpreters can quantify species composition
and delineate ecotones from aerial  photographs or airborne video  data. Driscoll and Reppert (1968) provide
a description of these techniques.   Field  measurements  of species  composition would be reported in  two
ways:  (1) number of individuals per unit area (density) of a species, and (2) the areal cover of a species as
a fraction  of the  total ground cover.  Methods for conducting field measurements are available (U.S.  BLM
1985).  Estimated costs for the analysis of aerial  photography is $1000 for each  resource sampling unit
Estimated  costs for field measurements of species composition  is $300 a resource sampling unit;  cost  will
vary with each type of vegetation community.

Although satellite  imagery is not useful for making diagnostic identifications of species composition, it can be
used to monitor ecotone movement. TM data would be used to delineate the ecotone of adjacent vegetation
types.  This can be performed by using the results of field and aerial photograph surveys to provide actual
species compositions.  Changes in ecotone location can then be monitored with repeated coverage by satellite
sensors.  Estimated cost is $500 per landscape sampling  unit.

VARIABILITY:  Estimates  of species composition  for annual plants are subject  to wide seasonal  variation;
perennial plant measurements would be less variable. The expected spatial variability of species composition
within a resource  sampling unit would produce a range that deviates 5-10% from the mean value and would
be different for each plant community.

PRIMARY  PROBLEMS:  The standardization  of  measurement practices would be the  foremost  problem
encountered in the application  of this indicator.

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REFERENCES:

Driscoll, R.S., and J.N. Reppert. 1968.  The identification and quantification of plant species, communities
and other resource features  in  herbland and shrubland environments from large-scale  aerial photography.
Annual Progress Report, Earth Resources Survey Program, NASA/OSSA. U.S. Department of Agriculture, Forest
Service, Rocky Mountain  Forest and Range Experiment Station, Ft Collins, CO.

U.S. BLM.  1985. Rangeland Monitoring:  Trend Studies.  Technical Reference TR 4400-4.  U.S.  Department
of the  Interior, Bureau of Land  Management, Washington, DC.
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E.8  INDICATOR:  Dendrochronology:  Trees and Shrubs

CATEGORY:   Response/ Population (Retrospective)

STATUS:  High-Priority Research

APPLICATION:  Changes in growth-ring characteristics (e.g., annual ring width)  over time can  be calibrated
with measures of plant productivity. For example, it would be feasible to reconstruct long-term (50 to several
hundred years) changes of above ground  biomass production in sagebrush or  pinyon-juniper  habitat types
(Ferguson 1964). Dendrochronology can also be used to date when trees and shrubs germinate and die and
when growth  is affected by anthropogenic factors such  as pollution and land management practices.

The purpose of using time series of growth ring widths  sampled from woody plants growing on  climatically
stressed sites is  to provide a proxy of past climatic variability,  including seasonal and  annual  temperature,
precipitation, drought,  and stream  discharge.  The long  reconstructions (from 500 years to several thousand
years) provide a sound basis for obtaining more reliable estimates  of central tendency, variability, and time
series characteristics than the normally short  period of instrumental data.  The  long reconstructions of past
climate  provided by tree and shrub rings can also be calibrated with other indicators of paleoenvironmental
change  sampled at reasonably  high  frequencies.  For example, pollen records from  locations  with  rapid
deposition rates have the potential to be  sampled at close intervals, so that each represents a brief period
of time.

Although  arid ecosystem research  has focused on tree rings of conifers such as pinyon, the dendrochrono-
logical/ecological approach is potentially applicable to any woody shrub, including sagebrush, with definable
annual growth layers.

INDEX  PERIOD:  Normally one ring a year is added,  although in years of extremely favorable climate, a
double  growth  flush   may occur; in drought  years  a tree may  not add a  ring completely  around  its
circumference.  Radial increment cores can be obtained from  trees and woody shrubs at any time of  the
year, but the complete growth ring for a given year will be present  only after the end  of the growing season.
For example,  in the Great Basin this  occurs in  early autumn.

MEASUREMENTS: Annual growth layers from radial cores are measured under  microscopic magnification to
the nearest 1 /im on a computer-compatible linear measurement apparatus.  A large body of micromainframe
software is available to analyze the resultant series of ring width measurements.  Each  time series  of tree ring
widths (representing one core) is standardized to a mean of 1  and relatively constant variance  over time by
fitting a growth  function to account for the age trend.  This allows the growth  records  from slower growing
older trees and faster  growing younger  trees to be averaged into  a mean value function.  The  older trees
normally contain a stronger  climatic  signal than younger trees, whose growth  often reflects  the effects of
competition for  water,  nutrients and sunlight,  and canopy position.  The mean value function represents tree
growth  for one species  at one location  (stand) over time.  The  annual growth  record  can  normally be
calibrated numerically  with time series of  climatic data during the years of common overlap.   If  a verifiable
numerical relationship  can be established for the period  of instrumental climatic  record, the equation can be
applied  to the lengthy tree ring  series to reconstruct past climate.  In  many arid regions the potential exists
to create  climatic reconstructions for  the  past 500 to several thousand years.   A concise  record of climate
may be  essential in establishing the frequency of climatic events that can dramatically alter ecological systems.

VARIABILITY:   The  quantitative variability in  ring-width index can  closely correspond  to  macroclimatic
variability, according to the sampling design employed.  Growth of  trees and shrubs sampled on  sites where
climate  limits  the physiological  processes controlling growth can be highly correlated with  climate (60-80%
variance in growth attributable to climate).   Because climate can limit growth simultaneously at many locations
in a region, it is not unrealistic to expect 50-75% variance in common among chronologies within a resource

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sampling unit.  Woody plants sampled  on sites where  climate is  not the primary stressor will reflect the
influence of other factors such as competition and microclimate and will  have little correlation with regional
climate or with one another.  Because the dendrochronological records are temporally integrative measures,
the variability during an index period is inconsequential.

PRIMARY PROBLEMS: Reconstructing climate from wood-ring  series is normally not a problem in the West
One factor of prime  importance is the  availability of meteorological data to calibrate  with  the wood-ring
series.  Lower elevations generally  have the greatest  density of weather  stations, whereas information from
higher  elevations is more sparse.   Wood-ring  series can be  used to reconstruct climate at  considerable
distances from where  the trees are  sampled.  One common misconception concerns the difference between
ring  counting  to date annual growth layers and a  dendrochronological approach  involving  a procedure
referred to as  cross-dating. Cross-dating establishes the  exact  calendrical date of every ring  in every radial
increment core, by an exacting comparison  of the growth  patterns among all specimens.  Ring counting does
not yield exact growth ring dates,  because  a ring may be locally absent along a radius, or there may be a
double growth flush in one year.  If a dendrochronological approach utilizing cross-dating is  not employed,
any environmental information present in the series may be lost

REFERENCE:

Ferguson, C.W.  1964.  Annual Rings  in  Big Sagebrush;  Papers of the Laboratory of Tree-Ring Research.
No. 1.  University  of  Arizona Press, Tucson.  95 pp.

BIBLIOGRAPHY:

Fritts, H.C.  1976.  Tree  Rings and  Climate. Academic Press,  London.

Cook, E.R.   1985.  A time series approach to tree-ring standardization.  Ph.D. dissertation.   Department of
Renewable Natural Resources, University of Arizona, Tucson.

Graybill, D.A., and M.R. Rose.  1989.  Analysis of growth trends and variation in conifers from Arizona and
New Mexico.   Final  report submitted to   U.S.  Environmental  Protection Agency and  U.S.  Forest  Service
Western  Conifers Research Cooperative, Corvallis, OR.   Laboratory of Tree-Ring Research, University of
Arizona,  Tucson.

Graybill, D.A.  1985. Western  U.S. tree-ring index chronology data for detection of arboreal response to
increasing carbon dioxide.  Laboratory of Tree-Ring Research,  University of Arizona, Tucson.

Holmes, R.L, R.K. Adams, and H.C. Fritts.   1986.  Tree ring  chronologies  of western North  America:
California,  eastern Oregon and northern  Great Basin.   NSF grants ATM-8026732 and  ATM-8303192.
Laboratory  of Tree-Ring Research,  University of Arizona, Tucson.
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E.9  INDICATOR:  Pollen Record

CATEGORY:   Response/ Population (Retrospective)

STATUS:  High-Priority Research

APPLICATION:  Pollen analysis can be used to identify past changes in  plant communities.  Interannual
comparisons of stress is reflected in a decrease in  both  pollen production and pollen size.   Once stress is
removed (e.g., increased precipitation) both pollen production and pollen size increase.  Over several years,
changing proportions of pollen  types will reflect community response to changing stress conditions.  Pollen
obtained from pollen  cores of intense sampling can  be used to extend these observations back  in time
beyond the brief period of environmental  monitoring that is reflected  in historical records.

INDEX PERIOD:  The collection of samples is best in the autumn after the late summer and early autumn
bloom is completed.  This way the annual pollen output can be fully characterized.

MEASUREMENTS:  In general, measurements of pollen production, size, and changing proportions are best
taken from samples obtained near the edge of a plant community, where plants are most stressed naturally.
Samples taken from  well within a plant community would be less sensitive.  Three types  of  measurements
must be taken:  (1) Pollen abundance:  Knowledge of the deposition rate through the use of tracers would
reveal changing pollen  production of both  individual species and the community as a whole.  (2) Pollen size:
Through measurements using an ocular micrometer, the mean and standard deviation of the pollen grain size
distribution would reveal annual changes.   (3) Relative abundance of pollen types:   Changing proportions
would reveal the community  response to  stress factors.  Some species respond to stress more  quickly and
reflect this in changed outputs in  pollen; longer  term  changes in species  distribution also affect  pollen
production.  The estimated processing cost is $180 a  sample; the collection of pollen  cores adds about $50
a sample.  A sampling frequency of one year is adequate for revealing evidence of stress or changes in the
plant community.

VARIABILITY:   The expected  spatial variability of  pollen records within a resource sampling  unit were not
estimated.  Because  the pollen  records are temporally integrative measures, the variability during the index
period is inconsequential.

PRIMARY  PROBLEMS: The viability of this indicator is mainly constrained by logistics.  To best implement
such an indicator, hundreds of pollen collection localities would have to be  located and traps prepared within
each  resource sampling unit  Collection would have to  be completed by  winter snowfall, but after bloom-
time. A network of  pollen traps would have to be placed along elevation  gradients and be  in areas where
they would not be disturbed by vandals or human activities (e.g.,  plowing) that would resuspend pollen  in
the air.

BIBLIOGRAPHY:

Birks, A.J.B., and A.D. Gordon. 1985. Numerical Methods in Quaternary Pollen Analysis. Academic Press,
New  York.

Birks, H.J.B., and H.H. Birks.   1980.  Quaternary Paleoecology.  Edward  Arnold  Publishers  Limited.

Grosse-Brauckmann,  G.    1978.    Absolute  jahrliche  Pollenederschlagsmengen  an  vershichdenen
Beobachtungsorten in der Bundesrepublik  Deutschland.  Flora 167:209-247.

Watts, W.A.  1973.  Rates of change  and  stability in vegetation in the perspective of long periods of time.
In:  H.J.B. Birks and R.G. West, eds.  Quaternary Plant  Ecology.  Blackwell.

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E.10  INDICATOR:  Woodrat Midden Record

CATEGORY:  Response/ Population (Retrospective)

STATUS:  High-Priority Research

APPLICATION: The plant remains in strata of woodrat midden (den content) reveal long-term plant species
community response to climatic and other environmental changes. Both  plant species presence and health
can be monitored  on the scale  of decades to tens of thousands of years. Community composition can be
assessed  by the presence or absence of plant species, and community health can be monitored by actual
measurement of plant remains.

INDEX PERIOD:  This indicator is insensitive to time of year and can be sampled at  any time.

MEASUREMENTS:  The sample  interval depends upon the spacing of woodrat midden strata in time and the
period included in  each stratum. This can range from a woodrat midden sample with a decade to a century
of material in each stratum and a century to many millenia between strata. The three primary measurements
of woodrat midden data are as follows.

   1.  The materials  of identified plant  species are weighed  separately  to  arrive at  nonparametric
      quantifying of the midden materials.

   2.  The plant parts (e.g., seeds, fruits, leaves) of identified species are  enumerated.

   3.  The size of fruits and seeds is recorded, because a change in size through time and among strata
      can reflect stress upon  the plant community.

Both (1)  and (2) above are dependent  upon not only abundance in the plant community, but also upon the
foraging  behavior of the woodrat.  The estimated cost from collection to  analysis  is about $1000 a sample.

VARIABILITY:  Because woodrat midden strata are heterogeneous units, the spatial variability of plant species
composition within a  resource  sampling unit  can  be considerable.   Because  the  midden records are
temporally integrative measures, the variability during the  index period is inconsequential.

PRIMARY PROBLEMS: The primary problem is related to the influence of woodrat foraging behavior upon
the material collected for the den  and its placement in the den; therefore,  data  retrieved from a  woodrat
den cannot be quantified in the same way as dendrochronological, pollen, or charcoal data.

BIBLIOGRAPHY:

Spaulding, W.G.   1985.  Vegetation and  climates of the last 45,000 years in the vicinity of the Nevada Test
Site, south-central  Nevada.  Professional  Paper 1329.  U.S. Department  of the Interior, Geological Survey,
Government Printing Office, Washington, DC.

Spaulding, W.G., J.L Betancourt, L. K. Croft and K.L. Cole.  1990. Packrat midden analysis and vegetation
reconstruction.  In: J.L. Betancourt, T.R. Van Devender, and P.S. Martin,  eds.  Fossil Packrat Middens:  The
Last 40,000 Years  of Biotic Changes.  University of Arizona Press, Tucson.  In press.

Mehringer, P.J. Jr., and P.E. YVigand.   1990. Coming and goings of Western Juniper.  In:  J.L. Betancourt,
T.R. Van Devender, and P.S. Martin, eds.  Fossil Packrat Middens:  The Last 40,000 Years of Biotic Changes.
University of Arizona  Press, Tucson.  In press.
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E.11  INDICATOR:  Abundance and Species  Composition of Lichens and Cryptogamic Crusts

CATEGORY:  Response/ Community Structure

STATUS:  Research

APPLICATION:  Lichens, fungal, and algal crusts are widespread on rock and  soil  surfaces of arid lands
(Cameron 1969).   In chaparral and  woodlands,  these organisms occur on the outer bark of plants, as well
as on rocks and soils.  They are important locally to preserving the stability of soils.   Cryptogamic crusts
stabilize soil by binding it with their thallial filaments, by armoring  the surface, and by increasing surface
roughness (Cameron and Blank 1966). These cryptogamic crusts are strong enough to protect underlying soil
from raindrop impacts and wind erosion.  Cryptogamic crusts are known to be quite sensitive to mechanical
disturbance of soil  surfaces (Wilshire 1983). Once they have been disrupted, the forces of wind and water
are able accelerate erosion.  Lichens in forests are known to be sensitive indicators of air pollution damage
(Ferry et al. 1973; Anderson arid  Treshow  1984;  see  indicator D.11),  and it may  be possible  to  use
cryptogamic crusts as an  indicator of air pollution exposure in  arid  ecosystems.  This indicator would  be
applicable to all arid resource classes.

INDEX PERIOD:  The optimal sampling window would  be during the growing season from late spring to
early autumn.

MEASUREMENTS:   The measurement of lichens and  cryptogamic crusts  requires field work.   Techniques
which may be amenable to the measurement of abundance and species composition  for these organisms
include permanent photo plots, species richness, and density and areal cover by species.  Methods for these
measurements  are available (U.S. BLM 1985).   Estimated cost of measurement is $200 a resource sampling
unit.

VARIABILITY:   These organisms  expand during moist times and contract during dry times; therefore,  the
expected temporal variability of this indicator  during the  index period would  produce a  range that deviates
5-100% from the  mean value.  Spatial variability within a resource sampling unit would vary over a similar
range.

PRIMARY PROBLEMS:  Measurement of this indicator is labor intensive and requires a high degree of training
to identify species  and perform adequate  sampling.   The full value  of  this indicator for arid  lands  is  not
currently established.

REFERENCES:

Anderson, F.K., and  M, Treshow.  1984.  Response of lichens to atmospheric pollution.  In: Treshow,  M.,
ed. Air Pollution  and  Plant  Life.  John Wiley and Sons,  Ltd.,  New York.

Cameron, R.E.   1969. Abundance of microflora in soils of desert regions. Technical Report 32-1378.
National Aeronautics and Space Administration,  jet Propulsion Laboratory, Pasadena,  CA.

Cameron, R.E.,  and G.B. Blank.  1966.  Desert algae:  Soil crusts and diaphanous substrata as algal habitats.
Technical  Report  32-971.   National Aeronautics and  Space Administration, Jet Propulsion  Laboratory,
Pasadena, CA.

Ferry, B.W., M.S.  Baddeley, and D.L. Hawksworth.   1973. Air Pollution and Lichens.  The Athens  Press,
University of London.
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U.S. BLM.  1985.  Rangeland monitoring: Trend studies. Technical Reference TR 4400-4. U.S. Department
of the Interior, Bureau of Land Management, Washington, DC.

Wilshire, H.G.  1983. The impact of vehicles on desert soil stabilizers.  In:  R.H. Webb and H.G. Wilshire,
eds.  Environmental  Effects of Off-Road Vehicles:   Impacts  and Management  in Arid Regions.  Springer-
Verlag, New York.
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E.12  INDICATOR:  Foliar Chemistry

CATEGORY:   Exposure and Habitat/ Tissue Concentrations

STATUS:  High-Priority Research

APPLICATION:  Foliar analysis can be used as an indicator of elemental availability in soil or atmospheric
deposition.  The vector  analysis described below can be used to determine if vegetation is  experiencing
increases in nutrient or toxin concentrations that are in turn causing either negative or positive growth effects.
Through a combination of foliar concentration and content, a reliable estimate of probable growth response
to changes in  nutrient and toxin status can  be obtained.  This information can be combined  with stressor
indicator data to determine if external  inputs of nutrients are causing changes in productivity of a given
species  over its natural range.  In arid vegetation, foliar Si has been shown to  be a good indicator of grazing
pressure (Cid et al. 1989).

INDEX  PERIOD:  Sampling should occur  during periods of peak stable biomass.

MEASUREMENTS:  For dominant (by biomass) species on site, total tissue concentrations of N, P, K, Ca, Mg,
Na, S, Fe, Mn, Zn, Cu, B, Ti, Al, Mo, Cl, Si, Ni,  Pb (10 foliar samples per species per sampling unit) should
be measured.   Leaves should  be washed  (quickly) with mild  nonphosphate detergent solution. Unwashed
leaves could be included for comparison and to estimate dust accumulation on leaves (differences in  Al, Si,
and Ti,  especially).  Observations to be made include Mn/Mo  ratio changes (which indicate soil  pH changes:
Increased  ratio, greater acidity).

For vector analysis, both foliar concentration and  weight per leaf of new foliage are required.  Samples are
taken and analyzed as described  above.  Litter fall  sampling  is not  recommended as a substitute for live
foliage samples because the translocation of nutrients prior to litter fall to relatively constant concentrations
(e.g.,  Turner 1977) would  greatly  reduce  the sensitivity of litter fall  as an  indicator of nutrient status and
change.

Foliar concentrations and leaf  weights are plotted on a generic nomograph (Weetman and Fournier 1982),
which depicts  potential responses of first-year  needle weight and elemental concentration to elemental input
This method has proven successful in predicting growth responses to fertilization in balsam fir  (Timmer and
Stone 1978), jack pine (Timmer and Morrow 1984),  lodgepole pine (Weetman  and Fournier 1982),  and
loblolly  pine (Johnson  and  Todd 1988).  The predictions from these  nomographs should provide an early
indication  of plant response to  changes in nutrient or toxin availability from atmospheric deposition or various
site disturbances.  The cost would range from $50 to  $150 a sample.

VARIABILITY:   The expected  spatial  variability of foliar elements within a  resource sampling unit would
produce a range that deviate <20% from the mean value. Temporal variability during the index period was
not estimated.

PRIMARY  PROBLEMS:  Concerns  include the lack of baseline data on many species and of understanding
optimum sampling time.  The vector analysis has not been tested on several species and  may not work  well
in nondeterminate species  (e.g., species that  initiate growth whenever  conditions  are favorable rather than
during a specific season each year).

REFERENCES:

Cid, M.S., J.K. Detling, MA. Brizuela, and A.D. Whicker.  1989.  Patterns in grass silicification:  Response
to grazing history and defoliation.  Oecologia 50:268-271.
                                                E-25

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Johnson,  D.W., and  D.E.  Todd.  1988.   Nitrogen fertilization  of young yellow-poplar and loblolly  pine
plantations at differing frequencies.  Soil Sci. Soc. Am. J. 52:1468-1473.

Timmer, V.L, and LD. Morrow.  1984. Predicting fertilizer growth response and nutrient status of jack pine
by foliar diagnosis.  Pages 335-351.  In:  E.L. Stone, ed.  Forest  Soils and Treatment Impacts.  Proceedings
of the Sixth North American Forest Soils Conference, June 1984, Knoxville, TN.   University of Tennessee
Press, Knoxville.

Timmer, V.L, and E.L Stone.  1978.  Comparative foliar  analysis of young balsam fir fertilized with N, P,
K, and lime.  Soil Sci. Soc. Am. J. 42:125-130.

Turner, J.  1977.   Effect of nitrogen availability on nitrogen cycling in a  Douglas fir stand.   For.  Sci.
23:307-316.

Weetman, G.F., and  R. Fournier.  1982.  Graphical diagnoses of lodgepole  pine to fertilization.  Soil Sci.
Soc. Am.  J. 46:1280-1289.

BIBLIOGRAPHY:

Lajtha K., and W.H.  Scheslinger.  1986.  Plant response to  variations in nitrogen availability  in a desert
shrubland. Ecosystem Biogeochem. 2:29-37.
                                                E-26

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E.13  INDICATOR:  Physiochemical Soil Factors

CATEGORY:   Exposure and Habitat/ Ambient Concentrations

STATUS:  High-Priority Research

APPLICATION:  The application of this indicator to arid  lands is similar to  the "Soil  Productivity  Index"
indicator described for  forests (D.8), but it is somewhat more intensive, especially with respect to soil physical
properties. Quantitative pits (Hamburg 1984) in  conjunction with soil pedon  descriptions would be made
on each ground sampling plot on a one-time basis for the purpose of describing the soil type and obtaining
data on soil  bulk density and the proportion of  gravel  (the latter  to be used in  calculating  soil elemental
contents  on  an aerial  [kg/ha]  basis). The soil would be classified  according to the U.S. Soil Conservation
Service Great Group Level or  to a finer degree (e.g., series), if possible.

Periodic measurements of soil chemical properties would be  used to detect temporal changes in soils and
to correlate such changes with measures of soil moisture, temperature, plant growth, nutrient use efficiency,
and other indicators that may be relevant to soil  change.  This indicator provides a baseline against which
future changes in soils  may occur in response to long-term  climate change (e.g., soil C and  N content;  Post
et al. 1982,  1985).  Soil salinity changes also would be identified  in  response to changes in ground water
levels and surface water movement patterns.   Plant species composition or lack of plants (see Indicator E.7,
"Species Composition and Ecotone Location  of Vegetation") would  also reflect soil salinity status.

For grasslands, changes in climate or land use (e.g., cultivation) can strongly affect soil organic  matter storage
(Schimel  et  al.  1990).  Soil  organic  carbon (SOQ  levels represent the balance  between net primary
production and decomposition and are a sensitive indicator of ecosystem function  and  status (see  also
indicator C.1, "Sediment  and Organic Matter Accretion").   Decreases in grassland SOC  from increasing
decomposition rates are a likely consequence of global  warming; as a result, these systems would become
a net source  of CO2 and  provide feedbacks to the  climate system  (Schimel et al.  1990). SOC response in
shrubland or  woodland systems may not be as important or detectable as in grassland because soil organic
matter levels  are low,  but SOC responses  in ecotones  between forest and woodland would be a critical
indicator of the desertification processes that may occur as a  result of climatic change.

INDEX PERIOD: An optimal sampling time does not exist  However, because of the high seasonal variation,
soil chemistry must be measured during the same season on  repeat field visits.

MEASUREMENTS:   Quantitative  pits are soil  sampling units in which the exact volume, weight, and particle
size distribution of soil are measured  by  horizon (Hamburg  1984).  These parameters represent nutrient
concentration data on  an areal basis (e.g., kg/ha).  A minimum of 10 quantitative pits would be measured
initially at each resource sampling unit, and within  each pit a pedon description would be obtained.  Samples
for determination of long-term change  would be taken randomly from a permanently established grid (e.g.,
10 samples taken randomly within a 20- x 20-m  grid during each  sampling period).  All samples would be
analyzed  for total C (LECO), total N (Kjeldahl), soluble salts (Rhoades 1982), exchangeable cations (Al,  K, Ca,
Mg, Na, Zn,  Cu, Mo),  and cation exchange  capacity (1  M NH_Cl), extractable SO/" (0.016  M NaH2PO4),
extractable P  (bicarbonate), and extractable B (Bingham  1982).  Pedon samples would also  be analyzed for
total P, S,  K,  Ca, Mg, Na, Zn, Cu, and Mo.  All samples would be archived for potential additional analyses.

Pedon analyses and determination of soil bulk density and gravel fraction would be  conducted one time only;
soil nutrients  would  be sampled at five-year  intervals.   Costs  of collection  and analyses for soils would be
approximately $150-$200  a sample for periodic nutrient collections and $300-$350  a  sample for the
quantitative pit samples.  With a sufficient number of quantitative pits, measurement error (standard errors)
can be maintained at <20%.  Pedon analyses, soil bulk  density, and gravel fraction would be sampled  one
                                                E-27

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time only.  Soil nutrients would be sampled quarterly to allow an examination of seasonal variations, which
can be significant with respect to certain extractable nutrients (e.g., extractable P).

VARIABILITY:  The expected temporal variability of soil chemistry and structure in surface soil samples during
the index period would produce  a range that deviates <50% from the mean values. The spatial variability
within a resource sampling unit ranges between 20 and 80%, depending upon depth and sampling unit size.

PRIMARY PROBLEMS:  The high spatial variability of soil chemistry would be the greatest problem to be
encountered, especially with respect to soil physical properties.  This would  necessitate more replication  than
is typical of most soil sampling protocols.  Laboratory  techniques for trace  metals requires skill and care.

REFERENCES:

Bingham, F.T.  1982. Boron.  Pages 431-448.  In: A.L. Page,  R.H. Miller,  and D.R. Keeney, eds. Methods
of Soil Analyses, Part 2.  Chemical and  Microbial Properties, 2nd  Edition.  American Society of Agronomy,
Madison, Wl.

Hamburg,  S.P.  1984.  Effects of forest growth  on soil nitrogen and organic matter pools following release
from subsistence agriculture.   Pages  145-158.  In:   E.L. Stone,  ed.   Forest Soils  and  Treatment Impacts.
Proceedings of the Sixth North American Forest Soils Conference, University of Tennessee, Knoxville.

Post, W.M., W.R.  Emanuel, P.J. Zinke, and A.G. Stangenberger. 1982.  Soil carbon pools and world life
zones.  Nature 298:156-159.

Post, W.M., J. Pastor, P.J. Zinke,  and A.G. Stangenbergrer.  1985. Global patterns of soil nitrogen  storage.
Nature 317:613-616.

Rhoades, J.D.  1982.   Soluble salts.  Pages 167-180.  In:   A.L. Page, R.H. Miller, and D.R.  Keeney, eds.
Methods of Soil Analyses, Part 2. Chemical and Microbial Properties, 2nd Edition.  American  Society of
Agronomy, Madison, Wl.

Schimel, D.S., T.G. Kittel, T.R. Seastad, and W.J. Parton.  1990. Landscape variations in biomass, nitrogen,
and  light interception:  Constraints over interactions with the atmosphere.   Ecology.  Submitted.

BIBLIOGRAPHY:

Dickinson, R.E. 1985.   Climatic sensitivity.   Adv. Geophys. 28A:99-129.

Running, S.W., R.R. Neman!,  D.L Peterson, L.E. Bane, D.F. Potts, L.L. Pierce, and MA. Spanner.  1989.
Mapping regional  forest evapotranspiration and photosynthesis by coupling satellite data with  ecosystem
simulation.  Ecology 70:1090-1101.

Schmidlin, T.W., F.F. Peterson, and  R.O. Gilford.  1983.   Soil  temperature regimes in Nevada. Soil Sci.
Soc. Am. J. 47:977-982.

Segal M.,  R. Avissar, M.C. McCumber, and RA Pielke.  1988.  Evaluation of vegetation effects  on the
generation  and modification of mesoscale circulations.  J. Atmos. Sci. 45:2268-2392.

Sellers, P.J., Y. Mintz, Y.C. Sud, and A. Dalcher.  1986.  A  simple biosphere (SiB) model for  use within
general circulation models.   J.  Atmos. Sci. 43:505-531.
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Sellers, P.J., F.G. Hall, C. Asrar, D.E. Strebel, and R.E. Murphy.  1988.  The First ISLSCP Field Experiment
(FIFE).  Bull. Am. Meteorol.  Soc. 69:22-27.
                                                E-29

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E.14  INDICATOR:  Exotic Plants

CATEGORY:  Exposure and Habitat/ Exotics-CEOs

STATUS:  High-Priority Research

APPLICATION:   A number of the introduced species in the western United States are widely regarded as
indicators of degraded conditions, including the presence of cheat grass (Bromus tectorum), tamarisk (Tamar/x
sp.), and tumbleweed.  These plants have proliferated widely during the past 200 to 300 years since their
introduction because of their adaptation to thrive in disturbed  habitats.  The presence and abundance of
exotic plants can be used as an  indicator of the condition of arid lands and would  be applicable to all sites.

INDEX PERIOD:  The optimal sampling window would  be late spring to early summer.

MEASUREMENT:  Data required for this indicator would also be collected for the "Species Composition and
Ecotone Location of Vegetation" indicator (E.5) by aerial  photography and field surveys.  The estimated cost
is $100 a  resource sampling unit

VARIABILITY:   Estimates of species composition for annual plants are subject to wide seasonal  variation.
Perennial plant  measurements would be less variable.  The expected spatial variability of field measurements
of species composition within a  resource sampling unit would produce a range that deviates 5-10% from the
mean value.  The  expected temporal  variability of species composition  measures  during the index period
would  produce a range  that deviates  20% from the mean value.

PRIMARY PROBLEMS:   No major problems are anticipated when this indicator is  assessed.

BIBLIOGRAPHY:

Costing, H.J.   1956.  The Study of Plant Communities.  W.H.  Freeman and  Co., San Francisco.
                                               E-30

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E.15  INDICATOR:  Livestock Crazing

CATEGORY:  Exposure and Habitat/ Exotics-CEOs

STATUS:  High-Priority Research

APPLICATION: The majority of areas to be monitored by EMAP-Arid Lands is subjected to livestock grazing,
on both public and private lands.  Although grazing is restricted or prohibited at some federal land holdings
(national parks  and wilderness areas), these areas account for only a small fraction  of the total land area
under consideration.  Cattle  and  sheep grazing in the western United States has produced major impacts
on 40 to 80 million hectares (100 to 200 million acres) of federal land (U.S. CAO  1988).  In addition to
cattle and sheep, grazing by feral  animals (wild horses and burros) must be considered.  Grazing alters plant
species composition  and  vegetation cover, impacts riparian systems, and can accelerate erosion (U.S. BLM
1978).  This  indicator would provide estimates of grazing intensity, and it  is applicable to all arid resource
classes.

INDEX PERIOD:  A seasonal record  of actual use is required to document in terms of "animal  unit months"
the time over which an area has  been grazed.

MEASUREMENTS:  A livestock grazing record  should  be  acquired for each resource sampling unit  The U.S.
BLM (1978) describes the methods for collecting actual-use data. Actual use data consists of livestock counts,
the kind  or class of livestock, and the period(s) of time the livestock actually grazed  the sampling unit (e.g.,
animal unit month).  Several  sources of actual use data  exist and include the following.

(1)  Livestock Operator  Reports:   Operators of  grazing enterprises  can  be  asked  to submit  reports
documenting actual livestock grazing use.  The U.S. Bureau of Land Management (BLM) and U.S. Department
of Agriculture-Forest Service (USDA-FS) commonly request these reports to assess actual use and to calculate
billings for federal  land use.

(2) USDA-FS and  BLM Counts:  These two  land management agencies often conduct  head counts when
livestock  are  moved  onto  or  off their respective grazing  allotments.

(3) Direct Counts:  Field counts, aerial counts, and counts derived from low-altitude  aerial photography are
performed on localized areas by both the BLM and USDA-FS.  Similar counts should be performed  by EMAP
field and aerial crews at the sampling units.  This  is the  only technique that provides  data on actual grazing
by wild  horses  and burros.

The  actual  use data records  would  have  to  be acquired  and  processed to produce seasonal estimates of
actual use.  The estimated cost to acquire and process actual use  data for a resource sampling unit is $500
a season.

VARIABILITY: The expected spatial variability  of direct counts within a resource sampling unit would produce
a range that deviates up to  100% from  the  mean value; the large variability is  expected because  of the
mobility of livestock within  grazing  units  that include portions of sampling units.   Because the  measures
integrate grazing intensity throughout the season,  an  estimate of temporal variability  of direct counts  during
a season was not  required.

PRIMARY PROBLEMS:  The  primary problems with estimating grazing intensity would be the  development
of reliable and sustainable sources of actual use data. EMAP crews would  not be in  the field often enough
to produce an adequate record of actual  use  data; therefore, the reliability and coverage of the data  record
would have to  be  thoroughly reviewed and assessed.
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REFERENCES:

U.S. BLM.  1978.  The effects of surface disturbance (primarily livestock use) on the salinity of public lands
in the upper Colorado River Basin:  1977 status report BLM/YA/TR-78/01.  U.S. Department of the Interior,
Bureau of Land Management, Washington, DC.

U.S. GAO.  1988. Management of Public Rangelands by the Bureau of Land Management  GAO/T-RCED-
88-20.  U.S. General Accounting Office, Washington, DC.

BIBLIOGRAPHY:

U.S.  BLM.   1988.  Rangeland Monitoring:  Actual  Use Studies.  Technical  Reference  4400-2.   U.S.
Department of the Interior, Bureau of Land Management, Washington, DC.
                                              E-32

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E.16  INDICATOR:  Fire Regime

CATEGORY:   Exposure and Habitat/ Habitat

STATUS:  High-Priority Research

APPLICATION:  Fire occurs as a natural phenomenon  in shrubland, grassland, chaparral, and woodland
ecosystems.  Fire plays a crucial role in the availability of plant nutrients in arid and semiarid regions where
the rate of litter decay is  low.   In chaparral,  fire  removes overly matured stands of plants, allowing  the
vegetation  in  the  burnt zone to be  rejuvenated.  Since  the  early  1900s,  fires have  been suppressed
throughout the West Gradually, however, land managers have realized the valuable aspects of wildfires and
are reintroducing fire  as a management practice in the USDA  Forest Service and  U.S.  Bureau of Land
Management

Although fire is now recognized  as having beneficial ecological effects, the timing and frequency of fires must
be regulated to  protect property and to avoid detrimental ecological effects. If fire occurs too frequently or
if burning is too intensive,  environmental damage such as the loss of  soil  nutrients, acceleration of erosion,
or proliferation of  less desirable plant species such as cheat grass (Bromus tectorum)  can  occur.  For most
areas there is  an optimal frequency pattern for fire (e.g., once every 8-12 years) and  an optimal season for
producing a manageable and  beneficial fire.

Fire frequency would be tracked by using remotely sensed data and verified with selective field surveys.  In
addition, fire hazard maps  would be produced to indicate areas most prone  to burning.   This indicator is
applicable to all arid resource classes.

INDEX PERIOD: The optimal sampling period is the peak growing season from mid-summer to early autumn
or when the  incidence of fire is approaching zero.

MEASUREMENTS:    The location and  spatial  dimension of recent burns would  be determined  by using
Thematic Mapper  (TM) satellite data.  Burns retaining charcoal  can be recognized by the  unique spectral
characteristics  of charcoal:  low reflectance in the visible and high reflectance  in the TM bands at 1.65 and
2.22 /xm. Burnt areas without the charcoal spectral signature would be identified by a combination of factors
(Chuvieco and  Congalton  1988; Tanaka  et al.  1983), including (1) brighter reflectance than that  of
surrounding unburnt areas  due  the removal of litter and soil organic matter,  (2) diagnostic shapes such as
sharp boundaries, windblown stringers, and cleared firelines, and (3) a lower perennial vegetation cover inside
recent burns or a higher annual vegetation  cover than  that of the surroundings.  Burnt  areas frequently
remain visible to TM sensors for decades.  Estimated cost of TM data analysis is $200 for  each  landscape
sampling unit

Fire hazard maps (Chuvieco and Congalton  1989) indicating the  probability of burning would be prepared
by using (1) species composition and fuel loading,  (2) elevation,  (3) slope, (4)  aspect, and (5) proximity to
roads, trails,  or building.   This data  would  be available  from  EMAP-Characterization and the "Species
Composition and Ecotone Location of Vegetation" (indicator  E.7).  Estimated cost of constructing fire  hazard
maps is $200  for each  landscape sampling unit.

VARIABILITY:   The  expected  temporal variability during the index period for measurements of burn area
derived from  TM data  would produce a range that deviates  <30% from the mean value.  The variability is
induced primarily by systematic alterations in illumination conditions over the index period and can  be largely
eliminated by acquiring data with standardized illumination conditions.  Because the entire resource sampling
unit would be censused, the  spatial variability of fire regime  measures within a resource  sampling  unit is
inconsequential.
                                                E-33

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PRIMARY PROBLEMS:  Standardized measurement procedures must be developed.

REFERENCES:

Chuvieco, E., and R.G. Congalton.  1988. Mapping and inventory of forest fires from digital processing of
TM data.  Geocarto Int. 4:41-53.

Chuvieco, E., and R.G. Congalton.  1989. Application of remote sensing and geographic information systems
to forest fire hazard mapping.  Remote Sens. Environ. 29:147-159.

Tanaka, S., H. Kimura, and Y. Suga.  1983. Preparation of a 1:25,000 Landsat map for assessment of burnt
area on Etajima Island.  Int. J. Remote Sens. 4:17-31.
                                              E-34

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E.17  INDICATOR:  Mechanical Disturbance of Soils and Vegetation

CATEGORY:  Exposure and Habitat/ Habitat

STATUS:  High-Priority Research

APPLICATION:  Mechanical disturbance of  soils and vegetation  is closely linked to several of the primary
processes involved in land degradation (Webb and Wilshire 1983), including loss of plant cover, fragmentation
of habitat, acceleration of erosion, and introduction of exotic species.  Mechanical disturbances can be caused
by grazing animals (U.S. BLM  1978), off-road vehicles,  road and site constructions, mining, and mineral or
fuel exploration.

This indicator would track the development of  mechanical disturbance through time by using airborne and
satellite imagery.   It is conceded  that the mechanical disturbances induced  by grazing or single passes  by
vehicles on  an undisturbed landscape would not  be detected by this approach.   However,  it would  be
possible to detect and identify the new roads and trails,  plus mechanically disturbed areas, created by mining
and mineral or fuel exploration.  The indicator is applicable to all arid resource classes.

INDEX PERIOD:   The optimal sampling window is generally the growing season, from late spring to early
autumn,  when activities causing disturbances are likely  to occur.

MEASUREMENTS:  Standard change detection procedures would  be applied to  Thematic Mapper (TM)
satellite data and low-altitude  aerial  photography  or videography.  Mechanically disturbed areas would  be
recognized by their spatial and spectral signatures;  for example, they (1) are brighter than their surroundings,
(2) have low  to negligible  vegetation cover,  (3)  have sharp  boundaries, and (4)  are frequently linear in
shape.   The  identification and mapping can  be  accomplished  by using the visual  approach of photo
interpretation.  Automated  approaches may accelerate  this  procedure and reduce costs.  Estimated cost is
$400 for each landscape sampling unit.

VARIABILITY: The expected temporal variability for measurements derived from remotely sensed data during
the index period would produce a range that deviates  5-30%  from the mean value.  The variability is due
primarily to  the loss of details in heavily shadowed areas  of steep terrain;  variation in shadowing is  induced
by systematic alterations in illumination conditions both seasonally  and diurnally. The high sun angles of mid-
summer are best for reducing the obscuration  of details  in shadowed areas.  Because the entire resource
sampling  unit would   be  monitored   by   remote sensing,  the  spatial  variability  of  this   indicator  is
inconsequential.

PRIMARY PROBLEMS:  This indicator would  not measure  mechanical impacts  due to grazing or single vehicle
traverses on the landscape.

REFERENCES:

U.S. BLM.  1978.  The effects of surface disturbance (primarily livestock use) on the salinity of  public lands
in the upper Colorado  River Basin: 1977 status report.  BLM/YA/TR-78/01. U.S. Department of the Interior,
Bureau of Land Management,  Washington,  DC.

Webb, R.H., and  H.G. Wilshire,  eds.  1983.  Environmental Effects of  Off-Road  Vehicles:  Impacts and
Management in Arid Regions.  Springer-Verlag,  New York.
                                                E-35

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E.18  INDICATOR:  Chemical Contaminants in Wood

CATEGORY:  Exposure and Habitat/ Ambient Concentrations (Retrospective)

STATUS:  Research

APPLICATION:   Trees and woody shrubs may have recognizable annual  growth layers that can be exactly
dated to the year  in which they were formed (see indicator E.8, "Dendochronology:  Trees and Shrubs").
Samples of wood  representing individual years,  or intervals of years such  as 5  years or decades,  can be
analyzed by  various physical-chemical  techniques to  determine elemental concentrations with potential
environmental exposure/dose information. For example, changes in elemental concentrations may be related
to anthropogenic impacts such as air pollution. By constructing a contaminant record,  a natural or baseline
contaminant dose can be determined for any plant community from which to judge current or future trends
in exposure.  This indicator is  applicable to shrubland  and woodland resource classes.

INDEX PERIOD:  An optimal sampling window during the year is based  only on logistical constraints.

MEASUREMENTS:   (1) Sampling procedure:  usually  two cores a tree, obtained from  30-60 trees of a
particular species at a given  location.   (2) After  radial cores  are mounted and surfaced so  that growth
increments can be discerned, each core is cross-dated.  The cross-dating procedure assigns each ring in each
specimen to the exact calendar year in which it was formed.  This is different from simple ring counting,
which does not result in an exact chronological placement.  (3) After each specimen is dated, wood samples
associated with  each time  increment are  removed  (e.g.,  5 years, decade).  (4) The wood  can  then be
subjected to various types of  physiochemical analyses.   The most common are  inductively coupled plasma
(ICP)  optical  emission spectroscopy and  neutron  activation  analysis (NAA).   ICP analysis typically yields
information on the following elements: Ag, Al, As, B, Ba, Be, Bi, Ca, Cd, Co, Cr, Vu, Fe, K, Li, Mg, Mn, Mo,
Na, Ni,  P, Pb, Sb,  Se, Si, Sn, Sr, Ti, Tl, V, and Zn.  NAA can be used to  obtain concentrations of elements
such as  As, Au,  Ca, K, Mo, Na, Ba, Fe,  Hg, Sr, and Zn.

VARIABILITY:  The expected  spatial and temporal variabilities within a resource sampling unit and during
an index period  would produce ranges that deviate 50-100%  from their  respective mean values.

PRIMARY PROBLEMS:  It is difficult to determine and separate trends for anatomical distribution of elemental
concentrations that are caused by  changes in the environment from those for  concentrations due to the
normal  physiological processes of a tree.

BIBLIOGRAPHY:

Fritts, H.C.   1976.  Tree Rings and Climate.  Academic Press, London.

Jacoby,  G.C., and  J.W. Hornbeck, eds.   1987. International Symposium  on the  Ecological Aspects of Tree-
Ring Analyses, Sponsored by the U.S. Department  of Energy.  US DOE CONF 8608144.  Available from the
National Technical Information Service,  Springfield, VA.
                                               E-36

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APPENDIX F:  INDICATOR FACT SHEETS FOR AGROECOSYSTEMS
                            Authors
                        C. Lee Campbell
                  North Carolina State University
                  Department of Plant Pathology
                     Raleigh, North Carolina
                          Walter Heck
                  U.S. Department of Agriculture
         Agricultural Research Service Air Quality Program
                     Raleigh, North Carolina
                          Tom Moser
   NSI Technology Services Corporation - Environmental Sciences
           U.S. EPA Environmental Research Laboratory
                        Corvallis, Oregon
                     Robert P.  Breckenridge
              Idaho National Engineering Laboratory
                        Idaho Falls, Idaho
                          George Hess
                  North Carolina State University
                       Air Quality Program
                     Raleigh, North Carolina
                         Julie R. Meyer
   NSI Technology Services Corporation - Environmental Sciences
U.S. EPA Atmospheric Research and Exposure Assessment Laboratory
              Research Triangle Park, North Carolina

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F.I  INDICATOR:  Nutrient Budgets

CATEGORY:  Response/ Ecosystem  Process Rates & Storage
              Exposure and Habitat/ Ambient Concentrations

STATUS:  High-Priority Research

APPLICATION:   The cyclic  processing of chemical elements within an  ecosystem is fundamental to the
maintenance of its components. Organisms depend on the  constant availability of some 20 elements which
are required for all life processes. The effects of contaminants on nutrient availability ultimately translate into
effects on many other aspects of ecosystem structure and function   (Mittelman et al. 1987).

Nutrient concentrations  in  soil, and those in living  organisms, and  nutrient leaching from  soil can  be
determined  for an ecosystem; and major changes in these can provide an early warning of structural changes.
The most frequent limiting factor for primary productivity, aside from water, is available (i.e., mineralized) N.
Most soil  N, however, is present in organic forms that cannot be assimilated by plants (Follett et al. 1981).
A  measure of mineralizable  N would be  useful for assessing the capacity of a  soil to retain and supply N in
a timely fashion; this capacity is related to soil and ecosystem quality. Such a  measure would also be useful
as  an indicator  of  N  export  from  the  ecosystem  because a  portion  of  N  is lost as  gas  during the
mineralization process.  The amount of N that would be mineralized is difficult to predict, however, because
mineralization depends on  numerous environmental factors.

Elemental ratios are  another important metric of  nutrient budgets, because these  reflect nutrient storage in
plants.  Typical  C:N  ratios,  for example,  are  known for corn  stalks, small grain straw, clover,  and alfalfa.
Repeated  variations from these  typical ratios over  time  may be used to signal  a change in ecosystem process
rates.  Similarly, elemental  ratios that compare soil and plant tissues may be  used to signal a similar stress
response.  If the elements appear to be plentiful in the soil but are  not found in plant tissues, some stressor
may  be impeding assimilation.  Other  nutrient  (e.g., P  and  K)  budgets  among the  various  ecosystem
compartments (e.g.,  soil  and plants) could also be used as early-warning indicators of ecological resource
condition.

INDEX  PERIOD:  Soils should  be sampled  before agricultural fertilization in  the  spring or autumn.  Spring
sampling is  recommended for estimating export of gaseous NOX because microbial activity is greatest during
the spring flush of growth.  Vegetation could  be  sampled once or twice at the end of the growing season
prior to harvest.   Because  mosses  and  lichens  bioaccumulate contaminants continuously, their sampling
window would be more flexible.

MEASUREMENTS: Traditionally, potentially mineralizable N has been estimated from mineral N accumulation
during a 2- to 4-week incubation  of soil samples; however, a chemical test called Electroultra Filtration
includes an incubation  time of <1  h.   Data  analysis  for mineralizable N determination for each sample,
therefore, requires  either expensive equipment  ($20K) and  high  cost ($30) and  only  one day,  or less
expensive equipment ($2K)  and lower cost  ($3) and more than two weeks (for incubation  and  analyses).

Elemental ratios on a national scale would be most efficiently examined either  at state agricultural experiment
stations or through local extension agents who would be more familiar with typical ratios for local crops and
local  soils and with typical nutrient deficiencies for the region.  The experiment stations would be able to
quickly  identify the  most appropriate  ratios for any given  region.   A particular  ratio that  can  be applied
universally probably will not exist because of the high degree of variation in soil types, climatic effects, ratios
in  different species, etc.  Examples of elemental ratios that could be examined include N:B,  N:Ca, and C:N.
Laboratory analysis for determining  ratios  would be inexpensive with mass spectrometry.
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Concentrations of N,  P, and  K compounds would be determined  in each ecosystem  compartment, that is,
soil, roots and foliage.  Measuring soil leachate would provide a well-tested, sensitive method of monitoring
changes  in nutrient cycling because it  reflects the rate  that nutrients leave the system.   Changes  in  the
leaching  rate can reflect the breakdown in nutrient-cycling processes.   Leachate studies can detect small
changes in nutrient content that are not measurable by other methods (Mittelman et  al.  1987).  Generally,
in situ soil leaching analyses are performed with lysimeters.

Plant  and soil sample collection would be labor intensive, involving a technician for 4 h  and a scientist for
2  h  at each resource sampling unit.   Sample  collection would  be even  more  labor  intensive  for ratio
determinations, requiring that soil and  plant samples be  taken from the same area.  Analysis for metals by
inductively coupled plasma would cost about  $50-$75 per sample.

VARIABILITY:  The spatial variability of soil nutrients  is generally high,  particularly on intensely  managed
landscapes such as  agricultural  land.  The soil type would partially determine a soil's ability to provide or
retain  nutrients; soil experiencing different erosion rates would also affect its nutrient exchange  capacity.
Several soil types would often occur within a resource sampling unit, with varying degrees of erosion among
those types. Variability among nutrients in plants would depend somewhat on the variability in the associated
soils.   The expected  spatial  variability  of gaseous export within  a  resource sampling unit  has not been
determined.  The expected temporal variability of each nutrient-cycling parameter during the respective index
period is not available.

PRIMARY PROBLEMS:  Once expected nutrient ratios are established for each  region, no major  problems
are anticipated in the application of this indicator.

REFERENCES:

Follett, R.H.,  LS. Murphy, and R.L. Donahue.   1981.  Fertilizers  and Soil Amendments.  Prentice-Hall,
Inc., Englewood Cliffs, NJ.

Mittelman, A., J. Settel, K. Plourd, R.S. Fulton, III, G.  Sun, S. Chaube, and P. Sheehan.  1987.  Ecological
endpoint selection criteria.  Draft report prepared under Contract No. 68-02-4199 for the U.S. Environmental
Protection Agency, Office of Research and Development, Exposure Assessment Group.  Technical Resources,
Inc., Rockville, MD.
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F.2  INDICATOR:  Soil Erosion

CATEGORY:  Response/ Ecosystem Process Rates & Storage

STATUS:  High-Priority Research

APPLICATION:   Erosion  of  soil by water and wind is  a natural  process that is  accelerated  by human
manipulation of the soil to optimize crop production.  More than  one fourth of the nation's  167 million
hectares of  cropland  (413 million acres) is eroding  at a rate that lowers soil productivity.   Productivity  of
the land is  reduced  as the  more  productive topsoil is  lost and  part  of the lower, less fertile layers are
incorporated into the plow  layer.   As soil productivity  decreases,  additional fertilizer is often  needed  to
maintain crop production.  Excessive  soil  removal  by erosion changes the water-holding characteristics  of
the soil or reduces the depth of the root  zone.  The decrease in plant-available soil water capacity is the
major effect of erosion on crop productivity.

If the slope of the crop or pastureland is  more than 2%,  erosion by water is a  hazard.  Threats from this
hazard  can  be reduced  by  maintaining protective  plant cover, which also serves  to  increase  infiltration,
improve soil tilth, and provide nitrogen for subsequent plant growth.  The removal or  loss of plant cover
due to poor management or stressful environmental  conditions (e.g.,  drought, long-term climate change) can
result in wind and water  erosion and  degradation of the  ecosystem.

Erosion on farm lands allows sediments and chemicals to  enter aquatic systems at higher rates.  Controlling
erosion minimizes the pollution of  streams by sediment and  improves the suitability of water for municipal
use, recreation, and fish and other  animals. Erosion and its associated nonpoint source pollution of aquatic
systems are  of interest to  regulatory and most land  management agencies.

Quantifying  soil  erosion  provides a good indicator of the condition of our Nation's farmland.  Long-term
trends of soil loss can be evaluated  in  the proportion  of soils that are subnominal  according to their
classification  by the U.S. Soil Conservation Service  (SCS).   More than 601 million hectares (1,484,000,000
acres) had been mapped and were included in published soil surveys for  the U.S. and Caribbean territories
as of September  1987 (USDA 1983).

Soil surveys  provide an credibility factor (K) and a soil-loss tolerance factor (T) for each soil.  K is the soil's
susceptibility to erosion by water; T is the maximum rate of soil erosion from rain, wind, or environmental
quality that  can  occur without  reducing crop  production.  The soil-loss tolerance factor (T) is expressed  as
tons of annual soil loss per  acre.  Actual soil erosion (Tc)  can be calculated from the universal  soil loss
equation (USLE) and a wind  erosion equation (USDA 1983).   These  equations use the credibility and index
of a soil and related management, cropping, and environmental factors for the site to determine the annual
erosion rates.  The calculated erosion rates (Tc) can  be compared to the acceptable soil-loss tolerance factors
from  the  published  SCS  soil  surveys.   In addition to  calculations, remote  sensing  and photographic
interpretation  could be used to determine the number of larger rills and gullies formed due to erosion. This
type of assessment helps to complete  the picture in assessing an erosion  index indicator  because the  USLE
does not account for large-scale gully  erosion.

INDEX  PERIOD:   The best time for field sampling would  be in  late summer after cropping.  The best time
to count gullies would be in the spring prior to leafout.

MEASUREMENTS:  Data  can be collected by remote sensing and  field  visits.   Soil loss values would  be
calculated from  soil texture and slope length of fields (from soil surveys),  land  use and/or cropping data
(remotely  sensed  or from the SCS  National Resources Inventory),  and  sediment  delivery ratios and rainfall
data (from National Weather Service).  Much of this data  is  already available and has been used for other
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data (from National Weather Service).  Much of this data is already available and has  been used for other
purposes, but can  be adapted.  The recommended interannual sampling frequency is every year.

VARIABILITY:  The spatial variability of soil erosion would depend mostly on the variations in slopes and soil
types.  The  expected spatial and temporal variabilities of soil erosion within  a resource sampling unit and
during the index period,  respectively, were not estimated.

PRIMARY PROBLEMS: No major problems  are anticipated in the application of this indicator.

REFERENCES:

USDA.  1983.   Assistance  Available from the Soil Conservation Service.  Agriculture  Information Bulletin
345.  U.S. Department of Agriculture, Washington, DC.

BIBLIOGRAPHY:

USDA.  1981.  Soil erosion effects on soil productivity:  A research perspective.  National  Soil Erosion-Soil
Productivity  Research  Planning Committee.   U.S.  Department  of Agriculture, Science   and Education
Administration-Agricultural Research, Washington, DC.

Lowrance, R., J.K. Sharpe, and J.M. Sheridan.  1986.  Long-term sediment deposition in the riparian zone
of a coastal  plain watershed. J. Soil Wat. Conserv.  41:266-271.

Pierce, F.J.,  W.E. Larson, R.H.  Dowdy, and WAP.  Graham.  1983. Productivity of soils:   Assessing long-
term changes due to erosion.  J. Soil Wat. Conserv. 38:39-44.

Pierce, F.J.,  R.H. Dowdy, W.E. Larson, and WA P. Graham.  1984.  Soil  productivity in the Corn Belt:
An assessment of erosion's  long-term effects. J. Soil Wat. Conserv. 39:131-138.

Sheridan, J.M., C.V. Booram, Jr., and L.E. Asmussen.  Sediment and delivery ratios for a small coastal plain
agricultural watershed. Trans. ASAE 25:610-615.

USDA.  1989.  Agricultural Statistics, 1988.  U.S.  Department of Agriculture, Washington,  DC.
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F.3  INDICATOR:  Microbial Biomass in Soils

CATEGORY:  Response/ Populations

STATUS:  High-Priority Research

APPLICATION:  Soil microorganisms play an important role in the retention and release of nutrients and
energy in agricultural soils.  The role of soil microbes in soil energy and nutrient relationships was reviewed
by Paul and Voroney (1980).  Nutrient and energy fluxes influence the biological and chemical activity of an
ecosystem and are sensitive to structural  changes in that system.

Soil  microbial biomass is the living part of the soil organic matter, excluding plant roots and  soil  animals
larger than about 5000/zm3 (lenkinson  and Ladd 1981).   Although composed  of a variety of different
organisms, such as bacteria,  actinomycetes, fungi,  and microfauna,  soil microbial biomass is usually treated
as an undifferentiated whole.  The treatment of this soil component as a single compartment is useful and
integrative  in providing  a quantitative  indicator  of  the  flux  of  energy and  material  through  the soil
compartment of the ecosystem.  Soil samples collected  at a resource sampling unit  can  be partitioned to
permit measurements that will contribute to other indicators, such as metrics of the index of soil integrity and
the soils component of potential nematode stress (see indicator F.8, "Agricultural Pest Density1).

INDEX  PERIOD:   Soil samples can  be collected at any time of the year; however, collection of samples
when crop plants are actively growing would increase the certainty of obtaining the maximum soil microbial
biomass.

MEASUREMENTS:  Soil microbial  biomass will be estimated by a  chemical  technique that measures the
flush  of decomposition  of soil biomass  caused by fumigation with chloroform (CHCI3).   Soil organisms
subjected to CHCI3 vapors have their cell membranes  destroyed by the vapor. This allows cell contents to
leak into  the soil, where it can  subsequently  be  degraded by  living  microorganisms.   The  flush of
decomposition is  defined  as  the amount of CO2 evolved  by a fumigated soil over  a given time less the
amount of CO2 evolved by the same amount of untreated soil in the same time  (lenkinson and Ladd 1981;
Paul and Clark 1989).  Although the amount of C that is evolved varies for fungi  and bacteria, an average
of 41-45%  of fungal and bacterial  C is evolved as  CO2.

Five assumptions  are made in calculating biomass  from  the flush of CO2 evolved by a soil that has been
fumigated  and then incubated (Jenkinson 1976):

        1.    The C in dead organisms is mineralized more rapidly than in  living organisms (i.e.,
              the protected C in a living cell becomes  available to other organisms after cell death.)

        2.    Fumigation  kills all soil microbes.

        3.    The biomass of organisms  dying  in unfumigated soil during incubation is negligible
              compared with that in fumigated soil.

        4.    The fraction of biomass C from dead organisms mineralized over a given time period
              does not differ in different soils.

        5.    Fumigation  has no effect on the  soil other than  the  killing of soil microbes.

The  procedures for estimating microbial  biomass from the CO2 flush after soil fumigation were established
and refined in a series of papers (Jenkinson 1976; Jenkinson and Powlson 1976a, b; Powlson and Jenkinson
1976; Jenkinson et al. 1979)  and summarized by Parkinson and  Paul (1982).  The developed protocol,

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however, requires a  10-day incubation period before CO2 evolution  is measured.  This  incubation period
makes the procedure resource intensive.

A modified procedure proposed and evaluated by Vance et al. (1987) reduces the post-fumigation incubation
period, which makes the assay less resource intensive.  Two composite samples would be taken for each
resource sampling unit, each consisting of 10 soil  cores that are 5 cm wide and 20 cm deep.  After selecting
an arbitrary starting  point along one edge of a sample unit, soil cores would be collected along a zigzag
pattern every 10-20  paces.  Soil would  be well  mixed  and stored  at 5°C. Before  biological  analysis, soils
would be sieved (<6.35  mm) and incubated at  approximately 40% water-holding capacity at 25°C for  10
days.

The CHCI3 extraction technique (Vance et al. 1987) has been tested  on a limited number of soils, and its
validity should  be established on  a wider range of soils.   It is  simpler  than  direct microscopy for the
determination of soil  microbial biomass  (Paul  and Clark 1989)  and avoids  systematic  errors that are
introduced when biomass is  calculated  on the basis of biovolume from  the  shrinkage of microorganisms
with drying (Jenkinson and Ladd 1981).  The CHCI3 technique does  not require standardization on each soil
type  like that required for  the calculation  of  biomass C by  adenosine  5'-triphosphate (ATP)  extraction
Oenkinson and Ladd  1981).   In addition, the ATP extraction technique is  most successful for  characterizing
soils in which the microbial population  is in a resting  state at excess or  constant phosphorus levels  (Paul
and Clark 1989), whereas the CHCI3 extraction technique can be  used at any time.

Collection of soil for two composite samples from each  resource sampling unit would require 0.5-0.7 h.
The estimated cost of analysis is $25 per sample.

VARIABILITY: Variation among samples from a  resource sampling  unit should be reduced by compositing
of samples.   The coefficient  of variation (CV)  for four soils with three replicate samples assayed per soil
ranged from 11.5 to 15.8%,  with  a  mean CV  of  13.6% (Vance  et al.  1987).  The expected  temporal
variability throughout the year was not estimated.

PRIMARY PROBLEMS:  Establishment of  seasonal and regional standards for evaluating soil microbial biomass
would require considerable effort and extensive knowledge of soil type, crop history, and management inputs.
Extensive  temporal  variation  in  soil microbial biomass  within a  resource sampling unit may complicate
establishment of a subnominal threshold value.

REFERENCES:

Jenkinson, D.S.  1976.  The effects of  biocidal treatments on metabolism in soil-IV:  The decomposition
of fumigated organisms in soil.  Soil Biol.  Biochem.  8:203-208.

Jenkinson, D.S., and J.N. Ladd.   1981.  Microbial biomass in soil:   Measurement and turnover.  Pages
415-471.  In:  E.A.  Paul  and J.N. Ladd,  eds.  Soil Biochemistry. Marcel Dekker, Inc., New York.  480 pp.

Jenkinson, D.S., and D.S. Powlson.  1976a.   The effects of biocidal  treatments on metabolism  in soil-l:
Fumigation with chloroform.  Soil Biol. Biochem. 8:167-177.

Jenkinson, D.S., and D.S. Powlson.  1976b.  The effects of biocidal treatments on metabolisms in  soil-V:
A method for measuring soil biomass.  Soil Biol. Biochem. 8:209-213.

Jenkinson, D.S., S.A. Davidson, and D.S. Powlson. 1979. Adenosine triphosphate and microbial biomass
in soil.  Soil Biol. Biochem.  11:521-527.
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Parkinson, Dv and E.A. Paul.  1982.  Microbial biomass.  Pages 821-830.  In: A.L. Page, R.H. Miller, and
D.R. Keeney, eds.  Methods of Soil Analysis, Part 2:  Chemical and Microbiological Properties.  Agronomy
Monographs no. 9.  American Society of Agronomy, Madison, Wl.  1159 pp.

Paul,  E.A., and  F.E. Clark.   1989.   Soil Microbiology  and  Biochemistry.   Academic  Press,  San Diego.
271 pp.

Paul,  EA., and R. P. Voroney.   1980.  Nutrient and energy flows through soil microbial biomass.  Pages
215-237.  In:  R.C. Ellwood, ed.  Contemporary Microbial Ecology.  Academic Press, New York.

Powlson,  D.S., and D.S. Jenkinson.   1976.  The  effects of biocidal treatments on  metabolism in soil-ll:
Gamma irradiation, autoclaving, air-drying and fumigation.  Soil Biol. Biochem. 8:179-188.

Vance, E.D., P.C. Brookes, and D.S. Jenkinson. 1987.  An extraction method for measuring soil microbial
biomass C.  Soil Biol.  Biochem. 19:703-707.
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F.4  INDICATOR:  Land Use/ Extent of Noncrop Vegetation

CATEGORY:  Response/ Ecosystem Process Rates  and Storage
              Exposure and Habitat/ Habitat

STATUS:  High-Priority Research

APPLICATION:  The monitoring of land  use would indicate status and trends in resource condition, crop
preferences, and grower perceptions concerning market opportunities and profitability.  A shift in the ratio
of ancillary area to cultivated area would indicate alternation of perceptions with regard to set-aside and soil
conservation practices.   Land use would  be evaluated in relation to other indicators, such as soil  erosion
(F.2), soil productivity index (F.11), agricultural chemical use, and socioeconomic indicators. The areal extent
and spatial relations of areas of noncrop vegetation in agroecosystems would be a  habitat indicator  that
affects  native animal diversity.

INDEX PERIOD:   Land use can  be determined  any time when  crops are growing  in the field or when
growers have  made decisions  concerning crops to be planted for the growing season;  spring or summer
would  be preferable.

MEASUREMENTS:  Land  use within a  landscape  sampling  unit would  be assessed initially  by EMAP-
Characterization from remote sensing according to a classification scheme based primarily on the Anderson
classification scheme (Zev  and Lieberman  1984).  Subsequently, land use within a resource sampling  unit
would  be assessed by enumerators during field visits in cooperation with owners or operators.  Specific items
of interest are areas (1) under cultivation (rented, leased, or owned), by crop; (2) in permanent pasture, set-
aside  programs,  or in  fallow;  (3) in use for feeder  lots  or confined  animal  operations; (4)  in ponds for
irrigation or waste containment; (5)  in  border  area (e.g.,  woodlot,  hedge  row);  (6) devoted  to grass
waterways; and  (7) devoted to buildings, driveways, and paved  areas.   Information concerning grower
rationale for selecting specific crops would aid in the interpretation of land-use information, although such
information may be subjective.

Grower interviews would  last 0.5-1.0 h for each resource sampling unit.  Some errors  in estimation  may
occur because of grower responses.

VARIABILITY:  Because land use  within a sampling unit is a census measure, the spatial variability of  land
use within a resource sampling unit  is inconsequential. The expected temporal variability of land use during
the index period  is minimal.

PRIMARY PROBLEMS:   Reliable identification of actual crop species present is not possible from remotely
sensed data obtained from the thematic mapper (LANDSAT-V).  Grower interviews and site visits or current,
leaf-on aerial photography at 1:24,000 would thus be essential.

REFERENCES:

Zev, N., and A.S. Lieberman.   1984.  Landscape Ecology:  Theory and Application.  Springer-Verlag, New
York.  356 pp.

BIBLIOGRAPHY:

Forman, R.T., and M.  Gordon.  1986.  Landscape Ecology.  John Wiley and Sons, New York.  619 pp.

Vink, A.P.A.  1983. Landscape Ecology  and Land Use.  English translation  by D.A.  Davidson. Longman,
London.   264 pp.

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F.5  INDICATOR:  Crop Yield

CATEGORY:  Response/ Populations

STATUS:    High-Priority  Research

APPLICATION:  The principal function of an agroecosystem is the production of food and fiber for human
benefit  For crops, agricultural production  is quantitatively measured in yield that results in economic gain.
Crop yield must be incorporated as a response indicator to assess the status and trends of agroecosystem
condition.  Yield integrates the action of a  large array of biotic and abiotic factors experienced by the crop
during the growing season,  such as soil  factors  (e.g., type, moisture,  nutrients), climatic variables (e.g.,
temperature, light, relative humidity, precipitation, wind),  management practices (e.g., agricultural chemical
applications,  tillage, irrigation, crop  cultivars,  planting date and  density),  pests  (e.g., insects), diseases,
socioeconomic conditions (e.g., government  policies, markets), and  pollutants  (e.g., ozone, sulfur dioxide,
heavy metals).  Crop yield is normally expressed as  biomass per unit area.

The use of crop yield data to help determine the status and  trends  of agroecosystem condition must  be
qualified, because crop growth and subsequent yields depend on a number of external factors (e.g., national
and international markets, management practices).  Crop yield, however, is one  of the best known measures
of biological and chemical function in agroecosystems, and it has measurable responses to both natural (U.S.
EPA 1978; Lowrance et al. 1984) and anthropogenic stressors (Benson et al. 1982; Treshow  1984; U.S. EPA
1978, 1986; Heck et al. 1988). Because many agricultural areas are highly managed  and on-farm decisions
are driven largely by socioeconomic factors, attributing changes in crop yield directly to increases or decreases
in stressor frequency or intensity may be inappropriate.  For example, farmers respond to observed  reductions
in crop vigor  or yield  by adjusting management strategies to  minimize the  effect  These management
strategies might include changes  in irrigation, agricultural  chemical applications, or crop type/variety.  The
consequence of these changes is that the ecosystems remain highly productive,  at least over the short term,
and any decrease in yields due to stressors is postponed by management strategies.

INDEX PERIOD:  Field  sampling would occur at the end of the growing season.

MEASUREMENTS:  If the U.S. Department of Agriculture's National Agricultural Statistical Service (MASS) area
sampling frame were used to select resource sampling units, the cost  would be minimal  in comparison to
constructing an EMAP frame.   Most of the cost would be in retrieving, manipulating, and analyzing the NASS
data.  Yield data for  crops not currently monitored by  NASS  would  cost approximately $500-$1000  per
resource sampling  unit for NASS labor.  Crop yield  could  be measured  as biomass of marketable product
harvested, as a production efficiency index (see also indicator D.1, "Tree Growth Efficiency"), or  in relation
to specific  inputs such as  fertilizer applied.  All measurements would be conducted for crops annually.

VARIABILITY:   A  number  of factors,  discussed  above, influence variability in  crop yield.   Objective
measurement surveys of yield would produce relatively little  spatial variability (coefficient of variation <20%)
if cultivars, management practices, and soil were identical throughout the resource sampling unit Because
crop yield integrates growth over the entire period of productivity, the expected temporal variability  during
the index period is inconsequential.

PRIMARY PROBLEMS:  Development of a  yield index such as  net primary productivity (biomass produced
from  each  unit land  area) and production  efficiency (yield in relation to fertilizer/energy  input) will  be
necessary to compare yield among different agronomic crops. Also, the threshold value for subnominal yield
may depend on region.
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REFERENCES:

Benson, F.J., S.V. Krupa, P.S. Teng, D.E. Welsch, C. Chen, and K.  Kromroy.  1982.  Economic assessment
of air pollution damage to agricultural and silvicultural crops in Minnesota.  Report to Minnesota Pollution
Control Agency.  University of Minnesota, St.  Paul.  270 pp.

Heck, WAV., O.C. Taylor, and D.T. Tingey.  1988.  Assessment of Crop Loss From Air Pollutants.  Elsevier
Applied Science, New York.   552 pp.

Huff, F.A., and J.C. Neill.  1982.  Effects of  natural climatic fluctuations on  temporal  and spatial variation
in crop yields.  J. Appl. Meteorol. 21:540-550.

Lowrance, R.,  B.R. Stinner,  and G.J. House.  1984.   Agricultural  Ecosystems:  Unifying Concepts.   John
Wiley and Sons, Inc.,  New York.  233 pp.

Thompson, LM.  1986.  Climatic change, weather variability, and corn production.  Agron. J.  78:649-653.

Thompson, LM.   1988.  Effects of changes  in climate and weather variability  on the yields  of corn and
soybeans.   J. Prod. Agric. 1:20-27.

Treshow,  M.  1984.  Air Pollution and Plant Life.  John Wiley & Sons, Ltd.   486  pp.

U.S. EPA.   1978.  Methodologies for  valuation of agricultural crop yield changes:   A review.  EPA 600/5-
78/018.  U.S.  Environmental  Protection Agency,  Environmental Research Laboratory, Corvallis,  OR.

U.S. EPA.   1986.  Air  quality  criteria for ozone and other photochemical oxidants:  Volume III.  EPA 600/8-
84/020cF.  U.S. Environmental Protection Agency, Environmental Criteria and Assessment Office, Research
Triangle Park,  NC.
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F.6  INDICATOR:  Livestock Production

CATEGORY:  Response/ Populations

STATUS:  High-Priority Research

APPLICATION:  The productivity of livestock operations, like crop productivity, is an environmental value for
agroecosystems.  As with other aspects of agriculture, the definition of a "health/1 livestock system is difficult
to formulate.  Traditional measurements of these systems are economic, that is, measured in terms of profits.
Livestock  operations, like  crop production,  are highly  managed with  the  goal  of maximum yield  with
minimum  input  The operator provides  a certain  amount of food, water, and labor and expects a high
livestock production.  A certain amount of waste is produced  and exported to other ecosystems via air and
water media.

Livestock operations suggest two useful indicators, productivity and waste export; only the former is addressed
in this discussion.  Productivity is an  economic  barometer which quantifies livestock yield.  Use of existing
measures of productivity assumes that a productive  system  is a healthy one; although in the short term that
is true economically, it may not be true when ecological costs are included.  A  highly productive operation
with its waste exports  may not be an  ecologically healthy  system.

Productivity is controlled by both economic and  production criteria.  Production  factors include shelter, feed
quality  (nutrition), and  genetic  advances.   One way to determine the effects of production criteria on
productivity would be to define an  ideal environment that resulted in maximum productivity.  Research has
been accomplished to optimize housing, reproduction, and nutrition independently.  If the combination of
these optima are linearly additive, it might be possible to define an optimal environment  It would then be
possible to compare the deviations between productivity measurements from livestock operations and the
maximum potential productivity for use as a  response indicator of growth efficiency.

INDEX  PERIOD:  The sampling window  is  flexible, but for logistical efficiency  should  be handled as part
of a summer enumerative survey questionnaire, such as that conducted by the National Agricultural  Statistical
Service  of the U.S. Department of Agriculture (USDA).

MEASUREMENTS:  Because productivity measurements differ  with livestock type,  no universal indicator of
productivity could be applied across all livestock  operations.  For example, the days to reach 105 kg (230 Ib)
is a common  measurement for swine operations (Mayrose et al. 1985) but would be meaningless for  a
poultry  producer, who would  be more interested in eggs produced or live weight at slaughter (Bull 1989).
Reproductive and growth  rate statistics for various livestock are also  used and available (USDA 1989).

Growth rate and live weight at slaughter are accepted indexes  of productivity for broilers.  Rate of lay serves
as the measure  for egg-producing poultry.  These statistics are available  from the USDA (1989) on at least
a state basis. It would be useful to form a production:input ratio as an  indicator  of animal growth efficiency.
There are several commonly accepted measures of swine productivity.  They include live weight at slaughter,
number of days to reach  105 kg (230 Ib), feed  efficiency, breeding  efficiency,  and  pigs  per litter  (Mayrose
et al. 1985).

Data will  be available from grower interviews and should be part of a regular enumerative  survey.  This
part of a questionnaire should  require <1.0 h.

VARIABILITY:   Because livestock production,  like crop yield,  integrates growth over the  entire period  of
productivity,  the  expected temporal  variability  of livestock production  during the  index  period   is
inconsequential.   Because this  indicator would be  censusing the entire  resource sampling unit, the spatial
variability within a resource sampling  unit is also inconsequential.

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PRIMARY PROBLEMS:  Possible problems are currently under evaluation.  Integration of socioeconomic
programs into the indicator interpretation  may be difficult

REFERENCES:

Bull, L  1989.  Personal communication.  Conversation with George Hess, September.   North  Carolina
State University, Raleigh.

Mayrose, V.B., D.H. Bache, and G. Libal.  1985.   Pork Industry  Handbook.  North  Carolina Agricultural
Extension Service, Raleigh.

USDA.  1989.  Agricultural Statistics, 1988.  U.S. Department of Agriculture, Government Printing Office,
Washington, DC.
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F.7  INDICATOR:  Visual Symptoms of Foliar Damage:  Crops

CATEGORY: Response/ Pathology
       Exposure and Habitat/ Pathogens

STATUS:  High-Priority Research

APPLICATION:  Plants tolerate and adapt to most natural stresses caused primarily by variations in weather
without being visibly damaged or deformed.   Foliar symptoms  and deformation (other  than  diseases and
insect pests) are associated with abiotic stresses including pollutants, nutrient imbalance, or weather extremes;
this response indicator relates to the environmental value of productivity.

Although  various foliage symptoms (e.g., chlorosis and  nerosis)  may  be caused  by a variety  of stresses,
extension agents routinely use specific symptoms to diagnose plant problems.  Trained observers can often
identify specific stressors from the pattern of damage/deformation/chlorosis (Treshow 1984). Acute symptoms
from air  pollutants are readily diagnosed on selected indicator plants:  SO2  -ragweed,  bachelor button (cv.
Jubilee Gem), downy brore grass, alfalfa, Rue///a caroliniana; O3 - milkweed, potato (cv. Norland), soybean,
tobacco; and F - gladiolus, apricot (cv.  Royal or Chinese).

For nonleafy crops where foliar symptoms do not affect  crop productivity directly,  it may be possible to relate
the symptoms (in this case an exposure indicator) to a response indicator such as crop yield (F.5), which does
reflect productivity.  For some disease and insect problems, there  are models that relate pest densities  to
yield effects (Campbell and Madden 1990); these would require calibration.  Foliar symptoms are of greatest
value in revealing acute exposures to specific stressors and thus are not the most  sensitive indicator to reveal
chronic pollutant exposures.

INDEX PERIOD:  The sampling window is  the season of vegetative growth (May  to September); the optimal
time of data collection depends on the objective.  A monthly sampling frequency would be useful; a weekly
sampling frequency allows a more accurate  estimate of the episode periodicity but does not fit into the EMAP
design.  If symptoms are to be related to yield, index  period may be the same  as that for crop yield.

MEASUREMENTS: A field survey would assess plants for symptoms of fungal,  bacterial, insect, and nutritional
problems.  The observations  would include reporting and  diagnosing pathogen presence or symptoms and
a quantitative assessment (% cover) for plants with any  reported  symptom.  This  type of survey can provide
general information on the health of a field or crop and specific information on the major disease and  insect
stresses that are  present.  The sampling density would  depend on the desired  data resolution and sampling
frequency (Campbell and  Duthie 1989).  Some states may have pest survey data available.

Observers must be  trained (40-50 h  per observer) in  the diagnosis of foliar  symptoms  and trained  in
calibration if quantification is required.  Symptoms should be clearly  described with photographs,  if possible.
Development of disease identification aids would  cost $50-$100  for each crop. Data sheets  should provide
a standardized reporting format for easy  transfer to a data base.  The measurement is easy to acquire (in 30-
60 min a sampling unit) and  fairly inexpensive after training field crews.  Field calibration and validation  of
foliar symptoms are  necessary operations for field enumerators to reduce measurement error.

Foliar injury and  mortality depress leaf  area index values and increase the  intensity of the Iignin/cellulose
absorption in plant  canopies.   Recent laboratory and  airborne  spectral  measurements of  near-infrared
lignin/cellulose absorption suggest that spectral features of dry  plant materials could  be used  to indicate
vegetation stress.

VARIABILITY:  The  expected  spatial variability of foliar symptoms  within a resource sampling  unit  would
produce a range that deviates 10-20% from the mean  value; this deviation will be lower if presence versus

                                                F-13

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absence (incidence) is used.  The expected temporal  variability of foliar symptoms during the index period
might be great (coefficient of variation >50%).

PRIMARY PROBLEMS:  If identification of a specific disease is required,  it might be necessary to isolate the
pathogen; however, common insect and disease problems can be identified with  little or no ancillary data.
Confirmation of symptoms of mineral deficiency or toxicity might  require additional nutrient analysis data.

REFERENCES:

Campbell, C.L,  and JA  Duthie.  1989.  Sampling for Disease Assessment.  Biology and  Culture Tests
Volume 4: v-ix.  APS Press,  St. Paul, MN.

Campbell, C.L,  and LV. Madden.   1990.  Introduction to  Plant Disease Epidemiology.  John Wiley and
Sons, New York.  532  pp.

Treshow,  M.  1984. Air Pollutants  and Plant  Life. John Wiley and Sons, Chichester, UK.  486 pp.

BIBLIOGRAPHY:

Johnson,  K.B., E.B. Radcliffe, and P.S.  Teng.  1986.   Effects of interacting populations of Alternaria so/an/,
Verticillium  dahliae  and  the  potato leafhopper  (Empoasca  fabae) on  potato  yield.    Phytopathology
76:1046-1052.

Lauranee, JA, and GA Greituer.   1984.  Chapter  2.6.4.  In:   Development of a Biological Air Quality
Indexing System.  Minnesota Environmental Quality Board, St.  Paul,  MN.   380 pp.

Lucas, G.B., C.L.  Campbell, and L.T.  Lucas.   1985.  Introduction to  Plant Diseases.   AVI Publishing,
Westport, CT.  353 pp.

Teng, P.S., and K.W. Kromroy.  1984.   A biological system for indexing air quality and assessing vegetation
effects.  Minnesota  Bioindicators Study Annual Report, University of  Minnesota, St. Paul.  120 pp.
                                                F-14

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F.8  INDICATOR:  Agricultural Pest Density

CATEGORY:   Exposure and Habitat/ Pathogens

STATUS:  High-Priority Research

APPLICATION:  Agricultural  pest density is an  indicator of the amount of biological  stress  to which the
crops are exposed.  If higher pest densities are  monitored in agroecosystems that are in subnominal
condition, it would support biological stress as a contributing cause  to this undesirable status.  Two types
of pests or pathogens will be monitored:  weeds and  nematodes. Insects, fungi, and bacteria will probably
be present as pests in agroecosystems as well; however, techniques  for estimating the  population densities
of these pests  or pathogens are not currently amenable to the EMAP sampling design,  including the selection
of an index period during which all sampling will occur.  Indirect estimates of effective populations of insect
pests and fungal  and bacteria pathogens will  be obtained from the "Visual Symptoms of Foliar  Damage:
Crops" indicator (F.7).

Weeds are defined as any plant present in an agricultural field  other than the  intended  crop plant  The
number of weed  species present,  particularly noxious weed species,  indicates  the effectiveness of pest
management and cultural practices,  as well as the potential stress to which the associated crop is subjected.
The  presence of  certain noxious weeds  (e.g.,  sicklepod [Cassia obtusifolia], witch weed  [Str/ga lutea]) also
indicates potential for a high degree of stress on the system.

Plant-parasitic nematodes are stylet-bearing nematodes that depend on a host plant  for nutrition at one
or more life stages. The population size  of plant-parasitic nematodes can be related to  a damage threshold
or nematode hazard index for specific crops (Barker et  al. 1985; Ferris 1978).  Nematode assays and advisory
services (Rickard  and  Barker 1982), available  in several states in the Southeast,  currently provide specific
cultural and management recommendations for  minimizing nematode  damage to crop plants.   Counts of
plant-parasitic nematodes indicate success or failure of current season practices as well as potential for future
losses.

INDEX PERIOD:  The most appropriate time for an agricultural weed survey would be  early in the growing
season (spring to early summer), after crop emergence. The most appropriate index period for  plant-parasitic
nematodes would be at or  near crop maturity.  At crop maturity, usually in autumn,  populations of  most
plant-parasitic nematodes are at their  highest annual levels.  The  higher population  levels  maximize the
probability of  detection of rare but important species, and it is  the autumn  population  levels that usually
serve as a basis for damage threshold or hazard index determinations (Barker et al. 1985).

MEASUREMENTS:  Weeds would  be monitored in current-season production fields  only, not in fallow or
set-aside fields or in  areas adjacent to  production  fields.  Weeds would be  identified by species, and a
separate list would  be prepared for each resource sampling unit.  The enumerator would survey for weeds
along a diamond-shaped sampling path  (four transects or diagonals connecting the midpoints of the  field
edges).  Weed species within visual  range would be recorded on a  minimum of five equally spaced stops
along each diagonal.   From  this survey, the species  number (richness) and the presence of any noxious
weeds can be determined.  Enumeration of weed species would  require 0.5-1  h at each  resource sampling
unit   Because the  weed   information  is  merely an enumeration of  species  presence,  a sufficiently
representative sample should detect a!l species present.

The  plant-parasitic nematode count in 500 cm3 soil would  be determined from one  composite soil  sample
from  each resource sampling unit.   Soil would  be collected with  a sampling tube  (at  least 2  cm inside
diameter)  to a depth  of 20 cm.  Thirty  cores taken in  a zigzag pattern from at least  three  transects of a
resource sampling unit would be well mixed,  and a sample  of at least  500 cm3  would be submitted to a
nematode assay laboratory (Barker and Campbell 1981).  Nematodes would be extracted from the soil  by

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an elutriation  and centrifugal  flotation procedure (Byrd et al.  1976; Barker et al. 1986).  A dissecting
microscope would be used to tally population size of each  of the major parasitic species.  The collection of
soil samples for the nematode assay would require 0.5 h at each  resource sampling unit The estimated cost
of a  laboratory nematode assay is $5 per sample.   For plant-parasitic nematodes, the compositing of the
sample from at least 30 core samples should quantify variability  adequately.

VARIABILITY:  Some spatial variability in weed species  present and in  numbers of plant-parasitic nematodes
present is expected  within a resource sampling unit.  If repeated samples of 30 cores were taken from the
same field, after mixing and  extraction, the coefficient of  variability should not exceed  20% within most
sampling  units.  The expected temporal variabilities of agricultural pests over a 2-3 week index period would
produce  a range that deviates <10% from the mean value.

PRIMARY PROBLEMS:  Rare and  isolated weed species may not be detected.  Plant-parasitic  nematodes
may  be detected less frequently in the Northeast, Central, Great Plains, and  Northwest than in the Southeast
because  of the relative abundance of nematodes in these regions and because of the  projected  size of
sampling  units and the projected sampling frequency of the EMAP design.

REFERENCES:

Barker, K.R., and C.L. Campbell.  1981.  Sampling  nematode populations.   Pages 451-474.   In:   B.M.
Zuckerman and R.A. Rohde, eds.   Plant Parasitic Nematodes,  Volume III.   Academic Press, New York.

Barker, K.R., D.P. Schmitt, and J.L.  Imbriana.   1985.  Nematode population dynamics with emphasis on
determining damage potential to crops.  Pages 135-148.  In:  K.R.  Barker, C.C.  Carter, and J.N.  Sasser, eds.
An Advanced  Treatise on Meloidogyne, Vol.  II: Methodology.  North  Carolina State University Graphics,
Raleigh.

Barker,   K.R.,  J.L. Townshend, G.W.  Bird, I.J. Thomason,  and  D.W.  Dickson.   1986.  Determining
nematode population responses to control  agents.   Pages  283-296.  In:   K.D. Mickey, ed.  Methods for
Evaluating Pesticides for Control of Plant Pathogens.  APS Press, St. Paul, MN.

Byrd, D.W., Jr., K.R. Barker, H. Ferris, C.J. Nusbaum,  W.E.  Griffin, R.H. Small,  and CA. Stone.   1976.
Two semi-automatic elutriators for extracting nematodes and certain fungi from soil.  J. Nematol.  8:206-212.

Ferris, H. 1978.  Nematode economic thresholds:  Derivation, requirements, and theoretical considerations.
J.  Nematol.  10:341-350.

Rickard,  DA, and  K.R.  Barker.  1982.  Nematode assays and advisory services.   Pages 8-20. In:  R.D.
Riggs, ed.  Nematology in the southeastern region of the United States.  Southern Cooperative  Series Bull.
276.  Agricultural Experiment Station, University of Arkansas, Fayetteville.
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F.9  INDICATOR:  Lichens and  Mosses, Clover, Earthworm Bioassays

CATEGORY:  Exposure and Habitat/ Bioassays

STATUS:  High-Priority Research

APPLICATION:  Lichens and mosses have been used for more than a century as biomonitors of air pollution.
They are long-lived perennials lacking roots and cuticle;  because they absorb most of their  nutrients (and
contaminants) directly from atmospheric deposition, they  are  ideal organisms  to assess exposure to certain
airborne chemicals.   Several studies  have used  lichens and  mosses as accumulators  of  heavy  metal
contamination around point-emission sources  (Gilbert 1965;  Manning and Feder  1980).  They also may
accumulate persistent agricultural organic chemicals, if these chemicals do  not interfere with metabolism to
cause dysfunction or death.  Lichen and moss species differ in their sensitivity to SO2 and other compounds;
ecosystems that experience high ambient  levels  of  air contaminants (specifically  SO2) can lose sensitive
species. Lichen zone maps can indicate zones  of SO2 contamination downwind from a point source (Gilbert
1965).  Lichens can also  be transplanted into polluted zones for  use in observing the extent and severity of
the pollution problem.

A  number of higher plants also have been used successfully as biomonitors of air pollution stress; examples
are tobacco,  potato, alfalfa, cotton,  ragweed,  soybean (Manning and Feder  1980; Ashmore et al. 1988;
Kromroy et al. 1989). Recently, a  monitoring system using an O3 sensitive (S)  clone and an O3 resistant (R)
clone of ladino clover has been developed to diagnose O3 stress (Heagle et al. 1989).  This system, which
is  designed to provide information on the temporal and spatial distribution of elevated O3 and possibly other
atmospheric pollutant levels, has the potential to serve as a monitor of atmospheric pollutant effects on yield
of important crop species on a wider geographic and temporal basis than had been possible previously.  The
clover system also may be useful in developing  a production index that  includes short-term climatic variables.

Earthworms have a long  history of use as indicator organisms because  they occur in a majority of soils, are
large and easy to identify, are in intimate contact with soil, take up moisture through their integument, and
are noncontroversial experimental  animals; these attributes make earthworms suitable for monitoring  soil
contamination.  Earthworms make up a large proportion of the total biomass of soil invertebrates.  They play
a  key role  in the breakdown of decaying plant and animal material in the soil and maintain soil structure,
aeration, and fertility.  Earthworms are affected, via intake into  their tissues,  by  a  number of organic and
inorganic contaminants.

INDEX PERIOD:  The lichen-moss survey would occur in late summer or early autumn after application of
pesticides  and during the crop growing season.  Clover  would  be  monitored monthly during  its growing
season.  Containers with  contents of standardized  parameters that relate to earthworm activity (soil, species,
numbers, baseline  measurements) would be placed by field crews in the spring; the  assessment would  occur
upon their return in early autumn.

MEASUREMENTS:  Lichens and mosses:  Naturally occurring lichen and  moss species could  be surveyed,
or transplants (lichen strings,  lichen bark samples,  moss bags) could be monitored  for  chemical input and
accumulation or for presence or absence of the lichen and  moss species  (Gilbert 1965;  Huchabee 1973).
Lichen and moss collection would have a relatively low labor cost.  Plant material would  be dried and
stored; chemical analyses of  the material  would  be expensive.  The  recommended interannual sampling
frequency  for a lichen-moss survey is every year.

Clover system:  The S and  R clones  of  ladino clover were grown under several O3 regimes; and  foliar
injury, leaf chlorophyll  and biomass  were compared  (Heagle et al. 1989).   The S/R ratio of these  three
measures was  related to  the  O3 concentration and could be  calibrated to show  their relationship to yield
of important crop  species as well as  ambient O3 concentrations.  Clover would be monitored for biomass,

                                                F-17

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visible  injury, and leaf chlorophyll content  Mean seasonal  results of clover biomass accumulation are used
to perform correlation analyses with crop yields.  Chlorophyll analysis needs only leaf clip - EtOH vials and
a spectrophotometer; estimates of foliar injury require 0.5 h of a trained person for each resource sampling
unit; and  estimates of clover biomass require collection, drying, and weighing.

Earthworms:  Enclosed field containers with a known species and count of earthworms would be monitored
in both field crop and surrounding locations.  Measurements would include population parameters such as
mortality,  reproduction, and growth and exposure parameters such as tissue chemical residues and biomarkers
(see Appendix G-2) of enzyme induction (e.g., metallothionen) and enzyme depression (e.g., cholinesterases).
The cost for constructing earthworm containers would be approximately  $1.00 a plot, and  containers could
be reused. An adequate stock of earthworms would be inexpensive and  easy to locate and maintain.  Initial
setup and final assessments (earthworm count per container; baseline chemical analyses) would be relatively
laborious,  and chemical and  enzyme analyses would range from $100 to $1000.

VARIABILITY:  The expected spatial variability of the  bioassay measures within a resource sampling unit and
temporal variability of the bioassays during the index period are  not available for lichens and  mosses, clover,
or earthworms.

PRIMARY PROBLEMS:   The ladino clover system needs some developmental time so that tests can  be
conducted on (1) the effects of  climatic and  edaphic factors on the O3-induced S/R ratio,  (2)  the relative
sensitivity  of S and  R clones to  SO2, other gases,  parasites, and pests,  and  (3) calibrating clover response
parameters to yield  responses of crops.

Lichens, mosses and earthworms,  used in a bioexposure  mode (accumulation of contaminants), will not
bioaccumulate all possible contaminants of concern. Many contaminants will not persist in biological  tissues,
but are transitory (e.g., ozone) and may or may not result in a biological  response.  More information about
the physiochemical  properties of contaminants,  especially concerning pesticide active ingredients and other
synthetic organics (including transformation products)  is required to reduce analytical costs  of contaminants.
To provide contaminant flux (quantity/unit time)  information, more research is needed to determine retention
of initially absorbed contaminants.

REFERENCES:

Ashmore, M.R., J.N.B.  Bell, and  A. Mimmack.  1988.   Crop growth along a  gradient  of ambient  air
pollution.   Environ.  Pollut.  53:99-121.

Gilbert, O.L  1965.   Lichens as indicators of air pollution  in  the Tyne Valley.  Pages 35-47.   In:  G.T.
Goodman, R.U. Edwards,  and T.M.  Lambert, eds.   Ecology and   Industrial  Society.   Blackwell  Scientific,
Oxford, U.K.

Heagle, A.S.,  J. Rebbeck,  S.R.  Shafer, U. Blien, and  W.W.  Heck.  1989.   Effects of  long-term  ozone
exposure  and  soil moisture deficit on growth of ladino clover - tall  fescue pasture.  Phytopathology 79:128-
136.

Huchabee, J.W.  1973.   Mosses:  Sensitive indicators of airborne nursery pollution. Atmos. Environ. 7:749-
754.

Kromroy, K.W., P.S. Teng, M.F. Olson,  and D.R.  French. 1989.  A biological system for indexing  air
quality and averaging effects on  vegetation (Minnesota Bioindicators Study).  Environ. Pollut. 53:439-441.

Manning, W.J., and  W.A. Feder.    1980.   Biomonitoring  Air Pollutants with Plants.   Applied Science
Publishers, London.  142 pp.

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F.10  INDICATOR:  Quantity and Quality of Irrigation Waters

CATEGORY:  Exposure and Habitat/ Ambient Concentrations
             Stressor/ Physical, Chemical

STATUS:  High-Priority Research

APPLICATION:  The quality  of surface waters and ground water  impacts productivity,  sustainability, and
overall condition of agroecosystems. The concentrations of metals, salts, and toxic compounds are examples
of aquatic parameters that could have deleterious effects on crops (indicator F.5) or livestock (indicator F.6).
Some of these contaminants are already monitored through the National Stream Quality Accounting Network
(NASQAN) of the U.S. Geological Survey (USGS) and through individual  state departments of water resources.
Ground-water quality is monitored  by the USGS in cooperation with state agencies through the Federal-State
Cooperative Water-Resources Program (Gilbert and Mann 1988).  The USGS also maintains a variety of data
bases in its Water  Resources  Division  (USGS 1983).

The quantity of irrigation water available to agroecosystems may  also stress the system and often serves as
an  indicator of sustainability of farming practices.  When water is consumed faster than  it can be replaced,
the marginal  cost eventually  exceeds the possible return, and  continued operation cannot be sustained.

Irrigation water inputs for western U.S. water districts can be tracked  through the U.S. Bureau of Reclamation
(1987).  In addition, remote sensing techniques can be used to determine the number and extent of small
streams and farm impoundments within  or adjacent to a resource sampling unit.  Ground-water quantity is
monitored by the USGS through numerous programs.  USGS conducts the Regional Aquifer Systems Analysis
program,  which  focuses  on  defining regional  hydrology  and geology  and  establishing a  framework  of
background  information of geology, hydrology, and geochemistry of the nation's  important aquifer systems
(Sun 1985).

Depending on whether the parameters are obtained from the EMAP resource sampling units or from other
established networks, water quantity and quality can be either exposure or stressor indicators for the EMAP-
Agroecosystems indicator  strategy.

INDEX  PERIOD:   Water quality parameters  should  be examined  just  before or during irrigation; water
quantity should be examined before heavy water use.

MEASUREMENTS:   The acquisition of instrumentation necessary  to collect and analyze samples would  be
relatively costly; thus a thorough investigation  of the utility of available data bases is warranted.   Numerous
efforts have  been undertaken to inventory available data  bases that  could help support EMAP (Olson et  al.
1989; Olson  and Breckenridge 1989).  Coordination with  EMAP-lnland Surface Waters (Appendix B) would
be  imperative.  Assuming remote  sensing data are available from  EMAP-Characterization, effort and cost
would be  <$10 a  sample for water quantity determinations.  Bureau of Reclamation  data are summarized
annually for  western states  (U.S. Bureau  of Reclamation 1987).   The  National  Weather  Service has
precipitation data which would be useful for areas  that do not rely on irrigation.

If USGS data are being collected, the  approximate  data acquisition  costs per resource sampling unit would
include  4  h of a technician  and 2 h  of a scientist.  If USGS data  are not being collected, the  resources
would increase to about $800 for water analysis and  16 h of a technician and 4  h of a scientist to  collect,
analyze, and  interpret data.   Measurement  error for  most water parameters should  be low because most
require only simple, physical measurements.  The recommended interannual sampling frequency is every year
for  water quality parameters  and five years for water quantity measures.
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VARIABILITY:  The expected spatial and temporal variabilities of ground-water levels, surface water storage,
and water quality within a resource sampling unit and during the index period, respectively, are not available.
The variability associated with surface water storage  data would be related mostly to climatic factors (e.g.,
snowfall, evapotranspiration  rates).  Water  quality will  be more variable  because of differing management
practices (e.g., timing of manure spreading).

PRIMARY PROBLEMS:    Potential  lack  of coincidence of  optimum index  period with  that  of  other
EMAP-Agroecosystem indicators may necessitate an additional visit to a resource sampling unit if existing data
bases cannot be utilized.

REFERENCES:

Gilbert, B.K.,  and W.B.  Mann, IV.  1988.  The  U.S.  Geological Survey  Federal-State Cooperative Water-
Resources Program: Fiscal Year 1987. Open-File Report 88-174. U.S. Department of the Interior, Geological
Survey,  Denver,  CO.

Olson,  G.L, R.P.  Breckenridge, and  G.B. Wiersma.   1989. Assessment of federal  databases  to assess
productivity of U.S. agroecosystems.  Draft  Report.  Idaho National  Engineering Laboratory, Idaho Falls.

Olson,  G.L, and  R.P. Breckenridge.  1989.  Summary of  federal contaminant monitoring programs and
related databases:  A fish and  wildlife  perspective.   Draft Report.   Idaho National Engineering Laboratory,
Idaho Falls.

Sun, R.J., ed.  1985.   Regional aquifer-system analysis program of the USGS:  Summary of Projects, 1978-
84.  Circular 1002.  U.S. Department of the Interior, Geological Survey,  Denver, CO.  264 pp.

U.S. Bureau of Reclamation.  1987. Summary statistics:  Water, land and related data. Volume  1.  Similar
statistics compiled  annually.   U.S. Department of the Interior,  Bureau of  Reclamation, Washington, DC.

USGS.  1983.  Scientific and technical, spatial and bibliographic data bases  and systems of the U.S. Geological
Survey.  Circular 817 (revised edition).  U.S. Department of the Interior, Geological Survey, Washington, DC.
435  pp.

BIBLIOGRAPHY:

Leonard,  R.A.   1988.   Herbicides in  surface water.   In:  G.  Grover,  ed.   Environmental Chemistry of
Herbicides.  CRC  Publishing, Boca Raton, FL.

U.S. Bureau of  the Census.  Census of Agriculture.   (Note:  conducted  every five years.)   Department of
Commerce, Bureau of the Census, Washington, DC.

U.S. Department  of Agriculture.  Agricultural Statistics.  Conducted annually.  USDA, National Agricultural
Statistical Service,  Washington,  DC.

Newell, A.D., C.F. Powers,  and S.J. Christie.  1987.  Analysis of data from long-term monitoring of lakes.
EPA 600/4-87/014. U.S. Environmental Protection Agency, Environmental  Research Laboratory, Corvallis, OR.
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F.11  INDICATOR:  Soil Productivity Index

CATEGORY:   Exposure and Habitat/ Ambient Concentrations

STATUS:  High-Priority Research

APPLICATION:  Each  soil  has a productivity capacity for a specific crop or sequence of crops under a
defined set of management practices.  Soils high in  organic matter and nutrients, of medium texture, and
in good tilth, and  of 150-cm (59.1-in.) depth are  recognized as highly productive.  Key site characteristics
could be combined into a soil productivity index (SPI) that, if combined with  the other exposure indicators,
could be useful  in assessing the causes of subnominal condition within an agroecosystem resource class.

Several scientists have made major progress in development of an SPI (Pierce et al. 1983J 1984).  The  effect
of accumulated erosion on the potential productivity of different soil types has been considered. These data
show that  agricultural  practices can  impact soils  slowly  or  fairly  rapidly.   For  three  different  soil  types
investigated, the soils could  remain at a sustained level for some time; however, under improper management
the surface soil could be eroded  away to expose the less  productive subsoil.

The development  of an SPI along  with  an  erosion index could  generate  data like those  presented  in
Figure F-1; these data  show the major difference in the productivity index (PI) for three different  soil  series
in Minnesota.  An  SPI developed with additional soils data  (e.g.,  remote sensing data on crop residues)  could
                          10
20     30
40    50    60
  CM ERODED
70    80
MX)
             Figure F-1.  An example of the potential  use of soil productivity  index and
                         information on erosion  rates for three  soil series  in  Minnesota
                         to illustrate the relative  vulnerability of soils to erosion and the
                         potential use of selected soil series as sensitive indicators (Source:
                         Pierce et al. 1983).
                                                F-21

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be an important exposure indicator for agroecosystems.  Soil series that are more vulnerable to degradation
could serve as early-warning sites because  they  would  be the first to display  subnominal impacts from
mismanagement

The information in Figure F-1 demonstrates how available soils data can be applied in ecological assessments.
In a recent commentary, Dregne (1989) concluded that a wealth of information is available from  local, state,
regional, and national sources.  The  study noted, however, that in only one area of the  United States  has
research been conducted  on the effect of soil loss on potential soil productivity within a large geographic
area.  This region covers about 800,000  ha (2  million acres) in  the Palouse landscape of Washington and
Idaho.   The investigators  of the Palouse study (Krauss and Allmarai 1982)  concluded that over the past 90
years of farming,  soil productivity has decreased by  about 22% because of topsoil  losses.  By using an  SPI
in combination with erosion data (Indicator F.2), this type of assessment could determine the productivity of
agricultural soils.

INDEX  PERIOD:  Soil samples and remote sensing data should be collected in autumn, preferably after fall
management practices.  Some of the soils data  (i.e.,  texture,  clay fraction) are not  dependent on a season
or event and can be collected during any unfrozen  period.

MEASUREMENTS: Important factors  that need to be  considered in an SPI include soil rooting depth, topsoil
thickness, available water capacity, texture,  bulk  density,  permeability, clay fraction, pH, and soil organic
matter content (includes crop residue).   Approximate labor  required to collect soil samples from  a field
(sampling unit of  about 40 ha [100 acres]) would be 8 h of a technician and 2 h of a scientist.  Estimated
analysis cost is $3000 per soil series. The interannual sampling frequency would be 5 years or longer.

Data are generally not available on soil productivity; however, data bases like the SOILS-5 and the National
Resources Inventory (NRI) have been established by the U.S.  Soil Conservation Service since  1977 and can
serve as input to  an  SPI.

VARIABILITY:  Some of the soil  factors needed to  calculate an SPI for a soil series would  be considered
constant throughout the series (i.e., texture, depth). Although  some physical and chemical  parameters would
be temporally variable (e.g., pH,  bulk density),  procedures to determine ranges and averages for most soil
series have  been established.   The  expected spatial variability of an  SPI  within a  resource sampling unit
would  produce a range that deviates about  20% from the mean value.  The expected temporal variability
of SPI during the index period would produce a range that also deviates about 20% from the mean  value.

PRIMARY PROBLEMS:  Some developmental research  is  needed to combine the designated  measurements
into the soil productivity index.  The  importance of each factor would  need to  be evaluated on  a soil series
and regional basis.

REFERENCES:

Dregne, H. E. 1989.  Informed  opinion:  Filling the soil erosion gap.  J. Soil Wat. Conserv. 44:303-305.

Krauss, HA.,  and R.R. Allmarai.  1982.  Technology masks  the effects of soil erosion on wheat yields:   A
case study in Whitman County, Washington. Pages  75-86.   In:  B.L. Schmidt et al., eds.  Determinants of
Soil Loss Tolerance.  Spec. Publ. 45. American Society of Agronomy,  Madison,  Wl.

Larson, W.E., F.J. Pierce, and  R.H. Dowdy.  1983.  The threat of soil erosion to  long-term crop  production.
Science 219:458-465.

Pierce,  F.J., W.E. Larson, R.H. Dowdy, and W.A.P. Graham. 1983.  Productivity  of soils:  Assessing long-
term changes  due to erosion.  J. Soil Wat. Conserv.  38:39-44.

                                                F-22

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Pierce, F.J., R.H. Dowdy, W.E. Larson, and  W.A.P. Graham.  1984.  Soil  productivity in the corn belt:
An assessment of erosion's  long-term effects.  J. Soil Wat. Conserv. 39:131-138.
BIBLIOGRAPHY:
Runge,  C.F., W.E.  Larson, and G.  Roloff.   1986.   Using productivity measures to target  conservation
programs:  A comparative analysis.  J. Soil Wat. Conserv. 41:45-49.
                                               F-23

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APPENDIX C: INDICATORS OF RELEVANCE TO MULTIPLE RESOURCE CATEGORIES

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APPENDIX C.1:  INDICATOR FACT SHEETS FOR ANIMALS
                         /Authors

                   Dean E. Carpenter
NSI Technology Services Corporation -  Environmental Sciences
           Research Triangle Paik, North Carolina

                  Carolyn T. Hunsaker
              Oak Ridge  National Laboratory
              Environmental Sciences  Division
                  Oak Ridge, Tennessee

                      Reed  F. Noss
           U.S. Environmental Protection  Agency
             Environmental Research Laboratory
                    Corvallis, Oregon

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G.1.1  INDICATOR:   Relative Abundance:  Animals

CATEGORY:  Response/ Community Structure
              Exposure and Habitat/ GEMs-Exotics

STATUS:   High-Priority Research

APPLICATION:  The ecological condition of a community can sometimes be assessed by the condition of a
few species or categories of species (guilds) that play critical roles.  Although the indicator species concept
has  not fared well in  recent reviews (Landres et al. 1988), many ecologists agree that attention to particular
species is  valuable for community-level monitoring.   The relative  abundance,  or where  more feasible
presence/absence, of species including exotics, keystone species, and sensitive species (e.g., listed threatened
and endangered  species)  in a community should be tracked as an  index of  community condition.  This
indicator  is  related  to many environmental values, including  aesthetics,  biodiversity,  productivity, and
sustainability.

To begin  addressing which  species or guilds should be monitored in each ecological resource class,  EMAP
hosted an animal indicator workshop in March 1990 that included a select group of biologists and ecologists
whose specialties together spanned a range of animal types.  The result of the workshop suggested certain
animal types as appropriate indicators for each ecological resource category, based on the EMAP indicator
selection criteria and field experience.  Their relative ranking of animal types by these criteria and subsequent
recommendations are  summarized  in Tables 9-2 and 9-3, respectively.  Because of their consistently high
relative score among all resource categories, birds were selected as the animal type that should be  measured
in all categories.  Likewise, the low relative marks for large mammals and snakes prompted their elimination
from immediate  consideration.  The  nonavian  vertebrate and  invertebrate types that were suggested  as
appropriate indicators  of the  animal  condition in  each ecological resource category or subcategory  are as
follows:

Inland Waters
Vertebrate:  Turtles,  Frogs, and Salamanders
Invertebrate:  Snails

Wetlands
Vertebrate:  Turtles,  Frogs and Salamanders
Invertebrate:  Snails/Slugs

Coniferous Forests
Vertebrate:  Salamanders
Invertebrate:  Ground-Dwelling Beetles and Snails &  Slugs (Northwest only)

Deciduous  Forests
Vertebrate:  Salamanders and  Lizards (Southwest only)
Invertebrate:  Ground-Dwelling Beetles, Ants, and Snails &  Slugs

Tundra
Vertebrate:  Small Mammals
Invertebrate:  Bees

Arid Shrublands and Grasslands
Vertebrate:  Lizards and Tortoises
Invertebrate:  Grasshoppers, Ants,  Termites, and Ground-Dwelling Beetles (Great Basin only)

                                                 G-1

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Mesic Shrublands
Vertebrate:  Small Mammals and Lizards
Invertebrate:  Butterflies and Ants

Mesic Grasslands
Vertebrate:  Small Mammals and Lizards
Invertebrate:  Grasshoppers, Ground-Dwelling Beetles, and Termites

Agroecosystems
Vertebrate:  Small Mammals
Invertebrate:  Grasshoppers and Bees

INDEX PERIOD:  The optimal sampling window during a year depends on the season of peak activity of the
species  to  be  sampled.   The  suggested  sampling  season  is  spring for  lizards, tortoises,  frogs, toads,
salamanders,  and  bees;  summer  for turtles  and termites;  late summer  for small  mammals and
grasshoppers; early and late summer for  butterflies; and spring to autumn for  ground-dwelling beetles,
snails/slugs, and  ants.  The suggested  window for birds is a month in duration  and depends  on latitude,
ranging from May in the South to early July in the North.  Sampling should be avoided during moonlit nights
and stormy weather.

MEASUREMENTS:  Relative  abundances  or presence/absence of  the identified animal  types would  be
determined by  means of standard sampling techniques for the taxa; a leading sampling technique for small
mammals, ground-dwelling beetles, lizards, frogs,  toads, salamanders,  and some ants includes use of
permanent pitfall  can traps  (opened only  for optimal sampling periods).   The pitfall traps would contain
ethylene glycol  to kill and preserve  the specimens between site visits.  The sampling technique for snails and
slugs uses small squares of untreated lumber, whereas the technique for ants and termites involves placing
toilet paper rolls in the traps. Standard sweep-sample techniques currently  exist for sampling grasshoppers,
but sticky traps may become the standard grasshopper collection technique in the  future.   Bees and
butterflies can  be collected by netting along line transects; colony bees can be  collected from hives, and
cavity-nesting bees can be sampled by  using wood blocks with holes.

The most cost-effective census technique for birds would be point counts, whereby a trained observer notes
all birds seen or heard during a specified  length of time (usually 5-15 min).  A trained  birder can perform
5-10 point counts in one morning;  ideally three replicates should occur annually at a point.  Another possible
indicator taxon with high mobility is the  bat;  a sampling technique is being developed  that utilizes tape
recorders with  photocells that  record bat sonar at set intervals.  Tape  recorders are also being investigated
for use  in bird  censusing.

For animal types other than birds,  approximately 30 stations for each sampling technique  would be placed
along a  line transect within a resource sampling unit.  Stations must be located both in the center and edge
areas of this sampling unit  These provide relative abundances,  when  all species  in the taxonomic or
functional group of interest are tallied from the sample.   Absolute abundance, on the other hand, requires
intensive mark-recapture or repeated observations, which are cost-prohibitive over large  geographic regions.
The  recommended interannual sampling frequency would be  four years.

VARIABILITY:   The spatial variability of relative species abundances  or presence/absence within a resource
sampling unit using common techniques would be dependent on the taxa sampled.  The expected temporal
variability  of relative  species abundances during the index periods would also be dependent on taxa.
                                                G-2

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PRIMARY PROBLEMS:  (1)  Abundances of all but the most conspicuous species (such  as large birds and
mammals in open habitats) are notoriously difficult to assess with accuracy;  however, standardized census
techniques allow valid comparisons for a site (or better yet, a series  or group of sites) over time. (2)  Pitfall
traps result in the destructive sampling of organisms.  Sherman  live  traps can be used for small mammals;
however,  live traps would not be as  cost-effective because they are  not permanent,  capture fewer animals,
and  require  more frequent  site revisitation.   Destructive sampling, however,  will enable  the possible
application of other indicator types (e.g., contaminants in  tissues and biomarkers) and removes animals at
a resource sampling unit only once every four years.  (3)  Bird point counts introduce a bias toward calling
birds.  (4) Presence/Absence  measurements contain  less information that relative abundance but may be
logistically more feasible for EMAP.

REFERENCES:

landres, P.B., J.  Verner,  and J.W. Thomas.   1988.  Ecological  uses of vertebrate indicator species:  A
critique.  Conservation Biol. 2:316-329.

BIBLIOGRAPHY:

Bury, R.B., and P.S. Corn. 1987.  Evaluation of pitfall trapping  in Northwest forests:  Trap arrays with drift
fences.  J. Wildlife Manage. 51(1):112-119.

Cooperrider, A.Y., R.J. Boyd, and  H.R. Stuart, eds.  1986.  Inventory and Monitoring of Wildlife Habitat
U.S.  Department of the Interior, Bureau of Land Management, Washington, DC.

Ralph, C.J., and J.M.  Scott   1981.   Estimating numbers of terrestrial birds.  Stud. Avian Biol.  6:1-630.

Short,  H.L.  1983.  Wildlife guilds in Arizona desert habitat  Technical Note 362. U.S. Department of the
Interior, Bureau of Land Management

Thomas, J.W., ed.  1979.   Animal  Habitat in Managed  Forests:   The Blue Mountains of  Oregon and
Washington.  Agricultural Handbook No. 553.   U.S. Department of Agriculture, Forest Service, Washington,
DC.
                                                G-3

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G.I.2  INDICATOR:  Demographics:  Animals

CATEGORY:  Response/ Population Structure

STATUS:  Research

APPLICATION:   Population  vigor  is  reflected  in  the recruitment of individuals (birth  rate  and their
survivorship) into and  through  the  breeding population.  Analysis  of demographic variables such  as  age
structure,  sex  ratio, fertility, mortality, survivorship,  and  dispersal may be  particularly  worthwhile  for
populations  of keystone  species that are  known  to be  sensitive to  a particular disturbance.   These
measurements are the  traditional tools of  animal biologists and managers for assessing population  "health"
(Schemnitz 1980).

INDEX PERIOD:  The optimal sampling window during a year depends on species to be sampled, but should
be within  the season of peak activity regardless.  The suggested index periods for each animal type are listed
under Indicator G.1.1.

MEASUREMENTS:  A  detailed  life table is informative but its construction is  laborious.  Estimates of fertility
and mortality (birth and death rates) can be obtained through observations or  estimates, especially of marked
individuals.  Dispersal may be difficult to document with the  EMAP sampling design.  For species that  can
be separated into general age  classes, a portrayal of the age  structure  of the population may  be a good
indicator.  Temple and Wiens (1989) suggest that primary population parameters (birth, death, and dispersal
rates) for  birds  may provide  a better  indication of  response to  environmental change than  secondary
population parameters  (population size, density, habitat occupancy, age structure,  sex  ratio, proportion of
breeders).   In addition, numerous studies of bird nesting and fledgling success have  revealed that these may
be sensitive  indicators of response to stress.  Many fish and game agencies collect information on sex ratio,
density, birth/death,  harvest,  and dispersal  rates.  This data  could supplement data collected by EMAP
resource groups.  The  recommended  interannual sampling frequency is three or  four years, although some
parameters may  need more frequent monitoring.

VARIABILITY:  The expected spatial and temporal variabilities of demographic parameters within a resource
sampling unit and during  the index period,  respectively, were not estimated because specific demographic
parameters,  species, and  individuals were  not defined explicitly.
PRIMARY  PROBLEMS:   Most demographic variables can be measured only through detailed study.  EMAP
observations are expected to be limited to no more than two  brief field visits at a resource sampling unit in
any given year,  so demographic parameters will probably not be  estimated.   When  primary  population
parameters  are  used,  it  is important to  look  for compensatory  effects (e.g.,  an  increase in  mortality
accompanied by an increase in fecundity or survivorship).  If secondary population parameters are used, then
time lags,  site  fidelity, and compensation may prevent short-term  responses to environmental perturbations
from being noticed  (which is sometimes helpful, but also obscures response  to certain stressors).

REFERENCES:

Schemnitz, S.D., ed.   1980.  Animal  Management Techniques Manual.  The Animals Society, Washington,
DC.

Temple, SA.,  and J.A. Wiens.  1989.  Bird populations and environmental changes:   Can  birds  be  bio-
indicators?  Am. Birds  43:260-270.
                                                G-4

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G.1.3  INDICATOR:  Morphological Asymmetry:  Animals

CATEGORY:  Response/ Population Structure

STATUS:  Research

APPLICATION:  Fluctuating asymmetry (FA)  is the morphological variability demonstrated by an  individual
organism.    FA in  structures of bilaterally symmetrical organisms (e.g., fin rays, teeth, limb bones, fingertip
ridges, wing length) can be an early-warning  indicator of population responses to environmental and genetic
stress.  The application of this indicator may be worthwhile for species that are known to be sensitive to a
particular disturbance, including exposure to pesticides, heavy metals, and  other pollutants, hybridization, arid
inbreeding; each has been found to result in FA for various species.

INDEX PERIOD: The optimal sampling window during a year depends on species to be sampled, but should
be within  the season of peak  activity.  The  suggested index periods for  each animal type are  listed under
Indicator G.1.1.

MEASUREMENTS:   No single character may  provide an adequate measure of response; hence, a composite
index containing information from several characters is preferred.  Many of these indices,  including their
statistical strengths and weaknesses, are discussed by Palmer and Strobeck  (1986).  Leary and Allendorf (1989)
note that relatively sedentary organisms (closely associated with a local  environment) and ectotherms (whose
development may be more sensitive to environmental and genetic variation)  may be the best candidates for
measurement of FA. The recommended  interannual  sampling frequency is four or five years.

VARIABILITY:  The expected spatial variability of the fluctuating asymmetry index within a resource sampling
unit was not estimated because the index and species were  not explicitly defined; however, the relationship
of FA with character size is troubling, as variance increases with increasing  character size. Because differences
in FA among samples are usually small, confounding factors such as measurement error can be important
The expected temporal variability of demographic parameters during the index periods was not  estimated
because index periods  were not defined explicitly.

PRIMARY  PROBLEMS: Measurement error  may  be the largest obstacle  in discriminating differences in  FA
among populations; however, a rigorous quality management program  can keep such errors  to a minimum.

REFERENCES:

Leary,  R.F., and F.W. Allendorf.  1989.  Fluctuating asymmetry as an indicator of stress:  Implications  for
conservation biology.  Trends Ecol. Evolut 4:214-217.

Palmer, A.R., and C. Strobeck. 1986.  Fluctuating asymmetry: Measurement, analysis, patterns. Ann. Rev.
Ecol. Sys. 17:391-421.
                                                G-5

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APPENDIX G.2: INDICATOR FACT SHEETS FOR BIOMARKERS
                         Authors

                     John McCarthy
                     Lee R. Shugart
               Oak Ridge National Laboratory
               Environmental Sciences Division
                  Oak Ridge, Tennessee

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C.2.1  INDICATOR:  DNA Alteration:  Adducts

CATEGORY:  Exposure and  Habitat/ Biomarkers

STATUS:  Research

APPLICATION:  Recent field studies (Dunn et al. 1987; Varanasi et al. 1989; Stein et al. 1990) with benthic
fish  have begun  to  validate the use of DNA adducts (using the  32P-postlabeling assay  technique)  as  a
biomarker of exposure to genotoxic  compounds.    For example,  comparison of the levels of total hepatic
DNA adducts in  English  sole from Puget Sound, Washington, to  sediment levels of high-molecular-weight
polycyclic aromatic hydrocarbons revealed a general concordance  between these variables that suggests that
adduct counts appear to  be  reflective of the degree of exposure (Varanasi et al. 1989; Stein et al. 1990).

The  32P-postlabeling technique shows particular promise as a screening technique because it has a very low
limit of detection  (one adduct in 109-1010nucleotides) and does not require the characterization of individual
adducts before they are measured.  A further important advantage is that it is a nonspecific procedure that
can  detect a variety of bulky aromatic  adducts in  animals  exposed to complex mixtures of contaminants.
Although a positive response is indicative of exposure to chemical(s), with sufficient toxicological information
and  identification of particular adducts, data obtained by this technique may be  diagnostic of environmental
genotoxicity.

The  technique can be implemented immediately with  little or no lag time; however, only a few "dedicated"
laboratories are currently available to perform  this  type of analysis.  The geographical  range of test species
within a  resource class must be considered.

INDEX PERIOD:  No temporal limitations are known to exist; however, no  sampling period during the year
is known to have minimal temporal variability  in adduct measures.

MEASUREMENTS:  DNA adducts, enzymatically tagged with a radiolabeled component [33P], are separated
by thin-layer chromatography  (TLQ,  detected  by  autoradiography, and quantified  by Cerenkov counting
(Randerath et al. 1981; reviewed by Watson (1987]). In this  procedure (summarized by Gupta and  Randerath
[1988]), DNA is enzymatically hydrolyzed to 3'-monophosphates of normal DNA nucleotides  and adducts.
The  adducts are then enriched relative  to the  normal nucleotides, 32P label is incorporated (leading to [5'-
32P]-3',5'-biphosphates), and the remaining normal nucleotides and adducts are separated by multidimensional
TLC. Finally, the adducts are detected by autoradiography and quantitated by scintillation counting. Sample
collection times for a suite of biomarker indicators  is 0.5-1.0 day at each  resource sampling unit for two to
three technicians.  The estimated laboratory analysis cost for DNA adducts  is  $150-$200 per sample.

VARIABILITY:  The expected spatial variability of adduct measures within a resource  sampling unit and  their
temporal variability throughout the year were not estimated.

PRIMARY PROBLEMS: Although the cost is moderate, the 32P-postlabeling assay is currently more laborious
than other biomarker-type assays, requires substantial training of personnel before it can be routinely used,
and  involves the use of nigh-specific-activity 32P, which  necessitates  the  use of special precautions to minimize
exposure to radioactivity. Additionally, this technique is semiquantitative, and generally the  procedure for its
use varies from one laboratory to another.  Finally, in  TLC chromatograms of DNA from  organisms exposed
to complex mixtures of contaminants, a radioactive zone is routinely observed,  which  appears to represent
multiple overlapping adducts and makes it difficult  to  relate individual spots (adducts) to specific  chemicals.
Recent advances in chromatographic  conditions (Randerath et al. 1989), however, suggest that the resolution
of multiple adducts can  be  increased, which should  aid in characterizing  individual adducts in  organisms
                                                G-8

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exposed  to  unknown mixtures of chemicals, and thus may  increase the chemical specificity of the "P-
postlabeling assay.

A recent study by Kurelec et al. (1990) illustrates, however, the need for further in-depth field studies of DNA
adducts as a biomarker of exposure to genotoxic compounds.  In this study, five species of fish exhibited
several qualitatively similar adducts irrespective of whether the fish were sampled from apparently unpolluted
or polluted sites.   These  findings emphasize  the need  to  conduct future field  validation  studies  that
incorporate  additional measures  of contaminant  exposure in individual  organisms  in  order to  clearly
demonstrate that the levels of DNA adducts are related to exposure.  Furthermore, these studies illustrate a
disadvantage of  the 32P-postlabeling assay, in that careful selection of appropriate control sites is  a  critical
factor in the current use of this technique for measuring DNA adducts.

REFERENCES:

Dunn, B., J. Black, and A. Maccubbin.  1987.  32P-postlabeling analysis of aromatic DNA adducts  in fish
from polluted areas.   Cancer Res.  47:6543-6548.

Gupta,  R.C.,  and  K. Randerath.   1988.   Analysis  of  DNA adducts  by 32P-labeling  and thin layer
chromatography.  Pages 399-418.  In:  E. Friedberg and P.M.  Hanawalt,  eds.  DNA  Repair, Vol. 3.  Marcel
Dekker, Inc., New York.

Kurelec, B., M.  Chacko,  S. Krca, A. Garg, and R.C. Gupta.  1990,  DNA adducts  in marine  mussels and
freshwater  fishes.   In:   J.F.  McCarthy and L.R. Shugart, eds.   Biological Markers  of   Environmental
Contaminants.  Lewis Publ.  Inc., Chelsea, Ml.  In press.

Randerath, K., M. Reddy, and R.C. Gupta.  1981.  "P-postlabeling analysis for DNA damage.  Proc. Natl.
Acad. Sci. USA 78:6126-6129.

Randerath, K., E. Randerath, T.F.  Danna, K.L. van Golen, and K.L. Putnam.  1989. A new  sensitive 32P-
postlabeling assay based  on  the  specific enzymatic conversion  of bulky  DNA lesions  to   radiolabeled
dinucleotides and nucleoside 5'-monophosphates.  Carcinogenesis 10:1231-1239.

Stein, J.E., W.L. Reichert,  M. Nishimote,  and U.  Varanasi.  1989. "P-postlabeling of DNA:  A sensitive
method for assessing environmentally induced genotoxicity. Oceans 89.  In press.

Varanasi, U., W.L Reichert, and J. Stein.  1989.  "P-postlabeling analysis of DNA adducts in  liver of wild
English sole (Parophrys vetulus) and winter flounder (Pseudop/euronectes amer/canus).  Cancer Res. 49:1171-
1177.

Watson, W.P.  1987.  Post-radiolabeling for detecting DNA damage. Mutagenesis 2:319-331.
                                                C-9

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G.2.2  INDICATOR:  DMA Alteration:  Secondary Modification

CATEGORY:  Exposure and  Habitat/ Biomarkers

STATUS:  Research

APPLICATION:  Numerous toxic chemicals cause strand breaks in DNA, either directly or indirectly which
causes an unwinding or secondary modification of the DNA molecule.  The alkaline unwinding assay can
estimate  the  increase  in the  level  of breaks above background that  result from  exposure to toxins.  The
technique can be applied to the analysis of many samples without the need for costly reagents or laboratory
equipment   For field  studies, laboratory analyses  are performed on live animals or frozen tissues.   Data is
available within a few hours and is best interpreted along with data collected from other biomarkers.  The
method is ideally suited as a screening technique for the routine in situ monitoring of environmental species
because the  analysis is easy and its cost is low.  A positive  result can  be seen as a "red flag," because in
theory, exposure to any genotoxic  chemical will elicit such a response.

Using this method, Shugart (1988a,b) has demonstrated that  alkaline  unwinding is significantly faster in  the
DNA of  fish  that were chronically exposed to  genotoxic agents than in control  fish.  Additionally, it was
shown that fish DNA from polluted  areas unwound faster than DNA of fish from nonpolluted areas, indicating
sensitivity to  xenobiotic substances in their environment (Shugart 1990).  In addition, analyses have been
conducted in numerous environmental species including oysters and mussles (Nacci and Jackim 1990), desert
rodents (Shugart 1989), and turtles (Meyers et al.  1988).

The method  is sensitive, amenable to routine laboratory analyses, and  immediately available for field studies.
The geographical range of test species within  resource classes must be considered.

INDEX PERIOD:  No temporal limitations are known to exist; however, no sampling period during the year
is known  to  have minimal temporal variability for measuring  this indicator.

MEASUREMENTS: Alkaline unwinding is a sensitive analytical technique which has previously been used in
cell  cultures  to detect and  quantify DNA strand breaks  induced by  physical and chemical carcinogens
(Ahnstrom and Erixon 1980;  Kanter and Schwartz  1979, 1982; Daniel et al. 1985). To assess the level of
DNA strand breaks in environmental species intact, highly polymerized  DNA is required  (Shugart 1988a,b).
DNA isolation is accomplished by homogenizing appropriate tissue in 1 N NH4OH/0.2% Triton X-100.  DNA
is  recovered  by differential  extraction with chloroform/isoamyl alcohol/phenol  (24/1/25-v/v) and  passage
through a molecular sieve column (Sephadex C50).  Strand breaks are measured in the isolated DNA by an
alkaline unwinding assay (Kanter and Schwartz  1979, 1982; Shugart 1988a,b).  The technique is  based on
the time-dependent partial alkaline unwinding of  DNA followed by determination of the duplex:total DNA
ratio (F value).  Because DNA unwinding takes place at single-strand breaks within  the molecule, the amount
of double-stranded DNA remaining after a given period of alkaline unwinding will be  inversely proportional
to the number of strand  breaks  present at the  initiation of the alkaline exposure,  provided renaturation is
prevented. The amounts of these two types of DNA are quantified by  measuring the fluorescence that results
with bis-benzimidazole - Hoechst dye #33258.  Test response is quite sensitive to toxins, and changes are
readily discerned, provided proper baseline or reference data is available.

Rydberg (1975) has established  the theoretical background for  estimating strand breaks in DNA by alkaline
unwinding, which is summarized by the equation:

                                          In F =  -(K/M)(tb)
                                                G-10

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where K is a constant, t is time, M is the average molecular weight between two breaks, and b is a constant
less than 1 which is influenced by the conditions for alkaline unwinding.

The  relative number of DNA strand breaks  (N value) of an organism from a control site can be compared
to that from a reference site as follows (Kanter and Schwartz, 1982; Shugart, 1988a,b):

                                       N =  (In Fc/ln Fr) - 1

where Fc and  Fr are the mean  F values of  DNA from the control site and reference site, respectively.  N
values greater  than zero indicate that DNA  from the sampled sites has more strand breaks than  DNA from
the reference site; for example, an N value of 5  indicates five times more strand breakage.

Sample collection times for a set of biomarker indicators is 0.5-1.0  day at each resource sampling unit for
two  to three technicians.  The estimated laboratory analysis cost for alkaline unwinding is $25 a sample.

VARIABILITY:   Existing data suggest the spatial variability  of this indicator within a resource sampling unit
would be low. Its expected temporal variability throughout the year was not estimated.

PRIMARY PROBLEMS:  No major problem  is known to exist that would prevent its immediate  use  in the
field.

REFERENCES:

Ahnstrom  G.,  and  K.  Erixon.   1980.   Measurement of strand  breaks by  alkaline  denaturation and
hydroxyapatite chromatography. Pages 403-419.  In:  E.G. Friedberg and P.C. Hanawalt, eds. DNA Repair,
Vol.  1, Part A.  Marcel Dekker, Inc.,  New York.

Daniel, F.B., D.L. Haas, and S.M. Pyle. 1985.  Quantitation of chemically induced DNA strand breaks in
human cells via an alkaline unwinding assay.  Anal.  Biochem. 144:390-402.

Kanter, P.M.,  and  H.S. Schwartz.  1979.   A hydroxylapatite batch assay for quantitation of cellular DNA
damage.  Anal. Biochem. 97:77-84.

Kanter, P.M.,  and  H.S. Schwartz.   1982.   A fluorescence enhancement assay for cellular DNA  damage.
Molec. Pharmacol.  22:145-151.

Meyers, L.J., LR. Shugart, and B.T. Walton.  1988. Freshwater turtles as indicators of contaminated aquatic
environments.   Paper presented at the 9th Annual Meeting of the Society of Environmental Toxicology and
Chemistry,  November 15, Arlington,  VA.

Nacci, D.,  and G. jackim.  1990.  Using  the DNA alkaline unwinding assay to detect DNA damage in
laboratory and environmentally  exposed cells and tissue.  Mar. Environ. Res.  In press.

Rydberg, B. 1975.   The rate of strand separation in  alkali of DNA  of irradiated mammalian cells. RadiaL
Res.  61:274-285.

Shugart,  LR.   1988a. An alkaline unwinding assay for the detection of DNA damage in aquatic  organisms.
Mar. Environ. Res. 24:321-325.

Shugart,  LR.   1988b. Quantitation of chemically induced  damage to DNA of aquatic organisms by alkaline
unwinding assay. Aquat. Toxicol. 13:43-52.

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Shugart,  L R.  1989.  Personal communication.  Oak Ridge National Laboratory,  Environmental Sciences
Division,  Oak Ridge, TN.

Shugart,  LR.  1990.  Biological monitoring:  Testing for genotoxicity.  In:  J.F. McCarthy and L.R. Shugart,
eds.  Biological Markers of Environmental Contaminants.  Lewis Publ. Inc., Chelsea, Ml.  In press.
                                                G-12

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G.2.3  INDICATOR:  DNA Alteration:  Irreversible Event

CATEGORY:   Exposure and  Habitat/ Biomarkers

STATUS:  Research

APPLICATION: The measure of irreversible DNA alteration is a screening technique that indicates subclinical
expression of mutagenic damage.

INDEX PERIOD:  No constraints on the sampling period are recognized.  Because the period with minimum
temporal variability is unknown, no index period has been suggested.

MEASUREMENTS:  Flow cytometry (FCM)  measures various cellular variables  in  suspended cells (Shapiro
1988).   Measurable variables  include  levels of  DNA, RNA,  protein, and  specific  chemicals  (using
immunofluorescent probes), and numerous morphological attributes that affect time of flight and various light-
scatter parameters.  Some flow cytometers can analyze as  many as eight parameters from 10,000 cells a
second.  Cell-sorting capabilities are available on many flow cytometers.

The application of flow cytometry to the study of  environmental  mutagenesis was reviewed by Bickham
(1990).  The  primary parameter of interest in such studies is DNA content, which can be measured with a
high degree of precision and accuracy.  Laboratory challenge experiments  have shown  that exposure to
mutagenic chemicals and ionizing radiation result in  a broader nuclear or chromosomal DNA distribution; a
positive relationship  between exposure and a broader distribution exists, both in vivo (Bickham  1990) and
in vitro (Otto et al. 1981).  Bickham (1990) concluded that FCM  is  a highly sensitive assay for the effects of
environmental mutagens.  Advantages of FCM over other cytogenetic and cytometric techniques include lower
cost, greater rapidity, greater  sensitivity due to the vast  number of cells analyzed, and tremendous diversity
of application to which  FCM  is  suitable.  For example,  virtually  any  tissue can be examined (whereas
chromosomal assays are limited to rapidly proliferating tissues such as bone marrow), so the effects of organ-
specific mutagens can be investigated.  With  the use of multiparameter analysis,  specific cell types can be
differentiated  and  analyzed.   Moreover, FCM is easily adapted for use on species in which chromosomal
analysis is difficult (Bickham et  al. 1988;  Lamb et a!. 1990).

FCM has also identified a potential qualitative difference in the response  of animals to chronic environmental
and acute laboratory mutagen  exposure.  Aneuploid mosaicism  was observed in animals exposed at low
frequency to  environmental mutagens in  each of three studies (McBee  and  Bickham  1988; Bickham et al.
1988; Lamb et al.  1990).  Such  mosaicism was not observed in animals from control sites or  in animals
exposed  to acute  laboratory  doses (Bickham  1990).  This demonstrates the capability of  FCM  to identify
multiple populations of cells that might have subtle differences in DNA content and to identify low-frequency
variant cells.

For use as an initial screening procedure,  FCM has tremendous potential because of low cost and high
sensitivity.  Hundreds of  thousands of cells from scores of individuals can be analyzed quickly, in a matter
of a few  days if necessary.  This techniques can be useful both in the initial screening for effects  and  in the
subsequent evaluation of the level of damage of environmental mutagens. FCM can also be  used to evaluate
almost any species and tissue type, so the degree of impact of an environmental  insult can be investigated.
Sample collection times for a set of biomarker indicators  is 0.5-1.0 day at each resource sampling unit for
two to three technicians.   The  estimated  laboratory  analysis cost  for flow cytometry is $25-$75 a sample.

VARIABILITY:  FCM  has been extensively validated as a laboratory procedure for  evaluating acute exposure
to mutagenic  chemicals.  Field studies have demonstrated the efficiency of FCM in measuring the effects of
chronic exposure  to chemical pollutants (McBee and Bickham 1988) and low-level radioactivity (Bickham et

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al. 1988; Lamb et al. 1990).  The expected spatial variability of this indicator within a resource sampling unit
and its expected temporal variability throughout the year were not estimated.

PRIMARY PROBLEMS:  No  major problem  is known that would prevent its  immediate use.

REFERENCES:

Bickham, J.W., B.G. Hanks, M.J. Smolen, T. Lamb, and j.W. Gibbons. 1988.  Flow cytometric analysis of
the effects of low level radiation exposure on natural populations of slider turtles (Pseudemys scripta). Arch.
Environ. Contam. Toxicol. 17:837-841.

Bickham, J.W.  1990.  Flow cytometry as a technique to  monitor the effects of environmental genotoxins
on wildlife populations.  In:   S. Sandhu, ed.  First Symposium on In Situ Evaluation  of Biological Hazards of
Environmental  Pollutants.  Environmental Research  Series, Plenum  Press, New York.  In press.

Lamb,  T., J.W. Bickham, j.W. Gibbons, M.j. Smolen, and S. McDowell.  1990.   Genetic damage  in a
population of slider turtles (Trachemyus scripta) inhabiting a radioactive reservoir.   Environ. Mol. Mutagen.
In press.

McBee, K., and J.W. Bickham.  1988.  Petrochemical-related DNA damage in wild rodents detected by  flow
cytometry.  Bull. Environ. Contam. Toxicol. 40:343-349.

Otto, F.J., H. Oldiges, W. Gohde, and V.IC Jain.   1981.   Flow  cytometric  measurement  of nuclear DNA
content variations as a potential in vivo mutagenicity test  Cytometry 2:189-191.

Shapiro,  H.M.  1988. Practical Flow Cytometry, 2nd Edition.  Alan R. Liss, Inc., New York.  353  pp.
                                              G-14

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C.2.4  INDICATOR:  Cholinesterase  Levels

CATEGORY:  Exposure and Habitat/  Biomarkers

STATUS:  Research

APPLICATION:  The diagnosis of exposure to neurotoxic chemicals such as organophosphates and carbamates
(insecticides) usually  relies on  the measurement of acetylcholinesterase (ACh-ase) activity, because inhibition
of this critical enzyme is the mechanism by which these agents exert their neurotoxic effect Measurement
of ACh-ase  activity not only monitors physiological response; the technique is also diagnostic  because the
enzyme activity can be compared to results from previous studies on the sublethal and lethal effects of these
neurotoxic chemicals  in a variety of vertebrates and invertebrates.  Use of brain tissue is considered the most
reliable approach  because  inhibition  most  closely correlates with  other toxic effects,  including mortality.
However, nondestructive, sequential sampling can be accomplished in a single individual by examining blood
for ACh-ase activity.   Such repeated measures can  be  useful in a field situation to document exposure and
subsequent  recovery.  It is anticipated that the degree of ACh-ase depression from normal levels could be
used as an  integrative, functional,  nondestructive measure of exposure.

There is extensive literature available that is useful for interpreting ACh-ase activity data in a variety of species
(e.g., Fairbrother et a!. 1989).  The biomarker has  been extensively  field tested.

INDEX PERIOD: An important consideration is the effect of generally short half-lives of organophosphorous
compounds  and carbamates (in the environment and in biological tissues) on the duration of ACh-ase activity.
Seasonal effects also are a  factor.

MEASUREMENTS: ACh-ase activities  are measured in brain tissue and blood plasma.  Ellman et al. (1961)
is a generally cited reference describing the ACh-ase assay that is currently  undergoing the American  Society
for Testing and Materials (ASTM) standardization process. For monitoring avian and fish exposures,  greater
than 20% inhibition of ACh-ase activity has been used as an index for significant exposures and  greater than
50% inhibition as indicative of lethal  exposures.  Sample collection  times for a set of biomarker indicators
is 0.5-1.0 day for each resource sampling unit for two to three technicians.  The estimated laboratory analysis
cost for measuring ACh-ase activity is $25-$75 a sample.

VARIABILITY: ACh-ase activity can be affected by physiological factors, and these need to be considered in
interpreting  data. The expected spatial variability of Ach-ase measures within a resource sampling unit and
their temporal variability during the year were not estimated.

PRIMARY PROBLEMS:  No major  problems are recognized.

REFERENCES:
Ellman, C.L.,  K.D. Courtney, V. Andres, and  R.M. Featherstone.   1961.  A new and rapid  colorimetric
determination of acetylcholinesterase activity.  Biochem.  Pharmacol.  7:88-95.

Fairbrother, A., R.S.  Bennett, and J.K.  Bennet.   1989.  Sequential sampling of  plasma cholinesterase in
mallards as an  indicator of exposure to cholinesterase  inhibitors.  Environ.  Toxicol. Chem. 8:117-122.
                                                G-15

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C.2.5  INDICATOR:  Metabolites of Xenobiotic Chemicals

CATEGORY:   Exposure and Habitat/ Biomarkers

STATUS:  Research

APPLICATION:  The  identification of certain metabolites  of xenobiotic chemicals in animals confirms that
toxicants have entered cells and interacted with molecular targets; in this way, supporting evidence can be
provided that a population response is attributable to biochemical stress from xenobiotic compounds.  These
metabolite biomarkers can be used to assist in such diagnoses since the nature and proportions of metabolites
of xenobiotic chemicals in various tissues have been extensively studied (Creaven et al 1965; Lee et al. 1972;
Melancon and Lech 1976; Krahn and Malins 1982).

The feasibility of using xenobiotic metabolite  formation as a  biomarker depends on the sensitivity  of the
analytical  methods employed for their detection and quantitation.  The presence of such metabolites may be
assessed by detection  and quantitation of free and conjugated metabolites in tissues, body fluids, or excreta.
Determination of the metabolites of poiycyclic aromatic  hydrocarbons (PAHs) in tissues  and of PAHs and
chlorinated phenols in  bile of fish as a biomonitoring method is currently ready for use in environmental
monitoring (Lee  et al.  1972; Krahn and Malins 1982).

INDEX PERIOD:  If measurements of metabolites are to  be undertaken in wild populations of organisms,
initial sampling efforts must be designed so that temporal variability throughout  the year can be tested.  Also,
variability in  feeding  times (e.g., in  the  case  of  biliary metabolites),  sex,   maturity, and environmental
temperatures are ancillary  factors that must be considered, any of  which may dictate  a  stratified  sampling
program.  Several treatments of environmental sampling design are available  that can be  used to help in
design of  a statistically sound sampling strategy.

MEASUREMENTS:  Most  of the analytical  procedures used for measuring free and conjugated metabolites
involve chromatographic techniques including gas chromatography and high-pressure liquid chromatography
(with or without enzymatic hydrolysis).  A limitation of many of these  procedures is the lengthy preparation
time  required before the  sample is subjected  to analysis.  Thus,  the  efficiency and  cost-effectiveness of
metabolite biomarkers could be significantly improved  by developing procedures such as an immunoassay for
sensitive, rapid measurement of metabolites in  a  large number of samples.

A variety of factors, including  reproductive state, temperature, and  dietary status, can influence metabolite
production.  The influence of various factors on metabolite proportions  has been the focus of more limited
studies.  For  example, during  feeding,  the bile and its associated metabolites  are discharged from the gall
bladder.   In  females  during  egg production,  a number of  biochemical changes  occur that can  affect
production of metabolites.  These include steroid-synthesizing cytochrome P-450 isozymes that can  affect
oxidation  rates of xenobiotics and types of metabolites produced.  Also, there is increased lipid  synthesis
needed for egg production, which may facilitate metabolite production.  Eggs may sequester certain types of
metabolites.  Ancillary measures  of these influential factors  must be  made to account for their effects on
metabolite production.

Sample collection times for a set of biomarker  indicators is 0.5-1.0  day for each resource sampling unit for
two to three technicians. The estimated laboratory analysis cost for metabolite measures is $25-75 a sample.

VARIABILITY:  The expected spatial variability of metabolite measures within a  resource  sampling unit was
not estimated. The expected temporal variability of metabolites was not estimated because the index  period
was not defined;  however, the  temporal variability  could  be  significant  because  of the  relatively rapid
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pharmacodynamics  of  many metabolites.   Nevertheless, field trials have  demonstrated clear statistical
differences between exposed and unexposed populations (Melancon and Lech 1976; Krahn and Malins 1982).

PRIMARY PROBLEMS:  Species-specific information is needed to expand  the  utilization of metabolites as
biomarkers beyond fish and the aquatic environment  In addition, if metabolites are to be used as biomarkers
of effect, more information is needed to relate the presence of specific metabolites of xenobiotics in organisms
to toxic effect

REFERENCES:

Creaven, P.J., D.V. Parke, and R.T. Williams.  1965. A fluorometric  study of the hydroxylation of biphenyl
in vitro by liver preparations of various species.  Biochem. J. 96:879-885.

Krahn, M.E., and D.C. Malins.   1982. Gas chromatographic-mass spectrometric determination of aromatic
hydrocarbon metabolites from livers of fish  exposed to fuel oil.  J. Chromatogr. 248:99-107.

Lee, R.F., R. Sauerheber, and C.H. Dobbs.  1972.  Uptake,  metabolism and discharge of polycyclic aromatic
hydrocarbons by marine fish. Mar. Biol.  17:201-208.

Melancon, M.J., and J.). Lech.  1976.  Isolation and identification of a polar metabolite of tetrachlorobiphenyl
from bile of rainbow trout exposed to 14C-tetrachlorobiphenyl.  Bull. Environ.  Contam. Toxicol. 15:181-188.

Melancon, M.J., Jr., and  J.J. Lech.   1979.   Uptake,  biotransformation, disposition and elimination of
2-methylnaphthalene and naphthalene in several fish species.  Aquat. Toxicol. 667:5-22.

BIBLIOGRAPHY:

Oikari, A.O.J.  1986.   Metabolites of xenobiotics  in the bile  of fish  in  waterways polluted  by  pulpmill
effluents.  Bull. Environ. Contamin. Toxicol. 36:429-436.

Varanasi,  U., D.J.  Cmur, and  PA. Tressler.   1979.   Influence  of time and  mode of  exposure  on
biotransformation  of naphthalene by  juvenile starry flounder (Platichthys flesus)  and rock  sole (Lepidopsetta
bilineata).  Arch. Environ. Contam. Toxicol.  8:673-692.

Varanasi,  U., J.E. Stein, N. Nishimoto, and T. Horn.  1982. Benzo[a]pyrene metabolites in  liver,  muscle,
gonads and bile of adult English sole.   Pages 1221-1234.  In:  Polynuclear Aromatic Hydrocarbons:  Seventh
International Symposium on Formation, Metabolism  and  Measurement  Battelle Press,  Columbus, OH.
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C.2.6  INDICATOR:  Porphyrin Accumulation

CATEGORY:  Exposure  and Habitat/ Tissue Concentration

STATUS:  Research

APPLICATION:  Heme biosynthesis is a vital process for animals to maintain an adequate blood cell count,
because the heme molecule is the building block for blood cells.   When a chemical is  known  to  have a
specific effect on heme biosynthesis, abnormalities  of porphyrin metabolism  may provide a method for
assessing exposure (Elder and Urquhart 1987).  Conversely, patterns of porphyrin accumulation in tissues and
excreta may be used to predict the sites of action of chemicals within  the pathway of heme biosynthesis
(Marks 1985).  Thus the analyses of porphyrins may be used in a diagnostic manner.

Chlorinated aromatics such  as polychlorinated biphenyls  (PCBs) and heavy metals  such as  Pb may  disturb
porphyrin  metabolism in mammals and birds.  In chemically induced porphyrias, these chemicals or their
metabolites modify the activity  of one or more of the enzymes involved in heme  biosynthesis, resulting in
an  alteration  in the size and/or composition  of  the porphyrin pool  (Goldstein et al. 1973; Strik  1979).
Available evidence  in birds suggests that porphyrins are promising as a biomarker in field studies.  This
biomarker is currently accepted  as a biomarker in human studies.

INDEX PERIOD:  No temporal limitations are known to exist;  however,  no index period  during the year is
known to have minimum temporal variability.

MEASUREMENTS:  Analysis involves homogenizing  the  liver  in 3  N HCI to extract the  porphyrins and
determining individual protoporphyrins by their fluorescence.   Uroporphyrin can  be determined directly on
the HCl extract by its specific fluorescence.  The spectrum of protoporphyrins present can  be determined  by
high-pressure liquid chromatography with fluorescence detection.

An  example of the  use  of porphyrins in ecological studies is exposure of mallard ducks to  Pb.  Pb  inhibits
the activity of heme synthetase, the enzyme responsible for incorporating Fe into protoporphyrin IX to form
heme.  As a result, protoporphyrin accumulates in the  peripheral  blood, where it can be  measured by a
simple fluorescence technique.   Using a hematofluorometer, Roscoe et al. (1979) reported  increased levels
of protoporphyrin in a single drop of untreated blood following administration  of Pb shot to mallard ducks.
Following  the administration of 1 to 18 number 4 Pb shot, the blood concentrations  of  protoporphyrin IX
were  related  to clinical  signs  of  Pb poisoning.   Although  death  corresponded to  protoporphyrin  IX
concentrations >800 jig/dL  in penned ducks,  lesser concentrations would probably lead to death in the wild.
For a study of Pb exposure in two types  of pen-reared and wild ducks,  toxicity and lethality corresponding
to much less elevated blood protoporphyrin IX concentrations (<800 jLig/dL) are reported (Rattner et  al.
1989).

Sample collection times for  a set of biomarker indicators  is 0.5-1.0 day for each  resource sampling unit for
two to three technicians. The estimated laboratory analysis cost for porphyrin measures is $25-$75 a sample.

VARIABILITY: A study of herring gulls from  the Great Lakes may serve as an example of the spatial variability
of porphyrin  measures  within  a resource  sampling  unit  Fox et al. (1988)  have shown  that  gulls from
contaminated areas have considerably higher concentrations of highly  carboxylated porphyrins in liver than
gulls from "clean" areas.  In  the areas studied, the frequency of levels  >10 times the median of the  control
(clean) values ranged from  22  to 100%.   The expected temporal variability for porphyrin  in animal liver
throughout the year was not estimated.
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PRIMARY PROBLEMS:  Information on species other than birds will enable further utilization of porphyrins
in ecological monitoring programs.

REFERENCES:

Elder, C.H. and A.J. Urquhart.  1987.  Porphyrin metabolism as a target of exogenous chemicals.  Pages
221-230.  In:   V.  Foa, E.A. Emmett, M. Maroni, and A. Colombi, eds.  Occupational and Environmental
Chemical Hazards:  Cellular and Biochemical  Indices for Monitoring Toxicity.  Wiley-lnterscience, New York.

Fox, G.A., S.W. Kennedy, R.J. Norstrom, and  D.C. Wigfield.  1988. Porphyria in herring gulls: A biochemical
response to chemical contamination of Great Lakes food  chains.   Environ. Toxicol. Chem. 7:831-839.

Goldstein, J.A., P. Hichman, H. Bergman, and J.G. Vos.  1973.  Hepatic porphyria  induced  by 2,3,7,8-
tetrachlorodibenzo-p-dioxin in the mouse.  Res.  Commun. Chem. Pathol. Pharmacol. 6:919-929.

Marks,  G.S.   1985.  Exposure to toxic agents:  The heme  biosynthetic pathway and  hemoproteins as
indicator.  CRC Crit Rev. Toxicol. 15:151-179.

Rattner, B.A., W.J. Fleming, and C.M. Bunck.  1989.  Comparative toxicity of lead shot  in black ducks (Anas
rubripes) and  mallards (Anas platyrhynchos).  J. Wildlife Dis. 25:175-183.

Roscoe, D.E.,  S.W. Nielson, AA.  Lamola, and D. Zuckerman.   1979.  A  simple  quantitative test for
erythrocytic protoporphyrin  in lead-poisoned ducks. J. Wildlife Dis. 15:127-136.

Strik, J.J.T.WA.  1979. Porphyrins  in urine as an indication of exposure to chlorinated  hydrocarbons. Ann.
N.Y. Acad. Sci. 390:308-310.
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G.2.7  INDICATOR:  Histopathologic Alterations

CATEGORY:   Exposure and Habitat/ Biomarkers

STATUS:  Research

APPLICATION:  Histopathological alterations are most valuable as an indicator of exposure to a variety of
anthropogenic pollutants.   Because the alterations occur after physiologic or biochemical perturbation,  the
responses may be thought of as integration  of molecular changes at the cell, tissue, and organism level.  This
indicator is distinguished from (response) indicators of gross  pathology  in that its measures are not highly
perceptible to the  unaided eye.   The utility of histopathological alterations as biomarkers is most studied in
teleost fish, but changes in tissues and cells occur in all vertebrates and invertebrates, and laboratory studies
of histopathological  consequences  of  toxic exposure  are  well  documented.  No  resource  specificity  or
geographic limitations are apparent.

The  techniques  used to  monitor this indicator are ready for field use.   Considerable testing has  been
completed in  the  laboratory, but an  inadequate application  to  field studies is the major cause of lack of
historical data.  The Status and  Trends Program (mussel watch) of the U.S.  National Oceanographic and
Atmospheric Administration  (NOAA) and limited  monitoring efforts attest to the utility of these approaches.

INDEX PERIOD:  Although  season-related variation exists, no specific sampling window during the year was
proposed.

MEASUREMENTS:   Extensive  methodology exists for the determination of tissue, cellular and subcellular
responses.   New  plastic embedment procedures improve resolution without  appreciably increasing cost
Histopathologic measures  demonstrated to  be useful as biomarkers include the following.

    •   Hepatocellular necrosis and  sequelae:  This includes coagulative  necrosis associated with
        exposure to anthropogenic environmental toxicants  in  both  mammals and  fish (Wyllie et at.
        1980;  Meyers  and Hendricks 1985;  Popper 1988),  regenerative hyperplasia indicative of
        extensive  prior necrosis, and  bile  ductular/ductal hyperplasia,  a  lesion  of chronic  duration
        consistently found  in wild fish  from impacted sites  (May et al. 1987).

    •   Spongiosis hepatis: this results from fibroblastic transformation of Ito cells  (Yamamoto et al.
        1986) and observed in winter flounder of coastal New England, English sole in Puget Sound, and
        in fishes collected from  impacted sites in the Kanawha River  of West Virginia.

    •   Hepatocytomegaly:  enlarged  hepatocytes seen as  an  early change  in  English sole  of Puget
        Sound, Washington (Myers et al. 1987), in sea pen cultures of Atlantic salmon in Puget Sound
        (Kent  et al. 1988), and in livers of pond-cultured fingerling striped bass (Croff  1989), or as a rare
        form of chronic swelling  of endoplasmic reticulum cisternae encountered  in  high prevalence in
        winter flounder of Boston Harbor and  nearby estuaries  (Murchelano and Wolke 1985).

    •   Foci of staining alteration: an early stage in the spectrum of lesions seen  between normal and
        tumor-bearing liver that have been associated  with eventual tumor  formation (Hendricks et al.
        1984).

    •   Liver neoplastic lesions: examples are hepatic adenoma, hepatocellular carcinoma, cholangioma,
        and cholangiocarcinoma.
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Although somewhat subjective, user-oriented computer software for the quantification of lesions exists.  When
applied to characterize magnitude of response, data amenable to statistical evaluation are obtainable.  Sample
collection times for a set of biomarker indicators is 0.5-1.0 day for each resource sampling unit for two to
three technicians.  The estimated laboratory analysis cost for histopathologic measures is $50-$100 a sample.

VARIABILITY:  Responses are easily recognized provided that proper reference and control data are available.
Physiologic and sex-related  variation  exists  and must  be  taken  into account, but should not  prevent  the
immediate application of histopathologic biomarkers because normal variation is at cell and subcellular level
of organization, whereas effective biomarkers involve tissue components.  The expected spatial variability of
hispathologic alterations within a  resource  sampling  unit and  the expected temporal variability  of these
alterations  during the index  period were not estimated.

PRIMARY PROBLEMS:  An  experienced histologist is required for proper interpretations of slides.

REFERENCES:

Boyer J.L., J. Swartz, and N. Smith.   1976.  Biliary secretion in elasmobranchs:  II.  Hepatic uptake and
biliary excretion of organic anions. Am. J. Physiol. 230:974-981.

Gingerich, W.H.   1982.    Hepatic toxicology of fishes.   Pages  55-105.   In:   L. Weber,  ed.  Aquatic
Toxicology.  Raven Press,  New York.

Groff, J. 1989. Personal Communication.  Telephone conversation with D. Hinton.  University of California,
Department of Medicine, School of Veterinary Medicine, Davis.

Hendricks, J.D., R.O.  Sinnhuber, M.C Henderson, et al.  1981.  Liver and kidney  pathology  in rainbow
trout (Sa/mo gairdneri)  exposed to dietary  pyrrolizidine (Senec/b)  alkaloids.  Exp.  Mol. Pathol.  35:170-183.

Hendricks, J.D., T.R. Meyers, and D.W.  Skelton.  1984. Histological progression of hepatic neoplasia in
rainbow trout (Sa/mo gairdneri).  Natl. Cancer InsL Monogr. 65:321-336.

Hinton, D.E., J.A. Couch, S.J. Teh, et al.  1988.  Cytological changes during progression of neoplasia in
selected fish species. Aquat. Toxicol. 11:77-112.

Hinton, D.E., and D.J. Lauren.  1990. Liver structural alterations accompanying chronic toxicity in fishes:
Potential biomarkers of exposure.   In:  L.R. Shugart and J. McCarthy, eds. Biomarkers of Chemical Exposure
in Fishes.  Lewis Publishing  Co., Chelsea,  Ml.  In press.

Kent, M.L, M.S. Myers, D.E. Hinton, W.D. Eaton, and R.A. Elston. 1988.  Suspected toxicopathic hepatic
necrosis and megalocytosis in pen-reared Atlantic Salmon Sa/mo sa/ar in Puget Sound, Washington, USA.  Dis.
Aquat. Organisms  49:91-100.

May, E.B., R.  Lukacovic, H. King, and M.M.  Lipsky.   1987. Hyperplastic and neoplastic  alterations  in the
livers of white  perch (Morone amer/cana) from the Chesapeake Bay. J.  Natl. Cancer  InsL 79:137-143.

Meyers,  T.R., and J.D. Hendricks.   1985.  Histopathology.  Pages 283-331.   In:   G.M. Rand and  S.R.
Petrocelli, eds.   Fundamentals of Aquatic Toxicology.   Hemisphere  Publishing Corp., Washington, DC.

Moon, T.W., P.J. Walsh, and T.P. Mommsen.  1985.  Fish hepatocytes: A model metabolic system.  Can.
J. Fish. AquaL Sci. 42:1772-1782.
                                                G-21

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Murchelano, R.A. and R.E. Wolke.  1985.  Epizootic carcinoma in the winter flounder (Pseudbp/euronectes
americanus).  Science 228:587-589.

Myers, M.S., LD.  Rhodes, and B.B. McCain.  1987.  Pathologic anatomy and  patterns of occurrence  of
hepatic  neoplasms,  putative preneoplastic lesions,  and other idiopathic  hepatic conditions  in  English sole
(Parophrys vetulus)  from Puget Sound, Washington.  J. Natl. Cancer InsL  78(2):333-363.

Popper,  H.   1988.   Hepatocellular degeneration  and death.  Pages 1087-1103.  In:   I.M.  Arias, W.B.
Jackoby,  H.  Poppe  et al., eds.   The Liver:   Biology and  Pathobiology,  Second Edition.   Raven Press Ltd.,
New York.

Schmidt, D.C., and LJ. Weber.  1973.  Metabolism and biliary excretion of sulfobromophthalein by rainbow
trout (Sa/mo ga/rdneri).  J. Fish. Res. Bd. Can. 30:1301-1308.

Segner,  H.,  and H. MOller.   1984.   Electron microscopical  investigations on  starvation-induced  liver
pathology in  flounders Platichthys flesus.  Mar. Ecol. Prog. Ser. 19:193-196.

Segner, H., and J.V. Juario.  1986.  Histological observations on the  rearing of milkfish (Chanos chanos)  by
using different diets. J.  Appl. Ichthyol. 2:162-173.

Segner, H.,  and T. Braunbeck.  1988.  Hepatocellular adaptation to extreme nutritional  conditions in ide,
Leuciscus idus melanotus L (Cyprinidae).  A morphofunctional analysis.  Fish  Physiol. Biochem.  5(2):79-97.

Stegeman, J.J.,  R.L Binder, and A. Orren.  1979.  Hepatic and extrahepatic  microsomal  electron transport
components and mixed-function oxygenases in the  marine fish Stenotomus versicolor.  Biochem. Pharmacol.
28:3431-3439.

Vaillant, C, C.  Le Cuellec, F. Padkel, et al.  1988.  Vitellogenin gene expression  in primary  culture of male
rainbow  trout hepatocytes. Gen. Comp.  Endocrin. 70:284-290.

van Bohemen,  C.G., J.G.D. Lambert, and J. Peute. 1981.  Annual changes in plasma and  liver in relation
to vitellogenesis in the female rainbow trout Sa/mo gairdneri. Gen. Comp.  Endocrin. 44:94-107.

Walton,  M.J., and  C.B. Cowey.  1982.  Aspects of intermediary metabolism in salmonid fish.   Comp.
Biochem. Physiol. 73B:59-79.

Wolke, R.E.,  R.A. Murchelano, CD. Dickstein, et al.  1985. Preliminary evaluation of the use of  macrophage
aggregates (MA) as fish health  monitors.  Bull. Environ. Contam. Toxicol. 35:222-227.

Wyllie, A.H., j.F.G. Kerr, and A.R. Cumi.  1980. Cell death:  The significance of  apoptosis.  Int. Rev. Cytol.
68:251-306.

Yamamoto,  K., PA Sargent, M.M. Fisher, et al.  1986.   Periductal fibrosis and lipocytes (fat-storing cells  or
Ito cells) during biliary atresia in the lamprey.  Hepatology 6:54-59.
                                               G-22

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G.2.8  INDICATOR:  Macrophage Phagocytotic Activity

CATEGORY:  Exposure and Habitat/ Biomarkers

STATUS:  Research

APPLICATION:  The immune system, in its capacity to destroy foreign material and protect the host against
disease, can serve as a useful sentinel of the health status of environmentally stressed organisms.   Several
responses have been used as measures of immune function and status, including lymphocyte mitogenesis
(Laudenslager et al. 1983; Spitsbergen et al. 1986), antibody-producing cell formation (Anderson et al. 1983),
antibody production (O'Neill 1981), and nonspecific ma
:rophage activity (Weeks et al. 1986, 1987, 1988;
Weeks and Warinner 1984; Wishovsky et al. 1990).  These and other elements of the immune system have
been shown to be affected (depressed or stimulated) by exposure to toxicants.  The nonspecific macrophage
activity  assays have  been  extensively field  tested,  primarily in  fish,  and  are  suited to a  screening-level
evaluation of an important component of the immune system.

This indicator has been tested in fish specimens obtained from contaminated and uncontaminated estuarine
waters and in  experimentally exposed animals; it is considered ready  for regional evaluation.

INDEX PERIOD:  Although field experience with this assay has been limited to fish specimens sampled during
the late spring through autumn  months (these fish species are unavailable during the winter months), the
assay method  is believed to be applicable during all  seasons.

MEASUREMENTS: Macrophage  activity  is evaluated by isolating macrophages and measuring either directly
by microscopically observing the active  uptake of foreign particulate matter (phagocytosis), or indirectly by
measuring the  chemiluminescence resulting from the  production  of reactive oxygen  intermediates  that
accompanies macrophage ingestion of foreign matter.   The macrophage  phagocytosis assay measures the
percentage of  macrophages capable of ingesting formalir-killed Escherichia coli during an incubation period
of 90-120 min at 15°C  The macrophage suspension i> washed and  placed on  microscope slides, which
are differentially stained and examined under oil immersion microscopy. It is easily and inexpensively carried
out,  requiring only  standard  laboratory equipment and techniques.   In the chemiluminescence  assay,
macrophages are  stimulated by using soluble (phorbol myristate acetate) or particulate (zymosan  or bacteria)
stimuli in the presence of luminol,  which enhances the emitted luminescence.  Photon emission is measured
with a liquid scintillation counter  or luminometer.  The  procedure is rapid (30-60 min), inexpensive,  and
easily performed.

Sample  collection times for a set of biomarker indicators  is 0.5-1.0 day for each resource sampling unit for
two to three technicians, ihe estimated  laboratory analysis cost for phagocytotic activity measures is $25-$75
per sample.

VARIABILITY:  The variability of phagocytotic activity among replicates from fish populations maintained in
the laboratory has been minimal  (approximate coefficient of variation was  25-30%).   The expected spatial
variability  of phagocytotic  activity within  a  resource sampling  unit and its expected temporal variability
throughout the year  were not estimated.

PRIMARY PROBLEMS:  Despite the considerable similarity in immune system functions across species, some
development work is necessary to test and validate these  assays for invertebrates and  for  mammals.  Further
research is  needed to develop more quantitative relationships that may permit  this assay to  be considered
a biomarker  of potential adverse effects.  To increase this biomarker's utility for diagnosing  chemically induced
disfunction of the immune system  component, laboratory studies  of fish exposed  to  selected contaminants
should be performed to evaluate the immumodulatory effects of individual  aquatic contaminants.
                                                G-23

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REFERENCES:

Anderson,  D.P., B. Merchant,  O.W.  Dixon, C.F.  Schott, and  E.F. Halo.   1983.   Flush exposure  and
injection  immunization of rainbow trout to selected DNP conjugates.  Dev. Comp.  Immunol. 7:261-268.

Cleland,  G.B.  and RA.  Sonstegard.   1987.   Natural killer ceil activity in rainbow trout (Sa/mo gairdneri):
Effect of  dietary exposure to Arochlor  1254 and/or  Mirex.  Can. J. Fisher. Aquat Sci. 44:636-638.

Evans, D.L, S.S.  Craves,  V.S.  Blazer, D.L  Dawe,  and J.B. Gratzek.   1984.   Immunoregulation of fish
nonspecific cytotoxic cell activity by retinolacetate but not poly I:C.  Comp. Immunol. Microbiol.  Infect Dis.
7:91-100.

Ghoneum, M., Faisall, M., G. Peters,  I.I. Ahmed, and E.L. Cooper.  1988. Suppression of natural cytotoxic
cell activity by social aggressiveness in  Tilapia.  Dev. Comp.  Immunol. 12:595-602.

Laudenslager, M.  L.,  S. M. Ryan, R. C. Drugan,  R. L.  Hyson, and S. F. Maier.   1983.  Coping and
immunosuppression:   Inescapable but not escapable shock suppresses lymphocyte proliferation.  Science
221:568-569.

O'Neill, J.G.  1981. Effects of intraperitoneai lead and cadmium on the humoral immune response of Sa/mo
trutta.  Bull. Environ. Contain.  Toxicol. 27:42-48.

Spitsbergen,  J.M.,  KA.  Schat, J.M.  Kieeman,  and  R.E.  Peterson.   1986.    Interactions  of  2,3,7,8-
tetrachlorodibenzo-p-dioxin (TCDD) with immune response of rainbow trout.  Vet, Immunol.  Immunopathol.
12:263-280.

Warinner, J.E., E.S. Mathews, and  BA Weeks.  1988. Preliminary investigations of the chemiluminescent
response in normal and   pollutant-exposed fish.  Mar. Environ. Res. 24:281-284.

Weeks, BA, and  J.E. Warinner.  1984.  Effects of toxic chemicals  on  macrophage phagocytosis in two
estuarine fishes.  Mar. Environ.  Res. 14:327-335.

Weeks, BA., j.E. Warinner, P.L Mason, and D.S. McGinnis.   1986. Influence of toxic chemicals on the
chemotactic response of fish macrophages.  J. Fish Biol. 28:653-658.

Weeks, BA., A.S. Keisler, J.E.  Warinner, and  E.S. Mathews.  1987. Preliminary evaluation  of macrophage
pinocytosis as  a technique to monitor  fish health.  Mar. Environ.  Res. 22:205-213.

Wishkovsky,  A., E.S. Mathews, and BA. Weeks.   1990.  Effects  of tributyltin on  the chemiluminescent
response of phagocytes from three  species of estuarine fish.   Arch. Environ. Contam. Toxicol.   In press.
                                               G-24

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G.2.9  INDICATOR:   Blood Chemistry

CATEGORY:  Exposure and  Habitat/ Biomarkers

STATUS:   Research

APPLICATION:  Blood chemistry assays basically evaluate performance of an animal's organ systems in vivo.
Direct assessment of organ  function is  sometimes  useful when other tests  are  ineffectual or cannot be
performed.  Circulating concentrations of biochemicals associated with the General Adaptation Syndrome are
a  function  of their secretion  into  and  clearance  from the  blood.   Even though  these indicators are
representative of physiological functions in the organism, most are biochemical in nature and could serve  in
a restricted sense as screening indicators of exposure. Lag time between exposure to stress and biochemical
response is typically short (within minutes to hours), and the response may persist for some time (days  to
months) following exposure.

Blood  chemistry assays are simple to administer, objective, and in  many cases interpretable.  Many of these
types of measurements have been taken on a wide variety of fish under a variety of environmental conditions
and are basically ready for use in field situations.  The underlying physiological bases for measurable changes
are usually understood and  can be traced in many instances to specific tissue and organ dysfunctions.  For
many of the cell/tissue/organ dysfunction indicators,  however, use in routine monitoring is recommended
provided that they are used  in conjunction with other bioindicators at higher levels of biological organization
(e.g., histopathological or bioenergetic parameters, growth) until the link between blood chemistry and organ
dysfunction is better understood  (see also Indicator G.2.4, "Cholinesterase Levels").

INDEX PERIOD:  No  index period  is known to have minimal temporal variability.  Although season  may
affect the absolute values of some  parameters,  comparisons can be made between animal data collected
within  the  same season.

MEASUREMENTS:  Indicators of cell/tissue/organ dysfunction  represent a wide variety of assays including (1)
serum  enzymes  (i.e.,  lysosomal  enzymes,  transaminases),  (2) electrolyte  homeostasis (e.g.,  Na2+,  K+),
(3)  carbohydrate  and   lipid  metabolism  (glucose,   triglycerides),  (4)  endocrine-related hormones   (i.e.,
corticosteriods, catecholamines),  and (5)  reproductive hormones (i.e.,  estradiol,  testosterone).  These five
groups of circulating chemicals  represent myriad physiological processes and functions in  the organism, and
most groups should be chosen as indicators with care relative to the species, environmental conditions, state
of development, and sex of the organism which is being monitored.

Because many of these variables are biochemical level indicators, they are short-lived in the blood and should
be  measured  at discrete time periods of short  intervals.  Sample  collection  times for a set of biomarker
indicators is 0.5-1.0 day per  resource sampling unit for two to three technicians.  The estimated laboratory
analysis cost for blood chemistry is $25 per sample.

VARIABILITY:  Because of the short-lived nature of chemicals in  the blood, the sample variability for most
of these chemical  parameters is relatively high.  The  independent variables which can influence the timing
and magnitude of these variables in  the blood are size, sex, age, and state of development of the organism
and environmental factors such as season, temperature, food availability, habitat availability, and population
density.  Much information  exists in the literature  relative to the variability of many circulating blood
parameters, particularly the more common commercial and sport species of fish (i.e., salmonids, centrachids)
and domesticated  animals (e.g.,  cattle, sheep, horses).  The  expected spatial  variability of blood chemistry
measures within  a resource sampling unit and their temporal variability  throughout  the year were not
estimated.
                                                G-25

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PRIMARY PROBLEMS:  One major constraint is that normal or reference values for most species have not
been statistically established for field situations.  Before these types of measures serve as early warning signals
of impending effects at the organism, population, or community level, some research is needed to establish
the relationship between these types of assays  and  responses  observed at higher levels  of  biological
organization.

REFERENCES:

Curtis,  LR.  1983.   Glucuronidation and  biliary excretion  of phenolphthalein in temperature-acclimated
steelhead trout (Sa/mo gairdneri).  Comp. Biochem. Physiol. C  76:107-111.

Curtis, LR., C.J. Kemp,  and A.V. Svec.  1986.  Biliary excretion of [14C]taurocholate by  rainbow trout (Sa/mo
gairdneri) is stimulated at warmer acclimation temperature.  Comp. Biochem.  Physiol.  C 84:87-90.

Gingerich, W.H., J.L Weber,  and R.E. Larson.   1977.   Hepatic accumulation, metabolism and biliary
excretion of sulfobromophthalein  by rainbow trout (Sa/mo gairdneri).  Comp.  Biochem. Physiol. C 58:113-
120.

Han, Z., and Z. Yaron.  1980.  Effects of organochlorines and  interrenal activity and cortisol metabolism  in
Jilapia aurea.  Gen.  Comp.  Endocrin. 40:345.

Kemp, C.}., and LR. Curtis. 1987.  Thermally modulated biliary  excretion of [14C]taurocholate in rainbow
trout (Sa/mo gairdneri) and the  Na+,K+-ATPase.  Can. J.  Fish. AquaL Sci. 44:846-851.

Schmidt, D.C., and LJ. Weber. 1973.  Metabolism and biliary  excretion of sulfobromophthalein by rainbow
trout (Sa/mo gairdneri).  J. Fish. Res. Bd. Can. 30:1301-1308.

Schreck, C.B., R. Patino,  C.K. Pring, J.R. Winton, and  J.E.  Holway.  1985.  Effects of rearing density on
indices of smoltification  and performance of coho salmon, Oncorhynchus kisutch.  Aquaculture 45:345-358.
                                               C-26

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C.2.10 INDICATOR:  Cytochrome P-450 Monooxygenase System

CATEGORY:  Exposure and Habitat/ Biomarkers

STATUS:  Research

APPLICATION:  The cytochrome P-450 monooxygenase system is most valuable as a screening indicator of
exposure to a variety of petroleum hydrocarbons (particularly polycyclic aromatic hydrocarbons [PAHs]) and
halogenated hydrocarbons  (dioxins, polychlorinated biphenyls [PCBsl,  PBBs).  In some  cases such as PAHs,
it  may be  viewed as a diagnostic indicator  because monooxygenase activity is required for activation  to
ultimate carcinogens.  Lag  time between exposure and  response is typically short (within hours).  Response
generally persists throughout exposure and for some time thereafter (days to weeb), but method selection
is  important here (see Measurements).  Its utility as a biomarker is most studied in teleost fish,  but inductions
of the system  apparently occur in all vertebrates.  No  resource specificity or  geographic  limitations  exist

This technique  is ready for field testing;  in  fact,  considerable  field testing has occurred in  the  case  of
petroleum-related contamination of aquatic systems with encouraging results. Considerable basic and applied
research, however,  is required for this approach  to reach its potential as a  biomarker for routine  regional
monitoring.

INDEX PERIOD: As with biochemical indicators  in general, monooxygenase  responses can be measured at
discrete points in time.  No temporal constraints are known, although  active reproductive status may reduce
baseline enzyme activity and/or the induction response to contaminants.

MEASUREMENTS:  Several  approaches are available; see Payne et al. (1987) and  Stegeman and Kloepper-
Sams (1987) and references therein.  Simplest and  least expensive, and often most sensitive,  are associated
enzyme activities such as ethoxyresorufin O-deethylase and aryl hydrocarbon hydroxlyase; these are measured
spectrophotometrically, typically on microsomal fractions of hepatic  or other (kidney, gut, heart, gill) tissues
that are obtained by ultracentrifugation.  However, chronic exposures sometimes can result in  loss of activity
following inductions. Considerably more  involved, but  quite powerful, techniques  involve immunochemical
assays for specific cytochrome P-450 isozymes and CDNA probes for messenger RNAs for specific isozymes.
These have indicated inductions following losses of catalytic activity  (as indicated by the enzyme assays
mentioned  earlier).   Additionally, the  greater  specificity  of  these latter approaches can  provide  clues
concerning the chemical compounds underlying an observed induction.

Most techniques available  merit adaptations to  make them  simpler  and  more available  for  routine
biomonitoring. Sample collection times for a set of biomarker indicators is 0.5-1.0 day per resource sampling
unit for two to three technicians.  The estimated laboratory analysis cost for enzyme analysis is $25-$75 per
sample.  Test  responses are quite sensitive to petroleum hydrocarbons and changes are relatively easy   to
discern provided proper reference or baseline data are available.

VARIABILITY:   The expected spatial  variability of this indicator  within  a resource sampling unit  and  its
temporal variability throughout the year were not estimated.

PRIMARY PROBLEMS:  The types of compounds these responses  are useful for  appear to  be somewhat
limited, particularly in fish,  but considerable species (across vertebrate taxa) variation occurs, and research in
this area is needed.  It is also important to note that some compounds  (some solvents and metals) can inhibit
these responses  and could  lead to "false negatives" in  cases where  inducers  co-occur with such inhibitors.
A constraint here is the lack of adequate  historical data for establishing baseline values for most species  of
potential  interest Considerable research is needed in the area of chemical interactions before  this technique
is relied upon in situations  evaluating highly complex mixtures.

                                                G-27

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REFERENCES:

Payne, J.F., LL Fancey, A.D. Rahimtula, and  E.L Porter.   1987.  Review and perspective on the use of
mixed-function oxygenase enzymes in biological monitoring.  Comp. Biochem. Physiol. C 86:233-245.

Stegeman, J.J., and P.J. Kloepper-Sams.  1987.  Cytochrome P-450 isozymes and monooxygenase activity
in  aquatic animals.  Environ.  Health PerspecL 71:87-95.
                                              G-28

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G.2.11  INDICATOR:  Enzyme-Altered  Foci

CATEGORY:  Exposure and Habitat/ Biomarkers

STATUS:  Research

APPLICATION:  Enzyme-altered foci (EAF) refer to the appearance of hepatocytes (identifed by histochemical
changes) that are an early stage in a spectrum  of lesions in the progressive development of neoplasia.  With
histochemical  procedures to localize selected enzymes, altered phenotypes of "carcinogen-initiated" cells are
demonstrated.  EAF  are most valuable as an indicator of prior exposure of the host to one of a variety of
chemical carcinogens.  First described in rodent liver,  EAF have been shown to increase in a dose-dependent
fashion  with application of compounds to promote liver tumors (Pitot  1988;  Hinton et al. 1988; Hendricks
et al. 1984; Nakazawa et al. 1985), Lag time between exposure and effect is likely to be weeks in duration.
Because growth of foci occurs with application of promoters,  focal volume may indicate both initiation and
promotion.  Care in method selection is important because negative and positive markers exist (Peraino et
al. 1983).

This technique is ready for field testing, although developmental research (species specific) is required to bring
this approach  to its full potential  as a biomarker for  routine regional monitoring.

INDEX  PERIOD:   No optimal  sampling window  was  recommended;  however,  no apparent  temporal
constraints exist.

MEASUREMENTS: Field samples can be quenched in liquid N arid stored indefinitely.  Processing includes
routine  cryostat sectioning  of frozen tissue (livers  of  large fish) or freeze- drying and embedment in glycol
methacrylate using  nonexothermic polymerization steps  (small fish or early life forms).   With the  latter
processing, nine enzyme  reactions (Hinton and Lauren 1989)  have been proven useful. Several  approaches
are available (see Pretlow et al. 1987; Peiaino et al. 1983).

VARIABILITY:  The spatial and temporal variability needs to be assessed in various species of feral fish. The
expected  spatial variability of this indicator within  a  resource sampling  unit  and  its temporal variability
throughout the year  were not estimated, because no proper reference or  baseline data are available.

PRIMARY PROBLEMS:  The constraints to field implementation  is the lack of  adequate historical data for
establishing  baseline values  and  no prior field application.   Increasing volume of foci and  appearance of
enzyme-altered nodules may signify incipient promotion of carcinogen-initiated  tissue.

REFERENCES:

Boutwell, R.K.  1964.  Some biological  aspects of skin carcinogenesis. Prog. Exp. Tumor Res. 4:207-250.

Farber,  E., and D.S.R.  Sarma.  1987.   Hepatocarcinogenesis:   A dynamic  cellular perspective.  Lab. Invest
56:4-22.

Hendricks, J.D., T.R. Meyers, and D.W. Skefton.   1984. Histological progression of hepatic neoplasia in
rainbow trout  (Sa/mo gairdneri).  Natl. Cancer  InsL Monog.  65:321-336.

Hinton, D.E.,  and D.J. Lauren.  1990.  Liver  structural alterations accompanying chronic toxicity in fishes:
Potential biomarkers  of exposure.  In:  L.R. Shugart and J. McCarthy, eds.  Biomarkers of Chemical Exposure
in Fishes.  Lewis Publishing Co.,  Chelsea, Ml.  In  press.
                                               C-29

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Hinton, D.E.,  J.A. Couch, S.J. Teh, et al.   1988.   Cytoiogical  changes during progression of neoplasia in
selected fish species.  Aquat Toxicol.  11:77-112.

Nakazawa, T., S. Hamaguchi, and Y. Kyono-Hamaguichi. 1985.  Histochemistry of liver tumors induced by
diethylnitrosamine and differentia! sex susceptibility to carcinogenesis in Oryzias latipes.  J. Natl. Cancer InsL
75:567-573.

Peraino, C, W.L. Richards, and F.J. Stevens.  1983.   Multistage hepatocarcinogenesis.  Pages 1-53.   In:
T.J. Slaga, ed.   Mechanisms  of Tumor Promotion, Vol. 1.  CRC Press, Boca Raton, FL.

Pitot, H.C. 1988. Hepatic neoplasia:  Chemical induction.  Pages 1125-1146.  In:  I.M. Arias, W.B. Jakoby,
H. Popper, et al. The Liver: Biology and Pathology.  Raven  Press, Ltd., New  York.

Pretlow, T.P.,  et al.  1987.   Examination of enzyme-altered foci  with gamma-glutamyl transpeptidase,
aldehyde dehydrogenase, glucose-6-phosphate dehydrogenase, and other markers in methacrylate-embedded
liver.  Lab. Invest 56:96-100.

Solt,  D.B., and E.  Farber.   1976.  New principle for  the  analysis  of chemical  carcinogenesis.   Nature
263:702-703.

Spies, R.B., D.W. Rice, and J. Felton.  1988.  Effects of organic contaminants on reproduction of the starry
flounder (Platichthys  stellatus) in  San Francisco Bay. I.   Hepatic contamination and  mixed-function oxidase
(MFO) activity during the reproductive season.  Mar. Biol. 98:181-189.
                                                G-30

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APPENDIX G.3:  INDICATOR FACT SHEETS FOR LANDSCAPE AND HABITAT INDICATORS
                                Authors

                            Robert V. O'Neill
                          Carolyn T.  Hunsaker
                      Oak Ridge National Laboratory
                      Environmental Sciences Division
                          Oak Ridge,  Tennessee

                             Henry L Short
                      U.S. Fish and Wildlife Service
                            Arlington, Virginia
                               Contributor

                             Reed F. Noss
                   U.S. Environmental Protection Agency
                    Environmental Research Laboratory
                            Corvallis, Oregon

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G.3.1  INDICATOR:  Abundance or Density of Key Physical  Features and Structural Elements

CATEGORY:   Exposure and Habitat/ Habitat

STATUS:  High-Priority Research

APPLICATION:  Research in many different ecosystems has demonstrated that certain physical features of
habitats (e.g., cliffs, outcrops, sinks, seeps, talus slopes) and structural elements (e.g., snags, downed logs) are
critical to animal diversity and abundance.  Land-use practices, such as forestry, can alter the density and
distribution of many  important  structural features.  Many habitat features and  elements are specific to
particular resource classes,  but determining what to measure in a given class can be based  on existing
literature. This indicator is  related to Indicator G.3.2.

INDEX PERIOD:  No optimal sampling window exists for this indicator.

MEASUREMENTS: Identification of important features and elements in a particular resource class is the first
step.   This  is followed by an  inventory of these features  through  field  and/or aerial  surveys,  and  a
determination of their abundance or density in the resource sampling unit.  The recommended interannual
sampling frequency is four or five years.

VARIABILITY:  The expected spatial variability for this indicator within  a  resource sampling unit and its
expected temporal variability during the year were not estimated.

PRIMARY PROBLEMS:  Measuring the abundance or density of structural  elements is straightforward, and is
a long-standing tradition in wildlife biology.  No problems are  foreseen, except that the  effort may be  labor-
intensive.

BIBLIOGRAPHY:

Cooperrider, A.Y., R.J. Boyd, and  H.R.  Stuart, eds. 1986. Inventory and  Monitoring of  Wildlife Habitat
U.S. Department of the Interior, Bureau of Land Management; Washington,  DC.
                                                G-32

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G.3.2  INDICATOR:   Linear Classification and Physical Structure of Habitat

CATEGORY:  Exposure and Habitat/ Habitat

STATUS:   High-Priority Research

APPLICATION:  The  structure of animal habitats in many terrestrial and in some wetland communities can
be considered to consist of vertical layers and a horizontal distribution of habitat variables within  each layer.
The Habitat Linear Classification System (HLCS; Short 1990) is a simple way to translate the vertical and
horizontal  dimensions of habitats  into a numeric whose status and  trends can be  compared between sites
or regions.  The HLCS can be applied to different types and resolutions of monitoring data, such as satellite
imagery and aerial photography (both with ground-truthing) and field surveys.  Like the HLCS, The Habitat
Layers Index (HLI; Short and Williamson  1986) can be calculated from data at several  spatial scales, and
together these indices provide a way to evaluate and monitor habitat structure and predict potential animal
diversity from that structure.

Application of the  HLCS to  field  survey  data  from south central  Colorado indicated that the algorithm
provides values that  are linear as the number of clumped  cells within the grid is increased,  that an
interpretable distinction could be made between n-cells that were clumped or dispersed  within a grid, and
that signatures from different  habitats varied  in  a way that seemed related  to the way animal species used
those habitats.

It  is  important to have a habitat indicator that can measure fundamental land-use changes in  agroecosystems
and  forests and reflect plant succession, urbanization, desertification, etc., because these changes impact wild
animals.  Use of the  HLCS will  allow  EMAP  to characterize the effects of changing habitat on animals at a
regional and national scale.

INDEX  PERIOD:  Field surveys to measure habitat variables for the HLCS should be  conducted when
vegetation  is in full leaf.

MEASUREMENTS:  To calculate the HLCS, map-based  data are overlaid on a grid, or field survey data are
collected from a gridded sampling unit   While only habitat layers are distinguished from remotely sensed
data, a variety of habitat variables  are  distinguished from field surveys.  Two metrics are developed for data
about layers and variables: (1) the proportion of grid cells that contain a particular habitat layer or important
habitat variable; and  (2) the  proportion of grid-cell  perimeter segments that surround occupied cells.  The
HLCS will be most  useful if field surveys   in  all terrestrial  and  suitable wetland  resource  classes are
standardized, have a consistent format, and measure for the same variables.  Variables include habitat layers,
surface cover, and a variety of vegetative variables in surface, midstory and  overstory layers. The product of
the analysis is a series of linear traits describing the individual habitat variables or layers within the grid. This
series provides a "signature" that is descriptive of the habitat, and it is this  signature that whose status and
trends can be compared among sites.

The  analysis  of habitat structure to implement the  HLCS would cost approximately $500-$1000 for each
resource sampling unit,  the  amount  depending on habitat complexity.   The recommended  interannual
sampling frequency is 5 years.

VARIABILITY: The expected spatial and temporal variabilities within a resource sampling unit  and during the
index period,  respectively, were not established.

PRIMARY PROBLEMS:  Research is needed to determine  a best size for a  survey grid and survey grid cells
and  the most efficient and cost-effective method to sample habitat variables.

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REFERENCES:

Short, H.L   1990.  The  use of the  Habitat Linear Classification System (HLCS) to inventory  and monitor
wildlife habitat  Unpublished  manuscript  U.S.  Department  of  the  Interior, Fish and  Wildlife Service,
Arlington, VA.

Short, H.L, and S.C. Williamson.  1986.  Evaluating the structure of habitat for wildlife.  Pages 97-104.
In:  J. Verner, M.L.  Morrison, and  C.J. Ralph, eds.  Modeling Habitat Relationships of Terrestrial Vertebrates.
University of Wisconsin Press, Madison.
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C.3.3  INDICATOR:  Habitat Proportions (Cover Types)

CATEGORY:  Exposure and Habitat/ Landscape

STATUS:  High-Priority Research

APPLICATION:  Mapping and determining proportions  (P,) of various land use or vegetation cover types in
a landscape is a basic measurement when considering both extent and change in vegetation and associated
animal composition and diversity (Noss 1983).  In addition, land use is an  important factor in determining
the type and amount of nonpoint-source pollutants entering inland and coastal waters.

Habitat proportions should be valuable for  monitoring purposes because of the extensive development of P|
as a landscape property permitting application  of percolation  theory (Gardner et  al.  1987).  If  habitat is
randomly scattered on the  landscape, then  P,- =  0.59  represents a "percolating" habitat  Above this value of
Pit the  habitat tends to be connected throughout  the landscape, permitting animal populations to move across
the entire available habitat and fully utilize the resource.   If  P, is less than  0.59,  then the  habitat is
disconnected and  isolated into patches that make it much less available to animals (O'Neill et al. 1988).  If,
however,  the habitat of concern is susceptible  to disturbances such as  fire,  large values  of P-, permit the
disturbance  to propagate throughout the  landscape  (Turner et  al.  1989).   If the assumption of random
distribution  of the habitat is relaxed, then  percolation theory becomes more useful as an indicator for real
landscapes.

Near-Coastal and  Inland Waters:   Proportions of land  use have consistently explained variation in water
chemistry for large geographic areas, especially for sediments and  nutrients (Omernik 1977; Hunsaker 1986;
Osborne and Wiley 1988).  This relationship exists because of the biogeochemical cycling that links terrestrial
and aquatic systems and is dominated by nonpoint-source pollution in surface runoff from disturbed areas.

Forests:  Monitoring the distribution of tree  species is  important for  assessing the  total extent and rate of
change in extent of different forest types. Distribution patterns of vegetation result from interactions between
natural and  human altered climatic, terrestrial, and biological habitat conditions (Braun 1950; Daubenmire
1947;  Walter 1973).  Patterns of vegetation are thus in response to environmental conditions.  Changes in
conditions, either from natural (e.g., succession) or human-induced (e.g., timber harvest)  influences can affect
or stress the ecosystem  and can alter the distribution patterns.

Monitoring the vegetative and physical structure of ecosystems is important because changes in structure may
result in loss of desirable vegetation components (e.g.,  species, life forms, communities) or in acute cases,
alteration  of ecosystem function. An example of the latter case occurs in the northern forested region of the
upper  Midwest where historic logging, followed  by extensive fires, entirely altered the forest vegetation, the
forest floor litter,  and the associated surface water chemistry. Additionally, structural diversity has generally
been shown to have  a positive relationship  with animal  and plant species diversity (Short and Williamson
1986).

INDEX PERIOD:  The optimal sampling window  for remotely sensed data from which habitat proportions are
calculated is the growing season. An optimal measurement window for field surveys  in all terrestrial resource
classes is when perennial vegetation is in leaf-out condition; for  arid lands, late spring to early summer is
optimal.  For remote sensing images, a  high sun angle  is  good to reduce topographically induced illumination
differences.

MEASUREMENT:  Land use and land cover data can  be classified from remotely sensed data, and the area
of each type can  be determined.   If the images are in digital  form, areal measures  can be  calculated by
computer.  The  land use and vegetation cover data to calculate this  indicator would be provided primarily

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by EMAP-Characterization and augmented by field survey data such as the USDA Forest Service FIA and FPM
inventories.   Standard  digital image processing techniques  would be  employed, involving image-to-image
registration and change detection procedures coupled with  spectral classifications (Pilon et al. 1988).  The
recommended interannual measurement frequency  is 5 to 10 years.  The development of classifications for
relevance to animal indicators is more difficult Animal species can  be associated with different vegetation
types to estimate faunal composition, diversity, and relative abundances in sampling units.  Such relationships
will likely require field data collection to verify habitat classifications, but this work could be done when the
animal data are  being collected.

VARIABILITY:   Because the  remotely sensed data will provide 100% spatial coverage in the landscape
sampling units, the expected spatial variability of  habitat proportions within a resource sampling  unit is
inconsequential.  The temporal variability of habitat proportions during the index period will produce extreme
values that deviate <10% of the sample mean.

PRIMARY PROBLEMS:  Ground-truthing and  correspondence  with selected animal indicators will require
significant effort

REFERENCES:

Braun, E.L  1950.  Deciduous Forests of  Eastern North America.  The Free Press.   New York.

Cooperrider, A.Y., R.J. Boyd, and H.R. Stuart, eds. 1986.   Inventory and  Monitoring of Wildlife Habitat
U.S. Department of the Interior, Bureau of Land Management, Washington, DC.

Daubenmire, R.F.  1947.  Plants and  Environment: A  Textbook of Autecology.  Third Edition.  John Wiley
and  Sons, New York.

Gardner, R.H., B.T. Milne, M.G. Turner, and R.V. O'Neill.  1987.  Neutral models for the analysis of broad-
scale landscape pattern.  Landscape Ecol.  1:19-28.

Hunsaker, C.T., S.W. Christensen, J.J. Beauchamp, R.J. Olsen, R.S. Turner, and J.L Malanchuk.  1986.
Empirical relationships between  watershed  attributes and headwater lake chemistry in the Adirondack  region.
ORNL/TM-9838.  Oak Ridge National Laboratory, Oak Ridge,  TN.

Noss, R.F.  1983.  A regional landscape approach  to maintain diversity.  Bioscience 33:700-706.

Omernik, J.M.  1977.  Nonpoint source  - stream  nutrient level relationships:  A  nationwide study.  EPA
600/3-77/105.  U.S.  Environmental Protection Agency,  Corvallis, OR.

O'Neill,  R.V., B.T. Milne, M.G. Turner, and  R.H. Gardner.  1988.  Resource utilization scale and landscape
pattern.  Ecology 2:63-69.

Osborne, L.L., and M.J. Wiley.  1988.   Empirical relationships between landuse/cover and stream water
quality in an agricultural watershed.  J. Environ. Manage. 26:9-27.

Pilon, P.G., P.J. Howarth, R.A. Bullock, and P.O.  Adeniyi.  1988. An enhanced classification approach to
change detection in semi-arid environments.  Photogram. Eng.  Remote Sensing 54:1709- 1716.

Short,  H.L, and S.C. Williamson.  1986.  Evaluating  the  structure of habitat for wildlife.  Pages 97-104.
In:  J. Verner, ed.  Modeling Habitat Relationships  of Terrestrial Vertebrates.  University of Wisconsin Press,
Madison.

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Turner, M.G., R.H. Gardner, V.H. Dale, and R.V. O'Neill.  1989.  Predicting the spread of disturbance in
heterogeneous landscapes.  Oikos 55:121-129.

Walter, H.  1973.  Vegetation of the Earth.  Springer-Verlag,  New York.
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G.3.4  INDICATOR:  Patch Size and Perimeter-to-Area Ratio

CATEGORY:  Exposure and Habitat/ Landscape

STATUS:  High-Priority Research

APPLICATION:  Patch dynamics have implications for movements of biota, nutrient cycling, and energy flux.
Studies in many different ecosystems have demonstrated that a number of spatially related habitat attributes
or landscape indices are related to animal diversity and abundance.  This indicator will provide an index of
terrestrial  biotic integrity which can  provide  a measure  of population  viability, and will  provide  critical
information  which can  be  used for analysis and assessment of migratory patterns.   Patch size is  a  critical
consideration especially when connectivity between  patches is poor.  Many  habitat patches in fragmented
landscapes are too small to support viable populations, or even  a single  home range or  territory of  certain
large mammal or bird species.   The  distribution of suitable  habitat among patches of various size is just as
important for animals as the total area of suitable habitat in a landscape.

Patch perimeter to area (edge to interior) ratio is a measure  of habitat fragmentation.  It is useful for  forests,
where  the amount of forest edge relative to  forest  interior is  known to be an  important determinant of
vertebrate community structure  and  population viability of forest interior species.   The relationship  is best
documented for birds, where artificial edge favors "weed/1 species over native species and increases rates of
nest predation  and cowbird parasitism on many forest species.   Numerous studies have documented the
deterioration of bird  populations in  landscapes with high edge-interior ratios, yet this variable has seldom
been measured directly.  For wetlands, the significance of shape in a  monitoring context is in its relationship
to loss of acreage and function  over time.

The frequency of patches in various size categories would  be plotted  for each habitat type.  This distribution
could be compared with home range sizes or  minimum areas required for population persistence for native
vertebrates.   The fractal dimension of patches is a  measure of  perimeter to area  ratio  and quantifies the
dissectedness of boundaries (O'Neill et  al. 1988); this indicator is discussed separately (see  Indicator G.3.5,
"Fractal  Dimension").

INDEX  PERIOD:  The optimal sampling  window for remotely  sensed  data from  which  patch  size and
perimeter to area ratio are calculated is the growing season.

MEASUREMENTS:   Measurements  of patch areas  and  perimeters  are  relatively straightforward  in many
landscapes.   They  can be done manually on  an  aerial photo;  for large  areas,  however, the use  of a
Geographic  Information System  and  digital data are  necessary.  The land-use and vegetation-cover data to
calculate these  indices would   be  provided by EMAP-Characterization.   The  recommended interannual
sampling frequency is 5 to 10 years.

VARIABILITY:  Because the remotely sensed data will provide 100% spatial coverage in the sampling units,
the expected spatial variabilities of patch size and perimeter to area ratios are inconsequential. The temporal
variabilities of patch size and perimeter to area ratios during the  index period will  produce extreme values
that deviate <10% of the  sample mean.

PRIMARY PROBLEMS:  No significant problems are  anticipated.
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REFERENCES:

O'Neill, R.V., J.R.  Krummel, R.H. Gardner, G. Sugihara, B. Jackson, D.L DeAngelis, B.T. Milne, M.G.
Turner, B. Zygmunt, S.W. Christensen, V.H Dale, and R.L Graham.  1988.  Indices of landscape pattern.
Landscape Ecol. 1:153-162.

BIBLIOGRAPHY:

Harris,  LD.  1984.  The Fragmented Forest:  Island Biogeography Theory and the Preservation of Biotic
Diversity.  University of Chicago Press, Chicago, IL.

Noss, R.F.  1983.  A regional landscape approach to maintain diversity.  Bioscience 33:700-706.

Whitcomb, R.F., C.S. Robbins, J.F. Lynch, B.L Whitcomb, K. Klimkiewicz, and D. Bystrak. 1981.  Effects
of forest fragmentation on avifauna of the eastern deciduous forest.  Pages 125-205.  In:  R.L. Burgess  and
D.M. Sharpe, eds.  Forest Island Dynamics in  Man-Dominated Landscapes.  Springer-Verlag, New York.

Wilcove, D.S., C.H.  Me Lei Ian, and A.P. Dobson.   1986.  Habitat fragmentation  in the temperate zone.
Pages 237-256.  In:  M.E. Soule, ed.  Conservation Biology: The Science of Scarcity and Diversity. Sinauer,
Sunderland, MA.
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C.3.5  INDICATOR:  Fractal  Dimension

CATEGORY:  Exposure and Habitat/ Landscape

STATUS:  High-Priority Research

APPLICATION:  The fractal dimension, F,  is a measure of the fractal geometry (Mandelbrot 1983) and  an
index of the  complexity of shapes on  the  landscape.  If the landscape is composed of simple geometric
shapes like squares and rectangles, the fractal dimension will  be small,  approaching 1.0.  If the landscape
contains many patches with complex and convoluted shapes,  the fractal dimension will  be large (Krummel
et al. 1987).   F is calculated from maps  of land  use or  vegetation cover and  appears  to  be useful for
characterizing landscape pattern (Krummel  et al. 1987, O'Neill et al. 1988).

INDEX PERIOD:  The optimal sampling window for remotely sensed data from which fractal dimensions are
calculated is the period that best allows one to discriminate the habitat patterns of interest

MEASUREMENTS:   The fractal dimension  is estimated by regressing the  logarithm of  polygon perimeter
(dependent variable) against the logarithm of area (independent variable) for all patches on a digitized map.
The fractal dimension is related to the  slope of the regression, S, by the relationship (Lovejoy 1982):

                                             F  = 2 S

The  land-use and vegetation-cover  data  to  calculate  these indices would   be provided  by  EMAP-
Characterization.  The recommended interannual sampling frequency is  5 to  10 years.

O'Neill et al. (1988) analyzed the fractal dimension for 58 quadrangles in  the eastern United States.  This
study quantified  the regional  variability of  this indicator by using data with a spatial resolution of 200  m.
The fractal dimension ranged from 1.24 to 1.45 with a coefficient of variation of 0.04.  These values are
believed to be fairly representative of landscapes in North  America;  however, this needs to be verified for
the western United States.

VARIABILITY:  Because the  remotely  sensed  data will  provide 100%  spatial coverage in  the landscape
sampling units, the expected  spatial variability of the fractal dimension  within resource sampling units are
inconsequential.  The temporal variability of fractal dimension during the index period was not estimated.

PRIMARY PROBLEMS:  The fractal dimension, as with some other landscape indices, is probably not a good
indicator for  a single  measurement in  time because we lack currently knowledge of how this parameter
relates to ecosystem function.  However, as an indicator of landscape pattern change over large geographic
areas it should be  powerful.  Also, this indicator is probably most useful  when  used together with other
landscape indices such as contagion and proportion of land use.

REFERENCES:

Krummel, J.R., R.H. Gardner, G.Sugihara,  R.V. O'Neill, and  P.R. Coleman.  1987. Landscape pattern in
a disturbed environment.  Oikos 48:321-324.

Lovejoy,  S.  1982.  Area-perimeter  relation for rain and cloud areas. Science 216:185-187.

Mandelbrot,  B.  1983.  The  Fractal Geometry of Nature.  W.H. Freeman  and Co., New York.
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O'Neill, R.V., J.R. Krummel, R.H. Gardner, G. Sugihara, B. Jackson, D.L DeAngelis, B.T. Milne, M.G.
Turner, B. Zygmunt, S.W. Christensen, V.H Dale, and R.L Graham. 1988.  Indices of landscape pattern.
Landscape Ecol. 1:153-162.
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G.3.6  INDICATOR:  Contagion or Habitat Patchiness

CATEGORY:  Exposure and Habitat/ Landscape

STATUS:  High-Priority Research

APPLICATION:  The horizontal heterogeneity or patchiness of a habitat is a primary determinant of animal
diversity  and abundance.  Up to  a certain threshold, increases  in heterogeneity  correspond to increased
diversity  of  species,  resources, and abundances of animals dependent on those  resources.   Patchiness is
caused by resource heterogeneity  (e.g., sinkholes, seeps, outcrops, unusual soils) and by disturbances (e.g.,
treefall gaps, spot fires,  insects).

Contagion is a landscape index derived from information theory (Shannon and Weaver 1962) as applied to
landscape pattern (O'Neill  et al 1988). Contagion  measures the extent to which  land uses are aggregated
or clumped:

                                     C = 2n  In n + -, -y  P,j In P-t]

where P-t}  is  the probability  that a grid point of land use / will be found adjacent to a grid point of land  use
;'.  The term 2n In n represents a maximum in which all adjacency probabilities  are equal; that is,  for a
randomly chosen spot on the  landscape, there  is an equal probability that any  land use type is adjacent to
the chosen  point  At high values  of C, the landscape tends  to be composed of large, contiguous patches.
At low values, the landscape is dissected into many small patches.

The  index C used by O'Neill et al.  (1988) retains a sensitivity to the number of land-use types that is avoided
by using the recommended formulation:

                                     C =  •;  -j- Pj. In P;J/ 2n  In n

Their study  analyzed contagion for 94  quadrangles in the eastern United States, and  quantified the regional
variability of this indicator  by  using data with a spatial resolution of 200 m.  The contagion values ranged
from 9.5  to 22.8  with  a coefficient of variation of 0.16, and are believed to be fairly representative of
landscapes in North America;  however, this needs to be verified in the western United States.

INDEX PERIOD:  The optimal sampling window for landscape data from which contagion is calculated  is the
growing season.

MEASUREMENTS:  The land-use and vegetation-cover data to calculate this indicator would be provided by
EMAP-Characterization.   The  recommended interannual measurement frequency  is  4 to 5 years.  At  the
landscape scale, contagion  is a measure of patchiness.

Several measures of habitat patchiness for  small geographic areas have been proposed.  Roth (1976) used
the coefficient of variation  (CV) of distance to nearest trees and shrubs in point-quarter samples, and found
that  bird  richness and  abundance increased in more heterogeneous areas.   Noss  (1988)  tested several
measures of habitat patchiness in a Florida hardwood forest,  including CV of distances to nearest  trees,
nearest shrubs, and both combined; CV of shrub density; CV of canopy openness, diversity of tree species,
shrub species, and both combined; and proportion of plot area in canopy gaps, bayheads  (dense broadleaf
evergreen vegetation  in  seepage areas), and both combined.  The  best predictor of bird abundance was
proportion of area in canopy  gaps and bayheads combined.  Species richness was significantly related only
to variation in shrub density.  Mapping these patches on sample plots through field  surveys is straightforward,
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though time-consuming (but less time-consuming than  using point-quarter samples).  Current aerial photos,
when ground-truthed, may be more useful.

VARIABILITY:  Because the remotely sensed data will  provide 100% spatial coverage within the landscape
sampling units, the expected  spatial variability of contagion or habitat  patchiness within resource sampling
units is inconsequential.   The temporal variability  during the index period was not estimated.

PRIMARY PROBLEMS:  Some measures of habitat patchiness require detailed field surveys.  However, direct
measurement of  patchiness for EMAP will be calculated from the program's landscape characterization data.

REFERENCES:

Noss, R.F.   1988.  Effects of edge and  internal patchiness on habitat  use by  birds in a Florida  hardwood
forest  Ph.D. dissertation.  University of Florida,  Gainesville.

O'Neill,  R.V., J.R. Krummel, R.H. Gardner, C.  Sugihara, B. Jackson, D.L DeAngelis, B.T. Milne, M.C.
Turner, B. Zygmunt, S.W. Christensen, V.H. Dale, and R.L. Graham.  1988.  Indices of landscape pattern.
Landscape Ecol.  1:153-162.

Roth, R.R.   1976. Spatial heterogeneity and bird species diversity.  Ecology 57:773-782.

Shannon, C.E., and W. Weaver. 1962.  The Mathematical Theory of Communication.  University of Illinois
Press, Urbana.  125 pp.
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G.3.7  INDICATOR:  Gamma Index of Network Connectivity

CATEGORY:  Exposure and Habitat/ Landscape

STATUS:  Research

APPLICATION:  The connectivity of habitat in a landscape is a measure of how easily individuals of a given
animal species can travel about, which in turn is important to meeting daily and seasonal  life history needs,
allowing juvenile dispersal, escaping disturbance, and providing for gene flow. While in theory the usefulness
of such as index appears logical, the data are lacking to support the application of this index to a specific
species.

INDEX PERIOD:  No optimal sampling window exists for remotely sensed data from which gamma  indices
are calculated.

MEASUREMENTS:  The gamma index of network  connectivity is  the  ratio of links in a network  to the
maximum possible number of links in that network.  The formula is y = L/Lmax = L/3(V -  2), where L is the
number of links (i.e., corridors), Lmax  is the maximum possible number of links, and  V  is the number  of
nodes (i.e., habitat patches;  Forman and Godron 1986).  The recommended interannual sampling frequency
is 4 to 5 years.

VARIABILITY:   Because the remotely  sensed  data  will provide 100% spatial coverage  in the landscape
sampling units, the expected  spatial  variability of  the gamma index within  resource  sampling  units  is
inconsequential. The temporal variability of the Gamma index during the index period was not estimated.

PRIMARY PROBLEMS:  This is a simple measure, but its ecological relevance is unknown.  Connectivity  in
real landscapes would depend on habitat structure within corridors, the nature of surrounding habitat (matrix),
corridor width:length ratio, and  details of the autecology of species expected to use the corridor.

REFERENCE:

Forman, R.T.T., and M. Godron. 1986.  Landscape  Ecology.  John Wiley and Sons, New York.

BIBLIOGRAPHY:

Noss, R.F.  1987.  Corridors in  real landscapes:  A reply to Simberloff and Cox. Conserv. Biol. 1:159-164.
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G.3.8  INDICATOR:  Pattern's Diversity Index

CATEGORY:  Exposure and Habitat/ Landscape

STATUS:  Research

APPLICATION:  Ration's diversity index (Dl)  is actually a measure of the amount of edge within an area of
a given size (thus, it is a measure of habitat diversity).  The amount of artificial edge in a landscape is a good
index (inverse) of terrestrial biotic integrity, even though wildlife managers traditionally have considered edge
to be beneficial  because many game species are edge-adapted.  A landscape is "less natural" the larger the
amount of artificial edge.

INDEX PERIOD: The optimal sampling window for  landscape data from which Patton's diversity index is
calculated is the growing season.

MEASUREMENTS:  This index is based on measurements from aerial photos.  The land use and vegetation
cover data to calculate these indices  would be provided by the EMAP-Characterization. The formula is Dl
= TP/2A (Patton 1975), where TP is the total perimeter of an area plus any linear edge within the area, and
A  is the total area.  Thomas et al.  (1979) split Patton's index  into two indices, one each for inherent edge
(the natural boundary between two plant communities) and induced edge (a boundary caused  by disturbance,
human or otherwise).   Other considerations  suggest separating edge into natural (created by either natural
gradients or disturbances) and artificial (created by human land-use), because the latter tends to be longer-
lasting and often associated with continuing human impact   Edges may also be classified  on the basis of
contrast between the two habitats.  The recommended interannual sampling frequency is 5  to  10 years.

VARIABILITY:   Because  the  remotely sensed  data will  provide 100% spatial coverage in the landscape
sampling units, the expected spatial variability of this index within a resource sampling  unit is inconsequential.
The temporal variability of Patton's Dl during the index period was not estimated.

PRIMARY PROBLEMS:  Thomas et al. (1979) comment that Patton's Dl assumes that the total  perimeter of
an area is  actually edge, whereas  this is  usually not true in  an ecological  sense.   However,  it would be
simple to focus only  on  true edge  (and  perhaps, only artificially-created  edge) simply by not including
perimeter of the sample area that abuts similar habitat

REFERENCES:

Patton, D.R.  1975.  A  diversity  index for quantifying habitat "edge."  Wildl. Soc. Bull. 3:171-173.

Thomas, J.W., C. Maser, and J.E. Rodiek.   1979.   Edges.  Pages 48-59   In:  J.W.  Thomas, ed.  Wildlife
Habitats  in Managed Forests:  The Blue  Mountains  of Oregon and Washington.   Agricultural  Handbook
No. 553.  U.S. Department of Agriculture, Forest Service, Washington, DC.
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APPENDIX H: INDICATOR FACT SHEETS FOR ATMOSPHERIC STRESSORS
                              Authors

                         Allen S. Lefohn
                         A.S.L. & Associates
                          Helena, Montana

                            Tom Moser
      NSI Technology Services Corporation - Environmental Sciences
              U.S. EPA Environmental Research Laboratory
                          Corvallis, Oregon

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H.1  INDICATOR:  Ozone

CATEGORY:  Stressor/ Chemical

STATUS:  High-Priority Research

APPLICATION:  In the atmosphere, chemical emissions from natural ^nd man-made sources are subjected
to physical, chemical, and photochemical processes, which may produce transformation products that can be
as toxic as or  more toxic to ecological resources than the parent chemicals.  The atmosphere serves as the
conduit from emissions sources to  the biosphere.   Once chemicals are emitted  into the atmosphere,  their
pathways to receptors in terrestrial and aquatic ecosystems are a function of a variety of factors. Atmospheric
residence times depend upon characteristics such as mode and rate  of emission, atmospheric transformations,
and physical state.

Ozone is  regulated  by  EPA National Ambient Air Quality Standards and is  considered both a  health and
welfare risk.  Its degree of ecological impact depends on the  deposition rate, toxicity, environmental fate of
the pollutant,  and organism sensitivity.  Deposition of and exposure to ozone  affects vegetation  through
disruption of physiological  processes. Ozone  is recognized as a major problem in both human  health and
welfare (U.S. EPA 1986; U.S. EPA  1988a,  1988b).

Lefohn  and Moser  (1989)  focused  on  methods  available  for summarizing the  hourly  average ozone
concentration  information  in biologically  meaningful terms.   Exposure indices, such as the summation  of
hourly average concentration equal to or  greater  than  selected thresholds, the  number of hourly  average
concentrations equal to or greater than a  selected  threshold,  and the cumulative index based on a  sigmoid
weighing function, were described  as alternative measures of vegetation  exposure to ozone.

INDEX PERIOD:  Ozone concentrations would be monitored continuously throughout the year at all EMAP
deposition monitoring sites.  Currently, ozone concentrations are monitored continuously for specific months
of the year and are summarized as hourly average concentrations.  According to the geographic region  of
the United States, ozone is  currently monitored for as few as  5 months to as many as 12 months (see Table
H-1).

MEASUREMENTS: Peak ozone exposures  would be measured; data will be reported as peak values, seasonal
and annual averages. Currently, ozone measurements (in ppmv) can be obtained  from data bases of existing
monitoring networks (e.g.,  EPA  Atmospheric Information Retrieval System  [AIRS],  National Park  Service,
Mountain  Cloud Chemistry Program, and EPA National Dry  Deposition  Network).   Measurement  error is
expected to produce ranges that deviate 20% from the true value.

VARIABILITY:   The expected spatial variability of ozone concentrations within  a landscape sampling unit
would produce extreme measures that deviate <20% from the mean value.  The expected temporal variability
of ozone concentrations during the year could produce measures  that deviate as much as  100% from the
mean value, the variability depending upon release from emitters and seasonal climatic patterns.

PRIMARY  PROBLEMS:   (1)  Sites monitored in  existing atmospheric sampling networks are geographically
clumped,  usually in  association with urban  centers.   There are few data available from which to judge
changes in pollutant concentration over distance from these urban  centers.  Of the 1384 ozone  sites in the
EPA AIRS  data  base, only  33%  have been designated as rural or remote.  The National Park Service  does
monitor ozone  at several locations around the United States;  however, ozone data for only 1983 and 1985
are available.  Quality control confirmations have not been completed on data  collected between 1986 and
1988.  In future years, ozone data may be available from select statewide research networks (e.g., California).
                                                H-1

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Table H-1.  Ozone Monitoring Season by State  (Source: 40 CFR Part 58, Appendix O).
State
Begin
End
State
Begin
End
Alabama
Alaska
Arizona
Arkansas
California
Colorado
Connecticut
Delaware
D.C
Florida
Georgia
Hawaii
Idaho
Illinois
Indiana
Iowa
Kansas
Kentucky
Louisiana
Maine
Maryland
Massachusetts
Michigan
Minnesota
Mississippi
March
April
January
March
January
March
April
April
April
January
March
January
April
April
April
April
April
April
January
April
April
April
April
April
March
November
October
December
November
December
September
October
October
October
December
November
December
October
October
October
October
October
October
December
October
October
October
October
October
November
Missouri
Montana
Nebraska
Nevada
New Hampshire
New Jersey
New Mexico
New York
North Carolina
North Dakota
Ohio
Oklahoma
Oregon
Pennsylvania
Rhode Island
South Carolina
South Dakota
Tennessee
Utah
Vermont
Virginia
Washington
West Virginia
Wisconsin
Wyoming
April
June
April
January
April
April
January
April
April
May
April
March
April
April
April
April
June
April
May
April
April
April
April
April
April
October
September
October
December
October
October
December
October
October
September
October
November
October
October
October
October
September
October
September
October
October
October
October
October
October
(2)  Because annual ozone levels are closely linked with meteorological factors, it will be difficult to ascertain
the existence of a long-term directional trend in ozone exposures (U.S. EPA  1989).

(3)   Until  ozone effects  research on  trees can  provide  insight  on exposure  dynamics (e.g.,  the  relative
importance of high ozone concentrations versus low concentrations), it will be difficult to identify and defend
any one specific ozone exposure parameter.  However, on the basis of published results from agricultural
research, higher ozone concentrations should be more heavily weighted than lower concentrations.  Exposure
periods that contain high  hourly average concentrations over short periods of time should provide different
responses from exposure periods that contain  low hourly average concentrations over long periods of time.
According  to  EPA  (U.S.  EPA  1988a;  1988b), the use  of  long-term  average concentrations should be
discouraged.   Lefohn and Moser (1989) present a lucid discussion on the subject of selecting an exposure
index.

REFERENCES:

Lefohn, A.S.,  and T. Moser.  1989.   Exposure indicators for monitoring air  quality in the environment
Prepared  for the Environmental Monitoring and  Assessment Program  (EMAP) of the U.S. Environmental
Protection  Agency.   A.S.L. and Associates,  Helena, MT.
                                                 H-2

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U.S. EPA.  1986.  Air quality criteria for ozone and other photochemical oxidants.  Volume III.  EPA 600/8-
84/020cF.  U.S. Environmental Protection Agency,  Environmental Criteria  and Assessment Office, Research
Triangle Park, NC.

U.S. EPA.    1988a.  Summary of selected new information on effects of  ozone on health and vegetation:
Draft supplement  to  air quality criteria for ozone and other photochemical oxidants.  EPA 600/8-88/105A.
U.S. Environmental Protection Agency, Office of Health and Environmental Assessment, Washington,  DC.

U.S. EPA.  1988b.  Review of the National Ambient Air Quality Standards for ozone: Assessment of scientific
and  technical  information.  U.S. Environmental  Protection  Agency, Office  of Air  Quality  Planning and
Standards, Research Triangle Park, NC.

U.S. EPA.   1989.   National air  quality  and  emissions trends  report, 1987.   EPA  450/4-89/001.   U.S.
Environmental Protection Agency, Office of Air Quality Planning and Standards,  Research Triangle Park, NC.
                                               H-3

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H.2  INDICATOR:  Sulfur Dioxide

CATEGORY:  Stressor/ Chemical

STATUS:  High-Priority Research

APPLICATION:  In the atmosphere, chemical emissions from natural and man-made sources are subjected
to physical, chemical, and photochemical processes, which may produce transformation products that can be
as toxic as or more toxic to ecological  resources than the parent chemicals.  The atmosphere serves as the
conduit from emissions sources to the  biosphere.  Once chemicals  are emitted into the atmosphere,  their
pathways to receptors in terrestrial and aquatic ecosystems are a function of a variety of factors. Atmospheric
residence times depend upon characteristics such as mode and rate of emission, atmospheric transformations,
and physical state.

Sulfur dioxide is regulated by EPA National Ambient Air Quality Standards and is considered both a health
and welfare risk.  The degree of ecological impact depends  on the  deposition rate, toxicity,  environmental
fate of the pollutant,  and organism sensitivity.   Deposition of and  exposure  to SO2 has the potential for
affecting vegetation through  disruption  of ecological processes. The major emissions of SO2  are associated
with point sources (U.S. EPA 1989).  The  pollutant is recognized as both a  health  and  welfare problem.
Research investigations near  SO2 point  sources have linked foliar injury symptoms in vegetation to gradients
of SO2 exposure (Stratmann  1963).

Exposure  indices, such as the summation  of  hourly average concentrations equal  to or above selected
thresholds and  the number of hourly average concentrations equal to or greater than a selected threshold,
are ways to summarize SO2 information  in a potentially biologically useful format. Research results have been
published  indicating that higher SO2 concentrations  are more important than the  lower  concentrations  in
eliciting adverse effects in trees (Stratmann 1963).  Therefore,  it is possible to design  exposure  parameters for
SO2 that can be associated with adverse effects.  The use of a  long-term average concentration  for SO2 should
be discouraged (Lefohn and  Moser 1989).

INDEX PERIOD:  Sulfur dioxide concentrations would be measured  continuously throughout the year. For
existing long-term monitoring networks,  sulfur dioxide is continuously monitored throughout the year in  most
cases.

MEASUREMENTS: Sulfur dioxide concentrations would be measured on an integrated weekly basis, where
a one-week accumulated sample would be  collected.  Hourly averaged measurements of SO2 (in ppmv) can
be obtained from data bases of existing monitoring  networks (e.g.,  EPA Atmospheric Information  Retrieval
System [AIRS], National Park Service, and the Mountain  Cloud Chemistry Program).

 VARIABILITY:  The expected spatial variability of SO2 concentrations within a landscape sampling unit would
produce ranges that  deviate  <20% from  the  mean value.  The  expected temporal  variability  of SO2
concentrations  during  the year could produce extreme measures that deviate as  high as 100% from the
mean value,  the  variability  depending  upon release from emitters  and  seasonal climatic patterns.  Sulfur
dioxide is more likely to be associated  with the emission strength of local point sources than with sources
located many miles from a monitor.  Thus, a  network of higher  density is required to adequately quantify
SO2 exposures  to vegetation affected by local point sources as opposed to nonpoint, regional sources.

PRIMARY PROBLEMS:  (1)   Sites monitored in existing atmospheric sampling networks are  geographically
clumped,  usually in  association with urban centers.  There  are few data  available from which  to  judge
changes in pollutant concentration relative to distance from these urban centers.  Of the 1831  SO2 sites in
the EPA AIRS data base, approximately 33% are designated as rural or remote. The National Park Service
does monitor SO2 at several locations around  the United States.   However, the SO2 data are available for

                                                H-4

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1983 to 1985 only. Quality control confirmations have not been completed on the data collected between
1986 and 1988. Sulfur dioxide is not considered a  regional pollutant, and therefore, it is not expected that
extensive networks focusing on SO2 exposures would be introduced  in the near future.

REFERENCES:

Lefohn, A.S., and  T. Moser.  1989.  Exposure indicators for  monitoring air  quality  in the environment.
Prepared for the Environmental  Monitoring  and Assessment Program (EMAP)  of the  U.S. Environmental
Protection Agency.  A.S.L.  and Associates, Helena, MT.

Stratmann, H.  1963.  Freilandversuche zur Schwefeldioxidwirkungen auf die vegetation.  II.  Teil:  Messung
und Bewertung der SO2-Emissionen (Field experiments for the determination of the effects of sulfur dioxide
on vegetation.  Part II:  Measurement and assessment  of SO2  emissions).  Forschungsberichte  des landes
Nordrhein-Westfalen no. 1184.  Westdeutscher Verlag (Koln and Opladen).

U.S.  EPA.   1989.   National air  quality  and emissions  trends report,  1987.    EPA 450/4-89/O01.   U.S.
Environmental Protection Agency, Office of Air Quality Planning and Standards, Research Triangle Park, NC.
                                               H-5

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H.3  INDICATOR:  Nitric Acid

CATEGORY:  Stressor/ Chemical

STATUS:  High-Priority Research

APPLICATION:  Nitric acid  is formed in the atmosphere from the reactions of various nitrogen-containing
compounds.  Once formed, the amount of nitric acid actually deposited on the landscape depends on several
factors.  For example,  a moist receiving surface will retain more nitric acid than one that is dry, and for a
given atmospheric condition, complex terrain induces more deposition than one in which less turbulence is
generated.

Nitric acid  is not now a regulated  pollutant but is believed to have  potentially major impacts on  human
health and ecological  condition.  Both human health and  ecological studies are planned or are under way
to identify human health and ecological effects and to determine the threshold at which symptoms or changes
are apparent  The effects of both peak and chronic exposures  will be investigated.

INDEX PERIOD:  This indicator would be monitored throughout the year.

MEASUREMENTS:  Nitric acid concentrations would be measured  by  continuously collecting material on a
filter surface  over  a  7-day  period  (a weekly integrating technique).   An accumulation  of material on a
collection  medium over a  24-h period could also be used.  Weekly samples would  be combined  to report
monthly, quarterly (seasonal), and annual results.

VARIABILITY: The expected spatial variability of nitric acid  within a  landscape sampling unit would be similar
to that of sulfur dioxide (Indicator  H.2); assuming terrain is not complex, ranges should deviate less than 20%
from the mean value.  The  expected temporal variability of nitric acid throughout the year could  produce
ranges that deviate as much  as  100% from the mean, the variability depending on climatological factors and
emission releases.

PRIMARY PROBLEMS:  Very few  sites are presently collecting nitric acid samples on a routine basis.  Thus,
estimates of spatial variability are unavailable or are unreliable.  The  sites that are  operating are primarily
located east of the Mississippi River and in national parks.  Sampling procedures sometimes introduce artifacts
into the process.  The filter  pack  method measures  total nitrogen compounds rather than being specific  for
nitric acid.  Where paniculate nitrate is not high, this method offers  a reasonable approximation of nitric acid
exposure.   The annular denuder  system has not been  successfully tested  for sampling periods longer than
several days.   Field calibration  of sampling equipment is not possible  at this time.  A field  device that can
accurately introduce  nitric acid into either a filter pack or annular  denuder has not been developed.

BIBLIOGRAPHY:

Hicks, B.B., and J.D. Womack.  1990.  Dry Deposition Inferential Measurement,  Test of the Filterpack  for
Measuring  HNO3.  U.S. Department of  Commerce, National  Oceanic and  Atmospheric Administration,
Atmospheric  Turbulence and Diffusion Laboratory,  Oak Ridge, TN.  In preparation.

U.S. EPA.  1987.   National Dry Deposition Network, First Annual  Progress Report.  U.S. Environmental
Protection Agency, Research Triangle  Park, NC.  July 1987.  52  pp.
                                                H-6

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H.4  INDICATOR:  Ionic Constituents in Precipitation

CATEGORY:  Stressor/ Chemical

STATUS:  High-Priority Research

APPLICATION:  In the atmosphere, chemicals emissions from natural and man-made sources are subjected
to physical, chemical, and photochemical processes, which may produce transformation products that can be
as toxic as or more toxic to ecological resources than the parent chemicals.  The atmosphere serves as the
conduit from emissions sources to the biosphere.  Once chemicals are emitted  into the atmosphere, their
pathways to receptors in terrestrial and aquatic ecosystems are a function of a variety of factors.  Atmospheric
residence times depend upon characteristics such as mode and rate  of emission, atmospheric transformations,
and  physical state.

Chemical deposition in wetfall is monitored by several research projects  in the United States and  Canada.
Chemical  deposition,  coupled with  other  pollutants,  may  have  direct  and indirect adverse  impacts  on
terrestrial vegetation and  aquatic biota.  For  direct  impacts on vegetation,  H+  appears to be  the ion of
interest.  However, for indirect impacts on vegetation, all  nutrient cycles are important  Therefore,  the nine
major ions measured by the wetfall networks appear to be important.  Because  of the interest in assessing
the effects  of acidic deposition on vegetation and the possible interaction of acidic deposition  with other
pollutants, it is  important to continue to monitor these nine ions across the  United States.

Although much is known about the effects of gaseous pollutants, such as  episodic ozone (see indicator H.1)
and  SO3 (see indicator H.2) exposures on crops, little  is  known about ion concentrations that  may impact
vegetation through nutrient cycling modifications. Although Lefohn and Moser (1989) have discussed episodic
exposure concepts, it appears that the calculation of monthly or annual means may suffice at this time.  Little
research has been  performed to assess the importance of concentration, duration,  frequency, and time
between episodes for the ions on vegetation.  The use of long-term integrated values of pollutant loadings
may be sufficient to evaluate effects on nutrient cycling mechanisms.

Those assessing the possible effects of acidic deposition on soils  and aquatic resources  have used deposition
values integrated over an annual period to estimate effects.  However, the use of an annual deposition rate
assumes that episodic and  nonepisodic  wetfall events have no differential impacts on ecological resources.
The  use of a deposition exposure parameter for assessing possible effects ignores the temporal variability of
wetfall  and assumes that ecological  resources do  not respond differently  to similar exposures over  different
growth periods. When long-term averages are used, it is assumed that the  temporal variability associated with
biological  processes occurring within the soil ecosystem is decoupled from the  temporal nature of wetfall.
Future  research should explore the validity of these assumptions.

INDEX  PERIOD:  Concentrations of the ions would  be measured  throughout the year.   For existing  long-
term monitoring networks,  these ions are monitored  throughout the year in  most cases.

MEASUREMENTS:  Concentrations of the ions would be measured on an integrated weekly basis.  Candidate
ions  include H+, Ca2+, Mg2+, K+, Na+, NH/, SO/~, NO3", and CI"  in rain, snow,  fog, and cloud water.  The
most extensive precipitation chemistry monitoring activity has been associated with the National Atmospheric
Deposition  Network (NADP)/National Trends  Network (NTN).  Precipitation  samples are collected by the
NADP/NTN network on a weekly basis.  The wetfall is collected in a large  bucket by an apparatus that opens
only when  precipitation is sensed.   Concurrently,  a  rain  gauge measures the  amount  of  precipitation
associated with the wetfall event.  In addition, the sampling network of  the Multistate Atmospheric Power
Production Pollution Study/Research in Acidity from Industrial Emissions Program, the SURE sampling network
of the Electric Power Research Institute, and the Utility Acid Precipitation Study Program sampling network,
                                                H-7

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although not as geographically extensive, do provide wet deposition data on a daily and/or event basis.  The
Mountain Cloud Chemistry Program has collected cloud water samples and  precipitation samples.

VARIABILITY:  The expected spatial variability of ion concentrations within a  landscape sampling unit should
produce ranges that  deviate <20% from  the mean value.   The expected temporal  variability  of  ion
concentrations throughout the year could produce ranges that deviate up to 100% of the mean  value.

PRIMARY PROBLEMS:   Research results to date apparently  have not  indicated that  acidic  deposition
exposures at ambient levels result in direct effects on vegetation.  Little is known concerning the ion exposure
regimes (concentration,  frequency, duration, and time between  events)  that are responsible for eliciting
biological responses.   Therefore, it is difficult to recommend, for the nine ions measured, specific exposure
indices for summarizing the weekly or event wetfall measurements collected.

REFERENCES:

Lefohn, A.S., and T.  Moser.  1989.  Exposure  Indicators for Monitoring Air Quality in  the Environment
Prepared  for the Environmental Monitoring and  Assessment Program (EMAP) of the U.S.  Environmental
Protection Agency.  A.S.L. and Associates, Helena, MT.


BIBLIOGRAPHY:

NAPAP.  1987.  Interim assessment:  The causes and effects of acid  deposition.  Vol.!:  Executive summary.
National Acid  Precipitation Assessment  Program,  U.S.  Government Printing Office, Washington, DC.
                                                H-8

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H.5  INDICATOR:  Metals and Organics (Toxins)

CATEGORY:  Stressor/ Chemical

STATUS:  Research

APPLICATION: Airborne toxic chemicals, either individually or in combination, may threaten the productivity,
stability, and diversity of terrestrial and aquatic ecosystems.  The effects from chronic deposition of airborne
toxic chemicals on terrestrial and aquatic organisms and their potential interaction with other stressors (abiotic
and biotic) to induce antagonistic to synergistic effects are unknown. The low biodegradability of many toxic
compounds allows them to remain ecologically harmful for long periods.  The persistence of these compounds
can result in adverse biological effects by incorporation and accumulation into food chains and disruption of
ecological processes. The degree of ecological impact depends on the deposition rate, toxicity, environmental
fate of the pollutant(s), and  organism sensitivity.

Toxic chemicals enter the atmosphere from a variety of anthropogenic emission sources, such as  chemical,
metal, plastic, and pulp/paper industries; oil refineries; combustion of coal and municipal wastes; incinerators;
motor vehicles; aircraft; dry cleaning operations; agriculture stubble burning;  and agricultural use.   Broadly
grouped, these chemicals, gaseous and paniculate, fall into two categories:  (1) organic compounds (which
includes volatile organic compounds and pesticides) and (2) trace metals.

In the atmosphere, these and other  more benign chemical emissions are subjected  to physical, chemical, and
photochemical  processes, which may produce transformation products that can be as toxic as or more toxic
to ecological  resources than the parent chemicals.  Once chemicals are emitted into the atmosphere, their
pathways to receptors  in terrestrial  and aquatic ecosystems are a function of a variety of factors, including
local meteorological conditions and  the physical and chemical properties of the compounds themselves. Air
toxin concentrations and deposition  rates can be  measured and compared among all regions of the United
States.

INDEX PERIOD:  If airborne toxic chemicals are not monitored continuously by the EMAP-Air and Deposition
Monitoring Network (Bromberg et al.  1989), a synoptic  measure of air toxin concentrations should occur
during summer, when  vegetation growth and susceptibility to air pollutants is greatest.

MEASUREMENTS:  Assuming that the appropriate sampling equipment is incorporated in the EMAP-Air and
Deposition Monitoring  Network, monitoring costs for both  volatile organic carbons  and  pesticides are
estimated  as  $700 a sample at each  site  or  $350 for either  group alone  (Bromberg et al.  1989).   The
additional cost  to measure trace metals was not estimated.

If only air toxin emissions data such as the Toxic Release Inventory (Poje et al. 1989)  is available, ambient
concentrations  and deposition estimates for resource  sampling  units could be estimated from atmospheric
models. A reasonable qualitative understanding of atmospheric transport and  deposition processes currently
exists.  Knowledge of chemical transformations, especially of  the  large number  of organic  substances,  is
incomplete.  Despite the degree of knowledge that exists for atmospheric processes, it is difficult to quantify
the complete  atmospheric pathway for a  particular substance between source  and receptor.  However,
residence times of chemicals in the atmosphere determine how far a particular substance is likely  to move
away from  its emission source.  Therefore,  atmospheric residence time  is a useful indicator for determining
the likely  gross depositional distribution of a  particular chemical, given  known prevalent  meteorological
conditions. Models for estimating residence times have been generated.

VARIABILITY:  The expected spatial and temporal variabilities of airborne toxic chemicals within a landscape
sampling unit and during the year,  respectively, were  not estimated.
                                                H-9

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PRIMARY PROBLEMS: The environmental exposure of remote areas to airborne organic chemicals and trace
metals has not been continuously measured.  Methods development or improvement projects are needs for
some metals and most organic compounds.

REFERENCES:

Bromberg,  Sv  E.  Edgerton, J.  Gibson,  and  D. Holland.   1989. Air/deposition  monitoring for  the
Environmental Monitoring and Assessment Program (EMAP) (draft).  U.S.  Environmental Protection Agency,
Atmospheric Research and Exposure Assessment Laboratory, Research Triangle Park, NC.

Poje, J., N.L. Dean, and J.B. Randall.  1989.  Danger downwind:  A report on the  release of billions of
pounds of toxic air pollutants.  National Wildlife Federation, Washington, DC.
                                              H-10

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H.6  INDICATOR:  Free Radicals

CATEGORY:  Stressor/ Chemical

STATUS:  Research

APPLICATION:  The lowest few kilometers of the troposphere typically contain the highest concentration of
reactive gases emitted by both natural and anthropogenic sources.  A complex series of oxidation reactions
converts primary emissions to secondary compounds that manifest themselves as components of smog and
acidic  deposition.  The  products  of these reactions generally  have lifetimes of several  days.   In  the
photochemical process, unusually  high concentrations of free-radicals (e.g., OH, HO2, NO2) are generated.
These short-lived compounds (lifetime of seconds) are highly energetic and, upon exposure to vegetation,
could be disruptive to its normal biochemistry.

INDEX PERIOD:  The sampling period would be continuous throughout the year.

MEASUREMENTS:  Direct measurements of free radicals are  not possible at this time, although kinetic models
and surrogate species provide reasonable estimates.  Laboratory experiments should first be devised to test
this hypothesis.

VARIABILITY: The expected temporal variabilities of free radicals throughout the year would produce ranges
that  deviate 100%  from their mean values.   The expected spatial variabilities of free  radicals within a
landscape  sampling unit were not estimated.

PRIMARY  PROBLEMS:  Serious problems exist in accurate,  controlled  generation of free  radicals and in
measurements and estimations of their concentrations.

BIBLIOGRAPHY:

Possanzini, M., A. Febo, and A. Liberti. 1983.  New design of a high-performance denuder for the sampling
of atmospheric pollutants.  Atmos. Environ.  17:2605.
                                               H-11

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H.7  INDICATOR:  Carbon Dioxide

CATEGORY:  Stressor/ Chemical

STATUS:  Research

APPLICATION:   Although not the greatest  absorber of infrared radiation  per molecule, the huge flux of
carbon into the troposphere by fossil fuel sources has made CO2 a leading factor in long-term climate trends
(Schneider 1989).  Also, the potential  fertilization effect of increased CO2  concentrations on  all vegetation
types makes it a potentially important  atmospheric stressor indicator.

INDEX PERIOD: This indicator would be monitored throughout the year because of its high variability among
seasons.

MEASUREMENTS:  The Geophysical Monitoring for Climatic Change  Division  of the National Oceanic  and
Atmospheric Administration has been  monitoring CO2  at two  Alaskan coastal stations since 1980; a third
coastal station in Florida was added in 1984. Monitoring techniques need to be developed for  and extended
to continental interiors to increase the accuracy of estimating interior distributions of direct CO2  exposure  and
important sources and sinks areas  in the carbon cycle (Tans  et al. 1990).

VARIABILITY:   The expected spatial and temporal variabilities of CO2 concentration within a landscape
sampling unit and during the  year, respectively, were not estimated.

PRIMARY PROBLEMS:  Very sophisticated measurements are needed over  large spatial scales.

REFERENCES:

Schneider, S.H. 1989.  The  greenhouse effect:  Science and  policy.  Science 243:771-781.

Tans, P.P.,  I.Y.  Fung, and T.  Takahashi.  1990.  Observational constraints on the global atmospheric CO2
budget  Science 247:1431-1438.
                                               H-12

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H.8  INDICATOR  Other Greenhouse Cases

CATEGORY: Stressor/ Physical

STATUS:  Research

APPLICATION: There is evidence that the chemistry of the global troposphere has been changing and will
continue to change.   The result may be significant changes in  climate that could affect the condition  of
ecological resources.  The chemistry of the free troposphere is characterized by gases which absorb  infrared
radiation yet have little direct effect on vegetation (e.g., CH4, N2O, O2 hydrocarbons, and halocarbons), and
their increase has led to the greenhouse theory of global warming (Schneider 1989).

INDEX PERIOD:  The tropospheric gases would be monitored throughout the year because of their high
variabilities among seasons.

MEASUREMENTS:   Measurements of the free  troposphere  for greenhouse gases are needed at several
latitudes, integrating  over various time intervals; their  concentrations would  be reported  as annual  and
seasonal  averages.

VARIABILITY:  The expected spatial  variability and temporal variability of tropospheric chemistry within a
landscape sampling unit  and during the year,  respectively, were  not estimated.

PRIMARY PROBLEMS: Very sophisticated measurements are needed  over large spatial scales.

REFERENCES:

Schneider, S.H.  1989.  The greenhouse effect:  Science and policy.   Science 243:771-781.
                                               H-13

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H.9  INDICATOR:  Ultraviolet Type B Radiation

CATEGORY:  Stressor/ Physical

STATUS:  Research

APPLICATION: Molina and Rowland (1974) hypothesized that photochemical reactions involving halocarbons
could diminish the protective ozone layer in the stratosphere.  There is now evidence which validates this
hypothesis (Cicerone 1987;  McElroy and Salawitch 1989).  An important consequence of this stratospheric
change is increased ultraviolet type-B radiation (UV-B)  exposure in the lower atmosphere and at the earth's
surface.  Increased  UV-B  intensity to terrestrial organisms  may have a significant  effect  on reproduction.
Increased intensity may also stress aquatic microorganisms,  which  in turn would affect organisms of higher
trophic levels.

INDEX PERIOD:  Strong diurnal and seasonal variability would require near-continuous monitoring during
daylight hours.

MEASUREMENTS:  Continuous measurements of  incident UV-B  radiation are  needed at several latitudes.
Such measurements are made at several U.S.  laboratories,  and historical data  should be readily available.
These spectroradiometric measurements are sophisticated and of high precision.

VARIABILITY: Strong  diurnal and seasonal  variability  is anticipated.   The expected spatial and temporal
variabilities of UV-B radiation within a landscape sampling unit and during the year, respectively, were not
estimated.

PRIMARY PROBLEMS:  Calibration standards  for the  UV-B monitors  are also needed.   The sensitivity  of
ecological resources to ultraviolet radiation is largely unknown.

REFERENCES:

Molina, M.J., and  F.S. Rowland.  1974.   Stratospheric sink  for  chlorofluoromethanes:   Chlorine atorn-
catalyzed destruction of ozone.  Nature 249:810-812.

Cicerone, R.J.  1987.  Changes  in stratospheric ozone.  Science 237:35-42.

McElroy, M.B., and RJ.  Salawitch.  1989.   Changing composition of  the global stratosphere.   Science
243:763-770.
                                                H-14

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H.10  INDICATOR:  Airborne  Particles

CATEGORY: Stressor/ Physical

STATUS:  Research

APPLICATION:  Extremes of particle loading in the atmosphere could have short-term effects on ecological
resources.  Such  extremes include severe dust storms, volcanic  activity, and fumigation of emissions from
electricity-generating plants.   In addition  to possible health effects, particle loading is principally related to
atmospheric extinction and the  assessment endpoint of visibility.  Particle loading could be particularly acute
in arid regions where visibility is generally outstanding.

INDEX PERIOD:  The sampling period would be continuous over all seasons.

MEASUREMENTS:  Particle loading is readily  measured by sampling air through filters and processing filters
by gravimetric analysis.  Visibility measurements are much more  sophisticated and subjective.

VARIABILITY:  Diurnal and seasonal variability in particle loading is significant  Visibility is highly variable and
depends strongly  on sunlight and sun angle conditions.

PRIMARY PROBLEMS:  Visibility measurements are problematic,  as indicated above.

BIBLIOGRAPHY:

U.S. EPA.  1987.  National  Dry Deposition  Network, First Annual  Progress Report.  U.S. Environmental
Protection Agency, Research Triangle Park, NC.  July 1987.  52  pp.
                                               H-15

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                  APPENDIX I




WORKSHOP CONTRIBUTORS AND TECHNICAL REVIEWERS

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1.1  Contributors to the Identification of Research Indicators Relevant to Near-Coastal Waters
John Boreman
University of Massachusetts
National Oceanic and Atmospheric Administration
Amherst, MA

Denise  Breitburg
Academy of Natural  Science
Benedict Estuarine Research Laboratory
Benedict, MD

Linda A. Deegan
Marine  Biological Laboratory
Woods  Hole, MA

Tom DeMoss
U.S. Environmental Protection Agency
Region  III
Annapolis, MD

Robert  Diaz
Virginia  Institute of Marine Science
Gloucester Point, VA

Jeffrey B. Frithsen
Versar,  Inc.
Columbia, MD

Jonathan Garber
U.S. Environmental Protection Agency
Environmental Research Laboratory
Narragansett, Rl

Michael  Haire
Maryland Department of the  Environment
Baltimore, MD

Frank Hetrick
University of Maryland
College  Park, MD

Fred Holland
Versar,  Inc.
Columbia, MD

James R. Karr
Virginia  Polytechnic Institute and State University
Blacksburg, VA
John Kraeuter
Rutgers University
Port Norris, NJ

Mark Luckenback
Virginia Institute of Marine Science
Gloucester Point, VA

Sam Luoma
U.S. Geological Survey
Menlo Park, CA

Foster  "Sonny" Mayer
U.S. Environmental Protection Agency
Environmental Research Laboratory
Gulf Breeze,  FL

William Muir
U.S. Environmental Protection Agency
Region III
Philadelphia,  PA

William Nelson
U.S. Environmental Agency
Environmental Research Laboratory
Narragansett, Rl

Joel S. O'Connor
U.S. Environmental Protection Agency
Region II
New York, NY

Tom O'Connor
National Oceanic and Atmospheric Administration
National Status and Trends Program
Rockville, MD

John Paul
U.S. Environmental Protection Agency
Environmental Research Laboratory
Narragansett,  Rl

Fred Pinkney
Versar, Inc.
Columbia, MD
                                                1-1

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Nancy N.  Rabalais
Louisiana Universities Marine Consortium
Chauvin, LA

Ananda Ranasinghe
Versar, Inc.
Columbia, MD

Donald C. Rhoads
Science Applications International Corporation
Woods Hole, MA

William Richkus
Versar, Inc.
Columbia, MD

John Scott
Science Applications International Corporation
Narragansett, Rl

Anna Shaughnessy
Versar, Inc.
Columbia, MD

John Stein
National Oceanic and Atmospheric Administration
Seattle, WA

Kevin Summers
U.S. Environmental Agency
Environmental Research Laboratory
Gulf Breeze, FL

Steve Weisberg
Versar, Inc.
Columbia, MD
                                                 1-2

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1.2 Contributors to the Identification of Research  Indicators Relevant to Inland Surface Waters
Marshall Adams
Oak Ridge National Laboratory
Environmental Sciences Division
Oak Ridge, TN

Roger Bachmann
Iowa State University
Ames, IA

Joan Baker
Formerly with Kilkelly Environmental Associates
Raleigh, NC

Don Charles
Indiana  University
Bloomington, IN

Gary Collins
U.S. Environmental Protection  Agency
Environmental Monitoring Systems Laboratory
Cincinnati, OH

Joe Eilers
E&S Environmental Chemistry
Corvallis, OR

John Giese
Arkansas  Department of  Pollution  Control  and
Ecology
Little Rock, AR

Dave HalliweH
Massachusetts Division of Fisheries and Wildlife
Westboro, MA

Steve Heiskary
Minnesota Pollution Control Agency
Division of Water Quality
St Paul, MN

Terry Hollingsworth
U.S. Environmental Protection  Agency
Office of Water
Washington, DC

Jack Jones
University of Missouri
Columbia, MO
Bruce Kimmel
Oak Ridge National Laboratory
Environmental Sciences Division
Oak Ridge, TN

Dixon Landers
U.S. Environmental Protection Agency
Environmental Research Laboratory
Corvallis, OR

Rich Langdon
Vermont Department of Water Resources
Waterbury, VT

Stan Loeb
University of Kansas
Manhattan, KA

Dave Marmorek
Environmental and Social System Analysts,  Ltd.
Vancouver, BC

Dick Marzolf
Murray State  University
Hancock Biological Station
Murray, KY

Chuck McKnight
Ohio Environmental Protection Agency
Columbus, OH

Ken Mills
Freshwater Institute
Winnipeg, MB
Canada

Ron Pasch
Tennessee Valley Authority
Chattanooga,  TN

Frank Rahel
University of Wyoming
Lararnie, WY

Ken Reckhow
Duke University
School  of  Forestry and  Environmental Studies
Durham, NC
                                                1-3

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Dave Rosenberg
Freshwater Institute
Winnipeg, MB
Canada

Dave Schindler
Freshwater Institute
Winnipeg, MB
Canada

Kent Thornton
FTN Associates
Little  Rock, AR

Steve Twidwell
Texas Water Commission
Austin, TX

Bill Wilber
U. S. Geological Survey
Reston, VA

Ron Williams
Oregon Department of Fisheries and Wildlife
Corvallis, OR

Chris Yoder
Ohio Environmental Protection Agency
Columbus, OH
                                                1-4

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1.3  Contributors to the Identification of Research Indicators  Relevant to Wetlands
James Andreasen
U.S. Fish and Wildlife Service
Division of Environmental Contaminants
Washington, DC

Robert Brooks
Pennsylvania State University
State College, PA

David Cooper
Colorado School of Mines
Golden, CO

Sam Droege
U.S. Fish and Wildlife Service
Breeding Bird Survey
Laurel, MD

Cindy Hagley
AsCi Corp.
Duluth, MN

Steve Hedtke
U.S. Environmental Protection Agency
Environmental Research Laboratory
Duluth, MN

Richard Horner
University of Washington
Seattle, WA

Bob Hughes
NSI - Environmental Sciences
Corvallis, OR

James Karr
Virginia Polytechnic Institute and State  University
Blacksburg, VA

Barb Kleiss
U.S. Army Corp of Engineers
Waterways Experiment Station
Vicksburg, MS

Suzanne Marcy
U.S. Environmental Protection Agency
Office of Water  Regulations and Standards
Washington, DC
John Maxted
Formerly with U.S. Environmental Protection
  Agency
Office of Wetlands Protection
Washington, DC

Ruth Miller
U.S. Environmental Protection  Agency
Office of Policy and  Program Evaluation
Washington, DC

R. Wayne Nelson
Science Applications  International Corp.
Denver, CO

Doug Norton
U.S. Environmental Protection  Agency
Environmental Photographic Interpretation Center
Warrenton, VA

Dennis Peters
U.S. Fish and Wildlife Service
National Wetlands Inventory
Portland, OR

Mark Rejmanek
University of California
Davis, CA

Bill Sanville
U.S. Environmental Protection  Agency
Environmental Research Laboratory
Duluth, MN

Charles Sasser
Louisiana State University
Baton Rouge, LA

Rose Sullivan
U.S. Environmental Protection  Agency
Environmental Photographic Interpretation Center
Warrenton, VA

Milton  Weller
Texas A&M  University
College Station, TX
                                                 1-5

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Bill Wilen
U.S. Fish and Wildlife Service
National Wetlands Inventory
Washington, DC
                                                  1-6

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1.4  Contributors to the Identification of Research Indicators Relevant to  Forests
Jerry Barker
NSI - Environmental  Sciences
Corvallis, OR

Joe Barnard
U.S. Department of Agriculture
Forest Service
Research Triangle Park,  NC

David Bernard
Environmental Social Systems Analysts, Ltd.
Vancouver, BC
Canada

Dean Carpenter
NSI - Environmental  Sciences
Research Triangle Park,  NC

Mike Castellano
U.S Department of Agriculture
Forest Service
Corvallis, OR

Carl Fox
Desert Research Institute
Reno, NV

Henry Gholz
University of Florida
Gainesville, FL

John Hazard
SCS Consulting
Bend, OR

Bill Hogsett
U.S. Environmental Protection  Agency
Corvallis, OR

Rich Holdren
NSI - Environmental  Sciences
Corvallis, OR
Carolyn Hunsaker
Oak Ridge National Laboratory
Environmental Sciences Division
Oak Ridge, TN

Dale Johnson
Desert Research Institute
Reno, NV

Nancy Leibowitz
NSI - Environmental Sciences
Corvallis, OR

Bob Loomis
U.S. Department of Agriculture
Forest Service
Washington, DC

Tom Moser
NSI - Environmental Sciences
Corvallis, OR

Reed Noss
NSI - Environmental Sciences
Corvallis, OR

Dan Oswald
U.S. Department of Agriculture
Forest Service
Portland, OR

Michael Papp
Lockheed  Environmental Service Corp.
Las Vegas,  NV

David Perry
Oregon State University
Corvallis, OR

Doug Robson
Ottawa, ON
Canada
                                                1-7

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Henry Short
U.S.  Fish and Wildlife Service
Washington, DC

Paul  Van Deusen
U.S.  Department of Agriculture
Forest Service
New Orleans,  LA

Jack  Winjum
National Council for Air and  Stream Improvement
Corvallis, OR
                                                 1-8

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1.5  Contributors to the Identification of Research Indicators Relevant to Arid  Lands
Penny Amy
University of Nevada
Biology  Department
Las Vegas, NV

Tim Ball
Desert Research Institute
Biological Sciences Center
Reno, NV

Peter  Bed low
U.S. Environmental Protection Agency
Environmental Research Laboratory
Corvallis, OR

Bob Breckenridge
Idaho National  Engineering Laboratory
Idaho Falls,  ID

Roy Cameron
Lockheed Engineering and Science Corporation
Environmental Program Office
Las Vegas, NV

Dean  Carpenter
NSI -  Environmental Sciences
Research Triangle Park,  NC

Jim Collins
Arizona State University
Department of  Zoology
Tempe,  AZ

Allen  Cooperidder
U.S. Bureau of Land Management
Branch of Resources
Denver, CO

Rob DeVelice
Nature Conservancy
Montana Natural  Heritage Program
Helena, MT

Dick Egami
Energy and Environmental Engineering Center
Reno, NV
Chris Elvidge
Desert Research Institute
Biological Sciences Center
Reno, NV

Sandra Feldman
Bachtel  Geotechnical Services
San  Francisco, CA

Chris Field
Stanford  University
Department of Plant Biology
Carnegie Institute of Washington
Palo Alto, CA

John Flueck
University of Las Vegas
Environmental Research Center
Las Vegas,  NV

Carl Fox
Desert Research Institute
Biological Sciences Center
Reno, NV

Susan Fransin
U.S. Environmental  Protection  Agency
Environmental Monitoring Systems Laboratory
Las Vegas,  NV

Fred Cifford
University of Nevada
Range, Wildlife & Forestry
Reno, NV

Jack Hess
Desert Research Institute
Water Resources Center
Reno, NV

Wes Jarrell
Oregon  Graduate Center
Department of Environmental Science and
Engineering
Beaverton,  OR
                                                1-9

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Bruce Jones
U.S. Environmental Protection Agency
Environmental Monitoring Systems Laboratory
Las Vegas, NV

Bob Kinnison
Desert Research Institute
Water  Resources Center
Las Vegas, NV

Tim  Kittel
Colorado State University
Natural Resource Ecology Laboratory
Fort Collins, CO

Ross Lunetta
U.S. Environmental Protection Agency
Environmental Monitoring Systems Laboratory
Las Vegas, NV

Jim McMahon
Utah State University
Department of Biology
Logan, UT

Dave Miller
Desert Research Institute
Energy and  Environmental Engineering Center
Reno,  NV

Wally Miller
University of Nevada
Range, Wildlife, and  Forestry
Reno,  NV

Ken Moor
Idaho  National Engineering Laboratory

Dave Mouat
Desert Research Institute
Biological Sciences Center
Reno,  NV

Jan Nachlinger
The Nature Conservancy
Reno,  NV

Ron Neilsen
U.S. Environmental Protection Agency
Environmental Research Laboratory
Corvallis, OR
Tony Olson
Battelle Pacific Northwest Laboratory
Richland, WA

Michael Papp
Lockheed  Environmental Services Corp.
Las Vegas,  NV

Cliff Pereira
Oregon State University
Department of Statistics
Corvallis, OR

Kelly Redman
Desert Research  Institute
Atmospheric Sciences
Reno,  NV

Dick Reinhardt
Desert Research  Institute
Atmospheric Sciences Center
Reno,  NV

John Reuss
Fort Collins, CO

Marty  Rose
Desert Research  Institute
Biological  Sciences Center
Reno,  NV

Dave  Schimel
Colorado State University
Natural Resource Ecology Laboratory
Fort Collins, CO

Stan Smith
University of Nevada
Biology Department
Las Vegas,  NV

Robert Szaro
U.S. Department of Agriculture
Forest  Service
Washington,  DC

Robin  Tausch
U.S. Department of Agriculture
Forest  Service
Washington,  DC
                                                1-10

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Paul Tueller
University of Nevada
Range, Wildlife, and Forestry
Reno, NV

Joe Walker
Division of Water Resources
Canberra
Australia

Peter Wigand
Desert Research Institute
Reno, NV

Sam Williamson
U.S. Fish and Wildlife Service
Fort Collins, CO
                                                  1-11

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1.6  Contributors to the Identification of Research Indicators Relevant to Agroecosystems
Basil Acock
U.S. Department of Agriculture
Agricultural Research Service
Systems Research  Laboratory
Beltsville, MD

Robert P.  Brecken ridge
Idaho National  Engineering Laboratory
Idaho Falls, ID

Gerald Byers
Lockheed  Engineering & Sciences Co.
Environmental Programs Office
Las Vegas, NV

Roy Cameron
Lockheed  Engineering & Sciences Co.
Environmental Programs Office
Las Vegas, NV

Lee Campbell
North Carolina  State University
Department of  Plant Pathology
Raleigh, NC

Dean Carpenter
NSI-Environmental Sciences
Research Triangle Park, NC

Anton C.  Endress
State Natural  History Survey
Champaign, IL

William A. Feder
University of  Massachusetts
Professor of Plant Pathology
Waltham,  MA

Arthur Forer
York University
Biology Department
Toronto, Ontario
CANADA

Carl Fox
Desert Research Institute
Biological  Science Center
Reno, NV
Timothy j. Gish
U.S. Department of Agriculture
Agricultural Research Service
Hydrology Laboratory
Beltsville, MD

F.  Gregory  Hayden
University of  Nebraska
Department of Economics
Lincoln, NE

Alan Heagle
North Carolina State University
Air Quality Program
Raleigh, NC

Walter W.  Heck
U.S. Department of Agriculture
Agricultural Research Service
North Carolina State University
Air Quality Program
Raleigh, NC

George Hess
North Carolina State University
Air Quality Program
Raleigh, NC

Patrick G. Hunt
U.S. Department of Agriculture
Agriculture  Research  Service
Coastal Plains Soil and Water
  Conservation Research  Center
Florence, SC

Gordon L.  Hutchinson
U.S. Department of Agriculture
Agricultural Research Service
Soil-Plant Nutrient Research
Fort Collins, CO

Bruce Jones
U.S. Environmental Protection  Agency
Environmental Monitoring Systems  Laboratory
Las Vegas,  NV
                                                1-12

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Donald T.  Krizek
U.S. Department of Agriculturue
Agricultural Research Service
Plant Stress Laboratory
Beltsville, MD

Kathy W.  Kromroy
University of Minnesota
Department of Plant Pathology
St.  Paul, MN

John A, Laurence
Cornell University
Boyce Thompson Institute for Plant Research
Ithaca, NY

Ralph A. Leonard
U.S. Department of Agriculture
Agricultural Research Service
Southeast Watershed Research
Georgia Coastal Plain Experiment Station
Tifton, CA

Laurence V. Madden
Ohio State University
Department of Plant Pathology
Ohio Agricultural R &  D Center
Wooster, OH

Jay Messer
U.S. Environmental  Protection Agency
Atmospheric Research and  Exposure Assessment
Laboratory
Research Triangle Park, NC

Joseph E. Miller
North Carolina State University
U.S. Department of Agriculture
Agricultural Research Service
Air Quality Program
Raleigh, NC

Tom Moser
NSI Environmental Sciences
Corvallis, OR

James L. Olsen
North Carolina Department of Agriculture
Department of Agricultural  Statistics
Raleigh, NC
Michael Papp
Lockheed Engineering & Sciences Co.
Environmental Programs Office
Las Vegas,  NV

Katie Perry
North Carolina State University
Department of Horticultural Science
Raleigh, NC

John Rawlings
North Carolina State University
Department of Statistics
Raleigh, NC

Jack Sheets
North Carolina State University
Department of Entomology
Raleigh, NC

Henry Short
U.S. Fish & Wildlife Service
National Ecology  Research  Center
Fort Collins, CO

Lee Shugart
Oak Ridge National Laboratory
Oak Ridge, TN

Matthew Somerville
North Carolina State University
Air Quality Program
Raleigh, NC

Bob Swank
U.S. Environmental  Protection Agency
Environmental Research Laboratory
Athens,  GA

Ronald Tauchen
U.S. Department  of Agriculture
Research and Agricultural Development
Fairfax, VA

Robert D. Totora
U.S. Department  of Agriculture
Research and Agricultural Development,
National Agricultural Statistical Survey
Washington, DC
                                                1-13

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Mike Treshow
University of Utah
Biology Department
Salt Lake City, UT

Jonathan  Wacker
U.S.  Environmental Protection Agency
Environmental Monitoring Systems Laboratory
Las Vegas, NV
                                               1-14

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1.7  Contributors to the Identification of Research Indicators Relevant to Multiple  Resource Categories
Animals

Paul Adamus
U.S. Environmental Protection Agency
Environmental Research Laboratory
Corvallis, OR

Bob Breckenridge
Idaho National Engineering Laboratory
Idaho Falls, ID

Peter Brussard
University of Nevada
Reno, NV

Bruce Bury
U.S. Fish and Wildlife Service
Fort Collins, CO

Greg Butcher
Cornell  University
Laboratory of Ornithology
Ithaca, NY

Glenn Clemmer
The Nature Conservancy
Carson City, NV

Jim Collins
Arizona State University
Department of Zoology
Tempe, AZ

Sam Droege
U.S. Fish and Wildlife Service
Laurel, MD

Curt Flather
U.S. Forest Service
Fort Collins, CO

Bruce Jones
U.S. Environmental Protection Agency
Environmental Monitoring Systems Laboratory
Las Vegas,  NV

Jim MacMahon
Utah State  University
Logan, UT
Ken Moor
Idaho National Engineering Laboratory
Idaho Falls,  ID

John Sauer
U.S.  Fish and  Wildlife Service
Laurel, MD

Henry Short
U.S.  Fish and  Wildlife Service
Arlington, VA

Sam  Williamson
U.S.  Fish and  Wildlife Service
Fort Collins, CO

Biomarkers

William H.  Benson
University of Mississippi
University, MS

Richard T. DiGulio
Duke University
Durham, NC

David E. Hinton
University of California
Davis, CA

Foster Mayer
U.S.  Environmental Protection Agency
Environmental Research Laboratory
Gulf Breeze, FL

Ann  B. Weeks
University of Virginia
Charlottesville, VA
                                                1-15

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1.8  Technical Reviewers
Document Integration

Larry Barnthouse
Oak Ridge National Laboratory
Oak Ridge, TN

Glen Suter
Oak Ridge National Laboratory
Oak Ridge, TN

Near-Coasta/ Waters

James R. Karr
Virginia  Polytechnic Institute and State University
Department of Biology
Blackburg, VA

Sam Luoma
U.S. Geological Survey
Menlo Park, CA

Joel S. O'Connor
U.S. Environmental  Protection  Agency
Region II
New York, NY

Inland Surface Waters

David Lenat
North Carolina Department of Environmental
  and Natural Resources
Raleigh, NC

Wetlands

Robert Ohmart
Arizona State  University
Center for Environmental Studies
Tempe,  AZ

Forest Stearns
University of  Wisconsin
Department of Biological Sciences
Milwaukee, Wl
Forests

Kermit Cromack
Oregon State University
Department of  Forest Science
Corvallis, OR

Frank Davis
University of California
Department of  Geography
Santa Barbara,  CA

Ian Morrison
Forestry Canada, Ontario Region
Great Lakes Forestry Center
Sault St. Marie, Ontario
Canada

Arid Lands

James MacMahon
Utah State University
College of  Science
Logan, UT

Laurence L. Strong
Ames Research Center
Moffett Field, CA

/Agroecosystems

John A. Laurence
Cornell University
Boyce  Thompson Institute for Plant Research
Ithaca, NY

Ralph A. Leonard
U.S. Department of Agriculture
Agricultural Research Service
Tifton, GA
                                                1-16

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Animals

Bruce Bury
U.S. Fish and Wildlife Service
National Ecology Research Center
Fort Collins, CO

Bob Szaro
U.S. Forest Service
Washington, DC

Biomarkers

Larry Kapustka
U.S. Environmental  Protection Agency
Environmental Research Laboratory
Corvallis, OR

Bill Mauck
U.S. Fish and Wildlife Service
National Fisheries Contaminant Research Center
Columbia, MO

Landscape

Kevin Price
University of Kansas
Department of Geography
Lawrence, KS

Atmospheric Stressors

Eric  Edgerton
ESE, Inc.
Durham, NC

George  Taylor
Desert Research Institute
Reno, NV
                                                1-17


                                                    •fr U.S. GOVERNMENT PRINTING OFFICE 1990— 726-063  '0

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