EPA/630/R-95/002F
April 1998
Guidelines for
Ecological Risk Assessment
(Published on May 14, 1998, Federal Register 63(93):26846-26924)
Risk Assessment Forum
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Thursday
May 14, 1998
Part II
Environmental
Protection Agency
Guidelines for Ecological Risk
Assessment; Notice
2684
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26846
Federal Register/Vol. 63. No. 93/Thursday, May 14, 1998/Notices
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.
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 Forurru 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 47552-4763 I.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 l/z" 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: 513-569-7562; fax: 513-
569-7566. 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:
703-487-4650, fax: 703-321-8547.
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: 202-
564-3371; e-mail: Eco-
Guidellnes@epamail.epa.gov.,
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 "bookshelf1 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 ^aps 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
-------
Federal Register/Vol. 63, No. 93/Thursday, May 14, 1998/Notices
26847
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
••*
. ...
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 revaluation 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
stressbrs. their distribution in the
environment, and thelrcontact 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-
respons* 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.
-------
DISCLAIMER
This document has been reviewed in accordance with U.S. Environmental Protection Agency
policy and approved for publication. Mention of trade names or commercial products does not
constitute endorsement or recommendation for use.
NOTICE
This report contains the full text of the Guidelines for Ecological Risk Assessment. However,
the format of this version differs from the Federal Register version, as follows: text boxes that are
included in this document at their point of reference were instead listed at the end of the Federal
Register document as text notes, due to format limitations for Federal Register documents.
-------
GUIDELINES FOR ECOLOGICAL RISK ASSESSMENT
[FRL-6011-2]
AGENCY: U.S. Environmental Protection Agency
ACTION: Notice of availability of final Guidelines for Ecological Risk Assessment
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 April 30, 1998.
IV
-------
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) SVa" 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: 513-569-7562; fax: 513-569-7566. 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: 703-487-4650, fax: 703-321-8547. Please provide the NTIS PB No.
(PB98-117849) when ordering.
FOR FURTHER INFORMATION, CONTACT: Risk Assessment Forum (8061-D), U.S. Environmental
Protection Agency, 1200 Pennsylvania Avenue, N.W., Washington D.C. 20460; telephone (202) 564-3361,
facsimile (202) 565-0062, E-mail: risk.forum@epa.gov (Thispdf document has been updated to reflect currrent
point-of-contact information. The text of the document is otherwise unchanged from the original publication.)
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
VI
-------
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 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.
vu
-------
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 Carol M. Browner
Administrator
vui
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CONTENTS
PART A: GUIDELINES FOR ECOLOGICAL RISK ASSESSMENT
List of Figures ix
List of Text Boxes x
1. Introduction 1
1.1. The Ecological Risk Assessment Process 2
1.2. Ecological Risk Assessment in a Management Context 6
1.2.1. Contributions of Ecological Risk Assessment to Environmental
Decision Making 6
1.2.2. Factors Affecting the Value of Ecological Risk Assessment for Environmental
Decision Making 7
1.3. Scope and Intended Audience 8
1.4. Guidelines Organization 9
2. Planning the Risk Assessment 10
2.1. The Roles of Risk Managers, Risk Assessors, and Interested Parties in Planning 11
2.2. Products of Planning 12
2.2.1. Management Goals 14
2.2.2. Management Options to Achieve Goals 16
2.2.3. Scope and Complexity of the Risk Assessment 18
2.3. Planning Summary 19
3. Problem Formulation Phase 21
3.1. Products of Problem Formulation 22
3.2. Integration of Available Information 22
3.3. Selecting Assessment Endpoints 25
3.3.1. Criteria for Selection 25
3.3.1.1. Ecological Relevance 27
3.3.1.2. Susceptibility to Known or Potential Stressors 29
ix
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CONTENTS (continued)
3.3.1.3. Relevance to Management Goals 31
3.3.2. Defining Assessment Endpoints 32
3.4. Conceptual Models 36
3.4.1. Risk Hypotheses 38
3.4.2. Conceptual Model Diagrams 39
3.4.3. Uncertainty in Conceptual Models 40
3.5. Analysis Plan 41
3.5.1. Selecting Measures 42
3.5.2. Ensuring That Planned Analyses Meet Risk Managers' Needs 45
4. Analysis Phase 48
4.1. Evaluating Data and Models for Analysis 50
4.1.1. Strengths and Limitations of Different Types of Data 50
4.1.2. Evaluating Measurement or Modeling Studies 53
4.1.2.1. Evaluating the Purpose and Scope of the Study 55
4.1.2.2. Evaluating the Design and Implementation of the Study 55
4.1.3. Evaluating Uncertainty 56
4.2. Characterization of Exposure 60
4.2.1. Exposure Analyses 61
4.2.1.1. Describe the Source(s) 61
4.2.1.2. Describe the Distribution of the Stressors or Disturbed Environment 63
4.2.1.3. Describe Contact or Co-Occurrence 67
4.2.2. Exposure Profile 71
4.3. Characterization of Ecological Effects 73
4.3.1. Ecological Response Analysis 73
4.3.1.1. Stressor-Response Analysis 73
4.3.1.2. Establishing Cause-and-Effect Relationships (Causality) 78
4.3.1.3. Linking Measures of Effect to Assessment Endpoints 82
4.3.2. Stressor-Response Profile 89
5. Risk Characterization 92
5.1. Risk Estimation 92
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CONTENTS (continued)
5.1.1. Results of Field Observational Studies 92
5.1.2. Categories and Rankings 94
5.1.3. Single-Point Exposure and Effects Comparisons 95
5.1.4. Comparisons Incorporating the Entire Stressor-Response Relationship 99
5.1.5. Comparisons Incorporating Variability in Exposure and/or Effects 99
5.1.6. Application of Process Models 101
5.2. Risk Description 103
5.2.1. Lines of Evidence 103
5.2.2. Determining Ecological Adversity 106
5.3. Reporting Risks 109
6. Relating Ecological Information to Risk Management Decisions 112
Appendix A: Changes from EPA's Ecological Risk Assessment Framework A-l
Appendix B: Key Terms B-l
Appendix C: Conceptual Model Examples C-1
Appendix D: Analysis Phase Examples D-l
Appendix E: Criteria for Determining Ecological Adversity: A Hypothetical Example E-l
References R-l
PART B: RESPONSE TO SCIENCE ADVISORY BOARD AND PUBLIC COMMENTS
1. Introduction 1
2. Response to General Comments 1
XI
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CONTENTS (continued)
3. Response to Comments on Specific Questions
xu
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LIST OF FIGURES
Figure 1-1. The framework for ecological risk assessment 3
Figure 1-2. The ecological risk assessment framework, with an expanded
view of each phase 4
Figure 3-1. Problem formulation phase 23
Figure 4-1. Analysis phase 49
Figure 4-2. A simple example of a stressor-response relationship 74
Figure 4-3. Variations in stressor-response relationships 75
Figure 5-1. Risk characterization 93
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 96
Figure 5-3. Risk estimation techniques: comparison of point estimates
with associated uncertainties 98
Figure 5-4. Risk estimation techniques: stressor-response curve versus a
cumulative distribution of exposures 100
Figure 5-5. Risk estimation techniques: comparison of exposure distribution of an
herbicide in surface waters with freshwater single-species toxicity data 102
xui
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LIST OF TEXT BOXES
Text Box 1-1. Related Terminology 2
Text Box 1-2. Flexibility of the Framework Diagram 5
Text Box 2-1. Who Are Risk Managers? 10
Text Box 2-2. Who Are Risk Assessors? 11
Text Box 2-3. Who Are Interested Parties? 12
Text Box 2-4. Questions Addressed by Risk Managers and Risk Assessors 13
Text Box 2-5. Sustainability as a Management Goal 14
Text Box 2-6. Management Goals for Waquoit Bay 15
Text Box 2-7. What is the Difference Between a Management Goal and
Management Decision? 17
Text Box 2-8. Tiers and Iteration: When Is a Risk Assessment Done? 18
Text Box 2-9. Questions to Ask About Scope and Complexity 19
Text Box 3-1. Avoiding Potential Shortcomings Through Problem Formulation 21
Text Box 3-2. Uncertainty in Problem Formulation 23
Text Box 3-3. Initiating a Risk Assessment: What's Different When Stressors, Effects, or
Values Drive the Process? 24
Text Box 3-4. Assessing Available Information: Questions to Ask Concerning Source,
Stressor, and Exposure Characteristics, Ecosystem Characteristics, and Effects 26
Text Box 3-5. Salmon and Hydropower: Salmon as the Basis for an
Assessment Endpoint 27
Text Box 3-6. Cascading Adverse Effects: Primary (Direct) and Secondary (Indirect) 28
Text Box 3-7. Identifying Susceptibility 29
Text Box 3-8. Sensitivity and Secondary Effects: The Mussel-Fish Connection 31
Text Box 3-9. Examples of Management Goals and Assessment Endpoints 34
Text Box 3-10. Common Problems in Selecting Assessment Endpoints 36
Text Box 3-11. What Are the Benefits of Developing Conceptual Models? 37
Text Box 3-12. What Are Risk Hypotheses, and Why Are They Important? 38
Text Box 3-13. Examples of Risk Hypotheses 39
Text Box 3-14. Uncertainty in Problem Formulation 41
Text Box 3-15. Why W"as Measurement Endpoint Changed? 43
Text Box 3-16. Examples of a Management Goal, Assessment Endpoint, and Measures 44
xiv
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LIST OF TEXT BOXES (continued)
Text Box 3-17. How Do Water Quality Criteria Relate to Assessment Endpoints? 45
Text Box 3-18. The Data Quality Objectives Process 46
Text Box 4-1. Data Collection and the Analysis Phase 48
Text Box 4-2. The American National Standard for Quality Assurance 54
Text Box 4-3. Questions for Evaluating a Study's Utility for Risk Assessment 54
Text Box 4-4. Uncertainty Evaluation in the Analysis Phase 58
Text Box 4-5. Considering the Degree of Aggregation in Models 60
Text Box 4-6. Questions for Source Description 62
Text Box 4-7. Questions to Ask in Evaluating Stressor Distribution 64
Text Box 4-8. General Mechanisms of Transport and Dispersal 64
Text Box 4-9. Questions to Ask in Describing Contact or Co-occurrence 67
Text Box 4-10. Example of an Exposure Equation: Calculating a Potential Dose
via Ingestion 68
Text Box 4-11. Measuring Internal Dose Using Biomarkers and Tissue Residues 69
Text Box 4-12. Questions Addressed by the Exposure Profile 71
Text Box 4-13. Questions for Stressor-Response Analysis 74
Text Box 4-14. Qualitative Stressor-Response Relationships 75
Text Box 4-15. Median Effect Levels 76
Text Box 4-16. No-Effect Levels Derived From Statistical Hypothesis Testing 77
Text Box 4-17. General Criteria for Causality 79
Text Box 4-18. Koch's Postulates 80
Text Box 4-19. Examples of Extrapolations to Link Measures of Effect to Assessment
Endpoints 82
Text Box 4-20. Questions Related to Selecting Extrapolation Approaches 83
Text Box 4-21. Questions to Consider When Extrapolating From Effects Observed in the
Laboratory to Field Effects of Chemicals 85
Text Box 4-22. Questions Addressed by the Stressor-Response Profile 90
Text Box 5-1. An Example of Field Methods Used for Risk Estimation 94
Text Box 5-2. Using Qualitative Categories to Estimate Risks of an Introduced Species 95
Text Box 5-3. Applying the Quotient Method 97
Text Box 5-4. Comparing an Exposure Distribution With a Point Estimate of Effects 99
Text Box 5-5. Comparing Cumulative Exposure and Effects Distributions for Chemical
xv
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LIST OF TEXT BOXES (continued)
Stressors 101
Text Box 5-6. Estimating Risk With Process Models 103
Text Box 5-7. What Are Statistically Significant Effects? 107
Text Box 5-8. Possible Risk Assessment Report Elements 110
Text Box 5-9. Clear, Transparent, Reasonable, and Consistent Risk Characterizations Ill
Text Box 6-1. Questions Regarding Risk Assessment Results 112
Text Box 6-2. Risk Communication Considerations for Risk Managers 113
Text Box A-l. Stressor vs. Agent A-3
xvi
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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 box 1-1.
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Text Box 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
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
process1 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.
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
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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are then analyzed to characterize the nature of potential or actual exposure and the ecological responses
under the circumstances defined in the conceptual
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Figure 1-2. The ecological risk assessment framework, with an expanded view of each phas<
Within each phase, rectangles designate inputs, hexagons indicate actions, and circles
represent outputs. Problem formulation, analysis, and risk characterization are discussed in
sections 3, 4, and 5, respectively. Sections 2 and 6 describe interactions between risk
assessors and risk managers.
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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 box 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.
Text Box 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.
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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 box 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:
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• 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.
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,
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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
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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.
10
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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 boxes 2-1 and 2-2) and,
where appropriate, interested parties (see text
box 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).
Text Box 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 boxes
2-3 and 2-4.
11
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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 box 2-1).
Likewise, risk assessment may be conducted by
a single risk assessor or a team of risk assessors
(see text box 2-2). In some cases, interested
parties play an important role (see text box 2-3).
Careful consideration up front about who will
participate, and the character of that
participation, will determine the success of
planning.
Text Box 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 team members bring specific
expertise relevant to the locations, stressors,
ecosystems, scientific issues, and other expertise
as needed, depending on the type of
assessment.
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
12
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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 box
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 box 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
Text Box 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 box 2-1 and section 2.1.
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agreements are the products of planning. They include (1) clearly established and
Text Box 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)?
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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 boxes
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 box 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,
Text Box 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).
15
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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
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Text Box 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, 1996a).
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.
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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 area! 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 box 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 box 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 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 box 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
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Text Box 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.
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 box
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
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Text Box 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.
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
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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 box 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
Text Box 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?
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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 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.
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3. PROBLEM FORMULATION PHASE
Text Box 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, 1993a,
1994d). 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.
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 box 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 box 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.
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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.
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 box
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
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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
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\
PBOBIFV FORMULATION
-\
AN/LYSIS
RISK CH.4H«CTERIZATION
\
Planning
(Risk Assessor/
Risk Manner/
I [iterated Parthe
DMogue)
I ntegrtf e Aval able I nformafciwi
f
ANALYSIS
Figure 3-1. Problem formulation phase.
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 box 3-3).
Information (actual, inferred, or
estimated) is initially integrated in a scoping
process that provides the foundation for
developing problem formulation. Knowledge
Text Box 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).
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Text Box 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).
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
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assessors and decision makers. Text box 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 box 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
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Text Box 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)?
• 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?
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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.
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
box 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
Text Box 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.
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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 box 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
Text Box 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.
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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 box 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
Text Box 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.
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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 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 box 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
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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 box 3-7).
Text Box 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 offish. 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.
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
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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
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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 box 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 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.
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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
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Text Box 3-9. Examples of Management Goals and Assessment Endpoints
Case
Regulatory context/management goal
Assessment endpoint
Assessing Risks of
New Chemical Under
Toxic Substances
Control Act (Lynch et
al., 1994)
Protect "the environment" from "an unreasonable
risk of injury" (TSCA §2[b][l] and [2]); protect the
aquatic environment. Goal was to exceed a
concentration of concern on no more than 20 days
a year.
Survival, growth, and
reproduction of fish,
aquatic invertebrates,
and algae
Special Review of
Granular Carbofuran
Based on Adverse
Effects on Birds
(Houseknecht, 1993)
Prevent. . . "unreasonable adverse effects on the
environment" (FIFRA §§3[c][5] and 3[c][6]); using
cost-benefit considerations. Goal was to have no
regularly repeated bird kills.
Individual bird survival
Modeling Future
Losses of Bottomland
Forest Wetlands
(Brodyetal, 1993)
National Environmental Policy Act may apply to
environmental impact of new levee construction;
also Clean Water Act §404.
(1) Forest community
structure and habitat
value to wildlife species
(2) Species composition
of wildlife community
Pest Risk Assessment
on Importation of Logs
From Chile (USDA,
1993)
Assessment was done to help provide a basis for
any necessary regulation of the importation of
timber and timber products into the United States.
Survival and growth of
tree species in the
western United States
Baird and McGuire
Superfund Site
(terrestrial component);
(Bur-master etal., 1991;
Callahan et al., 1991;
Menzieetal, 1992)
Protection of the environment (CERCLA/SARA).
(1) Survival of soil
invertebrates
(2) Survival and
reproduction of song
birds
Waquoit Bay Estuary
Watershed Risk
Assessment (U.S. EPA,
1996a)
Clean Water Act—wetlands protection; water
quality criteria—pesticides; endangered species.
National Estuarine Research Reserve,
Massachusetts, Area of Critical Environmental
Concern. Goal was to reestablish and maintain
water quality and habitat conditions to support
diverse self-sustaining commercial, recreational,
and native fish, water-dependent wildlife, and
shellfish and to reverse ongoing degradation.
(1) Estuarine eelgrass
habitat abundance and
distribution
(2) Estuarine fish
species diversity and
abundance
(3) Freshwater pond
benthic invertebrate
species diversity and
abundance
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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 box 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 box 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
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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 box 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
Text Box 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 Box 3-8).
Attribute is not sufficiently sensitive for
detecting important effects (e.g., survival
compared with recruitment for endangered
species)
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assessment. Some of the benefits gained by
developing conceptual models are featured in
text box 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 box 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, 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.
Text Box 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.
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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
box 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).
Text Box 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.
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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 box 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
Text Box 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 K^
tend to bioaccumulate. PMN chemical A has a
K^, of 5.5 and molecular structure similar to
known chemical stiessor B.
Hypotheses: Based on the K^ 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 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.
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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
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models during risk 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 box 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 briefer
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
Text Box 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.
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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 uncertainly 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.
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Text Box 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.
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 box 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 box 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
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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 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 phytogeny.
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 box 3-
Text Box 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 offish 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
48
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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' lexicological 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
Text Box 3-17. How Do Water Quality
Criteria Relate to Assessment Endpoints?
Water quality criteria (U.S. EPA, 1986a) have been
developed for the protection of aquatic life from
chemical stressors. This text box shows how the
elements of a water quality criterion correspond to
management goals, management decisions,
assessment endpoints, and measures.
Regulatory Goal
• Clean Water Act, § 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.
49
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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 box
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
Text Box 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.
50
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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.
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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 box 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)
Text Box 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.
Analyzes effects by examining stressor-response relationships, the evidence for
causality, and the relationship between measures of effect and assessment endpoints
(section 4.3)
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PROBLEM FORMULATION
ANALYSIS
XXN
RISK CHARACTERIZATION
PROBLEM FORMULATION
Characterization of Exposure i Characterization of Ecological Effects
Measures
Of
Exposure
^ ^
Measures of
Ecosystem and
Characteristics
Measures
Of
Effect
Exposure
Analysis.
Ecological Response
Analysis
RISK CHARACTERIZATION
Figure 4-1. Analysis phase.
4—*
rocass,
• Summarizes the conclusions about exposure (section 4.2.2) and effects (section 4.3.2).
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
53
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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,
54
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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
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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 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
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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
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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 box
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, 1994e; 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.
Text Box 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:
• Planning and scoping
• Designing data collection operations
• Implementing and monitoring planned
operations
• Assessing and verifying data usability.
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Text Box 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?
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 box 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 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)
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• 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 uncertainly 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, 1994e).
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
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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 box 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
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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;
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Text Box 4-4. Uncertainty Evaluation in the Analysis Phase
Example analysis phase strategies
Source of
uncertainty
Specific example
Unclear Contact principal investigator or other study
communication participants if obj ectives or methods of literature
studies are unclear.
Document decisions made during the course of
the assessment.
Clarify whether the study was designed
to characterize local populations or
regional populations.
Discuss rationale for selecting the
critical toxicity study.
Descriptive Verify that data sources followed appropriate
errors QA/QC procedures.
Double-check calculations and data
entry.
Variability Describe heterogeneity using point estimates
(e.g., central tendency and high end) or by
constructing probability or frequency
distributions.
Display differences in species
sensitivity using a cumulative
distribution function.
Differentiate from uncertainty due to lack of
knowledge.
Data gaps Collect needed data.
Describe approaches used for bridging gaps and
their rationales.
Discuss rationale for using a factor of 10
to extrapolate between a lowest-
observed-adverse-effect level (LOAEL)
andaNOAEL.
Differentiate science-based judgments from
policy-based judgments.
Uncertainty
about a
quantity's true
value
Use standard statistical methods to construct
probability distributions or point estimates (e.g.,
confidence limits).
Evaluate power of designed experiments to detect
differences.
Collect additional data.
Present the upper confidence limit on
the arithmetic mean soil concentration,
in addition to the best estimate of the
arithmetic mean.
Verify location of samples or other spatial
features.
Ground-truth remote sensing data.
Model structure
uncertainty
(process models)
Discuss key aggregations and model
simplifications.
Compare model predictions with data collected in
the system of interest.
Discuss combining different species into
a group based on similar feeding habits.
Uncertainty
about a model's
form
(empirical
models)
Evaluate whether alternative models should be
combined formally or treated separately.
Compare model predictions with data collected in
the system of interest.
Present results obtained using
alternative models.
Compare results of a plant uptake model
with data collected in the field.
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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 box 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 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 box 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
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Text Box 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.
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, 1996b, 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.
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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 box 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
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should also consider whether other constituents emitted by a source influence transport, transformation,
or bioavailability of the stressor of interest. The presence of
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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 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):
Text Box 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?
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• 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 box 4-7).
4.2.1.2.1. Evaluating Transport Pathways. Stressors can be transported via many pathways (see
text box 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.
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Text Box 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?
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
Text Box 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
Primarily biological stressors:
Splashing or raindrops
Human activity (boats, campers)
Passive transmittal by other organisms
Biological vectors
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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 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
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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,
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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 box 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.
Text Box 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?
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Text Box 4-10. Example of an Exposure
Equation: Calculating a Potential Dose via
Ingestion
ADD
h-l
(Pk
Where:
ADDpot =
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, 1993b)
(see text box 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,
NIRk =
m
Potential average daily dose (e.g.,
in mg/kg-day)
Average contaminant concentration
in the kth type of food (e.g., in
mg/kg wet weight)
Fraction of intake of the k"1 food
type that is from the contaminated
area (unitless)
Normalized ingestion rate of the kth
food type on a wet-weight basis
(e.g., in kg food/kg body-weight-
day).
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, 1993b.
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permeability), and the organism (sickness, active
uptake) (Suter et al., 1994). Because of
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 box 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
Text Box 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).
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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
lexicologically 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).
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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
Text Box 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?
likelihood that exposure will occur (see text box
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.
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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 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-l 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.
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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 box 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
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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 (Rolling, 1978).
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
Text Box 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?
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individual variables (e.g., species
a: Streseor-refiponse curves
(e.g., dose-% mortality)
b: Point estimates
Intensity- of stressor (e.fl., dose)
Figure 4-2. A simple example of a stressor-response relationship. Substantially more
complex relationships are typical of many ecological risk assessments, given the range
of stressors, endpoints, and environmental situations often encountered.
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Intensity of Stressor
(pesticide concentration)
Figure 4-3. Variations in stressor-response relationships. These curves illustrate a
range of responses to pesticide exposure of the intrinsic rate of increase of
zooplankton populations (adapted from Schindler, 1987).
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 box 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
Text Box 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.
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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 box 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 box 4-15) are frequently selected
because the level of uncertainty is minimized at
Text Box 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.
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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 box 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 box 3-
12). An example of this approach for deriving chemical no-effect
levels is provided in text box 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
Text Box 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).
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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, 1986b]), 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 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
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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 box 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
Text Box 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
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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 box 4-18) may be useful. For
Text Box 4-18. Koch's Postulates (Pelczar
and Reid, 1972)
• A pathogen must be consistently found in
association with a given disease.
• 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.
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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 box
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
Text Box 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
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should revisit the questions listed in text box 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 box 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
Text Box 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?
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.
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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
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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 box 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 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
Text Box 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?
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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 box 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.
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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
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(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) (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 (1993 a), 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
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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; Person 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 box 4-5). For example, if a decision is required about a particular species, a
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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
box 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
Text Box 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?
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
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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 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 L0kke (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.
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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).
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
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Field observational studies (surveys) can serve as risk estimation techniques because they
provide empirical evidence linking exposure to effects. Field surveys measure biological
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PROBLEM FORMULATION
ANALYSIS
RISK CHARACTERIZATION
ANALYSIS
Communicatiig Results to the Risk Manager
Risk Management and Communicating
Results to Interested Parties
Figure 5-1. Risk characterization.
s
Zi
•a
i-5
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
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natural systems, whereas estimates generated
from laboratory studies generally delineate either
exposures or effects under controlled or
prescribed conditions (see text box 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 box 5-2).
Text Box 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 Box 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).
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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.
Text Box 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.
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
box 5-3).
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.
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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
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a: Comparison of point estimates
Exposure stressor-response
estimate estjmatfi
(e.g., mean
concentration)
(a
v"'y''
b: Comparison of a point estimate of a stressor-response
relationship with uncertainty associated with an exposure
point estimate
tn
c
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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, 1993 a). 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
Text Box 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/ft2)
by the granules/LD50 derived from single-dose
avian toxicity tests. The calculation yields values
with units of LD50/ft2. 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/ft2 were estimated for songbirds,
upland game birds, and waterfowl that may
forage within or near 10 different agricultural
crops.
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,
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c
OJ
o
e.g., uncertainty around
mean concentration
e.g., uncertainty around
Intensity of Stressor (e.g., concentration)
e.g., probability that LC^ > mean concentration
Figure 5-3. Risk estimation techniques: comparison of point estimates with
associated 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 box 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?
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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
Text Box 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.
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0.90
o
c
CD
D
CT
CD
CD 0.50
E
D
O
0.10
cumulative
distribution of
exposures
'50
stressor-
response
curve
comparison of
90th percentile exposure
with EC10
comparison of
50th percentile exposure
with EC
Intensity of Stressor (e.g., concentration)
Figure 5-4. Risk estimation techniques: stressor-response curve versus a cumulative
distribution of exposures.
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
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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 box
5-5 and figure 5-5 illustrate the use of cumulative
exposure and effects distributions for estimating
risk.
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 box
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
Text Box 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 5-5 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?
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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
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(0
1
Compaiscn if Wth cattle
concentration with 1Cth centils
ifthflLC5E
J
10-2 1O1 10D 1D1 102 103
CONCENTRATION IN
Figure 5-5. Risk estimation techniques: comparison of exposure distribution of an
herbicide in surface waters with freshwater single-species toxicity data. See text
box 5-4 for further discussion. Redrawn from Baker et al., 1994. (Centile ranks
for species LC5 data were obtained using the formula (100 x «/[N+l]), where n is
the rank number of the LC5 and N is the total number of data points in the set;
adapted from Parkhurst et al., 1995).
multiple stressors, such as the effects of multiple chemicals on fish population sustainability (Barnthouse
etal., 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
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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.
Text Box 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.
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
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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-
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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
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 boxes 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-l). 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 box 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.
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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?
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Text Box 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 (a). 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.
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 box 5-7) may
not be relevant to an assessment endpoint offish
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
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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. 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
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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
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providing a detailed outline of reporting elements, the risk assessor should consider the elements listed
in text box 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 box 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 box 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.
Text Box 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.
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Text Box 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 maj or 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.
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6. RELATING ECOLOGICAL INFORMATION TO RISK MANAGEMENT DECISIONS
After characterizing risks and preparing a
discuss the results with risk managers (figure 5-
1). 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 box 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
risk assessment report (section 5), risk assessors
Text Box 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?
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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 box 6-2.
Along with discussions of risk and
communications with the public, it is important
for risk managers to consider whether additional
Text Box 6-2. Risk Communication
Considerations for Risk Managers (US
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.
• Listen to the public's specific concerns.
• Be honest, frank, and open.
• Speak clearly and with compassion.
• Meet the needs of the media.
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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.
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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.l. 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.
A-l
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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).
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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
box A-l). 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
Text Box A-l. 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.
A-3
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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 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).
A-4
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• 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.
A-5
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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).
B-l
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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-l).
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).
CDF for a Normal Distribution
CDF for a Log-Normal Distribution
Q
i?
Li-
ft
J
J
1
(N
CJi
Figure B-l. Plots of cumulative distribution function (CDF).
B-2
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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).
B-3
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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).
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.
B-4
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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.
B-5
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PDF for a Normal Distribution
PDF for a Log-Normal Distribution
5 15
Figure B-2. Plots of probability density functions (PDF)
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.
B-6
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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).
B-7
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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-l 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
Source
(e.g., logging plan)
Primary Stressor
(e.g., building logging roads)
Interaction with
ecosystem
(e.g., slope, soil type)
I
l
(No exposure off receptor
by this pathway)
Secondary
Stressor
(e-g., increased
sillation of stream)
Exposure
.of receptor
Primary Effect
(e.g., smothering of
benthic insects)
Interspecies interaction (e.g., food,
habitat, competition)
Secondary (Indirect) Effect
(e.g., decreased abundance
of insectivorous fish)
Figure C-l. Conceptual model for logging.
C-l
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deposition
Lead Shot
Lead Shot in
the Soil
ingestion
4, repro-
duction
t predation
^ infection
4 competi-
tiveness
Upland Bird
Tissue Lead
Ingested
Lead
Embedded
Lead
sublethal
intoxication
penetration
Increased Upland
Bird Morbidity
embedded
pellets
|etha| intoxication
Increased Upland
Bird Mortdil/
embedded
pelets
Increased
Predator Exposure
Upland Bird
Population
Effects
Increased
Predator
Mortality
FBD, 02/95
Figure C-2. Conceptual model for tracking stress associated with lead shot through upland
ecosystems. Reprinted from EnvironmentaLTcxicology and Chemistry by Kendall et al.
(1996) with permission of the Society of Environmental Toxicology and Chemistry
(copyright 1996).
-------
Agriculture Atmosphere Residential Dev. Industry
Marine Activities
Figure C-3. Waquoit Bay watershed conceptual model.
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).
Figure C-l 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
C-3
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/5-HdlliSll/ /
Fell
TESU6
/ Rtesfciii!/
Figure C-3. Waquoit Bay watershed conceptual model (continued).
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
C-4
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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.
C-4
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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.I. SPECIAL REVIEW OF GRANULAR FORMULATIONS OF CARBOFURAN BASED
ON ADVERSE EFFECTS ON BIRDS
Figure D-l 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.
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-l
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Measures of Exposure:
Application rates,
formulation attributes,
residues in invertebrates
and prey organisms
Describe Source:
Application of granular
pesticide
Measures of
Ecosystem and
Receptor
Characteristics:
Species
occurrences in
agri-ecosystems,
bird feeding habits
Measures of Effects:
Toxicity tests, field
studies of bird mortality
Describe Distribution in the
^Environment: number of exposed granules^
square foot estimated for differenj.
application methods
Describe Exposure:
Of birds to carbofuran in
^granules, soil invertebrates^,
and prey organisms
Describe
Stressor-Response
Relationship:
^Number of granules needed
for 50% mortality
in test species
Describe
Causal Evidence:
Experimental evidence
field studies,
biomarkers of
exposure
Stressor-Response Profile
Figure D-l. Example of the analysis phase process: special review of carbofuran.
Rectangles indicate inputs, hexagons indicate actions, and circles indicate outputs.
D-2
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D-3
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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.
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
D-4
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Measures of Exposure:
Levee attributes,
water-level measures
Describe Source:
Construction of
levees, decreased river
gradient
Measures of Ecosystem and
Receptor Characteristics:
Flow regime, location and extent
of bottomland hardwood
community, species occurrences
PRIMARY EFFECTS: Estimated
Using FORFLO Model
Describe Distribution
of Stressor in the Environment
Extent, frequency and location of
flooding
Describe
Exposure 1:
Of seeds and tree
seedlings to increased
soil moisture and
flooding
JT Describe Disturbed
f Environment:
V Plant community
^**+^^ composition
^ —
Describe Stressor-Respom
Relationship:
Soil moisture/flooding -
germination, survival, and growth
rates
Extrapolate to Plant Community
Using FORFLO model
Describe Exposure 2
Of wildlife species
to altered plant
community
SECONDARY EFFECTS: Estimated
Using Habitat Suitability Indices
Describe Stressor-Response
Relationship:
Plant community-
habitat suitability for wildlife
Describe Causal
Evidence:
' Mechanism of action, field ^
studies, laboratory
experimentation,
model validation
Combined Exposure and Stressor-Response Profile
Figure D-2. Example of the analysis phase process: modeling losses of bottomland
hardwoods. Rectangles indicate inputs, hexagons indicate actions, and circles indicate
outputs.
D-5
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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.
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
D-6
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Measures of Exposure:
Point of entry for logs, processing
status, and eventual destination,
attributes of insects and pathogens
(dispersal mechanisms, life-history
characteristics).
Describe
Source:
Entry of infested
logs into U.S.
Ecosystem/Receptor
Measures:
Climate, geographic
barriers, host suitability,
extent of potential host
species
Describe Distribution
in the Environment:
Consider colonization potential,
spread potential,
survival, and reproduction,
Describe Exposure:
Of resources of concern
Measures of Effect:
Infectivity of similar
pests on U.S. hosts,
infectivity of pests in
other countries
Characterize Effects:
Consider potential for
ecosystem destabilization,
reduction in biodiversity, and
loss of keystone or
endangered species
I
Exposure Profile
Stressor-Response Profile
Figure D-3. Example of the analysis phase process: pest risk assessment of the
importation of logs from Chile. Rectangles indicate inputs, hexagons indicate actions, and
circles indicate outputs.
D-7
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D-8
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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.
D-9
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APPENDIX E—CRITERIA FOR DETERMINING ECOLOGICAL ADVERSITY: A
HYPOTHETICAL EXAMPLE (Adapted from Hartwell et al., 1994)2
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.
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.
2 This example is simplified for illustrative purposes. In other situations, it may be considerably
more difficult to draw clear conclusions regarding relative ecological adversity.
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• 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.
E-2
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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
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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.
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,
1994d).
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.
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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.
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(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
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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 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 ofstressors, 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
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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.
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