United States Office of Research and EPA/620/R-99/005
Environmental Protection Development May 2000
Agency Washington DC 20460
&EPA
Evaluation
Guidelines for
Ecological Indicators
Environmental Monitoring and
Assessment Program
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EPA/620/R-99/005
May 2000
EVALUATION GUIDELINES
FOR ECOLOGICAL
INDICATORS
Edited by
Laura E. Jackson
Jam's C. Kurtz
William S. Fisher
U.S. Environmental Protection Agency
Office of Research and Development
Research Triangle Park, NC 27711
Printed on Recycled Paper
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Notice
The information in this document has been funded wholly or in part by the U.S.
Environmental Protection Agency. It has been subjected to the Agency's review, and it has
been approved for publication as EPA draft number NHEERL.-RTP-MS--00-08. Mention of
trade names or commercial products does not constitute endorsement or recommendation
for use.
Acknowledgements
The editors wish to thank the authors of Chapters Two, Three, and Four for their patience
and dedication during numerous document revisions, and for their careful attention to
review comments. Thanks also go to the members of the ORD Ecological Indicators
Working Group, which was instrumental in framing this document and highlighting potential
users. We are especially grateful to the 12 peer reviewers from inside and outside the U.S.
Environmental Protection Agency for their insights on improving the final draft.
This report should be cited as follows:
Jackson, Laura E., Janis C. Kurtz, and William S. Fisher, eds. 2000. Evaluation
Guidelines for Ecological Indicators. EPA/620/R-99/005. U.S. Environmental Protection
Agency, Office of Research and Development, Research Triangle Park, NC. 107 p.
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Abstract
This document presents fifteen technical guidelines to evaluate the suitability of an
ecological indicator for a particular monitoring program. The guidelines are organized
within four evaluation phases: conceptual relevance, feasibility of implementation,
response variability, and interpretation and utility. The |U.S. Environmental Protection
Agency's Office of Research and Development has adopted these guidelines as an iterative
process for internal and (EPA's) affiliated researchers during the course of indicator
development, and as a consistent framework for indicator review. Chapter One describes
the guidelines; Chapters Two, Three, and Four illustrate application of the guidelines to
three indicators in various stages of development. The example indicators include a direct
chemical measure, dissolved oxygen concentration, and two multi-metric biological indices,
an index of estuarine benthic condition and one based on;stream fish assemblages. The
purpose of these illustrations is to demonstrate the evaluation process using real data and
working with the limitations of research in progress. Furthermore, these chapters
demonstrate that an evaluation may emphasize individual guidelines differently, depending
on the type of indicator and the program design. The evaluation process identifies
weaknesses that may require further indicator research and modification. This document
represents a compilation and expansion of previous efforts, in particular, the initial guidance
developed for EPA's Environmental Monitoring and Assessment Program (EMAP).
Keywords: ecological indicators, EMAP, environmental monitoring, ecological assessment,
Environmental Monitoring and Assessment Program
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Preface
This document describes a process for the technical evaluation of ecological indicators. It
was developed by members of the U.S. Environmental Protection Agency's (EPA's) Office
of Research and Development (ORD), to assist primarily the indicator research component
of ORD's Environmental Monitoring and Assessment Program (EMAP). The Evaluation
Guidelines are intended to direct ORD scientists during the course of indicator
development, and provide a consistent framework for indicator review. The primary users
will evaluate indicators for their suitability in ORD-affiliated ecological monitoring and
assessment programs, including those involving other federal agencies. This document
may also serve technical needs of users who are evaluating ecological indicators for other
programs, including regional, state, and community-based initiatives.
The Evaluation Guidelines represent a compilation and expansion of previous ORD efforts,
in particular, the initial guidance developed for EMAP. General criteria for indicator
evaluation were identified for EMAP by Messer (1990) and incorporated into successive
versions of the EMAP Indicator Development Strategy (Knapp 1991, Barber 1994). The
early EMAP indicator evaluation criteria were included in program materials reviewed by
EPA's Science Advisory Board (EPA 1991) and the National Research Council (NRC1992,
1995). None of these reviews recommended changes to the evaluation criteria.
However, as one result of the National Research Council's review, EMAP incorporated
additional temporal and spatial scales into its research mission. EMAP also expanded its
indicator development component, through both internal and extramural research, to
address additional indicator needs. Along with indicator development and testing, EMAP's
indicator component is expanding the Indicator Development Strategy, and revising the
general evaluation criteria in the form of technical guidelines presented here with more
clarification, detail, and examples using ecological indicators currently under development.
The Ecological Indicators Working Group that compiled and detailed the Evaluation
Guidelines consists of researchers from all of ORD's National Research Laboratories-
Health and Environmental Effects, Exposure, and Risk Management-as well as ORD's
National Center for Environmental Assessment. This group began in 1995 to chart a
coordinated indicator research program. The working group has incorporated the
Evaluation Guidelines into the ORD Indicator Research Strategy, which applies also to the
extramural grants program, and is working with potential user groups in EPA Regions and
Program Offices, states, and other federal agencies to explore the use of the Evaluation
Guidelines for their indicator needs.
IV
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References
Barber, C.M., ed. 1994. Environmental Monitoring and Assessment Program: Indicator
Development Strategy. EPA/620/R-94/022. U.S. Environmental Protection Agency,
Office of Research and Development: Research Triangle Park, NC.
EPA Science Advisory Board. 1991. Evaluation of the Ecblogical Indicators Report for
EMAP; A Report of the Ecological Monitoring Subcommittee of the Ecological Processes
and Effects Committee. EPA/SAB/EPEC/91-01. U.S. Environmental Protection Agency,
Science Advisory Board: Washington, DC.
Knapp, C.M., ed. 1991. Indicator Development Strategy for the Environmental Monitoring
and Assessment Program. EPA/600/3-91/023. U.S. Environmental Protection Agency,
Office of Research and Development: Corvallis, OR.
Messer, J.J. 1990. EMAP indicator concepts. In: Environmental Monitoring and
Assessment Program: Ecological Indicators. EPA/600/3-90/060. Hunsaker, C.T. and
D.E. Carpenter, eds. United States Environmental Protection Agency, Office of Research
and Development: Research Triangle Park, NC, pp. 2-1 - 2-26.
National Research Council. 1992. Review of EPA's Environmental Monitoring and
Assessment Program: Interim Report. National Academy Press: Washington, DC.
National Research Council. 1995. Review of EPA's Environmental Monitoring and
Assessment Program: Overall Evaluation. National Academy Press: Washington, DC.
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Contents
Abstract iii
Preface iv
: i
i i
Introduction ... vii
Chapter 1 1-1
Presentation of the Guidelines
Chapter 2 2-1
Application of the Indicator Evaluation Guidelines to
Dissolved Oxygen Concentration as an Indicator of the
Spatial Extent of Hypoxia in Estuarine Waters
Charles J. Strobel and James Heltshe
Chapter 3 3-1
Application of the Indicator Evaluation Guidelines to an
Index of Benthic Condition for Gulf of Mexico Estuaries
Virginia D. Engle
Chapter 4 4-1
Application of the Indicator Evaluation Guidelines to a
Multimetric Indicator of Ecological Condition Based on
Stream Fish Assemblages
Frank H. McCormick and David V. Peck
VI
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Introduction
Worldwide concern about environmental threats and sustainable development has led to
increased efforts to monitor and assess status and trends in environmental condition.
Environmental monitoring initially focused on obvious, discrete sources of stress such as
chemical emissions. It soon became evident that remote and combined stressors, while
difficult to measure, also significantly alter environmental condition. Consequently,
monitoring efforts began to examine ecological receptors, since they expressed the effects
of multiple and sometimes unknown stressors and their status was recognized as a societal
concern. To characterize the condition of ecological receptors, national, state, and
community-based environmental programs increasingly explored the use of ecological
indicators.
An indicator is a sign or signal that relays a complex message, potentially from numerous
sources, in a simplified and useful manner. An ecological indicator is defined here as a
measure, an index of measures, or a model that characterizes an ecosystem or one of its
critical components. An indicator may reflect biological, chemical or physical attributes of
ecological condition. The primary uses of an indicator are to characterize current status and
to track or predict significant change. With a foundation of diagnostic research, an
ecological indicator may also be used to identify major ecosystem stress.
There are several paradigms currently available for selecting an indicator to estimate
ecological condition. They derive from expert opinion, assessment science, ecological
epidemiology, national and international agreements, and a variety of other sources (see
Noon 1998, Anonymous 1995, Cairns et al. 1993, HunSaker and Carpenter 1990, and
Rapport et al. 1985). The chosen paradigm can significantly affect the indicator that is
selected and is ultimately implemented in a monitoring program. One strategy is to work
through several paradigms, giving priority to those indicators that emerge repeatedly during
this exercise.
Under EPA's Framework for Ecological Risk Assessment (EPA 1992), indicators must
provide information relevant to specific assessment questions, which are developed to
focus monitoring data on environmental management issues. The process of identifying
environmental values, developing assessment questions, and identifying potentially
responsive indicators is presented elsewhere (Posner 1973, Bardwell 1991, Cowling 1992,
Barber 1994, Thornton et al. 1994). Nonetheless, the importance of appropriate assess-
ment questions cannot be overstated; an indicator may provide accurate information that is
ultimately useless for making management decisions.: In addition, development of
assessment questions can be controversial because of competing interests for
environmental resources. However important, it is not within the purview of this document
to focus on the development and utility of assessment questions. Rather, it is intended to
guide the technical evaluation of indicators within the presumed context of a pre-established
assessment question or known management application.
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Numerous sources have developed criteria to evaluate environmental indicators. This
document assembles those factors most relevant to ORD-affiliated ecological monitoring
and assessment programs into 15 guidelines and, using three ecological indicators as
examples, illustrates the types of information that should be considered under each
guideline. This format is intended to facilitate consistent and technically-defensible
indicator research and review. Consistency is critical to developing a dynamic and iterative
base of knowledge on the strengths and weaknesses of individual indicators; it allows
comparisons among indicators and documents progress in indicator development.
i
Building on Previous Efforts
The Evaluation Guidelines document is not the first effort of its kind, nor are indicator needs
and evaluation processes unique to EPA. As long as managers have accepted
responsibility for environmental programs, they have required measures of performance
(Reams etal. 1992). In an international effort to promote consistency in the collection and
interpretation of environmental information, the Organization for Economic Cooperation
and Development (OECD) developed a conceptual framework, known as the Pressure-
State-Response (PSR) framework, for categorizing environmental indicators (OECD
1993). The PSR framework encompasses indicators of human activities (pressure),
environmental condition (state), and resulting societal actions (response).
The PSR framework is used in OECD member countries including the Netherlands
(Adriaanse 1993) and the U.S., such as in the Department of Commerce's National Oceanic
and Atmospheric Administration (NOAA1990) and the Department of Interior's Task Force
on Resources and Environmental Indicators. Within EPA, the Office of Water adopted the
PSR framework to select indicators for measuring progress towards clean water and safe
drinking water (EPA 1996a). EPA's Office of Policy, Planning and Evaluation (OPPE) used
the PSR framework to support the State Environmental Goals and Indicators Project of the
Data Quality Action Team (EPA 1996b), and as a foundation for expanding the
Environmental Indicators Team of the Environmental Statistics and Information Division.
The Interagency Task Force on Monitoring Water Quality (ITFM 1995) refers to the PSR
framework, as does the International Joint Commission in the Great Lakes Water Quality
Agreement (IJC 1996).
OPPE expanded the PSR framework to include indicators of the interactions among
pressures, states and responses (EPA 1995). These types of measures add an "effects"
category to the PSR framework (now PSR/E). OPPE incorporated EMAP's indicator
evaluation criteria (Barber 1994) into the PSR/E framework's discussion of those indicators
that reflect the combined impacts of multiple stressors on ecological condition.
Measuring management success is now required by the U.S. Government Performance
and Results Act (GPRA) of 1993, whereby agencies must develop program performance
reports based on indicators and goals. In cooperation with EPA, the Florida Center for
Public Management used the GPRA and the PSR framework to develop indicator
evaluation criteria for EPA Regions and states. The Florida Center defined a hierarchy of
six indicator types, ranging from measures of administrative actions such as the number of
permits issued, to measures of ecological or human health, such as density of sensitive
species. These criteria have been adopoted by EPA Region IV (EPA 1996c), and by state
and local management groups. Generally, the focus for guiding environmental policy and
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decision-making is shifting from measures of program and administrative performance to
measures of environmental condition. i
ORD recognizes the need for consistency in indicator evaluation, and has adopted many of
the tenets of the PSR/E framework. ORD indicator research focuses primarily on ecological
condition (state), and the associations between condition and stressors (OPPE's "effects"
category). As such, ORD develops and implements science-based, rather than
administrative policy performance indicators. ORD researchers and clients have
determined the need for detailed technical guidelines to ensure the reliability of ecological
indicators for their intended applications. The Evaluation Guidelines expand on the
information presented in existing frameworks by describing the statistical and
implementation requirements for effective ecological indicator performance. This
document does not address policy indicators or indicators of administrative action, which
are emphasized in the PSR approach.
Four Phases of Evaluation
Chapter One presents 15 guidelines for indicator evaluation in four phases (originally
suggested by Barber 1994): conceptual foundation, feasibility of implementation, response
variability, and interpretation and utility. These phases describe an idealized progression
for indicator development that flows from fundamental concepts to methodology, to
examination of data from pilot or monitoring studies, and lastly to consideration of how the
indicator serves the program objectives. The guidelines are presented in this sequence
also because movement from one phase into the next can represent a large commitment
of resources (e.g., conceptual fallacies may be resolved less expensively than issues raised
during method development or a large pilot study). However, in practice, application of the
guidelines may be iterative and not necessarily sequential. For example, as new
information is generated from a pilot study, it may be necessary to revisit conceptual or
methodological issues. Or, if an established indicator is being modified for a new use, the
first step in an evaluation may concern the indicator's feasibility of implementation rather
than its well-established conceptual foundation.
Each phase in an evaluation process will highlight strengths or weaknesses of an indicator
in its current stage of development. Weaknesses may be overcome through further
indicator research and modification. Alternatively, weaknesses might be overlooked if an
indicator has strengths that are particularly important to program objectives. The protocol
in ORD is to demonstrate that an indicator performs satisfactorily in all phases before
recommending its use. However, the Evaluation Guidelines may be customized to suit the
needs and constraints of many applications. Certain guidelines may be weighted more
heavily or reviewed more frequently. The phased approach described here allows interim
reviews as well as comprehensive evaluations. Finally, there are no restrictions on the
types of information O'ournal articles, data sets, unpublished results, models, etc.) that can
be used to support an indicator during evaluation, so long as they are technically and
scientifically defensible.
IX
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References
i j
Adriaanse, A. 1993. Environmental Policy Performance Indicators: A Study on the
Development of Indicators for Environmental Policy in the Netherlands. Netherlands
Ministry of Housing, Physical Planning and Environment.
Anonymous, 1995. Sustaining the World's Forests: The Santiago Agreement. Journal of
Forestry 93:18-21.
Barber, M.C., ed. 1994. Indicator Development Strategy. EPA/620/R-94/022. U.S.
Environmental Protection Agency, Office of Research and Development: Research
Triangle Park, NC.
Bardwell, L.V. 1991. Problem-framing: a perspective on environmental problem-solving.
Environmental Management 15:603-612.
Cairns J. Jr., P.V. McCormick and B.R. Niederlehner. 1993. A proposed framework for
developing indicators of ecosystem health. Hydrobiologia 263:1-44.
Cowling, E.B. 1992. The performance and legacy of NAPAP. Ecological Applications
2:111-116.
EPA. 1992. Framework for Ecological Risk Assessment. EPA/630/R-92/001. U.S.
Environmental Protection Agency, Office of Research and Development: Washington,
DC.
EPA. 1995. A Conceptual Framework to Support Development and Use of Environmental
Information in Decision-Making. EPA 239-R-95-012. United States Environmental
Protection Agency, Office of Policy Planning and Evaluation, April, 1995.
EPA. 1996a. Environmental Indicators of Water Quality in the United States. EPA 841-R-
96-002, United States Environmental Protection Agency, Office of Water, Washington,
D.C.
EPA. 1996b. Revised Draft: Process for Selecting Indicators and Supporting Data; Second
Edition. United States Environmental Protection Agency, Office of Policy Planning and
Evaluation, Data Quality Action Team, May 1996.
EPA. 1996c. Measuring Environmental Progress for U.S. EPA and the States of Region IV:
Environmental Indicator System. United States Environmental Protection Agency,
Region IV, July, 1996.
Hunsaker, C.T. and D.E. Carpenter, eds. 1990. Ecological Indicators for the Environmental
Monitoring and Assessment Program. EPA 600/3-90/060. The U.S. Environmental
Protection Agency, Office of Research and Development, Research Triangle Park, NC.
IJC. 1996. Indicators to Evaluate Progress under the Great Lakes Water Quality
Agreement. Indicators for Evaluation Task Force; International Joint Commission.
ITFM. 1995. Strategy for Improving Water Quality Monitoring in the United States: Final
Report. Intergovernmental Task Force on Monitoring Water Quality. United States
Geological Survey, Washington, D.C.
NOAA. 1990. NOAA Environmental Digest - Selected Indicators of the United States and
the Global Environment. National Oceanographic and Atmospheric Administration.
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Noon, B.R., T.A. Spies, and M.G. Raphael. 1998. Conceptual Basis for Designing an
Effectiveness Monitoring Program. Chapter 2 In: The Strategy and Design of the
Effectiveness Monitoring Program for the Northwest Forest Plan, General Technical
Report PNW-GTR-437, Portland, OR: USDA Forest Service Pacific Northwest Research
Station, pp. 21-48.
OECD. 1993. OECD Core Set of Indicators for Environmental Performance Reviews.
Environmental Monograph No. 83. Organization for Economic Cooperation and
Development.
Posner, M.I. 1973. Cognition: An Introduction. Glenview, IL: Scott Foresman Publication.
Rapport, D.J., H.A. Reigier, and T.C. Hutchinson. 1985. Ecosystem Behavior under
Stress. American Naturalist 12(5: 617-640.
Reams, M.A., S.R. Coffee, A.R. Machen, and K.J. Poche. 1992. Use of Environmental
Indicators in Evaluating Effectiveness of State Environmental Regulatory Programs. In:
Ecological Indicators, vol 2, D.H. McKenzie, D.E.Hyatt and V.J. McDonald, Editors.
Elsevier Science Publishers, pp. 1245-1273.
Thornton, K.W., G.E. Saul, and D.E. Hyatt. 1994. Environmental Monitoring and
Assessment Program: Assessment Framework. EPA/620/R-94/016. U.S. Environmental
Protection Agency, Office of Research and Development: Research Triangle Park, NC.
XI
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Chapter 1
Presentation of the Guidelines
Phase 1: Conceptual Relevance
The indicator must provide information that is relevant to societal concerns about ecological condition. The
indicator should clearly pertain to one or more identified assessment questions. These, in turn, should be
germane to a management decision and clearly relate to ecological components or processes deemed
important in ecological condition. Often, the selection of a relevant indicator is obvious from the assessment
question and from professional judgement However, a conceptual model can be helpful to demonstrate and
ensure an indicator's ecological relevance, particularly if the indicator measurement is a surrogate for
measurement of the valued resource. This phase of indicator evaluation does not require field activities or
data analysis. Later in the process, however, information may come to light that necessitates re-evaluation
of the conceptual relevance, and possibly indicator modification or replacement. Likewise, new information
may lead to a refinement of the assessment question.
Guideline 1: Relevance to the Assessment
Early in the evaluation process, it must be demonstrated in concept that the proposed indicator is responsive
to an identified assessment question and will provide information useful to a management decision. For
indicators requiring multiple measurements (indices or aggregates), the relevance of each measurement to
the management objective should be identified. In addition, the indicator should be evaluated for its potential
to contribute information as part of a suite of indicators designed to address multiple assessment questions.
The ability of the proposed indicator to complement indicators iat other scales and levels of biological
organization should also be considered. Redundancy with existing indicators may be permissible,
particularly if improved performance or some unique and critical information is anticipated from the proposed
indicator.
Guideline 2: Relevance to Ecological Function
It must be demonstrated that the proposed indicator is conceptually linked to the ecological function of
concern. A straightforward link may require only a brief explanation. If the link is indirect or if the indicator
itself is particularly complex, ecological relevance should be clarified with a description, or conceptual model.
A conceptual model is recommended, for example, if an indicator is comprised of multiple measurements or
if it will contribute to a weighted index. In such cases, the relevance of each component to ecological function
and to the index should be described. At a minimum, explanations and models should include the principal
stressors that are presumed to impact the indicator, as well as the resulting ecological response. This
information should be supported by available environmental, ecological and resource management literature.
Phase 2: Feasibility of Implementation
Adapting an indicator for use in a large or long-term monitoring program must be feasible and practical.
Methods, logistics, cost, and other issues of implementation should be evaluated before routine data
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collection begins. Sampling, processing and analytical methods should be documented for all
measurements that comprise the indicator. The logistics and costs associated with training, travel,
equipment and field and laboratory work should be evaluated and plans for information management and
quality assurance developed.
Note: Need For a Pilot Study
If an indicator demonstrates conceptual relevance to the environmental issue(s) of concern, tests of
measurement practicality and reliability will be required before recommending the indicator for use. In
all likelihood, existing literature will provide a basis for estimating the feasibility of implementation (Phase
2) and response variability (Phase 3). Nonetheless, both new and previously-developed indicators
should undergo some degree of performance evaluation in the context of the program for which they are
being proposed.
A pilot study is recommended in a subset of the region designated for monitoring. To the extent possible,
pilot study sites should represent the range of elevations, biogeographic provinces, water temperatures,
or other features of the monitoring region that are suspected or known to affect the indicators) under
evaluation. Practical issues of data collection, such as time and equipment requirements, may be
evaluated at any site. However, tests of response variability require a priori knowledge of a site's
baseline ecological condition.
Pilot study sites should be selected to represent a gradient of ecological condition from best attainable
to severely degraded. With this design, it is possible to document an indicator's behavior under the range
of potential conditions that will be encountered during routine monitoring. Combining attributes of the
planned survey design with an experimental design may best estimate the variance components. The
pilot study will identify benchmarks of response for sensitive indicators so that routine monitoring sites
can be classified on the condition gradient. The pilot study will also identify indicators that are insensitive
to variations in ecological condition and therefore may not be recommended for use.
Clearly, determining the ecological condition of potential pilot study sites should be accomplished without
the use of any of the indicators under evaluation. Preferably, sites should be located where intensive
studies have already documented ecological status. Professional judgement may be required to select
additional sites for more complete representation of the region or condition gradient.
Guideline 3: Data Collection Methods
Methods for collecting all indicator measurements should be described. Standard, well-documented
methods are preferred. Novel methods should be defended with evidence of effective performance and, if
applicable, with comparisons to standard methods. If multiple methods are necessary to accommodate
diverse circumstances at different sites, the effects on data comparability across sites must be addressed.
Expected sources of error should be evaluated.
Methods should be compatible with the monitoring design of the program for which the indicator is intended.
Plot design and measurements should be appropriate for the spatial scale of analysis. Needs for specialized
equipment and expertise should be identified.
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Sampling activities for indicator measurements should not significantly disturb a site. Evidence should be
provided to ensure that measurements made during a single visit do not affect the same measurement at
subsequent visits or, in the case of integrated sampling regimes, simultaneous measurements at the site.
Also, sampling should not create an adverse impact on protected species, species of special concern, or
protected habitats.
Guideline 4: Logistics
The logistical requirements of an indicator can be costly and time-consuming. These requirements must be
evaluated to ensure the practicality of indicator implementation; and to plan for personnel, equipment,
training, and other needs. A logistics plan should be prepared that identifies requirements, as appropriate,
for field personnel and vehicles, training, travel, sampling instruments, sample transport, analytical
equipment, and laboratory facilities and personnel. The length of time required to collect, analyze and report
the data should be estimated and compared with the needs of the program.
Guideline 5: Information Management :
Management of information generated by an indicator, particularly in a long-term monitoring program, can
become a substantial issue. Requirements should be identified for data processing, analysis, storage, and
retrieval, and data documentation standards should be developed. Identified systems and standards must
be compatible with those of the program for which the indicator is intended and should meet the interpretive
needs of the program. Compatibility with other systems should also be considered, such as the internet,
established federal standards, geographic information systems, and systems maintained by intended
secondary data users.
Guidelines: Quality Assurance
For accurate interpretation of indicator results, it is necessary to understand their degree of validity. A quality"
assurance plan should outline the steps in collection and computation of data, and should identify the data
quality objectives for each step. It is important that means and methods to audit the quality of each step are
incorporated into the monitoring design. Standards of quality assurance for an indicator must meet those of
the targeted monitoring program.
Guideline 7: Monetary Costs
Cost is often the limiting factor in considering to implement an indicator. Estimates of all implementation costs
should be evaluated. Cost evaluation should incorporate economy of scale, since cost per indicator or cost
per sample may be considerably reduced v/hen data are collected for multiple indicators at a given site. Costs
of a pilot study or any other indicator development needs should be included if appropriate.
Phase 3: Response Variability
It is essential to understand the components of variability in indicator results to distinguish extraneous factors
from a true environmental signal. Total variability includes both measurement error introduced during field
and laboratory activities and natural variation, which includes influences ofstressors. Natural variability can
include temporal (within the field season and across years) and spatial (across sites) components.
Depending on the context of the assessment question, some of these sources must be isolated and
quantified in order to interpret indicator responses correctly. It may not be necessary or appropriate to
address all components of natural variability. Ultimately, an indicator must exhibit significantly different
responses at distinct points along a condition gradient. I fan indicator is composed of multiple measurements,
variability should be evaluated for each measurement as well as for the resulting indicator.
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Guidelines: Estimation of Measurement Error
The process of collecting, transporting, and analyzing ecological data generates errors that can obscure the
discriminatory ability of an indicator. Variability introduced by human and instrument performance must be
estimated and reported for all indicator measurements. Variability among field crews should also be
estimated, if appropriate! If standard methods and equipment are employed, information on measurement
error may be available in the literature. Regardless, this information should be derived or validated in
dedicated testing or a pilot study.
Guidelines: Temporal Variability - Within the Field Season
It is unlikely in a monitoring program that data can be collected simultaneously from a large number of sites.
Instead, sampling may require several days, weeks, or months to complete, even though the data are
ultimately to be consolidated into a single reporting period. Thus, within-field season variability should be
estimated and evaluated. For some monitoring programs, indicators are applied only within a particular
season, time of day, or other window of opportunity when their signals are determined to be strong, stable,
and reliable, or when stressor influences are expected to be greatest. This optimal time frame, or index
period, reduces temporal variability considered irrelevant to program objectives. The use of an index period
should be defended and the variability within the index period should be estimated and evaluated.
Guideline 10: Temporal Variability - Across Years
Indicator responses may change over time, even when ecological condition remains relatively stable.
Observed changes in this case may be attributable to weather, succession, population cycles or other natural
inter-annual variations. Estimates of variability across years should be examined to ensure that the indicator
reflects true trends in ecological condition for characteristics that are relevant to the assessment question.
To determine inter-annual stability of an indicator, monitoring must proceed for several years at sites known
to have remained in the same ecological condition.
Guideline 11: Spatial Variability
Indicator responses to various environmental conditions must be consistent across the monitoring region if
that region is treated as a single reporting unit. Locations within the reporting unit that are known to be in
similar ecological condition should exhibit similar indicator results. If spatial variability occurs due to regional
differences in physiography or habitat, it may be necessary to normalize the indicator across the region, or
to divide the reporting area into more homogeneous units.
Guideline 12: Discriminatory Ability
The ability of the indicator to discriminate differences among sites along a known condition gradient should
be critically examined. This analysis should incorporate all error components relevant to the program
objectives, and separate extraneous variability to reveal the true environmental signal in the indicator data.
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Phase 4: Interpretation and Utility
, \ ! .
A useful ecological indicator must produce results that are clearly understood and accepted by scientists,
policy makers, and the public. The statistical limitations of the indicator's performance should be
documented. A range of values should be established that defines ecological condition as acceptable,
marginal, and unacceptable in relation to indicator results. Finally, the presentation of indicator results should
highlight their relevance for specific management decisions and public acceptability.
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Guideline 13: Data Quality Objectives
The discriminatory ability of the indicator should be evaluated against program data quality objectives and
constraints. It should be demonstrated how sample size, monitoring duration, and other variables affect the
precision and confidence levels of reported results, and how these variables may be optimized to attain stated
program goals. For example, a program may require that an indicator be able to detect a twenty percent
change in some aspect of ecological condition over a ten-year period, with ninety-five percent confidence.
With magnitude, duration, and confidence level constrained, sample size and extraneous variability must be
optimized in order to meet the program's data quality objectives. Statistical power curves are recommended
to explore the effects of different optimization strategies on indicator performance.
Guideline 14: Assessment Thresholds
To facilitate interpretation of indicator results by the user community, threshold values or ranges of values
should be proposed that delineate acceptable from unacceptable ecological condition. Justification can be
based on documented thresholds, regulatory criteria, historical records, experimental studies, or observed
responses at reference sites along a condition gradient. Thresholds may also include safety margins or risk
considerations. Regardless, the basis for threshold selection must be documented.
Guideline 15: Linkage to Management Action
Ultimately, an indicator is useful only if it can provide information to support a management decision or to
quantify the success of past decisions. Policy makers and resource managers must be able to recognize the
implications of indicator results for stewardship, regulation, or research. An indicator with practical
application should display one or more of the following characteristics: responsiveness to a specific stressor,
linkage to policy indicators, utility in cost-benefit assessments, limitations and boundaries of application, and
public understanding and acceptance. Detailed consideration of an indicator's management utility may lead
to a re-examination of its conceptual relevance and to a refinement of the original assessment question.
Application of the Guidelines
This document was developed both to guide indicator development and to facilitate indicator review.
Researchers can use the guidelines informally to find weaknesses or gaps in indicators that may be corrected
with further development. Indicator development will also benefit from formal peer reviews, accomplished
through a panel or other appropriate means that bring experienced professionals together. It is important to
include both technical experts and environmental managers in such a review, since the Evaluation Guidelines
incorporate issues from both arenas. This document recommends that a review address information and
data supporting the indicator in the context of the four phases described. The guidelines included in each
phase are functionally related and allow the reviewers to focus on four fundamental questions:
Phase 1 - Conceptual Relevance: Is the indicator relevant to the assessment question (management
concern) and to the ecological resource or' function at risk?
Phase 2 - Feasibility of Implementation: Are the methods for sampling and measuring the environmental
variables technically feasible, appropriate, and efficient for use in a monitoring program?
Phase 3 - Response Variability: Are human errors of measurement and natural variability over time and
space sufficiently understood and documented?
Phase 4 - Interpretation and Utility: Will the indicator convey information on ecological condition that is
meaningful to environmental decision-making?
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Upon completion of a review, panel members should make written responses to each guideline.
Documentation of the indicator presentation and the panel comments and recommendations will establish a
knowledge base for further research and indicator comparisons. Information from ORD indicator reviews will
be maintained with public access so that scientists outside of EPA who are applying for grant support can
address the most critical weaknesses of an indicator or an indicator area.
1 | •!
It is important to recognize that the Evaluation Guidelines by themselves do not determine indicator
applicability or effectiveness. Users must decide the acceptability of an indicator in relation to their specific
needs and objectives, this document was developed to evaluate indicators for ORD-affiliated monitoring
programs, but it should be useful for other programs as well. To increase its potential utility, this document
avoids labeling individual guidelines as either essential or optional, and does not establish thresholds for
acceptable or unacceptable performance. Some users may be willing to accept a weakness in an indicator
if it provides vital information. Or, the cost may be too high for the information gained. These decisions should
be made on a case-by-case basis and are not prescribed here.
Example Indicators
Ecological indicators vary in methodology, type (biological, chemical, physical), resource application (fresh
water, forest, etc.), and system scale, among other ways. Because of the diversity and complexity of
ecological indicators, three different indicator examples are provided in the following chapters to illustrate
application of the guidelines. The examples include a direct measurement (dissolved oxygen concentration),
an index (benthic condition) and a multimetric indicator (stream fish assemblages) of ecological condition. All
three examples employ data from EMAP studies, but each varies in the type of information and extent of
analysis provided for each guideline, as well as the approach and terminology used. The authors of these
chapters present their best interpretations of the available information. Even though certain indicator
strengths and weaknesses may emerge, the examples are not evaluations, which should be performed in a
peer-review format. Rather, the presentations are intended to illustrate the types of information relevant to
each guideline.
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Chapter 2
Application of the Indicator Evaluation Guidelines to
Dissolved Oxygen Concentration as an Indicator of the Spatial Extent of
Hypoxia in Estuarine Waters
Charles J. Strobel, U.S. EPA, National Health and Environmental Effects
Research Laboratory, Atlantic Ecology Division, Narragansett, Rl
and James Heltshe, OAO Corporation,:Narragansett, Rl
This chapter provides an example of how ORD's indicator evaluation process can be applied to a simple
ecological indicator - dissolved oxygen (DO) concentration in estuarine water.
The intent of these guidelines is to provide a process for evaluating the utility of an ecological indicator in
answering a specific assessment question for a specific program. This is important to keep in mind because
any given indicator may be ideal for one application but inappropriate for another. The dissolved oxygen
indicator is being evaluated here in the context of a large-scale monitoring program such as EPA's
Environmental Monitoring and Assessment Program (EMAP). Program managers developed a series of
assessment questions early in the planning process to focus indicator selection and monitoring design. The
assessment question being addressed in this example is What percent of estuarine area is hypoxic/anoxic?
Note that this discussion is not intended to address the validity of the assessment question, whether or not
other appropriate indicators are available, or the, biological significance of hypoxia. It is intended only to
evaluate the utility of dissolved oxygen measurements as an indicator of hypoxia.
This example of how the indicator evaluation guidelines can be applied is a very simple one, and one in
which the proposed indicator, DO concentration, is nearly synonymous with the focus of the assessment
question, hypoxia. Relatively simple statistical techniques were jchosen for this analysis to illustrate the
ease with which the guidelines can be applied. More complex indicators, as discussed in subsequent
chapters, may require more sophisticated analytical techniques. '.
Phase 1: Conceptual Relevance
Guideline 1: Relevance to the Assessment
Early in the evaluation process, it must be demonstrated in concept that the proposed indicator is
responsive to an identified assessment question and will provide information useful to a management
decision. For indicators requiring multiple measurements (indices or aggregates), the relevance of
each measurement to the management objective should be identified. In addition, the indicator should
be evaluated for its potential to contribute information as part of a 'suite of indicators designed to address
multiple assessment questions. The ability of the proposed indicator to complement indicators at other
scales and levels of biological organization should also be considered. Redundancy with existing
indicators may be permissible, particularly if improved performance or some unique and critical information
is anticipated from the proposed indicator.
2-1 ;
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In this example, the assessment question is: What percent of estuarine area is hypoxic/anoxic? Since
hypoxia and anoxia are defined as low levels of oxygen and the absence of oxygen, respectively, the
relevance of the proposed indicator to the assessment is obvious. It is important to note that, in this evaluation,
we are examining the use of DO concentrations only to answer the specific assessment question, not to
comment on the eutrophic state of an estuary. This is a much larger issue that requires additional indicators.
Guideline 2: Relevance to Ecological Function
It must be demonstrated that the proposed indicator is conceptually linked to the ecological function of
concern. A straightforward link may require only a brief explanation. If the link is indirect or if the
Indicator itself is particularly complex, ecological relevance should be clarified with a description, or
conceptual model. A conceptual model is recommended, for example, if an indicator is comprised of
multiple measurements or if it will contribute to a weighted index. In such cases, the relevance of each
component to ecological function and to the index should be described. At a minimum, explanations
and models should include the principal stressors that are presumed to impact the indicator, as well as
the resulting ecological response. This information should be supported by available environmental,
ecological and resource management literature.
The presence of oxygen is critical to the proper functioning of most ecosystems. Oxygen is needed by
aquatic organisms for respiration and by sediment microorganisms for oxidative processes. It also affects
chemical processes, including the adsorption or release of pollutants in sediments. Low concentrations are
often associated with areas of little mixing and high oxygen consumption (from bacterial decomposition).
Figure 2-1 presents a conceptual model of oxygen dynamics in an estuarine ecosystem, and how hypoxic
conditions form. Oxygen enters the system from the atmosphere or via photosynthesis. Under certain
conditions, stratification of the water column may occur, creating two layers. The upper layer contains less
dense water (warmer, lower salinity). This segment is in direct contact with the atmosphere, and since it is
generally well illuminated, contains living phytoplankton. As a result, the dissolved oxygen concentration is
generally high. As plants in this upper layer die, they sink to the bottom where bacterial decomposition
occurs. This process uses oxygen. Since there is generally little mixing of water between these two layers,
oxygen is not rapidly replenished.
This may lead to hypoxic or anoxic conditions near the bottom. This problem is intensified by nutrient
enrichment commonly caused by anthropogenic activities. High nutrient levels often result in high
concentrations of phytoplankton and algae. They eventually die and add to the mass of decomposing
organic matter in the bottom layer, hence aggravating the problem of hypoxia.
2-2
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Sewage effluent
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thrives on nutrients
Dissolved Oxygen
from wave action
& photosynthesis
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D.O. trapped
in lighter layer
Lighter freshwater
e^scape
poxia
xg±ri
Decomposition of organic/
Figure 2-1 . Conceptual model showing the ecological relevance of dissolved
oxygen concentration in estuarine water.
2-3
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Phase 2. Feasibility of Implementation
Guideline 3: Data Collection Methods
Methods for collecting all indicator measurements should be described. Standard, well-documented
methods are preferred. Novel methods should be defended with evidence of effective performance
and, if applicable, with comparisons to standard methods. If multiple methods are necessary to
accommodate diverse circumstances at different sites, the effects on data comparability across sites
must be addressed. Expected sources of error should be evaluated.
Methods should be compatible with the monitoring design of the program for which the indicator is
intended. Plot design and measurements should be appropriate for the spatial scale of analysis. Needs
for specialized equipment and expertise should be identified.
Sampling activities for indicator measurements should not significantly disturb a site. Evidence should
be provided to ensure that measurements made during a single visit do not affect the same measurement
at subsequent visits or, in the case of integrated sampling regimes, simultaneous measurements at the
site. Also, sampling should not create an adverse impact on protected species, species of special
concern, or protected habitats.
Once it is determined that the proposed indicator is relevant to the assessment being conducted, the next
phase of evaluation consists of determining if the indicator can be implemented within the context of the
program. Are well-documented data collection and analysis methods currently available? Do the logistics
and costs associated with this indicator fit into the overall program plan? In some cases a pilot study may be
needed to adequately address these questions. As described below, the answer to all these questions is
yes for dissolved oxygen. Once again, this applies only to using DO to address the extent of hypoxia/anoxia
for a regional monitoring program.
A variety of well-documented methods are currently available for the collection of dissolved oxygen data in
estuarine waters. Electronic instruments are most commonly used. These include simple dissolved oxygen
meters as well as more sophisticated CTDs (instruments designed to measure conductivity, temperature,
and depth) equipped with DO probes. A less expensive, although more labor intensive method, is a Winkler
titration. This "wet chemistry" technique requires the collection and fixation of a water sample from the field,
and the subsequent titration of the sample with a thiosulphate solution either in the field or back in the
laboratory. Because this method is labor intensive, it is probably not appropriate for large monitoring
programs and will not be considered further. The remainder of this discussion will focus on the collection of
DO data using electronic instrumentation.
Other variations in methodology include differences in sampling period, duration, and location. The first
consideration is the time of year. Hypoxia is most severe during the summer months when water temperatures
are high and the biota are most active. This is therefore the most appropriate time to monitor DO, and it is
the field season for the program in which we are considering using this indicator. The next consideration is
whether to collect data at a single point in time or to deploy an instrument to collect data over an extended
period. Making this determination requires a priori knowledge of the DO dynamics of the area being studied.
This issue will be discussed further in Guideline 9. For the purpose of this evaluation guideline, we will
focus on single point-in-time measurements.
2-4
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The third aspect to be considered is where in the water column to make the measurements. Because
hypoxia is generally most severe near the bottom, a bottom measurement is critical. For this program, we
will be considering a vertical profile using a CTD. This provides uswith information on the DO concentration
not only at the bottom, but throughout the water column. The additional information can be used to deter-
mine the depth of the pycnocline (a sharp, vertical density gradient in the water column), and potentially the
volume of hypoxic water. Using a CTD instead of a DO meter provides ancillary information on the water
column (salinity, temperature, and depth of the measurements). This information is needed to characterize
the water column at the station, so using a CTD eliminates the need for multiple measurement with different
instruments.
The proposed methodology consists of lowering a CTD through the water column to obtain a vertical profile.
The instrument is connected to a surface display. Descent is halted at one meter intervals and the CTD held
at that depth until the DO reading stabilizes. This process is continued until the unit is one meter above the
bottom, which defines the depth of the bottom measurement. ;
Guideline 4: Logistics
The logistical requirements of an indicator can be costly and time-consuming. These requirements
must be evaluated to ensure the practicality of indicator implementation, and to plan for personnel,
equipment, training, and other needs. A logistics plan should be prepared that identifies requirements,
as appropriate, for field personnel and vehicles, training, travel, sampling instruments, sample transport,
analytical equipment, and laboratory facilities and personnel. The length of time required to collect,
analyze and report the data should be estimated and compared with the needs of the program.
The collection of dissolved oxygen data in the manner described under Guideline 3 requires little additional
planning over and above that required to mount a field effort involving sampling from boats. Collecting DO
data adds approximately 15 to 30 minutes at each station, depending on water depth and any problems that
may be encountered. The required gear is easily obtainable from a number of vendors (see Guideline 7 for
estimated costs), and is compact, requiring little storage space on the boat. Each field crew should be
provided with at least two CTD units, a primary unit and a backup unit. Operation of the equipment is fairly
simple, but at least one day of training and practice is recommended before personnel are allowed to collect
actual data. :
Dissolved oxygen probes require frequent maintenance, including changing membranes. This should be
conducted at least weekly, depending on the intensity of usage. This process needs to be worked into
logistics as the membrane must be allowed to "relax" for at least 12 hours after installation before the unit
can be recalibrated. In addition, the dissolved oxygen probe must be air-calibrated at least once per day.
This process takes about 30 minutes and can be easily conducted: prior to sampling while the boat is being
readied for the day. :
No laboratory analysis of samples is required for this indicator; however, the data collected by field crews
should be examined by qualified personnel.
In summary, with the proper instrumentation and training, field personnel can collect data supporting this
indicator with only minimal effort. -!
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Guideline 5: Information Management
Management of information generated by an indicator, particularly in a long-term monitoring program,
can become a substantial issue. Requirements should be identified for data processing, analysis,
storage, and retrieval, and data documentation standards should be developed. Identified systems and
standards must be compatible with those of the program for which the indicator is intended and should
meet the interpretive needs of the program. Compatibility with other systems should also be considered,
such as the internet, established federal standards, geographic information systems, and systems
maintained by intended secondary data users.
This indicator should present no significant problems from the perspective of information management.
Based on the proposed methodology, data are collected at one-meter intervals^ The values are written on
hard-copy datasheets and concurrently logged electronically in a surface unit attached to the CTD. (Note
that this process will vary with the method used. Other options include not using a deck unit and logging
data in the CTD itself for later uploading to a computer; or simply typing values from the hard-copy datasheet
directly into a computer spreadsheet). After sampling has been completed, data from the deck unit can be
uploaded to a computer and processed in a spreadsheet package. Processing would most likely consist of
plotting out dissolved oxygen with depth to view the profile. Data should be uploaded to a computer daily.
The user needs to pay particular attention to the memory size of the CTD or deck unit. Many instruments
may contain sufficient memory for only a few casts. To avoid data loss it is important that the data be
uploaded before the unit's memory is exhausted. The use of hard-copy datasheets provides a back-up in
case of the loss of electronic data.
Guideline 6: Quality Assurance
For accurate interpretation of indicator results, it is necessary to understand their degree of validity. A
quality assurance plan should outline the steps in collection and computation of data, and should identify
the data quality objectives for each step. It is important that means and methods to audit the quality of
each step are incorporated into the monitoring design. Standards of quality assurance for an indicator
must meet those of the targeted monitoring program.
The importance of a well-designed quality assurance plan to any monitoring program cannot be overstated.
One important aspect of any proposed ecological indicator is the ability to validate the results. Several
methods are available to assure the quality of dissolved oxygen data collected in this example. The simplest
method is to obtain a concurrent measurement with a second instrument, preferably a different type than is
used for the primary measurement (e.g., using a DO meter rather than a CTD). This is most easily performed
at the surface, and can be accomplished by hanging both the CTD and the meter's probe over the side of
the boat and allowing them to come to equilibrium. The DO measurements can then be compared and, if
they agree within set specifications (e.g., 0.5 mg/L), the CTD is assumed to be functioning properly. The DO
meter should be air-calibrated immediately prior to use at each station. One could argue against the use of
an electronic instrument to check another electronic instrument, but it is unlikely that both would be out of
calibration in the same direction, to the same magnitude. An alternative method is to collect a water sample
for Winkler titration; however, this would not provide immediate feedback. One would not know that the data
were questionable until the sample is returned to the laboratory and it is too late to repeat the CTD cast.
Although Winkler titrations can be performed in the field, the rocking of the boat can lead to erroneous
titration.
2-6
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Additional QA of the instrumentation can be conducted periodically in the laboratory under more controlled
conditions. This might include daily tests in air-saturated water in the laboratory, with Winkler titrations
verifying the results. Much of this depends upon the logistics of the program, for example, whether the
program is run in proximity to a laboratory or remotely.
Three potential sources of error could invalidate results for this indicator: 1) improper calibration of the CTD,
2) malfunction of the CTD, and 3) the operator not allowing sufficient time for the instrument to equilibrate
before each reading is taken. Taking a concurrent surface measurement should identify problems 1 and 2.
The third source of error is more difficult to control, but can be minimized with proper training. If data are not
uploaded directly from the CTD or surface unit into a computer, another source of error, transcription error,
is also possible. However, this can be easily determined through careful review of the data.
Guideline 7: Monetary Costs
Cost is often the limiting factor in considering to implement an indicator. Estimates of all implementation
costs should be evaluated. Cost evaluation should incorporate economy of scale, since cost per indicator
or cost per sample may be considerably reduced when data are collected for multiple indicators at a
given site. Costs of a pilot study or any other indicator development needs should be included if
appropriate.
Cost is not a major factor in the implementation of this indicator. The sampling platform (boat) and personnel
costs are spread across all indicators. As stated earlier, this indicator adds approximately 30 minutes to
each station; however, one person can be collecting DO data while other crew members are collecting other
types of data or samples. :
The biggest expense is the equipment itself. Currently the most commonly used type of CTD costs
approximately $6,000 each, the deck unit $3,000 and a DO meter approximately $1,500. A properly outfitted
crew would need two of each, which totals $21,000. Assuming this equipment lasts for four years at 150
stations per year, the average equipment cost per station would be only $35. Expendable supplies (DO
membranes and electrolyte) should be budgeted at approximately $200 per year, depending upon the size
of the program.
Phase 3: Response Variability
Once it is determined that an indicator is relevant and can be implemented within the context of a specific
monitoring program, the next phase consists of evaluating the expected variability in the response of that
indicator. In this phase of the evaluation, it is very important to keep in mind the specific assessment
question and the program design. For this example, the program js a large-scale monitoring program and
the assessment question is focused on the spatial extent of hypoxia. This is very different from evaluating
the hypoxic state at a specific station, as will be shown below in our evaluation of variability.
The data used in this evaluation come from two related sources. The majority of the data were collected as
part of EMAP's effort in the estuaries of the Virginian Province (Cape Cod, MA to Cape Henry, VA) from
1990 to 1993. The distribution of sampling locations is shown in Figure 2-2. This effort is described in
Holland (1990), Weisberg et al. (1993), and Strobel etal. (1995). Additional data from EPA's Mid-Atlantic
Integrated Assessment (MAIA) program, collected in 1997, were also used. These data were collected in
the small estuaries associated with Chesapeake Bay.
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Figure 2-2. Each dot identifies an EMAP-Virginian Province station location in estuaries, 1990-1993.
Guideline 8: Estimation of Measurement Error
The process of collecting, transporting, and analyzing ecological data generates errors that can obscure
the discriminatory ability of an indicator. Variability introduced by human and instrument performance
must be estimated and reported for all indicator measurements. Variability among field crews should
also be estimated, if appropriate. If standard methods and equipment are employed, information on
measurement error may be available in the literature. Regardless, this information should be derived
or validated in dedicated testing or a pilot study.
Using the QA information collected by EMAP over the period from 1991 to 1993 (a different method was
employed in 1990, so those data were excluded from this analysis), we can estimate the error associated
with this measurement Figure 2-3 is a frequency distribution for 784 stations of the absolute difference
between the DO measurements collected by the CTD and the DO meter used as a cross check (A DO). The
data included in this figure were collected over three years by nine different field crews. Therefore, the
figure illustrates the total measurement error—that associated with instrumentation as well as with operation
of the instruments. Of the 784 stations, the measurement quality objective of < 0.5 mg/L was met at over 90
percent. No bias was detected, meaning the CTD values were not consistently higher or lower than those
from the DO meter.
i i i,
It is of course possible to analyze instrumentation and operation errors separately. Such analyses would be
necessary if total error exceeded a program's measurement quality objectives, in order to isolate and attempt
2-8
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to minimize the source of error. In fact, EMAP-Estuaries field crews conducted side-by-side testing during
training to minimize between-crew differences. Good comparability between crews was achieved. However,
because this was considered a training exercise, these data were not saved. Such side-by-side testing
could be incorporated into any future analyses of the dissolved oxygen indicator. This would need to be
conducted in the laboratory rather than in the field to eliminate the1 inherent temporal and spatial varability
at any given site.
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A D.O. (mg/L)
Figure 2-3. Frequency distribution of EMAP dissolved oxygen quality assurance data. A DO
represents the absolute difference between the CTD measurement and that
from a second instrument. Over 90% of the stations met the measurement quality
objective (A DO < 0.5 mg/L).
Other potential sources of measurement error include inadequate thermal equilibration of the instrumentation
prior to conducting a cast, and allowing insufficient time for the DO probe to repond to changes in DO
concentration across an oxycline. Both can be addressed by proper training and evaluated by examining
the full vertical profile for several parameters (i.e., temperature arid DO).
Guideline 9: Temporal Variability - Within the Field Season
It is unlikely in a monitoring program that data can be collected simultaneously from a large number of
sites. Instead, sampling may require several days, weeks, or months to complete, even though the
data are ultimately to be consolidated into a single reporting period. Thus, within-field season variability
should be estimated and evaluated. For some monitoring programs, indicators are applied only within
a particular season, time of day, or other window of opportunity when their signals are determined to be
strong, stable, and reliable, or when stressor influences are expected to be greatest. This optimal time
frame, or index period, reduces temporal variability considered irrelevant to program objectives. The
use of an index period should be defended and the variability within the index period should be estimated
and evaluated. '.
2-9
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The dissolved oxygen concentration of estuarine water is highly dependent on a variety of factors, including
photosynthesis (which is affected by nutrient levels), temperature, salinity, tidal currents, stratification, winds,
and water depth. These factors make DO concentrations highly variable over relatively short time periods.
There is also a strong seasonal component, with lowest dissolved oxygen concentrations experienced
during the summer months of late July through September. In the EMAP program, estuarine monitoring
was conducted during the summer when the biotic community is most active. Since we are interested in
DO because of its effects on aquatic biota, and since summer is the season when organisms are most
active and dissolved oxygen concentrations are generally the lowest, it is also the most appropriate season
for evaluating the extent of hypoxic conditions. In 1990, EMAP conducted sampling in the Virginian Province
estuaries to determine the most appropriate index period within the summer season. A subset of stations
were sampled in each of three sampling intervals; 20 June to 18 July, 19 July to 31 August, and 1 September
to 22 September. The results of analysis of the data collected at these stations showed the DO concentrations
to be most consistent in Intervals 2 and 3, suggesting that July 19-September 22 is the most appropriate
definition of the index period for the study area. Similar reconnaissance would need to be performed in
other parts of the country where this indicator may be employed.
r
Even within the index period, DO concentrations at a given station vary hourly, daily and weekly. The high
degree of temporal variability in DO at one station over the period from July 28 through August 26 is shown
in Figure 2-4.
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9 11 13 15
Data (July/August)
17
19 21 23 25
Figure 2-4. Continuous plot of bottom dissolved oxygen concentration at EMAP
station 088 in Chesapeake Bay, 1990.
Figure 2-5 illustrates a 24-hour record of bottom dissolved oxygen from the same station. Although
concentrations vary throughout the day, most mid-Atlantic estuaries generally do not exhibit a strong diurnal
signal; most of the daily variability is associated with other factors such as tides (Weisberg et al. 1993). This
is not the case in other regions, such as the Gulf of Mexico, where EMAP showed a strong diurnal signal
(Summers et al. 1993). Such regional differences in temporal variability illustrate the need to tailor
implementation of the indicator to the specific study area.
,! ' ' I !
Short-term variability, as illustrated in Figures 2-4 and 2-5, makes this indicator, using single point-in-time
measurements, inappropriate for characterizing a specific station. However, single stations are not the
focus of the program for which this indicator is being evaluated in this example, the purpose of EMAP is to
evaluate ecological condition across a broad geographic expanse, not at individual stations. The percent
area hypoxic throughout the index period is more stable on a regional scale.
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CD
3 s
Time
Figure 2-5. A 24-hour segment of the DO plot from Figure 2-4 showing a
lack of strong diurnal signal.
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- Interval 3
4 6
Bottom D.O. (mg/U)
Figure 2-6. Comparison of cumulative distribution functions for
EMAP-Virginian Province Intervals 2 and 3.
By plotting the cumulative distribution of bottom DO concentrations as a function of their weighted frequency
(based on the spatial area represented by each station), we can estimate the percent area across the region
with a DO concentration below any given value. Figure 2-6 shows cumulative distribution functions (CDFs)
for point-in-time bottom DO measurements collected in 1990 during intervals 2 and 3 (i.e., the index period).
To determine the percent of estuarine area with a dissolved oxygen concentration less than 5 mg/L, one
would look for the point on the y axis where the curve intersects the value of 5 on the x axis (i.e., 15 to 20%
in Figure 2-6). Confidence intervals can also be constructed around these CDFs, but were eliminated here
for the sake of clarity. This figure shows that the percent area classified as hypoxic (i.e., DO <5 or <2 mg/L)
was approximately the same in the first half of the index period as it was in the second half. This stability
makes this indicator appropriate for monitoring programs documenting the spatial extent of environmental
condition on large (i.e., regional) scales.
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Guideline 10: Temporal Variability - Across Years
Indicator responses may change over time, even when ecological condition remains relatively stable.
Observed changes in this case may be attributable to weather, succession, population cycles or other
natural inter-annual variations. Estimates of variability across years should be examined to ensure that
the Indicator reflects true trends in ecological condition for characteristics that are relevant to the
assessment question. To determine inter-annual stability of an indicator, monitoring must proceed for
several years at sites known to have remained in the same ecological condition.
too
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Dissolved Oxygen (mg/L)
Figure 2-7. Annual cumulative distribution functions of bottom dissolved
oxygen concentration for the Virginian Province, 1990-1993.
As discussed above, point-in-time DO measurements can be highly variable at any given station. This
applies to across-year comparisons as well as within-season comparisons. However, when using this
Information to address the spatial extent of hypoxia across a broad region, this indicator is reasonably
stable. Figure 2-7 shows the similarity of individual CDFs of bottom dissolved oxygen concentration in the
estuaries of the Virginian Province for 1990 through 1993. Figure 2-8 shows the percent area below 5 and
2 mg/L (defined by EMA'P as criteria for hypoxic and very hypoxic conditions, respectively) for those same
years. Note that the percent area considered hypoxic by these criteria do not differ significantly from year to
year, despite differences in climatic conditions (temperature and rainfall: Figure 2-9).
2-12
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Figure 2-9. Climatic conditions in the Virginian Province, 1990-1993. (A) deviation from
mean air temperature, and (B) deviation from mean rainfall.
Guideline 11: Spatial Variability
Indicator responses to various environmental conditions must be consistent across the monitoring
region if that region is treated as a single reporting unit. Locations within the reporting unit that are
known to be in similar ecological condition should exhibit similar indicator results. If spatial variability
occurs due to regional differences in physiography or habitat, it maybe necessary to normalize the
indicator across the region, or to divide the reporting area into more homogeneous units.
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Guideline 12: Discriminatory Ability
The ability of the indicator to discriminate differences among sites along a known condition gradient
should be critically examined. This analysis should incorporate all error components relevant to the
program objectives, and separate extraneous variability to reveal the true environmental signal in the
indicator data.
Since we are evaluating the use of dissolved oxygen concentration as an indicator of hypoxia, which is
defined as a DO concentration below a certain value, there is no spatial variability associated with this
indicator. Simply stated, using a criterion of 5 mg/L to define hypoxia, a DO concentration of 4 mg/L will
Indicate hypoxic conditions regardless of where the sample is collected. Note that this does NOT mean that
adverse biological effects will always occur if the DO concentration falls below 5 mg/L Nor does it mean
that a given level of nutrient enrichment will result in the same degree of hypoxia in all areas. Both of these
components are spatially variable and are affected by a number of environmental factors. However, they do
not affect the relationship between dissolved oxygen concentration (the indicator) and hypoxia as defined
(the asssessment issue).
Because of the large number of variables known to affect the dissolved oxygen concentration in sea water,
most of which are not routinely measured, the utility of variability component analyses is limited. However,
this indicator is really a direct measurement of the focus of the assessment question; therefore, discriminatory
ability is inherently high.
; : ]
Since the program's objective is to estimate the percent of estuarine area with hypoxic/anoxic condition on
a broad geographic scale rather than to compare individual sites, an alternative way to look at this indicator's
discriminatory ability is to plot out the CDF along with its confidence intervals. Figure 2-10 illustrates such
a plot for the EMAP Virginian Province data collected from 1990 to 1993. (See Strobel ef a/. [1995] for a
discussion of how the confidence intervals were developed). The tight 95% confidence intervals suggest
that this indicator, as applied, has a high degree of discriminatory ability - a relatively small shift in the curve
2468
Bottom Dissolved Oxygen (mg/L)
Figure 2-10. Cumulative distribution function of bottom dissolved oxygen concentration
for the EMAP Virginian Province, 1990-1993. Error bars represent 95%
confidence intervals.
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can be determined to be significant. These confidence intervals are a function of both the variability in the
data and the sampling design. Alternative approaches might be needed to evaluate the utility of this indicator
for programs with significantly different designs.
Comparisons between curves can be made for those generated in two different regions (i.e., status
comparison) or from the same region at two different times (i.e., trends comparison). Although this analysis
does not separate out variability due to extraneous factors, it does provide insight into the utility of the
indicator to discriminate condition using the design of the EMAP-Virginian Province program.
Phase 4: Interpretation and Utility
Once it is determined that the indicator is relevant, applicable, and responsive, the final phase of evaluation
is to determine if the results can be clearly understood and useful;
Guideline 13: Data Quality Objectives
The discriminatory ability of the indicator should be evaluated against program data quality objectives
and constraints. It should be demonstrated how sample size, monitoring duration, and other variables
affect the precision and confidence levels of reported results, and how these variables may be optimized
to attain stated program goals. For example, a program may require that an indicator be able to detect
a twenty percent change in some aspect of ecological condition over a ten-year period, with ninety-five
percent confidence. With magnitude, duration, and confidence level constrained, sample size and
extraneous variability must be optimized in order to meet the program's data quality objectives. Statistical
power curves are recommended to explore the effects of different optimization strategies on indicator
performance.
The Data Quality Objective for trends in EMAP-Estuaries was to be able to detect a two percent change per
year over 12 years with 90% confidence. This indicator meets that requirement as shown in Figure 2-11.
This figure shows several power curves for annual changes ranging from one to three percent. Note that
these curves are based on data from more than 400 stations sampled over a period of four years. The ability
to detect trends will differ using different sampling designs. If fewer stations were to be sampled, a new set
of power curves could be generated to show the ability to detect trends with that number of stations.
Guideline 14: Assessment Thresholds I
To facilitate interpretation of indicator results by the user community, threshold values or ranges of
values should be proposed that delineate acceptable from unacceptable ecological condition. Justification
can be based on documented thresholds, regulatory criteria, historical records, experimental studies,
or observed responses at reference sites along a condition gradient. Thresholds may also include
safety margins or risk considerations. Regardless, the basis for threshold selection must be documented.
Although there is debate regarding their validity, assessment thresholds already exist for dissolved oxygen.
Several states have adopted 5 mg/L as a criterion for 24-hour continuous concentrations and 2 mg/L as a
point-in-time minimum concentration for supporting a healthy ecosystem. This is supported by EPA research
(U.S. EPA 1998) which shows long-term effects at 4.6 mg/L and acute effects at 2.1 mg/L. If these thresholds
change in the future, data collected on this indicator can easily be re-analyzed to produce new assessments
of the hypoxic area.
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1 ••
0.9 ••
o.a ••
0,7 ••
08 -•
0.5 ••
0.4 -•
O.3 -•
0.2 ••
O.1 ••
O 4-
-3%
1%
9
Years
12
J
Figure 2-11. Power curves for detecting annual changes of 1,1.5, 2, 2.5 and 3% in the
percent of area exhibiting hypoxia based on EMAP Virginian Province data,
1990-1993.
Guideline 15: Linkage to Management Action
Ultimately, an indicator is useful only if it can provide information to support a management decision or
to quantify the success of past decisions. Policy makers and resource managers must be able to
recognize the implications of indicator results for stewardship, regulation, or research. An indicator with
practical application should display one or more of the following characteristics: responsiveness to a
specific stressor, linkage to policy indicators, utility in cost-benefit assessments, limitations and boundaries
of application, and public understanding and acceptance. Detailed consideration of an indicator's
management utility may lead to a re-examination of its conceptual relevance and to a refinement of the
original assessment question.
Currently, hypoxia and eutrophication are important issues, particularly in the mid-Atlantic states. Millions of
dollars are being spent on sewage treatment upgrades and controls for non-point sources. If these actions
are successful, they will result in a decrease in the percent area with low dissolved oxygen, making this
indicator important for measuring the efficacy of these efforts.
Mitigation of hypoxia is a complicated issue. Sewage treatment plants, non-point source pollution, and a
variety of natural sources introduce nutrients to our estuaries. Increased nutrients can lead to hypoxic
conditions, but the effects of hypoxia are not always easy to predict. For example, increased turbidity may
inhibit phytoplankton growth, which, through a series of complicated interactions, may decrease a system's
susceptibility to reductions in DO. Management interest is not necessarily to reduce nutrient levels, but to
protect the biota of our estuaries from hypoxic conditions, which is exactly what this indicator is measuring.
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Summary
The results of this evaluation show that point-in-time bottom dissolved oxygen measurement can be an
appropriate indicator for determining the spatial extent of hypoxia in a regional monitoring program. The
indicator is conceptually relevant to both the assessment questibn and ecological function. It is easily
implemented at reasonable cost with well-defined methods. Probably the greatest concern in the
implementation of this indicator is the temporal and spatial variability of DO concentrations. This variability
limits the utility of point-in-time measurements in describing the conditions at a given station. However,
when the indicator is applied across a large region to generate an estimate of the overall percent area
hypoxic, this evaluation indicates reasonable stability of the indicator. This scale-dependent conclusion
clearly illustrates the need to evaluate an indicator in the context iof a specific monitoring program, as an
indicator that may be ideal for one type of program may be inappropriate for another. In this case, the
indicator itself could be applied to monitoring programs designed to characterize conditions at individual
stations if alternative methods were employed (e.g., continuous monitoring). Lastly, dissolved oxygen data
are easily interpretable relative to the assessment question on the extent of hypoxia, and are of high value
to environmental managers.
Acknowledgements
This work was supported in part by EPA contract number 68-W5-0065 to OAO Corporation. This chapter is
NHEERL-Atlantic Ecology Division contribution # NHEERL-NAR-2068.
References
Holland, A.F. (ed.). 1990. Near Coastal Program Plan for 1990: Estuaries. EPA/600/4-900/033. U.S.
Environmental Protection Agency, Office of Research and Development, Narragansett, Rl.
Strobel, C.J., H.W. Buffum, S.J. Benyi, E.A. Petrocelli, D.R. Reifsteck, and D.J.Keith. 1995. Statistical
Summary: EMAP-Estuaries Virginian Province -1990-1993. EPA/620/R-94/026.
Summers, J.K., J.M. Macauley, V.D. Engle, G.T. Brooks, P.T. Heitmuller, and M.T. Adams. 1993. Louisianian
Province Demonstration Project Report: EMAP-Estuaries-1991.' U.S. Environmental Protection Agency,
Office of Research and Development, Gulf Breeze, FL. '
U.S. EPA, 1992. What is the Long Island Sound Study? Fact sheet # 15, U.S. Environmental Protection
Agency, Long Island Sound Study, Stamford, CT.
U.S. EPA. 1998. (Draft) Ambient Water Quality Criteria- DissolvedOxygen. U.S. Environmental Protection
Agency, Office of Water, Washington, DC. (In Review).
Weisberg, S.B., J.B. Frithsen, A.F. Holland, J.F. Paul, K.J. Scott, J.K. Summers, H.T. Wilson, R.M.Valente,
D.G. Heimbuch, J. Gerritsen, S.C. Schimrnel, and R.W. Latimer. 1993. EMAP-Estuaries, Virginian Province
1990 Demonstration Project Report. EPA/620/R- 93/006. U.S. Environmental Protection Agency, Office
of Research and Development, Narragansett, Rl.
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Chapter Three
Application of the Indicator Evaluation Guidelines to an Index
of Benthic Condition for Gulf of Mexico Estuaries
Virginia D. Engle, U.S. EPA, National Health and Environmental Effects
Research Laboratory, Gulf Ecology Division, Gulf Breeze, FL
This section provides an example of how the Evaluation Guidelines for Ecological Indicators can be applied to
a multimetric ecological indicator - a benthic index for estuarine waters.
The intent of the Evaluation Guidelines is to provide a process for evaluating the utility of an ecological
indicator in answering a specific assessment question for a specific program. This is important to keep in
mind because any given indicator may be ideal for one application but inappropriate for another. The benthic
index is evaluated here in the context of a large-scale monitoring program, specifically EPA's Environmental
Monitoring and Assessment Program - Estuaries (EMAP-E). Program managers developed a series of
assessment questions early in the planning process and focused the monitoring design accordingly.
One of the primary goals of EMAP-E was to develop and monitor indicators of pollution exposure and habitat
condition in order to determine the magnitude and geographical distribution of resources that are adversely
affected by pollution and other environmental stresses (Messer et al. 1991). In its first year of implementation
in the estuaries of the Gulf of Mexico, EMAP-E collected data to develop a preliminary assessment of the
association between benthic communities, sediment contamination and hypoxia. A benthic index of estuarine
integrity was developed that incorporated measures of community composition and diversity, and discriminated
between areas of undegraded vs. degraded environmental conditions. In this way, a benthic index would
reflect the collective response of the benthic community to pollution exposure or adverse habitat conditions.
Information gained from monitoring benthic macroinvertebrate communities has been widely used to measure
the status of and trends in the ecological condition of estuaries. Benthic macroinvertebrates are good indicators
of estuarine condition because they are relatively sedentary within the sediment-water interface and deeper
sediments (Dauer et al. 1987). Both short-term disturbances such as hypoxia and long-term disturbances
such as accumulation of sediment contaminants affect the population and community dynamics of benthic
macroinvertebrates (Rosenberg 1977, Harper et al. 1981, Rygg 1986). Many of the effects of such disturbances
on the benthos have been documented and include changes in indicators such as benthic diversity, long-lived
to short-lived species, biomass, abundance of opportunistic or pollution-tolerant organisms, and the trophic or
functional structure of the community (Pearson and Rosenberg 1978, Santos and Simon 1980, Gaston 1985,
Warwick 1986, Gaston and Nasci 1988, Gaston and Young 1992).
The search for an index that both integrates parameters of macrobenthic community structure and distinguishes
between polluted and unpolluted areas has been a recent focus of marine and estuarine benthic monitoring
programs (Warwick 1986, Chapman 1989, McManus and Pauly 1990). An ideal indicator of the response of
benthic organisms to perturbations in the environment would not only quantify their present condition in
ecosystems but also would integrate the effects of anthropogenic and natural stressors on the organisms
overtime (Boesch and Rosenberg 1981, Messer et al. 1991).
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Recently, researchers have successfully developed multimetric indices that combine the various effects of
natural and anthropogenic disturbances on benthic communities. Although initially developed for freshwater
systems (Lenat 1988, Lang et al. 1989, Plafkin et al. 1989, Kerans and Karr 1994, Lang and Reymond
1995), variations of the benthic index of biotic integrity (B-IBI) concept have been successfully applied to
estuaries (Engle et al. 1994, Ranasinghe et al. 1994, Weisberg et al. 1997, Engle and Summers 1999, Van
Dolah et al. 1999). There are some basic differences between the approach we have used and the traditional
IBI approach. The parameters that comprise our benthic index were chosen empirically as the parameters
that provided the best statistical discrimination between sites with known degraded or undegradecl conditions
(where degraded is defined as having undesirable or unacceptable ecological condition). The weighting
factors applied to these parameters were also determined empirically based on the contribution of each
parameter to the fit of the model. The parameters included in a traditional IBI approach were chosen by the
researchers based on evaluations of cumulative ecological dose response curves. The rank scoring of each
parameter (e.g., as a 1,3, or 5) was based on a subjective weighting of the distribution of values from known
sites. The parameters in the IBI are equally weighted in the calculation of the overall rank score. Both
approaches to developing multimetric indices have advantages and criticisms; however, the ultimate goal is
the same - to combine complex community information into a meaningful index of condition.
Multimetric benthic indices can help environmental managers who require a standardized means of tracking
the ecological condition of estuaries. However, environmental managers and policy makers also desire an
easy, manageable method of identifying the extent of potentially degraded areas and a means of associating
biotic responses with environmental stressors (Summers ef al. 1995). In order for an indicator to be appropriate
for the assessment of estuarine health, it should incorporate geographic variation and should recognize the
inherent multivariate nature of estuarine systems (Karr 1993, Wilson and Jeffrey 1994). While the statistical
methods used to develop indicators may often be complex, it is the end product, an index of condition, that
is of interest to resource managers. By applying a mathematical formula to multivariate benthic data, resource
managers can calculate a single, scaled index that can then be used to evaluate the benthic condition of
estuaries in their region. Although indices have been accused of oversimplifying orovergeneralizing biological
processes, they play an important role in resource management (i.e., to provide criteria with which to
characterize a resource as impaired or healthy) (Rakocinski ef al. 1997). While ecological indicators were
developed to serve as tools for the preliminary assessment of ecological condition, they are not intended to
replace a complete analysis of the benthic biological dynamics nor were they intended to stand alone. They
also should be used in conjunction with other synoptic data on sediment toxicity and pollutant concentrations
to provide a weight-of-evidence basis for judging the incidence of anthropogenically induced disturbances
(Hylandefa/. 1998).
Phase 1: Conceptual Relevance
Guideline 1: Relevance To The Assessment
Early in the evaluation process, it must be demonstrated in concept that the proposed indicator is
responsive to an identified assessment question and will provide information useful to a management
decision. For indicators requiring multiple measurements (indices or aggregates), the relevance of each
measurement to the management objective should be identified. In addition, the indicator should be
evaluated for its potential to contribute information as part of a suite of indicators designed to address
multiple assessment questions. The ability of the proposed indicator to complement indicators at other
scales and levels of biological organization should also be considered. Redundancy with existing
indicators may be permissible, particularly if improved performance or some unique and critical
information is anticipated from the proposed indicator.
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The Environmental Monitoring and Assessment Program (EMAP) focused on providing much needed
information about the condition of the Nation's ecological resources. EMAP was designed to answer the
following questions (Summers etal. 1995):
1. What is the status, extent, and geographical distribution of our ecological resources?
2. What proportions of these resources are declining or improving? Where? At what rate?
3. What factors are likely to be contributing to declining conditions?
4. Are pollution control, reduction, mitigation, and prevention programs achieving overall
improvement in ecological condition?
To accomplish these management objectives, EMAP sought to develop a suite of indicators that would
represent the response of biota to environmental perturbations. These indicators were categorized as response,
exposure, habitat, or stressor. Our indicator, the benthic index, was classified as a response indicator because
it represents the response of the estuarine benthic community to environmental stressors (e.g., sediment
contaminants and hypoxia). A good response indicator should demonstrate the ability to associate responses
with well-defined exposures. EMAP-Estuaries (EMAP-E) sought to apply these management objectives to
the development of indicators to represent the condition of estuaries.
The specific assessment question addressed by the benthic index emerged from a hierarchy of assessment
questions that were relevant to EMAP-E management goals. The ibroad assessment question for EMAP-E
is: What is the condition of estuaries? Our project was geographically limited to estuaries in the Gulf of
Mexico; therefore, our regional assessment question became: What percent of estuarine area in the Gulf of
Mexico is in good (or degraded) ecological condition? Because biological integrity is one component of
ecological condition, the next logical assessment question was: What percent of estuarine area in the Gulf of
Mexico exhibited acceptable (or unacceptable) biological integrity? The condition of benthic biota is one
measure of biological integrity. This tenet led to the specific assessment question addressed by the benthic
index: What percent of estuarine area has degraded benthic communities? As a response indicator for
estuaries, the benthic index was intended to contribute information to the broad assessment question above
and to be used in conjunction with a suite of indicators to evaluate the overall condition of estuaries.
Macroinvertebrates provide an ideal measure of the response of the benthic community to environmental
perturbations for many reasons (e.g., see Boesch and Rosenberg 1981, Reish 1986). Benthos are primarily
sedentary and, thus, have limited escape mechanisms to avoid disturbances (Bilyard 1987). Benthic
invertebrates are relatively easy to monitor and tend to reflect the cumulative impacts of environmental
perturbations, thereby providing good indications of the changes in an ecosystem overtime. They have been
used extensively as indicators of the impacts of both pollution and natural fluctuations in the estuarine
environment (Gaston etal. 1985, Bilyard 1987, Holland etal. 1987, Boesch and Rabalais 1991). Benthic
assemblages are often comprised of a variety of species (across multiple phyla) that represent a range of
biotic responses to potential pollutant impacts.
The concept behind development of our benthic index begins with the assumption that adverse environmental
conditions (e.g., hypoxia and sediment contamination) affect benthic communities in predictable ways. The
basic tenets of Pearson and Rosenberg (1978) for organic pollution provide a good example of the biological
principles that operate in benthic communities. Pollution induces a decrease in diversity in favor of (sometimes
high) abundances of relatively few species labeled as pollution-tolerant or opportunist. In pristine areas or
areas unaffected ,by pollution, benthic communities exhibit higher diversity and stable populations of species
labeled as pollution-sensitive or equilibrium. In general, although pollution-tolerant species may thrive in
relatively undegraded areas, the converse is almost never true - pollution-sensitive species do not normally
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exist In polluted areas. Groups composed of higher-order levels of taxonomy are more often used in this
context than a single indicator species. The fact that indicator species are not found in certain areas may be
due to factors other than environmental condition (i.e., inability to disperse, seasonal absence, or biotic
competition; Sheehan 1984).
Through a mathematical process of determining which components of the benthic community best discriminate
between degraded and undegraded sites, the benthic index is composed of the following parameters: diversity,
proportional abundance of capitellids, bivalves, and amphipods, and the abundance of tubificids. Each parameter
is directly or indirectly related to the condition of the benthic community. Each component of the benthic
Index can be and has been used individually as an indicator of benthic community condition in various
monitoring programs. For our purposes, however, none of the components retains an individual relevance to
the management objective. The strength of an indicator like the benthic index lies in the checks and balances
associated with combining these components.
The benthic index does, indeed, indicate if a site has a degraded benthic community and this index is used by
EMAP-E to compute the proportion of estuarine area with this subnominal condition. The benthic index was
intended to be part of an overall assessment of ecological condition of estuaries that incorporated indicators
of biological integrity, sediment and water quality, and aesthetic values. As a component of biological integrity,
the benthic index provides insight into one aspect of the biotic community; complementary indicators could
be developed for fish, zooplankton, and phytoplankton if sufficient data were available. One step in the
validation of the benthic index showed that the benthic index had a greater success rate in classifying degraded
or undegraded sites than any of its components did individually. Although the component parameters that
make up the benthic index are useful for specific assessments, by combining them, the benthic index provides
a more comprehensive assessment of benthic condition without being redundant.
Guideline 2: Relevance to Ecological Function
It must be demonstrated that the proposed indicator is conceptually linked to the ecological function of
concern. A straightforward link may require only a brief explanation. If the link is indirect or if the indicator
itself is particularly complex, ecological relevance should be clarified with a description, or conceptual
model. A conceptual model is recommended, for example, if an indicator is comprised of multiple
measurements or if it will contribute to a weighted index. In such cases, the relevance of each component
to ecological function and to the index should be described. At a minimum, explanations and models
should include the principal stressors that are presumed to impact the indicator, as well as the resulting
ecological response. This information should be supported by available environmental, ecological and
resource management literature.
Benthos are vital to ecosystem structure and function as a food resource for demersal fish and as intermediate
links between higher and lower trophic levels. They provide a significant transfer of carbon in the energy
dynamics of an estuary, and act as agents of bioturbation and nutrient regeneration (Flint et al. 1982).
Benthic organisms often provide the first step in the bioaccumulation of pollutants in estuarine food chains,
especially heavy metals. An index of environmental condition based on benthos, therefore, would provide
Useful information for management decisions based on long-term trend analysis, spatial patterns of enrichment
or contamination, or the recognition of "hot spots" exhibited by total defaunation. An ideal indicator that
incorporates the characteristics of benthic community structure would be sensitive to contaminant and dissolved
oxygen stress and serve as a good integrator of estuarine sediment quality (Scott 1990).
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Adverse environmental conditions that may have human influence's and may affect the benthic community
can be grouped into five general categories (Karr 1991,1993; Fig. 3-1):
1. Water & Sediment Quality - hyppxia, salinity, temperature, contaminants
2. Habitat Structure - substrate type, water depth, complexity of physical habitat
3. Flow Regime - water volume and season flow distributions
4. Energy Source - characteristics of organic material entering waterbody
5. Biotic Interactions - competition, predation, disease, parasitism.
For the purposes of this assessment, we sought to evaluate the effects of water and sediment quality
(specifically, contaminants and hypoxia) on the benthic community. While the other factors are equally
important in determining benthic community structure, they were not included in this assessment. Figure 3-
1 illustrates the primary pathways by which contaminants enter estuaries. Contaminants enter the estuary
primarily via land-based non-point sources (e.g., runoff from agricultural or livestock operations or urban
runoff) and point sources (e.g., industrial effluent or municipal wastewater). Contaminant stress is evident if
the sediments are toxic to test organisms or if the levels of certain chemicals are high when compared to
established guidelines. The benthic community responds to contaminant stress by an overall reduction in
abundance and number of species, an increase in the proportion of pollution-tolerant or opportunistic species,
or both. !
Non-point
Sources , , Ultoan
Runoff
Agriculture
Livestock
Wastewater
Physical Disturbancet*' Contaminants,
Point
Sources
Industry
Marsh
Components
of the
Benthic Index
Benthic Community |
Diversity
Tul; tficicls
Capitellids
Bivalves
Amphipods
Figure 3-1. Conceptual diagram of a typical estuary showing the environmental stressors
that may contribute to altering benthic macroinvertebrate community structure
and the components of the benthic index.
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Nutrients enter an estuarine system via the same point and non-point source pathways as contaminants but
may also come from atmospheric deposition. Excess nutrients that lead to eutrophication can cause shifts in
species composition and abundance. Eutrophication can deplete the oxygen in the bottom waters or climatic
conditions may drive hypoxia because they influence the stratification of the water column. Oxygen depletion
causes acute stress to the benthic community resulting in die-offs or chronic stress that may lead to shifts in
species composition. Habitat disturbance includes physical scouring of the bottom as a result of storms,
trawling, or dredging, as well as salinity and temperature changes brought about by climatic events. Because
any of these stressors may induce alterations of the benthic community, monitoring community changes
reflects the environmental conditions to which the benthos are exposed.
The benthic index represents the response of the benthic community to stressors like contaminants and
hypoxia (Fig. 3-2). As a multimetric indicator, the benthic index is composed of the community measures that
best discriminate between stressed and unstressed areas. Although these components (diversity, proportional
abundance of capitellids, bivalves, and amphipods, and the abundance of tubificids) were chosen empirically
by statistical analyses, they have biological relevance to the function of the benthic community. Diversity is
directly related to the relative stability of a community. Sites that have been affected by contamination or
hypoxia exhibit lower diversity than sites that have not been so adversely affected. Capitellid worms are
often regarded as opportunists because of their high reproductive rate, small body size, and short life span.
They are often found in great abundance in organically enriched areas and are usually the first to colonize
disturbed sediments. Tubificid worms have life histories similar to capitellids but may extend the range of
habitats in which capitellids are commonly found. Bivalves are usually indicative of stable environments
because most bivalves are large-bodied, have slower reproductive rates, and longer life spans than worms.
As filter feeders, most bivalves are the first to show signs of stress when water quality becomes unsuitable.
Amphipods have long been used as test organisms in toxicity tests because of their demonstrated sensitivity
to contaminants.
Source
Stress
T Contaminated/Toxic Sediments
Organic chemicals
fieavymetals
T Organic Enrichment
Nitrogen
Carbon
4-0,
Habitat Disturbance
Physical
T Temperature
A Salinity
Effect
-I Diversity
4- Abundance
•I Richness
i Biomass
T Opportunists
T Tolerant spp.
•I sensitive spp.
A Community Composition
Figure 3-2. Conceptual model of the indicator, showing linkages between sources of stress, types
of stress, and effect on the benthic community.
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Phase 2: Feasibility of Implementation
Given that aspects of the benthic macroinvertebrate community can be used as indicators of degraded areas,
how does a benthic index represent degraded benthic communities? The development of the benthic index
is based on a simple question: if you have a set of known degraded and undegraded areas, what components
of the benthic community best discriminate between the two? The degraded and undegraded sites were
chosen from the EMAP-E database according to strict criteria involving environmental parameters that are
known to affect the benthic community (e.g., hypoxia, sediment contaminants, and sediment toxicity). This
was done without prior knowledge of the benthic communities at those sites. A suite of parameters that
represented aspects of benthic community structure and function were compiled for these sites (Engle et a/.
1994, Engle and Summers 1999). The statistical techniques of discriminant analysis were applied in order to
choose a subset of parameters that best discriminated between the degraded and undegraded sites.
Appropriate coefficients (or weights) were computed by canonical discriminant analysis. The benthic index is
a linear function of the weighted parameters that represent the subset that best discriminates between degraded
and undegraded sites. The benthic index was scaled to range from 0 to 10 and threshold values were
determined empirically from the data. The final result is an index that, when calculated to be below the
threshold value for a given site, indicates that the benthic community at that site is similar to benthic communities
found at known degraded sites. The summary of the development of EMAP-E's benthic index given here is
brief; more detail on the development, validation, and application of the benthic index may be found elsewhere
(Engle et a/. 1994, Engle and Summers 1998, Engle and Summers 1999).
Guideline 3: Data Collection Methods
Methods for collecting all indicator measurements should be described. Standard, well-documented
methods are preferred. Novel methods should be defended with evidence of effective performance and,
if applicable, with comparisons to standard methods. If multiple methods are necessary to
accommodate diverse circumstances at different sites, the effects on data comparability across sites
must be addressed. Expected sources of error should be evaluated.
Methods should be compatible with the monitoring design of the program for which the indicator is
intended. Plot design and measurements should be appropriate for the spatial scale of analysis. Needs
for specialized equipment and expertise should be identified.
Sampling activities for indicator measurements should not significantly disturb a site. Evidence should
be provided to ensure that measurements made during a single visit do not affect the same
measurement at subsequent visits or, in the case of integrated sampling regimes, simultaneous
measurements at the site. Also, sampling should not create an adverse impact on protected species,
species of special concern, or protected habitats.
Field and laboratory methods for collecting and processing benthic macroinvertebrate samples are thoroughly
documented in Heitmuller and Valente (1991), Macauley (1991), and U.S. EPA (1995).
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Field Sampling Methods
A1/25 m2, stainless steel, Young-modified Van Veen Grab sampler was used to collect 3 replicate sediment
grabs for enumeration of benthic invertebrate community composition. This grab sampled an area of 413
cm2 with a maximum depth of penetration in the sediment of 10 cm. Each acceptable benthic grab sample
was rinsed into a plastic dishpan for transport to the sieving station for immediate, aboard processing. The
sediment from an individual grab was sieved through a 500 |am sieve to wash away sediments and leave
organisms, detritus, sand and shell particles larger than 500 jim. The contents on the sieve were rinsed with
site water, into 500-ml wide-mouth polypropylene jar(s). The contents of each jar were preserved by the
addition of 100 ml of formalin:seawater (50:50) containing Rose Bengal vital stain to yield a final formalin
concentration of 10% by volume.
i •
Expected sources of error in the field sampling methods were reduced by mandatory training of all field
personnel in proper collection methods including determination of acceptable grabs, sieving techniques, and
preservation of the sample. However, slight measurement error could occur if the volume of sediment in a
grab was not consistent among grabs or if there was not sufficient water and formalin in the sample jars to fill
the jar (in order to prevent agitation of fixed organisms). Human error could occur also in the transfer of
samples from grab to sieve to sampling jar. These sources of measurement error were minimized by thorough
training of all field personnel and random quality control audits to ensure that proper sampling techniques
Were being employed.
The field sampling methods employed by EMAP-E are not expected to cause significant disturbance to a
site. The grab samples an area of 413 cm2 with a maximum penetration depth of 10 cm; this results in a
maximum sample volume of 4 liters. After 3 grabs were taken, whether successful or not, the anchor line
was let out to move the boat 5 m downstream to ensure that the exact 413 cm2 location was not sampled
repetitively. Because EMAP-E samples each station at a single point in time, the adverse effects of disturbance
at any site are minimal. There are no species of estuarine benthic macroinveiiebrates currently listed as
endangered, threatened, or protected in the Gulf of Mexico. Although most sampling occurred in open-bay
bottom, occasionally a site was located in a protected submerged aquatic vegetation (SAV) habitat, in which
case, special care was taken not to unduly disturb SAV.
Laboratory Processing Methods
The samples were shipped directly from the field to the laboratory where they were again washed through
500 [im mesh sieves. Benthic fauna were sorted from the sediments, identified to lowest practical taxa, and
enumerated. Only benthic macroinvertebrates were identified. Meiofauna and taxonomic groups having
only planktonic forms were excluded from the identification process. At least one qualified benthic taxonomist
was reqtiired in order to provide authority on identification of taxa.
Expected sources of error in laboratory processing included errors in handling of samples, inefficient sorting
and inaccurate identifications. These sources of error were minimized by rigorous quality control measures
that included random resorts and recounts and by having qualified and trained personnel performing the
sorting and identification of taxa.
, i
Data Manipulation Methods
The benthic macrofaunal count data were sent to a data manager in dBase® format. The data manager
translated this data into SAS® format. The data was then checked for transcription errors, and inconsistencies
in taxonomic coding as well as new taxonomic codes. Detailed methods for the development and application
of the benthic index are found elsewhere (Engle etal. 1994, Engle and Summers 1998, Engle and Summers
1999). The benthic parameters that make up the components of the benthic index were calculated for each
sampling station (Table 3-1). The benthic index was calculated by combining the components in a linear
fashion as illustrated in Table 3-2.
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Table 3-1. Methods used to calculate components of the benthic index from raw benthic data.
Component
Calculation Method
Proportion of Expected
Shannon-Wiener Diversity Index
Mean Abundance of Tubificids
Percent Capitellids
Percent Bivalves
Percent Amphipods
Shannon-Wiener Diversity Index: H' = -£ p, Iog2 p,
where p(. = proportion of total abundance represented by species /.
Proportion of Expected Shannon-Wiener Diversity Index =
H'H[ 2.618426 - 0.044795X + 0.007278X2 - 0.000119X3 ]
where X = bottom salinity (ppt)
Calculate sum of total abundance of members of Family:Tubificidae and
divide by number of grabs (3) :
Calculate sum of abundance of members of Family:Capitellidae in each grab
and divide by the total abundance of all fauna in that grab. Calculate average
proportion in 3 grabs and multiply by 100.
Calculate sum of abundance of members of Class:Bivalvia in each grab and
divide by the total abundance of all fauna in that grab. Calculate average
proportion in 3 grabs and multiply by 100.
Calculate sum of abundance of members of Order:Amphipoda in each grab
and divide by the total abundance of all fauna in that grab. Calculate average
proportion in 3 grabs and multiply by 100.
Table 3-2. Methods used to calculate the benthic index from the components listed in Table 1.
• Step 1: Log transform abundances and arc-sine transform proportions.
• Step 2: Standardize variables to mean = 0 and standard deviation = 1 by applying the
following formula to all of the data:
x.' = S * (x. -0)
where
x/ = new standardized value
S = desired standard deviation (1) .
M = desired mean (0)
x, = observation's original value
0 = variable's mean ;
sx = variable's standard deviation ;
Step 3: Calculate the discriminant score of the benthic index as follows using the
standardized variables from Step 2.
Discriminant Score = ( 1.571 Ox Proportion of expected diversity) +
(-1.0335 x Mean Abundance of tubificids) +
(-0.5607 x Percent capitellids) +
(-0.4470 x Percent bivalves) + '.
(0.5023 x Percent amphipods). \
Step 4: Calculate the benthic index by normalizing the discriminant scores from Step 3
using the following formula which includes the minimum (-3.21) and range (7.50)
of discriminant scores from the original test data used to develop the benthic index:
Benthic Index = ((Discriminant Score - (-3.21)) 77.50) * 10
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Guideline 4: Logistics
The logistical requirements of an indicator can be costly and time-consuming. These requirements must
be evaluated to ensure the practicality of indicator implementation, and to plan for personnel, equipment,
training, and other needs. A logistics plan should be prepared that identifies requirements, as
appropriate, for field personnel and vehicles, training, travel, sampling instruments, sample transport,
analytical equipment, and laboratory facilities and personnel. The length of time required to collect,
analyze and report the data should be estimated and compared with the needs of the program.
Monitoring programs that utilize sediment sampling for the collection of benthic macroinvertebrates vary in
their logistic requirements depending on the spatial extent and temporal duration of the monitoring design.
All field operations conducted by EMAP-E were planned and implemented according to an approved logistics
plan that was prepared following guidelines established for EMAP (Baker and Merritt 1990). Elements of the
logistics plan address major areas of project implementation, including project management, site access and
scheduling, safety and waste disposal, procurement and inventory control, training and data collection, and
the assessment of the operation upon completion. EMAP-E in the Lousianian Province was tasked with
sampling -150 stations that ranged hundreds of miles from Texas to Florida. The time frame was ~2 months
during the summer. Because the success of EMAP-E depended on standardized sampling and processing,
all boat crews underwent rigorous training that covered boat operation, collection methods, and proper QA
procedures. A field operations manual was prepared each year to give detailed instructions to the field
teams on safety, operation of equipment, handling of samples, and quality assurance (Macauley 1991). A
logistics plan was prepared each year that gave a day-to-day account of locations to be sampled, personnel
assignments, and suggested hotels, boat ramps, and other necessary resources. In addition, a quality
assurance project plan was prepared to identify quality control guidelines for collection, handling, and shipping
of field samples as well as laboratory analytical methods (Heitmuller and Valente 1991). Table 3-3 lists the
logistical requirements to carry out sampling of benthos by the EMAP-E field teams; however, the magnitude
of equipment required by EMAP-E would not necessarily be appropriate for small-scale, localized monitoring
programs.
i
EMAP-E set a goal of producing a statistical summary approximately 9 months after sampling concluded.
Collection and field processing of benthic samples was completed aboard the boat within a short time frame
(i.e., 1-2 hours). In the laboratory, processing of the samples was more tedious and, on average, it took 5-10
man-hours to process a sample, from the initial bench sieving to transfer of raw data from handwritten sheets
to an electronic file. The original development of the index from receiving the electronic data to publishing a
manuscript took on the order of several years. The application of the index, now that it has been finalized, is
relatively straightforward; the length of time from receipt of the data to calculating the index is on the order of
a few days.
Guideline 5: Information Management
Management of information generated by an indicator, particularly in a long-term monitoring program, can
become a substantial issue. Requirements should be identified for data processing, analysis, storage,
and retrieval, and data documentation standards should be developed. Identified systems and standards
must be compatible with those of the program for which the indicator is intended and should meet the
Interpretive needs of the program. Compatibility with other systems should also be considered, such as
the internet, established federal standards, geographic information systems, and systems maintained by
intended secondary data users.
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Table 3-3. Logistical requirements for sampling and processing of benthic macroinvertebrates by EMAP-E in the
Louisianian Province. ;
Field
Personnel
Vehicles
Training
Travel
Sampling Gear
Data Transport
Laboratory
Facilities
Laboratory
personnel
3 Teams consisting of 2 crews with 5 members each (1 crew chief, 2 boat crew
members, and 2 shore crew members)
1-ton, 4WD, dual rear wheel pickup truck with heavy duty, dual axle trailer (with brakes, winch,
spare tire and rollers)
25-foot SeaArk work boat equipped with 7.5 L gas engine fitted with Bravo II outdrive, an "A"
frame boom assembly and hydraulic winch. On-board electronics included Loran C, 2 VHF
radios, radar, compass, and depth finder.
Mobile laboratory - 15-foot truck equipped with VHF radio, GRiD laptop computer, shelves,
and work bench.
Full-size panel van for transporting crew members. I
A 2-week training course is mandatory for all crew members. Crew members must show
proficiency in towing and launching the boat, using navigation equipment, locating stations,
entering and retrieving data from computers, using all sampling gear, first aid, and safety.
The two crews comprising a team worked alternating 6-day schedules. Extensive travel from
the Field Operations Center to the staging area was required of all crews (as much as 1000
miles by road, trailering the boat). Site reconnaissance was performed prior to initiation of
field activities in order to determine locations of boat ramps and hotels and to identify any
stations unsuitable for sampling.
Stainless steel Young-modified Van Veen grab sampler (self-leveling with a hinged top)
Samples were transferred from 0.5 mm sieve to wide mouth Nalgene bottles and preserved
with 10% buffered formalin containing Rose Bengal stain. Bottles are labeled with bar code,
packed individually in ziploc plastic bags and placed into shipping container. Samples were
shipped via Federal Express to the benthic sample processing laboratory.
State-of-the-art benthic processing laboratory equipped with compound and
dissecting microscopes, magnifying lights, computers for data entry, complete specimen
voucher collection.
3 benthic taxonomists, 3-5 student sorters, 1 Ph.D. level benthic ecologist /supervisor.
Information management was thoroughly addressed in EMAP-E and one of its major goals was to disseminate
the data to the public in a timely manner. This goal requires that the data be collected with adequate sample
tracking methods and that it is stored in a standardized format that is made easily accessible to the public. All
samples that were collected by EMAP-E were tracked via a bar code system from the field to the laboratory.
At the laboratory, benthic taxa identifications and counts were handwritten onto data sheets that were then
transcribed into an electronic dBase® file. This file was sent via e-mail to the EMAP-E data manager who
translated the data into SAS® format. All data manipulations and calculations of the benthic index were
accomplished using SAS® on an Intel® PC. EMAP-E data is currently stored in three venues: 1) as SAS® data
sets on a Windows® NT server at the Louisianian Province office, 2) as Oracle 7™ DBMS files on a Windows®
NT server at the Louisianian Province office, and 3) as ASCII text downloadable files on the EMAP web page
(http://www.epa.gov/emap) which is housed on a server at the centralized EMAP Information Management
(EMAP-IM) office. EMAP-IM produced an Information management plan as an evolving document to outline
EMAP's strategy for maintaining, archiving, and distributing all of the data collected by EMAP (Hale et al.
1998). In addition to the data sets, metadata files are cataloged that describe methods, contacts, quality
assurance, and other information pertinent to the data.
3-11
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The complex information management requirements of EMAP were necessary to ensure that the large amounts
of data from this national monitoring program were consistently documented, standardized, and made available
to end-users in a timely and efficient manner. These might not be necessary for smaller monitoring programs.
At a minimum, the benthictaxa identifications and counts could be entered electronically into a spreadsheet
or database format and all calculations for the benthic index could be accomplished there. In this case the
hardware and software required would be a high-end PC with a package such as Excel, Lotus, or dBase
installed.
Guideline 6: Quality Assurance
For accurate interpretation of indicator results, it is necessary to understand their degree of validity. A
quality assurance plan should outline the steps in collection and computation of data, and should identify
the data quality objectives for each step. It is important that means and methods to audit the quality of
each step are incorporated into the monitoring design. Standards of quality assurance for an indicator
must meet those of the targeted monitoring program.
EMAP-E emphasized the collection of data via standardized methods. This ensured the comparability of
data collected by different field teams across large geographic areas. Rigorous quality control (QC) was
necessary to achieve this goal. All field crew personnel were trained prior to the sampling season in, among
other things, the proper techniques for grab sampling, sieving, and preservation of benthic samples. Both the
field crews and the laboratory personnel were provided with manuals that outlined the correct techniques for
handling the samples (see Macauley 1991 and U.S. EPA 1995). Random QC audits were performed by the
Quality Assurance Officer both in the field and at the laboratory. Table 3-4 lists the various aspects of quality
control for field and laboratory operations as well as information management for EMAP-E Louisianian Province
benthic data (Heitmuller and Valente 1991).
Guideline 7: Monetary Costs
Cost is often the limiting factor in considering to implement an indicator. Estimates of all implementation
costs should be evaluated. Cost evaluation should incorporate economy of scale, since cost per indicator
or cost per sample maybe considerably reduced when data are collected for multiple indicators at a given
site. Costs of a pilot study or any other indicator development needs should be included if appropriate.
It is difficult to separate the cost of implementing this particular indicator from the overall cost of implementing
EMAP. The cost of personnel, vehicles, and travel are spread across all indicators measured by the program.
Even the cost of the equipment is spread across several indicators (i.e., benthos, sediment characterization,
sediment toxicity, and sediment chemistry) as the same gear is used to collect all of these samples. The
Young-modified Van Veen grab cost $1250 to purchase initially. The sieve boxes, forceps, Nalgene bottles,
labels and other miscellaneous equipment for one team cost $350 for one year. The entire process of
collecting, sieving, and preserving benthic samples took an average of 1 hour per station. Given that each
team sampled an average of 50 stations per year and assuming that the grab lasts for four years, the average
equipment cost per station would be only $13.
3-12
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Table 3-4. Quality Control procedures for field sampling, laboratory processing, and data analysis of EMAP-E
Louisianian Province benthic data.
Field Operations
Sample Collection
• Sediment should not extrude from the upper face of sampler.
• Overlying water should be present.
• Sediment surface should be relatively flat.
• Entire surface of sample should be included in sampler.
• Grab must have penetrated sediment to a minimum depth of 7 cm.
• If these QC guidelines were not met, the sample was rejected.
Sample Processing •
• Gentle rinsing of sample through sieve - no forceful jets of water.
• Preservation of sample with 10% buffered formalin containing Rose Bengal stain.
Laboratory Operations
Sample Storage
• Samples should be stored between 5"C and 30"C to avoid freezing or evaporation.
• After sorting, organisms should be stored in vials with 70% ethanol.
• Minimize exposure of samples to direct sunlight.
Sorting (i.e., separating organisms from sediment and debris)
• 10% of all samples are resorted independently by a second, experienced sorter.
• Re-sorts are randomly chosen (1 out of 10) on a regular basis
• Sorting efficiency is calculated as:
# of organisms originally sorted x 100
# of organisms originally sorted + additional # found in re-sbrt
• Actions for unsatisfactory sorting efficiencies:
90-95% - Technician should be retrained.
< 90% - All samples in batch must be resorted and any organisms found
in re-sort will be added to the original data sheet.
Species Identification and Enumeration
• 10% of all samples are checked for accuracy by a senior taxonomist.
• Re-identification samples are randomly chosen (1 out of 10) on a regular basis
• Accuracy will be calculated as: ;
Total # of organisms in QC recount -Total # of errors x 100
Total # of organisms in QC recount
where errors include: ;
Counting error (e.g., counting 11 of a given species instead of 10)
Identification error (e.g., misidentifying species X as species Y)
Unrecorded taxa errors (e.g., not identifying species X when it is present)
• Actions for unsatisfactory level of taxonomic accuracy: ,
90-95% - Original technician advised, species identifications reviewed, and any changes to species
identifications recorded on original data sheet.
< 90% - Same as for 90-95% but numerical counts should also be corrected on original data sheet.
• Maintain voucher specimen collection and permanent undegraded collection.
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The bulk of the expense to implement a benthic monitoring program comes from the laboratory costs. The
benthic laboratory for this study charged $300 per sample for sorting and taxonomic identification. Three
replicate samples were collected at each station bringing the laboratory cost to $900 per station. This brings
the total cost of implementing the benthic index to $913 per station, excluding travel, lodging and personnel
costs. These costs were well within the budget allocated to this program.
i i
il i
Phase 3: Response Variability
t !| I
ij I
Once the indicator has been determined to be pertinent to the assessment question, ecologically relevant,
and feasible to implement, then the next phase is to evaluate the expected variability in the response of the
indicator. Variability can arise from many sources (i.e., human error in field or laboratory processing of
samples, temporal variability, and spatial variability). EMAP-E has incorporated, as part of its design,
mechanisms to address these sources of variability in any indicator. It is important to evaluate the variability
in an indicator in the context of the specific assessment question. The evaluation of response variability is
very different for an indicator that is designed to measure the current condition at a particular site versus an
indicator like EMAP-E's benthic index. This benthic index was designed to measure the proportion of estuarine
area in the Louisianian Province that had degraded benthic communities. In this example, the assessment
question was aimed at estimating the spatial extent of degraded benthic communities across a large
geographical area (~25,000 km2 of estuarine area in the Louisianian Province).
The data used in this evaluation came entirely from EMAP-E's efforts in the Louisianian Province estuaries
(northern Gulf of Mexico, from Anclote Key, Florida to Rio Grande, Texas) from 1991 to 1994. Figure 3-3
shows the distribution of sampling sites for a single year, 1991. The sample design, methods, results, and
statistical evaluations are documented in Summers et al. (1991), Summers et al. (1992), Summers et al.
(1993), Macauley et al. (1994), and Macauley et al. (1996).
Guideline 8: Estimation of Measurement Error
The process of collecting, transporting, and analyzing ecological data generates errors that can obscure
the discriminatory ability of an indicator. Variability introduced by human and instrument performance
must be estimated and reported for all indicator measurements. Variability among field crews should also
be estimated, if appropriate. If standard methods and equipment are employed, information on
measurement error may be available in the literature. Regardless, this information should be derived or
validated in dedicated testing or a pilot study.
While the parsing of overall variance into specific components (e.g., measurement error) is essential to the
estimation of trends, our program was initially more concerned with the estimation of status. We did not
evaluate measurement error specifically; to do so would require a redesign of our program. We did, however,
determine the most likely sources of measurement error and sought to minimize this error through rigorous
training and quality control. Measurement errors can be introduced into the benthic data from three primary
sources; collection of the sample, handling and preservation of the sample, and activities in the laboratory. In
the field, variability in the sample would be associated with the volume of the grab, incorporation of water in
the sample, and human error associated with field sieving and preservation of the sample. This variability
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Figure 3-3. Map of EMAP-E sampling sites for 1991 in the Louisianian province.
Table 3-5. Results of the quality control measures employed by the benthic laboratory in the
sorting, identification, and enumeration of benthic macroinvertebrate samples from
EMAP-E Louisianian Province, 1991 to 1994.
QC Measure 1991
Sorting QC
Total* jars sorted 558
Total* jars QC'd 64
# jars passed 57
Sorting success rate 89%
Corrective action yes
Taxonomy and Enumeration QC
Total*vials ID'd -800
Total*vials QC'd 83
# QC vials with >10% error 3
Taxonomy success rate 96%
Corrective action no
1992
nd
nd
nd
nd
nd
nd
nd
nd
nd
nd
1993
527
53
49
92%
yes
738
74
0
100%
no
1994
544
56
55
98%
no
895
92
3
97%
no
nd = no data available
3-15
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was minimized by rigorous training of the field crew prior to initiation of the sampling season and QC audits
of the field crew that were performed throughout the sampling season. Although the magnitude of variability
in the indicator that js associated wjth these sources of measurement error were pot quantified, field crews
consistently scored >95% efficiency in all field activities. We believe that measurement errors were minimal
due to the standardized methods employed and the thorough training and QC requirements imposed on the
personnel.
| ! •'
As detailed in Guideline 6: Quality Assurance, quality control measures were employed in the laboratory to
minimize the variability in the benthic data resulting from potential human processing error. In addition to the
QC requirements of 10% resorts and reidentifications, QC audits were performed on the laboratory as well.
The results of the QC for the benthic laboratory are listed in Table 3-5.
Guideline 9: Temporal Variability - Within the Field Season
it is unlikely in a monitoring program that data can be collected simultaneously from a large number of
sites. Instead, sampling may require several days, weeks, or months to complete, even though the data
are ultimately to be consolidated into a single reporting period. Thus, within-field season variability should
be estimated and evaluated. For some monitoring programs, indicators are applied only within a particular
season, time of day, orotherwindowof opportunity when their signals are determined to be strong, stable,
and reliable, or when stressor influences are expected to be greatest. This optimal time frame, or index
period, reduces temporal variability considered irrelevant to program objectives. The use of an index
period should be defended and the variability within the index period should be estimated and evaluated.
EMAP-E chose to implement sampling during the summer (July to September) because this was the period
during which the stressors of concern (i.e., contaminants and DO) would most severely impact the biota.
During the summer index period, benthic organisms are most active, temperatures are highest, hypoxia
occurs more frequently, and predation is at its peak. In estuaries especially, the added pressures attributed
to human populations (e.g., recreational boating, fishing, increased water usage and municipal effluent, nonpoint
source runoff of nutrients from agricultural lands) are at their highest during the summer. It is during this index
period that we are most likely to detect impacts of stressors on benthic communities.
The benthic index was computed for all sites sampled by EMAP-E in the Louisianian Province from 1991 to
1994. Because the index was developed from a subset of sites sampled in 1991 and 1992, validation of the
benthic index was accomplished by using an independent set of data from two subsequent years, 1993 and
1994, as well as data from special study sites representing between-year and within-year replicates. Validation
of the benthic index consisted of three steps: assessment of the correct classification by the index of an
independent set of degraded and undegraded sites, comparison of the cumulative distribution function of the
index among four years, and correct classification of replicate sites by the index.
Within each year (excluding 1991), 13 estuaries were visited more than once in order to evaluate spatial and
temporal replication. These sites were used to validate the consistency of classification of the benthic index.
The same classification by the benthic index should be given to a single site on replicate visits within a
sampling season. The distribution of benthic index values between the first and second visits to a site within
the same sampling period was compared (Fig. 3-4). The shaded areas indicate the marginal zone between
the threshold values of 3.0 for degraded sites and 5.0 for undegraded sites. Ideally, all of the points should fall
in quadrants 2 and 4 where sites were classified as degraded on both visits or as undegraded on both visits.
However, although several points fall within the lightly shaded area, indicating that the site classification
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changed from degraded or undegraded to marginal (or vice versa) from the first visit to the second visit, no
sites fell within quadrants 1 and 3, indicating that no sites were inversely classified as degraded or undegraded.
Correlation between the benthic index from the temporal replicates was significant (p <0.05; r = 0.83). The
Kappa statistic (K) was used to test the degree of agreement in classification by the benthic index between
site visits. The null hypothesis of no agreement (H0: K =0) was rejected at a = 0.5. The Kappa statistic (K =
.644) indicated moderate agreement in the classification between visits. This validation of the benthic index
was determined to be successful in showing minimal within-year temporal variability.
CM
.*->
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-------
A subset of 13 sites from 1991 was sampled every year to provide an estimate of between-year variation. An
analysis of variance was performed on the data from the between-year temporal replicates to test for a year
effect on the benthic index and a pain/vise T-test was used to detect significant differences in benthic index
values paired by station between any two years. For both tests, the null hypothesis of "no significant difference"
was not rejected (p > 0.05). The benthic index values among years at the thirteen stations are compared
categorically in Figure 3-5. We defined a change in classification as a change from degraded to undegraded
(or vice versa). Benthic index values between 3 and 5 represented moderate or marginal conditions. A
change in classification from degraded to moderate or undegraded to moderate was not viewed as a
misclassification by the index. Although the range of values for any given station is sometimes large, the
classification of a station does not change from degraded to undegraded (or vice versa) except in the case of
stations 1 and 10. the classification of a station does change from degraded or undegraded to moderate for
many stations, however. This validation exercise showed that the benthic index maintained relative inter-
annual stability in addition to minimal within-year variability.
•19
1 £.
10 -
8-
•i 6 ~
JE 4 -
.2 2-
i o-
00 -2 -
-4-
R -
undegraded
O
0 * A
° A A A •
^ O ii • M moderate
A I B • B n R
' A-0 o "i 5
i a
n ^
degraded
O1 991
• 1992
A 1993
O1 994
~o n i i i i i i i i i i i i i
0 1 2 3 4 5 6 7 8 9 1011 121314
Station
Figure 3-5. Comparison of benthic index values at stations that were sampled each year.
Horizontal lines at benthic index values of 3 and 5 indicate the boundaries of
degraded, moderate and undegraded benthic conditions.
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Guideline 11: Spatial Variability
Indicator responses to various environmental conditions must be consistent across the monitoring region
if that region is treated as a single reporting unit. Locations within the reporting unit that are known to be
in similar ecological condition should exhibit similar indicator results. If spatial variability occurs due to
regional differences in physiography or habitat, it may be necessary to normalize the indicator across the
region, or to divide the reporting area into more homogeneous units.
Monitoring programs that cover large geographic areas must contend with the inherent spatial variability in
the data. In the Louisianian Province, which spans estuaries from Texas to Florida, EMAP-E encountered
the full range of expected benthic habitat types (from sand to mud, tidal freshwater to marine, and impacted
to pristine) and identified more than 1000 different benthic invertebrate species. The 46 test sites that were
used to develop the benthic index were chosen, therefore, not only to represent extreme reference and
degraded environmental conditions, but also to cover the expected range of salinity, sediment types, and
biogeographical divisions found in the estuaries of the northern Gulf of Mexico (Engle et al. 1994, Engle and
Summers 1999). The sites ranged in salinity regimes from tidal-freshwater (0 ppt) to marine (>35 ppt) and
most sites were located in muddy (>80% silt-clay) sediment (Table 3-6). The location of the majority of sites
in Louisiana is simply an artifact of the EMAP probability-based sample design (Louisiana has proportionately
more estuarine area than the other four gulf states). In this way, we sought to minimize the spatial variability
in the benthic index.
Table 3- 6. Distribution of the number of degraded and undegraded test sites among
categories of salinity, sediment types, and states.
Salinity
Number of
Degraded Sites :
Number of
Undegraded Sites
Fresh (0 ppt)
Brackish (>0-5ppt)
Mesohaline (>5-18 ppt)
Polyhaline (> 18-35 ppt)
Marine (>35 ppt)
Sediment Type
Mud (>80% Silt-Clay)
Mud/Sand (20-80% Silt-Clay)
Sand (<20% Silt-Clay)
State
Florida
Alabama
Mississippi
Louisiana
Texas
8
2
4
7
1
17
5
0
4
3
2
12
1
2
3
5
11
3
10
10
4
2
3
2
10
7
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Guideline 12: Discriminatory Ability
The ability of the indicatorto discriminate differences among sites along a known condition gradient should
be critically examined. This analysis should incorporate all error components relevant to the program
objectives, and separate extraneous variability to reveal the true environmental signal in the indicator
data.
Sites from 1993 and 1994 were classified as degraded or undegraded based on our a priori criteria for
dissolved oxygen, sediment chemistry, and sediment toxicity that were used to choose test sites in the
development of the index. Of the 310 sites sampled in 1993 and 1994, only 195 could be classified as either
degraded or undegraded and these were used in the first validation step. In a Monte Carlo exercise, we
randomly chose 50 subsets of the 195 sites where each subset consisted of 50 degraded and 50 undegraded
sites. Correct classification occurred when the benthic index was either < 3 at degraded sites or > 5 at
undegraded sites. Misclassification occurred when the benthic index was < 3 at undegraded sites (false
negative) or > 5 at degraded sites (false positive). Using the 50 trials, we determined the percent of sites that
were correctly classified as degraded and undegraded by the benthic index. The benthic index correctly
classified 66-82% of degraded sites (x = 74%; SE = 0.5) and 70-84% of undegraded sites (x == 77%; SE =
0.4). The high degree of variability in the benthic communities in the Gulf of Mexico region influenced the
classification success. Although we attempted to minimize this variability during the development phase, we
may have sacrificed a level of precision in favor of a generalized index that is applicable across a wide
geographic area with an inherently large spatial variation. We also investigated the kappa coefficient (K) to
measure the degree of agreement (Stokes et al. 1995) between classification of a site by the benthic index
versus classification by sediment contaminants, toxicity, and dissolved oxygen. The average kappa coefficient
for the 50 trials was 0.509 where K > 0.4 indicates moderate agreement and the null hypothesis that there
was no agreement (H0: K = 0) was rejected at the a = 0.05 level of significance.
" i i
An important consideration for the benthic index was that it not be significantly correlated with any natural
habitat factors like salinity or sediment type. This was addressed during the development of the index by
adjusting any benthic parameters that were correlated with salinity, or sediment type. One of the components
of the benthic index, proportion of expected diversity, represents a salinity-adjusted variable because diversity
is highly correlated with salinity in estuarine waters. Figures 3-6 and 3-7 show the relationship between the
benthic index and salinity and percent silt-clay content of sediments. The benthic index was still significantly
correlated with salinity and percent silt-clay but the R2 for both of these correlations was <15%. We determined
that these relationships were insignificant from an ecological perspective with statistical significance primarily
driven by the large number of samples (n = 338).
Anthropogenic impacts may be correlated with salinity and silt-clay as well; therefore, residual correlations
between the benthic index and salinity or silt-clay may not indicate a lack of discriminatory power in the index.
This is important because the benthic index was designed to be an indicator of environmental condition that
is representative of the degree of sediment contamination and hypoxia experienced at a site, regardless of
the inherent salinity and sediment characteristics.
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3
TJ
2
10
20 so :
Bottom Salinity (ppt)
40
50
Figure 3-6. Benthic index versus bottom salinity for all sites from EMAP-E Louisianian
Province, 1991 to 1994.
120
100
x
01
o
a>
CQ
10
20 30
Silt-Clay (%)
40
50
Figure 3-7. Benthic index versus silt-clay content of sediments for all sites from EMAP-E
Louisianian Province, 1991-1994.
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Phase 4: Interpretation and Utility
Guideline 13: Data Quality Objectives
The discriminatory ability of the indicator should be evaluated against program data quality objectives and
constraints. It should be demonstrated how sample size, monitoring duration, and other variables affect
the precision and confidence levels of reported results, and how these variables may be optimized to attain
stated program goals. For example, a program may require that an indicator be able to detect a twenty
percent change in some aspect of ecological condition over a ten-year period, with ninety-five percent
confidence. With magnitude, duration, and confidence level constrained, sample size and extraneous
variability must be optimized in order to meet the program's data quality objectives. Statistical power
curves are recommended to explore the effects of different optimization strategies on indicator
performance.
Traditional power analyses are employed in hypothesis testing to evaluate the probability of failing to reject
the null hypothesis when the alternative hypothesis is true. This is called a "Type 11 error" and has been
described as "not seeing enough in the data" by Anderson (1966 as cited in Keppel and Saufley 1980).
Power is then the probability that a correct decision is made (i.e., the null hypothesis is rejected when the
alternative hypothesis is true). Power analyses are recommended as part of the experimental design process
to determine the number of samples needed in order to make a correct decision with a given level of power.
Afterward, power analyses may be used to determine the probability that a correct decision was made given
the number of samples and the sample variance.
Power analyses were included in the design of EMAP-E but were different from traditional analyses in that we
were evaluating the power to detect a trend rather than the power to reject a null hypothesis. EMAP-E set a
performance goal of detecting a 2% per year change in the province-wide proportion of area that exceeds a
pre-specified indicator threshold value over a period of 12 years with a probability exceeding 0.8 (Larsen et
al. 1995, Heimbuch et al. [in review]). In order to test whether EMAP-E is capable of meeting this target,
power analyses were performed to construct scenarios for detecting a 1 % to 3% change over 12 years.
• ,'
The power to detect a 2% trend over 12 years is calculated by first estimating the variance. The overall
variance consists of two components: a spatial component and a component that is dependent on the number
of years over which you wish to estimate a trend. Spatial variance was estimated for the four years during
which data were collected. We computed power curves for 1 % to 3% change per year (p = 0.01,0.015,0.02,
0.025,0.03) for years 6 to 12 at a = 0.10 according to the methods presented in Heimbuch et al. (in review)
for EMAP-E (Figure 3-8). According to the results presented here for the Louisianian Province, EMAP-E has
met its performance goal of detecting a 2% change per year in the proportion of area with degraded benthic
communities.
Guideline 14: Assessment Thresholds
To facilitate interpretation of indicator results by the user community, threshold values or ranges of values
should be proposed that delineate acceptable from unacceptable ecological condition. Justification can
be based on documented thresholds, regulatory criteria, historical records, experimental studies, or
observed responses at reference sites along a condition gradient. Thresholds may also include safety
margins or risk considerations. Regardless, the basis for threshold selection must be documented.
3-22
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Benthic index values were calculated for the test data set by applying the appropriate weighting factors to the
individual components according to the methods outlined in Guideline 3, Table 3-2. The benthic index values
were then compared to the original classification of sites as degraded or undegraded. This comparison
indicated an overlap in classification of degraded and undegraded sites between benthic index values of 3.5
and 4.5. To be conservative, we extended the threshold values for determining site classification using the
benthic index such that index values < 3.0 represented degraded sites, index values > 5.0 represented
undegraded sites, and index values between 3 and 5 represented sites with marginal conditions (these sites
were misclassified by the original analysis). The distribution of benthic index values among the test sites
(Fig. 3-9) shows the overlap of classification as degraded or undegraded by the benthic index with the
symbols indicating the original classification of test sites using a priori criteria.
1.1
1
0.9 -
0.8 -
0.7 -
fe 0.6 -
£ 0.5
0.4
0.3 -
0.2 -
0.1
0
Power Curves - Benthic Index
.03 ,
—r—
6
8 9
Years
10
11
12
Figure 3-8. Power curves for detecting annual trends of 1 % to 3% in the proportion of area with
degraded benthic communities based on EMAP-E Louisianian Province data from
1991-1994.
3-23
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Guideline 15: Linkage to Management Action
Ultimately, an indicator is useful only if it can provide information to support a management decision or to
quantify the success of past decisions. Policy makers and resource managers must be able to recognize
the implications of indicator results for stewardship, regulation, or research. An indicator with practical
application should display one or more of the following characteristics: responsiveness to a specific
stressor, linkage to policy indicators, utility in cost-benefit assessments, limitations and boundaries of
application, and public understanding and acceptance. Detailed consideration of an indicator's
management utility may lead to a re-examination of its conceptual relevance and to a refinement of the
original assessment question.
Degraded
Unknown
Reference
V)
to
ss
to
tfl
j
34567
Benthic Index
8
10
Figure 3-9. Distribution of benthic index values for the test sites where 4- = degraded sites and
o = undegraded sites determined by a priori criteria for dissolved oxygen, sediment
chemistry, and sediment toxicity. The cutoff value for degraded sites determined by the
benthic index is 3.0 and 5.0 is the cutoff value for undegraded sites determined by the
benthic index.
The value of an index lies in its applicability across large geographical areas and its ability to provide regional
assessments of ecological condition. The information derived from an index of environmental condition such
as the benthic index is useful to environmental managers and policy and decision makers who want to identify
areas of potential degradation and track the status of environmental condition over time. A benthic index can
be used to answer questions about the health of benthic communities in the estuaries of a large geographical
region, the spatial or temporal variation of degraded areas of benthic communities, and the status of benthic
ecological conditions between the estuaries of different regions.
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This benthic index, although developed for EMAP-Estuaries in the Louisianian Province, is easily applied to
benthic data from other sampling programs in the northern Gulf of Mexico (see the examples in Engle and
Summers [1998]). This benthic index has been successfully applied by others in order to assess benthic
conditions in specific estuaries on the Gulf coast. Alabama's Department of Environmental Management
effectively used this benthic index to assess the sediment quality in the estuaries of their state (Carlton et al.
1998). Similarly, this EMAP benthic index successfully discriminated degraded from undegraded sites in a
regional assessment of environmental conditions in Galveston Bay, Texas (C. Gorham-Test, unpublished
data). The Texas Natural Resource Conservation Commission applied this benthic index to sites in a targeted
study of land use in Galveston Bay and found significant correlations with site rankings based on sediment
toxicity tests and sediment chemical concentrations (G. Guillen and L. Broach, pers. comm.). Although the
Louisianian Province is geographically widespread, we caution the application of the index outside of this
biogeographic region. The environmental stresses affecting the benthos in Gulf of Mexico estuaries may
differ from those affecting other regions (e.g., the Mid-Atlantic or Pacific Northwest).
We have successfully synthesized benthic community information into a benthic index of ecological condition
that provides environmental managers with an alternate way to assess the status of benthic communities
over large geographical areas. A response indicator like the benthic index provides a numerical quantification
of the response of the benthic communities to environmental stresses (Summers et al. 1995). Because the
benthic index is scalable and the criteria for determining the classification of degraded or undegraded are
numeric, the application of the benthic index to other estuaries is straightforward.
Summary
The results of this evaluation show that the benthic index is an appropriate indicator for determining the
extent of degraded benthic communities in a regional monitoring program. The indicator is conceptually
relevant to both the assessment question and ecological function. When part of the collection for a suite of
indicators, collecting sediment for benthos is easily implemented and standardized methods are well-
established. The greatest cost of implementing the benthic index is in the laboratory processing charges.
Temporal and spatial variability have been minimized by both the EMAP-E sample design and rigorous
training and QC of the field and laboratory personnel. Probably the greatest concern in the implementation
of this indicator is the discriminatory ability of the benthic index. The percent efficiency of the benthic index
to classify independent sites was adequate but not as high as we had hoped. Because the EMAP-E design
is not limited to specific habitat types but characterizes a region as a whole, there is, inherently, a high degree
of variability in the benthic communities in the Gulf of Mexico region. We may have sacrificed a level of
precision in favor of a generalized index that is applicable across a wide geographic area.
Potential users have criticized this index approach because of various perceived difficulties in application.
Several reviewers have expressed that indices of biotic integrity (IBIs) are easier to understand. We would
agree that the IBIs modeled after Karr (1981) are more intuitive in that the models are forced to incorporate
the conceptual framework of the developer. The EMAP benthic index employs the same generalized approach
but assumes multi-stressor relationships and depends solely on the data to delineate which benthic parameters
relate to the observed situation. The IBIs are also perceived to be easier to employ. Clearly, they may be
easier to develop than the proposed benthic index but the scoring on multiple habitats of 4 to 7 parameters is
certainly more involved than inserting 5 parameters into an equation. The normalization to force the range to
be between 0 and 10 is for ease of interpretation and does not need to be done (i.e., if not normalized, the
cut-off between poor condition and marginal condition is 0.0). Finally, the index proposed here is applicable
over a wide range of environmental conditions and geography and provides comparable scores over these
3-25
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gradients. While the metrics used for an IBI should be selected for broad applicability (e.g., Kerans & Karr
1994), not all applications of the original IBI concept have followed this tenet.
The purpose of monitoring the condition of estuaries on a regional scale was to provide environmental managers
with an estimate of baseline conditions. EMAP-E provides regional estimates of estuarine condition against
which local managers can compare their estuaries. This further emphasizes the need to evaluate an indicator
in the context of a specific monitoring program, as an indicator that may be ideal for one type of program may
be inappropriate for another. In this case, the indicator itself could be applied to other, more spatially-specific,
monitoring programs if alternative methods were employed. The benthic index in this context is of high value
to environmental managers, especially to those concerned with estimating biotic or ecological condition in a
quantitative manner.
!l <
-------
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Chapter Four
Application of the Indicator Evaluation Guidelines to a
Multimetric Indicator of Ecological Condition
Based on Stream Fish Assemblages
Frank H. McCormick, U.S. EPA, National Exposure Research Laboratory,
Ecological Exposure Research Division, Cincinnati, OH
David V. Peck, U.S. EPA, National Health and Environmental Effects
Research Laboratory, Western Ecology Division, Corvallis, OR
In this chapter, we employ the guidelines presented in Chapter 1 to evaluate a complex (i.e., multiple
components) indicator of ecological condition based on stream fish assemblages. This indicator is being
modified and developed for implementation in a specific monitoring effort (the Mid-Atlantic Highlands
Assessment, described below) designed to address specific regional-scale assessment questions.
This chapter does not provide complete documentation of the indicator or the process of its
development. Our primary intent is to provide examples of the type of information that is appropriate to
address each evaluation guideline for the indicator. In some cases, examples are presented based on
hypothetical or simulated data, and in some cases, not all available information pertinent to a comprehensive
evaluation is provided. More complete documentation of the development and evaluation of the suitability of
this indicator exists or will be forthcoming in various scientific journals.
The indicator is a composite index, and its development is based on the multimetric Index of Biotic Integrity
(IBI) originally developed by Karr (Karr 1981, Karr etal. 1986). The IBI was developed to assess the condition
of water bodies by direct evaluation of biological attributes (Karr 11981, Karr and Dudley 1981, Karr 1991).
Multimetric indicators such as the IBI are based on the premise that biological data represent a means to
integrate various structural and functional attributes of an ecosystem and provide an overall assessment of
ecosystem condition (Fausch et a/. 1990, Karr 1991, Karr and Chu 1997). Biological and socioeconomic
characteristics of stream fish assemblages, including the capability to integrate the effects of a variety of
stressors across different time scales and levels of ecological organization, and the importance and familiarity
of fishes to the general public, make them conducive to the development of an indicator of ecological condition
(Table 4-1).
Some important features of the indicator are presented in Table 4-2. The development of the indicator is
based on accepted ecological and mathematical principles. Various critical structural and functional attributes
of the biotic components of an ecosystem (e.g., taxonomic richness, trophic structure) believed to respond
predictably to increasing intensities of human disturbance are represented by different metrics (Karr 1986,
Karr 1991, Barbour etal. 1995). Metrics are derived from species composition and relative abundance data
of a particular ecological assemblage or community (stream fish in this example) collected at individual
sampling sites. A final suite of metrics is selected for use in developing the indicator, based on responsiveness
4-1
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to biotic or abiotic conditions resulting from increasing human disturbance, and their biological importance.
For each sampling site, the response value for each metric selected is transformed to a metric score. The
score for each metric is based on the degree of deviation of the response value from that expected at a similar
site under conditions of minimal human disturbance. The individual metric scores are then aggregated to
produce an overall indicator score. A higher score indicates better ecological condition (i.e., closer to the
expected condition when human disturbance is minimal). More detailed descriptions of the general approach
used to develop multimetric indices can be found in Fausch et a/. (1984), Karr et al. (1986), Karr (1991),
Plafkin et al. (1989), Gibson (1994), Barbouref al. (1995), Simon and Lyons (1995), and Karr and Chu (1997).
Simon and Lyons (1995) and Karr and Chu (1997) summarize and address criticisms of the multimetric
approach to developing indicators of condition.
Multimetric indicators based on modifications to the original IBI concept of Karr (Simon and Lyons 1995) have
been developed for use in various geographic areas in the United States and elsewhere (Miller et al. 1988,
Lyons et al. 1995, Yoder and Rankin 1995, Lyons etal. 1996, Hughes and Oberdorf 1999), various systems
(Jordan etal. 1993) and taxa (Lenat 1993, Kerans and Karr 1994, Fore et al. 1994, DeShon 1995, Fore et al.
1996, Barbour et al. 1996). Many of these studies address evaluations of the indicator, approximating some
of ORD's evaluation guidelines as outlined in Chapter 1, and use a variety of approaches to address a
particular guideline.
Table 4-1. Rationale for indicators of ecological condition based on stream fish assemblages1
i
i i
• Historical data available
Autecology of most species described
• Integrates effects of stressors at various scales, time periods, and
levels of organization
• Includes long-lived and mobile species
Assemblage composed of populations, individuals
• Important resource to humans
• High level of familiarity with general public
1 Compiled and summarized from Karr et al. (1986), Plafkin et al. (1989), Simon (1991), and
Simon and Lyons (1995).
The stream fish assemblage indicator is being developed using data collected as part of the Mid-Atlantic
Highlands Assessment (MAHA). This study was funded by the EPA Environmental Monitoring and Assessment
Program (EMAP; e.g., Whittier and Paulsen 1992) in conjunction with a Regional-EMAP (R-EMAP) effort
(U.S. EPA 1997). The MAHA study represents a partnership between EMAP and EPA Region III to develop
and demonstrate EMAP approaches such as probability-based survey designs and appropriate indicators of
ecological condition to address specific assessment questions of interest to the Region.
The monitoring framework for MAHA consists of a regional-scale probability-based survey design to select
sampling sites. This design permits unbiased inferences to be made with known certainty from the subset of
sites where samples and data are collected to explicitly defined populations of ecological resource units
(Larsen 1995,1997, Diaz-Ramos et al. 1996). For MAHA, populations are defined based on the total length
of streams. The design allows one to estimate the total length of streams in the target population (e.g., all
permanent streams appearing on a particular scale of map) which meet some criteria (e.g., all first-order
target streams, all target streams within a specific ecoregion). The distribution of indicator scores can then be
examined for these defined populations to determine the estimated length of stream characterized by a
particular set of indicator values, with associated uncertainty in these estimates represented by confidence
bounds.
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Multimetric indicators developed for a particular geographic area and scale of monitoring effort should not be
applied to other scales of monitoring or other geographic areas without evaluation and modification. Because
of differences in the biological assemblage structure and composition, and different expectations of conditions
associated with minimal human disturbance, component metrics may require substitution and validation (Miller
et a/. 1988, Barbour ef a/. 1995). Thus, previously existing multimetric indicators may not be useful for the
MAHA geographic region and monitoring framework. Likewise, this!assemblage indicator should not be used
in any other monitoring context and/or geographic area.
Table 4-2. Characteristics of indicator
Basic data from fish assemblages required:
• Presence/absence of species
Abundance of individual species
Requires representative data on fish species composition and abundance be
collected at each sampling site.
Metrics: Categories of species or individuals representing various aspects of ecology and
life history in the assemblage
• Species richness and composition
Abundance and individual condition
• Trophic function
• Reproductive function
Each fish species assigned to categories within each metric based on life history characteristics.
Score for a particular metric based on comparison of observed responses to expectations under
conditions of minimal human disturbance.
Each metric is assigned a score of between 0 and 10 based on the similarity of the
observed response to expectations.
Indicator score is computed as the sum of individual metric scores.
Metrics are equally weighted
• Indicator score is re-scaled to be between 0 and 100
0 = no fish collected at site
100 = site meets all expectations for conditions under minimal human disturbance
Assessment question(s) addressed by extrapolating indicator values from probability sample of
sites to entire target resource population (stream length).
Public Perception
• Indicator score easily understood
Once developed and validated, indicator does not require sophisticated technical
expertise to interpret
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The example indicator is modified from other multimetric indicators developed previously to tailor it to a
specific geographic region (mid-Atlantic highlands), the characteristic ichthyofauna of the region, and the
proposed monitoring framework (regional scale survey design). Some metrics are modified to be more
generic (i.e., to include more species) to account for the fact that not all fish species are distributed throughout
the target region. Expectations for the responses of various metrics are modified to be more appropriate for
the geographic region and extant ichthyofauna.
In this presentation of a stream fish assemblage indicator for ecological condition, we have re-stated each
individual guideline presented in Chapter 1 for convenience and easy reference. In order to demonstrate the
application of relevant information to the guideline, we have developed Performance Objectives, or brief
descriptions of our interpretation of the specific needs for each guideline based on the specific type of
indicator and the proposed monitoring framework. After presentation and discussion of pertinent information,
we offer a summary of findings regarding the suitability of the indicator with respect to each guideline.
.I
Phasel: Conceptual Relevance
Guideline 1: Relevance to the Assessment
Early in the evaluation process, it must be demonstrated in concept that the proposed indicator is responsive
to an identified assessment question and will provide information useful to a management decision. For
indicators requiring multiple measurements (indices or aggregates), the relevance of each measurement
to the management objective should be identified. In addition, the indicator should be evaluated for its
potential to contribute information as part of a suite of indicators designed to address multiple assessment
questions. The ability of the proposed indicator to complement indicators at other scales and levels of
biological organization should also be considered. Redundancy with existing indicators may be permissible,
particularly if improved performance or some unique and critical information is anticipated from the proposed
indicator.
Performance objectives
1. Demonstrate that the indicator is linked to an identified assessment question
2. Discuss its role in contributing information to address multiple assessment questions
3. Demonstrate the complementarity and minimal redundancy with other potential indicators
'!
The design of the MAHA study was driven in part by a series of specific assessment questions that collectively
would provide the means to determine the status and extent of the condition of stream resources in the region
with respect to the societal value of biological integrity (as defined by Karr and Dudley 1981). The principal
questions pertaining to stream fish assemblages are presented in Table 4-3 (U.S. EPA 1997). The nature of
these questions suggests that an appropriate indicator should focus at the assemblage level and consist of
multiple components to address the various aspects of the questions. In addition, to yield representative
estimates of status and extent of stream resources with respect to biological integrity, a monitoring framework
based on a probability-based survey design is required. The example multimetric indicator, applied in
conjunction with the appropriate sampling design, meets all of the requirements to address the principal
assessment question. The indicator can address all three components of the principal assessment question
by Including appropriate metrics (e.g., the number of species considered to be sensitive to human disturbance).
i i ;
The indicator is also useful in that the basic fish species and abundance data used to develop it can also be
used with little or no additional effort to address other assessment questions of interest (Table 4-3) (U.S. EPA
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1997). These subsidiary questions are relevant to a separate societal value of interest to the MAHA study,
fishery health.
There are several possible complementary relationships with other indicators (Table 4-4). It can be used as
part of a suite of indicators to address multiple assessment questions, or can provide a more complete
assessment of biological integrity when combined with other indicators using biological assemblages.
Component metrics of the indicator are selected based on their hypothesized response to stressors which are
monitored at different scales and incorporate information from different levels of biological organization. Possible
causes of poor condition as determined by the indicator can be Identified (although specific cause-effect
relationships cannot always be ascertained) by examining correlations between the indicator or component
metrics and various measures of ecosystem stress (measurementivariables or multi-component indicators).
Finally, the potential exists that the indicator may provide information that is highly redundant with indicators
derived from stream macroinvertebrate assemblages. This has yet to be evaluated for the MAHA study.
DeShon (1995) reported that multimetric indicators for fish and benthic macroinvertebrates provided
complementary, rather than redundant, information when compared in Ohio streams.
Table 4-3. Assessment questions driving development of indicator
Principal Question Relevant to the Societal Value of Biological Integrity
What % of stream miles (and spatial distribution) have fish assemblages that differ from
"reference" condition as measured by:
Species richness?
Number of species and/or % individuals of species sensitive to human disturbance?
Cumulative index of biotic integrity based on fish assemblage?
Subsidiary Questions Relevant to the Societal Value of Fishery Health
What % of stream miles have game fish?
Which species are most abundant or widely distributed?
What is the % of stream miles with game fish classified by stream order?
What % of stream miles support coldwater vs. warmwater fisheries as determined by the fish
species?
Specific assemblages of interest include:
• Cold water (e.g., salmon, trout)
Cool water (e.g., smallmouth bass)
Warm water (e.g., largemouth bass, sunfish)
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Table 4-4. Relationship to other indicators
Complementarity
Potential to be combined with other condition indicators to provide a more complete idea of
overall biotic integrity or sustainability
• Macroinvertebrate assemblages
Periphyton assemblages
Index of Well Being (Gammon 1976)
Abiotic condition indicators (e.g., habitat quality or chemical quality)
Metrics can be selected that are linked to stressors that can be monitored at different scales:
site level, watershed level, and landscape level.
Indicator incorporates information at various levels of biological organization^
Assemblage (community): species richness, trophic composition, habitat guilds
Population: abundance, life history/reproductive strategies
Associations between indicator (or component metrics) and other stressor indicators
(e.g., habitat disturbance, chemical water quality) can be examined to identify possible
causes of impairment.
Redundancy
i i
Potentially redundant with other condition indicators based on assemblages (e.g., macro-
invertebrates, periphyton), but this has not been empirically demonstrated.
1 Modified from Table 7 in U.S. EPA (1997)
Summary
ii
'i
The indicator and associated monitoring framework are linked to a specific assessment question developed
for use in the mid-Atlantic highlands as part of a program to determine ecological condition of freshwater
streams. Ancillary questions related to separate societal values (e.g., fishery health) can also be addressed
with the indicator, its components, or its basic measurement data. The indicator, in conjunction with other
condition or stressor indicators monitored at other scales or levels of biological organization, can contribute
information to address multiple assessment questions or provide a capability to diagnose possible causes of
impairment. The potential for providing redundant information with other condition indicators based on different
types of assemblages or communities is identified, but has not been empirically demonstrated or evaluated
for this monitoring program.
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Guideline 2: Relevance to Ecological! Function
It must be demonstrated that the proposed indicator is conceptually linked to the ecological function of
concern. A straightforward link may require only a brief explanation. If the link is indirect or if the
indicator itself is particularly complex, ecological relevance should be clarified with a description, or
conceptual model. A conceptual model is recommended, for example, if an indicator is comprised of
multiple measurements or if it will contribute to a weighted index. In such cases, the relevance of each
component to ecological function and to the index should be described. At a minimum, explanations and
models should include the principal stressors that are presumed to impact the indicator, as well as the
resulting ecological response. This information should be supported by available environmental, ecological
and resource management literature.
Performance Objectives
1. Demonstrate conceptual linkages between ecosystem components and principal stressors.
2. Demonstrate conceptual linkages between principal stressors believed to cause impairment with
respect to the societal value of interest, ecosystem responses to these stressors, and the indicator
(or its components).
Basic relationships between major structural components and processes can be graphically represented
(Figure 4-1, modified from Hughes etal. 1994) to illustrate possible routes of exposure from anthropogenic
stressors. This diagram also points out the location and functional roles offish assemblages to demonstrate
those stressor-response relationships that can be effectively monitored with a fish assemblage indicator.
Fish assemblages can be used to assess condition both in the water column and bottom habitats, and can
provide information from multiple trophic levels.
More specific hypotheses have been developed regarding the relationship of indicator metrics with
anthropogenic stressors (Fig. 4-2). This approach is based on a model originally conceived by Karr et al.
(1986). We have modified the model to organize it by types of major stressors (following terminology presented
in U.S. EPA 1997). This representation shows direct linkages between individual metrics and each type of
stressor, and helps to illustrate the diagnostic capability of the indicator since different scores for individual
components can be associated with responses to certain groups of stressors.
The suite of candidate metrics represent those selected after a screening process to eliminate those that
were not responsive to hypothesized stressors of interest, were redundant in their information content, or
were otherwise not suited for application in the proposed monitoring framework and/or geographic region of
interest. The metrics shown in Table 4-5 provide information about the ecological relevance of each component
metric; consequently Table 4-5 could be considered one type of conceptual "model" of the indicator. It
demonstrates anticipated responses of component metrics to various types of stressors, based on
characteristics offish assemblages in environmentally degraded systems described by Fausch etal. (1990).
Further demonstrating the diagnostic capability and potential discriminatory ability of the indicator (addressed
more completely as part of Guideline 12), Figure 4-3 shows the range of response for each individual metric,
based on summaries in Angermeier and Karr (1986) and Karr (1991). Some metrics (e.g., species richness)
will exhibit a response over the entire range of condition, while others help to discriminate either very good
condition (e.g., sensitive species richness) or very poor condition (e.g., proportion of tolerant individuals).
4-7
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ATMOSPHERE
STREAM USE
Producer: woody plants
1° Consumer: birds
2° Consumer birds
Decomposers
LAND USE
Producer: macrophytes
1° Consumer: fish
2° Consumers: herpetofauna,
fish
Decomposers
"1''' ' :a*'"'
Producer: algae
1° Consumer: benthos
2° Consumer: benthos,
herpetofauna, fish
Decomposers: microbes
Stressor Sources
Movement of Materials
Figure 4-1. Conceptual model of major structural and functional components
of a stream ecosystem modified from Hughes et a/. (1994).
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CHEMICAL ALTERATIONS
Stressor Disturbance Metric Response
PHYSICAL HABITAT ALTERATIONS
Stressor Disturbance Metric Response
HYDROLOGIC ALTERATIONS
Stressor Disturbance Metric Response
BIOLOGICAL ALTERATIONS
Stressor Disturbance Metric Response
Figure 4-2. Conceptual model of indicator, showing linkages between various types
and classes of stressors and component metrics [Derived from Karr (1985),
Fausch etal. (1990), and U.S. EPA (1997)].
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Table 4-5. Description of component metrics of example indicator and conceptual linkages to stressors
Metric
Description, Rationale, and Linkage to Stressors
Response
To Increased
Human
Disturbance1
Species Richness and Composition Metrics2
Number of different native species collected. Measure of biodiversity. Decrease
!
Number of different families represented in sample. Assess degree to which Decrease
stream reach supports families represented by only one or a few species.
; i i j
Species adapted for utilizing bottom habitat. Measure of substrate habitat quality Decrease
and quality of riffle habitats, affected by sedimentation.
Species adapted for utilizing surface and midwater habitats. Measure of habitat Decrease
quality (esp. pools); affected by increased turbidity.
i
i
Abundance and Condition Metrics2
Number of individuals collected. Measure of relative productivity. Decrease
i
Indicator Species Metrics2
Measure of biological "pollution," also a biological stressor to native fish Increase
populations.
•I
i
First species lost following human disturbance, last to recover following restoration Decrease
Individuals that are tolerant of disturbance or extremes in environmental Decrease
restoration conditions.
j
Trophic Function Metrics2
i
Excluding omnivores; measure of trophic/food web complexity of fish assemblage. Decrease
! i|
Measure of ability of food chain to support top level; affected by toxics, turbidity. Decrease
Native Spedes Richness
Native Family Richness
Native Benthic Species
Richness
Native Water Column
Species Richness
Total Abundance
Proportion of Individuals
as Non-native Species
Sensitive Spedes Richness
Proportion of Individuals
from Tolerant Spedes
Number of Trophic Strategies
Proportion of Individuals
as Top Carnivores
Proportion of Individuals
as Invertivores
Proportion of Individuals
as Omnivores
Proportion of Individuals
as Herbivores
Number of Reproductive
Strategies
Proportion of Individuals
as Tolerant Spawners
Measure of capacity of system to support primary consumers (major trophic Decrease
group of fishes).
Trophic generalists feeding on variety of plant and animal material. Increase
Taxa that feed exclusively on plants and algae. Increase
Reproductive Function Metrics2
Excludes strategies tolerant to siltation. Measure of ability of stream reach to Decrease
support a variety of reproductive strategies; affected by toxics, turbidity,
sedimentation.
Species that can reproduce under a broad range of habitat conditions with no Increase
special requirements for spawning to occur.
'Based on Fausch et a/. (1990) and Hoefs and Boyle (1992)
^Terminology based on Simon and Lyons (1995)
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# Native Spp.
# Native Families
# Native Benthic Spp.
# Native WaterColumn Spp.
Abundance
# Sensitive Spp.
% Tolerants
% Non-natives
# Trophic Strategies
% Carnivores
•/• Invertivores
% Om nivores II
% Herbivores!
# Reproductive Strategies
% Clean Substrate Spawners
% Tolerant Spawners
POOR
BIOTIC INTEGRITY
GOOD
Figure 4-3. Range of ecological condition (as expressed by biotic integrity) over which
individual metrics comprising the indicator are expected to respond
[Modified from Karr and Angemeier (1986) and Karr (1991)].
Summary
Conceptual linkages between air, land and stream stressors of stream ecosystems with riparian, water column
and benthic receptors are presented. Principal stressor types include chemical, biological, hydrologic and
physical habitat alterations. Two different approaches were used to demonstrate conceptual linkages between
principal stressors believed to cause impairment with respect to the societal value of interest (biological
integrity), ecosystem responses to these stressors, and the indicator (or its components).
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Phase 2: Feasibility of Implementation
Guideline 3: Data Collection Methods
Methods for collecting all indicator measurements should be described. Standard, well-documented
methods are preferred. Novel methods should be defended with evidence of effective performance and,
If applicable, with compan'sons to standard methods. If multiple methods are necessary to accommodate
diverse circumstances at different sites, the effects on data comparability across sites must be addressed.
Expected sources of error should be evaluated.
Methods should be compatible with the monitoring design of the program for which the indicator is intended.
Plotdesign and measurements should be appropriate forthe spatial scale of analysis. Needs for specialized
equipment and expertise should be identified.
Sampling activities for indicator measurements should not significantly disturb a site. Evidence should be
provided to ensure that measurements made during a single visit do not affect the same measurement at
subsequent visits or, in the case of integrated sampling regimes, simultaneous measurements at the site.
Also, sampling should not create an adverse impact on protected species, species of special concern, or
protected habitats.
Performance Objectives
1. Clearly describe all methods required to obtain field measurement data for the indicator.
Demonstrate performance and compatibility with standard methods if necessary.
2. Demonstrate that the plot design (e.g., reach length, index period) associated with
methods is appropriate for proposed monitoring framework.
3. Describe equipment and technical expertise required to successfully implement methods.
4. Demonstrate that the proposed sampling design and methods have a low impact on the
environment and other potential indicator measurements.
5. Identify and evaluate sources of error associated with implementing a particular method.
!' . . j. I
Information regarding the basic procedure used at each sampling site to obtain values for the indicator is
presented in Table 4-6. Collection of field data at an individual sampling site is based on standard approaches
for stream fish assemblages (McCormick and Hughes 1998). References are presented in Table 4-6 that
more conripletely document the procedures, and point out possible compatibilities with other monitoring efforts.
Laboratory methods include confirming field identifications offish species; confirmation should be conducted
by a recognized taxonomic expert on the regional ichthyofauna. Other activities include compiling available
life history information on each fish species in preparation for making assignments to individual metric categories
(e.g., sensitive species, trophic function, type of reproductive strategy). Finally, information regarding the
composition and structure of fish assemblages in the region of interest that might be expected under conditions
of minimal human disturbance must be obtained to develop expectations for each metric.
i ",'';' ' ":
We also present analytical methods used to develop the indicator from measurements offish assemblages.
Most of these procedures are based on standard approaches published for multimetric indicators. We point
out deviations from the standard approach and their rationale. We also point out how indicator values are
coupled with the proposed monitoring framework to produce a distribution of indicator scores applicable to
stream resource populations as described in the Introduction.
• '• 'I | .
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Table 4-6. Summary of procedures to obtain measurement data and indicator values at each sampling site.
Field Procedures:
Standard sampling gears and techniques:
"Best Effort" sampling using a combination of gear types, standardized sampling times and
distances (40 times mean channel width (Lyons 1992) or 150 m, whichever is greater).
• Electrofishing
• Seining
Identify individual fish to species and enumerate.
Documented Protocols:
Lazorchak et al. (1998): EMAP Surface Waters-Streams Methods Manual
Similar protocols used by other large-scale monitoring programs.
• Meador et al. (1993): National Water Quality Assessment Program (NAWQA)
Laboratory Procedures:
Confirm species identifications from voucher specimens.
Compile autecological information for each species from published sources of life history data.
Obtain information regarding assemblage composition and structure under conditions of minimal
human disturbance.
Data Analysis Procedures:
Standard approaches for multimetric-based indicators (Karr et al. 1986, Plafkin et al. 1989,
Klemm ef al. 1993, Barbour et al. 1995, Simon and Lyons 1995).
Compute response values for each metric (e.g., species richness, proportion of tolerant individuals)
based on abundance data and autecological information for each fish species.
Determine expected conditions for each metric response.
• Metrics based on numbers of species require calibration for stream size.
Develop "maximum species richness lines" (Fausch et al. 1984)
Data from all sites used, rather than just "reference sites" (Simon and Lyons 1995).
• Metrics based on proportion of individuals require expectations based on composition
of a fish assemblage under conditions of minimal human disturbance.
Expectations modified when possible, based on knowledge of historical assemblages
prior to European settlement (Hughes 1995).
Compute scores for each metric based on deviation of response from expectations.
• 0 to 10 scale: Differs from standard approach (1,3, 5): Provides more continuous
distribution of scores (Hughes ef al. 1998).
Sum metric scores to produce indicsitor value.
• Rescale to be between 0 and 100: differs from standard approach (no rescaling)
Use EMAP techniques (e.g., Diaz-Ramos et al. 1995) to calculate distribution of indicator values in
estimated resource populations based on probability sample.
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Characteristics and issues associated with the application of methods to an individual sampling site within
the proposed monitoring framework are presented in Table 4-7 (Hughes 1993, Lazorchak et at. 1998). "Plot
design" refers to the approach required to obtain representative data on the fish assemblage from an individual
sampling site. Plot design involves considerations such as when to sample, where to sample within a
designated site, aricjl how many individual samples are required from each site. Probability-based survey
designs result in sites being selected at random, without regard to ease of access or other aspects of location.
Table 4-7. Features of monitoring framework and plot design
Monitoring Framework
Target resource is wadeable streams (Strahler order 1 through 3).
Survey design provides synoptic information about spatial distribution and extent of condition
in target resource populations.
, , , . I
Limited utility in describing condition at an individual site due to lack of replication.
Indicator specifically developed for use in mid-Atlantic highlands region.
Indicator is principally retrospective; anticipatory capability is low, although some metrics
(e.g., number of sensitive species) may provide an early warning of potential degradation.
Plot Design
Characteristics:
When to sample (Index period): Once per year during the summer baseflow period.
Where to sample: All habitats within a defined length of stream (based on mean width).
Number of samples per visit: One composite sample offish assemblage is created from
collections made with appropriate sampling gear.
Issues and constraints within proposed monitoring framework:
..I ij
Site Inaccessibility:
• Access rights to private land
• Remote locations away from roads
' I
Small proportion of sampling units are potentially affected by
, I
• Restrictions Imposed on State and Federal Scientific Collection Permits
• Species: threatened, endangered, economically valuable
• Sites: Wilderness areas, parks, preserves
• Gear types allowed at sites
Accuracy and consistency of field identifications among field crews.
Control measures include:
• Consistent training
• Performance evaluations against experts
• Use of experienced personnel (Federal, state and university)
• Consistent protocol for vouchering specimens for confirmation of species
identifications to allow for data correction when necessary
•". Field audits
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Thus, the possibility exists that some proportion of sampling sites will be located within protected areas or
within ranges of protected fish species. Eventual access to these sites may be denied or possibly restricted
(in terms of when sampling can occur or how samples may be obtained) by State or Federal agencies when
scientific collecting permits are issued. Finally, a large-scale sampling effort requiring multiple field crews
requires consistent implementation of the sampling and data acquisition procedures to permit robust
comparisons of data across sites sampled by different crews. Table 4-7 presents measures used to control
for crew differences.
Specialized equipment needs and technical requirements for field and laboratory personnel are presented in
Table 4-8. No specialized sampling or analytical equipment or instrumentation is required for the indicator.
Some level of technical expertise is required to support the collection of data for the indicator, especially in the
areas of ichthyology, fisheries biology and aquatic ecology. Some of this expertise can be gained through
specialized training programs, or addressed through staffing schemes considered under Guideline 4. Karr
(1991) points out the need for experienced professional fisheries biologists in the initial development and
subsequent interpretation of the indicator values.
Table 4- 8. Equipment and technical expertise requirements
Specialized Field or Analytical Equipment
Backpack, bank or boat-mounted electrofishing unit, seines, nets.
Technical Expertise
Field: •
Ability to identify majority of common fish species in field
• Especially state-listed species, larger species and sport fish (on which
sampling restrictions may be placed) which are identified and released
Ability to operate different types of sampling gear safely and effectively
• Electrofishing
• Seining !
Laboratory:
Ability to identify all fish species in region from preserved specimens
• Especially small, non-game fish (e.g., minnows)
Ability to review literature , compile information, and categorize individual fish
species regarding life history characteristics, tolerance to disturbance, etc.
Data analysis and interpretation:
Critical that professional fisheries biologists and ecologists be involved in the
selection and evaluation of metrics, the determining of expectations for each
metric, and the assignment of threshold values associated with different
classes of ecological condition.
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Potential consequences of the proposed plot design and collection methods are identified and presented in
Table 4-9. Adverse effects on fish populations are generally low, and the fish assemblage has sufficient time
to recover between sampling visits. Methods for the indicator can be a component of an integrated sampling
regime for several different indicators.
A critical aspect of obtaining a representative sample of the fish assemblage under the proposed plot design
is determining the length of stream that must be sampled at each site. For this indicator, a sample of the
assemblage must be collected from a single pass through a prescribed length of stream (Karr et al. 1986,
McCorrnjck and Hughes 1998). Repeated sampling of a stream reach is neither practical nor representative.
Thus, it Is imperative that the length of stream to be sampled maximizes the number of species collected. A
small pilot study on a few selected streams was conducted to make this determination. Based on this study
(Fig. 4-4), a stream length equal to 40 times the mean channel width was selected as the area to be sampled
at each stream. This length of stream is sufficient to obtain approximately 90 percent of the fish species
inhabiting the reach. Sampling additional lengths of a stream does not substantially increase the number of
species obtained. Lyons (1992) reported similar results.
I !
•I i
Table 4-9. Effects of sampling
Effects on stream fish assemblages and protected organisms, populations, habitats:
1
ii
Minimal impacts under normal conditions
Most individuals collected released alive (some retained as voucher specimens)
Some mortality due to electrofishing or seining, especially if large numbers
are collected and processed
• Sorne impact due to physical disturbance of stream channel during seining,
but scale of sampling is small relative to entire watershed
• May be minimal in some areas due to collection permit restrictions (no sampling)
'•; : . I •
Effects of a single visit on subsequent visits:
i
• Collecting replicate samples during a single visit not practical nor accurate
• Entire reach is disturbed during sampling
• Most individuals collected are released alive, but need time to recover and
redistribute after collection
• Studies indicate that fish assemblages recover after natural disturbances
such as floods or extended drought
Effects on concurrent measurement of other indicators:
! i
Methods are used in an integrated sampling regime for a variety of different
indicators.
* No impact if proper sampling sequence is followed (e.g., collect chemical
samples prior to obtaining fish assemblage samples)
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--. 100
o
£
w
Q.
w
40
20
0 B
0 10 20 30 40 50 60 70 '80 90
Stream Length (Channel Width Units)
Figure 4-4. Effort-return curves offish species richness versus length of
stream sampled. (McCormick, unpublished data).
Summary ;
Standard sampling and data analysis methods for this indicator are shown to be compatible with the proposed
plot design and monitoring framework, and are based on standard techniques for documenting stream fish
assemblages. Possible constraints associated with the proposed plot design using the monitoring framework
include site accessibility and consistency among crews. Equipment and technical expertise requirements are
defined, and include the need for experienced fisheries biologists during initial phases of a monitoring program
and for data analysis and interpretation. Field methods used for the indicator have little impact on either fish
populations or habitat, and are compatible with an integrated sampling regime for multiple indicators. A pilot
study was performed to determine the optimal stream length for sampling (i.e., to assure that the stream
length sampled is sufficient to obtain at least 90% of the fish species inhabiting the reach).
Guideline 4: Logistics
The logistical requirements of an indicator can be costly and time-consuming. These requirements must
be evaluated to ensure the practicality of indicator implementation, and to plan for personnel, equipment,
training, and other needs. A logistics plan should be prepared that identifies requirements, as appropriate,
for field personnel and vehicles, training, travel, sampling instruments, sample transport, analytical
equipment, and laboratory facilities and personnel. The length of time required to collect, analyze and
report the data should be estimated and compared with the needs of the program.
Performance Objective
1. Demonstrate the feasibility of data acquisition with respect to the proposed scale and intensity of
monitoring in terms of staffing, training, travel, equipment, laboratory facilities, and data turnaround
time.
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There are several critical features of the proposed monitoring framework (Table 4-16) that must be considered
in developing the logistics plan (e.g., Baker and Merritt 1991) for data acquisition. These features require that
a large number of sites be visited across a broad geographic area in a relatively brief time period each year.
Table 4-10 also presents field logistics issues that must be addressed in the context of the constraints imposed
by the monitoring program. Considerable effort is required to locate and obtain permission from landowners
who must be contacted to access sampling sites. A considerable amount of lead time is also required to
apply for and obtain all required scientific collecting permits. This is because of the number of different states
Included in the mid-Atlantic highlands region and the large number of sites that must be reviewed individually
for presence of protected species. As mentioned previously (Table 4-7), restrictions may be placed on
collecting at individual sites that harbor protected species.
i i
Based on experience from the MAHA study, it is feasible to implement the indicator as part of an even larger-
scale, long-term monitoring program under the proposed monitoring framework (Hughes 1993, Lazorchak et
a/. 1998). Several field crews are required to accomplish all sampling within the required time period. The
location of sites in different states imposes certain constraints that must be considered in determining the
best source of personnel; if State personnel are used, they may be restricted to travel within their home state.
Use of State personnel has advantages including: 1) shortening the process for obtaining scientific permits,
and 2) providing more familiarity with staging areas, access points, landowners, streams, and fishes in the
region. A crew of 3 or 4 people can accomplish all collecting from a stream in less than 4 hours, and complete
all processing activities within a single day. A crew of this size can also obtain data for other indicators during
the same visit. During the MAHA study, crews obtained samples or data for 8 additional indicators during a
single site visit (Lazorchak et al. 1998). Four to five streams a week can be visited by a single crew, allowing
for one day of travel between sites that are not necessarily close together because of the random selection
process. Because of the level of technical expertise required (Table 4-8), the use of volunteers is not
recommended for the indicator unless they can be included on crews with other personnel having sufficient
technical background and experience. Additional training beyond basic instruction in collection procedures
includes safety training associated with electrofishing and a workshop on field identification of the regional
fish fauna. If State personnel are used, training may be less intensive, as they will be more familiar with basic
collecting procedures and may have more experience with the identification of fishes in the field.
Several issues related to equipment and supplies (Table 4-10) should be considered in selecting the proposed
Indicator for use in a monitoring program. For example, the random selection of sites with no regard for
location or ease of access will result in a number of sites located in remote areas of the mid-Atlantic highlands
region. Experience with the MAHA program revealed these sites were accessible only by 4-wheel drive
vehicles, by foot, or by a combination of the two. Sources and availability of leased 4-wheel drive vehicles are
usually limited in many areas, and a long lead time may be required to obtain appropriate vehicles. If State
or Federal personnel are used, appropriate government vehicles may be available. Accessing sites by foot
may affect the crew size (i.e., additional people may be required to transport all the required equipment), and
possibly even the choice of equipment based on its portability. The use of hazardous material (formalin,
gasoline) requires knowledge of and compliance with all regulations related to personal protection and transport.
Depending on the sampling scenario developed, this usage may involve additional training requirements and
purchase of appropriate shipping and packaging materials.
Laboratory issues (Table 4-10) relate primarily to the selection of a qualified facility to confirm identifications
of voucher specimens and provide long-term archival of vouchers. A key constraint is the length of time that
may be required before results of confirmatory identifications are available. Confirmation may affect the time
required to complete validation of the data (Guideline 5) and report results for the indicator value. Under
most circumstances, the 9-month timeline specified in Table 4-10 should be achievable for the indicator.
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Table 4-10. Logistical considerations
Monitoring Requirements
50 to 200 site visits per year, sites to be revisited every 4 years
Sites selected at random with no consideration for location (e.g., public vs. private
ownership) or ease of access (e.g., bridge crossings vs. wilderness areas)
Sites located across mid-Atlantic highlands region (multiple States)
All site visits must be completed within summer baseflow (July-September)
Each stream visit is limited to a single day
Results (e.g., indicator values) should be reported within 9 months of collection
Field Logistics Issues
Site Access
Landowner permission required ;
Scientific collection permits required
Federal Endangered Species Permits
State permits may include limits for listed Threatened/Endangered species
Field Crew
3-4 person crew
Personnel sources
Partnerships with State/Federal Agencies ;
Contract :
Volunteers not recommended
Time/Effort Requirements j
1 site per day
Total sampling time: 45 min. - 3 hrs
Processing time varies based on catch ;
4-5 sites sampled per week
Additional training requirements :
Electrofishing requires safety training, first aid, CPR
Field identification of fishes
Equipment and Supplies
4-wheel drive vehicles may be necessary to access remote sites (unmaintained roads)
Equipment may need to be transported by foot over rugged terrain
Transport and handling of hazardous materials required (formalin, gasoline)
Laboratory Logistics Issues
Partnerships with State/Federal Agencies
Contracts with regional museum facilities
Time/Effort Requirements
Identification of voucher specimens: up to several months
Long-term archival of voucher specimens
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Summary
i
Data collection activities for the indicator are described within the constraints imposed by the proposed
monitoring framework. Considerable lead time is required to obtain permission to access sampling sites and
to obtain the required scientific collecting permits. Partnerships with local agencies can streamline collection
efforts. Various staffing options are presented to provide the required expertise; some safety training may be
necessary for crews. Equipment and supplies may have to be transported over harsh terrain. Validation of
fish identifications can be achieved by a competent local museum.
Guideline 5: Information Management
Management of information generated by an indicator, particularly in a long-term monitoring program,
can become a substantial issue. Requirements should be identified for data processing, analysis, storage,
and retrieval, and data documentation standards should be developed. Identified systems and standards
must be compatible with those of the program for which the indicator is intended and should meet the
interpretive needs of the program. Compatibility with other systems should also be considered, such as
the internet, established federal standards, geographic information systems, and systems maintained by
intended secondary data users.
Performance Objectives
1. Identify requirements for data processing, review, analysis, and storage, and demonstrate
compatibility with those capabilities available to the proposed monitoring program.
2. Describe the metadata necessary for primary and secondary users to access the data, to
reproduce the results, or to use the data in other types of analytical and interpretive activities.
There are important information management requirements and issues related to supporting the routine use
of the indicator within the proposed monitoring framework (Table 4-11). Experience with the MAHA study has
indicated that a fairly lengthy time period is required to complete review and validation of the measurement
data prior to their use in computing metric responses, scores, and the indicator value. This process may
inhibit the ability to achieve the desired reporting timeframe (9 months; Table 4-10). We anticipate this time
will be reduced as experience with the data is gained and automated routines are developed to facilitate
review and validation activities.
• !
The requirements for hardware and software (Table 4-11) were selected to be compatible with nearly all
potential participants in the proposed monitoring program. Some programming support may be needed to
develop the routines for computing metric responses, scores, and indicator values from validated measurement
data. Diaz-Ramos etal. (1996) provide statistical algorithms needed to compute resource population estimates
in spreadsheet-compatible format.
ij , i
The critical data sets and metadata required to support the development of the indicator and its component
metrics (Table 4-11) are few in number and fairly straightforward. A critical component of archival activities
for the indicator is the incorporation of voucher specimens into a permanent museum collection.
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Table 4-11. Information management requirements
Time to Validate and Analyze Data
Six-twelve months: Will be reduced with experience in the region and development of
automated check routines.
Hardware and Software Requirements
Hardware: High-end personal computer (PC)
Capable of performing all calculations required for indicator development and
evaluation and resource population estimates.
Measurement data files (species ID and abundance data) can become large;
sufficient storage capacity is required. '
Software: Data management
• Relational database software useful, but not required
Spreadsheets can be used to perform calculations and manage data files,
but may be cumbersome
Statistical analysis software, graphics software
SAS has been used to develop indicator in MAHA
Spreadsheet capable of computing basic statistics, regressions
Multivariate analyses can be beneficial in evaluating metrics and IBI,
but not required
Critical Data Sets ;
Validated species ID and count data for each site visit
Autecological data for each species, including taxonornic information, habitat, tolerance class,
feeding class and reproductive strategy
Individual metric values, scores, and IBI value for each site visit
File containing locational information for each site, site classification information
(e.g., ecoregion, drainage), and inclusion probability values to calculate resource population
estimates
Ancillary databases to allow metric and indicator evaluation of response to disturbance (chemistry,
physical habitat, watershed stressors, landscape-level data)
Metadata Requirements
Methods documentation •
Sources of autecological information
Documentation for how individual metrics are computed from basic count and ID data, and
how a final score is assigned for each metric
Database documentation regarding files and variables
Data and Sample Archival ,
Field data forms (paper or electronic)
Voucher specimens cataloged into permanent museum collection
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Summary
Information management requirements for the indicator are currently time consuming (6-12 months to develop
indicator values from the raw data), but time should be reduced with automated routines. The length of time
currently required to validate measurement data may affect the desired turnaround time established for the
proposed monitoring program. No specialized hardware, software, or programming support is required, and
data storage is compatible with other systems for retrieval. Critical data sets and associated metadata are not
extensive or complicated.
Guideline 6: Quality Assurance
For accurate interpretation of indicator results, it is necessary to understand their degree of validity. A
quality assurance plan should outline the steps in collection and computation of data, and should identify
the data quality objectives for each step. It is important that means and methods to audit the quality of
each step are incorporated into the monitoring design. Standards of quality assurance for an indicator
must meet those of the targeted monitoring program.
Performance Objective
1. Demonstrate that the critical components of an appropriate quality assurance program are established
for the indicator, and that techniques are available to monitor and control important sources of error in
the measurement data for the indicator.
. , ,j
The scale and time frame of the proposed monitoring framework and the need for multiple field crews (Table
4-10) require a rigorous quality assurance (QA) program to ensure consistency in data collection and
interpretation of indicator values (e.g., Chaloud and Peck 1994). There are important considerations (Table
4-12) for developing an appropriate QA program for EMAP-related studies. Resources are available, in the
form of guidance documents and existing quality assurance plans, that can be adapted or modified to other
types of monitoring efforts. No additional research is required to develop appropriate standards or other
techniques to monitor and control data quality. All field and laboratory procedures associated with the indicator
are amenable to the development of performance criteria and to internal or external audits by qualified personnel.
Measurement related errors can be identified (Guideline 8) and compared against established performance
criteria. The use of a qualified museum facility to confirm field identifications of voucher specimens and as a
permanent repository provides a means to control and correct for a critical source of error. Examination of
data from sites visited more than once during a single index period (Table 4-7) can be used to evaluate the
consistency and performance of collection methods and field personnel. Concurrent identification of fish
species in the field by a recognized authority in fish taxonomy can provide rapid identification and correction
of errors. Finally, a variety of procedures are available to provide a rigorous review and validation of data to
Identify and correct for entry errors, erroneous species identification, and abundance values.
Summary
An appropriate quality assurance program can be developed and implemented for the indicator and monitoring
framework using available resources and techniques. No additional research is required to provide appropriate
performance standards or other techniques to monitor and control data quality. Measurement errors can be
identified and evaluated at each critical step of indicator measurement.
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Table 4-12. Quality assurance considerations
QA program guidance available:
Environment Canada (1991)
• Draft guidance documents from U.S>. EPA National Center for
Environmental Research and Quality Assurance
EMAP-Surface Waters integrated QA project plan
(Chaloud and Peck 1994)
Controls and audits can be established for all field and laboratory protocols.
Performance evaluations can be accomplished using repeat visits or by comparisons
to results obtained by recognized experts.
Data review procedures available:
• Comparison of observed locations of species to known geographic range
Exploratory analysis to identify outliers and suspicious values
• Internal consistency of counts
Guideline 7: Monetary Costs
Cost is often the limiting factor in considering to implement an indicator. Estimates of all implementation
costs should be evaluated. Cost evaluation should incorporate economy of scale, since cost per indicator
or cost per sample may be considerably reduced when data are collected for multiple indicators at a
given site. Costs of a pilot study or any other indicator development needs should be included if appropriate.
Performance Objective
1. Provide information regarding costs associated with implementing the indicator within the proposed
monitoring framework. Compare these costs, if possible, to similar costs associated with other
indicators that could be implemented within the proposed monitoring framework.
There are a variety of costs associated with implementing data collection activities for the indicator under the
proposed monitoring framework (Table 4-13). These costs are based on collection activities within the MAHA
study, using private contract field crews. Also included are costs associated with permanent archival of
voucher specimens. Equipment costs are presented on a per-crew basis, under the assumption that new
equipment is required. Karr (1991) presents cost-related information that suggests that sampling fish
assemblages may be more economical than other types of biological or chemical samples. Yoder and Rankin
(1995) present costs required to implement a similar indicator within a statewide network of hand-selected
monitoring sites in Ohio. They also show that fish assemblage dataware less expensive to collect and analyze
than quantitative macroinvertebrate samples, chemical samples, or various types of bioassays. Their costs
are based on the capability for a small-sized crew (3) to sample 3 to 6 sites per day. Costs might increase
when field logistics or accessibility are difficult (Guideline 4, Table 4-10), but under the proposed monitoring
framework this is offset to some extent by visiting fewer sites.
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Table 4-13. Cost information
Sampling costs (per site)=$1,540
Field Crews: $1200
• Includes salary, benefits, per diem, and lodging for 4 persons
• Based on 1 site per day collecting data for multiple indicators
• Does not include costs associated with vehicles
•!
• Includes cost of acquisition of specimens for fish tissue, biomarkers,
and genetics indicators
Laboratory Costs: $325
• Data analysis and data management
• Identification, verification, and archiving voucher specimens
Supplies: $15
• Jars, waterproof paper, formalin
: ;; : • : '. j ; :
Field equipment (per crew): $3,515
• Backpack electrofishing unit: $3,000 (one-time cost for multiple years'use;
annual maintenance = $300)
!
• Dip nets and seines: $200
• Measuring board: $65
• Miscellaneous equipment: $250
• Estimate 15% annual maintenance and replacement cost
j ,
Summary
The greatest cost for this indicator is salary for a field crew. Per-site costs may depend on field logistics and
accessibility of sites. Much of the cost for supplies and equipment can be distributed over several years (for
the life pf the equipment or duration of the monitoring program). Similarly, costs for field crews and site visits
could be distributed to other indicator measurements made at the same sites. Examination of costs for this
indicator, using estimates presented here and with similar information obtained from other published sources,
suggests that data collection and analysis may be less expensive for this indicator than for other biological or
chemical indicators that might be implemented within the proposed monitoring.
; .. . / . | . . • ;,•.•
Phase 3: Response Variability
Guidelines: Estimation of Measurement Error
The process of collecting, transporting, and analyzing ecological data generates errors that can obscure
the discriminatory ability of an indicator. Variability introduced by human and instrument performance
must be estimated and reported for all indicator measurements. Variability among field crews should
also be estimated, if appropriate. If standard methods and equipment are employed, information on
measurement error may be available in the literature. Regardless, this information should be derived or
validated in dedicated testing or a pilot study.
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Performance Objective
1. Provide estimates of important measurement-related errors associated with the indicator, and
compare them to established performance criteria for the proposed monitoring framework.
Several different types of errors can affect either the measurement data or the development of indicator
values from measurement data (Table 4-14). Measurement-related errors of field collection data, in terms of
number of species collected, species composition, and number of individuals cannot be estimated directly for
the indicator by collecting replicate samples during a single visit to a site (Table 4-9, Fore et a/. 1994). In
terms of repeatability, other published studies may not be applicable to the entire mid-Atlantic highlands
region or to the proposed monitoring framework. Measurement-related errors can be treated as a part of
temporal indicator variability (Guideline 9) and can be indirectly evaluated as part of "within-year extraneous
variance." The other critical source of error in measurement data is incorrect identifications of fish species.
Various means of controlling this source of error have been presented previously, including the collection and
confirmation of voucher specimens (Table 4-7), using personnel experienced in fish identification (Table 4-8)
and additional training in field identification of regional fishes (Table 4-10).
Table 4-14. Potential sources of measurement error
Data Collection
Poor repeatability in number of species collected, species composition, and counts
• Cannot be estimated directly using replicate samples
• Controlled by standardized protocols and methods
Incorrect identification of species by field crews or data recording errors
• Performance objective is < 10% errors
• Controlled through training, field audits and performance checks, and confirmation
of voucher specimens
See Table 4-9 for additional details
A more quantitative evaluation of errors related to identification offish species by field crews was derived from
3 years of sampling for the MAHA study (Fig. 4-5). Five types of error are investigated. Transcription errors
occur when the wrong species code is recorded on the field data form. The remaining four relate to actual
errors in taxonomy. They include a cumulative estimate of errors for all species, errors specific to two groups
of fishes that are difficult to identify to species in the field (sculpihs, genus Cottus, and a cyprinid genus
Nocomis), and errors at the genus level. Over the 3-year period,; improvements were made to field data
forms, crew training, and the procedure for collecting voucher specimens. Performance improved each year,
with the virtual elimination of transcription errors, misidentification of sculpins, and misidentifications at the
genus level (which are potentially more serious than species level identification in terms of the impact on
metric responses). The remaining error levels for overall species misidentifications and identification of Nocomis
species declined to well below the performance objective initially established (< 10%; Table 4-14).
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1993 1994 1995
YEAR
QTranscrlptlon HSpocles ID ENocomls sp.
QCottussp. EGenusID
Figure 4-5. Comparison of different errors in the identification of fish species
(McCorrruck, unpublished data).
Summary
• ' i i ,.,'
i ,
Measurement error is difficult to assess because repeatability is not possible using single-sample data collection
methods. However, relevant information can be obtained indirectly through the estimation of other variance
components. Performance criteria established to control important sources of measurement error in the
indicator, cap be achieved with the implementation of appropriate control measures. Control measures applied
to field identifications of fishes resulted in a substantial reduction of errors to within the performance criteria
initially established for the monitoring program.
Guideline 9: Temporal Variability - Within the Field Season
It is unlikely in a monitoring program that data can be collected simultaneously from a large number of
sites. Instead, sampling may require several days, weeks, or months to complete, even though the data
are ultimately to be consolidated into a single reporting period. Thus, within-field season variability
should be estimated and evaluated. For some monitoring programs, indicators are applied only within a
particular season, time of day, or other window of opportunity when their signals are determined to be
strong, stable, and reliable, or when stressor influences are expected to be greatest. This optimal time
frame, or index period, reduces temporal variability considered irrelevant to program objectives. The use
of an index period should be defended and the variability within the index period should be estimated and
evaluated.
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Guideline 10: Temporal Variability - Across Years
Indicator responses may change over time, even when ecological condition remains relatively stable.
Observed changes in this case may be attributable to weather, succession, population cycles or other
natural inter-annual variations. Estimates of variability across years should be examined to ensure that
the indicator reflects true trends in ecological condition for characteristics that are relevant to the
assessment question. To determine inter-annual stability of an indicator, monitoring must proceed for
several years at sites known to have remained in the same ecological condition.
Combined Performance Objectives for Guidelines 9 and 10
1. Identify important components of variance based on the proposed monitoring framework and
sampling design.
2. Demonstrate that the magnitude of individual components is within performance criteria established
for the proposed monitoring program.
The use of a probability-based survey design as the monitoring framework requires a modified approach to
defining and estimating important components of an indicator's spatial and temporal variance. In the case of
a multi-metric index, it is also necessary to determine variability in individual candidate metrics in order to
select the final suite.
Important sources of variation for the indicator within the proposed monitoring framework and target
performance criteria for EMAP have been identified (Table 4-15). Note the inclusion of "population variance,"
which is the variation due to the relationships between the probability sample and the survey design. It is
solely a function of the number of probability-based samples used to estimate the resource population of
interest. It has been determined that 50 samples provides population estimates with 90 percent confidence
bounds that are approximately ±10 percent of the proportion (Larsen etal. 1995, Larsen 1997). The survey
design is flexible in allowing one to define & posteriori various resource subpopulations of interest within the
constraints of sample size.
Sources of temporal variation in the indicator value (or metric score variable) are included in "extraneous
variance" (Table 4-15). These components and descriptions are based on Larsen etal. (1995) and Urquhardt
etal. (1998), for indicators associated with monitoring frameworks'similar to that of the proposed indicator.
Within-year variability (Guideline 9) is estimated as index period variance. Variability across years (Guideline
10) for this indicator is addressed by two separate components of variance: the coherent variability of all sites
across years, and the among-year variability of individual sites, Within-year variability also includes
"measurement-related" errors described under Guideline 8. Because the proposed monitoring framework
emphasizes regional scales and populations of sites, rather than patterns at individual sites, the importance
of measurement-related error is reduced. Measurement-related ejrors are considered in detail only when
within-year variability is unacceptably large relative to the total extraneous variance of the indicator. In such
cases, it must be determined whether the variability is due primarily to temporal variability or to measurement-
related errors.
Variance components were estimated using all 298 sites in the mid-Atlantic highlands region (including repeat
visits within and across years), weighted by the appropriate population expansion factor. These expansion
factors are used to extrapolate the results from each site in the survey sample to the entire resource population
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Table 4-15, Principal variance components for proposed monitoring framework
Among-site variance: Variation due to differences in the indicator value among a sample of stream
sites, this component represents the environmental "signal" to be detected and interpreted with
respect to an ecological condition.
• Function of number of sites sampled (inclusion probability)
• Calculated based on routines in Diaz-Ramos et al. (1996)
• Performance Objective: Target subpopulation sample size of 50, with minimum of 30
Extraneous variance: Remaining temporal, spatial and measurement-related variation. Collectively,
these components represent "noise" that inhibit the ability to detect and interpret the environmental
"signal." Extraneous variance is characterized using a randomly-selected subset of the probability
Sample sites. These sites are revisited across and within years.
• Components of extraneous variance:
• Coherent variance across years: Amount that all sites in a region vary in
common due to regional-scale effects (e.g., climate, hydrology); important
component in ability to detect trends in regional population of sites
• Among-year variance: Interaction of site and annual variability; amount
an individual site varies among years
• Within-year variance: Temporal variance at a site within the defined
index period. Also contains measurement-related error, crew errors, etc.
Important component in determining status of resource population
Approach: If temporal variance is significantly less than total observed
variance, then measurement-related components are not important. If
temporal variance contributes substantially to total variance, then examine
measurement-related components for possible sources.
• Spatial variance: Variance among different ecological subregions
(see Guideline 11)
• Calculated based on 2-factor analysis of variance model (sites, coherent variance
across years), with an interaction term representing among-year variability at an
individual site
• Performance Objectives:
• Variance within the index period should be approximately 10% of total variance
to minimize effect on status estimation and maximize discriminatory ability.
II . , :j", ! , ' II I ; t •
• Variance between years must be minimal relative to total; target capability
is to detect a 2% change per year in a regional population mean with a Type la
error of 0,1 and a Type llb error of 0.2.
• Type I error (false positive) is the probability of concluding that a trend is present when in truth it is not.
bType II error (false negative) is the probability of concluding that a trend is absent when in truth it is present.
of interest, and are based on the probability that an individual site will be selected as part of the survey sample
from the universe of potential target population sites. The relative magnitudes of different components of
variability for the indicator and each candidate metric were identified (Fig. 4-6), following Urquhardt et al.
(1998). With respect to estimating the status of resource populations using the proposed indicator, within-
year (index period) variability should comprise 10 percent or less of the total extraneous variance (Larsen et
a/11995, Larsen 1997). The indicator itself achieved this target, with index variability contributing approximately
5% to the total extraneous variance. However, performance of individual metrics was mixed: Six metrics
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PERCENT OF TOTAL,
40% 60%
Q ERROR 1STRM ID'YEARDYEARHSTRM ID
100%
Indicator
No. Species
No. Families
No. Individuals
Sensitive Spp.
Tolerant Individuals
No. Benthic Spp.
No. Column Spp.
Non-native Individuals
No. Trophic Strategies
Carnivores
Invertivores
Omnivores
Herbivores
No. Reproductive Stategies
Tolerant Spawners
Clean Substrate Spawners
Figure 4-6. Relative contributions of important variance components for the proposed indicator
and for candidate metric score variables. ERROR = within year (index) variability.
STRM_ID*YEAR = among year variability of individual sites. YEAR = coherent
variability of all sites across years. STRMJD = among site variability.
4-29
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were within 10%, five exceeded this only slightly, and five were 20-40% of the total extraneous variance. The
latter group requires additional evaluation to determine if error variability can be reduced by modification of
the scoring approach, alteration of the fish species in a particular group, or possibly altering the index period
itself. Also, these estimates are based on only two years and a relatively small number of sites; more precise
estimates will be possible with additional years of repeat sampling. Some of these metrics represent the most
widely-used attributes for characterizing aquatic communities; deleting any of them for purely statistical reasons
might diminish the utility of the indicator (Karr and Chu 1977).
For trend detection capability, the coherent variability across years component should not be large relative to
the total extraneous variance. This cannot be completely evaluated at the present time because the coherent
variation component is estimated from only two years of data, and thus probably underestimates the true
cphererjt variation of all sites across years. Several years of data from repeat sampling are required to
rigorously estimate this component, trend detection capability is further evaluated as part of Guideline 13
(Data Quality Objectives).
i ' • : • ', . ' 'i • I •
Summary
Important components of variability, particularly within-year variability, were estimated for the indicator and
candidate metric score variables. The indicator and most individual metrics achieved or nearly achieved the
performance objective ^contributing < 10% to the total extraneous variance). Five metrics were well above
this and should be further evaluated. Performance of the indicator and candidate metrics with respect to
trend detection cannot be evaluated at this time, as several years of data are required to provide rigorous
estimates of coherent annual variability.
Guideline 11: Spatial Variability
Indicator responses to various environmental conditions must be consistent across the monitoring region
if that region is treated as a single reporting unit. Locations within the reporting unit that are known to be
in similar ecological condition should exhibit similar indicator results. If spatial variability occurs due to
regional differences in physiography or habitat, it may be necessary to normalize the indicator across the
region, or to divide the reporting area into more homogeneous units.
Performance Objective
11; i i
1. Demonstrate that indicator response will be consistent across the monitoring region of interest.
The geographic scale of the proposed monitoring framework is such that differences might be expected in
species composition and potential richness, general structure of stream fish assemblages, and general
abiotic characteristics of stream ecosystems. Aquatic ecoregions (Omernik 1987), along with consideration
of zoogeographic factors affecting fish distribution patterns, can serve as a basis for determining if normalizing
the indicator across the region of interest is necessary (Table 4-16). If major differences in the response
variables associated with individual candidate metrics (e.g., potential species richness, percent of carnivorous
individuals) are observed among ecoregions (or aggregates of similar ecoregions), the indicator will require
some type of normalization. Normalization can be attained by adjusting expectations (addressed under
Guideline 14) for individual metrics within ecoregions as necessary. For example, the expectation for the
percent of tolerant individuals may be 10% or less in one ecoregion, but be 20% or less in another because
of the natural occurrence of more tolerant species. The final indicator value remains consistent with this
approach, but its derivation is altered (i.e., an indicator value of 60 means the same across the entire region
of interest).
4-30
.i
". 8 1 111 ..i*n| l.""il'ii;i. .1 n
-------
Table 4-16. Use of aquatic ecoregions to evaluate regional consistency in interpretation of
indicator
Aquatic ecoregions (e.g., Omernik 1987, Omernik and Griffith 1991, Omernik 1995) can serve
as a regional framework to classify stream ecosystems in a target resource population
• Based on overall similarity in several natural features (e.g., climate, soils,
vegetation, physiography, land use).
Ecoregions correspond to spatial patterns in fish assemblages and abiotic characteristics of
streams (e.g., Pflieger 1975, Larsen et a/. 1986, Rohm etal. 1987, Whittier et a/. 1988, Hughes
and Larsen 1988, Jenkins and Burkhead 1993).
Ecoregions have been shown to be useful in improving the consistency of interpretation of other
multimetric indicators applied over large geographic scales (e.g., Yoder and Rankin 1995,
Barbourefa/. 1996).
Ecoregions serve as a basis to account for natural differences in potential biotic integrity under
minimal human disturbance.
• Can be used to define different expectations for individual metrics, or different
thresholds for indicator value (e.g., Yoder and Rankin 1995).
• Metric-based adjustment is more suitable for EMAP indicators because of focus
on regional resource population estimates.
Two examples (Fig. 4-7) are provided to demonstrate an evaluation of differences in fish assemblage
characteristics across the region of interest The distributions of metric response variables across two levels
of aquatic ecoregion aggregations are examined using box-and-whisker plots. Regions showing restricted or
expanded distributions in comparison to others should be considered for possible adjustment in metric
expectations. For both examples (Figure 4-7 (A), number of water column species; and (B) proportion of
individuals of tolerant species), examination of the boxplots suggests that no substantial differences exist in
the range or general distribution of response values across the tWo levels of ecoregion aggregation. For
these two metrics, adjustments of expectations do not appear to be necessary. Similar analyses applied to
other candidate metrics have provided similar results, and at present, the indicator is being developed without
normalization of component metrics.
Summary
Aquatic ecoregions, evaluated in the context of historical zoogeography affecting fish distributions, can be
used to assess the natural variation in metric responses. These results can be used to adjust the expectations
for individual metrics. Preliminary examination suggests that normalization is not necessary for the component
metrics or the indicator, but additional analyses, performed in conjunction with assessing the responsiveness
of the indicator (Guideline 12), are needed.
4-31
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AGGREGATED ECOREGIONS
AGGREGATED ECOREGIONS
Figure 4-7. Examples of evaluating possible differences in metric expectations across MAHA subdivided
by two levels of ecoregion aggregation. (A) number of water column species, a metric
based on species richness; (B) proportion of individuals of tolerant species, a metric based
on the proportion of individuals collected.
4-32
. nil ,„;.„:;', „ Hi,;,,,,, ,, iiiiiniii'jir ,,i' A, i
. j.iiHd'j Ji :.j !/„.„'„:, MJiir,, i ilri'illi . iliii : ., ,-1,111
-------
Guideline 12: Discriminatory Ability
The ability of the indicator to discriminate differences among sites along a known condition gradient
should be critically examined. This analysis should incorporate all error components relevant to the
program objectives, and separate extraneous variability to reveal the true environmental signal in the
indicator data.
Performance Objective
1. Demonstrate responsiveness of the indicator and its component metrics to individual
stressors or to the cumulative effects of multiple stressors.
Conceptual relationships between the indicator and its component metrics and various types of stressors
have been addressed (Guideline 2). Other studies using similar multimetric indicators have demonstrated
the potential responsiveness of the indicator (Table 4-17). For this indicator, a large number of sites,
representing a range of stressor intensities, are used rather than an experimental-based approach using
sites of known stress intensity. The proposed evaluation approach for this guideline is graphic (Fig. 4-8),
rather than statistical (Fore et al. 1996, Karr and Chu 1997). Indicator values or individual metric scores are
plotted against individual stressor variables, and/or against new variables derived from multivariate analyses
of suites of stressor variables (e.g., Hughes et al. 1998).
Table 4-17. Responsiveness of other multimetric fish assemblage indicators to stressors
• Karr et al. (1985): Chlorine
• Steedman (1988): Gradient of urban to forest land use
• Rankin (1995): Habitat quality in Ohio (Correlation coefficients between 0.45 and 0.7)
• Wang et al. (1997): Land use in Wisconsin
• Hughes et al. (1998): Intensity of human disturbance
Summary
Individual metrics respond predictably to specific stressors, though in some cases those specific responses
are weak. The individual metrics and the indicator exhibit the predicted responsiveness to a multivariate
"disturbance" variable derived from several individual chemical, habitat, and watershed stressor variables.
Individual metric responses to specific stresisor variables, as well as expectations for scoring metrics, should
be examined to determine if responsiveness of the indicator to suite's of stressor variables can be improved.
4-33
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30
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Figure 4-8. Examples of metric variables and indicator values plotted against a multivariate
"disturbance intensity" factor derived from individual chemistry, habitat, and
watershed stressor variables.
4-34
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Phase 4. Interpretation and Utility
Guideline 13: Data Quality Objectives
The discriminatory ability of the indicator should be evaluated against program data quality objectives
and constraints. It should be demonstrated how sample size, monitoring duration, and other variables
affect the precision and confidence levels of reported results, and how these variables may be optimized
to attain stated program goals. For example, a program may require that an indicator be able to detect a
twenty percent change in some aspect of ecological condition over a ten-year period, with ninety-five
percent confidence. With magnitude, duration, and confidence level constrained, sample size and
extraneous variability must be optimized in order to meet the program's data quality objectives. Statistical
power curves are recommended to explore the effects of different optimization strategies on indicator
performance.
Performance Objectives
1. Demonstrate the capability of the indicator to distinguish classes of ecological condition within the
proposed monitoring framework.
2. Demonstrate the capability of the indicator to detect trend in condition change within the proposed
monitoring framework.
The capacity to estimate status and detect trend in condition is primarily a function of variability. Variability is
due in part to natural differences that occur across a set of sampling sites (Guidelines 8 through 11), and also
to differences in the intensity of human disturbance across those sites (Guideline 12). An indicator can have
low variability (and thus high statistical power), but poor discriminatory capability because it cannot discern
differences in intensities of human disturbance. However, high variability serves to reduce the discriminatory
capability of an indicator.
Specific performance criteria for the indicator to detect trend in ecological condition have been developed for
the proposed monitoring framework (Table 4-18). These criteria were examined using several power curves
for the indicator to evaluate the effects of coherent variation across years, magnitude of trend, and sample
size (Fig. 4-9). These curves were developed using the initial variance component estimates from the 1993-
1994 MAHA study (Guidelines 10 and 11) and the approach described by Larsen et al. (1995) and Urquhardt
et al. (1998). Derived estimates of the coherent variation across! years were not used because they are
based on only two years of data. Instead, to provide a range of possible scenarios, values of coherent
variation (S2year) were substituted to range from 0-100, where 100 is approximately 1.7 times the within-year
variance (S2residual). Four different magnitudes of trend were also evaluated, ranging from 0.5 to 2 indicator
points per year (equal to 0.5 - 4% per year for an indicator score of 50 points). This represents a potential
trend in the indicator score of 5 to 20 points over a 10-year period.
With respect to estimating status, the indicator satisfies the performance criterion (Table 4-18) under the
conditions specified in Figure 4-9 (A). After 4 years of monitoring, the standard error of the indicator score
ranges between 1 and 2 points (depending on sample size), which would provide 95% confidence intervals
of about ±2 to ±4 points (which is less than 10% of the proposed impairment threshold of 50 points [Table 4-
18]). Intervals computed for a = 0.1 (90% confidence intervals) would be smaller. With continued monitoring,
the standard error of the estimate is stable through time.
4-35
-------
(A)
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Figure 4-9. Statistical power curves for indicator. (A) Effect of annual sample size on standard
estimate of indicator score; (B) Effect of the magnitude of coherent across-year variance
(indicator score units) on trend detection; (C) Capability to detect different magnitudes
of trend; (D) Effect of annual sample size on trend detection.
4-36
; i
-------
Figure 4-9(B) illustrates that, under the conditions specified, it would take between 5 and 20 years (equal to 1
to 5 sampling cycles) for the indicator to detect the specified magnitude of change (2% per year if the regional
median = 50 points) with a power of 0.8, given a coherent variance across years of between 0 and 100 points2
of indicator score. Figure 4-9(C) shows that, under the specified conditions, it would take between 5 and 13
years to detect various changes in indicator scores, representing 1 to 4% change per year in a regional
population median score of 50 points. Figure 4-9(D) shows that, under the specified conditions, it would take
between 7 and 9 years to detect the specified trend, depending on the number of sites visited per year. This
series of figures points out that the capability of the indicator to detect trend is affected most by the magnitude
of coherent across-year variance and by the desired magnitude of change that the monitoring program is
expected to detect, and is affected to a lesser degree by the sample size. These analyses need to be
repeated once a more robust estimate of coherent variance across years is obtained from several years'
worth of data to determine which of the scenarios presented in Figure 4-9 is the most realistic. If the coherent
across-year variance in the indicator score is relatively small (< 10 points2) the indicator should meet the
performance criteria for both status and trend established for EMAP-related monitoring frameworks.
Table 4-18. Statistical power capabilities
Power to discriminate among classes of ecological condition
Proposed monitoring framework: a minimum of 3, and preferably 4 classes of impairment in
condition are desired:
• Fore et al. (1996): Analysis of similar multimetric indicator suggests 5-6 classes of
condition can be distinguished at cc = 0.05 and p = 0.2
• Similar approach, using sites with repeat visits and/or resampling methods such as
bootstrap procedures, is potentially feasible with indicator
Performance criteria for proposed monitoring framework:
• 90% confidence interval should be < 10% of the estimated proportion of a resource
that is at or below a threshold value designating impairment
Power to detect trend in condition
Performance criteria for proposed monitoring framework:
• Magnitude of trend: 2% per year change in regional population median indicator
score (= 20% change over a 10-year period)
• Sample size=50 to 200 sites monitored in region per year
• Probability of false positive (a) = 0.1
• Probability of false negatives (p) = 0.2 (Power = 0.8)
Summary
Results from a previous study imply that 3 or 4 classes of condition can be distinguished over the potential
range of indicator scores. Preliminary analyses indicate performance criteria for both status and trend detection
can be met if across-year variance is relatively small compared to within-year variance. These analyses
must be repeated after more robust estimates of coherent variability among sites are obtained from several
years of data collection, and after the responsiveness of the indicator (Guideline 12) has been adequately
established. Power curves were used to demonstrate the effects of alternative monitoring requirements,
especially the importance of coherent across-year variance and desired magnitude of change.
4-37
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Guideline 14: Assessment Thresholds
To facilitate interpretation of indicator results by the user community, threshold values or ranges of
values should be proposed that delineate acceptable from unacceptable ecological condition. Justification
can be based on documented thresholds, regulatory criteria, historical records, experimental studies, or
observed responses at reference sites along a condition gradient. Thresholds may also include safety
margins or risk considerations. Regardless, the basis for threshold selection must be documented.
Performance Objectives
1 Present and justify approach used to describe expected conditions under a regime of minimal
human disturbance.
2. Present and justify proposed threshold values for the indicator to distinguish among classes
of ecological condition.
The approach to scoring individual metrics is based on comparison of an observed metric response at a
sampling site to the response expected under conditions of minimal human disturbance (see Table 4-6).
Expectations for individual metrics (Table 4-19) that are based on measures of species richness are derived
from a large number of sample sites from the MAHA study, as opposed to using a set of representative
"reference" sites believed to be minimally impacted by human activities. For metrics based on the percentage
of individuals, expectations are based primarily on values developed for similar indicators in other areas
(e.g., Karri986, Yoderand Rankin 1995).
Initial threshold values of the final indicator score have been proposed to classify different states of ecological
condition (Table 4-20). Four classes of condition are proposed, based in part oh the examination of the
distribution of values within resource populations of the 1993-1994 MAHA study. Impaired condition was
operationally defined as any score less than 50, which represents a level of biotic integrity less than one-half
of that score expected under minimal human disturbance. This number of classes is consistent with the
potential power of the indicator to distinguish differences in condition (Table 4-18). These thresholds are
also somewhat consistent with those proposed by other groups using similar multimetric indicators (e.g.,
Foreefa/. 1996).
These threshold values have not been quantitatively examined, a process that requires a better understanding
of indicator responsiveness (Guideline 12). Independent confirmation of appropriate threshold values is
also necessary to achieve the performance objectives established for this guideline and implement this
indicator in the proposed monitoring framework. Confirmation can be achieved by applying the indicator to
an independent set of sites of known levels of impairment. Peer review of the proposed thresholds by
professional ecologists and resource managers familiar with the development and interpretation of multimetric
indicators is also required to complete the evaluation of the indicator with respect to this guideline.
4-38
< ,. ,, ,i.,,.,'i!,il ,< ; 4!!,;;,: ; sits i,mi i • Vr ,i iiik':1
-------
Table 4-19. Thresholds defining expectations of indicator and metrics under
minimal human disturbance
Expected conditions based on large number of sample sites, as opposed to a set of
defined "reference" sites (Simon and Lyons 1995).
Expectations for metrics based on number of species calibrated for stream size or type
(watershed area, gradient, cold vs. warm water) (Fausch etal. 1984).
Taxonomic composition and abundance metrics
• Number of native species: Varies with watershed area
• Number of native families: Varies with watershed area
• Total Abundance: ;>500 individuals collected in standard effort sample
Indicator species metrics:
• Percent of non-native individuals: 0%
• Sensitive spp. richness: Varies with watershed area
• Percent tolerant individuals:<;20%
Habitat metrics ;
• Number of benthic species: Varies with watershed area
• Number of water column species: Varies with watershed area
Trophic metrics
• Number of trophic strategies: 1 to 5 (varies with watershed area)
• Percent individuals as carnivores: *5%
• Percent individuals as invertivores: ^50% '•.
• Percent individuals as omnivores: <;20%
• Percent individuals as herbivores: <10%
Reproductive guild metrics
• Number of reproductive strategies: 1 to 4 (varies with watershed area)
• Percent individuals as tolerant spawners: ^ 20%
Table 4-20. Threshold values for classifying condition
Range of indicator values = 1 to 100
Excellent: > 85
Acceptable: 70 to 85
Marginal: 50 to 69.9
Impaired: < 50
Summary
The approach to defining expected conditions for individual metrics under a regime of minimal human
disturbance is presented, and is based on standard documented approaches established for other multimetric
indicators. Thresholds for the final indicator score are proposed for four classes of ecological condition.
These thresholds are consistent with the potential capability of the indicator to distinguish among condition
states, and with schemes developed for similar multimetric indicators. Additional research on the expectation
for individual metrics remains, subsequent to achieving a better understanding of indicator responsiveness.
These threshold values should be confirmed, either empirically through application to sites representing a
known range of impairment, and/or through peer review by professional ecologists and resource managers.
4-39
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Guideline 15: Linkage to Management Action
Ultimately, an indicator is useful only if it can provide information to support a management decision or to
quantify the success of past decisions. Policy makers and resource managers must be able to recognize
the implications of indicator results for stewardship, regulation, or research. An indicator with practical
application should display one or more of the following characteristics: responsiveness to a specific
stressor, linkage to policy indicators, utility in cost-benefit assessments, limitations and boundaries of
application, and public understanding and acceptance. Detailed consideration of an indicator's
management utility may lead to a re-examination of its conceptual relevance and to a refinement of the
original assessment question.
Performance Objective
i '
1. Demonstrate how indicator values are to be interpreted and used to make management decisions
related to relative condition or risk.
: | i
Data derived from this indicator have not been assembled for management use, but EMAP has advanced an
approach (e.g., Paulsen et a/. 1991, U.S. EPA 1997) to present information regarding the status of resource
populations with respect to ecological condition (Fig. 4-10). Procedures are available (Diaz-Ramos et al.
1996) for developing cumulative distribution functions (cdfs) that show the proportion of a target resource
population (estimated as lengths of target stream resource) that is at or below any specific value of the
indicator (e.g., a threshold value for impaired condition). Additional information regarding uncertainty is
presented by computing confidence bounds about the cdf curve (e.g., Diaz-Ramos et al. 1996, Stewart-
Oaten 1996). In the example (Fig. 4-10), a threshold value of 50 (see Guideline 14, Table 4-20) is used to
distinguish impaired condition. Approximately 30 percent (with 95 percent confidence bounds of approximately
±8 percent) of the target resource population has indicator values at or below the threshold value.
Information regarding relative risks from different stressors can be obtained using a similar approach (i.e.,
developing cdf curves and evaluating the proportion of the target resource population that is at or below some
threshold of impairment). Figure 4-11 presents an example showing the relative ranking of different stressors,
based on the 1993-1994 MAHA study. Introduced fish species (based on presence) and watershed-level
disturbances are the most regionally extensive stressors in the MAHA region, whereas acidic deposition, a
larger-scale stressor, has a much lower impact across the region than might be expected. Once a suitably
responsive indicator has been developed, association or contingency analysis of indicator values (or condition
classes) and regionally important stressor variables (or impact classes) can be used to identify potential
sources of impairment in condition. These analyses have not yet been conducted for the indicator, pending
further research to improve the responsiveness of the indicator.
i
Summary
Approaches developed for EMAP can be used to graphically present results relating the distribution of indicator
values (and corresponding condition classes) across a target resource population. Relative impact of various
stressors on resource populations can also be determined and presented graphically. The combination of
these two tools allows for the estimation of the status of resource populations with respect to ecological
condition, and provides some indication of potential causes of impaired condition. Results from this indicator
of biotic integrity can be used in the development of resource policy.
4-40
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Cumulative Distribution Function
95% Confidence Interval
20
40 60
Indicator Value
80
100
Figure 4-10. Hypothetical example showing how results from indicator values and
monitoring framework will be used to estimate status of resource population.
Introduced Fish
Watershed & Nonpoint Source
Shoreline Hab i'ta t
Nutrient Enrich m ent
In -S tr e a m Hab Ilta t
Acid Depos itio n
Acid Mine Drainage
0 10 20 30 40 50
Percent of Stream Miles Impacted
Figure 4-11. Relative ranking of stressor variables, based on the proportion of target
resource population impacted (Source: 1993-1994 MAHA study).
4-41
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i,i i
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