National Center for Environmental Assessment

Office of Research and Development, Cincinnati, OH 45268

EPA/600/R-10/023F | March 2011 | www.epa.gov/ncea

A Field-Based Aquatic Life 

Benchmark for Conductivity 

in Central Appalachian Streams
[This page intentionally left blank.] 
EPA/600/R-10/023F

March 2011

A Field-Based Aquatic Life Benchmark for 

Conductivity in Central Appalachian Streams

National Center for Environmental Assessment

Office of Research and Development

U.S. Environmental Protection Agency

Washington, DC 20460
DISCLAIMER

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

Agency policy and approved for publication. Mention of trade names or commercial products 

does not constitute endorsement or recommendation for use. 

Cover Photo:

Used by permission, from Randall Sanger Photography. Photo of Ramsey Branch, West Virginia. 

Back Cover Photos:

Used by permission, from Guenter Schuster. Photo of Barbicambarus simmonsi.

Used by permission, from North American Benthological Society. Photos of various invertebrates. © 2009 North 

American Benthological Society. http://www.benthos.org/Education-and-Outreach/MediaGalleries/Invertebrates.aspx?Page=1

Preferred Citation:

U.S. EPA (Environmental Protection Agency). 2011. A Field-Based Aquatic Life Benchmark for Conductivity in 

Central Appalachian Streams. Office of Research and Development, National Center for Environmental 

Assessment, Washington, DC. EPA/600/R-10/023

.

F.

ii
LIST OF TABLES...........................................................................................................................v

LIST OF FIGURES ....................................................................................................................... vi

LIST OF ABBREVIATIONS AND ACRONYMS ..................................................................... vii

PREFACE.................................................................................................................................... viii

AUTHORS, CONTRIBUTORS, AND REVIEWERS ................................................................. ix

ACKNOWLEDGMENTS ........................................................................................................... xiii

EXECUTIVE SUMMARY ......................................................................................................... xiv

1. INTRODUCTION .......................................................................................................................1

1.1. CONDUCTIVITY ............................................................................................................. 1

1.2. APPROACH ...................................................................................................................... 3

2. DATA SETS ................................................................................................................................6

2.1. DATA SET SELECTION ................................................................................................. 6

2.2. DATA SOURCES ............................................................................................................. 6

2.3. DATA SET CHARACTERISTICS................................................................................... 9

3. METHODS ................................................................................................................................13

3.1. EXTIRPATION CONCENTRATION DERIVATION .................................................. 13

3.2. TREATMENT OF POTENTIAL CONFOUNDERS...................................................... 17

3.3. DEVELOPING THE SPECIES SENSITIVITY DISTRIBUTION ................................ 18

3.4. CONFIDENCE BOUNDS............................................................................................... 19

3.5. EVALUATING ADEQUACY OF NUMBER OF SAMPLES....................................... 21

3.6. ESTIMATING BACKGROUND.................................................................................... 22

4. RESULTS ..................................................................................................................................23

4.1. EXTIRPATION CONCENTRATIONS.......................................................................... 23

4.2. SPECIES SENSITIVITY DISTRIBUTIONS ................................................................. 23

4.3. HAZARDOUS CONCENTRATION VALUES AT THE 5th CENTILE ....................... 23

4.4. UNCERTAINTY ANALYSIS ........................................................................................ 23

5. CONSIDERATIONS.................................................................................................................25

5.1. CHOOSING TO USE FIELD VERSUS LABORATORY DATA................................. 25

5.2. SELECTION OF THE EFFECTS ENDPOINT .............................................................. 26

5.3. TREATMENT OF MIXTURES...................................................................................... 26

5.4. DEFINING THE REGION OF APPLICABILITY......................................................... 27

5.5. BACKGROUND ............................................................................................................. 33

5.6. SELECTION OF INVERTEBRATE GENERA ............................................................. 34

5.7. INCLUSION OF OTHER TAXA ................................................................................... 34

5.8. TREATMENT OF LISTED SPECIES............................................................................ 34

5.9. INCLUSION OF REFERENCE SITES .......................................................................... 35

CONTENTS

iii
CONTENTS (contuined)

5.10. SEASONALITY, LIFE HISTORY, AND SAMPLING METHODS........................... 35

5.11. FORMS OF EXPOSURE-RESPONSE RELATIONSHIPS.........................................

5.12. USE OF MODELED OR EMPIRICAL DISTRIBUTIONS......................................... 38

5.13. DUPLICATE SAMPLES .............................................................................................. 39

5.14. TREATMENT OF CAUSATION ................................................................................. 39

5.15. TREATMENT OF POTENTIAL CONFOUNDERS.................................................... 40

6. AQUATIC LIFE BENCHMARK..............................................................................................41

REFERENCES ..............................................................................................................................42

APPENDIX A. CAUSAL ASSESSMENT ............................................................................... A-1

APPENDIX B. ANALYSIS OF POTENTIAL CONFOUNDERS............................................B-1

APPENDIX C. DATA SOURCES AND METHODS OF LAND USE/LAND COVER 

ANALYSIS USED TO DEVELOP EVIDENCE OF SOURCES OF 

HIGH CONDUCTIVITY WATER ..................................................................C-1

APPENDIX D. EXTIRPATION CONCENTRATION VALUES FOR GENERA IN 

THE WEST VIRGINA DATA SET................................................................ D-1

APPENDIX E. GRAPHS OF OBSERVATION PROBABILITIES FOR GENERA IN 

THE WEST VIRGINIA DATA SET ...............................................................E-1

APPENDIX F. GRAPHS OF CUMULATIVE FREQUENCY DISTRIBUTIONS FOR 

GENERA IN THE WEST VIRGINIA DATA SET.........................................F-1

APPENDIX G. VALIDATION OF METHOD USING FIELD DATA TO DERIVE 

AMBIENT WATER QUALITY BENCHMARK FOR 

CONDUCTIVITY USING A KENTUCKY DATA SET............................... G-1

APPENDIX H. EXTIRPATION CONCENTRATION VALUES FOR GENERA IN A 

KENTUCKY DATA SET ............................................................................... H-1

APPENDIX I. GRAPHS OF OBSERVATION PROBABILITIES FOR GENERA IN 

KENTUCKY DATA SET .................................................................................I-1

APPENDIX J. GRAPHS OF CUMULATIVE FREQUENCY DISTRIBUTIONS FOR 

GENERA IN KENTUCKY DATA SET...........................................................J-1

37

iv
1. Differences between the method used to derive the conductivity benchmark and 

the method in Stephan et al. (1985) and the section of the report in which each is 

discussed ..............................................................................................................................3

2. Summary statistics of the measured water-quality parameters..........................................10

3. Number of samples with reported genera and conductivity meeting our acceptance 

criteria for calculating the benchmark value......................................................................11

4. Samples excluded from the original data sets of 2,668 samples used to develop 

benchmark value ................................................................................................................11

5. Genera excluded from 95th centile extirpation concentration calculation because 

they never occurred at reference sites................................................................................12

6. Genera of threatened species included in the SSD ............................................................35

LIST OF TABLES

v
1. Points are sampling locations used to develop the benchmark from Level III 

Ecoregions 69 and 70 in West Virginia. ..............................................................................5

2. Box plot showing seasonal variation of conductivity (μS/cm) in the reference 

streams of Ecoregions 69 and 70 in West Virginia from 1999 to 2006...............................7

3. Box plot showing seasonal variation of conductivity (μS/cm) from a probabilitybased set of sample streams of Ecoregions 69 and 70 in West Virginia from 1997 

to 2007. ................................................................................................................................7

4. Box plot showing seasonal variation of conductivity (μS/cm) from the data set 

used to develop the benchmark............................................................................................8

5. Histograms of the frequencies of observed conductivity values in samples from 

Ecoregions 69 and 70 from West Virginia sampled between 1999 and 2006...................14

6. Examples of weighted CDFs and the associated 95th centile extirpation 

concentration values...........................................................................................................16

7. Three typical distributions of observation probabilities ....................................................16

8. The species sensitivity distribution....................................................................................18

9. Species sensitivity distribution (expanded). ......................................................................19

10. Diagram depicting the process for estimating the uncertainty of the HC05. ......................20

11. The cumulative distribution of XC95 values for the 36 most sensitive genera and 

the bootstrap-derived means and two-tailed 95% confidence intervals.

12. Adequacy of the number of samples used to model the HC05 ...........................................21

13a. Anions................................................................................................................................28

13b. Cations ...............................................................................................................................29

13c. Dissolved metals ................................................................................................................30

13d. Total metals........................................................................................................................31

13e. Other water quality parameters..........................................................................................32

14. Comparison of full data set and subsets of spring and summer collected samples ...........36

15. Relationship of conductivity values sampled from the same site in spring and 

summer...............................................................................................................................37

............................20

LIST OF FIGURES

vi
LIST OF ABBREVIATIONS AND ACRONYMS

CDF cumulative distribution function

CVs chronic values

DCx depletion concentration

GAM Generalized Additive Model

HCx hazardous concentration

LCx lethal concentration

LOWESS locally weighted scatterplot smoothing

SSD species sensitivity distribution

TMDL total maximum daily load

U.S. EPA United States Environmental Protection Agency

WABbase Water Analysis Database

WVDEP West Virginia Department of Environmental Protection

WVSCI West Virginia Stream Condition Index

XCx extirpation concentration

vii
PREFACE

At the request of U.S. Environmental Protection Agency’s (EPA) Office of Water and 

Regions, the EPA Office of Research and Development has developed an aquatic life benchmark 

for conductivity for the Appalachian Region. The benchmark is applicable to mixtures of ions 

dominated by salts of Ca2+, Mg2+, SO4

2−

and HCO3

−

at a circum-neutral to alkaline pH. The 

impetus for the benchmark is the observation that high conductivities in streams below surface 

coal mining operations, especially mountaintop mining and valley fills, are associated with 

impairment of aquatic life. However, application of the benchmark is not limited to that source.

The benchmark was derived by a method modeled on the EPA’s 1985 methodology for 

deriving ambient water-quality criteria for the protection of aquatic life. The methodology was 

adapted for use of field data, by substituting the extirpation of stream macroinvertebrates for 

laboratory toxicity data. 

The methodology and derivation of the benchmark were reviewed by internal reviewers, 

external reviewers, and a review panel of the EPA’s Science Advisory Board (SAB). The SAB 

panel’s review was in turn reviewed by the Chartered SAB. The SAB review is available at 

http://yosemite.epa.gov/sab/sabproduct.nsf/02ad90b136fc21ef85256eba00436459/984d6747508

d92ad852576b700630f32!OpenDocument. 

The SAB concluded that the benchmark is applicable to the regions in which it was 

derived and the benchmark and the methodology may be applicable to other states and regions 

with appropriate validation. In addition, hundreds of public commenters provided their views. 

Comments from all of these sources were considered and used to improve the clarity and 

scientific rigor of the document.

viii
AUTHORS, CONTRIBUTORS, AND REVIEWERS

AUTHORS

Susan M. Cormier, PhD

U.S. Environmental Protection Agency

Office of Research and Development, National Center for Environmental Assessment

Cincinnati, OH 45268

Glenn W. Suter II, PhD

U.S. Environmental Protection Agency

Office of Research and Development, National Center for Environmental Assessment

Cincinnati, OH 45268

Lester L. Yuan, PhD

U.S. Environmental Protection Agency 

Office of Research and Development, National Center for Environmental Assessment

Washington, DC 20460

Lei Zheng, PhD

Tetra Tech, Inc.

Owings Mills, MD 21117

CONTRIBUTORS

R. Hunter Anderson, PhD

U.S. Environmental Protection Agency

Office of Research and Development, National Center for Environmental Assessment

Cincinnati, OH 45268

Jennifer Flippin, MS

Tetra Tech, Inc.

Owings Mills, MD 21117

Jeroen Gerritsen, PhD

Tetra Tech, Inc.

Owings Mills, MD 21117

Michael Griffith, PhD

U.S. Environmental Protection Agency

Office of Research and Development, National Center for Environmental Assessment

Cincinnati, OH 45268

ix
x

AUTHORS, CONTRIBUTORS, AND REVIEWERS (continued)

CONTRIBUTORS (continued)

Michael McManus, PhD

U.S. Environmental Protection Agency 

Office of Research and Development, National Center for Environmental Assessment 

Cincinnati, OH 45268 

John Paul, PhD 

U.S. Environmental Protection Agency 

National Health and Environmental Effects Research Laboratory

Research Triangle Park, NC 27711 

Gregory J. Pond, MS

U.S. Environmental Protection Agency 

Region III

Wheeling, WV 26003 

Samuel P. Wilkes, MS 

Tetra Tech, Inc.

Charleston, WV 25301 

REVIEWERS

John Paul, PhD 

U.S. Environmental Protection Agency 

National Health and Environmental Effects Research Laboratory

Research Triangle Park, NC 27711 

Samuel P. Wilkes, MS 

Tetra Tech, Inc.

Charleston, WV 25301 

Margaret Passmore, MS 

U.S. Environmental Protection Agency 

Office of Monitoring and Assessment, Freshwater Biology Team

Wheeling, WV 26003 

Charles Delos, MS

U.S. Environmental Protection Agency 

Office of Water, Health and Ecological Criteria Division

Washington, DC 20460 
xi

AUTHORS, CONTRIBUTORS, AND REVIEWERS (continued)

REVIEWERS (continued)

John Van Sickle, PhD 

U.S. Environmental Protection Agency 

National Health and Environmental Effects Research Laboratory, Western Ecology Division

Corvallis, OR 97333 

Charles P. Hawkins, PhD 

Western Center for Monitoring and Assessment of Freshwater Ecosystems 

Department of Watershed Sciences

Utah State University

Logan, UT 84322 

Christopher C. Ingersoll, PhD 

U.S. Geological Survey 

Columbia Environmental Research Center 

4200 New Haven Road 

Columbia, MO 65201 

Charles A. Menzie, PhD 

Exponent 

2 West Lane

Severna Park, MD 21146 

Science Advisory Board Panel on Ecological Impacts of Mountaintop Mining and Valley 

Fills

Duncan Patten, Chairman, PhD 

Montana State University, Bozeman, MT 

Elizabeth Boyer, PhD 

Pennsylvania State University, University Park, PA 

William Clements, PhD 

Colorado State University, Fort Collins, CO 

James Dinger, PhD 

University of Kentucky, Lexington, KY 

Gwendelyn Geidel, PhD 

University of South Carolina, Columbia, SC 
AUTHORS, CONTRIBUTORS, AND REVIEWERS (continued)

REVIEWERS (continued)

xii

Kyle Hartman, PhD 

West Virginia University, Morgantown, WV 

Robert Hilderbrand, PhD 

University of Maryland Center for Environmental Science, Frostburg, MD 

Alexander Huryn, PhD 

University of Alabama, Tuscaloosa, AL 

Lucinda Johnson, PhD 

University of Minnesota Duluth, Duluth, MN 

Thomas W. La Point, PhD 

University of North Texas, Denton, TX 

Samuel N. Luoma, PhD 

University of California – Davis, Sonoma, CA 

Douglas McLaughlin, PhD 

National Council for Air and Stream Improvement, Kalamazoo, MI 

Michael C. Newman, PhD 

College of William & Mary, Gloucester Point, VA

Todd Petty, PhD 

West Virginia University, Morgantown, WV 

Edward Rankin, MS 

Ohio University, Athens, OH 

David Soucek, PhD 

University of Illinois at Urbana-Champaign, Champaign, IL 

Bernard Sweeney, PhD 

Stroud Water Research Center, Avondale, PA 

Philip Townsend, PhD 

University of Wisconsin–Madison, Madison, WI 
xiii

AUTHORS, CONTRIBUTORS, AND REVIEWERS (continued)

REVIEWERS (continued)

Richard Warner, PhD 

University of Kentucky, Lexington, KY 

ACKNOWLEDGMENTS

Susan B. Norton, Teresa Norberg-King, Peter Husby, Peg Pelletier, Treda Grayson, Amy 

Bergdale, Candace Bauer, Brooke Todd, David Farar, Lana Wood, Heidi Glick, Cristopher 

Broyles, Linda Tackett, Stacey Herron, Bette Zwayer, Maureen Johnson, Debbie Kleiser, Crystal 

Lewis, Marie Nichols-Johnson, Sharon Boyde, Katherine Loizos, and Ruth Durham helped bring 

this document to completion by providing comments, essential fact checking, editing, formatting, 

and other key activities. Statistical review of the methodology was provided by Paul White, 

John Fox, and Leonid Kopylev. 
This report uses field data to derive an aquatic life benchmark for conductivity that can 

be applied to waters in the Appalachian Region that are dominated by salts of Ca2+, Mg2+, SO4

2−

and HCO3

−

at a circum-neutral to mildly alkaline pH. This benchmark is intended to protect the 

aquatic life in the region. It is derived by a method modeled on the EPA’s standard methodology 

for deriving water-quality criteria (i.e., Stephan et al., 1985). In particular, the methodology was 

adapted for use of field data. Field data were used because sufficient and appropriate laboratory 

data were not available and because high-quality field data were available to relate conductivity 

to effects on aquatic life. This report provides scientific evidence for a conductivity benchmark 

in a specific region rather than for the entire United States.

The method used in this report is based on the standard methodology for deriving 

water-quality criteria, as explained in Stephan et al. (1985), in that it used the 5th centile of a 

species sensitivity distribution (SSD) as the benchmark value. SSDs represent the response of 

aquatic life as a distribution with respect to exposure. Data analysis followed the standard 

methodology in aggregating species to genera and using interpolation to estimate the centile. It 

differs primarily in that the points in the SSDs are extirpation concentrations (XCs) rather than 

median lethal concentrations (LC50s) or chronic values. The XC is the level of exposure above 

which a genus is effectively absent from water bodies in a region. For this benchmark value, the 

95th centile of the distribution of the probability of occurrence of a genus with respect to 

conductivity was used as a 95th centile extirpation concentration. Hence, this aquatic life 

benchmark for conductivity is expected to avoid the local extirpation of 95% of native species 

(based on the 5th centile of the SSD) due to neutral to alkaline effluents containing a mixture of 

dissolved ions dominated by salts of SO4

2−

and HCO3

−

. Because it is not protective of all genera 

and protects against extirpation rather than reduction in abundance, this level is not fully 

protective of sensitive species or higher quality, exceptional waters designated by state and 

federal agencies. 

This field-based method has several advantages. Because it is based on biological 

surveys, it is inherently relevant to the streams where the benchmark may be applied and 

represents the actual aquatic life use in these streams. Another advantage is that the method 

assesses all life stages and ecological interactions of many species. Further, it represents the 

actual exposure conditions for elevated conductivity in the region, the actual temporal variation 

in exposure, and the actual mixture of ions that contribute to salinity as measured by 

conductivity.

The disadvantages of field data result from the fact that exposures are not controlled. As 

a result, the causal nature of the relationship between conductivity and the associated biological 

EXECUTIVE SUMMARY

xiv
xv

impairments must be assessed. Also, any variables that are correlated with conductivity and the 

biotic response may confound the relationship of biota to conductivity. Assessments of 

causation and confounding were performed and are presented in the appendices. They 

demonstrate that conductivity can cause impairments and the relationship between conductivity 

and biological responses apparently is not appreciably confounded. 

The chronic aquatic life benchmark value for conductivity derived from all-year data 

from West Virginia is 300 μS/cm. It is applicable to parts of West Virginia and Kentucky within 

Ecoregions 68, 69, and 70 (Omernick, 1987). It is expected to be applicable to the same 

ecoregions extending into Ohio, Pennsylvania, Tennessee, Virginia, Alabama, and Maryland, but 

data from those states have not been analyzed. This is because the salt matrix and background is 

expected to be similar throughout the ecoregions. The benchmark may also be appropriate for 

other nearby ecoregions, such as Ecoregion 67, but it has only been validated for use in 

Ecoregions 68, 69, and 70 at this time. This benchmark level might not apply when the relative 

concentrations of dissolved ions are not dominated by salts of Ca2+, Mg2+, SO4

2−

 and HCO3

− or 

the natural background exceeds the benchmark. However, the salt mixture dominated by salts of 

SO4

2−

 and HCO3

−

 is believed to be an insurmountable physiological challenge for some species. 
1. INTRODUCTION

At the request of U.S. Environmental Protection Agency’s (U.S. EPA) Office of Water

and Regions, the Office of Research and Development has developed an aquatic life benchmark 

for conductivity that may be applied in the Appalachian Region associated with mixtures of ions 

dominated by salts of Ca2+, Mg2+, SO 2−

4 and HCO −3 at a circum-neutral to alkaline pH. The 

benchmark is intended to protect the aquatic life in streams and rivers in the region. It is derived 

by a method modeled on the EPA’s standard methodology for deriving water-quality criteria 

(i.e., Stephan et al., 1985). In particular, the methodology was adapted for use of field data. 

Field data were used because sufficient and appropriate laboratory data were not available and 

because high quality field data were available to relate conductivity to effects on aquatic life in 

streams and rivers.

1.1. CONDUCTIVITY

Although the elements comprising the common mineral salts such as sodium chloride 

(NaCl) are essential nutrients, aquatic organisms are adapted to specific ranges of salinity and 

experience toxic effects from excess salinity. Salinity is the property of water that results from 

the combined influence of all disassociated mineral salts. The most common contributors to 

salinity in surface waters, referred to as matrix ions, are:

Cations: Ca2+, Mg2+, Na+, K+

Anions: HCO −, CO 2−, SO 2− −

3 3 4 , Cl

The salinity of water may be expressed in various ways, but the most common is specific 

conductivity. Specific conductivity (henceforth simply referred to as “conductivity”) is the 

ability of a material to conduct an electric current measured in microSiemens per centimeter 

(μS/cm) standardized to 25°C. (In this report, “conductivity” refers to the measurement, and 

resulting data and “salinity” refers to the environmental property that is measured.) Currents are 

carried by both cations and anions—but to different degrees depending on charge and mobility. 

Effectively, conductivity may be considered an estimate of the ionic strength of a salt solution. 

The ionic composition of mixtures of salts affects their toxicity (Mount et al., 1997). Therefore, 

a measure such as conductivity is necessary because the effects of the salts are a result of the 

magnitude of the exposure and the relative proportion of all of the ions in the mixture—not to 

any one individually. Hence, unless an individual ion occurs at a much higher concentration 

relative to its toxicity than other ions, the individual ion would not be the only potential cause,

and a benchmark based on an individual ion could be under-protective. Therefore, this aquatic 

1
2 

life benchmark for conductivity is only appropriate for a mixture of salts dominated by the Ca2+, 

Mg2+, SO4

2−

, and HCO3

−

Salinity has numerous sources (Ziegler et al., 2007). Freshwater can become increasingly 

salty due to evaporation, which concentrates salts such as those in irrigation return waters 

(Rengasamy, 2002) or diversions that reduce inflow relative to evaporation (e.g., Pyramid Lake, 

Nevada). Intrusion of saltwater occurs when ground water withdrawal exceeds recharge 

especially near coastal areas (Bear et al., 1999; Werner, 2009). Freshwater can also become 

salty with the additions of brines and wastes (Clark et al., 2001), minerals dissolved from 

weathering rocks (Pond, 2004; U.S. EPA, 2011), and runoff from treating pavements for icy 

conditions (Environment Canada and Health Canada, 2001; Evans and Frick, 2000; Kelly et al., 

2008). 

 ions at a circum-neutral to mildly alkaline pH (6.0−10.0) in the 

Appalachian Region. 

Exposure of aquatic organisms to salinity is direct. Fish, amphibians, mussels, and 

aquatic macroinvertebrates are especially exposed on their gills or other respiratory surfaces that 

are in direct contact with dissolved ions in water. All animals have specific structures to 

transport nutrient ions and control their ionic and osmotic balance (Bradley, 2009; Evans, 

2008a, b, 2009; Wood and Shuttleworth, 2008; Thorp and Covich, 2001; Komnick, 1977; Smith, 

2001; Sutcliff, 1962; Hille, 2001). However, these cell membrane and tissue structures function 

only within a range of salinities. For example, some aquatic insects, such as most 

Ephemeroptera (mayflies), have evolved in a low-salt environment. Because they would 

normally lose salt in freshwater, their epithelium is selectively permeable to the uptake of certain 

ions and less permeable to larger ions and water. Many freshwater organisms depend heavily on 

specialized external mitochondria-rich chloride cells on the epithelium of their gills for the 

uptake of salts and export of metabolic waste (Komnick, 1977). Some life stages of animals may 

be particularly sensitive. For instance, ionic concentrations and transport processes are essential 

to regulate membrane permeability during external fertilization of eggs, including those of fish 

(Tarin et al., 2000). 

Retention of ions is insufficient to maintain homeostasis and the actual uptake and export 

of ions occurs at semipermeable membranes. Anion, cation, and proton transport occurs by 

passive, active, uniport, and cotransport processes often in a coordinated fashion (Nelson and 

Cox, 2005; Hille, 2001). These numerous specific mechanisms are involved in the toxicity of 

solutions with relative ion concentrations different from what an organism typically encounters 

(see Appendix Section A.2.3 for more details on physiological mechanisms). 
1.2. APPROACH

The approach used to derive the benchmark is based on the standard method for the 

EPA’s published Section 304(a) Ambient Water-Quality Criteria. Those criteria are the 

5

th centiles of species sensitivity distributions (SSDs) based upon laboratory toxicity tests, such 

that the goal is to protect at least 95% of the species in an exposed community (Stephan et al.,

1985). SSDs are models of the distribution of exposure levels at which species respond to a 

stressor. That is, the most sensitive species respond at exposure level X1, the second most 

sensitive species respond at X2, etc. The species ranks are scaled from 0 to 1 so that they 

represent cumulative probabilities of responding, and the probabilities are plotted against the 

exposure levels (as seen in Posthuma et al., 2002). Centiles of the distribution can be derived 

using interpolation, parametric regression, or nonparametric regression. It should be noted that 

because SSDs are models of the distribution of sensitivity—and not just descriptions of the 

relative sensitivity of a particular set of species—they can be broadly applicable. In particular, 

SSDs derived using species from different continents are consistent for some chemicals (Hose

and Van den Brink, 2004; Maltby et al., 2005).

For the conductivity benchmark, the SSDs are derived from field data. Some pollutants, 

such as suspended and bedded sediments (U.S. EPA, 2006; Cormier et al., 2008), and some 

assessment endpoints do not lend themselves to laboratory testing, and field data have some 

advantages for benchmark development (see Section 5.1). The differences between the method 

used here and the traditional method for deriving water-quality criteria are presented in Table 1,

and the advantages are listed in Section 5.1.

Table 1. Differences between the method used to derive the conductivity 

benchmark and the method in Stephan et al. (1985) and the section of the 

report in which each is discussed

5.6, 5.7, and 5.8

test species

Used the macroinvertebrate taxa from biological surveys rather than 

Used data from a particular region 5.4

Used an integrative measure of a mixture rather than a single chemical 5.3

Used extirpation as the response rather than a LC50 or CV 5.2

Used field rather than laboratory data 5.1

Difference Section

3
The choice to use field data to derive benchmarks of any kind poses some challenges. 

Because causal relationships in the field are uncontrolled, unreplicated, and unrandomized, they 

are more subject to a broader array of responses and to confounding. Confounding is the 

appearance of apparently causal relationships that are due to noncausal correlations. In addition, 

noncausal correlations and the inherent noisiness of environmental data can obscure true causal 

relationships. The potential for confounding is reduced, as far as possible, by identifying 

potential confounding variables, determining their contributions, if any, to the relationships of 

interest, and eliminating their influence when possible and as appropriate based on credible and 

objective scientific reasoning (see Appendix B). In addition, the evidence for and against salts as 

a cause of biological impairment is weighed using causal criteria adapted from epidemiology 

(see Appendix A). 

Because relationships between conductivity and biological responses appear to vary 

among different mixtures of ions, this benchmark is limited to two contiguous regions with a 

particular dominant source of salinity. The regions are Level III 69 (Central Appalachian) and 

70 (Western Allegheny Plateau) (see Figure 1) (U.S. EPA, 2007; Omernik, 1987; Woods et al., 

1996). Low salinity rain water, sometimes so low as to not be accurately measured by 

conductivity, becomes salty as it interacts with the earth’s surface. Along surface and ground 

water paths to the ocean, water contacts rocks. The rock demineralizes and contributes salts that 

accumulate. A large surface to volume ratio of unweathered rock increases dissolution of rock. 

For the most part, these salts are not degraded by natural processes but can be diluted by more 

rain or by less salty tributaries. Drought increases salt concentrations. Addition of wastes or 

waste waters also contributes salts. The prominent sources of salts in Ecoregions 69 and 70 are 

mine overburden and valley fills from large-scale surface mining, but they may also come from 

slurry impoundments, coal refuse fills, or deep mines. Other sources include effluent from waste 

water treatment facilities and brines from natural gas drilling and coalbed methane production.

This benchmark for conductivity applies to waters influenced by current inputs from these 

sources in Ecoregions 69 and 70 with salts dominated by SO 2−

4 and HCO −3 anions at a 

circum-neutral to mildly alkaline pH. 

4
Figure 1. Points are sampling locations used to develop the benchmark from 

Level III Ecoregions 69 (light grey) and 70 in West Virginia. 

5
2. DATA SETS

Data are required to develop and substantiate the benchmark. This section explains how 

the data were selected, describes the data that were used, and explains how the data set was 

refined to make it useful for analysis.

2.1. DATA SET SELECTION

The Central Appalachia (69) and Western Allegheny Plateau (70) Ecoregions were 

selected for development of a benchmark for conductivity because available data were of 

sufficient quantity and quality, and because conductivity has been implicated as a cause of 

biological impairment in these ecoregions (Pond et al., 2008; Pond, 2010; Gerritsen et al., 2010). 

These regions were judged to be similar in terms of water quality including resident biota and 

sources of conductivity. Confidence in the quality of reference sites in West Virginia was 

relatively high owing to the extensively forested areas of the region and well-documented 

process by which West Virginia Department of Environmental Protection (WVDEP) assigns 

reference status. They use a tiered approach. Only Tier 1 was used when analyses involved the 

use of reference sites, thus avoiding the use of conductivity as a characteristic of reference 

condition. Conductivity values from WVDEP’s reference sites were low and similar in different 

months collected over several years (see Figure 2), providing evidence that the sites were 

reasonable reference sites. The 75th centiles were below 200 μS/cm in most months. The 

25th centiles from samples from a probability-based sample and from the full data set were below

200 μS/cm in most months (see Figures 3 and 4). Also, a wide range of conductivity levels were 

sampled, which is useful for modeling the response of organisms to different levels of salinity.

2.2. DATA SOURCES

All data used for benchmark derivation were taken from the WVDEP’s in-house Water

Analysis Database (WABbase) 1999−2007. The WABbase contains data from Level III 

Ecoregions 66, 67, 69, and 70 in West Virginia (U.S. EPA, 2000; Omernik, 1987; Woods et al., 

1996). In this assessment, only data from Ecoregions 69 and 70 were used (see Figure 1).

Chemical, physical, and/or biological samples were collected from 2,542 distinct locations 

(2,668 samples) during the sampling years 1999−2007. WVDEP uses a tiered sampling design 

collecting measurements from long-term monitoring stations; targeted sites within watersheds on 

a rotating basin schedule; probability-based sites (Smithson, 2007); and sites chosen to further 

define impaired stream segments in support of total maximum daily load (TMDL) development 

(WVDEP, 2008b). Most sites have been sampled once during an annual sampling period, but 

6
500

200

100

50

20

DecSepAugJulJunMayAprFebJan

S/cm)  Conductivity ( 

Month

Figure 2. Box plot showing seasonal variation of conductivity (μS/cm) in the

reference streams of Ecoregions 69 and 70 in West Virginia from 1999 to 

2006. A total of 97 samples from 70 reference stations were used for this 

analysis. The 75th centiles were below 200 μS/cm in all months except in June.

10000

1000

100

10

1

Apr May Jun Jul Aug Sep Oct

S/cm)  Conductivity ( 

Month

Figure 3. Box plot showing seasonal variation of conductivity (μS/cm) from a 

probability-based set of sample streams of Ecoregions 69 and 70 in West 

Virginia from 1997 to 2007. A total of 1,271 samples were used for this 

analysis. The 25th centiles were below 200 μS/cm (horizontal dashed line) except 

in the September and October samples.

7
10000

5000

2000

1000

500

200

100

50

20

DecNovOctSepAugJulJunMayAprMarFebJan

S/cm)  Conductivity ( 

Month

Figure 4. Box plot showing seasonal variation of conductivity (μS/cm) from 

the data set used to develop the benchmark. A total of 2,210 samples from 

2000 to 2007 from Ecoregions 69 and 70 in West Virginia are represented. The 

25th centiles were below 200 μS/cm except in the August and November (n = 2) 

samples. The wide range of conductivities allows the XC95 to be well 

characterized.

TMDL sites have been sampled monthly for water-quality parameters. Some targeted sites 

represent least disturbed or reference sites that have been selected by a combination of screening 

values and best professional judgment (Bailey, 2009). Water quality, habitat, watershed 

characteristics, macroinvertebrate data (both raw data and calculated metrics), and supporting 

information are used by the State to develop 305(b) and 303(d) reports to the EPA (WVDEP, 

2008b). All sites were in perennial reaches of streams.

Quality assurance and standard procedures are described by WVDEP (2006, 2008a). 

WVDEP collects macroinvertebrates from a 1-m2area of a 100-m reach at each site. When 

using 0.5-m-wide rectangular kicknet (595-μ mesh), four, 0.25-m2

riffle areas are sampled. In 

narrow or shallow water, nine areas are sampled with a 0.33-m-wide D-frame dipnet of the same 

mesh size. Composited samples are preserved in 95% denatured ethanol. A random subsample

of 200 individuals +20% are identified in the laboratory. All contracted analyses for chemistry 

and macroinvertebrate identification follow West Virginia’s internal quality-control and 

quality-assurance protocols. This is a well-documented, regulatory database. EPA judged the 

quality assurance to be excellent based on the database itself, supporting documentation, and the 

experience of EPA Region 3 personnel.

8
Information was also obtained from the literature and other sources for the assessments of 

causality and confounding (see Appendices A and B):

1) Toxicity test results were obtained from peer-reviewed literature.

2) Information on the effects of dissolved salts on freshwater invertebrates was taken from 

standard texts and other physiological reviews. 

3) An EPA Region 3 data set was obtained from Gregory Pond which includes the original 

data for Table 3 in Pond et al. (2008) and data collected for the Programmatic 

Environmental Impact Assessment (Bryant et al., 2002). It was used to evaluate the 

relative contribution of different ions in drainage from valley fills of large scale surface 

mining and for other analyses related to causation (see Appendix A) and confounding 

(see Appendix B). Some of these data were added since the 2010 public review draft.

4) The constituent ions for Marcellus Shale brine were provided by EPA Region 3 based on 

analyses by drilling operators (see Appendix A). 

5) Data for Kentucky are from the Kentucky Department of Water database and are 

described in Appendix G, and results are presented in Appendices A, G, H, I, and J. 

6) Geographic and related information are from WVDEP and various public sources and are 

described in Appendix C and also used in Appendix A.

2.3. DATA SET CHARACTERISTICS

Biological sampling usually occurred once per year with minimal repeat biological 

samples from the same location (5%). Multiple samples from the same location were not 

excluded from the data set (see Section 5.13). Summary statistics for ion concentrations and 

other parameters for the data set are provided in Table 2. The benchmark applies to waters with 

a similar composition to those in Table 2. 

A total of 2,210 samples from Ecoregions 69 and 70 were used in the determination of 

the benchmark (see Figure 1 and Table 3). Data from a sampling event at a site were excluded

from calculations if they lacked a conductivity measurement (see Table 4). Samples were 

excluded if the samples were identified as being from a large river (>155 km2) because the 

sampling methods differed (Flotermersch et al., 2001). They were excluded if the salt mixture 

was dominated by Cl−rather than SO 2−

4 (conductivity > 1,000 μS/cm, SO4 < 125 mg/L, and 

Cl−> 250 mg/L). Four sites with elevated conductivity, high chloride, and low sulfate were 

removed in response to concerns that the benchmark might be biased by sites with salts 

dominated by Marcellus Shale brines. 

9
+ +

Table 2. Summary statistics of the measured water-quality parameters

Habitat RBP score 49 115 130 145 192 127.8 2,186

C −0.28 15.1 18.4 21.3 31.9 17 2,210 o Temperature

0.173 2.311 6.965 25.836 153.014 7.644 717 2km

area

Catchment 

6.02 7.27 7.62 7.96 10.48 7.59 2,210

units

pH standard 

DO mg/L 1.02 8.2 9.2 10.3 18.35 9.3 2,182

0 36 170 600 250,000 151 2,035

mL100

Fecal coliform counts/

Se, total mg/L 0 0.001 0.001 0.005 1.26 0.002 496

Se, dissolved mg/L 0.001 0.001 0.001 0.001 1.26 0.001 313

mg/L 0.01 0.02 0.02 0.03 2.36 0.03 1,181

phosphate

Total 

Mn, dissolved mg/L 0.01 0.03 0.07 0.22 1.06 0.07 20

Mn, total mg/L 0.003 0.02 0.04 0.1 7.25 0.05 1,430

Fe, dissolved mg/L 0.001 0.02 0.042 0.06 11.8 0.05 1,259

Al, dissolved mg/L 0.01 0.02 0.05 0.06 0.93 0.04 1,287

Al, total mg/L 0.01 0.09 0.11 0.23 12 0.15 1,436

NO2 NO- 3 mg/L 0.01 0.1 0.2 0.37 30 0.20 1,178

Fe, total mg/L 0.005 0.123 0.26 0.5 110 0.26 1,433

TSS mg/L 1 3 4 6 190 4.3 1,442

Mg, total mg/L 0.05 3.7 6.3 14 204 7.3 1,150

Ca, total mg/L 0.002 13.6 25.1 49.2 430 25.5 1,154

mg/L 1 3 5.2 11.95 1,153 6.5 1,118 −

lC

mg/L 1 17 37 159 6,000 1,428651.

2−

SO4

Alkalinity mg/L 0.2 30.5 66.7 117 560 55 1,425

Hardness mg/L 0.5 50.2 91.1 188 1,492 97.1 1,148

Conductivity μS/cm 15.4 146 261 563 11,646 281.5 2,210

N

Valid 

centile Max Mean

th 75

centile Median

th 25

Parameter Units Min

Note: K and Na not measured; all means are geometric means except pH, DO, Temperature, and Habitat Score.

DO = dissolved oxygen; RBP = rapid bioassessment protocol; TSS = total suspended solids.

10
Table 3. Number of samples with reported genera and conductivity meeting 

our acceptance criteria (see Table 4) for calculating the benchmark value.

Number of samples is presented for each month and ecoregion

Total 2,2101026235250627328241925053712

70 1,2044281202371941792321874334

69 1,00660542322697910318763148

DecNovOctSepAugJulJunMayAprMarFebJan Total

Month

Region

Table 4. Samples excluded from the original data sets of 2,668 samples used 

to develop benchmark value

Ambiguous taxa ,family, or higher level 2

identification

Taxonomic 

mg/L>250 4

−

>1,000 μS/cm, SO4 <125 mg/L, and Cl − ClHigh 

pH <6 147

Conductivity No measurement 10

295 2 Catchment >155 km

xcludede

N of samples 

Parameter Exclusion level

The effects of low pH were eliminated by excluding sites with a pH of <6. This 

prevented potential confounding of conductivity effects by the effects of acid mine drainage (see 

Appendix B). The freshwater chronic water-quality criterion requires waters to be maintained 

between a pH of 6.5 and 9 (U.S. EPA, 1986). The conductivity benchmark was derived from

waters having a pH between 6.0 and 10. Thus the circum-neutral application brackets pH of 

7 primarily in the range where pH is usually not toxic to freshwater organisms. 

An organism was excluded from calculations if it was not identified to the genus level, 

and a genus was excluded if it was never observed at reference sites or it was observed in 

<25 samples. Invertebrate genera that did not occur at WVDEP Tier 1 reference sites 

represented 11.4% of all genera (see Table 5). They were excluded so that the data would be 

relevant to potentially unimpaired conditions—and so as to not include opportunistic salt-tolerant

organisms. Genera were excluded that were observed at fewer than 25 sampling locations in the 

composited ecoregion thus ensuring reasonable confidence in the evaluation of the relationship 

11
Table 5. Genera excluded from 95th centile extirpation concentration 

calculation because they never occurred at reference sites

Dineutus

Corbicula Nanocladius Paratendipes Stenochironomus Tricorythodes

Calopteryx Leucotrichia Paracladopelma Sphaerium Tribelos

Baetisca Fossaria Palpomyia Saetheria Tokunagaia

Argia Ferrissia Oecetis Prostoma Stictochironomus

between conductivity and the presence and absence of a genus. This decision was made because 

an analysis showed that the benchmark varied within <5% when SSD models were constructed 

from >20 occurrences of each genus; whereas the benchmark steadily became lower when 

XC95 values were derived from <15 occurrences. 

In the WABbase, 497 benthic invertebrate genera were identified in Ecoregions 69 

and 70. Those ecoregions had 308 genera in common. Of these, 220 genera occurred at least 

once at 1 of the 70 reference sites in the two ecoregions. Genera that did not occur at reference 

sites were excluded from the SSD (see Table 5). Greater than 95% of genera observed at 

reference sites as defined by WVDEP occur in both Ecoregions 69 and 70. This indicates that 

the same sensitive genera exist in both ecoregions. Of the 220 genera, 163 occurred at 

≥25 sampling locations in Ecoregions 69 and 70. Of the genera occurring at >25 sampling sites, 

162 genera occurred in Ecoregion 69 and 163 in Ecoregion 70.

12
3. METHODS

The derivation of the benchmark for conductivity includes three steps: first, the 

extirpation values (XCs) for each invertebrate genus was derived. Second, the XC95 values for 

all genera were used to generate an SSD and the 5th centile of the distribution, the 5th centile

hazardous concentration (HC05). (The HCX terminology for concentrations derived from SSDs 

is not in the EPA method [Stephan et al., 1985], but its usage has become common more recently 

[Posthuma et al., 2002]). Finally, background values were estimated for the regions to ensure

that the benchmark is not in the background range. These steps are explained in this section.

We used the statistical package R, version Version: 2.12.1 (December 2010), for all 

statistical analyses (http://www.r-project.org/).

3.1. EXTIRPATION CONCENTRATION DERIVATION

Extirpation is defined as the depletion of a population to the point that it is no longer a 

viable resource or is unlikely to fulfill its function in the ecosystem (U.S. EPA, 2003). In this 

report, extirpation is operationally defined for a genus as “the conductivity value below which 

95% of the observations of the genus occur and above which only 5% occur.” In other words, 

the probability is 0.05 that an observation of a genus occurs above its XC95 conductivity value. 

This is a chronic-duration endpoint because the field data set reflects exposure over the entire life 

cycle of the resident biota. The 95th centile was selected because it is more reliable than the 

maximum value, yet it still represents the extreme of a genus’s tolerance of conductivity. The 

maximum value is sensitive to occurrences due to drifting organisms, misidentifications, or other 

misleading occurrences.

The XC95 is estimated from the cumulative distribution of probabilities of observing a 

genus at a site with respect to the concurrently measured conductivity at that site. Observed 

conductivity values were nonuniformly distributed across a range of possible values (see 

Figure 5), and, therefore, we were more likely to observe a genus at certain conductivity values 

simply because more samples were collected at those values. To correct for the uneven sampling 

frequency, a weighted cumulative distribution function was used to estimate the XC95 values for 

each genus. The purpose of weighting is to avoid bias due to uneven distribution of observations 

with respect to conductivity by converting the sampling distribution to one that mimics an even 

distribution of sample across the gradient of conductivity. It creates a distribution more like the 

design of a toxicity test, which is appropriate when developing an exposure-response 

relationship. To compute weights for each sample, we first defined equally-sized bins, each 

13
120100806040200

32 100 316 1000 3162 10000

Frequency

Conductivity (μS/cm)

Figure 5. Histograms of the frequencies of observed conductivity values in 

samples from Ecoregions 69 and 70 from West Virginia sampled between 

1999 and 2006. Bins are each 0.017 log10 conductivity units wide.

0.017 log10 conductivity units wide, that spanned the range of observed conductivity values, a 

total of 60 bins. We then calculated the number of samples that occurred within each bin (see 

Figure 5). Each sample was then assigned a weight w

i = 1/

n

i

, where n

i is the number of samples 

in the i

th bin. 

The value of the weighted cumulative distribution function, F(x), of conductivity values 

associated with observations of a particular genus was computed for each unique observed value 

of conductivity, x, as follows:

14
( )

( )

( )

11

11

F x

w I x x and G

w I G

i ij ij

j

M

i

N

i ij

j

M

i

N

b i

b i













where xij is the conductivity value in the j

th sample of bin i,

Nb is the total number of bins,

Mi is the number of samples in the i

th bin,

Gij is true if the genus of interest was observed in j

th sample of bin i,

I is an indicator function that equals 1 if the indicated conditions are true, and 0 

otherwise.

The XC95 value is defined as the conductivity value, x where F(x) = 0.95. Equation 1 is 

an empirical cumulative distribution function, and the output is the proportion of observations of 

the genus that occur at or below a given conductivity level. However, the individual 

observations are weighted to account for the uneven distribution of observations across the range 

of conductivities.

An example of a weighted cumulative distribution function (CDF) is shown in Figure 6 

for the mayfly, Drunella. The horizontal dashed line indicates the point of extirpation where 

F(x) = 0.95 intersects the CDF. The vertical dashed line indicates the XC95 conductivity value 

on the x-axis. 

This method for calculating the XC95 will generate a value even if the genus is not 

extirpated. For example, the occurrence of Nigronia changes little with increasing conductivity 

(see Figure 6). In order to examine the trend of taxa occurrence along the conductivity gradient, 

we used a nonparametric function (Generalized Additive Model [GAM] with 3 degrees of 

freedom) to model the likelihood of a taxon being observed with increasing conductivity (see 

Figure 7). The solid line is the mean smoothing spline fit. The dots are the mean observed 

probabilities of occurrence, estimated as the proportion of samples within each conductivity bin. 

The conductivity at the red, vertical, dashed line is the estimated XC95 from the weighted 

cumulative distribution (see Appendix E). 

Because of the data distributions, not all 95th centiles correspond to extirpation, and some 

imprecisely estimate the extirpation threshold. The following rules were applied to the XC95

values. If the GAM mean curve at maximum conductivity is approximately equal to 0 (defined 

as less than 1% of the maximum modeled probability), then the XC95 is listed without 

qualification. If the GAM mean curve at maximum conductivity is >0, but the lower confidence 

(1)

15
1.00.80.60.40.20.0

31610032 1000 3162 10000

Cumulative Probability

Conductivity ( S/cm)

Nigronia

1.00.80.60.40.20.0

32 100 316 1000

Cumulative Probability

Conductivity ( S/cm)

Drunella

Figure 6. Examples of weighted CDFs and the associated 95th centile

extirpation concentration values. The step function shows weighted proportion 

of samples with Drunella or Nigronia present at or below the indicated 

conductivity value (μS/cm). The XC95 is the conductivity at the 95th centile of the 

cumulative distribution function (CDF) (vertical dashed line). In a CDF, genera 

that are affected by increasing conductivity (e.g., Drunella) show a steep slope 

and asymptote well below the maximum exposures; whereas, genera unaffected 

by increasing conductivity (e.g., Nigronia) have a steady increase over the entire 

range of measured exposure and do not reach a clear asymptote.

Cheumatopsyche

1.0 0.8 0.6 0.4 0.2 0.0

10032 316 1000 3162 10000

Capture probability

Conductivity ( S/cm)

Diploperla

0.20 0.15 0.10 0.05 0.00

32 100 316 1000 3162 10000

Capture probability

Conductivity ( S/cm)

Lepidostoma

0.8 0.6 0.4 0.2 0.0

32 100 316 1000 3162 10000

Capture probability

Conductivity ( S/cm)

Figure 7. Three typical distributions of observation probabilities. Open 

circles are the probabilities of observing the genus within a range of 

conductivities. Circles at zero probability indicate no individuals at any sites 

were found at these conductivities. The lines fitted to the probabilities are for 

visualization. The vertical red line indicates the XC95. Note that different genera 

respond differently to increasing salinity. Lepidostoma declines, Diploperla has 

an optimum, and Cheumatopsyche increases. The XC95 for genera like 

Cheumatopsyche are reported as “greater than” because extirpation did not occur 

in the measured range.

16
limit is approximating to 0 (<1% of the maximum mean modeled probability) the value is listed 

as approximate (~). If the GAM lower confidence limit is >0, then the XC95 is listed as greater 

than (>) the 95th centile. All model fits and the scatter of points were also visually inspected for 

anomalies and if the model poorly fit the data, the uncertainty level was increased to either (~) or 

(>). This procedure was applied to the distributions in Appendices E and I, and the results 

appear in Appendices D and H. Also, these models were used to evaluate when genera began to 

decline as evidence of alteration and sufficiency in Appendix A.

For example, the XC95 for Cheumatopsyche (an extremely salt tolerant genus) is 

>9,180 μS/cm (see Appendices D and E). Whereas, although Pteronarcys is declining, the upper 

confidence bound is >0; therefore, its XC95 is ~634 μS/cm. The assignation of (>) and (~) does 

not affect the HC05, but are provided to alert users of the uncertainty of the XC95 values for other 

uses such as comparison with toxicity test results or with results from other geographic regions. 

3.2. TREATMENT OF POTENTIAL CONFOUNDERS

Potentially confounding variables for the relationship of conductivity with the extirpation 

of stream invertebrates were evaluated in several ways, which are described in Appendix B. 

Based on the weight of evidence, only low pH was a likely confounder. As mentioned 

previously in Section 2.3, because low pH waters violate existing water-quality criteria and 

because the data set was large, sites were excluded with pH <6 before identifying the XC95

values. 

We evaluated the effects of spring benthic invertebrate emergence, temperature, and 

different conductivities associated with season by partitioning the data set into spring 

(March−June) and summer (July−October) subsets. However, we found that the SSDs for spring 

and all year were similar. Because high and low exposures occurred in all seasons, we chose to 

include the occurrence of a genus whenever it was observed. Therefore, although we explored 

season, we could not justify excluding an observation of a genus just because it was seen outside 

an imposed time frame.

Other potential confounders were evaluated by weighing the available evidence. Because 

confounders are by definition correlated with the cause of concern and the effect, we determined 

the degree of correlation of the confounder with conductivity and with the number of 

ephemeropteran genera. We also evaluated contingency tables of the occurrence of any 

Ephemeroptera at a site with respect to high and low levels of conductivity and the potential 

confounder. Ephemeroptera were selected as an effect endpoint that allowed us to evaluate a 

greater range of exposures and confounding factors than occurs for individual genera. The 

confounding analysis focused on Ephemeroptera because they are among the most sensitive 

genera. Other evidence of confounding was included when appropriate data were available.

17
3.3. DEVELOPING THE SPECIES SENSITIVITY DISTRIBUTION

The SSDs are cumulative distribution plots of XC95 values for each genus relative to 

conductivity (see Figures 8 and 9). The cumulative proportion for each genus P is calculated as 

P = R / (N + 1) where R is the rank of the genus and N is the number of genera. Some 

salinity-tolerant genera are not extirpated within the observed range of conductivity. So, like 

laboratory test endpoints reported as “greater than” values, we retained field data that do not 

show the field endpoint effect (extirpation) in the database. In this way, they can be included in 

N when calculating the proportions responding because they fall in the upper portion of the SSD. 

The HC05 was derived by using a 2-point interpolation to estimate the centile between the XC95

values bracketing P = 0.05 (i.e., the 5th centile of modeled genera). The benchmark is obtained 

by rounding the HC05 to two significant figures as directed by Stephan et al. (1985).

295 μS/cm

1.0 0.8 0.6 0.4 0.2 0.0

200 500 1000 2000 5000 10000

Proportion of Genera

Conductivity (μS/cm)

Figure 8. The species sensitivity distribution. Each point is an XC95 value for 

a genus. There are 163 genera. The HC05 (295 μs/cm) is the conductivity at the 

intercept of the SSD with the horizontal line at the 5th centile.

18
295 μS/cm

0.30 0.25 0.20 0.15 0.10 0.05 0.00

100 200 500 1000 2000

Proportion of Genera

Conductivity (μS/cm)

Figure 9. Species sensitivity distribution (expanded). The dotted horizontal 

line is the 5th centile. The vertical arrow indicates the HC05 of 295 μS/cm. Only 

the lower 50 genera are shown to better discriminate the points in the left side of 

the full distribution. 

3.4. CONFIDENCE BOUNDS

The purpose of this analysis is to characterize the uncertainty in the benchmark value by 

calculating confidence bounds on the HC05 values. Because the XC95 values were estimated 

from field data and then the HC05 values were derived from those XC95 values, we used a method 

that generated distributions and confidence bounds in the first step and propagated the statistical 

uncertainty of the first step through the second step (see Figure 10). Bootstrapping is commonly 

used in environmental studies to estimate confidence limits of a parameter, and the method has 

been used in the estimation of HC05 values (Newman et al., 2000, 2002). 

Bootstrap estimates of the XC95 were derived for each genus used in the derivation of the 

benchmark by resampling 2,210 times (the number of observations in the data set) with 

replacement (see Figure 10) (Efron and Tibshirani, 1993). From each bootstrap sample, the 

XC95 was calculated for each genus by the same method applied to the original data (see 

Section 3.1). That process was repeated 1,000 times to create a distribution of XC95 values for 

each genus. These distributions were used to calculate a two-tailed 95% confidence interval on 

19
the XC95 for each genus. The XC95s from the original data set, the mean XC95s of the bootstrap 

distributions, and the confidence intervals are shown for the 36 most sensitive genera (see 

Figure 11). 

observations

210 ,2

Original 

with replacement

from observations

samples210 ,2

Randomly pick 

samples

or more 25at 

taxa that occurred 

Select reference 

values

HC05

XC95 and 

Compute 

bounds

confidence 

%5Calculate 9

results 

Store 

Repeat 1000 times

Figure 10. Diagram depicting the process for estimating the uncertainty of 

the HC05.

0.20 0.15 0.10 0.05 0.00

100 200 500 1000

Proportion of Genera

Conductivity (μS/cm)

Figure 11. The cumulative distribution of XC95 values for the 36 most

sensitive genera (red circles) and the bootstrap-derived means (blue 

x symbol) and two-tailed 95% confidence intervals (whiskers). The 5th centile

is shown by the dashed line.

Uncertainty in the HC05 value was evaluated by generating an HC05 from each of the 

1,000 sets of bootstrapped XC95 estimates. The distribution of 1,000 HC05 values was used to 

generate two-tailed 95% confidence bounds on these bootstrap-derived values.

20
3.5. EVALUATING ADEQUACY OF NUMBER OF SAMPLES

Bootstrapping was performed to evaluate the effect of sample size on the HC05 and their 

confidence bounds. This process is similar to the method used to calculate confidence bounds on 

the HC05 values (see Figure 10). A data set with a selected sample size was randomly picked 

with replacement from the original 2,210 samples. From the bootstrap data set, the XC95 was 

calculated for each genus by the same method applied to the original data and the HC05 was also 

calculated. The uncertainty in the HC05 value was evaluated by repeating the sampling and HC05

calculation 1,000 for each sample size. The distribution of 1,000 HC05 values was used to 

generate two tailed 95% confidence bounds on these bootstrap-derived values. The whole 

process was repeated for a selected sample size range from 100 to 2,210 samples. The mean 

HC05 values, the numbers of genera used for HC05 calculation, and their 95% confidence bounds, 

were plotted to show the effects of sample sizes. The HC05 values stabilize at approximately 

800 samples in this data set, which suggests that 800 is a minimum sample size for this method 

(see Figure 12). Note that, the mean HC05 value is lower than the actual HC05 value at a similar 

sample size, because the Monte Carlo results are asymmetrical (i.e., there are more ways that the 

sample variance can result in lower values than higher values).

166

145

123

102

80

59

37

900

800

700

600

500

400

300

500 1000 1500 2000

Number of Genera

S/cm)

 HC05 Conductivity ( 

Sample Size

Figure 12. Adequacy of the number of samples used to model the HC05. As 

sample size increases the number of genera included in the SSD increases 

(triangles). As sample size increases, the confidence bounds on the HC05

decreases and the mean HC05 is asymptotic at <300 μs/cm (circles). 

21
3.6. ESTIMATING BACKGROUND

In general, a benchmark should be greater than natural background. The background 

conductivities of streams were estimated using that portion of the WABbase that consists of 

probability-based samples. Those are samples from locations that were selected to represent 

streams within a stream order with equal probability. The 25th centile of the probability-based 

samples was selected as the estimate of the upper limit of background because disturbed and 

even impaired sites are included in the sample (U.S. EPA, 2000). A total of 

1,271 probability-based samples were collected from Ecoregions 69 and 70. The background 

values on the 25th centile were 72 μS/cm for Ecoregion 69, 153 μS/cm for Ecoregion 70, and 

116 μS/cm when samples from Ecoregions 69 and 70 are combined (see Figure 3). We also 

estimated the background conductivity using reference sites in WABbase (see Figure 2). The 

75th centiles from 43 sites in Ecoregion 69 and 27 sites in Ecoregion 70 are 66 μS/cm for 

Ecoregion 69, 214 μS/cm for Ecoregion 70. When samples from Ecoregions 69 and 70 are 

combined, the 75th centile is 150 μS/cm. Sampling locations were among the least disturbed 

based on WVDEP’s best professional judgment (WVDEP, 2008a, b); therefore, the 75th centile

was selected (U.S. EPA, 2000). The bases for selecting centiles are explained in Section 5.5.

22
4. RESULTS

4.1. EXTIRPATION CONCENTRATIONS

The XC95 values are presented in Appendix D. Values are calculated for all 

macroinvertebrate genera that were observed at a reference site and at a minimum of 

25 sampling sites in the two ecoregions. Distributions of occurrence with respect to conductivity 

are presented for each genus of macroinvertebrate in Appendix E and the CDFs used to derive 

the XC95 values are presented in Appendix F.

4.2. SPECIES SENSITIVITY DISTRIBUTIONS

A SSD for invertebrates is derived from XC95 values of 163 genera (see Figure 8). The 

SSDs do not reach a horizontal asymptote at 100% of genera because salt-tolerant genera are 

included in the SSD that are not extirpated within the observed range of conductivity values. 

The lower third of the SSD is shown in Figure 10 for better viewing of the points near the 

5

th centile of genera.

4.3. HAZARDOUS CONCENTRATION VALUES AT THE 5TH CENTILE

The hazardous concentration value at the 5th centile of the SSDs is 295 μS/cm. Rounding 

the HC05 to two significant figures yields a benchmark value of 300 μS/cm.

4.4. UNCERTAINTY ANALYSIS

The bootstrap statistics yield 95% confidence bounds of 228 and 303 μS/cm (see 

Figure 11). The asymmetry of the confidence bounds with respect to the point estimate of 

295 μS/cm is not unusual. In bootstrap-generated estimates, such as those used here, asymmetry 

occurs because statistical resampling from the distribution of data generates more realizations 

that produce values lower than the point estimate than realizations that produce higher values. 

Confidence bounds represent the potential range of HC05 values using the SSD approach, 

given the data and the model. Conceptually, these confidence bounds may be thought of 

representing the potential range of HC05 values that one might obtain by returning to West 

Virginia and resampling the streams. The contributors to this uncertainty include measurement 

variance in determining conductivity and sampling variance in the locations for monitoring and 

in collecting and enumerating organisms. It also includes variance due to differences in stream 

reaches, weather, and other random factors.

The confidence bounds do not address potential systematic sources of variance such as 

differences between geographic areas or between different organizations performing the 

23
sampling using different protocols. The contributions of those sources of uncertainty—in 

addition to the sampling uncertainty—can best be evaluated by comparing the results of 

independent studies. One estimate of that uncertainty is provided by comparing the all-year 

HC05 values derived from West Virginia and Kentucky data. Even though the data were 

obtained in different areas by different agencies using different laboratory processing protocols, 

the values (West Virginia: 295 μS/cm, Kentucky: 282 μS/cm) differ by <5% (see Appendix G 

for details). In addition, the 95% confidence bounds on the HC05 values for the two states 

overlap, suggesting that the sampling variance (i.e., the uncertainty captured by the confidence 

intervals) may be the largest component of total uncertainty. While this result is from only one 

comparison of two states, it does provide a reassuring validation of the West Virginia results.

24
5. CONSIDERATIONS

Because of the complexity of field observations, decisions must be made when deriving 

field-based benchmark values that are not required when using laboratory data. In the case of 

conductivity, additional decisions must be made to address a pollutant that is a mixture and a 

naturally occurring constituent of water. 

5.1. CHOOSING TO USE FIELD VERSUS LABORATORY DATA

The standard methodology for deriving water-quality criteria uses results from laboratory 

toxicity studies (Stephan et al., 1985); however, we have adapted the method to use field data 

because suitable laboratory data are not available. Furthermore, SSDs based on laboratory 

studies cannot replicate the range of conditions, effects, or interactions that occur in the field 

(Suter et al., 2002). Although field data require additional assurance of attributable causation 

due to potential confounders (Section 5.15, Appendices A and B), field data have many 

advantages over laboratory data. 

1) Field exposures include realistic levels, proportions, and variability of pollutant mixtures.

2) Field exposures occur in inherently realistic physical and chemical conditions.

3) Field exposures include regionally appropriate taxa and relative abundances of taxa.

4) Field studies can include more taxa than are available in laboratory data sets.

5) Field data include appropriately sensitive taxa and life stages.

6) Field data include pollutant interactions with migration, predation, competition, and other

behaviors.

7) Organisms in the field have realistic nutrition and levels of stress.

8) Organisms in the field realistically integrate effects of pollutants and other conditions into 

a population response.

9) The field chronic endpoint (extirpation of a population) is inherently relevant, but the 

chronic laboratory test endpoints correspond to no particular effect (chronic values—

CVs).

This study can benefit from these inherent advantages of field data because of the availability of 

large, high quality data sets with clear effects of the pollutant and little evidence of confounding. 

25
5.2. SELECTION OF THE EFFECTS ENDPOINT

We have used the extirpation concentration as the effects endpoint because it is easy to 

understand that an adverse effect has occurred when a genus is lost from an ecosystem. 

However, for the same reason, it may not be considered protective. An alternative is to use a 

depletion concentration (DCx) based on a percent reduction in abundance or capture probability. 

Another option is to use only those taxa sensitive to the stressor of concern, thus developing an 

SSD for the most relevant taxa. DCx values or other more sensitive endpoints may be considered 

when managing exceptional resources. 

In this study, an invertebrate genus may represent several species, and this approach 

identifies the pollutant level that extirpates all sampled species within that genus, that is, the 

level at which the least sensitive among them is rarely observed. In a review of extrapolation 

methods, Suter (2007) indicated that although species within a genus respond similarly to 

toxicants, different species within a genus could have evolved to partition niches afforded by 

naturally occurring causal agents such as conductivity (Remane, 1971). Hence, an apparently 

salt tolerant genus may contain both sensitive species and tolerant species. A potential solution 

would be to use distinct species. However, this may not be practical because some taxa are very 

difficult to identify except as late instars. We chose to follow Stephan et al. (1985) by using 

genera until such time that the advantages and disadvantages of using species can be more fully 

studied. 

Because this endpoint is based on full life-cycle exposures and responses of populations 

to multigenerational exposures, it is considered a chronic-duration endpoint. 

5.3. TREATMENT OF MIXTURES

In natural waters, salinity is a result of mixtures of ions. A metric is required to express 

the strength of that mixture. We use conductivity because it is a measure of the ionic strength of 

the solution, because it is related to biological effects, and because it is readily measured 

accurately. However, conductivity per se is not the cause of toxic effects, and waters with 

different mixtures of salts but the same conductivity may have different toxicities. In this case, 

the benchmark value was calculated for a relatively uniform mixture of ions in those streams that 

exhibit elevated conductivity in the Appalachian Region associated with salts dominated by Ca+, 

Mg+, SO 2−

4 , and HCO −3 ions at circum-neutral to mildly alkaline pH (pH 6−10). Recent 

increases in drilling for natural gas may change the toxicity of salinity in this region, and 

monitoring should be designed to evaluate differences. The relative contributions of individual 

salts from large-scale surface coal mining are described by Pond et al. (2008). Whereas Ca2+,

Mg2+, SO 2−

4 , and HCO −3 are the four most common ions to drain from surface coal mines

(Bryant et al., 2002), ions of Na+and Cl−are the two most common in seawater and brines from 

26
Marcellus Shale drilling operations (see Appendix A, Table A-16). Because the few sites with 

very elevated Cl− were found to be outliers in the distributions of occurrence, they were deleted 

from the data set used to derive the XC95 values. Hence, the use of the benchmark value in other 

regions or in waters that are contaminated by other sources, such as road salt or irrigation return 

waters, may not be appropriate. However, for the circum-neutral to alkaline drainage from 

surface mines and valley fills, these four primary ions are highly correlated with conductivity 

(see Figures 13a−e).

5.4. DEFINING THE REGION OF APPLICABILITY

If the method for developing a benchmark as described here is applied to a large region, 

the increased range of environmental conditions and a greater diversity of anthropogenic 

disturbances may obscure the causal relationship. However, if the region is too small, the 

available data set may be inadequate, and the resulting benchmark value will have a small range 

of applicability. In this case, we chose two adjoining regions that have abundant data, >95% of 

genera in common, and a common dominant source of the stressor of concern.

Although Ecoregions 69 (Central Appalachia) and 70 (Western Allegheny Plateau) are 

very similar, including similar bedrock types, the relative abundances differ. The coal-bearing 

subregions of the Central Appalachians are 69a (Forested Hills and Mountains), and 69d

(Cumberland Mountains). According to Woods et al. (1996), “Ecoregion 69 ... is a high, 

dissected, and rugged plateau made up of sandstone, shale, conglomerate, and coal of 

Pennsylvanian and Mississippian age. The plateau is locally punctuated by a limestone valley 

(the Greenbrier Karst; subregion 69c) and a few anticlinal ridges” (p.30). Ecoregion 70 has more 

heterogeneous bedrock formations than subregions 69a and 69d. It is underlain by shale, 

siltstone, limestone, sandstone, and coal, including the interbedded limestone, shale, sandstone, 

and coal of the Monongahela Group and the Pennsylvanian sandstone, shale, and coal of the 

Conemaugh and Allegheny Groups (Woods et al., 1996). 

Individual analyses of Ecoregions 69 and 70 result in a somewhat lower HC05 value for 

Ecoregion 69 and a somewhat higher value for 70 (254 μS/cm in Ecoregion 69 and 345 μS/cm in 

Ecoregion 70). This difference might be attributed to the background water chemistry (see the 

following section). However, if the genera were adapted to high conductivity in Ecoregion 70 

and low conductivity in 69, or if they were represented by more resistant species in 70 and more 

sensitive species in 69, it would be expected that the XC95 values would consistently go up in 

Ecoregion 70 and down in Ecoregion 69 relative to the values in the combined data set. 

However, XC95 values go up and down in both ecoregions when they are analyzed individually. 

27
Chloride

3210

3.0 2.0 1.0 0.0

4.03.53.02.52.01.5

0.41

3210

Sulfate

Alkalinity 0.6 0.56

2.5 1.5 0.5 -0.5

0.64

4.03.53.02.52.01.5

0.0 1.0 2.0 3.0

0.78 0.89

-0.5 0.5 1.5 2.5

Conduct

Figure 13a. Anions. Matrix of scatter plots and absolute Spearman correlation 

coefficients between conductivity (μS/cm), alkalinity (mg/L), sulfate (mg/L), and 

chloride (mg/L) concentrations in streams of Ecoregions 69 and 70 in West 

Virginia. All variables are logarithm transformed. The smooth lines are the 

locally weighted scatter plot smoothing (LOWESS) lines (span = 2/3).

28
Ca

210-1

210-1-2

4.03.53.02.52.01.5

0.91

210-1

Mg

Hardn 0.96 0.99

3.0 2.0 1.0 0.0

0.92

4.03.53.02.52.01.5

210-1-2

0.95 0.93

0.0 1.0 2.0 3.0

Conduct

Figure 13b. Cations. Matrix of scatter plots and absolute Spearman correlation 

coefficients between conductivity (μS/cm), hardness (mg/L), Mg (mg/L), and Ca 

(mg/L), in the streams of Ecoregions 69 and 70 in West Virginia. All variables 

are logarithm transformed. The smooth lines are the locally weighted scatter plot 

smoothing (LOWESS) lines (span = 2/3).

29
Dis_Fe

10-1-2-3

1.5 2.5 3.5 -3.0 -2.0 -1.0 0.0 10-1-2-3

Dis_Al 0.07

0.0-0.5-1.0-1.5-2.0

0.06

0.0 -1.0 -2.0 -3.0

Dis_Se 0.21

Dis_Mn NA 0.01 0.28

0.0 -1.0 -2.0

0.09

3.5 2.5 1.5

0.12

0.0-0.5-1.0-1.5-2.0

0.64 0.14

-2.0 -1.0 0.0

Conduct

Figure 13c. Dissolved metals. Matrix of scatter plots and absolute Spearman 

correlation coefficients among conductivity (μS/cm) and dissolved metal 

concentrations (mg/L) in the streams of Ecoregions 69 and 70 in West Virginia. 

All variables are logarithm transformed. The smooth lines represent the locally 

weighted scatter plot smoothing (LOWESS) lines (span = 2/3).

30
Al

1.0 0.0 -1.0 -2.0

1.5 2.5 3.5 0-1-2-3 -2.0 -1.0 1.00.0

Fe 0.59

210-1-2

0.13

0-1-2-3

Se 0.08

Mn 0.13 0.57 0.27

0.5-0.5-1.5 -2.5

0.13

3.5 2.5 1.5

0.03

210-1-2

0.35 0.09

0.5-0.5-1.5-2.5

Conduct

Figure 13d. Total metals. Matrix of scatter plots and absolute Spearman 

correlation coefficients between conductivity (μS/cm) and total metal 

concentrations (mg/L) in the streams of Ecoregions 69 and 70 in West Virginia. 

All variables are logarithm transformed. The smooth lines represent the locally 

weighted scatter plot smoothing (LOWESS) lines (span = 2/3).

31
NO23

30150 1.0-1.0 20100 -0.5-2.0

0.0-2.0

3.01.5

0.14

-0.5-2.0

TP

DO 0.07 0.05

155

0.11

20100

Embed 0.08 0.09

Hab_Sc 0.64 0.13 0.03 0.23

14060

0.02

1.0-1.0

Watshed 0.1 0.1 0.14 0.05

Fecal 0.12 0.25 0.13 0.11 0.11 0.03

420

0.08

30150

Temp 0.29 0.35 0.22 0.08 0.49 0.06

pH 0.31 0.2 0.24 0.07 0.01 0.14 0.06 0.02

1086

0.07

3.01.5

0.0-2.0

0.11 0.04

155

0.25 0.18

14060

0.26 0.25

420

0.52 0.4

1086

Conduct

Figure 13e. Other water-quality parameters. Matrix of scatter plots and 

absolute Spearman correlation coefficients between environmental variables in 

the streams of Ecoregions 69 and 70 in West Virginia. The smooth lines are 

locally weighted scatter plot smoothing (LOWESS) lines (span = 2/3). 

Conductivity is logarithm transformed specific conductance (μS/cm); Temp is 

water temperature (°C); RBP is Rapid Bioassessment (Habitat) Protocol score 

(possible range from 0 to 200); Fecal is logarithm transformed fecal coliform 

bacteria count (per 100 mL water); Watershed is logarithm transformed watershed 

area (km2); embeddedness is a parameter score from the Rapid Bioassessment 

Protocol (possible range from 0 to 20); DO is dissolved oxygen (mg/L); TP is 

logarithm transformed total phosphorus (mg/L); NO23 is logarithm-transformed 

nitrate and nitrite (mg/L).

32
The differences in HC05 values appear to be due primarily to random differences in which rarer 

genera do not meet the minimum sample size of 25 occurrences in a region. When the data set is 

split by ecoregion, the SSD model is reduced by 31genera for Ecoregion 69 and 35 genera for

Ecoregion 70. Furthermore, the two Ecoregions had similar genera, and, although Ecoregion 70 

had a slightly higher estimated background, there were sites that had conductivity below 100 

suggesting that the truly undisturbed background would be low. Overall we could not we could 

not justify the increase in uncertainty associated with the reduced sample size and number of 

genera. Therefore, EPA did not derive benchmarks for individual ecoregions.

5.5. BACKGROUND

For naturally occurring stressors, it would not, in general, be appropriate to derive a 

benchmark value that is within the background range. Background levels may be estimated from 

reference sites, which are sites that are judged to be among the best within a category. However, 

because disturbance is pervasive, reference sites are not necessarily pristine or representative of 

natural background. Many reference sites have unrecognized disturbances in their watersheds or 

have recognized disturbances that are less than most others in their category. Some may have 

extreme values of a stressor because of measurement error or unusual conditions at the time the 

sample was taken. For those reasons, when estimating background concentrations, it is 

conventional to use only the best 75% of reference values. The cutoff centile is based on 

precedent and on the collective experience of EPA field ecologists (U.S. EPA, 2000). Estimated 

background conductivities for Ecoregions 69, 70, and both combined are 66, 214, and 

150 μS/cm, respectively, using 75th centiles of reference sites in West Virginia. 

Alternatively, background values may be estimated using samples from a 

probability-based design. Such samples include all waters within the sampling frame, including 

impaired sites, with defined probability. In some regions, there are no undisturbed streams. To 

characterize the best streams, the 25th centile is commonly used by EPA field ecologists 

(U.S. EPA, 2000). Based on the 25th centiles, estimated background conductivities for 

Ecoregions 69, 70, and both combined are 72, 153, and 116 μS/cm for probability-based samples 

in West Virginia. 

Background between Ecoregions 69 and 70 appear to be different; however, none of 

these values exceed the benchmark value of 300 μS/cm. The higher estimates of background 

conductivity in Ecoregion 70 relative to Ecoregion 69 may be attributed to the variable 

occurrence of limestone and limestone-derived soils. The higher level of development and 

population density in Ecoregion 70 may also contribute, but it was not evaluated.

33
5.6. SELECTION OF INVERTEBRATE GENERA

Selection of genera to model can affect the results. Using the data set of all taxa includes 

species that may occur due to a competitive advantage in polluted water. Some taxa, such as 

Corbicula, are not native to streams in North America. Using only genera found at sites with 

minimal disturbance as defined by reference sites somewhat alleviates this problem. The 

reference site genera are often linked to state narrative water-quality standards; thus, they 

represent the aquatic life use that state water-quality criteria should be designed to protect. 

Furthermore, the importance of losing species that inhabit minimally disturbed sites may be 

clearer to decision makers and stakeholders. In this particular case, using all genera, including 

invasive species, would increase the HC05 by less than 2%. 

Genera are also selected for statistical reasons. We restricted genera used in the analyses 

to those recorded at a minimum of 25 sampling sites to reduce the chance that an apparent 

extirpation is due to sampling variance and to increase the likelihood that the models and 

quantitative analyses for potential confounding are reasonably strong. 

5.7. INCLUSION OF OTHER TAXA

Inclusion of other taxa are recommended under the EPA’s 1985 criteria derivation 

methodology (Stephan et al., 1985) solely to ensure that other taxonomic groups are not more 

sensitive than those already evaluated. Fish were not included because their occurrence is 

strongly affected by stream size making it difficult to determine XC95 values. Indeed, some of 

the affected streams naturally have no fish. In addition, the WABbase data set used to derive the 

benchmark does not contain data for fish. Other data sets that do contain fish are not as large and 

do not contain as great a range of conductivity values. A separate SSD might be developed for 

fish, once these technical issues are resolved. Data for plants and amphibians are not available. 

Additional findings regarding mussels could change this analysis if they are found to be more 

sensitive to conductivity than the invertebrates used here. Mussels were not represented because 

genera did not occur in a minimum of 25 samples probably owing to the WVDEP sampling 

methods. Additional analyses may be necessary to ensure protection of federally or state listed 

rare, threatened, or endangered species of fish, amphibians, and mussels. 

5.8. TREATMENT OF LISTED SPECIES

Species listed by West Virginia Department of Natural Resources (WVDNR, 2007) as 

threatened were among the genera observed. Because taxa were identified to genus, we are not 

certain if the species are included. Therefore, we recommend that the invertebrate taxa in 

Table 6, that were included in the SSD, be identified to species in subsequent monitoring to 

evaluate the risk to these threatened taxa. Also, some genera of listed species were not included 

34
Table 6. Genera of threatened species included in the SSD (WVDNR, 2007)

Crangonyx amphipod Utaperla stonefly

Cordulegaster spiketail Sweltsa stonefly

Cambarus crayfish Stenonema mayfly

Calopteryx jewelwing Pteronarcys stonefly

Caecidotea isopod Orconectes stonefly

Alloperla stonefly Ephemera mayfly

Allocapnia stonefly Diploperla stonefly

Genus Common Family Name Genus Common Family Name

in the SSD because the genus was not collected in sufficient numbers, such as from the genera 

Gomphus, Hansonoperla, Macromia, and Ostrocerca. Furthermore, freshwater mussels were 

not well represented in the samples perhaps due to the sampling methods. Stephan et al. (1985) 

recommend lowering the concentration below the 5th centile when necessary to protect 

threatened, endangered, or otherwise important species. Rare species may be ecologically 

important. 

5.9. INCLUSION OF REFERENCE SITES

If high-quality (i.e., reference) sites are not included in the data set, effects on sensitive 

species will not be incorporated into the benchmark. That is, the lower end of the SSD will be 

missing. For example, in a region where all watersheds include tilled agricultural land uses, all 

sites are affected by sediment, so a legitimate SSD for sediment could not be derived by this 

method in that region. In this case, WVDEP’s reference sites were included as well as many 

probability-based sites with >90% forest cover, which are believed to be representative of 

good-to high-quality systems. 

5.10. SEASONALITY, LIFE HISTORY, AND SAMPLING METHODS

The seasonality of life history events such as emergence of aquatic insects can affect the 

probability of detecting a species because eggs and early instars are not captured by the sampling 

methods used. As a result, annual insects that emerge in the spring are present but unlikely to be 

detected in the summer, when conductivities increase in some streams. 

The effects of seasonality and life history were evaluated by comparing HC05 values 

partitioned for season. The data set was partitioned into spring and summer based on seasonal 

35
patterns of conductivity in the full data set (see Figure 4) and the HC05 was calculated. The 

spring season is March through June. The summer season is July through October. The HC05

values in the truncated data sets are 317 μS/cm for spring that included 132 genera and 

415 μS/cm for summer that included 120 genera. The greater summer HC05 is due to the loss of 

sensitive taxa from the SSD. The lower end of the SSD for the full data set and spring samples 

are fairly similar (see Figure 14). Lower effects levels in the spring were not due to an 

insufficient test range of conductivities because exposures as high as 5,200 μS/cm occurred in 

the spring samples. Because the spring data set included both sensitive genera and a full range of 

exposures, it was judged more reliable than the summer model. 1.0 0.8 0.6 0.4 0.2 0.0

200 500 1000 2000 5000 10000

Proportion of Genera

Conductivity (μS/cm)

Figure 14. Comparison of full data set (circles) and subsets of spring 

(inverted triangles) and summer (triangles) collected samples. Spring consists 

of 132 genera, summer of 120 genera. The SSD for the full data set and summer 

are similar until XC95s of 1,000 μS/cm. The summer SSD lacks sensitive genera.

Because we cannot be sure whether the greatest exposures in summer are tolerated by the 

spring-emergent genera, we estimated the likelihood that conductivity would increase in the 

summer. Sampling locations with at least one spring and summer conductivity measurement 

were identified. The spring season is March through June. The summer season is July through 

October. High and low conductivity streams are represented in both spring and summer samples. 

The conductivity in certain streams was three times greater in the summer than the spring. 

36
However, streams with conductivity <300 μS/cm in summer are below the benchmark in 

spring 98% of the time (see Figure 15). So, if a stream meets the benchmark in summer, it is 

likely to meet it year-round. Therefore, seasonal variation should be considered when planning 

monitoring of conductivity and should include ample samples in the spring to ensure inclusion of 

sensitive genera.

123 ( 18.2 %) 343 ( 50.7 %)

4 ( 0.6 %)

207 ( 30.6 %)

10000 1000 100 10

10 100 1000 10000

Summer Conductivity

Spring Conductivity

Figure 15. Relationship of conductivity values sampled from the same site in 

spring and summer. When conductivity is <300 μS/cm (broken lines) in March 

thru June, the conductivity is <300 μS/cm in the same stream 63% of the time 

July through October. When the conductivity is <300 μS/cm in July through 

October, the conductivity in the same stream March through June is <300 μS/cm 

98% of the time.

5.11. FORMS OF EXPOSURE-RESPONSE RELATIONSHIPS

The diversity of the forms of the exposure-response relationships (i.e., decreasing, 

unimodal, decreasing, and no relationship) (see Figure 7 and Appendix E) has required some 

37
methodological decisions. The forms are expected given the nature of the salts and the variance 

in sensitivity. The salt mixture includes nutrient elements, and, like other pollutants that are 

nutrients at low exposure levels (e.g., copper and selenium), the response to this mixture is 

expected to have a unimodal distribution (see Figure 7, Diploperla). In the ascending (left) limb, 

nutrient needs are increasingly being met. In the descending (right) limb, toxicity is increasing. 

However, many of the empirical exposure-response relationships do not display both limbs. 

They may show: (a) the descending portion of the curve because none of the observed 

conductivity levels are sufficiently low to show deficiency for the taxon (see Figure 7, 

Lepidostoma); (b) the ascending portion because none of the observed conductivity levels are 

sufficiently high to show toxicity for the taxon (see Figure 7, Cheumatopsyche); (c) the entire 

unimodal curve because their optimum is near the center of observed conductivities and the 

range from deficiency to toxicity is relatively narrow (see Figure 7, Diploperla); or (d) no trend 

because the optimum is more of a plateau than a peak so it extends across the range of observed 

conductivities (see Appendix E, Nigronia). 

In order to estimate effects to sensitive taxa, it may be necessary to exclude genera 

favored by the pollutant if the region is highly modified. This was not done with the 

Appalachian data set. All genera regardless of the exposure-response form were included in the 

SSD. However, the XC values for those such as Cheumatopsyche that do not descend to zero in 

the observed range are treated as “greater than values.” Because the 5th centile of the SSD is 

derived by interpolation, it is not necessary to provide point estimates of the XC values for 

resistant taxa. The setting of the benchmark in a conductivity range in which the occurrence of 

some genera is increasing suggests that the benchmark could result in the extirpation of some 

genera. However, that is not the case. All but one of the 163 genera occur in sites with low 

conductivity (<100 μS/cm). Even if that were not the case, the concern for resistant taxa is 

unwarranted. The EPA sets water-quality criteria to protect the taxa that occur prior to 

pollution—not taxa that require pollution.

5.12. USE OF MODELED OR EMPIRICAL DISTRIBUTIONS

When deriving XC and HC values, one might use a centile of an empirical distribution or 

fit a function to the data and calculate the value from the resulting model. Models use all of the 

data and, therefore, are resistant to biases associated with any peculiar data at the centiles of 

interest or to uneven distributions of data. However, there is no a priori reason to believe that 

these distributions have a prescribed mathematical form, and fitted models may fit the data 

poorly at the centiles of interest. In particular, standard models are symmetrical but many SSDs 

are not, so the data are poorly fit at the extremes. The use of a nonparametric regression method 

to alleviate the problem of assuming a particular functional form can result in biologically 

38
unlikely forms, may reduce the potential generality of the model, and is not readily understood. 

The use of empirical distribution functions without fitted models eliminates the problems of 

model selection and makes the method easier to understand and implement. With respect to 

SSDs, this issue is unresolved, and assessors are encouraged to consider the properties of their 

distributions when deciding whether to fit or not (Newman et al., 2002; Suter et al., 2002). In the 

interest of conceptual and operational simplicity, we identify the XC95 as the conductivity value 

at which the empirical cumulative probability is 0.95. The HC05 is determined by2-point

interpolation of points on the empirical distributions of XC95 values as described in Stephan et al. 

(1985). 

5.13. DUPLICATE SAMPLES

Although most sites in the WABbase were sampled only once, 4% were sampled more 

than once and 5% of samples were from sites with duplicates. This situation may be confused 

with pseudoreplication, but that statistical error is not an issue in this analysis because we are 

estimating a value rather than testing a hypothesis. Duplicates provide more information 

especially when samples originate from different seasons when different genera may be present, 

but they could be problematical if they introduce a bias (e.g., if low conductivity sites were more 

likely to be sampled repeatedly). However, the duplicated sites do not appear to be biased in this 

case. In fact, if a simple inverse weighting scheme is applied (e.g., if a site is sampled twice; 

each observation is weighted 0.5) it does not materially change the result (HC05 = 293).

Therefore, for the sake of simplicity and to avoid the possibility of inadvertently introducing bias 

by inappropriately weighting, we have not deleted or differentially weighted the duplicated 

samples. However, if there is a potential for bias due to duplication of some samples in future 

applications of this method, an appropriate weighting scheme could be applied. It was not 

necessary in this case. 

5.14. TREATMENT OF CAUSATION

Causation should not be an issue in laboratory toxicity tests, but, even with rigorous 

treatment of confounders, scientists will question whether observed field relationships are truly 

causal (Kriebel, 2009). Like many epidemiologists, we believe that statistical analysis of 

relationships should be supplemented by the consideration of qualitative criteria for causation. 

In this case, we used evidence of causal characteristics derived from Hill’s considerations 

(Cormier et al., 2010) to evaluate the causal relationship of conductivity and extirpation of 

organisms (see Appendix A). 

39
5.15. TREATMENT OF POTENTIAL CONFOUNDERS

The use of field data to understand and manipulate causal relationships is limited by the 

possibility that the apparent relationship used to estimate the benchmark is confounded. 

Confounding is a bias in the analysis of causal relationships due to the influence of extraneous 

factors (confounders). Confounding occurs when a variable is correlated with both the cause and 

its effect. The correlations are usually due to a common source of multiple, potentially causal 

agents. However, they may be observed for other reasons (e.g., when one variable is a 

by-product of another) or due to chance associations. Confounding can bias a causal model 

resulting in uncertainty concerning the actual magnitude of the effects. Therefore, a variety of 

types of evidence are used to determine whether confounders significantly affect the results (see 

Appendix B). This is done because statistics alone cannot determine the causal nature of 

relationships (Pearl, 2009; Stewart-Oaten, 1996). 

Potential confounders include the following: habitat, organic enrichment, nutrients, 

deposited sediments, pH, selenium, temperature, lack of headwaters, catchment area, settling 

ponds, dissolved oxygen, and metals. One potential confounder, low pH, was known to cause 

effects and was controlled by removing sites with pH <6 (see also Section 2.3). The influence of 

selenium is unclear due to poor data and should be investigated. The signal from conductivity 

was strong so that other potential confounders that were not strongly influential could be ignored 

with reasonable or greater confidence. These variables do affect species in the region, but their 

effects do not alter the signal from conductivity or the aquatic life benchmark.

40
6. AQUATIC LIFE BENCHMARK

The aquatic life benchmark of 300 μS/cm was developed for year-round application. 

This level is intended to prevent the extirpation of 95% of invertebrate genera in this region. The 

estimated two-tailed 95% lower confidence bound of the HC05 point estimate is 228 μS/cm and 

the upper bound is 303 μS/cm.

The aquatic life benchmark has been validated by an independent data set. Application 

of the same methodology to data from the State of Kentucky gave a very similar result, 

282 μS/cm with a lower confidence bound of 169 μS/cm and an upper bound of 380 μS/cm (see 

Appendix G).

The method used to develop the benchmark is an adaptation of the standard method for 

deriving water-quality criteria for aquatic life (i.e., Stephan et al., 1985), so it is supported by 

precedent. Because the organisms are exposed throughout their life cycle, this is a chronic value. 

Acute exposures were not evaluated. 

The aquatic life benchmark for conductivity is provided as scientific advice for reducing 

the increasing loss of aquatic life in the Appalachian Region associated with a mixture of salts 

dominated by Ca+, Mg+, SO 2−

4 , and HCO −3 at circum-neutral pH. The aquatic life benchmark 

for conductivity is applicable to the parts of West Virginia, that provided the data for its 

derivation, and to Kentucky, which gave essentially the same result. It may be applicable to 

Ohio, Tennessee, Pennsylvania, Virginia, Alabama, and Maryland in Ecoregions 68, 69, and 70. 

This is because the salt matrix and background is expected to be similar throughout the 

ecoregions. (Region 68 [Southwestern Appalachia] does not occur in WV and is not included in 

the derivation of the benchmark value, but it is included in the validation data set from Kentucky 

[see Appendix G]). The aquatic life benchmark may also be appropriate for other nearby 

regions. However, this benchmark level may not apply when the relative concentrations of 

dissolved ions are different (see Table 2 for the ranges of concentrations in the data set used to 

derive the benchmark value). 

41
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45
APPENDIX A 

CAUSAL ASSESSMENT

ABSTRACT

Because associations in the field are not necessarily causal, this appendix reviews the 

evidence that salts are a cause of extirpation of aquatic macroinvertebrates in streams in 

Ecoregions 69 and 70 of West Virginia. The goal is to establish that salts composed primarily of 

Ca+, Mg+, HCO3

−

, and SO4

−

are a general cause—not that they cause all impairments, nor that 

there are no other causes of impairment, nor that they cause the impairment at any particular site. 

The evidence is organized in terms of six characteristics of causation. The inferential approach 

is to weigh the body of evidence, as is done in epidemiology. The results are positive; the 

available evidence indicates that salts, as measured by conductivity, are a common cause of 

impairment of aquatic macroinvertebrates in the region of concern. Appendix B addresses the 

potential for other variables to confound the model of the effects of salts used to select the 

benchmark.

A.1. INTRODUCTION

To assure that the association of conductivity with the extirpation of aquatic taxa reflects 

a causal relationship, we use epidemiological arguments. The most widely accepted 

epidemiological approach was first used to show that smoking causes cancer in humans (Hill, 

1965; U.S. DHEW, 1964). It consists of weighing the available evidence on the basis of causal 

considerations. As in the case of tobacco smoke, conductivity represents a mixture, and its 

effects are not necessarily immediately apparent following exposure. Hill’s approach for 

establishing a probable causal relationship has been adapted for ecological applications (Fox, 

1991; U.S. EPA, 2000; Suter et al., 2002; Cormier et al., 2010). We rely on the same approach 

to demonstrate that mixtures of ions that elevate conductivity in streams in the Mountain and 

Plateau Regions of Central Appalachia are causing local extirpation of species. 

The causal characteristics used in this assessment are described in Cormier et al. (2010) 

and defined in Table A-1. They are related to Hill’s considerations and to the types of evidence 

in the Stressor Identification (SI) Guidance (U.S. EPA, 2000) and the Causal Analysis/Diagnosis 

Decision Information System (CADDIS) Web site (http://www.epa.gov/caddis). The SI and 

CADDIS types of evidence indicate the types of information which are potentially available to 

demonstrate characteristics of causation. Hill’s considerations are a mixture of types of 

evidence, sources of information, and quality of information. 

A-1
For the general causal question, “Can salts cause biological impairments in the region?”

the best support is evidence that salts have already caused biological impairment in the region. 

We have relied on this type of evidence whenever possible.

Table A-1. Definitions of causal characteristics

Characteristic Description

Co-occurrence The cause co-occurs with the unaffected entity in space and time

Preceding causation Each causal relationship is a result of a larger web of cause-and-effect 

relationships

Interaction The cause physically interacts with the entity in a way that induces the 

effect

Alteration The entity is changed by the interaction with the cause

Sufficiency The intensity, frequency, and duration of the cause are adequate, and the 

entity is susceptible to produce the type and magnitude of the effect

Time order The cause precedes the effect

Source: Cormier et al. (2010).

A.1.1. Assessment Endpoints

This causal assessment evaluates whether aqueous salinity, as measured by conductivity, 

is capable of causing local extirpation of stream biota in an area of Central Appalachia including 

Ecoregions 69 (Central Appalachia) and 70 (Western Alleghany Plateau) (Woods et al., 1996). 

These regions include parts of the states of Ohio, Pennsylvania, Maryland, West Virginia, 

Kentucky, Virginia, Alabama, and Tennessee. The entities of concern are benthic invertebrates, 

possibly including rare and threatened species. The effect is local extirpation of genera from 

streams in their natural range. Because the endpoint for the benchmark is the extirpation of 

multiple genera, a single measurement endpoint is sometimes needed to represent those multiple 

individual responses. Depending on the type of evidence, different biological measurement 

endpoints are used. In particular, the number of ephemeropteran genera is used in many of the 

quantitative analyses because many of the sensitive genera are Ephemeroptera and the number of 

genera is a summary of the consequences of extirpation (see Figure A-1). However, the 

assessment is of general causation in the regions of concern, not for any specific taxon or 

location. 

A-2
Figure A-1. The genera in the Order Ephemeroptera, as a group, are 

extirpated at lower conductivity levels than many other taxonomic groups. 

The plot is a species sensitivity distribution (SSD). Open circles represent the 

95th centile extirpation concentration (XC95) for a genus. The closed circles are 

genera of the Order Ephemeroptera. The genus at 230 μS/cm is Cinygmula and at 

3,923 μS/cm is Caenis.

A.1.2. 

The same data set used in the derivation of the aquatic life benchmark,the West Virginia 

Department of Environmental Protection’s (WVDEP’s) Water Analysis Data Base (WABbase),

was used in the causal assessment (see Sections 2.2 and 2.3). In addition, other sources were 

used. (1) Toxicity test results were obtained from peer-reviewed literature. (2) Information on 

the effects of dissolved salts on freshwater invertebrates was taken from standard texts and other 

physiological reviews. (3) An EPA Region 3 data set was obtained from Gregory Pond, which 

includes the original data for Table 3 in Pond et al. (2008a) and data collected for the 

Programmatic Environmental Impact Assessment (Bryant et al., 2002). (4) The constituent ions 

for Marcellus Shale brine were provided by EPA Region 3, based on analyses by drilling 

operators. (5) Data for Kentucky are from the Kentucky Department of Water database and are 

described in Appendix G. (6) Geographic and related information is from various public sources 

and WVDEP and is described in Appendix C. 

295 μS/cm

1.0

0.8

0.6

0.4

0.2

0.0

200 500 1000 2000 5000 10000

Pro

p

ortion of G

enera

Conductivity (μS/cm)

Data Sets

A-3
A.1.3. Analyzing and Weighing Evidence

Causal evidence is data that have been analyzed or organized in some way to show a 

characteristic of causation or a lack of one. In this assessment, most of the evidence was 

developed from analyses of the West Virginia field data. Other evidence was drawn from the 

literature involving manipulations in the laboratory, field observations in the region and 

elsewhere, and from general theories of physiology and ecology. Because the types of evidence 

are diverse, each is described as it is presented.

After the evidence is developed, we used a form of criteria-guided judgment to weight 

evidence, to weigh consolidated evidence for each causal characteristic, and to weigh the body of 

evidence of the causal relationship. The overall process for synthesizing the evidence is depicted 

in Figure A-2. (1) First, the evidence is sorted by type and by causal characteristic. (2) The 

types of evidence are then evaluated for relevance to the assessment, consistency with scientific 

theory, and quality of the study. Evidence that did not provide relevant or credible evidence was 

not used in the assessment. The remaining types of evidence are weighted by scoring them 

based on logical implications and the strength of the signal, and corroboration. (3) The overall 

qualities of the collected evidence for each characteristic are weighed and then scored. 

(4) Lastly, the body of evidence for the causal relationship is evaluated based on the evidence 

that the hypothesized relationship possesses the characteristics of causation. The methods for 

weighting and weighing steps are provided in Tables A-2 through A-8. The types of evidence 

are scored for the relative strength of quantitative evidence (see Tables A-4, A-5, and A

Figure A-2. A criteria-guided process to weight (score) and weigh the 

evidence for or against causation. Tables called out below each box contain the 

criteria for that step.

The evidence is weighted using a system of plus (+) for supporting conductivity as a 

cause, minus (−) for weakening, and zero (0) for no effect. (Both neutral evidence and 

evidence

• Score remaining 

evidence 

poor quality 

irrelevant or 

• Set aside 

(2) 

characteristic

type and causal 

Sort evidence by 

(1)

characteristic

causal 

evidence of each 

collected types of 

Weigh and score 

(3)

of evidence 

weighing the body 

causation by 

Determine 

(4)

Table A-3 Table A-7 Table A-8

-6).

A-4
ambiguous evidence have no effect on the inference.) One to three plus or minus symbols are 

used to indicate the weight of a piece of evidence. 

+ + + or − − − Strongly supports or discounts

+ + or − − Clearly supports or discounts

+ or − Somewhat supports or discounts

0 No effect

Note that these scores may be for particular types of evidence or a body of evidence for a 

causal characteristic, but not for causation as a whole. For example, several studies may 

convincingly demonstrate that a source exists that is associated with elevated conductivity in the 

region, so that causal characteristic is scored + + +, but alone, it is not convincing evidence that 

conductivity causes extirpation of biota. 

A.1.3.1. Sorting Evidence

Evidence is sorted into types by the kind of association or information, the source of the 

information (from observation, manipulation, or general knowledge), and the source of the 

association (from the case, from elsewhere, or from theory). For example in Table A-15, the first 

type of evidence includes three pieces of evidence in the form of contingency tables (the kind of 

association) of cause and effect from field surveys (the source of information: observational data 

from the region). Then, the types of evidence are grouped by causal characteristics (see 

Figure A-2, Step 1). The contingency table example is evidence of co-occurrence.

A.1.3.2. Scoring Types of Evidence

Each type of evidence is dichotomously evaluated as credible or not based on 

(1) relevance to the assessment, (2) coherence with scientific theory, and (3) quality of the study

(see Table A-2). Evidence that was not credible according to any of these criteria was not used 

in the assessment. For example, in evaluating sufficiency, we did not include toxicity test studies 

of taxa or ionic mixtures that were substantially different from those used to construct the causal 

model (see Table A-21). No studies were found to be inconsistent with scientific theory. Low 

relevance studies were rejected based on content. Data from non-peer-reviewed studies were not 

used, but an exception was made for a data set of chemical analysis of brine drilling waste.

A-5
The remaining evidence was weighted by scoring the types of evidence using a system of 

plus (+) for supporting conductivity as a cause, minus (−) for weakening, and (0) for ambiguous

qualities (see Figure A-2, Step 2). Three qualities of the evidence may contribute to the score. 

(1) A single score is applied to register the logical implication of the evidence: to decrease (−) or

increase (+) support for the causal relationship or to have neither tendency (0). (2) Especially 

strong evidence receives an additional score, based on logical properties (e.g., the effect occurred 

before the cause) or the quantitative strength of the evidence (e.g., high correlation coefficients 

or large quantitative differences (see Tables A-4, A-5, and A-6). (3) A type of evidence may 

receive an additional score if there is consistency among multiple studies for that type of 

evidence. For example, for Co-occurrence of Cause and Ephemeroptera, the evidence in 

Tables A-9, A-10, and A-11 all show that, where conductivity is high, individuals of the family 

Ephemeroptera are less likely to occur. This supports the causal hypothesis, and a + is assigned 

for logical implication. A change of 50% or more is large (see Table A-4), so another + is 

assigned for strength. The evidence was consistently corroborated in three independent data sets 

and, therefore, receives another + for a total of + + +

Table A-2. Abbreviations used for scoring the different types of evidence

Score Meaning

NE No evidence

na Quality is not applicable

0 Evidence is ambiguous or neutral

+ or − Logical implication

Table A-3. Standardized scoring for assigning weights to types of evidence

Rationale Description Score assignment

Logical implication Registers that the evidence is relevant and 

either supports or discounts the causal 

relationship.

+ or −

Especially strong or 

logically 

compelling

The association was quantitatively strong (see 

Tables A-4, A-5, and A-6), or predicted from 

first, principles of chemistry or physics, or 

logically excludes or confirms the relationship.

Increase score

Corroborated An independent data set corroborated the 

evidence.

Increase score

(see Table 15).

A-6
Table A-4. Scoring the logical implication and strength of evidence for 

co-occurrence from contingency table

Assessment Strength Score

Effect endpoints differ and are explained in the 

accompanying text for each association. For example, 

Table A-9 supports the causal hypothesis because high 

levels of conductivity increase the probability that a 

site lacks Ephemeroptera, and low levels of 

conductivity increase the probability that 

Ephemeroptera are present.

Increased effect >25% +

Increased effect >50% + +

Increased effect <25% 0

Increased effect <5% −

Decreased effect − −

An additional score may be added for corroboration, for a total not to exceed three pluses or 

minuses. 

Table A-5. Scoring the logical implication and strength of evidence for 

co-occurrence from correlations (for consistency all correlations are 

Spearman’s)

Assessment Strength Score

The sign of the correlation coefficient depends on the 

relationship. For toxic relationships, such as the 

correlation between conductivity and Ephemeroptera, 

the sign should be negative. Weak or positive 

correlations discount the causal relationship. For 

example, see Table A-22.

│0.75│ ≥ r ≥ │0.25│ +

r > │0.75│ + +

│0.1│ < r < │0.25│ 0

r < │0.1│ −

r has the wrong sign − −

An additional score may be added for corroboration, total not to exceed three pluses or minuses.

s

based on judgment and explained in each case. 

Tables A-4, A-5, and A-6. Other qualities, which are not simple and quantitative, must be scored 

comparisons, we used standard criteria for logical implication and strength described in 

When scoring evidence based on correlations, contingency tables, or quantitative 

A-7
Table A-6. Scoring the logical implication and strength of evidence for 

magnitude of effects

Assessment Different by a factor of Score

Differences among sites or in levels of exposures 

or effects were scored based on their magnitudes. 

Small differences are ambiguous, and differences 

counter to logical expectation are negative 

evidence for causation. For example, see scoring 

of Tables A-12 and A-13 summarized in 

Table A-

>2 +

>10 + +

<2

An additional score may be added for corroboration, for a total not to exceed three 

0

wrong sign −

wrong sign >2 − −

pluses or minuses.

A.1.3.3. Weighing and Scoring the Collected Evidence for Each Causal Characteristic

We continued the process to assess the causal relationship by weighing the strength, 

diversity, and consistency of the evidence for each causal characteristic and noting any 

discrepancies and any aspects of the body of evidence that could be improved (see Figure A-2, 

Step 3). The evidence is weighed using a system with the same symbols as for weighting the 

types of evidence. 

The summary score for each causal characteristic was assigned the median score for the 

body of evidence. A score was reduced if the evidence for that characteristic was inconsistent. 

The score was increased if the evidence included at least three types of consistent evidence not to 

exceed 3 +’s or −’s. (see Table A-7). 

Table A-7. Standardized scoring for assigning weights to collected types of 

evidence for each causal characteristic

Rationale

Score

Not to exceed three pluses or minuses

Median score of evidence with the logical implication 

indicated by the sign 

+, 0, −, ne

Inconsistent evidence Reduce by one or more + or −

Consistency among three or more types of evidence Increase by one + or −

NE = No evidence.

18.

A-8
A.1.3.4. Weighing the Body of Evidence

The scores for the evidence of the causal characteristics were used to evaluate the body of 

evidence for the causal relationship (see Figure A-2, Step 4). The system for evaluating the 

evidence is outlined in Table A-8. A causal relationship was judged to be reliable if there was no 

evidence that weakened the relationship and if there was supporting evidence for all six 

characteristics of causation. Evidence for some causal characteristics is difficult to obtain, thus, 

the cause was judged very likely if there was evidence of five characteristics and some of these 

were strong. In this assessment, several types of evidence were weighted and then weighed for 

five causal characteristics. 

A summary of the evidence for each of the causal characteristics is described in 

Section A.2.7 Evaluation of the Body of Evidence.

Table A-8. Rules for determining causation by weighing the body of 

evidence for the causal relationship

Body of evidence Causal relationship

Evidence refutinga1 or more characteristics Refuted causation

Evidence discountingb4, 5, or 6 characteristics Unlikely causation

Evidence discounting 1, 2, or 3 characteristics, others 

supporting

Unlikely causation but low 

confidence

Evidence strongly documenting 6 characteristics Confirmed causation

Evidence documenting 5 or 6 characteristics and none 

discounting

Very probable causation

Evidence strongly documenting 3 or 4 characteristics and none 

discounting

Probable causation

Evidence strongly documenting 2 characteristics and none 

discounting

Probable causation but low 

confidence

Evidence documenting 1 characteristic Insufficient evidence to make a 

determination

aRefuting is the logical process of demonstrating the impossibility of a candidate cause, thus allowing it to be 

eliminated from further consideration.

bDiscounting is the weighting of evidence that weakens the case for a candidate cause but is insufficient to refute.

A-9
A.2. EVIDENCE OF CHARACTERISTICS OF CAUSATION

A.2.1. Co-occurrence

Because causation requires that causal agents interact with unaffected entities; they must 

co-occur in space and time. Co-occurrence corresponds to Hill’s consistency, SI’s 

co-occurrence, and CADDIS’s co-occurrence in space and time (Hill, 1965; U.S. EPA, 2000). 

The summary of evidence is presented at the end of Section A.2.1 in Table

A.2.1.1. Co-occurrence of Cause and Ephemeroptera

The genera in the family Ephemeroptera, as a group, are extirpated at lower conductivity 

levels than many other taxonomic groups (see Figure A-1). We constructed a contingency table

of the presence of Ephemeroptera at sites near background conductivity (≤200 μS/cm) and high 

conductivities (>1,500 μS/cm) and recorded the number and relative percentage of the presence 

or absence of Ephemeroptera (see Table A-9). It shows that Ephemeroptera co-occur with low 

conductivity but that all ephemeropteran species are absent from more than 55% of sites where 

conductivity is high. This analysis emphasizes the difference between high and low conductivity 

sites with respect to a clear endpoint, the absence of all Ephemeroptera. 

We repeated the analysis with the EPA Region 3 data set and the Kentucky data set, with 

similar results. To ensure a sufficient number of samples, low conductivity was <300 μS/cm, 

and high conductivity was evaluated at >1,500 μS/cm. In the EPA Region 3 data set, 81% of 

high conductivity sites lacked Ephemeroptera (see Table A-10), and in the Kentucky data set 

30.8% of high conductivity sites lacked Ephemeroptera (see Table A-11)

We also compared the number of ephemeropteran genera at sites with lower 

conductivities and higher conductivities with and without the co-occurrence of other parameters 

that are somewhat correlated with conductivity or are known biological stressors (see 

Appendix B). Whatever the level of the other parameter, when conductivity was low, 

Ephemeroptera occurred, and they occurred much less often at high conductivity. Hence, those 

potentially confounding agents were not responsible for the observed co-occurrence of 

conductivity and biological impairments. Other analyses of potential confounders are described 

in Appendix B. 

Scoring—This evidence supports the causal relationship between conductivity and extirpation of 

genera (+). Where conductivity is high, individuals of the family Ephemeroptera are less likely 

to occur. A change of 50% or more is large (+). The evidence is corroborated in three 

independent data sets (+). The total score assigned is + + +.

.

A-15.

A-10
Ephemeroptera present Ephemeroptera absent Total

Near background conductivity 

(≤200 μS/cm)

852

(99.2%)

7

(0.8%)

859

High conductivity 

(>1,500 μS/cm)

50

(45%)

61

(55%)

111

Total 902 68 970

Source: data from WABbase.

Table A-10. Presence of Ephemeroptera contingent on stream conductivity 

(EPA Region 3 data set

Ephemeroptera present Ephemeroptera absent Total

Conductivity ≤300 7

(100%)

0

(0%)

7

Conductivity >1,500 4

(19%)

17

(81%)

21

Total 11 17 28

Source: data from EPA Region 3 data set.

Table A-11. Presence of Ephemeroptera contingent on stream conductivity 

(Kentucky data set

Ephemeroptera present Ephemeroptera absent Total

Conductivity ≤300 150

(97.4%)

4

(2.6%)

154

Conductivity >1,500 9

(69.2%)

4

(30.8%)

13

Total 159 8 167

Source: data from Kentucky data set.

A.2.1.2. Co-occurrence in Nearby Catchments

Two valley-filled tributaries and one unmined tributary were identified in the Twenty 

A

Mile Creek Watershed from the WABbase. The conductivity is lower in the unmined sites 

)

)

Table A-9. Presence of Ephemeroptera contingent on stream conductivity

-11
compared to the valley-filled streams, and all of the biological metrics are greater than in the 

mined sites (see Table A-12). In another study, sites in three reclaimed mined watersheds were 

compared with three nearby unmined watersheds by Pond et al. (2008a) (see Table A-13). The 

conductivity is lower in the unmined sites compared to the reclaimed mined sites, and all of the 

biological metrics are greater in the unmined sites, even though habitat scores are similar. The 

number of ephemeropteran genera is 2−3-fold greater in the unmined sites.

Scoring—This evidence supports the causal relationship (+); the biological effect is 2 to 3 times 

less than at the low conductivity sites (no additional score). The results are consistent and 

corroborated (+). Total score assigned is + +. 

Table A-12. Temporal increase of conductivity after permitting of mining 

operations

Never mined

Ash Fork

Permit 1994, 1996

Boardtree Branch

Permit 1996

Stillhouse Branch

1998 2003 2006 2007 1998 2003 2007 1998 2003 2007

μS/cm 44a39b51a37a1,396a3,015b3,390a511a3,199b3,970a

% E 27.23 29.21 31 1.23 0

# E 6 4 9 2 0

# P 5 6 8 0 0

# EPT 20 14 22 5 3

TT 41 24 27 20 8

a

Single measurement.

bMean value.

E = Ephemeroptera; P = Plecoptera; T = Trichoptera; TT = total taxa.

A-12
Table A-13. Multimetric indices, selected metric values, specific 

conductance, and total Rapid Bioassessment Protocol (RBP) habitat scores 

for reclaimed mined and unmined sites

Stream

Unmined Reclaimed Mined

Rushpatch Spring White Oak Ballard Stanley Fork Sugartree

1999 2006 1999 2006 1999 2006 1999 2006 1999 2006 2000 2007

Specific conductance 

(μS/cm) 

60 70 51 66 64 88 1,201 1,195 1,387 2,010 1,854 191

GLIMPSS 75 75 74 79 75 85 51 38 21 34 32 29

WVSCI 68 90 90 95 91 88 55 52 25 38 52 36

Total genus richness 42 40 33 37 32 30 33 20 14 28 22 20

EPT genus richness 17 19 17 21 17 20 12 9 2 6 4 4

Ephemeropteran genus

richness 

9 7 8 8 9 8 3 3 0 0 0 0

Total RBP habitat score 147 144 163a149 161 163 148 149 145 155 141 154

aRBP from spring 2000.

GLIMPSS = genus-level index of most probable stream status; WVSCI = West Virginia Stream Condition Index;

EPT = Ephemeroptera, Plecoptera, Trichoptera

Source: Pond et al. (2008a).

A.2.1.3. Co-occurrence between Conductivity and Extirpation of Genera

All 163 benthic invertebrate genera appearing in the West Virginia species sensitivity 

distribution (SSD) list are observed at some sites below 100 μS/cm except Hydroporus (lowest 

occurrence at 168 μS/cm); therefore, low conductivity is not a limiting factor. However, 24.5% 

of genera (40/163) are never observed above 1,500 μS/cm (see Table A-14).

Table A-14. Presence of genera contingent on stream conductivity

Genera present Genera absent

West Virginia Kentucky West Virginia Kentucky

Near background conductivity (<150 μS/cm) 163

(99.9%)

104 

(100%)

0

(0.01%)

0

(0.0%)

High conductivity (≥1,500 μS/cm) 123

(75.5%) 

58

(55.8%)

40

(24.5%)

46

(44.2%) 

Source: data from WABbase and Kentucky Division of Water database.

A-13
Scoring—This evidence supports the causal relationship (+); extirpation of 40 genera in West 

Virginia and 46 in Kentucky in streams with conductivity >1,500 μS/cm is a strong effect (+). 

The two analyses corroborated one another (+). The total score assigned is + + +. 

Table A-15. Weighing and scoring evidence for co-occurrence

Type of 

evidence Description of evidence

Logical 

implication Strength Corroborated

Co-occurrence 

of cause and 

Ephemeroptera

Contingency Tables A-9, A-10, and A-11 

provide quantitative evidence that high 

conductivity is strongly associated with severe 

effects. Ephemeroptera are present at >99% of 

low conductivity and absent at 55−73% of high 

conductivity sites in three data sets. 

+ + +

Co-occurrence 

in nearby 

watersheds 

In two studies (see Tables A-12 and A-13), 

there is a 2−3-fold difference between high and 

low conductivity sites for several effect 

endpoints despite similar habitat quality among 

sites. 

+ +

Co-occurrence 

between 

conductivity 

and extirpation 

of genera

Table A-14 show that 37% of genera are never 

seen >1,500 μs/cm, while all genera in the 

study set were observed at sites <150 μs/cm 

except for one. These findings were confirmed 

with independent data sets from West Virginia 

and Kentucky.

+ + +

Summary of co-occurrence—In summary, the causal relationship exhibits the causal characteristic of 

co-occurrence of loss of susceptible taxa with conductivity greater than natural background (+). Many 

genera are never seen at high conductivity in two independent data sets. Also, Ephemeroptera are present 

where conductivity is low even when other stressors are present. Ephemeroptera are frequently absent where 

conductivity is high, even when other stressors are absent. Loss of many genera is a strong effect (+). In 

paired watersheds, various biological metrics are diminished in co-occurrence with elevated conductivity. 

Each type of evidence was independently corroborated (+). A summary score of + + + was assigned.

A.2.2. Preceding Causation

Each causal relationship is a result of a web of preceding cause and effect relationships 

that begin with sources and include pathways of transport, transformation, and exposure. 

Evidence of sources of a causal agent increases confidence that the causal event actually 

occurred and was not a result of a measurement error, chance, or hoax (Bunge, 1979). Although 

preceding causation was not recognized by Hill, it corresponds to a type of evidence in the 

EPA’s SI and CADDIS process, causal pathway. The summary of evidence is presented at the 

end of Section A.2.2 in Table A-18.

A-14
A.2.2.1. Complete Source to Cause Pathway from the Literature

Because exposure to aqueous salts does not require transport or transformation (i.e., 

organisms are directly exposed to salts in water immediately below sources), only evidence of 

the occurrence of sources of aqueous salts is assessed for this type of evidence. Potential sources 

in the region include surface and underground coal mining, effluent from coal preparation plants 

and associated slurry impoundments, effluent from coal fly ash impoundments, winter road 

maintenance, brines from natural gas and coalbed methane operations, treatment of wastewater, 

human and animal waste, scrubbers at coal fired electric plants, and demineralization of crushed 

rock (Ziegler et al., 2007, U.S. EPA, 2011). In particular, high conductivity leachate has been 

shown to flow from valley fills created during coal mining operations (Bryant et al., 2002; 

Merricks et al., 2007). General ecological studies have shown that conductivity increases only 

slightly following clear-cutting and burning. Dissolved mineral loading may be increased 

slightly by harvesting but also declines quickly as vegetation re-establishes (Swank and 

Douglass, 1977). Golladay et al. (1992) and Arthur et al. (1998) found increases in nitrogen and 

phosphorus export in logged catchments in the Appalachians but minor differences in calcium, 

potassium, or sulfate concentrations between logged and undisturbed watersheds. Likens et al. 

(1970) actually found sulfate concentrations to decrease following clear cutting and experimental 

suppression of forest growth by herbicides.

Scoring—This evidence from the literature indicates that there are sources of aqueous salts in the 

region (+). Multiple studies are consistent in the description of the ion types associated with 

different sources (+). Strength is not scored. Total score is + +.

A.2.2.2. Co-occurrence of Sources and Conductivity from the Region

Conductivity is shown to increase after the construction of valley fill coal mining 

operations in two catchments (see Table A-12). Conductivity is elevated where surface mining 

operations occur in a watershed and not in an adjacent unmined watershed (see Tables A-12 and 

A-13) and salts are higher overall in mined watersheds with valley fill than in unmined 

watersheds (see Table A-16). Similar results are reported in mined and unmined sites in 

Kentucky (Pond, 2010). Principal component analysis sorted mined and residential sites from 

reference sites primarily on the basis of specific conductance and pH (Pond et al., 2008a). 

Scoring—This evidence supports the causal relationship (+). The magnitude of the difference in 

conductivity at mined sites is 10 to 50 times greater than at unmined sites (+). The source of 

increased conductivity is corroborated and consistent (+). Total score is + + +.

A-15
Table A-16. Total cations and anions measured in water originating from surface mined sites with valley fills, 

unmined sites, and Marcellus Shale brine. Individual ions are presented as a fraction of the total cations or anions. 

Measurements of HCO3

−

and NO3

−

/N were not available for Marcellus Shale brine sites. Mined and unmined data 

from Pond et al. (2008a). Marcellus from industry data submitted to Region 3.

Mined (Valley Fill) n = 13 Unmined n = 7 Marcellus Shale Brine n = 3

Mean Median Range Mean Median Range Mean Median Range

Total Cations (mg/L) 282.4 238.9 72.7−515.2 15.7 15.9 7.0−25.6 23,862.0 21,719.0 8,650.0−41,217.0

Ca+0.48 0.48 0.42−0.55 0.46 0.46 0.37−0.63 0.24 0.23 0.20−0.28

Mg2+ 0.42 0.42 0.28−0.51 0.28 0.27 0.22−0.36 0.02 0.02 0.02−0.02

K

+

0.04 0.04 0.02−0.05 0.11 0.11 0.06−0.18 0.02 0.01 0.005−0.05

Na+0.06 0.03 0.02−0.25 0.15 0.14 0.06−0.24 0.72 0.70 0.69−0.78

Total Anions (mg/L) 926.8 730.4 228.1−1,734.4 44.7 47.2 21.9−66.5 28,296.1a

18,620.8a14,326.3−51,941.3

a

HCO3

−b

0.25 0.25 0.06−0.48 0.54 0.57 0.34−0.66 NA NA NA

Cl−0.0076 0.0042 0.0032−0.0036 0.07 0.06 0.04−0.11 0.999 0.999 0.998−0.999

NO3−N 0.0036 0.0031 0.0013−0.011 0.01 0.01 0.002−0.04 NA NA NA

SO4

2_ 0.73 0.74 0.51−0.93 0.38 0.35 0.29−0.51 0.0013 0.0011 0.0011−0.0016

aTotal anions include only Cl−

and SO4

2−

.

bHCO3−

converted from measurement of alkalinity as CaCO3.

NA = not applicable due to lack of data.

16 - A
A.2.2.3. Characteristic Composition of Identified Sources

Correlation and regression analyses suggest that, in Ecoregions 69 and 70, conductivities 

above 500 μS/cm contain high levels of the ions of Ca2+, Mg2+, HCO3

−

, and SO4

2−

(see 

Figure 13a−b), which is consistent with surface coal mining and valley fill sources (Pond et al., 

2008a; Pond, 2010). In the WABbase data set, 98% of the sample sites were characterized by 

anions with (HCO3

−

+ SO4

2−

) / Cl−> 1. In mined and unmined sites, the dominant cations are 

Ca2+ and Mg2+, and anions are HCO3

−

and SO4

2−

. This pattern results from calcareous geology 

and the fact that, in these regions, surface mining is the activity that greatly increases the 

leaching of salts from those rocks. Other saline effluents including human and livestock wastes 

and road salts are dominated by NaCl. Particularly high concentrations of NaCl occur in 

Marcellus shale brines (see Table A-16). The median difference is very large; 99% of anions are 

HCO3

−

+ SO4

2−

in both mined and unmined sites, and >99% of the anions are Cl−in brines (see 

Table A-16). Therefore, the causal assessment relates primarily to mixtures of salts typical of 

alkaline coal mine drainage and associated valley fill discharges. 

Scoring—This evidence supports the causal relationship (+) by showing that there are sources of 

high conductivity with a consistent matrix of ions. Both mined and unmined sites have similar 

proportions of Ca2+, Mg2+, HCO3

−

, and SO4

2−

but very different concentrations. The difference 

between the ionic concentrations is very large, with a >99% difference from other sources of 

salts such as brines (+). The evidence from the WABbase data set and two other Appalachian 

studies consistently supported the ionic makeup associated with land disturbance, especially 

surface mining (+). The mined and unmined data are from a peer-reviewed publication (Pond et 

al., 2008a), and the brine values are from reports from extraction permittees. Although the brine 

analyses are not peer reviewed, the findings are qualitatively similar to other non-peer-reviewed 

reports of the makeup of these brines. Total score is + + +.

A.2.2.4. Correlation of Conductivity with Sources

Scatter plots of conductivity levels were generated for nine land cover classifications to 

determine if conductivity increased with any particular sources. The methods and results are 

presented in greater detail in Appendix C. Briefly, 190 records of <20-km2 watersheds in the 

WVDEP WABbase in Ecoregion 69D were found that had macroinvertebrate samples identified 

to the genus level, at least one chemistry sample, and total maximum daily load land cover 

information. Small (<20-km2) subwatersheds were selected to reduce confounding from multiple 

sources. These subwatersheds drained to the Coal, Upper Kanawha, Gauley, and New Rivers. 

Scatter plots and Spearman rank correlations of nine land use categories and geometric mean 

A

conductivity are shown in Figure A-3: total percentage area in mining (% Total Mining); 

-17
percentage in mountaintop mining valley fill (% MTM-valley fill); percentage of abandoned 

mine lands (% Abandoned Mine); percentage of mining (% Mining) minus % MTM-Valley Fill 

and % Abandoned Mine; percentage barren land use (% Barren); percentage of residences, 

buildings, and roads (% Urban/residential); percentage in agriculture and pasture 

(% Agricultural); percentage in forest (% Forest), and percentage in open water (% Water).

The two land use types that are most strongly and positively correlated with conductivity 

are % MTM-Valley Fill and % Total Mining (see Table A-17). In contrast, % Forest is 

negatively correlated with ion concentrtions. % Urban/residential is not well correlated and in 

this region is confounded somewhat by mining land uses. The ions that are more strongly 

correlated with land use are total calcium and magnesium (also captured together as hardness), 

bicarbonate measured as alkalinity, and sulfate. Noticeably, chloride is not strongly correlated, 

owing to fewer measurements of chloride, but also due to the low concentrations except at one 

site. Chloride was 629 mg/L at the site with the greatest residential and mining land uses.

At relatively low % Urban/residential, conductivity is highly variable (see Figure A-3). 

In contrast, there is a clear pattern of increasing conductivity as % MTM-Valley Fill increases 

and of decreasing conductivity with increasing % Forest. When area in valley fill is subtracted 

from the total nonacid mining area, the correlation decreases by 25% (see Figure A-3d). The 

scatter plots illustrate that there are clear sources of increased conductivity, but that 

% MTM-Valley Fill has the strongest correlation with conductivity (r = 0.65) and the percentage 

of mining without a valley fill has a moderate correlation (r = 0.39). 

Of the land uses in the small watersheds analyzed, only mining especially associated with 

valley fills is a substantial source of the salts that are measured as conductivity. Disturbances 

associated with agriculture and human habitation may also contribute, but the densities of 

agricultural and urban land cover are relatively low, and a clear pattern of increasing 

conductivity and increasing land use is not evident. Furthermore, despite the natural bedrock of 

shale, limestone, dolomite, and calcareous cemented sandstone, natural background is 

exceedingly low. 

Although conductivity typically increases with increasing land use (Herlihy et al., 1998), 

at relatively low urban land use, conductivity is highly variable. This may be caused by 

unknown mine drainage, deep mine break-outs, road applications, poor infrastructure condition 

(e.g., leaking sewers or combined sewers), or other practices. In contrast, there is a clear pattern 

of increasing conductivity as percentage of area in valley fill increases and decreasing 

conductivity with increasing forest cover. 

Scoring—This evidence supports the causal relationship (+). The correlations for mountaintop 

mining with valley fill (r = 0.65), mining minus valley fill and abandoned mine lands (r = 0.39), 

A-18
and forestry (r = −0.55) are moderately strong. This study has not been independently 

corroborated, although it is consistent with the findings of Pond et al. (2008a). The association 

seems to be specific for extensive geologic disturbances, which in these regions, are from mining 

and valley fills. The total score is +.

Table A-17. Correlation coefficients between pairs of land use and water 

quality parameters in the land use data set

Water quality 

parameter

% MTM-Valley 

Fill % Total Mining % Mining % Forest

Conductivity 0.65 0.52 0.39 −0.54

Alkalinity 0.51 0.49 0.37 −0.51

Hardness 0.69 0.63 0.55 −0.63

Sulfate 0.64 0.52 0.39 −0.53

Calcium total 0.67 0.61 0.52 −0.64

Magnesium total 0.66 0.65 0.58 −0.59

A-19
h. i.

g.

e. f.

b. c.

d.

a.

Figure A-3. Geometric mean conductivity associated with different land uses 

in 190 watersheds in Ecoregion 69D and Spearman’s correlation coefficient.

Conductivity increases with increasing % MTM-Valley Fill and % Total Mining, 

and decreases with increasing % Forest, but there is less clear or no pattern with 

other land use. From left to right, they are (a) % Total Mining (percentage 

of deep, surface, quarry mining, MTM-Valley Fill, and abandoned mine land), 

(b) % MTM-Valley Fill (from mountaintop mining overburden), 

(c) % Abandoned Mine, (d) % Mining (inclusive of all types of mining except

MTM-Valley Fill and Abandoned Mine), (e) % Barren, (f) % Urban/residential, 

(g) % Agricultural, (h) % Forest, and (i) % Water. Fitted LOWESS line with span 

set at 

3162 1000 316 100 32

r  0.27

0 0.3 0.6 1 1.5 2.2

S/cm)  Conductivity (

% Water

3162 1000 316 100 32

r  -0.55

0 20 40 60 80 100

S/cm)  Conductivity (

% Forest

3162 1000 316 100 32

r  -0.03

0 0.6 1.5 3 5.3 9 14.8

S/cm)  Conductivity (

% Agricultural

3162 1000 316 100 32

r  0.13

0 0.6 1.5 3 5.3 9 14.8

S/cm)  Conductivity (

% Urban/residential

3162 1000 316 100 32

r  0

0 2.2 9 30.6

S/cm)  Conductivity (

% Barren

3162 1000 316 100 32

r  0.4

0 2.2 9 30.6 99

S/cm)  Conductivity (

% Mining

3162 1000 316 100 32

r  0.02

0 2.2 9 30.6

S/cm)  Conductivity (

% Abandoned Mine

3162 1000 316 100 32

r  0.65

0 2.2 9 30.6

S/cm)  Conductivity (

% MTM-Valley Fill

3162 1000 316 100 32

r  0.53

0 2.2 9 30.6 99

S/cm)  Conductivity (

%Total Mining

0.75.

A-20
Table A-18. Weighing and scoring evidence for preceding causation

Type of evidence Description of evidence

Logical 

implication Strength Corroboration

Complete 

source-to-cause 

pathway from 

literature 

Multiple publications link conductivity 

to sources in the region and eliminate 

some other land uses as sources. Sources 

are present, and no intermediate steps in 

the pathway are required.

+ +

Co-occurrence of 

sources and 

conductivity in the 

region

When valley fills are present, 

conductivity is 14- to 90-fold greater 

than at unmined sites (see Tables A-12 

and A-13). This is very strong 

quantitative evidence from the case.

+ + +

Characteristic 

composition of 

identified sources

Ambient mixtures of ions have 

characteristic compositions that can be 

associated with particular sources. Most 

sites with elevated conductivities have 

compositions characteristic of coal 

mining with valley fill. The salt mixture 

consistently contains ions of HCO3

−

+ 

SO4

−2

/ Cl−that are >1 (see Table 1 and 

Table A-16).

+ + +

Correlation of 

conductivity with 

sources 

Correlation of % MTM Valley Fill is 

r = 0.65; see Figure A-3. This is 

moderately strong quantitative evidence 

from the case.

+

Summary of Preceding Causation. In summary, large-scale surface mining and associated valley fills 

constitute a common source of high conductivity water in this region (+). Some of the evidence is very 

strong and specific to sources associated with coal mining (+). Four types of evidence are provided from 

different investigators (+). A summary score of + + + was assigned. Hence, the evidence of preceding 

causation leading to high conductivity is conclusive.

A.2.3. Interaction and Physiological Mechanisms

Causal agents alter affected entities by interacting with them through a physical 

mechanism. Evidence that a mechanism of interaction exists for a proposed causal relationship 

strengthens the argument for that relationship. This characteristic corresponds to Hill’s 

plausibility, SI’s mechanism, and CADDIS’s mechanistically plausible cause. The summary of 

evidence is presented at the end of Section A.2.3 in Table A-19.

A-21
A.2.3.1. Mechanism of Exposure

Aqueous salts are dissolved ions that are readily available for uptake by aquatic 

organisms as they pass over their respiratory and other permeable surfaces (Sutcliff, 1962; 

Bradley, 2009; Evans, 2008a, b, 2009; Wood and Shuttleworth, 2008; Thorp and Covich, 2001).

Ionic concentration is greater than natural background levels (see Section 3.6 and Figures 2, 3, 

and 4). Many benthic invertebrates inhabit low conductivity streams (see Appendix D). 

Therefore, the pollutant is present and the animals are exposed. 

Scoring—Evidence is from knowledge that the ions are present in Appalachian waters (see 

Table 1 and Table A-16) and from general knowledge of animal physiology and the anatomy of 

Ephemeroptera and other aquatic invertebrates (+). The exposure is 15 to 100 times greater than 

background (+) (see Tables 1, A-12, A-13, and A-16). Many studies support this inference (+). 

The total score is + + +. 

A.2.3.2. Biochemical Mechanism of Effect

Living cells, and the organisms they comprise, must maintain a relatively narrowly 

defined internal composition of ions that varies with function and that is different from their 

environment. Maintaining homeostasis involves osmotic and ionic regulation by cells and 

tissues. Homeostasis is achieved by surrounding cellular compartments with selectively 

permeable and energy-converting membranes equipped with ion-transport proteins.

The internal fluids of freshwater organisms are saltier than the water in which they live. 

As a result, freshwater organisms must use many physical structures and physiological 

mechanisms to maintain water content, charge balance, and specific ionic concentrations. To 

maintain the balance of ions, they excrete hypotonic urine; possess impermeable scales, cuticles, 

or exoskeletons; and use semipermeable membranes to redistribute ions (Bradley, 2009; Evans, 

2008a, b, 2009; Wood and Shuttleworth, 2008; Thorp and Covich, 2001; Komnick, 1977; Smith, 

2001; Sutcliff, 1962; O’Donnell, 2011). Many freshwater invertebrates have mitochondrion-rich 

chloride cells on gills and other surfaces that take up chloride and other ions (Komnick, 1977; 

Bradley, 2009, Evans 2009). Exclusion of ions is insufficient to maintain homeostasis, and the 

actual uptake and export of ions occurs at semipermeable membranes. Anion, cation, and proton 

transport occurs by passive, active, uniport, and cotransport processes often in a coordinated 

fashion (Nelson and Cox, 2005). 

Numerous specific mechanisms are involved in the toxicity of high-conductivity 

solutions. One that is used by invertebrates and vertebrates is discussed here to illustrate how 

ions are moved against a concentration gradient through a selectively permeable membrane. The 

example ion-regulation system involves antiport anion exchange proteins that cotransport Cl−

A-22
against the concentration gradient into the cell simultaneously with HCO3

− movement down the 

concentration gradient and out of the cell (Larsen et al., 1996; Nelson and Cox, 2005; Bradley, 

2009, Evans 2009) (see Figure A-4). Normally, HCO3

−

concentrations are relatively low in the 

water and HCO3

−

can be made from the waste products of respiration so that HCO3

−

concentration becomes greater inside the cell than in the surrounding water. Under these 

conditions the HCO3

−

gradient is strong enough and the antiport protein swaps out HCO3

−

for Cl−

despite the higher amounts of Cl−inside the cell compared to in the water. However, when

external HCO3

−

is high, the gradient is not favorable for HCO3

−

export and Cl−uptake (Avenet 

and Lingnon, 1985). As a result, internal regulation of the Cl−concentration must depend on the 

active transport of Cl−against a concentration gradient, which is energetically costly or 

impossible to maintain. In addition, the normal export of HCO3

− must occur against a gradient to 

rid cells of metabolic waste CO2 and to balance internal pH. Furthremore, there is also some 

evidence that SO4

2−

can pass through some HCO3

−

channels and high external concentration of 

SO4

2−

could also affect the concentration gradient and outward flow of HCO3

−

(Pritchard and 

Renfro, 1983). Furthermore, the internal concentration of Cl−affects the balance of other ions 

such as Na+, K+, H

+

, and NH4

+

. This example illustrates how membrane-transport pathways are 

inhibited by too much ambient salinity in the form of bicarbonate salts, which interfere with the 

uptake and balance of necessary chloride and sodium ions. The gills of Ephemeroptera have an 

abundance of mitochondrion-rich chloride cells that use the cellular physiological mechanisms 

illustrated in Figure AThe previous example illustrates only two types of passive co-transport proteins and four 

ions. It does not show the roles of other ions on the stream side of the cell and does not depict 

any of the mechanisms on the basal side (organism-side) of the cell. The full complement and 

relative abundance of ions are necessary for homeostasis. Because all dissolved ions interact, 

there are many types of ionic transport proteins that work together to regulate pH and ionic

concentrations and cell volume. Some of the types of transport proteins are depicted in 

Figure A-5. These proteins are folded into the plasma membrane and are specific for certain 

ions. Some are passive channels (depicted as tubes). Others require the expenditure of energy 

(indicated by the ATP as part of the protein symbol). For these, the conversion of ATP to ADP 

momentarily changes the shape of the protein to regulate transport or to move an ion against a 

concentration gradient. Some transporters move one ion (single arrow and circle). Others 

co-transport more than one type of ion thereby leveraging the electromotive force of the 

concentration gradient of another ion to reduce the concentration of some ions and increase the 

concentration of others (two or three arrows and circle[s]). 

4. 

A-23
Figure A-4. Schematic of a mechanism altered by elevated bicarbonate salts.

(a) Dilute water with low HCO3

−

and Cl−. (b) High conductivity water with high 

HCO3

−

and low Cl−. Filled arrows indicate transport readily occurs in (a) but 

unfilled arrows in (b) indicate transport is inhibited. 

Gill epithelium

b)

Stream

Na+

Na+

− HCO3

Cl−

Cl−

H+

− HCO3

Anhydrase

Carbonic 

CO2

O + H2

Na+

Na+

− HCO3

body

Organism’s 

a)

Cl−

Cl−

H+

− HCO3

Anhydrase

Carbonic 

CO2

O + H2

A-24
Figure A-5. Depiction of a variety of types of transport proteins. Passive 

transport by individual ions and water (tube and arrow), passive co-transport of 

ions (two arrows and circle), and energy dependent transport (circle with ATP). 

Transport proteins are depicted to show many types rather than a functional 

example as in Figure A-4.

The type, distribution and abundance of transport proteins are different on each cell 

membrane and on different sides of a cell, thus creating arrangements that concentrate the 

different ions at different levels in organelles, cell types, and bodily fluids. The different 

concentrations of ions in body compartments create a complex ionic circuit that stores specific 

ions as potential energy that enables all cell functions and creates conditions for the proper 

chemical reactions that cells and organisms use to grow, reproduce, and continue living. Some 

transport proteins are altered by pressure and affect the regulation of water volume or signal 

touch in a sensory cell. Some voltage-gated channels are involved in embryonic activation, 

secretion, and nerve and muscle activity. The variety of organized combinations is as various as 

life itself. Selectively permeable membranes are a universal attribute of living things. Every 

physiological process of animals, plants, and microbes uses ionic gradients made possible by 

these membranes and their ionic transport proteins. In all living things, when the ionic balance is 

disrupted, organs fail and death ensues.

Na+

H+

Ca2+ ATP

Na+

+ NH4

ATP

K+ Cl−

K+

K+

Na+

ATP

H+ ATP

Cl− Na+ H+ Ca2+

H2O

Mg2+

Cl−

K+

Na+

Cl−

− HCO3

Na+

Ca2+

2− SO4

Cl−

A-25
Scoring—This evidence supports the causal relationship by providing evidence that the typical 

ion matrix in the region can create ionic gradients that can interfere with proper homeostasis (+). 

However, direct evidence of the ionic regulatory mechanism or membrane potential 

measurement from affected species and tolerant species in Appalachia is not available. Evidence 

from the literature about mitochondrion-rich chloride cells in aquatic animals including insects, 

amphibians, and fish, logically leads to disruption of ionic regulation in organisms highly 

dependent on passive ionic regulation by a HCO3

−

/ Cl−antiport anion exchange, such as is 

present on ephemeropteran gill epithelium. Other ion transport systems are also affected by 

increases in the concentration of the ion mixture, which is measured as increased conductivity in 

the region of concern. A large body of peer-reviewed physiological studies supports this 

inference (+). The total score is + +.

A.2.3.3. Physiological Mechanism of Effect

In aquatic systems, organisms are capable of coping with different environmental 

challenges presented by different concentrations of dissolved ions. However, the extent and rate 

of adaptation to changes of salinity varies depending on the physiological potential of a 

particular species (Bradley, 2009; Evans, 2009). As noted previously, osmotic and ionic cellular 

mechanisms involve selectively permeable membranes. However, it is the disruption of the ionic 

balance throughout a physiological system of specialized tissues and organs with specialized 

functions that reduces fitness and survival. Some examples include slight or large differences in 

ionic composition between cell compartments, cells, or external media that are used to release 

energy from food; transcribe and translate RNA into proteins; regulate pH and water volume; 

excrete metabolic waste (ammonia and CO2); enable secretion of enzymes, hormones, and 

neurotransmitters; guide embryonic development (Evans, 2009; Bradley, 2009); and propagate 

action potentials in nerves and muscles, thus enabling complex behaviors and activation of 

fertilized eggs (Evans, 2009; Hagiwara and Jaffe, 1979; Tarin et al., 2000). These physiological 

functions enable organisms to develop, grow, move, and sense their environment. When the pH 

or ionic balance is disrupted, death is usually near at hand.

For the sake of illustration, the role of chloride ions within inhibitory neural circuits is 

described. Chemical transmission of nerve impulses can excite or inhibit nerve conduction, thus 

modulating signaling. Gama-aminobutyric acid is an inhibitory neurotransmitter that binds and 

opens chloride channels on the postsynaptic membrane (Bloomquist, 1993, 1996). Cl−ions flow 

into the postsynaptic neuron, hyperpolarizing the cell (i.e., making the cell more negative than a 

normal resting neuron and interfering with the propagation of action potentials). Too much or 

too little Cl−disrupts normal neural activity. Too much Cl−excessively inhibits nerve activity, 

whereas, insufficient Cl−results in hyperexcitability. Interruption of the function of chloride 

A-26
channels has been exploited to develop insecticides, such as dieldrin, endrin, lindane, and 

endosulfan that block Cl−permeability, resulting in ataxia and insecticides such as avermectins

that activate Cl−channels, resulting in paralysis. Exposure to these Cl−channel blockers and 

enhancers have similar effects in insects, fish, and mammals. 

In dilute water, mitochondrion-rich epithelial cells and tissues of many aquatic organisms 

help maintain the balance of Cl−, which enables modulation of neural activity as well as 

regulating pH and other ions. This example provides evidence that disruption of ionic imbalance 

in insects is a known mechanism that can cause dysfunction of the nervous system, leading to 

death. In this causal assessment, the ionic imbalance is not caused by chemicals binding to ionic 

channels as with insecticides, but by altering the amount of ions dissolved in the water (see 

Section A.2.3.2). Classic neurophysiological studies have demonstrated that changing the ionic 

constituents outside and inside cells can block the propagation of neural signaling in all animals

with a nervous system (Hille, 2001). 

Scoring—This evidence supports the causal relationship (+) by demonstrating that the loss of 

ionic regulation can affect an animal’s physiology leading to severe effects. Studies of the 

physiology of affected species and tolerant species in Appalachia are not available. The effects 

of ionic disruption are supported by a large body of peer-reviewed physiological studies, some of 

which are presented in an example (+). The total score is + +.

A-27
Table A-19. Weighing and scoring evidence for interaction

Type of 

evidence Description of evidence

Logical 

implication Strength Corroboration

Mechanism of 

exposure 

Salts readily dissolve in water and interact 

directly with aquatic organisms.

+ + +

Biochemical 

mechansim of 

effect

Organisms living in dilute streams 

exchange intracellular bicarbonate for Cl−

and H+and NH4

+

for Na+and K+. This 

transport is blocked when the 

concentration gradient does not favor 

movement of HCO3

−

out of the cell. No 

studies of ionic compensation were found 

for invertebrates in the region, but the 

basic mechanism is well established for 

the example and other ion channels.

+ +

Physiological 

mechanism of 

effect 

Many mechanistic studies show that 

disruption of ion and water regulation 

leads to organ failure by interfering with 

cell functions such as enzyme and 

hormone secretion, nerve conduction, 

muscle contraction, waste removal, and 

other physiological functions. No studies 

are available for invertebrates in the 

region.

+ +

Summary of interaction—In summary, aquatic organisms are directly exposed to aqueous salts, and 

the relative amounts and concentration of salts may exceed the capacity of organisms to regulate their 

internal pH and ionic composition (+). The importance of osmoregulation and ionic homeostasis has 

been demonstrated in diverse animal models with results published in the peer-reviewed literature. The 

evidence is drawn from a long history of physiological investigations (+). A summary score of + + is 

assigned. 

A.2.4. Alteration

A cause alters or changes a susceptible entity. In this case, the alteration is failure to 

maintain viable populations of sensitive species. Documentation that a change occurs is 

evidence of causation, but that evidence is much stronger if a specific effect of a cause is 

characterized. If the specific effect of a cause occurs with no other causes, it can be diagnostic 

of that cause. This characteristic corresponds to specificity in Hill’s considerations and in the 

SI’s types of evidence and to symptoms in CADDIS. The summary of evidence is presented at 

the end of Section A.2.4 in Table A-20.

A-28
A.2.4.1. Change of Occurrence of Genera

Ephemeroptera and Plecoptera do not occur in mesohaline waters, whereas other insect 

families do occasionally occur in brackish water (Remane, 1971) (see also Figure A-1). In a 

paper focusing on Ephemeroptera (Pond et al., 2008a), a nonmetric multidimensional scaling 

model strongly associated Cinygmula, Drunella, Ephemerella, Epeorus, and Ameletus with the 

low conductivity reference sites and Stenonema, Isonychia, Baetis, and Caenis with the high 

conductivity sites. The first group has 95th centile extirpation concentration (XC95) values of 

230, 297, 299, 307, and 591 μS/cm, and the second group has XC95 values of 745, 1,180, 1,395, 

and 3,923 μS/cm (see Appendix D). Another study using data from Kentucky showed similar 

results (Pond, 2010); however, habitat alteration may have confounded the relationship with 

conductivity in that data set. Nevertheless, the relative frequency of the sensitive genera 

identified in the West Virginia study (Pond et al., 2008a) decreased by more than half at mined 

sites in Kentucky and, except for Baetis, which was relatively unchanged, the relative frequency 

of the insensitive genera increased at mined sites with high conductivity. This evidence indicates 

that specific genera tend to be more or less tolerant of ionic stress found in the region. Johansen 

(1918, as cited in Remane [1971]) also mentioned isolated records of Baetis and Caenis at 

1.6 ppt; however, these salt matrices are marine in nature. 

Both the XC95 values and species sensitivity distributions in this document demonstrate 

that a characteristic set of genera, including many Ephemeroptera, were extirpated at relatively 

low conductivities and others were resistant. The relative sensitivities are consistent with the 

findings of Pond et al. (2008a), Pond (2010), and with our analyses of data from Kentucky (see 

U.S. EPA [2010], Appendix E). This is not meant to suggest that conductivity is the only 

possible cause of loss of these genera. Rather, it indicates that the loss of those genera 

consistently occurs where conductivity is elevated. If a random set of genera were lost, it might 

suggest that various causes were acting that co-occur with elevated conductivity, but that was not 

the case.

Taxa that are sensitive to high conductivity are similar in Kentucky and West Virginia.

Extirpation levels can be found in Appendix D for West Virginia and Appendix H for Kentucky. 

Genera that began to decrease in occurrence at levels 500 μs/cm were identified from the fitted 

lines on generalized additive model plots in Appendix E for West Virginia and Appendix I for 

Kentucky. 

 In the WABbase data set, 14 genera with XC95 values below 500 μs/cm also occur in the 

Kentucky data set. Among these 14 genera, 9 (64.3%) have XC95 values below 

500 μs/cm in the Kentucky data set. 

A-29
 A total of 88 genera (85%) of the 104 in Kentucky used to develop the SSD were also 

used in the West Virginia SSD. Of these 104 genera, 54 showed declines below 

500 μS/cm in at least one data set (44 declined in both data sets, 4 only in Kentucky, and 

6 only in West Virginia). Therefore, the West Virginia and Kentucky data sets had 44 of 

54 genera (81.5%) in common that showed declines below <500 μS/cm. 

Scoring—This evidence supports the causal relationship (+) by demonstrating that conductivity 

greater than background levels causes a consistent set of sensitive animals to be extirpated. 

Genera affected by increasing conductivity are consistent. The number of genera with similar 

XC95 values (less than 10% difference) in Kentucky and West Virginia with XC95 < 500 μs/cm is 

71.4% and for those with a similar pattern of decline is 81.5% (+). Multiple studies and data sets 

confirmed the evidence (+). The total score is + + +.

A.2.4.2. Models of Change of Genera

Empirical models based on macroinvertebrate assemblage composition were used to 

identify probable causes of biological impairments in a case study in Clear Fork Watershed in 

West Virginia (Gerritsen et al., 2010). Eight weighted averaging regression models were 

developed and tested using four groups of candidate stressors based on genus-level abundance. 

The strongest predictive models were for acidic metals (dissolved aluminum) and conductivity, 

r

2

= 0.76 and r

2

= 0.54, respectively. 

In another approach, nonmetric multidimensional scaling and multiple responses were 

used to examine the separation of “dirty” reference groups from “clean” reference groups based 

on the biological communities observed in the two groups. Four “dirty” reference groups were 

identified consisting of sites primarily affected by one of the following stressor categories: 

dissolved metals (Al and Fe), excessive sedimentation, high nutrients and organic enrichment 

(using fecal coliform as a surrogate measure of wastewater and livestock runoff), and increased 

ionic strength (using sulfate concentration as a surrogate measure). Of the “dirty” reference 

groups, the dissolved metals group was significantly different from the other three “dirty” 

reference groups (p < 0.001). The other three “dirty” reference groups, though overlapping in 

ordination space to some extent, were also significantly different from one another (p < 0.05). 

Overall, each of the five reference models (the fifth model was “clean” reference sites) was 

significantly different from the others (p < 0.001), indicating that differences among stressors, 

including ionic strength, apparently led to unique macroinvertebrate assemblages.

In another study with a different data set collected in West Virginia, nonmetric 

multidimensional scaling was applied to invertebrate genera, and sites were sorted into distinct 

ordination space characterized by low, medium, and high conductivities associated with surface 

A-30
mines with valley fills (Pond et al., 2008a). A study in Kentucky found similar results (Pond, 

2010).

Scoring—This evidence supports the causal relationship (+) by demonstrating that conductivity 

greater than background levels causes a consistent set of sensitive animals to be extirpated. The 

prediction was statistically strong (+). The effect is specific enough to be used to clearly 

separate groups by nonparametric statistical methods in two different data sets. Independent data 

sets and investigators confirmed that different assemblages of invertebrates occur with different 

stressors, including neutral-to-alkaline waters with increased salinity (+). The total score is 

+ + +.

Table A-20. Weighing and scoring evidence for alteration

Type of 

evidence Description of evidence

Logical 

implication Strength Corroboration

Change in 

occurrence of 

genera

Many genera exhibit sensitivity to 

increasing conductivity. These same genera 

are consistently sensitive to conductivity in 

another data set from Kentucky. This 

quantitative evidence is independently 

confirmed. Although the effect is consistent 

and strong, other causes may extirpate the 

same genera. 

+ + +

Models of 

Change of 

Genera

Empirical models based on specific biology 

discriminated effects of conductivity 

associated with mining.

+ + +

Summary of alteration. In summary, exposure to saline waters in Appalachia is associated with the 

declines of specific genera (+). The specific genera are not diagnostic because they may be affected by 

other causes; however, statistical tests could reliably sort and predict stressors based on biological 

assemblages (+) in different data sets from two states (+). The total score is + + +.

A.2.5. 

For an effect to occur, susceptible entities must experience a sufficient magnitude of 

exposure, and the magnitude of the alteration should be commensurate. This characteristic 

corresponds to biological gradient in Hill’s considerations. In SI and CADDIS, multiple types 

of evidence may demonstrate sufficiency including stressor-response in the field, laboratory 

tests of site media, manipulation of exposure, and stressor-response from laboratory studies. 

The summary of evidence is presented at the end of Section A.2.5 in Table A-22.

Sufficiency

A-31
In this section, we describe evidence that can be credibly used to evaluate whether the 

level of ionic stress is sufficient or not to cause extirpation. The evidence is primarily from field 

observations. Several laboratory studies (see Table A-21) were not used to evaluate sufficiency 

for the following reasons: (1) the ionic constituents were not similar to those in high salinity 

waters in the region of concern; (2) the study organisms infrequently or never occurred in 

streams in the region and are not closely related to the affected species; (3) the test species are 

physiologically tolerant of higher salinity; or (4) only acute lethality effects were reported. Such 

toxicity tests serve to show that the salt mixture is highly toxic at some levels to some test 

species, but they do not provide evidence to support or discount that the levels observed are 

sufficient to cause the extirpation of genera found by the analyses in this report. The fact that 

these test were not useful for this purpose does not imply that they are not useful for other 

purposes such as WET testing or criterion development.

Table A-21. Laboratory toxicity tests of saline mixtures and reasons that 

they were not useful for determining the sufficiency of the field salts to cause 

the field effects

Reference Mixture Test species Summary

Reason to 

exclude

Mount et al. (1997) Binary salt 

mixtures 

Ceriodaphnia dubia, 

Daphnia magna, 

Pimephales 

promelas

Acute lethality tests 

indicated that high levels of 

mixtures of common salts 

can be toxic to common 

laboratory organisms

1, 2, 3, 4

Lasier and Hardin 

(2010)

Salts of HCO3

−

, 

SO4

2−

, and Cl−and 

effluents 

dominated by Na 

salts

Ceriodaphnia dubia Reproductive tests showed 

that bicarbonate is the most 

toxic of the anions

1, 2, 3

Merricks et al. 

(2007)

Waters from 

below valley fills

Ceriodaphnia dubia Waters with high levels of 

conductivity had a higher 

prevalance of toxicity in 48-

hr tests than waters with 

lower levels of conductivity

2, 3, 4

Echols et al. (2010) Coal-processing 

effluent

Isonychia bicolor 7-d lethality tests of an 

NaCl-dominated effluent

1, 4

Kefford et al. 

(2003, 2004, 2005, 

2006, 2007), 

Hassell et al. 

(2006)

Tests of 

NaCl-dominated 

waters in Australia

Various Australian 

macroinvertebrates

Various test protocols and 

endpoints

1, 2

A-32
A.2.5.1. Laboratory Tests of Reconstituted Mine Discharges

Kennedy et al. (2003, 2004, 2005) tested simulated coal mine discharge waters in Ohio 

with the cladoceran crustacean Ceriodaphnia dubia and an ephemeropteran (Isonychia bicolor). 

In 7-day lethality tests, the ephemeropteran was about three times more sensitive than the 

crustacean. Lowest observed effect concentrations (LOECs) for survival of Isonychia (mid-tolate-instars) at 20°C occurred at 1,562, 966, and 987 μS/cm in three tests. These values bracket

the Isonychia XC95 of 1,180 μS/cm. However, when the assay was conducted at 12oC, the 

LOEC was 4,973 μS/cm, suggesting that longer exposures are needed before effects occur at

cold temperatures. Ceriodaphnia tests with simulated effluent containing only major ions 

indicated that the toxicity of this effluent was not due to heavy metals or selenium (Kennedy et 

al., 2005).

Scoring—The laboratory tests by Kennedy et al. (2003, 2004, 2005) establish that the effect for 

one insensitive ephemeropteran species, Isonychia bicolor, in the laboratory, occurred at a 

similar conductivity level to that in the field. A total score of + was assigned. 

A.2.5.2. Field Exposure-Response Relationships of Composite Metrics

As Hill (1965) suggested, a biological gradient in the field suggests that the exposures 

reach levels that are sufficient to cause effects. Evidence from several studies was evaluated. 

Our analyses, using the WABbase data sets, show that as conductivity increases, the total 

number of genera and the number of ephemeropteran genera decrease at conductivity levels 

shown to extirpate sensitive genera (r = −0.61) (see Figure A-6). This analysis shows not only 

the co-occurrence of elevated conductivity and the loss of stream biota but also that there is a 

regular exposure-response relationship that extends to the lowest-observed concentrations 

(evidence of sufficiency). 

This relationship holds even when elevated levels of potential alternative causes 

(confounders) are removed (see Figure A-7). The same data set was modeled after partitioning 

for potential confounding parameters. Streams with higher temperatures (>22°C), low pH (<6), 

poor habitat (<135), and high fecal coliform (>400 colonies/100 mL) were excluded. The effect 

of conductivity was still moderately strong (r = −0.53) (see Figure A-7). The correlation of the 

number of genera and conductivity increased slightly, from −0.41 to −0.49. See Appendix B for 

additional evaluation of potential cofounders.

A-33
Figure A-6. As conductivity increases, the number of total genera and 

ephemeropteran genera decreases. The fitted lines are locally weighted scatter 

plot smoothing (LOWESS) lines (span = 0.75). Data source: WABase.

Figure A-7. As conductivity increases, the number of total and 

ephemeropteran genera decreases, even when potentially confounding 

parameters are removed. (Excluded: streams with higher temperatures [>22°C], 

low pH [<6], poor habitat [<135], and high fecal coliform 

[>400 colonies/100 mL]). The fitted lines are LOWESS lines (span = 0.75).

r  -0.53

12

10

8

6

4

2

0

10 100 1000

Number of Emphemeroptera Genera

Conductivity ( S/cm)

50 r  -0.49

40

30

20

10

10 100 1000

Number of Total Genera

Conductivity ( S/cm)

r  -0.61 14

12

10

8

6

4

2

0

10 100 1000 10000

Number of Emphemeroptera Genera

Conductivity ( S/cm)

r  -0.41

50

40

30

20

10

0

10 100 1000 10000

Number of Total Genera

Conductivity ( S/cm)

A-34
Figure A-8. As conductivity increases, the number of total genera and 

Ephemeroptera genera decreases. The fitted lines are LOWESS lines 

(span = 0.75). Data from EPA Region 3.

Scoring—The field observations show that as conductivity increases, the number of 

Ephemeroptera and total number of genera decrease and, thus, the level of salt in streams is 

sufficient to cause effects (+). The correlation is strong to moderately strong depending on the 

data set. The effect was specific for the types of salts and species native to the region. The 

correlations were corroborated with independent data sets and different investigators (+). A total 

score of + + was assigned.

A.2.5.3. Field Exposure-Response Relationships of Composite Indices

The relationship between conductivity and the West Virginia Stream Condition Index

(WVSCI) score, which is a composite of six family level metrics, was also modeled from the 

r  -0.72 7

6

5

4

3

2

1

0

50 100 200 500 1000 2000

Number of Emphemeroptera Genera

Conductivity ( S/cm)

r  -0.35

35

30

25

20

15

10

5

0

50 100 200 500 1000 2000

Number of Total Genera

Conductivity ( S/cm)

negatively correlated with conductivity (r = −0.72 and −0.35, respectively) (see Figure A-8).

EPA Region 3 data set, the ephemeropteran genera and total genera were both moderately 

increases. In a recalculation of the Pond et al. (2008a) data with additional data to create the 

reported that the number of Ephemeroptera and the number of taxa decreases as conductivity 

conductivity and less so with habitat (r = −0.64). Pond (2010) and Pond et al. (2008a, b) also 

ephemeropteran genera and conductivity were highly negatively correlated (r = −0.90) with 

In a study of the effects of valley fills in West Virginia by Pond et al. (2008a, b), 

A-35
WABbase data set. A low WVSCI score indicates poorer stream condition. Mean WVSCI 

scores from 60 bins were regressed with conductivity (see Figure A-9). A stream location with a 

WVSCI score of <68 attained on multiple visits is assessed by WVDEP as impaired (Gerritsen et 

al. 2000, WVDEP 2010). Based on the modeled relationship, a WVSCI score of 68 corresponds 

to 180 μS/cm. At the benchmark of 300 μS/cm, the corresponding WVSCI score is 64, which is

impaired based on West Virginia’s biocriteria. Using logistic regression, the probability of 

impairment at 500 μS/cm is 0.72 and at 300 μS/cm is 0.59.

Figure A-9. As conductivity increases, the West Virginia Stream Condition 

Index (WVSCI) score decreases. Points represent mean WVSCI score for 

conductivity bins. Bars are 90% confidence intervals. The dotted line is the 

95% confidence bound for the modeled line. A WVSCI impairment score of 68 

intercepts the regression line at 180 μS/cm (dashed arrow). The model estimates 

a WVSCI value of 64 at 300 μS/cm (solid arrow).

In Pond et al. (2008a), the genus-level index of most probable stream status (GLIMPSS)

and WVSCI scores were strongly correlated with conductivity (r = −0.90 and −0.80, 

respectively). In an earlier study completed in 2006 and published in 2010, Gerritsen et al. 

identified 180 μS/cm as a plausible stressor response threshold and 300 μS/cm as a substantial 

10

0

8

0

6

0

4

0

2

0

100 1000 10000

W

V

S

CI

Conductivity ( S/cm)

A-36
effects threshold for the association of conductivity and the WVSCI biological index using a data 

set from the WABbase. 

Scoring—This set of evidence indicates that, in multiple data sets and by a variety of biological 

responses and analytical methods, as conductivity levels observed in the region increase, stream 

condition becomes impaired, and the assemblage of macroinvertebrates is different from best 

available reference sites in the region. This is supporting evidence of sufficient salt in the 

streams to cause widespread effects (+). The correlations are strong (+). The correlations were 

corroborated with different methods in three studies (+). A total score of + + + was assigned. 

A.2.5.4. Field Exposure-Response Relationships: Susceptible Genera

As conductivity increases, the occurrence and capture probability decreases for many 

genera in West Virginia (see Appendices C, D, and E) and Kentucky (see Appendices H, I, and 

J) at the conductivity levels predicted to cause effects. The loss of these genera is a severe and 

clear effect. 

In the West Virginia data set at 500 μS/cm, 17% of genera (14/163) are extirpated and an 

additional 50% of genera are declining. In the Kentucky data set, 11.5% of genera (12/104) are 

extirpated at 500 μS/cm, and a total of 76% of genera are in decline. This evidence shows that 

exposures are sufficient to extirpate susceptible genera in two geographic areas. The 

associations show that relatively low exposures are sufficient to adversely affect susceptible 

genera.

Scoring—The observed effects logically support the causal relationship between increased 

conductivity and declining survival of susceptible genera and indicate that effects occur at 

relatively low conductivity levels (+). The effect is strong, with complete extirpation of many 

genera (+). The results were corroborated with a separate data set from Kentucky (+). The total 

score is + + +.

A-37
Table A-22. Weighing and scoring evidence for sufficiency

Type of 

evidence Description of evidence

Logical 

implication Strength

Corroboration

Laboratory 

tests of 

ambient 

waters

These tests showed acute lethality to an apparently 

resistant species, Isonychia bicolor, at 

conductivity levels similar to its XC95. 

+

Field 

exposureresponse 

relationships 

of composite 

metrics

Ephemeroptera were negatively correlated with 

conductivity in two data sets r = −0.61 and −0.72 

(see Figures A-6 and A-8) and r = −0.90 in Pond

et al. (2008a). This evidence is highly relevant 

and was obtained independently in two separate 

data sets, with moderate-to-strong correlations. 

Exposures were in the field with native species.

Removal of sites with poor habitat had little effect 

on the correlation (see Figure A-7), the SSD or 

benchmark (see Appendix B).

+ +

Field 

exposureresponse 

relationships 

of composite 

indices

The field observations show that as conductivity 

increases, indices of stream condition (WVSCI 

and GLIMPSS) decrease (see Figure A-9). 

Correlations were strong (r = −0.80; r = −0.90 in

Pond et al. [2008 a, b]). Results were further 

corroborated by Gerritsen et al. (2000). 

Exposures were in the field with native species.

+ + +

Field 

exposureresponse 

relationships: 

susceptible 

genera

At 500 μS/cm, the capture probabilities of more 

than 65% of genera have begun to decline. 

Similar results were obtained with West Virginia 

and Kentucky data sets.

+ + +

Summary of sufficiency. In summary, exposure to saline waters in Appalachia is sufficient to cause the 

declines of genera (+) with the salts found in the region’s streams. The increases in effects of conductivity 

are strong even when other stressors are present (+). Different analytical approaches demonstrate the level 

of salinity associated with different effect endpoints in different data sets in two states (+). The evidence is 

consistent. The total score is + + +.

GLIMPSS = genus-level index of most probable stream status.

A.2.6. Time Order

Logically, a causal event occurs before an effect is observed. Evidence of time order 

would be provided by changes in the invertebrate assemblages after the introduction of a source 

that increased conductivity. This characteristic corresponds to temporality in Hill’s 

considerations, in the SI types of evidence, and to temporal sequence in CADDIS.

A-38
We could not obtain conductivity and biological survey data for before and after a valley 

fill or other source of saline effluents began operation. Hence, this characteristic of causation is 

scored no evidence (NE).

Scoring—NE 

A.2.7. Evaluation of the Body of Evidence

In this assessment, the body of evidence is assessed based on completeness of evidence 

for most characteristics of causation, and the logical implications, strength, consistency, and 

diversity of the overall body of evidence 

This causal assessment found that the available evidence supports a causal relationship 

between mixtures of matrix ions in streams of Ecoregions 69 and 70 and resulting biological 

impairments. That conclusion is based on evidence showing that the relationship of conductivity 

to the loss of aquatic genera has the characteristics of causation.

1. Co-occurrence―The loss of genera occurs where conductivity is high even when 

potential confounding causes are low but is rare when conductivity is low (+ + +).

2. Preceding causation―Sources of conductivity are present and are shown to increase

stream conductivity in the region (+ + +).

3. Interaction―Aquatic organisms are directly exposed to dissolved salts. Based on first 

principals of physics, ionic gradients in high conductivity streams would not favor the 

exchange of ions across gill epithelia. Physiological studies over the last 100 years have 

documented the many ways that physiological functions of organisms are affected by 

excess salt (i.e., combinations of ions that they do not have mechanisms or the capacity to 

regulate) (+ +).

4. Alteration―Some genera, composite metrics, and assemblages are affected at sites with 

higher conductivity, while others are not. These differences are characteristic of high 

conductivity (+ + +).

5. Sufficiency―Laboratory analyses report results of effects for tolerant taxa, but taxa, 

ionic compositions and durations are not representative of exposure in streams. 

However, increased exposure in both concentration and duration to salt affects 

invertebrates based on field observations (+ + +).

6. Time order―Conductivity increases and local extirpation occurs after mining permits are

issued, but conductivity and biological data before and after mining are not available 

(NE).

A-39
A.3. CONCLUSION

This causal assessment presents clear evidence that the deleterious effects to benthic 

invertebrates are caused by, not just associated with, the ionic strength of the water. Because this 

is an assessment of general causation, the causal relationship describes how Ephemeroptera and 

other salinity intolerant invertebrates, in general, respond to ionic stress and does not require that 

the species or genera be the same in all applications or at all locations. Therefore, we expect that 

ionic stress sufficient to cause extirpations would occur with a similar ionic matrix in other 

regions with naturally low conductivity.

Other potential causes of the loss of genera in the region include elevated temperatures 

associated with loss of shade or increased impervious surfaces, siltation from various land use 

activities, low pH from atmospheric deposition and abandoned mines, aluminum toxicity from 

abandoned mines, and nutrient enrichment from various sources. When these causes are absent 

or removed, a relationship between conductivity and ephemeropteran richness is still evident (see 

Appendix B). 

This causal assessment does not attempt to identify constituents of the mixture that 

account for the effects. Rather, it shows that the mixture of ions in streams with elevated 

conductivity and neutral or somewhat alkaline waters in the region of concern is causing the 

extirpation of sensitive genera of macroinvertebrates. The dominant ions, that is, those in the 

greatest relative amounts, are HCO3

−

, SO4

2−

, Ca2+, and Mg2+. 

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A-43
B-1 

APPENDIX B 

ANALYSIS OF POTENTIAL CONFOUNDERS

ABSTRACT

The purpose of Appendix B is to evaluate the ability of factors that may co-occur with 

conductivity (i.e., potential confounders) to weaken our ability to model the relationship between 

conductivity and occurrence of genera. The analyses in this appendix do not determine whether 

those factors cause effects in the region. Rather, they evaluate how the potential confounders 

may affect our ability to model the relationship between conductivity and the loss of 

macroinvertebrate genera. 

The appendix addresses its purpose in two ways. First, it supports Appendix A by 

demonstrating that none of the potential confounders is responsible for the association between 

conductivity and biological effects. Second, it supports the development of the benchmark value 

by determining whether the confounders have substantive influence on the causal relationship 

between salts and macroinvertebrate assemblages. Twelve potential confounders were 

evaluated: habitat, organic enrichment, nutrients, deposited sediments, pH, selenium, 

temperature, lack of headwaters, catchment area, settling ponds, dissolved oxygen, and metals. 

The inference was performed by identifying potential confounders and then determining the 

occurrence and strength of 10 types of evidence of confounding for each of them. The term 

“confounding” refers to a bias in the analysis of causal relationships due to the influence of 

extraneous factors (confounders), in this case, the stressors listed above. 

The effect of confounders was found to be minimal and manageable. Potential 

confounding by low pH was minimized by removing sites with pH <6 from the data set when 

calculating the aquatic life benchmark. The signal from conductivity was strong, so that 

potential confounders that were not strongly influential could be ignored with reasonable or 

greater confidence. No analysis can demonstrate that these variables have no influence at any 

place or time, but, this analysis does demonstrate that their influence on the relationship of 

conductivity and extirpation of genera is minimal given the streams that would be affected by the 

aquatic life benchmark.

B.1. INTRODUCTION

Having established that salt mixtures dominated by bicarbonate and sulfate cause 

biological impairments in the region (see Appendix A), this appendix addresses other potential 

causes of impairment in the region that might confound that relationship. The goal of this 

analysis is not to eliminate confounding variables. They are natural variables such as 

temperature and habitat structure that cannot be literally eliminated like eliminating smokers in 
B-2 

an epidemiological study. Nor is the goal to equate the levels of confounders to an ideal or 

pristine level. High conductivity effluents do not enter wilderness streams. Rather, the streams 

are subject to some level of current or historic disturbance. The overall goal of the Report is to 

estimate conductivity levels that would protect against the unacceptable effects of salts in those 

streams (i.e., typical streams receiving salty effluents in the region of concern). The goal of the 

assessment in Appendix B is to determine if the model developed for that purpose is a reliable 

predictor of harmful effects and protective levels. We do this by trying to discover if there are 

factors that bias that model. 

Confounding is a bias in the analysis of causal relationships due to the influence of 

extraneous factors (confounders). Confounding occurs when a variable is correlated with both 

the cause and its effect. The correlations are usually due to a common source of multiple, 

potentially causal agents. However, they may be observed for other reasons (e.g., when one 

variable is a by-product of another) or due to chance associations. 

Confounding may have two consequences. First, it can result in identification of a cause 

that is in fact a noncausal correlate. That possibility is commonly addressed by applying Hill’s 

(1965) considerations or some equivalent set of criteria for causation as in Appendix A. This is 

done because statistics alone cannot determine the causal nature of relationships (Pearl, 2009; 

Stewart-Oaten, 1996). Second, confounding can bias a causal model resulting in uncertainty 

concerning the actual magnitude of the effects. That can be addressed by considering the 

magnitudes of correlations with and without the potential confounder or by considering the 

change in the results when the potential confounder is removed. 

A variety of types of evidence may be used to determine whether confounders 

significantly affect the results; we have identified 10 types of evidence. They are related to three 

of the characteristics of causation used to determine that elevated conductivity is a cause of 

impairment of stream communities in Appendix A: co-occurrence, sufficiency, and alteration. 

1. Co-occurrence of confounder and cause: Confounders are correlated with the cause

2. Co-occurrence of confounder and effect: Confounders are correlated with the 

of 

interest. A low correlation coefficient is evidence against the potential confounder. 

effect

3. Co-occurrence of confounder and cause: Even when the confounder is not correlated 

with the cause of interest, it may be influential at extreme levels. A lack of influence at 

extreme levels of the cause and the potential confounder is evidence against the potential 

confounder. 

of 

interest. A low correlation coefficient is evidence against the potential confounder. 
B-3 

4. Co-occurrence of confounder and effect: If the frequency of the effect does not 

diminish when the potential confounder is never present or is present in all cases, the 

confounder can be discounted in that subset. 

5. Sufficient confounder: The magnitude of the potential confounder (e.g., concentration 

of a co-contaminant) may be compared to exposure-response relationships from 

elsewhere (e.g., laboratory toxicity tests) to determine if the exposure to the potential 

confounder is sufficient. If it is not sufficient, that is evidence that it is not acting as a 

confounder. 

6. Sufficient confounder: If the confounder is estimated to be sufficient in a subset of 

cases, those cases may be removed from the data set, and the remaining set reanalyzed to 

determine the influence of their removal on the results. 

7. Sufficient confounder: Multivariate statistical techniques may be used to estimate the 

magnitude of confounding or to adjust the causal model for confounding—if their 

assumptions hold. 

8. Sufficient confounder: If the potential confounder occurs in a sufficiently small 

proportion of cases, it can be ignored. 

9. Alteration: If a potential confounder has characteristic effects that are distinct from those 

of the cause of concern, then the absence of those effects can eliminate the potential 

confounder as a concern in either individual cases or the entire data set. 

10. Alteration: If the effects are characteristic of the cause of concern and not of the 

potential confounder, then the potential confounder can be eliminated as a concern in 

either individual cases or the entire data set.

Weighing evidence for confounding differs from weighing evidence for causation. The 

causal assessment in Appendix A determines whether dissolved salts are an important cause of 

biological impairment in the region. This assessment of confounding accepts the result of the 

causal assessment and attempts to determine whether any of the known potential confounders 

interfere with estimating the effects of conductivity to a significant degree. If there is significant 

interference, the confidence in the model predictions would be weakened unless the model is 

modified. That requires a different weighting and weighing method from the one in 

Appendix A, which would be used if the goal were to determine whether the potential 

confounder is itself a cause. 

As in Appendix A, the number of ephemeropteran (mayfly) genera is used as a standard 

metric for the effects of conductivity, which may or may not be confounded. Because the 

endpoint effect is extirpation of 5% of genera and the sensitive genera are primarily 
B-4 

Ephemeroptera, this is an appropriate metric. However, because of a resistant mayfly genus (see 

Figure A-1), it is not expected that all Ephemeroptera will be missing at high conductivities.

Some commenters recommended using multivariate statistics in place of weight-ofevidence analysis as the sole means to address potential confounders. However, because of the 

goals of the analysis and the nature of the data, it is not appropriate to use multivariate statistics 

alone to try to model the relationship between conductivity and extirpation or to eliminate the 

effects of confounders or estimate the magnitude of their effects. First, no statistical test can 

demonstrate that an association is causal. Second, violation of assumptions prevents reliable 

estimation of the influence of one potentially causal variable on another. Multiple regression 

depends on assumptions of independence, additivity, and normality that are not met. In sum, 

multivariate statistical associations are just associations, and association is not causation. 

However, they can be used as evidence in the weight-of-evidence analysis along with other 

incomplete or imperfect pieces of evidence to help reach the best-supported conclusion. 

B.2. WEIGHTING

The evidence is weighted using a system of plus (+) for supporting the potential 

confounder (i.e., the evidence suggests that the potential confounder is actually causing the effect 

to a significant degree), minus (−) for weakening the potential confounder (i.e., the evidence 

suggests that the potential confounder does not contribute to the effect to a significant degree), 

and zero (0) for no effect. One to three plus or minus symbols are used to indicate the weight of 

a piece of evidence. 

+ + + or − − − Convincingly supports or weakens

+ + or − − Strongly supports or weakens

+ or − Somewhat supports or weakens

0 No effect

Any relevant evidence receives a single plus, minus, or zero to register the evidence as 

relevant and to indicate a decreased or increased potential for confounding (see Table B-1). The 

strength of evidence is considered next. Criteria for scoring the strength of evidence are 

presented below for the common types. They were developed for transparency and consistency 

and are based on the best professional judgments. After strength, the other possible unit of 

weight is assigned depending on the type of evidence. 
B-5 

Table B-1. Relationships between qualities of evidence and scores for 

weighing evidence

Qualities of the evidence Score, not to exceed three minus or three plus

Logical implications and relevance +, 0, −

Strength Increase score

Other qualities Increase score

For co-occurrence (Evidence Types 1−4), strength or consistency of the association is the 

primary consideration. The primary measure of association is Spearman’s correlation 

coefficients. For comparison to the potential confounders, the correlation coefficient for 

conductivity and number of ephemeropteran genera are −0.61 for the West Virginia (WV) data 

set and −0.72 for the EPA Region 3 data set, values in the upper end of the moderate range. 

Correlations, as measures of co-occurrence, can be scored as in Table B-2. 

The scores in this appendix are based on conventional expectations for a confounder that 

is itself a cause. That is, a potential confounder—such as a metal by itself—might cause 

extirpation of invertebrate genera (independent combined action) or might act in combination 

with conductivity to extirpate invertebrate genera (additive or more than additive combined 

action). However, sometimes correlations are anomalous. For example, a potential confounder 

may actually decrease effects as when calcium reduces effects of metals. Such anomalous 

results require case-specific interpretation based on knowledge of mechanisms and 

characteristics of the ecosystems being analyzed.

Table B-2. Weighting co-occurrence using correlations for Evidence 

Types 1−2

Assessment Strength Score

Absent r < |0.1| − −

Weak |0.1| < r < |0.25| −

Moderate |0.75| > r > |0.25| +

High r > |0.75| + +

Anomalous results may also result from violation of the expectation that a confounder 

should be correlated with both conductivity and the effect. If only one of the correlations is 
B-6 

observed, that result requires additional interpretation. If the potential confounder is correlated 

with the effect, but not with conductivity, the result may be due to chance or to a partitioning of 

causation in space. That is, they are independent because the confounder impairs communities at 

different locations than conductivity. This could occur if the potential confounder and 

conductivity have different sources. In any case, it is not a confounder of conductivity. 

In the contingency tables (Evidence Type 3), the frequency of occurrence of any 

Ephemeroptera (i.e., of the failure to extirpate all ephemeropteran genera) is presented for 

combinations of high and low levels of conductivity and of the potential confounder. If the 

frequency of occurrence is much lower when the potential confounder is present at high levels, 

this is supporting evidence for confounding. Note that the goal here is not to determine the 

effects of exceeding a criterion or other benchmark. Rather the goal is to clarify the 

co-occurrence of conductivity, confounders, and effects by determining the frequency of effects 

at each possible combination of extremely high and low levels of conductivity and the potential 

confounder. It is expected that, if a variable is indeed a confounder, its influence on the 

occurrence of effects would be seen at an extreme level. This use of contingency tables could 

reveal influences of confounders that are obscured when the entire ranges of data are correlated 

by, for example, a step function or other discontinuity in the relationship. Therefore, clearly high 

and low levels of conductivity and the potential confounder are used in contingency tables. 

When scoring evidence from contingency tables, a potential confounder gets a plus score 

if its presence at a high level reduces the probability of occurrence by more than 25% and a 

minus score if it does not (see Table B-3). It gets a double plus score if its presence at a high 

level reduces the probability of occurrence by more than 75% and a double minus score if it 

raises it by less than 10%. Any decrease in effects at high levels of a potential confounder is 

anomalous and is treated as strong negative evidence.

Table B-3. Weighting co-occurrence for Evidence Type 3 using contingency 

tables

Assessment Strength Score

High levels of a confounder 

should increase the 

probability that a site lacks 

Ephemeroptera at low 

conductivity, and low levels 

of the confounder should 

decrease the effect at high 

conductivities

Increased effect >25% + for co-occurrence 

Increased effect >75% + + for co-occurrence and strength

Increased effect <25% − for co-occurrence

Increased effect <10% or 

decreased effect

− − for co-occurrence and strength
B-7 

The evidence concerning sufficiency of the confounder (Evidence Types 5−8) is diverse. 

Only Evidence Type 6 was sufficiently common and consistent to develop scoring criteria. For 

Evidence Type 6, the primary consideration is the degree of departure of the correlation in the 

truncated data set from the correlation of conductivity and Ephemeroptera in the full data set (see 

Table B-4). However, no more than one negative score was given if less than 10% of the data 

were removed.

Table B-4. Weighting sufficiency for Evidence Type 6: alteration of the 

correlation of conductivity with the number of ephemeropteran genera after 

removal of elevated levels of a confounder 

Assessment Strength Score

Removal of elevated 

levels of a confounder 

should change the 

correlation coefficient 

Coefficients decrease by <10%

(0.55 < r for WV data)

− − for a lack of change in effect 

with removal of confounder

Coefficients decrease by <20%

(0.49 < r for WV data)

− for a small change in effect with 

removal of confounder

Coefficients decrease by >20%

(0.49 > r for WV data)

+ for a strong change in effect 

with removal of confounder 

Coefficients increase − − because removal of a true 

confounder should decrease the 

effect of conductivity 

For alteration, the primary consideration is the degree of specificity of the effects of the 

confounder relative to those of the salts. This type of evidence is rare and is scored ad hoc when 

it occurs. Additional considerations that may result in a higher score are presented in Table B-5. 

The primary data source for evidence of confounding is the Watershed Analysis Data 

Base (WABbase), which was used to derive the benchmark. Except where indicated, reported 

results are derived from those data, which are referred to as the West Virginia data. However, 

where possible and appropriate, the EPA Region 3 data set from West Virginia samples (referred 

to as the EPA data set) is used for independent corroboration. The EPA data set is much smaller 

and often does not have enough extreme values of the potential confounder to calculate reliable 

contingency tables or regressions of censored data. 
B-8 

Table B-5. Considerations used to weight the evidence concerning the 

influence of potentially confounding variables

Quality of evidence Descriptor

Logical implication Negative or positive

Directness of cause Proximate cause, sources, or intermediate causal connections

Specificity Effect attributable to only one cause or to multiple causes

Relevance to effect From the case or from other similar situations

Nature of the association Quantitative or qualitative

Strength of association Strong relationships and large range or weak relationships and 

small range

Consistency of information All consistent or some inconsistencies

Quantity of information Many data or few data 

Quality of information Good study or poor study

Source: Cormier et al. (2010).

B.3. WEIGHING

After the individual pieces of evidence have been weighted, the body of evidence for a 

potential confounder is weighed based primarily on the consistency of the evidence and 

secondarily on the strength of the pieces of evidence (see Table B-6). The body of evidence—

rather than any one piece of evidence—determines how strongly these potential confounders 

might affect the model. 

B.4. POTENTIAL CONFOUNDERS

Potential confounders were chosen because they were believed to be associated with 

mountaintop mining, valley fills, or other sources of salts or because of suggestions from 

reviewer or public comments. Each of the discussions in this section begins with a statement of 

the reason that the potential confounder was chosen for evaluation. 
B-9 

Table B-6. Weighing confidence in the body of evidence for a potential 

confounder

Assessment Score Body of evidence Action

Very confident − − − All minus, some strongly negative 

evidence

No treatment for confounding

Moderately confident − − All minus, no strongly negative 

evidence

No treatment for confounding

Reasonably confident − Majority minus No treatment for confounding 

Undetermined 0 Approximately equal positive and 

negative, ambiguous evidence, or 

low quality evidence

Additional study advised 

Potential confounding + Majority plus Correction for confounding may 

be advised

B.4.1. Habitat Quality

Stream habitat may be modified by physical disturbance, changes in flow or increased 

sediment loads in reaches that receive high conductivity effluents. Habitat quality was 

represented by a qualitative index, the Rapid Bioassessment Protocol Habitat Evaluation (RBP) 

derived by the WVDEP, which increases as habitat quality increases. Component metrics were 

not used because they were less correlated with Ephemeroptera than the index.

Habitat quality was analyzed as part of groups of variables that were judged a priori to be 

more likely than others to have combined effects. Therefore, sites at which RBP and pH were 

low and fecal coliform count was high were removed to determine whether the 5th centile 

hazardous concentration (HC05) was affected (see Figure B-1). Similarly, RBP was used with 

fecal coliform count and temperature in a multiple linear regression with conductivity (see 

Table B-7).

The body of evidence was mixed. Habitat scores were moderately correlated with both 

conductivity and biological response, which indicates a potential for confounding. However, 

removal of poor habitat had little effect on the correlation of conductivity with Ephemeroptera or 

on the derivation of the HC05 for conductivity (see Table B-7 and Figure B-1). Habitat score had 

a very slight effect on the intercept and the slope for conductivity in a multiple regression (see 

Table B-7). In addition, Ephemeroptera occur even when habitat is poor (see Table B-8). The 

weight of the scored body of evidence indicated habitat was not a confounder (see Table B-9). 
B-10 

a. 

b. 

Conductivity (μS/cm)

Proportion of Genera

100 200 500 1000 2000 5000 10000

0.0 0.2 0.4 0.6 0.8 1.0

Conductivity (μS/cm)

Proportion of Genera

100 200 500 1000 2000

0.0 0.1 0.2 0.3 0.4 0.5

Figure B-1. Species sensitivity distribution for all year, pH >6 and all sites 

(open circles) and for sites with pH >6, Rapid Bioassessment Protocol score 

>135 and fecal coliform <400 colonies/100 mL (closed circles). (a) Uncensored 

with 163 genera and censored dataset with 117 genera. (b) Only the lower half of 

the SSD is shown to better discriminate the points in the left side of the full 

distribution. Habitat disturbance and organic enrichment have little influence; the 

HC05 for the constrained data set is 326 μS/cm based on 117 genera. The upper 

and lower confidence bounds on that value are 229 μS/cm and 343 μS/cm, 

respectively.
B-11 

Table B-7. An output table for two linear regression models. The first is the 

simple model predicting ephemeropteran genera from conductivity. The second 

is a multivariate model with the additional covariates RBP score, temperature, and 

fecal coliform count. These variables were chosen a priori as likely confounders 

that could co-occur and have combined effects.

Parameter Estimate Standard error

Univariate model

Intercept 3.65 0.055

Conductivity slope −0.93 0.024

Multivariate model

Intercept 3.39 0.11

Conductivity slope −0.92 0.029

RBP slope 0.0014 0.0005

Temperature slope 0.0068 0.0026

Fecal coliform slope 0.037 0.012

Table B-8. Number and percent of sites with high and low quality habitat 

and high and low conductivity with Ephemeroptera in streams (pH >6)

Conductivity <200 μS/cm Conductivity >1,500 μS/cm

Habitat score <115 140/142

(99%)

12/31

(39%)

Habitat score >140 373/375

(99%)

13/22

(59%)
B-12 

Table B-9. Evidence and weight for confounding by habitat quality

Type Score Evidence

1. Correlation of 

cause and 

confounder 

+ RBP score was barely moderately correlated with conductivity, 

(r = −0.25, n = 2,192). 

2. Correlation of 

effect and 

confounder

+ RBP score was barely moderately correlated with the number of 

ephemeropteran genera (r = 0.26, n = 2,192). 

3. Contingency of 

high level of 

cause and 

confounder 

− In a contingency table (see Table B-8), Ephemeroptera are 

present at 99% of sites with low conductivity (<200 μS/cm) even 

when habitat is poor (<115). However, with high conductivity, 

Ephemeroptera are present at only about half of sites regardless 

of habitat. 

6. Removal of 

confounder 

− When sites with moderate to poor habitat (an RBP score <140) 

were removed from the analysis, conductivity is a little less 

negatively correlated with the number of Ephemeroptera 

(r = −0.55, n = 747). 

− − The SSD and HC05 are very similar when the XC95 values were 

calculated with a subset of the data set with sites removed with 

pH of <6, RBP score <135, and fecal coliform 

>400 colonies/100 mL (see Figure B-1).

7. Multivariate 

statistics

− − Habitat quality, temperature and fecal coliform together had 

essentially no effect on the slope in multiple regression and the 

slope for RBP score is particularly small (see Table B-7).

Weight of evidence − Reasonably confident. The correlations are marginal; RBP 

explains only 6.7% of the variance in ephemeropteran 

occurrence, based on r

2

. However, the contingency table gives 

relatively strong negative evidence (Ephemeroptera occur even 

when habitat is poor), and elimination of poor habitat (along with 

high coliform counts) has almost no effect on the SSD or HC05

(see Figure B-1). Habitat has very little effect in the multiple 

regression. Therefore, we did not correct for habitat, but more 

detailed habitat studies could be worthwhile.

SSD = species sensitivity distribution.
B-13 

B.4.2. Organic Enrichment

Sources of organic enrichment such as domestic sewage and animal wastes are also 

sources of salts that contribute to conductivity. Fecal coliform counts are an indicator of organic 

enrichment and the presence of sources that may contain other toxicants such as household 

waste. The evidence is mixed, but, overall, the evidence against significant confounding 

associated with fecal coliform counts was much stronger than the supporting evidence (see 

Tables B-7, B-10, and B-11). 

Conductivity <200 μS/cm Conductivity >1,500 μS/cm

Coliform <400 colonies/100 mL 610/613

(99%)

30/69

(43%)

Coliform >400 colonies/100 mL 184/187

(98%)

14/34

(41%)

Table B-11. Evidence and weights for confounding by organic enrichment

Type Score Evidence

1. Correlation of cause 

and confounder

+ Fecal coliform counts were barely moderately correlated with 

conductivity (r = 0.26, n = 2,040).

2. Correlation of effect 

and confounder

+ Coliform counts were barely moderately correlated with the 

number of ephemeropteran genera (r = −0.25, n = 2.040).

3. Contingency of high 

level of cause and 

confounder

− − In a contingency table (see Table B-10), the presence of high 

coliform counts did not change the probability of finding 

Ephemeroptera at either high or low conductivity.

6. Removal of 

confounder

− − When samples >400 colonies/100 mL were removed from the 

analysis, the correlation of conductivity with Ephemeroptera 

barely changed (r = −0.61, n = 1,364).

− − The species sensitivity distribution (SSD) and HC05 are very similar 

to those used in the benchmark, when calculated from subset of 

the data with sites removed with pH of <6, RBP score <135, and 

fecal coliform >400 colonies/100 mL (see Figure B-1).

7. Multivariate statistics − − Habitat quality, temperature and fecal coliform together had 

essentially no effect on the slope for conductivity in multiple 

regression (see Table B-7).

Weight of evidence − Reasonably confident: the correlations producing the two positive 

scores were exactly on the margin, and negative evidence was 

strong. No treatment for confounding.
B-14 

B.4.3. Nutrients

Nitrogen and phosphorus may come from sewage and animal wastes or from fertilizers 

used in agriculture or mine reclamation. Because neither nutrient was correlated with 

conductivity or Ephemeroptera, effects could not be confounded by nutrients when conductivity 

increased (see Table B-12).

Table B-12. Evidence and weights for confounding by nutrients

NA = not applicable.

Type Score Evidence

1. Correlation of 

cause and 

confounder 

− − Conductivity was uncorrelated with nitrate and nitrite in the 

WV data set (r = 0.07, n = 1,182) and moderately correlated 

in the EPA data set (r = 0.33, n = 39). 

− − Conductivity was uncorrelated with total phosphorus in the 

WV data set (r = 0.04, n = 1,185) and the EPA data set 

(r = 0.03, n = 45). 

2. Correlation of 

effect and 

confounder 

− Ephemeroptera was uncorrelated with nitrate and nitrite in 

the WV data set (r = −0.04, n = 1,182) and barely 

moderately correlated in the EPA data set (r = −0.26, 

n = 39). 

− − Ephemeroptera was uncorrelated with total phosphorous 

(r = 0.001, n = 1,185) and the EPA data set (r = 0.06, 

n = 45).

3. Contingency of 

high level of cause 

and confounder

NA Contingency table analyses were not used because extreme 

nutrient levels were rare at high conductivities.

6. Removal of 

confounder 

− When samples with nitrate plus nitrite >0.6 mg/L were 

removed from the analysis, the correlation of conductivity 

with the number of Ephemeroptera was little changed 

(r = −0.54, n = 999). 

− When samples with total phosphorus >0.04 mg/L were 

removed from the analysis, the correlation of conductivity 

with the number of Ephemeroptera was little changed 

(r = −0.56, n = 998). 

Weight of evidence − − − Very confident: all negative, some strongly negative. No 

treatment for confounding.
B-15 

B.4.4. Deposited Sediment

Mining and other activities that result in crushing and exposing rocks are sources of salts 

and potentially of silt that may affect stream organisms. A qualitative measure of embeddedness 

(WABase embeddedness score) was evaluated by contingency table and by correlation (see 

Table B-13 and B-14). No evidence supported embeddedness as a confounder (see Table B-14).

Table B-13. Number of sites with high and low embeddedness scores and 

high and low conductivity with Ephemeroptera present in streams (pH >6)

Conductivity <200 μS/cm Conductivity >1,500 μS/cm

Embeddedness score <7 42/44

(95%)

7/16

(44%)

Embeddedness score >15 210/211

(99%)

6/15

(40%)

Table B-14. Evidence and weights for confounding by deposited sediment

Type Score Evidence

1. Correlation of 

cause and 

confounder 

− The WABbase embeddedness score is weakly correlated 

with conductivity (r = −0.18, n = 2,197). 

2. Correlation of 

effect and 

confounder

− The WABbase embeddedness score is weakly correlated 

with Ephemeroptera (r = 0.22, n = 2,197). 

3. Contingency of 

high level of cause 

and confounder

− − In a contingency table (see Table B-13), high 

embeddedness (score >15) has little effect at either high or 

low conductivity. 

6. Removal of 

confounder 

− − When samples with an embeddedness score <13 are 

removed from the analysis, the correlation of conductivity 

with the number of Ephemeroptera was virtually 

unchanged (r = −0.62, n = 1,088). 

Weight of evidence − − − Very confident: all negative, some strongly. No treatment 

for confounding.
B-16 

B.4.5. High pH

The dissolution of limestone and dolomite increases as unweathered surface area of rock 

increases. Waters draining crushed limestone and dolomite contain HCO3

−

, which contributes to 

higher pH and alkalinity. The HCO3

− that raises the pH is also a major anion moiety that 

contributes to conductivity. Hence, pH directly reflects a major constituent of conductivity 

(HCO3

−

), so it could not be a conventional confounder. In addition, salts influence hydrogen ion 

activity—which is measured as pH. In any case, available evidence indicates that the variance in 

pH has little effect on the derivation of the HC05 for conductivity in waters above pH 7 (see 

Tables B-15 and B-16). 

B.4.6. Low pH

Because low pH from acid mine drainage is known to be an important cause of 

impairment where it occurs and was judged a priori to be a potentially important environmental 

variable. That preconception was supported by the evidence summarized here (see Table B-17). 

Therefore, sites with pH <6 were not used to calculate the XC values. However, 

Table B-15 suggests that even below pH 4.5, conductivity is more important than acidity to the 

occurrence of Ephemeroptera (see Tables B-15 and B-17). In sum, although the benchmark 

applies to waters with neutral or basic pH, high conductivity appears to also cause effects at 

low pH. 

Table B-15. Number of sites with high and low conductivity with high and 

low levels of pH with Ephemeroptera present

Conductivity <200 μS/cm Conductivity >1,500 μS/cm

pH <4.5 16/19

(84%)

0/14

(0%)

pH >8.5 3/3

(100%)

4/8

(50%)
B-17 

Table B-16. Evidence and weights for confounding by high pH

Type Score Evidence

1. Correlation of cause 

and confounder 

0 Conductivity was moderately correlated with pH between 7 

and 9 in the WV data set (r = 0.45, n = 1,900) and weakly 

correlated in the EPA data set (r = 0.14, n = 45). 

2. Correlation of effect 

and confounder 

− High pH was weakly correlated with Ephemeroptera in the 

WV data set (r = −0.19, n = 1,906) and in the EPA data set 

(r = −0.10, n = 45).

3. Contingency of high 

level of cause and 

confounder 

− In a contingency table (see Table B-15), high pH at high 

conductivities has the same frequency of Ephemeroptera as 

high conductivity without elevated levels of another variable 

in other contingency tables (approximately 50%).

5. Levels of confounder 

known to cause effects 

− EPA (1976) Water Quality Standards indicate that water 

with pH 6.5−9 is protective of freshwater fish and nearly all 

data were within that range. 

− Tests of the mayfly Isonychia bicolor found sublethal effects 

at pH 10 and lethality at pH 11 (Peters et al., 1985). 

6. Removal of 

confounder shows it is 

important 

− When samples with pH >8.5 are removed from the analysis, 

the correlation of conductivity with the number of 

Ephemeroptera was unchanged (r = −0.62, n = 2,151). 

However, this evidence is weak because relatively few sites 

were removed.

8. Potential confounding 

evaluated by 

frequency 

− The number of sites with a pH >8.5 is a very small 

proportion of the sample (<2.5%), so high pH is unlikely to 

influence the conductivity relationship. 

Weight of evidence − Reasonably confident: majority negative. No treatment for 

confounding. 
B-18 

Table B-17. Evidence and weights for confounding by low pH

Type Score Evidence

1. Correlation of cause 

and confounder

+ Conductivity was moderately correlated with pH <6 

(r = −0.48, n = 145). 

2. Correlation of effect 

and confounder

+ Low pH was moderately correlated with Ephemeroptera 

(r = 0.46, n = 145).

3. Contingency of high 

level of cause and 

confounder 

− Even at low pH some low conductivity streams support 

some Ephemeroptera but not at high conductivities (see 

Table B-15). 

5. Levels of confounder 

known to cause 

effects

+ Hatching success of the mayfly Habrophlebia vibrans was 

reduced a pH of 5.0 and lower (Rowe et al., 1988). 

− WVSCI was not reduced at pH 4−6 unless aluminum was 

elevated in the Clear Fork, WV, study (Gerritson et al., 

2010).

Weight of evidence + Potential confounding: majority positive. Correction for 

confounding was preformed.

B.4.7. Selenium

Selenium (Se) is a potential confounder because it is commonly associated with coal, and 

elevated levels have been reported in the region, but the evidence does not support confounding 

(see Table B-18). No correlations were found between selenium and Ephemeroptera or between 

selenium and conductivity in the West Virginia data set or in the EPA Region 3 data set. This 

result is unreliable because most of the selenium values were detection limits, and many of the 

detection limits were relatively high, even equaling the water quality criterion of 5.0 μg/L. In 

addition, there were too few high selenium concentrations in the West Virginia data to perform a 

contingency table analysis. For these reasons, correlational evidence of confounding was 

ambiguous. 

Evidence of the sufficiency of observed selenium levels to cause extirpation of stream 

macroinvertebrates is weakly negative. The National Ambient Water Quality Criterion (5 μg/L) 

is irrelevant because it is based on more sensitive vertebrates (U.S. EPA, 2004). Field and 

laboratory studies have found invertebrates to be relatively insensitive and unaffected at levels 

observed in WV streams (Lemly, 1993; Chapman et al., 2010). In outdoor artificial streams 

dosed with selenium, insects were less sensitive than fish, crustaceans, and oligochaetes; baetid 

mayfly nymphs (Baetis, Callibaetis), damselfly nymphs (Enallagma), and chironomid larvae 

were not statistically significantly reduced—even at 30 μg/L (Swift, 2002). Relatively few 

invertebrate species have been tested and highly sensitive species may be identified in the future 
B-19 

(DeBruyn and Chapman, 2007), but the available toxicological evidence does not indicate that 

selenium confounds the relationship between conductivity and invertebrate extirpation. 

The effects of removing high selenium on the conductivity relationship (Evidence 

Type 6) were addressed using the West Virginia data set. When data from streams with 

selenium concentrations above the water quality criterion (5 μg/L) were removed, the linear 

correlation coefficient for number of ephemeropteran genera and log conductivity is barely 

changed (r = −0.56, n = 339) relative to the full data set. When the same analysis was performed 

with the EPA data set, the correlation was actually greater than that for the full data set 

(r = −0.84, n = 32) (see Figure B-2), which is contrary to expectations for a confounder. This 

result indicates that the conductivity relationship is not confounded by toxic effects of selenium. 

Figure B-2. Spearman’s correlation coefficient and scatterplot between the 

number of ephemeropteran genera and conductivity for 32 sites with low 

selenium concentrations (<5 μg/L). Data from the EPA Region 3 data set. 

Conductivity ( S/cm)

Number of Ephemeropt 

50 100 200 500 1000 2000

0

2

4

6

8

-0.84
B-20 

Table B-18. Evidence and weights for confounding by selenium

Type Score Evidence

1. Correlation of cause 

and confounder 

0 Conductivity was not correlated with total selenium in the 

WV data set (r = 0.09, n = 501) and in the EPA data set 

(r = −0.07, n = 46), but the evidence is ambiguous due to 

poor selenium data. 

2. Correlation of effect 

and confounder 

0 Ephemeroptera were not correlated with total selenium in the 

WV data set (r = −0.04, n = 501) and in the EPA data set 

(r = −0.07, n = 46), but the evidence is ambiguous due to 

poor selenium data. 

5. Levels of confounder 

known to cause 

effects 

− In the most relevant toxicity test, effects on insects in an 

artificial stream over an exposure of >2 years, occurred at 

>0.030 mg/L (Swift, 2002). The 90th centiles for dissolved 

and total selenium were (0.003 and 0.005 mg/L). 

6. Removal of 

confounder shows it 

is important

− After removing high selenium sites (>5 μg/L), the 

correlation of Ephemeroptera with conductivity is barely 

changed (r = −0.56, n = 339) relative to the full data set, but 

the evidence is not strong because few sites have high 

selenium. The same analysis performed with the EPA data 

set also found no reduction in correlation (Figure B-2).

9. Specific effects of the 

confounder 

− − Selenium affects fish more than invertebrates and, in Swift 

(2002), crustaceans and oligochaetes more than insects, 

which is not the pattern seen in the streams.

− − Selenium causes characteristic deformities in fish, which 

have not been seen in the streams.

− − Selenium effects occur primarily in top predators, not 

herbivores and detritivores such as the Ephemeroptera.

− − Selenium at ambient concentrations causes effects in lentic 

systems but not lotic systems such as the streams sampled in 

WV. Deformities typical of selenium have been found in a 

reservoir in the region but not in streams (WVDEP, 2010).

Weight of evidence − Reasonably confident: majority negative. No treatment for 

confounding. 

Consideration of the specific effects of selenium (Evidence Type 9) suggests that it is not 

an important contributor to the impairment. First, the most sensitive organisms to aqueous 

selenium are fish and other oviparous vertebrates (Chapman et al., 2010) but, in this case, 

relatively selenium-insensitive insects are most affected. Second, selenium causes characteristic 

deformities in fish, which have not been reported in WV streams. Third, the effects of selenium 
B-21 

at low concentrations are seen in lentic ecosystems (lakes, reservoirs, ponds, wetlands)—not in 

streams like those from which the conductivity relationship and benchmark were derived 

(Chapman et al., 2010). Finally, because selenium is biomagnified, it primarily affects top 

predators not the herbivores and detritivores that are affected in this case. This specificity is 

supported by the fact that, in the region, the only reported effects of selenium are greatly elevated 

body burdens and associated deformities in a top predator fish (largemouth bass) in a lentic 

system (Upper Mud River Reservoir) (WVDEP, 2009, 2010). 

The weight of evidence does not support confounding by selenium, so no action was 

taken to adjust the dataset or analysis. However, because existing selenium data are poor, the 

occurrence of selenium in central Appalachian streams should be investigated further. 

B.4.8. Temperature

Elevated temperature may occur with elevated conductivity if the sources of salts are 

associated with reduced stream shading or if saline effluents are warmed. In an evaluation using 

contingency tables, Ephemeroptera were present at 99−100% of sites at low conductivity at both 

high and low temperature (see Table B-19). However, the differences between low and high 

temperature are not large and that in itself suggests that temperature would not be a confounder 

(a variant of Evidence Type 5). Correlations of temperature with conductivity are inconsistent 

(see Table B-20). More importantly, elevated temperature does not appear to be associated with 

the loss of Ephemeroptera and the relationship of conductivity to Ephemeroptera is not 

influenced by elevated temperatures. 

Table B-19. Number of sites with high and low temperatures and high and 

low conductivity with Ephemeroptera present in streams (pH >6)

Conductivity <200 μS/cm Conductivity >1,500 μS/cm

Temperature <17°C 468/474

(99%)

9/27

(33%)

Temperature >22°C 78/78

(100%)

24/43

(56%)
B-22 

Table B-20. Evidence and weights for confounding by temperature

Type Score Evidence

1. Correlation of cause 

and confounder 

0 Temperature was moderately correlated with conductivity 

year-round in the WV data set (r = 0.39, n = 2,216) but 

weakly correlated in the EPA data set (r = 0.17, n = 46).

2. Correlation of effect 

and confounder 

− Temperature was weakly correlated with Ephemeroptera 

year round in the WV data set (r = −0.22, n = 2,216) and 

uncorrelated in the EPA data set (r = −0.06, n = 46) 

3. Contingency of high 

level of cause and 

confounder 

− − − Ephemeroptera were present at 99−100% of sites at low 

conductivity at both high and low temperature. In the 

high conductivity categories, Ephemeroptera occurred in 

more sites with elevated temperatures (see Table B-19), 

which is contrary to expectations, if temperature were 

contributing to the impairment. 

5. Levels of confounder 

known to cause 

effects 

− Temperature limits are highly taxon specific but 

temperatures rarely exceeded the WV limits for reference 

sites (<30.6oC May−November and <22.8oC 

December−April) and, therefore, are not likely to cause 

extirpation.

6. Removal of 

confounder shows it 

is important

− − When high temperatures (>22°C) were deleted, the 

correlation of conductivity and Ephemeroptera was 

unchanged (r = −0.61, n = 1,787). 

7. Multivariate statistics − − Habitat quality, temperature and fecal coliform together 

had essentially no effect on the slope in multiple 

regression (see Table B-7).

Weight of evidence − − Moderately confident: none positive, some strongly 

negative. No treatment for confounding.

B.4.9. Lack of Headwaters

The loss of headwaters due to mining and valley fill eliminates a source of recolonization 

for downstream reaches. Hypothetically, this could result in extirpation of invertebrates if the 

sampled sites are sink habitats that must be recolonized by headwater source habitats. This is 

plausible in stream reaches immediately below valley fills. However, where there are other 

headwaters on tributaries above the sampling site, they serve as alternative sources for 

recolonization. No regional data are available to address this issue. However, examination of 

individual watersheds shows that many if not most of the sampled sites have at least one 

upstream intact headwater. Two examples are presented here.
B-23 

Ballard Fork, a tributary to the Mud River in West Virginia, is downstream of several 

valley fills but has unmined tributaries upstream such as Spring Branch (see Figures B-3, B-4, 

and B-5). Conductivity in Spring Branch measured <44−66 μS/cm. Conductivity in Ballard 

Fork was 464−2,300 μS/cm. In Spring Branch, the benthic invertebrate assemblages in the 

springs of 1999, 2000, and 2006 had 6−8 genera of Ephemeroptera representing 29−45% of the 

sample. In contrast, on the same dates Ballard Fork had 1−3 genera of Ephemeroptera 

representing only 2−4% of the sample and those may be immigrant specimens. Hence, even 

when a source of recolonization was available from Spring Branch, ephemeropteran genera were 

extirpated in Ballard Fork where conductivity was elevated. Also, habitat quality (total RBP 

habitat score), embeddedness, and pH are not related to biological quality, so they are not 

confounders in these streams (see Table B-21). 

In the Twentymile Creek watershed, the most upstream catchment above river kilometer 

(RKm) 44 is a small headwater that is 99% forested. Between RKm 44 and 13, the tributary 

catchments are heavily mined with valley fills. Below RKm 25 to the mouth, benthic 

invertebrate assemblages are depauperate. Two catchments that enter Twentymile Creek near 

RKm 17 and 14 are 100% forested with diverse benthic invertebrate assemblages. Nevertheless, 

at RKm 12, the benthic assemblage in Twentymile Creek remains depressed. 

Figure B-3. Topographical map of Spring Branch (blue triangle) and 

Ballard Fork (red triangle) sampling stations. 
B-24 

Figure B-4. Aerial imagery (June 13, 2007) with superimposed sampling 

locations of Spring Branch (turquoise square) and Ballard Fork (yellow 

square). Mined land drains into Ballard Fork (upper section of image) and 

forested land drains into Spring Branch (lower right quadrant). Two valley fills 

indicated by white arrows as examples. 

Figure B-5. Aerial imagery (April 10, 1996) with superimposed sampling 

locations of spring branch (turquoise square) and Ballard Fork (yellow 

square). Same area as Figure 3. The many upstream valley fills in Ballard Fork 

are easily seen.
B-25 

Table B-21. Comparison of low conductivity Spring Branch with high 

conductivity Ballard Fork 

Stream name Date Embed.

Total RBP 

score pH μS/cm # E % E

Total 

count

Spring Branch 5/9/2006 16 149 7.7 66 8 29.27 205

Spring Branch 4/18/2000 16 163 7.5 44 6 44.76 143

Spring Branch 4/20/1999 7.7 51 8 34.72 337

Ballard Fork 5/9/2006 14 149 8.1 1,195 3 2.96 203

Ballard Fork 4/18/2000 12 148 7.1 464 1 2.08 48

Ballard Fork 1/25/2000 7.9 1,050 0 0 52

Ballard Fork 7/26/1999 8.2 2,300 0 0 88

Ballard Fork 4/20/1999 8.1 1,201 3 4.12 291

Embed. = embeddedness score from RBP; RBP = Rapid Bioassessment Protocol Habitat Evaluation; # E = Number 

of ephemeropteran genera; % E = percent of ephemeropteran individuals in the sample; Total count = count of all 

individuals of all taxa.

Source: data from U.S. EPA mountaintop mining studies (Green et al., 2000; Pond et al., 2008).

Downstream from RKm 12, there are mixed mining and forest land uses. Near RKm 2 

there are legacy mining and urban land uses (see Table B-22). WVSCI scores, number of 

ephemeropteran families and number of ephemeropteran, plecopteran, and trichopteran (EPT)

families were low when conductivity was high regardless of the condition of catchments that 

provided sources of benthic macroinvertebrates including salt-sensitive genera. 

In these two examples, the evidence indicates that the reduction in ephemeropteran 

genera or EPT is not caused by a lack of sources of recolonization from headwaters. This is not 

to say that recolonization is never an issue. The sources of salts in this region are primarily 

chronic and localized, so lack of recolonization is unlikely to confound their effects. However, if 

an episodic agent caused the loss of aquatic organisms (e.g., drought or forest treatment with 

insecticides), sources of recolonization could be important. 
B-26 

Table B-22. Twentymile Creek sampling locations, conductivity, habitat 

score, number of EPT taxa, and WVSCI scores 

Year

River 

kilometer

Tributary 

catchment

land usea

Max reported 

conductivity 

(μS/cm)

RBP 

habitat 

score # E # EPT WVSCI

2003 44.6 Forested 44 148 4 15 90.72

2004 44.6 Forested 37 −

1998 25.1 Mined 805 155 3 7 67.62

2003 25.1 Mined 2,087 153 1 5 58.45

2003 11.9 Mixed Forest 

and Mine

1,702 157 2 7 64.74

2004 11.9 Mixed Forest 

and Mine

1,282 − − − −

2003 1.8 Mixed Forest, 

Mine, & Urban

987 − − − −

2004 1.8 Mixed Forest, 

Mine, & Urban

1,138 − − − −

2003 0.5 Mixed Forest, 

Mine, & Urban

845 146 2 6 66.73

2004 0.5 Mixed Forest, 

Mine, & Urban

836 − − − −

1998 0 Mixed Forest, 

Mine, & Urban

590 131 3 8 65.94

a

Land use refers to catchment land use of tributaries upstream from the sampled sites in Twentymile Creek.

# E families = Number of ephemeroptera families; #EPT = ephemeropteran, plecopteran, and trichopteran families; 

WVSCI = West Virginia Stream Condition Index.

Source: data from WABbase.

B.4.10. Catchment Area

Larger streams tend to have more moderate chemical properties than small streams 

because they receive waters from more sources, both natural and anthropogenic. Consequently, 

extreme values, in this case both low and high conductivity, tend to occur less frequently in large 

streams. One of the initial data filters for this analysis was to exclude streams larger than 

155 km2 (or 60 mi2). Small streams are numerically more abundant than large streams and the 

inclusion of large streams might introduce extraneous variance. This raises the issue whether 
B-27 

stream size is a potential confounder and whether the results from small streams might be 

extrapolated to larger streams. That is, do the same effects of conductivity occur in larger 

streams as were found in the detailed analysis of smaller streams? We examined these issues by 

analyzing the influence of stream size (as catchment area) on the effects of conductivity and on 

the occurrence of Ephemeroptera. 

We categorized streams by catchment area into three groups: small catchments less than 

6 mi2

 (15.5 km2), medium catchments of 6 to 60 mi2 (15.5 km2 to 155 km2), and large 

catchments greater than 60 mi2 (155 km2). In all three stream size categories, if conductivity was 

<200 μS/cm, 99% or more of all streams had Ephemeroptera, but if conductivity was above 

1,500 μS/cm, fewer streams had Ephemeroptera (see Table B-23). The number of 

Ephemeroptera taxa declines with increasing conductivity in all streams with measured 

catchment areas, independent of classification of catchment area (r = −0.59). Correlation of log 

conductivity with log catchment area is weak (see Table B-24). 

The weight of evidence for confounding by catchment area (see Table B-24) is uniformly 

negative, so we conclude that catchment area has little or no effect on invertebrate response to 

conductivity. 

Table B-23. Number and percent of streams with Ephemeroptera present: 

small, medium and large streams and low and high conductivity (pH >6) 

Conductivity <200 μS/cm Conductivity >1,500 μS/cm

Small streams 

(<15.5 km2)

302/303

(100%)

6/15 

40%)

Medium streams

(>15.5 km2 and

<155 km2)

118/119 

(99%)

10/14 

(71%)

Large streams 

(>155 km2)

37/37

(100%)

1/2

(50%)
B-28 

Table B-24. Evidence and weights for confounding by catchment area

Type Score Evidence

1. Correlation of cause 

and confounder

− Log catchment area was very weakly correlated with log 

conductivity (r = 0.18, n = 926).

2. Correlation of effect 

and confounder

− − Log catchment area was not correlated with the number of 

ephemeropteran genera (r = −0.009, n = 926).

3. Contingency of high 

level of cause and 

confounder 

− In a contingency table (see Table B-23), catchment area did 

not affect the probability of finding Ephemeroptera at low 

conductivity. Medium size somewhat increased the 

probability of occurrence at high conductivity. 

6. Removal of 

confounder 

− − When large streams were removed, the correlation of 

conductivity and number of ephemeropteran genera was 

barely changed (r = −0.60, n = 837).

Weight of evidence − − − Very confident: all negative, some strongly negative. No 

treatment for confounding.

B.4.11. Ponds

The effluents from most valley fills flow into settling ponds, and it has been suggested 

that those ponds are the actual cause of downstream community impairments. This issue was 

addressed using the EPA Region 3 data set because it identifies the presence of ponds. When 

data from only streams with ponds are used (i.e., the occurrence of ponds is removed as a 

variable—Evidence Type 4), the correlation coefficient for number of ephemeropteran genera 

and log conductivity is r = −0.84 (see Figure B-6). This result is somewhat higher than those for 

the uncensored EPA Region 3 data set (r = −0.73), which is contrary to the expectation if ponds 

were the cause. This result clearly shows that the conductivity relationship is not a result of 

co-occurrence with ponds. In addition, when ponds are removed and the streams are reclaimed, 

conductivity remains high and the effects continue. For example, Venter’s Branch and Jones 

Branch in Martin County, KY, were mined in the mid 1990s, and the ponds were removed. 

When the streams were sampled in 2009, conductivity was >2,000 μS/cm and no Ephemeroptera 

were found in either stream (Greg Pond, U.S. EPA, personal communication). 

The weight of evidence for confounding from ponds is uniformly negative, so we 

conclude that the presences of ponds have little or no effect on invertebrate response to 

conductivity. 
B-29 

Figure B-6. Spearman’s correlation coefficient and scatterplot between the 

number of ephemeropteran genera and conductivity for 20 sites below 

settling ponds for valley fills. Data from the EPA Region 3 data set. 

B.4.12. Dissolved Oxygen 

 Dissolved oxygen (DO) is not expected to be a confounder because these relatively 

shallow and high gradient streams are generally well oxygenated, but reviewer comments 

suggested that DO might be a confounder. 

 The 30-day mean water quality criteria for DO are 6.5 mg/L for coldwater and 5.5 mg/L 

for warm water (U.S. EPA, 1986). Ephemeropterans showed slightly reduced body condition 

and survivorship at concentrations below 7 mg/L DO in laboratory studies (Love et al., 2005; 

Pucket and Cook, 2004). A recent assessment of the Clear Fork watershed, WV, derived a 

plausible threshold for DO of 5 mg/L and a substantial threshold of 4 mg/L (Gerritson et al., 

2010). DO is rarely low (see Table B-25), but because the hour of sampling was not consistent 

in the data set, some uncertainty remains. Nevertheless, Ephemeroptera are present at 99% of 

sites with low conductivity even when DO is low for these streams and at high conductivity the 

presence of Ephemeroptera is unaffected by DO. Correlations of DO with conductivity were 

weak and with Ephemeroptera were very weak (see Table B-26). The available evidence shows 

no signs of confounding by low DO (see Tables B-25 and B-26). 

Conductivity ( S/cm)

Number of Ephemeropte 

100 200 500 1000 2000

0

2

4

6

8 -0.84
B-30 

Table B-25. Number of sites with high and low dissolved oxygen and high 

and low conductivity with Ephemeroptera present in streams (pH >6)

Conductivity <200 μS/cm Conductivity >1,500 μS/cm

DO >10.3 mg/L 244/246

(99%)

11/28

(39%)

DO <8.2 mg/L 172/174

(99%)

12/30

(40%)

Table B-26. Evidence and weight for confounding by dissolved oxygen (DO) 

Type Score Evidence

1. Correlation of 

cause and 

confounder

− DO was weakly correlated with conductivity (r = −0.11, 

n = 2,188). 

2. Correlation of 

effect and 

confounder 

− − DO was uncorrelated with the number of ephemeropteran 

genera (r = 0.09, n = 2,188). 

3. Contingency of 

high level of cause 

and confounder 

− − In a contingency table (see Table B-25), Ephemeroptera are 

present at 99% of sites with low conductivity (<200 μS/cm) 

even when DO is low (<8 mg/L) and at high conductivity the 

presence of Ephemeroptera is unaffected by DO.

5. Level of 

confounder known 

to cause effects

− The 30 day mean water quality criteria for DO of 6.5 mg/L 

for coldwater and 5.5 mg/L for warm water (U.S. EPA, 

1986) are below the lower 10th centile of WV sites 

(7.3 mg/L). 

− Reduced body condition and survivorship in 

ephemeropterans occur below 7 mg/L DO in laboratory 

studies (Love et al., 2005; Pucket and Cook, 2004).

− In the Clear Fork watershed, WV, a plausible threshold for 

DO of 5 mg/L and a substantial threshold of 4 mg/L were 

derived (Gerritsen et al., 2010).

6. Removal of

confounder 

− − When sites with moderate to low DO (<8.2 mg/L) were 

removed from the analysis, the correlation of conductivity 

with the number of Ephemeroptera is slightly increased 

(r = −0.63, n = 1,642). 

Weight of evidence − − − Very confident: all negative, some strongly negative. No 

treatment for confounding. 
B-31 

B.4.13. Metals

Iron (Fe), aluminum (Al), and manganese (Mn) are the metals most associated with acid 

mine drainage and commenters have suggested that they may cause the impairment associated 

with conductivity. However, for the following reasons, the circum-neutral to moderately 

alkaline streams are unlikely to experience toxicity from these metals (Luoma and Rainbow, 

2008). The most toxic form of iron (free Fe2+) does not occur in oxygenated waters above pH 4. 

Under those conditions, iron occurs as hydroxide particles or, if significant dissolved organic 

matter is present, as iron colloids. In these forms, iron is thought to serve primarily to reduce the 

toxicity of co-occurring metals by adsorption and co-precipitation. Toxic divalent aluminum 

precipitates similarly above pH 5 as hydroxide flocs or polymeric aluminum. Divalent 

manganese is converted to insoluble Mn4+ in mildly alkaline waters. The precipitates of these 

metals may adversely modify habitats and directly affect organisms. However, the valley fill 

effluents that are primarily responsible for the relationship between conductivity and extirpation 

of invertebrates are not equivalent to the acid drainage into neutralizing streams that results in 

heavy accumulations of precipitates. Finally, the toxicity of these divalent anions is mitigated by 

divalent calcium, which is the dominant cation in the saline mixtures. Hence, it is expected that, 

as conductivity increases, the toxicity of these metals will decrease per unit concentration.

Because of concern for combined effects of metals, multiple linear regression of 

conductivity, iron, aluminum and manganese was performed. The metals reduced the coefficient 

for conductivity by only 8.6% (see Table B-27). 

Iron and aluminum are clearly not confounders, based on contingency table analyses (see 

Tables B-28 and B-29), weak correlations (see Tables B-31and B-32) and other evidence (see 

Tables B-31 and B-32). 

However, manganese is more ambiguous since it is moderately correlated with both 

conductivity and ephemeropteran genera (see Tables B-30 and B-33). Manganese has been 

relatively poorly studied because it has seldom been found at toxic levels. Like other divalent 

cationic metals, Mn2+ is less toxic in hard (i.e., high Ca) waters and the high conductivity waters 

in this region are inherently hard. Based on a linear relationship of hardness to conductivity in 

the WV data, 300 μS/cm conductivity is equivalent to a hardness of approximately 200 mg/L 

CaCO3. The equivalent hardness-adjusted British Columbia Chronic Water Quality Guideline 

for manganese is 1.5 mg/L (BC, 2001). Dittman and Buchwalter (2010) provide the laboratory 

study with the most directly relevant taxa: aquatic insects from Appalachia. They quantified 

bioaccumulation and performed biomarker studies that found reduced levels of cysteine and 

glutathione at 0.10 and 0.50 mg/L but they saw no overt toxic effects. The most relevant 

conventional toxicity tests of aquatic invertebrates were 21 day reproduction tests of Daphnia 

magna which yielded IC25 values of 5.4 and 9.4 mg/L for hardness levels of 100 and 250 mg/L, 
B-32 

respectively (Reimer, 1999). A recent assessment of the Clear Fork watershed, WV, concluded 

that total manganese at 0.002−0.50 mg/L was a minor contributor to biotic impairment, because 

manganese was weakly correlated (r = −0.16) with the WVSCI index when corrected for 

stronger causes (Gerritsen et al., 2010). 

 In sum, iron and aluminum are clearly not confounders. Equivocal evidence suggests 

that manganese is potentially a weak confounder. 

Table B-27. An output table for two linear regression models. The first is the 

simple model predicting ephemeropteran genera from conductivity. The second 

is a multivariate model with the additional covariates iron, aluminum, and 

manganese. 

Parameter Estimate Standard error

Univariate model

Intercept 3.65 0.056

Conductivity slope −0.93 0.024

Multivariate model

Intercept 3.05 0.092

Conductivity slope −0.85 0.031

Iron −0.028 0.042

Aluminum −0.066 0.044

Manganese −0.30 0.033

Table B-28. Number of sites with high and low total iron and high and low 

conductivity with Ephemeroptera present in streams (pH >6) 

Conductivity <200 μS/cm Conductivity >1,500 μS/cm

Iron >0.5 mg/L 122/124

(98%)

8/28

(29%)

Iron <0.12 mg/L 139/140

(99%)

13/24

(54%)
B-33 

Table B-29. Number of sites with high and low total aluminum and high and 

low conductivity with Ephemeroptera present in streams (pH >6)

Conductivity <200 μS/cm Conductivity >1,500 μS/cm

Aluminum >0.23 mg/L 177/178

(99%)

5/22

(23%)

Aluminum <0.09 mg/L 103/103

(100%)

14/31

(45%)

Table B-30. Number of sites with high and low total manganese and high 

and low conductivity with Ephemeroptera present in streams (pH >6)

Conductivity <200 μS/cm Conductivity >1,500 μS/cm

Mn >0.1 mg/L 69/72

(96%)

13/50

(26%)

Mn <0.02 mg/L 158/158

(100%)

3/5

(60%)
B-34 

Table B-31. Evidence and weight for confounding by iron

Type Score Evidence

1. Correlation of cause 

and confounder

− − − Dissolved iron was uncorrelated with conductivity in the WV data 

(r = −0.08, n = 1,265) and weakly correlated in the EPA data (r = −0.17, 

n = 12). Both signs are incorrect for confounding.

− − Total iron was uncorrelated with conductivity in the WV data (r = 0.03, 

n = 1,439) and weakly correlated with the wrong sign in the EPA data 

(r = −0.14, n = 46).

2. Correlation of effect 

and confounder

− − Dissolved iron was uncorrelated with the number of ephemeropteran 

genera in the WV data (r = −0.08, n = 1,265) and in the EPA data 

(r = −0.04, n = 12).

− Total iron was weakly correlated with the number of ephemeropteran 

genera in the WV data (r = −0.14, n = 1,436) and in the EPA data with 

the wrong sign (r = 0.12, n = 46).

3. Contingency of high 

level of cause and 

confounder

− − In a contingency table (see Table B-28), Ephemeroptera are present at 

>98% of sites with low conductivity (<200 μS/cm) even when total iron 

is high (>0.1 mg/L). There are too few observations at extreme 

conductivities to derive a contingency table for dissolved iron.

5. Level of confounder 

known to cause effects

− The most relevant criteria are the British Columbia Chronic Water 

Quality Guidelines of 1 mg/L for total iron and 0.35 mg/L for dissolved 

iron (BC, 2008), which are above the 90th centiles in WV (0.93 and 

0.14 mg/L, respectively).

− The most relevant conventional toxicity tests were 120 h tests of the 

mayfly Leptophlebia marginata with an LC50 of 106.3 mg/L and reduced 

predator avoidance at 70 mg/L at pH 7 and low conductivity (7.0 μS/cm) 

(Gerhardt, 1994) which are well above the maximum dissolved iron in 

WV. 

− Two highly relevant field studies use data from the same source. Total 

iron caused no or minimal change at 0.21 mg/L and slight to moderate 

changes at 1.74 mg/L using benthic macroinvertebrate abundances in the 

WVDEP data set (Linton et al., 2007). Acid drainage sites were not 

excluded. Gerritson et al. (2010) found no effects of iron in the WVDEP 

data set.

6. Removal of confounder − − When sites with moderate to high dissolved iron (>0.06 mg/L) were 

removed from the analysis, conductivity is more negatively correlated 

with the number of Ephemeroptera (r = −0.72, n = 949), which is 

contrary to expectations for a confounder. This result is corroborated by 

the EPA data set (r = −0.77, n = 9).

− − When sites with moderate to high total iron (>0.5 mg/L) were removed 

from the analysis, conductivity is slightly more negatively correlated 

with the number of Ephemeroptera (r = −0.66, n = 1,076), which is 

contrary to expectations for a confounder. This result is corroborated by 

the EPA data set (r = −0.64, n = 34).

7. Multivariate statistics − − In the multiple linear regression, the slope for iron is less than a tenth 

that of conductivity (see Table B-27).

Weight of evidence − − − Very confident: all negative, some strongly negative. No treatment for 

confounding.
B-35 

Table B-32. Evidence and weight for confounding by aluminum

Type Score Evidence

1. Correlation of cause 

and confounder

− Dissolved aluminum was weakly correlated with conductivity in the WV 

data (r = 0.12, n = 1,293) and in the EPA data (r = 0.18, n = 12).

− Total aluminum was weakly correlated with conductivity and in the 

wrong direction in the WV data (r = −0.12, n = 1,442) and uncorrelated 

in the EPA data (r = 0.03, n = 46).

2. Correlation of effect 

and confounder

− Dissolved aluminum was weakly correlated with the number of 

ephemeropteran genera in the WV data (r = −0.16, n = 1,293) and 

uncorrelated in the EPA data (r = −0.02, n = 12).

− − Total aluminum was uncorrelated with the number of ephemeropteran 

genera in the WV data (r = 0.03, n = 1,442) and weakly correlated in the 

EPA data (r = 0.15, n = 46); both have the wrong sign

3. Contingency of high 

level of cause and 

confounder

− In a contingency table (see Table B-29), Ephemeroptera are present at 

>99% of sites with low conductivity (<200 μS/cm) even when total 

aluminum is high (>0.1 mg/L). However, there are fewer Ephemeroptera 

at high conductivity with high total aluminum so some confounding is 

possible but only at levels far above the benchmark. There are too few 

observations at extreme conductivities to derive a contingency table for 

dissolved aluminum.

5. Level of confounder 

known to cause effects

0 The most relevant criteria are the British Columbia Acute and Chronic 

Water Quality Criteria of 0.1 and 0.05 mg/L, respectively, for dissolved 

aluminum above pH 6.5 (BC, 2001). The chronic value equals the 

median value in WV; the acute value equals the 90th centile. However, 

the criteria are based on effects on sensitive fish.

0 The most relevant conventional toxicity tests were 48 h tests of 

Ceriodaphnia dubia in neutralized acid mine drainage which gave a 

mean LC50 for total aluminum of 2.9 mg/L (Soucek et al., 2001). This 

value is well above the 90th centile (0.1 mg/L) but its relevance to stream 

insects is unclear.

− In the most relevant field study, the plausible and substantial effects 

thresholds were >0.2 mg/L and >0.4 mg/L dissolved aluminum in WV 

Ecoregion 69 (Gerritson et al., 2010). These are above the 90th centile in 

WV (0.1 mg/L).

6. Removal of confounder − When sites with moderate to high dissolved aluminum (>0.06 mg/L) 

were removed from the analysis, conductivity is slightly more negatively 

correlated with the number of Ephemeroptera (r = −0.68, n = 973) which 

is contrary to expectations for a confounder. 

− When sites with moderate to high total aluminum (>0.23 mg/L) were 

removed from the analysis, conductivity is slightly more negatively 

correlated with the number of Ephemeroptera (r = −0.66, n = 1,063) 

which is contrary to expectations for a confounder. This result is 

corroborated by the EPA data (r = −0.79, n = 15).

7. Multivariate statistics − − In the multiple regression, the slope for aluminum is less than a tenth that 

of conductivity (see Table B-27).

Weight of evidence − − Moderately confident: none positive, some strongly negative. No 

treatment for confounding.
B-36 

Table B-33. Evidence and weight for confounding by manganese

Type Score Evidence

1. Correlation of cause 

and confounder

0 Dissolved Mn was moderately correlated with conductivity in the WV 

data (r = 0.64, n = 20) but weakly correlated in the EPA data (r = 0.22, 

n = 12).

+ Total Mn was moderately correlated with conductivity in the WV data 

(r = 0.35, n = 1,436) and in the EPA data (r = 0.55, n = 46).

2. Correlation of effect 

and confounder

+ Dissolved Mn was moderately correlated with the number of 

ephemeropteran genera in the WV data (r = −0.73, n = 20) and in the 

EPA data (r = −0.37, n = 12).

+ Total Mn was moderately correlated with the number of ephemeropteran 

genera in the WV data (r = −0.41, n = 1,436) and in the EPA data 

(r = −0.49, n = 46).

3. Contingency of high 

level of cause and 

confounder

− In a contingency table (see Table B-30), Ephemeroptera are present at 

>96% of sites with low conductivity (<200 μS/cm) even when total Mn 

is high (>0.1 mg/L). However, there are fewer Ephemeroptera at high 

conductivity with high total Mn suggesting that some confounding is 

possible at levels far above the benchmark. There are too few dissolved 

Mn observations at extreme conductivities to derive a contingency table.

5. Level of confounder is 

known to cause effects

− The most relevant criterion is the British Columbia Chronic Water 

Quality Guideline for Mn of 1.5 mg/L (BC, 2001). This is above the 

maximum dissolved Mn.

0 The most relevant conventional toxicity tests were 21 day reproduction 

tests of Daphnia magna which yielded IC25 values of 5.4 and 9.4 mg/L 

for hardness levels of 100 and 250 mg/L, respectively (Reimer, 1999). 

This is far above the 90th centile and maximum dissolved Mn (0.29 and 

1.06 mg/L), but its relevance to stream insects is uncertain.

− In the most relevant field study, total Mn in the Clear Fork watershed, 

WV, was weakly correlated (r = −0.16) with the WVSCI index when 

corrected for stronger causes and there were no substantial effects 

(Gerriston et al., 2010).

6. Removal of confounder − When sites with moderate to high dissolved Mn (>0.05 mg/L) were 

removed from the analysis, the correlation of conductivity with the 

number of Ephemeroptera is little changed (r = −0.58, n = 16). There 

were too few high dissolved Mn sites in the EPA data to corroborate. 

− − When sites with moderate to high total Mn (>0.1 mg/L) were removed 

from the analysis, the correlation of conductivity with the number of 

Ephemeroptera is slightly increased which is contrary to expectations for 

a confounder (r = −0.63, n = 1,067). This result is corroborated by the 

EPA data (r = −0.74, n = 34, compared to r = −0.72).

7. Multivariate statistics − In the multiple regression, the slope for Mn is only 35% that of 

conductivity and the conductivity slope is reduced by only 8.6% relative 

to the univariate slope (see Table B-27).

Weight of evidence − Reasonably confident. Majority negative. No treatment for 

confounding.
B-37 

B.5. SUMMARY OF ACTIONS TAKEN TO ADDRESS POTENTIAL CONFOUNDING

Low pH is an apparent confounder, but sites with pH <6 were removed from the data set 

when calculating the benchmark value. Other potential confounders were eliminated from 

consideration with some confidence. We do not argue that these variables do not cause 

impairment at some locations in the region. Neither do we argue that they have no influence at 

all on salt-impaired sites. Rather, given the inevitable variability in sites to which the benchmark 

would be applied and the relatively strong relationship of conductivity and loss of sensitive 

genera, the evaluated confounders do not substantially affect the model that is used to develop 

and apply the conductivity benchmark. 

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BC (British Columbia) (2008) Ambient water quality guidelines for iron. Ministry of Environment, Province of 

British Columbia, Canada. Available online at 

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Chapman, PM; Adams, WJ; Brooks, ML; et al. (2010) Ecological assessment of selenium in the aquatic 

environment. Boca Raton: SETAC/CRC Press.

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loss, and Mn(II) thiol scavenging. Environ Sci Technol 44(23):9182−9188.

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(Insecta) in relation to freshwater acidification. Hydrobiologia 284:157−168.

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statement. Region 3, U.S. Environmental Protection Agency, Philadelphia, Pennsylvania. Available online at 

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Hill, AB. (1965) The environment and disease: Association or causation. Proc Royal Soc Med 58(5):295−300.

Lemly AD. (1993) Guidelines for evaluating selenium data from aquatic monitoring and assessment. Environ 

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B-38 

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oxygen consumption, and chloride cell ultrastructure. Can. J. Fish. Aquat. Sci. 42:1088−1095.

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biological conditions using family- and genus-level macroinvertebrate bioassessment tools. J N Am Benthol Soc 

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1649−1652.

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Swift, MC. (2002) Stream ecosystem response to, and recovery from, experimental exposure to selenium. J Aqua 

Ecosyst Stress Rec 9(3):159−184.

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EPA/440/9-76/023. Available online at 

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Washington, DC; EPA-822-D-04-001. Available online at 

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online at 

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C-1 

APPENDIX C 

DATA SOURCES AND METHODS OF LAND USE/LAND COVER ANALYSIS USED 

TO DEVELOP EVIDENCE OF SOURCES OF HIGH CONDUCTIVITY WATER

ABSTRACT

Potential sources of elevated conductivity were characterized for subwatersheds within 

the Coal, Upper Kanawha, Gauley, and New Rivers. From a large monitoring data set developed 

by the West Virginia Department of Environmental Protection (WVDEP), 190 <20-km2

watersheds were found for which there was total maximum daily load (TMDL) and land cover 

information in southwestern West Virginia and macroinvertebrate samples identified to the 

genus level with at least one chemistry sample. Small <20-km2 subwatersheds were selected to 

reduce confounding from multiple sources. Scatter plots of conductivity, SO4

2−, and Cl−

, and 

alkalinity levels were generated for nine land cover classifications: open water, agriculture, 

forest, residential, barren, total mining, valley fill, abandoned mine lands, and mining excluding 

valley fill and abandoned mine lands. Conductivity was negatively correlated with the 

percentage of forest area and most strongly negatively associated with catchments with the 

greatest percentages of valley fills, and the HCO3

−

 and SO4

2− concentrations were greater than 

Cl− concentration. Areas with more residences and farm buildings also had elevated conductivity 

but rarely exceeded 1,000 μS/cm, and Cl− often exceeded SO4

2− and HCO3−

 concentrations. 

These findings confirm sources of high conductivity waters that are used as evidence in the 

causal assessment that salts are a cause of impairment of aquatic macroinvertebrates in streams 

in West Virginia (see Section A.2.2.4). 

C.1. INTRODUCTION

Analysis of land use and cover was used to determine if there was a source of high 

conductivity, to assess if land use was associated with conductivity levels, and to confirm the 

relative proportion of ions associated with land use and cover types reported in the literature for 

different sources. This information was used as evidence of preceding causation in the causal 

assessment described in Appendix A of this report. 

C.2. METHODS

C.2.1. General Approach

Small catchments were delineated, and the proportions of land covers were regressed 

against water quality parameters. Watershed size was limited to <20 km2 to minimize the variety 

of land use and cover types within a single watershed, thereby providing a clearer signal for each 

potential source of salinity. However, because the region has a long history of mining and land 
C-2 

cover information may not include legacy mining, persistent effects of mining are potentially 

present even when there is no current record of past or present mining activity in the publically 

available land cover databases. Also, residences are present in areas where mining occurs. 

Therefore, there are potential influences from multiple sources in most of the 190 watersheds, 

but these are minimized by using small catchments. 

The final data set consisted of 190 small watersheds for which macroinvertebrate samples 

were identified to genus, water chemistry was available from at least one sampling effort, 

subwatershed area was <20 km2, and detailed land cover information was available. The 

190 sites are located in the Coal, Upper Kanawha, Gauley, and New Rivers of Ecoregion 69D. 

Water quality parameters are from the WVDEP’s Watershed Assessment Branch Data Base 

(WABbase). For each watershed, scatter plots for several parameters were generated for 

nine land cover classifications: open water, agriculture, forest, urban/residential, barren, total 

mining, valley fill, and mining excluding valley fill and abandoned mine lands. 

C.2.2. General Geographical Information Systems (GIS) Data Descriptions

Numerous geographic information system (GIS) data sets are available for the State of 

West Virginia. One such repository for data, the WVGISTC (2011), maintains publicly available 

shapefiles. WVDEP (2011a) also maintains a publicly available repository of statewide GIS data 

sets (http://gis.dep.wv.gov/). All relevant GIS metadata are available for the data housed at each 

repository site. All GIS coverages used in this EPA study are in universal transverse mercator 

(UTM) 1983 Zone 17, and the units are in meters. Table C-1 describes some of the publicly 

available GIS shapefiles that were originally used to develop base files for WVDEP’s TMDL 

program. These base files were the beginning point for determining the 190 stations selected for 

the analyses described in Section C.2.3 and were used to estimate land uses (see Table C-2). The 

area in valley fill was from a 2003 coverage developed by WVDEP. 
C-3 

Table C-1. Publicly available GIS data used to generate land cover estimates

Data information Data description Source

General sources of land use/land cover information

West Virginia GIS 

Technical Center

General West Virginia Universities 

GIS data repository location

http://wvgis.wvu.edu/data/data.php

(WVGISTC, 2011)

WVDEP GIS data sets General WVDEP’s GIS data 

repository location

http://gis.dep.wv.gov/ (WVDEP, 2011a)

Base Land use/land cover

GAP GAP land use http://wvgis.wvu.edu/data/dataset.php?ID

=62 (WVGISTC, 2002)

NLCD 2001 NLCD land use http://wvgis.wvu.edu/data/dataset.php?ID

=269 (WVGISTC, 2001)

Other files

Watershed Boundary 

Data sets

USGS 8-digit Hydrologic Unit 

Code boundaries

http://wvgis.wvu.edu/data/dataset.php?ID

=123 (WVGISTC, 2004)

NHD Streams National Hydrography Data set 

Streams

http://wvgis.wvu.edu/data/dataset.php?ID

=235(WVGISTC, 2010)

Abandoned Mine Lines 

(AML-Highwalls) and 

Polygons (AML Areas)

West Virginia abandoned mine 

lands coverages. Highwall mine 

coverage and AML area

http://wvgis.wvu.edu/data/dataset.php?ID

=150 (WVGISTC, 1996)

DMR Mining NPDES 

Permits and Outlets

WVDEP Office of Mining and 

Reclamation NPDES permit and 

outlet coverages

http://gis.dep.wv.gov/data/omr.html 

(WVDEP, 2011b)

Mining Related Fills, 

Southern West Virginia

WVDEP valley fills coverage from 

2003

http://gis.dep.wv.gov/data/omr.html

(WVDEP, 2011c)

Mining Permit 

Boundaries

WVDEP Mining permit boundaries http://gis.dep.wv.gov/data/omr.html

(WVDEP, 2011d)

http://wvgis.wvu.edu/data/dataset.php?ID

=149 (WVGISTC, 2011)

Roads Paved 2000 TIGER/Line GIS and 

WV_Roads shapefiles

http://www.census.gov/geo/www/tiger/tig

er2k/tgr2000.html (U.S. Census Bureau, 

2000a)

http://wvgis.wvu.edu/data/data.php

(WVGISTC, 2011)

Roads Unpaved 2000 TIGER/Line GIS shapefile 

and digitized from aerial 

photographs and topographic maps

http://www.census.gov/geo/www/tiger/tig

er2k/tgr2000.html (U.S. Census Bureau, 

2000b)

http://wvgis.wvu.edu/data/data.php

(WVGISTC, 2011)

GAP = Gap Analysis Program; GIS = geographic information system; NHD = National Hydrography Data Set; 

NLCD = National Land Cover Database; NPDES = National Pollutant Discharge Elimination System; DMR = 

Division of Mining Reclamation; USGS = U.S. Geological Survey; WVDEP = West Virginia Department of 

Environmental Protection. 
C-4 

Table C-2. Detailed WV TMDL land use category derivation and land use 

derivation used in Appendix A. Base land use categories are highlighted in 

grey. 

Detailed WV 

TMDL land use 

category Data source

Base land use from 

which new source area 

was subtracted

Land use categories 

used in scatter plots 

in Appendix A

Water Water—base LU coverage N/A Water

Wetland Wetland—base LU coverage N/A Water

Forest Forest—consolidated all 

forested types from base LU 

coverage

N/A Forest

Grassland Grassland—base LU 

coverage

N/A Agriculture

Cropland Cropland—consolidated all 

cropland types from base LU 

coverage

N/A Agriculture

Urban pervious Urban—consolidated 

urbanized types from base 

LU coverage

N/A Urban/residential

Urban impervious Urban—consolidated 

urbanized types from base 

LU coverage

N/A Urban/residential

Barren Barren—base LU coverage N/A Barren

Pasture Source tracking New area subtracted from 

Grassland

Agriculture

Paved roads Roads shapefiles New area subtracted from 

Urban Impervious

Urban/residential

Unpaved roads Roads shapefiles New area subtracted from 

Urban Pervious

Urban/residential

Revoked mining 

permits

AML information New area subtracted from 

Barren

AML

Abandoned mine 

land

AML shapefile New area subtracted from 

Barren

AML

Quarry Mining shapefile New area subtracted from 

Barren

Mining

Highwall AML shapefile New area subtracted from 

Barren

Mining

Oil and gas Oil and Gas shapefile New area subtracted from 

Barren

Mining
C-5 

Table C-2. Detailed WV TMDL land use category derivation and land use 

derivation used in Appendix A (continued)

Detailed WV 

TMDL land use 

category Data source

Base land use from 

which new source area 

was subtracted

Land use categories 

used in scatter plots 

in Appendix A

Surface Mine Water 

Quality permits

Mining shapefile New area subtracted from 

Barren

Mining

Surface Mine 

Technology permits

Mining shapefile New area subtracted from 

Barren

Mining

Comingled mine 

deep ground gravity 

discharge

Mining shapefile New area subtracted from 

Barren 

Mining

Comingled mine 

deep ground pump 

discharge

Mining shapefile New area subtracted from 

Barren 

Mining

Undeveloped surface 

mine WQ permits

Mining shapefile New area subtracted from 

Forest

Mining

Undeveloped surface 

mine technology 

permits

Mining shapefile New area subtracted from 

Forest

Mining

Undeveloped 

comingled mine 

gravity discharge

Mining shapefile New area subtracted from 

Forest

Mining

Undeveloped 

comingled mine 

pump discharge

Mining shapefile New area subtracted from 

Forest

Mining

Burned Forest Forestry Dept. information New area subtracted from 

Forest

Barren

Harvested Forest Forestry Dept. information New area subtracted from 

Forest

Barren

Skid Roads Forestry Dept. information New area subtracted from 

Forest

Barren

TMDL land use 

considers Valley 

Filla area as part of 

the Surface Mine 

Water Quality and 

Technology Permit 

information

WVDEP valley fills 

coverage from 2003

New area subtracted from 

Mining, Barren, and 

Forest, as appropriate

Valley fill

a

Valley fill land use was not part of the base TMDL land use and was specifically incorporated into the detailed land 

use analysis for this EPA report. See Table 1 for the source file.

AML = Abandoned Mine Line, LU = Land use, TMDL = total maximum daily load, WQ = water quality, 

WV = West Virginia. 
C-6 

C.2.3. Selection of Catchments

Catchments with available data that met the needs of the analysis involved a six-step 

selection process that resulted in 190 catchments. The steps were preformed in the following 

sequence:

• Select all WVDEP WAB stations located within Ecoregion 69D. This generated 

2,151 stations.

• Select stations where a macroinvertebrate sample was collected and identified to the 

genus level. During this selection process, stations had to have both a WVSCI and a 

GLIMPSS score. At least one chemistry sample was required to be associated with the 

macroinvertebrate sample from the same station location. This narrowed the available 

stations to 825. 

• Select stations with detailed TMDL-associated land use located within the Coal, Upper 

Kanawha, Gauley, and New River watersheds. This narrowed the selection to 

382 stations. 

• Eliminate stations if the detailed land use was not created during the TMDL process. 

This eliminated 38 stations for a total of 344 stations. 

• Eliminate stations located on undelineated tributary streams contained within a larger 

mainstem subwatershed. This eliminated an additional 33 stations for a total of 

311 stations.

• Select stations with a total watershed drainage area <20 km2

 (4,942.08 acres). The total 

number of remaining stations in TMDL watersheds within Ecoregion 69D after this last 

reduction was 190 (see Figure C-1), and the data from these stations were assembled 

from 1997 to 2007, with the majority of samples collected from 2001 to 2006. 

C.2.4. Land Use Analysis 

To create the land use for the 190 stations, the original TMDL land uses from the Coal, 

Upper Kanawha, Gauley, and New Rivers were used as the starting point. These land uses were 

originally created by consolidating the available base land use (Gap Analysis Program [GAP] 

2000 or National Land Cover Data [NLCD] 2001) into more general categories and then adding 

more detailed source land use categories (e.g., mining, oil and gas, roads) from detailed source 

information. To add these new land use categories, GIS shapefiles were used to locate sources 

and assign areas. These areas were then subtracted from the category they most likely would be 

attributed to in the original base land use. For example, a disturbed mine site would likely be 

classified as barren in GAP, so any area assigned as mining would be subtracted from barren to 

keep the total land use area in the watershed the same. Table C-2 contains the WVDEP TMDL 
C-7 

Figure C-1. Sampling locations used to develop evidence of sources of high 

conductivity inputs. The 190 stations (black dots) at the terminus of each 

>20-km2 catchment are shown within the larger 8-digit HUCs in southwestern 

West Virginia.

land use categories, the data source from which the extent of the area and its location were 

determined, and the base land use from which any newly created land use categories were 

subtracted. In brief, nine land use categories were generated: total percentage area in mining 

(% Total Mining) which is the sum of % Abandoned Mine, % MTM-Valley Fill and % Mining; 

percentage in mountaintop mining valley fill (% MTM-Valley Fill); percentage of abandoned 

mine lands (% Abandoned Mine); percentage of mining (% Mining) excluding % MTM-Valley 

Fill and % Abandoned Mine; percentage barren land use (% Barren); percentage of residences, 

buildings, and roads (% Urban/residential); percentage in agriculture and pasture 

(% Agricultural); percentage in forest (% Forest), and percentage in open water (% Water).

Because the WVDEP TMDL land use manipulation process has undergone revisions and 

enhancements since the initiation of the TMDL program, WVDEP TMDL land use data sets for 
C-8 

the Upper Kanawha, Coal, Gauley, and New Rivers were manipulated to have equivalent land 

use when necessary and resulted in the consolidated land use for the 190 sampling stations. The 

land use representation used for more recently developed TMDLs is more detailed than that for 

TMDLs completed in earlier efforts. Therefore, consolidation of the detailed TMDL land use to 

seven basic land use categories was necessary. The valley fill GIS coverage was then 

incorporated into the TMDL land use by subtracting the valley fill acreage from Shank (2004) 

from the mining land use category. If more area was present in the valley fill coverage than was 

present in the TMDL mining area for each TMDL subwatershed, the remainder was subtracted 

from barren and then forest, respectively. The eight land use categories calculated for each of 

the 190 WAB sampling stations used seven categories consolidated from the TMDL land use 

(see Table C-2) and then included the addition of the valley fill area. The % Total Mining 

category is simply the sum of the % Mining, % MTM-Valley Fill, and % Abandoned Mine land 

categories. The % Mining land use represents all other types of mining activities except for 

abandoned mines and valley fill areas. 

C.3. RESULTS

C.3.1. Characterization of Catchments and Ionic Matrix 

The 190 small catchments used in the analysis are located near the borders of the 8-digit 

hydrologic unit codes (HUCS) where elevations are greater and headwaters of these small 

perennial streams are located (see Figure C-1). The ionic composition of these waters is not 

uniform, but bicarbonate and sulfate are usually greater than chloride (see Table C-3) (see also 

Table 1 and Table A-16, Pond et al., 2008). Because we were interested in all ions as well as the 

mixture, we did not exclude high Cl−

 sites. Only one site, New West Hollow, had a conductivity 

measurement >300 μS/cm and higher chloride (629 mg/L) than sulfate (89 mg/L). That 

watershed had the greatest area in residences, 16.4% urban, and a conductivity of 2,767 μS/cm. 

The potential presence of methane coal brine production was not ruled out. 

C.3.2. Correlations with In-stream Biological and Water Quality Parameters

Pairs of land use and water quality parameters are listed in Table C-4 with at least one 

Pearson’s correlation coefficient with an r > |0.50|, except for a few with spurious points or 

composed of only two points. 

The two land use types that are most strongly and positively correlated with conductivity 

are percentage of mining and percentage of valley fill. Percentage of forest is negatively 

correlated with ion concentrations. Percentage of residential land use is not well correlated, and 

in this region, is somewhat confounded by mining land uses. Among the ions that are more 

strongly correlated, are total calcium and magnesium, also captured together as hardness, 
C-9 

Table C-3. Summary statistics of water quality parameters in the 

190 catchments

Parameter Units Min 25th

centile Median 75thcentile Max Mean Valid N

Conductivity μS/cm 6 254 474 851 3,964 445 1,671

Fecal counts/ml 1 4 42 330 60,000 47.5 1,181

Alkalinity mg/L 0.02 14.45 46 99.15 710 36.73 1,348

Hardness mg/L 11.26 37.95 84.24 235.57 862.6 85.11 48

Sulfate mg/L 5 84.55 192 358 2915 168.32 1,350

Chloride mg/L 1 2.0 3.8 12.3 629 5.5 45

TSS mg/L 0.3 3 5 8 1217 5.97 1,348

Al, total mg/L 0.02 0.06 0.15 0.59 23.6 0.21 1,342

Al, dissolved mg/L 0.02 0.02 0.05 0.07 23.5 0.063 1,335

Ca, total mg/L 1.93 7.63 22.5 49.43 184 17.716 50

Cu, total mg/L 0.001 0.003 0.004 0.005 0.014 0.004 24

Cu, dissolved mg/L 0.001 0.003 0.003 0.004 1.91 0.004 40

Fe, total mg/L 0.02 0.09 0.21 0.51 32.8 0.24 1,341

Fe, dissolved mg/L 0.02 0.02 0.03 0.07 13.1 0.045 1,329

Mg, total mg/L 1.28 4.3 8.0 26.3 97.9 9.97 49

Mn, total mg/L 0.003 0.025 0.10 0.40 27.3 0.116 1,340

Se, total mg/L 0.001 0.005 0.005 0.005 1.26 0.005 436

Se, dissolved mg/L 0.001 0.001 0.003 0.006 1.26 0.003 23

Zn, total mg/L 0.005 0.009 0.01 0.021 0.18 0.014 25

Zn, dissolved mg/L 0.005 0.005 0.005 0.01 0.726 0.009 40

Flow ft3/s 0.004 0.41 1.45 4.545 63.01 1.25 839

Temperature °C 0.05 8.705 12.65 17.77 30.72 13.139 1,672

pH standard 

units

3.03 7.105 7.6 7.97 12.99 7.355 1,671

DO mg/L 1.22 9.26 10.43 11.75 11.81 11.23 1,666

TSS = Total suspended solids, Mean is geometric mean except for temperature, pH, and DO =dissolved oxygen.
C-10 

Table C-4. Correlation coefficients between pairs of land use and water 

quality parameters in the land use data set

Water quality parameter % Valley fill % Total mining % Mining % Forest

Conductivity 0.65 0.52 0.39 −0.54

Alkalinity 0.51 0.49 0.37 −0.51

Hardness 0.69 0.63 0.55 −0.63

Sulfate 0.64 0.52 0.39 −0.53

Calcium Total 0.67 0.61 0.52 −0.64

Magnesium Total 0.66 0.65 0.58 −0.59

Parameters yielding only r < |0.50 are not shown. 

bicarbonate measured as alkalinity, and sulfate. Noticeably chloride is not strongly correlated, 

owing to fewer measurements of chloride, but also due to the low concentrations except at one 

site. Chloride was 629 mg/L chloride at the site with the greatest residential and mining land 

uses. 

Individual scatter plots and associated correlation coefficients for conductivity can be 

found in Appendix A, Section 2.2.2.4 but are reproduced here for the convenience of the reader 

(see Figure C-2). At relatively low urban land use, the range of conductivity is highly variable. 

In contrast, there is a clear pattern of increasing conductivity as percentage of area in valley fill 

increases and of decreasing conductivity with increasing forest cover. When area in valley fill is 

subtracted from the total nonacid mining area, the correlation decreases by 25% (see 

Figure C-2d). The scatter plots illustrate that there are clear sources of increased conductivity, 

but that percentage area in valley fill has the strongest correlation with conductivity (r = 0.65), 

and percentage mining without a valley fill has a moderate correlation (r = 0.39). 

Assuming that the lower conductivity values represent current best practices, we modeled 

the lower 25th quantile of the percent valley fill scatter plot (see Figure C-3). 

From the 10th quantile regression, the intercept for 300 μS/cm is 4% valley fill and the 

intercept for 500 μS/cm is 8% valley fill. Using logistic regression at 300 and 500 μS/cm, the 

probability of impairment, based on a WVSCI score <68, is around 0.59 and 0.72, respectively. 

At 300 μS/cm, 5% of genera are extirpated, and at 500 μS/cm, 17% of genera are extirpated (see 

Figure 9). Because these estimates do not take into account the volume of the fill, construction 

practices, distance from the fill, or dilution from tributaries, the estimate of conductivity 

associated with percent valley fill is useful as a general characterization but will vary for specific 

cases. 
C-11 

Figure C-2. Geometric mean conductivity associated with different land uses 

in 190 watersheds in Ecoregion 69D and Spearman’s correlation coefficient. 

Conductivity increases with increasing % MTM-Valley Fill and % Total Mining, 

and decreases with increasing % Forest, but there is less clear or no pattern with 

other land use. From left to right, they are (a) % Total Mining (percentage 

of deep, surface, quarry mining, MTM-Valley Fill, and abandoned mine land), 

(b) % MTM-Valley Fill (from mountaintop mining overburden), 

(c) % Abandoned Mine, (d) % Mining (inclusive of all types of mining except 

MTM-Valley Fill and Abandoned Mine), (e) % Barren, (f) % Urban/residential, 

(g) % Agricultural, (h) % Forest, and (i) % Water. Fitted LOWESS line with span 

set at 2/3. 

%Total Mining

Conductivity ( S/cm)

0 2.2 9 30.6 99

r  0.53

32 100 316 1000 3162

% MTM-Valley Fill

Conductivity ( S/cm)

0 2.2 9 30.6

r  0.65

32 100 316 1000 3162

% Abandoned Mine

Conductivity ( S/cm)

0 2.2 9 30.6

r  0.02

32 100 316 1000 3162

% Mining

Conductivity ( S/cm)

0 2.2 9 30.6 99

r  0.4

32 100 316 1000 3162

% Barren

Conductivity ( S/cm)

0 2.2 9 30.6

r  0

32 100 316 1000 3162

% Urban/residential

Conductivity ( S/cm)

0 0.6 1.5 3 5.3 9 14.8

r  0.13

32 100 316 1000 3162

% Agricultural

Conductivity ( S/cm)

0 0.6 1.5 3 5.3 9 14.8

r  -0.03

32 100 316 1000 3162

% Forest

Conductivity ( S/cm)

0 20 40 60 80 100

r  -0.55

32 100 316 1000 3162

% Water

Conductivity ( S/cm)

0 0.3 0.6 1 1.5 2.2

r  0.27

32 100 316 1000 3162

a. b. c. 

d. e. f. 

g. h. i. 
C-12 

Figure C-3. Quantile regression of percentage of area in valley fill and 

conductivity in 190 small watersheds in Ecoregion 69D. Assuming the lowest 

conductivity points represent some of the best fill construction practices, the 10th

and 25th quantile regression lines are shown. The intercepts for 500 μS/cm 

(horizontal dashed line) are approximately 4% and 8% valley fill and for 300 

μS/cm are 1.5% and 3.9% valley fill for the 10th and 25th quantiles, respectively. 

The mean model based on samples minus those with zero percent valley fill 

shows that the relationship is unaffected by the removal of sites without valley 

fills. 

% Valley Fill

Conductivity ( 

S/cm)

0 2 931

32 100 316 1000 3162

Alldata - Mean Model

NonZero - Mean Model

10th Quantile Model

25th Quantile Model
C-13 

C.4. CONCLUSIONS

Of the land uses in the small watersheds analyzed, only mining associated with valley 

fills are significant sources of the salts that are measured as conductivity. Disturbances 

associated with agriculture and human habitation may also contribute, but the densities of 

agricultural and urban land cover are relatively low, and a clear pattern of increasing 

conductivity and increasing land use is not evident. Furthermore, natural background is 

exceedingly low. For Ecoregion 69, the 25th centile from a probability-based sample from the 

WABbase data set was 72 μS/cm, N = 617 (see Section 5.5). 

Although conductivity typically increases with increasing land use (Herlihy et al., 1998), 

conductivity is highly variable at relatively low urban land use. This may be caused by unknown 

mine drainage, deep mine break-outs, road applications, poor infrastructure condition (e.g., 

leaking sewers or combined sewers), gas drilling, or other practices. In contrast, there is a clear 

pattern of increasing conductivity as the percentage of valley fill area increases and decreasing 

conductivity with increasing percentage of forest cover area. This is evidence of at least one 

strong source of high conductivity in the region (see Appendix A for causal assessment).

REFERENCES

Herlihy, AT; Stoddard, JL; Johnson, CB. (1998) The relationship between stream chemistry and watershed land 

cover data in the mid-Atlantic region, U.S. Water, Air Soil Pollut 105(1−2):377-386. 

Pond, GJ; Passmore, ME; Borsuk, FA; et al. (2008) Downstream effects of mountaintop coal mining: comparing 

biological conditions using family- and genus-level macroinvertebrate bioassessment tools. J N Am Benthol Soc 

27(3): 717−737.

Shank, M. (2004). Development of a mining fill inventory from multi-date elevation data. Presented at the 

Advanced Integration of Geospatial Technologies in Mining and Reclamation Conference, December 7-9, 2004, 

Atlanta, GA.

U.S. Census Bureau. (2000a) 2000 TIGER/Line GIS and WV roads shapefiles. Geography division. Available 

online at http://www.census.gov/geo/www/tiger/tiger2k/tgr2000.html.

U.S. Census Bureau. (2000b). 2000 TIGER/Line GIS shapefiles aerial photographs and topographic maps. 

Geography division. Available online at http://www.census.gov/geo/www/tiger/tiger2k/tgr2000.html.

WVDEP (West Virginia Department of Environmental Protection). (2011a) General WVDEP’s data repository 

locations. WVDEP GIS data sets. State of West Virginia, Charleston, WV. Available online at 

http://gis.dep.wv.gov/. 

WVDEP (West Virginia Department of Environmental Protection). (2011b). Mining permits, point locations. 

Division of Mining and Reclamation Data. State of West Virginia, Charleston, WV. Available online at 

http://gis.dep.wv.gov/data/omr.html. 

WVDEP (West Virginia Department of Environmental Protection). (2011c) Mining related fills, Southern West 

Virginia as of September 2003. Division of Mining and Reclamation Data. State of West Virginia, Charleston, WV. 

Available online at http://gis.dep.wv.gov/data/omr.html. 
C-14 

WVDEP (West Virginia Department of Environmental Protection). (2011d) Mining permit boundaries. Division of 

Mining and Reclamation Data. State of West Virginia, Charleston, WV. Available online at 

http://gis.dep.wv.gov/data/omr.html. 

WVGISTC (West Virginia GIS Technical Center). (1996) Mining – abandoned mine lands. University of West 

Virginia, Morgantown, WV. Available online at http://wvgis.wvu.edu/data/dataset.php?ID=150. 

WVGISTC (West Virginia GIS Technical Center). (2001) Land cover (NLCD 2001). WV NLCD (National land 

cover database). University of West Virginia, Morgantown, WV. Available online at 

http://wvgis.wvu.edu/data/dataset.php?ID=269. 

WVGISTC (West Virginia GIS Technical Center). (2002) Land cover (GAP). WV GAP analysis program. 

University of West Virginia, Morgantown, WV. Available online at http://wvgis.wvu.edu/data/dataset.php?ID=62. 

WVGISTC (West Virginia GIS Technical Center). (2004) Watershed boundary data sets (8, 10, and 12 digits). 

University of West Virginia, Morgantown, WV. Available online at http://wvgis.wvu.edu/data/dataset.php?ID=123. 

WVGISTC (West Virginia GIS Technical Center). (2010) Streams – national hydrography data sets (24k). 

University of West Virginia, Morgantown, WV. Available online at http://wvgis.wvu.edu/data/dataset.php?ID=235

WVGISTC (West Virginia GIS Technical Center). (2011) General West Virginia Universities GIS data repository 

locations. University of West Virginia, Morgantown, WV. Available online at http://wvgis.wvu.edu/data/data.php. 
D-1 

APPENDIX D

EXTIRPATION CONCENTRATION VALUES FOR 

GENERA IN THE WEST VIRGINIA DATA SET

ABSTRACT

The purpose of Appendix D is to provide the reader with a list of the extirpation 

concentration (XC95) values used to develop the species sensitivity distribution and the 

hazardous concentration (HC05). Genera are ordered alphabetically (see Table D-1). The 

numbers of occurrences in the data set and at West Virginia Department of Environmental 

Protection (WVDEP) reference sites are noted in the right-hand columns. 

Not all 95th centiles correspond to extirpation, and some imprecisely estimate the 

extirpation threshold. The following rules were applied to the XC95 values using the fitted curve 

and the confidence bounds from the plots in Appendix E. If the generalized additive model 

(GAM) mean curve at maximum conductivity is approximately equal to 0 (defined as less than 

1% of the maximum modeled probability), then the XC95 value is listed without qualification. If 

the GAM mean curve at maximum conductivity is >0 but the lower confidence limit is 

approximating to 0 (<1% of the maximum mean modeled probability), then the XC95 value is 

listed as approximate (~). If the GAM lower confidence limit is >0, then the XC95 value is listed 

as greater than (>) the 95th centile. All model fits and scatter of points were also visually 

inspected for anomalies, and if the model poorly fit the data, the uncertainty level was increased 

to either (~) or (>). 

The assignation of (~) and (>) does not affect the HC05. They are provided to alert users 

to the uncertainty of some XC95 values for other uses such as comparison with toxicity test 

results or with results from other geographic regions. 
D-2 

Table D-1. Extirpation concentration and sample size from West Virginia 

data set. XC95 values reported without a preceding symbol indicate evidence of 

extirpation within the tested range. XC95 values preceded by a (~) or (>) indicate 

extirpation with greater uncertainty or extirpation at a level above the reported 

value. 

Order Family Genus XC95 N

N from 

reference 

locations

Diptera Chironomidae Ablabesmyia >11,646 162 5

Ephemeroptera Baetidae Acentrella 1,337 752 31

Ephemeroptera Baetidae Acerpenna ~649 27 3

Plecoptera Perlidae Acroneuria >2,630 512 60

Trichoptera Glossosomatidae Agapetus 365 27 6

Plecoptera Capniidae Allocapnia 542 33 15

Plecoptera Chloroperlidae Alloperla 246 101 15

Ephemeroptera Ameletidae Ameletus 591 219 30

Plecoptera Nemouridae Amphinemura 812 589 42

Diptera Culicidae Anopheles >2,768 26 2

Diptera Tipulidae Antocha >6,468 565 18

Isopoda Asellidae Asellus 960 33 2

Diptera Athericidae Atherix >11,646 157 3

Diptera Ceratopogonidae Atrichopogon >2,257 43 3

Ephemeroptera Ephemerellidae Attenella ~698 34 1

Ephemeroptera Baetidae Baetis >1,395 1527 71

Diptera Ceratopogonidae Bezzia 380 62 2

Odonata Aeshnidae Boyeria >7,340 175 5

Diptera Tipulidae Brachypremna 408 27 2

Diptera Chironomidae Brillia >2,005 95 6

Isopoda Asellidae Caecidotea >4,713 141 1

Ephemeroptera Caenidae Caenis >3,923 552 8

Decapoda Cambaridae Cambarus >1,274 472 44

Diptera Chironomidae Cardiocladius >2,257 191 2
D-3 

Order Family Genus XC95 N

N from 

reference 

locations

Ephemeroptera Baetidae Centroptilum 1,092 90 6

Trichoptera Hydropsychidae Ceratopsyche >6,468 909 27

Diptera Chironomidae Chaetocladius >5,057 184 4

Diptera Empididae Chelifera >3,341 152 9

Trichoptera Hydropsychidae Cheumatopsyche >9,180 1665 57

Trichoptera Philopotamidae Chimarra >3,972 516 11

Diptera Chironomidae Chironomus >11,646 105 1

Diptera Tabanidae Chrysops >11,646 76 1

Ephemeroptera Heptageniidae Cinygmula 230 90 15

Diptera Chironomidae Cladotanytarsus >11,646 104 5

Diptera Empididae Clinocera >4,713 61 6

Diptera Chironomidae Conchapelopia 546 135 7

Odonata Cordulegastridae Cordulegaster >1,436 43 3

Megaloptera Corydalidae Corydalus >11,227 317 1

Diptera Chironomidae Corynoneura >2,006 149 4

Amphipoda Crangonyctidae Crangonyx >2,169 105 7

Diptera Chironomidae Cricotopus >11,227 617 21

Diptera Chironomidae Cryptochironomus >3,489 287 3

Diptera Ceratopogonidae Dasyhelea >3,341 66 3

Diptera Chironomidae Demicryptochironomus 322 81 6

Diptera Chironomidae Diamesa >4,713 486 14

Diptera Tipulidae Dicranota >7,010 355 43

Diptera Chironomidae Dicrotendipes >11,646 197 1

Ephemeroptera Baetidae Diphetor 632 148 17

Trichoptera Hydropsychidae Diplectrona >2,527 618 59

Plecoptera Perlodidae Diploperla 315 106 2

Diptera Dixidae Dixa >704 70 16

Trichoptera Philopotamidae Dolophilodes >863 356 46
D-4 

Order Family Genus XC95 N

N from 

reference 

locations

Ephemeroptera Ephemerellidae Drunella 297 176 18

Coleoptera Elmidae Dubiraphia >7,370 144 3

Plecoptera Perlidae Eccoptura 497 65 6

Coleoptera Psephenidae Ectopria >1,380 324 32

Ephemeroptera Heptageniidae Epeorus 307 414 53

Ephemeroptera Ephemeridae Ephemera 696 148 20

Ephemeroptera Ephemerellidae Ephemerella 299 405 38

Diptera Chironomidae Eukiefferiella >1,876 519 28

Ephemeroptera Ephemerellidae Eurylophella 490 189 19

Amphipoda Gammaridae Gammarus >4,713 216 10

Trichoptera Glossosomatidae Glossosoma >1,652 157 7

Trichoptera Goeridae Goera ~738 25 4

Plecoptera Chloroperlidae Haploperla 418 253 27

Diptera Chironomidae Heleniella >1,697 62 7

Coleoptera Dryopidae Helichus ~11,646 333 18

Diptera Empididae Hemerodromia >9,790 615 8

Ephemeroptera Heptageniidae Heptagenia 326 68 3

Diptera Tipulidae Hexatoma >9,790 846 65

Coleoptera Dytiscidae Hydroporus 822 32 1

Trichoptera Hydropsychidae Hydropsyche >7,010 999 21

Trichoptera Hydroptilidae Hydroptila >11,227 281 4

Ephemeroptera Isonychiidae Isonychia 1,180 740 16

Plecoptera Perlodidae Isoperla 460 520 39

Diptera Chironomidae Krenopelopia >2,320 62 2

Diptera Chironomidae Krenosmittia ~1,115 27 3

Odonata Gomphidae Lanthus >2,087 66 7

Diptera Chironomidae Larsia ~2,630 96 3

Trichoptera Lepidostomatidae Lepidostoma ~121 91 12
D-5 

Order Family Genus XC95 N

N from 

reference 

locations

Ephemeroptera Leptophlebiidae Leptophlebia 251 87 8

Ephemeroptera Heptageniidae Leucrocuta 424 225 29

Plecoptera Leuctridae Leuctra >2,087 1199 84

Diptera Tipulidae Limnophila ~1,503 54 10

Diptera Chironomidae Limnophyes >5,120 88 1

Diptera Tipulidae Limonia >5,057 62 1

Isopoda Asellidae Lirceus ~1,323 72 6

Ephemeroptera Heptageniidae Maccaffertium ~1,035 214 13

Coleoptera Elmidae Macronychus >1,890 44 4

Plecoptera Perlodidae Malirekus >904 27 6

Coleoptera Elmidae Microcylloepus >3,341 94 2

Diptera Chironomidae Micropsectra >6,468 227 24

Diptera Chironomidae Microtendipes >3,489 532 33

Hemiptera Veliidae Microvelia >2,523 46 3

Diptera Tipulidae Molophilus ~2,169 28 2

Diptera Chironomidae Natarsia >1,842 54 1

Trichoptera Uenoidae Neophylax 316 166 35

Megaloptera Corydalidae Nigronia >9,790 746 36

Diptera Chironomidae Nilotanypus >2,266 112 3

Ephemeroptera Heptageniidae Nixe 319 77 3

Trichoptera Hydroptilidae Ochrotrichia >2,791 32 1

Coleoptera Elmidae Optioservus >9,790 1471 63

Decapoda Cambaridae Orconectes >3,162 205 2

Diptera Chironomidae Orthocladius >3,427 277 9

Coleoptera Elmidae Oulimnius >2,791 227 27

Diptera Chironomidae Pagastia >1,800 46 2

Plecoptera Capniidae Paracapnia 334 37 13

Diptera Chironomidae Parachaetocladius >1,147 169 27
D-6 

Order Family Genus XC95 N

N from 

reference 

locations

Plecoptera Perlidae Paragnetina 2,087 40 3

Diptera Chironomidae Parakiefferiella >1,757 75 2

Ephemeroptera Leptophlebiidae Paraleptophlebia 463 449 46

Diptera Chironomidae Parametriocnemus >4,713 1501 72

Diptera Chironomidae Paraphaenocladius >6,468 71 2

Diptera Chironomidae Paratanytarsus >3,489 110 2

Plecoptera Peltoperlidae Peltoperla >694 126 12

Plecoptera Perlidae Perlesta 3,314 315 8

Diptera Chironomidae Phaenopsectra ~2,332 89 1

Basommatophora Physidae Physella >9,790 145 1

Veneroida Pisidiidae Pisidium >1,795 34 2

Ephemeroptera Baetidae Plauditus 996 289 12

Trichoptera Polycentropodidae Polycentropus >4,713 380 41

Diptera Chironomidae Polypedilum >4,884 1648 70

Diptera Chironomidae Potthastia >1,886 62 1

Diptera Chironomidae Procladius >11,227 28 1

Ephemeroptera Baetidae Procloeon 702 78 3

Coleoptera Elmidae Promoresia ~672 79 5

Diptera Simuliidae Prosimulium ~531 106 20

Coleoptera Psephenidae Psephenus >9,119 886 35

Diptera Chironomidae Pseudochironomus >11,646 31 2

Diptera Tipulidae Pseudolimnophila >1,357 135 11

Trichoptera Psychomyiidae Psychomyia >1,131 39 3

Plecoptera Pteronarcyidae Pteronarcys ~634 113 25

Trichoptera Limnephilidae Pycnopsyche 295 44 10

Plecoptera Perlodidae Remenus 121 35 3

Hemiptera Veliidae Rhagovelia >2,030 52 3

Diptera Chironomidae Rheocricotopus >3,489 559 11
D-7 

Order Family Genus XC95 N

N from 

reference 

locations

Diptera Chironomidae Rheopelopia ~1,457 126 4

Diptera Chironomidae Rheotanytarsus >3,489 949 28

Trichoptera Rhyacophilidae Rhyacophila >1,890 415 57

Ephemeroptera Ephemerellidae Serratella 535 49 2

Megaloptera Sialidae Sialis >11,227 264 3

Diptera Simuliidae Simulium >6,468 1095 26

Diptera Chironomidae Stempellina 644 35 8

Diptera Chironomidae Stempellinella >927 309 26

Ephemeroptera Heptageniidae Stenacron ~782 258 15

Coleoptera Elmidae Stenelmis >9,790 1232 26

Ephemeroptera Heptageniidae Stenonema 745 922 57

Odonata Gomphidae Stylogomphus >6,468 118 1

Diptera Chironomidae Sublettea >2,421 182 2

Plecoptera Chloroperlidae Sweltsa ~750 315 42

Diptera Tabanidae Tabanus >9,790 61 1

Plecoptera Taeniopterygidae Taeniopteryx 260 30 12

Plecoptera Peltoperlidae Tallaperla 478 89 16

Diptera Chironomidae Tanytarsus >9,180 1232 64

Diptera Chironomidae Thienemanniella >9,790 395 9

Diptera Chironomidae Thienemannimyia >6,468 1345 56

Diptera Tipulidae Tipula >1,979 621 36

Diptera Chironomidae Tvetenia >2,613 760 40

Plecoptera Chloroperlidae Utaperla 255 47 2

Trichoptera Philopotamidae Wormaldia >1,553 79 8

Plecoptera Perlodidae Yugus 655 75 12

Diptera Chironomidae Zavrelia 413 81 6

Diptera Chironomidae Zavrelimyia >2,768 244 11
E-1

APPENDIX E 

GRAPHS OF OBSERVATION PROBABILITIES 

FOR GENERA IN THE WEST VIRGINIA DATA SET

ABSTRACT

The purpose of Appendix E is to help the reader visualize the changes in the occurrence 

of each genus in the West Virginia data set as conductivity increases. Each figure depicts a 

general additive model (GAM) of the relationship between capture probabilities of a genus and 

conductivity. Genera are ordered from the lowest to the highest extirpation concentration (XC95)

value. Open circles are the probabilities of observing the genus within a range of conductivities. 

Circles at zero probability indicate no individuals were found in any sample with those 

conductivities. The GAM line (solid line) fitted to the probabilities is for visualization and 

dashed lines are 90% confidence bounds. The vertical dotted red line marks the XC95 as listed in 

Appendix D. Note that, because of differences in sensitivity, different genera respond differently 

within the observed range of salinity. For example, Lepidostoma declines, Diploperla has an 

optimum, and Cheumatopsyche increases. The fitted lines and confidence bounds were used to 

assign qualifiers to the XC95 values in Appendix D.
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●

●

●

●

●●●

●

●

●

●

●

●●

●

●

●

●

●

●

●●●●

●

●

●

●

●●●●●●●●●●●●●●●●●●●●●●●●●

15 81 423 2221 11646

0.00 0.05 0.10 0.15 0.20 0.25

Conductivity (μS/cm)

Capture Probability

Bezzia

●●●●●●●●●

●●

●●●

●

●

●

●

●

●

●

●

●

●

●

●●

●●●

●

●

●

●●●●●●●●●●●●●●●●●●●●●●●●●●●

15 81 423 2221 11646

0.00 0.02 0.04 0.06 0.08

Conductivity (μS/cm)

Capture Probability

Brachypremna

●●●

●

●

●

●●

●

●

●

●

●

●

●

●

15 8

0.00 0.05 0.10 0.15 0.20 0.25

Con

Capture Probability

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●●

●

●●

●●

●

●

●

●●

●

●●

●

●●

●

●

●

●●●●●●●●●●●●●●●●●●●●●●●

15 81 423 2221 11646

0.0 0.2 0.4 0.6 0.8 1.0

Conductivity (μS/cm)

Capture Probability

Haploperla

●

●●●●●

●

●

●

●●●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●●

●●●●

●●●●●●●●●●●●●●●●●●●

15 81 423 2221 11646

0.0 0.1 0.2 0.3 0.4 0.5

Conductivity (μS/cm)

Capture Probability

Leucrocuta ●

●●

●

●

●

●

●

●

●

●

●

●

●

●

●

15 8

0.0 0.2 0.4 0.6 0.8 1.0

Con

Capture Probability

E-5
●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●●●

●

●●

●

●

●

●

●

●

●

●

●

●

●

●●●

●

●

●

●●●●●●●●●●●●●●●●●

15 81 423 2221 11646

0.0 0.2 0.4 0.6

Conductivity (μS/cm)

Capture Probability

Paraleptophlebia

●

●

●

●

●

●

●

●

●

●●

●

●

●

●

●

●

●

●

●

●

●●

●

●

●

●

●

●

●

●

●●●

●

●

●●●●●

●

●●●●●●●●●●●●●●●●●●

15 81 423 2221 11646

0.0 0.1 0.2 0.3 0.4 0.5

Conductivity (μS/cm)

Capture Probability

Tallaperla

●●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

15 8

0.0 0.1 0.2 0.3 0.4

Con

Capture Probability

●●●

●

●

●

●

●

●

●

●

●

●

●

●

●●

●

●

●

●●

●

●

●

●●

●

●

●

●

●

●●

●

●

●●●●●●●●●●●●●●●●●●●●●●●●

15 81 423 2221 11646

0.00 0.05 0.10 0.15 0.20 0.25

Conductivity (μS/cm)

Capture Probability

Eccoptura

●●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●●

●

●

●

●

●

●

●

●●

●

●

●

●

●

●

●●●●

●

●●●●●●●●●●●●●●●●●●

15 81 423 2221 11646

0.0 0.1 0.2 0.3 0.4

Conductivity (μS/cm)

Capture Probability

Prosimulium

●●●●

●

●

●

●

●

●

●

●

●

●

●

●

15 8

0.00 0.05 0.10 0.15 0.20

Con

Capture Probability

E-6
●●●●●●●

●

●

●

●

●

●

●

●

●

●

●●

●

●

●

●●

●

●●

●

●●●●●●●●●●●●●

●

●●●●●●●●●●●●●●●●●●

15 81 423 2221 11646

0.00 0.02 0.04 0.06 0.08 0.10 0.12

Conductivity (μS/cm)

Capture Probability

Allocapnia

●●

●

●

●●●●●●

●

●

●

●

●

●

●

●

●

●

●

●●

●

●

●●●

●

●

●

●

●

●

●

●

●●●

●●

●●●●●●●●●●●●●●●●●●●

15 81 423 2221 11646

0.00 0.05 0.10 0.15 0.20 0.25

Conductivity (μS/cm)

Capture Probability

Conchapelopia

●

●●

●

●

●

●

●

●

●

●

●

●

●

●●

15 8

0.0 0.1 0.2 0.3 0.4 0.5

Con

Capture Probability

●●●

●

●●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●●●

●

●

●

●

●

●

●

●●●●●●●●●●●●●●●●●●●●●

15 81 423 2221 11646

0.00 0.05 0.10 0.15 0.20 0.25

Conductivity (μS/cm)

Capture Probability

Diphetor

●●

●

●

●

●

●

●

●

●

●

●●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●●

●●●

●

●

●

●

●

●●

●●●

●

●●

●

●●●●●●●●●●●●●

15 81 423 2221 11646

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7

Conductivity (μS/cm)

Capture Probability

Pteronarcys

●●●●●

●

●

●

●

●

●●

●

●

●●

15 8

0.00 0.05 0.10 0.15 0.20 0.25

Con

Capture Probability

E-7
●●●●

●

●●●●●

●

●

●

●

●●

●

●

●

●

●

●

●●

●

●●

●●●

●

●

●

●

●

●●●●●●●●●●●●●●●●●●●●●●●●●

15 81 423 2221 11646

0.00 0.04 0.08 0.12

Conductivity (μS/cm)

Capture Probability

Acerpenna

●●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●●

●

●

●

●

●●●

●

●

●●

●

●

●

●

●●

●

●

●

●●●●●●●●●●●●●●●●●●●●●●

15 81 423 2221 11646

0.0 0.1 0.2 0.3 0.4

Conductivity (μS/cm)

Capture Probability

Yugus

●●

●

●

●

●

●

●

●

●

●

●

●

●

●●

15 8

0.0 0.1 0.2 0.3 0.4 0.5

Con

Capture Probability

●

●

●

●

●

●

●

●

●

●

●●

●

●

●

●

●

●

●

●●

●●●●●

●●●●●●

●

●●●

●

●

●

●

●

●

●

●●●●●●●●●●●●●●●●●

15 81 423 2221 11646

0.0 0.2 0.4 0.6 0.8 1.0

Conductivity (μS/cm)

Capture Probability

Peltoperla

●●●●

●

●

●

●

●

●●

●

●

●

●

●●

●

●

●

●

●

●

●

●●

●

●

●

●

●

●

●

●

●

●

●●●

●

●●●

●

●●●●●●●●●●●●●●●●

15 81 423 2221 11646

0.0 0.1 0.2 0.3 0.4 0.5

Conductivity (μS/cm)

Capture Probability

Ephemera

●●●●

●

●●●

●

●

●

●

●

●

●

●

15 8

0.00 0.05 0.10 0.15

Con

Capture Probability

E-8
●●●●●●

●

●

●

●

●

●

●●

●

●●

●

●

●

●●

●

●●

●

●

●

●

●

●

●●

●

●

●

●

●●●

●

●●●●●●●●●●●●●●●●●●●

15 81 423 2221 11646

0.00 0.05 0.10 0.15

Conductivity (μS/cm)

Capture Probability

Procloeon

●

●

●

●

●

●

●

●

●

●●

●

●

●

●

●●

●

●

●

●●

●

●●

●

●

●

●

●

●●●●●●

●

●

●

●

●●

●

●●●●●●●●●●●●●●●●●

15 81 423 2221 11646

0.0 0.1 0.2 0.3 0.4 0.5

Conductivity (μS/cm)

Capture Probability

Dixa

●●●●●●●●●

●

●

●

●

●●

●

15 8

0.00 0.01 0.02 0.03 0.04 0.05

Con

Capture Probability

●

●

●

●

●

●

●

●●

●

●

●

●

●

●

●

●

●

●

●●●●●

●●●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●●

●●●●●●●●●●●●●●●●●

15 81 423 2221 11646

0.0 0.2 0.4 0.6 0.8 1.0

Conductivity (μS/cm)

Capture Probability

Stenonema ●

●●

●

●

●

●

●

●

●●

●

●

●

●

●

●

●

●

●

●●●

●●

●

●

●

●

●

●●●

●●

●

●

●

●

●●

●

●

●

●●●●●●●●●●●●●●●●

15 81 423 2221 11646

0.0 0.2 0.4 0.6 0.8 1.0

Conductivity (μS/cm)

Capture Probability

Sweltsa

●●●●

●

●

●

●

●

●

●

●

●

●

●

●

15 8

0.0 0.2 0.4 0.6

Con

Capture Probability

E-9
●

●●

●

●

●

●

●

●

●

●

●

●

●

●

●

●●●

●

●●●

●

●

●

●

●

●●

●

●

●●

●●

●●●

●

●

●

●

●

●●●●●

●

●●●●●●●●●●

15 81 423 2221 11646

0.0 0.2 0.4 0.6 0.8 1.0

Conductivity (μS/cm)

Capture Probability

Amphinemura

●●●●●●●●●●●●●●●●●●●●●

●

●

●

●

●

●

●

●

●●●●●●

●

●

●●●●●●●●●●●●●●●●●●●●●●●

15 81 423 2221 11646

0.00 0.01 0.02 0.03 0.04 0.05 0.06

Conductivity (μS/cm)

Capture Probability

Hydroporus

●●

●

●

●

●

●●

●

●

●

●

●

●

●

●

15 8

0.0 0.2 0.4 0.6 0.8 1.0

Con

Capture Probability

D

●●●

●

●●●

●

●●●●

●

●

●

●●

●

●

●

●

●

●●●

●

●

●

●

●●

●

●

●

●●

●

●

●●●●●●●●●●●●●●●●●●●●●●

15 81 423 2221 11646

0.00 0.05 0.10 0.15 0.20

Conductivity (μS/cm)

Capture Probability

Malirekus

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●●

●●

●

●

●

●

●

●

●●

●

●

●

●

●●

●

●

●

●

●

●

●

●

●

●●●●

●

●●●●●●●●●●●●●

15 81 423 2221 11646

0.0 0.2 0.4 0.6 0.8 1.0

Conductivity (μS/cm)

Capture Probability

Stempellinella

●●●●●●●●●

●●

●●

●

●●

15 8

0.00 0.02 0.04 0.06

Con

Capture Probability

E-10
●●●●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●●

●

●

●

●

●

●

●

●

●

●

●

●●

●

●

●

●●

●●●

●

●●●●●●●●●●●●●●

15 81 423 2221 11646

0.0 0.1 0.2 0.3

Conductivity (μS/cm)

Capture Probability

Plauditus

●●●●●●

●

●

●

●

●

●

●

●

●

●

●

●

●

●●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●●●●●●●●●●●●●●●●●●

15 81 423 2221 11646

0.00 0.05 0.10 0.15 0.20 0.25

Conductivity (μS/cm)

Capture Probability

Maccaffertium

●●●●

●

●●●

●

●

●

●

●

●

●

●

15 8

0.00 0.04 0.08 0.12

Con

Capture Probability

●●●

●

●●

●

●

●

●

●

●

●●

●

●

●

●

●

●●●

●

●●

●

●

●

●

●●●●

●

●●●●●

●●●

●●●●●●●●●●●●●●●●●●

15 81 423 2221 11646

0.0 0.1 0.2 0.3 0.4

Conductivity (μS/cm)

Capture Probability

Krenosmittia

●●●●●

●

●

●●

●

●

●

●

●●

●●

●

●

●

●

●●

●

●

●

●

●

●

●●

●

●

●

●●●

●

●

●

●

●●●●●●●●●●●●●●●●●●●

15 81 423 2221 11646

0.00 0.05 0.10 0.15 0.20 0.25

Conductivity (μS/cm)

Capture Probability

Psychomyia

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●●

15 8

0.0 0.1 0.2 0.3 0.4 0.5 0.6

Con

Capture Probability

Par

E-11
●

●

●●●

●

●

●

●

●●●

●●

●

●

●

●

●

●

●

●●

●●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●●●●●●●●●●●●●●●

15 81 423 2221 11646

0.0 0.1 0.2 0.3 0.4 0.5

Conductivity (μS/cm)

Capture Probability

Isonychia

●

●

●

●

●

●

●

●

●

●●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●●●

●

●

●

●

●●

●

●

●

●

●

●

●

●

●●●●●●●●●●●●●●●●

15 81 423 2221 11646

0.0 0.1 0.2 0.3 0.4 0.5 0.6

Conductivity (μS/cm)

Capture Probability

Cambarus

●●●●●●●●●●

●

●●●

●

●

15 8

0.00 0.02 0.04 0.06

Con

Capture Probability

●●

●

●

●

●

●

●

●

●

●

●●

●

●

●

●

●●

●

●

●

●●

●

●

●

●

●

●

●

●

●

●

●●

●

●

●

●

●

●●

●

●

●●●

●

●●●●●●●●●●●

15 81 423 2221 11646

0.0 0.2 0.4 0.6 0.8 1.0

Conductivity (μS/cm)

Capture Probability

Acentrella ●

●●●●●

●

●

●

●●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●●

●●●

●

●

●●

●

●

●

●

●

●

●

●

●

●●●●●●●●●●●●●●●●

15 81 423 2221 11646

0.0 0.1 0.2 0.3 0.4 0.5

Conductivity (μS/cm)

Capture Probability

Pseudolimnophila

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

15 8

0.0 0.1 0.2 0.3 0.4 0.5

Con

Capture Probability

E-12
●

●●

●

●

●

●

●

●

●●

●

●

●

●

●

●

●●●

●

●

●

●

●●

●●●

●

●

●●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●●

●

●●●●●●●●●●

15 81 423 2221 11646

0.0 0.2 0.4 0.6 0.8 1.0

Conductivity (μS/cm)

Capture Probability

Baetis

●●●●

●

●●●●

●

●

●

●

●

●

●

●

●

●

●

●

●●

●

●

●

●

●

●●

●

●

●

●

●

●

●

●●

●

●

●

●●●●●●●●●●●●●●●●●●

15 81 423 2221 11646

0.00 0.02 0.04 0.06 0.08

Conductivity (μS/cm)

Capture Probability

Cordulegaster

●

●

●●

●

●

●

●

●

●

●

●

●

●

●

●

15 8

0.0 0.1 0.2 0.3 0.4 0.5

Con

Capture Probability

●

●●●

●

●

●

●

●

●

●

●

●

●

●●

●●

●

●

●●●

●●●

●

●

●

●●●●●

●

●●

●●

●●●●●●●

●

●●●●●●●●●●●●●

15 81 423 2221 11646

0.0 0.1 0.2 0.3 0.4 0.5

Conductivity (μS/cm)

Capture Probability

Limnophila

●

●●●●●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●●

●

●●

●

●

●

●

●

●

●

●

●●

●●●

●

●

●

●●●●●●●●●●●●●●●●

15 81 423 2221 11646

0.0 0.1 0.2 0.3 0.4 0.5

Conductivity (μS/cm)

Capture Probability

Wormaldia

●

●

●

●

●

●

●

●

●

●

●

●●●

●

●

15 8

0.0 0.1 0.2 0.3 0.4 0.5

Con

Capture Probability

E-13
●

●

●

●●

●

●

●

●

●

●

●

●

●

●

●

●

●●●●

●

●●●

●

●

●

●●

●

●●

●

●

●

●

●

●

●

●

●

●

●●●

●

●●●●●●●●●●●●●

15 81 423 2221 11646

0.0 0.1 0.2 0.3 0.4 0.5

Conductivity (μS/cm)

Capture Probability

Heleniella

●●●●●●●●

●

●

●

●

●

●

●

●

●

●

●

●●

●

●

●

●

●

●

●

●

●●

●

●

●●●

●

●

●

●

●

●

●

●

●●●●●●●●●●●●●●●●

15 81 423 2221 11646

0.00 0.05 0.10 0.15

Conductivity (μS/cm)

Capture Probability

Parakiefferiella

●●●●

●

●●●

●

●●●●

●

●

●

15 8

0.00 0.04 0.08 0.12

Con

Capture Probability

●●●●●●

●

●●

●

●●●

●

●●

●

●

●

●●

●●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●●●●●●●●●●●●●●●●

15 81 423 2221 11646

0.00 0.02 0.04 0.06 0.08

Conductivity (μS/cm)

Capture Probability

Pagastia

●●●●●●●

●

●

●●

●

●●●●

●

●

●

●

●

●

●

●

●

●

●

●

●

●●

●

●

●

●

●

●

●

●

●

●

●

●

●

●●●●●●●●●●●●●●●●

15 81 423 2221 11646

0.00 0.05 0.10 0.15

Conductivity (μS/cm)

Capture Probability

Natarsia

●●

●

●

●

●

●

●●

●

●

●

●

●

●

●

15 8

0.0 0.2 0.4 0.6

Con

Capture Probability

E-14
●●●●●●●●●

●

●

●

●

●

●

●

●

●●

●

●

●

●

●

●

●●

●

●

●

●

●

●

●●

●

●

●

●●●

●

●

●

●●●●●●●●●●●●●●●●

15 81 423 2221 11646

0.00 0.02 0.04 0.06 0.08

Conductivity (μS/cm)

Capture Probability

Potthastia

●●●●●●

●

●

●●

●

●

●

●●●●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●●

●

●

●

●

●

●

●

●

●

●●●●●●●●●●●●●●●●

15 81 423 2221 11646

0.00 0.02 0.04 0.06 0.08 0.10 0.12

Conductivity (μS/cm)

Capture Probability

Macronychus

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

15 8

0.0 0.2 0.4 0.6 0.8 1.0

Con

Capture Probability

●●●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●●●●●●●●●●●●●

15 81 423 2221 11646

0.0 0.1 0.2 0.3 0.4

Conductivity (μS/cm)

Capture Probability

Tipula

●●

●

●

●●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●●●

●

●●●●●●●●●●●●●

15 81 423 2221 11646

0.00 0.05 0.10 0.15 0.20 0.25

Conductivity (μS/cm)

Capture Probability

Brillia

●

●

●●●●●●

●

●

●

●

●

●●

●

15 8

0.0 0.1 0.2 0.3 0.4 0.5

Con

Capture Probability

E-15
●●

●

●●●

●

●

●●

●

●

●

●●●●

●●

●

●

●

●

●

●

●

●

●

●

●●●●

●●

●

●●

●

●

●●

●●

●

●●●●●●●●●●●●●●●

15 81 423 2221 11646

0.00 0.05 0.10 0.15 0.20 0.25

Conductivity (μS/cm)

Capture Probability

Rhagovelia

●●●●

●

●

●●●

●

●

●

●

●

●

●●

●

●

●

●

●

●●

●

●

●

●

●●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●●●●●●●●●●●●●●●

15 81 423 2221 11646

0.00 0.05 0.10 0.15 0.20 0.25

Conductivity (μS/cm)

Capture Probability

Lanthus

●●●

●

●

●

●

●

●

●●

●

●●

●

●

15 8

0.0 0.2 0.4 0.6 0.8 1.0

Con

Capture Probability

●●●

●

●

●●

●

●

●

●

●

●

●

●

●

●

●●

●

●

●

●

●●

●

●

●●●

●

●●●●●●●●●●●

●

●

●

●●●●●●●●●●●●●●●

15 81 423 2221 11646

0.00 0.05 0.10 0.15 0.20 0.25 0.30

Conductivity (μS/cm)

Capture Probability

Paragnetina

●●

●

●

●

●●

●

●

●●●●●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●●●

●

●

●

●●●●●●●●●●●●●●●

15 81 423 2221 11646

0.00 0.05 0.10 0.15 0.20 0.25

Conductivity (μS/cm)

Capture Probability

Crangonyx

●●●●●●

●

●

●●●

●

●

●

●

●

15 8

0.00 0.05 0.10 0.15 0.20 0.25

Con

Capture Probability

E-16
●●●●

●

●●●

●

●

●●●●

●

●●

●

●

●●

●

●

●

●

●●

●●

●

●

●

●

●

●

●

●●

●

●

●

●●

●

●

●

●●●●●●●●●●●●●●

15 81 423 2221 11646

0.00 0.05 0.10 0.15 0.20

Conductivity (μS/cm)

Capture Probability

Atrichopogon

●●●

●

●●●●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●●●●●●●●●●●●●●

15 81 423 2221 11646

0.00 0.05 0.10 0.15 0.20

Conductivity (μS/cm)

Capture Probability

Cardiocladius

●●●●

●

●●●●●●●

●

●

●

●

15 8

0.00 0.05 0.10 0.15 0.20 0.25

Con

Capture Probability

●●●●●●●●●●

●

●●●

●

●

●

●

●

●

●●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●●

●

●

●

●●●●●●●●●●●●●●

15 81 423 2221 11646

0.00 0.05 0.10 0.15 0.20

Conductivity (μS/cm)

Capture Probability

Krenopelopia

●●●●●●●●●●●●●●●

●

●●

●

●●

●

●

●

●

●●

●

●

●●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●●●●●●●●●●●●●●

15 81 423 2221 11646

0.00 0.05 0.10 0.15 0.20

Conductivity (μS/cm)

Capture Probability

Phaenopsectra

●●●●●

●

●

●

●

●●●

●

●

●

●

15 8

0.0 0.1 0.2 0.3 0.4 0.5

Con

Capture Probability

E-17
●●●●●●●●●●

●

●

●

●

●

●●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●●

●●

●

●

●

●

●

●

●●●●●

●

●●●●●●●●●●●●●

15 81 423 2221 11646

0.00 0.02 0.04 0.06 0.08 0.10

Conductivity (μS/cm)

Capture Probability

Microvelia ●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●●

●

●

●

●

●●●

●

●

●

●●

●

●

●

●

●

●

●●

●●●●●●●●●●

15 81 423 2221 11646

0.0 0.2 0.4 0.6 0.8 1.0

Conductivity (μS/cm)

Capture Probability

Diplectrona

●●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

15 8

0.0 0.2 0.4 0.6

Con

Capture Probability

●●

●

●

●

●

●

●●●

●

●●

●

●●

●●

●

●

●●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●●

●

●

●

●

●

●

●

●●●●●●●●●●●●

●

15 81 423 2221 11646

0.0 0.2 0.4 0.6

Conductivity (μS/cm)

Capture Probability

Acroneuria

●●●●●●●●

●

●

●

●

●

●

●

●

●

●

●

●

●●●

●

●

●

●

●●

●

●

●

●

●●

●

●

●

●

●

●

●●

●

●●

●

●●●●●●●●●●●●●

15 81 423 2221 11646

0.00 0.05 0.10 0.15 0.20

Conductivity (μS/cm)

Capture Probability

Larsia

●●●●●●●●●●●

●

●●●

●

15 8

0.00 0.02 0.04 0.06 0.08 0.10

Con

Capture Probability

E-18
●●

●

●

●

●●●●

●

●

●

●

●

●

●

●

●

●

●●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●●

●

●

●

●

●

●

●

●

●

●●●●●

●

●●●●●●●

15 81 423 2221 11646

0.00 0.05 0.10 0.15 0.20 0.25

Conductivity (μS/cm)

Capture Probability

Zavrelimyia

●●●●●●●●●●●●●●●

●

●

●●●

●

●●

●●

●

●●●●

●

●

●●

●

●

●●

●●

●

●●

●

●●●

●

●●●●●●●●●●●●

15 81 423 2221 11646

0.0 0.1 0.2 0.3 0.4 0.5

Conductivity (μS/cm)

Capture Probability

Ochrotrichia

●●

●

●

●

●

●●

●

●

●

●

●

●

●

●

15 8

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7

Con

Capture Probability

●●●●

●

●●●●●●

●

●

●●

●

●

●

●

●●●

●

●

●

●

●

●

●

●●

●

●

●

●●

●

●

●

●

●

●

●

●

●

●●●

●

●●●●●●●●●●●

15 81 423 2221 11646

0.0 0.1 0.2 0.3 0.4 0.5

Conductivity (μS/cm)

Capture Probability

Orconectes

●●

●

●

●

●

●

●●●●

●

●

●

●

●

●

●●●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●●

●

●

●

●

●

●●●●●●

●

●●●●●●●●●●●

15 81 423 2221 11646

0.0 0.1 0.2 0.3 0.4 0.5 0.6

Conductivity (μS/cm)

Capture Probability

Perlesta

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

15 8

0.0 0.1 0.2 0.3 0.4 0.5

Con

Capture Probability

E-19
●●

●

●

●●●●

●

●●

●

●

●

●●

●

●

●

●

●

●

●

●●

●

●

●

●

●

●●

●●

●

●

●

●

●

●

●

●

●

●●●

●

●

●

●●●●●●●●●●●

15 81 423 2221 11646

0.00 0.10 0.20 0.30

Conductivity (μS/cm)

Capture Probability

Dasyhelea

●●●●●●●●

●

●

●

●

●

●

●

●

●

●

●

●●

●

●

●

●●

●●●

●

●●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●●

●

●●●●●●●●●●●

15 81 423 2221 11646

0.00 0.10 0.20 0.30

Conductivity (μS/cm)

Capture Probability

Microcylloepus

●●

●

●

●

●

●

●

●

●●

●

●

●

●

●

15 8

0.0 0.1 0.2 0.3 0.4 0.5

Con

Capture Probability

●●●●●●

●

●

●●

●

●

●

●

●

●

●

●

●

●

●

●

●●

●

●

●

●

●

●

●

●

●

●

●

●●

●

●

●

●

●

●

●

●

●

●

●

●●

●●●●●●●●●●

15 81 423 2221 11646

0.0 0.1 0.2 0.3 0.4 0.5

Conductivity (μS/cm)

Capture Probability

Cryptochironomus

●

●

●

●

●

●

●

●

●●

●

●

●

●

●

●

●

●

●

●●

●

●

●

●

●

●

●

●

●

●

●

●●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●●●●●●●●●

15 81 423 2221 11646

0.0 0.2 0.4 0.6

Conductivity (μS/cm)

Capture Probability

Microtendipes

●●●●

●

●

●●

●

●

●

●

●

●

●●

15 8

0.0 0.1 0.2 0.3 0.4 0.5 0.6

Con

Capture Probability

P

E-20
●●●

●

●●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●●

●

●

●

●

●

●

●●

●

●

●

●

●

●

●●

●

●

●

●

●●

●

●●

●

●●●●●●●

15 81 423 2221 11646

0.0 0.1 0.2 0.3 0.4

Conductivity (μS/cm)

Capture Probability

Rheocricotopus

●

●

●●

●

●

●

●

●●

●

●

●

●

●●

●

●

●

●

●

●

●

●

●

●

●

●

●

●●

●●

●

●●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●●●●●●●

15 81 423 2221 11646

0.0 0.1 0.2 0.3 0.4 0.5 0.6

Conductivity (μS/cm)

Capture Probability

Rheotanytarsus

●●●●●●

●

●●●

●●

●●

●

●

15 8

0.0 0.2 0.4 0.6 0.8 1.0

Con

Capture Probability

●●●●●●

●

●●●

●

●

●

●

●

●

●

●

●

●

●

●●

●

●

●

●

●

●●●

●●

●

●

●●

●

●●

●

●

●

●

●

●

●

●●

●

●

●

●

●●●●●●●

15 81 423 2221 11646

0.0 0.2 0.4 0.6 0.8 1.0

Conductivity (μS/cm)

Capture Probability

Chimarra

●●●●

●

●●

●

●

●

●

●

●

●●

●

●●

●

●●●●●

●

●●

●

●

●

●

●

●

●●

●●

●

●

●●

●

●

●

●●

●

●●●

●

●

●●●●●●●●

15 81 423 2221 11646

0.0 0.2 0.4 0.6 0.8 1.0

Conductivity (μS/cm)

Capture Probability

Caecidotea

●●●●●●

●

●

●

●

●●●

●

●●

15 8

0.0 0.2 0.4 0.6 0.8 1.0

Con

Capture Probability

E-21
●●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●●

●●

●

●

●

●

●●●

●●

●

●

●

●●

●

●●

●

●

●

●

●●●●●●●

15 81 423 2221 11646

0.0 0.2 0.4 0.6 0.8 1.0

Conductivity (μS/cm)

Capture Probability

Diamesa

●

●●●

●

●

●

●

●●

●

●

●

●

●

●

●●●●●●●

●

●

●

●

●

●

●

●●

●

●●

●

●●

●

●

●●

●

●

●

●

●

●●●●

●

●●●●●●●●

15 81 423 2221 11646

0.0 0.2 0.4 0.6 0.8 1.0

Conductivity (μS/cm)

Capture Probability

Gammarus ●

●●

●

●

●

●

●

●

●

●

●

●

●

●

●

15 8

0.0 0.2 0.4 0.6 0.8 1.0

Con

Capture Probability

Par

●●

●

●

●

●

●●●

●

●●

●

●

●

●

●

●

●●●

●

●

●

●

●

●

●

●

●

●

●●●

●

●●

●

●●

●

●

●

●

●●

●

●●●●

●

●●●●●●●●

15 81 423 2221 11646

0.0 0.2 0.4 0.6 0.8 1.0

Conductivity (μS/cm)

Capture Probability

Polycentropus

●

●

●

●

●

●

●

●

●

●

●

●●

●

●

●

●

●

●

●

●

●

●

●●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●●

●

●●

●

●

15 81 423 2221 11646

0.0 0.2 0.4 0.6 0.8 1.0

Conductivity (μS/cm)

Capture Probability

Polypedilum

●●●●●

●

●

●

●

●

●

●

●

●

●

●

15 8

0.00 0.05 0.10 0.15 0.20 0.25

Con

Capture Probability

C

E-22
●●

●

●●●●

●

●●●

●

●●●●●

●●

●

●●

●●●

●

●

●

●●●

●

●

●

●

●

●

●

●

●

●

●

●

●●

●

●

●●●●●

●

●●●●●●●

15 81 423 2221 11646

0.00 0.10 0.20 0.30

Conductivity (μS/cm)

Capture Probability

Limonia

●●

●

●

●●●●●

●●

●

●

●

●

●●

●

●

●●●

●

●

●●●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●●●●●●●●

●

●●●●●●●

15 81 423 2221 11646

0.00 0.05 0.10 0.15 0.20 0.25

Conductivity (μS/cm)

Capture Probability

Limnophyes

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●●

15 8

0.0 0.2 0.4 0.6 0.8 1.0

Con

Capture Probability

●●

●

●

●

●

●

●

●

●

●

●

●

●●

●

●

●

●●●●

●

●

●

●

●

●

●

●

●

●●

●●

●

●

●

●

●

●

●

●

●

●

●

●

●

●●

●●

●

●

●

●

●●●●

15 81 423 2221 11646

0.0 0.2 0.4 0.6 0.8 1.0

Conductivity (μS/cm)

Capture Probability

Ceratopsyche

●

●●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

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15 81 423 2221 11646

0.0 0.2 0.4 0.6 0.8 1.0

Conductivity (μS/cm)

Capture Probability

Micropsectra

●●●●●●●

●●●●●

●

●●

●

15 8

0.0 0.2 0.4 0.6 0.8 1.0

Con

Capture Probability

Par

E-23
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15 81 423 2221 11646

0.0 0.2 0.4 0.6 0.8 1.0

Conductivity (μS/cm)

Capture Probability

Simulium

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15 81 423 2221 11646

0.0 0.2 0.4 0.6 0.8 1.0

Conductivity (μS/cm)

Capture Probability

Stylogomphus

●●

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●

●

●

●

●

●

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●

●

●

●

●

15 8

0.0 0.2 0.4 0.6 0.8 1.0

Con

Capture Probability

Th

●

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15 81 423 2221 11646

0.0 0.2 0.4 0.6 0.8 1.0

Conductivity (μS/cm)

Capture Probability

Dicranota

●

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15 81 423 2221 11646

0.0 0.2 0.4 0.6 0.8 1.0

Conductivity (μS/cm)

Capture Probability

Hydropsyche

●●●●●●●

●

●

●

●

●●

●

●

●

15 8

0.00 0.10 0.20 0.30

Con

Capture Probability

E-24
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15 81 423 2221 11646

0.0 0.2 0.4 0.6 0.8 1.0

Conductivity (μS/cm)

Capture Probability

Dubiraphia

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15 81 423 2221 11646

0.0 0.2 0.4 0.6 0.8 1.0

Conductivity (μS/cm)

Capture Probability

Psephenus

●

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●

●

●

●

●

●

●

●

●

●

●

●

15 8

0.0 0.2 0.4 0.6 0.8 1.0

Con

Capture Probability

Ch

●

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15 81 423 2221 11646

0.0 0.2 0.4 0.6 0.8 1.0

Conductivity (μS/cm)

Capture Probability

Tanytarsus

●

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15 81 423 2221 11646

0.0 0.2 0.4 0.6 0.8 1.0

Conductivity (μS/cm)

Capture Probability

Hemerodromia

●●

●

●

●

●

●

●

●

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●

●

●

●

●

15 8

0.0 0.2 0.4 0.6 0.8 1.0

Con

Capture Probability

E-25
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15 81 423 2221 11646

0.0 0.2 0.4 0.6 0.8 1.0

Conductivity (μS/cm)

Capture Probability

Nigronia

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15 81 423 2221 11646

0.0 0.2 0.4 0.6 0.8 1.0

Conductivity (μS/cm)

Capture Probability

Optioservus

●●●●●●●●●●●●●

●

●●

15 8

0.0 0.2 0.4 0.6 0.8 1.0

Con

Capture Probability

●●●●

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15 81 423 2221 11646

0.0 0.2 0.4 0.6 0.8 1.0

Conductivity (μS/cm)

Capture Probability

Stenelmis

●●●●●●●●●

●

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15 81 423 2221 11646

0.0 0.2 0.4 0.6 0.8 1.0

Conductivity (μS/cm)

Capture Probability

Tabanus

●●

●

●

●

●

●

●●●

●

●

●

●

●

●

15 8

0.0 0.2 0.4 0.6 0.8 1.0

Con

Capture Probability

Th

E-26
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15 81 423 2221 11646

0.0 0.2 0.4 0.6 0.8 1.0

Conductivity (μS/cm)

Capture Probability

Corydalus

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15 81 423 2221 11646

0.0 0.2 0.4 0.6 0.8 1.0

Conductivity (μS/cm)

Capture Probability

Cricotopus

●●●●●

●

●

●

●

●

●

●

●

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●

15 8

0.0 0.2 0.4 0.6 0.8 1.0

Con

Capture Probability

●●●●●●●●●●●

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15 81 423 2221 11646

0.0 0.1 0.2 0.3 0.4 0.5

Conductivity (μS/cm)

Capture Probability

Procladius

●●

●

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15 81 423 2221 11646

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7

Conductivity (μS/cm)

Capture Probability

Sialis

●●●●●●●

●

●●●

●

●

●

●

●

15 8

0.0 0.2 0.4 0.6 0.8 1.0

Con

Capture Probability

A

E-27
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15 81 423 2221 11646

0.0 0.2 0.4 0.6 0.8 1.0

Conductivity (μS/cm)

Capture Probability

Atherix

●●●●●●●●●●

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15 81 423 2221 11646

0.0 0.2 0.4 0.6 0.8 1.0

Conductivity (μS/cm)

Capture Probability

Chironomus

●●●

●

●●●

●

●

●

●

●

●

●

●

●

15 8

0.0 0.2 0.4 0.6 0.8 1.0

Con

Capture Probability

●

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15 81 423 2221 11646

0.0 0.2 0.4 0.6 0.8 1.0

Conductivity (μS/cm)

Capture Probability

Cladotanytarsus

●●●●●●●●

●

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15 81 423 2221 11646

0.0 0.2 0.4 0.6 0.8 1.0

Conductivity (μS/cm)

Capture Probability

Dicrotendipes

●●●●●

●

●

●

●

●

●

●

●

●

●

●

15 8

0.0 0.2 0.4 0.6 0.8 1.0

Con

Capture Probability

E-28
●●●●●●

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15 81 423 2221 11646

0.0 0.2 0.4 0.6 0.8 1.0

Conductivity (μS/cm)

Capture Probability

Pseudochironomus

E-29
F-1 

APPENDIX F 

GRAPHS OF CUMULATIVE FREQUENCY DISTRIBUTIONS

FOR GENERA IN THE WEST VIRGINIA DATA SET

ABSTRACT

The purpose of Appendix F is to help the reader visualize the changes in the occurrence 

of each genus in the West Virginia data set as conductivity increases and understand how the 

extirpation concentration (XC95) values are derived. Each plot contains the weighted cumulative 

distribution function (CDF) for the occurrence of a genus with respect to conductivity. For each 

genus, the points in the CDF represent the weighted proportions of occurrences of the genus in 

samples less than the indicated conductivity value (μS/cm), calculated using Equation 1. In a 

CDF, genera that are affected by increasing conductivity (e.g., Drunella) show a steep slope and 

asymptote well below the maximum conductivity, whereas genera unaffected by increasing 

conductivity (e.g., Nigronia) have a steady increase over the entire range of measured exposure 

and do not reach a perceptible asymptote. The 95th centile is found at the intersection of the 

dashed horizontal line with the CDF. The conductivity at the 95th centile is the XC95 value and is 

found at the intersection of the vertical line and the x-axis. 
Lepidostoma Remenus 

Proportion <= x 

0.0 0.2 0.4 0.6 0.8 1.0 

Proportion <= x 

0.0 0.2 0.4 0.6 0.8 1.0 

Proportion <= x 

0.0 0.2 0.4 0.6 0.8 1.0 

15 81 423 2221 11646 15 81 423 2221 11646 15 

Proportion <= x 

0.0 0.2 0.4 0.6 0.8 1.0 

Alloperla

15 81 423 2221 11646 

Conductivity (μS/cm) 

Proportion <= x 

Conductivity (μS/cm) Conductivity (μS/cm) 

0.0 0.2 0.4 0.6 0.8 1.0 

Proportion <= x 

0.0 0.2 0.4 0.6 0.8 1.0 

Leptophlebia 

15 81 423 2221 11646 15 

Conductivity (μS/cm) 

F-2
Taeniopteryx Pycnopsyche 

0.0 0.2 0.4 0.6 0.8 

Proportion <= x 

Proportion <= x 

1.0 0.0 0.2 0.4 0.6 0.8 1.0 

Proportion <= x 

Conductivity (μS/cm) Conductivity (μS/cm) 

0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 

Proportion <= x Proportion <= x 

0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 15 81 423 2221 11646 15 81 423 2221 11646 15 

Ephemerella Epeorus 

15 81 423 2221 11646 15 81 423 2221 11646 15 

Conductivity (μS/cm) Conductivity (μS/cm) 

Proportion <= x 

F-3
Neophylax Nixe Dem

Proportion <= x Proportion <= x 

0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 

Proportion <= x Proportion <= x Conductivity (μS/cm) Conductivity (μS/cm) 

0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 

Proportion <= x Proportion <= x 

0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 15 81 423 2221 11646 15 81 423 2221 11646 15 

Heptagenia Paracapnia 

15 81 423 2221 11646 15 81 423 2221 11646 15 

Conductivity (μS/cm) Conductivity (μS/cm) 

F-4
Bezzia Brachypremna 

Proportion <= x Proportion <= x 

0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 

Proportion <= x Proportion <= x Conductivity (μS/cm) Conductivity (μS/cm) 

0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 

Proportion <= x Proportion <= x 

0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 15 81 423 2221 11646 15 81 423 2221 11646 15 

Haploperla Leucrocuta 

15 81 423 2221 11646 15 81 423 2221 11646 15 

Conductivity (μS/cm) Conductivity (μS/cm) 

F-5
Paraleptophlebia Tallaperla 

Proportion <= x Proportion <= x 

0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 

Proportion <= x Proportion <= x Conductivity (μS/cm) Conductivity (μS/cm) 

0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 

Proportion <= x Proportion <= x 

0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 15 81 423 2221 11646 15 81 423 2221 11646 15 

Eccoptura Prosimulium 

15 81 423 2221 11646 15 81 423 2221 11646 15 

Conductivity (μS/cm) Conductivity (μS/cm) 

F-6
Allocapnia Conchapelopia 

Proportion <= x Proportion <= x 

0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 

Proportion <= x Proportion <= x Conductivity (μS/cm) Conductivity (μS/cm) 

0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 

Proportion <= x Proportion <= x 

0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 15 81 423 2221 11646 15 81 423 2221 11646 15 

Diphetor Pteronarcys 

15 81 423 2221 11646 15 81 423 2221 11646 15 

Conductivity (μS/cm) Conductivity (μS/cm) 

F-7
Acerpenna Yugus 

Proportion <= x Proportion <= x 

0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 

Proportion <= x Proportion <= x Conductivity (μS/cm) Conductivity (μS/cm) 

0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 

Proportion <= x Proportion <= x 

0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 15 81 423 2221 11646 15 81 423 2221 11646 15 

Peltoperla Ephemera 

15 81 423 2221 11646 15 81 423 2221 11646 15 

Conductivity (μS/cm) Conductivity (μS/cm) 

F-8
Procloeon Dixa 

Proportion <= x Proportion <= x 

0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 

Proportion <= x Proportion <= x Conductivity (μS/cm) Conductivity (μS/cm) 

0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 

Proportion <= x Proportion <= x 

0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 15 81 423 2221 11646 15 81 423 2221 11646 15 

Stenonema Sweltsa 

15 81 423 2221 11646 15 81 423 2221 11646 15 

Conductivity (μS/cm) Conductivity (μS/cm) 

F-9
Amphinemura Hydroporus 

0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 

Proportion <= x Proportion <= x Conductivity (μS/cm) Conductivity (μS/cm) 

0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 

Proportion <= x Proportion <= x 

0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 15 81 423 2221 11646 15 81 423 2221 11646 15 

Malirekus Stempellinella 

15 81 423 2221 11646 15 81 423 2221 11646 15 

Conductivity (μS/cm) Conductivity (μS/cm) 

Proportion <= x Proportion <= x 

F-10
Plauditus Maccaffertium 

Proportion <= x Proportion <= x 

0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 

Proportion <= x Proportion <= x Conductivity (μS/cm) Conductivity (μS/cm) 

0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 

Proportion <= x Proportion <= x 

0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 15 81 423 2221 11646 15 81 423 2221 11646 15 

Krenosmittia Psychomyia P

15 81 423 2221 11646 15 81 423 2221 11646 15 

Conductivity (μS/cm) Conductivity (μS/cm) 

F-11
Isonychia Cambarus 

Proportion <= x Proportion <= x 

0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 

Proportion <= x Proportion <= x Conductivity (μS/cm) Conductivity (μS/cm) 

0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 

Proportion <= x Proportion <= x 

0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 15 81 423 2221 11646 15 81 423 2221 11646 15 

Acentrella Pseudolimnophila 

15 81 423 2221 11646 15 81 423 2221 11646 15 

Conductivity (μS/cm) Conductivity (μS/cm) 

F-12
Baetis Cordulegaster 

Proportion <= x Proportion <= x 

0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 

Proportion <= x Proportion <= x Conductivity (μS/cm) Conductivity (μS/cm) 

0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 

Proportion <= x Proportion <= x 

0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 15 81 423 2221 11646 15 81 423 2221 11646 15 

Limnophila Wormaldia 

15 81 423 2221 11646 15 81 423 2221 11646 15 

Conductivity (μS/cm) Conductivity (μS/cm) 

F-13
Heleniella Parakiefferiella 

Proportion <= x Proportion <= x 

0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 

Proportion <= x Proportion <= x Conductivity (μS/cm) Conductivity (μS/cm) 

0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 

Proportion <= x Proportion <= x 

0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 15 81 423 2221 11646 15 81 423 2221 11646 15 

Pagastia Natarsia 

15 81 423 2221 11646 15 81 423 2221 11646 15 

Conductivity (μS/cm) Conductivity (μS/cm) 

F-14
Potthastia Macronychus 

Proportion <= x Proportion <= x 

0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 

Proportion <= x Proportion <= x Conductivity (μS/cm) Conductivity (μS/cm) 

0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 

Proportion <= x Proportion <= x 

0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 15 81 423 2221 11646 15 81 423 2221 11646 15 

Tipula Brillia 

15 81 423 2221 11646 15 81 423 2221 11646 15 

Conductivity (μS/cm) Conductivity (μS/cm) 

F-15
Rhagovelia Lanthus 

Proportion <= x Proportion <= x 

0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 

Proportion <= x Proportion <= x Conductivity (μS/cm) Conductivity (μS/cm) 

0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 

Proportion <= x Proportion <= x 

0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 15 81 423 2221 11646 15 81 423 2221 11646 15 

Paragnetina Crangonyx 

15 81 423 2221 11646 15 81 423 2221 11646 15 

Conductivity (μS/cm) Conductivity (μS/cm) 

F-16
Atrichopogon Cardiocladius 

Proportion <= x Proportion <= x 

0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 

Proportion <= x Proportion <= x Conductivity (μS/cm) Conductivity (μS/cm) 

0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 

Proportion <= x Proportion <= x 

0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 15 81 423 2221 11646 15 81 423 2221 11646 15 

Krenopelopia Phaenopsectra 

15 81 423 2221 11646 15 81 423 2221 11646 15 

Conductivity (μS/cm) Conductivity (μS/cm) 

F-17
Microvelia Diplectrona 

Proportion <= x Proportion <= x 

0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 

Proportion <= x Proportion <= x Conductivity (μS/cm) Conductivity (μS/cm) 

0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 

Proportion <= x Proportion <= x 

0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 15 81 423 2221 11646 15 81 423 2221 11646 15 

Acroneuria Larsia 

15 81 423 2221 11646 15 81 423 2221 11646 15 

Conductivity (μS/cm) Conductivity (μS/cm) 

F-18
Zavrelimyia Ochrotrichia 

Proportion <= x Proportion <= x 

0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 

Proportion <= x Proportion <= x Conductivity (μS/cm) Conductivity (μS/cm) 

0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 

Proportion <= x Proportion <= x 

0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 15 81 423 2221 11646 15 81 423 2221 11646 15 

Orconectes Perlesta 

15 81 423 2221 11646 15 81 423 2221 11646 15 

Conductivity (μS/cm) Conductivity (μS/cm) 

F-19
Dasyhelea Microcylloepus 

Proportion <= x Proportion <= x 

0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 

Proportion <= x Proportion <= x Conductivity (μS/cm) Conductivity (μS/cm) 

0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 

Proportion <= x Proportion <= x 

0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 15 81 423 2221 11646 15 81 423 2221 11646 15 

Cryptochironomus Microtendipes 

15 81 423 2221 11646 15 81 423 2221 11646 15 

Conductivity (μS/cm) Conductivity (μS/cm) 

F-20
Rheocricotopus Rheotanytarsus 

Proportion <= x Proportion <= x 

0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 

Proportion <= x Proportion <= x Conductivity (μS/cm) Conductivity (μS/cm) 

0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 

Proportion <= x Proportion <= x 

0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 15 81 423 2221 11646 15 81 423 2221 11646 15 

Chimarra Caecidotea 

15 81 423 2221 11646 15 81 423 2221 11646 15 

Conductivity (μS/cm) Conductivity (μS/cm) 

F-21
Diamesa Gammarus Pa

Proportion <= x Proportion <= x 

0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 

Proportion <= x Proportion <= x Conductivity (μS/cm) Conductivity (μS/cm) 

0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 

Proportion <= x Proportion <= x 

0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 15 81 423 2221 11646 15 81 423 2221 11646 15 

Polycentropus Polypedilum 

15 81 423 2221 11646 15 81 423 2221 11646 15 

Conductivity (μS/cm) Conductivity (μS/cm) 

F-22
Limonia Limnophyes 

Proportion <= x Proportion <= x 

0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 

Proportion <= x Proportion <= x 

0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 

Proportion <= x Proportion <= x 

0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 15 81 423 2221 11646 15 81 423 2221 11646 15 

Conductivity (μS/cm) Conductivity (μS/cm) 

Ceratopsyche Micropsectra Pa

15 81 423 2221 11646 15 81 423 2221 11646 15 

Conductivity (μS/cm) Conductivity (μS/cm) 

F-23
Simulium Stylogomphus T

Proportion <= x Proportion <= x 

0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 

Proportion <= x Proportion <= x 

0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 

Proportion <= x Proportion <= x 

0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 15 81 423 2221 11646 15 81 423 2221 11646 15 

Conductivity (μS/cm) Conductivity (μS/cm) 

Dicranota Hydropsyche 

15 81 423 2221 11646 15 81 423 2221 11646 15 

Conductivity (μS/cm) Conductivity (μS/cm) 

F-24
Dubiraphia Psephenus C

Proportion <= x Proportion <= x 

0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 

Proportion <= x Proportion <= x 

0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 

Proportion <= x Proportion <= x 

0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 15 81 423 2221 11646 15 81 423 2221 11646 15 

Conductivity (μS/cm) Conductivity (μS/cm) 

Tanytarsus Hemerodromia 

15 81 423 2221 11646 15 81 423 2221 11646 15 

Conductivity (μS/cm) Conductivity (μS/cm) 

F-25
Nigronia Optioservus 

0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 

Proportion <= x Proportion <= x 

0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 

Proportion <= x Proportion <= x 

0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 15 81 423 2221 11646 15 81 423 2221 11646 15 

Conductivity (μS/cm) Conductivity (μS/cm) 

Stenelmis Tabanus T

15 81 423 2221 11646 15 81 423 2221 11646 15 

Conductivity (μS/cm) Conductivity (μS/cm) 

Proportion <= x Proportion <= x 

F-26
Corydalus Cricotopus 

Proportion <= x Proportion <= x 

0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 

Proportion <= x Proportion <= x 

0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 

Proportion <= x Proportion <= x 

0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 15 81 423 2221 11646 15 81 423 2221 11646 15 

Conductivity (μS/cm) Conductivity (μS/cm) 

Procladius Sialis 

15 81 423 2221 11646 15 81 423 2221 11646 15 

Conductivity (μS/cm) Conductivity (μS/cm) 

F-27
Atherix Chironomus 

Proportion <= x Proportion <= x 

0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 

Proportion <= x Proportion <= x 

0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 

Proportion <= x Proportion <= x 

0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 15 81 423 2221 11646 15 81 423 2221 11646 15 

Conductivity (μS/cm) Conductivity (μS/cm) 

Cladotanytarsus Dicrotendipes 

15 81 423 2221 11646 15 81 423 2221 11646 15 

Conductivity (μS/cm) Conductivity (μS/cm) 

F-28
Proportion <= x 

0.0 0.2 0.4 0.6 0.8 1.0 

Pseudochironomus

15 81 423 2221 11646
 

Conductivity (μS/cm)
 

F-29
G-1

APPENDIX G 

VALIDATION OF METHOD

USING FIELD DATA TO DERIVE AMBIENT WATER QUALITY BENCHMARK FOR 

CONDUCTIVITY USING A KENTUCKY DATA SET

ABSTRACT

The method for developing the aquatic life benchmark for conductivity was validated by 

developing extirpation concentration (XC95) and hazardous concentration (HC05) values using a 

data set independently collected by the Kentucky Division of Water (KDOW) and comparing 

results with those found using the larger West Virginia database. Because samples were also 

drawn from the Central Appalachians (Ecoregion 69) and Western Allegheny Plateau 

(Ecoregion 70), the two data sets were expected to give similar results. Some differences were 

expected due to the different collection and taxa identification protocols, shorter sampling 

window, inclusion of the Southwestern Appalachians (Ecoregion 68), and the fewer number of 

samples in the Kentucky data set. Nevertheless, the HC05 value was 282 μS/cm for the full 

Kentucky data set, which is very close to the West Virginia result. 

G.1. DATA SET SELECTION

The Southwestern Appalachians (68), Central Appalachia (69), and Western Allegheny 

Plateau (70) ecoregions were selected for validation, because they are physiographically similar 

to Ecoregions 69 and 70 in West Virginia (U.S. EPA, 2000; Omernik, 1987; Woods et al., 1996) 

(see Figures G-1 and G-2). Although the Kentucky data set is smaller than the West Virginia 

data set, it was judged to be large enough for validation of the method (see Section 3.5). These 

regions have heavily forested areas as well as extensive areas developed for coal mining, and, as 

in West Virginia, conductivity has been implicated as a cause of biological impairment in the 

three Kentucky ecoregions, which were judged to be similar within the state of Kentucky in 

terms of water quality, resident biota, and sources of conductivity (Pond 2004, 2010). 

Background conductivity was not estimated due to the lack of designation of reference sites in 

the data set or a probabilistic sample of sufficient size. However, the 25th centile of the entire 

data set, which includes impaired sites, is 118 μS/cm (see Figure G-3). Although not a 

background estimate, it does indicate conductivity levels are generally low in these ecoregions 

and is within the range of background values for West Virginia. 
G-2

Figure G-1. Location of Southern Appalachia (68), Central Appalachia (69), 

and Allegheny Plateau (70) and sampling points. 
G-3

Figure G-2. Location of sampling points used to develop the Kentucky HC05, 

shown with 8-digit HUC catchments.
 G-4

Figure G-3. Box plot showing seasonal variation of conductivity (μS/cm) 

from the data set used to develop the Kentucky HC05. A total of 291 samples 

from 1998−2004 from Ecoregions 68, 69, and 70 in Kentucky are 

represented. Small sample sizes and targeted sampling could obscure 

seasonal patterns, if any. 

G.2. DATA SOURCES 

All data used in this study were taken from the Kentucky Division of Water, Water 

Quality Branch database, Ecological Data Application System (KY EDAS). Chemical, physical, 

or biological samples were collected from 274 distinct locations during February−October from 

1998−2004 (see Table G-1). Like the West Virginia Department of Environmental Protection 

(WVDEP), the KDOW obtains biological data from both probability biosurvey and targeted 

ambient biological monitoring programs. The probability biosurvey program provides a 

condition assessment of the overall biological and water quality conditions for both basin and 

state levels. Targeted ambient biological monitoring involves intensive data-collection efforts 

Month

Conductivity ( 

S/cm)

Feb Mar Apr May Jun Jul Aug Sep Oct

20

50

100

200

500

1000

2000
G-5

Table G-1. Number of samples with reported genera and conductivity. 

Number of samples is presented for each month and ecoregion

Ecoregion

Month

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Total

68 0 0 10 0 6 18 2 3 0 39

69 7 14 44 16 16 42 18 0 25 182

70 0 9 21 2 17 21 0 0 0 70

291

for streams of interest as reference or impaired sites or for other reasons. Most sites have been 

sampled once during February to October. Quality assurance and standard procedures are 

described by KDOW (2008). Briefly, KDOW follows similar field and laboratory quality 

assurance methods as WVDEP. Macroinvertebrates are collected from mid-riffle/runs. 

Although KDOW also collects a separate sample from multihabitat survey, for consistency, only 

the riffle samples are quantified for this analysis. Four, 0.25-m2 samples were collected 

mid-riffle in a 100-m sampling reach using a 1-m-wide, 600-μm mesh net. The four samples 

were composited, and all macroinvertebrates were removed and stored in 95%-ethanol. Samples 

were composited, and macroinvertebrates were identified to the lowest possible taxonomic level. 

A notable difference in the WVDEP and KDOW methods is that KDOW picks the entire sample 

in the laboratory, as opposed to WVDEP’s fixed count of 200 organisms. 

All contracted chemical analyses and macroinvertebrate identifications followed internal 

quality control and quality assurance protocols. This is a well-documented, regulatory database. 

The quality assurance was judged to be excellent based on the database itself, supporting 

documentation, and experience of EPA Region 4 personnel. 

G.3. DATA SET CHARACTERISTICS

Biological sampling usually occurred once during February–October with the KDOW

(1998−2004) wadeable sampling protocol. The Kentucky data set was treated in the same way 

as the West Virginia data used to derive the aquatic life benchmark for conductivity. A sample 

was excluded from calculations if (1) it lacked a conductivity measurement, or (2) the pH was 

low. XC95 values were calculated for genera that occurred at >25 sampling locations. 

Organisms were not included unless identified to the genus level. Reference sites were not 

identified in the data set, so no genera were excluded in the species sensitivity distribution 

(SSD). Future analyses should identify invasive and opportunistic genera for a benchmark for 
G-6

Kentucky. Repeat biological samples from the same location at the same time (or within a 

month) were excluded, but samples collected in different months/years were not excluded from 

the data set. These repeat biological samples from different years were retained and represented 

about 8% of the samples. All samples were from wadeable streams. No measures of individual 

ions were available, so no sites with high chloride and low sulfate were identified or removed 

from the Kentucky data set. Eighty-five percent of the 104 genera used to develop the SSD for 

Kentucky also occurred in the West Virginia SSD. Genera from both states were judged to be 

similarly susceptible to the effects of conductivity after exploratory analysis (see also Sections 

A.2.4.1 and A.2.1.2). Conductivity ranged from 16−2,390 μS/cm for the Kentucky data set (see 

Table G-2, Figure G-4) and 15−11,646 μS/cm for the West Virginia data set. 

Table G-2. Summary statistics of the measured water quality parameters for 

the Kentucky data set

Min 25th 50th 75th Max Meana

Specific conductance 291 16 118.5 272.1 674.4 2,390 265.4

pH 291 6.03 7.1 7.5 7.92 9.26 7.49

Total HAB score 291 56 115 138 161 191 136.3

Embeddedness 291 0 8 13 16 19 12 a

Conductivity reported as geometric mean. 

In the Kentucky database, 359 benthic invertebrate genera were identified. Of the 

359 genera collected, 104 occurred in at least 25 sampling locations in Ecoregions 68, 69, and 70 

(see Appendix H). All genera used to construct the SSD occurred in all three ecoregions. 

Because of the data distributions, not all 95th centiles correspond to extirpation, and some 

imprecisely estimate the extirpation threshold. The following rules were applied to the XC95

values. If the generalized additive model (GAM) mean curve at maximum conductivity is 

approximately = 0 (<1% of the maximum modeled probability), then the XC95 is listed without 

qualification. If the GAM mean curve at maximum conductivity is >0 but the GAM lower

confidence limit is approximating to 0, the value is listed as approximate (~). If the GAM lower 

confidence limit is >0, then the XC95 is listed as greater than (>) the 95th centile. All models fits 

and the scatter of points were also visually inspected for anomalies, and if the model poorly fit 

the data, the uncertainty level was increased to either (~) or (>). This procedure was applied to 
 G-7

Figure G-4. Histograms of the frequencies of observed conductivity values in 

samples from Ecoregions 68, 69, and 70 in Kentucky sampled from 

1998−2004. 

plots in Appendix I, and the XC95 values appear in Appendix H. Also these models were used to 

evaluate when genera began to decline as evidence of alteration and sufficiency in Appendix A. 

Many genera are marked as approximate because the Kentucky data set is small, the XC95

models are based on a smaller number of occurrences, and the maximum conductivity measured 

is lower than in the West Virginia data set. The assignation of (>) and (~) does not affect the 

HC05 but alerts users of the uncertainty of the XC95 values for other uses such as comparison 

with toxicity test results or with results from other geographic regions. 

G.4. RESULTS 

Appendix H lists the genera used to construct the SSD from the Kentucky sample and 

their corresponding XC95 values. The cumulative distribution functions (CDFs) used to develop 

them can be found in Appendix J. The full SSD is shown in Figure G-5, and an enlargement of 

the lower half of the model is shown in Figure G-6. Despite the differences in sampling method 

and geographic location, the HC05 values were similar: 282 μS/cm for Kentucky compared to 

Conductivity (μS/cm)

Frequency

32 100 316 1000

0 2 4 6 8 10 12
 G-8

Figure G-5. Species sensitivity distribution (SSD) for Kentucky. A total of 

104 genera are included in the SSD. The HC05 is the conductivity at the 

intercept of the CDF while the horizontal line at the 5th centile is 282 μS/cm. 

 

Conductivity (μS/cm)

Proportion of Genera

200 500 1000 2000

0.0 0.2 0.4 0.6 0.8 1.0

282 μS/cm
 G-9

Figure G-6. Species sensitivity distribution (SSD) for Kentucky. Only the 

lower half of the total genera are shown to better discriminate the points in 

the left side of the SSD. 

295 μS/cm for West Virginia (see Figures G-2 and G-3, Table G-3). The 95% confidence 

bounds for the Kentucky HC05 are 169 and 380 μS/cm, which overlap with the West Virginia 

data set’s 95% confidence bounds of 228 and 303 μS/cm. Genera that exhibited a decreasing 

occurrence with increasing conductivity were among those with the lowest XC95 values in both 

states. Table G-4 shows the 10 lowest XC95 values for both West Virginia and Kentucky 

samples. The 5th centile occurs near the eighth genus for West Virginia samples and fifth genus 

for Kentucky samples. 50.40. anere

3

282 μS/cm

 G

0.

on ofitopor

20.

rP

10.00.

200 400 600 800

Conductivity (μS/cm)
G-10

Table G-3. HC05 values for Kentucky and West Virginia data sets 

Kentucky West Virginia

HC05 282 μS/cm 295 μS/cm

95% CI 169−380 228−303

Months 

represented

February−October January−December

Sample 291 2,210

Genera in SSD 104 163

Table G-4. Comparison of the sensitive genera and XC95 values 

West Virginia

Genus West Virginia XC95 Kentucky Genus Kentucky XC95

1 ~121 149

2 121 165

3 230 190

4 246 235

5 251 270

6 255 320

7 260 321

8 295 321

9 297 353

10 299 354

G.5. CONCLUSIONS

Based on the similar results, EPA judged the field-based method to be robust. The same 

aquatic life benchmark appears to be applicable to West Virginia and Kentucky streams in 

Ecoregions 68, 69, and 70. However, analysis of a larger statewide data set, removal of 

nonreference taxa, and verification of the basic water chemistry for the region are recommended.
G-11

REFERENCES 

KDOW (Kentucky Division of Water). (2008) Standard methods for assessing biological integrity of surface waters 

in Kentucky. Commonwealth of Kentucky Environmental and Public Protection Cabinet Department for 

Environmental Protection, Division of Water February 2008, Revision 3. 120 pp. Available online at 

http://www.water.ky.gov/sw/swmonitor/sop/ and at http://www.water.ky.gov/NR/rdonlyres/714984BB-54F8-46B9-

AF27-290EF7A6D5CE/0/BiologicalSOPMainDocument03_08.pdf (accessed 12/19/2009).

Omernik, JM. (1987) Ecoregions of the conterminous United States. Ann Assoc Am Geograph 77:118−125.

Pond, GJ. (2004) Effects of surface mining and residential land use on headwater stream biotic integrity in the 

eastern Kentucky coalfield region. Kentucky Department of Environmental Protection, Division of Water, 

Frankfort, KY. 

Pond, GJ. (2010) Patterns of Ephemeroptera taxa loss in Appalachian headwater streams (Kentucky, USA). 

Hydrobiologia. 641(1):185−201.

Stevens, DL, Jr.; Olsen, AR. (2004) Spatially balanced sampling of natural resources. J Am Stat Assoc 

99(465):262−278.

U.S. EPA (U.S. Environmental Protection Agency). (2000) Nutrient criteria technical guidance manual: rivers and 

streams. Office of Water, Office of Science and Technology, Washington, DC. EPA/822/B-00/002. Available 

online at http://www.epa.gov/waterscience/criteria/nutrient/guidance/rivers/rivers-streams-full.pdf. 

Woods, AJ; Omernik, JM; Brown, DD; et al. (1996) Level III and IV ecoregions of Pennsylvania and the Blue 

Ridge Mountains, the Ridge and Valley, and the Central Appalachians of Virginia, West Virginia, and Maryland. 

U.S. Environmental Protection Agency, National Health and Environmental Effects Research Laboratory, Corvallis, 

OR. EPA/600R-96/077. 50 pp.
H-1 

APPENDIX H 

EXTIRPATION CONCENTRATION VALUES FOR 

GENERA IN A KENTUCKY DATA SET

ABSTRACT

The purpose of Appendix H is to provide the reader with a list of the extirpation 

concentration (XC95) values used to develop the species sensitivity distribution and the 

hazardous concentration (HC05) for Kentucky. Genera are ordered alphabetically (see 

Table H-1). The numbers of occurrences in the data set are noted in the right-hand column. 

Genera highlighted in gray do not occur at West Virginia reference locations, but were included 

in the Kentucky species sensitivity distribution (SSD). If they were removed the hazardous 

concentration, HC05 would be slightly lower. 

Not all 95th centiles correspond to extirpation, and some imprecisely estimate the 

extirpation threshold. The following rules were applied to the XC95 values using the fitted curve 

and the confidence bounds from the plots in Appendix I. If the generalized additive model 

(GAM) mean curve at maximum conductivity is approximately equal to 0 (defined as less than 

1% of the maximum modeled probability), then the XC95 value is listed without qualification. If 

the GAM mean curve at maximum conductivity is >0 but the lower confidence limit is 

approximating to 0 (<1% of the maximum mean modeled probability), then the XC95 value is 

listed as approximate (~). If the GAM lower confidence limit is >0, then the XC95 value is listed 

as greater than (>) the 95th centile. All model fits and scatter of points were also visually 

inspected for anomalies, and if the model poorly fit the data, the uncertainty level was increased 

to either (~) or (>). 

The assignation of (~) and (>) does not affect the HC05. They are provided to alert users 

to the uncertainty of some XC95 values for other uses such as comparison with toxicity test 

results or with results from other geographic regions. 
H-2 

Table H-1. Extirpation concentration and sample size from Kentucky data 

set. Highlighted genera are not found at WV reference sites but were included in 

the SSD for Kentucky. XC95 values reported without a preceding symbol indicate 

evidence of extirpation within the tested range. XC95 values preceded by a (~) or 

(>) indicate extirpation with greater uncertainty or extirpation at a level above the 

reported value. Genera highlighted in gray do not occur at West Virginia 

reference locations.

Order Family Genus XC95 N

Diptera Chironomidae Ablabesmyia >1,410 43

Ephemeroptera Baetidae Acentrella >619 98

Plecoptera Perlidae Acroneuria >697 105

Ephemeroptera Ameletidae Ameletus >579 69

Plecoptera Nemouridae Amphinemura >1,269 107

Coleoptera Elmidae Ancyronyx 798 30

Diptera Tipulidae Antocha >958 49

Odonata Coenagrionidae Argia >1,410 51

Diptera Athericidae Atherix >1,650 61

Ephemeroptera Baetidae Baetis >1,410 170

Odonata Aeshnidae Boyeria >1,318 92

Ephemeroptera Caenidae Caenis >1,410 85

Odonata Calopterygidae Calopteryx >2,082 35

Decapoda Cambaridae Cambarus >1,090 157 

Trichoptera Hydropsychidae Ceratopsyche >1,577 102

Trichoptera Hydropsychidae Cheumatopsyche >1,630 230

Trichoptera Philopotamidae Chimarra >2,000 90

Diptera Chironomidae Chironomus >1,670 31

Ephemeroptera Heptageniidae Cinygmula 165 39

Veneroida Corbiculidae Corbicula >1,829 84

Megaloptera Corydalidae Corydalus >1,650 121

Diptera Chironomidae Cricotopus >2,037 98

Diptera Chironomidae Cryptochironomus >1,037 27

Diptera Chironomidae Diamesa >2,074 54
H-3 

Order Family Genus XC95 N

Diptera Tipulidae Dicranota >484 25

Diptera Chironomidae Dicrotendipes >1,437 29

Coleoptera Gyrinidae Dineutus >874 45

Ephemeroptera Baetidae Diphetor 190 25

Trichoptera Hydropsychidae Diplectrona >958 102

Plecoptera Perlodidae Diploperla >997 35

Trichoptera Philopotamidae Dolophilodes 270 31

Ephemeroptera Ephemerellidae Drunella 321 37

Coleoptera Elmidae Dubiraphia >1,650 86

Plecoptera Perlidae Eccoptura >1,649 31

Lumbriculida Lumbriculidae Eclipidrilus >1,294 92

Coleoptera Psephenidae Ectopria >582 66

Neotaenioglossa Pleuroceridae Elimia ~1,131 33

Odonata Coenagrionidae Enallagma >959 31

Ephemeroptera Heptageniidae Epeorus 321 65

Ephemeroptera Ephemeridae Ephemera ~559 42

Ephemeroptera Ephemerellidae Ephemerella ~467 70

Diptera Chironomidae Eukiefferiella >1,842 54

Ephemeroptera Ephemerellidae Eurylophella >499 84

Basommatophora Ancylidae Ferrissia >872 29

Odonata Gomphidae Gomphus >1,063 36

Plecoptera Chloroperlidae Haploperla 485 38

Coleoptera Dryopidae Helichus >1,050 148

Diptera Empididae Hemerodromia >2,000 123

Diptera Tipulidae Hexatoma >1,134 106

Trichoptera Hydropsychidae Hydropsyche >1,650 161

Trichoptera Hydroptilidae Hydroptila >1,680 58

Ephemeroptera Isonychiidae Isonychia >1,524 132

Plecoptera Perlodidae Isoperla >1,176 81
H-4 

Order Family Genus XC95 N

Odonata Gomphidae Lanthus >1,564 34

Trichoptera Lepidostomatidae Lepidostoma 149 30

Ephemeroptera Heptageniidae Leucrocuta >686 45

Plecoptera Leuctridae Leuctra >1,029 131

Isopoda Asellidae Lirceus ~958 35

Odonata Corduliidae Macromia ~772 27

Coleoptera Elmidae Macronychus >1,722 54

Diptera Chironomidae Micropsectra ~462 25

Diptera Chironomidae Microtendipes >681 58

Diptera Chironomidae Natarsia >1,630 45

Trichoptera Uenoidae Neophylax 353 73

Megaloptera Corydalidae Nigronia >1,197 153

Trichoptera Leptoceridae Oecetis >1,337 31

Coleoptera Elmidae Optioservus >1,563 178

Decapoda Cambaridae Orconectes >1,291 115

Diptera Chironomidae Orthocladius >1,480 50

Coleoptera Elmidae Oulimnius 320 31

Ephemeroptera Leptophlebiidae Paraleptophlebia ~420 76

Diptera Chironomidae Parametriocnemus >1,583 185

Plecoptera Peltoperlidae Peltoperla >1,520 37

Plecoptera Perlidae Perlesta >1,399 51

Basommatophora Physidae Physella >1,856 52

Ephemeroptera Baetidae Plauditus ~703 55

Trichoptera Polycentropodidae Polycentropus >570 82

Diptera Chironomidae Polypedilum >1,251 158

Ephemeroptera Baetidae Procloeon >800 42

Diptera Simuliidae Prosimulium >866 54

Coleoptera Psephenidae Psephenus >750 111

Ephemeroptera Baetidae Pseudocloeon ~861 36
H-5 

Order Family Genus XC95 N

Diptera Tipulidae Pseudolimnophila >1,051 40

Trichoptera Limnephilidae Pycnopsyche >775 64

Hemiptera Veliidae Rhagovelia >600 27

Diptera Chironomidae Rheocricotopus >1,117 51

Diptera Chironomidae Rheotanytarsus >1,601 115

Trichoptera Rhyacophilidae Rhyacophila >574 94

Megaloptera Sialidae Sialis >1,843 64

Diptera Simuliidae Simulium >1,580 179

Ephemeroptera Heptageniidae Stenacron >862 90

Coleoptera Elmidae Stenelmis >1,520 168

Diptera Chironomidae Stenochironomus >824 35

Ephemeroptera Heptageniidae Stenonema >993 178

Odonata Gomphidae Stylogomphus >1,720 90

Plecoptera Chloroperlidae Sweltsa 558 55

Diptera Chironomidae Tanytarsus >1,316 118

Diptera Chironomidae Thienemannimyia >1,697 155

Diptera Tipulidae Tipula >1,814 150

Trichoptera Leptoceridae Triaenodes >938 31

Ephemeroptera Leptohyphidae Tricorythodes >2,000 48

Diptera Chironomidae Tvetenia >1,254 46

Trichoptera Philopotamidae Wormaldia 235 38

Plecoptera Perlodidae Yugus 354 25
APPENDIX I 

GRAPHS OF OBSERVATION PROBABILITIES

FOR GENERA IN A KENTUCKY DATA SET

ABSTRACT

The purpose of Appendix I is to help the reader visualize the changes in the occurrence of 

each genus in the Kentucky data set as conductivity increases. Each figure depicts a general 

additive model (GAM) of the relationship between capture probabilities of a genus and 

conductivity. Genera are ordered from the lowest to the highest extirpation concentration (XC95) 

value. Open circles are the probabilities of observing the genus within a range of conductivities. 

Circles at zero probability indicate no individuals were found in any samples with those 

conductivities. The GAM line (solid line) fitted to the probabilities is for visualization and 

dashed lines are 90% confidence bounds. The vertical dashed red line indicates the XC95 taken 

from Appendix H. The fitted lines and confidence bounds were used to assign uncertainty levels 

of the XC95 values in Appendix H.

I-1
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16 56 196 684 2390

0.0 0.2 0.4 0.6 0.8 1.0

Conductivity (μS/cm)

Capture Probability

Lepidostoma

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16 56 196 684 2390

0.0 0.2 0.4 0.6 0.8 1.0

Conductivity (μS/cm)

Capture Probability

Cinygmula

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16 5

0.0 0.2 0.4 0.6 0.8 1.0

Con

Capture Probability

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16 56 196 684 2390

0.0 0.2 0.4 0.6 0.8 1.0

Conductivity (μS/cm)

Capture Probability

Wormaldia

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16 56 196 684 2390

0.0 0.2 0.4 0.6

Conductivity (μS/cm)

Capture Probability

Dolophilodes

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16 5

0.0 0.2 0.4 0.6 0.8 1.0

Con

Capture Probability

I-2
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16 56 196 684 2390

0.0 0.2 0.4 0.6 0.8 1.0

Conductivity (μS/cm)

Capture Probability

Drunella

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16 56 196 684 2390

0.0 0.2 0.4 0.6 0.8 1.0

Conductivity (μS/cm)

Capture Probability

Epeorus

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16 5

0.0 0.2 0.4 0.6 0.8 1.0

Con

Capture Probability

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16 56 196 684 2390

0.0 0.2 0.4 0.6 0.8 1.0

Conductivity (μS/cm)

Capture Probability

Yugus

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16 56 196 684 2390

0.0 0.2 0.4 0.6 0.8 1.0

Conductivity (μS/cm)

Capture Probability

Paraleptophlebia

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16 5

0.0 0.2 0.4 0.6 0.8 1.0

Con

Capture Probability

I-3
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16 56 196 684 2390

0.0 0.2 0.4 0.6 0.8 1.0

Conductivity (μS/cm)

Capture Probability

Ephemerella

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16 56 196 684 2390

0.0 0.2 0.4 0.6 0.8 1.0

Conductivity (μS/cm)

Capture Probability

Dicranota

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16 5

0.0 0.2 0.4 0.6 0.8 1.0

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Capture Probability

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16 56 196 684 2390

0.0 0.2 0.4 0.6 0.8 1.0

Conductivity (μS/cm)

Capture Probability

Eurylophella

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16 56 196 684 2390

0.0 0.2 0.4 0.6 0.8 1.0

Conductivity (μS/cm)

Capture Probability

Sweltsa

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16 5

0.0 0.2 0.4 0.6 0.8 1.0

Con

Capture Probability

I-4
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16 56 196 684 2390

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Conductivity (μS/cm)

Capture Probability

Polycentropus

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16 56 196 684 2390

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Conductivity (μS/cm)

Capture Probability

Rhyacophila

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16 5

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16 56 196 684 2390

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Conductivity (μS/cm)

Capture Probability

Ectopria

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16 56 196 684 2390

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Conductivity (μS/cm)

Capture Probability

Rhagovelia

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Capture Probability

I-5
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16 56 196 684 2390

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Conductivity (μS/cm)

Capture Probability

Microtendipes

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16 56 196 684 2390

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Conductivity (μS/cm)

Capture Probability

Leucrocuta

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0.0 0.2 0.4 0.6 0.8 1.0

Con

Capture Probability

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16 56 196 684 2390

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Conductivity (μS/cm)

Capture Probability

Plauditus

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16 56 196 684 2390

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Conductivity (μS/cm)

Capture Probability

Psephenus

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Capture Probability

I-6
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16 56 196 684 2390

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Conductivity (μS/cm)

Capture Probability

Pycnopsyche

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16 56 196 684 2390

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Conductivity (μS/cm)

Capture Probability

Ancyronyx

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Capture Probability

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16 56 196 684 2390

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Conductivity (μS/cm)

Capture Probability

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16 56 196 684 2390

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Conductivity (μS/cm)

Capture Probability

Pseudocloeon

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Con

Capture Probability

I-7
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16 56 196 684 2390

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Conductivity (μS/cm)

Capture Probability

Prosimulium

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16 56 196 684 2390

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Conductivity (μS/cm)

Capture Probability

Ferrissia

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0.0 0.2 0.4 0.6

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Capture Probability

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16 56 196 684 2390

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Conductivity (μS/cm)

Capture Probability

Triaenodes

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16 56 196 684 2390

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Conductivity (μS/cm)

Capture Probability

Antocha ●

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Con

Capture Probability

I-8
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16 56 196 684 2390

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Conductivity (μS/cm)

Capture Probability

Lirceus

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16 56 196 684 2390

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Conductivity (μS/cm)

Capture Probability

Enallagma

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●

●

●

16 5

0.0 0.2 0.4 0.6 0.8 1.0

Con

Capture Probability

●●

●

●●●

●●

●●

●

●

●

●

●

●

●

●●●

●

●●●●

●

●

●

●

●

●

●

●●

●

●●●

●

●

●

●

●

●

●

●

●●●●●

●

●●●●●●●

●

16 56 196 684 2390

0.0 0.2 0.4 0.6 0.8 1.0

Conductivity (μS/cm)

Capture Probability

Diploperla

●

●

●●●

●

●●●●●●●●●

●

●

●

●

●

●

●

●

●

●

●

●

●●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●●

●

●

●

●

●

●

16 56 196 684 2390

0.0 0.2 0.4 0.6 0.8 1.0

Conductivity (μS/cm)

Capture Probability

Leuctra

●●●●●●●●●●●●

●

●●●

16 5

0.00 0.10 0.20 0.30

Con

Capture Probability

Cry

I-9
●●●●

●

●

●

●

●

●

●

●

●

●

●

●

●

●●●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●●

●

●●

●

●●

16 56 196 684 2390

0.0 0.2 0.4 0.6 0.8 1.0

Conductivity (μS/cm)

Capture Probability

Helichus

●●●●●●●●●●

●

●

●

●

●

●

●

●●

●

●

●

●

●

●

●

●

●

●●

●

●

●

●

●●●●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●●●●●●●

16 56 196 684 2390

0.0 0.2 0.4 0.6

Conductivity (μS/cm)

Capture Probability

Pseudolimnophila

●●●●●●●●●●●●●●●●

16 5

0.0 0.1 0.2 0.3 0.4 0.5

Con

Capture Probability

●

●

●●●

●

●●

●●●

●

●

●●●●

●

●●●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●●

●

●

●

●

●

●

●

●

●

●

●●

●

●

●

●●●●●

16 56 196 684 2390

0.0 0.2 0.4 0.6 0.8 1.0

Conductivity (μS/cm)

Capture Probability

Cambarus

●●●●●●●●●●●

●

●●●●●●●●●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●●

●

●●●●●●●

16 56 196 684 2390

0.0 0.1 0.2 0.3 0.4 0.5

Conductivity (μS/cm)

Capture Probability

Rheocricotopus

●●●●●●●●●●●●●●●●

16 5

0.0 0.1 0.2 0.3 0.4 0.5

Con

Capture Probability

I-10
●

●

●●

●●

●

●●●●

●

●●

●●

●

●

●●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●●●●

●

●

●

16 56 196 684 2390

0.0 0.2 0.4 0.6 0.8 1.0

Conductivity (μS/cm)

Capture Probability

Hexatoma ●

●

●●●

●

●●

●

●

●

●

●

●

●●

●

●

●●

●

●

●

●

●

●

●

●

●

●

●

●

●●

●●

●

●

●

●

●

●

●

●

●

●●

●

●●

●

●

●●

●

●●●●

●

16 56 196 684 2390

0.0 0.2 0.4 0.6 0.8 1.0

Conductivity (μS/cm)

Capture Probability

Isoperla

●●

●●

●●

●

●●

●

●

●

●

●

●

●

16 5

0.0 0.2 0.4 0.6 0.8 1.0

Con

Capture Probability

●

●●

●

●●●●

●

●

●

●

●

●

●

●

●

●●

●

●

●●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●●

●

●

●●●●●●

16 56 196 684 2390

0.0 0.2 0.4 0.6 0.8 1.0

Conductivity (μS/cm)

Capture Probability

Polypedilum

●●●●●●●

●

●

●

●

●

●

●

●

●

●

●

●

●

●●●

●

●

●●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●●

●

●

●

●

●

●●●●●●

●

16 56 196 684 2390

0.0 0.1 0.2 0.3 0.4 0.5 0.6

Conductivity (μS/cm)

Capture Probability

Tvetenia ●

●

●●

●●

●●

●

●

●

●

●

●

●●

16 5

0.0 0.2 0.4 0.6 0.8 1.0

Con

Capture Probability

A

I-11
●●●●●●●●●●●●●●●●

●

●

●

●

●

●

●

●

●

●

●

●●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●●●●

16 56 196 684 2390

0.0 0.2 0.4 0.6 0.8 1.0

Conductivity (μS/cm)

Capture Probability

Orconectes

●●●●●●●●●●

●

●●●

●

●●●●●

●

●

●

●

●●

●

●

●●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●●●●

16 56 196 684 2390

0.0 0.2 0.4 0.6 0.8

Conductivity (μS/cm)

Capture Probability

Eclipidrilus

●●●

●

●●●●●

●

●

●

●

●

●

16 5

0.0 0.2 0.4 0.6 0.8 1.0

Con

Capture Probability

●●●

●

●●●

●

●

●●

●●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●●

●

●

●

●

●

●

●

●

●

●

●●

●

●

●●

●

●

●●●●●●

16 56 196 684 2390

0.0 0.2 0.4 0.6 0.8 1.0

Conductivity (μS/cm)

Capture Probability

Boyeria

●●●●●●●●●●●●●●●●●●●●●●

●

●●●●

●

●

●

●

●

●

●

●●

●

●

●

●

●

●

●

●●●

●

●

●

●●●●●●●●

●

●●

16 56 196 684 2390

0.0 0.1 0.2 0.3 0.4 0.5 0.6

Conductivity (μS/cm)

Capture Probability

Oecetis

●●●●●●●●●●

●

●●●●●

16 5

0.0 0.2 0.4 0.6 0.8

Con

Capture Probability

I-12
●●●●●●●●●●●●

●

●●●●●●●●●

●

●●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●●●

●

●

●

●

●

●

●

●

●●●●●●

16 56 196 684 2390

0.0 0.2 0.4 0.6 0.8 1.0

Conductivity (μS/cm)

Capture Probability

Ablabesmyia

●●●●●●●●●●●●●●●●●●

●

●●●

●

●

●

●

●

●

●

●●

●

●

●

●●

●

●

●

●

●

●

●

●

●

●

●●

●

●

●

●●

●

●●●●●●

16 56 196 684 2390

0.0 0.2 0.4 0.6 0.8 1.0

Conductivity (μS/cm)

Capture Probability

Argia

●●●

●

●●

●

●●●

●

●

●

●●

●

16 5

0.0 0.2 0.4 0.6 0.8 1.0

Con

Capture Probability

●●●●●●●●●●●●●●●●●●●●●●

●

●

●

●

●

●●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●●

●●

●

●

●

●

●

●

●●●

●

16 56 196 684 2390

0.0 0.2 0.4 0.6 0.8 1.0

Conductivity (μS/cm)

Capture Probability

Caenis

●●●●●●●●

●

●●●●●●●●●●●●●

●

●●●●

●

●

●

●●

●

●

●

●●

●

●

●

●

●

●

●

●

●

●

●

●

●●

●

●

●

●

●●●●●

16 56 196 684 2390

0.0 0.2 0.4 0.6 0.8 1.0

Conductivity (μS/cm)

Capture Probability

Dicrotendipes

●●●●●●

●

●●●●●●●

●

●

16 5

0.0 0.1 0.2 0.3 0.4 0.5

Con

Capture Probability

I-13
●

●

●

●●●●

●

●●

●

●

●

●

●

●

●

●

●

●

●

●

●

●●

●

●●●

●

●

●

●●●

●

●

●

●

●

●

●●●●●●●●●

●

●●●

●

●

●●●●

16 56 196 684 2390

0.0 0.2 0.4 0.6 0.8 1.0

Conductivity (μS/cm)

Capture Probability

Peltoperla

●●

●●

●●●

●●

●

●

●

●

●

●

●

●

●

●●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

16 56 196 684 2390

0.0 0.2 0.4 0.6 0.8 1.0

Conductivity (μS/cm)

Capture Probability

Stenelmis

●●●●●●●●

●

●

●

●●●●

●

16 5

0.0 0.2 0.4 0.6 0.8 1.0

Con

Capture Probability

●

●

●●

●●

●

●

●●

●

●

●

●

●●

●

●

●●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

16 56 196 684 2390

0.0 0.2 0.4 0.6 0.8 1.0

Conductivity (μS/cm)

Capture Probability

Optioservus

●

●

●

●●●●●

●

●●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●●

●

●

●

●●

●

●

●

●●

●

●

●●●●●●

●

●●

●

●

●

●●●

●

●

●●●●

16 56 196 684 2390

0.0 0.2 0.4 0.6 0.8 1.0

Conductivity (μS/cm)

Capture Probability

Lanthus

●

●

●

●

●

●●●●

●

●

●●

●

●

16 5

0.0 0.2 0.4 0.6 0.8 1.0

Con

Capture Probability

C

I-14
●

●●

●●

●

●

●●●

●

●

●

●

●●

●

●

●

●

●

●

●

●

●●

●

●●

●

●

●

●●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

16 56 196 684 2390

0.0 0.2 0.4 0.6 0.8 1.0

Conductivity (μS/cm)

Capture Probability

Simulium

●●

●●●

●

●●

●

●●

●

●

●

●

●

●

●●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●●

●

●

●

●

●

●

●

●

●

●

●

16 56 196 684 2390

0.0 0.2 0.4 0.6 0.8 1.0

Conductivity (μS/cm)

Capture Probability

Parametriocnemus

●●●●●●●●

●

●

●

●

●

●

●

16 5

0.0 0.2 0.4 0.6 0.8 1.0

Con

Capture Probability

R

●●

●

●

●

●

●

●

●

●

●

●

●

●

●●

●

●

●●

●

●

●

●

●

●

●

●

●

●

●

●

●●

●●

●

●

●

●

●

●

●

●●

●

●

●

●●●●●●●●

●

●

●

●

16 56 196 684 2390

0.0 0.2 0.4 0.6 0.8 1.0

Conductivity (μS/cm)

Capture Probability

Cheumatopsyche

●●●●●●●●●●●●●●●●●

●

●●

●

●●

●

●

●

●

●●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●●

●

●

●

●

●●●

●

16 56 196 684 2390

0.0 0.1 0.2 0.3 0.4 0.5

Conductivity (μS/cm)

Capture Probability

Natarsia

●●

●●

●●●●●●

●

●

●

●

●●

16 5

0.0 0.2 0.4 0.6 0.8 1.0

Con

Capture Probability

I-15
●●●●●●●●●

●

●●●

●

●●

●

●

●●

●

●

●

●

●

●●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●●

●

●

●

●

●

●

●●

16 56 196 684 2390

0.0 0.1 0.2 0.3 0.4 0.5 0.6

Conductivity (μS/cm)

Capture Probability

Atherix

●●●●●●●●●●

●

●●●●●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●●

●

●

●●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

16 56 196 684 2390

0.0 0.2 0.4 0.6 0.8 1.0

Conductivity (μS/cm)

Capture Probability

Corydalus

●●●●●●●●●

●

●●●●●●

16 5

0.0 0.2 0.4 0.6 0.8

Con

Capture Probability

●●

●●●

●

●

●●

●

●●

●

●

●●

●

●

●●

●●

●

●

●

●

●

●

●

●

●

●

●●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●●

●

●●●

●

●

●

●

16 56 196 684 2390

0.0 0.2 0.4 0.6 0.8 1.0

Conductivity (μS/cm)

Capture Probability

Hydropsyche

●●●●●●●●●●●●●●●●●●●●●●

●

●

●

●

●

●

●

●

●

●

●

●

●●

●●

●

●●

●

●

●

●

●

●

●

●

●

●

●

●●●●●●●

●

16 56 196 684 2390

0.0 0.1 0.2 0.3 0.4

Conductivity (μS/cm)

Capture Probability

Chironomus

●●●●●●

●

●●

●

●●

●

●●●

16 5

0.0 0.2 0.4 0.6 0.8 1.0

Con

Capture Probability

I-16
●●●

●

●●

●●

●●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●●

●

●

●

●

●

●

●

●

●

●●

●

●

●

●

●

●

●

●

●

●●

●

●

●

●

●

●

●

●

●

16 56 196 684 2390

0.0 0.2 0.4 0.6 0.8 1.0

Conductivity (μS/cm)

Capture Probability

Thienemannimyia

●

●●

●

●●●●●

●

●

●

●

●

●●

●

●

●

●

●

●

●

●

●

●●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●●

●

●

●

16 56 196 684 2390

0.0 0.2 0.4 0.6 0.8 1.0

Conductivity (μS/cm)

Capture Probability

Stylogomphus

●●●●●●●●●●●●●●●●

16 5

0.0 0.2 0.4 0.6

Con

Capture Probability

M

●

●●

●●

●

●●

●

●

●

●●

●

●●

●

●

●●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

16 56 196 684 2390

0.0 0.2 0.4 0.6 0.8 1.0

Conductivity (μS/cm)

Capture Probability

Tipula

●●●●●●●●●●●●●●●●●●●●●●

●

●

●

●

●

●

●

●

●

●

●●

●●

●

●

●

●

●

●

●●

●

●

●

●

●●

●

●

●

●●

●

●

●

●●

16 56 196 684 2390

0.0 0.2 0.4 0.6

Conductivity (μS/cm)

Capture Probability

Corbicula

●

●●

●

●●

●

●●●

●

●

●

●

●

16 5

0.0 0.2 0.4 0.6 0.8 1.0

Con

Capture Probability

I-17
●

●●

●

●●●●

●

●

●

●●●●●●●●●●●

●

●

●

●

●

●

●

●

●

●

●

●

●●

●

●

●

●

●●

●

●

●

●

●

●

●

●

●

●

●

●●●●

●

●

●

16 56 196 684 2390

0.0 0.2 0.4 0.6 0.8 1.0

Conductivity (μS/cm)

Capture Probability

Sialis

●●●●●●●●●●●●●●●●●●

●

●●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●●

●

●

●

●

●●

●

●

●

●

●

●●●

●

16 56 196 684 2390

0.0 0.2 0.4 0.6 0.8 1.0

Conductivity (μS/cm)

Capture Probability

Physella

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0.0 0.2 0.4 0.6 0.8 1.0

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16 56 196 684 2390

0.0 0.2 0.4 0.6 0.8 1.0

Conductivity (μS/cm)

Capture Probability

Hemerodromia

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16 56 196 684 2390

0.0 0.2 0.4 0.6

Conductivity (μS/cm)

Capture Probability

Tricorythodes

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16 5

0.0 0.2 0.4 0.6 0.8 1.0

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I-18
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16 56 196 684 2390

0.0 0.2 0.4 0.6 0.8 1.0

Conductivity (μS/cm)

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Diamesa

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16 56 196 684 

0.0 0.1 0.2 0.3 0.4 0.5

Conductivity (μS/cm)

Capture Probability

Calopteryx

I-19
J-1 

APPENDIX J 

GRAPHS OF CUMULATIVE FREQUENCY DISTRIBUTIONS

FOR GENERA IN A KENTUCKY DATA SET

ABSTRACT

The purpose of Appendix J is to help the reader visualize the changes in the occurrence of 

each genus in the Kentucky data set as conductivity increases and understand how the extirpation 

concentration (XC95) values are derived. Each plot contains the weighted cumulative 

distribution function (CDF) for the occurrence of a genus with respect to conductivity. For each 

genus, the points in the CDF represent the weighted proportions of occurrences of the genus in 

samples less than the indicated conductivity value (μS/cm), calculated using Equation 1. The 

95th centile is found at the intersection of the dashed horizontal line with the CDF. The 

conductivity for the 95th centile is the XC95 value and is found at the intersection of the vertical 

line and the x-axis. 
Lepidostoma Cinygmula 

Cumulative Probability Cumulative Probability 

0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 

Cumulative Probability Cumulative Probability Conductivity (μS/cm) Conductivity (μS/cm) C

0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 

Cumulative Probability Cumulative Probability 

0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 16 56 196 684 2390 16 56 196 684 2390 16 5

Wormaldia Dolophilodes 

16 56 196 684 2390 16 56 196 684 2390 16 5

Conductivity (μS/cm) Conductivity (μS/cm) C

J-2
Drunella Epeorus 

Cumulative Probability Cumulative Probability 

0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 

Cumulative Probability Cumulative Probability Conductivity (μS/cm) Conductivity (μS/cm) C

0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 

Cumulative Probability Cumulative Probability 

0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 16 56 196 684 2390 16 56 196 684 2390 16 5

Yugus Paraleptophlebia 

16 56 196 684 2390 16 56 196 684 2390 16 5

Conductivity (μS/cm) Conductivity (μS/cm) C

J-3
Ephemerella Dicranota 

Cumulative Probability Cumulative Probability 

0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 

Cumulative Probability Cumulative Probability Conductivity (μS/cm) Conductivity (μS/cm) C

0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 

Cumulative Probability Cumulative Probability 

0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 16 56 196 684 2390 16 56 196 684 2390 16 5

Eurylophella Sweltsa 

16 56 196 684 2390 16 56 196 684 2390 16 5

Conductivity (μS/cm) Conductivity (μS/cm) C

J-4
Polycentropus Rhyacophila 

Cumulative Probability Cumulative Probability 

0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 

Cumulative Probability Cumulative Probability Conductivity (μS/cm) Conductivity (μS/cm) C

0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 

Cumulative Probability Cumulative Probability 

0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 16 56 196 684 2390 16 56 196 684 2390 16 5

Ectopria Rhagovelia 

16 56 196 684 2390 16 56 196 684 2390 16 5

Conductivity (μS/cm) Conductivity (μS/cm) C

J-5
Microtendipes Leucrocuta 

Cumulative Probability Cumulative Probability 

0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 

Cumulative Probability Cumulative Probability Conductivity (μS/cm) Conductivity (μS/cm) C

0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 

Cumulative Probability Cumulative Probability 

0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 16 56 196 684 2390 16 56 196 684 2390 16 5

Plauditus Psephenus 

16 56 196 684 2390 16 56 196 684 2390 16 5

Conductivity (μS/cm) Conductivity (μS/cm) C

J-6
Pycnopsyche Ancyronyx 

Cumulative Probability Cumulative Probability 

0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 

Cumulative Probability Cumulative Probability Conductivity (μS/cm) Conductivity (μS/cm) C

0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 

Cumulative Probability Cumulative Probability 

0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 16 56 196 684 2390 16 56 196 684 2390 16 5

Stenochironomus Pseudocloeon 

16 56 196 684 2390 16 56 196 684 2390 16 5

Conductivity (μS/cm) Conductivity (μS/cm) C

J-7
Prosimulium Ferrissia 

Cumulative Probability Cumulative Probability 

0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 

Cumulative Probability Cumulative Probability Conductivity (μS/cm) Conductivity (μS/cm) C

0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 

Cumulative Probability Cumulative Probability 

0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 16 56 196 684 2390 16 56 196 684 2390 16 5

Triaenodes Antocha 

16 56 196 684 2390 16 56 196 684 2390 16 5

Conductivity (μS/cm) Conductivity (μS/cm) C

J-8
Lirceus Enallagma 

0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 

Cumulative Probability Cumulative Probability Conductivity (μS/cm) Conductivity (μS/cm) C

0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 

Cumulative Probability Cumulative Probability 

0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 16 56 196 684 2390 16 56 196 684 2390 16 5

Diploperla Leuctra C

16 56 196 684 2390 16 56 196 684 2390 16 5

Conductivity (μS/cm) Conductivity (μS/cm) C

Cumulative Probability Cumulative Probability 

J-9
Helichus Pseudolimnophila 

Cumulative Probability Cumulative Probability 

0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 

Cumulative Probability Cumulative Probability Conductivity (μS/cm) Conductivity (μS/cm) C

0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 

Cumulative Probability Cumulative Probability 

0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 16 56 196 684 2390 16 56 196 684 2390 16 5

Cambarus Rheocricotopus 

16 56 196 684 2390 16 56 196 684 2390 16 5

Conductivity (μS/cm) Conductivity (μS/cm) C

J-10
Hexatoma Isoperla 

Cumulative Probability Cumulative Probability 

0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 

Cumulative Probability Cumulative Probability Conductivity (μS/cm) Conductivity (μS/cm) C

0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 

Cumulative Probability Cumulative Probability 

0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 16 56 196 684 2390 16 56 196 684 2390 16 5

Polypedilum Tvetenia 

16 56 196 684 2390 16 56 196 684 2390 16 5

Conductivity (μS/cm) Conductivity (μS/cm) C

J-11
Orconectes Eclipidrilus 

Cumulative Probability Cumulative Probability 

0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 

Cumulative Probability Cumulative Probability Conductivity (μS/cm) Conductivity (μS/cm) C

0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 

Cumulative Probability Cumulative Probability 

0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 16 56 196 684 2390 16 56 196 684 2390 16 5

Boyeria Oecetis 

16 56 196 684 2390 16 56 196 684 2390 16 5

Conductivity (μS/cm) Conductivity (μS/cm) C

J-12
Ablabesmyia Argia 

Cumulative Probability Cumulative Probability 

0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 

Cumulative Probability Cumulative Probability Conductivity (μS/cm) Conductivity (μS/cm) C

0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 

Cumulative Probability Cumulative Probability 

0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 16 56 196 684 2390 16 56 196 684 2390 16 5

Caenis Dicrotendipes 

16 56 196 684 2390 16 56 196 684 2390 16 5

Conductivity (μS/cm) Conductivity (μS/cm) C

J-13
Peltoperla Stenelmis 

Cumulative Probability Cumulative Probability 

0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 

Cumulative Probability Cumulative Probability Conductivity (μS/cm) Conductivity (μS/cm) C

0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 

Cumulative Probability Cumulative Probability 

0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 16 56 196 684 2390 16 56 196 684 2390 16 5

Optioservus Lanthus 

16 56 196 684 2390 16 56 196 684 2390 16 5

Conductivity (μS/cm) Conductivity (μS/cm) C

J-14
Simulium Parametriocnemus 

Cumulative Probability Cumulative Probability 

0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 

Cumulative Probability Cumulative Probability Conductivity (μS/cm) Conductivity (μS/cm) C

0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 

Cumulative Probability Cumulative Probability 

0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 16 56 196 684 2390 16 56 196 684 2390 16 5

Cheumatopsyche Natarsia 

16 56 196 684 2390 16 56 196 684 2390 16 5

Conductivity (μS/cm) Conductivity (μS/cm) C

J-15
Atherix Corydalus 

Cumulative Probability Cumulative Probability 

0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 

Cumulative Probability Cumulative Probability Conductivity (μS/cm) Conductivity (μS/cm) C

0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 

Cumulative Probability Cumulative Probability 

0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 16 56 196 684 2390 16 56 196 684 2390 16 5

Hydropsyche Chironomus 

16 56 196 684 2390 16 56 196 684 2390 16 5

Conductivity (μS/cm) Conductivity (μS/cm) C

J-16
Thienemannimyia Stylogomphus 

Cumulative Probability Cumulative Probability 

0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 

Cumulative Probability Cumulative Probability Conductivity (μS/cm) Conductivity (μS/cm) C

0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 

Cumulative Probability Cumulative Probability 

0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 16 56 196 684 2390 16 56 196 684 2390 16 5

Tipula Corbicula 

16 56 196 684 2390 16 56 196 684 2390 16 5

Conductivity (μS/cm) Conductivity (μS/cm) C

J-17
Sialis Physella 

Cumulative Probability Cumulative Probability 

0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 

Cumulative Probability Cumulative Probability Conductivity (μS/cm) Conductivity (μS/cm) 

0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 

Cumulative Probability Cumulative Probability 

0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 16 56 196 684 2390 16 56 196 684 2390 16 

Hemerodromia Tricorythodes 

16 56 196 684 2390 16 56 196 684 2390 16 

Conductivity (μS/cm) Conductivity (μS/cm) 

J-18
Diamesa Calopteryx

Cumulative Probability 

0.0 0.2 0.4 0.6 0.8 1.0 

Cumulative Probability

0.0 0.2 0.4 0.6 0.8 1.0 

16 56 196 684 2390 16 56 196 684 

Conductivity (μS/cm) Conductivity (μS/cm)
 

J-19