[Federal Register Volume 61, Number 175 (Monday, September 9, 1996)]
[Notices]
[Pages 47552-47631]
From the Federal Register Online via the Government Publishing Office [www.gpo.gov]
[FR Doc No: 96-22648]
[[Page 47551]]
_______________________________________________________________________
Part II
Environmental Protection Agency
_______________________________________________________________________
Proposed Guidelines for Ecological Risk Assessment; Notice
Federal Register / Vol. 61, No. 175 / Monday, September 9, 1996 /
Notices
[[Page 47552]]
ENVIRONMENTAL PROTECTION AGENCY
[FRL-5605-9]
Proposed Guidelines for Ecological Risk Assessment
AGENCY: U.S. Environmental Protection Agency.
ACTION: Notice of Availability and Opportunity to Comment on Proposed
Guidelines for Ecological Risk Assessment.
-----------------------------------------------------------------------
SUMMARY: The U.S. Environmental Protection Agency (EPA) is today
publishing a document entitled Proposed Guidelines for Ecological Risk
Assessment (hereafter ``Proposed Guidelines''). These Proposed
Guidelines were developed as part of an interoffice Guidelines
development program by a Technical Panel of the Risk Assessment Forum.
The Proposed Guidelines expand upon the previously published EPA report
Framework for Ecological Risk Assessment (EPA/630/R-92/001, February
1992), while retaining the report's broad scope. When final, these
Proposed 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.
DATES: The Proposed Guidelines are being made available for a 90-day
public review and comment period. Comments must be in writing and must
be postmarked by December 9, 1996. See Addresses section for guidance
on submitting comments.
FOR FURTHER INFORMATION CONTACT: Bill van der Schalie, National Center
for Environmental Assessment-Washington Office, telephone: 202-260-
4191.
ADDRESSES: The Proposed Guidelines will be made available in the
following ways:
(1) The electronic version will be accessible on EPA's Office of
Research and Development home page on the Internet at http://
www.epa.gov/ORD/WebPubs/fedreg.
(2) 3\1/2\'' high-density computer diskettes in Wordperfect 5.1
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-95/002B) when ordering.
(3) This notice contains the full proposed guideline. In addition,
copies 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 No. PB96-193198; Price Code A13: ($47.00)
when ordering.
Submitting Comments
Comments on the Proposed Guidelines should be submitted to: U.S.
Environmental Protection Agency, Air and Radiation Docket and
Information Center (6102), Attn: File ORD-ERA-96-01, Waterside Mall,
401 M St. SW, Washington, DC 20460. Please submit one unbound original
with pages numbered consecutively, and three copies. For attachments,
provide an index, number pages consecutively, provide comment on how
the attachments relate to the main comment(s), and submit an unbound
original and three copies. Please identify all comments and attachments
with the file number ORD-ERA-96-01. Mailed comments must be postmarked
by the date indicated. Comments may also be submitted electronically by
sending electronic mail (e-mail) to: A-and-R-D[email protected].
Electronic comments must be submitted as an ASCII file avoiding the use
of special characters and any form of encryption. Comments and data
will also be accepted on disks in WordPerfect 5.1 file format or ASCII
file format. All comments in electronic form also must be identified by
the file number ORD-ERA-96-01.
The Air and Radiation Docket and Information Center is open for
public inspection and copying between 8:00 a.m. and 5:30 p.m.,
weekdays, in Room M-1500, Waterside Mall, 401 M St. SW, Washington, DC
20460. The Center is located on the ground floor in the commercial area
of Waterside Mall. The file index, materials, and comments are
available for review in the information center or copies may be mailed
on request from the Air and Radiation Docket and Information Center by
calling (202) 260-7548 or -7549. The FAX number for the Center is (202)
260-4400. A reasonable fee may be charged for copying materials.
Please note that all technical comments received in response to
this notice will be placed in the public record. For that reason,
commentors should not submit personal information such as medical data
or home addresses, confidential business information, or information
protected by copyright. Due to limited resources, acknowledgments will
not be sent.
SUPPLEMENTARY INFORMATION: These Proposed Guidelines are EPA's first
Agency-wide ecological risk assessment guidelines. They are broad in
scope, describing general principles and providing numerous examples to
show how ecological risk assessment can be applied to a wide range of
systems, stressors, and biological/spatial/temporal scales. This
general approach provides sufficient flexibility to permit EPA's
offices and regions to develop specific guidance suited to their
particular needs. Because of their broad scope, the Proposed Guidelines
do not provide detailed guidance in specific areas nor are they highly
prescriptive. Frequently, rather than requiring that certain procedures
always be followed, the Proposed Guidelines describe the strengths and
limitations of alternate approaches. Agency preferences are expressed
where possible, but because ecological risk assessment is a relatively
new, rapidly evolving discipline, requirements for specific approaches
could soon become outdated. EPA is working to expand the references in
the Proposed Guidelines to include additional review articles or key
publications that will help provide a ``window to the literature'' as
recommended by peer reviewers. In the future, EPA intends to develop a
series of shorter, more detailed guidance documents on specific
ecological risk assessment topics after these Proposed Guidelines have
been finalized.
These Proposed Guidelines were prepared during a time of increasing
interest in the field of ecological risk assessment and reflect input
from many sources outside as well as inside the Agency. Over the last
few years, the National Research Council proposed an ecological risk
paradigm (NRC, 1993), there has been a marked increase in discussion of
ecological risk assessment issues at meetings of professional
organizations, and numerous articles and books on the subject have been
published. Agency work on the Proposed Guidelines has proceeded in a
step-wise fashion during this time. Preliminary work began in 1989 and
included a series of colloquia sponsored by EPA's Risk Assessment Forum
to identify and discuss significant issues in ecological risk
assessment (U.S. EPA, 1991). Based on this early work and on a
consultation with EPA's Science Advisory Board (SAB), the Agency
decided to produce ecological risk assessment guidance sequentially,
beginning with basic terms and concepts and continuing with the
development of source materials for these Proposed Guidelines. The
first product of this effort was the Risk Assessment Forum report,
Framework
[[Page 47553]]
for Ecological Risk Assessment (Framework Report; U.S. EPA, 1992a,b),
which proposes principles and terminology for the ecological risk
assessment process. Since then, the Agency has solicited suggestions
for ecological risk assessment guidelines structuring (U.S. EPA, 1992c)
and has sponsored the development of other peer-reviewed materials,
including ecological assessment case studies (U.S. EPA, 1993a, 1994a),
and a set of issue papers that highlight important principles and
approaches that EPA scientists should consider in preparing these
Proposed Guidelines (U.S. EPA, 1994b,c).
The nature and content of these Proposed Guidelines have been
shaped by these documents as well as numerous meetings and discussions
with individuals both within and outside of EPA. In late 1994 and early
1995, the Agency solicited responses to the planned nature and
structure of these Proposed Guidelines at three colloquia with Agency
program offices and regions, other Federal agencies, and the public.
Draft Proposed Guidelines were discussed at an external peer review
workshop in December, 1995 (U.S. EPA, In Press). Subsequent reviews
have included the Agency's Risk Assessment Forum and the Regulatory and
Policy Development Committee, and interagency comment by members of
subcommittees of the Committee on the Environment and Natural Resources
of the Office of Science and Technology Policy. The EPA appreciates the
efforts of all participants in the process and has tried to address
their recommendations in these Proposed Guidelines.
The EPA's Science Advisory Board will review these Proposed
Guidelines at a future meeting. Following public and SAB reviews,
Agency staff will prepare comment summaries. Appropriate comments will
be incorporated, and the revised Guidelines will be submitted to EPA's
Risk Assessment Forum for review. The Agency will consider comments
from the public, the SAB, and the Risk Assessment Forum when finalizing
these Proposed Guidelines.
The public is invited to provide comments to be considered in EPA
decisions about the content of the final Guidelines. EPA asks those who
respond to this notice to include their views on the following:
(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 to 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
between risk assessors, risk managers, and other interested parties?
(2) The Proposed Guidelines are intended to provide a starting
point for Agency program 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?
(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?
(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?
(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 to 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 on general
approaches for evaluating causality of an observed effect. What
additional principles can be added?
(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?
(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 attributes 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?
Dated: August 21, 1996.
Carol M. Browner,
Administrator.
Proposed Guidelines for Ecological Risk Assessment
Contents
Lists of Figures, Tables, and Text Notes
Executive Summary
1. Introduction
1.1. Ecological Risk Assessment in a Management Context
1.1.1. Contributions of Ecological Risk Assessment to
Environmental Decisionmaking
1.1.2. Risk Management Considerations
1.2. Scope and Intended Audience
1.3. Guidelines Organization
2. Planning the Risk Assessment: Dialogue Between Risk Managers and
Risk Assessors
2.1. Establishing Management Goals
2.2. Management Decisions
[[Page 47554]]
2.3. Scope and Complexity of the Risk Assessment
2.4. Planning Outcome
3. Problem Formulation Phase
3.1. Products of Problem Formulation
3.2. Integration of Available Information
3.3. Selecting Assessment Endpoints
3.3.1. Selecting What to Protect
3.3.1.1. Ecological Relevance
3.3.1.2. Susceptibility to Known or Potential Stressors
3.3.1.3. Representation of 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. Relating Analysis Plans to Decisions
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
4.2.1.2. Describe the Distribution of the Stressor 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. Risk Estimates Expressed as Qualitative Categories
5.1.2. Single-Point Estimates
5.1.3. Estimates Incorporating the Entire Stressor-Response
Relationship
5.1.4. Estimates Incorporating Variability in Exposure or
Effects
5.1.5. Estimates Based on Process Models
5.1.6. Field Observational Studies
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 3-2. Elements of a conceptual model diagram.
Figure 4-1. Analysis phase.
Figure 4-2. A simple example of a stressor-response relationship.
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.
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 Tables
Table 4-1. Uncertainty Evaluation in the Analysis Phase.
List of Text Notes
Text Note 1-1. Related Terminology.
Text Note 1-2. Flexibility of the Framework Diagram.
Text Note 1-3. The Iterative Nature of Ecological Risk Assessment.
Text Note 2-1. Who Are Risk Managers?
Text Note 2-2. Who Are Risk Assessors?
Text Note 2-3. Questions Addressed by Risk Managers and Risk
Assessors.
Text Note 2-4. The Role of Interested Parties.
Text Note 2-5. Sustainability as a Management Goal.
Text Note 2-6. Management Goals for Waquoit Bay.
Text Note 2-7. 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: Why Salmon Would Provide the
Basis for an Assessment Endpoint.
Text Note 3-6. Cascading Adverse Effects: Primary (Direct) and
Secondary (Indirect).
Text Note 3-7. Sensitivity and Secondary Effects: The Mussel-Fish
Connection.
Text Note 3-8. Examples of Management Goals and Assessment
Endpoints.
Text Note 3-9. Common Problems in Selecting Assessment Endpoints.
Text Note 3-10. What Are Risk Hypotheses and Why Are They Important?
Text Note 3-11. Examples of Risk Hypotheses.
Text Note 3-12. What Are the Benefits of Develoving Conceptual
Models?
Text Note 3-13. Uncertainty in Problem Formulation.
Text Note 3-14. Examples of Assessment Endpoints and Measures.
Text Note 3-15. Selecting What to Measure.
Text Note 3-16. How Do Water Quality Criteria Relate to Assessment
Endpoints?
Text Note 3-17. Data Quality Objectives (DQO) 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. Considering the Degree of Aggregation in Models.
Text Note 4-5. Questions for Source Description.
Text Note 4-6. Questions to Ask in Evaluating Stressor Distribution.
Text Note 4-7. General Mechanisms of Transport and Dispersal.
Text Note 4-8. Questions to Ask in Describing Contact or Co-
occurrence.
Text Note 4-9. Example of an Exposure Equation: Calculating a
Potential Dose via Ingestion.
Text Note 4-10. Measuring Internal Dose Using Biomarkers and Tissue
Residues.
Text Note 4-11. Questions Addressed by the Exposure Profile.
Text Note 4-12. Questions for Stressor-Response Analysis.
Text Note 4-13. Qualitative Stressor-Response Relationships.
Text Note 4-14. Median Effect Levels.
Text Note 4-15. No-Effect Levels Derived From Statistical Hypothesis
Testing.
Text Note 4-16. General Criteria for Causality.
Text Note 4-17. Koch's Postulates.
Text Note 4-18. Examples of Extrapolations to Link Measures of
Effect to Assessment Endpoints.
Text Note 4-19. Questions Related to Selecting Extrapolation
Approaches.
Text Note 4-20. Questions to Consider When Extrapolating From
Effects Observed in the Laboratory to Field Effects of Chemicals.
Text Note 4-21. Questions Addressed by the Stressor-Response
Profile.
Text Note 5-1. Using Qualitative Categories to Estimate Risks of an
Introduced Species.
Text Note 5-2. Applying the Quotient Method.
Text Note 5-3. Comparing an Exposure Distribution With a Point
Estimate of Effects.
Text Note 5-4. Comparing Cumulative Exposure and Effects
Distributions for Chemical Stressors.
Text Note 5-5. Estimating Risk With Process Models.
Text Note 5-6. An Example of Field Methods Used for Risk Estimation.
[[Page 47555]]
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.
Executive Summary
The ecological problems facing environmental scientists and
decisionmakers are numerous and varied. Growing concern over potential
global climate change, loss of biodiversity, acid precipitation,
habitat destruction, and the effects of multiple chemicals on
ecological systems has highlighted the need for flexible problem-
solving approaches that can link ecological measurements and data with
the decisionmaking needs of environmental managers. Increasingly,
ecological risk assessment is being suggested as a way to address this
wide array of ecological problems.
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 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 decisionmaking 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 EPA'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 may be
useful to others outside the Agency (e.g., Agency contractors, state
agencies, and other interested parties). These guidelines are based on
and replace the 1992 report, Framework for Ecological Risk Assessment
(referred to as the Framework Report). They were written by a Forum
work group and have been extensively revised based on comments from
outside peer reviewers as well as Agency staff. The guidelines retain
the Framework Report's broad scope, while expanding on some framework
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 inferences (default assumptions) to
bridge gaps in knowledge.
Ecological risk assessment includes three primary phases (problem
formulation, analysis, and risk characterization). Within problem
formulation, important areas include identifying goals and assessment
endpoints, preparing the conceptual model, and developing an analysis
plan. The analysis phase involves evaluating exposure to stressors and
the relationship between stressor levels and ecological effects. In
risk characterization, key elements are estimating risk through
integration of exposure and stressor-response profiles, describing risk
by discussing lines of evidence and determining ecological adversity,
and preparing a report. The interface between risk assessors and risk
managers at the beginning and end of the risk assessment is critical
for ensuring that the results of the assessment can be used to support
a management decision.
Both risk assessors and risk managers bring valuable perspectives
to the initial planning activities for an ecological risk assessment.
Risk managers charged with protecting environmental values can ensure
that the risk assessment will provide information relevant to a
decision. Ecological risk assessors ensure that science is effectively
used to address ecological concerns. Both evaluate the potential value
of conducting a risk assessment to address identified problems. Further
objectives of the initial planning process are to establish management
goals that are agreed upon, clearly articulated, and contain a way to
measure success; determine the purpose for the risk assessment by
defining the decisions to be made within the context of the management
goals; and agree upon the scope, complexity, and focus of the risk
assessment, including the expected output and available resources.
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 inherently interactive and
iterative, not linear, substantial reevaluation is expected to occur
within and among all products of problem formulation.
Assessment endpoints are ``explicit expressions of the actual
environmental value that is to be protected'' (U.S. EPA, 1992a) that
link the risk assessment to management concerns. Assessment endpoints
include both a valued ecological entity and an attribute of that entity
that is important to protect and potentially at risk (e.g., nesting and
feeding success of piping plovers or areal extent and patch size of
eelgrass). For a risk assessment to have scientific validity,
assessment endpoints must be ecologically relevant to the ecosystem
they represent and susceptible to the stressors of concern. Assessment
endpoints that represent societal values and management goals are more
effective in that they increase the likelihood that the risk assessment
will be used in management decisions. Assessment endpoints that fulfill
all three criteria provide the best foundation for an effective risk
assessment.
Potential interactions between assessment endpoints and stressors
are explored by developing a conceptual model. Conceptual models link
anthropogenic activities with stressors and evaluate interrelationships
between exposure pathways, ecological effects, and ecological
receptors. Conceptual models include two principal components: risk
hypotheses and a conceptual model diagram.
Risk hypotheses describe predicted relationships between stressor,
exposure, and assessment endpoint response. Risk hypotheses are
hypotheses in the broad scientific sense; they do not necessarily
involve statistical testing of null and alternative hypotheses or any
particular analytical approach. Risk hypotheses may predict the effects
of a stressor (e.g., a chemical release) or they may postulate what
stressors may have caused observed ecological effects. Key risk
hypotheses are identified for subsequent evaluation in the risk
assessment.
A useful way to express the relationships described by the risk
hypotheses is through a diagram of a conceptual model. Conceptual model
diagrams are useful tools for communicating important pathways in a
clear and concise way and for identifying major sources of uncertainty.
Risk assessors can use these diagrams and risk hypotheses to identify
the most important pathways and relationships that will be evaluated in
the analysis phase. Risk assessors justify what will be done as well as
what will not be done in the assessment in an analysis plan.
[[Page 47556]]
The analysis plan also describes the data and measures to be used in
the risk assessment and how risks will be characterized.
The analysis phase, which follows problem formulation, includes two
principal activities: characterization of exposure and characterization
of ecological effects. The process is flexible, and interaction between
the ecological effects and exposure evaluations is recommended. Both
activities include an evaluation of available data for scientific
credibility and relevance to assessment endpoints and the conceptual
model. In exposure characterization, data analyses describe the
source(s) of stressors, the distribution of stressors in the
environment, and the contact or co-occurrence of stressors with
ecological receptors. In ecological effects characterization, data
analyses may evaluate stressor-response relationships or evidence that
exposure to a stressor causes an observed response.
The products of analysis are summary profiles that describe
exposure and the stressor-response relationships. Exposure and
stressor-response profiles may be written documents or modules of a
larger process model. Alternatively, documentation may be deferred
until risk characterization. In any case, the objective is to ensure
that the information needed for risk characterization has been
collected and evaluated.
The exposure profile identifies receptors and exposure pathways and
describes the intensity and spatial and temporal extent of exposure.
The exposure profile also describes the impact of variability and
uncertainty on exposure estimates and reaches a conclusion about the
likelihood that exposure will occur.
The stressor-response profile may evaluate single species,
populations, general trophic levels, communities, ecosystems, or
landscapes--whatever is appropriate for the assessment endpoints. For
example, if a single species is affected, effects should represent
appropriate parameters such as effects on mortality, growth, and
reproduction, while at the community level, effects may be summarized
in terms of structure or function depending on the assessment endpoint.
The stressor-response profile summarizes the nature and intensity of
effect(s), the time scale for recovery (where appropriate), causal
information linking the stressor with observed effects, and
uncertainties associated with the analysis.
Risk characterization is the final phase of an ecological risk
assessment. During risk characterization, risks are estimated and
interpreted and the strengths, limitations, assumptions, and major
uncertainties are summarized. Risks are estimated by integrating
exposure and stressor-response profiles using a wide range of
techniques such as comparisons of point estimates or distributions of
exposure and effects data, process models, or empirical approaches such
as field observational data.
Risk assessors describe risks by evaluating the evidence supporting
or refuting the risk estimate(s) and interpreting the adverse effects
on the assessment endpoint. Criteria for evaluating adversity include
the nature and intensity of effects, spatial and temporal scales, and
the potential for recovery. Agreement among different lines of evidence
of risk increases confidence in the conclusions of a risk assessment.
When risk characterization is complete, a report describing the
risk assessment can be prepared. The report may be relatively brief or
extensive depending on the nature and the resources available for the
assessment and the information required to support a risk management
decision. Report elements may include:
A description of risk assessor/risk manager planning
results.
A review of the conceptual model and the assessment
endpoints.
A discussion of the major data sources and analytical
procedures used.
A review of the stressor-response and exposure profiles.
A description of risks to the assessment endpoints,
including risk estimates and adversity evaluations.
A summary of major areas of uncertainty and the approaches
used to address them.
A discussion of science policy judgments or default
assumptions used to bridge information gaps, and the basis for these
assumptions.
To facilitate understanding, risk assessors should characterize risks
``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, 1995c).
After the risk assessment is completed, risk managers may consider
whether additional follow-up activities are required. Depending on the
importance of the assessment, confidence level in the assessment
results, and available resources, it may be advisable to conduct
another iteration of the risk assessment in order to facilitate a final
management decision. Ecological risk assessments are frequently
designed in sequential tiers that proceed from simple, relatively
inexpensive evaluations to more costly and complex assessments. Initial
tiers are based on conservative assumptions, such as maximum exposure
and ecological sensitivity. When an early tier cannot sufficiently
define risk to support a management decision, a higher assessment tier
that may require either additional data or applying more refined
analysis techniques to available data may be needed. Higher tiers
provide more ecologically realistic assessments while making less
conservative assumptions about exposure and effects.
Another option is to proceed with a management decision based on
the risk assessment and develop a monitoring plan to evaluate the
results of the decision. For example, if the decision was to mitigate
risks through exposure reduction, monitoring could help determine
whether the desired reduction in exposure (and effects) was achieved.
Monitoring is also critical for determining the extent and nature of
any ecological recovery that may be occurring. Experience obtained by
using focused monitoring results to evaluate risk assessment
predictions can help improve the risk assessment process and is
encouraged.
Communicating ecological risks to the public is usually the
responsibility of risk managers. 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. It is important to
clearly describe the ecological resources at risk, their value, and the
costs of protecting (and failing to protect) the resources (U.S. EPA,
1995c). The degree of confidence in the risk assessment and the
rationale for risk management decisions and options for reducing risk
are also important (U.S. EPA, 1995c).
1. Introduction
Ecological risk assessment is a 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
decisionmaking. This document, which is structured by the stages of the
ecological risk assessment process, provides Agency personnel with
broad guidelines that can be adapted to their specific requirements.
The full definition of ecological risk assessment is:
[[Page 47557]]
``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.'' (U.S. EPA, 1992a)
Several terms within this definition require further explanation:
`` * * * likelihood * * * '' Descriptions of risk may
range from qualitative judgments to quantitative probabilities. While
risk assessments may include quantitative risk estimates, the present
state of the science often may not support such quantitation. It is
preferable to convey qualitatively the relative magnitude of
uncertainties to a decision maker than to ignore them because they may
not be easily understood or estimated.
`` * * * adverse ecological effects * * * '' Ecological
risk assessments deal with anthropogenic changes that are considered
undesirable because they alter valued structural or functional
characteristics of ecological systems. An evaluation of adversity may
consider the type, intensity, and scale of the effect as well as the
potential for recovery.
`` * * * may occur or are occurring * * * '' Ecological
risk assessments may be prospective or retrospective. Retrospective
ecological risk assessments evaluate the likelihood that observed
ecological effects are associated with previous or current exposures to
stressors. Many of the same methods and approaches are used for both
prospective and retrospective assessments, and in the best case, even
retrospective assessments contain predictive elements linking sources,
stressors and effects.
`` * * * one or more stressors * * * '' Ecological risk
assessments may address single or multiple chemical, physical, or
biological stressors. (See Appendix A for definitions of stressor
types.) Because risk assessments are conducted to provide input to
management decisions, this document focuses on stressors generated or
influenced by anthropogenic activity.
The overall ecological risk assessment process is shown in figure
1-1.1 Problem formulation is the first phase of the process where
the assessment purpose is stated, the problem defined, and the plan for
analyzing and characterizing risk determined. In the analysis phase,
data on potential effects of and exposures to stressor(s) identified
during problem formulation are technically evaluated and summarized as
exposure and stressor-response profiles. These profiles are integrated
in risk characterization to estimate the likelihood of adverse
ecological effects. Major uncertainties, assumptions, and strengths and
limitations of the assessment are summarized during this phase. While
discussions between risk assessors and risk managers are emphasized
both at risk assessment initiation (planning) and completion
(communicating results), these guidelines maintain a distinction
between risk assessment and risk management. Risk assessment focuses on
evaluating the likelihood of adverse effects, and risk management
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.
Section 1.1 briefly discusses how risk assessments fit into a
decisionmaking context.
---------------------------------------------------------------------------
\1\ Changes in process and terminology from EPA's previous
ecological risk assessment framework (U.S. EPA, 1992a) are
summarized in Appendix A.
BILLING CODE 6560-50-P
[[Page 47558]]
[GRAPHIC] [TIFF OMITTED] TN09SE96.000
BILLING CODE 6560-50-C
[[Page 47559]]
The bar along the right side of figure 1-1 shows several activities
that are associated with risk assessments: data acquisition, iteration,
and monitoring. While the risk assessment may focus on data analysis
and interpretation, acquiring the appropriate quantity and quality of
data for use in the process is critical. If such data are lacking, the
risk assessment may stop until the necessary data are acquired. As
discussed in text note 1-3, the process is more frequently iterative
than linear, since the evaluation of new data or information may
require revisiting a part of the process or conducting a new
assessment.
Monitoring data can provide important input to all phases of the
risk assessment process. For example, monitoring can provide the
impetus for initiating a risk assessment by identifying changes in
ecological condition. In addition, monitoring data can be used to
evaluate the results predicted by the risk assessment. For example,
follow-up studies could be used to determine whether techniques used to
mitigate pesticide exposures in field situations in fact reduce
exposure and effects as predicted by the risk assessment. Or, for a
hazardous waste site, monitoring might help verify whether source
reduction resulted in anticipated ecological changes. Monitoring is
also critical for determining the extent and nature of any ecological
recovery that may occur. The experience gained by comparing monitoring
results to evaluate risk assessment predictions can help improve the
risk assessment process and is encouraged.
1.1. Ecological Risk Assessment in a Management Context
Ecological risk assessment is important for environmental
decisionmaking because of the high cost of eliminating environmental
risks associated with human activities and the necessity of making
regulatory decisions in the face of uncertainty (Ruckelshaus, 1983;
Suter, 1993a). Even so, ecological risk assessment provides only a
portion of the information required to make risk management decisions.
This section describes how ecological risk assessments fit into a
larger management framework.
1.1.1. Contributions of Ecological Risk Assessment to Environmental
Decisionmaking
At EPA, ecological risk assessments provide input to a diverse set
of environmental decisionmaking processes, such as the regulation of
hazardous waste sites, industrial chemicals, and pesticides, or the
management of watersheds affected by multiple nonchemical and chemical
stressors. The ecological risk assessment process has several features
that contribute to managing ecological risks:
In a risk assessment, changes in ecological effects can be
expressed as a function of changes in exposure to a stressor. This
inherently predictive aspect of risk assessment may be particularly
useful to the decision maker who must evaluate tradeoffs and examine
different alternatives.
Risk assessments include an explicit evaluation of
uncertainties. Uncertainty analysis lends credibility and a degree of
confidence to the assessment that can strengthen its use in
decisionmaking and can help the risk manager focus research on those
areas that will lead to the greatest reductions in uncertainty.
Risk assessments can provide a basis for comparing,
ranking, and prioritizing risks. The risk manager can use such
information to help decide among several management alternatives.
Risk assessments emphasize consistent use of well-defined
and relevant endpoints. This is especially important for ensuring that
the results of the risk assessment will be expressed in a way that the
risk manager can use.
1.1.2. Risk Management Considerations
Although risk assessors and risk managers interact both at the
initiation and completion of an ecological risk assessment (sections 2,
3, 5 and 6), risk managers decide how to use the results of an
assessment and whether a risk assessment should be conducted. While a
detailed review of management issues is beyond the scope of these
guidelines, key areas are highlighted below.
A risk assessment is not always required for management
action. When faced with compelling ecological risks and an immediate
need to make a decision, a risk manager might proceed without an
assessment, depending on professional judgment and statutory
requirements (U.S. EPA, 1992a).
Because initial management decisions or statutory
requirements significantly affect the scope of an assessment, it is
important, where possible, for risk managers to consider a broader
scope or alternative actions for a risk assessment. Sometimes a
particular statute may require the risk assessment to focus on one type
of stressor (e.g., chemicals) when there are other, perhaps more
important, stressors in the system (e.g., habitat alteration). In other
situations, however, it may be possible to evaluate a range of options.
For example, before requesting an ecological risk assessment of
alternative sites for the construction and operation of a dam for
hydroelectric power, risk managers may consider larger issues such as
the need for the additional power and the feasibility of using other
power-generating options.
Risk managers consider many factors in making regulatory
decisions. Legal mandates may require the risk manager to take certain
courses of action. Political and social considerations may lead the
risk manager to make decisions that are either more or less
ecologically protective. Economic factors may also be critical. For
example, a course of action that has the least ecological risk may be
too expensive or technologically infeasible. If cost-benefit analysis
is applied, ecological risks may be translated into monetary terms to
be compared against other monetary considerations. Thus, while
ecological risk assessment provides critical information to risk
managers, it is only part of the whole environmental decisionmaking
process.
1.2. Scope and Intended Audience
These guidelines replace the EPA report, Framework for Ecological
Risk Assessment (referred to as the Framework Report, U.S. EPA, 1992a).
As a next step in developing Agency-wide guidance, the guidelines
expand on and modify framework concepts to reflect Agency experience in
the several years since the Framework Report was published (see
Appendix A). Like the Framework Report, these guidelines are broad in
scope, describing general principles and providing numerous examples to
show how ecological risk assessment can be applied to a wide range of
systems, stressors, and biological, spatial, and temporal scales. This
approach provides flexibility to permit EPA's offices and regions to
develop specific guidance suited to their particular needs.
The proposed policies set out in this document are intended as
internal guidance for EPA. Risk assessors and risk managers at EPA are
the primary audience for this document, although these guidelines may
be useful to others outside the Agency (e.g., Agency contractors, state
agencies, and other interested parties). These Proposed Guidelines are
not intended, nor can they be relied upon, to create any rights
enforceable by any party in litigation with the United States. This
document is not a regulation and is not intended for EPA regulations.
These Proposed Guidelines set forth current scientific thinking and
approaches for conducting and evaluating ecological risk
[[Page 47560]]
assessments. As with other EPA guidelines (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 evolves.
These guidelines do not provide detailed guidance in specific areas
nor are they intended to be highly prescriptive. These guidelines
describe the strengths and limitations of alternate approaches and may
not apply to a particular situation based upon the circumstances.
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 guidance documents on specific
ecological risk assessment topics after these guidelines have been
finalized.
These guidelines emphasize processes and approaches for analyzing
data rather than specific data collection techniques, methods, or
models. Also, while these guidelines discuss the interface between the
risk assessor and risk manager, a detailed discussion of the use of
ecological risk assessment information in the risk management process
(e.g., the economic, legal, political, or social implications of the
risk assessment results) is beyond the scope of these guidelines. Other
EPA publications discuss how ecological concerns have been addressed in
decisionmaking at EPA (U.S. EPA, 1994g) and provide an introduction to
ecological risk assessment for risk managers (U.S. EPA, 1995b).
1.3. Guidelines Organization
These guidelines are structured according to the ecological risk
assessment process as shown in figure 1-2. Within problem formulation
(section 3), important areas addressed include identifying goals and
assessment endpoints, preparing the conceptual model, and developing an
analysis plan. The analysis phase (section 4) involves evaluating
exposure to stressors and the relationship between stressor levels and
ecological effects. In risk characterization (section 5), key elements
are estimating risk through integration of exposure and stressor-
response profiles and describing risk by discussing lines of evidence,
interpreting adversity, and summarizing uncertainty. In addition,
discussions between the risk assessor and risk manager at the beginning
(section 2) and end of the risk assessment (section 6) are highlighted.
BILLING CODE 6560-50-P
[[Page 47561]]
[GRAPHIC] [TIFF OMITTED] TN09SE96.001
BILLING CODE 6560-50-C
[[Page 47562]]
The reader may notice that cross-cutting topics are covered in
several sections. These include uncertainty, models, evaluating data,
causality, linking measures of effect to assessment endpoints, and
identifying ecological effects. Considerations appropriate to the
different phases of ecological risk assessment are discussed.
2. Planning The Risk Assessment: Dialogue Between Risk Managers and
Risk Assessors
The purpose for an ecological risk assessment is to produce a
scientific evaluation of ecological risk that enables managers to make
informed environmental decisions. To ensure that ecological risk
assessments meet risk managers' needs, a planning dialogue between risk
managers and risk assessors (see text notes 2-1 and 2-2) is a critical
first step toward initiating problem formulation and plays a continuing
role during the conduct of the risk assessment. Planning is the
beginning of a necessary interface between risk managers and risk
assessors and is represented by a side box in the ecological risk
assessment diagram (see figure 1-2). It is due to the importance of
planning and the significant role it plays in ecological risk
assessments that this section on planning is incorporated into
guidelines on ecological risk assessment. However, it is imperative to
remember that the 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 bias the scientific
evaluation of risk.
During the planning dialogue, risk managers and risk assessors each
bring important perspectives to the table. In general, risk managers
are charged with protecting societal values (e.g., human health and the
environment) and must ensure that the risk assessment will provide
information relevant to a decision. To meet this charge, risk managers
describe why the risk assessment is needed, what decisions it will
support, and what they want to receive from the risk assessor. It is
also helpful for managers to consider what problems they have
encountered in the past when trying to use risk assessments for
decisionmaking. In turn, it is the ecological risk assessors' role to
ensure that science is effectively used to address ecological concerns.
Risk assessors describe what they can provide to the risk manager,
where problems are likely to occur, and where uncertainty may be
problematic. Both evaluate the potential value of conducting a risk
assessment to address identified problems.
Both 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
endpoints, and publicly perceived environmental values to interpret the
goals for use in the ecological risk assessment. Examples of questions
risk managers and risk assessors may address during planning are
provided in text note 2-3.
The first step in planning may be to determine if a risk assessment
is the best option for making the decision required. Questions
concerning 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 are asked during these
discussions. In some cases, a risk assessment may add little value to
the decision process. It is important for the risk manager and risk
assessor to explore alternative options for addressing possible risk
before continuing to the next planning stage (see section 1.1.2).
Once the decision is made to conduct a risk assessment, planning
focuses on (1) establishing management goals that are agreed on,
clearly articulated, and contain a way to measure success; (2) defining
the decisions to be made within the context of the management goals;
and (3) agreeing on the scope, complexity, and focus of the risk
assessment, including the expected output and the technical and
financial support available to complete it. To achieve these
objectives, risk managers and risk assessors must each play an active
role in planning the risk assessment.
2.1. Establishing Management Goals
Management goals for a risk assessment are established by risk
managers but are derived in a variety of ways. Many Agency risk
assessments are conducted based on legally established management goals
(e.g., national regulatory programs generally have management goals
written into the law governing the program). In this case, goal setting
was previously completed through public debate in establishing the law.
In most cases, legally established management goals do not provide
sufficient guidance to the risk assessor. For example, the objectives
under the Clean Water Act to ``protect and maintain the chemical,
physical and biological integrity of the nation's waters'' are open to
considerable interpretation. Agency managers and staff often interpret
the law in regulations and guidance. Significant interaction between
the risk assessor and risk manager may be needed to translate the law
into management goals for a particular location or circumstance.
As the Agency increasingly emphasizes ``place-based'' or
``community-based'' management of ecological resources as recommended
in the Edgewater Consensus (U.S. EPA, 1994e), management goals take on
new significance for the ecological risk assessor. Management goals for
``places'' such as watersheds are formed as a consensus based on
diverse values reflected in Federal, state, and local regulations;
constituency group agendas; and public concerns. Significant
interactions among a variety of interested parties are required to
generate agreed-on management goals for the resource (see text note 2-
4). Public meetings, constituency group meetings, evaluation of
resource management organization charters, and other means of looking
for management goals shared by these diverse groups may be necessary.
Diverse risk management teams may elect to use social scientists
trained in consensus-building methods to help establish management
goals. While management goals derived in this way may require further
definition (see text note 2-5), there is increased confidence that
these goals are supported by the audience for the risk assessment.
Regardless of how management goals are established, goals that
explicitly define which ecological values are to be protected are more
easily used to design a risk assessment for decisionmaking than general
management goals. Whenever goals are general, risk assessors must
interpret those goals into ecological values that can be measured or
estimated and ensure that the managers agree with their interpretation
(see text note 2-6). Legally mandated goals generally are interpreted
by Agency managers and staff. This interpretation may be performed once
and then applied to the multiple similar assessments (e.g. evaluation
of new chemicals). For other risk assessments, the interpretation is
unique to the ecosystem being assessed and must be done on a case-by-
case basis as part of the planning process.
2.2. Management Decisions
A risk assessment is shaped by the kind of decision it will
support. When a management decision is explicitly
[[Page 47563]]
stated and closely aligned to management actions, the scope, focus, and
conduct of the risk assessment are well defined by the specificity of
the decision to be made. Some of these risk assessments are used to
help establish national policy that will be applied consistently across
the country (e.g., premanufacture notices for new chemicals, protection
of endangered species). Other risk assessments are designed for a
specific site (e.g., hazardous waste site clean-up level). When
decision options (e.g., decision criteria in the data quality
objectives process, U.S. EPA, 1994d; see section 3.5.2 for more
details) are known prior to the risk assessment, a number of
assumptions are inherent in those options that need to be explicitly
stated during planning. This ensures that the decision criteria are not
altering the scientific validity of the risk assessment by
inappropriately applying assumptions or unnecessarily limiting the
variables. For many risk assessments, there may be a range of possible
management options for managing risk. When different management options
have been identified (e.g., leave alone, clean up, or pave a
contaminated site), risk assessment can be used to predict potential
risk across the range of these management options.
Risk assessments may be designed to provide guidance for management
initiatives for a region or watershed where multiple stressors,
ecological values, and political factors influence decisionmaking.
These risk assessments require great flexibility and breadth and may
use national risk-based information and site-specific risk information
in conjunction with regional evaluations of risk. As risk assessment is
more frequently used to support landscape-scale management decisions,
the diversity, breadth, and complexity of the risk assessments increase
significantly and may include evaluations that focus on understanding
ecological processes influenced by a diversity of human actions and
management options. Risk assessments used in this application are often
based on a general goal statement and require significant planning to
establish the purpose, scope, and complexity of the assessment.
2.3. Scope and Complexity of the Risk Assessment
Although the purpose for the risk assessment determines whether it
is national, regional, or local, the resources available for conducting
the risk assessment determines how extensive and complex it can be
within this framework and the level of uncertainty that can be
expected. Each risk assessment is constrained by the availability of
data, scientific understanding, expertise, and financial resources.
Within these constraints there is much to consider when designing a
risk assessment. Risk managers and risk assessors must discuss in
detail the nature of the decision (e.g., national policy, local
economic impact), available resources, opportunities for increasing the
resource base (e.g., partnering, new data collection, alternative
analytical tools), and the output that will provide the best
information for decisions required (see text note 2-7).
Part of the agreement on scope and complexity is based on the
maximum uncertainty that is acceptable in whatever decision the risk
assessment supports. The lower the tolerance for uncertainty, the
greater the scope and complexity needed in the risk assessment. Risk
assessments completed in response to legal mandates and likely to be
challenged in court often require rigorous attention to acceptable
levels of uncertainty to ensure that the assessment will be used in a
decision. A frank discussion is needed between the risk manager and
risk assessor on sources of uncertainty in the risk assessment and ways
uncertainty can be reduced (if necessary) through selective investment
of resources. Where appropriate, planning could account for the
iterative nature of risk assessment and include explicitly defined
steps. These steps may take the form of ``tiers'' that represent
increasing levels of complexity and investment, with each tier designed
to reduce uncertainty. The plan may include an explicit definition of
iterative steps with a description of levels of investment and decision
criteria for each tier. Guidance 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, 1994d).
2.4. Planning Outcome
The planning phase is complete when agreements are reached on the
management goals, assessment objectives, the focus and scope of the
risk assessment, resource availability, and the type of decisions the
risk assessment is to support. 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 temporal scale (e.g., the time frame over which
stressors or effects will be evaluated).
In mandated risk assessments, planning agreements are often
codified in regulations, and little documentation of agreements is
warranted. In risk assessments where planning decisions can be highly
variable, a summary of planning agreements may be important for
ensuring that the risk assessment remains consistent with early
agreements. A summary can provide a point of reference for determining
if early decisions may need to be changed in response to new
information. There is no defined format, length, or complexity for a
planning summary. It is a useful reference only and should be tailored
to the complexity of the risk assessment it represents. However, a
summary is recommended to help ensure quality communication between and
among risk managers and risk assessors and to document the decisions
that have been agreed upon.
Once planning is complete, the formal process of risk assessment
begins through the initiation of problem formulation. During problem
formulation, risk assessors should continue the dialogue with risk
managers following assessment endpoint selection and once the analysis
plan is completed. At these points, potential problems can be
identified before the risk assessment proceeds.
3. Problem Formulation Phase
Problem formulation is a formal process for generating and
evaluating preliminary hypotheses about why ecological effects have
occurred, or may occur, from human activities. As the first stage of an
ecological risk assessment, it provides the foundation on which the
entire assessment depends. During problem formulation, management goals
developed during planning are evaluated to establish objectives for the
risk assessment, the problem is defined, and the plan for analyzing
data and characterizing risk is determined. Any deficiencies in problem
formulation will compromise all subsequent work on the risk assessment
(see text note 3-1).
3.1. Products of Problem Formulation
Successful completion of problem formulation depends on the quality
of 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 among several stressors and assessment
[[Page 47564]]
endpoints, and (3) an analysis plan. Essential to the development of
these products are the effective integration and evaluation of
available information.
The following discussion focuses on the products of problem
formulation and the information that determines the nature of those
products. The products are featured in the problem formulation diagram
as circles (see figure 3-1). The types of information that must be
evaluated to generate those products are shown in the hexagon.
BILLING CODE 6560-50-P
[[Page 47565]]
[GRAPHIC] [TIFF OMITTED] TN09SE96.002
BILLING CODE 6560-50-C
[[Page 47566]]
To enhance clarity, the organization of the following discussion
follows the above topics. However, problem formulation is not
necessarily completed in the order presented here. First, the order in
which products are produced is directly related to why the ecological
risk assessment is initiated, as addressed in section 3.2. Second,
problem formulation is inherently interactive and iterative, not
linear. Substantial reevaluation is expected to occur within and among
all products of problem formulation.
3.2. Integration of Available Information
The foundation for problem formulation is the integration of
available information on the sources of stressors and stressor
characteristics, exposure, the ecosystem(s) potentially at risk, and
ecological effects (see figure 3-1). When key information is of the
appropriate type and sufficient quality and quantity, problem
formulation can proceed effectively. When key information is
unavailable in one or more areas, the risk assessment may be
temporarily suspended while new data are collected. If new data cannot
be collected, then the risk assessment will depend on what is known and
what can be extrapolated from that information. Complete information is
not available at the beginning of many risk assessments. When this is
the case, the process of problem formulation assists in identifying
where key data are missing and provides the framework for further
research where more data are needed. Where data are few, a clear
articulation of the limitations of conclusions, or uncertainty, from
the risk assessment becomes increasingly critical in risk
characterization (see text note 3-2).
The reason why an ecological risk assessment is initiated directly
influences what information is available at the outset, and what
information must be found. A risk assessment can be initiated because a
known or potential stressor may be released into the environment, an
adverse effect or change in condition is observed, or better management
of an important ecological value (e.g., valued ecological entities such
as species, communities, ecosystems or places) is desired. Risk
assessments are sometimes initiated for two or all three of these
reasons.
Risk assessors beginning with information about the source or
stressor will seek available information on the effects the stressor
might be associated with and the ecosystems that it will likely be
found in. Risk assessors beginning with information about an observed
effect or change in condition will need to seek information about
potential stressors and sources. Risk assessors starting with concern
over a particular ecological value may need additional information on
the specific condition or effect of interest, the ecosystems
potentially at risk, and potential stressors and sources.
The initial use of available information is a scoping process
similar to that used to develop environmental impact statements. During
this process, data and information (i.e., actual, inferred, or
estimated) are considered to ensure that nothing important is
overlooked. A comprehensive evaluation of all information provides the
framework for generating a large array of risk hypotheses to consider
(see section 3.4.1). After the initial scoping process, information
quality and applicability to the particular problem of concern are
increasingly scrutinized as the risk assessor proceeds through problem
formulation. When analysis plans are formed, data validity becomes a
significant factor to consider. Issues relating to evaluating data
quality are discussed in the analysis phase (see section 4.1).
As the complexity and spatial scale of a risk assessment increase,
information needs escalate. Ecosystems 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 understanding of 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 limitations on what is known about ecosystems and the
stressors influencing them, the process of problem formulation offers a
valuable systematic approach for organizing and evaluating available
information on all stressors and possible effects in a way that can be
useful to risk assessors and decisionmakers. Text note 3-3 provides a
series of questions that risk assessors should attempt to answer using
available information, many of which were drawn from Barnthouse and
Brown (1994). 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 the analysis
plan (discussed below). However, the order in which these task are done
is influenced by the reason for initiating the assessment (text note 3-
4). Early recognition that initiation effects the order of product
generation will help facilitate the development of problem formulation.
3.3. Selecting Assessment Endpoints
Assessment endpoints are ``explicit expressions of the actual
environmental value that is to be protected'' (U.S. EPA, 1992a).
Assessment endpoints are critical to problem formulation because they
link the risk assessment to management concerns and they are central to
conceptual model development. Their relevance to ecological risk
assessment is determined by how well they target susceptible ecological
entities. Their ability to support risk management decisions depends on
how well they represent measurable characteristics of the ecosystem
that adequately represent management goals. The selection of ecological
concerns and assessment endpoints in EPA has traditionally been done
internally by individual Agency program offices (U.S. EPA, 1994g). More
recently, Agency activities such as the watershed protection approach
and community-based environmental protection have used contributions by
interested parties in the selection of ecological concerns and
assessment endpoints. This section describes criteria for selecting and
defining assessment endpoints.
3.3.1. Selecting What To Protect
The ecological resources selected to represent management goals for
environmental protection are reflected in the assessment endpoints that
drive ecological risk assessments. Assessment endpoints often reflect
environmental values that are protected by law, provide critical
resources, or provide an ecological function that would be
significantly impaired (or that society would perceive as having been
impaired) if the resource were altered.
Although many potential assessment endpoints may be identified,
considering the practicality of using particular assessment endpoints
will help refine selections. For example, when the attributes of an
assessment endpoint can be measured directly, extrapolation is
unnecessary; therefore this uncertainty is not introduced into the
results. Assessment endpoints that cannot be measured directly but can
be represented by measures that are easily monitored and modeled still
provide a good foundation for the risk assessment. Assessment endpoints
that cannot be linked with measurable attributes are not appropriate
for a risk assessment.
Three principal criteria are used when selecting assessment
endpoints: (1) their
[[Page 47567]]
ecological relevance, (2) their susceptibility to the known or
potential stressors, and (3) whether they represent management goals.
Of these three criteria, ecological relevance and susceptibility are
essential for selecting assessment endpoints that are scientifically
valid. Rigorous selection based on these criteria must be maintained.
However, to increase the likelihood that the risk assessment will be
used in management decisions, assessment endpoints that represent
societal values and management goals are more effective. Given the
complex functioning of ecosystems and the interdependence of ecological
entities, it is likely that assessment endpoints can be selected that
are responsive to management goals while meeting scientific criteria.
This provides a way to address changes that may occur over time in the
public's perception of ecological value (e.g., wetlands viewed as
infested swamps 30 years ago are considered prime wildlife habitat
today; Suter, 1993a). 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). These are endpoints that help sustain the natural
structure, function, and biodiversity of an ecosystem. For example,
ecologically relevant endpoints may contribute to the food base (e.g.,
primary production), provide habitat, 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). Changes in ecologically relevant
endpoints can result in unpredictable and widespread effects.
Ecological relevance becomes most important when risk assessors are
identifying the potential cascade of adverse effects that could result
from the loss or reduction of one or more species or a change in
ecosystem function (see text note 3-6). Careful selection of assessment
endpoints that address both specific organisms of concern and
landscape-level ecosystem processes becomes increasingly important in
landscape-level risk assessments. In some cases, it may be possible to
select one or more species and an ecosystem process to represent larger
functional community or ecosystem processes.
Determining ecological relevance in specific cases requires expert
judgment based on site-specific information, preliminary site surveys,
or other available information. The less information available, the
more critical it is to have informed expert judgment to ensure
appropriate selections. If assessment endpoints in a risk assessment
are not ecologically relevant, the results of the risk assessment may
predict risk to the assessment endpoints selected but seriously
misrepresent risk to the ecosystem of concern, which could lead to
misguided management.
3.3.1.2. Susceptibility to Known or Potential Stressors
Ecological resources are considered susceptible when they are
sensitive to a human-induced stressor to which they are exposed.
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. For example, chemical sensitivity is
influenced by individual physiology and metabolic pathways. Sensitivity
also is influenced by individual and community life-history
characteristics. For example, species with long life cycles and low
reproductive rates will be more vulnerable to extinction from increases
in mortality than those 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 those with smaller home ranges 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 exchange among subpopulations.
Sensitivity may 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 example, Pacific salmon eggs and fry are
very sensitive to sedimentation from forest logging practices and road
building because they can be smothered. Age-dependent sensitivity,
however, is not only in the young. In many species, special events like
migration (e.g., in birds) and molting (e.g., in harbor seals)
represent significant energy investments that make these organisms more
vulnerable to an array of possible stressors. Finally, sensitivity may
be increased 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).
Measures of sensitivity may include mortality or adverse
reproductive effects from exposure to toxics, behavioral abnormalities,
avoidance of significant food sources or nesting sites, or 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 other key determinant in susceptibility. Exposure
can mean co-occurrence, contact, or the absence of contact, depending
on the stressor and assessment endpoint (see section 4 for more
discussion). The amount and conditions of exposure directly influence
how an ecological entity will respond to a stressor. Thus, to determine
what entities are susceptible, it is important to consider information
on the proximity of an ecological resource to the stressor, the timing
of exposure (both in terms of frequency and duration), and the
intensity of exposure occurring during sensitive life stages of the
organisms.
Adverse effects of a particular stressor may be important during
one part of an organism's life cycle, such as early development or
reproduction. Adverse effects may result from exposure to a stressor or
to the absence of a necessary resource during a critical life stage.
For example, if fish are unable to find suitable nesting sites during
their reproductive phase, risk is significant even when water quality
is high and food sources abundant. The interplay between life stage and
stressors can be very complex (e.g., see text note 3-7).
Exposure may occur in one place or time, and effects may not occur
until another place or time. Both life history characteristics, as
described under sensitivity, and the circumstances of exposure,
influence susceptibility in this case. For example, the temperature of
the incubation medium of marine turtle eggs affects the sex ratio of
the offspring. But the population impacts of a change in incubation
temperature may not be observable until years later when the cohort of
affected turtles begins to reproduce. Delayed effects and multiple
stressor exposures add complexity to evaluations of susceptibility. For
example, although toxicity tests may determine receptor sensitivity to
one stressor, the degree of 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 is unlikely to be exposed to the stressor of
concern, it is inappropriate as an assessment endpoint.
3.3.1.3. Representation of Management Goals
Ultimately, the value of a risk assessment depends on whether it
can
[[Page 47568]]
support quality management decisions. Risk managers are more willing to
use a risk assessment for making decisions when the assessment is based
on values and organisms that people care about. These values,
interpreted from management goals (see section 2) into assessment
endpoints, provide a defined and measurable entity for the risk
assessment. Candidates for assessment endpoints might include entities
such as endangered species, commercially or recreationally important
species, functional attributes that support food sources or flood
control (wetland water sequestration, for example), or aesthetic
values, such as clean air in national parks or the existence of
charismatic species like eagles or whales.
Selection of assessment endpoints based on public perceptions alone
could lead to management decisions that do not consider important
ecological information. While being responsive to the public is
important, it does not obviate the requirement for scientific validity
as represented by the sections on ecological relevance and
susceptibility. Many ecological entities and attributes meet the
necessary scientific rigor as assessment endpoints; some will be
recognized as valuable by risk managers and the public, and others will
not. Midges, for example, can represent the base of a complex food web
that supports a popular sports fishery. They may also be considered
pests. While both midges and fish are important ecological entities in
this ecosystem and represent key components of the aquatic community,
selecting the fishery as the assessment endpoint and using midges as a
critical ecological entity to measure allow both entities to be used in
the risk assessment. This choice maintains the scientific validity of
the risk assessment and is responsive to management concerns. In those
cases where the risk assessor identifies a critical assessment endpoint
that is unpopular with the public, the risk assessor may find it
necessary to present a persuasive case in its favor based on scientific
arguments.
3.3.2. Defining Assessment Endpoints
Assessment endpoints provide a transition between broad management
goals and the specific measures used in an assessment. They help
assessors identify measurable attributes to quantify and predict
change. Assessment endpoints also help the risk assessor determine
whether management goals have been or can be achieved (see text note 3-
8).
Two elements are required to define an assessment endpoint. The
first is the valued ecological entity. This can be a species (e.g.,
eelgrass, piping plover), a functional group of species (e.g.,
raptors), an ecosystem function or characteristic (e.g., nutrient
cycling), a specific valued habitat (e.g., wet meadows) or a unique
place (e.g., a remnant of native prairie). The second is the
characteristic about the entity of concern that is important to protect
and potentially at risk. For example, it is necessary to define what is
important for piping plovers (e.g., nesting and feeding success),
eelgrass (e.g., areal extent and patch size), and wetlands (e.g.,
endemic wet meadow community structure and function). For an assessment
endpoint to provide a clear interpretation of the management goals and
the basis for measurement in the risk assessment, both an entity and an
attribute are required.
Assessment endpoints are distinct from management goals. They do
not represent what the managers or risk assessors want to achieve. As
such they do not contain words like ``protect,'' ``maintain,'' or
``restore,'' or indicate a direction for change such as ``loss'' or
``increase.''
Defining assessment endpoints can be difficult. They 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 an even more vague 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 and processes of an
ecosystem and describing characteristics that best represent integrity
for that system. For example, general goals for Waquoit Bay were
translated into several assessment endpoints, including ``estuarine
eelgrass abundance and distribution'' (see text note 2-6).
Expert judgment and an understanding of the characteristics and
function of an ecosystem are important for translating general goals
into usable assessment endpoints. Endpoints that are too narrowly
defined, however, may not support effective risk management. For
example, if an assessment is focused on protecting the habitat of an
endangered species, the risk assessment may overlook important
characteristics of the ecosystem and fail to include critical variables
(see text note 3-7).
Assessment endpoints must be appropriate for the ecosystem of
concern. Selecting a game fish that grows well in reservoirs may meet a
``feasible'' management goal, but 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 the fishable goal and may be highly desired by local
fishermen, a reservoir species does not represent the ecosystem at
risk. A vague ``viable fish populations'' assessment endpoint
substituted by ``reproducing populations of indigenous salmonids''
could therefore prevent the development of an inappropriate risk
assessment.
Clearly defined assessment endpoints provide direction and
boundaries for the risk assessment and can minimize miscommunication
and reduce uncertainty. Assessment endpoints directly influence the
type, characteristics, and interpretation of data and information used
for analyses and the scale and character of the assessment. For
example, an assessment endpoint such as ``egg production of pond
invertebrates'' defines local population characteristics and requires
very different types of data and ecosystem characterization compared
with ``watershed aquatic community structure and function.'' If
concerns are local, the assessment endpoints should not focus on
landscape concerns. Where ecosystem processes and landscape mosaics are
of concern, survival of a particular species would provide inadequate
representation. Assessment endpoints that are poorly defined,
inappropriate, or at the incorrect scale can be very problematic.
Common problems encountered in selecting assessment endpoints are
summarized in text note 3-9.
The presence of multiple stressors should influence the selection
of assessment endpoints. When it is possible to select one assessment
endpoint that is sensitive to many of the identified stressors, yet
responds in different ways to different stressors, it is possible to
consider the combined effects of multiple stressors while still
discriminating among effects. For example, if recruitment of a fish
population is the assessment endpoint, it is important to recognize
that recruitment may be adversely affected at several life stages, in
different habitats, through different ways, by different stressors. The
measures of effect, exposure, and ecosystem and receptor
characteristics chosen to evaluate recruitment provide a basis for
discriminating among different stressors, individual effects, and their
combined effect.
The assessment endpoint can provide a basis for comparing a range
of stressors if carefully selected. For example, the National Crop Loss
Assessment Network (Heck, 1993)
[[Page 47569]]
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 allowed them 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., the abundance of 1-year-old fish
to assess the effects from toxicity, fishing pressure, and habitat
loss). These considerations are important when selecting assessment
endpoints for addressing the combined effect of multiple stressors.
However, in situations where multiple stressors act on the structure
and function of aquatic and terrestrial communities in a watershed
ecosystem, an array of assessment endpoints that represent the
ecosystem community and processes is more effective than a single
endpoint. When based on differing susceptibility to an array of
stressors, the careful selection of assessment endpoints can help risk
assessors distinguish among effects from diverse stressors. Exposure to
multiple stressors may lead to effects at different levels of
biological organization, for a cascade of adverse responses that should
be considered.
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. If the response of an assessment endpoint
cannot be directly measured, it may be predicted from responses of
surrogate or similar entities. Although for practical reasons it is
helpful to use assessment endpoints that have well-developed test
methods, field measurement techniques, and predictive models (see
Suter, 1993a), it is not necessary for these methods to be established
protocols. Measures that will be used to evaluate assessment endpoint
response to exposures for the risk assessment are often identified
during conceptual model development and specified in the analysis plan.
See section 3.5 for issues surrounding the selection of measures.
It is important for risk assessors and risk managers to agree that
selected assessment endpoints represent the management goals for the
particular ecological value. The rationale for their selection should
be clear. Assessment endpoint selection is an important risk manager-
risk assessor checkpoint during problem formulation.
3.4. Conceptual Models
A conceptual model in problem formulation is a written description
and visual representation of predicted responses by ecological entities
to stressors to which they are exposed, and the model includes
ecosystem processes that influence these responses. Conceptual models
represent many relationships (e.g., exposure scenarios may
qualitatively link land-use activities to sources and their stressors,
may describe primary, secondary, and tertiary exposure pathways, and
may describe co-occurrence between exposure pathways, ecological
effects, and ecological receptors).
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 is known at the outset. The process of creating conceptual
models helps identify the unknown elements.
The complexity of the conceptual model depends on the complexity of
the problem, number of stressors, number of assessment endpoints,
nature of effects, and characteristics of the ecosystem. For single
stressors and single assessment endpoints, conceptual models can be
relatively simple relationships. In situations where conceptual models
describe both the pathways of individual stressors and assessment
endpoints and the interaction of multiple and diverse stressors and
assessment endpoints (e.g., assessments initiated because of important
values), several submodels normally will be required to describe
individual pathways. Other models may then be used to explore how these
individual pathways interact.
Conceptual models consist of two principal products:
A set of risk hypotheses that describe predicted
relationships between 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 (Merriam-Webster, 1972). Risk hypotheses are
statements of assumptions about risk based on available information
(see text note 3-10). They are formulated using a combination of expert
judgment and 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 event before it
happens, or they may postulate why observed ecological effects occurred
and ultimately what sources and stressors caused the effect. Depending
on the scope of the risk assessment, the set of 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.
Although risk hypotheses should be developed even when information
is incomplete, the amount and quality of data 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 among 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 intended to be broad in scope,
identifying as many potential relationships as possible. As more
information is incorporated, the plausibility of specific risk
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 selecting hypotheses are documented. Examples of risk
hypotheses are provided in text note 3-11.
3.4.2. Conceptual Model Diagrams
Conceptual model diagrams may be based on theory and logic,
empirical data, mathematical models, or probability models. They are
useful tools for communicating important pathways in a clear and
concise way and can be used to ask new questions about relationships
that help generate plausible risk hypotheses. Some of the benefits
gained by developing conceptual models are featured in text note 3-12.
Conceptual model diagrams frequently contain boxes and arrows to
illustrate relationships (see figure 3-2
[[Page 47570]]
and Appendix C). When constructing these kinds of flow diagrams, 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 for conceptual model
diagrams. Pictorial representations can be more effective (e.g.,
Bradley and Smith, 1989). Regardless of the configuration, a
significant part of the usefulness of a diagram is linked to the
detailed written descriptions and justifications for the pathways and
relationships shown. Without this, diagrams can misrepresent the
processes illustrated.
BILLING CODE 6560-50-P
[[Page 47571]]
[GRAPHIC] [TIFF OMITTED] TN09SE96.003
BILLING CODE 6560-50-C
[[Page 47572]]
When developing diagrams to represent a conceptual model, factors
to consider include the number of relationships depicted, the
comprehensiveness of the information, the certainty surrounding a
pathway, and the potential for measurement. The number of relationships
that can be depicted in one flow diagram depends on how comprehensive
each relationship is. The more comprehensive, the fewer relationships
that can be shown with clarity. Flow diagrams that highlight where data
are abundant or scarce can provide insights on how the analyses should
be approached and can be used to show the degree of confidence the risk
assessor has in the relationship. Such flow diagrams can also help
communicate 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, risks could be
seriously under- or overestimated in the risk characterization phase.
Uncertainty can arise from lack of knowledge on how the ecosystem
functions, failing to identify and interrelate temporal and spatial
parameters, not describing a stressor or suite of stressors, or not
recognizing secondary effects. In some cases, little may be known about
how a stressor moves through the environment or causes adverse effects.
In most cases, multiple stressors are the norm and a source of
confounding variables, particularly for conceptual models that focus on
a single stressor. Opinions of experts on the appropriate conceptual
model configuration may differ. 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 reduced by
developing alternative conceptual models for a particular assessment to
explore possible relationships. In cases where more than one conceptual
model is plausible, the risk assessor must decide whether it is
feasible to follow separate models through the analysis phase or
whether the models can be combined into a better conceptual model. It
is important to revisit, and if necessary revise, conceptual models
during risk assessments to incorporate new information and recheck the
rationale. It is valuable to present conceptual models to risk managers
to ensure the models communicate well and address key concerns the
managers have. This check for completeness and clarity provides an
opportunity to assess the need for changes before analysis begins.
Throughout the process of 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 clear description of the nature of the uncertainties should be
clearly summarized at the close of the problem formulation. Text note
3-13 provides recommendations for describing uncertainty in problem
formulation.
The hypotheses considered most likely to contribute to risk are
pursued in the analysis phase. As discussed previously, it is important
to provide the rationale for selecting and omitting risk hypotheses and
to acknowledge data gaps and uncertainties.
3.5. Analysis Plan
An analysis plan can be a usual final stage of problem formulation,
particularly in the case of complex assessments. Here, risk hypotheses
are evaluated to determine how they will be assessed using available
and new data. The analysis plan can also delineate the assessment
design, data needs, measures, and methods for conducting the analysis
phase of the risk assessment. The analysis plan may be relatively brief
or extensive depending on the nature of the assessment.
The analysis plan includes the most important pathways and
relationships identified during problem formulation that will be
pursued in the analysis phase. It is important for the risk assessor to
describe what will be done and, in particular, what will not be done.
It is important to address issues concerning the level of confidence
needed for the management decision relative to the confidence that can
be expected from an analysis in order to determine data needs and
evaluate whether one analytical approach may be better than another.
When new data are needed to conduct analyses, the feasibility of
obtaining the data should be taken into account.
The selection of critical relationships in the conceptual model to
pursue in analysis is based on several criteria, including:
Availability of information.
Strength of information about relationships between
stressors and effects.
The assessment endpoints and their relationship to
ecosystem function.
Relative importance or influence and mode of action of
stressors.
Completeness of known exposure pathways.
In situations where data are few and new data cannot be collected,
it is possible to combine existing data with extrapolation models so
that alternative data sources may be used. This allows the use of data
from other locations or on other organisms where similar problems exist
and data are available. For example, the relationship between nutrient
availability and algal growth is well established. Although there will
be differences in how the relationship is manifested based on the
dynamics of a particular ecosystem, the relationship itself will tend
to be consistent. When using data that require extrapolation, it is
important to identify the source of the data, justify the extrapolation
method and discuss major uncertainties apparent at this point.
Where data are not available, recommendations for new data
collection should be part of problem formulation. An iterative, phased,
or tiered approach (see text note 1-3) to the risk assessment may be
selected to provide an opportunity for early management decisions on
issues that can be addressed using available data. A decision to
conduct a new iteration is based on the results of any previous
iteration and proceeds using new data collected as specified in the
analysis plan. When new data collection cannot be obtained, pathways
that cannot be assessed are a source of uncertainty and should be
described in the analysis plan.
3.5.1. Selecting Measures
It is in the analysis planning stage that measures are identified
to evaluate the risk hypotheses. There are three categories of
measures. Measures of effect are measures used to evaluate the response
of the assessment endpoint when exposed to a stressor (formerly
measurement endpoints). Measures of exposure are measures of how
exposure may be occurring, including how a stressor moves through the
environment and how it may co-occur with the assessment endpoint.
Measures of
[[Page 47573]]
ecosystem and receptor characteristics include ecosystem
characteristics that influence the behavior and location of assessment
endpoints, the distribution of a stressor, and life history
characteristics of the assessment endpoint that may affect exposure or
response to the stressor. These diverse measures increase in importance
as the complexity of the assessment increases and are particularly
important for risk assessments initiated to protect ecological values
(see text notes 3-14 and 3-15 for more information).
Text note 3-16, which describes water quality criteria, provides
one example of how goals, endpoints, and measures are related. Although
water quality criteria are often considered risk-based, they do not
measure exposure. Instead, the water quality criteria provide an
effects benchmark for decisionmaking. 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). Assumptions embedded in
decision rules should be articulated (see section 3.5.2).
The analysis plan provides a synopsis of measures that will be used
to evaluate risk hypotheses. Potential extrapolations, model
characteristics, types of data (including quality), and planned
analyses (with specific tests for different types of data) are
described. The plan should discuss how the results will be presented
upon completion. The analysis plan provides the basis for making
selections of data sets that will be used for the risk assessment.
The plan includes explanations of how data analyses will
distinguish among hypotheses, an explicit expression of the approach to
be used, and justifications for the elimination of some hypotheses and
selection of others. It includes the measures selected, analytical
methods planned, and the nature of the risk characterization options
and considerations that will be generated (e.g., quotients, narrative
discussion, stressor-response curve with probabilities). An analysis
plan is enhanced if it contains explicit statements for how measures
were selected, what they are intended to evaluate, and which analyses
they support. During analysis planning, uncertainties associated with
selected measures and analyses are articulated and, where possible,
plans for addressing them are made.
3.5.2. Relating Analysis Plans to Decisions
The analysis plan is a risk manager-risk assessor checkpoint and an
appropriate time for technical review. Discussions between the risk
assessors and risk managers can help ensure that the analyses will
provide the type and extent of information that the manager can use for
decisionmaking. These discussions may also identify what can and cannot
be done based on the preliminary evaluation of problem formulation,
including which relationships to portray for the risk management
decision. A reiteration of the planning discussion is important to
ensure that the appropriate balance among the requirements for the
decision, data availability, and resource constraints is established
for the risk assessment.
The elements of an analysis plan share significant similarities
with the data quality objectives (DQO) process (see text note 3-17),
which emphasizes identifying the problem by establishing study
boundaries and determining necessary data quality, quantity, and
applicability to the problem being evaluated. The DQO guidance is a
valuable reference for risk assessors (U.S. EPA, 1994d).
The most important difference between problem formulation and DQO
is the presence of a decision rule 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. This approach provides the basis
for establishing null and alternative hypotheses appropriate for
statistical testing for significance. While this approach is
appropriate for some risk assessments, many risk assessments are not
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.
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 it is determined that the problem is clearly defined and there are
enough data to proceed, analysis begins.
4. Analysis Phase
The analysis phase consists of the technical evaluation of data to
reach conclusions about ecological exposure and the relationships
between the stressor and ecological effects. During analysis, risk
assessors use measures of exposure, effects, and ecosystem and receptor
attributes to evaluate questions and issues that were identified in
problem formulation. The products of analysis are summary profiles that
describe exposure and the stressor-response relationship. When
combined, these profiles provide the basis for reaching conclusions
about risk during the risk characterization phase.
The conceptual model and analysis plan developed during problem
formulation provide the basis for the analysis phase. By the start of
analysis, the assessor should know which stressors and ecological
effects are the focus of investigation and whether secondary exposures
or effects will be considered. In the analysis plan, the assessor
identified the information needed to perform the analysis phase. By the
start of analysis, these data should be available (text note 4-1).
The analysis phase is composed of two principal activities, the
characterization of exposure and characterization of ecological effects
(figure 4-1). Both activities begin by evaluating data (i.e., the
measures of exposure, ecosystem and receptor characteristics, and
effects) in terms of their scientific credibility and relevance to the
assessment endpoint and conceptual model (discussed in section 4.1). In
exposure characterization (section 4.2), these data are then analyzed
to describe the source, the distribution of the stressor in the
environment, and the contact or co-occurrence of the stressor with
ecological receptors. In ecological effects characterization (section
4.3), data are analyzed to describe the relationship between the
stressor and response and to evaluate the evidence that exposure to the
stressor causes the response (i.e., stressor-response analyses). In
many cases, extrapolation will be necessary to link the measures of
effect with the assessment endpoint.
BILLING CODE 6560-50-P
[[Page 47574]]
[GRAPHIC] [TIFF OMITTED] TN09SE96.004
BILLING CODE 6560-50-C
[[Page 47575]]
Conclusions about exposure and the relationship between the
stressor and response are summarized in profiles. The exposure and
stressor-response profiles (sections 4.2.2 and 4.3.2, respectively)
provide the opportunity to review what has been learned during the
analysis phase and summarize this information in the most useful format
for risk characterization. Depending on the risk assessment, these
profiles may take the form of a written document or modules of a larger
process model. Alternatively, documentation may be deferred until risk
characterization. In any case, the purpose of these profiles is to
ensure that the information needed for risk characterization has been
collected and evaluated.
This process is intended to be flexible, and interaction between
the ecological effects characterization and exposure characterization
is recommended. When secondary stressors and effects are of concern,
exposure and effects analyses are conducted iteratively for different
ecological entities, and the analyses can become so intertwined that
they are difficult to differentiate. The bottomland hardwoods example
(Appendix D) illustrates this type of assessment. This assessment
examined potential changes in the plant and animal communities under
different flooding scenarios. The stressor-response and exposure
analyses were combined 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 the analysis phase and risk
estimation can become blurred. For example, the model results developed
for the bottomland hardwoods example were used directly in risk
characterization.
The nature of the stressor (that is, whether it is chemical,
physical, or biological) will influence the types of analyses conducted
and the details of implementation. Thus, the results of the analysis
phase may range from highly quantitative to qualitative, depending on
the stressor and the scope of the assessment. The estimation of
exposure to chemicals emphasizes contact and uptake into the organism,
and the estimation of effects often entails extrapolation from test
organisms to the organism of interest. For physical stressors, the
initial disturbance may be most closely related to the assessment
endpoint (e.g., change of wetland to upland). In many cases, however,
secondary effects (e.g., effects on wildlife that use the wetland) are
the principal concern. The point of view taken during the analysis
phase will depend on the assessment endpoints identified during problem
formulation. 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 evaluates 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 be quantified with confidence.
Accordingly, exposure and effects are often assessed qualitatively by
eliciting expert opinion (Simberloff and Alexander, 1994).
4.1. Evaluating Data and Models for Analysis
In problem formulation, the assessor identifies the information
needed to perform the analysis phase and plans for collecting new data.
The first step of the analysis phase is the critical evaluation of data
and models to ensure that they can support the risk assessment. The
sources and evaluation of data and models are discussed in sections
4.1.1 and 4.1.2, respectively. The evaluation of uncertainty, an
important consideration when evaluating data and also throughout the
analysis phase, is discussed in section 4.1.3.
4.1.1. Strengths and Limitations of Different Types of Data
The analysis phase relies on the measures identified in the
analysis plan; these may come from laboratory or field studies or may
be produced as output from a model. Data may have been developed for a
specific risk assessment or for another purpose. A strategy that builds
on the strengths of each type of data can improve confidence in the
conclusions of a 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
can be less variable and smaller differences easier to detect. However,
the controls may limit the range of responses (for example, animals
cannot seek alternate food sources), so they may not reflect responses
in the environment. Field surveys are usually more representative of
both exposures and effects (including secondary effects) found in
natural systems than are estimates generated from laboratory studies or
theoretical models. However, because conditions are not controlled,
variability may be higher and it may be difficult to detect
differences. Field studies are most useful for linking stressors with
effects when stressor and effect levels are measured concurrently. In
addition, the presence of confounding stressors can make it difficult
to attribute observed effects to specific stressors. Preferred field
studies use designs that minimize effects of potentially confounding
factors. Intermediate between laboratory and field are studies that use
environmental media collected from the field to conduct studies of
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 into indices, and the index
values are reported. 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.
Although indices are very useful, they have several drawbacks, many
of which are associated with combining heterogeneous variables. For
example, 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. Such indices may need to be separated into their components
to investigate causality (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. Exposure and effects measures 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 also an important data source
that is particularly useful when predicting effects of stressors that
have not yet
[[Page 47576]]
been released. For example, 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 the evaluation of toxicity of 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 the compound for which data are lacking. More advanced
applications involve the use of quantitative structure-activity
relationships (QSARs). QSARs describe 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, these data may be the
only option available.
While models are often developed and used as part of the risk
assessment, sometimes the risk assessor relies on output of a
previously developed model as input to the risk assessment. Models are
particularly useful when measurements cannot be taken, for example when
the assessment is predicting the effects of a chemical yet to be
manufactured. Models can also provide estimates for times or locations
that are impractical to measure and provide a basis for extrapolating
beyond the range of observation. Starfield and Bleloch (1991) caution
that ``the quality of the model does not depend on how realistic it is,
but on how well it performs in relation to the purpose for which it was
built.'' Thus, the assessor must review the questions that need to be
answered and then ensure that a model can answer those questions.
Because models are simplifications of reality, they may not include
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 text note 1-3). 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 by 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. It is important to evaluate
tiered data in light of the decision they are intended to support; data
collected for early tiers may not be able to support more sophisticated
needs.
4.1.2. Evaluating Measurement or Modeling Studies
Much of the information used in the analysis phase is available
through published or unpublished studies that describe the purpose of
the study, the methods used to collect data, and the results.
Evaluating the utility of these studies relies on careful comparison of
the objectives of the studies with the objectives of the risk
assessment. In addition, study methods are examined to ensure that the
intended objectives were met and that the data are of sufficient
quality to support the risk assessment. Confidence in the information
and the implications of using different studies should be described
during risk characterization, when the overall confidence in the
assessment is discussed. In addition, the risk assessor should identify
areas where existing data do not meet risk assessment needs. In these
cases, we recommend collecting new data.
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
are also referred to Smith and Shugart, 1994; U.S. EPA, 1994f; and U.S.
EPA, 1990, for more information on evaluating data and models.)
A study's documentation directly influences the ability to evaluate
its utility for 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. An
additional advantage is the ability 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, a
description of any parameter estimation techniques, and tables or
graphs of results.
Papers or reports describing studies may not provide all of the
information needed to evaluate a study's utility for risk assessment.
Assessors are encouraged to communicate with the principal investigator
or other study participants to gain information on study plans and
their implementation. Questions useful for evaluating studies are shown
in text note 4-3.
4.1.2.1. Evaluating the Purpose and Scope of the Study
The assessor must often evaluate the utility of a study that was
designed for a purpose other than risk assessment. In these cases, it
is important that the objectives and scope of the original study be
examined to evaluate their compatibility with the objectives and needs
of the current risk assessment.
An examination of objectives can identify important uncertainties
and ensure that the information is used appropriately in the
assessment. 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 effects
measures identified in problem formulation, to support a causal
argument, effects measures must be linked with stressors. In the best
case, this means that the stressor should be measured at the same time
and place as the effect.
Similarly, a model may have been developed for purposes other than
risk assessment. The model 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 may have 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 variables and conditions identified during problem
formulation. In addition, the range of variability explored in the
study should be compared with the range of variability of interest for
the risk assessment. For example, a study that examines habitat needs
of an animal during the winter may miss important breeding-season
requirements. In
[[Page 47577]]
general, studies that minimize the amount of extrapolation needed are
preferred. These are the studies that are designed to represent:
The measures identified in the analysis plan (i.e.,
measures of exposure, effects, and ecosystem and receptor
characteristics).
The time frame of interest, considering seasonality and
intermittent events.
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 design and implementation of the study are evaluated to ensure
that the study objectives were met and that the information is of
sufficient quality to support the purposes of 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 for
studies of effects is whether a study had sufficient 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).
Risk assessors should evaluate evidence that 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 the
identification and control of potentially confounding variables and the
careful selection of reference sites. For models, issues include the
program's structure and logic and the correct specification of
algorithms in the model code (U.S. EPA, 1994f).
Study evaluation is easier if a standard method or standard quality
assurance/quality control (QA/QC) protocols are available and followed
by the study. However, the assessor still needs to consider whether the
precision and accuracy goals identified in the standard method were
achieved and whether these goals are appropriate for the purposes of
the risk assessment. For example, detection limits identified for one
environmental matrix may not be achievable for another and may be
higher than concentrations of interest for the risk assessment. Study
results can still be useful even if a standard method was not used.
However, it does place 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 an ongoing 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 credibility by explicitly
describing the magnitude and direction of uncertainties, and they
provide the basis for efficient data collection of or application of
refined methods.
U.S. EPA (1992d) discusses sources of uncertainty that arise during
the evaluation of information and conceptual model development
(combined under the subject of scenario uncertainty), when evaluating
the value of a parameter (e.g., an environmental measurement or the
results of a toxicity test), and during the development and application
of models. Uncertainty in conceptual model development is discussed in
section 3.4.3. Many of the sources of uncertainty discussed by EPA
(U.S. EPA, 1992d) are relevant to characterizing both exposure and
ecological effects; these sources and example strategies for the
analysis phase are shown in table 4-1.
Table 4-1.--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 and methods of literature populations or regional populations.
studies are unclear.
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 and sensitivity using a cumulative
high end) or by constructing distribution function.
probability or frequency
distributions.
Differentiate from uncertainty due to
lack of knowledge.
Data gaps........................ Describe approaches used for bridging Discuss rationale for using a factor
gaps and their rationales. of 10 to extrapolate between a LOAEL
Differentiate science-based judgments and a NOAEL.
from policy-based judgments.
Uncertainty about a quantity's Use standard statistical methods to Present the upper confidence limit on
true value. construct probability distributions the arithmetic mean soil
or point estimates (e.g., confidence concentration, in addition to the
limits). best estimate of the arithmetic
Evaluate power of designed experiments mean.
to detect differences. Ground-truth remote sensing data.
Consider taking additional data if
sampling error is too large.
Verify location of samples or other
spatial features
Model structure uncertainty Discuss key aggregations and model Discuss combining different species
(process models). simplifications. into a group based on similar
Compare model predictions with data feeding habits.
collected in the system of interest.
[[Page 47578]]
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.
----------------------------------------------------------------------------------------------------------------
Sources of uncertainty that are factors primarily when evaluating
information include unclear communication of the information to the
assessor, unclear communication about how the assessor handled the
information, and errors in the information itself (descriptive errors).
These sources are usually characterized by critically examining sources
of information and documenting the rationales for the decisions made
when handling it. The discussion should allow the reader to make an
independent judgment about the validity of the decisions reached by the
assessor.
Sources of uncertainty that arise primarily 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 the true heterogeneity in a characteristic influencing
exposure or effects. Examples include the variability in soil organic
carbon, seasonal differences in animal diets, or differences in
chemical sensitivity among different species. This heterogeneity 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 the ability of the study to detect effects. Properly designed
studies will specify sample sizes that are sufficiently large 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 been applied primarily in the arena of
human epidemiology and are still controversial (Mann, 1990).
Interest in quantifying spatial uncertainty has increased with the
increasing use of geographic information systems. Strategies include
verifying the locations of remotely sensed features, ensuring that the
spatial resolution of data or a method is commensurate with the needs
of the assessment, and using methods to describe and use the spatial
structure of data (e.g., Cressie, 1993).
Nearly every assessment encounters situations where data are
unavailable or where information is available on parameters that are
different from those of interest for the assessment. Examples include
using laboratory animal data to estimate a wild animal's response or
using a bioaccumulation measurement from an ecosystem other than the
one interest. These data gaps are usually bridged based on a
combination of scientific data or analyses, scientific judgement, and
policy judgement. For example, in deriving an ambient water quality
criterion (text note 3-16), data and analyses are used to construct
distributions of species sensitivity for a particular chemical.
Scientific judgment is used to infer that species selected for testing
will adequately represent the range of sensitivity of species in the
environment. Policy judgment is used to define the extent to which
individual species should be protected (e.g., 90 vs 95 percent of the
species). It is important to differentiate among these elements when
key assumptions and the approach used are documented.
In some circumstances scientists may disagree on the best way to
bridge data gaps. This lack of consensus can increase uncertainty.
Confidence can be increased through consensus building techniques such
as peer reviews, workshops, and other methods to elicit expert opinion.
Data gaps can often be filled by completing additional studies on the
unknown parameter. Opportunities for reducing this source of
uncertainty should be noted and carried through to risk
characterization. Data gaps that preclude the analysis of exposure or
ecological effects should also be noted and discussed in risk
characterization.
An important objective of characterizing uncertainty in the
analysis phase is to distinguish variability from uncertainties arising
from lack of knowledge (e.g., uncertainty about a quantity's true
value) (U.S. EPA, 1995c). This distinction facilitates the
interpretation and communication of results. For example, in their food
web models of herons and mink, MacIntosh et al. (1994) separated
variability expected among feeding habits of individual animals from
the uncertainty in the mean concentration of chemical in prey species.
In this way, the assessors could place error bounds on the distribution
of exposure among 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 the development
and application of models include the structure of process models and
the description of the relationship between two or more variables in
empirical models. Process model description should include key
assumptions, simplifications, and aggregations of variables (see text
note 4-4). Empirical model descriptions should include the rationale
for selection, and statistics on model performance (e.g., goodness of
fit). Uncertainty in process or empirical models can be quantitively
evaluated by comparing model results to measurements taken in the
system of interest or by comparing the results obtained using different
model alternatives.
Methods for analyzing and describing uncertainty can range from
simple to complex. The calculation of one or more point estimates is
one of the most common approaches to presenting analysis results; point
estimates that reflect different aspects of uncertainty can have great
value if appropriately developed and communicated. Classical
statistical methods (e.g., confidence limits, percentiles) can be
readily applied to describing uncertainty in parameters. When a
modeling approach
[[Page 47579]]
is used, 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 of the assessment. The availability of
software for Monte-Carlo analysis has greatly increased the use of
probabilistic methods; readers are encouraged to follow best practices
that have been suggested (e.g., Burmaster and Anderson, 1994; Haimes et
al. 1994). 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). These
guidelines do not endorse the use of any one method over others and
note 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 the contact or co-occurrence of
stressors with ecological receptors. The characterization is based on
measures of exposure and of ecosystem and receptor characteristics (the
evaluation of this information is discussed in section 4.1). These
measures 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 results to be useful, they
must be compatible with the stressor-response relationship generated in
the effects characterization.
4.2.1. Exposure Analyses
Exposure is analyzed by describing the source and releases, the
distribution of the stressor in the environment, and the extent and
pattern of contact or co-occurrence. The order of discussion of these
topics is not necessarily the order in which they are evaluated in a
particular assessment. For example, the assessor may start with
information about tissue residues, and attempt to link these residues
with a source.
4.2.1.1. Describe the Source
A source description identifies where the stressor originates,
describes what stressors are generated, and considers other sources of
the stressor. Exposure analyses may start with the source when it is
known, but some analyses may begin with known exposures and attempt to
link them to sources, while other analyses may start with known
stressors and attempt to identify sources and quantify contact. The
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.
Text note 4-5 provides some useful questions.
A source can be defined in several ways--as the place where the
stressor is released (e.g., a smoke stack, historically contaminated
sediments) or the management practice or action (e.g., dredging) that
produces stressors. In some assessments, the original source no longer
exists and the source is defined as the current origin of the
stressors. For example, the source may be defined as contaminated
sediments because the industrial plant that produced the contaminants
no longer operates.
In addition to identifying the source, the assessor describes the
generation of stressors in terms of intensity, timing, and location.
The location of the source and the environmental medium that first
receives stressors are two attributes that deserve particular
attention. In addition, the source characterization should consider
whether other constituents emitted by the source influence transport,
transformation, or bioavailability of the stressor of interest. For
example, 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). 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, and
the characterization of these other sources can be an important
component of the analysis phase. For example, many chemicals occur
naturally (e.g., most metals), are generally widespread due to 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 example,
construction activities may add fine sediments to a stream in addition
to those from a naturally undercut bank. In addition, human activities
may 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 way multiple sources are evaluated during the analysis phase
depends on the objectives of the assessment articulated during problem
formulation. Options include (in order of increasing complexity):
Focus only on the source under evaluation and calculate
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 new
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.
For example, in their analysis of risks from importation of Chilean
logs, the assessment team concluded that the beetle Hylurgus ligniperda
had a high potential for entry into the United States. They based this
conclusion on the fact that they are attracted to freshly cut logs and
tend to burrow under the bark and thus would be protected during
transport (USDA, 1993).
The description of the source can set the stage for the second
objective of exposure analysis, which is describing the distribution of
the stressor in the environment.
4.2.1.2. Describe the Distribution of the Stressor or Disturbed
Environment
The second objective of exposure analyses is to describe the
spatial and temporal distribution of the stressor in
[[Page 47580]]
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 where receptors co-occur with or contact stressors in
the environment, characterizing the spatial and temporal distribution
of a stressor is a necessary precursor to estimating exposure. The
stressor's distribution in the environment is described by evaluating
the pathways that stressors take from the source as well as the
formation and subsequent distribution of secondary stressors.
Evaluating Transport Pathways. There are many pathways by which
stressors can be transported in the environment (see text note 4-7). An
evaluation of transport pathways can help ensure that measurements are
taken in the appropriate media and locations and that models include
the most important processes.
For chemical stressors, the evaluation of pathways usually begins
by determining into which media a chemical will partition. Key
considerations include physicochemical properties such as solubility
and vapor pressure. For example, lipophilic chemicals 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
constituents of chemical mixtures may have different properties, it is
important to 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 is beyond the scope of these
guidelines; readers are referred to the exposure assessment guidelines
(U.S. EPA, 1992d) for additional information.
The attributes of physical stressors may also influence where the
stressors will go. For example, the size of silt particles determines
where they will eventually deposit in a stream. Physical stressors that
eliminate ecosystems or portions of them (e.g., logging activity or the
construction of dams or parking lots) may require no modeling of
pathways--the wetland is filled, the fish are harvested, or the valley
is flooded. For these direct disturbances, the challenge is usually to
evaluate the formation of secondary stressors and the effects
associated with the disturbance.
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
a function primarily of reproductive rates and motility. The other
movement pattern, 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. Biological stressors can use both
diffusion and jump-dispersal strategies, and often one or more
mechanisms are important. This makes dispersal rates very difficult to
predict. Key considerations include the availability of vectors,
whether the organism has natural attributes that enhance dispersal
(e.g., ability to fly, adhere to objects, disperse reproductive units),
and the habitat or host needs of the organism.
For biological stressors, assessors must consider the additional
factors of survival and reproduction. There is a wide range of
strategies organisms use to survive in adverse conditions, for example,
fungi form resting stages such as sclerotia and chlamydospores and some
amphibians became 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 some 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 must be interpreted with caution.
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. In other cases, much more professional
judgment is needed. For example, when evaluating the likelihood that an
introduced organism will become established, it is useful to know
whether the ecosystem is generally similar to or different from the one
where the biological stressor originated. In this case, professional
judgment is needed to determine which characteristics of the current
and original ecosystems should be compared.
Evaluating Secondary Stressors. The creation of secondary stressors
can greatly alter conclusions about risk. Secondary stressors can be
formed through biotic or abiotic transformation processes and may be of
greater or lesser concern than the primary stressor. Evaluating the
formation of secondary stressors is usually done as part of exposure
characterization; however, coordination with the ecological effects
characterization is important to ensure that all potentially important
secondary stressors are evaluated.
For chemicals, the evaluation of secondary stressors usually
focuses on metabolites or degradation products or chemicals formed
through abiotic processes. For example, microbial action increases the
bioaccumulation of mercury by transforming it from inorganic form to
organic forms. 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. In addition, secondary
stressors can be formed through ecosystem processes. For example,
nutrient inputs into an estuary can decrease dissolved oxygen
concentrations because they increase primary production and subsequent
decomposition. While the possibility and rates of 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., degradation of oil constituents by microorganisms) and transport
processes (e.g., loss of oil constituents through volatilization).
Disturbances can also generate secondary stressors, and identifying
the specific consequences that will affect the assessment endpoint can
be a difficult task. For example, the removal of riparian vegetation
can generate many secondary stressors, including increased nutrients,
stream temperature, sedimentation, and altered stream flow. However, it
may be the resulting increase in stream temperature that is the primary
cause of adult salmon mortality in a particular stream.
The distribution of stressors in the environment can be described
using measurements, models, or a combination of the two. If stressors
have already been released, direct measurements of environmental media
or a combination of modeling and measurement is preferred. However, a
modeling approach may be necessary if the assessment is intended to
predict future scenarios or if measurements are
[[Page 47581]]
not possible or practicable. Considerations for evaluating data
collection and modeling studies are discussed in section 4.1. For
chemical stressors, we also refer readers to the exposure assessment
guidelines (U.S. EPA, 1992d). For biological stressors, the
distribution in the environment is difficult to predict quantitatively.
If measurements in the environment cannot be taken, distribution 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 can be an important precursor to the next objective of
exposure analysis--estimating the contact or co-occurrence of the
stressor with ecological entities. In cases where the extent of contact
is known, describing the environmental distribution of the stressor can
help identify potential sources, and ensure that all important
exposures have been addressed. In addition, by identifying the pathways
a stressor takes from a source, the second component of an exposure
pathway is described.
4.2.1.3. Describe Contact or Co-occurrence
The third objective of the exposure analysis is to describe the
extent and pattern of co-occurrence or contact between a stressor and a
receptor (i.e., exposure). The objective of this step is to describe
the intensity and temporal and spatial extent of exposure in a form
that can be compared with the stressor-response profile generated in
the effects assessment. The description of exposure is a critical
element of estimating risk--if there is no exposure, there can be no
risk. Questions for describing contact or co-occurrence are shown in
text note
4-8.
Exposure can be described in terms of co-occurrence of the stressor
with receptors, of the actual contact of a stressor with receptors, or
of the uptake of a stressor into a receptor. The terms by which
exposure is described depend on how the stressor causes adverse
effects. Co-occurrence is particularly useful for evaluating stressors
that can cause effects without actually contacting ecological
receptors. For example, whooping cranes use sandbars in rivers for
their nesting areas, and they prefer sandbars with unobstructed views.
Manmade obstructions, such as bridges, can interfere with nesting
behavior without ever actually contacting the birds. Most stressors,
however, must contact receptors to cause an effect. For example, flood
waters must contact tree roots before their growth is impaired.
Finally, some stressors must not only be contacted, but also must be
internally absorbed. For example, a toxicant that causes liver tumors
in fish must be absorbed through the gills and reach the target organ
to cause the effect.
Co-occurrence is evaluated by comparing the distribution of the
stressor with the distribution of the ecological receptor. For example,
maps of the stressor may be overlaid with maps of ecological receptors
(e.g., the placement of bridges overlaid on maps showing habitat
historically used for crane nests). The increased availability of
geographic information systems (GIS) has provided new tools for
evaluating co-occurrence.
Contact is a function of the amount of a stressor in an
environmental medium and activities or behavior that brings receptors
into contact with the stressor. For biological stressors, this step
relies extensively on professional judgment; contact is often assumed
to occur in areas where the two overlap. For chemicals, contact is
quantified as the amount of a chemical ingested, inhaled, or in
material applied to the skin (i.e., the potential dose). In its
simplest form, it is quantified as an environmental concentration, with
the assumptions that the chemical is well mixed and that the organism
contacts a representative concentration. This approach is commonly used
for respired media (e.g., water for aquatic organisms, air for
terrestrial organisms). For ingested media (e.g., food, soil), another
common approach combines modeled or measured concentrations of the
contaminant with assumptions or parameters describing the contact rate
(U.S. EPA, 1993c) (see text note 4-9).
Uptake is evaluated by considering the amount of stressor that is
internally absorbed into an organism. Uptake is a function of the
stressor (e.g., a chemical's form or valence state), the medium (e.g.,
sorptive properties or presence of solvents), the biological membrane
(e.g., integrity, permeability), and the organism (e.g., sickness,
active uptake) (Suter et al., 1994). Because of interactions among
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 (i.e., 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 4-10). 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 using estimates of uptake for risk
estimation. 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, the presence of naturally anoxic areas above
contaminated sediments in an estuary may reduce the amount of time that
bottom-feeding fish spend in contact with the contaminated sediments
and thereby reduce exposure to the contamination. Biotic interactions
can also influence exposure. For example, competition for high-quality
resources may force some organisms to utilize disturbed areas. The
interaction between exposure and receptor behavior can influence both
the initial and subsequent exposures. For example, some chemicals
reduce the prey's ability to escape predators 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 must 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, exceeds some threshold intensity, or over which
intensity is integrated. If exposure occurs as repeated, discrete
events of about the same duration (e.g., floods), frequency is the
important temporal dimension of exposure. If the repeated events have
significant and variable durations, both duration and frequency must be
considered. In addition, the timing of exposure, including the order or
sequence of events, can be an important factor to describe. For
example, in the Northeast, lakes receive high concentrations of
hydrogen ions and aluminum during
[[Page 47582]]
snow melt; this period also corresponds to the sensitive life stages of
some aquatic organisms.
In chemical assessments, the dimensions of intensity and time are
often combined by averaging intensity over time. The duration over
which intensity is averaged is determined by considering both 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 to use (U.S. EPA, 1992d).
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 toxic
dynamic 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 filled wetland,
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, the emerging field of landscape ecology and the
increased availability of geographic information systems have greatly
expanded the options for analyzing and presenting the spatial dimension
of exposure.
This step completes exposure analysis. Exposure should be described
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 to the receptors,
completing the exposure pathway. 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 a summary profile of what
has been learned. Depending on the risk assessment, the profile may be
a written document, or a module of a larger process model.
Alternatively, documentation may be deferred until risk
characterization. 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 (text note 4-11).
The profile should describe the relevant exposure pathways. If
exposure can occur through many pathways, it may be useful to rank
them, perhaps by contribution to total exposure. For example, consider
an assessment of risks to grebes feeding on 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 describe the ecological entity that is exposed
and represented by the exposure estimates described below. For example,
the exposure profile may focus on the local population of grebes
feeding on a specific lake during the summer months.
The assessor should state how each of the three general dimensions
of exposure (intensity, time, and space) was treated and why that
treatment is necessary or appropriate. 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 variability in receptor
attributes or stressor levels can change exposure. For example,
variability in receptor attributes of the grebes may be addressed by
using data on how the proportion of fish in the diet varies among
individuals. If several lakes were the subject of the assessment and
individual grebes tended to feed on the same lake throughout the
season, variability in stressor levels could be addressed by comparing
exposures among the lakes.
Variability can be described by using a distribution or by
describing where a point estimate is expected to fall on a
distribution. Cumulative-distribution functions (CDFs) and probability-
density functions (PDFs) are two common presentation formats; (see
Appendix B, figures B1 and B2). Figures 5-4 to 5-6 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. We recommend using the descriptors discussed
in U.S. EPA, 1992d, 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
(i.e., 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. The exposure profile is
one of the products of the analysis phase. It 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
Characterization of ecological effects describes the effects that
are elicited by a stressor, links these effects with the assessment
endpoints, and evaluates how the effects change with varying stressor
levels. Ecological effects characterization begins by evaluating
effects data (discussed generally in section 4.1) to further specify
the effects that are elicited, confirm that the effects are consistent
with the assessment endpoints, and confirm that the conditions under
which they occur are
[[Page 47583]]
consistent with the conceptual model. Once the effects of interest are
identified, then an ecological response analysis (section 4.3.1) is
conducted to evaluate how the magnitude of the effects change with
varying stressor levels, evaluate the evidence that the stressor causes
the effect, and link the effects with the assessment endpoint. The
conclusions of the ecological effects characterization are summarized
in a stressor-response profile (section 4.3.2).
4.3.1. Ecological Response Analysis
Ecological response analysis has three primary elements:
determining the relationship between stressor levels and ecological
effects (section 4.3.1.1), evaluating the plausibility that effects may
occur or are occurring as a result of exposure to stressors (section
4.3.1.2), and linking measurable ecological effects with the assessment
endpoints when assessment endpoints cannot be directly measured
(section 4.3.1.3).
4.3.1.1. Stressor-Response Analysis
Evaluating ecological risks requires an understanding of the
relationships between stressor levels and resulting ecological
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
critical for determining the presence or absence of an effects
threshold or for evaluating incremental risks, or stressor-response
curves may be used as input for ecological 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
(section 4.3.1.3). Some questions for stressor-response analysis are
provided in text note 4-12.
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). While
quantifying this relationship is encouraged, qualitative stressor-
response evaluations are also possible (text note 4-13). In addition,
many stressor-response relationships are more complex than the simple
curve shown in this figure. Ecological systems frequently show
responses to stressors that may involve abrupt shifts to new community
or system types (Holling, 1978).
BILLING CODE 6560-50-P
[[Page 47584]]
[GRAPHIC] [TIFF OMITTED] TN09SE96.005
BILLING CODE 6560-50-C
[[Page 47585]]
In simple cases, the response will be one variable (e.g.,
mortality, incidence of abnormalities), 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 statistical
techniques may be useful. These techniques 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.
Stressor-response relationships can be described using any of the
dimensions of exposure (i.e., intensity, time, or space). Intensity is
probably the most familiar dimension and is often used for chemicals
(e.g., dose, concentration). The duration of exposure 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 hr, 48 hr, 96 hr). As noted in text note 4-13, the timing of
exposure was the critical dimension in evaluating the relationship
between seed germination and flooding (Pearlstine et al., 1985). The
spatial dimension is often of concern for physical stressors. For
example, the spatial 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 the growth of tree species 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 ml; propagules per unit of
substrate) may be related to the level of 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, developing 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, frequently this is not the case for
toxicity tests with wildlife species.
Risk assessors sometimes use curve-fitting analyses to determine
particular levels of effect for evaluation. 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 4-14)
are frequently selected because the level of uncertainty is minimized
at the midpoint of the regression curve. While a 50% effect 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 5-2).
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'' levels of a
stressor 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 3-10). An
example of this approach for deriving chemical no-effect levels is
provided in text note 4-15. An advantage 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, including Hurlbert (1984), Stewart-Oaten et al.
(1986), Wiens and Parker (1995), and Eberhardt and Thomas (1991). 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. An advantage
of experimental field studies is that treatments can be replicated,
increasing the confidence that observed differences are due to the
treatment.
Data available from multiple experiments can be used to generate
multiple point estimates that can be displayed as cumulative
distribution functions. Figure 5-6 shows an example of a cumulative
distribution function for species sensitivity derived from multiple
point estimates (EC5s) for freshwater algae exposed to a herbicide.
These distributions facilitate identification of stressor levels that
affect a minority or majority of species. A limiting factor in the use
of cumulative frequency distributions is the amount of data needed as
input. Cumulative effects distribution functions can also be derived
from models that use Monte Carlo or other methods to generate
distributions based on measured or estimated variation in input
parameters for the models.
When multiple stressors are present, stressor-response analysis is
particularly challenging. Stressor-response relationships can be
constructed for each stressor separately and then combined.
Alternatively, the relationship between response and the suite of
stressors can be combined in one analysis. It is preferable to directly
evaluate complex chemical mixtures present in environmental media
(e.g., wastewater effluents, contaminated soils; U.S. EPA, 1986b), but
it is important to consider the relationship between the samples tested
and the potential spatial and temporal variability in the mixture. The
approach taken for multiple stressors depends on the feasibility of
measuring the suite of stressors 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 and a response. Detenbeck (1994)
used this approach to evaluate change in the water quality of
[[Page 47586]]
wetlands resulting from multiple physical stressors. Multiple
regression analysis can be difficult to interpret if the explanatory
variables (i.e., the stressors) are not independent. Principal
components analysis can be used to extract independent explanatory
variables formed from linear combinations of the original variables
(Pielou, 1984).
4.3.1.2. Establishing Cause and Effect Relationships (Causality)
Causality is the relationship between cause (one or more stressors)
and effect (assessment endpoint response to one or more stressors).
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 proposes considerations for evaluating causality based on
criteria primarily for observational data developed by Fox (1991) 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 (e.g., 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 4-16 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 of evaluating available information.
The strength of association between stressor and response is often
the main reason that adverse effects (such as bird kills) are first
noticed. 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
decreasing effects downstream of 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-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 the occurrence of
an association in more than one species and species population is very
strong evidence for causation. An example would be the numerous species
of birds that were killed as a result of carbofuran application
(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 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 result in a
diverse array of 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 (text note 4-17) may be useful. For chemicals,
ecotoxicologists have slightly modified Koch's postulates to provide
evidence of causality (Adams, 1963; Woodman and Cowling, 1987). 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 normal 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.
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 the toxicity of a mixture relates to the
toxicity of individual components. The choice of method depends on the
goal of the assessment and the resources and test data that are
available.
Laboratory toxicity identification evaluations (TIEs) can be used
to help determine which components of a chemical mixture are causing
toxic effects. By using fractionation and other methods, the TIE
approach can help identify chemicals responsible for toxicity and show
the relative contributions to toxicity 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
[[Page 47587]]
allows for manipulation of the mixture and investigation of how varying
the components that are present or their ratios may affect mixture
toxicity but also requires additional assumptions about the
relationship between effects of the synthetic mixture and those of the
environmental mixture.
When the modes of action of chemicals in a mixture are known to be
similar, an additive model has been successful in predicting combined
effects (Konemann, 1981; Hermens et al., 1984a; McCarty and Mackay,
1993; Sawyer and Safe, 1985; Broderius et al., 1995). In this
situation, the contribution of each chemical to the overall toxicity of
the mixture can be evaluated. However, the situation is more
complicated when the modes of action of the chemical constituents are
unknown or partially known (see additional discussion in section
5.1.2).
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 the two are needed. Risk assessors may make
these linkages in the analysis phase or, especially when linkages rely
on expert judgment, risk assessors may work with measures of effect
through risk estimation (in risk characterization) and then make the
connection with the assessment endpoints. Common extrapolations used to
link measures of effect with assessment endpoints are shown in text
note 4-18.
General Considerations. During the preparation of the analysis plan
in problem formulation, 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 4-19 before proceeding with specific extrapolation
approaches to use.
The scope and nature of the risk assessment and the environmental
decision to be made help determine the degree of uncertainty (and type
of extrapolation) that is acceptable. At an early stage of a tiered
risk assessment, extrapolations from minimal data that involve large
uncertainties are acceptable when the primary purpose is to determine
whether a risk exists given worst-case exposure and effects scenarios.
To define risk further at later stages of the assessment, additional
data and more sophisticated extrapolation approaches are usually
required.
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 fish and birds,
may extrapolate among 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 anticipated. If spatial scales are
broadened, additional receptors may need to be included in
extrapolation models. If a stressor persists for an extended time in
the environment, it may be necessary to extrapolate short-term
responses over a longer period of exposure, 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 the methods in a manner
consistent with sound ecological principles and the availability of an
appropriate database. 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 from upland avian species to waterfowl may be more
credible if factors such as differences in food preferences, body mass,
physiology, and seasonal behavior (e.g., mating and migration habits)
are considered. Extrapolations made in a rote manner or that are
biologically implausible will erode the overall credibility of the
assessment.
Finally, many extrapolation methods are limited by the availability
of suitable databases. Although these databases are generally largest
for chemical stressors and aquatic species, data do not exist for all
taxa or effects. Chemical effects databases for mammals, amphibians, or
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 expert judgment. This approach 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 are based on
some level of understanding of the underlying operations of the system
under consideration.
Judgment Approaches for Linking Measures of Effect to Assessment
Endpoints. Expert judgment approaches rely on the professional
expertise of risk assessors, expert panels, or others to relate changes
in measures of effect to changes in the assessment endpoint. They are
essential when databases are inadequate to support empirical models and
process models are unavailable or inappropriate. Expert 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 expert
judgment extrapolations between species, from laboratory data to field
effects, and between geographic areas.
Because of the uncertainties in predicting the effects of
biological stressors such as introduced species, expert 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 4-20 summarizes some of
the considerations for risk assessors when extrapolating from
laboratory toxicity test results to field situations for chemical
stressors. Factors altering exposure in the field are among the most
important factors limiting extrapolations from laboratory test results,
but indirect effects on exposed
[[Page 47588]]
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 expert 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 exposure to
chemical stressors can be accurately estimated and are expected to be
similar (e.g., see text note 4-20), 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
environmental conditions.
For physical stressors that have natural counterparts, such as
fire, flooding, or temperature variations, effects may depend on the
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 facilitated 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 stressor effects from one system to
another.
The following references may be useful when assessing effects over
different geographical areas: Bedford and Preston (1988), Detenbeck et
al. (1992), Gibbs (1993), Gilbert (1987), Gosselink et al. (1990),
Preston and Bedford (1988), and Risser (1988).
Empirical and Process-Based Approaches for Linking Measures of
Effect to Assessment Endpoints. There are a variety of empirical and
process-based approaches 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
effects measures 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.
Empirical Approaches. 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 effects measures 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 mostly been developed for chemicals because of the extensive
ecotoxicologic databases 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, uncertainty factors vary inversely
with the quantity and type of effects measures data available (Zeeman,
1995). Uncertainty factors 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, 1995d).
In spite of 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 judgement and policy judgement (see section 4.1.3). It is
important to differentiate among these three elements when documenting
the basis for the uncertainty factors used.
Empirical data can be used to facilitate extrapolations between
species to species, genera, families, or orders or functional groups
(e.g., feeding guilds) (Suter, 1993a). Suter et al. (1983), Suter
(1993a), and Barnthouse et al. (1987, 1990) developed methods to
extrapolate toxicity among freshwater and marine fish and arthropods.
As noted by Suter
[[Page 47589]]
(1993a), the uncertainties associated with extrapolating between
orders, classes, and phyla tend to be very high. However,
extrapolations can be made 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.
Dose-scaling or allometric regression has also been used to
extrapolate the effects of a chemical stressor to another species. The
method is used for human health risk assessment but has not been
applied extensively to ecological effects (Suter, 1993a).
Allometric regression has been used with avian species (Kenaga,
1973) and to a limited extent for estimating effects to marine
organisms based on their length. For chemical stressors, allometric
relationships can enable an assessor to estimate toxic effects to
species not commonly tested, such as native mammalian species. It is
important that the assessor consider the taxonomic relationship between
the known species and the species of interest. The closer the two are
related, the more likely that 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.
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; Barnthouse et al., 1990).
Population models are useful in answering questions related to short-
or long-term changes of population size and structure and can be used
to estimate the probability that a population will decline below or
grow above a specified abundance (Ginzburg et al., 1982; Ferson et al.,
1989). This latter application may be useful when assessing risks
associated with biological stressors such as introduced or pest
species. Excellent reviews of population models are presented by
Barnthouse et al. (1986) and Wiegert and Bartell (1994). Emlen (1989)
has reviewed population models that can be used for terrestrial risk
assessment.
Proper use of the 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 may be important (Hassell, 1986)
and should be considered in the analysis of uncertainty.
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 of the system potentially at risk. These
models 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 larger environmental system.
Risk assessors should evaluate the degree of aggregation in
population or multispecies model parameters that is appropriate based
both on the input data available and on the desired output of the
model. 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 discussion of uncertainty.
4.3.2. Stressor-Response Profile
The final product of ecological response analysis is a summary
profile of what has been learned. Depending on the risk assessment, the
profile may be a written document, or a module of a larger process
model. Alternatively, documentation may be deferred until risk
characterization. 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. Using this approach, the assessor may be better able
to extract the information most important to the risk characterization
phase. In addition, compiling the stressor-response profile provides an
opportunity to verify that the assessment and measures of effect
identified in the conceptual model were evaluated.
Risk assessors should address several questions in the stressor-
response profile (text note 4-21). Depending on the type of risk
assessment, affected ecological entities could 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 could 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 as stressor-response curves or point estimates were provided in
section 4.3.1.1. Other information such as the spatial area or time to
recovery may be appropriate, depending on the scope of the assessment.
Causal analyses are important, especially for assessments that include
field observational data.
While ideally the stressor-response profile should express effects
in terms of the assessment endpoint, this will not always be possible.
Especially where it is necessary to use qualitative extrapolations
between assessment endpoints and measures of effect, the stressor-
response profile may only contain information on measures of effect.
Under these circumstances, risk will be estimated using the measures of
effects, and extrapolation to the assessment endpoints will occur
during risk characterization.
Risk assessors need to be descriptive and candid about any
uncertainties
[[Page 47590]]
associated with the ecological response analysis. If it was necessary
to extrapolate from measures of effect to the assessment endpoint,
describe both the extrapolation and its basis. Similarly, if a
benchmark or similar reference dose or concentration was calculated,
discuss the extrapolations and uncertainties associated with its
development. 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 Lkke (1991),
and Okkerman et al. (1993). Finally, the assessor should clearly
indicate major assumptions and default values used in 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. Its goals are to use the results of the analysis phase
to estimate risk to the assessment endpoints identified in problem
formulation (section 5.1), interpret the risk estimate (section 5.2),
and report the results (section 5.3).
BILLING CODE 6560-50-P
[[Page 47591]]
[GRAPHIC] [TIFF OMITTED] TN09SE96.006
BILLING CODE 6560-50-C
[[Page 47592]]
Risk characterization is a major element of the risk assessment
report. To be successful, it should provide clear information to the
risk manager to use in environmental decision making (NRC, 1994; see
section 6). If the risks are not sufficiently defined to support a
management decision, the risk manager may elect to proceed with another
iteration of the risk assessment process. Additional research or a
monitoring program may improve the risk estimate or help to evaluate
the consequences of a risk management decision.
5.1. Risk Estimation
Risk estimation determines the likelihood of adverse effects to
assessment endpoints by integrating exposure and effects data and
evaluating any associated uncertainties. The process uses exposure and
stressor-response profiles which are developed according to the
analysis plan (section 3.5). Risks can be estimated by one or more of
the following approaches: (1) estimates expressed as qualitative
categories, (2) estimates comparing single-point estimates of exposure
and effects, (3) estimates incorporating the entire stressor-response
relationship, (4) estimates incorporating variability in exposure and
effects estimates, (5) estimates based on process models that rely
partially or entirely on theoretical approximations of exposure and
effects, and (6) estimates based on empirical approaches, including
field observational data.
5.1.1. Risk Estimates Expressed as Qualitative Categories
In some cases, best professional judgment may be used to express
risks qualitatively 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 not easily expressed in quantitative terms.
A U.S. Forest Service assessment used qualitative categories because of
limitations on both the exposure and effects data for the introduced
species of concern as well as the resources available for the
assessment. (text note 5-1)
5.1.2. Single-Point Estimates
When sufficient data are available to quantify exposure and effects
estimates, the simplest approach for comparing the estimates is to use
a ratio of two numbers (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 (text note
5-2).
BILLING CODE 6560-50-P
[[Page 47593]]
[GRAPHIC] [TIFF OMITTED] TN09SE96.007
BILLING CODE 6560-50-C
[[Page 47594]]
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. The quotient method 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. In this approach, quotients for the individual
constituents in a mixture are generated by dividing each exposure level
by a corresponding toxicity endpoint (e.g., an LC50). Although the
toxicity of a chemical mixture may be greater (synergism) or less
(antagonism) than predicted from the toxicities of individual
constituents of the mixture, a quotient addition approach assumes that
toxicities are additive or close to additive, which may be true when
the modes of action of chemicals in a mixture are similar (e.g.,
Konemann, 1981; Broderius et al., 1995; Hermens et al., 1984a,b;
McCarty and Mackay, 1993; Sawyer and Safe, 1985).
For mixtures of chemicals having dissimilar modes of action, there
is some evidence from fish acute toxicity tests with industrial organic
chemicals that strict additivity or less-than-strict additivity is
common, while antagonistic and synergistic responses are rare
(Broderius, 1991). These experiences suggest that caution should be
used when predicting that chemicals in a mixture will act independently
of one another. However, these relationships observed with aquatic
organisms may not be relevant for other endpoints, exposure scenarios,
and species. When the mode of action for constituent chemicals are
unknown, the assumptions and rationale concerning chemical interactions
must be clearly stated.
The application of the quotient method is restricted by a number of
limitations (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 a 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.
Another potential difficulty with the quotient method is that the
point estimate of effect may not reflect the appropriate intensity of
effect or exposure pattern for the assessment. 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.
The quotient method cannot evaluate secondary effects. Interactions
and effects beyond what is predicted from the simple quotient may be
critical to characterizing the full extent of impacts from exposure to
the stressors (e.g., bioaccumulation).
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). However, some uncertainties 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 5-3. Further discussion
of comparisons between point estimates of effects and distributions of
exposure may be found in Suter et al., 1983.
BILLING CODE 6560-50-P
[[Page 47595]]
[GRAPHIC] [TIFF OMITTED] TN09SE96.008
BILLING CODE 6560-50-P
[[Page 47596]]
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?
5.1.3. Estimates Incorporating the Entire Stressor-Response
Relationship
If the stressor-response profile described a curve relating the
stressor level to the magnitude of response, 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.
BILLING CODE 6560-50-P
[[Page 47597]]
[GRAPHIC] [TIFF OMITTED] TN09SE96.009
BILLING CODE 6560-50-C
[[Page 47598]]
There are both advantages and limitations to comparing a stressor-
response curve with an exposure distribution. The steepness 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. While comparing
exposure and stressor-response curves provides a predictive ability
lacking in the quotient method, this approach shares the quotient
method's limitations of not evaluating secondary effects, assuming that
the exposure pattern used to derive the stressor-response curve is
comparable to the environmental exposure pattern, and not explicitly
considering uncertainties, such as extrapolations from tested species
to the species or community of concern.
5.1.4. Estimates Incorporating Variability in Exposure 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 describe
risks to moderately or highly exposed members of a population being
investigated, while variability in effects can be used to describe
risks to average or sensitive population members.
A major advantage of this approach is the capability to predict
changes in the magnitude and likelihood of effects for different
exposure scenarios, thus providing 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-
4 and figure 5-5 illustrate the use of cumulative exposure and effects
distributions for estimating risk.
BILLING CODE 6560-50-P
[[Page 47599]]
[GRAPHIC] [TIFF OMITTED] TN09SE96.010
BILLING CODE 6560-50-C
[[Page 47600]]
5.1.5. Estimates Based on 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 both in the analysis phase (see
section 4.1.2.) and the risk characterization phase of ecological risk
assessment. For illustrative purposes, we distinguish between process
models used for risk estimation that integrate exposure and effects
information (text note 5-5) and process models used in the analysis
phase that focus on either exposure or effects evaluations.
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 risk estimation techniques
based 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 may be capable of forecasting the
combined effects of multiple stressors (e.g., Barnthouse et al., 1990).
Process model outputs may be point estimates or distributions. In
either case, risk assessors should interpret these outputs with care.
Process model outputs may imply a higher level of certainty than is
appropriate and all too often are 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) with
respect to the risk hypotheses presented in problem formulation.
5.1.6. Field Observational Studies
Field observational studies (surveys) can serve as risk estimation
techniques because they provide direct evidence linking exposure to
stressors and effects. Field surveys measure biological changes in
uncontrolled situations through collection of exposure and effects data
at sites identified in problem formulation. A key issue with field
surveys is establishing causal relationships between stressors and
effects (section 4.3.1.2).
A major advantage of field surveys is that they provide a reality
check on other risk estimates, since field surveys are usually more
representative of both exposures and effects (including secondary
effects) found in natural systems than are estimates generated from
laboratory studies or theoretical models (text note 5-6). On the other
hand, field data may not constitute reality if they are flawed due to
poor experimental design, biased in sampling or analytical techniques,
or fail to measure critical components of the system or random
variations (Johnson, 1995). A lack of observed effects in a field
survey may occur because the measurements are insufficiently sensitive
to detect ecological effects, and, unless causal relationships are
carefully examined, effects that are observed may be caused by factors
unrelated to the stressor(s) of concern. Finally, field surveys taken
at one point in time are usually not predictive; they describe effects
associated with only one scenario (i.e., the one that exists).
5.2. Risk Description
After risks have been estimated, risk assessors need to integrate
and interpret the available information into conclusions about risks to
the assessment endpoints. In some cases, risk assessors may have
quantified the relationship between assessment endpoints and measures
of effect in the analysis stage (section 4.3.1.3). In other situations,
qualitative links to assessment endpoints are part of the risk
description. For example, if the assessment endpoints are survival of
fish, aquatic invertebrates, and algae, risks may be estimated using a
quotient method based on LC50c. Regardless of the risk estimation
technique, the technical narrative supporting the estimates is as
important as the risk estimates themselves.
Risk descriptions include an evaluation of the lines of evidence
supporting or refuting the risk estimate(s) and an interpretation of
the adverse effects on the assessment endpoint.
5.2.1. Lines of Evidence
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,
field experiments, or field observations. (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. We use the
phrase lines of evidence to emphasize that both qualitative evaluation
and quantitative weightings may be used.)
Some of the factors that the risk assessor should consider when
evaluating separate lines of evidence are:
The relevance of evidence to the assessment endpoints
The relevance of evidence to the conceptual model
The sufficiency and quality of data and experimental designs
used in key studies
The strength of cause/effect relationships
The relative uncertainties of each line of evidence and their
direction.
This process involves more than just listing the factors that support
or refute the risk. The risk assessor should carefully examine each
factor and evaluate its contribution to the risk assessment.
For example, consider the two lines of evidence described for the
carbofuran example (text notes 5-2 and 5-6): quotients and field
studies. 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 (figure 3-2). However, the quotients 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 (text note 5-6).
Nevertheless, because of the great preponderance of the data, the
strong evidence of causal relationships from the field studies, and the
consistency between these two lines of evidence, confidence in a
conclusion of high risk to the assessment endpoint is supported.
Sometimes lines of evidence do not point toward the same
conclusion. When they disagree, it is important 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.
[[Page 47601]]
Conversely, the model may have been unrealistic in its predictions.
While it may be possible to use numerical weighting techniques for
evaluating various lines of evidence, in most cases qualitative
evaluations based on professional judgment are appropriate for sorting
through conflicting lines of evidence. While iteration of the risk
assessment process and collection of additional data may help resolve
uncertainties, this option is not always available.
5.2.2. Determining Ecological Adversity
At this point in risk characterization, the changes expected in the
assessment endpoints have been estimated and described. The next step
is to interpret whether these changes are considered adverse. Adverse
changes are those of concern ecologically or socially (section 1).
Determining adversity is not always an easy task and frequently depends
on the best professional judgment of the risk assessor.
Five criteria are proposed for evaluating adverse changes in
assessment endpoints:
Nature of effects.
Intensity of effects.
Spatial scale.
Temporal scale.
Potential for recovery.
The extent to which the five criteria are evaluated depends on the
scope and complexity of the ecological risk assessment. However,
understanding the underlying assumptions and science policy judgments
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 exceedences of the benchmark are
considered adverse should be clearly understood.
To distinguish ecological changes that are adverse from those
ecological events that are within the normal pattern of ecosystem
variability or result 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 (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 (e.g., bird migration,
tides) are very important in natural systems. 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 must consider factors such as
statistical power to detect differences, natural variability, and other
lines of evidence in reaching their conclusions.
Spatial and temporal scales need to be considered in assessing the
adversity of the effects. 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. 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
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 some of
these microhabitats may present a significant risk to the entire
system.
Spatial factors are important for many species because of the
linkages between ecological landscapes and population dynamics.
Linkages between one or more landscapes can provide refugia for
affected populations, and species may require adequate 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 ecological 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 that responses from a stressor may be delayed. Thus, it is
important to distinguish the long-term impacts of a stressor from the
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 a condition that existed before
the introduction of a stressor. (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 are constantly changing in response to changes in the
physical environment (weather, natural catastrophes, etc.) 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 must be carefully defined. Examples might include
productivity declines in an eutrophic system, reestablishment of a
species at a particular density, species recolonization of a damaged
habitat, or the restoration of health of diseased organisms.
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., recovery of a stream from sewage effluent discharge), frequently
irreversible (e.g., establishment of introduced species), and always
irreversible (e.g., species extinction). It is important for risk
assessors to consider whether significant structural or functional
[[Page 47602]]
changes have occurred in a system that might render changes
irreversible. For example, physical alterations such as deforestation
in the coastal hills of Venezuela in recent history and Britain in the
Neolithic period changed soil structure and seed sources such that
forests cannot easily grow again (Fisher and Woodmansee, 1994).
Risk assessors should note natural disturbance patterns when
evaluating the likelihood of recovery from anthropogenic stressors.
Ecosystems that have been subjected to repeated natural disturbances
may be more vulnerable to anthropogenic stressors (e.g., overfishing,
logging of old-growth forest). Alternatively, if an ecosystem has
become adapted to a disturbance pattern, it may be affected when the
disturbance is removed (fire-maintained grasslands). The lack of
natural analogues make it difficult to predict recovery from novel
anthropogenic stressors (e.g., synthetic chemicals).
The relative rate of recovery can also be estimated. For example,
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 several decades, a benthic infaunal community in years, and a
planktonic community in weeks to months.
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 any
cleanup efforts affect the recovery.
5.3. Reporting Risks
When risk characterization is complete, the 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). This section describes
elements that risk assessors should consider 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 below, risk assessors must judge the appropriate
level of detail required. The report need not be overly complex or
lengthy, depending on the nature of the risk assessment and the
information required to support a risk management decision. In fact, it
is important that information be presented clearly and concisely.
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.
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 1995c). 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 the 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). Risk managers use risk assessment results along with
other factors (e.g., economic or legal concerns) in making
environmental decisions. The results also provide a basis for
communicating risks to the public.
Mutual understanding between risk assessors and risk managers can
be facilitated if the questions listed in text note 6-1 are addressed.
Risk managers need to know what the major risks (or potential risks)
are with respect 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. When there is insufficient information to
characterize risk at an appropriate level of detail due to a lack of
resources, a lack of a consensus on how to interpret information, or
other reasons, the issues, obstacles, and correctable deficiencies
should be clearly articulated for the risk manager's consideration.
In making a decision regarding ecological risks, risk managers use
risk assessment results along with other information that may include
social, economic, political, or legal issues. For example, the risk
assessment may be used as part of a risk/benefit analysis, which may
require translating resources (identified through the assessment
endpoints) into monetary values. One difficulty with this approach is
that traditional economic considerations may not adequately address
things that are not considered commodities, intergenerational resource
values or issues of long-term or irreversible effects (U.S. EPA,
1995b). Risk managers may also consider risk mitigation options or
alternative strategies for reducing risks. 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 new pesticide. Further,
risk managers may consider relative as well as absolute risk, for
example, by comparing the risk of a new pesticide to other pesticides
currently in use. Finally, risk managers consider public opinion and
political demands in their decisions. Taken together, these other
factors may render very high risks acceptable or very low risks
unacceptable.
Risk characterization provides the basis for communicating
ecological risks to the public. This task is usually the responsibility
of risk managers. 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. 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, 1995b).
Managers should clearly describe the sources and causes of risks,
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, 1995b). Other risk communication considerations
are provided in text note 6-2.
Along with the discussions of risk and communications with the
public, it is
[[Page 47603]]
important for risk managers to consider whether additional follow-on
activities are required. Depending on the importance of the assessment,
confidence level in the assessment 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 facilitate
a final management decision. Another option is to proceed with the
decision and develop a monitoring plan to evaluate the results of the
decision (see section 1). For example, if the decision was to mitigate
risks through exposure reduction, monitoring could help determine
whether the desired reduction in exposure (and effects) was achieved.
7. Text Notes
Text Note 1-1. Related Terminology
The following terms overlap to varying degrees with the broad
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, but this diagram should not be
viewed as rigid and prescriptive. Rather, as illustrated by the
examples below, broad applicability of the framework requires a
flexible interpretation of the process.
In problem formulation, an assessment may begin with a
consideration of endpoints, stressors, or ecological effects. Problem
formulation is frequently interactive and iterative rather than linear.
In the analysis phase, it may be difficult to maintain a
clear distinction between exposure and effects analyses in all but the
simplest systems. Exposure and effects frequently become intertwined,
as when an initial exposure leads to a cascade of additional exposures
and effects. It is important that a risk assessment is based on 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 1-3. The Iterative Nature of Ecological Risk Assessment
The ecological risk assessment process is by nature iterative. For
example, it may take more than one pass through problem formulation to
complete planning for the risk assessment, or information gathered in
the analysis phase may suggest further problem formulation activities
such as modification of the endpoints selected.
To maximize efficient use of limited resources, ecological risk
assessments are frequently designed in sequential tiers that proceed
from simple, relatively inexpensive evaluations to more costly and
complex assessments. Initial tiers are based on conservative
assumptions, such as maximum exposure and ecological sensitivity. When
an early tier cannot define risk to support a management decision, a
higher assessment tier is used that may require either additional data
or applying more refined analysis techniques to available data.
Iterations proceed until sufficient information is available to support
a sound management decision, within the constraints of available
resources.
Because a tiered approach can incorporate standardized decision
points and supporting analyses, it can be particularly useful for
multiple assessments of similar stressors or situations. However, it is
difficult to generalize further concerning tiered risk assessments
because they are used to answer so many different questions. Examples
of organizations that use, are considering, or have advocated using
tiered ecological risk assessments include the Canadian government
(proposed, Gaudet, 1994), the European Community (E.C., 1993), industry
(Cowan et al., 1995), the Aquatic Dialogue Group (SETAC 1994a), and the
U.S. EPA Offices of Pesticide Programs (Urban and Cook, 1986),
Pollution Prevention and Toxics (Lynch et al., 1994), and Superfund
(document in preparation).
Text Note 2-1. Who Are Risk Managers?
Risk managers are individuals and organizations that take
responsibility for, 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 decisionmaker in agencies like EPA or
state environmental offices who has the authority to protect or manage
a resource. However, risk managers often represent a diverse group of
interested parties that influence the outcome of resource protection
efforts. Particularly as the scope of environmental management expands
to communities, the meaning of risk manager significantly expands to
include decision officials in Federal, state, and local governments, as
well as private-sector leaders in commercial, industrial, and private
organizations. Risk managers may also include constituency groups,
other interested parties, and the public. In situations where a complex
of ecosystem values (e.g., watershed resources) is at risk from
multiple stressors, many of these groups may act together as risk
management teams. 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. When a specific risk assessment
process is well defined through regulations and guidance, one trained
individual may be able to complete a risk assessment if needed
information is available (e.g., premanufacture notice of a chemical).
However, as more complex risk assessments become common, it will be
rare that one individual can provide the necessary breadth of
expertise. Every risk assessment team should include at least one
professional who is knowledgeable and experienced in using the risk
assessment process. Other team members bring specific expertise
relevant to the location, the stressors, the ecosystem, and the
scientific issues and other expertise as determined by the type of
assessment.
Text Note 2-3. Questions Addressed by Risk Managers and Risk Assessors
Questions Principally for Risk Managers:
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 of concern?
What are the policy considerations (law, corporate stewardship,
societal concerns, environmental justice)?
What precedents are set by previous risk assessments and decisions?
What is the context of the assessment (e.g., industrial, national
park)?
What resources (e.g., personnel, time, money) are available?
What level of uncertainty is acceptable?
Questions Principally for Risk Assessors
What is the scale of the risk assessment?
[[Page 47604]]
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 on 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 2-4. The Role of Interested Parties
The involvement of all interested and affected parties, which
``stakeholder'' is commonly used to represent, is important to the
development of management goals for some risk assessments. The greater
the involvement, the broader the base of consensus about those goals.
With strong consensus on management goals, decisions are more likely to
be supported by all community groups during implementation of
management plans. However, the context of this involvement can vary
widely, and the ability to achieve consensus often decreases as the
size of the management team increases. Where large diverse groups need
to come to consensus, social science professionals and methods for
consensus building become increasingly important. Interested parties
become risk managers when they influence risk reduction. See additional
discussion in text note 2-1 and section 2.2.
Text Note 2-5. Sustainability as a Management Goal
Sustainability is used repeatedly as a management goal in a variety
of settings (see U.S. EPA, 1995b). To sustain is to prolong, to hold up
under, or endure (Merriam-Webster, 1972). Sustainability and other
concepts such as biotic or community integrity are very useful as
guiding principles for management goals. However, in each case these
principles must be explicitly interpreted 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
Waquoit Bay is a small estuary on Cape Cod showing signs of
degradation, including loss of eelgrass, fish, and shellfish and
increasing 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
shell fish populations, and (2) reverse ongoing degradation of
ecological resources in the watershed.
To define this goal, it was interpreted into 10 objectives, two of
which are:
Reestablish a self-sustaining scallop population in the
bay that can support a viable sport fishery.
Reduce or eliminate nuisance macroalgal growth.
From these objectives, specific ecological resources in the bay
were identified to provide the basis for the risk assessment, one of
which is:
Areal extent and patch size of eelgrass beds.
Eelgrass was selected because scallops are dependent directly on
eelgrass beds for survival and eelgrass is highly sensitive to excess
macroalgal growth.
Text Note 2-7. Questions to Ask About Scope and Complexity
Is this risk assessment legally mandated, addressing a court-
ordered decision, or providing guidance to a community?
Are decisions more likely 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 kinds of information are 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, 1993a, 1994a). Consistent shortcomings 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
In each product of problem formulation there are elements of
uncertainty, a consideration of what is known and not known about a
problem and its setting. 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, because uncertainty in
problem formulation is articulated in these models.
Text Note 3-3. Assessing Available Information: Questions to Ask
Concerning Source, Stressor, and Exposure Characteristics, Ecosystem
Characteristics, and Effects
Source and Stressor Characteristics
What is the source? Is it anthropogenic, natural, point
source, or diffuse nonpoint?
What type of stressor is it: chemical, physical, or
biological?
What is the intensity of the stressor (e.g., the dose or
concentration of a chemical, the magnitude or extent of physical
disruption, the density or population size of a biological stressor)?
What is the mode of action? How does the stressor act on
organisms or ecosystem functions?
Exposure Characteristics
With what frequency does a stressor event occur (e.g., is
it isolated, episodic, or continuous; is it subject to natural daily,
seasonal, or annual periodicity)?
What is its duration? How long does it persist in the
environment (e.g., for chemical, what is its half-life, does it
bioaccumulate; for physical, is habitat alteration sufficient to
prevent recovery; for biological, will it reproduce and proliferate)?
What is the timing of exposure? When does it occur in
relation to critical organism life cycles or ecosystem events (e.g.,
reproduction, lake overturn)?
What is the spatial scale of exposure? Is the extent or
influence of the stressor local, regional, global, habitat-specific, or
ecosystemwide?
[[Page 47605]]
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 3-4. Initiating a Risk Assessment: What's Different When
Stressors, Effects, or Values Drive the Process?
The reasons for initiating a risk assessment also influence how the
risk assessor proceeds through the process of problem formulation. When
the assessment is initiated due to concerns about stressors, risk
assessors use what is known about the characteristics of the stressor
and its source to focus the assessment. Goals are articulated based on
how the stressor is likely to cause risk to possible receptors that may
become exposed. 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. Often these endpoints involve affected
ecological entities and their response. 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 up front by goals for the ecological value 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 them, and describing the diversity of
potential effects. This information is then captured in the conceptual
model(s).
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 must 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 (basis for management goals).
``Salmon reproduction and population maintenance'' is a good assessment
endpoint for this risk assessment, and 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
result in the 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 response of an
ecological entity to a stressor becomes a stressor to another entity.
Secondary effects are not limited in number. They often are 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 black birds, alteration of
wetland hydrology that changes spawning habitat for fish, and so forth.
Text Note 3-7. 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,
exposure to the absence of a critical resource is the source of risk.
[[Page 47606]]
Text Note 3-8.--Examples of Management Goals and Assessment Endpoints
----------------------------------------------------------------------------------------------------------------
Case Regulatory context/management goal Assessment endpoint
----------------------------------------------------------------------------------------------------------------
Assessing Risks of New Chemical Protect ``the environment'' from ``an Survival, growth, and
Under Toxic Substances Control Act unreasonable risk of injury'' (TSCA Sec. 2[b] reproduction of fish,
(Lynch et al., 1994). [1] and [2]); protect the aquatic environment. aquatic invertebrates,
Goal was to exceed a concentration of concern and algae.
by no more than 20 days a year.
Special Review of Granular Prevent * * * ``unreasonable adverse effects on Individual bird survival.
Carbofuran Based on Adverse the environment'' (FIFRA Secs. 3[c][5] and
Effects on Birds (Houseknecht, 3[c][6]); using cost-benefit considerations.
1993). Goal was no regularly repeated bird kills.
Modeling Future Losses of National Environmental Policy Act may apply to (1) Forest community
Bottomland Forest Wetlands (Brody environmental impact of new levee structure and habitat
et al., 1993). construction; also Clean Water Act Sec. 404. value to wildlife
species.
(2) Species composition of
wildlife community.
Pest Risk Assessment on Importation This assessment was done to help provide a Survival and growth of
of Logs From Chile (USDA, 1993). basis for any necessary regulation of the tree species in the
importation of timber and timber products into western United States.
the United States.
Baird and McGuire Superfund Site Protection of the environment (CERCLA/SARA).... (1) Survival of soil
(terrestrial component); invertebrates.
(Burmaster et al., 1991; Callahan (2) Survival and
et al., 1991; Menzie et al., 1992). reproduction of song
birds.
Waquoit Bay Estuary Watershed Risk Clean Water Act--wetlands protection; water (1) Estuarine eelgrass
Assessment. quality criteria--pesticides; endangered habitat abundance and
species. National Estuarine Research Reserve, distribution.
Massachusetts, Area of Critical Environmental (2) Estuarine fish species
Concern. Goal was to reestablish and maintain diversity and abundance.
water quality and habitat conditions to (3) Freshwater pond
support diverse self-sustaining commercial, benthic invertebrate
recreational, and native fish, water-dependent species diversity and
wildlife, and shellfish, and reverse ongoing abundance.
degradation.
----------------------------------------------------------------------------------------------------------------
Text Note 3-9. 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., measure
emergence of midges for endpoint on feeding of fish).
Ecological entity may not be as sensitive to the stressor
(e.g., catfish versus salmon for sedimentation).
Ecological resource is not exposed to the stressor (e.g.,
using insectivorous birds for avian risk of pesticide application to
seeds).
Ecological resources are irrelevant to the assessment
(e.g., lake fish in salmon stream).
Value of a species or attributes of an ecosystem are not
fully considered (e.g., mussel-fish connection, see text note
3-7).
Attribute is not sufficiently sensitive for detecting
important effects (e.g., survival compared with recruitment for
endangered species).
Text Note 3-10. What Are Risk Hypotheses and Why Are They Important?
Risk hypotheses are proposed answers to questions risk assessors
have about what responses assessment endpoints (and measures) will show
when they are exposed to stressors and how exposure will occur. Risk
hypotheses clarify and codify relationships that are posited through
the consideration of available data, information from scientific
literature, and the best professional judgment by 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 3-11. 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. Premanufacture notice (PMN) chemical A has a Kow of
5.5 and similar molecular structure as 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 in 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 fish. Large mats of macroalgae clog the
estuary, most of the eelgrass has died, and scallops are gone.
Hypotheses: Nutrient loading from septic systems, air pollution, and
lawn fertilizers cause eelgrass loss by shading from algal growth, and
direct toxicity from nitrogen compounds. Fish and shellfish populations
are decreasing because of loss of eelgrass habitat and periodic
hypoxia.
Text Note 3-12. What Are the Benefits of Developing Conceptual Models?
The process of creating a conceptual model is a powerful
learning tool.
Conceptual models can be improved as knowledge increases.
[[Page 47607]]
Conceptual models highlight what we know and don't know
and can be used to plan future work.
Conceptual models can be a powerful communication tool.
They provide an explicit expression of our 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 3-13. Uncertainty in Problem Formulation
Uncertainties in problem formulation are manifested in the quality
of conceptual models. To describe uncertainty:
Be explicit in defining assessment endpoints; include both
entity and 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 3-14. Examples of Assessment Endpoints and Measures (see also
section 3.5.1)
Assessment Endpoint: Coho salmon breeding success and fry survival.
Measures of Effects
Egg and fry response to low dissolved oxygen.
Adult behavior in response to obstacles.
Spawning behavior and egg survival in response to
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 reproductive cycles.
Natural population structure (proportion of different size
and age classes).
Laboratory evaluation of reproduction, growth, and
mortality.
Measures of Exposure
Number and height of hydroelectric dams.
Toxic chemical concentrations in water, sediment, and fish
tissue.
Nutrient and dissolved oxygen levels in ambient waters.
Text Note 3-15. Selecting What To Measure
Direct measurement of assessment endpoint responses is often not
possible. Under these circumstances, the selection of a surrogate
response measure is necessary. The selection of what, where, and how to
measure determines whether the risk assessment is still relevant to
management decisions about an assessment endpoint. For example, a risk
assessment may be conducted to evaluate the potential risk of a
pesticide used on seeds. Birds and mammals may be selected as the
entities for assessment endpoints. However, to ensure that the
organisms selected are susceptible to the pesticide, only those that
eat seeds should be chosen. While insectivorous birds may serve as a
good surrogate measure for determining the sensitivity of birds to the
pesticide, they do not address issues of exposure. To evaluate
susceptibility, the appropriate assessment endpoints in this case would
be seed-eating birds and mammals. Problem formulations based on
assessment endpoints that are both sensitive and likely to be exposed
to the stressor will be relevant to management concerns. If assessment
endpoints are not susceptible, their use in assessing risk can lead to
poor management decisions.
Text Note 3-16. How Do Water Quality Criteria Relate to Assessment
Endpoints?
Water quality criteria (U.S. EPA, 1986a) have been developed for
the protection of aquatic life from chemical stressors. This text note
shows how the elements of a water quality criterion correspond to
management goals, assessment endpoints, and measures.
Regulatory Goal
Clean Water Act, Sec. 101: Protection of the chemical,
physical, and biological integrity of the Nation's waters.
Program Management Objective
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 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 3-17. Data Quality Objectives (DQO) Process
The 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 they are
collected or the assessor 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
[[Page 47608]]
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) recognizes several areas that are important to ensuring
that environmental data will meet study objectives, including:
Planning and scoping.
Design of data collection operations.
Implementation and monitoring of planned operations.
Assessment and verification of data usability.
Text Note 4-3. Questions for Evaluating a Study's Utility for Risk
Assessment
How do study objectives compare with those of the risk assessment?
Are the variables and conditions the study represents compared to
those important to the risk assessment?
Was the study design adequate to meet its objectives?
Was the study conducted properly?
How were variability and uncertainty treated and reported?
Text Note 4-4. 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 rates of
fluxes;
(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; and
(3) Disaggregate models only insofar as required by the goals of
the model to facilitate testing.
Text Note 4-5. Questions for Source Description
Where does the stressor originate?
What environmental medium first receives 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 4-6. 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 4-7. General Mechanisms of Transport and Dispersal
Physical, Chemical and Biological Stressors
By air current.
In surface water (rivers, lakes, streams).
Over and/or through the soil surface.
Through ground water.
Primarily Chemical Stressors
Through the food web.
Primarily Biological Stressors
Splashing or raindrops.
Human activity (boats, campers).
Passive transmittal by other organisms.
Biological vectors.
Text Note 4-8. 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 4-9. Example of an Exposure Equation: Calculating a Potential
Dose via Ingestion
[GRAPHIC] [TIFF OMITTED] TN09SE96.020
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 g food/g body-weight-day).
m=Number of contaminated food types
Source: U.S. EPA, 1993c
Text Note 4-10. Measuring Internal Dose Using Biomarkers and Tissue
Residues
Biomarkers, tissue residues, or other bioassessment methods may be
useful in estimating or confirming exposure in cases where
bioavailability is expected to be a significant issue, but the factors
influencing it are not known. They can also be very useful when the
metabolism and accumulation kinetics are important factors (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 residues or biomarkers. Additional information and some
considerations for their development can be found in Huggett et al.
(1992).
Text Note 4-11. 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 4-12. 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?
Text Note 4-13. 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
[[Page 47609]]
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 4-14. 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 hr). Because these
tests seldom exceed 96 hr, 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 4-15. 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
concentration for which effects are not statistically different from
the controls (the no observed adverse effect concentration, NOAEC) and
the lowest concentration at which effects were statistically
significant from the control (the lowest observed adverse effect
concentration, LOAEC). The range between the NOAEC and the LOAEC is
sometimes called the maximum acceptable toxicant concentration, or
MATC. The MATC, which can also be reported as the geometric mean of the
NOAEC and the LOAEC, 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 4-16. 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 4-17. Koch's Postulates (Pelczar and Reid, 1972)
A pathogen must be consistently found in association with
a given disease.
The pathogen must be isolated from the host and grown in
pure culture.
When inoculated into test animals, the same disease
symptoms must be expressed.
The pathogen must again be isolated from the test
organism.
Text Note 4-18. Examples of Extrapolations to Link Measures of Effect
to Assessment Endpoints
Every risk assessment has data gaps that must 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 timeframe to longer-term
effects.
Text Note 4-19. 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 4-20. 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
effect 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 4-21. 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. 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 to 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 5-2. 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 modifying factors prior to 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
[[Page 47610]]
estimating the risks to the survival of birds that forage in
agricultural areas where the pesticide granular carbofuran is applied
(Houseknecht, 1993). In this case, EPA calculated the quotient by
dividing the estimated exposure levels of carbofuran granules in
surface soils (number/ft2) by the granules/LD50 derived from
single-dose avian toxicity tests. The calculation yields values with
units of LD50/ft2. It was assumed that a higher quotient
value corresponded to an increased likelihood that a bird would be
exposed to lethal levels of granular carbofuran at the soil surface.
Minimum and maximum values for LD50/ft2 were estimated for
songbirds, upland game birds, and waterfowl that may forage within or
near 10 different agricultural crops.
Text Note 5-3. 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 5-4. 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 derived from different species (e.g., HCN, 1993;
Cardwell et al., 1993; SETAC, 1994a; Solomon et al., 1996). Figure 5-5
shows a distribution of exposure concentrations of an herbicide
compared with single-species algal toxicity data 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 percentile 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 5-5. 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 5-6. An Example of Field Methods Used for Risk Estimation
Along with quotients comparing field measures of exposure with
laboratory acute toxicity data (text note 5-2), EPA evaluated the risks
of granular carbofuran to birds based on incidents of bird kills
following carbofuran applications. Over 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 5-7. What Are Statistically Significant Effects?
Statistical testing is the ``statistical procedure or decision rule
which 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
inter-treatment variance exceeds intra-treatment 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.
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:
[[Page 47611]]
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 stressor(s)
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, 1993d)
Questions principally for risk assessors to ask:
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:
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 results of the risk
management decision?
Text Note 6-2. Risk Communication Considerations for Risk Managers
(U.S. EPA, 1995c)
Plan carefully and evaluate the success of your
communication efforts.
Coordinate and collaborate with other credible sources.
Accept and involve the public as a legitimate partner.
Listen to the public's specific concerns.
Be honest, frank, and open.
Speak clearly and with compassion.
Meet the needs of the media.
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, 1992d) (i.e., with exposure defined as ``contact of a
chemical, physical, or biological agent''). These 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, proposed 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, rectangular boxes are used to designate inputs, hexagon-shaped
boxes indicate actions, and circular boxes represent outputs. There
have been only minor changes in the wording for the boxes outside of
the risk assessment process (planning and communications between risk
assessors and risk managers; acquire data, iterate process, monitor
results). ``Iterate process'' was added to emphasize the iterative (and
frequently tiered) nature of risk assessment.
The new diagram of problem formulation contains several changes.
The hexagon encloses information about stressors, sources, and
exposures, ecological effects, and the ecosystem at risk to better
reflect the importance of integrating this 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. It is in the analysis plan that
measures of ecological effects (measurement endpoints) are identified.
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. These two
aspects of analysis must closely interact to produce compatible output
that can be integrated in risk characterization. The dotted line and
hexagon that includes 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.
[[Page 47612]]
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. It is likely that these terms will
continue to generate considerable discussion among risk assessors.
A.2.1. Endpoint Terminology
The Framework Report uses the assessment and measurement endpoint
terminology of Suter (1990) but offers no specific terms for
measurements of stressor levels or ecosystem attributes. Experience has
shown that stressor measurements are sometimes inappropriately called
measurement endpoints; measurement endpoints should be ``* * *
measurable responses to a stressor that are related to the valued
characteristics chosen as assessment endpoints'' (U.S. EPA, 1992a;
Suter, 1990; emphasis added). These guidelines replace measurement
endpoint with measure of effect, which is defined as a measurable
ecological characteristic that is related to the valued characteristic
chosen as the assessment endpoint (Suter, 1990; U.S. EPA, 1992a). (An
assessment endpoint is ``an explicit expression of the environmental
value to be protected'' [U.S. EPA, 1992a].) 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 after the common definition of expose: ``to
submit or subject to an action or influence'' (Merriam-Webster, 1972).
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 component exposed
to the stressor. This term may refer to tissues, organisms,
populations, communities, and ecosystems. While either ``ecological
component'' (U.S. EPA, 1992a) or ``biological system'' (Cohrssen and
Covello, 1989) are alternative terms, ``receptor'' is usually clearer
in discussions of exposure where the emphasis is on the stressor-
receptor relationship. As discussed below, both disturbance and stress
regime have been suggested as alternative terms for exposure. Neither
term is used in these guidelines, which instead use exposure as broadly
defined above.
Disturbance. A disturbance is any event or series of
events that disrupts ecosystem, community, or population structure and
changes resources, substrate availability, or the physical environment
(modified slightly from White and Pickett, 1985). Defined in this way,
disturbance is clearly a kind of exposure (i.e., an event that subjects
a receptor, the disturbed system, to the actions of a stressor).
Disturbance may be a useful alternative to stressor specifically for
physical stressors that are deletions or modifications (e.g., logging,
dredging, flooding).
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.
[[Page 47613]]
Model structure uncertainty (process models).
Uncertainty about a model's form (empirical models).
Appendix B.--Key Terms (Adapted From U.S. EPA, 1992a)
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. An assessment endpoint includes both an
ecological entity and specific attributes of that entity. For example,
salmon are a valued ecological entity; reproduction and population
maintenance of salmon form an assessment endpoint.
Characterization of ecological effects--A portion of the analysis
phase of ecological risk assessment that evaluates the ability of a
stressor 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 an
expert judgment approach to evaluate the relative magnitude of effects
and set priorities among a wide range of environmental problems (e.g.,
U.S. EPA, 1993b). 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 expert judgment and an ecological risk
assessment approach to analyze future ecological risk scenarios and
risk management alternatives (U.S. EPA, 1995a).
Conceptual model--The conceptual model describes a series of
working hypotheses of how the stressor might affect ecological
entities. The conceptual model also describes the ecosystem potentially
at risk, the relationship between measures of effect and assessment
endpoints, and exposure scenarios.
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).
BILLING CODE 6560-50-P
[GRAPHIC] [TIFF OMITTED] TN09SE96.011
[[Page 47614]]
BILLING CODE 6560-50-C
Cumulative ecological risk assessment--A process that involves
consideration of ``the aggregate ecologic risk to the target entity
caused by the accumulation of risk from multiple stressors'' (Bender,
1996).
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).
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 can be one component of an
assessment endpoint.
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--Assessments are required under the
National Environmental Policy Act (NEPA) to fully evaluate
environmental effects associated with proposed major Federal actions.
Like ecological risk assessments, environmental impact statements (EIS)
typically require a ``scoping process'' analogous to problem
formulation, an analysis by multidisciplinary teams, and a presentation
of uncertainties (CEQ, 1986, cited in Suter, 1993a). By virtue of
special expertise, EPA may cooperate with other agencies by preparing
EISs or otherwise participating in the NEPA process.
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).
Lines of evidence--Information derived from different sources or by
different techniques that can be used to interpret and compare risk
estimates. While this term is similar to 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 for a particular test. The geometric mean is also known as
the chronic value.
Measure of ecosystem and receptor characteristics--A measurable
characteristic of the ecosystem or receptor that is used in support of
exposure or effects analysis.
Measure of effect--A measurable ecological characteristic that is
related to the valued characteristic chosen as the assessment endpoint.
Measure of exposure--A measurable stressor characteristic that is
used to help quantify exposure.
Measurement endpoint--See ``measure of effect.''
Median lethal concentration (LC