NOTICE

This file does not contain all of the figures in the Proposed Guidelines for Ecological Risk
Assessment.  Paper copies are provided with this disk.  Scanned copies are included in the
Internet version:  http://www.epa.gov/ORD/WebPubs/ecorisk
                                                     EPA/630/R-95/002B
                                                     August 1996
                         IProposed Guidelines
                                     for
                     Ecological Risk Assessment
                            Risk Assessment Forum
                      U.S. Environmental Protection Agency
                               Washington, DC

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                          PROPOSED GUIDELINES FOR




                        ECOLOGICAL RISK ASSESSMENT




                                    FRL-5605-9








AGENCY: U.S. Environmental Protection Agency








ACTION:  Notice of Availability and Opportunity to Comment on Proposed Guidelines for




Ecological Risk Assessment.








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




document entitled Proposed Guidelines for Ecological Risk Assessment (hereafter "Proposed




Guidelines").  These Proposed Guidelines were developed as part of an interoffice Guidelines



development program by a Technical Panel of the Risk Assessment Forum.  The Proposed



Guidelines expand upon the previously published EPA report Framework for Ecological Risk




Assessment (EPA/630/R-92/001, February 1992), while retaining the report's broad scope.




When final, these Proposed Guidelines will help improve the quality of ecological risk



assessments at EPA while increasing the consistency of assessments among the Agency's




program offices and regions.








DATES: The Proposed Guidelines are being made available for a 90-day public review and




comment period.  Comments must be in writing and must be postmarked by December 9,



1996.  See Addresses section for guidance on submitting comments.






                                         1                      Proposed Guidelines

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    yferbatim
           U. S. Environmental Protection
           Agency(8801)
           Washington. DC 20460
EPA/630/R-95/Op2Ba
Proposed Guidelines for
Ecological Risk Assessment
August 1996
ECOGDLNS
WordPerfect 5.1 file

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FOR FURTHER INFORMATION, CONTACT:  Bill van der Schalie, National Center for

Environmental Assessment-Washington Office, telephone:  202-260-4191.



ADDRESSES:

      The Proposed Guidelines will be made available in the following ways:

1) the electronic version will be accessible on EPA's Office of Research and Development

home page on the Internet at http://www.epa.gov/ORD/WebPubs/fedreg.



2) 3V2" high-density computer diskettes in Wordperfect 5.1 format will be available from

ORD Publications, Technology Transfer and Support Division, National Risk Management

Research Laboratory,  Cincinnati, OH; telephone: 513-569-7562; fax: 513-569-7566. Please

provide the EPA No.  (EPA/630/R-95/002B) when ordering.



3) This notice contains the full proposed guideline.  In addition, copies will be available for

inspection at EPA headquarters and regional libraries, through the U.S. Government

Depository Library program,  and for purchase from the National Technical Information

Service (NTIS), Springfield, VA; telephone: 703-487-4650, fax: 703-321-8547. Please

provide the NTIS PB  No. PB96-193198; Price Code A13: ($47.00) when ordering.



Submitting Comments

      Comments on  the Proposed Guidelines should be submitted to: U.S. Environmental

Protection Agency, Air and Radiation Docket and Information Center (6102), Attn: File
                                                                                  v

                                         2                      Proposed Guidelines

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ORD-ERA-96-01, Waterside Mall, 401 M St. SW, Washington, DC 20460.  Please submit




one unbound original with pages numbered consecutively, and three copies. For



attachments, provide an index, number pages consecutively, provide comment on how the




attachments relate to the main comment(s), and submit an unbound original and three copies.



Please identify all comments and attachments with the file number (ORD-ERA-96-01).



Mailed comments must be postmarked by the date indicated.  Comments may also be



submitted electronically by sending electronic mail (e-mail) to: A-and-R-




Docket@epamail.epa.gov. Electronic comments must be submitted as an ASCII file avoiding




the use of special characters and any form of encryption.  Comments and data will also be




accepted on disks in WordPerfect 5.1 file format or ASCII file format.  All comments in



electronic form also must be identified by the file number ORD-ERA-96-01.




       The Air and  Radiation Docket and Information Center is open for public inspection




and copying between 8:00 a.m. and 5:30 p.m., weekdays, in Room M-1500, Waterside



Mall, 401 M St. SW, Washington, DC 20460.  The Center is located on the ground floor in



the commercial area of Waterside Mall.  The file index, materials, and comments are




available for review in the information center or copies may be mailed on request from the



Air and Radiation Docket and Information Center by calling (202) 260-7548 or -7549. The




FAX number for the Center is (202) 260-4400.  A reasonable fee may be charged for



copying materials.








       Please note that all technical comments received in response to this notice will be




placed in the public  record. For that reason, commentors should not submit personal







                                         3                      Proposed Guidelines

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information such as medical data or home addresses, confidential business information, or




information protected by copyright. Due to limited resources, acknowledgments will not be




sent.








SUPPLEMENTARY INFORMATION: These Proposed Guidelines are EPA's first




Agency-wide ecological risk assessment guidelines.  They are broad in scope, describing




general principles and providing numerous examples to show how ecological risk assessment




can be applied to a wide range of systems, stressors, and biological/spatial/temporal scales.




This general approach provides sufficient flexibility to permit EPA's offices and regions to




develop specific guidance suited to their particular needs. Because of their broad scope, the




Proposed Guidelines do not provide detailed guidance in specific areas nor are they highly




prescriptive.  Frequently, rather than requiring that certain procedures  always be followed,




the Proposed Guidelines describe the strengths and limitations of alternate approaches.



Agency preferences are expressed where possible, but because ecological risk assessment is a



relatively new, rapidly evolving discipline, requirements for specific approaches could soon




become outdated.  EPA is working to expand the references in the Proposed Guidelines to




include additional review articles or key publications that will help provide a "window to the




literature"  as recommended by peer reviewers.  In the future, EPA intends to develop a




series of shorter, more detailed guidance documents on specific  ecological risk assessment




topics after these Proposed Guidelines have been finalized.



       These Proposed  Guidelines were prepared during a time  of increasing interest in the




field of ecological risk assessment and reflect input from many sources outside as well as






                                           4                       Proposed Guidelines

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inside the Agency.  Over the last few years, the National Research Council proposed an




ecological risk paradigm (NRC, 1993), there has been a marked increase in discussion of



ecological risk assessment issues at meetings of professional organizations, and numerous



articles and books on the subject have been published.  Agency work on the Proposed




Guidelines has proceeded in a step-wise fashion during this time.  Preliminary work began in




1989  and included a series of colloquia sponsored by EPA's Risk Assessment Forum to




identify and discuss significant issues in ecological risk assessment (U.S. EPA, 1991).  Based




on this early work and on a consultation with EPA's Science Advisory Board (SAB), the



Agency decided to produce ecological risk assessment guidance sequentially, beginning with




basic  terms and concepts and continuing with the development of source materials for these



Proposed Guidelines.  The first product of this  effort was the Risk Assessment Forum report,




Framework for Ecological Risk Assessment (Framework Report; U.S. EPA, 1992a,b), which




proposes principles and terminology for the ecological risk assessment process.  Since then,



the Agency has solicited suggestions for ecological risk assessment guidelines structuring




(U.S. EPA, 1992c) and has sponsored the development of other peer-reviewed materials,



including ecological assessment case studies (U.S. EPA, 1993a, 1994a), and a set of issue



papers that highlight important principles and approaches that EPA scientists should consider



in preparing these Proposed Guidelines  (U.S. EPA, 1994b,c).



      The nature and content of these Proposed Guidelines have been shaped by these




documents  as well as numerous meetings and discussions with individuals both within and



outside of EPA.  In late 1994 and early 1995, the Agency solicited responses to the planned



nature and  structure of these Proposed Guidelines at three colloquia with Agency  program







                                          5                      Proposed Guidelines

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offices and regions, other Federal agencies, and the public. Draft Proposed Guidelines were




discussed at an external peer review workshop in December, 1995 (U.S. EPA, In Press).




Subsequent reviews have included the Agency's Risk Assessment Forum and the Regulatory




and Policy Development Committee, and interagency comment by members of




subcommittees of the Committee on the Environment and Natural Resources of the Office of




Science and Technology Policy.  The EPA appreciates the efforts of all participants in the




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




       The EPA's  Science Advisory Board will review these Proposed Guidelines at a future




meeting. Following public and SAB reviews, Agency staff will prepare comment




summaries.  Appropriate comments will be incorporated, and the revised Guidelines will be




submitted to EPA's Risk Assessment Forum for review.  The Agency will  consider




comments from the public, the SAB, and the Risk Assessment Forum when finalizing these




Proposed Guidelines.




       The public is invited to provide comments to be considered in EPA  decisions about




the content of the final Guidelines.  EPA asks those who respond to this notice to include



their views on the folio whig:








      • (1) Consistent with a recent  National Research Council report (NRC, 1996), these




Proposed Guidelines emphasize the importance of interactions between risk assessors and risk




managers as well as the  critical role of problem formulation to  ensuring that the results of the




risk assessment can be used for decision-making.  Overall, how compatible are these




Proposed Guidelines with the National Research Council concept of the risk assessment







                                          6                       Proposed Guidelines

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process and the interactions between risk assessors, risk managers, and other interested




parties?








       (2) The Proposed Guidelines are intended to provide a starting point for Agency




program and regional offices that wish to prepare ecological risk assessment guidance suited




to their needs.  In addition, the Agency intends to sponsor development of more detailed



guidance on certain ecological risk assessment topics.  Examples might include identification




and selection of assessment endpoints, selection of surrogate or indicator species, or the



development and application of uncertainty factors. Considering the state of the science of




ecological risk assessment and Agency needs and priorities,  what topics most require




additional guidance?








       (3)  Some reviewers have suggested that the Proposed Guidelines should provide more




discussion of topics related to the use of field observational data in ecological risk



assessments,  such as selection of reference sites, interpretation of positive and negative field



data, establishing causal linkages, identifying measures of ecological condition, the role and



uses of monitoring, and resolving conflicting lines of evidence between field and laboratory




data.  Given  the general scope of these Proposed Guidelines, what,  if any, additional material



should be added on these topics and, if so, what principles should be highlighted?
       (4)  The scope of the Proposed Guidelines is intentionally broad.  However, while the




intent is to cover the full range of stressors, ecosystem types, levels of biological







                                           7                       Proposed Guidelines

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organization, and spatial/temporal scales, the contents of the Proposed Guidelines are limited




by the present  state of the science and the relative lack of experience in applying risk




assessment principles to some areas.  In particular, given the Agency's present interest in




evaluating risks at larger spatial scales, how could the principles of landscape ecology be




more fully incorporated into the Proposed Guidelines?








       (5) Assessing risks when  multiple stressors are present is a challenging task.  The




problem may be how to aggregate risks attributable to individual stressors or to identify the




principal stressors responsible for an observed effect. Although some approaches for




evaluating risks associated with chemical mixtures are available, our ability to conduct risk




assessments involving multiple chemical,  physical, and biological stressors,  especially at




larger spatial scales, is limited.  Consequently, the Proposed Guidelines primarily discuss




predicting the effects of chemical  mixtures and on general approaches for evaluating causality




of an observed effect.  What additional principles can be added?








       (6)  Ecological risk assessments are frequently conducted in tiers that proceed from



simple evaluations of exposure and effects to more complex assessments.  While the




Proposed Guidelines acknowledge the importance of tiered assessments, the wide range of




applications of tiered assessments  make further generalizations difficult. Given the broad



scope of the Proposed Guidelines, what additional principles for conducting tiered



assessments can be discussed?
                                                                     Proposed Guidelines

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       (7)  Assessment endpoints are "explicit expression of the environmental value that is

to be protected".  As used in the Proposed Guidelines, assessment endpoints include both an

ecological entity and a specific attributes of the entity (e.g., eagle reproduction or extent of

wetlands).   Some reviewers have recommended that assessment endpoints also include a

decision criterion that is defined early in the risk assessment process (e.g., no more than a

20% reduction in reproduction, no more than a 10% loss of wetlands). While not precluding

this possibility, the Proposed Guidelines suggest that such decisions are more appropriately

made during discussions between risk assessors and managers in risk characterization at the

end of the process.  What are the relative merits of each approach?
         Date                                        Carol M. Browner
                                                Administrator
                                                                    Proposed Guidelines

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                                CONTENTS


LISTS OF FIGURES, TABLES, AND TEXT NOTES  	. .	13

EXECUTIVE SUMMARY  .	16

1.  INTRODUCTION .	.24
   1.1.  ECOLOGICAL RISK ASSESSMENT IN A MANAGEMENT CONTEXT ... 28
        1.1.1.  Contributions of Ecological Risk Assessment to Environmental
             DecisionmaMng	,	29
        1.1.2.  Risk Management Considerations	 30
   1.2.  SCOPE AND INTENDED AUDIENCE	31
   1.3.  GUIDELINES ORGANIZATION	33

2.  PLANNING THE RISK ASSESSMENT: DIALOGUE BETWEEN RISK MANAGERS
AND RISK ASSESSORS	36
   2.1.  ESTABLISHING MANAGEMENT GOALS  	38
   2.2.  MANAGEMENT DECISIONS	39
   2.3.  SCOPE AND COMPLEXITY OF THE RISK ASSESSMENT	 41
   2.4.  PLANNING OUTCOME	42

3.  PROBLEM FORMULATION PHASE  .	44
   3.1.  PRODUCTS OF PROBLEM FORMULATION  	44
   3.2.  INTEGRATION OF AVAILABLE INFORMATION	46
   3.3.  SELECTING ASSESSMENT ENDPOINTS	48
        3.3.1.  Selecting What to Protect  	49
             3.3.1.1.  Ecological Relevance	50
             3.3.1.2.  Susceptibility to Known or Potential Stressors  	51
             3.3,1.3.  Representation of Management Goals	 54
        3.3.2.  Defining Assessment Endpoints	55
   3.4.  CONCEPTUAL MODELS	59
        3.4.1.  Risk Hypotheses	60
        3.4.2.  Conceptual Model Diagrams	61
        3.4.3.  Uncertainty in Conceptual Models	64
   3.5.  ANALYSIS PLAN	66
        3.5.1.  Selecting Measures	 67
        3.5.2.  Relating Analysis Plans to Decisions	69

4.  ANALYSIS PHASE	71
   4.1.  EVALUATING DATA AND MODELS FOR ANALYSIS . . .	75
        4.1.1.  Strengths and Limitations of Different Types of Data  	76
        4.1.2.  Evaluating Measurement or Modeling Studies	79
             4.1.2.1.  Evaluating the Purpose and Scope of the Study	80
                                    10                   Proposed Guidelines

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                            CONTENTS (Continued)


             4.1.2.2.  Evaluating the Design and Implementation of the Study  ....  82
       4.1.3.  Evaluating Uncertainty	.'	83
   4.2.  CHARACTERIZATION OF EXPOSURE	89
       4.2.1.  Exposure Analyses	90
             4.2.1.1.  Describe the Source	90
             4.2.1.2.  Describe the Distribution of the Stressor or Disturbed
                     Environment	93
             4.2.1.3.  Describe Contact or Co-occurrence	%	98
       4.2.2.  Exposure Profile	102
   4.3.  CHARACTERIZATION OF ECOLOGICAL EFFECTS	  105
       4.3.1.  Ecological Response Analysis	 105
             4.3.1.1.  Stressor-Response Analysis	 106
             4.3.1.2.  Establishing Cause and Effect Relationships (Causality)  ...  112
             4.3.1.3.  Linking Measures of Effect to Assessment Endpoints   	  116
       4.3.2.  Stressor-Response Profile	126

5.   RISK CHARACTERIZATION	129
   5.1.  RISK ESTIMATION	131
       5.1.1.  Risk Estimates Expressed as Qualitative Categories	  131
       5.1.2.  Single-Point Estimates  	132
       5.1.3.  Estimates Incorporating the Entire Stressor-Response Relationship .  . .  137
       5.1.4.  Estimates Incorporating Variability in Exposure or Effects  	  139
       5.1.5.  Estimates Based on Process Models	 142
       5.1.6.  Field Observational Studies  	143
   5.2.  RISK DESCRIPTION	144
       5.2.1.  Lines of Evidence	144
       5.2.2.  Determining Ecological Adversity 	 146
   5.3.  REPORTING RISKS	151

6.   RELATING ECOLOGICAL INFORMATION TO RISK MANAGEMENT
   DECISIONS	153

7.   TEXT NOTES	'.	156

APPENDIX A: CHANGES FROM EPA'S  ECOLOGICAL RISK ASSESSMENT
FRAMEWORK  	198

APPENDIX B:  KEY TERMS  	205

APPENDIX C: CONCEPTUAL MODEL EXAMPLES	214
APPENDIX D: ANALYSIS PHASE EXAMPLES	221

                                      11                    Proposed Guidelines

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                        CONTENTS (Continued)
APPENDIX E: CRITERIA FOR DETERMINING ECOLOGICAL ADVERSITY:A
            HYPOTHETICAL EXAMPLE	231

REFERENCES  	234
                                12                 Proposed Guidelines

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                                 LIST OF FIGURES
Figure 1-1.  The framework for ecological risk assessment	27
Figure 1-2.  The ecological risk assessment framework, with an expanded view of each
            phase  	34
Figure 3-1.  Problem formulation phase   	45
Figure 3-2.  Elements of a conceptual model diagram	63
Figure 4-1.  Analysis phase	73
Figure 4-2.  A simple example of a stressor-response relationship	  107
Figure 5-1.  Risk characterization 	130
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 . .  133
Figure 5-3. • Risk estimation techniques:  comparison of point estimates with associated
            uncertainties  	136
Figure 5-4.  Risk estimation techniques:  stressor-response curve versus a cumulative
            distribution of exposures	138
Figure 5-5.  Risk estimation techniques:  comparison of exposure distribution of an
            herbicide in surface waters with freshwater single-species toxicity data  . .  141
                                  LIST OF TABLES
Table 4-1.  Uncertainty Evaluation in the Analysis Phase	85
                                          13
Proposed Guidelines

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                              LIST OF TEXT NOTES


Text Note 1-1.  Related Terminology	156
Text Note 1-2.  Flexibility of the Framework Diagram	 .	  156
Text Note 1-3.  The Iterative Nature of Ecological Risk Assessment	  157
Text Note 2-1.  Who Are Risk Managers?	-.  .  158
Text Note 2-2.  Who Are Risk Assessors?	159
Text Note 2-3.  Questions Addressed by Risk Managers and Risk Assessors  	  159
Text Note 2-4.  The Role of Interested Parties	160
Text Note 2-5.  Sustainability as a Management Goal	  161
Text Note 2-6.  Management Goals for Waquoit Bay	  162
Text Note 2-7.  Questions to Ask About Scope and Complexity	  .  163
Text Note 3-1.  Avoiding Potential Shortcomings Through Problem Formulation  ....  163
Text Note 3-2.  Uncertainty in.Problem Formulation	  164
Text Note 3-3.  Initiating a Risk Assessment:  What's Different When Stressors, Effects, or
                 Values Drive the Process?	  .  164
Text Note 3-4.  Assessing Available Information:  Questions to Ask Concerning Source,
               Stressor, and Exposure Characteristics, Ecosystem Characteristics, and
               Effects	•. .	166
Text Note 3-5.  Salmon and Hydropower: Why Salmon Would Provide the Basis for an
               Assessment Endpoint	  167
Text Note 3-6.  Cascading Adverse Effects:  Primary (Direct) and Secondary (Indirect)    168
Text Note 3-7.  Sensitivity and Secondary Effects:  The Mussel-Fish Connection  ....  169
Text Note 3-8.  Examples of Management Goals and Assessment Endpoints  	  170
Text Note 3-9.  Common Problems in Selecting Assessment Endpoints  	  171
Text Note 3-10.         What Are Risk Hypotheses and Why Are They Important? . .  171
Text Note 3-11.      '   Examples of Risk Hypotheses	  172
Text Note 3-12.         What Are the Benefits of Developing Conceptual Models?  . .  173
Text Note 3-13.         Uncertainty in Problem Formulation	  173
Text Note 3-14.         Examples of Assessment Endpoints and Measures	  174
Text Note 3-15.         Selecting What to Measure	175
Text Note 3-16.         How Do Water Quality Criteria Relate to Assessment Endpoints?176
Text Note 3-17.         Data Quality Objectives (DQO) Process	  177
Text Note 4-1.  Data Collection and the Analysis Phase	'.	  178
Text Note 4-2.  The American National Standard for Quality Assurance	  178
Text Note 4-3.  Questions for Evaluating a Study's Utility for Risk Assessment	  179
Text Note 4-4.  Considering the Degree of Aggregation in Models	  179
Text Note 4-5.  Questions for Source Description	  179
Text Note 4-6.  Questions to Ask in Evaluating Stressor Distribution	  180
Text Note 4-7.  General Mechanisms of Transport and Dispersal .	  181
Text Note 4-8.  Questions to Ask in Describing  Contact or Co-occurrence   	  181

Text Note 4-9.  Example of an Exposure Equation:  Calculating a Potential Dose

                                        14                     Proposed Guidelines

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                         LIST OF TEXT NOTES (Continued)


               vialngestion	182
Text Note 4-10.         Measuring Internal Dose Using Biomarkers and Tissue Residues 183
Text Note 4-11.         Questions Addressed by the Exposure Profile  	  183
Text Note 4-12.         Questions for Stressor-Response Analysis	  184
Text Note 4-13.         Qualitative Stressor-Response Relationships	  184
Text Note 4-14.         Median Effect Levels	184
Text Note 4-15.         No-Effect Levels Derived From Statistical Hypothesis Testing  185
Text Note 4-16.         General Criteria for Causality	 186
Text Note 4-17.         Koch's Postulates  	186
Text Note 4-18.         Examples of Extrapolations to Link Measures of Effect to
   Assessment
               Endpoints	,	187
Text Note 4-19.         Questions Related to Selecting Extrapolation Approaches  ...  187
Text Note 4-20.         Questions to Consider When Extrapolating From Effects Observed
   in the
               Laboratory to Field Effects of Chemicals	 188
Text Note 4-21.         Questions Addressed by the Stressor-Response Profile	  189
Text Note 5-1.         Using Qualitative Categories to Estimate Risks of an Introduced
   Species  	189
Text Note 5-2.         Applying the Quotient Method	 190
Text Note 5-3. Comparing an Exposure Distribution With a Point Estimate of Effects .  190
Text Note 5-4. Comparing Cumulative Exposure and Effects Distributions for Chemical
               Stressors  	191
Text Note 5-5. Estimating Risk With Process Models	 192
Text Note 5-6. An Example of Field Methods Used for Risk Estimation	 193
Text Note 5-7. What Are Statistically Significant Effects?	 193
Text Note 5-8. Possible Risk Assessment Report Elements	 194
Text Note 5-9. Clear, Transparent, Reasonable, and Consistent Risk Characterizations  194
Text Note 6-1. Questions Regarding Risk Assessment Results	 196
Text Note 6-2. Risk Communication Considerations for Risk Managers	 197
                                          15                      Proposed Guidelines

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                               EXECUTIVE SUMMARY








       The ecological problems facing environmental scientists and decisionmakers are




numerous and varied.  Growing concern over potential global climate change, loss of




biodiversity,  acid precipitation, habitat destruction, and the effects of multiple chemicals on




ecological systems has highlighted the need for flexible problem-solving approaches that can




link ecological measurements and data with the decisionmaking needs of environmental




managers.  Increasingly, ecological risk assessment is being suggested as a way to address




this wide array of ecological problems.




       Ecological risk assessment "evaluates the likelihood that adverse ecological effects



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




1992a).  It is a process for organizing and analyzing  data, information, assumptions, and




uncertainties  to evaluate the likelihood of adverse ecological effects.  Ecological risk




assessment provides a critical element for environmental decisionmaking by giving risk




managers an  approach for considering available scientific information along with the other



factors they need to consider (e.g.,  social, legal, political, or economic) in selecting a course



of action.




       To help improve the quality and consistency of EPA's ecological risk assessments,




EPA's Risk Assessment Forum initiated development of these guidelines. The primary




audience for this document is risk assessors and risk  managers at EPA, although these




guidelines may be useful to others outside the Agency (e.g., Agency contractors, state




agencies, and other interested parties). These guidelines are based on and replace the 1992







                                          16                       Proposed Guidelines

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




They were written by a Forum work group and have been extensively revised based on




comments from outside peer reviewers as well as Agency staff.  The guidelines retain the




Framework Report's broad scope, while expanding on some framework concepts  and



modifying others to reflect Agency experiences.  EPA intends to follow these guidelines with




a series of shorter, more detailed documents that address specific ecological risk assessment



topics.  This "bookshelf approach provides the flexibility necessary to keep pace with




developments in the rapidly evolving field of ecological risk assessment while allowing time



to form consensus, where appropriate, on science policy inferences (default assumptions) to




bridge gaps in knowledge.



       Ecological risk assessment includes three primary phases (problem formulation,



analysis, and risk characterization).  Within problem formulation, important areas include



identifying goals and assessment endpoints, preparing the conceptual model, and developing



an analysis plan. The analysis phase involves  evaluating exposure to stressors and the



relationship between stressor levels and ecological effects. In risk characterization, key




elements are estimating risk through integration of exposure and stressor-response profiles,



describing risk by discussing lines of evidence and determining ecological adversity, and



preparing a report.  The interface between risk assessors and risk managers at the beginning



and end of the risk assessment is critical for ensuring that the results of the assessment can




be used to support a management decision.



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




planning activities for an ecological risk assessment.  Risk managers charged with protecting






                                           17                      Proposed Guidelines

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 environmental values can ensure that the risk assessment will provide information relevant to


 a decision.  Ecological risk assessors ensure that science is effectively used to address


 ecological concerns.  Both evaluate the potential value of conducting a risk assessment to


 address identified problems.  Further objectives of the initial planning process are to establish


 management goals that are agreed upon, clearly articulated, and contain a way to measure


 success; determine the purpose for the risk assessment by defining the decisions to be made


 within the context of the management goals;  and agree upon the scope, complexity, and


 focus of the risk assessment, including the expected output and available resources.


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


 upon which the entire risk assessment depends.  Successful completion of problem


 formulation depends on the quality of three products: assessment  endpoints, conceptual


 models, and an analysis plan. Since problem formulation is inherently interactive and


 iterative, not linear, substantial reevaluation is expected to occur within and among all


 products of problem formulation.


       Assessment endpoints are "explicit expressions of the actual environmental value that


 is to be protected" (U.S. EPA, 1992a) that link the risk assessment to management concerns.


 Assessment  endpoints include both a valued ecological entity and an attribute of that entity


 that is important to protect and potentially at  risk (e.g., nesting and feeding success of piping


plovers  or areal extent and patch size of eelgrass).  For a risk assessment to have scientific


validity, assessment endpoints must be ecologically relevant to the ecosystem they represent
      :                                 !

and susceptible to the stressors of concern. Assessment endpoints  that represent societal


values and management goals are more effective in that they increase the likelihood that the


         ...... I

                                           18                       Proposed Guidelines

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risk assessment will be used in management decisions.  Assessment endpoints that fulfill all




three criteria provide the best foundation for an effective risk assessment.



       Potential interactions between assessment endpoints and stressors are explored by




developing a conceptual model. Conceptual models link anthropogenic activities with



stressors and evaluate interrelationships between exposure pathways, ecological effects,  and



ecological receptors.  Conceptual models include two principal components:  risk hypotheses




and a conceptual model diagram.



       Risk hypotheses describe predicted relationships between stressor, exposure, and




assessment endpoint response.  Risk hypotheses are hypotheses in the broad scientific sense;




they do not necessarily involve statistical testing of null and alternative hypotheses or any



particular analytical approach.  Risk hypotheses may predict the effects of a stressor (e.g., a




chemical release) or they may postulate what stressors may have caused observed ecological



effects.  Key risk hypotheses are identified for subsequent evaluation in the risk assessment.



       A useful way to express the relationships described by the risk hypotheses is through




a  diagram of a conceptual model.  Conceptual model diagrams are useful tools for



communicating important pathways in a clear and concise way and for identifying major



sources of uncertainty. Risk assessors can use these diagrams and risk hypotheses to identify




the most important pathways and relationships that will be evaluated in the analysis phase.



Risk assessors justify what will be done as well as what will not be done in the assessment in



an analysis plan. The analysis plan also describes the data and measures to be used in  the




risk assessment and how risks  will be characterized.
                                            19                       Proposed Guidelines

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       The analysis phase, which follows problem formulation, includes two principal

activities: characterization of exposure and characterization of ecological effects.  The
                                                                                   *»
process is flexible, and interaction between the ecological effects and exposure evaluations is

recommended.  Both activities include an evaluation of available  data for scientific credibility

and relevance to assessment endpoints and the conceptual model.  In exposure

characterization, data analyses describe the source(s) of stressors, the distribution of stressors

in the environment, and the contact or co-occurrence of stressors with ecological receptors.

In ecological effects characterization,  data analyses may evaluate stressor-response

relationships or evidence that exposure to a stressor causes an observed response.

      ' The products of analysis are summary profiles that describe exposure and the stressor-

response relationships.  Exposure and stressor-response profiles may be written documents or

modules of a larger process model. Alternatively, documentation may be deferred until risk

characterization. In any case,  the objective is to ensure that the information needed for risk

characterization has been collected and evaluated.

       The exposure profile identifies receptors and exposure pathways and describes the

intensity and spatial and temporal extent of exposure.  The exposure profile also describes

the impact of variability and uncertainty on exposure estimates and reaches a conclusion

about the likelihood that exposure will occur.

       The stressor-response profile may evaluate single species,  populations, general trophic

levels, communities, ecosystems, or landscapes—whatever is appropriate for the assessment

endpoints.   For example, if a single species is affected, effects should represent appropriate

parameters such as effects on mortality, growth, and reproduction, while at the community


                                            20                       Proposed Guidelines

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level, effects may be summarized in terms of structure or function depending on the




assessment endpoint.  The stressor-response profile summarizes the nature and intensity of



effect(s), the time scale for recovery (where appropriate), causal information linking the




stressor with observed effects, and uncertainties associated with the analysis.



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




characterization, risks are estimated and interpreted and the strengths, limitations,



assumptions, and major uncertainties are summarized. Risks are estimated by integrating



exposure and stressor-response profiles using a wide range of techniques such as comparisons




of point estimates or distributions of exposure and effects data, process models, or empirical




approaches such as field observational data.



       Risk assessors describe risks by evaluating the evidence supporting or refuting the risk




estimate(s) and interpreting the adverse effects on the assessment endpoint.  Criteria for



evaluating adversity include the nature and intensity of effects, spatial and temporal scales,




and the potential for recovery.  Agreement among different lines of evidence of risk




increases  confidence in the conclusions of a risk assessment.



       When risk characterization is complete, a report describing the risk assessment can be




prepared. The report may be relatively brief or extensive depending on the nature and the




resources available for the assessment and the information required to  support a risk



management decision.  Report elements may include:



        •  A description of risk assessor/risk  manager planning results.



        •  A review of the conceptual model and the assessment endpoints.




        •  A discussion of the major data  sources and analytical procedures used.






                                           21                      Proposed Guidelines

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       •  A review of the stressor-response and exposure profiles.




      ; •  A description of risks to the assessment endpoints, including risk estimates and




          adversity evaluations.




       •  A summary of major areas of uncertainty and the approaches used to address



          them.




      ; •  A discussion of science policy judgments or default assumptions used to bridge



          information gaps, and the basis for these assumptions.




To facilitate understanding, risk assessors should characterize risks "in a manner that is




clear, transparent,  reasonable, and consistent with other risk characterizations of similar




scope prepared across programs in the Agency" (U.S. EPA, 1995c).




       After the risk assessment is completed,  risk managers may consider whether




additional follow-up activities are required.  Depending on the importance of the assessment,,




confidence level in  the assessment results, and available resources, it may be advisable to



conduct another iteration of the risk assessment in order to facilitate a final management



decision.  Ecological risk assessments are frequently designed in sequential tiers that proceed




from simple, relatively inexpensive evaluations to more costly and complex assessments.




Initial tiers are based on conservative assumptions, such as maximum exposure and




ecological sensitivity. When an early tier cannot sufficiently define risk to support a




management  decision, a higher assessment tier that may require either additional data or




applying more refined analysis techniques to available data may be needed.  Higher tiers




provide more ecologically realistic assessments while making less conservative assumptions



about exposure and effects.







                                           22                       Proposed Guidelines

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       Another option is to proceed with a management decision based on the risk




assessment and develop a monitoring plan to evaluate the results of the decision.  For



example, if the decision was to mitigate risks through exposure reduction, monitoring could




help determine whether the desired reduction in exposure (and effects) was achieved.



Monitoring is also critical for determining the extent and nature of any ecological recovery




that may be occurring.  Experience obtained by using focused monitoring results to evaluate




risk assessment predictions  can help improve the risk assessment process and is encouraged.



       Communicating ecological risks to the public is usually the responsibility of risk




managers.  Although the final risk  assessment document (including its risk characterization




sections) can be made available to the public, the risk communication process is best served




by tailoring information to a particular audience.  It is important to clearly describe the



ecological resources at risk, their value, and the costs of protecting (and failing to protect)



the resources (U.S. EPA, 1995c).  The degree of confidence in the risk assessment and the




rationale for risk management decisions and options for reducing risk are also important




(U.S. EPA, 1995c).
                                          23                       Proposed Guidelines

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                                1.   INTRODUCTION



       Ecological risk assessment is a process for organizing and analyzing data,

information, assumptions, and uncertainties to evaluate the likelihood of adverse ecological

effects. Ecological risk assessment provides a critical element for environmental

decisionmaMng. This document, which is structured by the stages of the ecological risk

assessment process, provides Agency personnel with broad guidelines that can be adapted to

their specific requirements.

      , The full definition of ecological risk assessment is:

       "Theprocess that evaluates the likelihood that adverse ecological effects may

       occur or are occurring as a result of exposure to one or more stressors."

       (U.S. EPA,  1992a)

Several terms within this definition require further explanation:

       •  "... likelihood ..."  Descriptions of risk may  range from qualitative

          judgements to quantitative probabilities. While risk assessments may include

          quantitative risk estimates, the present state of the science often may not support

          such quantisation.  It is preferable to convey qualitatively the relative magnitude of

          uncertainties to a decision maker than to ignore them because they may not be

          easily understood or estimated.

       •  "... adverse ecological effects,. . ."  Ecological risk assessments deal with

          anthropogenic changes that are considered undesirable because they alter valued

          structural or functional characteristics of ecological systems.  An evaluation of
      i              .          '

                                          24                      Proposed Guidelines

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          adversity may consider the type, intensity, and scale of the effect as well as the

          potential for recovery.

       •  "... may occur or are occurring ..."  Ecological risk assessments may be

          prospective or retrospective.  Retrospective ecological risk assessments evaluate

          the likelihood that observed ecological effects are associated with previous or

          current exposures to stressors.  Many of the same methods and approaches are

          used for both prospective and retrospective assessments, and in the best case, even

          retrospective assessments contain predictive elements linking sources, stressors

          and effects.

       •  "... one or more stressors."  Ecological risk assessments may address single or

          multiple chemical, physical, or biological stressors.  (See Appendix A for

          definitions of stressor types.) Because risk assessments are conducted to provide

          input to management decisions, this document focuses on stressors generated or

          influenced by anthropogenic activity.

       The overall ecological risk assessment process is shown in figure 1-1.1   Problem

formulation is the first phase of the process where the assessment purpose is stated, the

problem defined, and the plan for analyzing and characterizing risk determined.  In the

analysis phase, data on potential effects of and exposures to stressor(s) identified during

problem formulation are technically evaluated and summarized as exposure and stressor-

response profiles.  These profiles are integrated  in risk characterization to estimate the
    1 Changes in process and terminology from EPA's previous ecological risk assessment
framework (U.S. EPA, 1992a) are summarized in Appendix A.

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likelihood of adverse ecological effects.  Major uncertainties, assumptions, and strengths and




limitations of the assessment are summarized during this phase.  While discussions between




risk assessors and risk managers are emphasized both at risk assessment initiation (planning)




and completion (communicating results), these guidelines maintain a distinction between risk




assessment and risk management.  Risk assessment focuses on evaluating the likelihood of




adverse effects, and risk management involves the selection of a course of action in response




to an identified risk that is based on many factors (e.g., social, legal, political,  or economic)




in addition to the risk assessment results.   Section 1.1 briefly discusses how risk assessments



fit into a decisionmaking context.
                                           26                       Proposed Guidelines

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Figure 1-1.  Hie framework for ecological risk assessment (U.S. EPA, 1992a).
Ecological risk assessment is shown as a three-phase process including problem
formulation, analysis, and risk characterization.  Important activities associated with
ecological risk assessment include discussions between risk assessors and risk
managers and data acquisition and monitoring. Ecological risk assessments
frequently follow an iterative or tiered approach.


                                         27                      Proposed Guidelines

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       The bar along the right side of figure 1-1 shows several activities that are associated




with risk assessments:  data acquisition, iteration, and monitoring. While the risk assessment




may focus on data analysis and interpretation, acquiring the appropriate quantity and quality




of data for use in the process is critical. If such data are lacking, the risk assessment may




stop until the necessary data are acquired.  As discussed in text note  1-3, the process is more




frequently iterative than linear, since the evaluation of new data or information may require



revisiting a part of the process or conducting a new assessment.




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




process.  For example, monitoring can provide the impetus for initiating a risk assessment by




identifying changes in ecological condition.  In addition, monitoring data can be used to




evaluate the results predicted by the risk assessment. For example, follow-up studies could




be used to determine whether techniques used to mitigate pesticide exposures in field




situations hi fact reduce exposure and effects as predicted by the risk assessment.  Or,  for a



hazardous waste site, monitoring might help verify whether source reduction resulted in




anticipated ecological changes. Monitoring is also critical for determining the extent and




nature of any ecological recovery that may occur.  The experience gained by comparing



monitoring results  to evaluate risk assessment predictions can help improve the risk




assessment process and is encouraged.








1.1.  ECOLOGICAL RISK ASSESSMENT IN A MANAGEMENT CONTEXT




       Ecological risk assessment is important for environmental decisionmaking because of



the high cost of eliminating environmental risks associated with human activities and the







                                          28                      Proposed Guidelines

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necessity of making regulatory decisions in the face of uncertainty (Ruckelshaus, 1983;




Suter, 1993a).  Even so, ecological risk assessment provides only a portion of the



information required to make risk management decisions. This section describes how




ecological risk assessments fit into a larger management framework.








1.1.1.   Contributions of Ecological Risk Assessment to Environmental Decisionmaking




       At EPA, ecological risk assessments provide input to a diverse set of environmental




decisionmaking processes, such as the regulation of hazardous waste sites, industrial



chemicals, and pesticides, or the management of watersheds affected by multiple nonchemical




and chemical stressors.  The ecological risk assessment process has several features that




contribute to managing ecological risks:



       •  In a risk assessment, changes in ecological effects can be expressed as a function



          of changes in exposure to a stressor.  This inherently predictive aspect of risk



          assessment may be particularly useful to the decision maker who must evaluate




          tradeoffs and examine different alternatives.



       •  Risk assessments include an explicit evaluation of uncertainties. Uncertainty



          analysis lends credibility and a degree of confidence to the assessment that can



          strengthen its use in decisionmaking and can help the risk  manager focus research



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



       •  Risk assessments can provide a basis for comparing, ranking, and prioritizing



          risks.  The risk manager can use such information to help  decide among several




           management alternatives.






                                           29                      Proposed Guidelines

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       •  Risk assessments emphasize consistent use of well-defined and relevant endpoints.




          This is especially important for ensuring that the results of the risk assessment will



          be expressed in a way that the risk manager can use.








1.1.2.  Risk Management Considerations




       Although risk assessors and risk managers interact both at the initiation and




completion of an ecological risk assessment (sections 2, 3, 5 and 6), risk managers decide




how to use the results of an assessment and whether a risk assessment should be conducted.




While a detailed review of management issues is beyond the scope of these guidelines, key



areas are highlighted below.




       •  A risk assessment is not always required for management action.  When faced




          with compelling  ecological risks and an immediate need to make a decision, a risk




          manager might proceed without an assessment,  depending on professional




          judgment and statutory requirements (U.S.  EPA, 1992a).




       •  Because initial management decisions or statutory requirements significantly affect



          the scope of an assessment,  it is important, where possible,  for risk managers to



          consider a broader scope or alternative actions for a risk assessment.  Sometimes a




          particular statute may require the risk assessment to focus on one type of stressor




          (e.g., chemicals) when there are other, perhaps more important, stressors in the




          system (e.g., habitat alteration). In other situations, however, it may be possible



          to evaluate a range of options.  For example, before requesting an ecological risk



          assessment of alternative sites for the construction and operation of a dam for







                                         30                      Proposed Guidelines

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         hydroelectric power, risk managers may consider larger issues such as the need




         for the additional power and the feasibility of using other power-generating




         options.



      •  Risk managers consider many factors in making regulatory decisions.  Legal



         mandates may require the risk manager to take certain courses of action.  Political




         and social considerations may lead the risk manager to make decisions that are



         either more or less ecologically protective.  Economic factors may  also be critical.




         For example, a course of action that has the least ecological risk may be too



         expensive or technologically infeasible.  If cost-benefit analysis is applied,




         ecological risks may be translated into monetary terms to be compared against




         other monetary considerations.  Thus, while ecological risk assessment provides




         critical information to risk managers, it is only part of the whole environmental




         decisionmaking process.








1.2.   SCOPE AND INTENDED AUDIENCE



      These guidelines replace the EPA report, Framework for Ecological Risk Assessment




(referred to as the Framework Report,  U.S.  EPA, 1992a). As a next step in  developing



Agency-wide guidance, the  guidelines expand on and modify framework concepts to reflect



Agency experience in  the several years since the Framework Report was published (see




Appendix A). Like the Framework Report,  these guidelines are broad in scope,  describing



general principles and providing numerous examples to show how ecological risk assessment




can be applied to a wide range of systems, stressors, and biological, spatial, and temporal






                                          31                      Proposed Guidelines

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scales.  This approach provides flexibility to permit EPA's offices and regions to develop




specific guidance suited to their particular needs.




       The proposed policies set out in this document are intended as internal guidance for




EPA.   Risk assessors and risk managers at EPA are the primary audience for this document,




although these guidelines may be useful to others outside the Agency (e.g., Agency




contractors, state agencies, and other interested parties). These Proposed Guidelines are not




intended, nor can they be relied upon, to create any rights enforceable by any party in




litigation with the United States,,  This document is not a regulation and is not intended for




EPA regulations. These Proposed Guidelines set forth current scientific thinking and




approaches for conducting and evaluating ecological risk assessments. As with other EPA




guidelines (developmental toxicity, 56 PR 63798-63826; exposure assessment, 57 PR 22888-




22938; and carcinogenicity, 61 PR 17960-18011), EPA will revisit these guidelines as



experience and scientific consensus evolves.




       These  guidelines do not provide detailed guidance in specific areas  nor are they



intended to be highly prescriptive.  These guidelines describe the strengths and limitations of




alternate approaches and may not apply to a particular situation based upon the



circumstances. Agency preferences  are expressed where possible, but because ecological risk




assessment is  a rapidly evolving discipline, requirements for specific approaches could soon




become outdated.  EPA intends to develop a series of shorter, more detailed guidance




documents on specific ecological risk assessment topics after these guidelines have been



finalized.
                                           32                       Proposed Guidelines

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      These guidelines emphasize processes and approaches for analyzing data rather than




specific data collection techniques, methods, or models.  Also, while these guidelines discuss




the interface between the risk assessor and risk manager, a detailed discussion of the use of




ecological risk assessment information in the risk management process (e.g., the economic,



legal, political,  or social implications of the risk assessment results) is beyond the scope of




these guidelines.  Other EPA publications discuss how ecological concerns have been




addressed in decisionmaking at EPA (U.S. EPA, 1994g) and provide an introduction to




ecological risk assessment for risk managers (U.S. EPA, 1995b).








1.3.  GUIDELINES ORGANIZATION



       These guidelines are structured according to the ecological risk assessment process as




shown in figure 1-2.  Within problem formulation (section 3), important areas addressed



include identifying goals  and assessment endpoints, preparing the conceptual model, and




developing an analysis plan.  The analysis phase (section 4) involves evaluating exposure to



stressors and the relationship between stressor levels and ecological effects.  In risk



characterization (section 5), key elements are estimating  risk through integration of exposure



and stressor-response profiles and describing risk by discussing lines of evidence, interpreting




adversity, and summarizing uncertainty. In addition, discussions between the risk assessor



and risk manager at the beginning (section 2) and end of the risk assessment (section 6) are




highlighted.
                                           33                      Proposed Guidelines

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Figure 1-2.  The ecological risk assessment framework, with an expanded view of each
phase. Within each phase, rectangular boxes designate inputs, hexagon-shaped boxes
indicate actions, and circular boxes 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.

                                         34                      Proposed Guidelines

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       The reader may notice that cross-cutting topics are covered in several sections.  These




include uncertainty, models, evaluating data, causality, linking measures of effect to



assessment endpoints,  and identifying ecological effects.  Considerations appropriate to the




different phases of ecological risk assessment are discussed.
                                            35                       Proposed Guidelines

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                     2.  PLAJXNING THE RISK ASSESSMENT:




         DIALOGUE BETWEEN RISK MANAGERS AND RISK ASSESSORS








       The purpose for an ecological risk assessment is to produce a scientific evaluation of




ecological risk that enables managers to make informed environmental decisions.  To ensure




that ecological risk assessments meet risk managers' needs, a planning dialogue between risk




managers and risk assessors (see text notes 2-1 and 2-2) is a critical first step toward




initiating problem formulation and plays a continuing role during the conduct of the risk



assessment.  Planning is the beginning of a necessary interface between risk managers and




risk assessors and is represented by a side box in the ecological risk assessment diagram (see




figure 1-2).  It is due to the importance  of planning and the significant role it plays in




ecological risk assessments that this section on planning is incorporated into guidelines on




ecological risk assessment.  However, it is imperative to remember that the planning process




is distinct from the scientific conduct of an ecological risk assessment.  This distinction helps



ensure that political and  social issues, while helping to define the objectives for the risk



assessment, do not bias the scientific evaluation of risk.




       During the planning dialogue, risk managers and risk assessors each bring important




perspectives to the table.  In general, risk managers are charged with protecting societal




values  (e.g., human health and the environment) and must ensure that the risk assessment




will provide information relevant to a decision,  To meet this charge, risk managers describe



why the risk assessment  is needed, what decisions it will support, and what they want to




receive from the risk assessor.  It is also helpful for managers to consider what problems







                                         36                      Proposed Guidelines

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they have encountered in the past when trying to use risk assessments for decisionmaking.




In turn, it is the ecological risk assessors' role to ensure that science is effectively used to



address ecological concerns.  Risk assessors describe what they can provide to the risk




manager, where problems are likely to occur, and where uncertainty may be problematic.



Both evaluate the potential value of conducting a risk assessment to address identified




problems.



       Both risk managers and risk assessors are responsible for coming to agreement on the




goals,  scope, and timing of a risk assessment and the resources that are available and



necessary to achieve the goals.  Together they use information on the area's ecosystems,




regulatory endpoints, and publicly perceived environmental values to interpret the goals for



use in  the ecological risk assessment.   Examples of questions risk managers and risk




assessors may address during planning are provided in text note 2-3.



       The first step in planning may be to determine if a risk assessment is the best option




for making the decision required.  Questions concerning what is known about the degree  of



risk, what management options are available to mitigate or  prevent it, and the value of



conducting a risk assessment compared with other ways of learning about and addressing



environmental concerns are asked during these discussions.  In some cases, a risk assessment




may add little value to the decision process.  It is important for the risk manager and risk



assessor to explore alternative options for addressing possible risk before continuing to the




next planning stage (see section 1.1.2).



       Once the decision is made to conduct a risk assessment, planning focuses on (1)



establishing management goals that are agreed on, clearly articulated, and contain a way to






                                           37                       Proposed Guidelines

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measure success; (2) defining the decisions to be made within the context of the management




goals; and (3) agreeing on the scope, complexity, and focus of the risk assessment, including




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




achieve these objectives, risk managers and risk assessors must each play an active role in



planning the risk assessment.








2.1.  ESTABLISHING MANAGEMENT GOALS




       Management goals for a risk assessment are established by risk managers but are




derived in a variety of ways. Many Agency risk assessments are conducted based on legally




established management goals (e.g., national regulatory programs generally have




management goals written into the law governing the program).  In this case, goal setting




was previously completed through public debate in establishing the law.  In most cases,




legally established management goals do not provide sufficient guidance to the risk assessor.




For example, the objectives under the Clean Water Act to "protect and maintain the




chemical, physical and biological integrity of the nation's waters" are open to considerable



interpretation.  Agency managers and staff often interpret the law in regulations and




guidance. Significant interaction between the risk assessor and risk manager may be needed




to translate the law into  management goals for a particular location or circumstance.




      As the Agency increasingly emphasizes "place-based" or "community-based"



management of ecological resources  as recommended in the Edgewater Consensus  (U.S.




EPA, 1994e), management goals  take on new!significance for the ecological risk assessor.




Management goals for "places" such as watersheds are formed  as a consensus based on







                                         38                      Proposed  Guidelines

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diverse values reflected in Federal, state, and local regulations; constituency group agendas;




and public concerns.  Significant interactions among a variety of interested parties are



required to generate agreed-on management goals for the resource (see text note 2-4).  Public




meetings,  constituency group meetings, evaluation of resource management organization



charters, and other means of looking for management goals shared by these diverse groups




may be necessary.  Diverse risk management teams may elect to use social scientists trained



in consensus-building methods to help establish management goals.   While management goals




derived in this way may require further definition (see text note 2-5), there is increased




confidence that these goals are supported by the audience for the risk assessment.




       Regardless of how management goals are established,  goals that explicitly define



which ecological values are to be protected are more easily used to design a risk assessment




for decisionmaking than general management goals.  Whenever goals are general, risk



assessors  must interpret those goals into ecological values that can be measured or estimated



and ensure that the managers agree with their interpretation (see text note 2-6).  Legally



mandated goals generally are interpreted by  Agency managers and staff.  This interpretation



may be performed once and then applied to the multiple similar assessments  (e.g. evaluation



of new chemicals).  For other risk assessments, the interpretation is unique to the ecosystem




being assessed and  must be done on a case-by-case basis as part of the planning process.








2.2.   MANAGEMENT DECISIONS



       A  risk assessment is shaped by the kind of decision it will support. When a




management decision is explicitly stated and closely aligned to management actions, the






                                           39                       Proposed Guidelines

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scope, focus, and conduct of the risk assessment are well defined by the specificity of the

decision to be made.  Some of these risk assessments are used to help establish national

policy that will be applied consistently across the country (e.g., premanufacture notices for

new chemicals, protection of endangered species).  Other risk assessments are designed for a

specific site (e.g., hazardous waste site clean-up level).  When decision options,(e.g.,

decision criteria in the data quality objectives process, U.S. EPA, 1994d; see section 3.5.2

for more details) are known prior to the risk assessment, a number of assumptions are

inherent in those options that need to be explicitly stated during planning.  This ensures that

the decision criteria are not alteiing the scientific validity of the risk assessment by

inappropriately applying assumptions or unnecessarily limiting the variables.  For many risk

assessments, there may be a range of possible management options for managing risk.  When

different management options have been identified (e.g., leave alone, clean up, or pave a

contaminated site), risk assessment can be used to predict potential risk across the range of

these management options.

       Risk assessments may be designed to provide guidance for management initiatives for

a region or watershed where  multiple stressors,  ecological values, and political factors

influence decisionmaking. These risk assessments require great flexibility and breadth and

may use national risk-based information and site-specific risk information in conjunction with

regional evaluations of risk.   As risk assessment is more frequently used to support

landscape-scale management decisions, the diversity, breadth, and complexity of the risk

assessments increase significantly and may include evaluations that focus on understanding

ecological processes influenced by a! diversity of human actions and management options.
                       |

                                          40                      Proposed Guidelines

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




require significant planning to establish the purpose, scope, and complexity of the




assessment.








2.3.  SCOPE AND COMPLEXITY OF THE RISK ASSESSMENT



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



regional, or local, the resources available for conducting the risk assessment determines how




extensive and  complex it can be within this framework and the level of uncertainty that can




be expected.  Each  risk assessment is constrained by the availability of data, scientific




understanding, expertise, and financial resources.  Within these constraints there is much to



consider when designing a risk assessment.  Risk managers and risk assessors must discuss in




detail the nature of the decision (e.g., national policy, local economic impact), available



resources, opportunities for increasing the resource base (e.g., partnering, new data



collection, alternative analytical tools), and the output that will provide the best information




for decisions required (see text note 2-7).



       Part of the agreement on scope and complexity is based on the maximum uncertainty




that is acceptable in whatever decision the risk assessment supports. The lower the tolerance




for uncertainty, the greater the scope and complexity needed in the risk assessment.  Risk



assessments completed in response to legal mandates and likely to be challenged in court



often require rigorous attention to acceptable levels of uncertainty to ensure that the



assessment will be  used in a decision.  A frank discussion is needed between the risk




manager and risk assessor on sources of uncertainty in the risk assessment and ways






                                           41                      Proposed Guidelines

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 uncertainty can be reduced (if necessary) through selective investment of resources.  Where




 appropriate, planning could account for the iterative nature of risk assessment and include




 explicitly defined  steps.  These steps may take the form of "tiers" that represent increasing




 levels of complexity and investment, with each tier designed to reduce uncertainty. The plan




 may include an explicit definition of iterative steps  with a description of levels of investment




 and decision criteria for each  tier.  Guidance on addressing  the interplay of management




 decisions, study boundaries, data needs, uncertainty, and specifying limits on decision errors




 may be found in EPA's guidance on data quality objectives  (U.S. EPA, 1994d).








 2.4.   PLANNING OUTCOME




       The planning phase is complete when agreements are reached on the management




 goals, assessment  objectives, the focus and scope of the risk assessment, resource




 availability, and the type of decisions the risk assessment is  to support.  Agreements may



 encompass the technical approach to be taken in a risk assessment as determined by the



 regulatory or management context and reason for initiating the risk assessment (see section




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



 frame over which  stressors or effects will be evaluated).




       In mandated risk assessments, planning agreements are often codified in regulations,



 and little documentation of agreements is warranted. In risk assessments where planning




 decisions can be highly variable, a summary of planning agreements may be important for




 ensuring that the.risk assessment remains consistent with early agreements. A summary can




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







                                          42                        Proposed  Guidelines

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response to new information.  There is no defined format, length, or complexity for a




planning summary.  It is a useful reference only and should be tailored to the complexity of




the risk assessment it represents. However, a summary is recommended to help ensure




quality communication between and among risk managers and risk assessors and to document




the decisions that have been agreed upon.



       Once planning is complete, the formal process of risk assessment begins through the




initiation of problem formulation.  During problem formulation, risk assessors should




continue the dialogue with risk managers following assessment endpoint selection and once



the analysis plan is completed.  At these points,  potential problems can be identified before




the risk assessment proceeds.
                                          43                       Proposed Guidelines

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                       3.   PROBLEM FORMULATION PHASE





       Problem formulation is a formal process for generating and evaluating preliminary


hypotheses about why ecological effects have occurred, or may occur, from human activities.


As the first stage of an ecological risk assessment, it provides the foundation on which the


entire assessment depends.  During problem formulation, management goals developed


during planning are evaluated to establish objectives for the risk assessment, the problem is


defined, and the plan for analyzing data and characterizing risk is determined.   Any


deficiencies in problem formulation will compromise all subsequent work on the risk


assessment (see text note 3-1).





3.1.  PRODUCTS OF PROBLEM FORMULATION


       Successful completion of problem formulation depends on the quality of three


products:  (1) assessment endpoints that adequately reflect management goals and the


ecosystem they represent, (2) conceptual models that describe key relationships between a


stressor and assessment endpoint or among several stressors and assessment endpoints, and


(3) an analysis plan. Essential to the development of these products are the effective


integration and evaluation of available information.


       The following discussion focuses on the products of problem formulation and the


information that determines the nature of those products.  The products are featured in the


problem formulation diagram as circles (see figure 3-1). The types of information that must


be evaluated to generate those products are shown in the hexagon.
                                                           i



                                         44                       Proposed Guidelines

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Figure 3-1. Problem formulation phase.






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       To enhance clarity, the organization of the following discussion follows the above


topics.  However, problem formulation is not necessarily completed in the order presented


here. First, the order in which products are produced is directly related to why the


ecological risk assessment is initiated, as addressed in section 3.2.  Second, problem


formulation is inherently interactive and iterative, not linear.  Substantial reevaluation is


expected to occur within and among all products of problem  formulation.





3.2.  INTEGRATION OF AVAILABLE INFORMATION


       The foundation for problem formulation is the integration of available information on


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


risk, and ecological effects (see figure 3-1).  When key information is of the appropriate type


and sufficient quality and  quantity, problem formulation can proceed effectively.  When key


information is unavailable in one or more areas, the risk assessment may be temporarily


suspended while new data are collected.  If new data cannot be collected, then the risk


assessment will depend on what is known and what can be extrapolated from that


information.   Complete information is not available at the beginning of many risk


assessments.  When this is the case, the process of problem formulation assists in identifying


where key data are missing and provides the  framework for further research where more data


are needed.  Where data are few, a clear articulation of the limitations of conclusions, or
                                            }

uncertainty, from the risk assessment  becomes increasingly critical in risk characterization


(see text note 3-2).
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       The reason why an ecological risk assessment is initiated directly influences what




information is available at the outset, and what information must be found.  A risk



assessment can be initiated because a known or potential stressor may be released into the



environment, an adverse  effect or change in condition is observed, or better management of



an important ecological value (e.g., valued ecological entities such as species, communities,




ecosystems or places) is desired.  Risk assessments are sometimes initiated for two or all




three of these reasons.



       Risk assessors beginning with information about the source or stressor will seek




available information on  the effects the stressor might be associated with and the ecosystems




that it will likely be found in. Risk assessors beginning with information about an observed




effect or change in condition will need to seek information about potential stressors and



sources. Risk assessors  starting with concern  over a particular ecological value may need



additional information on the specific condition or effect of interest, the ecosystems




potentially at risk, and potential stressors and sources.



       The initial use of available information is a scoping process similar to that used to




develop environmental impact statements.  During this process, data  and information (i.e.,



actual, inferred, or estimated) are considered to ensure that nothing important is overlooked.



A comprehensive evaluation  of all information provides the framework for generating a large



array of risk hypotheses  to consider (see section 3.4.1). After the initial scoping process,




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




scrutinized as the risk assessor proceeds through problem  formulation.   When analysis plans
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are formed, data validity becomes a significant factor to consider.  Issues relating to




evaluating data quality are discussed in the analysis phase (see section 4.1).




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




escalate.  Ecosystems characteristics directly influence when, how, and why particular




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




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




an understanding of their interactions.  Risk assessments for a region or watershed, where




multiple stressors are the rule, require consideration of ecological processes operating at



larger .spatial scales.




       Despite limitations on what is known about ecosystems and the stressors influencing




them, the process of problem formulation offers a valuable systematic approach for




organizing and evaluating available information on all stressors  and possible effects in a way




that can be useful to risk assessors and decisionmakers.  Text note 3-3 provides a series of



questions that risk assessors should attempt to answer using available information, many of




which were  drawn from  Barnthouse and Brown (1994).  This exercise will help risk




assessors identify known and unknown relationships, both of which are important in problem



formulation.




       Problem formulation proceeds  with the identification of assessment endpoints,  and the



development of conceptual models and the analysis plan (discussed below).  However, the




order in which these task are done i.s influenced by the  reason for initiating  the assessment



(text note 3-4). Early recognition that initiation effects the order of product generation  will



help facilitate the development of problem formulation.







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3.3.  SELECTING ASSESSMENT ENDPOINTS



       Assessment endpoints are "explicit expressions of the actual environmental value that



is to be protected" (U.S. EPA,  1992a).  Assessment endpoints are critical to problem




formulation because they link the risk assessment to management concerns and they are




central to conceptual model development.  Their relevance to ecological risk assessment is




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



risk management decisions depends on how well they represent measurable characteristics of



the ecosystem that adequately represent management goals.  The selection of ecological



concerns and assessment endpoints in  EPA has traditionally been done internally by




individual Agency program offices (U.S. EPA,  1994g). More recently, Agency activities




such as the watershed protection approach and community-based environmental protection




have used contributions by interested parties in the selection of ecological concerns and



assessment endpoints.  This  section describes criteria for selecting and defining assessment




endpoints.








3.3.1.  Selecting What to Protect



       The ecological resources selected to represent management goals for environmental



protection are reflected in the assessment endpoints that drive ecological risk assessments.



Assessment endpoints often reflect environmental values that are protected by law, provide




critical resources,  or provide an ecological function that would be significantly impaired (or



that society would perceive as having been impaired) if the resource were altered.
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       Although many potential, assessment endpoints may be identified, considering the


 practicality of using particular assessment endpoints will help refine selections.  For example,


 when the attributes of an assessment endpoint can be measured directly, extrapolation is


 unnecessary; therefore this uncertainty is not introduced into the results.  Assessment


 endpoints that cannot be measured directly but can be represented by measures that are easily


 monitored and modeled still provide a good foundation for the risk assessment.  Assessment


 endpoints that cannot be linked with measurable attributes are not appropriate for a risk


 assessment.


       Three principal criteria are used when selecting assessment endpoints: (1) their


 ecological relevance, (2) their susceptibility to the known or potential stressors, and (3)


 whether they represent management goals.  Of these three criteria, ecological relevance and
                  !

 susceptibility are essential for selecting assessment endpoints that are scientifically valid.


 Rigorous  selection based on these criteria must be maintained. However, to increase the


 likelihood that the risk assessment  will be used in management decisions, assessment


 endpoints that represent societal values and management goals are more effective.   Given the


 complex functioning of ecosystems and the interdependence of ecological entities, it is likely


 that assessment endpoints can be selected that are responsive to management goals while


 meeting scientific criteria.  This provides a way to address changes that may occur  over time


in the public's perception of ecological value (e.g., wetlands viewed as infested swamps 30


years ago  are considered prime wildlife habitat today; Suter, 1993a).  Assessment endpoints


that meet all three criteria provide the best foundation for an effective risk assessment (e.g.,


see text note 3-5).




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3.3.1.1.   Ecological Relevance



       Ecologically relevant endpoints reflect important characteristics of the system and are




functionally related to other endpoints (U.S. EPA,  1992a).  These are endpoints that help



sustain the natural structure, function, and biodiversity of an ecosystem.  For example,




ecologically relevant endpoints may contribute to the food base (e.g., primary production),



provide habitat, promote regeneration of critical resources (e.g.,  decomposition or nutrient




cycling), or reflect the structure of the community, ecosystem, or landscape (e.g., species




diversity or habitat mosaic). Changes in ecologically relevant endpoints can result in




unpredictable and widespread effects.



       Ecological relevance becomes most important when risk assessors are identifying the




potential cascade of adverse effects that could result from the loss or reduction of one or



more species or a change in ecosystem function (see text note 3-6).  Careful selection of



assessment endpoints that address both specific organisms of concern and landscape-level




ecosystem processes becomes increasingly important in landscape-level risk assessments. In




some cases, it may be possible to select one or more species and an ecosystem process to




represent larger functional community or ecosystem processes.



       Determining ecological relevance in specific cases requires expert judgment based on



site-specific information, preliminary site surveys,  or other available information.  The less




information available, the more critical it is to have informed  expert judgment to ensure




appropriate selections.  If assessment endpoints in  a risk assessment are not ecologically



relevant,  the results of the risk assessment may predict risk to the assessment endpoints
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selected but seriously misrepresent risk to the ecosystem of concern, which could lead to




misguided management.








3.3.1.2.   Susceptibility to Known or Potential Stressors




       Ecological resources are considered susceptible when they are sensitive to a human-




induced stressor to which they are exposed.  Sensitivity refers to how readily an ecological




entity is affected by a particular stressor.  Sensitivity is directly related to the mode of action




of the stressors.  For example, chemical sensitivity is influenced by individual physiology




and metabolic pathways.  Sensitivity also is influenced by individual and community life-




history characteristics.  For example, species with long life cycles and low reproductive rates




will be more vulnerable to extinction from increases in mortality than those with short life




cycles and high reproductive rates.  Species with large home ranges may be more sensitive to




habitat fragmentation when the fragment is smaller than their required home range compared



to those with smaller home ranges within a fragment.  However, habitat fragmentation may




also affect species with small home ranges where migration is a necessary part of their life




history and fragmentation prevents exchange among subpopulations.




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




Frequently,  young animals are more sensitive to stressors than adults.  For example, Pacific



salmon eggs and fry are very sensitive to sedimentation from forest logging practices and




road building because they can be smothered.  Age-dependent sensitivity, however, is not




only in the young.  In  many species, special events like migration (e.g., in birds) and




molting (e.g., in harbor seals) represent significant energy investments that make these






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organisms more vulnerable to an array of possible stressors. Finally, sensitivity may be




increased by the presence of other stressors or natural disturbances.  For example, the




presence of insect pests and disease may make plants more sensitive to damage from ozone




(Heck, 1993).



       Measures of sensitivity may include mortality or adverse reproductive effects from



exposure to toxics, behavioral abnormalities,  avoidance of significant food sources or nesting



sites, or  loss of offspring to predation because of the proximity of stressors such as noise,




habitat alteration or loss, community structural changes, or other factors.




       Exposure is the other key determinant in susceptibility.  Exposure can- mean co-




occurrence, contact,  or the absence of contact, depending on the stressor and assessment



endpoint (see section 4 for more discussion).  The amount and conditions of exposure



directly influence how an ecological entity will respond to a stressor. Thus, to determine




what entities are susceptible, it is important to consider information on the proximity of an




ecological resource to the stressor, the timing of exposure (both in terms of frequency and




duration), and the intensity of exposure occurring during sensitive life stages of the




organisms.



       Adverse effects of a particular stressor may be important during one part of an



organism's life cycle, such as early development or reproduction.  Adverse effects may result



from exposure to a stressor or to the absence of a necessary resource during a critical life



stage.  For example, if fish are unable to find suitable nesting sites during their reproductive




phase, risk is significant even when water quality is  high and food sources abundant.  The



interplay between life stage and stressors can be very complex (e.g., see text note 3-7).






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       Exposure may occur in one place or time, and effects may not occur until another




place pr time.  Both life history characteristics, as described under sensitivity, and the



circumstances of exposure, influence susceptibility in this case.  For example, the




temperature of the incubation medium of marine turtle eggs affects the sex ratio of the




offspring.  But the population impacts of a change in incubation temperature may not be




observable until years later when the cohort of affected turtles begins to reproduce. Delayed




effects and multiple stressor exposures add complexity to evaluations of susceptibility.  For




example, although toxicity tests may determine receptor sensitivity to one stressor, the degree




of susceptibility may depend on the co-occurrence of another stressor that significantly alters




receptor response. Conceptual models (see section 3.4) need to reflect these factors.  If a




species is unlikely to be exposed to the stressor of concern, it is inappropriate as an



assessment endpoint.








3.3.1.3.   Representation of Management Goals




       Ultimately, the value of a risk assessment depends on  whether it can support quality



management decisions.  Risk managers are more willing to use a risk assessment for making




decisions when the assessment is based on values and organisms that people care about.




These values, interpreted from management goals (see section 2) into assessment endpoints,




provide a defined and measurable entity for the risk assessment. Candidates for assessment



endpoints might include entities such as endangered species, commercially or recreationally




important species, functional attributes that support food .sources or flood control (wetland
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water sequestration, for example), or aesthetic values, such as clean air in national parks or




the existence of charismatic species like eagles or whales.



       Selection of assessment endpoints based on public perceptions alone could lead to




management decisions that do not consider important ecological information. While being




responsive to the public is important, it does not obviate the requirement for scientific




validity as represented by the sections on ecological relevance and susceptibility.  Many



ecological entities and attributes meet the necessary scientific rigor  as assessment endpoints;




some will be recognized as valuable by risk managers and the public, and others will not.




Midges, for example, can represent the base of a complex food web that supports a popular




sports fishery.  They may also be considered  pests.  While both midges and fish are



important ecological entities in this ecosystem and represent key components of the aquatic




community, selecting the fishery as the assessment endpoint and using midges as a critical



ecological entity to measure allow both entities to be used in the risk assessment. This




choice maintains the scientific validity  of the  risk assessment and is responsive to



management concerns.  In those cases  where  the risk assessor identifies a critical assessment



endpoint that is unpopular with the public, the risk assessor may find it necessary to present




a persuasive case in its  favor based on scientific arguments.








3.3.2.  Defining Assessment Endpoints



       Assessment endpoints provide a transition between broad management goals and the



specific measures used in an assessment.  They help assessors identify measurable attributes
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to quantify and predict change.  Assessment endpoints also help the risk assessor determine




whether management goals have been or can be achieved (see text note 3-8).




       Two elements are required to define an assessment endpoint. The first is the valued




ecological entity.  This can be a species (e.g., eelgrass, piping plover), a functional group of




species (e.g., raptors), an ecosystem function or characteristic (e.g., nutrient cycling), a




specific valued habitat (e.g.,  wet meadows) or a unique place (e.g., a remnant of native



prairie). The second is the characteristic about the entity of concern that is important to




protect and potentially at risk.  For example, it is necessary to define what is important for




piping plovers (e.g., nesting  and  feeding success), eelgrass (e.g., areal extent and patch




size), and wetlands (e.g., endemic wet meadow community  structure and function).  For an




assessment endpoint to provide a clear interpretation of the management goals and the basis




for measurement in the risk assessment, both an entity and an attribute are required.




       Assessment endpoints are distinct from management  goals.  They do not represent




what the managers or risk assessors want to achieve.  As such they do not contain words like




"protect," "maintain," or "restore," or indicate a direction for change such as "loss" or
"increase.'
       Defining assessment endpoints can be difficult. They may be too broad, vague, or




narrow, or they may be inappropriate for the ecosystem requiring protection.  "Ecological



integrity" is a frequently cited, but vague, goal and an even more vague assessment endpoint.



"Integrity" can only be used effectively when its meaning is explicitly characterized for a




particular ecosystem, habitat, or entity.  This may be done by selecting key entities and




processes of an ecosystem and describing characteristics that best represent integrity for that







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system. For example, general goals for Waquoit Bay were translated into several assessment




endpoints, including "estuarine eelgrass abundance and distribution" (see text note 2-6).



       Expert judgment and an understanding of the characteristics and function of an




ecosystem are important for translating general goals into usable assessment endpoints.



Endpoints that are too narrowly defined, however, may not support effective risk



management. For example, if an assessment is focused on protecting the habitat of an



endangered species, the risk assessment may overlook important characteristics of the




ecosystem and fail to include critical variables (see text note 3-7).




       Assessment endpoints must be  appropriate for the ecosystem of concern. Selecting a




game fish that grows well in reservoirs may meet a "feasible" management goal, but would



be inappropriate for evaluating risk from a new hydroelectric dam if the ecosystem of



concern is a stream in which salmon spawn  (see text note 3-5). Although the game fish will



satisfy the fishable goal and may be highly desired by local fishermen, a reservoir species



does not represent the ecosystem at risk.  A vague "viable fish populations" assessment



endpoint substituted by  "reproducing populations of indigenous salmonids" could therefore




prevent the development of an inappropriate risk assessment.



       Clearly defined assessment endpoints provide direction and boundaries for  the risk



assessment and can minimize miscommunication and reduce uncertainty.  Assessment



endpoints directly influence the type, characteristics, and interpretation of data and



information used for analyses and the scale and character of the assessment.  For example,



an assessment endpoint such as "egg production of pond invertebrates" defines local




population characteristics and requires very  different types of data and ecosystem






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 characterization compared with "watershed aquatic community structure and function."  If




 concerns are local, the assessment endpoints should not focus on landscape concerns.  Where




 ecosystem processes and landscape mosaics are of concern, survival of a particular species




 would provide inadequate representation.  Assessment endpoints that are poorly defined,




 inappropriate, or at the incorrect scale can be very problematic.  Common problems




 encountered in selecting assessment endpoints are summarized in text note 3-9.




       The presence of multiple stressors should  influence the selection of assessment




 endpoints. When it is possible to select one assessment endpoint that is sensitive to many of




 the identified stressors, yet responds in different ways to different stressors, it is possible to




 consider the combined effects of multiple stressors while still discriminating among effects.




 For example, if recruitment of a fish population is the assessment endpoint, it is important to




 recognize that recruitment  may be adversely affected at several life stages, in different




 habitats, through different ways, by different stressors.  The measures of effect, exposure,




 and ecosystem and receptor characteristics chosen to evaluate recruitment provide a basis for



 discriminating among different stressors, individual effects, and their combined effect.




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



 carefully selected.  For example, the National Crop Loss Assessment Network (Heck, 1993)




 selected crop yields as the assessment endpoint to evaluate the cumulative effects of multiple




 stressors.  Although the primary stressor was ozone, the crop-yield endpoint allowed them to




consider the effects of sulfur dioxide and soil moisture. As Barnthouse et al. (1990) pointed



out, an endpoint should be selected so that all the effects can be expressed in the same units




(e.g., the abundance of 1-year-old fish to assess the effects from toxicity, fishing pressure,







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and habitat loss).  These considerations are important when selecting assessment endpoints

for addressing the combined effect of multiple stressors. However, in situations where

multiple stressors act on the structure and function of aquatic and terrestrial communities in a

watershed ecosystem, an array of assessment endpoints that represent the ecosystem

community and processes is more effective than a single endpoint.  When based on differing

susceptibility to an array of stressors, the careful selection of assessment endpoints can help

risk assessors distinguish among effects from  diverse stressors.  Exposure to multiple

stressors may lead to effects at different levels of biological organization, for a cascade of

adverse responses that should be considered.
       f,
       Although assessment endpoints must be defined in terms of measurable attributes,

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

methods,  models, and data are currently available. If the response of an assessment endpoint

cannot be directly measured, it may be predicted from responses of surrogate or similar

entities. Although for practical reasons it is helpful to use assessment endpoints that have

well-developed test methods, field measurement techniques, and predictive models (see Suter,

1993a), it is not necessary for these methods  to be established protocols. Measures that will

be used to evaluate assessment endpoint response to exposures for the risk assessment are

often identified during conceptual model development and specified in the analysis plan.  See

section 3.5 for issues surrounding the selection of measures.

       It is important for risk assessors and risk managers to agree that selected assessment

endpoints represent the management goals for the particular ecological value. The rationale
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for their selection should be clear.  Assessment endpoint selection is an important risk

manager-risk assessor checkpoint during problem formulation.



3.4.  CONCEPTUAL MODELS

       A conceptual model in problem formulation is a written description and visual

representation of predicted responses by ecological entities to stressors to which they are

exposed, and the model includes ecosystem processes that influence these responses.

Conceptual models represent many relationships (e.g., exposure scenarios  may qualitatively

link lahd-use activities to sources and their stressors, may describe primary, secondary, and

tertiary exposure pathways, and may describe co-occurrence between exposure pathways,

ecological effects, and ecological receptors).

       Conceptual models for ecological risk assessments are developed from information

about stressors, potential exposure, and predicted effects on an ecological entity (the
                  i
assessment endpoint).  Depending on why a risk assessment is initiated, one or more of these

categories of information is known at the outset.  The process of creating conceptual models

helps identify the unknown elements.

       The complexity of the conceptual model depends on the complexity of the problem,

number of stressors, number of assessment endpoints, nature of effects, and characteristics of

the ecosystem!  For single stressors and  single assessment endpoints, conceptual  models can

be relatively simple relationships. In situations where conceptual models describe both the

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

diverse stressors and assessment  endpoints (e.g., assessments initiated because of important


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values), several submodels normally will be required to describe individual pathways. Other




models may then be used to explore how these individual pathways interact.



       Conceptual models consist of two principal products:



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



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




          selection



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








3.4.1.  Risk Hypotheses



       Hypotheses are assumptions made in order to evaluate logical or empirical



consequences (Merriam-Webster, 1972). Risk hypotheses are statements of assumptions




about risk based on available information (see text note 3-10).   They are formulated using a



combination of expert judgment and information on the ecosystem  at risk, potential sources




of stressors, stressor characteristics, and observed or predicted  ecological effects on selected



or potential assessment endpoints.  These hypotheses may predict the effects of a stressor



event before it happens, or they may postulate why observed ecological effects  occurred and



ultimately what sources and stressors caused the effect. Depending on the scope of the risk



assessment, the set of risk hypotheses may be very simple, predicting the potential effect of



one stressor on one receptor, or extremely complex, as is typical in value-initiated risk




assessments that often include prospective and retrospective hypotheses about the effects of




multiple complexes of stressors on diverse ecological receptors.
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       Although risk hypotheses should be developed even when information is incomplete,




the amount and quality of data will affect the specificity and level of uncertainty associated




with risk hypotheses and the conceptual models they form. When preliminary information is




conflicting, risk hypotheses can be constructed specifically to differentiate among competing




predictions.  The predictions can then be evaluated systematically either by using available




data during the analysis phase or by collecting new data before proceeding with the risk




assessment.  Hypotheses and predictions set a framework for using data to evaluate




functional relationships (e.g., stressor-response curves).




       Early conceptual models are intended to be broad in scope, identifying as  many




potential relationships as possible.  As more information is incorporated, the plausibility of




specific risk hypotheses helps risk assessors sort through potentially large numbers of




stressor-effect relationships and the ecosystem processes that influence them to identify those




risk hypotheses most appropriate for the analysis phase.  It is then that justifications for




selecting and omitting selecting hypotheses are documented.  Examples of risk hypotheses are




provided in  text note 3-11.








3.4.2.  Conceptual Model Diagrams



       Conceptual model diagrams may be based  on theory and logic, empirical data,




mathematical models, or probability models.  They are useful tools for communicating




important pathways in a clear and concise way and can be used to ask new questions about




relationships that help generate plausible risk hypotheses.  Some of the benefits gained by




developing conceptual models are featured in text note 3-12.
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       Conceptual model diagrams frequently contain boxes and arrows to illustrate




relationships (see figure 3-2 and Appendix C).  When constructing these kinds of flow



diagrams, it is helpful to use distinct and consistent shapes to distinguish stressors,




assessment endpoints, responses, exposure routes, and ecosystem processes.  Although flow




diagrams are often used to illustrate conceptual  models, there is no set configuration for




conceptual model diagrams.  Pictorial representations can be more effective (e.g., Bradley



and Smith, 1989). Regardless of the configuration, a significant part of the usefulness of a




diagram is linked to the detailed written descriptions and justifications for the pathways and



relationships shown.  Without this, diagrams can misrepresent the processes illustrated.
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Proposed Guidelines

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Figure 3-2. Elements of a conceptual model diagram. Illustrating the linkages between
sources, stressors, and responses is an important function of the conceptual model
diagram.  However, the arrows in the diagram do not necessarily reflect the order in
which this information is  developed. See Appendix C for specific examples.

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       When developing diagrams to represent a conceptual model, factors to consider



include the number of relationships depicted, the comprehensiveness of the information, the




certainty surrounding a pathway, and the potential for measurement.  The number of



relationships that can be depicted in one flow diagram depends on how comprehensive each



relationship is.  The more comprehensive, the fewer relationships that can be shown with




clarity.  Flow diagrams that highlight where data  are abundant or scarce can provide insights




on how the analyses should be approached and can be used to show the degree of confidence




the risk assessor has in the relationship.  Such flow diagrams can also help communicate why




certain pathways were pursued and others were not.



       Diagrams provide a working and dynamic representation of relationships.  They




should be used to explore different ways of looking at a problem before selecting one or




several to guide analysis. Once the risk hypotheses are selected and flow diagrams drawn,



they set the framework for final planning for the analysis phase.








3.4.3.  Uncertainty in Conceptual Models



       Conceptual model development may account for one of the most important sources of



uncertainty in a risk assessment.  If important relationships are missed or specified



incorrectly, risks could be seriously under- or overestimated in the risk characterization



phase. Uncertainty can arise from lack of knowledge on how the ecosystem functions,




failing to identify and interrelate temporal and spatial parameters, not describing a stressor or



suite of stressors, or not recognizing secondary effects.  In some cases, little may be known



about how a stressor moves through the environment or causes adverse effects.  In most






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cases, multiple stressors are the norm and a source of confounding variables, particularly for




conceptual models that focus on a single stressor.  Opinions of experts on the appropriate




conceptual model configuration may differ.  While simplification and lack of knowledge may




be unavoidable, risk assessors  should document what is known, justify the model, and rank




model components in terms  of uncertainty (see Smith and Shugart, 1994).




       Uncertainty associated with conceptual models can be reduced by developing




alternative conceptual models for a particular assessment to explore possible relationships. In




cases where more than one conceptual model is plausible, the risk assessor must decide




whether it is feasible to follow separate models through the analysis phase or whether the




models can be combined into a better conceptual model. It is important to revisit, and if




necessary revise,  conceptual models during risk assessments to incorporate new information




and recheck the rationale. It is valuable to present conceptual models to risk managers to



ensure the models communicate well and address key concerns the managers have.  This



check for completeness and  clarity provides an opportunity to assess the need for changes




before analysis begins.




       Throughout the process of problem formulation, ambiguities, errors, and




disagreements will occur, all of which contribute to uncertainty.  Wherever possible,  these




sources of uncertainty  should be eliminated through better planning.  Because all uncertainty



cannot be eliminated, a clear description of the nature of the uncertainties should be clearly




summarized at the close of the problem formulation. Text note 3-13 provides




recommendations for describing uncertainty in problem formulation.
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       The hypotheses considered most likely to contribute to risk are pursued in the analysis




phase.  As discussed previously, it is important to provide the rationale for selecting and




omitting risk hypotheses and to acknowledge data gaps and uncertainties.








3.5.  ANALYSIS PLAN




       An analysis plan can be a usual final stage of problem formulation, particularly in the




case of complex assessments.  Here, risk hypotheses are evaluated to determine how they



will be assessed using available and new data.  The analysis plan can also delineate the




assessment design, data needs, measures, and methods for conducting the analysis phase of




the risk assessment.  The analysis plan may be relatively brief or extensive depending on the



nature of the assessment.




       The analysis plan includes the most important pathways and relationships identified



during problem formulation that will be pursued in the analysis phase. It is important for the




risk assessor  to describe what will be done and, in particular, what will not be done.  It is



important to address issues concerning the level of confidence needed for the management




decision relative to the confidence that can be expected from an analysis in order to



determine data needs and evaluate whether one analytical approach may be better than




another. When new data are needed to conduct analyses,  the feasibility of obtaining the data



should be taken into account.




       The selection of critical relationships in the conceptual model to pursue' in analysis is



based on several criteria, including:



       •  Availability of information







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       •   Strength of information about relationships between stressors and effects




       •   The assessment endpoints and their relationship to ecosystem function




       •   Relative importance or influence and mode of action of stressors




       •   Completeness of known exposure pathways.




       In situations where data are few and new data cannot be collected, it is possible to




combine existing data with extrapolation models so that alternative data sources may be used.




This allows the use of data from  other locations or on other organisms where similar




problems exist and data are available.  For example, the relationship between nutrient




availability and algal growth is well established.  Although there will be differences in how




the relationship is manifested based on the dynamics of a particular ecosystem, the




relationship itself will tend to be consistent. When using data that require extrapolation, it is




important to identify the source of the data, justify the extrapolation method and discuss



major uncertainties apparent at this point.




       Where data are not available, recommendations for new data collection should be part



of problem formulation.  An iterative, phased, or tiered approach (see text note 1-3) to the



risk assessment may be selected to provide an opportunity for early management decisions on



issues that can be addressed using available data.  A decision to conduct a new iteration is




based on the results of any previous iteration  and proceeds using new data collected as




specified in the analysis plan.  When new data collection  cannot be obtained, pathways that




cannot be assessed are a source of uncertainty and should be described  in the analysis plan.
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3.5.1.  Selecting Measures



       It is in the analysis planning stage that measures are identified to evaluate the risk




hypotheses.  There are three categories of measures.  Measures of effect are measures used




to evaluate the response of the assessment endpoint when exposed to a stressor (formerly




measurement endpoints). Measures of exposure are measures of how exposure may be




occurring, including how a stressor moves through the environment and how it may  co-occur



with the assessment endpoint. Measures of ecosystem and receptor characteristics include




ecosystem characteristics that influence the behavior and location of assessment endpoints,




the distribution of a stressor, and life history characteristics  of the assessment endpoint that




may affect exposure or response to the stressor.  These diverse measures increase in



importance as the complexity of the assessment increases and are particularly important for



risk assessments initiated to protect ecological values (see text notes 3-14 and 3-15 for more




information).



       Text note 3-16, which describes water quality criteria, provides one example of how



goals, endpoints, and measures are related.  Although water quality criteria are often



considered risk-based, they do not measure exposure.  Instead, the water quality criteria




provide an effects benchmark for decisionmaking.  Within that benchmark there are  a



number of assumptions about significance (e.g., aquatic communities will be protected by




achieving a benchmark derived from individual species' toxicological responses to a single



chemical) and exposure (e.g., 1-hour and 4-day exposure averages).  Assumptions embedded




in decision rules should be articulated (see section 3.5.2).
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       The analysis plan provides a synopsis of measures that will be used to evaluate risk

hypotheses.  Potential extrapolations,  model characteristics,  types of data (including quality),

and planned analyses (with specific tests for different types of data) are described.  The plan

should discuss how the results will  be presented upon completion. The analysis plan

provides the basis for making selections of data sets that will be used for the risk assessment.

       The plan includes explanations of how data analyses  will distinguish among

hypotheses, an explicit expression of the approach to be used, and justifications for the
      '                        i
elimination of some hypotheses and selection of others. It includes the measures selected,

analytical methods planned, and the nature of the risk characterization options and

considerations that will be generated (e.g., quotients, narrative discussion,  stressor-response

curve with probabilities).  An analysis plan is enhanced if it contains explicit statements for

how measures were selected, what  they are intended to evaluate, and which analyses they

support. During analysis planning, uncertainties associated with selected measures and

analyses are articulated and, where possible,  plans for addressing them are made.

                                        i

3.5.2.  Relating Analysis Plans to Decisions

       The analysis plan is a risk manager-risk assessor checkpoint and an appropriate time

for technical review.  Discussions between the risk assessors and risk managers can help

ensure that the analyses will provide the type and extent of information that the manager can

use for decisionmaking.  These discussions may also identify what can and cannot be done

based on the preliminary evaluation of problem formulation, including which relationships to

portray for the risk management decision.  A reiteration of the planning discussion is
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important to ensure that the appropriate balance among the requirements for the decision,




data availability, and resource constraints is established for the risk assessment.




       The elements of an analysis plan share significant similarities with the data quality



objectives (DQO) process (see text note 3-17), which emphasizes identifying the problem by



establishing study boundaries and determining necessary data quality, quantity, and




applicability to the problem being evaluated.  The DQO guidance is a valuable reference for




risk assessors  (U.S. EPA, 1994d).



       The most important difference between problem formulation and DQO is the presence




of a decision rule that defines a benchmark for a management decision before the risk




assessment is completed.  The decision rule step specifies the statistical parameter that



characterizes the population,  specifies the action level for the study, and combines outputs




from the previous DQO steps into an "if . . . then" decision rule that defines conditions



under which the decision maker will choose alternative options.  This approach provides the




basis for establishing null and alternative hypotheses appropriate for statistical testing for



significance.  While this approach is appropriate for some risk assessments, many risk




assessments  are not based on benchmark decisions.  Presentation of stressor-response curves



with uncertainty bounds will  be more appropriate than statistical testing of decision criteria




where risk managers must evaluate the range of stressor effects to which they compare a



range of possible management options.



       The analysis plan is the final  synthesis before the risk assessment proceeds.  It



summarizes  what has been done during problem formulation, shows how the plan relates to




management decisions that must be made, and indicates how data and analyses will be used






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to estimate risks.  When it is determined that the problem is clearly defined and there are




enough data to proceed, analysis begins.
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                                4.  ANALYSIS PHASE








       The analysis phase consists of the technical evaluation of data to reach conclusions




about ecological exposure and the relationships between the stressor and ecological effects.




During analysis, risk assessors use measures of exposure, effects, and ecosystem and



receptor attributes to evaluate questions and issues that were identified in problem




formulation.  The products of analysis are summary profiles that describe exposure and the



stressor-response relationship.  When combined, these profiles provide the basis for reaching




conclusions about risk during the risk characterization phase.



       The conceptual model and analysis plan developed during problem formulation




provide the basis for the analysis phase. By the start of analysis, the assessor should know



which stressors and  ecological effects are the focus of investigation and whether secondary



exposures or effects will be considered.  In the analysis plan, the assessor identified the



information needed to perform the analysis phase.  By the start of analysis, these data should




be available (text note 4-1).



       The analysis  phase is composed of two principal activities, the characterization of




exposure and characterization of ecological effects (figure 4-1).   Both activities begin by



evaluating data (i.e., the measures of exposure, ecosystem and receptor characteristics, and




effects) in terms of their scientific credibility and  relevance to the assessment endpoint and



conceptual model (discussed in section 4.1). In exposure characterization (section 4.2), these




data are then analyzed to describe the source, the distribution of the stressor in the



environment, and the contact or co-occurrence of the stressor with ecological receptors. In






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ecological effects characterization (section 4.3), data are analyzed to describe the relationship




between the stressor and response and to evaluate the evidence that exposure to the stressor




causes the response (i.e., stressor-response analyses).  In many cases, extrapolation will be




necessary to link the measures of effect with the assessment endpoint.
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Figure 4-1.  Analysis phase.
                                        75
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        Conclusions about exposure and the relationship between the stressor and response are




 summarized in profiles.  The exposure and stressor-response profiles (sections 4.2.2 and




 4.3.2, respectively) provide the opportunity to review what has been learned during the




 analysis phase and summarize this information in the most useful format for risk




 characterization. Depending on the risk assessment, these profiles may take the form of a




 written document or modules of a larger process model. Alternatively, documentation may




 be deferred until risk characterization. In any case,  the purpose of these profiles is to ensure




 that the information needed for risk characterization  has been collected and evaluated.




       This process is intended to be flexible, and interaction between  the ecological effects




 characterization and exposure characterization is recommended.  When secondary stressors




 and  effects are of concern, exposure and effects analyses are conducted iteratively for




 different ecological entities, and the analyses can become so intertwined that they are difficult



 to differentiate.  The bottomland hardwoods example (Appendix D) illustrates this type of



 assessment. This assessment examined potential changes in the plant and animal




 communities under different flooding  scenarios.  The stressor-response  and exposure analyses




 were combined within the FORFLO model for primary effects on the plant community and



 within the Habitat Suitability  Index for secondary effects on the animal community.




       In addition,  the.distinction between the analysis phase and risk estimation can become



blurred.  For example, the model results developed for the bottomland hardwoods example



were used directly in risk characterization.




       The nature of the stressor (that is, whether it is chemical, physical, or biological) will




influence the types of analyses conducted and the details of implementation.  Thus, the







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results of the analysis phase may range from highly quantitative to qualitative, depending on




the stressor and the scope of the assessment.  The estimation of exposure to chemicals




emphasizes contact and uptake into the organism, and the estimation of effects often entails




extrapolation from test organisms to the organism of interest.  For physical stressors, the



initial disturbance may be most closely related to the assessment endpoint (e.g., change of



wefland to upland).  In many cases, however, secondary effects (e.g., effects on wildlife that



use the wetland) are the principal concern. The point of view taken during the analysis




phase will depend on the assessment endpoints identified during problem formulation.



Because adverse effects can occur even if receptors do not physically contact disturbed




habitat, exposure analyses may emphasize co-occurrence with physical stressors rather than



contact.  For biological stressors,  exposure analysis evaluates  entry, dispersal, survival, and




reproduction (Orr et al., 1993). Because biological stressors can reproduce, interact with



other organisms, and evolve over  time, exposure and effects cannot be quantified with



confidence.  Accordingly, exposure and effects are often assessed qualitatively by eliciting




expert opinion (Simberloff and Alexander, 1994).








4.1.   EVALUATING DATA AND MODELS FOR ANALYSIS



       In problem formulation, the assessor identifies the information needed to perform the



analysis phase and plans for collecting new data.  The first step of the analysis phase is the



critical evaluation of data and models  to ensure that they can support the risk assessment.



The  sources and evaluation of data and models are discussed in sections 4.1.1 and 4.1.2,
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respectively. The evaluation of uncertainty, an important consideration when evaluating data


and also throughout the analysis phase, is discussed in section 4.1.3.





4.1.1.  Strengths and Limitations of Different Types of Data


       The analysis phase relies on the measures identified in the analysis plan; these may


come from laboratory or field studies or may be produced as output from a model.  Data


may have been developed for a specific risk assessment or for another purpose. A strategy


that builds on the strengths of each type of data can improve confidence in the conclusions of


a risk assessment.


       Both laboratory and field studies (including field experiments and observational


studies) can provide useful data for risk assessment.  Because conditions can be controlled in


laboratory studies, responses can be less variable and smaller differences easier to detect.


However,  the controls may limit the range  of responses (for example,  animals  cannot seek


alternate food sources), so they may not reflect responses in the environment. Field surveys


are usually more representative of both exposures and effects (including secondary effects)


found in natural systems than are  estimates generated from laboratory  studies or theoretical


models. However, because conditions are not controlled, variability may be higher and it


may be difficult to detect differences. Field studies are most useful for linking stressors with

                                               i

effects when stressor and effect levels are measured concurrently.  In addition, the presence


of confounding stressors can make it difficult to attribute observed effects to specific


stressors.  Preferred field studies use designs that minimize effects of potentially confounding
                                                i

factors.  Intermediate between  laboratory and field are studies that use environmental media




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collected from the field to conduct studies of response in the laboratory.  Such studies may




improve the power to detect differences and may be designed to provide evidence of



causality.




       Most data will be reported as measurements for single variables such as a chemical



concentration or the number of dead organisms.  In some cases, however, variables are




combined into indices, and the index values are reported.  Several indices are used to




evaluate effects, for example,  the rapid bioassessment protocols (U.S. EPA, 1989a) and the




Index of Biotic Integrity, or IBI (Karr, 1981; Karr et al., 1986).  These have several



advantages (Barbour et al., 1995), including the ability to:




       •  Provide an overall indication of biological condition by incorporating many



          attributes of system structure and function, from individual to ecosystem levels




       •  Evaluate responses  from a broad range of anthropogenic stressors



       •  Minimize the limitations of individual  metrics for detecting specific types of



          responses.



       Although indices are very useful, they have several drawbacks, many of which are



associated with combining heterogeneous variables.  For example, the final value may



depend strongly on the function used to combine  variables.   Some indices  (e.g., the IBI)



combine only measures of effects.  Differential sensitivity or other factors may make it




difficult to attribute causality when many response variables are combined. Such indices may




need to be separated into their components to investigate causality (Suter,  1993b; Ott, 1978).



Interpretation becomes even more difficult when an index combines measures of exposure




and effects because double-counting may occur or changes in one variable can mask changes







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in another.  Exposure and effects measures may need to be separated in order to make


appropriate conclusions.  For these reasons, professional judgment plays a critical role in


developing and applying indices.


       Experience from similar situations is also an important data source that is particularly


useful when predicting effects of stressors that have not yet been released.  For example,


lessons learned from past experiences with related organisms are often critical in trying to


predict whether an organism will survive, reproduce,  and disperse in a new environment.


Another example is the evaluation of toxicity of new chemicals through  the use of structure-


activity relationships, or SARs (Auer et al., 1994;  Clements and Nabholz,  1994). The


simplest application of SARs is to identify a suitable analog for which data  are available to


estimate the toxicity of the compound for which data are lacking. More advanced


applications involve the use of quantitative structure-activity relationships (QSARs).  QSARs


describe the relationships between chemical structures and specific biological effects and are


derived using information on sets of related chemicals (Lipnick, 1995; Cronin and Dearden,


1995). The use of analogous da.ta without knowledge of the underlying  processes may


substantially increase the uncertainty in the risk assessment (e.g., Bradbury, 1994); however,


these data may be the only option available.


       While models  are often developed and used as part of the risk assessment, sometimes


the risk assessor relies on output of a previously developed model as input to the risk


assessment.  Models are particularly useful when measurements cannot be taken, for example


when the assessment is predicting the effects  of a chemical yet to be manufactured.  Models


can also provide estimates for times or locations that are impractical to measure and provide
                                                            f


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a basis for extrapolating beyond the range of observation. Starfield and Bleloch (1991)




caution that "the quality of the model does not depend on how realistic it is, but on how well



it performs in relation to the purpose for which it was built."  Thus, the assessor must




review the questions that need to be answered and then ensure that a model can answer those



questions.  Because models are simplifications of reality, they may not include important




processes for a particular system and may not reflect every condition in the real world.  In



addition, a model's output is only as good as the quality of its input variables, so critical




evaluation of input data is important, as is comparing model outputs with measurements in




the system of interest whenever possible.




       Data and models for risk assessment are often developed in a tiered fashion (also see




text note 1-3).  For example, simple models that err on the side of conservatism may be used



first, followed by more elaborate models that provide more realistic estimates.  Effects data



may also be collected by using a tiered approach.  Short-term tests designed to evaluate




effects such as lethality and immobility may be conducted first. If the chemical exhibits high




toxicity or a preliminary characterization indicates a risk, then more expensive, longer-term




tests that measure sublethal effects such as changes to growth and reproduction can be



conducted.  Later tiers may employ multispecies tests or field experiments.  It is important to



evaluate tiered data in light of the decision they are intended  to support; data collected for




early tiers may not be able to  support more sophisticated needs.
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4.1.2.  Evaluating Measurement or Modeling Studies

       Much of the information used in the analysis phase is available through published or

unpublished studies that describe the purpose of the study, the methods used to collect data,

and the results.  Evaluating the utility of these studies relies on careful comparison of the

objectives of the studies with the objectives of the risk assessment.  In addition, study

methods are examined to ensure that the intended objectives were met and that  the data are

of sufficient quality to support the risk assessment.  Confidence in the information and the

implications of using different studies should be described during risk characterization, when

the overall confidence in the assessment is discussed.  In addition, the risk assessor should

identify areas where existing date do not meet risk assessment needs.  In these  cases, we
                          i
recommend collecting new data.

       EPA is in the process of adopting the American Society for Quality Control's E-4

guidelines for assuring environmental data quality throughout the Agency (ASQC, 1994) (text

note 4-2).  These guidelines describe procedures for collecting  new data and provide a

valuable resource for evaluating existing studies.  (Readers are also referred to Smith and

Shugart, 1994; U.S. EPA, 1994f; and U.S. EPA, 1990,  for more  information on evaluating

data and models.)

       A study's documentation directly influences the ability to evaluate its utility for risk

assessment.  Studies should contain sufficient information so that results can be reproduced,

or at least so the details of the author's work can be accessed and  evaluated.  An additional

advantage is the ability to access findings in  their entirety; this provides the opportunity to

conduct additional analyses of the data, if needed. For models, a  number of factors increase



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the accessibility of methods and results.  These begin with model code and documentation




availability.  Reports describing model results should include all important equations, tables




of all parameter values, a description of any parameter estimation techniques, and tables or




graphs of results.



       Papers or reports describing studies may not provide all of the information needed to



evaluate a study's utility for risk assessment.  Assessors are encouraged to communicate with



the principal investigator or other study participants to gain information on study plans and




their implementation.  Questions useful for evaluating studies are shown in text note 4-3.








4.1.2.1.  Evaluating the Purpose and Scope of the Study




       The assessor must often evaluate the utility of a study that was designed for a purpose



other than risk assessment. In these cases, it is important that the objectives and scope of the




original study be examined to evaluate their compatibility with the objectives and needs of



the current risk assessment.




       An examination of objectives can identify important uncertainties and ensure that the



information is used appropriately in the assessment.  An example is the evaluation of studies



that measure condition (e.g., stream surveys, population surveys).  While the measurements



used to evaluate condition may be the same as the effects measures identified in problem




formulation, to support a causal argument, effects measures must be linked with stressors.



In the best case, this means that the stressor should be measured at the same time and place



as the effect.
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       Similarly, a model may have been developed for purposes other than risk assessment.




The model description should include the intended application, theoretical framework,




underlying assumptions, and limiting conditions.  This information can help assessors identify




important limitations in its application for risk assessment.  For example, a model developed




to evaluate chemical transport in the water column alone may have limited utility for a risk




assessment of a chemical that partitions readily into sediments.




       The variables and conditions examined by studies should also be compared with those



variables and conditions identified during problem formulation. In addition, the range of




variability explored in the study should be compared with the range of variability of interest




for the risk assessment.  For example, a study that examines  habitat needs of an animal




during the winter may miss important breeding-season requirements.  In general, studies that




minimize the amount of extrapolation needed are preferred.  These are the studies that are



designed to represent:




       •  The measures identified in the analysis plan (i.e., measures of exposure, effects,



          and ecosystem and receptor characteristics)




       •  The time frame of interest, considering seasonality and intermittent events



       •  The ecosystem and location of interest




       •  The environmental conditions of interest




       •  The exposure route of interest.
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4.1.2.2.   Evaluating the Design and Implementation of the Study




       The design and implementation of the study are evaluated to ensure that the study




objectives were met and that the information is of sufficient quality to support the purposes



of the risk assessment. The study design provides insight into the sources and magnitude of




uncertainly associated with the results (see section 4.1.3 for further discussion of



uncertainty). Among the most important design issues for studies of effects is whether a




study had sufficient power to detect important differences or changes. Because this




information is rarely reported (Peterman, 1990),  the assessor may need to calculate the




magnitude of an effect that could be detected under the study conditions (Rotenberry and




Wiens, 1985).



       Risk assessors should evaluate evidence that the study was conducted properly. For




laboratory studies, this may mean determining whether test conditions were properly




controlled and control responses were within acceptable bounds.  For field studies, issues



include the identification and control of potentially confounding variables and the careful



selection of reference sites.  For models, issues include the program's structure and logic and



the correct specification of algorithms in the model code (U.S. EPA, 1994f).



       Study evaluation is easier if a standard method or standard quality assurance/quality



control (QA/QC) protocols are available and followed by the study.  However, the assessor



still needs to consider whether the precision and accuracy goals identified in the standard



method were achieved and whether these goals are appropriate for the purposes of the risk



assessment.  For example, detection limits identified for one environmental matrix may not




be achievable for another and may be higher than concentrations of interest for the risk






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assessment.  Study results can still be useful even if a standard method was not used.




However, it does place an additional burden on both the authors and the assessors to provide



and evaluate evidence that the study was conducted properly.








4.1.3.   Evaluating Uncertainty




       Uncertainty evaluation is an ongoing theme throughout the analysis phase. The




objective is to describe,  and, where possible, quantify what is known and not known about




exposure and effects in the system of interest. Uncertainty analyses increase credibility by




explicitly describing  the magnitude and direction of uncertainties, and they provide the basis




for efficient data collection of or application of refined methods.




       U.S. EPA (1992d) discusses sources of uncertainty that arise during the evaluation of




information and conceptual model development (combined under the subject of scenario




uncertainty), when evaluating the value of a parameter (e.g., an environmental measurement




or the results of a toxicity test), and during  the development and application of models.




Uncertainty in conceptual model development is discussed in section 3.4.3.  Many of the




sources of uncertainty discussed by EPA (U.S. EPA, 1992d) are relevant to characterizing




both exposure and ecological effects; these sources  and example strategies for the analysis




phase are shown in table 4-1.
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Table 4-1. 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 and 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
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 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).         _„
                          o/
Evaluate power of designed experiments to detect
differences.

Consider taking additional data if sampling error is
too larse.
Present the upper confidence
limit on the arithmetic mean
soil'
to the~b~esFestfmate~of
arithmetic mean.
                                                                         Ground-truth remote sensins

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       Sources of uncertainty that are factors primarily when evaluating information include




 unclear communication of the information to the assessor, unclear communication about how




 the assessor handled the information, and errors in the information itself (descriptive errors).




 These sources are usually characterized by critically examining sources of information and




 documenting the rationales for the decisions made when handling it.  The discussion should




 allow the reader to make an independent judgment about the validity of the decisions reached



 by the assessor.




       Sources of uncertainty that arise primarily when estimating the value of a parameter




 include variability, uncertainty about a quantity's true value,  and data gaps.  The term




 variability is used here to describe the true heterogeneity in a characteristic influencing




 exposure or effects. Examples include the variability in soil  organic carbon, seasonal




 differences in animal diets, or differences in chemical sensitivity among different species.




 This heterogeneity is usually described during uncertainty analysis, although heterogeneity




 may not reflect a lack  of knowledge and cannot usually be reduced by further measurement.



 Variability can be described by presenting a distribution or specific percentiles from it (e.g.,



 mean and 95th percentile).




       Uncertainty about a quantity's true value may include uncertainty about its magnitude,




location, or time of occurrence. This uncertainty can usually be reduced by taking additional



measurements.  Uncertainty about a quantity's true magnitude is usually described by




sampling error (or variance in experiments) or measurement error. When the quantity of



interest is biological response, sampling error can greatly influence the ability of the study to




detect effects. Properly designed studies will specify sample  sizes that are sufficiently  large







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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 Hosteller,  1990; Petitti, 1994).  However, these




approaches have been applied primarily in the arena of human epidemiology and are still




controversial (Mann, 1990).



       Interest in quantifying spatial uncertainty has increased with the increasing  use of




geographic information systems.  Strategies include verifying the locations of remotely




sensed features,  ensuring that the spatial resolution of data or a method is commensurate with




the needs of the assessment, and using methods to describe and use the spatial  structure of




data (e.g., Cressie, 1993).



       Nearly every assessment encounters situations where data are unavailable or where




information is available on parameters that are different from those of interest for the



assessment. Examples include using laboratory  animal data to estimate a wild  animal's



response or using a bioaccumulation measurement from an ecosystem other than the one  •



interest. These  data gaps are usually bridged based on a combination of scientific data or




analyses, scientific judgement,  and policy judgement.  For example, in deriving an ambient




water quality criterion (text note 3-16), data and analyses are used to construct distributions




of species sensitivity for a particular chemical.   Scientific judgement is used to infer that



species selected for testing will adequately represent the range of sensitivity of species  in the



environment.  Policy judgement is used to define the extent to which individual species






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 should be protected (e.g., 90 vs 95 percent of the species).  It is important to differentiate




 among these elements when key assumptions and the approach used are documented.




       In some circumstances scientists may disagree on the best way to bridge data gaps.




 This lack of consensus can increase uncertainty.  Confidence can be increased through




 consensus building techniques such as peer reviews, workshops, and other methods to elicit




 expert opinion.  Data gaps can often be filled by completing additional studies on the




 unknown parameter. Opportunities for reducing this source of uncertainty should be noted




 and carried  through to risk characterization. Data gaps that preclude the analysis of exposure




 or ecological effects should also be noted and discussed in risk characterization.




       An important objective of characterizing uncertainty in the analysis phase is to




 distinguish variability from uncertainties arising from lack of knowledge (e.g., uncertainty




 about a quantity's true value) (U.S. EPA,  1995c).  This distinction  facilitates the




 interpretation and communication of results. For example, in their  food web models of




 herons and mink, Macintosh et  al.  (1994)  separated variability expected among feeding habits



 of individual animals from the uncertainty in the mean concentration of chemical in prey




 species.  In  this way, the assessors could place error bounds on the distribution of exposure



 among the animals using the site and estimate the proportion of the  animal population that



 might exceed a toxicity threshold.




      Sources of uncertainty that arise primarily during the development and application of




 models include the structure of process models and the description of the relationship



between two or more variables in empirical models. Process model description should




include key  assumptions, simplifications, and aggregations of variables (see text note 4-4).







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Empirical model descriptions should include the rationale for selection, and statistics on




model performance (e.g., goodness of fit).  Uncertainty in process or empirical models can



be quantitively evaluated by comparing model results to measurements taken in the system of




interest or by comparing the results obtained using different model alternatives.



       Methods for analyzing and describing uncertainty can range from simple to complex.




The calculation of one or more point estimates is one of the most common approaches to




presenting analysis results;  point estimates that reflect different aspects of uncertainty can




have great value if appropriately developed and communicated.  Classical statistical methods



(e.g., confidence limits, percentiles) can be readily applied to describing uncertainty in




parameters.  When a modeling approach is used, sensitivity analysis can be used to evaluate



how model output changes  with changes in input variables, and uncertainty propagation can




be analyzed to examine how uncertainty in individual parameters can affect the overall



uncertainty of the  assessment. The availability of software for Monte-Carlo analysis has



greatly increased the use of probabilistic methods; readers are encouraged to follow best



practices that have been suggested (e.g., Burmaster and Anderson, 1994; Haimes et al.




1994). Other methods (e.g., fuzzy mathematics, Bayesian methodologies) are available, but



have not yet been  extensively applied to ecological risk assessment (Smith and Shugart,



1994). These guidelines do not endorse the use of any one method over others and note that



the poor execution of any method can obscure rather than clarify the impact of uncertainty on




an assessment's results. No matter what technique is used, the sources of uncertainty




discussed above should be  addressed.
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4.2.  CHARACTERIZATION OF EXPOSURE




       Exposure characterization describes the contact or co-occurrence of stressors with




ecological receptors. The characterization is based on measures of exposure and of




ecosystem and receptor characteristics (the evaluation of this information is discussed in




section 4.1).  These measures are used to analyze stressor sources, their distribution in the




environment, and the extent and pattern of contact or co-occurrence (discussed in section




4.2.1). The objective is to produce a summary exposure profile (section 4.2.2) that identifies




the receptor (i.e., the exposed ecological entity), describes the course a stressor takes from




the source to the receptor (i.e., the exposure pathway), and describes the intensity and  spatial




and temporal extent of co-occurrence or contact. The profile also describes the impact of




variability and uncertainty on exposure estimates and reaches a conclusion about the




likelihood that exposure will occur.



       The exposure profile is combined with an effects profile (discussed in section 4.3.2)




to estimate risks.  For the results to be useful, they must be compatible with the stressor-




response relationship generated in the effects  characterization.








4.2.1.  Exposure Analyses



       Exposure is analyzed by describing the source and releases, the distribution of the




stressor in the environment, and the extent and pattern of contact or co-occurrence.  The




order of discussion of these topics is not necessarily the order in which they are evaluated in



a particular assessment.  For example, the assessor may start with information about tissue




residues, and attempt to link these residues with a source.






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4.2.1.1.  Describe the Source


       A source description identifies where the stressor originates, describes what stressors


are generated, and considers other sources of the stressor. Exposure analyses may start With


the source when it is known, but some analyses may begin with known exposures and


attempt to link them to sources, while other analyses may start with known stressors and


attempt to identify sources and quantify contact.  The source is the first component of the


exposure pathway and significantly influences where and when stressors eventually will be
                                                        /

found.  In addition, many management alternatives focus  on modifying the source. Text note


4-5 provides some useful questions.


       A source can be defined in several ways—as the place where the stressor is released


(e.g., a smoke stack, historically contaminated sediments)  or the management practice or


action (e.g., dredging) that produces stressors.  In some assessments, the original  source no


longer exists and the source is defined as the current origin of the stressors.  For example,


the source may be defined as contaminated sediments because the industrial plant that


produced the contaminants no longer operates.


      In addition to identifying the source, the assessor describes the generation of stressors


in terms of intensity, timing, and location.  The location of the source and the environmental


medium that first receives stressors are two attributes that deserve particular attention. In


addition, the source characterization should consider  whether other constituents emitted by


the source influence transport, transformation, or bioavailability of the stressor of interest.


For example, the presence of chloride in the feedstock of a coal-fired power plant influences


whether mercury is emitted in divalent (e.g., as mercuric  chloride) or elemental form (Meij,




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1991).  In the best case, stressor generation is measured or modeled quantitatively; however,




sometimes it can only be qualitatively described.




       Many stressors have natural counterparts or multiple sources, and the characterization




of these other sources can be an important component of the analysis phase.  For example,




many chemicals occur naturally (e.g., most metals), are generally widespread due to other




sources (e.g.,  polycyclic aromatic hydrocarbons in urban ecosystems),  or may have




significant sources outside the boundaries of the current assessment (e.g., atmospheric




nitrogen deposited in Chesapeake Bay).  Many physical stressors also have natural




counterparts.  For example, construction activities may add fine sediments to a stream in




addition to those from a naturally undercut bank.  In addition, human activities may change




the magnitude or frequency of natural disturbance cycles.  For example, development may




decrease the frequency but increase the severity of fires or may increase the frequency and



severity of flooding in a watershed.




       The way multiple sources are evaluated during  the analysis phase depends on the




objectives of the assessment articulated during problem formulation. Options include (in



order of increasing complexity):




       •  Focus only on the  source under evaluation and calculate incremental risks



          attributable to that  source (common for assessments initiated with an identified



          source or stressor).




       •  Consider all sources of a stressor and calculate total risks attributable to that




          stressor.  Relative  source attribution can be accomplished as a separate step
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          (common for assessments initiated with an observed effect or an identified




          stressor).



       •  Consider all stressors influencing an assessment endpoint and calculate cumulative




          risks to that endpoint (common for assessments initiated because of concern for an




          ecological value).



       Source characterization can be particularly important for new biological stressors,



since many of the strategies for reducing risks focus on preventing entry in the first place.



Once the source is identified, the likelihood of entry may be characterized qualitatively.  For




example, in their analysis of risks from importation of Chilean logs, the assessment team




concluded that the beetle Hylurgus ligniperda had a high potential for entry into the United




States.  They based this conclusion on the fact that they are attracted to freshly cut logs and




tend to burrow under the bark and thus would be protected during transport (USDA, 1993).



       The description of the source can set the stage for the second objective of  exposure




analysis, which is describing the distribution of the stressor in the environment.








4.2.1.2.  Describe the Distribution of the  Stressor or Disturbed Environment



       The second objective of exposure analyses is to describe the spatial and temporal




distribution of the stressor in the environment. For physical stressors that directly alter or




eliminate portions of the environment, the assessor describes the temporal and spatial



distribution of the disturbed environment. Because exposure occurs where receptors co-occur




with or contact stressors in the environment, characterizing the spatial and temporal



distribution of a stressor is a necessary precursor to estimating exposure.  The stressor's






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distribution in the environment is described by evaluating the pathways that stressors take




from the source as well as the formation and subsequent distribution of secondary stressors.




       Evaluating Transport Pathways. There are many pathways by which stressors can




be transported in the environment (see text note 4-7). An evaluation of transport pathways




can help ensure that measurements are taken in the appropriate media and locations and that




models include the most important processes.




       For chemical stressors, the evaluation of pathways usually begins by determining into




which  media a chemical will partition.  Key considerations include physicochemical




properties such as solubility and vapor pressure. For example, lipophilic chemicals tend to




be found in environmental compartments with higher proportions of organic carbon, such as




soils, sediments, and biota. From there, the evaluation may examine the transport of the




contaminated medium. Because constituents of chemical mixtures may have different




properties, it is important to consider how the composition of a mixture may change over



time or as it moves through the environment.  Guidance on evaluating the fate and transport




of chemicals is beyond the scope of these guidelines; readers are referred to the exposure



assessment guidelines (U.S. EPA, 1992d) for additional information.




       The attributes of physical stressors may  also influence where the stressors will go.



For example, the size of silt particles determines where they will eventually deposit in a




stream.  Physical stressors that eliminate ecosystems or portions of them (e.g.,  logging




activity or the construction of dams  or parking lots)  may require no modeling of pathways—




the wetland is filled, the fish are harvested, or the valley is flooded.  For these direct
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disturbances, the challenge is usually to evaluate the formation of secondary stressors and the




effects associated with the disturbance.



       The dispersion of biological stressors has been described in two ways,  as diffusion




and jump-dispersal (Simberloff and Alexander, 1994). Diffusion involves a gradual spread




from the establishment site and is a function primarily of reproductive rates and motility.




The other movement pattern, jump-dispersal, involves erratic spreads over periods of time,



usually by




means of a vector.  The gypsy moth and zebra mussel have spread this way; the gypsy moth




via egg masses on vehicles and the zebra mussel via boat ballast water.  Biological stressors




can use both diffusion and jump-dispersal strategies, and often one or more mechanisms are •



important.  This makes dispersal rates very difficult to predict.  Key considerations include




the availability of vectors, whether the organism has natural attributes that enhance dispersal



(e.g., ability to fly, adhere to objects, disperse reproductive units), and the habitat or host



needs of the organism.




       For biological stressors, assessors must consider the additional factors of survival and



reproduction.  There is  a wide range  of strategies organisms use to survive in adverse



conditions, for example, fungi form resting stages such as sclerotia and chlamydospores and



some amphibians became dormant during drought.  The survival of some organisms can be



measured to some extent under laboratory conditions.  However, it may be impossible to



determine how long some resting stages (e.g., spores) can survive under adverse conditions;



many can remain viable for years.  Similarly, reproductive rates may vary substantially,




depending  on specific environmental conditions.  Therefore, while life-history data such as







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temperature and substrate preferences, important predators, competitors or diseases, habitat




needs, and reproductive rates are of great value, they must be interpreted with caution.




       Ecosystem characteristics influence the transport of all types of stressors.  The




challenge is to determine the particular aspects of the ecosystem that are most important.  In




some cases, ecosystem characteristics that influence distribution are known.  For example,




fine sediments tend to accumulate in areas of low energy in streams such as pools and




backwaters.  In other cases, much more professional judgment is needed. For example,




when evaluating the likelihood that an introduced organism will become established, it is




useful to know whether the ecosystem is generally similar to  or different from the one where




the biological stressor originated.  In  this case, professional judgment is needed to determine




which characteristics of the current and original ecosystems should be compared.




       Evaluating Secondary Stressors.  The creation of secondary stressors can greatly




alter conclusions  about risk.  Secondary stressors can be formed through biotic or abiotic



transformation processes and may be  of greater or lesser concern than the primary stressor.




Evaluating the formation of secondary stressors is usually done as part of exposure



characterization; however, coordination with the ecological effects characterization is




important to ensure that all potentially important secondary stressors are  evaluated.




       For chemicals, the evaluation of secondary  stressors usually focuses on metabolites or




degradation products or chemicals formed through  abiotic processes. For example,  microbial



action increases the bioaccumulation of mercury  by transforming it from inorganic form to




organic forms.  Many azo dyes are not toxic because of their large molecular size but,  in  an




anaerobic environment,  the polymer is hydrolyzed  into more  toxic water-soluble units.  In







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addition, secondary stressors can be formed through ecosystem processes.  For example,




nutrient inputs into an estuary can decrease dissolved oxygen concentrations because they



increase primary production and subsequent decomposition. While the possibility and rates




of transformation can be investigated in the laboratory, rates in the field may differ



substantially, and some processes may be difficult or impossible to replicate in a laboratory.




When evaluating field information, though, it may be difficult to distinguish between



transformation processes (e.g., degradation of oil constituents by microorganisms) and




transport processes (e.g., loss of oil constituents through volatilization).



       Disturbances can also generate secondary stressors, and identifying the specific




consequences that will affect the assessment endpoint can be a difficult task.  For example,



the removal of riparian vegetation can generate  many secondary stressors, including




increased nutrients, stream temperature, sedimentation, and altered stream flow. However, it



may be the resulting increase in stream temperature that is the primary cause of adult




salmon mortality in a particular stream.



       The distribution of stressors in the environment can be described using measurements,




models, or a combination of the two.  If stressors  have already been released, direct



measurements of environmental media or a combination of modeling and measurement is



preferred.   However, a modeling approach may be necessary if the assessment is intended to



predict future scenarios or if measurements are not possible or practicable.   Considerations




for evaluating data collection and modeling studies are discussed in section 4.1.  For



chemical stressors, we also refer readers to the exposure assessment guidelines (U.S. EPA,




1992d). For biological stressors, the distribution in the environment is difficult to predict






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quantitatively. If measurements in the environment cannot be taken, distribution can be




evaluated qualitatively by considering the potential for transport, survival, and reproduction




(see above).



       By the end of this step, the environmental distribution of the stressor or the disturbed




environment should be described.  This description can be an important precursor to the next




objective of exposure analysis—estimating the contact or co-occurrence of the stressor with




ecological entities.  In cases where the extent of contact is known, describing  the




environmental distribution of the stressor can help identify potential sources, and ensure that




all important exposures have been addressed. In addition, by identifying the pathways a




stressor takes from  a source, the second component of an exposure pathway is described.








4.2.1.3.  Describe Contact or Co-occurrence



       The third objective of the exposure analysis is to describe the extent and pattern of




co-occurrence or contact between a stressor and a receptor (i.e., exposure). The objective of




this step is to describe the intensity and temporal and spatial extent of exposure in a form




that can be compared with the stressor-response profile generated in the effects assessment.




The description of exposure is a critical element of estimating risk—if there is no exposure,



there can be no risk.  Questions for describing contact or co-occurrence are shown in text




note 4-8.



       Exposure can be described in terms of co-occurrence of the stressor with receptors, of




the actual contact of a stressor with receptors, or of the uptake of a stressor into a receptor.




The terms by which exposure is described depend on how the stressor causes  adverse effects.






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Co-occurrence is particularly useful for evaluating stressors that can cause effects without




actually contacting ecological receptors.  For example, whooping cranes use sandbars in




rivers for their nesting areas, and they prefer sandbars with unobstructed views.  Manmade




obstructions, such as bridges, can interfere with nesting behavior without ever actually




contacting the birds.  Most stressors, however, must contact receptors to cause an effect.



For example, flood waters must contact tree roots before their growth is impaired.  Finally,



some stressors  must not only be contacted, but also  must be internally absorbed.  For




example, a toxicant that causes liver tumors in fish must be absorbed through the gills and




reach the target organ to cause the effect.




       Co-occurrence is evaluated by comparing the distribution of the stressor with the



distribution of the ecological receptor. For example, maps of the stressor may be overlaid




with maps of ecological receptors (e.g., the placement of bridges overlaid on maps showing



habitat historically used for crane nests).  The increased availability of geographic



information systems  (GIS) has provided new tools for  evaluating co-occurrence.



       Contact is a function of the amount of a stressor in an environmental medium and



activities or behavior that brings receptors  into contact with the stressor.  For biological



stressors, this step relies extensively on professional judgment; contact is often assumed to




occur in areas where the two overlap.  For chemicals, contact is quantified as the amount of



a chemical ingested,  inhaled, or in material applied to  the skin (i.e., the potential dose). In



its simplest form, it is quantified as an environmental concentration, with the assumptions



that the chemical is well mixed and that the organism contacts a representative concentration.




This approach is commonly used for respired media (e.g., water for aquatic organisms, air







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 for terrestrial organisms).  For ingested media (e.g., food,  soil), another common approach




 combines modeled or measured concentrations of the contaminant with assumptions or




 parameters describing the contact rate (U.S. EPA, 1993c) (see text note 4-9).




       Uptake is evaluated by considering the amount of stressor that is internally absorbed




 into an organism. Uptake is a function of the stressor (e.g., a chemical's form or valence




 state), the medium (e.g., sorptive properties or presence of solvents), the biological




 membrane (e.g., integrity, permeability), and the organism  (e.g., sickness, active uptake)




 (Suter et al., 1994).  Because of interactions among these four factors,  uptake will  vary on a




 situation-specific basis.  Uptake is usually assessed by modifying an estimate of contact with




 a factor indicating the proportion of the stressor that is available for uptake (i.e., the




 bioavailable fraction) or actually absorbed.  Absorption factors and bioavailability measured




 for the chemical, ecosystem, and organism of interest are preferred.  Internal dose  can also



 be evaluated by  using a pharmacokmetic model or by measuring biomarkers or residues in




 receptors  (see text note 4-10).  Most stressor-response relationships express the amount of



 stressor in terms of media concentration or potential dose rather than internal dose; this




 limits the utility of using estimates of uptake for  risk estimation. However, biomarkers and




 tissue residues can provide valuable confirmatory evidence that exposure has occurred, and




tissue residues in prey organisms can be used for estimating risks to their predators.




       The characteristics of the ecosystem and receptors must be considered to reach



appropriate conclusions about exposure. Abiotic attributes may increase or decrease the



amount of a stressor contacted by receptors.  For example,  the presence of naturally anoxic




areas above contaminated sediments in an estuary may reduce the amount of time that
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bottom-feeding fish spend in contact with the contaminated sediments and thereby reduce




exposure to the contamination.  Biotic interactions can also influence exposure. For



example, competition for high-quality resources may force some organisms to utilize




disturbed areas.  The interaction between exposure and receptor behavior can influence both




the initial and subsequent exposures.  For example, some chemicals reduce the prey's ability




to escape predators and thereby may  increase predator exposure to the chemical as well as



the prey's risk of predation.  Alternatively, organisms may avoid areas, food, or water with




contamination they can detect.  While avoidance can reduce exposure to  chemicals, it may




increase other risks by altering habitat usage or other behavior.



       Three dimensions must be considered when estimating exposure:  intensity, time, and




space.  Intensity is the most familiar dimension for chemical and  biological stressors and may




be expressed as the amount of chemical contacted per day or the  number of pathogenic




organisms per unit area.



       The temporal dimension of exposure has aspects of duration, frequency, and timing.




Duration can be expressed  as the time over which  exposure occurs, exceeds some threshold



intensity, or over which intensity is integrated.  If exposure occurs as repeated, discrete




events of about the same duration (e.g., floods), frequency is the important temporal



dimension  of exposure. If the repeated events have significant and variable durations, both



duration and frequency must be considered.  In addition,  the timing  of exposure,  including




the order or sequence of events, can be an important factor to describe.  For example, in the



Northeast,  lakes receive high concentrations of hydrogen ions and aluminum during snow




melt; this period also corresponds to the sensitive life stages of some aquatic organisms.






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       In chemical assessments, the dimensions of intensity and time are often combined by




averaging intensity over time.  The duration over which intensity is averaged is determined




by considering both the ecological effects of concern and the likely pattern of exposure.  For




example, an assessment of bird Mils associated with granular carbofuran focused on short-




term exposures because the effect of concern was  acute lethality (Houseknecht,  1993).




Because lexicological tests are usually conducted using constant exposures, the most realistic




comparisons between exposure and effects are made when exposure in the real world does




not vary substantially.  In these cases, the arithmetic average exposure over the time period




of toxicological significance is the appropriate statistic to use (U.S. EPA, 1992d).  However,




as concentrations or contact rates become more episodic or variable, the arithmetic average




may not reflect the lexicologically significant aspect of the exposure pattern.  In extreme




cases, averaging may not be appropriate at all, and assessors may need to use a toxic




dynamic model to assess chronic effects.




       Spatial extent is another dimension of exposure.  It is most commonly expressed in



terms of area (e.g., hectares of filled wetland, square meters that exceed a particular




chemical threshold).  At larger spatial scales, however, the shape or arrangement of exposure




may be an important issue, and area alone may not be the appropriate descriptor of spatial




extent for risk assessment.  A general solution to the problem of incorporating pattern into



ecological assessments has yet to be developed; however,  the emerging field of landscape



ecology and the increased availability of geographic information systems have greatly



expanded the options for analyzing and presenting the spatial dimension of exposure.
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       This step completes exposure analysis. Exposure should be described in terms of




intensity, space, and time, in units that can be combined with the effects assessment.  In



addition, the assessor should be able to trace the paths of stressors from the source to the




receptors, completing the exposure pathway. The results of exposure analysis are




summarized in the exposure profile, which is discussed in the next section.








4.2.2.   Exposure Profile



       The final product of exposure analysis is a summary profile of what has been learned.




Depending on the risk assessment, the profile may be a written document, or a module  of a




larger process model.  Alternatively,  documentation may be deferred until risk




characterization.  In any case, the objective is to ensure that the information needed for risk



characterization has been collected and evaluated.  In addition, compiling the exposure



profile provides an opportunity to verify that the important exposure pathways identified in



the conceptual model were evaluated.



       The exposure profile identifies the receptor and describes the exposure pathways and




intensity and spatial and temporal extent of co-occurrence or contact. It also describes the



impact of variability and uncertainty on exposure estimates and reaches a conclusion about




the likelihood that exposure will occur (text note 4-11).



       The profile should  describe the relevant exposure pathways.  If exposure can occur




through many pathways, it may be useful to rank them, perhaps by  contribution to total




exposure. For example, consider an  assessment of risks to grebes feeding on a mercury-



contaminated lake.  The grebes  may be exposed to methyl mercury  in fish that originated






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from historically contaminated sediments.  They may also be exposed by drinking lake water,




but comparing the two exposure pathways may show that the fish pathway contributes the




vast majority of exposure to mercury.




       The profile should describe the ecological entity that is exposed and represented by




the exposure estimates described, below. For example,  the exposure profile may focus on the




local population of grebes feeding on a specific lake during the summer months.




       The assessor should state how each of the three  general dimensions of exposure




(intensity, time, and space) was treated and why that treatment is necessary or appropriate.




Continuing with the grebe example, exposure might be  expressed as the daily potential dose




averaged over the summer months and over the extent of the lake.




       The profile should also describe how variability  in receptor attributes or stressor




levels can change exposure.  For example, variability in receptor attributes of the grebes may




be addressed by using data on how the proportion of fish in the diet varies  among




individuals.  If several lakes were the subject of the assessment and individual grebes tended



to feed on the same lake throughout  the season, variability in stressor levels could be




addressed by comparing exposures among the lakes.



       Variability can be described by using a distribution or by describing where a point




estimate is expected to fall on a distribution.  Cumulative-distribution functions (CDFs) and



probability-density functions (PDFs) are two common presentation formats; (see Appendix B,




figures Bl and B2).  Figures 5-4 to 5-6 show examples of cumulative frequency plots of



exposure data.  The point estimate/descriptor approach  is used when there is not enough




information to describe a distribution.  We recommend using the descriptors discussed in







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U.S. EPA, 1992d, including central tendency to refer to the mean or median of the




distribution, high end to refer to exposure estimates that are expected to fall between the 90th




and 99.9th percentile of the exposure distribution, and bounding estimates to refer to those




higher than any actual exposure. The exposure profile should summarize important



uncertainties (i.e., lack of knowledge) (see section 4.1.3 for a discussion of the different




sources of uncertainty).  In particular, the assessor should:




       •  Identify key assumptions and describe how they were handled




       •  Discuss (and quantify if possible) the magnitude of sampling and/or measurement




          error




       •  Identify the most sensitive variables influencing exposure




       •  Identify which uncertainties can be reduced through the collection of more data.



       Uncertainty about a quantity's true value can be  shown by calculating error bounds on



a point estimate, as shown in figure 5-2. All of the above information is synthesized to reach




a conclusion about the likelihood that exposure will occur. The exposure profile is one of



the products of the analysis phase.  It is combined with  the stressor-response profile (the




product of the ecological effects characterization discussed in the next section) during risk



characterization.








4.3.   CHARACTERIZATION OF ECOLOGICAL EFFECTS




       Characterization of ecological effects describes the effects that are elicited by a



stressor, links these effects with the assessment endpoints, and evaluates how the effects




change with varying stressor levels. Ecological effects characterization begins by evaluating






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effects data (discussed generally in section 4.1) to further specify the effects that are elicited,




confirm that the effects are consistent with the assessment endpoints, and confirm that the




conditions under which they  occur are consistent with the conceptual model.  Once the




effects of interest are identified, then an ecological response analysis (section 4.3.1) is




conducted to evaluate how the magnitude of the effects change with varying stressor levels,




evaluate the evidence that the stressor causes the effect, and link the effects with the




assessment endpoint. The conclusions of the ecological effects characterization are




summarized in a stressor-response profile (section 4.3.2).








4.3.1.  Ecological Response Analysis




       Ecological response analysis has  three primary elements:   determining the relationship




between stressor levels and ecological effects (section 4.3.1.1), evaluating the plausibility



that effects  may occur or are occurring as a result of exposure to stressors (section 4.3.1.2),




and linking measurable ecological effects with the assessment endpoints when assessment




endpoints cannot be directly  measured (section 4.3.1.3).








4.3.1.1.  Stressor-Response Analysis



       Evaluating ecological risks requires an understanding of the relationships between



stressor levels and resulting  ecological responses. The stressor-response relationships used in




a particular assessment depend on the scope and nature of the ecological risk assessment as




defined in problem formulation and reflected in the analysis plan.  For example, an assessor




may need a point estimate of an effect (such as an LC50) to compare with point estimates
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from other stressors. The shape of the stressor-response curve may be critical for




determining the presence or absence of an effects threshold or for evaluating incremental




risks, or stressor-response curves may be used as input for ecological effects models.  If




sufficient data are available, the risk assessor may construct cumulative distribution functions




using multiple point estimates of effects.  Or the assessor may use process models that



already incorporate empirically derived stressor-response relationships (section 4.3.1.3).



Some questions for stressor-response analysis are provided in text note 4-12.




       This section describes a range of stressor-response aproaches available to risk




assessors following a theme of variations on the classical stressor-response relationship (e.g.,




figure 4-2). While quantifying this relationship is encouraged, qualitative stressor-response



evaluations are also possible (text note 4-13). In  addition, many stressor-response



relationships are more complex than the simple curve shown in this figure.  Ecological



systems frequently show responses to stressors that may involve abrupt shifts to new




community or system types (Holling, 1978).
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Figure 4-2.  A simple example of a stressor-response relationship.




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       In simple cases, the response will be one variable (e.g., mortality, incidence of




abnormalities), and most quantitative techniques have been developed for univariate analysis.



If the response of interest is composed of many individual variables (e.g., species abundances



in an aquatic community), multivariate statistical techniques may be useful.  These techniques




have a long history of use in ecology (see texts by Gauch, 1982; Pielou,  1984; Ludwig and




Reynolds,  1988) but have not yet been extensively applied in risk assessment. Stressor-




response relationships can  be described using any of the dimensions of exposure (i.e.,




intensity, time, or space).  Intensity is probably the most familiar dimension and is often




used for chemicals (e.g., dose, concentration). The duration  of exposure is also commonly




used for chemical stressor-response relationships; for example, median acute effects levels  .



are always associated with a time parameter (e.g., 24 hr, 48 hr, 96 hr).  As noted in text




note 4-13,  the timing of exposure was the critical dimension in evaluating the relationship



between seed germination and flooding (Pearlstine et al., 1985).  The spatial dimension is




often of concern for physical stressors. For example, the spatial extent of suitable habitat



was related to the probability of sighting a spotted owl (Thomas et al., 1990),  and water-



table depth was  related to the growth of tree species  by Phipps (1979).



       Single-point estimates and  stressor-response curves can be generated for some




biological stressors.  For pathogens such as bacteria and fungi, inoculum levels (e.g., spores



per ml; propagules per unit of substrate) may be related to the level of symptoms in a host




(e.g., lesions per area of leaf surface, total number of plants infected) or actual signs of the



pathogen (asexual or sexual fruiting bodies, sclerotia, etc.). For other biological stressors
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such as introduced species, developing simple stressor-response relationships may be




inappropriate.



       Data from individual experiments can be used to develop curves and point estimates




both with and without associated uncertainty estimates (see figures 5-2 and 5-3). The




advantages of curve-fitting approaches include using all of the available experimental data




and the ability to interpolate to values other than the data points measured. If extrapolation




outside the range of experimental data is required, risk assessors should justify that the




observed experimental relationships remain valid. A disadvantage of curve fitting  is that the




number of data points required to complete an analysis may not always be available.  For




example, while standard toxicity tests with aquatic organisms frequently contain sufficient




experimental treatments to permit regression analysis, frequently this is not the case for




toxicity tests with wildlife species. Risk assessors sometimes use curve-fitting analyses to




determine particular levels of effect for evaluation.  These point estimates are  interpolated



from the fitted line.  Point estimates may be adequate for simple assessments or comparative



studies of risk and  are also useful if a decision rule for the assessment was identified during




the planning phase (see section 2).  Median effect levels (text note 4-14)  are frequently




selected because the level of uncertainty is minimized at the midpoint of the regression




curve.  While a 50% effect for an endpoint such as survival may not be appropriately



protective for the assessment endpoint, median effect levels  can be used for preliminary




assessments or comparative purposes, especially when used in combination with uncertainty



modifying factors (see text note 5-2).  Selection of a different effect level ,(10%, 20%, etc.)




can be arbitrary unless there is  some clearly defined benchmark for the assessment endpoint.






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Thus, it is preferable to carry several levels of effect or the entire stressor-response curve



forward to risk estimation.




       When risk assessors are particularly interested in effects at lower stressor levels, they



may seek to establish "no-effect" levels of a stressor based on comparisons between




experimental treatments and controls.  Statistical hypothesis testing is frequently used for this




purpose.  (Note that statistical hypotheses are different from the risk hypotheses discussed in




problem formulation; see text note 3-10).  An example of this approach for deriving




chemical no-effect levels is provided in text note 4-15.  An advantage of statistical hypothesis



testing is that the risk assessor is not required to pick a particular effect level of concern.




The no-effect level is determined instead by experimental conditions such as the number of



replicates as well as the variability inherent in the data.  Thus it is important to consider the




level of effect detectable in the experiment (i.e., its power) in addition to reporting the no-



effect level.  Another drawback of this approach is that it is difficult to evaluate effects




associated with stressor levels other than the actual treatments tested.  Several investigators



(Stephan and Rogers, 1985; Suter,  1993a) have proposed using regression analysis as an



alternative to statistical hypothesis testing.



       In observational field studies, statistical hypothesis testing is often  used to compare




site conditions with a reference site(s).  The difficulties of drawing proper conclusions from



these types of studies (which frequently cannot employ replication)  have been discussed by




many investigators, including Hurlbert (1984), Stewart-Oaten et al. (1986),  Wiens and Parker



(1995), and Eberhardt and Thomas (1991). Risk assessors should examine  whether sites



were carefully matched to minimize differences  other than  the stressor and consider whether







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potential covariates should be included in any analysis.  An advantage of experimental field




studies is that treatments can be replicated, increasing the confidence that observed




differences are due to the treatment.




       Data available from multiple experiments can be used to generate multiple point




estimates that can be displayed as cumulative distribution functions. Figure 5-6 shows an




example of a cumulative distribution function for species sensitivity derived from multiple




point estimates (EC5s) for freshwater algae exposed to a herbicide.  These distributions




facilitate identification of stressor levels that affect a minority or majority of species.  A




limiting factor in the use of cumulative frequency distributions is the amount of data needed




as input.  Cumulative effects distribution functions can also be derived from models that use




Monte Carlo or other methods to generate distributions based on measured  or estimated



variation in input parameters  for the models.




       When multiple stressors are present, stressor-response analysis is particularly




challenging.  Stressor-response relationships can be constructed for each stressor separately



and then combined.  Alternatively,  the relationship between response and the suite of




stressors can be combined in  one analysis.  It is preferable to directly evaluate complex



chemical mixtures present in  environmental media (e.g., wastewater effluents,  contaminated




soils; U.S. EPA,  1986b), but it is important to  consider the relationship between the samples




tested and the potential spatial and temporal variability in the mixture. The approach taken




for multiple stressors depends on the feasibility  of measuring  the suite of stressors and



whether an objective of the assessment is to project different stressor combinations.
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       In some cases, multiple regression analysis can be used to empirically relate multiple




stressors and a response.  Detenbeck (1994) used this approach to evaluate change in the



water quality of wetlands resulting from multiple physical stressors.  Multiple regression




analysis can be difficult to interpret if the explanatory variables (i.e., the stressors) are not




independent. Principal components analysis can be used to extract independent explanatory



variables formed from linear combinations of the original variables  (Pielou, 1984).








4.3.1.2.  Establishing Cause and Effect Relationships (Causality)



       Causality is the relationship between cause (one or more stressors) and effect




(assessment endpoint response to one or more stressors). Without a sound basis for linking




cause and effect, uncertainty in the conclusions of an ecological risk assessment is likely to



be high. Developing causal relationships is especially important for risk assessments driven



by observed adverse ecological effects such as bird or fish kills or a shift in the species



composition of an area.  This section proposes considerations for evaluating causality based



on criteria primarily for  observational data developed by Fox (1991) and additional criteria




for experimental evaluation of causality modified from Koch's postulates (e.g. see Woodman




and Cowling, 1987).



       Evidence of causality may be derived from observational evidence (e.g., bird kills are




associated  with field application of a pesticide) or experimental data (e.g., laboratory tests



with the pesticides in question show bird kills at levels similar to those found in the field),




and causal associations can be strengthened when both types of information are available.



But since not all situations lend themselves to formal experimentation, scientists have looked






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for other criteria, based largely on observation rather than experiment, to support a plausible




argument for cause and effect. Text note 4-16 provides criteria based on Fox (1991) that are



very similar to others reviewed by Fox (U.S. Department of Health, Education, and Welfare,




1964; Hill, 1965; Susser, 1986a,b).  While data to support some criteria may be incomplete




or missing for any given assessment, these criteria offer a useful way of evaluating available




information.



       The strength of association between  stressor and response is often the main reason




that adverse effects (such as bird Mils) are first noticed. A stronger response to a




hypothesized cause is more likely to indicate true causation.  Additional strong evidence of




causation is when a response follows after a change in the hypothesized cause (predictive




performance).



       The presence of a biological gradient or stressor-response relationship is another




important criterion for causality. The stressor-response relationship need not be linear.  It




can be a threshold, sigmoidal, or parabolic  phenomenon,  but in any case it is important that




it can be demonstrated.  Biological gradients, such as decreasing effects downstream of a




toxic discharge, are frequently used as  evidence of causality. To be credible, such



relationships should be consistent with current biological or ecological knowledge (biological




plausibility).



       A cause-effect relationship that is demonstrated  repeatedly (consistency of association)




provides strong evidence of causality.  Consistency may be shown by a greater number of



instances of association between stressor and response, occurrences  in diverse ecological




systems,  or associations demonstrated by diverse methods (Hill,  1965).  Fox (1991) adds
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that in ecoepidemiology the occurrence of an association in more than one species and




species population is very strong evidence for causation.  An example would be the




numerous species of birds that were killed as a result of carbofuran application



(Houseknecht, 1993).  Fox (1991) also believes that causality is supported if the same



incident is observed by different persons under different circumstances and at different times.








       Conversely, inconsistency  in association between stressor and response is strong




evidence against causality (e.g., the stressor is present without the expected effect, or the



effect occurs but the stressor is not found).  Temporal incompatibility (i.e., the presumed




cause does not precede the effect) and incompatibility with experimental or observational



evidence (factual implausibility) are also indications against a causal relationship.




       Two other criteria may be of some help in defining causal relationships:  specificity



of an association and probability.  The more specific the effect, the more likely it is to have



a consistent cause.  However, Fox (1991) argues that effect specificity does little to



strengthen a causal claim. Disease can have multiple causes, a substance can behave



differently in different environments or cause several different effects, and biochemical



events may result in a diverse array of biological responses.  But in general, the more




specific or localized the effects, the easier it is to identify the cause.  Sometimes,  a stressor



may have a distinctive mode of action that suggests its role.  Yoder and Rankin (1995) found



that patterns of change observed in fish and benthic invertebrate communities could serve as



indicators for  different types of anthropogenic impact (e.g., nutrient enrichment vs. toxicity).
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       For some pathogenic biological stressors, the causal evaluations proposed by Koch

(text note 4-17) may be useful. For chemicals, ecotoxicologists have slightly modified

Koch's postulates to provide evidence of causality (Adams, 1963; Woodman and Cowling,

1987). The modifications are:

       •  The injury, dysfunction,  or other putative effect of the toxicant must be regularly

          associated with exposure to the toxicant and any contributory  causal factors.

       •  Indicators of exposure to the toxicant must be found in the affected organisms.

       •  The toxic effects must be seen when normal organisms or communities are

          exposed to the toxicant under controlled conditions, and  any contributory factors

          should be manifested in the same way during controlled  exposures.

       •  The same indicators of exposure and effects must be identified in the controlled

          exposures as in the field.

       These modifications are conceptually identical to Koch's postulates.  While useful,

this approach may not be practical if resources for experimentation are not available or if an

adverse effect may be occurring  over such a wide spatial extent that experimentation and

correlation may prove difficult or yield equivocal results.

       Experimental techniques are frequently used for evaluating causality  in complex

chemical mixtures.  Options include evaluating separated components of the mixture,

developing and testing a synthetic mixture,  or determining how the toxicity  of a mixture

relates to the toxicity of individual components.  The choice of method depends on the goal
                        t                                             :
of the assessment and the resources and test data that are available*
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       Laboratory toxicity identification evaluations (TIEs) can be used to help determine




which components of a chemical mixture are causing toxic effects.  By using fractionation



and other methods, the TIE approach can help identify chemicals responsible for toxicity and




show the relative contributions to toxicity of different chemicals in aqueous effluents (U.S.




EPA, 1988a, 1989b,c) and sediments (e.g., Ankley et al., 1990).




       Risk assessors may utilize data from synthetic chemical mixtures if the individual



chemical components are well characterized. This approach allows for manipulation of the



mixture and investigation of how varying the components that are present or their ratios may



affect mixture toxicity but also requires additional assumptions about the relationship between




effects of the synthetic mixture and those of the environmental mixture.



       When the modes of action of chemicals  in a mixture are known to be similar, an




additive model has been successful in predicting combined effects (Konemann, 1981;



Hermens et al.,  1984a; McCarty and Mackay,  1993; Sawyer and Safe, 1985; Broderius et




al., 1995).   In this situation, the contribution of each chemical to the overall toxicity of the



mixture can be evaluated. However, the situation is more complicated when the modes of




action of the chemical constituents are unknown or partially  known (see additional discussion




in section 5.1.2).








4.3.1.3.  linking Measures  of Effect to Assessment Endpoints



       Assessment endpoints  express the environmental values of concern for a risk



assessment, but they cannot always be measured directly.  When measures of effect differ



from assessment endpoints, sound and explicit linkages between the two are needed. Risk






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assessors may make these linkages in the analysis phase or, especially when linkages rely on




expert judgment, risk assessors may work with measures of effect through risk estimation (in




risk characterization) and then make the connection with the assessment endpoints.  Common




extrapolations used to link measures of effect with assessment endpoints are shown in text




note 4-18.



       General Considerations.  During the preparation of the analysis plan in problem




formulation, risk assessors identify the extrapolations required between assessment endpoints




and measures  of effect. During the analysis phase, risk assessors should revisit the questions




listed in text note 4-19 before proceeding with specific extrapolation approaches to  use.



       The scope and nature of the risk assessment and the environmental decision to be




made help determine the degree of uncertainty (and type of extrapolation) that is acceptable.




At an early stage of a tiered risk assessment, extrapolations from minimal data that involve




large uncertainties are acceptable when the primary purpose is to determine whether a risk



exists given worst-case exposure and effects scenarios. To define risk further at later  stages




of the assessment, additional data and more sophisticated extrapolation approaches are




usually required.



       The scope  of the risk assessment also influences extrapolation through the nature of




the assessment endpoint.  Preliminary assessments that evaluate risks to general trophic




levels, such as fish and birds, may extrapolate among different genera or families to obtain a




range of sensitivity to the stressor. On the other hand, assessments concerned with




management strategies for a particular species may employ population models.
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       Analysis phase activities may suggest additional extrapolation needs.  Evaluation of




 exposure may indicate different spatial or temporal scales than originally anticipated. If



 spatial scales are broadened, additional receptors may need to be included in extrapolation




 models.  If a stressor persists for an extended time in the environment, it may be necessary




 to extrapolate short-term responses over a longer period of exposure, and population level




 effects may become more important.




       Whatever methods are employed  to link assessment endpoints with measures of effect,




 it is important to apply the methods in a manner consistent with sound ecological principles



 and the availability of an appropriate database.  For example, it is inappropriate to use




 structure-activity relationships to predict toxicity from chemical structure unless the chemical




 under consideration has a similar mode of toxic action to the reference chemicals (Bradbury,



 1994).  Similarly, extrapolations from upland avian species to waterfowl may be more



 credible if factors such as differences in  food preferences, body mass, physiology, and




 seasonal behavior (e.g., mating and migration habits) are considered. Extrapolations made in



 a rote manner or that are biologically implausible will erode the overall credibility of the



 assessment.




       Finally, many extrapolation methods are limited by the availability of suitable



 databases.  Although these databases are generally largest for chemical stressors and aquatic




 species, data do not exist for all taxa or effects. Chemical effects databases for mammals,



amphibians, or reptiles are extremely limited, and there is even less information on most



biological and physical stressors.  Risk assessors should be aware that extrapolations and




models are only as useful as the data on  which they are based and should recognize the great







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uncertainties associated with extrapolations that lack an adequate empirical or process-based




rationale.




       The rest of this section addresses the approaches used by risk assessors to link




measures of effect to assessment: endpoints, as noted below.



       •  Linkages based on expert judgment.  This approach is not as desirable as




          empirical or process-based approaches, but is the only option when data are




          lacking.




       •  Linkages based on empirical or process models.  Empirical extrapolations use




          experimental or observational data  that may or may not be organized into a




          database.  Process-based approaches are based on some level of understanding of




          the underlying operations of the system under consideration.




       Judgment Approaches for Linking Measures of Effect to Assessment Endpoints.




Expert judgment approaches rely on the professional expertise of risk assessors,  expert



panels, or others to relate changes in measures of effect to changes in the assessment




endpoint.  They are essential when databases  are inadequate to support empirical models and




process models are unavailable or inappropriate.  Expert judgment linkages between




measures of effect and assessment endpoints can be just as credible as empirical or process-



based expressions, provided they have a sound scientific basis. This section highlights expert




judgment extrapolations between species, from laboratory data to field effects, and between




geographic areas.




       Because of the uncertainties in predicting the effects of biological stressors such as




introduced species, expert judgment approaches are commonly used.  For example, there






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may be measures of effect data on a foreign pathogen that attacks a certain tree species not

found in the United States, but the assessment endpoint concerns the survival of a

commercially important tree found only in the United States.  In this case, a careful

evaluation and comparison of the life history and environmental requirements of both the

pathogen and the two tree species may  contribute toward a useful determination of potential

effects, even though the uncertainty may be high.  Expert panels are typically used for this

kind of evaluation (USDA,  1993).

       Risks to organisms in field situations are best estimated from studies at the site of

interest. However, such data are not always available.   Frequently, risk assessors must

extrapolate from laboratory toxicity test data to field effects.  Text note 4-20 summarizes
                                                        i
some of the considerations for risk assessors when extrapolating from laboratory toxicity test

results to field situations for chemical stressors.  Factors altering exposure in the field are

among the most important factors limiting extrapolations from laboratory test results, but

indirect effects on exposed 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.



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       Extrapolations based on expert judgment are frequently required when assessors wish

to use field data obtained from one geographic area and apply them to a different area of


concern, or to extrapolate from the results of laboratory tests to more than one geographic


region. In either case, risk assessors should consider variations between regions in

environmental conditions, spatial scales and heterogeneities, and ecological forcing functions.

(see below).

       Variations in environmental conditions in different geographic regions may alter


stressor exposure and effects.  If exposure to chemical stressors can be accurately estimated

and are expected to be similar (e.g., see text note 4-20), the same species in different areas


may respond similarly.  For example, if the pesticide granular carbofuran were applied at

comparable rates throughout the country, seed-eating birds could be expected to be similarly


affected by the pesticide (Houseknecht, 1993).  Nevertheless, the influence of environmental

conditions on stressor exposure and effects can be substantial.

       For biological stressors, environmental conditions such as  climate,  habitat, and

suitable hosts play major roles in determining whether a biological stressor becomes

established.   For example, climate would prevent establishment of the Mediterranean fruit fly

in the much colder northeastern United States.  Thus, a thorough evaluation of environmental

conditions in the area versus the natural habitat of the stressor is important.  Even so, many

biological stressors can adapt readily to varying environmental conditions, and the absence of

natural predators or diseases maty play an even  more important role than abiotic
                                                                      i    i •
environmental conditions.                                                  .
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       For physical stressors that have natural counterparts, such as fire, flooding, or




temperature variations, effects may depend on the natural variations in these parameters for a



particular region.  Thus, the comparability of two regions depends on both the pattern and




range of natural disturbances.



       Spatial scales and heterogeneities affect comparability between regions.  Effects




observed over a large scale may be difficult to extrapolate from one geographical location to



another mainly because the spatial heterogeneity is likely to differ. Factors such as number




and size of land-cover patches, distance between patches, connectivity and conductivity of




patches (e.g., migration routes), and patch shape may be important.  Extrapolations can be




facilitated by using appropriate reference sites, such as sites in comparable ecoregions




(Hughes,  1995).



       Ecological forcing functions may differ between geographic regions.  Forcing




functions are critical abiotic variables that exert a major influence on the structure and



function of ecological systems. Examples include temperature fluctuations, fire frequency,



light intensity, and hydrologic regime.  If these differ significantly between sites,  it may be



inappropriate to extrapolate stressor effects from one system to another.



       The following references may be useful when assessing effects over different




geographical areas: Bedford and Preston (1988), Detenbeck et al.  (1992), Gibbs (1993),



Gilbert (1987), Gosselink et al. (1990), Preston and Bedford (1988), and Risser (1988).



       Empirical and Process-Based Approaches for Linking Measures of Effect to



Assessment Endpoints.  There are a variety of empirical and process-based approaches




available to risk assessors depending on the scope of the  assessment and the data and







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resources available.  Empirical and process-based approaches include numerical



extrapolations between effects measures and assessment endpoints. These linkages range in



sophistication from applying an uncertainty factor to using a complex model requiring



extensive measures of effects and measures of ecosystem and receptor characteristics as



input.  But even the  most sophisticated quantitative models involve qualitative elements and



assumptions and thus require professional judgment for evaluation. Individuals who use



models and interpret their results should be familiar with the underlying assumptions and



components contained in the model.



       Empirical Approaches.  Empirically based uncertainty factors or taxonomic



extrapolations may be used when adequate effects databases  are available but the



understanding of underlying mechanisms of action or ecological principles is limited.  When



sufficient information on stressors and receptors is available, process-based approaches such



as pharmacokinetic/pharmacodynamic models or population or ecosystem process models



may be used. Regardless of the options used, risk assessors should justify and adequately
                       I


document the approach selected.



       Uncertainty factors are used to ensure that effects measures are sufficiently  protective



of assessment endpoints.  Uncertainty factors are empirically derived numbers  that are



divided into measure of effects values to give an estimated stressor level that should not



cause adverse effects to the assessment endpoint.  Uncertainty factors have mostly  been



developed for chemicals because of the  extensive ecotoxicologic databases available,
                                                         ;     t


especially for aquatic organisms.  Uncertainty factors are useful when decisions must be



made about stressors in a short time and with little information.
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       Uncertainty factors have been used to compensate for assessment endpoint/effect




measures differences between endpoints (acute to chronic effects), between species, and



between test situations (e.g., laboratory to field).  Typically, uncertainty factors vary



inversely with the quantity and type of effects measures data available (Zeeman, 1995).




Uncertainty factors have been used in screening-level assessments of new chemicals




(Nabholz, 1991), in assessing the risks of pesticides to aquatic and terrestrial organisms




(Urban and Cook, 1986), and in developing benchmark dose levels for human health effects




(U.S. EPA, 1995d).




       In spite of their usefulness, uncertainty factors can also be misused, especially when




used in an overly conservative fashion, as when chains of factors are multiplied together



without sufficient justification. Like other approaches to bridging data gaps, uncertainty



factors are often based on a combination of scientific analysis, scientific judgement and



policy judgement (see section 4.1.3).  It is important to differentiate among these three




elements when documenting the basis  for the uncertainty factors used.



       Empirical data can be used to facilitate extrapolations between species to species,



genera, families, or orders or functional groups (e.g., feeding guilds) (Suter,  1993a).  Suter



et al. (1983), Suter (1993a), and Barnthouse et al. (1987, 1990) developed methods to




extrapolate toxicity among freshwater  and marine fish and arthropods.  As noted by Suter



(1993a), the uncertainties associated with extrapolating between orders, classes, and phyla




tend to be very high. However,  extrapolations can be made with fair certainty between



aquatic species within genera and genera within families. Further applications of this
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approach (e.g., for chemical stressors and terrestrial organisms) are limited by a lack of




suitable databases.




       Dose-scaling or allometric regression has also been used to extrapolate the effects of a




chemical stressor to another species.  The method is used for human health risk assessment




but has not been applied extensively to ecological  effects (Suter, 1993a).



       Allometric regression has been used with avian species (Kenaga,  1973) and to a




limited extent for estimating effects to marine organisms based on their length.  For chemical




stressors, allometric relationships can enable an assessor to estimate toxic effects to species




not commonly tested, such as native mammalian species.  It is important that the assessor




consider the taxonomic relationship between the known species and the species of interest.




The closer the two are related, the more likely that the toxic response will be similar.




Allometric approaches should not be applied to species that differ greatly in uptake,




metabolism, or depuration of a chemical.




       Process-Based Approaches. Process models for extrapolation are representations or




abstractions of a system  or process (Starfield and Bleloch,  1991) that incorporate causal



relationships and provide a predictive capability that does not depend on the availability of




existing stressor-response information as empirical models do (Wiegert and Bartell, 1994).




Process models enable assessors to translate data on individual effects (e.g., mortality,



growth, and reproduction) to potential alterations in specific populations, communities, or



ecosystems.  Such models can be used to evaluate risk hypotheses about the duration and




severity of a stressor  on an assessment endpoint that cannot be tested readily in the




laboratory.






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       There are two major types of models:  single-species population models and




multispecies community and ecosystem models.  Population models describe the dynamics of



a finite group of individuals through time and have been used extensively in ecology and




fisheries management and to assess the impacts of power plants and toxicants on specific fish



populations (Barnthouse et al., 1987; Barnthouse et al., 1990).  Population models are useful




in answering questions related to short- or long-term changes of population size and structure



and can be used to estimate the probability that a population will decline below or grow




above a specified abundance (Ginzburg et al., 1982; Person et al., 1989).  This latter




application may be useful when assessing risks associated with biological stressors  such as




introduced or pest species.  Excellent reviews of population models are presented by



Barnthouse et al. (1986) and Wiegert and Bartell (1994).  Emlen (1989) has reviewed




population models that can  be used for terrestrial risk assessment.



       Proper use of the population models requires a thorough understanding of the natural




history of the species under consideration, as well as knowledge of how the stressor



influences its biology.  Model input can include somatic growth rates, physiological rates,




fecundity, survival rates of various classes within the population, and how these change when



the population is exposed to the stressor and other environmental factors.  In addition, the




effects of population density on these parameters may be important (Hassell, 1986) and



should be considered in the analysis of uncertainty.



       Community and ecosystem models  (e.g., Bartell et al., 1992; O'Neill et al.  1982) are



particularly useful when the assessment endpoint involves structural (e.g.,  community




composition) or functional (e.g., primary production) elements of the system potentially at
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risk.  These models can also be useful when secondary effects are of concern.  Changes in




various community or ecosystem components such as populations, functional types, feeding




guilds, or environmental processes can be estimated. By incorporating submodels describing




the dynamics of individual system components, these models permit evaluation of risk to




multiple assessment endpoints within the context of the larger environmental system.




       Risk assessors  should evaluate the degree of aggregation in population or multispecies




model parameters that is appropriate based both on the input data available and on the




desired output of the model. For example, if a decision is required about a particular




species, a model that lumps species  into trophic levels or feeding guilds will not be very




useful. Assumptions concerning aggregation in model parameters should be included in the




discussion of uncertainty.








4.3.2.  Stressor-Response Profile




       The final product of ecological response analysis is a summary profile of what has



been learned.  Depending on the risk assessment, the profile may be a written document, or




a module of a larger process model.  Alternatively, documentation may be deferred until risk




characterization.  In any case, the objective is to ensure that the information needed for risk




characterization has been collected and evaluated.  A useful approach in preparing the




stressor-response profile is to imagine that it will be used by someone else to perform the



risk characterization.  Using this approach, the assessor may be better able to extract the



information most important to the risk characterization phase.  In addition, compiling the
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stressor-response profile provides an opportunity to verify that the assessment and measures




of effect identified in the conceptual model were evaluated.



       Risk assessors should address several questions in the stressor-response profile (text




note 4-21).  Depending on the type of risk assessment, affected ecological entities could



include single species, populations, general trophic levels, communities, ecosystems, or



landscapes.  The nature of the effect(s) should be germane to the assessment  endpoint(s).




Thus if a single species is affected, the effects should represent parameters appropriate for




that level of organization.  Examples include effects on mortality, growth, and reproduction.




Short- and long-term effects should be reported as appropriate.  At the community level,




effects could be summarized in terms of structure or function depending on the assessment




endpoint At the landscape level, there may be a suite of assessment endpoints and each




should be addressed separately.



       Examples of different approaches for displaying the intensity  of effects as stressor-



response curves or point estimates were provided in section 4.3.1.1.  Other information such



as the spatial area or time to recovery may be appropriate, depending on the  scope of the



assessment.   Causal analyses are important, especially for assessments that include field



observational data.




       While ideally the stressor-response profile should express effects in terms of the



assessment endpoint, this will not always be possible.  Especially where it is  necessary to use




qualitative extrapolations between assessment endpoints and measures of effect, the stressor-



response profile may only contain information on measures of effect. Under  these
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circumstances, risk will be estimated using the measures of effects, and extrapolation to the

assessment endpoints will occur during risk characterization.

       Risk assessors  need to be descriptive and candid about any uncertainties associated

with the ecological response analysis. If it was necessary to  extrapolate from measures of

effect to the assessment endpoint, describe both the extrapolation and its basis. Similarly, if
                                         t
a benchmark or similar reference dose or concentration was calculated, discuss the

extrapolations and uncertainties associated with its development.  For additional information

on establishing reference concentrations, see Nabholz (1991), Urban and Cook (1986),

Stephan et al. (1985), Van Leeuwen et al. (1992), Wagner and L0kke (1991), and Okkerman

et al. (1993).  Finally, the assessor should clearly indicate major assumptions and default

values used in models.                  !

       At the end of the analysis phase, the stressor-response and exposure profiles are used

to estimate risks.  These profiles provide the opportunity to review what has been learned

and to summarize this information in the most useful format for risk characterization.

Whatever form the profiles take, they ensure that the necessary information is available for

risk characterization.
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                          5.  RISK CHARACTERIZATION








       Risk characterization (figure 5-1) is the final phase of ecological risk assessment.  Its



goals are to use the results of the analysis phase to estimate risk to the assessment endpoints




identified in problem formulation (section 5.1), interpret the risk  estimate (section 5.2), and



report the results (section 5.3).
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Figure 5-1.  Risk characterization.




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       Risk characterization is a major element of the risk, assessment report.  To be




successful, it should provide clear information to the risk manager to use in environmental



decision making (NRC, 1994;  see section 6).  If the risks are not sufficiently defined to



support a management decision, the risk manager may elect to proceed with another




iteration of the risk assessment process.  Additional research or a monitoring program may




improve the risk estimate or help to evaluate the consequences of a risk management



decision.








5.1.  RISK ESTIMATION




       Risk estimation determines the likelihood of adverse effects to assessment endpoints




by integrating exposure and effects data and evaluating any associated uncertainties. The




process uses exposure and stressor-response profiles which are developed according to the



analysis plan (section 3.5).  Risks can be estimated by one or more of the following




approaches:  (1) estimates expressed as qualitative categories, (2) estimates comparing single-



point estimates of exposure and effects, (3) estimates incorporating the entire stressor-



response relationship,  (4) estimates incorporating variability in exposure and effects



estimates,  (5) estimates based on process models that rely partially or entirely on theoretical




approximations of exposure and effects, and (6)  estimates based on empirical approaches,



including field observational data.
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5.1.1.  Risk Estimates Expressed as Qualitative Categories




       In some cases, best professional judgment may be used to express risks qualitatively




using categories such as low, medium, and high or yes and no.  This approach is most




frequently used when exposure and effects data are limited or not easily expressed in




quantitative terms.  A U.S. Forest Service assessment used qualitative categories because of




limitations on both the exposure and effects data for the introduced species of concern as




well as the resources available for the assessment, (text note 5-1)








5.1.2.  Single-Point Estimates



       When sufficient data are available to quantify exposure and effects estimates, the




simplest approach for comparing the estimates is to use a ratio of two numbers (figure 5-2a).




Typically, the ratio (or quotient) is expressed as an exposure concentration divided by an




effects concentration.  Quotients are commonly used for chemical stressors, where reference




or benchmark toxicity values are widely  available (text note 5-2).
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Figure 5-2.  Risk estimation techniques,  a.  Comparison of exposure and stressor-
response point estimates,  b.  Comparison of point estimates from the stressor-response
relationship with uncertainty associated with an exposure point estimate.
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       The principal advantages of the quotient method are that it is simple and quick to use




and risk assessors and managers are familiar with its application.  The quotient method




provides an efficient, inexpensive means of identifying high or low risk situations that can




allow risk management decisions to be made without the need for further information.




       Quotients have also been used to integrate the risks of multiple chemical stressors.  In




this approach, quotients for the. individual constituents in a mixture are generated by dividing




each exposure level by a corresponding toxicity endpoint (e.g., an LC50).  Although the




toxicity of a chemical mixture may be greater (synergism) or less (antagonism) than predicted




from the toxicities of individual constituents of the mixture,  a quotient addition approach




assumes that  toxicities are additive or close to additive, which may be true when the modes




of action of chemicals in a mixture are similar (e.g., Konemann, 1981; Broderius et al.,




1995; Hermens et al., 1984a,b; McCarty and Mackay,  1993; Sawyer and Safe, 1985).




       For mixtures of chemicals having  dissimilar modes of action, there is some evidence




from fish acute toxicity tests with industrial organic  chemicals that strict additivity or less-




than-strict additivity is common., while antagonistic and synergistic responses are rare




(Broderius, 1991).  These experiences suggest  that caution should be used when predicting




that chemicals in a mixture will act independently of one another. However, these




relationships  observed with aquatic organisms may not be relevant for other endpoints,



exposure scenarios, and species.  When the mode of action for constituent chemicals are




unknown, the assumptions and rationale concerning  chemical interactions  must be clearly




stated.
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       The application of the quotient method is restricted by a number of limitations (see




Smith and Cairns, 1993; Suter, 1993a).  While a quotient can be useful in answering whether



risks are high or low, it may not be helpful to a risk manager who needs to make a decision




requiring  a quantification of risks.  For example, it is seldom useful to say that a risk




mitigation approach will reduce a quotient value from 25 to 12, since this reduction cannot



by itself be clearly interpreted in terms of effects on an assessment endpoint.




       Another potential difficulty with the quotient method is that the point estimate of




effect may not reflect the appropriate intensity of effect or exposure pattern for the



assessment. For example, an LC50 derived from a 96-hour laboratory test using constant




exposure levels may not be appropriate for an assessment of effects on reproduction resulting



from short-term, pulsed exposures.




       The quotient method cannot evaluate secondary effects.  Interactions and effects



beyond what is predicted from the simple quotient may be critical to characterizing the full



extent of impacts from exposure to the stressors (e.g., bioaccumulation).



       Finally, in  most cases, the quotient method does not explicitly consider uncertainty



(e.g., extrapolation from tested species to the species or community of concern). However,




some uncertainties can be incorporated into single-point estimates to provide a statement of




likelihood that the effects point estimate exceeds the exposure point estimate (figures 5-2b



and 5-3).  If exposure variability is quantified, then the point estimate of effects can be



compared with a cumulative exposure distribution as described in text note 5-3. Further



discussion of comparisons between point estimates of effects and distributions of exposure



may be found in Suter et al., 1983.







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

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       In view of the advantages and limitations of the quotient method, it is important for




risk assessors to consider the points listed below when evaluating quotient method estimates.




       •  How does the effect concentration relate to the assessment endpoint?



       •  What extrapolations are involved?




       •  How does the point estimate of exposure relate to potential spatial and temporal




          variability in exposure?




       •  Are data sufficient  to provide confidence intervals on the endpoints?








5.1.3.  Estimates Incorporating the Entire Stressor-Response Relationship




       If the stressor-response profile described a curve relating the stressor level to the



magnitude of response, then risk estimation can examine risks associated with many different




levels of exposure (figure 5-4).  These estimates are particularly useful when the risk



assessment outcome  is not based on exceedance of a predetermined decision rule such as a



toxicity benchmark level.
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Figure 5-4.  Risk estimation techniques: stressor-response curve versus a cumulative
distribution of exposures.

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       There are both advantages and limitations to comparing a stressor-response curve with



an exposure distribution.  The steepness of the effects curve shows the magnitude of change



in effects associated with incremental changes in exposure, and the capability to predict




changes in the magnitude and likelihood of effects for different exposure scenarios can be




used to compare different risk management options. Also, uncertainty can be incorporated




by calculating uncertainty bounds on the stressor-response or exposure estimates. While



comparing exposure and stressor-response curves provides a predictive ability lacking in the



quotient method, this approach shares the quotient method's limitations of not evaluating




secondary effects, assuming that the exposure pattern used to derive the stressor-response




curve is comparable to the environmental exposure pattern, and not explicitly considering




uncertainties, such as extrapolations from tested species to the species or community of



concern.
5.1.4.  Estimates Incorporating Variability in Exposure or Effects



       If the exposure or stressor-response profiles describe the variability in exposure or




effects, then many different risk estimates can be calculated.  Variability in exposure can be



used to describe risks to moderately or highly exposed members of a population being




investigated, while variability in effects can be used to describe risks to average or sensitive



population members.  A major advantage of this approach is the capability to predict changes



in the magnitude and likelihood of effects for different exposure scenarios, thus providing a



means for comparing different risk management options. As noted above, comparing




distributions also allows one to identify and quantify risks to different segments of the
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population.  Limitations include the increased data requirements compared with previously




described techniques and the implicit assumption that the full range of variability in the




exposure and effects data is adequately represented.  As with the quotient method, secondary




effects are not readily evaluated with this technique.  Thus, it is desirable to corroborate




risks estimated by distributional comparisons with field studies or other lines of evidence.




Text note 5-4 and figure 5-5 illustrate  the use of cumulative exposure and effects




distributions for estimating risk.
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Figure 5-5.  Risk estimation techniques:  comparison of exposure distribution of an
herbicide in'surface waters with freshwater single-species toxicity data. See Text note
5-4 for further discussion.  Redrawn from SETAC, 1994a.
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5.1.5.   Estimates Based on Process Models




       Process models are mathematical expressions that represent our understanding of the




mechanistic operation of a system under evaluation.  They can be useful tools both in the




analysis phase (see section 4.1.2.)  and the risk characterization phase of ecological risk




assessment. For illustrative purposes, we distinguish between process models used for risk




estimation that integrate exposure and effects information (text note 5-5) and process models




used in the analysis phase that focus on either exposure or effects evaluations.




       A major advantage of using process models for risk estimation is the ability to




consider "what if" scenarios and to forecast beyond  the limits of observed data that constrain




risk estimation techniques based on empirical data.  The process model can also consider




secondary effects, unlike other risk estimation techniques such as the quotient method or




comparisons of exposure and effect distributions.  In addition, some process models may be




capable of forecasting the combined effects of multiple stressors  (e.g.,  Barnthouse et aL,




1990).



       Process model outputs may be point estimates or distributions.  In either case, risk




assessors should interpret these outputs with care. Process model outputs may imply a




higher level of certainty than is appropriate and all too often are viewed without sufficient




attention to underlying assumptions.  The lack of knowledge on basic life histories for many




species and incomplete knowledge  on the structure and function of a particular ecosystem is




often lost in the model output. Since process models are only as good as the assumptions on



which  they are based, they should be treated as hypothetical representations of reality until




appropriately tested with empirical data.  Comparing model results to field data provides a






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check on whether our understanding of the system was correct (Johnson, 1995) with respect




to the risk hypotheses presented in problem formulation.








5.1.6.  Field Observational Studies



       Field observational studies (surveys) can serve as risk estimation techniques because




they provide direct evidence Unking exposure to stressors and effects.  Field surveys measure



biological changes in uncontrolled situations through collection of exposure and effects data



at sites identified in problem formulation.  A key issue with field surveys is establishing



causal relationships between stressors  and effects (section 4.3.1.2).




       A major advantage of field surveys is that they provide a reality check on other risk



estimates, since field surveys are usually more representative of both exposures and effects




(including secondary  effects) found in natural systems than are estimates generated from



laboratory  studies or  theoretical models (text note 5-6).  On the other hand, field data  may




not constitute reality if they are flawed due to poor experimental design, biased in sampling




or analytical techniques, or fail to measure critical components of the system or random




variations (Johnson, 1995). A lack of observed effects in a field  survey may occur because



the measurements are insufficiently sensitive to detect ecological effects,  and, unless causal



relationships are carefully examined, effects that are observed may be caused by factors



unrelated to the stressor(s) of concern. Finally, field surveys taken at one point in time are




usually not predictive; they describe effects associated with only one scenario (i.e., the one



that exists).
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5.2.  RISK DESCRIPTION




       After risks have been estimated, risk assessors need to integrate and interpret the




available information into conclusions about risks to the assessment endpoints.  In some




cases, risk assessors may have quantified the relationship between assessment endpoints and




measures of effect in the analysis stage (section 4.3.1.3).  In other situations, qualitative




links to assessment endpoints are part of the risk description. For example, if the assessment




endpoints are survival of fish, aquatic invertebrates, and algae, risks may be estimated using




a quotient method based on LC5t)c.  Regardless of the risk estimation technique, the technical




narrative supporting the estimates is as  important as the risk estimates themselves.




       Risk descriptions include an evaluation of the lines of evidence supporting or refuting




the risk estimate(s) and an interpretation of the adverse effects on the assessment endpoint.








5.2.1.  Lines of Evidence



       Confidence in the conclusions of a risk assessment may be increased by using several




lines of evidence to interpret and compare risk estimates.  These lines of evidence may be




derived from different sources or by different techniques relevant to adverse effects on the



assessment endpoints, such as quotient  estimates, modeling results, field experiments, or field



observations.  (Note that the term "weight of evidence" is sometimes used in legal



discussions or in other documents, e.g., Urban and Cook, 1986; Menzie et al., 1996.  We




use the phrase lines of evidence  to emphasize that both qualitative evaluation and quantitative




weightings may be used.)
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       Some of the factors that the risk assessor should consider when evaluating separate




lines of evidence are:




       •  The relevance of evidence to the assessment endpoints




       •  The relevance of evidence to the conceptual model




       •  The sufficiency and quality of data and experimental designs used in key studies




       •  The strength of cause/effect relationships



       •  The relative uncertainties of each line of evidence and their direction.




This process involves more than just listing the factors that support or refute the risk.  The




risk assessor should carefully examine each factor and evaluate its  contribution to the risk




assessment.




       For example, consider the two lines of evidence described for  the carbofuran example



(text notes 5-2 and 5-6): quotients and field studies.  Both approaches are relevant to the



assessment endpoint (survival of birds that forage in agricultural areas where carbofuran is



applied), and both are relevant to the exposure scenarios described in  the conceptual model



(figure 3-2).  However, the quotients are limited in their ability to  express incremental risks




(e.g., how much greater risk is expressed by a quotient of "2" versus a quotient of "4"),




while the field studies had some design flaws (text note 5-6). Nevertheless, because of the



great preponderance of the data, the strong evidence of causal relationships from the field



studies, and the consistency between these two lines of evidence, confidence in a conclusion



of high risk to the assessment endpoint is  supported.




       Sometimes lines  of evidence do not point toward the  same conclusion. When they




disagree, it is important to distinguish between true inconsistencies and those related to







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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 it may be possible




to use numerical weighting techniques for evaluating various lines of evidence, in most cases




qualitative evaluations based on professional judgment are appropriate for sorting through




conflicting lines of evidence.   While iteration of the risk assessment process and collection of




additional data may help resolve uncertainties, this option is not always available.








5.2.2.  Determining Ecological Adversity




       At this point in risk characterization, the changes expected in the assessment



endpoints have been estimated and described.  The next step is to interpret whether these




changes are considered adverse.  Adverse changes are those of concern ecologically or



socially (section 1).  Determining adversity is not always an easy task and frequently depends




on the best professional judgment of the risk assessor.



       Five criteria are proposed for evaluating adverse changes in assessment endpoints:




       •  Nature of effects



       •  Intensity of effects




       •  Spatial scale




       •  Temporal scale




       •  Potential for recovery.      '






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       The extent to which the five criteria are evaluated depends on the scope and

complexity of the ecological risk assessment.  However, understanding the underlying

assumptions and science policy judgments is important even in simple cases.  For example,

when exceedance of a previously established decision rule such as a benchmark stressor level

is used as evidence of adversity (e.g., see Urban and Cook, 1986, or Nabholz, 1991), the

reasons why exceedences of the benchmark are considered adverse should be clearly

understood.

       To distinguish ecological changes that are adverse from those ecological events that

are within the normal pattern of ecosystem variability or result in little or no significant

alteration of biota, it is important to consider the nature and intensity of effects.  For

example, for an assessment endpoint involving survival, growth,  and reproduction of a

species,  do  predicted effects involve survival and reproduction or only growth?  If survival of

offspring will be affected, by what percentage will it diminish?
                                                         i
       It is  important for risk assessors to consider both the ecological and statistical  contexts

of an effect when evaluating intensity.  For example, a statistically significant 1 % decrease in

fish growth (text note 5-7) may not be relevant to an assessment endpoint of fish population

viability, and a 10% decline in reproduction may be worse for a population of slowly

reproducing trees than for rapidly reproducing planktonic algae.

       Natural ecosystem variation can make it very difficult to observe (detect) stressor-

related perturbations.  For example, natural fluctuations in marine fish populations are often

large, with intra- and interannual variability in population levels covering several orders of

magnitude.  Furthermore, cyclic events (e.g., bird migration, tides) are very important in



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natural systems.  Predicting the effects of anthropogenic stressors against this background of




variation can be very difficult.  Thus, a lack of statistically significant effects in a field study




does not automatically mean that adverse ecological effects are absent.  Rather, risk assessors




must consider factors such as statistical power to detect differences, natural variability, and




other lines of evidence in reaching their conclusions.




       Spatial and temporal scales need to be considered in assessing the adversity of the




effects.  The spatial dimension encompasses both the extent and pattern of effect as well as




the context of the effect within the landscape.  Factors to  consider include the absolute area




affected, the extent of critical habitats affected compared with a larger area of interest, and




the role or use of the affected area within the landscape.




       Adverse effects to assessment endpoints vary with  the absolute area of the effect.  A




larger affected area  may be (1)  subject to a greater number of other stressors, increasing  the




complications from  stressor interactions; (2) more likely to contain sensitive species or




habitats; or (3) more susceptible to landscape-level changes because many ecosystems may  be




altered by the stressors.



       Nevertheless, a smaller area of effect is not always associated with lower risk.  The




function of an area within the lamdscape may be more important than the absolute area.




Destruction of small but unique areas, such as critical wetlands, may have important effects




on local wildlife populations. Also, in river systems, both riffle and  pool areas provide



important microhabitats  that maintain the structure and function of the total river ecosystem.




Stressors acting on some of these microhabitats  may present a significant risk to the entire




system.






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       Spatial factors are important for many species because of the linkages between

ecological landscapes and population dynamics.  Linkages between one or more landscapes

can provide refugia for affected populations,  and species may require adequate corridors

between habitat patches for successful migration.

       The temporal scale for ecosystems can vary from seconds (photosynthesis,

prokaryotic reproduction) to centuries (global climate change).  Changes within a forest

ecosystem can occur gradually over decades or centuries and may be affected by slowly

changing external factors such as climate.  When interpreting ecological adversity, risk

assessors should recognize that the time scale of stressor-induced changes operates within the

context of multiple natural time scales.  In addition, temporal responses for ecosystems may

involve intrinsic time lags, so that responses from a stressor may be delayed. Thus, it is

important to distinguish the long-term impacts of a stressor from the immediately visible

effects.  For example, visible changes resulting from eutrophication of aquatic systems

(turbidity, excessive macrophyte growth, population decline) may not become evident  for

many years after initial increases  in nutrient levels.
                              4
       Considering the temporal scale of adverse effects leads logically to a consideration of

recovery. Recovery is the rate and extent of return of a population or community to a

condition that existed before the introduction of a stressor.  (While this discussion deals with

recovery as a result of natural processes, risk mitigation options may include restoration

activities to facilitate or speed up the recovery process.)  Because ecosystems are dynamic

and even under natural conditions are constantly changing in response to changes in the

physical environment (weather, natural catastrophes,  etc.) or other factors, it is unrealistic  to



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expect that a system will remain static at some level or return to exactly the same state that it




was before it was disturbed (Landis et al., 1993). Thus, the attributes of a "recovered"




system must be carefully defined.   Examples might include productivity declines in an




eutrophic system, reestablishmerit  of a species at a particular density,  species recolonization




of a damaged habitat, or the restoration  of health of diseased organisms.




       Recovery can be evaluated in spite of the difficulty in predicting events in ecological




systems (e.g., Niemi et al., 1990).  For example, it is possible to distinguish changes that




are usually reversible (e.g., recovery of a stream from sewage effluent discharge), frequently




irreversible (e.g., establishment of introduced species), and always irreversible (e.g., species




extinction). It is important for risk assessors to consider whether significant structural or




functional changes  have occurred  in a system that might render changes irreversible.  For




example, physical alterations such as deforestation in  the coastal hills of Venezuela in recent




history and Britain in the Neolithic period changed soil structure and seed sources such that




forests cannot easily grow again. (Fisher and Woodmansee, 1994).



       Risk assessors should note natural disturbance patterns when evaluating the likelihood




of recovery from anthropogenic stressors.  Ecosystems that have been subjected to  repeated



natural disturbances may be more vulnerable to anthropogenic stressors (e.g., overfishing,




logging of old-growth forest).  Alternatively,  if an ecosystem has become adapted to a



disturbance pattern, it may be affected when the disturbance is removed (fire-maintained




grasslands). The lack  of natural analogues make it difficult to predict recovery from novel




anthropogenic stressors (e.g., synthetic chemicals).
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       The relative rate of recovery can also be estimated. For example,  fish populations in




 a stream are likely to recover much faster from exposure to a degradable chemical than from




 habitat alterations resulting from stream channelization. Risk assessors can use knowledge of




 factors such as the temporal scales of organisms' life histories, the availability of adequate



 stock for recruitment, and the interspecific and trophic dynamics of the populations in



 evaluating the relative rates of recovery. A fisheries stock or forest might recover in several




 decades, a benthic infaunal community in years, and a planktonic community in weeks  to



 months.




       Appendix E illustrates how the criteria for ecological adversity (nature and intensity



 of effects,  spatial and temporal scales, and recovery) might be used in evaluating two cleanup



 options for a marine oil spill.  This example also shows that recovery of a system depends




 not only on how quickly a stressor is removed but also on how any cleanup efforts affect the



 recovery.
5.3.  REPORTING RISKS




       When risk characterization is complete, the risk assessors should be able to estimate



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



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



Usually this information is included in a risk assessment report (sometimes referred to as a




risk characterization report because of the integrative nature of risk characterization).  This



section describes elements that risk assessors should consider when preparing a risk



assessment report.
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       Like the risk assessment itself, a risk assessment report may be brief or extensive




depending on the nature of and the resources available for the assessment.  While it is




important to address the elements described below, risk assessors must judge the appropriate




level of detail required. The report need not be overly complex or lengthy, depending on the




nature of the risk assessment and the information required to support a risk management




decision.  In fact, it is important that information be presented clearly and concisely.




       While the breadth of ecological risk assessment precludes providing a detailed outline




of reporting elements,  the risk assessor should consider the elements listed in text note 5-8




when preparing a risk  assessment report.




       To facilitate mutual understanding, it is critical that the risk assessment results are




properly presented. Agency policy requires that risk characterizations be prepared "in a




manner that is clear, transparent, reasonable, and consistent with other risk characterizations




of similar scope prepared across programs in the Agency" (U.S. EPA 1995c).  Ways to




achieve such characteristics are described in text note 5-9.



       After the risk assessment report is prepared, the results are discussed with risk




managers.  Section 6 provides information on communication between risk assessors and risk



managers,  describes the use of the risk assessment in a risk management context, and briefly




discusses communication of .risk assessment results from risk managers to the public.
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                 6.  RELATING ECOLOGICAL INFORMATION TO




                          RISK MANAGEMENT DECISIONS
       After characterizing risks and preparing a risk assessment report (section 5), risk




assessors discuss the results with risk managers (figure 5-1).  Risk managers use risk



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



environmental decisions.  The results  also provide a basis for communicating risks to the



pubHc.




       Mutual understanding between risk assessors and risk managers can be facilitated if




the questions listed in text note 6-1 are addressed.  Risk managers need to know what the



major risks (or potential risks) are with respect to assessment endpoints and have an idea of




whether the conclusions are supported by a large body of data or if there are significant data



gaps. When there is insufficient information to characterize risk at an appropriate level of



detail due to  a lack of resources, a lack of a consensus  on how to  interpret information, or



other reasons, the issues, obstacles, and correctable deficiencies should be clearly articulated



for the risk manager's consideration.




       In making a decision regarding ecological risks, risk managers use risk assessment



results along with other information that may include social, economic, political, or legal



issues. For example, the risk assessment may be used as part of a risk/benefit analysis,



which may require translating resources (identified through the assessment endpoints) into



monetary values. One difficulty with  this approach is that traditional economic




considerations may not adequately  address things that are not considered commodities,
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intergenerational resource values or issues of long-term or irreversible effects (U.S. EPA,




1995b).  Risk managers may also consider risk mitigation options or alternative strategies for



reducing risks.  For example, risk mitigation techniques such as buffer strips or lower field




application rates can be used to reduce the exposure (and risk) of a new pesticide. Further,




risk managers may consider relative as well as absolute risk, for example, by comparing the




risk of a new pesticide to other pesticides currently in use.  Finally, risk managers consider




public opinion and political demands in their decisions.  Taken together, these other factors




may render very high risks acceptable or very low risks unacceptable.



       Risk characterization provides the basis for communicating ecological risks to the




public.  This task  is usually the responsibility of risk managers.  Although the final risk




assessment document (including its risk characterization sections) can be made available to




the public, the risk communication process is best served by tailoring information to a




particular audience. It is important to clearly describe the ecological resources at risk, their




value, and the monetary and other costs of protecting (and failing to protect) the resources




(U.S. EPA, 19955).



       Managers should clearly describe the sources and causes of risks,  the potential



adversity of the risks (e.g.,  nature and intensity, spatial and temporal scale, and recovery




potential). The degree of confidence in the risk assessment, the rationale for the risk




management decision, and the options for reducing risk are also important (U.S. EPA,




1995b).  Other risk communication considerations are provided in text note 6r2.



   .    Along with the discussions of risk and communications with the public, it is important




for risk managers to consider whether  additional follow-on activities are required.






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Depending on the importance of the assessment, confidence level in the assessment results,




and available resources, it may be advisable to conduct another iteration of the risk



assessment (starting with problem formulation or analysis) in order to facilitate a final



management decision.  Another option is to proceed with the decision and develop a



monitoring plan to evaluate the results of the decision (see section 1).  For example, if the




decision was to mitigate risks through exposure reduction, monitoring could help determine




whether the desired reduction in exposure (and effects) was  achieved.
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7.  TEXT NOTES





Text Note 1-1. Related Terminology





       The following terms overlap to varying degrees with the broad concept of ecological


risk assessment used in these guidelines (see Appendix B for definitions):


•     Hazard assessment


•     Comparative risk assessment


•     Cumulative ecological risk assessment


•     Environmental impact statement





Text Note 1-2. Flexibility of the Framework Diagram





       The framework process (figure 1-1) is a general representation of a complex and


varied group of assessments, but this diagram should not be viewed as rigid and prescriptive.


Rather, as illustrated by the examples below, broad applicability of the framework requires a


flexible interpretation of the process.


•     In problem formulation, an assessment may begin with a consideration of endpoints,


       stressors, or ecological  effects. Problem formulation is frequently interactive and


       iterative rather than linear.
                                         i.

•     In the analysis phase, it may be difficult to maintain a clear distinction between


       exposure and effects analyses  in all but the simplest systems.  Exposure and effects




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       frequently become intertwined, as when an initial exposure leads to a cascade of




       additional exposures and effects. It is important that a risk assessment is based on an



       understanding of these complex relationships.




•     Analysis and risk characterization are shown as separate phases.  However, some



       models may combine the analysis of exposure and effects data with the integration of




       these data that occurs  in risk characterization.








Text Note 1-3.  The Iterative Nature of Ecological Risk Assessment








       The ecological risk assessment process is by nature iterative.  For example, it may



take more than one pass through problem formulation to complete planning for the risk




assessment, or information gathered in the analysis phase may suggest further problem



formulation activities such as modification of the endpoints selected.



       To maximize efficient use of limited resources,  ecological risk assessments are



frequently designed in sequential tiers that proceed from simple, relatively inexpensive




evaluations to more costly and complex assessments. Initial tiers are based on conservative



assumptions, such as maximum exposure and ecological sensitivity.  When an early tier



cannot define risk to support a management decision, a higher assessment tier is used that



may require either additional  data or applying more refined analysis techniques to available




data.  Iterations proceed until sufficient information is available to support a sound



management decision, within the constraints of available resources.
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       Because a tiered approach can incorporate standardized decision points and



supporting analyses, it can be particularly useful for multiple assessments of similar stressors



or situations.  However, it is difficult to generalize further concerning tiered risk assessments



because they are used to answer so many different questions. Examples of organizations that



use, are considering, or have advocated using tiered ecological risk assessments include the



Canadian government (proposed, Gaudet, 1994), the European Community (E.G., 1993),



industry (Cowan et al., 1995), the Aquatic Dialogue Group (SETAC 1994a), and the U.S.



EPA Offices of Pesticide Programs (Urban and Cook, 1986), Pollution Prevention and



Toxics (Lynch et al.,  1994), and Superfund (document in preparation).
                                             i


                                                                              V


Text Note 2-1.  Who Are Risk Managers?






       Risk managers are individuals and organizations that take responsibility for, or have



the authority to take action or require action, to mitigate an identified risk. The expression



"risk manager" is often used to represent a decisionmaker in agencies like EPA or state



environmental offices who has the authority to protect or manage a resource. However, risk



managers often represent a diverse group of interested parties that influence the outcome of



resource protection efforts.  Particularly as the scope of environmental management expands



to communities, the meaning of risk manager significantly expands to include decision



officials in Federal, state, and local governments, as well as private-sector leaders in



commercial, industrial, and private organizations.  Risk managers may also include


constituency groups, other interested parties, and the public.  In  situations where a complex

                              ' l    '  •                        ,• '  !


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of ecosystem values (e.g., watershed resources) is at risk from multiple stressors, many of




these groups may act together as risk management teams.  For additional insights on risk



management and manager roles,  see text notes 2-3 and 2-4.








Text Note 2-2. Who Are Risk Assessors?








       Risk assessors are a diverse group of professionals who bring a needed expertise to a




risk assessment. When a specific risk assessment process is well defined through regulations



and guidance,  one trained  individual may be able to complete a risk assessment if needed




information is available (e.g., premanufacture notice of a chemical).  However, as more



complex risk assessments become common, it will be rare that one individual can provide the




necessary breadth of expertise. Every risk assessment team should include at least one



professional who is knowledgeable and experienced in using the risk assessment process.




Other team members bring specific expertise relevant to the location, the stressors, the



ecosystem, and the scientific issues and other expertise as  determined by the type of



assessment.








Text Note 2-3. Questions Addressed by Risk Managers and Risk Assessors








Questions principally for risk managers:
What is the nature of the problem and the best scale for the assessment?
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What are the management goals and decisions needed, and how will risk assessment help?




What are the ecological values of concern?



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




       environmental justice)?



What precedents are set by previous risk assessments and decisions?




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




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




What level of uncertainty is acceptable?








Questions principally for risk assessors:








What is the scale of the risk assessment?



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




How likely is recovery and how long will it take?



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




What is our state of knowledge on the problem?



What data and data analyses are available and appropriate?



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




       data)?








Text Note 2-4.  The Role of Interested Parties
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       The involvement of all interested and affected parties, which "stakeholder" is




commonly used to represent, is important to the development of management goals for some



risk assessments. The greater the involvement, the broader the base of consensus about




those goals.  With strong consensus on management goals, decisions are more likely to be



supported by all community groups during implementation of management plans. However,




the context of this involvement can vary widely, and the ability to achieve consensus often



decreases as the size of the management team increases.  Where large diverse groups need to




come to consensus, social science professionals and methods for consensus building become




increasingly important.  Interested parties become risk managers when they influence risk




reduction.  See additional discussion in text note 2-1 and section 2.2.








Text Note 2-5.  Sustainability as a Management Goal
       Sustainability is used repeatedly as a management goal in a variety of settings (see



U.S. EPA, 1995b).  To sustain is to prolong, to hold up under, or endure (Merriam-



Webster, 1972).  Sustainability and other concepts such as biotic or community integrity are



very useful as guiding principles for management goals.  However, in each case these




principles must be explicitly interpreted to support a risk assessment.  To do this, key




questions need to be addressed: What does Sustainability or integrity mean for the particular



ecosystem?  What must be protected to meet sustainable goals or system integrity? Which



ecological resources and processes are to be sustained and why? How will we know we have



achieved it? Answers to these questions serve to clarify the goals for a particular ecosystem.
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Concepts like sustainability and integrity do not meet the criteria for an assessment endpoint

(see section 3.3.2).




Text Note 2-6.  Management Goals for Waquoit Bay




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

loss of eelgrass, fish, and shellfish and increasing macroalgae mats and fish kills. The

management goal for Waquoit Bay was established through public meetings, preexisting goals

from local organizations, and state and Federal regulations:

       Reestablish and maintain water quality and habitat conditions in Waquoit Bay and

       associated freshwater rivers and ponds to (1) support diverse self-sustaining

       commercial, recreational, and native fish and shell fish populations, and (2) reverse

       ongoing degradation of ecological resources in the watershed.

       To define this goal, it was interpreted into 10 objectives, two of which are:

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

       sport fishery

•     Reduce or eliminate nuisance macroalgal growth

       From these objectives, specific ecological resources in the bay were identified to

provide the basis for the risk assessment,  one of which is:

       Areal extent and patch size of eelgrass beds                    t

       Eelgrass was selected because scallops dependent directly on eelgrass beds for

survival and eelgrass is highly sensitive to excess macroalgal growth.


                                            .  i                                 *
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Text Note 2-7. Questions to Ask About Scope and Complexity








Is this risk assessment legally mandated, addressing a court-ordered decision, or providing




      guidance to a community?



Are decisions more likely based on assessments of a small area evaluated in-depth or a large-




      scale area in less detail?



What are the spatial and temporal boundaries of the problem?




What kinds of information are already available compared to what is needed?




How much time can be taken and how many resources are available?




What practicalities constraint data collection?



Is a tiered approach an option?








Text Note 3-1. Avoiding Potential Shortcomings Through Problem Formulation








       The importance of problem formulation has been shown repeatedly in the Agency's




analysis of ecological risk assessment case studies and in interactions with senior EPA



managers and  regional risk assessors (U.S. EPA, 1993a, 1994a).  Consistent shortcomings




identified in the case studies include (1) absence of clearly defined goals, (2) endpoints that



are ambiguous and difficult to define and measure, and (3) failure to identify important risks.




These and other shortcomings can be avoided through rigorous development of the products




of problem formulation as described in this  section of the guidelines.
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Text Note 3-2.  Uncertainty in Problem Formulation








       In each product of problem formulation there are elements of uncertainty, a




consideration of what is known and not known about a problem and its setting.  The explicit



treatment of uncertainty during problem formulation is particularly important because it will




have repercussions throughout the remainder of the assessment.  Uncertainty is discussed in



section 3.4, Conceptual Models, because uncertainty in problem formulation is articulated in




these models.








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



Stressor, and Exposure Characteristics, Ecosystem Characteristics, and Effects








Source and Stressor Characteristics




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



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



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




       the magnitude or extent of physical disruption, the density or population size of a



       biological Stressor)?




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



       functions?








Exposure Characteristics







                                         169                     Proposed Guidelines

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


       continuous; is it subject to natural daily, seasonal, or annual periodicity)?


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


       chemical, what is its half-life, does it bioaccumulate; for physical, is habitat alteration


       sufficient to prevent recovery; for biological, will it reproduce and proliferate)?


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


       life cycles or ecosystem events (e.g., reproduction, lake overturn)?


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


       regional, global, habitat-specific, or ecosystemwide?


•      What is the distribution? How does the stressor move through the  environment


       (e.g., for chemical, fate and transport; for physical, movement of physical structures;


       for biological, life history dispersal characteristics)?




Ecosystems Potentially at Risk


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


       characteristics of the ecosystem?


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


       geology, hydrology, soil type, water quality)?


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


       source and processing, nutrient cycling)?
                                              i             '

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


       abundance, trophic relationships)?



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 •     What habitat types are present?

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

       exposure) of the ecosystem to the stressor(s)?
     r
 •      Are there unique features that are particularly valued (e.g., the last representative of

       an ecosystem type)?

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




 Ecological Effects

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

       surveys, laboratory tests, or structure-activity relationships)?

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

       by the stressor?

 •     Under what circumstances will effects occur?




 Text Note 3-4.  Initiating a Risk Assessment:  What's Different When Stressors, Effects,

 or Values Drive the Process?
       The reasons for initiating a risk assessment also influence how the risk assessor

proceeds through the process of problem formulation.  When the assessment is initiated due

to concerns about stressors, risk assessors use what is known about the characteristics of the

stressor and its source to focus the assessment.  Goals are articulated based on how the

stressor is likely to cause risk to possible receptors that may become exposed. This
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information forms the basis for developing conceptual models and selecting assessment




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




normally established first.  Often these endpoints involve affected ecological entities and




their response.  Goals for protecting the assessment endpoints are then established, which




support the development of conceptual models.  The models aid in the identification of the




most likely stressor(s).  Value-initiated risk assessments are driven up front by goals for the




ecological value of concern. These values might involve ecological entities such as species,




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




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




for identifying an array of stressors that may be influencing them, and describing the




diversity of potential effects.  This information is then captured in the conceptual model(s).








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




Endpoint








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



anadromous fish such as salmon spawn.  Assessment endpoints must be selected to assess




potential ecological risk. Of the anadromous fish, salmon that spawn in the river are an




appropriate choice because they meet the criteria for good assessment endpoints.   Salmon




fry and adults are important food sources for a multitude of aquatic and terrestrial species



and are major predators of aquatic invertebrates (ecological relevance). Salmon are sensitive




to changes in sedimentation and substrate pebble size,  require quality cold water habitats, and






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have difficulty climbing fish ladders.  Hydroelectric dams represent significant and normally




fatal habitat alteration and physical obstacles to successful salmon breeding and fry survival



(susceptibility). Finally, salmon support a large commercial fishery, some species are




endangered, and they have ceremonial importance and are key food sources for Native




Americans (basis for management goals). "Salmon reproduction and population




maintenance"  is a good assessment endpoint for this risk assessment, and if salmon



populations are protected, other anadromous fish populations are likely to be protected as




well. However, one assessment endpoint can rarely provide the basis for a risk assessment



of complex ecosystems. These are better represented by a set of assessment  endpoints.








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








       The interrelationships among entities and processes in ecosystems result in the



potential for cascading  effects:  as one population, species, process, or other  entity in the



ecosystem is altered, other entities are affected as well.  Primary, or direct, effects occur




when a stressor acts directly on the assessment endpoint and causes an adverse response.




Secondary, or indirect, effects occur when the response of an ecological entity to a stressor




becomes a stressor to another entity.   Secondary effects are not limited in number.  They



often are a series of effects among a diversity of organisms  and processes that cascade



through the ecosystem. For example, application of an herbicide on a wet meadow results in



direct toxicity to plants. Death of the wetland plants leads to secondary effects such as loss
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of feeding habitat for ducks, breeding habitat for red-winged black birds, alteration of




wetland hydrology that changes spawning habitat for fish, and so forth.








Text Note 3-7.  Sensitivity and Secondary Effects:  The Mussel-Fish Connection








       Native freshwater mussels are endangered in many streams.  Management efforts have




focused on maintaining suitable habitat for mussels because habitat loss has been considered




the greatest threat to this group.  However, larval unionid mussels must attach to the gills of




a fish host for one month during development. Each species of mussel must attach to a




particular host species of fish.  In situations where the fish community has been changed,




perhaps due to stressors  to which mussels are insensitive, the host fish may no longer be




available.  Mussel larvae will die before reaching maturity as a result.  Regardless of how



well managers restore mussel habitat, mussels will be lost from this system unless the fish




community is restored.  In this case, exposure to the absence of a critical resource is the




source of risk.
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Text Note 3-8.  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 etal., 1994)
Protect "the environment" from "an unreasonable
risk of injury" (TSCA §2[b][l] and [2]); protect the
aquatic environment. Goal was to exceed a
concentration of concern by 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][6j); using
cost-benefit considerations.  Goal was 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)
This 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
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 reverse
ongoing degradation.
(1) Estuarine eelgrass
habitat abundance and
distribution
(2) Estuarine fish species
diversity and abundance
(3) Freshwater pond
benthic invertebrate
species diversity and
abundance
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Text Note 3-9.  Common Problems in Selecting Assessment Endpoints




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

•      Endpoint is vague (e.g., estuarine integrity instead of eelgrass abundance and

       distribution)

•      Ecological entity is better as a measure  (e.g., measure emergence of midges for

       endpoint on feeding of fish)

•      Ecological entity may not be as sensitive to the stressor (e.g., catfish versus salmon

       for sedimentation)

•      Ecological resource is not exposed to the stressor (e.g., using insectivorous birds for

       avian risk of pesticide application to seeds)

•      Ecological resources are irrelevant to the assessment (e.g., lake fish in salmon stream)
                               •
•      Value of a species or attributes of an ecosystem are not fully considered (e.g.,

       mussel-fish connection, see text note 3-7).

•      Attribute is not sufficiently sensitive for detecting important effects (e.g., survival

       compared with recruitment for endangered species)




Text Note 3-10. What Are Risk Hypotheses and Why Are They Important?



       Risk hypotheses are proposed answers to questions risk assessors have about what

responses assessment endpoints (and measures) will show when they are exposed to stressors

and how exposure will occur. Risk hypotheses clarify and codify relationships  that are



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posited through the consideration of available data, information from scientific literature, and




the best professional judgment by risk assessors developing the conceptual models.  This



explicit process opens the risk assessment to peer review and evaluation to ensure the




scientific validity of the work.  Risk hypotheses are not equivalent to statistical testing of null




and alternative hypotheses.  However, predictions generated from risk hypotheses can be




tested in a variety of ways, including standard statistical approaches.








Text Note 3-11.  Examples of Risk Hypotheses
       Hypotheses include known information that sets the problem in perspective and the



proposed relationships that need evaluation.




       Stressor-initiated: Chemicals with a high K^, tend to bioaccumulate.



Premanufacture notice (PMN) chemical A has a K^ of 5.5 and similar molecular structure as



known chemical stressor B.  Hypotheses: Based on the K^ of chemical A, the mode of




action of chemical B, and the food web of the target ecosystem, when the PMN chemical is



released at a specified rate, it will bioaccumulate sufficiently in 5 years to cause



developmental problems in wildlife and fish.




       Effects-initiated: Bird kills were repeatedly observed in golf courses following the




application of the pesticide carbofuran, which is highly toxic. Hypotheses:  Birds die when




they consume recently applied granulated carbofuran; as the level of application increases,



the number of dead birds increases.  Exposure occurs when dead and dying birds are
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consumed by other animals.  Birds of prey and scavenger species will die from eating




contaminated birds.



       Ecological value-initiated: Waquoit Bay, Massachusetts, supports recreational




boating and commercial and recreational shellfishing and is a significant nursery for fish.




Large mats of macroalgae clog the estuary, most of the eelgrass has died, and scallops are




gone.  Hypotheses: Nutrient loading from septic systems, air pollution,  and lawn fertilizers




cause eelgrass loss by shading from algal growth, and direct toxicity from nitrogen




compounds.  Fish and shellfish populations are decreasing because of loss of eelgrass habitat




and periodic hypoxia.








Text Note 3-12. What Are the Benefits of Developing Conceptual Models?








•     The process of creating a conceptual model is a powerful learning tool.




•     Conceptual models can be improved as knowledge increases.



•     Conceptual models highlight what we know and don't know and can be used to plan




       future work.



•     Conceptual models can be a powerful communication tool. They provide an explicit




       expression of our  assumptions and understanding of a system for  others to evaluate.




•     Conceptual models provide a framework for prediction and are the template for




       generating more risk hypotheses.








Text Note 3-13.  Uncertainty in Problem Formulation






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       Uncertainties in problem formulation are manifested in the quality of conceptual



models.  To describe uncertainty:




•     Be explicit in defining assessment endpoints; include both entity and measurable




       attributes.



•     Reduce or define variability by carefully defining boundaries for the assessment.




•     Be open and explicit about the strengths and limitations of pathways and relationships



       depicted in the conceptual model.




•     Identify and describe rationale for key assumptions made because of lack of




       knowledge, model simplification, approximation, or extrapolation.



•     Describe data limitations.








Text Note 3-14.  Examples of Assessment Endpoints and Measures (see also section



3.5.1)








Assessment Endpoint:  Coho salmon breeding success and fry survival.








Measures of Effects



•     Egg and fry response to low dissolved oxygen



•     Adult behavior in response to  obstacles




•     Spawning behavior and egg survival in response to sedimentation
Measures of Ecosystem and Receptor Characteristics
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•      Water temperature, water velocity, and physical obstructions


•      Abundance and distribution of suitable breeding substrate


•      Abundance and distribution of suitable food sources for fry


•      Feeding, resting, and reproductive cycles


•      Natural population structure (proportion of different size and age classes)


•      Laboratory evaluation of reproduction, growth, and mortality





Measures of Exposure


•      Number and height of hydroelectric dams


•      Toxic chemical concentrations in water,  sediment, and fish tissue


•      Nutrient and dissolved oxygen levels in ambient waters





Text Note 3-15.  Selecting What to Measure





       Direct measurement of assessment endpoint responses is often not possible.  Under


these circumstances, the selection of a surrogate response measure  is  necessary. The


selection of what, where, and how to measure determines whether  the risk assessment is still


relevant to management decisions about an assessment endpoint.  For example, a risk


assessment may be conducted to evaluate the potential risk of a pesticide used on seeds.


Birds and mammals may be selected as the entities for assessment endpoints.  However, to
                                             i

ensure that the organisms selected are susceptible to the pesticide, only those that eat seeds


should be chosen.  While insectivorous birds may serve as a good surrogate measure for
         i



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 determining the sensitivity of birds to the pesticide, they do not address issues of exposure.




 To evaluate susceptibility, the appropriate assessment endpoints in this case would be seed-



 eating birds and mammals. Problem formulations based on assessment endpoints that are




 both sensitive and likely to be exposed to the stressor will be relevant to management




 concerns. If assessment endpoints are not susceptible, their use in assessing risk can lead to



 poor management decisions.
Text Note 3-16.  How Do Water Quality Criteria Relate to Assessment Endpoints?








       Water quality criteria (U.S. EPA,  1986a) have been developed for the protection of



aquatic life from chemical stressors.  This text note shows how the elements of a water




quality criterion correspond to management goals, assessment endpoints, and measures.
Regulatory Goal:




•      Clean Water Act, §101:  Protection of the chemical, physical, and biological



       integrity of the Nation's waters



Program Management Objective:




•      Protect 99% of individuals in 95% of the species in aquatic communities from acute



       and chronic effects resulting from exposure to a chemical stressor



Assessment Endpoints:




•      Survival of fish, aquatic invertebrate, and algal species under acute exposure
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•      Survival, growth, and reproduction of fish, aquatic invertebrate, and algal species


       under chronic exposure

Measures of Effect:

•      Laboratory LC50s for at least eight species meeting certain requirements

•      Chronic NOAELs for at least three species meeting certain requirements


Measures of Ecosystem and Receptor Characteristics:

•      Water hardness (for some metals)

•      pH




       The water quality criterion is a benchmark level derived from a distributional analysis

of single-species toxicity data.  It is assumed that the species tested adequately represent the


composition and sensitivities of species in a natural community.




Text Note 3-17. Data Quality Objectives (DQO) Process




       The DQO process combines elements of both planning and problem formulation in its

seven-step format.




Step 1 -

        State the problem.  Review existing information to concisely describe the problem
                                                             i
        to be studied.
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Step 2 -
Step 3 -
Step 4
Step 5 -
Step 6 -
Step 7 -
Identify the decision.  Determine what questions the study will try to resolve and




what actions may result.








Identify inputs to the decision. Identify information and measures needed to




resolve the decision statement.








Define study boundaries.  Specify time and spatial parameters and where and when




data should be collected.








Develop decision rule.  Define statistical parameter, action level, and logical basis




for choosing alternatives.








Specify tolerable limits on decision errors. Define limits based on the



consequences of an incorrect decision.








Optimize the design. Generate alternative data collection designs and choose most




resource-effective design that meets all DQOs.
Text Note 4-1. Data Collection and the Analysis Phase
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   Data needs are identified during problem formulation (the analysis plan step), and data




are collected before the start of the analysis phase.  These data may be collected for the




specific purpose of a particular risk assessment, or they may be available from previous




studies. If additional data needs are identified as the assessment proceeds,  the analysis phase




may be temporarily halted while they are collected or the assessor may choose to iterate the




problem formulation again.   Data collection methods are not described in these guidelines.




However, the evaluation of data for the purposes of risk assessment is discussed in section




4.2.








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








   The Specifications and Guidelines for Quality Systems for Environmental Data Collection




and Environmental Technology Programs (ASQC,  1994) recognizes several areas that are




important to ensuring that environmental data will meet study objectives, including:



•  Planning and scoping




•  Design of data collection operations



•  Implementation and monitoring of planned operations




•  Assessment and verification of data usability








Text Note 4-3. Questions  for Evaluating a Study's Utility for Risk Assessment








How do study objectives compare with those of the risk assessment?






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Are the variables and conditions the study represents compared to those important to the risk

  .  assessment?

Was the study design adequate  to meet its objectives?

Was the study conducted properly?

How were variability and uncertainty treated and reported?



Text Note 4-4.  Considering the Degree of Aggregation in Models


                                                                i
    Wiegert and Bartell (1994) suggest the following considerations for evaluating the proper

degree of aggregation or disaggregation:

(1)    do not aggregate components with greatly disparate rates of fluxes;

(2)    do not greatly increase the disaggregation of the structural aspects of the model

       without a corresponding increase in the sophistication of the functional relationships

       and controls; and

(3)    disaggregate models only insofar as required by the goals of the model to facilitate

       testing.



Text Note 4-5.  Questions for  Source Description



Where does the stressor originate?

What environmental medium first receives stressors?
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Does the source generate other constituents that will influence a stressor's eventual

   distribution in the environment?

Are there other sources of the same stressor?

Are there background sources?

Is the source still active?

Does the source produce a distinctive signature that can be seen in the environment,

organisms or communities?



Additional questions for introduction of biological stressors:



Is there an opportunity for repeated introduction or escape into the new environment?

Will the organism be present on a transportable item?

Are  there mitigation requirements or conditions that would kill or impair the organism before

   entry, during transport, or at the port of entry?



Text Note 4-6. Questions to Ask in Evaluating Stressor Distribution



What are the important transport pathways?

What characteristics of the stressor influence transport?

What characteristics of the ecosystem will influence transport?
                                                                     !
What secondary stressors will be formed?

Where will they be transported?                                                   .      .


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 Text Note 4-7.  General Mechanisms of Transport and Dispersal


 Physical, chemical and biological stressors:
 •  By air current
 •  In surface water (rivers, lakes, streams)
 •  Over and/or through the soil surface
 •  Through ground water
                                                        i

 Primarily chemical stressors:
 •  Through the food web


 Primarily biological stressors:
 •  Splashing or raindrops
 •  Human activity (boats, campers)
 •  Passive transmittal by other organisms
 •  Biological vectors


Text Note 4-8.  Questions to Ask in Describing Contact or Co-occurrence
Must the receptor actually contact the stressor for adverse effects to occur?
Must the stressor be taken up into a receptor for adverse effects to occur?
What characteristics of the receptors will influence the extent of contact or co-occurrence?
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Will abiotic characteristics of the environment influence the extent of contact or co-

   occurrence?

Will ecosystem processes or community-level interactions influence the extent of contact or

   co-occurrence?


Text Note 4-9.  Example of an Exposure Equation:  Calculating a Potential Dose via

Ingestion
                                     k=i
Where:

ADDpot    =  Potential average daily dose (e.g., in mg/kg-day)

Q         =  Average contaminant concentration in the k^ type of food (e.g. , in mg/kg wet

              weight)

FRk       =  Fraction of intake of the k* food type that is from the contaminated area

              (unitless)

NIRk      =  Normalized ingestion rate of the k* food type on a wet-weight basis (e.g., in g

              food/g body-weight-day).

m         =  Number of contaminated food types


Source:  U.S. EPA, 1993c
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Text Note 4-10. Measuring Internal Dose Using Biomarkers and Tissue Residues








    Biomarkers, tissue residues, or other bioassessment methods may be useful in estimating




or confirming exposure in cases where bioavailability is expected to be a significant issue,




but the factors influencing it are not known. They can also be very useful when the




metabolism and accumulation kinetics are important factors (McCarty and Mackay, 1993).



These  methods are most useful when they can be quantitatively linked to the amount of




stressor originally contacted by the organism. In addition, they are most useful when the



stressor-response relationship expresses the amount of stressor in terms of the tissue  residues




or biomarkers.  Additional information and some considerations for their development can be



found in Huggett et al. (1992).








Text Note 4-11. Questions  Addressed by the Exposure Profile








How does exposure occur?



What is exposed?




How much exposure occurs? When and where does it occur?



How does exposure vary?




How uncertain are the exposure estimates?



What is the likelihood that exposure will occur?
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Text Note 4-12.  Questions for Stressor-Response Analysis




Does the assessment require point estimates or stressor-response curves?


Does the assessment require the establishment of a "no-effect" level?


Would cumulative effects distributions be useful?




Text Note 4-13.  Qualitative Stressor-Response Relationships




   The relationship between stressor and response can be described qualitatively, for


instance, using categories of high, medium, and low, to describe the intensity of response


given exposure to a stressor.  For example, Pearlstine et al. (1985) assumed that seeds would


not germinate if they were inundated with water at the critical time.  This stressor-response

                                             t
relationship was described simply as a yes or  no. In most cases,  however, the objective is


to describe quantitatively the intensity of response associated with  exposure, and in the best


case, to describe how intensity of response changes with incremental increases in exposure.




Text Note 4-14.  Median Effect Levels




   Median effects are those effects elicited in 50%  of the test organisms exposed to a


stressor, typically chemical stressors. Median effect concentrations can be expressed in


terms of lethality or mortality and are known as LC50 or LD50, depending on whether
                             . .                                   i

concentrations (in the diet or in water) or doses (mg/kg) were used.  Median effects other
                                           190                      Proposed Guidelines

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than lethality (e.g., effects on growth) are expressed as EC50 or ED50.  The median effect


level is always associated with a time parameter (e.g., 24 or 48 hr). Because these tests


seldom exceed 96 hr, their main value lies in evaluating short-term effects of chemicals.


Stephan (1977) discusses several statistical methods to estimate the median effect level.




Text Note 4-15. No-Effect Levels Derived From Statistical Hypothesis Testing




   Statistical hypothesis tests have typically been used with chronic toxicity tests of chemical


stressors that evaluate multiple endpoints.  For each endpoint, the objective is to determine


the highest test concentration for which effects are not statistically different from the controls
                                                       i

(the no observed adverse effect concentration, NOAEC) and the lowest concentration at


which effects were statistically significant from the control (the  lowest observed adverse


effect concentration, LOAEC).  The range between the NOAEC and the LOAEC is


sometimes called the maximum acceptable toxicant concentration,  or MATC.  The MATC,


which can also be reported as the geometric mean of the NOAEC and the LOAEC, provides


a useful reference with which to compare toxicities of various chemical stressors.


   Reporting the results of chronic tests in terms of the MATC or GMATC  has been widely


used within the Agency for evaluating pesticides and industrial chemicals (e.g., Urban and


Cook, 1986; Nabholz,  1991).
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Text Note 4-16.  General Criteria for Causality (Adapted From Fox, 1991)








Criteria strongly affirming causality:




•  Strength of association




•  Predictive performance




•  Demonstration of a stressor-response relationship




•  Consistency of association








Criteria providing a basis for rejecting causality:



•  Inconsistency in association




•  Temporal incompatibility




•  Factual implausibility








Other relevant criteria:



•  Specificity of association



•  Theoretical and biological plausibility








Text Note 4-17.  Koch's  Postulates (Pelczar and Reid, 1972)








•  A pathogen must be consistently found in association with a given disease.




•  The pathogen must be isolated from the host and grown in pure culture.
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•  When inoculated into test animals, the same disease symptoms must be expressed.




•  The pathogen must again be isolated from the test organism.








Text Note 4-18. Examples of Extrapolations to Link Measures of Effect to Assessment




Endpoints








   Every risk assessment has data gaps that must be addressed, but it is not always possible




to obtain more information.  When there is a lack of time, monetary resources, or a practical




means to acquire more data, extrapolations such as those listed below may be the only way




to bridge gaps in available data.  Extrapolations may be:



•  Between taxa (e.g., bluegill to rainbow trout)



•  Between responses (e.g., mortality to growth or reproduction)



•  From laboratory to field




•  Between geographic  areas



•  Between spatial scales



•  From data collected over a short timeframe to longer-term effects








Text Note 4-19. Questions Related to Selecting Extrapolation Approaches








How specific is the assessment endpoint?



Does the spatial or temporal extent of exposure suggest the need for additional receptors or



   extrapolation models?






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Are the quantity and quality of the data available sufficient for planned extrapolations and




    models?




Is the proposed extrapolation technique consistent with ecological information?




How much uncertainty is acceptable?








Text Note 4-20.  Questions to Consider When Extrapolating From Effects Observed in



the Laboratory to Field Effects of Chemicals








Exposure factors:








How will environmental fate and transformation of the chemical effect exposure in the field?




How comparable are exposure conditions and the timing of exposure?




How comparable are the routes of exposure?




How do abiotic factors influence bioavailability and exposure?



How likely are preference or avoidance behaviors?








Effects factors:








What is known about the biotic and abiotic factors controlling populations of the organisms



    of concern?




To  what degree arei critical life stage data available?
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How may exposure to the same or other stressors in the field have altered organism




    sensitivity?








Text Note 4-21.  Questions Addressed by the Stressor-Response Profile








What ecological entities are affected?



What is the nature of the effect(s)?




What is the intensity of the effect(s)?



Where appropriate, what is the time scale for recovery?




What causal information links the stressor with any observed effects?



How do changes in measures of effects relate to changes in assessment endpoints?




What is the uncertainty associated with the analysis?








Text Note 5-1.  Using Qualitative Categories to Estimate Risks of an Introduced Species








    The importation of logs from Chile required an assessment of the risks posed by the




potential introduction of the bark beetle, Hylurgus ligniperda (USDA,  1993).  Experts to



judged the potential for colonization and spread of the species, and their opinions were



expressed as high, medium, or low as  to the likelihood of establishment (exposure) or



consequential effects of the beetle.  Uncertainties were similarly expressed. A ranking




scheme was then used to sum the individual elements into an overall estimate of risk (high,




medium, or low).  Narrative explanations of risk accompanied the overall rankings.
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Text Note 5-2.  Applying the Quotient Method








   When applying the quotient method to chemical stressors, the effects concentration or




dose (e.g., an LC50, LD50, EC50, ED50, NOAEL, or LOAEL) is frequently adjusted by




uncertainty modifying factors prior to division into the exposure number (U.S. EPA, 1984;



Nabholz,  1991; Urban and Cook, 1986; see section 4.3.1.3), although EPA used a slightly



different approach in estimating the risks to the survival of birds that forage in agricultural




areas where the pesticide granular carbofuran is applied (Houseknecht, 1993). In this case,



EPA calculated the quotient by dividing the estimated exposure levels of carbofuran granules




in surface soils (number/ft2) by the granules/LD50 derived from single-dose avian toxicity




tests.  The calculation yields values with .units of LD50/ft2. It was  assumed that a higher




quotient value corresponded to an increased likelihood that a bird would be exposed to lethal



levels of granular carbofuran at the soil surface.  Minimum and maximum values for LD50/ft2



were estimated for songbirds, upland game birds, and waterfowl that may forage within or



near 10 different agricultural crops.








Text Note 5-3.  Comparing an Exposure Distribution With a Point Estimate of Effects








   The EPA Office of Pollution Prevention and Toxics uses a Probabilistic Dilution Model



(PDM3) to generate a distribution of daily average chemical concentrations based on



estimated variations in stream flow in a model system.  The PDM3 model compares this




exposure distribution with an aquatic toxicity test endpoint to estimate how many days in a 1-







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year period the endpoint concentration is exceeded (Nabholz et al., 1993; U.S. EPA, 1988b).




The frequency of exceedance is based on the duration of the toxicity test used to derive the




effects endpoint. Thus, if the endpoint was an acute toxicity level of concern, an exceedance




would be identified if the level of concern was exceeded for 4 days or more (not necessarily




consecutive). The exposure estimates are conservative in that they assume instantaneous




mixing of the chemical in the water column and no losses due to physical,  chemical, or




biodegradation effects.








Text Note 5-4.  Comparing Cumulative Exposure and Effects Distributions for Chemical




Stressors








    Exposure distributions for chemical stressors can be compared with effects distributions



derived from point estimates of acute or chronic toxicity values derived from different




species  (e.g., HCN, 1993; Cardwell et al., 1993; SETAC, 1994a; Solomon et al.,  1996).




Figure 5-5 shows a distribution of exposure concentrations of an herbicide compared with




single-species algal toxicity data, for the same chemical. The degree of overlap of the curves



indicates the likelihood that a certain percentage of species may be adversely affected.  For




example, figure 5-5 indicates that the 10th percentile of algal species' EC5  values is exceeded




less than 10%  of the time.



    The predictive value of this approach is evident.  The degree of risk reduction that could



be achieved by changes in exposure associated with proposed risk mitigation options can  be
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readily determined by comparing modified exposure distributions with the effects distribution




curve.



   When using effects distributions derived from single-species toxicity data, risk assessors




should consider the following questions:



• Does the subset of species for which toxicity test data are available represent the range of




   species present in the environment?



• Are particularly sensitive (or insensitive) groups of organisms represented in the




   distribution?




• If a criterion level is selected—e.g., protect 95% of species—does the 5% of potentially




   affected species include organisms  of ecological, commercial, or recreational




   significance?








Text Note 5-5.  Estimating Risk With Process Models








   Models that integrate both exposure and effects information can be used to estimate risk.



During risk estimation,  it is important  that both the strengths and limitations of a process



model approach be highlighted.  Brody et al. (1993; see Appendix D) linked two process



models to integrate exposure and effects  information and forecast spatial and temporal



changes in forest communities and their wildlife habitat value. While the models were useful



for projecting long-term effects based on an understanding of the underlying mechanisms of



change in forest communities and wildlife habitat,  they could not evaluate all possible




stressors of concern and were limited in  the plant and wildlife species they could consider.
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Understanding both the strengths and limitations of models is essential for accurately



representing the overall confidence in the assessment.
                                           200                       Proposed Guidelines

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                                                                      t  \1 1', I, ("
Text Note 5-6.  An Example of Field Methods Used for Risk Estimation








    Along with quotients comparing field measures of exposure with laboratory acute toxicity




data (text note 5-2), EPA evaluated the  risks of granular carbofuran to birds based on



incidents of bird kills following carbofuran applications.  Over 40 incidents involving nearly




30 species of birds were documented.  Although reviewers identified problems with




individual field studies (e.g., lack of appropriate control sites, lack of data on carcass-search




efficiencies, no examination of potential  synergistic effects of other pesticides, and lack of



consideration of other potential receptors such as small  mammals), there was so much




evidence of mortality associated with carbofuran application that the study deficiencies did



not alter the conclusions of high risk found by the assessment (Houseknecht,  1993).








Text Note 5-7.  What Are Statistically  Significant Effects?








    Statistical testing is the "statistical procedure or decision rule which leads to establishing




the truth or falsity of a hypothesis.  . ." (Alder and Roessler, 1972).  Statistical significance



is based on the number of data points, the nature of their distribution, whether inter-



treatment variance exceeds intra-treatment variance in the data, and the a priori significance




level (a).  The types of statistical tests and the appropriate protocols (e.g., power of test) for



these tests  should be established as  part of the analysis plan  during problem formulation.
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Text Note 5-8. Possible Risk Assessment Report Elements








•  Describe risk assessor/risk manager planning results.




•  Review the conceptual model and the assessment endpoints.




•  Discuss the major data sources and analytical procedures used.




•  Review the stressor-response and exposure profiles.




•  Describe risks to the assessment endpoints, including risk estimates and adversity




   evaluations.                            .




•  Review and summarize major areas  of uncertainty (as well as  their direction) and the



   approaches used to address them.




   +   Discuss the degree of scientific consensus in key areas of uncertainty.




   +   Identify major data gaps and, where appropriate, indicate whether gathering additional




       data would add significantly to the overall confidence in the assessment results.




   >   Discuss science policy judgments or default assumptions used to bridge information



       gaps, and the basis for these assumptions.








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








For clarity:




•  Be brief; avoid jargon.




•  Make language and organization understandable to risk managers and the informed lay



   person.     ;

          '; ..•   •( ,   •• ••       •• •    .    ..'->,-    '    - ,' W. ••,.('•',':•'•'  •/' ':',••:. • -'- •.•••
         '.'.'. I-.'. !•" ..,!•:• M.  :,    :' >  •  <  ,• i,.•.'•.,;,; t+t	T	-:	,i.-.-,[-.r;;Lij- .•• !•,.•,; ••..•'•• •>,. :••.,( V. j •*» ,..«• ..•••..

      : ',^:^mi^-£l-::W".<;'i: :^f.j;?.^^^$*W^&$&'$$M^-i;."

                                         202                      Proposed Guidelines

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•  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:
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•  Describe how the risks posed by one set of stressor(s) compare with the risks posed by a

                                               i

   similar stressor(s) or similar environmental conditions.



•  Indicate how the strengths and limitations of the assessment compare with past



   assessments.







Text Note 6-1. Questions Regarding Risk Assessment Results (Adapted From U.S. EPA,



1993d)







Questions principally for risk assessors to ask:



•  Are the risks sufficiently well defined (and data gaps small enough) to support a risk



   management decision?



•  Was the right problem analyzed?



•  Was the problem adequately characterized?







Questions principally for risk managers to ask:



•  What effects might occur?



•  How adverse are the effects?



•  How likely is it that effects will occur?



•  When and where do the effects occur?



•  How confident are you in the conclusions of the risk assessment?    -
                                                                  j


•  What are the critical data gaps, and will information be available in the near future to fill



   these gaps?


              i
                                                                *.


      t                                  204                      Proposed Guidelines

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•  Are more ecological risk assessment iterations required?




•  How could monitoring help evaluate results of the risk management decision?
                                         205                      Proposed Guidelines

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Text Note 6-2.  Risk Communication Considerations for Risk Managers (U.S. EPA,




1995c)








•  Plan carefully and evaluate the success of your communication efforts.




•  Coordinate and collaborate with other credible sources.




•  Accept and involve the public as a legitimate partner.




•  Listen to the public's specific concerns.




•  Be honest, frank, and open.




•  Speak clearly and with compassion.




•  Meet the needs of the media.








Text Note A-l.  Stressor vs. Agent








Agent has been suggested as an  alternative for the term stressor (Suter et al., 1994). Agent is




thought to be a more neutral term than stressor, but agent is also associated with certain




classes of chemicals (e.g., chemical warfare agents).  In addition, agent has the connotation




of the entity that is initially released from the source, whereas stressor has the connotation of




the entity that causes the response. Agent is used in EPA's Guidelines for Exposure




Assessment  (U.S. EPA, 1992d)  (i.e., with exposure defined as "contact of a chemical,




physical, or biological agent").  These  two terms are considered to be nearly synonymous,




but stressor is used throughout these guidelines for internal consistency.
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                                    APPENDIX A

    CHANGES FROM EPA'S ECOLOGICAL RISK ASSESSMENT FRAMEWORK



    EPA has gained much experience with the ecological risk assessment process since the

publication of the Framework Report (U.S. EPA, 1992a) and has received many suggestions

for modifications of both the process and the terminology.  While EPA is not recommending

major changes in the overall ecological risk assessment process, proposed modifications are

summarized here to assist those who may already be familiar with the Framework Report.

Changes in the diagram are discussed first, followed by changes in terminology and

definitions.
A.I.   CHANGES IN THE FRAMEWORK DIAGRAM

   The revised framework diagram is shown in figure 1-2.  Within each phase, rectangular

boxes are used to designate inputs, hexagon-shaped boxes indicate actions, and circular boxes

represent outputs. There have been only minor changes in the wording for the boxes outside

of the risk assessment process (planning and communications between risk assessors and risk

managers; acquire data, iterate process, monitor results). "Iterate process" was added to

emphasize the iterative (and frequently tiered) nature of risk assessment. The new diagram of

problem formulation contains several changes.  The hexagon encloses information about

stressors, sources, and exposures, ecological effects,  and the ecosystem at risk to better
                                                      i
reflect  the importance of integrating this information  before selecting assessment endpoints

and building conceptual models.  The three products  of problem formulation are enclosed  in
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circles.  Assessment endpoints axe shown as a key product that drives conceptual model




development.  The conceptual model remains a central product of problem formulation. The




analysis plan has been added as an explicit product of problem formulation to emphasize the




need to plan data evaluation and. interpretation before analyses begin. It is in the analysis




plan that measures of ecological effects (measurement endpoints) are identified.




   In the analysis phase, the left-hand side of figure 1-2 shows the general process of




characterization of exposure, and the right-hand side shows the characterization of ecological




effects. These two aspects of analysis must closely interact to produce compatible output that




can be integrated in risk characterization.  The dotted line and  hexagon that includes both the




exposure and ecological response analyses emphasize this interaction. In addition, the first




three boxes in analysis now include the measures of exposure,  effects, and ecosystem and




receptor characteristics that provide input to the exposure and ecological response analyses.




   Experience with the application of risk characterization as outlined in the Framework




Report suggests the need for several modifications in this process.  Risk estimation entails



the integration of exposure and effects estimates along with an  analysis  of uncertainties. The




process of risk estimation outlined in the Framework Report  separates integration and




uncertainty.  The original purpose for this separation was to  emphasize the importance of




estimating uncertainty. This separation is no longer needed since uncertainty analysis is now



explicitly addressed in most risk integration methods.                              .




   The description of risk is similar to the process described in the Framework Report.




Topics included in the risk description include the lines of evidence that support causality and
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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.  It is likely that these terms will continue to generate



considerable discussion among risk assessors.








A.2.1.  Endpoint Terminology




    The Framework Report uses the assessment and measurement endpoint terminology of



Suter (1990) but offers no specific terms for measurements of stressor levels or ecosystem




attributes. Experience has shown that stressor measurements are sometimes inappropriately



called measurement endpoints; measurement endpoints should be ". . . measurable responses




to a stressor that are related to the valued characteristics chosen as assessment endpoints"



(U.S. EPA,  1992a; Suter, 1990; emphasis added). These guidelines replace measurement




endpoint with measure of effect, which is defined as a measurable ecological characteristic




that is related to the valued characteristic chosen as the assessment endpoint (Suter,  1990;




U.S. EPA, 1992a). (An assessment endpoint is "an explicit expression of the environmental



value to be protected" [U.S. EPA,  1992a].)  Since data other than those required to evaluate




responses  (i.e., measures of effects) are required for an ecological risk assessment, two






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additional types of measures are used.  Measures of exposure include stressor and source




measurements, while measures of ecosystem and receptor characteristics include, for




example, habitat measures, soil parameters, water quality conditions, or life history




parameters that may be necessary to better characterize exposure or effects.  Any of the three




types of measures may be actual data (e.g., mortality),  summary statistics (e.g., an LC50),




or estimated values (e.g., an LC50 estimated from a structure-activity relationship).








A.2.2.  Exposure Terminology




   These guidelines define exposure in a manner that is relevant to any chemical, physical,




or biological entity.  While the broad concepts are the same, the language and approaches




vary depending on whether a chemical, physical, or biological entity is the subject of



assessment.  Key exposure-related terms and their definitions are:




   •  Source. A source is an entity or action that releases to the environment or imposes




       on the environment a chemical, physical,  or biological stressor or stressors.  Sources




       may include a waste treatment plant, a pesticide application, a logging operation,



       introduction of exotic organisms, or a dredging project.




   •  Stressor. A stressor is any physical, chemical, or biological entity that can induce an




       adverse response. This term is used broadly to encompass entities that cause primary




       effects and those primary effects that can  cause secondary (i.e.,  indirect) effects.




       Stressors may be chemical (e.g., toxics or nutrients), physical (e.g.,  dams, fishing




       nets,  or suspended sediments), or biological (e.g., exotic or genetically engineered




       organisms).  While risk assessment is concerned with the characterization of adverse






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   responses, under some circumstances a stressor may be neutral or produce effects that




   are beneficial to certain ecological components (see text note A-l).  Primary effects



   may also become stressors.  For example, a change in a bottomland hardwood plant




   community affected by rising water levels can be thought of as a stressor influencing




   the wildlife community.  Stressors may also be formed through abiotic interactions;




   for example, the increase in ultraviolet light reaching  the earth's surface results from



   the interaction of the original stressors released (chlorofluorocarbons) with the




   ecosystem (stratospheric ozone).



•  Exposure.  As discussed above, these guidelines use the term  exposure broadly after




   the common definition of expose:  "to submit or subject to an  action or influence"




   (Merriam-Webster, 1972).  Used in this way, exposure applies to physical and




   biological stressors as well as to chemicals (organisms are commonly said to be



   exposed to radiation, pathogens, or heat). Exposure is also applicable to higher levels



   of biological organization,  such as exposure of a benthic community to dredging,



   exposure of an owl population to habitat modification, or exposure of a wildlife




   population to hunting.  Although the operational definition of exposure, particularly



   the units of measure, depends on the stressor and receptor (defined below), the




   following general definition is applicable: Exposure is the contact or co-occurrence




   of a stressor with a receptor.



•  Receptor.  The receptor is the ecological component exposed to the stressor.  This




   term may refer to tissues, organisms, populations, communities, and ecosystems.




   While either "ecological component"  (U.S. EPA,  1992a) or "biological system"






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(Cohrssen and Covello, 1989) are alternative terms, "receptor" is usually clearer in




discussions of exposure where the emphasis is on the stressor-receptor relationship.




As discussed below, both disturbance and stress regime have been suggested as




alternative terms for exposure.  Neither term is used in these guidelines, which




instead use exposure as broadly defined above.




Disturbance.  A disturbance is any event or series of events that disrupts ecosystem,




community,  or population structure and changes resources, substrate availability, or




the physical environment (modified slightly from White and Pickett, 1985).  Defined




in this way, disturbance is clearly a kind of exposure (i.e., an event that subjects a




receptor, the disturbed system, to the actions of a stressor).  Disturbance may be a




useful alternative to stressor specifically for physical stressors that are deletions or



modifications (e.g., logging, dredging, flooding).




Stress Regime.  The term stress regime has been used in at least three distinct ways:




(1) to characterize exposure to multiple chemicals or to both chemical and




nonchemical stressors (more clearly described as multiple exposure, complex




exposure, or exposure to mixtures), (2) as a synonym for exposure that is intended to



avoid overemphasis on chemical exposures,  and (3) to describe the series of



interactions  of exposures and effects resulting in secondary exposures,  secondary



effects,  and, finally, ultimate effects (also known as risk cascade [Lipton et al.,




1993]) or causal chain, pathway,  or network (Andrewartha and Birch,  1984).



Because of the potential for confusion and the availability of other clearer terms, this




term is not used in these guidelines.






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A.2.3.   Uncertainty Tenninology



   The Framework Report divided uncertainty into conceptual model formation, information




and data, stochasticity, and error.  These guidelines discuss uncertainty throughout the




process, focusing on the conceptual model (section 3.4.3), the analysis phase (section 4.1.3),



and the incorporation of uncertainty in risk estimates  (section 5.1). The bulk of the



discussion appears in section 4.1.3, where the discussion is organized according to the




following sources of uncertainty:



   •  Unclear communication




   •  Descriptive errors




   •  Variability



   •  Data gaps



   •  Uncertainty about a quantity's true value



   •  Model structure uncertainty (process models)



   •  Uncertainty about a model's form (empirical models).
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                                    APPENDIX B




                    KEY TERMS (Adapted from U.S. EPA, 1992a)








agent—Any physical, chemical, or biological entity that can induce an adverse response



    (synonymous with stressor).




assessment endpoint—An explicit expression of the environmental value that is to be




    protected.  An assessment endpoint includes both an ecological entity and specific




    attributes of that entity. For example, salmon are a valued ecological entity; reproduction




    and population maintenance of salmon form an assessment endpoint.




characterization of ecological effects—A portion of the analysis phase of ecological risk




    assessment that evaluates the ability of a stressor to cause adverse effects under a



    particular set of circumstances.




characterization of exposure—A portion of the analysis phase of ecological risk assessment




    that evaluates the interaction of the stressor with one or more ecological entities.




    Exposure can be expressed as co-occurrence or contact, depending on the stressor and



    ecological component involved.




community—An assemblage of populations of different species within a  specified location in



    space and time.




comparative risk assessment—A process that generally uses an expert judgment approach to




    evaluate the relative magnitude of effects and set priorities among a wide range  of



- -  environmental problems (e.g., U.S. EPA, 1993b).  Some applications of this process  are




    similar to the problem formulation portion of an ecological risk assessment in that the
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   outcome may help select topics for further evaluation and help focus limited resources on




   areas having the greatest risk reduction potential.  In other situations, a comparative risk




   assessment is conducted more like a preliminary risk assessment. For example, EPA's




   Science Advisory Board used expert judgment and an ecological risk assessment approach



   to analyze future ecological risk scenarios and risk management alternatives (U.S. EPA,




   1995a).



conceptual model—The conceptual model describes a series of working hypotheses of how




   the stressor might affect ecological entities.  The conceptual model also describes the




   ecosystem potentially at risk, the relationship between measures of effect and assessment




   endpoints, and exposure scenarios.



cumulative distribution function (CDF)—Cumulative distribution functions are particularly




   useful for describing the likelihood that a variable will fall within different ranges of x.



   F(x) (i.e., the value of y at x in a CDF plot) is the probability that a variable will have a




   value less than or equal to x  (figure B-l).
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     CDF for a Normal Distribution
CDF for a Log-Normal Distribution
   -4-2      02
                 x
                         10
Figure B-l.  Plots of Cumulative Distribution Function (CDF)
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cumulative ecological risk assessment—A process that involves consideration of "the




   aggregate ecologic risk to the target entity caused by the accumulation of risk from




   multiple stressors" (Bender, 1996).



disturbance—Any event or series of events that disrupts ecosystem, community, or




   population structure and changes resources, substrate availability, or the physical




   environment (modified from White and Pickett, 1985).




ecological entity—A general term that may refer to a species, a group of species,  an



   ecosystem function or characteristic,  or a specific habitat.  An ecological entity can be




   one component of an assessment endpoint.




ecological risk assessment—The process that evaluates the likelihood that adverse ecological




   effects may occur or are occurring as a result of exposure to one or more stressors.



ecosystem—The biotic community and abiotic environment within a specified location in




   space and time.



environmental impact statement—Assessments are required under the National



   Environmental Policy Act (NEPA) to fully evaluate environmental effects associated with




   proposed  major Federal actions.  Like ecological risk assessments, environmental impact



   statements (EIS) typically require a "scoping process"  analogous to problem formulation,



   an analysis by multidisciplinary teams, and a presentation of uncertainties (CEQ,  1986,



   cited in Suter,  1993a).  By virtue of special expertise,  EPA may cooperate with other




   agencies by preparing EISs or otherwise participating in the NEPA process.



exposure—The contact or co-occurrence of a stressor with a receptor.
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exposure profile—The product of characterization of exposure in the analysis phase of




   ecological risk assessment.  The exposure profile summarizes the magnitude and spatial




   and temporal patterns of exposure for the scenarios described in the conceptual model.




exposure scenario—A set of assumptions concerning how an exposure may take place,




   including assumptions about the exposure setting, stressor characteristics, and activities




   that may lead to exposure.




hazard assessment—This term has been used to mean either (1) evaluating the intrinsic




   effects of a stressor (U.S. EPA, 1979) or (2) defining a margin of safety or quotient by




   comparing a toxicologic effects concentration with an exposure estimate (SETAC, 1987).




lines of evidence—Information derived from different sources or by  different techniques that




   can be used to interpret and compare risk estimates. While this term is similar to the




   term "weight of evidence," it does not necessarily imply assignment of quantitative




   weightings to information.



lowest observed adverse effect level (LOAEL)—The lowest level of a stressor evaluated in




   a test that causes statistically significant differences from the controls.




maximum acceptable toxic concentration  (MATC)—For a particular ecological effects




   test, this term  is used to mean either the range between the NOAEL and the LOAEL or



   the geometric mean of the NOAEL and the LOAEL for a particular test.  The geometric




   mean is also known as  the chronic value.




measure of ecosystem and receptor characteristics—A measurable characteristic of the




   ecosystem or receptor that is used in support of exposure or effects  analysis.
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measure of effect—A measurable ecological characteristic that is related to the valued




   characteristic chosen as the assessment endpoint.




measure of exposure—A measurable stressor characteristic that is used to help quantify




   exposure.



measurement endpoint—See "measure of effect."




median lethal concentration (LCSO)—A statistically or graphically estimated concentration



   that is expected to be lethal to 50% of a group of organisms under specified conditions




   (ASTM, 1990).



no observed adverse effect level (NOAEL)—The highest level of a stressor evaluated in a




   test that does not cause statistically  significant differences from the controls.




population—An aggregate of individuals of a species within a specified location in space and



   time.



primary effect—An effect where the stressor acts on the ecological component of interest




   itself, not through effects on other components of the ecosystem (synonymous with direct



   effect; compare with definition for secondary effect).




probability density function (PDF)—Probability density functions are particularly useful in




   describing the relative likelihood that a variable will have different particular values of x.



   The probability that a variable will have a value within a small interval around x can be



   approximated by multiplying f(x) (i.e., the value of y at x in a PDF plot) by the width of



   the interval (figure B-2).
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Figure B-2. Plots of Probability Density Functions (PDF)
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receptor—The ecological entity exposed to the stressor.



recovery—The rate and extent of return of a population or community to a condition that




   existed before the introduction of a stressor.  Due to the dynamic nature of ecological




   systems, the attributes of a "recovered" system must be carefully defined.



relative risk assessment—A process similar to comparative risk assessment.  It involves




   estimating the risks associated with different stressors  or management actions.  To some,



   relative risk connotes the use of quantitative risk techniques, while comparative risk



   approaches more often rely on expert judgment. Others do not make this distinction.




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.  The adversity of effects is discussed, including




   consideration of the nature and intensity of the  effects, the spatial and temporal scales,




   and the potential for recovery.



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.



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






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   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).



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.



trophic levels—A functional classification of taxa within a community that is based on




   feeding relationships (e.g., aquatic and terrestrial green plants comprise the first trophic




   level and herbivores comprise the second).
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                                    APPENDIX C




                        CONCEPTUAL MODEL EXAMPLES








   Conceptual model diagrams are visual representations of the conceptual models.  They



may be based on theory and logic, empirical data, mathematical models, and probability



models.  These diagrams are useful tools for communicating important pathways in a clear



and concise way.  They can be used to ask new questions about relationships that help




generate plausible risk hypotheses.  Further discussion of conceptual models is found in




section 3-4.




   Flow diagrams like those shown in figures C-l through C-3 are typical conceptual model




diagrams.  When constructing flow diagrams like these, it is helpful to use distinct and



consistent shapes to distinguish among stressors, assessment endpoints, responses, exposure



routes, and ecosystem processes.  Although flow diagrams are often used to illustrate



conceptual models, there is  no set configuration for conceptual model diagrams.  Pictorial




representations of the processes of an ecosystem can be more effective (e.g., Bradley and



Smith, 1989).
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Figure C-l.  Conceptual model for logging.






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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).

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Figure C-3.  Waquoit Bay watershed conceptual model.






                                       226                    Proposed Guidelines

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227                     Proposed Guidelines

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   Figure C-l illustrates the relationship between a primary physical stressor (logging roads)




and an effect on an assessment endpoint (fecundity in insectivorous fish).  This simple




diagram illustrates that building logging roads (which could be considered a stressor or a




source) in ecosystems where slope, soil type, low riparian cover, and other ecosystem




characteristics lead to the erosion of soil, which enters streams and smothers the benthic




organisms (exposure pathway is not explicit in this diagram). Because of the dependence of




insectivorous fish on  benthic organisms, the fish are believed to be at risk from the building




of logging roads.  Each arrow in this diagram represents a hypothesis about the proposed




relationship (e.g.,  human action and stressor, stressor and effect, primary effect to secondary




effect). Each risk hypothesis provides insights into the kinds of data that will be needed to




verify that the hypothesized relationships are valid.




   Figure C-2 is a conceptual model used by Kendall et al.  (1996) to track a contaminant




through upland ecosystems.  In this example, upland birds are exposed to lead shot when it



becomes embedded in their tissue after being shot and by ingesting lead accidentally when




feeding on the ground. Both are hypothesized  to result in increased morbidity (e.g., lower




reproduction and competitiveness and higher predation and infection) and mortality, either



directly (lethal intoxication) or indirectly (effects of morbidity leading to mortality).  These



effects are believed to result in changes  in upland  bird populations and,  due to hypothesized




exposure of predators to lead, to increase 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.







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   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.
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                                  APPENDIX D




                         ANALYSIS PHASE EXAMPLES








   The analysis phase process is illustrated here for a chemical, physical, and biological




stressor.  These examples do not represent all possible approaches but illustrate the analysis




phase process using information, from actual assessments.








D.I. SPECIAL REVIEW OF GRANULAR FORMULATIONS OF CARBOFURAN



     BASED ON ADVERSE EFFECTS ON BIRDS




      Figure D-l is based on an assessment of the risks of carbofuran to birds under the




Federal Insecticide, Fungicide, and Rodenticide Act (FIFRA) (Houseknecht, 1993).




Carbofuran is a broad-spectrum insecticide and nematicide applied primarily in granular form




on 27 crops as well as forests and pineseed orchards.  The assessment endpoint was survival



of birds that forage in agricultural areas where carbofuran is applied.
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Figure D-l. Example of the analysis phase process:  special review of carbofuran.
Rectangular boxes indicate inputs, hexagon-shaped boxes indicate actions, and circular
boxes indicate outputs.

                                        231                     Proposed Guidelines

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       The analysis phase focused on birds that may incidentally ingest granules as they




forage or that may eat other animals that contain granules or residues.  Measures of exposure




included application rates, attributes of the formulation (e.g., size of granules), and residues




in prey organisms.  Measures of the ecosystem and receptors included  an inventory of bird



species that may be exposed following applications for 10 crops.  The birds' respective




feeding behaviors  were considered in developing routes of exposure. Measures of effect




included laboratory toxicity studies and field investigations of bird mortality.




       The source of the chemical was application of the pesticide in granular form.  The




distribution of the pesticide in agricultural fields was estimated based on the application rate.




The number of exposed granules was estimated from literature data.  Based on 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 (AI) 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.
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D.2.  MODELING LOSSES OF BOTTOMLAND-FOREST WETLANDS




      Figure D-2 is based on an assessment of the ecological consequences (risks) of long-



term changes in hydrologic conditions (water-level elevations) for three habitat types in the




Lake Verret Basin of Louisiana (Brody et al., 1989, 1993; Connor and Brody, 1989).  The



project was intended to provide a habitat-based approach for assessing the environmental




impacts of Federal water projects under the National Environmental Policy Act and Section




404 of the Clean Water Act.  Output from the models provided risk managers  with



information on how changes in water elevation might alter the ecosystem.  The primary




anthropogenic stressor addressed in this assessment was artificial levee construction for flood




control, which contributes to land subsidence by reducing sediment deposition in the




floodplain. Assessment endpoints included forest community structure and habitat value to



wildlife species and the species composition of the wildlife community.
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Figure D-2. Example of the analysis phase process:  modeling losses of bottomland
hardwoods. Rectangular boxes indicate inputs, hexagon-shaped boxes indicate
actions, and circular boxes indicate outputs.
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       The analysis phase began by considering primary (direct) effects of water-level




changes on plant community composition and habitat characteristics.  Measures of exposure



included the attributes and placement of the levees and water-level measurements.




Ecosystem and receptor measures included location and extent of bottomland-hardwood




communities, plant species occurrences within these communities, and information on the




historic flow regimes.  Effects measures 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 onto a Geographic Information System



(GIS) along  with the hydrological information.  Then the changes  projected by FORFLO




were manually linked to the GIS to show how the spatial distribution of different







                                         235                      Proposed Guidelines

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communities would change. Evidence that flooding would actually cause these changes


included comparisons of model predictions with field measurements,  the laboratory studies of


plant response to moisture, and knowledge of the mechanisms by which flooding elicits


changes in plant communities.


       Secondary (indirect) effects on wildlife associated with changes in the habitat provided


by the plant community formed the second part of the analysis phase. Important measures
                         -rJ-

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) were 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 oth&r 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

                                                             $



                                         236                      Proposed Guidelines

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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.
                                           237                      Proposed Guidelines

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Figure D-3.  Example of the analysis phase process: pest risk assessment of the
importation of logs from Chile. Rectangular boxes indicate inputs, hexagon-shaped
boxes indicate actions, and circular boxes indicate outputs.
                                        238                     Proposed Guidelines

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       The analysis phase was carried out by eliciting professional opinions from a team of




experts.  Exposure measures 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.  Ecosystem and receptor measures included the climate of the




United States, location of geographic barriers, knowledge of host suitability, and ranges of



potential host species.  Effect measures 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 organisms given entry was evaluated by considering the




potential for colonization and spread beyond the point of entry as well as the likelihood of




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 based on 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, these guidelines consider those factors 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.
                                           239                       Proposed Guidelines

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                                    APPENDIX E




            CRITERIA FOR DETERMINING ECOLOGICAL ADVERSITY:




         A HYPOTHETICAL EXAMPLE (Adapted from Harwell et al., 1994)








       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




determined that if no action were taken, significant risks to the communities would result.




Additional risk assessments were 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, 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?
                                         240                     Proposed Guidelines

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•  Nature and severity 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 that generally are stable and have less spatial




   and temporal variability than planktonic forms, oil contamination will likely result




   in severe impacts on survival and chronic effects lasting for several years.  Thus,




   under both options, changes in the assessment endpoints will probably exceed  the




   natural variability for threatened communities  in both space and time.




•  Spatial scale.  The areal  extent of impacts is similar for each of the options.



   While extensive, the area of impact constitutes a small percentage of the



   landscape. This leaves considerable area available for replacement stocks and



   creates significant fragmentation of either the planktonic or inter- and subtidal



   habitats.  Ecological adversity is reduced because the area is not a mammalian or




   avian migratory corridor.



•  Temporal scale and recovery. Based on 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. Similarly,  although ichthyoplankton do not



   reproduce, they typically experience extensive natural mortality, and immigration






                                    241                      Proposed Guidelines

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           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  area!




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.
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