[Federal Register Volume 63, Number 93 (Thursday, May 14, 1998)]
[Notices]
[Pages 26846-26924]
From the Federal Register Online via the Government Publishing Office [www.gpo.gov]
[FR Doc No: 98-12302]
[[Page 26845]]
_______________________________________________________________________
Part II
Environmental Protection Agency
_______________________________________________________________________
Guidelines for Ecological Risk Assessment; Notice
Federal Register / Vol. 63, No. 93 / Thursday, May 14, 1998 /
Notices
[[Page 26846]]
ENVIRONMENTAL PROTECTION AGENCY
[FRL-6011-2 ]
Guidelines for Ecological Risk Assessment
AGENCY: Environmental Protection Agency.
ACTION: Notice of availability of final Guidelines for Ecological Risk
Assessment.
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SUMMARY: The U.S. Environmental Protection Agency (EPA) is today
publishing in final form a document entitled Guidelines for Ecological
Risk Assessment (hereafter ``Guidelines''). These Guidelines were
developed as part of an interoffice program by a Technical Panel of the
Risk Assessment Forum. These Guidelines will help improve the quality
of ecological risk assessments at EPA while increasing the consistency
of assessments among the Agency's program offices and regions.
These Guidelines were prepared during a time of increasing interest
in the field of ecological risk assessment and reflect input from many
sources both within and outside the Agency. The Guidelines expand upon
and replace the previously published EPA report Framework for
Ecological Risk Assessment (EPA/630/R-92/001, February 1992), which
proposed principles and terminology for the ecological risk assessment
process. From 1992 to 1994, the Agency focused on identifying a
structure for the Guidelines and the issues that the document would
address. EPA sponsored public and Agency colloquia, developed peer-
reviewed ecological assessment case studies, and prepared a set of
peer-reviewed issue papers highlighting important principles and
approaches. Drafts of the proposed Guidelines underwent formal external
peer review and were reviewed by the Agency's Risk Assessment Forum, by
Federal interagency subcommittees of the Committee on Environment and
Natural Resources of the Office of Science and Technology Policy, and
by the Agency's Science Advisory Board (SAB). The proposed Guidelines
were published for public comment in 1996 (61 FR 47552-47631, September
9, 1996). The final Guidelines incorporate revisions based on the
comments received from the public and the SAB on the proposed
Guidelines. EPA appreciates the efforts of all participants in the
process and has tried to address their recommendations in these
Guidelines.
DATES: The Guidelines will be effective on April 30, 1998.
ADDRESSES: The Guidelines will be made available in several ways:
(1) The electronic version will be accessible on the EPA National
Center for Environmental Assessment home page on the Internet at http:/
/www.epa.gov/ncea/.
(2) 3\1/2\'' high-density computer diskettes in WordPerfect format
will be available from ORD Publications, Technology Transfer and
Support Division, National Risk Management Research Laboratory,
Cincinnati, OH; telephone: 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-G[email protected].
SUPPLEMENTARY INFORMATION: Ecological risk assessment ``evaluates the
likelihood that adverse ecological effects may occur or are occurring
as a result of exposure to one or more stressors'' (U.S. EPA, 1992a).
It is a flexible process for organizing and analyzing data,
information, assumptions, and uncertainties to evaluate the likelihood
of adverse ecological effects. Ecological risk assessment provides a
critical element for environmental decision making by giving risk
managers an approach for considering available scientific information
along with the other factors they need to consider (e.g., social,
legal, political, or economic) in selecting a course of action.
To help improve the quality and consistency of the U.S.
Environmental Protection Agency's ecological risk assessments, EPA's
Risk Assessment Forum initiated development of these Guidelines. The
primary audience for this document is risk assessors and risk managers
at EPA, although these Guidelines also may be useful to others outside
the Agency. These Guidelines expand on and replace the 1992 report
Framework for Ecological Risk Assessment (referred to as the Framework
Report; see Appendix A). They were written by a Forum technical panel
and have been revised on the basis of extensive comments from outside
peer reviewers as well as Agency staff. The Guidelines retain the
Framework Report's broad scope, while expanding on some concepts and
modifying others to reflect Agency experiences. EPA intends to follow
these Guidelines with a series of shorter, more detailed documents that
address specific ecological risk assessment topics. This ``bookshelf''
approach provides the flexibility necessary to keep pace with
developments in the rapidly evolving field of ecological risk
assessment while allowing time to form consensus, where appropriate, on
science policy (default assumptions) to bridge gaps in knowledge. EPA
will revisit guidelines documents as experience and scientific
consensus evolve. The Agency recognizes that ecological risk assessment
is only one tool in the overall management of ecological risks.
Therefore, there are ongoing efforts within the Agency to develop other
tools and processes that can contribute to an overall approach to
ecological risk management, addressing topics such as ecological
benefits assessment and cost-benefit analyses.
Ecological risk assessment includes three primary phases: Problem
formulation, analysis, and risk characterization. In problem
formulation, risk assessors evaluate goals and select assessment
endpoints, prepare the conceptual model, and develop an analysis plan.
During the analysis phase, assessors evaluate exposure to stressors and
the relationship between stressor levels and ecological effects. In the
third phase, risk characterization, assessors estimate risk through
integration of exposure and stressor-response profiles, describe risk
by discussing lines of evidence and determining ecological adversity,
and prepare a report. The interface among risk assessors, risk
managers, and interested parties during planning at the beginning and
communication of risk at the end of the risk assessment is critical to
ensure that the results of the assessment can be used to support a
management decision. Because of the diverse expertise required
(especially in complex ecological risk assessments), risk assessors and
risk managers frequently work in multidisciplinary teams.
Both risk managers and risk assessors bring valuable perspectives
to the initial
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planning activities for an ecological risk assessment. Risk managers
charged with protecting the environment can identify information they
need to develop their decision, risk assessors can ensure that science
is effectively used to address ecological concerns, and together they
can evaluate whether a risk assessment can address identified problems.
However, this planning process is distinct from the scientific conduct
of an ecological risk assessment. This distinction helps ensure that
political and social issues, while helping to define the objectives for
the risk assessment, do not introduce undue bias.
Problem formulation, which follows these planning discussions,
provides a foundation upon which the entire risk assessment depends.
Successful completion of problem formulation depends on the quality of
three products: Assessment endpoints, conceptual models, and an
analysis plan. Since problem formulation is an interactive, nonlinear
process, substantial reevaluation is expected to occur during the
development of all problem formulation products.
The analysis phase includes two principal activities:
Characterization of exposure and characterization of ecological
effects. The process is flexible, and interaction between the two
evaluations is essential. Both activities evaluate available data for
scientific credibility and relevance to assessment endpoints and the
conceptual model. Exposure characterization describes sources of
stressors, their distribution in the environment, and their contact or
co-occurrence with ecological receptors. Ecological effects
characterization evaluates stressor-response relationships or evidence
that exposure to stressors causes an observed response. The bulk of
quantitative uncertainty analysis is performed in the analysis phase,
although uncertainty is an important consideration throughout the
entire risk assessment. The analysis phase products are summary
profiles that describe exposure and the stressor-response
relationships.
Risk characterization is the final phase of an ecological risk
assessment. During this phase, risk assessors estimate ecological
risks, indicate the overall degree of confidence in the risk estimates,
cite evidence supporting the risk estimates, and interpret the
adversity of ecological effects. To ensure mutual understanding between
risk assessors and managers, a good risk characterization will express
results clearly, articulate major assumptions and uncertainties,
identify reasonable alternative interpretations, and separate
scientific conclusions from policy judgments. Risk managers use risk
assessment results, along with other factors (e.g., economic or legal
concerns), in making risk management decisions and as a basis for
communicating risks to interested parties and the general public.
After completion of the risk assessment, risk managers may consider
whether follow-up activities are required. They may decide on risk
mitigation measures, then develop a monitoring plan to determine
whether the procedures reduced risk or whether ecological recovery is
occurring. Managers may also elect to conduct another planned tier or
iteration of the risk assessment if necessary to support a management
decision.
Dated: April 30, 1998.
Carol M. Browner,
Administrator.
Part A: Guidelines for Ecological Risk Assessment
Contents
List of Figures
List of Text Notes
1. Introduction
1.1. The Ecological Risk Assessment Process
1.2. Ecological Risk Assessment in a Management Context
1.2.1. Contributions of Ecological Risk Assessment to
Environmental Decision Making
1.2.2. Factors Affecting the Value of Ecological Risk Assessment
for Environmental Decision Making
1.3. Scope and Intended Audience
1.4. Guidelines Organization
2. Planning the Risk Assessment
2.1. The Roles of Risk Managers, Risk Assessors, and Interested
Parties in Planning
2.2. Products of Planning
2.2.1. Management Goals
2.2.2. Management Options to Achieve Goals
2.2.3. Scope and Complexity of the Risk Assessment
2.3. Planning Summary
3. Problem Formulation Phase
3.1. Products of Problem Formulation
3.2. Integration of Available Information
3.3. Selecting Assessment Endpoints
3.3.1. Criteria for Selection
3.3.1.1. Ecological Relevance
3.3.1.2. Susceptibility to Known or Potential Stressors
3.3.1.3. Relevance to Management Goals
3.3.2. Defining Assessment Endpoints
3.4. Conceptual Models
3.4.1. Risk Hypotheses
3.4.2. Conceptual Model Diagrams
3.4.3. Uncertainty in Conceptual Models
3.5. Analysis Plan
3.5.1. Selecting Measures
3.5.2. Ensuring That Planned Analyses Meet Risk Managers' Needs
4. Analysis Phase
4.1. Evaluating Data and Models for Analysis
4.1.1. Strengths and Limitations of Different Types of Data
4.1.2. Evaluating Measurement or Modeling Studies
4.1.2.1. Evaluating the Purpose and Scope of the Study
4.1.2.2. Evaluating the Design and Implementation of the Study
4.1.3. Evaluating Uncertainty
4.2. Characterization of Exposure
4.2.1. Exposure Analyses
4.2.1.1. Describe the Source(s)
4.2.1.2. Describe the Distribution of the Stressors or Disturbed
Environment
4.2.1.3. Describe Contact or Co-occurrence
4.2.2. Exposure Profile
4.3. Characterization of Ecological Effects
4.3.1. Ecological Response Analysis
4.3.1.1. Stressor-Response Analysis
4.3.1.2. Establishing Cause-and-Effect Relationships (Causality)
4.3.1.3. Linking Measures of Effect to Assessment Endpoints
4.3.2. Stressor-Response Profile
5. Risk Characterization
5.1. Risk Estimation
5.1.1. Results of Field Observational Studies
5.1.2. Categories and Rankings
5.1.3. Single-Point Exposure and Effects Comparisons
5.1.4. Comparisons Incorporating the Entire Stressor-Response
Relationship
5.1.5. Comparisons Incorporating Variability in Exposure and/or
Effects
5.1.6. Application of Process Models
5.2. Risk Description
5.2.1. Lines of Evidence
5.2.2. Determining Ecological Adversity
5.3. Reporting Risks
6. Relating Ecological Information to Risk Management Decisions
7. Text Notes
Appendix A: Changes from EPA's Ecological Risk Assessment Framework
Appendix B: Key Terms
Appendix C: Conceptual Model Examples
Appendix D: Analysis Phase Examples
Appendix E: Criteria for Determining Ecological Adversity: A
Hypothetical Example
References
List of Figures
Figure 1-1. The framework for ecological risk assessment
Figure 1-2. The ecological risk assessment framework, with an
expanded view of each phase
Figure 3-1. Problem formulation phase
Figure 4-1. Analysis phase
Figure 4-2. A simple example of a stressor-response relationship.
Figure 4-3. Variations in stressor-response relationships
Figure 5-1. Risk characterization
Figure 5-2. Risk estimation techniques. a. Comparison of exposure
and stressor-response point estimates. b. Comparison of point
estimates from the stressor-response relationship with uncertainty
associated with an exposure point estimate
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Figure 5-3. Risk estimation techniques: Comparison of point
estimates with associated uncertainties
Figure 5-4. Risk estimation techniques: Stressor-response curve
versus a cumulative distribution of exposures
Figure 5-5. Risk estimation techniques: Comparison of exposure
distribution of an herbicide in surface waters with freshwater
single-species toxicity data
List of Text Notes
Text Note 1-1. Related Terminology
Text Note 1-2. Flexibility of the Framework Diagram
Text Note 2-1. Who Are Risk Managers?
Text Note 2-2. Who Are Risk Assessors?
Text Note 2-3. Who Are Interested Parties?
Text Note 2-4. Questions Addressed by Risk Managers and Risk
Assessors
Text Note 2-5. Sustainability as a Management Goal
Text Note 2-6. Management Goals for Waquoit Bay
Text Note 2-7. What is the Difference Between a Management Goal and
Management Decision?
Text Note 2-8. Tiers and Iteration: When Is a Risk Assessment Done?
Text Note 2-9. Questions to Ask About Scope and Complexity
Text Note 3-1. Avoiding Potential Shortcomings Through Problem
Formulation
Text Note 3-2. Uncertainty in Problem Formulation
Text Note 3-3. Initiating a Risk Assessment: What's Different When
Stressors, Effects, or Values Drive the Process?
Text Note 3-4. Assessing Available Information: Questions to Ask
Concerning Source, Stressor, and Exposure Characteristics, Ecosystem
Characteristics, and Effects
Text Note 3-5. Salmon and Hydropower: Salmon as the Basis for an
Assessment Endpoint
Text Note 3-6. Cascading Adverse Effects: Primary (Direct) and
Secondary (Indirect)
Text Note 3-7. Identifying Susceptibility
Text Note 3-8. Sensitivity and Secondary Effects: The Mussel-Fish
Connection
Text Note 3-9. Examples of Management Goals and Assessment Endpoints
Text Note 3-10. Common Problems in Selecting Assessment Endpoints
Text Note 3-11. What Are the Benefits of Developing Conceptual
Models?
Text Note 3-12. What Are Risk Hypotheses, and Why Are They
Important?
Text Note 3-13. Examples of Risk Hypotheses
Text Note 3-14. Uncertainty in Problem Formulation
Text Note 3-15. Why Was Measurement Endpoint Changed?
Text Note 3-16. Examples of a Management Goal, Assessment Endpoint,
and Measures
Text Note 3-17. How Do Water Quality Criteria Relate to Assessment
Endpoints?
Text Note 3-18. The Data Quality Objectives Process
Text Note 4-1. Data Collection and the Analysis Phase
Text Note 4-2. The American National Standard for Quality Assurance
Text Note 4-3. Questions for Evaluating a Study's Utility for Risk
Assessment
Text Note 4-4. Uncertainty Evaluation in the Analysis Phase
Text Note 4-5. Considering the Degree of Aggregation in Models
Text Note 4-6. Questions for Source Description
Text Note 4-7. Questions to Ask in Evaluating Stressor Distribution
Text Note 4-8. General Mechanisms of Transport and Dispersal
Text Note 4-9. Questions to Ask in Describing Contact or Co-
occurrence
Text Note 4-10. Example of an Exposure Equation: Calculating a
Potential Dose via Ingestion
Text Note 4-11. Measuring Internal Dose Using Biomarkers and Tissue
Residues
Text Note 4-12. Questions Addressed by the Exposure Profile
Text Note 4-13. Questions for Stressor-Response Analysis
Text Note 4-14. Qualitative Stressor-Response Relationships
Text Note 4-15. Median Effect Levels
Text Note 4-16. No-Effect Levels Derived From Statistical Hypothesis
Testing
Text Note 4-17. General Criteria for Causality
Text Note 4-18. Koch's Postulates
Text Note 4-19. Examples of Extrapolations to Link Measures of
Effect to Assessment Endpoints
Text Note 4-20. Questions Related to Selecting Extrapolation
Approaches
Text Note 4-21. Questions to Consider When Extrapolating From
Effects Observed in the Laboratory to Field Effects of Chemicals
Text Note 4-22. Questions Addressed by the Stressor-Response Profile
Text Note 5-1. An Example of Field Methods Used for Risk Estimation
Text Note 5-2. Using Qualitative Categories to Estimate Risks of an
Introduced Species
Text Note 5-3. Applying the Quotient Method
Text Note 5-4. Comparing an Exposure Distribution With a Point
Estimate of Effects
Text Note 5-5. Comparing Cumulative Exposure and Effects
Distributions for Chemical Stressors
Text Note 5-6. Estimating Risk With Process Models
Text Note 5-7. What Are Statistically Significant Effects?
Text Note 5-8. Possible Risk Assessment Report Elements
Text Note 5-9. Clear, Transparent, Reasonable, and Consistent Risk
Characterizations
Text Note 6-1. Questions Regarding Risk Assessment Results
Text Note 6-2. Risk Communication Considerations for Risk Managers
Text Note A-1. Stressor vs. Agent
1. Introduction
Ecological risk assessment is a process that evaluates the
likelihood that adverse ecological effects may occur or are occurring
as a result of exposure to one or more stressors (U.S. EPA, 1992a). The
process is used to systematically evaluate and organize data,
information, assumptions, and uncertainties in order to help understand
and predict the relationships between stressors and ecological effects
in a way that is useful for environmental decision making. An
assessment may involve chemical, physical, or biological stressors, and
one stressor or many stressors may be considered.
Ecological risk assessments are developed within a risk management
context to evaluate human-induced changes that are considered
undesirable. As a result, these Guidelines focus on stressors and
adverse effects generated or influenced by anthropogenic activity.
Defining adversity is important because a stressor may cause adverse
effects on one ecosystem component but be neutral or even beneficial to
other components. Changes often considered undesirable are those that
alter important structural or functional characteristics or components
of ecosystems. An evaluation of adversity may include a consideration
of the type, intensity, and scale of the effect as well as the
potential for recovery. The acceptability of adverse effects is
determined by risk managers. Although intended to evaluate adverse
effects, the ecological risk assessment process can be adapted to
predict beneficial changes or risk from natural events.
Descriptions of the likelihood of adverse effects may range from
qualitative judgments to quantitative probabilities. Although risk
assessments may include quantitative risk estimates, quantitation of
risks is not always possible. It is better to convey conclusions (and
associated uncertainties) qualitatively than to ignore them because
they are not easily understood or estimated.
Ecological risk assessments can be used to predict the likelihood
of future adverse effects (prospective) or evaluate the likelihood that
effects are caused by past exposure to stressors (retrospective). In
many cases, both approaches are included in a single risk assessment.
For example, a retrospective risk assessment designed to evaluate the
cause for amphibian population declines may also be used to predict the
effects of future management actions. Combined retrospective and
prospective risk assessments are typical in situations where ecosystems
have a history of previous impacts and the potential for future effects
from multiple chemical, physical, or biological stressors. Other
terminology related to ecological risk assessment is referenced in text
note 1-1.
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1.1. The Ecological Risk Assessment Process
The ecological risk assessment process is based on two major
elements: Characterization of effects and characterization of exposure.
These provide the focus for conducting the three phases of risk
assessment: Problem formulation, analysis, and risk characterization.
The overall ecological risk assessment process 1 is
shown in figure 1-1. The format remains consistent with the diagram
from the 1992 report Framework for Ecological Risk Assessment (referred
to as the Framework Report). However, the process and products within
each phase have been refined, and these changes are detailed in figure
1-2. The three phases of risk assessment are enclosed by a dark solid
line. Boxes outside this line identify critical activities that
influence why and how a risk assessment is conducted and how it will be
used.
\1\ Changes in process and terminology from EPA's previous
ecological risk assessment framework (U.S. EPA, 1992a) are
summarized in Appendix A.
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Problem formulation, the first phase, is shown at the top. In
problem formulation, the purpose for the assessment is articulated, the
problem is defined, and a plan for analyzing and characterizing risk is
determined. Initial work in problem formulation includes the
integration of available information on sources, stressors, effects,
and ecosystem and receptor characteristics. From this information two
products are generated: Assessment endpoints and conceptual models.
Either product may be generated first (the order depends on the type of
risk assessment), but both are needed to complete an analysis plan, the
final product of problem formulation.
Analysis, shown in the middle box, is directed by the products of
problem formulation. During the analysis phase, data are evaluated to
determine how exposure to stressors is likely to occur
(characterization of exposure) and, given this exposure, the potential
and type of ecological effects that can be expected (characterization
of ecological effects). The first step in analysis is to determine the
strengths and limitations of data on exposure, effects, and ecosystem
and receptor characteristics. Data are then analyzed to characterize
the nature of potential or actual exposure and the ecological responses
under the circumstances defined in the conceptual model(s). The
products from these analyses are two profiles, one for exposure and one
for stressor response. These products provide the basis for risk
characterization.
During risk characterization, shown in the third box, the exposure
and stressor-response profiles are integrated through the risk
estimation process. Risk characterization includes a summary of
assumptions, scientific uncertainties, and strengths and limitations of
the analyses. The final product is a risk description in which the
results of the integration are presented, including an interpretation
of ecological adversity and descriptions of uncertainty and lines of
evidence.
Although problem formulation, analysis, and risk characterization
are presented sequentially, ecological risk assessments are frequently
iterative. Something learned during analysis or risk characterization
can lead to a reevaluation of problem formulation or new data
collection and analysis (see text note 1-2).
Interactions among risk assessors, risk managers, and other
interested parties are shown in two places in the diagram. The side box
on the upper left represents planning, where agreements are made about
the management goals, the purpose for the risk assessment, and the
resources available to conduct the work. The box following risk
characterization represents when the results of the risk assessment are
formally communicated by risk assessors to risk managers. Risk managers
generally communicate risk assessment results to interested parties.
These activities are shown outside the ecological risk assessment
process diagram to emphasize that risk assessment and risk management
are two distinct activities. The former involves the evaluation of the
likelihood of adverse effects, while the latter involves the selection
of a course of action in response to an identified risk that is based
on many factors (e.g., social, legal, political, or economic) in
addition to the risk assessment results.
The bar along the right side of figure 1-2 highlights data
acquisition, iteration, and monitoring. Monitoring data provide
important input to all phases of a risk assessment. They can provide
the impetus for a risk assessment by identifying changes in ecological
condition. They can also be used to evaluate a risk assessment's
predictions. For example, follow-up studies could determine whether
mitigation efforts were effective, help verify whether source reduction
was effective, or determine the extent and nature of ecological
recovery. It is important for risk assessors and risk managers to use
monitoring results to evaluate risk assessment predictions so they can
gain experience and help improve the risk assessment and risk
management process (Commission on Risk Assessment and Risk Management,
1997).
Even though the risk assessment focuses on data analysis and
interpretation, acquiring the appropriate quantity and quality of data
for use in the process is critical. If data are unavailable, the risk
assessment may stop until data are obtained. The process is more often
iterative than linear, since the evaluation of new data or information
may require revisiting a part of the process or conducting a new
assessment (see text note 2-8). The dotted line between the side bar
and the risk management box indicates that additional data acquisition,
iteration, or monitoring, while important, are not always required.
1.2. Ecological Risk Assessment in a Management Context
Ecological risk assessments are designed and conducted to provide
information to risk managers about the potential adverse effects of
different management decisions. Attempts to eliminate risks associated
with human activities in the face of uncertainties and potentially high
costs present a challenge to risk managers (Ruckelshaus, 1983; Suter,
1993a). Although many considerations and sources of information are
used by managers in the decision process, ecological risk assessments
are unique in providing a scientific evaluation of ecological risk that
explicitly addresses uncertainty.
1.2.1. Contributions of Ecological Risk Assessment to Environmental
Decision Making
At EPA, ecological risk assessments are used to support many types
of management actions, including the regulation of hazardous waste
sites, industrial chemicals, and pesticides, or the management of
watersheds or other ecosystems affected by multiple nonchemical and
chemical stressors. The ecological risk assessment process has several
features that contribute to effective environmental decision making:
Through an iterative process, new information can be
incorporated into risk assessments, which can be used to improve
environmental decision making. This feature is consistent with adaptive
management principles (Holling, 1978) used in managing natural
resources.
Risk assessments can be used to express changes in
ecological effects as a function of changes in exposure to stressors.
This capability may be particularly useful to the decision maker who
must evaluate tradeoffs, examine different alternatives, or determine
the extent to which stressors must be reduced to achieve a given
outcome.
Risk assessments explicitly evaluate uncertainty.
Uncertainty analysis describes the degree of confidence in the
assessment and can help the risk manager focus research on those areas
that will lead to the greatest reductions in uncertainty.
Risk assessments provide a basis for comparing, ranking,
and prioritizing risks. The results can also be used in cost-benefit
and cost-effectiveness analyses that offer additional interpretation of
the effects of alternative management options.
Risk assessments consider management goals and objectives
as well as scientific issues in developing assessment endpoints and
conceptual models during problem formulation. Such initial planning
activities help ensure that results will be useful to risk managers.
[[Page 26853]]
1.2.2. Factors Affecting the Value of Ecological Risk Assessment for
Environmental Decision Making
The wide use and important advantages of ecological risk
assessments do not mean they are the sole determinants of management
decisions; risk managers consider many factors. Legal mandates and
political, social, and economic considerations may lead risk managers
to make decisions that are more or less protective. Reducing risk to
the lowest level may be too expensive or not technically feasible.
Thus, although ecological risk assessments provide critical information
to risk managers, they are only part of the environmental decision-
making process.
In some cases, it may be desirable to broaden the scope of a risk
assessment during the planning phase. A risk assessment that is too
narrowly focused on one type of stressor in a system (e.g., chemicals)
could fail to consider more important stressors (e.g., habitat
alteration). However, options for modifying the scope of a risk
assessment may be limited when the scope is defined by statute.
In other situations, management alternatives may be available that
completely circumvent the need for a risk assessment. For example, the
risks associated with building a hydroelectric dam may be avoided by
considering alternatives for meeting power needs that do not involve a
new dam. In these situations, the risk assessment may be redirected to
assess the new alternative, or one may not be needed at all.
1.3. Scope and Intended Audience
These Guidelines describe general principles and give examples to
show how ecological risk assessment can be applied to a wide range of
systems, stressors, and biological, spatial, and temporal scales. They
describe the strengths and limitations of alternative approaches and
emphasize processes and approaches for analyzing data rather than
specifying data collection techniques, methods, or models. They do not
provide detailed guidance, nor are they prescriptive. This approach,
although intended to promote consistency, provides flexibility to
permit EPA's offices and regions to develop specific guidance suited to
their needs.
Agency preferences are expressed where possible, but because
ecological risk assessment is a rapidly evolving discipline,
requirements for specific approaches could soon become outdated. EPA
intends to develop a series of shorter, more detailed documents on
specific ecological risk assessment topics following publication of
these Guidelines.
The interface between risk assessors and risk managers is discussed
in the Guidelines. However, details on the use of ecological risk
assessment in the risk management process are beyond the scope of these
Guidelines. Other EPA publications discuss how ecological concerns have
been addressed in decision making at EPA (U.S. EPA, 1994a), propose
ecological entities that may be important to protect (U.S. EPA, 1997a),
and provide an introduction to ecological risk assessment for risk
managers (U.S. EPA, 1995a).
Policies in this document are intended as internal guidance for
EPA. Risk assessors and risk managers at EPA are the primary audience,
although these Guidelines may be useful to others outside the Agency.
This document is not a regulation and is not intended for EPA
regulations. The Guidelines set forth current scientific thinking and
approaches for conducting and evaluating ecological risk assessments.
They are not intended, nor can they be relied upon, to create any
rights enforceable by any party in litigation with the United States.
As with other EPA guidelines (e.g., developmental toxicity, 56 FR
63798-63826; exposure assessment, 57 FR 22888-22938; and
carcinogenicity, 61 FR 17960-18011), EPA will revisit these Guidelines
as experience and scientific consensus evolve.
These Guidelines replace the Framework Report (U.S. EPA, 1992a).
They expand on and modify framework concepts to reflect Agency
experience since the Framework Report was published (see Appendix A).
1.4. Guidelines Organization
These Guidelines follow the ecological risk assessment format as
presented in figures 1-1 and 1-2. Section 2 (planning) describes the
dialogue among risk assessors, risk managers, and interested parties
before the risk assessment begins. Section 3 (problem formulation)
describes how management goals are interpreted, assessment endpoints
selected, conceptual models constructed, and analysis plans developed.
Section 4 (analysis) addresses how to evaluate potential exposure of
receptors and the relationship between stressor levels and ecological
effects. Section 5 (risk characterization) describes the process of
estimating risk through the integration of exposure and stressor-
response profiles and discusses lines of evidence, interpretation of
adversity, and uncertainty. Finally, section 6 (on relating ecological
information to risk management decisions) addresses communicating the
results of the risk assessment to risk managers.
2. Planning the Risk Assessment
Ecological risk assessments are conducted to transform scientific
data into meaningful information about the risk of human activities to
the environment. Their purpose is to enable risk managers to make
informed environmental decisions. To ensure that risk assessments meet
this need, risk managers and risk assessors (see text notes 2-1 and 2-
2) and, where appropriate, interested parties (see text note 2-3),
engage in a planning dialogue as a critical first step toward
initiating problem formulation (see figure 1-2).
The planning dialogue is the beginning of a necessary interface
between risk managers and risk assessors. However, it is imperative to
remember that planning remains distinct from the scientific conduct of
a risk assessment. This distinction helps ensure that political and
social issues, though helping define the objectives for the assessment,
do not bias the scientific evaluation of risk.
The first step in planning may be to determine if a risk assessment
is the best option for supporting the decision. Risk managers and risk
assessors both consider the potential value of conducting a risk
assessment to address identified problems. Their discussion explores
what is known about the degree of risk, what management options are
available to mitigate or prevent it, and the value of conducting a risk
assessment compared with other ways of learning about and addressing
environmental concerns. In some cases, a risk assessment may add little
value to the decision process because management alternatives may be
available that completely circumvent the need for a risk assessment
(see section 1.2.2). In other cases, the need for a risk assessment may
be investigated through a simple tiered risk evaluation based on
minimal data and a simple model (see section 2.2.2).
Once the decision is made to conduct a risk assessment, the next
step is to ensure that all key participants are appropriately involved.
Risk management may be carried out by one decision maker in an agency
such as EPA or it may be implemented by several risk managers working
together as a team (see text note 2-1). Likewise, risk assessment may
be conducted by a single risk assessor or a team of risk assessors (see
text note 2-2). In some cases, interested parties play an important
role (see text note 2-3).
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Careful consideration up front about who will participate, and the
character of that participation, will determine the success of
planning.
2.1. The Roles of Risk Managers, Risk Assessors, and Interested Parties
in Planning
During the planning dialogue, risk managers and risk assessors each
bring important perspectives to the table. Risk managers, charged with
protecting human health and the environment, help ensure that risk
assessments provide information relevant to their decisions by
describing why the risk assessment is needed, what decisions it will
influence, and what they want to receive from the risk assessor. It is
also helpful for managers to consider and communicate problems they
have encountered in the past when trying to use risk assessments for
decision making.
In turn, risk assessors ensure that scientific information is
effectively used to address ecological and management concerns. Risk
assessors describe what they can provide to the risk manager, where
problems are likely to occur, and where uncertainty may be problematic.
In addition, risk assessors may provide insights to risk managers about
alternative management options likely to achieve stated goals because
the options are ecologically grounded.
In some risk assessments, interested parties also take an active
role in planning, particularly in goal development. The National
Research Council describes participation by interested parties in risk
assessment as an iterative process of ``analysis'' and ``deliberation''
(NRC, 1996). Interested parties may communicate their concerns to risk
managers about the environment, economics, cultural changes, or other
values potentially at risk from environmental management activities.
Where they have the ability to increase or mitigate risk to ecological
values of concern that are identified, interested parties may become
part of the risk management team (see text note 2-1). However,
involvement by interested parties is not always needed or appropriate.
It depends on the purpose of the risk assessment, the regulatory
requirements, and the characteristics of the management problem (see
section 2.2.1). When interested parties become risk managers on a team,
they directly participate in planning.
During planning, risk managers and risk assessors are responsible
for coming to agreement on the goals, scope, and timing of a risk
assessment and the resources that are available and necessary to
achieve the goals. Together they use information on the area's
ecosystems, regulatory requirements, and publicly perceived
environmental values to interpret the goals for use in the ecological
risk assessment. Examples of questions that risk managers and risk
assessors may address during planning are provided in text note 2-4.
2.2. Products of Planning
The characteristics of an ecological risk assessment are directly
determined by agreements reached by risk managers and risk assessors
during planning dialogues. These agreements are the products of
planning. They include (1) clearly established and articulated
management goals, (2) characterization of decisions to be made within
the context of the management goals, and (3) agreement on the scope,
complexity, and focus of the risk assessment, including the expected
output and the technical and financial support available to complete
it.
2.2.1. Management Goals
Management goals are statements about the desired condition of
ecological values of concern. They may range from ``maintain a
sustainable aquatic community'' (see text notes 2-5 and 2-6) to
``restore a wetland'' or ``prevent toxicity.'' Management goals driving
a specific risk assessment may come from the law, interpretations of
the law by regulators, desired outcomes voiced by community leaders and
the public, and interests expressed by affected parties. All involve
input from the public. However, the process used to establish
management goals influences how well they provide guidance to a risk
assessment team, how they foster community participation, and whether
the larger affected community will support implementation of management
decisions to achieve the goal.
A majority of Agency risk assessments incorporate legally
established management goals found in enabling legislation. In these
cases, goals were derived through public debate among interested
parties when the law was enacted. Such management goals (e.g., the
Clean Water Act goals to ``protect and restore the chemical, physical
and biological integrity of the Nation's waters'') are often open to
considerable interpretation and rarely provide sufficient guidance to a
risk assessor. To address this, the Agency has interpreted these goals
into regulations and guidance for implementation at the national scale
(e.g., water quality criteria, see text note 3-17). Mandated goals may
be interpreted by Agency managers and staff into a particular risk
assessment format and then applied consistently across stressors of the
same type (e.g., evaluation of new chemicals). In cases where laws and
regulations are specifically applied to a particular site, interaction
between risk assessors and risk managers is needed to translate the law
and regulations into management goals appropriate for the site or
ecosystem of concern (e.g., Superfund site cleanup).
Although this approach has been effective, most regulations and
guidance are stated in terms of measures or specific actions that must
or must not be taken rather than establishing a value-based management
goal or desired state. As environmental protection efforts shift from
implementing controls toward achieving measurable environmental
results, value-based management goals at the national scale will be
increasingly important as guidance for risk assessors. Such goals as
``no unreasonable effects on bird survival'' or ``maintaining areal
extent of wetlands'' will provide a basis for risk assessment design
(see also U.S. EPA, 1997a, for additional examples and discussion).
The ``place-based'' or ``community-based'' approach for managing
ecological resources recommended in the Edgewater Consensus (U.S. EPA,
1994b) generally requires that management goals be developed for each
assessment. Management goals for ``places'' such as watersheds are
formed as a consensus based on diverse values reflected in Federal,
State, tribal, and local regulations and on constituency-group and
public concerns. Public meetings, constituency-group meetings,
evaluation of resource management organizational charters, and other
means of looking for shared goals may be necessary to reach consensus
among these diverse groups, commonly called ``stakeholders'' (see text
note 2-3). However, goals derived by consensus are normally general.
For use in a risk assessment, risk assessors must interpret the goals
into more specific objectives about what must occur in a place in order
for the goal to be achieved and identify ecological values that can be
measured or estimated in the ecosystem of concern (see text note 2-6).
For these risk assessments, the interpretation is unique to the
ecosystem being assessed and is done on a case-by-case basis as part of
the planning process. Risk assessors and risk managers should agree on
the interpretations.
Early discussion on and selection of clearly established management
goals provide risk assessors with a fuller understanding of how
different risk management options under
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consideration may result in achieving the goal. Such information helps
the risk assessor identify and gather critical data and information.
Regardless of how management goals are established, those that
explicitly define ecological values to be protected provide the best
foundation for identifying actions to reduce risk and generating risk
assessment objectives. The objectives for the risk assessment derive
from the type of management decisions to be made.
2.2.2. Management Options To Achieve Goals
Risk managers must implement decisions to achieve management goals
(see text note 2-7). These risk management decisions may establish
national policy applied consistently across the country (e.g.,
premanufacture notices (PMN) for new chemicals, protection of
endangered species) or be applied to a specific site (e.g., hazardous
waste site cleanup level) or management concern (e.g., number of
combined sewer overflow events allowable per year) intended to achieve
an environmental goal when implemented. Management decisions often
begin as one of several management options identified during planning.
Management options may range from preventing the introduction of a
stressor to restoration of affected ecological values. When several
options are defined during planning for a particular problem (e.g.,
leave alone, clean up, or pave a contaminated site), risk assessments
can be used to predict potential risk across the range of these
management options and, in some cases, combined with cost-benefit
analyses to aid decision making. When risk assessors are made aware of
possible options, they can use them to ensure that the risk assessment
addresses a sufficient breadth of issues.
Explicitly stated management options provide a framework for
defining the scope, focus, and conduct of a risk assessment. Some risk
assessments are specifically designed to determine if a preestablished
decision criterion is exceeded (e.g., see the data quality objectives
process, U.S. EPA, 1994c, and section 3.5.2 for more details). Decision
criteria often contain inherent assumptions about exposure, the range
of possible stressors, or conditions under which the targeted stressor
is operating. To ensure that decision options include appropriate
assumptions and the risk assessment is designed to address management
issues, these assumptions need to be clearly stated.
Decision criteria are often used within a tiering framework to
determine how extensive a risk assessment should be. Early screening
tiers may have predetermined decision criteria to answer whether a
potential risk exists. Later tiers frequently do not because the
management question changes from ``yes-no'' to questions of ``what,
where, and how great is the risk.'' Results from these risk assessments
require risk managers to evaluate risk characterization and generate a
decision, perhaps through formal decision analysis (e.g., Clemen,
1996), or managers may request an iteration of the risk assessment to
address issues of continuing concern (see text note 2-8).
Risk assessments designed to support management initiatives for a
region or watershed where multiple stressors, ecological values, and
political and economic factors influence decision making require great
flexibility and more complex iterative risk assessments. They generally
require an examination of ecological processes most influenced by
diverse human actions. Risk assessments used in this application are
often based on a general goal statement and multiple potential
decisions. These require significant planning to determine which array
of management decisions may be addressed and to establish the purpose,
scope, and complexity of the risk assessment.
2.2.3. Scope and Complexity of the Risk Assessment
Although the purpose for conducting a risk assessment determines
whether it is national, regional, or local in scope, resource
availability determines its extent, complexity, and the level of
confidence in results that can be expected. Each risk assessment is
constrained by the availability of valid data and scientific
understanding, expertise, time, and financial resources. Risk managers
and risk assessors consider the nature of the decision (e.g., national
policy, local impact), available resources, opportunities for
increasing the resource base (e.g., partnering, new data collection,
alternative analytical tools), potential characteristics of the risk
assessment team, and the output that will provide the best information
for the required decisions (see text note 2-9). They must often be
flexible in determining what level of effort is warranted for a risk
assessment. The most detailed assessment process is neither applicable
nor necessary in every instance. Screening assessments may be the
appropriate level of effort. One approach for determining the needed
level of effort in the risk assessment is to set up tiered evaluations,
as discussed in section 2.2.2. Where tiers are used, specific
descriptions of management questions and decision criteria should be
included in the plan.
Part of the agreement on scope and complexity is based on the
maximum uncertainty that can be tolerated for the decision the risk
assessment supports. Risk assessments completed in response to legal
mandates and likely to be challenged in court often require rigorous
attention to potential sources of uncertainty to help ensure that
conclusions from the assessment can be defended. A frank discussion is
needed between the risk manager and risk assessor on the sources of
uncertainty and ways uncertainty can be reduced (if necessary or
possible) through selective investment of resources. Resource planning
may account for the iterative nature of risk assessment or include
explicitly defined steps, such as tiers that represent increasing cost
and complexity, each tier designed to increase understanding and reduce
uncertainty. Advice on addressing the interplay of management
decisions, study boundaries, data needs, uncertainty, and specifying
limits on decision errors may be found in EPA's guidance on data
quality objectives (U.S. EPA, 1994c).
2.3. Planning Summary
The planning phase is complete when agreements are reached on (1)
the management goals for ecological values, (2) the range of management
options the risk assessment is to support, (3) objectives for the risk
assessment, including criteria for success, (4) the focus and scope of
the assessment, and (5) resource availability. Agreements may encompass
the technical approach to be taken in a risk assessment as determined
by the regulatory or management context and reason for initiating the
risk assessment (see section 3.2), the spatial scale (e.g., local,
regional, or national), and the temporal scale (e.g., the time frame
over which stressors or effects will be evaluated).
In mandated risk assessments, planning agreements may be codified
in regulations, and little documentation of agreements is warranted. In
others, a summary of planning agreements may be important for ensuring
that the risk assessment remains consistent with its original intent. A
summary can provide a point of reference for determining if early
decisions need to be changed in response to new information. There is
no predetermined format, length, or complexity for a planning summary.
It is a useful reference only and should be tailored to the risk
assessment it represents. However, a summary will help ensure quality
communication
[[Page 26856]]
between risk managers and risk assessors and will document agreed-upon
decisions.
Once planning is complete, the formal process of risk assessment
begins. During problem formulation, risk assessors should continue the
dialogue with risk managers, particularly following assessment endpoint
selection and completion of the analysis plan. At these points,
potential problems can be identified before the risk assessment
proceeds.
3. Problem Formulation Phase
Problem formulation is a process for generating and evaluating
preliminary hypotheses about why ecological effects have occurred, or
may occur, from human activities. It provides the foundation for the
entire ecological risk assessment. Early in problem formulation,
objectives for the risk assessment are refined. Then the nature of the
problem is evaluated and a plan for analyzing data and characterizing
risk is developed. Any deficiencies in problem formulation will
compromise all subsequent work on the risk assessment (see text note 3-
1). The quality of the assessment will depend in part on the team
conducting the assessment and its responsiveness to the risk manager's
needs.
The makeup of the risk assessment team assembled to conduct problem
formulation depends on the requirements of the risk assessment. The
team should include professionals with expertise directly related to
the level and type of problem under consideration and the ecosystem
where the problem is likely to occur. Teams may range from one
individual calculating a simple quotient where the information and
algorithm are clearly established to a large interdisciplinary,
interagency team typical of ecosystem-level risk assessments involving
multiple stressors and ecological values.
Involvement by the risk management team and other interested
parties in problem formulation can be most valuable during final
selection of assessment endpoints, review of the conceptual models, and
adjustments to the analysis plan. The degree of participation is
commensurate with the complexity of the risk assessment and the
magnitude of the risk management decision to be faced. Participation
normally consists of approval and refinement rather than technical
input (but see text note 2-3). The format used to involve risk managers
needs to gain from, and be responsive to, their input without
compromising the scientific validity of the risk assessment. The level
of involvement by interested parties in problem formulation is
determined by risk managers.
3.1. Products of Problem Formulation
Problem formulation results in three products: (1) Assessment
endpoints that adequately reflect management goals and the ecosystem
they represent, (2) conceptual models that describe key relationships
between a stressor and assessment endpoint or between several stressors
and assessment endpoints, and (3) an analysis plan. The first step
toward developing these products is to integrate available information
as shown in the hexagon in figure 3-1; the products are shown as
circles. While the assessment of available information is begun up
front in problem formulation and the analysis plan is the final
product, the order in which assessment endpoints and conceptual models
are produced depends on why the risk assessment was initiated (see
section 3.2). To enhance clarity, the following discussion is presented
as a linear progression. However, problem formulation is frequently
interactive and iterative rather than linear. Reevaluation may occur
during any part of problem formulation.
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3.2. Integration of Available Information
The foundation for problem formulation is based on how well
available information on stressor sources and characteristics, exposure
opportunities, characteristics of the ecosystem(s) potentially at risk,
and ecological effects are integrated and used (see figure 3-1).
Integration of available information is an iterative process that
normally occurs throughout problem formulation. Initial evaluations
often provide the basis for generating preliminary conceptual models or
assessment endpoints, which in turn may lead risk assessors to seek
other types of available information not previously recognized as
needed.
The quality and quantity of information determine the course of
problem formulation. When key information is of the appropriate type
and sufficient quality and quantity, problem formulation can proceed
effectively. When data are unavailable, the risk assessment may be
suspended while additional data are collected or, if this is not
possible, may be developed on the basis of what is known and what can
be extrapolated from what is known. Risk assessments are frequently
begun without all needed information, in which case the problem
formulation process helps identify missing data and provides a
framework for further data collection. Where data are few, the
limitations of conclusions, or uncertainty, from the risk assessment
should be clearly articulated in risk characterization (see text note
3-2).
The impetus for an ecological risk assessment influences what
information is available at the outset and what information should be
collected. For example, a risk assessment can be initiated because a
known or potential stressor may enter the environment. Risk assessors
evaluating a source or stressor will seek data on the effects with
which the stressor might be associated and the ecosystems in which it
will likely be introduced or found. If an observed adverse effect or
change in ecological condition initiates the assessment, risk assessors
will seek information about potential stressors and sources that could
have caused the effect. When a risk assessment is initiated because of
a desire to better manage an ecological value or entity (e.g., species,
communities, ecosystems, or places), risk assessors will seek
information on the specific condition or effect of interest, the
characteristics of relevant ecosystems, and potential stressors and
sources (see text note 3-3).
Information (actual, inferred, or estimated) is initially
integrated in a scoping process that provides the foundation for
developing problem formulation. Knowledge gained during scoping is used
to identify missing information and potential assessment endpoints, and
it provides the basis for early conceptualization of the problem being
assessed. As problem formulation proceeds, information quality and
applicability to the particular problem of concern are increasingly
scrutinized. Where appropriate, further iterations may result in a
comprehensive evaluation that helps risk assessors generate an array of
risk hypotheses (see section 3.4.1). Once analysis plans are being
formed, data validity becomes a significant factor for risk assessors
to evaluate (see section 4.1 for a discussion of assessing data
quality). Thus an evaluation of available information is an ongoing
activity throughout problem formulation. The level of effort is driven
by the type of assessment.
As the complexity and spatial scale of a risk assessment increase,
information needs often escalate. Risk assessors consider the ways
ecosystem characteristics directly influence when, how, and why
particular ecological entities may become exposed and exhibit adverse
effects due to particular stressors. Predicting risks from multiple
chemical, physical, and biological stressors requires an effort to
understand their interactions. Risk assessments for a region or
watershed, where multiple stressors are the rule, require consideration
of ecological processes operating at larger spatial scales.
Despite our limited knowledge of ecosystems and the stressors
influencing them, the process of problem formulation offers a
systematic approach for organizing and evaluating available information
on stressors and possible effects. It can function as a preliminary
risk assessment that is useful to risk assessors and decision makers.
Text note 3-4 provides a series of questions that risk assessors should
attempt to answer. This exercise will help risk assessors identify
known and unknown relationships, both of which are important in problem
formulation.
Problem formulation proceeds with the identification of assessment
endpoints and the development of conceptual models and an analysis plan
(discussed below). Early recognition that the reasons for initiating
the risk assessment affect the order in which products are generated
will help facilitate the development of problem formulation (see text
note 3-3).
3.3. Selecting Assessment Endpoints
Assessment endpoints are explicit expressions of the actual
environmental value that is to be protected, operationally defined by
an ecological entity and its attributes (see section 3.3.2). Assessment
endpoints are critical to problem formulation because they structure
the assessment to address management concerns and are central to
conceptual model development. Their relevance is determined by how well
they target susceptible ecological entities. Their ability to support
risk management decisions depends on whether they are measurable
ecosystem characteristics that adequately represent management goals.
The selection of ecological concerns and assessment endpoints at EPA
has traditionally been done internally by individual Agency program
offices (U.S. EPA, 1994a). More recently, interested and affected
parties have helped identify management concerns and assessment
endpoints in efforts to implement watershed or community-based
environmental protection.
This section provides guidance on selecting and defining assessment
endpoints. It is presented in two parts. Section 3.3.1 establishes
three criteria (ecological relevance, susceptibility, and relevance to
management goals) for determining how to select, among a broad array of
possibilities, the specific ecological characteristics to target in the
risk assessment that are responsive to general management goals and are
scientifically defensible. Section 3.3.2 then provides specific
guidance on how to convert selected ecological characteristics into
operationally defined assessment endpoints that include both a defined
entity and specific attributes amenable to measurement.
3.3.1. Criteria for Selection
All ecosystems are diverse, with many levels of ecological
organization (e.g., individuals, populations, communities, ecosystems,
landscapes) and multiple ecosystem processes. It is rarely clear which
of these characteristics are most critical to ecosystem function, nor
do professionals or the public always agree on which are most valuable.
As a result, it is often a challenge to consider the array of
possibilities and choose which ecological characteristics to protect to
meet management goals. Those choices are critical, however, because
they become the basis for defining assessment endpoints, the transition
between broad management goals and the specific measures used in a risk
assessment.
[[Page 26859]]
Three principal criteria are used to select ecological values that
may be appropriate for assessment endpoints: (1) Ecological relevance,
(2) susceptibility to known or potential stressors, and (3) relevance
to management goals. Of these, ecological relevance and susceptibility
are essential for selecting assessment endpoints that are
scientifically defensible. However, to increase the likelihood that the
risk assessment will be used in management decisions, assessment
endpoints are more effective when they also reflect societal values and
management goals. Given the complex functioning of ecosystems and the
interdependence of ecological entities, it is likely that potential
assessment endpoints can be identified that are both responsive to
management goals and meet scientific criteria. Assessment endpoints
that meet all three criteria provide the best foundation for an
effective risk assessment (e.g., see text note 3-5).
3.3.1.1. Ecological Relevance
Ecologically relevant endpoints reflect important characteristics
of the system and are functionally related to other endpoints (U.S.
EPA, 1992a). Ecologically relevant endpoints may be identified at any
level of organization (e.g., individual, population, community,
ecosystem, landscape). The consequences of changes in these endpoints
may be quantified (e.g., alteration of community structure from the
loss of a keystone species) or inferred (e.g., survival of individuals
is needed to maintain populations). Ecological entities are not
ecologically relevant unless they are currently, or were historically,
part of the ecosystem under consideration.
Ecologically relevant endpoints often help sustain the natural
structure, function, and biodiversity of an ecosystem or its
components. They may contribute to the food base (e.g., primary
production), provide habitat (e.g., for food or reproduction), promote
regeneration of critical resources (e.g., decomposition or nutrient
cycling), or reflect the structure of the community, ecosystem, or
landscape (e.g., species diversity or habitat mosaic). In landscape-
level risk assessments, careful selection of assessment endpoints that
address both species of concern and landscape-level ecosystem processes
becomes important. It may be possible to select one or more species and
an ecosystem process to represent larger functional community or
ecosystem processes.
Ecological relevance is linked to the nature and intensity of
potential effects, the spatial and temporal scales where effects may
occur, and the potential for recovery (see Determining Ecological
Adversity, section 5.2.2). It is also linked to the level of ecological
organization that could be adversely affected (see U.S. EPA, 1997a, for
a discussion of how different levels of organization are used by the
Agency in defining assessment endpoints). When changes in selected
ecosystem entities are likely to cause multiple or widespread effects,
such entities can be powerful components of assessment endpoints. They
are particularly valuable when risk assessors are trying to identify
the potential cascade of adverse effects that could result from loss or
reduction of a species or a change in ecosystem function (see text note
3-6). Although a cascade of effects may be predictable, it is often
difficult to predict the nature of all potential effects. Determining
ecological relevance in specific cases requires professional judgment
based on site-specific information, preliminary surveys, or other
available information.
3.3.1.2. Susceptibility to Known or Potential Stressors
Ecological resources are considered susceptible when they are
sensitive to a stressor to which they are, or may be, exposed.
Susceptibility can often be identified early in problem formulation,
but not always. Risk assessors may be required to use their best
professional judgment to select the most likely candidates (see text
note 3-7).
Sensitivity refers to how readily an ecological entity is affected
by a particular stressor. Sensitivity is directly related to the mode
of action of the stressors (e.g., chemical sensitivity is influenced by
individual physiology and metabolic pathways). Sensitivity is also
influenced by individual and community life-history characteristics.
For example, stream species assemblages that depend on cobble and
gravel habitat for reproduction are sensitive to fine sediments that
fill in spaces between cobbles. Species with long life cycles and low
reproductive rates are often more vulnerable to extinction from
increases in mortality than species with short life cycles and high
reproductive rates. Species with large home ranges may be more
sensitive to habitat fragmentation when the fragment is smaller than
their required home range compared to species with smaller home ranges
that are encompassed within a fragment. However, habitat fragmentation
may also affect species with small home ranges where migration is a
necessary part of their life history and fragmentation prevents
migration and genetic exchange among subpopulations. Such life-history
characteristics are important to consider when evaluating potential
sensitivity.
Sensitivity can be related to the life stage of an organism when
exposed to a stressor. Frequently, young animals are more sensitive to
stressors than adults. For instance, Pacific salmon eggs and fry are
very sensitive to fine-grain sedimentation in river beds because they
can be smothered. Age-dependent sensitivity, however, is not only in
the young. In many species, events like migration (e.g., in birds) and
molting (e.g., in harbor seals) represent significant energy
investments that increase vulnerability to stressors. Finally,
sensitivity may be enhanced by the presence of other stressors or
natural disturbances. For example, the presence of insect pests and
disease may make plants more sensitive to damage from ozone (Heck,
1993). To determine how sensitivity at a particular life stage is
critical to population parameters or community-level assessment
endpoints may require further evaluation.
Measures of sensitivity may include mortality or adverse
reproductive effects from exposure to toxics. Other possible measures
of sensitivity include behavioral abnormalities; avoidance of
significant food sources and nesting sites; loss of offspring to
predation because of the proximity of stressors such as noise, habitat
alteration, or loss; community structural changes; or other factors.
Exposure is the second key determinant in susceptibility. Exposure
can mean co-occurrence, contact, or the absence of contact, depending
on the stressor and assessment endpoint. Questions concerning where a
stressor originates, how it moves through the environment, and how it
comes in contact with the assessment endpoint are evaluated to
determine susceptibility (see section 4.2 for more discussion on
characterizing exposure). The amount and conditions of exposure
directly influence how an ecological entity will respond to a stressor.
Thus, to determine which entities are susceptible, it is important that
the assessor consider the proximity of an ecological value to stressors
of concern, the timing of exposure (both in terms of frequency and
duration), and the intensity of exposure occurring during sensitive
periods.
Adverse effects of a particular stressor may be important during
one part of an organism's life cycle, such as early development or
reproduction. They may result from exposure to a stressor or to the
absence of a necessary resource
[[Page 26860]]
during a critical life stage. For example, if fish are unable to find
suitable nesting sites during their reproductive phase, risk is
significant even when water quality is high and food sources abundant.
The interplay between life stage and stressors can be very complex (see
text note 3-8).
Exposure may occur in one place or time, but effects may not be
observed until another place or time. Both life-history characteristics
and the circumstances of exposure influence susceptibility in this
case. For instance, the temperature of the egg incubation medium of
marine turtles affects the sex ratio of hatchlings, but population
impacts are not observed until years later when the cohort of affected
turtles begins to reproduce. Delayed effects and multiple-stressor
exposures add complexity to evaluations of susceptibility (e.g.,
although toxicity tests may determine receptor sensitivity to one
stressor, susceptibility may depend on the co-occurrence of another
stressor that significantly alters receptor response). Conceptual
models (see section 3.4) need to reflect these factors. If a species or
other ecological entity is unlikely to be directly or indirectly
exposed to the stressor of concern, or to the secondary effects of
stressor exposure, it may be inappropriate as an assessment endpoint
(see text note 3-7).
3.3.1.3. Relevance to Management Goals
Ultimately, the effectiveness of a risk assessment depends on
whether it is used and improves the quality of management decisions.
Risk managers are more willing to use a risk assessment for making
decisions when it is based on ecological values that people care about.
Thus, candidates for assessment endpoints include endangered species or
ecosystems, commercially or recreationally important species,
functional attributes that support food sources or flood control (e.g.,
wetland water sequestration), aesthetic values such as clean air in
national parks, or the existence of charismatic species such as eagles
or whales. However, selection of assessment endpoints based on public
perceptions alone could lead to management decisions that do not
consider important ecological information. While responsiveness to the
public is important, it does not obviate the requirement for scientific
validity.
The challenge is to find ecological values that meet the necessary
scientific rigor as assessment endpoints that are also recognized as
valuable by risk managers and the public. As an illustration, suppose
an assessment is designed to evaluate the risk of applying pesticide
around a lake to control insects. At this lake, however, midges are
susceptible to the pesticide and form the base of a complex food web
that supports a native fish population popular with sportsmen. While
both midges and fish represent key components of the aquatic community,
selecting the fishery as the value for defining the assessment endpoint
targets both ecological and community concerns. Selecting midges would
not. The risk assessment can then characterize the risk to the fishery
if the midge population is adversely affected. This choice maintains
the scientific validity of the risk assessment while being responsive
to management concerns. In those cases where a critical assessment
endpoint is identified that is unpopular with the public, the risk
assessor may find it necessary to present a persuasive case in its
favor to risk managers based on scientific arguments.
Practical issues may influence what values are selected as
potential assessment endpoints, such as what is required by statute
(e.g., endangered species) or whether it is possible to achieve a
particular management goal. For example, in a river already impounded
throughout its reach by multiple dams, goals for reestablishing
spawning habitat for free-living anadromous salmon may be feasible only
if dams are removed. If this will not be considered, selection of other
ecological values as potential endpoints in this highly modified system
may be the only option. Another concern may be whether it is possible
to directly measure important variables. Where it is possible to
directly measure attributes of an assessment endpoint, extrapolation is
unnecessary, thus preventing the introduction of a source of
uncertainty. Assessment endpoints that cannot be measured directly but
can be represented by measures that are easily monitored and modeled
may still provide a good foundation for a risk assessment. However,
while established measurement protocols are convenient and useful, they
do not determine whether an assessment endpoint is appropriate. Data
availability alone is not an adequate criterion for selection.
To ensure scientific validity, risk assessors are responsible for
selecting and defining potential assessment endpoints based on an
understanding of the ecosystem of concern. Risk managers and risk
assessors should then come to agreement on the final selection.
3.3.2. Defining Assessment Endpoints
Once ecological values are selected as potential assessment
endpoints, they need to be operationally defined. Two elements are
required to define an assessment endpoint. The first is the
identification of the specific valued ecological entity. This can be a
species (e.g., eelgrass, piping plover), a functional group of species
(e.g., piscivores), a community (e.g., benthic invertebrates), an
ecosystem (e.g., lake), a specific valued habitat (e.g., wet meadows),
a unique place (e.g., a remnant of native prairie), or other entity of
concern. The second is the characteristic about the entity of concern
that is important to protect and potentially at risk. Thus, it is
necessary to define what is important for piping plovers (e.g., nesting
and feeding conditions), a lake (e.g., nutrient cycling), or wet meadow
(e.g., endemic plant community diversity). For an assessment endpoint
to serve as a clear interpretation of the management goals and the
basis for measurement in the risk assessment, both an entity and an
attribute are required.
What distinguishes assessment endpoints from management goals is
their neutrality and specificity. Assessment endpoints do not represent
a desired achievement (i.e., goal). As such, they do not contain words
like ``protect,'' ``maintain,'' or ``restore,'' or indicate a direction
for change such as ``loss'' or ``increase.'' Instead they are
ecological values defined by specific entities and their measurable
attributes, providing a framework for measuring stress-response
relationships. When goals are very broad it may be difficult to select
appropriate assessment endpoints until the goal is broken down into
multiple management objectives. A series of management objectives can
clarify the inherent assumptions within the goal and help a risk
assessor determine which ecological entities and attributes best
represent each objective (see text box 2-6). From this, multiple
assessment endpoints may be selected. See text note 3-9 for examples of
management goals and assessment endpoints.
Assessment endpoints may or may not be distinguishable from
measures, depending on the assessment endpoints selected and the type
of measures. While it is the entity that influences the scale and
character of a risk assessment, it is the attributes of an assessment
endpoint that determine what to measure. Sometimes direct measures of
effect can be collected on the attribute of concern. Where this occurs,
the assessment endpoint and measure of
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effect are the same and no extrapolation is necessary (e.g., if the
assessment endpoint is ``reproductive success of blue jays,'' egg
production and fledgling success could potentially be directly measured
under different stressor exposure scenarios). In other cases, direct
measures may not be possible (e.g., toxicity in endangered species) and
surrogate measures of effect must be selected. Thus, although
assessment endpoints must be defined in terms of measurable attributes,
selection does not depend on the ability to measure those attributes
directly or on whether methods, models, and data are currently
available. For practical reasons, it may be helpful to use assessment
endpoints that have well-developed test methods, field measurement
techniques, and predictive models (see Suter, 1993a). However, it is
not necessary for methods to be standardized protocols, nor should
assessment endpoints be selected simply because standardized protocols
are readily available. The appropriate measures to use are generally
identified during conceptual model development and specified in the
analysis plan. Measures of ecosystem characteristics and exposure are
determined by the entity and attributes selected and serve as important
information in conceptual model development. See section 3.5.1 for
issues surrounding the selection of measures.
Clearly defined assessment endpoints provide direction and
boundaries for the risk assessment and can minimize miscommunication
and reduce uncertainty; where they are poorly defined, inappropriate,
or at the incorrect scale, they can be very problematic. Endpoints may
be too broad, vague, or narrow, or they may be inappropriate for the
ecosystem requiring protection. ``Ecological integrity'' is a
frequently cited but vague goal and is too vague for an assessment
endpoint. ``Integrity'' can only be used effectively when its meaning
is explicitly characterized for a particular ecosystem, habitat, or
entity. This may be done by selecting key entities or processes for an
ecosystem and describing attributes that best represent integrity for
that system. Assessment endpoints that are too narrowly defined may not
support effective risk management. If an assessment is focused only on
protecting the habitat of an endangered species, for example, the risk
assessment may overlook other equally important characteristics of the
ecosystem and fail to include critical variables (see text note 3-8).
Finally, the assessment endpoint could fail to represent the ecosystem
at risk. For instance, selecting a game fish that grows well in
reservoirs may meet a ``fishable'' management goal, but it would be
inappropriate for evaluating risk from a new hydroelectric dam if the
ecosystem of concern is a stream in which salmon spawn (see text note
3-5). Although the game fish will satisfy ``fishable'' goals and may be
highly desired by local fishermen, a reservoir species does not
represent the ecosystem at risk. Substituting ``reproducing populations
of indigenous salmonids'' for a vague ``viable fish populations''
assessment endpoint could therefore prevent the development of an
inappropriate risk assessment.
When well selected, assessment endpoints become powerful tools in
the risk assessment process. One endpoint that is sensitive to many of
the identified stressors, yet responds in different ways to different
stressors, may provide an opportunity to consider the combined effects
of multiple stressors while still distinguishing their effects. For
example, fish population recruitment may be adversely affected at
several life stages, in different habitats, through different ways, and
by different stressors. Therefore, measures of effect, exposure, and
ecosystem and receptor characteristics could be chosen to evaluate
recruitment and provide a basis for distinguishing different stressors,
individual effects, and their combined effects.
The assessment endpoint can provide a basis for comparing a range
of stressors if carefully selected. The National Crop Loss Assessment
Network (Heck, 1993) selected crop yields as the assessment endpoint to
evaluate the cumulative effects of multiple stressors. Although the
primary stressor was ozone, the crop-yield endpoint also allowed the
risk assessors to consider the effects of sulfur dioxide and soil
moisture. As Barnthouse et al. (1990) pointed out, an endpoint should
be selected so that all the effects can be expressed in the same units
(e.g., changes in the abundance of 1-year-old fish from exposure to
toxicity, fishing pressure, and habitat loss). This is especially true
when selecting assessment endpoints for multiple stressors. However, in
situations where multiple stressors act on the structure and function
of aquatic and terrestrial communities in a watershed, an array of
assessment endpoints that represent the community and associated
ecological processes is more effective than a single endpoint. When
based on differing susceptibility to an array of stressors, carefully
selected assessment endpoints can help risk assessors distinguish the
effects of diverse stressors. Exposure to multiple stressors may lead
to effects at different levels of biological organization, for a
cascade of adverse effects that should be considered.
Professional judgment and an understanding of the characteristics
and function of an ecosystem are important for translating general
goals into usable assessment endpoints. The less information available,
the more critical it is to have informed professionals help in the
selection. Common problems encountered in selecting assessment
endpoints are summarized in text note 3-10.
Final assessment endpoint selection is an important risk manager-
risk assessor checkpoint during problem formulation. Risk assessors and
risk managers should agree that selected assessment endpoints
effectively represent the management goals. In addition, the scientific
rationale for their selection should be made explicit in the risk
assessment.
3.4. Conceptual Models
A conceptual model in problem formulation is a written description
and visual representation of predicted relationships between ecological
entities and the stressors to which they may be exposed. Conceptual
models represent many relationships. They may include ecosystem
processes that influence receptor responses or exposure scenarios that
qualitatively link land-use activities to stressors. They may describe
primary, secondary, and tertiary exposure pathways (see section 4.2) or
co-occurrence among exposure pathways, ecological effects, and
ecological receptors. Multiple conceptual models may be generated to
address several issues in a given risk assessment. Some of the benefits
gained by developing conceptual models are featured in text note 3-11.
Conceptual models for ecological risk assessments are developed
from information about stressors, potential exposure, and predicted
effects on an ecological entity (the assessment endpoint). Depending on
why a risk assessment is initiated, one or more of these categories of
information are known at the outset (refer to section 3.2 and text note
3-3). The process of creating conceptual models helps identify the
unknown elements.
The complexity of the conceptual model depends on the complexity of
the problem: the number of stressors, number of assessment endpoints,
nature of effects, and characteristics of the ecosystem. For single
stressors and single assessment endpoints, conceptual models may be
simple. In some cases,
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the same basic conceptual model may be used repeatedly (e.g., in EPA's
new chemical risk assessments). However, when conceptual models are
used to describe pathways of individual stressors and assessment
endpoints and the interaction of multiple and diverse stressors and
assessment endpoints (e.g., assessments initiated to protect ecological
values), more complex models and several submodels will often be
needed. In this case, it can be helpful to create models that also
represent expected ecosystem characteristics and function when
stressors are not present.
Conceptual models consist of two principal components:
A set of risk hypotheses that describe predicted
relationships among stressor, exposure, and assessment endpoint
response, along with the rationale for their selection.
A diagram that illustrates the relationships presented in
the risk hypotheses.
3.4.1. Risk Hypotheses
Hypotheses are assumptions made in order to evaluate logical or
empirical consequences, or suppositions tentatively accepted to provide
a basis for evaluation. Risk hypotheses are specific assumptions about
potential risk to assessment endpoints (see text note 3-12) and may be
based on theory and logic, empirical data, mathematical models, or
probability models. They are formulated using a combination of
professional judgment and available information on the ecosystem at
risk, potential sources of stressors, stressor characteristics, and
observed or predicted ecological effects on selected or potential
assessment endpoints. These hypotheses may predict the effects of a
stressor before they occur, or they may postulate why observed
ecological effects occurred and ultimately what caused the effect.
Depending on the scope of the risk assessment, risk hypotheses may be
very simple, predicting the potential effect of one stressor on one
receptor, or extremely complex, as is typical in value-initiated risk
assessments that often include prospective and retrospective hypotheses
about the effects of multiple complexes of stressors on diverse
ecological receptors. Risk hypotheses represent relationships in the
conceptual model and are not designed for statistically testing null
and alternative hypotheses. However, they can be used to generate
questions appropriate for research.
Although risk hypotheses are valuable even when information is
limited, the amount and quality of data and information will affect the
specificity and level of uncertainty associated with risk hypotheses
and the conceptual models they form. When preliminary information is
conflicting, risk hypotheses can be constructed specifically to
differentiate between competing predictions. The predictions can then
be evaluated systematically either by using available data during the
analysis phase or by collecting new data before proceeding with the
risk assessment. Hypotheses and predictions set a framework for using
data to evaluate functional relationships (e.g., stressor-response
curves).
Early conceptual models are normally broad, identifying as many
potential relationships as possible. As more information is
incorporated, the plausibility of specific hypotheses helps risk
assessors sort through potentially large numbers of stressor-effect
relationships, and the ecosystem processes that influence them, to
identify those risk hypotheses most appropriate for the analysis phase.
It is then that justifications for selecting and omitting hypotheses
are documented. Examples of risk hypotheses are provided in text note
3-13.
3.4.2. Conceptual Model Diagrams
Conceptual model diagrams are a visual representation of risk
hypotheses. They are useful tools for communicating important pathways
clearly and concisely and can be used to generate new questions about
relationships that help formulate plausible risk hypotheses.
Typical conceptual model diagrams are flow diagrams containing
boxes and arrows to illustrate relationships (see Appendix C). When
this approach is used, it is helpful to use distinct and consistent
shapes to distinguish stressors, assessment endpoints, responses,
exposure routes, and ecosystem processes. Although flow diagrams are
often used to illustrate conceptual models, there is no set
configuration. Pictorial representations can be very effective (e.g.,
Bradley and Smith, 1989). Regardless of the configuration, a diagram's
usefulness is linked to the detailed written descriptions and
justifications for the relationships shown. Without this, diagrams can
misrepresent the processes they are intended to illustrate.
When developing conceptual model diagrams, factors to consider
include the number of relationships depicted, the comprehensiveness of
the information, the certainty surrounding a linkage, and the potential
for measurement. The number of relationships that can be depicted in
one flow diagram depends on their complexity. Several models that
increasingly show more detail for smaller portions can be more
effective than trying to create one model that shows everything at the
finest detail. Flow diagrams that highlight data abundance or scarcity
can provide insights on how the analyses should be approached and can
be used to show the risk assessor's confidence in the relationship.
They can also show why certain pathways were pursued and others were
not.
Diagrams provide a working and dynamic representation of
relationships. They should be used to explore different ways of looking
at a problem before selecting one or several to guide analysis. Once
the risk hypotheses are selected and flow diagrams drawn, they set the
framework for final planning for the analysis phase.
3.4.3. Uncertainty in Conceptual Models
Conceptual model development may account for one of the most
important sources of uncertainty in a risk assessment. If important
relationships are missed or specified incorrectly, the risk
characterization may misrepresent actual risks. Uncertainty arises from
lack of knowledge about how the ecosystem functions, failure to
identify and interrelate temporal and spatial parameters, omission of
stressors, or overlooking secondary effects. In some cases, little may
be known about how a stressor moves through the environment or causes
adverse effects. Multiple stressors are the norm and a source of
confounding variables, particularly for conceptual models that focus on
a single stressor. Professionals may not agree on the appropriate
conceptual model configuration. While simplification and lack of
knowledge may be unavoidable, risk assessors should document what is
known, justify the model, and rank model components in terms of
uncertainty (see Smith and Shugart, 1994).
Uncertainty associated with conceptual models can be explored by
considering alternative relationships. If more than one conceptual
model is plausible, the risk assessor may evaluate whether it is
feasible to follow separate models through analysis or whether the
models can be combined to create a better model.
Conceptual models should be presented to risk managers to ensure
that they communicate well and address managers' concerns. This check
for completeness and clarity is a way to assess the need for changes
before analysis begins. It is also valuable to revisit and where
necessary revise conceptual models during risk
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assessments to incorporate new information and recheck the rationale.
If this is not feasible, it is helpful to present any new information
during risk characterization along with associated uncertainties.
Throughout problem formulation, ambiguities, errors, and
disagreements will occur, all of which contribute to uncertainty.
Wherever possible, these sources of uncertainty should be eliminated
through better planning. Because all uncertainty cannot be eliminated,
a description of the nature of the uncertainties should be summarized
at the close of problem formulation. See text note 3-14 for
recommendations on how to address uncertainty.
3.5. Analysis Plan
The analysis plan is the final stage of problem formulation. During
analysis planning, risk hypotheses are evaluated to determine how they
will be assessed using available and new data. The plan includes a
delineation of the assessment design, data needs, measures, and methods
for conducting the analysis phase of the risk assessment. Analysis
plans may be brief or extensive depending on the assessment. For some
assessments (e.g., EPA's new chemical assessments), the analysis plan
is already part of the established protocol and a new plan is generally
unnecessary. As risk assessments become more unique and complex, the
importance of a good analysis plan increases.
The analysis plan includes pathways and relationships identified
during problem formulation that will be pursued during the analysis
phase. Those hypotheses considered more likely to contribute to risk
are targeted. The rationale for selecting and omitting risk hypotheses
is incorporated into the plan and includes acknowledgment of data gaps
and uncertainties. It also may include a comparison of the level of
confidence needed for the management decision with that expected from
alternative analyses in order to determine data needs and evaluate
which analytical approach is best. When new data are needed, the
feasibility of obtaining them can be taken into account.
Identification of the most critical relationships to evaluate in a
risk assessment is based on the relationship of assessment endpoints to
ecosystem structure and function, the relative importance or influence
and mode of action of stressors on assessment endpoints, and other
variables influencing ecological adversity (see section 5.2.2).
However, final selection of relationships that can be pursued in
analysis is based on the strength of known relationships between
stressors and effects, the completeness of known exposure pathways, and
the quality and availability of data.
In situations where data are few and new data cannot be collected,
it may be possible to extrapolate from existing data. Extrapolation
allows the use of data collected from other locations or organisms
where similar problems exist. For example, the relationship between
nutrient availability and algal growth is well established and
consistent. This relationship can be acknowledged despite differences
in how it is manifested in particular ecosystems. When extrapolating
from data, it is important to identify the source of the data, justify
the extrapolation method, and discuss recognized uncertainties.
A phased, or tiered, risk assessment approach (see section 2.2) can
facilitate management decisions in cases involving minimal data sets.
However, where few data are available, recommendations for new data
collection should be part of the analysis plan. When new data are
needed and cannot be obtained, relationships that cannot be assessed
are a source of uncertainty and should be described in the analysis
plan and later discussed in risk characterization.
When determining what data to analyze and how to analyze them,
consider how these analyses may increase understanding and confidence
in the conclusions of the risk assessment and address risk management
questions. During selection, risk assessors may ask questions such as:
How relevant will the results be to the assessment endpoint(s) and
conceptual model(s)? Are there sufficient data of high quality to
conduct the analyses with confidence? How will the analyses help
establish cause-and-effect relationships? How will results be presented
to address managers' questions? Where are uncertainties likely to
become a problem? Consideration of these questions during analysis
planning will improve future characterization of risk (see section
5.2.1 for discussion of lines of evidence).
3.5.1. Selecting Measures
Assessment endpoints and conceptual models help risk assessors
identify measurable attributes to quantify and predict change. However,
determining what measures to use to evaluate risk hypotheses is both
challenging and critical to the success of a risk assessment. There are
three categories of measures. Measures of effect are measurable changes
in an attribute of an assessment endpoint or its surrogate in response
to a stressor to which it is exposed (formerly measurement endpoints;
see text note 3-15). Measures of exposure are measures of stressor
existence and movement in the environment and their contact or co-
occurrence with the assessment endpoint. Measures of ecosystem and
receptor characteristics are measures of ecosystem characteristics that
influence the behavior and location of entities selected as the
assessment endpoint, the distribution of a stressor, and life-history
characteristics of the assessment endpoint or its surrogate that may
affect exposure or response to the stressor. Examples of the three
types of measures are provided in text note 3-16 (see also Appendix
A.2.1).
The selection of appropriate measures is particularly complicated
when a cascade of ecological effects is likely to occur from a
stressor. In these cases, the effect on one entity (i.e., the measure
of effect) may become a stressor for other ecological entities (i.e.,
become a measure of exposure) and may result in impacts on one or more
assessment endpoints. For example, if a pesticide reduces earthworm
populations, change in earthworm population density could be the direct
measure of effect of toxicity and in some cases may be an assessment
endpoint. However, the reduction of worm populations may then become a
secondary stressor to which worm-eating birds become exposed, measured
as lowered food supply. This exposure may then result in a secondary
measurable effect of starvation of young. In this case, although ``bird
fledgling success'' may be an assessment endpoint that could be
measured directly, measures of earthworm density, pesticide residue in
earthworms and other food sources, availability of alternative foods,
nest site quality, and competition for nests from other bird species
may all be useful measurements.
When direct measurement of assessment endpoint responses is not
possible, the selection of surrogate measures is necessary. The
selection of what, where, and how to measure surrogate responses
determines whether the risk assessment is still relevant to management
decisions about an assessment endpoint. As an example, an assessment
may be conducted to evaluate the potential risk of a pesticide used on
seeds to an endangered species of seed-eating bird. The assessment
endpoint entity is the endangered species. Example attributes include
feeding behavior, survival, growth, and reproduction. While it may be
possible
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to directly collect measures of exposure and assessment endpoint life-
history characteristics on the endangered species, it would not be
appropriate to expose the endangered species to the pesticide to
measure sensitivity. In this case, to evaluate susceptibility, the most
appropriate surrogate measures would be on seed-eating birds with
similar life-history characteristics and phylogeny. While insectivorous
birds may serve as an adequate surrogate measure for determining the
sensitivity of the endangered bird to the pesticide, they do not
address issues of exposure.
Problem formulations based on assessment endpoints and selected
measures that address both sensitivity and likely exposure to stressors
will be relevant to management concerns. If assessment endpoints are
not susceptible, their use in assessing risk can lead to poor
management decisions (see section 3.3.1). To highlight the
relationships among goals, assessment endpoints, and measures, text
note 3-17 illustrates how these are related in water quality criteria.
In this example, it is instructive to note that although water quality
criteria are considered risk-based, they are not full risk assessments.
Water quality criteria provide an effects benchmark for decision making
and do not incorporate measures of exposure in the environment. Within
that benchmark, there are a number of assumptions about significance
(e.g., aquatic communities will be protected by achieving a benchmark
derived from individual species' toxicological responses to a single
chemical) and exposure (e.g., 1-hour and 4-day exposure averages). Such
assumptions embedded in decision rules are important to articulate (see
section 3.5.2).
The analysis plan provides a synopsis of measures that will be used
to evaluate risk hypotheses. The plan is strongest when it contains
explicit statements for how measures were selected, what they are
intended to evaluate, and which analyses they support. Uncertainties
associated with selected measures and analyses and plans for addressing
them should be included in the plan when possible.
3.5.2. Ensuring That Planned Analyses Meet Risk Managers' Needs
The analysis plan is a risk manager-risk assessor checkpoint. Risk
assessors and risk managers review the plan to ensure that the analyses
will provide information the manager can use for decision making. These
discussions may also identify what can and cannot be done on the basis
of a preliminary evaluation of problem formulation. A reiteration of
the planning discussion helps ensure that the appropriate balance of
requirements for the decision, data availability, and resource
constraints is established for the risk assessment. This is also an
appropriate time to conduct a technical review of the planning outcome.
Analysis plans include the analytical methods planned and the
nature of the risk characterization options and considerations to be
generated (e.g., quotients, narrative discussion, stressor-response
curve with probabilities). A description of how data analyses will
distinguish among risk hypotheses, the kinds of analyses to be used,
and rationale for why different hypotheses were selected and eliminated
are included. Potential extrapolations, model characteristics, types of
data (including quality), and planned analyses (with specific tests for
different types of data) are described. Finally, the plan includes a
discussion of how results will be presented upon completion and the
basis used for data selection.
Analysis planning is similar to the data quality objectives (DQO)
process (see text note 3-18), which emphasizes identifying the problem
by establishing study boundaries and determining necessary data
quality, quantity, and applicability to the problem being evaluated
(U.S. EPA, 1994c). The most important difference between problem
formulation and the DQO process is the presence of a decision rule in a
DQO that defines a benchmark for a management decision before the risk
assessment is completed. The decision rule step specifies the
statistical parameter that characterizes the population, specifies the
action level for the study, and combines outputs from the previous DQO
steps into an ``if * * * then'' decision rule that defines conditions
under which the decision maker will choose alternative options (often
used in tiered assessments; see also section 2.2.2). This approach
provides the basis for establishing null and alternative hypotheses
appropriate for statistical testing for significance that can be
effective in this application. While this approach is sometimes
appropriate, only certain kinds of risk assessments are based on
benchmark decisions. Presentation of stressor-response curves with
uncertainty bounds will be more appropriate than statistical testing of
decision criteria where risk managers must evaluate the range of
stressor effects to which they compare a range of possible management
options (see Suter, 1996).
The analysis plan is the final synthesis before the risk assessment
proceeds. It summarizes what has been done during problem formulation,
shows how the plan relates to management decisions that must be made,
and indicates how data and analyses will be used to estimate risks.
When the problem is clearly defined and there are enough data to
proceed, analysis begins.
4. Analysis Phase
Analysis is a process that examines the two primary components of
risk, exposure and effects, and their relationships between each other
and ecosystem characteristics. The objective is to provide the
ingredients necessary for determining or predicting ecological
responses to stressors under exposure conditions of interest.
Analysis connects problem formulation with risk characterization.
The assessment endpoints and conceptual models developed during problem
formulation provide the focus and structure for the analyses. Analysis
phase products are summary profiles that describe exposure and the
relationship between the stressor(s) and response. These profiles
provide the basis for estimating and describing risks in risk
characterization.
At the beginning of the analysis phase, the information needs
identified during problem formulation should have already been
addressed (text note 4-1). During the analysis phase (figure 4-1), the
risk assessor:
Selects the data that will be used on the basis of their
utility for evaluating the risk hypotheses (section 4.1)
Analyzes exposure by examining the sources of stressors,
the distribution of stressors in the environment, and the extent of co-
occurrence or contact (section 4.2)
Analyzes effects by examining stressor-response
relationships, the evidence for causality, and the relationship between
measures of effect and assessment endpoints (section 4.3)
Summarizes the conclusions about exposure (section 4.2.2)
and effects (section 4.3.2).
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The analysis phase is flexible, with substantial interaction
between the effects and exposure characterizations as illustrated by
the dotted line in figure 4-1. In particular, when secondary stressors
and effects are of concern, exposure and effects analyses are conducted
iteratively for different ecological entities, and they can become
intertwined and difficult to differentiate. In the bottomland hardwoods
assessment, for example (Appendix D), potential changes in the plant
and animal communities under different flooding scenarios were
examined. Risk assessors combined the stressor-response and exposure
analyses within the FORFLO model for primary effects on the plant
community and within the Habitat Suitability Index for secondary
effects on the animal community. In addition, the distinction between
analysis and risk estimation can become blurred. The model results
developed for the bottomland hardwoods assessment were used directly in
risk characterization.
The nature of the stressor influences the types of analyses
conducted. The results may range from highly quantitative to
qualitative, depending on the stressor and the scope of the assessment.
For chemical stressors, exposure estimates emphasize contact and uptake
into the organism, and effects estimations often entail extrapolation
from test organisms to the organism of interest. For physical
stressors, the initial disturbance may cause primary effects on the
assessment endpoint (e.g., loss of wetland acreage). In many cases,
however, secondary effects (e.g., decline of wildlife populations that
depend on wetlands) may be the principal concern. The point of view
depends on the assessment endpoints. Because adverse effects can occur
even if receptors do not physically contact disturbed habitat, exposure
analyses may emphasize co-occurrence with physical stressors rather
than contact. For biological stressors, exposure analysis is an
evaluation of entry, dispersal, survival, and reproduction (Orr et al.,
1993). Because biological stressors can reproduce, interact with other
organisms, and evolve over time, exposure and effects cannot always be
quantified with confidence; therefore, they may be assessed
qualitatively by eliciting expert opinion (Simberloff and Alexander,
1994).
4.1. Evaluating Data and Models for Analysis
At the beginning of the analysis phase, the assessor critically
examines the data and models to ensure that they can be used to
evaluate the conceptual model developed in problem formulation (see
sections 4.1.1 and 4.1.2). Section 4.1.3 addresses uncertainty
evaluation.
4.1.1. Strengths and Limitations of Different Types of Data
Many types of data can be used for risk assessment. Data may come
from laboratory or field studies or may be produced as output from a
model. Familiarity with the strengths and limitations of different
types of data can help assessors build on strengths and avoid pitfalls.
Such a strategy improves confidence in the conclusions of the risk
assessment.
Both laboratory and field studies (including field experiments and
observational studies) can provide useful data for risk assessment.
Because conditions can be controlled in laboratory studies, responses
may be less variable and smaller differences easier to detect. However,
the controls may limit the range of responses (e.g., animals cannot
seek alternative food sources), so they may not reflect responses in
the environment. In addition, larger-scale processes are difficult to
replicate in the laboratory.
Field observational studies (surveys) measure biological changes in
uncontrolled situations. Ecologists observe patterns and processes in
the field and often use statistical techniques (e.g., correlation,
clustering, factor analysis) to describe an association between a
disturbance and an ecological effect. For instance, physical attributes
of streams and their watersheds have been associated with changes in
stream communities (Richards et al., 1997). Field surveys are often
reported as status and trend studies. Messer et al. (1991) correlated a
biotic index with acid concentrations to describe the extent and
proportion of lakes likely to be impacted.
Field surveys usually represent exposures and effects (including
secondary effects) better than estimates generated from laboratory
studies or theoretical models. Field data are more important for
assessments of multiple stressors or where site-specific factors
significantly influence exposure. They are also often useful for
analyses of larger geographic scales and higher levels of biological
organization. Field survey data are not always necessary or feasible to
collect for screening-level or prospective assessments.
Field surveys should be designed with sufficient statistical rigor
to define one or more of the following:
Exposure in the system of interest
Differences in measures of effect between reference sites
and study areas
Lack of differences. Because conditions are not controlled
in field studies, variability may be higher and it may be difficult to
detect differences. For this reason, it is important to verify that
studies have sufficient power to detect important differences.
Field surveys are most useful for linking stressors with effects
when stressor and effect levels are measured concurrently. The presence
of confounding factors can make it difficult to attribute observed
effects to specific stressors. For this reason, field studies designed
to minimize effects of potentially confounding factors are preferred,
and the evidence for causality should be carefully evaluated (see
section 4.3.1.2). In addition, because treatments may not be randomly
applied or replicated, classical statistical methods need to be applied
with caution (Hurlbert, 1984; Stewart-Oaten et al., 1986; Wiens and
Parker, 1995; Eberhardt and Thomas, 1991). Intermediate between
laboratory and field are studies that use environmental media collected
from the field to examine response in the laboratory. Such studies may
improve the power to detect differences and may be designed to provide
evidence of causality.
Most data will be reported as measurements for single variables
such as a chemical concentration or the number of dead organisms. In
some cases, however, variables are combined and reported as indices.
Several indices are used to evaluate effects, for example, the rapid
bioassessment protocols (U.S. EPA, 1989a) and the Index of Biotic
Integrity, or IBI (Karr, 1981; Karr et al., 1986). These have several
advantages (Barbour et al., 1995), including the ability to:
Provide an overall indication of biological condition by
incorporating many attributes of system structure and function, from
individual to ecosystem levels
Evaluate responses from a broad range of anthropogenic
stressors
Minimize the limitations of individual metrics for
detecting specific types of responses.
Indices also have several drawbacks, many of which are associated
with combining heterogeneous variables. The final value may depend
strongly on the function used to combine variables. Some indices (e.g.,
the IBI) combine only measures of effects. Differential sensitivity or
other factors may make it difficult to attribute causality when many
response variables are combined. To investigate causality, such indices
may need to be separated into their
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components, or analyzed using multivariate methods (Suter, 1993b; Ott,
1978). Interpretation becomes even more difficult when an index
combines measures of exposure and effects because double counting may
occur or changes in one variable can mask changes in another. Measures
of exposure and effects may need to be separated in order to make
appropriate conclusions. For these reasons, professional judgment plays
a critical role in developing and applying indices.
Experience from similar situations is particularly useful in
assessments of stressors not yet released (i.e., prospective
assessments). Lessons learned from past experiences with related
organisms are often critical in trying to predict whether an organism
will survive, reproduce, and disperse in a new environment. Another
example is toxicity evaluation for new chemicals through the use of
structure-activity relationships, or SARs (Auer et al., 1994; Clements
and Nabholz, 1994). The simplest application of SARs is to identify a
suitable analog for which data are available to estimate the toxicity
of a compound for which data are lacking. More advanced applications
use quantitative structure-activity relationships (QSARs), which
mathematically model the relationships between chemical structures and
specific biological effects and are derived using information on sets
of related chemicals (Lipnick, 1995; Cronin and Dearden, 1995). The use
of analogous data without knowledge of the underlying processes may
substantially increase the uncertainty in the risk assessment (e.g.,
Bradbury, 1994); however, use of these data may be the only option
available.
Even though models may be developed and used as part of the risk
assessment, sometimes the risk assessor relies on output of a
previously developed model. Models are particularly useful when
measurements cannot be taken, for example, when predicting the effects
of a chemical yet to be manufactured. They can also provide estimates
for times or locations that are impractical to measure and can provide
a basis for extrapolating beyond the range of observation. Because
models simplify reality, they may omit important processes for a
particular system and may not reflect every condition in the real
world. In addition, a model's output is only as good as the quality of
its input variables, so critical evaluation of input data is important,
as is comparing model outputs with measurements in the system of
interest whenever possible.
Data and models for risk assessment are often developed in a tiered
fashion (also see section 2.2). For example, simple models that err on
the side of conservatism may be used first, followed by more elaborate
models that provide more realistic estimates. Effects data may also be
collected using a tiered approach. Short-term tests designed to
evaluate effects such as lethality and immobility may be conducted
first. If the chemical exhibits high toxicity or a preliminary
characterization indicates a risk, then more expensive, longer-term
tests that measure sublethal effects such as changes to growth and
reproduction can be conducted. Later tiers may employ multispecies
tests or field experiments. Tiered data should be evaluated in light of
the decision they are intended to support; data collected for early
tiers may not support more sophisticated needs.
4.1.2. Evaluating Measurement or Modeling Studies
The assessor's first task in the analysis phase is to carefully
evaluate studies to determine whether they can support the objectives
of the risk assessment. Each study should include a description of the
purpose, methods used to collect data, and results of the work. The
assessor evaluates the utility of studies by carefully comparing study
objectives with those of the risk assessment for consistency. In
addition, the assessor should determine whether the intended objectives
were met and whether the data are of sufficient quality to support the
risk assessment. This is a good opportunity to note the confidence in
the information and the implications of different studies for use in
the risk characterization, when the overall confidence in the
assessment is discussed. Finally, the risk assessor should identify
areas where existing data do not meet risk assessment needs. In these
cases, collecting additional data is recommended.
EPA is in the process of adopting the American Society for Quality
Control's E-4 guidelines for assuring environmental data quality
throughout the Agency (ASQC, 1994) (text note 4-2). These guidelines
describe procedures for collecting new data and provide a valuable
resource for evaluating existing studies. Readers may also refer to
Smith and Shugart, 1994; U.S. EPA, 1994d; and U.S. EPA, 1990, for more
information on evaluating data and models.
A study's documentation determines whether it can be evaluated for
its utility in risk assessment. Studies should contain sufficient
information so that results can be reproduced, or at least so the
details of the author's work can be accessed and evaluated. Ideally,
one should be able to access findings in their entirety; this provides
the opportunity to conduct additional analyses of the data, if needed.
For models, a number of factors increase the accessibility of methods
and results. These begin with model code and documentation
availability. Reports describing model results should include all
important equations, tables of all parameter values, any parameter
estimation techniques, and tables or graphs of results.
Study descriptions may not provide all the information needed to
evaluate their utility for risk assessment. Assessors should
communicate with the principal investigator or other study participants
to gain information on study plans and their implementation. Useful
questions for evaluating studies are shown in text note 4-3.
4.1.2.1. Evaluating the Purpose and Scope of the Study
Assessors should pay particular attention to the objectives and
scope of studies that were designed for purposes other than the risk
assessment at hand. This can identify important uncertainties and
ensure that the information is used appropriately. An example is the
evaluation of studies that measure condition (e.g., stream surveys,
population surveys): While the measurements used to evaluate condition
may be the same as the measures of effects identified in problem
formulation, to support a causal argument they must be linked with
stressors. In the best case, this means that the stressor was measured
at the same time and place as the effect.
Similarly, a model may have been developed for purposes other than
risk assessment. Its description should include the intended
application, theoretical framework, underlying assumptions, and
limiting conditions. This information can help assessors identify
important limitations in its application for risk assessment. For
example, a model developed to evaluate chemical transport in the water
column alone is of limited utility for a risk assessment of a chemical
that partitions readily into sediments.
The variables and conditions examined by studies should also be
compared with those identified during problem formulation. In addition,
the range of variability explored in the study should be compared with
that of the risk assessment. A study that examines animal habitat needs
in the winter, for example, may miss important breeding-season
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requirements. Studies that minimize the amount of extrapolation needed
are preferred. These are studies that represent:
The measures identified in the analysis plan (i.e.,
measures of exposure, effects, and ecosystem and receptor
characteristics)
The time frame of interest
The ecosystem and location of interest
The environmental conditions of interest
The exposure route of interest.
4.1.2.2. Evaluating the Design and Implementation of the Study
The assessor evaluates study design and implementation to determine
whether the study objectives were met and the information is of
sufficient quality to support the risk assessment. The study design
provides insight into the sources and magnitude of uncertainty
associated with the results (see section 4.1.3 for further discussion
of uncertainty). Among the most important design issues of an effects
study is whether it has enough statistical power to detect important
differences or changes. Because this information is rarely reported
(Peterman, 1990), the assessor may need to calculate the magnitude of
an effect that could be detected under the study conditions (Rotenberry
and Wiens, 1985).
Part of the exercise examines whether the study was conducted
properly:
For laboratory studies, this may mean determining whether
test conditions were properly controlled and control responses were
within acceptable bounds.
For field studies, issues include identification and
control of potentially confounding variables and careful reference site
selection. (A discussion of reference site selection is beyond the
scope of these Guidelines; however, it has been identified as a
candidate topic for future development.)
For models, issues include the program's structure and
logic and the correct specification of algorithms in the model code
(U.S. EPA, 1994d).
Evaluation is easier if standard methods or quality assurance/
quality control (QA/QC) protocols are available and followed by the
study. However, the assessor should still consider whether the
identified precision and accuracy goals were achieved and whether they
are appropriate for the risk assessment. For instance, detection limits
identified for one environmental matrix may not be achievable for
another, and thus it may not be possible to detect concentrations of
interest. Study results can still be useful even if a standard method
was not used. However, this places an additional burden on both the
authors and the assessors to provide and evaluate evidence that the
study was conducted properly.
4.1.3. Evaluating Uncertainty
Uncertainty evaluation is a theme throughout the analysis phase.
The objective is to describe and, where possible, quantify what is
known and not known about exposure and effects in the system of
interest. Uncertainty analyses increase the credibility of assessments
by explicitly describing the magnitude and direction of uncertainties,
and they provide the basis for efficient data collection or application
of refined methods. Uncertainties characterized during the analysis
phase are used during risk characterization, when risks are estimated
(section 5.1) and the confidence in different lines of evidence is
described (see section 5.2.1).
This section discusses sources of uncertainty relevant to the
analysis of ecological exposure and effects; source and example
strategies are shown in text note 4-4. Section 3.4.3 discusses
uncertainty in conceptual model development. Readers are also referred
to the discussion of uncertainties in the exposure assessment
guidelines (U.S. EPA, 1992b).
Sources of uncertainty that are encountered when evaluating
information include unclear communication of the data or its
manipulation and errors in the information itself (descriptive errors).
These are usually characterized by critically examining the sources of
information and documenting the decisions made when handling it. The
documentation should allow the reader to make an independent judgment
about the validity of the assessor's decisions.
Sources of uncertainty that primarily arise when estimating the
value of a parameter include variability, uncertainty about a
quantity's true value, and data gaps. The term variability is used here
to describe a characteristic's true heterogeneity. Examples include the
variability in soil organic carbon, seasonal differences in animal
diets, or differences in chemical sensitivity in different species.
Variability is usually described during uncertainty analysis, although
heterogeneity may not reflect a lack of knowledge and cannot usually be
reduced by further measurement. Variability can be described by
presenting a distribution or specific percentiles from it (e.g., mean
and 95th percentile).
Uncertainty about a quantity's true value may include uncertainty
about its magnitude, location, or time of occurrence. This uncertainty
can usually be reduced by taking additional measurements. Uncertainty
about a quantity's true magnitude is usually described by sampling
error (or variance in experiments) or measurement error. When the
quantity of interest is biological response, sampling error can greatly
influence a study's ability to detect effects. Properly designed
studies will specify sample sizes large enough to detect important
signals. Unfortunately, many studies have sample sizes that are too
small to detect anything but gross changes (Smith and Shugart, 1994;
Peterman, 1990). The discussion should highlight situations where the
power to detect difference is low. Meta-analysis has been suggested as
a way to combine results from different studies to improve the ability
to detect effects (Laird and Mosteller, 1990; Petitti, 1994). However,
these approaches have thus far been applied primarily in human
epidemiology and are still controversial (Mann, 1990).
Interest in quantifying spatial uncertainty has increased with the
increasing use of geographic information systems (GIS). Strategies
include verifying the locations of remotely sensed features and
ensuring that the spatial resolution of data or a method is
commensurate with the needs of the assessment. A growing literature is
addressing other analytical challenges often associated with using
spatial data (e.g., collinearity and autocorrelation, boundary and
scale effects, lack of true replication) (Johnson and Gage, 1997;
Fotheringham and Rogerson, 1993; Wiens and Parker, 1995). Large-scale
assessments generally require aggregating information at smaller
scales. It is not known how aggregation affects uncertainty (Hunsaker
et al., 1990).
Nearly every assessment must treat situations where data are
unavailable or available only for parameters other than those of
interest. Examples include using laboratory data to estimate a wild
animal's response to a stressor or using a bioaccumulation measurement
from a different ecosystem. These data gaps are usually bridged with a
combination of scientific analyses, scientific judgment, and perhaps
policy decisions. In deriving an ambient water quality criterion (text
note 3-17), for example, data and analyses are used to construct
distributions of species sensitivity for a particular chemical.
Scientific judgment is used to infer that species selected for testing
will adequately represent the range of sensitivity of species in the
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environment. Policy defines the extent to which individual species
should be protected (e.g., 90% vs. 95% of the species). It is important
to distinguish these elements.
Data gaps can often be filled by completing additional studies on
the unknown parameter. When possible, the necessary data should be
collected. At the least, opportunities for filling data gaps should be
noted and carried through to risk characterization. Data or knowledge
gaps that are so large that they preclude the analysis of either
exposure or ecological effects should also be noted and discussed in
risk characterization.
An important objective is to distinguish variability from
uncertainties that arise from lack of knowledge (e.g., uncertainty
about a quantity's true value) (U.S. EPA, 1995b). This distinction
facilitates the interpretation and communication of results. For
instance, in their food web models of herons and mink, MacIntosh et al.
(1994) separated expected variability in individual animals' feeding
habits from the uncertainty in the mean concentration of chemical in
prey species. They could then place error bounds on the exposure
distribution for the animals using the site and estimate the proportion
of the animal population that might exceed a toxicity threshold.
Sources of uncertainty that arise primarily during model
development and application include process model structure and the
relationships between variables in empirical models. Process model
descriptions should include assumptions, simplifications, and
aggregations of variables (see text note 4-5). Empirical model
descriptions should include the rationale for selection and model
performance statistics (e.g., goodness of fit). Uncertainty in process
or empirical models can be quantitatively evaluated by comparing model
results to measurements taken in the system of interest or by comparing
the results of different models.
Methods for analyzing and describing uncertainty can range from
simple to complex. When little is known, a useful approach is to
estimate exposure and effects based on alternative sets of assumptions
(scenarios). Each scenario is carried through to risk characterization,
where the underlying assumptions and the scenario's plausibility are
discussed. Results can be presented as a series of point estimates with
different aspects of uncertainty reflected in each. Classical
statistical methods (e.g., confidence limits, percentiles) can readily
describe parameter uncertainty. For models, sensitivity analysis can be
used to evaluate how model output changes with changes in input
variables, and uncertainty propagation can be analyzed to examine how
uncertainty in individual parameters can affect the overall uncertainty
in the results. The availability of software for Monte Carlo analysis
has greatly increased the use of probabilistic methods; readers are
encouraged to follow suggested best practices (e.g., U.S. EPA, 1996a,
1997b). Other methods (e.g., fuzzy mathematics, Bayesian methodologies)
are available but have not yet been extensively applied to ecological
risk assessment (Smith and Shugart, 1994). The Agency does not endorse
the use of any one method and cautions that the poor execution of any
method can obscure rather than clarify the impact of uncertainty on an
assessment's results. No matter what technique is used, the sources of
uncertainty discussed above should be addressed.
4.2. Characterization of Exposure
Exposure characterization describes potential or actual contact or
co-occurrence of stressors with receptors. It is based on measures of
exposure and ecosystem and receptor characteristics that are used to
analyze stressor sources, their distribution in the environment, and
the extent and pattern of contact or co-occurrence (discussed in
section 4.2.1). The objective is to produce a summary exposure profile
(section 4.2.2) that identifies the receptor (i.e., the exposed
ecological entity), describes the course a stressor takes from the
source to the receptor (i.e., the exposure pathway), and describes the
intensity and spatial and temporal extent of co-occurrence or contact.
The profile also describes the impact of variability and uncertainty on
exposure estimates and reaches a conclusion about the likelihood that
exposure will occur.
The exposure profile is combined with an effects profile (discussed
in section 4.3.2) to estimate risks. For the exposure profile to be
useful, it should be compatible with the stressor-response relationship
generated in the effects characterization.
4.2.1. Exposure Analyses
Exposure is contact or co-occurrence between a stressor and a
receptor. The objective is to describe exposure in terms of intensity,
space, and time in units that can be combined with the effects
assessment. In addition, the assessor should be able to trace the paths
of stressors from the source(s) to the receptors (i.e., describe the
exposure pathway).
A complete picture of how, when, and where exposure occurs or has
occurred is developed by evaluating sources and releases, the
distribution of the stressor in the environment, and the extent and
pattern of contact or co-occurrence. The order of these topics here is
not necessarily the order in which they are executed. The assessor may
start with information about tissue residues, for example, and attempt
to link these residues with a source.
4.2.1.1. Describe the Source(s)
A source can be defined in two general ways: as the place where the
stressor originates or is released (e.g., a smokestack, historically
contaminated sediments) or the management practice or action (e.g.,
dredging) that produces stressors. In some assessments, the original
sources may no longer exist and the source may be defined as the
current location of the stressors. For example, contaminated sediments
might be considered a source because the industrial plant that produced
the contaminants no longer operates. A source is the first component of
the exposure pathway and significantly influences where and when
stressors eventually will be found. In addition, many management
alternatives focus on modifying the source.
Exposure analyses may start with the source when it is known, begin
with known exposures and attempt to link them to sources, or start with
known stressors and attempt to identify sources and quantify contact.
In any case, the objective of this step is to identify the sources,
evaluate what stressors are generated, and identify other potential
sources. Text note 4-6 provides some useful questions to ask when
describing sources.
In addition to identifying sources, the assessor examines the
intensity, timing, and location of stressors' release. The location of
a source and the environmental media that first receive stressors are
two attributes that deserve particular attention. For chemical
stressors, the source characterization should also consider whether
other constituents emitted by a source influence transport,
transformation, or bioavailability of the stressor of interest. The
presence of chloride in the feedstock of a coal-fired power plant
influences whether mercury is emitted in divalent (e.g., as mercuric
chloride) or elemental form (Meij, 1991), for example. In the best
case, stressor generation is measured or modeled quantitatively;
however, sometimes it can only be qualitatively described.
Many stressors have natural counterparts or multiple sources, so it
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may be necessary to characterize these as well. Many chemicals occur
naturally (e.g., most metals), are generally widespread from other
sources (e.g., polycyclic aromatic hydrocarbons in urban ecosystems),
or may have significant sources outside the boundaries of the current
assessment (e.g., atmospheric nitrogen deposited in Chesapeake Bay).
Many physical stressors also have natural counterparts. For instance,
construction activities may release fine sediments into a stream in
addition to those coming from a naturally undercut bank. Human
activities may also change the magnitude or frequency of natural
disturbance cycles. For example, development may decrease the frequency
but increase the severity of fires or may increase the frequency and
severity of flooding in a watershed.
The assessment scope identified during planning determines how
multiple sources are evaluated. Options include (in order of increasing
complexity):
Focus only on the source under evaluation and calculate
the incremental risks attributable to that source (common for
assessments initiated with an identified source or stressor).
Consider all sources of a stressor and calculate total
risks attributable to that stressor. Relative source attribution can be
accomplished as a separate step (common for assessments initiated with
an observed effect or an identified stressor).
Consider all stressors influencing an assessment endpoint
and calculate cumulative risks to that endpoint (common for assessments
initiated because of concern for an ecological value).
Source characterization can be particularly important for
introduced biological stressors, since many of the strategies for
reducing risks focus on preventing entry in the first place. Once the
source is identified, the likelihood of entry may be characterized
qualitatively. In their risk analysis of Chilean log importation, for
example, the assessment team concluded that the beetle Hylurgus
ligniperda had a high potential for entry into the United States. Their
conclusion was based on the beetle's attraction to freshly cut logs and
tendency to burrow under the bark, which would provide protection
during transport (USDA, 1993).
4.2.1.2. Describe the Distribution of the Stressors or Disturbed
Environment
The second objective of exposure analysis is to describe the
spatial and temporal distribution of stressors in the environment. For
physical stressors that directly alter or eliminate portions of the
environment, the assessor describes the temporal and spatial
distribution of the disturbed environment. Because exposure occurs when
receptors co-occur with or contact stressors, this characterization is
a prerequisite for estimating exposure. Stressor distribution in the
environment is examined by evaluating pathways from the source as well
as the formation and subsequent distribution of secondary stressors
(see text note 4-7).
4.2.1.2.1. Evaluating Transport Pathways
Stressors can be transported via many pathways (see text note 4-8).
A careful evaluation can help ensure that measurements are taken in the
appropriate media and locations and that models include the most
important processes.
For a chemical stressor, the evaluation usually begins by
determining into which media it can partition. Key considerations
include physicochemical properties such as solubility and vapor
pressure. For example, chemicals with low solubility in water tend to
be found in environmental compartments with higher proportions of
organic carbon such as soils, sediments, and biota. From there, the
evaluation may examine the transport of the contaminated medium.
Because chemical mixture constituents may have different properties,
the analysis should consider how the composition of a mixture may
change over time or as it moves through the environment. Guidance on
evaluating the fate and transport of chemicals (including
bioaccumulation) is beyond the scope of these Guidelines; readers are
referred to the exposure assessment guidelines (U.S. EPA, 1992b) for
additional information. The topics of bioaccumulation and
biomagnification have been identified as candidates for further
development.
The attributes of physical stressors also influence where they will
go. The size of suspended particles determines where they will
eventually deposit in a stream, for example. Physical stressors that
eliminate ecosystems or portions of them (e.g., fishing activities or
the construction of dams) may require no modeling of pathways--the fish
are harvested or the valley is flooded. For these direct disturbances,
the challenge is usually to evaluate secondary stressors and effects.
The dispersion of biological stressors has been described in two
ways, as diffusion and jump-dispersal (Simberloff and Alexander, 1994).
Diffusion involves a gradual spread from the establishment site and is
primarily a function of reproductive rates and motility. Jump-dispersal
involves erratic spreads over periods of time, usually by means of a
vector. The gypsy moth and zebra mussel have spread this way, the gypsy
moth via egg masses on vehicles and the zebra mussel via boat ballast
water. Some biological stressors can use both strategies, which may
make dispersal rates very difficult to predict. The evaluation should
consider factors such as vector availability, attributes that enhance
dispersal (e.g., ability to fly, adhere to objects, disperse
reproductive units), and habitat or host needs.
For biological stressors, assessors should consider the additional
factors of survival and reproduction. Organisms use a wide range of
strategies to survive in adverse conditions; for example, fungi form
resting stages such as sclerotia and chlamydospores and some amphibians
become dormant during drought. The survival of some organisms can be
measured to some extent under laboratory conditions. However, it may be
impossible to determine how long resting stages (e.g., spores) can
survive under adverse conditions: many can remain viable for years.
Similarly, reproductive rates may vary substantially depending on
specific environmental conditions. Therefore, while life-history data
such as temperature and substrate preferences, important predators,
competitors or diseases, habitat needs, and reproductive rates are of
great value, they should be interpreted with caution, and the
uncertainty should be addressed by using several different scenarios.
Ecosystem characteristics influence the transport of all types of
stressors. The challenge is to determine the particular aspects of the
ecosystem that are most important. In some cases, ecosystem
characteristics that influence distribution are known. For example,
fine sediments tend to accumulate in areas of low energy in streams
such as pools and backwaters. Other cases need more professional
judgment. When evaluating the likelihood that an introduced organism
will become established, for instance, it is useful to know whether the
ecosystem is generally similar to or different from the one where the
biological stressor originated. Professional judgment is used to
determine which characteristics of the current and original ecosystems
should be compared.
4.2.1.2.2. Evaluating Secondary Stressors
Secondary stressors can greatly alter conclusions about risk; they
may be of
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greater or lesser concern than the primary stressor. Secondary stressor
evaluation is usually part of exposure characterization; however, it
should be coordinated with the ecological effects characterization to
ensure that all potentially important secondary stressors are
considered.
For chemicals, the evaluation usually focuses on metabolites,
biodegradation products, or chemicals formed through abiotic processes.
As an example, microbial action increases the bioaccumulation of
mercury by transforming inorganic forms to organic species. Many azo
dyes are not toxic because of their large molecular size, but in an
anaerobic environment, the polymer is hydrolyzed into more toxic water-
soluble units. Secondary stressors can also be formed through ecosystem
processes. Nutrient inputs into an estuary can decrease dissolved
oxygen concentrations because they increase primary production and
subsequent decomposition. Although transformation can be investigated
in the laboratory, rates in the field may differ substantially, and
some processes may be difficult or impossible to replicate in a
laboratory. When evaluating field information, though, it may be
difficult to distinguish between transformation processes (e.g., oil
degradation by microorganisms) and transport processes (e.g.,
volatilization). Although they may be difficult to distinguish, the
assessor should be aware that these two different processes will
largely determine if secondary stressors are likely to be formed. A
combination of these factors will also determine how much of the
secondary stressor(s) may be bioavailable to receptors. These
considerations reinforce the need to have a chemical risk assessment
team experienced in physical/chemical as well as biological processes.
Physical disturbances can also generate secondary stressors, and
identifying the specific consequences that will affect the assessment
endpoint can be a difficult task. The removal of riparian vegetation,
for example, can generate many secondary stressors, including increased
nutrients, stream temperature, sedimentation, and altered stream flow.
However, it may be the temperature change that is most responsible for
adult salmon mortality in a particular stream.
Stressor distribution in the environment can be described using
measurements, models, or a combination of the two. If stressors have
already been released, direct measurement of environmental media or a
combination of modeling and measurement is preferred. Models enhance
the ability to investigate the consequences of different management
scenarios and may be necessary if measurements are not possible or
practicable. They are also useful if a quantitative relationship of
sources and stressors is desired. As examples, land use activities have
been related to downstream suspended solids concentrations (Oberts,
1981), and downstream flood peaks have been predicted from the extent
of wetlands in a watershed (Novitski, 1979; Johnston et al., 1990).
Considerations for evaluating data collection and modeling studies are
discussed in section 4.1. For chemical stressors, readers may also
refer to the exposure assessment guidelines (U.S. EPA, 1992b). For
biological stressors, distribution may be difficult to predict
quantitatively. If it cannot be measured, it can be evaluated
qualitatively by considering the potential for transport, survival, and
reproduction (see above).
By the end of this step, the environmental distribution of the
stressor or the disturbed environment should be described. This
description provides the foundation for estimating the contact or co-
occurrence of the stressor with ecological entities. When contact is
known to have occurred, describing the stressor's environmental
distribution can help identify potential sources and ensure that all
important exposures are addressed.
4.2.1.3. Describe Contact or Co-Occurrence
The third objective is to describe the extent and pattern of co-
occurrence or contact between stressors and receptors (i.e., exposure).
This is critical--if there is no exposure, there can be no risk.
Therefore, assessors should be careful to include situations where
exposure may occur in the future, where exposure has occurred in the
past but is not currently evident (e.g., in some retrospective
assessments), and where ecosystem components important for food or
habitat are or may be exposed, resulting in impacts to the valued
entity (e.g., see figure D-2). Exposure can be described in terms of
stressor and receptor co-occurrence, actual stressor contact with
receptors, or stressor uptake by a receptor. The terms in which
exposure is described depend on how the stressor causes adverse effects
and how the stressor-response relationship is described. Relevant
questions for examining contact or co-occurrence are shown in text note
4-9.
Co-occurrence is particularly useful for evaluating stressors that
can cause effects without physically contacting ecological receptors.
Whooping cranes provide a case in point: they use sandbars in rivers
for their resting areas, and they prefer sandbars with unobstructed
views. Manmade obstructions such as bridges can interfere with resting
behavior without ever actually contacting the birds. Co-occurrence is
evaluated by comparing stressor distributions with that of the
receptor. For instance, stressor location maps may be overlaid with
maps of ecological receptors (e.g., bridge placement overlaid on maps
showing historical crane resting habitat). Co-occurrence of a
biological stressor and receptor may be used to evaluate exposure when,
for example, introduced species and native species compete for the same
resources. GIS has provided new tools for evaluating co-occurrence.
Most stressors must contact receptors to cause an effect. For
example, tree roots must contact flood waters before their growth is
impaired. Contact is a function of the amount or extent of a stressor
in an environmental medium and activity or behavior of the receptors.
For biological stressors, risk assessors usually rely on professional
judgment; contact is often assumed to occur in areas and during times
where the stressor and receptor are both present. Contact variables
such as the mode of transmission between organisms may influence the
contact between biological stressors and receptors.
For chemicals, contact is quantified as the amount of a chemical
ingested, inhaled, or in material applied to the skin (potential dose).
In its simplest form, it is quantified as an environmental
concentration, with the assumptions that the chemical is well mixed or
that the organism moves randomly through the medium. This approach is
commonly used for respired media (water for aquatic organisms, air for
terrestrial organisms). For ingested media (food, soil), another common
approach combines modeled or measured contaminant concentrations with
assumptions or parameters describing the contact rate (U.S. EPA, 1993a)
(see text note 4-10).
Finally, some stressors must not only be contacted but also must be
internally absorbed. A toxicant that causes liver tumors in fish, for
example, must be absorbed and reach the target organ to cause the
effect. Uptake is evaluated by considering the amount of stressor
internally absorbed by an organism. It is a function of the stressor
(e.g., a chemical's form or a pathogen's size), the medium (sorptive
properties or presence of solvents), the biological membrane
(integrity, permeability), and the organism (sickness, active uptake)
(Suter et al., 1994). Because of
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interactions between these four factors, uptake will vary on a
situation-specific basis. Uptake is usually assessed by modifying an
estimate of contact with a factor indicating the proportion of the
stressor that is available for uptake (the bioavailable fraction) or
actually absorbed. Absorption factors and bioavailability measured for
the chemical, ecosystem, and organism of interest are preferred.
Internal dose can also be evaluated by using a pharmacokinetic model or
by measuring biomarkers or residues in receptors (see text note 4-11).
Most stressor-response relationships express the amount of stressor in
terms of media concentration or potential dose rather than internal
dose; this limits the utility of uptake estimates in risk calculations.
However, biomarkers and tissue residues can provide valuable
confirmatory evidence that exposure has occurred, and tissue residues
in prey organisms can be used for estimating risks to their predators.
The characteristics of the ecosystem and receptors must be
considered to reach appropriate conclusions about exposure. Abiotic
attributes may increase or decrease the amount of a stressor contacted
by receptors. For example, naturally anoxic areas above contaminated
sediments in an estuary may reduce the time bottom-feeding fish spend
in contact with sediments and thereby reduce their exposure to
contaminants. Biotic interactions can also influence exposure. For
example, competition for high-quality resources may force some
organisms into disturbed areas. The interaction between exposure and
receptor behavior can influence both initial and subsequent exposures.
Some chemicals reduce the prey's ability to escape predators, for
instance, and thereby may increase predator exposure to the chemical as
well as the prey's risk of predation. Alternatively, organisms may
avoid areas, food, or water with contamination they can detect. While
avoidance can reduce exposure to chemicals, it may increase other risks
by altering habitat usage or other behavior.
Three dimensions should be considered when estimating exposure:
intensity, time, and space. Intensity is the most familiar dimension
for chemical and biological stressors and may be expressed as the
amount of chemical contacted per day or the number of pathogenic
organisms per unit area.
The temporal dimension of exposure has aspects of duration,
frequency, and timing. Duration can be expressed as the time over which
exposure occurs, some threshold intensity is exceeded, or intensity is
integrated. If exposure occurs as repeated discrete events of about the
same duration, frequency is the important temporal dimension of
exposure (e.g., the frequency of high-flow events in streams). If the
repeated events have significant and variable durations, both duration
and frequency should be considered. In addition, the timing of
exposure, including the order or sequence of events, can be an
important factor. Adirondack Mountain lakes receive high concentrations
of hydrogen ions and aluminum during snow melt; this period also
corresponds to the sensitive life stages of some aquatic organisms.
In chemical assessments, intensity and time are often combined by
averaging intensity over time. The duration over which intensity is
averaged is determined by considering the ecological effects of concern
and the likely pattern of exposure. For example, an assessment of bird
kills associated with granular carbofuran focused on short-term
exposures because the effect of concern was acute lethality
(Houseknecht, 1993). Because toxicological tests are usually conducted
using constant exposures, the most realistic comparisons between
exposure and effects are made when exposure in the real world does not
vary substantially. In these cases, the arithmetic average exposure
over the time period of toxicological significance is the appropriate
statistic (U.S. EPA, 1992b). However, as concentrations or contact
rates become more episodic or variable, the arithmetic average may not
reflect the toxicologically significant aspect of the exposure pattern.
In extreme cases, averaging may not be appropriate at all, and
assessors may need to use a toxicodynamic model to assess chronic
effects.
Spatial extent is another dimension of exposure. It is most
commonly expressed in terms of area (e.g., hectares of paved habitat,
square meters that exceed a particular chemical threshold). At larger
spatial scales, however, the shape or arrangement of exposure may be an
important issue, and area alone may not be the appropriate descriptor
of spatial extent for risk assessment. A general solution to the
problem of incorporating pattern into ecological assessments has yet to
be developed; however, landscape ecology and GIS have greatly expanded
the options for analyzing and presenting the spatial dimension of
exposure (e.g., Pastorok et al., 1996).
The results of exposure analysis are summarized in the exposure
profile, which is discussed in the next section.
4.2.2. Exposure Profile
The final product of exposure analysis is an exposure profile.
Exposure should be described in terms of intensity, space, and time in
units that can be combined with the effects assessment. The assessor
should summarize the paths of stressors from the source to the
receptors, completing the exposure pathway. Depending on the risk
assessment, the profile may be a written document or a module of a
larger process model. In any case, the objective is to ensure that the
information needed for risk characterization has been collected and
evaluated. In addition, compiling the exposure profile provides an
opportunity to verify that the important exposure pathways identified
in the conceptual model were evaluated.
The exposure profile identifies the receptor and describes the
exposure pathways and intensity and spatial and temporal extent of co-
occurrence or contact. It also describes the impact of variability and
uncertainty on exposure estimates and reaches a conclusion about the
likelihood that exposure will occur (see text note 4-12).
The profile should describe the applicable exposure pathways. If
exposure can occur through many pathways, it may be useful to rank
them, perhaps by contribution to total exposure. As an illustration,
consider an assessment of risks to grebes feeding in a mercury-
contaminated lake. The grebes may be exposed to methyl mercury in fish
that originated from historically contaminated sediments. They may also
be exposed by drinking lake water, but comparing the two exposure
pathways may show that the fish pathway contributes the vast majority
of exposure to mercury.
The profile should identify the ecological entity that the exposure
estimates represent. For example, the exposure estimates may describe
the local population of grebes feeding on a specific lake during the
summer months.
The assessor should explain how each of the three general
dimensions of exposure (intensity, time, and space) was treated.
Continuing with the grebe example, exposure might be expressed as the
daily potential dose averaged over the summer months and over the
extent of the lake.
The profile should also describe how exposure can vary depending on
receptor attributes or stressor levels. For instance, the exposure may
be higher for grebes eating a larger proportion of bigger, more
contaminated fish. Variability can be described by using a distribution
or by describing where a
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point estimate is expected to fall on a distribution. Cumulative-
distribution functions (CDFs) and probability-density functions (PDFs)
are two common presentation formats (see Appendix B, figures B-1 and B-
2). Figures 5-3 to 5-5 show examples of cumulative frequency plots of
exposure data. The point estimate/descriptor approach is used when
there is not enough information to describe a distribution. Descriptors
discussed in U.S. EPA, 1992b, are recommended, including central
tendency to refer to the mean or median of the distribution, high end
to refer to exposure estimates that are expected to fall between the
90th and 99.9th percentile of the exposure distribution, and bounding
estimates to refer to those higher than any actual exposure.
The exposure profile should summarize important uncertainties
(e.g., lack of knowledge; see section 4.1.3 for a discussion of the
different sources of uncertainty). In particular, the assessor should:
Identify key assumptions and describe how they were
handled
Discuss (and quantify, if possible) the magnitude of
sampling and/or measurement error
Identify the most sensitive variables influencing exposure
Identify which uncertainties can be reduced through the
collection of more data.
Uncertainty about a quantity's true value can be shown by
calculating error bounds on a point estimate, as shown in figure 5-2.
All of the above information is synthesized to reach a conclusion
about the likelihood that exposure will occur, completing the exposure
profile. It is one of the products of the analysis phase and is
combined with the stressor-response profile (the product of the
ecological effects characterization discussed in the next section)
during risk characterization.
4.3. Characterization of Ecological Effects
To characterize ecological effects, the assessor describes the
effects elicited by a stressor, links them to the assessment endpoints,
and evaluates how they change with varying stressor levels. The
characterization begins by evaluating effects data to specify the
effects that are elicited, verify that they are consistent with the
assessment endpoints, and confirm that the conditions under which they
occur are consistent with the conceptual model. Once the effects of
interest are identified, the assessor conducts an ecological response
analysis (section 4.3.1), evaluating how the magnitude of the effects
change with varying stressor levels and the evidence that the stressor
causes the effect, and then linking the effects with the assessment
endpoint. Conclusions are summarized in a stressor-response profile
(section 4.3.2).
4.3.1. Ecological Response Analysis
Ecological response analysis examines three primary elements: the
relationship between stressor levels and ecological effects (section
4.3.1.1), the plausibility that effects may occur or are occurring as a
result of exposure to stressors (section 4.3.1.2), and linkages between
measurable ecological effects and assessment endpoints when the latter
cannot be directly measured (section 4.3.1.3).
4.3.1.1. Stressor-Response Analysis
To evaluate ecological risks, one must understand the relationships
between stressors and resulting responses. The stressor-response
relationships used in a particular assessment depend on the scope and
nature of the ecological risk assessment as defined in problem
formulation and reflected in the analysis plan. For example, an
assessor may need a point estimate of an effect (such as an
LC50) to compare with point estimates from other stressors.
The shape of the stressor-response curve may be needed to determine the
presence or absence of an effects threshold or for evaluating
incremental risks, or stressor-response curves may be used as input for
effects models. If sufficient data are available, the risk assessor may
construct cumulative distribution functions using multiple-point
estimates of effects. Or the assessor may use process models that
already incorporate empirically derived stressor-response relationships
(see section 4.3.1.3). Text note 4-13 provides some questions for
stressor-response analysis.
This section describes a range of stressor-response approaches
available to risk assessors following a theme of variations on the
classical stressor-response relationship (e.g., figure 4-2). More
complex relationships are shown in figure 4-3, which illustrates a
range of projected responses of zooplankton populations to pesticide
exposure based on laboratory tests. In field studies, the complexity of
these responses could increase even further, considering factors such
as potential indirect effects of pesticides on zooplankton populations
(e.g., competitive interactions between species). More complex patterns
can also occur at higher levels of biological organization; ecosystems
may respond to stressors with abrupt shifts to new community or system
types (Holling, 1978).
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In simple cases, one response variable (e.g., mortality, incidence
of abnormalities) is analyzed, and most quantitative techniques have
been developed for univariate analysis. If the response of interest is
composed of many individual variables (e.g., species abundances in an
aquatic community), multivariate techniques may be useful. These have a
long history of use in ecology (see texts by Gauch, 1982; Pielou, 1984;
Ludwig and Reynolds, 1988) but have not yet been extensively applied in
risk assessment. While quantifying stressor-response relationships is
encouraged, qualitative evaluations are also possible (text note 4-14).
Stressor-response relationships can be described using intensity,
time, or space. Intensity is probably the most familiar of these and is
often used for chemicals (e.g., dose, concentration). Exposure duration
is also commonly used for chemical stressor-response relationships; for
example, median acute effects levels are always associated with a time
parameter (e.g., 24 hours). As noted in text note 4-14, the timing of
exposure was the critical dimension in evaluating the relationship
between seed germination and soil moisture (Pearlstine et al., 1985).
The spatial dimension is often of concern for physical stressors. For
instance, the extent of suitable habitat was related to the probability
of sighting a spotted owl (Thomas et al., 1990), and water-table depth
was related to tree growth by Phipps (1979).
Single-point estimates and stressor-response curves can be
generated for some biological stressors. For pathogens such as bacteria
and fungi, inoculum levels (e.g., spores per milliliter; propagules per
unit of substrate) may be related to symptoms in a host (e.g., lesions
per area of leaf surface, total number of plants infected) or actual
signs of the pathogen (asexual or sexual fruiting bodies, sclerotia,
etc.). For other biological stressors such as introduced species,
simple stressor-response relationships may be inappropriate.
Data from individual experiments can be used to develop curves and
point estimates both with and without associated uncertainty estimates
(see figures 5-2 and 5-3). The advantages of curve-fitting approaches
include using all of the available experimental data and the ability to
interpolate to values other than the data points measured. If
extrapolation outside the range of experimental data is required, risk
assessors should justify that the observed experimental relationships
remain valid. A disadvantage of curve fitting is that the number of
data points required to complete an analysis may not always be
available. For example, while standard toxicity tests with aquatic
organisms frequently contain sufficient experimental treatments to
permit regression analysis, this is often not the case for toxicity
tests with wildlife species.
Risk assessors sometimes use curve-fitting analyses to determine
particular levels of effect. These point estimates are interpolated
from the fitted line. Point estimates may be adequate for simple
assessments or comparative studies of risk and are also useful if a
decision rule for the assessment was identified during the planning
phase (see section 2). Median effect levels (text note 4-15) are
frequently selected because the level of uncertainty is minimized at
the midpoint of the regression curve. While a 50% effect level for an
endpoint such as survival may not be appropriately protective for the
assessment endpoint, median effect levels can be used for preliminary
assessments or comparative purposes, especially when used in
combination with uncertainty modifying factors (see text note 5-3).
Selection of a different effect level (10%, 20%, etc.) can be arbitrary
unless there is some clearly defined benchmark for the assessment
endpoint. Thus, it is preferable to carry several levels of effect or
the entire stressor-response curve forward to risk estimation.
When risk assessors are particularly interested in effects at lower
stressor levels, they may seek to establish ``no-effect'' stressor
levels based on comparisons between experimental treatments and
controls. Statistical hypothesis testing is frequently used for this
purpose. (Note that statistical hypotheses are different from the risk
hypotheses discussed in problem formulation; see text note 3-12). An
example of this approach for deriving chemical no-effect levels is
provided in text note 4-16. A feature of statistical hypothesis testing
is that the risk assessor is not required to pick a particular effect
level of concern. The no-effect level is determined instead by
experimental conditions such as the number of replicates as well as the
variability inherent in the data. Thus it is important to consider the
level of effect detectable in the experiment (i.e., its power) in
addition to reporting the no-effect level. Another drawback of this
approach is that it is difficult to evaluate effects associated with
stressor levels other than the actual treatments tested. Several
investigators (Stephan and Rogers, 1985; Suter, 1993a) have proposed
using regression analysis as an alternative to statistical hypothesis
testing.
In observational field studies, statistical hypothesis testing is
often used to compare site conditions with a reference site(s). The
difficulties of drawing proper conclusions from these types of studies
(which frequently cannot employ replication) have been discussed by
many investigators (see section 4.1.1). Risk assessors should examine
whether sites were carefully matched to minimize differences other than
the stressor and consider whether potential covariates should be
included in any analysis. In contrast with observational studies, an
advantage of experimental field studies is that treatments can be
replicated, increasing the confidence that observed differences are due
to the treatment.
Experimental data can be combined to generate multiple-point
estimates that can be displayed as cumulative distribution functions.
Figure 5-5 shows an example for species sensitivity derived from
multiple-point estimates (EC5s) for freshwater algae (and
one vascular plant species) exposed to an herbicide. These
distributions can help identify stressor levels that affect a minority
or majority of species. A limiting factor in the use of cumulative
frequency distributions is the amount of data needed as input.
Cumulative effects distribution functions can also be derived from
models that use Monte Carlo or other methods to generate distributions
based on measured or estimated variation in input parameters for the
models.
When multiple stressors are present, stressor-response analysis is
particularly challenging. Stressor-response relationships can be
constructed for each stressor separately and then combined.
Alternatively, the relationship between response and the suite of
stressors can be combined in one analysis. It is preferable to directly
evaluate complex chemical mixtures present in environmental media
(e.g., wastewater effluents, contaminated soils (U.S. EPA, 1986a)), but
it is important to consider the relationship between the samples tested
and the potential spatial and temporal variability in the mixture. The
approach taken for multiple stressors depends on the feasibility of
measuring them and whether an objective of the assessment is to project
different stressor combinations.
In some cases, multiple regression analysis can be used to
empirically relate multiple stressors to a response. Detenbeck (1994)
used this approach to evaluate change in the water quality of wetlands
resulting from multiple physical stressors. Multiple regression
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analysis can be difficult to interpret if the explanatory variables
(i.e., the stressors) are not independent. Principal components
analysis can be used to extract independent explanatory variables
formed from linear combinations of the original variables (Pielou,
1984).
4.3.1.2. Establishing Cause-and-Effect Relationships (Causality)
Causality is the relationship between cause (one or more stressors)
and effect (response to the stressor(s)). Without a sound basis for
linking cause and effect, uncertainty in the conclusions of an
ecological risk assessment is likely to be high. Developing causal
relationships is especially important for risk assessments driven by
observed adverse ecological effects such as bird or fish kills or a
shift in the species composition of an area. This section describes
considerations for evaluating causality based on criteria developed by
Fox (1991) primarily for observational data and additional criteria for
experimental evaluation of causality modified from Koch's postulates
(e.g., see Woodman and Cowling, 1987).
Evidence of causality may be derived from observational evidence
(e.g., bird kills are associated with field application of a pesticide)
or experimental data (laboratory tests with the pesticides in question
show bird kills at levels similar to those found in the field), and
causal associations can be strengthened when both types of information
are available. But since not all situations lend themselves to formal
experimentation, scientists have looked for other criteria, based
largely on observation rather than experiment, to support a plausible
argument for cause and effect. Text note 4-17 provides criteria based
on Fox (1991) that are very similar to others reviewed by Fox (U.S.
Department of Health, Education, and Welfare, 1964; Hill, 1965; Susser,
1986a, b). While data to support some criteria may be incomplete or
missing for any given assessment, these criteria offer a useful way to
evaluate available information.
The strength of association between stressor and response is often
the main reason that adverse effects such as bird kills are linked to
specific events or actions. A stronger response to a hypothesized cause
is more likely to indicate true causation. Additional strong evidence
of causation is when a response follows after a change in the
hypothesized cause (predictive performance).
The presence of a biological gradient or stressor-response
relationship is another important criterion for causality. The
stressor-response relationship need not be linear. It can be a
threshold, sigmoidal, or parabolic phenomenon, but in any case it is
important that it can be demonstrated. Biological gradients, such as
effects that decrease with distance from a toxic discharge, are
frequently used as evidence of causality. To be credible, such
relationships should be consistent with current biological or
ecological knowledge (biological plausibility).
A cause-and-effect relationship that is demonstrated repeatedly
(consistency of association) provides strong evidence of causality.
Consistency may be shown by a greater number of instances of
association between stressor and response, occurrences in diverse
ecological systems, or associations demonstrated by diverse methods
(Hill, 1965). Fox (1991) adds that in ecoepidemiology, an association's
occurrence in more than one species and population is very strong
evidence for causation. An example would be the many bird species
killed by carbofuran applications (Houseknecht, 1993). Fox (1991) also
believes that causality is supported if the same incident is observed
by different persons under different circumstances and at different
times.
Conversely, inconsistency in association between stressor and
response is strong evidence against causality (e.g., the stressor is
present without the expected effect, or the effect occurs but the
stressor is not found). Temporal incompatibility (i.e., the presumed
cause does not precede the effect) and incompatibility with
experimental or observational evidence (factual implausibility) are
also indications against a causal relationship.
Two other criteria may be of some help in defining causal
relationships: specificity of an association and probability. The more
specific or diagnostic the effect, the more likely it is to have a
consistent cause. However, Fox (1991) argues that effect specificity
does little to strengthen a causal claim. Disease can have multiple
causes, a substance can behave differently in different environments or
cause several different effects, and biochemical events may elicit many
biological responses. But in general, the more specific or localized
the effects, the easier it is to identify the cause. Sometimes, a
stressor may have a distinctive mode of action that suggests its role.
Yoder and Rankin (1995) found that patterns of change observed in fish
and benthic invertebrate communities could serve as indicators for
different types of anthropogenic impact (e.g., nutrient enrichment vs.
toxicity).
For some pathogenic biological stressors, the causal evaluations
proposed by Koch (see text note 4-18) may be useful. For chemicals,
ecotoxicologists have slightly modified Koch's postulates to provide
evidence of causality (Suter, 1993a). The modifications are:
The injury, dysfunction, or other putative effect of the
toxicant must be regularly associated with exposure to the toxicant and
any contributory causal factors.
Indicators of exposure to the toxicant must be found in
the affected organisms.
The toxic effects must be seen when organisms or
communities are exposed to the toxicant under controlled conditions,
and any contributory factors should be manifested in the same way
during controlled exposures.
The same indicators of exposure and effects must be
identified in the controlled exposures as in the field.
These modifications are conceptually identical to Koch's
postulates. While useful, this approach may not be practical if
resources for experimentation are not available or if an adverse effect
may be occurring over such a wide spatial extent that experimentation
and correlation may prove difficult or yield equivocal results.
Woodman and Cowling (1987) provide a specific example of a causal
evaluation. They proposed three rules for establishing the effects of
airborne pollutants on the health and productivity of forests: (1) The
injury or dysfunction symptoms observed in the case of individual trees
in the forest must be associated consistently with the presence of the
suspected causal factors, (2) the same injury or dysfunction symptoms
must be seen when healthy trees are exposed to the suspected causal
factors under controlled conditions, and (3) natural variation in
resistance and susceptibility observed in forest trees also must be
seen when clones of the same trees are exposed to the suspected causal
factors under controlled conditions.
Experimental techniques are frequently used for evaluating
causality in complex chemical mixtures. Options include evaluating
separated components of the mixture, developing and testing a synthetic
mixture, or determining how a mixture's toxicity relates to that of
individual components. The choice of method depends on the goal of the
assessment and the resources and test data that are available.
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Laboratory toxicity identification evaluations (TIEs) can be used
to help determine which components of a chemical mixture cause toxic
effects. By using fractionation and other methods, the TIE approach can
help identify chemicals responsible for toxicity and show the relative
contributions of different chemicals in aqueous effluents (U.S. EPA,
1988a, 1989b, c) and sediments (e.g., Ankley et al., 1990).
Risk assessors may utilize data from synthetic chemical mixtures if
the individual chemical components are well characterized. This
approach allows for manipulation of the mixture and investigation of
how varying the components that are present or their ratios may affect
mixture toxicity, but it also requires additional assumptions about the
relationship between effects of the synthetic mixture and those of the
environmental mixture. (See section 5.1.3 for additional discussion of
mixtures.)
4.3.1.3. Linking Measures of Effect to Assessment Endpoints
Assessment endpoints express the environmental values of concern
for a risk assessment, but they cannot always be measured directly.
When measures of effect differ from assessment endpoints, sound and
explicit linkages between them are needed. Risk assessors may make
these linkages in the analysis phase or, especially when linkages rely
on professional judgment, work with measures of effect through risk
estimation (in risk characterization) and then connect them with
assessment endpoints. Common extrapolations used to link measures of
effect with assessment endpoints are shown in text note 4-19.
4.3.1.3.1. General Considerations
During the preparation of the analysis plan, risk assessors
identify the extrapolations required between assessment endpoints and
measures of effect. During the analysis phase, risk assessors should
revisit the questions listed in text note 4-20 before proceeding with
specific extrapolation approaches.
The nature of the risk assessment and the type and amount of data
that are available largely determine how conservative a risk assessment
will be. The early stages of a tiered risk assessment typically use
conservative estimates because the data needed to adequately assess
exposure and effects are usually lacking. When a risk has been
identified, subsequent tiers use additional data to address the
uncertainties that were incorporated into the initial assessment(s)
(see text note 2-8).
The scope of the risk assessment also influences extrapolation
through the nature of the assessment endpoint. Preliminary assessments
that evaluate risks to general trophic levels such as herbivores may
extrapolate between different genera or families to obtain a range of
sensitivity to the stressor. On the other hand, assessments concerned
with management strategies for a particular species may employ
population models.
Analysis phase activities may suggest additional extrapolation
needs. Evaluation of exposure may indicate different spatial or
temporal scales than originally planned. If spatial scales are
broadened, additional receptors may need to be included in
extrapolation models. If a stressor persists for an extended time, it
may be necessary to extrapolate short-term responses over a longer
exposure period, and population-level effects may become more
important. Whatever methods are employed to link assessment endpoints
with measures of effect, it is important to apply them in a manner
consistent with sound ecological principles and use enough appropriate
data. For example, it is inappropriate to use structure-activity
relationships to predict toxicity from chemical structure unless the
chemical under consideration has a similar mode of toxic action to the
reference chemicals (Bradbury, 1994). Similarly, extrapolations between
two species may be more credible if factors such as similarities in
food preferences, body mass, physiology, and seasonal behavior (e.g.,
mating and migration habits) are considered (Sample et al., 1996). Rote
or biologically implausible extrapolations will erode the assessment's
overall credibility.
Finally, many extrapolation methods are limited by the availability
of suitable databases. Although many data are available for chemical
stressors and aquatic species, they do not exist for all taxa or
effects. Chemical effects databases for wildlife, amphibians, and
reptiles are extremely limited, and there is even less information on
most biological and physical stressors. Risk assessors should be aware
that extrapolations and models are only as useful as the data on which
they are based and should recognize the great uncertainties associated
with extrapolations that lack an adequate empirical or process-based
rationale.
The rest of this section addresses the approaches used by risk
assessors to link measures of effect to assessment endpoints, as noted
below.
Linkages based on professional judgment. This is not as
desirable as empirical or process-based approaches, but is the only
option when data are lacking.
Linkages based on empirical or process models. Empirical
extrapolations use experimental or observational data that may or may
not be organized into a database. Process-based approaches rely on some
level of understanding of the underlying operations of the system of
interest.
4.3.1.3.2. Judgment Approaches for Linking Measures of Effect to
Assessment Endpoints.
Professional-judgment approaches rely on the professional expertise
of risk assessors, expert panels, or others to relate changes in
measures of effect to changes in assessment endpoints. They are
essential when databases are inadequate to support empirical models and
process models are unavailable or inappropriate. Professional-judgment
linkages between measures of effect and assessment endpoints can be
just as credible as empirical or process-based expressions, provided
they have a sound scientific basis. This section highlights
professional-judgment extrapolations between species, from laboratory
data to field effects, and between geographic areas.
Because of the uncertainty in predicting the effects of biological
stressors such as introduced species, professional-judgment approaches
are commonly used. For example, there may be measures of effect data on
a foreign pathogen that attacks a certain tree species not found in the
United States, but the assessment endpoint concerns the survival of a
commercially important tree found only in the United States. In this
case, a careful evaluation and comparison of the life history and
environmental requirements of both the pathogen and the two tree
species may contribute toward a useful determination of potential
effects, even though the uncertainty may be high. Expert panels are
typically used for this kind of evaluation (USDA, 1993).
Risks to organisms in field situations are best estimated from
studies at the site of interest. However, such data are not always
available. Frequently, risk assessors must extrapolate from laboratory
toxicity test data to field effects. Text note 4-21 summarizes some of
the considerations for risk assessors when extrapolating from
laboratory test results to field situations for chemical stressors.
Factors altering exposure in the field are among the most important
factors limiting extrapolations from laboratory test
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results, but indirect effects on exposed organisms due to predation,
competition, or other biotic or abiotic factors not evaluated in the
laboratory may also be significant. Variations in direct chemical
effects between laboratory tests and field situations may not
contribute as much to the overall uncertainty of the extrapolation.
In addition to single-species tests, laboratory multiple-species
tests are sometimes used to predict field effects. While these tests
have the advantage of evaluating some aspects of a real ecological
system, they also have inherent scale limitations (e.g., lack of top
trophic levels) and may not adequately represent features of the field
system important to the assessment endpoint.
Extrapolations based on professional judgment are frequently
required when assessors wish to use field data obtained from one
geographic area and apply them to a different area of concern, or to
extrapolate from the results of laboratory tests to more than one
geographic region. In either case, risk assessors should consider
variations between regions in environmental conditions, spatial scales
and heterogeneities, and ecological forcing functions (see below).
Variations in environmental conditions in different geographic
regions may alter stressor exposure and effects. If exposures to
chemical stressors can be accurately estimated and are expected to be
similar (e.g., see text note 4-21), the same species in different areas
may respond similarly. For example, if the pesticide granular
carbofuran were applied at comparable rates throughout the country,
seed-eating birds could be expected to be similarly affected by the
pesticide (Houseknecht, 1993). Nevertheless, the influence of
environmental conditions on stressor exposure and effects can be
substantial.
For biological stressors, environmental conditions such as climate,
habitat, and suitable hosts play major roles in determining whether a
biological stressor becomes established. For example, climate would
prevent establishment of the Mediterranean fruit fly in the much colder
northeastern United States. Thus, a thorough evaluation of
environmental conditions in the area versus the natural habitat of the
stressor is important. Even so, many biological stressors can adapt
readily to varying environmental conditions, and the absence of natural
predators or diseases may play an even more important role than abiotic
factors.
For physical stressors that have natural counterparts, such as
fire, flooding, or temperature variations, effects may depend on the
difference between human-caused and natural variations in these
parameters for a particular region. Thus, the comparability of two
regions depends on both the pattern and range of natural disturbances.
Spatial scales and heterogeneities affect comparability between
regions. Effects observed over a large scale may be difficult to
extrapolate from one geographical location to another, mainly because
the spatial heterogeneity is likely to differ. Factors such as number
and size of land-cover patches, distance between patches, connectivity
and conductivity of patches (e.g., migration routes), and patch shape
may be important. Extrapolations can be strengthened by using
appropriate reference sites, such as sites in comparable ecoregions
(Hughes, 1995).
Ecological forcing functions may differ between geographic regions.
Forcing functions are critical abiotic variables that exert a major
influence on the structure and function of ecological systems. Examples
include temperature fluctuations, fire frequency, light intensity, and
hydrologic regime. If these differ significantly between sites, it may
be inappropriate to extrapolate effects from one system to another.
Bedford and Preston (1988), Detenbeck et al. (1992), Gibbs (1993),
Gilbert (1987), Gosselink et al. (1990), Preston and Bedford (1988),
and Risser (1988) may be useful to risk assessors concerned with
effects in different geographical areas.
4.3.1.3.3. Empirical and Process-Based Approaches for Linking Measures
of Effect to Assessment Endpoints
A variety of empirical and process-based approaches are available
to risk assessors, depending on the scope of the assessment and the
data and resources available. Empirical and process-based approaches
include numerical extrapolations between measures of effects and
assessment endpoints. These linkages range in sophistication from
applying an uncertainty factor to using a complex model requiring
extensive measures of effects and measures of ecosystem and receptor
characteristics as input. But even the most sophisticated quantitative
models involve qualitative elements and assumptions and thus require
professional judgment for evaluation. Individuals who use models and
interpret their results should be familiar with the underlying
assumptions and components contained in the model.
4.3.1.3.3.1. Empirical Approaches
Empirical approaches are derived from experimental data or
observations Empirically based uncertainty factors or taxonomic
extrapolations may be used when adequate effects databases are
available but the understanding of underlying mechanisms of action or
ecological principles is limited. When sufficient information on
stressors and receptors is available, process-based approaches such as
pharmacokinetic/pharmacodynamic models or population or ecosystem
process models may be used. Regardless of the options used, risk
assessors should justify and adequately document the approach selected.
Uncertainty factors are used to ensure that measures of effects are
sufficiently protective of assessment endpoints. Uncertainty factors
are empirically derived numbers that are divided into measure of
effects values to give an estimated stressor level that should not
cause adverse effects to the assessment endpoint. Uncertainty factors
have been developed most frequently for chemicals because extensive
ecotoxicologic databases are available, especially for aquatic
organisms. Uncertainty factors are useful when decisions must be made
about stressors in a short time and with little information.
Uncertainty factors have been used to compensate for assessment
endpoint/effect measures differences between endpoints (acute to
chronic effects), between species, and between test situations (e.g.,
laboratory to field). Typically, they vary inversely with the quantity
and type of measures of effects data available (Zeeman, 1995). They
have been used in screening-level assessments of new chemicals
(Nabholz, 1991), in assessing the risks of pesticides to aquatic and
terrestrial organisms (Urban and Cook, 1986), and in developing
benchmark dose levels for human health effects (U.S. EPA, 1995c).
Despite their usefulness, uncertainty factors can also be misused,
especially when used in an overly conservative fashion, as when chains
of factors are multiplied together without sufficient justification.
Like other approaches to bridging data gaps, uncertainty factors are
often based on a combination of scientific analysis, scientific
judgment, and policy judgment (see section 4.1.3). It is important to
differentiate these three elements when documenting the basis for the
uncertainty factors used.
Empirical data can be used to facilitate extrapolations between
species, genera, families, or orders or functional groups (e.g.,
feeding guilds)
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(Suter, 1993a). Suter et al. (1983), Suter (1993a), and Barnthouse et
al. (1987, 1990) developed methods to extrapolate toxicity between
freshwater and marine fish and arthropods. As Suter notes (1993a), the
uncertainties associated with extrapolating between orders, classes,
and phyla tend to be very high. However, one can extrapolate with fair
certainty between aquatic species within genera and genera within
families. Further applications of this approach (e.g., for chemical
stressors and terrestrial organisms) are limited by a lack of suitable
databases.
In addition to taxonomic databases, dose-scaling or allometric
regression is used to extrapolate the effects of a chemical stressor to
another species. Allometry is the study of change in the proportions of
various parts of an organism as a consequence of growth and
development. Processes that influence toxicokinetics (e.g., renal
clearance, basal metabolic rate, food consumption) tend to vary across
species according to allometric scaling factors that can be expressed
as a nonlinear function of body weight. These scaling factors can be
used to estimate bioaccumulation and to improve interspecies
extrapolations (Newman, 1995; Kenaga, 1973; U.S. EPA 1992c, 1995d).
Although allometric relationships are commonly used for human health
risk assessments, they have not been applied as extensively to
ecological effects (Suter, 1993a). For chemical stressors, allometric
relationships can enable an assessor to estimate toxic effects to
species not commonly tested, such as native mammals. It is important
that the assessor consider the taxonomic relationship between the known
species and the one of interest. The closer they are related, the more
likely the toxic response will be similar. Allometric approaches should
not be applied to species that differ greatly in uptake, metabolism, or
depuration of a chemical.
4.3.1.3.3.2. Process-Based Approaches
Process models for extrapolation are representations or
abstractions of a system or process (Starfield and Bleloch, 1991) that
incorporate causal relationships and provide a predictive capability
that does not depend on the availability of existing stressor-response
information as empirical models do (Wiegert and Bartell, 1994). Process
models enable assessors to translate data on individual effects (e.g.,
mortality, growth, and reproduction) to potential alterations in
specific populations, communities, or ecosystems. Such models can be
used to evaluate risk hypotheses about the duration and severity of a
stressor on an assessment endpoint that cannot be tested readily in the
laboratory.
There are two major types of models: Single-species population
models and multispecies community and ecosystem models. Population
models describe the dynamics of a finite group of individuals through
time and have been used extensively in ecology and fisheries management
and to assess the impacts of power plants and toxicants on specific
fish populations (Barnthouse et al., 1987, 1990). They can help answer
questions about short- or long-term changes of population size and
structure and can help estimate the probability that a population will
decline below or grow above a specified abundance (Ginzburg et al.,
1982; Ferson et al., 1989). The latter application may be useful when
assessing the effects of biological stressors such as introduced or
pest species. Barnthouse et al. (1986) and Wiegert and Bartell (1994)
present excellent reviews of population models. Emlen (1989) has
reviewed population models that can be used for terrestrial risk
assessment.
Proper use of population models requires a thorough understanding
of the natural history of the species under consideration, as well as
knowledge of how the stressor influences its biology. Model input can
include somatic growth rates, physiological rates, fecundity, survival
rates of various classes within the population, and how these change
when the population is exposed to the stressor and other environmental
factors. In addition, the effects of population density on these
parameters are important (Hassell, 1986) and should be considered in
the uncertainty analysis.
Community and ecosystem models (e.g., Bartell et al., 1992; O'Neill
et al., 1982) are particularly useful when the assessment endpoint
involves structural (e.g., community composition) or functional (e.g.,
primary production) elements. They can also be useful when secondary
effects are of concern. Changes in various community or ecosystem
components such as populations, functional types, feeding guilds, or
environmental processes can be estimated. By incorporating submodels
describing the dynamics of individual system components, these models
permit evaluation of risk to multiple assessment endpoints within the
context of the ecosystem.
Risk assessors should determine the appropriate degree of
aggregation in population or multispecies model parameters based both
on the input data available and on the desired output of the model
(also see text note 4-5). For example, if a decision is required about
a particular species, a model that lumps species into trophic levels or
feeding guilds will not be very useful. Assumptions concerning
aggregation in model parameters should be included in the uncertainty
discussion.
4.3.2. Stressor-Response Profile
The final product of ecological response analysis is a summary
profile of what has been learned. This may be a written document or a
module of a larger process model. In any case, the objective is to
ensure that the information needed for risk characterization has been
collected and evaluated. A useful approach in preparing the stressor-
response profile is to imagine that it will be used by someone else to
perform the risk characterization. Profile compilation also provides an
opportunity to verify that the assessment endpoints and measures of
effect identified in the conceptual model were evaluated.
Risk assessors should address several questions in the stressor-
response profile (text note 4-22). Affected ecological entities may
include single species, populations, general trophic levels,
communities, ecosystems, or landscapes. The nature of the effect(s)
should be germane to the assessment endpoint(s). Thus if a single
species is affected, the effects should represent parameters
appropriate for that level of organization. Examples include effects on
mortality, growth, and reproduction. Short- and long-term effects
should be reported as appropriate. At the community level, effects may
be summarized in terms of structure or function depending on the
assessment endpoint. At the landscape level, there may be a suite of
assessment endpoints, and each should be addressed separately.
Examples of different approaches for displaying the intensity of
effects were provided in section 4.3.1.1. Other information such as the
spatial area or time to recovery may also be appropriate. Causal
analyses are important, especially for assessments that include field
observational data.
Ideally, the stressor-response profile should express effects in
terms of the assessment endpoint, but this is not always possible.
Where it is necessary to use qualitative extrapolations between
assessment endpoints and measures of effect, the stressor-response
profile may contain information only on measures of effect. Under these
circumstances, risk will be estimated using the measures of effects,
and extrapolation to the
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assessment endpoints will occur during risk characterization.
Risk assessors need to clearly describe any uncertainties
associated with the ecological response analysis. If it was necessary
to extrapolate from measures of effect to the assessment endpoint, both
the extrapolation and its basis should be described. Similarly, if a
benchmark or similar reference dose or concentration was calculated,
the extrapolations and uncertainties associated with its development
need to be discussed. For additional information on establishing
reference concentrations, see Nabholz (1991), Urban and Cook (1986),
Stephan et al. (1985), Van Leeuwen et al. (1992), Wagner and Lokke
(1991), and Okkerman et al. (1993). Finally, the assessor should
clearly describe major assumptions and default values used in the
models.
At the end of the analysis phase, the stressor-response and
exposure profiles are used to estimate risks. These profiles provide
the opportunity to review what has been learned and to summarize this
information in the most useful format for risk characterization.
Whatever form the profiles take, they ensure that the necessary
information is available for risk characterization.
5. Risk Characterization
Risk characterization (figure 5-1) is the final phase of ecological
risk assessment and is the culmination of the planning, problem
formulation, and analysis of predicted or observed adverse ecological
effects related to the assessment endpoints. Completing risk
characterization allows risk assessors to clarify the relationships
between stressors, effects, and ecological entities and to reach
conclusions regarding the occurrence of exposure and the adversity of
existing or anticipated effects. Here, risk assessors first use the
results of the analysis phase to develop an estimate of the risk posed
to the ecological entities included in the assessment endpoints
identified in problem formulation (section 5.1). After estimating the
risk, the assessor describes the risk estimate in the context of the
significance of any adverse effects and lines of evidence supporting
their likelihood (section 5.2). Finally, the assessor identifies and
summarizes the uncertainties, assumptions, and qualifiers in the risk
assessment and reports the conclusions to risk managers (section 5.3).
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Conclusions presented in the risk characterization should provide
clear information to risk managers in order to be useful for
environmental decision making (NRC, 1994; see section 6). If the risks
are not sufficiently defined to support a management decision, risk
managers may elect to proceed with another iteration of one or more
phases of the risk assessment process. Reevaluating the conceptual
model (and associated risk hypotheses) or conducting additional studies
may improve the risk estimate. Alternatively, a monitoring program may
help managers evaluate the consequences of a risk management decision.
5.1. Risk Estimation
Risk estimation is the process of integrating exposure and effects
data and evaluating any associated uncertainties. The process uses
exposure and stressor-response profiles developed according to the
analysis plan (section 3.5). Risk estimates can be developed using one
or more of the following techniques: (1) Field observational studies,
(2) categorical rankings, (3) comparisons of single-point exposure and
effects estimates, (4) comparisons incorporating the entire stressor-
response relationship, (5) incorporation of variability in exposure
and/or effects estimates, and (6) process models that rely partially or
entirely on theoretical approximations of exposure and effects. These
techniques are described in the following sections.
5.1.1. Results of Field Observational Studies
Field observational studies (surveys) can serve as risk estimation
techniques because they provide empirical evidence linking exposure to
effects. Field surveys measure biological changes in natural settings
through collection of exposure and effects data for ecological entities
identified in problem formulation.
A major advantage of field surveys is that they can be used to
evaluate multiple stressors and complex ecosystem relationships that
cannot be replicated in the laboratory. Field surveys are designed to
delineate both exposures and effects (including secondary effects)
found in natural systems, whereas estimates generated from laboratory
studies generally delineate either exposures or effects under
controlled or prescribed conditions (see text note 5-1).
While field studies may best represent reality, as with other kinds
of studies they can be limited by (1) a lack of replication, (2) bias
in obtaining representative samples, or (3) failure to measure critical
components of the system or random variations. Further, a lack of
observed effects in a field survey may occur because the measurements
lack the sensitivity to detect ecological effects. See section 4.1.1
for additional discussion of the strengths and limitations of different
types of data.
Several assumptions or qualifications need to be clearly
articulated when describing the results of field surveys. A primary
qualification is whether a causal relationship between stressors and
effects (section 4.3.1.2) is supported. Unless causal relationships are
carefully examined, conclusions about effects that are observed may be
inaccurate because the effects are caused by factors unrelated to the
stressor(s) of concern. In addition, field surveys taken at one point
in time are usually not predictive; they describe effects associated
only with exposure scenarios associated with past and existing
conditions.
5.1.2. Categories and Rankings
In some cases, professional judgment or other qualitative
evaluation techniques may be used to rank risks using categories, such
as low, medium, and high, or yes and no. This approach is most
frequently used when exposure and effects data are limited or are not
easily expressed in quantitative terms. The U.S. Forest Service risk
assessment of pest introduction from importation of logs from Chile
used qualitative categories owing to limitations in both the exposure
and effects data for the introduced species of concern as well as the
resources available for the assessment (see text note 5-2).
Ranking techniques can be used to translate qualitative judgment
into a mathematical comparison. These methods are frequently used in
comparative risk exercises. For example, Harris et al. (1994) evaluated
risk reduction opportunities in Green Bay (Lake Michigan), Wisconsin,
employing an expert panel to compare the relative risk of several
stressors against their potential effects. Mathematical analysis based
on fuzzy set theory was used to rank the risk from each stressor from a
number of perspectives, including degree of immediate risk, duration of
impacts, and prevention and remediation management. The results served
to rank potential environmental risks from stressors based on best
professional judgment.
5.1.3. Single-Point Exposure and Effects Comparisons
When sufficient data are available to quantify exposure and effects
estimates, the simplest approach for comparing the estimates is a ratio
(figure 5-2a). Typically, the ratio (or quotient) is expressed as an
exposure concentration divided by an effects concentration. Quotients
are commonly used for chemical stressors, where reference or benchmark
toxicity values are widely available (see text note 5-3).
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The principal advantages of the quotient method are that it is
simple and quick to use and risk assessors and managers are familiar
with its application. It provides an efficient, inexpensive means of
identifying high-or low-risk situations that can allow risk management
decisions to be made without the need for further information.
Quotients have also been used to integrate the risks of multiple
chemical stressors: quotients for the individual constituents in a
mixture are generated by dividing each exposure level by a
corresponding toxicity endpoint (e.g., LC50,
EC50, NOAEL). Although the toxicity of a chemical mixture
may be greater than or less than predicted from the toxicities of
individual constituents of the mixture, a quotient addition approach
assumes that toxicities are additive or approximately additive. This
assumption may be most applicable when the modes of action of chemicals
in a mixture are similar, but there is evidence that even with
chemicals having dissimilar modes of action, additive or near-additive
interactions are common (Konemann, 1981; Broderius, 1991; Broderius et
al., 1995; Hermens et al., 1984a, b; McCarty and Mackay, 1993; Sawyer
and Safe, 1985). However, caution should be used when assuming that
chemicals in a mixture act independently of one another, since many of
the supporting studies were conducted with aquatic organisms, and so
may not be relevant for other endpoints, exposure scenarios, or
species. When the modes of action for constituent chemicals are
unknown, the assumptions and rationale concerning chemical interactions
should be clearly stated.
A number of limitations restrict application of the quotient method
(see Smith and Cairns, 1993; Suter, 1993a). While a quotient can be
useful in answering whether risks are high or low, it may not be
helpful to a risk manager who needs to make a decision requiring an
incremental quantification of risks. For example, it is seldom useful
to say that a risk mitigation approach will reduce a quotient value
from 25 to 12, since this reduction cannot by itself be clearly
interpreted in terms of effects on an assessment endpoint.
Other limitations of quotients may be caused by deficiencies in the
problem formulation and analysis phases. For example, an
LC50 derived from a 96-hour laboratory test using constant
exposure levels may not be appropriate for an assessment of effects on
reproduction resulting from short-term, pulsed exposures.
In addition, the quotient method may not be the most appropriate
method for predicting secondary effects (although such effects may be
inferred). Interactions and effects beyond what are predicted from the
simple quotient may be critical to characterizing the full extent of
impacts from exposure to the stressors (e.g., bioaccumulation,
eutrophication, loss of prey species, opportunities for invasive
species).
Finally, in most cases, the quotient method does not explicitly
consider uncertainty (e.g., extrapolation from tested species to the
species or community of concern). Some uncertainties, however, can be
incorporated into single-point estimates to provide a statement of
likelihood that the effects point estimate exceeds the exposure point
estimate (figures 5-2b and 5-3). If exposure variability is quantified,
then the point estimate of effects can be compared with a cumulative
exposure distribution as described in text note 5-4. Further discussion
of comparisons between point estimates of effects and distributions of
exposure may be found in Suter et al., 1983.
In view of the advantages and limitations of the quotient method,
it is important for risk assessors to consider the points listed below
when evaluating quotient method estimates.
How does the effect concentration relate to the assessment
endpoint?
What extrapolations are involved?
How does the point estimate of exposure relate to
potential spatial and temporal variability in exposure?
Are data sufficient to provide confidence intervals on the
endpoints?
5.1.4. Comparisons Incorporating the Entire Stressor-Response
Relationship
If a curve relating the stressor level to the magnitude of response
is available, then risk estimation can examine risks associated with
many different levels of exposure (figure 5-4). These estimates are
particularly useful when the risk assessment outcome is not based on
exceedance of a predetermined decision rule, such as a toxicity
benchmark level.
There are advantages and limitations to comparing a stressor-
response curve with an exposure distribution. The slope of the effects
curve shows the magnitude of change in effects associated with
incremental changes in exposure, and the capability to predict changes
in the magnitude and likelihood of effects for different exposure
scenarios can be used to compare different risk management options.
Also, uncertainty can be incorporated by calculating uncertainty bounds
on the stressor-response or exposure estimates. Comparing exposure and
stressor-response curves provides a predictive ability lacking in the
quotient method. Like the quotient method, however, limitations from
the problem formulation and analysis phases may limit the utility of
the results. These limitations may include not fully considering
secondary effects, assuming the exposure pattern used to derive the
stressor-response curve is comparable to the environmental exposure
pattern, and failure to consider uncertainties, such as extrapolations
from tested species to the species or community of concern.
5.1.5. Comparisons Incorporating Variability in Exposure and/or Effects
If the exposure or stressor-response profiles describe the
variability in exposure or effects, then many different risk estimates
can be calculated. Variability in exposure can be used to estimate
risks to moderately or highly exposed members of a population being
investigated, while variability in effects can be used to estimate
risks to average or sensitive population members. A major advantage of
this approach is its ability to predict changes in the magnitude and
likelihood of effects for different exposure scenarios and thus provide
a means for comparing different risk management options. As noted
above, comparing distributions also allows one to identify and quantify
risks to different segments of the population. Limitations include the
increased data requirements compared with previously described
techniques and the implicit assumption that the full range of
variability in the exposure and effects data is adequately represented.
As with the quotient method, secondary effects are not readily
evaluated with this technique. Thus, it is desirable to corroborate
risks estimated by distributional comparisons with field studies or
other lines of evidence. Text note 5-5 and figure 5-5 illustrate the
use of cumulative exposure and effects distributions for estimating
risk.
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5.1.6. Application of Process Models
Process models are mathematical expressions that represent our
understanding of the mechanistic operation of a system under
evaluation. They can be useful tools in both analysis (see section
4.1.2) and risk characterization. For illustrative purposes, it is
useful to distinguish between analysis process models, which focus
individually on either exposure or effects evaluations, and risk
estimation process models, which integrate exposure and effects
information (see text note 5-6). The assessment of risks associated
with long-term changes in hydrologic conditions in bottomland forest
wetlands in Louisiana using the FORFLO model (Appendix D) linked the
attributes and placement of levees and corresponding water level
measurements (exposure) with changes in forest community structure and
wildlife habitat suitability (effects).
A major advantage of using process models for risk estimation is
the ability to consider ``what if'' scenarios and to forecast beyond
the limits of observed data that constrain techniques based solely on
empirical data. The process model can also consider secondary effects,
unlike other risk estimation techniques such as the quotient method or
comparisons of exposure and effect distributions. In addition, some
process models can forecast the combined effects of multiple stressors,
such as the effects of multiple chemicals on fish population
sustainability (Barnthouse et al., 1990).
Process model outputs may be point estimates, distributions, or
correlations; in all cases, risk assessors should interpret them with
care. They may imply a higher level of certainty than is appropriate
and are all too often viewed without sufficient attention to underlying
assumptions. The lack of knowledge on basic life histories for many
species and incomplete knowledge on the structure and function of a
particular ecosystem is often lost in the model output. Since process
models are only as good as the assumptions on which they are based,
they should be treated as hypothetical representations of reality until
appropriately tested with empirical data. Comparing model results to
field data provides a check on whether our understanding of the system
was correct (Johnson, 1995), particularly with respect to the risk
hypotheses presented in problem formulation.
5.2. Risk Description
Following preparation of the risk estimate, risk assessors need to
interpret and discuss the available information about risks to the
assessment endpoints. Risk description includes an evaluation of the
lines of evidence supporting or refuting the risk estimate(s) and an
interpretation of the significance of the adverse effects on the
assessment endpoints. During the analysis phase, the risk assessor may
have established the relationship between the assessment endpoints and
measures of effect and associated lines of evidence in quantifiable,
easily described terms (section 4.3.1.3). If not, the risk assessor can
relate the available lines of evidence to the assessment endpoints
using qualitative links. Regardless of the risk estimation technique,
the technical narrative supporting the risk estimate is as important as
the risk estimate itself.
5.2.1. Lines of Evidence
The development of lines of evidence provides both a process and a
framework for reaching a conclusion regarding confidence in the risk
estimate. It is not the kind of proof demanded by experimentalists
(Fox, 1991), nor is it a rigorous examination of weights of evidence.
(Note that the term ``weight of evidence'' is sometimes used in legal
discussions or in other documents, e.g., Urban and Cook, 1986; Menzie
et al., 1996.) The phrase lines of evidence is used to de-emphasize the
balancing of opposing factors based on assignment of quantitative
values to reach a conclusion about a ``weight'' in favor of a more
inclusive approach, which evaluates all available information, even
evidence that may be qualitative in nature. It is important that risk
assessors provide a thorough representation of all lines of evidence
developed in the risk assessment rather than simply reduce their
interpretation and description of the ecological effects that may
result from exposure to stressors to a system of numeric calculations
and results.
Confidence in the conclusions of a risk assessment may be increased
by using several lines of evidence to interpret and compare risk
estimates. These lines of evidence may be derived from different
sources or by different techniques relevant to adverse effects on the
assessment endpoints, such as quotient estimates, modeling results, or
field observational studies.
There are three principal categories of factors for risk assessors
to consider when evaluating lines of evidence: (1) Adequacy and quality
of data, (2) degree and type of uncertainty associated with the
evidence, and (3) relationship of the evidence to the risk assessment
questions (see also sections 3 and 4).
Data quality directly influences how confident risk assessors can
be in the results of a study and conclusions they may draw from it.
Specific concerns to consider for individual lines of evidence include
whether the experimental design was appropriate for the questions posed
in a particular study and whether data quality objectives were clear
and adhered to. An evaluation of the scientific understanding of
natural variability in the attributes of the ecological entities under
consideration is important in determining whether there were sufficient
data to satisfy the analyses chosen and to determine if the analyses
were sufficiently sensitive and robust to identify stressor-caused
perturbations.
Directly related to data quality issues is the evaluation of the
relative uncertainties of each line of evidence. One major source of
uncertainty comes from extrapolations. The greater the number of
extrapolations, the more uncertainty introduced into a study. For
example, were extrapolations used to infer effects in one species from
another, or from one temporal or spatial scale to another? Were
conclusions drawn from extrapolations from laboratory to field effects,
or were field effects inferred from limited information, such as
chemical structure-activity relationships? Were no-effect or low-effect
levels used to address likelihood of effects? Risk assessors should
consider these and any other sources of uncertainty when evaluating the
relative importance of particular lines of evidence.
Finally, how directly lines of evidence relate to the questions
asked in the risk assessment may determine their relative importance in
terms of the ecological entity and the attributes of the assessment
endpoint. Lines of evidence directly related to the risk hypotheses,
and those that establish a cause-and-effect relationship based on a
definitive mechanism rather than associations alone, are likely to be
of greatest importance.
The evaluation process, however, involves more than just listing
the evidence that supports or refutes the risk estimate. The risk
assessor should carefully examine each factor and evaluate its
contribution in the context of the risk assessment. The importance of
lines of evidence is that each and every factor is described and
interpreted. Data or study results are often not reported or carried
forward in the risk assessment because they are of insufficient
quality. If such data or results are eliminated from the evaluation
process, however, valuable information may be lost with respect to
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needed improvements in methodologies or recommendations for further
studies.
As a case in point, consider the two lines of evidence described
for the carbofuran example (see text notes 5-1 and 5-3), field studies
and quotients. Both approaches are relevant to the assessment endpoint
(survival of birds that forage in agricultural areas where carbofuran
is applied), and both are relevant to the exposure scenarios described
in the conceptual model (see figure D-1). The quotients, however, are
limited in their ability to express incremental risks (e.g., how much
greater risk is expressed by a quotient of ``2'' versus a quotient of
``4''), while the field studies had some design flaws (see text note 5-
1). Nevertheless, because of the strong evidence of causal
relationships from the field studies and consistency with the
laboratory-derived quotient, confidence in a conclusion of high risk to
the assessment endpoint is supported.
Sometimes lines of evidence do not point toward the same
conclusion. It is important to investigate possible reasons for any
disagreement rather than ignore inconvenient evidence. A starting point
is to distinguish between true inconsistencies and those related to
differences in statistical powers of detection. For example, a model
may predict adverse effects that were not observed in a field survey.
The risk assessor should ask whether the experimental design of the
field study had sufficient power to detect the predicted difference or
whether the endpoints measured were comparable with those used in the
model. Conversely, the model may have been unrealistic in its
predictions. While iteration of the risk assessment process and
collection of additional data may help resolve uncertainties, this
option is not always available.
Lines of evidence that are to be evaluated during risk
characterization should be defined early in the risk assessment (during
problem formulation) through the development of the conceptual model
and selection of assessment endpoints. Further, the analysis plan
should incorporate measures that will contribute to the interpretation
of the lines of evidence, including methods of reviewing, analyzing,
and summarizing the uncertainty in the risk assessment.
Also, risk assessments often rely solely on laboratory or in situ
bioassays to assess adverse effects that may occur as a result of
exposure to stressors. Although they may not be manifested in the
field, ecological effects demonstrated in the laboratory should not be
discounted as a line of evidence.
5.2.2. Determining Ecological Adversity
At this point in risk characterization, the changes expected in the
assessment endpoints have been estimated and the supporting lines of
evidence evaluated. The next step is to interpret whether these changes
are considered adverse. Adverse ecological effects, in this context,
represent changes that are undesirable because they alter valued
structural or functional attributes of the ecological entities under
consideration. The risk assessor evaluates the degree of adversity,
which is often a difficult task and is frequently based on the risk
assessor's professional judgment.
When the results of the risk assessment are discussed with the risk
manager (section 6), other factors, such as the economic, legal, or
social consequences of ecological damage, should be considered. The
risk manager will use all of this information to determine whether a
particular adverse effect is acceptable and may also find it useful
when communicating the risk to interested parties.
The following are criteria for evaluating adverse changes in
assessment endpoints:
Nature of effects and intensity of effects
Spatial and temporal scale
Potential for recovery.
The extent to which the criteria are evaluated depends on the scope
and complexity of the risk assessment. Understanding the underlying
assumptions and science policy judgments, however, is important even in
simple cases. For example, when exceedance of a previously established
decision rule, such as a benchmark stressor level, is used as evidence
of adversity (e.g., see Urban and Cook, 1986, or Nabholz, 1991), the
reasons why this is considered adverse should be clearly understood. In
addition, any evaluation of adversity should examine all relevant
criteria, since none are considered singularly determinative.
To distinguish adverse ecological changes from those within the
normal pattern of ecosystem variability or those resulting in little or
no significant alteration of biota, it is important to consider the
nature and intensity of effects. For example, for an assessment
endpoint involving survival, growth, and reproduction of a species, do
predicted effects involve survival and reproduction or only growth? If
survival of offspring will be affected, by what percentage will it
diminish?
It is important for risk assessors to consider both the ecological
and statistical contexts of an effect when evaluating intensity. For
example, a statistically significant 1% decrease in fish growth (see
text note 5-7) may not be relevant to an assessment endpoint of fish
population viability, and a 10% decline in reproduction may be worse
for a population of slowly reproducing trees than for rapidly
reproducing planktonic algae.
Natural ecosystem variation can make it very difficult to observe
(detect) stressor-related perturbations. For example, natural
fluctuations in marine fish populations are often large, with intra-
and interannual variability in population levels covering several
orders of magnitude. Furthermore, cyclic events of various periods
(e.g., bird migration, tides) are very important in natural systems and
may mask or delay stressor-related effects. Predicting the effects of
anthropogenic stressors against this background of variation can be
very difficult. Thus, a lack of statistically significant effects in a
field study does not automatically mean that adverse ecological effects
are absent. Rather, risk assessors should then consider other lines of
evidence in reaching their conclusions.
It is also important to consider the location of the effect within
the biological hierarchy and the mechanisms that may result in
ecological changes. The risk assessor may rely on mechanistic
explanations to describe complex ecological interactions and the
resulting effects that otherwise may be masked by variability in the
ecological components.
The boundaries (global, landscape, ecosystem, organism) of the risk
assessment are initially identified in the analysis plan prepared
during problem formulation. These spatial and temporal scales are
further defined in the analysis phase, where specific exposure and
effects scenarios are evaluated. The spatial dimension encompasses both
the extent and pattern of effect as well as the context of the effect
within the landscape. Factors to consider include the absolute area
affected, the extent of critical habitats affected compared with a
larger area of interest, and the role or use of the affected area
within the landscape.
Adverse effects to assessment endpoints vary with the absolute area
of the effect. A larger affected area may be (1) subject to a greater
number of other stressors, increasing the complications from stressor
interactions, (2) more likely to contain sensitive species or habitats,
or (3) more susceptible to landscape-level changes because many
ecosystems may be altered by the stressors.
Nevertheless, a smaller area of effect is not always associated
with lower risk.
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The function of an area within the landscape may be more important than
the absolute area. Destruction of small but unique areas, such as
critical wetlands, may have important effects on local and regional
wildlife populations. Also, in river systems, both riffle and pool
areas provide important microhabitats that maintain the structure and
function of the total river ecosystem. Stressors acting on these
microhabitats may result in adverse effects to the entire system.
Spatial factors are important for many species because of the
linkages between ecological landscapes and population dynamics.
Linkages between landscapes can provide refuge for affected
populations, and organisms may require corridors between habitat
patches for successful migration.
The temporal scale for ecosystems can vary from seconds
(photosynthesis, prokaryotic reproduction) to centuries (global climate
change). Changes within a forest ecosystem can occur gradually over
decades or centuries and may be affected by slowly changing external
factors such as climate. When interpreting adversity, risk assessors
should recognize that the time scale of stressor-induced changes
operates within the context of multiple natural time scales. In
addition, temporal responses for ecosystems may involve intrinsic time
lags, so responses to a stressor may be delayed. Thus, it is important
to distinguish a stressor's long-term impacts from its immediately
visible effects. For example, visible changes resulting from
eutrophication of aquatic systems (turbidity, excessive macrophyte
growth, population decline) may not become evident for many years after
initial increases in nutrient levels.
Considering the temporal scale of adverse effects leads logically
to a consideration of recovery. Recovery is the rate and extent of
return of a population or community to some aspect of its condition
prior to a stressor's introduction. (While this discussion deals with
recovery as a result of natural processes, risk mitigation options may
include restoration activities to facilitate or speed up the recovery
process.) Because ecosystems are dynamic and, even under natural
conditions, constantly changing in response to changes in the physical
environment (e.g., weather, natural disturbances) or other factors, it
is unrealistic to expect that a system will remain static at some level
or return to exactly the same state that it was before it was disturbed
(Landis et al., 1993). Thus, the attributes of a ``recovered'' system
should be carefully defined. Examples might include productivity
declines in a eutrophic system, reestablishment of a species at a
particular density, species recolonization of a damaged habitat, or the
restoration of health of diseased organisms. The Agency considered the
recovery rate of biological communities in streams and rivers from
disturbances in setting exceedance frequencies for chemical stressors
in waste effluents (U.S. EPA, 1991).
Recovery can be evaluated in spite of the difficulty in predicting
events in ecological systems (e.g., Niemi et al., 1990). For example,
it is possible to distinguish changes that are usually reversible
(e.g., stream recovery from sewage effluent discharge), frequently
irreversible (e.g., establishment of introduced species), and always
irreversible (e.g., extinction). Risk assessors should consider the
potential irreversibility of significant structural or functional
changes in ecosystems or ecosystem components when evaluating
adversity. Physical alterations such as deforestation in the coastal
hills of Venezuela in recent history and in Britain during the
Neolithic period, for example, changed soil structure and seed sources
such that forests cannot easily grow again (Fisher and Woodmansee,
1994).
The relative rate of recovery can also be estimated. For instance,
fish populations in a stream are likely to recover much faster from
exposure to a degradable chemical than from habitat alterations
resulting from stream channelization. Risk assessors can use knowledge
of factors, such as the temporal scales of organisms' life histories,
the availability of adequate stock for recruitment, and the
interspecific and trophic dynamics of the populations, in evaluating
the relative rates of recovery. A fisheries stock or forest might
recover in decades, a benthic invertebrate community in years, and a
planktonic community in weeks to months.
Risk assessors should note natural disturbance patterns when
evaluating the likelihood of recovery from anthropogenic stressors.
Alternatively, if an ecosystem has become adapted to a disturbance
pattern, it may be affected when the disturbance is removed (e.g.,
fire-maintained grasslands). The lack of natural analogs makes it
difficult to predict recovery from uniquely anthropogenic stressors
(e.g., synthetic chemicals).
Appendix E illustrates how the criteria for ecological adversity
(nature and intensity of effects, spatial and temporal scales, and
recovery) might be used in evaluating two cleanup options for a marine
oil spill. This example also shows that recovery of a system depends
not only on how quickly a stressor is removed, but also on how the
cleanup efforts themselves affect the recovery.
5.3. Reporting Risks
When risk characterization is complete, risk assessors should be
able to estimate ecological risks, indicate the overall degree of
confidence in the risk estimates, cite lines of evidence supporting the
risk estimates, and interpret the adversity of ecological effects.
Usually this information is included in a risk assessment report
(sometimes referred to as a risk characterization report because of the
integrative nature of risk characterization). While the breadth of
ecological risk assessment precludes providing a detailed outline of
reporting elements, the risk assessor should consider the elements
listed in text note 5-8 when preparing a risk assessment report.
Like the risk assessment itself, a risk assessment report may be
brief or extensive, depending on the nature of and the resources
available for the assessment. While it is important to address the
elements described in text note 5-8, risk assessors should judge the
level of detail required. The report need not be overly complex or
lengthy; it is most important that the information required to support
a risk management decision be presented clearly and concisely.
To facilitate mutual understanding, it is critical that the risk
assessment results are properly presented. Agency policy requires that
risk characterizations be prepared ``in a manner that is clear,
transparent, reasonable, and consistent with other risk
characterizations of similar scope prepared across programs in the
Agency'' (U.S. EPA, 1995b). Ways to achieve such characteristics are
described in text note 5-9.
After the risk assessment report is prepared, the results are
discussed with risk managers. Section 6 provides information on
communication between risk assessors and risk managers, describes the
use of the risk assessment in a risk management context, and briefly
discusses communication of risk assessment results from risk managers
to interested parties and the general public.
6. Relating Ecological Information to Risk Management Decisions
After characterizing risks and preparing a risk assessment report
(section 5), risk assessors discuss the results with risk managers
(figure 5-1).
[[Page 26892]]
Risk managers use risk assessment results, along with other factors
(e.g., economic or legal concerns), in making risk management decisions
and as a basis for communicating risks to interested parties and the
general public.
Mutual understanding between risk assessors and risk managers
regarding risk assessment results can be facilitated if the questions
listed in text note 6-1 are addressed. Risk managers need to know the
major risks to assessment endpoints and have an idea of whether the
conclusions are supported by a large body of data or if there are
significant data gaps. Insufficient resources, lack of consensus, or
other factors may preclude preparation of a detailed and well-
documented risk characterization. If this is the case, the risk
assessor should clearly articulate any issues, obstacles, and
correctable deficiencies for the risk manager's consideration.
In making decisions regarding ecological risks, risk managers
consider other information, such as social, economic, political, or
legal issues in combination with risk assessment results. For example,
the risk assessment results may be used as part of an ecological cost-
benefit analysis, which may require translating resources (identified
through the assessment endpoints) into monetary values. Traditional
economic considerations may only partially address changes in
ecological resources that are not considered commodities,
intergenerational resource values, or issues of long-term or
irreversible effects (U.S. EPA, 1995a; Costanza et al., 1997); however,
they may provide a means of comparing the results of the risk
assessment in commensurate units such as costs. Risk managers may also
consider alternative strategies for reducing risks, such as risk
mitigation options or substitutions based on relative risk comparisons.
For example, risk mitigation techniques, such as buffer strips or lower
field application rates, can be used to reduce the exposure (and risk)
of a pesticide. Further, by comparing the risk of a new pesticide to
other pesticides currently in use during the registration process,
lower overall risk may result. Finally, risk managers consider and
incorporate public opinion and political demands into their decisions.
Collectively, these other factors may render very high risks acceptable
or very low risks unacceptable.
Risk characterization provides the basis for communicating
ecological risks to interested parties and the general public. This
task is usually the responsibility of risk managers, but it may be
shared with risk assessors. Although the final risk assessment document
(including its risk characterization sections) can be made available to
the public, the risk communication process is best served by tailoring
information to a particular audience. Irrespective of the specific
format, it is important to clearly describe the ecological resources at
risk, their value, and the monetary and other costs of protecting (and
failing to protect) the resources (U.S. EPA, 1995a).
Managers should clearly describe the sources and causes of risks
and the potential adversity of the risks (e.g., nature and intensity,
spatial and temporal scale, and recovery potential). The degree of
confidence in the risk assessment, the rationale for the risk
management decision, and the options for reducing risk are also
important (U.S. EPA, 1995a). Other risk communication considerations
are provided in text note 6-2.
Along with discussions of risk and communications with the public,
it is important for risk managers to consider whether additional
follow-on activities are required. Depending on the importance of the
assessment, confidence in its results, and available resources, it may
be advisable to conduct another iteration of the risk assessment
(starting with problem formulation or analysis) in order to support a
final management decision. Another option is to proceed with the
decision, implement the selected management alternative, and develop a
monitoring plan to evaluate the results (see section 1). If the
decision is to mitigate risks through exposure reduction, for example,
monitoring could help determine whether the desired reduction in
exposure (and effects) is achieved.
7. Text Notes
Text Note 1-1. Related Terminology
The following terms overlap to varying degrees with the concept of
ecological risk assessment used in these Guidelines (see Appendix B for
definitions):
Hazard assessment
Comparative risk assessment
Cumulative ecological risk assessment
Environmental impact statement
Text Note 1-2. Flexibility of the Framework Diagram
The framework process (figure 1-1) is a general representation of a
complex and varied group of assessments. This diagram represents a
flexible process, as illustrated by the examples below.
In problem formulation, an assessment may begin with a
consideration of endpoints, stressors, or ecological effects. Problem
formulation is generally interactive and iterative, not linear.
In the analysis phase, characterization of exposure and
effects frequently become intertwined, as when an initial exposure
leads to a cascade of additional exposures and secondary effects. The
analysis phase should foster an understanding of these complex
relationships.
Analysis and risk characterization are shown as separate
phases. However, some models may combine the analysis of exposure and
effects data with the integration of these data that occurs in risk
characterization.
Text Note 2-1. Who Are Risk Managers?
Risk managers are individuals and organizations who have the
responsibility, or have the authority to take action or require action,
to mitigate an identified risk. The expression ``risk manager'' is
often used to represent a decision maker in agencies such as EPA or
State environmental offices who has legal authority to protect or
manage a resource. However, risk managers may include a diverse group
of interested parties who also have the ability to take action to
reduce or mitigate risk. In situations where a complex of ecosystem
values (e.g., watershed resources) is at risk from multiple stressors,
and management will be implemented through community action, these
groups may function as risk management teams. Risk management teams may
include decision officials in Federal, State, local, and tribal
governments; commercial, industrial, and private organizations; leaders
of constituency groups; and other sectors of the public such as
property owners. For additional insights on risk management and manager
roles, see text notes 2-3 and 2-4.
Text Note 2-2. Who Are Risk Assessors?
Risk assessors are a diverse group of professionals who bring a
needed expertise to a risk assessment team. When a specific risk
assessment process is well defined through regulations and guidance,
one trained individual may be able to complete a risk assessment given
sufficient information (e.g., premanufacture notice of a chemical).
However, for complex risk assessments, one individual can rarely
provide the necessary breadth of expertise. Every risk assessment team
should include at least one professional who is knowledgeable and
experienced in using the risk assessment process. Other
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team members bring specific expertise relevant to the locations,
stressors, ecosystems, scientific issues, and other expertise as
needed, depending on the type of assessment.
Text Note 2-3. Who Are Interested Parties?
Interested parties (commonly called ``stakeholders'') may include
Federal, State, tribal, and municipal governments, industrial leaders,
environmental groups, small-business owners, landowners, and other
segments of society concerned about an environmental issue at hand or
attempting to influence risk management decisions. Their involvement,
particularly during management goal development, may be key to
successful implementation of management plans since implementation is
more likely to occur when backed by consensus. Large diverse groups may
require trained facilitators and consensus-building techniques to reach
agreement.
In some cases, interested parties may provide important information
to risk assessors. Local knowledge, particularly in rural communities,
and traditional knowledge of native peoples can provide valuable
insights about ecological characteristics of a place, past conditions,
and current changes. This knowledge should be considered when assessing
available information during problem formulation (see section 3.2).
The context of involvement by interested parties can vary widely
and may or may not be appropriate for a particular risk assessment.
Interested parties may be limited to providing input to goal
development, or they may become risk managers, depending on the degree
to which they can take action to manage risk and the regulatory context
of the decision. When and how interested parties influence risk
assessments and risk management are areas of current discussion (NRC,
1996). See additional information in text note 2-1 and section 2.1.
Text Note 2-4. Questions Addressed by Risk Managers and Risk Assessors
Questions Principally for Risk Managers to Answer
What is the nature of the problem and the best scale for the
assessment?
What are the management goals and decisions needed, and how will
risk assessment help?
What are the ecological values (e.g., entities and ecosystem
characteristics) of concern?
What are the policy considerations (law, corporate stewardship,
societal concerns, environmental justice, intergenerational equity)?
What precedents are set by similar risk assessments and previous
decisions?
What is the context of the assessment (e.g., industrial site,
national park)?
What resources (e.g., personnel, time, money) are available?
What level of uncertainty is acceptable?
Questions Principally for Risk Assessors to Answer
What is the scale of the risk assessment?
What are the critical ecological endpoints and ecosystem and
receptor characteristics?
How likely is recovery, and how long will it take?
What is the nature of the problem: Past, present, future?
What is our state of knowledge of the problem?
What data and data analyses are available and appropriate?
What are the potential constraints (e.g., limits on expertise,
time, availability of methods and data)?
Text Note 2-5. Sustainability as a Management Goal
To sustain is to keep in existence, maintain, or prolong.
Sustainability is used as a management goal in a variety of settings
(see U.S. EPA, 1995a). Sustainability and other concepts such as biotic
or community integrity may be very useful as guiding principles for
management goals. However, in each case these principles should be
explicitly defined and interpreted for a place to support a risk
assessment. To do this, key questions need to be addressed: What does
sustainability or integrity mean for the particular ecosystem? What
must be protected to meet sustainable goals or system integrity? Which
ecological resources and processes are to be sustained and why? How
will we know we have achieved it? Answers to these questions serve to
clarify the goals for a particular ecosystem. Concepts like
sustainability and integrity do not meet the criteria for an assessment
endpoint (see section 3.3.2).
Text Note 2-6. Management Goals for Waquoit Bay
A key challenge for risk assessors when dealing with a general
management goal is interpreting the goal for a risk assessment. This
can be done by generating a set of management objectives that represent
what must be achieved in a particular ecosystem in order for the goal
to be met. An example of this process was developed in the Waquoit Bay
watershed risk assessment (U.S. EPA, 1996b).
Waquoit Bay is a small estuary on Cape Cod showing signs of
degradation, including loss of eelgrass, fish, and shellfish and an
increase in macroalgae mats and fish kills. The management goal for
Waquoit Bay was established through public meetings, preexisting goals
from local organizations, and State and Federal regulations:
Reestablish and maintain water quality and habitat conditions in
Waquoit Bay and associated freshwater rivers and ponds to (1) support
diverse self-sustaining commercial, recreational, and native fish and
shellfish populations and (2) reverse ongoing degradation of ecological
resources in the watershed.
To interpret this goal for the risk assessment, it was converted
into 10 management objectives that defined what must be true in the
watershed for the goal to be achieved and provide the foundation for
management decisions. The management objectives are:
Reduce or eliminate hypoxic or anoxic events.
Prevent toxic levels of contamination in water, sediments,
and biota.
Restore and maintain self-sustaining native fish
populations and their habitat.
Reestablish viable eelgrass beds and associated aquatic
communities in the bay.
Reestablish a self-sustaining scallop population in the
bay that can support a viable sport fishery.
Protect shellfish beds from bacterial contamination that
results in closures.
Reduce or eliminate nuisance macroalgal growth.
Prevent eutrophication of rivers and ponds.
Maintain diversity of native biotic communities.
Maintain diversity of water-dependent wildlife.
From these objectives, eight ecological entities and their
attributes in the bay were selected as assessment endpoints (see
section 3.3.2) to best represent the management goals and objectives,
one of which is areal extent and patch size of eelgrass beds. Eelgrass
was selected because (1) scallops and other benthic organisms and
juvenile finfish depend directly on eelgrass beds for survival, (2)
eelgrass is highly sensitive to excess macroalgal growth, and (3)
abundant eelgrass represents a healthy bay to human users.
[[Page 26894]]
Text Note 2-7. What Is the Difference Between a Management Goal and
Management Decision?
Management goals are desired characteristics of ecological values
that the public wants to protect. Clean water, protection of endangered
species, maintenance of ecological integrity, clear mountain views, and
fishing opportunities are all possible management goals. Management
decisions determine the means to achieve the end goal. For instance, a
goal may be ``fishable, swimmable'' waters. The management options
under consideration to achieve that goal may include increasing
enforcement of point-source discharges, restoring fish habitat,
designing alternative sewage treatment facilities, or implementing all
of the above.
Text Note 2-8. Tiers and Iteration: When Is a Risk Assessment Done?
Risk assessments range from very simple to complex and resource
demanding. How is it possible to decide the level of effort? How many
times should the risk assessor revisit data and assessment issues? When
is the risk assessment done?
Many of these questions can be addressed by designing a set of
tiered assessments. These are preplanned and prescribed sets of risk
assessments of progressive data and resource intensity. The outcome of
a given tier is to either make a management decision, often based on
decision criteria, or continue to the next level of effort. Many risk
assessors and public and private organizations use this approach (e.g.,
see Gaudet, 1994; European Community, 1993; Cowan et al., 1995; Baker
et al., 1994; Urban and Cook, 1986; Lynch et al., 1994).
An iteration is an unprescribed reevaluation of information that
may occur at any time during a risk assessment, including tiered
assessments. It is done in response to an identified need, new
information, or questions raised while conducting an assessment. As
such, iteration is a normal characteristic of risk assessments but is
not a formal planned step. An iteration may include redoing the risk
assessment with new assumptions and new data.
Setting up tiered assessments and decision criteria may reduce the
need for iteration. Up-front planning and careful development of
problem formulation will also reduce the need for revisiting data,
assumptions, and models. However, there are no rules to dictate how
many iterations will be necessary to answer management questions or
ensure scientific validity. A risk assessment can be considered
complete when risk managers have sufficient information and confidence
in the results of the risk assessment to make a decision they can
defend.
Text Note 2-9. Questions To Ask About Scope and Complexity
Is this risk assessment mandated, required by a court decision, or
providing guidance to a community?
Will decisions be based on assessments of a small area evaluated in
depth or a large-scale area in less detail?
What are the spatial and temporal boundaries of the problem?
What information is already available compared to what is needed?
How much time can be taken, and how many resources are available?
What practicalities constrain data collection?
Is a tiered approach an option?
Text Note 3-1. Avoiding Potential Shortcomings Through Problem
Formulation
The importance of problem formulation has been shown repeatedly in
the Agency's analysis of ecological risk assessment case studies and in
interactions with senior EPA managers and regional risk assessors (U.S.
EPA, 1993b, 1994e). Shortcomings consistently identified in the case
studies include (1) absence of clearly defined goals, (2) endpoints
that are ambiguous and difficult to define and measure, and (3) failure
to identify important risks. These and other shortcomings can be
avoided through rigorous development of the products of problem
formulation as described in this section of the Guidelines.
Text Note 3-2. Uncertainty in Problem Formulation
Throughout problem formulation, risk assessors consider what is
known and not known about a problem and its setting. Each product of
problem formulation contains uncertainty. The explicit treatment of
uncertainty during problem formulation is particularly important
because it will have repercussions throughout the remainder of the
assessment. Uncertainty is discussed in section 3.4 (Conceptual
Models).
Text Note 3-3. Initiating a Risk Assessment: What's Different When
Stressors, Effects, or Values Drive the Process?
The reasons for initiating a risk assessment influence when risk
assessors generate products in problem formulation. When the assessment
is initiated because of concerns about stressors, risk assessors use
what is known about the stressor and its source to focus the
assessment. Objectives for the assessment are based on determining how
the stressor is likely to come in contact with and affect possible
receptors. This information forms the basis for developing conceptual
models and selecting assessment endpoints. When an observed effect is
the basis for initiating the assessment, endpoints are normally
established first. Frequently, the affected ecological entities and
their response form the basis for defining assessment endpoints. Goals
for protecting the assessment endpoints are then established, which
support the development of conceptual models. The models aid in the
identification of the most likely stressor(s). Value-initiated risk
assessments are driven by goals for the ecological values of concern.
These values might involve ecological entities such as species,
communities, ecosystems, or places. Based on these goals, assessment
endpoints are selected first to serve as an interpretation of the
goals. Once selected, the endpoints provide the basis for identifying
an array of stressors that may be influencing the assessment endpoints
and describing the diversity of potential effects. This information is
then captured in the conceptual model(s).
Text Note 3-4. Assessing Available Information: Questions to Ask
Concerning Source, Stressor, and Exposure Characteristics, Ecosystem
Characteristics, and Effects (derived in part from Barnthouse and
Brown, 1994)
Source and Stressor Characteristics
What is the source? Is it anthropogenic, natural, point
source, or diffuse nonpoint?
What type of stressor is it: chemical, physical, or
biological?
What is the intensity of the stressor (e.g., the dose or
concentration of a chemical, the magnitude or extent of physical
disruption, the density or population size of a biological stressor)?
What is the mode of action? How does the stressor act on
organisms or ecosystem functions?
Exposure Characteristics
With what frequency does a stressor event occur (e.g., is
it isolated, episodic, or continuous; is it subject to natural daily,
seasonal, or annual periodicity)?
What is its duration? How long does it persist in the
environment (e.g., for chemical, what is its half-life, does it
bioaccumulate; for physical, is habitat alteration sufficient to
prevent recovery; for biological, will it reproduce and proliferate)?
[[Page 26895]]
What is the timing of exposure? When does it occur in
relation to critical organism life cycles or ecosystem events (e.g.,
reproduction, lake overturn)?
What is the spatial scale of exposure? Is the extent or
influence of the stressor local, regional, global, habitat-specific, or
ecosystemwide?
What is the distribution? How does the stressor move
through the environment (e.g., for chemical, fate and transport; for
physical, movement of physical structures; for biological, life-history
dispersal characteristics)?
Ecosystems Potentially at Risk
What are the geographic boundaries? How do they relate to
functional characteristics of the ecosystem?
What are the key abiotic factors influencing the ecosystem
(e.g., climatic factors, geology, hydrology, soil type, water quality)?
Where and how are functional characteristics driving the
ecosystem (e.g., energy source and processing, nutrient cycling)?
What are the structural characteristics of the ecosystem
(e.g., species number and abundance, trophic relationships)?
What habitat types are present?
How do these characteristics influence the susceptibility
(sensitivity and likelihood of exposure) of the ecosystem to the
stressor(s)?
Are there unique features that are particularly valued
(e.g., the last representative of an ecosystem type)?
What is the landscape context within which the ecosystem
occurs?
Ecological Effects
What are the type and extent of available ecological
effects information (e.g., field surveys, laboratory tests, or
structure-activity relationships)?
Given the nature of the stressor (if known), which effects
are expected to be elicited by the stressor?
Under what circumstances will effects occur?
Text Note 3-5. Salmon and Hydropower: Salmon as the Basis for an
Assessment Endpoint
A hydroelectric dam is to be built on a river in the Pacific
Northwest where anadromous fish such as salmon spawn. Assessment
endpoints should be selected to assess potential ecological risk. Of
the anadromous fish, salmon that spawn in the river are an appropriate
choice because they meet the criteria for good assessment endpoints.
Salmon fry and adults are important food sources for a multitude of
aquatic and terrestrial species and are major predators of aquatic
invertebrates (ecological relevance). Salmon are sensitive to changes
in sedimentation and substrate pebble size, require quality cold-water
habitats, and have difficulty climbing fish ladders. Hydroelectric dams
represent significant, and normally fatal, habitat alteration and
physical obstacles to successful salmon breeding and fry survival
(susceptibility). Finally, salmon support a large commercial fishery,
some species are endangered, and they have ceremonial importance and
are key food sources for Native Americans (relevance to management
goals). ``Salmon reproduction and population recruitment'' is a good
assessment endpoint for this risk assessment. In addition, if salmon
populations are protected, other anadromous fish populations are likely
to be protected as well. However, one assessment endpoint can rarely
provide the basis for a risk assessment of complex ecosystems. These
are better represented by a set of assessment endpoints.
Text Note 3-6. Cascading Adverse Effects: Primary (Direct) and
Secondary (Indirect)
The interrelationships among entities and processes in ecosystems
foster a potential for cascading effects: as one population, species,
process, or other entity in the ecosystem is altered, other entities
are affected as well. Primary, or direct, effects occur when a stressor
acts directly on the assessment endpoint and causes an adverse
response. Secondary, or indirect, effects occur when the entity's
response becomes a stressor to another entity. Secondary effects are
often a series of effects among a diversity of organisms and processes
that cascade through the ecosystem. For example, application of an
herbicide on a wet meadow results in direct toxicity to plants. Death
of the wetland plants leads to secondary effects such as loss of
feeding habitat for ducks, breeding habitat for red-winged blackbirds,
alteration of wetland hydrology that changes spawning habitat for fish,
and so forth.
Text Note 3-7. Identifying Susceptibility
Often it is possible to identify ecological entities most likely to
be susceptible to a stressor. However, in some cases where stressors
are not known at the initiation of a risk assessment, or specific
effects have not been identified, the most susceptible entities may not
be known. Where this occurs, professional judgment may be required to
make initial selections of potential endpoints.
Once done, available information on potential stressors in the
system can be evaluated to determine which of the endpoints are most
likely susceptible to identified stressors. If an assessment endpoint
is selected for a risk assessment that directly supports management
goals and is ultimately found not susceptible to stressors in the
system, then a conclusion of no risk is appropriate. However, where
there are multiple possible assessment endpoints that address
management goals and only some of those are susceptible to a stressor,
the susceptible endpoints should be selected. If the susceptible
endpoints are not initially selected for an assessment, an additional
iteration of the risk assessment with alternative assessment endpoints
may be needed to determine risk.
Text Note 3-8. Sensitivity and Secondary Effects: The Mussel-Fish
Connection
Native freshwater mussels are endangered in many streams.
Management efforts have focused on maintaining suitable habitat for
mussels because habitat loss has been considered the greatest threat to
this group. However, larval unionid mussels must attach to the gills of
a fish host for one month during development. Each species of mussel
must attach to a particular host species of fish. In situations where
the fish community has been changed, perhaps due to stressors to which
mussels are insensitive, the host fish may no longer be available.
Mussel larvae will die before reaching maturity as a result. Regardless
of how well managers restore mussel habitat, mussels will be lost from
this system unless the fish community is restored. In this case, risk
is caused by the absence of exposure to a critical resource.
Text Note 3-9. Examples of Management Goals and Assessment Endpoints
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----------------------------------------------------------------------------------------------------------------
Case Regulatory context/management goal Assessment endpoint
----------------------------------------------------------------------------------------------------------------
Assessing Risks of New Chemical Under Protect ``the environment'' from ``an Survival, growth, and
Toxic Substances Control Act (Lynch unreasonable risk of injury'' (TSCA reproduction of fish, aquatic
et al., 1994). Sec. 2[b][1] and [2]); protect the invertebrates, and algae.
aquatic environment. Goal was to exceed
a concentration of concern on no more
than 20 days a year.
Special Review of Granular Carbofuran Prevent * * * ``unreasonable adverse Individual bird survival.
Based on Adverse Effects on Birds effects on the environment'' (FIFRA
(Houseknecht, 1993). Secs. [c][5] and 3[c][6]); using cost-
benefit considerations. Goal was to
have no regularly repeated bird kills.
Modeling Future Losses of Bottomland National Environment Policy Act may (1) Forest community structure
Forest Wetlands (Brody et al., 1993). apply to environmental impact of new and habitat value to wildlife
levee construction; also Clean Water species
Act Sec. 404. (2) Species composition of
wildlife community.
Pest Risk Assessment on Importation of Assessment was done to help provide a Survival and growth of tree
Logs From Chile (USDA, 1993). basis for any necessary regulation of species in the western United
the importation of timber and timber States.
products into the United States.
Baird and McGuire Superfund Site Protection of the environment (CERCLA/ (1) Survival of soil
(terrestrial component) (Burmaster et SARA). invertebrates
al., 1991; Callahan et al., 1991; (2) Survival and reproduction
Menzie et al., 1992). of song birds.
Waquoit Bay Estuary Watershed Risk Clean Water Act--wetlands protection; (1) Estuarine eelgrass habitat
Assessment (U.S. EPA, 1996b). water quality criteria--pesticides; abundance and distribution
endangered species. National Estuarine (2) Estuarine fish species
Research Reserve, Massachusetts, Area diversity and abundance
of Critical Environment Concern. Goal (3) Freshwater pond benthic
was to reestablish and maintain water invertebrate species
quality and habitat conditions to diversity and abundance.
support diverse self-sustaining
commercial, recreational, and native
fish, water-dependent wildlife, and
shellfish and to reverse ongoing
degradation.
----------------------------------------------------------------------------------------------------------------
Text Note 3-10. Common Problems in Selecting Assessment Endpoints
Endpoint is a goal (e.g., maintain and restore endemic
populations).
Endpoint is vague (e.g., estuarine integrity instead of
eelgrass abundance and distribution).
Ecological entity is better as a measure (e.g., emergence
of midges can be used to evaluate an assessment endpoint for fish
feeding behavior).
Ecological entity may not be as sensitive to the stressor
(e.g., catfish versus salmon for sedimentation).
Ecological entity is not exposed to the stressor (e.g.,
using insectivorous birds for avian risk of pesticide application to
seeds).
Ecological entities are irrelevant to the assessment
(e.g., lake fish in salmon stream).
Importance of a species or attributes of an ecosystem are
not fully considered (e.g., mussel-fish connection, see text note 3-8).
Attribute is not sufficiently sensitive for detecting
important effects (e.g., survival compared with recruitment for
endangered species).
Text Note 3-11. What Are the Benefits of Developing Conceptual Models?
The process of creating a conceptual model is a powerful
learning tool.
Conceptual models are easily modified as knowledge
increases.
Conceptual models highlight what is known and not known
and can be used to plan future work.
Conceptual models can be a powerful communication tool.
They provide an explicit expression of the assumptions and
understanding of a system for others to evaluate.
Conceptual models provide a framework for prediction and
are the template for generating more risk hypotheses.
Text Note 3-12. What Are Risk Hypotheses, and Why Are They Important?
Risk hypotheses are proposed answers to questions risk assessors
have about what responses assessment endpoints will show when they are
exposed to stressors and how exposure will occur. Risk hypotheses
clarify and articulate relationships that are posited through the
consideration of available data, information from scientific
literature, and the best professional judgment of risk assessors
developing the conceptual models. This explicit process opens the risk
assessment to peer review and evaluation to ensure the scientific
validity of the work. Risk hypotheses are not equivalent to statistical
testing of null and alternative hypotheses. However, predictions
generated from risk hypotheses can be tested in a variety of ways,
including standard statistical approaches.
Text Note 3-13. Examples of Risk Hypotheses
Hypotheses include known information that sets the problem in
perspective and the proposed relationships that need evaluation.
Stressor-initiated: Chemicals with a high Kow tend to
bioaccumulate. PMN chemical A has a Kow of 5.5 and molecular
structure similar to known chemical stressor B.
Hypotheses: Based on the Kow of chemical A, the mode of
action of chemical B, and the food web of the target ecosystem, when
the PMN chemical is released at a specified rate, it will bioaccumulate
sufficiently in 5 years to cause developmental problems in wildlife and
fish.
Effects-initiated: Bird kills were repeatedly observed on golf
courses following the application of the pesticide carbofuran, which is
highly toxic.
Hypotheses: Birds die when they consume recently applied granulated
carbofuran; as the level of application increases, the number of dead
birds increases. Exposure occurs when dead and dying birds are consumed
by other animals. Birds of prey and scavenger species will die from
eating contaminated birds.
Ecological value-initiated: Waquoit Bay, Massachusetts, supports
recreational boating and commercial and recreational shellfishing and
is a significant nursery for finfish. Large mats of macroalgae clog the
estuary, most of the eelgrass has died, and the scallops are gone.
Hypotheses: Nutrient loading from septic systems, air pollution,
and lawn fertilizers causes eelgrass loss by
[[Page 26897]]
shading from algal growth and direct toxicity from nitrogen compounds.
Fish and shellfish populations are decreasing because of loss of
eelgrass habitat and periodic hypoxia from excess algal growth and low
dissolved oxygen.
Text Note 3-14. Uncertainty in Problem Formulation
Uncertainties in problem formulation are manifested in the quality
of conceptual models. To address uncertainty:
Be explicit in defining assessment endpoints; include both
an entity and its measurable attributes.
Reduce or define variability by carefully defining
boundaries for the assessment.
Be open and explicit about the strengths and limitations
of pathways and relationships depicted in the conceptual model.
Identify and describe rationale for key assumptions made
because of lack of knowledge, model simplification, approximation, or
extrapolation.
Describe data limitations.
Text Note 3-15. Why Was Measurement Endpoint Changed?
The original definition of measurement endpoint was ``a measurable
characteristic that is related to the valued characteristic chosen as
the assessment endpoint'' (Suter, 1989; U.S. EPA, 1992a). The
definition refers specifically to the response of an assessment
endpoint to a stressor. It does not include measures of ecosystem
characteristics, life-history considerations, exposure, or other
measures. Because measurement endpoint does not encompass these other
important measures and there was confusion about its meaning, the term
was replaced with measures of effect and supplemented by two other
categories of measures.
Text Note 3-16. Examples of a Management Goal, Assessment Endpoint, and
Measures
Goal: Viable, self-sustaining coho salmon population that supports
a subsistence and sport fishery.
Assessment Endpoint: Coho salmon breeding success, fry survival,
and adult return rates.
Measures of Effects
Egg and fry response to low dissolved oxygen.
Adult behavior in response to obstacles.
Spawning behavior and egg survival with changes in
sedimentation.
Measures of Ecosystem and Receptor Characteristics
Water temperature, water velocity, and physical
obstructions.
Abundance and distribution of suitable breeding substrate.
Abundance and distribution of suitable food sources for
fry.
Feeding, resting, and breeding behavior.
Natural reproduction, growth, and mortality rates.
Measures of Exposure
Number of hydroelectric dams and associated ease of fish
passage.
Toxic chemical concentrations in water, sediment, and fish
tissue.
Nutrient and dissolved oxygen levels in ambient waters.
Riparian cover, sediment loading, and water temperature.
Text Note 3-17. How Do Water Quality Criteria Relate to Assessment
Endpoints?
Water quality criteria (U.S. EPA, 1986b) have been developed for
the protection of aquatic life from chemical stressors. This text note
shows how the elements of a water quality criterion correspond to
management goals, management decisions, assessment endpoints, and
measures.
Regulatory Goal
Clean Water Act, Sec. 101: Protect the chemical, physical,
and biological integrity of the Nation's waters.
Program Management Decisions
Protect 99% of individuals in 95% of the species in
aquatic communities from acute and chronic effects resulting from
exposure to a chemical stressor.
Assessment Endpoints
Survival of fish, aquatic invertebrate, and algal species
under acute exposure.
Survival, growth, and reproduction of fish, aquatic
invertebrate, and algal species under chronic exposure.
Measures of Effect
Laboratory LC50s for at least eight species
meeting certain requirements.
Chronic no-observed-adverse-effect levels (NOAELs) for at
least three species meeting certain requirements.
Measures of Ecosystem and Receptor Characteristics
Water hardness (for some metals).
pH.
The water quality criterion is a benchmark level derived from a
distributional analysis of single-species toxicity data. It is assumed
that the species tested adequately represent the composition and
sensitivities of species in a natural community.
Text Note 3-18. The Data Quality Objectives Process
The data quality objectives (DQO) process combines elements of both
planning and problem formulation in its seven-step format.
Step 1. State the problem. Review existing information to concisely
describe the problem to be studied.
Step 2. Identify the decision. Determine what questions the study
will try to resolve and what actions may result.
Step 3. Identify inputs to the decision. Identify information and
measures needed to resolve the decision statement.
Step 4. Define study boundaries. Specify time and spatial
parameters and where and when data should be collected.
Step 5. Develop decision rule. Define statistical parameter, action
level, and logical basis for choosing alternatives.
Step 6. Specify tolerable limits on decision errors. Define limits
based on the consequences of an incorrect decision.
Step 7. Optimize the design. Generate alternative data collection
designs and choose most resource-effective design that meets all DQOs.
Text Note 4-1. Data Collection and the Analysis Phase
Data needs are identified during problem formulation (the analysis
plan step), and data are collected before the start of the analysis
phase. These data may be collected for the specific purpose of a
particular risk assessment, or they may be available from previous
studies. If additional data needs are identified as the assessment
proceeds, the analysis phase may be temporarily halted while data are
collected or the assessor (in consultation with the risk manager) may
choose to iterate the problem formulation again. Data collection
methods are not described in these Guidelines. However, the evaluation
of data for the purposes of risk assessment is discussed in section
4.2.
Text Note 4-2. The American National Standard for Quality Assurance
The Specifications and Guidelines for Quality Systems for
Environmental Data Collection and Environmental Technology Programs
(ASQC, 1994) recognize several areas that are important to ensuring
that environmental data will meet study objectives, including:
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Planning and scoping.
Designing data collection operations.
Implementing and monitoring planned operations.
Assessing and verifying data usability.
Text Note 4-3. Questions for Evaluating a Study's Utility for Risk
Assessment
Are the study objectives relevant to the risk assessment?
Are the variables and conditions the study represents comparable
with those important to the risk assessment?
Is the study design adequate to meet its objectives?
Was the study conducted properly?
How are variability and uncertainty treated and reported?
Text Note 4-4. Uncertainty Evaluation in the Analysis Phase
----------------------------------------------------------------------------------------------------------------
Source of uncertainty Example analysis phase strategies Specific example
----------------------------------------------------------------------------------------------------------------
Unclear communication.............. Contact principal investigator or Clarify whether the study was
other study participants if designed to characterize local
objectives or methods of literature populations or regional
studies are unclear. populations.
Document decisions made during the Discuss rationale for selecting the
course of the assessment. critical toxicity study.
Descriptive errors................. Verify that data sources followed Double-check calculations and data
appropriate QA/QC procedures. entry.
Variability........................ Describe heterogeneity using point Display differences in species
estimates (e.g., central tendency sensitivity using a cumulative
and high end) or by constructing distribution function.
probability or frequency
distributions.
Differentiate from uncertainty due to
lack of knowledge.
Data gaps.......................... Collect needed data.................. Discuss rationale for using a factor
of 10 to extrapolate between a
lowest-observed-adverse-effect
level (LOAEL) and a NOAEL.
Describe approaches used for bridging
gaps and their rationales.
Differentiate science-based judgments
from policy-based judgments.
Uncertainty about a quantity's true Use standard statistical methods to Present the upper confidence limit
value. construct probability distributions on the arithmetic mean soil
or point estimates (e.g., confidence concentration, in addition to the
limits). best estimate of the arithmetic
mean.
Evaluate power of designed
experiments to detect differences.
Collect additional data.
Verify location of samples or other Ground-truth remote sensing data.
spatial features.
Model structure uncertainty Discuss key aggregations and model Discuss combining different species
(process models). simplifications. into a group based on similar
feeding habits.
Compare model predictions with data
collected in the system of interest.
Uncertainty about a model's form Evaluate whether alternative models Present results obtained using
(empirical models). should be combined formally or alternative models.
treated separately.
Compare model predictions with data Compare results of a plant uptake
collected in the system of interest. model with data collected in the
field.
----------------------------------------------------------------------------------------------------------------
Text Note 4-5. Considering the Degree of Aggregation in Models
Wiegert and Bartell (1994) suggest the following considerations for
evaluating the proper degree of aggregation or disaggregation:
1. Do not aggregate components with greatly disparate flux rates.
2. Do not greatly increase the disaggregation of the structural
aspects of the model without a corresponding increase in the
sophistication of the functional relationships and controls.
3. Disaggregate models only insofar as required by the goals of the
model to facilitate testing.
Text Note 4-6. Questions for Source Description
Where does the stressor originate?
What environmental media first receive stressors?
Does the source generate other constituents that will influence a
stressor's eventual distribution in the environment?
Are there other sources of the same stressor?
Are there background sources?
Is the source still active?
Does the source produce a distinctive signature that can be seen in
the environment, organisms, or communities?
Additional Questions for Introduction of Biological Stressors
Is there an opportunity for repeated introduction or escape into
the new environment?
Will the organism be present on a transportable item?
Are there mitigation requirements or conditions that would kill or
impair the organism before entry, during transport, or at the port of
entry?
Text Note 4-7. Questions to Ask in Evaluating Stressor Distribution
What are the important transport pathways?
What characteristics of the stressor influence transport?
What characteristics of the ecosystem will influence transport?
What secondary stressors will be formed?
Where will they be transported?
Text Note 4-8. General Mechanisms of Transport and Dispersal
Physical, Chemical, and Biological Stressors
By air current.
In surface water (rivers, lakes, streams).
Over and/or through the soil surface.
Through ground water.
Primarily Chemical Stressors
Through the food web.
[[Page 26899]]
Primarily Biological Stressors
Splashing or raindrops.
Human activity (boats, campers).
Passive transmittal by other organisms.
Biological vectors.
Text Note 4-9. Questions To Ask in Describing Contact or Co-Occurrence
Must the receptor actually contact the stressor for adverse effects
to occur?
Must the stressor be taken up into a receptor for adverse effects
to occur?
What characteristics of the receptors will influence the extent of
contact or co-occurrence?
Will abiotic characteristics of the environment influence the
extent of contact or co-occurrence?
Will ecosystem processes or community-level interactions influence
the extent of contact or co-occurrence?
Text Note 4-10. Example of an Exposure Equation: Calculating a
Potential Dose via Ingestion
[GRAPHIC] [TIFF OMITTED] TN14MY98.020
Where:
ADDpot=Potential average daily dose (e.g., in mg/kg-day)
Ck=Average contaminant concentration in the kth type of food
(e.g., in mg/kg wet weight)
FRk=Fraction of intake of the kth food type that is from the
contaminated area (unitless)
NIRk=Normalized ingestion rate of the kth food type on a
wet-weight basis (e.g., in kg food/kg body-weight-day).
m=Number of contaminated food types
Note: A similar equation can be used to calculate uptake by adding
an absorption factor that accounts for the fraction of the chemical in
the kth food type that is absorbed into the organism. The choice of
potential dose or uptake depends on the form of the stressor-response
relationship. Source: U.S. EPA, 1993a.
Text Note 4-11. Measuring Internal Dose Using Biomarkers and Tissue
Residues
Biomarkers and tissue residues are particularly useful when
exposure across many pathways must be integrated and when site-specific
factors influence bioavailability. They can also be very useful when
metabolism and accumulation kinetics are important, although these
factors can make interpretation of results more difficult (McCarty and
Mackay, 1993). These methods are most useful when they can be
quantitatively linked to the amount of stressor originally contacted by
the organism. In addition, they are most useful when the stressor-
response relationship expresses the amount of stressor in terms of the
tissue residue or biomarker (van Gestel and van Brummelen, 1996).
Standard analytical methods are generally available for tissue
residues, making them more readily usable for routine assessments than
biomarkers. Readers are referred to the review in Ecotoxicology (Vol.
3, Issue 3, 1994), Huggett et al. (1992), and the debate in Human
Health and Ecological Risk Assessment (Vol. 2, Issue 2, 1996).
Text Note 4-12. Questions Addressed by the Exposure Profile
How does exposure occur?
What is exposed?
How much exposure occurs? When and where does it occur?
How does exposure vary?
How uncertain are the exposure estimates?
What is the likelihood that exposure will occur?
Text Note 4-13. Questions for Stressor-Response Analysis
Does the assessment require point estimates or stressor-response
curves?
Does the assessment require the establishment of a ``no-effect''
level?
Would cumulative effects distributions be useful?
Will analyses be used as input to a process model?
Text Note 4-14. Qualitative Stressor-Response Relationships
The relationship between stressor and response can be described
qualitatively, for instance, using categories of high, medium, and low,
to describe the intensity of response given exposure to a stressor. For
example, Pearlstine et al. (1985) assumed that seeds would not
germinate if they were inundated with water at the critical time. This
stressor-response relationship was described simply as a yes or no. In
most cases, however, the objective is to describe quantitatively the
intensity of response associated with exposure, and in the best case,
to describe how intensity of response changes with incremental
increases in exposure.
Text Note 4-15. Median Effect Levels
Median effects are those effects elicited in 50% of the test
organisms exposed to a stressor, typically chemical stressors. Median
effect concentrations can be expressed in terms of lethality or
mortality and are known as LC50 or LD50,
depending on whether concentrations (in the diet or in water) or doses
(mg/kg) were used. Median effects other than lethality (e.g., effects
on growth) are expressed as EC50 or ED50. The
median effect level is always associated with a time parameter (e.g.,
24 or 48 hours). Because these tests seldom exceed 96 hours, their main
value lies in evaluating short-term effects of chemicals. Stephan
(1977) discusses several statistical methods to estimate the median
effect level.
Text Note 4-16. No-Effect Levels Derived From Statistical Hypothesis
Testing
Statistical hypothesis tests have typically been used with chronic
toxicity tests of chemical stressors that evaluate multiple endpoints.
For each endpoint, the objective is to determine the highest test level
for which effects are not statistically different from the controls
(the no-observed-adverse-effect level, NOAEL) and the lowest level at
which effects were statistically significant from the control (the
lowest-observed-adverse-effect level, LOAEL). The range between the
NOAEL and the LOAEL is sometimes called the maximum acceptable toxicant
concentration, or MATC. The MATC, which can also be reported as the
geometric mean of the NOAEL and the LOAEL (i.e., GMATC), provides a
useful reference with which to compare toxicities of various chemical
stressors.
Reporting the results of chronic tests in terms of the MATC or
GMATC has been widely used within the Agency for evaluating pesticides
and industrial chemicals (e.g., Urban and Cook, 1986; Nabholz, 1991).
Text Note 4-17. General Criteria for Causality (Adapted From Fox, 1991)
Criteria Strongly Affirming Causality
Strength of association.
Predictive performance.
Demonstration of a stressor-response relationship.
Consistency of association.
Criteria Providing a Basis for Rejecting Causality
Inconsistency in association.
Temporal incompatibility.
Factual implausibility.
Other Relevant Criteria
Specificity of association.
Theoretical and biological plausibility.
Text Note 4-18. Koch's Postulates (Pelczar and Reid, 1972)
A pathogen must be consistently found in association with
a given disease.
[[Page 26900]]
The pathogen must be isolated from the host and grown in
pure culture.
When inoculated into test animals, the same disease
symptoms must be expressed.
The pathogen must again be isolated from the test
organism.
Text Note 4-19. Examples of Extrapolations To Link Measures of Effect
to Assessment Endpoints
Every risk assessment has data gaps that should be addressed, but
it is not always possible to obtain more information. When there is a
lack of time, monetary resources, or a practical means to acquire more
data, extrapolations such as those listed below may be the only way to
bridge gaps in available data. Extrapolations may be:
Between taxa (e.g., bluegill to rainbow trout).
Between responses (e.g., mortality to growth or
reproduction).
From laboratory to field.
Between geographic areas.
Between spatial scales.
From data collected over a short time frame to longer-term
effects.
Text Note 4-20. Questions Related to Selecting Extrapolation Approaches
How specific is the assessment endpoint?
Does the spatial or temporal extent of exposure suggest the need
for additional receptors or extrapolation models?
Are the quantity and quality of the data available sufficient for
planned extrapolations and models?
Is the proposed extrapolation technique consistent with ecological
information?
How much uncertainty is acceptable?
Text Note 4-21. Questions To Consider When Extrapolating From Effects
Observed in the Laboratory to Field Effects of Chemicals
Exposure Factors
How will environmental fate and transformation of the chemical
affect exposure in the field?
How comparable are exposure conditions and the timing of exposure?
How comparable are the routes of exposure?
How do abiotic factors influence bioavailability and exposure?
How likely are preference or avoidance behaviors?
Effects Factors
What is known about the biotic and abiotic factors controlling
populations of the organisms of concern?
To what degree are critical life-stage data available?
How may exposure to the same or other stressors in the field have
altered organism sensitivity?
Text Note 4-22. Questions Addressed by the Stressor-Response Profile
What ecological entities are affected?
What is the nature of the effect(s)?
What is the intensity of the effect(s)?
Where appropriate, what is the time scale for recovery?
What causal information links the stressor with any observed
effects?
How do changes in measures of effects relate to changes in
assessment endpoints?
What is the uncertainty associated with the analysis?
Text Note 5-1. An Example of Field Methods Used for Risk Estimation
Along with quotients comparing field measures of exposure with
laboratory acute toxicity data (see text note 5-3), EPA evaluated the
risks of granular carbofuran to birds based on incidents of bird kills
following carbofuran applications. More than 40 incidents involving
nearly 30 species of birds were documented. Although reviewers
identified problems with individual field studies (e.g., lack of
appropriate control sites, lack of data on carcass-search efficiencies,
no examination of potential synergistic effects of other pesticides,
and lack of consideration of other potential receptors such as small
mammals), there was so much evidence of mortality associated with
carbofuran application that the study deficiencies did not alter the
conclusions of high risk found by the assessment (Houseknecht, 1993).
Text Note 5-2. Using Qualitative Categories to Estimate Risks of an
Introduced Species
The importation of logs from Chile required an assessment of the
risks posed by the potential introduction of the bark beetle, Hylurgus
ligniperda (USDA, 1993). Experts judged the potential for colonization
and spread of the species, and their opinions were expressed as high,
medium, or low as to the likelihood of establishment (exposure) or
consequential effects of the beetle. Uncertainties were similarly
expressed. A ranking scheme was then used to sum the individual
elements into an overall estimate of risk (high, medium, or low).
Narrative explanations of risk accompanied the overall rankings.
Text Note 5-3. Applying the Quotient Method
When applying the quotient method to chemical stressors, the
effects concentration or dose (e.g., an LC50,
LD50, EC50, ED50, NOAEL, or LOAEL) is
frequently adjusted by uncertainty factors before division into the
exposure number (U.S. EPA, 1984; Nabholz, 1991; Urban and Cook, 1986;
see section 4.3.1.3), although EPA used a slightly different approach
in estimating the risks to the survival of birds that forage in
agricultural areas where the pesticide granular carbofuran is applied
(Houseknecht, 1993). In this case, EPA calculated the quotient by
dividing the estimated exposure levels of carbofuran granules in
surface soils (number/ft \2\) by the granules/LD50 derived
from single-dose avian toxicity tests. The calculation yields values
with units of LD50/ft \2\. It was assumed that a higher
quotient value corresponded to an increased likelihood that a bird
would be exposed to lethal levels of granular carbofuran at the soil
surface. Minimum and maximum values for LD50/ft \2\ were
estimated for songbirds, upland game birds, and waterfowl that may
forage within or near 10 different agricultural crops.
Text Note 5-4. Comparing an Exposure Distribution With a Point Estimate
of Effects.
The EPA Office of Pollution Prevention and Toxics uses a
Probabilistic Dilution Model (PDM3) to generate a distribution of daily
average chemical concentrations based on estimated variations in stream
flow in a model system. The PDM3 model compares this exposure
distribution with an aquatic toxicity test endpoint to estimate how
many days in a 1-year period the endpoint concentration is exceeded
(Nabholz et al., 1993; U.S. EPA, 1988b). The frequency of exceedance is
based on the duration of the toxicity test used to derive the effects
endpoint. Thus, if the endpoint was an acute toxicity level of concern,
an exceedance would be identified if the level of concern was exceeded
for 4 days or more (not necessarily consecutive). The exposure
estimates are conservative in that they assume instantaneous mixing of
the chemical in the water column and no losses due to physical,
chemical, or biodegradation effects.
Text Note 5-5. Comparing Cumulative Exposure and Effects Distributions
for Chemical Stressors
Exposure distributions for chemical stressors can be compared with
effects distributions derived from point estimates of acute or chronic
toxicity values for different species (e.g., HCN, 1993; Cardwell et
al., 1993; Baker et al., 1994; Solomon et al., 1996). Figure 5-
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5 shows a distribution of exposure concentrations of an herbicide
compared with single-species toxicity data for algae (and one vascular
plant species) for the same chemical. The degree of overlap of the
curves indicates the likelihood that a certain percentage of species
may be adversely affected. For example, figure 5-5 indicates that the
10th centile of algal species' EC5 values is exceeded less
than 10% of the time.
The predictive value of this approach is evident. The degree of
risk reduction that could be achieved by changes in exposure associated
with proposed risk mitigation options can be readily determined by
comparing modified exposure distributions with the effects distribution
curve.
When using effects distributions derived from single-species
toxicity data, risk assessors should consider the following questions:
Does the subset of species for which toxicity test data
are available represent the range of species present in the
environment?
Are particularly sensitive (or insensitive) groups of
organisms represented in the distribution?
If a criterion level is selected'e.g., protect 95% of
species--does the 5% of potentially affected species include organisms
of ecological, commercial, or recreational significance?
Text Note 5-6. Estimating Risk With Process Models
Models that integrate both exposure and effects information can be
used to estimate risk. During risk estimation, it is important that
both the strengths and limitations of a process model approach be
highlighted. Brody et al. (1993; see Appendix D) linked two process
models to integrate exposure and effects information and forecast
spatial and temporal changes in forest communities and their wildlife
habitat value. While the models were useful for projecting long-term
effects based on an understanding of the underlying mechanisms of
change in forest communities and wildlife habitat, they could not
evaluate all possible stressors of concern and were limited in the
plant and wildlife species they could consider. Understanding both the
strengths and limitations of models is essential for accurately
representing the overall confidence in the assessment.
Text Note 5-7. What Are Statistically Significant Effects?
Statistical testing is the ``statistical procedure or decision rule
that leads to establishing the truth or falsity of a hypothesis * * *''
(Alder and Roessler, 1972). Statistical significance is based on the
number of data points, the nature of their distribution, whether
intertreatment variance exceeds intratreatment variance in the data,
and the a priori significance level (). The types of
statistical tests and the appropriate protocols (e.g., power of test)
for these tests should be established as part of the analysis plan
during problem formulation.
Text Note 5-8. Possible Risk Assessment Report Elements
Describe risk assessor/risk manager planning results.
Review the conceptual model and the assessment endpoints.
Discuss the major data sources and analytical procedures
used.
Review the stressor-response and exposure profiles.
Describe risks to the assessment endpoints, including risk
estimates and adversity evaluations.
Review and summarize major areas of uncertainty (as well
as their direction) and the approaches used to address them.
Discuss the degree of scientific consensus in key areas of
uncertainty.
' Identify major data gaps and, where appropriate, indicate whether
gathering additional data would add significantly to the overall
confidence in the assessment results.
' Discuss science policy judgments or default assumptions used to
bridge information gaps and the basis for these assumptions.
' Discuss how the elements of quantitative uncertainty analysis are
embedded in the estimate of risk.
Text Note 5-9. Clear, Transparent, Reasonable, and Consistent Risk
Characterizations
For Clarity
Be brief; avoid jargon.
Make language and organization understandable to risk
managers and the informed lay person.
Fully discuss and explain unusual issues specific to a
particular risk assessment.
For Transparency
Identify the scientific conclusions separately from policy
judgments.
Clearly articulate major differing viewpoints of
scientific judgments.
Define and explain the risk assessment purpose (e.g.,
regulatory purpose, policy analysis, priority setting).
Fully explain assumptions and biases (scientific and
policy).
For Reasonableness
Integrate all components into an overall conclusion of
risk that is complete, informative, and useful in decision making.
Acknowledge uncertainties and assumptions in a forthright
manner.
Describe key data as experimental, state-of-the-art, or
generally accepted scientific knowledge.
Identify reasonable alternatives and conclusions that can
be derived from the data.
Define the level of effort (e.g., quick screen, extensive
characterization) along with the reason(s) for selecting this level of
effort.
Explain the status of peer review.
For Consistency with Other Risk Characterizations
Describe how the risks posed by one set of stressors
compare with the risks posed by a similar stressor(s) or similar
environmental conditions.
Indicate how the strengths and limitations of the
assessment compare with past assessments.
Text Note 6-1. Questions Regarding Risk Assessment Results (Adapted
From U.S. EPA, 1993c)
Questions Principally for Risk Assessors To Ask Risk Managers
Are the risks sufficiently well defined (and data gaps
small enough) to support a risk management decision?
Was the right problem analyzed?
Was the problem adequately characterized?
Questions Principally for Risk Managers To Ask Risk Assessors
What effects might occur?
How adverse are the effects?
How likely is it that effects will occur?
When and where do the effects occur?
How confident are you in the conclusions of the risk
assessment?
What are the critical data gaps, and will information be
available in the near future to fill these gaps?
Are more ecological risk assessment iterations required?
How could monitoring help evaluate the results of the risk
management decision?
Text Note 6-2. Risk Communication Considerations for Risk Managers
(U.S. EPA, 1995b)
Plan carefully and evaluate the success of your
communication efforts.
Coordinate and collaborate with other credible sources.
Accept and involve the public as a legitimate partner.
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Listen to the public's specific concerns.
Be honest, frank, and open.
Speak clearly and with compassion.
Meet the needs of the media.
Text Note A-1. Stressor vs. Agent
Agent has been suggested as an alternative for the term stressor
(Suter et al., 1994). Agent is thought to be a more neutral term than
stressor, but agent is also associated with certain classes of
chemicals (e.g., chemical warfare agents). In addition, agent has the
connotation of the entity that is initially released from the source,
whereas stressor has the connotation of the entity that causes the
response. Agent is used in EPA's Guidelines for Exposure Assessment
(U.S. EPA, 1992b) (i.e., with exposure defined as ``contact of a
chemical, physical, or biological agent''). The two terms are
considered to be nearly synonymous, but stressor is used throughout
these Guidelines for internal consistency.
Appendix A--Changes From EPA's Ecological Risk Assessment Framework
EPA has gained much experience with the ecological risk
assessment process since the publication of the Framework Report
(U.S. EPA, 1992a) and has received many suggestions for
modifications of both the process and the terminology. While EPA is
not recommending major changes in the overall ecological risk
assessment process, modifications are summarized here to assist
those who may already be familiar with the Framework Report. Changes
in the diagram are discussed first, followed by changes in
terminology and definitions.
A.1. Changes in the Framework Diagram
The revised framework diagram is shown in figure 1-2. Within
each phase, rectangles are used to designate inputs, hexagons
indicate actions, and circles represent outputs. There have been
some minor changes in the wording for the boxes outside of the risk
assessment process (planning; communicating results to the risk
manager; acquire data, iterate process, monitor results). ``Iterate
process'' was added to emphasize the iterative (and frequently
tiered) nature of risk assessment. The term ``interested parties''
was added to the planning and risk management boxes to indicate
their increasing role in the risk assessment process (Commission on
Risk Assessment and Risk Management, 1997). The new diagram of
problem formulation contains several changes. The hexagon emphasizes
the importance of integrating available information before selecting
assessment endpoints and building conceptual models. The three
products of problem formulation are enclosed in circles. Assessment
endpoints are shown as a key product that drives conceptual model
development. The conceptual model remains a central product of
problem formulation. The analysis plan has been added as an explicit
product of problem formulation to emphasize the need to plan data
evaluation and interpretation before analyses begin.
In the analysis phase, the left-hand side of figure 1-2 shows
the general process of characterization of exposure, and the right-
hand side shows the characterization of ecological effects. It is
important that evaluation of these two aspects of analysis is an
interactive process to ensure compatible outputs that can be
integrated in risk characterization. The dotted line and hexagon
that include both the exposure and ecological response analyses
emphasize this interaction. In addition, the first three boxes in
analysis now include the measures of exposure, effects, and
ecosystem and receptor characteristics that provide input to the
exposure and ecological response analyses.
Experience with the application of risk characterization as
outlined in the Framework Report suggests the need for several
modifications in this process. Risk estimation entails the
integration of exposure and effects estimates along with an analysis
of uncertainties. The process of risk estimation outlined in the
Framework Report separates integration and uncertainty. The original
purpose for this separation was to emphasize the importance of
estimating uncertainty. This separation is no longer needed since
uncertainty analysis is now explicitly addressed in most risk
integration methods.
The description of risk is similar to the process described in
the Framework Report. Topics included in the risk description
include the lines of evidence that support causality and a
determination of the ecological adversity of observed or predicted
effects. Considerations for reporting risk assessment results are
also described.
A.2. Changes in Definitions and Terminology
Except as noted below, these Guidelines retain definitions used
in the Framework Report (see Appendix B). Some definitions have been
revised, especially those related to endpoints and exposure. Some
changes in the classification of uncertainty from the Framework
Report are also described in this section.
A.2.1. Endpoint Terminology
The Framework Report uses the assessment and measurement
endpoint terminology of Suter (1990), but offers no specific terms
for measures of stressor levels or ecosystem characteristics.
Experience has demonstrated that measures unrelated to effects are
sometimes inappropriately called measurement endpoints, which were
defined by Suter (1990) as ``measurable responses to a stressor that
are related to the valued characteristic chosen as assessment
endpoints.'' These Guidelines replace measurement endpoint with
measure of effect, which is ``a change in an attribute of an
assessment endpoint or its surrogate in response to a stressor to
which it is exposed.'' An assessment endpoint is an explicit
expression of the environmental value to be protected, operationally
defined by an entity and its attributes. Since data other than those
required to evaluate responses (i.e., measures of effects) are
required for an ecological risk assessment, two additional types of
measures are used. Measures of exposure include stressor and source
measurements, while measures of ecosystem and receptor
characteristics include, for example, habitat measures, soil
parameters, water quality conditions, or life-history parameters
that may be necessary to better characterize exposure or effects.
Any of the three types of measures may be actual data (e.g.,
mortality), summary statistics (e.g., an LC50), or
estimated values (e.g., an LC50 estimated from a
structure-activity relationship).
A.2.2. Exposure Terminology
These Guidelines define exposure in a manner that is relevant to
any chemical, physical, or biological entity. While the broad
concepts are the same, the language and approaches vary depending on
whether a chemical, physical, or biological entity is the subject of
assessment. Key exposure-related terms and their definitions are:
Source. A source is an entity or action that releases
to the environment or imposes on the environment a chemical,
physical, or biological stressor or stressors. Sources may include a
waste treatment plant, a pesticide application, a logging operation,
introduction of exotic organisms, or a dredging project.
Stressor. A stressor is any physical, chemical, or
biological entity that can induce an adverse response. This term is
used broadly to encompass entities that cause primary effects and
those primary effects that can cause secondary (i.e., indirect)
effects. Stressors may be chemical (e.g., toxics or nutrients),
physical (e.g., dams, fishing nets, or suspended sediments), or
biological (e.g., exotic or genetically engineered organisms). While
risk assessment is concerned with the characterization of adverse
responses, under some circumstances a stressor may be neutral or
produce effects that are beneficial to certain ecological components
(see text note A-1). Primary effects may also become stressors. For
example, a change in a bottomland hardwood plant community affected
by rising water levels can be thought of as a stressor influencing
the wildlife community. Stressors may also be formed through abiotic
interactions; for example, the increase in ultraviolet light
reaching the Earth's surface results from the interaction of the
original stressors released (chlorofluorocarbons) with the ecosystem
(stratospheric ozone).
Exposure. As discussed above, these Guidelines use the
term exposure broadly to mean ``subjected to some action or
influence.'' Used in this way, exposure applies to physical and
biological stressors as well as to chemicals (organisms are commonly
said to be exposed to radiation, pathogens, or heat). Exposure is
also applicable to higher levels of biological organization, such as
exposure of a benthic community to dredging, exposure of an owl
population to habitat modification, or exposure of a wildlife
population to hunting. Although the operational definition of
exposure, particularly the units of measure, depends on the stressor
and receptor (defined below), the following general definition is
applicable: Exposure is the contact or co-occurrence of a stressor
with a receptor.
Receptor. The receptor is the ecological entity exposed
to the stressor. This term may
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refer to tissues, organisms, populations, communities, and
ecosystems. While either ``ecological component'' (U.S. EPA, 1992a)
or ``biological system'' (Cohrssen and Covello, 1989) are
alternative terms, ``receptor'' is usually clearer in discussions of
exposure where the emphasis is on the stressor-receptor
relationship.
As discussed below, both disturbance and stress regime have been
suggested as alternative terms for exposure. Neither term is used in
these Guidelines, which instead use exposure as broadly defined
above.
Disturbance. A disturbance is any event or series of
events that disrupts ecosystem, community, or population structure
and changes resources, substrate availability, or the physical
environment (modified slightly from White and Pickett, 1985).
Defined in this way, disturbance is clearly a kind of exposure
(i.e., an event that subjects a receptor, the disturbed system, to
the actions of a stressor). Disturbance may be a useful alternative
to stressor specifically for physical stressors that are deletions
or modifications (e.g., logging, dredging, flooding).
Stress Regime. The term stress regime has been used in
at least three distinct ways: (1) To characterize exposure to
multiple chemicals or to both chemical and nonchemical stressors
(more clearly described as multiple exposure, complex exposure, or
exposure to mixtures), (2) as a synonym for exposure that is
intended to avoid overemphasis on chemical exposures, and (3) to
describe the series of interactions of exposures and effects
resulting in secondary exposures, secondary effects, and, finally,
ultimate effects (also known as risk cascade [Lipton et al., 1993]),
or causal chain, pathway, or network (Andrewartha and Birch, 1984).
Because of the potential for confusion and the availability of
other, clearer terms, this term is not used in these Guidelines.
A.2.3. Uncertainty Terminology
The Framework Report divided uncertainty into conceptual model
formation, information and data, stochasticity, and error. These
Guidelines discuss uncertainty throughout the process, focusing on
the conceptual model (section 3.4.3), the analysis phase (section
4.1.3), and the incorporation of uncertainty in risk estimates
(section 5.1). The bulk of the discussion appears in section 4.1.3,
where the discussion is organized according to the following sources
of uncertainty:
Unclear communication.
Descriptive errors.
Variability.
Data gaps.
Uncertainty about a quantity's true value.
Model structure uncertainty (process models).
Uncertainty about a model's form (empirical models).
A.2.4. Lines of Evidence
The Framework Report used the phrase weight of evidence to
describe the process of evaluating multiple lines of evidence in
risk characterization. These Guidelines use the phrase lines of
evidence instead to de-emphasize the balancing of opposing factors
based on assignment of quantitative values to reach a conclusion
about a ``weight'' in favor of a more inclusive approach, which
evaluates all available information, even evidence that may be
qualitative in nature.
Appendix B--Key Terms (Adapted From U.S. EPA, 1992a)
Adverse ecological effects--Changes that are considered
undesirable because they alter valued structural or functional
characteristics of ecosystems or their components. An evaluation of
adversity may consider the type, intensity, and scale of the effect
as well as the potential for recovery.
Agent--Any physical, chemical, or biological entity that can
induce an adverse response (synonymous with stressor).
Assessment endpoint--An explicit expression of the environmental
value that is to be protected, operationally defined by an
ecological entity and its attributes. For example, salmon are valued
ecological entities; reproduction and age class structure are some
of their important attributes. Together ``salmon reproduction and
age class structure'' form an assessment endpoint.
Attribute--A quality or characteristic of an ecological entity.
An attribute is one component of an assessment endpoint.
Characterization of ecological effects--A portion of the
analysis phase of ecological risk assessment that evaluates the
ability of a stressor(s) to cause adverse effects under a particular
set of circumstances.
Characterization of exposure--A portion of the analysis phase of
ecological risk assessment that evaluates the interaction of the
stressor with one or more ecological entities. Exposure can be
expressed as co-occurrence or contact, depending on the stressor and
ecological component involved.
Community--An assemblage of populations of different species
within a specified location in space and time.
Comparative risk assessment--A process that generally uses a
professional judgment approach to evaluate the relative magnitude of
effects and set priorities among a wide range of environmental
problems (e.g., U.S. EPA, 1993d). Some applications of this process
are similar to the problem formulation portion of an ecological risk
assessment in that the outcome may help select topics for further
evaluation and help focus limited resources on areas having the
greatest risk reduction potential. In other situations, a
comparative risk assessment is conducted more like a preliminary
risk assessment. For example, EPA's Science Advisory Board used
professional judgment and an ecological risk assessment approach to
analyze future ecological risk scenarios and risk management
alternatives (U.S. EPA, 1995e).
Conceptual model--A conceptual model in problem formulation is a
written description and visual representation of predicted
relationships between ecological entities and the stressors to which
they may be exposed.
Cumulative distribution function (CDF)--Cumulative distribution
functions are particularly useful for describing the likelihood that
a variable will fall within different ranges of x. F(x) (i.e., the
value of y at x in a CDF plot) is the probability that a variable
will have a value less than or equal to x (figure B-1).
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Cumulative ecological risk assessment--A process that involves
consideration of the aggregate ecological risk to the target entity
caused by the accumulation of risk from multiple stressors.
Disturbance--Any event or series of events that disrupts
ecosystem, community, or population structure and changes resources,
substrate availability, or the physical environment (modified from
White and Pickett, 1985).
EC50--A statistically or graphically estimated
concentration that is expected to cause one or more specified
effects in 50% of a group of organisms under specified conditions
(ASTM, 1996).
Ecological entity--A general term that may refer to a species, a
group of species, an ecosystem function or characteristic, or a
specific habitat. An ecological entity is one component of an
assessment endpoint.
Ecological relevance--One of the three criteria for assessment
endpoint selection. Ecologically relevant endpoints reflect
important characteristics of the system and are functionally related
to other endpoints.
Ecological risk assessment--The process that evaluates the
likelihood that adverse ecological effects may occur or are
occurring as a result of exposure to one or more stressors.
Ecosystem--The biotic community and abiotic environment within a
specified location in space and time.
Environmental impact statement (EIS)--Environmental impact
statements are prepared under the National Environmental Policy Act
by Federal agencies as they evaluate the environmental consequences
of proposed actions. EISs describe baseline environmental
conditions; the purpose of, need for, and consequences of a proposed
action; the no-action alternative; and the consequences of a
reasonable range of alternative actions. A separate risk assessment
could be prepared for each alternative, or a comparative risk
assessment might be developed. However, risk assessment is not the
only approach used in EISs.
Exposure--The contact or co-occurrence of a stressor with a
receptor.
Exposure profile--The product of characterization of exposure in
the analysis phase of ecological risk assessment. The exposure
profile summarizes the magnitude and spatial and temporal patterns
of exposure for the scenarios described in the conceptual model.
Exposure scenario--A set of assumptions concerning how an
exposure may take place, including assumptions about the exposure
setting, stressor characteristics, and activities that may lead to
exposure.
Hazard assessment--This term has been used to mean either (1)
evaluating the intrinsic effects of a stressor (U.S. EPA, 1979) or
(2) defining a margin of safety or quotient by comparing a
toxicologic effects concentration with an exposure estimate (SETAC,
1987).
LC50--A statistically or graphically estimated
concentration that is expected to be lethal to 50% of a group of
organisms under specified conditions (ASTM, 1996).
Lines of evidence--Information derived from different sources or
by different techniques that can be used to describe and interpret
risk estimates. Unlike the term ``weight of evidence,'' it does not
necessarily imply assignment of quantitative weightings to
information.
Lowest-observed-adverse-effect level (LOAEL)--The lowest level
of a stressor evaluated in a test that causes statistically
significant differences from the controls.
Maximum acceptable toxic concentration (MATC)--For a particular
ecological effects test, this term is used to mean either the range
between the NOAEL and the LOAEL or the geometric mean of the NOAEL
and the LOAEL. The geometric mean is also known as the chronic
value.
Measure of ecosystem and receptor characteristics--Measures that
influence the behavior and location of ecological entities of the
assessment endpoint, the distribution of a stressor, and life-
history characteristics of the assessment endpoint or its surrogate
that may affect exposure or response to the stressor.
Measure of effect--A change in an attribute of an assessment
endpoint or its surrogate in response to a stressor to which it is
exposed.
Measure of exposure--A measure of stressor existence and
movement in the environment and its contact or co-occurrence with
the assessment endpoint.
Measurement endpoint--See ``measure of effect.''
No-observed-adverse-effect level (NOAEL)--The highest level of a
stressor evaluated in a test that does not cause statistically
significant differences from the controls.
Population--An aggregate of individuals of a species within a
specified location in space and time.
Primary effect--An effect where the stressor acts on the
ecological component of interest itself, not through effects on
other components of the ecosystem (synonymous with direct effect;
compare with definition for secondary effect).
Probability density function (PDF)--Probability density
functions are particularly useful in describing the relative
likelihood that a variable will have different particular values of
x. The probability that a variable will have a value within a small
interval around x can be approximated by multiplying f(x) (i.e., the
value of y at x in a PDF plot) by the width of the interval (figure
B-2).
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Prospective risk assessment--An evaluation of the future risks
of a stressor(s) not yet released into the environment or of future
conditions resulting from an existing stressor(s).
Receptor--The ecological entity exposed to the stressor.
Recovery--The rate and extent of return of a population or
community to some aspect(s) of its previous condition. Because of
the dynamic nature of ecological systems, the attributes of a
``recovered'' system should be carefully defined.
Relative risk assessment--A process similar to comparative risk
assessment. It involves estimating the risks associated with
different stressors or management actions. To some, relative risk
connotes the use of quantitative risk techniques, while comparative
risk approaches more often rely on professional judgment. Others do
not make this distinction.
Retrospective risk assessment--An evaluation of the causal
linkages between observed ecological effects and stressor(s) in the
environment.
Risk characterization--A phase of ecological risk assessment
that integrates the exposure and stressor response profiles to
evaluate the likelihood of adverse ecological effects associated
with exposure to a stressor. Lines of evidence and the adversity of
effects are discussed.
Secondary effect--An effect where the stressor acts on
supporting components of the ecosystem, which in turn have an effect
on the ecological component of interest (synonymous with indirect
effects; compare with definition for primary effect).
Source--An entity or action that releases to the environment or
imposes on the environment a chemical, physical, or biological
stressor or stressors.
Source term--As applied to chemical stressors, the type,
magnitude, and patterns of chemical(s) released.
Stressor--Any physical, chemical, or biological entity that can
induce an adverse response (synonymous with agent).
Stressor-response profile--The product of characterization of
ecological effects in the analysis phase of ecological risk
assessment. The stressor-response profile summarizes the data on the
effects of a stressor and the relationship of the data to the
assessment endpoint.
Stress regime--The term ``stress regime'' has been used in at
least three distinct ways: (1) To characterize exposure to multiple
chemicals or to both chemical and nonchemical stressors (more
clearly described as multiple exposure, complex exposure, or
exposure to mixtures), (2) as a synonym for exposure that is
intended to avoid overemphasis on chemical exposures, and (3) to
describe the series of interactions of exposures and effects
resulting in secondary exposures, secondary effects and, finally,
ultimate effects (also known as risk cascade [Lipton et al., 1993]),
or causal chain, pathway, or network (Andrewartha and Birch, 1984).
Trophic levels--A functional classification of taxa within a
community that is based on feeding relationships (e.g., aquatic and
terrestrial green plants make up the first trophic level and
herbivores make up the second).
Appendix C--Conceptual Model Examples
Conceptual model diagrams are visual representations of the
conceptual models. They may be based on theory and logic, empirical
data, mathematical models, or probability models. These diagrams are
useful tools for communicating important pathways in a clear and
concise way. They can be used to ask new questions about
relationships that help generate plausible risk hypotheses. Further
discussion of conceptual models is found in section 3.4.
Flow diagrams like those shown in figures C-1 through C-3 are
typical conceptual model diagrams. When constructing flow diagrams,
it is helpful to use distinct and consistent shapes to distinguish
between stressors, assessment endpoints, responses, exposure routes,
and ecosystem processes. Although flow diagrams are often used to
illustrate conceptual models, there is no set configuration for
conceptual model diagrams, and the level of complexity may vary
considerably depending on the assessment. Pictorial representations
of the processes of an ecosystem can be more effective (e.g.,
Bradley and Smith, 1989).
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Figure C-1 illustrates the relationship between a primary
physical stressor (logging roads) and an effect on an assessment
endpoint (fecundity in insectivorous fish). This simple diagram
illustrates the effect of building logging roads (which could be
considered a stressor or a source) in ecosystems where slope, soil
type, low riparian cover, and other ecosystem characteristics lead
to the erosion of soil, which enters streams and smothers the
benthic organisms (exposure pathway is not explicit in this
diagram). Because of the dependence of insectivorous fish on benthic
organisms, the fish are believed to be at risk from the building of
logging roads. Each arrow in this diagram represents a hypothesis
about the proposed relationship (e.g., human action and stressor,
stressor and effect, primary effect to secondary effect). Each risk
hypothesis provides insights into the kinds of data that will be
needed to verify that the hypothesized relationships are valid.
Figure C-2 is a conceptual model used by Kendall et al. (1996)
to track a contaminant through upland ecosystems. In this example,
upland birds are exposed to lead shot when it becomes embedded in
their tissue after being shot and by ingesting lead accidentally
when feeding on the ground. Both are hypothesized to result in
increased morbidity (e.g., lower reproduction and competitiveness
and higher predation and infection) and mortality, either directly
(lethal intoxication) or indirectly (effects of morbidity leading to
mortality). These effects are believed to result in changes in
upland bird populations and, because of hypothesized exposure of
predators to lead, to increased predator mortality. This example
shows multiple exposure pathways for effects on two assessment
endpoints. Each arrow contains within it assumptions and hypotheses
about the relationship depicted that provide the basis for
identifying data needs and analyses.
Figure C-3 is a conceptual model adapted from the Waquoit Bay
watershed risk assessment. At the top of the model, multiple human
activities that occur in the watershed are shown in rectangles.
Those sources of stressors are linked to stressor types depicted in
ovals. Multiple sources are shown to contribute to an individual
stressor, and each source may contribute to more than one stressor.
The stressors then lead to multiple ecological effects depicted
again in rectangles. Some rectangles are double-lined to indicate
effects that can be directly measured for data analysis. Finally,
the effects are linked to particular assessment endpoints. The
connections show that one effect can result in changes in many
assessment endpoints. To fully depict exposure pathways and types of
effects, specific portions of this conceptual model would need to be
expanded to illustrate those relationships.
Appendix D--Analysis Phase Examples
The analysis phase process is illustrated here for a chemical,
physical, and biological stressor. These examples do not represent
all possible approaches, but they illustrate the analysis phase
process using information from actual assessments.
D.1. Special Review of Granular Formulations of Carbofuran Based on
Adverse Effects on Birds
Figure D-1 is based on an assessment of the risks of carbofuran
to birds under the Federal Insecticide, Fungicide, and Rodenticide
Act (FIFRA) (Houseknecht, 1993). Carbofuran is a broad-spectrum
insecticide and nematicide applied primarily in granular form on 27
crops as well as forests and pine seed orchards. The assessment
endpoint was survival of birds that forage in agricultural areas
where carbofuran is applied.
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The analysis phase focused on birds that may incidentally ingest
granules as they forage or that may eat other animals that contain
granules or residues. Measures of exposure included application
rates, attributes of the formulation (e.g., size of granules), and
residues in prey organisms. Measures of the ecosystem and receptors
included an inventory of bird species that may be exposed following
applications for 10 crops. The birds' respective feeding behaviors
were considered in developing routes of exposure. Measures of effect
included laboratory toxicity studies and field investigations of
bird mortality.
The source of the chemical was application of the pesticide in
granular form. The distribution of the pesticide in agricultural
fields was estimated on the basis of the application rate. The
number of exposed granules was estimated from literature data. On
the basis of a review of avian feeding behavior, seed-eating birds
were assumed to ingest any granules left uncovered in the field. The
intensity of exposure was summarized as the number of exposed
granules per square foot.
The stressor-response relationship was described using the
results of toxicity tests. These data were used to construct a
toxicity statistic expressed as the number of granules needed to
kill 50% of the test birds (i.e., granules per LD50),
assuming 0.6 mg of active ingredient per granule and average body
weights for the birds tested. Field studies were used to document
the occurrence of bird deaths following applications and provide
further causal evidence. Carbofuran residues and cholinesterase
levels were used to confirm that exposure to carbofuran caused the
deaths.
D.2. Modeling Losses of Bottomland-Forest Wetlands
Figure D-2 is based on an assessment of the ecological
consequences (risks) of long-term changes in hydrologic conditions
(water-level elevations) for three habitat types in the Lake Verret
Basin of Louisiana (Brody et al., 1989, 1993; Conner and Brody,
1989). The project was intended to provide a habitat-based approach
for assessing the environmental impacts of Federal water projects
under the National Environmental Policy Act and Section 404 of the
Clean Water Act. Output from the models provided risk managers with
information on how changes in water elevation might alter the
ecosystem. The primary anthropogenic stressor addressed in this
assessment was artificial levee construction for flood control,
which contributes to land subsidence by reducing sediment deposition
in the floodplain. Assessment endpoints included forest community
structure and habitat value to wildlife species and the species
composition of the wildlife community.
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The analysis phase began by considering primary (direct) effects
of water-level changes on plant community composition and habitat
characteristics. Measures of exposure included the attributes and
placement of the levees and water-level measurements. Measures of
ecosystem and receptor characteristics included location and extent
of bottomland-hardwood communities, plant species occurrences within
these communities, and information on historic flow regimes.
Measures of effects included laboratory studies of plant response to
moisture and field measurements along moisture gradients.
While the principal stressor under evaluation was the
construction of levees, the decreased gradient of the river due to
sediment deposition at its mouth also contributed to increased water
levels. The extent and frequency of flooding were simulated by the
FORFLO model based on estimates of net subsidence rates from levee
construction and decreased river gradient. Seeds and seedlings of
the tree species were assumed to be exposed to the altered flooding
regime. Stressor-response relationships describing plant response to
moisture (e.g., seed germination, survival) were embedded within the
FORFLO model. This information was used by the model to simulate
changes in plant communities: The model tracks the species type,
diameter, and age of each tree on simulated plots from the time the
tree enters the plot as a seedling or sprout until it dies. The
FORFLO model calculated changes in the plant community over time
(from 50 to 280 years). The spatial extent of the three habitat
types of interest--wet bottomland hardwoods, dry bottomland
hardwoods, and cypress-tupelo swamp--was mapped into a GIS along
with the hydrological information. The changes projected by FORFLO
were then manually linked to the GIS to show how the spatial
distribution of different communities would change. Evidence that
flooding would actually cause these changes included comparisons of
model predictions with field measurements, the laboratory studies of
plant response to moisture, and knowledge of the mechanisms by which
flooding elicits changes in plant communities.
Secondary (indirect) effects on wildlife associated with changes
in the habitat provided by the plant community formed the second
part of the analysis phase. Important measures included life-history
characteristics and habitat needs of the wildlife species. Effects
on wildlife were inferred by evaluating the suitability of the plant
community as habitat. Specific aspects of the community structures
calculated by the FORFLO model provided the input to this part of
the analysis. For example, the number of snags was used to evaluate
habitat value for woodpeckers. Resident wildlife (represented by
five species) was assumed to co-occur with the altered plant
community. Habitat value was evaluated by calculating the Habitat
Suitability Index (HSI) for each habitat type multiplied by the
habitat type's area.
A combined exposure and stressor-response profile is shown in
figure D-2; these two elements were combined with the models used
for the analysis and then used directly in risk characterization.
D.3. Pest Risk Assessment of Importation of Logs from Chile
Figure D-3 is based on the assessment of potential risks to U.S.
forests due to the incidental introduction of insects, fungi, and
other pests inhabiting logs harvested in Chile and transported to
U.S. ports (USDA, 1993). This risk assessment was used to determine
whether actions to restrict or regulate the importation of Chilean
logs were needed to protect U.S. forests and was conducted by a team
of six experts under the auspices of the U.S. Department of
Agriculture Forest Service. Stressors include insects, forest
pathogens (e.g., fungi), and other pests. The assessment endpoint
was the survival and growth of tree species (particularly conifers)
in the western United States. Damage that would affect the
commercial value of the trees as lumber was clearly of interest.
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The analysis phase was carried out by eliciting professional
opinions from a team of experts. Measures of exposure used by the
team included distribution information for the imported logs and
attributes of the insects and pathogens such as dispersal mechanisms
and life-history characteristics. Measures of ecosystem and receptor
characteristics included the climate of the United States, location
of geographic barriers, knowledge of host suitability, and ranges of
potential host species. Measures of effect included knowledge of the
infectivity of these pests in other countries and the infectivity of
similar pests on U.S. hosts.
This information was used by the risk assessment team to
evaluate the potential for exposure. They began by evaluating the
likelihood of entry of infested logs into the United States. The
distribution of the organism's given entry was evaluated by
considering the potential for colonization and spread beyond the
point of entry as well as the likelihood of the organisms surviving
and reproducing. The potential for exposure was summarized by
assigning each of the above elements a judgment-based value of high,
medium, or low.
The evaluation of ecological effects was also conducted on the
basis of collective professional judgment. Of greatest relevance to
this guidance was the consideration of environmental damage
potential, defined as the likelihood of ecosystem destabilization,
reduction in biodiversity, loss of keystone species, and reduction
or elimination of endangered or threatened species. (The team also
considered economic damage potential and social and political
influences; however, for the purposes of these Guidelines, those
factors are considered to be part of the risk management process.)
Again, each consideration was assigned a value of high, medium, or
low to summarize the potential for ecological effects.
Appendix E--Criteria for Determining Ecological Adversity: A
Hypothetical Example (Adapted From Harwell et al., 1994) \1\
As a result of a collision at sea, an oil tanker releases 15
million barrels of #2 fuel oil 3 km offshore. It is predicted that
prevailing winds will carry the fuel onshore within 48 to 72 hours.
The coastline has numerous small embayments that support an
extensive shallow, sloping subtidal community and a rich intertidal
community. A preliminary assessment determines that if no action is
taken, significant risks to the communities will result. Additional
risk assessments are conducted to determine which of two options
should be used to clean up the oil spill.
---------------------------------------------------------------------------
\1\ This example is simplified for illustrative purposes. In
other situations, it may be considerably more difficult to draw
clear conclusions regarding relative ecological adversity.
---------------------------------------------------------------------------
Option 1 is to use a dispersant to break up the slick, which
would reduce the likelihood of extensive onshore contamination but
would cause extensive mortality to the phytoplankton, zooplankton,
and ichthyoplankton (fish larvae), which are important for
commercial fisheries. Option 2 is to try to contain and pump off as
much oil as possible; this option anticipates that a shift in wind
direction will move the spill away from shore and allow for natural
dispersal at sea. If this does not happen, the oil will contaminate
the extensive sub-and intertidal mud flats, rocky intertidal
communities, and beaches and pose an additional hazard to avian and
mammalian fauna. It is assumed there will be a demonstrable change
beyond natural variability in the assessment endpoints (e.g.,
structure of planktonic, benthic, and intertidal communities). What
is the adversity of each option?
Nature and intensity of the effect. For both options,
the magnitude of change in the assessment endpoints is likely to be
severe. Planktonic populations often are characterized by extensive
spatial and temporal variability. Nevertheless, within the spatial
boundaries of the spill, the use of dispersants is likely to produce
complete mortality of all planktonic forms within the upper 3 m of
water. For benthic and intertidal communities, which generally are
stable and have less spatial and temporal variability than
planktonic forms, oil contamination will likely result in severe
impacts on survival and chronic effects lasting for several years.
Thus, under both options, changes in the assessment endpoints will
probably exceed the natural variability for threatened communities
in both space and time.
Spatial scale. The areal extent of impacts is similar
for each of the options. While extensive, the area of impact
constitutes a small percentage of the landscape. This leaves
considerable area available for replacement stocks and creates
significant fragmentation of either the planktonic or inter-and
subtidal habitats. Ecological adversity is reduced because the area
is not a mammalian or avian migratory corridor.
Temporal scale and recovery. On the basis of experience
with other oil spills, it is assumed that the effects are reversible
over some time period. The time needed for reversibility of changes
in phytoplankton and zooplankton populations should be short (days
to weeks) given their rapid generation times and easy immigration
from adjacent water masses. There should not be a long recovery
period for ichthyoplankton, since they typically experience
extensive natural mortality, and immigration is readily available
from surrounding water masses. On the other hand, the time needed
for reversibility of changes in benthic and intertidal communities
is likely to be long (years to decades). First, the stressor (oil)
would be likely to persist in sediments and on rocks for several
months to years. Second, the life histories of the species
comprising these communities span 3 to 5 years. Third, the
reestablishment of benthic intertidal community and ecosystem
structure (hierarchical composition and function) often requires
decades.
Both options result in (1) assessment endpoint effects that are
of great severity, (2) exceedances of natural variability for those
endpoints, and (3) similar estimates of areal impact. What
distinguishes the two options is temporal scale and reversibility.
In this regard, changes to the benthic and intertidal ecosystems are
considerably more adverse than those to the plankton. On this basis,
the option of choice would be to disperse the oil, effectively
preventing it from reaching shore where it would contaminate the
benthic and intertidal communities.
References
Alder, HL; Roessler, EB. (1972) Introduction to probability and
statistics. San Francisco, CA: WH Freeman and Co.
American Society for Quality Control (ASQC). (1994) American
National Standard: specifications and guidelines for quality systems
for environmental data collection and environmental technology
programs. ANSI/ASQC E4-1994. Milwaukee, WI: SQC.
American Society for Testing and Materials. (1996) Standard
terminology relating to biological effects and environmental fate.
E943-95a. In: ASTM; 1996 Annual Book of ASTM Standards, Section 11,
Water and Environmental Technology. Philadelphia, PA: ASTM.
Andrewartha, HG; Birch, LC. (1984) The ecological web: more on
the distribution and abundance of animals. Chicago, IL: University
of Chicago Press.
Ankley, GT; Katko, A; Arthur, JW. (1990) Identification of
ammonia as an important sediment-associated toxicant in the Lower
Fox River and Green Bay, Wisconsin. Environ Toxicol Chem 9:313-322.
Auer, CM; Zeeman, M; Nabholz, JV; Clements, RG. (1994) SAR--the
U.S. regulatory perspective. SAR QSAR Environ Res 2:29-38.
Baker, JL; Barefoot, AC; Beasley, LE; Burns, LA; Caulkins, PP;
Clark, JE; Feulner, RL; Giesy, JP; Graney, RL; Griggs, RH; Jacoby,
HM; Laskowski, DA; Maciorowski, AF; Mihaich, EM; Nelson, HP, Jr.;
Parrish, PR; Siefert, RE; Solomon, KR; van der Schalie, WH, eds.
(1994) Aquatic dialogue group: pesticide risk assessment and
mitigation. Pensacola, FL: SETAC Press.
Barbour, MT; Stribling, JB; Karr, JR. (1995) Multimetric
approach for establishing biocriteria and measuring biological
condition. In: Biological assessment and criteria, tools for water
resource planning and decision making. Davis, WS; Simon, TP, eds.
Boca Raton, FL: Lewis Publishers, pp. 63-77.
Barnthouse, LW; Brown, J. (1994) Issue paper on conceptual model
development. In: Ecological risk assessment issue papers.
Washington, DC: Risk Assessment Forum, U.S. Environmental Protection
Agency, pp. 3-1 to 3-70. EPA/630/R-94/009.
Barnthouse, LW; O'Neill, RV; Bartell, SM; Suter, GW, II. (1986)
Population and ecosystem theory in ecological risk assessment. In:
Aquatic ecology and hazard assessment, 9th symposium. Poston, TM;
Purdy, R, eds. Philadelphia, PA: American Society for Testing and
Materials, pp. 82-96.
Barnthouse, LW; Suter, GW, II; Rosen, AE; Beauchamp, JJ. (1987)
Estimating responses of fish populations to toxic contaminants.
Environ Toxicol Chem 6:811-824.
Barnthouse, LW; Suter, GW, II; Rosen, AE. (1990) Risks of toxic
contaminants to exploited fish populations: influence of life
history, data uncertainty, and exploitation intensity. Environ
Toxicol Chem 9:297-311.
Bartell, SM; Gardner, RH; O'Neill, RV. (1992) Ecological risk
estimation. Boca Raton, FL: Lewis Publishers.
[[Page 26919]]
Bedford, BL; Preston, EM. (1988) Developing the scientific basis
for assessing cumulative effects of wetland loss and degradation on
landscape functions: status, perspectives, and prospects. Environ
Manage 12:751-771.
Bradbury, SP. (1994) Predicting modes of toxic action from
chemical structure: an overview. SAR QSAR Environ Res 2:89-104.
Bradley, CE; Smith, DG. (1989) Plains cottonwood recruitment and
survival on a prairie meandering river floodplain. Milk River,
Southern Alberta in Northern Canada. Can J Botany 64:1433-1442.
Broderius, SJ. (1991) Modeling the joint toxicity of xenobiotics
to aquatic organisms: basic concepts and approaches In: Aquatic
toxicology and risk assessment: fourteenth volume. Mayes, MA;
Barron, MG, eds. ASTM STP 1124. Philadelphia, PA: American Society
for Testing and Materials, pp. 107-127.
Broderius, SJ; Kahl, MD; Hoglund, MD. (1995) Use of joint toxic
response to define the primary mode of toxic action for diverse
industrial organic chemicals. Environ Toxicol Chem 9:1591-1605.
Brody, M; Conner, W; Pearlstine, L; Kitchens, W. (1989) Modeling
bottomland forest and wildlife habitat changes in Louisiana's
Atchafalaya Basin. In: Freshwater wetlands and wildlife. Sharitz,
RR; Gibbons, JW, eds. U.S. Department of Energy Symposium Series,
No. 61. CONF-8603100. Oak Ridge, TN: Office of Science and Technical
Information, U.S. Department of Energy.
Brody, MS; Troyer, ME; Valette, Y. (1993) Ecological risk
assessment case study: modeling future losses of bottomland forest
wetlands and changes in wildlife habitat within a Louisiana basin.
In: A review of ecological assessment case studies from a risk
assessment perspective. Washington, DC: Risk Assessment Forum, U.S.
Environmental Protection Agency, pp. 12-1 to 12-39. EPA/630/R-92/
005.
Burmaster, DE; Menzie, CA; Freshman, JS; Burris, JA; Maxwell,
NI; Drew, SR. (1991) Assessment of methods for estimating aquatic
hazards at Superfund-type sites: a cautionary tale. Environ Toxicol
Chem 10:827-842.
Callahan, CA; Menzie, CA; Burmaster, DE; Wilborn, DC; Ernst, T.
(1991) On-site methods for assessing chemical impacts on the soil
environment using earthworms: a case study at the Baird and McGuire
Superfund site, Holbrook, Massachusetts. Environ Toxicol Chem
10:817-826.
Cardwell, RD; Parkhurst, BR; Warren-Hicks, W; Volosin, JS.
(1993) Aquatic ecological risk. Water Environ Technol 5:47-51.
Clemen, RT. (1996) Making hard decisions. 2nd ed. New York:
Duxbury Press, 664 pp.
Clements, RG; Nabholz, JV. (1994) ECOSAR: a computer program for
estimating the ecotoxicity of industrial chemicals based on
structure activity relationships, user's guide. Washington, DC:
Environmental Effects Branch, Health and Environmental Review
Division (7402), U.S. Environmental Protection Agency. EPA/748/R-93/
002.
Cohrssen, J; Covello, VT. (1989) Risk analysis: a guide to
principles and methods for analyzing health and ecological risks.
Washington, DC: Council on Environmental Quality.
Commission on Risk Assessment and Risk Management. (1997)
Framework for environmental health risk management. Final Report.
Volume 1. Washington, DC: Commission on Risk Assessment and Risk
Management.
Conner, WH; Brody, M. (1989) Rising water levels and the future
of southeastern Louisiana swamp forests. Estuaries 12(4):318-323.
Costanza, R; d'Arge, R; de Groot, R; Farber, S; Grasso, M;
Hannon, B; Limburg, K; Naeem, S; O'Neill, RV; Paruelo, J; Raskinm,
RG; Sutton, P; van den Belt, M. (1997) The value of the world's
ecosystem services and natural capital. Nature 387:253-260.
Cowan, CE; Versteeg, DJ; Larson, RJ; Kloepper-Sams, PJ. (1995)
Integrated approach for environmental assessment of new and existing
substances. Regul Toxicol Pharmacol 21:3-31.
Cronin, MTD; Dearden, JC. (1995) QSAR in toxicology. 1.
Prediction of aquatic toxicity. Quant Struct-Act Relat 14:1-7.
Detenbeck, N. (1994) Ecological risk assessment case study:
effects of physical disturbance on water quality status and water
quality improvement function of urban wetlands. In: A review of
ecological assessment case studies from a risk assessment
perspective, vol. II. Washington, DC: Risk Assessment Forum, U.S.
Environmental Protection Agency, pp. 4-1 to 4-58. EPA/630/R-94/003.
Detenbeck, NE; DeVore, PW; Niemi, GJ; Lima, A. (1992) Recovery
of temperate-stream fish communities from disturbance: a review of
case studies and synthesis of theory. Environ Manage 16(1):33-53.
Eberhardt, LL; Thomas, JM. (1991) Designing environmental field
studies. Ecol Mono 61(1):53-73.
Emlen, JM. (1989) Terrestrial population models for ecological
risk assessment: a state-of-the-art review. Environ Toxicol Chem
8:831-842.
European Community (EC). (1993) Technical guidance document in
support of the risk assessment Commission Directive (93/67/EEC) for
new substances notified in accordance with the requirements of
Council Directive 67/548/EEC. Brussels, Belgium.
Ferson, S; Ginzburg, L; Silvers, A. (1989) Extreme event risk
analysis for age-structured populations. Ecol Model 47:175-187.
Fisher, SG; Woodmansee, R. (1994) Issue paper on ecological
recovery. In: Ecological risk assessment issue papers. Washington,
DC: Risk Assessment Forum, U.S. Environmental Protection Agency, pp.
7-1 to 7-54. EPA/630/R-94/009.
Fotheringham, AS; Rogerson, PA. (1993) GIS and spatial
analytical problems. Int J Geograph Inf Syst 7(1):3-19.
Fox, GA. (1991) Practical causal inference for
ecoepidemiologists. J Toxicol Environ Health 33:359-373.
Gauch, HG. (1982) Multivariate analysis in community ecology.
Cambridge, MA: Cambridge University Press.
Gaudet, C. (1994) A framework for ecological risk assessment at
contaminated sites in Canada: review and recommendations. Ottawa,
Canada: Environment Canada.
Gibbs, JP. (1993) Importance of small wetlands for the
persistence of local populations of wetland-associated animals.
Wetlands 13(1):25-31.
Gilbert, RO. (1987) Statistical methods for environmental
pollution monitoring. New York, NY: Van Nostrand Reinhold.
Ginzburg, LR; Slobodkin, LB; Johnson, K; Bindman, AG. (1982)
Quasiextinction probabilities as a measure of impact on population
growth. Risk Anal 2:171-182.
Gosselink, JG; Shaffer, GP; Lee, LC; Burdick, DL; Childer, NC;
Leibowitz, NC; Hamilton, SC; Boumans, R; Cushmam, D; Fields, S;
Koch, M; Visser, JM. (1990) Landscape conservation in a forested
wetland watershed: can we manage cumulative impacts? Bioscience
40(8):588-600.
Harris, HJ; Wenger, RB; Harris, VA; Devault, DS. (1994) A method
for assessing environmental risk: a case study of Green Bay, Lake
Michigan, USA. Environ Manage 18(2):295-306.
Harwell, MJ; Norton, B; Cooper, W; Gentile, J. (1994) Issue
paper on ecological significance. In: Ecological risk assessment
issue papers. Washington, DC: Risk Assessment Forum, U.S.
Environmental Protection Agency, pp. 2-1 to 2-49. EPA/630/R-94/009.
Hassell, MP. (1986) Detecting density dependence. Trends Ecol
Evol 1:90-93.
Health Council of the Netherlands (HCN). (1993) Ecotoxicological
risk assessment and policy-making in the Netherlands--dealing with
uncertainties. Network 6(3)/7(1):8-11.
Heck, WW. (1993) Ecological assessment case study: the National
Crop Loss Assessment Network. In: A review of ecological assessment
case studies from a risk assessment perspective. Washington, DC:
Risk Assessment Forum, U.S. Environmental Protection Agency, pp. 6-1
to 6-32. EPA/630/R-92/005.
Hermens, J; Canton, H; Janssen, P; De Jong, R. (1984a)
Quantitative structure-activity relationships and toxicity studies
of mixtures of chemicals with anaesthetic potency: acute lethal and
sublethal toxicity to Daphnia magna. Aquatic Toxicol 5:143-154.
Hermens, J; Canton, H; Steyger, N; Wegman, R. (1984b) Joint
effects of a mixture of 14 chemicals on mortality and inhibition of
reproduction of Daphnia magna. Aquatic Toxicol 5:315-322.
Hill, AB. (1965) The environment and disease: association or
causation? Proc R Soc Med 58:295-300.
Holling, CS, ed. (1978) Adaptive environmental assessment and
management. Chichester, UK: John Wiley & Sons.
Houseknecht, CR. (1993) Ecological risk assessment case study:
special review of the granular formulations of carbofuran based on
adverse effects on birds. In: A review of ecological assessment case
studies from a risk assessment perspective. Washington, DC: Risk
Assessment Forum, U.S. Environmental Protection Agency, pp. 3-1 to
3-25. EPA/630/R-92/005.
Huggett, RJ; Kimerle, RA; Merhle, PM, Jr.; Bergman, HL, eds.
(1992) Biomarkers: biochemical, physiological, and histological
markers of anthropogenic stress. Boca Raton, FL: Lewis Publishers.
Hughes, RM. (1995) Defining acceptable biological status by
comparing with reference
[[Page 26920]]
conditions. In: Biological assessment and criteria: tools for water
resource planning and decision making. Davis, WS; Simon, TP, eds.
Boca Raton, FL: Lewis Publishers, pp. 31-47.
Hunsaker, CT; Graham, RL; Suter, GW, II; O'Neill, RV;
Barnthouse, LW; Gardner, RH. (1990) Assessing ecological risk on a
regional scale. Environ Manage 14(3):325-332.
Hurlbert, SH. (1984) Pseudoreplication and the design of
ecological field experiments. Ecol Mono 54:187-211.
Johnson, BL. (1995) Applying computer simulation models as
learning tools in fishery management. North Am J Fisheries Manage
15:736-747.
Johnson, LB; Gage, SH. (1997) Landscape approaches to the
analysis of aquatic ecosystems. Freshwater Biol 37:113-132.
Johnston, CA; Detenbeck, NE; Niemi, GJ. (1990) The cumulative
effect of wetlands on stream water quality and quantity: a landscape
approach. Biogeochemistry 10:105-141.
Karr, JR. (1981) Assessment of biotic integrity using fish
communities. Fisheries 6(6):21-27.
Karr, JR; Fausch, KD; Angermeier, PL; Yant, PR; Schlosser, IJ.
(1986) Assessing biological integrity in running waters: a method
and its rationale. Illinois Natural History Survey. Special
Publication 5. Champaign, IL.
Kenaga, EE. (1973) Factors to be considered in the evaluation of
the toxicity of pesticides to birds in their environment. Environ
Qual Saf 2:166-181.
Kendall, RJ; Lacher, TE; Bunck, C; Daniel, B; Driver, C; Grue,
CE; Leighton, F; Stansley, W; Watanabe, PG; Whitworth, M. (1996) An
ecological risk assessment of lead shot exposure in non-waterfowl
avian species: upland game birds and raptors. Environ Toxicol Chem
15:4-20.
Konemann, H. (1981) Fish toxicity tests with mixtures of more
than two chemicals: a proposal for a quantitative approach and
experimental results. Aquatic Toxicol 19:229-238.
Laird, NM; Mosteller, R. (1990) Some statistical methods for
combining experimental results. Int J Technol Assess Health Care
6:5-30.
Landis, WG; Matthews, RA; Markiewicz, AJ; Matthews, GB. (1993)
Multivariate analysis of the impacts of the turbine fuel JP-4 in a
microcosm toxicity test with implications for the evaluation of
ecosystem dynamics and risk assessment. Ecotoxicology 2:271-300.
Lipnick, RL. (1995) Structure-activity relationships. In:
Fundamentals of aquatic toxicology effects, environmental fate, and
risk assessment. Rand, GM; Petrocelli, SR, eds. London, UK: Taylor
and Francis, pp. 609-655.
Lipton, J; Galbraith, H; Burger, J; Wartenberg, D. (1993) A
paradigm for ecological risk assessment. Environ Manage 17:1-5.
Ludwig, JA; Reynolds, JF. (1988) Statistical ecology. New York,
NY: Wiley-Interscience, 337 pp.
Lynch, DG; Macek, GJ; Nabholz, JV; Sherlock, SM; Wright, R.
(1994) Ecological risk assessment case study: assessing the
ecological risks of a new chemical under the Toxic Substances
Control Act. In: A review of ecological assessment case studies from
a risk assessment perspective, vol. II. Washington, DC: Risk
Assessment Forum, U.S. Environmental Protection Agency, pp. 1-1 to
1-35. EPA/630/R-94/003.
MacIntosh, DL; Suter GW, II; Hoffman, FO. (1994) Uses of
probabilistic exposure models in ecological risk assessments of
contaminated sites. Risk Anal 14(4):405-419.
Mann, C. (1990) Meta-analysis in the breech. Science 249:476-
480.
McCarty, LS; Mackay, D. (1993) Enhancing ecotoxicological
modeling and assessment: body residues and modes of toxic action.
Environ Sci Technol 27:1719-1728.
Meij, R. (1991) The fate of mercury in coal-fired power plants
and the influence of wet flue-gas desulphurization. Water Air Soil
Pollut 56:21-33.
Menzie, CA; Burmaster, DE; Freshman, JS; Callahan, CA. (1992)
Assessment of methods for estimating ecological risk in the
terrestrial component: a case study at the Baird & McGuire Superfund
Site in Holbrook, Massachusetts. Environ Toxicol Chem 11:245-260.
Menzie, C; Henning, MH; Cura, J; Finkelstein, K; Gentile, J;
Maughan J; Mitchell, D; Petron, S; Potocki, B; Svirsky, S; Tyler, P.
(1996) Special report of the Massachusetts weight-of-evidence
workgroup: a weight of evidence approach for evaluating ecological
risks. Human Ecol Risk Assess 2:277-304.
Messer, JJ; Linthurst, RA; Overton, WS. (1991) An EPA program
for monitoring ecological status and trends. Environ Monitor Assess
17:67-78.
Nabholz, JV. (1991) Environmental hazard and risk assessment
under the United States Toxic Substances Control Act. Science Total
Environ 109/110: 649-665.
Nabholz, JV; Miller, P; Zeeman, M. (1993) Environmental risk
assessment of new chemicals under the Toxic Substances Control Act
(TSCA) section five. In: Environmental toxicology and risk
assessment. Landis, WG; Hughes, SG; Lewis, M; Gorsuch, JW, eds. ASTM
STP 1179. Philadelphia, PA: American Society for Testing and
Materials, pp. 40-55.
National Research Council. (1994) Science and judgment in risk
assessment. Washington, DC: National Academy Press.
National Research Council. (1996) Understanding risk: informing
decisions in a democratic society. Washington, DC: National Academy
Press.
Newman, MC. (1995) Advances in trace substances research:
quantitative methods in aquatic ecotoxicology. Boca Raton, FL: Lewis
Publishers.
Niemi, GJ; DeVore, P; Detenbeck, N; Taylor, D; Lima, A; Pastor,
J; Yount, JD; Naiman, RJ. (1990) Overview of case studies on
recovery of aquatic systems from disturbance. Environ Manage 14:571-
587.
Novitski, RP. (1979) Hydrologic characteristics of Wisconsin's
wetlands and their influence on floods, stream flow, and sediment.
In: Wetland functions and values: the state of our understanding.
Greeson, PE; Clark, JR; Clark, JE, eds. Minneapolis, MN: American
Water Resources Association, pp. 377-388.
Oberts, GL. (1981) Impact of wetlands on watershed water
quality. In: Selected proceedings of the Midwest Conference on
Wetland Values and Management. Richardson, B, ed. Navarre, MN:
Freshwater Society, pp. 213-226.
Okkerman, PC; Plassche, EJVD; Emans, HJB. (1993) Validation of
some extrapolation methods with toxicity data derived from
multispecies experiments. Ecotoxicol Environ Saf 25:341-359.
O'Neill, RV; Gardner, RH; Barnthouse, LW; Suter, GW, II;
Hildebrand, SG; Gehrs, CW. (1982) Ecosystem risk analysis: a new
methodology. Environ Toxicol Chem 1:167-177.
Orr, RL; Cohen, SD; Griffin, RL. (1993) Generic non-indigenous
pest risk assessment process. Beltsville, MD: USDA Animal and Plant
Health Inspection Service.
Ott, WR. (1978) Environmental indices--theory and practice. Ann
Arbor, MI: Ann Arbor Science. Cited in: Suter, GW, II. (1993) A
critique of ecosystem health concepts and indexes. Environ Toxicol
Chem 12:1533-1539.
Parkhurst, BR; Warren-Hicks, W; Etchison, T; Butcher, JB;
Cardwell, RD; Voloson, J. (1995) Methodology for aquatic ecological
risk assessment. RP91 AER-1 1995. Alexandria, VA: Water Environment
Research Foundation.
Pastorok, RA; Butcher, MK; Nielsen, RD. (1996) Modeling wildlife
exposure to toxic chemicals: trends and recent advances. Human Ecol
Risk Assess 2:444-480.
Pearlstine, L; McKellar, H; Kitchens, W. (1985) Modelling the
impacts of a river diversion on bottomland forest communities in the
Santee River Floodplain, South Carolina. Ecol Model 29:281-302.
Pelczar, MJ; Reid, RD. (1972) Microbiology. New York, NY:
McGraw-Hill Company.
Peterman, RM. (1990) The importance of reporting statistical
power: the forest decline and acidic deposition example. Ecology
71:2024-2027.
Petitti, DB. (1994) Meta-analysis, decision analysis and cost-
effectiveness analysis: methods for quantitative synthesis in
medicine. Monographs in epidemiology and biostatistics, vol. 24. New
York, NY: Oxford University Press.
Phipps, RL. (1979) Simulation of wetlands forest vegetation
dynamics. Ecol Model 7:257-288.
Pielou, EC. (1984) The interpretation of ecological data. A
primer on classification and ordination. New York, NY: Wiley-
Interscience, 263 pp.
Preston, EM; Bedford, BL. (1988) Evaluating cumulative effects
on wetland functions: a conceptual overview and generic framework.
Environ Manage 12(5):565-583.
Richards, C; Haro, RJ; Johnson, LB; Host, GE. (1997) Catchment
and reach-scale properties as indicators of macroinvertebrate
species traits. Freshwater Biol 37:219-230.
Risser, PG. (1988) General concepts for measuring cumulative
impacts on wetland ecosystems. Environ Manage 12(5):585-589.
Rotenberry, JT; Wiens, JA. (1985) Statistical power analysis and
community-wide patterns. Am Naturalist 125:164-168.
Ruckelshaus, WD. (1983) Science, risk, and public policy.
Science 221:1026-1028.
[[Page 26921]]
Sample, BE; Opresko, DM; Suter, GW, II. (1996) Toxicological
benchmarks for wildlife: 1996 revision. ES/ER/TM-86/R3. Oak Ridge,
TN: Oak Ridge National Laboratory, Health Sciences Research
Division.
Sawyer, TW; Safe, S. (1985) In vitro AHH induction by
polychlorinated biphenyl and dibenzofuran mixtures: additive
effects. Chemosphere 14:79-84.
Schindler, DW. (1987) Detecting ecosystem responses to
anthropogenic stress. Can J Fish Aquat Sci 44(Suppl.1):6-25.
Simberloff, D; Alexander, M. (1994) Issue paper on biological
stressors. In: Ecological risk assessment issue papers. Washington,
DC: Risk Assessment Forum, U.S. Environmental Protection Agency, pp.
6-1 to 6-59. EPA/630/R-94/009.
Smith, EP; Cairns, J, Jr. (1993) Extrapolation methods for
setting ecological standards for water quality: statistical and
ecological concerns. Ecotoxicology 2:203-219.
Smith, EP; Shugart, HH. (1994) Issue paper on uncertainty in
ecological risk assessment. In: Ecological risk assessment issue
papers. Washington, DC: Risk Assessment Forum, U.S. Environmental
Protection Agency, pp. 8-1 to 8-53. EPA/630/R-94/009.
Society of Environmental Toxicology and Chemistry (SETAC).
(1987) Research priorities in environmental risk assessment. Report
of a workshop held in Breckenridge, CO, August 16-21, 1987.
Washington, DC: SETAC.
Solomon, KR; Baker, DB; Richards, RP; Dixon, KR; Klaine, SJ; La
Point, TW; Kendall, RJ; Weisskopf, CP; Giddings, JM; Geisy, JP;
Hall, LW; Williams, WM. (1996) Ecological risk assessment of
atrazine in North American surface waters. Environ Toxicol Chem
15(1):31-76.
Starfield, AM; Bleloch, AL. (1991) Building models for
conservation and wildlife management. Edina, MN: Burgess
International Group, Inc.
Stephan, CE. (1977) Methods for calculating an LC50.
In: ASTM Special Technical Publication 634. Philadelphia, PA:
American Society for Testing and Materials, pp. 65-88.
Stephan, CE; Rogers, JR. (1985) Advantages of using regression
analysis to calculate results of chronic toxicity tests. In: Aquatic
toxicology and hazard assessment. Eighth symposium. Bahner, RC;
Hanse, DJ, eds. Philadelphia, PA: American Society for Testing and
Materials, pp. 328-339.
Stephan, CE; Mount, DI; Hansen, DJ; Gentile, JH; Chapman, GA;
Brungs, WA. (1985) Guidelines for deriving numerical national water
quality criteria for the protection of aquatic organisms and their
uses. Duluth, MN: Office of Research and Development, U.S.
Environmental Protection Agency. PB85-227049.
Stewart-Oaten, A; Murdoch, WW; Parker, KR. (1986) Environmental
impact assessment: ``pseudoreplication'' in time? Ecology 67(4):929-
940.
Susser, M. (1986a) Rules of inference in epidemiology. Regul
Toxicol Pharmacol 6:116-128.
Susser, M. (1986b) The logic of Sir Carl Popper and the practice
of epidemiology. Am J Epidemiol 124:711-718.
Suter, GW, II. (1989) Ecological endpoints. In: Ecological
assessments of hazardous waste sites: a field and laboratory
reference document. Warren-Hicks, W; Parkhurst, BR; Baker, SS, Jr,
eds. Washington, DC: U.S. Environmental Protection Agency. EPA 600/
3-89/013.
Suter, GW, II. (1990) Endpoints for regional ecological risk
assessments. Environ Manage 14:19-23.
Suter, GW, II. (1993a) Ecological risk assessment. Boca Raton,
FL: Lewis Publishers.
Suter, GW, II. (1993b) A critique of ecosystem health concepts
and indexes. Environ Toxicol Chem 12:1533-1539.
Suter, GW, II. (1996) Abuse of hypothesis testing statistics in
ecological risk assessment. Human Ecol Risk Assess 2:331-347.
Suter, GW, II; Vaughan, DS; Gardner, RH. (1983) Risk assessment
by analysis of extrapolation error. A demonstration for effects of
pollutants on fish. Environ Toxicol Chem 2:369-377.
Suter, GW, II; Gillett, JW; Norton, SB. (1994) Issue paper on
characterization of exposure. In: Ecological risk assessment issue
papers. Washington, DC: Risk Assessment Forum, U.S. Environmental
Protection Agency, pp. 4-1 to 4-64. EPA/630/R-94/009.
Thomas, JW; Forsman, ED; Lint, JB; Meslow, EC; Noon, BR; Verner,
J. (1990) A conservation strategy for the spotted owl. Interagency
Scientific Committee to Address the Conservation of the Northern
Spotted Owl. 1990-791/20026. Washington, DC: U.S. Government
Printing Office.
Urban, DJ; Cook, JN. (1986) Ecological risk assessment. Hazard
Evaluation Division standard procedure. Washington, DC: Office of
Pesticide Programs, U.S. Environmental Protection Agency. EPA-54019-
83-001.
U.S. Department of Agriculture. (1993) Pest risk assessment of
the importation of Pinus radiata, Nothofagus dombeyi, and Laurelia
philippiana logs from Chile. Forest Service Miscellaneous
Publication 1517.
U.S. Department of Health, Education, and Welfare. (1964)
Smoking and health. Report of the Advisory Committee to the Surgeon
General. Public Health Service Publication 1103. Washington, DC:
U.S. Department of Health, Education, and Welfare.
U.S. Environmental Protection Agency. (1979) Toxic Substances
Control Act. Discussion of premanufacture testing policies and
technical issues: request for comment. Federal Register 44:16240-
16292.
U.S. Environmental Protection Agency. (1984) Estimating concern
levels for concentrations of chemical substances in the environment.
Washington, DC: U.S. Environmental Protection Agency, Health and
Environmental Review Division, Environmental Effects Branch.
U.S. Environmental Protection Agency. (1986a) Guidelines for the
health risk assessment of chemical mixtures. Federal Register
52:34014-34025.
U.S. Environmental Protection Agency. (1986b) Quality criteria
for water. Washington, DC: Office of Water, U.S. Environmental
Protection Agency. EPA/440/5-86/001.
U.S. Environmental Protection Agency. (1988a) Methods for
aquatic toxicity identification evaluations: phase I toxicity
characterization procedures. Duluth, MN: Environmental Research
Laboratory, U.S. Environmental Protection Agency. EPA/600/3-88/034.
U.S. Environmental Protection Agency. (1988b) User's guide to
PDM3, final report. Prepared by Versar, Inc., for Exposure
Assessment Branch, Exposure Evaluation Division, U.S. Environmental
Protection Agency, Washington, DC, under EPA contract no. 68-02-
4254, task no. 117.
U.S. Environmental Protection Agency. (1989a) Rapid
bioassessment protocols for use in streams and rivers: benthic
macroinvertebrates and fish. Washington, DC: Office of Water, U.S.
Environmental Protection Agency. EPA/440/4-89/001.
U.S. Environmental Protection Agency. (1989b) Methods for
aquatic toxicity identification evaluations: phase II toxicity
identification procedures. Duluth, MN: Environmental Research
Laboratory, U.S. Environmental Protection Agency. EPA/600/3-88/035.
U.S. Environmental Protection Agency. (1989c) Methods for
aquatic toxicity identification evaluations: phase III toxicity
confirmation procedures. Duluth, MN: Environmental Research
Laboratory, U.S. Environmental Protection Agency. EPA/600/3-88/035.
U.S. Environmental Protection Agency. (1990) Guidance for data
useability in risk assessment. Washington, DC: U.S. Environmental
Protection Agency. EPA/540/G-90/008.
U.S. Environmental Protection Agency. (1991) Technical support
document for water quality-based toxics control. Washington, DC:
Office of Water, U.S. Environmental Protection Agency. EPA/505/2-90/
001.
U.S. Environmental Protection Agency. (1992a) Framework for
ecological risk assessment. Washington, DC: Risk Assessment Forum,
U.S. Environmental Protection Agency. EPA/630/R-92/001.
U.S. Environmental Protection Agency. (1992b) Guidelines for
exposure assessment: notice. Federal Register 57:22888-22938.
U.S. Environmental Protection Agency. (1992c) A cross-species
scaling factor for carcinogenic risk assessment based on equivalence
of mg/kg\3/4\/day: draft report. Federal Register
57(109):24152-24173.
U.S. Environmental Protection Agency. (1993a) Wildlife exposure
factors handbook. Washington, DC: Office of Research and
Development, U.S. Environmental Protection Agency. EPA/600/R-93/187a
and 187b.
U.S. Environmental Protection Agency. (1993b) A review of
ecological risk assessment case studies from a risk assessment
perspective. Washington, DC: Risk Assessment Forum, U.S.
Environmental Protection Agency. EPA/630/R-92/005.
U.S. Environmental Protection Agency. (1993c) Communicating risk
to senior EPA policy makers: a focus group study. Research Triangle
Park, NC: Office of Air Quality Planning and Standards, U.S.
Environmental Protection Agency.
U.S. Environmental Protection Agency. (1993d) A guidebook to
comparing risks and setting environmental priorities. Washington,
DC: Office of Policy, Planning, and
[[Page 26922]]
Evaluation, U.S. Environmental Protection Agency. EPA/230/B-93/003.
U.S. Environmental Protection Agency. (1994a) Managing
ecological risks at EPA: issues and recommendations for progress.
Washington, DC: Center for Environmental Research Information, U.S.
Environmental Protection Agency. EPA/600/R-94/183.
U.S. Environmental Protection Agency. (1994b) ``Ecosystem
protection.'' Memorandum from Robert Perciaspe, David Gardiner, and
Johnathan Cannon to Carol Browner, March 1994.
U.S. Environmental Protection Agency. (1994c) Guidance for the
data quality objectives process. Washington, DC: Quality Assurance
Management Staff. EPA QA/G-4.
U.S. Environmental Protection Agency. (1994d) Environmental
Services Division guidelines. Hydrogeologic modeling. Seattle, WA:
Region X, U.S. Environmental Protection Agency.
U.S. Environmental Protection Agency. (1994e) A review of
ecological assessment case studies from a risk assessment
perspective, vol. II. Washington, DC: Risk Assessment Forum, U.S.
Environmental Protection Agency. EPA/630/R-94/003.
U.S. Environmental Protection Agency. (1995a) Ecological risk: a
primer for risk managers. Washington, DC: U.S. Environmental
Protection Agency. EPA/734/R-95/001.
U.S. Environmental Protection Agency. (1995b) ``EPA risk
characterization program.'' Memorandum to EPA managers from
Administrator Carol Browner, March 1995.
U.S. Environmental Protection Agency. (1995c) The use of the
benchmark dose approach in health risk assessment. Washington, DC:
Risk Assessment Forum, U.S. Environmental Protection Agency. EPA/
630/R-94/007.
U.S. Environmental Protection Agency. (1995d) Great Lakes water
quality initiative technical support document for wildlife.
Washington, DC: Office of Water, U.S. Environmental Protection
Agency. EPA/820/B-95/009.
U.S. Environmental Protection Agency. (1995e) An SAB report:
ecosystem management--imperative for a dynamic world. Washington,
DC: Science Advisory Board. EPA-SAB-EPEC-95-003.
U.S. Environmental Protection Agency. (1996a) Summary report for
the workshop on Monte Carlo analysis. Washington, DC: Office of
Research and Development, U.S. Environmental Protection Agency. EPA/
630/R-96/010.
U.S. Environmental Protection Agency. (1996b) Waquoit Bay
watershed. Ecological risk assessment planning and problem
formulation (draft). Washington, DC: Risk Assessment Forum, U.S.
Environmental Protection Agency. EPA/630/R-96/004a.
U.S. Environmental Protection Agency. (1997a) Priorities for
ecological protection: an initial list and discussion document for
EPA. Washington, DC: Office of Research and Development, U.S.
Environmental Protection Agency. EPA/600/S-97/002.
U.S. Environmental Protection Agency. (1997b) Policy for use of
probabilistic analysis in risk assessment: guiding principles for
Monte Carlo analysis. Washington, DC: Office of Research and
Development, U.S. Environmental Protection Agency. EPA/630/R-97/001.
van Gestel, CAM; van Brummelen, TC. (1996) Incorporation of the
biomarker concept--ecotoxicology calls for a redefinition of terms.
Ecotoxicol 5:217-225.
Van Leeuwen, CJ; Van der Zandt, PTJ; Aldenberg, T; Verhar, HJM;
Hermens, JLM. (1992) Extrapolation and equilibrium partitioning in
aquatic effects assessment. Environ Toxicol Chem 11:267-282.
Wagner, C; Lkke, H. (1991) Estimation of
ecotoxicological protection levels from NOEC toxicity data. Water
Res 25:1237-1242.
White, PS; Pickett, STA. (1985) Natural disturbance and patch
dynamics: an introduction. In: The ecology of natural disturbance
and patch dynamics. Pickett, STA; White, PS, eds. Orlando, FL:
Academic Press, pp. 3-13.
Wiegert, RG; Bartell, SM. (1994) Issue paper on risk integration
methods. In: Ecological risk assessment issue papers. Washington,
DC: Risk Assessment Forum, Environmental Protection Agency, pp. 9-1
to 9-66. EPA/630/R-94/009.
Wiens, JA; Parker, KR. (1995) Analyzing the effects of
accidental environmental impacts: approaches and assumptions. Ecol
Appl 5(4):1069-1083.
Woodman, JN; Cowling, EB. (1987) Airborne chemicals and forest
health. Environ Sci Technol 21:120-126.
Yoder, CO; Rankin, ET. (1995) Biological response signatures and
the area of degradation value: new tools for interpreting multi-
metric data. In: Biological assessment and criteria: tools for water
resource planning and decision making. Davis, WS; Simon, TP, eds.
Boca Raton, FL: Lewis Publishers.
Zeeman, M. (1995) EPA's framework for ecological effects
assessment. In: Screening and testing chemicals in commerce. OTA-BP-
ENV-166. Washington, DC: Office of Technology Assessment, pp. 69-78.
Part B: Response to Science Advisory Board and Public Comments
1. Introduction
This section summarizes the major issues raised in public comments
and by EPA's Science Advisory Board (SAB) on the previous draft of
these Guidelines (the Proposed Guidelines for Ecological Risk
Assessment, hereafter ``Proposed Guidelines''). A notice of
availability for public comment of the Proposed Guidelines was
published September 9, 1996 (61 FR 47552-47631). Forty-four responses
were received. The Ecological Processes and Effects Committee of the
SAB reviewed the Proposed Guidelines on September 19-20, 1996, and
provided comments in January 1997 (EPA-SAB-EPEC-97-002).
The SAB and public comments were diverse, reflecting the different
perspectives of the reviewers. Many of the comments were favorable,
expressing agreement with the overall approach to ecological risk
assessment. Many comments were beyond the scope of the Guidelines,
including requests for guidance on risk management issues (such as
considering social or economic impacts in decision making). Major
issues raised by reviewers are summarized below. In addition to
providing general comments (section 2), reviewers were asked to comment
on seven specific questions (section 3).
2. Response to General Comments
Probably the most common request was for greater detail in specific
areas. In some cases, additional discussion was added (for example, on
the use of tiering and iteration and the respective roles of risk
assessors, risk managers, and interested parties throughout the
process). In other areas, topics for additional discussion were
included in a list of potential areas for further development (see
response to question 2, below). Still other topics are more
appropriately addressed by regional or program offices within the
context of a certain regulation or issue, and are deferred to those
sources.
A few reviewers felt that since ecological risk assessment is a
relatively young science, it is premature to issue guidelines at this
time. The Agency feels that it is appropriate to issue guidance at this
time, especially since the Guidelines contain major principles but
refrain from recommending specific methodologies that might become
rapidly outdated. To help ensure the continued relevance of the
Guidelines, the Agency intends to develop documents addressing specific
topics (see response to question 2 below) and will revise these
Guidelines as experience and scientific consensus evolve.
Some reviewers asked whether the Guidelines would be applied to
previous or ongoing ecological risk assessments, and whether existing
regional or program office guidance would be superseded in conducting
ecological risk assessments. As described in section 1.3 (Scope and
Intended Audience), the Guidelines are principles, and are not
regulatory in nature. It is anticipated that guidance from program and
regional offices will evolve to implement the principles set forth in
these Guidelines. Similarly, some reviewers requested that assessments
require a comparison of the risks of alternative scenarios (including
background or baseline conditions) or an assignment of particular
levels of ecological significance to habitats. These decisions would be
most appropriately made on a case-by-case basis, or by a program office
in response to program-specific needs.
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Several Native American groups noted a lack of acknowledgment of
tribal governments in the document. This Agency oversight was corrected
by including tribal governments at points in the Guidelines where other
governmental organizations are mentioned.
Several reviewers noted that the Proposed Guidelines mentioned the
need for ``expert judgment'' in several places and asked how the Agency
defined ``expert'' and what qualifications such an individual should
have. At present, there is no standard set of qualifications for an
ecological risk assessor, and such a standard would be very difficult
to produce, since ecological assessments are frequently done by teams
of individuals with expertise in many areas. To avoid this problem, the
Guidelines now use the term ``professional judgment,'' and note that it
is important to document the rationale for important decisions.
Some reviewers felt that the Guidelines should address effects only
at the population level and above. The Guidelines do not make this
restriction for several reasons. First, some assessments, such as those
involving endangered species, do involve considerations of individual
effects. Second, the decision as to which ecological entity to protect
should be the result, on a case-by-case basis, of the planning process
involving risk assessors, risk managers, and interested parties, if
appropriate. Some suggestions have been proposed (U.S. EPA, 1997a).
Finally, there appears to be some confusion among reviewers between
conducting an assessment concerned with population-level effects, and
using data from studies of effects on individuals (e.g., toxicity test
results) to infer population-level effects. These inferences are
commonly used (and generally accepted) in chemical screening programs,
such as the Office of Pollution Prevention and Toxics Premanufacturing
Notification program (U.S. EPA, 1994e).
The use of environmental indices received a number of comments.
Some reviewers wanted the Guidelines to do more to encourage the use of
indices, while others felt that the disadvantages of indices should
receive greater emphasis. The Guidelines discuss both the advantages
and limitations of using indices to guide risk assessors in their
proper use.
Other reviewers requested that the Guidelines take a more
definitive position on the use of ``realistic exposure assumptions,''
such as those proposed in the Agency's exposure guidelines (U.S. EPA,
1992b). Although the exposure guidelines offer many useful suggestions
that are applicable to human health risk assessment, it was not
possible to generalize the concepts to ecological risk assessment,
given the various permutations of the exposure concept for different
types of stressors or levels of biological organization. The Guidelines
emphasize the importance of documenting major assumptions (including
exposure assumptions) used in an assessment.
Several reviewers requested more guidance and examples using
nonchemical stressors, i.e., physical or biological stressors. This
topic has been included in the list of potential subjects for future
detailed treatment (see response to question 2, below).
3. Response to Comments on Specific Questions
Both the Proposed Guidelines and the charge to the SAB for its
review contained a set of seven questions asked by the Agency. These
questions, along with the Agency's response to comments received, are
listed below.
(1) Consistent with a recent National Research Council report (NRC,
1996), these Proposed Guidelines emphasize the importance of
interactions between risk assessors and risk managers as well as the
critical role of problem formulation in ensuring that the results of
the risk assessment can be used for decision making. Overall, how
compatible are these Proposed Guidelines with the National Research
Council concept of the risk assessment process and the interactions
among risk assessors, risk managers, and other interested parties?
Most reviewers felt there was general compatibility between the
Proposed Guidelines and the NRC report, although some emphasized the
need for continued interactions among risk assessors, risk managers,
and interested parties (or stakeholders) throughout the ecological risk
assessment process and asked that the Guidelines provide additional
details concerning such interactions. To give greater emphasis to these
interactions, the ecological risk assessment diagram was modified to
include ``interested parties'' in the planning box at the beginning of
the process and ``communicating with interested parties'' in the risk
management box following the risk assessment. Some additional
discussion concerning interactions among risk assessors, risk managers,
and interested parties was added, particularly to section 2 (planning).
However, although risk assessor/risk manager interrelationships are
discussed, too great an emphasis in this area is inconsistent with the
scope of the Guidelines, which focus on the interface between risk
assessors and risk managers, not on providing risk management guidance.
(2) The Proposed Guidelines are intended to provide a starting
point for Agency programs and regional offices that wish to prepare
ecological risk assessment guidance suited to their needs. In addition,
the Agency intends to sponsor development of more detailed guidance on
certain ecological risk assessment topics. Examples might include
identification and selection of assessment endpoints, selection of
surrogate or indicator species, or the development and application of
uncertainty factors. Considering the state of the science of ecological
risk assessment and Agency needs and priorities, what topics most
require additional guidance?
Reviewers recommended numerous topics for further development.
Examples include:
Landscape ecology.
Data sources and quality.
Physical and biological stressors.
Multiple stressors.
Defining reference areas for field studies.
Ecotoxicity thresholds.
The role of biological and other types of indicators.
Bioavailability, bioaccumulation, and bioconcentration.
Uncertainty factors.
Stressor-response relationships (e.g., threshold vs.
continuous).
Risk characterization techniques.
Risk communication to the public.
Public participation.
Comparative ecological risk.
Screening and tiering assessments.
Identifying and selecting assessment endpoints.
These suggestions will be included in a listing of possible topics
proposed to the Agency's Risk Assessment Forum for future development.
(3) Some reviewers have suggested that the Proposed Guidelines
should provide more discussion of topics related to the use of field
observational data in ecological risk assessments, such as selection of
reference sites, interpretation of positive and negative field data,
establishing causal linkages, identifying measures of ecological
condition, the role and uses of monitoring, and resolving conflicting
lines of evidence between field and laboratory data. Given the general
scope of these Proposed Guidelines, what, if any, additional material
should be added on these topics and, if so, what principles should be
highlighted?
In response to a number of comments, the discussion of field data
in the
[[Page 26924]]
Guidelines was expanded, especially in section 4.1. Nevertheless, many
suggested topics requested a level of detail that was inconsistent with
the scope of the Guidelines. Some areas may be covered through the
development of future Risk Assessment Forum documents.
(4) The scope of the Proposed Guidelines is intentionally broad.
However, while the intent is to cover the full range of stressors,
ecosystem types, levels of biological organization, and spatial/
temporal scales, the contents of the Proposed Guidelines are limited by
the present state of the science and the relative lack of experience in
applying risk assessment principles to some areas. In particular, given
the Agency's present interest in evaluating risks at larger spatial
scales, how could the principles of landscape ecology be more fully
incorporated into the Proposed Guidelines?
Landscape ecology is critical to many aspects of ecological risk
assessment, especially assessments conducted at larger spatial scales.
However, given the general nature of these Guidelines and the responses
received to this question, the Guidelines could not be expanded
substantially at this time. This topic has been added to the list of
potential subjects for future development.
(5) Assessing risks when multiple stressors are present is a
challenging task. The problem may be how to aggregate risks
attributable to individual stressors or identify the principal
stressors responsible for an observed effect. Although some approaches
for evaluating risks associated with chemical mixtures are available,
our ability to conduct risk assessments involving multiple chemical,
physical, and biological stressors, especially at larger spatial
scales, is limited. Consequently, the Proposed Guidelines primarily
discuss predicting the effects of chemical mixtures and general
approaches for evaluating causality of an observed effect. What
additional principles can be added?
Few additional principles were provided that could be included in
the Guidelines. To further progress in evaluating multiple stressors,
EPA cosponsored a workshop on this issue, held by the Society of
Environmental Toxicology and Chemistry in September 1997. In addition,
evaluating multiple stressors is one of the proposed topics for further
development.
(6) Ecological risk assessments are frequently conducted in tiers
that proceed from simple evaluations of exposure and effects to more
complex assessments. While the Proposed Guidelines acknowledge the
importance of tiered assessments, the wide range of applications of
tiered assessments make further generalizations difficult. Given the
broad scope of the Proposed Guidelines, what additional principles for
conducting tiered assessments can be discussed?
Many reviewers emphasized the importance of tiered assessments, and
in response the discussion of tiered assessments was significantly
expanded in the planning phase of ecological risk assessment. Including
more detailed information (such as specific decision criteria to
proceed from one tier to the next) would require a particular context
for an assessment. Such specific guidance is left to the EPA program
offices and regions.
(7) Assessment endpoints are ``explicit expression of the
environmental value that is to be protected.'' As used in the Proposed
Guidelines, assessment endpoints include both an ecological entity and
a specific attribute of the entity (e.g., eagle reproduction or extent
of wetlands). Some reviewers have recommended that assessment endpoints
also include a decision criterion that is defined early in the risk
assessment process (e.g., no more than a 20% reduction in reproduction,
no more than a 10% loss of wetlands). While not precluding this
possibility, the Proposed Guidelines suggest that such decisions are
more appropriately made during discussions between risk assessors and
managers in risk characterization at the end of the process. What are
the relative merits of each approach?
Reviewer reaction was quite evenly divided between those who felt
strongly that decision criteria should be defined in problem
formulation and those who felt just as strongly that such decisions
should be delayed until risk characterization. Although the Guidelines
contain more discussion of this topic, they still take the position
that assessment endpoints need not contain specific decision criteria.
[FR Doc. 98-12302 Filed 5-13-98; 8:45 am]
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