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
-------
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.
-------
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.
-------
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
-------
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
-------
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
-------
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
vi
-------
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
vii
-------
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
viii
-------
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
-------
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
-------
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
-------
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
xii
-------
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
-------
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 :
^^ ^^
S"^ ^^+*^
/ SPATIAL \
/ ASSOCIATIONS \
1 i——~ —
V TEMPORAL /
\ ASSOCIATIONS /
— T""\
f
/ \ ^
/ , \
Exposure-Habitat
Indicators (E)
Bio markers
Pathogens
Btoassays
Tissue Concentrations
Ambient Concentrations
-Water, Air. Soil, Sediment
Exotics /GenefcaJry-
Englneered Organisms
Habitat Structure
Landscape Pattern
/ ; >•
1 \
Hazard Indicators Natural Process Indicators
Atmospheric Deposition / Emissions
Demographic* Climatic Fluctuations
Discharge Estimate* Pest-Disease Relations
***** P««dde Applications Predator-Prey Relations
Permits
Successtonal Stage
Pollutant Loadings 1
>- ^^^ J ^ _J
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
-------
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
xv
-------
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.
XVI
-------
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.
xvii
-------
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
XVIII
-------
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
XIX
-------
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
xx
-------
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.
xxi
-------
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.
XXII
-------
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.
XXIII
-------
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.
XXIV
-------
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.
XXV
-------
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.
xxvi
-------
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.
1-1
-------
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
1-2
-------
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.
1-3
-------
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.
1-4
-------
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
2-1
-------
Response
Indicators
-------
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.
2-3
-------
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
2-4
-------
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
2-5
-------
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.
2-6
-------
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
2-7
-------
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.
2-8
-------
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
2-9
-------
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,
2-10
-------
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
2-11
-------
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.
2-12
-------
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.
2-13
-------
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).
2-14
-------
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
2-15
-------
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
2-16
-------
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
2-17
-------
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.
2-18
-------
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
2-19
-------
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.
2-20
-------
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
-------
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
-------
IS
IQ
> "
111
b
-Si
11
< Q.
o
£3
^ c
_ 2
•o o
^3
N
u.5
Clil.
o
2-23
-------
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-
35.
Cairns, J., Jr., and J.R. Pratt. 1986. Developing a sampling strategy. Pages 168-186. In: B.C. Isom, ed.
Rationale for sampling and interpretation of ecological data in the assessment of freshwater ecosystems.
ASTM Special Technical Publication 894. American Society for Testing and Materials, Philadelphia, PA.
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.
Environmental Protection Agency, Environmental Research Laboratory, Corvallis, OR.
Fava, J.A., W.J. Adams, R.J. Larson, G.W. Dickson, K.L Dickson, and W.E. Bishop. 1987. Research
priorities in environmental risk assessment. Society of Environmental Toxicology and Chemistry. Washington,
DC.
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,
New York.
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
using cokriging. Bull. Ecol. Soc. Am. (Suppl.) 70(2):154.
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 Rationale. Special Publication 5. Illinois Natural History Survey,
Champaign, IL.
Lotspeich, F.B. 1980. Watersheds as the basic ecosystem: This conceptual framework provides a basis
for a natural classification system. Water Resour. Bull. 16(4):581-586.
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.
2-24
-------
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
13:12-20.
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.
371 pp.
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.
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. Aquat. Sci.
44(Suppl. 1):6-25.
Shannon, C.E, and W. Weaver. 1963. The Mathematical Theory of Communication. University of Illinois
Press, Urbana. 117 pp.
Shelford, V.E. 1963. The Ecology of North America. University of Illinois Press, Urbana. 610 pp.
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.
Shugart, eds., Biomarkers of Environmental Contamination. Lewis Publishing, New York. In Press.
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,
ed. Advances in Plant Nutrition, Vol. 3. Elsevier, New York.
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.
2-25
-------
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
-------
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
-------
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
-------
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
3-3
-------
• 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
-------
1
II
E
3
II
•6
X
X
.2
.2
u
c
o
1
$
EL.
S
m
f
1
_
i
u
S
^
o
S
c
~
(3
01
E
S
"o
c
•i
S
3
•
fi
JU
i
il
(j£
Ji
•jj
'?
Ju
.S
5
.S _
lol
=2
^ ^
II
^S
*2
ft
< c
ts
O
.£
I
1
n>
|
J
ffS
u
«1
<^5
fe
— hU
c
§1
t?c
41 ^
lib
0 C V
Candidate
Indicator
SS I ^^ r_,5^ iS_,^ -i _, Z _,
51 _i 55 5^^I 55lj -,5 5_,
55 S 5-, Z _, z z SIXX _,Z XS
51 5 5_, ISSI IIXX _i z XI
^5 5 ^ _, ^555 z 2 -i _, 51 15
II 5 SX ISXX ^XXX 55 II
XI 5 II 555-, 55^^ SI IS
X^ 5 ^_ 5^-jS X 2 _, _ _i -j Z _i
55 5 55 55X1 ^ I z z 5_, _, I
II S II 1155 _i _i Z Z Z _i _i _i
X5 _. 51 x z z x x z 5 x -.-. XX
|
5 T
=| r z 5 _,5 15X5 5555 Js_, ^ 5
3-5
-------
. (Continued)
i
rt
Jf
JD
&
'1
ll
Uuj
J>
1
*3
r(
O
T5
o
u
•a
•2 _
Jd
Z2m
0)
=2
5 3 °
— j5v*j
"2%
i|
5s
•^
II
< a
^
w
c
c
c
0£
•%
c
J
tn
fll
.§5,3
w^O-s:
*v
i
c
a> ^
^ £
a>^
c^5
^
?J5
£b'
fi/
IS
IN
T3''
§^
5b
o
K<
ft/ i
53
* C^Tl
ar
£
<§!
Z -> Z
z _. z
z z z
Z Z r
-i X Z
Z Z I
_i _i T
z _,
z z _,
z z r
Z Z I
111 ^
II 1 -1
s
g
3
n
•D
^
4 I
1 i
•T, S
$1 s^
fp- M
2 *> Mi>
«f "c
1 ^*
1? ll
^E --8
lift*
s-rE s-5
= S3 *1
S = So-"
§i!s5?
0 5 oSS"
111 If*
§ a.^ 5 ^ S
S^I^f
silil!
-S^il
g«:s.3
5"S ^-T 5 ^
i^r^5^
rx B^i O T- C
51
3?
•**
85
s§
•f H
1 fr
1 II
1 sll
w »• Js
1 .ill
3y> 5 P V
_. $ 6.S *
JO 3. -n O
Si 3 23^
e-c ««
= = =^§
i changes in processes or other
•road range of teaioriat ecosytier
rts ovef time and space.
iiguousfy and mofKrtonically to l
iiied fay synoptic mom ton no or I
> the ovctall structuie and luna
^1!
SSjIS-a
|J||-*|
5 fc|» S—
U<£MUJ:
.-rN^^rm*
3-6
-------
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
-------
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
-------
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
-------
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
3-10
-------
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.
3-11
-------
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.
3-12
-------
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.
3-13
-------
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.
3-14
-------
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.
3-15
-------
• 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.
3-16
-------
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.
3-17
-------
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.
3-18
-------
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
-------
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
-------
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.
4-3
-------
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
-------
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)
4-5
-------
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
-------
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
-------
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
-------
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
-------
•£
WD
z
II
S.X
.2
I
u
§
I
1S2
o^ t
"O.C
II
ft
n
i/)
•o
_-o
1
V
.a
u
ri
4
V
rir 2 IXI---1XS-1-.
S S 5
rrz 2 r^r^rs2
xss 2
SSS 5
r r r r
IZ5 i
r rrxxi-.
I S S 5 SI
2 r r -•
r irrrxr-.-j
i r J- r rrsrrsssi s 5 5 _,-_,_,
2 I 2 2
122 i 2^z^-"r2r
2 2 r r zi
.S -i i r x i-,-
» 1 J-S
I 2 -> -•
s
^
II
2-
t
i i
B
25"
5
PPP
4-10
-------
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
-------
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
-------
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
-------
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
-------
&.
<
^
,0
ft
_o
T3
^
u
c
o
s"
^
•o
c
fc
I/I
'S
5
£
c
I
2 S .*
2£oje*tj;=3jS3?
i£ £
* -^ " S.
Hi" *B|
" 5 5 •*
c ' c SR ./> c ^ S?
'•« •§ J* i 1 $ S
Oj nj v,p -p C °° xP
- 1
^ ~% £b 2 2 6O"o
| ~ I | 1 1 8
1 1 1 1 8 1 j
Si
5
„
i
ill 1
sP O-f V *9
^ S o> c £
k cL-g .92^3
V Wl *fl t/i flj * iA
e ^ ^ ^ " *> «T
| £ E E^rjzl
-S |"|- = ^| ^^ 4,
"Xt *• £ C 4* 3
f- - •« "§^i""^ci ^ ^^^"5
i-,,,, |;a Ii lii.^ H If
= 'l~^ ^S"? .£PC LLJ-SC^S-^UjSjSLlJ-O'i
— 3>*-*o ^w.c — js ^°a>oi<*'>=v^c=:^sE
Jjcoy oTJo. oE D.SSo^tDnDoa
o o o 0 _r*o® o^ Ooo *^c £6 T^ o c o
sis ?
,*9 IS " •— ^-. J-T1"
5C ^ T5 P c aj4>'>-SO™o>-n!(5
IIHIHlil ^"-~Hi|1|-e
'51 " 1 ,r! -= _^'l^ 0) °Q.a-O.CL-TV = X = cQ-
u. i/i C 1/7 «•> JZ U. t/> C (/) tA 1/1 m i i CJ^ v C ._ 3 •£
= Si o> '0 a. ^
C> -C O. D. fl; o c
v? % u*n"lQ_0,C'
§ 1 s I .$- i s .11 : l .s 1 1 §
•S | g Jr-§ z |b S -o -S s '€ = - g 1 1 =
1-1 £^ f lls-|2^5|||j
a-l^y s ; il.c.£.s.s.|-B^^
o p* JS *A •— ^19 iA>/iOft) U (jj ft, —
u^So-teC^^ u-i y^a>4>DS4>S;i'«
i.ll|S.S5i 1 '§=l?y ss||sssg
ll^IIIll 1 llllilHilll
u
1
•*i
w
—
c
.2
oP
c
o
"o
-c
s
2
*
•£
^
G
2
1
^
00
-D
a>
2
^
|
>
C
U
(^
•o
C
*s
_c
-=
w
1
"S
: Condition Index (BCD will be u:
'€
m
_c
c
S
.§"
oo
I
— >
ir streams.
•£
"2
.5
c
^
i/i
for lakes; macroinvertcbrates, fi
es.
"2 ~i
15 -*
"c *
" t>
- V
-C c
*~- ^s
£ 1
™ "^
=5 §
fc-SP
2 Includes sedlmenta
3 Determined from r
4-15
-------
•^
c
fl
_c
0.
<
a
o
id Exposure Indicators
u.
n
a
c
V
E
o>
S
1
1
LU
c
O
E
S
<£
1
1
E
§
•5
c
UJ
_
•s
c
CL
C
fij
< £
f S
CQ -5
£0 (j
2 I/)
V
3
H
Indicators
c
V
CJ
^
%
S
1
1
•o
c
LU
0
E
<
O>
D
1
c
O
LfeJ
55
>n of consumption criteria >1
w
1
-^
1
o
£
i^>
—
o
o>
X
E
"o
c
o
%
concen
1
2
vfi?
JT
<=
<
y
CL ,/.
O "U
1 1
I 1
if I! B
|| if |
3 -X T> -* g
1/1 Q- C -C £
U 0 ^ §- S
"o. ^ — "^ LJ"
li 15 <
_^ DO
^
a
4* 7"
E
oo -,-
°"
Q. n ^ O i
£ » *J t)
g- O -O
i 2 •€
i* 1
LT,
"^r i^
^5 >^
0> -^
5 "5
>- 5
Q_ •-
O
§ 1
s? &
f*.
'£
3
•& ?
1 E
c o
i 1
C w
£ H
W V
< <
-§
o
u
-£
1
C>
u
WD
'0
1
f 1
S" i
c ^
S 0
1 1
c §
ll
S H
1-1
2
^
o-
o
«J
1
i
"O
1/1
<
o
T)
_C
^
fl3
a
^
<^
a*
"™
^ •£ d
5 g-. e
1/1" o
S o SP
O -j >
Q. u ~O
, c
8^0
• 5 "^
g e--2
e? ^ S
C "^ P
•5 = §"
|||
— u
111
— -^ ^C
• • £ o
s •»- ^
< i S
_ ^ >
CO
_c
'^
c
i
~£
5
c
u
"?B
w
.*;
ffl ^
1
||
-— «1>
1 J
5 -C
L/l
A
uS"
rX
,1
to
Lft
^O
^ ^
X —
aj
^" l>
S ^
£"V t*
*TJ ^9
S .«
^o _™
cf
o
S
If
>
1
1"
2
1/1
_^
1
-^ lj
ll
% 1
S.I
4-16
-------
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
-------
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.
4-18
-------
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
-------
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
-------
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
-------
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
-------
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
-------
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
-------
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
-------
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
-------
3
II
5
I
.2
i
u
*
CL
sl
i
2
3
1
2
T5
1
I
0
a
2
LLJ
LO
ui
-p.
"
.a
.a
u
11
Jl
X 2 2 X 2 2
I S I IS
I 2 Z Z-' I 5SS
X I x 21 i
i
X2 2 r x -. -i r s
55 2
ids
X I 2
X X I 2 12 2
"•I" 3
I X I 52 X 121 X5
I 2 2 2r 2 222 -2
i 2 I 12 r rr2
r x -• 2 252 -i x
r r 2
-• 5 -J
5 2^555
si
I
J|
r i r
i iii ii i r x x -i x
idii
111
2 X 5 ->2 2 121 XX 5^1115X2
I i fi I ! I j jlj
11 in I III lil III III III
i
i i
.
5-7
-------
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
-------
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
-------
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
5-10
-------
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
5-11
-------
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
5-12
-------
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.
5-13
-------
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.
5-14
-------
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.
5-15
-------
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
6-1
-------
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
6-2
-------
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).
6-3
-------
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
-------
?
II
•fc
3
1
ii
^"
ii
i
.2
—
0
e
^o
^j
•5
a.
1
*
0
u.
O
5
'•u
13
•3
•o
3
u
3
'B
c
3
T3
>
LU
7
*
jl
2a
if a
a>
f
h.
I
^3
s
^
|3|
Tl
is
«S
•V
'll
< Q.
fx
1
c
O
a.
£
^
\
1
e
|||
I
Ii
SI
fe
II
c
o
11
tf a.
5 ?
4* ^~
UD5
V
II
•o 3
51
5Z-2S ISSZ^I^
IIS5
2SSSSS
rS5i5:s
xi:rr SrSrrrS
If
il^ P
s*^ If
5-c Si u
E»3^
*c5 2~2
ofsg jv
11"8 =^^
g |5 S 3 e
1 *% N*
1^ I - S
"" >-^ C ^ >
mi
3 f 2 E r
g jjj |-|
H«l
i^fl;
jSjiS-|
am
SZs&d*
6-5
-------
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
-------
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
-------
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
-------
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
-------
• 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
-------
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
-------
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
-------
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
-------
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
-------
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
-------
?
2
ii
•J
E"
a
1
5
II
*.
6C
Z
II
r
.2
3
b
u
c
_o
1
*
0.
kU
I/)
c
b»
O
o
c
7s
15
c
'o
1
i
tx
2
_2
0
_
'£
U
3
^ n
.2^1
3=
S ? c
f SiS
"il
1^
^2
•is
< c.
„
o
c
0
%
c
c
a
I/-
11
E
CJ C
^? c
il
a.
&€
IS
c
_ c
1?0.
o c ^
Candidate
Indicator
= * = * * 5 r
2 r 5 5 2 2 2
X S 5 S - 5 S
,
2 S ~ ~ 2 5 2
- - - - Z - ~
z r 2 r - ? ~
- - - - 2 5 r
, - 5
1
» = 1 4 -i S ,9
7S-c"*ffl|"5 a-
= S "
!I
X 2 S
r s s
r r 2
r z r
: 2 2
= 22
r 2 r
4 = F •$-!
?£=?-£
S-B £ S2
5 S 5 X - 5
r 2 2 r r 2
- 2 - 2 12
22 22 22
II II
7-3
-------
f
3
.E
1
t
15
.2
s
*
-
u
3
4;
l|
•c -
|S o
HOc
3-
IJ§
Tl
II
6v
c ~
< ex
c
c.
c
c
c.
J
^c
-pr C C
£ = 5
i7,o*
.^
E
o §
^? c
21
w
£bt^
II
C
— ^
c 0
.2-=
c7c_
&<
£ ^
'r? !£
Hi
UD5
Candidate
Indicator
55 5 __z-5-
-J— 1 — _(__ _ __
. _
- - _ _ 5 - _ 5 _
J _ » _ _ TJ
-5 5 rszjjr
"5 5 55S - 55
55 - -55 5 -5
55 5 - _ : _ ?s
-5 - -55 - rr
-5 5^515 - 55
1/1
1
! J . * *, *
•5S - 1T3CS' .-?!'
|l^ i t?J 1 |i T-f S-« sl^- 'ifs c
is^ s xcl 8 ^c fj 1= ^=- 5z^ 5
•dQj? CD UU> U 5cc <£c- \ju >£- Zo^: E
3
"
2
-c
-1
_c
£
1 1
1 i
II Ll
£i Dc3
tS s c
f» JI
!ll It
]|||j|
-; S.J-2 if
- - ^ . —
-% t
|1
Si
J J
"f*
I 2
li
£ c i
C «e ^
1 II-
c E r
i ill
ii iii
* E t-C: ^
!£ s c i
C ?" ^-Vr^
Co "** ="c
llljlj
£J^ i E j i
il!!l
^~C C C_ *•
u c i: S.t"^
liilli
u < £ c£ L; JT
7-4
-------
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
-------
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
-------
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)
7-7
-------
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
-------
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
-------
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.
7-10
-------
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
-------
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
8-1
-------
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
8-2
-------
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.
8-3
-------
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
-------
f
II
*.
3
1
II
•a
X
II
X
.2
v
u
.2
1
#
5
'>.
1
1
1
<
h.
1
_u
e
3
3
^
<3
E
"o
c
TS
^
UJ
•
2
il
i
z
1
"3
*c
u
00,
13
2 5 **
XGffl
o>
|S 2
-o<*_
li
^5
ft
< a
_>
'^
I
1
f"3
£
*&
="3 ^
^a^
'c
c
S c
-r; o
2C5
4^
?J-1
^ 1*
IS
C
O
^ ?3
ff S
«8
w p1^
0 c i
[^J_J 3
Si
-i I -i r X S Z S-. S S Z -^ Z -- ^ Z
Z X - I X Z - - -, ? S - 2 ? 5 -1 2
- Z - X X X Z Z j S S S 5 | . - |
S I S I IXS XS S 5 S SX^ ZX
ZXZX - - S -X X X Z X^Z - -
ZXXX XXX 2 S j - j ZZ5 !!
XXXX XZZ XX Z X Z ZZj ZZ
-, s - r zz- -- z - z _ _ - -z
ZZ-x zrx ZZ Z Z - ZSZ --
zxxzxxz - I I - zzrz!z
SX-zzzz r?zr - S - - - -
lixzx xxx z? Z z |z szz Z-^
~ J!
t -s
3 z
i«-hllfu«i it iJllfiJ ItlllliWiili
I I-l ^ si ^zE =- j-f 3|j| =2 J|= |/| il 1 J^> 1 N "3|| ^8 TSS
3 ll a zl --^ a 5£ 5jrio ?i ^c* 5^5 /= 3 cc= 2 ?-: scz 5z ±<3
s
_>
1
3
|
§
^
1 1
1 |
_ J Jj Q
ii| li
- ^ 5 t"2
5 = ? I'5 '
ffiJ
3 — ^ -5 ** S
•var-^;^;^
li
T S
i 3
> c
jl
5 "
p-=
i ll
1 111
^ "*-p ^
!«• ill
H ^j,;
l| 35|
3^1|1
|li-|||
c^alfl
5 c^ £.^|
c - 1"5^°
-c"^ ° 5"S —
-£^^-53 o
•JlilsJ
8-5
-------
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
-------
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
-------
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
8-8
-------
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.
8-9
-------
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
-------
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
-------
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
-------
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
-------
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
-------
If
.2
I
u
J
1
a
h.
1
^Q
J5
a.
^
5
w
f
$
O
*<
1
H
MM
1
*E
o
•c
4«
«g
j»x
•tf
.0
2.
fl{
^^
l
£
1
t
V
t
£
£
1
1
:>
1
V)
s
.t
1
€
<
s
CO
«
m <£
•t> ef «
ill
U Q CO
g I-
0-c
-s
m
c
15
Ic
g
s
'S.
o?
0>
f
i
E
5 =
•2 nj
CO
c
o
•«
t>
•*?•
•a$
l|
u JE
X 2 22 X X -J2-'-J -1 -1 -1
2 2 X2 X -1 -'-'2-' -1 -1 -1
2 x-' -' 2 -J25-J -1 I 5
2 X 2-1 -1 X -•xx-' 2 X X
X X X-1 2 X 2-Jz-' -1 2 J
2 2 X2 -1 X -1xx-J 2 X ->
X X X-1 2 X 22X-1 X X X
X X X-1 2 -1 -'-'2-' -1 -1 -1
2 -1 ^ -1 2 2 22X-1 -1 2 -1
- X -x - - ^^x-, 5 x X
X 2 X-1 -J X 2~'X-1 X X ->
e •§ e -o
1 ^ a " « 1 ^
•— -ii *i ^* *9 aj 03
«.,""« V % " =
E 1 S 1" ^ I" i SB- 1 2>- P *9 _o c 5
sJi'g «g sS'o^s; s~a - •« "«
do u c ^ — S — "0 3 = SJiu .- S >
!! ii 1 11 si 11 1 .1 i !! |* II 1
i« il i ii H ii 1 1 j ^s i! ii !
11 11 f II I! ii s 1 1 si It II 1
1
II
E"
I
2
•S
.c
II
X
m
9-4
-------
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
-------
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
-------
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
-------
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
9-8
-------
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
9-9
-------
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.
9-10
-------
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
9-11
-------
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).
9-12
-------
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.
9-13
-------
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
9-14
-------
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
9-15
-------
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
9-16
-------
(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.
9-17
-------
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
9-18
-------
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.
9-19
-------
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.
9-20
-------
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:
9-21
-------
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.
9-22
-------
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.
9-23
-------
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
-------
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
-------
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
10-1
-------
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
-------
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
-------
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
-------
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
-------
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
-------
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
-------
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
-------
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
-------
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
-------
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
-------
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
-------
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
-------
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
-------
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
-------
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
-------
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
-------
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
-------
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
-------
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
-------
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
-------
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
-------
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
-------
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
-------
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
-------
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
-------
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
-------
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
-------
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
-------
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
-------
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
-------
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
-------
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
-------
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
-------
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
-------
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
-------
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.
B-1
-------
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.
B-2
-------
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.
B-3
-------
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
B-4
-------
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.
B-5
-------
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
B-6
-------
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.
B-7
-------
Wiggins, G.B. 1977. Larvae of the North American Caddisfly Genera. University of Toronto Press, Toronto.
401 pp.
B-8
-------
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.
B-9
-------
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.
B-10
-------
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.
B-11
-------
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
-------
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.
B-13
-------
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
-------
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.
B-15
-------
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
-------
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.
B-17
-------
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.
B-18
-------
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
-------
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.
B-20
-------
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.
B-21
-------
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
B-22
-------
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.
B-23
-------
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.
B-24
-------
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
B-25
-------
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.
B-26
-------
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.
B-27
-------
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).
B-28
-------
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.
B-29
-------
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
B-30
-------
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.
B-31
-------
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.
B-32
-------
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
-------
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.
C-1
-------
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.
C-2
-------
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
C-3
-------
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.
C-4
-------
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
C-5
-------
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.
C-6
-------
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.
C-7
-------
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.
C-8
-------
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.
C-9
-------
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.
C-10
-------
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.
C-11
-------
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.
C-12
-------
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.
C-13
-------
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
C-14
-------
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.
C-15
-------
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.
C-16
-------
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.
C-17
-------
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.
C-18
-------
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.
C-19
-------
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.
C-20
-------
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.
C-21
-------
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
-------
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
-------
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.
D-1
-------
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
-------
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
-------
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
-------
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
-------
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
-------
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)
D-7
-------
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
-------
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.
D-9
-------
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.
D-10
-------
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.
D-11
-------
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
D-12
-------
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
-------
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
-------
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.
D-15
-------
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.
D-16
-------
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-
D-17
-------
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
-------
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.
D-19
-------
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.
D-20
-------
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.
D-21
-------
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.
D-22
-------
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.
D-23
-------
(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.
D-24
-------
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.
D-25
-------
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.
D-26
-------
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
-------
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
E-1
-------
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.
E-2
-------
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
-------
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.
E-4
-------
U.S. CAO. 1988. Restoring degraded riparian areas on western rangelands. GAO/T/RCED-88-20. U.S.
General Accounting Office, Washington, DC.
E-5
-------
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
-------
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)
E-7
-------
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.
E-8
-------
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.
E-9
-------
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
E-10
-------
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.
E-11
-------
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.
E-12
-------
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
E-13
-------
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.
E-14
-------
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.
E-15
-------
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
-------
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.
E-17
-------
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.
E-18
-------
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
E-19
-------
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.
E-20
-------
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.
E-21
-------
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.
E-22
-------
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.
E-23
-------
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.
E-24
-------
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
-------
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
-------
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
-------
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.
E-28
-------
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
-------
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
-------
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.
E-31
-------
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
-------
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
-------
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
-------
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
-------
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
-------
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
-------
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.
F-1
-------
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.
F-2
-------
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
F-3
-------
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.
F-4
-------
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,
F-5
-------
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.
F-6
-------
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.
F-7
-------
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.
F-8
-------
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.
F-9
-------
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.
F-10
-------
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.
F-11
-------
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.
F-12
-------
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
-------
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
-------
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
F-15
-------
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.
F-16
-------
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
-------
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.
F-18
-------
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.
F-19
-------
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.
F-20
-------
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
-------
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
-------
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
-------
APPENDIX C: INDICATORS OF RELEVANCE TO MULTIPLE RESOURCE CATEGORIES
-------
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
-------
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
-------
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
-------
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
-------
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
-------
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
-------
APPENDIX G.2: INDICATOR FACT SHEETS FOR BIOMARKERS
Authors
John McCarthy
Lee R. Shugart
Oak Ridge National Laboratory
Environmental Sciences Division
Oak Ridge, Tennessee
-------
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
-------
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
-------
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
-------
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.
G-11
-------
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
-------
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
G-13
-------
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
-------
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
-------
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
G-16
-------
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.
G-17
-------
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.
G-18
-------
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.
G-19
-------
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.
G-20
-------
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
-------
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
-------
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
-------
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
-------
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
-------
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
-------
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
-------
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
-------
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
-------
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
-------
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
-------
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
-------
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.
G-33
-------
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.
G-34
-------
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
G-35
-------
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.
G-36
-------
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.
G-37
-------
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.
G-38
-------
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.
G-39
-------
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.
G-40
-------
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.
C-41
-------
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,
G-42
-------
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.
G-43
-------
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.
G-44
-------
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.
C-45
-------
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
-------
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
-------
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
-------
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
-------
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
-------
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
-------
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
-------
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
-------
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
-------
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
-------
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
-------
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
-------
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
-------
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
-------
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
-------
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
-------
APPENDIX I
WORKSHOP CONTRIBUTORS AND TECHNICAL REVIEWERS
-------
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
-------
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
-------
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
-------
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
-------
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
-------
Bill Wilen
U.S. Fish and Wildlife Service
National Wetlands Inventory
Washington, DC
1-6
-------
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
-------
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
-------
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
-------
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
-------
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
-------
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
-------
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
-------
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
-------
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
-------
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
-------
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
------- |