STRESSOR IDENTIFICATION 

GUIDANCE DOCUMENT

U.S. Environmental Protection Agency

Office of Water

Washington, DC 20460

Office of Research and Development

Washington, DC 20460

EPA-822-B-00-025

December 2000
Disclaimer

This Stressor Identification Guidance Document provides guidance to assist EPA Regions, States, and

Tribes in their efforts to protect the biological integrity of the Nation’s waters, one of the primary

objectives of the Clean Water Act (CWA). It also provides guidance to the public and the regulated

community on identifying stressors that cause biological impairment. While this document constitutes

the U.S. Environmental Protection Agency’s (EPA’s) scientific recommendations regarding stressor

identification, this document does not substitute for the CWA or EPA’s regulations, nor is it a regulation

itself. Thus, it cannot impose legally binding requirements on EPA, States, Tribes, or the regulated

community, and may not apply to a particular situation based upon the circumstances. When appropriate,

State and Tribal decisionmakers retain the discretion to adopt approaches on a case-by-case basis that

differ from this guidance. EPA may change this guidance in the future.
Stressor Identification Guidance Document

iii

Acknowledgments

Primary Authors:

EPA, Office of Research and Development: 

Susan Cormier, Ph.D.

Susan Braen Norton, Ph.D.

Glenn Suter II, Ph.D.

EPA, Office of Science and Technology:

Donna Reed-Judkins, Ph.D.

Contributing Authors:

EPA, Office of Science and Technology:

Jennifer Mitchell

William Swietlik

Marjorie Coombs Wellman

EPA, Office of Wetlands, Oceans and Watersheds:

Thomas Danielson

Chris Faulkner

Laura Gabanski, Ph.D.

Molly Whitworth, Ph.D.

EPA, Office of Research and Development:

Edith Lin, Ph. D.

Bhagya Subramanian

EPA, Office of Enforcement and Compliance Assurance

Brad Mahanes

Other Affiliations:

David Altfater, Ohio Environmental Protection Agency

William Clements, Ph.D., Colorado State University, Fort Collins, Colorado

Susan P. Davies, Ph.D., Maine Department of Environmental Protection, Augusta, Maine 

Jeroen Gerritsen, Ph.D., Tetra Tech, Owings Mills, Maryland

Martina Keefe, Tetra Tech, Owings Mills, Maryland

Sandy Page, Tetra Tech, Owings Mills, Maryland

Jeffrey Stinson, Ph.D., U.S. Air Force

Technical Editors:

EPA, Office of Research and Development, National Risk Management and Restoration Lab:

Jean Dye, Ph.D.

Scott Minamyer
Stressor Identification Guidance Document

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Tetra Tech:

Abby Markowitz

Sandra Page

Colin Hill

Brenda Fowler

Stressor Identification and Evaluation Workgroup Members:

Co-leads:

Office of Water: Donna Reed-Judkins, Ph.D., Office of Science and Technology

 Office of Research and Development: Susan Cormier, Ph.D., National Exposure Research Lab

Members:

Office of Water:

Office of Science and Technology:

Tom Gardner, Susan Jackson, Jennifer Mitchell, Keith Sappington, Treda Smith,

William Swietlik, Brian Thompson, Marjorie Wellman

 Office of Wetlands, Oceans, and Watersheds:

 Thomas Danielson, Laura Gabanski, Chris Faulkner, Molly Whitworth, Ph.D.

Office of Research and Development:

National Center for Environmental Assessment: 

Susan Norton, Ph.D., Glenn Suter II, Ph.D.

National Health and Environmental Effects Laboratory: 

Naomi Detenbeck, Ph.D., Wayne Munns, Ph.D.

National Risk Management and Restoration Laboratory: 

Alan Everson, Scott Minamyer

Office of Enforcement and Compliance Assurance

Brad Mahanes

EPA Regions

Toney Ott, Region 8

Other Federal Agencies:

Jeffrey Stinson, Ph.D., U.S. Air Force

States:

Susan Davies, Maine Department of Environmental Protection, Augusta, Maine 

Chris O. Yoder, Ohio EPA, Columbus, Ohio

Other Supporting EPA Members:

Don Brady, Alan Hais, Margarete Heber, Mary Sullivan

Contract Support, Tetra Tech, Owings Mills, Maryland:

Michael Barbour, Ph.D., Jeroen Gerritsen, Ph.D., Martina Keefe, Sandy Page
Stressor Identification Guidance Document

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Peer Reviewers:

A. Fred Holland, Ph.D., Director, Marine Resources Research Institute of South Carolina.

Kent Thornton, Ph.D., FTN Associates

Wayne Landis, Ph.D., Director, Institute of Environment Toxicology and Chemistry, Western

Washington University

The authors wish to gratefully acknowledge all others, not named above, who helped to prepare this

document. The sum of these efforts contributed to the success of this guidance. Special thanks also to all

the EPA and State scientists who participated in the video conference in October, 1999; the Region III

Mid-Atlantic Water Pollution Biology Workshop at Cacapon, West Virginia, in March 2000; and the

Biological Advisory Committee meeting in Cincinnati, in May, 2000. Comments were also provided by

a number of EPA scientists and regulators and by other stakeholders, including the Kansas Department

of Health and Environment, Arizona Department of Environmental Quality, Denver Metro Wastewater

Reclamation District, Pennsylvania Department of Environmental Protection, Proctor and Gamble, The

Nature Conservancy, and the U.S. Geological Survey. Comments from the workshops and other

commenters helped shape the guidance.

The cover illustration was provided by a fifth grade student at Ursula Villa Elementary School, Mount

Lookout, OH. According to the illustrator, the front cover is the river when you first pick up this book,

and the back cover is the river after you’ve followed the instructions.
Stressor Identification Guidance Document

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Table of Contents

Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii

Acronym List . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii

Executive Summary

ES.1 The Clean Water Act, Biological Integrity, and Stressor 

 Identification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ES-1

ES.2 Intended Audience . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ES-2

ES.3 Applications of the SI Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ES-2

ES.4 Document Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ES-3

Chapter 1: Introduction to the Stressor Identification (SI) Process

 1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-1

1.2 Scope of this Guidance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-2

1.3 Data Quality Issues . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-2

1.4 Overview of the SI Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-3

1.4.1 The SI Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-3

1.4.2 SI Process Iterations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-5

1.4.3 Using the Results of Stressor Identification . . . . . . . . . . . . . . . . . . 1-5

1.5 Use of the SI Process in Water Quality Management Programs . . . . . . 1-6

Chapter 2: Listing Candidate Causes

2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-1

2.2 Describe the Impairment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-1

2.3 Define the Scope of the Investigation . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-3

2.4 Make the List . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-4

2.5 Develop Conceptual Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-5

Chapter 3: Analyzing the Evidence

3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-1

3.2 Associations Between Measurements of Candidate Causes and 

Effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-2

3.3 Using Effects Data from Elsewhere . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-6

3.4 Measurements Associated with the Causal Mechanism . . . . . . . . . . . . . 3-9

3.5 Associations of Effects with Mitigation or Manipulation of Causes . . 3-10

Chapter 4: Characterizing Causes

4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-1

4.2 Methods for Causal Characterization . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-1

4.2.1 Eliminating Alternatives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-3

4.2.2 Diagnostic Protocols or Keys . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-7

4.2.3 Strength of Evidence Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-8

4.2.3.1 Causal Considerations for Strength of Evidence 

 Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-9

4.2.3.2 Matching Evidence with Causal Considerations . . . . . . 4-14

4.2.3.3 Weighing Causal Considerations . . . . . . . . . . . . . . . . . . 4-14

4.3 Identify Probable Cause and Evaluate Confidence . . . . . . . . . . . . . . . . 4-17
Stressor Identification Guidance Document

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Table of Contents (continued)

Chapter 5: Iteration Options

5.1 Reconsider the Impairment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-1

5.2 Collect More Information on Previous and Additional Scenarios . . . . . . . 5-2

Chapter 6: Presumpscot River, Maine

6.1 Executive Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-1

6.2 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-3

6.3 List Candidate Causes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-5

6.4 Analyze Evidence and Characterize Causes: Eliminate . . . . . . . . . . . . 6-8

6.5 Analyze Evidence and Characterize Causes: Strength of Evidence . . 6-11

6.6 Characterize Causes: Identify Probable Cause . . . . . . . . . . . . . . . . . . 6-17

6.7 Significance and Use of Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-18

6.8 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-18

Chapter 7: Little Scioto River, Ohio

7.1 Executive Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-1

7.2 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-4

7.3 Evidence of Impairment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-5

7.4 List Candidate Causes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-10

7.5 Analyze Evidence to Eliminate Alternatives . . . . . . . . . . . . . . . . . . . . 7-13

7.5.1 Data Analyzed . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-13

7.5.2 Associations between Candidate Causes and Effects . . . . . . . . . 7-14

7.5.3 Measurements Associated with the Causal Mechanism: 

 Exposure Pathways . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-24

7.5.4 Summary of Analyses for Elimination . . . . . . . . . . . . . . . . . . . . . 7-26

7.6 Characterize Causes: Eliminate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-26

7.7 Analyze Evidence for Diagnosis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-28

7.8 Analyze Evidence to Compare Strength of Evidence . . . . . . . . . . . . . . 7-28

7.9 Characterize Causes: Strength of Evidence . . . . . . . . . . . . . . . . . . . . . 7-31

7.10 Characterize Causes: Identify Probable Cause . . . . . . . . . . . . . . . . . . 7-47

7.11 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-48

7.12 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-50

7.13 Additional Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-54

APPENDICES

 A Overview of Water Management Programs Supported by the SI

 B Worksheet Model

 C Glossary of Terms

 D Literature Cited

INDEX
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List of Figures

Figure Page

 

 1-1 The management context of the SI process . . . . . . . . . . . . . . . . . . . . . . . . . 1-4

 

 2-1 A conceptual model for ecological risk assessment illustrating the effect of

logging in salmon production in a forest stream . . . . . . . . . . . . . . . . . . . . . . . . 2-7

 3-1 The flow of information from data acquisition to the analysis phase of the 

SI process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-3

 3-2 Plot of toxicity data from a 7-day subchronic test of ambient waters and a

community metric obtained on a common stream gradient . . . . . . . . . . . . . . . 3-4

 4-1 A logic for characterizing the causes of ecological injuries at specific sites . . 4-2

 6-1 Map of the Presumpscot River showing biomonitoring stations, potential 

sources of impairment, and their location relative to the Androscogginn 

River . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-4

 6-2 Species richness and number of EPT taxa in the Presumpscot River upstream

and downstream of a pulp and paper mill effluent discharge . . . . . . . . . . . . . . 6-6

 6-3 Conceptual model showing the potential impact of stressors on the benthic

community of the Presumpscot River . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-7

 6-4 Bottom dissolved oxygen concentration in the Presumpscot River . . . . . . . . 6-10

 7-1 Map of the Little Scioto River, Ohio, showing sites where fish were 

sampled . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-6

 7-2 Spatial changes in fish IBI (A) and benthic macroinvertebrate ICI (B) 

values in the Little Scioto River in 1987 (OEPA 1988) and 1992 

(OEPA 1994) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-7

 7-3 Changes in IBI and ICI scores over distance in the Little Scioto River, 

1992 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-9

 7-4 A conceptual model of the six candidate causes for the Little Scioto 

stressor identification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-12

 7-5 Selected QHEI metrics for 1987 and 1992 . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-13

 7-6 Mean PAH concentrations from the sediment (mg/kg) in the Little Scioto 

River 1987-1998 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-15

 7-7 Mean metal concentrations from the sediment(mg/kg) in the Little Scioto 

River 1987-1998 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-17
Stressor Identification Guidance Document

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List of Figures (continued)

Figure Page

 7-8 Mean water chemistry values from the Little Scioto River from 1987-1998 . 7-18

 7-9 Bile metabolites (μg/mg protein) measured in white suckers from the

Little Scioto River in 1992 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-25
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List of Tables

Table Page

 ES-1 Summary of the use of Stressor Identification (SI) in water quality 

management programs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ES-3

 1-1 The role of SI in various water management programs . . . . . . . . . . . . . . . . . . 1-6

 3-1 Types of associations between measurements of causes and effects among 

site data and the evidence that may be derived from each . . . . . . . . . . . . . . . . 3-4

 3-2 Example associations between site-derived measures of exposure and 

measures of effects from controlled studies for different types of stressors . . 3-8

 3-3 Example associations between site data and the processes by which 

stressors induce effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-9

 3-4 Types of field experiments and the evidence that may be derived from 

each . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-10

 4-1 Application of common types of evidence in eliminating alternatives . . . . . . 4-5

 4-2 Types of evidence (columns) that contribute to each causal 

consideration (rows) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-15

 4-3 Format for a table used to summarize results of an inference concerning 

causation in case-specific ecoepidemiology . . . . . . . . . . . . . . . . . . . . . . . . . . 4-16

 6-1 Evidence of biological impairment in the Presumpscot River upstream and

downstream of a pulp and paper mill effluent discharge . . . . . . . . . . . . . . . . . 6-5

 6-2 Physical and chemical parameters measured in the Presumpscot River 

upstream and downstream of a pulp and paper mill effluent discharge . . . . . . 6-9

 6-3 Considerations for eliminating candidate causes . . . . . . . . . . . . . . . . . . . . . . 6-12

 6-4 Comparison of TSS loadings in the Presumpscot and Androscoggin 

Rivers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-12

 6-5 1996 - 1999 metal concentrations in the pulp and paper mill effluent . . . . . . 6-13

 6-6 Strength of evidence of non-attainment in the Presumpscot River . . . . . . . . 6-14

 7-1 Summary of the three impairments that were considered in the Little 

Scioto River . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-10

 7-2 Spearman rank correlations with selected metrics and the IBI and ICI 

from 1992 and selected PAHs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-19
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List of Tables (continued)

Table Page

 7-3 Spearman rank correlations with selected metrics and the IBI and ICI 

from 1992 and selected metals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-19

 7-4 Spearman rank correlations with selected metrics and the IBI and ICI 

from 1992 and selected water quality and habitat quality measurements . . . 7-20

 7-5 Evidence for eliminating candidates causes at Impairments A, B, and C . . . 7-21

 7-6 Candidate causes remaining after elimination . . . . . . . . . . . . . . . . . . . . . . . . 7-28

 7-7 Cumulative toxic units for PAHs and metals based on the PEL values . . . . . 7-30

 7-8 Comparison of the reported concentration of water quality parameters 

(mg/L) with exceedances . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-31

 7-9 Strength of evidence analysis for the three candidate causes of 

Impairment A, RM 7.9 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-32

 7-10 Strength of evidence analysis for the five candidate causes of 

Impairment B, RM 6.5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-36

 7-11 Strength of evidence analysis for the three candidate causes of 

Impairment C, RM 5.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-43

 7-12 Causal characterization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-48

 7-13 Fish metrics for the Little Scioto River 1987 and 1992 . . . . . . . . . . . . . . . . . 7-54

 7-14 Macroinvertebrate metrics for the Little Scioto River 1987 and 1992 . . . . . . 7-55

 7-15 QHEI metrics for the Little Scioto River 1987 and 1992 . . . . . . . . . . . . . . . . 7-56

 7-16 Average concentrations of selected sediment organic compounds (mg/kg) 

in the Little Scioto River, Ohio, by river mile in 1987, 1991, 1992 and 

1998 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-57

 7-17 Average concentrations (mg/kg) of selected metals in sediment from the 

Little Scioto River, Ohio, by river mile in 1987, 1991, 1992 and 1998 . . . . . 7-61

 7-18 Average concentration of selected water chemistry parameters (mg/L) in 

the Little Scioto River, Ohio, by river mile in 1987, 1992 and 1998 . . . . . . . 7-63

7-19 PAH concentrations at nearest upstream location and locations of 

impairments (mg/kg) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-64

 7-20 Metals concentrations at nearest upstream location and locations of 

impairments (mg/kg) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-65
Stressor Identification Guidance Document

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Acronym List

303(d) The section of the Clean Water Act that requires a listing by states,

territories, and authorized tribes of impaired waters, which do not meet

the water quality standards that states, territories, and authorized tribes

have set for them, even after point sources of pollution have installed the

minimum required levels of pollution control technology.

305(b) The section of the Clean Water Act that requires EPA to assemble and

submit a report to Congress on the condition of all water bodies across

the Country as determined by a biennial collection of data and other

information by States and Tribes.

7Q10 Lowest average 7 consecutive days flow with average recurrence

frequency of once every 10 years

BAP Benzo[a]pyrene

BOD Biological Oxygen Demand

CERCLA Comprehensive Environmental Response, Compensation, and Liability

Act

COD Chemical Oxygen Demand

CSOs Combined Sewer Outfalls

CWA Clean Water Act

DELTA Deformities, Erosions, Lesions, Tumors, and Anomalies

DDT Dichlorodiphenyltrichloroethane

DNR Department of Natural Resources

DO Dissolved Oxygen

DQA Data Quality Assessment

DQO Data Quality Objectives

ECBP Eastern Cornbelt Plains

EMAP Environmental Monitoring and Assessment Program

EPA U.S. Environmental Protection Agency

EPT Ephemeroptera-Plecoptera-Tricoptera

EROD Ethoxy Resorufin[o]deethylase
Stressor Identification Guidance Document

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FACA Federal Advisory Committee Act

GIS Geographic Information System

IBI Index of Biotic Integrity

ICI Invertebrate Community Index

IFIM Instream Flow and Incremental Methodology

KBI Kansas Biotic Index

KDHE Kansas Department of Environmental Protection

MBI Macroinvertebrate Biotic Index

MIWB Modified Index of Well-Being

MWH Modified Warmwater Habitat

NA Not Applicable/Available

NAPH Naphthalene

NE No Evidence

ND Not Detected

NEP National Estuaries Program

NIH National Institute of Health

NOx Nitrites

NPDES National Pollution Discharge Elimination Act

NPS Non-point Source

NRC National Research Council

OEPA Ohio Environmental Protection Agency

PAHs Polycyclic Aromatic Hydrocarbons

PEL Probable Effect Level

PO4 Ortho-phosphate

POTWs Publicly Owned Treatment Works

QHEI Qualitative Habitat Evaluation Index
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RM River Mile

SECs Sediment Effect Concentrations

SEP Supplemental Environmental Protection

SI Stressor Identification

TEL Threshold Effect Level

TKN Total Kjeldahl Nitrogen

TMDL Total Maximum Daily Load

TP Total Phosphorus

TIE Toxicity Identification Evaluation

TRE Toxicity Reduction Evaluation

TSS Total Suspended Solids

USEPA U.S. Environmental Protection Agency

WET Whole Effluent Toxicity

WWH Warm Water Habitat

WWTP Waste Water Treatment Plant
Stressor Identification Guidance Document

Executive Summary ES-1

In this Summary:

ES.1 The Clean Water Act, Biological

Integrity, and Stressor Identification

ES.2 Intended Audience

ES.3 Application of the SI Process

ES.4 Document Overview

Executive Summary

The ability to accurately

identify stressors and

defend the evidence

supporting those

findings is a critical step

in developing strategies

that will improve the

quality of aquatic

resources.

ES.1 The Clean Water Act, Biological

Integrity, and Stressor

Identification

Since the inception of the Clean Water Act (CWA) in 1972, the rivers, lakes, estuaries,

and wetlands of the United States have indeed become cleaner. The standard for

measuring these improvements are both chemical and biological. Yet, we know that

many waterbodies still fail to meet the goal of the Clean Water Act – to maintain the

chemical, physical and biological integrity of the nation's waters. 

Biological assessments have become increasingly important

tools for managing water quality to meet the goals of the

CWA. These methods, which use measurements of aquatic

biological communities, are particularly important for

evaluating the impacts of chemicals for which there are no

water quality standards, and for non-chemical stressors such

as flow alteration, siltation, and invasive species. However,

although biological assessments are critical tools for detecting

impairment, they do not identify the cause or causes of the

impairment. 

The Office of Water and Office of Research and

Development of the US EPA have developed a process for

identifying any type of stressor or combination of stressors

that cause biological impairment. The Stressor Identification

(SI) Guidance is intended to lead water resource managers

through a formal and rigorous process that

 identifies stressors causing biological impairment in aquatic ecosystems, and

 provides a structure for organizing the scientific evidence supporting the

conclusions.

The ability to accurately identify stressors and defend the evidence supporting those

findings is a critical step in developing strategies that will improve the quality of aquatic

resources. 

The Stressor Identification process (SI) is prompted by biological assessment data

indicating that a biological impairment has occurred. The general SI process entails

critically reviewing available information, forming possible stressor scenarios that might

explain the impairment, analyzing those scenarios, and producing conclusions about

which stressor or stressors are causing the impairment. The SI process is iterative,

usually beginning with a retrospective analysis of available data. The accuracy of the

identification depends on the quality of data and other information used in the SI

process. In some cases, additional data collection may be necessary to accurately

identify the stressor(s). The conclusions can be translated into management actions and

the effectiveness of those management actions can be monitored.
Stressor Identification Guidance Document

ES-2 U.S. Environmental Protection Agency

Although the Stressor

Identification process is

scientifically rigorous, it

is flexible enough to

support various water

management

requirements.

ES.2 Intended Audience

This guidance should prove useful to anyone involved in managing impaired aquatic

ecosystems. The results of Stressor Identification investigations are valuable to many

types of environmental managers— including land-use

planners, industrial and municipal dischargers, reclamation

companies, and any individuals or organizations involved in

activities that directly or indirectly affect water quality or

aquatic habitats.

The process of stressor identification draws upon a broad

variety of disciplines and is most effective when the SI

investigator has input from professionals in a number of

environmental areas such as aquatic ecology, biology,

geology, geomorphology, statistics, chemistry, environmental

risk assessment, and toxicology. Sophisticated knowledge in

certain fields may increase the tools available to investigators

(e.g., physiological responses to certain stressors), but the SI

process also can be used by investigators with very general tools (e.g., fish population

estimates). Results of general measures, however, may not be as precise as when more

specialized measures are used (e.g., stomach-lining histological evaluations).

ES.3 Applications of the SI Process

Although the Stressor Identification process is scientifically rigorous, it is flexible

enough to support various water management requirements. Some potential applications

of the SI process include the following:

 Characterizing the Quality of the Nation’s Waters: Stressor Identification

procedures can assist states in more accurately identifying the causes of

biological impairment in 305(b) reporting.

 Identifying Waterbodies and Wetlands that Exceed Water Quality

Standards: Accurate, reliable stressor identification procedures are necessary

for EPA and the states/tribes to accurately identify the cause(s) of water quality

standards violations for 303(d) listing and Total Maximum Daily Load (TMDL)

calculations. The SI process can help achieve higher degrees of accuracy and

reliability in identifying pollutants causing impacts. The SI process is not

designed, however, to allocate the amount of responsibility for an impact to a

particular source, especially when multiple sources of a stressor are present.

 Regulatory and Non-Regulatory Pollution Management Programs: Stressor

identification procedures can help identify different types of stressors within a

watershed that are contributing to biological impairment. Stressors can then be

prioritized and controlled through a combination of voluntary and mandatory

programs.

Other types of programs in which the SI process is useful include: State/Local Watershed

Management Programs, National Pollutant Discharge Elimination System (NPDES)

Permitting Programs, Dredge and Fill Permitting, Compliance and Enforcement Actions,

Risk Assessments, Preservation and Restoration Programs, and Control Effectiveness

Assessments.
Stressor Identification Guidance Document

Executive Summary ES-3

If a legal challenge to the conclusions drawn is possible, or if costly remediation efforts

are indicated as the means to control a stressor, it is essential to have a high level of

confidence in the accuracy of the identification. However, because requirements for

confidence levels and stressor precision can vary with the intended use of the findings,

managers also require flexibility in evaluation systems. Table ES.1 summarizes various

levels of rigor required in eight water quality management programs where the SI

process can be applied.

Table ES.1. Summary of the use of Stressor Identification (SI) in water quality

management programs.

Water Program

Type of Program Level of Rigor Needed for SI

Advisory Regulatory Enforcement Low Medium High ID Source

305(b) Water

Quality Reports

8 88 8

303(d) Impaired

Waterbody Lists

8 88

319 Non-point

Source Control

8 88 8

402 Point Source

Permitting

8 8 88

316(b) Cooling

Water Intake

Permitting

88 8 8

401 Water Quality

Certifications

8 8

404 Wetlands

Permitting

88 8 8

Water

Enforcement

8 88

ES.4 Document Overview

The SI guidance document describes the organization and analysis of available evidence

to determine the cause of biological impairment. The document does not directly

address biological assessment, impairment detection, source allocation, management

actions, or data collection, although these activities interact with SI in significant ways. 

This document is intended to guide water resource managers through the Stressor

Identification process. 

Section One: The Stressor Identification Process

Introduces SI process and provides detailed guidance on implementing a stressor

identification program. The guidance applies principles of ecoepidemiology to

evaluating causes of biological impairment at specific locations.

Chapter 1: Introduction to the SI Process

Provides the background and justification for the SI process.
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ES-4 U.S. Environmental Protection Agency

Chapter 2: Listing Candidate Causes

Provides an overview of and guidance on the first step of the SI process, listing

candidate causes for the impairment.

Chapter 3: Analyzing Evidence

Provides an overview of and guidance on the second step of the SI process,

analyzing new and previously existing data to generate evidence.

Chapter 4: Characterizing Causes

Provides an overview of and guidance on the third step of the SI process, using the

evidence from Step 2 to draw conclusions about the stressors that are most likely to

have caused the impairment.

Chapter 5: Iteration Options

Provides options for stressor identification if no clear cause is found in the first

iteration.

Section Two: Case Studies

Provides two case studies illustrating the SI process.

Chapter 6: Presumpscot River, Maine

Chapter 7: Little Scioto River, Ohio

Appendix A: Overview of Water Management Programs Supported by the SI

Process

Appendix B: Worksheet Model

Appendix C: Glossary of Terms

Appendix D: Literature Cited
Stressor Identification Guidance Document

Chapter 1: Introduction to the Stressor Identification (SI) Process 1-1

In this Chapter:

1.1 Introduction

1.2 Scope of this Guidance

1.3 Data Quality Issues

1.4 Overview of the SI Process

1.5 Use of the SI Process in Water

Quality Management Programs

Chapter 1:

Introduction to the Stressor

Identification (SI) Process

SI is an invaluable

component of any

bioassessment/biocriteria

program concerned with

protecting the biological

integrity of aquatic

ecosystems.

Defining Terms– Aquatic Life Use and Biocriteria

Aquatic Life Use is a beneficial use designation, identified by a state, in which a waterbody

provides suitable habitat for the survival and reproduction of desirable fish, shellfish, and other

aquatic organisms. Beneficial Use Designation is a management objective defining desirable

uses that water quality should support. Examples include drinking water supply, primary

contact recreation (swimming), and aquatic life use.

Biocriteria are narrative expressions (qualitative) or numeric values (quantitative) describing

the biological characteristics of aquatic communities based on appropriate reference

conditions.

1.1 Introduction

The use of biological assessments and biocriteria in state and tribal water quality

standards programs is a top priority of the U.S. Environmental Protection Agency (EPA). 

As such, one of the agency’s objectives is to ensure that all States and Tribes develop

water quality standards and programs that 

 use bioassessment information to evaluate the

condition of aquatic life in all waterbodies,

 establish biologically-based aquatic life use

designations,

 protect aquatic life use standards with narrative or

numeric biocriteria (see box below),

 regulate pollution sources, 

 assess the effectiveness of water quality

management efforts, and

 communicate the condition of their waters. 

Although bioassessments are useful for identifying biological impairments, they do not

identify the causes of impairments. Linking biological effects with their causes is

particularly complex when multiple stressors impact a waterbody. Investigation

procedures are needed that can successfully identify the stressor(s) and lead to

appropriate corrective measures through habitat restoration, point and non-point source

controls, or invasive species control. Water management programs have historically

shown that aquatic life protection is best accomplished using integrated information from

various sources. For example, the whole effluent toxicity program has utilized methods

for more than a decade that help resource managers understand and control the toxicity
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1-2 U.S. Environmental Protection Agency

The SI process may be

applied to any level of

biological organization

(e.g., individuals,

populations,

communities) and to any

type of waterbody (e.g.,

freshwater streams,

estuaries, wetlands,

etc.).

of complex effluents. Similarly, the Stressor Identification process will enable water

resource managers to better understand and control stressors affecting aquatic biota. SI

is an invaluable component of any bioassessment/biocriteria program concerned with

protecting the biological integrity of aquatic ecosystems.

1.2 Scope of this Guidance

The SI guidance covers the organization and analysis of available evidence to determine

the cause of biological impairment. It does not directly address biological assessment,

reference condition, impairment detection, source allocation,

management actions, data collection, or stakeholder

involvement– although these activities interact with SI in

significant ways. After stressors are identified, the

appropriate management actions depend on the nature of

those stressors, and on other factors– including economics. 

Identifying appropriate management actions is beyond the

scope of this document, but examples of management actions

are included in the case studies described in Chapters 6-7 of

this document. 

Many methods exist for measuring impacts, exposure, landuse, habitat changes and other parameters that are important

pieces of evidence in an SI investigation. Descriptions of

those methods are beyond the scope of this guidance. The SI

guidance, however, relies on the proper use of many tools to

collect evidence. EPA recognizes the need for a tools

compendium as well as software to help organize evidence, to

make use of available databases and technical publications and to prompt proper

collection of additional data when needed. The SI process should be viewed as a “logic

backbone” in determining the cause of impacts to aquatic biota. 

1.3 Data Quality Issues

The SI process is a procedure for analyzing available evidence and determining if the

available evidence is adequate to draw a conclusion about the causes of impairment. 

Since evidence may be collected from a variety of sources using a variety of tools, proper

documentation of the data is critical. Each technique for collecting data has associated

quality control measures. The higher the quality of data analyzed, the better the chances

will be of correctly identifying stressors. Guidance on assessing data quality and making

use of various types of data may be found in the Comprehensive State Water Quality

Assessment (305b) guidelines (USEPA 1997) and Ecological Risk Assessment

guidelines (USEPA 1998a, also Chapter 3). Data of unknown or poor quality can

sometimes be used for very rough estimates if the goals of the study allow, but, in

general, the quality of all data should be acceptable and well documented. If the

available data are not adequate, the SI process can show where data are missing or

deficient, but it does not address designing new data collection efforts. Chapter 2,

however, does provide advice on quality control when new data are collected.

After stressors are identified, the appropriate management actions depend on the nature

of those stressors and on other factors, including economics. Evaluating whether stressor

controls have allowed biological recovery is critically important in verifying that the

stressors were accurately identified.
Stressor Identification Guidance Document

Chapter 1: Introduction to the Stressor Identification (SI) Process 1-3

1.4 Overview of the SI Process

The SI process may be applied to any level of biological organization (e.g., individuals,

populations, communities) and to any type of waterbody (e.g., freshwater streams,

estuaries, wetlands, etc.). Some of the criteria presented for evaluating evidence may be

specific, however, to a waterbody type (e.g., references to upstream/downstream

associations). Similarly, the logic of the SI process may be applied in straightforward,

single stressor situations or in complex situations with multiple stressors and cumulative

impacts. Complex situations may require investigators to refine the definition of the

study area, gather new data, or do multiple iterations of SI to identify all the important

stressors. The Little Scioto Case Study (Chapter 7) is given as an example of a complex

stressor situation where river segments were analyzed separately because impacts and

stressors differed at each location.

1.4.1 The SI Process

Figure 1-1 provides an overview of the Stressor Identification process within the context

of water quality management and data collection. The SI process is initiated by the

observation of a biological impairment (shown in the topmost box). Decision-maker and

stakeholder involvement is shown along the left-hand side; their involvement is

particularly important in defining the scope of the investigation and listing candidate

causes. At any point in the process of identifying stressors, a need for additional data

may be identified; the acquisition of this data is shown by the box on the right-hand side

of the diagram. The accurate characterization of the probable cause allows managers to

identify appropriate management action to restore or protect biological condition. Once

stressors are identified and management actions are in place to control them, the

effectiveness of the SI process (as demonstrated by improved conditions) can be

monitored using appropriate monitoring tools and designs.

The core of the SI process is shown within the bold line of Figure 1-1 and consists of

three main steps:

1. listing candidate causes of impairment (Chapter 2),

2. analyzing new and previously existing data to generate evidence for each

candidate cause (Chapter 3), and

3. producing a causal characterization using the evidence generated in Step 2 to

draw conclusions about the stressors that are most likely to have caused the

impairment (Chapter 4). 

The first step in the SI process is to develop a list of candidate causes, or stressors, that

will be evaluated. This is accomplished by carefully describing the effect that is

prompting the analysis (e.g., unexplained absence of brook trout) and gathering available

information on the situation and potential causes. Evidence may come from the case at

hand, other similar situations, or knowledge of biological processes or mechanisms. The

outputs of this initial step are a list of candidate causes and a conceptual model that

shows cause and effect relationships.
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1-4 U.S. Environmental Protection Agency

LIST CANDIDATE CAUSES

ANALYZE EVIDENCE

Decision-maker and Stakeholder involvement

As Necessary: Acquire Data, Iterate Process

CHARACTERIZE CAUSES

MANAGEMENT ACTION: 

Eliminate or Control Causes;

Monitor Results 

Detect or Suspect Biological Impairment

Biological Condition Restored or Protected

Identify/

Apportion

Sources 

Stressor Identification

Identify Probable Cause

Eliminate Diagnose Strength of Evidence

LIST CANDIDATE CAUSES

ANALYZE EVIDENCE

Decision-maker and Stakeholder involvement

As Necessary: Acquire Data, Iterate Process

CHARACTERIZE CAUSES

MANAGEMENT ACTION: 

Eliminate or Control Causes;

Monitor Results 

Detect or Suspect Biological Impairment

Biological Condition Restored or Protected

Identify/

Apportion

Sources 

Stressor Identification

Identify Probable Cause

Eliminate Diagnose Strength of Evidence

Figure 1-1. The management context of the SI process. (The SI process is shown in the

center box with bold line. SI is initiated with the detection of a biological impairment. 

Decision-maker and stakeholder involvement is particularly important in defining the

scope of the investigation and listing candidate causes. Data can be acquired at any time

during the process. The accurate characterization of the probable cause allows

managers to identify appropriate management action to restore or protect biological

condition.) 

.
Stressor Identification Guidance Document

Chapter 1: Introduction to the Stressor Identification (SI) Process 1-5

Although the SI process

cannot accurately

identify stressors

without adequate data,

completing the SI

process is helpful even

without adequate data

because the exercise

can help target future

data collection efforts.

The second step, analyzing evidence, involves analyzing the information related to each

of the potential causes. Virtually everything that is known about an impaired aquatic

ecosystem is potentially useful in this step. For example, useful data may come from

chemical analysis of effluents, organisms, ambient waters, and sediments; toxicity tests

of effluents, waters, and sediments; necropsies; biotic surveys; habitat analyses;

hydrologic records; and biomarker analyses. These data do not in themselves, however,

constitute evidence of causation. The investigator performing the analysis must organize

the data in terms of associations that could support or refute proposed causal scenarios. 

Chapter 3 discusses several levels of associations between:

 measurements of the candidate causes and responses,

 measures of exposure at the site and measures of effects from laboratory studies

 site measurements and intermediate steps in a chain of causal processes, and 

 cause and effect in deliberate manipulations of field situations or media. 

These associations comprise the body of evidence used to characterize the cause. 

In the third step, characterize causes, the investigator uses the evidence to eliminate, to

diagnose, and to compare the strength of evidence in order to identify a probable cause. 

The input information includes a description of the effects to be explained, the set of

potential causes, and the evidence relevant to the characterization. Evidence is brought

in and analyzed as needed until sufficient confidence in the causal characterization is

reached. In straightforward cases, the process may be completed in linear fashion. In

more complex cases, the causal characterization may require additional data or analyses,

and the investigator may iterate the process.

1.4.2 SI Process Iterations

The SI process may be iterative, beginning with retrospective

analysis of available data. If the stressor is not adequately

identified in the first attempt, the SI process continues using

better data or testing other suspected stressors. The process

repeats until the stressor is successfully identified. The

certainty of the identification depends on the quality of

information used in the SI process. In some cases, additional

data collection may be necessary to confidently identify the

stressor(s). Although the SI process cannot accurately identify

stressors without adequate data, completing the SI process is

helpful even without adequate data because the exercise can

help target future data collection efforts.

1.4.3 Using the Results of Stressor Identification 

Stressor Identification is only one of several activities required

to improve and protect biological condition (Figure 1-1). In

some cases, the most effective management action will be obvious after the probable

cause has been identified. In many cases, however, the investigation must identify

sources and apportion responsibility among them. This can be even more difficult than

identifying the stress in the first place (e.g., quantifying the sources of sediment in a
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1-6 U.S. Environmental Protection Agency

large watershed), and may require environmental process models. The identification and

implementation of management alternatives can also be a complex process that requires

additional analyses (e.g., economic comparisons, engineering feasibility) and stakeholder

involvement. Once a management alternative is selected and implemented, monitoring

its effectiveness can ensure that biological goals are attained, and provides valuable

feedback to the SI process. All of these important activities are outside the scope of the

current document. However, accurate and defensible identification of the cause through

the SI process is the key component that directs management efforts towards solutions

that have the best chance of improving biological condition. 

1.5 Use of the SI Process in Water Quality Management Programs

Identifying the cause of biological impairments is an essential element of many water

quality management programs. Table 1-1 summarizes the stressor identification needs of

several water management programs. An extended discussion of some major regulatory

programs and their requirements is presented in Appendix A.

Table 1-1. The role of SI in various water management programs.

Program

Type/Name Purpose Role of SI

305(b)

Characterizing

the Quality of

the Nation’s

Waters

Under section 305(b) of the Clean

Water Act (CWA), states and tribes

are required to assess the general

status of their waterbodies and

identify, in general terms, known or

suspected causes of water quality

impairments, including biological

impairments. 

Stressor identification procedures will

assist states and tribes to accurately

identify the causes of biological

impairment. This is a non-regulatory,

information reporting effort. A high

degree of certainty in identifying the

causes of impairment is not always

needed for 305(b) reports.

303(d) Listings

and TMDLs

Identifying

Waterbodies

and Wetlands

that Exceed

Water Quality

Standards

Under section 303(d) of the CWA,

states and tribes are required to

prepare and submit to EPA lists of

specific waterbodies that currently

violate, or have the potential to violate

water quality standards, including

designated uses and numeric or

narrative criteria such as biocriteria. 

Wetlands assessment programs are

also being developed and wetlands

may be listed on 303(d) lists. 

Accurate, reliable stressor

identification procedures are

necessary for EPA and the

states/tribes to accurately identify the

cause(s) of water quality standards

violations. A high degree of accuracy

and reliability in the stressor

identification process is necessary

and sources will need to be identified.

State/Local

Watershed

Management

Programs

Managing water resources on a

watershed basis involves examining

the quality of a waterbody relative to

all the stressors within its watershed. 

Stressors, once identified, are

prioritized and controlled through a

combination of voluntary and

mandatory programs, possibly

employing the CWA 402, 319, 404,

401, and other programs.

Stressor identification procedures will

help to identify the different types of

stressors within a watershed that may

be contributing to biological

impairment. A high degree of

certainty in identifying the causes of

impairment is needed.
Stressor Identification Guidance Document

Chapter 1: Introduction to the Stressor Identification (SI) Process 1-7

Table 1-1 (continued). The role of SI in various water management programs.

Program

Type/Name Purpose Role of SI

319 Non-point

Source Control

Program

The 319 Program is a voluntary,

advisory program under which the

states develop plans for controlling

the impacts of non-point source runoff

using guidance and information about

different types of non-point source

pollution.

Stressor identification procedures will

help to identify the different types of

non-point sources within a watershed

that may be contributing to biological

impairment. A high degree of

certainty in identifying the causes of

impairment is not always needed.

NPDES Permit

Program 

Under Section 402 of the CWA, it is

illegal to discharge pollutants to

waters of the United States from any

“point source” (a discrete

conveyance) unless authorized by a

National Pollutant Discharge

Elimination System permit issued by

either the states or EPA. NPDES 

permits are required whenever a

discharge is found to be causing a

violation of water quality, including

biological impairment.

Accurate stressor identification can

be very critical in NPDES permitting

cases, both for fairness and success

in stressor control. The SI process

can help to determine if the discharge

is the cause of biological impairment. 

This is especially important when sitespecific modifications of state

standards or national criteria are

used. A high degree of accuracy and

reliability in the stressor identification

process is necessary and sources will

need to be identified. The SI process

is not designed to allocate the amount

of responsibility for an impact when

multiple sources for a stressor are

present.

316(b) Cooling

Water Intake

Program

Under Section 316(b) of the CWA,

any NPDES permitted discharger

which also intakes cooling water must

not cause an adverse environmental

impact to the waterbody.

To determine if a cooling water intake

structure is causing adverse

environmental impacts to the

waterbody, the overall health of the

waterbody should be known. Where

biological impairments are found,

stressor identification procedures

should be used to identify the different

stressors causing the waterbody to be

impaired, including the intake

structure. A high degree of certainty

is needed.

401 Water

Quality

Certifications

Under Section 401 of the CWA,

different types of federal permitting

activities (such as wetlands dredge

and fill permitting) require a

certification that there will be no

adverse impact on water quality as a

result of the activity. This certification

process is the 401 Water Quality

Certification.

Stressor identification procedures will

help to identify the different types of

stress an activity may place on water

quality that can then be addressed

through conditions in the 401

Certification.
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Table 1-1 (continued). The role of SI in various water management programs.

Program

Type/Name Purpose Role of SI

Wetlands

Permitting

Under Section 404 of the CWA, the

discharge of dredge and fill materials

into a wetland is illegal unless

authorized by a 404 Permit. The 404

Permit must receive a 401 Water

Quality Certification.

Stressor identification procedures

may help to identify unanticipated

stress from a dredge and fill activity

on water quality or the biological

community after the activity is

underway. Stressor identification

procedures will also help in prepermitting evaluations of the potential

impacts of 404 permitting by

assessing different potential stressors

on the wetland in advance.

Compliance

and

Enforcement

Whenever an enforcement action is

taken by a regulatory authority, the

type of pollution, the source, and

other stressors that play a role in

causing the violation need to be

clearly identified and related to the

violating source.

Stressor identification procedures

must be able to clearly identify the

different types of pollution causing the

violation with a high degree of

confidence. Legal defensibility is

required. Identifying the source with a

high degree of confidence is also

needed, though the current SI

process does not provide that

guidance.

Risk

Assessments

Results of bioassessment studies can

be used in watershed ecological risk

assessments to predict risk from

specific stressors and anticipate the

success of management actions. 

Accurate stressor identification is an

integral part of this process and can

help ensure that management actions

are properly targeted and efficient in

producing the desired results.

Wetlands

Assessments

States are beginning to develop

wetlands assessment procedures. In

the future, wetlands protection is

expected to be increasingly

incorporated into state water quality

standards.

Stressor identification procedures, as

well as future tools specific to wetland

investigations, are very much needed

by wetlands managers. The biological

assessment methods will allow

resource managers to evaluate the

condition of wetlands and may

provide some indication of the type of

stressor damaging a wetland. Once

bioassessment methods are

completed and incorporated into

monitoring programs, wetlands may

be listed on 305(b) lists as impaired

due to biological impairment. The SI

process should help identify stressors

causing biological impairment so

resource managers can better

remedy the problems.
Stressor Identification Guidance Document

Chapter 1: Introduction to the Stressor Identification (SI) Process 1-9

Table 1-1 (continued). The role of SI in various water management programs.

Program

Type/Name Purpose Role of SI

Preservation

Programs

The National Estuary Program (NEP)

was established in 1987 by

amendments to the Clean Water Act

to identify, restore, and protect

nationally significant estuaries of the

United States. The program focuses

on improving water quality in an

estuary, and on maintaining the

integrity of the whole system --its

chemical, physical, and biological

properties--as well as its economic,

recreational, and aesthetic values.

Stressor identification procedures

should be useful to the NEP, and

other preservation programs, by

helping stakeholders identify causes

of impairments. This information

would feed into the development of a

management plan.

Restoration

Programs

The Comprehensive Environmental

Response, Compensation, and

Liability Act (CERCLA), commonly

known as Superfund, was enacted in

1980 (and amended in 1986) for

hazardous waste cleanup.

As in enforcement and compliance

programs, stressor identification

procedures must be able to clearly

identify the different types of pollution

causing the impairment with a high

degree of confidence. Legal

defensibility is required. Identifying

the source with a high degree of

confidence is also needed, though the

current SI process does not provide

that guidance.

Pollution

Control

Effectiveness

A key component of any pollution

control program or watershed

management effort is the ability to

ascertain (or predict) the likely

effectiveness of pollution control

measures or management strategies. 

Stressor identification procedures will

help to identify the different types of

pollution a control measure needs to

reduce and the different types of

stressors a management strategy

needs to address.
Stressor Identification Guidance Document

Chapter 2: Listing Candidate Causes 2-1

LIST CANDIDATE CAUSES

ANALYZE EVIDENCE

CHARACTERIZE CAUSES

Stressor Identification

Identify Probable Cause

Eliminate Diagnose Strength of Evidence

LIST CANDIDATE CAUSES

ANALYZE EVIDENCE

CHARACTERIZE CAUSES

Stressor Identification

Identify Probable Cause

Eliminate Diagnose Strength of Evidence

Defining Terms – Exposures, Effects, Causes, Sources

An effect is a biological change traceable to a cause. 

Exposure is the co-occurrence or contact of a stressor with the biological resource. 

A cause is defined as a stressor that occurs at an intensity, duration, and frequency of exposure

that results in a change in the ecological condition.

A source is the origin of a stressor. It is an entity or action that releases or imposes a stressor

into the waterbody.

note: the processes of detecting impairment and identifying sources are beyond the scope of

this document

2.1 Introduction

The first step in the stressor identification

process is to develop the list of candidate

causes, or stressors. This is accomplished by

carefully describing the effect that is

prompting the analysis, and gathering

available information on the situation and

potential causes (see the box below for

definitions of some key terms). Potential

causes are evaluated and those that are

sufficiently credible are retained as candidate

causes used in the analysis stage. The outputs

of this initial step are a list of candidate causes

and a conceptual model that shows the

relationship between the causes and the effect.

2.2 Describe the Impairment

The first important piece of information to be documented is a careful description of the

effect that prompted the evaluation. Whenever possible, the impairment should be

described in terms of its nature, magnitude, and spatial and temporal extent (see

worksheet in Appendix B, Unit I, page B-4). Making inferences about causes is easier

when the impairment is defined in terms of a specific effect, or response. The response

should be quantified as a count (abundance of darter species) or continuous variable

(mean length of darters). If multiple effects with different causes are described as a

single impairment, it may be mistakenly assumed that there is only a single cause.

The importance of biological entities as resources and as sentinels of the overall integrity

of ecosystems is recognized in the Clean Water Act as well as in subsequent legislation

and regulations (See Chapter 1). Observations made in streams and rivers can alert

In this Chapter:

2.1 Introduction

2.2 Describe the Impairment

2.3 Define the Scope of the Investigation

2.4 Make the List

2.5 Develop Conceptual Models

Chapter 2

Listing Candidate Causes
Stressor Identification Guidance Document

2-2 U.S. Environmental Protection Agency

environmental managers or the public to a potential problem. If the biological or

ecological impairment is of sufficient magnitude, it may necessitate identifying the cause

and the potential management controls needed to prevent further damage or to restore the

ecosystem. Observations that might prompt the initiation of a stressor identification

investigation include:

 kills of fish, invertebrates, plants, domestic animals, or wildlife,

 anomalies in any life form, such as tumors, lesions, parasites, disease,

 altered community structure such as the absence, reduction, or dominance of a

particular taxon–this can include increased algal blooms, loss of mussels,

increase of tolerant species, etc.,

 loss of species or shifts in abundance,

 response of indicators designed to monitor or detect biological, community, or

ecological condition, such as the Index of Biotic Integrity (IBI) or the

Invertebrate Community Index (ICI),

 changes in the reproductive cycle, population structure, or genetic similarity,

 alteration of ecosystem function, such as nutrient cycles, respiration, and

photosynthetic rates, and

 alteration of the aerial extent and pattern of different ecosystems: for example,

shrinking wetlands, change in the mosaic of open water, wet meadows, sandbars

and riparian shrubs and trees.

It can be important to describe how the observed condition makes the waterbody unfit for

its intended use. This makes the purpose and relative importance of the assessment

clear. For instance, if the fish are covered with lesions, no one wants to fish for them. 

In addition to describing the impairment, it is useful to prepare a background statement

articulating the steps taken that revealed the biological impairment. For example, it

might be appropriate to refer to a numerical or narrative biocriterion, or a reference

condition that has been created for this type of waterbody, including the documentation

for its derivation. 

If conditions are below expectations, it is important to discuss how the quality or

condition of the stream compares to other streams, or to the same stream in other places

or times. Photographs of the water body provide visual evidence of a lost resource and

can later be used in describing potential pathways that may have lead to the impairment. 

Equally important are photographs of what the resource could be like (e.g., taken from

other locations), what it used to be like, or what valued attributes are still retained. 

Maps or other geographical representations that show the location and severity of

impairments are essential for orienting the investigators, examining spatial relationships,

and eliciting information from stakeholders (see worksheet in Appendix B, Unit I, page
Stressor Identification Guidance Document

Chapter 2: Listing Candidate Causes 2-3

The scope of the

investigation determines

the extent of the data

sets that will be

analyzed. It defines the

geographic area and

time frame under

consideration and the 

types of data that will be

examined.

Early communication

with the stakeholders

will help ensure that

relevant information has

been identified and that

potential causes are

considered. 

B-5). Maps can range from simple hand-drawn to computergenerated versions. Useful geographic information includes

location of the impairment and known point sources, cities,

roads, dams, tributaries, and land use. Examples of maps are

included in the case studies presented in Section 2 of this

document (Chapters 6 and 7). The depiction of this

geographic information is also used to determine the scope of

the evaluation; that is, the overall spatial and temporal extent

of the study.

2.3 Define the Scope of the Investigation

The scope of the investigation influences the selection of

candidate causes, and has ramifications for the final outcome

and the practical use of the entire stressor identification

effort. In a sense, the scope reflects perceptions about the

ecosystem and beliefs about the level of restoration, or

change, that is possible.

The scope of the investigation determines the extent of the

data sets that will be analyzed (see worksheet in Appendix

B, Unit I, page B-4). It defines the geographic area and time

frame under consideration as well as the types of data that

will be examined. The scope of the investigation may be

limited or broad. An example of a limited scope is an

evaluation of whether a particular stressor is responsible for

an impairment. A broader objective would be to evaluate

which, among several candidate causes, could be

responsible for the observed effect. This broader approach

might be appropriate for waters that are not attaining their

designated use, and for which TMDLs (Total Maximum

Daily Loads; see glossary) must be developed.

Several factors influence the overall scope of the investigation, including:

 the regulatory context,

 the purpose of the investigation, 

 the relative importance of stressors emanating from outside the watershed,

 stakeholder expectations and interests,

 logistical constraints,

 cost, 

 personnel, and

 available data.

Other factors to consider are the geographic extent of the impairment, and the extent of

knowledge about the impairment. Early communication with the stakeholders will help

ensure that relevant information has been identified, and that potential causes are

considered. After these factors are carefully reviewed, a definition of the geographical

area should be clearly stated. The regulatory context sometimes limits the scope of the

study. For acid rain regulation, the geographical area is very large, whereas an NPDES

violation may involve less than a kilometer of stream reach. The investigators should
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2-4 U.S. Environmental Protection Agency

In some cases, two or more

stressors must be present

for the effect to occur.

Finding and Using Existing Lists of Stressors

Monitoring programs conducted by government agencies and non-governmental organizations

may identify types and levels of stressors. For example, EPA’s Environmental Monitoring and

Assessment Program (EMAP) has monitored common stressors found in estuarine systems.1

Among those listed are elevated nutrient concentrations, prolonged phytoplankton blooms, low

dissolved oxygen, and sediment contamination.

State agencies and volunteer monitoring programs may also be good sources of information

on stressors. Maryland’s Department of Natural Resources (DNR), for example, maintains a

website on which are links to maps indicating long term trends in total nitrogen, total

phosphorus, and total suspended solids for 3rd order and larger streams in the state of

Maryland.2

1 See EPA “Condition of the Mid-Atlantic Estuaries.” Office of Research and Development,

Washington, D.C. #600-R-98-147. November, 1998.

2 See Maryland DNR website, http://www.dnr.state.md.us/streams/status_trend/index.html

document any regulatory authorities involved and discuss the regulatory requirement for

making a causal determination of the impairment.

The depth of the study may be limited by a paucity of data. In this case, it may still be

appropriate to attempt a causal determination with the available data, and then indicate

what additional information is needed to more confidently ascribe the cause. 

2.4 Make the List

In developing a list of candidate causes, investigators should consider available evidence

from the case at hand, other similar situations, and knowledge of biological processes or

mechanisms (see text box entitled “Using Existing Programs to List Candidate Causes”

and worksheet in Appendix B, Unit I, page B-6). The causes of ecological condition

usually involve multiple spatial and temporal scales; both of which must be considered in

defining the scope of the study and in listing candidate causes. Recent environmental

events are overlaid on historical events, even those spanning geological time. Global and

regional influences form the backdrop for local factors. 

Where multiple stressors contribute to cause an effect, the

stressor that makes the largest contribution is the

principal cause. Usually a principal cause is so dominant

that removing other causes has no effect on the condition

of the resource. For example, if benthic habitat is both

physically altered and chemically contaminated, restoring

the physical habitat may have no effect until the chemical

contamination is removed. In this situation the chemical contamination is the principal

cause. The habitat alteration is still a cause of impairment, but it is ancillary and masked

by the toxic chemical impact. Nevertheless, pervasive ancillary causes like habitat

alteration, nutrient enrichment, and sediment loading can lower the potential

improvement to the waterbody even after the controlling or principal cause is removed.
Stressor Identification Guidance Document

Chapter 2: Listing Candidate Causes 2-5

The conceptual model

can help the investigator

see the pathway

between the candidate

cause and the eventual

impact.

In some cases, two or more stressors must be present for the effect to occur. For

example, a moderate level of nutrients poses no toxicological threat, but if sparse

riparian cover permits sufficient sunlight to allow algal growth, then eutrophication can

occur, with a subsequent cascade of effects. Another example is when a combination of

reduced stream flow and lack of shading cause an elevation of temperature beyond the

limit that native species can tolerate. Stressors acting together to cause an effect should

be listed as a single scenario.

There are some ways to simplify the process of identifying and listing candidate causes. 

In the beginning, it helps to make a relatively long list and then pare the list down to the

most likely causes. For the initial long list, it is a good idea to include all stressors

known to occur in a waterbody. Even if these stressors have not previously been shown

to cause this type of impairment, someone is likely to want proof that they were not

causal agents. Include stressors that stakeholders have good reason to believe may be

important. Consult other ecologists for potential causes of the impairment. 

Knowledge about pollution sources near the waterbody can also suggest potential

stressors. Point sources, such as drainage pipes, outfalls, and ditches are easily identified

as sources. Constituents of the effluent can be listed as candidate causes. Other sources

may be located some distance from the resource, such as motor vehicles and smoke

stacks that generate candidate causes such as acid rain or nitrogen enrichment. Particular

land uses often generate a consistent suite of stressors. For example, siltation and

pesticides are commonly associated with agriculture. Locations of sources and stressors

should be added to the impairment maps developed in Section 2.2.

Once an exhaustive list of candidate causes is developed, the next step is to pare the list

down. Including very unlikely causes can make the identification process unwieldy and

will distract stakeholders and managers from the more likely candidates. Unlikely

stressors are those that are believed to be mechanistically implausible or absent from the

watershed. Although they need not be evaluated, we recommend that you document the

rationale for not including the less likely causes. 

2.5 Develop Conceptual Models

The final part of this initial step is to develop conceptual models for the candidate

causes, linking the cause with the effect (see worksheet in Appendix B, Unit I, page B-6). 

This part of the process documents a likely explanation of how the stressor could have

caused the impairment. Conceptual models provide a good way to communicate

hypotheses and assumptions about how and why effects are occurring. Models can also

show where different causes may interact and where additional data collection may

provide useful information.

Conceptual models will vary in complexity, depending on the

mechanisms and ecological processes involved. A

generalized conceptual model might show land uses in the

watershed that generate in-stream stressors impacting valued

resources. For instance, if fish communities are impacted by

moderate levels of nutrients in a sunlit stream, it is important

to show that the effect could have occurred via several

possible pathways, or a combination of pathways, such as:
Stressor Identification Guidance Document

2-6 U.S. Environmental Protection Agency

 decaying algal blooms that result in low dissolved oxygen,

 the dominance of prey, causing a change in abundance of species,

 conditions favorable for opportunistic pathogens, 

 diatom-rich water that is so turbid that sight-feeding fish cannot find prey and

starve, and

 embedded substrates smothered with decaying and overgrown algal mats that

reduce habitat for foraging, refugia, and reproduction.

The primary causes in this example are nutrients and incident sunlight. The secondary

cause in the pathway could be any of the stressors that are formed from the initial cause. 

It is usually a good idea to consult with ecologists experienced with similar streams

when developing conceptual models, especially when complex pathways and ecological

process are involved.

Using a pictorial, poster-style conceptual model is useful to introduce the ecological

relationships. Then a box and arrow diagram can be used to show details of the

relationships among stressors, receptors, and intermediate processes. Some models get

too complicated to be helpful. The diagram should show only the pathways and causes

considered in the study. Separate diagrams for each stressor or pathway can keep the

focus on the analysis steps that will follow. Figure 2-1 is an example of a box and arrow

conceptual model illustrating the impacts of logging on salmon production in a forest

stream. Additional examples and advice on conceptual model development can be found

in Jorgensen (1994), Suter (1999), Cormier et al. (2000c), USEPA (1998a) (especially

Appendix C), and in the case studies shown in Chapters 6 and 7.

In addition to helping the investigators to elucidate the relationships among multiple

cause and multiple effects, conceptual models are also powerful tools for communicating

among the investigative team and obtaining additional insights from stakeholders and

managers.
Stressor Identification Guidance Document

Chapter 2: Listing Candidate Causes 2-7

Figure 2-1. A conceptual model for ecological risk assessment illustrating the effect of

logging in salmon production in a forest stream. (The assessment includes a series of

exposures and responses. In the diagram, the circles are stressors, the rectangles are

states of receptors, and the hexagons are processes of receptors. The rectangle with

rounded corners is an intervention, establishment of buffer zones, that is being

considered (Suter et al. 1994).)
Stressor Identification Guidance Document

Chapter 3: Analyzing the Evidence 3-1

Existing data are often

sufficient to determine

the cause of impairment.

LIST CANDIDATE CAUSES

ANALYZE EVIDENCE

CHARACTERIZE CAUSES

Stressor Identification

Identify Probable Cause

Eliminate Diagnose Strength of Evidence

LIST CANDIDATE CAUSES

ANALYZE EVIDENCE

CHARACTERIZE CAUSES

Stressor Identification

Identify Probable Cause

Eliminate Diagnose Strength of Evidence

3.1 Introduction

The second step in the SI process is to analyze

the information that is related to each of the

candidate causes identified in Chapter 2.

Virtually everything that is known about an

impaired aquatic ecosystem and about the

candidate causes of the impairment may be

useful for inferring causality. Potentially

useful data that may come from studies of the

site include chemical analysis of effluents,

organisms, ambient waters, and sediments;

toxicity tests of effluents, waters, and

sediments; necropsies; biotic surveys; habitat

analyses; hydrologic records; and biomarker

analyses. A similar array of data may be

obtained from other sites and from laboratory

studies (performed ad hoc or reported in the

literature). However, these data do not in themselves constitute evidence of causation. 

The investigators performing the causal analysis must

organize and analyze the data in terms of associations that

might support or refute proposed causal scenarios. 

The SI process does not require a minimum data set, and

existing data are often sufficient to determine the cause of

impairment. However, the investigator has the responsibility

of evaluating whether the data used are sufficient to support

the SI process. If the investigator decides to generate additional data, its quality must be

assured (see text box entitled “Data Quality Objectives”). 

The primary inputs to the analysis step are the list of candidate causes and the associated

conceptual models that link the causes with the observed effects (developed in Chapter

2). Other inputs include data and information that come from the case at hand, other

similar cases, the laboratory, and the literature that synthesizes biological and ecological

knowledge (Figure 3-1). In the analysis step, this information is converted into causal

evidence that falls into four general categories of relationships:

1. associations between measurements of the candidate causes and effects

(Section 3.2),

2. associations between measures of exposure at the site and measures of

effects from laboratory studies (Section 3.3),

3. associations of site measurements with intermediate steps in a chain of

causal processes (Section 3.4), and

In this Chapter:

3.1 Introduction

3.2 Associations Between Measurements of

Candidate Causes and Effects

3.3 Using Effects Data from Elsewhere

3.4 Measurements Associated with the

Causal Mechanism

3.5 Associations of Effects with Mitigation or 

 Manipulation of Causes

Chapter 3

Analyzing the Evidence
Stressor Identification Guidance Document

3-2 U.S. Environmental Protection Agency

Data Quality Objectives

If new data will be generated for an SI investigation, consider following U.S. EPA’s Data Quality

Objectives (DQO) process. The DQO process combines a problem formulation exercise with

conventional sampling statistics to determine the type, quantity, and quality of data needed to

make an environmental decision with a desired probability of error (Quality Management Staff

1994). The DQO process is not directly applicable to SI since it is designed to determine the

probability of exceeding a threshold. However, using a formal process to define the problem,

examine information needs, and determine study boundaries is important in planning any

sampling and analysis program. The criteria for defining an optimum design for an SI study will

vary depending on the circumstances. Following sampling and analysis, a Data Quality

Assessment (DQA) should be performed to determine whether the goals of the DQO process

have been achieved (Quality Assurance Division 1998). The EPA’s Quality System, including

requirements for non-EPA organizations, can be found at www.epa.gov/quality/index.html.

Quality Assurance Division. 1998. Guidance for Data Quality Assessment. EPA QA/G-9, QA97

Version, or EPA/600/R-96/084. U.S. EPA, Washington, D.C.

Quality Management Staff. 1994. Guidance for the Data Quality Objectives Process. EPA

QA/G-4, or EPA/600/R-96/055. U.S. EPA, Washington D.C.

4. associations of cause and effect in deliberate manipulations of field

situations or media (Section 3.5).

The evidence produced in the analysis step is used to characterize the cause or causes of

the observed effect (see Chapter 4). The analysis and characterization of causes is

usually done iteratively and interactively, as illustrated by the two-way arrows between

the analysis and characterization boxes in Figures 1-1 and 3-1. Evidence is brought in

and analyzed as needed until there is sufficient confidence in the causal characterization. 

In straightforward cases, the process may be completed in linear fashion. In more

complex cases, the causal characterization may require additional data or analyses, and

the investigator may repeat the process. 

3.2 Associations Between Measurements of Candidate Causes and

Effects

The first type of evidence of causation is associations among measurements of candidate

causes and effects (Table 3-1). The objective of this analysis is to provide evidence that:

 the candidate cause and the effect are observed at the same time or place,

 when the candidate cause is not observed, the effect is also not observed, or

 the intensity of the causal factor is related to the magnitude of the effect.

Causal evaluations often begin by examining associations from the case at hand. For

example, effects are observed downstream, but not upstream of a candidate cause. These

associations provide the core of information used for characterizing causes (see

worksheet in Appendix B, Unit II, page B-7). Associations may be revealed by plotting

data on common axes, as shown in Figure 3-2. In this figure, the spatial pattern of a

toxicity bioassay results are clearly associated with the spatial pattern of a community

metric. Causal inference is easier when the stressors and effects are located together (co-
Stressor Identification Guidance Document

Chapter 3: Analyzing the Evidence 3-3

Causal evaluations often

begin by examining

associations

from the case at hand.

Acquire Data 

Characterize Causes

Analyze Evidence

List Candidate Causes

From the

Laboratory/

Literature

From the Case

Combine measurements of site exposures with

effects data from elsewhere

Associate measurements of candidate causes with 

observed effects 

Associate effects with mitigation or manipulation of 

causes

Associate measurements with the causal 

mechanism

From Other 

Similar Cases

Acquire Data 

Characterize Causes

Analyze Evidence

List Candidate Causes

From the

Laboratory/

Literature

From the Case

Combine measurements of site exposures with

effects data from elsewhere

Associate measurements of candidate causes with 

observed effects 

Associate effects with mitigation or manipulation of 

causes

Associate measurements with the causal 

mechanism

From Other 

Similar Cases

Acquire Data 

Characterize Causes

Analyze Evidence

List Candidate Causes

From the

Laboratory/

Literature

From the Case

Combine measurements of site exposures with

effects data from elsewhere

Associate measurements of candidate causes with 

observed effects 

Associate effects with mitigation or manipulation of 

causes

Associate measurements with the causal 

mechanism

From Other 

Similar Cases

Figure 3-1. The flow of information from data acquisition to the analysis phase of the SI

process.

located) in time and space. Inference becomes more difficult

as stressors are dispersed over larger scales, occur

intermittently, or cannot be measured. Inference is also more

difficult when there is a time lag between exposures. For

example, if a stressor, such as a diversion of water flow

prevents salmon from reaching the sea on their out-migration,

the effect (i.e., destruction of the salmon run) may not be

observed until three years later. In some cases, models may

be useful for extrapolating inferences from available measurements.
Stressor Identification Guidance Document

3-4 U.S. Environmental Protection Agency PERCENT REDUCTION

Table 3-1. Types of associations between measurements of causes and effects

among site data and the evidence that may be derived from each.

Type of Association Example Evidence

Spatial co-location Effects are occurring at same place as exposure

Effects do not occur where there is no exposure 

For candidates with discrete sources on streams and

rivers:

Effects occur downstream of a source 

Effects do not occur upstream of a source

For candidates with dispersed sources:

Effects occur where there is exposure, but not at

carefully matched reference sites where exposure

does not occur

Spatial gradient Effects decline as exposure declines over space

Temporal relationship Exposure precedes effects in time

Effects are occurring simultaneously with exposure

(allowing for response and recovery rates)

Intermittent sources are associated with intermittent

exposure and effects

Temporal gradient Effects increase or decline as exposure increases or

declines over time

Figure 3-2. Plot of toxicity data from a 7-day subchronic test of ambient waters and a

community metric obtained on a common stream gradient (Norberg-King and Mount

1986).
Stressor Identification Guidance Document

Chapter 3: Analyzing the Evidence 3-5

In some cases, the

candidate cause is the

lack of a required

resource, such as

nesting habitat.

The evaluation of associations must consider whether potentially affected organisms may

have moved since exposure. It is helpful to consider the mobility of organisms relative

to the extent of the observed exposed and unexposed reaches or areas. Clearly, fish are

capable of swimming long distances and invertebrates may drift downstream or fly

upstream. However, extensive experience with bioassessment of fish and invertebrate

communities has demonstrated that the movements of these organisms are usually not so

great as to prevent the observation of spatial associations. The movement of a few

individual organisms from contaminated reaches to upstream reaches will diminish, but

generally not eliminate, the contrast or gradient among reaches. However, salmon and

other species that regularly move long distances require special consideration when

analyzing spatial associations. In such cases, consider the logic of the situation and

possibly use a GIS as a platform for modeling spatial relationships.

Obtaining measurements of the stressor that can be associated with the effect can be

challenging. In the most straightforward cases, the

measurements of the stressor itself are available; for example,

nutrient concentrations, degree of siltation, dissolved oxygen

concentrations, or chemical concentrations. In some cases, the

candidate cause is the lack of a required resource, such as

nesting habitat. In these cases, measurements can establish that

the resource is indeed missing at the place and time it would be

required by an organism. When measurements of the stressor

are not available, surrogates can be used, although the

uncertainty in the analysis will increase. Information on the

location and attributes of sources can be useful surrogates. 

This information can be particularly important for stressors that are intermittent in nature

(e.g., high flow events), or degrade quickly (e.g., some pesticides). In these cases, source

information may be used as a surrogate for the stressors. As sources become larger in

scale and more diffuse, information on the sources becomes more difficult to use in sitespecific causal evaluation.

Similarly, measuring the immediate or direct response to a stressor increases the

confidence in a causal evaluation. For example, a fish kill may be associated with

nutrient enrichment, acting through algal growth, decomposition, and oxygen depletion. 

Measurements of the initial algal growth and oxygen depletion would increase an

investigator’s confidence that nutrient enrichment was the cause of the fish kill. 

Conceptual models are very useful for illustrating linkages between complex pathways

of cause and effects, and for illustrating where measurements are (and are not) available. 

Whenever possible, associations should be quantified. For categorical data, calculate the

frequencies of associations. For count or continuous data use, linear or nonlinear

models. For example, the abundance of Ephemeroptera at a site may be regressed

against concentration of total sediment PAHs. Similarly, the community data plotted in

Figure 3-2 might be regressed against the toxicity data. If effects data are categorical or

heterogeneous and exposure data are continuous, categorical regression may be used

(Dourson et al. 1997). Select the analysis technique that best illuminates the association,

based on the amounts and types of data available. Some statistical descriptions of the

associations include correlation coefficients, confidence intervals, and p-values.

However, avoid statistical hypothesis testing of the associations (see text box entitled

“Using Statistics and Statistical Hypothesis Testing for Analyzing Observational Data in

Stressor Identification”). Because groups are not randomly assigned in a way that

minimizes the influence of confounding variables, a significant outcome in a hypothesis

test may be falsely attributed to a candidate cause, when in fact it is due to another
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3-6 U.S. Environmental Protection Agency

factor. On the other hand, the small sample sizes that are usually seen in these studies

decrease the ability to statistically discriminate groups, and may lead to mistakenly

eliminating a true cause.

Often associations between candidate causes and effects can be improved by identifying

and isolating confounding factors in either the receptors or the environment. For

example, the frequency of hepatic neoplasms in fish is associated both with the age

structure of the fish population and the concentration of PAHs in sediment (Baumann et

al.1996). Correction for age of fish would increase the consistency and, potentially, the

biological gradient in the relationship between hepatic neoplasm frequency and industrial

contaminants. Similarly, a decline in fish species richness is a common measure of

impairment, but the number of species present generally increases with increasing stream

size (e.g., OEPA 1988a). Therefore, including a correction for stream size could

strengthen the association between the degradation and species loss.

Associations observed from other studies can provide useful supporting information,

particularly when the specific type or constellation of effects is consistently observed in

association with a candidate stressor. Keep in mind that, as evidence, associations

observed from other sites is not as strong as those observed from the study site. 

Therefore, if associations of effects and potential causes are analyzed at other sites, they

should be evaluated separately from those at the site of concern.

3.3 Using Effects Data from Elsewhere

Measures of exposure from the case at hand can also be matched with measures of effect

from other situations. The objective of this analysis is to provide evidence showing that

the stressor is present at the study site in sufficient quantity or frequency that the

investigator would expect to see a particular effect based on effect information from

laboratory tests, field tests, or exposure-response relationships developed at other sites

(see worksheet in Appendix B, Unit II, page B-12). This type of evidence is familiar to

ecotoxicologists who combine measures of exposure from the study site with measures

of effect from laboratory tests. For example, concentrations of chemicals measured in

water may be compared to concentrations that are thresholds for effects in toxicity tests,

or they may be used in concentration-response models to estimate the frequency or

magnitude of effects. When doing these comparisons, the investigator should keep in

mind that laboratory conditions or organisms may not accurately represent field

conditions or organisms.

Equivalent measures of exposure and effects are available for non-chemical stressors

(Table 3-2). As in toxicological assessments, it is important to choose the most

applicable high-quality effect measurements. It is also important to ensure that the

measures of exposure and effects are consistent. For example, long-term field exposures

are most appropriately compared with chronic test data. In some cases, exposureresponse information will not be available for a candidate cause, but will be available for

an analogous agent, such as an effluent with a structurally similar chemical or an

introduced species with similar feeding behavior.
Stressor Identification Guidance Document

Chapter 3: Analyzing the Evidence 3-7

Using Statistics and Statistical Hypothesis Testing 

for Analyzing Observational Data in Stressor Identification

Statistical techniques are essential tools for summarizing and analyzing environmental measurements for SI. 

Good SI uses a variety of techniques, including descriptive statistics (e.g., means, ranges, variances),

exploratory statistics (e.g., multivariate correlations), statistical modeling (e.g, exposure-response relationships),

quality assurance statistics (e.g., accuracy and precision of analyses of duplicates and standard reference

materials) and comparison of alternative models of candidate causes (e.g., goodness-of-fit or maximum

likelihood). However, the use of statistical hypothesis tests is problematic. Statistical hypothesis testing was

designed for analyzing data from experiments, where treatments are replicated and randomly assigned to

experimental units that are isolated from one another. The application of these tests to data from observational

studies can result in erroneous conclusions. In observational studies, treatments are very seldom replicated and

are never randomly assigned to experimental units. 

If experimental units are replicated at all, they are replicated within the same water body and hence are likely to

influence one another. As a result, samples are replicated rather than treatments. This is known as

pseudoreplication (Hurlbert 1984). Finally, the location of a candidate cause is a given, rather than being

randomly placed, so it is likely that candidate causes will co-vary with each other and with important natural

attributes of the system (e.g., salinity, depth). The following table summarizes several common analytical

techniques and discusses their use in SI.

Activity Application

to 

observational

data in SI 

Comments

Using summary statistics

(e.g., mean water

concentrations, 7Q10 flow

rates) to summarize

measurements

Encouraged Pay attention to the biological or physical relevance of the

summary statistic used. For example, the mean of chemical

concentrations over time is often the most relevant (USEPA

1998a). As another example, the bankfull flow event is

considered to be an important determinant of stream

morphology (Rosgen 1996).

Using statistics to determine

the probability that two sets

or samples are drawn from

the same distribution, or

that they differ by a

prescribed amount

Use Caution Note that this use is not hypothesis testing in that it does not

test a null hypothesis about a treatment (cause). It simply

tells you the likelihood that differences are due to sampling

variance. Also, the conventional criteria for statistically

significant differences are not relevant; the differences must

be shown to be biologically significant and the probabilities

must be shown to affect the overall strength of evidence.

Because the sample sizes are often small relative to

variance, the power to detect real differences may be small.

Using the results of

statistical hypothesis tests

to conclude that a candidate

is (or is not) the cause

Wrong The assumptions of statistical hypothesis testing are

violated. In observational studies, replicate treatments

cannot be randomly assigned in a way that minimizes the

influence of confounding variables. For this reason, a

significant outcome in a hypothesis test may be falsely

attributed to a candidate cause when in fact it is due to

another factor.

Using correlations or

regression techniques to

quantify relationships

between variables. 

Encouraged The type of data (continuous, ordinal, or categorical) and the

type of relationship (e.g., linear, non linear) will determine

the best technique to use. 

Using statistics to determine

the probability that a

relationship is nonrandom,

or that the slope of a

regression differs from zero.

Use Caution Note that this analysis indicates only the probability that an

apparent relationship is due to sampling variance. It does

not test the hypothesis that the relationship is causal. Also,

the number of samples is likely to be low, so even

correlations or models that are not statistically significant

can be biologically significant and contribute to the strength

of evidence.

Concluding that statistically

significantly correlated

variables have a causal

relationship 

Wrong Correlation does not indicate causation, and a highly

improbable regression model does not indicate that the

independent variable caused the relationship. Because

stressors often covary with each other and with natural

environmental attributes, a strong relationship between a

candidate cause and a biological variable may be due to a

factor other than the candidate cause.
Stressor Identification Guidance Document

3-8 U.S. Environmental Protection Agency

Table 3-2. Example associations between site-derived measures of exposure and

measures of effects from controlled studies for different types of stressors.

Stressor

Characterization of

Exposure:

Intensity, Time, and Space

Characterization of 

Exposure-Response

Chemical External concentration in

medium

Internal concentration in

organism

Biomarker

Concentration-response or timeresponse relationships from

laboratory or other field studies

Effluent Dilution of effluent Effluent dilution - response in the

laboratory (WET)

Contaminated

Ambient

Media

Location and time of

collection

Analysis of medium

Lab or in situ tests using the

medium:

Medium dilution - response

Medium gradient - response

Habitat Structural attributes Empirical models (e.g., Habitat

suitability models)

Water

withdrawal/

drought

Hydrograph and associated

summary statistics (e.g.,

7Q10)

In-stream flow models (e.g., IFIM)

Thermal

energy

Temperature Thermal tolerances

Siltation

(suspended)

Suspended concentration

(e.g., TSS) 

Concentration-Response

relationships from laboratory or other

field studies

Dissolved

oxygen and

oxygendemanding

contaminants

(e.g. BOD,

COD)

Dissolved Oxygen Oxygen concentration-response

relationships from laboratory or other

field studies.

Siltation

(bed load)

Degree of embeddedness,

texture

Empirical siltation-response

relationships from laboratory or other

field studies.

Excess

mineral

nutrients

Dissolved concentration Empirical concentration-response

relationships from laboratory or other

field studies. 

Eutrophication models

Pathogen Presence or abundance of

pathogen

Disease, Symptoms

Nonindigenous

invasive

species

Presence or abundance of

the species

Ecological models (food web,

energetics, predator-prey, etc.)
Stressor Identification Guidance Document

Chapter 3: Analyzing the Evidence 3-9

In developing

mechanistic conceptual

models depicting the

induction of effects, it is

often apparent that there

are intermediate steps in

the causal process that

may be observed or

measured.

Laboratory toxicity tests and other controlled studies provide

the bases for models depicting the induction of effects by

particular causes. For example, an acute lethality test of a

chemical provides a concentration-response model which may

be used to determine whether fish kills might be attributable

to observed or estimated ambient concentrations. More

complex causal mechanisms, particularly those involving

indirect causation, require more complex mechanistic models. 

As models of causal processes become more complex, it

becomes more difficult to judge whether an individual model

provides an acceptable representation of the causes of

ecological degradation at a site. In such cases, the best

strategy is to generate mechanistic models of each proposed

causal scenario and determine which model best explains the

site data (Hilborn and Mangel 1997).

3.4 Measurements Associated with the Causal Mechanism

In developing mechanistic conceptual models depicting the induction of effects, it is

often apparent that there are intermediate steps in the causal process that may be

observed or measured. Documenting those intermediate steps increases confidence in

the proposed causal mechanism (see worksheet in Appendix B, Unit II, page B-8). This

type of evidence is particularly useful when the ultimate effects of multiple candidate

causes are similar, but act through different mechanistic pathways. Types and examples

of intermediate steps are presented in Table 3-3. In some cases it is sufficient to

document the occurrence of the intermediate step, but in many cases, the level of the

metric must be shown to be adequate. For example, if competition for prey by an

introduced species is the proposed mechanism by which an endpoint species has been

lost, then the investigator should show that the number of prey are reduced sufficiently.

Table 3-3. Example associations between site data and the processes by which

stressors induce effects.

Type of Measurement Example Mechanistic Association

Symptoms (i.e., responses

specific to, or characteristic

of, a type of stressor and

causing the overt

impairment)

Fish have lesions characteristic of a bacterium

Biomarkers Metalothionine induction is an intermediate step in the

glomerular toxicity of cadmium

Intermediate product of an

ecological process

Algal abundance and DO are measures of intermediate

steps in the induction of fish kill by nutrient additions

Changes in abundance of

predators, prey, or

competitors 

Abundance of prey decreases upon introduction of a

new predator
Stressor Identification Guidance Document

3-10 U.S. Environmental Protection Agency

Table 3-3 (continued). Example associations between site data and processes by

which stressors induce effects.

Type of Measurement Example Mechanistic Association

Effects on other receptors If impairment is defined in terms of effects on fish, then

the responses of invertebrates or plants may suggest

what causes are operating

Distributions of stressors

and receptors coincide

For a stressor to cause an effect, it must contact or cooccur with the receptor organisms. For causes that act

through the deprivation of a resource, the deprivation

must actually occur

3.5 Associations of Effects with Mitigation or Manipulation of Causes 

Strong causal evidence can be provided by deliberately eliminating or reducing a

candidate cause and noting whether the effects disappear or remain (see worksheet in

Appendix B, Unit II, page B-10). Causes can be eliminated as a part of a field

experiment or by bringing site media into the laboratory (Table 3-4). Field experiments

may also be performed by manipulating the source (see text box entitled “Associating

Effects with Mitigation or Manipulation of a Cause”). For example, cattle may be

fenced away from some locations where they usually have access to a stream channel, or

an effluent may be eliminated for a time due to plant shut-down. These experiments may

be conducted at the site being assessed, or may be conducted at other sites where the

same type of source operates. Occasionally, a regulatory or remedial action may be

treated as an experimental manipulation. Alternatively, experiments may be conducted

that control the exposure of organisms or communities to potential causes. Examples

include caging previously unexposed organisms at contaminated locations, placing

containers of uncontaminated sediments in locations with contaminated water. These

field experiments typically cannot be replicated, so their results are potentially subject to

confounding (see text box “Using Statistics and Statistical Hypothesis Testing for

Analyzing Observational Data in Stressor Identification”). Finally, site media can be

brought into the laboratory and manipulated to eliminate different candidate causes. 

Then the results of the manipulation can be tested using laboratory organisms. These

methods have been most extensively developed for the purpose of attributing causality

among different chemicals in effluents.

Table 3-4. Types of field experiments and the evidence that may be derived from each. 

Example Experiment Example Evidence Derived from the Experiment

Manipulation of a source

in the field

Elimination of a source reduces or eliminates the effect.

Manipulation of exposure

in the field

Introduction of previously unexposed organisms results in

effects.

Isolation of organisms from one cause reveals the effects

of others.

Laboratory manipulation

and testing of media from

the case

Extracting site media into fractions containing different

chemical classes results in toxicity being associated with

only one fraction.
Stressor Identification Guidance Document

Chapter 3: Analyzing the Evidence 3-11

Associating Effects with Mitigation or Manipulation of a Cause

Biological data collected by the Kansas Department of Health and Environment (KDHE) have

played an increasingly important role in the state’s efforts to document water quality

impairments. KDHE historically has applied a modification of Davenport and Kelly’s (1983)

macroinvertebrate biotic index (MBI) to identify impairments resulting from nutrient loading and

organic enrichment. Recently, a genus- and species-level indicator known as the Kansas

Biotic Index (KBI) was developed to specifically respond to different stressor categories,

including nutrients and oxygen demanding substances (KBIorg). Data collected by KDHE have

shown that declines in the MBI and KBIorg have been consistently associated with increased

organic enrichment, nutrient loading, and ammonia contamination.

The MBI and KBIorg were used to document the association between effects and the

mitigation or manipulation of causes. After a nitrification process was installed at the city of

Wichita’s municipal wastewater treatment facility, median concentrations of total ammonianitrogen in the Arkansas River decreased from 1.1 mg/L (1982-91) to 0.06 mg/L (1992-99).

Concomitant decreases in the upper quartile MBI and KBIorg values were sufficiently large to

justify a formal change in the Arkansas River’s 305(b) impairment status. Moreover, city

officials documented the recolonization of this river by several rare or previously extirpated fish

species. Comparable improvements in MBI and KBIorg scores were documented in the Smoky

Hill River below the city of Salina sewage treatment plant after ammonia levels were reduced

by implementing wastewater nitrification and an industrial pretreatment initiative.

Outcome

In the 2000 KDHE 305(b) assessment, the Smoky Hill River was upgraded from non-supporting

to fully supporting of aquatic life. 

References

Davenport, E. and H. Kelly. (1983); Huggins, G. and F. Moffett. (1988); KDHE. (1993, 1998,

2000).
Stressor Identification Guidance Document

Chapter 4: Characterizing Causes 4-1

LIST CANDIDATE CAUSES

ANALYZE EVIDENCE

CHARACTERIZE CAUSES

Stressor Identification

Identify Probable Cause

Eliminate Diagnose Strength of Evidence

LIST CANDIDATE CAUSES

ANALYZE EVIDENCE

CHARACTERIZE CAUSES

Stressor Identification

Identify Probable Cause

Eliminate Diagnose Strength of Evidence

Although this approach

uses a combination of

methods for

characterizing causes,

each method may also

be used independently. 

4.1 Introduction

Characterizing causes involves using the

evidence analyzed in Chapter 3 to reach a

conclusion and to state the levels of confidence

in that conclusion. The input information in

this process includes a description of the

effects to be explained, the set of candidate

causes developed in Chapter 2, and the causal

evidence analyzed in Chapter 3.

4.2 Methods for Causal 

Characterization

After available evidence has been compiled and analyzed, the cause(s) may be obvious. 

In other cases, a more systematic method for reaching a conclusion may be needed. The

use of clearly documented inferential logic increases the defensibility of causal

attribution. This chapter describes three methods for using the evidence developed in

Chapter 3 to characterize the cause: (1) eliminating alternatives, (2) using diagnostic

protocols, and (3) weighing the strength of evidence supporting each candidate cause. 

Figure 4-1 depicts a procedure that combines these multiple methods to reach a

conclusion of causality. Although this approach uses a combination of methods for

characterizing causes, each method may also be used independently.

This integrated approach does not include all possible methods of causal analysis,

particularly the use of expert judgment. When evidence is ambiguous, the process of

developing consensus among a panel of experts may be more acceptable to stakeholders

than any systematic evaluation of evidence. Utilizing expert judgment is certainly a

more flexible approach in that it does not require any particular data set or type of model. 

In addition, experts can reach conclusions on the basis of experience and pattern

recognition. For example, an experienced extension agent

may visit a farm pond that is not producing bass and, without

taking any measurements, know that the pond is too small or

receives too much manure runoff from surrounding pastures

to support bass reproduction. However, when the issue of

causation is contentious, the attempt to develop consensus

may be complicated by experts who represent the interests of

the contending parties. Even when the experts are neutral,

expert consensus may not be acceptable to some parties due

to subjectivity. Finally, the process of developing expert

consensus may not be practical. An NIH consensus

development conference or an NRC panel may be practical

for large-scale issues, such as the carcinogenicity of

electromagnetic fields. It may not be practical to convene an expert panel for each

outfall causing ecological injuries.

In this Chapter:

4.1 Introduction

4.2 Methods for Causal Characterization

4.3 Identify Probable Cause and Evaluate

Confidence

Chapter 4

Characterizing Causes
Stressor Identification Guidance Document

4-2 U.S. Environmental Protection Agency

List of Candidate Causes

and Conceptual Models

Eliminate Causes

Repeat

Scenario

Formulation

How Many 

Remaining?

Causes Subject to 

Diagnosis?

Diagnostic

Analysis

Strength of 

Evidence 

Analysis

Identify Probable

Cause

Sufficient

Confidence?

Report Results

Reconsider

Effects

Effects

Real?

Collect

More

Information

Cause(s) 

Diagnosed?

Causal

Evidence

Causal

Evidence

Causal

Evidence

Criteria for

Sufficiency

Zero

Yes

One

No

Yes

No

More than One 

or Unclear

No

No

Yes

Yes

List of Candidate Causes

and Conceptual Models

Eliminate Causes

Repeat

Scenario

Formulation

How Many 

Remaining?

Causes Subject to 

Diagnosis?

Diagnostic

Analysis

Strength of 

Evidence 

Analysis

Identify Probable

Cause

Sufficient

Confidence?

Report Results

Reconsider

Effects

Effects

Real?

Collect

More

Information

Cause(s) 

Diagnosed?

Causal

Evidence

Causal

Evidence

Causal

Evidence

Criteria for

Sufficiency

Zero

Yes

One

No

Yes

No

More than One 

or Unclear

No

No

Yes

Yes

List of Candidate Causes

and Conceptual Models

Eliminate Causes

Repeat

Scenario

Formulation

How Many 

Remaining?

Causes Subject to 

Diagnosis?

Diagnostic

Analysis

Strength of 

Evidence 

Analysis

Identify Probable

Cause

Sufficient

Confidence?

Report Results

Reconsider

Effects

Effects

Real?

Collect

More

Information

Cause(s) 

Diagnosed?

Causal

Evidence

Causal

Evidence

Causal

Evidence

Criteria for

Sufficiency

Zero

Yes

One

No

Yes

No

More than One 

or Unclear

No

No

Yes

Yes

Figure 4-1. A logic for characterizing the causes of ecological injuries at specific sites.

(Processes are rectangles, and the three inferential methods have heavy borders. 

Decisions are diamonds, and inputs are parallelograms.)
Stressor Identification Guidance Document

Chapter 4: Characterizing Causes 4-3

Eliminate Diagnose Strength of Evidence

CHARACTERIZE CAUSES

Identify Probable Cause

Inputs to the characterization process (the parallelogram at the top of Figure 4-1) include

a description of the effects to be explained, the list of candidate causes, and the

associated conceptual models (Chapter 2). The set of candidate causes should include

stressors that consist of multiple factors that act together and are not individually

sufficient to cause the effect (i.e., causal scenarios). Other inputs to characterization

include the causal evidence produced in the analysis step (the three parallelograms on the

left side of Figure 4-1). As discussed above, analyses are usually conducted in

combination, as needed, throughout the characterization process. For example, the

evidence necessary for eliminating candidate causes is analyzed first, then evidence for

diagnosis, and, finally if necessary, the strength of evidence for each candidate cause is

analyzed.

4.2.1 Eliminating Alternatives

The causal characterization methods shown in

Figure 4-1 are presented in order, from the

most conclusive to the least conclusive. The

first method, eliminating alternatives, is a

powerful approach to evaluating information. 

The ability to eliminate all but one alternative

is a strong standard of proof for causality, and

it is easily understood and widely practiced. 

It is the basic technique of literature’s most

famous master of inference, Sherlock

Holmes:

"When you have eliminated the impossible, whatever remains, however

improbable, must be the truth."

-- (Sir Arthur Conan Doyle, Sign of Four, 1890).

Elimination is also an effective way of reducing the numbers of alternatives to be

considered before using another method (e.g., strength of evidence, Section 4.1.3, and

see worksheet in Appendix B, Unit III, page B-15). Eliminating evidence is a

particularly good option for SI when the set of alternatives is limited, and when disproof

does not rely on statistics (see text box in Chapter 3 entitled “Using Statistics and

Statistical Hypothesis Testing for Analyzing Observational Data in Stressor

Identification”). Specifically, if the SI is conducted to support a permitting action,

logical elimination of the permitted source as a potential cause of the observed injury is a

sufficient causal analysis. Because of the complexity associated with ecological systems

and multiple stressors, many SI investigations will not have the evidence necessary to

confidently eliminate causes. These evaluations will rely on a strength of evidence

analysis (Section 4.1.3).

Elimination as a method for establishing causality has strong roots in the philosophy of

science. Popper, Platt, and other conventional philosophers of science have argued that

it is logically impossible to prove a hypothesized relationship, but it is possible to

disprove hypotheses (Platt 1964, Popper 1968). If a set of possible causes has been

identified, once all but one alternative has been eliminated, the remaining hypothesis

must be true. For example, if a body of water is found to be acidic, it is possible to

establish the cause as acid deposition by eliminating acid mine drainage, geologic

sulphate, and biogenic acids as causes (Thornton et al. 1994). 
Stressor Identification Guidance Document

4-4 U.S. Environmental Protection Agency

Only associations

derived from

measurements taken

from the case under 

evaluation are strong

enough to eliminate an

alternative.

The elimination of alternatives has three major limitations: 

 Due to limited knowledge, it may not be possible to identify a complete set of

candidate. Also, the array of possible causes is potentially infinite, as there is no

clear boundary between plausible and absurd hypothetical causes (Susser 1986b,

Susser 1988). 

 The process of elimination is limited by the ability to perform reliable tests and

obtain unambiguous results. Such tests are often difficult in ecology. One may

fail to reject a hypothesis but be uncertain of that result due to sampling

variance, biases, and temporal variance. If all but one cause is rejected on

uncertain grounds, it is difficult to accept the remaining candidate cause with

confidence. 

 Elimination of causes should be done with particular care when multiple

sufficient causes may be operating. The evidence for one cause may be so strong

that it masks the effects of another sufficient cause and appears to be the sole

cause. In addition, beware that the temporal sequence of cause and effect may

appear to be wrong when one sufficient cause precedes another. For example, an

industrial effluent may impair a biological community. If the stream is

subsequently channelized, the effects would be obscured by the industrial

effluent. The channelization would have been sufficient to degrade biological

communities within a pristine stream and therefore should be retained as a

candidate cause. As shown in Table 4-1, similar issues are also relevant to

spatial sequences such as those occurring in streams or rivers.

Most often the objective of SI is to identify all sufficient causes (for example, when the

goal is to remediate or restore a water body). In these cases, the elimination step should

be performed iteratively. That is, each cause eliminated during the first round should be

reevaluated to determine if its effects may have been masked by another cause. If so, the

candidate cause should be retained. In extreme cases, the masked secondary causes will

remain unidentified, because the primary causes are so conspicuous. For example, if

channelization has eliminated nearly all fish, it may not be apparent that episodic

pesticide runoff would affect sensitive species. Such occult secondary causes will

become apparent only after the primary causes have been remediated.

Some types of evidence can be used to eliminate candidate causes, and when those

causes might be retained because of masking. Only associations derived from

measurements taken from the case under evaluation are strong

enough to eliminate an alternative. Associations derived from

similar cases cannot be used to eliminate alternatives, but are

useful in strength of evidence analyses which allow for

uncertain or indecisive evidence (Section 4.1.3).

A stressor can be confidently eliminated if case-specific

measurements clearly show that a necessary step in the causal

chain of events has not occurred. For example, if a chemical

must be taken up by an organism in order to cause an effect,

and it can be demonstrated that uptake has not occurred (e.g.,

though biomarkers or body burdens), the chemical can be

eliminated as a cause. Similarly, if sedimentation causes
Stressor Identification Guidance Document

Chapter 4: Characterizing Causes 4-5

effects by silting-in riffles, and riffles can be demonstrated to be free of silt,

sedimentation can be eliminated as a cause.

Although another potential way to eliminate a candidate cause is through experimental

manipulations, the results of field experiments are seldom sufficiently conclusive to

eliminate a cause. Uncertainties exist in field experiments due to a lack of thorough

knowledge of recovery and recolonization rates following exposure. As a result,

reduction or elimination of exposure may not appear to eliminate the effects. Field

experiment data can, however, be used in the strength of evidence analysis discussed in

Section 4.1.3. In addition, removal of one sufficient cause may unmask the effects of

another. The protocols associated with the Toxicity Identification and Evaluation (TIE)

program can be applied here, but not all effects of concern occur in these tests (e.g.,

tumors). Further, there may be questions concerning the sensitivity of the 7-day tests and

test species relative to field durations and species (USEPA 1993a,b). TIE, therefore, is

considered as part of the strength of evidence analysis.

Table 4-1. Application of common types of evidence in eliminating alternatives.

Type of Evidence

(See Chapter 3)

Reason for

Rejection

Masking

Considerations

Causal

Consideration 1

(See Section

4.1.3)

Associations

between

measurements of

candidate causes

and effects: Did

the stressor

precede the effect

in time?

If the effects

preceded a

candidate cause in

time, it cannot be

the primary cause.

If the candidate cause

is preceded by both

the effect and another

sufficient cause, its

effects may be

masked, and it should

be retained.

Temporality

Associations

between

measurements of

candidate causes

and effects: Is

there an

upstream/

downstream

conjunction of

candidate cause

and effect?

If the effect occurs

upstream of the

candidate cause’s

source or does not

occur regularly

downstream (e.g., is

distributed spatially

independently of a

plume, sediment

deposition areas,

etc.), it cannot be

the primary cause.

If the candidate cause

is downstream of

another sufficient

cause, its effects may

be masked and it

should be retained.

Co-occurrence

Associations

between

measurements of

candidate causes

and effects: Is

there a reference

site/test site

conjunction of

candidate cause

and effect?

If a candidate cause

occurs at reference

sites and occurs at

equal or greater

levels, it can be

eliminated.

Co-occurrence
Stressor Identification Guidance Document

4-6 U.S. Environmental Protection Agency

Table 4-1 (continued). Application of common types of evidence in eliminating

Alternatives.

Type of Evidence

(See Chapter 3)

Reason for

Rejection

Masking

Considerations

Causal

Consideration 1

(See Section

4.1.3)

Associations

between

measurements of

candidate causes

and effects: Is a

decrease in the

magnitude or

proportion of an

effect seen along

a decreasing

gradient of the

stressor?

A constant or

increasing level of

effect with

significantly

decreasing

exposure would

eliminate a cause.

If a decreasing

gradient of one

sufficient cause

coincides with an

increasing gradient of

second, recovery from

the first cause may be

obscured.

Biological

Gradient

Measurements

associated with

the causal

mechanism: Has

the stressor cooccurred with,

contacted, or

entered the

receptor(s)

showing the

effect? 

If the candidate

cause never

contacted or cooccurred with the

receptor organisms,

the cause may be

eliminated. For

appropriate

stressors, if tissue

burdens or other

measures of

exposure are found

not to occur in

affected organisms,

the cause may be

eliminated. For

stressors that act

through a known

chain of events, if a

link in the chain can

be shown to be

missing, the

candidate cause can

be eliminated. 

Complete

Exposure

Pathway

Association of

effects with

mitigation or

manipulation of

causes:

Did effects

continue when a

source or stressor

was removed?

If the effect

continues even after

the stressor is

removed, then the

candidate cause can

be eliminated. This

assumes that there

is no impediment to

recolonization. 

The effect may also

continue if another

sufficient cause is

present.

Experiment,

Temporality

1 Many of the same types of evidence can also be used in the strength of evidence analysis (see

Section 4.1.3). This column denotes the corresponding causal consideration used there.
Stressor Identification Guidance Document

Chapter 4: Characterizing Causes 4-7

Eliminate Diagnose Strength of Evidence

CHARACTERIZE CAUSES

Identify Probable Cause

The diagnostic approach

is a good alternative for

SI when organisms are

available for

examination, when the

candidate causes are

familiar enough that they

have made it into the

protocols, and when

there is a high degree of

specificity in the cause,

the effect, or both.

In some cases all causes but one will be eliminated, and the part of the process is to

describe the level of confidence in the characterization. It is often desirable to perform a

strength of evidence analysis of that cause to demonstrate that it is probable, given all

available evidence. If the true cause was not identified as a candidate, it may be possible

to eliminate all candidate causes. In that case, one must repeat the process of identifying

candidate causes (Chapter 2). In most cases, the elimination of causes will simply

narrow the set of candidates, which is always helpful. Then the process continues to the

next step, which is the use of diagnostic protocols or keys.

4.2.2 Diagnostic Protocols or Keys

If more than one cause remains after the

elimination step, the next step is to consider

whether any of the causes are subject to a

diagnostic analysis. Whereas the elimination

step relies on negative evidence (e.g., an

exposure pathway is not present), diagnostic

protocols rely on positive evidence (e.g., a

particular symptom is present). Diagnostic

symptoms are also used in the strength of

evidence analysis (under consistency of

association and specificity; see Section 4.1.3). The diagnostic protocols referred to here

have been used and tested sufficiently to be considered authoritative and some have been

formalized into a set of rules or a key (e.g., Meyer and Barclay 1990).

In medicine, diagnostic protocols identify a disease by examining its signs and

symptoms. The diagnostic process requires an understanding of mechanism, so most of

the evidence comes from measurements associated with the causal mechanism (see

Section 3.4 and worksheet in Appendix B, Unit III, page B19). As in medical practice, diagnostic information in the SI

process comes from the exposed organisms and includes

symptomatology (i.e., signs of the action of the causal agent

on the organisms), measures of internal exposure (e.g.,

isolation of pathogens or analysis of chemicals in

organisms), or measurements of intermediate processes (e.g.,

a depressed pre-dawn dissolved oxygen level).

The diagnostic approach is a good alternative for SI when

organisms are available for examination, when the candidate

causes are familiar enough that they have made it into the

protocols, and when there is a high degree of specificity in

the cause, the effect, or both. As an example, protocols for

the investigation of fish kills are particularly well established

(e.g., Meyer and Barclay 1990) and consist of collection of

site data concerning candidate causes (e.g., oxygen, pH,

temperature, contaminant levels, and presence of toxic

algae), site data concerning effects (e.g., taxa killed, duration

of event, behavior of live fish), and necropsy results (e.g.,

lesions, pathogens, tissue contamination, or clinical signs

such as blue stomach which indicates molybdenum toxicity).

Meyer and Barclay (1990) even provide a dichotomous key for determining the causes of

fish kills. Since an SI investigation is more likely to examine current biological
Stressor Identification Guidance Document

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Eliminate Diagnose Strength of Evidence

CHARACTERIZE CAUSES

Identify Probable Cause

community compositions that might reflect past chronic exposures rather than the effects

of acute lethality, the methods for fish kill investigations often are not directly

applicable. However, a diagnostic approach can potentially be employed.

Diagnostic tools are well developed for pathogens and to a slightly lesser extent for

chemicals (e.g., certain bill deformities are diagnostic of exposure to dioxin-like

compounds) (Gilbertson et al. 1991). Diagnostics are also well developed for a few

other agents such as low dissolved oxygen (low blood oxygen, gasping at the surface,

etc.). For many other stressors and for most non-vertebrate aquatic organisms, reliable

diagnostics are seldom available. Expert judgment has been used to assign tolerance

values to taxonomic groups for nutrients and this concept has been extended to other

stressor types (Hilsenhoff 1987, Huggins and Moffet 1988). The utility of using these 

tolerance values in multimetric indices along with some recent statistical analyses

indicate that the structure of fish and invertebrate communities may prove valuable for

diagnosis (Yoder and Rankin 1995b, Norton et al. 2000). Although the use of

multimetric information for diagnosing cause and effect is not yet widely accepted or

validated, this information can be brought into the strength of evidence analysis

discussed in the next section.

4.2.3 Strength of Evidence Analysis

In many SI cases, the candidate causes are not identified by elimination or diagnosis, and 

an analysis of the strength of evidence for each of the candidate causes is required (see

worksheet in Appendix B, Unit III, page B-20). This analysis organizes information so

that the evidence that supports, or doesn’t

support, each candidate cause can be easily

compared and communicated. When there are

many candidate causes or when evidence is

ambiguous, strength of evidence analysis is

more useful than elimination of alternatives

because it identifies the alternative that is best

supported by the evidence. Even when a cause

has been identified by a process of elimination

or diagnosis, it is often desirable to complete

the strength of evidence analysis in order to 

organize all of the evidence for the decision makers and stakeholders.

The strength of evidence analysis discussed in the remainder of this section defines a

group of causal considerations used to organize the information concerning each

alternative. Causal considerations are logical categories of evidence that are consistently

applied to support or refute a hypothesized cause. They are defined in Section 4.2.3.1. 

Section 4.2.3.2 discusses how the types of evidence described in Chapter 3 provide

information relevant to each consideration. Finally, Section 4.2.3.3 shows how to

evaluate the strength of each piece of evidence in supporting or refuting a candidate

cause.

For the purposes of this approach, we treat Koch’s postulates (see text box entitled

“Koch’s Postulates”) as a special case of analysis of the strength of evidence. That is,

for pathogens or chemical contaminants, if Koch’s postulates are satisfied, the strength

of evidence is particularly high.
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Chapter 4: Characterizing Causes 4-9

Koch's Postulates

Koch's postulates combine different lines of evidence in a formal way to provide compelling

evidence for causation. The approach was originally developed for pathogen-induced

diseases. It has been adapted for demonstrating that particular toxicants cause human

diseases (Yerushalmy and Palmer 1959, Hackney and Kinn 1979) or ecological effects

(Adams 1963, Woodman and Cowling 1987, Suter 1990, Suter 1993), and has been

recommended for ecological risk assessment (EPA 1998a). The following is an adaptation of

Koch's postulates for causal inference in ecological epidemiology for effects of pathogens or

chemicals.

1. The injury, dysfunction, or other potential effect of the pathogen or toxicant must be

regularly associated with exposure to the pathogen or toxicant in association with any

contributory causal factors.

2. The pathogen, toxicant, or a specific indicator of exposure must be found in the

affected organisms.

3. The effects must be seen when healthy organisms are exposed to the pathogen or

toxicant under controlled conditions, and any contributory factors should contribute in

the same way during the controlled exposures.

4. The pathogen, toxicant, or a specific indicator of exposure must be found in the

experimentally affected organisms.

The power of Koch’s postulates arises from the way the four types of evidence are combined.

The requirement of regular association in the field ensures that the association is relevant to

the field, but, because field observations are uncontrolled, one cannot determine whether the

association is, in fact, caused by an another agent that happens to be correlated with the

proposed cause. In addition, associations in field data fail to demonstrate the temporal 

sequence between the candidate cause and effect. The requirement that the candidate causal

agent induce the effect under controlled conditions eliminated the potential for confounding

and demonstrates that the cause precedes the effect. However, the artificial conditions of

toxicity tests and other experimental studies means that the demonstrated causal association

may not be relevant to the field. The second and fourth postulates provide the ties that bind

the two lines of evidence together. That is, evidence of exposure must be obtained in the field

and must correspond to the experimental exposure. This correspondence of the exposure

metrics makes it highly unlikely that the correspondence of effects in the field and the

experiment are coincidental.

Koch's four postulates were derived for addressing the general issue of whether a stressor

could be a cause at all (i.e., could DDT cause reproductive failure in birds). SI investigations

typically choose among causal scenarios that have already been established as having the

ability to produce impairment. For this reason, the emphasis is placed on postulate 2,

identifying the pathogen, toxicant, or specific indicator of exposure in the affected organisms. 

This case-specific information is then combined with previously established information

discussed in postulates 1, 3, and 4. This approach works best for simple causal agents that

have a known indicator of exposure. When causal scenarios have multiple insufficient causes,

the requirements of regular association and experimental evidence can rarely be met for the

specific mixture that is encountered in the field situation. In cases where multiple sufficient

causes can be assumed to be acting independently, the evidence for each cause can be

evaluated separately. 

4.2.3.1 Causal Considerations for Strength of Evidence Analysis

This section describes various causal considerations used for strength of evidence

analyses. These considerations draw on the work of epidemiologists and ecologists over

the last 30 years (Fox 1991, Hill 1965, Susser 1986a). 
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The first four considerations, co-occurrence, temporality, biological gradient, and

complete exposure pathway draw primarily on associations that are derived from the case

itself. These considerations form the strongest basis for causal inference. The next two

considerations, consistency of association and experiment can be based either on data

from the case at hand or may draw from similar situations. The next four considerations,

plausibility, specificity, analogy, and predictive performance, combine information from

the case at hand with experiences from other cases or test situations, or from knowledge

of biological, physical, and chemical mechanisms. These considerations provide

corroborative information that can be used to supplement the basic observations of

association of observed effects and potential causes from the case. The last two

considerations, consistency and coherency of evidence, evaluate the relationships among

all of the available lines of evidence.

Each of these causal considerations is discussed below:

Co-occurrence – The spatial co-location of the candidate cause and effect. In SI,

this consideration is case-specific; for example, effects may be occurring downstream 

but not upstream of an identified source (see text box entitled “Arkansas River Case

Study”). This consideration should be interpreted with caution when several sufficient

causes may be present and when the objective of the analysis is to identify all potential

and contributing causes. In this situation, the causes occurring the furthest upstream may

mask the effects of causes occurring later in the downstream sequence. 

Temporality – A cause must always precede its effects. For example, a baseline

monitoring study showing a productive trout population before a dam was built provides

some evidence that the dam caused the subsequent population decline. As with cooccurrence, this criterion should be applied with caution when several sufficient causes

may be present and when the objective of the analysis is to identify all potential and

contributing causes. In this situation, the causes occurring early in the time sequence

may mask the effects of causes occurring later.

Biological Gradient – The effect should increase with increasing exposure. This

is the classic toxicological requirements that effects must be shown to increase with

dose. Biological gradient is also applicable to other types of causes (see text box entitled

“Arkansas River Case Study”). For example, if fine substrate texture is believed to cause

reduced diversity of benthic invertebrates, then diversity should decline along a gradient

of texture. In SI, evidence for biological gradient is case-specific. Examples include

demonstrating recovery of a community downstream of an outfall, or evidence that an

effect decreases with decreasing concentration of an effluent or with increasing mean

flow. Investigators should be aware that some stressors elicit non-linear response. For

example, community diversity can increase at low levels of nutrient enrichment, then

decline again as enrichment increases. Regression and correlation analyses are common

tools used to quantify biological gradient; both high slopes and large correlation

coefficients increase the strength of evidence. 

Complete Exposure Pathway – The physical course a stressor takes from the

source to the receptors (e.g., organisms or community) of interest. If the exposure

pathway is incomplete, the stressor does not reach the receptor, and cannot cause an

effect. Evidence for a complete exposure pathway is case-specific and may include

measurements such as body burdens of chemicals, presence of parasites or pathogens, or

biomarkers of exposure (see text box entitled “Arkansas River Case Study”). For

stressors that do not leave internal evidence (e.g., siltation), measurements that show the
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Chapter 4: Characterizing Causes 4-11

Arkansas River Case Study:

Using Strength of Evidence Analysis

This example highlights strength of evidence evaluations used in the SI process. Specifically, the

example presents several lines of evidence used to support the hypothesis that heavy metal exposure

impairs benthic macroinvertebrate communities.

Several sites in the Arkansas River (CO) were monitored over a 10-year span to examine the effects

of cadmium (Cd), zinc (Zn), and copper (Cu) on benthic macroinvertebrates. More specifically, metal

contamination was related to the abundance of heptageniid mayflies. It was found that heptageniid

mayflies were abundant upstream of known metal inputs, and sparse downstream of these inputs, an

example of spatial co-occurrence. In addition, a complete exposure pathway was evident:

concentrations of Cd, Cu and Zn were elevated in benthic invertebrates collected at stations

downstream of the source. Evidence of a biological gradient was observed using multiple regression

analysis; the abundance of heptageniid mayflies decreased with increasing zinc concentrations.

Evidence from other studies was also available and demonstrated that effects from metals would be 

plausible based on stressor-response relationships observed in the laboratory. Chronic toxicity

tests of water collected from the Arkansas using Ceriodaphnia dubia and microcosm tests using

mayflies established that effects would be expected at the concentrations of Zn, Cu, and Cd measured

in the Arkansas. 

Evidence from other studies also supported the hypothesis that heavy metal exposures reduce

abundance of mayflies. Regional Environmental Monitoring and Assessment Program (R-EMAP) data

from other locations in the Rocky Mountains showed a consistent association between metal

exposures and reduced abundance of heptageniid mayflies.

Finally, efforts were undertaken by several agencies to reduce ambient metal concentrations, an

example of a remedial experiment. Increases in the abundance of heptageniid mayflies were

observed at the sites with greatest metal reduction. Further, little biological improvement was

observed where metal levels have remained elevated.

 

References: Clements and Kiffney 1994, Kiffney and Clements 1994a, Kiffney and Clements 1994b,

Clements 1994, Clements et al. 2000, Nelson and Roline 1996.

stressor co-occurring in space and time with the receptor may be useful. For causes that

induce effects indirectly, observations or measurements of the intermediate products or

conditions are evidence of a complete exposure pathway (see Chapter 7, Little Scioto

case study).

Consistency of Association – Refers to the repeated observation of the effect and

candidate cause in different places or times (see text box entitled “Lake Washington

Case Study”). A consistent association of an effect with a candidate cause is likely to

indicate true causation. The case for causation is stronger if the number of instances of

consistency is greater, if the systems in which consistency is observed are diverse, and if

the methods of measurement are diverse. Consistency can be demonstrated using

evidence from the case at hand, or may draw on evidence from many cases. For

example, if fish kills repeatedly occur below a particular outfall, there is a consistent

association over time of those incidents with a candidate cause. Less commonly, a

particular case may have multiple instances of exposure to an agent spread over space

rather than time. Consistent association can also be demonstrated across multiple sites

or cases. For example, a decrease in benthic arthropod diversity may be consistently

observed at many different sites having low dissolved oxygen levels. Consistency of

association across many sites is seldom demonstrated because the same causal agent

seldom occurs at multiple sites that are sufficiently similar to demonstrate a consistent

response. However, when it is demonstrated, consistency across sites is stronger

evidence for causation than the simple co-occurrence or temporal association of the

agent with the response in a single case.
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Experiment – Refers to the manipulation of a cause by eliminating a source or

altering exposure (Hill 1965) (see text boxes entitled “Lake Washington Case Study” and

“Arkansas River Case Study”). Experiments of greatest relevance to SI (see Section 3.3)

include manipulating and testing site media in the laboratory (e.g., using TIE), and

conducting field experiments by controlling a source (e.g., fencing cattle) (USEPA

1991b, 1993a, 1993b). The strongest evidence is case-specific. If evidence from

experiments conducted on a similar situation is used, the relevance to the case at hand

should be described. 

Plausibility – Refers to the degree to which a cause and effect relationship would

be expected given known facts. Two types of plausibility are discussed below: 

Mechanism: Given what is known about the biology, physics, and chemistry of the

candidate cause, the receiving environment, and the affected organisms, is it

plausible that the effect resulted from the cause? It is important to distinguish a

lack of information concerning a mechanism (e.g., the ability of chemical x to

induce tumors is unknown) from evidence that a mechanism is implausible (e.g.,

chemical x is not tumorogenic). It is also important to carefully consider whether

some indirect mechanism may be responsible. For example, increased nutrient

levels cause algal blooms that decompose and reduce epibenthic oxygen

concentrations, which in turn decrease invertebrate diversity. If a mechanism is

known and there is evidence that the mechanism is operating in a specific case, the

positive evidence is particularly strong.

Stressor-Response: Given a known relationship between the candidate cause and

the effect, would effects be expected at the level of stressor seen in the

environment? The comparison of environmental concentrations to laboratoryderived concentration-response relationships is a common approach used in

chemical risk assessments. It provides strong evidence of causality if

concentrations are higher than a level that causes a relevant effect (see Table 3-2)

(see text box entitled “Arkansas River Case Study”). Note that exceedence of

water quality criteria or standards does not necessarily imply causation because 

regulatory values are intended to be set at safe levels. Whole effluent toxicity tests

may be used with dilution models. Although used mostly for chemical stressors, a

similar approach could also be used for other types of stressors, such as siltation.

Analogy – Examines whether the hypothesized relationship between cause and

effect is similar to any well-established cases. Hill (1965) used the criterion of analogy

to refer specifically to similar causes. For example, a new pesticide with a similar

structure to another one may induce similar effects. The idea can be extended to other

types of stressors. For example, an introduced species that has similar natural history

characteristics to one that had been previously introduced may have similar impacts on

the ecological system.
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Chapter 4: Characterizing Causes 4-13

Lake Washington Case Study1

Lake Washington, located in Seattle and draining into Puget Sound, first began receiving street

runoff and raw sewage input from Seattle at the turn of the 20th century. Although the sewer

outlets were eventually replaced by wastewater treatment plant effluents, the growing human

population in the surrounding area put increasing demands on the lake. By 1953, 10

wastewater treatment plants discharged into Lake Washington. Shortly thereafter, the first

report describing nutrient loadings in the lake was issued by researchers at the University of

Washington.

While the problems associated with eutrophication were not widely recognized by the public at

the time, a University of Washington professor, W.T. Edmondson, used the concept of

consistency of association to make an important observation: the recent discovery of a bluegreen alga (Oscillatoria rubescens) in Lake Washington coincided with other documented

cases where water quality had declined in response to nutrient input. The lakes described in

these reports ranged geographically from Wisconsin to western Europe, yet the highly specific

occurrence of Oscillatoria was identified in each case as an early response to water

enrichment. Thus, Edmondson asserted that the water quality in Lake Washington was

declining in response to nutrient input, and would continue to decline in predictable ways.

Edmondson developed a model based on principles of mass balance and stoichiometry to

define the quantitative relationships between nutrient levels and algal biomass. He used the

model to forecast that water quality in Lake Washington would continue to decline in

predictable ways. This is an example of predictive performance, since continued monitoring

confirmed his assertions. 

Outcome

Edmondson's letters and popular science articles describing the problems of the lake

successfully brought about public and political support for the eventual clean-up of Lake

Washington. Between 1963 and 1968, all 10 wastewater treatment plant discharges were

diverted out of Lake Washington and sent to a common collection system that ultimately

discharged deep within Puget Sound.

Until the diversions were constructed, water quality had continued to decline as predicted by

Edmondson, with water transparency at less than 1 m in 1962. However, in the years following

the improvements, nutrient levels decreased substantially. By the 1970s, visibility had reached

12 m, and the presence of the blue-green alga O. rubescens was undetectable. The swift

recovery of Lake Washington following the removal of nutrient inputs in this field experiment

left little uncertainty about the true cause of its water quality decline.

1

 Summarized from J. T. Lehman (1986).

Specificity of Cause – Applicable only if the proposed cause is plausible or if it

has been consistently associated with the effect. Specific cause-effect relationships are

more likely to be demonstrated to be causal (see text box “Lake Washington Case

Study”). If an effect (e.g., hepatic tumors in fish) observed at the site has only one or a

few known causes (e.g., PAHs), then the occurrence of one of those causes in association

with the effect is strong evidence of causation. In the extreme, causation is clear when

both effects and causes are specific (x causes specific effect y, and y is caused only by x). 

One implication of this consideration is that both effects and causes should be defined as

specifically as possible in order to increase the specificity of the association. For

example, a specific cause such as highly embedded substrate can be more clearly

associated with identified effects than a general cause like overall poor habitat quality.

Predictive Performance – Refers to whether the candidate cause has any initially

unobserved properties that were predicted to occur. Was that prediction confirmed at the

site? The ability to make and confirm predictions is one of the hallmarks of a good
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scientific process. For example, if the proposed cause of a fish kill is drift of an

organophosphate insecticide into a stream, one could make the specific prediction that

cholinesterase levels would be reduced, or the more general prediction that insects and

crustaceans would also be killed. If these predicted conditions are then observed at the

site, it increases confidence in the causal relationship (see text box entitled “Lake

Washington Case Study”). Multiple predictions in both the positive and negative

direction would strengthen this criterion (e.g., plants and protozoa would not be harmed,

but arthropods would be).

Consistency of Evidence – Refers to whether the hypothesized relationship

between cause and effect is consistent with all available evidences. The strength of this

consideration increases with the number of lines of evidence (Yerushalmy and Palmer

1959).

Coherence of Evidence – Examines whether a conceptual or mathematical model

can explain any apparent inconsistencies among the lines of evidence. For example 

metal concentrations at the site may be sufficient to impair reproduction in fish, and yet

both juvenile and adult fish occur at the site. This evidence may be coherent if

reproduction is not occurring at the site, but juvenile fish re-colonize the site from

unexposed locations. Another explanation may be that the measured total metal

concentration is not 100% bioavailable. The strength of these explanations depend on

the expertise and judgment of the assessors. It is a weak line of evidence, because of the

possibility that post hoc explanations are wrong. However, the hypotheses may lead to

experiments or predictions in future iterations of the causal assessment (e.g., testing the

bioavailability of the metals), which could support stronger inferences.

4.2.3.2 Matching Evidence with Causal Considerations

Table 4-2 illustrates the different types of evidence discussed in Chapter 3 with the

causal considerations they support. The relationship between types of evidence and

causal considerations is not one-to-one. Each type of evidence may be relevant to

several causal considerations, and a causal consideration may be evaluated using several

different types of evidence. In any specific application of SI, evidence will probably

exist for only some of the causal considerations, and the evidence will be uneven across

the candidate causes. After the evidence relevant to each consideration is identified, it is

evaluated as discussed in the next section. 

4.2.3.3 Weighing Causal Considerations

Epidemiologists and ecoepidemiologists have attempted to develop guidance for

weighing the causal considerations described below (Fox 1991, Hill 1965, Susser

1986a). Table 4-3 presents the possible outcomes for each consideration and provides

symbols to represent the influence of each outcome on the inference.

Table 4-3 illustrates a format that can be applied to specific SI cases. In this table, the

causal considerations are listed in the left-hand column. Each of the other columns 

presents results for a candidate cause. The rows show the appropriate number of +, -, or

0 symbols associated with the strength of evidence for each consideration evaluated for

each candidate cause. Supporting narratives should describe how the scores were

obtained from the evidence. We do not recommend adding up the scores for each

candidate cause. Adding the scores erroneously implies that each consideration is of

equal importance and is equitable only if the same types of evidence are available across
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Chapter 4: Characterizing Causes 4-15

all candidates. In difficult cases, it may be valuable to compare the evidence for each

individual consideration across the candidate causes. Particular attention should be paid

to negative results, which are more likely to be decisive. 

Table 4-2. Types of evidence (columns) that contribute to each causal consideration (rows).

Types of Evidence

Causal

Considerations

Associations of

Measurements

of Cause and

Effect

Measurements Related

to Causal Mechanisms

Case

Exposure/

Other

Exposure

Response Experiments

Spatial Co-locationSpatial GradientTemporal Co-occurrenceTemporal GradientSymptomsBiomarkersIndirect Effects (Abundances of other Species)Effects on Other ReceptorsCause and Receptor are Co-locatedLaboratory Exposure ResponseField-derived Exposure-responseMechanistic model ofExposure-responseManipulation of SourcesManipulation of ExposureLab Manipulation and Testing of Media from Site

Co-occurrence x

Temporality x x

Biological

Gradient

x x

Consistency of

Association

x x

Complete

Exposure

Pathway

xx x xx

Specificity of

Cause

x x

Plausibility:

Mechanism

xx x xx x

Plausibility:

StressorResponse

x xx

Experiment xxx

Analogy x x x x x x x

Predictive

Performance

xx x x xx
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Table 4-3. Format for a table used to summarize results of an inference concerning

causation in case-specific ecoepidemiology. (Table adapted from Susser (1986a), Fox

(1991), Suter (1998), Beyers (1998).) 

Consideration Results Score1

Case-Specific Considerations

Co-occurrence Compatible, Uncertain, Incompatible +, 0, – – –

Temporality Compatible, Uncertain, Incompatible +, 0, – – –

Consistency of

Association

Invariant, In many places and times,

At background frequencies or many

exceptions to the association

++, +, –

Biological Gradient Strong and monotonic, Weak or other

than monotonic, None, Clear

association but wrong sign

+++, +, –, – – – 

Complete Exposure

Pathway

Evidence for all steps, Incomplete

evidence, Ambiguous, Some steps

missing or implausible

++, +, 0, –

Experiment Experimental studies: Concordant,

Ambiguous, Inconcordant

+++, 0, – – –

Considerations Based on Other Situations or Biological Knowledge

Plausibility

Mechanism Actual Evidence, Plausible, Not

known, Implausible

++, +, 0, –

Stressor-Response2

 Quantitatively consistent, Concordant,

Ambiguous, Inconcordant

+++, +, 0, –

Consistency of

Association

Invariant, In most places, In some

places, At background frequency or

many exceptions to the association

+++, ++, +, –

Specificity of cause3 Only possible cause, One of a few,

One of many

+++, ++, 0

Analogy

 Positive

 Negative

Analogous cases: Many or few but

clear, Few or unclear ++, +

– –, –

Experiment Experimental studies: Concordant,

Ambiguous, Inconcordant

+++, 0, – – –

Predictive Performance Prediction: Confirmed specific or

multiple, Confirmed general,

Ambiguous, Failed

+++, ++, 0, – – –

Considerations Based on Multiple Lines of Evidence

Consistency of Evidence Evidence: All consistent, Most

consistent, Multiple inconsistencies

+++, +, – – –

Coherence of Evidence Evidence: Inconsistency explained by

a credible mechanism, No known

explanation

+, 0

1 In addition to the scores noted, there ay be No Evidence (NE) available relevant to the

consideration, or the consideration may be Not Applicable (NA) for the particular case (see

especially stressor-response and specificity). 2 Stressor-response is not applicable (NA) if the mechanism is clearly implausible. 3 Specificity of cause is not applicable (NA) if either the mechanism is clearly implausible, or if

there are many exceptions to the association.
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Chapter 4: Characterizing Causes 4-17

Eliminate Diagnose Strength of Evidence

CHARACTERIZE CAUSES

Identify Probable Cause

Other methods for combining different lines of evidence include expert systems based on

the logic of abduction and Bayesian statistical approaches (Josephson and Josephson

1996, Clemens 1986). As of this writing, these more quantitative approaches have not

yet been developed for combining evidence for SI.

4.3 Identify Probable Cause and Evaluate Confidence

Whichever method is used to infer causation,

the results of the characterization must be

summarized. That is, the cause must be

described, the logical basis for its

determination summarized, and the

uncertainties concerning that determination

presented. As discussed above, there may be

multiple sufficient causes, all of which should

be characterized. In extreme cases, the

effects of the primary causes are so severe

that other potential causes will remain unidentified.

The level of confidence in causal identification may be assessed in quantitative or

qualitative terms. Confidence is determined in part by uncertainty concerning the data,

the models, and the observations that contribute to the inference. The uncertainty

associated with the data may be partially estimated by conventional statistical analysis

(see text box in Chapter 3 entitled “Data Quality Objectives,” and “Using Statistics and

Statistical Hypothesis Testing”), but also includes uncertainty concerning the

applicability of the data. If data must be extrapolated between species or life stages, if

old data are used to estimate current conditions, or if, for some other reason, data are not

directly applicable, the associated uncertainty should be estimated. The uncertainty in

statistical models, such as regressions of biological properties against levels of potential

causes, may be estimated using goodness-of-fit statistics or confidence bounds. The

uncertainty due to the parameters in mathematical models, such as models of dissolved

oxygen depression due to nutrient input, may be estimated analytically or by Monte

Carlo simulation (USEPA 1996a, 1999). If a causal inference is logically clear and is

based predominantly on the results of a statistical or mathematical model, the

uncertainties concerning the results may serve to estimate the uncertainties concerning

the inference. 

 In most cases, unquantified uncertainties will dominate. These include lack of data

concerning the presence or levels of particular stressors, incomplete biological data,

uncertainty concerning the time when the impairment began, and many more. In

addition, most causal inferences are based on the strength of evidence, so that no single

source of uncertainty characterizes the uncertainty concerning the conclusion. 

Therefore, the uncertainty concerning most identifications of causes must be

characterized qualitatively. That qualitative judgement should be accompanied by a list

of major sources of uncertainty and their possible influence on the results.

In some cases, investigators will be able to clearly demonstrate that a particular cause is

responsible for the ecological injuries of concern. However, in many if not most cases,

there will be significant uncertainty concerning the relative contributions of alternative

causal factors. In such cases, it is necessary to determine whether the evidence is

sufficient to justify a management action. Standards and criteria for establishing

epidemiological causation are not generally agreed upon. In particular, there is no
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consistent standard for adequacy of proof. While conventional science sets a high

standard to prove causation, the precautionary principle begins by assuming that an agent

is harmful and requires disproof of causation (Botti et al. 1996). Such decisions are

made by risk managers, rather than risk assessors, and may be based on considerations

such as the cost of remediation and the nature and magnitude of the ecological injury. 

Ideally, that judgment would be made on the basis of a priori criteria. That is, each

program that uses SI should specify a standard basis for deciding whether the

characterization of the cause is sufficient for the management purpose. For example, for

the permitting of POTW effluents, a particular state might develop standards for proof

that those effluents cause particular types of injuries. However, standards and criteria

for establishing causation are not generally agreed upon, and many decisions are made

ad hoc. That is, the evidence concerning causation may be presented to the risk manager

as a best estimate of causation along with an accompanying analysis of uncertainties. 

The risk manager may use that result to help reach a decision. 

As discussed in Chapter 1, the SI process may be conducted iteratively until sufficient

confidence in the causal characterization is reached. In the most uncertain and complex

cases, the SI process may best serve to guide further data collection, modeling, or

analysis efforts. Options for iterating the process are discussed further in Chapter 5,

below. If the cause is confidently identified, then the investigation may proceed to

identifying sources, developing and implementing management options, and monitoring

their effectiveness (Figure 1-1). 
Stressor Identification Guidance Document

Chapter 5: Iteration Options 5-1

This chapter describes iterations if no clear cause can be identified. If the SI process has

yielded no clear cause for the biological impairment, it may be because (1) there is

actually no effect (Section 5.1), or (2) there is insufficient information concerning the

identified causes or the true cause was not among the list of candidate causes (Section

5.2). These alternatives, all leading to a reiteration of the investigation (Figure 4-1), are

discussed in this section.

5.1 Reconsider the Impairment

When no cause was identified, it may be that there is actually no effect, or the actual

effect may be different from the identified impairment (see worksheet in Appendix B,

Unit V, page B-35). This situation is known as a false positive, or in statistical terms, a

Type I error. It should be noted that both false positive and false negative errors (failure

to detect an effect that exists) are inherent to any detection system, whether it is medical

diagnostics, aircraft radar, or environmental monitoring. 

A false positive might result from errors in a biological survey or in the analysis of data. 

The samples may have been collected improperly; therefore, the biotic community

appears to be less abundant or species rich than it truly is. The individuals performing

the identifications may have misidentified organisms. There may have been errors in

data recording or analysis. Any of these errors may artificially obscure the responses. 

A quality assurance program can minimize, but not entirely eliminate these errors. If the

causal analysis reveals weaknesses in the evidence for the occurrence of a real effect, a

careful audit of the biological survey may be appropriate.

Other reasons for a false positive result include sampling error and the natural variability

of the biological indicators. In any monitoring program, sampling is stratified among

perceived natural classes and subdivisions of systems (e.g., habitat type, salinity,

sediment, elevation, biogeographic region), and often by season (sampling index period

in defined season). A sample may have been taken outside of an index period. A site

may belong to a poorly characterized system type or may have been incorrectly classified

(e.g., cold water system evaluated using warm water criteria). Any unrecognized

misclassification can result in either a false positive or false negative. Intensive

monitoring and characterization of natural systems, combined with quality assurance and

peer review of results, can reduce both types of errors.

In other cases, the impairment may have been defined too broadly or investigators may

have made wrong assumptions about mechanisms when developing their conceptual

model. For example, the first investigations into bird population declines and DDT

focused on mortality rather than egg-shell thinning, and failed to find a connection with

DDT (see text box entitled “Revisiting the Impairment in the Case of DDT”). Careful

reconsideration of the nature of the impairment can put the investigation back on the

right track.

Finally, natural variability of the indicators, not due to any measurement or analytical

errors, can result in both false positives and false negatives. Environmental criteria may

In this Chapter:

5.1 Reconsider the Impairment

5.2 Collect More Information

Chapter 5

Iteration Options
Stressor Identification Guidance Document

5-2 U.S. Environmental Protection Agency

Revisiting the Impairment in the Case of DDT1

The fact that DDT played a role in the decline of bald eagle and other bird-of-prey populations

(e.g., osprey, brown pelicans) is now commonly appreciated among most biologists. However,

the link between DDT and the eggshell thinning that caused reproductive failure in these birds

was not initially recognized. Ultimately, the connection was made by re-examining the

description of the impairment.

The first link between DDT and diminishing bald eagle and other bird-of-prey populations was

the consistent observation of high body burdens of DDT metabolites. In other words, there was

co-occurrence of the declining bird populations and the candidate cause, DDT. There was

also evidence of a complete exposure pathway to birds based on body burden of DDT. 

However, extensive toxicity testing of DDT on adult bird mortality revealed no relationship. This

suggested that the proposed mechanism, toxicity, was implausible. However, lethality was not

the impairment; decline of birds-of prey was the impairment. A new conceptual model was

required that considered other mechanisms that could result in declines in bird populations. In

re-examination of the overall analysis, it became apparent that the species chosen for testing

had been relatively tolerant of DDT exposure compared to those that were affected in the wild,

and that the endpoint observed in these tests (lethality) would not reflect reproductive success

or failure resulting from DDT exposure.

Field observations eventually revealed a potential plausible mechanism of reproductive failure

due to eggshell thinning among bald eagles and other birds-of-prey. Laboratory experiments

showed that DDE could cause eggshell thinning. Field studies showed that field exposures to

DDE, a metabolite of DDT, were sufficient to cause effects in many species of birds based on

the stressor-response relationship. Together these findings provided lines of evidence by

which DDT might cause eggshell thinning and reduce reproductive success, a more specific

impairment than declines in bird population.

Outcome

In 1972, DDT was banned from most uses in the United States. In the years following the ban,

bald eagle and other bird-of-prey populations slowly recovered. The recovery of bird

populations after banning the use of DDT, is an example of mitigation of the effect following

manipulation of the cause, and is very strong evidence that the use of DDT was, in fact, the true

cause of bald eagle and other bird-of-prey population declines.

References

Grier, J.W. 1982; Blus, L.J., and C.J. Henny, 1997

be defined by exceedence of a percentile or extreme value of some statistical

distribution. This means that natural, or unimpaired conditions, may also exceed the

criteria at some frequency. Ideally, acceptable error rates should be specified for

decisions resulting from the biological assessment system. If confidence in a finding of

biological impairment is low (that is, if the indicator just exceeds the threshold value),

then increased sampling may reduce uncertainty and increase confidence (see next

section).

5.2 Collect More Information on Previous and Additional Scenarios

If a causal scenario has not been established with sufficient confidence and the effect

appears to be real, management should be consulted to discover if knowing the cause is 

still required for decision-making. If so, then more information must be collected (see

worksheet in Appendix B, Unit VI, page B-36). Because the cost of field data collection

and data analysis increases with each iteration, it is important to carefully plan what

additional information is needed to determine the cause of impairment. This information

may include previously considered scenarios for which information was inadequate, or

candidate causal scenarios that were not previously considered.
Stressor Identification Guidance Document

Chapter 5: Iteration Options 5-3

Even when the characterization of causes has not determined the cause with sufficient

confidence, the set of candidate causes should have been reduced, and the critical

evidence should be apparent. In particular, it should be possible to design experiments

or observations that will potentially eliminate certain causes (Chapter 4.1.1). However,

such experiments are not always feasible. Alternatively, one may identify critical pieces

of positive evidence that would strongly support one scenario and none of the others. In

most cases, it will be appropriate and prudent to plan a sampling and testing program that

will generate a set of potentially decisive positive and negative evidence.

If all of the most common causes have been eliminated or have been determined to be

unlikely, then additional causal scenarios need to be identified. The process is similar to

that described in Chapter 2. New data may have come to light during the first iteration

of the SI process. These data should be carefully reviewed to determine if there are any

clues to suggest additional causal scenarios. Details of the available data should be

considered, such as weather patterns, new construction, or land use information. If the

descriptions of the effect or the scope were too broad, they may need to be refined or

more clearly defined. Additional potential causal scenarios may include new stressors or

combinations of stressors that occur simultaneously or in a specific sequence. After the

additional candidate causal scenarios are developed, key evidence should be identified

that is likely to allow identification of the cause.

The most important tools to bring to the SI process are experience in multiple disciplines

(especially ecology), careful, deliberate critical thinking, and a strong desire to find the

true cause of biological impairment.
Stressor Identification Guidance Document

Chapter 6: Presumpscot River, Maine 6-1

6.1 Executive Summary

The Presumpscot River is located in southern Maine and forms the outlet of one of

Maine’s largest lakes, Sebago Lake. From 1984 to 1996, biological monitoring

downstream of a pulp and paper mill discharge consistently revealed non-attainment of

Maine’s Class C aquatic life standards. The river is impounded above and below the

discharge. The discharge releases high concentrations of TSS and total phosphorus, and

on occasion releases metals above the chronic criteria but below acute criteria. Upstream

samples consistently indicated attainment of Class C or better standards.

Description of the Impairment

Biological impairment was characterized by a shift in the benthic macroinvertebrate

community from 90% insects upstream of a pulp and paper mill discharge to about 50%

insects downstream. This shift included a 15-35% loss of taxonomic richness, and 40-

60% loss of Ephemeroptera-Plecoptera-Trichoptera (EPT) taxa. Moreover, many insect

taxa found upstream of the discharge were pollution-sensitive, while those found

downstream were primarily pollution-tolerant species, such as snails and worms.

List Candidate Causes

Eight candidate causes for non-attainment were considered in the Stressor Identification

process:

1. Excess toxic chemicals from the discharge;

2. High TSS combined with floc causes high BOD and reduced DO;

3. High TSS combined with floc causes smothering;

4. Excess nutrients (from POTWs, nonpoint sources, and the mill) cause excess

algal growth;

5. Impoundment increases sedimentation that smothers biota;

6. Impoundment decreases flow velocity and causes algal growth, leading to

reduced DO;

7. Impoundment causes low DO; and

8. Impoundment causes loss of suitable habitat.

Characterizing Causes: Eliminate

Four of the eight candidate causes were logically eliminated from examination of the

evidence. Reduced DO sufficient to cause the impairment was not observed in the

Presumpscot River, and bottom-water DO concentrations were stable throughout the

Chapter 6

Presumpscot River, Maine
Stressor Identification Guidance Document

6-2 U.S. Environmental Protection Agency

river, above and below the discharge. Therefore, causal scenarios #2, #6 and #7 could be

eliminated. Although elevated concentrations of total phosphorus (TP) were observed

below the discharge, the increase in chlorophyll a concentration was negligible. Water

column chlorophyll a is a surrogate measure for algal biomass. Because excess algal

growth was necessary for causal pathway #4, and there was none, it was also eliminated

from further consideration.

Characterizing Causes: Diagnose

No evidence strong enough to support diagnosis was available for any of the candidate

causes.

Characterizing Causes: Strength of Evidence

A strength of evidence approach was then used to examine the remaining four candidate

causes. The four remaining causes were toxic chemicals, flocculent TSS causing

smothering, impoundment increasing sedimentation, and impoundment causing loss of

suitable habitat. There was no strong evidence for or against the toxic chemical

hypothesis (#1). Several lines of strong evidence favored the TSS hypothesis (#3):

 The exposure pathway from discharge to biological impairment was complete

and plausible.

 Other rivers with similar elevated flocculent TSS also had impaired biological

assemblage.

 Removal of flocculent TSS from a nearby river resulted in recovery of the

biological assemblage.

Two lines of evidence disfavored the two impoundment hypotheses (#5 and #8):

 Other impoundments with similar potential sediment loadings (not from mill

discharge) and similar habitat support diverse invertebrate assemblages that meet

aquatic life use criteria; and 

 a site upstream of the mill effluent, and within the same impoundment, met

aquatic life use criteria.

Characterizing Causes: Identify Probable Cause

The evidence supporting scenario #3, that non-attainment was due to high loads of

flocculent TSS from the discharge, was consistent throughout the lines of evidence. 

Strength of association, spatial co-occurrence, and experimental lines of evidence

strongly supported this scenario. Evidence for the toxicity scenario (#1) was extremely

weak. Evidence for the two impoundment scenarios (#5 and #8) was negative. The State

of Maine concluded that high TSS was sufficient for causing the biological impairment. 

Quality of the data were adequate, and confidence in the conclusion was high. 

Subsequently, the State took management action to reduce loadings of TSS through a

TMDL that was approved by EPA. This was the first time in New England that

bioassessment findings had served as the quantitative response variable for development

of a TMDL and resulting pollutant discharge limits, including the pulp and paper mill. 
Stressor Identification Guidance Document

Chapter 6: Presumpscot River, Maine 6-3

Moreover, it provided a means for Maine to control a pollutant (TSS) for which it had no

specific criterion in its water quality standards.

6.2 Background

This case study is presented as an example of how the stressor identification (SI) process

could have been used to determine the cause of non-attainment of aquatic life use in a

small river in Maine. The case study begins with the presentation of background

information on the regulations in the State of Maine and the geographical location of the

case study. This is followed by a brief discussion of the evidence found at the site and in

other situations. Several causal scenarios are then presented and analyzed separately to

illustrate how the SI process could be used to eliminate four of the eight candidate

causes. A strength of evidence analysis is then used to identify the most likely cause. 

The case study concludes with a brief discussion of the management actions taken to

remedy the situation. One of the most significant results of this effort was that the State

of Maine, Department of Environmental Protection, used bioassessment findings to

control a stressor for which the State has no standards.

Impairment Trigger: Biological monitoring in the Presumpscot River in Westbrook,

below a pulp and paper mill discharge, has consistently revealed non-attainment of Class

C aquatic life standards (1984, 1994, 1995, 1996) using standard Maine Department of

Environmental Protection methods (invertebrate) (Davies and Tsomides 1997). 

Regulatory Authority

The Maine Department of Environmental Protection (MDEP) issues wastewater

discharge licenses that set the allowable amounts of pollutants that industries may

discharge to waters of the State. These limits are scientifically determined in order to

preserve water quality sufficient to maintain all designated uses and criteria established,

by law, for the river. In recent years USEPA has required that a Total Maximum Daily

Load (TMDL) be established for impaired river systems, such as the Presumpscot, for

which existing, required pollution controls are inadequate to attain applicable water

quality standards. 

The State of Maine established minimum standards for three water quality

classifications, Class A, Class B, and Class C. These classes specify designated aquatic

life uses from Class C, the minimum state standard, to the most protected waters with the

Class A/AA designation. Class C requires that the structure and function of the

biological community be maintained and provides for the support of all indigenous fish

species.

Under this system, attainment of the aquatic life classification standards for a given

water body is evaluated using numeric biological criteria. The MDEP numeric aquatic

life criteria are based on statewide data collections over a 14-year period with analysis of

over 400 sampling events. Artificial substrates (rock baskets) are incubated on the

bottom at stream sites, retrieved, and benthic macroinvertebrates that have colonized the

substrates are identified and enumerated (Davies and Tsomides 1997). Aquatic life

classification standards for a given water body are evaluated using numeric biological

criteria that were statistically derived from the statewide database. The criteria are in the

form of a statistical model (linear discriminant model) which yields the probability that a

test sample belongs to one of the 3 water quality classes, or non-attainment of the lowest
Stressor Identification Guidance Document

6-4 U.S. Environmental Protection Agency

Figure 6-1. Map of the Presumpscot River showing biomonitoring stations, potential

sources of impairment, and their location relative to the Androscoggin River (inset).

class (Davies et al. 1995). The model uses a set of metrics derived from the species

composition and abundance enumerated from the substrates.

Geography

The Presumpscot River is the outlet of Sebago Lake. The river flows through the most

densely-populated county in the State of Maine, crossing the towns of Gorham,

Windham, Westbrook, Portland, and Falmouth. The Presumpscot then empties into

Casco Bay at the Martins Point Bridge (Figure 6-1). 
Stressor Identification Guidance Document

Chapter 6: Presumpscot River, Maine 6-5

Compared to industrial receiving waters in the State of Maine, the Presumpscot is a

relatively small river, having a drainage area of only 647 square miles. These

circumstances contribute to a low dilution ratio in the lower Presumpscot River. 

The river has six impoundments and four industrial and municipal waste discharges. 

This study comprises an area immediately downstream of a pulp and paper mill effluent

discharge. Approximately 3.2 km, downstream of the discharge is an impoundment;

upstream is a municipal discharge and (further upstream) two impoundments.

Evidence of Impairment

Biological Evidence: Biological monitoring in the Presumpscot River in Westbrook,

below a pulp and paper mill discharge, consistently revealed non-attainment of Class C

aquatic life standards (1984, 1994, 1995, 1996) using the standard Maine Department of

Environmental Protection methods (Davies et al. 1995, Davies and Tsomides 1997). 

Biological evidence indicating impairment on the lower Presumpscot River is

summarized in Table 6-1 and Figure 6-2. Upstream samples consistently indicated

attainment of Class C or better aquatic life standards (Davies et al. 1999). Three

kilometers downstream the Presumpscot within the impounded area did not attain Class

C aquatic life standards. 

Table 6-1. Evidence of biological impairment in the Presumpscot River upstream and

downstream of a pulp and paper mill effluent discharge.

Evidence

Upstream of

Effluent Downstream of Effluent

Aquatic Life Standard Class C Non-Attainment

Benthic

Macroinvertebrate

Community

90% insects 50% insects

Taxonomic Richness -- 15%-35% decrease relative to upstream

Sensitive Species

(EPT)

-- 46%-60% decrease relative to upstream

Snails and Worms Low High

The Presumpscot River biological monitoring samples reveal a shift in the benthic

macroinvertebrate community from 90% insects above the mill to about 50% insects

below the mill, with 15%-35% loss of taxonomic richness and 46%-60% loss of the

sensitive Ephemeroptera-Plecoptera-Trichoptera (EPT) groups (Mitnik 1998). Pollutionsensitive insect taxa found in the upstream samples were replaced by a predominance of

snails and worms, which are more tolerant of pollution, in the downstream samples.

6.3 List Candidate Causes

Eight candidate causes for the non-attainment of biological standards were considered. 

The candidate causes for the biological impairment of the Presumpscot River are shown

in terms of a conceptual model (Figure 6-3), wherein the candidate causes are ordered

from left to right. Each scenario is explained below:

1. Excess Toxic Chemicals - Potentially toxic compounds may be discharged from the

paper mill and these compounds adversely affect aquatic life.
Stressor Identification Guidance Document

6-6 U.S. Environmental Protection Agency

Biological Indicators of Non-Attainment

0

5

10

15

20

25

30

35

40

45

upstream immediately

dow nstream

approx. 1 mile

dow nstream

Value

Species Richness

No. of EPT Taxa

Figure 6-2. Species richness and number of EPT taxa in the Presumpscot River

upstream and downstream of a pulp and paper mill effluent discharge.

LIST CANDIDATE CAUSES

ANALYZE EVIDENCE

CHARACTERIZE CAUSES

Stressor Identification

Identify Probable Cause

Eliminate Diagnose Strength of Evidence

LIST CANDIDATE CAUSES

ANALYZE EVIDENCE

CHARACTERIZE CAUSES

Stressor Identification

Identify Probable Cause

Eliminate Diagnose Strength of Evidence

2. BOD (produced by high TSS with floc) reduces

DO - Excess total suspended solids (TSS with floc)

may be released by the paper mill effluent, and

these solids create biological oxygen demand

(BOD), reducing dissolved oxygen (DO) levels in

the river. Consequently, the river has insufficient

oxygen to support sensitive species of benthic

invertebrates.

3. TSS with floc - The increased levels of TSS

discharged to the river could impact the benthic

communities by accumulating as (nonbiodegradable) sediment, resulting in fewer

interstitial spaces in which animals can live, and

possibly smothering benthic biota.

4. Excess Nutrients - Excess nutrients, deriving from either upstream, non-point sources

or from the paper mill effluent, may affect water quality by promoting algal blooms. In

this scenario, an overabundance of plant nutrients such as phosphorus is delivered to the

stream, and over-stimulates algal growth (a process known as eutrophication). An

increase of algae in the river may affect benthic macroinvertebrates in two ways. If the

algal growth is severe, the resulting detritus becomes a source of BOD, reducing

dissolved oxygen levels in the river. If the growth is modest, the algae may still affect

the benthic macroinvertebrate community by providing an increased food supply for

opportunistic invertebrates that use algae as a food source. Consequently, the

community would shift in such a way that the opportunistic species would thrive and

outcompete other, less opportunistic species.
Chapter 6: Presumpscot River, Maine 6-7 Sources: Stressors: Interactions: Effect: Paper Mill Point Source Discharge Upstream Discharges Altered Hydrology (Impoundment) Direct Toxicity Reduced Dissolved Oxygen Increased Food Supply Reduced Interstitial Space/ Smothering Loss of Optimal Habitat BOD Increased TSS Algal Growth Excess Nutrients Increased Sedimentation Pool Condition

reduced flow vel

stream wideni

deepened wat

Toxic Chemicals 1 2 3 4 4 567 8 severe moderate Shift in BenthicMacroinvertebrateCommunity Sources: Stressors: Interactions: Effect: Paper Mill Point Source Discharge Upstream Discharges Altered Hydrology (Impoundment) Direct Toxicity Reduced Dissolved Oxygen Increased Food Supply Reduced Interstitial Space/ Smothering Loss of Optimal Habitat BOD Increased TSS Algal Growth Excess Nutrients Increased Sedimentation Pool Condition

reduced flow vel

stream wideni

deepened wat

Toxic Chemicals 1 2 3 4 4 567 8 severe moderate Shift in BenthicMacroinvertebrateCommunity Figure 6-3. Conceptual model showing the potential impact of stressors on the benthic community of the Presu

River. (Arrow with minus sign (-) indicates inhibition.)
Stressor Identification Guidance Document

6-8 U.S. Environmental Protection Agency

LIST CANDIDATE CAUSES

ANALYZE EVIDENCE

CHARACTERIZE CAUSES

Stressor Identification

Identify Probable Cause

Eliminate Diagnose Strength of Evidence

LIST CANDIDATE CAUSES

ANALYZE EVIDENCE

CHARACTERIZE CAUSES

Stressor Identification

Identify Probable Cause

Eliminate Diagnose Strength of Evidence

The fifth through eighth candidate causes are based on impoundment of the river just

downstream of the paper mill effluent. Each cause begins with the idea that the

impoundment is causing adverse changes in the physical nature of the Presumpscot.

Impoundments generally widen and deepen a stream corridor, reducing flow velocity and

creating pool-like conditions. Such alterations can have several effects:

5. Impoundment Increases Sedimentation - One effect of impoundment is increased

sedimentation due to reduced flow velocity, which leads to fewer interstitial spaces in

which animals can live, and potentially smothers benthic ones. 

6. Impoundment Promotes Algal Growth - The pool-like conditions created by the

impoundment become a better habitat for algal growth, and algal blooms occur. 

Subsequently, benthic communities shift as a result of oxygen depletion or the

dominance of algae-consuming invertebrates, as described previously.

7. DO Reduction in Impoundment - An impounded river is deeper and slower, which

results in less potential for mixing and more potential for stratification, particularly in

warmer months. As a result, underlying water may not be sufficiently aerated, and

benthic diversity decreases in response to low dissolved oxygen levels. 

8. Habitat Degradation caused by Impoundment - Changes in physical conditions of the

river caused by impoundment reduce optimal habitat for benthic organisms. The effect is

a direct one: native benthic macroinvertebrates are unable to thrive under the altered

conditions. Dissolved oxygen levels and other water quality parameters are not a factor.

6.4 Analyze Evidence and Characterize Causes: Eliminate

Physical and Chemical Evidence: Physical and

chemical evidence indicating impairment on the

lower Presumpscot River is summarized in Table 6-

2. Upstream of the pulp and paper mill outfall, it

was possible to see samplers on the river bottom at

2.5 meters of depth, whereas in the effluent plume,

just 600 m downstream, visibility was less than 0.5

meter. Visibility at a sampling station 3.2 km

downstream of the outfall remained significantly

impaired. This evidence was used to eliminate

candidate causes.

1. Toxic Chemicals - No in-stream or sediment

chemistry data were available. Therefore, toxic

chemicals cannot be eliminated as a candidate

cause.
Stressor Identification Guidance Document

Chapter 6: Presumpscot River, Maine 6-9

Table 6-2. Physical and chemical parameters measured in the Presumpscot River

upstream and downstream of a pulp and paper mill effluent discharge.

Observation Source Upstream of

Mill

Downstream of Mill

Visibility Mitnik 1998 2.5 m <0.5 m (600 m below outfall)

and visibility remained

“significantly impaired” 3.2

km downstream

Observations on

Sampling Equipment

(e.g., ropes, nets)

Mitnik 1998 Free of brown

floc

Coated with brown floc

Mean TSS (ppm)1 Courtemanch

et al. 1997

3 ppm 5.9 ppm

Mean BOD (ppm)2 Mitnik 1994 3.96 6.19 

DO range (ppm) 3

 Mitnik 1994 5.9 - 8.4 5.8 - 8.0

Mean nitrate - nitrite

(ppm)2

Mitnik 1994 0.03 0.05

Mean ammonia

(ppm)2

Mitnik 1994 0.03 0.12

Mean Total

phosphorus (ppb)2

Mitnik 1994 12.8 61.2

Mean Orthophosphate

(ppb)2

Mitnik 1994 3.5 44.3

Mean Chlorophyll a

(ppb)2

Mitnik 1994 2.1 2.3

Notes

1 Observations from 1995-96; number unknown

2 4 sites above mill and 5 sites below on 3 consecutive days

3 Bottom water; 9 sites above mill and 8 sites below on 6 non-consecutive days

2. BOD (produced by high TSS with floc) reduces DO - Elevated BOD was associated

with the biological impairment in the Presumpscot River. In this candidate scenario,

reduced DO is the actual stressor that acts on the organisms to cause impairment. 

Monitoring in the Presumpscot River above and below the mill discharge indicated that

DO concentrations did not decrease upstream to downstream (Table 6-2 and Figure 6-4). 

The reported DO measurements were taken at stations indicated on the map (Figure 1). 

Most of the sites shown in Figure 1 were impounded water; only 7.7, and 6.3 were freeflowing. The results reported in Tables 6-2 and 6-3 were all sampled between 0640 and

0850 hours, within 1m of the bottom, in July and August, 1993 (Mitnik 1994). This is

the time, depth, and season at which minimum DO is found in lakes and impoundments,

because of the diurnal cycle of photosynthesis and respiration, and because

photosynthesis (but not respiration) is inhibited in deeper and darker waters. This

analysis demonstrated that low DO does not occur in the Presumpscot River under any of

the candidate causes involving reduced dissolved oxygen. Therefore, candidate causal

scenarios # 2 (High TSS with floc causes high BOD and reduced DO) # 6

(Impoundment promotes algal growth that in turn reduces dissolved oxygen), and # 7

(Impoundment causes low DO through decreased water flow rate) could be eliminated

without further analysis. The elimination of scenario #6 is reinforced by the evidence

described in scenario #4, below.
Stressor Identification Guidance Document

6-10 U.S. Environmental Protection Agency

0

1

2

3

4

5

6

7

8

9

16

14.813.512.6

7.77.7

7

6.76.35.54.73.32.41.81.20.2

0

Approximate Riv e r M ile

Dissolv e d Oxyge n (mg/L)

Max.

Min.

Avg.

Figure 6-4. Bottom dissolved oxygen concentration in the Presumpscot River. (Means

of 6 observations on 6 days in July and August, 1993, for each site. All observations

within 1m of bottom. Whiskers extend to minimum and maximum measurements. All

measurements were taken between 06:40 and 08:50 am. Arrow indicates location of

the pulp and paper mill discharge: sites to the left of arrow are incrementally upstream

of the discharge, and all sites to the right are incrementally downstream. The

darkened square represents the average DO measurement. The lines above and

below the square represent the maximum and minimum measurements, respectively.)

3. TSS with floc - In the Presumpscot River, TSS and floc are elevated at the impaired

site (Tables 6-2 and 6-3). TSS with floc cannot be eliminated as a candidate cause.

4. Nutrients and Algal Growth - The nitrogen to phosphorus ratio ((TKN + NO3 +

NO2)/TP ) upstream of the paper mill discharge was approximately 25, indicating

phosphorus limitation, as is typical of New England fresh waters. Below the paper mill

discharge, elevated phosphorus concentrations were associated with biological

impairment (Table 6-2). Specifically, total phosphorus (TP) and ortho-phosphate (PO4)

increase 5- to 10-fold downstream of the discharge (Table 6-2). Moreover, the discharge

alone contained an average of 723 mg/L TP. A five-fold increase in TP (ten-fold in PO4)

would normally result in increased algal growth (measured as chlorophyll a

concentration). However, the observed increase in chlorophyll a with the phosphorus

enrichment below the discharge was negligible, increasing by just 0.2 ppb. Because

excess algal growth is necessary for the causal pathway to be complete, causal scenario

#4 was eliminated from further consideration.

5. Impoundment Increases Sedimentation - Biological impairment downstream of the

paper mill discharge coincided with the presence of an impoundment (Table 6-3). 

However, no measurements of sediment loadings were available to determine if the

biological impairment was the result of increased sedimentation caused by the

impoundment. Therefore, scenario # 5 could not be eliminated.
Stressor Identification Guidance Document

Chapter 6: Presumpscot River, Maine 6-11

LIST CANDIDATE CAUSES

ANALYZE EVIDENCE

CHARACTERIZE CAUSES

Stressor Identification

Identify Probable Cause

Eliminate Diagnose Strength of Evidence

LIST CANDIDATE CAUSES

ANALYZE EVIDENCE

CHARACTERIZE CAUSES

Stressor Identification

Identify Probable Cause

Eliminate Diagnose Strength of Evidence

8. Habitat Degradation caused by Impoundment - Again, the biological impairment

found downstream of the paper mill discharge coincided with the presence of an

impoundment (Table 6-3). However, no measurements of habitat quality were available

to determine if the biological impairment was the result of habitat loss caused by the

impoundment. Therefore, scenario #8 could not be eliminated.

Following the process of elimination, 4 candidate causes remained:

1. Excess toxic chemicals.

3. High TSS with floc causes smothering.

5. Impoundment increases sedimentation that smothers biota (with or without

 discharge of TSS and floc).

8. Impoundment causes loss of

 suitable habitat.

6.5 Analyze Evidence and Characterize Causes: Strength of Evidence

Direct observations in the Presumpscot River

during macroinvertebrate and fish tissue sampling

revealed a heavy suspended and settled solids load. 

Samplers and gill nets were coated with flocculent

fibers and water clarity was dramatically reduced.

In comparison to other paper mills in the State, the

pulp and paper mill effluent released to the

Presumpscot was considered high strength for

solids. However, the conditions faced on the

Presumpscot were similar to those found below the

discharge from another paper mill on the

Androscoggin River in Jay, Maine. Because of this,

observations in the vicinity of the paper mill on the

Androscoggin River were used to support the

evidence found for this case study.

A comparison of the two rivers and discharge loadings to each is given in Table 6-4. 

Paper mill discharges on both rivers were subject to impoundments with similar

hydraulic properties (e.g., velocity and depth) and background TSS concentrations

(about 3 ppm). Two or more dams impounded both rivers upstream of the discharges. 
Stressor Identification Guidance Document

6-12 U.S. Environmental Protection Agency

Table 6-3. Considerations for eliminating candidate causes.

Candidate

Cause

Impairments

occur same

place as

exposure?

Exposure

increased

over

closest

upstream

location?

Gradient of

recovery at

reduced

exposure?

Exposure

pathway

complete?

Candidate

Causes

Remaining

Toxic

Chemicals

NE NE NE NE X

BOD

(produced by

TSS) reduces

DO

BOD Yes;

TSS Yes;

DO No

BOD Yes;

TSS Yes;

DO No

NE No

TSS with floc Yes Yes NE Yes X

Nutrients and

algal growth

Nutrients

Yes;

Algal Yes

Nutrients 

Yes;

Algal No

NE No

Impoundment

increases

sediment

Yes NE NA NE X

Impoundment

promotes

algal growth

Algal Yes;

DO No

Algal No;

DO No

NA No

DO reduction

in

Impoundment

Imp. Yes;

DO No

Imp. Yes;

DO No

NA No

Habitat

degradation

caused by

impoundment

Yes NE NA NE X

Table 6-4. Comparison of TSS loadings in the Presumpscot and Androscoggin Rivers. 

(Sample points were located below a pulp and paper mill effluent discharge.)

Presumpscot Androscoggin

Mill & Year 

Sampled

1995 1996 1995 1996 1997

Aquatic Life Status NonAttainmentNonAttainmentNonAttainmentAttainment Attainment

TSS treatment none none none TSS 

removal

TSS

removal

Sampling Months June-Aug Aug-Sept June-Aug Aug-Sept June-Aug

Flow, cubic

feet/second (cfs)

418 463 2114 2982 4116

TSS Discharged,

pounds/day

7454 8795 19804 5750 13495

TSS discharged/flow 3.31 3.52 1.74 0.36 0.61
Stressor Identification Guidance Document

Chapter 6: Presumpscot River, Maine 6-13

Moreover, the upstream impoundments on both rivers attained at least Class C aquatic

life standards. However, both rivers were found to be in non-attainment of aquatic life

standards downstream of the paper mill discharges in 1995. Calculated mean ambient

concentrations of TSS in the Presumpscot downstream of the mill were 32% to 39%

greater than ambient levels downstream of the mill on the Androscoggin River. For the

most part, the incremental TSS increase on the Androscoggin River, due to paper mill

discharges, was within 1 ppm of background, while on the Presumpscot, the mill

discharge was about 3 ppm greater than background.

In 1996, efforts were made by another paper mill on the Androscoggin River to reduce

TSS discharge into the Androscoggin River. Following these efforts, the site’s

biological score improved and the river met Class C aquatic life standards. This

recovery of biological conditions following TSS reduction provided experimental

evidence that TSS could also be the cause of ecological stress in the Presumpscot River. 

Table 6-6 summarizes the types of evidence weighed in the analysis of potential stressors

in the Presumpscot River.

Other evidence used in the strength of evidence comparison is shown in Table 6-5. 

Some metals exceeded chronic criteria when the maximum concentration in the effluent

was evaluated with a low flow scenario (Table 6-5). Although low DO was eliminated in

the previous step of this case study. Maine DEP performed an extensive modeling effort

to investigate the potential for low DO below the mill outfall. The modeling results

supported the conclusion that the DO concentrations did not fall below minimum levels

for Class C aquatic life uses (Mitnik 1998). Furthermore, during the same time period as

the biological monitoring, there were not violations of criteria for DO.

Table 6-5. 1996 - 1999 metal concentrations in the pulp and paper mill effluent.

Metals Range g/L 

in Effluent Grab

Samples

1996-1999

Maximum

Receiving Water

Concentration

(g/L) at Low

Flow1

 (7Q102)

Chronic

Criteria

(g/L)

Acute

Criteria

(g/L)

Aluminum 108 - 1920 207.9 87 750

Lead 3 - 14 1.52 0.41 10.52

Mercury 0.0001 - 0.9 0.097 0.012 2.4

Silver 10 1.083 0.12 0.92

Notes

1 The receiving water concentration is calculated from the maximum effluent concentration divided

by a dilution factor of 9. 

2 7Q10 + 7-day low flow over a ten year period.
6-14 U.S. Environmental Protection Agency Table 6-6: Strength of evidence of non-attainment in the Presumpscot River. Consideration TSS with Floc Toxic Compound Impoundment increases Sedimentation Im

Results Score Results Score Score 

Case-Specific Evidence: Spatial Co-occurrence Compatible: Non- attainment observed in area of high TSS and floc loading. Attainment observed in upstream areas without TSS loading. + Evidence unavailable. NE Uncertain: Non- attainment observed in area of impoundment, but no measurements of sedimentation were available. 0 Un

attin imobhaav

Temporality No observations prior to paper mill discharge. NE No observations prior to paper mill discharge. NE No observations prior to impounding NE No 

to 

Consistency of Association No evidence NE No evidence NE A site within the same impoundment, upstream of the mill met aquatic life uses. _ A s

saupme

Biological Gradient No evidence NE No evidence NE Not Applicable NA No

Complete Exposure Pathway Evidence for all steps: High TSS and floc discharge into river well-documented. ++ No evidence NE No evidence NE No 

Experiment No evidence NE No evidence NE No evidence NE No 
Chapter 6: Presumpscot River, Maine 6-15 Table 6-6 (continued): Strength of evidence for causes of non-attainment in the Presumpscot River. Consideration TSS with Floc Toxic Compound Impoundment increases Sedimentation Im

Results Score Results Score Score 

Information from Other Situations or Biological Knowledge: Plausibility - Mechanism Plausible: Snails and worms are adapted to utilization of settled solids. + Plausible: Toxic compounds could alter community composition. + Plausible: Sediment could alter habitat and community composition. + Pla

hacoco

Plausibility - Stressor- Response TSS response from Androscoggin study and modeling sufficient to cause impairment. ++ Ambiguous: Assuming low flow conditions and at the highest concentrations reported for effluent from the mill, chronic aquatic life criteria might be exceeded for aluminum, lead, mercury and silver. However, if we assume high flows at the time of sampling then neither acute nor chronic aquatic life criteria are likely to be exceeded. 0 Other impoundments with similar potential sediment loadings support diverse invertebrate communities. _ Ot

witsuinvco

Consistency of Association Invariant: Other sites on other rivers with TSS have impaired biological communities. +++ In some places: Possibly could cause effects if at maximum values most of the time, but unlikely 0 Other impoundments on other rivers are not impaired. _ Ot

on no

Specificity of Cause Low: Other causes elicit similar responses. 0 Low: Other causes elicit similar responses. 0 Low: Other causes elicit similar responses. 0 Lo

elires

Analogy No evidence NE No evidence NE No evidence NE No 

Experiment Concordant: Removal of TSS in the Androscoggin river improved invertebrate assembleges. +++ No evidence NE No evidence NE No 
6-16 U.S. Environmental Protection Agency Table 6-6 (continued): Strength of evidence for causes of non-attainment in the Presumpscot River. Consideration TSS Toxic Compound Impoundment increases Sedimentation Im

Results Score Results Score Score 

Predictive Performance No evidence NE No evidence NE No evidence NE No 

Considerations Based on Multiple Lines of Evidence: Consistency of Evidence All Consistent. +++ Not consistent: data collected during the same time period as the biological monitoring indicated that there were no violations of criteria for toxic materials (Mitnik 1998). 0 Not consistent: Other sites with impoundments maintained diverse communities. 0 No

Otimmaco

Coherence of Evidence Could be due to unmeasured chemical or episodic exposure. 0 No known explanation. 0 No 

ex
Stressor Identification Guidance Document

Chapter 6: Presumpscot River, Maine 6-17

Eliminate Diagnose Strength of Evidence

CHARACTERIZE CAUSES

Identify Probable Cause

6.6 Characterize Causes: Identify Probable Cause

Following the process of elimination, four

causal scenarios remained to compare for

strength of analysis (Table 6-6). These

scenarios were: #1 (excess toxic

chemicals), #3 (high TSS with floc causing

smothering), #5 (impoundment increasing

sedimentation that smothers biota, with or

without discharge of TSS and floc), and #8

(impoundment causing loss of suitable

habitat).

The evidence supporting scenario #3, that non-attainment was due to high TSS loads

combined with floc, was consistent throughout the lines of evidence. Moreover, the

strength of association, spatial co-occurrence, plausible stressor-response and experiment

lines of evidence strongly supported this scenario. Therefore, high TSS with floc was

sufficient for causing the biological impairment. The quality of the data are adequate for

this conclusion, and our confidence is high.

In contrast, evidence for the toxicity scenario was weak, because the stressor-response

association was unlikely based on levels of chemicals in the effluent and the likely

dilution provided by the river at the time of discharge. If greater certainty was required,

ambient receiving water toxicity tests could be used. 

Likewise, evidence for the candidate causes involving impoundments lacked field

measurements of sedimentation and habitat quality. However, our confidence in

rejecting these scenarios as the primary cause of impairment is strengthened by the fact

that several upstream sites along the Presumpscot River were impounded with no

associated biological impairment (Mitnik 1998, Davies et al. 1999), and within the same

impoundment upstream from the mill, the Presumpscot met aquatic life uses. 

Furthermore, several other impounded rivers of the state are able to meet Class B and C

biological criteria (Davies et al. 1999).

Nutrient levels were elevated; however, the algal concentration was not different from

the nearest upstream sampling location. As a result, candidate cause # 4, excess

nutrients, was eliminated; however, it is possible that the growth of algae was inhibited

by other factors, such as shading from floc. If floc were removed, then effects due to

eutrophication might become evident.

Low dissolved oxygen was also eliminated based on spatial patterns of DO along the

river. Other data is also available that increases the confidence that could have been

presented in a strength of evidence analysis. At the site, DO was not below 6 ppm. The

minimum DO level for Class C waters is 5 ppm. Maine DEP also performed an

extensive modeling effort to investigate the potential for low DO below the mill outfall. 

The modeling results supported the conclusion that the DO concentrations did not fall

below minimum levels for Class C aquatic life uses (Mitnik 1998). 
Stressor Identification Guidance Document

6-18 U.S. Environmental Protection Agency

6.7 Significance and Use of Results

In December 1998, the U.S. Environmental Protection Agency approved a Total

Maximum Daily Load (TMDL) finding, prepared by Maine Department of

Environmental Protection, for the Presumpscot River. This approval was significant for

several reasons:

1. It was the first TMDL that addressed a listed 303(d) water to be approved in

Region 1 USEPA (the New England States);

2. It was the first time in New England that bioassessment findings had served as

the quantitative response variable from which a pollutant discharge limit was

developed. 

The wastewater discharge license that has resulted from this effort requires an initial

30% reduction in the TSS discharge from a pulp and paper mill in Westbrook. 

Provisions are included in the license for further reductions (up to 61%) if the initial

levels still fail to provide for attainment of aquatic life standards. 

Main Factors Influencing Success

The Department was able to apply this innovative approach to improving water quality

and aquatic life conditions in the Presumpscot River because of the convergence of

several factors:

 The State has a sound legal basis for use of biological monitoring findings to

force action. Clearly defined aquatic life standards exist in the Water Quality

Classification law and technically-defensible numeric criteria have been

established by the Department;

 Data essential to the modeling of the recommended total suspended solids load

reductions on the Presumpscot River had been collected to assess aquatic life

issues on the Androscoggin River (under State requirements for a 401 Water

Quality certification for a hydropower license renewal);

 Teamwork and collaboration between DEP, water quality modelers, and aquatic

biologists resulted in an approach that integrated technical information and

expertise from both disciplines. It also provided a means for the Department to

control a stressor (TSS) for which the State has no standards.

6.8 References

Courtemanch, D.L., P. Mitnik, and L. Tsomides. 1997. Dec. 8, Memorandum to Greg

Wood, Maine Department of Environmental Protection Licensing Section, Augusta,

Maine.

Davies, S.P., L. Tsomides, D.L. Courtemanch, and F. Drummond. 1995. Maine

biological monitoring and biocriteria development program. Maine Department of

Environmental Protection, Augusta, Maine.
Stressor Identification Guidance Document

Chapter 6: Presumpscot River, Maine 6-19

Davies, S.P. and L. Tsomides. 1997. Methods for biological sampling and analysis of

Maine’s inland waters. DEP-LW/07-A97. Maine Department of Environmental

Protection, Augusta, Maine.

Davies, S.P., L. Tsomides, J.L. DiFranco, and D.L. Courtemanch. 1999. Biomonitoring

retrospective: Fifteen year summary for Maine rivers and streams. DEPLW1999-

26. Maine Department of Environmental Protection, Augusta, Maine.

Hilsenhoff, W.L. 1987. An improved biotic index of organic stream pollution. Great

Lakes Entomol. 20:31-39.

Mitnik, P. 1994. Presumpscot River waste load allocation. Maine Department of

Environmental Protection, Augusta, Maine.

 . 1998. Presumpscot River supplemental report to waste load allocation. Maine

Department of Environmental Protection, Augusta, Maine.
Stressor Identification Guidance Document

Chapter 7: Little Scioto River, Ohio 7-1

7.1 Executive Summary

This case study of the Little Scioto River represents an application of the SI process to a

complicated system. Impairment of the Little Scioto River reflected several impacts

caused by different stressors. Originally, the data on the Little Scioto were collected and

analyzed as part of the Ohio Environmental Protection Agency (OEPA) state monitoring

program during 1987, 1991, 1992 and 1998 (OEPA 1988b, 1992, 1994, unpublished data

from 1998) and as research for a USEPA methods development program. The

monitoring data were subsequently analyzed for this SI case study to demonstrate how

data collected from monitoring programs could be used to identify probable causes of

biological impairment.

The SI investigation was initiated because criteria in the state of Ohio’s water quality

standards were violated in parts of the Little Scioto, a small river in north-central Ohio

(Yoder and Rankin 1995b). The SI investigation involved a 9-mile stretch of the Little

Scioto River near Marion, Ohio, where there was evidence of biological impairment.

The State of Ohio has a “tiered” set of aquatic life use designations based on narrative

definitions of specific aquatic uses that are protected by a set of numeric biocriteria,

chemical criteria, and habitat criteria. Ohio EPA determines biological impairment of

stream segments by comparing study sites to the numeric biocriteria in their water

quality standards. OEPA uses standard multimetric indices, including the Index of

Biotic Integrity (IBI), the Invertebrate Community Index (ICI) (OEPA 1989a), and the

Qualitative Habitat Evaluation Index (QHEI) (OEPA 1989c). Little Scioto River data

collected in 1987 and 1992 showed a condition of “fair” to “severe impairment” in the

stretch from river mile (RM) 9.2 to where the Little Scioto joins the Scioto River, just

downstream of RM 0.4.

Describe the Impairment

Three distinctive impairments (A, B, and C) were identified for the causal evaluation (at

RM 7.9, 6.5, and 5.7, respectively). Impairment A was characterized by a loss of fish

and benthic invertebrate species, a decrease in the number of individual fish, and an

increase in the relative weight of fish. Impairment B was characterized by a decrease in

the relative weight of fish and a large increase in deformities, fin erosion, lesions, tumors

and anomalies (DELTA). Impairment C was characterized as having a further increase

in DELTA and extirpation of a Tribe of midges, the Tanytarsini. 

List Candidate Causes

Stressors impacting the upper portion of the river were identified as mostly non-point

nutrient and sediment loadings associated with agriculture. Beginning at river mile 9.0

and continuing to the mouth, the river is channelized. The Little Scioto River at and

below Marion, Ohio, however, has been notably contaminated with elevated levels of

polycyclic aromatic hydrocarbons (PAH). Creosote and metals in sediment samples and

Chapter 7

Little Scioto River, Ohio
Stressor Identification Guidance Document

7-2 U.S. Environmental Protection Agency

ammonia, phosphorous (P), total nitrogen (N) were detected in water samples (OEPA

1994). 

Based on the knowledge about the site and effects, six candidate causes were

hypothesized to account for the three major biological impairments observed in the Little

Scioto study area:

1. Habitat alteration: embedded stream and deepened channel

2. Exposure to PAHs

3. Metal contamination

4. Ammonia Toxicity

5. Low Dissolved Oxygen/High Biological Oxygen Demand

6. Nutrient Enrichment

Characterize Causes: Eliminate

Candidate causes were eliminated because the level of exposure to the candidate cause

did not increase compared to the nearest upstream location. Candidate causes that

remained after the elimination step are listed below:

 Impairment A (RM 7.9) — habitat alteration, metal contamination, and nutrient

enrichment remained as probable causes.

 Impairment B (RM 6.5) — PAH contamination, metal contamination, ammonia

toxicity, low dissolved oxygen/high biological oxygen demand, and nutrient

enrichment remained as probable causes.

 Impairment C (RM 5.7) — metal contamination, ammonia, and nutrient

enrichment remained as probable causes.

Characterize Causes: Diagnose

No evidence strong enough to support diagnosis was available for any of the candidate

stressors. 

Characterize Causes: Strength of Evidence

A strength of evidence approach was used to examine the remaining causes with regard

to each impairment. Evidence based on other situations and biological knowledge were

especially important including consistency of association and plausibility of mechanism

and stressor-response.

Characterize Causes: Identify Probable Causes

 

Impairment A

At Impairment A, the increased relative weight is probably caused by the artificial

deepening of the channel that allows larger fish to live there. The mechanisms were
Stressor Identification Guidance Document

Chapter 7: Little Scioto River, Ohio 7-3

probable, and consistency of association and experiments from other sites in Ohio and

elsewhere supported this finding for the specific impairments. The extirpation of fish

and benthic invertebrates seems to be most likely due to embedded substrates. Although

low DO could also be a cause, upstream locations had even lower DO levels and yet had

a greater variety of fish and invertebrate species. 

Although metals were present, the likelihood of response at these concentrations is low. 

Furthermore, the types of changes in the community, especially an increase in the

relative weight of fish, is very unlikely with the candidate cause of metals. Although P

levels are slightly higher, effects are not associated with these phosphorous

concentrations elsewhere, and they do not exceed Ohio proposed criteria values for

effects. PAH and ammonia had already been eliminated because levels were the same or

lower than upstream. Low DO /BOD was also eliminated as an overall pathway;

however, low DO associated with channelization may still play a role, especially with

respect to the slight increase in the percentage of DELTA.

Impairment B

A single probable cause, toxic levels of PAH-contaminated sediments, is likely for the

three manifestations of Impairment B: decreased relative weight, increased DELTA, and

decreased species. All of the evidence support PAH contamination as the cause. There

is a complete exposure pathway at the location, and a clear mechanism of action for each

of the effects. The single most convincing piece of evidence is that the cumulative toxic

units of PAH were more than 300 times the probable effects level. 

Metals are at sufficient concentrations to cause effects; however, they are at levels close

to upstream concentrations, and are less than 2% as toxic as the lowest cumulative toxic

units of PAH. Metal concentrations are high enough that they should be considered a

potentially masked cause. Reduced DO resulting from increased BOD is unlikely

because, downstream, even greater levels of BOD did not cause reduction of dissolved

oxygen. Ammonia and nutrient enrichment are unlikely given that state criteria levels

were met and given the much stronger evidence for PAH. Habitat alteration continues to

impair the site, but it is not the cause of the increased DELTA, decreased relative weight,

or the additional decline in the number of species, because the level of embeddedness

was similar to upstream.

Impairment C

At Impairment C, increased % DELTA and % Tanytarsini may have different causes. 

Increased DELTA in fish is probably caused by increased P and N. Nutrients, especially

P, have been associated with increased fin erosion and lesions, but some uncertainty

exists since P acts indirectly. Another candidate cause is also probable, namely,

ammonia. Ammonia is slightly higher at Impairment C than at Impairment B, and

exceeded ammonia criterion values. Biological gradients were absent for ammonia;

however, this may have been a statistical artifact given the number of sites available to

perform the analysis, and the potential interference from other stressors downstream. 

Metals are considered unlikely, because very specific surface lesions are only

occasionally noted as effects from long-term exposure, and only some metal

concentrations were slightly greater than at Impairment B. Metal concentrations are high

enough that they should be considered a potentially masked cause.

The probable cause of extirpation of Tanytarsini at Impairment C is more uncertain

because less is known about the natural history and stressor-response relationships of
Stressor Identification Guidance Document

7-4 U.S. Environmental Protection Agency

these benthic invertebrates. Nutrient enrichment still seems to be the most likely cause

since all of the strength of evidence considerations were consistent.

PAH contamination and habitat alteration continue to impair the site, but they are not the

cause of the increased percent DELTA or extirpation of Tanytarsini.

Identify Probable Cause

The most probable causes were:

 Impairment A (RM 7.9) — Siltation and deepened channel are consistent with

impairment A. The magnitude of the alteration and clear difference from

upstream locations strongly support this cause. 

 Impairment B (RM 6.5) — PAH-contaminated sediments are likely causes for

the three manifestations of Impairment B. 

 Impairment C (RM 5.7) — The causal characterization at Impairment C is less

certain, but the strength of evidence favors increased nutrient enrichment as the

cause.

The Little Scioto case study is a good example of a complex system requiring a detailed

analysis. Although it was possible to identify the dominant causes of specific

impairments, other causes were present that had the potential to cause impairments if the

dominant cause was removed. For instance, habitat alteration associated with

channelization would still impair the entire river below RM 9.0. 

7.2 Introduction

The Little Scioto case study involves a nine-mile stretch of a river suffering from several

impairments with different causes. Typical of similar stressor investigations, the data

examined for this case study were not collected or originally analyzed specifically for the

Stressor Identification Technical Guidance Document. Rather, they were collected as a

part of the Ohio EPA state monitoring program during 1987, 1991, 1992 and 1998

(OEPA 1988b, 1992, 1994, unpublished data from 1998), and as research for a USEPA

methods development program. These monitoring data were subsequently analyzed in

this study to demonstrate how data collected from existing monitoring programs could be

used to identify probable causes of biological impairment.

Various types of data were used in this case study, including chemical analyses

(sediment, water, and fish tissue) and biological assessment (biological community and

physical habitat). Methods for the collection and analysis of chemical data are described

in Ohio EPA (1989c). In 1992, one grab sample was taken, whereas in 1987, multiple

grab samples were taken. Other Ohio EPA data sets included biological assessment data

on fish and invertebrate assemblages and physical habitat measurements. In Ohio,

impairment of stream aquatic life uses are defined by standard multimetric indices

including the Index of Biotic Integrity (IBI) and the Invertebrate Community Index (ICI)

(OEPA 1989a). These indices have been promulgated as numeric biocriteria in the

State’s water quality standards. The quality of the habitat is characterized using the

Qualitative Habitat Evaluation Index (QHEI) (OEPA 1989c). These methods are

described in detail by Ohio EPA (1989c). Biochemical measurements of impairment

included bile metabolites measured according to Lin et al. (1996) and ethoxy
Stressor Identification Guidance Document

Chapter 7: Little Scioto River, Ohio 7-5

resorufin[O]deethylase (EROD) activity measured according to Cormier et al. (2000b). 

Although the attempt was made to use biological and chemical data from the same

locations, in some cases, chemical measurements were recorded at a location that did not

exactly coincide with the location of biological assessment (e.g., RM 5.8 and RM 5.7,

respectively). However, the distance between the chemical and biological sample sites

was negligible or overlapped, and the data were able to be used to analyze associations

between candidate causes and the biological impairment.

The Little Scioto River is a small river in north-central Ohio that empties into the Scioto

River (Figure 7-1). It drains primarily farmland in the northeastern quadrant of the

Eastern Corn Belt Plains ecoregion. The soils in this area are glacial till overlying

limestone, dolomite, and shale bedrock. The water table has been lowered in much of

the watershed by extensive use of tile drainage in crop fields. Near Marion, Ohio, the

Little Scioto is biologically impaired.

This causal investigation was initiated because the State of Ohio water quality standards

related to biological criteria were violated (Yoder and Rankin 1995a). The State of Ohio

has a “tiered” set of aquatic life use designations based on narrative definitions of

specific aquatic uses, which are protected by numeric criteria. 

The majority of Ohio rivers and streams are designated as Warmwater Habitat (WWH)

(Yoder and Rankin 1995a). This designation is narratively defined as supporting a

balanced, reproducing aquatic community. Quantitatively, the minimum criteria

required to be in attainment of WWH standards are defined as the 25th percentile values

of reference condition scores for a given index, site type, and ecoregion. The choice of

the 25th percentile is considered to be conservative and will likely be influenced by the

inclusion of marginal sites as well as reference quality sites. 

The Little Scioto River is considered Warmwater Habitat above RM 7.9 and a Modified

Warmwater Habitat at and below RM 7.9 (see Figure 7-1). The Modified Warmwater

Habitat (MWH) criteria are based on comparisons to a different reference condition than

are used for the WWH criteria (Yoder and Rankin 1995a). The MWH designation is a

non-fishable aquatic life use, and is designed to protect streams that have been too

impacted, or modified, to meet WWH standards. MWH streams are unlikely to recover

sufficiently to meet WWH designation. Consequently, MWH criteria are typically lower

than WWH criteria. In spite of poorer water quality conditions (such as low dissolved

oxygen, high ammonia concentration, and increased nutrient input), MWH streams are

nonetheless able to support permanent assemblages of tolerant species. 

7.3 Evidence of Impairment

In 1987 and 1992, sampling and measurements for community and habitat indices (IBI,

ICI, QHEI) were conducted by OEPA along the Little Scioto River. Standardized field,

laboratory and data processing methods followed OEPA procedural guidelines (OEPA

1988a, OEPA 1989a,b,c, Rankin 1989). Fish and macroinvertebrates were sampled at

seven sites along the river, from river mile (RM) 9.5 to 0.4 (Figure 7-1). Index and 

metric scores for IBI, ICI, and QHEI used in this study were obtained from data sets that

were generated and made available by OEPA as well as various OEPA reports (1988b,

1992, and 1994).
Stressor Identification Guidance Document

7-6 U.S. Environmental Protection Agency

Figure 7-1. Map of the Little Scioto River, Ohio, showing sites where fish were

sampled. (Approximate locations of significant physical features, tributaries and point

source inputs are noted. The small inset shows the location of the study area in the

state of Ohio. Locations of Impairments A, B and C are also shown.)

Of the seven sites sampled in 1987, the highest IBI score was 34 (out of a possible score

of 60), which occurred at RM 9.2. This score translates to a fair ranking according to

WWH standards. The remainder of sites were described as severely impaired, with IBI

scores between 25 and 12 (the lowest possible IBI score) (OEPA 1994, Yoder and

Rankin 1995a). In 1992, the IBI score at RM 9.2 decreased by one point to 33. 

However, in 1992, the IBI score dropped 9 points to a score of 24 between RM 9.2 and
Stressor Identification Guidance Document

Chapter 7: Little Scioto River, Ohio 7-7

Figure 7-2. Spatial changes in fish IBI (A) and benthic macroinvertebrate ICI (B)

values in the Little Scioto River in 1987 (OEPA 1988) and 1992 (OEPA 1994).

RM 7.9. Another 5 point drop occurred at RM 6.5 and scores stayed between 19 and 20

through RM 2.7. At RM 0.4, the IBI score climbed back to 25, greater than the adjacent

upstream site’s score, but still indicating impairment. Figure 7-2A illustrates the

fluctuation of the IBI at the seven sites during the two sampling years (1987 and 1992).

Figure 7-2B traces a similar pattern of impairment for the invertebrate index during the 

1987 and 1992 sampling years. The ICI met WWH aquatic life use standards in 1987

and 1992 at RM 9.2, with scores of 40 and 38, respectively (Figure 7-2). In 1992, the ICI

score declined 22 points at RM 7.9 with a score of 16, considered fair, but below MWH

aquatic life use standards. Scores further declined 12 or more points at RM 6.5, 5.7 and

4.4, with scores ranging between 6 and 10. These scores are indicative of highly

impaired conditions (OEPA 1994, Yoder and Rankin 1995a). ICI scores increased to a

value of 18 downstream at RM 2.7 and RM 0.4. In 1987, both IBI and ICI scores were

greater at RM 6.5 and then declined at RM 5.7, and remained very low to the mouth of

the Little Scioto.
Stressor Identification Guidance Document

7-8 U.S. Environmental Protection Agency

The impairments seen below RM 9.2 were more specifically described by examining the

metrics that make up the IBI and the ICI. This information was combined with the

changes seen in the overall IBI and ICI scores to determine whether distinctive patterns

of impairment could be identified. Each distinctive impairment required a separate

causal evaluation.

A subset of the fish and macroinvertebrate metrics, selected to highlight differences in

community patterns, is shown in Figures 7-3A (fish) and 7-3B (macroinvertebrates). The

complete list of values for the metrics is shown in Tables 7-13 and Table 7-14 (Please

note that Tables 7-13 through 7-20 are located in Section 7.13, “Additional Data

Tables”). One of the metrics, relative weight of fish, is not a component of the IBI but a

component of another index, the Modified Index of Well-being (MIWB).

Examination of the spatial distribution of the IBI, ICI, and metric patterns in 1992

indicates that at least three distinct impairments occurred:

 Impairment A was seen at RM 7.9 where a marked drop in both the IBI and ICI

occurred relative to the upstream location at RM 9.2. Specific fish metrics that

appeared to correspond to this drop included decreases in the number of

individuals minus tolerant fish, decreased total number of species, and increased

relative weight. In addition, the percentage of mayfly species decreased. 

 Impairment B occurred at RM 6.5 and corresponded with an additional decrease

in both the IBI and the ICI. Relative the upstream location at RM 7.9, fish

relative weight decreased, the number of deformities, erosions, lesions, tumors

and anomalies (DELTA) increased, and the percentages of mayflies and

Tanytarsini midges also decreased while the percentage of tolerant organisms

increased.

 Impairment C occurred at RM 5.7. There was no change in the IBI relative to

RM 6.5, although relative weight of fish decreased and DELTA increased. The

invertebrates had variable changes depending on the sampling year. In 1987 and

1992, the % Tanytarsini midges decreased or disappeared entirely. Changes in

the metrics at these three locations are summarized in Table 7-1.

The biological assessment data for the remaining locations showed a pattern similar to

Impairment C, with the possibility of intensification at RM 4.4 and some improvement in

metric scores occurring at RM 2.7 and 0.4. A fourth impairment was not hypothesized

for RM 4.4 because the pattern of fish and invertebrate metrics were fairly similar to

those seen at RM 5.7. 
Stressor Identification Guidance Document

Chapter 7: Little Scioto River, Ohio 7-9

Figure 7-3. Changes in the IBI and ICI scores over distance in the Little Scioto

River, 1992. ((A) Changes in the relative scores for the total number of individual

fish minus tolerant fish (# fish minus tolerant), the number of species (# species),

the relative weight of fish (relative weight) and the percentage of DELTA. (B)

Changes in the relative abundances of percent Ephemeroptera, Tanytarsini,

tolerant organisms, and Cricotopus, in the Little Scioto River. Normalized values

were calculated by dividing the value at the individual site by the highest value for

all sites.)
Stressor Identification Guidance Document

7-10 U.S. Environmental Protection Agency

LIST CANDIDATE CAUSES

ANALYZE EVIDENCE

CHARACTERIZE CAUSES

Stressor Identification

Identify Probable Cause

Eliminate Diagnose Strength of Evidence

LIST CANDIDATE CAUSES

ANALYZE EVIDENCE

CHARACTERIZE CAUSES

Stressor Identification

Identify Probable Cause

Eliminate Diagnose Strength of Evidence

Table 7-1. Summary of the three impairments that were considered in the Little

Scioto River. (Each location is scored relative to the location immediately upstream,

based on 1992 data.)

Response

Impairment A

RM 7.9

Impairment B

RM 6.5

Impairment C

RM 5.7

Fish

 # of individuals minus

tolerant individuals

-+ -

# Species - - 0

Relative Weight 7.9 - -

DELTA 0 0 5.7

Invertebrates

% Mayflies - - -

% Tanytarsini midges 0 - - 

% Tolerant taxa 0 0 -

% Cricotopus sp. - 0 0

(+) indicates an increase in the metric relative to the next upstream location

(-) indicates a decrease

(0) indicates no change.

7.4 List Candidate Causes

Evidence Used to Develop Candidate Causes

Many point and non-point sources of pollutants are

associated with the Little Scioto River. Stressors

impacting the upper portion of the river are mostly

non-point nutrient and sediment loadings associated

with agriculture. However, the Little Scioto River,

at and below Marion, Ohio, has been notably

contaminated with elevated levels of polycyclic

aromatic hydrocarbons (PAH). Creosote and metals

were found in sediment samples, and ammonia was

detected in water samples (OEPA 1994). The

OEPA has, in fact, recently requested Superfund

support in the clean-up of an abandoned wood

creosote plant suspected of polluting the river since

the 1860's (Edwards and Riepenhoff 1998). An oily

sheen was noted on the river between river miles 6.5 and 5.8 during a site visit in 1992

(Cormier, pers. observ.). In-stream habitat quality was also degraded by channelization

that took place in the early 1900's (OEPA 1994). Locations of the potential sources and

stressors, including a landfill and wastewater treatment plant (WWTP), are shown in

Figure 7-1.
Stressor Identification Guidance Document

Chapter 7: Little Scioto River, Ohio 7-11

List of Candidate Causes and Scenarios

As noted previously, three distinctive impairments were identified for the causal

evaluation. Based on the knowledge of the sources and effects, six candidate causes

were formulated to account for the impairment observed at each site. A conceptual

model of these candidates is provided in Figure 7-4.

1. Habitat Alteration - Habitat alteration, resulting from channelization, combines a

complex interaction of several stressors. These stressors are evident at RM 7.9 and

continue to the mouth of the river. Channelization can alter biological communities by

changing the physical structure of the stream and the flow characteristics of the water,

ultimately lowering dissolved oxygen, increasing siltation, and reducing substrate

complexity. This complex suite of stressors also includes: decreased woody debris,

which reduces available substrate and changes the energy source; decreased sinuosity,

which changes flow characteristics; erosional patterns and substrates; increased channel

depth that favors larger species of fish; loss of pools that act as refugia; and loss of riffles

that oxygenate water and transport sediment (Tarplee et al. 1971, Karr and Schlosser

1977, Yount and Niemi 1990, Allan 1995).

2. PAH and 3. Metals - Biological impairment could also have been caused by toxic

stress. Historically, the river has provided a means of waste disposal for various

industries, whose effluents have contained metals, PAH, and creosote. Waste materials

may have also been buried in the landfill below RM 6.5 (OEPA 1994). All are

potentially toxic to aquatic life, and some have the ability to bioaccumulate through the

food web (Eisler 2000a,b). Thus, two candidate causes emerge: candidate cause #2 is

that biological impairment has occurred due to PAH exposure (with PAH emanating

from creosote deposits), and candidate cause #3 attributes impairment to metal

contamination. 

4. Ammonia Toxicity - Ammonia is directly discharged into streams by point sources

(Russo 1985, Miltner and Rankin 1998). Ammonia can also be formed as the result of

nutrient enrichment. When dissolved oxygen levels are low, nitrates are reduced to

ammonium ion. If pH is high, some of the ammonium ion is converted to un-ionized

ammonia, which is toxic to aquatic organisms (Russo 1985). Moreover, pH may rise

during periods of high photosynthetic rates from bicarbonate depletion. High amounts of

nutrients often lead to increased algal growth rates, and the conversion of ammonium to

un-ionized ammonia is expedited (Dodds and Welsh 2000).

5. Low Dissolved Oxygen/ High Biological Oxygen Demand - Depletion of DO

commonly occurs from organic enrichment (Smith et al. 1999). Organic enrichment is

the most common cause of increased biological oxygen demand (BOD) (Allan 1995). 

Potential sources of excess organic matter within the study area include a waste water

treatment plant (WWTP) and several combined sewer outfalls (CSOs), as well as

upstream, non-point sources. Organic matter is also produced by excess algal growth

from nutrient enrichment (Dodds and Welsh 2000). Algal blooms themselves result in

increased organic matter regardless of DO depletion. The algal bloom may suffice to

raise BOD so that DO is depleted. Because no chlorophyll a or algal biomass data were

collected in this study, the cause of BOD to the river can only be estimated from BOD,

measured at several points, and COD (chemical oxygen demand) measured at point

sources such as the WWTP above RM 5.4 in 1998.
7-12 U.S. Environmental Protection Agency Sources Channelization Landfill Hazardous Waste Site Historical Industrial Discharge Combined Sewer Overflows Municipal Waste Treatment Plant 

Altered stream morphology. decreased wood debris, decreased sinuosity, decreased spatial diversity of flow and depth, decreased do, increased maximum stream flow, increased sediment deposition Increased Toxic Substances. PAHs and Metals Increased Total Ammonia (NH4+ & NH3) Increased Un-ionized Ammonia (NH3) Effects Excess Nutrie

Nitrates Phosp

Organic Carb

Increased BOD 

Reduced Dissolved Oxygen 

Stressors 1 23 4 5 

S

SEVERE Shift in Fish and Benthic Macroinvertebrate Community Structure Sources Channelization Landfill Hazardous Waste Site Historical Industrial Discharge Combined Sewer Overflows Municipal Waste Treatment Plant 

Altered stream morphology. decreased wood debris, decreased sinuosity, decreased spatial diversity of flow and depth, decreased do, increased maximum stream flow, increased sediment deposition Increased Toxic Substances. PAHs and Metals Increased Total Ammonia (NH4+ & NH3) Increased Un-ionized Ammonia (NH3) Effects Excess Nutrie

Nitrates Phosp

Organic Carb

Increased BOD 

Reduced Dissolved Oxygen 

Stressors 1 23 4 5 

S

SEVERE Shift in Fish and Benthic Macroinvertebrate Community Structure Figure 7-4. A conceptual model of the six candidate causes for the Little Scioto stressor identification. (Potential sources a

listed in top most rectangles. Potential stressors and interactions are located in ovals. Candidate causes are numbered 1 through 6. Note that some causes have more than one stressor or more than one step associated with it. The impairments are located in the lower rectangle.)
Stressor Identification Guidance Document

Chapter 7: Little Scioto River, Ohio 7-13

LIST CANDIDATE CAUSES

ANALYZE EVIDENCE

CHARACTERIZE CAUSES

Stressor Identification

Identify Probable Cause

Eliminate Diagnose Strength of Evidence

LIST CANDIDATE CAUSES

ANALYZE EVIDENCE

CHARACTERIZE CAUSES

Stressor Identification

Identify Probable Cause

Eliminate Diagnose Strength of Evidence

0

5

10

15

20

9. 5 7. 9 6. 5 5. 8 4. 4 2. 7 0. 4

0

5

10

15

20

9. 5 7. 9 6. 5 5. 8 4. 4 2. 7 0. 4

1987

1992

Figure 7-5. Selected QHEI metrics for 1987 and 1992. (Scores are qualitative

ranks.) 

6. Nutrient Enrichment - The sixth and final candidate cause is a less extreme form of

nutrient enrichment. Primary production and organic matter loading to the sediments are

increased, but not enough to reduce DO. This can cause changes in fish and benthic

macroinvertebrate assemblages, including changes in dominant species, and greatly

increased abundance and biomass (Carpenter et al. 1988, Rankin et al.1999, Smith et al.

1999, Dodds and Welsh 2000, Edwards et al. 2000). This form of nutrient enrichment is

also associated with fin erosion (Rankin et al. 1999).

7.5 Analyze Evidence to Eliminate Alternatives

7.5.1 Data Analyzed

Habitat alteration-related data 

Data on the spatial location of habitat alteration was

obtained by using the Qualitative Habitat Evaluation

Index (QHEI). The QHEI incorporates measures of

habitat condition and has been correlated with the

IBI. This index uses eight interrelated metrics,

which assess substrate type and quality; in-stream

cover type and amount; channel morphology;

riparian width and quality and bank erosion; pool /

riffle characteristics including depth, current, pool

morphology, substrate stability and riffle

embeddedness; and finally gradient (Rankin 1989). 

Based on these metrics, a total score is assigned to a

stream reach out of a possible 100 points, with

greater scores indicating higher quality. The channel morphology and substrate metrics

are particularly relevant for this case because of the channelization (Figure 7-5). Values

for the QHEI and its component metrics are given in Table 7-15 (see Section 7.13).
Stressor Identification Guidance Document

7-14 U.S. Environmental Protection Agency

Chemical Data 

Data on sediment and in-stream chemistry were used to evaluate the spatial location of

the remaining candidate causes (#2-6). Nutrient concentrations measured in water

included ammonia, nitrates and nitrites (NOX), phosphorus (P), and BOD. Ambient

levels of potential toxic chemicals were determined for sediment and water. Results of

chemical analyses are presented in Tables 7-16, 7-17 and 7-18 (See Section 7.13), and

Figures 7-6, 7-7, and 7-8.

While PAHs were not detectable at the upstream sites (RM 9.5 and 7.9), many PAHs

were detected between RM 6.5 and 0.4 (Table 7-16) (Figure 7-6). Spearman Rank

Correlations between chemical and biological data from 1992 at RM 5.7 to 0.4 are

shown in Table 7-2 through 7-5.

Metals were found in sediments at relatively high concentrations at RM 6.5 and

downstream (Table 7-17; see Section 7.13) (Figure 7-7). These included lead, cadmium,

copper, chromium, zinc, and mercury. Arsenic was relatively high at upstream reference

and study sites. Spearman rank correlations between metals and biological data from

1992 at RM 5.8 to 0.4 are shown in Table 7-3. Strong correlations having the sign that is

consistent with the hypothesis were noted for copper and mercury.

The water quality parameters ammonia, nitrates and nitrites (NOx), and BOD increased

substantially at RM 5.8, and remained elevated. Dissolved oxygen declined at 7.9 and

remained low to RM 0.4 (Table 7-18; see Section 7.13) (Figure 7-8). Spearman rank

correlations of water chemistry and biological endpoints are presented in Table 7-4. 

Percent Tanytarsini are significantly correlated with DO, BOD, NOx and P, and the

negative direction of the slope was consistent with ecological theory. Percent DELTA

was correlated with the same parameters (DO, BOD, NOx and P) but at the 0.8 level,

whereas percent Cricotopus was associated with ammonia and the QHEI.

7.5.2 Associations between Candidate Causes and Effects

The associations between candidate causes and effects were analyzed by combining data

on the location of the three impairments with data on habitat quality and chemical

concentrations in water and sediments. The analyses evaluated whether the candidate

causes and each of the three impairments were spatially co-located, and whether a

gradient in recovery corresponded with a decrease in the candidate cause. These

associations are organized in table format (Table 7-5).

The first objective of the analysis was to determine if there was evidence that the

candidate cause occurred at the same place as the impairment but not where that

particular impairment was absent. Plots of the channel quality and substrate metrics

from the QHEI are shown in Figure 7-5. The chemistry values relevant to each of the

causal scenarios are shown in Figures 7-6, 7-7 and 7-8. Each graph shows the level or

concentration of the parameter. The presence or absence of candidate causes at the

locations of Impairments A, B, and C are summarized in Table 7-5.
Stressor Identification Guidance Document

Chapter 7: Little Scioto River, Ohio 7-15

Figure 7-6. Mean PAH concentrations from the sediment (mg/kg) in the Little Scioto

River 1987-1998. ((o) indicates below detection limit. Absence of bar indicates no data

available.)
Stressor Identification Guidance Document

7-16 U.S. Environmental Protection Agency

Figure 7-6 (continued). Mean PAH concentrations from the sediment (mg/kg) in the

Little Scioto River 1987-1998. ((o) indicates below detection limit. Absence of bar

indicates no data available.)
Stressor Identification Guidance Document

Chapter 7: Little Scioto River, Ohio 7-17

Figure 7-7. Mean metal concentrations from the sediment (mg/kg) in the Little Scioto

River from 1987-1998. (Absence of bar indicates no data available.)
Stressor Identification Guidance Document

7-18 U.S. Environmental Protection Agency

Figure 7-8. Mean water chemistry values from the Little Scioto River from 1987-1998. 

(BOD, NOx, Ammonia, CaCO3, PO4, are all mg/L, Temperature (0

C). DO is also mg/L

and is the minimum value obtained from grab samples for each year. Absence of a bar

indicates no data for that year.)
Stressor Identification Guidance Document

Chapter 7: Little Scioto River, Ohio 7-19

Table 7-2. Spearman rank correlations with selected metrics and the IBI and ICI from

1992 and selected PAHs. (Reflects only values from RM 5.8 to 0.4. Correlations N=4).

Parameter DELTA

% Tanytarsini

Midges % Cricotopus

Anthracene (#2) 0.60 -0.74 -0.20

Benzo[a]anthracene (#2) 0.00 -0.21 -0.40

Benzo[ghi]perylene(#2) 0.00 -0.21 -0.40

Benzo[a]pyrene (#2) 0.00 -0.21 -0.40

Chrysene (#2) 0.80* -0.95* 0.40

Dibenzo[a,h]anthracene

(#2)

-0.21 -0.06 -0.21

Fluoranthene (#2) 0.00 -0.21 -0.40

Fluorene (#2) 0.74 -0.89* 0.11

Naphthalene (#2) 0.26 -0.54 0.26

Phenanthrene (#2) 0.60 -0.74 -0.20

Pyrene (#2) 0.00 -0.21 -0.40

* Correlations above 0.8

Table 7-3. Spearman rank correlations with selected metrics and the IBI and ICI from

1992 and selected metals. (Reflects only values from RM 5.8 to 0.4. Correlations N=4). 

Parameter (Candidate

Cause) DELTA

% Tanytarsini

Midges % Cricotopus

Arsenic (#3) 0.74 -0.89* 0.11

Cadmium (#3) 0.20 0.11 -0.60

Chromium (#3) 0.80* -0.63 0.40

Copper (#3) 1.00* -0.95* 0.20

Lead (#3) 0.40 -0.32 0.80*

Mercury (#3) 1.00* -0.95* 0.20

Zinc (#3) 0.40 -0.32 0.80*

* Correlations above 0.8
Stressor Identification Guidance Document

7-20 U.S. Environmental Protection Agency

Table 7-4. Spearman rank correlations with selected metrics and the IBI and ICI from

1992 and selected water quality and habitat quality measurements. (Reflects only

values from RM 5.8 to 0.4. Correlations N=4). 

Parameter (Candidate

Cause) DELTA

% Tanytarsini

Midges % Cricotopus

Channel Metric (#1) 0.77 -0.82* 0.77

QHEI (#1) 0.20 -0.32 1.00*

Ammonia, N (#4) 0.40 -0.32 0.80*

Dissolved oxygen maximum

(#5)

0.80* -0.95* 0.40

Dissolved oxygen minimum

(#5)

0.60 -0.74 -0.20

BOD (#5) 0.80* -0.95* 0.40

Nitrate-nitrite, N (#4,5,6) 0.80* -0.95* 0.40

Phosphorus, total P (#5,6) 0.80* -0.95* 0.40

* Correlations above 0.8

The second objective was to determine if the cause increased compared to the nearest

upstream location. Statistical analyses were not used to determine an increase because

the power would be very weak due to small sample sizes. Even a small increase was

accepted since it might represent a threshold for the effect (Table 7-5).

The third objective of the analyses was to evaluate whether a gradient in the intensity of

the potential cause corresponded to a gradient of recovery in impairment. The gradient

analysis was conducted only for Impairment C, which was observed at four contiguous

locations (i.e., RM 5.8 to 0.4). The recovery of Impairment B could not be analyzed

since it would be masked by Impairment C. Similarly, any recovery of Impairment A

would be masked by both B and C. The gradients in environmental parameters and the

IBI and ICI were examined visually by comparing Figures 7-2 and 7-3 with Figures 7-5

through 7-8. The IBI and ICI metrics for 1987 and 1992 data are shown in Table 7-13

and Table 7-14, respectively. In addition, Spearman's rank correlations were calculated

using the 1992 data set to relate the biological metrics (shown in Figure 7-3) with each of

the parameters related to the candidate causes. The results of this analysis are shown in

Tables 7-2 through 7-4.

Two metrics are more severe at Impairment C: % DELTA and % Tanytarsini midges

decrease and % Cricotopus increases. Percent DELTA were significantly correlated

with copper and mercury, and moderately correlated with chrysene, chromium, BOD,

nitrate, phosphorous, and maximum DO. The change in tanytarsini midges was

negatively and strongly correlated with chrysene, copper, mercury, BOD, nitrate,

phosphorous, maximum dissolved oxygen, and moderately correlated with fluorene,

arsenic, and the channel metric. The change in % Cricotopus was strongly positively

correlated with QHEI and moderately correlated with lead, zinc, and ammonia.
Stressor Identification Guidance Document

Chapter 7: Little Scioto River, Ohio 7-21

Table 7-5. Evidence for eliminating candidates causes at Impairments A, B, and C.

 Impairment A Impairment B Impairment C

Habitat Alteration (Candidate Cause 1)

Is there exposure at the

same location as the

impairment? 

Yes Yes Yes

Is exposure increased

over the closest

upstream location?

Yes No No

Is there a gradient of

recovery as exposure

decreases?

NA* 

(Gradient in

impairment is

masked by B

and C) 

NA 

 (Gradient in

impairment is

masked by C) 

No 

(Correlation

coefficients have

the wrong signs,

with % DELTA and

% Tanytarsini)

Is the exposure pathway

complete?

Yes Yes Yes

PAH Contamination (Candidate Cause 2)

Is there exposure at the

same location as the

impairment? 

No Yes Yes

Is exposure increased

over the closest

upstream location?

No Yes No

(based on

metabolite values

in fish)

Is there a gradient of

recovery as exposure

decreases?

NA 

(Gradient in

impairment is 

masked by B

and C) 

NA 

 (Gradient in

impairment is

masked by C) 

Inconclusive 

(Mixed results)

Is the exposure pathway

complete?

No Yes Yes
Stressor Identification Guidance Document

7-22 U.S. Environmental Protection Agency

Table 7-5 (continued). Evidence for eliminating candidates causes at Impairments

A, B, and C. 

 Impairment A Impairment B Impairment C

Metal Contamination (Candidate Cause 3)

Is there exposure at the

same location as the

impairment? 

Yes Yes Yes

Is exposure increased

over the closest

upstream location?

Yes

(all metals

greater in some

years)

Yes 

(all metals

greater)

Yes

(copper and zinc

increased)

Is there a gradient of

recovery as exposure

decreases?

NA 

(Gradient in

impairment is

masked by B

and C) 

NA 

(Gradient in

impairment is

masked by C) 

Yes 

(Tanytarsini

midges and %

DELTA are

strongly correlated

with copper and

mercury)

Is the exposure pathway

complete?

 Yes Yes Yes

Ammonia (Candidate Cause 4)

Is there exposure at the

same location as the

impairment? 

Yes Yes Yes

Is exposure increased

over the closest

upstream location?

No Yes Yes

Is there a gradient of

recovery as exposure

decreases?

NA

 (Gradient in

impairment is

masked by B

and C) 

NA 

(Gradient in

impairment is

masked by C) 

NA 

(ammonia

increases below

RM 5.8)

Is the exposure pathway

complete?

No Yes Yes
Stressor Identification Guidance Document

Chapter 7: Little Scioto River, Ohio 7-23

Table 7-5 (continued). Evidence for eliminating candidates causes at Impairments

A, B, and C. 

 Impairment A Impairment B Impairment C

Low Dissolved Oxygen/High BOD (Candidate Cause 5)

Is there exposure at the

same location as the

impairment? 

Yes Yes Yes

Is exposure increased

over the closest

upstream location?

No

(DO is

depressed,

BOD

unchanged)

Yes

 (BOD is two

times greater in

1992, DO slightly

less)

No

 (BOD is elevated,

but DO is greater

than either RM 7.9

or RM 6.5)

Is there a gradient of

recovery as exposure

decreases?

NA

 (Gradient in

impairment is

masked by B

and C) 

NA 

(Gradient in

impairment is

masked by C) 

NA 

(ammonia

increases below

RM 5.8)

Is the exposure pathway

complete?

Yes Yes No

Nutrient Enrichment (Candidate Cause 6)

Is there exposure at the

same location as the

impairment? 

Yes Yes Yes

Is exposure increased

over the closest

upstream location?

Yes Yes Yes

Is there a gradient of

recovery as exposure

decreases?

NA 

(Gradient in

impairment is

masked by B

and C) 

NA 

(Gradient in

impairment is

masked by C) 

Yes

(% Tanytarsini and

% DELTA are

strongly correlated

with NOx and Total

P)

Is the exposure pathway

complete?

Yes Yes Yes 

NA* = not applicable
Stressor Identification Guidance Document

7-24 U.S. Environmental Protection Agency

7.5.3 Measurements Associated with the Causal Mechanism: Exposure Pathways

The exposure pathways are shown in Figure 7-4. Lines of evidence for each exposure

pathway are discussed below and are summarized in Table 7-11. To refute an

hypothesis, a step in the pathway must be absent.

Habitat Alteration (#1) - Channelization results in a constellation of stressors, including

loss of riffles with increased sediment deposition, and decreased DO. The QHEI metrics

can yield insights into specific changes: for example, riffle scores are zero throughout

the channelized portion of the stream (Table 7-15; see Section 7.13), substrate quality

and embeddedness due to fine sediment drops at RM 7.9, and DO also drops at RM 7.9. 

The co-occurrence of macroinvertebrates with changes in physical structure may be

somewhat lessened, because Hester-Dendy samplers create an artificial solid substrate

for colonization. The ICI score does include a qualitative kick net sample that is

independent of the artificial substrates. The exposure pathway for habitat alteration is

complete for Impairments A, B, and C.

PAHs (#2) - Exposure to PAHs involves two steps: direct contact with external tissues

and uptake into the organism. Because the PAH information in this case is from the

sediments, we assume that fish and benthic invertebrates between river miles 6.5 and 0.4

will contact this contamination. Concentrations of PAH in the sediment were used only

from samples collected in 1992, as it was the only year in which we were confident that

the samples were collected from the top six inches. It is unlikely that fish or

invertebrates would be exposed to deeper sediments. 

The exposure pathway for PAHs could be interrupted if there was no sign of internal

exposure. Aquatic contaminants such as PAHs have been monitored by measuring the

metabolites of xenobiotics in fish bile (Roubal et al. 1977, Gmur and Varanasi 1982,

Varanasi et al. 1983). Samples from white suckers (Catostomus commersoni) taken in

1992 from the Little Scioto River were analyzed for concentrations of benzo[a]pyrene

(BAP) and naphthalene (NAPH)-type metabolites. Results of the analysis of PAH bile

metabolites in white suckers from the Little Scioto River are shown in Figure 7-9. 

Biomarkers of NAPH and BAP are elevated from RM 6.5 to the mouth of the river,

providing evidence that the exposure pathway is complete at these locations. Exposure

criteria, concentrations considered to be above background, were exceeded at RMs 6.5

through 0.4. PAHs are also known to cause induction of detoxifying enzymes such as

EROD. EROD activity was elevated at RM 6.5 - 0.4. Based on the absence or presence

of bile metabolites, the exposure pathway for PAHs is incomplete at Impairment A, and

complete at Impairments B and C.

Metals (#3) - Metals must be taken into organisms to cause adverse effects. Data from

fish tissue sampled in 1992 confirm uptake of lead and zinc. For common carp

(Cyprinus carpio) at RM 9.2, zinc concentrations were 79.6 mg/kg, at RM 6.5, zinc

concentrations were 68.3 mg/kg. For white suckers at RM 6.5, zinc concentrations were

17.8 mg/kg, and lead concentrations were 81.4 mg/kg. At RM 2.7, fish tissues levels

were 15.8 mg/kg for zinc and 0.34 mg/kg for lead. For the other metals, we have

conservatively assumed that external exposure will represent internal exposure for fish. 

Making this assumption, increased exposure to at least one of the metals occurs at all

sampling locations in the reach RM 7.9 to 0.4. Concentrations of metals in sediment

were from samples taken in 1992 from the top six inches of sediment. For 1987 and

1998 data, the depth of samples is unknown.
Stressor Identification Guidance Document

Chapter 7: Little Scioto River, Ohio 7-25

Figure. 7-9. Bile metabolites (μg/mg protein) measured in white suckers

from the Little Scioto River in 1992. (Median levels of PAH metabolites

below RM 7.9 were up as much as 4 times the Exposure Criteria, (dashed

horizontal line) which are upper limits of background for the state of Ohio. 

The numbers above the bars equal number of fish sampled. Vertical lines

are standard errors.) 

Ammonia (#4) - There are several interweaving pathways by which ammonia can be

produced in the river and cause effects. We have evidence for two of these steps: total

ammonia, and nitrate and nitrite concentrations that are converted to ammonia when DO

is low. Toxic unionized ammonia is formed at high pH. Hard water streams of the

Eastern Corn Belt Plains typically have pH from 7.5-8; pH may rise even above 9.0 in

the summer during maximum photosynthesis in nutrient-enriched waters. Data on pH

are not available in 1992, however, in 1998 grab samples, pH ranged between 7.4 to 8.0. 

The Little Scioto is highly enriched, and it is highly likely that there are periods when pH

is greater than indicated by grab samples. Thus, we assume that the exposure pathway is

complete in the Little Scioto when total ammonia is present. This occurs from RM 11.1

to 0.4. Because ammonia concentrations are measured in the water column, both fish

and macroinvertebrates are exposed.

Low Dissolved Oxygen/High Biological Oxygen Demand (#5) - Dissolved oxygen can be

depleted by high BOD due to the bacterial respiration associated with allochthonous

organic matter or decaying algal mats. We have measurements of several relevant

parameters: NOx, total P, BOD and DO concentrations. This exposure pathway is

considered complete under two scenarios: (1) BOD is elevated and DO is reduced

compared with the most upstream location, or (2) if BOD data is unavailable, NOx and P

are elevated and assumed to cause algal growth, and DO is reduced as compared with the

most upstream location. At RM 7.9, DO is reduced, but BOD is unchanged, so that the

exposure pathway is considered incomplete. RM 6.5 is more difficult to evaluate

because data are scanty and are used from different years. In 1987, DO data were low at
Stressor Identification Guidance Document

7-26 U.S. Environmental Protection Agency

Eliminate Diagnose Strength of Evidence

CHARACTERIZE CAUSES

Identify Probable Cause

RM 6.5, and in 1992, the BOD was slightly elevated; thus, the pathway is complete. At

RM 5.8 to 0.4, because BOD is elevated but DO is similar or greater than at 7.9, the

exposure pathway is considered incomplete.

Nutrient Enrichment (#6) - We have evidence for the presence of elevated levels of both

NOx and total P concentrations. This exposure pathway appears to be complete at RM

5.8 to 0.4, and at RM 7.9. 

7.5.4 Summary of Analyses for Elimination

The results of the analysis of spatial associations are summarized in Table 7-5 (pages 7-

21 to 7-23). The table addresses four questions for each combination of impairment and

candidate cause. If any of the answers are no, then the candidate cause can be

eliminated: 

 The first question is whether a candidate cause and impairment are spatially colocated. Regardless of concentration, the answer is yes if the stressor is present. 

If the stressor is not present, the answer is no and the impairment could not have

been caused by exposure to that stressor.

 The second question asks whether the exposure is elevated compared to the

closest upstream location where the impairment does not occur. The candidate

cause could have been responsible for the impairment only if exposure increased. 

The candidate cause can be eliminated if the answer to the second question is no.

 The third question asks whether there is a decrease in exposure that corresponds

with recovery of the impairment. As discussed above, this question is relevant

only to Impairment C. If the answer is no with results clearly showing a

lessening of impairment with consistent exposure, then the candidate cause can

be eliminated.

 The last question asks if the exposure pathway is complete. If it is interrupted or

clearly incomplete so that exposure could not have taken place, then it can be

eliminated as a potential cause.

7.6 Characterize Causes: Eliminate

Potential causes may be eliminated if the

evidence indicates that they do not co-occur

with effects, if effects decrease with increasing

influence of the cause, or if the exposure

pathway is incomplete. Each of the three

Impairments (A, B, and C) are discussed below

in relation to the elimination of specific causes. 

Conclusions about which candidate causes

remain for each impairment are also listed.

Impairment A: RM 7.9

Habitat alteration and metal contamination are the only candidate causes known to

co-occur at RM 7.9 and to increase compared to upstream locations. All metals were

slightly greater at RM 7.9 compared to RM 9.2. PAHs and ammonia were not

elevated at RM 7.9 relative to the upstream reference, thus candidate causes #2 and
Stressor Identification Guidance Document

Chapter 7: Little Scioto River, Ohio 7-27

#4 are eliminated. DO concentrations were about 30% lower than upstream, but

BOD concentrations were not different from the upstream reference location (RM

9.2), thus candidate cause #5 is eliminated. NOx increased from 1.2 mg/L to 1.4

mg/L. The shift is small, but precludes elimination of candidate cause #6. 

Conclusion: Habitat Alteration (#1), Metal Contamination (#3), and Nutrient

Enrichment (#6) remain.

Impairment B: RM 6.5. 

At this site, only candidate cause #1 can be eliminated because the degree of habitat

alteration is not elevated compared with those at RM 7.9. The decline in QHEI score

is associated with the obvious presence of organic chemical contamination rather

than physical stream characteristics. Organic chemicals, including benzo[a]pyrene

and naphthalene, were present and were elevated above concentrations at RM 7.9. 

Exposure to these organic chemicals was demonstrated by internal concentrations of

metabolites. The metals chromium, copper, lead, and mercury were elevated

compared to upstream concentrations in all years for which there is data, including

1988, 1991, 1992 and 1998. Dissolved oxygen levels were among the lowest in the

river in 1987, and BOD levels were slightly greater than upstream locations. 

Ammonia concentrations were also slightly greater, and total P concentrations were

0.02 mg/L greater. 

Conclusion: PAH Contamination (#2) Metal Contamination (#3), Ammonia

Toxicity (#4), Low Dissolved Oxygen/High Biological Oxygen Demand (#5), and

Nutrient Enrichment (#6) remain.

Impairment C: RM 5.7. 

In this reach of the river, the degree of habitat alteration and PAH levels were similar

or lower than at RM 6.5, thus candidates #1 and #2 are eliminated. Candidate cause

#5, low DO/high BOD, can be eliminated, even though BOD, P and NOx are elevated

because the subsequent event in the pathway, decreased DO, did not occur. DO is

unchanged from RM 7.9 in 1992, and RM 6.5 in 1987. The metals (copper and zinc)

increased slightly, and the copper gradient was significantly correlated with %

Tanytarsini midges and % DELTA, thus candidate cause #3 remains. NOx and P

were elevated in the reach compared to upstream locations and were significantly

correlated with % Tanytarsini midges. Candidate cause #6 remains. Candidate

cause #4 could be eliminated since ammonia was not correlated with the specific

impairments. However, the increase in ammonia was 10 times greater than

upstream, and because the data available for correlations were very limited, a

conservative decision could be made to retain this cause for further evaluation by the

strength of evidence approach.

Conclusion: Metal Contamination (#3), Ammonia (#4), and Nutrient

Enrichment (#6) remain. 

A summary of the candidate causes that remain after the elimination process are listed in

Table 7-6. Only those causes remaining need to be evaluated by diagnostic or strength of

evidence analyses. 
Stressor Identification Guidance Document

7-28 U.S. Environmental Protection Agency

LIST CANDIDATE CAUSES

ANALYZE EVIDENCE

CHARACTERIZE CAUSES

Stressor Identification

Identify Probable Cause

Eliminate Diagnose Strength of Evidence

LIST CANDIDATE CAUSES

ANALYZE EVIDENCE

CHARACTERIZE CAUSES

Stressor Identification

Identify Probable Cause

Eliminate Diagnose Strength of Evidence

LIST CANDIDATE CAUSES

ANALYZE EVIDENCE

CHARACTERIZE CAUSES

Stressor Identification

Identify Probable Cause

Eliminate Diagnose Strength of Evidence

LIST CANDIDATE CAUSES

ANALYZE EVIDENCE

CHARACTERIZE CAUSES

Stressor Identification

Identify Probable Cause

Eliminate Diagnose Strength of Evidence

Table 7-6. Candidate causes remaining after elimination.

Impairment A Impairment B Impairment C

#1 Habitat alteration X

#2 PAH Contamination X

#3 Metal Contamination X X X

#4 Ammonia X X

#5 Low DO/BOD X

#6 Nutrient Enrichment X X X

7.7 Analyze Evidence for Diagnosis

Diagnosis is the identification of causes based on

characteristic signs or symptoms (see 4.2.2). No

evidence strong enough to support diagnosis was

available for any of the candidate stressors. 

However, the pattern of community change is

considered to be suggestive, and is used in the

strength of evidence analysis below.

The deformities, fin erosion, tumors, physical

lesions and anomalies on fish that constitute the

DELTA are pathologies that are also potentially

subject to diagnosis. Some DELTA are strongly

associated with known toxic substances and others

with increased nutrients (Yoder and Rankin 1995b). 

However, no pathologist has examined the fish in

question. DELTA cannot be used to distinguish among toxic substances unless specific

anomalies are identified, and even these may be too non-specific to diagnose without

additional information (e.g., histopathology).

7.8 Analyze Evidence to Compare Strength of Evidence

All of the remaining candidate causes are subjected

to a strength of evidence analysis to verify the

elimination step and to identify the most likely

cause from the multiple hypothesized causes that

remained after the elimination process. The

strength of evidence analysis examined case

specific evidence as well as evidence from other

situation and biological knowledge. 

Case Specific Evidence

The evidence presented earlier for the elimination

step is useful here as well. In addition, some data

on loadings are available from the Waste Water

Treatment Plant (WWTP), which discharges at RM
Stressor Identification Guidance Document

Chapter 7: Little Scioto River, Ohio 7-29

6.2 and also has combined sewer overflows that discharge during wet weather periods. 

No clear trends were evident in the loadings of total non-filterable residue or biological

oxygen demand between 1977 and 1992. Ammonia values were generally low, with fifty

percent of loading below 10 kg/day between 1977 and 1991. The highest ammonia

loading occurred during 1992, with a median of 12.6 kg/day and a maximum of 130

kg/day (OEPA 1994).

Evidence from Other Situations or Biological Knowledge

This section presents evidence that uses information from other studies that are related to

either exposures or effects found in segments of the Little Scioto River. In particular,

associations are made between the exposures known at the site and reports of effects

caused by similar exposures. This section also uses levels of effects seen at the site and

effects seen at other sites where the same candidate cause occurred. It also considers

special experimental evidence; that is, reports about places with similar stressors and

effects that improved when the stressor was removed, and laboratory studies of candidate

cause-effect relationships.

Exposure-response data are available for PAHs and metals, although not for the

community parameters of greatest interest for this study. Sediment effect concentrations

(SECs) developed for Hyalella azteca and Chironomus riparius were considered, but

only Hyalella azteca was used since Chironomus riparius values were always less

sensitive. Sediment effect concentrations for Hyalella azteca are expressed as threshold

effect level (TEL) and probable effect level (PEL) (Table 7-19; see Section 7.13)

(USEPA 1996b).

The TEL and PEL are sediment concentrations associated with toxicity in laboratory

tests. The interpretation is that toxicity rarely occurs below the TEL and frequently

occurs above the PEL (USEPA 1996b). Values were derived from a data set consisting

of many similar studies, and they consider both effect and no-effect data for fieldcontaminated sediments. The TEL and PEL values used in this study are listed in Tables

7-19 and 7-20 (see Section 7.13). Since many metals and PAHs were present at sites,

partial toxicity contributed by individual chemicals were calculated and summed to

estimate the overall toxicity of metals and PAH at each site. TELs and PELs are used

with caution because they are based on sediments with multiple contaminants.

The TEL and PEL values were compared with the concentrations seen at the locations of

impairment in Table 7-19. As shown in Table 7-19, the most striking result is that no

PAH exceeded any criterion level at Impairment A for 1992. For metals only, the TEL

for arsenic was exceeded at Impairment A in 1992. At Impairment B and C, the Hyalella

azteca PEL and TEL were exceeded for all PAH that were measured and in every year

except 1992, when there were more samples below the detection limit. Hyalella azteca

TEL values were exceeded for most metals, but only a few PEL values were exceeded,

including those for lead, copper, and chromium.

For PAHs, the cumulative toxic units were exceeded at Impairments B and C in every

year (Table 7-7). Exceedances ranged from 339 to 18,820 times the value that would

probably kill Hyalella azteca. For metals, the cumulative toxic units were also exceeded

at Impairments B and C in every year. However, exceedances were never more than six

times the cumulative probable effect level.
Stressor Identification Guidance Document

7-30 U.S. Environmental Protection Agency

Table 7-7. Cumulative toxic units for PAHs and metals based on the PEL values. 

(Values greater than 1.0 exceed PEL*).

Chemical

Cumulative Toxic Units

Nearest

Upstream

Location Impairment A Impairment B Impairment C

PAH /0\

(0)

[1.2]*

/0\

(0)

[2.5]*

/604.5\*

(339.4)*

[18819.9]*

/9697.8\*

(821)*

[1633.4]*

Metals /0.4\

(0.6)

[0.9]

/0.7\

(1.1)*

[0.9]

/4.3\*

(5.1)*

[1.6]*

/1.5\*

(2.8)*

[5.8]*

* Exceeds PEL and TEL. / \ = 1987-1991, ( ) = 1992, [ ] = 1998. Zero = below

 detection.

Criteria are also available for ammonia (USEPA 1998b) (Table 7-8). The toxicity of

total ammonia (which includes NH3 and NH4

+

) varies with pH. Dehydration of

ammonium ion (NH4

+

) to un-ionized ammonia is controlled by ambient pH, such that

excess hydroxide ions (high pH) increase the concentration of the more toxic, un-ionized

form. Hard water streams of the Eastern Corn Belt Plains (ECBP) typically have pH

from 7.5-8; in the summer, during maximum photosynthesis in nutrient enriched waters,

pH may rise above 9.0. In 1998, pH values ranged between 7.4 and 8.4, and appeared to

be independent of location. Total ammonia concentrations at RM 5.8 through 2.7 would

have exceeded the ammonia criterion for water having a pH 8.0 to 8.5 in 1992 (Table 7-

8). In 1998, the criterion would have been exceeded at pH 8.5. 

Ohio's criteria for dissolved oxygen (causal candidate #5) are 4.0 mg/l for warm water,

and 3.0 mg/l for modified warm water. In 1992, no locations had dissolved oxygen

below the modified warm water criterion, and only RM 2.7 had dissolved oxygen

concentrations below the warm water criterion, based on a single measurement. 

However, in 1987, continuous data were collected by Datasonde (in-stream Hydrolab)

and violations were detected at Impairments A and B (Table 7-8).

Ohio’s proposed state-wide criterion for modified warm-water habitat for nitrate and

nitrite is 1.6 mg/L for wadeable streams in the ECBP having a drainage greater than 20

mi2

 and less than 200 mi2. For total phosphorus, the proposed state-wide criterion for

modified warm-water habitat is 0.28 mg/L (Rankin et al. 1999). These are exceeded at

RM 5.8 (Table 7-8).

A state-wide study by Yoder and Rankin (1995b) indirectly examined the plausibility of

specific community changes associated with nine types of sources, including waste water

treatment plants, industrial point sources, conventional municipal sources, combined

sewer overflows, channelization, and agricultural non-point sources. They found that

deformities, erosions, lesions, tumors and anomalies (DELTA) in fish were associated

with industrial discharges (Yoder and Rankin 1995b) and nutrient enrichment (Rankin et

al. 1999). In the Little Scioto, the greatest % DELTA values are associated with the

greatest nutrient concentrations. Among macroinvertebrates, the loss of Tanytarsini

midges and the increase of Cricotopus sp. are both associated with industrial discharges

(Yoder and Rankin 1995b). In the Little Scioto, the disappearance of Tanytarsini midges

and an increase in Cricotopus are associated with Impairment C.
Stressor Identification Guidance Document

Chapter 7: Little Scioto River, Ohio 7-31

Eliminate Diagnose Strength of Evidence

CHARACTERIZE CAUSES

Identify Probable Cause

Table 7-8. Comparison of the reported concentration of water quality parameters

(mg/L) with exceedances.

Sediment

Parameter

 Criteria mg/L

RM 7.9 

[RM 7.1]

RM 6.5 RM 5.8

[RM 6.2]

Ammoniaa

0.57 mg/L at pH 8.5

1.27 mg/L at pH 8.0

(<0.05)

[0.11, <0.05]

(0.1) (1.2)

[0.35, 0.69]

Dissolved Oxygenb

3.0 mg/L for MWH

{4.6-2.8}*

(7.9, 5.7)

{7.2-1.9}*

(NA)

{8.3, 4.2}

(8.23, 4.21)

Nitrate-nitritec

1.6 mg/L

(1.4)

[0.73, <0.1]

(0.8) (8.1)*

[0.33, 2.37]*

Total phosphorusd

0.28 mg/L

(0.07) 

[0.36*, 0.13]

(0.09) {1.65}*

(2.17)*

[1.9, 1.21]*

No Entry = No data for that year. { } = 1987, ( ) = 1992, [ ] = 1998. a

 USEPA (1998b) recommended ammonia criterion

b OEPA (1994) dissolved oxygen criterion

c

 Rankin et al. (1999) proposed nitrate-nitrite criterion

d

 Rankin et al. (1999) proposed total phosphorus criterion

* Exceedance of criterion

Dissolved oxygen values are maximum and minimum. Ammonia, nitrate-nitrite, total

phosphorus measured in August and October, 1998.

7.9 Characterize Causes: Strength of Evidence

Strength of evidence analysis uses all of

the evidence generated in the analysis

phase to examine the credibility of each

remaining candidate cause. The causal

considerations for the strength of evidence

analyses used three types of evidence:

case-specific evidence, evidence from

other situations or biological knowledge,

and evidence based on multiple lines of

evidence (Section 4.3.3). All the evidence

was evaluated for consistency or

coherence with the hypothesized causes.

The results of the strength of evidence analysis are presented in Tables 7-9 to 7-11. 

Following the strength of evidence analysis, the candidate causes are characterized

(Table 7-12). This involves describing the causal evidence and identifying the probable

cause.
Stressor Identification Guidance Document

7-32 U.S. Environmental Protection Agency

Table 7-9. Strength of evidence analysis for the three candidate causes of Impairment A, RM 7.9.

Causal

Consideration Evidence Score Evidence Score Evidence Score

Case-Specific Considerations

Habitat Alteration Metals Contamination Nutrient Enrichment

CooccurrenceCompatible: At and

below RM 7.9, the

habitat of the Little

Scioto is altered as a

result of channelization.

The degree of habitat

alteration remains about

the same to the mouth

of the river. The

upstream reference is

not channelized and

habitat is good.

+ Compatible: All

sediment metal

concentrations were

slightly higher at RM 7.9

compared to upstream. 

+ Compatible: N was

elevated by 0.2mg/L in

1992 compared to

upstream.

P is the same or

decreases compared to

upstream.

+

Temporality No evidence NE No evidence NE No evidence NE

Consistency

of

Association

No evidence NE No evidence NE No evidence NE

Biological

Gradient

Not applicable: Other

downstream candidate

causes interfere with

this consideration.

NA Not applicable: Other

downstream candidate

causes interfere with

this consideration.

NA Not applicable: Other

downstream candidate

causes interfere with

this consideration.

NA

Complete

Exposure

Pathway

Evidence for all steps: 

The fish and

invertebrates inhabit the

channelized reach

where the habitat is

altered. 

Channel was deepened.

DO was depressed.

Substrate was

embedded.

++ Incomplete evidence: 

No internal

concentrations of

metals were measured. 

Metals were present in

sediment and exposure

could occur from

ingestion or by

respiration of epibenthic

water or sediment

particles or through the

food chain. 

+ Incomplete evidence:

Fish and invertebrates

inhabit stream where

nutrients are elevated.

Concentrations of algae

or chlorophyll a were not

measured.

+

Experiment No evidence. NE No evidence. NE No evidence. NE

NE = no evidence; NA = not applicable/not available
Stressor Identification Guidance Document

Chapter 7: Little Scioto River, Ohio 7-33

Table 7-9 (continued). Strength of evidence analysis for the three candidate causes of Impairment

A, RM 7.9.

Causal

Consideration Evidence Score Evidence Score Evidence Score

Considerations Based on Other Situations or Biological Knowledge

Habitat Alteration Metals Contamination Nutrient Enrichment

Plausibility: 

Mechanism

Increased Relative

Weight: Plausible:

Artificially deepened

channel allows larger

sized fish to survive.

+ Increased Relative

Weight: Implausible: No

known mechanism for

metals. Metals usually

cause a decrease in the

relative weight of fish

(Eisler 2000b).

- Increased Relative

Weight: Implausible: N

is a nutrient for algal

growth. Greater

production of algae

could provide additional

food, increasing fish

growth. However, the

mechanism is

implausible because N

is generally not limiting

(Allan 1995).

-

Increased DELTA: Not

known: No obvious

mechanism other than

stress.

0 Increased DELTA:

Implausible: Metals do

not cause fin erosion

and lesions (Eisler

2000b).

- Increased DELTA:

Plausible: Nutrients are

believed to create

conditions that favor

opportunistic pathogens

and fungi that cause

lesions, fin erosion and

interfere with wound

healing.

+

Loss of species:

Plausible: Embedded

sediments remove

forage, reproductive,

and cover habitats for

benthic fish including

darters and benthic

invertebrates including

mayflies. Low DO is not

tolerated by many

species (Karr and

Schlosser 1977, Yount

and Niemi 1990, Rankin

1995).

+ Loss of species:

Plausible: Metals are

known to cause lethal

and sub-lethal effects to

invertebrates and fish

that can extirpate

species from a site

(Eisler 2000b). 

Metals usually cause a

decrease in the relative

weight of fish (Eisler

2000b).

+ Loss of species:

Plausible: Switching to

an autochthonous

energy source could

alter species survival

and community

composition of fish and

invertebrates.

+

Plausibility: 

StressorResponse

Increased Relative

Weight: No evidence.

NE Increased Relative

Weight: Not applicable:

Implausible mechanism.

NA Increased Relative

Weight: Not applicable:

Implausible mechanism.

NA

Increased DELTA: No

evidence.

NE Increased DELTA: Not

applicable: implausible

mechanism.

NA Increased DELTA:

Inconcordant:

magnitude of nutrient

change too small to

cause effect.

-

Loss of species: No

evidence.

No quantitative

evidence. 

Habitat alteration

associated with

channelization is

generally believed to be

an all or none situation

affected by it’s spatial

extent and severity. 

NE Loss of species:

Inconcordant.

No metals exceeded

Hyalella azteca PEL

values in 1987, 1992 or

1998. The TEL value

for arsenic was

exceeded only in 1992.

Metals cumulative toxic

units exceeded PEL in

1992, but only by 0.1

units (USEPA 1996b).

- Loss of species:

Inconcordant.

The magnitude of

nutrient change was too

small to account for the

dramatic shifts in

invertebrate and fish

metrics. Proposed

nitrogen criterion for

Ohio was not exceeded

(Rankin et al. 1999).

-

NE = no evidence; NA = not applicable/not available
Stressor Identification Guidance Document

7-34 U.S. Environmental Protection Agency

Table 7-9 (continued). Strength of evidence analysis for the three candidate causes of Impairment

A, RM 7.9.

Causal

Consideration Evidence Score Evidence Score Evidence Score

Considerations Based on Other Situations or Biological Knowledge (cont’d)

Habitat Alteration Metals Contamination Nutrient Enrichment

Consistency

of

Association

Increased Relative

Weight: In most places.

++ Increased Relative

Weight: Many

exceptions.

- Increased Relative

Weight: No evidence.

NE

Increased DELTA: In

most places.

++ Increased DELTA:

Many exceptions.

- Increased DELTA:

Many exceptions. At

many sites in Ohio,

DELTA was not

increased by these

levels of N (Rankin et al.

1999).

-

Loss of species: In most

places.

Moderate increase in

DELTA and loss of

species are commonly

associated with habitat

alteration associated

with channelization

(Yoder and Rankin

1995b). Increased

Relative Weight is also

commonly increased

with deepened channels

(Personal Observation).

Agricultural areas with

channelization having

similar stressors

showed decreases in

IBI and ICI component

metrics (Edwards et al.

1984, Sheilds et al.

1998).

++ Loss of species: Many

exceptions.

At other sites in Ohio

with similar metals

concentrations, Relative

Weight and DELTA

were not increased and

species were abundant. 

Personal observation of

Ohio database.

- Loss of species: Many

exceptions. At many

sites in Ohio, IBI and ICI

scores were high at

these levels of N

(Rankin et al. 1999).

High IBI and ICI cannot

be achieved when many

species are lost.

-

NE = no evidence; NA = not applicable/not available
Stressor Identification Guidance Document

Chapter 7: Little Scioto River, Ohio 7-35

Table 7-9 (continued). Strength of evidence analysis for the three candidate causes of Impairment

A, RM 7.9.

Causal

Consideration Evidence Score Evidence Score Evidence Score

Considerations Based on Other Situations or Biological Knowledge (cont’d)

Habitat Alteration Metals Contamination Nutrient Enrichment

Specificity of

Cause

Increased Relative

Weight: One of a few: 

Deep channels or pools

required for larger fish. 

Relative weight of fish is

significantly correlated

with drainage area, a

surrogate for channel

depth (Norton 1999).

++ Increased Relative

Weight: Not applicable:

Implausible mechanism.

NA Increased Relative

Weight: Not applicable:

Implausible mechanism.

NA

Increased DELTA: One

of many.

0 Increased DELTA: Not

applicable: Implausible

mechanism.

NA Increased DELTA: One

of many.

0

Loss of species: One of

many.

0 Loss of species: One of

many. 

0 Loss of species: One of

many.

0

Analogy Not applicable NA Not applicable NA Not applicable NA

Experiment Increased Relative

Weight: No evidence

NE Increased Relative

Weight: No evidence

NE Increased Relative

Weight: No evidence

NE

Increased DELTA: No

evidence

NE Increased DELTA: No

evidence

NE Increased DELTA: No

evidence

NE

Loss of species: 

Concordant: Artificial

riffle and pools

improved invertebrate

assemblage in the

channelized Olentangy

River (Edwards et al.

1984), and fish in

Mississippi River

(Sheilds et al. 1998).

+++

Predictive

Performance

No evidence NE No evidence NE No evidence NE

Considerations from Multiple Lines of Evidence

Habitat Alteration Metals Contamination Nutrient Enrichment

Consistency

of Evidence

Increased Relative

Weight: All consistent.

+++ Increased Relative

Weight: Inconsistent:

Implausible mechanism.

--- Increased Relative

Weight: Inconsistent:

Magnitude of change

inconsistent with

magnitude of effect.

---

Increased DELTA: All

consistent.

+++ Increased DELTA:

Inconsistent:

Implausible mechanism.

--- Increased DELTA:

Inconsistent: Magnitude

of change inconsistent

with magnitude of effect.

----

Loss of species: All

consistent. 

+++ Loss of species:

Inconsistent - Although

metals are present, the

concentrations are

unlikely to cause

species extirpation.

--- Loss of species:

Inconsistent: Magnitude

of change inconsistent

with magnitude of effect.

---

Coherence

of Evidence

Increased Relative

Weight, Increased

DELTA, Loss of

species: None.

0 Increased Relative

Weight, Increased

DELTA, Loss of

species: None.

0 Increased Relative

Weight, Increased

DELTA, Loss of

species: None.

0

NE = no evidence; NA = not applicable/not available
Stressor Identification Guidance Document

7-36 U.S. Environmental Protection Agency

Table 7-10. Strength of evidence analysis for the five candidate causes of Impairment B, RM 6.5.

Causal

Consideration

Evidence Score Evidence Score

Case-Specific Considerations

PAH contamination Metals Contamination

Co-occurrence Compatible: Sediment PAH

concentrations were several orders

of magnitude greater at RM 6.5 than

upstream (Table 13). 

+ Compatible: Lead, chromium, copper

and mercury concentrations in

sediment were two to ten times

greater at RM 6.5 than upstream. 

Cadmium and zinc were also greater,

but to a lesser degree.

+

Temporality No evidence NE No evidence NE

Consistency of

Association

No evidence: only one location. NE No evidence: only one location. NE

Biological Gradient Not Applicable: Other candidate

causes downstream interfere with

this consideration. 

NA Not Applicable: Other candidate

causes downstream interfere with this

consideration. 

NA

Complete Exposure

Pathway

Actual evidence for all steps: PAHs

were present in the sediment, and

bottom-feeding fish and benthic

invertebrates are typically exposed

to sediment contaminants. Both BAP

and NAPH metabolites were found in

fish. EROD, a detoxifying enzyme

known to be induced by PAH, was

elevated. 

+ + Actual evidence for all steps: Metals

were present in sediment and

exposure could occur from ingestion

or by respiration of epibenthic water of

sediment particles or through the food

chain. Zinc and lead were detected

in fish tissues.

+ +

Experiment No evidence NE No evidence NE

NE = no evidence; NA = not applicable/not available
Stressor Identification Guidance Document

Chapter 7: Little Scioto River, Ohio 7-37

Table 7-10 (continued). Strength of evidence analysis for the five candidate causes of

Impairment B, RM 6.5.

Causal

Consideration

Evidence Score Evidence Score

Considerations Based on Other Situations or Biological Knowledge

PAH contamination Metals Contamination

Plausibility:

Mechanism

Decreased relative weight: 

Plausible: PAHs are known to

reduce growth. Toxic compounds

can shorten life span resulting in

smaller fish (Eisler 2000a).

+ Decreased relative weight: Plausible:

Metals are known to reduce growth. 

Toxic compounds can shorten life

span resulting in smaller fish (Eisler

2000b).

+

Increased DELTA: Plausible: PAHs

are known to cause eroded barbels,

fin erosion, lesions and internal and

external tumors (Eisler 2000a).

+ Increased DELTA: Implausible:

Metals do not cause fin erosion and

lesions

-

Decreased species: Plausible: PAHs

are known to be toxic and cause

reproductive impairments which

could extirpate species (Eisler

2000a). 

+ Decreased species: Plausible: Metals

are known to cause lethal and sublethal effects to invertebrates and fish

that can extirpate species from a site

(Eisler 2000b). 

+

Plausibility: 

Stressor-Response

Decreased relative weight:

Concordant: Toxic levels are

consistent with decreased fish

growth.

+ Decreased relative weight:

Ambiguous. Toxic levels are

consistent with decreased fish growth

(Eisler 2000b).

0

Increased DELTA: Quantitatively

consistent: PAHs are at levels that

cause tumors and other DELTA.

+++ Increased DELTA: Not applicable: 

mechanism is implausible.

NA

Decreased species: Quantitatively

consistent: The Hyalella azteca

PEL’s were exceeded for all PAHs. 

The cumulative PAH toxic units

ranged between 339 to 18,820 times

the PEL value (USEPA 1996b). 

+++ Decreased species: Quantitatively

consistent. Lead exceeded Hyalella

azteca PEL values in 1988-1991 and

1992 and chromium in 1992. The

cumulative toxic units values for all

metals range from 1.6 to 5.1 (USEPA

1996b). 

+++

Consistency of

Association

Decreased relative weight: In most

places: Decreased relative weight is

associated with complex toxic

exposures (Yoder and Rankin

1995b).

++ Decreased relative weight: In most

places: Decreased relative weight is

associated with complex toxic

exposures (Yoder and Rankin 1995).

++

Increased DELTA: Invariant: Tumors

and other DELTA are associated

with fish exposed to high

concentrations of PAH in fresh and

marine waters (Albers 1995). 

+++ Increased DELTA: Not applicable. NA

Decreased species: Invariant: At

more than 25 locations associated

with PAH contamination that

exceeded exposure criteria in Ohio,

IBI and ICI scores were below 30 

(Cormier et al. 2000a). IBI and ICI

are known to be depressed even

when habitat quality is high (Cormier

et al. 2000b, OEPA 1992a). IBI and

ICI scores of less than 30 only occur

when some species are extirpated.

+++ Decreased species: In most places:

Hickey and Clements (1998) reviewed

changes in invertebrate community

associated with metals in water

column. 

++

Specificity of Cause Decreased relative weight: One of

many.

0 Decreased relative weight: One of

many.

0

Increased DELTA: One of many. 

PAHs are known to cause external

lesions seen at Impairment B.

0 Increased DELTA: Not applicable. NA

Decreased species: One of many. 0 Decreased species: One of many. 0

NE = no evidence; NA = not applicable/not available
Stressor Identification Guidance Document

7-38 U.S. Environmental Protection Agency

Table 7-10 (continued). Strength of evidence analysis for the five candidate causes of

Impairment B, RM 6.5.

Causal

Consideration

Evidence Score Evidence Score

Considerations Based on Other Situations or Biological Knowledge (cont’d)

PAH contamination Metals Contamination

Analogy Not applicable NA Not applicable NA

Experiment Decreased relative weight:

Concordant: Following dredging in

the Black River, Ohio, the age

structure of the brown bullheads

increased (Baumann and

Harshbarger 1995).

+++ No evidence: No references sought. NE

Increased DELTA: Concordant: In

the Black River Ohio, removal of

PAHs by dredging resulted in lower

levels of DELTA (Baumann and

Harshbarger 1995) and PAH bile

metabolites (Lin et al. submitted).

+++ No evidence: No references sought.

Decreased species: Concordant:

Following dredging the composition

of species at this site also changed

(Baumann, pers. comm.).

+++ No evidence: No references sought.

Predictive

Performance

No evidence NE No evidence NE

Considerations from Multiple Lines of Evidence

PAH contamination Metals Contamination

Consistency of

Evidence

Decreased relative weight: All

consistent.

+++ Decreased relative weight: All

consistent.

+++

Increased DELTA: All consistent. +++ Increased DELTA: Multiple

inconsistencies.

---

Decreased species: All consistent. +++ Decreased species: All consistent. +++

Coherence of

Evidence

Increased DELTA: No known

explanation.

0

NE = no evidence; NA = not applicable/not available
Stressor Identification Guidance Document

Chapter 7: Little Scioto River, Ohio 7-39

Table 7-10 (continued). Strength of evidence analysis for the five candidate causes of

Impairment B, RM 6.5.

Causal

Consideration

Evidence Score Evidence Score Evidence Score

Case-Specific Considerations

Ammonia Toxicity Low Dissolved oxygen/High

BOD

Nutrient Enrichment

Co-occurrence Compatible:

Ammonia

concentration was

doubled relative to

Impairment A.

+ Compatible: In

1992, BOD was

double the

upstream value

and the lowest DO

levels measured

were 0.9 mg/L less

than upstream.

+ Compatible:

Compared to RM

7.9, P was

elevated by 0.02

mg/L. N was less.

+

Temporality No evidence NE No evidence NE No evidence NE

Consistency of

Association

No evidence: Only

one location.

NE No evidence: Only

one location.

NE No evidence: Only

one location.

NE

Biological

Gradient

Not applicable: 

Other downstream

candidate causes

interfere with this

consideration. 

NA Not applicable: 

Other downstream

candidate causes

interfere with this

consideration.

NA Not applicable: 

Other downstream

candidate causes

interfere with this

consideration. 

NA

Complete

Exposure

Pathway

Evidence for all

steps: Fish and

invertebrates

inhabited stream

where ammonia

was present.

+ + Evidence for all

steps: Fish and

invertebrates

inhabited stream 

where conditions

of low DO and high

BOD occurred.

+ + Evidence for all

steps: Fish and

invertebrates

inhabit stream

where P was

elevated.

+ +

Experiment No evidence NE No evidence NE No evidence NE

NE = no evidence; NA = not applicable/not available
Stressor Identification Guidance Document

7-40 U.S. Environmental Protection Agency

Table 7-10 (continued). Strength of evidence analysis for the five candidate causes of

Impairment B, RM 6.5.

Causal

Consideration

Evidence Score Evidence Score Evidence Score

Considerations Based on Other Situations or Biological Knowledge

Ammonia Toxicity Low Dissolved oxygen/High

BOD

Nutrient Enrichment

Plausibility:

Mechanism

Decreased relative

weight: Plausible:

Ammonia toxicity

could reduce

growth and

survival. Low

survival could alter

the age structure

resulting in

smaller, younger

fish. 

+ Decreased relative

weight: Plausible: 

Stress could

reduce growth and

survival. Low

survival could alter

the age structure

resulting in more

smaller, younger

fish.

+ Decreased relative

weight:

Implausible:

Increased nutrients

are usually

associated with

increased algal

growth that

augment the

energy available

for growth. 

_

Increased DELTA:

Plausible: 

Ammonia has

been associated

with anomalies

(Dyer, pers.

comm.).

+ Increased DELTA: 

Not known: No

known mechanism.

0 Increased DELTA:

Plausible: Nutrients

are believed to

create conditions

that favor

opportunistic

pathogens and

fungi that cause

lesions, fin erosion,

and interfere with

wound healing

(Rankin et al.

1999).

+

Decreased

species: Plausible: 

Ammonia is known

to be toxic to fish

and invertebrates

(USEPA 1998b). 

+ Decreased

species: Plausible:

Low DO can kill

fish and

invertebrates

(Allan 1995).

+ Loss of species:

Plausible:

Switching to an

autochthonous

energy source

could alter species

survival and

community

composition for

fish and

invertebrates (Allan

1995). 

+

Plausibility: 

StressorResponse

Decreased relative

weight: No

evidence.

NE Decreased relative

weight: No

evidence.

NE Decreased relative

weight:

Inconcordant.

-

Increased DELTA:

No evidence.

NE Increased DELTA: 

Not applicable.

NA Increased DELTA:

Inconcordant.

-

Decreased

species:

Inconcordant: The

ammonia

concentrations

were not great

enough to cause

the dramatic

effects seen at

Impairment B. 

Ammonia criteria

were not

exceeded.

(USEPA 1998b). 

- Decreased

species: DO levels

are below Ohio

criteria for MWH

(OEPA 1992b).

+ Decreased

species:

Inconcordant: The

magnitude of P

change was not

great enough to

cause dramatic

effects seen at

Impairment B. 

Proposed P

criterion was not

exceeded (Rankin

et al. 1999).

-

NE = no evidence; NA = not applicable/not available
Stressor Identification Guidance Document

Chapter 7: Little Scioto River, Ohio 7-41

Table 7-10 (continued). Strength of evidence analysis for the five candidate causes of

Impairment B, RM 6.5.

Causal

Consideration

Evidence Score Evidence Score Evidence Score

Considerations Based on Other Situations or Biological Knowledge (cont’d)

Ammonia Toxicity Low Dissolved oxygen/High

BOD

Nutrient Enrichment

Consistency of

Association

No evidence NE No evidence. NE Decreased relative

weight: Many

exceptions.

-

Increased DELTA:

Many exceptions:

DELTA are

associated with

increased P at

many sites in Ohio,

but at a higher

concentration of P

(Rankin et al.

1999). 

-

Decreased

species: Many

exceptions:

Reduced species

are associated with

many sites in Ohio

increased P, but at

a higher

concentration

(Rankin et al.

1999).

-

Specificity of

Cause

Decreased relative

weight: One of

many.

0 Decreased relative

weight: One of

many

0 Decreased relative

weight: Not

applicable

NA

Increased DELTA:

One of many.

0 Increased DELTA:

Not applicable.

NA Increased DELTA:

One of many.

0

Decreased

species: One of

many.

0 Decreased

species: One of

many.

0 Decreased

species: One of

many.

0

Analogy Not applicable NA Not applicable NA Not applicable NA

Experiment No evidence: No

reference sought.

NE No evidence: No

reference sought.

NE No evidence: No

references sought.

NE

Predictive

Performance

No evidence NE No evidence NE No evidence NE

NE = no evidence; NA = not applicable/not available
Stressor Identification Guidance Document

7-42 U.S. Environmental Protection Agency

Table 7-10 (continued). Strength of evidence analysis for the five candidate causes of

Impairment B, RM 6.5.

Causal

Consideration

Evidence Score Evidence Score Evidence Score

Considerations from Multiple Lines of Evidence 

Ammonia Toxicity Low Dissolved oxygen/High

BOD

Nutrient Enrichment

Consistency of

Evidence

Decreased relative

weight: All

consistent. 

+++ Decreased relative

weight: Most

consistent. 

+ Decreased relative

weight: Many

inconsistencies.

_ _ _

Increased DELTA:

All consistent. 

+++ Increased DELTA:

Many

inconsistencies:

No known

mechanism.

_ _ _ Increased DELTA:

Many

inconsistencies:

Magnitude of

change

inconsistent with

magnitude of

effect. 

_ _ _

Decreased

species: 

Inconsistent: 

Magnitude of

change

inconsistent with

magnitude of

effect. 

_ _ _ Decreased

species: Most

consistent. 

+ Decreased

species: Many

inconsistencies: 

Magnitude of

change

inconsistent with

magnitude of

effect. 

_ _ _

Coherence of

Evidence

Decreased

species: No

known explanation.

0 Increased DELTA:

No known

explanation.

0 Decreased relative

weight, Increased

DELTA, Decreased

species: No

known explanation.

0

NE = no evidence; NA = not applicable/not available
Stressor Identification Guidance Document

Chapter 7: Little Scioto River, Ohio 7-43

Table 7-11. Strength of evidence analysis for the three candidate causes of Impairment C, 

RM 5.7.

Causal

Consideration

Evidence Score Evidence Score Evidence Score

Case-Specific Considerations

 

 

Metals Contamination Ammonia Toxicity Nutrient Enrichment

Co-occurrence Uncertain: There

were only slight

changes in metal

concentrations in

sediment at RM

5.7 compared to

RM 6.5. Only

copper and zinc

increased slightly

and possibly

cadmium. All

others declined. 

0 Compatible:

Ammonia

concentrations

were 10X or

greater than at RM

6.5. from RM 5.7 to

RM 2.7 

+ Compatible: Total

phosphorus and

nitrogen

concentrations are

elevated at RM 5.7

through 2.7. P

values are more

than 24X greater

than at RM 6.5 and

more than 10X

greater for nitrogen

than upstream.

+

Temporality No evidence NE No evidence NE No evidence NE

Consistency of

Association

Similar patterns of

fish and

invertebrate

communities are

seen at RM 5.7,

4.4 and 2.7 

+ Similar patterns of

fish and

invertebrate

communities are

seen at RM 5.7,

4.4 and 2.7. 

+ Similar patterns of

fish communities

are seen at RM 5.7,

4.4 and 2.7. 

+ 

Biological

Gradient

Increased DELTA:

Strong and

monotonic: From

RM 5.7 to RM 0.4,

copper and

mercury are

strongly correlated

with % DELTA.

++ Increased DELTA:

None: No

correlation of

ammonia with %

DELTA. 

- Increased DELTA:

Strong and

monotonic: %

DELTA was 

moderately

correlated with

BOD, N and P.

++

Decreased

Tanytarsini: Strong

and monotonic: 

The decline in %

tanytarsini was

also strongly

correlated with

copper and

mercury.

++ Decreased

Tanytarsini: None: 

No correlation of

ammonia with the

decline in %

Tanytarsini.

- Decreased

Tanytarsini: Strong

and monotonic: 

BOD, nitrate-nitrite

and phosphorus

were all strongly

correlated with

decline in %

Tanytarsini midges

and the ICI. 

++

NE = no evidence; NA = not applicable/not available
Stressor Identification Guidance Document

7-44 U.S. Environmental Protection Agency

Table 7-11 (continued). Strength of evidence analysis for the three candidate causes of

Impairment C, RM 5.8

Causal

Consideration

Evidence Score Evidence Score Evidence Score

Case-Specific Considerations (cont’d)

Metals Contamination Ammonia Toxicity Nutrient Enrichment

Complete

Exposure

Pathway

Incomplete

evidence: Lead

and zinc were

detected in water

samples (OEPA

1992a). In

sediment, many

metals were

detected. No

internal

concentrations of

metals were

measured. Water

hardness may

have reduced

metal availability. 

+ Evidence for all

steps: Ammonia

levels measured in

water column, so

exposure possible

for fish and

invertebrates. 

Ammonia is

directly discharged

into streams by

point sources. 

Temperature and

pH conditions are

favorable for

forming unionized

ammonia, the toxic

form of ammonia.

Conditions are

favorable for

conversion of

nitrites to ammonia

(low DO). 

+ + Incomplete

evidence: Nutrient

and phosphorus

concentrations were

measured in water

column, and would

be available for

algal, fungal and

bacterial growth.

Neither algal nor

chlorophyll a

concentrations, the

direct effect of

nutrient enrichment,

nor bacterial

concentrations were

not measured.

+ 

 Experiment No evidence. NE No evidence. NE No evidence. NE

Considerations Based on Other Situations or Biological Knowledge

Plausibility:

Mechanism

Increased DELTA:

Implausible: Metals

do not cause fin

erosion and

lesions (Eisler

2000b).

- Increased DELTA: 

Plausible: 

Ammonia has

been associated

with DELTA (Dyer,

pers. comm.).

+ Increased DELTA: 

Plausible: Nutrients

are believed to

create conditions

that favor

opportunistic

pathogens and fungi

that cause lesions,

fin erosion and

interfere with wound

healing (Rankin et

al. 1999).

+

Decreased

Tanytarsini:

Plausible: Metals

are known to

cause lethal and

sub-lethal effects

to invertebrates

that can extirpate

species from a

site. In a literature

review, lead and

copper were

associated with

mortality and other

metals with

mortality,

reproduction,

growth and

behavior changes

(Eisler 2000b).

+ Decreased

Tanytarsini:

Plausible:

Ammonia is toxic

to benthic

macroinvertebrates

(USEPA 1998b). 

+ Decreased

Tanytarsini:

Increased nutrients

are known to

change community

structure primarily

by changing the

food source (Allan

1995).

+

NE = no evidence; NA = not applicable/not available
Stressor Identification Guidance Document

Chapter 7: Little Scioto River, Ohio 7-45

Table 7-11 (continued). Strength of evidence analysis for the three candidate causes of

Impairment C, RM 5.8.

Causal

Consideration

Evidence Score Evidence Score Evidence Score

Considerations Based on Other Situations or Biological Knowledge (cont’d)

 

 

Metals Contamination Ammonia Toxicity Nutrient Enrichment

Plausibility:

StressorResponse

Increased DELTA:

Not applicable. 

Mechanism not

plausible.

NA Increased DELTA:

Concordant

+ Increased DELTA: 

Quantitatively

consistent:

%DELTA consistent

with associations of

P concentrations

found in streams

throughout Ohio

(Rankin et al, 1999)

+++

Decreased

Tanytarsini:

Ambiguous: The

cumulative toxic

units exceed PEL

by 1.5 to 2.8 times

in 1988/91 and

1992, respectively. 

The cumulative

toxic units for PEL

decreased

compared to

upstream in

1988/91 and 1992. 

In 1998,

cumulative PEL

was 3.5 times

greater than at

Impairment B, but

this occurred after

the impairment had

already occurred

(USEPA 1996b).

0 Decreased

Tanytarsini:

Quantitatively

consistent:

Ammonia

concentrations are

in a plausible

range to cause

toxic effects

especially on

warm, sunny days. 

Conservatively,

ammonia was two

times the USEPA

chronic criteria

(USEPA 1996b).

+++ Decreased

Tanytarsini:

Concordant.

Nutrient criteria are

proposed for Ohio

and were exceeded

at RM 5.7 through

RM 0.4 for both

nitrate-nitrite and

phosphorus. At RM

5.7, nitrogen 

concentration was

five times the

proposed criterion

value. P

concentration was

more than seven

times the proposed

phosphorus criterion

(Rankin et al. 1999).

+

Consistency of

Association

Increased DELTA:

Many exceptions. 

Ohio EPA

database.

- Increased DELTA:

In most places

(Rankin et al.

1999).

++ Increased DELTA:

In most places

(Rankin et al. 1999).

+ +

Decreased

Tanytarsini: No

evidence.

NE Decreased

Tanytarsini: No

evidence.

NE Decreased

Tanytarsini: No

evidence.

NE

Specificity of

Cause and Effect

Increased DELTA:

Not applicable.

NA Increased DELTA:

One of a few.

++ Increased DELTA:

One of a few.

++

Decreased

Tanytarsini: One of

many. 

0 Decreased

Tanytarsini: One of

many.

++ Decreased

Tanytarsini: One of

many.

++

Analogy Not applicable NA Not applicable NA Not applicable NA

Experiment No evidence NE No evidence NE No evidence NE

Predictive

Performance

No evidence NE No evidence NE No evidence NE

NE = no evidence; NA = not applicable/not available
Stressor Identification Guidance Document

7-46 U.S. Environmental Protection Agency

Table 7-11 (continued). Strength of evidence analysis for the three candidate causes of

Impairment C, RM 5.8.

Causal

Consideration

Evidence Score Evidence Score Evidence Score

Considerations from Multiple Lines of Evidence

 

 

Metals Contamination Ammonia Toxicity Nutrient Enrichment

Consistency of

Evidence

Increased DELTA:

Multiple

inconsistencies.

--- Increased DELTA:

Most consistent.

+ Increased DELTA:

All consistent.

+++ 

Decreased

Tanytarsini: Most

consistent.

Although metals

are toxic the

magnitude and

type of effect do

not seem to

indicate that

metals caused

either the increase

% DELTA or shifts

in invertebrate

metrics. However,

mercury and

copper are both

significantly

correlated with %

DELTA and %

tanytarsini.

0 Decreased

Tanytarsini: Most

consistent.

Ammonia may

have toxic effects,

but % DELTA not

likely to be caused

by ammonia. No

biological

correlation.

+ Decreased

Tanytarsini: All

consistent.

Reasonable

evidence to suspect

that nitrogen and

phosphorus are

creating conditions

that favor

opportunistic

pathogens. 

Proposed criteria

values are

exceeded and high

% DELTA

consistent with

effects seen even in

the absence of

toxics. Shifts in

invertebrate metrics

more uncertain.

++

Coherence of

Evidence

Increased DELTA: 

No known

explanation.

0 Increased DELTA:

Biological gradient

based on few

observations and

may be

confounded by

other stressors

downstream.

0

0 Decreased

Tanytarsini: 

Biological gradient

based on few

observations and

may be

confounded by

other stressors

downstream.

0

NE = no evidence; NA = not applicable/not available
Stressor Identification Guidance Document

Chapter 7: Little Scioto River, Ohio 7-47

Eliminate Diagnose Strength of Evidence

CHARACTERIZE CAUSES

Identify Probable Cause

7.10 Characterize Causes: Identify Probable Causes

Impairment A (RM 7.9). At RM 7.9,

there is a decline in IBI and ICI that is

characterized by an increase in the

relative weight of fish and percent

DELTA, a decreased number of fish

and species of fish, and a decreased 

percentage of mayflies. Candidate

Causes #2, PAH, #4, ammonia, and #5,

low DO/BOD were eliminated (Tables

7-5 and 7-6). Candidate causes #1,

habitat alteration, #3, metal

contamination, and #6, nutrient enrichment, were evaluated in a strength of evidence

analysis (Tables 7-9, 7-10 and 7-11). An artificially deepened channel was identified

as the probable cause for an increase in the relative weight of fish. An embedded

stream bed was identified as the probable cause for decreased numbers and species

of fish and decreased percentage of mayflies. The stream bed may have been

susceptible to becoming embedded due to a lower gradient than upstream. The

probable cause for the low but measurable increase in percent DELTA remained

uncertain. The strength of evidence analysis strongly supports this causal

relationship. The quality of the data is high, and the consistency of the evidence is

good.

Impairment B (RM 6.5). At RM 6.5, there is a further decline in the IBI and ICI. 

Specific impairments include an increase in % DELTA, a decrease in the relative

weight and numbers of species of fish, and an additional decrease in percent

mayflies. Habitat alteration was eliminated as a candidate cause (Tables 7-5 and 7-

6). In the strength of evidence analysis a single probable cause, PAHs, was found to

be sufficient to cause all of the specific impairments (Tables 7-10 and 7-12). Habitat

alteration continued to impair the site but was not the cause of the increased DELTA,

decreased relative weight, or the additional decline in the number of species. The

strength of evidence analysis strongly supports this causal relationship. The quality

of the data is high, and the consistency of the evidence is very good. 

Impairment C (RM 5.7). At RM 5.7, there is a notable further increase in %

DELTA and a decrease in % Tanytarsini. Altered habitat and PAH still cause

impairments, but since the level of alteration remains about the same or decreases,

these candidate causes were eliminated (Tables 7-5 and 7-6). In the strength of

evidence analysis, nutrient enrichment, candidate cause #6, was identified as the

probable cause for both impairments. Nevertheless, ammonia toxicity may still be

important. We have moderate confidence in this characterization.

The causal characterization of the Little Scioto River could be strengthened by evidence

from published literature that reports associations applying to plausible mechanism and

stressor-response, consistency of association, specificity, and others. It was not the

intent of this document to prepare an exhaustive list of appropriate evidence, but such a

resource is certainly needed to make these types of evidence accessible for future

characterizations. This case study does demonstrate the stressor identification process

and the importance of clearly presenting the reasoning and evidence.
Stressor Identification Guidance Document

7-48 U.S. Environmental Protection Agency

Table 7-12. Causal characterization.

Impairment A - RM 7.9 Impairment B - RM 6.5 Impairment C - RM 5.7

Probable Cause: Habitat Alteration Probable Cause: PAH

Contamination

Probable Cause: Nutrient Enrichment

Increased Relative Weight: Is

probably caused by the artificial

deepening of the channel that allows

larger fish to live there.

Increased DELTA: The percentage of

DELTA is commonly associated with

channelized streams, but the specific

aspect of the channelization that

increased DELTA is unknown. 

Loss of species: Many factors could

contribute to the loss of fish and

benthic invertebrate species; however,

embedded substrates seem to be the

most likely stressor since upstream

locations had even lower DO levels

and yet had a greater variety of fish

and invertebrate species.

Although metals are present, the

likelihood of response at the these

concentrations are low. Furthermore,

the types of changes in the

community, especially an increase in

the relative weight of fish, is very

unlikely with the candidate cause of

metals. 

Although P levels are slightly higher,

effects are not associated with these

phosphorous concentration elsewhere

and they do not exceed Ohio’s

proposed criteria values for effects. 

Candidate Causes #2, PAH, and #4,

Ammonia, were eliminated because

levels were the same or lower than

upstream. Candidate Cause #5, Low

DO /BOD , was also eliminated as an

overall pathway; however, low DO

associated with channelization may

still play a roll especially in DELTA.

Siltation and deepened channel are

consistent with Impairment A. The

magnitude of the alteration and clear

difference from upstream location

strongly support this cause. 

A single cause is likely for the

three manifestations of

Impairment B: decreased

relative weight, increased

DELTA, and decreased

species: 

The probable cause of

Impairment B is toxic levels

of PAH-contaminated

sediments. All of the

evidence support PAH

contamination as the cause. 

There is a complete exposure

pathway at the location and

clear mechanism of action for

each of the effects. The

single most convincing piece

of evidence is that the

cumulative toxic units of PAH

were more than 300 times

the probable effects level. 

Metals are at sufficient 

concentrations to cause

effects; however, they were

sometimes at levels close to

upstream levels and were

less than 2% as toxic as the

lowest cumulative toxic units

of PAH. Metal concentrations

are high enough that they

should be considered a

potentially masked cause.

Candidate cause #5 is

unlikely because even

greater levels of BOD did not

cause reduction of dissolved

oxygen downstream.

Candidate Causes #4,

Ammonia, and #6, Nutrient

Enrichment, are unlikely

given that state criteria levels

were met and the much

stronger evidence for PAH.

Habitat alteration continues

to impair the site, but it is not

the cause of the increased

DELTA, decreased relative

weight, or the additional

decline in the number of

species.

At Impairment C increased % DELTA

and % Tanytarsini may have different

causes. Increased DELTA in fish is

probably caused by increased P and

NOx. Nutrients, especially P, have

been associated with increased fin

erosion and lesions but some

uncertainty exists since P acts

indirectly. 

Ammonia is slightly higher than at

Impairment B and exceeded ammonia

criteria values. Biological gradients

were absent; however, this may have

been a statistical artifact given the

number of sites available to perform

the analysis and potential interference

from other stressors downstream. 

Metals are considered unlikely

because surface lesions are only

occasionally noted as effects from long

term exposure and only some metal

concentrations were slightly greater

than at Impairment B. Metal

concentrations are high enough that

they should be considered a

potentially masked cause.

The probable cause of extirpation of

Tanytarsini at Impairment C is more

uncertain because less is known about

the natural history and stressor

response relationships of these

benthic invertebrates. Candidate

cause #6, nutrient enrichment, still

seems to be the most likely cause

since all of the strength of evidence

considerations were consistent.

PAH contamination and habitat

alteration continue to impair the site,

but they are not the cause of the

increased % DELTA or extirpation of

Tanytarsini. 

The causal characterization at

Impairment C is less certain, but the

strength of evidence favors cause #6,

increased nutrients.

7.11 Discussion

An important, practical aspect of this study is that even though the primary cause was

identified in each case, it is obvious that other causes are also present that would

constrain the biological community if the dominant cause was removed. For instance, if

PAHs could be independently removed from the river, metals might be high enough to
Stressor Identification Guidance Document

Chapter 7: Little Scioto River, Ohio 7-49

impair the biological assemblage. Likewise, if metals were removed, habitat alteration

would still affect the biological community and would lower IBI and ICI scores at

Impairments B and C.

Another issue is the impact of habitat alteration and its influence on modifying the

assimilative capacity of the river. In other words, if the physical habitat were improved,

would the impacts of PAH contamination be lessened? At Impairment B, this is unlikely

based on evidence from at least one river elsewhere that has very good physical habitat

qualities, yet has an impoverished biological community replete with high levels of %

DELTA due to high PAH concentrations (OEPA 1992b, Cormier et al. 2000b). The

strength of evidence analysis can provide these insights for the next step in managing

ecosystems, which is to find ways to identify and apportion the sources for the identified

causes and then take action to restore and protect the resource.

At Impairment C, a physical habitat that included wetlands, riparian wetlands, and

riparian cover might improve the assimilative capacity of the river by providing sinks for

the nutrient and ammonia loadings. However, since PAH and metals contamination are

still high at Impairment C, removal of nutrient loading alone would result in only a very

small improvement in biological condition.

At Impairment B, nutrient enrichment was retained as a candidate cause, even though the

increase in phosphorous was minute. Nutrient enrichment was an unlikely cause, but the

reasons for it being improbable come from ecological knowledge from examples in other

watersheds, not from evidence that permits elimination. The reason nutrient enrichment

was retained was because it failed to meet the criteria for elimination. The strength of

evidence is the proper way to show this evidence.

There are other uncertainties. Wet weather flow data was not available for review. 

Events, especially near the combined sewer overflow at RM 6.0, could be undetected

sources of candidate causes. Downstream from Impairment C, persistent impairments

may have other causes. For instance, BOD is elevated at RM 5.8; however, its effects

are usually associated with a certain lag time that results in low DO. 

The results from this particular causal analysis could have several practical applications. 

If it is determined that the river conditions must be improved due to state regulations,

federal TMDL (total maximum daily load) rules, citizen action, or other reasons, one

option is to remove or decrease all potential stressors identified in the causal analysis;

that is, remove both channel modification as well as water and sediment contamination. 

However, there may be intermediate pathways that may be more cost effective. Factors

that should be considered in choosing an option include the desired or expected level of

improvement in river condition, and the usefulness of the river’s resources versus the

cost to restore the river. Another factor to consider is the mode of restoration. For

instance, both PAH and metal remediation may require dredging of the contaminated

sediments. Knowing which agents (PAH, metals, or a combination of the two) may

satisfy our curiosity, but it may not change the management action or ecological

outcome. However, it might be determined that knowing the cause is important for

assigning the financial responsibility for clean-up. In the latter case, additional

information may be needed, especially if restoration costs are high.
Stressor Identification Guidance Document

7-50 U.S. Environmental Protection Agency

7.12 References

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330-355 in Hoffman, David J. et al, ed. Handbook of Ecotoxicology, CRC Press,

Boca Raton, Florida.

Allan, J.D. 1995. Stream ecology: structure and function of running waters. Chapman

and Hall Publishers, London.

Baumann, P.C., and Harshbarger, J.C. 1995. Decline in liver neoplasms in wild brown

bullhead catfish after coking plant closes and environmental PAHs plummet. 

Environ. Health Perspect. 103:168-170.

Carpenter, S.R., N.F. Caraco, D.L. Correll, R.W. Howarth, A.N. Sharpley and V.H.

Smith. 1988. Non-point pollution of surface waters with phosphorus and nitrogen. 

Ecological Applications 8(3):559-568.

Cormier, S.M., E.L.C. Lin, F.A. Fulk and B. Subramanian. 2000a. Estimation of

exposure criteria values for biliary polycyclic aromatic hydrocarbon metabolite

concentration in white suckers (Catostomus commersoni). Environmental

Toxicology and Chemistry 19:1120-1126.

Cormier, S.M., E.L.C. Lin, M.R. Millward, M.K. Schubauer-Berigan, D. Williams, B.

Subramanian, R. Sanders, B. Counts and D. Altfater. 2000b. Using regional

exposure criteria and upstream reference data to characterize spatial and temporal

exposures to chemical contaminants. Environmental Toxicology and Chemistry

19:1127-1135.

Dodds, K. and E.B. Welsh. 2000. Establishing nutrient criteria in streams. The North

American Benthological Society 19:186-196.

Edwards, R., and J. Riepenhoff. 1998. State turns to feds for cleanup. Columbus

Dispatch, April 28, 1998.

Edwards, A.C., H. Twist and G.A. Codd. 2000. Assessing the impact of terrestrially

derived phosphorus on flowing water systems. Journal of Environmental Quality.

29:117-124.

Edwards, C.J., B.L. Griswold, R.A. Tubb, E.C. Weber, L.C. Woods. 1984. Mitigating

effects of artificial riffles and pools on the fauna of a channelized warmwater stream. 

North American Journal of Fisheries Management 4:194-203 

Eisler, R. 2000a. Polycyclic aromatic hydrocarbons. Pages 1343-1411 in Handbook of

Chemical Risk Assessment. Vol. II. Lewis Publishers, Boca Raton, FL.

_______. 2000b. Handbook of Chemical Risk Assessment. Vol. I. Lewis Publishers,

Boca Raton, FL.

Gmur, D.J., and U. Varanasi. 1982. Characterization of benzo[a]pyrene metabolites

isolated from muscle, liver, and bile of a juvenile flatfish. Carcinogenesis 5:1397-

1403.
Stressor Identification Guidance Document

Chapter 7: Little Scioto River, Ohio 7-51

Hickey, C.W. and W.H. Clements. 1998. Effects of heavy metals on benthic

macroinvertebrate communities in New Zealand streams. Environmental Toxicology

and Chemistry 17:2338-2346

Karr, J.R., and I.J. Schlosser. 1977. Impact of nearstream vegetation and stream

morphology on water quality and stream biota. EPA-600-3-77-097. U.S.

Environmental Protection Agency, Environmental Research Laboratory, Athens, GA.

Lin, E.L.C., S.M. Cormier, and J.A. Torsella. 1996. Fish biliary polycyclic aromatic

hydrocarbon metabolites estimated by fixed-wavelength fluorescence: comparison

with HPLC-fluorescent detection. Ecotoxicol. Environ. Safety 35:16-23.

Lin, E.L.C., T.W. Neiheisel, B. Subramanian, D.E. Williams, M.R. Millward, and S.M.

Cormier. Historical monitoring of biomarkers of exposure of brown bullhead in the

remediated Black River, Ohio and two other Lake Erie tributaries. Submitted to

Journal of Great Lakes Research. 

Long, E.R., L.J. Field, and D.D. MacDonald. 1998. Predicting toxicity in marine

sediments with numerical sediment quality guidelines. Envir. Toxicol. Chem.

17:714-727.

Meyer, F.P. and L.A. Barclay. 1990. Field manual for the investigation of fish kills.

Resource Pub. 177. U.S. Fish and Wildlife Service, Washington, D.C.

Miltner, R.J. and E.T. Rankin. 1998. Primary nutrients and the biotic integrity of rivers

and streams. Freshwater Biology 40:145-158.

Norton, S.B. 1999. Using biological monitoring data to distinguish among types of

stress in streams of the Eastern Cornbelt Plains Ecoregion. Ph.D. Dissertation,

Georgetown University, Fairfax, VA.

Ohio Environmental Protection Agency (OEPA). 1988a. Biological criteria for the

protection of aquatic life: Vol. II. users manual for biological assessment of ohio

surface waters. Division of Water Quality Planning and Assessment, Ecological

Assessment Section, Columbus, OH.

__________. 1988b. Biological and water quality study of the Little Scioto River

watershed, Marion County, OH. OEPA Technical Report prepared by State of Ohio

Environmental Protection Agency, Division of Surface Water, Columbus, OH.

__________. 1989a. Addendum to: Biological criteria for the protection of aquatic life:

Volume II. users manual for biological assessment of Ohio surface waters. Division

of Water Quality Planning and Assessment, Ecological Assessment Section,

Columbus, OH.

__________. 1989b. Biological criteria for the protection of aquatic life: Volume III.

standardized field and laboratory methods for assessing fish and macroinvertebrate

communities. Division of Water Quality Planning and Assessment, Ecological

Assessment Section, Columbus, OH.

__________. 1989c. Manual of Ohio EPA surveillance methods and quality assurance

practices. Division of Environmental Services, Columbus, OH.
Stressor Identification Guidance Document

7-52 U.S. Environmental Protection Agency

__________. 1992a. Bottom sediment evaluation, Little Scioto River, Marion, Ohio. 

Division of Water Quality Planning and Ecological Assessment Section, Columbus,

OH.

__________. 1992b. Biological and water quality study of the Ottawa River, Hog

Creek, Little Hog Creek, and Pike Run. OEPA Technical Report EAS/1992-9-7. 

Prepared by State of Ohio Environmental Protection Agency, Division of Surface

Water, Columbus, OH.

__________. 1994. Biological, sediment, and water quality study of the Little Scioto

River, Marion, Ohio. OEPA Technical Report EAS/1994-1-1. Division of Surface

Water, Ecological Assessment Section, Columbus, OH.

Rankin, E.T. 1989. The qualitative habitat evaluation index (QHEI): rationale,

methods, and application. State of Ohio Environmental Protection Agency, Division

of Water Quality Planning and Assessment, Ecological Assessment Section,

Columbus, OH.

__________. 1995. Habitat indices in water resource quality assessments. Pages 181-

208 in W.S. Davis and T.P. Simon (editors). Biological Assessment and Criteria.

Lewis Publishers, Boca Raton, Florida.

Rankin E., R. Miltner, C. Yoder and D. Mishne. 1999. Association between nutrients,

habitat, and the aquatic biota in Ohio rivers and streams. Ohio EPA Technical

bulletin MAS/1999-1. Ohio EPA, Columbus, OH.

Roubal, W.T., T.K. Lallier, and D.C. Malins. 1977. Accumulation and metabolism of

C-14 labeled benzene, naphthalene, and anthracene by young coho salmon

(Oncorhynchus kisutch) and starry flounder (Platichthys stellatus). Arch. Environ.

Contam. Toxicol. 5: 513-529.

Russo, R.C. 1985. Ammonia, nitrate and nitrite. Pages 455-471 in G.M. Rand and S.A.

Petrocelli (editors). Fundamentals of Aquatic Toxicology. McGraw Hill,

Washington, D.C.

Sheilds, F.D. Jr., S.S. Knight and C.M. Cooper. 1998. Rehabilitation of aquatic habitats

in warmwater streams damaged by channel incision in Mississippi. Hydrobiologica

382:63-86

Smith, V.H., G.D. Tilman and J.C. Nekola. 1999. Eutrophication: impacts of excess

nutrient inputs on freshwater, marine and terrestrial ecosystems. Environmental

Pollution 100:179-196.

Tarplee, W.H. Jr., D.E. Louder, and A.J. Weber. 1971. Evaluation of the effects of

channelization on fish populations in North Carolina's coastal plain streams. North

Carolina Wildlife Resources Commission, Raleigh, NC.

U.S. Environmental Protection Agency (USEPA). 1996b. Calculation and evaluation of

sediment effect concentrations for the amphipod Hyallela azteca and the midge

Chironomus riparius. Assessment and Remediation of Contaminated Sediments

(ARCS) Program. Great Lakes National Program Office, Chicago, IL. EPA 905-

R96-008. 
Stressor Identification Guidance Document

Chapter 7: Little Scioto River, Ohio 7-53

__________. 1998b. 1998 update of ambient water quality criteria for ammonia. Office

of Water, Washington, DC. EPA 822-R-98-008.

Varanasi, U., J.E. Stein, M. Nishimoto, and T. Hom. 1983. Benzo[a]pyrene metabolites

in liver, muscle, gonads, and bile of adult English sole (Parophrys vetulus). Pages

1221-1234 in Cooke, M. and A.J. Dennis, eds. Polynuclear Aromatic Hydrocarbons:

Formation, Metabolism, and Measurement. Battelle, Columbus, OH, USA.

Yoder, C.O., and E.T. Rankin. 1995a. Biological criteria program development and

implementation in Ohio. Pages 109-144 in W.S. Davis and T.P. Simon, eds.

Biological Assessment and Criteria: Tools for Water Resource Planning and

Decision Making. Lewis Publishers, Boca Raton, FL.

__________. 1995b. Biological response signatures and the area of degradation value:

New tools for interpreting multi-metric data. Pages 236-286 in Biological

Assessment and Criteria: Tools for Water Resource Planning and Decisionmaking

Lewis Publishers, Boca Raton, FL..

Yount, J.D., and G.J. Niemi. 1990. Recovery of lotic communities and ecosystems from

disturbance; A narrative review of case studies. Environmental Management

14:547-569.
Stressor Identification Guidance Document

7-54 U.S. Environmental Protection Agency

Table 7-13. Fish metrics for the Little Scioto River 1987 and 1992.*

Response River Mile

7.13 Additional Tables

{9.2}

(9.2) (7.9)

{6.5}

(6.5)

{6.0}

(5.7) (4.4)

(3.1) {2.7}

(2.7)

{0.1}

(0.3)

Total No. of Species {19.3} 

(22 ) (13)

{13} 

(8.3)

{10} 

(10.7) (7.7)

{3.3} {6} 

(7.7)

{8.7}

(9.7)

No. of Darter Species {5 }

(5.5) (0)

{0}

(0)

{0}

(0) (0)

{0} {0}

(0)

{0.3}

No. of Sunfish species {3}

(4) (5)

{3}

(2.3)

{1.3}

(3.3) (3)

{0.7} {0.3}

(2.7)

{1} 

(2.7)

No. of Sucker Species {1.3}

(2.5) (3)

{1}

(1.3)

{1.7}

(1.7) (1)

{0.7} {1}

(1.3)

{2} 

(2.7)

No. of Intolerant Species {1}

(1) 0

{0}

(0)

{0} 

(0) (0)

{0} {0}

(0)

{0}

Percent Tolerant Species {35.45}

(60.69) (69.12)

{82.43}

(85.12)

{94.28}

(68.14) (82.75)

{98.2} {94.95}

(70.85)

{63.41}

(38.64)

Percent Omnivores {33.28}

(56.21) (44.95)

{57.2}

(56.72)

{72.47}

(46.84) (71.37)

{94.15} {85.4}

(51.77)

{62.72}

(31.92)

Percent Insectivores {53.41}

(35.96) (53.07)

{40.99}

(39.77)

{16.73}

(47.8) (21.91)

{3.95} {10.18}

(42.51)

{32.74}

(55.36)

Percent Pioneering Species {35.49}

(69.85) (28.41)

{22.52}

(26.39)

{21.94}

(21.76) (17.81)

{4.05} {7.58}

(23.31)

{5.33}

(22.1)

No. of Individuals {808.5}

(1104.6) (206)

{416.3} 

(335)

{237.33} 

(174) (137)

{84.7} {237.33

} 

(94)

{78} 

(75)

Percent Simple Lithophilic

Species

{21.24}

(12.8) (18.19)

{2.7}

(42.69)

{24.01}

(26.2) (31.45)

{5.88} {9.14}

(24.91)

{19.01}

(28.08)

Percent DELTA {0.13}

(0.14) (1.64)

{0.0}

(9.98)

{16.46}

(14.51) (22.37)

{32.8} {14.22}

(10.99)

{16.19}

(10.04)

Relative Weight {4.021}

(8.6) (74.9)

{34.2}

(38.7)

{29.773}

(17.031) (7.2)

{10.7} {24.482 }

(6.3)

{46.079}

(21.1)

IBI {332

(33) (23)

{24} 

(19)

{14} 

(19) (18)

{12} {13} 

(19)

{14}

(25)
Chapter 7: Little Scioto River, Ohio 7-55 Table 7-14. Macroinvertebrate metrics for the Little Scioto River 1987 and 1992.* Response River Mile {9.2} (9.2) (7.9) {6.5} (6.5) {5.8} (5.7) (4.4) {3.2} {2.7} (2.1) 

Total Number of Macroinvertebrates {773} (1464) (1952) {1116} (2815) {207} (1600) (1899) {763} {1779} (5242) 

Total No. of Taxa collected at a Site, both Qualitative and Quantitative {51} (47) (38) {38} (29) {26} (32) (27) {28} {28} (41) 

Total No. of Quantitative Taxa {34} (36) (30) {25} (18) {13} (18) (20) {14} {13} (23) No. of Mayfly Taxa {6} (7) (2) {3} (2) {2} (2) (1) {1} {0} (2) 

No. of Caddisfly Taxa {2} (3) (0) {2} (0) {0} (0) (1) {0} {0} (3) No. of Dipteran Taxa {19} (20) (18) {15} (12) {7} (13) (13) {10} {11} (14) 

No. of Qualitative EPT Taxa {10} (8) (1) {2} (0) {1} (1) (0) {4} {2} (6) 

Percent Mayfly Taxa {56.016} (58.811) (16.393) {20.251} (5.009) {3.382} (2) (0.263) {1.573} {0} (0.114) 

Percent Caddisfly Taxa {4.657} (6.557) (0) {0.179} (0) {0} (0) (0.053) {0} {0} (0.267) 

Percent Tanytarsini Midges {1.552} (3.347) (3.381) {4.48} (2.345) {0.966} (0) (0) {3.67} {0} (0.343) 

Percent Dipterans {26.132} (32.445) (74.795) {55.018} (57.336) {37.198} (91) (74.829) {95.937} {23.834} (97.138) 

Percent Non-insects {7.762} (1.639) (5.43) {20.43} (37.549) {56.039} (7) (21.959) {0.524} {75.998} (2.461) 

Percent Tolerant Organisms {4.916} (8.607) (15.061) {37.993} (77.371) {61.353} (67.75) (54.766) {20.315} {89.545} (29.569) 

Percent Cricotopus {0.388} (0.48) (0) {6.631} (0) {0} (8) (2.53) {0} {4.947} (0.301) 

ICI {40} (38) (16) {22} (8) {8} (6) (10) {8} {4} (18) * { } = 1987; ( ) = 1992
Stressor Identification Guidance Document

7-56 U.S. Environmental Protection Agency

Table 7-15. QHEI metrics for the Little Scioto River 1987 and 1992.*

Metric River Mile

{9.2}

(9.2) ( 7.9)

{6.5}

(6.5)

{6.0

} (5.7

)

(4.4)

{3.1} {2.7}

(2.7)

{0.1}

(0.3)

Substrate {18} 

 (16) (5)

{1} 

(1)

{1} 

(1) (5)

{1} {1} 

(5)

{1} 

(5)

Cover {10} 

(14) (10)

{9} 

(11)

{13} 

(10) (10)

{11} {13} 

(11)

{12} 

(9) 

Cover

Types

{3} 

(6) (4)

{2} 

(6)

{6}

(4) (4)

{4} {6} 

(6)

{5} 

(6)

Channel {18} 

 (17) (10)

{6} 

(10)

{10}

(10) (10)

{11} {10.5} 

(10)

{10} 

(7)

Riparian {9} 

(6) (5.5)

{4} 

(4)

{4}

(6) (6)

{5} {6} 

(8)

{8} 

(5.5) 

Pool {8} 

(11) (8)

{6} 

(8)

{8}

(9) (6)

{6} {8} 

(8)

{8} 

(8)

Riffle {5} 

(6) (0)

{0} 

(0)

{0}

(0) (0)

{0} {0} 

(0)

{0} 

(0)

Gradient {6} 

(6) (4)

{4} 

(4)

{4}

(4) (2)

{2} {2} 

(2)

{4} 

(4)

QHEI {74} 

(76) (42.5)

{30} 

(38.5)

{40}

(40) (39)

{36} {40.5} 

(42)

{43} 

(38.5)

* { } = 1987; ( ) = 1992
Chapter 7: Little Scioto River, Ohio 7-57 Table 7-16. Average concentrations of selected sediment organic compounds (mg/kg) in the Little Scioto River, Ohio

in 1987, 1991, 1992 and 1998.* Compound River Mile [11.1] /9.42\ (9.5) [9.21] (7.9) /7.15 \ [7.09] {6.5} /6.6\ (6.5) [6.6] /5.8\ (5.8) [6.2] (4.4) {2.

/2.

(2.

[2.6

Acenaphthene [0.59]ND (ND) [0.7]ND (ND) [0.047]J {14.8} (ND) [760]J /150\ (5) [5] (4.3) {1.

(N

[0.93

Anthracene [0.59]ND (ND) [0.70]ND (ND) [0.037]J {66.8} (ND) [100]J /360\ (27.1) [41] (7.9) {2.

(N

[3.

Benzo(a)anthracene [0.072]J (ND) [0.043]J (ND) /15\J [0.059]J {44.7} /15\J (8.2)J [310]J /185\ (16.5) [42]J (6.9) {4.

(2

[8.

Benzo(b)fluoranthene [0.068]J (ND) [0.052]J (ND) /25\ [0.051]J {23.6} /20\J (18.1) [200]J /215\ (16.8) [95] (6.9) {2.

(1.6

[12

Benzo(k)fluoranthene [0.058]J (ND) [0.052]J (ND) [0.046]J {213.2} (9.9)J [160]J (12.87) [80] (4.6) {21

(N

[10
7-58 U.S. Environmental Protection Agency Table 7-16 (continued). Average concentrations of selected sediment organic compounds (mg/kg) in the Little Sciot

by river mile in 1987, 1991, 1992 and 1998.* Compound River Mile [11.1] /9.42\ (9.5) [9.21] (7.9) /7.15 \ [7.09] {6.5} /6.6\ (6.5) [6.6] /5.8\ (5.8) [6.2] (4.4) {2.

/2.

(2.

[2.6

Benzo(ghi)perylene [0.052]J (ND) [0.044]J (ND) /10\J [0.030]J {144.1} (49.5) [150]ND /65\ (11.2) [19] (4.9) {16

(N

[13

Benzo(a)pyrene [0.067]J (ND) [0.053]J (ND) /10\J [0.043]J {141.1} (14.8)J [210]J /125\ (15.8) [14] (7.2) {11

(N

[12

Chrysene [0.087]J (ND) [0.065]J (ND) /15\J [0.081]J {119.5} /15\J (16.5) [390]J /305\ (20.8) [13] (9.9) {9.

(1.6

[13

Dibenzo(a,h)anthracene [0.59]ND (ND) [0.7]ND (ND) [0.56]N D {33.3} (ND) [150]ND (4.6) [16]ND (ND) {2.

(N

[3.

Fluoranthene [0.19]J (ND) [0.097]J (ND) /20\J [0.20]J {78.4} /50\ (8.2)J [100]J /550\ (37.6) [44]J (13.5) {6.

(N

[14
Chapter 7: Little Scioto River, Ohio 7-59 Table 7-16 (continued). Average concentrations of selected sediment organic compounds (mg/kg) in the Little Sciot

by river mile in 1987, 1991, 1992 and 1998.* Compound River Mile [11.1] /9.42\ (9.5) [9.21] (7.9) /7.15 \ [7.09] {6.5} /6.6\ (6.5) [6.6] /5.8\ (5.8) [6.2] (4.4) {2.

/2.

(2.

[2.6

Fluorene [0.590]ND (ND) [0.70]ND (ND) [0.059]J {18.3} (ND) [830]J /200\ (7.0) [20] (4.0) {1.

(N

[0.9

Indeno(1,2,3-cd)pyrene [0.045]J (ND) [0.037]J (ND) /5\J [0.56]ND {156.0} (13.2)J [150]ND /60\ (14.5) [16] (6.6) {18

(N

[10

Naphthalene [0.59]ND (ND) [0.70]ND (ND) [0.56]ND {22.9} (ND) [260] /70\ (4.6) [18]J (ND) {1.

(N

[0.2

Phenanthrene [0.14]J (ND) [0.053]J (ND) [0.11]J {88.3} /40\J (ND) [230] /470\ (24.1) [38]J (12.9) {2.

(N

[2.8

* { } = 1987; / \ = 1991; ( ) = 1992; [ ] = 1998
7-60 U.S. Environmental Protection Agency Table 7-16 (Continued). Average concentrations of selected sediment organic compounds (mg/kg) in the Little Scio

by river mile in 1987, 1991, 1992 and 1998.* Compound River Mile [11.1] /9.42\ (9.5) [9.21] (7.9) /7.15 \ [7.09] {6.5} /6.6\ (6.5) [6.6] /5.8\ (5.8) [6.2] (4.4) {2.

/2.

(2.

[2.6

Pyrene [0.2]J (ND) [0.1]J (ND) /15\J [0.2]J {67.5} /30\J (ND) [810]J /405\ (23.8) [32]J (10.2) {5.

(N

[10

{ } = 1987 data from OEPA 1988, sample depth unknown / \ = 1991 data from OEPA 1992a, sample depth unknown ( ) = 1992-93 data from OEPA 1994, sample from 1-6" except RM 7.9 sample from 8-12" [ ] = 1998 data from OEPA unpublished, sample depth unknown J is an estimated value that is above zero but below the practical quantitation limit.
Chapter 7: Little Scioto River, Ohio 7-61 Table 7-17. Average concentrations (mg/kg) of selected metals in sediment from the Little Scioto River, Ohio, by rive

1991,1992 and 1998.* Metal River Mile [11.1] /9.4\ ( 9.5) [9.2] (7.9) /7.2\ [7.1] {6.5} /6.6\ (6.5) [6.6] /5.8\ (5.8) [6.2] (4.4) {2

/2

(2

[2

Arsenic [3.6]J /<10\ (<10) [8.3]J (12.4) /<10\ [6.0]J {11.2} /<10\ (<10) [10.8]J /<10\ (13.8) [9.8]J (11.3) {9

(<

[9

Cadmium [0.1]ND /<1.0\ (<1.0) [0.1]ND (<1.0) /<1.0\ [0.2] {1.8} /3.4\ (<1.0) [0.1]ND /1.0\ (<1.0) [2.0] (10.5) {4

(

[

Chromium [8.1]J /5.8\ (7.3) [14.3]J (13.6) /13.2\ [8.9] {47.6} /415\ (208) [32.3]J /39.2\ (60.9) [50.4] (302) {1

(7

[7

Copper [15.7] (7.4) [24.1] (17.2) [22.9] {68} (79) [39.2] (56.0) [133] (76.8) {

(4

[7

Lead [23.8] /<10\ (12.1) [20.4] (19.1) /25.5\ [24]J {170} /175.5\ (172) [46.4] /59.5\ (84.6) [220]J (93.4) {1

(1

[1

Mercury [0.1]ND /<0.1\ (<0.1) [0.2]J (<0.1) /<0.1\ [0.1]J /0.3\ (0.33) [0.3]J /0.2\ (0.2) [0.6]J (0.8) (0

[0
7-62 U.S. Environmental Protection Agency Table 7-17 (continued). Average concentrations (mg/kg) of selected metals in sediment from the Little Scioto River, 

1987, 1991,1992 and 1998.* Metal River Mile [11.1] /9.4\ ( 9.5) [9.2] (7.9) /7.2\ [7.1] {6.5} /6.6\ (6.5) [6.6] /5.8\ (5.8) [6.2] (4.4) {2

/2

(2

[2

Zinc [48.2] (30.6) [81.4] (79.0) [66.6] {187} (173) [89.2] (141) [280]J (226) {7

(4

[3

{ } = 1987 data from OEPA 1988, sample depth unknown / \ = 1991 data from OEPA 1992, sample depth unknown ( ) = 1992-93 data from OEPA 1994, sample from 1-6" except RM 7.9 sample from 8-12" [ ] = 1998 data from OEPA unpublished, sample depth unknown J is an estimated value that is above zero but below the practical quantitation limit.
Chapter 7: Little Scioto River, Ohio 7-63 Table 7-18. Average concentrations of selected water chemistry parameters (mg/L) in the Little Scioto River, Ohio, b

1992 and 1998.* Compound River Mile [11.1] (9.2) [9.2] {7.9} (7.9) [7.1] {6.5} (6.5) {5.8} (5.8) [6.2] {4.4} ( 4.4) {2.7

(2.7[2.7

Ammonia [0.1,0.3] (<0.05) [<0.05,<0.0 5] (<0.05) [0.11, <0.05] (0.12) (1.16) [0.35, 0.69] (1.44) (2.1

[0.6

Dissolved oxygen** (12.2, 8.8) {4.6, 2.8} (7.9, 5.7) {7.27, 1.9} {8.3, 4.2} (8.23, 4.21) {8.8, 3.2} (5.2, 4.3) {6.6

(4.1

BOD [<2.0, 6.6] (1.0) [<2.0, <2.0] (1.0) [<2.0, 2.1] (2.3) (4.7) [4.6,13] (4.2) (3.5

[3.3

Nitrate-nitrite, NOx [0.7,3.3] (1.2) [0.4, 0.2] (1.4) [0.73, <0.1] (0.8) (8.1) [0.33, 2.37] (6.6) (4.5

[3.5

Phosphorus, total P [0.5,0.6] (0.06) [1.8, 0.1] (0.07) [0.36, 0.13] (0.09) {1.65} (2.17) [1.9, 1.21] (1.96) {2.7

(1.8[1.1

Hardness, CaCO3 [222,250] (329) [275, 269] (327) [281, 407] (389) (278) [224, 261] (280) (306

[228

* { } = 1987 (OEPA 1988b; ( ) = 1992-1993 (OEPA 1994) [ ] = 1998 (OEPA August and October, unpublished data). ** Dissolved Oxygen {maximum, minimum}, data from 1987 (OEPA, 1988b). (maximum, minimum from box plots), data from 1992 (OEPA, 1994.
Stressor Identification Guidance Document

7-64 U.S. Environmental Protection Agency

Table 7-19. PAH concentrations at nearest upstream location and locations of

impairments (mg/kg). (Hyalella azteca sediment effects concentrations, PEL and TEL,

normalized to sediment WET weight.)

Chemical PAH sediment concentration

 PEL TEL Nearest

Upstream

Location

Impairment

A 

Impairment

B 

Impairment

C 

Benzo(a)pyrene (BAP)

 0.32 0.03 

(0)

[0.053] #

(0)

[0.043] #

/141.1\ *

(14.8) *

[210] *

/125\ *

(15.8) *

[14] *

Naphthalene (NAPH)

 0.14 0.02 

(0)

[0]

(0)

[0]

/22.9\*

(0)

[260]*

/70\ *

(4.6) *

[18] *

Fluorene

 0.15 0.01 

(0)

[0]

(0)

[0.059] #

(0)

[830] *

/200\ *

(7) *

[20] *

Phenanthrene

 0.41 0.02 

(0)

[0.053] #

(0)

[0.11] #

(0)

[230] *

/470\ *

(24.1) *

[38] *

Anthracene

 0.17 0.03 

(0)

[0]

(0)

[0.037] #

(0)

[100] *

/360\ *

(27.1) *

[41] *

Fluoranthene

 0.32 0.04 

(0)

[0.097] #

(0)

[0.2] #

(8.2) *

[100] *

/550\ *

(37.6) *

[44] *

Pyrene

 0.49 0.02 

(0)

[0.076] #

(0)

[0.16] #

(0)

[810] *

/405\ *

(23.8) *

[32] *

Benzo[a]anthracene

 0.28 0.03 

(0)

[0.043] #

(0)

[0.059] #

(8.2) *

[310] *

/185\ *

(16.5) *

[42] *

Chrysene

 0.41 0.02 

(0)

[0.065] #

(0)

[0.081] #

(16.5) *

[390] *

/305\ *

(20.8) *

[13] *

Benzo(g,h,i)perylene

 0.25 0.01 

(0)

[0.044] #

(0)

[0.03] #

(49.5) *

[150] *

/65\ *

(11.2) *

[19] *

(*) exceeds PEL and TEL; (#) exceeds TEL. / \ = 1987-1991, ( ) = 1992, [ ] = 1998. 

Zero = below detection; No Entry = No data for that year
Stressor Identification Guidance Document

Chapter 7: Little Scioto River, Ohio 7-65

Table 7-20. Metals concentrations at nearest upstream location and locations of

impairments (mg/kg). (Hyalella azteca sediment effects concentrations, PEL and TEL,

normalized to sediment wet weight.)

Chemical

Nearest

Upstream

Location

Impairment

A

Impairment

B

Impairment

C

 PEL TEL

As

 48.4 10.8 

/5\

(5)

[8.3]

/8\

(12.4) #

[6]

/11.2\ #

(0)

[10.8] #

/8\

(13.8) #

[9.8]

Cd

 3.2 0.58 

/0.5\

(0.5)

[0]

/0.5\

(0.5)

[0.2]

/1.8\ #

(0.5)

[0.1]

/1\

(0.5)

[2] #

Cr

 119.4 32.3 

/5.8\

(7.3)

[14.3]

/13.2\

(13.6)

[8.9]

/47.6\ # 

(208) *

[32.3] #

/39.2\ #

(60.9) #

[50.4] #

Cu

 101.2 28 

(7.4)

[24.1]

(17.2)

[22.9]

/68\ #

(79) #

[39.2] #

(56) #

[133] *

Pb

 81.7 37.2 

(12.1)

[20.4]

(19.1)

[24]

/170\ * 

(172) *

[46.4] #

/59.5\ # 

(84.6) *

[220] *

Zn

 544 98.1 

(30.6)

[81.4]

(79)

[66.6]

/187\ #

(173) #

[89.2]

(141) #

[280] #

(*) exceeds PEL and TEL; (#) exceeds TEL. *ND= not detected, NA = not available, / \ =

1987-1991, ( ) = 1992, [ ] = 1998. Zero = below detection; No Entry = No data for that

year
APPENDIX A

OVERVIEW OF WATER

MANAGEMENT PROGRAMS

SUPPORTED BY THE SI
Stressor Identification Guidance Document

Appendix A: Overview of Water Management Programs A-1

The following sections describe several major water management programs and how the

SI process can support them.

A.1 Water Quality Assessment Reports Under CWA Section 305(b)

In 1987, EPA’s Office of Water recommended that regulatory authorities increase the

use of biological monitoring to better characterize aquatic systems. State and Tribal

agencies were directed to protect the fishable and swimmable goals of the Clean Water

Act. Under Section 305(b), States, Territories, the District of Columbia, interstate water

commissions, and participating American Indian Tribes are required to assess and report

on the quality of their waters (USEPA 1997). The results of 305(b) assessments are not

raw data, but rather are statements about the degree to which each waterbody supports

the uses designated in state or tribal water quality standards. Each State and Tribe

aggregates these assessments and extensive programmatic information in a 305(b) report,

which is a detailed document usually including information from multiple agencies. 

EPA then uses individual 305(b) reports to prepare a biennial National Water Quality

Inventory Report to Congress. This report is the primary vehicle for informing Congress

and the public about water quality conditions in the United States.

Most of the information contained in 305(b) assessments is based on data collected and

evaluated by states, tribes, and other jurisdictions over the two-year period immediately

preceding issuance of the report. The Report to Congress contains national summary

information about water quality conditions in rivers, lakes, estuaries, wetlands, coastal

waters, the Great Lakes, and groundwater. The report also contains information about

public health and aquatic ecosystem concerns, water quality monitoring, and state and

federal water pollution management programs.

States and Tribes base their 305(b) water quality determinations on whether waterbodies

are clean enough to support basic uses, such as aquatic life, swimming, fishing, and

drinking supply. These uses, along with appropriate national criteria and antidegradation statements, are part of the water quality standards set by each state or tribe

to protect its waters. These standards must be approved by EPA. 

Water quality for each individual use is rated as either:

 Good/Fully Supporting

 Good/Threatened

 Fair/Partially Supporting

 Poor/Not Supporting

 Poor/Not Attainable

Appendix A

Overview of Water

Management Programs

Supported by the SI
Stressor Identification Guidance Document

A-2 U.S. Environmental Protection Agency

For waterbodies with more than one use, information is consolidated into a summary use

support designation of general water quality conditions. These uses are characterized as

either:

 Good/Fully Supporting All Uses

 Good/Threatened for One or More Uses

 Impaired for One or More Uses

Once a state or tribe has determined, under section 305(b), that a waterbody is impaired

for one or more uses, the state or tribe is required to identify the source and cause of

impairment. Some causes are much easier to identify than others. For example, a case

where impairment is caused by a specific chemical from a point source discharge might

be straightforward and easily analyzed. Monitoring programs, however, must deal with

impacts caused not only by chemical toxicity, but also conventional pollutants (e.g.,

temperature, pH and dissolved oxygen) and anthropogenic pollutants from non-point

sources. Monitoring agencies need the ability to evaluate the relative impact that a

particular pollutant or other stressor has on the biological integrity of a receiving water.

A.2 303(d) Lists and TMDLs

Section 303 of the 1972 Clean Water Act requires States, Territories and authorized

Tribes to establish water quality standards and Total Maximum Daily Loads TMDLs) for

EPA review and approval. Water quality standards identify the uses for each waterbody

(e.g., drinking water supply, contact recreation, aquatic life support) and the water

quality criteria to support that use. Water quality criteria can be either numeric (e.g., no

more than 10 g/L of copper) or narrative (e.g., nutrients are not to exceed levels which

cause an imbalance of aquatic flora and fauna). Water quality standards also include

antidegradation policies to prevent deterioration of existing high quality waters.

Under Section 303(d), States, Territories and authorized Tribes must identify impaired

waters and establish TMDLs for these waters. Impaired waters are those that do not

meet applicable water quality standards, even after point sources of pollution have

installed the minimum required levels of pollution control technology. States, Territories

and authorized Tribes are required to submit their list of impaired every two years.

States, Territories and authorized Tribes are required to establish priority rankings for

impaired waters on the 303(d) lists and develop TMDLs for these waters. A TMDL

specifies the maximum amount of a pollutant that a waterbody can receive and still meet

water quality standards, and allocates pollutant loadings among point and nonpoint

pollutant sources. EPA must approve or disapprove lists and TMDLs established by

States, Territories and authorized Tribes. If a State, Territory or authorized Tribe

submission is inadequate, EPA must identify the impaired waters and establish the

TMDL.

TMDLs are a critical component of the water quality program. They provide the analytic

underpinning for watershed decisions and promote integrated program planning,

implementation, and funding. For example, controlling sediment and/or nutrient

loadings can protect aquatic habitat, wetlands, endangered species, and drinking water

sources. As requirements are strengthened and public communication emphasized,

sound procedures for identifying stressors and management solutions will become more

important.
Stressor Identification Guidance Document

Appendix A: Overview of Water Management Programs A-3

Development of a TMDL varies based on numerous factors including environmental

setting, waterbody type, source type/behavior, and pollutant type/behavior. However,

TMDL development generally includes the following activities:

1. Problem Identification: characterization of the impairment and

identification of the pollutant causing the impairment;

2. Identification of Water Quality Targets: establishment of the TMDL

endpoint or target value, which is typically the applicable numeric water

quality criterion or a numeric interpretation of the narrative water quality

standard;

3. Source Assessment: estimation of the point, nonpoint and background

sources of pollutants of concern, including magnitude and location of

sources;

4. Allocations: identification of appropriate wasteload allocations for point

sources and load allocations for nonpoint sources;

5. Link Between Numeric Target(s) and Pollutant(s) of Concern: Analysis

of the relationship between numeric target(s) and identified pollutant

sources. For each pollutant, describes the analytical basis for conclusion

that sum of wasteload allocations, load allocations, and margin of safety

does not exceed the loading capacity of the receiving water(s).

6. Calculation of the explicit or implicit margin of safety for each pollutant

and description of accounting for seasonal variations and critical

conditions in the TMDL.

A.2.1 Causes for Impairment: Pollutants and Pollution

Waterbodies are impaired by a variety of stressors. Recent data indicate that the top

causes for impairment include sedimentation/siltation/turbidity and suspended solids

(16%), nutrients (13%), pathogens (13%), and dissolved oxygen ( 10%). These stressors

are often associated with sources or activities that fall under the Clean Water Act

definition of pollutant, or pollution. Pollution is defined in Section 502(19) as the “manmade or man-induced alteration of the chemical, physical, biological, and radiological

integrity of water.”

Section 303(d) requires the identification and listing of all impaired waterbodies

regardless of the origin or source of the pollution or pollutant. Current regulations

require that TMDLs be calculated only for pollutants. Pollutants are defined in Section

502(6) as “dredged spoil, solid waste, incinerator residue, sewage, garbage, heat, and

industrial, municipal, and agricultural waste discharged into water.” 

Both pollution and pollutants are “stressors” that can be identified and evaluated using

the SI process. Under current regulations, those calculating TMDLs will benefit directly

from guidance on identifying stressors considered pollutants under the Clean Water Act. 

The SI guidance can also assist in establishing the causal linkage between a pollutant and

the biological impairment, and thus provide a basis for the development of a TMDL. For

example, if a pollutant causes ecosystem changes that alter the fish community, the
Stressor Identification Guidance Document

A-4 U.S. Environmental Protection Agency

altered biological community is an impairment that can be traced to a pollutant for which

a TMDL can be calculated.

A.2.2 EPA Actions to Implement the TMDL Program

In an effort to speed the Nation’s progress toward achieving water quality standards and

improving the TMDL program, EPA began, in 1996, a comprehensive evaluation of

EPA’s and the states’ implementation of their Clean Water Act section 303(d)

responsibilities. EPA convened a committee under the Federal Advisory Committee Act,

composed of 20 individuals with diverse backgrounds, including agriculture, forestry,

environmental advocacy, industry, and state, local, and tribal governments. The

committee issued its recommendations in 1998. These recommendations were used to

guide the development of proposed changes to the TMDL regulations, which EPA issued

in draft in August, 1999. After a long comment period, hundreds of meetings and

conference calls, much debate, and the Agency’s review and serious consideration of

over 34,000 comments, the final rule was published on July 13, 2000. However,

Congress added a “rider” to one of their appropriations bills that prohibits EPA from

spending FY2000 and FY2001 money to implement this new rule. The current rule

remains in effect until 30 days after Congress permits EPA to implement the new rule. 

TMDLs continue to be developed and completed under the current rule, as required by

the 1972 law and many court orders. The regulations that currently apply are those that

were issued in 1985 and amended in 1992 (40 CFR Part 130, section 130.7). These

regulations mandate that states, territories, and authorized tribes list impaired and

threatened waters and develop TMDLs.

A.2.3 Stressor Identification and the TMDL Program

EPA developed the SI process to assist water resource managers in identifying and

delineating stressors causing biological impairments to waterbodies. While not all water

quality impairments listed under 303(d) are linked directly to biological components of

waterbodies, a sample of submittals from 19 states indicate that approximately one-half

of waterbodies listed as impaired under 303(d) are not meeting biological designated

uses (e.g., aquatic life, cold water fishery). The SI process will have direct utility to

States, Tribes, and EPA by providing sound approaches to evaluating the causes of

biological impairments under the TMDL Program.

As used in the SI process, the term stressor is synonymous with the terms pollutant and

pollution which, under Section 303(d), are considered causes of impairment. The

identification of pollutant stressors resulting in biological impairment to waterbodies,

and the diagnostic evaluation of the sources of these stressors, is an essential first step in

calculating Total Maximum Daily Loads under Section 303(d) of the Clean Water Act. 

For pollution stressors (e.g., habitat degradation, water control structures), for which

TMDLs are not calculated, SI results can be used to identify the sources of the pollution

for use in alternative watershed management activities.

A.3 State/Local Watershed Management

Since 1991, EPA has promoted a watershed protection approach to help address the

nation’s remaining water resource challenges (USEPA 1991a). The watershed approach

is an integrated, holistic strategy for protecting and managing surface water and

groundwater resources by watershed, a naturally defined hydrologic unit. For any given

watershed, the approach considers not only the water resource; such as a stream, river,
Stressor Identification Guidance Document

Appendix A: Overview of Water Management Programs A-5

lake, estuary, or aquifer; but all of the land from which water drains into that resource.

The watershed approach uses all aspects of water resource quality—physical (e.g.,

temperature, flow, mixing, habitat); chemical (e.g., conventional and toxic pollutants,

such as nutrients and pesticides); and biological (e.g., health and integrity of biotic

communities, biodiversity). EPA’s Office of Water has worked to orient and coordinate

point source, non-point source, surface water, wetlands, coastal, groundwater, and

drinking water programs within a watershed context.

The watershed approach is not a program but a way to organize programs, so that the use

of SI will vary with the program conducting the investigation. The watershed approach,

however, can facilitate an SI investigation since information is already integrated from

various sources, such as point source discharges and non-point source runoff. This

integrated information can help investigators make sense of disturbances through

knowledge of potential sources of stressors that might feed into that location or might

affect the food source or some other essential ecosystem component by affecting the

natural continuum (Vannote et al. 1980).

The challenge for identifying stressors for watershed-based programs is proper scaling. 

Even though the SI may be initiated by a program using the watershed approach, the

impairment may not be watershed wide. Impairment to the biological system may be

difficult to determine on a watershed scale. Similarities among biota tend to follow

ecoregions, rather than watersheds. Several ecoregions may exist within a watershed,

especially where elevation differences are great. The biota within any given ecoregion

may respond differently to a given stressor than the biota within a neighboring ecoregion. 

Accurate scaling of the problem is important any time a biological impairment is found,

but especially with the watershed approach, to ensure that the information is used to full

advantage in identifying and characterizing stressors.

A.4 Non-point Source 319 Management

The 1987 Water Quality Act Amendments to the Clean Water Act added section 319,

which established a national program to assess and control non-point source (NPS)

pollution. Under this program, states and tribes are asked to assess their NPS pollution

problems and submit their assessments to EPA. The assessments included a list of

navigable waters within the State or Tribal Territories, which without additional action

to control NPS pollution, cannot reasonably be expected to attain or maintain applicable

water quality standards or the goals and requirements of the Clean Water Act. Section

319 also requires identification of categories and subcategories of NPS pollution that

contribute to impairment of waters, descriptions of procedures for identifying and

implementing best management practices, control measures for reducing NPS pollution,

and descriptions of State, Tribal, and local programs used to abate NPS pollution.

NPS programs need to identify and control NPS pollutants. Since NPS pollutants can be

difficult to trace, identifying the source of these pollutants is probably the greatest

challenge for NPS programs. The SI process can help investigators obtain greater

confidence that stressors have been accurately identified. Attributing responsibility to a

particular source can be very straightforward and obvious or very difficult. Mechanisms

used to attribute responsibility need to be assessed for each situation, and common sense

should be used. For example, runoff may be obviously coming from one farm. In

another situation, runoff may encounter multiple potential sources of pollution, including

a poultry farm, a cattle feedlot, and an abandoned mine. In the latter situation, if nutrient
Stressor Identification Guidance Document

A-6 U.S. Environmental Protection Agency

loading is the identified stressor, attributing responsibility between the poultry farm and

cattle feedlot may be difficult, but ruling out the abandoned mine would be simple.

A.5 Permitting Programs

A.5.1 NPDES Permits

All discrete sources of wastewater are required to obtain a National Pollutant Discharge

Elimination System (NPDES) permit (or State equivalent) that regulates the facility’s

discharge of pollutants. This approach to controlling and eliminating water pollution is

focused on pollutants determined to be harmful to receiving waters and sources of such

pollutants. Authority for issuing NPDES permits is established under Section 402 of the

CWA. A summary of the Water Quality-based “Standards to Permits” Process for

Toxics Control (adapted from the Technical Support Document for WQ-based Toxics

Control, TSD, USEPA 1991a) lists nine steps:

1. Define water quality objectives, criteria, and standards;

2. Establish priority waterbodies;

3. Characterize effluent - chemical-specific or Whole Effluent Toxicity

(WET);

a) evaluate for excursions above standards,

b) determine reasonable potential, and

c) generate effluent data;

4. Evaluate exposure (critical flow, fate modeling, and mixing) and

calculate wasteload allocation;

5. Define required discharge characteristics by the waste load allocation;

6. Derive permit requirements;

7. Evaluate toxicity reduction and/or investigate indicator parameters (as

needed, for permits containing WET monitoring or limits);

8. Issue final permit with monitoring requirements – average monthly and

maximum daily average weekly for publicly operated treatment works)

limits; and

9. Track compliance.

Sometimes the monitoring requirements include biological assessment of the receiving

water. The permit can contain a reopener clause to allow the limits and monitoring

requirements to be adjusted if biological impairment is found in the receiving water.

The SI guidance is somewhat analogous in function to the Toxicity Reduction Evaluation

(TRE) and Toxicity Identification Evaluation (TIE) guidance used in Step 7 above

(USEPA 1988a,b,c, 1991b, 1993a,b). In the permitting process, toxicity is controlled

through limits for specific chemicals and limits for whole effluent toxicity. When permit

monitoring shows that an effluent has toxicity above the amount allowed by the permit,

the discharger is often required to conduct a TRE to determine if a simple solution exists
Stressor Identification Guidance Document

Appendix A: Overview of Water Management Programs A-7

for reducing the toxicity, e.g., housekeeping procedures for cleaning fluids, or pH

buffering of the effluent. If the solution is not apparent from the TRE, additional TIE

procedures may be required. TIE procedures guide investigators through additional data

collection to determine the toxic component(s) of the waste stream. These procedures

include both aquatic toxicity methods and chemistry methods.

When WET or chemical testing show that the effluent is toxic, this does not mean that an

impairment will necessarily be found in the aquatic biota within the zone of influence of

the discharge. Effluent limits include safety factors in their calculations. The waste load

allocation (Step 4, above) is calculated based on worst-case estimations. For example,

effluent limits for toxicity or for a toxic chemical are based on low-flow conditions in

streams and rivers (often the lowest seven-day flow in a ten-year period). Effluent limits

may be exceeded, a TRE/TIE conducted, and the problem solved without incurring

measurable impairment in the receiving water biota. The current trend is to lessen this

safety buffer by customizing water quality-based permit limits to local conditions

through such mechanisms as dynamic modeling of waste load allocation (USEPA 1991a)

and recalculation of water quality standards or use of the water-effects ratio (USEPA

1994). 

Conversely, ambient biological assessments may show impairment in the aquatic biota

below a permitted discharge without a measured permit limit exceedence. The role of

the effluent in causing the impairment is not readily apparent in this case. The effluent

stream could have been toxic during periods when toxic parameters were not being

measured; effluent toxicity tests could have been insufficiently sensitive through

inappropriate selection of test organisms or operator error; or impairment could have

been caused by stressors other than effluent discharge. Accurate attribution of

responsibility can be very critical in NPDES permitting cases, both for fairness and

success in stressor control. A SI should be conducted to distinguish effects caused by the

effluent discharge and effects from other stressors.

A.5.2 Cooling Tower Intake 316(b) Permitting

Under section 316(b) of the CWA, any NPDES permitted discharger which intakes

cooling water must not cause an adverse environmental impact to the waterbody. To

determine if a cooling water intake structure is causing adverse environmental impacts to

the waterbody, the overall health of the waterbody should be known. Where biological

impairments are found, stressor identification procedures should help investigators

identify the different stressors causing the waterbody to be impaired, including the intake

structure. A high degree of certainty is needed.

A.5.3 Dredge and Fill Permitting

Under Section 401 of the CWA, different types of federal permitting activities (such as

wetlands dredge and fill permitting) require a certification that there will be no adverse

impact on water quality as a result of the activity. This certification process is the 401

Water Quality Certification. Under Section 404 of the CWA, the discharge of dredge

and fill materials into a wetland is illegal unless authorized by a 404 Permit. The 404

Permit must receive a 401 Water Quality Certification.

Stressor identification procedures will help investigators identify the different types of

stress an activity may place on water quality that can then be addressed through

conditions in the 401 Certification. Stressor identification procedures may help to
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A-8 U.S. Environmental Protection Agency

identify unanticipated stress from a dredge and fill activity on water quality or the

biological community after the activity is underway. Stressor identification procedures

may also help in pre-permitting evaluations of the potential impacts of 404 permitting by

assessing different potential stressors on the wetland in advance.

A.6 Compliance and Enforcement

Since 1972, Section 309 of the Clean Water Act has provided statutory authority for a

range of enforcement responses for entities or individuals who fail to comply with the

Act. At the extreme end of this range, actions can result in criminal penalties. EPA has

national and regional programs in place to investigate and prosecute cases. States and

Tribes may have their own compliance and enforcement investigation programs.

A.6.1 Investigations

When a violation occurs, an investigator must first ascertain what must be done to

achieve compliance with the Clean Water Act. Under a Section 309 order, the violator

must come in full compliance with the Clean Water Act; which, under Article 101,

directs the restoration and maintenance of the biological integrity of the nation’s waters.

When non-compliance is due to biological impairment or non-attainment of biological

integrity, the investigator must determine the cause of the impairment before

implementing a program to restore biological integrity and achieve compliance. This is a

direct use of the SI process.

The degree of environmental harm is a very important factor that investigators and

judges evaluate when assessing criminal penalties. The SI process should be helpful in

determining whether the causes of impairment are consistent with the causes that would

likely have resulted from the source under investigation. The SI process can also help to

determine the likelihood that one stressor versus another caused the impairment. In

cases where separation of stressor mechanisms is fairly clear cut, the SI process can help

investigators determine the significance of the available evidence in determining whether

the alleged stressor caused the noted environmental harm. However, the SI process is

limited to evaluating causes. If more than one stressor or source are involved, allocating

the relative contribution of each stressor or source to the environmental harm may

require additional tools, such as allocation methodologies, that are beyond the scope of

this document.

A.6.2 Enforcement Proceedings

In an enforcement action, the enforcement official seeks for a court to order the

defendant to cease the harmful action, or give injunctive relief. Identifying the causes of

impairment is a crucial step in identifying the actions that would constitute injunctive

relief. The SI process should benefit enforcement officials and expert witnesses by

helping them identify responsible stressors and organize cogent evidence supporting the

identified causal scenario. The SI process adds uniformity to the organization and

analysis of data. 

A special program that is often used to grant injunctive relief is the Supplemental

Environmental Project (SEP). Under this program, a judge may allow a defendant to

improve the environment in lieu of paying a portion of a federal fine to the National

Treasury. The environmental benefit gained through an SEP may not directly alter the

harm that the defendant caused originally, but is seen as alternate compensation. For
Stressor Identification Guidance Document

Appendix A: Overview of Water Management Programs A-9

example, rather than paying a fine of $1 million, a defendant might pay a $600,000 fine

and build a bike path with a 30-foot riparian buffer zone (for runoff reduction) along the

impacted creek, or even a neighboring stream.

When the SI process identifies multiple stressors as the cause of impairment, the

information can still be valuable to the SEP program because the alternate stressors may

help direct compensatory action. If, for example, the SI process identifies a stressor

scenario with two stressors working in conjunction and the defendant is responsible for

only one of the two stressors, a judge might approve a plan for the defendant to use

resources to conduct an SEP project that reduces the second stressor, in lieu of a portion

of the fine.

Targeting resources is very important to investigation and enforcement efforts. EPA

often uses 303d lists of impaired waterbodies to target these efforts. The SI process can

supplement the information in the 303d lists so that stressors may be targeted within

targeted waterbodies. Targeting may also be important in assessing future legislative

needs when mechanisms for stressor control are inadequate in national rules and policies,

and in current state and tribal statutes. Targeting stressors for increased control may

identify changes to instigate.

A.7 Risk Assessment

Risk assessment is a scientific process that includes stressor identification, receptor

characterization and endpoint selection, exposure assessment, stress-response

assessment, and risk characterization (USEPA 1998a, Suter 1993). Risk management is

a decision-making process that combines human-health and ecological assessment results

with political, legal, economic, and ethical values to develop and enforce environmental

standards, criteria, and regulations. Risk assessment can be performed on a site-specific

basis, or can be geographically-based (e.g., watershed scale). It can be used to assess

human health or ecological risks.

Results of bioassessment studies can be used in watershed ecological risk assessments to

develop broad-scale empirical models of biological responses to stressors. Such models

can be combined with exposure information to predict risk from specific stressors and

anticipate the success of management actions. Accurate stressor identification is an

integral part of this process and can help ensure that management actions are properly

targeted and efficient in producing the desired results.

A.8 Wetlands Assessments

Although few states have fully incorporated wetlands into water quality standards or

biological assessment programs, a growing number have started to develop biological

assessment methods for wetlands. During the past five years, several state and federal

agencies have independently started to develop bioassessment methods for wetlands. 

Minnesota, Montana, North Dakota, and Ohio have been pioneers among the states. The

Biological Resource Division of the U.S. Geological Service, Wetlands Science Institute

of the Natural Resources Conservation Service, and EPA have been the leading federal

agencies.

The SI process and tools specific to wetlands investigations are very much needed by

wetlands managers. In recent 305(b) Reports, states identified sedimentation, nutrient

enrichment, fill and drainage, pesticides, and flow alterations as the major causes of
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A-10 U.S. Environmental Protection Agency

wetlands degradation. Biological assessment methods will allow resource managers to

evaluate the condition of wetlands and may provide some indication of the types of

stressors involved. Once bioassessment methods are completed and incorporated into

monitoring programs, wetlands may be listed as impaired due to biological impairment. 

SI methods will be needed to identify stressors causing biological impairment so that

resource managers can better remedy the problems. More information about wetland

bioassessments is available at the EPA Wetlands Division web page

(www.epa.gov/owow/wetlands).

A.9 Preservation and Restoration Programs

Preservation and restoration programs like the National Estuary Program and the

Superfund Program can also benefit from the SI process.

A.9.1 National Estuary Program

The National Estuary Program (NEP) was established in 1987 by amendments to the

Clean Water Act to identify, restore, and protect nationally significant estuaries of the

United States. Unlike traditional regulatory approaches to environmental protection, the

NEP targets a broad range of issues and engages local communities in the process. The

program focuses not only on improving water quality in an estuary, but also on

maintaining the integrity of the whole system, its chemical, physical, and biological

properties, and its economic, recreational, and aesthetic values. 

The NEP is designed to encourage local communities to take responsibility for managing

their own estuaries. Each NEP is made up of representatives from federal, state and

local government agencies responsible for managing the estuary's resources, as well as

members of the community -- citizens, business leaders, educators, and researchers. 

These stakeholders work together to identify problems in the estuary, develop specific

actions to address those problems, and create and implement a formal management plan

to restore and protect the estuary. Twenty-eight estuary programs are currently working

to safeguard the health of some of our nation’s most important coastal waters. 

The SI process should be useful to the NEP, and other preservation programs, by helping

stakeholders identify sources and causes of impairments. This information would feed

into the development of a management plan.

A.9.2 Superfund

The Comprehensive Environmental Response, Compensation, and Liability Act

(CERCLA), commonly known as Superfund, was enacted in 1980 (and amended in

1986) for hazardous waste cleanup. This law created a tax on the chemical and

petroleum industries and provided federal authority to respond to releases or threatened

releases of hazardous substances that may endanger public health or the environment. 

The money collected from the taxation went to a trust fund for cleaning up abandoned or

uncontrolled hazardous waste sites. CERCLA also established prohibitions and

requirements for closed and abandoned hazardous waste sites; defined liability of

persons responsible for releases of hazardous waste at these sites; and established

funding for cleanup when no responsible party could be identified.

Since the basis for actions is whether the hazardous substance may endanger public

health or the environment, identifying the stressor(s) causing environmental harm is
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Appendix A: Overview of Water Management Programs A-11

important. For cleanup sites where other stressors (e.g., habitat alteration) are also likely

causes of impairment, any cleanup and ecosystem recovery plans would need to take into

account the effects of these stressors. Allocating the amount of responsibility that may

be attributed to each stressor is beyond the scope of the SI process, but knowledge of any

additional stressors that may be causing effects can be valuable in determining expected

outcomes of recovery activities.
APPENDIX B

WORKSHEET MODEL
Stressor Identification Guidance Document

Appendix B: Worksheet Model B-1

Appendix B

Worksheet Model

The following pages contain a worksheet model that may be used with the SI process. 

This is only an example and may not fit every case without alterations.

B.1 Instructions for Using the Worksheet Model

This worksheet follows the SI process outlined in this document. The worksheet was

designed to be flexible. At certain points, the user will be asked to stop ( ) and

consider the evidence gathered thus far, in order to determine whether the process is

complete or requires further analysis. For detailed guidance, the user will need to refer

to the sections of the document that are cited at each step.

1. To begin, write the name of the investigator and date for reference.

2. Fill in the appropriate information in Unit I: List Candidate Causes. To

determine the types of information to include throughout the worksheet,

please refer to the cited sections of the document.

3. Summarize and document the data and analyses in Unit II, Part A. Then,

you may use either of the following options:

 Option 1: Analyze the strongest evidence. If you feel that you have

enough case specific data to eliminate some causes, analyze this data

using Unit II, Part B and proceed to Unit III, Step 1: Eliminate

Alternatives. Note: You may also look at other types of evidence that

can be used for elimination in Unit II, Parts C and D. To do this, fill in

only the blanks in Parts C and D that are designated by the letter E (for

elimination) under the heading Associated Causal Characterization

Method in Unit III. Review this additional evidence to see if it allows

you to eliminate any alternatives. 

 If you still have more than one likely causal scenario that could not be

readily eliminated, or if you want to thoroughly review all evidence,

proceed to Unit II, Parts C and D. Complete relevant sections of Parts

C and D for each candidate cause that you listed in Unit I. Then

proceed to Unit III and characterize the cause using diagnosis or strength

of evidence, as appropriate (described under #4 below).

 Option 2: List all available evidence in Unit II before going on to Unit

III: Characterize Causes. Using either option, you may still choose to

do additional iterations if the available evidence is insufficient.

 Go to Unit III, Characterize Causes. For those candidate causes listed

in Unit I that were not eliminated while analyzing the evidence listed in

Unit II (i.e., those causes not designated as E in Parts C and D under

the heading Associated Causal Characterization Method in Unit III),

complete Step 1: Eliminate Alternatives and try to further eliminate
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B-2 U.S. Environmental Protection Agency

causes. Analyze this evidence carefully; if the evidence is not strong

enough to eliminate a candidate, it still may be useful for the strength of

evidence analysis. Using the worksheet in Unit III, Step 1, determine:

 If the primary cause is so dominant that it masks the effects of others,

then re-evaluate whether the other stressors should be retained. A cause

should not be eliminated if it is potentially masked. Instead, strength of

analysis should be used.

 If only one candidate cause remains, go to Unit IV: Sufficiency of

Evidence. Note: You still may want to look at the diagnostic and

strength of evidence information to strengthen your case. If so, go to

Unit III, Step 2.

 If more than one candidate cause remains, go to Unit III, Step 2 to look

for diagnostic evidence.

 If no candidate causes remain, go to Unit V. You will need to do another

iteration with more information.

 Next, try diagnosis. Look for evidence designated as D under the

column labeled Associated Causal Characterization Method in Unit III

in Unit II, Part C tables. Using the worksheet in Unit III, Step 2,

determine:

 If only one candidate cause remains, go to Unit IV: Sufficiency of

Evidence. Note: You may still want to do a strength of evidence

analysis to strengthen your case. If so, go to Unit III, Step 3.

 If more than one candidate cause remains, go to Unit III, Step 3

(Strength of Evidence Analysis).

 If no candidate causes remain go to Unit V and do another iteration with

more information.

 Many investigators will want to complete the strength of evidence

analysis even if elimination or diagnosis have identified the stressor. 

This part of the SI process helps determine how strong a case an

investigator can make for a particular stressor. Look for evidence

designated as S under the column labeled Associated Causal

Characterization Method in Unit III in Unit II Part C, and also consider

the evidence gathered in Part D. Analyze this evidence carefully using

the worksheet in Unit III, Steps 3, 4, and 5.

 Unit III Steps 3, 4, and 5 allow the investigator to compare evidence,

side-by-side, for candidate causes. The step used depends on the type of

evidence. Scores are assigned to each candidate cause to reflect that

cause’s relevance to each causal consideration. (For more detailed

information on comparing stressors, refer to the sections cited in the

worksheets). Compare scores among the candidate causes, and then go

to Unit IV, Sufficiency of Evidence.
 List the most likely cause in Unit IV, and determine if the evidence is

sufficient for the intended use. 

 If yes, your SI is complete, report results.

 If no, go to Unit V, Reconsider Impairment.

 Reconsider whether the impairment was real and describe the results.

 If no, your SI is complete, report results.

 If yes, go to Unit VI, Collect More Data.

 Determine whether all reasonable causes were analyzed.

 If no, complete Unit VI, Follow-on 1 to determine whether additional

scenarios should be analyzed (back to Unit I), or whether the process

should be ended and the results reported as inconclusive.

 If yes, go to Unit VI, Follow-on 2 to determine whether additional data

should be collected and another iteration begun (back to Unit I), or

whether the process should be ended and the results reported as

inconclusive.
Stressor Identification Guidance Document

B-4 U.S. Environ

Stressor Identification Worksheet

Investigator ____________________________ Date Completed________

UNIT I. LIST CANDIDATE CAUSES

Results / Notes

Describe the impairment.(see Chapter 2.2)Make a map. (Unit I part A) (see Chapter 2.2)Define the Scope of the Investigation.(see Chapter 2.3)List the candidate causes(see Chapter 2.4)Develop a conceptual model for the case. (UnitI, part B)(see Chapter 2.5)

Candidate Causes

# 1.# 2.# 3.

Go to Unit II, Analyze Evidence.
Stressor Identification Guidance Document

Appendix B: Worksheet Model 

UNIT I. LIST CANDIDATE CAUSES

Part A. Make a map to document geographic features relevant to the analysis. 

• Draw a map or insert map of study area.• Include natural and man-made features such as dams, sources, tributaries, landfills, dredge areas, je

bars, waterfalls, wetlands, salt water intrusion, etc. See Chapter 2.2.

• Show location of impairment.
Stressor Identification Guidance Document

B-6 U.S. Environ

UNIT I. LIST CANDIDATE CAUSES

Part B. Make a conceptual model of the case. 

• Draw a conceptual model of the case. See Chapter 2.5.

• Include hypothesized sources, stressors and important environmental processes that lead to the imp• Label candidate causes.
Stressor Identification Guidance Document

Appendix B: Worksheet Model 

UNIT II

Part A. Summarize and document associations between the candidate cause and th

• Insert tables, graphs and/or figures of relevant data. See Chapter 3.1.

• Insert statistical analyses including correlations, geographic associations, etc. See Chapter 3, textbo• You may want to look at other types of evidence that can be used for elimination in Unit II, Part B 

If you feel that you have enough case specific data to eliminate some causes, proceed Step 1 (Eliminate Alternatives). If not, proceed to Unit II Part B.
Stressor Identification Guidance Document

B-8 U.S. Environ

UNIT II

Part B. Measurements associated with the causal mechanism (Chapter 3.3).

• Evidence can be used for Elimination (E) Diagnosis (D) or Strength of Evidence (S), as noted below.• Prepare a separate table for each candidate cause.• Use this as a reminder of types of data that could be used in the analysis. Not all questions may be app

Candidate Cause: _______________________

Example Questions:

Yes/No/

Question Not

Relevant

Associated Causal

Characterization

Method in Unit

III* Supportin

Are symptoms or other responses specific toor characteristic of a type of stressor found inorganisms from the impaired community?

D, S

Are there internal measures of exposure (e.g.,body burdens, biomarkers) found inorganisms from the impaired community?

E, D, S

Is an intermediate product of an ecologicalprocess present?

 E, S

Do distributions of stressors and receptorscoincide?

 E, S
Stressor Identification Guidance Document

Example Questions:

Yes/No/

Question Not

Relevant

Associated Causal

Characterization

Method in Unit

III* Supportin

Appendix B: Worksheet Model 

Have there been expected changes in theabundance of predators, prey, or competitors?

S

Are there expected effects on other receptors? SOther 

*E = Elimination; D = Diagnosis; S = Strength of Evidence

If you feel that your evidence can be used to identify the cause through diagnosis, go Step 2. If not, continue with the analysis of evidence in Unit II Parts C and D.
Stressor Identification Guidance Document

B-10 U.S. Environ

UNIT II

Part C. Associations of effects mitigation with manipulation of causes (Chapter 3.4).

• Evidence can be used for elimination ONLY if it is from the site.• Prepare a separate table for each candidate cause.• Use this as a reminder of the types of data that could be used in the analysis. Not all questions may be 

Candidate Cause: _______________________

Questions:

Yes/No/

Information not

available/

Question not

Applicable

Associated

Causal

Characterization

Method

in Unit

III* Supporting An

Does elimination of the source reduce oreliminate the effect?

 S, E

Does the introduction of previouslyunexposed organisms result in an effect?

 S

Does the isolation of organisms from onecause reveal the effects of others?

 S
Stressor Identification Guidance Document

Questions:

Yes/No/

Information not

available/

Question not

Applicable

Associated

Causal

Characterization

Method

in Unit

III* Supporting An

Appendix B: Worksheet Model 

Does the testing of chemical fractions of sitemedia result in toxicity being associated witha particular fraction (i.e., TIE)?

 S

Other 

*E = Elimination; D = Diagnosis; S = Strength of Evidence

If you have enough data to determine the cause, proceed to Unit III Step 1 (Eliminat2 (Diagnosis) or Step 3 (Strength of Evidence), as appropriate. If not or uncertain, proII Part D.
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B-12 U.S. Environ

UNIT II

Part D. Using effects data from elsewhere (Chapter 3.2).

• Use this table to incorporate data from other situations that support the analysis. Not all questions may 

given candidate cause.

• This evidence is applicable to Strength of Evidence (S) characterization method.• Prepare a separate table for each candidate cause.

Candidate Cause: _______________________

Type of 

Candidate 

Cause

Characterization

of Exposure

(Intensity, Time,

and Space) 

Data

Available?

Yes (note

location of

data)/No

Exposure-Response

(E-R) Relationship

E-R Available?

Yes (note

location of data)

/No/Not

Relevant

Would effects

be expected at

the

environmental

conditions

seen in the

case? 

(Yes/No)

Chemical What is the

concentration inthe medium at thesite?

What is theconcentration-responserelationship (seen in thelab or the field)? 

What is theinternalconcentration inorganisms at thesite?

What is the internalexternal concentrationresponse relationship(seen in the lab or thefield)? 

What is theconcentration inthe biomarker atthe site?

What is the biomarkerresponse relationship? 
Stressor Identification Guidance Document

Type of 

Candidate 

Cause

Characterization

of Exposure

(Intensity, Time,

and Space) 

Data

Available?

Yes (note

location of

data)/No

Exposure-Response

(E-R) Relationship

E-R Available?

Yes (note

location of data)

/No/Not

Relevant

Would effects

be expected at

the

environmental

conditions

seen in the

case? 

(Yes/No)

Appendix B: Worksheet Model 

Effluent What is the

dilution of theeffluent at thelocation of theimpairment?

What are the laboratorytest (i.e., WET) resultsfrom 100% effluent or diluted effluent?

Contaminated ambientmedia

What were thelocation and timeof collection andthe results ofanalyses?

What are the results oflaboratory tests ofambient media? 

Habitat What are the

structuralattributes of thehabitat?

Are empirical modelsavailable that relatehabitat characteristicsto biological responses? 

Water Withdrawal or Drought

Are hydrographreadings andsummary statistics(e.g., 7Q10)available?

What are the results ofinstream flow models(e.g., IFIM)?

Thermal Energy Are temperature

records available?

What are the thermaltolerances of theimpacted organisms?
Stressor Identification Guidance Document

Type of 

Candidate 

Cause

Characterization

of Exposure

(Intensity, Time,

and Space) 

Data

Available?

Yes (note

location of

data)/No

Exposure-Response

(E-R) Relationship

E-R Available?

Yes (note

location of data)

/No/Not

Relevant

Would effects

be expected at

the

environmental

conditions

seen in the

case? 

(Yes/No)

B-14 U.S. Environ

Siltation (Suspended)

What is the totalsuspended solids(TSS)concentration?

What is theconcentration-responserelationship (seen in thelab or field)? 

Siltation(Bed-load)

What is the degreeof embeddednessand texture of thesilt?

 Are empirical modelsavailable tocharacterize theeffects?

Dissolved Oxygen andOxygen-DemandingContaminants(e.g., BOD, COD)

Review thedissolved oxygendata (esp.predawn).

What is theconcentration-responserelationship (from labor other field studies)? 

Review the BOD,COD data fromthe source.

Are there oxygendemand models thatcan be used to predicteffects? 

Excess MineralNutrients

What were thedissolved mineralnutrientconcentrations?

 What is theconcentration-responserelationship (from labor other field studies)?Are therenutrient/eutrophication models that can be usedto predict effects?
Stressor Identification Guidance Document

Type of 

Candidate 

Cause

Characterization

of Exposure

(Intensity, Time,

and Space) 

Data

Available?

Yes (note

location of

data)/No

Exposure-Response

(E-R) Relationship

E-R Available?

Yes (note

location of data)

/No/Not

Relevant

Would effects

be expected at

the

environmental

conditions

seen in the

case? 

(Yes/No)

Appendix B: Worksheet Model 

NonindigenousSpecies

Is a nonindigenousspecies present orabundant?

 Are ecological modelsavailable tocharacterize theeffects?

Pathogen Is a pathogen

present? If so, is itabundant?

Are any symptoms ordiseases observed?

Other

Go to Unit III, Characterize Causes.
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B-16 U.S. Environ

UNIT III. CHARACTERIZE CAUSES

Step 1. Eliminate Alternatives (Section 4.1.1) and compare supporting evidence where 

eliminated.

• For each candidate cause indicate Yes, No, No Evidence (NE), or Not Applicable (NA).

• If more than one stressor is necessary for a cause to be sufficient (i.e., temperature and dissolved ox

response for each stressor.

• Use extra pages for more than 3 candidate causes.• Provide comments as necessary.

Case-Specific 

Consideration

Candidate Cause # 1

(Yes / No / NE / NA)

Candidate Cause # 2

(Yes / No / NE / NA)

Ca

(Y

Temporal Co-occurrenceDid the effect precede the stressor in time?(If the effects preceded a proposed causeand effects are not obscured by anothersufficient cause, then it cannot be theprimary cause.)Temporal GradientDid the effect increase or decrease overtime in association with an increase ordecrease in the stressor?(If the effect increases or decreases overtime without a corresponding increase ordecrease in the stressor, then the stressorcannot be the primary cause.)
Stressor Identification Guidance Document

Case-Specific 

Consideration

Candidate Cause # 1

(Yes / No / NE / NA)

Candidate Cause # 2

(Yes / No / NE / NA)

Ca

(Y

Appendix B: Worksheet Model 

Spatial Co-occurrenceIs there an upstream/downstreamconjunction of candidate cause and effect?(If the effect occurs upstream of the sourceor does not occur regularly downstream,e.g., is distributed spatially independentlyof a plume, sediment deposition areas, etc.,and effects are not obscured by anothersufficient cause, then the candidate cannotbe the primary cause).Co-occurrence with Reference Site(s)Is there a reference site/impaired siteconjunction of candidate cause and effect?(If the cause occurs at reference sites aswell as the impaired sit, it can beeliminated.)Spatial GradientDoes the effect increase or decrease acrossa given region in association with anincrease or decrease in the stressor?(If the effect increases or decreases over agiven region without a correspondingincrease or decrease in the stressor, thenthe stressor cannot be the primary cause.)
Stressor Identification Guidance Document

Case-Specific 

Consideration

Candidate Cause # 1

(Yes / No / NE / NA)

Candidate Cause # 2

(Yes / No / NE / NA)

Ca

(Y

B-18 U.S. Environ

Biological Gradient Is a decrease in the

magnitude or proportion of an effect seenalong a decreasing gradient of the stressor?(A constant or increasing level of effectwith decreasing exposure would eliminatea cause.)Complete Exposure Pathway, Question 1: Is there evidence that the

stressor did not co-occur with, contact, orenter the receptor(s) showing the effect?(If there is no route of exposure, or, forappropriate stressors, if tissue burdens orother measures of exposure were not foundto occur in affected organisms, the causemay be eliminated.)Complete Exposure Pathway, Question 2: Is there evidence that anecessary intermediate step in the causalchain of events did not occur?(If a link in a known chain of events can beshown to be missing, the cause may beeliminated.)
Stressor Identification Guidance Document

Case-Specific 

Consideration

Candidate Cause # 1

(Yes / No / NE / NA)

Candidate Cause # 2

(Yes / No / NE / NA)

Ca

(Y

Appendix B: Worksheet Model 

Experiment, TemporalityDid the effects continue when the candidatecause was removed (allowing for rates ofrecovery)?(If effects continue despite elimination ofthe candidate cause, that cause can beeliminated.)Other

After completing Step 1 (above) for each candidate cause listed in Unit I:

• If only one candidate cause remains, elimination is definitive. Go to Unit IV.• If more than one candidate cause remains, go back to Unit II, Part B. If Uni

complete, go to Unit III Step 2.

• If no candidate causes remains, go to Unit V.
Stressor Identification Guidance Document

B-20 U.S. Environ

UNIT III

Step 2. Characterize cause using diagnostic evidence (Section 4.1.2).

• If diagnostic evidence was found in Unit II Part D, determine if the evidence is sufficient to define t

table.

• If evidence is not sufficient to diagnose the cause, it may still be used in the strength of evidence in • Use extra pages for more than 3 candidate causes.

Candidate Cause

Type of Diagnostic

Evidence Description of Evidence

# 1# 2# 3

After completing Step 2 for all causes remaining after the elimination step (Step 1):

• If diagnosis is definitive. Go to Unit IV.• If diagnosis is uncertain, go back to Unit II Parts B, C and D. If Unit II Parts 

are complete, proceed to Unit III Step 3.
Stressor Identification Guidance Document

Appendix B: Worksheet Model 

UNIT III

Step 3. Analyze strength of evidence (Section 4.1.3) for Case-Specific Considerations.

• Use extra pages for more than 3 candidate causes.

Causal

Considerations

and possible

scores

Candidate Cause # 1 Candidate Cause # 2 Candid

Evidence and Literature

Citation Score

Evidence and Literature

Citation Score

Evidence and 

Citat

Co-occurrenceCompatible (+),Uncertain (0),Incompatible (- - -),No evidence (NE)(The stressor haseither contactedthe affectedorganisms, theirfood source, orsome parameterthat can affect theorganisms.)
Stressor Identification Guidance Document

Causal

Considerations

and possible

scores

Candidate Cause # 1 Candidate Cause # 2 Candid

Evidence and Literature

Citation Score

Evidence and Literature

Citation Score

Evidence and 

Citat

B-22 U.S. Environ

TemporalityCompatible (+),Uncertain (0),Incompatible (- - -),No evidence (NE)(A cause mustalways precede itseffects.)Consistency ofAssociationInvariant (++), In many places andtimes (+), At backgroundfrequencies (-),No Evidence (NE)(The repeatedobservation of asimilarrelationship of theeffect andcandidate cause indifferent placesand times.)
Stressor Identification Guidance Document

Causal

Considerations

and possible

scores

Candidate Cause # 1 Candidate Cause # 2 Candid

Evidence and Literature

Citation Score

Evidence and Literature

Citation Score

Evidence and 

Citat

Appendix B: Worksheet Model 

BiologicalGradientStrong and monotonic (+++),Weak or other thanmonotonic (+), None (-), Clear associationbut wrong sign (- - -),Not applicable(NA)(The effectincreases in aregular mannerwith increasingexposure.)
Stressor Identification Guidance Document

Causal

Considerations

and possible

scores

Candidate Cause # 1 Candidate Cause # 2 Candid

Evidence and Literature

Citation Score

Evidence and Literature

Citation Score

Evidence and 

Citat

B-24 U.S. Environ

CompleteExposurePathwayEvidence for allsteps (++),Incompleteevidence (+),Ambiguous (0),Some steps missingor implausible (-),No evidence (NE)(The stressor cooccurs with orcontacts thereceptor(s).)
Stressor Identification Guidance Document

Causal

Considerations

and possible

scores

Candidate Cause # 1 Candidate Cause # 2 Candid

Evidence and Literature

Citation Score

Evidence and Literature

Citation Score

Evidence and 

Citat

Appendix B: Worksheet Model 

ExperimentExperimentalstudies Concordant(+++), Ambiguous (0),Inconcordant (- - -)No evidence (NE)(Toxicity tests orother controlledexperimentalstudiesdemonstrated thatthe candidatecause can inducethe observedeffect.)
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UNIT III

Step 4. Analyze strength of evidence (Section 4.1.3) using Evidence from Other Situat

Biological Knowledge.

• Use extra pages for more than 3 candidate causes.

Causal

Consideration and

possible scores

Candidate Cause # 1 Candidate Cause # 2 Ca

Evidence and

Literature Citation Score

Evidence and Literature

Citation Score

Evid

Literat

Plausibility:MechanismEvidence ofMechanism (++),Plausible (+),Not Known (0),Implausible (-)

(It is plausible thatthe effect resultedfrom the cause givenwhat is known aboutthe biology, physics,and chemistry of thecandidate cause, thereceivingenvironment, and theaffected organisms.)
Stressor Identification Guidance Document

Causal

Consideration and

possible scores

Candidate Cause # 1 Candidate Cause # 2 Ca

Evidence and

Literature Citation Score

Evidence and Literature

Citation Score

Evid

Literat

Appendix B: Worksheet Model 

Plausibility:Stressor-ResponseQuantitativelyconsistent (+++),Concordant (+),Ambiguous (0),Inconcordant (-), Noevidence (NE)(Given a knownrelationship betweenthe candidate causeand the effect, effectswould be expected atthe level of stressorseen in theenvironment.)
Stressor Identification Guidance Document

Causal

Consideration and

possible scores

Candidate Cause # 1 Candidate Cause # 2 Ca

Evidence and

Literature Citation Score

Evidence and Literature

Citation Score

Evid

Literat

B-28 U.S. Environ

Consistency ofAssociationInvariant (+++), Inmost places (++), In some places (+),At backgroundfrequency (-),Not applicable (NA)(The repeatedobservation of theeffect and candidatecause is similar indifferent places andtimes.)
Stressor Identification Guidance Document

Causal

Consideration and

possible scores

Candidate Cause # 1 Candidate Cause # 2 Ca

Evidence and

Literature Citation Score

Evidence and Literature

Citation Score

Evid

Literat

Appendix B: Worksheet Model 

Analogy: PositiveAnalogous cases:Many or few butclear (++), Few or unclear (+),None (0)(The hypothesizedrelationship betweencause and effectsimilar to other wellestablished cases.)Analogy: NegativeAnalogous cases:Many or few butclear (- -), Few or unclear (-), None (0)(The hypothesizedrelationship betweencause and effect isdissimilar to otherwell-establishedcases.)
Stressor Identification Guidance Document

Causal

Consideration and

possible scores

Candidate Cause # 1 Candidate Cause # 2 Ca

Evidence and

Literature Citation Score

Evidence and Literature

Citation Score

Evid

Literat

B-30 U.S. Environ

Specificity ofCause*Note: onlyapplicable if thecause is plausible oris consistentlyassociated with theeffect.Only possible cause(+++), One of a few (+),One of many (0), Notapplicable (NA)(The effect observedat the site is knownto have only one or afew known causes.)
Stressor Identification Guidance Document

Causal

Consideration and

possible scores

Candidate Cause # 1 Candidate Cause # 2 Ca

Evidence and

Literature Citation Score

Evidence and Literature

Citation Score

Evid

Literat

Appendix B: Worksheet Model 

ExperimentExperimentalstudies: Concordant(+++), Ambiguous (0),Inconcordant(- - -), No evidence (NE)(Toxicity tests orother controlledexperimental studiesdemonstrated thatthe candidate causecan induce theobserved effect.)
Stressor Identification Guidance Document

Causal

Consideration and

possible scores

Candidate Cause # 1 Candidate Cause # 2 Ca

Evidence and

Literature Citation Score

Evidence and Literature

Citation Score

Evid

Literat

B-32 U.S. Environ

PredictivePerformancePrediction:Confirmed specificor multiple (+++), Confirmed general(++), Ambiguous (0),Failed (- - -),No evidence (NE)(The candidate causehas any initiallyunobservedproperties that werepredicted to occurand the predictionwas subsequentlyconfirmed at thesite.)
Stressor Identification Guidance Document

Appendix B: Worksheet Model 

UNIT III

Step 5. Analyze strength of evidence (Section 4.1.3) based on multiple lines of evidence

• Use extra pages for more than 3 candidate causes.

Causal

Consideration and

possible scores

Candidate Cause # 1 Candidate Cause # 2 Ca

Evidence and

Literature Citation

Score Evidence and Literature

Citation

Score Evid

Literat

Consistency ofEvidence All consistent (+++), Most consistent (+),Multipleinconsistencies (- - -)(The hypothesizedrelationship betweenthe cause and effectis consistent acrossall availableevidence.)
Stressor Identification Guidance Document

Causal

Consideration and

possible scores

Candidate Cause # 1 Candidate Cause # 2 Ca

Evidence and

Literature Citation

Score Evidence and Literature

Citation

Score Evid

Literat

B-34 U.S. Environ

Coherence ofEvidenceEvidence:Inconsistencyexplained by acredible mechanism(+),No knownexplanation (0)No entry if allconsistent(A mechanisticconceptual modelexplains anyapparentinconsistenciesamong the lines ofevidence.)

Compare evidence among the candidate causes, then go to Unit IV to summarize you
Stressor Identification Guidance Document

Appendix B: Worksheet Model 

IV. SUFFICIENCY OF EVIDENCE (Chapter 4.2)

Most Likely Candidate Cause: _______________________________________________________________Is Evidence Sufficient for the Management Purpose?

 YES SI COMPLETE, REPORT RESULTS  NO GO TO UNIT V, RECONS

IMPAIRMENT

Summary of Characterization

Candidate Cause Cause Reasoning & Confidence

# 1.# 2.# 3.
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B-36 U.S. Environ

V. RECONSIDER IMPAIRMENT

Does Biological Impairment Really Exist? 

(Section 5.1)

Reconsider the impairment by auditing the quality of the methods used to generate and manage the data, banalysis tools, and by eliminating any suspicious data or analyses. Describe Reconsideration:Were effects real?

 NO SI COMPLETE, REPORT RESULTS. YES GO TO UNIT VI, COLLECT MORE INFORMATION.
Stressor Identification Guidance Document

Appendix B: Worksheet Model 

VI. COLLECT MORE INFORMATION (Section 5.2)

Were all reasonable causes analyzed?

 NO Go to Follow-on 1. YES Go to Follow-on 2.

Follow-on 1: Make sure that all reasonable causes were analyzed.

• If additional scenarios are indicated, repeat process, beginning at Unit 1.• If a good faith effort was implemented with reasonable time and resource expenditures, consult 

and determine if the process should be ended with inconclusive results.SI COMPLETED, REPORT RESULTS AS INCONCLUSIVE.

Follow-on 2: Look at the supporting evidence in Unit II, Analyze Evidence.

• Prioritize information needs for likely candidate causes, collect new information and repeat the 

at Unit 1.

• If a good faith effort was implemented with reasonable time and resource expenditures, consult 

and determine if the process should be ended with inconclusive results.SI COMPLETED, REPORT RESULTS AS INCONCLUSIVE.
APPENDIX C

GLOSSARY OF TERMS
Stressor Identification Guidance Document

Appendix C: Glossary of Terms C-1

Ambient monitoring: All forms of monitoring conducted beyond the

immediate influence of a discharge pipe or injection

well and may include sampling of sediments and living

resources.

Ambient waters: water bodies that are in the environment. 

Analogy: a comparison of two things, based on their similarity in

one or more respects. In SI, the criterion of an analogy

refers specifically to similar causes.

Bioassessment 

(biological assessment): evaluation of the condition of an ecosystem that uses

biological surveys and other direct measurements of the

resident biota.

Biocriteria 

(biological criteria): numerical values or narrative expressions that describe

the reference biological condition of aquatic

communities inhabiting waters of a given designated

aquatic life use. Biocriteria are benchmarks for

evaluation and management of water resources

Biogenic: produced by biological processes. For example, organic

acids produced by decomposition of plant litter are

biogenic acids.

Biological gradient: a regular increase or decrease in a measured biological

attribute with respect to space (e.g., below an outfall),

time (e.g., since a flood), or an environmental property

(e.g., temperature).

Biomarker: contaminant-induced physiological, biochemical, or

histological response of an organism.

Body burden: the concentration of a contaminant in a whole organism

or a specified organ or tissue.

Candidate cause: a hypothesized cause of an environmental impairment

which is sufficiently credible to be analyzed. 

Categorical regression: regression analysis in which the dependent variable is

defined by a categorical scale rather than as a count or

continuous variable.

Appendix C

Glossary of Terms
Stressor Identification Guidance Document

C-2 U.S. Environmental Protection Agency

Causal analysis: a process in which data and other information are

organized and evaluated using quantitative and logical

techniques to determine the likely cause of an observed

condition.

Causal mechanism: the process by which a cause induces an effect.

Causal relationship: the relationship between a cause and its effect.

Causal association: a correlation or other association between measures or

observations of two entities or processes which occurs

because of an underlying causal relationship.

Causal evidence: the results of an analysis of data to reveal an association

between the environmental condition and a candidate

cause.

Causal inference: the component of a causal analysis that is specifically

concerned with the interpretation of the evidence to

determine the most likely cause.

Causal characterization: a step in the stressor identification process in which the

proposed cause is described, the evidence for its causal

relationship to the impairment is summarized, and

uncertainties are presented.

Causal considerations: logical categories of evidence that are consistently

applied to support or refute a hypothesized cause. A

causal consideration (e.g., biological gradient) is

evaluated using causal evidence (e.g., a regression of

benthic invertebrate diversity against sediment PCB

concentration). 

Cause: 1. that which produces an effect (a general definition). 

2. a stressor or set of stressors that occur at an intensity,

duration and frequency of exposure that results in a

change in the ecological condition (a SI-specific

definition).

Co-occurrence: the spatial co-location of the candidate cause and effect.

Coherency of evidence: the final consideration in a strength of evidence analysis. 

If the results of all of the causal considerations in a

strength of evidence analysis are not consistent, they

may still be coherent, if a mechanistic conceptual or

mathematical model explains the apparent

inconsistencies.
Stressor Identification Guidance Document

Appendix C: Glossary of Terms C-3

Complete exposure 

pathway: the physical course a stressor takes from the source to

the receptors (e.g., organisms or community) of interest. 

(Evidence for a complete exposure pathway is casespecific and may include measurements such as body

burdens of chemicals, presence of parasites or

pathogens, or biomarkers of exposure.) 

Concentration-response

 model: a quantitative (usually statistical) model of the

relationship between the concentration of a chemical to

which a population or community of organisms is

exposed and the frequency or magnitude of a biological

response.

Consideration: see Causal consideration.

Consistency of association: the degree to which an effect and candidate cause have

been determined to co-occur in different places or times.

Consistency of evidence: the degree to which the causal considerations in a

strength of evidence analysis are in agreement

concerning a candidate cause.

Diagnostic analysis: a type of causal analysis in which effects that are

characteristic of a particular cause are used to determine

whether that candidate cause may be responsible for an

impairment.

Diagnostic protocol: a standard procedure for performing a diagnostic

analysis.

Dilution ratio: the ratio of the stream flow to the wastewater flow

Ecoepidemiology: the study of the nature and causes of effects on

ecological systems.

Endpoint species: a species that is the object of an assessment or test.

Eutrophication: enrichment of a water body with nutrients, resulting in

high levels of primary production, often leading to

depletion of dissolved oxygen.

Experiment: the manipulation of a candidate cause by eliminating a

source or altering exposure so as to evaluate its

relationship to an effect.

Expert judgement: a method of causal inference based on the knowledge

and skill of the assessors rather than a formal method.

Exposure: the co-occurrence or contact of a stressor and the

resource that becomes impaired.
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C-4 U.S. Environmental Protection Agency

Exposure-response 

relationships: a qualitative or quantitative (usually statistical) model of

the relationship between an exposure metric (e.g., the

concentration of a chemical or the abundance or an

exotic species) to which a population or community of

organisms is exposed and the frequency or magnitude of

a biological response.

Impairment: a detrimental effect on the biological integrity of a water

body that prevents attainment of the designated use.

Indirect causation: the induction of effects through a series of cause-effect

relationships, so that the impaired resource may not

even be exposed to the initial cause.

Indirect effects: changes in a resource that are due to a series of causeeffect relationships rather than to direct exposure to a

contaminant or other stressor.

Inferential logic: a process for reasoning from the evidence to a necessary

and specific conclusion.

Initial response: the response of an organism, population or community

to direct exposure to a stressor. 

Intermediate processes: processes that occur between the occurrence of a

stressor in an ecosystem and the induction of the effect

of concern. For example, the reduction in algal

abundance is an intermediate process between the

introduction of a non-native filter feeder and the

reduction in abundance of native planktivorous species.

Internal exposure: exposure of an organism to bioaccumulated

contaminants.

Logic of abduction: inference from data to the hypothesis that best accounts

for the data.

Mechanism: the process by which a system is changed.

Necropsy: a post-mortem examination or inspection intended to

determine the cause of death or the nature of

pathological changes.

Negative evidence: evidence that tends to refute a candidate cause.

Opportunistic: having the ability to exploit newly available habitats or

resources.

Pathogens: organisms that are capable of inducing a disease in a

susceptible host.
Stressor Identification Guidance Document

Appendix C: Glossary of Terms C-5

Plausibility: the degree to which a cause and effect relationship

would be expected, given known facts.

Positive evidence: evidence that tends to support a candidate cause.

Predictive performance: the degree to which a candidate cause has led to

predictions concerning conditions in the receiving

system which have been subsequently confirmed by

observation or measurement.

Principal cause: the cause that makes the largest contribution to the

effect.

Pseudoreplication: the treatment of multiple samples that are subject to the

same treatment as replicates for statistical purposes. For

example, multiple samples of benthic invertebrates taken

in a channelized stream are pseudo- replicates because

they are not independent. True replicates would be

taken from different channelized streams. 

Publicly Owned Treatment 

Works (POTW): a water treatment facility, as defined by Section 212 of

the Clean Water Act, that is used in the storage,

treatment, recycling, and reclamation of municipal

sewage or industrial wastes of a liquid nature and is

owned by a municipality or other governmental entity. 

It usually refers to sewage treatment plants.

Receptors: organisms, populations, or ecosystems that are exposed

to a contaminant or other stressor.

Replicate: (a) one of a set of independent systems which have been

randomly assigned a treatment; or (b) to generate a set

of such systems.

Source: an origination point, area, or entity that releases or emits

a stressor. A source can alter the normal intensity,

frequency, or duration of a natural attribute, whereby the

attribute then becomes a stressor. 

Spatial gradient: a graded change in the magnitude of some quantity or

dimension measured on a transect

Specificity: the quality of being specific rather than general. 

Specificity of cause: only one candidate cause or a few similar causes can

induce the observed effect.

Specificity of effect: one type of effect is characteristically induced by a

candidate cause. The absence of that effect is evidence

for eliminating the candidate cause.
Stressor Identification Guidance Document

C-6 U.S. Environmental Protection Agency

Strength-of-evidence 

analysis: an inferential process that uses all relevant evidence in a

systematic process to determine which candidate cause

is most likely to have induced the effect of concern.

Strength of association: the size of the effect produced by an increment in the

candidate cause. A candidate cause that is associated

with a large change in the level of effect is more likely

to be the true cause than one that is weakly associated.

Stressor: any physical, chemical, or biological entity that can

induce an adverse response.

Supplemental 

Environmental Project

 (SEP): a special program that is often used to grant injunctive

relief.

Symptomatology: a set of signs of the action of a causal agent on

organisms. A set of symptoms with a common cause

constitutes a symptomatology.

Temporal relationship: the relationship between the time of occurrence of a

candidate cause and of the effect of concern.

Temporal gradient: a graded change in the magnitude of some quantity or

dimension measured over time.

Total Maximum Daily 

Load (TMDL): the total allowable pollutant load to a receiving water

such that any additional loading will produce a violation

of water-quality standards.

Toxicity Reduction 

Evaluation (TRE): a site-specific study conducted in a stepwise process

designed to identify the causative agent(s) of effluent

toxicity, isolate the sources of toxicity, evaluate the

effectiveness of toxicity control options, and then

confirm the reduction in effluent toxicity.

Toxicity Identification 

and Evaluation (TIE): a process that identifies the toxic components of an

effluent or ambient medium by a process of chemically

manipulating the effluent or medium and testing the

resulting material.
APPENDIX D

LITERATURE CITED
Stressor Identification Guidance Document

Appendix D: Literature Cited D-1

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330-355 in Hoffman, David J. et al, ed. Handbook of Ecotoxicology, CRC Press,

Boca Raton, Florida.

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Allan, J.D. 1995. Stream ecology: structure and function of running waters. Chapman

and Hall Publishers, London.

Baumann, P.C., and J.C. Harshbarger. 1995. Decline in liver neoplasms in wild brown

bullhead catfish after coking plant closes and environmental PAHs plummet.

Environ. Health Perspect. 103:168-170.

Baumann, P.C., I.R. Smith, and C.D. Metcalfe. 1996. Linkage between chemical

contaminants and tumors in benthic Great Lakes fish. J. Great Lakes Res.

22(2):131-152.

Beyers, D.W. 1998. Causal inference in environmental impact studies. J. North Amer.

Benthol. Soc. 17: 367-373.

Botti, C., P. Comba, F. Forastiere, and L. Settimi. 1996. Causal inference in

environmental epidemiology: the role of implicit values. Sci. Total Environ.

184:97-101.

Blus, L.J., and C.F. Henny. 1997. Field studies on pesticides and birds: unexpected and

unique relations. Ecological Applications. 7:1125-1132.

Carpenter, S.R., N.F. Caraco, D.L. Correll, R.W. Howarth, A.N. Sharpley, and V.H.

Smith. 1988. Non-point pollution of surface waters with phosphorus and

nitrogen. Ecological Applications 8(3):559-568.

Clemen, R.T. 1986. Making hard decisions. An introduction to decision analysis. 2nd

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Clements, W.H. 1994. Benthic invertebrate community responses to heavy metals in the

Upper Arkansas River Basin, Colorado. J. N. Am. Benthol. Soc. 13:30-44.

Clements, W.H., and P.M. Kiffney. 1994. Integrated laboratory and field approach for

assessing impacts of heavy metals at the Arkansas River, Colorado.

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Clements, W.H., D.M. Carlisle, J.M. Lazorchak, and P.C. Johnson. 2000. Heavy metals

structure benthic communities in Colorado mountain streams. Ecol. Appl.

10:626-638.

Appendix D

Literature Cited
Stressor Identification Guidance Document

D-2 U.S. Environmental Protection Agency

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Cormier, S.M., E.L.C. Lin, M.R. Millward, M.K. Schubauer-Berigan, D. Williams, B.

Subramanian, R. Sanders, B. Counts, and D. Altfater. 2000b. Using regional

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Davenport, T.E., and M.H. Kelly. 1983. Water resource data and preliminary trend

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Davies, S.P., L. Tsomides, D.L. Courtemanch, and F. Drummond. 1995. Maine

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Davies, S.P., and L. Tsomides. 1997. Methods for biological sampling and analysis of

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Davies, S.P., L. Tsomides, J.L. DiFranco, and D.L. Courtemanch. 1999. Biomonitoring

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Dodds, K., and E.B. Welsh. 2000. Establishing nutrient criteria in streams. The North

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Dourson, M.L., L.K. Teuschler, P.R. Durkin, and W.M. Stiteler. 1997. Categorical

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Edwards, R., and J. Riepenhoff. 1998. State turns to feds for cleanup. Columbus

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Edwards, A.C., H. Twist, and G.A. Codd. 2000. Assessing the impact of terrestrially

derived phosphorus on flowing water systems. Journal of Environmental

Quality. 29:117-124.
Stressor Identification Guidance Document

Appendix D: Literature Cited D-3

Edwards, C.J., B.L. Griswold, R.A. Tubb, E.C. Weber, and L.C. Woods. 1984.

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203.

Eisler, R. 2000a. Polycyclic aromatic hydrocarbons. Pages 1343-1411 in Handbook of

Chemical Risk Assessment. Vol. II. Lewis Publishers, Boca Raton, FL.

_______. 2000b. Handbook of Chemical Risk Assessment. Vol. I. Lewis Publishers,

Boca Raton, FL.

Fox, G.A. 1991. Practical causal inference for ecoepidemiologists. J. Toxicol. Environ.

Health 33:359-373.

Gilbertson, M., T. Kubiak, J. Ludwig, and G. Fox. 1991. Great Lakes embryo mortality,

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similarity to chick-edema disease. J. Toxicology and Environmental Health

33:455-520.

Gmur, D.J., and U. Varanasi. 1982. Characterization of benzo[a]pyrene metabolites

isolated from muscle, liver, and bile of a juvenile flatfish. Carcinogenesis

5:1397-1403.

Grier, J.W. 1982. Ban of DDT and subsequent recovery of reproduction in bald eagles.

Science 218:1232-1234.

Hackney, J.D., and W.S. Kinn. 1979. Koch's postulates updated: A potentially useful

application to laboratory research and policy analysis in environmental

toxicology. Amer. Rev. Respir. Dis. 1119:849-852.

Hickey, C.W., and W.H. Clements. 1998. Effects of heavy metals on benthic

macroinvertebrate communities in New Zealand streams. Environmental

Toxicology and Chemistry 17:2338-2346.

Hilborn, R., and M. Mangel. 1997. The ecological detective: confronting models with

data. Princeton U. Press, Princeton, NJ.

Hill, A.B. 1965. The environment and disease: Association or causation. Proceed. Royal

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Hilsenhoff, W.L. 1987. An improved biotic index of organic stream pollution. Great

Lakes Entomol. 20:31-39.

Huggins, D.G., and M.F. Moffett. 1988. Proposed biotic and habitat indices for use in

Kansas streams. Report No. 35, Kansas Biological Survey, Lawrence.

Hurlbert, S.H. 1984. Pseudoreplication and the design of ecological field experiments.

Ecological Mono. 54:187-211.

Josephson, J.R., and S.G. Josephson. 1996. Abductive Inference. Cambridge University

Press, Cambridge.
Stressor Identification Guidance Document

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Jorgensen, S.E. 1994. Fundamentals of ecological modelling. Elsevier, Amsterdam.

Kansas Department of Health and Environment (KDHE). 1993. Kansas list of water

quality limited surface waters (303(d) list). Kansas Department of Health and

Environment, Division of Environment, Office of Science and Support, Topeka.,

KS.

__________. 1998. Kansas water quality limited segments (303(d) list). Kansas

Department of Health and Environment, Division of Environment, Bureau of

Environmental Field Services, Topeka, KS.

__________. 2000. Kansas water quality assessment (305(b) report). Kansas

Department of Health and Environment, Division of Environment, Bureau of

Environmental Field Services, Topeka, KS.

Karr, J.R., and I.J. Schlosser. 1977. Impact of nearstream vegetation and stream

morphology on water quality and stream biota. EPA-600-3-77-097. U.S.

Environmental Protection Agency, Environmental Research Laboratory, Athens,

GA.

Kiffney, P.M., and W.H. Clements. 1994a. Structural responses of benthic

macroinvertebrate communities from different stream orders to zinc.

Environmental Toxicology and Chemistry 13:389-395.

__________. 1994b. Effects of heavy metals on a macroinvertebrate assemblage from a

Rocky Mountain stream in experimental microcosms. J. N. Am. Benthol. Soc.

13:511-523.

Lehman, J.T. 1986. Control of eutrophication in Lake Washington: case study. Pages

301-316 in Ecological knowledge and environmental problem-solving: Concepts

and case studies. National Academy Press, Washington, D.C.

Lin, E.L.C., S.M. Cormier, and J.A. Torsella. 1996. Fish biliary polycyclic aromatic

hydrocarbon metabolites estimated by fixed-wavelength fluorescence:

comparison with HPLC-fluorescent detection. Ecotoxicol. Environ. Safety

35:16-23.

Lin, E.L.C., T.W. Neiheisel, B. Subramanian, D.E. Williams, M.R. Millward, and S.M.

Cormier. Historical monitoring of biomarkers of exposure of brown bullhead in

the remediated Black River, Ohio and two other Lake Erie tributaries. Submitted

to Journal of Great Lakes Research.

Long, E.R., L.J. Field, and D.D. MacDonald. 1998. Predicting toxicity in marine

sediments with numerical sediment quality guidelines. Envir. Toxicol. Chem.

17:714-727.

Meyer, F.P., and L.A. Barclay. 1990. Field manual for the investigation of fish kills.

Resource Pub. 177. U.S. Fish and Wildlife Service, Washington, D.C.

Miltner, R.J., and E.T. Rankin. 1998. Primary nutrients and the biotic integrity of rivers

and streams. Freshwater Biology 40:145-158.
Stressor Identification Guidance Document

Appendix D: Literature Cited D-5

Mitnik, P. 1994. Presumpscot River waste load allocation. Maine Department of

Environmental Protection, Augusta, Maine.

________. 1998. Presumpscot River supplemental report to waste load allocation.

Maine Department of Environmental Protection, Augusta, Maine.

Nelson S.M., and R.A. Roline. 1996. Recovery of a stream macroinvertebrate

community from mine drainage disturbance. Hydrobiologia 339:73-84.

Norberg-King, T., and D. Mount. 1986. Validity of effluent and ambient toxicity tests

for predicting biological impact. Skeleton Creek, Enid, Oklahoma. EPA/600/30-

85-044. Duluth Environmental Research Laboratory, Minnesota.

Norton, S.B. 1999. Using biological monitoring data to distinguish among types of

stress in streams of the Eastern Cornbelt Plains Ecoregion. Ph.D. Dissertation,

Georgetown University, Fairfax, VA.

Norton S.B., S.M. Cormier, M. Smith, and R.C. Jones. 2000. Can biological assessment

discriminate among types of stress? A case study for the eastern cornbelt plains

ecoregion. Environ. Toxicol. Chem. 19(4):1113-1119.

Ohio Environmental Protection Agency (OEPA). 1988a. Biological criteria for the

protection of aquatic life: Vol. II. users manual for biological assessment of

Ohio surface waters. Division of Water Quality Planning and Assessment,

Ecological Assessment Section, Columbus, OH.

__________. 1988b. Biological and water quality study of the Little Scioto River

watershed, Marion County, OH. OEPA Technical Report prepared by State of

Ohio Environmental Protection Agency, Division of Surface Water, Columbus,

OH.

__________. 1989a. Addendum to: Biological criteria for the protection of aquatic life:

Volume II. users manual for biological assessment of Ohio surface waters.

Division of Water Quality Planning and Assessment, Ecological Assessment

Section, Columbus, OH.

__________. 1989b. Biological criteria for the protection of aquatic life: Volume III.

standardized field and laboratory methods for assessing fish and

macroinvertebrate communities. Division of Water Quality Planning and

Assessment, Ecological Assessment Section, Columbus, OH.

__________. 1989c. Manual of Ohio EPA surveillance methods and quality assurance

practices. Division of Environmental Services, Columbus, OH.

__________. 1992a. Bottom sediment evaluation, Little Scioto River, Marion, Ohio.

Division of Water Quality Planning and Ecological Assessment Section,

Columbus, OH.

__________. 1992b. Biological and water quality study of the Ottawa River, Hog

Creek, Little Hog Creek, and Pike Run. OEPA Technical Report EAS/1992-9-7.

Prepared by State of Ohio Environmental Protection Agency, Division of

Surface Water, Columbus, OH.
Stressor Identification Guidance Document

D-6 U.S. Environmental Protection Agency

__________. 1994. Biological, sediment, and water quality study of the Little Scioto

River, Marion, Ohio. OEPA Technical Report EAS/1994-1-1. Division of

Surface Water, Ecological Assessment Section, Columbus, OH.

Platt, J.R. 1964. Strong inference. Science 146:347-353.

Popper, K.R. 1968. The logic of scientific discovery. Harper and Row, New York.

Rankin, E.T. 1989. The qualitative habitat evaluation index (QHEI): rationale,

methods, and application. State of Ohio Environmental Protection Agency,

Division of Water Quality Planning and Assessment, Ecological Assessment

Section, Columbus, OH.

__________. 1995. Habitat indices in water resource quality assessments. Pages 181-

208 in W.S. Davis and T.P. Simon (editors). Biological Assessment and

Criteria. Lewis Publishers, Boca Raton, Florida.

Rankin E., R. Miltner, C. Yoder and D. Mishne. 1999. Association between nutrients,

habitat, and the aquatic biota in Ohio rivers and streams. Ohio EPA Technical

bulletin MAS/1999-1. Ohio EPA, Columbus, OH.

Rosgen, D. 1996. Applied river morphology. Wildland Hydrology Books, Pagosa

Springs, CO.

Roubal, W.T., T.K. Lallier, and D.C. Malins. 1977. Accumulation and metabolism of

C-14 labeled benzene, naphthalene, and anthracene by young coho salmon

(Oncorhynchus kisutch) and starry flounder (Platichthys stellatus). Arch.

Environ. Contam. Toxicol. 5: 513-529.

Russo, R.C. 1985. Ammonia, nitrate and nitrite. Pages 455-471 in G.M. Rand and S.A.

Petrocelli (editors). Fundamentals of Aquatic Toxicology. McGraw Hill,

Washington, D.C.

Sheilds, F.D. Jr., S.S. Knight, and C.M. Cooper. 1998. Rehabilitation of aquatic habitats

in warmwater streams damaged by channel incision in Mississippi.

Hydrobiologica 382:63-86

Smith, V.H., G.D. Tilman, and J.C. Nekola. 1999. Eutrophication: impacts of excess

nutrient inputs on freshwater, marine and terrestrial ecosystems. Environmental

Pollution 100:179-196.

Susser, M. 1986a. Rules of inference in epidemiology. Regulatory Toxicology and

Pharmacology 6:116-186.

________. 1986b. The logic of Sir Karl Popper and the practice of epidemiology. Am.

J. Epidemiol. 124:711-718.

________. 1988. Falsification, verification and causal inference in epidemiology:

Reconsideration in light of Sir Karl Popper's philosophy. Pages 33-58 in K.J.

Rothman (ed.). Causal Inference. Epidemiology Resources Inc., Chestnut Hill,

MA.
Stressor Identification Guidance Document

Appendix D: Literature Cited D-7

Suter, G.W., II. 1990. Use of biomarkers in ecological risk assessment. Pages 419-426

in J.F. McCarthy and L. L. Shugart (eds.). Biomarkers of Environmental

Contamination. Lewis Publishers, Ann Arbor, Michigan.

__________. 1993. Ecological risk assessment. Lewis Publishers, Boca Raton, FL.

__________. 1998. Retrospective assessment, ecoepidemiology, and ecological

monitoring. Pages 177-217 in P. Calow (ed.). Handbook of Environmental Risk

Assessment and Management. Blackwell Scientific, Oxford, UK.

__________. 1999. Developing conceptual models for complex ecological risk

assessments. Human & Ecolog. Risk Assess. 5:375-396.

Suter, G.W. II, J.W. Gillett, and S. Norton. 1994. Characterization of exposure.

Chapter 4 in Ecological Risk Assessment Issue Papers. EPA/630/R-94/009.

U.S. Environmental Protection Agency, Washington, D.C.

Tarplee, W.H. Jr., D.E. Louder, and A.J. Weber. 1971. Evaluation of the effects of

channelization on fish populations in North Carolina's coastal plain streams.

North Carolina Wildlife Resources Commission, Raleigh, NC.

Thornton, K.W., G.E. Saul, and D.E. Hyatt. 1994. Environmental monitoring and

assessment program assessment framework. EPA/620/R-94/016. U.S.

Environmental Protection Agency, Research Triangle Park, NC.

U.S. Environmental Protection Agency (USEPA). 1988a. Generalized methodology for

conducting industrial toxicity reduction evaluations. EPA/600-2-88/070.

__________. 1988b. Toxicity reduction evaluation protocol for municipal wastewater

treatment plants. EPA/600/2-88/062.

__________. 1988c. Methods for aquatic toxicity identification evaluations: phase 1,

toxicity characterization procedures (EPA/600/3-88/034); Phase 2, toxicity

identification procedures (EPA/600/3-88/035); and Phase 3, toxicity

confirmation procedures. EPA/600/3-88/036.

__________. 1991a. The watershed protection approach: An overview. Office of

Water. Washington, DC. EPA 503/9-92-001.

__________. 1991b. Methods for aquatic toxicity identification evaluations, phase I

toxicity characterization procedures, 2nd ed. U.S. Environmental Protection

Agency, Office of Research and Development, Environmental Research

Laboratory, Duluth, MN. EPA/600/6-91/003.

__________. 1993a. Methods for aquatic toxicity identification evaluations, phase II

toxicity identification procedures for samples exhibiting acute and chronic

toxicity. EPA/600/R-92/080. (NTIS: PB94-114907).
Stressor Identification Guidance Document

D-8 U.S. Environmental Protection Agency

__________. 1993b. Methods for aquatic toxicity identification evaluations, phase III

toxicity confirmation procedures for samples exhibiting acute and chronic

toxicity. U.S. Environmental Protection Agency, Office of Research and

Development, Environmental Research Laboratory, Duluth, MN.

EPA/600/R-92/081.

__________. 1994. Interim guidance on determination and use of water-effect ratios

for metals. U.S. Environmental Protection Agency, Office of Science and

Technology, Washington, DC. EPA/823/B-94/001.

__________. 1996a. Summary report of workshop on Monte Carlo analysis. Office of

Research and Development, Risk Assessment Forum, Washington, DC.

EPA/630/R-96/010.

__________. 1996b. Calculation and evaluation of sediment effect concentrations for

the amphipod Hyallela azteca and the midge Chironomus riparius.

Assessment and Remediation of Contaminated Sediments (ARCS) Program.

Great Lakes National Program Office, Chicago, IL. EPA 905-R96-008.

__________. 1997. Guidelines for preparation of the comprehensive state water quality

assessments (305(b) Reports) and electronic updates: Report contents. U.S.

Environmental Protection Agency, Office of Water. Washington, DC 20460.

EPA-841-B-97-002A.

__________. 1998a. Guidelines for ecological risk assessment. Office of Research and

Development. Risk Assessment Forum, Washington, D.C. EPA/630/R-95/002F.

__________. 1998b. 1998 update of ambient water quality criteria for ammonia. Office

of Water, Washington, DC. EPA 822-R-98-008.

__________. 1999. Report of the workshop on selecting input distributions for

probabilistic assessments. Office of Research and Development, Risk

Assessment Forum, Washington, DC. EPA/630/R-98/004

Vannote, R.L., G.W. Minshall, K.W. Cummins, J.R. Sedell, and C.E. Cushing. 1980.

The river continuum concept. Canadian Journal of Fisheries and Aquatic

Sciences 37:130-137.

Varanasi, U., J.E. Stein, M. Nishimoto, and T. Hom. 1983. Benzo[a]pyrene metabolites

in liver, muscle, gonads, and bile of adult English sole (Parophrys vetulus).

Pages 1221-1234 in Cooke, M. and A.J. Dennis, eds. Polynuclear Aromatic

Hydrocarbons: Formation, Metabolism, and Measurement. Battelle, Columbus,

OH, USA.

Woodman, J.N., and E.B. Cowling. 1987. Airborne chemicals and forest health.

Environ. Sci. 21:120-126.

Yerushalmy, J., and C.E. Palmer. 1959. On the methodology of investigations of

etiologic factors in chronic disease. J. Chronic Disease 10(1):27-40.
Stressor Identification Guidance Document

Appendix D: Literature Cited D-9

Yoder, C.O., and E.T. Rankin. 1995a. Biological criteria program development and

implementation in Ohio. Pages 109-144 in W.S. Davis and T.P. Simon, eds.

Biological Assessment and Criteria: Tools for Water Resource Planning and

Decision Making. Lewis Publishers, Boca Raton, FL.

__________. 1995b. Biological response signatures and the area of degradation value:

New tools for interpreting multi-metric data. Pages 236-286 in Biological

Assessment and Criteria: Tools for Water Resource Planning and

Decisionmaking Lewis Publishers, Boca Raton, FL..

Yount, J.D., and G.J. Niemi. 1990. Recovery of lotic communities and ecosystems from

disturbance; A narrative review of case studies. Environmental Management

14:547-569.
Stressor Identification Guidance Document

Index I-1

Index

A

Algal growth, 6-6–6-8, 6-10, 6-12, 7-11

Ammonia concentrations, 7-2–7-3, 7-11, 7-14, 7-18–7-22, 7-25–7-31, 7-39–7-48, 7-63

Analogy

description, 4-12

Little Scioto River case study, 7-35, 7-38, 7-41, 7-45

Presumpscot River case study, 6-15

Androscoggin River case study, 6-11–6-13

Aquatic life standards, 6-1, 6-3, 6-5, 6-13

Aquatic life use

defined, 1-1

Arkansas River case study, 4-11

B

Beneficial use designation

defined, 1-1

Benthic macroinvertebrates

effects of heavy metal exposure, 4-11

Little Scioto River case study, 7-1–7-8, 7-24

Presumpscot River case study, 6-1–6-18

Biocriteria

defined, 1-1

Biological gradient

Arkansas River case study, 4-11

described, 4-10

Little Scioto River case study, 7-32, 7-36, 7-39, 7-43

Presumpscot River case study, 6-14

Biological integrity

describing impairments, 2-1–2-3

overview of Stressor Identification, 1-3–1-5

role of Stressor Identification process in water management programs, 1-6–1-9

Stressor Identification process, ES-1

using results of Stressor Identification, 1-5–1-6

water quality management, ES-2

Biological oxygen demand

Little Scioto River case study, 7-11, 7-14, 7-18, 7-20, 7-23–7-28, 7-39–7-49,

7-63

Presumpscot River case study, 6-6–6-7, 6-9, 6-12

BOD. See Biological oxygen demand

C

Candidate causes

analyzing evidence, 3-1–3-11

categories of relationships, 3-1–3-2

characterizing causes, 4-1–4-18

conceptual models, 2-5–2-7

describing the impairment, 2-1–2-3

key terms, 2-1

listing, 2-4–2-5
Stressor Identification Guidance Document

I-2 U.S. Environmental Protection Agency

overview of Stressor Identification, 1-3–1-5

principal causes, 2-4

scope of the investigation, 2-3–2-4

unlikely stressors, 2-5

using existing lists of stressors, 2-4

Case studies

Androscoggin River, 6-11–6-13

Arkansas River, 4-11

DDT, 5-2

Lake Washington, 4-13

Little Scioto River, 7-1–7-65

Presumpscot River, 6-1–6-18

Causal evidence

associations between measurements of candidate causes and effects, 3-2–3-6,

4-5–4-6

associations of effects with mitigation or manipulation of causes, 3-10–3-11, 4-6

causal considerations, 4-9–4-14

confidence evaluation, 4-17–4-18

diagnostic analysis, 4-7–4-8

eliminating alternatives, 4-3–4-7

identifying probable cause, 4-17–4-18

matching evidence with causal considerations, 4-14

measurements associated with the causal mechanism, 3-9–3-10, 4-6

methods for characterization, 4-1–4-17

strength of evidence analysis, 4-8–4-17

using effects data from elsewhere, 3-6–3-9

weighing causal considerations, 4-14–4-17

Cause

defined, 2-1

CERCLA. See Comprehensive Environmental Response, Compensation, and Liability

Act

Channelization, 7-6, 7-11, 7-13, 7-24, 7-47, 7-48

Chemical contaminants. See also Toxic compounds

Little Scioto River Case Study, 7-11, 7-14–7-22, 7-24–7-25, 7-27, 7-29–7-30, 7-

32–7-48

Koch's postulates, 4-9

Chemical oxygen demand, 7-11

Chironomus riparius, 7-29

Chlorophyll a, 6-2, 6-10

Class C aquatic life standards, 6-1, 6-3, 6-5, 6-13

Clean Water Act, 1-6, 1-9, A-1–A-11, ES-1

319 program, 1-7, A-5–A-6

404 Permits, 1-8, A-7–A-8

section 303(d), 1-6, A-2–A-4

section 305(b), 1-6, A-1

section 309, A-8

section 316(b), 1-7, A-7

section 319, A-5

section 401, 1-7, A-7–A-8

section 402, 1-7, A-6

section 502, A-3
Stressor Identification Guidance Document

Index I-3

Co-occurrence

Arkansas River case study, 4-11

DDT case study, 5-2

description, 4-10

Little Scioto River case study, 7-32, 7-36, 7-39, 7-43

Presumpscot River case study, 6-14

COD. See Chemical oxygen demand

Coherence of evidence

description, 4-14

Little Scioto River case study, 7-35, 7-38, 7-42, 7-46

Presumpscot River case study, 6-16

Colorado

Arkansas River case study, 4-11

Combined sewer outfalls, 7-11

Community data plot, 3-4

Complete exposure pathway

DDT case study, 5-2

description, 4-10–4-11

Little Scioto River case study, 7-32, 7-36, 7-39, 7-44

Presumpscot River case study, 6-15

Comprehensive Environmental Response, Compensation, and Liability Act, 1-9, 7-10, A10–A-11

Comprehensive State Water Quality Assessment, 1-2

Conceptual models, 2-5–2-7, 5-2

Consistency of association

description, 4-11

Lake Washington case study, 4-13

Little Scioto River case study, 7-32–7-39, 7-41, 7-43, 7-45

Presumpscot River case study, 6-14–6-15

Consistency of evidence

description, 4-14

Little Scioto River case study, 7-35, 7-38, 7-42, 7-46

Presumpscot River case study, 6-16

Cooling tower intake permitting, A-7

Cooling water intake program, 1-7

Cricotopus sp., 7-9–7-10, 7-19–7-20, 7-30

CSOs. See Combined sewer outfalls

CWA. See Clean Water Act

D

Data Quality Assessment, 3-2

Data quality issues, 1-2

Data Quality Objectives process, 3-2

DDT case study, 5-2

Deformities, fin erosion, lesions, tumors and anomalies, 7-1–7-4, 7-8–7-10, 7-20–7-23,

7-27–7-30, 7-33–7-49

DELTA. See Deformities, fin erosion, lesions, tumors and anomalies

Department of Natural Resources (Maryland)

website, 2-4

Dissolved oxygen

Little Scioto River case study, 7-3, 7-11, 7-14, 7-20, 7-23–7-31, 7-39–7-49, 7-63

Presumpscot River case study, 6-6–6-10, 6-13, 6-17
Stressor Identification Guidance Document

I-4 U.S. Environmental Protection Agency

DNR. See Department of Natural Resources

DO. See Dissolved oxygen

DQA. See Data Quality Assessment

DQO. See Data Quality Objectives process

Dredge and fill permitting, A-7–A-8

E

Eastern Corn Belt Plains, 7-30

Ecological Risk Assessment, 1-2

Edmondson, W.T., 4-13

Effect

defined, 2-1

Elimination of alternatives, 4-3–4-7, 6-8–6-11, 7-26–7-27

EMAP. See Environmental Monitoring and Assessment Program

Enforcement actions

EPA responsibilities, A-8–A-9

role of Stressor Identification process, 1-8

Environmental Monitoring and Assessment Program, 2-4

EPA. See U.S. Environmental Protection Agency

Ephemeroptera-Plecoptera-Trichoptera, 6-1, 6-5–6-6, 7-9

EPT. See Ephemeroptera-Plecoptera-Trichoptera

EROD. See Ethoxy resorufin[O]deethylase

Ethoxy resorufin[O]deethylase, 7-5, 7-24

Eutrophication, 6-6

Experiments

Arkansas River case study, 4-11

DDT case study, 5-2

description, 4-12

Lake Washington case study, 4-13

Little Scioto River case study, 7-35–7-36, 7-38–7-39, 7-41, 7-44–7-45

Presumpscot River case study, 6-14–6-15

Expert judgment, 4-1

Exposure

defined, 2-1

F

False positives, 5-1

Federal Advisory Committee Act, A-4

Field experiments

types of, 3-10

Fill permitting, A-7–A-8

Fish kills

diagnostic protocols, 4-7

Floc. See TSS with floc

G

Glossary of terms, C-1–C-6

H

Habitat degradation

Little Scioto River case study, 7-11, 7-13, 7-21, 7-24–7-28, 7-32–7-36

Presumpscot River case study, 6-8, 6-11, 6-12, 6-14–6-17
Stressor Identification Guidance Document

Index I-5

Heavy metals. See Metals

Hyalella azteca, 7-29, 7-33, 7-64–7-65

I

IBI. See Index of Biotic Integrity

ICI. See Invertebrate Community Index

Impoundment, 6-7–6-8, 6-10–6-12, 6-14–6-17

Index of Biotic Integrity, 2-2, 7-1, 7-4–7-9, 7-13, 7-19–7-20, 7-47

Invertebrate Community Index, 2-2, 7-1, 7-4–7-9, 7-19–7-20, 7-47

K

Kansas Biotic Index, 3-11

Kansas Department of Health and Environment

water quality documentation, 3-11

KBI. See Kansas Biotic Index

KDHE. See Kansas Department of Health and Environment

Koch's postulates, 4-9

L

Lake Washington case study, 4-13

Landfills, 7-6, 7-10

Little Scioto River case study

analyzing evidence for diagnosis, 7-28

characterizing causes, 7-26–7-28

comparing strength of evidence, 7-28–7-31

conceptual model of candidate causes for stressor identification, 7-12

discussion, 7-48–7-49

eliminating alternatives, 7-13–7-26

evidence of impairment, 7-5–7-10

executive summary, 7-1–7-4

fish metrics, 7-54

identifying probable causes, 7-47–7-48

introduction, 7-4–7-5

list of candidate causes, 7-10–7-13

macroinvertebrate metrics, 7-55

map, 7-6

metals concentrations, 7-61–7-62, 7-65

PAH concentrations, 7-64

QHEI metrics, 7-56

sediment organic compounds concentrations, 7-57–7-60

strength of evidence analysis, 7-28–7-46

water chemistry parameters, 7-63

M

Macroinvertebrate biotic index, 3-11

Macroinvertebrates. See Benthic macroinvertebrates

Maine

Presumpscot River case study, 6-1–6-18

Maine Department of Environmental Protection, 6-3, 6-18

Maps

describing impairments, 2-2–2-3

Little Scioto River case study, 7-6
Stressor Identification Guidance Document

I-6 U.S. Environmental Protection Agency

Presumpscot River case study, 6-4

Maryland

Department of Natural Resources website, 2-4

Mayflies, 7-9–7-10

MBI. See Macroinvertebrate biotic index

MDEP. See Maine Department of Environmental Protection

Mechanisms

description, 4-12

Little Scioto River case study, 7-33, 7-37, 7-40, 7-44

Presumpscot River case study, 6-15

Mechanistic conceptual models, 3-9–3-10

Metals

Arkansas River case study, 4-11

Little Scioto River case study, 7-2–7-3, 7-11, 7-14, 7-17, 7-19, 7-22, 7-24–7-38,

7-61–7-62, 7-65

Presumpscot River case study, 6-13

Midges. See Tanytarsini midges

MIWB. See Modified Index of Well-being

Modified Index of Well-being, 7-8

Modified Warmwater Habitat, 7-5, 7-7

Monte Carlo simulation, 4-17

MWH. See Modified Warmwater Habitat

N

National Estuary Program, 1-9, A-10

National Pollutant Discharge Elimination System permit program

monitoring requirements, A-6–A-7

role of Stressor Identification process, 1-7

National Water Quality Inventory Report to Congress, A-1

NEP. See National Estuary Program

Nitrates, 7-14, 7-18, 7-20, 7-27, 7-30–7-31, 7-63

Nitrification, 3-11

Nitrites, 7-14, 7-18, 7-20, 7-27, 7-30–7-31, 7-63

Nitrogen, 7-2

Non-point source pollution

management under section 319 of the CWA, A-5–A-6

role of Stressor Identification process in control program, 1-7

NPDES. See National Pollutant Discharge Elimination System permit program

NPS. See Non-point source pollution

Nutrients

enrichment, 7-13, 7-23, 7-26–7-28, 7-32–7-36, 7-39–7-49

excess, 6-6–6-7, 6-10, 6-12

loading, 3-11

O

OEPA. See Ohio Environmental Protection Agency

Ohio

Little Scioto River case study, 7-1–7-65

Ohio Environmental Protection Agency, 7-1, 7-5, 7-10

Organic enrichment, 3-11, 7-11

Ortho-phosphate, 6-10
Stressor Identification Guidance Document

Index I-7

P

PAH. See Polycyclic aromatic hydrocarbons

Pathogens

Koch's postulates, 4-9

PEL. See Probable effect levels

Permitting programs, A-6–A-8

pH levels, 7-30

Phosphorous, 7-2–7-3, 7-14, 7-18

Phosphorus, total

Little Scioto River case study, 7-20, 7-27, 7-30–7-31, 7-63

Presumpscot River case study, 6-1–6-18

Plausibility

Arkansas River case study, 4-11

DDT case study, 5-2

description, 4-12

Little Scioto River case study, 7-33, 7-37, 7-40, 7-44–7-45

Presumpscot River case study, 6-15

Pollutants

defined, A-3

Pollution

defined, A-3

Pollution control

measuring effectiveness, 1-9

Polycyclic aromatic hydrocarbons, 7-1–7-4, 7-10–7-30, 7-36–7-38, 7-47–7-49, 7-64

Predictive performance

description, 4-13–4-14

Little Scioto River case study, 7-35, 7-38, 7-41, 7-45

Presumpscot River case study, 6-16

Preservation programs, 1-9, A-10

Presumpscot River case study

background information, 6-3–6-5

biological indicators of non-attainment, 6-6

comparison with Androscoggin River, 6-11–6-13

conceptual model of stressor impact, 6-7

eliminating candidate causes, 6-8–6-12

executive summary, 6-1–6-3

identifying probable cause, 6-17

list of candidate causes, 6-5–6-8

map, 6-4

significance of results, 6-18

strength of evidence analysis, 6-11–6-16

using results, 6-18

Probable effect levels, 7-29–7-30, 7-33, 7-64–7-65

Pseudoreplication, 3-7

Pulp and paper mill discharge, 6-1–6-18

Q

QHEI. See Qualitative Habitat Evaluation Index

Qualitative Habitat Evaluation Index, 7-1, 7-4–7-5, 7-13–7-14, 7-20, 7-24, 7-27, 7-56

Quality System website, 3-2
Stressor Identification Guidance Document

I-8 U.S. Environmental Protection Agency

R

R-EMAP. See Regional Environmental Monitoring and Assessment Program

Regional Environmental Monitoring and Assessment Program, 4-11

Restoration programs, 1-9, A-10–A-11

Risk assessment, 1-8, A-9

S

SECs. See Sediment effect concentrations

Sediment effect concentrations, 7-29

Sediment organic compounds, 7-57–7-60

Sedimentation, 6-7–6-8, 6-10, 6-12, 6-14–6-17

SEP. See Supplemental Environmental Project

SI. See Stressor Identification

Source

defined, 2-1

Spatial co-location associations, 3-4

Spatial co-occurrence, 4-11, 6-14

Spatial gradient associations, 3-4

Spearman rank correlations, 7-14, 7-19–7-20

Specificity of cause

description, 4-13

Little Scioto River case study, 7-35, 7-37, 7-41, 7-45

Presumpscot River case study, 6-15

Statistical techniques

analyzing observational data in Stressor Identification, 3-7

evaluating confidence in causal identification, 4-17

Stressor Identification

analyzing evidence, 3-1–3-11

applications of the process, ES-2–ES-3

associations between measures of exposure and measures of effects, 3-8

characterizing causes, 4-1–4-18

data quality issues, 1-2

document overview, ES-3–ES-4

EPA objectives, 1-1

flow of information from data acquisition to analysis phase, 3-3

function and description, ES-1

intended audience, ES-2

iteration options, 5-1–5-3

listing candidate causes, 2-1–2-7

management context, 1-4

mechanistic association with site data, 3-9–3-10

overview of process, 1-3–1-5

process iterations, 1-5

role in water management programs, 1-6–1-9

scope of guidance, 1-2

TMDL program and, A-4

using results, 1-5–1-6

using statistical techniques for analyzing observational data, 3-7

water management programs, A-1–A-11

worksheet model, B-1–B-37

Stressor-responses

Arkansas River case study, 4-11
Stressor Identification Guidance Document

Index I-9

DDT case study, 5-2

description, 4-12

Little Scioto River case study, 7-33, 7-37, 7-40, 7-45

Presumpscot River case study, 6-15

Superfund, 1-9, 7-10, A-10–A-11

Supplemental Environmental Project, A-8–A-9

T

Tanytarsini midges, 7-1, 7-3–7-4, 7-8–7-10, 7-19–7-23, 7-27, 7-43–7-48

TEL. See Threshold effect levels

Temporal gradient associations, 3-4

Temporal relationships, 3-4

Temporality

description, 4-10

Little Scioto River case study, 7-32, 7-36, 7-39, 7-43

Presumpscot River case study, 6-14

Threshold effect levels, 7-29–7-30, 7-33, 7-64–7-65

TIE. See Toxicity Identification and Evaluation program

TMDL. See Total Maximum Daily Load

Total Maximum Daily Load

Clear Water Act requirements, A-2–A-3

EPA actions, A-4

Presumpscot River case study, 6-2–6-3, 6-18

Stressor Identification process, 1-6, ES-2

Total phosphorus

Presumpscot River case study, 6-1–6-18

Toxic compounds. See also Chemical contaminants

Little Scioto River Case Study, 7-11, 7-14–7-22, 7-24–7-25, 7-27, 7-29–7-30, 7-

32–7-48

Presumpscot River case study, 6-5–6-8, 6-12, 6-14–6-17

Toxicity data plot, 3-4

Toxicity Identification and Evaluation program, 4-5, A-6–A-7

Toxicity Reduction Evaluation, A-6–A-7

TP. See Total phosphorus

TRE. See Toxicity Reduction Evaluation

TSS with floc

Presumpscot River case study, 6-1–6-18

Type I error, 5-1

U

U.S. Environmental Protection Agency

compliance and enforcement of CWA, A-8–A-9

Data Quality Objectives process, 3-2

Environmental Monitoring and Assessment Program, 2-4

Quality System website, 3-2

TMDL program implementation, A-4

Wetlands Division website, A-10

W

Warmwater Habitat, 7-5–7-7

Washington

Lake Washington case study, 4-13
Stressor Identification Guidance Document

I-10 U.S. Environmental Protection Agency

Waste water treatment plants, 7-6, 7-10–7-11, 7-28

Water chemistry parameters, 7-63

Water hardness, 7-18, 7-63

Water management programs, A-1–A-11

Water quality

overview of Stressor Identification, 1-3–1-5

ratings, A-1–A-2

Stressor Identification process, 1-6–1-9, ES-2–ES-3

Water Quality Act Amendments, A-5

Water Quality Certification

dredge and fill permitting, A-7

role of Stressor Identification process, 1-7

Water Quality Classification, 6-18

Watershed management programs

role of Stressor Identification process, 1-6

state and local programs, A-4–A-5

Wetlands assessments

methods, A-9–A-10

role of Stressor Identification process, 1-8

Wetlands permitting

role of Stressor Identification process, 1-8

Worksheet model, B-1–B-37

WWH. See Warmwater Habitat

WWTP. See Waste water treatment plants