98-12302. Guidelines for Ecological Risk Assessment

[Federal Register Volume 63, Number 93 (Thursday, May 14, 1998)]
[Notices]
[Pages 26846-26924]
From the Federal Register Online via the Government Publishing Office [www.gpo.gov]
[FR Doc No: 98-12302]


      

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Part II





Environmental Protection Agency





_______________________________________________________________________



Guidelines for Ecological Risk Assessment; Notice

Federal Register / Vol. 63, No. 93 / Thursday, May 14, 1998 / 
Notices

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ENVIRONMENTAL PROTECTION AGENCY

[FRL-6011-2 ]


Guidelines for Ecological Risk Assessment

AGENCY: Environmental Protection Agency.

ACTION: Notice of availability of final Guidelines for Ecological Risk 
Assessment.

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SUMMARY: The U.S. Environmental Protection Agency (EPA) is today 
publishing in final form a document entitled Guidelines for Ecological 
Risk Assessment (hereafter ``Guidelines''). These Guidelines were 
developed as part of an interoffice program by a Technical Panel of the 
Risk Assessment Forum. These Guidelines will help improve the quality 
of ecological risk assessments at EPA while increasing the consistency 
of assessments among the Agency's program offices and regions.
    These Guidelines were prepared during a time of increasing interest 
in the field of ecological risk assessment and reflect input from many 
sources both within and outside the Agency. The Guidelines expand upon 
and replace the previously published EPA report Framework for 
Ecological Risk Assessment (EPA/630/R-92/001, February 1992), which 
proposed principles and terminology for the ecological risk assessment 
process. From 1992 to 1994, the Agency focused on identifying a 
structure for the Guidelines and the issues that the document would 
address. EPA sponsored public and Agency colloquia, developed peer-
reviewed ecological assessment case studies, and prepared a set of 
peer-reviewed issue papers highlighting important principles and 
approaches. Drafts of the proposed Guidelines underwent formal external 
peer review and were reviewed by the Agency's Risk Assessment Forum, by 
Federal interagency subcommittees of the Committee on Environment and 
Natural Resources of the Office of Science and Technology Policy, and 
by the Agency's Science Advisory Board (SAB). The proposed Guidelines 
were published for public comment in 1996 (61 FR 47552-47631, September 
9, 1996). The final Guidelines incorporate revisions based on the 
comments received from the public and the SAB on the proposed 
Guidelines. EPA appreciates the efforts of all participants in the 
process and has tried to address their recommendations in these 
Guidelines.

DATES: The Guidelines will be effective on April 30, 1998.

ADDRESSES: The Guidelines will be made available in several ways:
    (1) The electronic version will be accessible on the EPA National 
Center for Environmental Assessment home page on the Internet at http:/
/www.epa.gov/ncea/.
    (2) 3\1/2\'' high-density computer diskettes in WordPerfect format 
will be available from ORD Publications, Technology Transfer and 
Support Division, National Risk Management Research Laboratory, 
Cincinnati, OH; telephone: [phone]; fax: [phone]. Please 
provide the EPA No. (EPA/630/R-95/002Fa) when ordering.
    (3) This notice contains the full document. (However, because of 
Federal Register format limitations, text boxes that would normally be 
included at their point of reference in the document are instead listed 
at the end of the Guidelines as text notes.) Copies of the Guidelines 
will be available for inspection at EPA headquarters and regional 
libraries, through the U.S. Government Depository Library program, and 
for purchase from the National Technical Information Service (NTIS), 
Springfield, VA; telephone: [phone], fax: [phone]. Please 
provide the NTIS PB No. (PB98-117849) when ordering.

FOR FURTHER INFORMATION, CONTACT: Dr. Bill van der Schalie, National 
Center for Environmental Assessment-Washington Office (8623), U.S. 
Environmental Protection Agency, 401 M Street, SW, Washington, DC 
20460; telephone: [phone]; e-mail: [e-mail].

SUPPLEMENTARY INFORMATION: Ecological risk assessment ``evaluates the 
likelihood that adverse ecological effects may occur or are occurring 
as a result of exposure to one or more stressors'' (U.S. EPA, 1992a). 
It is a flexible process for organizing and analyzing data, 
information, assumptions, and uncertainties to evaluate the likelihood 
of adverse ecological effects. Ecological risk assessment provides a 
critical element for environmental decision making by giving risk 
managers an approach for considering available scientific information 
along with the other factors they need to consider (e.g., social, 
legal, political, or economic) in selecting a course of action.
    To help improve the quality and consistency of the U.S. 
Environmental Protection Agency's ecological risk assessments, EPA's 
Risk Assessment Forum initiated development of these Guidelines. The 
primary audience for this document is risk assessors and risk managers 
at EPA, although these Guidelines also may be useful to others outside 
the Agency. These Guidelines expand on and replace the 1992 report 
Framework for Ecological Risk Assessment (referred to as the Framework 
Report; see Appendix A). They were written by a Forum technical panel 
and have been revised on the basis of extensive comments from outside 
peer reviewers as well as Agency staff. The Guidelines retain the 
Framework Report's broad scope, while expanding on some concepts and 
modifying others to reflect Agency experiences. EPA intends to follow 
these Guidelines with a series of shorter, more detailed documents that 
address specific ecological risk assessment topics. This ``bookshelf'' 
approach provides the flexibility necessary to keep pace with 
developments in the rapidly evolving field of ecological risk 
assessment while allowing time to form consensus, where appropriate, on 
science policy (default assumptions) to bridge gaps in knowledge. EPA 
will revisit guidelines documents as experience and scientific 
consensus evolve. The Agency recognizes that ecological risk assessment 
is only one tool in the overall management of ecological risks. 
Therefore, there are ongoing efforts within the Agency to develop other 
tools and processes that can contribute to an overall approach to 
ecological risk management, addressing topics such as ecological 
benefits assessment and cost-benefit analyses.
    Ecological risk assessment includes three primary phases: Problem 
formulation, analysis, and risk characterization. In problem 
formulation, risk assessors evaluate goals and select assessment 
endpoints, prepare the conceptual model, and develop an analysis plan. 
During the analysis phase, assessors evaluate exposure to stressors and 
the relationship between stressor levels and ecological effects. In the 
third phase, risk characterization, assessors estimate risk through 
integration of exposure and stressor-response profiles, describe risk 
by discussing lines of evidence and determining ecological adversity, 
and prepare a report. The interface among risk assessors, risk 
managers, and interested parties during planning at the beginning and 
communication of risk at the end of the risk assessment is critical to 
ensure that the results of the assessment can be used to support a 
management decision. Because of the diverse expertise required 
(especially in complex ecological risk assessments), risk assessors and 
risk managers frequently work in multidisciplinary teams.
    Both risk managers and risk assessors bring valuable perspectives 
to the initial

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planning activities for an ecological risk assessment. Risk managers 
charged with protecting the environment can identify information they 
need to develop their decision, risk assessors can ensure that science 
is effectively used to address ecological concerns, and together they 
can evaluate whether a risk assessment can address identified problems. 
However, this planning process is distinct from the scientific conduct 
of an ecological risk assessment. This distinction helps ensure that 
political and social issues, while helping to define the objectives for 
the risk assessment, do not introduce undue bias.
    Problem formulation, which follows these planning discussions, 
provides a foundation upon which the entire risk assessment depends. 
Successful completion of problem formulation depends on the quality of 
three products: Assessment endpoints, conceptual models, and an 
analysis plan. Since problem formulation is an interactive, nonlinear 
process, substantial reevaluation is expected to occur during the 
development of all problem formulation products.
    The analysis phase includes two principal activities: 
Characterization of exposure and characterization of ecological 
effects. The process is flexible, and interaction between the two 
evaluations is essential. Both activities evaluate available data for 
scientific credibility and relevance to assessment endpoints and the 
conceptual model. Exposure characterization describes sources of 
stressors, their distribution in the environment, and their contact or 
co-occurrence with ecological receptors. Ecological effects 
characterization evaluates stressor-response relationships or evidence 
that exposure to stressors causes an observed response. The bulk of 
quantitative uncertainty analysis is performed in the analysis phase, 
although uncertainty is an important consideration throughout the 
entire risk assessment. The analysis phase products are summary 
profiles that describe exposure and the stressor-response 
relationships.
    Risk characterization is the final phase of an ecological risk 
assessment. During this phase, risk assessors estimate ecological 
risks, indicate the overall degree of confidence in the risk estimates, 
cite evidence supporting the risk estimates, and interpret the 
adversity of ecological effects. To ensure mutual understanding between 
risk assessors and managers, a good risk characterization will express 
results clearly, articulate major assumptions and uncertainties, 
identify reasonable alternative interpretations, and separate 
scientific conclusions from policy judgments. Risk managers use risk 
assessment results, along with other factors (e.g., economic or legal 
concerns), in making risk management decisions and as a basis for 
communicating risks to interested parties and the general public.
    After completion of the risk assessment, risk managers may consider 
whether follow-up activities are required. They may decide on risk 
mitigation measures, then develop a monitoring plan to determine 
whether the procedures reduced risk or whether ecological recovery is 
occurring. Managers may also elect to conduct another planned tier or 
iteration of the risk assessment if necessary to support a management 
decision.

    Dated: April 30, 1998.
Carol M. Browner,
Administrator.

Part A: Guidelines for Ecological Risk Assessment

Contents

List of Figures
List of Text Notes
1. Introduction
    1.1. The Ecological Risk Assessment Process
    1.2. Ecological Risk Assessment in a Management Context
    1.2.1. Contributions of Ecological Risk Assessment to 
Environmental Decision Making
    1.2.2. Factors Affecting the Value of Ecological Risk Assessment 
for Environmental Decision Making
    1.3. Scope and Intended Audience
    1.4. Guidelines Organization
2. Planning the Risk Assessment
    2.1. The Roles of Risk Managers, Risk Assessors, and Interested 
Parties in Planning
    2.2. Products of Planning
    2.2.1. Management Goals
    2.2.2. Management Options to Achieve Goals
    2.2.3. Scope and Complexity of the Risk Assessment
    2.3. Planning Summary
3. Problem Formulation Phase
    3.1. Products of Problem Formulation
    3.2. Integration of Available Information
    3.3. Selecting Assessment Endpoints
    3.3.1. Criteria for Selection
    3.3.1.1. Ecological Relevance
    3.3.1.2. Susceptibility to Known or Potential Stressors
    3.3.1.3. Relevance to Management Goals
    3.3.2. Defining Assessment Endpoints
    3.4. Conceptual Models
    3.4.1. Risk Hypotheses
    3.4.2. Conceptual Model Diagrams
    3.4.3. Uncertainty in Conceptual Models
    3.5. Analysis Plan
    3.5.1. Selecting Measures
    3.5.2. Ensuring That Planned Analyses Meet Risk Managers' Needs
4. Analysis Phase
    4.1. Evaluating Data and Models for Analysis
    4.1.1. Strengths and Limitations of Different Types of Data
    4.1.2. Evaluating Measurement or Modeling Studies
    4.1.2.1. Evaluating the Purpose and Scope of the Study
    4.1.2.2. Evaluating the Design and Implementation of the Study
    4.1.3. Evaluating Uncertainty
    4.2. Characterization of Exposure
    4.2.1. Exposure Analyses
    4.2.1.1. Describe the Source(s)
    4.2.1.2. Describe the Distribution of the Stressors or Disturbed 
Environment
    4.2.1.3. Describe Contact or Co-occurrence
    4.2.2. Exposure Profile
    4.3. Characterization of Ecological Effects
    4.3.1. Ecological Response Analysis
    4.3.1.1. Stressor-Response Analysis
    4.3.1.2. Establishing Cause-and-Effect Relationships (Causality)
    4.3.1.3. Linking Measures of Effect to Assessment Endpoints
    4.3.2. Stressor-Response Profile
5. Risk Characterization
    5.1. Risk Estimation
    5.1.1. Results of Field Observational Studies
    5.1.2. Categories and Rankings
    5.1.3. Single-Point Exposure and Effects Comparisons
    5.1.4. Comparisons Incorporating the Entire Stressor-Response 
Relationship
    5.1.5. Comparisons Incorporating Variability in Exposure and/or 
Effects
    5.1.6. Application of Process Models
    5.2. Risk Description
    5.2.1. Lines of Evidence
    5.2.2. Determining Ecological Adversity
    5.3. Reporting Risks
6. Relating Ecological Information to Risk Management Decisions
7. Text Notes
Appendix A: Changes from EPA's Ecological Risk Assessment Framework
Appendix B: Key Terms
Appendix C: Conceptual Model Examples
Appendix D: Analysis Phase Examples
Appendix E: Criteria for Determining Ecological Adversity: A 
Hypothetical Example
References

List of Figures

Figure 1-1. The framework for ecological risk assessment
Figure 1-2. The ecological risk assessment framework, with an 
expanded view of each phase
Figure 3-1. Problem formulation phase
Figure 4-1. Analysis phase
Figure 4-2. A simple example of a stressor-response relationship.
Figure 4-3. Variations in stressor-response relationships
Figure 5-1. Risk characterization
Figure 5-2. Risk estimation techniques. a. Comparison of exposure 
and stressor-response point estimates. b. Comparison of point 
estimates from the stressor-response relationship with uncertainty 
associated with an exposure point estimate

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Figure 5-3. Risk estimation techniques: Comparison of point 
estimates with associated uncertainties
Figure 5-4. Risk estimation techniques: Stressor-response curve 
versus a cumulative distribution of exposures
Figure 5-5. Risk estimation techniques: Comparison of exposure 
distribution of an herbicide in surface waters with freshwater 
single-species toxicity data

List of Text Notes

Text Note 1-1. Related Terminology
Text Note 1-2. Flexibility of the Framework Diagram
Text Note 2-1. Who Are Risk Managers?
Text Note 2-2. Who Are Risk Assessors?
Text Note 2-3. Who Are Interested Parties?
Text Note 2-4. Questions Addressed by Risk Managers and Risk 
Assessors
Text Note 2-5. Sustainability as a Management Goal
Text Note 2-6. Management Goals for Waquoit Bay
Text Note 2-7. What is the Difference Between a Management Goal and 
Management Decision?
Text Note 2-8. Tiers and Iteration: When Is a Risk Assessment Done?
Text Note 2-9. Questions to Ask About Scope and Complexity
Text Note 3-1. Avoiding Potential Shortcomings Through Problem 
Formulation
Text Note 3-2. Uncertainty in Problem Formulation
Text Note 3-3. Initiating a Risk Assessment: What's Different When 
Stressors, Effects, or Values Drive the Process?
Text Note 3-4. Assessing Available Information: Questions to Ask 
Concerning Source, Stressor, and Exposure Characteristics, Ecosystem 
Characteristics, and Effects
Text Note 3-5. Salmon and Hydropower: Salmon as the Basis for an 
Assessment Endpoint
Text Note 3-6. Cascading Adverse Effects: Primary (Direct) and 
Secondary (Indirect)
Text Note 3-7. Identifying Susceptibility
Text Note 3-8. Sensitivity and Secondary Effects: The Mussel-Fish 
Connection
Text Note 3-9. Examples of Management Goals and Assessment Endpoints
Text Note 3-10. Common Problems in Selecting Assessment Endpoints
Text Note 3-11. What Are the Benefits of Developing Conceptual 
Models?
Text Note 3-12. What Are Risk Hypotheses, and Why Are They 
Important?
Text Note 3-13. Examples of Risk Hypotheses
Text Note 3-14. Uncertainty in Problem Formulation
Text Note 3-15. Why Was Measurement Endpoint Changed?
Text Note 3-16. Examples of a Management Goal, Assessment Endpoint, 
and Measures
Text Note 3-17. How Do Water Quality Criteria Relate to Assessment 
Endpoints?
Text Note 3-18. The Data Quality Objectives Process
Text Note 4-1. Data Collection and the Analysis Phase
Text Note 4-2. The American National Standard for Quality Assurance
Text Note 4-3. Questions for Evaluating a Study's Utility for Risk 
Assessment
Text Note 4-4. Uncertainty Evaluation in the Analysis Phase
Text Note 4-5. Considering the Degree of Aggregation in Models
Text Note 4-6. Questions for Source Description
Text Note 4-7. Questions to Ask in Evaluating Stressor Distribution
Text Note 4-8. General Mechanisms of Transport and Dispersal
Text Note 4-9. Questions to Ask in Describing Contact or Co-
occurrence
Text Note 4-10. Example of an Exposure Equation: Calculating a 
Potential Dose via Ingestion
Text Note 4-11. Measuring Internal Dose Using Biomarkers and Tissue 
Residues
Text Note 4-12. Questions Addressed by the Exposure Profile
Text Note 4-13. Questions for Stressor-Response Analysis
Text Note 4-14. Qualitative Stressor-Response Relationships
Text Note 4-15. Median Effect Levels
Text Note 4-16. No-Effect Levels Derived From Statistical Hypothesis 
Testing
Text Note 4-17. General Criteria for Causality
Text Note 4-18. Koch's Postulates
Text Note 4-19. Examples of Extrapolations to Link Measures of 
Effect to Assessment Endpoints
Text Note 4-20. Questions Related to Selecting Extrapolation 
Approaches
Text Note 4-21. Questions to Consider When Extrapolating From 
Effects Observed in the Laboratory to Field Effects of Chemicals
Text Note 4-22. Questions Addressed by the Stressor-Response Profile
Text Note 5-1. An Example of Field Methods Used for Risk Estimation
Text Note 5-2. Using Qualitative Categories to Estimate Risks of an 
Introduced Species
Text Note 5-3. Applying the Quotient Method
Text Note 5-4. Comparing an Exposure Distribution With a Point 
Estimate of Effects
Text Note 5-5. Comparing Cumulative Exposure and Effects 
Distributions for Chemical Stressors
Text Note 5-6. Estimating Risk With Process Models
Text Note 5-7. What Are Statistically Significant Effects?
Text Note 5-8. Possible Risk Assessment Report Elements
Text Note 5-9. Clear, Transparent, Reasonable, and Consistent Risk 
Characterizations
Text Note 6-1. Questions Regarding Risk Assessment Results
Text Note 6-2. Risk Communication Considerations for Risk Managers
Text Note A-1. Stressor vs. Agent

1. Introduction

    Ecological risk assessment is a process that evaluates the 
likelihood that adverse ecological effects may occur or are occurring 
as a result of exposure to one or more stressors (U.S. EPA, 1992a). The 
process is used to systematically evaluate and organize data, 
information, assumptions, and uncertainties in order to help understand 
and predict the relationships between stressors and ecological effects 
in a way that is useful for environmental decision making. An 
assessment may involve chemical, physical, or biological stressors, and 
one stressor or many stressors may be considered.
    Ecological risk assessments are developed within a risk management 
context to evaluate human-induced changes that are considered 
undesirable. As a result, these Guidelines focus on stressors and 
adverse effects generated or influenced by anthropogenic activity. 
Defining adversity is important because a stressor may cause adverse 
effects on one ecosystem component but be neutral or even beneficial to 
other components. Changes often considered undesirable are those that 
alter important structural or functional characteristics or components 
of ecosystems. An evaluation of adversity may include a consideration 
of the type, intensity, and scale of the effect as well as the 
potential for recovery. The acceptability of adverse effects is 
determined by risk managers. Although intended to evaluate adverse 
effects, the ecological risk assessment process can be adapted to 
predict beneficial changes or risk from natural events.
    Descriptions of the likelihood of adverse effects may range from 
qualitative judgments to quantitative probabilities. Although risk 
assessments may include quantitative risk estimates, quantitation of 
risks is not always possible. It is better to convey conclusions (and 
associated uncertainties) qualitatively than to ignore them because 
they are not easily understood or estimated.
    Ecological risk assessments can be used to predict the likelihood 
of future adverse effects (prospective) or evaluate the likelihood that 
effects are caused by past exposure to stressors (retrospective). In 
many cases, both approaches are included in a single risk assessment. 
For example, a retrospective risk assessment designed to evaluate the 
cause for amphibian population declines may also be used to predict the 
effects of future management actions. Combined retrospective and 
prospective risk assessments are typical in situations where ecosystems 
have a history of previous impacts and the potential for future effects 
from multiple chemical, physical, or biological stressors. Other 
terminology related to ecological risk assessment is referenced in text 
note 1-1.

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1.1. The Ecological Risk Assessment Process

    The ecological risk assessment process is based on two major 
elements: Characterization of effects and characterization of exposure. 
These provide the focus for conducting the three phases of risk 
assessment: Problem formulation, analysis, and risk characterization.
    The overall ecological risk assessment process 1 is 
shown in figure 1-1. The format remains consistent with the diagram 
from the 1992 report Framework for Ecological Risk Assessment (referred 
to as the Framework Report). However, the process and products within 
each phase have been refined, and these changes are detailed in figure 
1-2. The three phases of risk assessment are enclosed by a dark solid 
line. Boxes outside this line identify critical activities that 
influence why and how a risk assessment is conducted and how it will be 
used.

    \1\ Changes in process and terminology from EPA's previous 
ecological risk assessment framework (U.S. EPA, 1992a) are 
summarized in Appendix A.
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    Problem formulation, the first phase, is shown at the top. In 
problem formulation, the purpose for the assessment is articulated, the 
problem is defined, and a plan for analyzing and characterizing risk is 
determined. Initial work in problem formulation includes the 
integration of available information on sources, stressors, effects, 
and ecosystem and receptor characteristics. From this information two 
products are generated: Assessment endpoints and conceptual models. 
Either product may be generated first (the order depends on the type of 
risk assessment), but both are needed to complete an analysis plan, the 
final product of problem formulation.
    Analysis, shown in the middle box, is directed by the products of 
problem formulation. During the analysis phase, data are evaluated to 
determine how exposure to stressors is likely to occur 
(characterization of exposure) and, given this exposure, the potential 
and type of ecological effects that can be expected (characterization 
of ecological effects). The first step in analysis is to determine the 
strengths and limitations of data on exposure, effects, and ecosystem 
and receptor characteristics. Data are then analyzed to characterize 
the nature of potential or actual exposure and the ecological responses 
under the circumstances defined in the conceptual model(s). The 
products from these analyses are two profiles, one for exposure and one 
for stressor response. These products provide the basis for risk 
characterization.
    During risk characterization, shown in the third box, the exposure 
and stressor-response profiles are integrated through the risk 
estimation process. Risk characterization includes a summary of 
assumptions, scientific uncertainties, and strengths and limitations of 
the analyses. The final product is a risk description in which the 
results of the integration are presented, including an interpretation 
of ecological adversity and descriptions of uncertainty and lines of 
evidence.
    Although problem formulation, analysis, and risk characterization 
are presented sequentially, ecological risk assessments are frequently 
iterative. Something learned during analysis or risk characterization 
can lead to a reevaluation of problem formulation or new data 
collection and analysis (see text note 1-2).
    Interactions among risk assessors, risk managers, and other 
interested parties are shown in two places in the diagram. The side box 
on the upper left represents planning, where agreements are made about 
the management goals, the purpose for the risk assessment, and the 
resources available to conduct the work. The box following risk 
characterization represents when the results of the risk assessment are 
formally communicated by risk assessors to risk managers. Risk managers 
generally communicate risk assessment results to interested parties. 
These activities are shown outside the ecological risk assessment 
process diagram to emphasize that risk assessment and risk management 
are two distinct activities. The former involves the evaluation of the 
likelihood of adverse effects, while the latter involves the selection 
of a course of action in response to an identified risk that is based 
on many factors (e.g., social, legal, political, or economic) in 
addition to the risk assessment results.
    The bar along the right side of figure 1-2 highlights data 
acquisition, iteration, and monitoring. Monitoring data provide 
important input to all phases of a risk assessment. They can provide 
the impetus for a risk assessment by identifying changes in ecological 
condition. They can also be used to evaluate a risk assessment's 
predictions. For example, follow-up studies could determine whether 
mitigation efforts were effective, help verify whether source reduction 
was effective, or determine the extent and nature of ecological 
recovery. It is important for risk assessors and risk managers to use 
monitoring results to evaluate risk assessment predictions so they can 
gain experience and help improve the risk assessment and risk 
management process (Commission on Risk Assessment and Risk Management, 
1997).
    Even though the risk assessment focuses on data analysis and 
interpretation, acquiring the appropriate quantity and quality of data 
for use in the process is critical. If data are unavailable, the risk 
assessment may stop until data are obtained. The process is more often 
iterative than linear, since the evaluation of new data or information 
may require revisiting a part of the process or conducting a new 
assessment (see text note 2-8). The dotted line between the side bar 
and the risk management box indicates that additional data acquisition, 
iteration, or monitoring, while important, are not always required.

1.2. Ecological Risk Assessment in a Management Context

    Ecological risk assessments are designed and conducted to provide 
information to risk managers about the potential adverse effects of 
different management decisions. Attempts to eliminate risks associated 
with human activities in the face of uncertainties and potentially high 
costs present a challenge to risk managers (Ruckelshaus, 1983; Suter, 
1993a). Although many considerations and sources of information are 
used by managers in the decision process, ecological risk assessments 
are unique in providing a scientific evaluation of ecological risk that 
explicitly addresses uncertainty.
1.2.1. Contributions of Ecological Risk Assessment to Environmental 
Decision Making
    At EPA, ecological risk assessments are used to support many types 
of management actions, including the regulation of hazardous waste 
sites, industrial chemicals, and pesticides, or the management of 
watersheds or other ecosystems affected by multiple nonchemical and 
chemical stressors. The ecological risk assessment process has several 
features that contribute to effective environmental decision making:
     Through an iterative process, new information can be 
incorporated into risk assessments, which can be used to improve 
environmental decision making. This feature is consistent with adaptive 
management principles (Holling, 1978) used in managing natural 
resources.
     Risk assessments can be used to express changes in 
ecological effects as a function of changes in exposure to stressors. 
This capability may be particularly useful to the decision maker who 
must evaluate tradeoffs, examine different alternatives, or determine 
the extent to which stressors must be reduced to achieve a given 
outcome.
     Risk assessments explicitly evaluate uncertainty. 
Uncertainty analysis describes the degree of confidence in the 
assessment and can help the risk manager focus research on those areas 
that will lead to the greatest reductions in uncertainty.
     Risk assessments provide a basis for comparing, ranking, 
and prioritizing risks. The results can also be used in cost-benefit 
and cost-effectiveness analyses that offer additional interpretation of 
the effects of alternative management options.
     Risk assessments consider management goals and objectives 
as well as scientific issues in developing assessment endpoints and 
conceptual models during problem formulation. Such initial planning 
activities help ensure that results will be useful to risk managers.

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1.2.2. Factors Affecting the Value of Ecological Risk Assessment for 
Environmental Decision Making
    The wide use and important advantages of ecological risk 
assessments do not mean they are the sole determinants of management 
decisions; risk managers consider many factors. Legal mandates and 
political, social, and economic considerations may lead risk managers 
to make decisions that are more or less protective. Reducing risk to 
the lowest level may be too expensive or not technically feasible. 
Thus, although ecological risk assessments provide critical information 
to risk managers, they are only part of the environmental decision-
making process.
    In some cases, it may be desirable to broaden the scope of a risk 
assessment during the planning phase. A risk assessment that is too 
narrowly focused on one type of stressor in a system (e.g., chemicals) 
could fail to consider more important stressors (e.g., habitat 
alteration). However, options for modifying the scope of a risk 
assessment may be limited when the scope is defined by statute.
    In other situations, management alternatives may be available that 
completely circumvent the need for a risk assessment. For example, the 
risks associated with building a hydroelectric dam may be avoided by 
considering alternatives for meeting power needs that do not involve a 
new dam. In these situations, the risk assessment may be redirected to 
assess the new alternative, or one may not be needed at all.

1.3. Scope and Intended Audience

    These Guidelines describe general principles and give examples to 
show how ecological risk assessment can be applied to a wide range of 
systems, stressors, and biological, spatial, and temporal scales. They 
describe the strengths and limitations of alternative approaches and 
emphasize processes and approaches for analyzing data rather than 
specifying data collection techniques, methods, or models. They do not 
provide detailed guidance, nor are they prescriptive. This approach, 
although intended to promote consistency, provides flexibility to 
permit EPA's offices and regions to develop specific guidance suited to 
their needs.
    Agency preferences are expressed where possible, but because 
ecological risk assessment is a rapidly evolving discipline, 
requirements for specific approaches could soon become outdated. EPA 
intends to develop a series of shorter, more detailed documents on 
specific ecological risk assessment topics following publication of 
these Guidelines.
    The interface between risk assessors and risk managers is discussed 
in the Guidelines. However, details on the use of ecological risk 
assessment in the risk management process are beyond the scope of these 
Guidelines. Other EPA publications discuss how ecological concerns have 
been addressed in decision making at EPA (U.S. EPA, 1994a), propose 
ecological entities that may be important to protect (U.S. EPA, 1997a), 
and provide an introduction to ecological risk assessment for risk 
managers (U.S. EPA, 1995a).
    Policies in this document are intended as internal guidance for 
EPA. Risk assessors and risk managers at EPA are the primary audience, 
although these Guidelines may be useful to others outside the Agency. 
This document is not a regulation and is not intended for EPA 
regulations. The Guidelines set forth current scientific thinking and 
approaches for conducting and evaluating ecological risk assessments. 
They are not intended, nor can they be relied upon, to create any 
rights enforceable by any party in litigation with the United States. 
As with other EPA guidelines (e.g., developmental toxicity, 56 FR 
63798-63826; exposure assessment, 57 FR 22888-22938; and 
carcinogenicity, 61 FR 17960-18011), EPA will revisit these Guidelines 
as experience and scientific consensus evolve.
    These Guidelines replace the Framework Report (U.S. EPA, 1992a). 
They expand on and modify framework concepts to reflect Agency 
experience since the Framework Report was published (see Appendix A).

1.4. Guidelines Organization

    These Guidelines follow the ecological risk assessment format as 
presented in figures 1-1 and 1-2. Section 2 (planning) describes the 
dialogue among risk assessors, risk managers, and interested parties 
before the risk assessment begins. Section 3 (problem formulation) 
describes how management goals are interpreted, assessment endpoints 
selected, conceptual models constructed, and analysis plans developed. 
Section 4 (analysis) addresses how to evaluate potential exposure of 
receptors and the relationship between stressor levels and ecological 
effects. Section 5 (risk characterization) describes the process of 
estimating risk through the integration of exposure and stressor-
response profiles and discusses lines of evidence, interpretation of 
adversity, and uncertainty. Finally, section 6 (on relating ecological 
information to risk management decisions) addresses communicating the 
results of the risk assessment to risk managers.

2. Planning the Risk Assessment

    Ecological risk assessments are conducted to transform scientific 
data into meaningful information about the risk of human activities to 
the environment. Their purpose is to enable risk managers to make 
informed environmental decisions. To ensure that risk assessments meet 
this need, risk managers and risk assessors (see text notes 2-1 and 2-
2) and, where appropriate, interested parties (see text note 2-3), 
engage in a planning dialogue as a critical first step toward 
initiating problem formulation (see figure 1-2).
    The planning dialogue is the beginning of a necessary interface 
between risk managers and risk assessors. However, it is imperative to 
remember that planning remains distinct from the scientific conduct of 
a risk assessment. This distinction helps ensure that political and 
social issues, though helping define the objectives for the assessment, 
do not bias the scientific evaluation of risk.
    The first step in planning may be to determine if a risk assessment 
is the best option for supporting the decision. Risk managers and risk 
assessors both consider the potential value of conducting a risk 
assessment to address identified problems. Their discussion explores 
what is known about the degree of risk, what management options are 
available to mitigate or prevent it, and the value of conducting a risk 
assessment compared with other ways of learning about and addressing 
environmental concerns. In some cases, a risk assessment may add little 
value to the decision process because management alternatives may be 
available that completely circumvent the need for a risk assessment 
(see section 1.2.2). In other cases, the need for a risk assessment may 
be investigated through a simple tiered risk evaluation based on 
minimal data and a simple model (see section 2.2.2).
    Once the decision is made to conduct a risk assessment, the next 
step is to ensure that all key participants are appropriately involved. 
Risk management may be carried out by one decision maker in an agency 
such as EPA or it may be implemented by several risk managers working 
together as a team (see text note 2-1). Likewise, risk assessment may 
be conducted by a single risk assessor or a team of risk assessors (see 
text note 2-2). In some cases, interested parties play an important 
role (see text note 2-3).

[[Page 26854]]

Careful consideration up front about who will participate, and the 
character of that participation, will determine the success of 
planning.

2.1. The Roles of Risk Managers, Risk Assessors, and Interested Parties 
in Planning

    During the planning dialogue, risk managers and risk assessors each 
bring important perspectives to the table. Risk managers, charged with 
protecting human health and the environment, help ensure that risk 
assessments provide information relevant to their decisions by 
describing why the risk assessment is needed, what decisions it will 
influence, and what they want to receive from the risk assessor. It is 
also helpful for managers to consider and communicate problems they 
have encountered in the past when trying to use risk assessments for 
decision making.
    In turn, risk assessors ensure that scientific information is 
effectively used to address ecological and management concerns. Risk 
assessors describe what they can provide to the risk manager, where 
problems are likely to occur, and where uncertainty may be problematic. 
In addition, risk assessors may provide insights to risk managers about 
alternative management options likely to achieve stated goals because 
the options are ecologically grounded.
    In some risk assessments, interested parties also take an active 
role in planning, particularly in goal development. The National 
Research Council describes participation by interested parties in risk 
assessment as an iterative process of ``analysis'' and ``deliberation'' 
(NRC, 1996). Interested parties may communicate their concerns to risk 
managers about the environment, economics, cultural changes, or other 
values potentially at risk from environmental management activities. 
Where they have the ability to increase or mitigate risk to ecological 
values of concern that are identified, interested parties may become 
part of the risk management team (see text note 2-1). However, 
involvement by interested parties is not always needed or appropriate. 
It depends on the purpose of the risk assessment, the regulatory 
requirements, and the characteristics of the management problem (see 
section 2.2.1). When interested parties become risk managers on a team, 
they directly participate in planning.
    During planning, risk managers and risk assessors are responsible 
for coming to agreement on the goals, scope, and timing of a risk 
assessment and the resources that are available and necessary to 
achieve the goals. Together they use information on the area's 
ecosystems, regulatory requirements, and publicly perceived 
environmental values to interpret the goals for use in the ecological 
risk assessment. Examples of questions that risk managers and risk 
assessors may address during planning are provided in text note 2-4.

2.2. Products of Planning

    The characteristics of an ecological risk assessment are directly 
determined by agreements reached by risk managers and risk assessors 
during planning dialogues. These agreements are the products of 
planning. They include (1) clearly established and articulated 
management goals, (2) characterization of decisions to be made within 
the context of the management goals, and (3) agreement on the scope, 
complexity, and focus of the risk assessment, including the expected 
output and the technical and financial support available to complete 
it.
2.2.1. Management Goals
    Management goals are statements about the desired condition of 
ecological values of concern. They may range from ``maintain a 
sustainable aquatic community'' (see text notes 2-5 and 2-6) to 
``restore a wetland'' or ``prevent toxicity.'' Management goals driving 
a specific risk assessment may come from the law, interpretations of 
the law by regulators, desired outcomes voiced by community leaders and 
the public, and interests expressed by affected parties. All involve 
input from the public. However, the process used to establish 
management goals influences how well they provide guidance to a risk 
assessment team, how they foster community participation, and whether 
the larger affected community will support implementation of management 
decisions to achieve the goal.
    A majority of Agency risk assessments incorporate legally 
established management goals found in enabling legislation. In these 
cases, goals were derived through public debate among interested 
parties when the law was enacted. Such management goals (e.g., the 
Clean Water Act goals to ``protect and restore the chemical, physical 
and biological integrity of the Nation's waters'') are often open to 
considerable interpretation and rarely provide sufficient guidance to a 
risk assessor. To address this, the Agency has interpreted these goals 
into regulations and guidance for implementation at the national scale 
(e.g., water quality criteria, see text note 3-17). Mandated goals may 
be interpreted by Agency managers and staff into a particular risk 
assessment format and then applied consistently across stressors of the 
same type (e.g., evaluation of new chemicals). In cases where laws and 
regulations are specifically applied to a particular site, interaction 
between risk assessors and risk managers is needed to translate the law 
and regulations into management goals appropriate for the site or 
ecosystem of concern (e.g., Superfund site cleanup).
    Although this approach has been effective, most regulations and 
guidance are stated in terms of measures or specific actions that must 
or must not be taken rather than establishing a value-based management 
goal or desired state. As environmental protection efforts shift from 
implementing controls toward achieving measurable environmental 
results, value-based management goals at the national scale will be 
increasingly important as guidance for risk assessors. Such goals as 
``no unreasonable effects on bird survival'' or ``maintaining areal 
extent of wetlands'' will provide a basis for risk assessment design 
(see also U.S. EPA, 1997a, for additional examples and discussion).
    The ``place-based'' or ``community-based'' approach for managing 
ecological resources recommended in the Edgewater Consensus (U.S. EPA, 
1994b) generally requires that management goals be developed for each 
assessment. Management goals for ``places'' such as watersheds are 
formed as a consensus based on diverse values reflected in Federal, 
State, tribal, and local regulations and on constituency-group and 
public concerns. Public meetings, constituency-group meetings, 
evaluation of resource management organizational charters, and other 
means of looking for shared goals may be necessary to reach consensus 
among these diverse groups, commonly called ``stakeholders'' (see text 
note 2-3). However, goals derived by consensus are normally general. 
For use in a risk assessment, risk assessors must interpret the goals 
into more specific objectives about what must occur in a place in order 
for the goal to be achieved and identify ecological values that can be 
measured or estimated in the ecosystem of concern (see text note 2-6). 
For these risk assessments, the interpretation is unique to the 
ecosystem being assessed and is done on a case-by-case basis as part of 
the planning process. Risk assessors and risk managers should agree on 
the interpretations.
    Early discussion on and selection of clearly established management 
goals provide risk assessors with a fuller understanding of how 
different risk management options under

[[Page 26855]]

consideration may result in achieving the goal. Such information helps 
the risk assessor identify and gather critical data and information. 
Regardless of how management goals are established, those that 
explicitly define ecological values to be protected provide the best 
foundation for identifying actions to reduce risk and generating risk 
assessment objectives. The objectives for the risk assessment derive 
from the type of management decisions to be made.
2.2.2. Management Options To Achieve Goals
    Risk managers must implement decisions to achieve management goals 
(see text note 2-7). These risk management decisions may establish 
national policy applied consistently across the country (e.g., 
premanufacture notices (PMN) for new chemicals, protection of 
endangered species) or be applied to a specific site (e.g., hazardous 
waste site cleanup level) or management concern (e.g., number of 
combined sewer overflow events allowable per year) intended to achieve 
an environmental goal when implemented. Management decisions often 
begin as one of several management options identified during planning. 
Management options may range from preventing the introduction of a 
stressor to restoration of affected ecological values. When several 
options are defined during planning for a particular problem (e.g., 
leave alone, clean up, or pave a contaminated site), risk assessments 
can be used to predict potential risk across the range of these 
management options and, in some cases, combined with cost-benefit 
analyses to aid decision making. When risk assessors are made aware of 
possible options, they can use them to ensure that the risk assessment 
addresses a sufficient breadth of issues.
    Explicitly stated management options provide a framework for 
defining the scope, focus, and conduct of a risk assessment. Some risk 
assessments are specifically designed to determine if a preestablished 
decision criterion is exceeded (e.g., see the data quality objectives 
process, U.S. EPA, 1994c, and section 3.5.2 for more details). Decision 
criteria often contain inherent assumptions about exposure, the range 
of possible stressors, or conditions under which the targeted stressor 
is operating. To ensure that decision options include appropriate 
assumptions and the risk assessment is designed to address management 
issues, these assumptions need to be clearly stated.
    Decision criteria are often used within a tiering framework to 
determine how extensive a risk assessment should be. Early screening 
tiers may have predetermined decision criteria to answer whether a 
potential risk exists. Later tiers frequently do not because the 
management question changes from ``yes-no'' to questions of ``what, 
where, and how great is the risk.'' Results from these risk assessments 
require risk managers to evaluate risk characterization and generate a 
decision, perhaps through formal decision analysis (e.g., Clemen, 
1996), or managers may request an iteration of the risk assessment to 
address issues of continuing concern (see text note 2-8).
    Risk assessments designed to support management initiatives for a 
region or watershed where multiple stressors, ecological values, and 
political and economic factors influence decision making require great 
flexibility and more complex iterative risk assessments. They generally 
require an examination of ecological processes most influenced by 
diverse human actions. Risk assessments used in this application are 
often based on a general goal statement and multiple potential 
decisions. These require significant planning to determine which array 
of management decisions may be addressed and to establish the purpose, 
scope, and complexity of the risk assessment.
2.2.3. Scope and Complexity of the Risk Assessment
    Although the purpose for conducting a risk assessment determines 
whether it is national, regional, or local in scope, resource 
availability determines its extent, complexity, and the level of 
confidence in results that can be expected. Each risk assessment is 
constrained by the availability of valid data and scientific 
understanding, expertise, time, and financial resources. Risk managers 
and risk assessors consider the nature of the decision (e.g., national 
policy, local impact), available resources, opportunities for 
increasing the resource base (e.g., partnering, new data collection, 
alternative analytical tools), potential characteristics of the risk 
assessment team, and the output that will provide the best information 
for the required decisions (see text note 2-9). They must often be 
flexible in determining what level of effort is warranted for a risk 
assessment. The most detailed assessment process is neither applicable 
nor necessary in every instance. Screening assessments may be the 
appropriate level of effort. One approach for determining the needed 
level of effort in the risk assessment is to set up tiered evaluations, 
as discussed in section 2.2.2. Where tiers are used, specific 
descriptions of management questions and decision criteria should be 
included in the plan.
    Part of the agreement on scope and complexity is based on the 
maximum uncertainty that can be tolerated for the decision the risk 
assessment supports. Risk assessments completed in response to legal 
mandates and likely to be challenged in court often require rigorous 
attention to potential sources of uncertainty to help ensure that 
conclusions from the assessment can be defended. A frank discussion is 
needed between the risk manager and risk assessor on the sources of 
uncertainty and ways uncertainty can be reduced (if necessary or 
possible) through selective investment of resources. Resource planning 
may account for the iterative nature of risk assessment or include 
explicitly defined steps, such as tiers that represent increasing cost 
and complexity, each tier designed to increase understanding and reduce 
uncertainty. Advice on addressing the interplay of management 
decisions, study boundaries, data needs, uncertainty, and specifying 
limits on decision errors may be found in EPA's guidance on data 
quality objectives (U.S. EPA, 1994c).

2.3. Planning Summary

    The planning phase is complete when agreements are reached on (1) 
the management goals for ecological values, (2) the range of management 
options the risk assessment is to support, (3) objectives for the risk 
assessment, including criteria for success, (4) the focus and scope of 
the assessment, and (5) resource availability. Agreements may encompass 
the technical approach to be taken in a risk assessment as determined 
by the regulatory or management context and reason for initiating the 
risk assessment (see section 3.2), the spatial scale (e.g., local, 
regional, or national), and the temporal scale (e.g., the time frame 
over which stressors or effects will be evaluated).
    In mandated risk assessments, planning agreements may be codified 
in regulations, and little documentation of agreements is warranted. In 
others, a summary of planning agreements may be important for ensuring 
that the risk assessment remains consistent with its original intent. A 
summary can provide a point of reference for determining if early 
decisions need to be changed in response to new information. There is 
no predetermined format, length, or complexity for a planning summary. 
It is a useful reference only and should be tailored to the risk 
assessment it represents. However, a summary will help ensure quality 
communication

[[Page 26856]]

between risk managers and risk assessors and will document agreed-upon 
decisions.
    Once planning is complete, the formal process of risk assessment 
begins. During problem formulation, risk assessors should continue the 
dialogue with risk managers, particularly following assessment endpoint 
selection and completion of the analysis plan. At these points, 
potential problems can be identified before the risk assessment 
proceeds.

3. Problem Formulation Phase

    Problem formulation is a process for generating and evaluating 
preliminary hypotheses about why ecological effects have occurred, or 
may occur, from human activities. It provides the foundation for the 
entire ecological risk assessment. Early in problem formulation, 
objectives for the risk assessment are refined. Then the nature of the 
problem is evaluated and a plan for analyzing data and characterizing 
risk is developed. Any deficiencies in problem formulation will 
compromise all subsequent work on the risk assessment (see text note 3-
1). The quality of the assessment will depend in part on the team 
conducting the assessment and its responsiveness to the risk manager's 
needs.
    The makeup of the risk assessment team assembled to conduct problem 
formulation depends on the requirements of the risk assessment. The 
team should include professionals with expertise directly related to 
the level and type of problem under consideration and the ecosystem 
where the problem is likely to occur. Teams may range from one 
individual calculating a simple quotient where the information and 
algorithm are clearly established to a large interdisciplinary, 
interagency team typical of ecosystem-level risk assessments involving 
multiple stressors and ecological values.
    Involvement by the risk management team and other interested 
parties in problem formulation can be most valuable during final 
selection of assessment endpoints, review of the conceptual models, and 
adjustments to the analysis plan. The degree of participation is 
commensurate with the complexity of the risk assessment and the 
magnitude of the risk management decision to be faced. Participation 
normally consists of approval and refinement rather than technical 
input (but see text note 2-3). The format used to involve risk managers 
needs to gain from, and be responsive to, their input without 
compromising the scientific validity of the risk assessment. The level 
of involvement by interested parties in problem formulation is 
determined by risk managers.

3.1. Products of Problem Formulation

    Problem formulation results in three products: (1) Assessment 
endpoints that adequately reflect management goals and the ecosystem 
they represent, (2) conceptual models that describe key relationships 
between a stressor and assessment endpoint or between several stressors 
and assessment endpoints, and (3) an analysis plan. The first step 
toward developing these products is to integrate available information 
as shown in the hexagon in figure 3-1; the products are shown as 
circles. While the assessment of available information is begun up 
front in problem formulation and the analysis plan is the final 
product, the order in which assessment endpoints and conceptual models 
are produced depends on why the risk assessment was initiated (see 
section 3.2). To enhance clarity, the following discussion is presented 
as a linear progression. However, problem formulation is frequently 
interactive and iterative rather than linear. Reevaluation may occur 
during any part of problem formulation.

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3.2. Integration of Available Information

    The foundation for problem formulation is based on how well 
available information on stressor sources and characteristics, exposure 
opportunities, characteristics of the ecosystem(s) potentially at risk, 
and ecological effects are integrated and used (see figure 3-1). 
Integration of available information is an iterative process that 
normally occurs throughout problem formulation. Initial evaluations 
often provide the basis for generating preliminary conceptual models or 
assessment endpoints, which in turn may lead risk assessors to seek 
other types of available information not previously recognized as 
needed.
    The quality and quantity of information determine the course of 
problem formulation. When key information is of the appropriate type 
and sufficient quality and quantity, problem formulation can proceed 
effectively. When data are unavailable, the risk assessment may be 
suspended while additional data are collected or, if this is not 
possible, may be developed on the basis of what is known and what can 
be extrapolated from what is known. Risk assessments are frequently 
begun without all needed information, in which case the problem 
formulation process helps identify missing data and provides a 
framework for further data collection. Where data are few, the 
limitations of conclusions, or uncertainty, from the risk assessment 
should be clearly articulated in risk characterization (see text note 
3-2).
    The impetus for an ecological risk assessment influences what 
information is available at the outset and what information should be 
collected. For example, a risk assessment can be initiated because a 
known or potential stressor may enter the environment. Risk assessors 
evaluating a source or stressor will seek data on the effects with 
which the stressor might be associated and the ecosystems in which it 
will likely be introduced or found. If an observed adverse effect or 
change in ecological condition initiates the assessment, risk assessors 
will seek information about potential stressors and sources that could 
have caused the effect. When a risk assessment is initiated because of 
a desire to better manage an ecological value or entity (e.g., species, 
communities, ecosystems, or places), risk assessors will seek 
information on the specific condition or effect of interest, the 
characteristics of relevant ecosystems, and potential stressors and 
sources (see text note 3-3).
    Information (actual, inferred, or estimated) is initially 
integrated in a scoping process that provides the foundation for 
developing problem formulation. Knowledge gained during scoping is used 
to identify missing information and potential assessment endpoints, and 
it provides the basis for early conceptualization of the problem being 
assessed. As problem formulation proceeds, information quality and 
applicability to the particular problem of concern are increasingly 
scrutinized. Where appropriate, further iterations may result in a 
comprehensive evaluation that helps risk assessors generate an array of 
risk hypotheses (see section 3.4.1). Once analysis plans are being 
formed, data validity becomes a significant factor for risk assessors 
to evaluate (see section 4.1 for a discussion of assessing data 
quality). Thus an evaluation of available information is an ongoing 
activity throughout problem formulation. The level of effort is driven 
by the type of assessment.
    As the complexity and spatial scale of a risk assessment increase, 
information needs often escalate. Risk assessors consider the ways 
ecosystem characteristics directly influence when, how, and why 
particular ecological entities may become exposed and exhibit adverse 
effects due to particular stressors. Predicting risks from multiple 
chemical, physical, and biological stressors requires an effort to 
understand their interactions. Risk assessments for a region or 
watershed, where multiple stressors are the rule, require consideration 
of ecological processes operating at larger spatial scales.
    Despite our limited knowledge of ecosystems and the stressors 
influencing them, the process of problem formulation offers a 
systematic approach for organizing and evaluating available information 
on stressors and possible effects. It can function as a preliminary 
risk assessment that is useful to risk assessors and decision makers. 
Text note 3-4 provides a series of questions that risk assessors should 
attempt to answer. This exercise will help risk assessors identify 
known and unknown relationships, both of which are important in problem 
formulation.
    Problem formulation proceeds with the identification of assessment 
endpoints and the development of conceptual models and an analysis plan 
(discussed below). Early recognition that the reasons for initiating 
the risk assessment affect the order in which products are generated 
will help facilitate the development of problem formulation (see text 
note 3-3).

3.3. Selecting Assessment Endpoints

    Assessment endpoints are explicit expressions of the actual 
environmental value that is to be protected, operationally defined by 
an ecological entity and its attributes (see section 3.3.2). Assessment 
endpoints are critical to problem formulation because they structure 
the assessment to address management concerns and are central to 
conceptual model development. Their relevance is determined by how well 
they target susceptible ecological entities. Their ability to support 
risk management decisions depends on whether they are measurable 
ecosystem characteristics that adequately represent management goals. 
The selection of ecological concerns and assessment endpoints at EPA 
has traditionally been done internally by individual Agency program 
offices (U.S. EPA, 1994a). More recently, interested and affected 
parties have helped identify management concerns and assessment 
endpoints in efforts to implement watershed or community-based 
environmental protection.
    This section provides guidance on selecting and defining assessment 
endpoints. It is presented in two parts. Section 3.3.1 establishes 
three criteria (ecological relevance, susceptibility, and relevance to 
management goals) for determining how to select, among a broad array of 
possibilities, the specific ecological characteristics to target in the 
risk assessment that are responsive to general management goals and are 
scientifically defensible. Section 3.3.2 then provides specific 
guidance on how to convert selected ecological characteristics into 
operationally defined assessment endpoints that include both a defined 
entity and specific attributes amenable to measurement.
3.3.1. Criteria for Selection
    All ecosystems are diverse, with many levels of ecological 
organization (e.g., individuals, populations, communities, ecosystems, 
landscapes) and multiple ecosystem processes. It is rarely clear which 
of these characteristics are most critical to ecosystem function, nor 
do professionals or the public always agree on which are most valuable. 
As a result, it is often a challenge to consider the array of 
possibilities and choose which ecological characteristics to protect to 
meet management goals. Those choices are critical, however, because 
they become the basis for defining assessment endpoints, the transition 
between broad management goals and the specific measures used in a risk 
assessment.

[[Page 26859]]

    Three principal criteria are used to select ecological values that 
may be appropriate for assessment endpoints: (1) Ecological relevance, 
(2) susceptibility to known or potential stressors, and (3) relevance 
to management goals. Of these, ecological relevance and susceptibility 
are essential for selecting assessment endpoints that are 
scientifically defensible. However, to increase the likelihood that the 
risk assessment will be used in management decisions, assessment 
endpoints are more effective when they also reflect societal values and 
management goals. Given the complex functioning of ecosystems and the 
interdependence of ecological entities, it is likely that potential 
assessment endpoints can be identified that are both responsive to 
management goals and meet scientific criteria. Assessment endpoints 
that meet all three criteria provide the best foundation for an 
effective risk assessment (e.g., see text note 3-5).
3.3.1.1. Ecological Relevance
    Ecologically relevant endpoints reflect important characteristics 
of the system and are functionally related to other endpoints (U.S. 
EPA, 1992a). Ecologically relevant endpoints may be identified at any 
level of organization (e.g., individual, population, community, 
ecosystem, landscape). The consequences of changes in these endpoints 
may be quantified (e.g., alteration of community structure from the 
loss of a keystone species) or inferred (e.g., survival of individuals 
is needed to maintain populations). Ecological entities are not 
ecologically relevant unless they are currently, or were historically, 
part of the ecosystem under consideration.
    Ecologically relevant endpoints often help sustain the natural 
structure, function, and biodiversity of an ecosystem or its 
components. They may contribute to the food base (e.g., primary 
production), provide habitat (e.g., for food or reproduction), promote 
regeneration of critical resources (e.g., decomposition or nutrient 
cycling), or reflect the structure of the community, ecosystem, or 
landscape (e.g., species diversity or habitat mosaic). In landscape-
level risk assessments, careful selection of assessment endpoints that 
address both species of concern and landscape-level ecosystem processes 
becomes important. It may be possible to select one or more species and 
an ecosystem process to represent larger functional community or 
ecosystem processes.
    Ecological relevance is linked to the nature and intensity of 
potential effects, the spatial and temporal scales where effects may 
occur, and the potential for recovery (see Determining Ecological 
Adversity, section 5.2.2). It is also linked to the level of ecological 
organization that could be adversely affected (see U.S. EPA, 1997a, for 
a discussion of how different levels of organization are used by the 
Agency in defining assessment endpoints). When changes in selected 
ecosystem entities are likely to cause multiple or widespread effects, 
such entities can be powerful components of assessment endpoints. They 
are particularly valuable when risk assessors are trying to identify 
the potential cascade of adverse effects that could result from loss or 
reduction of a species or a change in ecosystem function (see text note 
3-6). Although a cascade of effects may be predictable, it is often 
difficult to predict the nature of all potential effects. Determining 
ecological relevance in specific cases requires professional judgment 
based on site-specific information, preliminary surveys, or other 
available information.
3.3.1.2. Susceptibility to Known or Potential Stressors
    Ecological resources are considered susceptible when they are 
sensitive to a stressor to which they are, or may be, exposed. 
Susceptibility can often be identified early in problem formulation, 
but not always. Risk assessors may be required to use their best 
professional judgment to select the most likely candidates (see text 
note 3-7).
    Sensitivity refers to how readily an ecological entity is affected 
by a particular stressor. Sensitivity is directly related to the mode 
of action of the stressors (e.g., chemical sensitivity is influenced by 
individual physiology and metabolic pathways). Sensitivity is also 
influenced by individual and community life-history characteristics. 
For example, stream species assemblages that depend on cobble and 
gravel habitat for reproduction are sensitive to fine sediments that 
fill in spaces between cobbles. Species with long life cycles and low 
reproductive rates are often more vulnerable to extinction from 
increases in mortality than species with short life cycles and high 
reproductive rates. Species with large home ranges may be more 
sensitive to habitat fragmentation when the fragment is smaller than 
their required home range compared to species with smaller home ranges 
that are encompassed within a fragment. However, habitat fragmentation 
may also affect species with small home ranges where migration is a 
necessary part of their life history and fragmentation prevents 
migration and genetic exchange among subpopulations. Such life-history 
characteristics are important to consider when evaluating potential 
sensitivity.
    Sensitivity can be related to the life stage of an organism when 
exposed to a stressor. Frequently, young animals are more sensitive to 
stressors than adults. For instance, Pacific salmon eggs and fry are 
very sensitive to fine-grain sedimentation in river beds because they 
can be smothered. Age-dependent sensitivity, however, is not only in 
the young. In many species, events like migration (e.g., in birds) and 
molting (e.g., in harbor seals) represent significant energy 
investments that increase vulnerability to stressors. Finally, 
sensitivity may be enhanced by the presence of other stressors or 
natural disturbances. For example, the presence of insect pests and 
disease may make plants more sensitive to damage from ozone (Heck, 
1993). To determine how sensitivity at a particular life stage is 
critical to population parameters or community-level assessment 
endpoints may require further evaluation.
    Measures of sensitivity may include mortality or adverse 
reproductive effects from exposure to toxics. Other possible measures 
of sensitivity include behavioral abnormalities; avoidance of 
significant food sources and nesting sites; loss of offspring to 
predation because of the proximity of stressors such as noise, habitat 
alteration, or loss; community structural changes; or other factors.
    Exposure is the second key determinant in susceptibility. Exposure 
can mean co-occurrence, contact, or the absence of contact, depending 
on the stressor and assessment endpoint. Questions concerning where a 
stressor originates, how it moves through the environment, and how it 
comes in contact with the assessment endpoint are evaluated to 
determine susceptibility (see section 4.2 for more discussion on 
characterizing exposure). The amount and conditions of exposure 
directly influence how an ecological entity will respond to a stressor. 
Thus, to determine which entities are susceptible, it is important that 
the assessor consider the proximity of an ecological value to stressors 
of concern, the timing of exposure (both in terms of frequency and 
duration), and the intensity of exposure occurring during sensitive 
periods.
    Adverse effects of a particular stressor may be important during 
one part of an organism's life cycle, such as early development or 
reproduction. They may result from exposure to a stressor or to the 
absence of a necessary resource

[[Page 26860]]

during a critical life stage. For example, if fish are unable to find 
suitable nesting sites during their reproductive phase, risk is 
significant even when water quality is high and food sources abundant. 
The interplay between life stage and stressors can be very complex (see 
text note 3-8).
    Exposure may occur in one place or time, but effects may not be 
observed until another place or time. Both life-history characteristics 
and the circumstances of exposure influence susceptibility in this 
case. For instance, the temperature of the egg incubation medium of 
marine turtles affects the sex ratio of hatchlings, but population 
impacts are not observed until years later when the cohort of affected 
turtles begins to reproduce. Delayed effects and multiple-stressor 
exposures add complexity to evaluations of susceptibility (e.g., 
although toxicity tests may determine receptor sensitivity to one 
stressor, susceptibility may depend on the co-occurrence of another 
stressor that significantly alters receptor response). Conceptual 
models (see section 3.4) need to reflect these factors. If a species or 
other ecological entity is unlikely to be directly or indirectly 
exposed to the stressor of concern, or to the secondary effects of 
stressor exposure, it may be inappropriate as an assessment endpoint 
(see text note 3-7).
3.3.1.3. Relevance to Management Goals
    Ultimately, the effectiveness of a risk assessment depends on 
whether it is used and improves the quality of management decisions. 
Risk managers are more willing to use a risk assessment for making 
decisions when it is based on ecological values that people care about. 
Thus, candidates for assessment endpoints include endangered species or 
ecosystems, commercially or recreationally important species, 
functional attributes that support food sources or flood control (e.g., 
wetland water sequestration), aesthetic values such as clean air in 
national parks, or the existence of charismatic species such as eagles 
or whales. However, selection of assessment endpoints based on public 
perceptions alone could lead to management decisions that do not 
consider important ecological information. While responsiveness to the 
public is important, it does not obviate the requirement for scientific 
validity.
    The challenge is to find ecological values that meet the necessary 
scientific rigor as assessment endpoints that are also recognized as 
valuable by risk managers and the public. As an illustration, suppose 
an assessment is designed to evaluate the risk of applying pesticide 
around a lake to control insects. At this lake, however, midges are 
susceptible to the pesticide and form the base of a complex food web 
that supports a native fish population popular with sportsmen. While 
both midges and fish represent key components of the aquatic community, 
selecting the fishery as the value for defining the assessment endpoint 
targets both ecological and community concerns. Selecting midges would 
not. The risk assessment can then characterize the risk to the fishery 
if the midge population is adversely affected. This choice maintains 
the scientific validity of the risk assessment while being responsive 
to management concerns. In those cases where a critical assessment 
endpoint is identified that is unpopular with the public, the risk 
assessor may find it necessary to present a persuasive case in its 
favor to risk managers based on scientific arguments.
    Practical issues may influence what values are selected as 
potential assessment endpoints, such as what is required by statute 
(e.g., endangered species) or whether it is possible to achieve a 
particular management goal. For example, in a river already impounded 
throughout its reach by multiple dams, goals for reestablishing 
spawning habitat for free-living anadromous salmon may be feasible only 
if dams are removed. If this will not be considered, selection of other 
ecological values as potential endpoints in this highly modified system 
may be the only option. Another concern may be whether it is possible 
to directly measure important variables. Where it is possible to 
directly measure attributes of an assessment endpoint, extrapolation is 
unnecessary, thus preventing the introduction of a source of 
uncertainty. Assessment endpoints that cannot be measured directly but 
can be represented by measures that are easily monitored and modeled 
may still provide a good foundation for a risk assessment. However, 
while established measurement protocols are convenient and useful, they 
do not determine whether an assessment endpoint is appropriate. Data 
availability alone is not an adequate criterion for selection.
    To ensure scientific validity, risk assessors are responsible for 
selecting and defining potential assessment endpoints based on an 
understanding of the ecosystem of concern. Risk managers and risk 
assessors should then come to agreement on the final selection.
3.3.2. Defining Assessment Endpoints
    Once ecological values are selected as potential assessment 
endpoints, they need to be operationally defined. Two elements are 
required to define an assessment endpoint. The first is the 
identification of the specific valued ecological entity. This can be a 
species (e.g., eelgrass, piping plover), a functional group of species 
(e.g., piscivores), a community (e.g., benthic invertebrates), an 
ecosystem (e.g., lake), a specific valued habitat (e.g., wet meadows), 
a unique place (e.g., a remnant of native prairie), or other entity of 
concern. The second is the characteristic about the entity of concern 
that is important to protect and potentially at risk. Thus, it is 
necessary to define what is important for piping plovers (e.g., nesting 
and feeding conditions), a lake (e.g., nutrient cycling), or wet meadow 
(e.g., endemic plant community diversity). For an assessment endpoint 
to serve as a clear interpretation of the management goals and the 
basis for measurement in the risk assessment, both an entity and an 
attribute are required.
    What distinguishes assessment endpoints from management goals is 
their neutrality and specificity. Assessment endpoints do not represent 
a desired achievement (i.e., goal). As such, they do not contain words 
like ``protect,'' ``maintain,'' or ``restore,'' or indicate a direction 
for change such as ``loss'' or ``increase.'' Instead they are 
ecological values defined by specific entities and their measurable 
attributes, providing a framework for measuring stress-response 
relationships. When goals are very broad it may be difficult to select 
appropriate assessment endpoints until the goal is broken down into 
multiple management objectives. A series of management objectives can 
clarify the inherent assumptions within the goal and help a risk 
assessor determine which ecological entities and attributes best 
represent each objective (see text box 2-6). From this, multiple 
assessment endpoints may be selected. See text note 3-9 for examples of 
management goals and assessment endpoints.
    Assessment endpoints may or may not be distinguishable from 
measures, depending on the assessment endpoints selected and the type 
of measures. While it is the entity that influences the scale and 
character of a risk assessment, it is the attributes of an assessment 
endpoint that determine what to measure. Sometimes direct measures of 
effect can be collected on the attribute of concern. Where this occurs, 
the assessment endpoint and measure of

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effect are the same and no extrapolation is necessary (e.g., if the 
assessment endpoint is ``reproductive success of blue jays,'' egg 
production and fledgling success could potentially be directly measured 
under different stressor exposure scenarios). In other cases, direct 
measures may not be possible (e.g., toxicity in endangered species) and 
surrogate measures of effect must be selected. Thus, although 
assessment endpoints must be defined in terms of measurable attributes, 
selection does not depend on the ability to measure those attributes 
directly or on whether methods, models, and data are currently 
available. For practical reasons, it may be helpful to use assessment 
endpoints that have well-developed test methods, field measurement 
techniques, and predictive models (see Suter, 1993a). However, it is 
not necessary for methods to be standardized protocols, nor should 
assessment endpoints be selected simply because standardized protocols 
are readily available. The appropriate measures to use are generally 
identified during conceptual model development and specified in the 
analysis plan. Measures of ecosystem characteristics and exposure are 
determined by the entity and attributes selected and serve as important 
information in conceptual model development. See section 3.5.1 for 
issues surrounding the selection of measures.
    Clearly defined assessment endpoints provide direction and 
boundaries for the risk assessment and can minimize miscommunication 
and reduce uncertainty; where they are poorly defined, inappropriate, 
or at the incorrect scale, they can be very problematic. Endpoints may 
be too broad, vague, or narrow, or they may be inappropriate for the 
ecosystem requiring protection. ``Ecological integrity'' is a 
frequently cited but vague goal and is too vague for an assessment 
endpoint. ``Integrity'' can only be used effectively when its meaning 
is explicitly characterized for a particular ecosystem, habitat, or 
entity. This may be done by selecting key entities or processes for an 
ecosystem and describing attributes that best represent integrity for 
that system. Assessment endpoints that are too narrowly defined may not 
support effective risk management. If an assessment is focused only on 
protecting the habitat of an endangered species, for example, the risk 
assessment may overlook other equally important characteristics of the 
ecosystem and fail to include critical variables (see text note 3-8). 
Finally, the assessment endpoint could fail to represent the ecosystem 
at risk. For instance, selecting a game fish that grows well in 
reservoirs may meet a ``fishable'' management goal, but it would be 
inappropriate for evaluating risk from a new hydroelectric dam if the 
ecosystem of concern is a stream in which salmon spawn (see text note 
3-5). Although the game fish will satisfy ``fishable'' goals and may be 
highly desired by local fishermen, a reservoir species does not 
represent the ecosystem at risk. Substituting ``reproducing populations 
of indigenous salmonids'' for a vague ``viable fish populations'' 
assessment endpoint could therefore prevent the development of an 
inappropriate risk assessment.
    When well selected, assessment endpoints become powerful tools in 
the risk assessment process. One endpoint that is sensitive to many of 
the identified stressors, yet responds in different ways to different 
stressors, may provide an opportunity to consider the combined effects 
of multiple stressors while still distinguishing their effects. For 
example, fish population recruitment may be adversely affected at 
several life stages, in different habitats, through different ways, and 
by different stressors. Therefore, measures of effect, exposure, and 
ecosystem and receptor characteristics could be chosen to evaluate 
recruitment and provide a basis for distinguishing different stressors, 
individual effects, and their combined effects.
    The assessment endpoint can provide a basis for comparing a range 
of stressors if carefully selected. The National Crop Loss Assessment 
Network (Heck, 1993) selected crop yields as the assessment endpoint to 
evaluate the cumulative effects of multiple stressors. Although the 
primary stressor was ozone, the crop-yield endpoint also allowed the 
risk assessors to consider the effects of sulfur dioxide and soil 
moisture. As Barnthouse et al. (1990) pointed out, an endpoint should 
be selected so that all the effects can be expressed in the same units 
(e.g., changes in the abundance of 1-year-old fish from exposure to 
toxicity, fishing pressure, and habitat loss). This is especially true 
when selecting assessment endpoints for multiple stressors. However, in 
situations where multiple stressors act on the structure and function 
of aquatic and terrestrial communities in a watershed, an array of 
assessment endpoints that represent the community and associated 
ecological processes is more effective than a single endpoint. When 
based on differing susceptibility to an array of stressors, carefully 
selected assessment endpoints can help risk assessors distinguish the 
effects of diverse stressors. Exposure to multiple stressors may lead 
to effects at different levels of biological organization, for a 
cascade of adverse effects that should be considered.
    Professional judgment and an understanding of the characteristics 
and function of an ecosystem are important for translating general 
goals into usable assessment endpoints. The less information available, 
the more critical it is to have informed professionals help in the 
selection. Common problems encountered in selecting assessment 
endpoints are summarized in text note 3-10.
    Final assessment endpoint selection is an important risk manager-
risk assessor checkpoint during problem formulation. Risk assessors and 
risk managers should agree that selected assessment endpoints 
effectively represent the management goals. In addition, the scientific 
rationale for their selection should be made explicit in the risk 
assessment.

3.4. Conceptual Models

    A conceptual model in problem formulation is a written description 
and visual representation of predicted relationships between ecological 
entities and the stressors to which they may be exposed. Conceptual 
models represent many relationships. They may include ecosystem 
processes that influence receptor responses or exposure scenarios that 
qualitatively link land-use activities to stressors. They may describe 
primary, secondary, and tertiary exposure pathways (see section 4.2) or 
co-occurrence among exposure pathways, ecological effects, and 
ecological receptors. Multiple conceptual models may be generated to 
address several issues in a given risk assessment. Some of the benefits 
gained by developing conceptual models are featured in text note 3-11.
    Conceptual models for ecological risk assessments are developed 
from information about stressors, potential exposure, and predicted 
effects on an ecological entity (the assessment endpoint). Depending on 
why a risk assessment is initiated, one or more of these categories of 
information are known at the outset (refer to section 3.2 and text note 
3-3). The process of creating conceptual models helps identify the 
unknown elements.
    The complexity of the conceptual model depends on the complexity of 
the problem: the number of stressors, number of assessment endpoints, 
nature of effects, and characteristics of the ecosystem. For single 
stressors and single assessment endpoints, conceptual models may be 
simple. In some cases,

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the same basic conceptual model may be used repeatedly (e.g., in EPA's 
new chemical risk assessments). However, when conceptual models are 
used to describe pathways of individual stressors and assessment 
endpoints and the interaction of multiple and diverse stressors and 
assessment endpoints (e.g., assessments initiated to protect ecological 
values), more complex models and several submodels will often be 
needed. In this case, it can be helpful to create models that also 
represent expected ecosystem characteristics and function when 
stressors are not present.
    Conceptual models consist of two principal components:
     A set of risk hypotheses that describe predicted 
relationships among stressor, exposure, and assessment endpoint 
response, along with the rationale for their selection.
     A diagram that illustrates the relationships presented in 
the risk hypotheses.
3.4.1. Risk Hypotheses
    Hypotheses are assumptions made in order to evaluate logical or 
empirical consequences, or suppositions tentatively accepted to provide 
a basis for evaluation. Risk hypotheses are specific assumptions about 
potential risk to assessment endpoints (see text note 3-12) and may be 
based on theory and logic, empirical data, mathematical models, or 
probability models. They are formulated using a combination of 
professional judgment and available information on the ecosystem at 
risk, potential sources of stressors, stressor characteristics, and 
observed or predicted ecological effects on selected or potential 
assessment endpoints. These hypotheses may predict the effects of a 
stressor before they occur, or they may postulate why observed 
ecological effects occurred and ultimately what caused the effect. 
Depending on the scope of the risk assessment, risk hypotheses may be 
very simple, predicting the potential effect of one stressor on one 
receptor, or extremely complex, as is typical in value-initiated risk 
assessments that often include prospective and retrospective hypotheses 
about the effects of multiple complexes of stressors on diverse 
ecological receptors. Risk hypotheses represent relationships in the 
conceptual model and are not designed for statistically testing null 
and alternative hypotheses. However, they can be used to generate 
questions appropriate for research.
    Although risk hypotheses are valuable even when information is 
limited, the amount and quality of data and information will affect the 
specificity and level of uncertainty associated with risk hypotheses 
and the conceptual models they form. When preliminary information is 
conflicting, risk hypotheses can be constructed specifically to 
differentiate between competing predictions. The predictions can then 
be evaluated systematically either by using available data during the 
analysis phase or by collecting new data before proceeding with the 
risk assessment. Hypotheses and predictions set a framework for using 
data to evaluate functional relationships (e.g., stressor-response 
curves).
    Early conceptual models are normally broad, identifying as many 
potential relationships as possible. As more information is 
incorporated, the plausibility of specific hypotheses helps risk 
assessors sort through potentially large numbers of stressor-effect 
relationships, and the ecosystem processes that influence them, to 
identify those risk hypotheses most appropriate for the analysis phase. 
It is then that justifications for selecting and omitting hypotheses 
are documented. Examples of risk hypotheses are provided in text note 
3-13.
3.4.2. Conceptual Model Diagrams
    Conceptual model diagrams are a visual representation of risk 
hypotheses. They are useful tools for communicating important pathways 
clearly and concisely and can be used to generate new questions about 
relationships that help formulate plausible risk hypotheses.
    Typical conceptual model diagrams are flow diagrams containing 
boxes and arrows to illustrate relationships (see Appendix C). When 
this approach is used, it is helpful to use distinct and consistent 
shapes to distinguish stressors, assessment endpoints, responses, 
exposure routes, and ecosystem processes. Although flow diagrams are 
often used to illustrate conceptual models, there is no set 
configuration. Pictorial representations can be very effective (e.g., 
Bradley and Smith, 1989). Regardless of the configuration, a diagram's 
usefulness is linked to the detailed written descriptions and 
justifications for the relationships shown. Without this, diagrams can 
misrepresent the processes they are intended to illustrate.
    When developing conceptual model diagrams, factors to consider 
include the number of relationships depicted, the comprehensiveness of 
the information, the certainty surrounding a linkage, and the potential 
for measurement. The number of relationships that can be depicted in 
one flow diagram depends on their complexity. Several models that 
increasingly show more detail for smaller portions can be more 
effective than trying to create one model that shows everything at the 
finest detail. Flow diagrams that highlight data abundance or scarcity 
can provide insights on how the analyses should be approached and can 
be used to show the risk assessor's confidence in the relationship. 
They can also show why certain pathways were pursued and others were 
not.
    Diagrams provide a working and dynamic representation of 
relationships. They should be used to explore different ways of looking 
at a problem before selecting one or several to guide analysis. Once 
the risk hypotheses are selected and flow diagrams drawn, they set the 
framework for final planning for the analysis phase.
3.4.3. Uncertainty in Conceptual Models
    Conceptual model development may account for one of the most 
important sources of uncertainty in a risk assessment. If important 
relationships are missed or specified incorrectly, the risk 
characterization may misrepresent actual risks. Uncertainty arises from 
lack of knowledge about how the ecosystem functions, failure to 
identify and interrelate temporal and spatial parameters, omission of 
stressors, or overlooking secondary effects. In some cases, little may 
be known about how a stressor moves through the environment or causes 
adverse effects. Multiple stressors are the norm and a source of 
confounding variables, particularly for conceptual models that focus on 
a single stressor. Professionals may not agree on the appropriate 
conceptual model configuration. While simplification and lack of 
knowledge may be unavoidable, risk assessors should document what is 
known, justify the model, and rank model components in terms of 
uncertainty (see Smith and Shugart, 1994).
    Uncertainty associated with conceptual models can be explored by 
considering alternative relationships. If more than one conceptual 
model is plausible, the risk assessor may evaluate whether it is 
feasible to follow separate models through analysis or whether the 
models can be combined to create a better model.
    Conceptual models should be presented to risk managers to ensure 
that they communicate well and address managers' concerns. This check 
for completeness and clarity is a way to assess the need for changes 
before analysis begins. It is also valuable to revisit and where 
necessary revise conceptual models during risk

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assessments to incorporate new information and recheck the rationale. 
If this is not feasible, it is helpful to present any new information 
during risk characterization along with associated uncertainties.
    Throughout problem formulation, ambiguities, errors, and 
disagreements will occur, all of which contribute to uncertainty. 
Wherever possible, these sources of uncertainty should be eliminated 
through better planning. Because all uncertainty cannot be eliminated, 
a description of the nature of the uncertainties should be summarized 
at the close of problem formulation. See text note 3-14 for 
recommendations on how to address uncertainty.

3.5. Analysis Plan

    The analysis plan is the final stage of problem formulation. During 
analysis planning, risk hypotheses are evaluated to determine how they 
will be assessed using available and new data. The plan includes a 
delineation of the assessment design, data needs, measures, and methods 
for conducting the analysis phase of the risk assessment. Analysis 
plans may be brief or extensive depending on the assessment. For some 
assessments (e.g., EPA's new chemical assessments), the analysis plan 
is already part of the established protocol and a new plan is generally 
unnecessary. As risk assessments become more unique and complex, the 
importance of a good analysis plan increases.
    The analysis plan includes pathways and relationships identified 
during problem formulation that will be pursued during the analysis 
phase. Those hypotheses considered more likely to contribute to risk 
are targeted. The rationale for selecting and omitting risk hypotheses 
is incorporated into the plan and includes acknowledgment of data gaps 
and uncertainties. It also may include a comparison of the level of 
confidence needed for the management decision with that expected from 
alternative analyses in order to determine data needs and evaluate 
which analytical approach is best. When new data are needed, the 
feasibility of obtaining them can be taken into account.
    Identification of the most critical relationships to evaluate in a 
risk assessment is based on the relationship of assessment endpoints to 
ecosystem structure and function, the relative importance or influence 
and mode of action of stressors on assessment endpoints, and other 
variables influencing ecological adversity (see section 5.2.2). 
However, final selection of relationships that can be pursued in 
analysis is based on the strength of known relationships between 
stressors and effects, the completeness of known exposure pathways, and 
the quality and availability of data.
    In situations where data are few and new data cannot be collected, 
it may be possible to extrapolate from existing data. Extrapolation 
allows the use of data collected from other locations or organisms 
where similar problems exist. For example, the relationship between 
nutrient availability and algal growth is well established and 
consistent. This relationship can be acknowledged despite differences 
in how it is manifested in particular ecosystems. When extrapolating 
from data, it is important to identify the source of the data, justify 
the extrapolation method, and discuss recognized uncertainties.
    A phased, or tiered, risk assessment approach (see section 2.2) can 
facilitate management decisions in cases involving minimal data sets. 
However, where few data are available, recommendations for new data 
collection should be part of the analysis plan. When new data are 
needed and cannot be obtained, relationships that cannot be assessed 
are a source of uncertainty and should be described in the analysis 
plan and later discussed in risk characterization.
    When determining what data to analyze and how to analyze them, 
consider how these analyses may increase understanding and confidence 
in the conclusions of the risk assessment and address risk management 
questions. During selection, risk assessors may ask questions such as: 
How relevant will the results be to the assessment endpoint(s) and 
conceptual model(s)? Are there sufficient data of high quality to 
conduct the analyses with confidence? How will the analyses help 
establish cause-and-effect relationships? How will results be presented 
to address managers' questions? Where are uncertainties likely to 
become a problem? Consideration of these questions during analysis 
planning will improve future characterization of risk (see section 
5.2.1 for discussion of lines of evidence).
3.5.1. Selecting Measures
    Assessment endpoints and conceptual models help risk assessors 
identify measurable attributes to quantify and predict change. However, 
determining what measures to use to evaluate risk hypotheses is both 
challenging and critical to the success of a risk assessment. There are 
three categories of measures. Measures of effect are measurable changes 
in an attribute of an assessment endpoint or its surrogate in response 
to a stressor to which it is exposed (formerly measurement endpoints; 
see text note 3-15). Measures of exposure are measures of stressor 
existence and movement in the environment and their contact or co-
occurrence with the assessment endpoint. Measures of ecosystem and 
receptor characteristics are measures of ecosystem characteristics that 
influence the behavior and location of entities selected as the 
assessment endpoint, the distribution of a stressor, and life-history 
characteristics of the assessment endpoint or its surrogate that may 
affect exposure or response to the stressor. Examples of the three 
types of measures are provided in text note 3-16 (see also Appendix 
A.2.1).
    The selection of appropriate measures is particularly complicated 
when a cascade of ecological effects is likely to occur from a 
stressor. In these cases, the effect on one entity (i.e., the measure 
of effect) may become a stressor for other ecological entities (i.e., 
become a measure of exposure) and may result in impacts on one or more 
assessment endpoints. For example, if a pesticide reduces earthworm 
populations, change in earthworm population density could be the direct 
measure of effect of toxicity and in some cases may be an assessment 
endpoint. However, the reduction of worm populations may then become a 
secondary stressor to which worm-eating birds become exposed, measured 
as lowered food supply. This exposure may then result in a secondary 
measurable effect of starvation of young. In this case, although ``bird 
fledgling success'' may be an assessment endpoint that could be 
measured directly, measures of earthworm density, pesticide residue in 
earthworms and other food sources, availability of alternative foods, 
nest site quality, and competition for nests from other bird species 
may all be useful measurements.
    When direct measurement of assessment endpoint responses is not 
possible, the selection of surrogate measures is necessary. The 
selection of what, where, and how to measure surrogate responses 
determines whether the risk assessment is still relevant to management 
decisions about an assessment endpoint. As an example, an assessment 
may be conducted to evaluate the potential risk of a pesticide used on 
seeds to an endangered species of seed-eating bird. The assessment 
endpoint entity is the endangered species. Example attributes include 
feeding behavior, survival, growth, and reproduction. While it may be 
possible

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to directly collect measures of exposure and assessment endpoint life-
history characteristics on the endangered species, it would not be 
appropriate to expose the endangered species to the pesticide to 
measure sensitivity. In this case, to evaluate susceptibility, the most 
appropriate surrogate measures would be on seed-eating birds with 
similar life-history characteristics and phylogeny. While insectivorous 
birds may serve as an adequate surrogate measure for determining the 
sensitivity of the endangered bird to the pesticide, they do not 
address issues of exposure.
    Problem formulations based on assessment endpoints and selected 
measures that address both sensitivity and likely exposure to stressors 
will be relevant to management concerns. If assessment endpoints are 
not susceptible, their use in assessing risk can lead to poor 
management decisions (see section 3.3.1). To highlight the 
relationships among goals, assessment endpoints, and measures, text 
note 3-17 illustrates how these are related in water quality criteria. 
In this example, it is instructive to note that although water quality 
criteria are considered risk-based, they are not full risk assessments. 
Water quality criteria provide an effects benchmark for decision making 
and do not incorporate measures of exposure in the environment. Within 
that benchmark, there are a number of assumptions about significance 
(e.g., aquatic communities will be protected by achieving a benchmark 
derived from individual species' toxicological responses to a single 
chemical) and exposure (e.g., 1-hour and 4-day exposure averages). Such 
assumptions embedded in decision rules are important to articulate (see 
section 3.5.2).
    The analysis plan provides a synopsis of measures that will be used 
to evaluate risk hypotheses. The plan is strongest when it contains 
explicit statements for how measures were selected, what they are 
intended to evaluate, and which analyses they support. Uncertainties 
associated with selected measures and analyses and plans for addressing 
them should be included in the plan when possible.
3.5.2. Ensuring That Planned Analyses Meet Risk Managers' Needs
    The analysis plan is a risk manager-risk assessor checkpoint. Risk 
assessors and risk managers review the plan to ensure that the analyses 
will provide information the manager can use for decision making. These 
discussions may also identify what can and cannot be done on the basis 
of a preliminary evaluation of problem formulation. A reiteration of 
the planning discussion helps ensure that the appropriate balance of 
requirements for the decision, data availability, and resource 
constraints is established for the risk assessment. This is also an 
appropriate time to conduct a technical review of the planning outcome.
    Analysis plans include the analytical methods planned and the 
nature of the risk characterization options and considerations to be 
generated (e.g., quotients, narrative discussion, stressor-response 
curve with probabilities). A description of how data analyses will 
distinguish among risk hypotheses, the kinds of analyses to be used, 
and rationale for why different hypotheses were selected and eliminated 
are included. Potential extrapolations, model characteristics, types of 
data (including quality), and planned analyses (with specific tests for 
different types of data) are described. Finally, the plan includes a 
discussion of how results will be presented upon completion and the 
basis used for data selection.
    Analysis planning is similar to the data quality objectives (DQO) 
process (see text note 3-18), which emphasizes identifying the problem 
by establishing study boundaries and determining necessary data 
quality, quantity, and applicability to the problem being evaluated 
(U.S. EPA, 1994c). The most important difference between problem 
formulation and the DQO process is the presence of a decision rule in a 
DQO that defines a benchmark for a management decision before the risk 
assessment is completed. The decision rule step specifies the 
statistical parameter that characterizes the population, specifies the 
action level for the study, and combines outputs from the previous DQO 
steps into an ``if * * * then'' decision rule that defines conditions 
under which the decision maker will choose alternative options (often 
used in tiered assessments; see also section 2.2.2). This approach 
provides the basis for establishing null and alternative hypotheses 
appropriate for statistical testing for significance that can be 
effective in this application. While this approach is sometimes 
appropriate, only certain kinds of risk assessments are based on 
benchmark decisions. Presentation of stressor-response curves with 
uncertainty bounds will be more appropriate than statistical testing of 
decision criteria where risk managers must evaluate the range of 
stressor effects to which they compare a range of possible management 
options (see Suter, 1996).
    The analysis plan is the final synthesis before the risk assessment 
proceeds. It summarizes what has been done during problem formulation, 
shows how the plan relates to management decisions that must be made, 
and indicates how data and analyses will be used to estimate risks. 
When the problem is clearly defined and there are enough data to 
proceed, analysis begins.

4. Analysis Phase

    Analysis is a process that examines the two primary components of 
risk, exposure and effects, and their relationships between each other 
and ecosystem characteristics. The objective is to provide the 
ingredients necessary for determining or predicting ecological 
responses to stressors under exposure conditions of interest.
    Analysis connects problem formulation with risk characterization. 
The assessment endpoints and conceptual models developed during problem 
formulation provide the focus and structure for the analyses. Analysis 
phase products are summary profiles that describe exposure and the 
relationship between the stressor(s) and response. These profiles 
provide the basis for estimating and describing risks in risk 
characterization.
    At the beginning of the analysis phase, the information needs 
identified during problem formulation should have already been 
addressed (text note 4-1). During the analysis phase (figure 4-1), the 
risk assessor:
     Selects the data that will be used on the basis of their 
utility for evaluating the risk hypotheses (section 4.1)
     Analyzes exposure by examining the sources of stressors, 
the distribution of stressors in the environment, and the extent of co-
occurrence or contact (section 4.2)
     Analyzes effects by examining stressor-response 
relationships, the evidence for causality, and the relationship between 
measures of effect and assessment endpoints (section 4.3)
     Summarizes the conclusions about exposure (section 4.2.2) 
and effects (section 4.3.2).

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    The analysis phase is flexible, with substantial interaction 
between the effects and exposure characterizations as illustrated by 
the dotted line in figure 4-1. In particular, when secondary stressors 
and effects are of concern, exposure and effects analyses are conducted 
iteratively for different ecological entities, and they can become 
intertwined and difficult to differentiate. In the bottomland hardwoods 
assessment, for example (Appendix D), potential changes in the plant 
and animal communities under different flooding scenarios were 
examined. Risk assessors combined the stressor-response and exposure 
analyses within the FORFLO model for primary effects on the plant 
community and within the Habitat Suitability Index for secondary 
effects on the animal community. In addition, the distinction between 
analysis and risk estimation can become blurred. The model results 
developed for the bottomland hardwoods assessment were used directly in 
risk characterization.
    The nature of the stressor influences the types of analyses 
conducted. The results may range from highly quantitative to 
qualitative, depending on the stressor and the scope of the assessment. 
For chemical stressors, exposure estimates emphasize contact and uptake 
into the organism, and effects estimations often entail extrapolation 
from test organisms to the organism of interest. For physical 
stressors, the initial disturbance may cause primary effects on the 
assessment endpoint (e.g., loss of wetland acreage). In many cases, 
however, secondary effects (e.g., decline of wildlife populations that 
depend on wetlands) may be the principal concern. The point of view 
depends on the assessment endpoints. Because adverse effects can occur 
even if receptors do not physically contact disturbed habitat, exposure 
analyses may emphasize co-occurrence with physical stressors rather 
than contact. For biological stressors, exposure analysis is an 
evaluation of entry, dispersal, survival, and reproduction (Orr et al., 
1993). Because biological stressors can reproduce, interact with other 
organisms, and evolve over time, exposure and effects cannot always be 
quantified with confidence; therefore, they may be assessed 
qualitatively by eliciting expert opinion (Simberloff and Alexander, 
1994).

4.1. Evaluating Data and Models for Analysis

    At the beginning of the analysis phase, the assessor critically 
examines the data and models to ensure that they can be used to 
evaluate the conceptual model developed in problem formulation (see 
sections 4.1.1 and 4.1.2). Section 4.1.3 addresses uncertainty 
evaluation.
4.1.1. Strengths and Limitations of Different Types of Data
    Many types of data can be used for risk assessment. Data may come 
from laboratory or field studies or may be produced as output from a 
model. Familiarity with the strengths and limitations of different 
types of data can help assessors build on strengths and avoid pitfalls. 
Such a strategy improves confidence in the conclusions of the risk 
assessment.
    Both laboratory and field studies (including field experiments and 
observational studies) can provide useful data for risk assessment. 
Because conditions can be controlled in laboratory studies, responses 
may be less variable and smaller differences easier to detect. However, 
the controls may limit the range of responses (e.g., animals cannot 
seek alternative food sources), so they may not reflect responses in 
the environment. In addition, larger-scale processes are difficult to 
replicate in the laboratory.
    Field observational studies (surveys) measure biological changes in 
uncontrolled situations. Ecologists observe patterns and processes in 
the field and often use statistical techniques (e.g., correlation, 
clustering, factor analysis) to describe an association between a 
disturbance and an ecological effect. For instance, physical attributes 
of streams and their watersheds have been associated with changes in 
stream communities (Richards et al., 1997). Field surveys are often 
reported as status and trend studies. Messer et al. (1991) correlated a 
biotic index with acid concentrations to describe the extent and 
proportion of lakes likely to be impacted.
    Field surveys usually represent exposures and effects (including 
secondary effects) better than estimates generated from laboratory 
studies or theoretical models. Field data are more important for 
assessments of multiple stressors or where site-specific factors 
significantly influence exposure. They are also often useful for 
analyses of larger geographic scales and higher levels of biological 
organization. Field survey data are not always necessary or feasible to 
collect for screening-level or prospective assessments.
    Field surveys should be designed with sufficient statistical rigor 
to define one or more of the following:
     Exposure in the system of interest
     Differences in measures of effect between reference sites 
and study areas
     Lack of differences. Because conditions are not controlled 
in field studies, variability may be higher and it may be difficult to 
detect differences. For this reason, it is important to verify that 
studies have sufficient power to detect important differences.
    Field surveys are most useful for linking stressors with effects 
when stressor and effect levels are measured concurrently. The presence 
of confounding factors can make it difficult to attribute observed 
effects to specific stressors. For this reason, field studies designed 
to minimize effects of potentially confounding factors are preferred, 
and the evidence for causality should be carefully evaluated (see 
section 4.3.1.2). In addition, because treatments may not be randomly 
applied or replicated, classical statistical methods need to be applied 
with caution (Hurlbert, 1984; Stewart-Oaten et al., 1986; Wiens and 
Parker, 1995; Eberhardt and Thomas, 1991). Intermediate between 
laboratory and field are studies that use environmental media collected 
from the field to examine response in the laboratory. Such studies may 
improve the power to detect differences and may be designed to provide 
evidence of causality.
    Most data will be reported as measurements for single variables 
such as a chemical concentration or the number of dead organisms. In 
some cases, however, variables are combined and reported as indices. 
Several indices are used to evaluate effects, for example, the rapid 
bioassessment protocols (U.S. EPA, 1989a) and the Index of Biotic 
Integrity, or IBI (Karr, 1981; Karr et al., 1986). These have several 
advantages (Barbour et al., 1995), including the ability to:
     Provide an overall indication of biological condition by 
incorporating many attributes of system structure and function, from 
individual to ecosystem levels
     Evaluate responses from a broad range of anthropogenic 
stressors
     Minimize the limitations of individual metrics for 
detecting specific types of responses.
    Indices also have several drawbacks, many of which are associated 
with combining heterogeneous variables. The final value may depend 
strongly on the function used to combine variables. Some indices (e.g., 
the IBI) combine only measures of effects. Differential sensitivity or 
other factors may make it difficult to attribute causality when many 
response variables are combined. To investigate causality, such indices 
may need to be separated into their

[[Page 26867]]

components, or analyzed using multivariate methods (Suter, 1993b; Ott, 
1978). Interpretation becomes even more difficult when an index 
combines measures of exposure and effects because double counting may 
occur or changes in one variable can mask changes in another. Measures 
of exposure and effects may need to be separated in order to make 
appropriate conclusions. For these reasons, professional judgment plays 
a critical role in developing and applying indices.
    Experience from similar situations is particularly useful in 
assessments of stressors not yet released (i.e., prospective 
assessments). Lessons learned from past experiences with related 
organisms are often critical in trying to predict whether an organism 
will survive, reproduce, and disperse in a new environment. Another 
example is toxicity evaluation for new chemicals through the use of 
structure-activity relationships, or SARs (Auer et al., 1994; Clements 
and Nabholz, 1994). The simplest application of SARs is to identify a 
suitable analog for which data are available to estimate the toxicity 
of a compound for which data are lacking. More advanced applications 
use quantitative structure-activity relationships (QSARs), which 
mathematically model the relationships between chemical structures and 
specific biological effects and are derived using information on sets 
of related chemicals (Lipnick, 1995; Cronin and Dearden, 1995). The use 
of analogous data without knowledge of the underlying processes may 
substantially increase the uncertainty in the risk assessment (e.g., 
Bradbury, 1994); however, use of these data may be the only option 
available.
    Even though models may be developed and used as part of the risk 
assessment, sometimes the risk assessor relies on output of a 
previously developed model. Models are particularly useful when 
measurements cannot be taken, for example, when predicting the effects 
of a chemical yet to be manufactured. They can also provide estimates 
for times or locations that are impractical to measure and can provide 
a basis for extrapolating beyond the range of observation. Because 
models simplify reality, they may omit important processes for a 
particular system and may not reflect every condition in the real 
world. In addition, a model's output is only as good as the quality of 
its input variables, so critical evaluation of input data is important, 
as is comparing model outputs with measurements in the system of 
interest whenever possible.
    Data and models for risk assessment are often developed in a tiered 
fashion (also see section 2.2). For example, simple models that err on 
the side of conservatism may be used first, followed by more elaborate 
models that provide more realistic estimates. Effects data may also be 
collected using a tiered approach. Short-term tests designed to 
evaluate effects such as lethality and immobility may be conducted 
first. If the chemical exhibits high toxicity or a preliminary 
characterization indicates a risk, then more expensive, longer-term 
tests that measure sublethal effects such as changes to growth and 
reproduction can be conducted. Later tiers may employ multispecies 
tests or field experiments. Tiered data should be evaluated in light of 
the decision they are intended to support; data collected for early 
tiers may not support more sophisticated needs.
4.1.2. Evaluating Measurement or Modeling Studies
    The assessor's first task in the analysis phase is to carefully 
evaluate studies to determine whether they can support the objectives 
of the risk assessment. Each study should include a description of the 
purpose, methods used to collect data, and results of the work. The 
assessor evaluates the utility of studies by carefully comparing study 
objectives with those of the risk assessment for consistency. In 
addition, the assessor should determine whether the intended objectives 
were met and whether the data are of sufficient quality to support the 
risk assessment. This is a good opportunity to note the confidence in 
the information and the implications of different studies for use in 
the risk characterization, when the overall confidence in the 
assessment is discussed. Finally, the risk assessor should identify 
areas where existing data do not meet risk assessment needs. In these 
cases, collecting additional data is recommended.
    EPA is in the process of adopting the American Society for Quality 
Control's E-4 guidelines for assuring environmental data quality 
throughout the Agency (ASQC, 1994) (text note 4-2). These guidelines 
describe procedures for collecting new data and provide a valuable 
resource for evaluating existing studies. Readers may also refer to 
Smith and Shugart, 1994; U.S. EPA, 1994d; and U.S. EPA, 1990, for more 
information on evaluating data and models.
    A study's documentation determines whether it can be evaluated for 
its utility in risk assessment. Studies should contain sufficient 
information so that results can be reproduced, or at least so the 
details of the author's work can be accessed and evaluated. Ideally, 
one should be able to access findings in their entirety; this provides 
the opportunity to conduct additional analyses of the data, if needed. 
For models, a number of factors increase the accessibility of methods 
and results. These begin with model code and documentation 
availability. Reports describing model results should include all 
important equations, tables of all parameter values, any parameter 
estimation techniques, and tables or graphs of results.
    Study descriptions may not provide all the information needed to 
evaluate their utility for risk assessment. Assessors should 
communicate with the principal investigator or other study participants 
to gain information on study plans and their implementation. Useful 
questions for evaluating studies are shown in text note 4-3.
4.1.2.1. Evaluating the Purpose and Scope of the Study
    Assessors should pay particular attention to the objectives and 
scope of studies that were designed for purposes other than the risk 
assessment at hand. This can identify important uncertainties and 
ensure that the information is used appropriately. An example is the 
evaluation of studies that measure condition (e.g., stream surveys, 
population surveys): While the measurements used to evaluate condition 
may be the same as the measures of effects identified in problem 
formulation, to support a causal argument they must be linked with 
stressors. In the best case, this means that the stressor was measured 
at the same time and place as the effect.
    Similarly, a model may have been developed for purposes other than 
risk assessment. Its description should include the intended 
application, theoretical framework, underlying assumptions, and 
limiting conditions. This information can help assessors identify 
important limitations in its application for risk assessment. For 
example, a model developed to evaluate chemical transport in the water 
column alone is of limited utility for a risk assessment of a chemical 
that partitions readily into sediments.
    The variables and conditions examined by studies should also be 
compared with those identified during problem formulation. In addition, 
the range of variability explored in the study should be compared with 
that of the risk assessment. A study that examines animal habitat needs 
in the winter, for example, may miss important breeding-season

[[Page 26868]]

requirements. Studies that minimize the amount of extrapolation needed 
are preferred. These are studies that represent:
     The measures identified in the analysis plan (i.e., 
measures of exposure, effects, and ecosystem and receptor 
characteristics)
     The time frame of interest
     The ecosystem and location of interest
     The environmental conditions of interest
     The exposure route of interest.
4.1.2.2. Evaluating the Design and Implementation of the Study
    The assessor evaluates study design and implementation to determine 
whether the study objectives were met and the information is of 
sufficient quality to support the risk assessment. The study design 
provides insight into the sources and magnitude of uncertainty 
associated with the results (see section 4.1.3 for further discussion 
of uncertainty). Among the most important design issues of an effects 
study is whether it has enough statistical power to detect important 
differences or changes. Because this information is rarely reported 
(Peterman, 1990), the assessor may need to calculate the magnitude of 
an effect that could be detected under the study conditions (Rotenberry 
and Wiens, 1985).
    Part of the exercise examines whether the study was conducted 
properly:
     For laboratory studies, this may mean determining whether 
test conditions were properly controlled and control responses were 
within acceptable bounds.
     For field studies, issues include identification and 
control of potentially confounding variables and careful reference site 
selection. (A discussion of reference site selection is beyond the 
scope of these Guidelines; however, it has been identified as a 
candidate topic for future development.)
     For models, issues include the program's structure and 
logic and the correct specification of algorithms in the model code 
(U.S. EPA, 1994d).
    Evaluation is easier if standard methods or quality assurance/
quality control (QA/QC) protocols are available and followed by the 
study. However, the assessor should still consider whether the 
identified precision and accuracy goals were achieved and whether they 
are appropriate for the risk assessment. For instance, detection limits 
identified for one environmental matrix may not be achievable for 
another, and thus it may not be possible to detect concentrations of 
interest. Study results can still be useful even if a standard method 
was not used. However, this places an additional burden on both the 
authors and the assessors to provide and evaluate evidence that the 
study was conducted properly.
4.1.3. Evaluating Uncertainty
    Uncertainty evaluation is a theme throughout the analysis phase. 
The objective is to describe and, where possible, quantify what is 
known and not known about exposure and effects in the system of 
interest. Uncertainty analyses increase the credibility of assessments 
by explicitly describing the magnitude and direction of uncertainties, 
and they provide the basis for efficient data collection or application 
of refined methods. Uncertainties characterized during the analysis 
phase are used during risk characterization, when risks are estimated 
(section 5.1) and the confidence in different lines of evidence is 
described (see section 5.2.1).
    This section discusses sources of uncertainty relevant to the 
analysis of ecological exposure and effects; source and example 
strategies are shown in text note 4-4. Section 3.4.3 discusses 
uncertainty in conceptual model development. Readers are also referred 
to the discussion of uncertainties in the exposure assessment 
guidelines (U.S. EPA, 1992b).
    Sources of uncertainty that are encountered when evaluating 
information include unclear communication of the data or its 
manipulation and errors in the information itself (descriptive errors). 
These are usually characterized by critically examining the sources of 
information and documenting the decisions made when handling it. The 
documentation should allow the reader to make an independent judgment 
about the validity of the assessor's decisions.
    Sources of uncertainty that primarily arise when estimating the 
value of a parameter include variability, uncertainty about a 
quantity's true value, and data gaps. The term variability is used here 
to describe a characteristic's true heterogeneity. Examples include the 
variability in soil organic carbon, seasonal differences in animal 
diets, or differences in chemical sensitivity in different species. 
Variability is usually described during uncertainty analysis, although 
heterogeneity may not reflect a lack of knowledge and cannot usually be 
reduced by further measurement. Variability can be described by 
presenting a distribution or specific percentiles from it (e.g., mean 
and 95th percentile).
    Uncertainty about a quantity's true value may include uncertainty 
about its magnitude, location, or time of occurrence. This uncertainty 
can usually be reduced by taking additional measurements. Uncertainty 
about a quantity's true magnitude is usually described by sampling 
error (or variance in experiments) or measurement error. When the 
quantity of interest is biological response, sampling error can greatly 
influence a study's ability to detect effects. Properly designed 
studies will specify sample sizes large enough to detect important 
signals. Unfortunately, many studies have sample sizes that are too 
small to detect anything but gross changes (Smith and Shugart, 1994; 
Peterman, 1990). The discussion should highlight situations where the 
power to detect difference is low. Meta-analysis has been suggested as 
a way to combine results from different studies to improve the ability 
to detect effects (Laird and Mosteller, 1990; Petitti, 1994). However, 
these approaches have thus far been applied primarily in human 
epidemiology and are still controversial (Mann, 1990).
    Interest in quantifying spatial uncertainty has increased with the 
increasing use of geographic information systems (GIS). Strategies 
include verifying the locations of remotely sensed features and 
ensuring that the spatial resolution of data or a method is 
commensurate with the needs of the assessment. A growing literature is 
addressing other analytical challenges often associated with using 
spatial data (e.g., collinearity and autocorrelation, boundary and 
scale effects, lack of true replication) (Johnson and Gage, 1997; 
Fotheringham and Rogerson, 1993; Wiens and Parker, 1995). Large-scale 
assessments generally require aggregating information at smaller 
scales. It is not known how aggregation affects uncertainty (Hunsaker 
et al., 1990).
    Nearly every assessment must treat situations where data are 
unavailable or available only for parameters other than those of 
interest. Examples include using laboratory data to estimate a wild 
animal's response to a stressor or using a bioaccumulation measurement 
from a different ecosystem. These data gaps are usually bridged with a 
combination of scientific analyses, scientific judgment, and perhaps 
policy decisions. In deriving an ambient water quality criterion (text 
note 3-17), for example, data and analyses are used to construct 
distributions of species sensitivity for a particular chemical. 
Scientific judgment is used to infer that species selected for testing 
will adequately represent the range of sensitivity of species in the

[[Page 26869]]

environment. Policy defines the extent to which individual species 
should be protected (e.g., 90% vs. 95% of the species). It is important 
to distinguish these elements.
    Data gaps can often be filled by completing additional studies on 
the unknown parameter. When possible, the necessary data should be 
collected. At the least, opportunities for filling data gaps should be 
noted and carried through to risk characterization. Data or knowledge 
gaps that are so large that they preclude the analysis of either 
exposure or ecological effects should also be noted and discussed in 
risk characterization.
    An important objective is to distinguish variability from 
uncertainties that arise from lack of knowledge (e.g., uncertainty 
about a quantity's true value) (U.S. EPA, 1995b). This distinction 
facilitates the interpretation and communication of results. For 
instance, in their food web models of herons and mink, MacIntosh et al. 
(1994) separated expected variability in individual animals' feeding 
habits from the uncertainty in the mean concentration of chemical in 
prey species. They could then place error bounds on the exposure 
distribution for the animals using the site and estimate the proportion 
of the animal population that might exceed a toxicity threshold.
    Sources of uncertainty that arise primarily during model 
development and application include process model structure and the 
relationships between variables in empirical models. Process model 
descriptions should include assumptions, simplifications, and 
aggregations of variables (see text note 4-5). Empirical model 
descriptions should include the rationale for selection and model 
performance statistics (e.g., goodness of fit). Uncertainty in process 
or empirical models can be quantitatively evaluated by comparing model 
results to measurements taken in the system of interest or by comparing 
the results of different models.
    Methods for analyzing and describing uncertainty can range from 
simple to complex. When little is known, a useful approach is to 
estimate exposure and effects based on alternative sets of assumptions 
(scenarios). Each scenario is carried through to risk characterization, 
where the underlying assumptions and the scenario's plausibility are 
discussed. Results can be presented as a series of point estimates with 
different aspects of uncertainty reflected in each. Classical 
statistical methods (e.g., confidence limits, percentiles) can readily 
describe parameter uncertainty. For models, sensitivity analysis can be 
used to evaluate how model output changes with changes in input 
variables, and uncertainty propagation can be analyzed to examine how 
uncertainty in individual parameters can affect the overall uncertainty 
in the results. The availability of software for Monte Carlo analysis 
has greatly increased the use of probabilistic methods; readers are 
encouraged to follow suggested best practices (e.g., U.S. EPA, 1996a, 
1997b). Other methods (e.g., fuzzy mathematics, Bayesian methodologies) 
are available but have not yet been extensively applied to ecological 
risk assessment (Smith and Shugart, 1994). The Agency does not endorse 
the use of any one method and cautions that the poor execution of any 
method can obscure rather than clarify the impact of uncertainty on an 
assessment's results. No matter what technique is used, the sources of 
uncertainty discussed above should be addressed.

4.2. Characterization of Exposure

    Exposure characterization describes potential or actual contact or 
co-occurrence of stressors with receptors. It is based on measures of 
exposure and ecosystem and receptor characteristics that are used to 
analyze stressor sources, their distribution in the environment, and 
the extent and pattern of contact or co-occurrence (discussed in 
section 4.2.1). The objective is to produce a summary exposure profile 
(section 4.2.2) that identifies the receptor (i.e., the exposed 
ecological entity), describes the course a stressor takes from the 
source to the receptor (i.e., the exposure pathway), and describes the 
intensity and spatial and temporal extent of co-occurrence or contact. 
The profile also describes the impact of variability and uncertainty on 
exposure estimates and reaches a conclusion about the likelihood that 
exposure will occur.
    The exposure profile is combined with an effects profile (discussed 
in section 4.3.2) to estimate risks. For the exposure profile to be 
useful, it should be compatible with the stressor-response relationship 
generated in the effects characterization.
4.2.1. Exposure Analyses
    Exposure is contact or co-occurrence between a stressor and a 
receptor. The objective is to describe exposure in terms of intensity, 
space, and time in units that can be combined with the effects 
assessment. In addition, the assessor should be able to trace the paths 
of stressors from the source(s) to the receptors (i.e., describe the 
exposure pathway).
    A complete picture of how, when, and where exposure occurs or has 
occurred is developed by evaluating sources and releases, the 
distribution of the stressor in the environment, and the extent and 
pattern of contact or co-occurrence. The order of these topics here is 
not necessarily the order in which they are executed. The assessor may 
start with information about tissue residues, for example, and attempt 
to link these residues with a source.
4.2.1.1. Describe the Source(s)
    A source can be defined in two general ways: as the place where the 
stressor originates or is released (e.g., a smokestack, historically 
contaminated sediments) or the management practice or action (e.g., 
dredging) that produces stressors. In some assessments, the original 
sources may no longer exist and the source may be defined as the 
current location of the stressors. For example, contaminated sediments 
might be considered a source because the industrial plant that produced 
the contaminants no longer operates. A source is the first component of 
the exposure pathway and significantly influences where and when 
stressors eventually will be found. In addition, many management 
alternatives focus on modifying the source.
    Exposure analyses may start with the source when it is known, begin 
with known exposures and attempt to link them to sources, or start with 
known stressors and attempt to identify sources and quantify contact. 
In any case, the objective of this step is to identify the sources, 
evaluate what stressors are generated, and identify other potential 
sources. Text note 4-6 provides some useful questions to ask when 
describing sources.
    In addition to identifying sources, the assessor examines the 
intensity, timing, and location of stressors' release. The location of 
a source and the environmental media that first receive stressors are 
two attributes that deserve particular attention. For chemical 
stressors, the source characterization should also consider whether 
other constituents emitted by a source influence transport, 
transformation, or bioavailability of the stressor of interest. The 
presence of chloride in the feedstock of a coal-fired power plant 
influences whether mercury is emitted in divalent (e.g., as mercuric 
chloride) or elemental form (Meij, 1991), for example. In the best 
case, stressor generation is measured or modeled quantitatively; 
however, sometimes it can only be qualitatively described.
    Many stressors have natural counterparts or multiple sources, so it

[[Page 26870]]

may be necessary to characterize these as well. Many chemicals occur 
naturally (e.g., most metals), are generally widespread from other 
sources (e.g., polycyclic aromatic hydrocarbons in urban ecosystems), 
or may have significant sources outside the boundaries of the current 
assessment (e.g., atmospheric nitrogen deposited in Chesapeake Bay). 
Many physical stressors also have natural counterparts. For instance, 
construction activities may release fine sediments into a stream in 
addition to those coming from a naturally undercut bank. Human 
activities may also change the magnitude or frequency of natural 
disturbance cycles. For example, development may decrease the frequency 
but increase the severity of fires or may increase the frequency and 
severity of flooding in a watershed.
    The assessment scope identified during planning determines how 
multiple sources are evaluated. Options include (in order of increasing 
complexity):
     Focus only on the source under evaluation and calculate 
the incremental risks attributable to that source (common for 
assessments initiated with an identified source or stressor).
     Consider all sources of a stressor and calculate total 
risks attributable to that stressor. Relative source attribution can be 
accomplished as a separate step (common for assessments initiated with 
an observed effect or an identified stressor).
     Consider all stressors influencing an assessment endpoint 
and calculate cumulative risks to that endpoint (common for assessments 
initiated because of concern for an ecological value).
    Source characterization can be particularly important for 
introduced biological stressors, since many of the strategies for 
reducing risks focus on preventing entry in the first place. Once the 
source is identified, the likelihood of entry may be characterized 
qualitatively. In their risk analysis of Chilean log importation, for 
example, the assessment team concluded that the beetle Hylurgus 
ligniperda had a high potential for entry into the United States. Their 
conclusion was based on the beetle's attraction to freshly cut logs and 
tendency to burrow under the bark, which would provide protection 
during transport (USDA, 1993).
4.2.1.2. Describe the Distribution of the Stressors or Disturbed 
Environment
    The second objective of exposure analysis is to describe the 
spatial and temporal distribution of stressors in the environment. For 
physical stressors that directly alter or eliminate portions of the 
environment, the assessor describes the temporal and spatial 
distribution of the disturbed environment. Because exposure occurs when 
receptors co-occur with or contact stressors, this characterization is 
a prerequisite for estimating exposure. Stressor distribution in the 
environment is examined by evaluating pathways from the source as well 
as the formation and subsequent distribution of secondary stressors 
(see text note 4-7).
4.2.1.2.1. Evaluating Transport Pathways
    Stressors can be transported via many pathways (see text note 4-8). 
A careful evaluation can help ensure that measurements are taken in the 
appropriate media and locations and that models include the most 
important processes.
    For a chemical stressor, the evaluation usually begins by 
determining into which media it can partition. Key considerations 
include physicochemical properties such as solubility and vapor 
pressure. For example, chemicals with low solubility in water tend to 
be found in environmental compartments with higher proportions of 
organic carbon such as soils, sediments, and biota. From there, the 
evaluation may examine the transport of the contaminated medium. 
Because chemical mixture constituents may have different properties, 
the analysis should consider how the composition of a mixture may 
change over time or as it moves through the environment. Guidance on 
evaluating the fate and transport of chemicals (including 
bioaccumulation) is beyond the scope of these Guidelines; readers are 
referred to the exposure assessment guidelines (U.S. EPA, 1992b) for 
additional information. The topics of bioaccumulation and 
biomagnification have been identified as candidates for further 
development.
    The attributes of physical stressors also influence where they will 
go. The size of suspended particles determines where they will 
eventually deposit in a stream, for example. Physical stressors that 
eliminate ecosystems or portions of them (e.g., fishing activities or 
the construction of dams) may require no modeling of pathways--the fish 
are harvested or the valley is flooded. For these direct disturbances, 
the challenge is usually to evaluate secondary stressors and effects.
    The dispersion of biological stressors has been described in two 
ways, as diffusion and jump-dispersal (Simberloff and Alexander, 1994). 
Diffusion involves a gradual spread from the establishment site and is 
primarily a function of reproductive rates and motility. Jump-dispersal 
involves erratic spreads over periods of time, usually by means of a 
vector. The gypsy moth and zebra mussel have spread this way, the gypsy 
moth via egg masses on vehicles and the zebra mussel via boat ballast 
water. Some biological stressors can use both strategies, which may 
make dispersal rates very difficult to predict. The evaluation should 
consider factors such as vector availability, attributes that enhance 
dispersal (e.g., ability to fly, adhere to objects, disperse 
reproductive units), and habitat or host needs.
    For biological stressors, assessors should consider the additional 
factors of survival and reproduction. Organisms use a wide range of 
strategies to survive in adverse conditions; for example, fungi form 
resting stages such as sclerotia and chlamydospores and some amphibians 
become dormant during drought. The survival of some organisms can be 
measured to some extent under laboratory conditions. However, it may be 
impossible to determine how long resting stages (e.g., spores) can 
survive under adverse conditions: many can remain viable for years. 
Similarly, reproductive rates may vary substantially depending on 
specific environmental conditions. Therefore, while life-history data 
such as temperature and substrate preferences, important predators, 
competitors or diseases, habitat needs, and reproductive rates are of 
great value, they should be interpreted with caution, and the 
uncertainty should be addressed by using several different scenarios.
    Ecosystem characteristics influence the transport of all types of 
stressors. The challenge is to determine the particular aspects of the 
ecosystem that are most important. In some cases, ecosystem 
characteristics that influence distribution are known. For example, 
fine sediments tend to accumulate in areas of low energy in streams 
such as pools and backwaters. Other cases need more professional 
judgment. When evaluating the likelihood that an introduced organism 
will become established, for instance, it is useful to know whether the 
ecosystem is generally similar to or different from the one where the 
biological stressor originated. Professional judgment is used to 
determine which characteristics of the current and original ecosystems 
should be compared.
4.2.1.2.2. Evaluating Secondary Stressors
    Secondary stressors can greatly alter conclusions about risk; they 
may be of

[[Page 26871]]

greater or lesser concern than the primary stressor. Secondary stressor 
evaluation is usually part of exposure characterization; however, it 
should be coordinated with the ecological effects characterization to 
ensure that all potentially important secondary stressors are 
considered.
    For chemicals, the evaluation usually focuses on metabolites, 
biodegradation products, or chemicals formed through abiotic processes. 
As an example, microbial action increases the bioaccumulation of 
mercury by transforming inorganic forms to organic species. Many azo 
dyes are not toxic because of their large molecular size, but in an 
anaerobic environment, the polymer is hydrolyzed into more toxic water-
soluble units. Secondary stressors can also be formed through ecosystem 
processes. Nutrient inputs into an estuary can decrease dissolved 
oxygen concentrations because they increase primary production and 
subsequent decomposition. Although transformation can be investigated 
in the laboratory, rates in the field may differ substantially, and 
some processes may be difficult or impossible to replicate in a 
laboratory. When evaluating field information, though, it may be 
difficult to distinguish between transformation processes (e.g., oil 
degradation by microorganisms) and transport processes (e.g., 
volatilization). Although they may be difficult to distinguish, the 
assessor should be aware that these two different processes will 
largely determine if secondary stressors are likely to be formed. A 
combination of these factors will also determine how much of the 
secondary stressor(s) may be bioavailable to receptors. These 
considerations reinforce the need to have a chemical risk assessment 
team experienced in physical/chemical as well as biological processes.
    Physical disturbances can also generate secondary stressors, and 
identifying the specific consequences that will affect the assessment 
endpoint can be a difficult task. The removal of riparian vegetation, 
for example, can generate many secondary stressors, including increased 
nutrients, stream temperature, sedimentation, and altered stream flow. 
However, it may be the temperature change that is most responsible for 
adult salmon mortality in a particular stream.
    Stressor distribution in the environment can be described using 
measurements, models, or a combination of the two. If stressors have 
already been released, direct measurement of environmental media or a 
combination of modeling and measurement is preferred. Models enhance 
the ability to investigate the consequences of different management 
scenarios and may be necessary if measurements are not possible or 
practicable. They are also useful if a quantitative relationship of 
sources and stressors is desired. As examples, land use activities have 
been related to downstream suspended solids concentrations (Oberts, 
1981), and downstream flood peaks have been predicted from the extent 
of wetlands in a watershed (Novitski, 1979; Johnston et al., 1990). 
Considerations for evaluating data collection and modeling studies are 
discussed in section 4.1. For chemical stressors, readers may also 
refer to the exposure assessment guidelines (U.S. EPA, 1992b). For 
biological stressors, distribution may be difficult to predict 
quantitatively. If it cannot be measured, it can be evaluated 
qualitatively by considering the potential for transport, survival, and 
reproduction (see above).
    By the end of this step, the environmental distribution of the 
stressor or the disturbed environment should be described. This 
description provides the foundation for estimating the contact or co-
occurrence of the stressor with ecological entities. When contact is 
known to have occurred, describing the stressor's environmental 
distribution can help identify potential sources and ensure that all 
important exposures are addressed.
4.2.1.3. Describe Contact or Co-Occurrence
    The third objective is to describe the extent and pattern of co-
occurrence or contact between stressors and receptors (i.e., exposure). 
This is critical--if there is no exposure, there can be no risk. 
Therefore, assessors should be careful to include situations where 
exposure may occur in the future, where exposure has occurred in the 
past but is not currently evident (e.g., in some retrospective 
assessments), and where ecosystem components important for food or 
habitat are or may be exposed, resulting in impacts to the valued 
entity (e.g., see figure D-2). Exposure can be described in terms of 
stressor and receptor co-occurrence, actual stressor contact with 
receptors, or stressor uptake by a receptor. The terms in which 
exposure is described depend on how the stressor causes adverse effects 
and how the stressor-response relationship is described. Relevant 
questions for examining contact or co-occurrence are shown in text note 
4-9.
    Co-occurrence is particularly useful for evaluating stressors that 
can cause effects without physically contacting ecological receptors. 
Whooping cranes provide a case in point: they use sandbars in rivers 
for their resting areas, and they prefer sandbars with unobstructed 
views. Manmade obstructions such as bridges can interfere with resting 
behavior without ever actually contacting the birds. Co-occurrence is 
evaluated by comparing stressor distributions with that of the 
receptor. For instance, stressor location maps may be overlaid with 
maps of ecological receptors (e.g., bridge placement overlaid on maps 
showing historical crane resting habitat). Co-occurrence of a 
biological stressor and receptor may be used to evaluate exposure when, 
for example, introduced species and native species compete for the same 
resources. GIS has provided new tools for evaluating co-occurrence.
    Most stressors must contact receptors to cause an effect. For 
example, tree roots must contact flood waters before their growth is 
impaired. Contact is a function of the amount or extent of a stressor 
in an environmental medium and activity or behavior of the receptors. 
For biological stressors, risk assessors usually rely on professional 
judgment; contact is often assumed to occur in areas and during times 
where the stressor and receptor are both present. Contact variables 
such as the mode of transmission between organisms may influence the 
contact between biological stressors and receptors.
    For chemicals, contact is quantified as the amount of a chemical 
ingested, inhaled, or in material applied to the skin (potential dose). 
In its simplest form, it is quantified as an environmental 
concentration, with the assumptions that the chemical is well mixed or 
that the organism moves randomly through the medium. This approach is 
commonly used for respired media (water for aquatic organisms, air for 
terrestrial organisms). For ingested media (food, soil), another common 
approach combines modeled or measured contaminant concentrations with 
assumptions or parameters describing the contact rate (U.S. EPA, 1993a) 
(see text note 4-10).
    Finally, some stressors must not only be contacted but also must be 
internally absorbed. A toxicant that causes liver tumors in fish, for 
example, must be absorbed and reach the target organ to cause the 
effect. Uptake is evaluated by considering the amount of stressor 
internally absorbed by an organism. It is a function of the stressor 
(e.g., a chemical's form or a pathogen's size), the medium (sorptive 
properties or presence of solvents), the biological membrane 
(integrity, permeability), and the organism (sickness, active uptake) 
(Suter et al., 1994). Because of

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interactions between these four factors, uptake will vary on a 
situation-specific basis. Uptake is usually assessed by modifying an 
estimate of contact with a factor indicating the proportion of the 
stressor that is available for uptake (the bioavailable fraction) or 
actually absorbed. Absorption factors and bioavailability measured for 
the chemical, ecosystem, and organism of interest are preferred. 
Internal dose can also be evaluated by using a pharmacokinetic model or 
by measuring biomarkers or residues in receptors (see text note 4-11). 
Most stressor-response relationships express the amount of stressor in 
terms of media concentration or potential dose rather than internal 
dose; this limits the utility of uptake estimates in risk calculations. 
However, biomarkers and tissue residues can provide valuable 
confirmatory evidence that exposure has occurred, and tissue residues 
in prey organisms can be used for estimating risks to their predators.
    The characteristics of the ecosystem and receptors must be 
considered to reach appropriate conclusions about exposure. Abiotic 
attributes may increase or decrease the amount of a stressor contacted 
by receptors. For example, naturally anoxic areas above contaminated 
sediments in an estuary may reduce the time bottom-feeding fish spend 
in contact with sediments and thereby reduce their exposure to 
contaminants. Biotic interactions can also influence exposure. For 
example, competition for high-quality resources may force some 
organisms into disturbed areas. The interaction between exposure and 
receptor behavior can influence both initial and subsequent exposures. 
Some chemicals reduce the prey's ability to escape predators, for 
instance, and thereby may increase predator exposure to the chemical as 
well as the prey's risk of predation. Alternatively, organisms may 
avoid areas, food, or water with contamination they can detect. While 
avoidance can reduce exposure to chemicals, it may increase other risks 
by altering habitat usage or other behavior.
    Three dimensions should be considered when estimating exposure: 
intensity, time, and space. Intensity is the most familiar dimension 
for chemical and biological stressors and may be expressed as the 
amount of chemical contacted per day or the number of pathogenic 
organisms per unit area.
    The temporal dimension of exposure has aspects of duration, 
frequency, and timing. Duration can be expressed as the time over which 
exposure occurs, some threshold intensity is exceeded, or intensity is 
integrated. If exposure occurs as repeated discrete events of about the 
same duration, frequency is the important temporal dimension of 
exposure (e.g., the frequency of high-flow events in streams). If the 
repeated events have significant and variable durations, both duration 
and frequency should be considered. In addition, the timing of 
exposure, including the order or sequence of events, can be an 
important factor. Adirondack Mountain lakes receive high concentrations 
of hydrogen ions and aluminum during snow melt; this period also 
corresponds to the sensitive life stages of some aquatic organisms.
    In chemical assessments, intensity and time are often combined by 
averaging intensity over time. The duration over which intensity is 
averaged is determined by considering the ecological effects of concern 
and the likely pattern of exposure. For example, an assessment of bird 
kills associated with granular carbofuran focused on short-term 
exposures because the effect of concern was acute lethality 
(Houseknecht, 1993). Because toxicological tests are usually conducted 
using constant exposures, the most realistic comparisons between 
exposure and effects are made when exposure in the real world does not 
vary substantially. In these cases, the arithmetic average exposure 
over the time period of toxicological significance is the appropriate 
statistic (U.S. EPA, 1992b). However, as concentrations or contact 
rates become more episodic or variable, the arithmetic average may not 
reflect the toxicologically significant aspect of the exposure pattern. 
In extreme cases, averaging may not be appropriate at all, and 
assessors may need to use a toxicodynamic model to assess chronic 
effects.
    Spatial extent is another dimension of exposure. It is most 
commonly expressed in terms of area (e.g., hectares of paved habitat, 
square meters that exceed a particular chemical threshold). At larger 
spatial scales, however, the shape or arrangement of exposure may be an 
important issue, and area alone may not be the appropriate descriptor 
of spatial extent for risk assessment. A general solution to the 
problem of incorporating pattern into ecological assessments has yet to 
be developed; however, landscape ecology and GIS have greatly expanded 
the options for analyzing and presenting the spatial dimension of 
exposure (e.g., Pastorok et al., 1996).
    The results of exposure analysis are summarized in the exposure 
profile, which is discussed in the next section.
4.2.2. Exposure Profile
    The final product of exposure analysis is an exposure profile. 
Exposure should be described in terms of intensity, space, and time in 
units that can be combined with the effects assessment. The assessor 
should summarize the paths of stressors from the source to the 
receptors, completing the exposure pathway. Depending on the risk 
assessment, the profile may be a written document or a module of a 
larger process model. In any case, the objective is to ensure that the 
information needed for risk characterization has been collected and 
evaluated. In addition, compiling the exposure profile provides an 
opportunity to verify that the important exposure pathways identified 
in the conceptual model were evaluated.
    The exposure profile identifies the receptor and describes the 
exposure pathways and intensity and spatial and temporal extent of co-
occurrence or contact. It also describes the impact of variability and 
uncertainty on exposure estimates and reaches a conclusion about the 
likelihood that exposure will occur (see text note 4-12).
    The profile should describe the applicable exposure pathways. If 
exposure can occur through many pathways, it may be useful to rank 
them, perhaps by contribution to total exposure. As an illustration, 
consider an assessment of risks to grebes feeding in a mercury-
contaminated lake. The grebes may be exposed to methyl mercury in fish 
that originated from historically contaminated sediments. They may also 
be exposed by drinking lake water, but comparing the two exposure 
pathways may show that the fish pathway contributes the vast majority 
of exposure to mercury.
    The profile should identify the ecological entity that the exposure 
estimates represent. For example, the exposure estimates may describe 
the local population of grebes feeding on a specific lake during the 
summer months.
    The assessor should explain how each of the three general 
dimensions of exposure (intensity, time, and space) was treated. 
Continuing with the grebe example, exposure might be expressed as the 
daily potential dose averaged over the summer months and over the 
extent of the lake.
    The profile should also describe how exposure can vary depending on 
receptor attributes or stressor levels. For instance, the exposure may 
be higher for grebes eating a larger proportion of bigger, more 
contaminated fish. Variability can be described by using a distribution 
or by describing where a

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point estimate is expected to fall on a distribution. Cumulative-
distribution functions (CDFs) and probability-density functions (PDFs) 
are two common presentation formats (see Appendix B, figures B-1 and B-
2). Figures 5-3 to 5-5 show examples of cumulative frequency plots of 
exposure data. The point estimate/descriptor approach is used when 
there is not enough information to describe a distribution. Descriptors 
discussed in U.S. EPA, 1992b, are recommended, including central 
tendency to refer to the mean or median of the distribution, high end 
to refer to exposure estimates that are expected to fall between the 
90th and 99.9th percentile of the exposure distribution, and bounding 
estimates to refer to those higher than any actual exposure.
    The exposure profile should summarize important uncertainties 
(e.g., lack of knowledge; see section 4.1.3 for a discussion of the 
different sources of uncertainty). In particular, the assessor should:
     Identify key assumptions and describe how they were 
handled
     Discuss (and quantify, if possible) the magnitude of 
sampling and/or measurement error
     Identify the most sensitive variables influencing exposure
     Identify which uncertainties can be reduced through the 
collection of more data.
    Uncertainty about a quantity's true value can be shown by 
calculating error bounds on a point estimate, as shown in figure 5-2.
    All of the above information is synthesized to reach a conclusion 
about the likelihood that exposure will occur, completing the exposure 
profile. It is one of the products of the analysis phase and is 
combined with the stressor-response profile (the product of the 
ecological effects characterization discussed in the next section) 
during risk characterization.

4.3. Characterization of Ecological Effects

    To characterize ecological effects, the assessor describes the 
effects elicited by a stressor, links them to the assessment endpoints, 
and evaluates how they change with varying stressor levels. The 
characterization begins by evaluating effects data to specify the 
effects that are elicited, verify that they are consistent with the 
assessment endpoints, and confirm that the conditions under which they 
occur are consistent with the conceptual model. Once the effects of 
interest are identified, the assessor conducts an ecological response 
analysis (section 4.3.1), evaluating how the magnitude of the effects 
change with varying stressor levels and the evidence that the stressor 
causes the effect, and then linking the effects with the assessment 
endpoint. Conclusions are summarized in a stressor-response profile 
(section 4.3.2).
4.3.1. Ecological Response Analysis
    Ecological response analysis examines three primary elements: the 
relationship between stressor levels and ecological effects (section 
4.3.1.1), the plausibility that effects may occur or are occurring as a 
result of exposure to stressors (section 4.3.1.2), and linkages between 
measurable ecological effects and assessment endpoints when the latter 
cannot be directly measured (section 4.3.1.3).
4.3.1.1. Stressor-Response Analysis
    To evaluate ecological risks, one must understand the relationships 
between stressors and resulting responses. The stressor-response 
relationships used in a particular assessment depend on the scope and 
nature of the ecological risk assessment as defined in problem 
formulation and reflected in the analysis plan. For example, an 
assessor may need a point estimate of an effect (such as an 
LC50) to compare with point estimates from other stressors. 
The shape of the stressor-response curve may be needed to determine the 
presence or absence of an effects threshold or for evaluating 
incremental risks, or stressor-response curves may be used as input for 
effects models. If sufficient data are available, the risk assessor may 
construct cumulative distribution functions using multiple-point 
estimates of effects. Or the assessor may use process models that 
already incorporate empirically derived stressor-response relationships 
(see section 4.3.1.3). Text note 4-13 provides some questions for 
stressor-response analysis.
    This section describes a range of stressor-response approaches 
available to risk assessors following a theme of variations on the 
classical stressor-response relationship (e.g., figure 4-2). More 
complex relationships are shown in figure 4-3, which illustrates a 
range of projected responses of zooplankton populations to pesticide 
exposure based on laboratory tests. In field studies, the complexity of 
these responses could increase even further, considering factors such 
as potential indirect effects of pesticides on zooplankton populations 
(e.g., competitive interactions between species). More complex patterns 
can also occur at higher levels of biological organization; ecosystems 
may respond to stressors with abrupt shifts to new community or system 
types (Holling, 1978).

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    In simple cases, one response variable (e.g., mortality, incidence 
of abnormalities) is analyzed, and most quantitative techniques have 
been developed for univariate analysis. If the response of interest is 
composed of many individual variables (e.g., species abundances in an 
aquatic community), multivariate techniques may be useful. These have a 
long history of use in ecology (see texts by Gauch, 1982; Pielou, 1984; 
Ludwig and Reynolds, 1988) but have not yet been extensively applied in 
risk assessment. While quantifying stressor-response relationships is 
encouraged, qualitative evaluations are also possible (text note 4-14).
    Stressor-response relationships can be described using intensity, 
time, or space. Intensity is probably the most familiar of these and is 
often used for chemicals (e.g., dose, concentration). Exposure duration 
is also commonly used for chemical stressor-response relationships; for 
example, median acute effects levels are always associated with a time 
parameter (e.g., 24 hours). As noted in text note 4-14, the timing of 
exposure was the critical dimension in evaluating the relationship 
between seed germination and soil moisture (Pearlstine et al., 1985). 
The spatial dimension is often of concern for physical stressors. For 
instance, the extent of suitable habitat was related to the probability 
of sighting a spotted owl (Thomas et al., 1990), and water-table depth 
was related to tree growth by Phipps (1979).
    Single-point estimates and stressor-response curves can be 
generated for some biological stressors. For pathogens such as bacteria 
and fungi, inoculum levels (e.g., spores per milliliter; propagules per 
unit of substrate) may be related to symptoms in a host (e.g., lesions 
per area of leaf surface, total number of plants infected) or actual 
signs of the pathogen (asexual or sexual fruiting bodies, sclerotia, 
etc.). For other biological stressors such as introduced species, 
simple stressor-response relationships may be inappropriate.
    Data from individual experiments can be used to develop curves and 
point estimates both with and without associated uncertainty estimates 
(see figures 5-2 and 5-3). The advantages of curve-fitting approaches 
include using all of the available experimental data and the ability to 
interpolate to values other than the data points measured. If 
extrapolation outside the range of experimental data is required, risk 
assessors should justify that the observed experimental relationships 
remain valid. A disadvantage of curve fitting is that the number of 
data points required to complete an analysis may not always be 
available. For example, while standard toxicity tests with aquatic 
organisms frequently contain sufficient experimental treatments to 
permit regression analysis, this is often not the case for toxicity 
tests with wildlife species.
    Risk assessors sometimes use curve-fitting analyses to determine 
particular levels of effect. These point estimates are interpolated 
from the fitted line. Point estimates may be adequate for simple 
assessments or comparative studies of risk and are also useful if a 
decision rule for the assessment was identified during the planning 
phase (see section 2). Median effect levels (text note 4-15) are 
frequently selected because the level of uncertainty is minimized at 
the midpoint of the regression curve. While a 50% effect level for an 
endpoint such as survival may not be appropriately protective for the 
assessment endpoint, median effect levels can be used for preliminary 
assessments or comparative purposes, especially when used in 
combination with uncertainty modifying factors (see text note 5-3). 
Selection of a different effect level (10%, 20%, etc.) can be arbitrary 
unless there is some clearly defined benchmark for the assessment 
endpoint. Thus, it is preferable to carry several levels of effect or 
the entire stressor-response curve forward to risk estimation.
    When risk assessors are particularly interested in effects at lower 
stressor levels, they may seek to establish ``no-effect'' stressor 
levels based on comparisons between experimental treatments and 
controls. Statistical hypothesis testing is frequently used for this 
purpose. (Note that statistical hypotheses are different from the risk 
hypotheses discussed in problem formulation; see text note 3-12). An 
example of this approach for deriving chemical no-effect levels is 
provided in text note 4-16. A feature of statistical hypothesis testing 
is that the risk assessor is not required to pick a particular effect 
level of concern. The no-effect level is determined instead by 
experimental conditions such as the number of replicates as well as the 
variability inherent in the data. Thus it is important to consider the 
level of effect detectable in the experiment (i.e., its power) in 
addition to reporting the no-effect level. Another drawback of this 
approach is that it is difficult to evaluate effects associated with 
stressor levels other than the actual treatments tested. Several 
investigators (Stephan and Rogers, 1985; Suter, 1993a) have proposed 
using regression analysis as an alternative to statistical hypothesis 
testing.
    In observational field studies, statistical hypothesis testing is 
often used to compare site conditions with a reference site(s). The 
difficulties of drawing proper conclusions from these types of studies 
(which frequently cannot employ replication) have been discussed by 
many investigators (see section 4.1.1). Risk assessors should examine 
whether sites were carefully matched to minimize differences other than 
the stressor and consider whether potential covariates should be 
included in any analysis. In contrast with observational studies, an 
advantage of experimental field studies is that treatments can be 
replicated, increasing the confidence that observed differences are due 
to the treatment.
    Experimental data can be combined to generate multiple-point 
estimates that can be displayed as cumulative distribution functions. 
Figure 5-5 shows an example for species sensitivity derived from 
multiple-point estimates (EC5s) for freshwater algae (and 
one vascular plant species) exposed to an herbicide. These 
distributions can help identify stressor levels that affect a minority 
or majority of species. A limiting factor in the use of cumulative 
frequency distributions is the amount of data needed as input. 
Cumulative effects distribution functions can also be derived from 
models that use Monte Carlo or other methods to generate distributions 
based on measured or estimated variation in input parameters for the 
models.
    When multiple stressors are present, stressor-response analysis is 
particularly challenging. Stressor-response relationships can be 
constructed for each stressor separately and then combined. 
Alternatively, the relationship between response and the suite of 
stressors can be combined in one analysis. It is preferable to directly 
evaluate complex chemical mixtures present in environmental media 
(e.g., wastewater effluents, contaminated soils (U.S. EPA, 1986a)), but 
it is important to consider the relationship between the samples tested 
and the potential spatial and temporal variability in the mixture. The 
approach taken for multiple stressors depends on the feasibility of 
measuring them and whether an objective of the assessment is to project 
different stressor combinations.
    In some cases, multiple regression analysis can be used to 
empirically relate multiple stressors to a response. Detenbeck (1994) 
used this approach to evaluate change in the water quality of wetlands 
resulting from multiple physical stressors. Multiple regression

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analysis can be difficult to interpret if the explanatory variables 
(i.e., the stressors) are not independent. Principal components 
analysis can be used to extract independent explanatory variables 
formed from linear combinations of the original variables (Pielou, 
1984).
4.3.1.2. Establishing Cause-and-Effect Relationships (Causality)
    Causality is the relationship between cause (one or more stressors) 
and effect (response to the stressor(s)). Without a sound basis for 
linking cause and effect, uncertainty in the conclusions of an 
ecological risk assessment is likely to be high. Developing causal 
relationships is especially important for risk assessments driven by 
observed adverse ecological effects such as bird or fish kills or a 
shift in the species composition of an area. This section describes 
considerations for evaluating causality based on criteria developed by 
Fox (1991) primarily for observational data and additional criteria for 
experimental evaluation of causality modified from Koch's postulates 
(e.g., see Woodman and Cowling, 1987).
    Evidence of causality may be derived from observational evidence 
(e.g., bird kills are associated with field application of a pesticide) 
or experimental data (laboratory tests with the pesticides in question 
show bird kills at levels similar to those found in the field), and 
causal associations can be strengthened when both types of information 
are available. But since not all situations lend themselves to formal 
experimentation, scientists have looked for other criteria, based 
largely on observation rather than experiment, to support a plausible 
argument for cause and effect. Text note 4-17 provides criteria based 
on Fox (1991) that are very similar to others reviewed by Fox (U.S. 
Department of Health, Education, and Welfare, 1964; Hill, 1965; Susser, 
1986a, b). While data to support some criteria may be incomplete or 
missing for any given assessment, these criteria offer a useful way to 
evaluate available information.
    The strength of association between stressor and response is often 
the main reason that adverse effects such as bird kills are linked to 
specific events or actions. A stronger response to a hypothesized cause 
is more likely to indicate true causation. Additional strong evidence 
of causation is when a response follows after a change in the 
hypothesized cause (predictive performance).
    The presence of a biological gradient or stressor-response 
relationship is another important criterion for causality. The 
stressor-response relationship need not be linear. It can be a 
threshold, sigmoidal, or parabolic phenomenon, but in any case it is 
important that it can be demonstrated. Biological gradients, such as 
effects that decrease with distance from a toxic discharge, are 
frequently used as evidence of causality. To be credible, such 
relationships should be consistent with current biological or 
ecological knowledge (biological plausibility).
    A cause-and-effect relationship that is demonstrated repeatedly 
(consistency of association) provides strong evidence of causality. 
Consistency may be shown by a greater number of instances of 
association between stressor and response, occurrences in diverse 
ecological systems, or associations demonstrated by diverse methods 
(Hill, 1965). Fox (1991) adds that in ecoepidemiology, an association's 
occurrence in more than one species and population is very strong 
evidence for causation. An example would be the many bird species 
killed by carbofuran applications (Houseknecht, 1993). Fox (1991) also 
believes that causality is supported if the same incident is observed 
by different persons under different circumstances and at different 
times.
    Conversely, inconsistency in association between stressor and 
response is strong evidence against causality (e.g., the stressor is 
present without the expected effect, or the effect occurs but the 
stressor is not found). Temporal incompatibility (i.e., the presumed 
cause does not precede the effect) and incompatibility with 
experimental or observational evidence (factual implausibility) are 
also indications against a causal relationship.
    Two other criteria may be of some help in defining causal 
relationships: specificity of an association and probability. The more 
specific or diagnostic the effect, the more likely it is to have a 
consistent cause. However, Fox (1991) argues that effect specificity 
does little to strengthen a causal claim. Disease can have multiple 
causes, a substance can behave differently in different environments or 
cause several different effects, and biochemical events may elicit many 
biological responses. But in general, the more specific or localized 
the effects, the easier it is to identify the cause. Sometimes, a 
stressor may have a distinctive mode of action that suggests its role. 
Yoder and Rankin (1995) found that patterns of change observed in fish 
and benthic invertebrate communities could serve as indicators for 
different types of anthropogenic impact (e.g., nutrient enrichment vs. 
toxicity).
    For some pathogenic biological stressors, the causal evaluations 
proposed by Koch (see text note 4-18) may be useful. For chemicals, 
ecotoxicologists have slightly modified Koch's postulates to provide 
evidence of causality (Suter, 1993a). The modifications are:
     The injury, dysfunction, or other putative effect of the 
toxicant must be regularly associated with exposure to the toxicant and 
any contributory causal factors.
     Indicators of exposure to the toxicant must be found in 
the affected organisms.
     The toxic effects must be seen when organisms or 
communities are exposed to the toxicant under controlled conditions, 
and any contributory factors should be manifested in the same way 
during controlled exposures.
     The same indicators of exposure and effects must be 
identified in the controlled exposures as in the field.
    These modifications are conceptually identical to Koch's 
postulates. While useful, this approach may not be practical if 
resources for experimentation are not available or if an adverse effect 
may be occurring over such a wide spatial extent that experimentation 
and correlation may prove difficult or yield equivocal results.
    Woodman and Cowling (1987) provide a specific example of a causal 
evaluation. They proposed three rules for establishing the effects of 
airborne pollutants on the health and productivity of forests: (1) The 
injury or dysfunction symptoms observed in the case of individual trees 
in the forest must be associated consistently with the presence of the 
suspected causal factors, (2) the same injury or dysfunction symptoms 
must be seen when healthy trees are exposed to the suspected causal 
factors under controlled conditions, and (3) natural variation in 
resistance and susceptibility observed in forest trees also must be 
seen when clones of the same trees are exposed to the suspected causal 
factors under controlled conditions.
    Experimental techniques are frequently used for evaluating 
causality in complex chemical mixtures. Options include evaluating 
separated components of the mixture, developing and testing a synthetic 
mixture, or determining how a mixture's toxicity relates to that of 
individual components. The choice of method depends on the goal of the 
assessment and the resources and test data that are available.

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    Laboratory toxicity identification evaluations (TIEs) can be used 
to help determine which components of a chemical mixture cause toxic 
effects. By using fractionation and other methods, the TIE approach can 
help identify chemicals responsible for toxicity and show the relative 
contributions of different chemicals in aqueous effluents (U.S. EPA, 
1988a, 1989b, c) and sediments (e.g., Ankley et al., 1990).
    Risk assessors may utilize data from synthetic chemical mixtures if 
the individual chemical components are well characterized. This 
approach allows for manipulation of the mixture and investigation of 
how varying the components that are present or their ratios may affect 
mixture toxicity, but it also requires additional assumptions about the 
relationship between effects of the synthetic mixture and those of the 
environmental mixture. (See section 5.1.3 for additional discussion of 
mixtures.)
4.3.1.3. Linking Measures of Effect to Assessment Endpoints
    Assessment endpoints express the environmental values of concern 
for a risk assessment, but they cannot always be measured directly. 
When measures of effect differ from assessment endpoints, sound and 
explicit linkages between them are needed. Risk assessors may make 
these linkages in the analysis phase or, especially when linkages rely 
on professional judgment, work with measures of effect through risk 
estimation (in risk characterization) and then connect them with 
assessment endpoints. Common extrapolations used to link measures of 
effect with assessment endpoints are shown in text note 4-19.
4.3.1.3.1. General Considerations
    During the preparation of the analysis plan, risk assessors 
identify the extrapolations required between assessment endpoints and 
measures of effect. During the analysis phase, risk assessors should 
revisit the questions listed in text note 4-20 before proceeding with 
specific extrapolation approaches.
    The nature of the risk assessment and the type and amount of data 
that are available largely determine how conservative a risk assessment 
will be. The early stages of a tiered risk assessment typically use 
conservative estimates because the data needed to adequately assess 
exposure and effects are usually lacking. When a risk has been 
identified, subsequent tiers use additional data to address the 
uncertainties that were incorporated into the initial assessment(s) 
(see text note 2-8).
    The scope of the risk assessment also influences extrapolation 
through the nature of the assessment endpoint. Preliminary assessments 
that evaluate risks to general trophic levels such as herbivores may 
extrapolate between different genera or families to obtain a range of 
sensitivity to the stressor. On the other hand, assessments concerned 
with management strategies for a particular species may employ 
population models.
    Analysis phase activities may suggest additional extrapolation 
needs. Evaluation of exposure may indicate different spatial or 
temporal scales than originally planned. If spatial scales are 
broadened, additional receptors may need to be included in 
extrapolation models. If a stressor persists for an extended time, it 
may be necessary to extrapolate short-term responses over a longer 
exposure period, and population-level effects may become more 
important. Whatever methods are employed to link assessment endpoints 
with measures of effect, it is important to apply them in a manner 
consistent with sound ecological principles and use enough appropriate 
data. For example, it is inappropriate to use structure-activity 
relationships to predict toxicity from chemical structure unless the 
chemical under consideration has a similar mode of toxic action to the 
reference chemicals (Bradbury, 1994). Similarly, extrapolations between 
two species may be more credible if factors such as similarities in 
food preferences, body mass, physiology, and seasonal behavior (e.g., 
mating and migration habits) are considered (Sample et al., 1996). Rote 
or biologically implausible extrapolations will erode the assessment's 
overall credibility.
    Finally, many extrapolation methods are limited by the availability 
of suitable databases. Although many data are available for chemical 
stressors and aquatic species, they do not exist for all taxa or 
effects. Chemical effects databases for wildlife, amphibians, and 
reptiles are extremely limited, and there is even less information on 
most biological and physical stressors. Risk assessors should be aware 
that extrapolations and models are only as useful as the data on which 
they are based and should recognize the great uncertainties associated 
with extrapolations that lack an adequate empirical or process-based 
rationale.
    The rest of this section addresses the approaches used by risk 
assessors to link measures of effect to assessment endpoints, as noted 
below.
     Linkages based on professional judgment. This is not as 
desirable as empirical or process-based approaches, but is the only 
option when data are lacking.
     Linkages based on empirical or process models. Empirical 
extrapolations use experimental or observational data that may or may 
not be organized into a database. Process-based approaches rely on some 
level of understanding of the underlying operations of the system of 
interest.
4.3.1.3.2. Judgment Approaches for Linking Measures of Effect to 
Assessment Endpoints.
    Professional-judgment approaches rely on the professional expertise 
of risk assessors, expert panels, or others to relate changes in 
measures of effect to changes in assessment endpoints. They are 
essential when databases are inadequate to support empirical models and 
process models are unavailable or inappropriate. Professional-judgment 
linkages between measures of effect and assessment endpoints can be 
just as credible as empirical or process-based expressions, provided 
they have a sound scientific basis. This section highlights 
professional-judgment extrapolations between species, from laboratory 
data to field effects, and between geographic areas.
    Because of the uncertainty in predicting the effects of biological 
stressors such as introduced species, professional-judgment approaches 
are commonly used. For example, there may be measures of effect data on 
a foreign pathogen that attacks a certain tree species not found in the 
United States, but the assessment endpoint concerns the survival of a 
commercially important tree found only in the United States. In this 
case, a careful evaluation and comparison of the life history and 
environmental requirements of both the pathogen and the two tree 
species may contribute toward a useful determination of potential 
effects, even though the uncertainty may be high. Expert panels are 
typically used for this kind of evaluation (USDA, 1993).
    Risks to organisms in field situations are best estimated from 
studies at the site of interest. However, such data are not always 
available. Frequently, risk assessors must extrapolate from laboratory 
toxicity test data to field effects. Text note 4-21 summarizes some of 
the considerations for risk assessors when extrapolating from 
laboratory test results to field situations for chemical stressors. 
Factors altering exposure in the field are among the most important 
factors limiting extrapolations from laboratory test

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results, but indirect effects on exposed organisms due to predation, 
competition, or other biotic or abiotic factors not evaluated in the 
laboratory may also be significant. Variations in direct chemical 
effects between laboratory tests and field situations may not 
contribute as much to the overall uncertainty of the extrapolation.
    In addition to single-species tests, laboratory multiple-species 
tests are sometimes used to predict field effects. While these tests 
have the advantage of evaluating some aspects of a real ecological 
system, they also have inherent scale limitations (e.g., lack of top 
trophic levels) and may not adequately represent features of the field 
system important to the assessment endpoint.
    Extrapolations based on professional judgment are frequently 
required when assessors wish to use field data obtained from one 
geographic area and apply them to a different area of concern, or to 
extrapolate from the results of laboratory tests to more than one 
geographic region. In either case, risk assessors should consider 
variations between regions in environmental conditions, spatial scales 
and heterogeneities, and ecological forcing functions (see below).
    Variations in environmental conditions in different geographic 
regions may alter stressor exposure and effects. If exposures to 
chemical stressors can be accurately estimated and are expected to be 
similar (e.g., see text note 4-21), the same species in different areas 
may respond similarly. For example, if the pesticide granular 
carbofuran were applied at comparable rates throughout the country, 
seed-eating birds could be expected to be similarly affected by the 
pesticide (Houseknecht, 1993). Nevertheless, the influence of 
environmental conditions on stressor exposure and effects can be 
substantial.
    For biological stressors, environmental conditions such as climate, 
habitat, and suitable hosts play major roles in determining whether a 
biological stressor becomes established. For example, climate would 
prevent establishment of the Mediterranean fruit fly in the much colder 
northeastern United States. Thus, a thorough evaluation of 
environmental conditions in the area versus the natural habitat of the 
stressor is important. Even so, many biological stressors can adapt 
readily to varying environmental conditions, and the absence of natural 
predators or diseases may play an even more important role than abiotic 
factors.
    For physical stressors that have natural counterparts, such as 
fire, flooding, or temperature variations, effects may depend on the 
difference between human-caused and natural variations in these 
parameters for a particular region. Thus, the comparability of two 
regions depends on both the pattern and range of natural disturbances.
    Spatial scales and heterogeneities affect comparability between 
regions. Effects observed over a large scale may be difficult to 
extrapolate from one geographical location to another, mainly because 
the spatial heterogeneity is likely to differ. Factors such as number 
and size of land-cover patches, distance between patches, connectivity 
and conductivity of patches (e.g., migration routes), and patch shape 
may be important. Extrapolations can be strengthened by using 
appropriate reference sites, such as sites in comparable ecoregions 
(Hughes, 1995).
    Ecological forcing functions may differ between geographic regions. 
Forcing functions are critical abiotic variables that exert a major 
influence on the structure and function of ecological systems. Examples 
include temperature fluctuations, fire frequency, light intensity, and 
hydrologic regime. If these differ significantly between sites, it may 
be inappropriate to extrapolate effects from one system to another.
    Bedford and Preston (1988), Detenbeck et al. (1992), Gibbs (1993), 
Gilbert (1987), Gosselink et al. (1990), Preston and Bedford (1988), 
and Risser (1988) may be useful to risk assessors concerned with 
effects in different geographical areas.
4.3.1.3.3. Empirical and Process-Based Approaches for Linking Measures 
of Effect to Assessment Endpoints
    A variety of empirical and process-based approaches are available 
to risk assessors, depending on the scope of the assessment and the 
data and resources available. Empirical and process-based approaches 
include numerical extrapolations between measures of effects and 
assessment endpoints. These linkages range in sophistication from 
applying an uncertainty factor to using a complex model requiring 
extensive measures of effects and measures of ecosystem and receptor 
characteristics as input. But even the most sophisticated quantitative 
models involve qualitative elements and assumptions and thus require 
professional judgment for evaluation. Individuals who use models and 
interpret their results should be familiar with the underlying 
assumptions and components contained in the model.
4.3.1.3.3.1. Empirical Approaches
    Empirical approaches are derived from experimental data or 
observations Empirically based uncertainty factors or taxonomic 
extrapolations may be used when adequate effects databases are 
available but the understanding of underlying mechanisms of action or 
ecological principles is limited. When sufficient information on 
stressors and receptors is available, process-based approaches such as 
pharmacokinetic/pharmacodynamic models or population or ecosystem 
process models may be used. Regardless of the options used, risk 
assessors should justify and adequately document the approach selected.
    Uncertainty factors are used to ensure that measures of effects are 
sufficiently protective of assessment endpoints. Uncertainty factors 
are empirically derived numbers that are divided into measure of 
effects values to give an estimated stressor level that should not 
cause adverse effects to the assessment endpoint. Uncertainty factors 
have been developed most frequently for chemicals because extensive 
ecotoxicologic databases are available, especially for aquatic 
organisms. Uncertainty factors are useful when decisions must be made 
about stressors in a short time and with little information.
    Uncertainty factors have been used to compensate for assessment 
endpoint/effect measures differences between endpoints (acute to 
chronic effects), between species, and between test situations (e.g., 
laboratory to field). Typically, they vary inversely with the quantity 
and type of measures of effects data available (Zeeman, 1995). They 
have been used in screening-level assessments of new chemicals 
(Nabholz, 1991), in assessing the risks of pesticides to aquatic and 
terrestrial organisms (Urban and Cook, 1986), and in developing 
benchmark dose levels for human health effects (U.S. EPA, 1995c).
    Despite their usefulness, uncertainty factors can also be misused, 
especially when used in an overly conservative fashion, as when chains 
of factors are multiplied together without sufficient justification. 
Like other approaches to bridging data gaps, uncertainty factors are 
often based on a combination of scientific analysis, scientific 
judgment, and policy judgment (see section 4.1.3). It is important to 
differentiate these three elements when documenting the basis for the 
uncertainty factors used.
    Empirical data can be used to facilitate extrapolations between 
species, genera, families, or orders or functional groups (e.g., 
feeding guilds)

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(Suter, 1993a). Suter et al. (1983), Suter (1993a), and Barnthouse et 
al. (1987, 1990) developed methods to extrapolate toxicity between 
freshwater and marine fish and arthropods. As Suter notes (1993a), the 
uncertainties associated with extrapolating between orders, classes, 
and phyla tend to be very high. However, one can extrapolate with fair 
certainty between aquatic species within genera and genera within 
families. Further applications of this approach (e.g., for chemical 
stressors and terrestrial organisms) are limited by a lack of suitable 
databases.
    In addition to taxonomic databases, dose-scaling or allometric 
regression is used to extrapolate the effects of a chemical stressor to 
another species. Allometry is the study of change in the proportions of 
various parts of an organism as a consequence of growth and 
development. Processes that influence toxicokinetics (e.g., renal 
clearance, basal metabolic rate, food consumption) tend to vary across 
species according to allometric scaling factors that can be expressed 
as a nonlinear function of body weight. These scaling factors can be 
used to estimate bioaccumulation and to improve interspecies 
extrapolations (Newman, 1995; Kenaga, 1973; U.S. EPA 1992c, 1995d). 
Although allometric relationships are commonly used for human health 
risk assessments, they have not been applied as extensively to 
ecological effects (Suter, 1993a). For chemical stressors, allometric 
relationships can enable an assessor to estimate toxic effects to 
species not commonly tested, such as native mammals. It is important 
that the assessor consider the taxonomic relationship between the known 
species and the one of interest. The closer they are related, the more 
likely the toxic response will be similar. Allometric approaches should 
not be applied to species that differ greatly in uptake, metabolism, or 
depuration of a chemical.
4.3.1.3.3.2. Process-Based Approaches
    Process models for extrapolation are representations or 
abstractions of a system or process (Starfield and Bleloch, 1991) that 
incorporate causal relationships and provide a predictive capability 
that does not depend on the availability of existing stressor-response 
information as empirical models do (Wiegert and Bartell, 1994). Process 
models enable assessors to translate data on individual effects (e.g., 
mortality, growth, and reproduction) to potential alterations in 
specific populations, communities, or ecosystems. Such models can be 
used to evaluate risk hypotheses about the duration and severity of a 
stressor on an assessment endpoint that cannot be tested readily in the 
laboratory.
    There are two major types of models: Single-species population 
models and multispecies community and ecosystem models. Population 
models describe the dynamics of a finite group of individuals through 
time and have been used extensively in ecology and fisheries management 
and to assess the impacts of power plants and toxicants on specific 
fish populations (Barnthouse et al., 1987, 1990). They can help answer 
questions about short- or long-term changes of population size and 
structure and can help estimate the probability that a population will 
decline below or grow above a specified abundance (Ginzburg et al., 
1982; Ferson et al., 1989). The latter application may be useful when 
assessing the effects of biological stressors such as introduced or 
pest species. Barnthouse et al. (1986) and Wiegert and Bartell (1994) 
present excellent reviews of population models. Emlen (1989) has 
reviewed population models that can be used for terrestrial risk 
assessment.
    Proper use of population models requires a thorough understanding 
of the natural history of the species under consideration, as well as 
knowledge of how the stressor influences its biology. Model input can 
include somatic growth rates, physiological rates, fecundity, survival 
rates of various classes within the population, and how these change 
when the population is exposed to the stressor and other environmental 
factors. In addition, the effects of population density on these 
parameters are important (Hassell, 1986) and should be considered in 
the uncertainty analysis.
    Community and ecosystem models (e.g., Bartell et al., 1992; O'Neill 
et al., 1982) are particularly useful when the assessment endpoint 
involves structural (e.g., community composition) or functional (e.g., 
primary production) elements. They can also be useful when secondary 
effects are of concern. Changes in various community or ecosystem 
components such as populations, functional types, feeding guilds, or 
environmental processes can be estimated. By incorporating submodels 
describing the dynamics of individual system components, these models 
permit evaluation of risk to multiple assessment endpoints within the 
context of the ecosystem.
    Risk assessors should determine the appropriate degree of 
aggregation in population or multispecies model parameters based both 
on the input data available and on the desired output of the model 
(also see text note 4-5). For example, if a decision is required about 
a particular species, a model that lumps species into trophic levels or 
feeding guilds will not be very useful. Assumptions concerning 
aggregation in model parameters should be included in the uncertainty 
discussion.
4.3.2. Stressor-Response Profile
    The final product of ecological response analysis is a summary 
profile of what has been learned. This may be a written document or a 
module of a larger process model. In any case, the objective is to 
ensure that the information needed for risk characterization has been 
collected and evaluated. A useful approach in preparing the stressor-
response profile is to imagine that it will be used by someone else to 
perform the risk characterization. Profile compilation also provides an 
opportunity to verify that the assessment endpoints and measures of 
effect identified in the conceptual model were evaluated.
    Risk assessors should address several questions in the stressor-
response profile (text note 4-22). Affected ecological entities may 
include single species, populations, general trophic levels, 
communities, ecosystems, or landscapes. The nature of the effect(s) 
should be germane to the assessment endpoint(s). Thus if a single 
species is affected, the effects should represent parameters 
appropriate for that level of organization. Examples include effects on 
mortality, growth, and reproduction. Short- and long-term effects 
should be reported as appropriate. At the community level, effects may 
be summarized in terms of structure or function depending on the 
assessment endpoint. At the landscape level, there may be a suite of 
assessment endpoints, and each should be addressed separately.
    Examples of different approaches for displaying the intensity of 
effects were provided in section 4.3.1.1. Other information such as the 
spatial area or time to recovery may also be appropriate. Causal 
analyses are important, especially for assessments that include field 
observational data.
    Ideally, the stressor-response profile should express effects in 
terms of the assessment endpoint, but this is not always possible. 
Where it is necessary to use qualitative extrapolations between 
assessment endpoints and measures of effect, the stressor-response 
profile may contain information only on measures of effect. Under these 
circumstances, risk will be estimated using the measures of effects, 
and extrapolation to the

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assessment endpoints will occur during risk characterization.
    Risk assessors need to clearly describe any uncertainties 
associated with the ecological response analysis. If it was necessary 
to extrapolate from measures of effect to the assessment endpoint, both 
the extrapolation and its basis should be described. Similarly, if a 
benchmark or similar reference dose or concentration was calculated, 
the extrapolations and uncertainties associated with its development 
need to be discussed. For additional information on establishing 
reference concentrations, see Nabholz (1991), Urban and Cook (1986), 
Stephan et al. (1985), Van Leeuwen et al. (1992), Wagner and Lokke 
(1991), and Okkerman et al. (1993). Finally, the assessor should 
clearly describe major assumptions and default values used in the 
models.
    At the end of the analysis phase, the stressor-response and 
exposure profiles are used to estimate risks. These profiles provide 
the opportunity to review what has been learned and to summarize this 
information in the most useful format for risk characterization. 
Whatever form the profiles take, they ensure that the necessary 
information is available for risk characterization.

5. Risk Characterization

    Risk characterization (figure 5-1) is the final phase of ecological 
risk assessment and is the culmination of the planning, problem 
formulation, and analysis of predicted or observed adverse ecological 
effects related to the assessment endpoints. Completing risk 
characterization allows risk assessors to clarify the relationships 
between stressors, effects, and ecological entities and to reach 
conclusions regarding the occurrence of exposure and the adversity of 
existing or anticipated effects. Here, risk assessors first use the 
results of the analysis phase to develop an estimate of the risk posed 
to the ecological entities included in the assessment endpoints 
identified in problem formulation (section 5.1). After estimating the 
risk, the assessor describes the risk estimate in the context of the 
significance of any adverse effects and lines of evidence supporting 
their likelihood (section 5.2). Finally, the assessor identifies and 
summarizes the uncertainties, assumptions, and qualifiers in the risk 
assessment and reports the conclusions to risk managers (section 5.3).

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    Conclusions presented in the risk characterization should provide 
clear information to risk managers in order to be useful for 
environmental decision making (NRC, 1994; see section 6). If the risks 
are not sufficiently defined to support a management decision, risk 
managers may elect to proceed with another iteration of one or more 
phases of the risk assessment process. Reevaluating the conceptual 
model (and associated risk hypotheses) or conducting additional studies 
may improve the risk estimate. Alternatively, a monitoring program may 
help managers evaluate the consequences of a risk management decision.

5.1. Risk Estimation

    Risk estimation is the process of integrating exposure and effects 
data and evaluating any associated uncertainties. The process uses 
exposure and stressor-response profiles developed according to the 
analysis plan (section 3.5). Risk estimates can be developed using one 
or more of the following techniques: (1) Field observational studies, 
(2) categorical rankings, (3) comparisons of single-point exposure and 
effects estimates, (4) comparisons incorporating the entire stressor-
response relationship, (5) incorporation of variability in exposure 
and/or effects estimates, and (6) process models that rely partially or 
entirely on theoretical approximations of exposure and effects. These 
techniques are described in the following sections.
5.1.1. Results of Field Observational Studies
    Field observational studies (surveys) can serve as risk estimation 
techniques because they provide empirical evidence linking exposure to 
effects. Field surveys measure biological changes in natural settings 
through collection of exposure and effects data for ecological entities 
identified in problem formulation.
    A major advantage of field surveys is that they can be used to 
evaluate multiple stressors and complex ecosystem relationships that 
cannot be replicated in the laboratory. Field surveys are designed to 
delineate both exposures and effects (including secondary effects) 
found in natural systems, whereas estimates generated from laboratory 
studies generally delineate either exposures or effects under 
controlled or prescribed conditions (see text note 5-1).
    While field studies may best represent reality, as with other kinds 
of studies they can be limited by (1) a lack of replication, (2) bias 
in obtaining representative samples, or (3) failure to measure critical 
components of the system or random variations. Further, a lack of 
observed effects in a field survey may occur because the measurements 
lack the sensitivity to detect ecological effects. See section 4.1.1 
for additional discussion of the strengths and limitations of different 
types of data.
    Several assumptions or qualifications need to be clearly 
articulated when describing the results of field surveys. A primary 
qualification is whether a causal relationship between stressors and 
effects (section 4.3.1.2) is supported. Unless causal relationships are 
carefully examined, conclusions about effects that are observed may be 
inaccurate because the effects are caused by factors unrelated to the 
stressor(s) of concern. In addition, field surveys taken at one point 
in time are usually not predictive; they describe effects associated 
only with exposure scenarios associated with past and existing 
conditions.
5.1.2. Categories and Rankings
    In some cases, professional judgment or other qualitative 
evaluation techniques may be used to rank risks using categories, such 
as low, medium, and high, or yes and no. This approach is most 
frequently used when exposure and effects data are limited or are not 
easily expressed in quantitative terms. The U.S. Forest Service risk 
assessment of pest introduction from importation of logs from Chile 
used qualitative categories owing to limitations in both the exposure 
and effects data for the introduced species of concern as well as the 
resources available for the assessment (see text note 5-2).
    Ranking techniques can be used to translate qualitative judgment 
into a mathematical comparison. These methods are frequently used in 
comparative risk exercises. For example, Harris et al. (1994) evaluated 
risk reduction opportunities in Green Bay (Lake Michigan), Wisconsin, 
employing an expert panel to compare the relative risk of several 
stressors against their potential effects. Mathematical analysis based 
on fuzzy set theory was used to rank the risk from each stressor from a 
number of perspectives, including degree of immediate risk, duration of 
impacts, and prevention and remediation management. The results served 
to rank potential environmental risks from stressors based on best 
professional judgment.
5.1.3. Single-Point Exposure and Effects Comparisons
    When sufficient data are available to quantify exposure and effects 
estimates, the simplest approach for comparing the estimates is a ratio 
(figure 5-2a). Typically, the ratio (or quotient) is expressed as an 
exposure concentration divided by an effects concentration. Quotients 
are commonly used for chemical stressors, where reference or benchmark 
toxicity values are widely available (see text note 5-3).

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    The principal advantages of the quotient method are that it is 
simple and quick to use and risk assessors and managers are familiar 
with its application. It provides an efficient, inexpensive means of 
identifying high-or low-risk situations that can allow risk management 
decisions to be made without the need for further information.
    Quotients have also been used to integrate the risks of multiple 
chemical stressors: quotients for the individual constituents in a 
mixture are generated by dividing each exposure level by a 
corresponding toxicity endpoint (e.g., LC50, 
EC50, NOAEL). Although the toxicity of a chemical mixture 
may be greater than or less than predicted from the toxicities of 
individual constituents of the mixture, a quotient addition approach 
assumes that toxicities are additive or approximately additive. This 
assumption may be most applicable when the modes of action of chemicals 
in a mixture are similar, but there is evidence that even with 
chemicals having dissimilar modes of action, additive or near-additive 
interactions are common (Konemann, 1981; Broderius, 1991; Broderius et 
al., 1995; Hermens et al., 1984a, b; McCarty and Mackay, 1993; Sawyer 
and Safe, 1985). However, caution should be used when assuming that 
chemicals in a mixture act independently of one another, since many of 
the supporting studies were conducted with aquatic organisms, and so 
may not be relevant for other endpoints, exposure scenarios, or 
species. When the modes of action for constituent chemicals are 
unknown, the assumptions and rationale concerning chemical interactions 
should be clearly stated.
    A number of limitations restrict application of the quotient method 
(see Smith and Cairns, 1993; Suter, 1993a). While a quotient can be 
useful in answering whether risks are high or low, it may not be 
helpful to a risk manager who needs to make a decision requiring an 
incremental quantification of risks. For example, it is seldom useful 
to say that a risk mitigation approach will reduce a quotient value 
from 25 to 12, since this reduction cannot by itself be clearly 
interpreted in terms of effects on an assessment endpoint.
    Other limitations of quotients may be caused by deficiencies in the 
problem formulation and analysis phases. For example, an 
LC50 derived from a 96-hour laboratory test using constant 
exposure levels may not be appropriate for an assessment of effects on 
reproduction resulting from short-term, pulsed exposures.
    In addition, the quotient method may not be the most appropriate 
method for predicting secondary effects (although such effects may be 
inferred). Interactions and effects beyond what are predicted from the 
simple quotient may be critical to characterizing the full extent of 
impacts from exposure to the stressors (e.g., bioaccumulation, 
eutrophication, loss of prey species, opportunities for invasive 
species).
    Finally, in most cases, the quotient method does not explicitly 
consider uncertainty (e.g., extrapolation from tested species to the 
species or community of concern). Some uncertainties, however, can be 
incorporated into single-point estimates to provide a statement of 
likelihood that the effects point estimate exceeds the exposure point 
estimate (figures 5-2b and 5-3). If exposure variability is quantified, 
then the point estimate of effects can be compared with a cumulative 
exposure distribution as described in text note 5-4. Further discussion 
of comparisons between point estimates of effects and distributions of 
exposure may be found in Suter et al., 1983.
    In view of the advantages and limitations of the quotient method, 
it is important for risk assessors to consider the points listed below 
when evaluating quotient method estimates.
     How does the effect concentration relate to the assessment 
endpoint?
     What extrapolations are involved?
     How does the point estimate of exposure relate to 
potential spatial and temporal variability in exposure?
     Are data sufficient to provide confidence intervals on the 
endpoints?
5.1.4. Comparisons Incorporating the Entire Stressor-Response 
Relationship
    If a curve relating the stressor level to the magnitude of response 
is available, then risk estimation can examine risks associated with 
many different levels of exposure (figure 5-4). These estimates are 
particularly useful when the risk assessment outcome is not based on 
exceedance of a predetermined decision rule, such as a toxicity 
benchmark level.
    There are advantages and limitations to comparing a stressor-
response curve with an exposure distribution. The slope of the effects 
curve shows the magnitude of change in effects associated with 
incremental changes in exposure, and the capability to predict changes 
in the magnitude and likelihood of effects for different exposure 
scenarios can be used to compare different risk management options. 
Also, uncertainty can be incorporated by calculating uncertainty bounds 
on the stressor-response or exposure estimates. Comparing exposure and 
stressor-response curves provides a predictive ability lacking in the 
quotient method. Like the quotient method, however, limitations from 
the problem formulation and analysis phases may limit the utility of 
the results. These limitations may include not fully considering 
secondary effects, assuming the exposure pattern used to derive the 
stressor-response curve is comparable to the environmental exposure 
pattern, and failure to consider uncertainties, such as extrapolations 
from tested species to the species or community of concern.
5.1.5. Comparisons Incorporating Variability in Exposure and/or Effects
    If the exposure or stressor-response profiles describe the 
variability in exposure or effects, then many different risk estimates 
can be calculated. Variability in exposure can be used to estimate 
risks to moderately or highly exposed members of a population being 
investigated, while variability in effects can be used to estimate 
risks to average or sensitive population members. A major advantage of 
this approach is its ability to predict changes in the magnitude and 
likelihood of effects for different exposure scenarios and thus provide 
a means for comparing different risk management options. As noted 
above, comparing distributions also allows one to identify and quantify 
risks to different segments of the population. Limitations include the 
increased data requirements compared with previously described 
techniques and the implicit assumption that the full range of 
variability in the exposure and effects data is adequately represented. 
As with the quotient method, secondary effects are not readily 
evaluated with this technique. Thus, it is desirable to corroborate 
risks estimated by distributional comparisons with field studies or 
other lines of evidence. Text note 5-5 and figure 5-5 illustrate the 
use of cumulative exposure and effects distributions for estimating 
risk.

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5.1.6. Application of Process Models
    Process models are mathematical expressions that represent our 
understanding of the mechanistic operation of a system under 
evaluation. They can be useful tools in both analysis (see section 
4.1.2) and risk characterization. For illustrative purposes, it is 
useful to distinguish between analysis process models, which focus 
individually on either exposure or effects evaluations, and risk 
estimation process models, which integrate exposure and effects 
information (see text note 5-6). The assessment of risks associated 
with long-term changes in hydrologic conditions in bottomland forest 
wetlands in Louisiana using the FORFLO model (Appendix D) linked the 
attributes and placement of levees and corresponding water level 
measurements (exposure) with changes in forest community structure and 
wildlife habitat suitability (effects).
    A major advantage of using process models for risk estimation is 
the ability to consider ``what if'' scenarios and to forecast beyond 
the limits of observed data that constrain techniques based solely on 
empirical data. The process model can also consider secondary effects, 
unlike other risk estimation techniques such as the quotient method or 
comparisons of exposure and effect distributions. In addition, some 
process models can forecast the combined effects of multiple stressors, 
such as the effects of multiple chemicals on fish population 
sustainability (Barnthouse et al., 1990).
    Process model outputs may be point estimates, distributions, or 
correlations; in all cases, risk assessors should interpret them with 
care. They may imply a higher level of certainty than is appropriate 
and are all too often viewed without sufficient attention to underlying 
assumptions. The lack of knowledge on basic life histories for many 
species and incomplete knowledge on the structure and function of a 
particular ecosystem is often lost in the model output. Since process 
models are only as good as the assumptions on which they are based, 
they should be treated as hypothetical representations of reality until 
appropriately tested with empirical data. Comparing model results to 
field data provides a check on whether our understanding of the system 
was correct (Johnson, 1995), particularly with respect to the risk 
hypotheses presented in problem formulation.

5.2. Risk Description

    Following preparation of the risk estimate, risk assessors need to 
interpret and discuss the available information about risks to the 
assessment endpoints. Risk description includes an evaluation of the 
lines of evidence supporting or refuting the risk estimate(s) and an 
interpretation of the significance of the adverse effects on the 
assessment endpoints. During the analysis phase, the risk assessor may 
have established the relationship between the assessment endpoints and 
measures of effect and associated lines of evidence in quantifiable, 
easily described terms (section 4.3.1.3). If not, the risk assessor can 
relate the available lines of evidence to the assessment endpoints 
using qualitative links. Regardless of the risk estimation technique, 
the technical narrative supporting the risk estimate is as important as 
the risk estimate itself.
5.2.1. Lines of Evidence
    The development of lines of evidence provides both a process and a 
framework for reaching a conclusion regarding confidence in the risk 
estimate. It is not the kind of proof demanded by experimentalists 
(Fox, 1991), nor is it a rigorous examination of weights of evidence. 
(Note that the term ``weight of evidence'' is sometimes used in legal 
discussions or in other documents, e.g., Urban and Cook, 1986; Menzie 
et al., 1996.) The phrase lines of evidence is used to de-emphasize the 
balancing of opposing factors based on assignment of quantitative 
values to reach a conclusion about a ``weight'' in favor of a more 
inclusive approach, which evaluates all available information, even 
evidence that may be qualitative in nature. It is important that risk 
assessors provide a thorough representation of all lines of evidence 
developed in the risk assessment rather than simply reduce their 
interpretation and description of the ecological effects that may 
result from exposure to stressors to a system of numeric calculations 
and results.
    Confidence in the conclusions of a risk assessment may be increased 
by using several lines of evidence to interpret and compare risk 
estimates. These lines of evidence may be derived from different 
sources or by different techniques relevant to adverse effects on the 
assessment endpoints, such as quotient estimates, modeling results, or 
field observational studies.
    There are three principal categories of factors for risk assessors 
to consider when evaluating lines of evidence: (1) Adequacy and quality 
of data, (2) degree and type of uncertainty associated with the 
evidence, and (3) relationship of the evidence to the risk assessment 
questions (see also sections 3 and 4).
    Data quality directly influences how confident risk assessors can 
be in the results of a study and conclusions they may draw from it. 
Specific concerns to consider for individual lines of evidence include 
whether the experimental design was appropriate for the questions posed 
in a particular study and whether data quality objectives were clear 
and adhered to. An evaluation of the scientific understanding of 
natural variability in the attributes of the ecological entities under 
consideration is important in determining whether there were sufficient 
data to satisfy the analyses chosen and to determine if the analyses 
were sufficiently sensitive and robust to identify stressor-caused 
perturbations.
    Directly related to data quality issues is the evaluation of the 
relative uncertainties of each line of evidence. One major source of 
uncertainty comes from extrapolations. The greater the number of 
extrapolations, the more uncertainty introduced into a study. For 
example, were extrapolations used to infer effects in one species from 
another, or from one temporal or spatial scale to another? Were 
conclusions drawn from extrapolations from laboratory to field effects, 
or were field effects inferred from limited information, such as 
chemical structure-activity relationships? Were no-effect or low-effect 
levels used to address likelihood of effects? Risk assessors should 
consider these and any other sources of uncertainty when evaluating the 
relative importance of particular lines of evidence.
    Finally, how directly lines of evidence relate to the questions 
asked in the risk assessment may determine their relative importance in 
terms of the ecological entity and the attributes of the assessment 
endpoint. Lines of evidence directly related to the risk hypotheses, 
and those that establish a cause-and-effect relationship based on a 
definitive mechanism rather than associations alone, are likely to be 
of greatest importance.
    The evaluation process, however, involves more than just listing 
the evidence that supports or refutes the risk estimate. The risk 
assessor should carefully examine each factor and evaluate its 
contribution in the context of the risk assessment. The importance of 
lines of evidence is that each and every factor is described and 
interpreted. Data or study results are often not reported or carried 
forward in the risk assessment because they are of insufficient 
quality. If such data or results are eliminated from the evaluation 
process, however, valuable information may be lost with respect to

[[Page 26890]]

needed improvements in methodologies or recommendations for further 
studies.
    As a case in point, consider the two lines of evidence described 
for the carbofuran example (see text notes 5-1 and 5-3), field studies 
and quotients. Both approaches are relevant to the assessment endpoint 
(survival of birds that forage in agricultural areas where carbofuran 
is applied), and both are relevant to the exposure scenarios described 
in the conceptual model (see figure D-1). The quotients, however, are 
limited in their ability to express incremental risks (e.g., how much 
greater risk is expressed by a quotient of ``2'' versus a quotient of 
``4''), while the field studies had some design flaws (see text note 5-
1). Nevertheless, because of the strong evidence of causal 
relationships from the field studies and consistency with the 
laboratory-derived quotient, confidence in a conclusion of high risk to 
the assessment endpoint is supported.
    Sometimes lines of evidence do not point toward the same 
conclusion. It is important to investigate possible reasons for any 
disagreement rather than ignore inconvenient evidence. A starting point 
is to distinguish between true inconsistencies and those related to 
differences in statistical powers of detection. For example, a model 
may predict adverse effects that were not observed in a field survey. 
The risk assessor should ask whether the experimental design of the 
field study had sufficient power to detect the predicted difference or 
whether the endpoints measured were comparable with those used in the 
model. Conversely, the model may have been unrealistic in its 
predictions. While iteration of the risk assessment process and 
collection of additional data may help resolve uncertainties, this 
option is not always available.
    Lines of evidence that are to be evaluated during risk 
characterization should be defined early in the risk assessment (during 
problem formulation) through the development of the conceptual model 
and selection of assessment endpoints. Further, the analysis plan 
should incorporate measures that will contribute to the interpretation 
of the lines of evidence, including methods of reviewing, analyzing, 
and summarizing the uncertainty in the risk assessment.
    Also, risk assessments often rely solely on laboratory or in situ 
bioassays to assess adverse effects that may occur as a result of 
exposure to stressors. Although they may not be manifested in the 
field, ecological effects demonstrated in the laboratory should not be 
discounted as a line of evidence.
5.2.2. Determining Ecological Adversity
    At this point in risk characterization, the changes expected in the 
assessment endpoints have been estimated and the supporting lines of 
evidence evaluated. The next step is to interpret whether these changes 
are considered adverse. Adverse ecological effects, in this context, 
represent changes that are undesirable because they alter valued 
structural or functional attributes of the ecological entities under 
consideration. The risk assessor evaluates the degree of adversity, 
which is often a difficult task and is frequently based on the risk 
assessor's professional judgment.
    When the results of the risk assessment are discussed with the risk 
manager (section 6), other factors, such as the economic, legal, or 
social consequences of ecological damage, should be considered. The 
risk manager will use all of this information to determine whether a 
particular adverse effect is acceptable and may also find it useful 
when communicating the risk to interested parties.
    The following are criteria for evaluating adverse changes in 
assessment endpoints:
     Nature of effects and intensity of effects
     Spatial and temporal scale
     Potential for recovery.
    The extent to which the criteria are evaluated depends on the scope 
and complexity of the risk assessment. Understanding the underlying 
assumptions and science policy judgments, however, is important even in 
simple cases. For example, when exceedance of a previously established 
decision rule, such as a benchmark stressor level, is used as evidence 
of adversity (e.g., see Urban and Cook, 1986, or Nabholz, 1991), the 
reasons why this is considered adverse should be clearly understood. In 
addition, any evaluation of adversity should examine all relevant 
criteria, since none are considered singularly determinative.
    To distinguish adverse ecological changes from those within the 
normal pattern of ecosystem variability or those resulting in little or 
no significant alteration of biota, it is important to consider the 
nature and intensity of effects. For example, for an assessment 
endpoint involving survival, growth, and reproduction of a species, do 
predicted effects involve survival and reproduction or only growth? If 
survival of offspring will be affected, by what percentage will it 
diminish?
    It is important for risk assessors to consider both the ecological 
and statistical contexts of an effect when evaluating intensity. For 
example, a statistically significant 1% decrease in fish growth (see 
text note 5-7) may not be relevant to an assessment endpoint of fish 
population viability, and a 10% decline in reproduction may be worse 
for a population of slowly reproducing trees than for rapidly 
reproducing planktonic algae.
    Natural ecosystem variation can make it very difficult to observe 
(detect) stressor-related perturbations. For example, natural 
fluctuations in marine fish populations are often large, with intra- 
and interannual variability in population levels covering several 
orders of magnitude. Furthermore, cyclic events of various periods 
(e.g., bird migration, tides) are very important in natural systems and 
may mask or delay stressor-related effects. Predicting the effects of 
anthropogenic stressors against this background of variation can be 
very difficult. Thus, a lack of statistically significant effects in a 
field study does not automatically mean that adverse ecological effects 
are absent. Rather, risk assessors should then consider other lines of 
evidence in reaching their conclusions.
    It is also important to consider the location of the effect within 
the biological hierarchy and the mechanisms that may result in 
ecological changes. The risk assessor may rely on mechanistic 
explanations to describe complex ecological interactions and the 
resulting effects that otherwise may be masked by variability in the 
ecological components.
    The boundaries (global, landscape, ecosystem, organism) of the risk 
assessment are initially identified in the analysis plan prepared 
during problem formulation. These spatial and temporal scales are 
further defined in the analysis phase, where specific exposure and 
effects scenarios are evaluated. The spatial dimension encompasses both 
the extent and pattern of effect as well as the context of the effect 
within the landscape. Factors to consider include the absolute area 
affected, the extent of critical habitats affected compared with a 
larger area of interest, and the role or use of the affected area 
within the landscape.
    Adverse effects to assessment endpoints vary with the absolute area 
of the effect. A larger affected area may be (1) subject to a greater 
number of other stressors, increasing the complications from stressor 
interactions, (2) more likely to contain sensitive species or habitats, 
or (3) more susceptible to landscape-level changes because many 
ecosystems may be altered by the stressors.
    Nevertheless, a smaller area of effect is not always associated 
with lower risk.

[[Page 26891]]

The function of an area within the landscape may be more important than 
the absolute area. Destruction of small but unique areas, such as 
critical wetlands, may have important effects on local and regional 
wildlife populations. Also, in river systems, both riffle and pool 
areas provide important microhabitats that maintain the structure and 
function of the total river ecosystem. Stressors acting on these 
microhabitats may result in adverse effects to the entire system.
    Spatial factors are important for many species because of the 
linkages between ecological landscapes and population dynamics. 
Linkages between landscapes can provide refuge for affected 
populations, and organisms may require corridors between habitat 
patches for successful migration.
    The temporal scale for ecosystems can vary from seconds 
(photosynthesis, prokaryotic reproduction) to centuries (global climate 
change). Changes within a forest ecosystem can occur gradually over 
decades or centuries and may be affected by slowly changing external 
factors such as climate. When interpreting adversity, risk assessors 
should recognize that the time scale of stressor-induced changes 
operates within the context of multiple natural time scales. In 
addition, temporal responses for ecosystems may involve intrinsic time 
lags, so responses to a stressor may be delayed. Thus, it is important 
to distinguish a stressor's long-term impacts from its immediately 
visible effects. For example, visible changes resulting from 
eutrophication of aquatic systems (turbidity, excessive macrophyte 
growth, population decline) may not become evident for many years after 
initial increases in nutrient levels.
    Considering the temporal scale of adverse effects leads logically 
to a consideration of recovery. Recovery is the rate and extent of 
return of a population or community to some aspect of its condition 
prior to a stressor's introduction. (While this discussion deals with 
recovery as a result of natural processes, risk mitigation options may 
include restoration activities to facilitate or speed up the recovery 
process.) Because ecosystems are dynamic and, even under natural 
conditions, constantly changing in response to changes in the physical 
environment (e.g., weather, natural disturbances) or other factors, it 
is unrealistic to expect that a system will remain static at some level 
or return to exactly the same state that it was before it was disturbed 
(Landis et al., 1993). Thus, the attributes of a ``recovered'' system 
should be carefully defined. Examples might include productivity 
declines in a eutrophic system, reestablishment of a species at a 
particular density, species recolonization of a damaged habitat, or the 
restoration of health of diseased organisms. The Agency considered the 
recovery rate of biological communities in streams and rivers from 
disturbances in setting exceedance frequencies for chemical stressors 
in waste effluents (U.S. EPA, 1991).
    Recovery can be evaluated in spite of the difficulty in predicting 
events in ecological systems (e.g., Niemi et al., 1990). For example, 
it is possible to distinguish changes that are usually reversible 
(e.g., stream recovery from sewage effluent discharge), frequently 
irreversible (e.g., establishment of introduced species), and always 
irreversible (e.g., extinction). Risk assessors should consider the 
potential irreversibility of significant structural or functional 
changes in ecosystems or ecosystem components when evaluating 
adversity. Physical alterations such as deforestation in the coastal 
hills of Venezuela in recent history and in Britain during the 
Neolithic period, for example, changed soil structure and seed sources 
such that forests cannot easily grow again (Fisher and Woodmansee, 
1994).
    The relative rate of recovery can also be estimated. For instance, 
fish populations in a stream are likely to recover much faster from 
exposure to a degradable chemical than from habitat alterations 
resulting from stream channelization. Risk assessors can use knowledge 
of factors, such as the temporal scales of organisms' life histories, 
the availability of adequate stock for recruitment, and the 
interspecific and trophic dynamics of the populations, in evaluating 
the relative rates of recovery. A fisheries stock or forest might 
recover in decades, a benthic invertebrate community in years, and a 
planktonic community in weeks to months.
    Risk assessors should note natural disturbance patterns when 
evaluating the likelihood of recovery from anthropogenic stressors. 
Alternatively, if an ecosystem has become adapted to a disturbance 
pattern, it may be affected when the disturbance is removed (e.g., 
fire-maintained grasslands). The lack of natural analogs makes it 
difficult to predict recovery from uniquely anthropogenic stressors 
(e.g., synthetic chemicals).
    Appendix E illustrates how the criteria for ecological adversity 
(nature and intensity of effects, spatial and temporal scales, and 
recovery) might be used in evaluating two cleanup options for a marine 
oil spill. This example also shows that recovery of a system depends 
not only on how quickly a stressor is removed, but also on how the 
cleanup efforts themselves affect the recovery.

5.3. Reporting Risks

    When risk characterization is complete, risk assessors should be 
able to estimate ecological risks, indicate the overall degree of 
confidence in the risk estimates, cite lines of evidence supporting the 
risk estimates, and interpret the adversity of ecological effects. 
Usually this information is included in a risk assessment report 
(sometimes referred to as a risk characterization report because of the 
integrative nature of risk characterization). While the breadth of 
ecological risk assessment precludes providing a detailed outline of 
reporting elements, the risk assessor should consider the elements 
listed in text note 5-8 when preparing a risk assessment report.
    Like the risk assessment itself, a risk assessment report may be 
brief or extensive, depending on the nature of and the resources 
available for the assessment. While it is important to address the 
elements described in text note 5-8, risk assessors should judge the 
level of detail required. The report need not be overly complex or 
lengthy; it is most important that the information required to support 
a risk management decision be presented clearly and concisely.
    To facilitate mutual understanding, it is critical that the risk 
assessment results are properly presented. Agency policy requires that 
risk characterizations be prepared ``in a manner that is clear, 
transparent, reasonable, and consistent with other risk 
characterizations of similar scope prepared across programs in the 
Agency'' (U.S. EPA, 1995b). Ways to achieve such characteristics are 
described in text note 5-9.
    After the risk assessment report is prepared, the results are 
discussed with risk managers. Section 6 provides information on 
communication between risk assessors and risk managers, describes the 
use of the risk assessment in a risk management context, and briefly 
discusses communication of risk assessment results from risk managers 
to interested parties and the general public.

6. Relating Ecological Information to Risk Management Decisions

    After characterizing risks and preparing a risk assessment report 
(section 5), risk assessors discuss the results with risk managers 
(figure 5-1).

[[Page 26892]]

Risk managers use risk assessment results, along with other factors 
(e.g., economic or legal concerns), in making risk management decisions 
and as a basis for communicating risks to interested parties and the 
general public.
    Mutual understanding between risk assessors and risk managers 
regarding risk assessment results can be facilitated if the questions 
listed in text note 6-1 are addressed. Risk managers need to know the 
major risks to assessment endpoints and have an idea of whether the 
conclusions are supported by a large body of data or if there are 
significant data gaps. Insufficient resources, lack of consensus, or 
other factors may preclude preparation of a detailed and well-
documented risk characterization. If this is the case, the risk 
assessor should clearly articulate any issues, obstacles, and 
correctable deficiencies for the risk manager's consideration.
    In making decisions regarding ecological risks, risk managers 
consider other information, such as social, economic, political, or 
legal issues in combination with risk assessment results. For example, 
the risk assessment results may be used as part of an ecological cost-
benefit analysis, which may require translating resources (identified 
through the assessment endpoints) into monetary values. Traditional 
economic considerations may only partially address changes in 
ecological resources that are not considered commodities, 
intergenerational resource values, or issues of long-term or 
irreversible effects (U.S. EPA, 1995a; Costanza et al., 1997); however, 
they may provide a means of comparing the results of the risk 
assessment in commensurate units such as costs. Risk managers may also 
consider alternative strategies for reducing risks, such as risk 
mitigation options or substitutions based on relative risk comparisons. 
For example, risk mitigation techniques, such as buffer strips or lower 
field application rates, can be used to reduce the exposure (and risk) 
of a pesticide. Further, by comparing the risk of a new pesticide to 
other pesticides currently in use during the registration process, 
lower overall risk may result. Finally, risk managers consider and 
incorporate public opinion and political demands into their decisions. 
Collectively, these other factors may render very high risks acceptable 
or very low risks unacceptable.
    Risk characterization provides the basis for communicating 
ecological risks to interested parties and the general public. This 
task is usually the responsibility of risk managers, but it may be 
shared with risk assessors. Although the final risk assessment document 
(including its risk characterization sections) can be made available to 
the public, the risk communication process is best served by tailoring 
information to a particular audience. Irrespective of the specific 
format, it is important to clearly describe the ecological resources at 
risk, their value, and the monetary and other costs of protecting (and 
failing to protect) the resources (U.S. EPA, 1995a).
    Managers should clearly describe the sources and causes of risks 
and the potential adversity of the risks (e.g., nature and intensity, 
spatial and temporal scale, and recovery potential). The degree of 
confidence in the risk assessment, the rationale for the risk 
management decision, and the options for reducing risk are also 
important (U.S. EPA, 1995a). Other risk communication considerations 
are provided in text note 6-2.
    Along with discussions of risk and communications with the public, 
it is important for risk managers to consider whether additional 
follow-on activities are required. Depending on the importance of the 
assessment, confidence in its results, and available resources, it may 
be advisable to conduct another iteration of the risk assessment 
(starting with problem formulation or analysis) in order to support a 
final management decision. Another option is to proceed with the 
decision, implement the selected management alternative, and develop a 
monitoring plan to evaluate the results (see section 1). If the 
decision is to mitigate risks through exposure reduction, for example, 
monitoring could help determine whether the desired reduction in 
exposure (and effects) is achieved.

7. Text Notes

Text Note 1-1. Related Terminology

    The following terms overlap to varying degrees with the concept of 
ecological risk assessment used in these Guidelines (see Appendix B for 
definitions):
     Hazard assessment
     Comparative risk assessment
     Cumulative ecological risk assessment
     Environmental impact statement

Text Note 1-2. Flexibility of the Framework Diagram

    The framework process (figure 1-1) is a general representation of a 
complex and varied group of assessments. This diagram represents a 
flexible process, as illustrated by the examples below.
     In problem formulation, an assessment may begin with a 
consideration of endpoints, stressors, or ecological effects. Problem 
formulation is generally interactive and iterative, not linear.
     In the analysis phase, characterization of exposure and 
effects frequently become intertwined, as when an initial exposure 
leads to a cascade of additional exposures and secondary effects. The 
analysis phase should foster an understanding of these complex 
relationships.
     Analysis and risk characterization are shown as separate 
phases. However, some models may combine the analysis of exposure and 
effects data with the integration of these data that occurs in risk 
characterization.

Text Note 2-1. Who Are Risk Managers?

    Risk managers are individuals and organizations who have the 
responsibility, or have the authority to take action or require action, 
to mitigate an identified risk. The expression ``risk manager'' is 
often used to represent a decision maker in agencies such as EPA or 
State environmental offices who has legal authority to protect or 
manage a resource. However, risk managers may include a diverse group 
of interested parties who also have the ability to take action to 
reduce or mitigate risk. In situations where a complex of ecosystem 
values (e.g., watershed resources) is at risk from multiple stressors, 
and management will be implemented through community action, these 
groups may function as risk management teams. Risk management teams may 
include decision officials in Federal, State, local, and tribal 
governments; commercial, industrial, and private organizations; leaders 
of constituency groups; and other sectors of the public such as 
property owners. For additional insights on risk management and manager 
roles, see text notes 2-3 and 2-4.

Text Note 2-2. Who Are Risk Assessors?

    Risk assessors are a diverse group of professionals who bring a 
needed expertise to a risk assessment team. When a specific risk 
assessment process is well defined through regulations and guidance, 
one trained individual may be able to complete a risk assessment given 
sufficient information (e.g., premanufacture notice of a chemical). 
However, for complex risk assessments, one individual can rarely 
provide the necessary breadth of expertise. Every risk assessment team 
should include at least one professional who is knowledgeable and 
experienced in using the risk assessment process. Other

[[Page 26893]]

team members bring specific expertise relevant to the locations, 
stressors, ecosystems, scientific issues, and other expertise as 
needed, depending on the type of assessment.

Text Note 2-3. Who Are Interested Parties?

    Interested parties (commonly called ``stakeholders'') may include 
Federal, State, tribal, and municipal governments, industrial leaders, 
environmental groups, small-business owners, landowners, and other 
segments of society concerned about an environmental issue at hand or 
attempting to influence risk management decisions. Their involvement, 
particularly during management goal development, may be key to 
successful implementation of management plans since implementation is 
more likely to occur when backed by consensus. Large diverse groups may 
require trained facilitators and consensus-building techniques to reach 
agreement.
    In some cases, interested parties may provide important information 
to risk assessors. Local knowledge, particularly in rural communities, 
and traditional knowledge of native peoples can provide valuable 
insights about ecological characteristics of a place, past conditions, 
and current changes. This knowledge should be considered when assessing 
available information during problem formulation (see section 3.2).
    The context of involvement by interested parties can vary widely 
and may or may not be appropriate for a particular risk assessment. 
Interested parties may be limited to providing input to goal 
development, or they may become risk managers, depending on the degree 
to which they can take action to manage risk and the regulatory context 
of the decision. When and how interested parties influence risk 
assessments and risk management are areas of current discussion (NRC, 
1996). See additional information in text note 2-1 and section 2.1.

Text Note 2-4. Questions Addressed by Risk Managers and Risk Assessors

Questions Principally for Risk Managers to Answer

    What is the nature of the problem and the best scale for the 
assessment?
    What are the management goals and decisions needed, and how will 
risk assessment help?
    What are the ecological values (e.g., entities and ecosystem 
characteristics) of concern?
    What are the policy considerations (law, corporate stewardship, 
societal concerns, environmental justice, intergenerational equity)?
    What precedents are set by similar risk assessments and previous 
decisions?
    What is the context of the assessment (e.g., industrial site, 
national park)?
    What resources (e.g., personnel, time, money) are available?
    What level of uncertainty is acceptable?

Questions Principally for Risk Assessors to Answer

    What is the scale of the risk assessment?
    What are the critical ecological endpoints and ecosystem and 
receptor characteristics?
    How likely is recovery, and how long will it take?
    What is the nature of the problem: Past, present, future?
    What is our state of knowledge of the problem?
    What data and data analyses are available and appropriate?
    What are the potential constraints (e.g., limits on expertise, 
time, availability of methods and data)?

Text Note 2-5. Sustainability as a Management Goal

    To sustain is to keep in existence, maintain, or prolong. 
Sustainability is used as a management goal in a variety of settings 
(see U.S. EPA, 1995a). Sustainability and other concepts such as biotic 
or community integrity may be very useful as guiding principles for 
management goals. However, in each case these principles should be 
explicitly defined and interpreted for a place to support a risk 
assessment. To do this, key questions need to be addressed: What does 
sustainability or integrity mean for the particular ecosystem? What 
must be protected to meet sustainable goals or system integrity? Which 
ecological resources and processes are to be sustained and why? How 
will we know we have achieved it? Answers to these questions serve to 
clarify the goals for a particular ecosystem. Concepts like 
sustainability and integrity do not meet the criteria for an assessment 
endpoint (see section 3.3.2).

Text Note 2-6. Management Goals for Waquoit Bay

    A key challenge for risk assessors when dealing with a general 
management goal is interpreting the goal for a risk assessment. This 
can be done by generating a set of management objectives that represent 
what must be achieved in a particular ecosystem in order for the goal 
to be met. An example of this process was developed in the Waquoit Bay 
watershed risk assessment (U.S. EPA, 1996b).
    Waquoit Bay is a small estuary on Cape Cod showing signs of 
degradation, including loss of eelgrass, fish, and shellfish and an 
increase in macroalgae mats and fish kills. The management goal for 
Waquoit Bay was established through public meetings, preexisting goals 
from local organizations, and State and Federal regulations:
    Reestablish and maintain water quality and habitat conditions in 
Waquoit Bay and associated freshwater rivers and ponds to (1) support 
diverse self-sustaining commercial, recreational, and native fish and 
shellfish populations and (2) reverse ongoing degradation of ecological 
resources in the watershed.
    To interpret this goal for the risk assessment, it was converted 
into 10 management objectives that defined what must be true in the 
watershed for the goal to be achieved and provide the foundation for 
management decisions. The management objectives are:
     Reduce or eliminate hypoxic or anoxic events.
     Prevent toxic levels of contamination in water, sediments, 
and biota.
     Restore and maintain self-sustaining native fish 
populations and their habitat.
     Reestablish viable eelgrass beds and associated aquatic 
communities in the bay.
     Reestablish a self-sustaining scallop population in the 
bay that can support a viable sport fishery.
     Protect shellfish beds from bacterial contamination that 
results in closures.
     Reduce or eliminate nuisance macroalgal growth.
     Prevent eutrophication of rivers and ponds.
     Maintain diversity of native biotic communities.
     Maintain diversity of water-dependent wildlife.
    From these objectives, eight ecological entities and their 
attributes in the bay were selected as assessment endpoints (see 
section 3.3.2) to best represent the management goals and objectives, 
one of which is areal extent and patch size of eelgrass beds. Eelgrass 
was selected because (1) scallops and other benthic organisms and 
juvenile finfish depend directly on eelgrass beds for survival, (2) 
eelgrass is highly sensitive to excess macroalgal growth, and (3) 
abundant eelgrass represents a healthy bay to human users.

[[Page 26894]]

Text Note 2-7. What Is the Difference Between a Management Goal and 
Management Decision?

    Management goals are desired characteristics of ecological values 
that the public wants to protect. Clean water, protection of endangered 
species, maintenance of ecological integrity, clear mountain views, and 
fishing opportunities are all possible management goals. Management 
decisions determine the means to achieve the end goal. For instance, a 
goal may be ``fishable, swimmable'' waters. The management options 
under consideration to achieve that goal may include increasing 
enforcement of point-source discharges, restoring fish habitat, 
designing alternative sewage treatment facilities, or implementing all 
of the above.

Text Note 2-8. Tiers and Iteration: When Is a Risk Assessment Done?

    Risk assessments range from very simple to complex and resource 
demanding. How is it possible to decide the level of effort? How many 
times should the risk assessor revisit data and assessment issues? When 
is the risk assessment done?
    Many of these questions can be addressed by designing a set of 
tiered assessments. These are preplanned and prescribed sets of risk 
assessments of progressive data and resource intensity. The outcome of 
a given tier is to either make a management decision, often based on 
decision criteria, or continue to the next level of effort. Many risk 
assessors and public and private organizations use this approach (e.g., 
see Gaudet, 1994; European Community, 1993; Cowan et al., 1995; Baker 
et al., 1994; Urban and Cook, 1986; Lynch et al., 1994).
    An iteration is an unprescribed reevaluation of information that 
may occur at any time during a risk assessment, including tiered 
assessments. It is done in response to an identified need, new 
information, or questions raised while conducting an assessment. As 
such, iteration is a normal characteristic of risk assessments but is 
not a formal planned step. An iteration may include redoing the risk 
assessment with new assumptions and new data.
    Setting up tiered assessments and decision criteria may reduce the 
need for iteration. Up-front planning and careful development of 
problem formulation will also reduce the need for revisiting data, 
assumptions, and models. However, there are no rules to dictate how 
many iterations will be necessary to answer management questions or 
ensure scientific validity. A risk assessment can be considered 
complete when risk managers have sufficient information and confidence 
in the results of the risk assessment to make a decision they can 
defend.

Text Note 2-9. Questions To Ask About Scope and Complexity

    Is this risk assessment mandated, required by a court decision, or 
providing guidance to a community?
    Will decisions be based on assessments of a small area evaluated in 
depth or a large-scale area in less detail?
    What are the spatial and temporal boundaries of the problem?
    What information is already available compared to what is needed?
    How much time can be taken, and how many resources are available?
    What practicalities constrain data collection?
    Is a tiered approach an option?

Text Note 3-1. Avoiding Potential Shortcomings Through Problem 
Formulation

    The importance of problem formulation has been shown repeatedly in 
the Agency's analysis of ecological risk assessment case studies and in 
interactions with senior EPA managers and regional risk assessors (U.S. 
EPA, 1993b, 1994e). Shortcomings consistently identified in the case 
studies include (1) absence of clearly defined goals, (2) endpoints 
that are ambiguous and difficult to define and measure, and (3) failure 
to identify important risks. These and other shortcomings can be 
avoided through rigorous development of the products of problem 
formulation as described in this section of the Guidelines.

Text Note 3-2. Uncertainty in Problem Formulation

    Throughout problem formulation, risk assessors consider what is 
known and not known about a problem and its setting. Each product of 
problem formulation contains uncertainty. The explicit treatment of 
uncertainty during problem formulation is particularly important 
because it will have repercussions throughout the remainder of the 
assessment. Uncertainty is discussed in section 3.4 (Conceptual 
Models).

Text Note 3-3. Initiating a Risk Assessment: What's Different When 
Stressors, Effects, or Values Drive the Process?

    The reasons for initiating a risk assessment influence when risk 
assessors generate products in problem formulation. When the assessment 
is initiated because of concerns about stressors, risk assessors use 
what is known about the stressor and its source to focus the 
assessment. Objectives for the assessment are based on determining how 
the stressor is likely to come in contact with and affect possible 
receptors. This information forms the basis for developing conceptual 
models and selecting assessment endpoints. When an observed effect is 
the basis for initiating the assessment, endpoints are normally 
established first. Frequently, the affected ecological entities and 
their response form the basis for defining assessment endpoints. Goals 
for protecting the assessment endpoints are then established, which 
support the development of conceptual models. The models aid in the 
identification of the most likely stressor(s). Value-initiated risk 
assessments are driven by goals for the ecological values of concern. 
These values might involve ecological entities such as species, 
communities, ecosystems, or places. Based on these goals, assessment 
endpoints are selected first to serve as an interpretation of the 
goals. Once selected, the endpoints provide the basis for identifying 
an array of stressors that may be influencing the assessment endpoints 
and describing the diversity of potential effects. This information is 
then captured in the conceptual model(s).

Text Note 3-4. Assessing Available Information: Questions to Ask 
Concerning Source, Stressor, and Exposure Characteristics, Ecosystem 
Characteristics, and Effects (derived in part from Barnthouse and 
Brown, 1994)

Source and Stressor Characteristics

     What is the source? Is it anthropogenic, natural, point 
source, or diffuse nonpoint?
     What type of stressor is it: chemical, physical, or 
biological?
     What is the intensity of the stressor (e.g., the dose or 
concentration of a chemical, the magnitude or extent of physical 
disruption, the density or population size of a biological stressor)?
     What is the mode of action? How does the stressor act on 
organisms or ecosystem functions?

Exposure Characteristics

     With what frequency does a stressor event occur (e.g., is 
it isolated, episodic, or continuous; is it subject to natural daily, 
seasonal, or annual periodicity)?
     What is its duration? How long does it persist in the 
environment (e.g., for chemical, what is its half-life, does it 
bioaccumulate; for physical, is habitat alteration sufficient to 
prevent recovery; for biological, will it reproduce and proliferate)?

[[Page 26895]]

     What is the timing of exposure? When does it occur in 
relation to critical organism life cycles or ecosystem events (e.g., 
reproduction, lake overturn)?
     What is the spatial scale of exposure? Is the extent or 
influence of the stressor local, regional, global, habitat-specific, or 
ecosystemwide?
     What is the distribution? How does the stressor move 
through the environment (e.g., for chemical, fate and transport; for 
physical, movement of physical structures; for biological, life-history 
dispersal characteristics)?

Ecosystems Potentially at Risk

     What are the geographic boundaries? How do they relate to 
functional characteristics of the ecosystem?
     What are the key abiotic factors influencing the ecosystem 
(e.g., climatic factors, geology, hydrology, soil type, water quality)?
     Where and how are functional characteristics driving the 
ecosystem (e.g., energy source and processing, nutrient cycling)?
     What are the structural characteristics of the ecosystem 
(e.g., species number and abundance, trophic relationships)?
     What habitat types are present?
     How do these characteristics influence the susceptibility 
(sensitivity and likelihood of exposure) of the ecosystem to the 
stressor(s)?
     Are there unique features that are particularly valued 
(e.g., the last representative of an ecosystem type)?
     What is the landscape context within which the ecosystem 
occurs?

Ecological Effects

     What are the type and extent of available ecological 
effects information (e.g., field surveys, laboratory tests, or 
structure-activity relationships)?
     Given the nature of the stressor (if known), which effects 
are expected to be elicited by the stressor?
     Under what circumstances will effects occur?

Text Note 3-5. Salmon and Hydropower: Salmon as the Basis for an 
Assessment Endpoint

    A hydroelectric dam is to be built on a river in the Pacific 
Northwest where anadromous fish such as salmon spawn. Assessment 
endpoints should be selected to assess potential ecological risk. Of 
the anadromous fish, salmon that spawn in the river are an appropriate 
choice because they meet the criteria for good assessment endpoints. 
Salmon fry and adults are important food sources for a multitude of 
aquatic and terrestrial species and are major predators of aquatic 
invertebrates (ecological relevance). Salmon are sensitive to changes 
in sedimentation and substrate pebble size, require quality cold-water 
habitats, and have difficulty climbing fish ladders. Hydroelectric dams 
represent significant, and normally fatal, habitat alteration and 
physical obstacles to successful salmon breeding and fry survival 
(susceptibility). Finally, salmon support a large commercial fishery, 
some species are endangered, and they have ceremonial importance and 
are key food sources for Native Americans (relevance to management 
goals). ``Salmon reproduction and population recruitment'' is a good 
assessment endpoint for this risk assessment. In addition, if salmon 
populations are protected, other anadromous fish populations are likely 
to be protected as well. However, one assessment endpoint can rarely 
provide the basis for a risk assessment of complex ecosystems. These 
are better represented by a set of assessment endpoints.

Text Note 3-6. Cascading Adverse Effects: Primary (Direct) and 
Secondary (Indirect)

    The interrelationships among entities and processes in ecosystems 
foster a potential for cascading effects: as one population, species, 
process, or other entity in the ecosystem is altered, other entities 
are affected as well. Primary, or direct, effects occur when a stressor 
acts directly on the assessment endpoint and causes an adverse 
response. Secondary, or indirect, effects occur when the entity's 
response becomes a stressor to another entity. Secondary effects are 
often a series of effects among a diversity of organisms and processes 
that cascade through the ecosystem. For example, application of an 
herbicide on a wet meadow results in direct toxicity to plants. Death 
of the wetland plants leads to secondary effects such as loss of 
feeding habitat for ducks, breeding habitat for red-winged blackbirds, 
alteration of wetland hydrology that changes spawning habitat for fish, 
and so forth.

Text Note 3-7. Identifying Susceptibility

    Often it is possible to identify ecological entities most likely to 
be susceptible to a stressor. However, in some cases where stressors 
are not known at the initiation of a risk assessment, or specific 
effects have not been identified, the most susceptible entities may not 
be known. Where this occurs, professional judgment may be required to 
make initial selections of potential endpoints.
    Once done, available information on potential stressors in the 
system can be evaluated to determine which of the endpoints are most 
likely susceptible to identified stressors. If an assessment endpoint 
is selected for a risk assessment that directly supports management 
goals and is ultimately found not susceptible to stressors in the 
system, then a conclusion of no risk is appropriate. However, where 
there are multiple possible assessment endpoints that address 
management goals and only some of those are susceptible to a stressor, 
the susceptible endpoints should be selected. If the susceptible 
endpoints are not initially selected for an assessment, an additional 
iteration of the risk assessment with alternative assessment endpoints 
may be needed to determine risk.

Text Note 3-8. Sensitivity and Secondary Effects: The Mussel-Fish 
Connection

    Native freshwater mussels are endangered in many streams. 
Management efforts have focused on maintaining suitable habitat for 
mussels because habitat loss has been considered the greatest threat to 
this group. However, larval unionid mussels must attach to the gills of 
a fish host for one month during development. Each species of mussel 
must attach to a particular host species of fish. In situations where 
the fish community has been changed, perhaps due to stressors to which 
mussels are insensitive, the host fish may no longer be available. 
Mussel larvae will die before reaching maturity as a result. Regardless 
of how well managers restore mussel habitat, mussels will be lost from 
this system unless the fish community is restored. In this case, risk 
is caused by the absence of exposure to a critical resource.

Text Note 3-9. Examples of Management Goals and Assessment Endpoints

[[Page 26896]]



----------------------------------------------------------------------------------------------------------------
                 Case                      Regulatory context/management goal           Assessment endpoint     
----------------------------------------------------------------------------------------------------------------
Assessing Risks of New Chemical Under   Protect ``the environment'' from ``an     Survival, growth, and         
 Toxic Substances Control Act (Lynch     unreasonable risk of injury'' (TSCA       reproduction of fish, aquatic
 et al., 1994).                          Sec.  2[b][1] and [2]); protect the       invertebrates, and algae.    
                                         aquatic environment. Goal was to exceed                                
                                         a concentration of concern on no more                                  
                                         than 20 days a year.                                                   
Special Review of Granular Carbofuran   Prevent * * * ``unreasonable adverse      Individual bird survival.     
 Based on Adverse Effects on Birds       effects on the environment'' (FIFRA                                    
 (Houseknecht, 1993).                    Secs.  [c][5] and 3[c][6]); using cost-                                
                                         benefit considerations. Goal was to                                    
                                         have no regularly repeated bird kills.                                 
Modeling Future Losses of Bottomland    National Environment Policy Act may       (1) Forest community structure
 Forest Wetlands (Brody et al., 1993).   apply to environmental impact of new      and habitat value to wildlife
                                         levee construction; also Clean Water      species                      
                                         Act Sec.  404.                           (2) Species composition of    
                                                                                   wildlife community.          
Pest Risk Assessment on Importation of  Assessment was done to help provide a     Survival and growth of tree   
 Logs From Chile (USDA, 1993).           basis for any necessary regulation of     species in the western United
                                         the importation of timber and timber      States.                      
                                         products into the United States.                                       
Baird and McGuire Superfund Site        Protection of the environment (CERCLA/    (1) Survival of soil          
 (terrestrial component) (Burmaster et   SARA).                                    invertebrates                
 al., 1991; Callahan et al., 1991;                                                (2) Survival and reproduction 
 Menzie et al., 1992).                                                             of song birds.               
Waquoit Bay Estuary Watershed Risk      Clean Water Act--wetlands protection;     (1) Estuarine eelgrass habitat
 Assessment (U.S. EPA, 1996b).           water quality criteria--pesticides;       abundance and distribution   
                                         endangered species. National Estuarine   (2) Estuarine fish species    
                                         Research Reserve, Massachusetts, Area     diversity and abundance      
                                         of Critical Environment Concern. Goal    (3) Freshwater pond benthic   
                                         was to reestablish and maintain water     invertebrate species         
                                         quality and habitat conditions to         diversity and abundance.     
                                         support diverse self-sustaining                                        
                                         commercial, recreational, and native                                   
                                         fish, water-dependent wildlife, and                                    
                                         shellfish and to reverse ongoing                                       
                                         degradation.                                                           
----------------------------------------------------------------------------------------------------------------

Text Note 3-10. Common Problems in Selecting Assessment Endpoints

     Endpoint is a goal (e.g., maintain and restore endemic 
populations).
     Endpoint is vague (e.g., estuarine integrity instead of 
eelgrass abundance and distribution).
     Ecological entity is better as a measure (e.g., emergence 
of midges can be used to evaluate an assessment endpoint for fish 
feeding behavior).
     Ecological entity may not be as sensitive to the stressor 
(e.g., catfish versus salmon for sedimentation).
     Ecological entity is not exposed to the stressor (e.g., 
using insectivorous birds for avian risk of pesticide application to 
seeds).
     Ecological entities are irrelevant to the assessment 
(e.g., lake fish in salmon stream).
     Importance of a species or attributes of an ecosystem are 
not fully considered (e.g., mussel-fish connection, see text note 3-8).
     Attribute is not sufficiently sensitive for detecting 
important effects (e.g., survival compared with recruitment for 
endangered species).

Text Note 3-11. What Are the Benefits of Developing Conceptual Models?

     The process of creating a conceptual model is a powerful 
learning tool.
     Conceptual models are easily modified as knowledge 
increases.
     Conceptual models highlight what is known and not known 
and can be used to plan future work.
     Conceptual models can be a powerful communication tool. 
They provide an explicit expression of the assumptions and 
understanding of a system for others to evaluate.
     Conceptual models provide a framework for prediction and 
are the template for generating more risk hypotheses.

Text Note 3-12. What Are Risk Hypotheses, and Why Are They Important?

    Risk hypotheses are proposed answers to questions risk assessors 
have about what responses assessment endpoints will show when they are 
exposed to stressors and how exposure will occur. Risk hypotheses 
clarify and articulate relationships that are posited through the 
consideration of available data, information from scientific 
literature, and the best professional judgment of risk assessors 
developing the conceptual models. This explicit process opens the risk 
assessment to peer review and evaluation to ensure the scientific 
validity of the work. Risk hypotheses are not equivalent to statistical 
testing of null and alternative hypotheses. However, predictions 
generated from risk hypotheses can be tested in a variety of ways, 
including standard statistical approaches.

Text Note 3-13. Examples of Risk Hypotheses

    Hypotheses include known information that sets the problem in 
perspective and the proposed relationships that need evaluation.
    Stressor-initiated: Chemicals with a high Kow tend to 
bioaccumulate. PMN chemical A has a Kow of 5.5 and molecular 
structure similar to known chemical stressor B.
    Hypotheses: Based on the Kow of chemical A, the mode of 
action of chemical B, and the food web of the target ecosystem, when 
the PMN chemical is released at a specified rate, it will bioaccumulate 
sufficiently in 5 years to cause developmental problems in wildlife and 
fish.
    Effects-initiated: Bird kills were repeatedly observed on golf 
courses following the application of the pesticide carbofuran, which is 
highly toxic.
    Hypotheses: Birds die when they consume recently applied granulated 
carbofuran; as the level of application increases, the number of dead 
birds increases. Exposure occurs when dead and dying birds are consumed 
by other animals. Birds of prey and scavenger species will die from 
eating contaminated birds.
    Ecological value-initiated: Waquoit Bay, Massachusetts, supports 
recreational boating and commercial and recreational shellfishing and 
is a significant nursery for finfish. Large mats of macroalgae clog the 
estuary, most of the eelgrass has died, and the scallops are gone.
    Hypotheses: Nutrient loading from septic systems, air pollution, 
and lawn fertilizers causes eelgrass loss by

[[Page 26897]]

shading from algal growth and direct toxicity from nitrogen compounds. 
Fish and shellfish populations are decreasing because of loss of 
eelgrass habitat and periodic hypoxia from excess algal growth and low 
dissolved oxygen.

Text Note 3-14. Uncertainty in Problem Formulation

    Uncertainties in problem formulation are manifested in the quality 
of conceptual models. To address uncertainty:
     Be explicit in defining assessment endpoints; include both 
an entity and its measurable attributes.
     Reduce or define variability by carefully defining 
boundaries for the assessment.
     Be open and explicit about the strengths and limitations 
of pathways and relationships depicted in the conceptual model.
     Identify and describe rationale for key assumptions made 
because of lack of knowledge, model simplification, approximation, or 
extrapolation.
     Describe data limitations.

Text Note 3-15. Why Was Measurement Endpoint Changed?

    The original definition of measurement endpoint was ``a measurable 
characteristic that is related to the valued characteristic chosen as 
the assessment endpoint'' (Suter, 1989; U.S. EPA, 1992a). The 
definition refers specifically to the response of an assessment 
endpoint to a stressor. It does not include measures of ecosystem 
characteristics, life-history considerations, exposure, or other 
measures. Because measurement endpoint does not encompass these other 
important measures and there was confusion about its meaning, the term 
was replaced with measures of effect and supplemented by two other 
categories of measures.

Text Note 3-16. Examples of a Management Goal, Assessment Endpoint, and 
Measures

    Goal: Viable, self-sustaining coho salmon population that supports 
a subsistence and sport fishery.
    Assessment Endpoint: Coho salmon breeding success, fry survival, 
and adult return rates.

Measures of Effects

     Egg and fry response to low dissolved oxygen.
     Adult behavior in response to obstacles.
     Spawning behavior and egg survival with changes in 
sedimentation.

Measures of Ecosystem and Receptor Characteristics

     Water temperature, water velocity, and physical 
obstructions.
     Abundance and distribution of suitable breeding substrate.
     Abundance and distribution of suitable food sources for 
fry.
     Feeding, resting, and breeding behavior.
     Natural reproduction, growth, and mortality rates.

Measures of Exposure

     Number of hydroelectric dams and associated ease of fish 
passage.
     Toxic chemical concentrations in water, sediment, and fish 
tissue.
     Nutrient and dissolved oxygen levels in ambient waters.
     Riparian cover, sediment loading, and water temperature.

Text Note 3-17. How Do Water Quality Criteria Relate to Assessment 
Endpoints?

    Water quality criteria (U.S. EPA, 1986b) have been developed for 
the protection of aquatic life from chemical stressors. This text note 
shows how the elements of a water quality criterion correspond to 
management goals, management decisions, assessment endpoints, and 
measures.

Regulatory Goal

     Clean Water Act, Sec. 101: Protect the chemical, physical, 
and biological integrity of the Nation's waters.

Program Management Decisions

     Protect 99% of individuals in 95% of the species in 
aquatic communities from acute and chronic effects resulting from 
exposure to a chemical stressor.

Assessment Endpoints

     Survival of fish, aquatic invertebrate, and algal species 
under acute exposure.
     Survival, growth, and reproduction of fish, aquatic 
invertebrate, and algal species under chronic exposure.

Measures of Effect

     Laboratory LC50s for at least eight species 
meeting certain requirements.
     Chronic no-observed-adverse-effect levels (NOAELs) for at 
least three species meeting certain requirements.

Measures of Ecosystem and Receptor Characteristics

     Water hardness (for some metals).
     pH.
    The water quality criterion is a benchmark level derived from a 
distributional analysis of single-species toxicity data. It is assumed 
that the species tested adequately represent the composition and 
sensitivities of species in a natural community.

Text Note 3-18. The Data Quality Objectives Process

    The data quality objectives (DQO) process combines elements of both 
planning and problem formulation in its seven-step format.
    Step 1. State the problem. Review existing information to concisely 
describe the problem to be studied.
    Step 2. Identify the decision. Determine what questions the study 
will try to resolve and what actions may result.
    Step 3. Identify inputs to the decision. Identify information and 
measures needed to resolve the decision statement.
    Step 4. Define study boundaries. Specify time and spatial 
parameters and where and when data should be collected.
    Step 5. Develop decision rule. Define statistical parameter, action 
level, and logical basis for choosing alternatives.
    Step 6. Specify tolerable limits on decision errors. Define limits 
based on the consequences of an incorrect decision.
    Step 7. Optimize the design. Generate alternative data collection 
designs and choose most resource-effective design that meets all DQOs.

Text Note 4-1. Data Collection and the Analysis Phase

    Data needs are identified during problem formulation (the analysis 
plan step), and data are collected before the start of the analysis 
phase. These data may be collected for the specific purpose of a 
particular risk assessment, or they may be available from previous 
studies. If additional data needs are identified as the assessment 
proceeds, the analysis phase may be temporarily halted while data are 
collected or the assessor (in consultation with the risk manager) may 
choose to iterate the problem formulation again. Data collection 
methods are not described in these Guidelines. However, the evaluation 
of data for the purposes of risk assessment is discussed in section 
4.2.

Text Note 4-2. The American National Standard for Quality Assurance

    The Specifications and Guidelines for Quality Systems for 
Environmental Data Collection and Environmental Technology Programs 
(ASQC, 1994) recognize several areas that are important to ensuring 
that environmental data will meet study objectives, including:

[[Page 26898]]

     Planning and scoping.
     Designing data collection operations.
     Implementing and monitoring planned operations.
     Assessing and verifying data usability.

Text Note 4-3. Questions for Evaluating a Study's Utility for Risk 
Assessment

    Are the study objectives relevant to the risk assessment?
    Are the variables and conditions the study represents comparable 
with those important to the risk assessment?
    Is the study design adequate to meet its objectives?
    Was the study conducted properly?
    How are variability and uncertainty treated and reported?

Text Note 4-4. Uncertainty Evaluation in the Analysis Phase

----------------------------------------------------------------------------------------------------------------
       Source of uncertainty           Example analysis phase strategies              Specific example          
----------------------------------------------------------------------------------------------------------------
Unclear communication..............  Contact principal investigator or      Clarify whether the study was       
                                      other study participants if            designed to characterize local     
                                      objectives or methods of literature    populations or regional            
                                      studies are unclear.                   populations.                       
                                     Document decisions made during the     Discuss rationale for selecting the 
                                      course of the assessment.              critical toxicity study.           
Descriptive errors.................  Verify that data sources followed      Double-check calculations and data  
                                      appropriate QA/QC procedures.          entry.                             
Variability........................  Describe heterogeneity using point     Display differences in species      
                                      estimates (e.g., central tendency      sensitivity using a cumulative     
                                      and high end) or by constructing       distribution function.             
                                      probability or frequency                                                  
                                      distributions.                                                            
                                     Differentiate from uncertainty due to                                      
                                      lack of knowledge.                                                        
Data gaps..........................  Collect needed data..................  Discuss rationale for using a factor
                                                                             of 10 to extrapolate between a     
                                                                             lowest-observed-adverse-effect     
                                                                             level (LOAEL) and a NOAEL.         
                                     Describe approaches used for bridging                                      
                                      gaps and their rationales.                                                
                                     Differentiate science-based judgments                                      
                                      from policy-based judgments.                                              
Uncertainty about a quantity's true  Use standard statistical methods to    Present the upper confidence limit  
 value.                               construct probability distributions    on the arithmetic mean soil        
                                      or point estimates (e.g., confidence   concentration, in addition to the  
                                      limits).                               best estimate of the arithmetic    
                                                                             mean.                              
                                     Evaluate power of designed                                                 
                                      experiments to detect differences.                                        
                                     Collect additional data.                                                   
                                     Verify location of samples or other    Ground-truth remote sensing data.   
                                      spatial features.                                                         
Model structure uncertainty          Discuss key aggregations and model     Discuss combining different species 
 (process models).                    simplifications.                       into a group based on similar      
                                                                             feeding habits.                    
                                     Compare model predictions with data                                        
                                      collected in the system of interest.                                      
Uncertainty about a model's form     Evaluate whether alternative models    Present results obtained using      
 (empirical models).                  should be combined formally or         alternative models.                
                                      treated separately.                                                       
                                     Compare model predictions with data    Compare results of a plant uptake   
                                      collected in the system of interest.   model with data collected in the   
                                                                             field.                             
----------------------------------------------------------------------------------------------------------------

Text Note 4-5. Considering the Degree of Aggregation in Models

    Wiegert and Bartell (1994) suggest the following considerations for 
evaluating the proper degree of aggregation or disaggregation:
    1. Do not aggregate components with greatly disparate flux rates.
    2. Do not greatly increase the disaggregation of the structural 
aspects of the model without a corresponding increase in the 
sophistication of the functional relationships and controls.
    3. Disaggregate models only insofar as required by the goals of the 
model to facilitate testing.

Text Note 4-6. Questions for Source Description

    Where does the stressor originate?
    What environmental media first receive stressors?
    Does the source generate other constituents that will influence a 
stressor's eventual distribution in the environment?
    Are there other sources of the same stressor?
    Are there background sources?
    Is the source still active?
    Does the source produce a distinctive signature that can be seen in 
the environment, organisms, or communities?

Additional Questions for Introduction of Biological Stressors

    Is there an opportunity for repeated introduction or escape into 
the new environment?
    Will the organism be present on a transportable item?
    Are there mitigation requirements or conditions that would kill or 
impair the organism before entry, during transport, or at the port of 
entry?

Text Note 4-7. Questions to Ask in Evaluating Stressor Distribution

    What are the important transport pathways?
    What characteristics of the stressor influence transport?
    What characteristics of the ecosystem will influence transport?
    What secondary stressors will be formed?
    Where will they be transported?

Text Note 4-8. General Mechanisms of Transport and Dispersal

Physical, Chemical, and Biological Stressors

     By air current.
     In surface water (rivers, lakes, streams).
     Over and/or through the soil surface.
     Through ground water.

Primarily Chemical Stressors

     Through the food web.

[[Page 26899]]

Primarily Biological Stressors

     Splashing or raindrops.
     Human activity (boats, campers).
     Passive transmittal by other organisms.
     Biological vectors.

Text Note 4-9. Questions To Ask in Describing Contact or Co-Occurrence

    Must the receptor actually contact the stressor for adverse effects 
to occur?
    Must the stressor be taken up into a receptor for adverse effects 
to occur?
    What characteristics of the receptors will influence the extent of 
contact or co-occurrence?
    Will abiotic characteristics of the environment influence the 
extent of contact or co-occurrence?
    Will ecosystem processes or community-level interactions influence 
the extent of contact or co-occurrence?

Text Note 4-10. Example of an Exposure Equation: Calculating a 
Potential Dose via Ingestion
[GRAPHIC] [TIFF OMITTED] TN14MY98.020

Where:
ADDpot=Potential average daily dose (e.g., in mg/kg-day)
Ck=Average contaminant concentration in the kth type of food 
(e.g., in mg/kg wet weight)
FRk=Fraction of intake of the kth food type that is from the 
contaminated area (unitless)
NIRk=Normalized ingestion rate of the kth food type on a 
wet-weight basis (e.g., in kg food/kg body-weight-day).
m=Number of contaminated food types

    Note: A similar equation can be used to calculate uptake by adding 
an absorption factor that accounts for the fraction of the chemical in 
the kth food type that is absorbed into the organism. The choice of 
potential dose or uptake depends on the form of the stressor-response 
relationship. Source: U.S. EPA, 1993a.

Text Note 4-11. Measuring Internal Dose Using Biomarkers and Tissue 
Residues

    Biomarkers and tissue residues are particularly useful when 
exposure across many pathways must be integrated and when site-specific 
factors influence bioavailability. They can also be very useful when 
metabolism and accumulation kinetics are important, although these 
factors can make interpretation of results more difficult (McCarty and 
Mackay, 1993). These methods are most useful when they can be 
quantitatively linked to the amount of stressor originally contacted by 
the organism. In addition, they are most useful when the stressor-
response relationship expresses the amount of stressor in terms of the 
tissue residue or biomarker (van Gestel and van Brummelen, 1996). 
Standard analytical methods are generally available for tissue 
residues, making them more readily usable for routine assessments than 
biomarkers. Readers are referred to the review in Ecotoxicology (Vol. 
3, Issue 3, 1994), Huggett et al. (1992), and the debate in Human 
Health and Ecological Risk Assessment (Vol. 2, Issue 2, 1996).

Text Note 4-12. Questions Addressed by the Exposure Profile

    How does exposure occur?
    What is exposed?
    How much exposure occurs? When and where does it occur?
    How does exposure vary?
    How uncertain are the exposure estimates?
    What is the likelihood that exposure will occur?

Text Note 4-13. Questions for Stressor-Response Analysis

    Does the assessment require point estimates or stressor-response 
curves?
    Does the assessment require the establishment of a ``no-effect'' 
level?
    Would cumulative effects distributions be useful?
    Will analyses be used as input to a process model?

Text Note 4-14. Qualitative Stressor-Response Relationships

    The relationship between stressor and response can be described 
qualitatively, for instance, using categories of high, medium, and low, 
to describe the intensity of response given exposure to a stressor. For 
example, Pearlstine et al. (1985) assumed that seeds would not 
germinate if they were inundated with water at the critical time. This 
stressor-response relationship was described simply as a yes or no. In 
most cases, however, the objective is to describe quantitatively the 
intensity of response associated with exposure, and in the best case, 
to describe how intensity of response changes with incremental 
increases in exposure.

Text Note 4-15. Median Effect Levels

    Median effects are those effects elicited in 50% of the test 
organisms exposed to a stressor, typically chemical stressors. Median 
effect concentrations can be expressed in terms of lethality or 
mortality and are known as LC50 or LD50, 
depending on whether concentrations (in the diet or in water) or doses 
(mg/kg) were used. Median effects other than lethality (e.g., effects 
on growth) are expressed as EC50 or ED50. The 
median effect level is always associated with a time parameter (e.g., 
24 or 48 hours). Because these tests seldom exceed 96 hours, their main 
value lies in evaluating short-term effects of chemicals. Stephan 
(1977) discusses several statistical methods to estimate the median 
effect level.

Text Note 4-16. No-Effect Levels Derived From Statistical Hypothesis 
Testing

    Statistical hypothesis tests have typically been used with chronic 
toxicity tests of chemical stressors that evaluate multiple endpoints. 
For each endpoint, the objective is to determine the highest test level 
for which effects are not statistically different from the controls 
(the no-observed-adverse-effect level, NOAEL) and the lowest level at 
which effects were statistically significant from the control (the 
lowest-observed-adverse-effect level, LOAEL). The range between the 
NOAEL and the LOAEL is sometimes called the maximum acceptable toxicant 
concentration, or MATC. The MATC, which can also be reported as the 
geometric mean of the NOAEL and the LOAEL (i.e., GMATC), provides a 
useful reference with which to compare toxicities of various chemical 
stressors.
    Reporting the results of chronic tests in terms of the MATC or 
GMATC has been widely used within the Agency for evaluating pesticides 
and industrial chemicals (e.g., Urban and Cook, 1986; Nabholz, 1991).

Text Note 4-17. General Criteria for Causality (Adapted From Fox, 1991)

Criteria Strongly Affirming Causality

     Strength of association.
     Predictive performance.
     Demonstration of a stressor-response relationship.
     Consistency of association.

Criteria Providing a Basis for Rejecting Causality

     Inconsistency in association.
     Temporal incompatibility.
     Factual implausibility.

Other Relevant Criteria

     Specificity of association.
     Theoretical and biological plausibility.

Text Note 4-18. Koch's Postulates (Pelczar and Reid, 1972)

     A pathogen must be consistently found in association with 
a given disease.

[[Page 26900]]

     The pathogen must be isolated from the host and grown in 
pure culture.
     When inoculated into test animals, the same disease 
symptoms must be expressed.
     The pathogen must again be isolated from the test 
organism.

Text Note 4-19. Examples of Extrapolations To Link Measures of Effect 
to Assessment Endpoints

    Every risk assessment has data gaps that should be addressed, but 
it is not always possible to obtain more information. When there is a 
lack of time, monetary resources, or a practical means to acquire more 
data, extrapolations such as those listed below may be the only way to 
bridge gaps in available data. Extrapolations may be:
     Between taxa (e.g., bluegill to rainbow trout).
     Between responses (e.g., mortality to growth or 
reproduction).
     From laboratory to field.
     Between geographic areas.
     Between spatial scales.
     From data collected over a short time frame to longer-term 
effects.

Text Note 4-20. Questions Related to Selecting Extrapolation Approaches

    How specific is the assessment endpoint?
    Does the spatial or temporal extent of exposure suggest the need 
for additional receptors or extrapolation models?
    Are the quantity and quality of the data available sufficient for 
planned extrapolations and models?
    Is the proposed extrapolation technique consistent with ecological 
information?
    How much uncertainty is acceptable?

Text Note 4-21. Questions To Consider When Extrapolating From Effects 
Observed in the Laboratory to Field Effects of Chemicals

Exposure Factors

    How will environmental fate and transformation of the chemical 
affect exposure in the field?
    How comparable are exposure conditions and the timing of exposure?
    How comparable are the routes of exposure?
    How do abiotic factors influence bioavailability and exposure?
    How likely are preference or avoidance behaviors?

Effects Factors

    What is known about the biotic and abiotic factors controlling 
populations of the organisms of concern?
    To what degree are critical life-stage data available?
    How may exposure to the same or other stressors in the field have 
altered organism sensitivity?

Text Note 4-22. Questions Addressed by the Stressor-Response Profile

    What ecological entities are affected?
    What is the nature of the effect(s)?
    What is the intensity of the effect(s)?
    Where appropriate, what is the time scale for recovery?
    What causal information links the stressor with any observed 
effects?
    How do changes in measures of effects relate to changes in 
assessment endpoints?
    What is the uncertainty associated with the analysis?

Text Note 5-1. An Example of Field Methods Used for Risk Estimation

    Along with quotients comparing field measures of exposure with 
laboratory acute toxicity data (see text note 5-3), EPA evaluated the 
risks of granular carbofuran to birds based on incidents of bird kills 
following carbofuran applications. More than 40 incidents involving 
nearly 30 species of birds were documented. Although reviewers 
identified problems with individual field studies (e.g., lack of 
appropriate control sites, lack of data on carcass-search efficiencies, 
no examination of potential synergistic effects of other pesticides, 
and lack of consideration of other potential receptors such as small 
mammals), there was so much evidence of mortality associated with 
carbofuran application that the study deficiencies did not alter the 
conclusions of high risk found by the assessment (Houseknecht, 1993).

Text Note 5-2. Using Qualitative Categories to Estimate Risks of an 
Introduced Species

    The importation of logs from Chile required an assessment of the 
risks posed by the potential introduction of the bark beetle, Hylurgus 
ligniperda (USDA, 1993). Experts judged the potential for colonization 
and spread of the species, and their opinions were expressed as high, 
medium, or low as to the likelihood of establishment (exposure) or 
consequential effects of the beetle. Uncertainties were similarly 
expressed. A ranking scheme was then used to sum the individual 
elements into an overall estimate of risk (high, medium, or low). 
Narrative explanations of risk accompanied the overall rankings.

Text Note 5-3. Applying the Quotient Method

    When applying the quotient method to chemical stressors, the 
effects concentration or dose (e.g., an LC50, 
LD50, EC50, ED50, NOAEL, or LOAEL) is 
frequently adjusted by uncertainty factors before division into the 
exposure number (U.S. EPA, 1984; Nabholz, 1991; Urban and Cook, 1986; 
see section 4.3.1.3), although EPA used a slightly different approach 
in estimating the risks to the survival of birds that forage in 
agricultural areas where the pesticide granular carbofuran is applied 
(Houseknecht, 1993). In this case, EPA calculated the quotient by 
dividing the estimated exposure levels of carbofuran granules in 
surface soils (number/ft \2\) by the granules/LD50 derived 
from single-dose avian toxicity tests. The calculation yields values 
with units of LD50/ft \2\. It was assumed that a higher 
quotient value corresponded to an increased likelihood that a bird 
would be exposed to lethal levels of granular carbofuran at the soil 
surface. Minimum and maximum values for LD50/ft \2\ were 
estimated for songbirds, upland game birds, and waterfowl that may 
forage within or near 10 different agricultural crops.

Text Note 5-4. Comparing an Exposure Distribution With a Point Estimate 
of Effects.

    The EPA Office of Pollution Prevention and Toxics uses a 
Probabilistic Dilution Model (PDM3) to generate a distribution of daily 
average chemical concentrations based on estimated variations in stream 
flow in a model system. The PDM3 model compares this exposure 
distribution with an aquatic toxicity test endpoint to estimate how 
many days in a 1-year period the endpoint concentration is exceeded 
(Nabholz et al., 1993; U.S. EPA, 1988b). The frequency of exceedance is 
based on the duration of the toxicity test used to derive the effects 
endpoint. Thus, if the endpoint was an acute toxicity level of concern, 
an exceedance would be identified if the level of concern was exceeded 
for 4 days or more (not necessarily consecutive). The exposure 
estimates are conservative in that they assume instantaneous mixing of 
the chemical in the water column and no losses due to physical, 
chemical, or biodegradation effects.

Text Note 5-5. Comparing Cumulative Exposure and Effects Distributions 
for Chemical Stressors

    Exposure distributions for chemical stressors can be compared with 
effects distributions derived from point estimates of acute or chronic 
toxicity values for different species (e.g., HCN, 1993; Cardwell et 
al., 1993; Baker et al., 1994; Solomon et al., 1996). Figure 5-

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5 shows a distribution of exposure concentrations of an herbicide 
compared with single-species toxicity data for algae (and one vascular 
plant species) for the same chemical. The degree of overlap of the 
curves indicates the likelihood that a certain percentage of species 
may be adversely affected. For example, figure 5-5 indicates that the 
10th centile of algal species' EC5 values is exceeded less 
than 10% of the time.
    The predictive value of this approach is evident. The degree of 
risk reduction that could be achieved by changes in exposure associated 
with proposed risk mitigation options can be readily determined by 
comparing modified exposure distributions with the effects distribution 
curve.
    When using effects distributions derived from single-species 
toxicity data, risk assessors should consider the following questions:
     Does the subset of species for which toxicity test data 
are available represent the range of species present in the 
environment?
     Are particularly sensitive (or insensitive) groups of 
organisms represented in the distribution?
     If a criterion level is selected'e.g., protect 95% of 
species--does the 5% of potentially affected species include organisms 
of ecological, commercial, or recreational significance?

Text Note 5-6. Estimating Risk With Process Models

    Models that integrate both exposure and effects information can be 
used to estimate risk. During risk estimation, it is important that 
both the strengths and limitations of a process model approach be 
highlighted. Brody et al. (1993; see Appendix D) linked two process 
models to integrate exposure and effects information and forecast 
spatial and temporal changes in forest communities and their wildlife 
habitat value. While the models were useful for projecting long-term 
effects based on an understanding of the underlying mechanisms of 
change in forest communities and wildlife habitat, they could not 
evaluate all possible stressors of concern and were limited in the 
plant and wildlife species they could consider. Understanding both the 
strengths and limitations of models is essential for accurately 
representing the overall confidence in the assessment.

Text Note 5-7. What Are Statistically Significant Effects?

    Statistical testing is the ``statistical procedure or decision rule 
that leads to establishing the truth or falsity of a hypothesis * * *'' 
(Alder and Roessler, 1972). Statistical significance is based on the 
number of data points, the nature of their distribution, whether 
intertreatment variance exceeds intratreatment variance in the data, 
and the a priori significance level (). The types of 
statistical tests and the appropriate protocols (e.g., power of test) 
for these tests should be established as part of the analysis plan 
during problem formulation.

Text Note 5-8. Possible Risk Assessment Report Elements

     Describe risk assessor/risk manager planning results.
     Review the conceptual model and the assessment endpoints.
     Discuss the major data sources and analytical procedures 
used.
     Review the stressor-response and exposure profiles.
     Describe risks to the assessment endpoints, including risk 
estimates and adversity evaluations.
     Review and summarize major areas of uncertainty (as well 
as their direction) and the approaches used to address them.
     Discuss the degree of scientific consensus in key areas of 
uncertainty.
    ' Identify major data gaps and, where appropriate, indicate whether 
gathering additional data would add significantly to the overall 
confidence in the assessment results.
    ' Discuss science policy judgments or default assumptions used to 
bridge information gaps and the basis for these assumptions.
    ' Discuss how the elements of quantitative uncertainty analysis are 
embedded in the estimate of risk.

Text Note 5-9. Clear, Transparent, Reasonable, and Consistent Risk 
Characterizations

For Clarity

     Be brief; avoid jargon.
     Make language and organization understandable to risk 
managers and the informed lay person.
     Fully discuss and explain unusual issues specific to a 
particular risk assessment.

For Transparency

     Identify the scientific conclusions separately from policy 
judgments.
     Clearly articulate major differing viewpoints of 
scientific judgments.
     Define and explain the risk assessment purpose (e.g., 
regulatory purpose, policy analysis, priority setting).
     Fully explain assumptions and biases (scientific and 
policy).

For Reasonableness

     Integrate all components into an overall conclusion of 
risk that is complete, informative, and useful in decision making.
     Acknowledge uncertainties and assumptions in a forthright 
manner.
     Describe key data as experimental, state-of-the-art, or 
generally accepted scientific knowledge.
     Identify reasonable alternatives and conclusions that can 
be derived from the data.
     Define the level of effort (e.g., quick screen, extensive 
characterization) along with the reason(s) for selecting this level of 
effort.
     Explain the status of peer review.

For Consistency with Other Risk Characterizations

     Describe how the risks posed by one set of stressors 
compare with the risks posed by a similar stressor(s) or similar 
environmental conditions.
     Indicate how the strengths and limitations of the 
assessment compare with past assessments.

Text Note 6-1. Questions Regarding Risk Assessment Results (Adapted 
From U.S. EPA, 1993c)

Questions Principally for Risk Assessors To Ask Risk Managers

     Are the risks sufficiently well defined (and data gaps 
small enough) to support a risk management decision?
     Was the right problem analyzed?
     Was the problem adequately characterized?

Questions Principally for Risk Managers To Ask Risk Assessors

     What effects might occur?
     How adverse are the effects?
     How likely is it that effects will occur?
     When and where do the effects occur?
     How confident are you in the conclusions of the risk 
assessment?
     What are the critical data gaps, and will information be 
available in the near future to fill these gaps?
     Are more ecological risk assessment iterations required?
     How could monitoring help evaluate the results of the risk 
management decision?

Text Note 6-2. Risk Communication Considerations for Risk Managers 
(U.S. EPA, 1995b)

     Plan carefully and evaluate the success of your 
communication efforts.
     Coordinate and collaborate with other credible sources.
     Accept and involve the public as a legitimate partner.

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     Listen to the public's specific concerns.
     Be honest, frank, and open.
     Speak clearly and with compassion.
     Meet the needs of the media.

Text Note A-1. Stressor vs. Agent

    Agent has been suggested as an alternative for the term stressor 
(Suter et al., 1994). Agent is thought to be a more neutral term than 
stressor, but agent is also associated with certain classes of 
chemicals (e.g., chemical warfare agents). In addition, agent has the 
connotation of the entity that is initially released from the source, 
whereas stressor has the connotation of the entity that causes the 
response. Agent is used in EPA's Guidelines for Exposure Assessment 
(U.S. EPA, 1992b) (i.e., with exposure defined as ``contact of a 
chemical, physical, or biological agent''). The two terms are 
considered to be nearly synonymous, but stressor is used throughout 
these Guidelines for internal consistency.

Appendix A--Changes From EPA's Ecological Risk Assessment Framework

    EPA has gained much experience with the ecological risk 
assessment process since the publication of the Framework Report 
(U.S. EPA, 1992a) and has received many suggestions for 
modifications of both the process and the terminology. While EPA is 
not recommending major changes in the overall ecological risk 
assessment process, modifications are summarized here to assist 
those who may already be familiar with the Framework Report. Changes 
in the diagram are discussed first, followed by changes in 
terminology and definitions.

A.1. Changes in the Framework Diagram

    The revised framework diagram is shown in figure 1-2. Within 
each phase, rectangles are used to designate inputs, hexagons 
indicate actions, and circles represent outputs. There have been 
some minor changes in the wording for the boxes outside of the risk 
assessment process (planning; communicating results to the risk 
manager; acquire data, iterate process, monitor results). ``Iterate 
process'' was added to emphasize the iterative (and frequently 
tiered) nature of risk assessment. The term ``interested parties'' 
was added to the planning and risk management boxes to indicate 
their increasing role in the risk assessment process (Commission on 
Risk Assessment and Risk Management, 1997). The new diagram of 
problem formulation contains several changes. The hexagon emphasizes 
the importance of integrating available information before selecting 
assessment endpoints and building conceptual models. The three 
products of problem formulation are enclosed in circles. Assessment 
endpoints are shown as a key product that drives conceptual model 
development. The conceptual model remains a central product of 
problem formulation. The analysis plan has been added as an explicit 
product of problem formulation to emphasize the need to plan data 
evaluation and interpretation before analyses begin.
    In the analysis phase, the left-hand side of figure 1-2 shows 
the general process of characterization of exposure, and the right-
hand side shows the characterization of ecological effects. It is 
important that evaluation of these two aspects of analysis is an 
interactive process to ensure compatible outputs that can be 
integrated in risk characterization. The dotted line and hexagon 
that include both the exposure and ecological response analyses 
emphasize this interaction. In addition, the first three boxes in 
analysis now include the measures of exposure, effects, and 
ecosystem and receptor characteristics that provide input to the 
exposure and ecological response analyses.
    Experience with the application of risk characterization as 
outlined in the Framework Report suggests the need for several 
modifications in this process. Risk estimation entails the 
integration of exposure and effects estimates along with an analysis 
of uncertainties. The process of risk estimation outlined in the 
Framework Report separates integration and uncertainty. The original 
purpose for this separation was to emphasize the importance of 
estimating uncertainty. This separation is no longer needed since 
uncertainty analysis is now explicitly addressed in most risk 
integration methods.
    The description of risk is similar to the process described in 
the Framework Report. Topics included in the risk description 
include the lines of evidence that support causality and a 
determination of the ecological adversity of observed or predicted 
effects. Considerations for reporting risk assessment results are 
also described.

A.2. Changes in Definitions and Terminology

    Except as noted below, these Guidelines retain definitions used 
in the Framework Report (see Appendix B). Some definitions have been 
revised, especially those related to endpoints and exposure. Some 
changes in the classification of uncertainty from the Framework 
Report are also described in this section.

A.2.1. Endpoint Terminology

    The Framework Report uses the assessment and measurement 
endpoint terminology of Suter (1990), but offers no specific terms 
for measures of stressor levels or ecosystem characteristics. 
Experience has demonstrated that measures unrelated to effects are 
sometimes inappropriately called measurement endpoints, which were 
defined by Suter (1990) as ``measurable responses to a stressor that 
are related to the valued characteristic chosen as assessment 
endpoints.'' These Guidelines replace measurement endpoint with 
measure of effect, which is ``a change in an attribute of an 
assessment endpoint or its surrogate in response to a stressor to 
which it is exposed.'' An assessment endpoint is an explicit 
expression of the environmental value to be protected, operationally 
defined by an entity and its attributes. Since data other than those 
required to evaluate responses (i.e., measures of effects) are 
required for an ecological risk assessment, two additional types of 
measures are used. Measures of exposure include stressor and source 
measurements, while measures of ecosystem and receptor 
characteristics include, for example, habitat measures, soil 
parameters, water quality conditions, or life-history parameters 
that may be necessary to better characterize exposure or effects. 
Any of the three types of measures may be actual data (e.g., 
mortality), summary statistics (e.g., an LC50), or 
estimated values (e.g., an LC50 estimated from a 
structure-activity relationship).

A.2.2. Exposure Terminology

    These Guidelines define exposure in a manner that is relevant to 
any chemical, physical, or biological entity. While the broad 
concepts are the same, the language and approaches vary depending on 
whether a chemical, physical, or biological entity is the subject of 
assessment. Key exposure-related terms and their definitions are:
     Source. A source is an entity or action that releases 
to the environment or imposes on the environment a chemical, 
physical, or biological stressor or stressors. Sources may include a 
waste treatment plant, a pesticide application, a logging operation, 
introduction of exotic organisms, or a dredging project.
     Stressor. A stressor is any physical, chemical, or 
biological entity that can induce an adverse response. This term is 
used broadly to encompass entities that cause primary effects and 
those primary effects that can cause secondary (i.e., indirect) 
effects. Stressors may be chemical (e.g., toxics or nutrients), 
physical (e.g., dams, fishing nets, or suspended sediments), or 
biological (e.g., exotic or genetically engineered organisms). While 
risk assessment is concerned with the characterization of adverse 
responses, under some circumstances a stressor may be neutral or 
produce effects that are beneficial to certain ecological components 
(see text note A-1). Primary effects may also become stressors. For 
example, a change in a bottomland hardwood plant community affected 
by rising water levels can be thought of as a stressor influencing 
the wildlife community. Stressors may also be formed through abiotic 
interactions; for example, the increase in ultraviolet light 
reaching the Earth's surface results from the interaction of the 
original stressors released (chlorofluorocarbons) with the ecosystem 
(stratospheric ozone).
     Exposure. As discussed above, these Guidelines use the 
term exposure broadly to mean ``subjected to some action or 
influence.'' Used in this way, exposure applies to physical and 
biological stressors as well as to chemicals (organisms are commonly 
said to be exposed to radiation, pathogens, or heat). Exposure is 
also applicable to higher levels of biological organization, such as 
exposure of a benthic community to dredging, exposure of an owl 
population to habitat modification, or exposure of a wildlife 
population to hunting. Although the operational definition of 
exposure, particularly the units of measure, depends on the stressor 
and receptor (defined below), the following general definition is 
applicable: Exposure is the contact or co-occurrence of a stressor 
with a receptor.
     Receptor. The receptor is the ecological entity exposed 
to the stressor. This term may

[[Page 26903]]

refer to tissues, organisms, populations, communities, and 
ecosystems. While either ``ecological component'' (U.S. EPA, 1992a) 
or ``biological system'' (Cohrssen and Covello, 1989) are 
alternative terms, ``receptor'' is usually clearer in discussions of 
exposure where the emphasis is on the stressor-receptor 
relationship.
    As discussed below, both disturbance and stress regime have been 
suggested as alternative terms for exposure. Neither term is used in 
these Guidelines, which instead use exposure as broadly defined 
above.
     Disturbance. A disturbance is any event or series of 
events that disrupts ecosystem, community, or population structure 
and changes resources, substrate availability, or the physical 
environment (modified slightly from White and Pickett, 1985). 
Defined in this way, disturbance is clearly a kind of exposure 
(i.e., an event that subjects a receptor, the disturbed system, to 
the actions of a stressor). Disturbance may be a useful alternative 
to stressor specifically for physical stressors that are deletions 
or modifications (e.g., logging, dredging, flooding).
     Stress Regime. The term stress regime has been used in 
at least three distinct ways: (1) To characterize exposure to 
multiple chemicals or to both chemical and nonchemical stressors 
(more clearly described as multiple exposure, complex exposure, or 
exposure to mixtures), (2) as a synonym for exposure that is 
intended to avoid overemphasis on chemical exposures, and (3) to 
describe the series of interactions of exposures and effects 
resulting in secondary exposures, secondary effects, and, finally, 
ultimate effects (also known as risk cascade [Lipton et al., 1993]), 
or causal chain, pathway, or network (Andrewartha and Birch, 1984). 
Because of the potential for confusion and the availability of 
other, clearer terms, this term is not used in these Guidelines.

A.2.3. Uncertainty Terminology

    The Framework Report divided uncertainty into conceptual model 
formation, information and data, stochasticity, and error. These 
Guidelines discuss uncertainty throughout the process, focusing on 
the conceptual model (section 3.4.3), the analysis phase (section 
4.1.3), and the incorporation of uncertainty in risk estimates 
(section 5.1). The bulk of the discussion appears in section 4.1.3, 
where the discussion is organized according to the following sources 
of uncertainty:
     Unclear communication.
     Descriptive errors.
     Variability.
     Data gaps.
     Uncertainty about a quantity's true value.
     Model structure uncertainty (process models).
     Uncertainty about a model's form (empirical models).

A.2.4. Lines of Evidence

    The Framework Report used the phrase weight of evidence to 
describe the process of evaluating multiple lines of evidence in 
risk characterization. These Guidelines use the phrase lines of 
evidence instead to de-emphasize the balancing of opposing factors 
based on assignment of quantitative values to reach a conclusion 
about a ``weight'' in favor of a more inclusive approach, which 
evaluates all available information, even evidence that may be 
qualitative in nature.

Appendix B--Key Terms (Adapted From U.S. EPA, 1992a)

    Adverse ecological effects--Changes that are considered 
undesirable because they alter valued structural or functional 
characteristics of ecosystems or their components. An evaluation of 
adversity may consider the type, intensity, and scale of the effect 
as well as the potential for recovery.
    Agent--Any physical, chemical, or biological entity that can 
induce an adverse response (synonymous with stressor).
    Assessment endpoint--An explicit expression of the environmental 
value that is to be protected, operationally defined by an 
ecological entity and its attributes. For example, salmon are valued 
ecological entities; reproduction and age class structure are some 
of their important attributes. Together ``salmon reproduction and 
age class structure'' form an assessment endpoint.
    Attribute--A quality or characteristic of an ecological entity. 
An attribute is one component of an assessment endpoint.
    Characterization of ecological effects--A portion of the 
analysis phase of ecological risk assessment that evaluates the 
ability of a stressor(s) to cause adverse effects under a particular 
set of circumstances.
    Characterization of exposure--A portion of the analysis phase of 
ecological risk assessment that evaluates the interaction of the 
stressor with one or more ecological entities. Exposure can be 
expressed as co-occurrence or contact, depending on the stressor and 
ecological component involved.
    Community--An assemblage of populations of different species 
within a specified location in space and time.
    Comparative risk assessment--A process that generally uses a 
professional judgment approach to evaluate the relative magnitude of 
effects and set priorities among a wide range of environmental 
problems (e.g., U.S. EPA, 1993d). Some applications of this process 
are similar to the problem formulation portion of an ecological risk 
assessment in that the outcome may help select topics for further 
evaluation and help focus limited resources on areas having the 
greatest risk reduction potential. In other situations, a 
comparative risk assessment is conducted more like a preliminary 
risk assessment. For example, EPA's Science Advisory Board used 
professional judgment and an ecological risk assessment approach to 
analyze future ecological risk scenarios and risk management 
alternatives (U.S. EPA, 1995e).
    Conceptual model--A conceptual model in problem formulation is a 
written description and visual representation of predicted 
relationships between ecological entities and the stressors to which 
they may be exposed.
    Cumulative distribution function (CDF)--Cumulative distribution 
functions are particularly useful for describing the likelihood that 
a variable will fall within different ranges of x. F(x) (i.e., the 
value of y at x in a CDF plot) is the probability that a variable 
will have a value less than or equal to x (figure B-1).

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    Cumulative ecological risk assessment--A process that involves 
consideration of the aggregate ecological risk to the target entity 
caused by the accumulation of risk from multiple stressors.
    Disturbance--Any event or series of events that disrupts 
ecosystem, community, or population structure and changes resources, 
substrate availability, or the physical environment (modified from 
White and Pickett, 1985).
    EC50--A statistically or graphically estimated 
concentration that is expected to cause one or more specified 
effects in 50% of a group of organisms under specified conditions 
(ASTM, 1996).
    Ecological entity--A general term that may refer to a species, a 
group of species, an ecosystem function or characteristic, or a 
specific habitat. An ecological entity is one component of an 
assessment endpoint.
    Ecological relevance--One of the three criteria for assessment 
endpoint selection. Ecologically relevant endpoints reflect 
important characteristics of the system and are functionally related 
to other endpoints.
    Ecological risk assessment--The process that evaluates the 
likelihood that adverse ecological effects may occur or are 
occurring as a result of exposure to one or more stressors.
    Ecosystem--The biotic community and abiotic environment within a 
specified location in space and time.
    Environmental impact statement (EIS)--Environmental impact 
statements are prepared under the National Environmental Policy Act 
by Federal agencies as they evaluate the environmental consequences 
of proposed actions. EISs describe baseline environmental 
conditions; the purpose of, need for, and consequences of a proposed 
action; the no-action alternative; and the consequences of a 
reasonable range of alternative actions. A separate risk assessment 
could be prepared for each alternative, or a comparative risk 
assessment might be developed. However, risk assessment is not the 
only approach used in EISs.
    Exposure--The contact or co-occurrence of a stressor with a 
receptor.
    Exposure profile--The product of characterization of exposure in 
the analysis phase of ecological risk assessment. The exposure 
profile summarizes the magnitude and spatial and temporal patterns 
of exposure for the scenarios described in the conceptual model.
    Exposure scenario--A set of assumptions concerning how an 
exposure may take place, including assumptions about the exposure 
setting, stressor characteristics, and activities that may lead to 
exposure.
    Hazard assessment--This term has been used to mean either (1) 
evaluating the intrinsic effects of a stressor (U.S. EPA, 1979) or 
(2) defining a margin of safety or quotient by comparing a 
toxicologic effects concentration with an exposure estimate (SETAC, 
1987).
    LC50--A statistically or graphically estimated 
concentration that is expected to be lethal to 50% of a group of 
organisms under specified conditions (ASTM, 1996).
    Lines of evidence--Information derived from different sources or 
by different techniques that can be used to describe and interpret 
risk estimates. Unlike the term ``weight of evidence,'' it does not 
necessarily imply assignment of quantitative weightings to 
information.
    Lowest-observed-adverse-effect level (LOAEL)--The lowest level 
of a stressor evaluated in a test that causes statistically 
significant differences from the controls.
    Maximum acceptable toxic concentration (MATC)--For a particular 
ecological effects test, this term is used to mean either the range 
between the NOAEL and the LOAEL or the geometric mean of the NOAEL 
and the LOAEL. The geometric mean is also known as the chronic 
value.
    Measure of ecosystem and receptor characteristics--Measures that 
influence the behavior and location of ecological entities of the 
assessment endpoint, the distribution of a stressor, and life-
history characteristics of the assessment endpoint or its surrogate 
that may affect exposure or response to the stressor.
    Measure of effect--A change in an attribute of an assessment 
endpoint or its surrogate in response to a stressor to which it is 
exposed.
    Measure of exposure--A measure of stressor existence and 
movement in the environment and its contact or co-occurrence with 
the assessment endpoint.
    Measurement endpoint--See ``measure of effect.''
    No-observed-adverse-effect level (NOAEL)--The highest level of a 
stressor evaluated in a test that does not cause statistically 
significant differences from the controls.
    Population--An aggregate of individuals of a species within a 
specified location in space and time.
    Primary effect--An effect where the stressor acts on the 
ecological component of interest itself, not through effects on 
other components of the ecosystem (synonymous with direct effect; 
compare with definition for secondary effect).
    Probability density function (PDF)--Probability density 
functions are particularly useful in describing the relative 
likelihood that a variable will have different particular values of 
x. The probability that a variable will have a value within a small 
interval around x can be approximated by multiplying f(x) (i.e., the 
value of y at x in a PDF plot) by the width of the interval (figure 
B-2).

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    Prospective risk assessment--An evaluation of the future risks 
of a stressor(s) not yet released into the environment or of future 
conditions resulting from an existing stressor(s).
    Receptor--The ecological entity exposed to the stressor.
    Recovery--The rate and extent of return of a population or 
community to some aspect(s) of its previous condition. Because of 
the dynamic nature of ecological systems, the attributes of a 
``recovered'' system should be carefully defined.
    Relative risk assessment--A process similar to comparative risk 
assessment. It involves estimating the risks associated with 
different stressors or management actions. To some, relative risk 
connotes the use of quantitative risk techniques, while comparative 
risk approaches more often rely on professional judgment. Others do 
not make this distinction.
    Retrospective risk assessment--An evaluation of the causal 
linkages between observed ecological effects and stressor(s) in the 
environment.
    Risk characterization--A phase of ecological risk assessment 
that integrates the exposure and stressor response profiles to 
evaluate the likelihood of adverse ecological effects associated 
with exposure to a stressor. Lines of evidence and the adversity of 
effects are discussed.
    Secondary effect--An effect where the stressor acts on 
supporting components of the ecosystem, which in turn have an effect 
on the ecological component of interest (synonymous with indirect 
effects; compare with definition for primary effect).
    Source--An entity or action that releases to the environment or 
imposes on the environment a chemical, physical, or biological 
stressor or stressors.
    Source term--As applied to chemical stressors, the type, 
magnitude, and patterns of chemical(s) released.
    Stressor--Any physical, chemical, or biological entity that can 
induce an adverse response (synonymous with agent).
    Stressor-response profile--The product of characterization of 
ecological effects in the analysis phase of ecological risk 
assessment. The stressor-response profile summarizes the data on the 
effects of a stressor and the relationship of the data to the 
assessment endpoint.
    Stress regime--The term ``stress regime'' has been used in at 
least three distinct ways: (1) To characterize exposure to multiple 
chemicals or to both chemical and nonchemical stressors (more 
clearly described as multiple exposure, complex exposure, or 
exposure to mixtures), (2) as a synonym for exposure that is 
intended to avoid overemphasis on chemical exposures, and (3) to 
describe the series of interactions of exposures and effects 
resulting in secondary exposures, secondary effects and, finally, 
ultimate effects (also known as risk cascade [Lipton et al., 1993]), 
or causal chain, pathway, or network (Andrewartha and Birch, 1984).
    Trophic levels--A functional classification of taxa within a 
community that is based on feeding relationships (e.g., aquatic and 
terrestrial green plants make up the first trophic level and 
herbivores make up the second).

Appendix C--Conceptual Model Examples

    Conceptual model diagrams are visual representations of the 
conceptual models. They may be based on theory and logic, empirical 
data, mathematical models, or probability models. These diagrams are 
useful tools for communicating important pathways in a clear and 
concise way. They can be used to ask new questions about 
relationships that help generate plausible risk hypotheses. Further 
discussion of conceptual models is found in section 3.4.
    Flow diagrams like those shown in figures C-1 through C-3 are 
typical conceptual model diagrams. When constructing flow diagrams, 
it is helpful to use distinct and consistent shapes to distinguish 
between stressors, assessment endpoints, responses, exposure routes, 
and ecosystem processes. Although flow diagrams are often used to 
illustrate conceptual models, there is no set configuration for 
conceptual model diagrams, and the level of complexity may vary 
considerably depending on the assessment. Pictorial representations 
of the processes of an ecosystem can be more effective (e.g., 
Bradley and Smith, 1989).

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    Figure C-1 illustrates the relationship between a primary 
physical stressor (logging roads) and an effect on an assessment 
endpoint (fecundity in insectivorous fish). This simple diagram 
illustrates the effect of building logging roads (which could be 
considered a stressor or a source) in ecosystems where slope, soil 
type, low riparian cover, and other ecosystem characteristics lead 
to the erosion of soil, which enters streams and smothers the 
benthic organisms (exposure pathway is not explicit in this 
diagram). Because of the dependence of insectivorous fish on benthic 
organisms, the fish are believed to be at risk from the building of 
logging roads. Each arrow in this diagram represents a hypothesis 
about the proposed relationship (e.g., human action and stressor, 
stressor and effect, primary effect to secondary effect). Each risk 
hypothesis provides insights into the kinds of data that will be 
needed to verify that the hypothesized relationships are valid.
    Figure C-2 is a conceptual model used by Kendall et al. (1996) 
to track a contaminant through upland ecosystems. In this example, 
upland birds are exposed to lead shot when it becomes embedded in 
their tissue after being shot and by ingesting lead accidentally 
when feeding on the ground. Both are hypothesized to result in 
increased morbidity (e.g., lower reproduction and competitiveness 
and higher predation and infection) and mortality, either directly 
(lethal intoxication) or indirectly (effects of morbidity leading to 
mortality). These effects are believed to result in changes in 
upland bird populations and, because of hypothesized exposure of 
predators to lead, to increased predator mortality. This example 
shows multiple exposure pathways for effects on two assessment 
endpoints. Each arrow contains within it assumptions and hypotheses 
about the relationship depicted that provide the basis for 
identifying data needs and analyses.
    Figure C-3 is a conceptual model adapted from the Waquoit Bay 
watershed risk assessment. At the top of the model, multiple human 
activities that occur in the watershed are shown in rectangles. 
Those sources of stressors are linked to stressor types depicted in 
ovals. Multiple sources are shown to contribute to an individual 
stressor, and each source may contribute to more than one stressor. 
The stressors then lead to multiple ecological effects depicted 
again in rectangles. Some rectangles are double-lined to indicate 
effects that can be directly measured for data analysis. Finally, 
the effects are linked to particular assessment endpoints. The 
connections show that one effect can result in changes in many 
assessment endpoints. To fully depict exposure pathways and types of 
effects, specific portions of this conceptual model would need to be 
expanded to illustrate those relationships.

Appendix D--Analysis Phase Examples

    The analysis phase process is illustrated here for a chemical, 
physical, and biological stressor. These examples do not represent 
all possible approaches, but they illustrate the analysis phase 
process using information from actual assessments.

D.1. Special Review of Granular Formulations of Carbofuran Based on 
Adverse Effects on Birds

    Figure D-1 is based on an assessment of the risks of carbofuran 
to birds under the Federal Insecticide, Fungicide, and Rodenticide 
Act (FIFRA) (Houseknecht, 1993). Carbofuran is a broad-spectrum 
insecticide and nematicide applied primarily in granular form on 27 
crops as well as forests and pine seed orchards. The assessment 
endpoint was survival of birds that forage in agricultural areas 
where carbofuran is applied.

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    The analysis phase focused on birds that may incidentally ingest 
granules as they forage or that may eat other animals that contain 
granules or residues. Measures of exposure included application 
rates, attributes of the formulation (e.g., size of granules), and 
residues in prey organisms. Measures of the ecosystem and receptors 
included an inventory of bird species that may be exposed following 
applications for 10 crops. The birds' respective feeding behaviors 
were considered in developing routes of exposure. Measures of effect 
included laboratory toxicity studies and field investigations of 
bird mortality.
    The source of the chemical was application of the pesticide in 
granular form. The distribution of the pesticide in agricultural 
fields was estimated on the basis of the application rate. The 
number of exposed granules was estimated from literature data. On 
the basis of a review of avian feeding behavior, seed-eating birds 
were assumed to ingest any granules left uncovered in the field. The 
intensity of exposure was summarized as the number of exposed 
granules per square foot.
    The stressor-response relationship was described using the 
results of toxicity tests. These data were used to construct a 
toxicity statistic expressed as the number of granules needed to 
kill 50% of the test birds (i.e., granules per LD50), 
assuming 0.6 mg of active ingredient per granule and average body 
weights for the birds tested. Field studies were used to document 
the occurrence of bird deaths following applications and provide 
further causal evidence. Carbofuran residues and cholinesterase 
levels were used to confirm that exposure to carbofuran caused the 
deaths.

D.2. Modeling Losses of Bottomland-Forest Wetlands

    Figure D-2 is based on an assessment of the ecological 
consequences (risks) of long-term changes in hydrologic conditions 
(water-level elevations) for three habitat types in the Lake Verret 
Basin of Louisiana (Brody et al., 1989, 1993; Conner and Brody, 
1989). The project was intended to provide a habitat-based approach 
for assessing the environmental impacts of Federal water projects 
under the National Environmental Policy Act and Section 404 of the 
Clean Water Act. Output from the models provided risk managers with 
information on how changes in water elevation might alter the 
ecosystem. The primary anthropogenic stressor addressed in this 
assessment was artificial levee construction for flood control, 
which contributes to land subsidence by reducing sediment deposition 
in the floodplain. Assessment endpoints included forest community 
structure and habitat value to wildlife species and the species 
composition of the wildlife community.

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    The analysis phase began by considering primary (direct) effects 
of water-level changes on plant community composition and habitat 
characteristics. Measures of exposure included the attributes and 
placement of the levees and water-level measurements. Measures of 
ecosystem and receptor characteristics included location and extent 
of bottomland-hardwood communities, plant species occurrences within 
these communities, and information on historic flow regimes. 
Measures of effects included laboratory studies of plant response to 
moisture and field measurements along moisture gradients.
    While the principal stressor under evaluation was the 
construction of levees, the decreased gradient of the river due to 
sediment deposition at its mouth also contributed to increased water 
levels. The extent and frequency of flooding were simulated by the 
FORFLO model based on estimates of net subsidence rates from levee 
construction and decreased river gradient. Seeds and seedlings of 
the tree species were assumed to be exposed to the altered flooding 
regime. Stressor-response relationships describing plant response to 
moisture (e.g., seed germination, survival) were embedded within the 
FORFLO model. This information was used by the model to simulate 
changes in plant communities: The model tracks the species type, 
diameter, and age of each tree on simulated plots from the time the 
tree enters the plot as a seedling or sprout until it dies. The 
FORFLO model calculated changes in the plant community over time 
(from 50 to 280 years). The spatial extent of the three habitat 
types of interest--wet bottomland hardwoods, dry bottomland 
hardwoods, and cypress-tupelo swamp--was mapped into a GIS along 
with the hydrological information. The changes projected by FORFLO 
were then manually linked to the GIS to show how the spatial 
distribution of different communities would change. Evidence that 
flooding would actually cause these changes included comparisons of 
model predictions with field measurements, the laboratory studies of 
plant response to moisture, and knowledge of the mechanisms by which 
flooding elicits changes in plant communities.
    Secondary (indirect) effects on wildlife associated with changes 
in the habitat provided by the plant community formed the second 
part of the analysis phase. Important measures included life-history 
characteristics and habitat needs of the wildlife species. Effects 
on wildlife were inferred by evaluating the suitability of the plant 
community as habitat. Specific aspects of the community structures 
calculated by the FORFLO model provided the input to this part of 
the analysis. For example, the number of snags was used to evaluate 
habitat value for woodpeckers. Resident wildlife (represented by 
five species) was assumed to co-occur with the altered plant 
community. Habitat value was evaluated by calculating the Habitat 
Suitability Index (HSI) for each habitat type multiplied by the 
habitat type's area.
    A combined exposure and stressor-response profile is shown in 
figure D-2; these two elements were combined with the models used 
for the analysis and then used directly in risk characterization.

D.3. Pest Risk Assessment of Importation of Logs from Chile

    Figure D-3 is based on the assessment of potential risks to U.S. 
forests due to the incidental introduction of insects, fungi, and 
other pests inhabiting logs harvested in Chile and transported to 
U.S. ports (USDA, 1993). This risk assessment was used to determine 
whether actions to restrict or regulate the importation of Chilean 
logs were needed to protect U.S. forests and was conducted by a team 
of six experts under the auspices of the U.S. Department of 
Agriculture Forest Service. Stressors include insects, forest 
pathogens (e.g., fungi), and other pests. The assessment endpoint 
was the survival and growth of tree species (particularly conifers) 
in the western United States. Damage that would affect the 
commercial value of the trees as lumber was clearly of interest.

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    The analysis phase was carried out by eliciting professional 
opinions from a team of experts. Measures of exposure used by the 
team included distribution information for the imported logs and 
attributes of the insects and pathogens such as dispersal mechanisms 
and life-history characteristics. Measures of ecosystem and receptor 
characteristics included the climate of the United States, location 
of geographic barriers, knowledge of host suitability, and ranges of 
potential host species. Measures of effect included knowledge of the 
infectivity of these pests in other countries and the infectivity of 
similar pests on U.S. hosts.
    This information was used by the risk assessment team to 
evaluate the potential for exposure. They began by evaluating the 
likelihood of entry of infested logs into the United States. The 
distribution of the organism's given entry was evaluated by 
considering the potential for colonization and spread beyond the 
point of entry as well as the likelihood of the organisms surviving 
and reproducing. The potential for exposure was summarized by 
assigning each of the above elements a judgment-based value of high, 
medium, or low.
    The evaluation of ecological effects was also conducted on the 
basis of collective professional judgment. Of greatest relevance to 
this guidance was the consideration of environmental damage 
potential, defined as the likelihood of ecosystem destabilization, 
reduction in biodiversity, loss of keystone species, and reduction 
or elimination of endangered or threatened species. (The team also 
considered economic damage potential and social and political 
influences; however, for the purposes of these Guidelines, those 
factors are considered to be part of the risk management process.) 
Again, each consideration was assigned a value of high, medium, or 
low to summarize the potential for ecological effects.

Appendix E--Criteria for Determining Ecological Adversity: A 
Hypothetical Example (Adapted From Harwell et al., 1994) \1\

    As a result of a collision at sea, an oil tanker releases 15 
million barrels of #2 fuel oil 3 km offshore. It is predicted that 
prevailing winds will carry the fuel onshore within 48 to 72 hours. 
The coastline has numerous small embayments that support an 
extensive shallow, sloping subtidal community and a rich intertidal 
community. A preliminary assessment determines that if no action is 
taken, significant risks to the communities will result. Additional 
risk assessments are conducted to determine which of two options 
should be used to clean up the oil spill.
---------------------------------------------------------------------------

    \1\ This example is simplified for illustrative purposes. In 
other situations, it may be considerably more difficult to draw 
clear conclusions regarding relative ecological adversity.
---------------------------------------------------------------------------

    Option 1 is to use a dispersant to break up the slick, which 
would reduce the likelihood of extensive onshore contamination but 
would cause extensive mortality to the phytoplankton, zooplankton, 
and ichthyoplankton (fish larvae), which are important for 
commercial fisheries. Option 2 is to try to contain and pump off as 
much oil as possible; this option anticipates that a shift in wind 
direction will move the spill away from shore and allow for natural 
dispersal at sea. If this does not happen, the oil will contaminate 
the extensive sub-and intertidal mud flats, rocky intertidal 
communities, and beaches and pose an additional hazard to avian and 
mammalian fauna. It is assumed there will be a demonstrable change 
beyond natural variability in the assessment endpoints (e.g., 
structure of planktonic, benthic, and intertidal communities). What 
is the adversity of each option?
     Nature and intensity of the effect. For both options, 
the magnitude of change in the assessment endpoints is likely to be 
severe. Planktonic populations often are characterized by extensive 
spatial and temporal variability. Nevertheless, within the spatial 
boundaries of the spill, the use of dispersants is likely to produce 
complete mortality of all planktonic forms within the upper 3 m of 
water. For benthic and intertidal communities, which generally are 
stable and have less spatial and temporal variability than 
planktonic forms, oil contamination will likely result in severe 
impacts on survival and chronic effects lasting for several years. 
Thus, under both options, changes in the assessment endpoints will 
probably exceed the natural variability for threatened communities 
in both space and time.
     Spatial scale. The areal extent of impacts is similar 
for each of the options. While extensive, the area of impact 
constitutes a small percentage of the landscape. This leaves 
considerable area available for replacement stocks and creates 
significant fragmentation of either the planktonic or inter-and 
subtidal habitats. Ecological adversity is reduced because the area 
is not a mammalian or avian migratory corridor.
     Temporal scale and recovery. On the basis of experience 
with other oil spills, it is assumed that the effects are reversible 
over some time period. The time needed for reversibility of changes 
in phytoplankton and zooplankton populations should be short (days 
to weeks) given their rapid generation times and easy immigration 
from adjacent water masses. There should not be a long recovery 
period for ichthyoplankton, since they typically experience 
extensive natural mortality, and immigration is readily available 
from surrounding water masses. On the other hand, the time needed 
for reversibility of changes in benthic and intertidal communities 
is likely to be long (years to decades). First, the stressor (oil) 
would be likely to persist in sediments and on rocks for several 
months to years. Second, the life histories of the species 
comprising these communities span 3 to 5 years. Third, the 
reestablishment of benthic intertidal community and ecosystem 
structure (hierarchical composition and function) often requires 
decades.
    Both options result in (1) assessment endpoint effects that are 
of great severity, (2) exceedances of natural variability for those 
endpoints, and (3) similar estimates of areal impact. What 
distinguishes the two options is temporal scale and reversibility. 
In this regard, changes to the benthic and intertidal ecosystems are 
considerably more adverse than those to the plankton. On this basis, 
the option of choice would be to disperse the oil, effectively 
preventing it from reaching shore where it would contaminate the 
benthic and intertidal communities.

References

    Alder, HL; Roessler, EB. (1972) Introduction to probability and 
statistics. San Francisco, CA: WH Freeman and Co.
    American Society for Quality Control (ASQC). (1994) American 
National Standard: specifications and guidelines for quality systems 
for environmental data collection and environmental technology 
programs. ANSI/ASQC E4-1994. Milwaukee, WI: SQC.
    American Society for Testing and Materials. (1996) Standard 
terminology relating to biological effects and environmental fate. 
E943-95a. In: ASTM; 1996 Annual Book of ASTM Standards, Section 11, 
Water and Environmental Technology. Philadelphia, PA: ASTM.
    Andrewartha, HG; Birch, LC. (1984) The ecological web: more on 
the distribution and abundance of animals. Chicago, IL: University 
of Chicago Press.
    Ankley, GT; Katko, A; Arthur, JW. (1990) Identification of 
ammonia as an important sediment-associated toxicant in the Lower 
Fox River and Green Bay, Wisconsin. Environ Toxicol Chem 9:313-322.
    Auer, CM; Zeeman, M; Nabholz, JV; Clements, RG. (1994) SAR--the 
U.S. regulatory perspective. SAR QSAR Environ Res 2:29-38.
    Baker, JL; Barefoot, AC; Beasley, LE; Burns, LA; Caulkins, PP; 
Clark, JE; Feulner, RL; Giesy, JP; Graney, RL; Griggs, RH; Jacoby, 
HM; Laskowski, DA; Maciorowski, AF; Mihaich, EM; Nelson, HP, Jr.; 
Parrish, PR; Siefert, RE; Solomon, KR; van der Schalie, WH, eds. 
(1994) Aquatic dialogue group: pesticide risk assessment and 
mitigation. Pensacola, FL: SETAC Press.
    Barbour, MT; Stribling, JB; Karr, JR. (1995) Multimetric 
approach for establishing biocriteria and measuring biological 
condition. In: Biological assessment and criteria, tools for water 
resource planning and decision making. Davis, WS; Simon, TP, eds. 
Boca Raton, FL: Lewis Publishers, pp. 63-77.
    Barnthouse, LW; Brown, J. (1994) Issue paper on conceptual model 
development. In: Ecological risk assessment issue papers. 
Washington, DC: Risk Assessment Forum, U.S. Environmental Protection 
Agency, pp. 3-1 to 3-70. EPA/630/R-94/009.
    Barnthouse, LW; O'Neill, RV; Bartell, SM; Suter, GW, II. (1986) 
Population and ecosystem theory in ecological risk assessment. In: 
Aquatic ecology and hazard assessment, 9th symposium. Poston, TM; 
Purdy, R, eds. Philadelphia, PA: American Society for Testing and 
Materials, pp. 82-96.
    Barnthouse, LW; Suter, GW, II; Rosen, AE; Beauchamp, JJ. (1987) 
Estimating responses of fish populations to toxic contaminants. 
Environ Toxicol Chem 6:811-824.
    Barnthouse, LW; Suter, GW, II; Rosen, AE. (1990) Risks of toxic 
contaminants to exploited fish populations: influence of life 
history, data uncertainty, and exploitation intensity. Environ 
Toxicol Chem 9:297-311.
    Bartell, SM; Gardner, RH; O'Neill, RV. (1992) Ecological risk 
estimation. Boca Raton, FL: Lewis Publishers.

[[Page 26919]]

    Bedford, BL; Preston, EM. (1988) Developing the scientific basis 
for assessing cumulative effects of wetland loss and degradation on 
landscape functions: status, perspectives, and prospects. Environ 
Manage 12:751-771.
    Bradbury, SP. (1994) Predicting modes of toxic action from 
chemical structure: an overview. SAR QSAR Environ Res 2:89-104.
    Bradley, CE; Smith, DG. (1989) Plains cottonwood recruitment and 
survival on a prairie meandering river floodplain. Milk River, 
Southern Alberta in Northern Canada. Can J Botany 64:1433-1442.
    Broderius, SJ. (1991) Modeling the joint toxicity of xenobiotics 
to aquatic organisms: basic concepts and approaches In: Aquatic 
toxicology and risk assessment: fourteenth volume. Mayes, MA; 
Barron, MG, eds. ASTM STP 1124. Philadelphia, PA: American Society 
for Testing and Materials, pp. 107-127.
    Broderius, SJ; Kahl, MD; Hoglund, MD. (1995) Use of joint toxic 
response to define the primary mode of toxic action for diverse 
industrial organic chemicals. Environ Toxicol Chem 9:1591-1605.
    Brody, M; Conner, W; Pearlstine, L; Kitchens, W. (1989) Modeling 
bottomland forest and wildlife habitat changes in Louisiana's 
Atchafalaya Basin. In: Freshwater wetlands and wildlife. Sharitz, 
RR; Gibbons, JW, eds. U.S. Department of Energy Symposium Series, 
No. 61. CONF-8603100. Oak Ridge, TN: Office of Science and Technical 
Information, U.S. Department of Energy.
    Brody, MS; Troyer, ME; Valette, Y. (1993) Ecological risk 
assessment case study: modeling future losses of bottomland forest 
wetlands and changes in wildlife habitat within a Louisiana basin. 
In: A review of ecological assessment case studies from a risk 
assessment perspective. Washington, DC: Risk Assessment Forum, U.S. 
Environmental Protection Agency, pp. 12-1 to 12-39. EPA/630/R-92/
005.
    Burmaster, DE; Menzie, CA; Freshman, JS; Burris, JA; Maxwell, 
NI; Drew, SR. (1991) Assessment of methods for estimating aquatic 
hazards at Superfund-type sites: a cautionary tale. Environ Toxicol 
Chem 10:827-842.
    Callahan, CA; Menzie, CA; Burmaster, DE; Wilborn, DC; Ernst, T. 
(1991) On-site methods for assessing chemical impacts on the soil 
environment using earthworms: a case study at the Baird and McGuire 
Superfund site, Holbrook, Massachusetts. Environ Toxicol Chem 
10:817-826.
    Cardwell, RD; Parkhurst, BR; Warren-Hicks, W; Volosin, JS. 
(1993) Aquatic ecological risk. Water Environ Technol 5:47-51.
    Clemen, RT. (1996) Making hard decisions. 2nd ed. New York: 
Duxbury Press, 664 pp.
    Clements, RG; Nabholz, JV. (1994) ECOSAR: a computer program for 
estimating the ecotoxicity of industrial chemicals based on 
structure activity relationships, user's guide. Washington, DC: 
Environmental Effects Branch, Health and Environmental Review 
Division (7402), U.S. Environmental Protection Agency. EPA/748/R-93/
002.
    Cohrssen, J; Covello, VT. (1989) Risk analysis: a guide to 
principles and methods for analyzing health and ecological risks. 
Washington, DC: Council on Environmental Quality.
    Commission on Risk Assessment and Risk Management. (1997) 
Framework for environmental health risk management. Final Report. 
Volume 1. Washington, DC: Commission on Risk Assessment and Risk 
Management.
    Conner, WH; Brody, M. (1989) Rising water levels and the future 
of southeastern Louisiana swamp forests. Estuaries 12(4):318-323.
    Costanza, R; d'Arge, R; de Groot, R; Farber, S; Grasso, M; 
Hannon, B; Limburg, K; Naeem, S; O'Neill, RV; Paruelo, J; Raskinm, 
RG; Sutton, P; van den Belt, M. (1997) The value of the world's 
ecosystem services and natural capital. Nature 387:253-260.
    Cowan, CE; Versteeg, DJ; Larson, RJ; Kloepper-Sams, PJ. (1995) 
Integrated approach for environmental assessment of new and existing 
substances. Regul Toxicol Pharmacol 21:3-31.
    Cronin, MTD; Dearden, JC. (1995) QSAR in toxicology. 1. 
Prediction of aquatic toxicity. Quant Struct-Act Relat 14:1-7.
    Detenbeck, N. (1994) Ecological risk assessment case study: 
effects of physical disturbance on water quality status and water 
quality improvement function of urban wetlands. In: A review of 
ecological assessment case studies from a risk assessment 
perspective, vol. II. Washington, DC: Risk Assessment Forum, U.S. 
Environmental Protection Agency, pp. 4-1 to 4-58. EPA/630/R-94/003.
    Detenbeck, NE; DeVore, PW; Niemi, GJ; Lima, A. (1992) Recovery 
of temperate-stream fish communities from disturbance: a review of 
case studies and synthesis of theory. Environ Manage 16(1):33-53.
    Eberhardt, LL; Thomas, JM. (1991) Designing environmental field 
studies. Ecol Mono 61(1):53-73.
    Emlen, JM. (1989) Terrestrial population models for ecological 
risk assessment: a state-of-the-art review. Environ Toxicol Chem 
8:831-842.
    European Community (EC). (1993) Technical guidance document in 
support of the risk assessment Commission Directive (93/67/EEC) for 
new substances notified in accordance with the requirements of 
Council Directive 67/548/EEC. Brussels, Belgium.
    Ferson, S; Ginzburg, L; Silvers, A. (1989) Extreme event risk 
analysis for age-structured populations. Ecol Model 47:175-187.
    Fisher, SG; Woodmansee, R. (1994) Issue paper on ecological 
recovery. In: Ecological risk assessment issue papers. Washington, 
DC: Risk Assessment Forum, U.S. Environmental Protection Agency, pp. 
7-1 to 7-54. EPA/630/R-94/009.
    Fotheringham, AS; Rogerson, PA. (1993) GIS and spatial 
analytical problems. Int J Geograph Inf Syst 7(1):3-19.
    Fox, GA. (1991) Practical causal inference for 
ecoepidemiologists. J Toxicol Environ Health 33:359-373.
    Gauch, HG. (1982) Multivariate analysis in community ecology. 
Cambridge, MA: Cambridge University Press.
    Gaudet, C. (1994) A framework for ecological risk assessment at 
contaminated sites in Canada: review and recommendations. Ottawa, 
Canada: Environment Canada.
    Gibbs, JP. (1993) Importance of small wetlands for the 
persistence of local populations of wetland-associated animals. 
Wetlands 13(1):25-31.
    Gilbert, RO. (1987) Statistical methods for environmental 
pollution monitoring. New York, NY: Van Nostrand Reinhold.
    Ginzburg, LR; Slobodkin, LB; Johnson, K; Bindman, AG. (1982) 
Quasiextinction probabilities as a measure of impact on population 
growth. Risk Anal 2:171-182.
    Gosselink, JG; Shaffer, GP; Lee, LC; Burdick, DL; Childer, NC; 
Leibowitz, NC; Hamilton, SC; Boumans, R; Cushmam, D; Fields, S; 
Koch, M; Visser, JM. (1990) Landscape conservation in a forested 
wetland watershed: can we manage cumulative impacts? Bioscience 
40(8):588-600.
    Harris, HJ; Wenger, RB; Harris, VA; Devault, DS. (1994) A method 
for assessing environmental risk: a case study of Green Bay, Lake 
Michigan, USA. Environ Manage 18(2):295-306.
    Harwell, MJ; Norton, B; Cooper, W; Gentile, J. (1994) Issue 
paper on ecological significance. In: Ecological risk assessment 
issue papers. Washington, DC: Risk Assessment Forum, U.S. 
Environmental Protection Agency, pp. 2-1 to 2-49. EPA/630/R-94/009.
    Hassell, MP. (1986) Detecting density dependence. Trends Ecol 
Evol 1:90-93.
    Health Council of the Netherlands (HCN). (1993) Ecotoxicological 
risk assessment and policy-making in the Netherlands--dealing with 
uncertainties. Network 6(3)/7(1):8-11.
    Heck, WW. (1993) Ecological assessment case study: the National 
Crop Loss Assessment Network. In: A review of ecological assessment 
case studies from a risk assessment perspective. Washington, DC: 
Risk Assessment Forum, U.S. Environmental Protection Agency, pp. 6-1 
to 6-32. EPA/630/R-92/005.
    Hermens, J; Canton, H; Janssen, P; De Jong, R. (1984a) 
Quantitative structure-activity relationships and toxicity studies 
of mixtures of chemicals with anaesthetic potency: acute lethal and 
sublethal toxicity to Daphnia magna. Aquatic Toxicol 5:143-154.
    Hermens, J; Canton, H; Steyger, N; Wegman, R. (1984b) Joint 
effects of a mixture of 14 chemicals on mortality and inhibition of 
reproduction of Daphnia magna. Aquatic Toxicol 5:315-322.
    Hill, AB. (1965) The environment and disease: association or 
causation? Proc R Soc Med 58:295-300.
    Holling, CS, ed. (1978) Adaptive environmental assessment and 
management. Chichester, UK: John Wiley & Sons.
    Houseknecht, CR. (1993) Ecological risk assessment case study: 
special review of the granular formulations of carbofuran based on 
adverse effects on birds. In: A review of ecological assessment case 
studies from a risk assessment perspective. Washington, DC: Risk 
Assessment Forum, U.S. Environmental Protection Agency, pp. 3-1 to 
3-25. EPA/630/R-92/005.
    Huggett, RJ; Kimerle, RA; Merhle, PM, Jr.; Bergman, HL, eds. 
(1992) Biomarkers: biochemical, physiological, and histological 
markers of anthropogenic stress. Boca Raton, FL: Lewis Publishers.
    Hughes, RM. (1995) Defining acceptable biological status by 
comparing with reference

[[Page 26920]]

conditions. In: Biological assessment and criteria: tools for water 
resource planning and decision making. Davis, WS; Simon, TP, eds. 
Boca Raton, FL: Lewis Publishers, pp. 31-47.
    Hunsaker, CT; Graham, RL; Suter, GW, II; O'Neill, RV; 
Barnthouse, LW; Gardner, RH. (1990) Assessing ecological risk on a 
regional scale. Environ Manage 14(3):325-332.
    Hurlbert, SH. (1984) Pseudoreplication and the design of 
ecological field experiments. Ecol Mono 54:187-211.
    Johnson, BL. (1995) Applying computer simulation models as 
learning tools in fishery management. North Am J Fisheries Manage 
15:736-747.
    Johnson, LB; Gage, SH. (1997) Landscape approaches to the 
analysis of aquatic ecosystems. Freshwater Biol 37:113-132.
    Johnston, CA; Detenbeck, NE; Niemi, GJ. (1990) The cumulative 
effect of wetlands on stream water quality and quantity: a landscape 
approach. Biogeochemistry 10:105-141.
    Karr, JR. (1981) Assessment of biotic integrity using fish 
communities. Fisheries 6(6):21-27.
    Karr, JR; Fausch, KD; Angermeier, PL; Yant, PR; Schlosser, IJ. 
(1986) Assessing biological integrity in running waters: a method 
and its rationale. Illinois Natural History Survey. Special 
Publication 5. Champaign, IL.
    Kenaga, EE. (1973) Factors to be considered in the evaluation of 
the toxicity of pesticides to birds in their environment. Environ 
Qual Saf 2:166-181.
    Kendall, RJ; Lacher, TE; Bunck, C; Daniel, B; Driver, C; Grue, 
CE; Leighton, F; Stansley, W; Watanabe, PG; Whitworth, M. (1996) An 
ecological risk assessment of lead shot exposure in non-waterfowl 
avian species: upland game birds and raptors. Environ Toxicol Chem 
15:4-20.
    Konemann, H. (1981) Fish toxicity tests with mixtures of more 
than two chemicals: a proposal for a quantitative approach and 
experimental results. Aquatic Toxicol 19:229-238.
    Laird, NM; Mosteller, R. (1990) Some statistical methods for 
combining experimental results. Int J Technol Assess Health Care 
6:5-30.
    Landis, WG; Matthews, RA; Markiewicz, AJ; Matthews, GB. (1993) 
Multivariate analysis of the impacts of the turbine fuel JP-4 in a 
microcosm toxicity test with implications for the evaluation of 
ecosystem dynamics and risk assessment. Ecotoxicology 2:271-300.
    Lipnick, RL. (1995) Structure-activity relationships. In: 
Fundamentals of aquatic toxicology effects, environmental fate, and 
risk assessment. Rand, GM; Petrocelli, SR, eds. London, UK: Taylor 
and Francis, pp. 609-655.
    Lipton, J; Galbraith, H; Burger, J; Wartenberg, D. (1993) A 
paradigm for ecological risk assessment. Environ Manage 17:1-5.
    Ludwig, JA; Reynolds, JF. (1988) Statistical ecology. New York, 
NY: Wiley-Interscience, 337 pp.
    Lynch, DG; Macek, GJ; Nabholz, JV; Sherlock, SM; Wright, R. 
(1994) Ecological risk assessment case study: assessing the 
ecological risks of a new chemical under the Toxic Substances 
Control Act. In: A review of ecological assessment case studies from 
a risk assessment perspective, vol. II. Washington, DC: Risk 
Assessment Forum, U.S. Environmental Protection Agency, pp. 1-1 to 
1-35. EPA/630/R-94/003.
    MacIntosh, DL; Suter GW, II; Hoffman, FO. (1994) Uses of 
probabilistic exposure models in ecological risk assessments of 
contaminated sites. Risk Anal 14(4):405-419.
    Mann, C. (1990) Meta-analysis in the breech. Science 249:476-
480.
    McCarty, LS; Mackay, D. (1993) Enhancing ecotoxicological 
modeling and assessment: body residues and modes of toxic action. 
Environ Sci Technol 27:1719-1728.
    Meij, R. (1991) The fate of mercury in coal-fired power plants 
and the influence of wet flue-gas desulphurization. Water Air Soil 
Pollut 56:21-33.
    Menzie, CA; Burmaster, DE; Freshman, JS; Callahan, CA. (1992) 
Assessment of methods for estimating ecological risk in the 
terrestrial component: a case study at the Baird & McGuire Superfund 
Site in Holbrook, Massachusetts. Environ Toxicol Chem 11:245-260.
    Menzie, C; Henning, MH; Cura, J; Finkelstein, K; Gentile, J; 
Maughan J; Mitchell, D; Petron, S; Potocki, B; Svirsky, S; Tyler, P. 
(1996) Special report of the Massachusetts weight-of-evidence 
workgroup: a weight of evidence approach for evaluating ecological 
risks. Human Ecol Risk Assess 2:277-304.
    Messer, JJ; Linthurst, RA; Overton, WS. (1991) An EPA program 
for monitoring ecological status and trends. Environ Monitor Assess 
17:67-78.
    Nabholz, JV. (1991) Environmental hazard and risk assessment 
under the United States Toxic Substances Control Act. Science Total 
Environ 109/110: 649-665.
    Nabholz, JV; Miller, P; Zeeman, M. (1993) Environmental risk 
assessment of new chemicals under the Toxic Substances Control Act 
(TSCA) section five. In: Environmental toxicology and risk 
assessment. Landis, WG; Hughes, SG; Lewis, M; Gorsuch, JW, eds. ASTM 
STP 1179. Philadelphia, PA: American Society for Testing and 
Materials, pp. 40-55.
    National Research Council. (1994) Science and judgment in risk 
assessment. Washington, DC: National Academy Press.
    National Research Council. (1996) Understanding risk: informing 
decisions in a democratic society. Washington, DC: National Academy 
Press.
    Newman, MC. (1995) Advances in trace substances research: 
quantitative methods in aquatic ecotoxicology. Boca Raton, FL: Lewis 
Publishers.
    Niemi, GJ; DeVore, P; Detenbeck, N; Taylor, D; Lima, A; Pastor, 
J; Yount, JD; Naiman, RJ. (1990) Overview of case studies on 
recovery of aquatic systems from disturbance. Environ Manage 14:571-
587.
    Novitski, RP. (1979) Hydrologic characteristics of Wisconsin's 
wetlands and their influence on floods, stream flow, and sediment. 
In: Wetland functions and values: the state of our understanding. 
Greeson, PE; Clark, JR; Clark, JE, eds. Minneapolis, MN: American 
Water Resources Association, pp. 377-388.
    Oberts, GL. (1981) Impact of wetlands on watershed water 
quality. In: Selected proceedings of the Midwest Conference on 
Wetland Values and Management. Richardson, B, ed. Navarre, MN: 
Freshwater Society, pp. 213-226.
    Okkerman, PC; Plassche, EJVD; Emans, HJB. (1993) Validation of 
some extrapolation methods with toxicity data derived from 
multispecies experiments. Ecotoxicol Environ Saf 25:341-359.
    O'Neill, RV; Gardner, RH; Barnthouse, LW; Suter, GW, II; 
Hildebrand, SG; Gehrs, CW. (1982) Ecosystem risk analysis: a new 
methodology. Environ Toxicol Chem 1:167-177.
    Orr, RL; Cohen, SD; Griffin, RL. (1993) Generic non-indigenous 
pest risk assessment process. Beltsville, MD: USDA Animal and Plant 
Health Inspection Service.
    Ott, WR. (1978) Environmental indices--theory and practice. Ann 
Arbor, MI: Ann Arbor Science. Cited in: Suter, GW, II. (1993) A 
critique of ecosystem health concepts and indexes. Environ Toxicol 
Chem 12:1533-1539.
    Parkhurst, BR; Warren-Hicks, W; Etchison, T; Butcher, JB; 
Cardwell, RD; Voloson, J. (1995) Methodology for aquatic ecological 
risk assessment. RP91 AER-1 1995. Alexandria, VA: Water Environment 
Research Foundation.
    Pastorok, RA; Butcher, MK; Nielsen, RD. (1996) Modeling wildlife 
exposure to toxic chemicals: trends and recent advances. Human Ecol 
Risk Assess 2:444-480.
    Pearlstine, L; McKellar, H; Kitchens, W. (1985) Modelling the 
impacts of a river diversion on bottomland forest communities in the 
Santee River Floodplain, South Carolina. Ecol Model 29:281-302.
    Pelczar, MJ; Reid, RD. (1972) Microbiology. New York, NY: 
McGraw-Hill Company.
    Peterman, RM. (1990) The importance of reporting statistical 
power: the forest decline and acidic deposition example. Ecology 
71:2024-2027.
    Petitti, DB. (1994) Meta-analysis, decision analysis and cost-
effectiveness analysis: methods for quantitative synthesis in 
medicine. Monographs in epidemiology and biostatistics, vol. 24. New 
York, NY: Oxford University Press.
    Phipps, RL. (1979) Simulation of wetlands forest vegetation 
dynamics. Ecol Model 7:257-288.
    Pielou, EC. (1984) The interpretation of ecological data. A 
primer on classification and ordination. New York, NY: Wiley-
Interscience, 263 pp.
    Preston, EM; Bedford, BL. (1988) Evaluating cumulative effects 
on wetland functions: a conceptual overview and generic framework. 
Environ Manage 12(5):565-583.
    Richards, C; Haro, RJ; Johnson, LB; Host, GE. (1997) Catchment 
and reach-scale properties as indicators of macroinvertebrate 
species traits. Freshwater Biol 37:219-230.
    Risser, PG. (1988) General concepts for measuring cumulative 
impacts on wetland ecosystems. Environ Manage 12(5):585-589.
    Rotenberry, JT; Wiens, JA. (1985) Statistical power analysis and 
community-wide patterns. Am Naturalist 125:164-168.
    Ruckelshaus, WD. (1983) Science, risk, and public policy. 
Science 221:1026-1028.

[[Page 26921]]

    Sample, BE; Opresko, DM; Suter, GW, II. (1996) Toxicological 
benchmarks for wildlife: 1996 revision. ES/ER/TM-86/R3. Oak Ridge, 
TN: Oak Ridge National Laboratory, Health Sciences Research 
Division.
    Sawyer, TW; Safe, S. (1985) In vitro AHH induction by 
polychlorinated biphenyl and dibenzofuran mixtures: additive 
effects. Chemosphere 14:79-84.
    Schindler, DW. (1987) Detecting ecosystem responses to 
anthropogenic stress. Can J Fish Aquat Sci 44(Suppl.1):6-25.
    Simberloff, D; Alexander, M. (1994) Issue paper on biological 
stressors. In: Ecological risk assessment issue papers. Washington, 
DC: Risk Assessment Forum, U.S. Environmental Protection Agency, pp. 
6-1 to 6-59. EPA/630/R-94/009.
    Smith, EP; Cairns, J, Jr. (1993) Extrapolation methods for 
setting ecological standards for water quality: statistical and 
ecological concerns. Ecotoxicology 2:203-219.
    Smith, EP; Shugart, HH. (1994) Issue paper on uncertainty in 
ecological risk assessment. In: Ecological risk assessment issue 
papers. Washington, DC: Risk Assessment Forum, U.S. Environmental 
Protection Agency, pp. 8-1 to 8-53. EPA/630/R-94/009.
    Society of Environmental Toxicology and Chemistry (SETAC). 
(1987) Research priorities in environmental risk assessment. Report 
of a workshop held in Breckenridge, CO, August 16-21, 1987. 
Washington, DC: SETAC.
    Solomon, KR; Baker, DB; Richards, RP; Dixon, KR; Klaine, SJ; La 
Point, TW; Kendall, RJ; Weisskopf, CP; Giddings, JM; Geisy, JP; 
Hall, LW; Williams, WM. (1996) Ecological risk assessment of 
atrazine in North American surface waters. Environ Toxicol Chem 
15(1):31-76.
    Starfield, AM; Bleloch, AL. (1991) Building models for 
conservation and wildlife management. Edina, MN: Burgess 
International Group, Inc.
    Stephan, CE. (1977) Methods for calculating an LC50. 
In: ASTM Special Technical Publication 634. Philadelphia, PA: 
American Society for Testing and Materials, pp. 65-88.
    Stephan, CE; Rogers, JR. (1985) Advantages of using regression 
analysis to calculate results of chronic toxicity tests. In: Aquatic 
toxicology and hazard assessment. Eighth symposium. Bahner, RC; 
Hanse, DJ, eds. Philadelphia, PA: American Society for Testing and 
Materials, pp. 328-339.
    Stephan, CE; Mount, DI; Hansen, DJ; Gentile, JH; Chapman, GA; 
Brungs, WA. (1985) Guidelines for deriving numerical national water 
quality criteria for the protection of aquatic organisms and their 
uses. Duluth, MN: Office of Research and Development, U.S. 
Environmental Protection Agency. PB85-227049.
    Stewart-Oaten, A; Murdoch, WW; Parker, KR. (1986) Environmental 
impact assessment: ``pseudoreplication'' in time? Ecology 67(4):929-
940.
    Susser, M. (1986a) Rules of inference in epidemiology. Regul 
Toxicol Pharmacol 6:116-128.
    Susser, M. (1986b) The logic of Sir Carl Popper and the practice 
of epidemiology. Am J Epidemiol 124:711-718.
    Suter, GW, II. (1989) Ecological endpoints. In: Ecological 
assessments of hazardous waste sites: a field and laboratory 
reference document. Warren-Hicks, W; Parkhurst, BR; Baker, SS, Jr, 
eds. Washington, DC: U.S. Environmental Protection Agency. EPA 600/
3-89/013.
    Suter, GW, II. (1990) Endpoints for regional ecological risk 
assessments. Environ Manage 14:19-23.
    Suter, GW, II. (1993a) Ecological risk assessment. Boca Raton, 
FL: Lewis Publishers.
    Suter, GW, II. (1993b) A critique of ecosystem health concepts 
and indexes. Environ Toxicol Chem 12:1533-1539.
    Suter, GW, II. (1996) Abuse of hypothesis testing statistics in 
ecological risk assessment. Human Ecol Risk Assess 2:331-347.
    Suter, GW, II; Vaughan, DS; Gardner, RH. (1983) Risk assessment 
by analysis of extrapolation error. A demonstration for effects of 
pollutants on fish. Environ Toxicol Chem 2:369-377.
    Suter, GW, II; Gillett, JW; Norton, SB. (1994) Issue paper on 
characterization of exposure. In: Ecological risk assessment issue 
papers. Washington, DC: Risk Assessment Forum, U.S. Environmental 
Protection Agency, pp. 4-1 to 4-64. EPA/630/R-94/009.
    Thomas, JW; Forsman, ED; Lint, JB; Meslow, EC; Noon, BR; Verner, 
J. (1990) A conservation strategy for the spotted owl. Interagency 
Scientific Committee to Address the Conservation of the Northern 
Spotted Owl. 1990-791/20026. Washington, DC: U.S. Government 
Printing Office.
    Urban, DJ; Cook, JN. (1986) Ecological risk assessment. Hazard 
Evaluation Division standard procedure. Washington, DC: Office of 
Pesticide Programs, U.S. Environmental Protection Agency. EPA-54019-
83-001.
    U.S. Department of Agriculture. (1993) Pest risk assessment of 
the importation of Pinus radiata, Nothofagus dombeyi, and Laurelia 
philippiana logs from Chile. Forest Service Miscellaneous 
Publication 1517.
    U.S. Department of Health, Education, and Welfare. (1964) 
Smoking and health. Report of the Advisory Committee to the Surgeon 
General. Public Health Service Publication 1103. Washington, DC: 
U.S. Department of Health, Education, and Welfare.
    U.S. Environmental Protection Agency. (1979) Toxic Substances 
Control Act. Discussion of premanufacture testing policies and 
technical issues: request for comment. Federal Register 44:16240-
16292.
    U.S. Environmental Protection Agency. (1984) Estimating concern 
levels for concentrations of chemical substances in the environment. 
Washington, DC: U.S. Environmental Protection Agency, Health and 
Environmental Review Division, Environmental Effects Branch.
    U.S. Environmental Protection Agency. (1986a) Guidelines for the 
health risk assessment of chemical mixtures. Federal Register 
52:34014-34025.
    U.S. Environmental Protection Agency. (1986b) Quality criteria 
for water. Washington, DC: Office of Water, U.S. Environmental 
Protection Agency. EPA/440/5-86/001.
    U.S. Environmental Protection Agency. (1988a) Methods for 
aquatic toxicity identification evaluations: phase I toxicity 
characterization procedures. Duluth, MN: Environmental Research 
Laboratory, U.S. Environmental Protection Agency. EPA/600/3-88/034.
    U.S. Environmental Protection Agency. (1988b) User's guide to 
PDM3, final report. Prepared by Versar, Inc., for Exposure 
Assessment Branch, Exposure Evaluation Division, U.S. Environmental 
Protection Agency, Washington, DC, under EPA contract no. 68-02-
4254, task no. 117.
    U.S. Environmental Protection Agency. (1989a) Rapid 
bioassessment protocols for use in streams and rivers: benthic 
macroinvertebrates and fish. Washington, DC: Office of Water, U.S. 
Environmental Protection Agency. EPA/440/4-89/001.
    U.S. Environmental Protection Agency. (1989b) Methods for 
aquatic toxicity identification evaluations: phase II toxicity 
identification procedures. Duluth, MN: Environmental Research 
Laboratory, U.S. Environmental Protection Agency. EPA/600/3-88/035.
    U.S. Environmental Protection Agency. (1989c) Methods for 
aquatic toxicity identification evaluations: phase III toxicity 
confirmation procedures. Duluth, MN: Environmental Research 
Laboratory, U.S. Environmental Protection Agency. EPA/600/3-88/035.
    U.S. Environmental Protection Agency. (1990) Guidance for data 
useability in risk assessment. Washington, DC: U.S. Environmental 
Protection Agency. EPA/540/G-90/008.
    U.S. Environmental Protection Agency. (1991) Technical support 
document for water quality-based toxics control. Washington, DC: 
Office of Water, U.S. Environmental Protection Agency. EPA/505/2-90/
001.
    U.S. Environmental Protection Agency. (1992a) Framework for 
ecological risk assessment. Washington, DC: Risk Assessment Forum, 
U.S. Environmental Protection Agency. EPA/630/R-92/001.
    U.S. Environmental Protection Agency. (1992b) Guidelines for 
exposure assessment: notice. Federal Register 57:22888-22938.
    U.S. Environmental Protection Agency. (1992c) A cross-species 
scaling factor for carcinogenic risk assessment based on equivalence 
of mg/kg\3/4\/day: draft report. Federal Register 
57(109):24152-24173.
    U.S. Environmental Protection Agency. (1993a) Wildlife exposure 
factors handbook. Washington, DC: Office of Research and 
Development, U.S. Environmental Protection Agency. EPA/600/R-93/187a 
and 187b.
    U.S. Environmental Protection Agency. (1993b) A review of 
ecological risk assessment case studies from a risk assessment 
perspective. Washington, DC: Risk Assessment Forum, U.S. 
Environmental Protection Agency. EPA/630/R-92/005.
    U.S. Environmental Protection Agency. (1993c) Communicating risk 
to senior EPA policy makers: a focus group study. Research Triangle 
Park, NC: Office of Air Quality Planning and Standards, U.S. 
Environmental Protection Agency.
    U.S. Environmental Protection Agency. (1993d) A guidebook to 
comparing risks and setting environmental priorities. Washington, 
DC: Office of Policy, Planning, and

[[Page 26922]]

Evaluation, U.S. Environmental Protection Agency. EPA/230/B-93/003.
    U.S. Environmental Protection Agency. (1994a) Managing 
ecological risks at EPA: issues and recommendations for progress. 
Washington, DC: Center for Environmental Research Information, U.S. 
Environmental Protection Agency. EPA/600/R-94/183.
    U.S. Environmental Protection Agency. (1994b) ``Ecosystem 
protection.'' Memorandum from Robert Perciaspe, David Gardiner, and 
Johnathan Cannon to Carol Browner, March 1994.
    U.S. Environmental Protection Agency. (1994c) Guidance for the 
data quality objectives process. Washington, DC: Quality Assurance 
Management Staff. EPA QA/G-4.
    U.S. Environmental Protection Agency. (1994d) Environmental 
Services Division guidelines. Hydrogeologic modeling. Seattle, WA: 
Region X, U.S. Environmental Protection Agency.
    U.S. Environmental Protection Agency. (1994e) A review of 
ecological assessment case studies from a risk assessment 
perspective, vol. II. Washington, DC: Risk Assessment Forum, U.S. 
Environmental Protection Agency. EPA/630/R-94/003.
    U.S. Environmental Protection Agency. (1995a) Ecological risk: a 
primer for risk managers. Washington, DC: U.S. Environmental 
Protection Agency. EPA/734/R-95/001.
    U.S. Environmental Protection Agency. (1995b) ``EPA risk 
characterization program.'' Memorandum to EPA managers from 
Administrator Carol Browner, March 1995.
    U.S. Environmental Protection Agency. (1995c) The use of the 
benchmark dose approach in health risk assessment. Washington, DC: 
Risk Assessment Forum, U.S. Environmental Protection Agency. EPA/
630/R-94/007.
    U.S. Environmental Protection Agency. (1995d) Great Lakes water 
quality initiative technical support document for wildlife. 
Washington, DC: Office of Water, U.S. Environmental Protection 
Agency. EPA/820/B-95/009.
    U.S. Environmental Protection Agency. (1995e) An SAB report: 
ecosystem management--imperative for a dynamic world. Washington, 
DC: Science Advisory Board. EPA-SAB-EPEC-95-003.
    U.S. Environmental Protection Agency. (1996a) Summary report for 
the workshop on Monte Carlo analysis. Washington, DC: Office of 
Research and Development, U.S. Environmental Protection Agency. EPA/
630/R-96/010.
    U.S. Environmental Protection Agency. (1996b) Waquoit Bay 
watershed. Ecological risk assessment planning and problem 
formulation (draft). Washington, DC: Risk Assessment Forum, U.S. 
Environmental Protection Agency. EPA/630/R-96/004a.
    U.S. Environmental Protection Agency. (1997a) Priorities for 
ecological protection: an initial list and discussion document for 
EPA. Washington, DC: Office of Research and Development, U.S. 
Environmental Protection Agency. EPA/600/S-97/002.
    U.S. Environmental Protection Agency. (1997b) Policy for use of 
probabilistic analysis in risk assessment: guiding principles for 
Monte Carlo analysis. Washington, DC: Office of Research and 
Development, U.S. Environmental Protection Agency. EPA/630/R-97/001.
    van Gestel, CAM; van Brummelen, TC. (1996) Incorporation of the 
biomarker concept--ecotoxicology calls for a redefinition of terms. 
Ecotoxicol 5:217-225.
    Van Leeuwen, CJ; Van der Zandt, PTJ; Aldenberg, T; Verhar, HJM; 
Hermens, JLM. (1992) Extrapolation and equilibrium partitioning in 
aquatic effects assessment. Environ Toxicol Chem 11:267-282.
    Wagner, C; Lkke, H. (1991) Estimation of 
ecotoxicological protection levels from NOEC toxicity data. Water 
Res 25:1237-1242.
    White, PS; Pickett, STA. (1985) Natural disturbance and patch 
dynamics: an introduction. In: The ecology of natural disturbance 
and patch dynamics. Pickett, STA; White, PS, eds. Orlando, FL: 
Academic Press, pp. 3-13.
    Wiegert, RG; Bartell, SM. (1994) Issue paper on risk integration 
methods. In: Ecological risk assessment issue papers. Washington, 
DC: Risk Assessment Forum, Environmental Protection Agency, pp. 9-1 
to 9-66. EPA/630/R-94/009.
    Wiens, JA; Parker, KR. (1995) Analyzing the effects of 
accidental environmental impacts: approaches and assumptions. Ecol 
Appl 5(4):1069-1083.
    Woodman, JN; Cowling, EB. (1987) Airborne chemicals and forest 
health. Environ Sci Technol 21:120-126.
    Yoder, CO; Rankin, ET. (1995) Biological response signatures and 
the area of degradation value: new tools for interpreting multi-
metric data. In: Biological assessment and criteria: tools for water 
resource planning and decision making. Davis, WS; Simon, TP, eds. 
Boca Raton, FL: Lewis Publishers.
    Zeeman, M. (1995) EPA's framework for ecological effects 
assessment. In: Screening and testing chemicals in commerce. OTA-BP-
ENV-166. Washington, DC: Office of Technology Assessment, pp. 69-78.

Part B: Response to Science Advisory Board and Public Comments

1. Introduction

    This section summarizes the major issues raised in public comments 
and by EPA's Science Advisory Board (SAB) on the previous draft of 
these Guidelines (the Proposed Guidelines for Ecological Risk 
Assessment, hereafter ``Proposed Guidelines''). A notice of 
availability for public comment of the Proposed Guidelines was 
published September 9, 1996 (61 FR 47552-47631). Forty-four responses 
were received. The Ecological Processes and Effects Committee of the 
SAB reviewed the Proposed Guidelines on September 19-20, 1996, and 
provided comments in January 1997 (EPA-SAB-EPEC-97-002).
    The SAB and public comments were diverse, reflecting the different 
perspectives of the reviewers. Many of the comments were favorable, 
expressing agreement with the overall approach to ecological risk 
assessment. Many comments were beyond the scope of the Guidelines, 
including requests for guidance on risk management issues (such as 
considering social or economic impacts in decision making). Major 
issues raised by reviewers are summarized below. In addition to 
providing general comments (section 2), reviewers were asked to comment 
on seven specific questions (section 3).

2. Response to General Comments

    Probably the most common request was for greater detail in specific 
areas. In some cases, additional discussion was added (for example, on 
the use of tiering and iteration and the respective roles of risk 
assessors, risk managers, and interested parties throughout the 
process). In other areas, topics for additional discussion were 
included in a list of potential areas for further development (see 
response to question 2, below). Still other topics are more 
appropriately addressed by regional or program offices within the 
context of a certain regulation or issue, and are deferred to those 
sources.
    A few reviewers felt that since ecological risk assessment is a 
relatively young science, it is premature to issue guidelines at this 
time. The Agency feels that it is appropriate to issue guidance at this 
time, especially since the Guidelines contain major principles but 
refrain from recommending specific methodologies that might become 
rapidly outdated. To help ensure the continued relevance of the 
Guidelines, the Agency intends to develop documents addressing specific 
topics (see response to question 2 below) and will revise these 
Guidelines as experience and scientific consensus evolve.
    Some reviewers asked whether the Guidelines would be applied to 
previous or ongoing ecological risk assessments, and whether existing 
regional or program office guidance would be superseded in conducting 
ecological risk assessments. As described in section 1.3 (Scope and 
Intended Audience), the Guidelines are principles, and are not 
regulatory in nature. It is anticipated that guidance from program and 
regional offices will evolve to implement the principles set forth in 
these Guidelines. Similarly, some reviewers requested that assessments 
require a comparison of the risks of alternative scenarios (including 
background or baseline conditions) or an assignment of particular 
levels of ecological significance to habitats. These decisions would be 
most appropriately made on a case-by-case basis, or by a program office 
in response to program-specific needs.

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    Several Native American groups noted a lack of acknowledgment of 
tribal governments in the document. This Agency oversight was corrected 
by including tribal governments at points in the Guidelines where other 
governmental organizations are mentioned.
    Several reviewers noted that the Proposed Guidelines mentioned the 
need for ``expert judgment'' in several places and asked how the Agency 
defined ``expert'' and what qualifications such an individual should 
have. At present, there is no standard set of qualifications for an 
ecological risk assessor, and such a standard would be very difficult 
to produce, since ecological assessments are frequently done by teams 
of individuals with expertise in many areas. To avoid this problem, the 
Guidelines now use the term ``professional judgment,'' and note that it 
is important to document the rationale for important decisions.
    Some reviewers felt that the Guidelines should address effects only 
at the population level and above. The Guidelines do not make this 
restriction for several reasons. First, some assessments, such as those 
involving endangered species, do involve considerations of individual 
effects. Second, the decision as to which ecological entity to protect 
should be the result, on a case-by-case basis, of the planning process 
involving risk assessors, risk managers, and interested parties, if 
appropriate. Some suggestions have been proposed (U.S. EPA, 1997a). 
Finally, there appears to be some confusion among reviewers between 
conducting an assessment concerned with population-level effects, and 
using data from studies of effects on individuals (e.g., toxicity test 
results) to infer population-level effects. These inferences are 
commonly used (and generally accepted) in chemical screening programs, 
such as the Office of Pollution Prevention and Toxics Premanufacturing 
Notification program (U.S. EPA, 1994e).
    The use of environmental indices received a number of comments. 
Some reviewers wanted the Guidelines to do more to encourage the use of 
indices, while others felt that the disadvantages of indices should 
receive greater emphasis. The Guidelines discuss both the advantages 
and limitations of using indices to guide risk assessors in their 
proper use.
    Other reviewers requested that the Guidelines take a more 
definitive position on the use of ``realistic exposure assumptions,'' 
such as those proposed in the Agency's exposure guidelines (U.S. EPA, 
1992b). Although the exposure guidelines offer many useful suggestions 
that are applicable to human health risk assessment, it was not 
possible to generalize the concepts to ecological risk assessment, 
given the various permutations of the exposure concept for different 
types of stressors or levels of biological organization. The Guidelines 
emphasize the importance of documenting major assumptions (including 
exposure assumptions) used in an assessment.
    Several reviewers requested more guidance and examples using 
nonchemical stressors, i.e., physical or biological stressors. This 
topic has been included in the list of potential subjects for future 
detailed treatment (see response to question 2, below).

3. Response to Comments on Specific Questions

    Both the Proposed Guidelines and the charge to the SAB for its 
review contained a set of seven questions asked by the Agency. These 
questions, along with the Agency's response to comments received, are 
listed below.
    (1) Consistent with a recent National Research Council report (NRC, 
1996), these Proposed Guidelines emphasize the importance of 
interactions between risk assessors and risk managers as well as the 
critical role of problem formulation in ensuring that the results of 
the risk assessment can be used for decision making. Overall, how 
compatible are these Proposed Guidelines with the National Research 
Council concept of the risk assessment process and the interactions 
among risk assessors, risk managers, and other interested parties?
    Most reviewers felt there was general compatibility between the 
Proposed Guidelines and the NRC report, although some emphasized the 
need for continued interactions among risk assessors, risk managers, 
and interested parties (or stakeholders) throughout the ecological risk 
assessment process and asked that the Guidelines provide additional 
details concerning such interactions. To give greater emphasis to these 
interactions, the ecological risk assessment diagram was modified to 
include ``interested parties'' in the planning box at the beginning of 
the process and ``communicating with interested parties'' in the risk 
management box following the risk assessment. Some additional 
discussion concerning interactions among risk assessors, risk managers, 
and interested parties was added, particularly to section 2 (planning). 
However, although risk assessor/risk manager interrelationships are 
discussed, too great an emphasis in this area is inconsistent with the 
scope of the Guidelines, which focus on the interface between risk 
assessors and risk managers, not on providing risk management guidance.
    (2) The Proposed Guidelines are intended to provide a starting 
point for Agency programs and regional offices that wish to prepare 
ecological risk assessment guidance suited to their needs. In addition, 
the Agency intends to sponsor development of more detailed guidance on 
certain ecological risk assessment topics. Examples might include 
identification and selection of assessment endpoints, selection of 
surrogate or indicator species, or the development and application of 
uncertainty factors. Considering the state of the science of ecological 
risk assessment and Agency needs and priorities, what topics most 
require additional guidance?
    Reviewers recommended numerous topics for further development. 
Examples include:
     Landscape ecology.
     Data sources and quality.
     Physical and biological stressors.
     Multiple stressors.
     Defining reference areas for field studies.
     Ecotoxicity thresholds.
     The role of biological and other types of indicators.
     Bioavailability, bioaccumulation, and bioconcentration.
     Uncertainty factors.
     Stressor-response relationships (e.g., threshold vs. 
continuous).
     Risk characterization techniques.
     Risk communication to the public.
     Public participation.
     Comparative ecological risk.
     Screening and tiering assessments.
     Identifying and selecting assessment endpoints.
    These suggestions will be included in a listing of possible topics 
proposed to the Agency's Risk Assessment Forum for future development.
    (3) Some reviewers have suggested that the Proposed Guidelines 
should provide more discussion of topics related to the use of field 
observational data in ecological risk assessments, such as selection of 
reference sites, interpretation of positive and negative field data, 
establishing causal linkages, identifying measures of ecological 
condition, the role and uses of monitoring, and resolving conflicting 
lines of evidence between field and laboratory data. Given the general 
scope of these Proposed Guidelines, what, if any, additional material 
should be added on these topics and, if so, what principles should be 
highlighted?
    In response to a number of comments, the discussion of field data 
in the

[[Page 26924]]

Guidelines was expanded, especially in section 4.1. Nevertheless, many 
suggested topics requested a level of detail that was inconsistent with 
the scope of the Guidelines. Some areas may be covered through the 
development of future Risk Assessment Forum documents.
    (4) The scope of the Proposed Guidelines is intentionally broad. 
However, while the intent is to cover the full range of stressors, 
ecosystem types, levels of biological organization, and spatial/
temporal scales, the contents of the Proposed Guidelines are limited by 
the present state of the science and the relative lack of experience in 
applying risk assessment principles to some areas. In particular, given 
the Agency's present interest in evaluating risks at larger spatial 
scales, how could the principles of landscape ecology be more fully 
incorporated into the Proposed Guidelines?
    Landscape ecology is critical to many aspects of ecological risk 
assessment, especially assessments conducted at larger spatial scales. 
However, given the general nature of these Guidelines and the responses 
received to this question, the Guidelines could not be expanded 
substantially at this time. This topic has been added to the list of 
potential subjects for future development.
    (5) Assessing risks when multiple stressors are present is a 
challenging task. The problem may be how to aggregate risks 
attributable to individual stressors or identify the principal 
stressors responsible for an observed effect. Although some approaches 
for evaluating risks associated with chemical mixtures are available, 
our ability to conduct risk assessments involving multiple chemical, 
physical, and biological stressors, especially at larger spatial 
scales, is limited. Consequently, the Proposed Guidelines primarily 
discuss predicting the effects of chemical mixtures and general 
approaches for evaluating causality of an observed effect. What 
additional principles can be added?
    Few additional principles were provided that could be included in 
the Guidelines. To further progress in evaluating multiple stressors, 
EPA cosponsored a workshop on this issue, held by the Society of 
Environmental Toxicology and Chemistry in September 1997. In addition, 
evaluating multiple stressors is one of the proposed topics for further 
development.
    (6) Ecological risk assessments are frequently conducted in tiers 
that proceed from simple evaluations of exposure and effects to more 
complex assessments. While the Proposed Guidelines acknowledge the 
importance of tiered assessments, the wide range of applications of 
tiered assessments make further generalizations difficult. Given the 
broad scope of the Proposed Guidelines, what additional principles for 
conducting tiered assessments can be discussed?
    Many reviewers emphasized the importance of tiered assessments, and 
in response the discussion of tiered assessments was significantly 
expanded in the planning phase of ecological risk assessment. Including 
more detailed information (such as specific decision criteria to 
proceed from one tier to the next) would require a particular context 
for an assessment. Such specific guidance is left to the EPA program 
offices and regions.
    (7) Assessment endpoints are ``explicit expression of the 
environmental value that is to be protected.'' As used in the Proposed 
Guidelines, assessment endpoints include both an ecological entity and 
a specific attribute of the entity (e.g., eagle reproduction or extent 
of wetlands). Some reviewers have recommended that assessment endpoints 
also include a decision criterion that is defined early in the risk 
assessment process (e.g., no more than a 20% reduction in reproduction, 
no more than a 10% loss of wetlands). While not precluding this 
possibility, the Proposed Guidelines suggest that such decisions are 
more appropriately made during discussions between risk assessors and 
managers in risk characterization at the end of the process. What are 
the relative merits of each approach?
    Reviewer reaction was quite evenly divided between those who felt 
strongly that decision criteria should be defined in problem 
formulation and those who felt just as strongly that such decisions 
should be delayed until risk characterization. Although the Guidelines 
contain more discussion of this topic, they still take the position 
that assessment endpoints need not contain specific decision criteria.

[FR Doc. 98-12302 Filed 5-13-98; 8:45 am]
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