EPA/630/R-95/002F

April 1998

 

Guidelines for

Ecological Risk Assessment

(Published on May 14, 1998, Federal Register 63(93):26846-26924)

Risk Assessment Forum
U.S. Environmental Protection Agency

Washington, DC
DISCLAIMER

This document has been reviewed in accordance with U.S. Environmental Protection Agency

policy and approved for publication. Mention of trade names or commercial products does not

constitute endorsement or recommendation for use.

NOTICE

This report contains the full text of the Guidelines for Ecological Risk Assessment. However,

the format of this version differs from the Federal Register version, as follows: text boxes that are

included in this document at their point of reference were instead listed at the end of the Federal

Register document as text notes, due to format limitations for Federal Register documents.
iv

GUIDELINES FOR ECOLOGICAL RISK ASSESSMENT

[FRL-6011-2]

AGENCY: U.S. Environmental Protection Agency

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

SUMMARY: The U.S. Environmental Protection Agency (EPA) is today publishing in final form a

document entitled Guidelines for Ecological Risk Assessment (hereafter “Guidelines”). These

Guidelines were developed as part of an interoffice program by a Technical Panel of the Risk

Assessment Forum. These Guidelines will help improve the quality of ecological risk assessments at

EPA while increasing the consistency of assessments among the Agency’s program offices and regions.

These Guidelines were prepared during a time of increasing interest in the field of ecological risk

assessment and reflect input from many sources both within and outside the Agency. The Guidelines

expand upon and replace the previously published EPA report Framework for Ecological Risk

Assessment (EPA/630/R-92/001, February 1992), which proposed principles and terminology for the

ecological risk assessment process. From 1992 to 1994, the Agency focused on identifying a structure

for the Guidelines and the issues that the document would address. EPA sponsored public and Agency

colloquia, developed peer-reviewed ecological assessment case studies, and prepared a set of peerreviewed issue papers highlighting important principles and approaches. Drafts of the proposed

Guidelines underwent formal external peer review and were reviewed by the Agency’s Risk

Assessment Forum, by Federal interagency subcommittees of the Committee on Environment and

Natural Resources of the Office of Science and Technology Policy, and by the Agency’s Science

Advisory Board (SAB). The proposed Guidelines were published for public comment in 1996 (61 FR

47552-47631, September 9, 1996). The final Guidelines incorporate revisions based on the comments

received from the public and the SAB on the proposed Guidelines. EPA appreciates the efforts of all

participants in the process and has tried to address their recommendations in these Guidelines.

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

ADDRESSES: The Guidelines will be made available in several ways:
vi

(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-D), 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
vii

Framework Report’s broad scope, while expanding on some concepts and modifying others to reflect

Agency experiences. EPA intends to follow these Guidelines with a series of shorter, more detailed

documents that address specific ecological risk assessment topics. This “bookshelf” approach provides

the flexibility necessary to keep pace with developments in the rapidly evolving field of ecological risk

assessment while allowing time to form consensus, where appropriate, on science policy (default

assumptions) to bridge gaps in knowledge. EPA will revisit guidelines documents as experience and

scientific consensus evolve. The Agency recognizes that ecological risk assessment is only one tool in

the overall management of ecological risks. Therefore, there are ongoing efforts within the Agency to

develop other tools and processes that can contribute to an overall approach to ecological risk

management, addressing topics such as ecological benefits assessment and cost-benefit analyses.

Ecological risk assessment includes three primary phases: problem formulation, analysis, and

risk characterization. In problem formulation, risk assessors evaluate goals and select assessment

endpoints, prepare the conceptual model, and develop an analysis plan. During the analysis phase,

assessors evaluate exposure to stressors and the relationship between stressor levels and ecological

effects. In the third phase, risk characterization, assessors estimate risk through integration of exposure

and stressor-response profiles, describe risk by discussing lines of evidence and determining ecological

adversity, and prepare a report. The interface among risk assessors, risk managers, and interested

parties during planning at the beginning and communication of risk at the end of the risk assessment is

critical to ensure that the results of the assessment can be used to support a management decision. 

Because of the diverse expertise required (especially in complex ecological risk assessments), risk

assessors and risk managers frequently work in multidisciplinary teams.

Both risk managers and risk assessors bring valuable perspectives to the initial planning

activities for an ecological risk assessment. Risk managers charged with protecting the environment can

identify information they need to develop their decision, risk assessors can ensure that science is

effectively used to address ecological concerns, and together they can evaluate whether a risk

assessment can address identified problems. However, this planning process is distinct from the

scientific conduct of an ecological risk assessment. This distinction helps ensure that political and social

issues, while helping to define the objectives for the risk assessment, do not introduce undue bias.

Problem formulation, which follows these planning discussions, provides a foundation upon

which the entire risk assessment depends. Successful completion of problem formulation depends on

the quality of three products: assessment endpoints, conceptual models, and an analysis plan. Since

problem formulation is an interactive, nonlinear process, substantial reevaluation is expected to occur

during the development of all problem formulation products.
viii

The analysis phase includes two principal activities: characterization of exposure and

characterization of ecological effects. The process is flexible, and interaction between the two

evaluations is essential. Both activities evaluate available data for scientific credibility and relevance to

assessment endpoints and the conceptual model. Exposure characterization describes sources of

stressors, their distribution in the environment, and their contact or co-occurrence with ecological

receptors. Ecological effects characterization evaluates stressor- response relationships or evidence

that exposure to stressors causes an observed response. The bulk of quantitative uncertainty analysis is

performed in the analysis phase, although uncertainty is an important consideration throughout the entire

risk assessment. The analysis phase products are summary profiles that describe exposure and the

stressor-response relationships.

Risk characterization is the final phase of an ecological risk assessment. During this phase, risk

assessors estimate ecological risks, indicate the overall degree of confidence in the risk estimates, cite

evidence supporting the risk estimates, and interpret the adversity of ecological effects. To ensure

mutual understanding between risk assessors and managers, a good risk characterization will express

results clearly, articulate major assumptions and uncertainties, identify reasonable alternative

interpretations, and separate scientific conclusions from policy judgments. Risk managers use risk

assessment results, along with other factors (e.g., economic or legal concerns), in making risk

management decisions and as a basis for communicating risks to interested parties and the general

public.

After completion of the risk assessment, risk managers may consider whether follow-up

activities are required. They may decide on risk mitigation measures, then develop a monitoring plan to

determine whether the procedures reduced risk or whether ecological recovery is occurring. Managers

may also elect to conduct another planned tier or iteration of the risk assessment if necessary to support

a management decision.

 

Dated Carol M. Browner

 Administrator
ix

CONTENTS

PART A: GUIDELINES FOR ECOLOGICAL RISK ASSESSMENT

List of Figures ................................................................ ix

List of Text Boxes .............................................................. x

1. Introduction ................................................................. 1

1.1. The Ecological Risk Assessment Process ....................................... 2

1.2. Ecological Risk Assessment in a Management Context ............................. 6

1.2.1. Contributions of Ecological Risk Assessment to Environmental 

Decision Making ............................................................. 6

1.2.2. Factors Affecting the Value of Ecological Risk Assessment for Environmental 

Decision Making ............................................................. 7

1.3. Scope and Intended Audience ............................................... 8

1.4. Guidelines Organization .................................................... 9

2. Planning the Risk Assessment .................................................. 10

2.1. The Roles of Risk Managers, Risk Assessors, and Interested Parties in Planning ......... 11

2.2. Products of Planning ..................................................... 12

2.2.1. Management Goals ..................................................... 14

2.2.2. Management Options to Achieve Goals ...................................... 16

2.2.3. Scope and Complexity of the Risk Assessment ................................ 18

2.3. Planning Summary ....................................................... 19

3. Problem Formulation Phase .................................................... 21

3.1. Products of Problem Formulation ............................................ 22

3.2. Integration of Available Information .......................................... 22

3.3. Selecting Assessment Endpoints ............................................. 25

3.3.1. Criteria for Selection .................................................... 25

3.3.1.1. Ecological Relevance .................................................. 27

3.3.1.2. Susceptibility to Known or Potential Stressors ............................... 29
CONTENTS (continued)

x

3.3.1.3. Relevance to Management Goals ......................................... 31

3.3.2. Defining Assessment Endpoints ............................................ 32

3.4. Conceptual Models ...................................................... 36

3.4.1. Risk Hypotheses ....................................................... 38

3.4.2. Conceptual Model Diagrams .............................................. 39

3.4.3. Uncertainty in Conceptual Models .......................................... 40

3.5. Analysis Plan ........................................................... 41

3.5.1. Selecting Measures ..................................................... 42

3.5.2. Ensuring That Planned Analyses Meet Risk Managers’ Needs ..................... 45

4. Analysis Phase ............................................................. 48

4.1. Evaluating Data and Models for Analysis ...................................... 50

4.1.1. Strengths and Limitations of Different Types of Data ............................ 50

4.1.2. Evaluating Measurement or Modeling Studies ................................. 53

4.1.2.1. Evaluating the Purpose and Scope of the Study .............................. 55

4.1.2.2. Evaluating the Design and Implementation of the Study ......................... 55

4.1.3. Evaluating Uncertainty ................................................... 56

4.2. Characterization of Exposure ............................................... 60

4.2.1. Exposure Analyses ..................................................... 61

4.2.1.1. Describe the Source(s) ................................................ 61

4.2.1.2. Describe the Distribution of the Stressors or Disturbed Environment ............... 63

4.2.1.3. Describe Contact or Co-Occurrence ..................................... 67

4.2.2. Exposure Profile ....................................................... 71

4.3. Characterization of Ecological Effects ......................................... 73

4.3.1. Ecological Response Analysis ............................................. 73

4.3.1.1. Stressor-Response Analysis ............................................. 73

4.3.1.2. Establishing Cause-and-Effect Relationships (Causality) ........................ 78

4.3.1.3. Linking Measures of Effect to Assessment Endpoints .......................... 82

4.3.2. Stressor-Response Profile ................................................ 89

5. Risk Characterization ........................................................ 92

5.1. Risk Estimation ......................................................... 92
CONTENTS (continued)

xi

5.1.1. Results of Field Observational Studies ....................................... 92

5.1.2. Categories and Rankings ................................................. 94

5.1.3. Single-Point Exposure and Effects Comparisons ............................... 95

5.1.4. Comparisons Incorporating the Entire Stressor-Response Relationship ............... 99

5.1.5. Comparisons Incorporating Variability in Exposure and/or Effects .................. 99

5.1.6. Application of Process Models ........................................... 101

5.2. Risk Description ....................................................... 103

5.2.1. Lines of Evidence ..................................................... 103

5.2.2. Determining Ecological Adversity ......................................... 106

5.3. Reporting Risks ........................................................ 109

6. Relating Ecological Information to Risk Management Decisions ........................ 112

Appendix A: Changes from EPA’s Ecological Risk Assessment Framework ................ A-1

Appendix B: Key Terms ....................................................... B-1

Appendix C: Conceptual Model Examples ......................................... C-1

Appendix D: Analysis Phase Examples ........................................... D-1

Appendix E: Criteria for Determining Ecological Adversity: A Hypothetical Example .......... E-1

References .................................................................. R-1

PART B: RESPONSE TO SCIENCE ADVISORY BOARD AND PUBLIC COMMENTS

1. Introduction ................................................................. 1

2. Response to General Comments ................................................. 1
CONTENTS (continued)

xii

3. Response to Comments on Specific Questions ....................................... 3
xiii

LIST OF FIGURES

Figure 1-1. The framework for ecological risk assessment ................................ 3

Figure 1-2. The ecological risk assessment framework, with an expanded 

view of each phase .............................................................. 4

Figure 3-1. Problem formulation phase ............................................. 23

Figure 4-1. Analysis phase ...................................................... 49

Figure 4-2. A simple example of a stressor-response relationship. ......................... 74

Figure 4-3. Variations in stressor-response relationships. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75

Figure 5-1. Risk characterization .................................................. 93

Figure 5-2. Risk estimation techniques. a. Comparison of exposure and 

stressor-response point estimates. b. Comparison of point estimates from the 

stressor-response relationship with uncertainty associated with an exposure 

point estimate ................................................................. 96

Figure 5-3. Risk estimation techniques: comparison of point estimates

with associated uncertainties ...................................................... 98

Figure 5-4. Risk estimation techniques: stressor-response curve versus a

cumulative distribution of exposures ............................................... 100

Figure 5-5. Risk estimation techniques: comparison of exposure distribution of an

herbicide in surface waters with freshwater single-species toxicity data ...................... 102
xiv

LIST OF TEXT BOXES

Text Box 1-1. Related Terminology ................................................. 2

 Text Box 1-2. Flexibility of the Framework Diagram .................................... 5

Text Box 2-1. Who Are Risk Managers? ........................................... 10

Text Box 2-2. Who Are Risk Assessors? ........................................... 11

Text Box 2-3. Who Are Interested Parties? .......................................... 12

Text Box 2-4. Questions Addressed by Risk Managers and Risk Assessors .................. 13

Text Box 2-5. Sustainability as a Management Goal .................................... 14

Text Box 2-6. Management Goals for Waquoit Bay .................................... 15

Text Box 2-7. What is the Difference Between a Management Goal and 

Management Decision? ......................................................... 17

Text Box 2-8. Tiers and Iteration: When Is a Risk Assessment Done? ...................... 18

Text Box 2-9. Questions to Ask About Scope and Complexity ........................... 19

Text Box 3-1. Avoiding Potential Shortcomings Through Problem Formulation ................ 21

Text Box 3-2. Uncertainty in Problem Formulation ..................................... 23

Text Box 3-3. Initiating a Risk Assessment: What’s Different When Stressors, Effects, or 

Values Drive the Process? ....................................................... 24

Text Box 3-4. Assessing Available Information: Questions to Ask Concerning Source, 

Stressor, and Exposure Characteristics, Ecosystem Characteristics, and Effects ................ 26

Text Box 3-5. Salmon and Hydropower: Salmon as the Basis for an

Assessment Endpoint ........................................................... 27

Text Box 3-6. Cascading Adverse Effects: Primary (Direct) and Secondary (Indirect) .......... 28

Text Box 3-7. Identifying Susceptibility ............................................. 29

Text Box 3-8. Sensitivity and Secondary Effects: The Mussel-Fish Connection ............... 31

Text Box 3-9. Examples of Management Goals and Assessment Endpoints .................. 34

Text Box 3-10. Common Problems in Selecting Assessment Endpoints ..................... 36

Text Box 3-11. What Are the Benefits of Developing Conceptual Models? .................. 37

Text Box 3-12. What Are Risk Hypotheses, and Why Are They Important? ................. 38

Text Box 3-13. Examples of Risk Hypotheses ........................................ 39

Text Box 3-14. Uncertainty in Problem Formulation .................................... 41

Text Box 3-15. Why Was Measurement Endpoint Changed? ........................... 43

Text Box 3-16. Examples of a Management Goal, Assessment Endpoint, and Measures ......... 44
LIST OF TEXT BOXES (continued)

xv

Text Box 3-17. How Do Water Quality Criteria Relate to Assessment Endpoints? ............. 45

Text Box 3-18. The Data Quality Objectives Process .................................. 46

Text Box 4-1. Data Collection and the Analysis Phase .................................. 48

Text Box 4-2. The American National Standard for Quality Assurance ...................... 54

Text Box 4-3. Questions for Evaluating a Study’s Utility for Risk Assessment ................. 54

Text Box 4-4. Uncertainty Evaluation in the Analysis Phase .............................. 58

Text Box 4-5. Considering the Degree of Aggregation in Models .......................... 60

Text Box 4-6. Questions for Source Description ...................................... 62

Text Box 4-7. Questions to Ask in Evaluating Stressor Distribution ........................ 64

Text Box 4-8. General Mechanisms of Transport and Dispersal ........................... 64

Text Box 4-9. Questions to Ask in Describing Contact or Co-occurrence ................... 67

Text Box 4-10. Example of an Exposure Equation: Calculating a Potential Dose

via Ingestion .................................................................. 68

Text Box 4-11. Measuring Internal Dose Using Biomarkers and Tissue Residues .............. 69

Text Box 4-12. Questions Addressed by the Exposure Profile ............................ 71

Text Box 4-13. Questions for Stressor-Response Analysis ............................... 74

Text Box 4-14. Qualitative Stressor-Response Relationships ............................. 75

Text Box 4-15. Median Effect Levels .............................................. 76

Text Box 4-16. No-Effect Levels Derived From Statistical Hypothesis Testing ................ 77

Text Box 4-17. General Criteria for Causality ........................................ 79

Text Box 4-18. Koch’s Postulates ................................................. 80

Text Box 4-19. Examples of Extrapolations to Link Measures of Effect to Assessment

Endpoints .................................................................... 82

Text Box 4-20. Questions Related to Selecting Extrapolation Approaches ................... 83

Text Box 4-21. Questions to Consider When Extrapolating From Effects Observed in the

Laboratory to Field Effects of Chemicals ............................................ 85

Text Box 4-22. Questions Addressed by the Stressor-Response Profile ..................... 90

Text Box 5-1. An Example of Field Methods Used for Risk Estimation ..................... 94

Text Box 5-2. Using Qualitative Categories to Estimate Risks of an Introduced Species ......... 95

Text Box 5-3. Applying the Quotient Method ........................................ 97

Text Box 5-4. Comparing an Exposure Distribution With a Point Estimate of Effects ............ 99

Text Box 5-5. Comparing Cumulative Exposure and Effects Distributions for Chemical
LIST OF TEXT BOXES (continued)

xvi

Stressors ................................................................... 101

Text Box 5-6. Estimating Risk With Process Models .................................. 103

Text Box 5-7. What Are Statistically Significant Effects? ............................... 107

Text Box 5-8. Possible Risk Assessment Report Elements .............................. 110

Text Box 5-9. Clear, Transparent, Reasonable, and Consistent Risk Characterizations ......... 111

Text Box 6-1. Questions Regarding Risk Assessment Results ............................ 112

Text Box 6-2. Risk Communication Considerations for Risk Managers .................... 113

Text Box A-1. Stressor vs. Agent ............................................... A-3
1

1. INTRODUCTION

Ecological risk assessment is a process that evaluates the likelihood that adverse ecological

effects may occur or are occurring as a result of exposure to one or more stressors (U.S. EPA, 1992a). 

The process is used to systematically evaluate and organize data, information, assumptions, and

uncertainties in order to help understand and predict the relationships between stressors and ecological

effects in a way that is useful for environmental decision making. An assessment may involve chemical,

physical, or biological stressors, and one stressor or many stressors may be considered.

Ecological risk assessments are developed within a risk management context to evaluate

human-induced changes that are considered undesirable. As a result, these Guidelines focus on

stressors and adverse effects generated or influenced by anthropogenic activity. Defining adversity is

important because a stressor may cause adverse effects on one ecosystem component but be neutral or

even beneficial to other components. Changes often considered undesirable are those that alter

important structural or functional characteristics or components of ecosystems. An evaluation of

adversity may include a consideration of the type, intensity, and scale of the effect as well as the

potential for recovery. The acceptability of adverse effects is determined by risk managers. Although

intended to evaluate adverse effects, the ecological risk assessment process can be adapted to predict

beneficial changes or risk from natural events.

Descriptions of the likelihood of adverse effects may range from qualitative judgments to

quantitative probabilities. Although risk assessments may include quantitative risk estimates,

quantitation of risks is not always possible. It is better to convey conclusions (and associated

uncertainties) qualitatively than to ignore them because they are not easily understood or estimated.

Ecological risk assessments can be used to predict the likelihood of future adverse effects

(prospective) or evaluate the likelihood that effects are caused by past exposure to stressors

(retrospective). In many cases, both approaches are included in a single risk assessment. For

example, a retrospective risk assessment designed to evaluate the cause for amphibian population

declines may also be used to predict the effects of future management actions. Combined retrospective

and prospective risk assessments are typical in situations where ecosystems have a history of previous

impacts and the potential for future effects from multiple chemical, physical, or biological stressors. 

Other terminology related to ecological risk assessment is referenced in text box 1-1.
 

1Changes in process and terminology from EPA’s previous ecological risk assessment framework

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

2

Text Box 1-1. Related Terminology

The following terms overlap to varying degrees

with the concept of ecological risk assessment

used in these Guidelines (see Appendix B for

definitions):

• Hazard assessment

• Comparative risk assessment

• Cumulative ecological risk assessment

• Environmental impact statement

1.1. THE ECOLOGICAL RISK

ASSESSMENT PROCESS

The ecological risk assessment process

is based on two major elements: 

characterization of effects and characterization of

exposure. These provide the focus for

conducting the three phases of risk assessment: 

problem formulation, analysis, and risk

characterization.

The overall ecological risk assessment

process1 is shown in figure 1-1. The format remains consistent with the diagram from the 1992 report

Framework for Ecological Risk Assessment (referred to as the Framework Report). However, the

process and products within each phase have been refined, and these changes are detailed in figure 1-

2. The three phases of risk assessment are enclosed by a dark solid line. Boxes outside this line

identify critical activities that influence why and how a risk assessment is conducted and how it will be

used.

Problem formulation, the first phase, is shown at the top. In problem formulation, the purpose

for the assessment is articulated, the problem is defined, and a plan for analyzing and characterizing risk

is determined. Initial work in problem formulation includes the integration of available information on

sources, stressors, effects, and ecosystem and receptor characteristics. From this information two

products are generated: assessment endpoints and conceptual models. Either product may be

generated first (the order depends on the type of risk assessment), but both are needed to complete an

analysis plan, the final product of problem formulation.

Analysis, shown in the middle box, is directed by the products of problem formulation. During

the analysis phase, data are evaluated to determine how exposure to stressors is likely to occur

(characterization of exposure) and, given this exposure, the potential and type of ecological effects that

can be expected (characterization of ecological effects). The first step in analysis is to determine the

strengths and limitations of data on exposure, effects, and ecosystem and receptor characteristics. Data
3

are then analyzed to characterize the nature of potential or actual exposure and the ecological responses

under the circumstances defined in the conceptual
4

Figure 1-1. The framework for ecological risk assessment (modified from U.S. EPA,

1992a).
5

Figure 1-2. The ecological risk assessment framework, with an expanded view of each phase. 

Within each phase, rectangles designate inputs, hexagons indicate actions, and circles

represent outputs. Problem formulation, analysis, and risk characterization are discussed in

sections 3, 4, and 5, respectively. Sections 2 and 6 describe interactions between risk

assessors and risk managers.
6

Text Box 1-2. Flexibility of the Framework

Diagram

The framework process (figure 1-1) is a general

representation of a complex and varied group of

assessments. This diagram represents a flexible

process, as illustrated by the examples below.

• In problem formulation, an assessment may

begin with a consideration of endpoints,

stressors, or ecological effects. Problem

formulation is generally interactive and

iterative, not linear.

• In the analysis phase, characterization of

exposure and effects frequently become

intertwined, as when an initial exposure

leads to a cascade of additional exposures

and secondary effects. The analysis phase

should foster an understanding of these

complex relationships.

• Analysis and risk characterization are shown

as separate phases. However, some models

may combine the analysis of exposure and

effects data with the integration of these data

that occurs in risk characterization.

model(s). The products from these analyses are two profiles, one for exposure and one for stressor

response. These products provide the basis for risk characterization.

During risk characterization, shown in the third box, the exposure and stressor-response

profiles are integrated through the risk estimation process. Risk characterization includes a summary of

assumptions, scientific uncertainties, and strengths and limitations of the analyses. The final product is a

risk description in which the results of the integration are presented, including an interpretation of

ecological adversity and descriptions of uncertainty and lines of evidence.

Although problem formulation, analysis,

and risk characterization are presented

sequentially, ecological risk assessments are

frequently iterative. Something learned during

analysis or risk characterization can lead to a

reevaluation of problem formulation or new data

collection and analysis (see text box 1-2). 

Interactions among risk assessors, risk

managers, and other interested parties are shown

in two places in the diagram. The side box on

the upper left represents planning, where

agreements are made about the management

goals, the purpose for the risk assessment, and

the resources available to conduct the work. 

The box following risk characterization

represents when the results of the risk

assessment are formally communicated by risk

assessors to risk managers. Risk managers

generally communicate risk assessment results to

interested parties. These activities are shown

outside the ecological risk assessment process

diagram to emphasize that risk assessment and

risk management are two distinct activities. The

former involves the evaluation of the likelihood of adverse effects, while the latter involves the selection

of a course of action in response to an identified risk that is based on many factors (e.g., social, legal,

political, or economic) in addition to the risk assessment results.
7

The bar along the right side of figure 1-2 highlights data acquisition, iteration, and monitoring. 

Monitoring data provide important input to all phases of a risk assessment. They can provide the

impetus for a risk assessment by identifying changes in ecological condition. They can also be used to

evaluate a risk assessment’s predictions. For example, follow-up studies could determine whether

mitigation efforts were effective, help verify whether source reduction was effective, or determine the

extent and nature of ecological recovery. It is important for risk assessors and risk managers to use

monitoring results to evaluate risk assessment predictions so they can gain experience and help improve

the risk assessment and risk management process (Commission on Risk Assessment and Risk

Management, 1997).

Even though the risk assessment focuses on data analysis and interpretation, acquiring the

appropriate quantity and quality of data for use in the process is critical. If data are unavailable, the risk

assessment may stop until data are obtained. The process is more often iterative than linear, since the

evaluation of new data or information may require revisiting a part of the process or conducting a new

assessment (see text box 2-8). The dotted line between the side bar and the risk management box

indicates that additional data acquisition, iteration, or monitoring, while important, are not always

required.

1.2. ECOLOGICAL RISK ASSESSMENT IN A MANAGEMENT CONTEXT

Ecological risk assessments are designed and conducted to provide information to risk

managers about the potential adverse effects of different management decisions. Attempts to eliminate

risks associated with human activities in the face of uncertainties and potentially high costs present a

challenge to risk managers (Ruckelshaus, 1983; Suter, 1993a). Although many considerations and

sources of information are used by managers in the decision process, ecological risk assessments are

unique in providing a scientific evaluation of ecological risk that explicitly addresses uncertainty.

1.2.1. Contributions of Ecological Risk Assessment to Environmental Decision Making

At EPA, ecological risk assessments are used to support many types of management actions,

including the regulation of hazardous waste sites, industrial chemicals, and pesticides, or the

management of watersheds or other ecosystems affected by multiple nonchemical and chemical

stressors. The ecological risk assessment process has several features that contribute to effective

environmental decision making:
8

• Through an iterative process, new information can be incorporated into risk

assessments, which can be used to improve environmental decision making. This

feature is consistent with adaptive management principles (Holling, 1978) used in

managing natural resources.

• Risk assessments can be used to express changes in ecological effects as a function of

changes in exposure to stressors. This capability may be particularly useful to the

decision maker who must evaluate tradeoffs, examine different alternatives, or

determine the extent to which stressors must be reduced to achieve a given outcome.

• Risk assessments explicitly evaluate uncertainty. Uncertainty analysis describes the

degree of confidence in the assessment and can help the risk manager focus research on

those areas that will lead to the greatest reductions in uncertainty.

• Risk assessments provide a basis for comparing, ranking, and prioritizing risks. The

results can also be used in cost-benefit and cost-effectiveness analyses that offer

additional interpretation of the effects of alternative management options.

• Risk assessments consider management goals and objectives as well as scientific issues

in developing assessment endpoints and conceptual models during problem formulation. 

Such initial planning activities help ensure that results will be useful to risk managers.

1.2.2. Factors Affecting the Value of Ecological Risk Assessment for Environmental Decision

Making

The wide use and important advantages of ecological risk assessments do not mean they are the

sole determinants of management decisions; risk managers consider many factors. Legal mandates and

political, social, and economic considerations may lead risk managers to make decisions that are more

or less protective. Reducing risk to the lowest level may be too expensive or not technically feasible. 

Thus, although ecological risk assessments provide critical information to risk managers, they are only

part of the environmental decision-making process.

In some cases, it may be desirable to broaden the scope of a risk assessment during the

planning phase. A risk assessment that is too narrowly focused on one type of stressor in a system

(e.g., chemicals) could fail to consider more important stressors (e.g., habitat alteration). However,
9

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
10

toxicity, 56 FR 63798-63826; exposure assessment, 57 FR 22888-22938; and carcinogenicity, 61 FR

17960-18011), EPA will revisit these Guidelines as experience and scientific consensus evolve.

These Guidelines replace the Framework Report (U.S. EPA, 1992a). They expand on and

modify framework concepts to reflect Agency experience since the Framework Report was published

(see Appendix A).

1.4. GUIDELINES ORGANIZATION

These Guidelines follow the ecological risk assessment format as presented in figures 1-1 and

1-2. Section 2 (planning) describes the dialogue among risk assessors, risk managers, and interested

parties before the risk assessment begins. Section 3 (problem formulation) describes how management

goals are interpreted, assessment endpoints selected, conceptual models constructed, and analysis

plans developed. Section 4 (analysis) addresses how to evaluate potential exposure of receptors and

the relationship between stressor levels and ecological effects. Section 5 (risk characterization)

describes the process of estimating risk through the integration of exposure and stressor-response

profiles and discusses lines of evidence, interpretation of adversity, and uncertainty. Finally, section 6

(on relating ecological information to risk management decisions) addresses communicating the results

of the risk assessment to risk managers.
11

Text Box 2-1. Who Are Risk Managers?

Risk managers are individuals and organizations

who have the responsibility, or have the

authority to take action or require action, to

mitigate an identified risk. The expression “risk

manager” is often used to represent a decision

maker in agencies such as EPA or State

environmental offices who has legal authority to

protect or manage a resource. However, risk

managers may include a diverse group of

interested parties who also have the ability to

take action to reduce or mitigate risk. In

situations where a complex of ecosystem values

(e.g., watershed resources) is at risk from

multiple stressors, and management will be

implemented through community action, these

groups may function as risk management teams. 

Risk management teams may include decision

officials in Federal, State, local, and tribal

governments; commercial, industrial, and private

organizations; leaders of constituency groups;

and other sectors of the public such as property

owners. For additional insights on risk

management and manager roles, see text boxes

2-3 and 2-4.

2. PLANNING THE RISK ASSESSMENT

Ecological risk assessments are

conducted to transform scientific data into

meaningful information about the risk of human

activities to the environment. Their purpose is to

enable risk managers to make informed

environmental decisions. To ensure that risk

assessments meet this need, risk managers and

risk assessors (see text boxes 2-1 and 2-2) and,

where appropriate, interested parties (see text

box 2-3), engage in a planning dialogue as a

critical first step toward initiating problem

formulation (see figure 1-2).

The planning dialogue is the beginning of

a necessary interface between risk managers and

risk assessors. However, it is imperative to

remember that planning remains distinct from the

scientific conduct of a risk assessment. This

distinction helps ensure that political and social

issues, though helping define the objectives for

the assessment, do not bias the scientific

evaluation of risk.

The first step in planning may be to

determine if a risk assessment is the best option for supporting the decision. Risk managers and risk

assessors both consider the potential value of conducting a risk assessment to address identified

problems. Their discussion explores what is known about the degree of risk, what management options

are available to mitigate or prevent it, and the value of conducting a risk assessment compared with

other ways of learning about and addressing environmental concerns. In some cases, a risk assessment

may add little value to the decision process because management alternatives may be available that

completely circumvent the need for a risk assessment (see section 1.2.2). In other cases, the need for a

risk assessment may be investigated through a simple tiered risk evaluation based on minimal data and a

simple model (see section 2.2.2).
12

Text Box 2-2. Who Are Risk Assessors?

Risk assessors are a diverse group of

professionals who bring a needed expertise to a

risk assessment team. When a specific risk

assessment process is well defined through

regulations and guidance, one trained individual

may be able to complete a risk assessment given

sufficient information (e.g., premanufacture

notice of a chemical). However, for complex

risk assessments, one individual can rarely

provide the necessary breadth of expertise. 

Every risk assessment team should include at

least one professional who is knowledgeable

and experienced in using the risk assessment

process. Other team members bring specific

expertise relevant to the locations, stressors,

ecosystems, scientific issues, and other expertise

as needed, depending on the type of

assessment.

Once the decision is made to conduct a

risk assessment, the next step is to ensure that all

key participants are appropriately involved. 

Risk management may be carried out by one

decision maker in an agency such as EPA or it

may be implemented by several risk managers

working together as a team (see text box 2-1). 

Likewise, risk assessment may be conducted by

a single risk assessor or a team of risk assessors

(see text box 2-2). In some cases, interested

parties play an important role (see text box 2-3). 

Careful consideration up front about who will

participate, and the character of that

participation, will determine the success of

planning.

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
13

Text Box 2-3. Who Are Interested Parties?

Interested parties (commonly called

“stakeholders”) may include Federal, State,

tribal, and municipal governments, industrial

leaders, environmental groups, small-business

owners, landowners, and other segments of

society concerned about an environmental issue

at hand or attempting to influence risk

management decisions. Their involvement,

particularly during management goal

development, may be key to successful

implementation of management plans since

implementation is more likely to occur when

backed by consensus. Large diverse groups

may require trained facilitators and consensusbuilding techniques to reach agreement.

In some cases, interested parties may provide

important information to risk assessors. Local

knowledge, particularly in rural communities,

and traditional knowledge of native peoples can

provide valuable insights about ecological

characteristics of a place, past conditions, and

current changes. This knowledge should be

considered when assessing available information

during problem formulation (see section 3.2).

The context of involvement by interested parties

can vary widely and may or may not be

appropriate for a particular risk assessment. 

Interested parties may be limited to providing

input to goal development, or they may become

risk managers, depending on the degree to

which they can take action to manage risk and

the regulatory context of the decision. When

and how interested parties influence risk

assessments and risk management are areas of

current discussion (NRC, 1996). See additional

information in text box 2-1 and section 2.1.

may communicate their concerns to risk managers about the environment, economics, cultural changes,

or other values potentially at risk from environmental management activities. 

Where they have the ability to increase or

mitigate risk to ecological values of concern that

are identified, interested parties may become

part of the risk management team (see text box

2-1). However, involvement by interested

parties is not always needed or appropriate. It

depends on the purpose of the risk assessment,

the regulatory requirements, and the

characteristics of the management problem (see

section 2.2.1). When interested parties become

risk managers on a team, they directly participate

in planning.

During planning, risk managers and risk

assessors are responsible for coming to

agreement on the goals, scope, and timing of a

risk assessment and the resources that are

available and necessary to achieve the goals. 

Together they use information on the area’s

ecosystems, regulatory requirements, and

publicly perceived environmental values to

interpret the goals for use in the ecological risk

assessment. Examples of questions that risk

managers and risk assessors may address during

planning are provided in text box 2-4.

2.2. PRODUCTS OF PLANNING

The characteristics of an ecological risk

assessment are directly determined by

agreements reached by risk managers and risk

assessors during planning dialogues. These
14

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

Questions principally for risk managers to answer:

C What is the nature of the problem and the best scale for the assessment?

C What are the management goals and decisions needed, and how will risk assessment help?

C What are the ecological values (e.g., entities and ecosystem characteristics) of concern?

C What are the policy considerations (law, corporate stewardship, societal concerns,

environmental justice, intergenerational equity)?

C What precedents are set by similar risk assessments and previous decisions?

C What is the context of the assessment (e.g., industrial site, national park)?

C What resources (e.g., personnel, time, money) are available?

C What level of uncertainty is acceptable?

Questions principally for risk assessors to answer:

C What is the scale of the risk assessment?

C What are the critical ecological endpoints and ecosystem and receptor characteristics?

C How likely is recovery, and how long will it take?

C What is the nature of the problem: past, present, future?

C What is our state of knowledge of the problem?

C What data and data analyses are available and appropriate?

C What are the potential constraints (e.g., limits on expertise, time, availability of methods and

data)?

agreements are the products of planning. They include (1) clearly established and
15

Text Box 2-5. Sustainability as a

Management Goal

To sustain is to keep in existence, maintain, or

prolong. Sustainability is used as a management

goal in a variety of settings (see U.S. EPA,

1995a). Sustainability and other concepts such

as biotic or community integrity may be very

useful as guiding principles for management

goals. However, in each case these principles

should be explicitly defined and interpreted for a

place to support a risk assessment. To do this,

key questions need to be addressed: What

does sustainability or integrity mean for the

particular ecosystem? What must be protected

to meet sustainable goals or system integrity? 

Which ecological resources and processes are

to be sustained and why? How will we know

we have achieved it? Answers to these

questions serve to clarify the goals for a

particular ecosystem. Concepts like

sustainability and integrity do not meet the

criteria for an assessment endpoint (see section

3.3.2).

articulated management goals, (2) characterization of decisions to be made within the context of the

management goals, and (3) agreement on the scope, complexity, and focus of the risk assessment,

including the expected output and the technical and financial support available to complete it.

2.2.1. Management Goals

Management goals are statements about

the desired condition of ecological values of

concern. They may range from “maintain a

sustainable aquatic community” (see text boxes

2-5 and 2-6) to “restore a wetland” or “prevent

toxicity.” Management goals driving a specific

risk assessment may come from the law,

interpretations of the law by regulators, desired

outcomes voiced by community leaders and the

public, and interests expressed by affected

parties. All involve input from the public. 

However, the process used to establish

management goals influences how well they

provide guidance to a risk assessment team, how

they foster community participation, and whether

the larger affected community will support

implementation of management decisions to

achieve the goal.

A majority of Agency risk assessments

incorporate legally established management goals found in enabling legislation. In these cases, goals

were derived through public debate among interested parties when the law was enacted. Such

management goals (e.g., the Clean Water Act goals to “protect and restore the chemical, physical and

biological integrity of the Nation’s waters”) are often open to considerable interpretation and rarely

provide sufficient guidance to a risk assessor. To address this, the Agency has interpreted these goals

into regulations and guidance for implementation at the national scale (e.g., water quality criteria, see

text box 3-17). Mandated goals may be interpreted by Agency managers and staff into a particular

risk assessment format and then applied consistently across stressors of the same type (e.g., evaluation

of new chemicals). In cases where laws and regulations are specifically applied to a particular site,
16

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
17

Text Box 2-6. Management Goals for Waquoit Bay

A key challenge for risk assessors when dealing with a general management goal is interpreting the goal

for a risk assessment. This can be done by generating a set of management objectives that represent what

must be achieved in a particular ecosystem in order for the goal to be met. An example of this process

was developed in the Waquoit Bay watershed risk assessment (U.S. EPA, 1996a).

Waquoit Bay is a small estuary on Cape Cod showing signs of degradation, including loss of eelgrass, fish,

and shellfish and an increase in macroalgae mats and fish kills. The management goal for Waquoit Bay

was established through public meetings, preexisting goals from local organizations, and State and Federal

regulations:

Reestablish and maintain water quality and habitat conditions in Waquoit

Bay and associated freshwater rivers and ponds to (1) support diverse

self-sustaining commercial, recreational, and native fish and shellfish

populations and (2) reverse ongoing degradation of ecological resources in

the watershed.

To interpret this goal for the risk assessment, it was converted into 10 management objectives that

defined what must be true in the watershed for the goal to be achieved and provide the foundation for

management decisions. The management objectives are:

• Reduce or eliminate hypoxic or anoxic events

• Prevent toxic levels of contamination in water, sediments, and biota

• Restore and maintain self-sustaining native fish populations and their habitat

• Reestablish viable eelgrass beds and associated aquatic communities in the bay

• Reestablish a self-sustaining scallop population in the bay that can support a viable sport fishery

• Protect shellfish beds from bacterial contamination that results in closures

• Reduce or eliminate nuisance macroalgal growth

• Prevent eutrophication of rivers and ponds

• Maintain diversity of native biotic communities

• Maintain diversity of water-dependent wildlife

From these objectives, eight ecological entities and their attributes in the bay were selected as assessment

endpoints (see section 3.3.2) to best represent the management goals and objectives, one of which is

areal extent and patch size of eelgrass beds. Eelgrass was selected because (1) scallops and other

benthic organisms and juvenile finfish depend directly on eelgrass beds for survival, (2) eelgrass is highly

sensitive to excess macroalgal growth, and (3) abundant eelgrass represents a healthy bay to human

users.
18

value-based management goal or desired state. As environmental protection efforts shift from

implementing controls toward achieving measurable environmental results, value-based management

goals at the national scale will be increasingly important as guidance for risk assessors. Such goals as

“no unreasonable effects on bird survival” or “maintaining areal extent of wetlands” will provide a basis

for risk assessment design (see also U.S. EPA, 1997a, for additional examples and discussion).

The “place-based” or “community-based” approach for managing ecological resources

recommended in the Edgewater Consensus (U.S. EPA, 1994b) generally requires that management

goals be developed for each assessment. Management goals for “places” such as watersheds are

formed as a consensus based on diverse values reflected in Federal, State, tribal, and local regulations

and on constituency-group and public concerns. Public meetings, constituency-group meetings,

evaluation of resource management organizational charters, and other means of looking for shared goals

may be necessary to reach consensus among these diverse groups, commonly called “stakeholders”

(see text box 2-3). However, goals derived by consensus are normally general. For use in a risk

assessment, risk assessors must interpret the goals into more specific objectives about what must occur

in a place in order for the goal to be achieved and identify ecological values that can be measured or

estimated in the ecosystem of concern (see text box 2-6). For these risk assessments, the interpretation

is unique to the ecosystem being assessed and is done on a case-by-case basis as part of the planning

process. Risk assessors and risk managers should agree on the interpretations.

Early discussion on and selection of clearly established management goals provide risk

assessors with a fuller understanding of how different risk management options under consideration may

result in achieving the goal. Such information helps the risk assessor identify and gather critical data and

information. Regardless of how management goals are established, those that explicitly define

ecological values to be protected provide the best foundation for identifying actions to reduce risk and

generating risk assessment objectives. The objectives for the risk assessment derive from the type of

management decisions to be made.

2.2.2. Management Options to Achieve Goals

Risk managers must implement decisions to achieve management goals (see text box 2-7). 

These risk management decisions may establish national policy applied consistently across the country

(e.g., premanufacture notices [PMN] for new chemicals, protection of endangered species) or be

applied to a specific site (e.g., hazardous waste site cleanup level) or management concern (e.g.,

number of combined sewer overflow events allowable per year) intended to achieve an environmental

goal when implemented. Management decisions often begin as one of
19

Text Box 2-7. What Is the Difference

Between a Management Goal and

Management Decision?

Management goals are desired characteristics of

ecological values that the public wants to

protect. Clean water, protection of endangered

species, maintenance of ecological integrity,

clear mountain views, and fishing opportunities

are all possible management goals. 

Management decisions determine the means to

achieve the end goal. For instance, a goal may

be “fishable, swimmable” waters. The

management options under consideration to

achieve that goal may include increasing

enforcement of point-source discharges,

restoring fish habitat, designing alternative

sewage treatment facilities, or implementing all of

the above.

several management options identified during

planning. Management options may range from

preventing the introduction of a stressor to

restoration of affected ecological values. When

several options are defined during planning for a

particular problem (e.g., leave alone, clean up,

or pave a contaminated site), risk assessments

can be used to predict potential risk across the

range of these management options and, in some

cases, combined with cost-benefit analyses to

aid decision making. When risk assessors are

made aware of possible options, they can use

them to ensure that the risk assessment

addresses a sufficient breadth of issues.

Explicitly stated management options

provide a framework for defining the scope,

focus, and conduct of a risk assessment. Some risk assessments are specifically designed to determine

if a preestablished decision criterion is exceeded (e.g., see the data quality objectives process, U.S.

EPA, 1994c, and section 3.5.2 for more details). Decision criteria often contain inherent assumptions

about exposure, the range of possible stressors, or conditions under which the targeted stressor is

operating. To ensure that decision options include appropriate assumptions and the risk assessment is

designed to address management issues, these assumptions need to be clearly stated.

Decision criteria are often used within a tiering framework to determine how extensive a risk

assessment should be. Early screening tiers may have predetermined decision criteria to answer

whether a potential risk exists. Later tiers frequently do not because the management question changes

from “yes-no” to questions of “what, where, and how great is the risk.” Results from these risk

assessments require risk managers to evaluate risk characterization and generate a decision, perhaps

through formal decision analysis (e.g., Clemen, 1996), or managers may request an iteration of the risk

assessment to address issues of continuing concern (see text box 

2-8).

Risk assessments designed to support management initiatives for a region or watershed where

multiple stressors, ecological values, and political and economic factors influence decision making

require great flexibility and more complex iterative risk assessments. They generally
20

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

Risk assessments range from very simple to complex and resource demanding. How is it possible to

decide the level of effort? How many times should the risk assessor revisit data and assessment

issues? When is the risk assessment done?

Many of these questions can be addressed by designing a set of tiered assessments. These are

preplanned and prescribed sets of risk assessments of progressive data and resource intensity. The

outcome of a given tier is to either make a management decision, often based on decision criteria, or

continue to the next level of effort. Many risk assessors and public and private organizations use this

approach (e.g., see Gaudet, 1994; European Community, 1993; Cowan et al., 1995; Baker et al.,

1994; Urban and Cook, 1986; Lynch et al., 1994).

An iteration is an unprescribed reevaluation of information that may occur at any time during a risk

assessment, including tiered assessments. It is done in response to an identified need, new

information, or questions raised while conducting an assessment. As such, iteration is a normal

characteristic of risk assessments but is not a formal planned step. An iteration may include redoing

the risk assessment with new assumptions and new data.

Setting up tiered assessments and decision criteria may reduce the need for iteration. Up-front

planning and careful development of problem formulation will also reduce the need for revisiting

data, assumptions, and models. However, there are no rules to dictate how many iterations will be

necessary to answer management questions or ensure scientific validity. A risk assessment can be

considered complete when risk managers have sufficient information and confidence in the results of

the risk assessment to make a decision they can defend.

require an examination of ecological processes most influenced by diverse human actions. Risk

assessments used in this application are often based on a general goal statement and multiple potential

decisions. These require significant planning to determine which array of management decisions may be

addressed and to establish the purpose, scope, and complexity of the risk assessment.

2.2.3. Scope and Complexity of the Risk Assessment

Although the purpose for conducting a risk assessment determines whether it is national,

regional, or local in scope, resource availability determines its extent, complexity, and the level of

confidence in results that can be expected. Each risk assessment is constrained by the availability of

valid data and scientific understanding, expertise, time, and financial resources. 

Risk managers and risk assessors consider the nature of the decision (e.g., national policy, local

impact), available resources, opportunities for increasing the resource base (e.g., partnering, new data
21

Text Box 2-9. Questions to Ask About

Scope and Complexity

C Is this risk assessment mandated, required

by a court decision, or providing guidance to

a community?

C Will decisions be based on assessments of a

small area evaluated in depth or a largescale area in less detail?

C What are the spatial and temporal

boundaries of the problem?

C What information is already available

compared to what is needed?

C How much time can be taken, and how

many resources are available?

C What practicalities constrain data collection?

C Is a tiered approach an option?

collection, alternative analytical tools), potential

characteristics of the risk assessment team, and

the output that will provide the best information

for the required decisions (see text box 2-9). 

They must often be flexible in determining what

level of effort is warranted for a risk assessment. 

The most detailed assessment process is neither

applicable nor necessary in every instance. 

Screening assessments may be the appropriate

level of effort. One approach for determining the

needed level of effort in the risk assessment is to

set up tiered evaluations, as discussed in section

2.2.2. Where tiers are used, specific

descriptions of management questions and

decision criteria should be included in the plan.

Part of the agreement on scope and

complexity is based on the maximum uncertainty

that can be tolerated for the decision the risk

assessment supports. Risk assessments

completed in response to legal mandates and

likely to be challenged in court often require rigorous attention to potential sources of uncertainty to help

ensure that conclusions from the assessment can be defended. A frank discussion is needed between

the risk manager and risk assessor on the sources of uncertainty and ways uncertainty can be reduced

(if necessary or possible) through selective investment of resources. Resource planning may account

for the iterative nature of risk assessment or include explicitly defined steps, such as tiers that represent

increasing cost and complexity, each tier designed to increase understanding and reduce uncertainty. 

Advice on addressing the interplay of management decisions, study boundaries, data needs, uncertainty,

and specifying limits on decision errors may be found in EPA’s guidance on data quality objectives

(U.S. EPA, 1994c).

2.3. PLANNING SUMMARY

The planning phase is complete when agreements are reached on (1) the management goals for

ecological values, (2) the range of management options the risk assessment is to support, (3) objectives
22

for the risk assessment, including criteria for success, (4) the focus and scope of the assessment, and

(5) resource availability. Agreements may encompass the technical approach to be taken in a risk

assessment as determined by the regulatory or management context and reason for initiating the risk

assessment (see section 3.2), the spatial scale (e.g., local, regional, or national), and the temporal scale

(e.g., the time frame over which stressors or effects will be evaluated).

In mandated risk assessments, planning agreements may be codified in regulations, and little

documentation of agreements is warranted. In others, a summary of planning agreements may be

important for ensuring that the risk assessment remains consistent with its original intent. A summary

can provide a point of reference for determining if early decisions need to be changed in response to

new information. There is no predetermined format, length, or complexity for a planning summary. It is

a useful reference only and should be tailored to the risk assessment it represents. However, a

summary will help ensure quality communication between risk managers and risk assessors and will

document agreed-upon decisions.

Once planning is complete, the formal process of risk assessment begins. During problem

formulation, risk assessors should continue the dialogue with risk managers, particularly following

assessment endpoint selection and completion of the analysis plan. At these points, potential problems

can be identified before the risk assessment proceeds.
23

Text Box 3-1. Avoiding Potential

Shortcomings Through Problem Formulation

The importance of problem formulation has been

shown repeatedly in the Agency’s analysis of

ecological risk assessment case studies and in

interactions with senior EPA managers and

regional risk assessors (U.S. EPA, 1993a,

1994d). Shortcomings consistently identified in

the case studies include (1) absence of clearly

defined goals, (2) endpoints that are ambiguous

and difficult to define and measure, and (3)

failure to identify important risks. These and

other shortcomings can be avoided through

rigorous development of the products of

problem formulation as described in this section

of the Guidelines.

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 box 3-1). The

quality of the assessment will depend in part on

the team conducting the assessment and its

responsiveness to the risk manager’s needs.

The makeup of the risk assessment team assembled to conduct problem formulation depends

on the requirements of the risk assessment. The team should include professionals with expertise

directly related to the level and type of problem under consideration and the ecosystem where the

problem is likely to occur. Teams may range from one individual calculating a simple quotient where

the information and algorithm are clearly established to a large interdisciplinary, interagency team typical

of ecosystem-level risk assessments involving multiple stressors and ecological values.

Involvement by the risk management team and other interested parties in problem formulation

can be most valuable during final selection of assessment endpoints, review of the conceptual models,

and adjustments to the analysis plan. The degree of participation is commensurate with the complexity

of the risk assessment and the magnitude of the risk management decision to be faced. Participation

normally consists of approval and refinement rather than technical input (but see text box 2-3). The

format used to involve risk managers needs to gain from, and be responsive to, their input without

compromising the scientific validity of the risk assessment. The level of involvement by interested

parties in problem formulation is determined by risk managers.
24

3.1. PRODUCTS OF PROBLEM FORMULATION

Problem formulation results in three products: (1) assessment endpoints that adequately reflect

management goals and the ecosystem they represent, (2) conceptual models that describe key

relationships between a stressor and assessment endpoint or between several stressors and assessment

endpoints, and (3) an analysis plan. The first step toward developing these products is to integrate

available information as shown in the hexagon in figure 3-1; the products are shown as circles. While

the assessment of available information is begun up front in problem formulation and the analysis plan is

the final product, the order in which assessment endpoints and conceptual models are produced

depends on why the risk assessment was initiated (see section 3.2). To enhance clarity, the following

discussion is presented as a linear progression. However, problem formulation is frequently interactive

and iterative rather than linear. Reevaluation may occur during any part of problem formulation.

3.2. INTEGRATION OF AVAILABLE INFORMATION

The foundation for problem formulation is based on how well available information on stressor

sources and characteristics, exposure opportunities, characteristics of the ecosystem(s) potentially at

risk, and ecological effects are integrated and used (see figure 3-1). Integration of available information

is an iterative process that normally occurs throughout problem formulation. Initial evaluations often

provide the basis for generating preliminary conceptual models or assessment endpoints, which in turn

may lead risk assessors to seek other types of available information not previously recognized as

needed.

The quality and quantity of information determine the course of problem formulation. When

key information is of the appropriate type and sufficient quality and quantity, problem formulation can

proceed effectively. When data are unavailable, the risk assessment may be suspended while additional

data are collected or, if this is not possible, may be developed on the basis of what is known and what

can be extrapolated from what is known. Risk assessments are frequently begun without all needed

information, in which case the problem formulation process helps identify missing data and provides a

framework for further data collection. Where data are few, the limitations of conclusions, or

uncertainty, from the risk assessment should be clearly articulated in risk characterization (see text box

3-2).

The impetus for an ecological risk assessment influences what information is available at the

outset and what information should be collected. For example, a risk assessment can be initiated

because a known or potential stressor may enter the environment. Risk assessors
25

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 
26

Figure 3-1. Problem formulation phase.

Text Box 3-2. Uncertainty in Problem

Formulation

Throughout problem formulation, risk assessors

consider what is known and not known about a

problem and its setting. Each product of

problem formulation contains uncertainty. The

explicit treatment of uncertainty during problem

formulation is particularly important because it

will have repercussions throughout the remainder

of the assessment. Uncertainty is discussed in

section 3.4 (Conceptual Models).

adverse effect or change in ecological condition initiates the assessment, risk assessors will seek

information about potential stressors and sources that could have caused the effect. When a risk

assessment is initiated because of a desire to better manage an ecological value or entity (e.g., species,

communities, ecosystems, or places), risk

assessors will seek information on the specific

condition or effect of interest, the characteristics

of relevant ecosystems, and potential stressors

and sources (see text box 3-3).

Information (actual, inferred, or

estimated) is initially integrated in a scoping

process that provides the foundation for

developing problem formulation. Knowledge 
27

Text Box 3-3. Initiating a Risk Assessment: What’s Different When Stressors, Effects, or

Values Drive the Process?

The reasons for initiating a risk assessment influence when risk assessors generate products in

problem formulation. When the assessment is initiated because of concerns about stressors, risk

assessors use what is known about the stressor and its source to focus the assessment. Objectives

for the assessment are based on determining how the stressor is likely to come in contact with and

affect possible receptors. This information forms the basis for developing conceptual models and

selecting assessment endpoints. When an observed effect is the basis for initiating the assessment,

endpoints are normally established first. Frequently, the affected ecological entities and their

response form the basis for defining assessment endpoints. Goals for protecting the assessment

endpoints are then established, which support the development of conceptual models. The models

aid in the identification of the most likely stressor(s). Value-initiated risk assessments are driven by

goals for the ecological values of concern. These values might involve ecological entities such as

species, communities, ecosystems, or places. Based on these goals, assessment endpoints are

selected first to serve as an interpretation of the goals. Once selected, the endpoints provide the

basis for identifying an array of stressors that may be influencing the assessment endpoints and

describing the diversity of potential effects. This information is then captured in the conceptual

model(s).

gained during scoping is used to identify missing information and potential assessment endpoints, and it

provides the basis for early conceptualization of the problem being assessed. As problem formulation

proceeds, information quality and applicability to the particular problem of concern are increasingly

scrutinized. Where appropriate, further iterations may result in a comprehensive evaluation that helps

risk assessors generate an array of risk hypotheses (see section 3.4.1). Once analysis plans are being

formed, data validity becomes a significant factor for risk assessors to evaluate (see section 4.1 for a

discussion of assessing data quality). Thus an evaluation of available information is an ongoing activity

throughout problem formulation. The level of effort is driven by the type of assessment.

As the complexity and spatial scale of a risk assessment increase, information needs often

escalate. Risk assessors consider the ways ecosystem characteristics directly influence when, how, and

why particular ecological entities may become exposed and exhibit adverse effects due to particular

stressors. Predicting risks from multiple chemical, physical, and biological stressors requires an effort to

understand their interactions. Risk assessments for a region or watershed, where multiple stressors are

the rule, require consideration of ecological processes operating at larger spatial scales.

 Despite our limited knowledge of ecosystems and the stressors influencing them, the process of

problem formulation offers a systematic approach for organizing and evaluating available information on

stressors and possible effects. It can function as a preliminary risk assessment that is useful to risk
28

assessors and decision makers. Text box 3-4 provides a series of questions that risk assessors should

attempt to answer. This exercise will help risk assessors identify known and unknown relationships,

both of which are important in problem formulation.

Problem formulation proceeds with the identification of assessment endpoints and the

development of conceptual models and an analysis plan (discussed below). Early recognition that the

reasons for initiating the risk assessment affect the order in which products are generated will help

facilitate the development of problem formulation (see text box 3-3).

3.3. SELECTING ASSESSMENT ENDPOINTS

Assessment endpoints are explicit expressions of the actual environmental value that is to be

protected, operationally defined by an ecological entity and its attributes (see section 3.3.2). 

Assessment endpoints are critical to problem formulation because they structure the assessment to

address management concerns and are central to conceptual model development. Their relevance is

determined by how well they target susceptible ecological entities. Their ability to support risk

management decisions depends on whether they are measurable ecosystem characteristics that

adequately represent management goals. The selection of ecological concerns and assessment

endpoints at EPA has traditionally been done internally by individual Agency program offices (U.S.

EPA, 1994a). More recently, interested and affected parties have helped identify management

concerns and assessment endpoints in efforts to implement watershed or community-based

environmental protection.

This section provides guidance on selecting and defining assessment endpoints. It is presented

in two parts. Section 3.3.1 establishes three criteria (ecological relevance, susceptibility, and relevance

to management goals) for determining how to select, among a broad array of possibilities, the specific

ecological characteristics to target in the risk assessment that are responsive to general management

goals and are scientifically defensible. Section 3.3.2 then provides specific guidance on how to convert

selected ecological characteristics into operationally defined assessment endpoints that include both a

defined entity and specific attributes amenable to measurement.

3.3.1. Criteria for Selection

All ecosystems are diverse, with many levels of ecological organization (e.g., individuals,

populations, communities, ecosystems, landscapes) and multiple ecosystem processes. It is rarely clear

which of these characteristics are most critical to ecosystem function, nor do professionals or the public

always agree on which are most valuable. As a result, it is often a
29

Text Box 3-4. Assessing Available Information: Questions to Ask Concerning Source,

Stressor, and Exposure Characteristics, Ecosystem Characteristics, and Effects (derived in

part from Barnthouse and Brown, 1994)

Source and Stressor Characteristics

• What is the source? Is it anthropogenic, natural, point source, or diffuse nonpoint?

• What type of stressor is it: chemical, physical, or biological?

• What is the intensity of the stressor (e.g., the dose or concentration of a chemical, the magnitude or extent of

physical disruption, the density or population size of a biological stressor)?

• What is the mode of action? How does the stressor act on organisms or ecosystem functions?

Exposure Characteristics

• With what frequency does a stressor event occur (e.g., is it isolated, episodic, or continuous; is it subject to

natural daily, seasonal, or annual periodicity)?

• What is its duration? How long does it persist in the environment (e.g., for chemical, what is its half-life, does

it bioaccumulate; for physical, is habitat alteration sufficient to prevent recovery; for biological, will it

reproduce and proliferate)?

• What is the timing of exposure? When does it occur in relation to critical organism life cycles or ecosystem

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

• What is the spatial scale of exposure? Is the extent or influence of the stressor local, regional, global, habitatspecific, or ecosystemwide?

• What is the distribution? How does the stressor move through the environment (e.g., for chemical, fate and

transport; for physical, movement of physical structures; for biological, life-history dispersal characteristics)?

Ecosystems Potentially at Risk

• What are the geographic boundaries? How do they relate to functional characteristics of the ecosystem?

• What are the key abiotic factors influencing the ecosystem (e.g., climatic factors, geology, hydrology, soil

type, water quality)?

• Where and how are functional characteristics driving the ecosystem (e.g., energy source and processing,

nutrient cycling)?

• What are the structural characteristics of the ecosystem (e.g., species number and abundance, trophic

relationships)?

• What habitat types are present?

• How do these characteristics influence the susceptibility (sensitivity and likelihood of exposure) of the

ecosystem to the stressor(s)?

• Are there unique features that are particularly valued (e.g., the last representative of an ecosystem type)?

• What is the landscape context within which the ecosystem occurs?

Ecological Effects

• What are the type and extent of available ecological effects information (e.g., field surveys, laboratory tests, or

structure-activity relationships)?

• Given the nature of the stressor (if known), which effects are expected to be elicited by the stressor?

• Under what circumstances will effects occur?
30

Text Box 3-5. Salmon and Hydropower: 

Salmon as the Basis for an Assessment

Endpoint

A hydroelectric dam is to be built on a river in the

Pacific Northwest where anadromous fish such

as salmon spawn. Assessment endpoints should

be selected to assess potential ecological risk. Of

the anadromous fish, salmon that spawn in the

river are an appropriate choice because they

meet the criteria for good assessment endpoints. 

Salmon fry and adults are important food sources

for a multitude of aquatic and terrestrial species

and are major predators of aquatic invertebrates

(ecological relevance). Salmon are sensitive to

changes in sedimentation and substrate pebble

size, require quality cold-water habitats, and have

difficulty climbing fish ladders. Hydroelectric

dams represent significant, and normally fatal,

habitat alteration and physical obstacles to

successful salmon breeding and fry survival

(susceptibility). Finally, salmon support a large

commercial fishery, some species are

endangered, and they have ceremonial

importance and are key food sources for Native

Americans (relevance to management goals). 

“Salmon reproduction and population

recruitment” is a good assessment endpoint for

this risk assessment. In addition, if salmon

populations are protected, other anadromous fish

populations are likely to be protected as well. 

However, one assessment endpoint can rarely

provide the basis for a risk assessment of

complex ecosystems. These are better

represented by a set of assessment endpoints.

challenge to consider the array of possibilities and choose which ecological characteristics to protect to

meet management goals. Those choices are critical, however, because they become the basis for

defining assessment endpoints, the transition between broad management goals and the specific

measures used in a risk assessment.

Three principal criteria are used to select

ecological values that may be appropriate for

assessment endpoints: (1) ecological relevance,

(2) susceptibility to known or potential stressors,

and (3) relevance to management goals. Of

these, ecological relevance and susceptibility are

essential for selecting assessment endpoints that

are scientifically defensible. However, to

increase the likelihood that the risk assessment

will be used in management decisions,

assessment endpoints are more effective when

they also reflect societal values and management

goals. Given the complex functioning of

ecosystems and the interdependence of

ecological entities, it is likely that potential

assessment endpoints can be identified that are

both responsive to management goals and meet

scientific criteria. Assessment endpoints that

meet all three criteria provide the best foundation

for an effective risk assessment (e.g., see text

box 3-5).

3.3.1.1. Ecological Relevance

Ecologically relevant endpoints reflect

important characteristics of the system and are

functionally related to other endpoints (U.S.

EPA, 1992a). Ecologically relevant endpoints may be identified at any level of organization (e.g.,

individual, population, community, ecosystem, landscape). The consequences of changes in these

endpoints may be quantified (e.g., alteration of community structure from the loss of a keystone
31

Text Box 3-6. Cascading Adverse Effects: 

Primary (Direct) and Secondary (Indirect)

The interrelationships among entities and

processes in ecosystems foster a potential for

cascading effects: as one population, species,

process, or other entity in the ecosystem is

altered, other entities are affected as well. 

Primary, or direct, effects occur when a stressor

acts directly on the assessment endpoint and

causes an adverse response. Secondary, or

indirect, effects occur when the entity’s response

becomes a stressor to another entity. 

Secondary effects are often a series of effects

among a diversity of organisms and processes

that cascade through the ecosystem. For

example, application of an herbicide on a wet

meadow results in direct toxicity to plants. 

Death of the wetland plants leads to secondary

effects such as loss of feeding habitat for ducks,

breeding habitat for red-winged blackbirds,

alteration of wetland hydrology that changes

spawning habitat for fish, and so forth.

species) or inferred (e.g., survival of individuals is needed to maintain populations). Ecological entities

are not ecologically relevant unless they are currently, or were historically, part of the ecosystem under

consideration.

Ecologically relevant endpoints often help sustain the natural structure, function, and biodiversity

of an ecosystem or its components. They may contribute to the food base (e.g., primary production),

provide habitat (e.g., for food or reproduction), promote regeneration of critical resources (e.g.,

decomposition or nutrient cycling), or reflect the structure of the community, ecosystem, or landscape

(e.g., species diversity or habitat mosaic). In landscape-level risk assessments, careful selection of

assessment endpoints that address both species of concern and landscape-level ecosystem processes

becomes important. It may be possible to select one or more species and an ecosystem process to

represent larger functional community or ecosystem processes.

Ecological relevance is linked to the

nature and intensity of potential effects, the

spatial and temporal scales where effects may

occur, and the potential for recovery (see

Determining Ecological Adversity, section

5.2.2). It is also linked to the level of ecological

organization that could be adversely affected

(see U.S. EPA, 1997a, for a discussion of how

different levels of organization are used by the

Agency in defining assessment endpoints). 

When changes in selected ecosystem entities are

likely to cause multiple or widespread effects,

such entities can be powerful components of

assessment endpoints. They are particularly

valuable when risk assessors are trying to

identify the potential cascade of adverse effects

that could result from loss or reduction of a

species or a change in ecosystem function (see

text box 3-6). Although a cascade of effects

may be predictable, it is often difficult to predict

the nature of all potential effects. Determining ecological relevance in specific cases requires
32

Text Box 3-7. Identifying Susceptibility

Often it is possible to identify ecological entities

most likely to be susceptible to a stressor. 

However, in some cases where stressors are not

known at the initiation of a risk assessment, or

specific effects have not been identified, the

most susceptible entities may not be known. 

Where this occurs, professional judgment may

be required to make initial selections of potential

endpoints.

Once done, available information on potential

stressors in the system can be evaluated to

determine which of the endpoints are most likely

susceptible to identified stressors. If an

assessment endpoint is selected for a risk

assessment that directly supports management

goals and is ultimately found not susceptible to

stressors in the system, then a conclusion of no

risk is appropriate. However, where there are

multiple possible assessment endpoints that

address management goals and only some of

those are susceptible to a stressor, the

susceptible endpoints should be selected. If the

susceptible endpoints are not initially selected for

an assessment, an additional iteration of the risk

assessment with alternative assessment

endpoints may be needed to determine risk.

professional judgment based on site-specific information, preliminary surveys, or other available

information.

3.3.1.2. Susceptibility to Known or Potential Stressors

Ecological resources are considered

susceptible when they are sensitive to a stressor

to which they are, or may be, exposed. 

Susceptibility can often be identified early in

problem formulation, but not always. Risk

assessors may be required to use their best

professional judgment to select the most likely

candidates (see text box 3-7).

Sensitivity refers to how readily an

ecological entity is affected by a particular

stressor. Sensitivity is directly related to the

mode of action of the stressors (e.g., chemical

sensitivity is influenced by individual physiology

and metabolic pathways). Sensitivity is also

influenced by individual and community lifehistory 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
33

subpopulations. Such life-history characteristics are important to consider when evaluating potential

sensitivity.

Sensitivity can be related to the life stage of an organism when exposed to a stressor. 

Frequently, young animals are more sensitive to stressors than adults. For instance, Pacific salmon eggs

and fry are very sensitive to fine-grain sedimentation in river beds because they can be smothered. 

Age-dependent sensitivity, however, is not only in the young. In many species, events like migration

(e.g., in birds) and molting (e.g., in harbor seals) represent significant energy investments that increase

vulnerability to stressors. Finally, sensitivity may be enhanced by the presence of other stressors or

natural disturbances. For example, the presence of insect pests and disease may make plants more

sensitive to damage from ozone (Heck, 1993). To determine how sensitivity at a particular life stage is

critical to population parameters or community-level assessment endpoints may require further

evaluation.

Measures of sensitivity may include mortality or adverse reproductive effects from exposure to

toxics. Other possible measures of sensitivity include behavioral abnormalities; avoidance of significant

food sources and nesting sites; loss of offspring to predation because of the proximity of stressors such

as noise, habitat alteration, or loss; community structural changes; or other factors.

Exposure is the second key determinant in susceptibility. Exposure can mean co-occurrence,

contact, or the absence of contact, depending on the stressor and assessment endpoint. Questions

concerning where a stressor originates, how it moves through the environment, and how it comes in

contact with the assessment endpoint are evaluated to determine susceptibility (see section 4.2 for more

discussion on characterizing exposure). The amount and conditions of exposure directly influence how

an ecological entity will respond to a stressor. Thus, to determine which entities are susceptible, it is

important that the assessor consider the proximity of an ecological value to stressors of concern, the

timing of exposure (both in terms of frequency and duration), and the intensity of exposure occurring

during sensitive periods.

Adverse effects of a particular stressor may be important during one part of an organism’s life

cycle, such as early development or reproduction. They may result from exposure to a stressor or to

the absence of a necessary resource during a critical life stage. For example, if fish are unable to find

suitable nesting sites during their reproductive phase, risk is significant even when water quality is high

and food sources abundant. The interplay between life stage and stressors can be very complex (see

text box 3-8).

Exposure may occur in one place or time, but effects may not be observed until another place

or time. Both life-history characteristics and the circumstances of exposure influence susceptibility in
34

Text Box 3-8. Sensitivity and Secondary

Effects: The Mussel-Fish Connection

Native freshwater mussels are endangered in

many streams. Management efforts have

focused on maintaining suitable habitat for

mussels because habitat loss has been

considered the greatest threat to this group. 

However, larval unionid mussels must attach to

the gills of a fish host for one month during

development. Each species of mussel must

attach to a particular host species 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.

this case. For instance, the temperature of the egg incubation medium of marine turtles affects the sex

ratio of hatchlings, but population impacts are not observed until years later when the cohort of affected

turtles begins to reproduce. Delayed effects and multiple-stressor exposures add complexity to

evaluations of susceptibility (e.g., although toxicity tests may

determine receptor sensitivity to one stressor,

susceptibility may depend on the co-occurrence

of another stressor that significantly alters

receptor response). Conceptual models (see

section 3.4) need to reflect these factors. If a

species or other ecological entity is unlikely to be

directly or indirectly exposed to the stressor of

concern, or to the secondary effects of stressor

exposure, it may be inappropriate as an

assessment endpoint (see text box 3-7).

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
35

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
36

piping plovers (e.g., nesting and feeding conditions), a lake (e.g., nutrient cycling), or wet meadow

(e.g., endemic plant community diversity). For an assessment endpoint to serve as a clear interpretation

of the management goals and the basis for measurement in the risk assessment, both an entity and an

attribute are required.

What distinguishes assessment endpoints from management goals is their neutrality and

specificity. Assessment endpoints do not represent a desired achievement (i.e., goal). As such, they

do not contain words like “protect,” “maintain,” or “restore,” or indicate a direction for change such as

“loss” or “increase.” Instead they are ecological values defined by specific entities and their measurable

attributes, providing a framework for measuring stress-response relationships. When goals are very

broad it may be difficult to select appropriate assessment endpoints until the goal is broken down into

multiple management objectives. A series of management objectives can clarify the inherent

assumptions within the goal and help a risk assessor determine which ecological entities and attributes

best represent each objective (see text 

box 2-6). From this, multiple assessment endpoints may be selected. See text box 3-9 for examples of

management goals and assessment endpoints.

Assessment endpoints may or may not be distinguishable from measures, depending on the

assessment endpoints selected and the type of measures. While it is the entity that influences the scale

and character of a risk assessment, it is the attributes of an assessment endpoint that determine what to

measure. Sometimes direct measures of effect can be collected on the attribute of concern. Where this

occurs, the assessment endpoint and measure of effect are the same and no extrapolation is necessary

(e.g., if the assessment endpoint is “reproductive success of blue jays,” egg production and fledgling

success could potentially be directly measured under different stressor exposure scenarios). In other

cases, direct measures may not be possible (e.g., toxicity in endangered species) and surrogate

measures of effect must be selected. Thus, although assessment endpoints must be defined in terms of

measurable attributes, selection does not depend on the ability to measure those attributes directly or on

whether methods, models, and data are currently available. For practical reasons, it may be helpful to

use assessment endpoints that have well-developed test methods, field measurement techniques, and

predictive models (see Suter, 1993a). However, it is not necessary for methods to be standardized

protocols, nor should assessment endpoints be selected simply because standardized protocols are

readily available. The appropriate measures to use are generally identified during conceptual model

development and specified in the analysis plan. Measures of ecosystem characteristics and exposure

are determined by the entity and attributes selected and serve as important information in conceptual

model development. See section 3.5.1 for issues surrounding the selection of measures.
37

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
38

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

Case Regulatory context/management goal Assessment endpoint

Assessing Risks of

New Chemical Under

Toxic Substances

Control Act (Lynch et

al., 1994)

Protect “the environment” from “an unreasonable

risk of injury” (TSCA §2[b][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 §§3[c][5] and 3[c][6]); using

cost-benefit considerations. Goal was to have no

regularly repeated bird kills.

Individual bird survival

Modeling Future

Losses of Bottomland

Forest Wetlands

(Brody et al., 1993)

National Environmental Policy Act may apply to

environmental impact of new levee construction;

also Clean Water Act §404.

(1) Forest community

structure and habitat

value to wildlife species

(2) Species composition

of wildlife community

Pest Risk Assessment

on Importation of Logs

From Chile (USDA,

1993)

Assessment was done to help provide a basis for

any necessary regulation of the importation of

timber and timber products into the United States.

Survival and growth of

tree species in the

western United States

Baird and McGuire

Superfund Site

(terrestrial component);

(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,

1996a)

Clean Water Act—wetlands protection; water

quality criteria—pesticides; endangered species. 

National Estuarine Research Reserve,

Massachusetts, Area of Critical Environmental

Concern. Goal was to reestablish and maintain

water quality and habitat conditions to support

diverse self-sustaining commercial, recreational,

and native fish, water-dependent wildlife, and

shellfish and to reverse ongoing degradation.

(1) Estuarine eelgrass

habitat abundance and

distribution

(2) Estuarine fish

species diversity and

abundance

(3) Freshwater pond

benthic invertebrate

species diversity and

abundance
39

processes for an ecosystem and describing attributes that best represent integrity for that system. 

Assessment endpoints that are too narrowly defined may not support effective risk management. If an

assessment is focused only on protecting the habitat of an endangered species, for example, the risk

assessment may overlook other equally important characteristics of the ecosystem and fail to include

critical variables (see text box 3-8). Finally, the assessment endpoint could fail to represent the

ecosystem at risk. For instance, selecting a game fish that grows well in reservoirs may meet a

“fishable” management goal, but it would be inappropriate for evaluating risk from a new hydroelectric

dam if the ecosystem of concern is a stream in which salmon spawn (see text box 3-5). Although the

game fish will satisfy “fishable” goals and may be highly desired by local fishermen, a reservoir species

does not represent the ecosystem at risk. Substituting “reproducing populations of indigenous

salmonids” for a vague “viable fish populations” assessment endpoint could therefore prevent the

development of an inappropriate risk assessment.

When well selected, assessment endpoints become powerful tools in the risk assessment

process. One endpoint that is sensitive to many of the identified stressors, yet responds in different

ways to different stressors, may provide an opportunity to consider the combined effects of multiple

stressors while still distinguishing their effects. For example, fish population recruitment may be

adversely affected at several life stages, in different habitats, through different ways, and by different

stressors. Therefore, measures of effect, exposure, and ecosystem and receptor characteristics could

be chosen to evaluate recruitment and provide a basis for distinguishing different stressors, individual

effects, and their combined effects.

The assessment endpoint can provide a basis for comparing a range of stressors if carefully

selected. The National Crop Loss Assessment Network (Heck, 1993) selected crop yields as the

assessment endpoint to evaluate the cumulative effects of multiple stressors. Although the primary

stressor was ozone, the crop-yield endpoint also allowed the risk assessors to consider the effects of

sulfur dioxide and soil moisture. As Barnthouse et al. (1990) pointed out, an endpoint should be

selected so that all the effects can be expressed in the same units (e.g., changes in the abundance of 1-

year-old fish from exposure to toxicity, fishing pressure, and habitat loss). This is especially true when

selecting assessment endpoints for multiple stressors. However, in situations where multiple stressors

act on the structure and function of aquatic and terrestrial communities in a watershed, an array of

assessment endpoints that represent the community and associated ecological processes is more

effective than a single endpoint. When based on differing susceptibility to an array of stressors,

carefully selected assessment endpoints can help risk assessors distinguish the effects of diverse
40

Text Box 3-10. Common Problems in

Selecting Assessment Endpoints

• Endpoint is a goal (e.g., maintain and restore

endemic populations)

• Endpoint is vague (e.g., estuarine integrity

instead of eelgrass abundance and

distribution)

• Ecological entity is better as a measure (e.g.,

emergence of midges can be used to evaluate

an assessment endpoint for fish feeding

behavior)

• Ecological entity may not be as sensitive to

the stressor (e.g., catfish versus salmon for

sedimentation)

• Ecological entity is not exposed to the

stressor (e.g., using insectivorous birds for

avian risk of pesticide application to seeds)

• Ecological entities are irrelevant to the

assessment (e.g., lake fish in salmon stream)

• Importance of a species or attributes of an

ecosystem are not fully considered (e.g.,

mussel-fish connection, see Text Box 3-8).

• Attribute is not sufficiently sensitive for

detecting important effects (e.g., survival

compared with recruitment for endangered

species)

stressors. Exposure to multiple stressors may lead to effects at different levels of biological

organization, for a cascade of adverse effects that should be considered.

Professional judgment and an

understanding of the characteristics and function

of an ecosystem are important for translating

general goals into usable assessment endpoints. 

The less information available, the more critical it

is to have informed professionals help in the

selection. Common problems encountered in

selecting assessment endpoints are summarized

in text box 3-10.

Final assessment endpoint selection is an

important risk manager-risk assessor checkpoint

during problem formulation. Risk assessors and

risk managers should agree that selected

assessment endpoints effectively represent the

management goals. In addition, the scientific

rationale for their selection should be made

explicit in the risk assessment.

3.4. CONCEPTUAL MODELS

A conceptual model in problem

formulation is a written description and visual

representation of predicted relationships

between ecological entities and the stressors to

which they may be exposed. Conceptual models

represent many relationships. They may include

ecosystem processes that influence receptor

responses or exposure scenarios that

qualitatively link land-use activities to stressors. 

They may describe primary, secondary, and tertiary exposure pathways (see section 4.2) or cooccurrence among exposure pathways, ecological effects, and ecological receptors. Multiple

conceptual models may be generated to address several issues in a given risk
41

Text Box 3-11. What Are the Benefits of

Developing Conceptual Models?

• The process of creating a conceptual model

is a powerful learning tool.

• Conceptual models are easily modified as

knowledge increases.

• Conceptual models highlight what is known

and not known and can be used to plan

future work.

• Conceptual models can be a powerful

communication tool. They provide an explicit

expression of the assumptions and

understanding of a system for others to

evaluate.

• Conceptual models provide a framework for

prediction and are the template for generating

more risk hypotheses.

assessment. Some of the benefits gained by

developing conceptual models are featured in

text box 3-11.

Conceptual models for ecological risk

assessments are developed from information

about stressors, potential exposure, and

predicted effects on an ecological entity (the

assessment endpoint). Depending on why a risk

assessment is initiated, one or more of these

categories of information are known at the outset

(refer to section 3.2 and text box 3-3). The

process of creating conceptual models helps

identify the unknown elements.

The complexity of the conceptual model

depends on the complexity of the problem: the

number of stressors, number of assessment

endpoints, nature of effects, and characteristics

of the ecosystem. For single stressors and single

assessment endpoints, conceptual models may

be simple. In some cases, the same basic conceptual model may be used repeatedly (e.g., in EPA’s

new chemical risk assessments). However, when conceptual models are used to describe pathways of

individual stressors and assessment endpoints and the interaction of multiple and diverse stressors and

assessment endpoints (e.g., assessments initiated to protect ecological values), more complex models

and several submodels will often be needed. In this case, it can be helpful to create models that also

represent expected ecosystem characteristics and function when stressors are not present.

Conceptual models consist of two principal components:

• A set of risk hypotheses that describe predicted relationships among stressor,

exposure, and assessment endpoint response, along with the rationale for their selection

• A diagram that illustrates the relationships presented in the risk hypotheses.
42

Text Box 3-12. What Are Risk

Hypotheses, and Why Are They Important?

Risk hypotheses are proposed answers to

questions risk assessors have about what

responses assessment endpoints will show when

they are exposed to stressors and how exposure

will occur. Risk hypotheses clarify and articulate

relationships that are posited through the

consideration of available data, information from

scientific literature, and the best professional

judgment of risk assessors developing the

conceptual models. This explicit process opens

the risk assessment to peer review and

evaluation to ensure the scientific validity of the

work. Risk hypotheses are not equivalent to

statistical testing of null and alternative

hypotheses. However, predictions generated

from risk hypotheses can be tested in a variety

of ways, including standard statistical

approaches.

3.4.1. Risk Hypotheses

Hypotheses are assumptions made in

order to evaluate logical or empirical

consequences, or suppositions tentatively

accepted to provide a basis for evaluation. Risk

hypotheses are specific assumptions about

potential risk to assessment endpoints (see text

box 3-12) and may be based on theory and

logic, empirical data, mathematical models, or

probability models. They are formulated using a

combination of professional judgment and

available information on the ecosystem at risk,

potential sources of stressors, stressor

characteristics, and observed or predicted

ecological effects on selected or potential

assessment endpoints. These hypotheses may

predict the effects of a stressor before they

occur, or they may postulate why observed

ecological effects occurred and ultimately what

caused the effect. Depending on the scope of the risk assessment, risk hypotheses may be very simple,

predicting the potential effect of one stressor on one receptor, or extremely complex, as is typical in

value-initiated risk assessments that often include prospective and retrospective hypotheses about the

effects of multiple complexes of stressors on diverse ecological receptors. Risk hypotheses represent

relationships in the conceptual model and are not designed for statistically testing null and alternative

hypotheses. However, they can be used to generate questions appropriate for research.

Although risk hypotheses are valuable even when information is limited, the amount and quality

of data and information will affect the specificity and level of uncertainty associated with risk hypotheses

and the conceptual models they form. When preliminary information is conflicting, risk hypotheses can

be constructed specifically to differentiate between competing predictions. The predictions can then be

evaluated systematically either by using available data during the analysis phase or by collecting new

data before proceeding with the risk assessment. Hypotheses and predictions set a framework for

using data to evaluate functional relationships (e.g., stressor-response curves).
43

Text Box 3-13. Examples of Risk

Hypotheses

Hypotheses include known information that sets

the problem in perspective and the proposed

relationships that need evaluation.

Stressor-initiated: Chemicals with a high 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 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.

Early conceptual models are normally

broad, identifying as many potential relationships

as possible. As more information is

incorporated, the plausibility of specific

hypotheses helps risk assessors sort through

potentially large numbers of stressor-effect

relationships, and the ecosystem processes that

influence them, to identify those risk hypotheses

most appropriate for the analysis phase. It is

then that justifications for selecting and omitting

hypotheses are documented. Examples of risk

hypotheses are provided in text box 3-13.

3.4.2. Conceptual Model Diagrams

Conceptual model diagrams are a visual

representation of risk hypotheses. They are

useful tools for communicating important

pathways clearly and concisely and can be used

to generate new questions about relationships

that help formulate plausible risk hypotheses.

Typical conceptual model diagrams are

flow diagrams containing boxes and arrows to

illustrate relationships (see Appendix C). When

this approach is used, it is helpful to use distinct

and consistent shapes to distinguish stressors,

assessment endpoints, responses, exposure

routes, and ecosystem processes. Although flow

diagrams are often used to illustrate conceptual

models, there is no set configuration. Pictorial

representations can be very effective (e.g.,

Bradley and Smith, 1989). Regardless of the

configuration, a diagram’s usefulness is linked to
44

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
45

Text Box 3-14. Uncertainty in Problem

Formulation

Uncertainties in problem formulation are

manifested in the quality of conceptual models. 

To address uncertainty:

• Be explicit in defining assessment endpoints;

include both an entity and its measurable

attributes.

• Reduce or define variability by carefully

defining boundaries for the assessment.

• Be open and explicit about the strengths and

limitations of pathways and relationships

depicted in the conceptual model.

• Identify and describe rationale for key

assumptions made because of lack of

knowledge, model simplification,

approximation, or extrapolation.

• Describe data limitations.

models during risk assessments to incorporate new information and recheck the rationale. If this is not

feasible, it is helpful to present any new information during risk characterization along with associated

uncertainties.

Throughout problem formulation,

ambiguities, errors, and disagreements will

occur, all of which contribute to uncertainty. 

Wherever possible, these sources of uncertainty

should be eliminated through better planning. 

Because all uncertainty cannot be eliminated, a

description of the nature of the uncertainties

should be summarized at the close of problem

formulation. See text box 3-14 for

recommendations on how to address uncertainty.

3.5. ANALYSIS PLAN

The analysis plan is the final stage of

problem formulation. During analysis planning,

risk hypotheses are evaluated to determine how

they will be assessed using available and new

data. The plan includes a delineation of the

assessment design, data needs, measures, and

methods for conducting the analysis phase of the

risk assessment. Analysis plans may be 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
46

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. 
47

Text Box 3-15. Why Was Measurement

Endpoint Changed?

The original definition of measurement

endpoint was “a measurable characteristic that

is related to the valued characteristic chosen as

the assessment endpoint” (Suter, 1989; U.S.

EPA, 1992a). The definition refers specifically

to the response of an assessment endpoint to a

stressor. It does not include measures of

ecosystem characteristics, life-history

considerations, exposure, or other measures. 

Because measurement endpoint does not

encompass these other important measures and

there was confusion about its meaning, the term

was replaced with measures of effect and

supplemented by two other categories of

measures.

There are three categories of measures. 

Measures of effect are measurable changes in an

attribute of an assessment endpoint or its

surrogate in response to a stressor to which it is

exposed (formerly measurement endpoints; see

text box 3-15). Measures of exposure are

measures of stressor existence and movement in

the environment and their contact or 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 box 3-16 (see

also Appendix A.2.1).

The selection of appropriate measures is particularly complicated when a cascade of ecological

effects is likely to occur from a stressor. In these cases, the effect on one entity (i.e., the measure of

effect) may become a stressor for other ecological entities (i.e., become a measure of exposure) and

may result in impacts on one or more assessment endpoints. For example, if a pesticide reduces

earthworm populations, change in earthworm population density could be the direct measure of effect

of toxicity and in some cases may be an assessment endpoint. However, the reduction of worm

populations may then become a secondary stressor to which worm-eating birds become exposed,

measured as lowered food supply. This exposure may then result in a

secondary measurable effect of starvation of young. In this case, although “bird fledgling success” may

be an assessment endpoint that could be measured directly, measures of earthworm density, pesticide

residue in earthworms and other food sources, availability of alternative foods, nest site quality, and

competition for nests from other bird species may all be useful measurements.

When direct measurement of assessment endpoint responses is not possible, the selection of

surrogate measures is necessary. The selection of what, where, and how to measure surrogate
48

Text Box 3-16. Examples of a

Management Goal, Assessment Endpoint,

and Measures 

Goal: Viable, self-sustaining coho salmon

population that supports a subsistence and sport

fishery.

Assessment Endpoint: Coho salmon breeding

success, fry survival, and adult return rates.

Measures of Effects

• Egg and fry response to low dissolved

oxygen

• Adult behavior in response to obstacles

• Spawning behavior and egg survival with

changes in sedimentation

Measures of Ecosystem and Receptor

Characteristics

• Water temperature, water velocity, and

physical obstructions

• Abundance and distribution of suitable

breeding substrate

• Abundance and distribution of suitable food

sources for fry

• Feeding, resting, and breeding behavior

• Natural reproduction, growth, and mortality

rates

Measures of Exposure

• Number of hydroelectric dams and

associated ease 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

responses determines whether the risk

assessment is still relevant to management

decisions about an assessment endpoint. As an

example, an assessment may be conducted to

evaluate the

potential risk of a pesticide used on seeds to an

endangered species of seed-eating bird. The

assessment endpoint entity is the endangered

species. Example attributes include feeding

behavior, survival, growth, and reproduction. 

While it may be possible to directly collect

measures of exposure and assessment endpoint

life-history characteristics on the endangered

species, it would not be appropriate to expose

the endangered species to the pesticide to

measure sensitivity. In this case, to evaluate

susceptibility, the most appropriate surrogate

measures would be on seed-eating birds with

similar life-history characteristics and 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 box 3-
49

Text Box 3-17. How Do Water Quality

Criteria Relate to Assessment Endpoints?

Water quality criteria (U.S. EPA, 1986a) have been

developed for the protection of aquatic life from

chemical stressors. This text box shows how the

elements of a water quality criterion correspond to

management goals, management decisions,

assessment endpoints, and measures.

Regulatory Goal

• Clean Water Act, §101: Protect the chemical,

physical, and biological integrity of the Nation’s

waters

Program Management Decisions

• Protect 99% of individuals in 95% of the species in

aquatic communities from acute and chronic effects

resulting from exposure to a chemical stressor

Assessment Endpoints

• Survival of fish, aquatic invertebrate, and algal

species under acute exposure

• Survival, growth, and reproduction of fish, aquatic

invertebrate, and algal species under chronic

exposure

Measures of Effect

• Laboratory LC50s for at least eight species meeting

certain requirements

• Chronic no-observed-adverse-effect levels

(NOAELs) for at least three species meeting certain

requirements

Measures of Ecosystem and Receptor Characteristics

• Water hardness (for some metals)

• pH

The water quality criterion is a benchmark level

derived from a distributional analysis of single-species

toxicity data. It is assumed that the species tested

adequately represent the composition and sensitivities

of species in a natural community.

17 illustrates how these are related in water

quality criteria. In this example, it is instructive to

note that although water quality criteria are

considered risk-based, they are not full risk

assessments. Water quality criteria

provide an effects benchmark for decision

making and do not incorporate measures of

exposure in the environment. Within that

benchmark, there are a number of assumptions

about significance (e.g., aquatic communities will

be protected by achieving a benchmark derived

from individual species’ toxicological responses

to a single chemical) and exposure (e.g., 1-hour

and 4-day exposure averages). Such

assumptions embedded in decision rules are

important to articulate (see section 3.5.2).

The analysis plan provides a synopsis of

measures that will be used to evaluate risk

hypotheses. The plan is strongest when it

contains explicit statements for how measures

were selected, what they are intended to

evaluate, and which analyses they support. 

Uncertainties associated with selected measures

and analyses and plans for addressing them

should be included in the plan when possible.

3.5.2. Ensuring That Planned Analyses

Meet Risk Managers’ Needs

The analysis plan is a risk manager-risk

assessor checkpoint. Risk assessors and risk

managers review the plan to ensure that the

analyses will provide information the manager

can use for decision making. These discussions
50

Text Box 3-18. The Data Quality

Objectives Process

The data quality objectives (DQO) process

combines elements of both planning and

problem formulation in its seven-step format.

Step 1. State the problem. Review existing

information to concisely describe the problem to

be studied.

Step 2. Identify the decision. Determine

what questions the study will try to resolve and

what actions may result.

Step 3. Identify inputs to the decision.

Identify information and measures needed to

resolve the decision statement.

Step 4. Define study boundaries. Specify

time and spatial parameters and where and

when data should be collected.

Step 5. Develop decision rule. Define

statistical parameter, action level, and logical

basis for choosing alternatives.

Step 6. Specify tolerable limits on

decision errors. Define limits based on the

consequences of an incorrect decision.

Step 7. Optimize the design. Generate

alternative data collection designs and choose

most resource-effective design that meets all

DQOs.

may also identify what can and cannot be done on the basis of a preliminary evaluation of problem

formulation. A reiteration of the planning discussion helps ensure that the appropriate balance of

requirements for the decision, data availability, and resource constraints is established for the risk

assessment. This is also an appropriate time to conduct a technical review of the planning outcome.

Analysis plans include the analytical methods planned and the nature of the risk characterization

options and considerations to be generated (e.g.,

quotients, narrative discussion, stressor-response

curve with probabilities). A description of how

data analyses will distinguish among risk

hypotheses, the kinds of analyses to be used,

and rationale for why different hypotheses were

selected and eliminated are included. Potential

extrapolations, model characteristics, types of

data (including quality), and planned analyses

(with specific tests for different types of data) are

described. Finally, the plan includes a discussion

of how results will be presented upon completion

and the basis used for data selection.

Analysis planning is similar to the data

quality objectives (DQO) process (see text box

3-18), which emphasizes identifying the problem

by establishing study boundaries and determining

necessary data quality, quantity, and applicability

to the problem being evaluated (U.S. EPA,

1994c). The most important difference between

problem formulation and the DQO process is the

presence of a decision rule in a DQO that

defines a benchmark for a management decision

before the risk assessment is completed. The

decision rule step specifies the statistical

parameter that characterizes the population,

specifies the action level for the study, and

combines outputs from the previous DQO steps
51

into an “if . . . then” decision rule that defines conditions under which the decision maker will choose

alternative options (often used in tiered assessments; see also section 2.2.2). This approach provides

the basis for establishing null and alternative hypotheses appropriate for statistical testing for significance

that can be effective in this application. While this approach is sometimes appropriate, only certain

kinds of risk assessments are based on benchmark decisions. Presentation of stressor-response curves

with uncertainty bounds will be more appropriate than statistical testing of decision criteria where risk

managers must evaluate the range of stressor effects to which they compare a range of possible

management options (see Suter, 1996).

The analysis plan is the final synthesis before the risk assessment proceeds. It summarizes what

has been done during problem formulation, shows how the plan relates to management decisions that

must be made, and indicates how data and analyses will be used to estimate risks. When the problem

is clearly defined and there are enough data to proceed, analysis begins.
52

Text Box 4-1. Data Collection and the

Analysis Phase

Data needs are identified during problem

formulation (the analysis plan step), and data are

collected before the start of the analysis phase. 

These data may be collected for the specific

purpose of a particular risk assessment, or they

may be available from previous studies. If

additional data needs are identified as the

assessment proceeds, the analysis phase may be

temporarily halted while data are collected or

the assessor (in consultation with the risk

manager) may choose to iterate the problem

formulation again. Data collection methods are

not described in these Guidelines. However, the

evaluation of data for the purposes of risk

assessment is discussed in section 4.2.

4. ANALYSIS PHASE

Analysis is a process that examines the two primary components of risk, exposure and effects,

and their relationships between each other and ecosystem characteristics. The objective is to provide

the ingredients necessary for determining or predicting ecological responses to stressors under exposure

conditions of interest.

Analysis connects problem formulation with risk characterization. The assessment endpoints

and conceptual models developed during problem formulation provide the focus and structure for the

analyses. Analysis phase products are summary profiles that describe exposure and the relationship

between the stressor(s) and response. These profiles provide the basis for estimating and describing

risks in risk characterization.

At the beginning of the analysis phase,

the information needs identified during problem

formulation should have already been addressed

(text box 4-1). During the analysis phase (figure

4-1), the risk assessor:

• Selects the data that will be used

on the basis of their utility for

evaluating the risk hypotheses

(section 4.1)

• Analyzes exposure by examining

the sources of stressors, the

distribution of stressors in the

environment, and the extent of

co-occurrence or contact

(section 4.2)

• Analyzes effects by examining stressor-response relationships, the evidence for

causality, and the relationship between measures of effect and assessment endpoints

(section 4.3)
53

Figure 4-1. Analysis phase.

• Summarizes the conclusions about exposure (section 4.2.2) and effects (section 4.3.2).

The analysis phase is flexible, with substantial interaction between the effects and exposure

characterizations as illustrated by the dotted line in figure 4-1. In particular, when secondary stressors

and effects are of concern, exposure and effects analyses are conducted iteratively for different

ecological entities, and they can become intertwined and difficult to differentiate. In the bottomland
54

hardwoods assessment, for example (Appendix D), potential changes in the plant and animal

communities under different flooding scenarios were examined. Risk assessors combined the stressorresponse and exposure analyses within the FORFLO model for primary effects on the plant community

and within the Habitat Suitability Index for secondary effects on the animal community. In addition, the

distinction between analysis and risk estimation can become blurred. The model results developed for

the bottomland hardwoods assessment were used directly in risk characterization.

The nature of the stressor influences the types of analyses conducted. The results may range

from highly quantitative to qualitative, depending on the stressor and the scope of the assessment. For

chemical stressors, exposure estimates emphasize contact and uptake into the organism, and effects

estimations often entail extrapolation from test organisms to the organism of interest. For physical

stressors, the initial disturbance may cause primary effects on the assessment endpoint (e.g., loss of

wetland acreage). In many cases, however, secondary effects (e.g., decline of wildlife populations that

depend on wetlands) may be the principal concern. The point of view depends on the assessment

endpoints. Because adverse effects can occur even if receptors do not physically contact disturbed

habitat, exposure analyses may emphasize co-occurrence with physical stressors rather than contact. 

For biological stressors, exposure analysis is an evaluation of entry, dispersal, survival, and

reproduction (Orr et al., 1993). Because biological stressors can reproduce, interact with other

organisms, and evolve over time, exposure and effects cannot always be quantified with confidence;

therefore, they may be assessed qualitatively by eliciting expert opinion (Simberloff and Alexander,

1994).

4.1. EVALUATING DATA AND MODELS FOR ANALYSIS

At the beginning of the analysis phase, the assessor critically examines the data and models to

ensure that they can be used to evaluate the conceptual model developed in problem formulation (see

sections 4.1.1 and 4.1.2). Section 4.1.3 addresses uncertainty evaluation.

4.1.1. Strengths and Limitations of Different Types of Data

Many types of data can be used for risk assessment. Data may come from laboratory or field

studies or may be produced as output from a model. Familiarity with the strengths and limitations of

different types of data can help assessors build on strengths and avoid pitfalls. Such a strategy

improves confidence in the conclusions of the risk assessment.

Both laboratory and field studies (including field experiments and observational studies) can

provide useful data for risk assessment. Because conditions can be controlled in laboratory studies,
55

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
56

et al., 1986; Wiens and Parker, 1995; Eberhardt and Thomas, 1991). Intermediate between

laboratory and field are studies that use environmental media collected from the field to examine

response in the laboratory. Such studies may improve the power to detect differences and may be

designed to provide evidence of causality.

Most data will be reported as measurements for single variables such as a chemical

concentration or the number of dead organisms. In some cases, however, variables are combined and

reported as indices. Several indices are used to evaluate effects, for example, the rapid bioassessment

protocols (U.S. EPA, 1989a) and the Index of Biotic Integrity, or IBI (Karr, 1981; Karr et al., 1986). 

These have several advantages (Barbour et al., 1995), including the ability to:

• Provide an overall indication of biological condition by incorporating many attributes of

system structure and function, from individual to ecosystem levels

• Evaluate responses from a broad range of anthropogenic stressors

• Minimize the limitations of individual metrics for detecting specific types of responses.

Indices also have several drawbacks, many of which are associated with combining

heterogeneous variables. The final value may depend strongly on the function used to combine

variables. Some indices (e.g., the IBI) combine only measures of effects. Differential sensitivity or

other factors may make it difficult to attribute causality when many response variables are combined. 

To investigate causality, such indices may need to be separated into their components, or analyzed

using multivariate methods (Suter, 1993b; Ott, 1978). Interpretation becomes even more difficult when

an index combines measures of exposure and effects because double counting may occur or changes in

one variable can mask changes in another. Measures of exposure and effects may need to be

separated in order to make appropriate conclusions. For these reasons, professional judgment plays a

critical role in developing and applying indices.

Experience from similar situations is particularly useful in assessments of stressors not yet

released (i.e., prospective assessments). Lessons learned from past experiences with related organisms

are often critical in trying to predict whether an organism will survive, reproduce, and disperse in a new

environment. Another example is toxicity evaluation for new chemicals through the use of structureactivity 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
57

of a compound for which data are lacking. More advanced applications use quantitative structureactivity 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
58

Text Box 4-2. The American National

Standard for Quality Assurance

The Specifications and Guidelines for Quality

Systems for Environmental Data Collection and

Environmental Technology Programs (ASQC,

1994) recognize several areas that are important

to ensuring that environmental data will meet

study objectives, including:

• Planning and scoping

• Designing data collection operations

• Implementing and monitoring planned

operations

• Assessing and verifying data usability.

characterization, when the overall confidence in the assessment is discussed. Finally, the risk assessor

should identify areas where existing data do not meet risk assessment needs. In these cases, collecting

additional data is recommended.

EPA is in the process of adopting the

American Society for Quality Control’s E-4

guidelines for assuring environmental data quality

throughout the Agency (ASQC, 1994) (text box

4-2). These guidelines describe procedures for

collecting new data and provide a valuable

resource for evaluating existing studies. Readers

may also refer to Smith and Shugart, 1994; U.S.

EPA, 1994e; and U.S. EPA, 1990, for more

information on evaluating data and models.

A study’s documentation determines

whether it can be evaluated for its utility in risk

assessment. Studies should contain sufficient

information so that results can be reproduced, or

at least so the details of the author’s work can be

accessed and evaluated. Ideally, one should be

able to access findings in their entirety; this provides the opportunity to conduct additional analyses of

the data, if needed. For models, a number of factors increase the accessibility of methods and results. 

These begin with model code and documentation availability. Reports describing model results should

include all important equations, tables of all parameter values, any parameter estimation techniques, and

tables or graphs of results.
59

Text Box 4-3. Questions for Evaluating a

Study’s Utility for Risk Assessment

C Are the study objectives relevant to the risk

assessment? 

C Are the variables and conditions the study

represents comparable with those important

to the risk assessment?

C Is the study design adequate to meet its

objectives?

C Was the study conducted properly?

C How are variability and uncertainty treated

and reported?

Study descriptions may not provide all

the information needed to evaluate their utility for

risk assessment. Assessors should communicate

with the principal investigator or other study

participants to gain information on study plans

and their implementation. Useful questions for

evaluating studies are shown in text box 4-3.

4.1.2.1. Evaluating the Purpose and Scope

of the Study

Assessors should pay particular attention

to the objectives and scope of studies that were

designed for purposes other than the risk

assessment at hand. This can identify important

uncertainties and ensure that the information is

used appropriately. An example is the evaluation of studies that measure condition (e.g., stream

surveys, population surveys): While the measurements used to evaluate condition may be the same as

the measures of effects identified in problem formulation, to support a causal argument they must be

linked with stressors. In the best case, this means that the stressor was measured at the same time and

place as the effect.

Similarly, a model may have been developed for purposes other than risk assessment. Its

description should include the intended application, theoretical framework, underlying assumptions, and

limiting conditions. This information can help assessors identify important limitations in its application for

risk assessment. For example, a model developed to evaluate chemical transport in the water column

alone is of limited utility for a risk assessment of a chemical that partitions readily into sediments.

The variables and conditions examined by studies should also be compared with those identified

during problem formulation. In addition, the range of variability explored in the study should be

compared with that of the risk assessment. A study that examines animal habitat needs in the winter,

for example, may miss important breeding-season requirements. Studies that minimize the amount of

extrapolation needed are preferred. These are studies that represent:

• The measures identified in the analysis plan (i.e., measures of exposure, effects, and

ecosystem and receptor characteristics)
60

• 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, 1994e).

Evaluation is easier if standard methods or quality assurance/quality control (QA/QC) protocols

are available and followed by the study. However, the assessor should still consider whether the

identified precision and accuracy goals were achieved and whether they are appropriate for the risk
61

assessment. For instance, detection limits identified for one environmental matrix may not be achievable

for another, and thus it may not be possible to detect concentrations of interest. Study results can still

be useful even if a standard method was not used. However, this places an additional burden on both

the authors and the assessors to provide and evaluate evidence that the study was conducted properly.

4.1.3. Evaluating Uncertainty

Uncertainty evaluation is a theme throughout the analysis phase. The objective is to describe

and, where possible, quantify what is known and not known about exposure and effects in the system

of interest. Uncertainty analyses increase the credibility of assessments by explicitly describing the

magnitude and direction of uncertainties, and they provide the basis for efficient data collection or

application of refined methods. Uncertainties characterized during the analysis phase are used during

risk characterization, when risks are estimated (section 5.1) and the confidence in different lines of

evidence is described (see section 5.2.1). 

This section discusses sources of uncertainty relevant to the analysis of ecological exposure and

effects; source and example strategies are shown in text box 4-4. Section 3.4.3 discusses uncertainty

in conceptual model development. Readers are also referred to the discussion of uncertainties in the

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

Sources of uncertainty that are encountered when evaluating information include unclear

communication of the data or its manipulation and errors in the information itself (descriptive errors). 

These are usually characterized by critically examining the sources of information and documenting the

decisions made when handling it. The documentation should allow the reader to make an independent

judgment about the validity of the assessor’s decisions.

Sources of uncertainty that primarily arise when estimating the value of a parameter include

variability, uncertainty about a quantity’s true value, and data gaps. The term variability is used here

to describe a characteristic’s true heterogeneity. Examples include the variability in soil organic carbon,

seasonal differences in animal diets, or differences in chemical sensitivity in different species. Variability

is usually described during uncertainty analysis, although heterogeneity may not reflect a lack of

knowledge and cannot usually be reduced by further measurement. Variability can be described by

presenting a distribution or specific percentiles from it (e.g., mean and 95th percentile).

Uncertainty about a quantity’s true value may include uncertainty about its magnitude, location,

or time of occurrence. This uncertainty can usually be reduced by taking additional measurements. 

Uncertainty about a quantity’s true magnitude is usually described by sampling error (or variance in

experiments) or measurement error. When the quantity of interest is biological response, sampling error
62

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;
63

Text Box 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. 

Document decisions made during the course of

the assessment.

Clarify whether the study was designed

to characterize local populations or

regional populations.

Discuss rationale for selecting the

critical toxicity study.

Descriptive

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.

Differentiate from uncertainty due to lack of

knowledge.

Display differences in species

sensitivity using a cumulative

distribution function.

Data gaps Collect needed data.

Describe approaches used for bridging gaps and

their rationales.

Differentiate science-based judgments from

policy-based judgments.

Discuss rationale for using a factor of 10

to extrapolate between a lowestobserved-adverse-effect level (LOAEL)

and a NOAEL.

Uncertainty

about a

quantity’s true

value

Use standard statistical methods to construct

probability distributions or point estimates (e.g.,

confidence limits).

Evaluate power of designed experiments to detect

differences.

Collect additional data.

Verify location of samples or other spatial

features.

Present the upper confidence limit on

the arithmetic mean soil concentration,

in addition to the best estimate of the

arithmetic mean.

Ground-truth remote sensing data.

Model structure

uncertainty

(process models)

Discuss key aggregations and model

simplifications.

Compare model predictions with data collected in

the system of interest.

Discuss combining different species into

a group based on similar feeding habits.

Uncertainty

about a model’s

form

(empirical

models)

Evaluate whether alternative models should be

combined formally or treated separately.

Compare model predictions with data collected in

the system of interest.

Present results obtained using

alternative models.

Compare results of a plant uptake model

with data collected in the field.
64

Wiens and Parker, 1995). Large-scale assessments generally require aggregating information at smaller

scales. It is not known how aggregation affects uncertainty (Hunsaker et al., 1990).

Nearly every assessment must treat situations where data are unavailable or available only for

parameters other than those of interest. Examples include using laboratory data to estimate a wild

animal’s response to a stressor or using a bioaccumulation measurement from a different ecosystem. 

These data gaps are usually bridged with a combination of scientific analyses, scientific judgment, and

perhaps policy decisions. In deriving an ambient water quality criterion (text box 3-17), for example,

data and analyses are used to construct distributions of species sensitivity for a particular chemical. 

Scientific judgment is used to infer that species selected for testing will adequately represent the range

of sensitivity of species in the environment. Policy defines the extent to which individual species should

be protected (e.g., 90% vs. 95% of the species). It is important to distinguish these elements.

Data gaps can often be filled by completing additional studies on the unknown parameter. 

When possible, the necessary data should be collected. At the least, opportunities for filling data gaps

should be noted and carried through to risk characterization. Data or knowledge gaps that are so large

that they preclude the analysis of either exposure or ecological effects should also be noted and

discussed in risk characterization.

An important objective is to distinguish variability from uncertainties that arise from lack of

knowledge (e.g., uncertainty about a quantity’s true value) (U.S. EPA, 1995b). This distinction

facilitates the interpretation and communication of results. For instance, in their food web models of

herons and mink, MacIntosh et al. (1994) separated expected variability in individual animals’ feeding

habits from the uncertainty in the mean concentration of chemical in prey species. They could then

place error bounds on the exposure distribution for the animals using the site and estimate the

proportion of the animal population that might exceed a toxicity threshold.

Sources of uncertainty that arise primarily during model development and application include

process model structure and the relationships between variables in empirical models. Process model

descriptions should include assumptions, simplifications, and aggregations of variables (see text box 4-

5). Empirical model descriptions should include the rationale for selection and model performance

statistics (e.g., goodness of fit). Uncertainty in process or empirical models can be quantitatively

evaluated by comparing model results to measurements taken in the system of interest or by comparing

the results of different models.

Methods for analyzing and describing uncertainty can range from simple to complex. When

little is known, a useful approach is to estimate exposure and effects based on alternative sets of

assumptions (scenarios). Each scenario is carried through to risk characterization, where
65

Text Box 4-5. Considering the Degree of

Aggregation in Models

Wiegert and Bartell (1994) suggest the following

considerations for evaluating the proper degree

of aggregation or disaggregation:

1. Do not aggregate components with greatly

disparate flux rates.

2. Do not greatly increase the disaggregation of

the structural aspects of the model without a

corresponding increase in the sophistication

of the functional relationships and controls.

3. Disaggregate models only insofar as required

by the goals of the model to facilitate testing.

the underlying assumptions and the scenario’s

plausibility are discussed. Results can be

presented as a series of point estimates with

different aspects of uncertainty reflected in each. 

Classical statistical methods (e.g., confidence

limits, percentiles) can readily describe

parameter uncertainty. For models, sensitivity

analysis can be used to evaluate how model

output changes with changes in input variables,

and uncertainty propagation can be analyzed to

examine how uncertainty in individual parameters

can affect the overall uncertainty in the results. 

The availability of software for Monte Carlo

analysis has greatly increased the use of

probabilistic methods; readers are encouraged to

follow suggested best practices (e.g., U.S. EPA, 1996b, 1997b). Other methods (e.g., fuzzy

mathematics, Bayesian methodologies) are available but have not yet been extensively applied to

ecological risk assessment (Smith and Shugart, 1994). The Agency does not endorse the use of any

one method and cautions that the poor execution of any method can obscure rather than clarify the

impact of uncertainty on an assessment’s results. No matter what technique is used, the sources of

uncertainty discussed above should be addressed.

4.2. CHARACTERIZATION OF EXPOSURE

Exposure characterization describes potential or actual contact or co-occurrence of stressors

with receptors. It is based on measures of exposure and ecosystem and receptor characteristics that

are used to analyze stressor sources, their distribution in the environment, and the extent and pattern of

contact or co-occurrence (discussed in section 4.2.1). The objective is to produce a summary

exposure profile (section 4.2.2) that identifies the receptor (i.e., the exposed ecological entity),

describes the course a stressor takes from the source to the receptor (i.e., the exposure pathway), and

describes the intensity and spatial and temporal extent of co-occurrence or contact. The profile also

describes the impact of variability and uncertainty on exposure estimates and reaches a conclusion

about the likelihood that exposure will occur.
66

The exposure profile is combined with an effects profile (discussed in section 4.3.2) to estimate

risks. For the exposure profile to be useful, it should be compatible with the stressor-response

relationship generated in the effects characterization.

4.2.1. Exposure Analyses

Exposure is contact or co-occurrence between a stressor and a receptor. The objective is to

describe exposure in terms of intensity, space, and time in units that can be combined with the effects

assessment. In addition, the assessor should be able to trace the paths of stressors from the source(s)

to the receptors (i.e., describe the exposure pathway).

 A complete picture of how, when, and where exposure occurs or has occurred is developed

by evaluating sources and releases, the distribution of the stressor in the environment, and the extent

and pattern of contact or co-occurrence. The order of these topics here is not necessarily the order in

which they are executed. The assessor may start with information about tissue residues, for example,

and attempt to link these residues with a source.

4.2.1.1. Describe the Source(s)

A source can be defined in two general ways: as the place where the stressor originates or is

released (e.g., a smokestack, historically contaminated sediments) or the management practice or

action (e.g., dredging) that produces stressors. In some assessments, the original sources may no

longer exist and the source may be defined as the current location of the stressors. For example,

contaminated sediments might be considered a source because the industrial plant that produced the

contaminants no longer operates. A source is the first component of the exposure pathway and

significantly influences where and when stressors eventually will be found. In addition, many

management alternatives focus on modifying the source.

Exposure analyses may start with the source when it is known, begin with known exposures

and attempt to link them to sources, or start with known stressors and attempt to identify sources and

quantify contact. In any case, the objective of this step is to identify the sources, evaluate what

stressors are generated, and identify other potential sources. Text box 4-6 provides some useful

questions to ask when describing sources.

In addition to identifying sources, the assessor examines the intensity, timing, and location of

stressors’ release. The location of a source and the environmental media that first receive stressors are

two attributes that deserve particular attention. For chemical stressors, the source characterization
67

should also consider whether other constituents emitted by a source influence transport, transformation,

or bioavailability of the stressor of interest. The presence of
68

Text Box 4-6. Questions for Source

Description

C Where does the stressor originate?

C What environmental media first receive

stressors?

C Does the source generate other constituents

that will influence a stressor’s eventual

distribution in the environment?

C Are there other sources of the same stressor?

C Are there background sources?

C Is the source still active?

C Does the source produce a distinctive

signature that can be seen in the environment,

organisms, or communities?

Additional questions for introduction of

biological stressors:

C Is there an opportunity for repeated

introduction or escape into the new

environment?

C Will the organism be present on a

transportable item?

C Are there mitigation requirements or

conditions that would kill or impair the

organism before entry, during transport, or at

the port of entry?

chloride in the feedstock of a coal-fired power

plant influences whether mercury is emitted in

divalent (e.g., as mercuric chloride) or elemental

form (Meij, 1991), for example. In the best

case, stressor generation is measured or

modeled quantitatively; however, sometimes it

can only be qualitatively described.

Many stressors have natural counterparts

or multiple sources, so it may be necessary to

characterize these as well. Many chemicals

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

widespread from other sources (e.g., polycyclic

aromatic hydrocarbons in urban ecosystems), or

may have significant sources outside the

boundaries of the current assessment (e.g.,

atmospheric nitrogen deposited in Chesapeake

Bay). Many physical stressors also have natural

counterparts. For instance, construction

activities may release fine sediments into a

stream in addition to those coming from a

naturally undercut bank. Human activities may

also change the magnitude or frequency of

natural disturbance cycles. For example,

development may decrease the frequency but

increase the severity of fires or may increase the

frequency and severity of flooding in a

watershed.

The assessment scope identified during

planning determines how multiple sources are

evaluated. Options include (in order of

increasing complexity):
69

• Focus only on the source under evaluation and calculate the incremental risks

attributable to that source (common for assessments initiated with an identified source

or stressor).

• Consider all sources of a stressor and calculate total risks attributable to that stressor. 

Relative source attribution can be accomplished as a separate step (common for

assessments initiated with an observed effect or an identified stressor).

• Consider all stressors influencing an assessment endpoint and calculate cumulative risks

to that endpoint (common for assessments initiated because of concern for an

ecological value).

Source characterization can be particularly important for introduced biological stressors, since

many of the strategies for reducing risks focus on preventing entry in the first place. Once the source is

identified, the likelihood of entry may be characterized qualitatively. In their risk analysis of Chilean log

importation, for example, the assessment team concluded that the beetle Hylurgus ligniperda had a

high potential for entry into the United States. Their conclusion was based on the beetle’s attraction to

freshly cut logs and tendency to burrow under the bark, which would provide protection during

transport (USDA, 1993).

4.2.1.2. Describe the Distribution of the Stressors or Disturbed Environment

The second objective of exposure analysis is to describe the spatial and temporal distribution of

stressors in the environment. For physical stressors that directly alter or eliminate portions of the

environment, the assessor describes the temporal and spatial distribution of the disturbed environment. 

Because exposure occurs when receptors co-occur with or contact stressors, this characterization is a

prerequisite for estimating exposure. Stressor distribution in the environment is examined by evaluating

pathways from the source as well as the formation and subsequent distribution of secondary stressors

(see text box 4-7).

4.2.1.2.1. Evaluating Transport Pathways. Stressors can be transported via many pathways (see

text box 4-8). A careful evaluation can help ensure that measurements are taken in the appropriate

media and locations and that models include the most important processes.
70

Text Box 4-7. Questions to Ask in

Evaluating Stressor Distribution

C What are the important transport pathways?

C What characteristics of the stressor influence

transport?

C What characteristics of the ecosystem will

influence transport?

C What secondary stressors will be formed?

C Where will they be transported?

Text Box 4-8. General Mechanisms of

Transport and Dispersal

Physical, chemical, and biological stressors:

• By air current

• In surface water (rivers, lakes, streams)

• Over and/or through the soil surface

• Through ground water

Primarily chemical stressors:

• Through the food web

Primarily biological stressors:

• Splashing or raindrops

• Human activity (boats, campers)

• Passive transmittal by other organisms

• Biological vectors

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
71

of reproductive rates and motility. Jump-dispersal involves erratic spreads over periods of time, usually

by means of a vector. The gypsy moth and zebra mussel have spread this way, the gypsy moth via egg

masses on vehicles and the zebra mussel via boat ballast water. Some biological stressors can use both

strategies, which may make dispersal rates very difficult to predict. The evaluation should consider

factors such as vector availability, attributes that enhance dispersal (e.g., ability to fly, adhere to objects,

disperse reproductive units), and habitat or host needs.

For biological stressors, assessors should consider the additional factors of survival and

reproduction. Organisms use a wide range of strategies to survive in adverse conditions; for example,

fungi form resting stages such as sclerotia and chlamydospores and some amphibians become dormant

during drought. The survival of some organisms can be measured to some extent under laboratory

conditions. However, it may be impossible to determine how long resting stages (e.g., spores) can

survive under adverse conditions: many can remain viable for years. Similarly, reproductive rates may

vary substantially depending on specific environmental conditions. Therefore, while life-history data

such as temperature and substrate preferences, important predators, competitors or diseases, habitat

needs, and reproductive rates are of great value, they should be interpreted with caution, and the

uncertainty should be addressed by using several different scenarios.

Ecosystem characteristics influence the transport of all types of stressors. The challenge is to

determine the particular aspects of the ecosystem that are most important. In some cases, ecosystem

characteristics that influence distribution are known. For example, fine sediments tend to accumulate in

areas of low energy in streams such as pools and backwaters. Other cases need more professional

judgment. When evaluating the likelihood that an introduced organism will become established, for

instance, it is useful to know whether the ecosystem is generally similar to or different from the one

where the biological stressor originated. Professional judgment is used to determine which

characteristics of the current and original ecosystems should be compared.

4.2.1.2.2. Evaluating Secondary Stressors. Secondary stressors can greatly alter conclusions

about risk; they may be of greater or lesser concern than the primary stressor. Secondary stressor

evaluation is usually part of exposure characterization; however, it should be coordinated with the

ecological effects characterization to ensure that all potentially important secondary stressors are

considered.

For chemicals, the evaluation usually focuses on metabolites, biodegradation products, or

chemicals formed through abiotic processes. As an example, microbial action increases the

bioaccumulation of mercury by transforming inorganic forms to organic species. Many azo dyes are not
72

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,
73

Text Box 4-9. Questions To Ask in

Describing Contact or Co-Occurrence

C Must the receptor actually contact the

stressor for adverse effects to occur?

C Must the stressor be taken up into a receptor

for adverse effects to occur?

C What characteristics of the receptors will

influence the extent of contact or cooccurrence?

C Will abiotic characteristics of the environment

influence the extent of contact or cooccurrence?

C Will ecosystem processes or community-level

interactions influence the extent of contact or

co-occurrence?

describing the stressor’s environmental distribution can help identify potential sources and ensure that all

important exposures are addressed.

4.2.1.3. Describe Contact or Co-Occurrence 

The third objective is to describe the

extent and pattern of co-occurrence or contact

between stressors and receptors (i.e., exposure). 

This is critical—if there is no exposure, there can

be no risk. Therefore, assessors should be

careful to include situations where exposure may

occur in the future, where exposure has occurred

in the past but is not currently evident (e.g., in

some retrospective assessments), and where

ecosystem components important for food or

habitat are or may be exposed, resulting in

impacts to the valued entity (e.g., see figure D2). Exposure can be described in terms of

stressor and receptor co-occurrence, actual

stressor contact with receptors, or stressor

uptake by a receptor. The terms in which

exposure is described depend on how the

stressor causes adverse effects and how the stressor-response relationship is described. Relevant

questions for examining contact or co-occurrence are shown in text box 4-9.

Co-occurrence is particularly useful for evaluating stressors that can cause effects without

physically contacting ecological receptors. Whooping cranes provide a case in point: they use

sandbars in rivers for their resting areas, and they prefer sandbars with unobstructed views. Manmade

obstructions such as bridges can interfere with resting behavior without ever actually contacting the

birds. Co-occurrence is evaluated by comparing stressor distributions with that of the receptor. For

instance, stressor location maps may be overlaid with maps of ecological receptors (e.g., bridge

placement overlaid on maps showing historical crane resting habitat). Co-occurrence of a biological

stressor and receptor may be used to evaluate exposure when, for example, introduced species and

native species compete for the same resources. GIS has provided new tools for evaluating cooccurrence.
74

Text Box 4-10. Example of an Exposure

Equation: Calculating a Potential Dose via

Ingestion

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, 1993b.

Most stressors must contact receptors to cause an effect. For example, tree roots must contact

flood waters before their growth is impaired. Contact is a function of the amount or extent of a stressor

in an environmental medium and activity or behavior of the receptors. For biological stressors, risk

assessors usually rely on professional judgment; contact is often assumed to occur in areas and during

times where the stressor and receptor are both present. Contact variables such as the mode of

transmission between organisms may influence the contact between biological stressors and receptors. 

For chemicals, contact is quantified as

the amount of a chemical ingested, inhaled, or in

material applied to the skin (potential dose). In

its simplest form, it is quantified as an

environmental concentration, with the

assumptions that the chemical is well mixed or

that the organism moves randomly through the

medium. This approach is commonly used for

respired media (water for aquatic organisms, air

for terrestrial organisms). For ingested media

(food, soil), another common approach

combines modeled or measured contaminant

concentrations with assumptions or parameters

describing the contact rate (U.S. EPA, 1993b)

(see text box 4-10).

 Finally, some stressors must not only be

contacted but also must be internally absorbed. 

A toxicant that causes liver tumors in fish, for

example, must be absorbed and reach the target

organ to cause the effect. Uptake is evaluated

by considering the amount of stressor internally

absorbed by an organism. It is a function of the

stressor (e.g., a chemical’s form or a pathogen’s

size), the medium (sorptive properties or

presence of solvents), the biological membrane

(integrity,
75

Text Box 4-11. Measuring Internal Dose

Using Biomarkers and Tissue Residues

Biomarkers and tissue residues are particularly

useful when exposure across many pathways

must be integrated and when site-specific factors

influence bioavailability. They can also be very

useful when metabolism and accumulation

kinetics are important, although these factors can

make interpretation of results more difficult

(McCarty and Mackay, 1993). These methods

are most useful when they can be quantitatively

linked to the amount of stressor originally

contacted by the organism. In addition, they are

most useful when the stressor-response

relationship expresses the amount of stressor in

terms of the tissue residue or biomarker (van

Gestel and van Brummelen, 1996). Standard

analytical methods are generally available for

tissue residues, making them more readily usable

for routine assessments than biomarkers. 

Readers are referred to the review in

Ecotoxicology (Vol. 3, Issue 3, 1994), Huggett

et al. (1992), and the debate in Human Health

and Ecological Risk Assessment (Vol. 2, Issue

2, 1996).

permeability), and the organism (sickness, active

uptake) (Suter et al., 1994). Because of

interactions between these four factors, uptake

will vary on a situation-specific basis. Uptake is

usually assessed by modifying an estimate of

contact with a factor indicating the proportion of

the stressor that is available for uptake (the

bioavailable fraction) or actually absorbed. 

Absorption factors and bioavailability measured

for the chemical, ecosystem, and organism of

interest are preferred. Internal dose can also be

evaluated by using a pharmacokinetic model or

by measuring biomarkers or residues in

receptors (see text box 4-11). Most stressorresponse 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
76

avoid areas, food, or water with contamination they can detect. While avoidance can reduce exposure

to chemicals, it may increase other risks by altering habitat usage or other behavior.

Three dimensions should be considered when estimating exposure: intensity, time, and space. 

Intensity is the most familiar dimension for chemical and biological stressors and may be 

expressed as the amount of chemical contacted per day or the number of pathogenic organisms per unit

area.

The temporal dimension of exposure has aspects of duration, frequency, and timing. Duration

can be expressed as the time over which exposure occurs, some threshold intensity is exceeded, or

intensity is integrated. If exposure occurs as repeated discrete events of about the same duration,

frequency is the important temporal dimension of exposure (e.g., the frequency of high-flow events in

streams). If the repeated events have significant and variable durations, both duration and frequency

should be considered. In addition, the timing of exposure, including the order or sequence of events,

can be an important factor. Adirondack Mountain lakes receive high concentrations of hydrogen ions

and aluminum during snow melt; this period also corresponds to the sensitive life stages of some aquatic

organisms.

In chemical assessments, intensity and time are often combined by averaging intensity over time. 

The duration over which intensity is averaged is determined by considering the ecological effects of

concern and the likely pattern of exposure. For example, an assessment of bird kills associated with

granular carbofuran focused on short-term exposures because the effect of concern was acute lethality

(Houseknecht, 1993). Because toxicological tests are usually conducted using constant exposures, the

most realistic comparisons between exposure and effects are made when exposure in the real world

does not vary substantially. In these cases, the arithmetic average exposure over the time period of

toxicological significance is the appropriate statistic (U.S. EPA, 1992b). However, as concentrations

or contact rates become more episodic or variable, the arithmetic average may not reflect the

toxicologically significant aspect of the exposure pattern. In extreme cases, averaging may not be

appropriate at all, and assessors may need to use a toxicodynamic model to assess chronic effects.

Spatial extent is another dimension of exposure. It is most commonly expressed in terms of

area (e.g., hectares of paved habitat, square meters that exceed a particular chemical threshold). At

larger spatial scales, however, the shape or arrangement of exposure may be an important issue, and

area alone may not be the appropriate descriptor of spatial extent for risk assessment. A general

solution to the problem of incorporating pattern into ecological assessments has yet to be developed;

however, landscape ecology and GIS have greatly expanded the options for analyzing and presenting

the spatial dimension of exposure (e.g., Pastorok et al., 1996).
77

Text Box 4-12. Questions Addressed by

the Exposure Profile

C How does exposure occur?

C What is exposed?

C How much exposure occurs? When and

where does it occur?

C How does exposure vary?

C How uncertain are the exposure estimates?

C What is the likelihood that exposure will

occur?

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 box

4-12).

The profile should describe the

applicable exposure pathways. If exposure can

occur through many pathways, it may be useful

to rank them, perhaps by contribution to total

exposure. As an illustration, consider an

assessment of risks to grebes feeding in a

mercury-contaminated lake. The grebes may be exposed to methyl mercury in fish that originated from

historically contaminated sediments. They may also be exposed by drinking lake water, but comparing

the two exposure pathways may show that the fish pathway contributes the vast majority of exposure to

mercury.

The profile should identify the ecological entity that the exposure estimates represent. For

example, the exposure estimates may describe the local population of grebes feeding on a specific lake

during the summer months.
78

The assessor should explain how each of the three general dimensions of exposure (intensity,

time, and space) was treated. Continuing with the grebe example, exposure might be expressed as the

daily potential dose averaged over the summer months and over the extent of the lake.

The profile should also describe how exposure can vary depending on receptor attributes or

stressor levels. For instance, the exposure may be higher for grebes eating a larger proportion of

bigger, more contaminated fish. Variability can be described by using a distribution or by describing

where a point estimate is expected to fall on a distribution. Cumulative-distribution functions (CDFs)

and probability-density functions (PDFs) are two common presentation formats (see Appendix B,

figures B-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.
79

4.3. CHARACTERIZATION OF ECOLOGICAL EFFECTS

To characterize ecological effects, the assessor describes the effects elicited by a stressor, links

them to the assessment endpoints, and evaluates how they change with varying stressor levels. The

characterization begins by evaluating effects data to specify the effects that are elicited, verify that they

are consistent with the assessment endpoints, and confirm that the conditions under which they occur

are consistent with the conceptual model. Once the effects of interest are identified, the assessor

conducts an ecological response analysis (section 4.3.1), evaluating how the magnitude of the effects

change with varying stressor levels and the evidence that the stressor causes the effect, and then linking

the effects with the assessment endpoint. Conclusions are summarized in a stressor-response profile

(section 4.3.2).

4.3.1. Ecological Response Analysis

Ecological response analysis examines three primary elements: the relationship between

stressor levels and ecological effects (section 4.3.1.1), the plausibility that effects may occur or are

occurring as a result of exposure to stressors (section 4.3.1.2), and linkages between measurable

ecological effects and assessment endpoints when the latter cannot be directly measured (section

4.3.1.3).

4.3.1.1. Stressor-Response Analysis

To evaluate ecological risks, one must understand the relationships between stressors and

resulting responses. The stressor-response relationships used in a particular assessment depend on the

scope and nature of the ecological risk assessment as defined in problem formulation and reflected in

the analysis plan. For example, an assessor may need a point estimate of an effect (such as an LC50) to

compare with point estimates from other stressors. The shape of the stressor-response curve may be

needed to determine the presence or absence of an effects threshold or for evaluating incremental risks,

or stressor-response curves may be used as input for effects models. If sufficient data are available, the

risk assessor may construct cumulative distribution functions using multiple-point estimates of effects. 

Or the assessor may use process models that already incorporate empirically derived stressor-response

relationships (see section 4.3.1.3). Text box 4-13 provides some questions for stressor-response

analysis.

This section describes a range of stressor-response approaches available to risk assessors

following a theme of variations on the classical stressor-response relationship (e.g., figure 4-2). More

complex relationships are shown in figure 4-3, which illustrates a range of projected responses of
80

Text Box 4-13. Questions for StressorResponse Analysis

C Does the assessment require point estimates

or stressor-response curves?

C Does the assessment require the

establishment of a “no-effect” level?

C Would cumulative effects distributions be

useful?

C Will analyses be used as input to a process

model?

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).

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
81

Figure 4-2. A simple example of a stressor-response relationship. Substantially more

complex relationships are typical of many ecological risk assessments, given the range

of stressors, endpoints, and environmental situations often encountered.

individual variables (e.g., species 
82

Figure 4-3. Variations in stressor-response relationships. These curves illustrate a

range of responses to pesticide exposure of the intrinsic rate of increase of

zooplankton populations (adapted from Schindler, 1987).

Text Box 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 stressor-response relationship was

described simply as a yes or no. In most cases,

however, the objective is to describe

quantitatively the intensity of response

associated with exposure, and in the best case,

to describe how intensity of response changes

with incremental increases in exposure.

abundances in an aquatic community),

multivariate techniques may be useful. These

have a long history of use in ecology (see texts

by Gauch, 1982; Pielou, 1984; Ludwig and

Reynolds, 1988) but have not yet been

extensively applied in risk assessment. While

quantifying stressor-response relationships is

encouraged, qualitative evaluations are also

possible (text box 4-14). 

Stressor-response relationships can be

described using intensity, time, or space. 

Intensity is probably the most familiar of these

and is often used for chemicals (e.g., dose,

concentration). Exposure duration is also
83

Text Box 4-15. Median Effect Levels

Median effects are those effects elicited in 50%

of the test organisms exposed to a stressor,

typically chemical stressors. Median effect

concentrations can be expressed in terms of

lethality or mortality and are known as LC50 or

LD50, depending on whether concentrations (in

the diet or in water) or doses (mg/kg) were

used. Median effects other than lethality (e.g.,

effects on growth) are expressed as EC50 or

ED50. The median effect level is always

associated with a time parameter (e.g., 24 or 48

hours). Because these tests seldom exceed 96

hours, their main value lies in evaluating shortterm effects of chemicals. Stephan (1977)

discusses several statistical methods to estimate

the median effect level.

commonly used for chemical stressor-response relationships; for example, median acute effects levels

are always associated with a time parameter (e.g., 24 hours). As noted in text box 4-14, the timing of

exposure was the critical dimension in evaluating the relationship between seed germination and soil

moisture (Pearlstine et al., 1985). The spatial dimension is often of concern for physical stressors. For

instance, the extent of suitable habitat was related to the probability of sighting a spotted owl (Thomas

et al., 1990), and water-table depth was related to tree growth by Phipps (1979).

Single-point estimates and stressor-response curves can be generated for some biological

stressors. For pathogens such as bacteria and fungi, inoculum levels (e.g., spores per milliliter;

propagules per unit of substrate) may be related to symptoms in a host (e.g., lesions per area of leaf

surface, total number of plants infected) or actual signs of the pathogen (asexual or sexual fruiting

bodies, sclerotia, etc.). For other biological stressors such as introduced species, simple stressorresponse 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 box 4-15) are frequently selected

because the level of uncertainty is minimized at
84

Text Box 4-16. No-Effect Levels Derived

From Statistical Hypothesis Testing

Statistical hypothesis tests have typically been

used with chronic toxicity tests of chemical

stressors that evaluate multiple endpoints. For

each endpoint, the objective is to determine the

highest test level for which effects are not

statistically different from the controls (the noobserved-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).

the midpoint of the regression curve. While a 50% effect level for an endpoint such as survival may not

be appropriately protective for the assessment endpoint, median effect levels can be used for

preliminary assessments or comparative purposes, especially when used in combination with uncertainty

modifying factors (see text box 5-3). Selection of a different effect level (10%, 20%, etc.) can be

arbitrary unless there is some clearly defined benchmark for the assessment endpoint. Thus, it is

preferable to carry several levels of effect or the entire stressor-response curve forward to risk

estimation.

When risk assessors are particularly interested in effects at lower stressor levels, they may seek

to establish “no-effect” stressor levels based on comparisons between experimental treatments and

controls. Statistical hypothesis testing is frequently used for this purpose. (Note that statistical

hypotheses are different from the risk hypotheses discussed in problem formulation; see text box 3-

12). An example of this approach for deriving chemical no-effect

levels is provided in text box 4-16. A feature of

statistical hypothesis testing is that the risk

assessor is not required to pick a particular effect

level of concern. The no-effect level is

determined instead by experimental conditions

such as the number of replicates as well as the

variability inherent in the data. Thus it is

important to consider the level of effect

detectable in the experiment (i.e., its power) in

addition to reporting the no-effect level. Another

drawback of this approach is that it is difficult to

evaluate effects associated with stressor levels

other than the actual treatments tested. Several

investigators (Stephan and Rogers, 1985; Suter,

1993a) have proposed using regression analysis

as an alternative to statistical hypothesis testing.

In observational field studies, statistical

hypothesis testing is often used to compare site

conditions with a reference site(s). The

difficulties of drawing proper conclusions from

these types of studies (which frequently cannot
85

employ replication) have been discussed by many investigators (see section 4.1.1). Risk assessors

should examine whether sites were carefully matched to minimize differences other than the stressor and

consider whether potential covariates should be included in any analysis. In contrast with observational

studies, an advantage of experimental field studies is that treatments can be replicated, increasing the

confidence that observed differences are due to the treatment.

Experimental data can be combined to generate multiple-point estimates that can be displayed

as cumulative distribution functions. Figure 5-5 shows an example for species sensitivity derived from

multiple-point estimates (EC5s) for freshwater algae (and one vascular plant species) exposed to an

herbicide. These distributions can help identify stressor levels that affect a minority or majority of

species. A limiting factor in the use of cumulative frequency distributions is the amount of data needed

as input. Cumulative effects distribution functions can also be derived from models that use Monte

Carlo or other methods to generate distributions based on measured or estimated variation in input

parameters for the models.

When multiple stressors are present, stressor-response analysis is particularly challenging. 

Stressor-response relationships can be constructed for each stressor separately and then combined. 

Alternatively, the relationship between response and the suite of stressors can be combined in one

analysis. It is preferable to directly evaluate complex chemical mixtures present in environmental media

(e.g., wastewater effluents, contaminated soils [U.S. EPA, 1986b]), but it is important to consider the

relationship between the samples tested and the potential spatial and temporal variability in the mixture. 

The approach taken for multiple stressors depends on the feasibility of measuring them and whether an

objective of the assessment is to project different stressor combinations.

In some cases, multiple regression analysis can be used to empirically relate multiple stressors

to a response. Detenbeck (1994) used this approach to evaluate change in the water quality of

wetlands resulting from multiple physical stressors. Multiple regression analysis can be difficult to

interpret if the explanatory variables (i.e., the stressors) are not independent. Principal components

analysis can be used to extract independent explanatory variables formed from linear combinations of

the original variables (Pielou, 1984).

4.3.1.2. Establishing Cause-and-Effect Relationships (Causality)

Causality is the relationship between cause (one or more stressors) and effect (response to the

stressor[s]). Without a sound basis for linking cause and effect, uncertainty in the conclusions of an

ecological risk assessment is likely to be high. Developing causal relationships is especially important

for risk assessments driven by observed adverse ecological effects such as bird or fish kills or a shift in
86

Text Box 4-17. General Criteria for

Causality (Adapted From Fox, 1991)

Criteria strongly affirming causality:

• Strength of association

• Predictive performance

• Demonstration of a stressor-response

relationship

• Consistency of association

Criteria providing a basis for rejecting

causality:

• Inconsistency in association

• Temporal incompatibility

• Factual implausibility

Other relevant criteria:

• Specificity of association

• Theoretical and biological plausibility

the species composition of an area. This section describes considerations for evaluating causality based

on criteria developed by Fox (1991) primarily for observational data and additional criteria for

experimental evaluation of causality modified from Koch’s postulates (e.g., see Woodman and

Cowling, 1987).

Evidence of causality may be derived from observational evidence (e.g., bird kills are

associated with field application of a pesticide) or experimental data (laboratory tests with the

pesticides in question show bird kills at levels similar to those found in the field), and causal associations

can be strengthened when both types of information are available. But since not all situations lend

themselves to formal experimentation, scientists have looked for other criteria, based largely on

observation rather than experiment, to support a plausible argument for cause and effect. Text box 4-

17 provides criteria based on Fox (1991) that

are very similar to others reviewed by Fox (U.S.

Department of Health, Education, and Welfare,

1964; Hill, 1965; Susser,

1986a, b). While data to support some criteria

may be incomplete or missing for any given

assessment, these criteria offer a useful way to

evaluate available information.

The strength of association between

stressor and response is often the main reason

that adverse effects such as bird kills are linked

to specific events or actions. A stronger

response to a hypothesized cause is more likely

to indicate true causation. Additional strong

evidence of causation is when a response follows

after a change in the hypothesized cause

(predictive performance).

The presence of a biological gradient or

stressor-response relationship is another

important criterion for causality. The stressorresponse relationship need not be linear. It can

be a threshold, sigmoidal, or parabolic

phenomenon, but in any case it is important that
87

Text Box 4-18. Koch’s Postulates (Pelczar

and Reid, 1972)

• A pathogen must be consistently found in

association with a given disease.

• The pathogen must be isolated from the host

and grown in pure culture.

• When inoculated into test animals, the same

disease symptoms must be expressed.

• The pathogen must again be isolated from the

test organism.

it can be demonstrated. Biological gradients, such as effects that decrease with distance from a toxic

discharge, are frequently used as evidence of causality. To be credible, such relationships should be

consistent with current biological or ecological knowledge (biological plausibility).

A cause-and-effect relationship that is demonstrated repeatedly (consistency of association)

provides strong evidence of causality. Consistency may be shown by a greater number of instances of

association between stressor and response, occurrences in diverse ecological systems, or associations

demonstrated by diverse methods (Hill, 1965). Fox (1991) adds that in ecoepidemiology, an

association’s occurrence in more than one species and population is very strong evidence for causation. 

An example would be the many bird species killed by carbofuran applications (Houseknecht, 1993). 

Fox (1991) also believes that causality is supported if the same incident is observed by different

persons under different circumstances and at different times.

Conversely, inconsistency in association between stressor and response is strong evidence

against causality (e.g., the stressor is present without the expected effect, or the effect occurs but the

stressor is not found). Temporal incompatibility (i.e., the presumed cause does not precede the effect)

and incompatibility with experimental or observational evidence (factual implausibility) are also

indications against a causal relationship.

Two other criteria may be of some help in defining causal relationships: specificity of an

association and probability. The more specific or diagnostic the effect, the more likely it is to have a

consistent cause. However, Fox (1991) argues that effect specificity does little to strengthen a causal

claim. Disease can have multiple causes, a substance can behave differently in different environments or

cause several different effects, and biochemical events may elicit many biological responses. But in

general, the more specific or localized the

effects, the easier it is to identify the cause. 

Sometimes, a stressor may have a distinctive

mode of action that suggests its role. Yoder and

Rankin (1995) found that patterns of change

observed in fish and benthic invertebrate

communities could serve as indicators for

different types of anthropogenic impact (e.g.,

nutrient enrichment vs. toxicity).

For some pathogenic biological

stressors, the causal evaluations proposed by

Koch (see text box 4-18) may be useful. For
88

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.
89

Text Box 4-19. Examples of Extrapolations

To Link Measures of Effect to Assessment

Endpoints

Every risk assessment has data gaps that should

be addressed, but it is not always possible to

obtain more information. When there is a lack

of time, monetary resources, or a practical

means to acquire more data, extrapolations such

as those listed below may be the only way to

bridge gaps in available data. Extrapolations

may be:

• Between taxa (e.g., bluegill to rainbow trout)

• Between responses (e.g., mortality to growth

or reproduction)

• From laboratory to field

• Between geographic areas

• Between spatial scales

• From data collected over a short time frame to

longer-term effects

Laboratory toxicity identification evaluations (TIEs) can be used to help determine which

components of a chemical mixture cause toxic effects. By using fractionation and other methods, the

TIE approach can help identify chemicals responsible for toxicity and show the relative contributions of

different chemicals in aqueous effluents (U.S. EPA, 1988a, 1989b, c) and sediments (e.g., Ankley et

al., 1990).

Risk assessors may utilize data from synthetic chemical mixtures if the individual chemical

components are well characterized. This approach allows for manipulation of the mixture and

investigation of how varying the components that are present or their ratios may affect mixture toxicity,

but it also requires additional assumptions about the relationship between effects of the synthetic mixture

and those of the environmental mixture. (See section 5.1.3 for additional discussion of mixtures.)

4.3.1.3. Linking Measures of Effect to Assessment Endpoints

Assessment endpoints express the

environmental values of concern for a risk

assessment, but they cannot always be measured

directly. When measures of effect differ from

assessment endpoints, sound and explicit

linkages between them are needed. Risk

assessors may make these linkages in the

analysis phase or, especially when linkages rely

on professional judgment, work with measures of

effect through risk estimation (in risk

characterization) and then connect them

with assessment endpoints. Common

extrapolations used to link measures of effect

with assessment endpoints are shown in text box

4-19.

4.3.1.3.1. General Considerations. During

the preparation of the analysis plan, risk

assessors identify the extrapolations required

between assessment endpoints and measures of

effect. During the analysis phase, risk assessors
90

Text Box 4-20. Questions Related to

Selecting Extrapolation Approaches

C How specific is the assessment endpoint?

C Does the spatial or temporal extent of

exposure suggest the need for additional

receptors or extrapolation models?

C Are the quantity and quality of the data

available sufficient for planned extrapolations

and models?

C Is the proposed extrapolation technique

consistent with ecological information?

C How much uncertainty is acceptable?

should revisit the questions listed in text box 4-20 before proceeding with specific extrapolation

approaches.

The nature of the risk assessment and the type and amount of data that are available largely

determine how conservative a risk

assessment will be. The early stages of a tiered

risk assessment typically use conservative

estimates because the data needed to adequately

assess exposure and effects are usually lacking. 

When a risk has been identified, subsequent tiers

use additional data to address the uncertainties

that were incorporated into the initial

assessment(s) (see text box 2-8).

The scope of the risk assessment also

influences extrapolation through the nature of the

assessment endpoint. Preliminary assessments

that evaluate risks to general trophic levels such

as herbivores may extrapolate between different

genera or families to obtain a range of sensitivity

to the stressor. On the other hand, assessments

concerned with management strategies for a particular species may employ population models.

Analysis phase activities may suggest additional extrapolation needs. Evaluation of exposure

may indicate different spatial or temporal scales than originally planned. If spatial scales are broadened,

additional receptors may need to be included in extrapolation models. If a stressor persists for an

extended time, it may be necessary to extrapolate short-term responses over a longer exposure period,

and population-level effects may become more important. Whatever methods are employed to link

assessment endpoints with measures of effect, it is important to apply them in a manner consistent with

sound ecological principles and use enough appropriate data. For example, it is inappropriate to use

structure-activity relationships to predict toxicity from chemical structure unless the chemical under

consideration has a similar mode of toxic action to the reference chemicals (Bradbury, 1994). 

Similarly, extrapolations between two species may be more credible if factors such as similarities in

food preferences, body mass, physiology, and seasonal behavior (e.g., mating and migration habits) are

considered (Sample et al., 1996). Rote or biologically implausible extrapolations will erode the

assessment’s overall credibility.
91

Finally, many extrapolation methods are limited by the availability of suitable databases. 

Although many data are available for chemical stressors and aquatic species, they do not exist for all

taxa or effects. Chemical effects databases for wildlife, amphibians, and reptiles are extremely limited,

and there is even less information on most biological and physical stressors. Risk assessors should be

aware that extrapolations and models are only as useful as the data on which they are based and should

recognize the great uncertainties associated with extrapolations that lack an adequate empirical or

process-based rationale.

The rest of this section addresses the approaches used by risk assessors to link measures of

effect to assessment endpoints, as noted below.

• Linkages based on professional judgment. This is not as desirable as empirical or

process-based approaches, but is the only option when data are lacking.

• Linkages based on empirical or process models. Empirical extrapolations use

experimental or observational data that may or may not be organized into a database. 

Process-based approaches rely on some level of understanding of the underlying

operations of the system of interest.

4.3.1.3.2. Judgment Approaches for Linking Measures of Effect to Assessment Endpoints.

Professional-judgment approaches rely on the professional expertise of risk assessors, expert panels, or

others to relate changes in measures of effect to changes in assessment endpoints. They are essential

when databases are inadequate to support empirical models and process models are unavailable or

inappropriate. Professional-judgment linkages between measures of effect and assessment endpoints

can be just as credible as empirical or process-based expressions, provided they have a sound

scientific basis. This section highlights professional-judgment extrapolations between species, from

laboratory data to field effects, and between geographic areas.

Because of the uncertainty in predicting the effects of biological stressors such as introduced

species, professional-judgment approaches are commonly used. For example, there may be measures

of effect data on a foreign pathogen that attacks a certain tree species not found in the United States,

but the assessment endpoint concerns the survival of a commercially important tree found only in the

United States. In this case, a careful evaluation and comparison of the life history and environmental

requirements of both the pathogen and the two tree species may contribute toward a useful
92

Text Box 4-21. Questions To Consider

When Extrapolating From Effects Observed

in the Laboratory to Field Effects of

Chemicals

Exposure factors:

C How will environmental fate and

transformation of the chemical affect

exposure in the field?

C How comparable are exposure conditions

and the timing of exposure?

C How comparable are the routes of exposure?

C How do abiotic factors influence

bioavailability and exposure?

C How likely are preference or avoidance

behaviors?

Effects factors:

C What is known about the biotic and abiotic

factors controlling populations of the

organisms of concern?

C To what degree are critical life-stage data

available?

C How may exposure to the same or other

stressors in the field have altered organism

sensitivity?

determination of potential effects, even though the uncertainty may be high. Expert panels are typically

used for this kind of evaluation (USDA, 1993).

Risks to organisms in field situations are best estimated from studies at the site of interest. 

However, such data are not always available. Frequently, risk assessors must extrapolate from

laboratory toxicity test data to field effects. Text box 4-21 summarizes some of the considerations for

risk assessors when extrapolating from laboratory test results to field

situations for chemical stressors. Factors altering

exposure in the field are among the most

important factors limiting extrapolations from

laboratory test results, but indirect effects on

exposed organisms due to predation,

competition, or other biotic or abiotic factors not

evaluated in the laboratory may also be

significant. Variations in direct chemical effects

between laboratory tests and field situations may

not contribute as much to the overall uncertainty

of the extrapolation.

In addition to single-species tests,

laboratory multiple-species tests are sometimes

used to predict field effects. While these tests

have the advantage of evaluating some aspects

of a real ecological system, they also have

inherent scale limitations (e.g., lack of top trophic

levels) and may not adequately represent

features of the field system important to the

assessment endpoint.

Extrapolations based on professional

judgment are frequently required when assessors

wish to use field data obtained from one

geographic area and apply them to a different

area of concern, or to extrapolate from the

results of laboratory tests to more than one

geographic region. In either case, risk assessors
93

should consider variations between regions in environmental conditions, spatial scales and

heterogeneities, and ecological forcing functions (see below).

Variations in environmental conditions in different geographic regions may alter stressor

exposure and effects. If exposures to chemical stressors can be accurately estimated and are expected

to be similar (e.g., see text box 4-21), the same species in different areas may respond similarly. For

example, if the pesticide granular carbofuran were applied at comparable rates throughout the country,

seed-eating birds could be expected to be similarly affected by the pesticide (Houseknecht, 1993). 

Nevertheless, the influence of environmental conditions on stressor exposure and effects can be

substantial.

For biological stressors, environmental conditions such as climate, habitat, and suitable hosts

play major roles in determining whether a biological stressor becomes established. For example,

climate would prevent establishment of the Mediterranean fruit fly in the much colder northeastern

United States. Thus, a thorough evaluation of environmental conditions in the area versus the natural

habitat of the stressor is important. Even so, many biological stressors can adapt readily to varying

environmental conditions, and the absence of natural predators or diseases may play an even more

important role than abiotic factors.

For physical stressors that have natural counterparts, such as fire, flooding, or temperature

variations, effects may depend on the difference between human-caused and natural variations in these

parameters for a particular region. Thus, the comparability of two regions depends on both the pattern

and range of natural disturbances.

Spatial scales and heterogeneities affect comparability between regions. Effects observed over

a large scale may be difficult to extrapolate from one geographical location to another, mainly because

the spatial heterogeneity is likely to differ. Factors such as number and size of land-cover patches,

distance between patches, connectivity and conductivity of patches (e.g., migration routes), and patch

shape may be important. Extrapolations can be strengthened by using appropriate reference sites, such

as sites in comparable ecoregions (Hughes, 1995).

Ecological forcing functions may differ between geographic regions. Forcing functions are

critical abiotic variables that exert a major influence on the structure and function of ecological systems. 

Examples include temperature fluctuations, fire frequency, light intensity, and hydrologic regime. If

these differ significantly between sites, it may be inappropriate to extrapolate effects from one system to

another.
94

Bedford and Preston (1988), Detenbeck et al. (1992), Gibbs (1993), Gilbert (1987),

Gosselink et al. (1990), Preston and Bedford (1988), and Risser (1988) may be useful to risk

assessors concerned with effects in different geographical areas.

4.3.1.3.3. Empirical and Process-Based Approaches for Linking Measures of Effect to

Assessment Endpoints. A variety of empirical and process-based approaches are available to risk

assessors, depending on the scope of the assessment and the data and resources available. Empirical

and process-based approaches include numerical extrapolations between measures of effects and

assessment endpoints. These linkages range in sophistication from applying an uncertainty factor to

using a complex model requiring extensive measures of effects and measures of ecosystem and receptor

characteristics as input. But even the most sophisticated quantitative models involve qualitative

elements and assumptions and thus require professional judgment for evaluation. Individuals who use

models and interpret their results should be familiar with the underlying assumptions and components

contained in the model.

4.3.1.3.3.1. Empirical Approaches. Empirical approaches are derived from experimental data or

observations. Empirically based uncertainty factors or taxonomic extrapolations may be used when

adequate effects databases are available but the understanding of underlying mechanisms of action or

ecological principles is limited. When sufficient information on stressors and receptors is available,

process-based approaches such as pharmacokinetic/pharmacodynamic models or population or

ecosystem process models may be used. Regardless of the options used, risk assessors should justify

and adequately document the approach selected.

Uncertainty factors are used to ensure that measures of effects are sufficiently protective of

assessment endpoints. Uncertainty factors are empirically derived numbers that are divided into

measure of effects values to give an estimated stressor level that should not cause adverse effects to the

assessment endpoint. Uncertainty factors have been developed most frequently for chemicals because

extensive ecotoxicologic databases are available, especially for aquatic organisms. Uncertainty factors

are useful when decisions must be made about stressors in a short time and with little information.

Uncertainty factors have been used to compensate for assessment endpoint/effect measures

differences between endpoints (acute to chronic effects), between species, and between test situations

(e.g., laboratory to field). Typically, they vary inversely with the quantity and type of measures of

effects data available (Zeeman, 1995). They have been used in screening-level assessments of new

chemicals (Nabholz, 1991), in assessing the risks of pesticides to aquatic and terrestrial organisms
95

(Urban and Cook, 1986), and in developing benchmark dose levels for human health effects (U.S.

EPA, 1995c).

Despite their usefulness, uncertainty factors can also be misused, especially when used in an

overly conservative fashion, as when chains of factors are multiplied together without sufficient

justification. Like other approaches to bridging data gaps, uncertainty factors are often based on a

combination of scientific analysis, scientific judgment, and policy judgment (see section 4.1.3). It is

important to differentiate these three elements when documenting the basis for the uncertainty factors

used.

Empirical data can be used to facilitate extrapolations between species, genera, families, or

orders or functional groups (e.g., feeding guilds) (Suter, 1993a). Suter et al. (1983), Suter (1993a),

and Barnthouse et al. (1987, 1990) developed methods to extrapolate toxicity between freshwater and

marine fish and arthropods. As Suter notes (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
96

and provide a predictive capability that does not depend on the availability of existing stressor-response

information as empirical models do (Wiegert and Bartell, 1994). Process models enable assessors to

translate data on individual effects (e.g., mortality, growth, and reproduction) to potential alterations in

specific populations, communities, or ecosystems. Such models can be used to evaluate risk

hypotheses about the duration and severity of a stressor on an assessment endpoint that cannot be

tested readily in the laboratory.

There are two major types of models: single-species population models and multispecies

community and ecosystem models. Population models describe the dynamics of a finite group of

individuals through time and have been used extensively in ecology and fisheries management and to

assess the impacts of power plants and toxicants on specific fish populations (Barnthouse et al., 1987,

1990). They can help answer questions about short- or long-term changes of population size and

structure and can help estimate the probability that a population will decline below or grow above a

specified abundance (Ginzburg et al., 1982; Ferson et al., 1989). The latter application may be useful

when assessing the effects of biological stressors such as introduced or pest species. Barnthouse et al.

(1986) and Wiegert and Bartell (1994) present excellent reviews of population models. Emlen (1989)

has reviewed population models that can be used for terrestrial risk assessment.

Proper use of population models requires a thorough understanding of the natural history of the

species under consideration, as well as knowledge of how the stressor influences its biology. Model

input can include somatic growth rates, physiological rates, fecundity, survival rates of various classes

within the population, and how these change when the population is exposed to the stressor and other

environmental factors. In addition, the effects of population density on these parameters are important

(Hassell, 1986) and should be considered in the uncertainty analysis.

Community and ecosystem models (e.g., Bartell et al., 1992; O’Neill et al., 1982) are

particularly useful when the assessment endpoint involves structural (e.g., community composition) or

functional (e.g., primary production) elements. They can also be useful when secondary effects are of

concern. Changes in various community or ecosystem components such as populations, functional

types, feeding guilds, or environmental processes can be estimated. By incorporating submodels

describing the dynamics of individual system components, these models permit evaluation of risk to

multiple assessment endpoints within the context of the ecosystem.

Risk assessors should determine the appropriate degree of aggregation in population or

multispecies model parameters based both on the input data available and on the desired output of the

model (also see text box 4-5). For example, if a decision is required about a particular species, a
97

Text Box 4-22. Questions Addressed by

the Stressor-Response Profile

C What ecological entities are affected?

C What is the nature of the effect(s)?

C What is the intensity of the effect(s)?

C Where appropriate, what is the time scale for

recovery?

C What causal information links the stressor

with any observed effects?

C How do changes in measures of effects relate

to changes in assessment endpoints?

C What is the uncertainty associated with the

analysis?

model that lumps species into trophic levels or feeding guilds will not be very useful. Assumptions

concerning aggregation in model parameters should be included in the uncertainty discussion.

4.3.2. Stressor-Response Profile

The final product of ecological response analysis is a summary profile of what has been learned. 

This may be a written document or a module of a larger process model. In any case, the objective is to

ensure that the information needed for risk characterization has been collected and evaluated. A useful

approach in preparing the stressor-response profile is to imagine that it will be used by someone else to

perform the risk characterization. Profile compilation also provides an opportunity to verify that the

assessment endpoints and measures of effect identified in the conceptual model were evaluated.

Risk assessors should address several

questions in the stressor-response profile (text

box 4-22). Affected ecological entities may

include single species, populations, general

trophic levels, communities, ecosystems, or

landscapes. The nature of the effect(s) should be

germane to the assessment endpoint(s). Thus if a

single species is affected, the effects should

represent parameters appropriate for that level

of organization. Examples include effects on

mortality, growth, and reproduction. Short- and

long-term effects should be reported as

appropriate. At the community level, effects

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
98

between assessment endpoints and measures of effect, the stressor-response profile may contain

information only on measures of effect. Under these circumstances, risk will be estimated using the

measures of effects, and extrapolation to the assessment endpoints will occur during risk

characterization.

Risk assessors need to clearly describe any uncertainties associated with the ecological

response analysis. If it was necessary to extrapolate from measures of effect to the assessment

endpoint, both the extrapolation and its basis should be described. Similarly, if a benchmark or similar

reference dose or concentration was calculated, the extrapolations and uncertainties associated with its

development need to be discussed. For additional information on establishing reference concentrations,

see Nabholz (1991), Urban and Cook (1986), Stephan et al. (1985), Van Leeuwen et al. (1992),

Wagner and Løkke (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.
99

5. RISK CHARACTERIZATION

Risk characterization (figure 5-1) is the final phase of ecological risk assessment and is the

culmination of the planning, problem formulation, and analysis of predicted or observed adverse

ecological effects related to the assessment endpoints. Completing risk characterization allows risk

assessors to clarify the relationships between stressors, effects, and ecological entities and to reach

conclusions regarding the occurrence of exposure and the adversity of existing or anticipated effects. 

Here, risk assessors first use the results of the analysis phase to develop an estimate of the risk posed to

the ecological entities included in the assessment endpoints identified in problem formulation (section

5.1). After estimating the risk, the assessor describes the risk estimate in the context of the significance

of any adverse effects and lines of evidence supporting their likelihood (section 5.2). Finally, the

assessor identifies and summarizes the uncertainties, assumptions, and qualifiers in the risk assessment

and reports the conclusions to risk managers (section 5.3).

Conclusions presented in the risk characterization should provide clear information to risk

managers in order to be useful for environmental decision making (NRC, 1994; see section 6). If the

risks are not sufficiently defined to support a management decision, risk managers may elect to proceed

with another iteration of one or more phases of the risk assessment process. Reevaluating the

conceptual model (and associated risk hypotheses) or conducting additional studies may improve the

risk estimate. Alternatively, a monitoring program may help managers evaluate the consequences of a

risk management decision.

5.1. RISK ESTIMATION

Risk estimation is the process of integrating exposure and effects data and evaluating any

associated uncertainties. The process uses exposure and stressor-response profiles developed

according to the analysis plan (section 3.5). Risk estimates can be developed using one or more of the

following techniques: (1) field observational studies, (2) categorical rankings, (3) comparisons of

single-point exposure and effects estimates, (4) comparisons incorporating the entire stressor-response

relationship, (5) incorporation of variability in exposure and/or effects estimates, and (6) process

models that rely partially or entirely on theoretical approximations of exposure and effects. These

techniques are described in the following sections.

5.1.1. Results of Field Observational Studies
100

Field observational studies (surveys) can serve as risk estimation techniques because they

provide empirical evidence linking exposure to effects. Field surveys measure biological 
101

Figure 5-1. Risk characterization.

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
102

Text Box 5-1. An Example of Field

Methods Used for Risk Estimation

Along with quotients comparing field measures

of exposure with laboratory acute toxicity data

(see Text Box 5-3), EPA evaluated the risks of

granular carbofuran to birds based on incidents

of bird kills following carbofuran applications. 

More than 40 incidents involving nearly 30

species of birds were documented. Although

reviewers identified problems with individual

field studies (e.g., lack of appropriate control

sites, lack of data on carcass-search efficiencies,

no examination of potential synergistic effects of

other pesticides, and lack of consideration of

other potential receptors such as small

mammals), there was so much evidence of

mortality associated with carbofuran application

that the study deficiencies did not alter the

conclusions of high risk found by the assessment

(Houseknecht, 1993).

natural systems, whereas estimates generated

from laboratory studies generally delineate either

exposures or effects under controlled or

prescribed conditions (see text box 5-1).

While field studies may best represent

reality, as with other kinds of studies they can be

limited by (1) a lack of replication, (2) bias in

obtaining representative samples, or (3) failure to

measure critical components of the system or

random variations. Further, a lack of observed

effects in a field survey may occur because the

measurements lack the sensitivity to detect

ecological effects. See section 4.1.1 for

additional discussion of the strengths and

limitations of different types of data.

Several assumptions or qualifications

need to be clearly articulated when describing

the results of field surveys. A primary

qualification is whether a causal relationship between stressors and effects (section 4.3.1.2) is

supported. Unless causal relationships are carefully examined, conclusions about effects that are

observed may be inaccurate because the effects are caused by factors unrelated to the stressor(s) of

concern. In addition, field surveys taken at one point in time are usually not predictive; they describe

effects associated only with exposure scenarios associated with past and existing conditions.

5.1.2. Categories and Rankings

In some cases, professional judgment or other qualitative evaluation techniques may be used to

rank risks using categories, such as low, medium, and high, or yes and no. This approach is most

frequently used when exposure and effects data are limited or are not easily expressed in quantitative

terms. The U.S. Forest Service risk assessment of pest introduction from importation of logs from

Chile used qualitative categories owing to limitations in both the exposure and effects data for the

introduced species of concern as well as the resources available for the assessment (see text box 5-2).
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Text Box 5-2. Using Qualitative Categories

to Estimate Risks of an Introduced Species

The importation of logs from Chile required an

assessment of the risks posed by the potential

introduction of the bark beetle, Hylurgus

ligniperda (USDA, 1993). Experts judged the

potential for colonization and spread of the

species, and their opinions were expressed as

high, medium, or low as to the likelihood of

establishment (exposure) or consequential

effects of the beetle. Uncertainties were

similarly expressed. A ranking scheme was then

used to sum the individual elements into an

overall estimate of risk (high, medium, or low). 

Narrative explanations of risk accompanied the

overall rankings.

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

box 5-3).

The principal advantages of the quotient method are that it is simple and quick to use and risk

assessors and managers are familiar with its application. It provides an efficient, inexpensive means of

identifying high- or low-risk situations that can allow risk management decisions to be made without the

need for further information.

Quotients have also been used to integrate the risks of multiple chemical stressors: quotients for

the individual constituents in a mixture are generated by dividing each exposure level by a

corresponding toxicity endpoint (e.g., LC50, EC50, NOAEL). Although the toxicity of a chemical

mixture may be greater than or less than predicted from the toxicities of individual constituents of the

mixture, a quotient addition approach assumes that toxicities are additive or approximately additive. 
104

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
105

Figure 5-2. Risk estimation techniques. a. Comparison of exposure and

stressor-response point estimates. b. Comparison of a point estimate from the

stressor-response relationship with uncertainty associated with an exposure

point estimate.

dissimilar modes of action, additive or near-additive interactions are common (Könemann, 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
106

Text Box 5-3. Applying the Quotient

Method

When applying the quotient method to chemical

stressors, the effects concentration or dose (e.g.,

an LC50, LD50, EC50, ED50, NOAEL, or

LOAEL) is frequently adjusted by uncertainty

factors before division into the exposure number

(U.S. EPA, 1984; Nabholz, 1991; Urban and

Cook, 1986; see section 4.3.1.3), although

EPA used a slightly different approach in

estimating the risks to the survival of birds that

forage in agricultural areas where the pesticide

granular carbofuran is applied (Houseknecht,

1993). In this case, EPA calculated the quotient

by dividing the estimated exposure levels of

carbofuran granules in surface soils (number/ft2)

by the granules/LD50 derived from single-dose

avian toxicity tests. The calculation yields values

with units of LD50/ft2. It was assumed that a

higher quotient value corresponded to an

increased likelihood that a bird would be

exposed to lethal levels of granular carbofuran at

the soil surface. Minimum and maximum values

for LD50/ft2 were estimated for songbirds,

upland game birds, and waterfowl that may

forage within or near 10 different agricultural

crops.

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,
107

Figure 5-3. Risk estimation techniques: comparison of point estimates with

associated uncertainties.

however, can be incorporated into single-point estimates to provide a statement of likelihood that the

effects point estimate exceeds the exposure point estimate (figures 5-2b and 5-3). If exposure

variability is quantified, then the point estimate of effects can be compared with a cumulative

exposure distribution as described in text box 5-4. Further discussion of comparisons between point

estimates of effects and distributions of exposure may be found in Suter et al., 1983.

In view of the advantages and limitations of the quotient method, it is important for risk

assessors to consider the points listed below when evaluating quotient method estimates.

• How does the effect concentration relate to the assessment endpoint?

• What extrapolations are involved?

• How does the point estimate of exposure relate to potential spatial and temporal

variability in exposure?
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Text Box 5-4. Comparing an Exposure

Distribution With a Point Estimate of

Effects

The EPA Office of Pollution Prevention and

Toxics uses a Probabilistic Dilution Model

(PDM3) to generate a distribution of daily

average chemical concentrations based on

estimated variations in stream flow in a model

system. The PDM3 model compares this

exposure distribution with an aquatic toxicity test

endpoint to estimate how many days in a 1-year

period the endpoint concentration is exceeded

(Nabholz et al., 1993; U.S. EPA, 1988b). The

frequency of exceedance is based on the

duration of the toxicity test used to derive the

effects endpoint. Thus, if the endpoint was an

acute toxicity level of concern, an exceedance

would be identified if the level of concern was

exceeded for 4 days or more (not necessarily

consecutive). The exposure estimates are

conservative in that they assume instantaneous

mixing of the chemical in the water column and

no losses due to physical, chemical, or

biodegradation effects.

• 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
109

Cumulative Frequency

0.10

0.50

0.90

cumulative

distribution of

exposures

Intensity of Stressor (e.g., concentration)

comparison of

90th percentile exposure

with EC10

comparison of

50th percentile exposure

with EC50

stressorresponse

curve

0.90

0.50

0.10

Fraction of Test Population Responding

Figure 5-4. Risk estimation techniques: stressor-response curve versus a cumulative

distribution of exposures.

moderately or highly exposed members of a population being investigated, while variability in effects

can be used to estimate risks to average or sensitive population

members. A major advantage of this approach is its ability to predict changes in the magnitude and

likelihood of effects for different exposure scenarios and thus provide a means for comparing different

risk management options. As noted above, comparing distributions also allows one to identify and

quantify risks to different segments of the population. Limitations include the increased data
110

Text Box 5-5. Comparing Cumulative

Exposure and Effects Distributions for

Chemical Stressors

Exposure distributions for chemical stressors can

be compared with effects distributions derived

from point estimates of acute or chronic toxicity

values for different species (e.g., HCN, 1993;

Cardwell et al., 1993; Baker et al., 1994; Solomon

et al., 1996). Figure 5-5 shows a distribution of

exposure concentrations of an herbicide

compared with single-species toxicity data for

algae (and one vascular plant species) for the

same chemical. The degree of overlap of the

curves indicates the likelihood that a certain

percentage of species may be adversely affected. 

For example, figure 5-5 indicates that the 10th

centile of algal species’ EC5 values is exceeded

less than 10% of the time.

The predictive value of this approach is evident. 

The degree of risk reduction that could be

achieved by changes in exposure associated with

proposed risk mitigation options can be readily

determined by comparing modified exposure

distributions with the effects distribution curve.

When using effects distributions derived from

single-species toxicity data, risk assessors should

consider the following questions:

• Does the subset of species for which toxicity

test data are available represent the range of

species present in the environment?

• Are particularly sensitive (or insensitive) groups

of organisms represented in the distribution?

• If a criterion level is selected—e.g., protect

95% of species—does the 5% of potentially

affected species include organisms of

ecological, commercial, or recreational

significance?

requirements compared with previously described techniques and the implicit assumption that the full

range of variability in the exposure and effects data is adequately represented. As with the quotient

method, secondary effects are not readily

evaluated with this technique. Thus, it is

desirable to corroborate risks estimated by

distributional comparisons with

field studies or other lines of evidence. Text box

5-5 and figure 5-5 illustrate the use of cumulative

exposure and effects distributions for estimating

risk.

5.1.6. Application of Process Models

Process models are mathematical

expressions that represent our understanding of

the mechanistic operation of a system under

evaluation. They can be useful tools in both

analysis (see section 4.1.2) and risk

characterization. For illustrative purposes, it is

useful to distinguish between analysis process

models, which focus individually on either

exposure or effects evaluations, and risk

estimation process models, which integrate

exposure and effects information (see text box

5-6). The assessment of risks associated with

long-term changes in hydrologic conditions in

bottomland forest wetlands in Louisiana using the

FORFLO model (Appendix D) linked the

attributes and placement of levees and

corresponding water level measurements

(exposure) with changes in forest community

structure and wildlife habitat suitability (effects).

A major advantage of using process

models for risk estimation is the ability to
111

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
112

Figure 5-5. Risk estimation techniques: comparison of exposure distribution of an

herbicide in surface waters with freshwater single-species toxicity data. See text

box 5-4 for further discussion. Redrawn from Baker et al., 1994. (Centile ranks

for species LC5 data were obtained using the formula (100 x n/[N+1]), where n is

the rank number of the LC5 and N is the total number of data points in the set;

adapted from Parkhurst et al., 1995).

multiple stressors, such as the effects of multiple chemicals on fish population sustainability (Barnthouse

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
113

Text Box 5-6. Estimating Risk With

Process Models

Models that integrate both exposure and effects

information can be used to estimate risk. During

risk estimation, it is important that both the

strengths and limitations of a process model

approach be highlighted. Brody et al. (1993;

see Appendix D) linked two process models to

integrate exposure and effects information and

forecast spatial and temporal changes in forest

communities and their wildlife habitat value. 

While the models were useful for projecting

long-term effects based on an understanding of

the underlying mechanisms of change in forest

communities and wildlife habitat, they could not

evaluate all possible stressors of concern and

were limited in the plant and wildlife species they

could consider. Understanding both the

strengths and limitations of models is essential

for accurately representing the overall

confidence in the assessment.

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
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Cook, 1986; Menzie et al., 1996.) The phrase lines of evidence is used to de-emphasize the

balancing of opposing factors based on assignment of quantitative values to reach a conclusion about a

“weight” in favor of a more inclusive approach, which evaluates all available information, even evidence

that may be qualitative in nature. It is important that risk assessors provide a thorough representation of

all lines of evidence developed in the risk assessment rather than simply reduce their interpretation and

description of the ecological effects that may result from exposure to stressors to a system of numeric

calculations and results.

Confidence in the conclusions of a risk assessment may be increased by using several lines of

evidence to interpret and compare risk estimates. These lines of evidence may be derived from

different sources or by different techniques relevant to adverse effects on the assessment endpoints,

such as quotient estimates, modeling results, or field observational studies.

There are three principal categories of factors for risk assessors to consider when evaluating

lines of evidence: (1) adequacy and quality of data, (2) degree and type of uncertainty associated with

the evidence, and (3) relationship of the evidence to the risk assessment questions (see also sections 3

and 4).

Data quality directly influences how confident risk assessors can be in the results of a study and

conclusions they may draw from it. Specific concerns to consider for individual lines of evidence

include whether the experimental design was appropriate for the questions posed in a particular study

and whether data quality objectives were clear and adhered to. An evaluation of the scientific

understanding of natural variability in the attributes of the ecological entities under consideration is

important in determining whether there were sufficient data to satisfy the analyses chosen and to

determine if the analyses were sufficiently sensitive and robust to identify stressor-caused perturbations.

Directly related to data quality issues is the evaluation of the relative uncertainties of each line of

evidence. One major source of uncertainty comes from extrapolations. The greater the number of

extrapolations, the more uncertainty introduced into a study. For example, were extrapolations used to

infer effects in one species from another, or from one temporal or spatial scale to another? Were

conclusions drawn from extrapolations from laboratory to field effects, or were field effects inferred

from limited information, such as chemical structure-activity relationships? Were no-effect or low-effect

levels used to address likelihood of effects? Risk assessors should consider these and any other

sources of uncertainty when evaluating the relative importance of particular lines of evidence.

Finally, how directly lines of evidence relate to the questions asked in the risk assessment may

determine their relative importance in terms of the ecological entity and the attributes of the assessment

endpoint. Lines of evidence directly related to the risk hypotheses, and those that establish a cause-
115

and-effect relationship based on a definitive mechanism rather than associations alone, are likely to be

of greatest importance.

The evaluation process, however, involves more than just listing the evidence that supports or

refutes the risk estimate. The risk assessor should carefully examine each factor and evaluate its

contribution in the context of the risk assessment. The importance of lines of evidence is that each and

every factor is described and interpreted. Data or study results are often not reported or carried

forward in the risk assessment because they are of insufficient quality. If such data or results are

eliminated from the evaluation process, however, valuable information may be lost with respect to

needed improvements in methodologies or recommendations for further studies.

As a case in point, consider the two lines of evidence described for the carbofuran example

(see text boxes 5-1 and 5-3), field studies and quotients. Both approaches are relevant to the

assessment endpoint (survival of birds that forage in agricultural areas where carbofuran is applied), and

both are relevant to the exposure scenarios described in the conceptual model (see figure D-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 box 5-1). Nevertheless, because of the strong evidence of causal relationships from the field

studies and consistency with the laboratory-derived quotient, confidence in a conclusion of high risk to

the assessment endpoint is supported.

Sometimes lines of evidence do not point toward the same conclusion. It is important to

investigate possible reasons for any disagreement rather than ignore inconvenient evidence. A starting

point is to distinguish between true inconsistencies and those related to differences in statistical powers

of detection. For example, a model may predict adverse effects that were not observed in a field

survey. The risk assessor should ask whether the experimental design of the field study had sufficient

power to detect the predicted difference or whether the endpoints measured were comparable with

those used in the model. Conversely, the model may have been unrealistic in its predictions. While

iteration of the risk assessment process and collection of additional data may help resolve uncertainties,

this option is not always available.

Lines of evidence that are to be evaluated during risk characterization should be defined early in

the risk assessment (during problem formulation) through the development of the conceptual model and

selection of assessment endpoints. Further, the analysis plan should incorporate measures that will

contribute to the interpretation of the lines of evidence, including methods of reviewing, analyzing, and

summarizing the uncertainty in the risk assessment.
116

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? 
117

Text Box 5-7. What Are Statistically

Significant Effects?

Statistical testing is the “statistical procedure or

decision rule that leads to establishing the truth

or falsity of a hypothesis . . .” (Alder and

Roessler, 1972). Statistical significance is based

on the number of data points, the nature of their

distribution, whether intertreatment variance

exceeds intratreatment variance in the data, and

the a priori significance level ("). The types of

statistical tests and the appropriate protocols

(e.g., power of test) for these tests should be

established as part of the analysis plan during

problem formulation.

It is important for risk assessors to

consider both the ecological and statistical

contexts of an effect when evaluating intensity. 

For example, a statistically significant 1%

decrease in fish growth (see text box 5-7) may

not be relevant to an assessment endpoint 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
118

from stressor interactions, (2) more likely to contain sensitive species or habitats, or (3) more

susceptible to landscape-level changes because many ecosystems may be altered by the stressors.

Nevertheless, a smaller area of effect is not always associated with lower risk. The function of

an area within the landscape may be more important than the absolute area. Destruction of small but

unique areas, such as critical wetlands, may have important effects on local and regional wildlife

populations. Also, in river systems, both riffle and pool areas provide important microhabitats that

maintain the structure and function of the total river ecosystem. Stressors acting on these microhabitats

may result in adverse effects to the entire system.

Spatial factors are important for many species because of the linkages between ecological

landscapes and population dynamics. Linkages between landscapes can provide refuge for affected

populations, and organisms may require corridors between habitat patches for successful migration.

The temporal scale for ecosystems can vary from seconds (photosynthesis, prokaryotic

reproduction) to centuries (global climate change). Changes within a forest ecosystem can occur

gradually over decades or centuries and may be affected by slowly changing external factors such as

climate. When interpreting adversity, risk assessors should recognize that the time scale of stressorinduced 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
119

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
120

Text Box 5-8. Possible Risk Assessment

Report Elements

C Describe risk assessor/risk manager planning

results.

C Review the conceptual model and the

assessment endpoints.

C Discuss the major data sources and analytical

procedures used.

C Review the stressor-response and exposure

profiles.

C Describe risks to the assessment endpoints,

including risk estimates and adversity

evaluations.

C 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.

providing a detailed outline of reporting elements, the risk assessor should consider the elements listed

in text box 5-8 when preparing a risk assessment

report.

Like the risk assessment itself, a risk

assessment report may be brief or extensive,

depending on the nature of and the resources

available for the assessment. While it is

important to address the elements described in

text box 5-8, risk assessors should judge the

level of detail required. The report need not be

overly complex or lengthy; it is most important

that the information required to support a risk

management decision be presented clearly and

concisely.

To facilitate mutual understanding, it is

critical that the risk assessment results are properly

presented. Agency policy requires that risk

characterizations be prepared “in a manner that

is clear, transparent, reasonable, and

consistent with other risk characterizations of

similar scope prepared across programs in the

Agency” (U.S. EPA, 1995b). Ways to achieve

such characteristics are described in text box 5-

9.

After the risk assessment report is

prepared, the results are discussed with risk

managers. Section 6 provides information on

communication between risk assessors and risk

managers, describes the use of the risk

assessment in a risk management context, and

briefly discusses communication of risk

assessment results from risk managers to

interested parties and the general public.
121

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

For clarity:

• Be brief; avoid jargon.

• Make language and organization understandable to risk managers and the informed lay person.

• Fully discuss and explain unusual issues specific to a particular risk assessment.

For transparency:

• Identify the scientific conclusions separately from policy judgments. 

• Clearly articulate 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.
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Text Box 6-1. Questions Regarding Risk

Assessment Results (Adapted From U.S.

EPA, 1993c)

Questions principally for risk assessors to

ask risk managers:

• Are the risks sufficiently well defined (and

data gaps small enough) to support a risk

management decision?

• Was the right problem analyzed?

• Was the problem adequately characterized?

Questions principally for risk managers to

ask risk assessors:

• What effects might occur?

• How adverse are the effects?

• How likely is it that effects will occur?

• When and where do the effects occur?

• How confident are you in the conclusions of

the risk assessment?

• What are the critical data gaps, and will

information be available in the near future to

fill these gaps?

• Are more ecological risk assessment

iterations required?

• How could monitoring help evaluate the

results of the risk management decision?

6. RELATING ECOLOGICAL INFORMATION TO RISK MANAGEMENT DECISIONS

After characterizing risks and preparing a risk assessment report (section 5), risk assessors

discuss the results with risk managers (figure 5-

1). Risk managers use risk assessment results,

along with other factors (e.g., economic or legal

concerns), in making risk management decisions

and as a basis for communicating risks to

interested parties and the general public.

Mutual understanding between risk

assessors and risk managers regarding risk

assessment results can be facilitated if the

questions listed in text box 6-1 are addressed. 

Risk managers need to know the major risks to

assessment endpoints and have an idea of

whether the conclusions are supported by a large

body of data or if there are significant data gaps. 

Insufficient resources, lack of consensus, or

other factors may preclude preparation of a

detailed and well-documented risk

characterization. If this is the case, the risk

assessor should clearly articulate any issues,

obstacles, and correctable deficiencies for the

risk manager’s consideration.

In making decisions regarding ecological

risks, risk managers consider other information,

such as social, economic, political, or legal issues

in combination with risk assessment results. For

example, the risk assessment results may be used

as part of an ecological cost-benefit analysis,

which may require translating resources

(identified through the assessment endpoints) into

monetary values. Traditional economic
123

Text Box 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.

• Listen to the public’s specific concerns.

• Be honest, frank, and open.

• Speak clearly and with compassion.

• Meet the needs of the media.

considerations may only partially address changes in ecological resources that are not considered

commodities, intergenerational resource values, or issues of long-term or irreversible effects (U.S. EPA,

1995a; Costanza et al., 1997); however, they may provide a means of comparing the results of the risk

assessment in commensurate units such as costs. Risk managers may also consider alternative

strategies for reducing risks, such as risk mitigation options or substitutions based on relative risk

comparisons. For example, risk mitigation techniques, such as buffer strips or lower field application

rates, can be used to reduce the exposure (and risk) of a pesticide. Further, by comparing the risk of a

new pesticide to other pesticides currently in use during the registration process, lower overall risk may

result. Finally, risk managers consider and incorporate public opinion and political demands into their

decisions. Collectively, these other factors may render very high risks acceptable or very low risks

unacceptable.

Risk characterization provides the basis for communicating ecological risks to interested parties

and the general public. This task is usually the responsibility of risk managers, but it may be shared with

risk assessors. Although the final risk assessment document (including its risk characterization sections)

can be made available to the public, the risk communication process is best served by tailoring

information to a particular audience. Irrespective of the specific format, it is important to clearly

describe the ecological resources at risk, their

value, and the monetary and other costs of

protecting (and failing to protect) the resources

(U.S. EPA, 1995a).

Managers should clearly describe the

sources and causes of risks and the potential

adversity of the risks (e.g., nature and intensity,

spatial and temporal scale, and recovery

potential). The degree of confidence in the risk

assessment, the rationale for the risk

management decision, and the options for

reducing risk are also important (U.S. EPA,

1995a). Other risk communication

considerations are provided in text box 6-2.

Along with discussions of risk and

communications with the public, it is important

for risk managers to consider whether additional
124

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.
A-1

APPENDIX A—CHANGES FROM EPA’S ECOLOGICAL RISK ASSESSMENT

FRAMEWORK

EPA has gained much experience with the ecological risk assessment process since the

publication of the Framework Report (U.S. EPA, 1992a) and has received many suggestions for

modifications of both the process and the terminology. While EPA is not recommending major changes

in the overall ecological risk assessment process, modifications are summarized here to assist those who

may already be familiar with the Framework Report. Changes in the diagram are discussed first,

followed by changes in terminology and definitions.

A.1. CHANGES IN THE FRAMEWORK DIAGRAM

The revised framework diagram is shown in figure 1-2. Within each phase, rectangles are used

to designate inputs, hexagons indicate actions, and circles represent outputs. There have been some

minor changes in the wording for the boxes outside of the risk assessment process (planning;

communicating results to the risk manager; acquire data, iterate process, monitor results). “Iterate

process” was added to emphasize the iterative (and frequently tiered) nature of risk assessment. The

term “interested parties” was added to the planning and risk management boxes to indicate their

increasing role in the risk assessment process (Commission on Risk Assessment and Risk Management,

1997). The new diagram of problem formulation contains several changes. The hexagon emphasizes

the importance of integrating available information before selecting assessment endpoints and building

conceptual models. The three products of problem formulation are enclosed in circles. Assessment

endpoints are shown as a key product that drives conceptual model development. The conceptual

model remains a central product of problem formulation. The analysis plan has been added as an

explicit product of problem formulation to emphasize the need to plan data evaluation and interpretation

before analyses begin.

In the analysis phase, the left-hand side of figure 1-2 shows the general process of

characterization of exposure, and the right-hand side shows the characterization of ecological effects. It

is important that evaluation of these two aspects of analysis is an interactive process to ensure

compatible outputs that can be integrated in risk characterization. The dotted line and hexagon that

include both the exposure and ecological response analyses emphasize this interaction. In addition, the

first three boxes in analysis now include the measures of exposure, effects, and ecosystem and receptor

characteristics that provide input to the exposure and ecological response analyses.
A-2

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 lifehistory 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-3

Text Box 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.

A.2.2. Exposure Terminology

These Guidelines define exposure in a manner that is relevant to any chemical, physical, or

biological entity. While the broad concepts are the same, the language and approaches vary depending

on whether a chemical, physical, or biological entity is the subject of assessment. Key exposure-related

terms and their definitions are:

• Source. A source is an entity or action that releases to the environment or imposes on

the environment a chemical, physical, or biological stressor or stressors. Sources may

include a waste treatment plant, a pesticide application, a logging operation,

introduction of exotic organisms, or a dredging project.

• Stressor. A stressor is any

physical, chemical, or biological

entity that can induce an adverse

response. This term is used

broadly to encompass entities

that cause primary effects and

those primary effects that can

cause secondary (i.e., indirect)

effects. Stressors may be

chemical (e.g., toxics or

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

fishing nets, or suspended

sediments), or biological (e.g.,

exotic or genetically engineered

organisms). While risk

assessment is concerned with the

characterization of adverse responses, under some circumstances a stressor may be

neutral or produce effects that are beneficial to certain ecological components (see text

box A-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
A-4

surface results from the interaction of the original stressors released

(chlorofluorocarbons) with the ecosystem (stratospheric ozone).

• Exposure. As discussed above, these Guidelines use the term exposure broadly to

mean “subjected to some action or influence.” Used in this way, exposure applies to

physical and biological stressors as well as to chemicals (organisms are commonly said

to be exposed to radiation, pathogens, or heat). Exposure is also applicable to higher

levels of biological organization, such as exposure of a benthic community to dredging,

exposure of an owl population to habitat modification, or exposure of a wildlife

population to hunting. Although the operational definition of exposure, particularly the

units of measure, depends on the stressor and receptor (defined below), the following

general definition is applicable: Exposure is the contact or co-occurrence of a stressor

with a receptor.

• Receptor. The receptor is the ecological entity exposed to the stressor. This term

may refer to tissues, organisms, populations, communities, and ecosystems. While

either “ecological component” (U.S. EPA, 1992a) or “biological system” (Cohrssen

and Covello, 1989) are alternative terms, “receptor” is usually clearer in discussions of

exposure where the emphasis is on the stressor-receptor relationship. 

As discussed below, both disturbance and stress regime have been suggested as alternative

terms for exposure. Neither term is used in these Guidelines, which instead use exposure as broadly

defined above.

• Disturbance. A disturbance is any event or series of events that disrupts ecosystem,

community, or population structure and changes resources, substrate availability, or the

physical environment (modified slightly from White and Pickett, 1985). Defined in this

way, disturbance is clearly a kind of exposure (i.e., an event that subjects a receptor,

the disturbed system, to the actions of a stressor). Disturbance may be a useful

alternative to stressor specifically for physical stressors that are deletions or

modifications (e.g., logging, dredging, flooding).
A-5

• 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)

C Uncertainty about a model’s form (empirical models).

A.2.4. Lines of Evidence

The Framework Report used the phrase weight of evidence to describe the process of

evaluating multiple lines of evidence in risk characterization. These Guidelines use the phrase lines of

evidence instead to de-emphasize the balancing of opposing factors based on assignment of quantitative

values to reach a conclusion about a “weight” in favor of a more inclusive approach, which evaluates all

available information, even evidence that may be qualitative in nature.
B-1

APPENDIX B—KEY TERMS (Adapted from U.S. EPA, 1992a)

Adverse ecological effects—Changes that are considered undesirable because they alter valued

structural or functional characteristics of ecosystems or their components. An evaluation of

adversity may consider the type, intensity, and scale of the effect as well as the potential for

recovery.

Agent—Any physical, chemical, or biological entity that can induce an adverse response (synonymous

with stressor).

Assessment endpoint—An explicit expression of the environmental value that is to be protected,

operationally defined by an ecological entity and its attributes. For example, salmon are valued

ecological entities; reproduction and age class structure are some of their important attributes. 

Together “salmon reproduction and age class structure” form an assessment endpoint.

Attribute—A quality or characteristic of an ecological entity. An attribute is one component of an

assessment endpoint.

Characterization of ecological effects—A portion of the analysis phase of ecological risk

assessment that evaluates the ability of a stressor(s) to cause adverse effects under a particular

set of circumstances.

Characterization of exposure—A portion of the analysis phase of ecological risk assessment that

evaluates the interaction of the stressor with one or more ecological entities. Exposure can be

expressed as co-occurrence or contact, depending on the stressor and ecological component

involved.

Community—An assemblage of populations of different species within a specified location in space

and time.

Comparative risk assessment—A process that generally uses a professional judgment approach to

evaluate the relative magnitude of effects and set priorities among a wide range of environmental

problems (e.g., U.S. EPA, 1993d). Some applications of this process are similar to the

problem formulation portion of an ecological risk assessment in that the outcome may help

select topics for further evaluation and help focus limited resources on areas having the greatest

risk reduction potential. In other situations, a comparative risk assessment is conducted more

like a preliminary risk assessment. For example, EPA’s Science Advisory Board used

professional judgment and an ecological risk assessment approach to analyze future ecological

risk scenarios and risk management alternatives (U.S. EPA, 1995e).
B-2

Figure B-1. Plots of cumulative distribution function (CDF).

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).

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).
B-3

EC50—A statistically or graphically estimated concentration that is expected to cause one or more

specified effects in 50% of a group of organisms under specified conditions (ASTM, 1996).

Ecological entity—A general term that may refer to a species, a group of species, an ecosystem

function or characteristic, or a specific habitat. An ecological entity is one component of an

assessment endpoint.

Ecological relevance—One of the three criteria for assessment endpoint selection. Ecologically

relevant endpoints reflect important characteristics of the system and are functionally related to

other endpoints.

Ecological risk assessment—The process that evaluates the likelihood that adverse ecological effects

may occur or are occurring as a result of exposure to one or more stressors.

Ecosystem—The biotic community and abiotic environment within a specified location in space and

time.

Environmental impact statement (EIS)—Environmental impact statements are prepared under the

National Environmental Policy Act by Federal agencies as they evaluate the environmental

consequences of proposed actions. EISs describe baseline environmental conditions; the

purpose of, need for, and consequences of a proposed action; the no-action alternative; and the

consequences of a reasonable range of alternative actions. A separate risk assessment could

be prepared for each alternative, or a comparative risk assessment might be developed. 

However, risk assessment is not the only approach used in EISs.

Exposure—The contact or co-occurrence of a stressor with a receptor.

Exposure profile—The product of characterization of exposure in the analysis phase of ecological risk

assessment. The exposure profile summarizes the magnitude and spatial and temporal patterns

of exposure for the scenarios described in the conceptual model.

Exposure scenario—A set of assumptions concerning how an exposure may take place, including

assumptions about the exposure setting, stressor characteristics, and activities that may lead to

exposure.

Hazard assessment—This term has been used to mean either (1) evaluating the intrinsic effects of a

stressor (U.S. EPA, 1979) or (2) defining a margin of safety or quotient by comparing a

toxicologic effects concentration with an exposure estimate (SETAC, 1987).

LC50—A statistically or graphically estimated concentration that is expected to be lethal to 50% of a

group of organisms under specified conditions (ASTM, 1996).
B-4

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 lifehistory characteristics of the assessment endpoint or its surrogate that may affect exposure or

response to the stressor.

Measure of effect—A change in an attribute of an assessment endpoint or its surrogate in response to

a stressor to which it is exposed.

Measure of exposure—A measure of stressor existence and movement in the environment and its

contact or co-occurrence with the assessment endpoint.

Measurement endpoint—See “measure of effect.”

No-observed-adverse-effect level (NOAEL)—The highest level of a stressor evaluated in a test that

does not cause statistically significant differences from the controls.

Population—An aggregate of individuals of a species within a specified location in space and time.

Primary effect—An effect where the stressor acts on the ecological component of interest itself, not

through effects on other components of the ecosystem (synonymous with direct effect; compare

with definition for secondary effect).

Probability density function (PDF)—Probability density functions are particularly useful in describing

the relative likelihood that a variable will have different particular values of x. The probability

that a variable will have a value within a small interval around x can be approximated by

multiplying f(x) (i.e., the value of y at x in a PDF plot) by the width of the interval (figure B-2).

Prospective risk assessment—An evaluation of the future risks of a stressor(s) not yet released into

the environment or of future conditions resulting from an existing stressor(s).

Receptor—The ecological entity exposed to the stressor.
B-5

Recovery—The rate and extent of return of a population or community to some aspect(s) of its

previous condition. Because of the dynamic nature of ecological systems, the attributes of a

“recovered” system should be carefully defined.
B-6

Figure B-2. Plots of probability density functions (PDF).

Relative risk assessment—A process similar to comparative risk assessment. It involves estimating

the risks associated with different stressors or management actions. To some, relative risk

connotes the use of quantitative risk techniques, while comparative risk approaches more often

rely on professional judgment. Others do not make this distinction.

Retrospective risk assessment—An evaluation of the causal linkages between observed ecological

effects and stressor(s) in the environment.

Risk characterization—A phase of ecological risk assessment that integrates the exposure and

stressor response profiles to evaluate the likelihood of adverse ecological effects associated

with exposure to a stressor. Lines of evidence and the adversity of effects are discussed.
B-7

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).
C-1

Figure C-1. Conceptual model for logging.

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 
C-2

Figure C-2. Conceptual model for tracking stress associated with lead shot through upland

ecosystems. Reprinted from Environmental Toxicology and Chemistry by Kendall et al.

(1996) with permission of the Society of Environmental Toxicology and Chemistry

(copyright 1996).
C-3

Figure C-3. Waquoit Bay watershed conceptual model.

processes. Although flow diagrams are often used to illustrate conceptual models, there is no set

configuration for conceptual model diagrams, and the level of complexity may vary considerably

depending on the assessment. Pictorial representations of the processes of an ecosystem can be more

effective (e.g., Bradley and Smith, 1989).

Figure C-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
C-4

Figure C-3. Waquoit Bay watershed conceptual model (continued).

about the proposed relationship (e.g., human action and stressor, stressor and effect, primary effect to

secondary effect). Each risk hypothesis

provides insights into the kinds of data that will be needed to verify that the hypothesized relationships

are valid.

Figure C-2 is a conceptual model used by Kendall et al. (1996) to track a contaminant through

upland ecosystems. In this example, upland birds are exposed to lead shot when it becomes embedded

in their tissue after being shot and by ingesting lead accidentally when feeding on the ground. Both are

hypothesized to result in increased morbidity (e.g., lower reproduction and competitiveness and higher

predation and infection) and mortality, either directly (lethal intoxication) or indirectly (effects of

morbidity leading to mortality). These effects are believed to result in changes in upland bird populations
C-4

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.
D-1

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 broadspectrum insecticide and nematicide applied primarily in granular form on 27 crops as well as forests and

pine seed orchards. The assessment endpoint was survival of birds that forage in agricultural areas

where carbofuran is applied.

The analysis phase focused on birds that may incidentally ingest granules as they forage or that

may eat other animals that contain granules or residues. Measures of exposure included application

rates, attributes of the formulation (e.g., size of granules), and residues in prey organisms. Measures of

the ecosystem and receptors included an inventory of bird species that may be exposed following

applications for 10 crops. The birds’ respective feeding behaviors were considered in developing routes

of exposure. Measures of effect included laboratory toxicity studies and field investigations of bird

mortality.

The source of the chemical was application of the pesticide in granular form. The distribution of

the pesticide in agricultural fields was estimated on the basis of the application rate. The number of

exposed granules was estimated from literature data. On the basis of a review of avian feeding behavior,

seed-eating birds were assumed to ingest any granules left uncovered in the field. The intensity of

exposure was summarized as the number of exposed granules per square foot.

The stressor-response relationship was described using the results of toxicity tests. These data

were used to construct a toxicity statistic expressed as the number of granules needed to kill 50% of the

test birds (i.e., granules per LD50), assuming 0.6 mg of active ingredient per granule and average body

weights for the birds tested. Field studies were used to document the occurrence of bird deaths

following applications and provide further causal evidence. Carbofuran residues and cholinesterase

levels were used to confirm that exposure to carbofuran caused the deaths.
D-2

Describe Exposure:

Of birds to carbofuran in 

granules, soil invertebrates, 

and prey organisms

Describe

Stressor-Response

Relationship:

Number of granules needed

for 50% mortality 

in test species

Describe

Causal Evidence:

Experimental evidence,

field studies,

biomarkers of

exposure

Describe Source:

Application of granular

pesticide

Describe Distribution in the

Environment: number of exposed granules

per square foot estimated for different

application methods

 Exposure

 Profile Stressor-Response Profile

 Measures of Effects: 

Toxicity tests, field

studies of bird mortality

Measures of

Ecosystem and

Receptor

Characteristics:

Species

occurrences in

agri-ecosystems,

bird feeding habits

Measures of Exposure:

Application rates,

formulation attributes,

residues in invertebrates 

and prey organisms

Figure D-1. Example of the analysis phase process: special review of carbofuran. 

Rectangles indicate inputs, hexagons indicate actions, and circles indicate outputs.
D-3
D-4

D.2. MODELING LOSSES OF BOTTOMLAND-FOREST WETLANDS

Figure D-2 is based on an assessment of the ecological consequences (risks) of long-term

changes in hydrologic conditions (water-level elevations) for three habitat types in the Lake Verret Basin

of Louisiana (Brody et al., 1989, 1993; Conner and Brody, 1989). The project was intended to

provide a habitat-based approach for assessing the environmental impacts of Federal water projects

under the National Environmental Policy Act and Section 404 of the Clean Water Act. Output from the

models provided risk managers with information on how changes in water elevation might alter the

ecosystem. The primary anthropogenic stressor addressed in this assessment was artificial levee

construction for flood control, which contributes to land subsidence by reducing sediment deposition in

the floodplain. Assessment endpoints included forest community structure and habitat value to wildlife

species and the species composition of the wildlife community.

The analysis phase began by considering primary (direct) effects of water-level changes on plant

community composition and habitat characteristics. Measures of exposure included the attributes and

placement of the levees and water-level measurements. Measures of ecosystem and receptor

characteristics included location and extent of bottomland-hardwood communities, plant species

occurrences within these communities, and information on historic flow regimes. Measures of effects

included laboratory studies of plant response to moisture and field measurements along moisture

gradients.

While the principal stressor under evaluation was the construction of levees, the decreased

gradient of the river due to sediment deposition at its mouth also contributed to increased water levels. 

The extent and frequency of flooding were simulated by the FORFLO model based on estimates of net

subsidence rates from levee construction and decreased river gradient. Seeds and seedlings of the tree

species were assumed to be exposed to the altered flooding regime. Stressor-response relationships

describing plant response to moisture (e.g., seed germination, survival) were embedded within the

FORFLO model. This information was used by the model to simulate changes in plant communities: the

model tracks the species type, diameter, and age of each tree on simulated plots from the time the tree

enters the plot as a seedling or sprout until it dies. The FORFLO model calculated changes in the plant

community over time (from 50 to 280 years). The spatial extent of the three habitat types of

interest—wet bottomland hardwoods, dry bottomland hardwoods, and cypress-tupelo swamp—was

mapped into a GIS along with the hydrological information. The changes projected by FORFLO were

then manually linked to the GIS to show how the spatial distribution of different communities would

change. Evidence that flooding would actually cause these changes included comparisons of model

predictions with field measurements, the laboratory studies of plant response to 
D-5

Measures of

Effects:

Field studies, 

laboratory tests

Describe Distribution

of Stressor in the Environment:

Extent, frequency and location of

 flooding

Measures of Ecosystem and

Receptor Characteristics:

Flow regime, location and extent

of bottomland hardwood

community, species occurrences

Describe Source:

Construction of 

levees, decreased river

gradient

Describe Stressor-Response

Relationship:

Soil moisture/flooding -

germination, survival, and growth

rates

Describe 

Exposure 1:

Of seeds and tree 

seedlings to increased 

soil moisture and 

flooding

Describe Exposure 2

Of wildlife species

to altered plant

community

Describe Stressor-Response

Relationship:

Plant communityhabitat suitability for wildlife

Describe Causal

Evidence:

Mechanism of action, field

studies, laboratory

experimentation,

model validation

PRIMARY EFFECTS: Estimated 

Using FORFLO Model

SECONDARY EFFECTS: Estimated

Using Habitat Suitability Indices

Combined Exposure and Stressor-Response Profile

Measures of Exposure:

Levee attributes,

water-level measures

Describe Disturbed 

Environment:

Plant community

composition

Extrapolate to Plant Community:

Using FORFLO model

Figure D-2. Example of the analysis phase process: modeling losses of bottomland

hardwoods. Rectangles indicate inputs, hexagons indicate actions, and circles indicate

outputs.
D-6

moisture, and knowledge of the mechanisms by which flooding elicits changes in plant communities.

Secondary (indirect) effects on wildlife associated with changes in the habitat provided by the

plant community formed the second part of the analysis phase. Important measures included life-history

characteristics and habitat needs of the wildlife species. Effects on wildlife were inferred by evaluating

the suitability of the plant community as habitat. Specific aspects of the community structures calculated

by the FORFLO model provided the input to this part of the analysis. For example, the number of snags

was used to evaluate habitat value for woodpeckers. Resident wildlife (represented by five species) was

assumed to co-occur with the altered plant community. Habitat value was evaluated by calculating the

Habitat Suitability Index (HSI) for each habitat type multiplied by the habitat type’s area.

A combined exposure and stressor-response profile is shown in figure D-2; these two elements

were combined with the models used for the analysis and then used directly in risk characterization.

D.3. PEST RISK ASSESSMENT OF IMPORTATION OF LOGS FROM CHILE

Figure D-3 is based on the assessment of potential risks to U.S. forests due to the incidental

introduction of insects, fungi, and other pests inhabiting logs harvested in Chile and transported to U.S.

ports (USDA, 1993). This risk assessment was used to determine whether actions to restrict or regulate

the importation of Chilean logs were needed to protect U.S. forests and was conducted by a team of six

experts under the auspices of the U.S. Department of Agriculture Forest Service. Stressors include

insects, forest pathogens (e.g., fungi), and other pests. The assessment endpoint was the survival and

growth of tree species (particularly conifers) in the western United States. Damage that would affect the

commercial value of the trees as lumber was clearly of interest.

The analysis phase was carried out by eliciting professional opinions from a team of experts. 

Measures of exposure used by the team included distribution information for the imported logs and

attributes of the insects and pathogens such as dispersal mechanisms and life-history characteristics. 

Measures of ecosystem and receptor characteristics included the climate of the United States, location

of geographic barriers, knowledge of host suitability, and ranges of potential host species. Measures of

effect included knowledge of the infectivity of these pests in other countries and the infectivity of similar

pests on U.S. hosts.

This information was used by the risk assessment team to evaluate the potential for exposure. 

They began by evaluating the likelihood of entry of infested logs into the United States. The distribution

of the organism’s given entry was evaluated by considering the potential
D-7

Describe

Source: 

Entry of infested

logs into U.S.

Ecosystem/Receptor

Measures:

Climate, geographic 

barriers, host suitability,

 extent of potential host 

species

Describe Exposure: 

Of resources of concern

Characterize Effects:

Consider potential for 

ecosystem destabilization, 

reduction in biodiversity, and

loss of keystone or

endangered species

Exposure Profile Stressor-Response Profile

Measures of Exposure:

Point of entry for logs, processing

status, and eventual destination,

attributes of insects and pathogens

(dispersal mechanisms, life-history

characteristics).

Measures of Effect:

Infectivity of similar

pests on U.S. hosts,

infectivity of pests in

other countries

Describe Distribution

in the Environment:

Consider colonization potential,

spread potential,

survival, and reproduction

Figure D-3. Example of the analysis phase process: pest risk assessment of the

importation of logs from Chile. Rectangles indicate inputs, hexagons indicate actions, and

circles indicate outputs.
D-8
D-9

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.
2

 This example is simplified for illustrative purposes. In other situations, it may be considerably

more difficult to draw clear conclusions regarding relative ecological adversity.

E-1

APPENDIX E—CRITERIA FOR DETERMINING ECOLOGICAL ADVERSITY: A

HYPOTHETICAL EXAMPLE (Adapted from Hartwell et al., 1994)2

As a result of a collision at sea, an oil tanker releases 15 million barrels of #2 fuel oil 3 km

offshore. It is predicted that prevailing winds will carry the fuel onshore within 48 to 72 hours. The

coastline has numerous small embayments that support an extensive shallow, sloping subtidal community

and a rich intertidal community. A preliminary assessment determines that if no action is taken,

significant risks to the communities will result. Additional risk assessments are conducted to determine

which of two options should be used to clean up the oil spill.

Option 1 is to use a dispersant to break up the slick, which would reduce the likelihood of

extensive onshore contamination but would cause extensive mortality to the phytoplankton, zooplankton,

and ichthyoplankton (fish larvae), which are important for commercial fisheries. Option 2 is to try to

contain and pump off as much oil as possible; this option anticipates that a shift in wind direction will

move the spill away from shore and allow for natural dispersal at sea. If this does not happen, the oil will

contaminate the extensive sub- and intertidal mud flats, rocky intertidal communities, and beaches and

pose an additional hazard to avian and mammalian fauna. It is assumed there will be a demonstrable

change beyond natural variability in the assessment endpoints (e.g., structure of planktonic, benthic, and

intertidal communities). What is the adversity of each option?

• Nature and intensity of the effect. For both options, the magnitude of change in the

assessment endpoints is likely to be severe. Planktonic populations often are

characterized by extensive spatial and temporal variability. Nevertheless, within the

spatial boundaries of the spill, the use of dispersants is likely to produce complete

mortality of all planktonic forms within the upper 3 m of water. For benthic and intertidal

communities, which generally are stable and have less spatial and temporal variability

than planktonic forms, oil contamination will likely result in severe impacts on survival

and chronic effects lasting for several years. Thus, under both options, changes in the

assessment endpoints will probably exceed the natural variability for threatened

communities in both space and time.
E-2

• 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.
R-1

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.
R-2

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.

Bedford, BL; Preston, EM. (1988) Developing the scientific basis for assessing cumulative effects of wetland loss and

degradation on landscape functions: status, perspectives, and prospects. Environ Manage 12:751-771.

Bradbury, SP. (1994) Predicting modes of toxic action from chemical structure: an overview. SAR QSAR Environ Res

2:89-104.

Bradley, CE; Smith, DG. (1989) Plains cottonwood recruitment and survival on a prairie meandering river floodplain.

Milk River, Southern Alberta in Northern Canada. Can J Botany 64:1433-1442.

Broderius, SJ. (1991) Modeling the joint toxicity of xenobiotics to aquatic organisms: basic concepts and approaches.

In: Aquatic toxicology and risk assessment: fourteenth volume. Mayes, MA; Barron, MG, eds. ASTM STP 1124.

Philadelphia, PA: American Society for Testing and Materials, pp. 107-127.

Broderius, SJ; Kahl, MD; Hoglund, MD. (1995) Use of joint toxic response to define the primary mode of toxic action

for diverse industrial organic chemicals. Environ Toxicol Chem 9:1591-1605.

Brody, M; Conner, W; Pearlstine, L; Kitchens, W. (1989) Modeling bottomland forest and wildlife habitat changes in

Louisiana’s Atchafalaya Basin. In: Freshwater wetlands and wildlife. Sharitz, RR; Gibbons, JW, eds. U.S. Department

of Energy Symposium Series, No. 61. CONF-8603100. Oak Ridge, TN: Office of Science and Technical Information,

U.S. Department of Energy.

Brody, MS; Troyer, ME; Valette, Y. (1993) Ecological risk assessment case study: modeling future losses of

bottomland forest wetlands and changes in wildlife habitat within a Louisiana basin. In: A review of ecological

assessment case studies from a risk assessment perspective. Washington, DC: Risk Assessment Forum, U.S.

Environmental Protection Agency, pp. 12-1 to 12-39. EPA/630/R-92/005.

Burmaster, DE; Menzie, CA; Freshman, JS; Burris, JA; Maxwell, NI; Drew, SR. (1991) Assessment of methods for

estimating aquatic hazards at Superfund-type sites: a cautionary tale. Environ Toxicol Chem 10:827-842.

Callahan, CA; Menzie, CA; Burmaster, DE; Wilborn, DC; Ernst, T. (1991) On-site methods for assessing chemical

impacts on the soil environment using earthworms: a case study at the Baird and McGuire Superfund site, Holbrook,

Massachusetts. Environ Toxicol Chem 10:817-826.

Cardwell, RD; Parkhurst, BR; Warren-Hicks, W; Volosin, JS. (1993) Aquatic ecological risk. Water Environ Technol

5:47-51.

Clemen, RT. (1996) Making hard decisions. 2nd ed. New York: Duxbury Press, 664 pp. 
R-3

Clements, RG; Nabholz, JV. (1994) ECOSAR: a computer program for estimating the ecotoxicity of industrial chemicals

based on structure activity relationships, user’s guide. Washington, DC: Environmental Effects Branch, Health and

Environmental Review Division (7402), U.S. Environmental Protection Agency. EPA/748/R-93/002.

Cohrssen, J; Covello, VT. (1989) Risk analysis: a guide to principles and methods for analyzing health and ecological

risks. Washington, DC: Council on Environmental Quality.

Commission on Risk Assessment and Risk Management. (1997) Framework for environmental health risk

management. Final Report. Volume 1. Washington, DC: Commission on Risk Assessment and Risk Management.

Conner, WH; Brody, M. (1989) Rising water levels and the future of southeastern Louisiana swamp forests. Estuaries

12(4):318-323.

Costanza, R; d’Arge, R; de Groot, R; Farber, S; Grasso, M; Hannon, B; Limburg, K; Naeem, S; O’Neill, RV; Paruelo, J;

Raskinm, RG; Sutton, P; van den Belt, M. (1997) The value of the world’s ecosystem services and natural capital.

Nature 387:253-260.

Cowan, CE; Versteeg, DJ; Larson, RJ; Kloepper-Sams, PJ. (1995) Integrated approach for environmental assessment of

new and existing substances. Regul Toxicol Pharmacol 21:3-31.

Cronin, MTD; Dearden, JC. (1995) QSAR in toxicology. 1. Prediction of aquatic toxicity. Quant Struct-Act Relat 14:1-7.

Detenbeck, N. (1994) Ecological risk assessment case study: effects of physical disturbance on water quality status

and water quality improvement function of urban wetlands. In: A review of ecological assessment case studies from a

risk assessment perspective, vol. II. Washington, DC: Risk Assessment Forum, U.S. Environmental Protection

Agency, pp. 4-1 to 4-58. EPA/630/R-94/003.

Detenbeck, NE; DeVore, PW; Niemi, GJ; Lima, A. (1992) Recovery of temperate-stream fish communities from

disturbance: a review of case studies and synthesis of theory. Environ Manage 16(1):33-53.

Eberhardt, LL; Thomas, JM. (1991) Designing environmental field studies. Ecol Mono 61(1):53-73.

Emlen, JM. (1989) Terrestrial population models for ecological risk assessment: a state-of-the-art review. Environ

Toxicol Chem 8:831-842.

European Community (EC). (1993) Technical guidance document in support of the risk assessment Commission

Directive (93/67/EEC) for new substances notified in accordance with the requirements of Council Directive

67/548/EEC. Brussels, Belgium.

Ferson, S; Ginzburg, L; Silvers, A. (1989) Extreme event risk analysis for age-structured populations. Ecol Model

47:175-187.
R-4

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.
R-5

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 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.
R-6

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.

Könemann, H. (1981) Fish toxicity tests with mixtures of more than two chemicals: a proposal for a quantitative

approach and experimental results. Aquatic Toxicol 19:229-238.

Laird, NM; Mosteller, R. (1990) Some statistical methods for combining experimental results. Int J Technol Assess

Health Care 6:5-30.

Landis, WG; Matthews, RA; Markiewicz, AJ; Matthews, GB. (1993) Multivariate analysis of the impacts of the turbine

fuel JP-4 in a microcosm toxicity test with implications for the evaluation of ecosystem dynamics and risk assessment.

Ecotoxicology 2:271-300.

Lipnick, RL. (1995) Structure-activity relationships. In: Fundamentals of aquatic toxicology effects, environmental fate,

and risk assessment. Rand, GM; Petrocelli, SR, eds. London, UK: Taylor and Francis, pp. 609-655.

Lipton, J; Galbraith, H; Burger, J; Wartenberg, D. (1993) A paradigm for ecological risk assessment. Environ Manage

17:1-5.

Ludwig, JA; Reynolds, JF. (1988) Statistical ecology. New York, NY: Wiley-Interscience, 337 pp.

Lynch, DG; Macek, GJ; Nabholz, JV; Sherlock, SM; Wright, R. (1994) Ecological risk assessment case study:

assessing the ecological risks of a new chemical under the Toxic Substances Control Act. In: A review of ecological

assessment case studies from a risk assessment perspective, vol. II. Washington, DC: Risk Assessment Forum, U.S.

Environmental Protection Agency, pp. 1-1 to 1-35. EPA/630/R-94/003.

MacIntosh, DL; Suter GW, II; Hoffman, FO. (1994) Uses of probabilistic exposure models in ecological risk

assessments of contaminated sites. Risk Anal 14(4):405-419.

Mann, C. (1990) Meta-analysis in the breech. Science 249:476-480.

McCarty, LS; Mackay, D. (1993) Enhancing ecotoxicological modeling and assessment: body residues and modes of

toxic action. Environ Sci Technol 27:1719-1728.

Meij, R. (1991) The fate of mercury in coal-fired power plants and the influence of wet flue-gas desulphurization.

Water Air Soil Pollut 56:21-33.
R-7

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.
R-8

O’Neill, RV; Gardner, RH; Barnthouse, LW; Suter, GW, II; Hildebrand, SG; Gehrs, CW. (1982) Ecosystem risk analysis:

a new methodology. Environ Toxicol Chem 1:167-177.

Orr, RL; Cohen, SD; Griffin, RL. (1993) Generic non-indigenous pest risk assessment process. Beltsville, MD: USDA

Animal and Plant Health Inspection Service.

Ott, WR. (1978) Environmental indices—theory and practice. Ann Arbor, MI: Ann Arbor Science. Cited in: Suter, GW,

II. (1993) A critique of ecosystem health concepts and indexes. Environ Toxicol Chem 12:1533-1539.

Parkhurst, BR; Warren-Hicks, W; Etchison, T; Butcher, JB; Cardwell, RD; Voloson, J. (1995) Methodology for aquatic

ecological risk assessment. RP91-AER-1 1995. Alexandria, VA: Water Environment Research Foundation.

Pastorok, RA; Butcher, MK; Nielsen, RD. (1996) Modeling wildlife exposure to toxic chemicals: trends and recent

advances. Human Ecol Risk Assess 2:444-480.

Pearlstine, L; McKellar, H; Kitchens, W. (1985) Modelling the impacts of a river diversion on bottomland forest

communities in the Santee River Floodplain, South Carolina. Ecol Model 29:281-302.

Pelczar, MJ; Reid, RD. (1972) Microbiology. New York, NY: McGraw-Hill Company.

Peterman, RM. (1990) The importance of reporting statistical power: the forest decline and acidic deposition example.

Ecology 71:2024-2027.

Petitti, DB. (1994) Meta-analysis, decision analysis and cost-effectiveness analysis: methods for quantitative

synthesis in medicine. Monographs in epidemiology and biostatistics, vol. 24. New York, NY: Oxford University

Press.

Phipps, RL. (1979) Simulation of wetlands forest vegetation dynamics. Ecol Model 7:257-288.

Pielou, EC. (1984) The interpretation of ecological data. A primer on classification and ordination. New York, NY:

Wiley-Interscience, 263 pp.

Preston, EM; Bedford, BL. (1988) Evaluating cumulative effects on wetland functions: a conceptual overview and

generic framework. Environ Manage 12(5):565-583.

Richards, C; Haro, RJ; Johnson, LB; Host, GE. (1997) Catchment and reach-scale properties as indicators of

macroinvertebrate species traits. Freshwater Biol 37:219-230.

Risser, PG. (1988) General concepts for measuring cumulative impacts on wetland ecosystems. Environ Manage

12(5):585-589.

Rotenberry, JT; Wiens, JA. (1985) Statistical power analysis and community-wide patterns. Am Naturalist 125:164-168.
R-9

Ruckelshaus, WD. (1983) Science, risk, and public policy. Science 221:1026-1028.

Sample, BE; Opresko, DM; Suter, GW, II. (1996) Toxicological benchmarks for wildlife: 1996 revision. ES/ER/TM86/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.
R-10

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.
R-11

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) Quality criteria for water. Washington, DC: Office of Water, U.S.

Environmental Protection Agency. EPA/440/5-86/001.

U.S. Environmental Protection Agency. (1986b) Guidelines for the health risk assessment of chemical mixtures. Federal

Register 52:34014-34025.

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.
R-12

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) 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. (1993b) 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. (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 Evaluation, U.S. Environmental Protection Agency. EPA/230/B93/003.

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) 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. (1994e) Environmental Services Division guidelines. Hydrogeologic modeling.

Seattle, WA: Region X, U.S. Environmental Protection Agency.

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.
R-13

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) 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. (1996b) 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. (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; Løkke, 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.
R-14

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.
1

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). Fortyfour 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 (EPASAB-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
2

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-bycase basis, or by a program office in response to program-specific needs.

Several Native American groups noted a lack of acknowledgment of tribal governments in the

document. This Agency oversight was corrected by including tribal governments at points in the

Guidelines where other governmental organizations are mentioned.

Several reviewers noted that the Proposed Guidelines mentioned the need for “expert judgment”

in several places and asked how the Agency defined “expert” and what qualifications such an individual

should have. At present, there is no standard set of qualifications for an ecological risk assessor, and

such a standard would be very difficult to produce, since ecological assessments are frequently done by

teams of individuals with expertise in many areas. To avoid this problem, the Guidelines now use the

term “professional judgment,” and note that it is important to document the rationale for important

decisions.

Some reviewers felt that the Guidelines should address effects only at the population level and

above. The Guidelines do not make this restriction for several reasons. First, some assessments, such

as those involving endangered species, do involve considerations of individual effects. Second, the

decision as to which ecological entity to protect should be the result, on a case-by-case basis, of the

planning process involving risk assessors, risk managers, and interested parties, if appropriate. Some

suggestions have been proposed (U.S. EPA, 1997a). Finally, there appears to be some confusion

among reviewers between conducting an assessment concerned with population-level effects, and using

data from studies of effects on individuals (e.g., toxicity test results) to infer population-level effects. 

These inferences are commonly used (and generally accepted) in chemical screening programs, such as

the Office of Pollution Prevention and Toxics Premanufacturing Notification program (U.S. EPA,

1994d).

The use of environmental indices received a number of comments. Some reviewers wanted the

Guidelines to do more to encourage the use of indices, while others felt that the disadvantages of indices

should receive greater emphasis. The Guidelines discuss both the advantages and limitations of using

indices to guide risk assessors in their proper use.
3

Other reviewers requested that the Guidelines take a more definitive position on the use of

“realistic exposure assumptions,” such as those proposed in the Agency’s exposure guidelines (U.S.

EPA, 1992b). Although the exposure guidelines offer many useful suggestions that are applicable to

human health risk assessment, it was not possible to generalize the concepts to ecological risk

assessment, given the various permutations of the exposure concept for different types of stressors or

levels of biological organization. The Guidelines emphasize the importance of documenting major

assumptions (including exposure assumptions) used in an assessment.

Several reviewers requested more guidance and examples using nonchemical stressors, i.e.,

physical or biological stressors. This topic has been included in the list of potential subjects for future

detailed treatment (see response to question 2, below).

3. RESPONSE TO COMMENTS ON SPECIFIC QUESTIONS

Both the Proposed Guidelines and the charge to the SAB for its review contained a set of seven

questions asked by the Agency. These questions, along with the Agency’s response to comments

received, are listed below.

(1) Consistent with a recent National Research Council report (NRC, 1996), these

Proposed Guidelines emphasize the importance of interactions between risk assessors and risk

managers as well as the critical role of problem formulation in ensuring that the results of the risk

assessment can be used for decision making. Overall, how compatible are these Proposed

Guidelines with the National Research Council concept of the risk assessment process and the

interactions among risk assessors, risk managers, and other interested parties?

Most reviewers felt there was general compatibility between the Proposed Guidelines and the

NRC report, although some emphasized the need for continued interactions among risk assessors, risk

managers, and interested parties (or stakeholders) throughout the ecological risk assessment process

and asked that the Guidelines provide additional details concerning such interactions. To give greater

emphasis to these interactions, the ecological risk assessment diagram was modified to include

“interested parties” in the planning box at the beginning of the process and “communicating with

interested parties” in the risk management box following the risk assessment. Some additional discussion

concerning interactions among risk assessors, risk managers, and interested parties was added,

particularly to section 2 (planning). However, although risk assessor/risk manager interrelationships are

discussed, too great an emphasis in this area is inconsistent with the scope of the Guidelines, which focus

on the interface between risk assessors and risk managers, not on providing risk management guidance.
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(2) The Proposed Guidelines are intended to provide a starting point for Agency

programs and regional offices that wish to prepare ecological risk assessment guidance suited to

their needs. In addition, the Agency intends to sponsor development of more detailed guidance on

certain ecological risk assessment topics. Examples might include identification and selection of

assessment endpoints, selection of surrogate or indicator species, or the development and

application of uncertainty factors. Considering the state of the science of ecological risk

assessment and Agency needs and priorities, what topics most require additional guidance?

Reviewers recommended numerous topics for further development. Examples include:

• landscape ecology 

• data sources and quality 

• physical and biological stressors

• multiple stressors 

• defining reference areas for field studies 

• ecotoxicity thresholds 

• the role of biological and other types of indicators 

• bioavailability, bioaccumulation, and bioconcentration 

• uncertainty factors 

• stressor-response relationships (e.g., threshold vs. continuous)

• risk characterization techniques 

• risk communication to the public

• public participation 

• comparative ecological risk

• screening and tiering assessments 

• identifying and selecting assessment endpoints.

These suggestions will be included in a listing of possible topics proposed to the Agency’s Risk

Assessment Forum for future development.

(3) Some reviewers have suggested that the Proposed Guidelines should provide more

discussion of topics related to the use of field observational data in ecological risk assessments,

such as selection of reference sites, interpretation of positive and negative field data, establishing

causal linkages, identifying measures of ecological condition, the role and uses of monitoring, and

resolving conflicting lines of evidence between field and laboratory data. Given the general
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scope of these Proposed Guidelines, what, if any, additional material should be added on these

topics and, if so, what principles should be highlighted?

In response to a number of comments, the discussion of field data in the Guidelines was

expanded, especially in section 4.1. Nevertheless, many suggested topics requested a level of detail that

was inconsistent with the scope of the Guidelines. Some areas may be covered through the

development of future Risk Assessment Forum documents.

(4) The scope of the Proposed Guidelines is intentionally broad. However, while the

intent is to cover the full range 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
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assessments make further generalizations difficult. Given the broad scope of the Proposed

Guidelines, what additional principles for conducting tiered assessments can be discussed?

Many reviewers emphasized the importance of tiered assessments, and in response the

discussion of tiered assessments was significantly expanded in the planning phase of ecological risk

assessment. Including more detailed information (such as specific decision criteria to proceed from one

tier to the next) would require a particular context for an assessment. Such specific guidance is left to

the EPA program offices and regions.

(7) Assessment endpoints are “explicit expression of the environmental value that is to be

protected.” As used in the Proposed Guidelines, assessment endpoints include both an ecological

entity and a specific attribute of the entity (e.g., eagle reproduction or extent of wetlands). Some

reviewers have recommended that assessment endpoints also include a decision criterion that is

defined early in the risk assessment process (e.g., no more than a 20% reduction in reproduction,

no more than a 10% loss of wetlands). While not precluding this possibility, the Proposed

Guidelines suggest that such decisions are more appropriately made during discussions between

risk assessors and managers in risk characterization at the end of the process. What are the

relative merits of each approach?

Reviewer reaction was quite evenly divided between those who felt strongly that decision criteria

should be defined in problem formulation and those who felt just as strongly that such decisions should

be delayed until risk characterization. Although the Guidelines contain more discussion of this topic, they

still take the position that assessment endpoints need not contain specific decision criteria.