26846 Federal Register / Vol. 63, No. 93 /Thursday, May 14, 1998 / Notices

ENVIRONMENTAL PROTECTION

AGENCY

[FRL–6011–2 ]

Guidelines for Ecological Risk

Assessment

AGENCY: Environmental Protection

Agency.

ACTION: Notice of availability of final

Guidelines for Ecological Risk

Assessment.

SUMMARY: The U.S. Environmental

Protection Agency (EPA) is today

publishing in final form a document

entitled Guidelines for Ecological Risk

Assessment (hereafter ‘‘Guidelines’’).

These Guidelines were developed as

part of an interoffice program by a

Technical Panel of the Risk Assessment

Forum. These Guidelines will help

improve the quality of ecological risk

assessments at EPA while increasing the

consistency of assessments among the

Agency’s program offices and regions.

These Guidelines were prepared

during a time of increasing interest in

the field of ecological risk assessment

and reflect input from many sources

both within and outside the Agency.

The Guidelines expand upon and

replace the previously published EPA

report Framework for Ecological Risk

Assessment (EPA/630/R–92/001,

February 1992), which proposed

principles and terminology for the

ecological risk assessment process.

From 1992 to 1994, the Agency focused

on identifying a structure for the

Guidelines and the issues that the

document would address. EPA

sponsored public and Agency colloquia,

developed peer-reviewed ecological

assessment case studies, and prepared a

set of peer-reviewed issue papers

highlighting important principles and

approaches. Drafts of the proposed

Guidelines underwent formal external

peer review and were reviewed by the

Agency’s Risk Assessment Forum, by

Federal interagency subcommittees of

the Committee on Environment and

Natural Resources of the Office of

Science and Technology Policy, and by

the Agency’s Science Advisory Board

(SAB). The proposed Guidelines were

published for public comment in 1996

(61 FR 47552–47631, September 9,

1996). The final Guidelines incorporate

revisions based on the comments

received from the public and the SAB

on the proposed Guidelines. EPA

appreciates the efforts of all participants

in the process and has tried to address

their recommendations in these

Guidelines.

DATES: The Guidelines will be effective

on April 30, 1998.

ADDRESSES: The Guidelines will be

made available in several ways:

(1) The electronic version will be

accessible on the EPA National Center

for Environmental Assessment home

page on the Internet at http://

www.epa.gov/ncea/.

(2) 31⁄2′′ high-density computer

diskettes in WordPerfect format will be

available from ORD Publications,

Technology Transfer and Support

Division, National Risk Management

Research Laboratory, Cincinnati, OH;

telephone: 513–569–7562; fax: 513–

569–7566. Please provide the EPA No.

(EPA/630/R–95/002Fa) when ordering.

(3) This notice contains the full

document. (However, because of

Federal Register format limitations, text

boxes that would normally be included

at their point of reference in the

document are instead listed at the end

of the Guidelines as text notes.) Copies

of the Guidelines will be available for

inspection at EPA headquarters and

regional libraries, through the U.S.

Government Depository Library

program, and for purchase from the

National Technical Information Service

(NTIS), Springfield, VA; telephone:

703–487–4650, fax: 703–321–8547.

Please provide the NTIS PB No. (PB98–

117849) when ordering.

FOR FURTHER INFORMATION, CONTACT: Dr.

Bill van der Schalie, National Center for

Environmental Assessment-Washington

Office (8623), U.S. Environmental

Protection Agency, 401 M Street, SW,

Washington, DC 20460; telephone: 202–

564–3371; e-mail: EcoGuidelines@epamail.epa.gov.

SUPPLEMENTARY INFORMATION: Ecological

risk assessment ‘‘evaluates the

likelihood that adverse ecological effects

may occur or are occurring as a result

of exposure to one or more stressors’’

(U.S. EPA, 1992a). It is a flexible process

for organizing and analyzing data,

information, assumptions, and

uncertainties to evaluate the likelihood

of adverse ecological effects. Ecological

risk assessment provides a critical

element for environmental decision

making by giving risk managers an

approach for considering available

scientific information along with the

other factors they need to consider (e.g.,

social, legal, political, or economic) in

selecting a course of action.

To help improve the quality and

consistency of the U.S. Environmental

Protection Agency’s ecological risk

assessments, EPA’s Risk Assessment

Forum initiated development of these

Guidelines. The primary audience for

this document is risk assessors and risk

managers at EPA, although these

Guidelines also may be useful to others

outside the Agency. These Guidelines

expand on and replace the 1992 report

Framework for Ecological Risk

Assessment (referred to as the

Framework Report; see Appendix A).

They were written by a Forum technical

panel and have been revised on the

basis of extensive comments from

outside peer reviewers as well as

Agency staff. The Guidelines retain the

Framework Report’s broad scope, while

expanding on some concepts and

modifying others to reflect Agency

experiences. EPA intends to follow

these Guidelines with a series of shorter,

more detailed documents that address

specific ecological risk assessment

topics. This ‘‘bookshelf’’ approach

provides the flexibility necessary to

keep pace with developments in the

rapidly evolving field of ecological risk

assessment while allowing time to form

consensus, where appropriate, on

science policy (default assumptions) to

bridge gaps in knowledge. EPA will

revisit guidelines documents as

experience and scientific consensus

evolve. The Agency recognizes that

ecological risk assessment is only one

tool in the overall management of

ecological risks. Therefore, there are

ongoing efforts within the Agency to

develop other tools and processes that

can contribute to an overall approach to

ecological risk management, addressing

topics such as ecological benefits

assessment and cost-benefit analyses.

Ecological risk assessment includes

three primary phases: Problem

formulation, analysis, and risk

characterization. In problem

formulation, risk assessors evaluate

goals and select assessment endpoints,

prepare the conceptual model, and

develop an analysis plan. During the

analysis phase, assessors evaluate

exposure to stressors and the

relationship between stressor levels and

ecological effects. In the third phase,

risk characterization, assessors estimate

risk through integration of exposure and

stressor-response profiles, describe risk

by discussing lines of evidence and

determining ecological adversity, and

prepare a report. The interface among

risk assessors, risk managers, and

interested parties during planning at the

beginning and communication of risk at

the end of the risk assessment is critical

to ensure that the results of the

assessment can be used to support a

management decision. Because of the

diverse expertise required (especially in

complex ecological risk assessments),

risk assessors and risk managers

frequently work in multidisciplinary

teams.

Both risk managers and risk assessors

bring valuable perspectives to the initial
Federal Register / Vol. 63, No. 93 /Thursday, May 14, 1998 / Notices 26847

planning activities for an ecological risk

assessment. Risk managers charged with

protecting the environment can identify

information they need to develop their

decision, risk assessors can ensure that

science is effectively used to address

ecological concerns, and together they

can evaluate whether a risk assessment

can address identified problems.

However, this planning process is

distinct from the scientific conduct of

an ecological risk assessment. This

distinction helps ensure that political

and social issues, while helping to

define the objectives for the risk

assessment, do not introduce undue

bias.

Problem formulation, which follows

these planning discussions, provides a

foundation upon which the entire risk

assessment depends. Successful

completion of problem formulation

depends on the quality of three

products: Assessment endpoints,

conceptual models, and an analysis

plan. Since problem formulation is an

interactive, nonlinear process,

substantial reevaluation is expected to

occur during the development of all

problem formulation products.

The analysis phase includes two

principal activities: Characterization of

exposure and characterization of

ecological effects. The process is

flexible, and interaction between the

two evaluations is essential. Both

activities evaluate available data for

scientific credibility and relevance to

assessment endpoints and the

conceptual model. Exposure

characterization describes sources of

stressors, their distribution in the

environment, and their contact or cooccurrence with ecological receptors.

Ecological effects characterization

evaluates stressor-response

relationships or evidence that exposure

to stressors causes an observed

response. The bulk of quantitative

uncertainty analysis is performed in the

analysis phase, although uncertainty is

an important consideration throughout

the entire risk assessment. The analysis

phase products are summary profiles

that describe exposure and the stressorresponse relationships.

Risk characterization is the final

phase of an ecological risk assessment.

During this phase, risk assessors

estimate ecological risks, indicate the

overall degree of confidence in the risk

estimates, cite evidence supporting the

risk estimates, and interpret the

adversity of ecological effects. To ensure

mutual understanding between risk

assessors and managers, a good risk

characterization will express results

clearly, articulate major assumptions

and uncertainties, identify reasonable

alternative interpretations, and separate

scientific conclusions from policy

judgments. Risk managers use risk

assessment results, along with other

factors (e.g., economic or legal

concerns), in making risk management

decisions and as a basis for

communicating risks to interested

parties and the general public.

After completion of the risk

assessment, risk managers may consider

whether follow-up activities are

required. They may decide on risk

mitigation measures, then develop a

monitoring plan to determine whether

the procedures reduced risk or whether

ecological recovery is occurring.

Managers may also elect to conduct

another planned tier or iteration of the

risk assessment if necessary to support

a management decision.

Dated: April 30, 1998.

Carol M. Browner,

Administrator.

Part A: Guidelines for Ecological Risk

Assessment

Contents

List of Figures

List of Text Notes

1. Introduction

1.1. The Ecological Risk Assessment

Process

1.2. Ecological Risk Assessment in a

Management Context

1.2.1. Contributions of Ecological Risk

Assessment to Environmental Decision

Making

1.2.2. Factors Affecting the Value of

Ecological Risk Assessment for

Environmental Decision Making

1.3. Scope and Intended Audience

1.4. Guidelines Organization

2. Planning the Risk Assessment

2.1. The Roles of Risk Managers, Risk

Assessors, and Interested Parties in

Planning

2.2. Products of Planning

2.2.1. Management Goals

2.2.2. Management Options to Achieve

Goals

2.2.3. Scope and Complexity of the Risk

Assessment

2.3. Planning Summary

3. Problem Formulation Phase

3.1. Products of Problem Formulation

3.2. Integration of Available Information

3.3. Selecting Assessment Endpoints

3.3.1. Criteria for Selection

3.3.1.1. Ecological Relevance

3.3.1.2. Susceptibility to Known or

Potential Stressors

3.3.1.3. Relevance to Management Goals

3.3.2. Defining Assessment Endpoints

3.4. Conceptual Models

3.4.1. Risk Hypotheses

3.4.2. Conceptual Model Diagrams

3.4.3. Uncertainty in Conceptual Models

3.5. Analysis Plan

3.5.1. Selecting Measures

3.5.2. Ensuring That Planned Analyses

Meet Risk Managers’ Needs

4. Analysis Phase

4.1. Evaluating Data and Models for

Analysis

4.1.1. Strengths and Limitations of

Different Types of Data

4.1.2. Evaluating Measurement or

Modeling Studies

4.1.2.1. Evaluating the Purpose and Scope

of the Study

4.1.2.2. Evaluating the Design and

Implementation of the Study

4.1.3. Evaluating Uncertainty

4.2. Characterization of Exposure

4.2.1. Exposure Analyses

4.2.1.1. Describe the Source(s)

4.2.1.2. Describe the Distribution of the

Stressors or Disturbed Environment

4.2.1.3. Describe Contact or Co-occurrence

4.2.2. Exposure Profile

4.3. Characterization of Ecological Effects

4.3.1. Ecological Response Analysis

4.3.1.1. Stressor-Response Analysis

4.3.1.2. Establishing Cause-and-Effect

Relationships (Causality)

4.3.1.3. Linking Measures of Effect to

Assessment Endpoints

4.3.2. Stressor-Response Profile

5. Risk Characterization

5.1. Risk Estimation

5.1.1. Results of Field Observational

Studies

5.1.2. Categories and Rankings

5.1.3. Single-Point Exposure and Effects

Comparisons

5.1.4. Comparisons Incorporating the

Entire Stressor-Response Relationship

5.1.5. Comparisons Incorporating

Variability in Exposure and/or Effects

5.1.6. Application of Process Models

5.2. Risk Description

5.2.1. Lines of Evidence

5.2.2. Determining Ecological Adversity

5.3. Reporting Risks

6. Relating Ecological Information to Risk

Management Decisions

7. Text Notes

Appendix A: Changes from EPA’s Ecological

Risk Assessment Framework

Appendix B: Key Terms

Appendix C: Conceptual Model Examples

Appendix D: Analysis Phase Examples

Appendix E: Criteria for Determining

Ecological Adversity: A Hypothetical

Example

References

List of Figures

Figure 1–1. The framework for ecological risk

assessment

Figure 1–2. The ecological risk assessment

framework, with an expanded view of

each phase

Figure 3–1. Problem formulation phase

Figure 4–1. Analysis phase

Figure 4–2. A simple example of a stressorresponse relationship.

Figure 4–3. Variations in stressor-response

relationships

Figure 5–1. Risk characterization

Figure 5–2. Risk estimation techniques. a.

Comparison of exposure and stressorresponse point estimates. b. Comparison

of point estimates from the stressorresponse relationship with uncertainty

associated with an exposure point

estimate
26848 Federal Register / Vol. 63, No. 93 /Thursday, May 14, 1998 / Notices

Figure 5–3. Risk estimation techniques:

Comparison of point estimates with

associated uncertainties

Figure 5–4. Risk estimation techniques:

Stressor-response curve versus a

cumulative distribution of exposures

Figure 5–5. Risk estimation techniques:

Comparison of exposure distribution of

an herbicide in surface waters with

freshwater single-species toxicity data

List of Text Notes

Text Note 1–1. Related Terminology

Text Note 1–2. Flexibility of the Framework

Diagram

Text Note 2–1. Who Are Risk Managers?

Text Note 2–2. Who Are Risk Assessors?

Text Note 2–3. Who Are Interested Parties?

Text Note 2–4. Questions Addressed by Risk

Managers and Risk Assessors

Text Note 2–5. Sustainability as a

Management Goal

Text Note 2–6. Management Goals for

Waquoit Bay

Text Note 2–7. What is the Difference

Between a Management Goal and

Management Decision?

Text Note 2–8. Tiers and Iteration: When Is

a Risk Assessment Done?

Text Note 2–9. Questions to Ask About Scope

and Complexity

Text Note 3–1. Avoiding Potential

Shortcomings Through Problem

Formulation

Text Note 3–2. Uncertainty in Problem

Formulation

Text Note 3–3. Initiating a Risk Assessment:

What’s Different When Stressors, Effects,

or Values Drive the Process?

Text Note 3–4. Assessing Available

Information: Questions to Ask

Concerning Source, Stressor, and

Exposure Characteristics, Ecosystem

Characteristics, and Effects

Text Note 3–5. Salmon and Hydropower:

Salmon as the Basis for an Assessment

Endpoint

Text Note 3–6. Cascading Adverse Effects:

Primary (Direct) and Secondary

(Indirect)

Text Note 3–7. Identifying Susceptibility

Text Note 3–8. Sensitivity and Secondary

Effects: The Mussel-Fish Connection

Text Note 3–9. Examples of Management

Goals and Assessment Endpoints

Text Note 3–10. Common Problems in

Selecting Assessment Endpoints

Text Note 3–11. What Are the Benefits of

Developing Conceptual Models?

Text Note 3–12. What Are Risk Hypotheses,

and Why Are They Important?

Text Note 3–13. Examples of Risk

Hypotheses

Text Note 3–14. Uncertainty in Problem

Formulation

Text Note 3–15. Why Was Measurement

Endpoint Changed?

Text Note 3–16. Examples of a Management

Goal, Assessment Endpoint, and

Measures

Text Note 3–17. How Do Water Quality

Criteria Relate to Assessment Endpoints?

Text Note 3–18. The Data Quality Objectives

Process

Text Note 4–1. Data Collection and the

Analysis Phase

Text Note 4–2. The American National

Standard for Quality Assurance

Text Note 4–3. Questions for Evaluating a

Study’s Utility for Risk Assessment

Text Note 4–4. Uncertainty Evaluation in the

Analysis Phase

Text Note 4–5. Considering the Degree of

Aggregation in Models

Text Note 4–6. Questions for Source

Description

Text Note 4–7. Questions to Ask in

Evaluating Stressor Distribution

Text Note 4–8. General Mechanisms of

Transport and Dispersal

Text Note 4–9. Questions to Ask in

Describing Contact or Co-occurrence

Text Note 4–10. Example of an Exposure

Equation: Calculating a Potential Dose

via Ingestion

Text Note 4–11. Measuring Internal Dose

Using Biomarkers and Tissue Residues

Text Note 4–12. Questions Addressed by the

Exposure Profile

Text Note 4–13. Questions for StressorResponse Analysis

Text Note 4–14. Qualitative StressorResponse Relationships

Text Note 4–15. Median Effect Levels

Text Note 4–16. No-Effect Levels Derived

From Statistical Hypothesis Testing

Text Note 4–17. General Criteria for Causality

Text Note 4–18. Koch’s Postulates

Text Note 4–19. Examples of Extrapolations

to Link Measures of Effect to Assessment

Endpoints

Text Note 4–20. Questions Related to

Selecting Extrapolation Approaches

Text Note 4–21. Questions to Consider When

Extrapolating From Effects Observed in

the Laboratory to Field Effects of

Chemicals

Text Note 4–22. Questions Addressed by the

Stressor-Response Profile

Text Note 5–1. An Example of Field Methods

Used for Risk Estimation

Text Note 5–2. Using Qualitative Categories

to Estimate Risks of an Introduced

Species

Text Note 5–3. Applying the Quotient

Method

Text Note 5–4. Comparing an Exposure

Distribution With a Point Estimate of

Effects

Text Note 5–5. Comparing Cumulative

Exposure and Effects Distributions for

Chemical Stressors

Text Note 5–6. Estimating Risk With Process

Models

Text Note 5–7. What Are Statistically

Significant Effects?

Text Note 5–8. Possible Risk Assessment

Report Elements

Text Note 5–9. Clear, Transparent,

Reasonable, and Consistent Risk

Characterizations

Text Note 6–1. Questions Regarding Risk

Assessment Results

Text Note 6–2. Risk Communication

Considerations for Risk Managers

Text Note A–1. Stressor vs. Agent

1. Introduction

Ecological risk assessment is a process

that evaluates the likelihood that

adverse ecological effects may occur or

are occurring as a result of exposure to

one or more stressors (U.S. EPA, 1992a).

The process is used to systematically

evaluate and organize data, information,

assumptions, and uncertainties in order

to help understand and predict the

relationships between stressors and

ecological effects in a way that is useful

for environmental decision making. An

assessment may involve chemical,

physical, or biological stressors, and one

stressor or many stressors may be

considered.

Ecological risk assessments are

developed within a risk management

context to evaluate human-induced

changes that are considered undesirable.

As a result, these Guidelines focus on

stressors and adverse effects generated

or influenced by anthropogenic activity.

Defining adversity is important because

a stressor may cause adverse effects on

one ecosystem component but be

neutral or even beneficial to other

components. Changes often considered

undesirable are those that alter

important structural or functional

characteristics or components of

ecosystems. An evaluation of adversity

may include a consideration of the type,

intensity, and scale of the effect as well

as the potential for recovery. The

acceptability of adverse effects is

determined by risk managers. Although

intended to evaluate adverse effects, the

ecological risk assessment process can

be adapted to predict beneficial changes

or risk from natural events.

Descriptions of the likelihood of

adverse effects may range from

qualitative judgments to quantitative

probabilities. Although risk assessments

may include quantitative risk estimates,

quantitation of risks is not always

possible. It is better to convey

conclusions (and associated

uncertainties) qualitatively than to

ignore them because they are not easily

understood or estimated.

Ecological risk assessments can be

used to predict the likelihood of future

adverse effects (prospective) or evaluate

the likelihood that effects are caused by

past exposure to stressors

(retrospective). In many cases, both

approaches are included in a single risk

assessment. For example, a retrospective

risk assessment designed to evaluate the

cause for amphibian population

declines may also be used to predict the

effects of future management actions.

Combined retrospective and prospective

risk assessments are typical in situations

where ecosystems have a history of

previous impacts and the potential for

future effects from multiple chemical,

physical, or biological stressors. Other

terminology related to ecological risk

assessment is referenced in text note

1–1.
Federal Register / Vol. 63, No. 93 /Thursday, May 14, 1998 / Notices 26849

1Changes in process and terminology from EPA’s

previous ecological risk assessment framework

(U.S. EPA, 1992a) are summarized in Appendix A.

1.1. The Ecological Risk Assessment

Process

The ecological risk assessment

process is based on two major elements:

Characterization of effects and

characterization of exposure. These

provide the focus for conducting the

three phases of risk assessment: Problem

formulation, analysis, and risk

characterization.

The overall ecological risk assessment

process 1 is shown in figure 1–1. The

format remains consistent with the

diagram from the 1992 report

Framework for Ecological Risk

Assessment (referred to as the

Framework Report). However, the

process and products within each phase

have been refined, and these changes

are detailed in figure 1–2. The three

phases of risk assessment are enclosed

by a dark solid line. Boxes outside this

line identify critical activities that

influence why and how a risk

assessment is conducted and how it will

be used.

BILLING CODE 6560–50–P
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BILLING CODE 6560–50–C
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Problem formulation, the first phase,

is shown at the top. In problem

formulation, the purpose for the

assessment is articulated, the problem is

defined, and a plan for analyzing and

characterizing risk is determined. Initial

work in problem formulation includes

the integration of available information

on sources, stressors, effects, and

ecosystem and receptor characteristics.

From this information two products are

generated: Assessment endpoints and

conceptual models. Either product may

be generated first (the order depends on

the type of risk assessment), but both are

needed to complete an analysis plan,

the final product of problem

formulation.

Analysis, shown in the middle box, is

directed by the products of problem

formulation. During the analysis phase,

data are evaluated to determine how

exposure to stressors is likely to occur

(characterization of exposure) and,

given this exposure, the potential and

type of ecological effects that can be

expected (characterization of ecological

effects). The first step in analysis is to

determine the strengths and limitations

of data on exposure, effects, and

ecosystem and receptor characteristics.

Data are then analyzed to characterize

the nature of potential or actual

exposure and the ecological responses

under the circumstances defined in the

conceptual model(s). The products from

these analyses are two profiles, one for

exposure and one for stressor response.

These products provide the basis for

risk characterization.

During risk characterization, shown in

the third box, the exposure and stressorresponse profiles are integrated through

the risk estimation process. Risk

characterization includes a summary of

assumptions, scientific uncertainties,

and strengths and limitations of the

analyses. The final product is a risk

description in which the results of the

integration are presented, including an

interpretation of ecological adversity

and descriptions of uncertainty and

lines of evidence.

Although problem formulation,

analysis, and risk characterization are

presented sequentially, ecological risk

assessments are frequently iterative.

Something learned during analysis or

risk characterization can lead to a

reevaluation of problem formulation or

new data collection and analysis (see

text note 1–2).

Interactions among risk assessors, risk

managers, and other interested parties

are shown in two places in the diagram.

The side box on the upper left

represents planning, where agreements

are made about the management goals,

the purpose for the risk assessment, and

the resources available to conduct the

work. The box following risk

characterization represents when the

results of the risk assessment are

formally communicated by risk

assessors to risk managers. Risk

managers generally communicate risk

assessment results to interested parties.

These activities are shown outside the

ecological risk assessment process

diagram to emphasize that risk

assessment and risk management are

two distinct activities. The former

involves the evaluation of the likelihood

of adverse effects, while the latter

involves the selection of a course of

action in response to an identified risk

that is based on many factors (e.g.,

social, legal, political, or economic) in

addition to the risk assessment results.

The bar along the right side of figure

1–2 highlights data acquisition,

iteration, and monitoring. Monitoring

data provide important input to all

phases of a risk assessment. They can

provide the impetus for a risk

assessment by identifying changes in

ecological condition. They can also be

used to evaluate a risk assessment’s

predictions. For example, follow-up

studies could determine whether

mitigation efforts were effective, help

verify whether source reduction was

effective, or determine the extent and

nature of ecological recovery. It is

important for risk assessors and risk

managers to use monitoring results to

evaluate risk assessment predictions so

they can gain experience and help

improve the risk assessment and risk

management process (Commission on

Risk Assessment and Risk Management,

1997).

Even though the risk assessment

focuses on data analysis and

interpretation, acquiring the appropriate

quantity and quality of data for use in

the process is critical. If data are

unavailable, the risk assessment may

stop until data are obtained. The process

is more often iterative than linear, since

the evaluation of new data or

information may require revisiting a

part of the process or conducting a new

assessment (see text note 2–8). The

dotted line between the side bar and the

risk management box indicates that

additional data acquisition, iteration, or

monitoring, while important, are not

always required.

1.2. Ecological Risk Assessment in a

Management Context

Ecological risk assessments are

designed and conducted to provide

information to risk managers about the

potential adverse effects of different

management decisions. Attempts to

eliminate risks associated with human

activities in the face of uncertainties and

potentially high costs present a

challenge to risk managers

(Ruckelshaus, 1983; Suter, 1993a).

Although many considerations and

sources of information are used by

managers in the decision process,

ecological risk assessments are unique

in providing a scientific evaluation of

ecological risk that explicitly addresses

uncertainty.

1.2.1. Contributions of Ecological Risk

Assessment to Environmental Decision

Making

At EPA, ecological risk assessments

are used to support many types of

management actions, including the

regulation of hazardous waste sites,

industrial chemicals, and pesticides, or

the management of watersheds or other

ecosystems affected by multiple

nonchemical and chemical stressors.

The ecological risk assessment process

has several features that contribute to

effective environmental decision

making:

• Through an iterative process, new

information can be incorporated into

risk assessments, which can be used to

improve environmental decision

making. This feature is consistent with

adaptive management principles

(Holling, 1978) used in managing

natural resources.

• Risk assessments can be used to

express changes in ecological effects as

a function of changes in exposure to

stressors. This capability may be

particularly useful to the decision maker

who must evaluate tradeoffs, examine

different alternatives, or determine the

extent to which stressors must be

reduced to achieve a given outcome.

• Risk assessments explicitly evaluate

uncertainty. Uncertainty analysis

describes the degree of confidence in

the assessment and can help the risk

manager focus research on those areas

that will lead to the greatest reductions

in uncertainty.

• Risk assessments provide a basis for

comparing, ranking, and prioritizing

risks. The results can also be used in

cost-benefit and cost-effectiveness

analyses that offer additional

interpretation of the effects of

alternative management options.

• Risk assessments consider

management goals and objectives as

well as scientific issues in developing

assessment endpoints and conceptual

models during problem formulation.

Such initial planning activities help

ensure that results will be useful to risk

managers.
Federal Register / Vol. 63, No. 93 /Thursday, May 14, 1998 / Notices 26853

1.2.2. Factors Affecting the Value of

Ecological Risk Assessment for

Environmental Decision Making

The wide use and important

advantages of ecological risk

assessments do not mean they are the

sole determinants of management

decisions; risk managers consider many

factors. Legal mandates and political,

social, and economic considerations

may lead risk managers to make

decisions that are more or less

protective. Reducing risk to the lowest

level may be too expensive or not

technically feasible. Thus, although

ecological risk assessments provide

critical information to risk managers,

they are only part of the environmental

decision-making process.

In some cases, it may be desirable to

broaden the scope of a risk assessment

during the planning phase. A risk

assessment that is too narrowly focused

on one type of stressor in a system (e.g.,

chemicals) could fail to consider more

important stressors (e.g., habitat

alteration). However, options for

modifying the scope of a risk assessment

may be limited when the scope is

defined by statute.

In other situations, management

alternatives may be available that

completely circumvent the need for a

risk assessment. For example, the risks

associated with building a hydroelectric

dam may be avoided by considering

alternatives for meeting power needs

that do not involve a new dam. In these

situations, the risk assessment may be

redirected to assess the new alternative,

or one may not be needed at all.

1.3. Scope and Intended Audience

These Guidelines describe general

principles and give examples to show

how ecological risk assessment can be

applied to a wide range of systems,

stressors, and biological, spatial, and

temporal scales. They describe the

strengths and limitations of alternative

approaches and emphasize processes

and approaches for analyzing data

rather than specifying data collection

techniques, methods, or models. They

do not provide detailed guidance, nor

are they prescriptive. This approach,

although intended to promote

consistency, provides flexibility to

permit EPA’s offices and regions to

develop specific guidance suited to their

needs.

Agency preferences are expressed

where possible, but because ecological

risk assessment is a rapidly evolving

discipline, requirements for specific

approaches could soon become

outdated. EPA intends to develop a

series of shorter, more detailed

documents on specific ecological risk

assessment topics following publication

of these Guidelines.

The interface between risk assessors

and risk managers is discussed in the

Guidelines. However, details on the use

of ecological risk assessment in the risk

management process are beyond the

scope of these Guidelines. Other EPA

publications discuss how ecological

concerns have been addressed in

decision making at EPA (U.S. EPA,

1994a), propose ecological entities that

may be important to protect (U.S. EPA,

1997a), and provide an introduction to

ecological risk assessment for risk

managers (U.S. EPA, 1995a).

Policies in this document are

intended as internal guidance for EPA.

Risk assessors and risk managers at EPA

are the primary audience, although

these Guidelines may be useful to others

outside the Agency. This document is

not a regulation and is not intended for

EPA regulations. The Guidelines set

forth current scientific thinking and

approaches for conducting and

evaluating ecological risk assessments.

They are not intended, nor can they be

relied upon, to create any rights

enforceable by any party in litigation

with the United States. As with other

EPA guidelines (e.g., developmental

toxicity, 56 FR 63798–63826; exposure

assessment, 57 FR 22888–22938; and

carcinogenicity, 61 FR 17960–18011),

EPA will revisit these Guidelines as

experience and scientific consensus

evolve.

These Guidelines replace the

Framework Report (U.S. EPA, 1992a).

They expand on and modify framework

concepts to reflect Agency experience

since the Framework Report was

published (see Appendix A).

1.4. Guidelines Organization

These Guidelines follow the

ecological risk assessment format as

presented in figures 1–1 and 1–2.

Section 2 (planning) describes the

dialogue among risk assessors, risk

managers, and interested parties before

the risk assessment begins. Section 3

(problem formulation) describes how

management goals are interpreted,

assessment endpoints selected,

conceptual models constructed, and

analysis plans developed. Section 4

(analysis) addresses how to evaluate

potential exposure of receptors and the

relationship between stressor levels and

ecological effects. Section 5 (risk

characterization) describes the process

of estimating risk through the

integration of exposure and stressorresponse profiles and discusses lines of

evidence, interpretation of adversity,

and uncertainty. Finally, section 6 (on

relating ecological information to risk

management decisions) addresses

communicating the results of the risk

assessment to risk managers.

2. Planning the Risk Assessment

Ecological risk assessments are

conducted to transform scientific data

into meaningful information about the

risk of human activities to the

environment. Their purpose is to enable

risk managers to make informed

environmental decisions. To ensure that

risk assessments meet this need, risk

managers and risk assessors (see text

notes 2–1 and 2–2) and, where

appropriate, interested parties (see text

note 2–3), engage in a planning dialogue

as a critical first step toward initiating

problem formulation (see figure 1–2).

The planning dialogue is the

beginning of a necessary interface

between risk managers and risk

assessors. However, it is imperative to

remember that planning remains

distinct from the scientific conduct of a

risk assessment. This distinction helps

ensure that political and social issues,

though helping define the objectives for

the assessment, do not bias the scientific

evaluation of risk.

The first step in planning may be to

determine if a risk assessment is the best

option for supporting the decision. Risk

managers and risk assessors both

consider the potential value of

conducting a risk assessment to address

identified problems. Their discussion

explores what is known about the

degree of risk, what management

options are available to mitigate or

prevent it, and the value of conducting

a risk assessment compared with other

ways of learning about and addressing

environmental concerns. In some cases,

a risk assessment may add little value to

the decision process because

management alternatives may be

available that completely circumvent

the need for a risk assessment (see

section 1.2.2). In other cases, the need

for a risk assessment may be

investigated through a simple tiered risk

evaluation based on minimal data and a

simple model (see section 2.2.2).

Once the decision is made to conduct

a risk assessment, the next step is to

ensure that all key participants are

appropriately involved. Risk

management may be carried out by one

decision maker in an agency such as

EPA or it may be implemented by

several risk managers working together

as a team (see text note 2–1). Likewise,

risk assessment may be conducted by a

single risk assessor or a team of risk

assessors (see text note 2–2). In some

cases, interested parties play an

important role (see text note 2–3).
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Careful consideration up front about

who will participate, and the character

of that participation, will determine the

success of planning.

2.1. The Roles of Risk Managers, Risk

Assessors, and Interested Parties in

Planning

During the planning dialogue, risk

managers and risk assessors each bring

important perspectives to the table. Risk

managers, charged with protecting

human health and the environment,

help ensure that risk assessments

provide information relevant to their

decisions by describing why the risk

assessment is needed, what decisions it

will influence, and what they want to

receive from the risk assessor. It is also

helpful for managers to consider and

communicate problems they have

encountered in the past when trying to

use risk assessments for decision

making.

In turn, risk assessors ensure that

scientific information is effectively used

to address ecological and management

concerns. Risk assessors describe what

they can provide to the risk manager,

where problems are likely to occur, and

where uncertainty may be problematic.

In addition, risk assessors may provide

insights to risk managers about

alternative management options likely

to achieve stated goals because the

options are ecologically grounded.

In some risk assessments, interested

parties also take an active role in

planning, particularly in goal

development. The National Research

Council describes participation by

interested parties in risk assessment as

an iterative process of ‘‘analysis’’ and

‘‘deliberation’’ (NRC, 1996). Interested

parties may communicate their concerns

to risk managers about the environment,

economics, cultural changes, or other

values potentially at risk from

environmental management activities.

Where they have the ability to increase

or mitigate risk to ecological values of

concern that are identified, interested

parties may become part of the risk

management team (see text note 2–1).

However, involvement by interested

parties is not always needed or

appropriate. It depends on the purpose

of the risk assessment, the regulatory

requirements, and the characteristics of

the management problem (see section

2.2.1). When interested parties become

risk managers on a team, they directly

participate in planning.

During planning, risk managers and

risk assessors are responsible for coming

to agreement on the goals, scope, and

timing of a risk assessment and the

resources that are available and

necessary to achieve the goals. Together

they use information on the area’s

ecosystems, regulatory requirements,

and publicly perceived environmental

values to interpret the goals for use in

the ecological risk assessment.

Examples of questions that risk

managers and risk assessors may

address during planning are provided in

text note 2–4.

2.2. Products of Planning

The characteristics of an ecological

risk assessment are directly determined

by agreements reached by risk managers

and risk assessors during planning

dialogues. These agreements are the

products of planning. They include (1)

clearly established and articulated

management goals, (2) characterization

of decisions to be made within the

context of the management goals, and

(3) agreement on the scope, complexity,

and focus of the risk assessment,

including the expected output and the

technical and financial support

available to complete it.

2.2.1. Management Goals

Management goals are statements

about the desired condition of

ecological values of concern. They may

range from ‘‘maintain a sustainable

aquatic community’’ (see text notes 2–

5 and 2–6) to ‘‘restore a wetland’’ or

‘‘prevent toxicity.’’ Management goals

driving a specific risk assessment may

come from the law, interpretations of

the law by regulators, desired outcomes

voiced by community leaders and the

public, and interests expressed by

affected parties. All involve input from

the public. However, the process used to

establish management goals influences

how well they provide guidance to a

risk assessment team, how they foster

community participation, and whether

the larger affected community will

support implementation of management

decisions to achieve the goal.

A majority of Agency risk assessments

incorporate legally established

management goals found in enabling

legislation. In these cases, goals were

derived through public debate among

interested parties when the law was

enacted. Such management goals (e.g.,

the Clean Water Act goals to ‘‘protect

and restore the chemical, physical and

biological integrity of the Nation’s

waters’’) are often open to considerable

interpretation and rarely provide

sufficient guidance to a risk assessor. To

address this, the Agency has interpreted

these goals into regulations and

guidance for implementation at the

national scale (e.g., water quality

criteria, see text note 3–17). Mandated

goals may be interpreted by Agency

managers and staff into a particular risk

assessment format and then applied

consistently across stressors of the same

type (e.g., evaluation of new chemicals).

In cases where laws and regulations are

specifically applied to a particular site,

interaction between risk assessors and

risk managers is needed to translate the

law and regulations into management

goals appropriate for the site or

ecosystem of concern (e.g., Superfund

site cleanup).

Although this approach has been

effective, most regulations and guidance

are stated in terms of measures or

specific actions that must or must not be

taken rather than establishing a valuebased management goal or desired state.

As environmental protection efforts

shift from implementing controls

toward achieving measurable

environmental results, value-based

management goals at the national scale

will be increasingly important as

guidance for risk assessors. Such goals

as ‘‘no unreasonable effects on bird

survival’’ or ‘‘maintaining areal extent of

wetlands’’ will provide a basis for risk

assessment design (see also U.S. EPA,

1997a, for additional examples and

discussion).

The ‘‘place-based’’ or ‘‘communitybased’’ approach for managing

ecological resources recommended in

the Edgewater Consensus (U.S. EPA,

1994b) generally requires that

management goals be developed for

each assessment. Management goals for

‘‘places’’ such as watersheds are formed

as a consensus based on diverse values

reflected in Federal, State, tribal, and

local regulations and on constituencygroup and public concerns. Public

meetings, constituency-group meetings,

evaluation of resource management

organizational charters, and other means

of looking for shared goals may be

necessary to reach consensus among

these diverse groups, commonly called

‘‘stakeholders’’ (see text note 2–3).

However, goals derived by consensus

are normally general. For use in a risk

assessment, risk assessors must interpret

the goals into more specific objectives

about what must occur in a place in

order for the goal to be achieved and

identify ecological values that can be

measured or estimated in the ecosystem

of concern (see text note 2–6). For these

risk assessments, the interpretation is

unique to the ecosystem being assessed

and is done on a case-by-case basis as

part of the planning process. Risk

assessors and risk managers should

agree on the interpretations.

Early discussion on and selection of

clearly established management goals

provide risk assessors with a fuller

understanding of how different risk

management options under
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consideration may result in achieving

the goal. Such information helps the

risk assessor identify and gather critical

data and information. Regardless of how

management goals are established, those

that explicitly define ecological values

to be protected provide the best

foundation for identifying actions to

reduce risk and generating risk

assessment objectives. The objectives for

the risk assessment derive from the type

of management decisions to be made.

2.2.2. Management Options To Achieve

Goals

Risk managers must implement

decisions to achieve management goals

(see text note 2–7). These risk

management decisions may establish

national policy applied consistently

across the country (e.g., premanufacture

notices (PMN) for new chemicals,

protection of endangered species) or be

applied to a specific site (e.g., hazardous

waste site cleanup level) or management

concern (e.g., number of combined

sewer overflow events allowable per

year) intended to achieve an

environmental goal when implemented.

Management decisions often begin as

one of several management options

identified during planning. Management

options may range from preventing the

introduction of a stressor to restoration

of affected ecological values. When

several options are defined during

planning for a particular problem (e.g.,

leave alone, clean up, or pave a

contaminated site), risk assessments can

be used to predict potential risk across

the range of these management options

and, in some cases, combined with costbenefit analyses to aid decision making.

When risk assessors are made aware of

possible options, they can use them to

ensure that the risk assessment

addresses a sufficient breadth of issues.

Explicitly stated management options

provide a framework for defining the

scope, focus, and conduct of a risk

assessment. Some risk assessments are

specifically designed to determine if a

preestablished decision criterion is

exceeded (e.g., see the data quality

objectives process, U.S. EPA, 1994c, and

section 3.5.2 for more details). Decision

criteria often contain inherent

assumptions about exposure, the range

of possible stressors, or conditions

under which the targeted stressor is

operating. To ensure that decision

options include appropriate

assumptions and the risk assessment is

designed to address management issues,

these assumptions need to be clearly

stated.

Decision criteria are often used within

a tiering framework to determine how

extensive a risk assessment should be.

Early screening tiers may have

predetermined decision criteria to

answer whether a potential risk exists.

Later tiers frequently do not because the

management question changes from

‘‘yes-no’’ to questions of ‘‘what, where,

and how great is the risk.’’ Results from

these risk assessments require risk

managers to evaluate risk

characterization and generate a

decision, perhaps through formal

decision analysis (e.g., Clemen, 1996),

or managers may request an iteration of

the risk assessment to address issues of

continuing concern (see text note 2–8).

Risk assessments designed to support

management initiatives for a region or

watershed where multiple stressors,

ecological values, and political and

economic factors influence decision

making require great flexibility and

more complex iterative risk

assessments. They generally require an

examination of ecological processes

most influenced by diverse human

actions. Risk assessments used in this

application are often based on a general

goal statement and multiple potential

decisions. These require significant

planning to determine which array of

management decisions may be

addressed and to establish the purpose,

scope, and complexity of the risk

assessment.

2.2.3. Scope and Complexity of the Risk

Assessment

Although the purpose for conducting

a risk assessment determines whether it

is national, regional, or local in scope,

resource availability determines its

extent, complexity, and the level of

confidence in results that can be

expected. Each risk assessment is

constrained by the availability of valid

data and scientific understanding,

expertise, time, and financial resources.

Risk managers and risk assessors

consider the nature of the decision (e.g.,

national policy, local impact), available

resources, opportunities for increasing

the resource base (e.g., partnering, new

data collection, alternative analytical

tools), potential characteristics of the

risk assessment team, and the output

that will provide the best information

for the required decisions (see text note

2–9). They must often be flexible in

determining what level of effort is

warranted for a risk assessment. The

most detailed assessment process is

neither applicable nor necessary in

every instance. Screening assessments

may be the appropriate level of effort.

One approach for determining the

needed level of effort in the risk

assessment is to set up tiered

evaluations, as discussed in section

2.2.2. Where tiers are used, specific

descriptions of management questions

and decision criteria should be included

in the plan.

Part of the agreement on scope and

complexity is based on the maximum

uncertainty that can be tolerated for the

decision the risk assessment supports.

Risk assessments completed in response

to legal mandates and likely to be

challenged in court often require

rigorous attention to potential sources of

uncertainty to help ensure that

conclusions from the assessment can be

defended. A frank discussion is needed

between the risk manager and risk

assessor on the sources of uncertainty

and ways uncertainty can be reduced (if

necessary or possible) through selective

investment of resources. Resource

planning may account for the iterative

nature of risk assessment or include

explicitly defined steps, such as tiers

that represent increasing cost and

complexity, each tier designed to

increase understanding and reduce

uncertainty. Advice on addressing the

interplay of management decisions,

study boundaries, data needs,

uncertainty, and specifying limits on

decision errors may be found in EPA’s

guidance on data quality objectives

(U.S. EPA, 1994c).

2.3. Planning Summary

The planning phase is complete when

agreements are reached on (1) the

management goals for ecological values,

(2) the range of management options the

risk assessment is to support, (3)

objectives for the risk assessment,

including criteria for success, (4) the

focus and scope of the assessment, and

(5) resource availability. Agreements

may encompass the technical approach

to be taken in a risk assessment as

determined by the regulatory or

management context and reason for

initiating the risk assessment (see

section 3.2), the spatial scale (e.g., local,

regional, or national), and the temporal

scale (e.g., the time frame over which

stressors or effects will be evaluated).

In mandated risk assessments,

planning agreements may be codified in

regulations, and little documentation of

agreements is warranted. In others, a

summary of planning agreements may

be important for ensuring that the risk

assessment remains consistent with its

original intent. A summary can provide

a point of reference for determining if

early decisions need to be changed in

response to new information. There is

no predetermined format, length, or

complexity for a planning summary. It

is a useful reference only and should be

tailored to the risk assessment it

represents. However, a summary will

help ensure quality communication
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between risk managers and risk

assessors and will document agreedupon decisions.

Once planning is complete, the formal

process of risk assessment begins.

During problem formulation, risk

assessors should continue the dialogue

with risk managers, particularly

following assessment endpoint selection

and completion of the analysis plan. At

these points, potential problems can be

identified before the risk assessment

proceeds.

3. Problem Formulation Phase

Problem formulation is a process for

generating and evaluating preliminary

hypotheses about why ecological effects

have occurred, or may occur, from

human activities. It provides the

foundation for the entire ecological risk

assessment. Early in problem

formulation, objectives for the risk

assessment are refined. Then the nature

of the problem is evaluated and a plan

for analyzing data and characterizing

risk is developed. Any deficiencies in

problem formulation will compromise

all subsequent work on the risk

assessment (see text note 3–1). The

quality of the assessment will depend in

part on the team conducting the

assessment and its responsiveness to the

risk manager’s needs.

The makeup of the risk assessment

team assembled to conduct problem

formulation depends on the

requirements of the risk assessment. The

team should include professionals with

expertise directly related to the level

and type of problem under

consideration and the ecosystem where

the problem is likely to occur. Teams

may range from one individual

calculating a simple quotient where the

information and algorithm are clearly

established to a large interdisciplinary,

interagency team typical of ecosystemlevel risk assessments involving

multiple stressors and ecological values.

Involvement by the risk management

team and other interested parties in

problem formulation can be most

valuable during final selection of

assessment endpoints, review of the

conceptual models, and adjustments to

the analysis plan. The degree of

participation is commensurate with the

complexity of the risk assessment and

the magnitude of the risk management

decision to be faced. Participation

normally consists of approval and

refinement rather than technical input

(but see text note 2–3). The format used

to involve risk managers needs to gain

from, and be responsive to, their input

without compromising the scientific

validity of the risk assessment. The level

of involvement by interested parties in

problem formulation is determined by

risk managers.

3.1. Products of Problem Formulation

Problem formulation results in three

products: (1) Assessment endpoints that

adequately reflect management goals

and the ecosystem they represent, (2)

conceptual models that describe key

relationships between a stressor and

assessment endpoint or between several

stressors and assessment endpoints, and

(3) an analysis plan. The first step

toward developing these products is to

integrate available information as shown

in the hexagon in figure 3–1; the

products are shown as circles. While the

assessment of available information is

begun up front in problem formulation

and the analysis plan is the final

product, the order in which assessment

endpoints and conceptual models are

produced depends on why the risk

assessment was initiated (see section

3.2). To enhance clarity, the following

discussion is presented as a linear

progression. However, problem

formulation is frequently interactive and

iterative rather than linear. Reevaluation

may occur during any part of problem

formulation.

BILLING CODE 6560–50–P
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3.2. Integration of Available Information

The foundation for problem

formulation is based on how well

available information on stressor

sources and characteristics, exposure

opportunities, characteristics of the

ecosystem(s) potentially at risk, and

ecological effects are integrated and

used (see figure 3–1). Integration of

available information is an iterative

process that normally occurs throughout

problem formulation. Initial evaluations

often provide the basis for generating

preliminary conceptual models or

assessment endpoints, which in turn

may lead risk assessors to seek other

types of available information not

previously recognized as needed.

The quality and quantity of

information determine the course of

problem formulation. When key

information is of the appropriate type

and sufficient quality and quantity,

problem formulation can proceed

effectively. When data are unavailable,

the risk assessment may be suspended

while additional data are collected or, if

this is not possible, may be developed

on the basis of what is known and what

can be extrapolated from what is

known. Risk assessments are frequently

begun without all needed information,

in which case the problem formulation

process helps identify missing data and

provides a framework for further data

collection. Where data are few, the

limitations of conclusions, or

uncertainty, from the risk assessment

should be clearly articulated in risk

characterization (see text note 3–2).

The impetus for an ecological risk

assessment influences what information

is available at the outset and what

information should be collected. For

example, a risk assessment can be

initiated because a known or potential

stressor may enter the environment.

Risk assessors evaluating a source or

stressor will seek data on the effects

with which the stressor might be

associated and the ecosystems in which

it will likely be introduced or found. If

an observed adverse effect or change in

ecological condition initiates the

assessment, risk assessors will seek

information about potential stressors

and sources that could have caused the

effect. When a risk assessment is

initiated because of a desire to better

manage an ecological value or entity

(e.g., species, communities, ecosystems,

or places), risk assessors will seek

information on the specific condition or

effect of interest, the characteristics of

relevant ecosystems, and potential

stressors and sources (see text note 3–

3).

Information (actual, inferred, or

estimated) is initially integrated in a

scoping process that provides the

foundation for developing problem

formulation. Knowledge gained during

scoping is used to identify missing

information and potential assessment

endpoints, and it provides the basis for

early conceptualization of the problem

being assessed. As problem formulation

proceeds, information quality and

applicability to the particular problem

of concern are increasingly scrutinized.

Where appropriate, further iterations

may result in a comprehensive

evaluation that helps risk assessors

generate an array of risk hypotheses (see

section 3.4.1). Once analysis plans are

being formed, data validity becomes a

significant factor for risk assessors to

evaluate (see section 4.1 for a discussion

of assessing data quality). Thus an

evaluation of available information is an

ongoing activity throughout problem

formulation. The level of effort is driven

by the type of assessment.

As the complexity and spatial scale of

a risk assessment increase, information

needs often escalate. Risk assessors

consider the ways ecosystem

characteristics directly influence when,

how, and why particular ecological

entities may become exposed and

exhibit adverse effects due to particular

stressors. Predicting risks from multiple

chemical, physical, and biological

stressors requires an effort to

understand their interactions. Risk

assessments for a region or watershed,

where multiple stressors are the rule,

require consideration of ecological

processes operating at larger spatial

scales.

Despite our limited knowledge of

ecosystems and the stressors influencing

them, the process of problem

formulation offers a systematic

approach for organizing and evaluating

available information on stressors and

possible effects. It can function as a

preliminary risk assessment that is

useful to risk assessors and decision

makers. Text note 3–4 provides a series

of questions that risk assessors should

attempt to answer. This exercise will

help risk assessors identify known and

unknown relationships, both of which

are important in problem formulation.

Problem formulation proceeds with

the identification of assessment

endpoints and the development of

conceptual models and an analysis plan

(discussed below). Early recognition

that the reasons for initiating the risk

assessment affect the order in which

products are generated will help

facilitate the development of problem

formulation (see text note 3–3).

3.3. Selecting Assessment Endpoints

Assessment endpoints are explicit

expressions of the actual environmental

value that is to be protected,

operationally defined by an ecological

entity and its attributes (see section

3.3.2). Assessment endpoints are critical

to problem formulation because they

structure the assessment to address

management concerns and are central to

conceptual model development. Their

relevance is determined by how well

they target susceptible ecological

entities. Their ability to support risk

management decisions depends on

whether they are measurable ecosystem

characteristics that adequately represent

management goals. The selection of

ecological concerns and assessment

endpoints at EPA has traditionally been

done internally by individual Agency

program offices (U.S. EPA, 1994a). More

recently, interested and affected parties

have helped identify management

concerns and assessment endpoints in

efforts to implement watershed or

community-based environmental

protection.

This section provides guidance on

selecting and defining assessment

endpoints. It is presented in two parts.

Section 3.3.1 establishes three criteria

(ecological relevance, susceptibility, and

relevance to management goals) for

determining how to select, among a

broad array of possibilities, the specific

ecological characteristics to target in the

risk assessment that are responsive to

general management goals and are

scientifically defensible. Section 3.3.2

then provides specific guidance on how

to convert selected ecological

characteristics into operationally

defined assessment endpoints that

include both a defined entity and

specific attributes amenable to

measurement.

3.3.1. Criteria for Selection

All ecosystems are diverse, with many

levels of ecological organization (e.g.,

individuals, populations, communities,

ecosystems, landscapes) and multiple

ecosystem processes. It is rarely clear

which of these characteristics are most

critical to ecosystem function, nor do

professionals or the public always agree

on which are most valuable. As a result,

it is often a challenge to consider the

array of possibilities and choose which

ecological characteristics to protect to

meet management goals. Those choices

are critical, however, because they

become the basis for defining

assessment endpoints, the transition

between broad management goals and

the specific measures used in a risk

assessment.
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Three principal criteria are used to

select ecological values that may be

appropriate for assessment endpoints:

(1) Ecological relevance, (2)

susceptibility to known or potential

stressors, and (3) relevance to

management goals. Of these, ecological

relevance and susceptibility are

essential for selecting assessment

endpoints that are scientifically

defensible. However, to increase the

likelihood that the risk assessment will

be used in management decisions,

assessment endpoints are more effective

when they also reflect societal values

and management goals. Given the

complex functioning of ecosystems and

the interdependence of ecological

entities, it is likely that potential

assessment endpoints can be identified

that are both responsive to management

goals and meet scientific criteria.

Assessment endpoints that meet all

three criteria provide the best

foundation for an effective risk

assessment (e.g., see text note 3–5).

3.3.1.1. Ecological Relevance

Ecologically relevant endpoints reflect

important characteristics of the system

and are functionally related to other

endpoints (U.S. EPA, 1992a).

Ecologically relevant endpoints may be

identified at any level of organization

(e.g., individual, population,

community, ecosystem, landscape). The

consequences of changes in these

endpoints may be quantified (e.g.,

alteration of community structure from

the loss of a keystone species) or

inferred (e.g., survival of individuals is

needed to maintain populations).

Ecological entities are not ecologically

relevant unless they are currently, or

were historically, part of the ecosystem

under consideration.

Ecologically relevant endpoints often

help sustain the natural structure,

function, and biodiversity of an

ecosystem or its components. They may

contribute to the food base (e.g., primary

production), provide habitat (e.g., for

food or reproduction), promote

regeneration of critical resources (e.g.,

decomposition or nutrient cycling), or

reflect the structure of the community,

ecosystem, or landscape (e.g., species

diversity or habitat mosaic). In

landscape-level risk assessments,

careful selection of assessment

endpoints that address both species of

concern and landscape-level ecosystem

processes becomes important. It may be

possible to select one or more species

and an ecosystem process to represent

larger functional community or

ecosystem processes.

Ecological relevance is linked to the

nature and intensity of potential effects,

the spatial and temporal scales where

effects may occur, and the potential for

recovery (see Determining Ecological

Adversity, section 5.2.2). It is also

linked to the level of ecological

organization that could be adversely

affected (see U.S. EPA, 1997a, for a

discussion of how different levels of

organization are used by the Agency in

defining assessment endpoints). When

changes in selected ecosystem entities

are likely to cause multiple or

widespread effects, such entities can be

powerful components of assessment

endpoints. They are particularly

valuable when risk assessors are trying

to identify the potential cascade of

adverse effects that could result from

loss or reduction of a species or a

change in ecosystem function (see text

note 3–6). Although a cascade of effects

may be predictable, it is often difficult

to predict the nature of all potential

effects. Determining ecological

relevance in specific cases requires

professional judgment based on sitespecific information, preliminary

surveys, or other available information.

3.3.1.2. Susceptibility to Known or

Potential Stressors

Ecological resources are considered

susceptible when they are sensitive to a

stressor to which they are, or may be,

exposed. Susceptibility can often be

identified early in problem formulation,

but not always. Risk assessors may be

required to use their best professional

judgment to select the most likely

candidates (see text note 3–7).

Sensitivity refers to how readily an

ecological entity is affected by a

particular stressor. Sensitivity is directly

related to the mode of action of the

stressors (e.g., chemical sensitivity is

influenced by individual physiology

and metabolic pathways). Sensitivity is

also influenced by individual and

community life-history characteristics.

For example, stream species

assemblages that depend on cobble and

gravel habitat for reproduction are

sensitive to fine sediments that fill in

spaces between cobbles. Species with

long life cycles and low reproductive

rates are often more vulnerable to

extinction from increases in mortality

than species with short life cycles and

high reproductive rates. Species with

large home ranges may be more

sensitive to habitat fragmentation when

the fragment is smaller than their

required home range compared to

species with smaller home ranges that

are encompassed within a fragment.

However, habitat fragmentation may

also affect species with small home

ranges where migration is a necessary

part of their life history and

fragmentation prevents migration and

genetic exchange among

subpopulations. Such life-history

characteristics are important to consider

when evaluating potential sensitivity.

Sensitivity can be related to the life

stage of an organism when exposed to

a stressor. Frequently, young animals

are more sensitive to stressors than

adults. For instance, Pacific salmon eggs

and fry are very sensitive to fine-grain

sedimentation in river beds because

they can be smothered. Age-dependent

sensitivity, however, is not only in the

young. In many species, events like

migration (e.g., in birds) and molting

(e.g., in harbor seals) represent

significant energy investments that

increase vulnerability to stressors.

Finally, sensitivity may be enhanced by

the presence of other stressors or natural

disturbances. For example, the presence

of insect pests and disease may make

plants more sensitive to damage from

ozone (Heck, 1993). To determine how

sensitivity at a particular life stage is

critical to population parameters or

community-level assessment endpoints

may require further evaluation.

Measures of sensitivity may include

mortality or adverse reproductive effects

from exposure to toxics. Other possible

measures of sensitivity include

behavioral abnormalities; avoidance of

significant food sources and nesting

sites; loss of offspring to predation

because of the proximity of stressors

such as noise, habitat alteration, or loss;

community structural changes; or other

factors.

Exposure is the second key

determinant in susceptibility. Exposure

can mean co-occurrence, contact, or the

absence of contact, depending on the

stressor and assessment endpoint.

Questions concerning where a stressor

originates, how it moves through the

environment, and how it comes in

contact with the assessment endpoint

are evaluated to determine

susceptibility (see section 4.2 for more

discussion on characterizing exposure).

The amount and conditions of exposure

directly influence how an ecological

entity will respond to a stressor. Thus,

to determine which entities are

susceptible, it is important that the

assessor consider the proximity of an

ecological value to stressors of concern,

the timing of exposure (both in terms of

frequency and duration), and the

intensity of exposure occurring during

sensitive periods.

Adverse effects of a particular stressor

may be important during one part of an

organism’s life cycle, such as early

development or reproduction. They may

result from exposure to a stressor or to

the absence of a necessary resource
26860 Federal Register / Vol. 63, No. 93 /Thursday, May 14, 1998 / Notices

during a critical life stage. For example,

if fish are unable to find suitable nesting

sites during their reproductive phase,

risk is significant even when water

quality is high and food sources

abundant. The interplay between life

stage and stressors can be very complex

(see text note 3–8).

Exposure may occur in one place or

time, but effects may not be observed

until another place or time. Both lifehistory characteristics and the

circumstances of exposure influence

susceptibility in this case. For instance,

the temperature of the egg incubation

medium of marine turtles affects the sex

ratio of hatchlings, but population

impacts are not observed until years

later when the cohort of affected turtles

begins to reproduce. Delayed effects and

multiple-stressor exposures add

complexity to evaluations of

susceptibility (e.g., although toxicity

tests may determine receptor sensitivity

to one stressor, susceptibility may

depend on the co-occurrence of another

stressor that significantly alters receptor

response). Conceptual models (see

section 3.4) need to reflect these factors.

If a species or other ecological entity is

unlikely to be directly or indirectly

exposed to the stressor of concern, or to

the secondary effects of stressor

exposure, it may be inappropriate as an

assessment endpoint (see text note 3–7).

3.3.1.3. Relevance to Management Goals

Ultimately, the effectiveness of a risk

assessment depends on whether it is

used and improves the quality of

management decisions. Risk managers

are more willing to use a risk

assessment for making decisions when

it is based on ecological values that

people care about. Thus, candidates for

assessment endpoints include

endangered species or ecosystems,

commercially or recreationally

important species, functional attributes

that support food sources or flood

control (e.g., wetland water

sequestration), aesthetic values such as

clean air in national parks, or the

existence of charismatic species such as

eagles or whales. However, selection of

assessment endpoints based on public

perceptions alone could lead to

management decisions that do not

consider important ecological

information. While responsiveness to

the public is important, it does not

obviate the requirement for scientific

validity.

The challenge is to find ecological

values that meet the necessary scientific

rigor as assessment endpoints that are

also recognized as valuable by risk

managers and the public. As an

illustration, suppose an assessment is

designed to evaluate the risk of applying

pesticide around a lake to control

insects. At this lake, however, midges

are susceptible to the pesticide and form

the base of a complex food web that

supports a native fish population

popular with sportsmen. While both

midges and fish represent key

components of the aquatic community,

selecting the fishery as the value for

defining the assessment endpoint targets

both ecological and community

concerns. Selecting midges would not.

The risk assessment can then

characterize the risk to the fishery if the

midge population is adversely affected.

This choice maintains the scientific

validity of the risk assessment while

being responsive to management

concerns. In those cases where a critical

assessment endpoint is identified that is

unpopular with the public, the risk

assessor may find it necessary to present

a persuasive case in its favor to risk

managers based on scientific arguments.

Practical issues may influence what

values are selected as potential

assessment endpoints, such as what is

required by statute (e.g., endangered

species) or whether it is possible to

achieve a particular management goal.

For example, in a river already

impounded throughout its reach by

multiple dams, goals for reestablishing

spawning habitat for free-living

anadromous salmon may be feasible

only if dams are removed. If this will

not be considered, selection of other

ecological values as potential endpoints

in this highly modified system may be

the only option. Another concern may

be whether it is possible to directly

measure important variables. Where it is

possible to directly measure attributes of

an assessment endpoint, extrapolation is

unnecessary, thus preventing the

introduction of a source of uncertainty.

Assessment endpoints that cannot be

measured directly but can be

represented by measures that are easily

monitored and modeled may still

provide a good foundation for a risk

assessment. However, while established

measurement protocols are convenient

and useful, they do not determine

whether an assessment endpoint is

appropriate. Data availability alone is

not an adequate criterion for selection.

To ensure scientific validity, risk

assessors are responsible for selecting

and defining potential assessment

endpoints based on an understanding of

the ecosystem of concern. Risk

managers and risk assessors should then

come to agreement on the final

selection.

3.3.2. Defining Assessment Endpoints

Once ecological values are selected as

potential assessment endpoints, they

need to be operationally defined. Two

elements are required to define an

assessment endpoint. The first is the

identification of the specific valued

ecological entity. This can be a species

(e.g., eelgrass, piping plover), a

functional group of species (e.g.,

piscivores), a community (e.g., benthic

invertebrates), an ecosystem (e.g., lake),

a specific valued habitat (e.g., wet

meadows), a unique place (e.g., a

remnant of native prairie), or other

entity of concern. The second is the

characteristic about the entity of

concern that is important to protect and

potentially at risk. Thus, it is necessary

to define what is important for piping

plovers (e.g., nesting and feeding

conditions), a lake (e.g., nutrient

cycling), or wet meadow (e.g., endemic

plant community diversity). For an

assessment endpoint to serve as a clear

interpretation of the management goals

and the basis for measurement in the

risk assessment, both an entity and an

attribute are required.

What distinguishes assessment

endpoints from management goals is

their neutrality and specificity.

Assessment endpoints do not represent

a desired achievement (i.e., goal). As

such, they do not contain words like

‘‘protect,’’ ‘‘maintain,’’ or ‘‘restore,’’ or

indicate a direction for change such as

‘‘loss’’ or ‘‘increase.’’ Instead they are

ecological values defined by specific

entities and their measurable attributes,

providing a framework for measuring

stress-response relationships. When

goals are very broad it may be difficult

to select appropriate assessment

endpoints until the goal is broken down

into multiple management objectives. A

series of management objectives can

clarify the inherent assumptions within

the goal and help a risk assessor

determine which ecological entities and

attributes best represent each objective

(see text box 2–6). From this, multiple

assessment endpoints may be selected.

See text note 3–9 for examples of

management goals and assessment

endpoints.

Assessment endpoints may or may

not be distinguishable from measures,

depending on the assessment endpoints

selected and the type of measures.

While it is the entity that influences the

scale and character of a risk assessment,

it is the attributes of an assessment

endpoint that determine what to

measure. Sometimes direct measures of

effect can be collected on the attribute

of concern. Where this occurs, the

assessment endpoint and measure of
Federal Register / Vol. 63, No. 93 /Thursday, May 14, 1998 / Notices 26861

effect are the same and no extrapolation

is necessary (e.g., if the assessment

endpoint is ‘‘reproductive success of

blue jays,’’ egg production and fledgling

success could potentially be directly

measured under different stressor

exposure scenarios). In other cases,

direct measures may not be possible

(e.g., toxicity in endangered species)

and surrogate measures of effect must be

selected. Thus, although assessment

endpoints must be defined in terms of

measurable attributes, selection does not

depend on the ability to measure those

attributes directly or on whether

methods, models, and data are currently

available. For practical reasons, it may

be helpful to use assessment endpoints

that have well-developed test methods,

field measurement techniques, and

predictive models (see Suter, 1993a).

However, it is not necessary for methods

to be standardized protocols, nor should

assessment endpoints be selected

simply because standardized protocols

are readily available. The appropriate

measures to use are generally identified

during conceptual model development

and specified in the analysis plan.

Measures of ecosystem characteristics

and exposure are determined by the

entity and attributes selected and serve

as important information in conceptual

model development. See section 3.5.1

for issues surrounding the selection of

measures.

Clearly defined assessment endpoints

provide direction and boundaries for the

risk assessment and can minimize

miscommunication and reduce

uncertainty; where they are poorly

defined, inappropriate, or at the

incorrect scale, they can be very

problematic. Endpoints may be too

broad, vague, or narrow, or they may be

inappropriate for the ecosystem

requiring protection. ‘‘Ecological

integrity’’ is a frequently cited but vague

goal and is too vague for an assessment

endpoint. ‘‘Integrity’’ can only be used

effectively when its meaning is

explicitly characterized for a particular

ecosystem, habitat, or entity. This may

be done by selecting key entities or

processes for an ecosystem and

describing attributes that best represent

integrity for that system. Assessment

endpoints that are too narrowly defined

may not support effective risk

management. If an assessment is

focused only on protecting the habitat of

an endangered species, for example, the

risk assessment may overlook other

equally important characteristics of the

ecosystem and fail to include critical

variables (see text note 3–8). Finally, the

assessment endpoint could fail to

represent the ecosystem at risk. For

instance, selecting a game fish that

grows well in reservoirs may meet a

‘‘fishable’’ management goal, but it

would be inappropriate for evaluating

risk from a new hydroelectric dam if the

ecosystem of concern is a stream in

which salmon spawn (see text note 3–

5). Although the game fish will satisfy

‘‘fishable’’ goals and may be highly

desired by local fishermen, a reservoir

species does not represent the

ecosystem at risk. Substituting

‘‘reproducing populations of indigenous

salmonids’’ for a vague ‘‘viable fish

populations’’ assessment endpoint

could therefore prevent the

development of an inappropriate risk

assessment.

When well selected, assessment

endpoints become powerful tools in the

risk assessment process. One endpoint

that is sensitive to many of the

identified stressors, yet responds in

different ways to different stressors, may

provide an opportunity to consider the

combined effects of multiple stressors

while still distinguishing their effects.

For example, fish population

recruitment may be adversely affected at

several life stages, in different habitats,

through different ways, and by different

stressors. Therefore, measures of effect,

exposure, and ecosystem and receptor

characteristics could be chosen to

evaluate recruitment and provide a basis

for distinguishing different stressors,

individual effects, and their combined

effects.

The assessment endpoint can provide

a basis for comparing a range of

stressors if carefully selected. The

National Crop Loss Assessment Network

(Heck, 1993) selected crop yields as the

assessment endpoint to evaluate the

cumulative effects of multiple stressors.

Although the primary stressor was

ozone, the crop-yield endpoint also

allowed the risk assessors to consider

the effects of sulfur dioxide and soil

moisture. As Barnthouse et al. (1990)

pointed out, an endpoint should be

selected so that all the effects can be

expressed in the same units (e.g.,

changes in the abundance of 1-year-old

fish from exposure to toxicity, fishing

pressure, and habitat loss). This is

especially true when selecting

assessment endpoints for multiple

stressors. However, in situations where

multiple stressors act on the structure

and function of aquatic and terrestrial

communities in a watershed, an array of

assessment endpoints that represent the

community and associated ecological

processes is more effective than a single

endpoint. When based on differing

susceptibility to an array of stressors,

carefully selected assessment endpoints

can help risk assessors distinguish the

effects of diverse stressors. Exposure to

multiple stressors may lead to effects at

different levels of biological

organization, for a cascade of adverse

effects that should be considered.

Professional judgment and an

understanding of the characteristics and

function of an ecosystem are important

for translating general goals into usable

assessment endpoints. The less

information available, the more critical

it is to have informed professionals help

in the selection. Common problems

encountered in selecting assessment

endpoints are summarized in text note

3–10.

Final assessment endpoint selection is

an important risk manager-risk assessor

checkpoint during problem formulation.

Risk assessors and risk managers should

agree that selected assessment

endpoints effectively represent the

management goals. In addition, the

scientific rationale for their selection

should be made explicit in the risk

assessment.

3.4. Conceptual Models

A conceptual model in problem

formulation is a written description and

visual representation of predicted

relationships between ecological entities

and the stressors to which they may be

exposed. Conceptual models represent

many relationships. They may include

ecosystem processes that influence

receptor responses or exposure

scenarios that qualitatively link landuse activities to stressors. They may

describe primary, secondary, and

tertiary exposure pathways (see section

4.2) or co-occurrence among exposure

pathways, ecological effects, and

ecological receptors. Multiple

conceptual models may be generated to

address several issues in a given risk

assessment. Some of the benefits gained

by developing conceptual models are

featured in text note 3–11.

Conceptual models for ecological risk

assessments are developed from

information about stressors, potential

exposure, and predicted effects on an

ecological entity (the assessment

endpoint). Depending on why a risk

assessment is initiated, one or more of

these categories of information are

known at the outset (refer to section 3.2

and text note 3–3). The process of

creating conceptual models helps

identify the unknown elements.

The complexity of the conceptual

model depends on the complexity of the

problem: the number of stressors,

number of assessment endpoints, nature

of effects, and characteristics of the

ecosystem. For single stressors and

single assessment endpoints, conceptual

models may be simple. In some cases,
26862 Federal Register / Vol. 63, No. 93 /Thursday, May 14, 1998 / Notices

the same basic conceptual model may

be used repeatedly (e.g., in EPA’s new

chemical risk assessments). However,

when conceptual models are used to

describe pathways of individual

stressors and assessment endpoints and

the interaction of multiple and diverse

stressors and assessment endpoints (e.g.,

assessments initiated to protect

ecological values), more complex

models and several submodels will

often be needed. In this case, it can be

helpful to create models that also

represent expected ecosystem

characteristics and function when

stressors are not present.

Conceptual models consist of two

principal components:

• A set of risk hypotheses that

describe predicted relationships among

stressor, exposure, and assessment

endpoint response, along with the

rationale for their selection.

• A diagram that illustrates the

relationships presented in the risk

hypotheses.

3.4.1. Risk Hypotheses

Hypotheses are assumptions made in

order to evaluate logical or empirical

consequences, or suppositions

tentatively accepted to provide a basis

for evaluation. Risk hypotheses are

specific assumptions about potential

risk to assessment endpoints (see text

note 3–12) and may be based on theory

and logic, empirical data, mathematical

models, or probability models. They are

formulated using a combination of

professional judgment and available

information on the ecosystem at risk,

potential sources of stressors, stressor

characteristics, and observed or

predicted ecological effects on selected

or potential assessment endpoints.

These hypotheses may predict the

effects of a stressor before they occur, or

they may postulate why observed

ecological effects occurred and

ultimately what caused the effect.

Depending on the scope of the risk

assessment, risk hypotheses may be very

simple, predicting the potential effect of

one stressor on one receptor, or

extremely complex, as is typical in

value-initiated risk assessments that

often include prospective and

retrospective hypotheses about the

effects of multiple complexes of

stressors on diverse ecological receptors.

Risk hypotheses represent relationships

in the conceptual model and are not

designed for statistically testing null

and alternative hypotheses. However,

they can be used to generate questions

appropriate for research.

Although risk hypotheses are valuable

even when information is limited, the

amount and quality of data and

information will affect the specificity

and level of uncertainty associated with

risk hypotheses and the conceptual

models they form. When preliminary

information is conflicting, risk

hypotheses can be constructed

specifically to differentiate between

competing predictions. The predictions

can then be evaluated systematically

either by using available data during the

analysis phase or by collecting new data

before proceeding with the risk

assessment. Hypotheses and predictions

set a framework for using data to

evaluate functional relationships (e.g.,

stressor-response curves).

Early conceptual models are normally

broad, identifying as many potential

relationships as possible. As more

information is incorporated, the

plausibility of specific hypotheses helps

risk assessors sort through potentially

large numbers of stressor-effect

relationships, and the ecosystem

processes that influence them, to

identify those risk hypotheses most

appropriate for the analysis phase. It is

then that justifications for selecting and

omitting hypotheses are documented.

Examples of risk hypotheses are

provided in text note 3–13.

3.4.2. Conceptual Model Diagrams

Conceptual model diagrams are a

visual representation of risk hypotheses.

They are useful tools for communicating

important pathways clearly and

concisely and can be used to generate

new questions about relationships that

help formulate plausible risk

hypotheses.

Typical conceptual model diagrams

are flow diagrams containing boxes and

arrows to illustrate relationships (see

Appendix C). When this approach is

used, it is helpful to use distinct and

consistent shapes to distinguish

stressors, assessment endpoints,

responses, exposure routes, and

ecosystem processes. Although flow

diagrams are often used to illustrate

conceptual models, there is no set

configuration. Pictorial representations

can be very effective (e.g., Bradley and

Smith, 1989). Regardless of the

configuration, a diagram’s usefulness is

linked to the detailed written

descriptions and justifications for the

relationships shown. Without this,

diagrams can misrepresent the processes

they are intended to illustrate.

When developing conceptual model

diagrams, factors to consider include the

number of relationships depicted, the

comprehensiveness of the information,

the certainty surrounding a linkage, and

the potential for measurement. The

number of relationships that can be

depicted in one flow diagram depends

on their complexity. Several models that

increasingly show more detail for

smaller portions can be more effective

than trying to create one model that

shows everything at the finest detail.

Flow diagrams that highlight data

abundance or scarcity can provide

insights on how the analyses should be

approached and can be used to show the

risk assessor’s confidence in the

relationship. They can also show why

certain pathways were pursued and

others were not.

Diagrams provide a working and

dynamic representation of relationships.

They should be used to explore different

ways of looking at a problem before

selecting one or several to guide

analysis. Once the risk hypotheses are

selected and flow diagrams drawn, they

set the framework for final planning for

the analysis phase.

3.4.3. Uncertainty in Conceptual Models

Conceptual model development may

account for one of the most important

sources of uncertainty in a risk

assessment. If important relationships

are missed or specified incorrectly, the

risk characterization may misrepresent

actual risks. Uncertainty arises from

lack of knowledge about how the

ecosystem functions, failure to identify

and interrelate temporal and spatial

parameters, omission of stressors, or

overlooking secondary effects. In some

cases, little may be known about how a

stressor moves through the environment

or causes adverse effects. Multiple

stressors are the norm and a source of

confounding variables, particularly for

conceptual models that focus on a single

stressor. Professionals may not agree on

the appropriate conceptual model

configuration. While simplification and

lack of knowledge may be unavoidable,

risk assessors should document what is

known, justify the model, and rank

model components in terms of

uncertainty (see Smith and Shugart,

1994).

Uncertainty associated with

conceptual models can be explored by

considering alternative relationships. If

more than one conceptual model is

plausible, the risk assessor may evaluate

whether it is feasible to follow separate

models through analysis or whether the

models can be combined to create a

better model.

Conceptual models should be

presented to risk managers to ensure

that they communicate well and address

managers’ concerns. This check for

completeness and clarity is a way to

assess the need for changes before

analysis begins. It is also valuable to

revisit and where necessary revise

conceptual models during risk
Federal Register / Vol. 63, No. 93 /Thursday, May 14, 1998 / Notices 26863

assessments to incorporate new

information and recheck the rationale. If

this is not feasible, it is helpful to

present any new information during risk

characterization along with associated

uncertainties.

Throughout problem formulation,

ambiguities, errors, and disagreements

will occur, all of which contribute to

uncertainty. Wherever possible, these

sources of uncertainty should be

eliminated through better planning.

Because all uncertainty cannot be

eliminated, a description of the nature

of the uncertainties should be

summarized at the close of problem

formulation. See text note 3–14 for

recommendations on how to address

uncertainty.

3.5. Analysis Plan

The analysis plan is the final stage of

problem formulation. During analysis

planning, risk hypotheses are evaluated

to determine how they will be assessed

using available and new data. The plan

includes a delineation of the assessment

design, data needs, measures, and

methods for conducting the analysis

phase of the risk assessment. Analysis

plans may be brief or extensive

depending on the assessment. For some

assessments (e.g., EPA’s new chemical

assessments), the analysis plan is

already part of the established protocol

and a new plan is generally

unnecessary. As risk assessments

become more unique and complex, the

importance of a good analysis plan

increases.

The analysis plan includes pathways

and relationships identified during

problem formulation that will be

pursued during the analysis phase.

Those hypotheses considered more

likely to contribute to risk are targeted.

The rationale for selecting and omitting

risk hypotheses is incorporated into the

plan and includes acknowledgment of

data gaps and uncertainties. It also may

include a comparison of the level of

confidence needed for the management

decision with that expected from

alternative analyses in order to

determine data needs and evaluate

which analytical approach is best. When

new data are needed, the feasibility of

obtaining them can be taken into

account.

Identification of the most critical

relationships to evaluate in a risk

assessment is based on the relationship

of assessment endpoints to ecosystem

structure and function, the relative

importance or influence and mode of

action of stressors on assessment

endpoints, and other variables

influencing ecological adversity (see

section 5.2.2). However, final selection

of relationships that can be pursued in

analysis is based on the strength of

known relationships between stressors

and effects, the completeness of known

exposure pathways, and the quality and

availability of data.

In situations where data are few and

new data cannot be collected, it may be

possible to extrapolate from existing

data. Extrapolation allows the use of

data collected from other locations or

organisms where similar problems exist.

For example, the relationship between

nutrient availability and algal growth is

well established and consistent. This

relationship can be acknowledged

despite differences in how it is

manifested in particular ecosystems.

When extrapolating from data, it is

important to identify the source of the

data, justify the extrapolation method,

and discuss recognized uncertainties.

A phased, or tiered, risk assessment

approach (see section 2.2) can facilitate

management decisions in cases

involving minimal data sets. However,

where few data are available,

recommendations for new data

collection should be part of the analysis

plan. When new data are needed and

cannot be obtained, relationships that

cannot be assessed are a source of

uncertainty and should be described in

the analysis plan and later discussed in

risk characterization.

When determining what data to

analyze and how to analyze them,

consider how these analyses may

increase understanding and confidence

in the conclusions of the risk

assessment and address risk

management questions. During

selection, risk assessors may ask

questions such as: How relevant will the

results be to the assessment endpoint(s)

and conceptual model(s)? Are there

sufficient data of high quality to

conduct the analyses with confidence?

How will the analyses help establish

cause-and-effect relationships? How

will results be presented to address

managers’ questions? Where are

uncertainties likely to become a

problem? Consideration of these

questions during analysis planning will

improve future characterization of risk

(see section 5.2.1 for discussion of lines

of evidence).

3.5.1. Selecting Measures

Assessment endpoints and conceptual

models help risk assessors identify

measurable attributes to quantify and

predict change. However, determining

what measures to use to evaluate risk

hypotheses is both challenging and

critical to the success of a risk

assessment. There are three categories of

measures. Measures of effect are

measurable changes in an attribute of an

assessment endpoint or its surrogate in

response to a stressor to which it is

exposed (formerly measurement

endpoints; see text note 3–15). Measures

of exposure are measures of stressor

existence and movement in the

environment and their contact or cooccurrence with the assessment

endpoint. Measures of ecosystem and

receptor characteristics are measures of

ecosystem characteristics that influence

the behavior and location of entities

selected as the assessment endpoint, the

distribution of a stressor, and lifehistory characteristics of the assessment

endpoint or its surrogate that may affect

exposure or response to the stressor.

Examples of the three types of measures

are provided in text note 3–16 (see also

Appendix A.2.1).

The selection of appropriate measures

is particularly complicated when a

cascade of ecological effects is likely to

occur from a stressor. In these cases, the

effect on one entity (i.e., the measure of

effect) may become a stressor for other

ecological entities (i.e., become a

measure of exposure) and may result in

impacts on one or more assessment

endpoints. For example, if a pesticide

reduces earthworm populations, change

in earthworm population density could

be the direct measure of effect of

toxicity and in some cases may be an

assessment endpoint. However, the

reduction of worm populations may

then become a secondary stressor to

which worm-eating birds become

exposed, measured as lowered food

supply. This exposure may then result

in a secondary measurable effect of

starvation of young. In this case,

although ‘‘bird fledgling success’’ may

be an assessment endpoint that could be

measured directly, measures of

earthworm density, pesticide residue in

earthworms and other food sources,

availability of alternative foods, nest site

quality, and competition for nests from

other bird species may all be useful

measurements.

When direct measurement of

assessment endpoint responses is not

possible, the selection of surrogate

measures is necessary. The selection of

what, where, and how to measure

surrogate responses determines whether

the risk assessment is still relevant to

management decisions about an

assessment endpoint. As an example, an

assessment may be conducted to

evaluate the potential risk of a pesticide

used on seeds to an endangered species

of seed-eating bird. The assessment

endpoint entity is the endangered

species. Example attributes include

feeding behavior, survival, growth, and

reproduction. While it may be possible
26864 Federal Register / Vol. 63, No. 93 /Thursday, May 14, 1998 / Notices

to directly collect measures of exposure

and assessment endpoint life-history

characteristics on the endangered

species, it would not be appropriate to

expose the endangered species to the

pesticide to measure sensitivity. In this

case, to evaluate susceptibility, the most

appropriate surrogate measures would

be on seed-eating birds with similar lifehistory characteristics and phylogeny.

While insectivorous birds may serve as

an adequate surrogate measure for

determining the sensitivity of the

endangered bird to the pesticide, they

do not address issues of exposure.

Problem formulations based on

assessment endpoints and selected

measures that address both sensitivity

and likely exposure to stressors will be

relevant to management concerns. If

assessment endpoints are not

susceptible, their use in assessing risk

can lead to poor management decisions

(see section 3.3.1). To highlight the

relationships among goals, assessment

endpoints, and measures, text note 3–17

illustrates how these are related in water

quality criteria. In this example, it is

instructive to note that although water

quality criteria are considered riskbased, they are not full risk assessments.

Water quality criteria provide an effects

benchmark for decision making and do

not incorporate measures of exposure in

the environment. Within that

benchmark, there are a number of

assumptions about significance (e.g.,

aquatic communities will be protected

by achieving a benchmark derived from

individual species’ toxicological

responses to a single chemical) and

exposure (e.g., 1-hour and 4-day

exposure averages). Such assumptions

embedded in decision rules are

important to articulate (see section

3.5.2).

The analysis plan provides a synopsis

of measures that will be used to evaluate

risk hypotheses. The plan is strongest

when it contains explicit statements for

how measures were selected, what they

are intended to evaluate, and which

analyses they support. Uncertainties

associated with selected measures and

analyses and plans for addressing them

should be included in the plan when

possible.

3.5.2. Ensuring That Planned Analyses

Meet Risk Managers’ Needs

The analysis plan is a risk managerrisk assessor checkpoint. Risk assessors

and risk managers review the plan to

ensure that the analyses will provide

information the manager can use for

decision making. These discussions may

also identify what can and cannot be

done on the basis of a preliminary

evaluation of problem formulation. A

reiteration of the planning discussion

helps ensure that the appropriate

balance of requirements for the

decision, data availability, and resource

constraints is established for the risk

assessment. This is also an appropriate

time to conduct a technical review of

the planning outcome.

Analysis plans include the analytical

methods planned and the nature of the

risk characterization options and

considerations to be generated (e.g.,

quotients, narrative discussion, stressorresponse curve with probabilities). A

description of how data analyses will

distinguish among risk hypotheses, the

kinds of analyses to be used, and

rationale for why different hypotheses

were selected and eliminated are

included. Potential extrapolations,

model characteristics, types of data

(including quality), and planned

analyses (with specific tests for different

types of data) are described. Finally, the

plan includes a discussion of how

results will be presented upon

completion and the basis used for data

selection.

Analysis planning is similar to the

data quality objectives (DQO) process

(see text note 3–18), which emphasizes

identifying the problem by establishing

study boundaries and determining

necessary data quality, quantity, and

applicability to the problem being

evaluated (U.S. EPA, 1994c). The most

important difference between problem

formulation and the DQO process is the

presence of a decision rule in a DQO

that defines a benchmark for a

management decision before the risk

assessment is completed. The decision

rule step specifies the statistical

parameter that characterizes the

population, specifies the action level for

the study, and combines outputs from

the previous DQO steps into an ‘‘if

* * * then’’ decision rule that defines

conditions under which the decision

maker will choose alternative options

(often used in tiered assessments; see

also section 2.2.2). This approach

provides the basis for establishing null

and alternative hypotheses appropriate

for statistical testing for significance that

can be effective in this application.

While this approach is sometimes

appropriate, only certain kinds of risk

assessments are based on benchmark

decisions. Presentation of stressorresponse curves with uncertainty

bounds will be more appropriate than

statistical testing of decision criteria

where risk managers must evaluate the

range of stressor effects to which they

compare a range of possible

management options (see Suter, 1996).

The analysis plan is the final

synthesis before the risk assessment

proceeds. It summarizes what has been

done during problem formulation,

shows how the plan relates to

management decisions that must be

made, and indicates how data and

analyses will be used to estimate risks.

When the problem is clearly defined

and there are enough data to proceed,

analysis begins.

4. Analysis Phase

Analysis is a process that examines

the two primary components of risk,

exposure and effects, and their

relationships between each other and

ecosystem characteristics. The objective

is to provide the ingredients necessary

for determining or predicting ecological

responses to stressors under exposure

conditions of interest.

Analysis connects problem

formulation with risk characterization.

The assessment endpoints and

conceptual models developed during

problem formulation provide the focus

and structure for the analyses. Analysis

phase products are summary profiles

that describe exposure and the

relationship between the stressor(s) and

response. These profiles provide the

basis for estimating and describing risks

in risk characterization.

At the beginning of the analysis

phase, the information needs identified

during problem formulation should

have already been addressed (text note

4–1). During the analysis phase (figure

4–1), the risk assessor:

• Selects the data that will be used on

the basis of their utility for evaluating

the risk hypotheses (section 4.1)

• Analyzes exposure by examining

the sources of stressors, the distribution

of stressors in the environment, and the

extent of co-occurrence or contact

(section 4.2)

• Analyzes effects by examining

stressor-response relationships, the

evidence for causality, and the

relationship between measures of effect

and assessment endpoints (section 4.3)

• Summarizes the conclusions about

exposure (section 4.2.2) and effects

(section 4.3.2).

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BILLING CODE 6560–50–C
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The analysis phase is flexible, with

substantial interaction between the

effects and exposure characterizations

as illustrated by the dotted line in figure

4–1. In particular, when secondary

stressors and effects are of concern,

exposure and effects analyses are

conducted iteratively for different

ecological entities, and they can become

intertwined and difficult to

differentiate. In the bottomland

hardwoods assessment, for example

(Appendix D), potential changes in the

plant and animal communities under

different flooding scenarios were

examined. Risk assessors combined the

stressor-response and exposure analyses

within the FORFLO model for primary

effects on the plant community and

within the Habitat Suitability Index for

secondary effects on the animal

community. In addition, the distinction

between analysis and risk estimation

can become blurred. The model results

developed for the bottomland

hardwoods assessment were used

directly in risk characterization.

The nature of the stressor influences

the types of analyses conducted. The

results may range from highly

quantitative to qualitative, depending

on the stressor and the scope of the

assessment. For chemical stressors,

exposure estimates emphasize contact

and uptake into the organism, and

effects estimations often entail

extrapolation from test organisms to the

organism of interest. For physical

stressors, the initial disturbance may

cause primary effects on the assessment

endpoint (e.g., loss of wetland acreage).

In many cases, however, secondary

effects (e.g., decline of wildlife

populations that depend on wetlands)

may be the principal concern. The point

of view depends on the assessment

endpoints. Because adverse effects can

occur even if receptors do not

physically contact disturbed habitat,

exposure analyses may emphasize cooccurrence with physical stressors

rather than contact. For biological

stressors, exposure analysis is an

evaluation of entry, dispersal, survival,

and reproduction (Orr et al., 1993).

Because biological stressors can

reproduce, interact with other

organisms, and evolve over time,

exposure and effects cannot always be

quantified with confidence; therefore,

they may be assessed qualitatively by

eliciting expert opinion (Simberloff and

Alexander, 1994).

4.1. Evaluating Data and Models for

Analysis

At the beginning of the analysis

phase, the assessor critically examines

the data and models to ensure that they

can be used to evaluate the conceptual

model developed in problem

formulation (see sections 4.1.1 and

4.1.2). Section 4.1.3 addresses

uncertainty evaluation.

4.1.1. Strengths and Limitations of

Different Types of Data

Many types of data can be used for

risk assessment. Data may come from

laboratory or field studies or may be

produced as output from a model.

Familiarity with the strengths and

limitations of different types of data can

help assessors build on strengths and

avoid pitfalls. Such a strategy improves

confidence in the conclusions of the risk

assessment.

Both laboratory and field studies

(including field experiments and

observational studies) can provide

useful data for risk assessment. Because

conditions can be controlled in

laboratory studies, responses may be

less variable and smaller differences

easier to detect. However, the controls

may limit the range of responses (e.g.,

animals cannot seek alternative food

sources), so they may not reflect

responses in the environment. In

addition, larger-scale processes are

difficult to replicate in the laboratory.

Field observational studies (surveys)

measure biological changes in

uncontrolled situations. Ecologists

observe patterns and processes in the

field and often use statistical techniques

(e.g., correlation, clustering, factor

analysis) to describe an association

between a disturbance and an ecological

effect. For instance, physical attributes

of streams and their watersheds have

been associated with changes in stream

communities (Richards et al., 1997).

Field surveys are often reported as

status and trend studies. Messer et al.

(1991) correlated a biotic index with

acid concentrations to describe the

extent and proportion of lakes likely to

be impacted.

Field surveys usually represent

exposures and effects (including

secondary effects) better than estimates

generated from laboratory studies or

theoretical models. Field data are more

important for assessments of multiple

stressors or where site-specific factors

significantly influence exposure. They

are also often useful for analyses of

larger geographic scales and higher

levels of biological organization. Field

survey data are not always necessary or

feasible to collect for screening-level or

prospective assessments.

Field surveys should be designed

with sufficient statistical rigor to define

one or more of the following:

• Exposure in the system of interest

• Differences in measures of effect

between reference sites and study areas

• Lack of differences. Because

conditions are not controlled in field

studies, variability may be higher and it

may be difficult to detect differences.

For this reason, it is important to verify

that studies have sufficient power to

detect important differences.

Field surveys are most useful for

linking stressors with effects when

stressor and effect levels are measured

concurrently. The presence of

confounding factors can make it

difficult to attribute observed effects to

specific stressors. For this reason, field

studies designed to minimize effects of

potentially confounding factors are

preferred, and the evidence for causality

should be carefully evaluated (see

section 4.3.1.2). In addition, because

treatments may not be randomly applied

or replicated, classical statistical

methods need to be applied with

caution (Hurlbert, 1984; Stewart-Oaten

et al., 1986; Wiens and Parker, 1995;

Eberhardt and Thomas, 1991).

Intermediate between laboratory and

field are studies that use environmental

media collected from the field to

examine response in the laboratory.

Such studies may improve the power to

detect differences and may be designed

to provide evidence of causality.

Most data will be reported as

measurements for single variables such

as a chemical concentration or the

number of dead organisms. In some

cases, however, variables are combined

and reported as indices. Several indices

are used to evaluate effects, for example,

the rapid bioassessment protocols (U.S.

EPA, 1989a) and the Index of Biotic

Integrity, or IBI (Karr, 1981; Karr et al.,

1986). These have several advantages

(Barbour et al., 1995), including the

ability to:

• Provide an overall indication of

biological condition by incorporating

many attributes of system structure and

function, from individual to ecosystem

levels

• Evaluate responses from a broad

range of anthropogenic stressors

• Minimize the limitations of

individual metrics for detecting specific

types of responses.

Indices also have several drawbacks,

many of which are associated with

combining heterogeneous variables. The

final value may depend strongly on the

function used to combine variables.

Some indices (e.g., the IBI) combine

only measures of effects. Differential

sensitivity or other factors may make it

difficult to attribute causality when

many response variables are combined.

To investigate causality, such indices

may need to be separated into their
Federal Register / Vol. 63, No. 93 /Thursday, May 14, 1998 / Notices 26867

components, or analyzed using

multivariate methods (Suter, 1993b; Ott,

1978). Interpretation becomes even

more difficult when an index combines

measures of exposure and effects

because double counting may occur or

changes in one variable can mask

changes in another. Measures of

exposure and effects may need to be

separated in order to make appropriate

conclusions. For these reasons,

professional judgment plays a critical

role in developing and applying indices.

Experience from similar situations is

particularly useful in assessments of

stressors not yet released (i.e.,

prospective assessments). Lessons

learned from past experiences with

related organisms are often critical in

trying to predict whether an organism

will survive, reproduce, and disperse in

a new environment. Another example is

toxicity evaluation for new chemicals

through the use of structure-activity

relationships, or SARs (Auer et al.,

1994; Clements and Nabholz, 1994). The

simplest application of SARs is to

identify a suitable analog for which data

are available to estimate the toxicity of

a compound for which data are lacking.

More advanced applications use

quantitative structure-activity

relationships (QSARs), which

mathematically model the relationships

between chemical structures and

specific biological effects and are

derived using information on sets of

related chemicals (Lipnick, 1995;

Cronin and Dearden, 1995). The use of

analogous data without knowledge of

the underlying processes may

substantially increase the uncertainty in

the risk assessment (e.g., Bradbury,

1994); however, use of these data may

be the only option available.

Even though models may be

developed and used as part of the risk

assessment, sometimes the risk assessor

relies on output of a previously

developed model. Models are

particularly useful when measurements

cannot be taken, for example, when

predicting the effects of a chemical yet

to be manufactured. They can also

provide estimates for times or locations

that are impractical to measure and can

provide a basis for extrapolating beyond

the range of observation. Because

models simplify reality, they may omit

important processes for a particular

system and may not reflect every

condition in the real world. In addition,

a model’s output is only as good as the

quality of its input variables, so critical

evaluation of input data is important, as

is comparing model outputs with

measurements in the system of interest

whenever possible.

Data and models for risk assessment

are often developed in a tiered fashion

(also see section 2.2). For example,

simple models that err on the side of

conservatism may be used first,

followed by more elaborate models that

provide more realistic estimates. Effects

data may also be collected using a tiered

approach. Short-term tests designed to

evaluate effects such as lethality and

immobility may be conducted first. If

the chemical exhibits high toxicity or a

preliminary characterization indicates a

risk, then more expensive, longer-term

tests that measure sublethal effects such

as changes to growth and reproduction

can be conducted. Later tiers may

employ multispecies tests or field

experiments. Tiered data should be

evaluated in light of the decision they

are intended to support; data collected

for early tiers may not support more

sophisticated needs.

4.1.2. Evaluating Measurement or

Modeling Studies

The assessor’s first task in the analysis

phase is to carefully evaluate studies to

determine whether they can support the

objectives of the risk assessment. Each

study should include a description of

the purpose, methods used to collect

data, and results of the work. The

assessor evaluates the utility of studies

by carefully comparing study objectives

with those of the risk assessment for

consistency. In addition, the assessor

should determine whether the intended

objectives were met and whether the

data are of sufficient quality to support

the risk assessment. This is a good

opportunity to note the confidence in

the information and the implications of

different studies for use in the risk

characterization, when the overall

confidence in the assessment is

discussed. Finally, the risk assessor

should identify areas where existing

data do not meet risk assessment needs.

In these cases, collecting additional data

is recommended.

EPA is in the process of adopting the

American Society for Quality Control’s

E–4 guidelines for assuring

environmental data quality throughout

the Agency (ASQC, 1994) (text note

4–2). These guidelines describe

procedures for collecting new data and

provide a valuable resource for

evaluating existing studies. Readers may

also refer to Smith and Shugart, 1994;

U.S. EPA, 1994d; and U.S. EPA, 1990,

for more information on evaluating data

and models.

A study’s documentation determines

whether it can be evaluated for its

utility in risk assessment. Studies

should contain sufficient information so

that results can be reproduced, or at

least so the details of the author’s work

can be accessed and evaluated. Ideally,

one should be able to access findings in

their entirety; this provides the

opportunity to conduct additional

analyses of the data, if needed. For

models, a number of factors increase the

accessibility of methods and results.

These begin with model code and

documentation availability. Reports

describing model results should include

all important equations, tables of all

parameter values, any parameter

estimation techniques, and tables or

graphs of results.

Study descriptions may not provide

all the information needed to evaluate

their utility for risk assessment.

Assessors should communicate with the

principal investigator or other study

participants to gain information on

study plans and their implementation.

Useful questions for evaluating studies

are shown in text note 4–3.

4.1.2.1. Evaluating the Purpose and

Scope of the Study

Assessors should pay particular

attention to the objectives and scope of

studies that were designed for purposes

other than the risk assessment at hand.

This can identify important

uncertainties and ensure that the

information is used appropriately. An

example is the evaluation of studies that

measure condition (e.g., stream surveys,

population surveys): While the

measurements used to evaluate

condition may be the same as the

measures of effects identified in

problem formulation, to support a

causal argument they must be linked

with stressors. In the best case, this

means that the stressor was measured at

the same time and place as the effect.

Similarly, a model may have been

developed for purposes other than risk

assessment. Its description should

include the intended application,

theoretical framework, underlying

assumptions, and limiting conditions.

This information can help assessors

identify important limitations in its

application for risk assessment. For

example, a model developed to evaluate

chemical transport in the water column

alone is of limited utility for a risk

assessment of a chemical that partitions

readily into sediments.

The variables and conditions

examined by studies should also be

compared with those identified during

problem formulation. In addition, the

range of variability explored in the

study should be compared with that of

the risk assessment. A study that

examines animal habitat needs in the

winter, for example, may miss

important breeding-season
26868 Federal Register / Vol. 63, No. 93 /Thursday, May 14, 1998 / Notices

requirements. Studies that minimize the

amount of extrapolation needed are

preferred. These are studies that

represent:

• The measures identified in the

analysis plan (i.e., measures of

exposure, effects, and ecosystem and

receptor characteristics)

• The time frame of interest

• The ecosystem and location of

interest

• The environmental conditions of

interest

• The exposure route of interest.

4.1.2.2. Evaluating the Design and

Implementation of the Study

The assessor evaluates study design

and implementation to determine

whether the study objectives were met

and the information is of sufficient

quality to support the risk assessment.

The study design provides insight into

the sources and magnitude of

uncertainty associated with the results

(see section 4.1.3 for further discussion

of uncertainty). Among the most

important design issues of an effects

study is whether it has enough

statistical power to detect important

differences or changes. Because this

information is rarely reported

(Peterman, 1990), the assessor may need

to calculate the magnitude of an effect

that could be detected under the study

conditions (Rotenberry and Wiens,

1985).

Part of the exercise examines whether

the study was conducted properly:

• For laboratory studies, this may

mean determining whether test

conditions were properly controlled and

control responses were within

acceptable bounds.

• For field studies, issues include

identification and control of potentially

confounding variables and careful

reference site selection. (A discussion of

reference site selection is beyond the

scope of these Guidelines; however, it

has been identified as a candidate topic

for future development.)

• For models, issues include the

program’s structure and logic and the

correct specification of algorithms in the

model code (U.S. EPA, 1994d).

Evaluation is easier if standard

methods or quality assurance/quality

control (QA/QC) protocols are available

and followed by the study. However, the

assessor should still consider whether

the identified precision and accuracy

goals were achieved and whether they

are appropriate for the risk assessment.

For instance, detection limits identified

for one environmental matrix may not

be achievable for another, and thus it

may not be possible to detect

concentrations of interest. Study results

can still be useful even if a standard

method was not used. However, this

places an additional burden on both the

authors and the assessors to provide and

evaluate evidence that the study was

conducted properly.

4.1.3. Evaluating Uncertainty

Uncertainty evaluation is a theme

throughout the analysis phase. The

objective is to describe and, where

possible, quantify what is known and

not known about exposure and effects in

the system of interest. Uncertainty

analyses increase the credibility of

assessments by explicitly describing the

magnitude and direction of

uncertainties, and they provide the basis

for efficient data collection or

application of refined methods.

Uncertainties characterized during the

analysis phase are used during risk

characterization, when risks are

estimated (section 5.1) and the

confidence in different lines of evidence

is described (see section 5.2.1).

This section discusses sources of

uncertainty relevant to the analysis of

ecological exposure and effects; source

and example strategies are shown in text

note 4–4. Section 3.4.3 discusses

uncertainty in conceptual model

development. Readers are also referred

to the discussion of uncertainties in the

exposure assessment guidelines (U.S.

EPA, 1992b).

Sources of uncertainty that are

encountered when evaluating

information include unclear

communication of the data or its

manipulation and errors in the

information itself (descriptive errors).

These are usually characterized by

critically examining the sources of

information and documenting the

decisions made when handling it. The

documentation should allow the reader

to make an independent judgment about

the validity of the assessor’s decisions.

Sources of uncertainty that primarily

arise when estimating the value of a

parameter include variability,

uncertainty about a quantity’s true

value, and data gaps. The term

variability is used here to describe a

characteristic’s true heterogeneity.

Examples include the variability in soil

organic carbon, seasonal differences in

animal diets, or differences in chemical

sensitivity in different species.

Variability is usually described during

uncertainty analysis, although

heterogeneity may not reflect a lack of

knowledge and cannot usually be

reduced by further measurement.

Variability can be described by

presenting a distribution or specific

percentiles from it (e.g., mean and 95th

percentile).

Uncertainty about a quantity’s true

value may include uncertainty about its

magnitude, location, or time of

occurrence. This uncertainty can

usually be reduced by taking additional

measurements. Uncertainty about a

quantity’s true magnitude is usually

described by sampling error (or variance

in experiments) or measurement error.

When the quantity of interest is

biological response, sampling error can

greatly influence a study’s ability to

detect effects. Properly designed studies

will specify sample sizes large enough

to detect important signals.

Unfortunately, many studies have

sample sizes that are too small to detect

anything but gross changes (Smith and

Shugart, 1994; Peterman, 1990). The

discussion should highlight situations

where the power to detect difference is

low. Meta-analysis has been suggested

as a way to combine results from

different studies to improve the ability

to detect effects (Laird and Mosteller,

1990; Petitti, 1994). However, these

approaches have thus far been applied

primarily in human epidemiology and

are still controversial (Mann, 1990).

Interest in quantifying spatial

uncertainty has increased with the

increasing use of geographic

information systems (GIS). Strategies

include verifying the locations of

remotely sensed features and ensuring

that the spatial resolution of data or a

method is commensurate with the needs

of the assessment. A growing literature

is addressing other analytical challenges

often associated with using spatial data

(e.g., collinearity and autocorrelation,

boundary and scale effects, lack of true

replication) (Johnson and Gage, 1997;

Fotheringham and Rogerson, 1993;

Wiens and Parker, 1995). Large-scale

assessments generally require

aggregating information at smaller

scales. It is not known how aggregation

affects uncertainty (Hunsaker et al.,

1990).

Nearly every assessment must treat

situations where data are unavailable or

available only for parameters other than

those of interest. Examples include

using laboratory data to estimate a wild

animal’s response to a stressor or using

a bioaccumulation measurement from a

different ecosystem. These data gaps are

usually bridged with a combination of

scientific analyses, scientific judgment,

and perhaps policy decisions. In

deriving an ambient water quality

criterion (text note 3–17), for example,

data and analyses are used to construct

distributions of species sensitivity for a

particular chemical. Scientific judgment

is used to infer that species selected for

testing will adequately represent the

range of sensitivity of species in the
Federal Register / Vol. 63, No. 93 /Thursday, May 14, 1998 / Notices 26869

environment. Policy defines the extent

to which individual species should be

protected (e.g., 90% vs. 95% of the

species). It is important to distinguish

these elements.

Data gaps can often be filled by

completing additional studies on the

unknown parameter. When possible, the

necessary data should be collected. At

the least, opportunities for filling data

gaps should be noted and carried

through to risk characterization. Data or

knowledge gaps that are so large that

they preclude the analysis of either

exposure or ecological effects should

also be noted and discussed in risk

characterization.

An important objective is to

distinguish variability from

uncertainties that arise from lack of

knowledge (e.g., uncertainty about a

quantity’s true value) (U.S. EPA, 1995b).

This distinction facilitates the

interpretation and communication of

results. For instance, in their food web

models of herons and mink, MacIntosh

et al. (1994) separated expected

variability in individual animals’

feeding habits from the uncertainty in

the mean concentration of chemical in

prey species. They could then place

error bounds on the exposure

distribution for the animals using the

site and estimate the proportion of the

animal population that might exceed a

toxicity threshold.

Sources of uncertainty that arise

primarily during model development

and application include process model

structure and the relationships between

variables in empirical models. Process

model descriptions should include

assumptions, simplifications, and

aggregations of variables (see text note

4–5). Empirical model descriptions

should include the rationale for

selection and model performance

statistics (e.g., goodness of fit).

Uncertainty in process or empirical

models can be quantitatively evaluated

by comparing model results to

measurements taken in the system of

interest or by comparing the results of

different models.

Methods for analyzing and describing

uncertainty can range from simple to

complex. When little is known, a useful

approach is to estimate exposure and

effects based on alternative sets of

assumptions (scenarios). Each scenario

is carried through to risk

characterization, where the underlying

assumptions and the scenario’s

plausibility are discussed. Results can

be presented as a series of point

estimates with different aspects of

uncertainty reflected in each. Classical

statistical methods (e.g., confidence

limits, percentiles) can readily describe

parameter uncertainty. For models,

sensitivity analysis can be used to

evaluate how model output changes

with changes in input variables, and

uncertainty propagation can be analyzed

to examine how uncertainty in

individual parameters can affect the

overall uncertainty in the results. The

availability of software for Monte Carlo

analysis has greatly increased the use of

probabilistic methods; readers are

encouraged to follow suggested best

practices (e.g., U.S. EPA, 1996a, 1997b).

Other methods (e.g., fuzzy mathematics,

Bayesian methodologies) are available

but have not yet been extensively

applied to ecological risk assessment

(Smith and Shugart, 1994). The Agency

does not endorse the use of any one

method and cautions that the poor

execution of any method can obscure

rather than clarify the impact of

uncertainty on an assessment’s results.

No matter what technique is used, the

sources of uncertainty discussed above

should be addressed.

4.2. Characterization of Exposure

Exposure characterization describes

potential or actual contact or cooccurrence of stressors with receptors. It

is based on measures of exposure and

ecosystem and receptor characteristics

that are used to analyze stressor sources,

their distribution in the environment,

and the extent and pattern of contact or

co-occurrence (discussed in section

4.2.1). The objective is to produce a

summary exposure profile (section

4.2.2) that identifies the receptor (i.e.,

the exposed ecological entity), describes

the course a stressor takes from the

source to the receptor (i.e., the exposure

pathway), and describes the intensity

and spatial and temporal extent of cooccurrence or contact. The profile also

describes the impact of variability and

uncertainty on exposure estimates and

reaches a conclusion about the

likelihood that exposure will occur.

The exposure profile is combined

with an effects profile (discussed in

section 4.3.2) to estimate risks. For the

exposure profile to be useful, it should

be compatible with the stressorresponse relationship generated in the

effects characterization.

4.2.1. Exposure Analyses

Exposure is contact or co-occurrence

between a stressor and a receptor. The

objective is to describe exposure in

terms of intensity, space, and time in

units that can be combined with the

effects assessment. In addition, the

assessor should be able to trace the

paths of stressors from the source(s) to

the receptors (i.e., describe the exposure

pathway).

A complete picture of how, when, and

where exposure occurs or has occurred

is developed by evaluating sources and

releases, the distribution of the stressor

in the environment, and the extent and

pattern of contact or co-occurrence. The

order of these topics here is not

necessarily the order in which they are

executed. The assessor may start with

information about tissue residues, for

example, and attempt to link these

residues with a source.

4.2.1.1. Describe the Source(s)

A source can be defined in two

general ways: as the place where the

stressor originates or is released (e.g., a

smokestack, historically contaminated

sediments) or the management practice

or action (e.g., dredging) that produces

stressors. In some assessments, the

original sources may no longer exist and

the source may be defined as the current

location of the stressors. For example,

contaminated sediments might be

considered a source because the

industrial plant that produced the

contaminants no longer operates. A

source is the first component of the

exposure pathway and significantly

influences where and when stressors

eventually will be found. In addition,

many management alternatives focus on

modifying the source.

Exposure analyses may start with the

source when it is known, begin with

known exposures and attempt to link

them to sources, or start with known

stressors and attempt to identify sources

and quantify contact. In any case, the

objective of this step is to identify the

sources, evaluate what stressors are

generated, and identify other potential

sources. Text note 4–6 provides some

useful questions to ask when describing

sources.

In addition to identifying sources, the

assessor examines the intensity, timing,

and location of stressors’ release. The

location of a source and the

environmental media that first receive

stressors are two attributes that deserve

particular attention. For chemical

stressors, the source characterization

should also consider whether other

constituents emitted by a source

influence transport, transformation, or

bioavailability of the stressor of interest.

The presence of chloride in the

feedstock of a coal-fired power plant

influences whether mercury is emitted

in divalent (e.g., as mercuric chloride)

or elemental form (Meij, 1991), for

example. In the best case, stressor

generation is measured or modeled

quantitatively; however, sometimes it

can only be qualitatively described.

Many stressors have natural

counterparts or multiple sources, so it
26870 Federal Register / Vol. 63, No. 93 /Thursday, May 14, 1998 / Notices

may be necessary to characterize these

as well. Many chemicals occur naturally

(e.g., most metals), are generally

widespread from other sources (e.g.,

polycyclic aromatic hydrocarbons in

urban ecosystems), or may have

significant sources outside the

boundaries of the current assessment

(e.g., atmospheric nitrogen deposited in

Chesapeake Bay). Many physical

stressors also have natural counterparts.

For instance, construction activities may

release fine sediments into a stream in

addition to those coming from a

naturally undercut bank. Human

activities may also change the

magnitude or frequency of natural

disturbance cycles. For example,

development may decrease the

frequency but increase the severity of

fires or may increase the frequency and

severity of flooding in a watershed.

The assessment scope identified

during planning determines how

multiple sources are evaluated. Options

include (in order of increasing

complexity):

• Focus only on the source under

evaluation and calculate the

incremental risks attributable to that

source (common for assessments

initiated with an identified source or

stressor).

• Consider all sources of a stressor

and calculate total risks attributable to

that stressor. Relative source attribution

can be accomplished as a separate step

(common for assessments initiated with

an observed effect or an identified

stressor).

• Consider all stressors influencing

an assessment endpoint and calculate

cumulative risks to that endpoint

(common for assessments initiated

because of concern for an ecological

value).

Source characterization can be

particularly important for introduced

biological stressors, since many of the

strategies for reducing risks focus on

preventing entry in the first place. Once

the source is identified, the likelihood

of entry may be characterized

qualitatively. In their risk analysis of

Chilean log importation, for example,

the assessment team concluded that the

beetle Hylurgus ligniperda had a high

potential for entry into the United

States. Their conclusion was based on

the beetle’s attraction to freshly cut logs

and tendency to burrow under the bark,

which would provide protection during

transport (USDA, 1993).

4.2.1.2. Describe the Distribution of the

Stressors or Disturbed Environment

The second objective of exposure

analysis is to describe the spatial and

temporal distribution of stressors in the

environment. For physical stressors that

directly alter or eliminate portions of

the environment, the assessor describes

the temporal and spatial distribution of

the disturbed environment. Because

exposure occurs when receptors cooccur with or contact stressors, this

characterization is a prerequisite for

estimating exposure. Stressor

distribution in the environment is

examined by evaluating pathways from

the source as well as the formation and

subsequent distribution of secondary

stressors (see text note 4–7).

4.2.1.2.1. Evaluating Transport

Pathways

Stressors can be transported via many

pathways (see text note 4–8). A careful

evaluation can help ensure that

measurements are taken in the

appropriate media and locations and

that models include the most important

processes.

For a chemical stressor, the evaluation

usually begins by determining into

which media it can partition. Key

considerations include physicochemical

properties such as solubility and vapor

pressure. For example, chemicals with

low solubility in water tend to be found

in environmental compartments with

higher proportions of organic carbon

such as soils, sediments, and biota.

From there, the evaluation may examine

the transport of the contaminated

medium. Because chemical mixture

constituents may have different

properties, the analysis should consider

how the composition of a mixture may

change over time or as it moves through

the environment. Guidance on

evaluating the fate and transport of

chemicals (including bioaccumulation)

is beyond the scope of these Guidelines;

readers are referred to the exposure

assessment guidelines (U.S. EPA, 1992b)

for additional information. The topics of

bioaccumulation and biomagnification

have been identified as candidates for

further development.

The attributes of physical stressors

also influence where they will go. The

size of suspended particles determines

where they will eventually deposit in a

stream, for example. Physical stressors

that eliminate ecosystems or portions of

them (e.g., fishing activities or the

construction of dams) may require no

modeling of pathways—the fish are

harvested or the valley is flooded. For

these direct disturbances, the challenge

is usually to evaluate secondary

stressors and effects.

The dispersion of biological stressors

has been described in two ways, as

diffusion and jump-dispersal

(Simberloff and Alexander, 1994).

Diffusion involves a gradual spread

from the establishment site and is

primarily a function of reproductive

rates and motility. Jump-dispersal

involves erratic spreads over periods of

time, usually by means of a vector. The

gypsy moth and zebra mussel have

spread this way, the gypsy moth via egg

masses on vehicles and the zebra mussel

via boat ballast water. Some biological

stressors can use both strategies, which

may make dispersal rates very difficult

to predict. The evaluation should

consider factors such as vector

availability, attributes that enhance

dispersal (e.g., ability to fly, adhere to

objects, disperse reproductive units),

and habitat or host needs.

For biological stressors, assessors

should consider the additional factors of

survival and reproduction. Organisms

use a wide range of strategies to survive

in adverse conditions; for example,

fungi form resting stages such as

sclerotia and chlamydospores and some

amphibians become dormant during

drought. The survival of some

organisms can be measured to some

extent under laboratory conditions.

However, it may be impossible to

determine how long resting stages (e.g.,

spores) can survive under adverse

conditions: many can remain viable for

years. Similarly, reproductive rates may

vary substantially depending on specific

environmental conditions. Therefore,

while life-history data such as

temperature and substrate preferences,

important predators, competitors or

diseases, habitat needs, and

reproductive rates are of great value,

they should be interpreted with caution,

and the uncertainty should be addressed

by using several different scenarios.

Ecosystem characteristics influence

the transport of all types of stressors.

The challenge is to determine the

particular aspects of the ecosystem that

are most important. In some cases,

ecosystem characteristics that influence

distribution are known. For example,

fine sediments tend to accumulate in

areas of low energy in streams such as

pools and backwaters. Other cases need

more professional judgment. When

evaluating the likelihood that an

introduced organism will become

established, for instance, it is useful to

know whether the ecosystem is

generally similar to or different from the

one where the biological stressor

originated. Professional judgment is

used to determine which characteristics

of the current and original ecosystems

should be compared.

4.2.1.2.2. Evaluating Secondary

Stressors

Secondary stressors can greatly alter

conclusions about risk; they may be of
Federal Register / Vol. 63, No. 93 /Thursday, May 14, 1998 / Notices 26871

greater or lesser concern than the

primary stressor. Secondary stressor

evaluation is usually part of exposure

characterization; however, it should be

coordinated with the ecological effects

characterization to ensure that all

potentially important secondary

stressors are considered.

For chemicals, the evaluation usually

focuses on metabolites, biodegradation

products, or chemicals formed through

abiotic processes. As an example,

microbial action increases the

bioaccumulation of mercury by

transforming inorganic forms to organic

species. Many azo dyes are not toxic

because of their large molecular size,

but in an anaerobic environment, the

polymer is hydrolyzed into more toxic

water-soluble units. Secondary stressors

can also be formed through ecosystem

processes. Nutrient inputs into an

estuary can decrease dissolved oxygen

concentrations because they increase

primary production and subsequent

decomposition. Although

transformation can be investigated in

the laboratory, rates in the field may

differ substantially, and some processes

may be difficult or impossible to

replicate in a laboratory. When

evaluating field information, though, it

may be difficult to distinguish between

transformation processes (e.g., oil

degradation by microorganisms) and

transport processes (e.g., volatilization).

Although they may be difficult to

distinguish, the assessor should be

aware that these two different processes

will largely determine if secondary

stressors are likely to be formed. A

combination of these factors will also

determine how much of the secondary

stressor(s) may be bioavailable to

receptors. These considerations

reinforce the need to have a chemical

risk assessment team experienced in

physical/chemical as well as biological

processes.

Physical disturbances can also

generate secondary stressors, and

identifying the specific consequences

that will affect the assessment endpoint

can be a difficult task. The removal of

riparian vegetation, for example, can

generate many secondary stressors,

including increased nutrients, stream

temperature, sedimentation, and altered

stream flow. However, it may be the

temperature change that is most

responsible for adult salmon mortality

in a particular stream.

Stressor distribution in the

environment can be described using

measurements, models, or a

combination of the two. If stressors have

already been released, direct

measurement of environmental media or

a combination of modeling and

measurement is preferred. Models

enhance the ability to investigate the

consequences of different management

scenarios and may be necessary if

measurements are not possible or

practicable. They are also useful if a

quantitative relationship of sources and

stressors is desired. As examples, land

use activities have been related to

downstream suspended solids

concentrations (Oberts, 1981), and

downstream flood peaks have been

predicted from the extent of wetlands in

a watershed (Novitski, 1979; Johnston et

al., 1990). Considerations for evaluating

data collection and modeling studies are

discussed in section 4.1. For chemical

stressors, readers may also refer to the

exposure assessment guidelines (U.S.

EPA, 1992b). For biological stressors,

distribution may be difficult to predict

quantitatively. If it cannot be measured,

it can be evaluated qualitatively by

considering the potential for transport,

survival, and reproduction (see above).

By the end of this step, the

environmental distribution of the

stressor or the disturbed environment

should be described. This description

provides the foundation for estimating

the contact or co-occurrence of the

stressor with ecological entities. When

contact is known to have occurred,

describing the stressor’s environmental

distribution can help identify potential

sources and ensure that all important

exposures are addressed.

4.2.1.3. Describe Contact or CoOccurrence

The third objective is to describe the

extent and pattern of co-occurrence or

contact between stressors and receptors

(i.e., exposure). This is critical—if there

is no exposure, there can be no risk.

Therefore, assessors should be careful to

include situations where exposure may

occur in the future, where exposure has

occurred in the past but is not currently

evident (e.g., in some retrospective

assessments), and where ecosystem

components important for food or

habitat are or may be exposed, resulting

in impacts to the valued entity (e.g., see

figure D–2). Exposure can be described

in terms of stressor and receptor cooccurrence, actual stressor contact with

receptors, or stressor uptake by a

receptor. The terms in which exposure

is described depend on how the stressor

causes adverse effects and how the

stressor-response relationship is

described. Relevant questions for

examining contact or co-occurrence are

shown in text note 4–9.

Co-occurrence is particularly useful

for evaluating stressors that can cause

effects without physically contacting

ecological receptors. Whooping cranes

provide a case in point: they use

sandbars in rivers for their resting areas,

and they prefer sandbars with

unobstructed views. Manmade

obstructions such as bridges can

interfere with resting behavior without

ever actually contacting the birds. Cooccurrence is evaluated by comparing

stressor distributions with that of the

receptor. For instance, stressor location

maps may be overlaid with maps of

ecological receptors (e.g., bridge

placement overlaid on maps showing

historical crane resting habitat). Cooccurrence of a biological stressor and

receptor may be used to evaluate

exposure when, for example, introduced

species and native species compete for

the same resources. GIS has provided

new tools for evaluating co-occurrence.

Most stressors must contact receptors

to cause an effect. For example, tree

roots must contact flood waters before

their growth is impaired. Contact is a

function of the amount or extent of a

stressor in an environmental medium

and activity or behavior of the receptors.

For biological stressors, risk assessors

usually rely on professional judgment;

contact is often assumed to occur in

areas and during times where the

stressor and receptor are both present.

Contact variables such as the mode of

transmission between organisms may

influence the contact between biological

stressors and receptors.

For chemicals, contact is quantified as

the amount of a chemical ingested,

inhaled, or in material applied to the

skin (potential dose). In its simplest

form, it is quantified as an

environmental concentration, with the

assumptions that the chemical is well

mixed or that the organism moves

randomly through the medium. This

approach is commonly used for respired

media (water for aquatic organisms, air

for terrestrial organisms). For ingested

media (food, soil), another common

approach combines modeled or

measured contaminant concentrations

with assumptions or parameters

describing the contact rate (U.S. EPA,

1993a) (see text note 4–10).

Finally, some stressors must not only

be contacted but also must be internally

absorbed. A toxicant that causes liver

tumors in fish, for example, must be

absorbed and reach the target organ to

cause the effect. Uptake is evaluated by

considering the amount of stressor

internally absorbed by an organism. It is

a function of the stressor (e.g., a

chemical’s form or a pathogen’s size),

the medium (sorptive properties or

presence of solvents), the biological

membrane (integrity, permeability), and

the organism (sickness, active uptake)

(Suter et al., 1994). Because of
26872 Federal Register / Vol. 63, No. 93 /Thursday, May 14, 1998 / Notices

interactions between these four factors,

uptake will vary on a situation-specific

basis. Uptake is usually assessed by

modifying an estimate of contact with a

factor indicating the proportion of the

stressor that is available for uptake (the

bioavailable fraction) or actually

absorbed. Absorption factors and

bioavailability measured for the

chemical, ecosystem, and organism of

interest are preferred. Internal dose can

also be evaluated by using a

pharmacokinetic model or by measuring

biomarkers or residues in receptors (see

text note 4–11). Most stressor-response

relationships express the amount of

stressor in terms of media concentration

or potential dose rather than internal

dose; this limits the utility of uptake

estimates in risk calculations. However,

biomarkers and tissue residues can

provide valuable confirmatory evidence

that exposure has occurred, and tissue

residues in prey organisms can be used

for estimating risks to their predators.

The characteristics of the ecosystem

and receptors must be considered to

reach appropriate conclusions about

exposure. Abiotic attributes may

increase or decrease the amount of a

stressor contacted by receptors. For

example, naturally anoxic areas above

contaminated sediments in an estuary

may reduce the time bottom-feeding fish

spend in contact with sediments and

thereby reduce their exposure to

contaminants. Biotic interactions can

also influence exposure. For example,

competition for high-quality resources

may force some organisms into

disturbed areas. The interaction

between exposure and receptor behavior

can influence both initial and

subsequent exposures. Some chemicals

reduce the prey’s ability to escape

predators, for instance, and thereby may

increase predator exposure to the

chemical as well as the prey’s risk of

predation. Alternatively, organisms may

avoid areas, food, or water with

contamination they can detect. While

avoidance can reduce exposure to

chemicals, it may increase other risks by

altering habitat usage or other behavior.

Three dimensions should be

considered when estimating exposure:

intensity, time, and space. Intensity is

the most familiar dimension for

chemical and biological stressors and

may be expressed as the amount of

chemical contacted per day or the

number of pathogenic organisms per

unit area.

The temporal dimension of exposure

has aspects of duration, frequency, and

timing. Duration can be expressed as the

time over which exposure occurs, some

threshold intensity is exceeded, or

intensity is integrated. If exposure

occurs as repeated discrete events of

about the same duration, frequency is

the important temporal dimension of

exposure (e.g., the frequency of highflow events in streams). If the repeated

events have significant and variable

durations, both duration and frequency

should be considered. In addition, the

timing of exposure, including the order

or sequence of events, can be an

important factor. Adirondack Mountain

lakes receive high concentrations of

hydrogen ions and aluminum during

snow melt; this period also corresponds

to the sensitive life stages of some

aquatic organisms.

In chemical assessments, intensity

and time are often combined by

averaging intensity over time. The

duration over which intensity is

averaged is determined by considering

the ecological effects of concern and the

likely pattern of exposure. For example,

an assessment of bird kills associated

with granular carbofuran focused on

short-term exposures because the effect

of concern was acute lethality

(Houseknecht, 1993). Because

toxicological tests are usually conducted

using constant exposures, the most

realistic comparisons between exposure

and effects are made when exposure in

the real world does not vary

substantially. In these cases, the

arithmetic average exposure over the

time period of toxicological significance

is the appropriate statistic (U.S. EPA,

1992b). However, as concentrations or

contact rates become more episodic or

variable, the arithmetic average may not

reflect the toxicologically significant

aspect of the exposure pattern. In

extreme cases, averaging may not be

appropriate at all, and assessors may

need to use a toxicodynamic model to

assess chronic effects.

Spatial extent is another dimension of

exposure. It is most commonly

expressed in terms of area (e.g., hectares

of paved habitat, square meters that

exceed a particular chemical threshold).

At larger spatial scales, however, the

shape or arrangement of exposure may

be an important issue, and area alone

may not be the appropriate descriptor of

spatial extent for risk assessment. A

general solution to the problem of

incorporating pattern into ecological

assessments has yet to be developed;

however, landscape ecology and GIS

have greatly expanded the options for

analyzing and presenting the spatial

dimension of exposure (e.g., Pastorok et

al., 1996).

The results of exposure analysis are

summarized in the exposure profile,

which is discussed in the next section.

4.2.2. Exposure Profile

The final product of exposure analysis

is an exposure profile. Exposure should

be described in terms of intensity,

space, and time in units that can be

combined with the effects assessment.

The assessor should summarize the

paths of stressors from the source to the

receptors, completing the exposure

pathway. Depending on the risk

assessment, the profile may be a written

document or a module of a larger

process model. In any case, the objective

is to ensure that the information needed

for risk characterization has been

collected and evaluated. In addition,

compiling the exposure profile provides

an opportunity to verify that the

important exposure pathways identified

in the conceptual model were evaluated.

The exposure profile identifies the

receptor and describes the exposure

pathways and intensity and spatial and

temporal extent of co-occurrence or

contact. It also describes the impact of

variability and uncertainty on exposure

estimates and reaches a conclusion

about the likelihood that exposure will

occur (see text note 4–12).

The profile should describe the

applicable exposure pathways. If

exposure can occur through many

pathways, it may be useful to rank them,

perhaps by contribution to total

exposure. As an illustration, consider an

assessment of risks to grebes feeding in

a mercury-contaminated lake. The

grebes may be exposed to methyl

mercury in fish that originated from

historically contaminated sediments.

They may also be exposed by drinking

lake water, but comparing the two

exposure pathways may show that the

fish pathway contributes the vast

majority of exposure to mercury.

The profile should identify the

ecological entity that the exposure

estimates represent. For example, the

exposure estimates may describe the

local population of grebes feeding on a

specific lake during the summer

months.

The assessor should explain how each

of the three general dimensions of

exposure (intensity, time, and space)

was treated. Continuing with the grebe

example, exposure might be expressed

as the daily potential dose averaged over

the summer months and over the extent

of the lake.

The profile should also describe how

exposure can vary depending on

receptor attributes or stressor levels. For

instance, the exposure may be higher for

grebes eating a larger proportion of

bigger, more contaminated fish.

Variability can be described by using a

distribution or by describing where a
Federal Register / Vol. 63, No. 93 /Thursday, May 14, 1998 / Notices 26873

point estimate is expected to fall on a

distribution. Cumulative-distribution

functions (CDFs) and probabilitydensity functions (PDFs) are two

common presentation formats (see

Appendix B, figures B–1 and B–2).

Figures 5–3 to 5–5 show examples of

cumulative frequency plots of exposure

data. The point estimate/descriptor

approach is used when there is not

enough information to describe a

distribution. Descriptors discussed in

U.S. EPA, 1992b, are recommended,

including central tendency to refer to

the mean or median of the distribution,

high end to refer to exposure estimates

that are expected to fall between the

90th and 99.9th percentile of the

exposure distribution, and bounding

estimates to refer to those higher than

any actual exposure.

The exposure profile should

summarize important uncertainties (e.g.,

lack of knowledge; see section 4.1.3 for

a discussion of the different sources of

uncertainty). In particular, the assessor

should:

• Identify key assumptions and

describe how they were handled

• Discuss (and quantify, if possible)

the magnitude of sampling and/or

measurement error

• Identify the most sensitive variables

influencing exposure

• Identify which uncertainties can be

reduced through the collection of more

data.

Uncertainty about a quantity’s true

value can be shown by calculating error

bounds on a point estimate, as shown in

figure 5–2.

All of the above information is

synthesized to reach a conclusion about

the likelihood that exposure will occur,

completing the exposure profile. It is

one of the products of the analysis

phase and is combined with the

stressor-response profile (the product of

the ecological effects characterization

discussed in the next section) during

risk characterization.

4.3. Characterization of Ecological

Effects

To characterize ecological effects, the

assessor describes the effects elicited by

a stressor, links them to the assessment

endpoints, and evaluates how they

change with varying stressor levels. The

characterization begins by evaluating

effects data to specify the effects that are

elicited, verify that they are consistent

with the assessment endpoints, and

confirm that the conditions under

which they occur are consistent with

the conceptual model. Once the effects

of interest are identified, the assessor

conducts an ecological response

analysis (section 4.3.1), evaluating how

the magnitude of the effects change with

varying stressor levels and the evidence

that the stressor causes the effect, and

then linking the effects with the

assessment endpoint. Conclusions are

summarized in a stressor-response

profile (section 4.3.2).

4.3.1. Ecological Response Analysis

Ecological response analysis examines

three primary elements: the relationship

between stressor levels and ecological

effects (section 4.3.1.1), the plausibility

that effects may occur or are occurring

as a result of exposure to stressors

(section 4.3.1.2), and linkages between

measurable ecological effects and

assessment endpoints when the latter

cannot be directly measured (section

4.3.1.3).

4.3.1.1. Stressor-Response Analysis

To evaluate ecological risks, one must

understand the relationships between

stressors and resulting responses. The

stressor-response relationships used in a

particular assessment depend on the

scope and nature of the ecological risk

assessment as defined in problem

formulation and reflected in the analysis

plan. For example, an assessor may

need a point estimate of an effect (such

as an LC50) to compare with point

estimates from other stressors. The

shape of the stressor-response curve

may be needed to determine the

presence or absence of an effects

threshold or for evaluating incremental

risks, or stressor-response curves may be

used as input for effects models. If

sufficient data are available, the risk

assessor may construct cumulative

distribution functions using multiplepoint estimates of effects. Or the

assessor may use process models that

already incorporate empirically derived

stressor-response relationships (see

section 4.3.1.3). Text note 4–13 provides

some questions for stressor-response

analysis.

This section describes a range of

stressor-response approaches available

to risk assessors following a theme of

variations on the classical stressorresponse relationship (e.g., figure 4–2).

More complex relationships are shown

in figure 4–3, which illustrates a range

of projected responses of zooplankton

populations to pesticide exposure based

on laboratory tests. In field studies, the

complexity of these responses could

increase even further, considering

factors such as potential indirect effects

of pesticides on zooplankton

populations (e.g., competitive

interactions between species). More

complex patterns can also occur at

higher levels of biological organization;

ecosystems may respond to stressors

with abrupt shifts to new community or

system types (Holling, 1978).

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In simple cases, one response variable

(e.g., mortality, incidence of

abnormalities) is analyzed, and most

quantitative techniques have been

developed for univariate analysis. If the

response of interest is composed of

many individual variables (e.g., species

abundances in an aquatic community),

multivariate techniques may be useful.

These have a long history of use in

ecology (see texts by Gauch, 1982;

Pielou, 1984; Ludwig and Reynolds,

1988) but have not yet been extensively

applied in risk assessment. While

quantifying stressor-response

relationships is encouraged, qualitative

evaluations are also possible (text note

4–14).

Stressor-response relationships can be

described using intensity, time, or

space. Intensity is probably the most

familiar of these and is often used for

chemicals (e.g., dose, concentration).

Exposure duration is also commonly

used for chemical stressor-response

relationships; for example, median

acute effects levels are always

associated with a time parameter (e.g.,

24 hours). As noted in text note 4–14,

the timing of exposure was the critical

dimension in evaluating the

relationship between seed germination

and soil moisture (Pearlstine et al.,

1985). The spatial dimension is often of

concern for physical stressors. For

instance, the extent of suitable habitat

was related to the probability of sighting

a spotted owl (Thomas et al., 1990), and

water-table depth was related to tree

growth by Phipps (1979).

Single-point estimates and stressorresponse curves can be generated for

some biological stressors. For pathogens

such as bacteria and fungi, inoculum

levels (e.g., spores per milliliter;

propagules per unit of substrate) may be

related to symptoms in a host (e.g.,

lesions per area of leaf surface, total

number of plants infected) or actual

signs of the pathogen (asexual or sexual

fruiting bodies, sclerotia, etc.). For other

biological stressors such as introduced

species, simple stressor-response

relationships may be inappropriate.

Data from individual experiments can

be used to develop curves and point

estimates both with and without

associated uncertainty estimates (see

figures 5–2 and 5–3). The advantages of

curve-fitting approaches include using

all of the available experimental data

and the ability to interpolate to values

other than the data points measured. If

extrapolation outside the range of

experimental data is required, risk

assessors should justify that the

observed experimental relationships

remain valid. A disadvantage of curve

fitting is that the number of data points

required to complete an analysis may

not always be available. For example,

while standard toxicity tests with

aquatic organisms frequently contain

sufficient experimental treatments to

permit regression analysis, this is often

not the case for toxicity tests with

wildlife species.

Risk assessors sometimes use curvefitting analyses to determine particular

levels of effect. These point estimates

are interpolated from the fitted line.

Point estimates may be adequate for

simple assessments or comparative

studies of risk and are also useful if a

decision rule for the assessment was

identified during the planning phase

(see section 2). Median effect levels (text

note 4–15) are frequently selected

because the level of uncertainty is

minimized at the midpoint of the

regression curve. While a 50% effect

level for an endpoint such as survival

may not be appropriately protective for

the assessment endpoint, median effect

levels can be used for preliminary

assessments or comparative purposes,

especially when used in combination

with uncertainty modifying factors (see

text note 5–3). Selection of a different

effect level (10%, 20%, etc.) can be

arbitrary unless there is some clearly

defined benchmark for the assessment

endpoint. Thus, it is preferable to carry

several levels of effect or the entire

stressor-response curve forward to risk

estimation.

When risk assessors are particularly

interested in effects at lower stressor

levels, they may seek to establish ‘‘noeffect’’ stressor levels based on

comparisons between experimental

treatments and controls. Statistical

hypothesis testing is frequently used for

this purpose. (Note that statistical

hypotheses are different from the risk

hypotheses discussed in problem

formulation; see text note 3–12). An

example of this approach for deriving

chemical no-effect levels is provided in

text note 4–16. A feature of statistical

hypothesis testing is that the risk

assessor is not required to pick a

particular effect level of concern. The

no-effect level is determined instead by

experimental conditions such as the

number of replicates as well as the

variability inherent in the data. Thus it

is important to consider the level of

effect detectable in the experiment (i.e.,

its power) in addition to reporting the

no-effect level. Another drawback of

this approach is that it is difficult to

evaluate effects associated with stressor

levels other than the actual treatments

tested. Several investigators (Stephan

and Rogers, 1985; Suter, 1993a) have

proposed using regression analysis as an

alternative to statistical hypothesis

testing.

In observational field studies,

statistical hypothesis testing is often

used to compare site conditions with a

reference site(s). The difficulties of

drawing proper conclusions from these

types of studies (which frequently

cannot employ replication) have been

discussed by many investigators (see

section 4.1.1). Risk assessors should

examine whether sites were carefully

matched to minimize differences other

than the stressor and consider whether

potential covariates should be included

in any analysis. In contrast with

observational studies, an advantage of

experimental field studies is that

treatments can be replicated, increasing

the confidence that observed differences

are due to the treatment.

Experimental data can be combined to

generate multiple-point estimates that

can be displayed as cumulative

distribution functions. Figure 5–5 shows

an example for species sensitivity

derived from multiple-point estimates

(EC5s) for freshwater algae (and one

vascular plant species) exposed to an

herbicide. These distributions can help

identify stressor levels that affect a

minority or majority of species. A

limiting factor in the use of cumulative

frequency distributions is the amount of

data needed as input. Cumulative effects

distribution functions can also be

derived from models that use Monte

Carlo or other methods to generate

distributions based on measured or

estimated variation in input parameters

for the models.

When multiple stressors are present,

stressor-response analysis is particularly

challenging. Stressor-response

relationships can be constructed for

each stressor separately and then

combined. Alternatively, the

relationship between response and the

suite of stressors can be combined in

one analysis. It is preferable to directly

evaluate complex chemical mixtures

present in environmental media (e.g.,

wastewater effluents, contaminated soils

(U.S. EPA, 1986a)), but it is important

to consider the relationship between the

samples tested and the potential spatial

and temporal variability in the mixture.

The approach taken for multiple

stressors depends on the feasibility of

measuring them and whether an

objective of the assessment is to project

different stressor combinations.

In some cases, multiple regression

analysis can be used to empirically

relate multiple stressors to a response.

Detenbeck (1994) used this approach to

evaluate change in the water quality of

wetlands resulting from multiple

physical stressors. Multiple regression
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analysis can be difficult to interpret if

the explanatory variables (i.e., the

stressors) are not independent. Principal

components analysis can be used to

extract independent explanatory

variables formed from linear

combinations of the original variables

(Pielou, 1984).

4.3.1.2. Establishing Cause-and-Effect

Relationships (Causality)

Causality is the relationship between

cause (one or more stressors) and effect

(response to the stressor(s)). Without a

sound basis for linking cause and effect,

uncertainty in the conclusions of an

ecological risk assessment is likely to be

high. Developing causal relationships is

especially important for risk

assessments driven by observed adverse

ecological effects such as bird or fish

kills or a shift in the species

composition of an area. This section

describes considerations for evaluating

causality based on criteria developed by

Fox (1991) primarily for observational

data and additional criteria for

experimental evaluation of causality

modified from Koch’s postulates (e.g.,

see Woodman and Cowling, 1987).

Evidence of causality may be derived

from observational evidence (e.g., bird

kills are associated with field

application of a pesticide) or

experimental data (laboratory tests with

the pesticides in question show bird

kills at levels similar to those found in

the field), and causal associations can be

strengthened when both types of

information are available. But since not

all situations lend themselves to formal

experimentation, scientists have looked

for other criteria, based largely on

observation rather than experiment, to

support a plausible argument for cause

and effect. Text note 4–17 provides

criteria based on Fox (1991) that are

very similar to others reviewed by Fox

(U.S. Department of Health, Education,

and Welfare, 1964; Hill, 1965; Susser,

1986a, b). While data to support some

criteria may be incomplete or missing

for any given assessment, these criteria

offer a useful way to evaluate available

information.

The strength of association between

stressor and response is often the main

reason that adverse effects such as bird

kills are linked to specific events or

actions. A stronger response to a

hypothesized cause is more likely to

indicate true causation. Additional

strong evidence of causation is when a

response follows after a change in the

hypothesized cause (predictive

performance).

The presence of a biological gradient

or stressor-response relationship is

another important criterion for

causality. The stressor-response

relationship need not be linear. It can be

a threshold, sigmoidal, or parabolic

phenomenon, but in any case it is

important that it can be demonstrated.

Biological gradients, such as effects that

decrease with distance from a toxic

discharge, are frequently used as

evidence of causality. To be credible,

such relationships should be consistent

with current biological or ecological

knowledge (biological plausibility).

A cause-and-effect relationship that is

demonstrated repeatedly (consistency of

association) provides strong evidence of

causality. Consistency may be shown by

a greater number of instances of

association between stressor and

response, occurrences in diverse

ecological systems, or associations

demonstrated by diverse methods (Hill,

1965). Fox (1991) adds that in

ecoepidemiology, an association’s

occurrence in more than one species

and population is very strong evidence

for causation. An example would be the

many bird species killed by carbofuran

applications (Houseknecht, 1993). Fox

(1991) also believes that causality is

supported if the same incident is

observed by different persons under

different circumstances and at different

times.

Conversely, inconsistency in

association between stressor and

response is strong evidence against

causality (e.g., the stressor is present

without the expected effect, or the effect

occurs but the stressor is not found).

Temporal incompatibility (i.e., the

presumed cause does not precede the

effect) and incompatibility with

experimental or observational evidence

(factual implausibility) are also

indications against a causal

relationship.

Two other criteria may be of some

help in defining causal relationships:

specificity of an association and

probability. The more specific or

diagnostic the effect, the more likely it

is to have a consistent cause. However,

Fox (1991) argues that effect specificity

does little to strengthen a causal claim.

Disease can have multiple causes, a

substance can behave differently in

different environments or cause several

different effects, and biochemical events

may elicit many biological responses.

But in general, the more specific or

localized the effects, the easier it is to

identify the cause. Sometimes, a stressor

may have a distinctive mode of action

that suggests its role. Yoder and Rankin

(1995) found that patterns of change

observed in fish and benthic

invertebrate communities could serve as

indicators for different types of

anthropogenic impact (e.g., nutrient

enrichment vs. toxicity).

For some pathogenic biological

stressors, the causal evaluations

proposed by Koch (see text note 4–18)

may be useful. For chemicals,

ecotoxicologists have slightly modified

Koch’s postulates to provide evidence of

causality (Suter, 1993a). The

modifications are:

• The injury, dysfunction, or other

putative effect of the toxicant must be

regularly associated with exposure to

the toxicant and any contributory causal

factors.

• Indicators of exposure to the

toxicant must be found in the affected

organisms.

• The toxic effects must be seen when

organisms or communities are exposed

to the toxicant under controlled

conditions, and any contributory factors

should be manifested in the same way

during controlled exposures.

• The same indicators of exposure

and effects must be identified in the

controlled exposures as in the field.

These modifications are conceptually

identical to Koch’s postulates. While

useful, this approach may not be

practical if resources for

experimentation are not available or if

an adverse effect may be occurring over

such a wide spatial extent that

experimentation and correlation may

prove difficult or yield equivocal

results.

Woodman and Cowling (1987)

provide a specific example of a causal

evaluation. They proposed three rules

for establishing the effects of airborne

pollutants on the health and

productivity of forests: (1) The injury or

dysfunction symptoms observed in the

case of individual trees in the forest

must be associated consistently with the

presence of the suspected causal factors,

(2) the same injury or dysfunction

symptoms must be seen when healthy

trees are exposed to the suspected

causal factors under controlled

conditions, and (3) natural variation in

resistance and susceptibility observed in

forest trees also must be seen when

clones of the same trees are exposed to

the suspected causal factors under

controlled conditions.

Experimental techniques are

frequently used for evaluating causality

in complex chemical mixtures. Options

include evaluating separated

components of the mixture, developing

and testing a synthetic mixture, or

determining how a mixture’s toxicity

relates to that of individual components.

The choice of method depends on the

goal of the assessment and the resources

and test data that are available.
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Laboratory toxicity identification

evaluations (TIEs) can be used to help

determine which components of a

chemical mixture cause toxic effects. By

using fractionation and other methods,

the TIE approach can help identify

chemicals responsible for toxicity and

show the relative contributions of

different chemicals in aqueous effluents

(U.S. EPA, 1988a, 1989b, c) and

sediments (e.g., Ankley et al., 1990).

Risk assessors may utilize data from

synthetic chemical mixtures if the

individual chemical components are

well characterized. This approach

allows for manipulation of the mixture

and investigation of how varying the

components that are present or their

ratios may affect mixture toxicity, but it

also requires additional assumptions

about the relationship between effects of

the synthetic mixture and those of the

environmental mixture. (See section

5.1.3 for additional discussion of

mixtures.)

4.3.1.3. Linking Measures of Effect to

Assessment Endpoints

Assessment endpoints express the

environmental values of concern for a

risk assessment, but they cannot always

be measured directly. When measures of

effect differ from assessment endpoints,

sound and explicit linkages between

them are needed. Risk assessors may

make these linkages in the analysis

phase or, especially when linkages rely

on professional judgment, work with

measures of effect through risk

estimation (in risk characterization) and

then connect them with assessment

endpoints. Common extrapolations used

to link measures of effect with

assessment endpoints are shown in text

note 4–19.

4.3.1.3.1. General Considerations

During the preparation of the analysis

plan, risk assessors identify the

extrapolations required between

assessment endpoints and measures of

effect. During the analysis phase, risk

assessors should revisit the questions

listed in text note 4–20 before

proceeding with specific extrapolation

approaches.

The nature of the risk assessment and

the type and amount of data that are

available largely determine how

conservative a risk assessment will be.

The early stages of a tiered risk

assessment typically use conservative

estimates because the data needed to

adequately assess exposure and effects

are usually lacking. When a risk has

been identified, subsequent tiers use

additional data to address the

uncertainties that were incorporated

into the initial assessment(s) (see text

note 2–8).

The scope of the risk assessment also

influences extrapolation through the

nature of the assessment endpoint.

Preliminary assessments that evaluate

risks to general trophic levels such as

herbivores may extrapolate between

different genera or families to obtain a

range of sensitivity to the stressor. On

the other hand, assessments concerned

with management strategies for a

particular species may employ

population models.

Analysis phase activities may suggest

additional extrapolation needs.

Evaluation of exposure may indicate

different spatial or temporal scales than

originally planned. If spatial scales are

broadened, additional receptors may

need to be included in extrapolation

models. If a stressor persists for an

extended time, it may be necessary to

extrapolate short-term responses over a

longer exposure period, and populationlevel effects may become more

important. Whatever methods are

employed to link assessment endpoints

with measures of effect, it is important

to apply them in a manner consistent

with sound ecological principles and

use enough appropriate data. For

example, it is inappropriate to use

structure-activity relationships to

predict toxicity from chemical structure

unless the chemical under consideration

has a similar mode of toxic action to the

reference chemicals (Bradbury, 1994).

Similarly, extrapolations between two

species may be more credible if factors

such as similarities in food preferences,

body mass, physiology, and seasonal

behavior (e.g., mating and migration

habits) are considered (Sample et al.,

1996). Rote or biologically implausible

extrapolations will erode the

assessment’s overall credibility.

Finally, many extrapolation methods

are limited by the availability of suitable

databases. Although many data are

available for chemical stressors and

aquatic species, they do not exist for all

taxa or effects. Chemical effects

databases for wildlife, amphibians, and

reptiles are extremely limited, and there

is even less information on most

biological and physical stressors. Risk

assessors should be aware that

extrapolations and models are only as

useful as the data on which they are

based and should recognize the great

uncertainties associated with

extrapolations that lack an adequate

empirical or process-based rationale.

The rest of this section addresses the

approaches used by risk assessors to

link measures of effect to assessment

endpoints, as noted below.

• Linkages based on professional

judgment. This is not as desirable as

empirical or process-based approaches,

but is the only option when data are

lacking.

• Linkages based on empirical or

process models. Empirical

extrapolations use experimental or

observational data that may or may not

be organized into a database. Processbased approaches rely on some level of

understanding of the underlying

operations of the system of interest.

4.3.1.3.2. Judgment Approaches for

Linking Measures of Effect to

Assessment Endpoints.

Professional-judgment approaches

rely on the professional expertise of risk

assessors, expert panels, or others to

relate changes in measures of effect to

changes in assessment endpoints. They

are essential when databases are

inadequate to support empirical models

and process models are unavailable or

inappropriate. Professional-judgment

linkages between measures of effect and

assessment endpoints can be just as

credible as empirical or process-based

expressions, provided they have a

sound scientific basis. This section

highlights professional-judgment

extrapolations between species, from

laboratory data to field effects, and

between geographic areas.

Because of the uncertainty in

predicting the effects of biological

stressors such as introduced species,

professional-judgment approaches are

commonly used. For example, there

may be measures of effect data on a

foreign pathogen that attacks a certain

tree species not found in the United

States, but the assessment endpoint

concerns the survival of a commercially

important tree found only in the United

States. In this case, a careful evaluation

and comparison of the life history and

environmental requirements of both the

pathogen and the two tree species may

contribute toward a useful

determination of potential effects, even

though the uncertainty may be high.

Expert panels are typically used for this

kind of evaluation (USDA, 1993).

Risks to organisms in field situations

are best estimated from studies at the

site of interest. However, such data are

not always available. Frequently, risk

assessors must extrapolate from

laboratory toxicity test data to field

effects. Text note 4–21 summarizes

some of the considerations for risk

assessors when extrapolating from

laboratory test results to field situations

for chemical stressors. Factors altering

exposure in the field are among the

most important factors limiting

extrapolations from laboratory test
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results, but indirect effects on exposed

organisms due to predation,

competition, or other biotic or abiotic

factors not evaluated in the laboratory

may also be significant. Variations in

direct chemical effects between

laboratory tests and field situations may

not contribute as much to the overall

uncertainty of the extrapolation.

In addition to single-species tests,

laboratory multiple-species tests are

sometimes used to predict field effects.

While these tests have the advantage of

evaluating some aspects of a real

ecological system, they also have

inherent scale limitations (e.g., lack of

top trophic levels) and may not

adequately represent features of the

field system important to the assessment

endpoint.

Extrapolations based on professional

judgment are frequently required when

assessors wish to use field data obtained

from one geographic area and apply

them to a different area of concern, or

to extrapolate from the results of

laboratory tests to more than one

geographic region. In either case, risk

assessors should consider variations

between regions in environmental

conditions, spatial scales and

heterogeneities, and ecological forcing

functions (see below).

Variations in environmental

conditions in different geographic

regions may alter stressor exposure and

effects. If exposures to chemical

stressors can be accurately estimated

and are expected to be similar (e.g., see

text note 4–21), the same species in

different areas may respond similarly.

For example, if the pesticide granular

carbofuran were applied at comparable

rates throughout the country, seedeating birds could be expected to be

similarly affected by the pesticide

(Houseknecht, 1993). Nevertheless, the

influence of environmental conditions

on stressor exposure and effects can be

substantial.

For biological stressors,

environmental conditions such as

climate, habitat, and suitable hosts play

major roles in determining whether a

biological stressor becomes established.

For example, climate would prevent

establishment of the Mediterranean fruit

fly in the much colder northeastern

United States. Thus, a thorough

evaluation of environmental conditions

in the area versus the natural habitat of

the stressor is important. Even so, many

biological stressors can adapt readily to

varying environmental conditions, and

the absence of natural predators or

diseases may play an even more

important role than abiotic factors.

For physical stressors that have

natural counterparts, such as fire,

flooding, or temperature variations,

effects may depend on the difference

between human-caused and natural

variations in these parameters for a

particular region. Thus, the

comparability of two regions depends

on both the pattern and range of natural

disturbances.

Spatial scales and heterogeneities

affect comparability between regions.

Effects observed over a large scale may

be difficult to extrapolate from one

geographical location to another, mainly

because the spatial heterogeneity is

likely to differ. Factors such as number

and size of land-cover patches, distance

between patches, connectivity and

conductivity of patches (e.g., migration

routes), and patch shape may be

important. Extrapolations can be

strengthened by using appropriate

reference sites, such as sites in

comparable ecoregions (Hughes, 1995).

Ecological forcing functions may

differ between geographic regions.

Forcing functions are critical abiotic

variables that exert a major influence on

the structure and function of ecological

systems. Examples include temperature

fluctuations, fire frequency, light

intensity, and hydrologic regime. If

these differ significantly between sites,

it may be inappropriate to extrapolate

effects from one system to another.

Bedford and Preston (1988),

Detenbeck et al. (1992), Gibbs (1993),

Gilbert (1987), Gosselink et al. (1990),

Preston and Bedford (1988), and Risser

(1988) may be useful to risk assessors

concerned with effects in different

geographical areas.

4.3.1.3.3. Empirical and Process-Based

Approaches for Linking Measures of

Effect to Assessment Endpoints

A variety of empirical and processbased approaches are available to risk

assessors, depending on the scope of the

assessment and the data and resources

available. Empirical and process-based

approaches include numerical

extrapolations between measures of

effects and assessment endpoints. These

linkages range in sophistication from

applying an uncertainty factor to using

a complex model requiring extensive

measures of effects and measures of

ecosystem and receptor characteristics

as input. But even the most

sophisticated quantitative models

involve qualitative elements and

assumptions and thus require

professional judgment for evaluation.

Individuals who use models and

interpret their results should be familiar

with the underlying assumptions and

components contained in the model.

4.3.1.3.3.1. Empirical Approaches

Empirical approaches are derived

from experimental data or observations

Empirically based uncertainty factors or

taxonomic extrapolations may be used

when adequate effects databases are

available but the understanding of

underlying mechanisms of action or

ecological principles is limited. When

sufficient information on stressors and

receptors is available, process-based

approaches such as pharmacokinetic/

pharmacodynamic models or

population or ecosystem process models

may be used. Regardless of the options

used, risk assessors should justify and

adequately document the approach

selected.

Uncertainty factors are used to ensure

that measures of effects are sufficiently

protective of assessment endpoints.

Uncertainty factors are empirically

derived numbers that are divided into

measure of effects values to give an

estimated stressor level that should not

cause adverse effects to the assessment

endpoint. Uncertainty factors have been

developed most frequently for

chemicals because extensive

ecotoxicologic databases are available,

especially for aquatic organisms.

Uncertainty factors are useful when

decisions must be made about stressors

in a short time and with little

information.

Uncertainty factors have been used to

compensate for assessment endpoint/

effect measures differences between

endpoints (acute to chronic effects),

between species, and between test

situations (e.g., laboratory to field).

Typically, they vary inversely with the

quantity and type of measures of effects

data available (Zeeman, 1995). They

have been used in screening-level

assessments of new chemicals (Nabholz,

1991), in assessing the risks of

pesticides to aquatic and terrestrial

organisms (Urban and Cook, 1986), and

in developing benchmark dose levels for

human health effects (U.S. EPA, 1995c).

Despite their usefulness, uncertainty

factors can also be misused, especially

when used in an overly conservative

fashion, as when chains of factors are

multiplied together without sufficient

justification. Like other approaches to

bridging data gaps, uncertainty factors

are often based on a combination of

scientific analysis, scientific judgment,

and policy judgment (see section 4.1.3).

It is important to differentiate these

three elements when documenting the

basis for the uncertainty factors used.

Empirical data can be used to

facilitate extrapolations between

species, genera, families, or orders or

functional groups (e.g., feeding guilds)
26880 Federal Register / Vol. 63, No. 93 /Thursday, May 14, 1998 / Notices

(Suter, 1993a). Suter et al. (1983), Suter

(1993a), and Barnthouse et al. (1987,

1990) developed methods to extrapolate

toxicity between freshwater and marine

fish and arthropods. As Suter notes

(1993a), the uncertainties associated

with extrapolating between orders,

classes, and phyla tend to be very high.

However, one can extrapolate with fair

certainty between aquatic species

within genera and genera within

families. Further applications of this

approach (e.g., for chemical stressors

and terrestrial organisms) are limited by

a lack of suitable databases.

In addition to taxonomic databases,

dose-scaling or allometric regression is

used to extrapolate the effects of a

chemical stressor to another species.

Allometry is the study of change in the

proportions of various parts of an

organism as a consequence of growth

and development. Processes that

influence toxicokinetics (e.g., renal

clearance, basal metabolic rate, food

consumption) tend to vary across

species according to allometric scaling

factors that can be expressed as a

nonlinear function of body weight.

These scaling factors can be used to

estimate bioaccumulation and to

improve interspecies extrapolations

(Newman, 1995; Kenaga, 1973; U.S. EPA

1992c, 1995d). Although allometric

relationships are commonly used for

human health risk assessments, they

have not been applied as extensively to

ecological effects (Suter, 1993a). For

chemical stressors, allometric

relationships can enable an assessor to

estimate toxic effects to species not

commonly tested, such as native

mammals. It is important that the

assessor consider the taxonomic

relationship between the known species

and the one of interest. The closer they

are related, the more likely the toxic

response will be similar. Allometric

approaches should not be applied to

species that differ greatly in uptake,

metabolism, or depuration of a

chemical.

4.3.1.3.3.2. Process-Based Approaches

Process models for extrapolation are

representations or abstractions of a

system or process (Starfield and

Bleloch, 1991) that incorporate causal

relationships and provide a predictive

capability that does not depend on the

availability of existing stressor-response

information as empirical models do

(Wiegert and Bartell, 1994). Process

models enable assessors to translate data

on individual effects (e.g., mortality,

growth, and reproduction) to potential

alterations in specific populations,

communities, or ecosystems. Such

models can be used to evaluate risk

hypotheses about the duration and

severity of a stressor on an assessment

endpoint that cannot be tested readily in

the laboratory.

There are two major types of models:

Single-species population models and

multispecies community and ecosystem

models. Population models describe the

dynamics of a finite group of

individuals through time and have been

used extensively in ecology and

fisheries management and to assess the

impacts of power plants and toxicants

on specific fish populations (Barnthouse

et al., 1987, 1990). They can help

answer questions about short- or longterm changes of population size and

structure and can help estimate the

probability that a population will

decline below or grow above a specified

abundance (Ginzburg et al., 1982;

Ferson et al., 1989). The latter

application may be useful when

assessing the effects of biological

stressors such as introduced or pest

species. Barnthouse et al. (1986) and

Wiegert and Bartell (1994) present

excellent reviews of population models.

Emlen (1989) has reviewed population

models that can be used for terrestrial

risk assessment.

Proper use of population models

requires a thorough understanding of

the natural history of the species under

consideration, as well as knowledge of

how the stressor influences its biology.

Model input can include somatic

growth rates, physiological rates,

fecundity, survival rates of various

classes within the population, and how

these change when the population is

exposed to the stressor and other

environmental factors. In addition, the

effects of population density on these

parameters are important (Hassell, 1986)

and should be considered in the

uncertainty analysis.

Community and ecosystem models

(e.g., Bartell et al., 1992; O’Neill et al.,

1982) are particularly useful when the

assessment endpoint involves structural

(e.g., community composition) or

functional (e.g., primary production)

elements. They can also be useful when

secondary effects are of concern.

Changes in various community or

ecosystem components such as

populations, functional types, feeding

guilds, or environmental processes can

be estimated. By incorporating

submodels describing the dynamics of

individual system components, these

models permit evaluation of risk to

multiple assessment endpoints within

the context of the ecosystem.

Risk assessors should determine the

appropriate degree of aggregation in

population or multispecies model

parameters based both on the input data

available and on the desired output of

the model (also see text note 4–5). For

example, if a decision is required about

a particular species, a model that lumps

species into trophic levels or feeding

guilds will not be very useful.

Assumptions concerning aggregation in

model parameters should be included in

the uncertainty discussion.

4.3.2. Stressor-Response Profile

The final product of ecological

response analysis is a summary profile

of what has been learned. This may be

a written document or a module of a

larger process model. In any case, the

objective is to ensure that the

information needed for risk

characterization has been collected and

evaluated. A useful approach in

preparing the stressor-response profile

is to imagine that it will be used by

someone else to perform the risk

characterization. Profile compilation

also provides an opportunity to verify

that the assessment endpoints and

measures of effect identified in the

conceptual model were evaluated.

Risk assessors should address several

questions in the stressor-response

profile (text note 4–22). Affected

ecological entities may include single

species, populations, general trophic

levels, communities, ecosystems, or

landscapes. The nature of the effect(s)

should be germane to the assessment

endpoint(s). Thus if a single species is

affected, the effects should represent

parameters appropriate for that level of

organization. Examples include effects

on mortality, growth, and reproduction.

Short- and long-term effects should be

reported as appropriate. At the

community level, effects may be

summarized in terms of structure or

function depending on the assessment

endpoint. At the landscape level, there

may be a suite of assessment endpoints,

and each should be addressed

separately.

Examples of different approaches for

displaying the intensity of effects were

provided in section 4.3.1.1. Other

information such as the spatial area or

time to recovery may also be

appropriate. Causal analyses are

important, especially for assessments

that include field observational data.

Ideally, the stressor-response profile

should express effects in terms of the

assessment endpoint, but this is not

always possible. Where it is necessary to

use qualitative extrapolations between

assessment endpoints and measures of

effect, the stressor-response profile may

contain information only on measures of

effect. Under these circumstances, risk

will be estimated using the measures of

effects, and extrapolation to the
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assessment endpoints will occur during

risk characterization.

Risk assessors need to clearly describe

any uncertainties associated with the

ecological response analysis. If it was

necessary to extrapolate from measures

of effect to the assessment endpoint,

both the extrapolation and its basis

should be described. Similarly, if a

benchmark or similar reference dose or

concentration was calculated, the

extrapolations and uncertainties

associated with its development need to

be discussed. For additional information

on establishing reference

concentrations, see Nabholz (1991),

Urban and Cook (1986), Stephan et al.

(1985), Van Leeuwen et al. (1992),

Wagner and Lokke (1991), and

Okkerman et al. (1993). Finally, the

assessor should clearly describe major

assumptions and default values used in

the models.

At the end of the analysis phase, the

stressor-response and exposure profiles

are used to estimate risks. These profiles

provide the opportunity to review what

has been learned and to summarize this

information in the most useful format

for risk characterization. Whatever form

the profiles take, they ensure that the

necessary information is available for

risk characterization.

5. Risk Characterization

Risk characterization (figure 5–1) is

the final phase of ecological risk

assessment and is the culmination of the

planning, problem formulation, and

analysis of predicted or observed

adverse ecological effects related to the

assessment endpoints. Completing risk

characterization allows risk assessors to

clarify the relationships between

stressors, effects, and ecological entities

and to reach conclusions regarding the

occurrence of exposure and the

adversity of existing or anticipated

effects. Here, risk assessors first use the

results of the analysis phase to develop

an estimate of the risk posed to the

ecological entities included in the

assessment endpoints identified in

problem formulation (section 5.1). After

estimating the risk, the assessor

describes the risk estimate in the

context of the significance of any

adverse effects and lines of evidence

supporting their likelihood (section 5.2).

Finally, the assessor identifies and

summarizes the uncertainties,

assumptions, and qualifiers in the risk

assessment and reports the conclusions

to risk managers (section 5.3).

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Conclusions presented in the risk

characterization should provide clear

information to risk managers in order to

be useful for environmental decision

making (NRC, 1994; see section 6). If the

risks are not sufficiently defined to

support a management decision, risk

managers may elect to proceed with

another iteration of one or more phases

of the risk assessment process.

Reevaluating the conceptual model (and

associated risk hypotheses) or

conducting additional studies may

improve the risk estimate. Alternatively,

a monitoring program may help

managers evaluate the consequences of

a risk management decision.

5.1. Risk Estimation

Risk estimation is the process of

integrating exposure and effects data

and evaluating any associated

uncertainties. The process uses

exposure and stressor-response profiles

developed according to the analysis

plan (section 3.5). Risk estimates can be

developed using one or more of the

following techniques: (1) Field

observational studies, (2) categorical

rankings, (3) comparisons of singlepoint exposure and effects estimates, (4)

comparisons incorporating the entire

stressor-response relationship, (5)

incorporation of variability in exposure

and/or effects estimates, and (6) process

models that rely partially or entirely on

theoretical approximations of exposure

and effects. These techniques are

described in the following sections.

5.1.1. Results of Field Observational

Studies

Field observational studies (surveys)

can serve as risk estimation techniques

because they provide empirical

evidence linking exposure to effects.

Field surveys measure biological

changes in natural settings through

collection of exposure and effects data

for ecological entities identified in

problem formulation.

A major advantage of field surveys is

that they can be used to evaluate

multiple stressors and complex

ecosystem relationships that cannot be

replicated in the laboratory. Field

surveys are designed to delineate both

exposures and effects (including

secondary effects) found in natural

systems, whereas estimates generated

from laboratory studies generally

delineate either exposures or effects

under controlled or prescribed

conditions (see text note 5–1).

While field studies may best represent

reality, as with other kinds of studies

they can be limited by (1) a lack of

replication, (2) bias in obtaining

representative samples, or (3) failure to

measure critical components of the

system or random variations. Further, a

lack of observed effects in a field survey

may occur because the measurements

lack the sensitivity to detect ecological

effects. See section 4.1.1 for additional

discussion of the strengths and

limitations of different types of data.

Several assumptions or qualifications

need to be clearly articulated when

describing the results of field surveys. A

primary qualification is whether a

causal relationship between stressors

and effects (section 4.3.1.2) is

supported. Unless causal relationships

are carefully examined, conclusions

about effects that are observed may be

inaccurate because the effects are

caused by factors unrelated to the

stressor(s) of concern. In addition, field

surveys taken at one point in time are

usually not predictive; they describe

effects associated only with exposure

scenarios associated with past and

existing conditions.

5.1.2. Categories and Rankings

In some cases, professional judgment

or other qualitative evaluation

techniques may be used to rank risks

using categories, such as low, medium,

and high, or yes and no. This approach

is most frequently used when exposure

and effects data are limited or are not

easily expressed in quantitative terms.

The U.S. Forest Service risk assessment

of pest introduction from importation of

logs from Chile used qualitative

categories owing to limitations in both

the exposure and effects data for the

introduced species of concern as well as

the resources available for the

assessment (see text note 5–2).

Ranking techniques can be used to

translate qualitative judgment into a

mathematical comparison. These

methods are frequently used in

comparative risk exercises. For example,

Harris et al. (1994) evaluated risk

reduction opportunities in Green Bay

(Lake Michigan), Wisconsin, employing

an expert panel to compare the relative

risk of several stressors against their

potential effects. Mathematical analysis

based on fuzzy set theory was used to

rank the risk from each stressor from a

number of perspectives, including

degree of immediate risk, duration of

impacts, and prevention and

remediation management. The results

served to rank potential environmental

risks from stressors based on best

professional judgment.

5.1.3. Single-Point Exposure and Effects

Comparisons

When sufficient data are available to

quantify exposure and effects estimates,

the simplest approach for comparing the

estimates is a ratio (figure 5–2a).

Typically, the ratio (or quotient) is

expressed as an exposure concentration

divided by an effects concentration.

Quotients are commonly used for

chemical stressors, where reference or

benchmark toxicity values are widely

available (see text note 5–3).

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The principal advantages of the

quotient method are that it is simple

and quick to use and risk assessors and

managers are familiar with its

application. It provides an efficient,

inexpensive means of identifying highor low-risk situations that can allow risk

management decisions to be made

without the need for further

information.

Quotients have also been used to

integrate the risks of multiple chemical

stressors: quotients for the individual

constituents in a mixture are generated

by dividing each exposure level by a

corresponding toxicity endpoint (e.g.,

LC50, EC50, NOAEL). Although the

toxicity of a chemical mixture may be

greater than or less than predicted from

the toxicities of individual constituents

of the mixture, a quotient addition

approach assumes that toxicities are

additive or approximately additive. This

assumption may be most applicable

when the modes of action of chemicals

in a mixture are similar, but there is

evidence that even with chemicals

having dissimilar modes of action,

additive or near-additive interactions

are common (Ko ̈nemann, 1981;

Broderius, 1991; Broderius et al., 1995;

Hermens et al., 1984a, b; McCarty and

Mackay, 1993; Sawyer and Safe, 1985).

However, caution should be used when

assuming that chemicals in a mixture

act independently of one another, since

many of the supporting studies were

conducted with aquatic organisms, and

so may not be relevant for other

endpoints, exposure scenarios, or

species. When the modes of action for

constituent chemicals are unknown, the

assumptions and rationale concerning

chemical interactions should be clearly

stated.

A number of limitations restrict

application of the quotient method (see

Smith and Cairns, 1993; Suter, 1993a).

While a quotient can be useful in

answering whether risks are high or

low, it may not be helpful to a risk

manager who needs to make a decision

requiring an incremental quantification

of risks. For example, it is seldom useful

to say that a risk mitigation approach

will reduce a quotient value from 25 to

12, since this reduction cannot by itself

be clearly interpreted in terms of effects

on an assessment endpoint.

Other limitations of quotients may be

caused by deficiencies in the problem

formulation and analysis phases. For

example, an LC50 derived from a 96-

hour laboratory test using constant

exposure levels may not be appropriate

for an assessment of effects on

reproduction resulting from short-term,

pulsed exposures.

In addition, the quotient method may

not be the most appropriate method for

predicting secondary effects (although

such effects may be inferred).

Interactions and effects beyond what are

predicted from the simple quotient may

be critical to characterizing the full

extent of impacts from exposure to the

stressors (e.g., bioaccumulation,

eutrophication, loss of prey species,

opportunities for invasive species).

Finally, in most cases, the quotient

method does not explicitly consider

uncertainty (e.g., extrapolation from

tested species to the species or

community of concern). Some

uncertainties, however, can be

incorporated into single-point estimates

to provide a statement of likelihood that

the effects point estimate exceeds the

exposure point estimate (figures 5–2b

and 5–3). If exposure variability is

quantified, then the point estimate of

effects can be compared with a

cumulative exposure distribution as

described in text note 5–4. Further

discussion of comparisons between

point estimates of effects and

distributions of exposure may be found

in Suter et al., 1983.

In view of the advantages and

limitations of the quotient method, it is

important for risk assessors to consider

the points listed below when evaluating

quotient method estimates.

• How does the effect concentration

relate to the assessment endpoint?

• What extrapolations are involved?

• How does the point estimate of

exposure relate to potential spatial and

temporal variability in exposure?

• Are data sufficient to provide

confidence intervals on the endpoints?

5.1.4. Comparisons Incorporating the

Entire Stressor-Response Relationship

If a curve relating the stressor level to

the magnitude of response is available,

then risk estimation can examine risks

associated with many different levels of

exposure (figure 5–4). These estimates

are particularly useful when the risk

assessment outcome is not based on

exceedance of a predetermined decision

rule, such as a toxicity benchmark level.

There are advantages and limitations

to comparing a stressor-response curve

with an exposure distribution. The

slope of the effects curve shows the

magnitude of change in effects

associated with incremental changes in

exposure, and the capability to predict

changes in the magnitude and

likelihood of effects for different

exposure scenarios can be used to

compare different risk management

options. Also, uncertainty can be

incorporated by calculating uncertainty

bounds on the stressor-response or

exposure estimates. Comparing

exposure and stressor-response curves

provides a predictive ability lacking in

the quotient method. Like the quotient

method, however, limitations from the

problem formulation and analysis

phases may limit the utility of the

results. These limitations may include

not fully considering secondary effects,

assuming the exposure pattern used to

derive the stressor-response curve is

comparable to the environmental

exposure pattern, and failure to consider

uncertainties, such as extrapolations

from tested species to the species or

community of concern.

5.1.5. Comparisons Incorporating

Variability in Exposure and/or Effects

If the exposure or stressor-response

profiles describe the variability in

exposure or effects, then many different

risk estimates can be calculated.

Variability in exposure can be used to

estimate risks to moderately or highly

exposed members of a population being

investigated, while variability in effects

can be used to estimate risks to average

or sensitive population members. A

major advantage of this approach is its

ability to predict changes in the

magnitude and likelihood of effects for

different exposure scenarios and thus

provide a means for comparing different

risk management options. As noted

above, comparing distributions also

allows one to identify and quantify risks

to different segments of the population.

Limitations include the increased data

requirements compared with previously

described techniques and the implicit

assumption that the full range of

variability in the exposure and effects

data is adequately represented. As with

the quotient method, secondary effects

are not readily evaluated with this

technique. Thus, it is desirable to

corroborate risks estimated by

distributional comparisons with field

studies or other lines of evidence. Text

note 5–5 and figure 5–5 illustrate the

use of cumulative exposure and effects

distributions for estimating risk.

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5.1.6. Application of Process Models

Process models are mathematical

expressions that represent our

understanding of the mechanistic

operation of a system under evaluation.

They can be useful tools in both

analysis (see section 4.1.2) and risk

characterization. For illustrative

purposes, it is useful to distinguish

between analysis process models, which

focus individually on either exposure or

effects evaluations, and risk estimation

process models, which integrate

exposure and effects information (see

text note 5–6). The assessment of risks

associated with long-term changes in

hydrologic conditions in bottomland

forest wetlands in Louisiana using the

FORFLO model (Appendix D) linked

the attributes and placement of levees

and corresponding water level

measurements (exposure) with changes

in forest community structure and

wildlife habitat suitability (effects).

A major advantage of using process

models for risk estimation is the ability

to consider ‘‘what if’’ scenarios and to

forecast beyond the limits of observed

data that constrain techniques based

solely on empirical data. The process

model can also consider secondary

effects, unlike other risk estimation

techniques such as the quotient method

or comparisons of exposure and effect

distributions. In addition, some process

models can forecast the combined

effects of multiple stressors, such as the

effects of multiple chemicals on fish

population sustainability (Barnthouse et

al., 1990).

Process model outputs may be point

estimates, distributions, or correlations;

in all cases, risk assessors should

interpret them with care. They may

imply a higher level of certainty than is

appropriate and are all too often viewed

without sufficient attention to

underlying assumptions. The lack of

knowledge on basic life histories for

many species and incomplete

knowledge on the structure and

function of a particular ecosystem is

often lost in the model output. Since

process models are only as good as the

assumptions on which they are based,

they should be treated as hypothetical

representations of reality until

appropriately tested with empirical

data. Comparing model results to field

data provides a check on whether our

understanding of the system was correct

(Johnson, 1995), particularly with

respect to the risk hypotheses presented

in problem formulation.

5.2. Risk Description

Following preparation of the risk

estimate, risk assessors need to interpret

and discuss the available information

about risks to the assessment endpoints.

Risk description includes an evaluation

of the lines of evidence supporting or

refuting the risk estimate(s) and an

interpretation of the significance of the

adverse effects on the assessment

endpoints. During the analysis phase,

the risk assessor may have established

the relationship between the assessment

endpoints and measures of effect and

associated lines of evidence in

quantifiable, easily described terms

(section 4.3.1.3). If not, the risk assessor

can relate the available lines of evidence

to the assessment endpoints using

qualitative links. Regardless of the risk

estimation technique, the technical

narrative supporting the risk estimate is

as important as the risk estimate itself.

5.2.1. Lines of Evidence

The development of lines of evidence

provides both a process and a

framework for reaching a conclusion

regarding confidence in the risk

estimate. It is not the kind of proof

demanded by experimentalists (Fox,

1991), nor is it a rigorous examination

of weights of evidence. (Note that the

term ‘‘weight of evidence’’ is sometimes

used in legal discussions or in other

documents, e.g., Urban and Cook, 1986;

Menzie et al., 1996.) The phrase lines of

evidence is used to de-emphasize the

balancing of opposing factors based on

assignment of quantitative values to

reach a conclusion about a ‘‘weight’’ in

favor of a more inclusive approach,

which evaluates all available

information, even evidence that may be

qualitative in nature. It is important that

risk assessors provide a thorough

representation of all lines of evidence

developed in the risk assessment rather

than simply reduce their interpretation

and description of the ecological effects

that may result from exposure to

stressors to a system of numeric

calculations and results.

Confidence in the conclusions of a

risk assessment may be increased by

using several lines of evidence to

interpret and compare risk estimates.

These lines of evidence may be derived

from different sources or by different

techniques relevant to adverse effects on

the assessment endpoints, such as

quotient estimates, modeling results, or

field observational studies.

There are three principal categories of

factors for risk assessors to consider

when evaluating lines of evidence: (1)

Adequacy and quality of data, (2) degree

and type of uncertainty associated with

the evidence, and (3) relationship of the

evidence to the risk assessment

questions (see also sections 3 and 4).

Data quality directly influences how

confident risk assessors can be in the

results of a study and conclusions they

may draw from it. Specific concerns to

consider for individual lines of evidence

include whether the experimental

design was appropriate for the questions

posed in a particular study and whether

data quality objectives were clear and

adhered to. An evaluation of the

scientific understanding of natural

variability in the attributes of the

ecological entities under consideration

is important in determining whether

there were sufficient data to satisfy the

analyses chosen and to determine if the

analyses were sufficiently sensitive and

robust to identify stressor-caused

perturbations.

Directly related to data quality issues

is the evaluation of the relative

uncertainties of each line of evidence.

One major source of uncertainty comes

from extrapolations. The greater the

number of extrapolations, the more

uncertainty introduced into a study. For

example, were extrapolations used to

infer effects in one species from another,

or from one temporal or spatial scale to

another? Were conclusions drawn from

extrapolations from laboratory to field

effects, or were field effects inferred

from limited information, such as

chemical structure-activity

relationships? Were no-effect or loweffect levels used to address likelihood

of effects? Risk assessors should

consider these and any other sources of

uncertainty when evaluating the relative

importance of particular lines of

evidence.

Finally, how directly lines of

evidence relate to the questions asked in

the risk assessment may determine their

relative importance in terms of the

ecological entity and the attributes of

the assessment endpoint. Lines of

evidence directly related to the risk

hypotheses, and those that establish a

cause-and-effect relationship based on a

definitive mechanism rather than

associations alone, are likely to be of

greatest importance.

The evaluation process, however,

involves more than just listing the

evidence that supports or refutes the

risk estimate. The risk assessor should

carefully examine each factor and

evaluate its contribution in the context

of the risk assessment. The importance

of lines of evidence is that each and

every factor is described and

interpreted. Data or study results are

often not reported or carried forward in

the risk assessment because they are of

insufficient quality. If such data or

results are eliminated from the

evaluation process, however, valuable

information may be lost with respect to
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needed improvements in methodologies

or recommendations for further studies.

As a case in point, consider the two

lines of evidence described for the

carbofuran example (see text notes 5–1

and 5–3), field studies and quotients.

Both approaches are relevant to the

assessment endpoint (survival of birds

that forage in agricultural areas where

carbofuran is applied), and both are

relevant to the exposure scenarios

described in the conceptual model (see

figure D–1). The quotients, however, are

limited in their ability to express

incremental risks (e.g., how much

greater risk is expressed by a quotient of

‘‘2’’ versus a quotient of ‘‘4’’), while the

field studies had some design flaws (see

text note 5–1). Nevertheless, because of

the strong evidence of causal

relationships from the field studies and

consistency with the laboratory-derived

quotient, confidence in a conclusion of

high risk to the assessment endpoint is

supported.

Sometimes lines of evidence do not

point toward the same conclusion. It is

important to investigate possible

reasons for any disagreement rather than

ignore inconvenient evidence. A starting

point is to distinguish between true

inconsistencies and those related to

differences in statistical powers of

detection. For example, a model may

predict adverse effects that were not

observed in a field survey. The risk

assessor should ask whether the

experimental design of the field study

had sufficient power to detect the

predicted difference or whether the

endpoints measured were comparable

with those used in the model.

Conversely, the model may have been

unrealistic in its predictions. While

iteration of the risk assessment process

and collection of additional data may

help resolve uncertainties, this option is

not always available.

Lines of evidence that are to be

evaluated during risk characterization

should be defined early in the risk

assessment (during problem

formulation) through the development

of the conceptual model and selection of

assessment endpoints. Further, the

analysis plan should incorporate

measures that will contribute to the

interpretation of the lines of evidence,

including methods of reviewing,

analyzing, and summarizing the

uncertainty in the risk assessment.

Also, risk assessments often rely

solely on laboratory or in situ bioassays

to assess adverse effects that may occur

as a result of exposure to stressors.

Although they may not be manifested in

the field, ecological effects

demonstrated in the laboratory should

not be discounted as a line of evidence.

5.2.2. Determining Ecological Adversity

At this point in risk characterization,

the changes expected in the assessment

endpoints have been estimated and the

supporting lines of evidence evaluated.

The next step is to interpret whether

these changes are considered adverse.

Adverse ecological effects, in this

context, represent changes that are

undesirable because they alter valued

structural or functional attributes of the

ecological entities under consideration.

The risk assessor evaluates the degree of

adversity, which is often a difficult task

and is frequently based on the risk

assessor’s professional judgment.

When the results of the risk

assessment are discussed with the risk

manager (section 6), other factors, such

as the economic, legal, or social

consequences of ecological damage,

should be considered. The risk manager

will use all of this information to

determine whether a particular adverse

effect is acceptable and may also find it

useful when communicating the risk to

interested parties.

The following are criteria for

evaluating adverse changes in

assessment endpoints:

• Nature of effects and intensity of

effects

• Spatial and temporal scale

• Potential for recovery.

The extent to which the criteria are

evaluated depends on the scope and

complexity of the risk assessment.

Understanding the underlying

assumptions and science policy

judgments, however, is important even

in simple cases. For example, when

exceedance of a previously established

decision rule, such as a benchmark

stressor level, is used as evidence of

adversity (e.g., see Urban and Cook,

1986, or Nabholz, 1991), the reasons

why this is considered adverse should

be clearly understood. In addition, any

evaluation of adversity should examine

all relevant criteria, since none are

considered singularly determinative.

To distinguish adverse ecological

changes from those within the normal

pattern of ecosystem variability or those

resulting in little or no significant

alteration of biota, it is important to

consider the nature and intensity of

effects. For example, for an assessment

endpoint involving survival, growth,

and reproduction of a species, do

predicted effects involve survival and

reproduction or only growth? If survival

of offspring will be affected, by what

percentage will it diminish?

It is important for risk assessors to

consider both the ecological and

statistical contexts of an effect when

evaluating intensity. For example, a

statistically significant 1% decrease in

fish growth (see text note 5–7) may not

be relevant to an assessment endpoint of

fish population viability, and a 10%

decline in reproduction may be worse

for a population of slowly reproducing

trees than for rapidly reproducing

planktonic algae.

Natural ecosystem variation can make

it very difficult to observe (detect)

stressor-related perturbations. For

example, natural fluctuations in marine

fish populations are often large, with

intra- and interannual variability in

population levels covering several

orders of magnitude. Furthermore,

cyclic events of various periods (e.g.,

bird migration, tides) are very important

in natural systems and may mask or

delay stressor-related effects. Predicting

the effects of anthropogenic stressors

against this background of variation can

be very difficult. Thus, a lack of

statistically significant effects in a field

study does not automatically mean that

adverse ecological effects are absent.

Rather, risk assessors should then

consider other lines of evidence in

reaching their conclusions.

It is also important to consider the

location of the effect within the

biological hierarchy and the

mechanisms that may result in

ecological changes. The risk assessor

may rely on mechanistic explanations to

describe complex ecological interactions

and the resulting effects that otherwise

may be masked by variability in the

ecological components.

The boundaries (global, landscape,

ecosystem, organism) of the risk

assessment are initially identified in the

analysis plan prepared during problem

formulation. These spatial and temporal

scales are further defined in the analysis

phase, where specific exposure and

effects scenarios are evaluated. The

spatial dimension encompasses both the

extent and pattern of effect as well as

the context of the effect within the

landscape. Factors to consider include

the absolute area affected, the extent of

critical habitats affected compared with

a larger area of interest, and the role or

use of the affected area within the

landscape.

Adverse effects to assessment

endpoints vary with the absolute area of

the effect. A larger affected area may be

(1) subject to a greater number of other

stressors, increasing the complications

from stressor interactions, (2) more

likely to contain sensitive species or

habitats, or (3) more susceptible to

landscape-level changes because many

ecosystems may be altered by the

stressors.

Nevertheless, a smaller area of effect

is not always associated with lower risk.
Federal Register / Vol. 63, No. 93 /Thursday, May 14, 1998 / Notices 26891

The function of an area within the

landscape may be more important than

the absolute area. Destruction of small

but unique areas, such as critical

wetlands, may have important effects on

local and regional wildlife populations.

Also, in river systems, both riffle and

pool areas provide important

microhabitats that maintain the

structure and function of the total river

ecosystem. Stressors acting on these

microhabitats may result in adverse

effects to the entire system.

Spatial factors are important for many

species because of the linkages between

ecological landscapes and population

dynamics. Linkages between landscapes

can provide refuge for affected

populations, and organisms may require

corridors between habitat patches for

successful migration.

The temporal scale for ecosystems can

vary from seconds (photosynthesis,

prokaryotic reproduction) to centuries

(global climate change). Changes within

a forest ecosystem can occur gradually

over decades or centuries and may be

affected by slowly changing external

factors such as climate. When

interpreting adversity, risk assessors

should recognize that the time scale of

stressor-induced changes operates

within the context of multiple natural

time scales. In addition, temporal

responses for ecosystems may involve

intrinsic time lags, so responses to a

stressor may be delayed. Thus, it is

important to distinguish a stressor’s

long-term impacts from its immediately

visible effects. For example, visible

changes resulting from eutrophication of

aquatic systems (turbidity, excessive

macrophyte growth, population decline)

may not become evident for many years

after initial increases in nutrient levels.

Considering the temporal scale of

adverse effects leads logically to a

consideration of recovery. Recovery is

the rate and extent of return of a

population or community to some

aspect of its condition prior to a

stressor’s introduction. (While this

discussion deals with recovery as a

result of natural processes, risk

mitigation options may include

restoration activities to facilitate or

speed up the recovery process.) Because

ecosystems are dynamic and, even

under natural conditions, constantly

changing in response to changes in the

physical environment (e.g., weather,

natural disturbances) or other factors, it

is unrealistic to expect that a system

will remain static at some level or return

to exactly the same state that it was

before it was disturbed (Landis et al.,

1993). Thus, the attributes of a

‘‘recovered’’ system should be carefully

defined. Examples might include

productivity declines in a eutrophic

system, reestablishment of a species at

a particular density, species

recolonization of a damaged habitat, or

the restoration of health of diseased

organisms. The Agency considered the

recovery rate of biological communities

in streams and rivers from disturbances

in setting exceedance frequencies for

chemical stressors in waste effluents

(U.S. EPA, 1991).

Recovery can be evaluated in spite of

the difficulty in predicting events in

ecological systems (e.g., Niemi et al.,

1990). For example, it is possible to

distinguish changes that are usually

reversible (e.g., stream recovery from

sewage effluent discharge), frequently

irreversible (e.g., establishment of

introduced species), and always

irreversible (e.g., extinction). Risk

assessors should consider the potential

irreversibility of significant structural or

functional changes in ecosystems or

ecosystem components when evaluating

adversity. Physical alterations such as

deforestation in the coastal hills of

Venezuela in recent history and in

Britain during the Neolithic period, for

example, changed soil structure and

seed sources such that forests cannot

easily grow again (Fisher and

Woodmansee, 1994).

The relative rate of recovery can also

be estimated. For instance, fish

populations in a stream are likely to

recover much faster from exposure to a

degradable chemical than from habitat

alterations resulting from stream

channelization. Risk assessors can use

knowledge of factors, such as the

temporal scales of organisms’ life

histories, the availability of adequate

stock for recruitment, and the

interspecific and trophic dynamics of

the populations, in evaluating the

relative rates of recovery. A fisheries

stock or forest might recover in decades,

a benthic invertebrate community in

years, and a planktonic community in

weeks to months.

Risk assessors should note natural

disturbance patterns when evaluating

the likelihood of recovery from

anthropogenic stressors. Alternatively, if

an ecosystem has become adapted to a

disturbance pattern, it may be affected

when the disturbance is removed (e.g.,

fire-maintained grasslands). The lack of

natural analogs makes it difficult to

predict recovery from uniquely

anthropogenic stressors (e.g., synthetic

chemicals).

Appendix E illustrates how the

criteria for ecological adversity (nature

and intensity of effects, spatial and

temporal scales, and recovery) might be

used in evaluating two cleanup options

for a marine oil spill. This example also

shows that recovery of a system

depends not only on how quickly a

stressor is removed, but also on how the

cleanup efforts themselves affect the

recovery.

5.3. Reporting Risks

When risk characterization is

complete, risk assessors should be able

to estimate ecological risks, indicate the

overall degree of confidence in the risk

estimates, cite lines of evidence

supporting the risk estimates, and

interpret the adversity of ecological

effects. Usually this information is

included in a risk assessment report

(sometimes referred to as a risk

characterization report because of the

integrative nature of risk

characterization). While the breadth of

ecological risk assessment precludes

providing a detailed outline of reporting

elements, the risk assessor should

consider the elements listed in text note

5–8 when preparing a risk assessment

report.

Like the risk assessment itself, a risk

assessment report may be brief or

extensive, depending on the nature of

and the resources available for the

assessment. While it is important to

address the elements described in text

note 5–8, risk assessors should judge the

level of detail required. The report need

not be overly complex or lengthy; it is

most important that the information

required to support a risk management

decision be presented clearly and

concisely.

To facilitate mutual understanding, it

is critical that the risk assessment

results are properly presented. Agency

policy requires that risk

characterizations be prepared ‘‘in a

manner that is clear, transparent,

reasonable, and consistent with other

risk characterizations of similar scope

prepared across programs in the

Agency’’ (U.S. EPA, 1995b). Ways to

achieve such characteristics are

described in text note 5–9.

After the risk assessment report is

prepared, the results are discussed with

risk managers. Section 6 provides

information on communication between

risk assessors and risk managers,

describes the use of the risk assessment

in a risk management context, and

briefly discusses communication of risk

assessment results from risk managers to

interested parties and the general

public.

6. Relating Ecological Information to

Risk Management Decisions

After characterizing risks and

preparing a risk assessment report

(section 5), risk assessors discuss the

results with risk managers (figure 5–1).
26892 Federal Register / Vol. 63, No. 93 /Thursday, May 14, 1998 / Notices

Risk managers use risk assessment

results, along with other factors (e.g.,

economic or legal concerns), in making

risk management decisions and as a

basis for communicating risks to

interested parties and the general

public.

Mutual understanding between risk

assessors and risk managers regarding

risk assessment results can be facilitated

if the questions listed in text note 6–1

are addressed. Risk managers need to

know the major risks to assessment

endpoints and have an idea of whether

the conclusions are supported by a large

body of data or if there are significant

data gaps. Insufficient resources, lack of

consensus, or other factors may

preclude preparation of a detailed and

well-documented risk characterization.

If this is the case, the risk assessor

should clearly articulate any issues,

obstacles, and correctable deficiencies

for the risk manager’s consideration.

In making decisions regarding

ecological risks, risk managers consider

other information, such as social,

economic, political, or legal issues in

combination with risk assessment

results. For example, the risk

assessment results may be used as part

of an ecological cost-benefit analysis,

which may require translating resources

(identified through the assessment

endpoints) into monetary values.

Traditional economic considerations

may only partially address changes in

ecological resources that are not

considered commodities,

intergenerational resource values, or

issues of long-term or irreversible effects

(U.S. EPA, 1995a; Costanza et al., 1997);

however, they may provide a means of

comparing the results of the risk

assessment in commensurate units such

as costs. Risk managers may also

consider alternative strategies for

reducing risks, such as risk mitigation

options or substitutions based on

relative risk comparisons. For example,

risk mitigation techniques, such as

buffer strips or lower field application

rates, can be used to reduce the

exposure (and risk) of a pesticide.

Further, by comparing the risk of a new

pesticide to other pesticides currently in

use during the registration process,

lower overall risk may result. Finally,

risk managers consider and incorporate

public opinion and political demands

into their decisions. Collectively, these

other factors may render very high risks

acceptable or very low risks

unacceptable.

Risk characterization provides the

basis for communicating ecological risks

to interested parties and the general

public. This task is usually the

responsibility of risk managers, but it

may be shared with risk assessors.

Although the final risk assessment

document (including its risk

characterization sections) can be made

available to the public, the risk

communication process is best served

by tailoring information to a particular

audience. Irrespective of the specific

format, it is important to clearly

describe the ecological resources at risk,

their value, and the monetary and other

costs of protecting (and failing to

protect) the resources (U.S. EPA, 1995a).

Managers should clearly describe the

sources and causes of risks and the

potential adversity of the risks (e.g.,

nature and intensity, spatial and

temporal scale, and recovery potential).

The degree of confidence in the risk

assessment, the rationale for the risk

management decision, and the options

for reducing risk are also important

(U.S. EPA, 1995a). Other risk

communication considerations are

provided in text note 6–2.

Along with discussions of risk and

communications with the public, it is

important for risk managers to consider

whether additional follow-on activities

are required. Depending on the

importance of the assessment,

confidence in its results, and available

resources, it may be advisable to

conduct another iteration of the risk

assessment (starting with problem

formulation or analysis) in order to

support a final management decision.

Another option is to proceed with the

decision, implement the selected

management alternative, and develop a

monitoring plan to evaluate the results

(see section 1). If the decision is to

mitigate risks through exposure

reduction, for example, monitoring

could help determine whether the

desired reduction in exposure (and

effects) is achieved.

7. Text Notes

Text Note 1–1. Related Terminology

The following terms overlap to

varying degrees with the concept of

ecological risk assessment used in these

Guidelines (see Appendix B for

definitions):

• Hazard assessment

• Comparative risk assessment

• Cumulative ecological risk

assessment

• Environmental impact statement

Text Note 1–2. Flexibility of the

Framework Diagram

The framework process (figure 1–1) is

a general representation of a complex

and varied group of assessments. This

diagram represents a flexible process, as

illustrated by the examples below.

• In problem formulation, an

assessment may begin with a

consideration of endpoints, stressors, or

ecological effects. Problem formulation

is generally interactive and iterative, not

linear.

• In the analysis phase,

characterization of exposure and effects

frequently become intertwined, as when

an initial exposure leads to a cascade of

additional exposures and secondary

effects. The analysis phase should foster

an understanding of these complex

relationships.

• Analysis and risk characterization

are shown as separate phases. However,

some models may combine the analysis

of exposure and effects data with the

integration of these data that occurs in

risk characterization.

Text Note 2–1. Who Are Risk Managers?

Risk managers are individuals and

organizations who have the

responsibility, or have the authority to

take action or require action, to mitigate

an identified risk. The expression ‘‘risk

manager’’ is often used to represent a

decision maker in agencies such as EPA

or State environmental offices who has

legal authority to protect or manage a

resource. However, risk managers may

include a diverse group of interested

parties who also have the ability to take

action to reduce or mitigate risk. In

situations where a complex of

ecosystem values (e.g., watershed

resources) is at risk from multiple

stressors, and management will be

implemented through community

action, these groups may function as

risk management teams. Risk

management teams may include

decision officials in Federal, State, local,

and tribal governments; commercial,

industrial, and private organizations;

leaders of constituency groups; and

other sectors of the public such as

property owners. For additional insights

on risk management and manager roles,

see text notes 2–3 and 2–4.

Text Note 2–2. Who Are Risk Assessors?

Risk assessors are a diverse group of

professionals who bring a needed

expertise to a risk assessment team.

When a specific risk assessment process

is well defined through regulations and

guidance, one trained individual may be

able to complete a risk assessment given

sufficient information (e.g.,

premanufacture notice of a chemical).

However, for complex risk assessments,

one individual can rarely provide the

necessary breadth of expertise. Every

risk assessment team should include at

least one professional who is

knowledgeable and experienced in

using the risk assessment process. Other
Federal Register / Vol. 63, No. 93 /Thursday, May 14, 1998 / Notices 26893

team members bring specific expertise

relevant to the locations, stressors,

ecosystems, scientific issues, and other

expertise as needed, depending on the

type of assessment.

Text Note 2–3. Who Are Interested

Parties?

Interested parties (commonly called

‘‘stakeholders’’) may include Federal,

State, tribal, and municipal

governments, industrial leaders,

environmental groups, small-business

owners, landowners, and other

segments of society concerned about an

environmental issue at hand or

attempting to influence risk

management decisions. Their

involvement, particularly during

management goal development, may be

key to successful implementation of

management plans since

implementation is more likely to occur

when backed by consensus. Large

diverse groups may require trained

facilitators and consensus-building

techniques to reach agreement.

In some cases, interested parties may

provide important information to risk

assessors. Local knowledge, particularly

in rural communities, and traditional

knowledge of native peoples can

provide valuable insights about

ecological characteristics of a place, past

conditions, and current changes. This

knowledge should be considered when

assessing available information during

problem formulation (see section 3.2).

The context of involvement by

interested parties can vary widely and

may or may not be appropriate for a

particular risk assessment. Interested

parties may be limited to providing

input to goal development, or they may

become risk managers, depending on

the degree to which they can take action

to manage risk and the regulatory

context of the decision. When and how

interested parties influence risk

assessments and risk management are

areas of current discussion (NRC, 1996).

See additional information in text note

2–1 and section 2.1.

Text Note 2–4. Questions Addressed by

Risk Managers and Risk Assessors

Questions Principally for Risk

Managers to Answer

What is the nature of the problem and

the best scale for the assessment?

What are the management goals and

decisions needed, and how will risk

assessment help?

What are the ecological values (e.g.,

entities and ecosystem characteristics)

of concern?

What are the policy considerations

(law, corporate stewardship, societal

concerns, environmental justice,

intergenerational equity)?

What precedents are set by similar

risk assessments and previous

decisions?

What is the context of the assessment

(e.g., industrial site, national park)?

What resources (e.g., personnel, time,

money) are available?

What level of uncertainty is

acceptable?

Questions Principally for Risk

Assessors to Answer

What is the scale of the risk

assessment?

What are the critical ecological

endpoints and ecosystem and receptor

characteristics?

How likely is recovery, and how long

will it take?

What is the nature of the problem:

Past, present, future?

What is our state of knowledge of the

problem?

What data and data analyses are

available and appropriate?

What are the potential constraints

(e.g., limits on expertise, time,

availability of methods and data)?

Text Note 2–5. Sustainability as a

Management Goal

To sustain is to keep in existence,

maintain, or prolong. Sustainability is

used as a management goal in a variety

of settings (see U.S. EPA, 1995a).

Sustainability and other concepts such

as biotic or community integrity may be

very useful as guiding principles for

management goals. However, in each

case these principles should be

explicitly defined and interpreted for a

place to support a risk assessment. To

do this, key questions need to be

addressed: What does sustainability or

integrity mean for the particular

ecosystem? What must be protected to

meet sustainable goals or system

integrity? Which ecological resources

and processes are to be sustained and

why? How will we know we have

achieved it? Answers to these questions

serve to clarify the goals for a particular

ecosystem. Concepts like sustainability

and integrity do not meet the criteria for

an assessment endpoint (see section

3.3.2).

Text Note 2–6. Management Goals for

Waquoit Bay

A key challenge for risk assessors

when dealing with a general

management goal is interpreting the goal

for a risk assessment. This can be done

by generating a set of management

objectives that represent what must be

achieved in a particular ecosystem in

order for the goal to be met. An example

of this process was developed in the

Waquoit Bay watershed risk assessment

(U.S. EPA, 1996b).

Waquoit Bay is a small estuary on

Cape Cod showing signs of degradation,

including loss of eelgrass, fish, and

shellfish and an increase in macroalgae

mats and fish kills. The management

goal for Waquoit Bay was established

through public meetings, preexisting

goals from local organizations, and State

and Federal regulations:

Reestablish and maintain water

quality and habitat conditions in

Waquoit Bay and associated freshwater

rivers and ponds to (1) support diverse

self-sustaining commercial, recreational,

and native fish and shellfish

populations and (2) reverse ongoing

degradation of ecological resources in

the watershed.

To interpret this goal for the risk

assessment, it was converted into 10

management objectives that defined

what must be true in the watershed for

the goal to be achieved and provide the

foundation for management decisions.

The management objectives are:

• Reduce or eliminate hypoxic or

anoxic events.

• Prevent toxic levels of

contamination in water, sediments, and

biota.

• Restore and maintain selfsustaining native fish populations and

their habitat.

• Reestablish viable eelgrass beds and

associated aquatic communities in the

bay.

• Reestablish a self-sustaining scallop

population in the bay that can support

a viable sport fishery.

• Protect shellfish beds from bacterial

contamination that results in closures.

• Reduce or eliminate nuisance

macroalgal growth.

• Prevent eutrophication of rivers and

ponds.

• Maintain diversity of native biotic

communities.

• Maintain diversity of waterdependent wildlife.

From these objectives, eight ecological

entities and their attributes in the bay

were selected as assessment endpoints

(see section 3.3.2) to best represent the

management goals and objectives, one of

which is areal extent and patch size of

eelgrass beds. Eelgrass was selected

because (1) scallops and other benthic

organisms and juvenile finfish depend

directly on eelgrass beds for survival, (2)

eelgrass is highly sensitive to excess

macroalgal growth, and (3) abundant

eelgrass represents a healthy bay to

human users.
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Text Note 2–7. What Is the Difference

Between a Management Goal and

Management Decision?

Management goals are desired

characteristics of ecological values that

the public wants to protect. Clean water,

protection of endangered species,

maintenance of ecological integrity,

clear mountain views, and fishing

opportunities are all possible

management goals. Management

decisions determine the means to

achieve the end goal. For instance, a

goal may be ‘‘fishable, swimmable’’

waters. The management options under

consideration to achieve that goal may

include increasing enforcement of

point-source discharges, restoring fish

habitat, designing alternative sewage

treatment facilities, or implementing all

of the above.

Text Note 2–8. Tiers and Iteration:

When Is a Risk Assessment Done?

Risk assessments range from very

simple to complex and resource

demanding. How is it possible to decide

the level of effort? How many times

should the risk assessor revisit data and

assessment issues? When is the risk

assessment done?

Many of these questions can be

addressed by designing a set of tiered

assessments. These are preplanned and

prescribed sets of risk assessments of

progressive data and resource intensity.

The outcome of a given tier is to either

make a management decision, often

based on decision criteria, or continue

to the next level of effort. Many risk

assessors and public and private

organizations use this approach (e.g.,

see Gaudet, 1994; European

Community, 1993; Cowan et al., 1995;

Baker et al., 1994; Urban and Cook,

1986; Lynch et al., 1994).

An iteration is an unprescribed

reevaluation of information that may

occur at any time during a risk

assessment, including tiered

assessments. It is done in response to an

identified need, new information, or

questions raised while conducting an

assessment. As such, iteration is a

normal characteristic of risk

assessments but is not a formal planned

step. An iteration may include redoing

the risk assessment with new

assumptions and new data.

Setting up tiered assessments and

decision criteria may reduce the need

for iteration. Up-front planning and

careful development of problem

formulation will also reduce the need

for revisiting data, assumptions, and

models. However, there are no rules to

dictate how many iterations will be

necessary to answer management

questions or ensure scientific validity. A

risk assessment can be considered

complete when risk managers have

sufficient information and confidence in

the results of the risk assessment to

make a decision they can defend.

Text Note 2–9. Questions To Ask About

Scope and Complexity

Is this risk assessment mandated,

required by a court decision, or

providing guidance to a community?

Will decisions be based on

assessments of a small area evaluated in

depth or a large-scale area in less detail?

What are the spatial and temporal

boundaries of the problem?

What information is already available

compared to what is needed?

How much time can be taken, and

how many resources are available?

What practicalities constrain data

collection?

Is a tiered approach an option?

Text Note 3–1. Avoiding Potential

Shortcomings Through Problem

Formulation

The importance of problem

formulation has been shown repeatedly

in the Agency’s analysis of ecological

risk assessment case studies and in

interactions with senior EPA managers

and regional risk assessors (U.S. EPA,

1993b, 1994e). Shortcomings

consistently identified in the case

studies include (1) absence of clearly

defined goals, (2) endpoints that are

ambiguous and difficult to define and

measure, and (3) failure to identify

important risks. These and other

shortcomings can be avoided through

rigorous development of the products of

problem formulation as described in

this section of the Guidelines.

Text Note 3–2. Uncertainty in Problem

Formulation

Throughout problem formulation, risk

assessors consider what is known and

not known about a problem and its

setting. Each product of problem

formulation contains uncertainty. The

explicit treatment of uncertainty during

problem formulation is particularly

important because it will have

repercussions throughout the remainder

of the assessment. Uncertainty is

discussed in section 3.4 (Conceptual

Models).

Text Note 3–3. Initiating a Risk

Assessment: What’s Different When

Stressors, Effects, or Values Drive the

Process?

The reasons for initiating a risk

assessment influence when risk

assessors generate products in problem

formulation. When the assessment is

initiated because of concerns about

stressors, risk assessors use what is

known about the stressor and its source

to focus the assessment. Objectives for

the assessment are based on

determining how the stressor is likely to

come in contact with and affect possible

receptors. This information forms the

basis for developing conceptual models

and selecting assessment endpoints.

When an observed effect is the basis for

initiating the assessment, endpoints are

normally established first. Frequently,

the affected ecological entities and their

response form the basis for defining

assessment endpoints. Goals for

protecting the assessment endpoints are

then established, which support the

development of conceptual models. The

models aid in the identification of the

most likely stressor(s). Value-initiated

risk assessments are driven by goals for

the ecological values of concern. These

values might involve ecological entities

such as species, communities,

ecosystems, or places. Based on these

goals, assessment endpoints are selected

first to serve as an interpretation of the

goals. Once selected, the endpoints

provide the basis for identifying an

array of stressors that may be

influencing the assessment endpoints

and describing the diversity of potential

effects. This information is then

captured in the conceptual model(s).

Text Note 3–4. Assessing Available

Information: Questions to Ask

Concerning Source, Stressor, and

Exposure Characteristics, Ecosystem

Characteristics, and Effects (derived in

part from Barnthouse and Brown, 1994)

Source and Stressor Characteristics

• What is the source? Is it

anthropogenic, natural, point source, or

diffuse nonpoint?

• What type of stressor is it: chemical,

physical, or biological?

• What is the intensity of the stressor

(e.g., the dose or concentration of a

chemical, the magnitude or extent of

physical disruption, the density or

population size of a biological stressor)?

• What is the mode of action? How

does the stressor act on organisms or

ecosystem functions?

Exposure Characteristics

• With what frequency does a stressor

event occur (e.g., is it isolated, episodic,

or continuous; is it subject to natural

daily, seasonal, or annual periodicity)?

• What is its duration? How long does

it persist in the environment (e.g., for

chemical, what is its half-life, does it

bioaccumulate; for physical, is habitat

alteration sufficient to prevent recovery;

for biological, will it reproduce and

proliferate)?
Federal Register / Vol. 63, No. 93 /Thursday, May 14, 1998 / Notices 26895

• What is the timing of exposure?

When does it occur in relation to critical

organism life cycles or ecosystem events

(e.g., reproduction, lake overturn)?

• What is the spatial scale of

exposure? Is the extent or influence of

the stressor local, regional, global,

habitat-specific, or ecosystemwide?

• What is the distribution? How does

the stressor move through the

environment (e.g., for chemical, fate and

transport; for physical, movement of

physical structures; for biological, lifehistory dispersal characteristics)?

Ecosystems Potentially at Risk

• What are the geographic

boundaries? How do they relate to

functional characteristics of the

ecosystem?

• What are the key abiotic factors

influencing the ecosystem (e.g., climatic

factors, geology, hydrology, soil type,

water quality)?

• Where and how are functional

characteristics driving the ecosystem

(e.g., energy source and processing,

nutrient cycling)?

• What are the structural

characteristics of the ecosystem (e.g.,

species number and abundance, trophic

relationships)?

• What habitat types are present?

• How do these characteristics

influence the susceptibility (sensitivity

and likelihood of exposure) of the

ecosystem to the stressor(s)?

• Are there unique features that are

particularly valued (e.g., the last

representative of an ecosystem type)?

• What is the landscape context

within which the ecosystem occurs?

Ecological Effects

• What are the type and extent of

available ecological effects information

(e.g., field surveys, laboratory tests, or

structure-activity relationships)?

• Given the nature of the stressor (if

known), which effects are expected to

be elicited by the stressor?

• Under what circumstances will

effects occur?

Text Note 3–5. Salmon and

Hydropower: Salmon as the Basis for an

Assessment Endpoint

A hydroelectric dam is to be built on

a river in the Pacific Northwest where

anadromous fish such as salmon spawn.

Assessment endpoints should be

selected to assess potential ecological

risk. Of the anadromous fish, salmon

that spawn in the river are an

appropriate choice because they meet

the criteria for good assessment

endpoints. Salmon fry and adults are

important food sources for a multitude

of aquatic and terrestrial species and are

major predators of aquatic invertebrates

(ecological relevance). Salmon are

sensitive to changes in sedimentation

and substrate pebble size, require

quality cold-water habitats, and have

difficulty climbing fish ladders.

Hydroelectric dams represent

significant, and normally fatal, habitat

alteration and physical obstacles to

successful salmon breeding and fry

survival (susceptibility). Finally, salmon

support a large commercial fishery,

some species are endangered, and they

have ceremonial importance and are key

food sources for Native Americans

(relevance to management goals).

‘‘Salmon reproduction and population

recruitment’’ is a good assessment

endpoint for this risk assessment. In

addition, if salmon populations are

protected, other anadromous fish

populations are likely to be protected as

well. However, one assessment

endpoint can rarely provide the basis for

a risk assessment of complex

ecosystems. These are better represented

by a set of assessment endpoints.

Text Note 3–6. Cascading Adverse

Effects: Primary (Direct) and Secondary

(Indirect)

The interrelationships among entities

and processes in ecosystems foster a

potential for cascading effects: as one

population, species, process, or other

entity in the ecosystem is altered, other

entities are affected as well. Primary, or

direct, effects occur when a stressor acts

directly on the assessment endpoint and

causes an adverse response. Secondary,

or indirect, effects occur when the

entity’s response becomes a stressor to

another entity. Secondary effects are

often a series of effects among a

diversity of organisms and processes

that cascade through the ecosystem. For

example, application of an herbicide on

a wet meadow results in direct toxicity

to plants. Death of the wetland plants

leads to secondary effects such as loss

of feeding habitat for ducks, breeding

habitat for red-winged blackbirds,

alteration of wetland hydrology that

changes spawning habitat for fish, and

so forth.

Text Note 3–7. Identifying Susceptibility

Often it is possible to identify

ecological entities most likely to be

susceptible to a stressor. However, in

some cases where stressors are not

known at the initiation of a risk

assessment, or specific effects have not

been identified, the most susceptible

entities may not be known. Where this

occurs, professional judgment may be

required to make initial selections of

potential endpoints.

Once done, available information on

potential stressors in the system can be

evaluated to determine which of the

endpoints are most likely susceptible to

identified stressors. If an assessment

endpoint is selected for a risk

assessment that directly supports

management goals and is ultimately

found not susceptible to stressors in the

system, then a conclusion of no risk is

appropriate. However, where there are

multiple possible assessment endpoints

that address management goals and only

some of those are susceptible to a

stressor, the susceptible endpoints

should be selected. If the susceptible

endpoints are not initially selected for

an assessment, an additional iteration of

the risk assessment with alternative

assessment endpoints may be needed to

determine risk.

Text Note 3–8. Sensitivity and

Secondary Effects: The Mussel-Fish

Connection

Native freshwater mussels are

endangered in many streams.

Management efforts have focused on

maintaining suitable habitat for mussels

because habitat loss has been

considered the greatest threat to this

group. However, larval unionid mussels

must attach to the gills of a fish host for

one month during development. Each

species of mussel must attach to a

particular host species of fish. In

situations where the fish community

has been changed, perhaps due to

stressors to which mussels are

insensitive, the host fish may no longer

be available. Mussel larvae will die

before reaching maturity as a result.

Regardless of how well managers restore

mussel habitat, mussels will be lost

from this system unless the fish

community is restored. In this case, risk

is caused by the absence of exposure to

a critical resource.

Text Note 3–9. Examples of

Management Goals and Assessment

Endpoints
26896 Federal Register / Vol. 63, No. 93 /Thursday, May 14, 1998 / Notices

Case Regulatory context/management goal Assessment endpoint

Assessing Risks of New Chemical

Under Toxic Substances Control Act

(Lynch et al., 1994).

Protect ‘‘the environment’’ from ‘‘an unreasonable risk of injury’’ (TSCA § 2[b][1] and [2]); protect the aquatic environment. Goal was to exceed a concentration of concern on

no more than 20 days a year.

Survival, growth, and reproduction of

fish, aquatic invertebrates, and algae.

Special Review of Granular Carbofuran

Based on Adverse Effects on Birds

(Houseknecht, 1993).

Prevent * * * ‘‘unreasonable adverse effects on the environment’’ (FIFRA §§ [c][5] and 3[c][6]); using cost-benefit

considerations. Goal was to have no regularly repeated

bird kills.

Individual bird survival.

Modeling Future Losses of Bottomland

Forest Wetlands (Brody et al., 1993).

National Environment Policy Act may apply to environmental impact of new levee construction; also Clean

Water Act § 404.

(1) Forest community structure and

habitat value to wildlife species

(2) Species composition of wildlife community.

Pest Risk Assessment on Importation of

Logs From Chile (USDA, 1993).

Assessment was done to help provide a basis for any necessary regulation of the importation of timber and timber

products into the United States.

Survival and growth of tree species in

the western United States.

Baird and McGuire Superfund Site (terrestrial component) (Burmaster et al.,

1991; Callahan et al., 1991; Menzie

et al., 1992).

Protection of the environment (CERCLA/SARA) ................... (1) Survival of soil invertebrates

(2) Survival and reproduction of song

birds.

Waquoit Bay Estuary Watershed Risk

Assessment (U.S. EPA, 1996b).

Clean Water Act—wetlands protection; water quality criteria—pesticides; endangered species. National Estuarine

Research Reserve, Massachusetts, Area of Critical Environment Concern. Goal was to reestablish and maintain

water quality and habitat conditions to support diverse

self-sustaining commercial, recreational, and native fish,

water-dependent wildlife, and shellfish and to reverse ongoing degradation.

(1) Estuarine eelgrass habitat abundance and distribution

(2) Estuarine fish species diversity and

abundance

(3) Freshwater pond benthic invertebrate species diversity and abundance.

Text Note 3–10. Common Problems in

Selecting Assessment Endpoints

• Endpoint is a goal (e.g., maintain

and restore endemic populations).

• Endpoint is vague (e.g., estuarine

integrity instead of eelgrass abundance

and distribution).

• Ecological entity is better as a

measure (e.g., emergence of midges can

be used to evaluate an assessment

endpoint for fish feeding behavior).

• Ecological entity may not be as

sensitive to the stressor (e.g., catfish

versus salmon for sedimentation).

• Ecological entity is not exposed to

the stressor (e.g., using insectivorous

birds for avian risk of pesticide

application to seeds).

• Ecological entities are irrelevant to

the assessment (e.g., lake fish in salmon

stream).

• Importance of a species or attributes

of an ecosystem are not fully considered

(e.g., mussel-fish connection, see text

note 3–8).

• Attribute is not sufficiently

sensitive for detecting important effects

(e.g., survival compared with

recruitment for endangered species).

Text Note 3–11. What Are the Benefits

of Developing Conceptual Models?

• The process of creating a

conceptual model is a powerful learning

tool.

• Conceptual models are easily

modified as knowledge increases.

• Conceptual models highlight what

is known and not known and can be

used to plan future work.

• Conceptual models can be a

powerful communication tool. They

provide an explicit expression of the

assumptions and understanding of a

system for others to evaluate.

• Conceptual models provide a

framework for prediction and are the

template for generating more risk

hypotheses.

Text Note 3–12. What Are Risk

Hypotheses, and Why Are They

Important?

Risk hypotheses are proposed answers

to questions risk assessors have about

what responses assessment endpoints

will show when they are exposed to

stressors and how exposure will occur.

Risk hypotheses clarify and articulate

relationships that are posited through

the consideration of available data,

information from scientific literature,

and the best professional judgment of

risk assessors developing the conceptual

models. This explicit process opens the

risk assessment to peer review and

evaluation to ensure the scientific

validity of the work. Risk hypotheses

are not equivalent to statistical testing of

null and alternative hypotheses.

However, predictions generated from

risk hypotheses can be tested in a

variety of ways, including standard

statistical approaches.

Text Note 3–13. Examples of Risk

Hypotheses

Hypotheses include known

information that sets the problem in

perspective and the proposed

relationships that need evaluation.

Stressor-initiated: Chemicals with a

high Kow tend to bioaccumulate. PMN

chemical A has a Kow of 5.5 and

molecular structure similar to known

chemical stressor B.

Hypotheses: Based on the Kow of

chemical A, the mode of action of

chemical B, and the food web of the

target ecosystem, when the PMN

chemical is released at a specified rate,

it will bioaccumulate sufficiently in 5

years to cause developmental problems

in wildlife and fish.

Effects-initiated: Bird kills were

repeatedly observed on golf courses

following the application of the

pesticide carbofuran, which is highly

toxic.

Hypotheses: Birds die when they

consume recently applied granulated

carbofuran; as the level of application

increases, the number of dead birds

increases. Exposure occurs when dead

and dying birds are consumed by other

animals. Birds of prey and scavenger

species will die from eating

contaminated birds.

Ecological value-initiated: Waquoit

Bay, Massachusetts, supports

recreational boating and commercial

and recreational shellfishing and is a

significant nursery for finfish. Large

mats of macroalgae clog the estuary,

most of the eelgrass has died, and the

scallops are gone.

Hypotheses: Nutrient loading from

septic systems, air pollution, and lawn

fertilizers causes eelgrass loss by
Federal Register / Vol. 63, No. 93 /Thursday, May 14, 1998 / Notices 26897

shading from algal growth and direct

toxicity from nitrogen compounds. Fish

and shellfish populations are decreasing

because of loss of eelgrass habitat and

periodic hypoxia from excess algal

growth and low dissolved oxygen.

Text Note 3–14. Uncertainty in Problem

Formulation

Uncertainties in problem formulation

are manifested in the quality of

conceptual models. To address

uncertainty:

• Be explicit in defining assessment

endpoints; include both an entity and

its measurable attributes.

• Reduce or define variability by

carefully defining boundaries for the

assessment.

• Be open and explicit about the

strengths and limitations of pathways

and relationships depicted in the

conceptual model.

• Identify and describe rationale for

key assumptions made because of lack

of knowledge, model simplification,

approximation, or extrapolation.

• Describe data limitations.

Text Note 3–15. Why Was Measurement

Endpoint Changed?

The original definition of

measurement endpoint was ‘‘a

measurable characteristic that is related

to the valued characteristic chosen as

the assessment endpoint’’ (Suter, 1989;

U.S. EPA, 1992a). The definition refers

specifically to the response of an

assessment endpoint to a stressor. It

does not include measures of ecosystem

characteristics, life-history

considerations, exposure, or other

measures. Because measurement

endpoint does not encompass these

other important measures and there was

confusion about its meaning, the term

was replaced with measures of effect

and supplemented by two other

categories of measures.

Text Note 3–16. Examples of a

Management Goal, Assessment

Endpoint, and Measures

Goal: Viable, self-sustaining coho

salmon population that supports a

subsistence and sport fishery.

Assessment Endpoint: Coho salmon

breeding success, fry survival, and adult

return rates.

Measures of Effects

• Egg and fry response to low

dissolved oxygen.

• Adult behavior in response to

obstacles.

• Spawning behavior and egg survival

with changes in sedimentation.

Measures of Ecosystem and Receptor

Characteristics

• Water temperature, water velocity,

and physical obstructions.

• Abundance and distribution of

suitable breeding substrate.

• Abundance and distribution of

suitable food sources for fry.

• Feeding, resting, and breeding

behavior.

• Natural reproduction, growth, and

mortality rates.

Measures of Exposure

• Number of hydroelectric dams and

associated ease of fish passage.

• Toxic chemical concentrations in

water, sediment, and fish tissue.

• Nutrient and dissolved oxygen

levels in ambient waters.

• Riparian cover, sediment loading,

and water temperature.

Text Note 3–17. How Do Water Quality

Criteria Relate to Assessment

Endpoints?

Water quality criteria (U.S. EPA,

1986b) have been developed for the

protection of aquatic life from chemical

stressors. This text note shows how the

elements of a water quality criterion

correspond to management goals,

management decisions, assessment

endpoints, and measures.

Regulatory Goal

• Clean Water Act, § 101: Protect the

chemical, physical, and biological

integrity of the Nation’s waters.

Program Management Decisions

• Protect 99% of individuals in 95%

of the species in aquatic communities

from acute and chronic effects resulting

from exposure to a chemical stressor.

Assessment Endpoints

• Survival of fish, aquatic

invertebrate, and algal species under

acute exposure.

• Survival, growth, and reproduction

of fish, aquatic invertebrate, and algal

species under chronic exposure.

Measures of Effect

• Laboratory LC50s for at least eight

species meeting certain requirements.

• Chronic no-observed-adverse-effect

levels (NOAELs) for at least three

species meeting certain requirements.

Measures of Ecosystem and Receptor

Characteristics

• Water hardness (for some metals).

• pH.

The water quality criterion is a

benchmark level derived from a

distributional analysis of single-species

toxicity data. It is assumed that the

species tested adequately represent the

composition and sensitivities of species

in a natural community.

Text Note 3–18. The Data Quality

Objectives Process

The data quality objectives (DQO)

process combines elements of both

planning and problem formulation in its

seven-step format.

Step 1. State the problem. Review

existing information to concisely

describe the problem to be studied.

Step 2. Identify the decision.

Determine what questions the study will

try to resolve and what actions may

result.

Step 3. Identify inputs to the decision.

Identify information and measures

needed to resolve the decision

statement.

Step 4. Define study boundaries.

Specify time and spatial parameters and

where and when data should be

collected.

Step 5. Develop decision rule. Define

statistical parameter, action level, and

logical basis for choosing alternatives.

Step 6. Specify tolerable limits on

decision errors. Define limits based on

the consequences of an incorrect

decision.

Step 7. Optimize the design. Generate

alternative data collection designs and

choose most resource-effective design

that meets all DQOs.

Text Note 4–1. Data Collection and the

Analysis Phase

Data needs are identified during

problem formulation (the analysis plan

step), and data are collected before the

start of the analysis phase. These data

may be collected for the specific

purpose of a particular risk assessment,

or they may be available from previous

studies. If additional data needs are

identified as the assessment proceeds,

the analysis phase may be temporarily

halted while data are collected or the

assessor (in consultation with the risk

manager) may choose to iterate the

problem formulation again. Data

collection methods are not described in

these Guidelines. However, the

evaluation of data for the purposes of

risk assessment is discussed in section

4.2.

Text Note 4–2. The American National

Standard for Quality Assurance

The Specifications and Guidelines for

Quality Systems for Environmental Data

Collection and Environmental

Technology Programs (ASQC, 1994)

recognize several areas that are

important to ensuring that

environmental data will meet study

objectives, including:
26898 Federal Register / Vol. 63, No. 93 /Thursday, May 14, 1998 / Notices

• Planning and scoping.

• Designing data collection

operations.

• Implementing and monitoring

planned operations.

• Assessing and verifying data

usability.

Text Note 4–3. Questions for Evaluating

a Study’s Utility for Risk Assessment

Are the study objectives relevant to

the risk assessment?

Are the variables and conditions the

study represents comparable with those

important to the risk assessment?

Is the study design adequate to meet

its objectives?

Was the study conducted properly?

How are variability and uncertainty

treated and reported?

Text Note 4–4. Uncertainty Evaluation

in the Analysis Phase

Source of uncertainty Example analysis phase strategies Specific example

Unclear communication .................. Contact principal investigator or other study participants if objectives or methods of literature studies

are unclear.

Clarify whether the study was designed to characterize local populations or regional populations.

Document decisions made during the course of the

assessment.

Discuss rationale for selecting the critical toxicity

study.

Descriptive errors ........................... Verify that data sources followed appropriate QA/QC

procedures.

Double-check calculations and data entry.

Variability ........................................ Describe heterogeneity using point estimates (e.g.,

central tendency and high end) or by constructing

probability or frequency distributions.

Display differences in species sensitivity using a cumulative distribution function.

Differentiate from uncertainty due to lack of knowledge.

Data gaps ....................................... Collect needed data ................................................... Discuss rationale for using a factor of 10 to extrapolate between a lowest-observed-adverse-effect

level (LOAEL) and a NOAEL.

Describe approaches used for bridging gaps and

their rationales.

Differentiate science-based judgments from policybased judgments.

Uncertainty about a quantity’s true

value.

Use standard statistical methods to construct probability distributions or point estimates (e.g., confidence limits).Present the upper confidence limit on the arithmetic

mean soil concentration, in addition to the best

estimate of the arithmetic mean.

Evaluate power of designed experiments to detect

differences.

Collect additional data.

Verify location of samples or other spatial features .. Ground-truth remote sensing data.

Model structure uncertainty (process models).Discuss key aggregations and model simplifications Discuss combining different species into a group

based on similar feeding habits.

Compare model predictions with data collected in

the system of interest.

Uncertainty about a model’s form

(empirical models).

Evaluate whether alternative models should be combined formally or treated separately.Present results obtained using alternative models.

Compare model predictions with data collected in

the system of interest.

Compare results of a plant uptake model with data

collected in the field.

Text Note 4–5. Considering the Degree

of Aggregation in Models

Wiegert and Bartell (1994) suggest the

following considerations for evaluating

the proper degree of aggregation or

disaggregation:

1. Do not aggregate components with

greatly disparate flux rates.

2. Do not greatly increase the

disaggregation of the structural aspects

of the model without a corresponding

increase in the sophistication of the

functional relationships and controls.

3. Disaggregate models only insofar as

required by the goals of the model to

facilitate testing.

Text Note 4–6. Questions for Source

Description

Where does the stressor originate?

What environmental media first

receive stressors?

Does the source generate other

constituents that will influence a

stressor’s eventual distribution in the

environment?

Are there other sources of the same

stressor?

Are there background sources?

Is the source still active?

Does the source produce a distinctive

signature that can be seen in the

environment, organisms, or

communities?

Additional Questions for Introduction

of Biological Stressors

Is there an opportunity for repeated

introduction or escape into the new

environment?

Will the organism be present on a

transportable item?

Are there mitigation requirements or

conditions that would kill or impair the

organism before entry, during transport,

or at the port of entry?

Text Note 4–7. Questions to Ask in

Evaluating Stressor Distribution

What are the important transport

pathways?

What characteristics of the stressor

influence transport?

What characteristics of the ecosystem

will influence transport?

What secondary stressors will be

formed?

Where will they be transported?

Text Note 4–8. General Mechanisms of

Transport and Dispersal

Physical, Chemical, and Biological

Stressors

• By air current.

• In surface water (rivers, lakes,

streams).

• Over and/or through the soil

surface.

• Through ground water.

Primarily Chemical Stressors

• Through the food web.
Federal Register / Vol. 63, No. 93 /Thursday, May 14, 1998 / Notices 26899

Primarily Biological Stressors

• Splashing or raindrops.

• Human activity (boats, campers).

• Passive transmittal by other

organisms.

• Biological vectors.

Text Note 4–9. Questions To Ask in

Describing Contact or Co-Occurrence

Must the receptor actually contact the

stressor for adverse effects to occur?

Must the stressor be taken up into a

receptor for adverse effects to occur?

What characteristics of the receptors

will influence the extent of contact or

co-occurrence?

Will abiotic characteristics of the

environment influence the extent of

contact or co-occurrence?

Will ecosystem processes or

community-level interactions influence

the extent of contact or co-occurrence?

Text Note 4–10. Example of an

Exposure Equation: Calculating a

Potential Dose via Ingestion

ADD C FR NIR pot k k k

k

m

= × × ( ) =

∑

1

Where:

ADDpot=Potential average daily dose

(e.g., in mg/kg-day)

Ck=Average contaminant concentration

in the kth type of food (e.g., in mg/

kg wet weight)

FRk=Fraction of intake of the kth food

type that is from the contaminated

area (unitless)

NIRk=Normalized ingestion rate of the

kth food type on a wet-weight basis

(e.g., in kg food/kg body-weightday).

m=Number of contaminated food types

Note: A similar equation can be used

to calculate uptake by adding an

absorption factor that accounts for the

fraction of the chemical in the kth food

type that is absorbed into the organism.

The choice of potential dose or uptake

depends on the form of the stressorresponse relationship. Source: U.S. EPA,

1993a.

Text Note 4–11. Measuring Internal

Dose Using Biomarkers and Tissue

Residues

Biomarkers and tissue residues are

particularly useful when exposure

across many pathways must be

integrated and when site-specific factors

influence bioavailability. They can also

be very useful when metabolism and

accumulation kinetics are important,

although these factors can make

interpretation of results more difficult

(McCarty and Mackay, 1993). These

methods are most useful when they can

be quantitatively linked to the amount

of stressor originally contacted by the

organism. In addition, they are most

useful when the stressor-response

relationship expresses the amount of

stressor in terms of the tissue residue or

biomarker (van Gestel and van

Brummelen, 1996). Standard analytical

methods are generally available for

tissue residues, making them more

readily usable for routine assessments

than biomarkers. Readers are referred to

the review in Ecotoxicology (Vol. 3,

Issue 3, 1994), Huggett et al. (1992), and

the debate in Human Health and

Ecological Risk Assessment (Vol. 2,

Issue 2, 1996).

Text Note 4–12. Questions Addressed by

the Exposure Profile

How does exposure occur?

What is exposed?

How much exposure occurs? When

and where does it occur?

How does exposure vary?

How uncertain are the exposure

estimates?

What is the likelihood that exposure

will occur?

Text Note 4–13. Questions for StressorResponse Analysis

Does the assessment require point

estimates or stressor-response curves?

Does the assessment require the

establishment of a ‘‘no-effect’’ level?

Would cumulative effects

distributions be useful?

Will analyses be used as input to a

process model?

Text Note 4–14. Qualitative StressorResponse Relationships

The relationship between stressor and

response can be described qualitatively,

for instance, using categories of high,

medium, and low, to describe the

intensity of response given exposure to

a stressor. For example, Pearlstine et al.

(1985) assumed that seeds would not

germinate if they were inundated with

water at the critical time. This stressorresponse relationship was described

simply as a yes or no. In most cases,

however, the objective is to describe

quantitatively the intensity of response

associated with exposure, and in the

best case, to describe how intensity of

response changes with incremental

increases in exposure.

Text Note 4–15. Median Effect Levels

Median effects are those effects

elicited in 50% of the test organisms

exposed to a stressor, typically chemical

stressors. Median effect concentrations

can be expressed in terms of lethality or

mortality and are known as LC50 or

LD50, depending on whether

concentrations (in the diet or in water)

or doses (mg/kg) were used. Median

effects other than lethality (e.g., effects

on growth) are expressed as EC50 or

ED50. The median effect level is always

associated with a time parameter (e.g.,

24 or 48 hours). Because these tests

seldom exceed 96 hours, their main

value lies in evaluating short-term

effects of chemicals. Stephan (1977)

discusses several statistical methods to

estimate the median effect level.

Text Note 4–16. No-Effect Levels

Derived From Statistical Hypothesis

Testing

Statistical hypothesis tests have

typically been used with chronic

toxicity tests of chemical stressors that

evaluate multiple endpoints. For each

endpoint, the objective is to determine

the highest test level for which effects

are not statistically different from the

controls (the no-observed-adverse-effect

level, NOAEL) and the lowest level at

which effects were statistically

significant from the control (the lowestobserved-adverse-effect level, LOAEL).

The range between the NOAEL and the

LOAEL is sometimes called the

maximum acceptable toxicant

concentration, or MATC. The MATC,

which can also be reported as the

geometric mean of the NOAEL and the

LOAEL (i.e., GMATC), provides a useful

reference with which to compare

toxicities of various chemical stressors.

Reporting the results of chronic tests

in terms of the MATC or GMATC has

been widely used within the Agency for

evaluating pesticides and industrial

chemicals (e.g., Urban and Cook, 1986;

Nabholz, 1991).

Text Note 4–17. General Criteria for

Causality (Adapted From Fox, 1991)

Criteria Strongly Affirming Causality

• Strength of association.

• Predictive performance.

• Demonstration of a stressorresponse relationship.

• Consistency of association.

Criteria Providing a Basis for Rejecting

Causality

• Inconsistency in association.

• Temporal incompatibility.

• Factual implausibility.

Other Relevant Criteria

• Specificity of association.

• Theoretical and biological

plausibility.

Text Note 4–18. Koch’s Postulates

(Pelczar and Reid, 1972)

• A pathogen must be consistently

found in association with a given

disease.
26900 Federal Register / Vol. 63, No. 93 /Thursday, May 14, 1998 / Notices

• The pathogen must be isolated from

the host and grown in pure culture.

• When inoculated into test animals,

the same disease symptoms must be

expressed.

• The pathogen must again be

isolated from the test organism.

Text Note 4–19. Examples of

Extrapolations To Link Measures of

Effect to Assessment Endpoints

Every risk assessment has data gaps

that should be addressed, but it is not

always possible to obtain more

information. When there is a lack of

time, monetary resources, or a practical

means to acquire more data,

extrapolations such as those listed

below may be the only way to bridge

gaps in available data. Extrapolations

may be:

• Between taxa (e.g., bluegill to

rainbow trout).

• Between responses (e.g., mortality

to growth or reproduction).

• From laboratory to field.

• Between geographic areas.

• Between spatial scales.

• From data collected over a short

time frame to longer-term effects.

Text Note 4–20. Questions Related to

Selecting Extrapolation Approaches

How specific is the assessment

endpoint?

Does the spatial or temporal extent of

exposure suggest the need for additional

receptors or extrapolation models?

Are the quantity and quality of the

data available sufficient for planned

extrapolations and models?

Is the proposed extrapolation

technique consistent with ecological

information?

How much uncertainty is acceptable?

Text Note 4–21. Questions To Consider

When Extrapolating From Effects

Observed in the Laboratory to Field

Effects of Chemicals

Exposure Factors

How will environmental fate and

transformation of the chemical affect

exposure in the field?

How comparable are exposure

conditions and the timing of exposure?

How comparable are the routes of

exposure?

How do abiotic factors influence

bioavailability and exposure?

How likely are preference or

avoidance behaviors?

Effects Factors

What is known about the biotic and

abiotic factors controlling populations

of the organisms of concern?

To what degree are critical life-stage

data available?

How may exposure to the same or

other stressors in the field have altered

organism sensitivity?

Text Note 4–22. Questions Addressed by

the Stressor-Response Profile

What ecological entities are affected?

What is the nature of the effect(s)?

What is the intensity of the effect(s)?

Where appropriate, what is the time

scale for recovery?

What causal information links the

stressor with any observed effects?

How do changes in measures of

effects relate to changes in assessment

endpoints?

What is the uncertainty associated

with the analysis?

Text Note 5–1. An Example of Field

Methods Used for Risk Estimation

Along with quotients comparing field

measures of exposure with laboratory

acute toxicity data (see text note 5–3),

EPA evaluated the risks of granular

carbofuran to birds based on incidents

of bird kills following carbofuran

applications. More than 40 incidents

involving nearly 30 species of birds

were documented. Although reviewers

identified problems with individual

field studies (e.g., lack of appropriate

control sites, lack of data on carcasssearch efficiencies, no examination of

potential synergistic effects of other

pesticides, and lack of consideration of

other potential receptors such as small

mammals), there was so much evidence

of mortality associated with carbofuran

application that the study deficiencies

did not alter the conclusions of high risk

found by the assessment (Houseknecht,

1993).

Text Note 5–2. Using Qualitative

Categories to Estimate Risks of an

Introduced Species

The importation of logs from Chile

required an assessment of the risks

posed by the potential introduction of

the bark beetle, Hylurgus ligniperda

(USDA, 1993). Experts judged the

potential for colonization and spread of

the species, and their opinions were

expressed as high, medium, or low as to

the likelihood of establishment

(exposure) or consequential effects of

the beetle. Uncertainties were similarly

expressed. A ranking scheme was then

used to sum the individual elements

into an overall estimate of risk (high,

medium, or low). Narrative explanations

of risk accompanied the overall

rankings.

Text Note 5–3. Applying the Quotient

Method

When applying the quotient method

to chemical stressors, the effects

concentration or dose (e.g., an LC50,

LD50, EC50, ED50, NOAEL, or LOAEL) is

frequently adjusted by uncertainty

factors before division into the exposure

number (U.S. EPA, 1984; Nabholz, 1991;

Urban and Cook, 1986; see section

4.3.1.3), although EPA used a slightly

different approach in estimating the

risks to the survival of birds that forage

in agricultural areas where the pesticide

granular carbofuran is applied

(Houseknecht, 1993). In this case, EPA

calculated the quotient by dividing the

estimated exposure levels of carbofuran

granules in surface soils (number/ft 2) by

the granules/LD50 derived from singledose avian toxicity tests. The

calculation yields values with units of

LD50/ft 2. It was assumed that a higher

quotient value corresponded to an

increased likelihood that a bird would

be exposed to lethal levels of granular

carbofuran at the soil surface. Minimum

and maximum values for LD50/ft 2 were

estimated for songbirds, upland game

birds, and waterfowl that may forage

within or near 10 different agricultural

crops.

Text Note 5–4. Comparing an Exposure

Distribution With a Point Estimate of

Effects.

The EPA Office of Pollution

Prevention and Toxics uses a

Probabilistic Dilution Model (PDM3) to

generate a distribution of daily average

chemical concentrations based on

estimated variations in stream flow in a

model system. The PDM3 model

compares this exposure distribution

with an aquatic toxicity test endpoint to

estimate how many days in a 1-year

period the endpoint concentration is

exceeded (Nabholz et al., 1993; U.S.

EPA, 1988b). The frequency of

exceedance is based on the duration of

the toxicity test used to derive the

effects endpoint. Thus, if the endpoint

was an acute toxicity level of concern,

an exceedance would be identified if the

level of concern was exceeded for 4

days or more (not necessarily

consecutive). The exposure estimates

are conservative in that they assume

instantaneous mixing of the chemical in

the water column and no losses due to

physical, chemical, or biodegradation

effects.

Text Note 5–5. Comparing Cumulative

Exposure and Effects Distributions for

Chemical Stressors

Exposure distributions for chemical

stressors can be compared with effects

distributions derived from point

estimates of acute or chronic toxicity

values for different species (e.g., HCN,

1993; Cardwell et al., 1993; Baker et al.,

1994; Solomon et al., 1996). Figure 5–
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5 shows a distribution of exposure

concentrations of an herbicide

compared with single-species toxicity

data for algae (and one vascular plant

species) for the same chemical. The

degree of overlap of the curves indicates

the likelihood that a certain percentage

of species may be adversely affected.

For example, figure 5–5 indicates that

the 10th centile of algal species’ EC5

values is exceeded less than 10% of the

time.

The predictive value of this approach

is evident. The degree of risk reduction

that could be achieved by changes in

exposure associated with proposed risk

mitigation options can be readily

determined by comparing modified

exposure distributions with the effects

distribution curve.

When using effects distributions

derived from single-species toxicity

data, risk assessors should consider the

following questions:

• Does the subset of species for which

toxicity test data are available represent

the range of species present in the

environment?

• Are particularly sensitive (or

insensitive) groups of organisms

represented in the distribution?

• If a criterion level is selected’e.g.,

protect 95% of species—does the 5% of

potentially affected species include

organisms of ecological, commercial, or

recreational significance?

Text Note 5–6. Estimating Risk With

Process Models

Models that integrate both exposure

and effects information can be used to

estimate risk. During risk estimation, it

is important that both the strengths and

limitations of a process model approach

be highlighted. Brody et al. (1993; see

Appendix D) linked two process models

to integrate exposure and effects

information and forecast spatial and

temporal changes in forest communities

and their wildlife habitat value. While

the models were useful for projecting

long-term effects based on an

understanding of the underlying

mechanisms of change in forest

communities and wildlife habitat, they

could not evaluate all possible stressors

of concern and were limited in the plant

and wildlife species they could

consider. Understanding both the

strengths and limitations of models is

essential for accurately representing the

overall confidence in the assessment.

Text Note 5–7. What Are Statistically

Significant Effects?

Statistical testing is the ‘‘statistical

procedure or decision rule that leads to

establishing the truth or falsity of a

hypothesis * * *’’ (Alder and Roessler,

1972). Statistical significance is based

on the number of data points, the nature

of their distribution, whether

intertreatment variance exceeds

intratreatment variance in the data, and

the a priori significance level (α). The

types of statistical tests and the

appropriate protocols (e.g., power of

test) for these tests should be

established as part of the analysis plan

during problem formulation.

Text Note 5–8. Possible Risk Assessment

Report Elements

• Describe risk assessor/risk manager

planning results.

• Review the conceptual model and

the assessment endpoints.

• Discuss the major data sources and

analytical procedures used.

• Review the stressor-response and

exposure profiles.

• Describe risks to the assessment

endpoints, including risk estimates and

adversity evaluations.

• Review and summarize major areas

of uncertainty (as well as their

direction) and the approaches used to

address them.

• Discuss the degree of scientific

consensus in key areas of uncertainty.

′ Identify major data gaps and, where

appropriate, indicate whether gathering

additional data would add significantly

to the overall confidence in the

assessment results.

′ Discuss science policy judgments or

default assumptions used to bridge

information gaps and the basis for these

assumptions.

′ Discuss how the elements of

quantitative uncertainty analysis are

embedded in the estimate of risk.

Text Note 5–9. Clear, Transparent,

Reasonable, and Consistent Risk

Characterizations

For Clarity

• Be brief; avoid jargon.

• Make language and organization

understandable to risk managers and the

informed lay person.

• Fully discuss and explain unusual

issues specific to a particular risk

assessment.

For Transparency

• Identify the scientific conclusions

separately from policy judgments.

• Clearly articulate major differing

viewpoints of scientific judgments.

• Define and explain the risk

assessment purpose (e.g., regulatory

purpose, policy analysis, priority

setting).

• Fully explain assumptions and

biases (scientific and policy).

For Reasonableness

• Integrate all components into an

overall conclusion of risk that is

complete, informative, and useful in

decision making.

• Acknowledge uncertainties and

assumptions in a forthright manner.

• Describe key data as experimental,

state-of-the-art, or generally accepted

scientific knowledge.

• Identify reasonable alternatives and

conclusions that can be derived from

the data.

• Define the level of effort (e.g., quick

screen, extensive characterization) along

with the reason(s) for selecting this level

of effort.

• Explain the status of peer review.

For Consistency with Other Risk

Characterizations

• Describe how the risks posed by

one set of stressors compare with the

risks posed by a similar stressor(s) or

similar environmental conditions.

• Indicate how the strengths and

limitations of the assessment compare

with past assessments.

Text Note 6–1. Questions Regarding

Risk Assessment Results (Adapted From

U.S. EPA, 1993c)

Questions Principally for Risk

Assessors To Ask Risk Managers

• Are the risks sufficiently well

defined (and data gaps small enough) to

support a risk management decision?

• Was the right problem analyzed?

• Was the problem adequately

characterized?

Questions Principally for Risk

Managers To Ask Risk Assessors

• What effects might occur?

• How adverse are the effects?

• How likely is it that effects will

occur?

• When and where do the effects

occur?

• How confident are you in the

conclusions of the risk assessment?

• What are the critical data gaps, and

will information be available in the near

future to fill these gaps?

• Are more ecological risk assessment

iterations required?

• How could monitoring help

evaluate the results of the risk

management decision?

Text Note 6–2. Risk Communication

Considerations for Risk Managers (U.S.

EPA, 1995b)

• Plan carefully and evaluate the

success of your communication efforts.

• Coordinate and collaborate with

other credible sources.

• Accept and involve the public as a

legitimate partner.
26902 Federal Register / Vol. 63, No. 93 /Thursday, May 14, 1998 / Notices

• Listen to the public’s specific

concerns.

• Be honest, frank, and open.

• Speak clearly and with compassion.

• Meet the needs of the media.

Text Note A–1. Stressor vs. Agent

Agent has been suggested as an

alternative for the term stressor (Suter et

al., 1994). Agent is thought to be a more

neutral term than stressor, but agent is

also associated with certain classes of

chemicals (e.g., chemical warfare

agents). In addition, agent has the

connotation of the entity that is initially

released from the source, whereas

stressor has the connotation of the entity

that causes the response. Agent is used

in EPA’s Guidelines for Exposure

Assessment (U.S. EPA, 1992b) (i.e., with

exposure defined as ‘‘contact of a

chemical, physical, or biological

agent’’). The two terms are considered to

be nearly synonymous, but stressor is

used throughout these Guidelines for

internal consistency.

Appendix A—Changes From EPA’s

Ecological Risk Assessment Framework

EPA has gained much experience with the

ecological risk assessment process since the

publication of the Framework Report (U.S.

EPA, 1992a) and has received many

suggestions for modifications of both the

process and the terminology. While EPA is

not recommending major changes in the

overall ecological risk assessment process,

modifications are summarized here to assist

those who may already be familiar with the

Framework Report. Changes in the diagram

are discussed first, followed by changes in

terminology and definitions.

A.1. Changes in the Framework Diagram

The revised framework diagram is shown

in figure 1–2. Within each phase, rectangles

are used to designate inputs, hexagons

indicate actions, and circles represent

outputs. There have been some minor

changes in the wording for the boxes outside

of the risk assessment process (planning;

communicating results to the risk manager;

acquire data, iterate process, monitor results).

‘‘Iterate process’’ was added to emphasize the

iterative (and frequently tiered) nature of risk

assessment. The term ‘‘interested parties’’

was added to the planning and risk

management boxes to indicate their

increasing role in the risk assessment process

(Commission on Risk Assessment and Risk

Management, 1997). The new diagram of

problem formulation contains several

changes. The hexagon emphasizes the

importance of integrating available

information before selecting assessment

endpoints and building conceptual models.

The three products of problem formulation

are enclosed in circles. Assessment

endpoints are shown as a key product that

drives conceptual model development. The

conceptual model remains a central product

of problem formulation. The analysis plan

has been added as an explicit product of

problem formulation to emphasize the need

to plan data evaluation and interpretation

before analyses begin.

In the analysis phase, the left-hand side of

figure 1–2 shows the general process of

characterization of exposure, and the righthand side shows the characterization of

ecological effects. It is important that

evaluation of these two aspects of analysis is

an interactive process to ensure compatible

outputs that can be integrated in risk

characterization. The dotted line and

hexagon that include both the exposure and

ecological response analyses emphasize this

interaction. In addition, the first three boxes

in analysis now include the measures of

exposure, effects, and ecosystem and receptor

characteristics that provide input to the

exposure and ecological response analyses.

Experience with the application of risk

characterization as outlined in the

Framework Report suggests the need for

several modifications in this process. Risk

estimation entails the integration of exposure

and effects estimates along with an analysis

of uncertainties. The process of risk

estimation outlined in the Framework Report

separates integration and uncertainty. The

original purpose for this separation was to

emphasize the importance of estimating

uncertainty. This separation is no longer

needed since uncertainty analysis is now

explicitly addressed in most risk integration

methods.

The description of risk is similar to the

process described in the Framework Report.

Topics included in the risk description

include the lines of evidence that support

causality and a determination of the

ecological adversity of observed or predicted

effects. Considerations for reporting risk

assessment results are also described.

A.2. Changes in Definitions and Terminology

Except as noted below, these Guidelines

retain definitions used in the Framework

Report (see Appendix B). Some definitions

have been revised, especially those related to

endpoints and exposure. Some changes in

the classification of uncertainty from the

Framework Report are also described in this

section.

A.2.1. Endpoint Terminology

The Framework Report uses the assessment

and measurement endpoint terminology of

Suter (1990), but offers no specific terms for

measures of stressor levels or ecosystem

characteristics. Experience has demonstrated

that measures unrelated to effects are

sometimes inappropriately called

measurement endpoints, which were defined

by Suter (1990) as ‘‘measurable responses to

a stressor that are related to the valued

characteristic chosen as assessment

endpoints.’’ These Guidelines replace

measurement endpoint with measure of

effect, which is ‘‘a change in an attribute of

an assessment endpoint or its surrogate in

response to a stressor to which it is exposed.’’

An assessment endpoint is an explicit

expression of the environmental value to be

protected, operationally defined by an entity

and its attributes. Since data other than those

required to evaluate responses (i.e., measures

of effects) are required for an ecological risk

assessment, two additional types of measures

are used. Measures of exposure include

stressor and source measurements, while

measures of ecosystem and receptor

characteristics include, for example, habitat

measures, soil parameters, water quality

conditions, or life-history parameters that

may be necessary to better characterize

exposure or effects. Any of the three types of

measures may be actual data (e.g., mortality),

summary statistics (e.g., an LC50), or

estimated values (e.g., an LC50 estimated from

a structure-activity relationship).

A.2.2. Exposure Terminology

These Guidelines define exposure in a

manner that is relevant to any chemical,

physical, or biological entity. While the

broad concepts are the same, the language

and approaches vary depending on whether

a chemical, physical, or biological entity is

the subject of assessment. Key exposurerelated terms and their definitions are:

• Source. A source is an entity or action

that releases to the environment or imposes

on the environment a chemical, physical, or

biological stressor or stressors. Sources may

include a waste treatment plant, a pesticide

application, a logging operation, introduction

of exotic organisms, or a dredging project.

• Stressor. A stressor is any physical,

chemical, or biological entity that can induce

an adverse response. This term is used

broadly to encompass entities that cause

primary effects and those primary effects that

can cause secondary (i.e., indirect) effects.

Stressors may be chemical (e.g., toxics or

nutrients), physical (e.g., dams, fishing nets,

or suspended sediments), or biological (e.g.,

exotic or genetically engineered organisms).

While risk assessment is concerned with the

characterization of adverse responses, under

some circumstances a stressor may be neutral

or produce effects that are beneficial to

certain ecological components (see text note

A–1). Primary effects may also become

stressors. For example, a change in a

bottomland hardwood plant community

affected by rising water levels can be thought

of as a stressor influencing the wildlife

community. Stressors may also be formed

through abiotic interactions; for example, the

increase in ultraviolet light reaching the

Earth’s surface results from the interaction of

the original stressors released

(chlorofluorocarbons) with the ecosystem

(stratospheric ozone).

• Exposure. As discussed above, these

Guidelines use the term exposure broadly to

mean ‘‘subjected to some action or

influence.’’ Used in this way, exposure

applies to physical and biological stressors as

well as to chemicals (organisms are

commonly said to be exposed to radiation,

pathogens, or heat). Exposure is also

applicable to higher levels of biological

organization, such as exposure of a benthic

community to dredging, exposure of an owl

population to habitat modification, or

exposure of a wildlife population to hunting.

Although the operational definition of

exposure, particularly the units of measure,

depends on the stressor and receptor (defined

below), the following general definition is

applicable: Exposure is the contact or cooccurrence of a stressor with a receptor.

• Receptor. The receptor is the ecological

entity exposed to the stressor. This term may
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refer to tissues, organisms, populations,

communities, and ecosystems. While either

‘‘ecological component’’ (U.S. EPA, 1992a) or

‘‘biological system’’ (Cohrssen and Covello,

1989) are alternative terms, ‘‘receptor’’ is

usually clearer in discussions of exposure

where the emphasis is on the stressorreceptor relationship.

As discussed below, both disturbance and

stress regime have been suggested as

alternative terms for exposure. Neither term

is used in these Guidelines, which instead

use exposure as broadly defined above.

• Disturbance. A disturbance is any event

or series of events that disrupts ecosystem,

community, or population structure and

changes resources, substrate availability, or

the physical environment (modified slightly

from White and Pickett, 1985). Defined in

this way, disturbance is clearly a kind of

exposure (i.e., an event that subjects a

receptor, the disturbed system, to the actions

of a stressor). Disturbance may be a useful

alternative to stressor specifically for

physical stressors that are deletions or

modifications (e.g., logging, dredging,

flooding).

• Stress Regime. The term stress regime

has been used in at least three distinct ways:

(1) To characterize exposure to multiple

chemicals or to both chemical and

nonchemical stressors (more clearly

described as multiple exposure, complex

exposure, or exposure to mixtures), (2) as a

synonym for exposure that is intended to

avoid overemphasis on chemical exposures,

and (3) to describe the series of interactions

of exposures and effects resulting in

secondary exposures, secondary effects, and,

finally, ultimate effects (also known as risk

cascade [Lipton et al., 1993]), or causal chain,

pathway, or network (Andrewartha and

Birch, 1984). Because of the potential for

confusion and the availability of other,

clearer terms, this term is not used in these

Guidelines.

A.2.3. Uncertainty Terminology

The Framework Report divided uncertainty

into conceptual model formation,

information and data, stochasticity, and

error. These Guidelines discuss uncertainty

throughout the process, focusing on the

conceptual model (section 3.4.3), the analysis

phase (section 4.1.3), and the incorporation

of uncertainty in risk estimates (section 5.1).

The bulk of the discussion appears in section

4.1.3, where the discussion is organized

according to the following sources of

uncertainty:

• Unclear communication.

• Descriptive errors.

• Variability.

• Data gaps.

• Uncertainty about a quantity’s true

value.

• Model structure uncertainty (process

models).

• Uncertainty about a model’s form

(empirical models).

A.2.4. Lines of Evidence

The Framework Report used the phrase

weight of evidence to describe the process of

evaluating multiple lines of evidence in risk

characterization. These Guidelines use the

phrase lines of evidence instead to deemphasize the balancing of opposing factors

based on assignment of quantitative values to

reach a conclusion about a ‘‘weight’’ in favor

of a more inclusive approach, which

evaluates all available information, even

evidence that may be qualitative in nature.

Appendix B—Key Terms (Adapted From

U.S. EPA, 1992a)

Adverse ecological effects—Changes that

are considered undesirable because they alter

valued structural or functional characteristics

of ecosystems or their components. An

evaluation of adversity may consider the

type, intensity, and scale of the effect as well

as the potential for recovery.

Agent—Any physical, chemical, or

biological entity that can induce an adverse

response (synonymous with stressor).

Assessment endpoint—An explicit

expression of the environmental value that is

to be protected, operationally defined by an

ecological entity and its attributes. For

example, salmon are valued ecological

entities; reproduction and age class structure

are some of their important attributes.

Together ‘‘salmon reproduction and age class

structure’’ form an assessment endpoint.

Attribute—A quality or characteristic of an

ecological entity. An attribute is one

component of an assessment endpoint.

Characterization of ecological effects—A

portion of the analysis phase of ecological

risk assessment that evaluates the ability of

a stressor(s) to cause adverse effects under a

particular set of circumstances.

Characterization of exposure—A portion of

the analysis phase of ecological risk

assessment that evaluates the interaction of

the stressor with one or more ecological

entities. Exposure can be expressed as cooccurrence or contact, depending on the

stressor and ecological component involved.

Community—An assemblage of

populations of different species within a

specified location in space and time.

Comparative risk assessment—A process

that generally uses a professional judgment

approach to evaluate the relative magnitude

of effects and set priorities among a wide

range of environmental problems (e.g., U.S.

EPA, 1993d). Some applications of this

process are similar to the problem

formulation portion of an ecological risk

assessment in that the outcome may help

select topics for further evaluation and help

focus limited resources on areas having the

greatest risk reduction potential. In other

situations, a comparative risk assessment is

conducted more like a preliminary risk

assessment. For example, EPA’s Science

Advisory Board used professional judgment

and an ecological risk assessment approach

to analyze future ecological risk scenarios

and risk management alternatives (U.S. EPA,

1995e).

Conceptual model—A conceptual model in

problem formulation is a written description

and visual representation of predicted

relationships between ecological entities and

the stressors to which they may be exposed.

Cumulative distribution function (CDF)—

Cumulative distribution functions are

particularly useful for describing the

likelihood that a variable will fall within

different ranges of x. F(x) (i.e., the value of

y at x in a CDF plot) is the probability that

a variable will have a value less than or equal

to x (figure B–1).

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BILLING CODE 6560–50–C
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Cumulative ecological risk assessment—A

process that involves consideration of the

aggregate ecological risk to the target entity

caused by the accumulation of risk from

multiple stressors.

Disturbance—Any event or series of events

that disrupts ecosystem, community, or

population structure and changes resources,

substrate availability, or the physical

environment (modified from White and

Pickett, 1985).

EC50—A statistically or graphically

estimated concentration that is expected to

cause one or more specified effects in 50%

of a group of organisms under specified

conditions (ASTM, 1996).

Ecological entity—A general term that may

refer to a species, a group of species, an

ecosystem function or characteristic, or a

specific habitat. An ecological entity is one

component of an assessment endpoint.

Ecological relevance—One of the three

criteria for assessment endpoint selection.

Ecologically relevant endpoints reflect

important characteristics of the system and

are functionally related to other endpoints.

Ecological risk assessment—The process

that evaluates the likelihood that adverse

ecological effects may occur or are occurring

as a result of exposure to one or more

stressors.

Ecosystem—The biotic community and

abiotic environment within a specified

location in space and time.

Environmental impact statement (EIS)—

Environmental impact statements are

prepared under the National Environmental

Policy Act by Federal agencies as they

evaluate the environmental consequences of

proposed actions. EISs describe baseline

environmental conditions; the purpose of,

need for, and consequences of a proposed

action; the no-action alternative; and the

consequences of a reasonable range of

alternative actions. A separate risk

assessment could be prepared for each

alternative, or a comparative risk assessment

might be developed. However, risk

assessment is not the only approach used in

EISs.

Exposure—The contact or co-occurrence of

a stressor with a receptor.

Exposure profile—The product of

characterization of exposure in the analysis

phase of ecological risk assessment. The

exposure profile summarizes the magnitude

and spatial and temporal patterns of

exposure for the scenarios described in the

conceptual model.

Exposure scenario—A set of assumptions

concerning how an exposure may take place,

including assumptions about the exposure

setting, stressor characteristics, and activities

that may lead to exposure.

Hazard assessment—This term has been

used to mean either (1) evaluating the

intrinsic effects of a stressor (U.S. EPA, 1979)

or (2) defining a margin of safety or quotient

by comparing a toxicologic effects

concentration with an exposure estimate

(SETAC, 1987).

LC50—A statistically or graphically

estimated concentration that is expected to

be lethal to 50% of a group of organisms

under specified conditions (ASTM, 1996).

Lines of evidence—Information derived

from different sources or by different

techniques that can be used to describe and

interpret risk estimates. Unlike the term

‘‘weight of evidence,’’ it does not necessarily

imply assignment of quantitative weightings

to information.

Lowest-observed-adverse-effect level

(LOAEL)—The lowest level of a stressor

evaluated in a test that causes statistically

significant differences from the controls.

Maximum acceptable toxic concentration

(MATC)—For a particular ecological effects

test, this term is used to mean either the

range between the NOAEL and the LOAEL or

the geometric mean of the NOAEL and the

LOAEL. The geometric mean is also known

as the chronic value.

Measure of ecosystem and receptor

characteristics—Measures that influence the

behavior and location of ecological entities of

the assessment endpoint, the distribution of

a stressor, and life-history characteristics of

the assessment endpoint or its surrogate that

may affect exposure or response to the

stressor.

Measure of effect—A change in an attribute

of an assessment endpoint or its surrogate in

response to a stressor to which it is exposed.

Measure of exposure—A measure of

stressor existence and movement in the

environment and its contact or co-occurrence

with the assessment endpoint.

Measurement endpoint—See ‘‘measure of

effect.’’

No-observed-adverse-effect level

(NOAEL)—The highest level of a stressor

evaluated in a test that does not cause

statistically significant differences from the

controls.

Population—An aggregate of individuals of

a species within a specified location in space

and time.

Primary effect—An effect where the

stressor acts on the ecological component of

interest itself, not through effects on other

components of the ecosystem (synonymous

with direct effect; compare with definition

for secondary effect).

Probability density function (PDF)—

Probability density functions are particularly

useful in describing the relative likelihood

that a variable will have different particular

values of x. The probability that a variable

will have a value within a small interval

around x can be approximated by

multiplying f(x) (i.e., the value of y at x in

a PDF plot) by the width of the interval

(figure B–2).

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Prospective risk assessment—An

evaluation of the future risks of a stressor(s)

not yet released into the environment or of

future conditions resulting from an existing

stressor(s).

Receptor—The ecological entity exposed to

the stressor.

Recovery—The rate and extent of return of

a population or community to some aspect(s)

of its previous condition. Because of the

dynamic nature of ecological systems, the

attributes of a ‘‘recovered’’ system should be

carefully defined.

Relative risk assessment—A process

similar to comparative risk assessment. It

involves estimating the risks associated with

different stressors or management actions. To

some, relative risk connotes the use of

quantitative risk techniques, while

comparative risk approaches more often rely

on professional judgment. Others do not

make this distinction.

Retrospective risk assessment—An

evaluation of the causal linkages between

observed ecological effects and stressor(s) in

the environment.

Risk characterization—A phase of

ecological risk assessment that integrates the

exposure and stressor response profiles to

evaluate the likelihood of adverse ecological

effects associated with exposure to a stressor.

Lines of evidence and the adversity of effects

are discussed.

Secondary effect—An effect where the

stressor acts on supporting components of

the ecosystem, which in turn have an effect

on the ecological component of interest

(synonymous with indirect effects; compare

with definition for primary effect).

Source—An entity or action that releases to

the environment or imposes on the

environment a chemical, physical, or

biological stressor or stressors.

Source term—As applied to chemical

stressors, the type, magnitude, and patterns

of chemical(s) released.

Stressor—Any physical, chemical, or

biological entity that can induce an adverse

response (synonymous with agent).

Stressor-response profile—The product of

characterization of ecological effects in the

analysis phase of ecological risk assessment.

The stressor-response profile summarizes the

data on the effects of a stressor and the

relationship of the data to the assessment

endpoint.

Stress regime—The term ‘‘stress regime’’

has been used in at least three distinct ways:

(1) To characterize exposure to multiple

chemicals or to both chemical and

nonchemical stressors (more clearly

described as multiple exposure, complex

exposure, or exposure to mixtures), (2) as a

synonym for exposure that is intended to

avoid overemphasis on chemical exposures,

and (3) to describe the series of interactions

of exposures and effects resulting in

secondary exposures, secondary effects and,

finally, ultimate effects (also known as risk

cascade [Lipton et al., 1993]), or causal chain,

pathway, or network (Andrewartha and

Birch, 1984).

Trophic levels—A functional classification

of taxa within a community that is based on

feeding relationships (e.g., aquatic and

terrestrial green plants make up the first

trophic level and herbivores make up the

second).

Appendix C—Conceptual Model Examples

Conceptual model diagrams are visual

representations of the conceptual models.

They may be based on theory and logic,

empirical data, mathematical models, or

probability models. These diagrams are

useful tools for communicating important

pathways in a clear and concise way. They

can be used to ask new questions about

relationships that help generate plausible risk

hypotheses. Further discussion of conceptual

models is found in section 3.4.

Flow diagrams like those shown in figures

C–1 through C–3 are typical conceptual

model diagrams. When constructing flow

diagrams, it is helpful to use distinct and

consistent shapes to distinguish between

stressors, assessment endpoints, responses,

exposure routes, and ecosystem processes.

Although flow diagrams are often used to

illustrate conceptual models, there is no set

configuration for conceptual model diagrams,

and the level of complexity may vary

considerably depending on the assessment.

Pictorial representations of the processes of

an ecosystem can be more effective (e.g.,

Bradley and Smith, 1989).

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Figure C–1 illustrates the relationship

between a primary physical stressor (logging

roads) and an effect on an assessment

endpoint (fecundity in insectivorous fish).

This simple diagram illustrates the effect of

building logging roads (which could be

considered a stressor or a source) in

ecosystems where slope, soil type, low

riparian cover, and other ecosystem

characteristics lead to the erosion of soil,

which enters streams and smothers the

benthic organisms (exposure pathway is not

explicit in this diagram). Because of the

dependence of insectivorous fish on benthic

organisms, the fish are believed to be at risk

from the building of logging roads. Each

arrow in this diagram represents a hypothesis

about the proposed relationship (e.g., human

action and stressor, stressor and effect,

primary effect to secondary effect). Each risk

hypothesis provides insights into the kinds of

data that will be needed to verify that the

hypothesized relationships are valid.

Figure C–2 is a conceptual model used by

Kendall et al. (1996) to track a contaminant

through upland ecosystems. In this example,

upland birds are exposed to lead shot when

it becomes embedded in their tissue after

being shot and by ingesting lead accidentally

when feeding on the ground. Both are

hypothesized to result in increased morbidity

(e.g., lower reproduction and

competitiveness and higher predation and

infection) and mortality, either directly

(lethal intoxication) or indirectly (effects of

morbidity leading to mortality). These effects

are believed to result in changes in upland

bird populations and, because of

hypothesized exposure of predators to lead,

to increased predator mortality. This example

shows multiple exposure pathways for effects

on two assessment endpoints. Each arrow

contains within it assumptions and

hypotheses about the relationship depicted

that provide the basis for identifying data

needs and analyses.

Figure C–3 is a conceptual model adapted

from the Waquoit Bay watershed risk

assessment. At the top of the model, multiple

human activities that occur in the watershed

are shown in rectangles. Those sources of

stressors are linked to stressor types depicted

in ovals. Multiple sources are shown to

contribute to an individual stressor, and each

source may contribute to more than one

stressor. The stressors then lead to multiple

ecological effects depicted again in

rectangles. Some rectangles are double-lined

to indicate effects that can be directly

measured for data analysis. Finally, the

effects are linked to particular assessment

endpoints. The connections show that one

effect can result in changes in many

assessment endpoints. To fully depict

exposure pathways and types of effects,

specific portions of this conceptual model

would need to be expanded to illustrate those

relationships.

Appendix D—Analysis Phase Examples

The analysis phase process is illustrated

here for a chemical, physical, and biological

stressor. These examples do not represent all

possible approaches, but they illustrate the

analysis phase process using information

from actual assessments.

D.1. Special Review of Granular

Formulations of Carbofuran Based on

Adverse Effects on Birds

Figure D–1 is based on an assessment of

the risks of carbofuran to birds under the

Federal Insecticide, Fungicide, and

Rodenticide Act (FIFRA) (Houseknecht,

1993). Carbofuran is a broad-spectrum

insecticide and nematicide applied primarily

in granular form on 27 crops as well as

forests and pine seed orchards. The

assessment endpoint was survival of birds

that forage in agricultural areas where

carbofuran is applied.

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The analysis phase focused on birds that

may incidentally ingest granules as they

forage or that may eat other animals that

contain granules or residues. Measures of

exposure included application rates,

attributes of the formulation (e.g., size of

granules), and residues in prey organisms.

Measures of the ecosystem and receptors

included an inventory of bird species that

may be exposed following applications for 10

crops. The birds’ respective feeding

behaviors were considered in developing

routes of exposure. Measures of effect

included laboratory toxicity studies and field

investigations of bird mortality.

The source of the chemical was application

of the pesticide in granular form. The

distribution of the pesticide in agricultural

fields was estimated on the basis of the

application rate. The number of exposed

granules was estimated from literature data.

On the basis of a review of avian feeding

behavior, seed-eating birds were assumed to

ingest any granules left uncovered in the

field. The intensity of exposure was

summarized as the number of exposed

granules per square foot.

The stressor-response relationship was

described using the results of toxicity tests.

These data were used to construct a toxicity

statistic expressed as the number of granules

needed to kill 50% of the test birds (i.e.,

granules per LD50), assuming 0.6 mg of active

ingredient per granule and average body

weights for the birds tested. Field studies

were used to document the occurrence of

bird deaths following applications and

provide further causal evidence. Carbofuran

residues and cholinesterase levels were used

to confirm that exposure to carbofuran

caused the deaths.

D.2. Modeling Losses of Bottomland-Forest

Wetlands

Figure D–2 is based on an assessment of

the ecological consequences (risks) of longterm changes in hydrologic conditions

(water-level elevations) for three habitat

types in the Lake Verret Basin of Louisiana

(Brody et al., 1989, 1993; Conner and Brody,

1989). The project was intended to provide

a habitat-based approach for assessing the

environmental impacts of Federal water

projects under the National Environmental

Policy Act and Section 404 of the Clean

Water Act. Output from the models provided

risk managers with information on how

changes in water elevation might alter the

ecosystem. The primary anthropogenic

stressor addressed in this assessment was

artificial levee construction for flood control,

which contributes to land subsidence by

reducing sediment deposition in the

floodplain. Assessment endpoints included

forest community structure and habitat value

to wildlife species and the species

composition of the wildlife community.

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The analysis phase began by considering

primary (direct) effects of water-level changes

on plant community composition and habitat

characteristics. Measures of exposure

included the attributes and placement of the

levees and water-level measurements.

Measures of ecosystem and receptor

characteristics included location and extent

of bottomland-hardwood communities, plant

species occurrences within these

communities, and information on historic

flow regimes. Measures of effects included

laboratory studies of plant response to

moisture and field measurements along

moisture gradients.

While the principal stressor under

evaluation was the construction of levees, the

decreased gradient of the river due to

sediment deposition at its mouth also

contributed to increased water levels. The

extent and frequency of flooding were

simulated by the FORFLO model based on

estimates of net subsidence rates from levee

construction and decreased river gradient.

Seeds and seedlings of the tree species were

assumed to be exposed to the altered flooding

regime. Stressor-response relationships

describing plant response to moisture (e.g.,

seed germination, survival) were embedded

within the FORFLO model. This information

was used by the model to simulate changes

in plant communities: The model tracks the

species type, diameter, and age of each tree

on simulated plots from the time the tree

enters the plot as a seedling or sprout until

it dies. The FORFLO model calculated

changes in the plant community over time

(from 50 to 280 years). The spatial extent of

the three habitat types of interest—wet

bottomland hardwoods, dry bottomland

hardwoods, and cypress-tupelo swamp—was

mapped into a GIS along with the

hydrological information. The changes

projected by FORFLO were then manually

linked to the GIS to show how the spatial

distribution of different communities would

change. Evidence that flooding would

actually cause these changes included

comparisons of model predictions with field

measurements, the laboratory studies of plant

response to moisture, and knowledge of the

mechanisms by which flooding elicits

changes in plant communities.

Secondary (indirect) effects on wildlife

associated with changes in the habitat

provided by the plant community formed the

second part of the analysis phase. Important

measures included life-history characteristics

and habitat needs of the wildlife species.

Effects on wildlife were inferred by

evaluating the suitability of the plant

community as habitat. Specific aspects of the

community structures calculated by the

FORFLO model provided the input to this

part of the analysis. For example, the number

of snags was used to evaluate habitat value

for woodpeckers. Resident wildlife

(represented by five species) was assumed to

co-occur with the altered plant community.

Habitat value was evaluated by calculating

the Habitat Suitability Index (HSI) for each

habitat type multiplied by the habitat type’s

area.

A combined exposure and stressorresponse profile is shown in figure D–2; these

two elements were combined with the

models used for the analysis and then used

directly in risk characterization.

D.3. Pest Risk Assessment of Importation of

Logs from Chile

Figure D–3 is based on the assessment of

potential risks to U.S. forests due to the

incidental introduction of insects, fungi, and

other pests inhabiting logs harvested in Chile

and transported to U.S. ports (USDA, 1993).

This risk assessment was used to determine

whether actions to restrict or regulate the

importation of Chilean logs were needed to

protect U.S. forests and was conducted by a

team of six experts under the auspices of the

U.S. Department of Agriculture Forest

Service. Stressors include insects, forest

pathogens (e.g., fungi), and other pests. The

assessment endpoint was the survival and

growth of tree species (particularly conifers)

in the western United States. Damage that

would affect the commercial value of the

trees as lumber was clearly of interest.

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1This example is simplified for illustrative

purposes. In other situations, it may be

considerably more difficult to draw clear

conclusions regarding relative ecological adversity.

The analysis phase was carried out by

eliciting professional opinions from a team of

experts. Measures of exposure used by the

team included distribution information for

the imported logs and attributes of the insects

and pathogens such as dispersal mechanisms

and life-history characteristics. Measures of

ecosystem and receptor characteristics

included the climate of the United States,

location of geographic barriers, knowledge of

host suitability, and ranges of potential host

species. Measures of effect included

knowledge of the infectivity of these pests in

other countries and the infectivity of similar

pests on U.S. hosts.

This information was used by the risk

assessment team to evaluate the potential for

exposure. They began by evaluating the

likelihood of entry of infested logs into the

United States. The distribution of the

organism’s given entry was evaluated by

considering the potential for colonization

and spread beyond the point of entry as well

as the likelihood of the organisms surviving

and reproducing. The potential for exposure

was summarized by assigning each of the

above elements a judgment-based value of

high, medium, or low.

The evaluation of ecological effects was

also conducted on the basis of collective

professional judgment. Of greatest relevance

to this guidance was the consideration of

environmental damage potential, defined as

the likelihood of ecosystem destabilization,

reduction in biodiversity, loss of keystone

species, and reduction or elimination of

endangered or threatened species. (The team

also considered economic damage potential

and social and political influences; however,

for the purposes of these Guidelines, those

factors are considered to be part of the risk

management process.) Again, each

consideration was assigned a value of high,

medium, or low to summarize the potential

for ecological effects.

Appendix E—Criteria for Determining

Ecological Adversity: A Hypothetical

Example (Adapted From Harwell et al.,

1994) 1

As a result of a collision at sea, an oil

tanker releases 15 million barrels of #2 fuel

oil 3 km offshore. It is predicted that

prevailing winds will carry the fuel onshore

within 48 to 72 hours. The coastline has

numerous small embayments that support an

extensive shallow, sloping subtidal

community and a rich intertidal community.

A preliminary assessment determines that if

no action is taken, significant risks to the

communities will result. Additional risk

assessments are conducted to determine

which of two options should be used to clean

up the oil spill.

Option 1 is to use a dispersant to break up

the slick, which would reduce the likelihood

of extensive onshore contamination but

would cause extensive mortality to the

phytoplankton, zooplankton, and

ichthyoplankton (fish larvae), which are

important for commercial fisheries. Option 2

is to try to contain and pump off as much oil

as possible; this option anticipates that a shift

in wind direction will move the spill away

from shore and allow for natural dispersal at

sea. If this does not happen, the oil will

contaminate the extensive sub-and intertidal

mud flats, rocky intertidal communities, and

beaches and pose an additional hazard to

avian and mammalian fauna. It is assumed

there will be a demonstrable change beyond

natural variability in the assessment

endpoints (e.g., structure of planktonic,

benthic, and intertidal communities). What is

the adversity of each option?

• Nature and intensity of the effect. For

both options, the magnitude of change in the

assessment endpoints is likely to be severe.

Planktonic populations often are

characterized by extensive spatial and

temporal variability. Nevertheless, within the

spatial boundaries of the spill, the use of

dispersants is likely to produce complete

mortality of all planktonic forms within the

upper 3 m of water. For benthic and

intertidal communities, which generally are

stable and have less spatial and temporal

variability than planktonic forms, oil

contamination will likely result in severe

impacts on survival and chronic effects

lasting for several years. Thus, under both

options, changes in the assessment endpoints

will probably exceed the natural variability

for threatened communities in both space

and time.

• Spatial scale. The areal extent of impacts

is similar for each of the options. While

extensive, the area of impact constitutes a

small percentage of the landscape. This

leaves considerable area available for

replacement stocks and creates significant

fragmentation of either the planktonic or

inter-and subtidal habitats. Ecological

adversity is reduced because the area is not

a mammalian or avian migratory corridor.

• Temporal scale and recovery. On the

basis of experience with other oil spills, it is

assumed that the effects are reversible over

some time period. The time needed for

reversibility of changes in phytoplankton and

zooplankton populations should be short

(days to weeks) given their rapid generation

times and easy immigration from adjacent

water masses. There should not be a long

recovery period for ichthyoplankton, since

they typically experience extensive natural

mortality, and immigration is readily

available from surrounding water masses. On

the other hand, the time needed for

reversibility of changes in benthic and

intertidal communities is likely to be long

(years to decades). First, the stressor (oil)

would be likely to persist in sediments and

on rocks for several months to years. Second,

the life histories of the species comprising

these communities span 3 to 5 years. Third,

the reestablishment of benthic intertidal

community and ecosystem structure

(hierarchical composition and function) often

requires decades.

Both options result in (1) assessment

endpoint effects that are of great severity, (2)

exceedances of natural variability for those

endpoints, and (3) similar estimates of areal

impact. What distinguishes the two options

is temporal scale and reversibility. In this

regard, changes to the benthic and intertidal

ecosystems are considerably more adverse

than those to the plankton. On this basis, the

option of choice would be to disperse the oil,

effectively preventing it from reaching shore

where it would contaminate the benthic and

intertidal communities.

References

Alder, HL; Roessler, EB. (1972)

Introduction to probability and statistics. San

Francisco, CA: WH Freeman and Co.

American Society for Quality Control

(ASQC). (1994) American National Standard:

specifications and guidelines for quality

systems for environmental data collection

and environmental technology programs.

ANSI/ASQC E4–1994. Milwaukee, WI: SQC.

American Society for Testing and

Materials. (1996) Standard terminology

relating to biological effects and

environmental fate. E943–95a. In: ASTM;

1996 Annual Book of ASTM Standards,

Section 11, Water and Environmental

Technology. Philadelphia, PA: ASTM.

Andrewartha, HG; Birch, LC. (1984) The

ecological web: more on the distribution and

abundance of animals. Chicago, IL:

University of Chicago Press.

Ankley, GT; Katko, A; Arthur, JW. (1990)

Identification of ammonia as an important

sediment-associated toxicant in the Lower

Fox River and Green Bay, Wisconsin.

Environ Toxicol Chem 9:313–322.

Auer, CM; Zeeman, M; Nabholz, JV;

Clements, RG. (1994) SAR—the U.S.

regulatory perspective. SAR QSAR Environ

Res 2:29–38.

Baker, JL; Barefoot, AC; Beasley, LE; Burns,

LA; Caulkins, PP; Clark, JE; Feulner, RL;

Giesy, JP; Graney, RL; Griggs, RH; Jacoby,

HM; Laskowski, DA; Maciorowski, AF;

Mihaich, EM; Nelson, HP, Jr.; Parrish, PR;

Siefert, RE; Solomon, KR; van der Schalie,

WH, eds. (1994) Aquatic dialogue group:

pesticide risk assessment and mitigation.

Pensacola, FL: SETAC Press.

Barbour, MT; Stribling, JB; Karr, JR. (1995)

Multimetric approach for establishing

biocriteria and measuring biological

condition. In: Biological assessment and

criteria, tools for water resource planning and

decision making. Davis, WS; Simon, TP, eds.

Boca Raton, FL: Lewis Publishers, pp. 63–77.

Barnthouse, LW; Brown, J. (1994) Issue

paper on conceptual model development. In:

Ecological risk assessment issue papers.

Washington, DC: Risk Assessment Forum,

U.S. Environmental Protection Agency, pp.

3–1 to 3–70. EPA/630/R–94/009.

Barnthouse, LW; O’Neill, RV; Bartell, SM;

Suter, GW, II. (1986) Population and

ecosystem theory in ecological risk

assessment. In: Aquatic ecology and hazard

assessment, 9th symposium. Poston, TM;

Purdy, R, eds. Philadelphia, PA: American

Society for Testing and Materials, pp. 82–96.

Barnthouse, LW; Suter, GW, II; Rosen, AE;

Beauchamp, JJ. (1987) Estimating responses

of fish populations to toxic contaminants.

Environ Toxicol Chem 6:811–824.

Barnthouse, LW; Suter, GW, II; Rosen, AE.

(1990) Risks of toxic contaminants to

exploited fish populations: influence of life

history, data uncertainty, and exploitation

intensity. Environ Toxicol Chem 9:297–311.

Bartell, SM; Gardner, RH; O’Neill, RV.

(1992) Ecological risk estimation. Boca Raton,

FL: Lewis Publishers.
Federal Register / Vol. 63, No. 93 /Thursday, May 14, 1998 / Notices 26919

Bedford, BL; Preston, EM. (1988)

Developing the scientific basis for assessing

cumulative effects of wetland loss and

degradation on landscape functions: status,

perspectives, and prospects. Environ Manage

12:751–771.

Bradbury, SP. (1994) Predicting modes of

toxic action from chemical structure: an

overview. SAR QSAR Environ Res 2:89–104.

Bradley, CE; Smith, DG. (1989) Plains

cottonwood recruitment and survival on a

prairie meandering river floodplain. Milk

River, Southern Alberta in Northern Canada.

Can J Botany 64:1433–1442.

Broderius, SJ. (1991) Modeling the joint

toxicity of xenobiotics to aquatic organisms:

basic concepts and approaches In: Aquatic

toxicology and risk assessment: fourteenth

volume. Mayes, MA; Barron, MG, eds. ASTM

STP 1124. Philadelphia, PA: American

Society for Testing and Materials, pp. 107–

127.

Broderius, SJ; Kahl, MD; Hoglund, MD.

(1995) Use of joint toxic response to define

the primary mode of toxic action for diverse

industrial organic chemicals. Environ

Toxicol Chem 9:1591–1605.

Brody, M; Conner, W; Pearlstine, L;

Kitchens, W. (1989) Modeling bottomland

forest and wildlife habitat changes in

Louisiana’s Atchafalaya Basin. In: Freshwater

wetlands and wildlife. Sharitz, RR; Gibbons,

JW, eds. U.S. Department of Energy

Symposium Series, No. 61. CONF–8603100.

Oak Ridge, TN: Office of Science and

Technical Information, U.S. Department of

Energy.

Brody, MS; Troyer, ME; Valette, Y. (1993)

Ecological risk assessment case study:

modeling future losses of bottomland forest

wetlands and changes in wildlife habitat

within a Louisiana basin. In: A review of

ecological assessment case studies from a risk

assessment perspective. Washington, DC:

Risk Assessment Forum, U.S. Environmental

Protection Agency, pp. 12–1 to 12–39. EPA/

630/R–92/005.

Burmaster, DE; Menzie, CA; Freshman, JS;

Burris, JA; Maxwell, NI; Drew, SR. (1991)

Assessment of methods for estimating aquatic

hazards at Superfund-type sites: a cautionary

tale. Environ Toxicol Chem 10:827–842.

Callahan, CA; Menzie, CA; Burmaster, DE;

Wilborn, DC; Ernst, T. (1991) On-site

methods for assessing chemical impacts on

the soil environment using earthworms: a

case study at the Baird and McGuire

Superfund site, Holbrook, Massachusetts.

Environ Toxicol Chem 10:817–826.

Cardwell, RD; Parkhurst, BR; WarrenHicks, W; Volosin, JS. (1993) Aquatic

ecological risk. Water Environ Technol 5:47–

51.

Clemen, RT. (1996) Making hard decisions.

2nd ed. New York: Duxbury Press, 664 pp.

Clements, RG; Nabholz, JV. (1994)

ECOSAR: a computer program for estimating

the ecotoxicity of industrial chemicals based

on structure activity relationships, user’s

guide. Washington, DC: Environmental

Effects Branch, Health and Environmental

Review Division (7402), U.S. Environmental

Protection Agency. EPA/748/R–93/002.

Cohrssen, J; Covello, VT. (1989) Risk

analysis: a guide to principles and methods

for analyzing health and ecological risks.

Washington, DC: Council on Environmental

Quality.

Commission on Risk Assessment and Risk

Management. (1997) Framework for

environmental health risk management. Final

Report. Volume 1. Washington, DC:

Commission on Risk Assessment and Risk

Management.

Conner, WH; Brody, M. (1989) Rising water

levels and the future of southeastern

Louisiana swamp forests. Estuaries

12(4):318–323.

Costanza, R; d’Arge, R; de Groot, R; Farber,

S; Grasso, M; Hannon, B; Limburg, K; Naeem,

S; O’Neill, RV; Paruelo, J; Raskinm, RG;

Sutton, P; van den Belt, M. (1997) The value

of the world’s ecosystem services and natural

capital. Nature 387:253–260.

Cowan, CE; Versteeg, DJ; Larson, RJ;

Kloepper-Sams, PJ. (1995) Integrated

approach for environmental assessment of

new and existing substances. Regul Toxicol

Pharmacol 21:3–31.

Cronin, MTD; Dearden, JC. (1995) QSAR in

toxicology. 1. Prediction of aquatic toxicity.

Quant Struct-Act Relat 14:1–7.

Detenbeck, N. (1994) Ecological risk

assessment case study: effects of physical

disturbance on water quality status and water

quality improvement function of urban

wetlands. In: A review of ecological

assessment case studies from a risk

assessment perspective, vol. II. Washington,

DC: Risk Assessment Forum, U.S.

Environmental Protection Agency, pp. 4–1 to

4–58. EPA/630/R–94/003.

Detenbeck, NE; DeVore, PW; Niemi, GJ;

Lima, A. (1992) Recovery of temperate-stream

fish communities from disturbance: a review

of case studies and synthesis of theory.

Environ Manage 16(1):33–53.

Eberhardt, LL; Thomas, JM. (1991)

Designing environmental field studies. Ecol

Mono 61(1):53–73.

Emlen, JM. (1989) Terrestrial population

models for ecological risk assessment: a stateof-the-art review. Environ Toxicol Chem

8:831–842.

European Community (EC). (1993)

Technical guidance document in support of

the risk assessment Commission Directive

(93/67/EEC) for new substances notified in

accordance with the requirements of Council

Directive 67/548/EEC. Brussels, Belgium.

Ferson, S; Ginzburg, L; Silvers, A. (1989)

Extreme event risk analysis for age-structured

populations. Ecol Model 47:175–187.

Fisher, SG; Woodmansee, R. (1994) Issue

paper on ecological recovery. In: Ecological

risk assessment issue papers. Washington,

DC: Risk Assessment Forum, U.S.

Environmental Protection Agency, pp. 7–1 to

7–54. EPA/630/R–94/009.

Fotheringham, AS; Rogerson, PA. (1993)

GIS and spatial analytical problems. Int J

Geograph Inf Syst 7(1):3–19.

Fox, GA. (1991) Practical causal inference

for ecoepidemiologists. J Toxicol Environ

Health 33:359–373.

Gauch, HG. (1982) Multivariate analysis in

community ecology. Cambridge, MA:

Cambridge University Press.

Gaudet, C. (1994) A framework for

ecological risk assessment at contaminated

sites in Canada: review and

recommendations. Ottawa, Canada:

Environment Canada.

Gibbs, JP. (1993) Importance of small

wetlands for the persistence of local

populations of wetland-associated animals.

Wetlands 13(1):25–31.

Gilbert, RO. (1987) Statistical methods for

environmental pollution monitoring. New

York, NY: Van Nostrand Reinhold.

Ginzburg, LR; Slobodkin, LB; Johnson, K;

Bindman, AG. (1982) Quasiextinction

probabilities as a measure of impact on

population growth. Risk Anal 2:171–182.

Gosselink, JG; Shaffer, GP; Lee, LC;

Burdick, DL; Childer, NC; Leibowitz, NC;

Hamilton, SC; Boumans, R; Cushmam, D;

Fields, S; Koch, M; Visser, JM. (1990)

Landscape conservation in a forested wetland

watershed: can we manage cumulative

impacts? Bioscience 40(8):588–600.

Harris, HJ; Wenger, RB; Harris, VA;

Devault, DS. (1994) A method for assessing

environmental risk: a case study of Green

Bay, Lake Michigan, USA. Environ Manage

18(2):295–306.

Harwell, MJ; Norton, B; Cooper, W;

Gentile, J. (1994) Issue paper on ecological

significance. In: Ecological risk assessment

issue papers. Washington, DC: Risk

Assessment Forum, U.S. Environmental

Protection Agency, pp. 2–1 to 2–49. EPA/

630/R–94/009.

Hassell, MP. (1986) Detecting density

dependence. Trends Ecol Evol 1:90–93.

Health Council of the Netherlands (HCN).

(1993) Ecotoxicological risk assessment and

policy-making in the Netherlands—dealing

with uncertainties. Network 6(3)/7(1):8–11.

Heck, WW. (1993) Ecological assessment

case study: the National Crop Loss

Assessment Network. In: A review of

ecological assessment case studies from a risk

assessment perspective. Washington, DC:

Risk Assessment Forum, U.S. Environmental

Protection Agency, pp. 6–1 to 6–32. EPA/

630/R–92/005.

Hermens, J; Canton, H; Janssen, P; De Jong,

R. (1984a) Quantitative structure-activity

relationships and toxicity studies of mixtures

of chemicals with anaesthetic potency: acute

lethal and sublethal toxicity to Daphnia

magna. Aquatic Toxicol 5:143–154.

Hermens, J; Canton, H; Steyger, N;

Wegman, R. (1984b) Joint effects of a mixture

of 14 chemicals on mortality and inhibition

of reproduction of Daphnia magna. Aquatic

Toxicol 5:315–322.

Hill, AB. (1965) The environment and

disease: association or causation? Proc R Soc

Med 58:295–300.

Holling, CS, ed. (1978) Adaptive

environmental assessment and management.

Chichester, UK: John Wiley & Sons.

Houseknecht, CR. (1993) Ecological risk

assessment case study: special review of the

granular formulations of carbofuran based on

adverse effects on birds. In: A review of

ecological assessment case studies from a risk

assessment perspective. Washington, DC:

Risk Assessment Forum, U.S. Environmental

Protection Agency, pp. 3–1 to 3–25. EPA/

630/R–92/005.

Huggett, RJ; Kimerle, RA; Merhle, PM, Jr.;

Bergman, HL, eds. (1992) Biomarkers:

biochemical, physiological, and histological

markers of anthropogenic stress. Boca Raton,

FL: Lewis Publishers.

Hughes, RM. (1995) Defining acceptable

biological status by comparing with reference
26920 Federal Register / Vol. 63, No. 93 /Thursday, May 14, 1998 / Notices

conditions. In: Biological assessment and

criteria: tools for water resource planning and

decision making. Davis, WS; Simon, TP, eds.

Boca Raton, FL: Lewis Publishers, pp. 31–47.

Hunsaker, CT; Graham, RL; Suter, GW, II;

O’Neill, RV; Barnthouse, LW; Gardner, RH.

(1990) Assessing ecological risk on a regional

scale. Environ Manage 14(3):325–332.

Hurlbert, SH. (1984) Pseudoreplication and

the design of ecological field experiments.

Ecol Mono 54:187–211.

Johnson, BL. (1995) Applying computer

simulation models as learning tools in fishery

management. North Am J Fisheries Manage

15:736–747.

Johnson, LB; Gage, SH. (1997) Landscape

approaches to the analysis of aquatic

ecosystems. Freshwater Biol 37:113–132.

Johnston, CA; Detenbeck, NE; Niemi, GJ.

(1990) The cumulative effect of wetlands on

stream water quality and quantity: a

landscape approach. Biogeochemistry

10:105–141.

Karr, JR. (1981) Assessment of biotic

integrity using fish communities. Fisheries

6(6):21–27.

Karr, JR; Fausch, KD; Angermeier, PL;

Yant, PR; Schlosser, IJ. (1986) Assessing

biological integrity in running waters: a

method and its rationale. Illinois Natural

History Survey. Special Publication 5.

Champaign, IL.

Kenaga, EE. (1973) Factors to be considered

in the evaluation of the toxicity of pesticides

to birds in their environment. Environ Qual

Saf 2:166–181.

Kendall, RJ; Lacher, TE; Bunck, C; Daniel,

B; Driver, C; Grue, CE; Leighton, F; Stansley,

W; Watanabe, PG; Whitworth, M. (1996) An

ecological risk assessment of lead shot

exposure in non-waterfowl avian species:

upland game birds and raptors. Environ

Toxicol Chem 15:4–20.

Ko ̈nemann, H. (1981) Fish toxicity tests

with mixtures of more than two chemicals:

a proposal for a quantitative approach and

experimental results. Aquatic Toxicol

19:229–238.

Laird, NM; Mosteller, R. (1990) Some

statistical methods for combining

experimental results. Int J Technol Assess

Health Care 6:5–30.

Landis, WG; Matthews, RA; Markiewicz,

AJ; Matthews, GB. (1993) Multivariate

analysis of the impacts of the turbine fuel JP–

4 in a microcosm toxicity test with

implications for the evaluation of ecosystem

dynamics and risk assessment. Ecotoxicology

2:271–300.

Lipnick, RL. (1995) Structure-activity

relationships. In: Fundamentals of aquatic

toxicology effects, environmental fate, and

risk assessment. Rand, GM; Petrocelli, SR,

eds. London, UK: Taylor and Francis, pp.

609-655.

Lipton, J; Galbraith, H; Burger, J;

Wartenberg, D. (1993) A paradigm for

ecological risk assessment. Environ Manage

17:1–5.

Ludwig, JA; Reynolds, JF. (1988) Statistical

ecology. New York, NY: Wiley-Interscience,

337 pp.

Lynch, DG; Macek, GJ; Nabholz, JV;

Sherlock, SM; Wright, R. (1994) Ecological

risk assessment case study: assessing the

ecological risks of a new chemical under the

Toxic Substances Control Act. In: A review

of ecological assessment case studies from a

risk assessment perspective, vol. II.

Washington, DC: Risk Assessment Forum,

U.S. Environmental Protection Agency, pp.

1–1 to 1–35. EPA/630/R–94/003.

MacIntosh, DL; Suter GW, II; Hoffman, FO.

(1994) Uses of probabilistic exposure models

in ecological risk assessments of

contaminated sites. Risk Anal 14(4):405–419.

Mann, C. (1990) Meta-analysis in the

breech. Science 249:476–480.

McCarty, LS; Mackay, D. (1993) Enhancing

ecotoxicological modeling and assessment:

body residues and modes of toxic action.

Environ Sci Technol 27:1719–1728.

Meij, R. (1991) The fate of mercury in coalfired power plants and the influence of wet

flue-gas desulphurization. Water Air Soil

Pollut 56:21–33.

Menzie, CA; Burmaster, DE; Freshman, JS;

Callahan, CA. (1992) Assessment of methods

for estimating ecological risk in the terrestrial

component: a case study at the Baird &

McGuire Superfund Site in Holbrook,

Massachusetts. Environ Toxicol Chem

11:245–260.

Menzie, C; Henning, MH; Cura, J;

Finkelstein, K; Gentile, J; Maughan J;

Mitchell, D; Petron, S; Potocki, B; Svirsky, S;

Tyler, P. (1996) Special report of the

Massachusetts weight-of-evidence

workgroup: a weight of evidence approach

for evaluating ecological risks. Human Ecol

Risk Assess 2:277–304.

Messer, JJ; Linthurst, RA; Overton, WS.

(1991) An EPA program for monitoring

ecological status and trends. Environ Monitor

Assess 17:67–78.

Nabholz, JV. (1991) Environmental hazard

and risk assessment under the United States

Toxic Substances Control Act. Science Total

Environ 109/110: 649–665.

Nabholz, JV; Miller, P; Zeeman, M. (1993)

Environmental risk assessment of new

chemicals under the Toxic Substances

Control Act (TSCA) section five. In:

Environmental toxicology and risk

assessment. Landis, WG; Hughes, SG; Lewis,

M; Gorsuch, JW, eds. ASTM STP 1179.

Philadelphia, PA: American Society for

Testing and Materials, pp. 40–55.

National Research Council. (1994) Science

and judgment in risk assessment.

Washington, DC: National Academy Press.

National Research Council. (1996)

Understanding risk: informing decisions in a

democratic society. Washington, DC:

National Academy Press.

Newman, MC. (1995) Advances in trace

substances research: quantitative methods in

aquatic ecotoxicology. Boca Raton, FL: Lewis

Publishers.

Niemi, GJ; DeVore, P; Detenbeck, N;

Taylor, D; Lima, A; Pastor, J; Yount, JD;

Naiman, RJ. (1990) Overview of case studies

on recovery of aquatic systems from

disturbance. Environ Manage 14:571–587.

Novitski, RP. (1979) Hydrologic

characteristics of Wisconsin’s wetlands and

their influence on floods, stream flow, and

sediment. In: Wetland functions and values:

the state of our understanding. Greeson, PE;

Clark, JR; Clark, JE, eds. Minneapolis, MN:

American Water Resources Association, pp.

377–388.

Oberts, GL. (1981) Impact of wetlands on

watershed water quality. In: Selected

proceedings of the Midwest Conference on

Wetland Values and Management.

Richardson, B, ed. Navarre, MN: Freshwater

Society, pp. 213–226.

Okkerman, PC; Plassche, EJVD; Emans,

HJB. (1993) Validation of some extrapolation

methods with toxicity data derived from

multispecies experiments. Ecotoxicol

Environ Saf 25:341–359.

O’Neill, RV; Gardner, RH; Barnthouse, LW;

Suter, GW, II; Hildebrand, SG; Gehrs, CW.

(1982) Ecosystem risk analysis: a new

methodology. Environ Toxicol Chem 1:167–

177.

Orr, RL; Cohen, SD; Griffin, RL. (1993)

Generic non-indigenous pest risk assessment

process. Beltsville, MD: USDA Animal and

Plant Health Inspection Service.

Ott, WR. (1978) Environmental indices—

theory and practice. Ann Arbor, MI: Ann

Arbor Science. Cited in: Suter, GW, II. (1993)

A critique of ecosystem health concepts and

indexes. Environ Toxicol Chem 12:1533–

1539.

Parkhurst, BR; Warren-Hicks, W; Etchison,

T; Butcher, JB; Cardwell, RD; Voloson, J.

(1995) Methodology for aquatic ecological

risk assessment. RP91 AER–1 1995.

Alexandria, VA: Water Environment

Research Foundation.

Pastorok, RA; Butcher, MK; Nielsen, RD.

(1996) Modeling wildlife exposure to toxic

chemicals: trends and recent advances.

Human Ecol Risk Assess 2:444–480.

Pearlstine, L; McKellar, H; Kitchens, W.

(1985) Modelling the impacts of a river

diversion on bottomland forest communities

in the Santee River Floodplain, South

Carolina. Ecol Model 29:281–302.

Pelczar, MJ; Reid, RD. (1972) Microbiology.

New York, NY: McGraw-Hill Company.

Peterman, RM. (1990) The importance of

reporting statistical power: the forest decline

and acidic deposition example. Ecology

71:2024–2027.

Petitti, DB. (1994) Meta-analysis, decision

analysis and cost-effectiveness analysis:

methods for quantitative synthesis in

medicine. Monographs in epidemiology and

biostatistics, vol. 24. New York, NY: Oxford

University Press.

Phipps, RL. (1979) Simulation of wetlands

forest vegetation dynamics. Ecol Model

7:257–288.

Pielou, EC. (1984) The interpretation of

ecological data. A primer on classification

and ordination. New York, NY: WileyInterscience, 263 pp.

Preston, EM; Bedford, BL. (1988)

Evaluating cumulative effects on wetland

functions: a conceptual overview and generic

framework. Environ Manage 12(5):565–583.

Richards, C; Haro, RJ; Johnson, LB; Host,

GE. (1997) Catchment and reach-scale

properties as indicators of macroinvertebrate

species traits. Freshwater Biol 37:219–230.

Risser, PG. (1988) General concepts for

measuring cumulative impacts on wetland

ecosystems. Environ Manage 12(5):585–589.

Rotenberry, JT; Wiens, JA. (1985) Statistical

power analysis and community-wide

patterns. Am Naturalist 125:164–168.

Ruckelshaus, WD. (1983) Science, risk, and

public policy. Science 221:1026–1028.
Federal Register / Vol. 63, No. 93 /Thursday, May 14, 1998 / Notices 26921

Sample, BE; Opresko, DM; Suter, GW, II.

(1996) Toxicological benchmarks for wildlife:

1996 revision. ES/ER/TM–86/R3. Oak Ridge,

TN: Oak Ridge National Laboratory, Health

Sciences Research Division.

Sawyer, TW; Safe, S. (1985) In vitro AHH

induction by polychlorinated biphenyl and

dibenzofuran mixtures: additive effects.

Chemosphere 14:79–84.

Schindler, DW. (1987) Detecting ecosystem

responses to anthropogenic stress. Can J Fish

Aquat Sci 44(Suppl.1):6–25.

Simberloff, D; Alexander, M. (1994) Issue

paper on biological stressors. In: Ecological

risk assessment issue papers. Washington,

DC: Risk Assessment Forum, U.S.

Environmental Protection Agency, pp. 6–1 to

6–59. EPA/630/R–94/009.

Smith, EP; Cairns, J, Jr. (1993)

Extrapolation methods for setting ecological

standards for water quality: statistical and

ecological concerns. Ecotoxicology 2:203–

219.

Smith, EP; Shugart, HH. (1994) Issue paper

on uncertainty in ecological risk assessment.

In: Ecological risk assessment issue papers.

Washington, DC: Risk Assessment Forum,

U.S. Environmental Protection Agency, pp.

8–1 to 8–53. EPA/630/R–94/009.

Society of Environmental Toxicology and

Chemistry (SETAC). (1987) Research

priorities in environmental risk assessment.

Report of a workshop held in Breckenridge,

CO, August 16–21, 1987. Washington, DC:

SETAC.

Solomon, KR; Baker, DB; Richards, RP;

Dixon, KR; Klaine, SJ; La Point, TW; Kendall,

RJ; Weisskopf, CP; Giddings, JM; Geisy, JP;

Hall, LW; Williams, WM. (1996) Ecological

risk assessment of atrazine in North

American surface waters. Environ Toxicol

Chem 15(1):31–76.

Starfield, AM; Bleloch, AL. (1991) Building

models for conservation and wildlife

management. Edina, MN: Burgess

International Group, Inc.

Stephan, CE. (1977) Methods for

calculating an LC50. In: ASTM Special

Technical Publication 634. Philadelphia, PA:

American Society for Testing and Materials,

pp. 65–88.

Stephan, CE; Rogers, JR. (1985) Advantages

of using regression analysis to calculate

results of chronic toxicity tests. In: Aquatic

toxicology and hazard assessment. Eighth

symposium. Bahner, RC; Hanse, DJ, eds.

Philadelphia, PA: American Society for

Testing and Materials, pp. 328–339.

Stephan, CE; Mount, DI; Hansen, DJ;

Gentile, JH; Chapman, GA; Brungs, WA.

(1985) Guidelines for deriving numerical

national water quality criteria for the

protection of aquatic organisms and their

uses. Duluth, MN: Office of Research and

Development, U.S. Environmental Protection

Agency. PB85–227049.

Stewart-Oaten, A; Murdoch, WW; Parker,

KR. (1986) Environmental impact

assessment: ‘‘pseudoreplication’’ in time?

Ecology 67(4):929–940.

Susser, M. (1986a) Rules of inference in

epidemiology. Regul Toxicol Pharmacol

6:116–128.

Susser, M. (1986b) The logic of Sir Carl

Popper and the practice of epidemiology. Am

J Epidemiol 124:711–718.

Suter, GW, II. (1989) Ecological endpoints.

In: Ecological assessments of hazardous

waste sites: a field and laboratory reference

document. Warren-Hicks, W; Parkhurst, BR;

Baker, SS, Jr, eds. Washington, DC: U.S.

Environmental Protection Agency. EPA 600/

3–89/013.

Suter, GW, II. (1990) Endpoints for regional

ecological risk assessments. Environ Manage

14:19–23.

Suter, GW, II. (1993a) Ecological risk

assessment. Boca Raton, FL: Lewis

Publishers.

Suter, GW, II. (1993b) A critique of

ecosystem health concepts and indexes.

Environ Toxicol Chem 12:1533–1539.

Suter, GW, II. (1996) Abuse of hypothesis

testing statistics in ecological risk

assessment. Human Ecol Risk Assess 2:331–

347.

Suter, GW, II; Vaughan, DS; Gardner, RH.

(1983) Risk assessment by analysis of

extrapolation error. A demonstration for

effects of pollutants on fish. Environ Toxicol

Chem 2:369–377.

Suter, GW, II; Gillett, JW; Norton, SB.

(1994) Issue paper on characterization of

exposure. In: Ecological risk assessment issue

papers. Washington, DC: Risk Assessment

Forum, U.S. Environmental Protection

Agency, pp. 4–1 to 4–64. EPA/630/R–94/009.

Thomas, JW; Forsman, ED; Lint, JB;

Meslow, EC; Noon, BR; Verner, J. (1990) A

conservation strategy for the spotted owl.

Interagency Scientific Committee to Address

the Conservation of the Northern Spotted

Owl. 1990–791/20026. Washington, DC: U.S.

Government Printing Office.

Urban, DJ; Cook, JN. (1986) Ecological risk

assessment. Hazard Evaluation Division

standard procedure. Washington, DC: Office

of Pesticide Programs, U.S. Environmental

Protection Agency. EPA–54019–83–001.

U.S. Department of Agriculture. (1993) Pest

risk assessment of the importation of Pinus

radiata, Nothofagus dombeyi, and Laurelia

philippiana logs from Chile. Forest Service

Miscellaneous Publication 1517.

U.S. Department of Health, Education, and

Welfare. (1964) Smoking and health. Report

of the Advisory Committee to the Surgeon

General. Public Health Service Publication

1103. Washington, DC: U.S. Department of

Health, Education, and Welfare.

U.S. Environmental Protection Agency.

(1979) Toxic Substances Control Act.

Discussion of premanufacture testing policies

and technical issues: request for comment.

Federal Register 44:16240–16292.

U.S. Environmental Protection Agency.

(1984) Estimating concern levels for

concentrations of chemical substances in the

environment. Washington, DC: U.S.

Environmental Protection Agency, Health

and Environmental Review Division,

Environmental Effects Branch.

U.S. Environmental Protection Agency.

(1986a) Guidelines for the health risk

assessment of chemical mixtures. Federal

Register 52:34014–34025.

U.S. Environmental Protection Agency.

(1986b) Quality criteria for water.

Washington, DC: Office of Water, U.S.

Environmental Protection Agency. EPA/440/

5–86/001.

U.S. Environmental Protection Agency.

(1988a) Methods for aquatic toxicity

identification evaluations: phase I toxicity

characterization procedures. Duluth, MN:

Environmental Research Laboratory, U.S.

Environmental Protection Agency. EPA/600/

3–88/034.

U.S. Environmental Protection Agency.

(1988b) User’s guide to PDM3, final report.

Prepared by Versar, Inc., for Exposure

Assessment Branch, Exposure Evaluation

Division, U.S. Environmental Protection

Agency, Washington, DC, under EPA contract

no. 68–02–4254, task no. 117.

U.S. Environmental Protection Agency.

(1989a) Rapid bioassessment protocols for

use in streams and rivers: benthic

macroinvertebrates and fish. Washington,

DC: Office of Water, U.S. Environmental

Protection Agency. EPA/440/4–89/001.

U.S. Environmental Protection Agency.

(1989b) Methods for aquatic toxicity

identification evaluations: phase II toxicity

identification procedures. Duluth, MN:

Environmental Research Laboratory, U.S.

Environmental Protection Agency. EPA/600/

3–88/035.

U.S. Environmental Protection Agency.

(1989c) Methods for aquatic toxicity

identification evaluations: phase III toxicity

confirmation procedures. Duluth, MN:

Environmental Research Laboratory, U.S.

Environmental Protection Agency. EPA/600/

3–88/035.

U.S. Environmental Protection Agency.

(1990) Guidance for data useability in risk

assessment. Washington, DC: U.S.

Environmental Protection Agency. EPA/540/

G–90/008.

U.S. Environmental Protection Agency.

(1991) Technical support document for water

quality-based toxics control. Washington, DC:

Office of Water, U.S. Environmental

Protection Agency. EPA/505/2–90/001.

U.S. Environmental Protection Agency.

(1992a) Framework for ecological risk

assessment. Washington, DC: Risk

Assessment Forum, U.S. Environmental

Protection Agency. EPA/630/R–92/001.

U.S. Environmental Protection Agency.

(1992b) Guidelines for exposure assessment:

notice. Federal Register 57:22888–22938.

U.S. Environmental Protection Agency.

(1992c) A cross-species scaling factor for

carcinogenic risk assessment based on

equivalence of mg/kg3⁄4/day: draft report.

Federal Register 57(109):24152–24173.

U.S. Environmental Protection Agency.

(1993a) Wildlife exposure factors handbook.

Washington, DC: Office of Research and

Development, U.S. Environmental Protection

Agency. EPA/600/R–93/187a and 187b.

U.S. Environmental Protection Agency.

(1993b) A review of ecological risk

assessment case studies from a risk

assessment perspective. Washington, DC:

Risk Assessment Forum, U.S. Environmental

Protection Agency. EPA/630/R–92/005.

U.S. Environmental Protection Agency.

(1993c) Communicating risk to senior EPA

policy makers: a focus group study. Research

Triangle Park, NC: Office of Air Quality

Planning and Standards, U.S. Environmental

Protection Agency.

U.S. Environmental Protection Agency.

(1993d) A guidebook to comparing risks and

setting environmental priorities. Washington,

DC: Office of Policy, Planning, and
26922 Federal Register / Vol. 63, No. 93 /Thursday, May 14, 1998 / Notices

Evaluation, U.S. Environmental Protection

Agency. EPA/230/B–93/003.

U.S. Environmental Protection Agency.

(1994a) Managing ecological risks at EPA:

issues and recommendations for progress.

Washington, DC: Center for Environmental

Research Information, U.S. Environmental

Protection Agency. EPA/600/R–94/183.

U.S. Environmental Protection Agency.

(1994b) ‘‘Ecosystem protection.’’

Memorandum from Robert Perciaspe, David

Gardiner, and Johnathan Cannon to Carol

Browner, March 1994.

U.S. Environmental Protection Agency.

(1994c) Guidance for the data quality

objectives process. Washington, DC: Quality

Assurance Management Staff. EPA QA/G–4.

U.S. Environmental Protection Agency.

(1994d) Environmental Services Division

guidelines. Hydrogeologic modeling. Seattle,

WA: Region X, U.S. Environmental

Protection Agency.

U.S. Environmental Protection Agency.

(1994e) A review of ecological assessment

case studies from a risk assessment

perspective, vol. II. Washington, DC: Risk

Assessment Forum, U.S. Environmental

Protection Agency. EPA/630/R–94/003.

U.S. Environmental Protection Agency.

(1995a) Ecological risk: a primer for risk

managers. Washington, DC: U.S.

Environmental Protection Agency. EPA/734/

R–95/001.

U.S. Environmental Protection Agency.

(1995b) ‘‘EPA risk characterization program.’’

Memorandum to EPA managers from

Administrator Carol Browner, March 1995.

U.S. Environmental Protection Agency.

(1995c) The use of the benchmark dose

approach in health risk assessment.

Washington, DC: Risk Assessment Forum,

U.S. Environmental Protection Agency. EPA/

630/R–94/007.

U.S. Environmental Protection Agency.

(1995d) Great Lakes water quality initiative

technical support document for wildlife.

Washington, DC: Office of Water, U.S.

Environmental Protection Agency. EPA/820/

B–95/009.

U.S. Environmental Protection Agency.

(1995e) An SAB report: ecosystem

management—imperative for a dynamic

world. Washington, DC: Science Advisory

Board. EPA–SAB–EPEC–95–003.

U.S. Environmental Protection Agency.

(1996a) Summary report for the workshop on

Monte Carlo analysis. Washington, DC: Office

of Research and Development, U.S.

Environmental Protection Agency. EPA/630/

R–96/010.

U.S. Environmental Protection Agency.

(1996b) Waquoit Bay watershed. Ecological

risk assessment planning and problem

formulation (draft). Washington, DC: Risk

Assessment Forum, U.S. Environmental

Protection Agency. EPA/630/R–96/004a.

U.S. Environmental Protection Agency.

(1997a) Priorities for ecological protection: an

initial list and discussion document for EPA.

Washington, DC: Office of Research and

Development, U.S. Environmental Protection

Agency. EPA/600/S–97/002.

U.S. Environmental Protection Agency.

(1997b) Policy for use of probabilistic

analysis in risk assessment: guiding

principles for Monte Carlo analysis.

Washington, DC: Office of Research and

Development, U.S. Environmental Protection

Agency. EPA/630/R–97/001.

van Gestel, CAM; van Brummelen, TC.

(1996) Incorporation of the biomarker

concept—ecotoxicology calls for a

redefinition of terms. Ecotoxicol 5:217–225.

Van Leeuwen, CJ; Van der Zandt, PTJ;

Aldenberg, T; Verhar, HJM; Hermens, JLM.

(1992) Extrapolation and equilibrium

partitioning in aquatic effects assessment.

Environ Toxicol Chem 11:267–282.

Wagner, C; Lφkke, H. (1991) Estimation of

ecotoxicological protection levels from NOEC

toxicity data. Water Res 25:1237–1242.

White, PS; Pickett, STA. (1985) Natural

disturbance and patch dynamics: an

introduction. In: The ecology of natural

disturbance and patch dynamics. Pickett,

STA; White, PS, eds. Orlando, FL: Academic

Press, pp. 3–13.

Wiegert, RG; Bartell, SM. (1994) Issue

paper on risk integration methods. In:

Ecological risk assessment issue papers.

Washington, DC: Risk Assessment Forum,

Environmental Protection Agency, pp. 9–1 to

9–66. EPA/630/R–94/009.

Wiens, JA; Parker, KR. (1995) Analyzing

the effects of accidental environmental

impacts: approaches and assumptions. Ecol

Appl 5(4):1069–1083.

Woodman, JN; Cowling, EB. (1987)

Airborne chemicals and forest health.

Environ Sci Technol 21:120–126.

Yoder, CO; Rankin, ET. (1995) Biological

response signatures and the area of

degradation value: new tools for interpreting

multi-metric data. In: Biological assessment

and criteria: tools for water resource planning

and decision making. Davis, WS; Simon, TP,

eds. Boca Raton, FL: Lewis Publishers.

Zeeman, M. (1995) EPA’s framework for

ecological effects assessment. In: Screening

and testing chemicals in commerce. OTA–

BP–ENV–166. Washington, DC: Office of

Technology Assessment, pp. 69–78.

Part B: Response to Science Advisory

Board and Public Comments

1. Introduction

This section summarizes the major

issues raised in public comments and by

EPA’s Science Advisory Board (SAB) on

the previous draft of these Guidelines

(the Proposed Guidelines for Ecological

Risk Assessment, hereafter ‘‘Proposed

Guidelines’’). A notice of availability for

public comment of the Proposed

Guidelines was published September 9,

1996 (61 FR 47552–47631). Forty-four

responses were received. The Ecological

Processes and Effects Committee of the

SAB reviewed the Proposed Guidelines

on September 19–20, 1996, and

provided comments in January 1997

(EPA–SAB–EPEC–97–002).

The SAB and public comments were

diverse, reflecting the different

perspectives of the reviewers. Many of

the comments were favorable,

expressing agreement with the overall

approach to ecological risk assessment.

Many comments were beyond the scope

of the Guidelines, including requests for

guidance on risk management issues

(such as considering social or economic

impacts in decision making). Major

issues raised by reviewers are

summarized below. In addition to

providing general comments (section 2),

reviewers were asked to comment on

seven specific questions (section 3).

2. Response to General Comments

Probably the most common request

was for greater detail in specific areas.

In some cases, additional discussion

was added (for example, on the use of

tiering and iteration and the respective

roles of risk assessors, risk managers,

and interested parties throughout the

process). In other areas, topics for

additional discussion were included in

a list of potential areas for further

development (see response to question

2, below). Still other topics are more

appropriately addressed by regional or

program offices within the context of a

certain regulation or issue, and are

deferred to those sources.

A few reviewers felt that since

ecological risk assessment is a relatively

young science, it is premature to issue

guidelines at this time. The Agency feels

that it is appropriate to issue guidance

at this time, especially since the

Guidelines contain major principles but

refrain from recommending specific

methodologies that might become

rapidly outdated. To help ensure the

continued relevance of the Guidelines,

the Agency intends to develop

documents addressing specific topics

(see response to question 2 below) and

will revise these Guidelines as

experience and scientific consensus

evolve.

Some reviewers asked whether the

Guidelines would be applied to

previous or ongoing ecological risk

assessments, and whether existing

regional or program office guidance

would be superseded in conducting

ecological risk assessments. As

described in section 1.3 (Scope and

Intended Audience), the Guidelines are

principles, and are not regulatory in

nature. It is anticipated that guidance

from program and regional offices will

evolve to implement the principles set

forth in these Guidelines. Similarly,

some reviewers requested that

assessments require a comparison of the

risks of alternative scenarios (including

background or baseline conditions) or

an assignment of particular levels of

ecological significance to habitats.

These decisions would be most

appropriately made on a case-by-case

basis, or by a program office in response

to program-specific needs.
Federal Register / Vol. 63, No. 93 /Thursday, May 14, 1998 / Notices 26923

Several Native American groups

noted a lack of acknowledgment of

tribal governments in the document.

This Agency oversight was corrected by

including tribal governments at points

in the Guidelines where other

governmental organizations are

mentioned.

Several reviewers noted that the

Proposed Guidelines mentioned the

need for ‘‘expert judgment’’ in several

places and asked how the Agency

defined ‘‘expert’’ and what

qualifications such an individual should

have. At present, there is no standard

set of qualifications for an ecological

risk assessor, and such a standard

would be very difficult to produce,

since ecological assessments are

frequently done by teams of individuals

with expertise in many areas. To avoid

this problem, the Guidelines now use

the term ‘‘professional judgment,’’ and

note that it is important to document the

rationale for important decisions.

Some reviewers felt that the

Guidelines should address effects only

at the population level and above. The

Guidelines do not make this restriction

for several reasons. First, some

assessments, such as those involving

endangered species, do involve

considerations of individual effects.

Second, the decision as to which

ecological entity to protect should be

the result, on a case-by-case basis, of the

planning process involving risk

assessors, risk managers, and interested

parties, if appropriate. Some suggestions

have been proposed (U.S. EPA, 1997a).

Finally, there appears to be some

confusion among reviewers between

conducting an assessment concerned

with population-level effects, and using

data from studies of effects on

individuals (e.g., toxicity test results) to

infer population-level effects. These

inferences are commonly used (and

generally accepted) in chemical

screening programs, such as the Office

of Pollution Prevention and Toxics

Premanufacturing Notification program

(U.S. EPA, 1994e).

The use of environmental indices

received a number of comments. Some

reviewers wanted the Guidelines to do

more to encourage the use of indices,

while others felt that the disadvantages

of indices should receive greater

emphasis. The Guidelines discuss both

the advantages and limitations of using

indices to guide risk assessors in their

proper use.

Other reviewers requested that the

Guidelines take a more definitive

position on the use of ‘‘realistic

exposure assumptions,’’ such as those

proposed in the Agency’s exposure

guidelines (U.S. EPA, 1992b). Although

the exposure guidelines offer many

useful suggestions that are applicable to

human health risk assessment, it was

not possible to generalize the concepts

to ecological risk assessment, given the

various permutations of the exposure

concept for different types of stressors

or levels of biological organization. The

Guidelines emphasize the importance of

documenting major assumptions

(including exposure assumptions) used

in an assessment.

Several reviewers requested more

guidance and examples using

nonchemical stressors, i.e., physical or

biological stressors. This topic has been

included in the list of potential subjects

for future detailed treatment (see

response to question 2, below).

3. Response to Comments on Specific

Questions

Both the Proposed Guidelines and the

charge to the SAB for its review

contained a set of seven questions asked

by the Agency. These questions, along

with the Agency’s response to

comments received, are listed below.

(1) Consistent with a recent National

Research Council report (NRC, 1996),

these Proposed Guidelines emphasize

the importance of interactions between

risk assessors and risk managers as well

as the critical role of problem

formulation in ensuring that the results

of the risk assessment can be used for

decision making. Overall, how

compatible are these Proposed

Guidelines with the National Research

Council concept of the risk assessment

process and the interactions among risk

assessors, risk managers, and other

interested parties?

Most reviewers felt there was general

compatibility between the Proposed

Guidelines and the NRC report,

although some emphasized the need for

continued interactions among risk

assessors, risk managers, and interested

parties (or stakeholders) throughout the

ecological risk assessment process and

asked that the Guidelines provide

additional details concerning such

interactions. To give greater emphasis to

these interactions, the ecological risk

assessment diagram was modified to

include ‘‘interested parties’’ in the

planning box at the beginning of the

process and ‘‘communicating with

interested parties’’ in the risk

management box following the risk

assessment. Some additional discussion

concerning interactions among risk

assessors, risk managers, and interested

parties was added, particularly to

section 2 (planning). However, although

risk assessor/risk manager

interrelationships are discussed, too

great an emphasis in this area is

inconsistent with the scope of the

Guidelines, which focus on the interface

between risk assessors and risk

managers, not on providing risk

management guidance.

(2) The Proposed Guidelines are

intended to provide a starting point for

Agency programs and regional offices

that wish to prepare ecological risk

assessment guidance suited to their

needs. In addition, the Agency intends

to sponsor development of more

detailed guidance on certain ecological

risk assessment topics. Examples might

include identification and selection of

assessment endpoints, selection of

surrogate or indicator species, or the

development and application of

uncertainty factors. Considering the

state of the science of ecological risk

assessment and Agency needs and

priorities, what topics most require

additional guidance?

Reviewers recommended numerous

topics for further development.

Examples include:

• Landscape ecology.

• Data sources and quality.

• Physical and biological stressors.

• Multiple stressors.

• Defining reference areas for field

studies.

• Ecotoxicity thresholds.

• The role of biological and other

types of indicators.

• Bioavailability, bioaccumulation,

and bioconcentration.

• Uncertainty factors.

• Stressor-response relationships

(e.g., threshold vs. continuous).

• Risk characterization techniques.

• Risk communication to the public.

• Public participation.

• Comparative ecological risk.

• Screening and tiering assessments.

• Identifying and selecting

assessment endpoints.

These suggestions will be included in

a listing of possible topics proposed to

the Agency’s Risk Assessment Forum

for future development.

(3) Some reviewers have suggested

that the Proposed Guidelines should

provide more discussion of topics

related to the use of field observational

data in ecological risk assessments, such

as selection of reference sites,

interpretation of positive and negative

field data, establishing causal linkages,

identifying measures of ecological

condition, the role and uses of

monitoring, and resolving conflicting

lines of evidence between field and

laboratory data. Given the general scope

of these Proposed Guidelines, what, if

any, additional material should be

added on these topics and, if so, what

principles should be highlighted?

In response to a number of comments,

the discussion of field data in the
26924 Federal Register / Vol. 63, No. 93 /Thursday, May 14, 1998 / Notices

Guidelines was expanded, especially in

section 4.1. Nevertheless, many

suggested topics requested a level of

detail that was inconsistent with the

scope of the Guidelines. Some areas

may be covered through the

development of future Risk Assessment

Forum documents.

(4) The scope of the Proposed

Guidelines is intentionally broad.

However, while the intent is to cover

the full range of stressors, ecosystem

types, levels of biological organization,

and spatial/temporal scales, the

contents of the Proposed Guidelines are

limited by the present state of the

science and the relative lack of

experience in applying risk assessment

principles to some areas. In particular,

given the Agency’s present interest in

evaluating risks at larger spatial scales,

how could the principles of landscape

ecology be more fully incorporated into

the Proposed Guidelines?

Landscape ecology is critical to many

aspects of ecological risk assessment,

especially assessments conducted at

larger spatial scales. However, given the

general nature of these Guidelines and

the responses received to this question,

the Guidelines could not be expanded

substantially at this time. This topic has

been added to the list of potential

subjects for future development.

(5) Assessing risks when multiple

stressors are present is a challenging

task. The problem may be how to

aggregate risks attributable to individual

stressors or identify the principal

stressors responsible for an observed

effect. Although some approaches for

evaluating risks associated with

chemical mixtures are available, our

ability to conduct risk assessments

involving multiple chemical, physical,

and biological stressors, especially at

larger spatial scales, is limited.

Consequently, the Proposed Guidelines

primarily discuss predicting the effects

of chemical mixtures and general

approaches for evaluating causality of

an observed effect. What additional

principles can be added?

Few additional principles were

provided that could be included in the

Guidelines. To further progress in

evaluating multiple stressors, EPA

cosponsored a workshop on this issue,

held by the Society of Environmental

Toxicology and Chemistry in September

1997. In addition, evaluating multiple

stressors is one of the proposed topics

for further development.

(6) Ecological risk assessments are

frequently conducted in tiers that

proceed from simple evaluations of

exposure and effects to more complex

assessments. While the Proposed

Guidelines acknowledge the importance

of tiered assessments, the wide range of

applications of tiered assessments make

further generalizations difficult. Given

the broad scope of the Proposed

Guidelines, what additional principles

for conducting tiered assessments can

be discussed?

Many reviewers emphasized the

importance of tiered assessments, and in

response the discussion of tiered

assessments was significantly expanded

in the planning phase of ecological risk

assessment. Including more detailed

information (such as specific decision

criteria to proceed from one tier to the

next) would require a particular context

for an assessment. Such specific

guidance is left to the EPA program

offices and regions.

(7) Assessment endpoints are

‘‘explicit expression of the

environmental value that is to be

protected.’’ As used in the Proposed

Guidelines, assessment endpoints

include both an ecological entity and a

specific attribute of the entity (e.g., eagle

reproduction or extent of wetlands).

Some reviewers have recommended that

assessment endpoints also include a

decision criterion that is defined early

in the risk assessment process (e.g., no

more than a 20% reduction in

reproduction, no more than a 10% loss

of wetlands). While not precluding this

possibility, the Proposed Guidelines

suggest that such decisions are more

appropriately made during discussions

between risk assessors and managers in

risk characterization at the end of the

process. What are the relative merits of

each approach?

Reviewer reaction was quite evenly

divided between those who felt strongly

that decision criteria should be defined

in problem formulation and those who

felt just as strongly that such decisions

should be delayed until risk

characterization. Although the

Guidelines contain more discussion of

this topic, they still take the position

that assessment endpoints need not

contain specific decision criteria.

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

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