United States Office of EPA/600/4-91/018
Environmental Protection Research and Development June 1991
Agency Washington, DC 20460
&EPA I Arid Ecosystems
Monitoring Plan,
1991
Environmental Monitoring and
Assessment Program
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EPA/600/4-91/018
June 1991
Arid Ecosystems Strategic Monitoring Plan, 1991
Environmental Monitoring and Assessment Program
Environmental Monitoring Systems Laboratory
Office of Research and Development
U.S. Environmental Protection Agency
Las Vegas, Nevada, 89143-3478
Printed on Recycled Paper
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Environmental Monitoring and Assessment
Program (EMAP)
STRATEGIC MONITORING PLAN:
ARID ECOSYSTEMS
Arid Ecosystems Resource Group
William G. Kepner3, Technical Director
Carl A. Fox1, Principal Scientist
John Baker5, Bob Breckenridge4, Chris Elvidge1, Virginia Eno2, John Flueck6, Susan
Franson3, Janet Jackson1, Bruce Jones3, Michael Meyer1, David Mouat1, Martin Rose1,
Carol Thompson2
NOTICE: The information in this document has been funded wholly or in part by the United
States Environmental Protection Agency under cooperative agreement CR-816385-01-0
with the Desert Research Institute of the University of Nevada Systems, Reno, Nevada;
interagency agreement DW 89934398 with the U.S. Department of Energy, Idaho Operations
Office, Idaho National Engineering Laboratory; contract number 68-CO-0049 with
Lockheed Engineering and Sciences Company; and cooperative agreement CR-814701
with the Environmental Research Center of the University of Nevada, Las Vegas. It has been
subjected to the Agency's peer and administrative 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.
1 Desert Research Institute, Biological Sciences Center, Reno, Nevada
2 Desert Research Institute, Water Resources Center, Las Vegas, Nevada
3 Environmental Protection Agency, Environmental Monitoring Systems Laboratory, Las Vegas, Nevada
4 Idaho National Engineering Laboratory, Idaho Falls, Idaho
5 Lockheed Engineering and Sciences, Environmental Program Office, Las Vegas, Nevada
6 University of Nevada, Las Vegas, Environmental Research Center, Las Vegas, Nevada
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TABLE OF CONTENTS
NOTICE ii
FIGURES . viii
TABLES x
ACRONYMS xi
ACKNOWLEDGEMENTS xvi
1.0 INTRODUCTION 1-1
1.1 OVERVIEW OF EMAP 1-2
1.2 ECOLOGICAL ASSESSMENT FRAMEWORK 1-4
1.3 EMAP DESIGN 1-6
1.4 EMAP - ARID ECOSYSTEMS 1-10
2.0 RATIONALE AND APPROACH 2-1
2.1 INTRODUCTION 2-1
2.2 BACKGROUND FOR ARID ECOSYSTEMS MONITORING
AND ASSESSMENT 2-1
2.2.1 Grazing 2-3
2.2.2 Biodiversity 2-4
2.2.3 Desertification 2-4
2.2.4 Water Resources . . 2-5
2.2.5 Air Quality 2-6
2.2.6 Global Change 2-7
2.2.7 Summary 2-8
2.3 ARID ECOSYSTEMS - DEFINITION 2-9
2.4 CONCEPTUAL APPROACH 2-9
2.5 DEFINING EXPECTATIONS AND GOALS - STEP 1 2-12
2.5.1 Issues of Concern 2-13
2.5.2 Ecological Endpoints .. ... 2-13
2.5.3 Legislative Mandate ... 2-14
2.5.4 Critical Scientific Questions 2-15
2.5.5 Existing Information 2-17
2.5.6 Goals and Objectives 2-17
2.6 IMPLEMENTATION 2-18
2.6.1 Analysis of Existing Data, Research and Monitoring . 2-19
2.6.2 Pilot Studies 2-20
2.6.3 Regional Demonstration Projects 2-20
111
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TABLE OF CONTENTS (continued)
2.6.4 National Implementation 2-20
2.6.5 Implementation Priorities and Schedule 2-21
2.7 PROGRAM PRODUCTS 2-21
3.0 MONITORING NETWORK DESIGN STRATEGY 3-1
3.1 OVERVIEW OF THE GENERAL EMAP DESIGN 3-1
3.1.1 EMAP Tier 1 Activities: Resource Extent 3-2
3.1.2 EMAP Tier 2 Activities: Resource Condition 3-4
3.2. FEATURES OF THE ARID LANDS MONITORING DESIGNS 3-7
3.3 EMAP-ARID DESIGN BACKGROUNDS 3-7
3.3.1 Populations, Sub-Populations, and Methodology ... 3-8
3.3.2 The Sample Units 3-12
3.3.3 Frame Development 3-13
3.3.4 Sample Selection 3-13
3.4 THREE CLASSES OF ARID LANDS DESIGNS 3-14
3.4.1 Discrete Resource Design 3-14
3.4.2 Elongated Resources 3-14
3.4.3 Extensive Resources 3-16
3.5 OTHER DESIGN ISSUES 3-17
3.5.1 Reserve Sample of Grid Point Hexagons 3-17
3.5.2 Plot Designs 3-18
3.5.3 Shorter Time and Space Scales 3-18
3.5.4 Spatial Association 3-18
3.5.5 Fixed Historical Site Data 3-18
4.0 ARID ECOSYSTEMS CLASSIFICATION 4-1
5.0 INDICATORS 5-1
5.1 INTRODUCTION 5-1
5.2 DEVELOPMENT OF INDICATORS FOR ARID
ECOSYSTEMS 5-2
5.2.1 Field Sampling and Sample-based Measurements .. 5-7
5.2.2 Synoptic Measurements 5-9
5.2.3 Retrospective Indicators 5-12
5.3 HIERARCHICAL EXAMINATION OF INDICATORS 5-16
5.4 SUMMARY AND CONCLUSIONS 5-18
6.0 ASSESSMENT AND USE OF EXISTING DATA 6-1
6.1 IDENTIFICATION AND EVALUATION OF
EXISTING DATA SOURCES 6-1
6.1.2 Evaluation of Data Sources Related to Criteria 6-12
6.1.3 Relationship of Data Sources to Arid Ecosystem
Questions and Issues 6-24
IV
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TABLE OF CONTENTS (continued)
6.1.4 Research Needs to Address EMAP-Arid Questions . 6-26
6.1.5 Approach to Using Existing Data 6-27
7.0 LOGISTICS APPROACH 7-1
7.1 IMPLEMENTATION COMPONENTS 7-1
7.2 SUMMARY OF EMP LOGISTICS ELEMENTS 7-1
7.3 ORGANIZATIONAL STRUCTURE 7-3
7.4 LOGISTICS ISSUES 7-4
7.4.1 Staffing 7-5
7.4.2 Access 7-5
7.4.3 Data Confidentiality 7-6
7.5 FIELD OPERATION SCENARIO 7-6
7.6 DAILY ACTIVITIES SCENARIO 7-7
8.0 ANALYTICAL CONSIDERATIONS/MEASUREMENT TECHNIQUES 8-1
8.1 METHODS 8-1
8.1.1 Sample Timing 8-1
8.1.2 Site Selection 8-2
8.1.3 Sample Collection 8-2
8.1.4 Subjective Measurement and Training 8-2
8.2 LABORATORY ANALYSES 8-3
8.2.1 Analytical Precision And Accuracy 8-4
8.2.2 Detection Limits/Required Sample Size 8-5
8.3 SYNOPTIC SUPPORT REQUIREMENTS 8-5
8.3.1 Aerial Photography 8-6
8.3.2 Multispectral Data Quality Control 8-6
8.3.3 GIS Data File Development 8-6
9.0 A STRATEGY FOR QUALITY ASSURANCE . 9-1
9.1 INTRODUCTION 9-1
9.2 DATA QUALITY OBJECTIVES 9-1
9.3 QUALITY ASSURANCE REQUIREMENTS 9-3
9.3.1 Accuracy 9-3
9.3.2 Precision 9-3
9.3.3 Completeness 9-4
9.3.4 Representatives 9-4
9.3.5 Comparability 9-4
9.4 A TOTAL QUALITY APPROACH TO THE QUALITY
ASSURANCE PROGRAM 9-4
9.4.1 Products and Customers 9-6
9.4.2 Seven Stages of an Experiment 9-6
9.5 Conclusion .... 9-14
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TABLE OF CONTENTS (continued)
10.0 INFORMATION MANAGEMENT . - 1CM
10.1 ROLE OF INFORMATION MANAGEMENT . ... 10~1
10.1.1 Objectives of EMAP-Arid Information Management 10-2
10.1.2 Levels of Information Management Activities 10~2
10.2 USER REQUIREMENTS . ... . - 10~2
10.2.1 Levels of Data 10~2
10.2.2 Users ... 10~3
10.2.3 Issues 10~4
10.3 ARID LANDS RESOURCE GROUP REQUIREMENTS 10-5
10.3.1 Data and Sample Collection, Transfer,
and Tracking '
10.3.2 Quality Assurance 10~6
10.3.3 Data Management, Analysis and Reporting . . 10-9
10.3.4 Data Documentation, Access, and Archival 10-9
10.3.5 Communications Support .10-10
10.3.6 Existing Data 10-10
10 4 EMAP-ARID INFORMATION MANAGEMENT
INFORMATION CENTER (EAIMC) 10-10
10.5 IMPLEMENTATION STAGES . . 10-13
11.0 DATA ANALYSIS . 11-1
11.1 APPROACH TO DATA ANALYSIS 11-1
11.2 TEMPORAL IMAGE PROCESSING SYSTEM (TEMPIST) . . 11-3
11.2.1 Time Series Analysis Module 11-7
11.2.2 Designated Geographic Area Module (DGAM) . . 11-8
12.0 INTEGRATION FOR ARID ECOSYSTEMS 12-1
12.1 DEFINITION OF INTEGRATION 12-1
12.2 RELATIONSHIP TO OTHER EMAP GRAPHS 12-2
12.3 RELATIONSHIPS TO OTHER EPA PROGRAMS 12-4
12.4 RELATIONSHIPS TO NON EPA PROGRAMS 12-4
12.5 TECHNICAL INTEGRATION 12-5
13.0 EXPECTED OUTPUTS 13-1
13.1 Data Evaluation Reports 13-1
13.2 Internal Reports 13-2
13.3 Ecological Risk Assessment 13-2
13.4 Other Outputs 13-3
13.5 FUTURE RESEARCH, IMPLEMENTATION
AND TIMELINES 13~3
VI
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TABLE OF CONTENTS (continued)
14.0 LITERATURE CITED 14-1
Section 1.0 14-1
Section 2.0 ... ... 14-1
Section 3.0 . 14-2
Section 4.0 14-3
Section 5.0 14-3
Section 6.0 14-4
Section 7.0 14-6
Section 8.0 14-6
Section 9.0 . 14-7
Section 10.0 14-8
Section 11.0 ... . 14-8
APPENDIX A - Indicators
APPENDIX B - Site Location Maps
APPENDIX C - Analytical Capabilities
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LIST OF FIGURES
Figure 1 -1. Organization of the Environmental Monitoring and
Assessment Program into four major elements 1-3
Figure 1-2 The Environmental Monitoring and Assessment Program
provides a foundation for the ORD's Ecological Risk
Assessment Program 1~5
Figure 1 -3. Four tier structure of EMAP and the major activities
associated with each of the tiers 1-6
Figure 1 -4. The trunicated icosahedron model projects the familiar
soccer ball tessellation pattern onto the earth 1-7
Figure 1 -5. The base grid placed advantageously on the United States 1-8
Figure 1-6. Approximately 12,600 points over contiguous United States. ... 1-9
Figure 2-1. U.S. land and census water breakdown by ecosystems 2-2
Figure 2-2. The Status of desertification in the United States 2-5
Figure 2-3. Vulnerability to climate change based on availability
of water resources 2-8
Figure 2-4. Conceptual approach for EMAP-Arid 2-10
Figure 2-5. The elements of designing and implementing a
monitoring program 2-11
Figure 2-6. Process used to define expectations, goals, and
objectives of a monitoring program 2-12
Figure 2-7. EMAP regions. .. 2-23
Figure 2-8. EMAP-Arid Implementation Schedule by
resource category 2-24
Figure 3-1. The landscape characterization hexagons are
1/16th of the total area and centered on the sampling points. .. 3-3
Figure 3-2. Spatial and time distribution for field subset visits 3-5
Figure 3-3. Enhancement factor for increasing the base grid density 3-6
Figure 3-4. EMAP hexagons overlay on the Brown and Lowe biotic
communities classification 3-9
Figure 3-5. Possible Double Sampling Relationships between
Tasks 1 and 2 for both Primary and Secondary Resources 3-12
Figure 3-6. A 40-Hex, its Stream Component, and the Riparian
Corridor. 3_16
Figure 5-1. The EMAP conceptual model links ecological endpoints and
indicators to assess the status and evaluate trends
in the condition of arid ecosystems 5-3
Figure 5-2. Variations of growth (20-year means from 3431 BC)
in Bristlecone pine trees hear the upper tree line on the
White Mountains of California 5-13
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LIST OF FIGURES (continued)
Figure 5-3. Actual Palmer Drought Severity Index for July,
Nevada division 1 5-15
Figure 5-4. Indicator selection, prioritization, and evaluation
approach for EMAP 5-18
Figure 5-5. Temporal and spatial resolution of various indicators 5-20
Figure 7-1. Flow chart of EMAP Arid Ecosystems daily field activities. . .. 7-9
Figure 9-1. Relationships between traditional QA Tools and the
Total Quality's Primary Tenet "Customer Satisfaction". . 9-5
Figure 9-2. The typical stages, or "life cycle" of an experiment 9-7
Figure 10-1. EMAP-Arid information data and flow . . . 10-11
Figure 10-2. EMAP-Arid DRI connections to EMSL-LV 10-14
Figure 11-1. Basic display screen layout of the temporal
image processing system (TEMPIST) 11-5
Figure 11 -2. Conceptual model of the Temporal Image Processing
System analysis .. 11-5
Figure 11-3. Cumulative distribution function for PDSI data 11-10
Figure 11 -4. Cumulative distribution function for PDSI data . .... 11-11
Figure 11-5. Cumulative distribution function for PDSI data ... . 11-12
Figure 11 -6 Drylands risk index and the indices used in its formulation .. 11-17
IX
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LIST OF TABLES
Table 2-1. EMAP-Arid land ranked priority classes 2~22
Table 2-2. EMAP-Arid implementation schedule by EPA Region 2-23
Table 2-3. Proposed strategy for producing EMAP-Arid products 2-24
Table 4-1. Vegetation division, formation and vegetation types
included in EMAP-Arid 4-4
Table 5-1. Candidate indicators for arid ecosystems 5-4
Table 5-2. Sample protocols for arid ecosystems' indicators 5-6
Table 5-3. Remote sensing requirements for arid ecosystems
land degradation issues 5-12
Table 5-4. Indicator variables for inventorying, monitoring, and
assessing terrestrial biodiversity at four levels of
organization 5-17
Table 6-1. Database evaluation criteria 6-2
Table 6-2 Summary of data sources with respect to evaluation
criteria 6-4
Table 6-3. Summary of data source with respect to arid lands
indicators. . . 6-15
Table 6-4. Defines key coded values appearing under
indicator headings in Table 6-3 6-20
Table 8-1 Examples of acceptable precision required for
laboratory analyses 8-4
Table 8-2. Acceptable average spike recoveries for assessing
accuracy of the analysis 8-5
Table 9-1. An applications of the four basic steps of total
quality to the even stages of a hypothetical EMAP
arid field project 9-8
Table 9-2. Quality assurance related documentation of EMAP. 9-12
Table 11-1. Palmer drought severity index class intervals 11-3
Table 12-1. Some agencies and institutions with which the EMAP-Arid
ecosystems resource group will interact 12-3
Table 13-1. EMAP-Arid proposed research and development
objectives .... 13-4
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ABBREVIATIONS AND ACRONYMS
ADDNET
ADP
ADS
AIRS
ARMA
ASA
AVHRR
AVIRIS
AWUDS
BBS
BLM
CDD
CDF
CERCLA
COE
CSIRO
DBMS
DOE
DRI
DSI
DGAM
DOE
Acid Deposition Data Network
Automated Data Processing
Acid Deposition System
Aerometric Information Retrieval System
Auto Regressive Moring Average
American Statistical Association
Advanced Very High Resolution Radiometer
Airborne Visible and Infrared Imaging Spectrometer
Aggregated Water Use Data System
Breeding Bird Survey
U.S. Bureau of Land Management
Central Data Dictionary
Cumulative Distribution Function
Comprehensive Environmental Response Compension and
Liability Act
U.S. Army Corps of Engineers
Commonwealth Scientific and Industrial Research Organization
Data Base Management System
Department of Energy, National Environmental Research Parks,
and associated ecological regions.
Desert Research Institute
Data Set Index
Designated Geographic Area Module
U.S. Department of Energy
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EDA Exploratory Data Analysis
EMAP Environmental Monitoring and Assessment Program
EMAP-Arid Environmental Monitoring and Assessment Program Arid
Ecosystems Resource Group
EMSL-LV Environmental Monitoring Systems Laboratory, Las Vegas, Nevada
EPA U.S. Environmental Protection Agency
ESIS Earth Sciences Information System
FLPMA Federal Land Policy and Management Act of 1976
FRAMIS Forest Service Range Management Information System
FHS fixed historical site
FWS U.S. Fish and Wildlife Service
GAO General Accounting Office
GIS geographic information system
GLM General Linear Model
HAPDEX Hydrologic-Atmospheric Pilot Experiment
HBN hydrologic benchmark network
IBP International Biological Program
IDS Integrated Data System
IHICS Integrated Habitat Inventory and Classification System
IMPROVE Interagency Monitoring of Protected Visual Environments
INEL Idaho National Engineering Laboratory
LTER Long-Term Ecological Research
LCTA Land Condition Trend Analysis
MAP3S/PCN Multi-State Atmospheric Power Production Pollution
Study/Precipitation Chemistry Network
MSS Multispectral scanner
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NADP/NTN National Atmospheric Deposition Program/National Trends
Network
NAPAP National Acid Precipitation Assessment Program
NASA U.S. National Aeronautics and Space Administration
NASQAN National Stream Quality Accounting Network
NATSGO National Soil Geographic Database
NAWDEX National Water Data Index
NAWQA National Water Quality Assessment Program
NCBP National Contaminant Biomonitoring Program
NCSS National Cooperative Soil Survey
NDDN National Dry Deposition Network
NDVI Normalized Difference Vegetation Index
NERP National Environmental Research Parks
NGDC National Geophysical Data Center (Boulder, CO)
NOAA U.S. National Oceanographic and Atmospheric Administration
NPS U.S. National Park Service
NPS National Park Service Monitoring Sites Interagency Monitoring of
Protected Visual Environment (IMPROVE) and Visibility Monitoring
Programs
NRC National Research Council
NRDS National Range Data System
NRI National Resource Inventory
NSF National Science Foundation
NWI National Wetlands Inventory
NWWRC Northwest Watershed Research Center
PSDI Palmer Drought Severity Index
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PCA Principal Components Analysis
qA/QC Quality assurance/quality control
RAIDS Riparian and Aquatic Information Data Summary
RCRA Resource Conservation and Recovery Act
RGIM Resource Group Information Managers
RPA Resource Planning Assessment
SCS U.S. Soil Conservation Service
SSURGO Status of Soil Survey Geographic Database
STATSGO Status of State Soil Geographic Database
STORE! Storage and Retrieval of Water Quality Data
T&E or T/E threatened and endangered
TEDS Threatened and Endangered Data System
TM Thematic Mapper
TSAM Time Series Analysis Model
USDA U.S. Department of Agriculture
USFS U.S. Forest Service
USGS U.S. Geological Survey
USFWS U.S. Fish and Wildlife Service
USGS U.S. Geological Survey NASQAN Sampling Sites
USGS U.S. Geological Survey Hydrologic Benchmark Sampling Stations
USFWS U.S. Fish and Wildlife Service National Wildlife Refuge System
sources for arid lands are protected and managed through statutes
such as the Wilderness Act of 1964, the Land and Water
Conservation Fund Act of 1965, Wild and Scenic Rivers Act of 1968
and the Soil and Water Resource Conservation Act of 1977. Other
public laws, such as the Clean Air Act and Safe Drinking Water Act,
address mostly human health issues, but are limited in their
protection of ecological systems.
XIV
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WATSTORE National Water Data Storage and Retrieval System
WDMS Water Data Management System
WORDS Wildlife Observation Report Data System
XV
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ACKNOWLEDGEMENTS
The members of the EMAP Arid Ecosystems Resource Group would like to
acknowledge the advice and assistance of a number of individuals in the development and
preparation of the Strategic Monitoring and Research Plan. In particular, the following
individuals provided technical advice throughout the development of the plan: Timothy Ball,
Dale Johnson, Peter Wigand, and George Taylor Desert Research Institute; Peter
Brussard University of Nevada, Reno; Robin Tacusch U.S. Forest Service, and Steve
Leonard Bureau of Land Management.
Jim Wickham and Doug Norton of EMAP Landscape Characterization assisted in the
production of the Brown and Lowe biotic community maps. Tony Olson and Don Stevens
provided statistical guidance in the development of the design. Julie Muhilly, Barbie
Nauroth, and Susan Sawatzky provided word processing and graphics assistance for the
final manuscript. Numerous other individuals also participated in various workshops
associated with development of the plan and provided needed scientific information to
EMAP-Arid
XVI
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1.0 INTRODUCTION
Degradation of the environment as a result of man's activities is not a recent phenomenon.
The development of agriculture about 10,000 years ago gave rise to settled societies and placed
tremendous strains on local ecosystems. As populations increased so did the demand forfood
and other resources. Short-term demands outweighed any considerations for long-term
ecological stability. Eventually extensive deforestation, salinization, and soil erosion brought
about a severe decline in productivity and dramatic changes in the Earth's ecosystem. The
Mediterranean region is a good example where, beginning about 2000 B.C., 90 percent of the
original oak, beech, pine, and cedar forests were replaced by olive trees, vines, low bushes, and
strongly scented herbs in less than 2,000 years as a result of increasing demands for timber and
land.
While in the past man's impact on the environment was generally restricted to local and
regional areas, the scale of current environmental problems spans the globe. Environmental
issues such as global climatic change, acidic deposition, and toxic wastes are and will continue to
be of great worldwide economic, political, and social concern during the coming decades.
Ecological pressures on the Earth's limited natural resources are anticipated to increase as man's
activities continue to alter the Earth's ecosystems. Years of scientific study have heightened
environmental awareness and revealed the complexity of the Earth's ecosystems and their
response to natural and anthropogenic perturbations. Unfortunately, there has been atandency of
deal independently with environmental issues related to land, air, and water resources. Because
these components of the environment are intricately linked and often codependent, dealing with
them independently has led to the environmental degradation of both natural and developed areas
(Viessman, 1990).
Environmental degradation has also resulted from the inability to detect ecosystem change.
Much of this inability stems from a poorly documented environmental data base upon which to
base comparisons regarding environmental change. Thus, while it is generally perceived that
current policies and programs are protecting and improving the quality of the environment, it is
difficult to quantitatively prove it with the available data.
Recognizing the gap in the availability of baseline environmental data and the need for an
integrated approach to address environmental issues, the U.S. Environmental Protection Agency
(EPA), at the recommendation of the Agency Science Advisory Board, established the
Environmental Monitoring and Assessment Program (EMAP). This Program is part of the EPA's
Office of Research and Development (ORD) and represents the foundation for the ORD Ecological
Risk Assessment Program. When fully implemented in cooperation with other government
agencies that share resource monitoring responsibilities, EMAP will provide information needed
to: (1) document the current condition of the Nation's ecological resources; (2) understand why
that condition exists; and (3) predict future ecological conditions under various management
scenarios (EPA, 1990).
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1.1 OVERVIEW OF EMAP
The Environmental Monitoring and Assessment Program is designed as an interagency,
interdisciplinary program to characterize and evaluate the Nation's ecological resources on
regional and national scales over long periods of time. The overall goal of EMAP is to monitor the
status and assess trends in the condition of the Nation's ecological resources in order to help
evaluate current environmental policies and identify emerging environmental problems before
they become widespread or irreversible. EMAP provides a strategic approach to meet the growing
need to identify and bound the extent, magnitude, and location of degradation or improvement in
environmental condition. EMAP focuses specifically on regional-scale conditions over periods of
years to decades. EMAP is not intended to describe all components or attributes of an ecosystem
or resource type. It is not a process-oriented research program and will not describe how systems
function. It will, however, provide information on specific indicators measured during a specific
index period, as a "snapshot" of the overall condition of a system.
The EMAP approach to environmental monitoring is designed to: (1) ensure broad
geographic coverage; (2) enable quantitative and unbiased estimates of ecological status and
trends; (3) facilitate analyses of associations among measurements of habitat condition, pollutant
sources and exposure, and biological condition (indicators); and (4) have sufficient flexibility to
accommodate sampling of multiple types of resources and to identify emerging environmental
issues (EPA, 1990). Specific EMAP objectives are to:
Estimate the 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 resource managers and the public.
To meet these objectives and insurethe efficient execution of the program, EMAP is organized
into four major elements: resource monitoring, integration, coordination, and developmental
research (Figure 1-1).
Resource monitoring focuses on the collection and interpretation of field data on the
ecological condition of eight resource categories: agroecosystems, arid ecosystems, forests,
estuaries, Great Lakes, coastal waters, surface waters, and wetlands. These resource groups
were established by ecosystem type to assemble scientific teams with specific areas of expertise
and to facilitate interagency cooperation. EMAP scientists fully recognize that the activities of these
resource groups will have to be integrated due to the interacting nature of these systems in the
environment. Field and remote sensing-based biological, chemical, and physical measurements
will be made by each resource group on statistically selected sampling sites for each resource
class, e.g., oak-hickory forests, emergent estuarine wetlands, or sagebrush desert. These
measurements will be evaluated in conjunction with data on atmospheric deposition, climate, and
1-2
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Environmental Monitoring and Assessment Program
Resource Monitoring
Agroecosystems
Arid Lands
Forests
Estuaries
Great Lakes
Coastal Waters
Surface Waters
Wetlands
Integration Activities
Air and Deposition
Landscape
Characterization
Information
Management
Integration and
Assessment
Coordination Activities I Developmental Researc
Statistics and Design
Indicators
Logistics
Total Quality
Management
Technology Transfer
International Activities
Environmental
Statistics
Ecological Indicator
Development
Landscape Ecology
Ecological Risk
Characterization
Figure 1 -1. Organization of the Environmental Monitoring and Assessment Program into the
four major elements.
other environmental stressors in order to assess associations between environmental stress and
ecological condition.
Integration activities in EMAP will center around several functions that facilitate the
acquisition, management, and interpretation of monitoring data. Atmospheric deposition data,
landscape pattern and composition information, integration of monitoring data from non-EMAP
networks, and EMAP information management are some of the responsibilities of the Integration
Team. The Integration Team will also ensure that the scientific information collected during various
field activities is translated into a form that can be used to answer management questions
regarding environmental problems at regional and national scales.
Coordination activities of EMAP include network design and statistical analysis; indicator
selection, testing, and evaluation; logistics; and quality assurance. The Coordination Team will
implement the concept of "total quality management" by providing guidance, support, oversight,
and planning assistance to the resource groups on quality assurance and quality control
protocols. The Coordination Team will ensure that data collection activities by the resource groups
are conducted in standardized ways. Other coordination functions include technology transfer
and liaison activities with other state, federal, and private agencies, the EPA regions, and
international institutions and organizations (e.g., Commonwealth Scientific and Industrial
Research Organization.
The developmental research component is designed to be responsive to new issues and
improve scientific understanding. Research programs will include environmental statistics,
ecological indicators, landscape ecology, and ecological risk characterization. Additional studies
advancing monitoring methods, data analysis, and information transfer will be part of the research
1-3
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program. All of the research conducted within the EMAP framework will be integrated with the
ORD's Ecological Risk Assessment Program and similar programs in federal, state, and private
agencies and institutions. EMAP recognizes that research is a strong component of the overall
monitoring and assessment concept and will devote considerable resources to maintaining a
state-of-the-art capability.
1.2 ECOLOGICAL ASSESSMENT FRAMEWORK
The paradigm upon which EMAP is based is risk assessment. Risk assessment is the
scientific process in which facts and assumptions are used to estimate the potential for adverse
effects on the environment that might result from exposures to pollutants and other stresses. In the
past risk assessment relied almost exclusively on single species and single chemical toxicity
bioassays and media-specific exposure models. EMAP, however, will collect data that will be used
in the development of ecological risk assessments at the ecosystem, regional, or national scale.
EMAP will provide a foundation for the ecological risk assessment process consisting of six major
elements (Figure 1-2):
Environmental monitoring and assessment (hazard identification)
Ecological exposure (exposure assessment)
Ecological effects (dose-response)
Ecological risk characterization
Ecosystem restoration and management (risk management)
Risk communication
EMAP will take an epidemiological or top-down approach to ecological risk assessment. If
an effect is observed through monitoring, then associations will be made with exposure
information to identify potential hazards or stressors. The EMAP monitoring tools (indicators) will
generally be anticipatory rather than predictive of environmental effects. This approach enhances
the likelihood of detecting cumulative impacts of natural and anthropogenic influences on
ecological resources (Knapp et al., 1990).
1-4
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COMMUNICATIO
ECOSYSTEM
RESTORATION
& MANAGEMENT
RISK CHARACTERIZATION
ECOLOGICAL EFFECTS
ECOLOGICAL EXPOSURE
ENVIRONMENTAL MONITORING & ASSESSMENT PROGRAM
Figure 1-2. The Environmental Monitoring and Assessment Program provides a foundation
for the ORD Ecological Risk Assessment Program.
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1.3 EMAP DESIGN
The Environmental Monitoring and Assessment Program is designed to provide statistically
unbiased estimates of the status and trends in the condition of the Nation's ecological resources.
To accomplish this goal the EMAP design is probability-based and hierarchical with four distinct
tiers (Figure 1 -3). Tier 1 is the broadest monitoring tier with the greatest extent of spatial coverage.
TIERS
Sampling at
Increased Spatial or
Temporal Resolution
TIER 2
Status and Trends
Monitoring
TIER 1
Characterization
Figure 1 -3. Four-tier structure of EMAP and the major activities associated
with each of the tiers.
The design is based on dividing the Earth's surface using a truncated icosahedron (i.e., soccer
ball) (Figure 1-4). Using a triangular point grid with approximately 27 km between points in all
directions, 40 km2 hexagons centered on each point will be characterized by EMAP (Figure 1-5).
In the conterminous United States, approximately 12,600 hexagons (Figure 1-6), representing
about one-sixteenth of the total area of the United States, will be monitored and evaluated by using
remote sensing and existing data. Regional estimates of the areal extent of all extensive ecological
and landscape resources and estimates of the number of discrete resources (e.g., lakes, stream
reaches, or prairie potholes) will be made from the Tier 1 data (Overton et al., 1990).
Tier 2 will use probability methods to select a subset of the ecological resource units within the
40 km2 hexagons in proportion to their occurrence and importance. Tier 2 activities will rely heavily
on the use of specific indicators implemented by each resource group. Tier 2 sampling will involve
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Figure 1-4. The trunicated icosahedron model projects the familiar soccer ball
tessellation pattern onto the earth.
periodic visitation to selected points in the nationwide grid to collect samples and data of ecological
resource condition. The nominal density of these grid points is one point per 640 km 2 This
density will be increased or decreased to meet specific needs. Selection of specific sites for
monitoring will strongly depend on the location of the ecological resources within the grid. The grid
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Figure 1 -5. The base grid placed advantageously on the United States.
-------
Figure 1-6. Approximately 12,600 points over contiguous United States.
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used in Tier 2 can be extended to provide global as well as national coverage or enhanced
coverage for state or local resource assessment.
Tier 3 will focus on subpopulations of interest (e.g., redwood forests, saguaro cactus, low
alkalinity lakes) or provide additional diagnostic information beyond Tier 2. Sampling intensity will
be increased either spatially or temporally to increase confidence in the data relative to status and
trends of the subpopulations of interest.
Tier 4 includes process-level research that may be conducted at specific, nonrandomly
selected sites. Research at these sites will likely center on the development of new sampling,
measurement, or assessment methodologies; center on ecological indicators; or involve pilot or
demonstration scale studies incorporating all aspects of EMAR
The EMAP design has been developed to be adaptive, simple, rigorous, robust, and flexible.
Its flexibility is such that data from other agencies and monitoring programs can be directly
incorporated into the data base. The EMAP intent is to develop a monitoring network that will fill
critical data gaps in order to determine the status and evaluate trends in the condition of the
Nation's ecological resources.
1.4 EMAP - ARID ECOSYSTEMS
The purpose of this document is to describe a strategy for establishing an integrated
environmental monitoring and assessment program for arid ecosystems in the United States. This
Strategic Plan is designed to be a "living" document responsive to changes in the state of
knowledge concerning arid ecosystems. It represents an initial attempt at developing a strategy for
monitoring and assessing the condition of arid ecosystems within a spatial and temporal
framework.
The EMAP-Arid Ecosystems Resource Group's (EMAP-Arid) Strategic Plan is being
developed in cooperation with key natural resource management agencies (e.g., BLM, USFS,
NPS). Economic, scientific, and institutional resources are being combined into an integrated
program that will allow environmental issues to be addressed in a cost effective and scientifically
defensible manner. Negotiations for formal cooperative agreements (e.g., memoranda of
understanding) between EMAP-Arid and appropriate agencies and institutions are currently
underway.
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2.0 RATIONALE AND APPROACH
2.1 INTRODUCTION
EMAP is designed to be an interagency, interdisciplinary monitoring and assessment
program focused on ecological resources in arid regions of the United States. EMAP-Arid has fully
embraced the mission, goals, and objectives of the Environmental Monitoring and Assessment
Program as outlined in Chapter 1. EMAP-Arid is not intended to be a compliance monitoring
program and will not replace the need for these activities. Products from EMAP-Arid will be used to
determine the relative magnitude and geographic location of environmental problems and assist in
establishing objective mitigation and research priorities. EMAP-Arid will provide an ongoing
monitoring framework within which new variables can be added or regional modifications made,
so that the magnitude and extent of effects from newly identified problems can be determined on a
more timely basis.
This chapter discusses the rationale, conceptual approach, and implementation strategy for
EMAP-Arid. It provides background information on arid ecosystems within the EMAP framework
and establishes the basis for a strategic plan.
2.2 BACKGROUND FOR ARID ECOSYSTEMS MONITORING AND ASSESSMENT
Arid and semi-arid ecosystems (hereafter referred to simply as arid ecosystems) occur on
most continents of the Earth, comprise about one third of the land surface, and support about 20
percent of the human population (approximately 1 billion). In the United States these ecosystems
occupy nearly 40 percent of the land surface area (Figure 2-1) and are important commercial,
agricultural, mineralogical, and energy resource centers. For example, 80, 60, and 30 percent of
the total U.S. production of oil, natural gas, and coal occurs in arid regions of the western U.S.
(National Geographic Society, 1982).
Arid ecosystems are characterized by climatic conditions where sparse and erratic rainfall is
combined with long, warm summers and a pattern of descending air giving rise to extremely dry
conditions. Precipitation is both spatially and temporally highly variable. This high variability is as
important as the low quantity in determining the productive potential of arid systems (UCAR, 1990).
Nearly all the precipitation falling on arid ecosystems leaves through evaporation or transpiration
rather than through surface or ground-water drainage. Soils common in arid systems are deficient
in moisture and nitrogen, low in organic matter content and aggregate strength, and often low in
permeability. Natural vegetation in arid ecosystems includes grasslands, shrublands, and
savanna. Scattered trees are present in the emphemeral drainages and lush riparian forests are
present along some water courses.
Historically, overexploitation of the limited resources of arid ecosystems combined with a
restricted and unreliable water supply have led to severe land degradation and associated rapid
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Figure 2-1. U.S. land and census water breakdown by ecosystems.
declines in productivity. Once degraded, arid lands are generally unlikely to return to their
preimpacted state without extensive reclamation. Hence, arid ecosystems are often termed
"fragile" because they show little resistance or resilience in the face of disturbance (UCAR, 1990).
Existing environmental degradation of arid systems is evident. Complex environmental
problems, in part fostered by 19th century policies, programs, and resource management
practices, are found across most arid lands in the western United States. The Colorado River is
dead at its mouth and normally discharges no water into the Gulf of California because so much of
its waters are diverted for irrigation. On the east side of the Sierra Nevada, Owens Lake has been
dry for most of this century, Mono Lake and Pyramid Lake have been heavily drawn down, and the
survival of the animals that depend on these critical bodies of water has been threatened.
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Thousands of abandoned and working mines have discharged poisonous metals into aquifers and
streams, including the Clark Fork, the largest hazardous waste site requiring cleanup under the
Superfund Amendments and Reauthorization Act in the United States (Wilkinson, 1990). Because
of agricultural pollution, Kesterson, Stillwater, and other arid regions wildlife refuges have
experienced major bird mortality due to chemical contamination. The Ogallala Aquifer, stretching
from Texas to the Dakotas, has been depleted in some areas to the extent that land once irrigated
for agriculture is now abandoned due to lack of available water (Wilkinson, 1990).
While the preceding examples are some of the more serious environmental problems of arid
lands, other more regionally based environmental issues have and continue to affect these
ecosystems. Grazing, biodiversity, desertification, water resource management, air quality, and
global climatic change are regional issues important to the health of arid ecosystems. These
issues drive and will continue to drive much of the public, scientific, and policy making concern for
arid systems.
2.2.1 Grazing
Grazing of livestock on public and private arid lands over the entire western region of the
United States has been practiced for over 100 years. Current data indicate that over 70 percent of
the land in 16 western states is grazed (Duffus, 1988; Jacobs, 1988). Thefederal government (e.g.,
Bureau of Land Management and the U.S.Forest Service) controls most of the grazing through
allotments on about 268 million acres. Recent reports (Wald and Alberswerth, 1989; Duffus, 1988)
show that 68.4 percent of BLM rangelands and over 50 percent of all federally controlled
rangelands evaluated were in an unsatisfactory condition. These studies also showed that 19
percent of the surveyed allotments were threatened by livestock overgrazing and that about 8
percent were actually declining in quality. Riparian ecosystems associated with rangelands were
identified as particularly vulnerable and in poor condition as a result of grazing. These studies sited
insufficient monitoring data for management decisions as a principal reason for the unsatisfactory
condition of rangelands.
While grazing is an important ecological component of many arid ecosystems, man has
greatly increased its frequency, intensity, and extent with the introduction of livestock. Widespread
changes in soil and plant species composition have occurred with increased grazing pressure in
arid systems. Examples of changes in plant species composition include replacement of
grasslands and savannas by shrublands or woodlands dominated by unpalatable species and the
degradation of woodlands or perennial grasslands to communities dominated by unpalatable
perennials, ephemerals, or bare ground (UCAR, 1990). Grazing impacts on soils result from
decreases in plant cover which may cause higher summer soil temperatures, lower winter soil
temperatures, increased bare soil evaporation, and increased wind and water erosion.
Additionally, infiltration may decrease and runoff may increase due to soil compaction, nutrient
turnover increase, and carbon storage decrease, all as a result of grazing. Effects on plants and
soils can often be compounded or mitigated by variations in weather or changes in climate.
However, it appears that most arid systems have a critical threshold beyond which recovery is very
slow and difficult (UCAR, 1990).
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2.2.2 Biodiversity
Biological diversity has recently been recognized as an important global resource to be
indexed, used, and, above all, preserved (Wilson, 1988). The wide variety of genes, species,
communities, and ecosystems provide natural resources (e.g., food, medicine, and shelter) for
daily subsistence and ecological services (e.g., climate moderation, water and nutrient cycling,
and breakdown of wastes) necessary for the survival of life on Earth (Blockstein, 1989). Arid
ecosystems in the United States exhibit a tremendous variety of habitats and accommodate a vast
array of animal and plant species. Biodiversity (species richness) in these systems, particularly
Mediterranean-climate regions, can rival that found in tropical forests (Mooney, 1988).
Unfortunately, plant and animal species in arid ecosystems have not escaped the impact of
increasing urbanization, agricultural usage, mining, and recreation. Observed declines in species
such as the desert tortoise, bighorn sheep, desert pupfish, California condor, and saguaro cactus
are attributable to changes in land use, air and water quality, habitat availability, and climate.
Continued impacts on biodiversity in arid systems will undoubtedly have long-term global
ecological consequences.
2.2.3 Desertification
Desertification has been defined as "the decline or destruction of the potential or actual
biological productivity of arid and semi-arid lands caused by certain natural and man-made
stresses" (Bender, 1982). An extended period of drought, severe mismanagement of land, or
evaporation of water, leading to soil salinization, can bring about desertification. The U.N.
Environmental Programme estimates that about one third of the world's land surface and the
livelihoods of at least 850 million people are threatened by global desertification (Speth, 1988). In
the United States over 75 percent of arid and semi-arid lands have been impacted, and 10 percent
of these lands are in a state of severe or very severe desertification (Figure 2-2; Dregne, 1977).
Recent studies by Schlesinger et al. (1990) in a semi-arid grassland indicate that changes in
arid ecosystem function leading to desertification are the result of increases in the spatial and
temporal heterogeneity of water, nitrogen, and other soil resources. For example, increases in
heterogeneity from grazing, off-road vehicles, row-crop agriculture, or other perturbations can
lead to the invasion of grasslands by shrubs, concentrate soil resources under the shrubs, and
increase the removal of soil materials between shrubs by water and wind erosion.
Losses of desert soil by wind erosion may be globally significant. It is estimated, for example,
that 40 to 60 percent of the atmospheric nitrate over the North Pacific Ocean is derived from
continental aerosols (Schlesinger et al., 1990), presumably from the deserts of China. In the
United States, much of the nitrate deposited in eastern forests as dry deposition may be derived
from desert soils in the West. Desert soils also are likely to contribute significantly to global cycling
of sulfur, phosphorus, calcium, and other elements. Arid ecosystems also contribute to
atmospheric concentrations of methane and other hydrocarbons as well as nitrous oxide.
Increases in these gases may affect the production of tropospheric ozone but their contribution is
poorly understood at this time. With the area of arid lands expected to increase due to
desertification and other processes, any change in these resources may have global implications.
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g Slight
|§ Moderate
|H Severe
H Very severe
Figure 2-2. The status of desertification in the United States. Source: Dregne, 1977
2.2.4 Water Resources
Undoubtedly the most important and most limiting environmental factor in arid ecosystems is
water. Species (including man) occupying these systems and living at their ecological limit are
dependent upon and adapted to limited but reliable surface and ground-water supplies.
Exploitation of these limited water resources, principally to irrigate crops, continues in arid regions
worldwide. Crop irrigation accounts for 90 percent of the water use in the western U.S. (Wilkinson,
1990).
Reduction in water availability because of irrigation and domestic importation has had a
pronounced affect on arid systems, particularly riparian communities. Today, it is estimated that
only 20 to 30 percent of the riparian ecosystems that provide habitat for 40 to 90 percent of all
vertebrate wildlife remain in the United States. Of the remaining riparian systems, 80 percent are
considered to be in an unsatisfactory condition (Crumpacker, 1984). These estimates have
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recently been confirmed by statewide BLM estimates for Idaho and Colorado indicating that 80 and
90 percent, respectively, of the riparian communities along over 17,000 miles of streams were in
poor to fair condition (Duffus, 1988).
The conversion of natural or grazed systems to irrigated agriculture has also had a profound
effect on arid ecosystems. Agricultural conversion of biologically diverse plant communities
results in monocultures with a uniform canopy, declines in soil organic matter, increases in soil
bulk density, and decreases in infiltration and may lead to increases in soil salinity (UCAR, 1990).
The accumulation of salts is especially significant as soil and shallow ground water can eventually
become too saline for plant growth. Increased salinity combined with mobilized toxic trace
elements (e.g., selenium) from subsoils or agricultural pesticides and fertilizers may result in the
development of a mosaic of vegetation communities composed of exotic species not native to the
region. Changes in the vegetation and soil environment can alter surface energy budgets (i.e.,
albedo, evapotranspiration, and sensible heat flux) (UCAR, 1990). Resultant increases in air
temperatures will increase potential evapotranspiration, reduce relative humidity, decrease water
availability, and hence, increase aridity. Restoration of arid lands impacted by man is a difficult and
expensive proposition.
2.2.5 Air Quality
Continued rapid urbanization and industrialization of arid lands in the western U.S. has
caused considerable concern about air quality. Concentrations of air pollutants in the West have
generally increased over time in association with increases in population. Nitrogen oxide (NOx)
emissions, for example, have increased by 50 percent since 1960 (Young et al., 1988). Ozone (O3)
concentrations in many arid systems have also increased recently and some of the most extreme
values in the Nation are found in the South Coast Air Basin (Los Angeles area). Concentrations of
sulfur dioxide (SO2) in the West, however, have decreased by a factor of two since the late 1960s
(Young et al., 1988). Wet deposition of chemical anions and cations (i.e., H +, NH4+, Ca2+, N03,
and SO42) is not problematic but increases across arid systems from west to east (Young et al.,
1988).
Much of the recent concern over air quality in arid ecosystems is focused on ozone and
visibility. Numerous Class 1 areas (e.g., national parks and wilderness areas) occur in much of the
western United States and, hence, regional changes in air quality may have significant impacts.
Ozone occurs on a regional scale and is extremely problematic for vegetation due to its high
toxicity. Numerous studies have shown that ozone and photochemical smog have affected
vegetation in arid ecosystems particularly in California. Recent reviews of the data by Shriner et al.
(1990) clearly show the effects of ozone on vegetation. Most of these studies were conducted on
forest trees and agricultural crops because desert vegetation was considered to be relatively
insensitive. Research by Thompson et al. (1984), however, has shown that certain species of
desert annuals are extremely sensitive to ozone at concentrations below current ambient
standards.
Concern over visibility is linked to the atmospheric transport of pollutants from urban and
industrial sources into national parks, wilderness areas, and other Class 1 airsheds. Amendments
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to the Clean Air Act now provide for visibility protection for Federal Class 1 Areas and provide for the
conduct of visibility studies, establishment of visibility regions, and formation of regulatory
commissions. Continued expansion of man's activities in arid ecosystems is likely to increase
problems associated with air quality.
2.2.6 Global Change
Of all the issues facing arid ecosystems, global climatic change may be the most significant.
Several current models of climatic change predict that lower precipitation and shifts in precipitation
seasonality will occur in the interior of large continental areas where arid ecosystems are most
commonly located. While these models are somewhat speculative, even small changes in climate
in arid zones would greatly intensify the already high natural variability and could lead to permanent
degradation. Because vegetation in arid ecosystems is known to be very responsive to even small
changes in climate, these systems may be the first to be affected by global environmental change
(UCAR, 1990). Even if feedback mechanisms stabilize mean global temperatures at current levels,
small changes in circulation, cloudiness, or water vapor transport could significantly change arid
ecosystem climate. The sensitivity and vulnerability of the United States has been identified by
Maggs (1989) in Figure 2- 3. Clearly arid ecosystems in the western United States are the most
vulnerable to global climate change, and the Great Basin Desert appears to be the most sensitive.
A recent publication by Wharton et al. (1990) confirms the responsiveness of the Great Basin to
climatic change over the last 50,000 years.
Changes in the composition, structure, or function of arid ecosystems are also likely to affect
regional and global climate through feedback mechanisms. Changes in the biota of arid lands due
to climate change (i.e., increasing aridity) and changes in land use (e.g., conversion to irrigated
agriculture or urbanization) may alter mesoscale atmospheric circulation, albedo, soil moisture,
and evapotranspiration (UCAR, 1990). Changes in the vegetation of arid ecosystems may also
affect wind and water erosion (i.e., transport of aerosols to the atmosphere), trace gas exchange
(e.g..ammonia and methane), and fire frequency. The significance of these changes is enhanced
in view of the expansive global extent of arid systems over one third of the global land surface area.
There is past evidence to indicate that arid ecosystems have affected and are being affected
by climatic change. Extensive deposits of loess in Alaska and dust in Antarctic ice demonstrate
that during the last ice age, wind erosion from desert regions was a significant phenomenon and
may have greatly influenced planetary albedo. These changes in arid lands also may have
accelerated continental deglaciation. Recent data suggest that the climatic warming (increased
aridity) of the current interglacial period may be responsible for ecosystems in the southwestern
United States shifting from a sink to a large source of atmospheric carbon dioxide (UCAR, 1990).
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NUMBER OF VULNERABILITY FACTORS EXCEEDED
5
Figure 2-3. Vulnerability to climate change based on availability of water resources.
(Maggs, 1989)
2.2.7 Summary
In summary, while arid lands have been used throughout history by relatively small numbers
of people, recent population explosions in conjunction with development practices has
precipitated a rapid degradation of these regions. Slight shifts in climate may lead to further
degradation and affect the entire global environment. Arid ecosystems can no longer be
considered remote places of little value. They are intricately linked to all other ecosystems and may
greatly influence their condition.
20
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2.3 ARID ECOSYSTEMS-DEFINITION
The ecosystems considered by the EMAP-Arid resource group may not strictly follow those
traditionally considered as arid or semi-arid systems from a biogeographic perspective. The
working definition for the Arid Ecosystems Resource Group is given as follows
Arid ecosystems are terrestrial systems characterized by a climatic regime where
potential evaporation exceeds precipitation, annual precipitation ranges from <5
cm to 60 cm, and daily and seasonal temperatures range from -40 °Cto50°C. The
vegetation in arid ecosystems is dominated by woody perennials orgraminoids with
a low form physiognomy including drought resistant trees in open canopies. Arid
ecosystems also include associated riparian areas occurring within the arid zone.
Irrigated lands are not considered part ofEMAP arid ecosystems even though they
occur in the same climatic region.
This definition is designed to include the arid ecosystem resource classes considered
important to EMAR It also attempts to take into account boundaries (ecotones) between resource
classes that may be important in monitoring environmental change.
2.4 CONCEPTUAL APPROACH
The EMAP-Arid resource group is developing and implementing a holistic, ecological
resource approach to monitoring and assessment. The overall strategy (Figure 2-4) is to identify
issues and endpoints, measure and integrate indicators of ecological condition, and evaluate
spatial and temporal variability to determine the status and evaluate trends in the condition of arid
ecosystems. A key concept behind the approach is to integrate synoptic (complete landscape
coverage), sample-based (e.g., field measurements), and retrospective (historical) indicator data
with stressor information in order to evaluate correlative relationships and determine spatial and
temporal variability. Integrating the data derived from these types of indicators is essential to
defining the status of arid ecosystems and assessing trends in their condition. The main emphasis
is on the development of a detection level monitoring program that determines large-scale
patterns and trends. This approach will augment and add value to existing long term ecological
research sites whose primary aim is to determine cause and effect relationships.
To assist in the logical development of the arid ecosystem monitoring program, EMAP-Arid
has adopted guidelines developed by the National Research Council (NRC) for designing and
implementing environmental monitoring programs (Figure 2-5). The NRC process provides a
formula that leads from defining goals to disseminating information to decision makers. The NRC
framework will help ensure the program's effectiveness in addressing its user's needs and
promote compatibility across all EMAP resource groups. The EMAP-Arid resource group is
currently at Step 2 in the process.
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ARID ECOSYSTEMS - EMAP
CONCEPTUAL APPROACH
ISSUES:
ASSESSMENT ENDPOINTS:
Water
Resources
4DOIMTC-
Air Quality
Biodiversity
| | Desertification
Sustainability
Global
Chanae
Aesthetics
INDICATORS
Synoptic
\
Vegetation index
Albedo
Land cover/Landform
Erosion
\
Sample-Based
Biodiver
compos
Biomark
Visibility
sity/
tion
ers
Biomass
/
STRESSORS
Retrospective
/
Tree ring Increment
Macrobotanical
composition
Packrat middens
Fire frequency and
distribution
1
Climate
Anthropogenic
Natural processes
1
SPATIAL & TEMPORAL VARIABILITY
-*
DESCRIBE
STATUS
ASSESS
TRENDS
Figure 2-4. Conceptual approach for EMAP-Arid ecosystem.
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Stepl
Define
Expectations and Goals
Rethink
Monitoring
Approach
Step 2
Define
Study Strategy
Step 4
Develop
Sampling Design
Step 3
Conduct Exploratory ]
Studies if Needed
Refine
Objectives
Can
Changes Be
Detected
T
StepS
Implement Study
Make Decisions!
Step 6 1
Produce Information!
Is Information
Adequate?
Step 7
Disseminate
Information
(Source: NRC 1990)
Figure 2-5. The elements of designing and implementing a monitoring program.
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2.5 DEFINING EXPECTATIONS AND GOALS - STEP 1
The ultimate goal of EMAP is to provide scientifically based information on the condition of the
Nation's ecological resources that will enable natural resource managers and decision makers to
establish sound environmental policy. EMAP is in a unique position to foster communication
between scientists and users of scientific data through the development of interactive partnerships
(e.g., memoranda of understanding). The focus and mission of EMAP-Arid are defined as a
component of the overall EMAP (Section 1).
To establish a common ground upon which science and policy can interact, an expanded
approach to Step 1 (Figure 2-6) has been used by EMAP-Arid. The elements of this approach
(Figure 2-6) include: (1) identifying relevant societal and ecological issues; (2) determining
ecological endpoints; (3) evaluating pertinent environmental laws and regulations; (4) developing
questions of interest; (5) identifyinf and evaluating existing information and data gaps; and (6)
Identify Public
Concerns and
Ecological Issues
Focus Scientific
Understanding and
Define Mission
Identify Relevant
Laws and Regulations
that Provide Direction
Develop Questions of Interest
Identify what Information
is Currently Available to
Answer Questions
i
Identify Gaps in Information
Establish Goals and Objectives
to Cover Gaps and Answer Questions
Figure 2-6. Process used by the EMAP arid ecosystems resource group to define
expectations, goals, and objectives of a monitoring program.
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(Adapted fiom NKC1990)
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establishing goals and objectives to address the data gaps and answer the questions of interest.
The following sections will address these six points.
2.5.1 Issues of Public and ScientificConcern
Several major issues for arid ecosystems were discussed in Section 2.2 (i.e., grazing,
biodiversity, desertification, water resources, air quality, and global change). Much of the concern
over these issues stems from the tremendous increase in man's activity (i.e., population) in arid
regions of the United States over the last 40 years. The 1990 census data reveal that in the 11 arid
western states the population since 1950 and 1980 has increased 169.85 percent and 22.29
percent, respectively. A large share of this increase is due to a demographic shift to the western
United States These population increases can be linked to an observed deterioration in air quality
(e.g., ozone, particulates), increasing demands on limited supplies of water, and loss of critical
wildlife habitat (from desertification and urbanization). With the prospect of global climatic change,
the condition of arid ecosystems may be increasingly threatened because of their sensitivity and
responsiveness to climate.
2.5.2 Assessment Endpoints
Assessment endpoints for EMAP-Arid are quantitative or quantifiable expressions of
environmental values that have both social and biological relevance. They are quantities
accessible to prediction and measurement and susceptible to the environmental stressors of
concern. Of the three endpoints identified in Figure 2-4, sustainability is generally all-
encompassing and may be directly linked to biodiversity and aesthetics.
2.5.2.1 Sustainability
Sustainability is the capacity of a system to maintain its productivity when subject to stress.
Sustainability in arid ecosystems depends on the number of people present and the demands they
make on the system, the system's physical and biological processes, and the investment society is
willing and able to make to overcome constraints in the system (Orians, 1990). Arid ecosystem
sustainability involves careful conservation of biological resources, maintenance of material
balances into and out of the system, and implementation of resource management programs.
Sustainability in arid ecosystems can only be measured over decades, centuries, and
perhaps millenia. Its measurement is critical to interpreting changes in the status and evaluating
trends in the condition of arid ecosystems. Changes in air and water quality, land use, climate, and
other environmental stresses can lead to increases in erosion, salinization, chemical
contamination, and desertification which can directly influence arid ecosystem sustainability.
Sustainability can be measured in numerous ways (i.e., measurement endpoints). Net
primary productivity is principal among the possible measures of sustainability and can be derived
by evaluating indicators such as vegetation greeness, annual wood increment, standing biomass,
and root to shoot biomass ratios. Other indicators which reflect sustainability are associated with
energy and water balance, reproduction and regeneration, and species composition, frequency,
and distribution.
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2.5.2.2 Biodiversity
Biodiversity has recently become a popular environmental issue and is an environmental
attribute of concern to the public (Nash, 1989). Changes in global populations of amphibians,
historic declines in birds species such as the California condor, and a reduction in observed
numbers of saguaro cactus are popular examples of biodiversity losses that have drawn public
attention. Biodiversity, however, involves more than just species diversity or endangered species.
The issue is focused on biological impoverishment at multiple levels of organization. These levels
of organization include: regional landscape, community-ecosystem, population-species, and
genetic integrity (Noss, 1990). Biodiversity operates at and across all these levels of organization
and is attributable to three components of ecosystems-composition, structure, and function.
Indicators of biodiversity are numerous. Indicators range from perimeter to area ratios,
contagion or habitat patchiness, gamma index of connectivity, Ration's diversity index, and fractal
dimension at the landscape level to karyotypic analysis, DNA sequencing, and sib analysis at the
genetic level. Other indicators at the ecosystem-community and population-species levels
include Habitat Layers Index; species density, frequency, and diversity; and retrospective pollen
and woodrat midden analyses. These indicators are further discussed in Appendix A.
2.5.2.3 Aesthetics
Past studies of aesthetics have shown that the need to interact with other species is deeply
rooted in the human psyche (Orians, 1990). For example, people spend large sums of money to
have flowers in their homes, travel to see unusual organisms or environments, and to have views of
nature from their windows. The aesthetic value of a resource or experience often depends on the
number of people attempting to enjoy it and on the abundance or rarity of the species or habitat.
While this endpoint has perhaps more social, than ecological relevance, it is a reflection of
ecosystem composition and structure. Therefore, changes in these attributes will affect the
public's perception of the condition of the environment. Changes in the aesthetic condition of a
resource may alter its use and impact management and environmental policy.
2.5.3 Legislative Mandate
In order to determine the scope and rate of ecological degradation or improvement of
response to management and conservation programs, an integrated national approach for
monitoring ecological condition and anthropogenic stresses is necessary. Presently, the
responsibility for protection and management of the Nation's arid ecosystems is shared by
numerous agencies including the Bureau of Land Management (BLM), U.S. Fish and Wildlife
Service (USFWS), U.S. Forest Service (USFS), National Park Service (NPS) and EPA. These
agencies are mandated by public laws to protect the integrity and sustainability of these resources.
Broad federal statutes such as the National Environmental Policy Act of 1969, the Federal Land
Policy and Management Act of 1976, the Multiple-Use Sustained Yield Act of 1960, and the
National Forest Management Act of 1976 provide a sound basis for the management of multiple
resources on federal lands.
Numerous pieces of legislation have been enacted to protect specific resource components
from degradation and toxic pollution at both the federal and state levels. The Clean Water Act of
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1977; the Water Quality Act of 1987; the Comprehensive Environmental Reponse, Compensation
and Liability Act (CERCLA), and the Resource Conservation and Recovery Act (RCRA) are key
federal mandates controlling toxic substances and their effects on fish, wildlife, and recreation.
Administered by the EPA, these and other toxic chemical laws including the Federal Insecticide,
Fungicide and Rodenticide Act and the Toxic Substances Control Act, may apply to activities
conducted under the jurisdiction of other management agencies, for instance BLM or USFS insect
control programs. Although primarily administered by the BLM, mining activities may be subject to
EPA regulations for hazardous substance handling, disposal and cleanup. The Taylor Grazing Act
of 1934 and the Public Rangelands Improvement Act of 1978 are the principal mandates for
managing grazing resources on public rangelands. Wildlife management guidelines are provided
by the Fish and Wildlife Coordination Act of 1934, Public Rangelands Improvement Act, and the
Endangered Species Act of 1973. Recreational and landscape resources for arid lands are
protected and managed through statutes such as the Wilderness Act of 1964, the Land and Water
Conservation Fund Act of 1965, Wild and Scenic Rivers Act of 1968, and the Soil and Water
Resource Conservation Act of 1977. While many public laws, such as the Clean Air Act and Safe
Drinking Water Act, address primarily human health issues, they also are designed to protect
ecological resources in arid systems.
2.5.4 Critical Scientific Questions
Arid ecosystems have probably been the least studied and are likely the most misunderstood
of the resource systems (e.g., forests, agroecosystems) under study in EMAR While numerous
studies have been conducted, many scientific questions remain unanswered for arid ecosystems.
EMAP-Arid certainly will not address all questions of scientific interest and will not necessarily be
able to define cause and effect relationships based on the collected information. Nevertheless, it is
important to identify critical scientific questions that need, in the broadest terms, to be addressed
and are important to guide EMAP-Arid monitoring and assessment. This list of questions does not
encompass all of the scientific issues but is designed to provide a basis for EMAP-Arid planning
and development. These questions are as follows:
DesertificationWhat is the extent and rate of desertification?
What are the changes in distribution of shrub dominated versus perennial
graminoid dominate versus annual graminoid/forb dominated systems,
particularly in "tension zones"?
To what degree is desertification associated with grazing and/or climate change?
What is the relationship between desertification on: species composition; fire
frequency, intensity, distribution, and extent; and water quality?
Sustainability-What is the present biomass and rate of productivity?
Are vegetation productivity and species composition changing in association with
climatic variation, management practices, fire frequency, or other natural or
anthropogenic factors?
Are the dominant vegetation life-forms changing on temporal and spatial scales?
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What is the status and rate of change/replacement of native versusn. exotic
species?
Are soils changing in association with climatic variation, management practices,
etc.?
Is there a change in landscape pattern (distribution, density, or composition)?
Is the cycling of nutrients and water or the flow of energy through the system being
altered?
Is there a change in forage quality or quantity?
Water - What is the status and what trends are being observed in surface and ground water
quality and quantity?
What role do arid ecosystems play in global water cycling and related energy
balance processes?
To what extent do riparian ecosystems mitigate water quality and quantity?
What role do current land and water resource management practices play in
determining water quality and quantity in arid ecosystems?
Erosion-What is the rate of soil erosion in arid ecosystem watersheds?
What changes in quantity and quality of soil erosion are occurring in arid
ecosystems?
What are the rates for aeolian, slope, and channel erosion?
In what ways is resource use and management associated with erosional rates and
FireWhat is the role of fire in arid ecosystems and to what extent is associated with
environmental change?
To what extent is fire associated with weather, vegetation characteristics, arson, and
management practices?
How is the extent, distribution, frequency, and intensity of fires altered with changes
in arid ecosystem composition, etc.?
What is the relationship of fire with arid ecosystem structure and function (e.g., soil
carbon and nitrogen, productivity, species composition)?
Air Quality-To what degree are changes in air quality associated with the structure and
function of arid ecosystems?
To what extent are changes in air quality associated with anthropogenic emissions,
fire, erosion, etc.?
What changes are occurring in visibility, atmospheric deposition rates, and
chemical cycling that may be associated with arid ecosystems perturbations?
What is the relationshyip between air quality and recreation, ecosystem
productivity, water quality, etc.?
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2.5.5 Existing Information and Data Gaps
An extensive, but preliminary evaluation of existing data and monitoring programs is provided
in Section 6. The use and integration of existing information into EMAP-Arid is a difficult but
extremely important task. The EMAP-Arid approach is to use data collected from previous and
ongoing studies and monitoring networks to the fullest possible extent. Ecological research sites;
Federal, state, local, and private monitoring programs; and scientific studies will be used in all
phases of EMAP-Arid. Existing data will assist in formulating ecological research questions,
developing ecological indicators, defining historical baselines, and understanding ecological
processes. Direct linkages to ongoing monitoring programs will also be made to minimize
duplication and enhance interagency cooperation. Many other data bases and networks will be
explored over the next few years.
2.5.6 Goals and Objectives
The end result of the process outlined in Figure 2-6 is the establishment of goals and
objectives. The overall goal of the EMAP-Arid is to:
Provide an unbiased estimate with known confidence of the current and changing
conditions of ecological resources in arid ecosystems at the regional and national
level.
This goal is in concert with the EMAP goal and represents the focus of EMAP-Arid. Specific
objectives for EMAP-Arid that must be met to achieve this goal are:
Measure the status, evaluate trends, and estimate the extent of arid ecosystems using
synoptic, retrospective, and sample-based methodologies.
Determine the spatial and temporal correlation between stressor(s) (e.g., pollutants)
and ecological condition/trends.
Provide information to decision/policy makers and management, regulatory, and
research agencies and institutes that can be utilized for comprehensive regional
planning.
Develop a regional interagency communication and data transfer network.
EMAP-Arid has also established 5- and 10- year goals and objectives. Meeting these
goals and objectives will depend on the success of pilot and demonstration studies, development
of interagency agreements, program integration, and the availability of funding for monitoring and
research activities. The 5- and 10- year goals and associated objectives are:
5 Year Goal
Establish baseline conditions, develop a management structure and procedures,
secure interagency commitments and assess the ability of EMAP-Arid to integrate
information to determine regional ecological condition.
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Objectives
Identify and review critical issues and scientific questions on a regular basis.
Characterize and classify arid ecosystems in terms of extent and distribution.
Develop and select indicators to assess temporal and spatial variability and sensitivity.
Develop sampling protocols for selected indicators.
Acquire, assess, and integrate existing databases into EMAP-Arid.
Develop an information management network with cooperating agencies and research
institutions.
10 Year Goal
Determine regional trends in the condition and extent of selected arid ecosystem
resources and develop test scenarios to determine causes of regional alteration,
degradation, or enhancement.
Objectives
Establish a condition index for arid ecosystems.
Evaluate trends in selected core indicators.
Assess effectiveness of EMAP within arid ecosystems.
Implement an integrated interagency monitoring program.
Implement a long term strategic research plan.
These goals and objectives undoubtedly will change as EMAP-Arid progresses toward full
implementation. They will be periodically (at least annually) reviewed to determine their
applicability and appropriateness and will help to guide the program in an orderly and strategic
manner.
2.6 IMPLEMENTATION
Implementation of EMAP-Arid will generally be accomplished by following the steps outlined
in Figure 2-5. Currently EMAP-Arid is at Step 2, Define Study Strategy, which is the scope of this
document. Following an extensive review of the strategy, exploratory (i.e., pilot and
demonstration) studies will be conducted before a regional sampling design is finalized for full
implementation. The sampling design will be evaluated on the basis of its ability to detect change.
Following full implementation, the information produced will be assessed for its adequacy and
appropriateness for use by decision makers and resource managers. Implementation of
EMAP-Arid and the steps leading to full implementation are viewed as a dynamic process which
issensitive and flexible to advances in our understanding of monitoring and assessing arid
ecosystem conditions.
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Several important design, indicator, and logistical issues need to be evaluated, field tested,
and resolved before EMAP-Arid is ready for full scale implementation. The following subsections
describe the types of efforts that will be made to scientifically and operationally advance the
EMAP-Arid group toward national monitoring of all arid ecosystem resources. Within a given
region and formation, the proposed tasks will generally be completed in sequence.
2.6.1 Analysis of Existing Data, Research, and Monitoring Resources
The synthesis and evaluation of existing data, research sites, and monitoring networks
appropriate to each region and formation (see Section 4 for definition of formation) is a critical and
cost effective first step required before proceeding to field studies. Evaluation of existing data will
provide an up-to-date understanding of arid ecosystems necessary for the development of
conceptual models and indicators. Existing research and monitoring sites (e.g., LTERs, BLM
Range sites, National Parks) are likely candidates for calibration of synoptic indicators, "canary"
sites to address specific issues, and as locations for process-based research in the development
of ecological indicators. Integration of ongoing monitoring networks with EMAP-Arid will provide
an important mechanism for effectively and efficiently assessing the condition of arid ecosystems.
Examples of specific tasks that will be addressed include the following:
Identify or develop conceptual model(s) using existing data for each formation that will
integrate issues and ecological endpoints leading to the selection of specific indicators
for implementation.
Investigate the commonality between the EMAP-Arid design, existing monitoring
networks, and long-term research sites in terms of issues, indicators, logistics, data
base management, quality assurance, etc.
Quantify to the extent possible the spatial and temporal variability of candidate
indicators.
Evaluate trends in the condition of arid ecosystems using historical and
paleoenvironmental data at sites where information is sufficient.
Using geographic information systems, initiate an analysis of the relationships between
physical, biological, and stressor environmental variables.
Perform simulations of expected indicator performance and proposed data analysis
and interpretation techniques.
2.6.2 Workshops and Meetings
Further refinement of indicator selection criteria and core indicators will be done through
additional workships and scientific meetings. Recognized aird ecosystem experts will review
candidate indicators, conceptual models, and on-going monitoring and research programs in
order to develop a list of core indicators for evaluation in pilot studies. Similarly, issues and
methodologies concerning sampling design will be reviewed, evaluated, and designs
recommended in a series of workshops and meetings.
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2.6.2 Pilot Studies
Pilot studies are field projects that will be conducted on one arid ecosystem formation (e.g.,
grassland) generally in more than one region with a limited set of indicators to meet one or more of
the following objectives:
Develop, evaluate, and refine sampling methods and assessthe sensitivity of indicators
of arid ecosystem condition, for the specific formation and region of interest.
Evaluate the ability of the proposed indicators to assess ecosystem condition at sites
preselected, using expert judgement, to reflect both nominal and subnominal
conditions.
Quantify the temporal variability within the index sampling period to evaluate and refine
proposed sampling protocols.
Quantify the regional spatial variability for proposed indicators.
Pilot studies will be conducted in all arid land formations and all regions, prior to full-scale
implementation.
2.6.3 Regional Demonstration Projects
Regional demonstration projects are field studies conducted in a survey mode using the
EMAP-Arid frame and the proposed EMAP-Arid sampling protocols. Demonstration projects will
be conducted for each formation and region using a limited suite of indicators. The objectives of
these regional demonstrations include the following:
Identify and resolve logistical problems associated with the program design.
Gather the information necessary to evaluate alternative sampling designs and
establish appropriate data quality objectives for the program.
Evaluate the specificity, sensitivity, reliability, and repeatability of the responses of
selected indicators over a broad range of environmental conditions.
Generate data that can be used as tools to identify sensitive areas and assess the effect
of management and policy.
2.6.4 National Implementation
The full-scale implementation of EMAP-Arid will involve the monitoring of arid ecosystem
condition and extent in all of the proposed EMAP-Arid formations and in all regions of the country,
utilizing the full suite of proposed synoptic, sample-based, and retrospective indicators. Following
completion of pilot studies, the program will initiate one regional demonstration project,
monitoring one formation in one region, and then gradually scale up using the following proposed
implementation priorities:
Monitoring of the arid ecosystem formation in additional regions, adding new reaions
each year until the formation is monitored nationally;
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Yearly additions of new arid formations, starting in one region and then gradually
expanding to other areas; and
Expansion of the core list of indicators for national monitoring, as additional indicators
are developed, tested, and approved.
2.6.5 Implementation Priorities and Schedule
EMAP-Arid has established priority classes (formations) and an implementation schedule for
each priority class. Formations are ranked and shown in Table 2-1 for each EPA region identified in
Figure 2-7. The ranking of each formation is based principally on a review of arid ecosystem issues
and their importance to decision makers, resource management agencies, and scientists.
Rankings are somewhat arbitrary since formations often cross state and EPA Region boundaries
which are not based on ecologically units. In addition, these rankings must be considered
preliminary as they may change as progress is made towards implementation.
The implementation schedule for each region and formation is shown in Table 2-2 and Figure
2-8, respectively. The years when pilot studies, demonstration projects, and full implementation
are anticipated to begin are subject to progress made in each sequential element and the
adequacy of financial resources to complete the required tasks. The proposed schedule is very
ambitious but it is important that a long-term monitoring and assessment program be fully
implemented as soon as possible in order to address the critical issues facing arid and other
ecosystems in the U.S.
2.7 PROGRAM PRODUCTS
Proposed EMAP-Arid products for FY91-FY95 are identified in Table 2-3. The proposed
products represent the principal outputs of EMAP-Arid and will in many ways drive the program.
Details of the specific reports and other EMAP-Arid deliverables are contained in Section 13.
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TABLE 2-1. EMAP-ARID LAND RANKED PRIORITY CLASSES.
EPA REGIONS
Priority
1
2
3.
4.
5.
6.
1-5
Grassland
Formation
Riparian
Formation
Grassland
Formation
Woodland
Formation
Desertscrub
Formation
Scrubland
Formation
Grassland
Formation
Woodland
Formation
Riparian
Formation
8
Riparian
Formation
Desertscrub
Formation
Grassland
Formation
Woodland
Formation
Scrubland
Formation
Tundra
Fomration
Riparian
Formation
Desertscrub
Formation
Grassland
Formation
Scrubland
Formation
Woodland
Formation
Tundra
Formation
10
Riparian
Formation
Grassland
Formation
Woodland
Formation
Desertcrub
Formation
Tundra
Formation
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Figure 2-7 EPA Regions
TABLE 2-2. EMAP-ARID IMPLEMENTATION SCHEDULE - BY EPA REGION.
FISCAL YEAR
Region 92
6, 9 Pilot
7,8
5, 10
1,2,3,4
93 94
Demo Implement
Pilot Demo
Pilot
95
Implement
Implement
Demo
Pilot
96
Implement
Implement
Implement
Demo
97
Implement
Implement
Implement
Implement
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1992
1993
1994
1995
1996
1997
RIPARIAN
GRASSLAND
DESERT SCRUB
WOODLAND
SCRUBLAND
TUNDRA
LJJJJ FULL
DEMONSTRATION UUULJ IMPLEMENTATION
PILOT STUDY
Figure 2-8. EMAP-Arid Implementation Schedule-by Resource Category
TABLE 2-3. PROPOSED STRATEGY FOR PRODUCING EMAP-ARID PRODUCTS.
FISCAL YEAR
PRODUCTS
National Monitoring Strategy
Integrated Assessment Design
Annual Statistical Summaries
Interpretative Reports
Pilot Studies
Interagency Agreements / MOUs
Demonstration Projects
Field Methods Manuals
QA / QC Reports
Implementation Plan / Riparian
Implementation Plans
Presentations / Publications
1991
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v
10
1992
v
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is
is
12
1993
V
is
jX*
IX*
is
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12
1994
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12
1995
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3.0 MONITORING NETWORK DESIGN STRATEGY
This chapter presents information on the current EMAP-Arid monitoring network design
strategy and its relationship to the general EMAP design. Section 3.1 presents a brief overview of
the general EMAP monitoring network design. Sections 3.2 and 3.3 present the EMAP-Arid
monitoring strategies and designs for the estimation of resource land use and land cover, extent,
conditions, and stressors. Three classes of monitoring network designs (Section 3.4), all within the
general EMAP design structure, are proposed for handling all of the EMAP-Arid resources of
interest, and the EMAP tier structure is generalized to serve the EMAP-Arid requirements. The
chapter concludes (Section 3.5) with a brief discussion of some special design issues that need
further methodological attention from both the EMAP-Arid team and other EMAP groups.
3.1 OVERVIEW OF THE GENERAL EMAP DESIGN
The current EMAP strategy is to design and implement a permanent national probability-
based sampling network that will satisfy multi purpose customer-oriented needs in the monitoring
and assessment of the ecological health of the nation. The monitoring designs are being
developed to meet the following objectives as described by (Overton et al.,1989):
1. Provide rigorous and adequate statistical answers regarding any explicit
question of status and quality of any resource.
2. Enable an evaluation of the degree of association of status and quality
variables with appropriate measures of pollutants and other stressors.
3. Accommodate change in resource definition and classification and
correction of errors.
4. Incorporate information from existing environmental monitoring networks
and data bases, with immediate implementation with regard to a set of
selected monitoring and baseline inventory issues.
The general EMAP design strategy was introduced in Section 1. This design involves four
types of activities, or tiers, for accomplishing the ecological monitoring and assessment tasks:
landscape characterization to determine resource extent and distribution, termed Tier
1,
regional assessment of the status and trends in resource condition through
measurement of ecological indicators, termed Tier 2,
diagnostic studies of special subpopulations to address the associations between
indicators of ecological condition and indicators of exposure and stress, termed Tier 3,
and
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joint research studies to aid in the development and understanding of ecological
resource conceptual models, termed Tier 4.
These four types of EMAP activities define a hierarchical, or tiered, approach that relates
national and regional scale ecological monitoring to site-specific ecological diagnostic and
research studies. It also allows feed back from the latter to improve the performance of the former.
The greatest spatial coverage occurs for Tier 1 (T1) and Tier 2 (T2) activities, although T2 activities
may be conducted on a smaller spatial scale than T1. Tier 3 (T3) activities are expected to focus on
specific issues related to condition-stressor associations and undertaken on a regional or
subregional basis. Lastly, Tier 4 (T4) activities will rely, where possible, on existing site-specific
research programs, e.g., the National Science Foundation's Long-Term Ecological Research
program.
3.1.1 EMAP Tier 1 Activities: Resource Extent
EMAP provides for two alternative approaches at T1 to landscape characterization of the
extent and distribution of resources: a census, or complete coverage, and a sampling based
methodology. In some resource classes a remote sensing approach may be able to provide
appropriate and cost-effective indicator information on extent and distribution of a resource, and
thus the census approach will be possible. However, in other resource situations a remote sensing
system will not be able to provide the appropriate extent and distribution information, and hence a
probability-based sample of the resource will be selected to estimate the resource extent and
distribution.
To provide for the probability-based sampling approach, a hexagon was placed on the North
American continent. Grid points established on the basis of the six equilateral triangles that make
up the hexagon result in approximately 12,600 grid points in the conterminous United States,
which is the grid density for baseline sampling in EMAP. The distance between each of these points
is approximately 27 km. Base hexagons, established with these grid points as the center, tessellate
the larger hexagon and are about 640 km2.
Arounds a grid point chosen randomly within one of the base hexagons, and then located at
the same place within each of the other base hexagons, a standard hexagon of approximately 40
km2 forms standard support area (i.e., arbitary area representing the grid point sampling unit) for
the statistical design. Because each of these 40 km2 hexagons is 1/16 of the area of its base
hexagon (Figure 3.1), the selected Tier 1 characterization sample contains 6.25 percent of the
entire land mass of the conterminous United States; a sizable sampling rate for national surveys.
The two sets of hexagons (i.e., the 640-km2 base and the 40-km2 support) are available for
landscape description or characterization utilizing a combination of remote sensing systems (e.g.,
satellite based aerial photography) and other information (e.g., ecological maps, and auxiliary data
sets such as NOAA climate data). Some of the resource characterization may be on a census, or
complete national coverage basis (e.g., the AVHRR and the Landsat MSS characterization), and
some resources will be characterized only for selected subsets of the 40 km2 hexagon sample. The
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0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Figure 3-1. The landscape characterization hexagons are 1/16th of the total area and
centered on the sampling points. The randomly positioned sampling grid
occupies a common but randomly selected position in each of the base
tessellation hexagons.
resulting data will be used for a probability-based estimation of the type, extent, and distribution of
the various ecological resource populations.
The characterization and other resource information is also expected to be used in a double
sampling scheme (e.g., Cochran,1977) for more precise planning of the subsequent T2
investigations of the actual conditions and exposure to stressors of so-called primary T1 resources
(i.e., resources that are of particular interest to ecosystem issues and endpoints). Thus, the
monitoring network design attempts to guarantee that no "identifiable" ecological resources at the
selected level of characterization will be excluded from the sample due to the sampling design, and
the double sampling methodology allows for more precise monitoring of the conditions and
exposure of primary T1 resources.
Presently plans call for periodic update of the characterization information (e.g., every 5
years for the satellite derived data), and thus new estimates of population resource type, extent,
and distribution will be possible. However, because these new estimates will be conditional on the
previous sample selection, and they will require some additional qualifications (e.g., no effects of
the act of measurement on the results or the so-called "Hawthorne" effect).
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3.1.2 EMAP Tier 2 Activities: Resource Condition
Regional assessment of the status and trends of ecological resource conditions is the focus
of EMAP tier 2 activities. Ecological condition is assessed by measuring ecological indicators for
the resource class of interest. Many ecological indicators will require field sampling to obtain the
necessary measurements. The general EMAP design provides for the measurement of these
indicators to be made on a subsample of the T1 grid points and their associated hexagons. Other
ecological indicators may be measurable through the use of remote sensing, providing complete
coverage instead of a subsample of the T1 grid.
In keeping with the double sampling design, the T2 probability-based sample design is
based on the T1 grid structure. The T2 design focuses on the collection of condition and^tressor
information in order to make separate population estimates for primary T1 resources (e.g., Rocky
Mountain Alpine tundra). The selected 12,600 grid points, and their associated hexagons, are
available for field data collection visits on a rotating basis. One fourth of the total sample (i.e.,
3,125) will be available for field visits in each year, and these hexagons will be systematically
spaced throughout the United States, (Figure 3-2). However, there is no design requirement that
all 3,125 available T1 sample hexagons be visited in a given year, and currently it is not anticipated
that any EMAP ecosystem group will visit this number in any year. Rather, an a priori systematic
spatial proportion of the 3,125 hexagons will be visited each year (the proportion will depend on the
particular resource and the variability of the associated indicators). It is this interpenetration (in
time and space) of the sample hexagons that has been presented as one of the important
characteristics of the overall EMAP design.
The EMAP monitoring design also allows for a decrease or increase in the density of the point
grid (i.e., density changes by three-, four-, or sevenfold as well as any multiple), and thus one can
change the number and/or "size of area selected for" of both the T1 and the T2 samples, (Figure
3-3). This flexibility allows sampling at time and space densities that are appropriate for the
particular resource of interest. It provides a foundation for working with potential EMAP customers
on international, national, regional, state, and even local scales.
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Spatial Distribution
Time Cycle for Field Subset Visits
YEAR
1991 92 93 94 95 96 97 98 99 2000
VISITED
SUBSET
1 234123412
Figure 3-2. Spatial and time distribution for field subset visits.
3-5
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Figure 3-3. Enhancement factor for increasing the base grid density. Enhancement
will be made only in the sample grid, not in the base grid.
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3.2 FEATURES OF THE ARID ECOSYSTEMS MONITORING DESIGNS
The EMAP-Arid monitoring network component of the national EMAP design has the same
general objectives as those of the overall monitoring network design and will emphasize some
particular features including the following:
The explicit identification of the major customers (e.g., BLM, and USFW) and users
(e.g., EPA managers) of the monitoring results and a clear specification of their present
needs.
A monitoring hierarchy of four ecological levels that EMAP-Arid will use in addressing
Tier I through Tier 4 activities.
The construction of a set of probability-based multipurpose monitoring network
designs that will be used with remote sensing and/or field measurement systems to
answer specific arid ecosystem resource questions.
A clear working definition of each general resource population of interest (e.g., Arctic
Tundras) and its particular sub populations, (primary resources), of present interest
(e.g., Rocky Mountain Alpine and Alaskan Coastal), the resulting identified populations
(e.g., the frame or list of the locations of each cluster of Arctic Tundra), and the
respective sampling points and their support units.
A regular triangular point grid design structure that permits both increases or decreases
in the grid spacing and the support size in order to be able to focus properly on
ecological resources that occur on many scales.
The ability to use time series data from historical research site(e.g., the yearly "health"
of a particular cottonwood-willow community over the past 50 years) both in the design
process and possibly in conditional historical estimation or development of resource
condition and exposure for similar EMAP sites.
3.3 EMAP-ARID DESIGN BACKGROUNDS
The EMAP-Arid component of the EMAP has selected a hierarchy of four ecological
monitoring with which to address the general EMAP Tier 1 through Tier 4 activities. These tasks are
as follows:
1. Monitor the spatial extent and distribution of resources,
2. Monitor the condition of resources,
3. Perform diagnostic studies of the association between conditions and
stressors of resources, and
4. Promote the use of monitoring and other data to aid in the development of
resource conceptual models.
Although each of these tasks is important to the overall mission of EMAP, the following set of
EMAP-Arid designs will focus only on tasks 1 and 2. Furthermore, each of these tasks may be
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approached by collecting remote sensing or field measurement system data. All remote sensing
system data will be systematically validated with ground observations. Finally, the following design
presentations and discussions should be viewed as preliminary and largely indicative of current
strategy.
3.3.1 Populations. Subpopulations. and Methodology
One of the first steps in estimating the type, extent, distribution, and condition and stressors of
resources in EMAP-Arid is to define the area actually occupied by arid ecosystems; the land area
which will be designated as Arid Ecosystems. The Riparian Indicators Workshop began to
approach this problem by defining arid ecosystems (see Section 2.3).
Within this region, we are in the process of more closely defining the actual EMAP-Arid area,
and its various resource populations, by utilizing maps of resource categories. Two candidates
have been examined (i.e., an Omernik and Gallant map and a Brown and Lowe map), and we have
adopted the Brown etal., 1979, classification system and maps (Figure 3-4) because they provide
locations of actual biotic communities and are hierarchical. We also are currently considering the
possibility of creating a full digitization of the Brown et al. map (portions of Arizona, New Mexico,
and parts of other states) have already been digitized and then using a geographic information
system to produce maps and lists of populations and subpopulations of interest. However, the
Brown et al. (1979) map delineations will not be of fine enough resolution to produce a list frame
(potential sample sites) for rare resources such as alpine tundra. Additionally, linear resources
(e.g., riparian communities) are not provided for on these maps. EMAP-Arid will, therefore
investigate other potential mapping and site sources to identify sampling frames for these types of
resource classes.
The ecological populations and subpopulations of interest for EMAP-Arid are described in
Section 4. They include a small part of the "wetland" and a large part of the "upland" formations as
listed in Brown et al. (1979). The selection of these two general populations, and the specific
corresponding subpopulations (e.g., Rocky Mountain Alpine tundra, cottonwood-willow
communities) stem from customer and user interests in selected ecological issues and their
resulting endpoints (Section 1).
Many of the hierarchical subpopulations can be usefully counted using remote sensing and
existing prior information (e.g., AVHRR, Landsat MSS, Brown etal. digitized data). Consequently,
for some resources the estimates of type, extent, distribution, and condition may utilize remote
sensing data for all questions of interest under the first two ecological tasks. Furthermore,
substantial amounts of relevant prior information will be available from the census process for the
planning of the second stage of a double sampling design, if such a design is appropriate. In this
situation, the second stage will focus on validation and comparisons of the previous hierarchical
results.
If we assume we can conduct a census, via remote sensing, the following three riparian
resource classes: i.e., cottonwood-willow, mixed broad leaf, and mesquite communities, will be
candidates for the primary T1 resources in a double sampling design, will be classified as primary
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TIES AND
AP HEXAGONS (WESTERN SECTION)
14 /
-------
AND LOWE BIOTC
-if
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Subalpine Grassland (1414)
Alpine Tundras (111.5)
Sierran Montane Conifer Forest (122.5)
Petran Montane Conifer Forest (122.3)
Petran Subalpine Conifer Forest (1213)
Sierran Subalpine Conifer Forest (1214)
Great Basin Conifer Woodland (122.4)
Great Basin Montane Scrub (132.1)
Californian Coastalscrub (133.2)
Semidesert Grassland (143.1)
Californian Valley Grassland (143,2)
ins and Great Basin Grassland (142.1)
Chihuahuan Desertscrub (153.2)
Mohave Desertscrub (153.1)
Lower Colorado River Subdivision (154.11)
Arizona Upland Subdivision (154.12)
Interior Chaparral (133.3)
~~' Californian Chaparral (133.1)
~ Madrean Evergreen Woodland (123.3)
|f: Californian Evergreen Woodland (123.4)
Great Basin Desertscrub (152.1)
-------
T1 resources and assigned to individual strata. Assigning each of these primary resources to a
separate strata allows us to control their respective sample sizes in an effort to achieve the
precision specified by the customers and the resulting data quality objective (DQO). All of the other
resources identified by characterization will be termed secondary T1 resources and assigned to
the same separate stratum, (Figure 3.5).
Primary Resources
Secondary Resources
Task 1:
Initial Remote
Sensing
Characterization
(ri7>
18''
Task 2:
More detailed
characterization
and/or on-site
sampling
21
'22 '23
24
r*
25
'26
Figure 3-5. Possible double sampling Relationships between tasks 1 and 2 for both primary
and secondary resources.
If a secondary T1 resource becomes of special concern, then it can be designated a primary
resource, removed from the secondary category, and given a separate T1 stratum in order to
control its sample size and the resulting precision of its population estimates. If this situation arises
after characterization has been completed, then post-stratification would allow for separate
resource estimation but result in loss of control of precision of the estimate. It should be
remembered that only the primary resources will be monitored separately in the second stage, or
T2, of the monitoring design.
3.3.2 The Sample Units
As mentioned earlier, the sample units in the general EMAP statistical design are grid points.
However, because a point creates a very small area to sample, there are supports attached to each
grid point in the form of a hexagon of some size. Thus, the associated hexagon is the area that the
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sample unit will represent at the first level of a double sample design. Hence, the size of the
hexagonal support must be carefully examined in order to have it represent an effective ecological
unit for the given resource. For the present the standard, or default size, issetat40km2(Overtonet
al., 1989). Also, a procedure will be needed for handling edge problems (i.e., a sample unit whose
support has some portion of its area outside of the boundary of the particular resource class).
3.3.3 Frame Development
The two-dimensional regular triangular point grid and its associated hexagons has provided
one possible sampling frame (i .e., a sample of the entire population frame). This regular point grid
has promoted, but does not guarantee equal spatial coverage across all ecological resources. It
also provides for a complete spatial or list frame of the resources of interest within the hexagons
selected for sampling. However, cost-benefit analysis is needed to decide if it is better to use afirst
stage sample to accrue some detailed planning information to provide more focus for the second
stage sampling. An alternative frame could be provided by other materials such as maps, state
resource inventories, and results of other relevant studies, this kind of frame would use the given
resources to collect a larger single-stage sample. Section 3.4 will provide some details on frame
construction for the EMAP-Arid situation.
For relatively rare resources of special concern, such as Rocky Mountain Alpine tundra, the
regular triangular point grid and its supporting hexagons may not have sufficient resolution to catch
any or a sufficient number, of these resource units. Thus, other frame materials EMAP-Arid will
investigate alternative frame material source over the next five years will be needed to locate these
resources and allow for the construction of a list frame.
3.3.4 Sample Selection
At the initial level of the general EMAP design, the set of approximately 12,600 grid points in
the conterminous United States has been randomly selected and the supporting standard 40 km2
hexagons have been designated. For the EMAP-Arid study which actual locations and the number
are presently under investigation. However, initially it appears that about 4000 grid points fall within
areas designated as arid lands, and it would be possible to characterize a yet to be determined
systematic proportion of the associated hexagons at an appropriate level of detail.
An alternative to the above sample selection is a census of the entire EMAP-Arid region using
a remote sensing system such as AVHRR, Landsat MSS, or other imagery data. There currently
appears to be considerable interest in this alternative for a number of reasons. First, some of the
potential customers have expressed a strong interest in complete coverage data. Second, it
appears that some of the present maps may not be current or accurate enough to correctly
delineate the transition zones (ecotones) of various resources; these locations may be the most
telling in some ecological issues. Third, this alternative would give us an opportunity to partially
evaluate the selected EMAP Tier 1 sample by comparing a census with corresponding sample
estimates (e.g., the spatial extent land size distribution for grasslands). Finally, there is some
present evidence that the actual cost of such a census may be affordable. This census, or 100
percent sample, strategy is presently being given careful consideration.
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3.4 THREE CLASSES OF ARID LANDS DESIGNS
Important ecological endpoints for EMAP-Arid region include sustainability, biodiversity, and
aesthetics (see Section 2.5.4). For each of these endpoints, critical questions (based on
customers needs) already have been or will be identified, and the extent, distribution, conditions,
and stressors of the relevant resources will be investigated. Because available existing information
and the sampling problems can change from resource to resource, the EMAP Arid resource group
investigate the use of several classes of monitoring network designs. This investigation should
allow selection of a design appropriate to a given situation.
As a result of the investigation, propose that three classes of monitoring network designs be
utilized for EMAP-Arid population estimations (1) a discrete rare resource, (2) an elongated
resource, and (3) an extensive resource design class.
3.4.1 Discrete and Rare Resource Design
There are some resource populations that clearly are discrete, and often rare (e.g., Rocky
Mountain Alpine tundra, riparian communities and playa lakes), and thus special attention is
needed to properly sample these resources. Two approaches to this problem are being
considered by the EMAP-Arid team.
The first, and presently preferred, approach is to identify each and every discrete cluster or
unit of the particular primary resource throughout the EMAP-Arid region. This typically will require
substantial existing information (e.g., data drived from remote sensing, maps) of sufficient
resolution to identify and accurately locate each unit. Then an ordered (e.g., by elevation) list of the
resource units (the list frame) can be constructed, and a systematic sample can be drawn from this
list, assuring proper spatial coverage and an appropriate sample size. If the list is relatively short, a
systematic sample at interval 1, would achieve a census of the resource units on the list.
The second, more limited approach is to use the EMAP characterization information from a
given sample of the hexagons (probably with an enhanced point grid and/or enlarged hexagons) to
construct groups (strata) of hexagons based on the likelihood of the existence of the particular
resource (e.g., unlikely, possible, very likely). The strata would be sampled with a probability
proportional to the likelihood of the existence of the particular resource and a census performed in
each of the selected hexagons. This approach would result in more sampling of hexagons which a
priori had more evidence of the existence of the particular rare resource. The choice between these
two alternatives will depend substantially on the quality and quantity of the available information
and the relative costs of the two approaches. Finally, if a non-rare discrete resource can be readily
listed, then it too can be handled by this type of design. EMAP-Arid plans to evaluate these and
other alternatives for discrete and rare resources over the next five years.
3.4.2 Elongated Resources Design
In some situations the primary resource of special interest such as cottonwood-willow
communities, or the principal area of interest for the resource, such as grassland-desert ecotone,
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occurs in linear or elongated forms. In this case, special care must be exercised in order to capture
the resource and estimate the resource extent, distribution, condition, and stressors with the
desired precision.
The initial problem is to create an ecologically meaningful sample unit for the designated
primary resource (e.g., the riparian corridor along a stream reach for measuring the
cottonwood-willow communities) and to clearly define the entire desired population of all sample
units (e.g., all riparian corridors along all stream reaches.
A second problem is how to identify collection of possible sample units. (Note: the desired
population of sample units may differ from the identified set, the frame, in that one may desire to
investigate all riparian corridors but be restricted to corridors on "active" steams.) The typical
alternatives are map frames, list frames, or conditional population frames constructed from
existing information such as the characterization of some of the selected sample hexagons. The
latter is the EMAP double sample situation and if it is appropriate, this will be the preferred frame.
As an example, if the remote sensing characterization or other existing information (e.g., the
USGS 1:100,000 topographic map) has been able to identify all riparian forest communities, then
we can superimpose a set of selected hexagons on this prior information and have the basis for a
conditional sample frame for those cottonwood-willow communities. One approach to
generating an actual conditional frame would be to construct a within-hexagon list frame of all of
the identified possible riparian sample units in all of the EMAP-Arid 40 km2 hexagons using a given
membership rule. For example, if the upgradient node of the stream reach resides within the
hexagon, then the riparian corridor associated with that reach is a member of that hexagon, Figure
3-6. This list frame then could be used to select the second-stage sample units (e.g.,
cottonwood-willow communities).
With regard to the sample size question, it has been suggested that typically atotal of 50 to100
sample units over the full four-year cycle are needed to produce adequate population estimates
(Linthurst et al.,1986). However, the final desired number of samples should be related to the
actual performance of selected indicators and the desired precision of the estimator (these two
issues will be addressed in forthcoming EMAP-Arid pilot studies). Finally, if the sample size for a
particular resource sample size appears to be inadequate, the grid can be enhanced or the
hexagon enlarged or both.
In true double sampling fashion, it is assumed that EMAP-Arid typically will visit only a subset
of the selected 40-km2 hexagons for the second stage of the study. Thus, a probability-based rule
will be needed for selecting a spatially distributed set of hexagons for each primary resource of
interest. It should be noted that a different subset of hexagons may be visited for each resource.
However, for a number of reasons (e.g., cost, ecotone estimation, and integrated joint estimation),
the EMAP-Arid team believe a "core set" of hexagons in which all primary resources will be
measured is preferable.
The above mentioned stream-reach elongated design for riparian resources is very similar to
the EMAP Surface Waters stream design (Paulsen et al., 1990). Thus, for riparian resources
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Figure 3-6. A 40-km2 hexagon its stream component, and the riparian corridor.
identified by the EMAParidresourcesgroup.it will be necessary to combine efforts with the Surface
Waters group in order to allow for resource sharing and joint estimation of particular resources.
Clearly this possibility needs to be further evaluated. EMAP-Arid will be holding workshops over
the next few years to develop and evaluate sample designs for elongated resources.
3.4.3 Extensive Resource Design
The third situation occurring in the EMAP-Arid ecosystems is the presence of broadly located
or extensive resources. Examples include scrubland, grassland, and desertscrub formations. In
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this case the selected set of grid points can be used as sample units, and the supporting hexagons
(e.g., the standard 40 km2) become the sampling areas. A frame for a particular primary resource
can be constructed by using the relevant Brown and Lowe map and overlaying the EMAP point grid
and its standard hexagons. Those hexagons that intersect the particular resource become
members of the frame. This map frame or an ordered list frame can then be sampled using some
type of spatially proper systematic probability rule to select the second stage subsample of
hexagons which will be further characterized and visited in the field.
The typical initial primary resource sample size will contain approximately 50 to 100 total
hexagons over a complete field cycle (4 years) until detailed information is available on the total
variability of the particular indicator which may increase or decrease the number of hexagons.
Then, after weighing with the appropriate inclusion probabilities, the empirical frequency and
cumulative distribution functions can be calculated for each indicator of the condition of the
primary resource (e.g., size of the crown for the scrubland communities).
Lastly, it should be noted that for some primary resources of interest (e.g., creosote bush) the
comparison of results from two monitoring designs may be ussful to in assess the condition of the
resource. As an example, results from the pinyon-juniper-sagebrush ecotone elongated
monitoring design could be compared with the results from the extensive monitoring design to
determine the relationship between the two approaches for assessing resource conditions. The
ecotone results may prove to be a useful early warning predictor of forthcoming large-scale
changes.
3.5 OTHER DESIGN ISSUES
A number of statistical and data issues have been directly or indirectly raised by the proposed
EMAP-Arid monitoring designs. Some of the more important problems are briefly mentioned and
discussed below. Current suggested solutions are also provided in the discussion.
3.5.1 Reserve Sample of Grid Point Hexagons
There appears to be a need for a reserve set of hexagons. Two roles presently are planned for
this set. First, one needs to assess the "Hawthorne" effect does the rancher purposely graze
fewer cattle on the land that is within the 40 km2 hexagon than on land outside the hexagon.
Consequently, it maybe necessary to monitor pairs of adjacent hexagons and keep one
anonymous. Second, we probably need to follow other "rotating panel" type surveys, such as the
current population survey, and admit that some percentage of the selected hexagons will become
unavailable to us (i.e., access denied) at some point in time. These nonresponse units create both
a sample size and an inference problem, particularly if their proportion becomes sizable (e.g., 25
percent). Therefore, the EMAP-Arid group is considering the use of an over-sampling scheme
rather than attempting to construct a substitution rule for unavailable hexagons. The latter
approach typically is unacceptable if classical statistical inference statements such as confidence
interval statements are needed.
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3.5.2 Plot Designs
The statistical plot design (the spatial sampling design used to select a single sample unit of
the resource) should depend on a number of items including the within sample unit variability of the
particular resource, the complexity of the indicators being used, and the cost of taking and
processing measurements. The possible plot designs include the entire hexagon (a typical
characterization situation), a radial design using the three line segments that pass through the
center of a hexagon and connect its corners (a balanced spatial design suitable for sampling
clustered resources), and a sequence of systematically spaced parallel transects each
perpendicular to a single line which passes through the center point of the selected hexagon (a
balanced off-grid design possibly suitable for riparian communities). We plan to investigate
different plot designs both theoretically and empirically in future studies.
3.5.3 Shorter Time and Space Scales
There may be some ecological situations where a resource responds to a stressor on a very
short space and time scale (e.g., cottonwood-willows impacted by industrial, mine, or farm
effluents). In these cases, it may be important to monitor at least on a yearly basis to detect
changes early. Also, an internal or external EMAP-Arid customer may request annual monitoring
of the same sites. The EMAP design will accomodate such requests but the costs will likely be
incurred by the client.
3.5.4 Spatial Association
It is well known that systematic sampling can be viewed as cluster sampling (e.g., Cochran,
1977), and in this case the sampling design handles the intercluster correlation in the population
estimation process. In the present context this often is termed spatial association, and the
question of enlarging the supporting hexagon versus enhancing the point grid is directly related to
this problem). Hence, the EMAP-Arid team expects to consider cluster sampling methodology in
planning monitoring designs for both future empirical and simulation studies.
3.5.5 Fixed Historical Site Data
Four roles presently are planned for fixed historical site (FHS) data in the EMAP-Arid
monitoring network designs. The first use is to aid the designers in the assessment of potential
natural variability found in various arid ecosystem indicators over the past decades. The second
use is in the identification of new indicators of ecosystem conditions and exposure. The third use is
to establish the degree of correspondence between the behavior of the FHS resources and the
behavior of the same (other members of a local well-defined population) resources at surrounding
sample sites measured on the grid. Hence, we should be able to determine the representtiveness
of these historical sites by comparing to measures taken on the EMAP grid. The hope is that FHS
data can be used as a proper historical baseline for the evaluation of present resource trends for a
local well-defined resource population. Arid the fourth use is to provide information for diagnostic
and research efforts, tasks 3 and 4 listed in Section 3.3. It is presumed that some FHSs will become
EMAP diagnostic and research sites.
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4.0 ARID ECOSYSTEMS CLASSIFICATION
Arid regions may be classified according to a number of biotic and abiotic environmental
factors, including land use, climate, geology, physiography, and vegetation. While all of these
factors are interrelated, vegetation is perhaps the most effective integrator of the environment.
Changes in climate, soils, geology, and physiography are all reflected in the vegetation.
Vegetation is, therefore, very sensitive to these environmental changes. As such, EMAP-Arid is
adopting a classification based on vegetation. This classification can be considered at various
levels of detail and spatial resolution for characterization performed by using detailed and intensive
ground-based sampling or extensive satellite remote sensing.
Classification systems can be defined as the grouping or clustering of objects based on their
resemblance to each other (Ludwig and Reynolds, 1988). Vegetation is heterogeneous, the
approaches to its classification numerous, the purposes served by the classifications highly
varied, and the personal attitudes of the phytocenologists equally varied (adapted from Kuchler,
1967). There are, therefore, different approaches to an orderly arrangement of vegetation extent
and distribution, and each of these may lead to success in certain regions or for certain purposes.
The appropriate classification must be selected and developed for the specific purpose.
Vegetation classification (and concomitant mapping) necessarily presupposes that grouping
or clustering of similar vegetation species or characteristics exists and that groups of similar
species or plant characteristics repeat themselves in a pattern across the landscape. Some
ecologists argue (successfully) that the concept of groups or clusters of species represents an
arbitrary and anthropogenic view and that, in fact, species distribution follows a continuum across
a suite of various environmental parameters. From the landscape ecology view taken by EMAR
however, the importance of characterizing landscape mosaics is of paramount importance; hence
the need for a classification system.
Units within a classification system do not imply complete homogeneity of attributes, that is,
an equal chance to find a given species or vegetation characteristic throughout the unit, but rather a
relative homogeneity of attributes, that is a higher chance of finding one or more species (or
vegetation characteristic) within one unit than in another.
A vegetation classification presupposes that a number of vegetation categories can be
established. These categories can be arrived at in a number of ways, three of which follow (after
Ludwig and Reynolds, 1988):
1. Classifications may be hierarchical or reticulate. In a hierarchical
classification, groups at a lower level within the classification are exclusive
subgroups of those groups at higher levels. In a reticulate classification,
groups are defined separately and are linked together in a web-like
network. Nonhierarchical and nonreticulate classifications, or mere listings
of types or units, also exist and are common.
2. Classifications may be either divisive or agglomerative. In a divisive
classification, the entire collection of sample units (SUs) is divided and
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redivided to arrive at the final groupings (i.e., picture an inverted tree). In
an agglomerative classification, individual SUs are combined and
recombined to form larger groups of SUs (the tree).
3. Classifications may be either monothetic or polythetic. In a monothetic
classification, the similarity of any two SUs or groups is based on the value
of a single variable, for example, the presence or absence of a single
species. In a polythetic classification, the similarity of any two SUs or
groups is based on their overall similarity as measured by numerous
variables, for example, species abundances.
The EMAP-Arid classification is hierarchical, divisive, and polythetic. For EMAP-Arid, it is
highly important that resources be considered in a hierarchical context. This context allows
characterization of arid ecosystems at both very coarse or regional levels as well as at finer or local
levels, aierarchical system allows for characterization at a multitude of scales and levels of
intensity. In addition, it allows for characterization at a finer level of detail if ancillary information or
local knowledge is available and use dictates it. This kind of system also allows for characterization
at a coarser level should either a lack of available information or demand preclude the need for
characterization at the finer level. The following illustrates a general characterization of
ecosystems at varying levels within a digital hierarchical classification (Brownet al., 1979):
1,000 = Biogeographic (Continental) Realm
1,100 = Vegetation
1,110 = Formation-type
1,111 = Climatic (thermal) Zone
1,111.1 = Regional Formation (Biome)
1,111.11 = Series (Community of generic dominants)
1,111,111 = Association (Community of specific dominants)
1,111.1111 = Composition-structure-phase
In addition to the hierarchical systems suggested by Brown et al. (1979), the EMAP-Arid
examined a number of other classification systems. At the coarsest level, these included systems
developed by Omernik, Kuchler, Bailey, which all describe the basic cover types occurring within
EMAP-Arid. The basic uses for these systems is for identifying and delineating regional
boundaries.
The ecoregion concept developed by Omernik has been used to determine the entire arid
ecosystem geographic region of interest that can be defined through broad climatic and
physiographictypes. Broad boundary identification based upon actual present vegetation maybe
more useful for EMAP purposes than systems based upon potential vegetation, physiographic
types, or broad climatic types. Therefore, an integration of multidate AVHRR imagery with any of
these classification systems might be most useful for the delineation of those boundaries. One
problem even with the use of a coarse system such as the AVHRR lies with boundary placement.
The eastern boundary of EMAP-Arid broadly integrades with wetlands, agroecosystems, and
forests, and, therefore, complex decision criteria are required to identify the boundaries.
Small-scale characterization can be extremely useful for identifying changes that may not be
perceptible or noticeable at larger scales. An example of such a change might be manifest through
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albedo or total surface reflectance. Evidence of a gradual climatic trend toward increased aridity or
a tendency toward overgrazing on public lands might be more acceptable if it occurred over very
large areas (small scales) than over small areas. In such examples, a high radiometric resolution
remote sensing system such as the AVHRR with coarse spatial resolution and with a very short
repetitive coverage (twice a day) might be ideal. Coarse classification systems, such as some of
those mentioned earlier, or a higher (broader) level within a detailed hierarchical system would be
more appropriate for regional characterization.
The EMAP-Arid resource group has adopted the Brown, Lowe, and Pase (1979) classification
systems for use at finer levels of landscape characterization. This system allows for landscape
characterization for a variety of ecological research and management purposes. The fact that this
system is hierarchical adds to its utility for reasons previously mentioned. An example of the
Brown, Lowe, and Pase classification, which might be of use in portions of Nevada, is (the first digit
has been deleted):
150 Desertscrub Formation
151 Arctic-Boreal Desertlands
151.1 Polar Desertscrub
152 Cold Temperate Desertlands
152.1 Great Basin Desertscrub
152.11 Sagebrush Series
152.111 Artemisia tridentata Association
152.112 Artemisia tridentata-m'\xed scrub-grassAssociation
152.113 Artemisia nova Association
We have adapted this system to meet the characterization needs envisioned for EMAP-Arid.
Using the example of characterization in central Nevada, delineation and identification would begin
at the second digit to differentiate a Desertscrub Formation (150) from, for example, a Grassland
Formation (130). The third and fourth would identify the locale. At the fifth digit, there would be an
attempt to differentiate a sagebrush series (152.11) from, for example, a blackbrush series
(152.13). With ground sampling and verification, characterization to the sixth, seventh, or even
eighth digit might be possible. Digital remote sensing (Landsat TM or even MSS) might be able to
separate units at very fine levels depending upon percent cover, the differentiation of reflectance
properties among types, the association of the vegetation types with ancillary information
(elevation, parent materials, soils, slope and aspect), and ground verification.
The structure of our classification, then, proceeds from the formation through the series level
to progressively finer levels. The EMAP-Arid group plans to investigate flora/fauna classification
alternatives. Formation level categories are listed in Table 4-1.
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TABLE 4-1. VEGETATION DIVISION, FORMATION AND VEGETATION TYPES
INCLUDED IN EMAP-ARID.
Vegetation
Division
Formation
Vegetation
Type
Wetland
Upland
Riparian Forest
Riparian Scrub
Wet Tundra
Strandland
Other1
Desertscrub
Grassland
Scrubland (or chaparral)
Woodland
Tundra
Other2
Cottonwood/Willow
Mixed Broadleaf
Mesquite
Salt Cedar
Mixed Scrub
Salt Cedar
Mixed Scrub
Creosotebush-bursage
Sagebrush
Rocky Mtn. Alpine Tundra
1 Marshlands, submerged emergents, and similar wetland cateogories are
included in EMAP-Wetlands.
2 Forest lands and agricultural lands are included within separate EMAP
resource groups.
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5.0 INDICATORS
5.1 INTRODUCTION
As stated in the EMAP Indicator Development Strategy (ERL, 1990), EMAP success will
depend on the ability to characterize environmental condition (or health) and to suggest likely
causes of adverse changes. Because concepts such as environmental "condition" or "health"
cannot be directly measured, EMAP will monitor a set of environmental indicators that collectively
describe the overall condition of an ecosystem. An environmental indicator is defined as an
environmental attribute that, when measured, quantifies the magnitude of stress, habitat
characteristic, degree of exposure to the stressor, or degree of ecological response to the
exposure. Because of the importance of indicators in interpreting ecosystem condition, the
selection, development, and evaluation of these indicators for use in a broad-scale regional status
and trends program is a major component of EMAP activities.
Indicators will be used within EMAP to assess condition, or health, of ecological resources.
Rapport (1989) lists three approaches or criteria commonly used to assess ecosystem health: (1)
identification of systematic indicators of ecosystem functional and structural integrity; (2)
measurement of ecological sustainability or resiliency, i.e., the ability of the system to handle
stress loadings, either natural or anthropogenic; and (3) the absence of detectable symptoms of
ecosystem disease or stress. Additionally, an indicator should be (from Noss, 1990):
1. sufficiently sensitive to provide an early warning of change;
2. distributed over a broad geographical area;
3. capable of providing a continuous assessment over a wide range of stress;
4. relatively independent of sample size;
5. easy and cost-effective to measure, collect, assess, and/or calculate;
6. able to differentiate between natural cycles or trends and those induced by
anthropogenic stress; and
7. relevant to ecologically significant phenomena.
In more general terms, determination of ecosystem health involves measurement of systemic
indicators of ecosystem functional and structural integrity. Measurements of primary productivity,
nutrient cycling, species diversity, system stability, prevalence of disease, structure (e.g.
distribution of life forms), and the occurrence of contaminants are examples of parameters that can
be used to quantify ecosystem condition (Rapport, 1989). These measurements can be made and
are applicable to virtually all ecosystems including arid ecosystems. The parameters which are
measured are referred to as indicators.
5-1
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5.2 DEVELOPMENT OF INDICATORS FOR ARID ECOSYSTEMS
The overall strategy and process employed to specifically develop indicators of ecological
health for arid ecosystems is similar to that identified by Hunsaker and Carpenter (1990).
EMAP-Arid has developed a conceptual model linking ecological endpoints and indicators to
assess the status and evaluate trends in the condition of arid ecosystems. Hunsaker and
Carpenter (1990) divided indicators into three groups: stressor, exposure, and habitat indicators
(other investigators might add response). While the EMAP-Arid resource group recognizes the
importance of this trichotomy, EMAP-Arid has adopted a further characterization based on how
they're measured. This activity retains the process used throughout the EMAP as outlined by
Hunsaker and Carpenter (1990), but consists of three measurements categories of indicator types
(synoptic, sample-based, and retrospective) that have been identified for arid ecosystems Figure
5.1).
The initial broad list of indicators developed for EMAP-Arid were determined through a series
of workshops and interactive meetings involving EMAP-Arid team members and others from the
academic community and various resource agencies. Workshops were held in September 1989,
January 1990, and September 1990 to develop and discuss candidate indicators. The candidate
indicators were compiled by a number of working groups and presented for review to the workshop
participants. Selected indicators have been summarized and are presented in Table 5-1.
Table 5-2 lists the sample protocols for each indicator. Fact sheets discussing these indicators
have been compiled and are summarized in Appendix A.
As mentioned above, we have further classified indicators by the way in which they're
measured. Synoptic measurements of indicators provide full landscape coverage and essentially
eliminate the need for probability-based field sampling because they measure specific attributes
over the entire population. Advancements in remote sensing and image processing technologies
now make the use of synoptic indicators as measures of ecosystem health applicable to
environmental monitoring. Arid ecosystems are particularly well suited for the application of
synoptic indicators because of the numerous near by cloud-free days, the general lack of a closed
vegetation canopy, and the availability of imagery over large spatial and temporal frameworks.
Sample-based measurements of indicators are somewhat more traditional in nature and are
based on field and laboratory methodologies that estimate population values by sampling
selective parts (sites) of the ecosystem. Standard plot-level measurements of species
composition, density, cover, and diversity are examples of sample-based data. These types of
indicators can also be used to calibrate synoptic indicators to make them applicable for regional
assessments. They are good indicators of ecosystem function and process.
Retrospective measurements of indicators are ecological measurements which provide a
temporal and spatial framework for environmental monitoring. Because long-term ecological data
are very sparse, data derived from proxy historical and paleoenvironmental indicators can be used
to place synoptic and sample-based indicator measurements in a long-term (up to 50,000 years)
ecological perspective. This capability is particularly critical in order to determine if observed
changes are within expected values or are part of a natural long-term cycle. Use of retrospective
5-2
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i
U)
CLASSIFICATION
ASSESSMENT
ENDPOINTS
INDICATORS
ECOSYSTEM
PROCESSES
ARID ECOSYSTEMS
SOIL
DEVELOPMENT
HYDROLOGIC
CYCLES
BIOGEOCHEMICALl
CYCLING
ATMOSPHERIC
INTERACTIONS
SUCCESSION
CARBON
ALLOCATION
LANDSCAPE
PROCESSES
ENDPOINTS & ECOSYSTEM PROCESSES
Figure 5-1. The EMAP conceptual model links ecological endpoints and indicators to assess the status and evaluate
trends in the condition of arid ecosystems.
-------
TABLE 5-1. CANDIDATE INDICATORS FOR ARID ECOSYSTEMS
INDICATOR
LINKAGE RELEVANT
TYPE ENDPOINTS*
MEASUREMENTS
CATEGORY PRIORITY
| VEGETATION BIOMASS & CONDITION |
Leaf Area Index/
Vegetation Index
Areal Extent
Foliar Chemistry
Exotic Species
Response
Response
Habitat
Exposure
Habitat
Exposure
Habitat
S
B, S, A
S
B,A
Synoptic
Sample
Synoptic
Sample
Sample
High
High
High
High
| SOIL PRODUCTIVITY & CONDITION |
Life Form
Mechanical Disturbance
Soils and Vegetation
Soil Erosion
Lichens & Cryptogamic Crusts
Physicochemical Soil Factors
| WATER BALANCE |
Vegetation
Ground Water
Stream Flow
Precipitation
Energy Balance
| LANDSCAPE PATTERN & LAND
Patch Size &
Connectivity
Habitat Proportions
Linear Classification
Vertical Structure
Land Use
Abundance & Density
Physical Features
Response
Habitat
Exposure
Habitat
Response
Response
Exposure
Response
Response
Stressor
Response
Habitat/stressor
Stressor
Response
USE |
Exposure
Response
Habitat
Exposure
Habitat
Exposure
Habitat
Exposure
Habitat
A, B
B
B,S
B
S
B,S
B, S
B,S
B, S
S
B
B
B, S
B,S
B
Synoptic
Sample
Synoptic
Synoptic
Sample
Sample
Sample
Sample
Sample
Synoptic
Sample
Sample
Synoptic
Synoptic
Sample
Synoptic
Sample
Sample
Synoptic
Synoptic
High
High
High
Moderate
High
High
High
High
High
High
High
High
High
High
High
5-4
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Table 5-1. (continued)
INDICATOR
| FIRE |
Occurrence
and Area
RETROSPECTIVE |
Charcoal Record
Dendrochronology
Pollen Record
Woodrat Midden
WILDLIFE/HABITAT |
Species Composition
and Ecotone Location
Relative Abundance
Animals (Guilds)
Demographics for Animals
Morphological asymetry
of Animals
Biomarkers
Chemical Contaminants
in Plant Tissues
B = biodiversity
S = sustainability
A = aesthetics
LINKAGE
TYPE
Exposure
Habitat
Response
Response
Response
Response
response
habitat
response
response
response
exposure
habitat
exposure
habitat
RELEVANT
ENDPOINTS*
S, B
B
S
B
B
B, S
B, A
S
S,A
S
S
MEASUREMENTS
CATEGORY
Synoptic
Sample
Sample
Sample
Sample
Sample
Sample
Synoptic
Sample
Sample
Sample
Sample
Sample
PRIORITY
High
High
High
High
High
High
High
Moderate
Moderate
Moderate
Moderate
5-5
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TABLE 5-2. SAMPLE PROTOCOLS FOR ARID ECOSYSTEMS' INDICATORS
SAMPLING
INDICATOR FRAMF
VEGETATION BIOMASS |
Leaf Area Index/
Vegetation Index
Areal Extent
Foliar Chemistry
Exotic Species
SOIL PRODUCTIVITY & CONDITION
Life Form
Mechanical Disturbance
Soils and Vegetation
Soil Erosion
Lichens and Cryptogamic Crusts
Physicochemical Soil Factors
WATER BALANCE |
Vegetation
Ground Water
Stream Flow
Precipitation
Energy Balance
LANDSCAPE PATTERN |
Patch Size &
Connectivity
Habitat Proportions
Linear Classification
Vertical Structure
Livestock Grazing
Abundance & Density
Physical Features
Synoptic
Synoptic
Hexagon
Hexagon
Synoptic
1
Hexagon
Synoptic
Synoptic
Habitat
Synoptic
Hexagon
Hexagon
Hexagon
Hexagon
Hexagon
Linear
Synoptic
Hexagon
Hexagon
Synoptic
Synoptic
Hexagon
Synoptic
Hexagon
Hexagon
Synoptic
Synoptic
SUGGESTED
MFTHnnfi
Satellite
Satellite
aerial photos
tissue samples
Survey
aerial photos
Survey
aerial photos
Satellite
survey/aerial
satellite
abundance
species
quant, pits
pedon samples
OPTIMAL
PFRIOD
peak vegetation
early summer
peak biomass
spring/summer
seaonal
late summer
same season
summer/autumn
same season
stomatal conductance growing season
Isotopes
Wells
Weir
Satellite
Gauges
Radiometer
Satellite
Satellite
aerial photos
survey/aerial
Satellite
Survey
Survey
aerial photos
Satellite
aerial photos
Continuous
Continuous/spring
Continuous
Continuous
growing season
seasonal record
late summer
5-6
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Table 5-2. (continued)
INDICATOR
| FIRE |
Occurrence
and Area
| RETROSPECTIVE |
Charcoal Record
Dendrochronology
Pollen Record
Woodrat Midden
| WILDLIFE/HABITAT
SAMPLING
FRAME
Hexagon
Synoptic
Region
Region
Record
Region
Species Composition Hexagon
and Ecotone Location Synoptic
Relative Abundance of Hexagon
Animals (Guilds)
Demographics for Animals Hexagon
Morphological asymetry Hexagon
of Animals
Biomarkers
Hexagon
Chemical Contaminants Hexagon
in Plant Tissues
SUGGESTED
METHODS
Survey
Aerial photos
Abundance/size
ring width
abundance/size
species
Plants/seed
survey/aerial
Satellite
Counts
Keystone
Morphology
Samples
Elements
OPTIMAL
PERIOD
Autumn
Early winter
Autumn
growing season
summer
breeding season
species dependent
species dependent
spring/summer
growing season
meausements will provide a mechanism for determining trends from present day environmental
monitoring without having to wait decades or longer for collection of environmental data. The use
and integration of these three indicator measurement categories will determine critical spatial and
temporal variability for environmental monitoring. See Appendix A for specifics on measurement
techniques associated with these measurement categories.
5.2.1 Field Sampling and Sample-based Measurements
Field sampling is performed for a wide variety of purposes. In the context of indicator
development and verification, it provides field verification and characterization of aerial and
satellite derived patterns and signatures prior to landscape classification and characterization from
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those data sources. Sampling also provides information that can be used to assess both the
temporal and the spatial variability of the measured indicators. In addition, field sampling provides
detailed information to develop and characterize ground-based indicators such as species
composition.
The choice of a sampling strategy is clearly based on the needs of the experiment, the types of
questions asked, and overall project objectives. A sampling program must be designed to: (1)
Obtain appropriate data for statistical analysis; (2) reduce variability so that indicators are sensitive
to conditional changes in critical ecological processes and functions; (3) produce status, trends,
and associations among indicators There are many variations to this sequence that define a
particular study, and these variations usually determine the best sequence of sampling design and
statistical analysis (Green, 1979).
Three examples of specific sampled-based measurements of indicators applicable to arid
ecosystems are vegetation cover, density, and biomass. These parameters can be measured to
determine if a trend toward increasing desertification is present. Vegetation cover can be
measured either as basal area (that which is in contact with the ground) or as foliage cover by
projecting aerial parts of the vegetation on the ground. Point methods can be used to estimate
cover, whether singly, as in the step-point method, or in frames, as in the point-frame method.
Line-intercept methods of cover estimation involve laying out a transect and measuring the length
of a species intersected. Line-intercept data are more accurate and the data are obtained more
rapidly than by the use of quadrats in communities with different-sized individuals of any one plant
species. Quadrat methods may involve ocular estimates of percentage cover by species. Ocular
estimates often make use of cover classes and the use of midpoint values. These techniques allow
for repeatable observations made by different observers.
Most density measures use counts from plots or distance measures. The measurements
may make use of plot frames which may range from one meter square to ten meters square, and
are frequently nested, that is, the smaller plots are placed inside the larger plots with herbaceous
plants counted in the smaller plots and shrubs and trees in the larger. Line (such as a 50' or 100'
-meter tape) intercepts are also used to measure density.
Vegetation biomass is most often determined from plots of given areas. Vegetation in small
plots is clipped and weighed to yield an estimate of biomass. Measurements of cover and
frequency have been used successfully to estimate biomass production of some species with
correlation coefficients exceeding 90 percent (adapted from Bonham, 1989).
A critical element in estimating population attributes (i.e., sampling) is acquiring an
appropriate number of samples. Sample size is the number of observations made on a measured
characteristic of vegetation (such as cover or biomass). The general form of the equation for
sample adequacy is
n =
Eq. 1
5-S
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where n is the estimated number of observations needed to obtain the estimate within a given
probability; the value for t is the critical value of the t distribution, taken from atable; s is the sample
standard deviation, k is the proportion oj precision that the true difference of the sample mean
occurs from the population mean; and x is the sample average (Bonham, 1989).
Vegetation sampling for inventory and monitoring of arid ecosystems often makes use of
random sampling and the establishment of permanent plots within vegetation types or subtypes.
Sampling for monitoring purposes is usually restricted to selected locations within a vegetation
type. In monitoring arid ecosystems, a specific effort must be made to measure characteristics
which are likely to change over the monitoring period. Species composition, cover, density, and
biomass are all direct factors which can be measured to detect change. Field sampling techniques
can be augmented by remote sensing techniques for ancillary measurements of the above
characteristics as well as provide a synoptic overview of spatial differentiation of those attributes.
5.2.2 Synoptic Measurements
Synoptic measurements provide an overview or "single shot" perspective and typically
provide information on the status, condition, and trends of resources on large spatial scales. They
include various types of vegetation indices, indications of land use and land cover attributes, and
other spatially related features. They are especially amenable to discrimination by remote sensing
techniques. They are also crosscutting indicators which can serve equally well in all ecosystems.
Synoptic indicators are of critical importance to environmental monitoring programs such as
EMAP because they provide spatially related information that can tie together single-point and
sample-based measurements to form a picture of how ecosystems are performing spatially.
Measurements of synoptic indicators may be made to estimate the quantity of change of
environmental phenomena on an areal basis.
In arid systems, for example, remotely sensed information obtained by satellites and aircraft
can provide critical measurements of the areal extent and composition of surface phenomena as
well as indications of ecosystem vigor and health. Arid ecosystems are known to respond
dramatically to short-term climatic fluctuations, such as variations in annual precipitation. The use
of systems with high temporal as well as spatial resolution together with other data sets (including
meteorological and ground-based) are most amenable to those kinds of studies. While the NOAA
(National Oceanic and Atmospheric Administration), Advanced Very High Resolution Radiometer
(AVHRR) has very high temporal resolution (daily coverage), its spatial resolution of 1.1 km renders
it of little value for most detailed studies. For the purpose of more detailed studies, the landsat
multispectral scanner (MSS), thematic mapper (TM) or the French SPOT satellite system provides
considerably more spatial and spectral detail. Table 5-3 compares satellite measurements made
through the use of AVHRR, Landsat, and SPOT, with three types of land degradation.
Within the context of EMAP, two basic types of remote sensing will be considered: aerial
photography and satellite systems. Aerial photography, either current or historic can be used for
measurement and assessment of recent change. Color infrared (CIR) aerial photography
acquired for EMAP is being used to provide a means of "ground verification" for subsequent
satellite measurements and for separate and finer levels of landscape characterization.
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TABLE 5-3. REMOTE SENSING REQUIREMENTS FOR ARID ECOSYSTEMS LAND DEGRADATION
ISSUES
REQUIREMENTS
SATELLITE
MEASUREMENTS
Satellites
Map Accuracy
Resolution
Timing
Frequency
Algorithms/Models
Validate Data
(other data)
Ancillary Data
Current Capability
Desired Capability
DESERTIFICATION SALINITY
AVHRR, Landsat, SPOT, (ERS-1) Landsat, SPOT
1:5,000,000; 1:100,000; 1 :24,000 1:100,000; 1:24,000
Determined by sensors
Spring
Annual
Loss of vegetation
Seasonal changes
Change detection
Climate conditions/change
Higher resolution imagery
with ground observations
Climate conditions/changes
Population human and animal
Soil types
Wind erosion
Overgrazing
Cultural practices
Historical imagery
Visual observation of loss
of vegetation
Automate, quantify the
areal extent
Determine degree and intensity
of desertification
Predict and take corrective
action
Determined by sensors
Late spring/late summer
Annual
Loss of vegetation
Soil brightness (albedo)
Irrigated: saline water
Water table fluctuation
Higher resolution imagery
with ground observations
Soil samples
Water table depth
Soil
pH
Electrical conductivity
Irrigation practices
Water quality
Historical imagery
Brown or patchy
Low-quality vegetation
Provide early automated
detection so corrective
measures can be applied
Monitor progress and success
of restoration
EROSION
Landsat, SPOT, ERS-1
1:100,000; 1:24,000
Determined by sensors
Late spring
Annual
Decrease of vegetation
Universal Soil Loss Eq. USLE
Vegetation vs. topography
Higher resolution imagery
with ground observations
Physical measurements
Soil
Topography
Vegetation cover
Climate
Historical imagery
Sediment in rivers and lakes
Gullies
Slides
Identify and assess degree
of erosion in annotated
procedures
Provide maps and statistical
data for corrective structures
or practices
Taken from Linking Remote Sensing Technology and Global Needs: A Strategic Vision 1987.
A report to NASA by the Applications Working Group.
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Satellite systems include the use of Landsat, SPOT, and AVHRR. These systems are not only
of critical importance in landscape characterization (with the AVHRR being used forTM or SPOT
data selection and acquisition) but also for the assessment and discrimination of synoptic
indicators. Those indicators may take the form of spatial differentiation and variation in vegetation
composition and areal extent as well as providing information on landscape and vegetation
spectral indices.
Hyperspectral (numerous wavelength intervals or bands) experimental remote sensing
systems such as the Airborne Visible and Infrared Spectrometer (AVIRIS) and other airborne
systems may offer a third category of remote sensing research and applications to EMAP. An
example of their use might include the acquisition of precise measurements of foliar chemistry and
photosynthesis.
Albedo and Desertification
The use of Landsat MSS and TM and, especially, AVHRR, for deriving vegetation indices is of
considerable importance in desertification assessment. The albedo (reflectance integrated over
the upward hemisphere of directions; sometimes referred to as brightness) of land surfaces is an
excellent indication of degradation. Increasing albedos have long been thought to indicate
erosion, salinization, overgrazing, and other deleterious land surface effects. In addition it is
considered to be an important parameter for climate models (Dickinson et al., 1989). Observations
over large areas may be determined through satellite observations. Surface albedo helps
determine how much solar energy is absorbed and hence the surface temperature and
evapotranspiration. Desertification, frequently related to albedo, has been defined as the
dimunition or destruction of the biological potential of the land, and can lead ultimately to
desert-like conditions (UNEP, 1988). It is argued that land can not be desertified by natural
conditions but rather only by human interactions with the environment (see, for example,
Schlesingeretal., 1990). Various investigators (e.g., Justiceetal., 1985) have made use of remote
sensing for deriving vegetation indices for the purpose of assessing vegetation parameters
associated with desertification. One of the most useful indices is the "normalized difference
vegetation index" (NDVI) which has been shown to be highly correlated with vegetation parameters
such as green-leaf biomass and green-leaf area and hence can be of considerable value when
incorporated into a strategy aimed at assessing desertification. NDVI is typically given as:
NDVI = (CH2- Chl)/Ch2 + Chi) Eq. 2
where Ch1 represents data from a visible channel (AVHRR Channel 1 orTM Channel 3) and Ch2
represents data from a near-infrared channel (AVHRR Channel 2 or TM Channel 4). NDVI has
been used in Africa and elsewhere to demonstrate the effects of overgrazing, especially during
times of drought (Justice et al., 1985). Attempting to explain the effects of overgrazing upon the
environment is difficult and complicated. However, application of AVHRR has provided critical data
to identify damage caused directly by livestock and that caused by other management practices.
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5.2.3 Retrospective Measurements
Retrospective measurements of indicators of stress, exposure, and response can be used to
determine the current status, extent, changes, and trends in the condition of ecological resources
in arid ecosystems. These measurements are crossingcutting and can be used by most resource
groups to assess the status of resources over time. Future observations must be evaluated
against baseline conditions to determine changes in the environment, particularly given the
increase in complexity, scale, and social importance of environmental issues in today's world.
Critical quantitative and scientifically valid assessments are required within a continuous
monitoring framework.
Retrospective historical and paleoenvironmental indicator measurements offer an objective,
repeatable and quantitative method for determining trends in ecosystem health. Retrospective
indicator include instrumental hydrometeorological data, tree-ring series, pollen data, and
macrobotanical remains from packrat middens. The primary advantage there is that they allow
probabilistic evaluations of observations obtained during the first year or two of monitoring. They
are also applicable to and compatible with observations made in other regions. They make use of
existing databases, can be scaled up and down with different spatial sampling designs, and can be
used to create models to establish their covariance with synoptic and sample-based indicators.
Because retrospective measurements offer a lengthy temporal perspective they are also helpful in
formulating conceptual models of environmental change.
Assessment of the nation's ecological resources over large spatial areas and lengthy time
periods is a difficult task. Extensive geographic sampling designs can be established, but the
temporal aspect of evaluating annual to decadal or longer changes requires a wait-and-see
approach. Temporal change, by definition, implies a difference from one time to another, and it
seems germane and scientifically responsible to place decisions (when possible) about the
significance of such changes on a firm foundation, not just a few years of observations. The first
year of a monitoring program yields a reference point, but not a baseline indicative of long-term
behavior. Deviations from the reference point represent a change, but the change significance is
unknown. Furthermore, short-term spatial variability in an indicator is most likely not
representative of the long-term temporal variability (e.g., 100s or 1,000s of years) at any one
location.
5.2.3.1 Characteristics of Retrospective Measurements
Direct interpretation by statistical analysis of the observations available for each retrospective
measurement, or indirectly by calculating its covariance with other response, exposure, or stressor
indicators, can provide information about the mean of the process, its variability, and long-term
trends.
A comparison of a shrub or tree-ring index for the current year (species and location specific),
as a response indicator of productivity, with the time series of indices for the previous several
hundred years is an example of a direct analysis. Figure 5-2 illustrates a tree ring chronology, using
data derived by calculation 20-year averages, for a bristlecone pine in the White Mountains of
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California. The value for the current year can be evaluated with respect to long-term central
tendency, variability, and persistence, and a probabilistic value can be associated with its
interpretation. In the absence of retrospective information on shrub or tree growth, the value for the
current year could only be compared spatially to the tree-ring index values for the current year with
other species and locations. It would be impossible to state with any degree of confidence what
those values represent in terms of temporal behavior. Retrospective information thus allows
consideration of temporal and spatial variability.
A statistical transfer function relating tree-ring indices (independent variable) to annual
precipitation (dependent variable) during a recent period of temporal overlap can also be
established, validated, and then applied to the tree-ring series to reconstruct the past several
hundred years of annual precipitation. In this situation, a long-term record of a stressor indicator
can be produced, against which future values can be compared. This model-based approach is
described more extensively in the following text.
5.2.3.2 Present to Past to Future
The establishment of annual ecological monitoring programs at a regional level allows
interannual spatial change detection, but only after several years of data have been obtained. On a
minimal level it will be several years before results regarding even simple changes are forthcoming.
And, followed in the strictest sense, it will take many years to resolve temporal trends and apply
time series models with any degree of statistical significance. Even if temporal trends are
distinguishable with a few years of data, the problem remainshow to determine if they represent
"local" or "global" changes (global is used here in a numerical analysis sense and is not related in
any fashion to global climate change).
A simple example illustrates this point. Assume that 10 years of observations exist for variable
X at a particular location and that an overall downward trend has been detected. In the absence of a
longer time series one might reasonably conclude that conditions were becoming worse or
changing from what we might call nominal to marginal to subnominal. This conclusion especially
might be made if the same trends were observed in variable X at multiple nearby locations.
fcfo
040
3000
2000
1000
BC
AD
IOOO
2000
Figure 5-2. Variations of growth (20-year means from 3431 BC) in Bristlecone pine trees
near the upper tree line in the White Mountains of California. (Harding 1982).
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For purpose of illustration, embed this decadal decline in a longer time series and several
feasible interpretations result. The decadal decline could represent a local decrease when
conditions are considerably above average, it could represent a local decrease when conditions
are considerably below average, or the 10 year period could be centered about the mean level. In
the first situation conditions could be described as nominal, in the second they are marginal, and in
the third they are subnominal.
The same argument could be made for a decade marked by an overall upward trend. The
decadal increase could represent a local increase when conditions are above average, an increase
when conditions are considerably below average, or the increase could be centered about the
mean level. With long-term data, interpretations would actually be identical to those of the previous
example where a downward trend was considered! The objective of this simple exercise is to show
that numerous interpretations of the decadal sequence are possible, none of which is correct when
examined in the absence of a long-term perspective.
In a third example, the decadal downward trend could actually be embedded in a global
downward trend and the conclusion that conditions were worsening would essentially be correct,
but unknown, in the absence of additional data. Similarly, the decadal upward trend could be
embedded in a global upward trend and the conclusion would also be correct but unknown. Finally
the decadal increase and decrease could be embedded in globally decreasing and increasing
trends, respectively, and both interpretations would be erroneous.
The Palmer Drought Severity Index (PDSI) is a useful climatic integration that can link modern
climatic data to biological responses. It is a useful measure of recent (approximately 100 years)
climatic variations and is derived from a combination of monthly precipitation, temperature, and
soil moisture retention information. It offers an integrated measure of moisture availability (i.e.,
effective precipitation). It frequently exhibits a higher covariation than temperature or precipitation
alone with a tree-ring series, since a tree-ring responds to the integrated effects of temperature
and precipitation through its interface with the soil and atmosphere. A time-series plot of the PDSI
for the month of July is presented in Figure 5-3. Values near zero indicate normal meteorological
conditions, while with increasing distance from zero, positive values indicate increasingly mesic
conditions, and negative values indicate increasingly xeric conditions. Note the dust bowl years in
the early thirties (Wharton et al., 1990).
5.2.3.3 A Strategy for Using Retrospective Measurements
Temporal calibrations are established when a large set of temporally sequential values of a
Retrospective Measurements (RM) have a pairwise correspondence with values of the targeted
Stressor Indicator (SI) variable. This is basically a statistical problem in that the set of sample
observations must be large enough to allow the creation of a probabilistically valid transfer
function. Transfer functions can range from bivariate regression between one retrospective
indicator (independent variable) and one synoptic measurement (dependent variable) to
equations expressing the covariation between large space-time arrays of RMs and Sis.
The more complicated transfer functions usually require some type of data reduction scheme
(e.g., PCA) for the variables involved to minimize problems caused by multicollinearity. This
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1895
1925
1955
1985
Year
Figure 5-3. (upper) Actual Palmer Drought Severity llndex for July. Nevada division 1
for the years 1895 to 1983. (lower) Predicted Palmer Drought Severity
Index for July, Nevada division 1, 1895 to 1962.
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operation usually results in a more parsimonious description of the patterns involved, a desirable
feature of most scientific discussion. Also, it normally results in a transfer function based on more
degrees of freedom. The sample on which the transfer function is based should encompass the full
range of variability expected in the dependent climatic variable(s) during the period of interest.
A spatial calibration is required when the temporal overlap between the retrospective
measurements and synoptic measurements is not represented by a large set of observations with
a one-to-one time correspondence. In this situation, a transfer function can be established
between a large spatial set of observations of SI data (dependent variable) and RM data
(independent variable). A transfer function between a remotely sensed AVHRR-based greenness
index (dependent variable) and the Palmer Drought Severity Index (independent variable) is an
example of a spatial calibration. If a significant transfer function can be established between thetwo
data sets, then its coefficients can be applied to the temporal PDSI series to obtain estimates of
past greenness indices.
5.3 HIERARCHICAL EXAMINATION OF INDICATORS
While EMAP-Arid has further grouped indicators into sample-based, synoptic, and
retrospective measurement groups, it also considers indicators within a hierarchical context,
paralleling the hierarchy of the classification scheme (cf. Chapter 4). A hierarchical arrangement of
indicators can be illustrated through an example developed by Noss (1990) involving the
monitoring of biodiversity. His biodiversity hierarchy concept suggests that biodiversity be
monitored at multiple levels of organization and at multiple spatial and temporal scales. The spatial
and temporal variations are amenable to consideration through the use of many of the synoptic
and retrospective techniques previously discussed. Different levels of resolution are appropriate
for different issues. As stated by Noss, if we are interested in the associations between of climate
change on biodiversity, we may want to consider (1) climatic factors controlling major vegetation
ecotones and patterns of species richness throughout large regions; (2) availability of suitable
habitats and landscape linkages for species migration; (3) climatic controls on environmental
perturbations at regional and local scales; (4) physiological tolerances, autecological
requirements, and dispersal capabilities of individual species; and (5) genetically controlled
variation within and between populations of a species in response to climatic variables (Noss,
1990). Again, Noss states that the effects of environmental stresses will be expressed in different
ways at different levels of biological organization.
The preceding list can be considered to fall within the following hierarchy: regional landscape
(1 OOs to 100,000s of km2); community-ecosystem (a few to 10s of km2); population-species (this
is, historically, where most biodiversity monitoring has taken place); and genetic basis. Using this
hierarchy, Noss has developed a list of biodiversity indicators. (Table 5-4)
The development of indicators within EMAP-Arid is proceeding through a logical sequence
from the identification of candidate indicators through literature review, workshops (as has been
discussed), and other techniques, to core indicators for long-term implementation (Hunsaker and
Carpenter, 1990). Figure 5-4 illustrates the indicator selection, prioritization, and evaluation
approach for EMAP as depicted by Hunsaker and Carpenter (1990). As described earlier
5-16
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TABLE 5-4. INDICATOR VARIABLES FOR INVENTORYING, MONITORING, AND
ASSESSING TERRESTRIAL BIODIVERISTY AT FOUR LEVELS OF
ORGANIZATION (adapted from Noss, 1990).
Level of
Organization
Composition
CLASSES OF INDICATORS
Structure Function
Inventorying and
monitoring tools
REGIONAL Identify distribution
LANDSCAPE
richness and proportions
of patch (habitat) types
and multipatch landscape
types; collective patterns
of species distributions
(richness, endemism)
Heterogeneity; connectivity;
spatial linkage; patchiness;
porosity; contrast, grain
size; fragmentation;
configuration;
juxtaposition; patch size
frequency distribution;
perimeter-area ratio;
pattern of habitat layer
distribution
COMMUNITY Identity, relative abundance,
ECOSYSTEM frequency, richness,
evenness, and diversity of
species and guilds;
proportions of endemic,
exotic, threatened and
endangered species;
dominance-diversity
curves; life-form
proportions; similarity
coefficiency; C5_C3 plant
species ratios
POPULATION
SPECIES
Absolute or relative
abundance; frequency;
importance or cover
values; biomass, density
GENETIC
Disturbance processes (areal
extent, frequency, or return
interval, rotation period,
predictability, intensity,
severity, seasonality);
nutrient cycling rates; energy
flow rates; patch persistence
and turnover rates; rates of
erosion and geomorphic
and hydrologic processes;
human land-use trends
Allelic diversity; presence or
particular rare alleles,
deleterious recessives, or
karyotypic variants
Substrate and soil variables;
slope and aspect;
vegetation biomass and
physiognomy; foliage
density and layering;
horizontal patchiness;
canopy openness and gap
proportions; abundance,
density and distribution of
key physical features (e.g.,
cliffs, outcrops, sinks) and
structural elements (snags,
down logs): water and
resource (e.g., mast
availability; snow cover
Dispersion
(microdistribution); range
(macrodistribution);
population structure (sex
ratio, age ratio); habitat
vasriables (see
community-ecosystem
structure, above);
within-individual
morphological variability
Census and effective
population size;
heterozygosity;
chromosomal or
phenotypic polymorphism;
generation overlap
heritability
Biomass and resource
productivity; herbivory,
parasitism, and predation
rates; colonization and
local extinction rates;
patch dynamics
(fine-scale disturbance
processes), nutrient
cycling rates; human
intrusion rates and
intensities
Demographic processes
(fertility, recruitment rate.
survivorship, mortality);
metapopulation dynamics;
population genetics (see
below); population
fluctuations; physiology;
life history; phenology;
growth rate (of
individuals); accumulation;
adaptation
Inbreeding depression;
outbreeding rate; rate of
genetic drift; gene flow;
mutation rate; selection
intensity
Areal photographs (satellite
and conventional aircraft)
and other remote sensing
data; geographic
information systems (GIS)
technology; time series
analyses; spatial statistics;
mathematical indices (of
pattern, heterogeneity,
connectivity, layering.
diversity, edge, morphology,
autocorrelation, fractal
dimension)
Aerial photographs and
other remote sensing data;
ground-level photo
stations; time series
analysis; physical habitat
measures and resource
inventories; habitat
suitability indices (HSI,
multispecies);
observations, censuses
and inventories, captures,
and other sampling
methodologies;
mathematical indices (e.g.,
of diversity, heterogeneity,
layering dispersion, biotic
integrity)
Censuses (observations,
counts, captures, signs,
radio-tracking); remote
sensing; habitat suitability
index (HSI);
species-habitat modeling;
population viability
analysis
Electrophoresis; karyotypic
analysis; DMA sequencing;
offspring-parent
regression; sib analysis;
morphological analysis
5-17
-------
CANDIDATE INDICATORS
IDENTIFY AND PRIORITIZE
Expert Knowledge
Literature Review
Peer Review
RESEARCH INDICATORS
EVALUATE EXPECTED PERFORMANCE
Analysis of Existing Data
Simulations
Limited-Scale Field Tests
Peer Review
DEVELOPMENT OF INDICATORS
EVALUATE ACTUAL PERFORMANCE
Regional Demonstration Projects
Peer Review
CORE INDICATORS
IMPLEMENT REGIONAL AND NATIONAL MONITORING
PERIODIC REEVALUATION
Figure 5-4. Indicator selection, prioritization, and evaluation approach for EMAP
(Hunsaker and Carpenter, 1990).
EMAP-Arid will be conducting aseries of workshops to further refine indicator selection criteria and
reduce the suite of indicators. Pilot projects which address indicators as well as other EMAP issues
are of vital significance to the illustrated process.
5.4 SUMMARY AND CONCLUSIONS
The three categories of indicator measurements discussed, sample-based, synoptic, and
retrospective, are highly useful for characterizing arid and other ecosystem health, condition, and
vigor. Each indicator affords a unique perspective on the ecosystem's status and condition.
Combined, however, they afford a spatial temporal perspective on reconstructing past climates
and conditions. They allowfor the addition of vegetation, soils, and other information with which to
5-18
-------
characterize past environments. In addition, they provide a wider response function with which to
base predictions on future trends. Figure 5-5 illustrates the temporal and spatial resolution of
various indicators. When retrospective, climate, synoptic, and sample-based measurements are
integrated, a more holistic picture of future environments and climates can be made.
5-19
-------
Resolution
COARSE
FINE
(1-100yrs.)
SiLnKfi9£Dc
INDICATORS
(< yearly)
SYNOPTIC
INDICATORS
SAMPLE-BASED
INDICATORS
(variable)
/
/
i
i
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7
/
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I
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i
7
1800s
/
/
7
7
7~
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7
7 7 "
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/
1972
. . /
2,000-50,000 BP
Figure 5-5. Temporal and spatial resolution of various indicators.
-------
6.0 ASSESSMENT AND USE OF EXISTING DATA
An important issue to the EMAP-Arid study is the need to identify and summarize existing
data sets and monitoring programs and to assess whether they could be used to answer some
questions about the condition of arid ecosystems on appropriate temporal and spatial scales.
The initial scope of this effort was focused on evaluating existing data sources and sets to
determine if they could be used to make large-scale assessments consistent with the goals of
EMAR Applying this approach limited the consideration of many data sources that have
long-term data sets because they are site specific (e.g., LTER and many university research
stations). Thus, the scope was modified to look at data sources potentially useful to all levels of
monitoring in EMAP-Arid
Much of the existing data from research sites should be useful to help formulate ecological
research questions, investigate potential indicators, present historical baselines, and provide
understanding of how ecosystems function. It is also important to identify established agency
networks and sources of expertise to minimize duplication and enhance interagency
cooperation.
6.1 IDENTIFICATION AND EVALUATION OF EXISTING DATA SOURCES
The principle method used to identify potential sources of arid ecosystems ecological data
was an extensive literature search. Over 180 reports and documents were obtained related to
desert and rangeland studies conducted by government agencies, universities, and other
organizations currently involved in ecological research. In addition, nine directories
inventorying agency environmental data sets and databases were reviewed (Zarony, 1987;
Yardas et al., 1982; USGS, 1983; Tucker et al., 1980; Olson, 1984; NTIS, 1988; Abramovitz et
al., 1990; Olson and Breckenridge, 1990; Multer and Kemp, 1986). Existing research programs,
data sets, formal databases, and large scale assessments identified through the literature
search were categorized by departments of the Federal Government, agency, or organization
and representatives for each source were contacted by telephone.
Because monitoring networks on a large scale are of primary interest, Agency regional
offices were contacted to obtain an overview of monitoring activities. A database survey was
distributed to 26 contacts who were requested to supply general information and available
documentation. These surveys were followed by phone calls to clarify information. Many of the
telephone interviews resulted in identifying additional sources that needed investigation.
Documentation from potential databases, data sets, or research and monitoring programs
was systematically reviewed and follow-up contacts were made to acquire missing details.
Each candidate data source was reviewed for its ability to meet six attributes important in the
design of the EMAP: geographical coverage, data quality, temporal coverage, data
management, cost, and indicators. A summary of the criteria used for each of these attributes
is presented in Table 6-1.
A number of the criteria (e.g., spatial and temporal coverage, and data quality) were
selected to identify those data networks that are compatible with the EMAP concept and have
6-1
-------
TABLE 6-1. DATABASE EVALUATION CRITERIA.
ATTRIBUTE
CRITERION
DEFINITION
GEOGRAPHICAL COVERAGE
Spatial compatibility
Statistical design
Site location
Regional scale or larger
Documented
Specified
DATA QUALITY
Sample design
Collection methods
Data preparation
Protocols
Data summaries
Sample size
QA program
Documented
Documented
Methods documented
Documented
Are produced
Documented
Documented
TEMPORAL COVERAGE
Current
Long term
Collected in the past 5 years
5 years minimum
DATA MANAGEMENT
Computerized
Published form
Available
User friendly
PC compatible
Confidentiality concern
Support available
Manipulations available
Automated
Data reported in published form
Can be accessed by outside users
Easy to use system/minimal training
required
PC files available
Restrictions on data use and access
Knowledgeable personnel available to
assist users or provide data
Universal format files, graphics,
statistical packages available
COST
Low
Medium
High
Only phone access and report costs
Phone access plus one time use fee
Annual access fee and support charges
with specific equipment required
INDICATORS
Able to indicate (or provide informa-
tion on):
Vegetation biomass and condition
Landscape pattern and land use
Fire
Water balance
Retrospective measurements
(historical condition)
Wildlife/habitat condition
Provide indication for conditions of arid
ecosystems
6-2
-------
adequate documentation and data management measures to produce data of known quality
(some of the details, such as the statistical bases of the samples, are still to be determined).
Obtaining data summaries is important because they allow the use of confidential data that may
be restricted for use in EMAP in a raw form.
Existing data sources were also evaluated for their potential to provide data for
development of arid ecosystems indicators (Section 5). These were selected following several
workshops and input from numerous scientists, and a preliminary discussion of these
selections was presented in the EMAP Ecological indicator report (Hunsaker and Carpenter,
eds., 1990). This list was revised by the EMAP-Arid group and currently focuses on vegetation
changes, landscape pattern, fire, water balance, retrospective analysis, wildlife/habitat,
reference/canary sites, and riparian systems.
Table 6-2 summarizes how well each candidate database, data set or related resource
assessment program, met five of the six selection criterion. Because indicators (the sixth
criteria) are such an important part of the design, a separate section discusses their
relationships. In Table 6-2, the letters "Y" or "N" denote whether adequate documentation
exists to either meet the criteria ("Y") or not ("N"). If information is inconclusive in terms of
using it to evaluate a data source, a question mark (?) was inserted in the table. If information is
not available or could not be obtained, the section is left blank.
Failure to meet all criteria does not prevent the data source from being useful to scientists
and policymakers. Therefore, information on all relevant databases and data sets is provided
so that EMAP has the opportunity to utilize data from existing networks in its design and
implementation tasks.
The primary goal of this survey was to evaluate existing programs having extensive
temporal and spatial coverage. As a consequence, emphasis was placed on evaluation of
those data bases and data sets most likely to meet those criteria. Other sources were
identified, but most represent studies of short duration and/or small spatial scale and
documentation summarizing these programs is difficult to obtain or non-existent.
Universities are key contributors to the scientific community and independent research
conducted at these institutions is of primary importance to EMAP-Arid. For example,
researchers at Utah State University, involved in the International Biological Program (IBP),
along with personnel from other universities, have developed an extensive bibliography of arid
ecosystem studies and data sets generated during that program. In addition, USU is involved
in ongoing ecological studies at other arid sites. The University of Arizona's Arid Lands
Information System (ALIS) contained current satellite data sets, aerial photography and other
information associated with changes in landuse patterns for areas of the southwest. Many state
universities and private institutions are involved in federal ecological research programs, for
example, universities in Montana, Wyoming, Idaho, California, Utah, and Arizona participate in
the Service's cooperative programs for fish and wildlife. University involvement is also
important to many longer term projects including the ACOE's Land Condition Trend Analysis
(LCTA) program and NSF's Long Term Ecological Research (LTER).
6-3
-------
Table6-2 ' Summary of Data Sources with Respect to Evaluation Criteria
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FOREST SERVICE
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6-2: Sumnary of Data Sources with Respect to Evaluation Criteria (continued)
Database or Data
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table 6-2: Sunmary of Data Sources with Respect to Evaluation Criteria (continued)
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-?: SiniinAry of Data Sources with Respect to Evaluation Criteria (continued)
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-------
Suninjry of Data Sources with Respect to Evaluation Criteria (continued)
Database or Data
Set Title or
Acronym
_
U.S. DEPARTMENT OF
ENERGY
National
Environmental
Research Parks.
(Network Ing
(ParkNet)
underway) (INEL.
Los Alamos.
Hanford, Nevada
Test Site)
SIA1ES
Natura 1
Heritage
Programs
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Geographical
Coverage
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1 Statistical Design
1 Site laentif icat ion
Y
Y
Data Quality (Identified)
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exist for each site,
cannot be adequately
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II state to state II
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N Nn R-flpgional H/A-not applicable blank-unknown ?-to be determined 1-ltmlted l-lntermitient
-------
Under the administration of various federal and state agencies and universities, research
conducted at Experimental Ecological Reserves (EER) and Research Natural Areas ( RNA) has
resulted in large collections of ecological data maintained by various institutions. Although
most EERs and RNAs in arid regions are of relatively small size, they may be appropriate for use
as reference sites. Most studies conducted at these sites address specific questions and are of
short duration, however, some may support new research or be of use in other capacities.
Several private organizations also provide access to large data sets, for instance the The
Nature Conservancy's State Heritage Program. This program provides large potential for
access to data pertinent to threatened and endangered plants and wildlife. However, program
format varies somewhat from state to state and each must be reviewed independently. The
Audubon Society's Breeding Bird Survey provides annual counts country wide. An summary
report is issued yearly and some studies have been conducted to evaluate the statistical validity
of the survey. Considerable effort will be required, however, to assess the ability of these and
other private sources to meet the rigorous statistical design requirements of the EMAP
Time constraints prevented extensive review of these and other potential sources during
this survey. However, plans exist to examine these databases and data sets using criteria
developed for evaluating the applicability of smaller scale and site specific data to the EMAP
design.
6.1.2 Evaluation of Data Sources Related to Criteria
Only 13 of the candidate indicators presented in Appendix A qualified for use by
EMAP-Arid in terms of temporal and geographic coverage and data quality. Maps (where
available) identifying the location and coverage of these networks, relative to arid ecosystems,
are presented in Appendix B. These databases, representing large-scale monitoring programs
conducted by Federal agencies, include:
Storage and Retrieval of Water Quality Data (STORET) Aerometric Information
Retrieval System (AIRS), Acid Deposition System (ADS) databases
National Water Data Storage and Retrieval System (WATSTORE), National Water
Quality Assessment Program (NAWQA), National Stream Quality Accounting
Network (NASQAN)
National Climatic Data Center
National Wetland Inventory (NWI)
National Contaminant Biomonitoring Program (NCBP)
Visibility and Gaseous Pollutant Monitoring Programs
National Resource Inventory (NRI)
National Range Data System (NRDS).
6-12
-------
These major repositories house large volumes of hydrological, meteorological, soil,
vegetation, and air quality data collected by various federal, state, and local government
agencies.
Other sources meeting most of the selection criteria include the Bureau of Land
Management's Riparian and Aquatic Information Data System (RAIDS), Wildlife Observation
Report Data System (WORDS), Threatened and Endangered Species Data System (TEDS),
Integrated Data System (IDS), and Water Data Management System (WDMS); the Forest
Service's Range Forage and Resource Planning Assessment (RPA) databases; and the
National Science Foundation's Long-Term Ecological Research (LTER) programs. These data
sources should be useful in the diagnostics and research activities in EMAP-Arid and we
anticipate some joint ventures with these programs.
Although substantial amounts of data are collected by each agency surveyed, many of the
databases, data sets, and programs evaluated are site specific and most (with the exception of
the LTER program) do not represent long-term monitoring activities. Temporal and/or spatial
limitations of these data sources restrict their useful in designing the regional and national
aspects of EMAP. However, information on watersheds or range sites can be used to test the
interaction and application of indicators. This information will help foster understanding of how
well the indicators represent regional ecological conditions. Analysis of the data collected by
site specific programs can be used to formulate and help answer basic research questions. For
example, vegetation change data from an LTER site could be used as a benchmark for
evaluating the efficiency of the ecological system.
Land management agencies, including the NPS, FS and BLM data are usually not
integrated in a manner that can be used to effectively assess resources on an appropriate
temporal or spatial scale. Reasons cited for this deficiency include patchwork land
management, funding problems, and agency mission definitions (USFS 1988, Flather, 1989;
Joyce 1989). Many of these coordination possibilities are currently being addressed by the
respective agencies and are helping to influence interaction with the EMAP. As long as
EMAP-Arid indicators address long range ecological questions, interaction between EMAP and
the land management agencies will be necessary.
Every 10 years, the Forest Service conducts a Resource Planning Act Assessment (RPA)
intended to evaluate ecological resources on a national scale (Flather, 1989; Joyce, 1989). This
assessment combines multipleagency data to estimate the status of rangeland, fish and wildlife,
and water resources. The 1989 RPA presents the most current data available in summary form
and uses those data to determine the condition of, and forecast demands on, those resources.
As part of the report, observations are presented regarding data availability and quality. In
general, information about vegetation type and condition is incomplete and data suitable for
assessing rangeland characteristics on a regional scale are not available (Joyce, 1989; Flather,
1989). Inventory data for wildlife are inconsistent and inadequate to assess trends on a regional
level (Flather, 1989). Information used to assess water resources on a regional and national
scale were drawn from large hydrologic data sources such as the EPA STORET and USGS
WATSTORE databases.
6-13
-------
6.1.2.1 Identification of Missing Information for Indicator evaluation
Little information is available to make assessments at the current regional-scale design of
the EMAP, but much of the available information may be useful as a knowledge base to support
the research needs of EMAP-Arid. Some of the information at a smaller scale such as
watersheds, may be useful for the first phase of the double sampling process (Section 3). The
EMAP-Arid design would then need to be tested to see how well the watershed information can
be aggregated to make regional assessments.
The following discussion summarizes and reviews the agencies or organizations that
provide data for the endpoint indicators, discusses the limitations of the data, and identifies
missing information for each of those indicators. This information is provided in summary form
in Table 6-3. Data source parameters that measure EMAP-Arid endpoint indicators, or other
pertinent ecological measurements, are indicated by corresponding numbers as defined in
Table 6-4.
Vegetation Changes
To more consistently represent available information pertaining to vegetative change, data
sources providing measures of composition, stratification, and other measurements of
vegetation type and classification are categorized in Table 6-4 as measures of life form (4)
under Vegetative Change.
Soils data, vegetation inventories, and site classification data are available from the BLM
IDS, RAIDS, and IHICS; the SCS NRI, National Range Data System and the FS Range Forage
and RPA databases. However, most of this information was collected for a specific purpose at
each site and thus different measures of range condition were used by different agencies
(Joyce, 1989). Vegetation composition and productivity data are also collected as part of core
research studies for four LTER arid sites. Two of these sites, Jornada and the Central Plains
Experimental Range, are former International Biologic Program (IBP) sites where substantial
historical data have been collected. Some leaf area and vegetation index measurements also
are collected for LTER remote sensing programs and other site specific projects. These data
may be useful to validate EMAP measurements that will be obtained on a regional scale via
Landsat and other remote sensing techniques. To use these data, a pilot study should evaluate
the effort required and the availability of data to compare different collecting methods.
Landscape Pattern
Sources such as the National Resources Inventory, National Wetland Inventory, and USGS
mapping efforts can contribute general measures of landscape pattern. Remote sensing
projects, in conjunction with geographic information system (GIS) developments, such as those
at the Jornada and Sevilleta (New Mexico), Konza Prairie (Kansas), and Central Plains Range
(Colorado) LTER sites, provide integrated landscape characterization data. However, these
6-14
-------
: Sumnary of Data Sources with Respect to Arid Lands Indicators'
DATABASE OR DATA SET
TITLE OR ACRONYM
U.S. DIPARTHENT OF
AGRir.UUURE
FOREST SCRVICE
Range Forage
Database
Resource Planning
Act Assessment
RPA Range
Database
RPA Wildlife
Database
FRAHIS
Wlldland Fire
Statistics
RUNWILD
SOIL CONSERVATION
SERVICE
NRI
National Range
Data System
STATSGO
NATSGO
SSURGO
Wind Erosion
Comfit ions
Vegetation
Change
1
1
1
1
1
Landscape
Pattern
1
Fire
2.3
2,3
Water Balance
2.5
5
Retrospect Ive
5
Wildlife/
Habitat
1.5.6
1.5.6
Reference
Sites
3
1
1.4
Riparian
Systems
2
Other
1.2.3.4.8
1
1.3.8
8
2
1
2
2
2
ice Key (p.nii1 1C) for definitions.
-------
6-3: bunmary of Data Sources with Respect to Arid Lands Indicators" (continued)
DATABASE OR DATA SET
TITLE OR ACRONYM
U.S. DEPARTMENT OF
TIIE INTERIOR
FISH AND WILDLIFE
SERVICE
NUI
GAP Analysis
Refuge System
NCBP
BBS
BUREAU OF LAND
MANAGEMENT
RAIDS
WORDS
TEDS
IDS
WHOS
IHICS
NATIONAL PARK SERVICE
Visibility
Monitor Ing
Network
Gaseous Pollutant
Monitoring
Network
Vegetal Ion
Change
1.2
1.2.4
1
1.4
Landscape
Pattern
1
1
Fire
Water Balance
Retrospective
Wildlife/
Habitat
1.5.6
1.5.6
1.5.6
1.6
Reference
Sites
Riparian
Systems
1.2
1.2.3.4.7
4.5
Other
2
12.14
ii
6.9
6.9.14
Key (p."lo IG) for definitions.
-------
6-3: Sumnary of Data Sources with Respect to Arid Lands Indicators" (continued)
DATABASE OR DATA SET
TITLE OR ACRONYM
UNITED STATES
GEOLOGICAL SURVEY
UATSTORE
NAUDEX
NAUQA
NASQAN
ESIS
AWUDS/SWUDS
US GEODA1A
ENVIRONMENTAL
PROJECTION AGENCY
STORE!
AIRS
ADS
CLEAR
OTHER IJSDA
AGRICULTURAL RESEARCH
STATIONS
NWWRC
Other ARS
Watershed Sites
Vegetal ton
Change
2
1.2
Landscape
Pattern
Fire
Water Balance
4
1
4
1.2.4.5
Retrospect Ive
Wildlife/
Habitat
Reference
Sites
2
3
Riparian
Systems
1
Other
5.14
11
:>
5 1;
5.14
9
6.12
11
5.6
See Key (p.in.'-' 16) for definitions.
-------
6-3: Sumnary of Data Sources with Respect to Arid Lands Indicators* (continued)
DATABASE OR DATA SET
TITLE OR ACRONYM
U.S. DEPARTMENT OF
COHHfRCE
NOAA
Nat lonal Climate
Data Center
NATIONAI AERONAUTICS
AND SPACE
ADMINISTRATION
Land Process
Program
HAPDEX
1NIERAGENCY PROGRAMS
NAPAP/AODNET
HADP/NTN
NATIONAL SCIENCE
FOUNDATION
LTER
Jornada
Sevllleta
CPR
Konza
INTIRNATIONAI
BIOIOGICAI PROGRAM
Desert Biome
Vegetal Ion
Change
1
1.2.3.4
1 .4
1.4
1.2.4
Landscape
Pattern
1.2.3.6
1.3
1.3
1
Fire
1.2.3
1
1
1.2.3
?
Water Balance
5
1
1
3.5
2,5
2.5
Retrospect Ive
5
4
Wildlife/
Habitat
1,5.6
5.6
1.5.6
Reference
Sites
4
4
1.2.3.4
Riparian
Systems
Other
6.9
5.9
2
5.9.12
12
2.6.10
2.6.10
2.6.10
2.3.6.7.10
Key
16) for definitions.
-------
6-3: Seminary of Data Sources with Respect to Arid Lands Indicators" (continued)
DATABASE OR DATA SET
TITLE OR ACRONYM
Grassland Blocne
U.S. DEPARTMENT OF
DEFENSE
CORPS OF ENGINEERS
ICIA (proposed)
U.S. DEPARTMENT OF
ENERGT
Nat lonal
Environmental
Research Parks
INEL
Los Alamos
Hanford
STATES
Natural Heritage
Programs
i PRIVATE
TNC Preserve
Database
Vegetation
Change
1.2.4
I.Z.I
1
1
1
1.4
Landscape
Pattern
1
1
Fire
1.2.3
Water Balance
2.5
Retrospect ive
5
Wildlife/
Habitat
1.5.6
6
6
6
1
1.5.6
Reference
Sites
2.3
Riparian
Systems
4
4
4
Other I
2.3.6.7.10
i
j
6.7.10
6.7.10 1
6.2
6,2
6,2
13
11.8.13
Sen Key (|iiii|c IG) for definitions
-------
TABLE 6-4 DEFINES KEY TO CODED VALUES APPEARING UNDER INDICATOR
HEADINGS IN TABLE 6-3
VEGETATION CHANGE
1-General
2-Areal Extent
3-Leaf Area/Vegetation Index
4-Life Form
LANDSCAPE PATTERN
1-General
2-Patch Size
3-Composition
4-Connectivity
6-Vertical Stratification
6-Ecotone Location
WILDLIFE HABITAT
1 -General
2-Biomarkers
3-Guilds
4-Assymetry
6-Demographics
6-Species Composition
REFERENCE SITES
1 -General
2-Foliar Chemistry
3-Erosion
4-Cryptogamic Crusts
FIRE
1-General
2-Occurrence
3-Area
WATER BALANCE
1-General
2-Evapotranspiration
3-Radiometer
4-Stream Flow
6-Precipitation
RETROSPECTIVE
1-General
2-Dendrochronology
3-Pollen
4-Woodrat Middens
6-Fire History
RIPARIAN SYSTEMS
1 -General
2-Areal Extent
3-Species Composition
4-lnstream Flow
6-Water Table Flux
6-Water/Soil Conductivity
7-Channel Morphology
OTHER
1-Biomass
2-Soils
3-Grazing
4-Range Condition Index
5-Water Quality
6-Meteorological/Weather/Climate
7-Organic Matter Accumulation
8-Management and Use
9-Air Quality
10-Nutrients
11 -Support/Ancillary
12-Stress
13-Threatened and Endangered Species
14-Contaminants
6-20
-------
small-scale efforts will be of limited value to EMAR Consistent, nationwide classifications of
landcover and landuse patterns are being developed and are scheduled to be implemented by
the EMAP Landscape Characterization. Thus, EMAP can provide a good forum to encourage
interagency collaboration in development of compatible characterization schemes for planned
BLM, NFS, and USFS GIS and remote sensing programs.
Fire
Nationwide wildland fire data for public and private lands are collected at the Boise
Interagency Fire Center. These data (acreage, origin, trends) are compiled yearly and
published in statistical form by the U.S. Forest Service Fire and Aviation management staff
(Abramovitz et al., 1990). Historical fire data collected as part of SCS Range inventories are
maintained in the National Range Data System (SCS, 1976). Numerous fire-related studies are
being conducted at arid research sites. For example, experimental burn treatment plots are
maintained at the Konza Prairie Research Natural Area and fire disturbance studies are being
conducted at the Jornada, Sevilleta, and Central Plains LTER sites. These data, in conjunction
with retrospective fire data (Section 6), maybe useful for investigating the ecological
site-specific effects of fire on arid ecosystems.
Water Balance
Streamflow data (collected and stored by the USGS) and precipitation measurements
(collected by NOAA and other monitoring programs) represent the largest sources of organized
data for evaluating ecosystem water balance. As discussed in Section 6, WATSTORE, STORET,
and the National Climate Data Center provide access to these nationwide hydrologic and
precipitation data. Some site-specific evapotranspiration measurements are available for LTER
and IBP arid and grassland sites, and the USFS Range Forage Database includes both
precipitation zone and potential evapotranspiration zone data for range sites in 20 states,
including the West. In general, flow and precipitation data are well documented, automated,
and readily available. However, good sources of representative data measuring
evapotranspiration are not available and no large-scale radiometric data were located.
Retrospective
Limited fire history data are available for several arid sites associated with the IBP,
including Jornada and the Central Plains Experimental Range. Research involving fire scars
and tree growth chronology for areas of the Southwest is currently being conducted at the
University of Arizona Laboratory of Tree Ring Research (Swetnam and Betancourt, 1990).
Historical occurrence and coverage data are available from the National Archives (Swetnam and
Betancourt, 1990) and recent information is compiled by the Forest Service Fire and Aviation
management staff.
Most available fire data are associated with forest ecosystems and more research is
required to identify data specific to arid ecosystems. No sources were located for pollen
record, and research conducted at the Sevilleta LTER site is the only identified source of
6-21
-------
woodrat midden data. These data can be used to provide historical information on vegetative
coverage for an area. The data will be assessed to determine how useful it can be for
developing a baseline for making regional assessments.
An extensive data base of tree-ring information is available from the NOAA National
Geophysical Data Center (NGDC) in Boulder, Colorado. After a hiatus of 5 years, the
International Tree Ring Data Bank reopened and moved its physical operations to the NGDC
facility from the Laboratory of Tree-Ring Research at the University of Arizona, Tucson.
Over 1000 Chronologies and ringwidth (or density) measurements from over 600 sites
worldwide are now available. Seven hundred twelve of these chronologies are from North
America, and several hundred are from the western United States. Additionally, tHere are
chronologies from Europe (252), Australia/New Zealand (76), South America (35), Asia (14),
and Africa (1).
An American Pollen Database, coordinated by Eric Grimm at the Illinois State Museum, is
under development. The database will be a relational PC-based system, with exportable
database tables. The database will include, insofar as possible, original pollen counts, site
locations and descriptions, radiocarbon and other chronological data, and bibliographic
information. The goal is to incorporate all North American pollen data, both stratigraphic and
surface-sample data.
Wildlife Habitat
Most BLM and USFS inventory and monitoring activities assess wildlife habitat.
Composition and population data are collected by both agencies, but primarily involve big
game, and threatened and endangered (T/E) species. Data representing nongame wildlife is
generally not available in state or federal inventories (Flather, 1989). The Christmas bird survey
from the National Audubon Society, State Natural Heritage Programs, and the FWS Breeding
Bird Survey and migratory bird and nesting counts represent the largest collections of
organized wildlife data. The contaminants data collected by the FWS involve migratory
waterfowl and starlings for local scale issues and fish for regional scale issues (watershed
assessments). These data have been used primarily to assess concentrations of pesticides
and metals in tissue. They have proven useful to identify regions of the country that have
received insult from organochloride pesticides. Although banned in the U.S., they still are used
in Mexico and show up along the border. Numerous smaller scale or site specific programs
exist, for instance the BLM's raptor monitoring at the Snake River Birds of Prey Area in Idaho
(Steenhof ed., 1989), small mammal studies at LTER sites, and private efforts such as
HAWKWATCH in Nevada and Utah (Western Foundation for Raptor Conservation, Inc., 1990).
The Idaho Cooperative Fish and Wildlife Unit headed by Michael Scott, is conducting a
study called Gap Analysis to provide a rapid assessment of the distribution of biodiversity. The
process uses geographic information systems (GIS) (ARCINFO software) to develop a
multifaceted assessment of habitat and wildlife. Presently a vegetation map at a scale of
1:500,000 has been developed and compared to data collected in the field. The next step
6-22
-------
converts available information on vertebrate and invertebrate distributions to digitized format.
Land-ownership information is then added to generate a map depicting species richness. This
information can be combined with maps on species of special interest to identify minimum and
optimum areas required for protection of predetermined levels of statewide species richness.
The original assessment was done for the State of Idaho. Similar assessments are now being
conducted in Washington, Oregon, California, Utah, and Nevada. This process has the
potential to provide useful information to aid EMAP-Arid in assessing wildlife and habitat
conditions.
Very limited biomarkers, guilds, or asymmetry data were identified. Lack of these data, as
well as limited demographic and composition data, represents a significant deficiency for
evaluation of the wildlife/habitat indicator.
Reference/Canary Sites
Designation of a data collection site as a reference or canary site implies that the effects of
a single parameter may be observed without being confounded by other influences; for
instance a reference site being used to study the effects of fire on erosion should not be
subjected to another disturbance such as grazing. Without in-depth study of the sites
represented in this assessment, identification of sites appropriate for use as reference sites is
not possible. Consequently, all sources for which erosion, foliar chemistry, and cryptogamic
crust (i.e. soil crusts formed by algae) data are available have been included whether or not
they qualify as reference sites. These are provided so that further evaluations can be made of
these data.
Foliar chemistry data are collected as part of site specific studies for the LTER and IBP arid
sites. The soils and vegetation data collected by the SCS, BLM, and USFS, include rough
estimates of percent coverage for cryptogamic crusts. Data collected as part of the National
Acid Deposition Program evaluate conditionally the effects of acidic deposition on cryptogams
for selected sites.
Erosion data for range sites in the Western states are gathered by the USFS and SCS. In
addition, the SCS collects and summarizes yearly wind erosion data. Watershed erosion data
are available from numerous USDA Agricultural Research Service Watershed Research Units
and are important since the coverage may better represent the regional level concept of the
EMAP. These data do not provide sufficient coverage to be used to assess erosion in arid
regions. However, they provide useful baselines.
Riparian
With the exception of stream flow and other pertinent data contained in large, centralized
water databases, information of sufficient quality for riparian assessment is limited. Many
agencies have riparian programs to inventory and monitor riparian areas on a site specific or
watershed specific basis, but, with the exception of the BLM RAIDS database, few are
computerized and none are aggregated to provide a regional assessment (Mussallem, 1990).
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The National Soil-Range Team is currently developing a computerized system to characterize
riparian areas (National Soil-Range Team, 1988). This system will characterize a site using soil
and vegetation data to assess current conditions as well as evaluate site potential.
The FWS National Wetlands Inventory (NWI) is primarily concerned with mapping wetlands
and surface waters for migratory waterfowl. The Inventory consists of ata for wildlife refuges
and wetlands within arid regions are provided, but the coverage of riparian systems is limited.
Riparian systems, as defined by the EMAP-Arid group, might be delineated using some of the
NWI data, but a pilot study is required to evaluate this situation.
6.1.3 Relationship of Data Sources to Arid Ecosystem Questions and Issues
Hydrological, meteorological, air quality, and pollution information is crucial in assessing
status and evaluating trends in conditions across all ecosystems. The large air and water
databases are of particular importance due to the currently increased interest in interagency
cooperation. Data are collected, contributed, and used by various agencies. For example,
NFS Gaseous Pollutants data are available through the EPA AIRS database. The fact that the
information is collected, organized, and made available through a sophisticated,
well-supported data management network makes these databases potentially important to
EMAP-Arid scientists. Further work is needed to interpret these data, to identify missing pieces,
and to see how the collected data can be used in assessing the stressors of arid ecosystems.
Two major databases, WATSTORE and STORET, contain the bulk of available hydrologic
information. These database systems store water data collected by numerous networks.
NASQAN, NAWQA, and the Hydrologic Benchmark Network (HBN) are subsets of WATSTORE
and contain nationwide water data from the USGSi The HBN may be particularly useful for
establishing reference sites because the streams and basins monitored for water quality are
those that have not been altered by man (Abramovitz et al., 1989). Other subsets contain data
pertaining to aquatic species, stream flow, water demand, snowpack, and other information that
may be used to assess hydrologic aspects of arid ecosystems. The baseline, historical, and
chemical information may be of use to EMAP-Arid planning and indicator development work.
There are about 150 real-time collection sites (USGS data collection platforms transmitting data
to Geostationary Operational Environmental Satellites [USGS, 1986]) and numerous other
collection sites in areas currently considered by the arid ecosystems group.
Maps and data generated by the FWS's NWI provide information for determining the
extent and the increase and loss in wetland systems. These data are primarily useful to the
EMAP Surface Waters and Wetland resource groups in establishing ecosystem function and to
EMAP-Arid resource group in characterizing wetland system status. Although this system does
not currently specifically map riparian systems, the maps are generally available and could be
used to delineate riparian areas for locations previously mapped. Data from these sources can
be used to help define historical site-specific status and trends in surface and ground-water
quality and quantity and to identify degradation in water resources and riparian habitats.
However, there will be limitations because definitions of natural systems for the data sources
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may be different than those being used in EMAP; thus, some transitional definitions must be
developed if the data are to be used.
Two major sources for air quality and pollutant monitoring include the EPA ADS and AIRS
databases. Included within these large systems are data collected from networks such as the
National Atmospheric Deposition Program/National Trends Network, NPS Visibility and
Gaseous Pollutant Monitoring, and others. Like the water-related databases, these systems
provide organized access to air quality and acid deposition data collected through a number of
networks nationwide. These databases also provide criteria pollutant data suitable for use in
establishing baseline conditions for all EMAP ecosystems. Wet and dry deposition data
contained in the ADS database, and pesticide monitoring data collected by the FWS National
Contaminant Biomonitoring Program (NCBP), could be used to assess anthropogenic input of
selected chemicals to terrestrial systems. These data sources might be used by EMAP-Arid to
conditionally address the issue of urban population expansion and determine how increased
anthropogenic pressures and pollutants may affect the structure and function of arid
ecosystems at the research level.
The National Climatic Data Center is another important source of air quality
measurements. In addition, current and historical weather data available through the Center's
computerized system, together with climate data collected by other programs (air and water
quality monitoring programs and many ecological research programs collect site-specific
climate information), may be useful in exploring the link between pollution, climate change, and
arid ecosystem functions. An example would be the conditional evaluation (i.e., nonprobability)
of the issue of desertification (including increased salinity) by looking at rainfall patterns, stream
hydrographs and water quality data and attempting to relate these to distribution of vegetation
and wildlife communities. Data are available to do this type of assessment for some fairly large
watersheds, but the issue of the usefulness of this effort on a regional scale presently is
unresolved.
Several large, automated data management systems contain useful soil and vegetation
information. The SCS characterizes soil and vegetation in the NRI, the National Cooperative
Soil Survey (NCSS) and the National Range Data System (NRDS). This information is entered
in a data base, maintained, and updated on a periodic basis. The SCS also generates maps for
the State Soil Geographic Databases (STATSGO), National Soil Geographic Database
(NATSGO), and the Soil Survey Geographic Database (SSURGO). The SCS maps and
inventories can provide useful prior information for design and indicator development.
The Bureau of Land Management maintains the following databases, which represent an
effort to integrate inventory and monitoring data:
WORDS - Wildlife Observation Report Data System
TEDS - Threatened and Endangered Data System
RAIDS - Riparian and Aquatic Information Data System
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WDMS - Water Data Management System
IDS - Integrated Data System
These five databases employ a common software system, and the elements for all are
standardized to a BLM data element dictionary. This provides common definitions, codes, and
cross references from one database to another. In addition, BLM data collection methods and
forms are standardized and documented (BLM, 1981). Field data collection forms are used as
input records and most western states contribute information to these centrally maintained
databases (Ballantyne, 1990). The information collected is project oriented (site specific) and
although the data represent long-term inventory records, not all sites are characterized on ayearly
basis. However, numerous sites over a large geographic region are characterized. These data
sources may be useful in establishing baseline data for specific vegetation and soils relationships.
6.1.4 Research Needs to Address EMAP-Arid Questions
A wealth of information exists that can be used to address questions about the conditions
of site-specific arid ecosystems. However, the problem is that there are major inconsistencies
in the design for data collection and in how the data were collected, analyzed, and reported.
Ground and surface water, air quality, and climate data are in large supply and currently are
used by several agencies to assess status and trends and make predictions regarding resource
demands (e.g., USGS.1986). For large centralized databases such as WATSTORE, STORET,
and AIRS, however, the sampling design and QA plan for each contributing agency or
organization needs to be further evaluated for compatibility with the EMAP framework. Little
additional research is needed to support the data on surface and ground-water quality and
quantity currently collected by the USGS. These data probably can be accessed through
interagency cooperation. However, research is needed to determine how accurately data
collected at a site or on a watershed basis reflect the hydrologic regime of the region. The data
supplied by USGS needs to be evaluated to determine what, if any, techniques can be used to
extrapolate site-specific information to a larger scale population.
Data sets addressing land use issues such as rangeland condition, degradation of riparian
habitat, desertification, mining impacts, and urban encroachment are collected and
summarized by several agencies (BLM Public Land Statistics, for example), but much effort is
needed to gather, interpret, and integrate information. Some collected data may be of use for
prior information even though not currently available in an integrated form. For example, soils
data collected by the BLM closely follows the SCS methodology (BLM handbook), and most
BLM protocols and forms are standardized. For states and regions where large tracts are
managed by a single agency (Nevada for instance is nearly all BLM-managed land) the
inventory and monitoring data collected from district to district may be similar. Soil and water
parameters collected from FS districts are also similar; however it is less likely that the same
data collection methods are employed from station to station (USFS, 1988). GIS projects under
development by state and Federal agencies should provide help in integrating existing
information. Studies are required to evaluate the compatibility of methods of various agencies.
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In conducting these evaluations, emphasis should be placed on quality assurance and quality
control (QA/QC) checks. Under current circumstances, much of the data collected by the
various agencies is not comparable.
Although some large-scale databases or programs meet most selection criteria, several of
these sources may be only slightly useful for EMAP-Arid. For example, the NRI soils data
retained by the SCS represent only non-federal lands, and, although useful for other portions of
EMAR they may be of limited value for EMAP-Arid because of the extensive tracts of federal
lands in the Southwest. To address this concern, EMAP-Arid will need to work with the BLM
and SCS to develop RAIDS and IDS to produce data useful to EMAP. Both of these programs
are at a stage in their development suitable for coordination with EMAP. Over the next several
years they could help identify and collect the appropriate data to make assessments on the
conditions of much of the federal arid ecosystems.
The NWI, conducted by the FWS, is of primary value to EMAP Wetlands. The mapping
data for wetland areas lying within arid regions are not classified in a manner to address riparian
questions and issues. Future effort is needed to work with the NWI to determine the suitability
of using existing aerial photography and remote sensing data to delineate riparian systems.
This effort needs to identify the type of analyses required to ensure that adequate ground
verification is conducted to assess the quality of data on vegetation species, composition, and
distribution.
Future effort is needed to better develop indicators to represent the condition of
ecosystems over a large area. Information is generally available for site-specific assessments,
but methods are not adequate to integrate these into regional assessments that reflect the
condition of ecosystem processes. Good examples of this problem are the LTER and IBP
programs that have large collections of historical data on both deserts and grassland
ecosystems. These programs have data on selected EMAP indicators, but they have not been
evaluated to see if site-specific ecological relationships will generalize to any other location.
6.1.5 Using Existing Data
The EMAP-Arid assessment is embarking on a new mission to make large-scale
evaluations on the status and trends of arid ecosystems. However, this is not an uncharted
course. There are numerous researchers who are working to answer parts of the large
question. The challenge to EMAP-Arid will be to see how much of the existing data is useful to
answer the EMAP questions. In the case of arid ecosystem assessment, considerable
information is available. However, the effort required to incorporate this information into EMAP
is substantial because of problems in representation, quality, and full documentation. The effort
needed to secure and evaluate this existing data for EMAP purposes continues to be sizable.
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7.0 LOGISTICS
7.1 LOGISTICS IMPLEMENTATION COMPONENTS
The EMAP-Arid resource group proposes to collect ecological information on an annual
basis from sampling sites throughout the entire United States where arid ecosystems are present;
these sites will be primarily in the western United States. Implementation of this large-scale
program will require detailed, comprehensive logistics planning. Logistic considerations include
coordination and oversight of all implementation support activities (e.g., access permission and
procurement) in addition to actual data collection activities.
A pilot program will help to definethe logistical considerations that must be taken into account
for future field-based activities. Field activities for a pilot project are scheduled for 1992. One goal of
the pilot program is to conduct a monitoring and assessment program which exemplifies future
monitoring and assessment activities across the arid ecosystem. Arid zone ecosystems are
extremely different structurally and in terms of diversity and exhibit greater system complexity than
most other ecosystems. The pilot program will uncover a broad assortment of logistics concerns.
During the pilot program, a preliminary logistics plan will be evaluated and developed in more
detail. Modifications and improvements to the logistics plan will be determined during the pilot, and
the formal EMAP-Arid Logistics Plan will be developed prior to implementing further field activities.
This Plan will be implemented in subsequent years and will be appended and updated annually or
as needed to incorporate new knowledge and experience or to accommodate unique sampling
strategies or conditions.
7.2 SUMMARY OF EMAP LOGISTICS ELEMENTS
The EMAP-Arid Logistics Plan will address each of the elements presented in "Guidelines for
Preparing Logistics Plans" (EPA, 1990). These elements are summarized below. The major logistic
issues and those that are specific or unique to the EMAP-Arid Program are discussed in more
detail in Sections 7.3 through 7.6.
ELEMENT 1. Overview of Logistics ActivitiesSummarize the types of activities required to
complete the project. Maintain a timeline or Gantt chart showing all critical path milestones (e.g.,
project design, indicator selection, site selection, access permission, reconnaissance,
procurement, methods selection, development of standard operating procedures, and resolution
of specific quality assurance issues). Show required deliverable products such as plans, manuals,
and reports. Also provide logistics budget summaries.
ELEMENT 2. Staffing and Personnel RequirementsDescribe the number of personnel and
the organizational structure necessary to accomplish project objectives. Define who is responsible
for staffing and interagency and teaming mechanisms. Consider work schedules to determine
whether extra positions should be created or whether existing personnel should work overtime.
Create a contingency plan for replacing staff members when necessary. Identify key personnel and
provide plans for retaining them.
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ELEMENT 3. CommunicationsAddress communications among field crews, laboratory
crews, and supervisory personnel and between EMAP participants and any local organizations
who should be informed of EMAP field activities. Also include plans for tracking samples, data,
crews, and equipment and supplies. Discuss how field crews should interact with the public or with
the media. Explain how approved changes in standard operating procedures will be documented
and communicated for implementation.
ELEMENT 4. Sampling ScheduleBased on project, indicator, and statistical design or
other program requirements, devise an efficient scheduleforfield activities. Consider geographical
sampling windows within geographical areas and other factors such as climate and site access
constraints.
ELEMENT 5. Site AccessAddress issues related to gaining access to sampling sites
including scientific collection permits, if required. Develop a list of local contacts to discern
property ownership, jurisdiction, and the best site access methods. Address plans to obtain
appropriate access permission and applicable collection permits. Consider how to coordinate
activities of more than one resource task group in the same area. Discuss ways to arrange
long-term access rights, track changes in ownership of private sites and management of public
sites, notification of owners and managers before revisiting the sites for future monitoring, and
provide contingency plans in case of future failure to obtain reaccess permission.
ELEMENT 6. ReconnaissanceDefine criteria for selecting base operation sites (take into
consideration personnel and technical support requirements), geographical location with respect
to sampling sites, and time constraints imposed by sampling design or climate. Sampling sites
identified as having potentially difficult physical or legal access should be visited during field
reconnaissance. Additional resources needed for sampling should be identified if the access
problem is due to physical conditions. If the access problem is legal, one last attempt should be
made to obtain permission to sample.
ELEMENT 7. Waste Disposal PlanExplain how chemical and biological wastes will be
stored, transported, and disposed of safely and legally. Address what permits will be needed for
storage, transport, and disposal of wastes.
ELEMENTS. Safety PlanDiscuss how emergency situations will beevaluated and handled.
Determine emergency contact personnel and what emergency services will be available in the
field. Explain what procedures will be used to initiate search and rescue operations. List the training
or other preventive measures required to conduct field operations safely. Indicate how this field
safety plan will be developed in conjunction with laboratory, processing, and materials handling
safety plans.
ELEMENT 9. Procurement and Inventory ControlIdentify equipment, supply, inventory
control and resupply, and services requirements of the field program and the processes by which
they will be acquired and maintained. Determine where back-up equipment will be stored and how
sites will be resupplied. Consider shipping regulations, especially for chemical and biological
materials. Determine what analytical or other services will be needed and the best mechanisms for
acquiring them. A procurement schedule should be provided for all items.
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ELEMENT 10. Training ProgramDescribe who will prepare, review, and revise the field
training and operations manual and the procedures for field measurements, sampling, sample
handling, shipment, data recording, quality control, safety, waste disposal, and communications.
Outline a schedule for the completion of these items. Describe training needs and identify who will
conduct and review training. Address how personnel will be evaluated to ensure competency.
ELEMENT 11. Field OperationsIndicate the organizations that will perform each of the daily
field activities. Describe how and when the daily field activities will be performed. Discuss and
schedule the major events within field operations (i.e., mobilization, demobilization, and phase
changes in sampling activities). Consider contingencies such as back-up personnel in the event of
sickness. Require real-time evaluation to identify and resolve problems.
ELEMENT 12. Laboratory OperationsIndicate what organizations will be responsible for
each type of sample preparation or analysis and for formulating each laboratory operations
manual. If EPA conducts the activities directly, provide a development plan for providing
appropriate laboratory facilities.
ELEMENT 13. Information ManagementDescribe any data management activities that
might be affected directly by field operations. Establish guidelines for the timely and responsive
transferral of information from field personnel to data managers. Indicate the groups that will be
responsible for preparing and reviewing field data forms; provide a schedule for the completion of
these forms. Develop a schedule for completion of the information management plan by the
information management group.
ELEMENT 14. Quality AssuranceDescribe who will provide input to the QA plan on field
sampling, sample handling and preparation, sample shipment, sample disposition, and data
management. A schedule for completing the QA plan will be developed and included in the
logistics plan. The QA activities should be coordinated with other resource groups using similar
methods. This effort should identify common methods and standards when possible.
ELEMENT 15. Logistics Review and RecommendationsFor each year of study, summarize
logistic activities. Discuss how personnel will be debriefed to identify and resolve problems.
Discuss pilot studies and associated methods evaluation experiments; present logistics data
summaries within the full-scale annual ecosystem report.
7.3 ORGANIZATIONAL STRUCTURE
Standardization of EMAP-Arid field activities across a nearly nationwide network of field
teams is a critical aspect of the logistics process and requires a "Logistics Center." The overall
purpose of the Logistics Center will be to provide guidance, training, and ultimately, uniformity in
data collection. This uniformity is perceived as a factor critical to EMAP, since much of the data
collected during the program will be used to detect trends. If data is collected nonuniformly,
comparative analyses will be hampered.
The location and structure of the Logistics Center has not been defined at this time. However,
a possible mechanism for instituting the Center would be through regional or district offices of
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cooperating Federal resource management agencies. Good candidates for placement of logistics
centers would be locally-based BLM offices. Other possibilities include SCS or EPA regional
offices. Training and coordination through the Logistics Center would be implemented through a
consortium of Federal, state, and private cooperators, each of which could provide various levels
of scientific and administrative expertise. Cooperative interactions between EPA, state, and other
Federal resource management agencies, as well as the private sector are envisioned as a critical
component of the EMAP program.
EMAP-Arid is one of the seven EMAP resource groups that will be conducting annual field
operations. There may be overlap between the field objectives of EMAP-Arid and one or more of
the other resource groups (i.e., Wetlands, Great Lakes, Near Coastal, Agro, Forest, and Surface
Waters). Logistics efforts will be integrated to the extent possible. Sharing facilities such as the
Logistics Center or warehouse facilities may be ways of integrating efforts.
As mentioned previously, the long-term success of EMAP is dependent on the development
of an interagency program with common goals for the monitoring of the ecological condition of the
environment. Arid ecosystems monitoring alone could involve numerous agencies within the
Department of the Interior (e.g., BLM, USFWS, NPS, USER), the Department of Agriculture (e.g.,
USFS, SCS), the DOD, and the DOE. State game management agencies and natural heritage
programs may also support cooperative relationships. As EMAP evolves into an interagency
program, agreements between agencies will be established to define responsibilities. These
agencies have locally based, experienced field personnel, and it is anticipated that personnel from
these agencies will participate in both field and logistics center activities.
The Boise Interagency Fire Center may serve as a model for an EMAP Logistics Center. The
Boise Interagency Fire Center is the national logistical support center responsible for coordinating
and dispatching the closest suitable manpower, equipment, and aircraft for wildfires which exceed
the capabilities of local and regional resources of land management agencies. This center was
established in 1984 with an interagency agreement between the BLM, the U.S. Bureau of Indian
Affairs, the NPS, and the USFWS. The objectives of this agreement are:
1. Development of interagency programs and services through coordination
and cooperation;
2. Effective use of interagency programs and services by cooperating
agencies; and
3. Equitable cost sharing of interagency programs and services.
Similar objectives will be established for interagency EMAP logistics centers.
7.4 LOGISTICS ISSUES
The complexity of EMAP poses a number of logistics issues. Overlooking or ignoring
apparently minor issues or details may eventually jeopardize the success of the Program. These
issues will be fully addressed in the EMAP-Arid Logistics Plan. The following discussion provides
an overview of major issues for the EMAP-Arid Program.
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7.4.1 Staffing
Various types of field data must be collected for indicator evaluation; therefore, field crews
with varied research experience and expertise will be needed over the life of the program. At a
minimum, individuals with knowledge in ecological field sampling methods, soil sampling,
ground-water and surface water hydrology, and meteorological measurement will be required on
each field crew.
Collection of field data in arid ecosystems is envisioned as a multiteam effort, which will
eventually be conducted concurrently across each region by locally based field teams. All field
personnel will be required to undergo an intensive training program to insure that protocols are
understood and strictly adhered to and to provide consistency across the arid ecosystems
program. The overall costs associated with training may be reduced over the long term by retaining
field crews for long periods of time. The EMAP-Arid will use and train qualified personnel selected
from the permanent staff of the BLM, USFS, NFS, EPA or other Federal agencies; the state
resource management agencies, or from universities. Long-term agreements with these agencies
and institutions will be negotiated in order to maintain long-term monitoring programs by qualified
staff.
The use of personnel from local agencies rather than from short-term or seasonal hiring is
advantageous for three reasons: (1) an agreement between EMAP-Arid and other entities will
foster cooperative relationships which may lead to a more responsive and integrated sense of
commitment by all resource management agencies; (2) use of locally based, permanent resource
management personnel will result in greater efficiency because of the familiarity these individuals
have of the local environment; for example, these personnel will be familiar with the geography,
terrain, and field conditions of the local regions in general, and possibly site specific conditions in
particular; and (3) if permanent personnel are used, it is assumed that they will participate yearly,
and thus, new field crews will not have to be trained as frequently. The benefits of permanent field
crews will eventually be realized both in cost savings and greater accuracy.
To accomplish these types of cooperative agreements, EMAP-Arid will need to demonstrate
its utility to the states and other agencies by providing additional data and information addressing
their research problems. A concerted effort will be undertaken to inform these agencies of the goals
and objectives of EMAP, and to allow these agencies to become involved in the early planning
phases of the programs. A cooperative agreement with the BLM has been initiated, and similar
agreements will be sought with other resource management agencies, including SCS, USFWS,
NPS, USFS, and state management agencies.
7.4.2 Access
Obtaining access information and permission to visit sampling sites will be difficult under
some circumstances. Strategies for obtaining access permission are not yet fully developed. If
land is publicly owned and open to the public, approval may be obtained from the appropriate
authority; however, permission may be conditional (e.g., upon the use of nonmotorized transport).
Contingency plans for these types of conditions will be developed. Federal lands not open to the
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public (e.g., DOE and DOD facilities) may be accessible through Memoranda of Agreement;
however, access may be completely denied depending on the types of activities conducted at
those facilities. If land is owned privately, each landowner will be contacted and written access
permission obtained.
Sample collection permits may be required by the state resource management agencies for
collection of flora and fauna. Permits will be obtained from the state resource management
agencies regardless of land ownership. State collection permits are usually very specific relative to
site location, numbers and kinds of specimens, and identification of field personnel.
Gaining access permission and knowledge of access routes will require reconnaissance. The
amount of time required to sample a site is dependent upon the physical access conditions. Some
sites may be accessible only by foot, while others may require access by helicopter. Access
constraints will be determined prior to the field season with topographic maps and through
communication with local resource management agencies or other knowledgeable individuals.
Determinations can then be made on how sampling crews and gear will be transported to the
sampling site and how samples can be transported adequately.
Since the field sites will not be visited prior to the field season, in some cases the field crew
may determine that the site is not suitable for sampling (e.g., because of unanticipated disturbance
to the area or logistical sampling problems related to the area). Alternative sites or a strategy for
determining an alternative site will be developed and provided to the field crew in advance so a
decision can be made to relocate if necessary.
7.4.3 Data Confidentiality
Data confidentiality is an issue of particular concern to EMAP. Many landowners may be
reluctant to permit access to sites from their property because they fear regulatory and
enforcement actions. Access is potentially a design constraint, and denials by landowners could
affect the design of the program. Cooperating agencies within the Department of Agriculture often
conduct field programs under an agreement of confidentiality with landowners. EMAP data may be
aggregated in such a way that individuals cannot be identified to assure landowners and
cooperating agencies that site-specific data will not be used against their interests. Agreeing to
withhold certain information, however, is in direct conflict with the Freedom of Information Act and
the EPA policy on data confidentiality. This issue will be resolved in the near future. The EPA Office
of General Counsel is currently being consulted on this matter.
7.5 FIELD OPERATION SCENARIO
Arid ecosystems field data collection will be conducted by a team of field scientists and
overseen by a field supervisor who is directly responsible for successful completion of all data
collection activities. A primary function of the supervisor is to coordinate with the Logistics Center
on type, frequency, and specific procedures of data collection. This will require annual training and
information sessions led by Logistics Center personnel for all field supervisors prior to the
commencement of thefield season. The organizational, supervisory, administrative, scientific, and
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technical skills of the field supervisor are a key factor controlling successful data collection
activities.
The field crew will consist of diverse personnel such as plant ecologists, plant physiologists,
wildlife biologists, soil scientists, and other trained personnel, as needed. Each individual will be
responsible for the collection of field data in their respective disciplines. It is imperative that each
individual be trained in their own field methodology, data management, and quality assurance
activities. The Logistics Center will provide a mechanism for ensuring that data is collected
accurately and uniformly across the network of field crews.
A variety of information will be collected in the field, including such diverse parameters as soil
chemistry, vegetation ecological parameters, plant physiological response, wildlife populations,
and meteorological conditions. The level of complexity associated with the equipment involved in
these operations necessitates that a field engineer skilled in instrumentation maintenance and
repair be present on each team. Although this individual may also be involved in specific aspects of
the data collection, a primary responsibility of the field engineer is to ensure that all equipment is in
sound working order. This individual will also act as the assistant field supervisor, and provide
back-up assistance where needed to all field crew members. The importance of this crew member
cannot be underestimated inasmuch as any downtime related to field equipment may jeopardize
opportunities for complete data collection.
Prior to the planned date for data collection, the field supervisor will be provided with
information on field site locations and alternative sites. Problems with access or data collection will
be worked out prior to sending the entire field crew out to collect data. Problems related to access
or data collection logistics that are discovered during thefirst year of data collection will be resolved
prior to returning to the field in subsequent years.
Prior to conducting data collection for the first time in any given year, the field personnel,
assisted by the field engineer, will perform laboratory and field checks of all field equipment to
ensure that all instruments are in good working order. Standard field equipment will include all
necessary spare parts (in triplicate) (including batteries or other power supplies), a fully equipped
tool box, and first-aid kit. Mechanisms will be developed to properly harness the field equipment to
the field vehicle to eliminate the potential for damage enroute to the sites. The field engineer will
ensure that the equipment is in good working order at all times.
Unanticipated problems such as climatic conditions or equipment failures may require
personnel to remain in the field longer than was originally anticipated in order to accomplish all
required data collection. Therefore, all trips and field personnel will be scheduled for approximately
50 percent more time than theoretically will be needed.
7.6 DAILY ACTIVITIES SCENARIO
Prior to leaving base camp every morning, individual field crew members will be responsible
for consolidating and checking their field equipment. The field supervisor and field engineer will be
ultimately responsible for ensuring that all field supplies, including field instrumentation,
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equipment, forms, emergency and safety equipment, is stowed in the field vehicle prior to entering
the field.
Scheduled field activities will be preplanned; thus each field crew member will have specific
duties to perform on any given day. The field personnel will collect data, overseen and assisted by
the field supervisor. The field engineer will provide technical support for instrumentation and field
sampling as needed and time permits.
Field data collection will be automated to the extent possible. Other EMAP resource groups
have demonstrated the efficiency of the Paravant RHC44 portable data recorder (PDR) for many
types of field data collection. This computer-based data recorder is fully portable and has proven
to be relatively indestructible. These recorders will be fully programmed prior to the field season,
requiring little programming or maintenance while in the field. A major advantage to automated
data recording is that the information transfer step from hard copy to computer is eliminated,
thereby reducing the potential for error. Data input error is also reduced because the PDR can be
programmed to reject entries out of an acceptable range of values. Because automated data
collection may not always be practical, other procedures will be available as needed.
Upon return to the base camp at the end of each field day, the field crew will download any
data loggers or recorders to a portable computer, prepare, pack, and store or ship all samples
collected during the day; restock field supplies for the next day; and recalibrate their equipment.
The field engineer will clean, maintain, and assist in the calibration of all field equipment. The field
supervisor will immediately debrief the crew to determine progress made during the day and
prepare a schedule for the following day. Daily or at some other interval, samples will be shipped or
data will be transferred to the Logistics Center via modem. The field supervisor will oversee these
periodic operations, as well as keep complete records of all collection activities, data transfers, or
sample shipments. Figure 7-1 illustrates a flow chart of EMAP-Arid field activities.
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AM
Load equipment
and personnel
Access site
FIELD CREW
Data Collection
Data collection
A
I
Data collection
B
1
Data collection
C
I
Data collection
D
1
Data collection
E
<_
>.
1
T
Return to
base camp
>.
FIELD
SUPERVISOR
Oversee
operations
Provide
back-up
assistance
Monitor
timelines
FIELD
ENGINEER
Maintain
equipment
Provide
back-up
assistance
Collect
data
PM
Staff
progress
meeting
FIELD
CREW
Field
preparations
FIELD
SUPERVISOR
FIELD
ENGINEER
Download
field data to PC
Debrief
field crew
Maintain
and calibrate
equipment
Prepare and
pack samples
I
Proof
data
Proof data
Schedule
following
day
Restock
supplies
Restock
supplies
Data transfer
via modem
Provide
general crew
support
Figure 7-1. Flow chart of EMAP arid ecosystems daily field activities.
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8.0 ANALYTICAL CONSIDERATIONS AND MEASUREMENT TECHNIQUES
This section addresses the analytical considerations associated with three aspects of the
EMAP-Arid data collection and analysis: field data collection, laboratory analysis, and synoptic
(remote sensing) analysis. These factors are important considerations in total quality
management, which requires the application of qualified personnel, equipment, and techniques
under the scrutiny of rigorous process quality control to achieve results of measurable quality as
set by data quality objectives. Quality control is discussed in greater detail in Section 9.
8.1 METHODS
Methods for measuring habitat attributes in terrestrial and aquatic environments and
evaluating biotic condition are described in numerous places (e.g., Cox, 1976; Brower and Zar,
1977; Mikol, 1980; Call, 1981; Hays etal., 1981; Plattsetal., 1983;Cooperrideretal., 1986; Graves
and Dittberner, 1986; and Platts et al., 1987). The contribution of attributes to an ecosystem may
vary due to site-specific characteristics. Identification of limiting or enhancing environmental
factors is an important component process of defining the resource condition. Standardization of
measurement techniques will make it possible to compare monitoring information and evaluate
study results in a uniform manner. Factors to be considered during the planning of field sampling
include timing of sample collection, site selection, sample collection, subjective measurement,
and training.
8.1.1 Sample Timing
Sample timing will minimize the variability of the data yet permit objectives to be met. The best
time to collect a sample, the index period, is an important issue in the collection of remote or
ground verification data. The index period may change with time of year or day and is a critical value
to assess when considering data variability. Some QA concerns can be allayed by considering the
following issues:
1. Seasonal influence and multiple measurements over the year-there are
numerous instances when time of the year is critical to the objectives of the
project. Vegetation assessments, for example, are probably best made in
the spring or summer. Multiple measurements taken throughout the year
may be required to determine the seasonal variability for some parameters.
Sample timing for soil samples, however, may be less important in some
cases since fluctuations in soil properties are more stable .
2. Time of day-certain parameters, such as photosynthetic processes and
animal activity are influenced by time of day; thus diurnal sampling
constraints must be considered in the design.
3. Meteorologic activityseveral meteorologic parameters influence the
amount of variability in monitoring results. Although field situations do not
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necessarily allow for the minimization of such variability, significant sources
of variance.should be recorded.
4. Ecosystem cycles - measurement of certain ecosystem parameters, e.g.,
phenological status, or brood success in birds, must be conducted at the
appropriate time of year.
8.1.2 Site Selection
Selection criteria should be carefully chosen to ensure comparability between sites. For
example, if the terrestrial area to be monitored is a remote site, factors such as influence from
global and local pollution sources should be assessed and kept to a minimum, the area should be
located where a minimum level of economic development is expected in the conning decades, the
area should not be subject to intervention, and baseline data should be available for the site with
particular emphasis on long-term (30 to 50 years) meteorological records (Wiersma, 1983).
Site selection criteria will depend on a number of factors, including the scale of the program or
project. For monitoring a landscape or watershed vegetation type, slope, aspect, drainage class,
and soil type should be taken into consideration during selection. For monitoring regional or
national representation, geology, physiography, vegetation, precipitation, and temperature
regimes should be considered. Ideally, site selection criteria must be concise enough that two
independent researchers would choose similar locations. The criteria must minimize the amount of
subjectivity that enters into the site selection process.
8.1.3 Sample Collection
Natural sample variability may be reduced through various sampling methods. The most
common way to reduce sample variability is to composite samples. For example, soil or vegetation
samples are frequently composited to obtain an average. Another way to reduce variability is to
take a sufficiently large homogeneous sample from which to draw a smaller subsample. These
methods work well for small-scale or site-specific assessments; when, for example, the whole site
can be gridded.
8.1.4 Subjective Measurement and Training
Subjective measurements include those that rely on subjective estimates. Ecosystem studies
rely on subjective measurements for estimating such factors as vegetation cover percentages and
species composition (Barbouretal., 1987). One problem with subjective measurement can be the
high degree of variability between samplers.
One way to reduce the subjectivity from field estimates is by using standardized field forms
that provide multiple-choice type answers in addition to spaces for other data. For example, the
soil description forms used by the USDA allows individuals with little training to produce fairly
accurate soil descriptions by providing choices on all relevant information such as structure,
texture, soil color, and horizon depth. Standard methods for reporting properties such as the size,
strength, and shape of the soil bed are all described in the soil structure section. An individual
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making a soil profile description without a soil description form is much more likely to omit pertinent
information than the individual using the form. Similar forms could be developed for all ecosystem
studies that require documentation of data from the sampler.
Training is vital to the success of subjective measurement systems. The USFS, for example, is
addressing this concern now and working on standardizing data collection so that data can be
used in geographic information systems (GIS). For EMAP to be successful, all employees involved
in monitoring must receive the same level of training and their activities must be checked by
qualified field auditors.
8.2 LABORATORY ANALYSES
Analytical laboratories will generally assume that all samples were properly handled and
labeled prior to receipt, unless gross problems such as shipping delays are evident. Therefore, it is
the responsibility of all field personnel to properly collect, contain, label, preserve, document, and
transport their samples via standardized protocol to the analytical laboratories.
The quality of a measurement may be assured if the measurement technique is performed in a
technically competent manner by qualified personnel using appropriate methods and equipment.
The precision and accuracy of the measurement techniques are monitored to ensure consistency.
Acceptable quality may vary by analyte, matrix, or analysis technique. The quality of contracted
services is assured by contracting with reputable consulting groups, research institutes, and
laboratories that have demonstrated their competence in performing field measurements or
laboratory analyses and by monitoring their performance throughout the life of the contract
through a variety of quality assurance procedures. The following information will be required within
the report process for deliverables under all contractual agreements:
1. A description of the methods or techniques used.
2. The quantitative results.
3. The results of any quality control samples analyzed or measured in
conjunction with the report results.
4. Limits of detection for each analyte.
5. A description of any problems encountered in laboratory analysis or field
measurement.
Additionally, general quality assurance can be also be assessed via round-robin and
crosscheck analysis procedures between consulting groups and contract laboratories. A
preselected percentage, e.g., 5 percent, of all results could be selected for this purpose. A quality
assurance report will be prepared which evaluates the results provided by the contractor. This
report will discuss the quality control data, report any constraints such as detection limits or sample
size, and provide an estimate of the precision (as ±95 percent confidence interval) and accuracy
(as percent recovery of spiked analyte or reference material) of the analytical results.
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8.2.1 Analytical Precision and Accuracy
The precision of the method for each analyte is estimated by using a range ratio control chart
to calculate an estimate of the 95 percent confidence interval for the method. The precision of the
results of duplicate analyses reported in each submitted sample set are compared to precision that
is expected, based on the chart. If the precision of the duplicate analyses in a single report is within
the 95 percent confidence interval for the estimate of the laboratory precision, based on the control
chart, the precision of the analyses is acceptable. The precision of the laboratory is expected to be
within the standards established for each type of analysis (Table 8-1).
TABLE 8-1. EXAMPLES OF ACCEPTABLE PRECISION REQUIRED FOR
LABORATORY ANALYSES
METHOD
ICP Scan3
Atomic
Absorption
(AA)
Organochlorine
Scan
Petroleum
Hydrocarbon
Scan
±95% CONFIDENCE INTERVAL
REGION OF DETECTION1
30%
20%
30%
70%
REGION OF QUANTITATION2
15%
10%
15%
35%
1 Duplicate analyses fall within the region of detection when their average concentration is
between two and ten times the limit of detection. If the average of the duplicate analyses
is less than two times the limit of detection, no evaluation of the precision is made. The
confidence interval is defined by ±2 times the limit of detection.
2 Duplicate analyses fall within the region of quantitation when their average concentration
is greater than ten times the limit of detection.
3 ICP = inductively coupled plasma spectroscopy.
The accuracy of the analysis is assessed using percent recovery of spiked analytes. The
accuracy of the laboratory is expected to be within the standards established for each type of
analysis (Table 8-2). Mean spike recovery and the standard deviation of the mean recovery are
determined for each analyte. The recoveries reported with a single sample set are compared to the
average recovery for the analyte. If the reported recoveries are within the 95 percent confidence
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interval for the mean recovery, the accuracy of the analysis is considered acceptable. In addition to
spike recoveries, standard reference materials are frequently analyzed in conjunction with metal
analyses. Results from these determinations are compared to both the laboratory average and the
certified value. Procedural blanks are also utilized to assure quality and are analyzed to confirm that
no analyte is added during the processing of the samples. These data are expected to be below the
limit of detection and results are included in the quality assurance report.
TABLE 8-2. ACCEPTABLE AVERAGE SPIKE RECOVERIES FOR ASSESSING
ACCURACY OF THE ANALYSIS
ANALYTE
Metals (ICP)
Metals (AA)
Organochlorine Pesticides
Petroleum Hydrocarbons
ACCEPTABLE RECOVERY RANGE
80-120%
85-115%
80-120%
60-140%
8.2.2 Detection Limits and Required Sample Size
The tables provided in Appendix C summarize information for organic and inorganic
analytical capabilities that are typically requested (Tables C1 and C2), lower limits of detection per
analyte and minimum and optimal sample weights or volumes required by each method (Tables C3
through C7). Detection limits are directly affected by sample size and vary among laboratories due
to differences in methodology and instrumentation.
8.3 SYNOPTIC SUPPORT REQUIREMENTS
Synoptically based data collection, integration, and analysis is an integral part of the
EMAP-Arid research agenda, and there are many inherent analytical considerations that must be
considered for this aspect of the program. Developing a relationship between data derived from
remote sensing and the GIS will require that attention be given to the integration of the data sets.
Remote sensing and GIS error associated with data acquisition, processing analysis, conversion,
and final product presentation could have a significant impact on the confidence of decisions made
using the data. The error sources and issues present within this process are discussed in Lunetta et
al. (1991). This section highlights basic considerations in synoptic data analytical techniques.
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8.3.1 Aerial Photography
Acquisition and interpretation of aerial photographs are important features of the synoptic
data program. An orientation and training program that is task and function specific will be
implemented to ensure technical competence, compliance with standard operating procedures,
and maintain continuity and depth of expertise in the event of personnel turnover. CIR photography
will be used to the extent possible, and new photography will be obtained if baseline photography
is not available. All film will be labelled and provided with a data tracking form upon receipt, and
visual inspections will be immediately implemented to check for damage or flaws. The film will also
be analyzed to ensure adequate coverage, to determine scale, and to identify the hexagon center.
Film quality will be maintained through proper handling procedures.
In the data preparation stage, photographs and hexagons will be registered and mylar
composite maps will be generated. The analysis will delineate polygons and assign attribute codes
in accordance with a classification system. Comprehensive quality control and quality assurance
reviews are to be performed on this product to ensure consistency in applying the classification
system and accompanying user conventions and as a check for any inaccuracies in boundary
delineations or classifications.
8.3.2 Multispectral Data Quality Control
Analytical procedures included in the multispectral data analysis will include raw data
examination and evaluation, documentation of analytical procedures and data tracking, routine
data backups, audits of documentation and technical personnel, and corrective actions.
The raw data will be evaluated to ensure proper areal coverage and an absence of scanner
striping, clouds, or haze. An Analysis and Data Tracking Form will document the digital
characterization process and will cover all procedures that are applied to the data. Procedures will
also be developed for classification and raster-to-vector data conversion to ensure that the data
products are accurate.
8.3.3 GIS Data File Development
The transfer of information from aerial photographs or multispectral data to the GIS will
require that measures are imposed to ensure data integrity and map consistency. A Hexagon
Production Record sheet will provide record-keeping methods for ensuring that all operational
processes are completed. During GIS development, standard checks will be made to ensure that
all polygons are closed and coded and that all codes are valid and to check map registration.
Hexagon tracking forms will trace the development of each hexagon from the photo acquisition
stage through data base development.
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9.0 A STRATEGY FOR QUALITY ASSURANCE
9.1 INTRODUCTION
The arid ecosystem component of the EMAP will be collecting each year a sizable quantity of
data, in a sequence of locations, with numerous measurement systems, by a variety of field
personnel, and at a considerable cost. These data will be further checked, edited, and processed
at various laboratories and assembled, through a distributed information management system,
into regional and national data bases. Subsequently, population estimates of various ecological
parameters (e.g., extent and condition) will be made and the results will be utilized by EMAP
customers in many ways including the assessment of the effectiveness of present regulations and
policies in protecting the health of arid ecosystems. Clearly, the EMAP-Arid data will be a valuable
resource to many users, and thus it needs to be properly focused, identified, collected, processed,
assembled, documented, and preserved in order to provide these users with results of known
quality. The strategy used to assure the proper performance of these steps is the focus of quality
assurance (QA) plans and activities.
The origin of QA within the EPA stems from regulatory and compliance activities which
traditionally have differed considerably from the proposed EMAP activities. Hence, the EMAP-Arid
project will take a new approach to environmental QA, and it will emphasize the philosophy and
strategy of total quality (e.g., Snee, 1990) in its efforts to achieve excellence in design,
implementation, analysis, and reporting. Thus, particular attention will be placed on the:
1. Description of the EMAP-Arid information products.
2. Identification of the relevant customers, their potential decisions, and the
resulting data quality needs.
3. Building in quality, or excellence, in the design stage.
4. Training for, and standardization of, the implementation of the monitoring
design.
5. Early detection of problems and the use of "improvement teams" to
diagnose, recommend, and implement corrective action.
6. Need to recognize and reward individuals and teams for outstanding
performances.
Finally, all EMAP-Arid participants will be expected to pursue the philosophy of continual
improvement in all EMAP-Arid activities.
9.2 DATA QUALITY OBJECTIVES
The EPA has a policy that all data collections conducted under its sponsorship must be
properly planned and implemented so that the resulting data are of known and documented quality
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and meet the specifications of the users (Stanley and Verner, 1985). This policy was officially stated
in U.S. EPA Order 5360.1, "Policy and Program Requirements to Implement the Quality Assurance
Program" (EPA, 1984), and, in a memorandum accompanying the issuance of the Order, Deputy
Administrator Aim indicated two steps that are needed to accomplish the data quality assurance
task:
"... the user must first specify the quality of data he needs; then the degree of
quality control necessary to assure that the resultant data satisfy his
specifications must be determined."
These two steps presently are addressed by the data quality objectives (DQOs) process and
the corresponding quality assurance project plan (QAPP) required in EPA-sponsored projects.
The development of the DQO process stemmed from the EPA need for data that clearly would be
defendable in regulatory and judicial situations. The process should involve four steps: (1) the user
indicates the types of decisions to be made and the desired data quality; (2) these statements then
are translated into quality assurance requirements; (3) a sampling design is created in response to
the requirements; and (4) comparisons are made between what is desired and what is affordable.
The user statements typically are termed DQOs, and they have been defined as "qualitative
and quantitative statements of the quality of data needed to support specific decisions or
regulatory actions" (EPA, 1987a). The DQO process is iterative as it translates the DQOs into
quantitative and qualitative guidance for the sample design and the resulting quality assurance
plan. These iterations between what is desired and what is possible and affordable are not easy, but
they ultimately should lead to an acceptable resolution and plans.
One of the major components of the DQO process is the assessment of both the population
variability (Vp) and the measurement variability (Vm) that will occur with each ecological indicator.
These two components add to the total variability (Vt) (Vt = Vp + Vm) that any monitoring design
will face upon implementation (e.g., van Ee et al., 1990), and the desire is to keep this total
variability (i.e., noise) relatively small so that the indicator "signal" can be readily detected.
Temporal and spatial variability are additional factors that may affect the total noise, and they must
be identified and controlled. Lastly, systematic error (termed biasit can arise from many causes
such as a faulty sampling procedure) may be present and may accumulate during the
measurement process. Hence, the signal may not be accurate, and the resulting bias must be
assessed.
EMAP-Arid is a pioneering program, involving regional and national estimates of ecological
population parameters (e.g., population characteristics such as extent, distribution, and
condition) over an extended period and using a probability based design. The measurement
variability for the proposed indicators is essentially unknown at the present time. Consequently, the
DQO process cannot be utilized in the initial EMAP-Arid field efforts such as pilot studies. Instead,
the focus will be on assessing actual systematic and random measurement error of the different
measurement devices such as the AVHRR and MSS in order to determine what measurement
quality objectives (MQOs) can be met for the selected indicators. The plan is to gradually build the
indicator and other information data base for a future demonstration project that can attempt to
incorporate the DQO process.
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The QAPP plays the role of guiding the implementation of the sampling design to assure that
the DQOs are satisfied. This plan is a detailed document that focuses on the implementation of the
project and the accompanying quality assurance efforts. Although, there is some flexibility in the
material that must be covered in the report, depending on the purpose of the data collection effort
(EPA, 1987b), it typically contains 16 sections including the project description, DQOs, and data
characteristics (precision, accuracy, completeness, comparabilities), sampling procedures,
sample custody, calibration procedures, laboratory analytical procedures, internal quality control
checks, performance and system audits, and some details on the forthcoming quality assurance
reports to management. When completed, the QAPP becomes a basis for the justification of
sample selection and quality assurance.
9.3 QUALITY ASSURANCE REQUIREMENTS
The MQO and DQO processes presently require that at least the five fundamental data
characteristics of accuracy, precision, completeness, representativeness, and comparability be
properly addressed. The following definitions of these requirements (e.g., Barth et al., 1989)
present typical approaches to assessing each of them. Our initial proposal on how to handle each
requirement also is included.
9.3.1 Accuracy
The degree to which a measured value agrees with a known "true" value (typically reported as
amount of bias). This problem typically is controlled by the laboratory and its standard operating
procedures. It usually is assessed by laboratory blanks or spiked samples.
The EMAP-Arid resource group believes that the idea of accuracy must be extended to the
field and, hence, standard operating procedures (e.g., high-tech measuring devices,
computerized data entry, field screening software) must be emphasized as much in the field as in
the laboratory. This emphasis will require a system that will assure quick reporting to the laboratory
and the field if accuracy problems are encountered.
9.3.2 Precision
The amount of variability among repeated observations from the same population (generally
reported by calculating the variance or standard deviation of the observations). This quantitative
description of the variability of a process enters the quality assurance situation at many stages. The
particular sampling design, the sample taking, the sample handling, the laboratory analysis
method, and other related activities all have precision components. Replicate colocated samples
and splits typically are used to assess this dimension of data.
Attention to all of the above steps will be needed in order to reduce the random error which
ultimately affects the total variability of the estimate. This attention will be emphasized in the design
and implementation stages through training and periodic checks including field audit samples.
Again, quick communication of the results will be needed in order for the audit data to serve its
purpose.
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9.3.3 Completeness
A measure of the proportion of the sample size that actually results in validated data. This
requirement typically is handled by oversampling or by sampling in a phased approach.
Completeness is measured by the ratio of the amount of validated data to the amount of requested
data.
The EMAP-Arid team currently favors a phased or sequential approach to this problem
believing that considerable emphasis should be placed on attempting to secure the necessary
data from the designated site. We expect that it will not be possible to collect some proportion of the
data from the designated sites and will watch this situation closely.
9.3.4 Representativeness
The degree to which data truly represent a characteristic of an a priori designated population.
Representativeness largely is achieved by the monitoring network design, the field sampling
design, the field sampling, and the detailed knowledge of the ultimate use of the data.
For EMAP-Arid, the current plan is to emphasize the above techniques to handle this problem
and to include field training and self-reporting of problems in following the implementation
protocol.
9.3.5 Comparability
The degree of confidence that one has that two or more data sets have been subjected to the
same overall measurement process. This objective typically is addressed by proper training,
standardized methods, and uniform attention to the actual measurement process by field crews,
laboratory staff, data managers, and other participants. Field audit samples may help in assessing
this dimension.
Again, EMAP-Arid will emphasize standardized and robust methods, training, and reporting
of all problems encountered in the field or laboratory.
Although it will not be possible to fully address these requirements until after EMAP-Arid
customers are identified and some pilot studies are completed, the design team will evaluate these
design evaluation criteria frequently in order to build excellence into the design and
implementation stages. Clearly, the monitoring design team ultimately should attempt to settle on
appropriate values or levels for the above five data characteristics such that will serve both external
and internal users.
9.4 A TOTAL QUALITY APPROACH TO THE QUALITY ASSURANCE PROGRAM
As already indicated, the EMAP-Arid group plans to incorporate the old with the new to create
a QA system that is both responsive to customer needs and responsible for a unified team
approach to providing excellent products throughout the stages of EMAP-Arid activities. Figure
9-1 presents some of the relations between the traditional QA and aversion of the more recenttotal
-------
QA
QAPP/TQM > ESTABLISH PRINCIPLES AND
GUIDANCE FOR EMAP PERSONNEL
DQO > ESTABLISH CUSTOMER NEEDS
QAPJP > ESTABLISH THE PROCESS FOR
SATISFYING CUSTOMER NEEDS
AUDITS > EVALUATE THE PROCESS TO
IMPROVE IT AND TO JUDGE ITS
ABILITY TO SATISFY CUSTOMER
NEEDS
REPORTS * PROVIDE PRODUCT THAT MEET
CUSTOMER NEEDS
Figure 9-1. Relationships between traditional QA tools and the total quality primary tenet
"customer satisfaction."
quality approach (Graves, 1990). The total quality approach to the QA problem has strong
historical roots as evidenced in the 1920s statement by George Edwards, a member of a small
AT&T engineering and inspection group at Western Electric, who stated the following in proposing
the term "quality assurance":
This approach recognizes that good quality is not accidental and that it
does not result from mere wishful thinking, that it results rather from the
planned and interlocked activities of all the organizational parts of the
company, that it enters into design, engineering, technical and quality
planning specification, production layouts, standards, both workmanship
and personnel, and even into training and fostered point of view of
administrative, supervisory, and production personnel. This approach
means placing one of the officers of the company in charge of the quality
control program in a position at the same level as the controller or as the
other managers in the operation. (Edwards, 1926?, in Harrington, 1983)
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The following subsections will provide some initial views and a strategy for a total quality (TQ)
approach to the EMAP-Arid QA program (see Harrington, 1987, for how to organize for TQ).
Further ideas and results will be forthcoming as some of these ideas are tested in the field and in the
laboratory in the summer of 1991.
9.4.1 Products and Customers
All of the proposed EMAP-Arid product types should be identified early in order that the
potential final customers (e.g., BLM, USFWS, and EPA regions) can understand what they will be
purchasing in a joint venture. For all of the EMAP resource groups, the final product is information
such as tables of results, visual displays, and interpretative comments (see Section 10); this
information will focus on responding to the issues, endpoints, and critical questions of the
particular resource (e.g., What is the condition of the cottonwood-willow vegetation type in the
Southwest U.S.?).
A second, and equally important, question is who are the customers for whom the final
EMAP-Arid products will be produced? Here both external agencies such as the BLM and USFW
and internal decision makers such as the EPA Administrator and other EMAP ecosystems will be
identified. In keeping with Deputy Aim's guidance, the EMAP-Arid team also will clearly identify
what the users desire, need, and can afford. Although these questions may not be easy to answer
at this time, early answers are important for planning activities.
9.4.2 Seven Stages of an Experiment
To illustrate how a TQ strategy for EMAP-Arid QA might function, Figure 9-2 presents the
seven stages of the typical life cycle of a field monitoring project. The following text describes
typical research activities for each of these seven stages (Flueck, 1986) and examples of
associated TQ actions (Table 9-1). We will use four QA steps:
1. Goals: defining quality performance standards;
2. Evaluation: comparing results with the standard;
3. Corrections: taking corrective actions when needed; and
4. Improvements: planning for continued improvement to guide these
examples.
9.4.2.1 The Conception Stage
During the conception stage of an EMAP ecosystem survey, the planners have the
responsibility to select the high priority customer issues, formulate some critical questions, query
some experts and the relevant literature, develop some insights on possible endpoints, and
perform preliminary investigations. The major study decisions will be to determine if there is
sufficient interest and opportunity to continue.
The quality assistance role, under TQ, should emphasize the checks and balances needed to
assure that the project is carefully designed and implemented (much like the industrial view of
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START-
NOT FEASIBLE
STOP!
INDICATION!
EFFECT! NOISE!
***
SUBSTANTIAL PROBLEMS
STOP I
Figure 9-2. The typical stages or life cycle of an experiment.
"doing it right the first time", see Flueck, Paulsen, and Jones, 1990, for an EMAP example). Thus,
as shown in Table 9-1, the specification of goals could include: (1) identifying high priority
ecological issues, related EMAP critical questions, and important endpoints; (2) completing a
comprehensive literature search; and (3) conducting some preliminary investigations.
The evaluation could involve evidence of frequent (e.g., weekly) discussions between and
among the scientists and the Technical Director with monthly progress reports to the relevant
EMAP Associate Director. Finally, the EMAP Steering Committee should review periodically the
major results of each ecosystem task group's activities. The corrections could range from further
explanation and additional justification to stop and start again. Finally, the improvement process
could entail a gradual raising in the level of required knowledge, the sharpness of the critical
questions and the associated endpoints, and more focused preliminary investigations.
9.4.2.2 The Design Stage
The design stage is responsible for the construction of a formal research plan including the
EMAP-Arid goals and scope.the critical questions.the proposed conceptual model, and the
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TABLE 9-1 AN APPLICATION OF THE FOUR BASIC STEPS OF TOTAL QUALITY TO
THE SEVEN STAGES OF A HYPOTHETICAL FIELD PROJECT FOR
THE EMAP-ARID ECOSYSTEM RESOURCE GROUP
1. The Conceptual Stage
a. Goals
i. Identification of high priority issues and related EMAP critical questions and important
endpoints
ii. Completion of a comprehensive literature search
iii. Preliminary investigations and possible solutions
b. Evaluation/Verification
i. Weekly reporting to Technical Director and monthly progress reports to the relevant EMAP
Associate Director (AD)
ii. Review by EMAP Steering Committee at major junctures
c. Corrections
i. Additional explanation or small course changes
ii. Start again
d. Improvements
i. Better method to identify high priority issues, resulting critical questions, and endpoints
ii. Perform more focused preliminary investigations
2. The Design Stage
a. Goals
i. Clear statements of goals, products, customers, and scope of the project
ii. Description of the relevant conceptual model
iii. Clear specification of sampling design with quantitative evidence of its competitive advantage
iv. Specification of data quality objectives (DQOs) and related data quality requirements
b. Evaluation/Verification
i. Review of EMAP Design Report by internal committee
ii. Review of Design Report, or sections, by external panels
c. Corrections
i. Clarify sections of the Design Report
ii. Revise the Design Report
iii. Scrap Report and start over
d. Improvements
i. Better focus and clarity of Design Report
ii. Quicker redesign time
iii. Better QA coverage of activities
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TABLE 9-1. (continued)
3. The Feasibility Study Stage
a. Goals
i. All benefits and costs properly estimated
ii. Well-formulated questions for the pilot tests
iii. Prepare required QA documents
iv. Pilot testing gives needed answers the first time
b. Evaluation/Verification
i. Review of pilot tests by the ecosystem Technical Director and other scientists
ii. Review of results by the EMAP Steering Committee
c. Corrections
i. Revise the Design
ii. Revise the pilot study
d. Improvements
i. Plan more effective and efficient pilot studies
ii. Plan for iteration of pilot tests
4. The Implementation Stage
a. Goals
i. Utilization of a detailed Field Operations Plan and Manual
ii. Well-trained field workers
iii. Data completeness of 100%
iv. No deviations from the design
b. Evaluation/Verification
i. Daily and weekly discussion of operations by Technical Director, his staff, and other
scientists
ii. Frequent check of equipment and calibration
c. Corrections
i. Revision of the schedule and work assignment
ii. Revision of the operations plan
iii. Replace equipment and personnel
d. Improvements
i. Plan more realistically for future operations
ii. Provide better contingency plans
iii. Improved training of field workers
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TABLE 9-1. (continued)
5. The Analysis Stage
a. Goals
i. Clean data
ii. Appropriate estimators or tests
iii. Proper basis for inference
iv. All QA components of variance are small
b. Evaluation/Verification
i. Inspection of data for compliance
ii. Review of each analysis upon completion
iii. Duplication of some analyses
c. Corrections
i. Transform data to meet assumptions
ii. Select new estimators
iii. Delete the analysis
d. Improvements
i. Select better estimators initially
ii. Separate exploratory from confirmatory analyses
iii. Better estimators of QA components of variance
6. The Reporting Stage
a. Goals
i. Clear and complete reports
ii. Timeliness of reports
b. Evaluation/Verification
i. Internal review of reports
ii. Frequency and type of report
c. Corrections
i. Revise report
ii. Require more frequent reporting
d. Improvements
i. Improve formats of reports
ii. Plan for more journal articles
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TABLE 9-1. (continued)
7. The Ex-Post Studies Stage
a. Goals
i. Uncover other relevant results
ii. No mistakes in the old or new data
b. Evaluation /Verification
i. Review each analysis
ii. Re-check data and data-reduction
c. Corrections
i. Re-do analyses
ii. Revise data
d. Improvements
i. Plan for broader exploration
ii. Plan better data collection and reduction
two-tiered survey design, the relevant indicators and estimators, the measurement processes,
and the desired analyses. At the completion of this stage a formal design document should be
available.
The TQ opportunities at this stage are numerous, and they include the presence, clarity, and
succinctness of the study goals, scope, products, customers, conceptual models, and indicators.
In addition, as seen in Table 9-1, quality goals or objectives should be set for selecting the "best
compromise" sampling design and evidence of its advantages. Also, although the emphasis is on
DQOs, they often become measurement quality objectives (MQOs) and thus, the inherent
population variability is not considered.
The evaluation or monitoring of this stage appears to be the responsibility of both internal and
external groups (e.g., the design team, thetechnical directors, and the EMAP Steering Committee
on the inside and designated review panels and committees, such as the American Statistical
Association Statistical Design Review Committee, on the outside). As indicated in Table 9-1,
corrections can range from small revisions of the design report to scrapping of the entire report and
starting over. However, the extreme action (scrapping) typically occurs only after the feasibility
study stage. Finally, the improvements step is not much different from that mentioned in the
conception stage. But the reaction time typically is much shorter in the design stage because the
pressure is on to get to the field.
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9.4.2.3 The Feasibility Study Stage
The feasibility stage is dedicated to the examination, exploration, and testing of the current
proposed design (e.g., the EMAP-Arid Research Design). The activities include a benefit-cost
assessment of the project, a list of expected deliverables, and some pilot testing of the proposed
design.
The corresponding TQ activities (Table 9-1) should include the goals of properly estimating all
benefits and costs, answering well-formulated questions by the pilot test, efficient pilot tests, and
acquiring useful answers, as a result of the pilot test, the first time. Also, a number of standard QA
documents must be completed (Table 9-2). The evaluation should include review of test results by
TABLE 9-2. QUALITY ASSURANCE DOCUMENTATION FOR EMAP PROJECTS
EMAP Quality Assurance Program Plan (QAPP)
- Describes the philosophy and QA policies of EMAP and provides guidance for designing and
implementation QA programs within EMAP.
EMAP-Arid Quality Assurance Project Plans (QAPP)
- Detail the quality control and assessment activities that will be used in the EMAP-Arid QA program.
Field Operations Manuals
- Describe standard operating procedures for sample collection, handling, and processing;
collection of field data; and data management activities (including QA and QC procedures). Also
describes other logistical procedures (e.g., sample shipping, waste disposal, communications,
and safety) conducted in the field.
Analytical Methods Manuals
- Describe standard operating procedures for sample analysis (including QA and QC procedures).
Quality Assurance Project Plans from EMAP Support Groups
- Landscape Characterization
- Information Management
Other QAPPs and appropriate QAPPs for other participating groups (agencies, laboratories,
principal investigators).
the relevant scientists, the Technical Director, and ultimately the EMAP Steering Committee.
Corrections typically would be focused on revising the design, and improvements could aim at
better pilot testing of the design.
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9.4.2.4 The Implementation Stage
The implementation stage is focused on the field performance of the study guided by a
detailed field operations plan and manual. This document specifies steps and procedures for each
field activity including sampling preparation, sample taking, data recording, and field data
checking. At the end of field efforts for each year, a year-end summary document of all field
activities, by days, should be prepared for internal distribution and review.
The possible quality assistance or TQ activities could include the clear and concise goals from
the field operations plan and manual, solid training of all field workers, 100 percent data
completeness, and no deviations from the final study design (Table 9-1). The suggested
evaluations include daily and weekly discussion of operations among the Technical Director and
the associated scientists and frequent checking of equipment and calibration. Corrective actions
could range from revisions of the work schedule or assignments to replacement of equipment or
personnel. Improvements could include better planning, more complete contingency plans, and
better training of field workers.
9.4.2.5 The Analysis Stage
The analysis stage typically includes final data verification, data reduction, database
construction, and performance of the exploratory and confirmatory analyses.
The quality assistance activities will focus on obtaining clean data (e.g., validated data) and
appropriate and complete analyses. Thus, Table 9-1 presents goals of clean data, appropriate
estimators, and proper basis for inference. Evaluation or monitoring could then be accomplished
by data screening programs, close inspection of each analysis, and duplicate analyses, as in
pharmaceutical research and development. Corrections could take the form of transforming data
to better meet the statistical assumptions, utilization of more appropriate estimation or analyses, or
more careful accounting for multiplicity (e.g., exploratory analyses of the data and hence little basis
for formal inference). Improvements might focus on selection of better methods or analytical
procedures and the proper separation of exploratory from confirmatory results.
9.4.2.6 The Reporting Stage
This stage is concerned with the publication of the study design, results, conclusions, and
data with information on the availability of the latter. The suggested TQ activities (Table 9-1) include
the goals of clarity and completeness of reports, monitoring through internal review of all reports,
corrective action by report revisions, and improving the reporting process.
9.4.2.7 The Ex-Post Studies Stage
The final stage in a study, the often overlooked poststudy evaluation, is composed of
reanalyses of the data, revisions of the conceptual model, and evaluations of study performance.
The suggested TQ activities could be directed to uncovering additional results and evaluating the
performance of the study against the original expectations.
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9.5 CONCLUSION
Finally, it should be noted that the two management concepts of building quality into the
product at the beginning and making the quality task everyone's business (e.g., Juran, 1945) are
major characteristics of a TQ approach. For it is important to remember that one cannot inspect
excellence into a product.
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10.0 INFORMATION MANAGEMENT
The information collected and analyzed from during EMAP-Arid projects must be available for
many types of users: the field crews; the EMAP-Arid researchers; other EMAP resource groups;
local, state, and federal organizations; decision-makers; and academic institutions. The needs of
these users, in terms of type and format of the information and response time, may be different for
each group and at various times over the life of EM AP. The research needs of the EM AP program, in
general, and of the arid ecosystem component may also change in the future. These specifications
require that an information management system be created and used and that it be computerized,
flexible in design, efficient, and responsive to the user needs. This section describes the strategy
that will drive the EMAP-Arid Information Management System (EAIMS) and will satisfy the
requirements of these users over time.
10.1 ROLE OF INFORMATION MANAGEMENT
Information management plays a significant role in a research project of this size and
complexity. Its role is not defined by other aspects of the project (bottom-up approach). Instead it
shares complementary roles with the research objectives and quality assurance in defining how
the project will be implemented (top-down approach). Information management is the backbone
of the research effort and ties the pieces together from data collection, through data analysis and
trend assessment, to the use of the information by decision makers and the public.
EMAP-lnformation Management is working with the information managers for each resource
group (RGIMs), the Office of Administration and Resources Management (OARM), and the Office
of Information Resources Management (OIRM) to help each resource group define the
requirements for information management and automated data processing (ADP) resources as
early as possible in the research project. These requirements and the method for their
implementation will provide timely, cost-effective, and accurate access to all data generated or
utilized by the Program. The EMAP-Arid activities covered in this research strategy include, but are
not be limited to, the following areas:
1. Project management, planning, and logistics;
2. Field activities, including sample collection, tracking, and processing;
3. Archiving, cataloging, retrieving, and analysis and dissemination of all
pertinent data; and
4. Data base design and management.
The EMAP-Arid Information Management Group (EAIMG) will interact with the Technical
Director, project managers, researchers, statisticians, and quality assurance and quality control
(QA/QC) personnel to determine their requirements and to tailor the capabilities of the EAIMS to
suit their needs. The EAIMG will also interact with other EMAP information management staff to
address the needs of the project on the overall EMAP level.
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10.1.1 Objectives of EMAP-Arid Information Management
The objectives of the EAIMG are to:
Design an information management system that meets user requirements for
information from within the EMAP-Arid resource group and within and outside of
EMAP;
Develop a flexible information system that can adapt to the changing needs of the
program;
Provide state-of-the-art information management technology within the guidelines of
OIRM and OARM policy and the confines of available resources;
Coordinate with EMAP-Arid information management and other RGIMs to share
information management technology for the efficient use of EMAP resources;
Facilitate the wide use of EMAP-Arid data;
Ensure integration among users within EMAP and coordinate integration outside of
EMAP; and
Provide for the timely distribution of information.
10.1.2 Levels of Information Management Activities
Information management within EMAP will occur at three distinct levels. Although each level
will function independently, their individual activities will be integrated to form a data management
system that will encompass all ecosystem types and associated EMAP activities. These levels of
organization are described below:
1. RegionalEach region (e.g., arid regions of the southwest) within a
resource group;
2. NationalData for each resource group aggregated over all regions; and
3. Program wideThe overall EMAP, integrated among all resource groups
(i.e., national evaluations across multiple resources).
10.2 USER REQUIREMENTS
10.2.1 Levels of Data
Data will be disseminated to users in the categories listed below:
raw dataunmodified data collected in the field or analytical laboratory;
verified dataraw data that have been reviewed for completeness and accuracy;
validated dataverified data that have undergone validation analysis;
enhanced datavalidated data files that have missing values filled in using established
procedures; and
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summarized datadata that have been analyzed and summarized for presenting in
reports.
10.2.2 Users
The users of EMAP-Arid information can be described by the level of data processing
required to meet their needs:
(1) The EMAP-Arid resource group members who are individuals and groups involved with daily
field operations and tasked with the design, implementation (e.g., logistics and QA/QC
personnel), and interpretation of data from the field sampling programs or involved with the
assembly and interpretation of historical data, both synoptic and retrospective.
This group will require access to a comprehensive data set, on a real time basis, including:
- raw data files (from the field and laboratory),
- project management information,
- sample tracking,
- QA/QC reports,
- field logs,
- logistics,
- summary reports,
- maps,
- verified and validated data sets, and
- applicable historical data sets.
These individuals will work primarily with raw data that have not been verified or validated, but
may also require access to the data required by the other levels of users.
(2) The EMAP-Arid group members who are involved in the overall program, but not necessarily
involved with daily field operations. This category includes cooperating organizations (e.g., NPS
and USFWS), GIS support personnel, program reviewers, and EPA Headquarters personnel.
This group will require access to summary information related to project management and
logistics. It will require access to data that have been verified and validated. Group members will
not require real-time access to the comprehensive data sets.
(3) EMAP individuals and cooperating organizations directly involved in design, implementation,
and analyses for the overall program. These individuals include members of other ecosystem
resource groups, members of the Integration and Assessment Resource Group, and personnel in
other agencies directly involved in EMAP.
This group will require final summaries related to project management and logistics. These
participants may require access to some verified and validated data files. They do not require
real-time access, nor do they require access to a comprehensive data set. They need data in a
context which can be integrated with data from other disciplines. Document summaries with
interpretation and graphic outputs will be most useful.
(4) Other local, state, and Federal agencies involved in similar environmental monitoring
programs, including the EPA regions, other EPA offices, state environmental agencies, academic
institutions, and the scientific research community.
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This group will require access to verified and validated data sets and to data summaries. Its
members will also require access to an index of available EMAP and historical data. Documented
summaries with interpretation and graphic displays will be most useful to this user group.
(5) Legislators, environmental managers, and the public.
This group will require access to summarized, interpretive data through published reports or
specific retrievals.
10.2.3 Issues
10.2.3.1 Data Integrity and Security
Data base security is essential to help ensure the integrity of the data. Direct access to the
EAIMS will be restricted to users from Level 1 (Section 10.2.2). When data have been verified and
added to the EM AP-Arid data base, these users will be able to access the data for numerous tasks
(e.g., analysis, plotting, and transfer to other computers) from a read-only mode to prevent
accidentally compromising the integrity of the data base. Changes that need to be made to the
EAIMS after data verification will be handled through a procedure formalized for QA tracking
purposes. Access to data will be limited or flagged where the quality of the data are suspect. Other
measures that will be taken to maintain the integrity of the EAIMS include protection against
mismanagement (accidental alteration or destruction), viruses, unauthorized access, and
hardware and software failure. Written procedures will be defined and implemented to handle all
aspects of data base management and security.
10.2.3.2 Data Confidentiality
Other agencies may have data bases that EMAP-Arid needs to access. Agreements may be
necessary, depending on the agency, that ensure the confidentiality of and controlled access to
that data. Such an agreement would apply to use of the data base within or outside of the EAIMS.
The EMAP-Arid resource group does not expect to be using much confidential data, but
information about range sites from the BLM would be one example of such confidential data.
Issues related to EMAP, EPA, and interagency policies on data confidentiality are currently being
studied and addressed (Franson, 1990).
10.2.3.3 Interactions with Other Information Systems
Coordination with agencies that may have or will be collecting information useful to
EMAP-Arid should begin early in the EMAP-Arid research efforts. This early effort will enhance the
sharing of limited funding resources, provide some preliminary information to the research effort,
and result in better information products from both sides in the future.
If EMAP-Arid will be collecting information on certain indicators, but information on these
indicators is available from an existing agency information system, it is important that an interface
to that data be established to help the researchers assess variability in the indicators before new
sampling begins and explain conditions deriving from the interpretive or diagnostic stage of the
research efforts. EMAP-Arid resource group will also make an effort to discuss strategy and design
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with other agencies to enhance their monitoring efforts and to provide a better interface with future
designs of their information systems.
10.3 ARID ECOSYSTEM RESOURCE GROUP REQUIREMENTS
The information to be used by this resource group will be based on synoptic, retrospective,
and traditional field-sampled data. Some of the data will be current, some will be historical; some
will be collected by the EMAP-Arid resource group and some will come from other organizations.
EMAP-Arid has three functional levels of operation: Resource Group Projects, the Resource
Group Program, and the overall EMAP Program. In order to support the needs of these levels, the
EAIMG will provide the following products and services:
Resource Group Projects:
- field data collection (e.g., data loggers, bar code readers, communications),
- field user support and training,
- sample tracking,
- analytical laboratory data collection,
- QA/QC analysis and reporting,
- data transfer from other agencies, and
- configuration management;
Resource Group Program:
- handling and analysis of remote sensing data,
- access to EAIMS,
- data base security,
- data confidentiality,
- data base management,
- documentation,
- data integration and analysis,
- presentation and reports,
- archiving and backup of data, data base, and supporting documentation,
- EAIMS user training and support, and
- communications;
Overall EMAP:
- data base transfer and access,
- integration of data from multiple resource groups,
- data base cataloging, and
- archiving and backup of data.
The EAIMS will be developed by addressing the requirements that users can currently
identify. Its structure and management will be designed, given current technology, to allow for
changes in future requirements without a significant redesign of the system. An overall design for
the EAIMS and for the required management procedures will be developed before the field or
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analytic activities begin. Actual implementation of the EAIMS will take place in stages. A beta-level
information management function for a given activity will be available for use at the time of the pilot
test. Refinements will be made to the beta-level function based on the results of its operation
during the pilot study. This function will then be incorporated into a more permanent operational
mode for the EAIMS. Currently, identified requirements for the EMAP-Arid I MS are described in the
following subsections.
10.3.1 Data and Sample Collection, Transfer, and Tracking
This aspect of the EAIMS will be in place prior to initiating field activities. A sample data base
will be developed that includes information relevant to each sampling site to ensure that samples
and field data are collected at the proper sites. The EAIMG will monitor the transfer of data from field
collection, through laboratory analyses, and to the EAIMS.
Field data will be entered directly into portable field data recorders. These data include direct
field measurements (e.g., water conductivity) and site information (e.g., site identification number,
latitude/longitude, state and county, and classification code). Procedures will be in place to
automatically verify the data as described in Section 10.3.2. Field data will be downloaded from
these recorders in established time frames for further verification before they are included in the
EAIMS.
A sample number will be assigned by the field sampling coordinator and entered into the
EAIMS prior to the sampling event. All sample labels will be produced prior to the sampling event.
This procedure will ensure that all samples are properly identified and tracked.
When samples are transferred to a laboratory, a record of the exchange will be entered into the
EAIMS. A manifest of all samples collected will be produced and delivered with the samples. A
central log in the EAIMS containing the identity of each sample will track the status of all analyses
and results for a given sample. A flag will automatically be set when all the expected analyses for a
sample are completed. Bar codes on sample and shipping labels will be used where possible to
facilitate sample tracking and to reduce transcription errors. Laboratory data will be submitted to
the EAIMG in established time frames for QA processing before they are included in the EAIMS.
The EAIMS will keep track of the status of records received, processed, and still outstanding
for each EMAP-Arid sampling site. The addition of verified analytical results to the EAIMS will
indicate the completion of sampling and laboratory analysis for an arid ecosystem site.
10.3.2 Quality Assurance
Quality assurance for research data bases includes procedures to ensure that data entered
into the system are of high quality. A program of the magnitude of EMAP requires utmost
confidence in the validity and integrity of the final data bases.
Ensuring quality data begins with identifying sources of error in the data base system. There
are essentially two general types of errors: (1) incorrect information; and (2) missing, incomplete,
or nonretrievable information (Kanciruk et al., 1986). Examples of errors of the first type include
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typographical errors, incorrect plant species identification, and inaccurate instrument calibration.
Although all errors cannot be completely eliminated through data management protocols, the
potential for including incorrect information in the data base can be reduced. The EMAP results will
not be accepted without the backing of appropriate quality levels.
Where appropriate, data will be directly entered into portable data recorders. The principal QA
advantage of direct data entry is that data are computer scanned as they are entered for correct
type and range to prevent typographical errors. Corrections can be made immediately on site. The
EMAP-Arid data dictionary will contain ranges of acceptable values for each variable measured.
Data that are suspect based on these ranges, but are accepted by the computer, will be
automatically assigned a data quality flag. The flagged data will be reviewed at a later date on a
case-by-case basis. These data will then also be used to evaluate the appropriateness of the
ranges used in the data dictionary. Sample labels will include bar codes to facilitate accurate
sample number entry and identification in the field.
The second type of error is the omission of important information relating to a legitimate data
value (Kanciruk et al., 1986). Such pieces of information, called data qualifiers, assist in the correct
interpretation of data values. Standard operating procedures will document the use of field
computer systems and portable data recorders for entering field data. Again the use of direct data
entry into portable recorders provides a QA advantage. Pertinent information that has not been
entered can prevent entry of information for the next sample. Data qualifier flags can be assigned
based on the nature of the information. For example, flags will be assigned when data were
gathered without following the proper protocols. Default flags may be assigned based on known
data (e.g., sensor calibration). Qualifying information will be anticipated and a structured system
for recording and retrieval of this information will be developed. The system will be designed with
flexibility to allow for the inclusion of unanticipated qualifiers identified and added in the field.
Errors may arise in the transmission of data sets. QA protocols will be developed to ensure
proper data transmission. These data files may be verified by duplicating the transmission and
comparing the transmitted files.
Verification reports will be developed that summarize the data and highlight the flagged and
qualified data. The reports will be reviewed by personnel with adequate specific knowledge to allow
them to make decisions about whether to accept, modify, additionally flag, or reject data values
(Kanciruk et al., 1986). Whereas verification examines individual data values, validation evaluates
the entire data set using selected graphical and statistical techniques. Validation of a data set is the
next step in the QA process to ensure that the data are acceptable for data summaries or analyses
used to answer EMAP questions or for transfer to any other user. Figure 10-1 illustrates the data
flow through this process.
Quality assurance and information management staff members will work closely to develop
an information management system that prevents corruption of data throughout all phases of
activities. QA measures will be applied during field data collection, data transfer, sample tracking,
data entry and verification, data validation, data analyses, archiving and backup, and configuration
management.
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Field Data
Analytical
Lab Data
Synoptic
(Remote
Sensing Data)
Retrospective
Data
External
Data
Preliminary
Data Processing
o
oo
Raw Data
Base
QA/QC
Processing
Verified
Data Base
EAIMS
Archive
Analysis and
Processing
CIS and
Research
Analysis
Validated and
Enhanced
Data Base
EAIMS
Other Users
EMAPIC
(EMSL-LV)
Figure 10-1. EMAP-Arid information data and flow.
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10.3.3 Data Management. Analysis, and Reporting
The EAIMS will be developed and maintained on the network of computers resident at the
Desert Research Institute (DRI) facilities. The data base management system (DBMS) for
EMAP-Arid will be ACCELL/UNIFY 2000, which is currently available on the DRI system. At the
point where an EMAP relational DBMS is available on the computers at the EPA Laboratory in Las
Vegas, Nevada (EMSL-LV), an assessment and decision will be made about converting the EAIMS
to that system. Until that time, the design of the EAIMS will be made as consistent as possible with
what would be expected at EMSL-LV with such a DBMS. The design of the EAIMS will also take into
account any design decisions made for an EMAP data base at EMSL-LV.
Figure 10-1 describes the information and data flow for the EAIMS. Data exchange interfaces
will be developed between the DBMS, geographical information systems, and other tools available
for data analysis. The Statistical Analysis System (SAS) is an industry standard statistical package
capable of performing both simple and complex data analysis and modeling. Since SAS is
available on the EMSL-LV system, it will serve as one of the statistical tools to be used by the
EMAP-Arid researchers. Other tools will include software that will be available for the PCs or for the
networked computers at DRI.
A critical requirement of the EMAP-Arid resource group is the ability to generate maps and
perform geographically based analyses. Spatial analyses will be prepared on a geographical
information system using ARC-INFO software. The EAIMG will work with other information
management groups within EMAP and with other agencies to develop standards and coverages
for GIS applications. Standards will be developed for data accuracy, naming conventions, and
documentation and archiving of completed maps.
10.3.4 Data Documentation, Access, and Archival
All data sets received by the EAIMG will be converted into ASCII files and stored on tape at
specified points in the process to add them to the EAIMS archive (Figure 10-1). Complete
documentation of all data sets stored on these tapes or in the EAIMS is of paramount importance. A
Data Set Index (DSI) will be the principal source of information for these data sets. It will be
contained within the EAIMS and will provide users with important information about the contents of
each data set (e.g., variables measured, site locations). A Central Data Dictionary (CDD) will
document information on standards that have been developed for data sets which are generated
by EMAP and for external data sets which have been processed for incorporation into the EMAP
information system. These standards will include field names, formats, documentation,
acceptable ranges, and codes. A data base backup system will be developed for complete and
rapid recovery of all data and documentation in the event of a hardware or software system failure.
Access to the EAIMS for EMAP-Arid researchers will be available through the DRI network.
Access authorization will be established under the direction of the Technical Director and in
conjunction with the DRI computer systems managers. Where data are deemed confidential,
access will be limited. If the quality of the data are suspect, access may be limited. Data sets that
have served their purpose (e.g., quality assurance validation files) or which are not likely to be used
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(e.g., files containing non-EMAP data that do not meet QA standards) will be maintained on tape
and not in the EAIMS. Information about these data sets will be included in the DSI.
10.3.5 Communications Support
Research and information management efforts for the EMAP-Arid resource group will be
performed on PCs, the DRI network of SUN computers, portable data recorders, and the VAX at
EMSL-LV, with most of the effort focused on the first two. Connections from the DRI network to
EMSL-LV are currently available through dial-up communications. The current path of
communications is shown in Figure 10-2. This current connection allows EMAP-Arid staff to have
access to SAS, the EMAP IMS, other analytical tools, and mail services to other EMAP participants.
Other transfers of data may be facilitated by use of magnetic disks, magnetic tapes, and optical
disks. If necessary, an additional communications system and procedures will be set up to allow
field crews to transfer information from and to or to have access to the EAIMS.
10.3.6 Existing Data
Existing data bases constitute an important source of information for developing the
indicators, designing the sampling program, and interpreting the data (Section 5). Data bases
identified as pertinent to the EMAP-Arid monitoring program will be included in the DSI (Section
10.3.3) with descriptive information for each entry. Data sets that are frequently used will be
converted and added to the EAIMS with an annotation of the origin of the data.
10.4 EMAP-ARID INFORMATION MANAGEMENT CENTER
Data processing activities for the EMAP-Arid resource group will take place primarily in the
EMAP-Arid Information Management Center (EAIMC) at the DRI facilities as described in sections
10.3.3 and 10.3.4. In time, a secondary center for these activities will be the Computing Center at
EMSL-LV. The EAIMC staff will be responsible for:
designing and implementing an information management system which will meet the
needs of the users of EMAP-Arid ecosystems data;
interfacing the EMAP-Arid information management effort with the EMAP central
information management effort;
interfacing the EMAP-Arid information management effort with the efforts of other
EMAP resource groups;
establishing information management standards and procedures for EMAP-Arid
activities;
maintaining a comprehensive EMAP-Arid data inventory, data dictionary, and sample
tracking system;
maintaining and disseminating summary information;
supporting data processing requirements of the field crews;
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Desert
Research
Institute
Reno
56KB
University of
Nevada at
Reno
EMSL
-LV
Tl
Desert
Research
Institute
Las Vegas
56KB
University of
Nevada at
Las Vegas
San Diego
(Internet)
Figure 10-2. EMAP-Arid DPI connections to EMSL-LV.
-------
coordinating communications support; and
establishing liaisons with appropriate data management personnel in other agencies to
arrange for cooperative information exchange.
When EMAP-Arid is fully implemented, the EAIMC will be supported by professional staff with
responsibilities as indicated below.
EMAP-Arid Information Manager: Senior information management staff member who
directs the EAIMC and is responsible for planning, coordinating, and facilitating information
management activities. The Information Manager reports to the EMAP-Arid Technical Director and
is responsible for ensuring that data and information collected are properly captured, stored, and
transmitted. As an executive member of the IMC, the Information Manager serves as a liaison
between EMAP-Arid and the Information Management Director, representing the needs of the Arid
Ecosystems Resource Group.
Data Base Administrator: Responsible for assembling, documenting, and administering
the EAIMS described in the EMAP-Arid information management documents and for helping
coordinate the tasks of the following staff members.
Geographical Information System Programmer: Responsible for implementing and
maintaining the GIS portion of the EMAP-Arid information management requirements and for
interactions with the EMAP GIS support staff at EMSL-LV.
Programmer: Responsible for development of programs to manage data collection,
tracking, and dissemination including data entry screen design, reports, sample tracking, and
analysis.
Data Clerk/Librarian: Responsible for documenting and archiving EMAP-Arid data sets,
documenting and archiving other pertinent data sets received by the EMAP-Arid resource group,
preparing data for transfer by tape or diskette to other agencies, and preparing and reviewing
routine reports on data QA and day-to-day processing.
Technical Support: Responsible for the installation and maintenance of all hardware,
software, and communications.
The EMAP Information Management Program support structure includes the following:
EMAP Information Management Technical Director: Responsible for the development,
implementation, and administration of the overall EMAP Information Management Program.
EMAP Information Management Committee: A coordination and advisory committee
responsible for providing insight, recommendations, and guidance to the Information
Management Director on the information management requirements and activities for EMAP.
EPA OIRM and OARM: EMAP personnel will work with OIRM and OARM to ensure that the
EMAP Information Management Program is properly planned and coordinated with the Agency
program and that EMAP resource requirements are conveyed through the appropriate channels.
10-12
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10.5 IMPLEMENTATION STAGES
This section summarizes the phased approach to implementation of the EMAP-Arid
Information Management Program for FY91 through FY96. The EAIMC will be considered to be in
operational status when basic information management systems are in place and initial data
collection activities have begun. The plans and operations are subject to change as the program
develops.
Activities for FY91 will focus on staffing of the EAIMC and start-up of the EAIMS. EMAP-Arid
will make use of existing resources (staff, hardware, and software) to the extent possible, and will
only acquire additional resources as necessary.
The EMAP-Arid Information Manager will assess user needs and produce plans, as required,
for the EAIMC activities and will begin design of the EAIMS. Initial prototypes of the field system,
communications, and sample tracking data bases will be developed to be ready for the pilot
studies in 1992. Prototype data bases for the field and analytical activities for these pilots will also
be created. These data bases will be used in preliminary data validation and verification. External
data will be integrated with EMAP-Arid data as required.
The activities for FY92 will expand the areas of development and operation. The prototypes
developed in 1991 will be tested through the pilot studies and modified as necessary. This
procedure will allow for a more concrete implementation of the information management
procedures and the EAIMS. A core EAIMC staff will be in operation. External data will be integrated
with EMAP-Arid data and analyses will be initiated. Procedures for the inventory and archival of
data sets in the EAIMS will be defined and implemented.
In FY93, the EAIMC will expand human and hardware and software resources as required.
The EAIMS will be fully operational. Extensive use will be made of external data.
In FY94 through FY96, the EAIMC will continue to enhance the core systems as well as the
design of the EAIMS according to the needs of the resource group. More advanced applications
will be implemented as required.
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11.0 DATA ANALYSIS
11.1 APPROACH TO DATA ANALYSIS
The EMAP-Arid study is designed to provide assessments of ecosystem status and trends
on a regional scale. Arid ecosystems are known to respond dynamically to short-term climatic
fluctuations, such as variations in annual precipitation and temperature. Therefore, the detection
of trends and ecosystem impacts caused by stressors requires the integration of meteorological
data. In order to accomplish this need, three primary data bases will be collected and analyzed:
(1) regional and finer scale synoptic satellite images (e.g., SPOT, AVHRR); (2) hexagon-based
measurements of indicators and stressors; and (3) meteorological data. Additionally,
surface-interpolated data layers will also be used in assessments. This will be the primary source
of geographic data on air pollution and deposition. These composites are similar to climate
information and are considered as the same type of source. In essence, the scientists will be
working with time series and numerically removing or controlling data for climatic influences in
assessing ecosystem status and trends. The techniques for performing this style of transfer
function analysis have already been developed under the rubric of the general linear model (GLM).
The linkage of the three primary data bases is essential to conducting analyses that will
achieve EMAP objectives. The three general types of data bases are described below.
1) Synoptic satellite data available from the AVHRR instrument extends back to 1978. A raster
data base depicting vegetation vigor (a vegetation index) and surface albedo will be
constructed covering the period 1979 to present. The AVHRR data will be geometrically
corrected in order to register it to the map base selected by EMAP. The EMAP-Arid group
proposes to use one AVH RR scene every month for the period from 1979 to 1989. Beginning in
1990, we propose to add one scene every two weeks. This procedure will produce 186 scenes
covering a 15-year time span to the point of EMAP-Arid implementation (1993). This record
will be updated with the addition of new satellite data approximately every two weeks during
the period of EMAP-Arid field operation.
2) Ground-based sampling of indicators and stressors will be performed in EMAP Tier II
hexagons. Subsamples of Landsat Thematic Mapper imagery will be obtained for use in
hexagon-level landscape measurements and for potential calibration with the ground-based
measurements. These data records will begin to be collected in limited areas as early as 1991
with the inception of a pilot project. With full implementation (1995), the data record for
ground-based indicators will be initiated at all Tier II hexagons. A refined list of indicators will
be determined via additional workshops.
3) Meteorological data from the existing national system will be utilized in the analysis of the
satellite data and ground-based indicators. The meteorological data for factors such as
temperature, precipitation, and the Palmer Drought Severity Index (PDSI) will be contoured to
11-1
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produce raster images, registered to the selected map base. The contouring will incorporate
digital elevation data (from the USGS) in the development of transfer functions for use in
interpolating values across the raster grid. Other data layers such as air pollution and
deposition will be of this type.
Historical Climate Network data from 1895 to the present are available from the Department
of Energy, Oak Ridge National Laboratory. These data have been subjected to extensive
evaluation and corrections to ensure that the information is of the highest quality. Variables include
monthly minimum, maximum, and mean temperature, total monthly precipitation, and the Palmer
Drought Severity Index (PDSI).
The PDSI, described fully by Palmer (1965), will be used as an index of meteorological
drought, as opposed to a specific biological or hydrological measure. Thus, drought will be
evaluated as a meteorological anomaly characterized by a prolonged abnormal moisture
deficiency, with its severity depending on the duration and magnitude of the abnormality.
The definition of drought used in this study is taken from Palmer (1965:3). He defines a
drought period as "an interval of time, generally of the order of months or years in duration, during
which the actual moisture supply at a given place rather consistently falls short of the climatically
expected or climatically appropriate moisture supply. Further, the severity of drought may be
considered as being a function of both the duration and magnitude of the moisture deficiency."
The empirical method adopted by Palmer for dealing with the availability of water from the
soil is based on dividing the soil into two layers. The upper surface layer is assumed to retain 1
inch of moisture at field capacity. Evapotranspiration is assumed to take place from this surface
layer until all the available moisture in it is removed. Only then is moisture removed from the
underlying layer. It is further assumed that no recharge takes place in the underlying layer until the
surface layer is brought to field capacity. Available water capacity in the lower layer depends on
the depth of the effective root zone and the soil characteristics of the area. Water loss from the
underlying layer depends on initial moisture content, the computed potential evapotranspiration,
and the available water capacity of both layers. It is also assumed that no runoff occurs until both
layers reach field capacity.
Estimates of evapotranspiration require a realistic value for the available water capacity of the
soils in an area. This varies widely from one soil to another, but Palmer surmises no more so than
microclimate and suggests using a value more or less representative of the area in general. The
PDSI class intervals are given in Table 11-1.
The EMAP-Arid meteorological station grid will be comprised of the Historical Climate
Network (HCN) data supplemented by data from operating climate stations in areas with sparse
HCN coverage. When necessary the PDSI will be calculated on a monthly basis for the
supplementary stations where it is not routinely calculated. Existing software for PDSI
hydrological accounting computations has been extensively tested.
These three data types need to be linked into a single display and analysis system. Results
depicting the status and trends in the various ecosystem elements need to be summarized and
11-2
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TABLE 11-1. PALMER DROUGHT SEVERITY INDEX CLASS INTERVALS
PDSI
*»*
^
3.00 -
2.00 -
1.00 -
0.50 -
0.49 -
-0.50 -
-1.00 -
-2.00 -
-3.00 -
<
VALUE
> 4.00
3.99
2.99
1.99
0.99
-0.49
-0.99
-1.99
-2.99
-3.99
C-4.00
CLASS
Extremely wet
Very wet
Moderately wet
Slightly wet
Incipient wet spell
Near normal
Incipient drought
Mild drought
Moderate drought
Severe drought
Extreme drought
reported using several designated geographic boundary patterns. Examples of geographic
boundary options include state, congressional district, National Forest, or ecological unit (e.g.,
grasslands of central California).
To our knowledge, no software system has been developed to accomplish these objectives
in an effective manner. It will be extremely valuable to have a data analysis system which is
optimized for the visual display and analysis of the three data bases. The EMAP-Arid resource
group proposes to develop the required software system. The functioning of the software system
is described in the following section.
11.2 TEMPORAL IMAGE PROCESSING SYSTEM
Traditional image processing systems are optimized for the display and analysis of single
dates of imagery. The utilization of such systems for performing analyses of multiple dates of
imagery involve subtracting one date of imagery from the other to produce a change image. For
projects involving the analysis of two or three dates of imagery, such a system is adequate. To
perform an analysis of a long series of images (up to hundreds of dates) a new software system
will be required. The EMAP-Arid resource group proposes to develop such a system, optimized to
perform the functions required by EMAP. We call this software the Temporal Image Processing
System (TEMPIST). A prototype of this software is already under development as a collaborative
project between DRI and WRJ Software Services.
TEMPIST is designed to provide a display and analysis tool for long-term time series of
images and ancillary data. The basic display screen layout is shown in Figure 11-1. An example
formulated with hypothetical data is presented in Figurel 1- 2. The system will provide a display
11-3
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Panel A
AVHRR
Color Image
cursor
Panel C TEMPORAL CROSS SECTION
Panel B
Magnified Color
Image
Panel D Temporal
Spectra
Time Series Curves
I I I I I I 1 M I I I I I I I I I I I
1989 1990 2000
TIME
Figure 11-1. The basic display screen layout for the Temporal Image Processing System.
-------
ANNUAL STATISTICAL SUMMARY
t
ANALYSIS OF STATUS AND TRENDS
t
DESIGNATED
GEOGRAPHIC
AREAS
TIME SERIES ANALYSIS
t
TEMPORAL IMAGE PROCESSING SYSTEM
TEMPIST
1993
1979
1993
1979
AVHRR
DATA
CONTOURED
METEOROLOGICAL
DATA
1993
EMAP
HEXAGON
DATA
Figure 11 -2. Conceptual model of the Temporal Image Processing System analysis.
11-5
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and analysis link between the three primary data bases. To operate, the three data bases must be
spatially registered to a common geographic base. This spatial registration will be performed
outside of TEMPIST using the functions available on conventional image processing systems.
The TEMPIST workstation will consist of a microcomputer with a large hard disk (more than
500 megabytes), and an 8-bit color monitor (1024 by 1024 resolution) for displaying data from the
three databases. The initial software system will be capable of displaying and processing 300 data
layers. These layers could be 300 AVHRR vegetation index images covering a 20-year period. The
full databases will be stored on the hard disk. At any one instant TEMPIST will have a single line of
data from each data base in memory (RAM). The following is a description of the four panels
present on the TEMPIST display.
Panel A: Color Image. Size: 700 by 700 pixels. This panel will be used to display a color image of
the work area. It is anticipated that this panel will be used most frequently to display a color image
from a single AVHRR scene. However, it will be possible to specify any three image layers from the
data bases for display as a color image in this panel. Using AVHRR Global Area Coverage (4-km
pixels) this panel would provide coverage over an area 2,800 by 2,800 km, covering the entire
western United States. With the AVHRR Local Area Coverage (1.1-km pixels), the screen would
cover an area 770 by 770 km. The locations of EMAP sampling hexagons will be displayed in the
graphics overlay plane. In the normal mode of operation the cursor is confined to this panel. The
cursor location in panel A provides the required locational data for the displays in the other three
panels.
Panel B: Magnified Color Image. Size: 300 by 300 pixels. This panel will be a magnified version (3
times or more) of the color image, centered on the cursor location within panel A.
Panel C: Cross Section. 700 by 300 pixels. This panel provides a cross section of the data bases at
the line of the cursor located in panel A. The most recent data is at the top of the cross section and
the oldest data is at the bottom of the cross section. This panel can be interactively switched to
provide cross sections of the AVHRR vegetation index data, AVHRR albedo data, or the
meteorological data.
Panel D: Time Series Line-plots. Thetime series of data from the pixel designated by the cursor on
panel A will be plotted here. The X axis will be time. The default curve to be displayed will be the time
series from the data base selected for the cross section (panel C). Time series curves from the other
data bases can be added to the display. In addition, when the cursor is located on an EMAP
hexagon, it will be possible to display the time series curves for the various indicators or stressors
being measured at that hexagon.
There are two output modes from the above described display. The entire screen (panels A
through D) can be written to a file for printing on film or paper. The second output form is a digital
file for the time series curves present in panel D. This output from panel D will be used to develop
transfer functions via the time series analysis module (TSAM) described below.
11-6
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11.2.1 Time Series Analysis Module
Three possible scenarios for calibrating AVHRR and long-term meteorological data, such as
the PDSI, depend on whether the imagery is regarded as the dependent or independent variable
in a transfer function analysis.
In the first situation, the digital number (0 - 255) of each pixel in the image provides values of
the dependent variable and meteorological data obtained at weather stations provide values for
the independent variable. The locations of weather stations have a resolution of one pixel. The set
of values for the dependent variable is much larger than the set of values for the independent
variable because each 1 by 1-km pixel has a digital number, but meteorological data are only
available from a relatively sparse network of weather stations.
Pixel digital numbers at meteorological station locations from one to many images that
represent an array of meteorological conditions (dry to mesic) are calibrated with the temporally
equivalent meteorological datafield(s). If the calibration equation generated with the training data
set verifies satisfactorily with a test data set, it can be applied to the long-time series of
meteorological data to retrodict a long-term set of synthetic images. The merit of this approach is
that long-term estimates of central tendency and variability can be obtained for pixels or groups of
pixels in the vicinity of meteorological stations. Knowledge of the moments of the probability
distribution of pixel values at given locations allows an immediate probabilistic evaluation of newly
acquired images. For example, it allows the determination of locations with significant (e.g., alpha
= 0.05) departures above or below the mean and, similarly, of areas with no major changes.
The second context in which the imagery provides values of the dependent variable and the
meteorological data provide values of the independent data offers a methodological approach to
control for, or numerically remove, the effects of climate on images from two or more different time
periods. In this situation, separate calibration equations are created between imagery and
meteorological data for each time period. The sets of imagery residuals remaining after each
calibration represent the original image with the effects of climate removed. The residual images
can then be subjected to change analyses to determine the effects of other stressors, such as
grazing, fire, off-road vehicle use, and various land management practices.
In the third type of calibration between imagery and meteorological data, the imagery digital
numbers comprise the values of the independent variable and the meteorological data are the
dependent variable. If a viable calibration between the two data sets can be achieved, the
equation can be applied to the pixel dn's to generalize meteorological conditions over the
complete scene at very high resolution (1 by 1 km). This type of information is extremely valuable
in watershed studies and wildfire management.
The calibration equations defining relationships between imagery and meteorological data
are based on the general linear model. In the simplest situation one dependent variable is
regressed against one independent variable, while in more complex transfer function analyses a
set of dependent variables is calibrated with a set of independent variables. In situations where a
set of dependent or independent variables is available some type of data reduction operation,
11-7
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usually principal components analysis, must be performed. Creating a set of orthogonalized
(uncorrelated) variables from a set of correlated variables is an efficient procedure for removing
the complicating effects of multicollinearity in linear model analyses. It is also scientifically
parsimonious because a smaller set of principal components normally can be used to represent
the variability within a data set. In addition, this procedure allows transfer functions to be based on
more degrees of freedom than equations utilizing the original set of correlated variables.
11.2.2 Designated Geographic Area Module (DGAM)
After performing the numerical analysis to remove short-term climatic influences from the
data, the next step towards the final output will be to integrate the data within designated
geographic boundaries by using a designated geographic area module (DGAM). These
designated boundary areas will be the reporting units from EMAP-Arid. It is anticipated that the
data will be segmented in a variety of ways, for example, by:
EPA Regions
Congressional Districts
States
Counties
National Forests
BLM Land Management Units
Ecological Units
Physiographic Provinces
The outlines for these various geographic units will be stored in TEMPIST and used like
"cookie cutters" to retrieve data for use in the analysis of status and trends. However, the
field-based probability sampling will represent particular predesignated Tier 1 resources. This
makes it difficult to reallocate (poststratify) to some of these units and to integrate point data that
represent predefined units.
EMAP-Arid has proposed a tiered sampling design, with most of the measurements at the
top tier made via remote sensing techniques and most of the measurements at the bottom of the
tier obtained with intense field-based sampling procedures. It will be necessary to integrate
observations made on nominal, ordinal, interval, and interval-ratio scales of measurement within
and among tiers of the sampling design. Because the basic data and measurement scales
constitute the building blocks of more sophisticated numerical operations, they should be
thoroughly investigated at the earliest stages of analysis.
The most cogent reason for a thorough evaluation of the raw data is that the most frequently
used statistical techniques are parametric, that is, they rely on assumptions about the
distributions of the variables being analyzed. Single variables are usually assumed to be normally
11-8
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distributed, pairs of variables are assumed to have bivariate normal distributions, and sets of
variables are expected to have multivariate normal distributions. In our analytical strategy
variables will be tested for normality in order to determine whether additional statistical tests
should be performed with parametric or nonparametric techniques. Nonparametric techniques, in
contrast to the parametric alternatives, do not require as rigorous distributional assumptions.
Because it is virtually impossible, and unnecessary, to obtain all of the information contained
in the distribution of a variable, several descriptive properties that summarize its most important
attributes must suffice. The most frequently used types of information pertain to a distribution's
location and dispersion. Measures of location include the mean, median and mode, and various
quartiles, while the most common measure of dispersion is the variance (or standard deviation).
The other two moments that we will be concerned with are skewness and kurtosis.
The EMAP-Arid resource group will use cumulative distribution functions (CDFs) to analyze
the status and trends of ecosystems within designated geographic areas. CDFs were chosen
because they encapsulate the central tendency (e.g., mean, median) and extreme values for all
data categories in an easily interpreted graphical format. For example, consider CDFs of the
Palmer Drought Severity Index for normal, dry, and mesic years. Figure 11-3 depicts a simulated
CDF for 1,000 PDSI locations for a particular year, using a mean of 0 and a standard deviation of 1
(refer to Table 11-1 for the PDSI class intervals). Figure 11-4 depicts a simulated CDF for the same
number of locations using a mean of 2.5 and a standard deviation of 0.7. Finally, Figure 11-5
shows a simulated CDF for the same locations, but for a dry year, with a mean of -3.0 and a
standard deviation of 0.5. These CDFs graphically capture the main features of the distributions
for each year's drought conditions over space.
Of concern are the types of probability distributions assumed for each variable because
those distributions partially determine which statistical tests can be used to analyze and visually
portray the variables. While parametric tests are generally more powerful and more widely applied
than the nonparametric alternatives, their assumptions may not always be met, especially with
small sample sizes. For these reasons, especially for graphical summaries of single variables,
exploratory data analysis (EDA) techniques will be employed.
The EDA techniques emphasize the use of visual displays and robust and resilient numerical
summaries. The basic philosophy underlying EDA is one of searching a data set using a number
of alternative techniques in order to maximize what can be learned. In contrast to a more
traditional statistical approach, EDA does not impose a hypothetical pattern on the data; it lets the
pattern emerge from the data. It also emphasizes the re-expression of variables that are not
normally distributed or might be expressed better on a different scale. The EDA, like more
traditional parametric approaches, places a premium on analysis of single variables in an attempt
to understand the central tendency, variability, and shape of the distribution of each variable.
Techniques employed include the stem-and-leaf display, box-and-whisker plots, and resistant
number summaries. Resistance means that the measures are not highly sensitive to departures
from normality, and thus they are suitable indicators of location and dispersion for a wide variety of
distributions. In addition to numeric summaries for single variables, box-and-whisker plots will be
used to portray the major characteristics of a distribution. These plots have a higher information
11-9
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1.0
Q
cc
LL
0,8 I-
Q 0.6 r-
LL
O
0.4 h
0,2 I-
0.0
-4-20246
PDSI
Figure 11 -3. Cumulative distribution function for PDSI data.
11-10
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1.0
tt
Q
o
z
Q
cc
0.8 -
0.6 -
0.4 -
0.2 -
0.0
0 1 2
456
PDSI
Figure 11 -4. Cumulative distribution function for PDSI data.
ii-ii
-------
I
Q
LJ_
O
Q
O
<
DC
LL
1.0
0.8 h
0.6 h
0.4 h
0.2 I-
0.0
-5
-4
-3
-2
_ -i
PDSI
Figure 11 -5. Cumulative distribution function for PDSI data.
11-12
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content than simple mean and standard deviation limits and provide detail when it is often most
neededi.e., when the tails of a distribution contain extremely large or small values.
The use of the techniques outlined above as a prelude to constructing more complex
relationships is necessary for several reasons. First, they assist in determining which variables
meet the distributional assumptions of the parametric techniques (preferred over nonparametric
techniques). If the variables do not meet the criteria for parametric statistics, nonparametric
alternatives may be appropriate. Second, they make it easier to define the relationships between
pairs of variables. And third, they make it possible to determine, and untangle, the complex
relationships that can exist among several variables. Ultimately, all of these techniques help to
identify a set of stressor and response variables that are useful in monitoring environmental
processes. However, nonreductionist display and interpretation of data (e.g., suite of indicator
measurements) will also be critical to the assessment process.
Establishing a relationship between one or more interval- or ratio-level independent
variables and one or more interval- or ratio-level dependent variables can vary in terms of
complexity. At the simplest level, bivariate regression can be employed and, when more than one
independent variable is involved, a multiple regression scheme of one form or another can be
used. If the independent variables are intercorrelated, a modification to the normal approach may
be required. A principal components analysis can be used to create a new set of orthogonal
variables that are linear combinations of the original variables. Or, in multiple regression
situations, ridge regression or latent root regression may represent a viable alternative. When
there is a set of dependent variables in addition to the set of independent variables, canonical
regression with the original variables or with the principal components of each data set is required.
A final graphical summary product, useful for portraying a cumulative spatial view of how the
state of the environment might be portrayed on an annual basis, is a map of the Dryland Risk Index
(Figure 11-6). The Dryland Risk Index is a composite scale, not unlike the Dow Jones Index, that
includes variables for which annual information exists on a reasonably fine spatial scale. The
variables comprise observations made at several different tier levels. All of the variables may be
represented as polygons, lines, or points within a GIS.
At the highest tier, a vegetation density index derived from AVHRR imagery with a 1.1 -km
spatial resolution is derived. Another layer represents values of the Palmer Drought Severity
Index, described earlier, calculated for about 2,000 spatial locations in the western United States.
A third layer considers grazing pressure. Another layer, reflecting a proxy of public access to land,
could be comprised of different road classifications combined with distance to population centers
of different size. Additional data layers may be defined as appropriate arguments of relevance are
made for their inclusion. In the first analytical attempt to assemble this index, variables will be
normalized to zero mean and unit variance. Because some of the variables are not orthogonal to
one another, some type of data reduction scheme will also be employed. And, at some point,
weighting factors for the different layers may have to be introduced. While the Dryland Risk Index
will require a fair amount of exploratory research in terms of how it should be assembled, the end
product of an environmental risk assessment index would be extremely valuable to EMAP clients.
11-13
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Figures 11 -6a thru 6g.
Drylands Risk Index and the indices used in its formulation.
11-14
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The Status of Desertification in the
United States
Slight
Moderate
Severe
Very Severe
(Dregne, 1977)
-------
Palmer Drought Severity Index
TEN YEAR COMPOSITE
Category Score
Unusually Moist 1
r^ Moist 2
| " | Near Normal 3
Moderate Drought 4
Severe Drought 5
-------
Maximum Greenness Index
GROWING SEASON
TEN YEAR CHANGE, %
EH
m
Category
>1%
Oto 1%
1 to 0%
-1 to -2%
<-2%
Compos fte
Score
1
2
3
4
5
-------
Grazing Index
INTENSITY OF DRYLAND GRAZING
Hi
CD
SB
Category
Light
Low
Moderate
High
Highest
Composite
Score
1
2
3
4
5
Intensity of Dryland Grazing =
Max. Greenness Index
-------
Soil Carbon Nitrogen Ratio
TEN YEAR CHANGE,%
mm
cn
I
Category
> 2%
1 to 2%
-1 to 1%
-1 to -2%
<-2%
Score
1
2
3
4
5
-------
Flammable Exotics
PERCENT OF
Category
IB! 0-19%
20-34%
[ _J 35-49%
iBi 50-64%
HH * 35%
COVER
Score
0
1
2
3
4
-------
Drylands Risk Index
A COMPOSITE OF FIVE INDICES
Composite
Category Score
Lowest Risk 0-4
WW( Low Risk 5-9
I | Moderate Risk 10-14
Substantial Risk 15-19
Extreme Risk 20-24
-------
12.0 INTEGRATION FOR ARID ECOSYSTEMS
12.1 DEFINITION OF INTEGRATION
The Science Advisory Board has determined a need for the EPA to monitor the Nation's
ecological resources to assess their condition, look for changes and trends, and examine possible
causes for these changes. The EPA cannot do this independently and has envisioned EMAP as an
interagency program to accomplish these goals. Thus, one aspect of integration is the
coordination of agencies involved in environmental monitoring. Beyond the political realities within
which EMAP must operate, there are issues of integration in the scientific realm. Ecology is
primarily a science of integrating patterns and processes of natural systems and EMAP must
operate within this ecological perspective to succeed in its goal of monitoring the Nation's natural
resources. Three levels of activities related to integrationpolicy, program, and technicalhave
been identified within EMAP.
Policy integration is the process of evaluating and coordinating the needs of various EMAP
clients and constituencies and of ensuring that those collective needs are addressed by the
respective EMAP components. Several activities must occur to achieve policy integration. The
EMAP constituent groups, the appropriate contacts within each group, and the needs of each
group must be identified. Interactions with constituent groups will be facilitated by developing a
communication strategy that includes identification of effective presentation methods.
Program integration is the process of coordinating EMAP with existing monitoring and other
environmental programs and of communicating EMAP goals to those charged with exploring the
potential for consolidating programs in order to achieve a more comprehensive, efficient national
monitoring scheme. Program integration will involve the identification of existing environmental
monitoring programs and historical databases. A consistent strategy to determine the relevancy of
existing programs and data bases to EMAP should be developed. In some cases, EMAP could
identify modifications to existing monitoring plans that would allow the EPA and a cooperating
agency to consolidate programs to avoid duplication, improve efficiency, and enhance
significance. In cases of cooperation among agencies and institutions, appropriate agreements
will need to be put in place, such as memoranda of understanding and interagency agreements
between governmental bodies and cooperative agreements with not-for-profit institutions.
Technical integration is the process of selecting, analyzing, and evaluating data collected
from the various ecosystems sampled by EMAP, along with data from other sources, to transmit the
resultant interpretation of those data into an environmental policy framework. Technical integration
will facilitate our understanding of the association between anthropogenic and natural stressors on
individual and multiple resources at regional and national scales. Such integration will maximize
the likelihood of detecting changes in overall ecological condition.
The individual resource groups, including the EMAP-Arid group, will cooperate with the
Integration and Assessment Group to achieve the overall goals of EMAP. The Integration and
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Assessment Group will emphasize policy and program integration, while the individual resource
groups will deal primarily with technical integration.
A partial list of agencies and institutions with which EMAP-Arid will interact is presented in
Table 12-1. The details of how EMAP-Arid will accomplish this integration with other EM AP groups,
other EPA programs, and non-EPA programs are discussed in Sections 12.2 through 12.4,
respectively, along with some examples. Further details of the technical integration of EMAP-Arid
into the whole of EMAP are discussed in Section 12.5.
12.2 RELATIONSHIP TO OTHER EMAP GROUPS
The EMAP monitoring tasks have been split among seven resource groups:'Forests,
Agroecosystems, arid ecosystems, Surface Waters, Wetlands, Near Coastal, and Great Lakes.
This partition represents an operational division of the EMAP monitoring tasks rather than an
ecological reality. The condition of arid ecosystems must be interpreted in the context of other
nearby resources. Arid ecosystems will be influenced by, and in turn influence, the condition of
nearby forests, agricultural lands, surface waters, wetlands, and near coastal environments. These
interactions will be explored in more detail in Section 12.5 on Technical Integration.
While each resource group is responsible for a particular aspect of monitoring activities, many
activities are common to each of the individual resource groups. Whenever possible and
appropriate, EMAP-Arid will interact with these crosscutting groups, the responsibilities of which
are briefly described below:
Statistics and Design - Coordinate the design and statistical methods to be used in the
individual resource group monitoring programs with the overall EMAP approach.
Logistics - Facilitate field collection of data for each of the individual resource groups.
For further detail see Section 7.
Information Management - Ensure that EMAP data will be available. For further details
see Section 10.
Air and Deposition - Coordinate EMAP activities with those undertaken by the EPA
Office of Air Quality. In particular, existing data on air quality can provide critical
information on stressors to all resource groups.
Integration and Assessment - Ensure coordination among the resource groups in
policy, programs, and technical arenas.
Indicators - Several resource groups may use common indicators. In such cases, the
indicators crosscutting group will ensure that comparable methods are used to
facilitate integration among EMAP resource groups. For further details see Section 5.
Quality Assurance - This crosscutting group will advise all EMAP groups on methods to
achieve total quality management. For further details see Section 9.
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TABLE 12-1. SOME AGENCIES AND INSTITUTIONS WITH WHICH THE EMAP-ARID
ECOSYSTEMS RESOURCE GROUP WILL INTERACT
DEPARTMENT OF THE INTERIOR
Bureau of Land Management
U.S. Geological Survey
U.S. Fish and Wildlife Service
Bureau of Reclamation
National Park Service
Bureau of Indian Affairs
Mineral Indian Nations
Management Service
(e.g., Navajo)
STATE RESOURCE MANAGEMENT AGENCIES
NATURE CONSERVANCY
OTHER EMAP PROGRAMS
ENVIRONMENTAL PROTECTION AGENCY
Program Offices
Regional Offices
Administrator
DEPARTMENT OF AGRICULTURE
U.S. Forest Service
Soil Conservation Service
U.S. GENERAL ACCOUNTING OFFICE
DEPARTMENT OF DEFENSE
- U.S. Army
Corps of Engineers
Land Condition/
Trend Analysis Program
DEPARTMENT OF ENERGY
ParkNet
NATIONAL SCIENCE FOUNDATION
Long Term Ecological Research
(LTER) sites
U.S. CONGRESS
INTERNATIONAL
Commonwealth Scientific and
Industrial Research Organization
(CSIRO) of Australia
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Landscape Characterization - Provide the landscape context for interpreting
on-ground sampling and, in some cases, monitor condition of selected resources.
12.3 RELATIONSHIPS TO OTHER EPA PROGRAMS
Whenever possible, EMAP-Arid will cooperate with other EPA programs in joint monitoring
efforts or collaborative use of data. It is expected that the efforts to monitor air quality and visibility in
our Nation's National Parks, particularly the Grand Canyon, will be prime candidates for such
collaborative efforts. In addition, EMAP-Arid data will form the foundation for cumulative
environmental condition and risk assessments within EPA and other agency regions.
Information obtained from all EMAP activities will be available to the EPA Regional Offices,
EPA Program Offices, and the EPA Administrator for use in monitoring the success of existing
regulatory programs and in developing new regulatory programs to continue the EPA mission to
protect our environment. In these interactions, the EMAP information will provide a foundation for
the ecological risk assessment process as discussed in Section 1.
12.4 RELATIONSHIPS TO NON-EPA PROGRAMS (State, Federal, Private,
International)
Approximately 65 percent of arid lands in the continental United States are publicly owned.
Thus, many state and Federal agencies will be associated with EMAP-Arid. The extent of the
cooperative effort may vary among the different state and Federal agencies. All will likely be
interested in the results of EMAP-Arid; some may be active collaborators in the implementation of
EMAP-Arid. For example, the BLM manages extensive holdings of Federal lands that support
grazing programs. An Interagency Agreement between EPA and the BLM is currently being
developed to ensure through the experience of BLM in arid lands and the monitoring efforts of
EMAP-Arid that rangelands will be a sustainable resource long into the future.
For extensive areas of the Southwest, access is restricted because of the sensitive nature of
the activities conducted there. These are generally Department of Defense or Department of
Energy sites. Examples include the Nevada Test Site, Edwards Air Force Base, Yuma Proving
Grounds, and the White Sands Missile Range. In these cases, special negotiations will be needed
to determine if sampling, either by aerial overflights or on-ground visits, may be conducted on
these restricted access areas. The statistical implication of such constraints on the sampling
design must be addressed.
Interactions with universities and regional research institutes will continue to ensure that the
best expertise in arid ecosystems is used in developing, implementing, and interpreting the results
from the EMAP-Arid program. Additionally, collaboration in developing ecological monitoring
programs for arid resources has been initiated on an international level such as with the Division of
Water Resources of Australia's Commonwealth Scientific and Industrial Research Organization
(CSIRO).
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12.5 TECHNICAL INTEGRATION
Integration of data into ecologically meaningful assessments must occur at many levels. In
particular, it is important to understand and combine both natural and anthropogenic stressors to
make assessments on the condition of ecological resources. Integration begins within a resource
group through the statistical analyses of data and presentation of results. Data analysis is
discussed in detail in Section 11. The data collected in the field will be the result of measuring many
variables. Often measurements of these variables can be integrated into an index or evaluated as a
suite for concordance that has more meaning for ecological interpretation than do the values for
the individual variables taken separately. For example, species composition and abundance can
be combined into any one of a number of different diversity indices that each have particular value
for data interpretation. Additionally, trends in climate help us understand and interpret trends in
biotic and ecological indicators.
Data on anthropogenic factors such as land use and natural factors such as climate may be
combined into an integrated assessment. One example of such an assessment, the Drylands Risk
Index, was obtained through the integration of climate, grazing intensity, greenness, exotic
flammable species, and carbon/nitrogen ratio data layers (see Section 10). The types of such
indices are numerous and many more will be developed as EMAP-Arid begins to collect data from
pilot studies and evolves toward full-scale implementation.
Such indices currently are limited to describing a particular site from which data have been
obtained. To begin to make regional assessments of the condition of arid ecosystems, the data
from many sites must be integrated into estimates of condition. While the presentation of data in
cumulative frequency distributions begins to explain regionwide condition, multivariate statistical
methods will allow more detailed examination of trends in condition and lead to suggestions of
possible cause for a degraded condition (see Section 11). For example, it may be found that
riparian zones in areas of intense grazing are generally in poorer condition than those in areas
where grazing pressure is less. Such findings will begin to allow EMAP to provide scientific
evidence on the ecological soundness of current management practices.
Perhaps no other EMAP program reflects the need for integration among all the resource
groups as strongly as does EMAP-Arid. This need is most easily understood when one begins to
define what specific types of resources fall under the purview of EMAP-Arid. Arid ecosystems can
most simply be defined in terms of average yearly rainfall. By this definition, EMAP-Arid would
encompass much of the area west of the M ississippi. However, some landscapes within this region
do not fit well into the standard concept of arid ecosystems. For example, pinyon-juniper
communities occur in arid ecosystems but contain enough trees to conceptually qualify as a Forest
component, although the species are of little commercial value. Many otherwise arid ecosystems
have been irrigated to grow various crops and so function as Agroecosystems. EMAP-Arid will
cooperate with the other EMAP resource groups in joint monitoring efforts to achieve a more
comprehensive, efficient national monitoring program.
Because these close ties with other resource groups are best understood in terms of
landscape, EMAP-Arid will have close ties with EMAP landscape characterization. In addition,
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because of the vast areas covered by arid ecosystems, monitoring will most efficiently be
conducted with aggressive use of remote sensing. Many aspects of the condition of arid
ecosystems can be determined from satellite imagery and aerial photography. EMAP-Arid and
landscape characterization groups are currently collaborating on a joint study to refine the
techniques of multistage remote sensing in assessing the condition of arid ecosystems. This study
will help to evaluate the benefits of placing some on-ground sample sites in the watersheds for
which landscape characterization will be providing very detailed classification.
Data from EMAP-Arid can be integrated with those of other associated resource groups, and,
in particular, the Landscape Characterization Integration Activity (EMAP-LC). EMAP-LC will
provide nationwide coverage at coarse detail for qualities such as landscape types, soils,
geological formations, climate, and human population density. For about 10 percent of the USGS
hydrological units, more detailed land cover and land use data will be provided. Within these,
samples will be selected for complete landscape characterization. Some EMAP-Arid on-ground
sampling will occur in the watersheds that EMAP-LC has selected for detailed classification. This
overlap will provide the context for the interpretation of data on arid ecosystems. Landscape
indices such as amount of edge, perimeter to area ratio, patch size, and fractal index can help
associate anthropogenic stresses with the condition of an arid ecosystems. For example, it could
be found that arid ecosystems in relatively undisturbed areas are in better condition than are those
that are becoming fragmented by encroaching urbanization. Or perhaps, arid ecosystems
bordering irrigated agricultural lands where large amounts of fertilizers and pesticides are used are
in poorer condition than those in areas where agricultural practices are less intense and fewer
chemicals are used. These types of interpretations form the basis for risk assessments.
The possibilities for integrating data from all aspects of EMAP are almost endless. Ecological
interactions can occur at various spatial and temporal scales. To detect some interactions, data
must be collected from areas that have the potential to interact ecologically. This fact suggests
some spatial and temporal closeness for data collection. For example, extensive media such as air,
water, and wildlife can integrate the environmental response to stressors over large distances. The
exact degree needed of spatial and temporal closeness of sample sites for integrating data among
resource groups has not yet been determined and will vary with different types of ecological
interactions. However, EMAP-Arid is aware of this limitation in the ability to integrate data and is
considering strategies to answer the question of how close (in both space and time) areas must be
to interact in an ecological fashion. The EMAP-Arid resource group supports the concept of a
regional pilot involving all EMAP resource groups to test the programs ability to provide integrated
assessment.
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13.0 EXPECTED OUTPUTS
This section describes the types of products or outputs expected from the EM AP-Arid studies
over the next several years. The number and timing of products will depend on data collection and
analysis activities which will be a function of budget. A proposed implementation schedule is
highlighted in Section 2.
13.1 DATA EVALUATION REPORTS
A key element of EMAP-Arid is the production of information derived from collected data in a
timely manner and the dissemination of that information to users. Four types of reports are
currently envisioned:
annual statistical summaries;
periodic interpretive reports;
special interest reports; and
scientific articles.
The EMAP-Arid resource group recognizes the need to transform data into useful information
as quickly as possible. Therefore, EMAP-Arid will publish summaries of the preceding field season
surveys within 9 months of data collection. These summaries will provide information on ecological
indicators as well as climate and other factors that will help set an environmental context for that
collection period. Rigorous statistical evaluation of trends and associations between indicators will
not be made in these summaries; these are the subject of periodic interpretive reports. The
objective of these reports is to distribute data to interested parties as quickly as possible. Annual
statistical summaries are likely to be authored with other participating agencies, such as the BLM,
NPS, USFS, and USFWS, thus resulting in one document rather than several summaries of the
same data. An example Annual Statistical Summary is presented in Ball et al. (1990).
At a minimum, interpretive reports will be published following completion of 4-year sampling
cycles, although reports may precede this cycle if significant results are found. These reports will
attempt to summarize indicator results for the preceding 4 years and integrate information from a
suite of ecological and stressor indicators to determine the regional and national status of arid
ecosystems. Trends in indicators will be evaluated against trends in stressors in an effort to
determine connections between response patterns and associations. This evaluation will be
accomplished by analysis of the associations and relationships between trends in ecological and
exposure indicators and natural and anthropogenic stressors, such as climate (from NOAA),
human demographics (from the Census Bureau), and land-use (from the EMAP-LC). An example
of this type of analysis and assessment can be found in Section 11. In addition to developing
associations between stressors and ecological indicators, these reports will assess the cumulative
benefit of regulatory and control programs as they might relate to observed associations, patterns,
and trends. A more detailed example of an interpretive report will be developed in the upcoming
year.
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Investigations within EMAP and from programs outside of EMAP will be encouraged to
produce analyses of EMAP-Arid results for publication in peer-reviewed literature. One of the best
tests of the quality of the EMAP-Arid program will be the use of its data in both policy and
science-oriented publications.
13.2 INTERNAL REPORTS
A series of reports and plans will be required before the program can commence field
activities. These include but are not limited to:
implementation plans
methods manuals
quality assurance plans
information management plans
field operations manuals
quality assessment reports
Similar to the overall monitoring strategy, each of these plans should be considered a "living"
document. This approach implies that they will be updated prior to field implementation each year
as a result of activities and findings of the previous year. The implementation plan will contain the
objectives and outline of the field activities for that season. This plan will include listings and
locations of all sites to be visited along with indicators to be measured. Additional details for all
aspects of each season's activities can then be found in updated program plans and manuals
related to quality assurance, information management, and field operations. An assessment of the
quality of data collected will be required following each field season, since a fundamental objective
of EMAP-Arid is to have data of known quality. Additionally, since a great deal of data from other
monitoring networks, such as satellite imagery and climate networks, will be used to conduct
analyses and assessments, tight operational and quality assurance procedures for these types of
data must be included in these plans. Similar to the data evaluation reports, many of these plans
may be authored with other agencies.
13.3 ECOLOGICAL RISK ASSESSMENT
As pointed out earlier in this plan, EMAP data forms the baseline upon which ecological risk
assessments will be conducted. The EMAP-Arid data will help determine the extent and evaluate
trends in ecological resources within the western United States and should be the basis for
identifying ecological resources at risk; process models also will form the basis for this
assessment. By combining data on ecological indicators, natural and anthropogenic stressors
(see Drylands Risk Index in Section 11), and landscape distribution and condition (e.g., simplified
versus complex landscapes). EMAP-Arid will be able to determine ecosystems that are most
sensitive to and at highest risk from further stress. Further, these data will be useful to a wide variety
of managers in reducing overall risk within the western United States. EMAP-Arid proposes to use
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state-of-the-science GIS technology to display and communicate these data to cooperating
agencies and the public in addition to developing new tools (see Section 11). Reports that
characterize and report on ecological risk will likely be a combined effort with EPA Program Offices
and other Federal and state agencies.
13.4 OTHER OUTPUTS
In addition to reports, the EMAP-Arid resource group intends to make data available upon
request through the EM AP information management center (EIC). Some information transferred to
EMAP by other agencies may be confidential, but most EMAP data should be readily available
upon request (see Section 10). The EMAP-Arid data should be shared with all types of users,
ranging from the public to private industry. Additionally, EMAP-Arid will present findings at
symposia, workshops, and conferences and develop brochures and videotapes for general public
information. Frequent communication with cooperating and outside organizations on EMAP
findings is one of the highest priorities of this program.
13.5 FUTURE RESEARCH, IMPLEMENTATION, AND TIMELINES
This section summarizes proposed major research tasks, implementation strategies and
timelines, and projected budgets for the continued phased development and implementation of
EMAP-Arid. Three general types of activities are necessary to develop and implement the
program:
analysis of existing data and simulations;
field pilot studies; and
regional demonstration projects.
All three of these activities will be conducted to evaluate the overall design, indicator
performance, and field sampling; Table 13-1 provides a summary of these activities. We anticipate
continuous evaluation of design and indicator performance. One of the key developmental areas
for EMAP-Arid will be the use of remote sensing data to measure and determine status and trends
in ecological indicators. Initially, EMAP-Arid will use remote sensing data to determine the
distribution and extent of certain arid ecosystems and for the measurement of landscape indices (a
measure of landscape condition). As new sensors become available (e.g., NASA EOS platforms),
EMAP-Arid will evaluate the usefulness of using data generated from these sources in measuring
ecological indicators.
Other developmental areas of priority to the EMAP-Arid resource group include:
development of the "suite of indicators" concept whereby the resolving power of the
suite of indicators is greater than the sum of the resolving power of individual indicators;
development of nominal and subnominal classifications for indicators and indicator
suites. Here it is important to develop an ecologically meaningful definition that is
compatible with but not compromised by the variety of subjective definitions of
ecological and environmental health;
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TABLE 13-1. EMAP-ARID PROPOSED RESEARCH AND DEVELOPMENT OBJECTIVES
TASK
DESIGN
INDICATOR
Analysis of existing data
and simulation
Compare EMAP to previous
sampling grid designs
Assess efficiency of
proposed classification
Determine optimal sample
sizes for field
measurements
Evaluate indicator
responsiveness to
major stressors
Quantify spatial and
temporal variability of
proposed indicators
Initiate development of
metrics and evaluation
Develop statistical
procedures to integrate
satellite data with field
measurements
From existing data, develop
a suite of indicators likely to
determine arid land
conditions
Develop statistical
procedures to integrate
paleoenvironmental data
with field measurements
Field Pilot
Develop strategy to link
existing ecological
monitoring sites with
EMAP design
Develop approaches to
integrate assessments
Quantify the spatial and
temporal variability of
indicators
Determine whether to move
certain indicators to the
demonstration phase
(based on variability and
sensitivity)
Test and refine sampling
methods
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TABLE 13-1. (continued)
TASK
DESIGN
INDICATOR
Regional demonstration
projects
Other research
Evaluate adequacy of EMAP
grid
Quantify errors associated
with misclassification,
boundary delineation,
denied access
Link design to global
monitoring community
Evaluate indicator
applicability and
interpretability on a
regional scale
Obtain first data to produce
regional assessments of
arid land conditions
Develop nominal/subnominal
classification for indicator
measurements
Develop ecological indicator
measurement techniques
from satellite imagery
development of spatial statistics to enhance the understanding of trends, associations,
and patterns among indicators (stressor and ecological indicators);
incorporation of landscape condition data with field measurements to understand
overall ecosystem condition and risk, including determining hierarchical relationships
between measures on plots and landscapes.
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14.0 LITERATURE CITED
Section 1
EPA. 1990. An overview of the environmental monitoring and assessment program. EMAP
Monitor. EPA-600/M-90/022. pp 1-3.
Knapp C. M., D. R. Marmorek, J. P. Baker, K. W. Thornton, J. M. Klopatek, D. P. Charles. 1990.
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Overton, W. S. 1990. Design Report for EMAP, Environmental Monitoring and Assessment
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Viessman, W. 1990. A framework for reshaping water management. Environment. 32(4):
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Section 2
Bender, G. L. 1982. Reference Handbook on the Deserts of North America. Greenwood Press.
Westport, CT
Blockstein, D. E. 1989. Toward a federal plan for biological diversity. Issues in Science and
Technology V(4): 63-67
Crumpacker, D. W. 1984. Regional riparian research and a multi-university approach to the
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Dregne, H. 1977. Desertification of arid lands. Econ. Geog. 53(4): 322-331.
Duffus, J. III. 1988. Management of Public Rangelands by the Bureau of Land Management.
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Public Lands. Washington, DC.
Jacobs, L. 1988. Amazing graze. Desertification Control Bulletin 17: 13-17.
Maggs, W. W. 1989. Warming will alter water resources. EOS 70: 67, 74.
Mooney, H. A. 1988. Lessons from Mediterranean - Climate Regions. In: Biodiversity. E.G.
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Nash, R. F. 1989. The Rights of Nature: A History of Environmental Ethics. University of
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National Research Council. 1990. Managing Troubled Waters: The Role of Marine
Environmental Monitoring. National Academy Press. Washington, DC.
Noss, R. F. 1990. Indicators for monitoring biodiversity: a hierarchical approach.
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Shriner, D. S., W. W. Heck, S. B. Mclaughlin, D. W. Johnson, J. D. Joslin, and C. E. Peterson.
1990. Response of vegetation to atmospheric deposition and air pollution. State of
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Thompson, C. R., D. W. Olszyk, G. Kats, A. Bytnerowicz, P. J. Dawson, and J. W. Wolf. 1984.
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UCAR. 1990. Arid ecosystem interactions: lesson from North American experiences. Draft
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Wald, J., and D. Alberswerth. 1989. Our Ailing Public Rangelands: Condition Report - 1989.
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Ingraham, J. O. Davis, C. A. Fox, and J. T. Ball. 1990. The North American Great Basin: A
Sensitive Indicator of Climatic Change. In: Plant Biology of the Basin and Range. C. B.
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Section 3
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Kepner, W. et al.1990. Riparian Indicators Workshop (memo), September 25-27. 1990, Las
Vegas, NV.
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EPA-600/4-86/007a. Washington, DC,
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Corvallis, OR.
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Section 4
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Section 5
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Sons, 338 pp.
Crumpacker, D. W. 1984. Regional riparian research and a multi-university approach to the
special problem of livestock grazing in the Rocky Mountains and Great Plains. IN:
California Riparian Systems. R.E. Warner and K.M. Hendrix (eds.). University of California
Press. Berkeley, CA.
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remotely sensed data. Agricultural and Forest Meteorology, 52 109-131.
Dregne, H. 1977. Desertification of arid lands. Econ. Geog. 53(4):325.
Green, R. H. 1979. Sampling design and statistical methods for environmental biologists.
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Harding, A. F. 1982. Climate change in later prehistory. Edinburgh, Edinburgh University
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Monitoring and Assessment Program. EPA 600/3-90/060. U.S. EPA, Office of Research
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The Nature Conservancy. 1989. Personal Communication.
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Wharton, R.A., Jr., RE. Wigand, M.R. Rose, R.L. Reinhardt, D.A. Mouat, H.E. Kleiforth, N.L.
Ingraham, J.O. Davis, C.A. Fox, and J.T. Ball (1990) The North American Great Basin: A
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Section 6
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Monitoring and Assessment Program, U.S. Environmental Protection Agency, Preprint,
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Joyce, L. J. 1989. An analysis of the forage situation in the United States: 1989-2040.
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Mussallem, K., 1990. Private Communication.
National Research Council, 1990. Managing Troubled Waters: The Role of Marine
Environmental Monitoring, National Academy Press, Washington, DC.
National Soil-Range Team, 1988. SITEFORM: A Users Guide for Computer Entry and Retrieval
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National Technical Information Service, 1988. Directory of Computerized Data Files 1988: A
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Olson, G. L. and Breckenridge, R. P. (1987) (1989), Summary of Federal Contaminant
Monitoring Programs and Related Data Bases: A Fish and Wildlife Perspective, Idaho
National Engineering Laboratory
Olson, R. J. 1984. Review of existing environmental and natural resource data bases. Oak
Ridge National Laboratory, Oak Ridge, TN ORNL/TM-8928. 71 pp.
Riggins, R. E., E. B. Jones, R. Singh, and P. A. Rechard, eds., 1990. Watershed Planning and
Analysis in Action, American Society of Civil Engineers, New York, New York.
Robbins, Chandler S., Danny Bystrak, Paul H. Geissler, 1986. The breeding bird survey: its
first fifteen years, 1965-1979. Resour. Publ. 157. Washington, DC, U.S. Department of
Interior, Fish and Wildlife Service, 196 pp.
Soil Conservation Service, 1989. Summary Report: 1987 National Resources Inventory,
Statistical Bulletin Number 790, USDA-SCS, Iowa State Statistical Laboratory, 37 pp.
Soil Conservation Service, 1987. Basic Statistics: 1982 National Resources Inventory,
Statistical Bulletin Number 756, USDA-SCS, Iowa State Statistical Laboratory, 153 pp.
Soil Conservation Service, 1976. National Range Handbook, USDA-SCS, Washington, DC.
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Department of the Interior, Bureau of Land Management, Boise District, Idaho 151 pp.
Swetnam, T. W. and Betancount, J.L., 1990. Fire-southern Oscillation Relations in the
Southwestern United States. Science 249: 1017-1020.
Tucker, C. S. and E. E. Huber, 1980. Inventory of sources of computerized ecological
information. Oak Ridge National Laboratory, Oak Ridge, TN. ORNL-5441/Ri. 61pp.
U.S. Forest Service, 1988. Resource Information Project, USDA-Forest Service, Information
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14-5
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U.S. Geological Survey, 1983. Scientific and Technical, Spatial, and Bibliographical Data
Bases and Systems of the USGS, 1983, Geological Survey Circular 817, USGS, Alexandria,
VA.
U.S. Geological Survey, 1986. National Water Summary 1985-, Hydrologic Events and
Surface-Water Resources, United States Geological Survey Water-Supply Paper 2300,
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and files available from the Federal Government. Second Edition. Information USA,
Potomac, MD. 368 pp.
Section 7
NO CITATIONS
Section 8
Barbour, M. G., J. H. Burk, and W. D. Pitts. 1987. Terrestrial Plant Ecology.
Benjamin/Cummings Publishing Company. Menlo Park, California.
Brower, J. E., and J. H. Zar. 1977. Field and Laboratory Methods for General Ecology. Wm.
C. Brown Publishers. Dubuque, Iowa. 194 pp.
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PP-
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Wildlife Habitat. U.S. Bureau of Land Management, Denver Service Center, Denver,
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Fish and Wildlife Service, Western Energy and Land Use Team. FWS/OBS-81/47. Fort
Collins, Colorado 111 pp.
Lunetta, R. S., R. G. Congalton, L. K. Fenstermaker, J. R. Jenson, K. C. McGuire, and L. R.
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14-6
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Mikol, S. A. 1980. Field Guidelines for Using Transects to Sample Nongame Bird Populations.
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Platts, W. S., W. F. Megahan, and G. W. Minshall. 1983. Methods for Evaluating Stream,
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General Technical Report INT-221. Ogden, Utah. 177pp.
Section 9
Barth, D. S., B. J. Mason, T H. Starks, and K. Brown. 1989. Soil Sampling Quality Assurance
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Environmental Measurements. ASTM STP 867 ASTM, Philadelphia, PA.
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Quality Assurance Program", USEPA, Washington, DC.
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Vegas, NV.
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van Ee, J.J., L.J. Blume, and T.H. Starks, 1990. A Rationale for the Assessment of Errors in
the Sampling of Soils. EPA/600/4-90/013, USEPA-EMSL, Las Vegas, NV.
Section 10
Franson, S. E. 1990. Data confidentiality in the Environmental Monitoring and Assessment
Program (EMAP): Issues and recommendations. EPA600/X-90/219. Environmental
Monitoring Systems Laboratory. Las Vegas, NV.
Kanciruk, P., R.J. Olson, and R.A. McCord. 1986. Quality control in research databases: The
U.S. Environmental Protection Agency.
Section 11
Fox, C. A., C. D. Elvidge, and D.W. Johnson. 1990. Indicator strategy for arid lands. In: C.T.
Hunsaker and D.E. Carpenter, eds. Ecological Indicators for the Environmental Monitoring
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Development Research Triangle Park, NC
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Government Print Office, Washington, DC.
Section 12
NO CITATIONS
Section 13
NO CITATIONS
14-8
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APPENDIX A
Possible Indicators for Use by the
EMAP-Arid Ecology Resource Group
as Developed in EMAP-Arid Workshops
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VEGETATION BIOMASS
INDICATOR: Leaf Area Index/Vegetation Index
LINKAGE: Response
ENDPOINT: Sustainability
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 resource availability and the lack of major disturbances.
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.
Leaf Area Index/Vegetation Index: 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 remains 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.
MEASUREMENTS:
Leaf Area Index/Vegetation Index: 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 (AVH RR) 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 (verification) of remotely
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sensed measurements of vegetation is underway 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.
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 have not been 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 the
potential NPP of a species 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 nitrogen 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.
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 G. 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, H.A. Mooney. 1985. Resource limitation in plants - an economic
analogy. Annual Rev. Ecol. Syst. 16: 363-392.
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Botkin, D.B., et al. 1986. Remote Sensing of the Biosphere. National Academy Press,
Washington, DC. 135pp.
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 H.A. 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. Int. J. Remote Sens. 9:69-84.
Reppertand 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.
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INDICATOR: Aerial Extent
LINKAGE: Response, Habitat
ENDPOINTS: Biodiversity, Sustainability, Aesthetics
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).
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 afull leaf-out condition, and a high
sun angle reduces shadow effects in steep terrain.
MEASUREMENTS: The areal extent of grasslands, shrublands, woodlands, and 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 afield 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 $1,500. The recommended interannual sampling frequency would be approximately 5
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.
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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.
U.S. GAO. 1988. Restoring degraded riparian areas on western rangelands.
GAO/T/RCED-88-20. U.S. General Accounting Office, Washington, DC.
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INDICATOR: Life Form
LINKAGE: Response, Habitat
ENDPOINTS: Aesthetics, Biodiversity
STATUS: High-Priority Research
APPLICATION: An obvious characteristic that helps to distinguish ecosystems is the
predominance of certain types of life forms such as trees, shrubs, and grasses. Changes in the
relative proportion of life forms can be caused by natural processes (e.g., succession) and^human
intervention, and may presage a fundamental change in the system.
INDEX PERIOD: The optimal period for sampling would be during the peak growth period,
probably late spring or early summer. However, many annuals have peak growth periods during
different seasons.
MEASUREMENTS: Field surveys would be needed to establish the relative abundance per unit
area and as a percentage of ground cover. Much of this information can be extended in space and
time through aerial photography and satellite coverage. Images from the LANDSAT Thematic
Mapper can be used to monitor boundaries between predominant life forms.
VARIABILITY: Annual plants have a large seasonal and year-to-year variation. Shrubs and trees
are less prove to short time-scale changes more dependent on decadal influences (excluding
catastrophic). The variation, then, between sampled resources is highly dependent on the
resident species.
PRIMARY PROBLEMS: Ground truth information is required and the variability in survey results is
exacerbated by the high variability in ephemeral life forms.
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INDICATOR: Mechanical Disturbance of Soils and Vegetation
LINKAGE: Exposure, Habitat
ENDPOINT: Biodiversity
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 to 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 midsummer 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.
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Webb, R.H., and H.G. Wilshire, eds. 1983. Environmental Effects of Off-Road Vehicles: Impacts
and Management in Arid Regions. Springer-Verlag, New York.
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INDICATOR: Exotic Plants
LINKAGE: Exposure, Habitat
ENDPOINTS: Biodiversity, Aesthetics
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 cheat grass (Bromustectorum), tamarisk
(Tamarix sp.), and tumbleweed. These plants have proliferated widely during the past 200 to 300
years since their introduction because of their ability to adapt and 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" wildlife habitat indicator 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 to 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:
Oosting, H.J. 1956. The Study of Plant Communities. W.H. Freeman and Co., San Francisco.
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WATER BALANCE
APPLICATION: Water is a resource critical to arid ecosystems and four indicators are grouped
below. 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.
INDICATOR: Vegetation
LINKAGE: Response
ENDPOINTS: Biodiversity, Sustainability
STATUS: High-Priority Research
Application: 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, 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
10
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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.
INDICATOR: Ground-water Depth and Use
LINKAGE: Response, Stressor
ENDPOINTS: Biodiversity, Sustainability
STATUS: High-Priority Research
Application: 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
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.
INDICATOR: Stream Flows
LINKAGE: Response, Habitat, Stressor
ENDPOINTS: Biodiversity, Sustainability
STATUS: High-Priority Research
Application: 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
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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. Inthese 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.
INDICATOR: Precipitation
LINKAGE: Stressor
ENDPOINTS: Biodiversity, Sustainability
STATUS: High-Priority Research
Application: The amount and seasonal variation in precipitation has marked effects on the
structural, functional, and life history of arid ecosystems. Many plant species are dependent on
surface waters being available at certain times of the year. Long periods of drought and/or high
variability will reduce the overall productivity and recruitment into the area.
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 information about 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) Long-Term Records, for Some Areas, are Available from the National Weather Service:
Additional locations of particular interest can be monitored in conjunction with energy balance
measurements (see next indicator).
(3) 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.
(4) 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 afunction of height
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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 to 50-cm intervals), and 10 cores per unit, the
total cost is $3,000 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.
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, G.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.W.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 C02
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.
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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.
449 pp.
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INDICATOR: Energy Balance
LINKAGE: Response
ENDPOINTS: Sustainability
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 vegetation and
would change if the site 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.
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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 diurnal cycle or the annual cycle give a good
indication of the importance of soil heat flux to 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, 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.
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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 cannot
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 $8,000.
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)
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 collected for 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.J., and K. G. McNaughton. 1986. Stomatal control of transpiration: Scaling up from the
leaf to region. Adv. Ecol. Res. 15:1-49.
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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 njtrogen
storage. Nature 317:613-616.
Running, S.W., R.R. Nemani, 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.
BIBLIOGRAPHY:
Sellers, P.J., Y. Mintz, Y.C. Sud, and A. Dalcher. 1986. A simple biosphere (SiB) model for use
within general circulation models. J. Atmos. Sci. 43:505-531.
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LANDSCAPE PATTERN
INDICATOR: Patch Size and Perimeter-to-Area Ratio
LINKAGE: Response
ENDPOINT: Biodiversity
STATUS: High-Priority Research
Five indices for patch size and connectivity are considered: (1) perimeter-to-area ratio; (2)
contagion or habitat patchiness; (3) gamma index of network connectivity; (4) Ration's diversity
index; and (5) fractal dimension.
1. Perimeter-to-Area Ratio
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 "weedy" 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.
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.
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MEASUREMENTS: Measurements of patch areas and perimeters are relatively straightforward in
many landscapes. They can be made manually on an aerial photo; for large areas, however, the
use of a GIS and digital data are necessary. The land-use and vegetation-cover data to calculate
these indices would be provided by the EMAP-Characterization group. 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% from the sample mean.
PRIMARY PROBLEMS: No significant problems are anticipated.
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, L.D. 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. McLellan, 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.
2. Contagion or Habitat Patchiness
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 etal., 1988). Contagion (C) measures the extentto which land
uses are aggregated or clumped:
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C = 2n\nn + i»jPii lnP,y
where PJJ 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 = / / P;J In Pjjl2n 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 the EMAP-Characterization group. 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 nearesttrees 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 all 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, though
time-consuming (but less time-consuming than using point-quarter samples). Current aerial
photos, when observations are verified on the ground, 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.
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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, 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.
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.
3. Gamma Index of Network Connectivity
APPLICATION: The connectivity of habitat in a landscape is a measure of how easily individuals of
a given animal species can travel about, which in turn is important to meeting daily and seasonal life
history needs, allowing juvenile dispersal, escaping disturbance, and providing for gene flow.
While in theory the usefulness of such as index appears logical, the data are lacking to support the
application of this index to a specific species.
INDEX PERIOD: No optimal sampling window exists for remotely sensed data from which gamma
indices are calculated.
MEASUREMENTS: The gamma index of network connectivity is the ratio of links in a network to the
maximum possible number of links in that network. The formula is y = L/Lmax = L/3(V-2),where
L is the number of links (i.e., corridors), Lmax is the maximum possible number of links, and V is the
number of nodes (i.e., habitat patches; Forman and Godron 1986). The recommended interannual
sampling frequency is 4 to 5 years.
VARIABILITY: Because the remotely sensed data will provide 100% spatial coverage in the
landscape sampling units, the expected spatial variability of the gamma index within resource
sampling units is inconsequential. The temporal variability of the gamma index during the index
period was not estimated.
PRIMARY PROBLEMS: This is a simple measure, but its ecological relevance is unknown.
Connectivity in real landscapes would depend on habitat structure within corridors, the nature of
surrounding habitat (matrix), corridor width:length ratio, and details of the autecology of species
expected to use the corridor.
REFERENCE:
Forman, R.T.T., and M. Godron. 1986. Landscape Ecology. John Wiley and Sons, New York.
BIBLIOGRAPHY:
Noss, R.F. 1987. Corridors in real landscapes: A reply to Simberloff and Cox. Conserv. Biol.
1:159-164.
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4. Ration's Diversity Index
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 EM AP-Characterization
group. 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.
5. Fractal Dimension
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
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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 etal., 1987; O'Neill
etal., 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 = 2S
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 thefractal 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. Thefractal 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 index
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 currently lack knowledge of how
this parameter relates to ecosystem function. However, as an indicator of landscape pattern
change over large geographic areas it should be powerful. Also, this indicator is probably most
useful when used together with other landscape indices such as contagion and proportion of land
use.
REFERENCES:
Krummel, J.R., R.H. Gardner, G.Sugihara, R.V. O'Neill, and P.R. Coleman. 1987. Landscape
pattern in a disturbed environment. Oikos 48:321-324.
Lovejoy, S. 1982. Area-perimeter relation for rain and cloud areas. Science 216:185-187.
Mandelbrot, B. 1983. The Fractal Geometry of Nature. W.H. Freeman and Co., New York.
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O'Neill, R.V., J.R. Krummel, R.H. Gardner, G. Sugihara, B. Jackson, D.L. DeAngelis, B.T.
Milne, M.G. Turner, B. Zygmunt, S.W. Christensen, V.H Dale, and R.L. Graham. 1988.
Indices of landscape pattern. Landscape Ecol. 1:153-162.
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INDICATOR: Habitat Proportions (Cover Types)
LINKAGE: Response, Habitat
ENDPOINT: Biodiversity
STATUS: High-Priority Research
APPLICATION: Mapping and determining proportions (Pi) 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 Pi as a landscape property permitting application of percolation theory (Gardner et
al., 1987). If habitat is randomly scattered on the landscape, then Pi = 0.59 represents a
"percolating" habitat. Above this value of Pi, 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 Pi 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 Pi 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
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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 by EMAP-Characterization studies 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% from the sample mean.
PRIMARY PROBLEMS: Ground verification 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., RJ. 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 forthe 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.
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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 MJ. 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.
Turner, M.G., R.H. Gardner, V.H. Dale, and R.V. O'Neill. 1989. Predicting the spread of
disturbance in heterogeneous landscapes. Oikos 55:121-129.
Walter, H. 1973. Vegetation of the Earth. Springer-Verlag, New York.
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INDICATOR: Linear Classification and Physical Structure of Habitat
LINKAGE: Exposure, Habitat
ENDPOINTS: Biodiversity, Sustainability
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
verified on the ground 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 togetherthese indices
provide a way to evaluate and monitor habitat structure and to 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 changes such asplant succession, urbanization,
desertification, 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 to which status and
trends can be compared among sites.
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The analysis of habitat structure to implement the HLCS would cost approximately $500 to $1,000
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.
REFERENCES:
Short, H.L. 1990. The use of the Habitat Linear Classification System (HLCS) to inventory and
monitor wildlife habitat. Unpublished manuscript. U.S. Department of the Interior, Fish and
Wildlife Service, Arlington, VA.
Short, H.L., and S.C. Williamson. 1986. Evaluating the structure of habitat for wildlife. Pages
97-104. In: J. Verner, M.L. Morrison, and C.J. Ralph, eds. Modeling Habitat Relationships of
Terrestrial Vertebrates. University of Wisconsin Press, Madison.
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INDICATOR: Livestock Grazing
LINKAGE: Exposure, Habitat
ENDPOINTS: Biodiversity, Sustainability
STATUS: High-Priority Research
APPLICATION: The majority of areas to be monitored by the EMAP-Arid ecosystem group 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. GAO, 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
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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.
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.
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INDICATOR: Abundance or Density of Key Physical Features and Structural Elements
LINKAGE: Exposure, Habitat
ENDPOINT: Biodiversity
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.
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 aerial surveys
and a determination of their abundance or density in the resource sampling unit. The
recommended interannual sampling frequency is 4 or 5 years.
VARIABILITY: The expected spatial variability for this indicator within a resource sampling unit and
its expected temporal variability during the year have not been estimated.
PRIMARY PROBLEMS: Measuring the abundance or density of structural elements is
straightforward, and is a longstanding 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.
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FIRE
INDICATOR: Occurrence and Area
LINKAGE: Exposure, Habitat
ENDPOINTS: Biodiversity, Sustainability
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 BLM.
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 to 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 midsummer 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 p.m. 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;
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and (5) proximity to roads, trails, or buildings. These data will be available from
EMAP-Characterization studies and the "Species Composition and Ecotone Location of
Vegetation". 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.
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.
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RETROSPECTIVE
INDICATOR: Charcoal Record
LINKAGE: Response
ENDPOINTS: Biodiversity
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.
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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 atemporary 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: PS. 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.
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
ReportlNT-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 properties. 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 ll:66.
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INDICATOR: Dendrochronology: Trees and Shrubs
LINKAGE: Response
ENDPOINT: Sustainability
STATUS: High-Priority Research
APPLICATION: Changes in growth-ring characteristics (e.g., annual ring width) overtime 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
dendrochronological/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.
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MEASUREMENTS: Annual growth layers from radial cores are measured under microscopic
magnification to the nearest 1 p.m 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 procedure 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 to 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 to 75%
variance in common among chronologies within a resource 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.
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BIBLIOGRAPHY:
Fritts, H.C. 1976. Tree Rings and Climate. Academic Press, London.
Cook, E.R. 1985. A time series approach to tree-ring standardization. Ph.D. dissertation.
Department of Renewable Natural Resources, University of Arizona, Tucson.
Graybill, D.A., and M.R. Rose. 1989. Analysis of growth trends and variation in conifers from
Arizona and New Mexico. Final report submitted to U.S. Environmental Protection Agency and
U.S. Forest Service Western Conifers Research Cooperative, Corvallis, OR. Laboratory of
Tree-Ring Research, University of Arizona, Tucson.
Graybill, D.A. 1985. Western U.S. tree-ring index chronology data for detection of arboreal
response to increasing carbon dioxide. Laboratory of Tree-Ring Research, University of
Arizona, Tucson.
Holmes, R.L., R.K. Adams, and H.C. Fritts. 1986. Tree ring chronologies of western North
America: California, eastern Oregon and northern Great Basin. NSF grants ATM-8026732 and
ATM-8303192. Laboratory of Tree-Ring Research, University of Arizona, Tucson.
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INDICATOR: Pollen Record
LINKAGE: Response
ENDPOINTS: Biodiversity
STATUS: High-Priority Research
APPLICATION: Pollen analysis can be used to identify past changes in plant communities.
Interannual comparisons of stress are 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 response in changed outputs of
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
was 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
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winter snowfall, but after bloom-time. A network of pollen traps would have to be placed along
elevation gradients and in areas where they would not be disturbed by vandals or human activities
(e.g., plowing) that would resuspend pollen in the air.
BIBLIOGRAPHY:
Birks, A.J.B., and A.D. Gordon. 1985. Numerical Methods in Quaternary Pollen Analysis.
Academic Press, New York.
Birks, H.J.B.,and H.H. Birks. 1980. Quaternary Paleoecology. Edward Arnold Publishers Limited.
Grosse-Brauckmann, G. 1978. Absolute jahrliche Pollenederschlagsmengen an vershichdenen
Beobachtungsorten in der Bundesrepublik Deutschland. Flora 167:209-247.
Watts, W.A. 1973. Rates of change and stability in vegetation in the perspective of long periods of
time. In: H.J.B. Birks and R.G. West, eds. Quaternary Plant Ecology. Blackwell.
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INDICATOR: Woodrat Midden Record
LINKAGE: Response
ENDPOINT: Biodiversity
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
$1,000 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.
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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 PS. 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. Wigand. 1990. Comings and goings of Western Juniper. In: J.L.
Betancourt, T.R. Van Devender, and RS. Martin, eds. Fossil Packrat Middens: The Last 40,000
Years of Biotic Changes. University of Arizona Press, Tucson. In press.
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WILDLIFE HABITAT
INDICATOR: Species Composition and Ecotone Location of Vegetation
LINKAGE: Response, Habitat
ENDPOINTS: Biodiversity, Sustainability
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
and 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 $1,000 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.
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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 procedure 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 to 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.
REFERENCES:
Driscoll, R.S., and J.N. Reppert. 1968. The identification and quantification of plant species,
communities, and other resource features in herbland and shrubland environments from
large-scale aerial photography. Annual Progress Report, Earth Resources Survey Program,
NASA/OSSA. U.S. Department of Agriculture, Forest Service, Rocky Mountain Forest and
Range Experiment Station, Ft. Collins, CO.
U.S. BLM. 1985. Rangeland Monitoring: Trend Studies. Technical Reference TR 4400-4. U.S.
Department of the Interior, Bureau of Land Management, Washington, DC.
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INDICATOR: Relative Abundance: Animals
LINKAGE: Response
ENDPOINTS: Biodiversity, Aesthetics
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
Geologists agree that attention to particular species is valuable for community-level monitoring.
The relative abundance or, where more feasible presence or 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. 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 and Slugs (Northwest only)
Deciduous Forests
Vertebrate: Salamanders and Lizards (Southwest only)
Invertebrate: Ground-Dwelling Beetles, Ants, and Snails and Slugs
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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)
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 and 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 to 15 min). A
trained birder can perform 5 to 10 point counts in one morning; ideally three replicates should occur
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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 or 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.
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 or
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.
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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.
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INDICATOR: Demographics: Animals
LINKAGE: Response
ENDPOINTS: Sustainability
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.
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 3 or 4 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).
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REFERENCES:
Schemnitz, S.D., ed. 1980. Animal Management Techniques Manual. The Animals Society,
Washington, DC.
Temple, S.A., and J.A. Wiens. 1989. Bird populations and environmental changes: Can birds be
bio-indicators? Am. Birds 43:260-270.
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INDICATOR: Morphological Asymmetry: Animals
LINKAGE: Response
ENDPOINTS: Sustainability, Aesthetics
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 bean 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, and 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.
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 4 or 5 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.
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INDICATOR: Biomarkers
LINKAGE: Exposure, Habitat
ENDPOINTS: Sustainability
STATUS: Research
DNA Alteration: Adducts
APPLICATION: Recent field studies (Dunn etal., 1987; Varanasietal., 1989; Stein etal., 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 109-1010 nucleotides) 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 [32P], are
separated by thin-layer chromatography (TLC), 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 are from 0.5to 1.0
day at each resource sampling unit for two to three technicians. The estimated laboratory analysis
cost for DNA adducts is $150 to $200 per sample.
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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 high-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 DMA 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 exposed to unknown mixtures of chemicals, and thus may increase the
chemical specificity of the 32R-postlabeling assay.
A recent study by Kurelec et al. (1990) illustrates, however, the need for further in-depth field
studies of DMA 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. Fried berg and PH. 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. 32p-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.
55
-------
Stein, J.E., W.L. Reichert, M. Nishimote, and U. Varanasi. 1989. 32p-postlabeling of DNA: A
sensitive method for assessing environmentally induced genotoxicity. Oceans 89. In press.
Varanasi, U., W.L. Reichert, and J. Stein. 1989.32R-postlabeling analysis of DNA adducts in liver
of wild English sole (Parophrys vetulus) and winter flounder (Pseudopleuronectes
americanus). Cancer Res. 49:1171-1177.
Watson, W.P. 1987. Post-radiolabeling for detecting DNA damage. Mutagenesis 2:319-331.
DNA Alteration: Secondary Modification
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 etal., 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 etal.,
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 NH4
-------
the time-dependent partial alkaline unwinding of DMA followed by determination of the
duplex:total DNA ratio (F value). Because DMA 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. Thetest response is quite sensitive to toxins, and changes are readily discerned, provided
proper baseline or reference data are available.
Rydberg (1975) has established the theoretical background for estimating strand breaks in DNA by
alkaline unwinding, which is summarized by the equation:
InF = -(K/M)(tb)
where K is a constant, M is the average molecular weight between two breaks, t is time, 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 (Kanterand Schwartz, 1982; Shugart, 1988a,b):
N = (In Fc /In 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 are from 0.5 to 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 the immediate use
of this measurement 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 PC. 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.
57
-------
Kanter, P.M., and H.S. Schwartz. 1982. A fluorescence enhancement assay for cellular DNA
damage. Molec. Pharmacol. 22:145-151.
Meyers, L.J., L.R. Shugart, arid 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.
Radiat. Res. 61:274-285.
Shugart, L.R. 1988a. An alkaline unwinding assay for the detection of DNA damage in aquatic
organisms. Mar. Environ. Res. 24:321-325.
Shugart, L.R. 1988b. Quantitation of chemically induced damage to DNA of aquatic organisms by
alkaline unwinding assay. Aquat. Toxicol. 13:43-52.
Shugart, L. R. 1989. Personal communication. Oak Ridge National Laboratory, Environmental
Sciences Division, Oak Ridge, TN.
Shugart, L.R. 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.
DNA Alteration: Irreversible Event
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
58
-------
distribution exists, both in vivo (Bickham, 1990) and in vitro (Otto et a!., 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 al., 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 (McBeeand
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 DMA 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
are from 0.5 to 1.0 day at each resource sampling unit for two to three technicians. The estimated
laboratory analysis cost for flow cytometry is $25 to $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 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.
59
-------
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 DMA damage in wild rodents
detected by flow cytometry. Bull. Environ. Contam. Toxicol. 40:343-349.
Otto, F.J., H. Oldiges, W. Gohde, and V.K. Jain. 1981. Flow cytometric measurement of nuclear
DMA 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. 353pp.
Cholinesterase Levels
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 al., 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 are from 0.5 to 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 to $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.
60
-------
PRIMARY PROBLEMS: No major problems are recognized.
REFERENCES:
Ellman, G.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.
Metabolites of Xenobiotic Chemicals
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 (Creavenetal., 1965; Leeetal., 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 polycyclic 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
61
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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 are from 0.5 to1.0 day for each resource
sampling unit for two to three technicians. The estimated laboratory analysis cost for metabolite
measures is $25 to 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
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 G.H. Dobbs. 1972. Uptake, metabolism and discharge of
polycyclic aromatic hydrocarbons by marine fish. Mar. Biol. 17:201-208.
Melancon, M.J., and J.J. Lech. 1976. Isolation and identification of a polar metabolite of
tetrachlorobiphenyl from bile of rainbow trout exposed to i4C-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 offish in waterways polluted by pulpmill
effluents. Bull. Environ. Contamin. Toxicol. 36:429-436.
62
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APPENDIX B
SITE LOCATION MAPS
The maps contained in this Appendix provide site locations for the various monitoring and
sampling programs discussed in Chapter 6.
-------
APPENDIX C
Analytical Capabilities
-------
TABLE C1. ANALYTICAL CAPABILITIES FOR ORGANIC COMPOUNDS
Organochlorine Scan |
o,p'- DDE
o,p'-DDD
o,p' - DDT
p,p'-DDE
p,p'-DDD
p,p' - DDT
Lindane
Dieldrin
Endrin
Oxychlordane
Photomirex and Degradates [
8-monohydromirex
10-monohydromirex
2,8-dihydromirex
cis-5.10-dihydromirex
trans-5.10-dihydromix
Chlorophenoxy Acid Herbicides Scan
2,4-D
2,4-DB
2,4,5-T
Other Individual Organic Analyses
Dicofol
Endosulfan (I, II)
Endosulfan Sulfate
Kepone
Oil and Grease
Octachlorostyrene
cis-chlordane
trans-nonachlor
cis-nonachlor
trans-chlordane
Heptachlor Epoxide
Hexachlorobenzene
Mi rex
BHC
Toxaphene (estimated total)
PCBs (estimated total)
Dicamba
Silvex
Dichlorprop
PCB Aroclors:
PCB-1242
PCB-1248
PCB-1254
PCB-1260
-------
TABLE C1. (continued)
Anti-cholinesterase Compounds
Carbamate Scan
Methiocarb
Methomyl
Aldicarb
Carbaryl
Carbofuran
Oxamyl
Organophosphate Scan
Acephate
Azinphos-methyl
Chlorpyrifos (Dursban)
Coumaphos
Demeton (two peaks)
Diazinon
Dichlorvos
Dichrotophos
Disulfoton
EPN
Ethoprop
Dimethoate
Fensulfothion
Famphur
Fenthion
Malathion
Methamidophos
Methyl Parathion
Mevinphos
Monocrotophos
Parathion
Phorate
Terbufos
Trichlorfon
-------
TABLE C1. (continued)
Petroleum Hydrocarbon Compounds
Aliphatic Hydrocarbon Scan I f Polycyclic Aromatic Hydrocarbon (PAH) Scan
n-dodecane naphthalene
n-tridecane fluorene
n-tetradecane phenanthrene
octylcyclohexane anthracene
n-pentadecane fluoranthrene
noncylohexane pyrene
n-hexadecane 1,2-benzanthracene
n-heptadecane chrysene
pristane benzo (b) fluoranthene
n-octadecane benzo (k) fluoranthene
phytane benzo (e) pyrene
n-nonadecane benzo (a) pyrene
n-eicosane 1,2,5,6-dibenzanthracene
benzo (g,h,i) perylene
-------
TABLE C2 GUIDELINES FOR ESTIMATING DETECTION LIMITS FOR SELECTED
ELEMENTS ANALYZED BY INDUCTIVELY COUPLED PLASMA EMISSION
WATER
ELEMENT , _ ? n
w/o Precon2 Precon
Ag
Al
As
B
Ba
Be
Cd
Cr
Cu
Fe
Mg
Mn
Mo
Ni
Pb
Sb
Se
Sn
Sr
Tl
V
Zn
Sample Weights:
0.05
0.1
0.7
0.06
0.05
0.005
0.006
0.03
0.025
0.1
1
0.015
0.05
0.04
0.06
0.05
0.5
0.05
0.01
0.5
0.05
0.02
water
tissue
0.01
0.02
NA
0.01
0.01
0.001
0.001
0.003
0.005
0.02
0.2
0.003
0.01
0.008
0.02
0.01
0.05
0.009
0.002
0.05
0.01
0.004
- minimum 200 ml
- minimum 7.5 g,
sediment - minimum 10
1 Concentrations are
reported
~"-' ! \'^" J
SEDIMENT TISSUE
w/o Precon Precon w/o Precon Precon
8
20
20
10
5
0.5
1
5
2.5
10
100
1.7
8
6
12
30
50
30
2
50
5
10
2.5
5
NA
2.5
2.5
0.01
0.25
0.5
1.25
5
50
0.75
2.5
2
5
NA
NA
2.5
0.5
5
2.5
1
20
40
30
20
20
2
2
4
10
40
400
6
20
16
40
15
50
20
4
50
20
4
4
8
NA
4
4
0.4
0.4
0.8
2
8
80
1.2
4
3.2
8
2
2
4
0.8
0.5
4
1.6
, standard 500 ml
standard 25 g
g, standard 50 g
as u.g/g dry weight for tissue and
water. Detection limits
2 Precon = Preconcentration,
an acid digestion
are also greatly
sediment or )ig/ml for
affected by
(pH 3 or 6) to decrease the
sample size.
ower limit of
detection.
-------
TABLE C3. GUIDELINES FOR ESTIMATING DETECTION LIMITS FOR SELECTED
ELEMENTS ANALYZED USING GRAPHITE FURNACE ATOMIC ABSORPTION
SPECTROSCOPY*
ELEMENT WATER TISSUE
Ag
Al
As
Be
Cd
Cr
Cu
Fe
Mn
Mo
Ni
Pb
Sb
Se
Sn
Tl
V
Sample
*NOTE
0.001
0.10
0.005
0.002
0.001
0.003
0.01
0.002
0.001
0.002
0.003
0.001
0.004
0.004
0.01
0.004
0.003
Weights:
water - minimum 10 ml, standard 500 ml
tissue - minimum 0.1 g, standard 25 g
soil/sediment - minimum 0.2 g, standard 50 g
Concentrations are reported as p.g/g dry weight
water. Detection limits are also greatly affected
0.40
10
0.5
0.05
0.4
0.3
1.0
0.2
0.1
0.3
0.5
0.4
0.4
0.6
1.0
0.4
0.3
SOIL/SEDIMENT
0.2
5.0
0.7
0.2
0.2
0.5
0.5
0.4
0.2
0.8
0.6
0.5
0.5
1.0
2.0
0.6
0.6
for tissue and sediment or ^g/ml for
by sample size.
-------
TABLE C4. GUIDELINES FOR ESTIMATING DETECTION LIMITS FOR SELECTED
ELEMENTS ANALYZED USING HYDRIDE GENERATION ATOMIC
ABSORPTION SPECTROSCOPY*
ELEMENT WATER TISSUE SOIL/SEDIMENT
As 0.002 0.8 0.3
Sb 0.002 0.8 1.0
Se 0.002 0.2 0.3
Sample Weights:
water - minimum 10 ml, standard 50 ml
tissue - minimum 1 g, standard 1 g
soil/sediment - minimum 0.5 g, standard 1 g
*NOTE: Concentrations are reported as ug/g dry weight for tissue and sediment or ug/ml for
water. Detection limits are also greatly affected by sample size.
-------
TABLE C5. GUIDELINES FOR ESTIMATING DETECTION LIMITS FOR MERCURY
ANALYZED BY COLD VAPOR REDUCTION ATOMIC ABSORPTION SPECTROCOPY*
ELEMENT WATER TISSUE SOIL/SEDIMENT
Hg 0.0004 0.1 0.2
Sample Weights:
water - minimum 25 ml, standard 100 ml
tissue - minimum 5 g, standard 15 g
soil/sediment - minimum 1 g, standard 5 g
*NOTE: Concentrations are reported as ug/g dry weight for tissue and sediment or ug/ml for
water. Detection limits are also greatly affected by sample size.
-------
TABLE C6. DETECTION LIMITS AND SAMPLE WEIGHT REQUIREMENTS FOR
ORGANIC ANALYSES*
Detection Limit Sample Wt. (g)
MATRIX COMPOUNDS ppm, wet weight Minimum Optimal
TISSUE/
SEDIMENTS Pesticides*1)
PCBs
Toxaphene
Aromatics (PAHs)
Aliphatics
Kepone
Oil and Grease
Chlorophenoxy Acid
Herbicides
Organophosphates
Carbamates
WATER Pesticides^)
PCBs
Toxaphene
Aromatics (PAHs)
Aliphatics
Kepone
Chlorophenoxy Acid
Herbicides
Organophosphates
Carbamates
0) Includes organochlorines, kepone
octachlorostyrene.
0.01
0.05
0.05
0.01
0.01
0.01
500
0.01
0.10
0.10
0.005
0.100
0.100
0.005
0.005
0.005
0.005
0.001
0.001
10
10
10
10
10
10
20
50
10
10
100
100
100
500
500
100
200
500
500
100
100
100
100
100
100
100
100
20
20
1000
1000
1000
1000
1000
1000
1000
1000
1000
dicofol, endosulfans, photomirex, and
*NOTE: Detection limits are determined, to a large extent, by sample weights.
The detection limits given are based on optimal sample weights.
-------
TABLE C7. DETECTION LIMITS FOR POLYCHLORINATED DIBENZO-P-DIOXINS
AND DIBENZOFURANS (PCDD/PCDF)*
ANALYTE DETECTION LIMIT (pg/p)
TCDD, TCDF 1
PeCDD, PeCDF 2
HxCDD, HxCDF 4
HpCDD, HpCDF 10
OCDD, OCDF 40
* T = Tetra
Pe = Penta
Hx = Hexa
Hp = Hepta
Oc = Octa
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Varanasi, U., D.J. Gmur, and RA. 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.
Porphyrin Accumulation
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 HCI 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 g/dL in
penned ducks, lesser concentrations would probably lead to death in the wild. For a study of Pb
63
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exposure in two types of pen-reared and wild ducks, toxicity and lethality corresponding to much
less elevated blood protoporphyrin IX concentrations (<800 g/dL) are reported (Rattner et al.
1989).
Sample collection times for a set of biomarker indicators are from 0.5 to 1.0 day for each resource
sampling unit for two to three technicians. The estimated laboratory analysis cost for porphyrin
measures is $25 to $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. Foxetal. (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.
PRIMARY PROBLEMS: Information on species other than birds will enable further utilization of
porphyrins in ecological monitoring programs.
REFERENCES:
Elder, G.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, A.A. 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.W.A. 1979. Porphyrins in urine as an indication of exposure to chlorinated
hydrocarbons. Ann. N.Y Acad. Sci. 390:308-310.
Histopatholoaic Alterations
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
64
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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 teleostfish, 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.
(1) Hepatocellular necrosis and sequelae: This includes coagulative necrosis associated with
exposure to anthropogenic environmental toxicants in both mammals and fish (Wyllie et al. ,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).
(2) 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.
(3) 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 (Groff, 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).
(4) 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).
(5) Liver neoplastic lesions: examples are hepatic adenoma, hepatocellular carcinoma,
cholangioma, and cholangiocarcinoma.
Although somewhat subjective, user-oriented computer software for the quantification of lesions
exists. When applied to characterize magnitude of response, data amenable to statistical
65
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evaluation are obtainable. Sample collection times for a set of biomarker indicators are from 0.5 to
1.0 day for each resource sampling unit for two to three technicians. The estimated laboratory
analysis cost for histopathologic measures is $50 to $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
rainbowtrout (Salmo gairdneri) exposed to dietary pyrrolizidine (Senecio) alkaloids. Exp. Mol.
Pathol. 35:170-183.
Hendricks, J.D., T.R. Meyers, and D.W. Skelton. 1984. Histological progression of hepatic
neoplasia in rainbowtrout (Salmo gairdneri). Natl. Cancer Inst. 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. Shugartand 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 Salmo salar in Puget
Sound, Washington, USA. Dis. Aquat. Organisms 49:91-100.
May, E.B., R. Lukacovic, H. King, and M.M. Lipsky. 1987. Hyperplasticand neoplasticalterations
in the livers of white perch (Morone americana) from the Chesapeake Bay. J. Natl. Cancer Inst.
79:137-143.
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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.R Mommsen. 1985. Fish hepatocytes: A model metabolic system.
Can. J. Fish. Aquat. Sci. 42:1772-1782.
Murchelano, R.A. and R.E. Wolke. 1985. Epizootic carcinoma in the winter flounder
(Pseudopleuronectes americanus). Science 228:587-589.
Myers, M.S., L.D. 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
Inst. 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 L.J. Weber. 1973. Metabolism and biliary excretion of sulfobromophthalein by
rainbow trout (Salmo gairdneri). J. Fish. Res. Bd. Can. 30:1301-1308.
Segner, H., and H. MUller 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 Guellec, 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 Salmo 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. 738:59-79.
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Wolke, R.E., R.A. Murchelano, C.D. 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.M., J.F.G. Kerr, and A.R. Cumi. 1980. Cell death: The significance of apoptosis. Int. Rev.
Cytol. 68:251-306.
Yamamoto, K., P.A. 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.
Macrophage Phagocytotic Activity
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 macrophage 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
formalin-killed Escherichia coli during an incubation period of 90 to 120 min at 15C. The
macrophage suspension is 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
to 60 min), inexpensive, and easily performed.
68
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Sample collection times for a set of biomarker indicators are from 0.5 to 1.0 day for each resource
sampling unit for two to three technicians. The estimated laboratory analysis cost for phagocytotic
activity measures is $25 to $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 to
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 offish exposed to selected contaminants should be performed to
evaluate the immumodulatory effects of individual aquatic contaminants.
REFERENCES:
Anderson, D.P., B. Merchant, O.W. Dixon, C.F. Schott, and E.F. Lizzio. 1983. Flush exposure and
injection immunization of rainbow trout to selected DNP conjugates. Dev. Comp. Immunol.
7:261-268.
Cleland, G.B. and R.A. Sonstegard. 1987. Natural killer cell activity in rainbow trout (Salmo
gairdneri): Effect of dietary exposure to Arochlor 1254 and/or Mirex. Can. J. Fisher. Aquat.Sci.
44:636-638.
Evans, D.L., S.S. Graves, 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 intraperitoneal lead and cadmium on the humoral immune response
of Salmo trutta. Bull. Environ. Contam. Toxicol. 27:42-48.
Spitsbergen, J.M., K.A. Schat, J.M. Kleeman, 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 B.A. Weeks. 1988. Preliminary investigations of the
chemiluminescent response in normal and pollutant-exposed fish. Mar. Environ. Res.
24:281-284.
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Weeks, B.A., and J.E. Warinner. 1984. Effects of toxic chemicals on macrophage phagocytosis in
two estuarine fishes. Mar. Environ. Res. 14:327-335.
Weeks, B.A., 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, B.A., 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 B.A. Weeks. 1990. Effects of tributyltin on the
chemiluminescent response of phagocytes from three species of estuarine fish. Arch.
Environ. Contam. Toxicol. In press.
Blood Chemistry
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 "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
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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 are from 0.5 to 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.
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, L.R. 1983. Glucuronidation and biliary excretion of phenolphthalein in
temperature-acclimated steelhead trout (Salmo gairdneri). Comp. Biochem. Physiol. C
76:107-111.
Curtis, L.R., C.J. Kemp, and A.V. Svec. 1986. Biliary excretion of [i4C]taurocholate by rainbow
trout (Salmo 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 rain bow trout (Salmo gairdneri). Comp. Biochem.
Physiol. C 58:113-120.
Han Z., and Z. Yaron. 1980. Effects of organochlorines and interrenal activity and cortisol
metabolism in Tilapia aurea. Gen. Comp. Endocrin. 40:345.
Kemp, C.J., and L.R. Curtis. 1987. Thermally modulated biliary excretion of [i4C]taurocholate in
rainbow trout (Salmo gairdneri) and the Na+,K+-ATPase. Can. J. Fish. Aquat. Sci.
44:846-851.
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Schmidt, D.C.,and L.J.Weber. 1973. Metabolism and biliary excretion of sulfobromophthalein by
rainbow trout (Salmo 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.
Cytochrome P-450 Monooxvaenase System
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
[PCBs], 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 weeks), but method selection is important here
(see Measurements). Its utility as a biomarker is most studied in teleostfish, 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 are from 0.5 to 1.0 day per
resource sampling unit for two to three technicians. The estimated laboratory analysis cost for
enzyme analysis is $25 to $75 per sample. Test responses are quite sensitive to petroleum
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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.
REFERENCES:
Payne, J.F., L.L. 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 Perspect. 71:87-95.
Enzyme-Altered Foci
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 and stored indefinitely. Processing
includes routine cryostat sectioning of frozen tissue (livers of large fish) or freeze-drying and
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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; Peraino 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. Skelton. 1984. Histological progression of hepatic
neoplasia in rainbow trout (Salmo gairdneri). Natl. Cancer Inst. 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. Shugartand J. McCarthy, eds. Biomarkersof
Chemical Exposure in Fishes. Lewis Publishing Co., Chelsea, Ml. In press.
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.
Nakazawa, T., S. Hamaguchi, and Y Kyono-Hamaguichi. 1985. Histochemistry of liver tumors
induced by diethylnitrosamine and differential sex susceptibility to carcinogenesis in Oryzias
latipes. J. Natl. Cancer Inst. 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-phosphatedehydrogenase, 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.
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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.
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INDICATOR: Chemical Contaminants in Wood
LINKAGE: Exposure, Habitat
ENDPOINT: Sustainability
APPLICATION: Trees and woody shrubs may have recognizable annual growth layers that can be
exactly dated to the year in which they were formed. 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 and 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 to 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
procedure is different from si nple 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 to 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.
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REFERENCE/CANARY SITES
INDICATOR: Soil Erosion
LINKAGE: Response
ENDPOINTS: Biodiversity, Sustainability
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
("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.
INDEX 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 products of the
EMAP-Characterization group (soil information and slopes), meteorological data, and the
vegetation biomass and mechanical disturbance indicators. The Food and Agriculture
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Organization (FAO, 1979) provides 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/rill ratio, gully density, and alterations in
drainage density and drainage pattern would be measured for appropriate soil and landform units
by using low-altitude aerial photography or videography. The total cost of aerial data acquisition
($100) 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 and
meteorological data. These data would be integrated to identify areas at risk of accelerated erosion
by wind. The 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 to 50% from the mean
value. This variability is induced primarily by seasonal and diurnal alterations in illumination
conditions and can be largely eliminated by acquiring data with standardized illumination
conditions. Because the remote sensing data will census the entire resource sampling unit, the
spatial variability of measurements is inconsequential.
PRIMARY PROBLEMS: Measurement procedures must be standardized. The integration of
factors to obtain indices for wind and water erosion would require some effort but is achievable.
REFERENCES:
FAO. 1979. A Provisional Methodology for Soil Degradation Assessment. United Nations, Food
and Agriculture Organization, Rome, Italy.
Webb, R.H., and H.G. Wilshire, eds. 1983. Environmental Effects of Off-Road Vehicles: Impacts
and Management in Arid Regions. Springer-Verlag, New York.
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INDICATOR: Abundance and Species Composition of Lichens and Cryptogramic Crusts
LINKAGE: Response
ENDPOINT: Biodiversity
STATUS: Moderate Priority 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 to accelerate erosion.
Lichens in forests are known to be sensitive indicators of air pollution damage (Ferry et al., 1973;
Anderson and Treshow, 1984), 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 to 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. Thefull 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.
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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.
U.S. BLM. 1985. Rangeland monitoring: Trend studies. Technical Reference TR 4400-4. U.S.
Department of the Interior, Bureau of Land Management, Washington, DC.
Wilshire, H.G. 1983. The impact of vehicles on desert soil stabilizers. In: R.H. Webb and H.G.
Wilshire, eds. Environmental Effects of Off-Road Vehicles: Impacts and Management in Arid
Regions. Springer-Verlag, New York.
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INDICATOR: Foliar Chemistry
LINKAGE: Exposure, Habitat
ENDPOINT: Sustainability
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, R
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, andTi, 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.
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PRIMARY PROBLEMS: Concerns include the lack of baseline data on many species and of
understanding of 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, M.A. Brizuela, and A.D. Whicker. 1989. Patterns in grass silicification:
Response to grazing history and defoliation. Oecologia 50:268-271.
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 L.D. 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.,andR.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.
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INDICATOR: Physiochemical Soil Factors
LINKAGE: Exposure
ENDPOINTS: Sustainability
STATUS: High-Priority Research
APPLICATION: The application of this indicator to arid ecosystems is similar to indicators
described for forests, 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 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 (SOC) levels represent the balance
between net primary production and decomposition and are a sensitive indicator of ecosystem
function and status. 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 by
20-m grid during each sampling period). All samples would be analyzed for total C (LECO), total N
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(Kjeldahl), soluble salts (Rhoades, 1982), exchangeable cations (Al, K, Ca, Mg, Na, Zn, Cu, Mo),
and cation exchange capacity (1 M NH4CI), extractable SO4-2 (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 5-year intervals. Costs of collection and analyses for
soils would be approximately $150 to $200 a sample for periodic nutrient collections and $300 to
$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 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, FT. 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.
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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. Nemani, 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.
Segal M., R. Avissar, M.C. McCumber, and R.A. 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.
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.
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TECHNICAL REPORT DATA
(Please read Instructions on the reverse before completing)
. REPORT NO.
EPA/600/4-91/018
2.
3. RECIPIENT'S ACCESSION NO.
PB 93-100139
4. TITLE AND SUBTITLE
ARID ECOSYSTEMS STRATEGIC MONITORING PLAN, 1991
ENVIRONMENTAL MONITORING AND ASSESSMENT PROGRAM.
. AUTHOR(S)
W.G. Kepner3, C.A. Fox1, J. Baker1, B. Breckenrldge',
C. Elvidge', V. Eno2, J. Flueck', S. Franson3, J. Jackson1, B. Jones3,
H. Meyer', D. Houat', M. Rose', and C. Thompson2
9. PERFORMING ORGANIZATION NAME AND ADDRESS
U.S. EPA, EMSL-LV, EAD3, P.O. Box 93478, Las Vegas, NV 89119
Lockheed Engineering & Sciences Co.5, 980 Kelly Drive, Las Vegas, NV, 89119
Desert Research Institute1, P.O. Box 60220, Reno, NV, 89506
Desert Research Institute2' WSC, P.O. Box 19040, Las Vegas, NV, 89132-0040
Idaho National Engineering Lab.4. P.O. Box 1625, Idaho Falls, ID 83415-2213
University of Nevada, Las Vegas. Environmental Research Center6. Las Vegas. N"
12. SPONSORING AGENCY NAME AND ADDRESS
U.S. Environmental Protection Agency
Office of Research and Development
EMSL-LV, P.O. Box 93478, Las Vegas, NV 89193
5. REPORT DATE
June 1991
6. PERFORMING ORGANIZATION CODE
8. PERFORMING ORGANIZATION REPORT NO.
10. PROGRAM ELEMENT NO.
11. CONTRACT/GRANT NO.
68-CO-0049
13. T^PE OF REPORT AND PERIOD COVERED
14. SPONSORING AGENCY CODE
EPA/600/07
15. SUPPLEMENTARY NOTES
16. ABSTRACT
In response to the growing concerns regarding the condition of the nation's
ecological resources, the U.S. EPA has initiated the development of a national
interagency program to determine the status of, and to monitor the changes in,
ecological systems. This program is called the Environmental Monitoring and
Assessment Program (EMAP). The Arid Ecosystem portion of EMAP includes grasslands,
shrublands, woodlands, riparian zones, tundra and deserts in the arid (dryer)
regions, primarily the central and western U.S.. This document outlines a strategy
for the development and operation of the Arid Ecosystems portion of EMAP. Through
workshops, with interagency cooperation and technical support from universities,
indicators (measures of ecological parameters) will be evaluated, developed and
tested in small scale pilot field studies, before being implemented at larger scales.
Also discussed in the document are the sampling design; plans for the use of remote
sensing and CIS; development of the information management system; a quality
assurance program; means for implementation of a large scale field sampling
operation; and the guidelines for statistics, assessment and reporting for the
program.
17.
KEY WORDS AND DOCUMENT ANALYSIS
DESCRIPTORS
b.lDENTIFIERS/OPfcN ENDED TERMS
COSATI Field/Gioup
18. DISTRIBUTION STATEMENT
RELEASE TO PUBLIC
19. SECURITY CLASS (This Report)
UNCLASSIFIED
21. NO. OF PAGES
299
20. SECURITY CLASS (This page)
UNCLASSIFIED
22. PRICE
EPA Form 2220-1 (Rev. 4-77) PREVIOUS EDI TION is OBSOLETE
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