UNITED STATES ENVIRONMENTAL PROTECTION AGENCY
WASHINGTON D.C. 20460
OFFICE OF THE ADMINISTRATOR
SCIENCE ADVISORY BOARD
March 25, 2011
EPA-SAB-11-006
The Honorable Lisa P. Jackson
Administrator
U.S. Environmental Protection Agency
1200 Pennsylvania Avenue, N.W.
Washington, D.C. 20460
Subject: Review of Field-Based Aquatic Life Benchmark for Conductivity in
Central Appalachian Streams
Dear Administrator Jackson:
EPA's Office of Research and Development (ORD) requested that the Science Advisory
Board (SAB) review two Agency's draft reports: (1) The Effects of Mountaintop Mines and
Valley Fills on Aquatic Ecosystems of the Central Appalachian Coalfields and (2) A Field-based
Aquatic Life Benchmark for Conductivity in Central Appalachian Streams. These draft EPA
reports were developed by ORD's National Center for Environmental Assessment upon the
request of EPA's Office of Water and Regions 3, 4, and 5, and help provide scientific
information to support EPA's actions on environmental permitting requirements related to
Appalachian surface coal mining operations.
The EPA document, A Field-Based Aquatic Life Benchmark for Conductivity in Central
Appalachian Streams, derives an aquatic life benchmark for conductivity, intended to protect
95% of native genera in Appalachian streams impacted by mountaintop mining and valley fills.
The Charge to the Panel requested advice on the adequacy of the data and methods used to
develop the conductivity benchmark, as well as the likely transferability of the approach to other
regions and other pollutants. In the enclosed report, we provide responses to the specific
questions on the conductivity benchmark posed in the EPA Charge to the Panel.
Mountaintop mining and valley fills are important sources of stress to aquatic systems in
the Central Appalachian region, both from the perspective of localized and cumulative regional
impacts. In a companion report, the Panel provides a review of EPA's assessment of the impacts
associated with mountaintop mining and valley fills. There is clear evidence that valley fills are
associated with increased levels of dissolved ions (measured as conductivity) in downstream
waters, and that these increased levels of conductivity are associated with changes in the
composition of stream biological communities.
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The SAB applauds the Agency's efforts to assess the linkages between measured levels
of conductivity and the presence or absence of native aquatic insects in Appalachian streams.
The field-based methodology for establishing a conductivity benchmark provides greater realism
than traditional laboratory-based methods because it includes native taxa and a range of life
stages. Although conductivity is a surrogate measure for the constituent ions that may contribute
to toxicity, the resulting benchmark provides a degree of protection comparable to, if not greater
than, a conventional water quality criterion based on traditional chronic toxicity testing.
That said, the SAB Panel notes that the scientific credibility of the benchmark would be
strengthened by analysis relating the constituent ions to observed biological community changes.
We encourage EPA to undertake that analysis to improve the mechanistic understanding of the
toxicological effects associated with releases from MTM-VG activities.
The Panel also had concerns with the selection of ecological endpoints for the analysis,
including the decision to define the ecological effect as loss of an entire genus from a region, and
based only on common taxa. The benchmark is based almost exclusively on data for aquatic
insects, while the potential for impacts on other rare and/or sensitive taxa (such as mollusks, fish,
or water-dependent wildlife) was not evaluated in setting the benchmark. Nor were changes in
the abundance of taxa, short of extirpation, considered. While the choice of ecological endpoints
was dictated in part by the availability of data, these choices may allow the loss of important and
widespread aquatic taxa. If the necessary data are available, the Agency should consider an
ecological effect defined as a specified reduction in genera abundance rather than extirpation. If
the extirpation endpoint is retained, the Agency might consider incorporating into the benchmark
a safety factor, subject knowledge, or other protocol for added protection.
The extensive data set from West Virginia used to derive the benchmark provides broad
spatial coverage and includes a large number of streams with and without mountaintop mining
and valley fills. The similarity of the benchmark developed using an independent data set from
Kentucky was an important validation of the approach and the quality of the data. However, we
caution the Agency not to apply the conductivity benchmark beyond the environmental
conditions (e.g., geographic region, relative composition—or ionic signature—of the ions that
make up total conductivity) for which it has been validated. To guard against misuse of the
benchmark, the EPA document should be more explicit about conditions under which the 300
microsiemens per centimeter (jiS/cm) value is applicable, specifying the pH range and the
percent of conductivity from individual ions such as sulfate or bicarbonate.
The field-based approach for inferring stressor-response causality holds tremendous
promise for other regions (and other pollutants) if data sufficiency requirements are met. As
with conductivity, it will be important to assess potential confounding factors (i.e.,
environmental factors other than the stressor of concern) using multiple analytical approaches,
when establishing these causal relationships. If EPA moves forward with application of the
approach to other stressors, the SAB urges a detailed review of the scientific issues (e.g.,
interaction effects, speciation) associated with the particular pollutant.
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We appreciate the opportunity to review the technical documents relating to mountaintop
mining and valley fills and an associated conductivity benchmark. We look forward to your
response.
Sincerely,
/signed/ /signed/
Dr. Deborah L. Swackhamer, Chair Dr. Duncan T. Patten, Chair
Science Advisory Board SAB Mountaintop Mining Panel
Enclosure
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NOTICE
This report has been written as part of the activities of the EPA Science Advisory Board (SAB),
a public advisory group providing extramural scientific information and advice to the
Administrator and other officials of the Environmental Protection Agency. The SAB is
structured to provide balanced, expert assessment of scientific matters related to problems facing
the Agency. This report has not been reviewed for approval by the Agency and, hence, the
contents of this report do not necessarily represent the views and policies of the Environmental
Protection Agency, nor of other agencies in the Executive Branch of the Federal government, nor
does mention of trade names of commercial products constitute a recommendation for use.
Reports of the SAB are posted on the EPA Web site at http://www.epa.gov/sab.
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U.S. Environmental Protection Agency
Science Advisory Board
Panel on Ecological Impacts of
Mountaintop Mining and Valley Fills
(Mountaintop Mining Panel)
CHAIR
Dr. Duncan Patten, Research Professor, Hydroecology Research Program, Department of Land
Resources and Environmental Sciences, Montana State University, Bozeman, MT
MEMBERS
Dr. Elizabeth Boyer, Associate Professor, School of Forest Resources and Assistant Director,
Pennsylvania State Institutes of Energy & the Environment, and Director, Pennsylvania Water
Resources Research Center, Pennsylvania State University, University Park, PA
Dr. William Clements, Professor, Department of Fish, Wildlife, and Conservation Biology,
Colorado State University, Fort Collins, CO
Dr. James Dinger, Head, Water Resources Section, Kentucky Geological Survey, University of
Kentucky, Lexington, KY
Dr. Gwendelyn Geidel, Research Professor/Assistant Director, Dept of Earth & Ocean Sci/
School of the Environment, University of South Carolina, Columbia, SC
Dr. Kyle Hartman, Professor, Wildlife and Fisheries Resources, Division of Forestry and
Natural Resources, West Virgnia University, Morgantown, WV
Dr. Robert Hilderbrand, Professor, Appalachian Laboratory, University of Maryland Center
for Environmental Science, Frostburg, MD
Dr. Alexander Huryn, Professor, Department of Biological Sciences, College of Arts & Sci.,
University of Alabama, Tuscaloosa, AL
Dr. Lucinda Johnson, Center Director, Center for Water and the Environment, Natural
Resources Reserach Institute, University of Minnesota Duluth, Duluth, MN
Dr. Thomas W. La Point, Professor, Department of Biological Sciences, University of North
Texas, Denton, TX
Dr. Samuel N. Luoma, Senior Research Hydrologist, John Muir Institute of the Environment,
University of California - Davis, Sonoma, CA
11
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Dr. Douglas McLaughlin, Principal Research Scientist, Western Michigan University, National
Council for Air and Stream Improvement, Kalamazoo, MI
Dr. Michael C. Newman, Professor of Marine Science, School of Marine Sciences, Virginia
Institute of Marine Science, College of William & Mary, Gloucester Point, VA
Dr. Todd Petty, Associate Professor, Wildlife and Fisheries, Forestry and Natural Resources,
West Virginia University, Morgantown, WV
Mr. Edward Rankin, Environmental Researcher, Ohio University, Athens, OH
Dr. David Soucek, Associate Professional Scientist, Illinois Natural History Survey, Institute of
Natural Resource Sustainability, University of Illinois at Urbana-Champaign, Champaign, IL
Dr. Bernard Sweeney, Director, President, Senior Resaerch Scientist, Stroud Water Research
Center, Avondale, PA
Dr. Philip Townsend, Associate Professor, Forest & Wildlife Ecology, College of Agriculture
and Life Sciences, University of Wisconsin - Madison, Madison, WI
Dr. Richard Warner, Professor, Biosystems & Agr. Engr., College of Agriculture, University
of Kentucky, Lexington, KY
SCIENCE ADVISORY BOARD STAFF
Ms. Stephanie Sanzone, Designated Federal Officer, U.S. Environmental Protection Agency,
Science Advisory Board (1400R), 1200 Pennsylvania Avenue, NW, Washington, DC, Phone:
202-564-2067, Fax: 202-565-2098, (sanzone.stephanie@epa.gov)
in
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U.S. Environmental Protection Agency
Science Advisory Board
CHAIR
Dr. Deborah L. Swackhamer, Professor and Charles M. Denny, Jr., Chair in Science,
Technology and Public Policy, Hubert H. Humphrey School of Public Affairs, and Co-Director
of the Water Resources Center, University of Minnesota, St. Paul, MN
SAB MEMBERS
Dr. David T. Allen, Professor, Department of Chemical Engineering, University of Texas,
Austin, TX
Dr. Claudia Benitez-Nelson, Professor and Director of the Marine Science Program,
Department of Earth and Ocean Sciences, University of South Carolina, Columbia, SC
Dr. Timothy Buckley, Associate Professor and Chair, Division of Environmental Health
Sciences, College of Public Health, The Ohio State University, Columbus, OH
Dr. Patricia Buffler, Professor of Epidemiology and Dean Emerita, Department of
Epidemiology, School of Public Health, University of California, Berkeley, CA
Dr. Ingrid Burke, Director, Haub School and Ruckelshaus Institute of Environment and Natural
Resources, University of Wyoming, Laramie, WY
Dr. Thomas Burke, Professor, Department of Health Policy and Management, Johns Hopkins
Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD
Dr. Terry Daniel, Professor of Psychology and Natural Resources, Department of Psychology,
School of Natural Resources, University of Arizona, Tucson, AZ
Dr. George Daston, Victor Mills Society Research Fellow, Product Safety and Regulatory
Affairs, Procter & Gamble, Cincinnati, OH
Dr. Costel Denson, Managing Member, Costech Technologies, LLC, Newark, DE
Dr. Otto C. Doering III, Professor, Department of Agricultural Economics, Purdue University,
W. Lafayette, IN
Dr. David A. Dzombak, Walter J. Blenko Sr. Professor of Environmental Engineering ,
Department of Civil and Environmental Engineering, College of Engineering, Carnegie Mellon
University, Pittsburgh, PA
Dr. T. Taylor Eighmy, Vice President for Research, Office of the Vice President for Research,
Texas Tech University, Lubbock, TX
IV
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Dr. Elaine Faustman, Professor, Department of Environmental and Occupational Health
Sciences, School of Public Health and Community Medicine, University of Washington, Seattle,
WA
Dr. John P. Giesy, Professor and Canada Research Chair, Veterinary Biomedical Sciences and
Toxicology Centre, University of Saskatchewan, Saskatoon, Saskatchewan, Canada
Dr. Jeffrey K. Griffiths, Associate Professor, Department of Public Health and Community
Medicine, School of Medicine, Tufts University, Boston, MA
Dr. James K. Hammitt, Professor, Center for Risk Analysis, Harvard University, Boston, MA
Dr. Bernd Kahn, Professor Emeritus and Associate Director, Environmental Radiation Center,
Georgia Institute of Technology, Atlanta, GA
Dr. Agnes Kane, Professor and Chair, Department of Pathology and Laboratory Medicine,
Brown University, Providence, RI
Dr. Madhu Khanna, Professor, Department of Agricultural and Consumer Economics,
University of Illinois at Urbana-Champaign, Urbana, IL
Dr. Nancy K. Kim, Senior Executive, Health Research, Inc., Troy, NY
Dr. Catherine Kling, Professor, Department of Economics, Iowa State University, Ames, IA
Dr. Kai Lee, Program Officer, Conservation and Science Program, David & Lucile Packard
Foundation, Los Altos, CA
Dr. Cecil Lue-Hing, President, Cecil Lue-Hing & Assoc. Inc., Burr Ridge, IL
Dr. Floyd Malveaux, Executive Director, Merck Childhood Asthma Network, Inc., Washington,
DC
Dr. Lee D. McMullen, Water Resources Practice Leader, Snyder & Associates, Inc., Ankeny,
IA
Dr. Judith L. Meyer1, Professor Emeritus, Odum School of Ecology, University of Georgia,
Lopez Island, WA
Dr. James R. Mihelcic, Professor, Civil and Environmental Engineering, University of South
Florida, Tampa, FL
Dr. Jana Milford, Professor, Department of Mechanical Engineering, University of Colorado,
Boulder, CO
1 Did not participate in this review.
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Dr. Christine Moe, Eugene J. Gangarosa Professor, Hubert Department of Global Health,
Rollins School of Public Health, Emory University, Atlanta, GA
Dr. Horace Moo-Young, Dean and Professor, College of Engineering, Computer Science, and
Technology, California State University, Los Angeles, CA
Dr. Eileen Murphy, Grants Facilitator, Ernest Mario School of Pharmacy, Rutgers University,
Piscataway, NJ
Dr. Duncan Patten, Research Professor, Hydroecology Research Program , Department of Land
Resources and Environmental Sciences, Montana State University, Bozeman, MT
Dr. Stephen Polasky, Fesler-Lampert Professor of Ecological/Environmental Economics,
Department of Applied Economics, University of Minnesota, St. Paul, MN
Dr. Arden Pope, Professor, Department of Economics, Brigham Young University, Provo, UT
Dr. Stephen M. Roberts, Professor, Department of Physiological Sciences, Director, Center for
Environmental and Human Toxicology, University of Florida, Gainesville, FL
Dr. Amanda Rodewald, Professor of Wildlife Ecology, School of Environment and Natural
Resources, The Ohio State University, Columbus, OH
Dr. Jonathan M. Samet, Professor and Flora L. Thornton Chair, Department of Preventive
Medicine, University of Southern California, Los Angeles, CA
Dr. James Sanders, Director and Professor, Skidaway Institute of Oceanography, Savannah,
GA
Dr. Jerald Schnoor, Allen S. Henry Chair Professor, Department of Civil and Environmental
Engineering, Co-Director, Center for Global and Regional Environmental Research, University
of Iowa, Iowa City, IA
Dr. Kathleen Segerson, Philip E. Austin Professor of Economics , Department of Economics,
University of Connecticut, Storrs, CT
Dr. Herman Taylor, Director, Principal Investigator, Jackson Heart Study, University of
Mississippi Medical Center, Jackson, MS
Dr. Barton H. (Buzz) Thompson, Jr., Robert E. Paradise Professor of Natural Resources Law
at the Stanford Law School and Perry L. McCarty Director, Woods Institute for the
Environment, Stanford University, Stanford, CA
Dr. Paige Tolbert, Professor and Chair, Department of Environmental Health, Rollins School of
Public Health, Emory University, Atlanta, GA
VI
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Dr. John Vena, Professor and Department Head, Department of Epidemiology and
Biostatistics, College of Public Health, University of Georgia, Athens, GA
Dr. Thomas S. Wallsten, Professor and Chair, Department of Psychology, University of
Maryland, College Park, MD
Dr. Robert Watts, Professor of Mechanical Engineering Emeritus, Tulane University,
Annapolis, MD
Dr. R. Thomas Zoeller, Professor, Department of Biology, University of Massachusetts,
Amherst, MA
SCIENCE ADVISORY BOARD STAFF
Dr. Thomas Armitage, Designated Federal Officer, U.S. Environmental Protection Agency,
Washington, DC
Dr. Angela Nugent, Designated Federal Officer, U.S. Environmental Protection Agency,
Washington, DC
vn
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Table of Contents
1. EXECUTIVE SUMMARY
2. INTRODUCTION
2.1. BACKGROUND 5
2.2. CHARGE TO THE PANEL 5
3. RESPONSE TO CHARGE QUESTIONS 7
3.1. ADEQUACY OF DATA 7
3.2. FIELD-BASED METHODOLOGY 13
3.3. CAUSALITY BETWEEN EXTIRPATION AND CONDUCTIVITY 19
3.4. ADDRESSING CONFOUNDING FACTORS 21
3.5. UNCERTAINTY IN THE BENCHMARK 22
3.6. COMPARING THE BENCHMARK TO A CHRONIC ENDPOINT 23
3.7. TRANSFERABILITY OF THE METHOD TO OTHER REGIONS 25
3.8. TRANSFERABILITY OF THE METHOD TO OTHER POLLUTANTS 28
REFERENCES R-l
APPENDIX A: CHARGE TO THE PANEL A-l
Vlll
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1. EXECUTIVE SUMMARY
The draft EPA document, A Field-based Aquatic Life Benchmark for Conductivity in
Central Appalachian Streams, March 2010 draft (USEPA, 2010a), defines a benchmark value
for conductivity of streams. Conductivity is a measure of the electrical conductance in water,
and is related to the major charged ions that are dissolved in waters. The benchmark
conductivity value for streams in this region was determined to be 300 |iS/cm, with 95%
confidence bounds of 225 to 305 jiS/cm. This value was developed using field data relating
conductivity levels in streams with loss of aquatic insect genera. The benchmark is intended to
protect 95% of aquatic taxa in streams in the Appalachian Region influenced by mountaintop
mining and valley fill (MTM-VF). Using field measures of the presence or absence of
macroinvertebrate (insect) genera and conductivity, the Agency calculated the conductivity
concentration below which 95% of occurrences of a genus were observed. This value was
termed the extirpation concentration (XCgs) because the genus was effectively not found in areas
where conductivity exceeded that concentration. This procedure was repeated for genera that
naturally occur in high quality (i.e., reference) sites within the study area, and the calculated
XCgs values were used to construct a "species sensitivity distribution" (SSD) for
macroinvertebrate genera. The conductivity benchmark is based on the hazardous concentration
values at the 5th percentile of the SSD (the HCos).
An extensive field data set from West Virginia was used to estimate the conductivity
benchmark. A second, independent data set from Kentucky, where similar environmental
conditions and MTM-VF occur, was used to validate the method. Applying the methodology to
this second data set produced a benchmark value of 319 jiS/cm, with 95% confidence bounds of
180to429|iS/cm.
The draft EPA document also describes the weight-of-evidence supporting a causal
relationship between conductivity levels in Appalachian streams and the presence/absence of
stream taxa. Causal criteria similar to those used in epidemiology were applied to the stressor-
biological response relationship of concern. The report also summarizes analyses conducted to
evaluate the potential that other environmental stressors (confounding factors) were contributing
to observed patterns of genera occurrence.
The SAB Mountaintop Mining Panel (the Panel) met on July 20-22, 2010 to review the
draft conductivity report, and held a follow-up public teleconference call on October 20, 2010.
The Panel's responses to the charge questions are summarized below. (For the Panel's
comments on the EPA document on the effects on aquatic ecosystems of mountaintop mining
and valley fills, see the companion SAB report, EPA-SAB-11-005).
Adequacy of Data
The information used to develop the conductivity benchmark was derived from portions
of two ecoregions (Ecoregions 69 and 70) in WV and KY, and these data were deemed adequate
to establish a quantitative relationship between conductivity and benthic community responses in
the sampled region. The primary sample set from WV provides broad spatial coverage and
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includes a large number of streams with and without MTM-VF impacts. Therefore, the
relationships established between conductivity and the probability of extirpation for these genera
are relatively robust. The similarity of conductivity benchmarks derived from this analysis (300
|iS/cm) and from an independent dataset from KY (319 |iS/cm) provides an important validation
of the approach and the quality of the data, especially because data were collected by different
agencies using different techniques.
However, the background conductivity values at reference sites in the WV portions of the
two ecoregions were markedly different (75* percentiles were 110 and 198 |iS/cm in Ecoregions
69 and 70, respectively). The EPA document should comment on the reason for these
differences between reference sites and discuss the extent to which a benchmark conductivity
value developed for Ecoregion 70 also would protect sensitive species in Ecoregion 69. Further,
the Panel recommends that the benchmark value not be applied to other areas of Ecoregions 69
and 70, beyond the boundaries of the geographic coverage of the current data set, without
additional validation.
One of the most important considerations for the proposed approach is the decision to use
extirpation of genera as an effects endpoint. The complete loss of a genus is an extreme
ecological effect and not a chronic response. Thus, a benchmark based on extirpation may not be
protective of the stream ecosystem. A "depletion concentration", defined as the level of a
stressor that results in a specified reduction in abundance, may be a more appropriate endpoint
than extirpation for development of a conductivity benchmark.
In addition, the Panel was concerned that only macroinvertebrate genera were used to
develop the benchmark. Although the WV database did not include fish, amphibians, or long-
lived macroinvertebrates such as mollusks, it would be instructive to compare the differential
response to conductivity among organisms such as these where possible. Rare species also were
excluded from the analysis. Rare species often are among the most sensitive taxa in a
community, and their elimination from the data pool could skew the results towards more
tolerant organisms.
Field-Based Methodology
The Panel agreed that the use of a field-based approach to developing the benchmark was
justified. Neither the approach nor the benchmark is perfect, but they provide improvement over
a benchmark that might have been derived from laboratory data using test species that are not
native to the region and do not reflect the broad range of life stage and life history strategies.
Thus, the benchmark likely provides a degree of protection comparable to or greater than a
conventional ambient water quality criterion derived from traditional chronic toxicity testing.
However, the Panel was concerned with the use of HCos in the methodology. Accepting a loss of
5% of genera could eliminate entire groups of related species that are vulnerable to elevated
concentrations of particular dissolved ions for mechanistic reasons particular to their taxa. For
the streams in question, the HCos would allow the loss of headwater genera (primarily mayflies)
that are common in unaffected streams, and that might be key to certain ecological functions.
Subject knowledge (e.g., from peer-reviewed literature on relevant stream ecosystems) could be
employed to modify the benchmark if necessary to conserve important taxa of headwater
streams.
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Multiple analytical approaches (e.g., quantile regression, logistic regression, conditional
probability analysis), as well as other study types (e.g., mesocosm and/or intensive site-specific
field investigations) could be used to support and complement field-based SSDs in a weight-of-
evidence approach.
Although the field-based approach is sound, the report would be improved by further
justification of the methodology and the chosen benchmark. For example, the report should
more clearly describe the many limitations with the extrapolation of laboratory data to nature. In
addition, the report should better support the use of conductivity as an indicator rather than the
concentration of particular ions or ion ratios. The report also should discuss the sensitivity of the
benchmark to the assumptions and constraints on the data set.
Causality between Extirpation and Conductivity
Building a strong case for causality between conductivity and loss of genera requires that
two linkages be demonstrated: (1) a strong relationship between stream conductivity and the
amount of MTM-VF in the upstream catchment, and (2) a strong relationship between elevated
stream conductivity and loss of benthic macroinverterate taxa. The EPA document presents a
convincing case for both linkages. To further strengthen the scientific basis for the benchmark,
the Panel recommends that the document include more information on the constituent ions that
contribute to conductivity at the sampled sites, and on the likely mechanisms of extirpation
produced by the constituent ions.
Confounding Factors
The report has done a credible job in isolating the major, potential confounding factors
and providing a basis for their assessment relative to the potential effect of conductivity.
However, the report would be strengthened by further attention to potential confounding factors
such as selenium and other trace metals, dissolved organic carbon, and hydrologic flows.
Further use of quantitative statistical analyses would be helpful for understanding causality and
the potential role of confounding factors.
Uncertainty in the Benchmark
The Panel commends the Agency for providing a characterization of the uncertainty in
the benchmark, reflected in the XCgs values, but suggests that the EPA document provide
additional detail on how the confidence bounds were generated. In addition, the document
should note other categories of uncertainty in the benchmark (e.g., uncertainties in the
assignment of cause and effect) that are not included.
Comparing the Benchmark to Chronic Endpoints
The Panel found that the general approach, including the use of field data and the
resulting benchmark, is sound and provides a degree of protection comparable to or greater than
a conventional ambient water quality criterion derived from traditional chronic toxicity testing
because the approach includes native taxa and a range of life stages (i.e., early and late instar
larvae, and adults). The field-based benchmark is probably more reflective of changes in the
invertebrate community in response to changes in conductivity than would be chronic toxicity
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tests. The XCgs approach used in this report provides useful and ecologically sound insights;
however, the choice of extirpation as an endpoint and the exclusion of rare taxa may result in a
loss of sensitivity.
Transferability to Other Regions and Other Pollutants
The Panel concluded that the field-based method used to develop the conductivity
benchmark was quite general and sufficiently flexible to allow the approach (though not the
benchmark value) to be transferred to other regions with different ionic signatures, where
minimum data requirements are met. Important conditions that should be met include
availability of high quality reference sites, a common regional pool of genera, similar levels of
background conductivity and ionic composition across the region, and a large field data set. The
approach also seemed applicable to other stressors—particularly where there is a relatively direct
physiological mechanism and effect linking the stressor and the occurrence of taxa—where data
coverage and quality are complete. However, change points in taxa abundances might be the
more appropriate choice for SSD statistics than an extirpation curve.
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2. INTRODUCTION
2.1. Background
EPA's Office of Research and Development (ORD) requested that the Science Advisory
Board (SAB) review the Agency's draft reports entitled The Effects ofMountaintopMines and
Valley Fills on Aquatic Ecosystems of the Central Appalachian Coalfields (draft Aquatic
Ecosystem Effects Report) and A Field-based Aquatic Life Benchmark for Conductivity in
Central Appalachian Streams (draft Conductivity Benchmark Report; USEPA 2010a). The
reports were developed by ORD's National Center for Environmental Assessment at the request
of EPA's Office of Water (OW) and Regions 3, 4, and 5, to provide scientific information to
support a set of actions EPA is undertaking to clarify and strengthen environmental permitting
requirements for Appalachian surface coal mining operations.
In a detailed guidance memorandum (dated April 1, 2010), EPA lays out steps to be taken
by EPA Regions and states to strengthen permit decision-making for Appalachian surface coal
mining activities. The memorandum notes that the two technical documents mentioned above
are being sent to SAB for review. In the interim, the memorandum provides guidance on the
interpretation of narrative Water Quality Criteria for elevated conductivity, such that projects
resulting in "predicted conductivity levels below 300 jiS/cm generally will not cause a water
quality standard violation and that in-stream conductivity levels above 500 |iS/cm are likely to
be associated with ... exceedences of narrative state water quality standards." The memorandum
also notes that the Agency will evaluate whether changes to these conductivity benchmarks are
appropriate, based on the results of the SAB review.
The Panel met on July 20-22, 2010 to review and provide advice to ORD on the scientific
adequacy, suitability and appropriateness of the two ORD reports. The Panel reviewed the draft
reports and background materials provided by ORD, and considered public comments and oral
statements that were received. The Panel held a follow-up public teleconference on October 20,
2010, and the SAB conducted a quality review of the Panel report on January 19, 2011. The
Panel's advice is provided in two SAB advisory reports. The present document provides advice
on the Conductivity Benchmark Report and a companion SAB report (EPA-SAB-11-005)
discusses the draft Aquatic Ecosystem Effects Report.
2.2. Charge to the Panel
The Agency's Charge to the Panel (Appendix A) included a total of 14 questions, of
which the following 8 relate to the Conductivity Benchmark Report:
Charge Question 1: The data sets used to derive a conductivity benchmark were
developed primarily by two central Appalachian states (WV and KY). Please comment
on the adequacy of these data and their use in developing a conductivity benchmark.
Charge Question 2: The derivation of a benchmark value for conductivity was adapted
from EPA's methods for deriving water quality criteria. The water quality criteria
methodology relies on a lab-based procedure, whereas this report uses a field-based
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approach. Has the report adapted the water quality criteria methodology to derive a water
quality advisory for conductivity using field data in a way that is clear, transparent and
reasonable?
Charge Question 3: Appendix A of the EPA report describes the process used to
establish a causal relationship between the extirpation of invertebrate genera and levels of
conductivity. Has the report effectively made the case for a causal relationship between
species extirpation and high levels of conductivity due to surface coal mining activities?
Charge Question 4: In using field data, other variables and factors have to be accounted
for in determining causal relationships. Appendix B of the EPA report describes the
techniques for dealing with confounding factors. Does the report effectively consider
other factors that may confound the relationship between conductivity and extirpation of
invertebrates? If not, how can the analysis be improved?
Charge Question 5: Uncertainty values were analyzed using a boot-strapped statistical
approach. Does the SAB agree with the approach used to evaluate uncertainty in the
benchmark value? If not, how can the uncertainty analysis be improved?
Charge Question 6: The field-based method results in a benchmark value that the report
authors believe is comparable to a chronic endpoint. Does the Panel agree that the
benchmark derived using this method provides for a degree of protection comparable to
the chronic endpoint of conventional ambient water quality criteria?
Charge Question 7: As described, the conductivity benchmark is derived using central
Appalachian field data and has been validated within Ecoregions 68, 69, and 70. Under
what conditions does the SAB believe this method would be transferable to developing a
conductivity benchmark for other regions of the United States whose streams have a
different ionic signature?
Charge Question 8: The amount and quality of field data available from the states and the
federal government have substantially increased throughout the years. In addition, the
computing power available to analysts continues to increase. Given these enhancements
in data availability and quality and computing power, does the Panel feel it feasible and
advisable to apply this field-based method to other pollutants? What issues should be
considered when applying the method to other pollutants?
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3. RESPONSE TO CHARGE QUESTIONS
3.1. Adequacy of Data
Charge Question 1: The data sets used to derive a conductivity benchmark were developed
primarily by two central Appalachian states (WV andKY). Please comment on the
adequacy of these data and their use in developing a conductivity benchmark.
The information used to develop the conductivity benchmark was derived from portions
of two ecoregions (Ecoregions 69 and 70) in WV and KY , and these data were deemed
adequate by the Panel to establish a quantitative relationship between conductivity and benthic
community responses in the sampled region. The EPA document suggests (e.g., pages xiii and
20, and Figure 1) that the benchmark may be applicable to the entirety of Ecoregions 69 and 70,
including portions in OH, PA, TN and MD. However, as discussed below, the Panel
recommends that the benchmark not be applied outside the geographic bounds of the current data
set without further validation because of differences in the background conductivity levels in
other portions of these ecoregions.
Sample sites were excluded from the analysis if they were collected from large rivers or
had ionic concentrations or composition markedly different from those typically associated with
mountaintop mining and valley fills (MTM-VF). The authors also removed sites with low pH (<
6) from the analysis before identifying extirpation concentrations. Some of these decisions limit
the generality and broad applicability of the conductivity benchmark, but they are appropriate to
ensure that the relationships developed were a function of elevated conductivity and not spurious
correlations. The decision to omit data from sites where organisms were not identified to genus
also is appropriate and further enhances the quality of the results; Pond et al. (2008) reported that
data based on family-level identification were less effective for distinguishing effects associated
with high conductivity downstream from MTM-VF areas. In addition, the EPA document
correctly notes that there may be significant variation in sensitivity among species within the
same genus and that these differences should be considered when assessing effects associated
with elevated conductivity.
A total of 2145 samples (from an initial sample of 3286 sites) with macroinvertebrate and
conductivity data met the acceptance criteria and were evaluated from these two ecoregions.
This sample set provides broad spatial coverage and includes a large number of streams with and
without MTM-VF impacts. Therefore, the relationships established between conductivity and
the probability of extirpation for these genera are relatively robust. The similarity of
conductivity benchmarks derived from this analysis (300 |iS/cm) and from an independent
dataset from KY (319 |iS/cm) provides an important validation of the approach and the quality of
the data, especially because data were collected by different agencies using different techniques.
The EPA document states that the WV and KY datasets are well-documented, regulatory
databases with excellent quality assurance. However, more information on the specific methods
2 The KY data set used for validation also included samples from a small portion of Ecoregion 68.
-------
used to sample water conductivity and macroinvertebrates would help in evaluating the quality
of these data. For example, were conductivity measurements standardized and reported at 25
°C? For macroinvertebrates, were quantitative or semi-quantitative techniques employed? What
mesh size was used in the field and laboratory? Were macroinvertebrate samples sub-sampled,
and if so how many organisms were removed? Details of sampling protocols are provided in the
WVDEP reports cited. However, because these methodological details are essential for
evaluating the quality of these data, they also should be provided in EPA's conductivity
benchmark report.
Data from Ecoregions 69 (Central Appalachia) and 70 (Western Allegheny Plateau, or
WAP) were selected because of the high quality of data (water quality and macroinvertebrates),
because the region is currently undergoing significant MTM-VF impacts, and because the two
ecoregions have similar water quality and biota. However, the background conductivity values
at reference sites in the two ecoregions were markedly different (75* percentiles were 110 and
198 |iS/cm in Ecoregions 69 and 70, respectively3). The EPA document should comment on the
reason for these differences between reference sites. For example, do they reflect differences in
underlying geology between central Appalachia and the Allegheny Plateau? More importantly,
do these differences in background conductivity affect macroinvertebrate responses? Is it
possible to estimate HCos values from these 2 ecoregions separately? In other words, would a
benchmark conductivity value developed for Ecoregion 70 also be protective of sensitive species
in Ecoregion 69?
Even within an ecoregion, it is important to consider whether natural background levels
of conductivity are homogeneous enough to derive a single benchmark value for that ecoregion.
In the Ohio portion of Ecoregion 70, for example, water hardness related to conductivity is
higher relative to the datasets from the KY and WV portions of the ecoregion (see Figure 1,
below). In addition, a study of a random subset of wadeable reference sites supported the
generally higher background conductivity (mean of 416 uS/cm) in the Ohio portion of Ecoregion
70 (Figure 2) compared to southern parts of the ecoregion. These data suggest that most
reference sites in the WAP ecoregion in OH would have conductivity values greater than the 300
[j,S/cm benchmark developed using WV data. For subregions with high natural background
conductivity, the genera that comprise the species sensitivity distribution (SSD) might need to be
screened to account for the fact that genera associated with low conductivity/low hardness
conditions would not be expected at reference sites in those areas.
3 Although the draft review document reports 75th percentiles of 100 and 234 uS/cm in Ecoregions 69 and 70, EPA
staff indicated that the correct values are 110 and 198 uS/cm, respectively.
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Figure 1. Data illustrating concentration of hardness across the United States. Note
CONCENTRATION OF HARDNESS AS CALCIUM CARBONATE,
IN MILLIGRAMS PER LITER
Mean hardness as calcium carbonate at NASQAN water-monitoring sites during 1975 water year.
Colors represent site data representing streamflow from the hydrologic-unit rea.
(Map edited by USEPA.2005)
the elevated water hardness in southeast Ohio compared to Kentucky and West
Virginia within Ecoregion 70.
Conductivity - WAP Ecoregion
of Ohio - Wadeble Streams
E
u
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To illustrate the importance of regional differences in biota occurrence, plots are
provided (in Figure 3) of two sensitive genera (Leptophlebia and Ephemerella) sampled in areas
within the Ohio portion of the WAP ecoregion that have, on average, higher stream conductivity.
These plots are similar to those in Figure D-l of the EPA document, where the y-axis is the
probability of occurrence of taxa along a gradient of conductivity generated by dividing the
samples into 20 equal-sized bins and the midpoint of conductivity represents the mean
conductivity within that bin of data. Although the pattern of decline is similar for the WV and
OH data, the concentrations are shifted to the right. This suggests that XCgs values may be
higher if calculated from Ohio data4.
Thus, the conductivity benchmark derived using data from WV may not be applicable to
areas beyond the geographic bounds of the dataset, and the benchmark should not be applied to
other portions of the ecoregions without further validation. Figure 1 in the EPA document
should be revised so that the shaded area labeled "Advisory Area" is restricted to the sampled
region. Furthermore, the figure caption is misleading, and should be revised to note that data
used to develop the benchmark are from the WV portion of Ecoregions 69 and 70, not from the
full ecoregions (which span the states of PA, KY, TN, WV and MD). (See Section 3.7, response
to Charge Question 7, for discussion of the applicability of the method to other regions.)
4The Ohio data set includes some species-level data within these genera, and might permit differential sensitivity
between species to be tested and perhaps sub-ecoregion classifications could be examined. In addition, the Ohio
biological criteria were derived for tiered aquatic life uses (TALUs) and derivation of conductivity or other stressor
benchmarks could vary with the probability of different genera occurring among different aquatic life tiers.
10
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8
D
15 R -
O
B J
o
15
Leptophlebia
— n
id
q
D
_ s
61
123
2221
11646
Conductivity (|jS/cm)
Leptophlebia - Statewide vs. WAP Ecoregion
Conductivity (|jS/cm)
""• _
•
Ephemeral la
-I
--.
1S 61 423 2221 11646
Conductivity (uS/cm)
Ephemerella - Statewide vs. WAP Ecoregion
Conductivity (uS/cm)
Figure 3. Observation probabilities for two genera of aquatic insects used in the EPA conductivity
benchmark report - Leptophlebia (upper left) and Ephemerella (lower left) and similar plots
generated for Leptophlebia in Ohio (statewide and WAP ecoregion, upper right) and Ephemerella
in Ohio (statewide and WAP ecoregion, lower right). (Data for right-most figures from Ohio EPA)
11
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The decision to exclude rare genera (i.e., those that occurred at fewer than 30 sites) is a
necessary practical decision. However, it would be appropriate to acknowledge that rare taxa are
often important for biological assessments (Cao et al., 1998) and may be more sensitive to
elevated conductivity. Species are rare for many reasons, but one of the reasons is greater
sensitivity to environmental stressors (Clements and Newman, 2002). The document also should
provide a specific justification for using < 30 sites as the cutoff point for inclusion of genera in
the analysis. Is this a minimum amount of data necessary to generate a statistically rigorous
species sensitivity distribution (SSD)?
One of the most important considerations for the proposed approach to develop a
conductivity benchmark is the decision to use genera extirpation5 as an effects endpoint. This
issue is briefly addressed in Section 5.8 of the EPA report, but it requires additional
consideration from EPA. Unlike laboratory-derived SSDs, which are based on chronic responses
(e.g., growth, reproduction) or acute lethality (e.g., LCso values), the field-based approach
defines an adverse effect as the loss of a genus from a stream. The complete loss of a genus is an
extreme ecological effect and not a chronic response. Congeneric species can have vastly
different environmental requirements and sensitivities; thus, levels of any stressor need to be
relatively high before an entire species or genus is eliminated from a site. Therefore, as noted in
Section 5.8 of the EPA report, a benchmark based on extirpation may not be protective of the
stream ecosystem. A "depletion concentration", defined as the level of a stressor that results in a
specified reduction in abundance, may be a more appropriate endpoint for development of a
conductivity benchmark. (Additional discussion of extirpation as an endpoint is presented in
Section 3.6, response to Charge Question 6.)
A large data set was available for the development of a conductivity benchmark for the
region. However, the data apparently lack flow (volume/time) measurements and the EPA
document should clarify that data were collected only from perennial streams, and not
intermittent or ephemeral streams. A future effort to collect data on ephemeral streams (which
flow only in response to rainfall/runoff) is needed to fill the gap in data for these systems. A
second concern with the data set is the temporal distribution of the samples - Table 2 of the EPA
document gives a general breakdown, but the report should provide additional detail on month
and/or season of sampling. If, for example, most of the mined sites were sampled in late spring
as opposed to early spring, impacts on insect emergence (which is related to degree day
accumulations) might be missed.
A series of reports published by the USDA Forest Service and EPA (Dyer 1982a; 1982b;
1982c) provide additional water quality data from first-order streams in the Appalachian coal
fields, including conductivity data from unmined and mined first-order streams and watersheds.
While the Forest Service data do not include benthic samples, conductivity values (and other
parameters) from unmined sites would certainly expand the data on background conductivity
levels in the region.
5 Extirpation is the local loss of a species or other taxon, or depletion below levels necessary to maintain a viable
population and/or fulfill ecological community functions. In operational terms for development of the conductivity
benchmark, EPA characterizes a genus as "extirpated" if the probability of capture during field sampling falls below
5 percent.
12
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The Panel was concerned that only macroinvertebrate genera were used to develop the
benchmark. Although the WV database did not include fish, amphibians, or long-lived
macroinvertebrates such as mollusks, it would be instructive to compare the differential response
among organism such as these where possible.
The EPA document should describe the process for defining data quality objectives
(DQOs) and intended uses for the conductivity benchmark following, for example, EPA's
systematic planning and DQO process (U.S. EPA 2006). Although it is clear that the
conductivity benchmark is intended to provide an indication of macroinvertebrate impairment
connected to a causal variable, how this benchmark will be used, for example in regulatory
programs, is not well defined. This is important because the intended uses of the benchmark
may influence the degree of uncertainty that is tolerable or acceptable to decision-makers. If the
DQOs associated with benchmark derivation are defined to fit existing data rather than first
designing a field program necessary to achieve a set of objectives, then the resulting benchmark
may not protect the true 5th percentile genus from adverse impacts, which is the primary
objective of EPA's current aquatic life criteria development guidelines (Stephan et al. 1985).
In ideal circumstances, the data used for the conductivity benchmark would come from
highly controlled laboratory studies using macroinvertebrate species common to the Appalachian
coal-mining region or, in their absence, from a carefully executed project designed to produce
field data as a substitute. In the case presented here, it appears that the objective of developing
an aquatic life benchmark is being adapted to a macroinvertebrate data set used as part of a
Stream Condition Index (SCI) tool to evaluate biological impairment of aquatic life use (see
Pond et al., 2008, page 718). Nonetheless, developing the benchmark using pre-existing field
data gathered in the MTM-VF region is a reasonable, timely, and cost-effective approach. This
assumes, of course, that: (1) the QA/QC measures associated with the studies at the source of the
data were adequate (few details are given); (2) enough data were available even after culling out
data that were confounded for one reason or another; and (3) the source studies for the data
contained adequate reference sites. These assumptions appear to be largely met, although more
information regarding QA/QC would be helpful to put the data into perspective.
3.2. Field-Based Methodology
Charge Question 2: The derivation of a benchmark value for conductivity was adapted
from EPA 's methods for deriving water quality criteria. The water quality criteria
methodology relies on a lab-based procedure, whereas this report uses afield-based
approach. Has the report adapted the water quality criteria methodology to derive a
water quality advisory for conductivity using field data in a way that is clear,
transparent and reasonable?
The Panel agreed that the use of a field-based approach to developing the benchmark was
justified. Neither the approach nor the benchmark is perfect, perhaps because they borrow too
much from the traditional approach, but they provide improvement over a benchmark that might
have been derived from laboratory data using test species that are not native to the region and do
not reflect the broad range of life stage and life history strategies. However, there were a number
of areas where the report did not sufficiently justify the choices made and/or explain why a field-
based approach was a better choice than the traditional laboratory approach.
13
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The field-based approach was justified but not perfect. The goal of the EPA report
was to develop a benchmark to protect benthic communities from adverse effects associated with
elevated conductivity, and this goal was clearly stated. One of the criticisms raised in the public
comments on the field-based approach was that the final data set used in the analysis is highly
caveated, using about 10 different criteria to narrow the data set to circumstances where major
confounding variables are minimized. Constraining the data set is statistically justified in this
case because eliminating obvious confounding situations was the most reasonable way to
establish a benchmark that is minimally confounded by other stressors. The result is a
benchmark that is relevant to effects associated with conductivity
However, the Panel was concerned about the use of HCos in the methodology, an
approach directly derived from the traditional laboratory approach. Accepting a loss of 5% of
genera could have the effect of eliminating entire groups of related species that are vulnerable to
elevated concentrations of particular dissolved ions for mechanistic reasons particular to their
taxa. For the streams in question, the HCos would allow the loss of headwater genera (primarily
of mayflies) that are common in unaffected streams, and that might be key to certain ecological
functions. Better application of subject knowledge—for example, of key attributes of the
undisturbed communities and the role of taxonomic components in important ecosystem
functions—could be employed to modify the benchmark if necessary to conserve many food-
web-important taxa of headwater systems that have XCgs values less than 300 jiS/cm. A field-
based methodology is particularly suited to the use of subject knowledge to protect key taxa (that
are sensitive to elevated ion concentrations). It is not a methodology used in the traditional
laboratory-based approach because the use of surrogate species in toxicity testing is not suitable
to understanding sensitivities of native species. In this case, deviation from the traditional
approach is both justified and recommended.
Compare field-based benchmarks derived from multiple approaches. The use of
field data to derive benchmarks for stressor identification or Total Maximum Daily Load
(TMDL) development has been relatively widespread, although the methods have varied widely.
In a recent review of a draft EPA document, Empirical Approaches for Nutrient Criteria
Derivation, another SAB panel recommended that stressor-response relationships be evaluated
using multiple analytical approaches (e.g., ordinary least squares regression, quantile regression,
logistic regression, conditional probability analysis, and other other quantitative methods) and a
"weight-of-evidence" approach (U.S. EPA SAB 2010c). In the context of the conductivity
benchmark, a similar approach might be useful whereby targets developed by multiple
approaches would at a minimum lend support to the benchmarks derived using the field-derived
SSD.
Some of the other methodologies employ data used as indicators or metrics (e.g.,
Ephemeroptera, Plecotera, Trichoptera-EPT-taxa) in state programs that can provide a level of
comfort with results of the field-derived SSD methodology. State decision-making thresholds
(for Section 401 permitting, determining attainment or impairment of aquatic life uses, etc.) often
are tied directly to biological benchmarks. Demonstration of the links between the field-derived
benchmarks discussed here and assemblage benchmarks used by state programs could influence
how a state applies the proposed conductivity benchmarks. Benchmark values for TMDL
development or stressor identification have been derived using field data by a number of states
and more comparisons with these methodologies would be very useful.
14
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The report should provide clear, complete and transparent justification of the
methodology and the chosen benchmark. There are several areas where it is important that the
clarity and justification of the approach and benchmark be improved.
• The report appropriately references the 1985 guidelines approach, and recognizes the
common aspects of the two approachs; for example, the use of species sensitivity
distributions. However, it is critical to transparency that the report better (and more
explicitly) describe, or perhaps list in one place, the differences in the approach.
• A new methodology based on field data will come under especially heavy scrutiny.
Therefore, the report should more clearly describe the many limitations in extrapolating
from a laboratory approach to nature and reasons why field-based approaches, or a
combination of laboratory and field-based approaches, are preferred. Field data usually
include more taxa and more system-relevant taxa than can be achieved in laboratory tests.
In particular:
o Traditional laboratory surrogates (often crustaceans) are not suitable for testing
the effect of changing major ion concentrations. Mayflies and other groups are
especially sensitive because of common traits probably associated with
osmoregulation. Crustaceans, however, employ a different approach to
osmoregulation that makes them much less vulnerable to high concentrations of
major ions. For this reason, a field-based approach to develop a conductivity
benchmark is preferable to one based on laboratory tests using Ceriodaphnia, for
example, which would be under-protective and misleading.
o Routine testing protocols do not yet exist for the native species most sensitive to
high conductivity. Laboratory studies use species biased towards culture;
culturing methodologies do not exist yet for the species most sensitive to high
conductivity. Thus good methods for deploying a laboratory approach are not
available for evaluating potential toxicity associated with elevated conductivity.
• The report needs to be more explicit, and/or complete, in justifying the use of
conductivity as an indicator rather than particular ions or ion ratios. EPA should make a
strong case up front for how conductivity directly relates to key ionic stressors such that
it can be a surrogate for those parameters. (In Section 3.3, the Panel suggests additional
information that could be included on this topic.)
• The report could include examples relating conductivity to other aquatic effect endpoints
(other than mayflies) to further strengthen the conclusions.
• As mentioned in the previous section, the report should be clear about the extent to which
the data come from perennial streams only. However, the empirical relationship between
conductivity and genera occurrence likely would be applicable to intermittent (but not
ephemeral) streams in the WV area because intermittent streams have a component of
base flow, the traits of vulnerable species are common to all stream types, and because of
connected downstream influences. (Note: the Panel is not commenting on whether the
15
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legal jurisdiction of the NPDES permit program should include perennial or intermittent
streams.)
• The report should discuss the effect on the benchmark of each assumption used to
constrain the data set, including a summary of the sensitivity of the outcome to these
constraints and assumptions (i.e., how alternative approaches or assumptions would alter
the benchmark). Apparently some of this analysis has already been done by EPA but was
not presented in the report. While the Panel understands the Agency's desire to keep the
report of manageable length, a sensitivity analysis of this sort could be presented in
summary tables or figures and perhaps in an appendix where more discussion is
necessary. Examples of questions that could be considered include:
o What is the effect on the benchmark if the requirements for excluding rare species
are relaxed?
o What is the effect on the benchmark of including genera that do not appear at the
reference sites?
o How would adjustments to the choice of season affect the benchmark?
o What is the effect on the benchmark of including fish data (at least using examples
from the small data sets available), so as to address the Stephan et al. (1985) goal
of including all the fauna in the benchmark?
o Would a different benchmark result if the nutrient numerical limit methods
recently released by USEPA (U.S. EPA 201 Ob) were used as an alternative?
o What is the effect if individual major ions (suspected toxins) or ratios are included
instead of conductivity, where data are available?
o How does the benchmark change if abundance-weighted analyses are used instead
of presence/absence?
o How would quantile regression affect the choice of benchmark?
• Appendix E of the EPA document should provide additional detail on the analysis of
data from Kentucky that is used to support the validation of the conductivity benchmark
and the field-based approach. The authors apparently conduct a similar data analysis
process with an apparently similar data set and obtain "similar results" in terms of a
derived conductivity benchmark. The appendix includes XCgs values for all genera
(Tables E-3 and E-4) and presents results of SSDs for all-year, spring and summer
sampling periods (Figure E-2 and E-3). However, the appendix does not contain a
results/discussion section. Consequently, the authors seem to proceed directly from a
discussion of methods to a conclusion that the method is "robust." Also, no causal
analysis is presented in Appendix E. This is a critical element in support of the
conductivity benchmark, and it should be repeated as a part of the validation of the
approach.
Additional guidance is required on the conditions under which the conductivity
benchmark is applicable to a stream. In the EPA document, the authors note repeatedly (e.g.,
p. xii, xiii, 1, 2, 4, 6, 19, 20) that the "aquatic life benchmark for conductivity is applicable for
streams in the Appalachian Region where conductivity is dominated by salts of SO 4 and
HCOi at circum-neutral to mildly alkalinepH [emphasis added]." Such constraints on the
applicability of the benchmark are very important, but are not adequately defined in the
16
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document. In fact, the report never quantifies the percentage of conductivity generated by
individual ions or compounds such as sulfate or bicarbonate, a method that would be required to
assess the "dominant" contributors to conductivity. Rather, the report apparently uses
concentration thresholds, rather than dominance of conductivity as stated, to establish
applicability of the benchmark. This issue is presented only in the context of stream site data
that were excluded from developing the benchmark. For example, Page 6 of the EPA report
states that: "[Data] were excluded if the salt mixture was dominated by Cl" rather than 864 2"
(conductivity > 1000 |iS/cm, SO4 2"< 125 mg/L, and Cl"> 250 mg/L)." Similarly, the required
"circum-neutral" pH range is not defined explicitly. This is only presented in the context of
stream site data that were excluded from developing the XCgs values in the consideration of
confounding variables - with stream site data that were excluded if pH < 6, and no mention of an
upper pH bound. Additionally, background conductivity levels in some areas of the Appalachian
Region may limit applicability of the benchmark (see discussion in Section 3.1). Overall, the
criteria to establish applicability of the benchmark and methodology need to be defined explicitly
and clarified.
The EPA report should highlight that comparing values of concentration in mass units
(e.g., mg/L) for different ions is not a valid way to compare their quantities or to assess which
constituents are dominant. Concentrations in mass units (e.g., mg/L) are useful in practical
application and are used for values for drinking water standards, toxicity limits, etc, but they
should not be used when quantifying relationships between concentration and conductivity.
Given the focus here on conductivity - ability of water to conduct an electric current - defining
concentrations in equivalent units (e.g., |ieq/L) is appropriate. Equivalent weight units
(calculated as the formula weight divided by the electrical charge) incorporate the chemical
behavior of a solute; one equivalent is the amount of ion required to cancel out the electrical
charge of an oppositely charged monovalent ion. Thus, the Panel recommends that Figure 1
(page 24), Figure 1 la-e (Pages 36-40) and related information in the EPA report aiming to show
relations among ions and conductivity be re-cast in equivalent units (e.g., |ieq/L) rather than
mass units (mg/L). An excellent reference providing information on how to convert water
chemistry units is provided by Hem (1985). Further, it is important that information on
ions/compounds that dominate conductivity be presented as the percent of conductivity made up
by these individual constituents. The amount of conductivity generated by an equivalent unit of
sulfate is very different than the amount of conductivity generated by an equivalent unit of
chloride or bicarbonate. This can be done by calculating the equivalent ionic conductance of
each of the individual matrix ions, and their contributions to the overall conductance of the water
solution (e.g., following Laxen 1977, with summary tables presented by Boyd 2000).
To illustrate the importance of these comments, data are provided for 40 forested,
headwater streams in central Pennsylvania, relatively unimpacted by human activities, with
about half located in the Appalachian region of Ecoregions 67 and 70 (Table 1, below).
Information on concentration (table -left) portrays a very different picture of the importance of
individual ions when compared to information on the percent of conductivity they generate
(table-right). In these streams there is not a single one where the fraction of conductivity
generated by (sulfate + bicarbonate) is greater than 50%; rather, conductivity is dominated by the
other ions.
17
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Table 1. Conductivity and Ion Concentrations in 40 Headwater Pennsylvania Streams during Summer Base Flow
(Source: E. Boyer, unpub. data)
Code
BB21
BB14
BB27
BB12
BB33
BB15
BB2
BB20
BB22
BB11
BB36
BB3
BB37
BB34
BB29
BB35
BB8
BB38
BB24
BB31
BB6
BB25
BB13
BB19
BB1
BB4
BB7
BB16
BB23
BB9
BB32
BBS
BB18
BB10
Cond
uctivit
y
uS/cm
% from
matrix
ions
Cond.
203.6
100.2
174.7
89.7
59.1
75.2
47.3
126.4
233.5
189.8
106.8
94.6
121.3
58.9
210.1
23.2
49.8
137.0
22.1
214.1
68.6
220.8
110.1
25.9
44.1
81.1
255.2
198.5
49.6
220.6
199.2
109.7
158.9
56.7
99.4
99.6
99.6
99.7
99.7
99.7
99.8
98.7
99.8
99.6
99.9
99.2
99.7
99.6
99.6
99.7
98.6
99.0
99.6
99.9
99.9
99.4
98.5
99.9
99.6
99.9
99.9
99.9
99.4
99.8
99.1
99.4
99.7
99.6
Matrix Ions - concentrations
mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L
PH
7.0
6.4
7.4
6.7
7.2
6.7
6.8
7.0
7.7
7.1
6.9
7.0
7.6
6.7
7.8
7.0
7.5
7.9
6.2
7.8
7.4
7.9
7.4
6.6
6.8
7.3
8.0
7.6
7.6
7.5
8.0
7.5
7.0
7.4
SO4 DIC NO3 Q Ca Mg Na K
8.7 1.5 0.8 58.2 5.9 1.9 24.6 1.0
12.7 0.7 3.2 19.0 3.5 2.4 6.5 1.8
8.0 7.3 3.9 32.9 10.5 4.3 12.1 2.3
12.4 1.8 1.1 15.0 5.0 1.9 5.9 1.2
8.6 2.1 1.4 4.2 6.3 1.0 2.1 0.9
17.9 1.2 1.6 8.1 3.7 1.9 4.1 1.3
9.7 1.5 1.5 3.5 4.8 1.1 0.9 0.5
19.2 6.6 0.6 10.6 10.9 3.3 5.8 1.4
16.8 17.2 1.4 28.1 18.7 4.5 16.0 2.5
49.1 3.5 1.4 15.1 17.1 6.1 5.7 1.3
11.0 7.5 1.5 5.3 10.5 2.6 4.5 1.2
19.8 3.1 1.5 7.4 7.9 2.4 2.9 0.9
8.5 9.5 2.5 8.5 12.7 2.9 4.6 1.5
13.4 5.6 1.6 4.7 7.5 2.4 3.3 0.9
20.6 16.7 5.3 12.0 24.9 7.2 7.3 1.4
3.3 1.7 1.4 0.8 2.4 0.8 0.4 0.4
8.2 2.6 0.9 3.3 5.1 1.1 1.0 0.6
5.8 13.0 3.0 4.0 21.5 2.3 2.8 1.1
7.1 0.4 0.3 0.9 2.0 0.6 0.6 0.4
12.3 21.8 0.9 16.2 23.4 6.9 8.7 2.4
13.2 2.8 1.3 3.7 6.2 1.1 2.2 0.7
24.3 16.8 1.3 10.6 24.6 5.8 7.5 2.0
14.6 8.0 4.0 4.7 10.1 3.6 2.3 1.4
7.3 0.7 0.3 0.8 2.2 0.6 0.7 0.4
11.2 1.8 1.1 1.2 4.6 1.1 0.5 0.5
14.7 5.0 3.2 1.5 11.2 1.4 0.4 0.9
21.1 25.0 3.6 11.3 29.3 7.0 8.7 2.7
19.8 18.6 1.4 12.4 17.9 5.3 8.5 2.4
10.7 2.6 0.9 1.5 5.8 1.0 0.8 0.7
20.1 21.5 1.3 11.0 21.2 5.3 8.3 2.9
5.1 24.7 6.0 3.3 30.0 6.1 1.7 0.7
16.0 7.8 1.2 4.7 8.9 3.2 2.6 1.3
16.4 16.8 0.4 4.9 16.0 4.7 3.3 2.3
12.2 3.0 1.7 1.1 6.7 0.8 0.4 0.7
Matrix ions - % contribution to total conductivity
% % % % % % % %
SO4 HCO3 O NO3 Ca Mg Na K
5 3 47 0 8 5 31 1
17 2 34 3 11 13 18 3
5 18 29 2 16 11 16 2
17 7 28 1 17 10 17 2
18 16 12 2 32 8 9 2
30 5 18 2 15 13 14 3
25 12 12 3 30 11 5 2
18 19 13 0 24 12 11 2
8 30 17 0 20 8 15 1
30 7 13 1 25 15 7 1
12 26 81 28 12 10 2
26 13 13 1 25 12 8 1
8 32 10 2 27 10 9 2
19 22 92 25 13 9 2
10 30 82 28 14 7 1
15 26 55 27 15 4 2
19 22 10 2 29 10 5 2
4 38 42 39 7 4 1
39 3 61 26 12 7 3
6 37 10 0 25 12 8 1
24 19 92 27 8 8 2
12 32 70 29 11 8 1
15 29 63 25 15 5 2
36 9 51 26 12 7 3
31 14 52 31 12 3 2
20 25 33 37 8 1 2
8 38 61 27 11 7 1
10 36 91 22 11 9 2
24 23 42 32 9 4 2
10 38 70 24 10 8 2
2 45 22 34 11 2 0
17 31 71 23 14 6 2
11 37 40 26 13 5 2
26 23 33 34 7 2 2
so4+hco3
8
19
23
24
34
35
37
37
37
38
38
39
40
40
40
41
41
42
42
43
43
44
44
45
45
46
46
47
47
48
48
48
48
49
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3.3. Causality Between Extirpation and Conductivity
Charge Question 3: Appendix A of the EPA report describes the process used to establish a
causal relationship between the extirpation of invertebrate genera and levels of
conductivity. Has the report effectively made the case for a causal relationship between
species extirpation and high levels of conductivity due to surface coalmining?
To build a strong case for causality, two linkages must be demonstrated: (1) a strong
relationship between stream conductivity and the amount of MTM-VF in the upstream
catchment, and (2) a strong relationship between elevated stream conductivity and loss of benthic
macroinvertebrate taxa. The EPA document presents a convincing case for both linkages.
Linking stream conductivity and the amount of MTM-VF in the upstream catchment
The EPA document makes a convincing case that stream conductivity increases below
valley fills and that the greater the valley fill extent (as a percent of land use in the watershed),
the higher the level of conductivity. The authors further make a convincing case that high
conductivity waters dominated by sulfate and bicarbonate, but low chloride, are associated with
mining activity. Both natural (e.g., weathering-related) and anthropogenic (e.g., atmospheric
deposition) sources of conductivity exist, even in areas unimpacted by mining. However, the
correlation analysis and Figure A-3 in the EPA document show convincing support for a very
strong signal between the percent valley fill and conductivity (dominated by sulfate and
bicarbonate), while the same analyses show weak relationships between conductivity and other
potential suspect variables (e.g., percent forest, percent urban).
Linking elevated stream conductivity and loss of benthic macroinvertebrate genera
The general consensus of the Panel is that a convincing case has been made relating
elevated conductivity and extirpation of invertebrate genera. While the analyses primarily focus
on the mayflies (Ephemeroptera), supporting evidence from other groups was also included (as
shown in Fig.s A-l, A-2 of the EPA report). The authors demonstrated a negative correlation
between conductivity and the number of Ephemeroptera genera, and to a lesser extent, the total
number of genera. These correlations held when sites with elevated levels of potential
confounders were removed. The EPA document presents a plausible physiological mechanism
for the effect of exposure to elevated concentrations of ions (i.e., the need for freshwater
invertebrates to maintain internal osmotic pressure and ion balance in dilute media; the presence
of specialized ionoregulatory cells or tissues in some insect orders; the dependence of other
physiological processes on ion balance). The data demonstrate consistency in patterns of loss of
specific taxa associated with elevated conductivity; in the present study and another published
study, similar groups of genera were the most sensitive to conductivity. Finally, the authors
made a case for sufficiency, i.e., that exposed taxa experienced a sufficient magnitude of
exposure to elicit an effect (but see comments below). For example, effect levels for Isonychia
spp. from the literature were similar to the XCgs for that genus in the present study.
In the absence of major confounders, the field-based data are more indicative of actual
responses because the organisms are exposed to the potential stressor throughout their entire
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lives, and they show an integrated effect that accounts for the potential for additional stress that
laboratory studies simply cannot mimic.
Although we believe the authors have made a strong case linking elevated conductivity
and extirpation of genera, there are a number of important points and recommendations to
consider:
• Conductivity itself is not a pollutant, but is a surrogate measure for the maj or
constituent ions in the mixture. Thus, the supporting information presented by the
authors may be representative of a combination of effects of the constituent ions.
Furthermore, if there are unaccounted for factors that may be confounding the causal
relationship between stress from specific ions and taxa loss (e.g., dietary selenium
exposure or slight reductions in habitat quality), conductivity may still be interpreted
as a signal for the presence of the combination of factors resulting from the presence
of upstream VF. The EPA document should include more information on the likely
mechanisms of extirpation produced by the constituent ions because stress is not due
to conductivity itself, but rather is linked to volume regulation, ion regulation and
osmoregulation. There is a rich literature on this central physiological theme and
reference to this literature will further strengthen the case for conductivity as a
reliable surrogate measure (e.g., see Nemenz 1960; Gainey and Greenberg 1977;
Schoffeniels and Gilles 1979; Kapoor 1979; Pierce 1982; Dietz et al. 1998; Scholz
and Zerbst-Boroffka 1998). In addition, data figures in the document showing SSD
as a function of conductivity would be enhanced by the inclusion of a second x-axis
that indicates a metric of ionic strength or other measure more directly related to
osmotic/ionic/volume stress.
• Mixture calculations for the constituent ions should be made to better understand their
role and contribution, showing the percent contribution to conductivity from each of
the various matrix ion constituents (e.g., see Lax en 1977; Boyd 2000). EPA's
Environmental Monitoring and Assessment Program (EMAP) has used this method in
the past to explore surface water chemistry (EPA 1985). Conductivity balance
calculations may help to guide the transferability of the method to regions with
differing ionic signatures. However, the relationships between conductivity and
specific ions in the current report all appear to be strong and similar in distribution,
suggesting that ion ratios are relatively similar across the sites.
• The authors should take care to ensure that literature studies selected to support
"Sufficiency" in the analysis are drawn from areas with similar ionic signatures to the
advisory area. Supporting data for conductivity effect levels were based on
potassium salts, which are not present in important concentrations in the West
Virginia system. As stated above, going outside the ecotoxicological literature to the
ionoregulation literature may provide supporting evidence.
• We also caution the authors on the interpretation of evidence with respect to
"Alteration" (Section A.2.4 in the EPA document). The effect is consistent, but
perhaps not so specific. Metals may produce a similar effect (i.e., loss of mayfly
genera).
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3.4. Addressing Confounding Factors
Charge Question 4: In using field data, other variables and factors have to be accounted for
in determining causal relationships. Appendix B of the report describes the techniques for
dealing with confounding factors. Does the report effectively consider other factors that may
confound the relationship between conductivity and extirpation of invertebrates (genera)? If
not, how can the analysis be improved?
The Panel commends the authors for carefully considering factors that may confound the
relationship between conductivity and extirpation of invertebrate genera. This was accomplished
by: (1) removing some potentially confounding factors from the data set before determining the
benchmark concentrations; and (2) considering weight-of-evidence of a suite of other potentially
confounding factors that were not excluded from the data set - using correlations between
potential confounding factors, conductivity, and aquatic genera (mayflies). The report has done
a credible job in isolating the major, potential confounding factors and providing a basis for their
assessment relative to the potential effect associated with conductivity.
The use of mayflies as the aquatic response variable in the analyses of confounding
factors was appropriate. It would be helpful to reiterate in Appendix B that the hypothesis that
conductivity is the primary variable explaining patterns of mayfly taxonomic richness was
addressed earlier (in Appendix A of the EPA document), and that this hypothesis could not be
rejected due to weight of evidence.
The Panel emphasizes the importance of clarifying the relationship between conductivity
and the matrix ions that generate conductivity. The document as a whole has not provided
sufficient clarity regarding the relative importance of conductivity (i.e., the effect of
salinity/ionic strength on an organism's ionic balance) versus specific ionic constituents as causal
variables. This contributes to the lack of clarity in whether an individual constituent (e.g.,
sulfate), total ionic strength, or some other single or combination of chemicals is the most
appropriate causal factor. Further, questions remain about the potential effect on aquatic life of
minor constituents that do not greatly shape conductivity, including organics (e.g., dissolved
organic carbon), trace metals (e.g., iron, aluminum, zinc) and trace minerals (e.g., selenium).
Given the focus of the public comments, the discussion of confounding factors may well
be one of the most critical parts of the benchmark report. Thus, the Panel recommends that the
report be strengthened by considering the following additions:
• Address additional potential confounding factors, including further attention to selenium
and other trace metals, dissolved organic carbon, and flows.
o Trace metals and minerals (e.g., selenium) and organic matter (e.g., dissolved
organic carbon) may not contribute substantially to the conductivity of
freshwaters, but are tightly linked to other changes in flow and water quality.
o Flow conditions and base flows also may influence conductivity levels; in some
cases high flow is associated with high conductivity (particularly if sulfate
predominates) and in other cases high flow is associated with low conductivity
(more likely if bicarbonate dominates the system) (e.g., see Geidel 1979).
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o Several panelists suggested the potential importance of the undisturbed
hyporheos, noting that the survivorship of larval forms depends on an extant,
vibrant hyporheos and this was not covered, per se, in the report.
o A more detailed analysis of substrate composition and vegetation, factors known
to greatly affect macroinvertebrate communities, would improve the analysis of
macroinvertebrate responses to conductivity levels and potential confounding
factors.
• Consider further use of quantitative statistical analyses for understanding causality and
the potential role of confounding factors. Because parametric procedures have been used
successfully elsewhere to evaluate multivariate environmental data sets and can provide a
relatively objective, quantitative framework for data analysis, a more rigorous statistical
analysis should be contained in the document. Further, it would be helpful for the
authors to clarify whether nonparametric multivariate methods, such as non-metric
multidimensional scaling, were considered. At a minimum, the EPA document should
discuss the pros and cons of multivariate statistical methods (such as multiple linear
regressions, principal components analysis and canonical correlations, factor analyses,
and partial correlations) and explain why these approaches were not applied.
3.5. Uncertainty in the Benchmark
Charge Question 5: Uncertainty values were analyzed using a boot-strapped statistical
approach. Does the SAB agree with the approach used to evaluate uncertainty in the
benchmark value? If not, how can the uncertainty analysis be improved?
The Panel commends the Agency for providing a characterization of the uncertainty in
the benchmark, reflected in the XCgs values. Several authors (Barnett and O'Hagan 1997; Reiley
et al. 2003; Hope et al. 2007) describe the need for and value of quantitative expressions of
uncertainty in water quality criteria and guidance values (a water quality "benchmark" in this
case). Benefits include improved characterization and communication of the reliability of a
criterion; more realistic risk assessments; more frequent inclusion of uncertainty into decision-
making; and a better appreciation of the potential for a criterion to be over- or under-protective
(Reiley et al. 2003). Although the boot-strapped statistical approach is appropriate to
characterize uncertainty in the XCgs values, the EPA document also should discuss other sources
of uncertainty that are not reflected in the confidence limits.
The bootstrap resampling approach appears to be sound and consistent with techniques
found in peer-reviewed literature. Bootstrapping is commonly used in environmental studies to
estimate confidence limits of a parameter, and the method has been used in the estimation of
HCos values (e.g., Newman et al. 2000). However, in addition to the reference to Efron and
Tibshirani (1993), it would be helpful for the document to briefly discuss other examples of the
use of bootstrapping in relevant water resources applications.
In addition, certain aspects of the approach are not sufficiently clear. For example, with
the ranges of the confidence intervals for the 35 genera shown in Figure 7 of the EPA report,
how is the interval reported for the benchmark (confidence interval of 225 to 305 |iS/cm about
the benchmark of 300 jiS/cm) derived? We recommend that the authors provide a more detailed
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description of the method used, with both narrative and figures, detailing how to generate the
bootstrap means/confidence intervals for each genus of interest, and how the data generated from
the bootstrapping procedure is used to derive confidence limits on the proposed benchmark.
Some discussion also is needed of why 1000 was selected as the appropriate number of
resamples. What were the trade-offs between the reliability/repeatability of the confidence limits
versus a larger number of resampling events? Although 1000 is commonly used to derive
bootstrap confidence limits, the reader may benefit from more discussion of the basis for this
choice.
Finally, although confidence limits for the benchmark that reflect uncertainty and
variation in the extirpation data are important and useful, there are other uncertainties in the
benchmark that are not assessed using the bootstrap resampling procedure. For example,
uncertainties in the assignment of cause and effect between specific conductance and
macroinvertebrate extirpation are not reflected in the confidence limits. The authors state in
Section 3.4 (Confidence Bounds) that "[T]he purpose of this analysis is to characterize the
statistical uncertainty in the benchmark value," and in Section 4.4 (Uncertainty Analysis), the
authors discuss sources of uncertainty that are and are not reflected in the derived confidence
limits. This discussion is important to the utility of the document and to other uses of this
approach. It may be helpful to describe more clearly in Section 4.4 what is meant by "statistical
uncertainty" and we recommend that the authors ensure that this topic is addressed clearly and
comprehensively.
3.6. Comparing the Benchmark to a Chronic Endpoint
Charge Question 6: The field-based method results in a benchmark value that the report
authors believe is comparable to a chronic endpoint. Does the Panel agree that the
benchmark derived using this method provides for a degree of protection comparable to the
chronic endpoint of conventional ambient water quality criteria?
The general approach, including the use of field data and the resulting benchmark, is
sound and provides a degree of protection comparable to or greater than a conventional ambient
water quality criterion derived from traditional chronic toxicity testing. The field-based
benchmark is probably more reflective of how the invertebrate community responds to
conductivity than would be chronic toxicity tests. One reason is that chronic toxicity tests
usually involve abbreviated times of exposure (relative to generation times of species) and they
use surrogate species. Furthermore, as noted in Section 3.2 above, the surrogate species most
commonly employed to study effects of conductivity (e.g., crustaceans like Ceriodaphnia dubid)
are not especially sensitive to changes in major ion concentrations for physiological reasons.
The species most sensitive to conductivity are often very difficult to work with in demanding
tests like chronic toxicity tests. The ability to focus on the most sensitive groups of species in
the constrained field data set is a powerful connection to reality that routine toxicity testing
cannot achieve. In this sense, the result is a benchmark that is probably more sensitive to
changes in conductivity than would be a benchmark dependent upon traditional chronic toxicity
testing, but also one more realistic in terms of protecting invertebrate communities in streams
affected by MTM-VF.
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The XCgs approach used in this report provides useful and ecologically sound insights.
The specific manner in which the SSD approach was applied (i.e., using field survey data from
impacted locations) is reasonable and avoids many of the flaws of laboratory test-based SSD
analyses that ignore the importance of species interactions in the functioning of ecological
communities (termed synecology; Luoma 1995). The Executive Summary (page xii) of the EPA
document states that "SSDs represent the response of aquatic life as a distribution with respect to
exposure. It is implicitly assumed that if exposure level is kept below the 5th percentile of the
SSD, at least 95% of species will be protected." Although this assumption is frequently stated, it
is not ecologically supported (e.g., see Hopkin 1993; Newman and Clements 2008, pp. 205-208),
is not needed to support the report's conclusions, and should be omitted from the document.
As noted previously, the report could be improved if it more explicitly confronted the
issues surrounding use of laboratory testing to estimate ecological effects. Such tests ignore
aspects like physiological acclimation in extrapolation to the field. Laboratory tests are done
with individuals of a specific demographic class of a single species exposed to constant
concentrations without any co-stressor(s) for durations of somewhat arbitrary length. In contrast,
the survey data have exceptional ecological realism and provide a stronger basis for inferring
causality between concentrations of one or more constituent ions (using conductivity as a
surrogate measure) and presence/absence of genera in aquatic communities in streams below
MTM-VF activities.
The approach based on field surveys seeks "the level of exposure above which a genus is
effectively absent from water bodies in the region." The extirpation concentration (XC) is the
95% point of the surveyed data distribution. The data sets are large enough to allow good
estimation. Correctly, the EPA document notes that "this level is not fully protective of rare
species..." (page 8, lines 11-19). In fact, it is possible that the benchmark will not protect a
number of mayflies important to small streams in this region. The arbitrary choice to protect
95% of genera is partly mitigated by constraining the data set, so as to protect 95% of genera
highly sensitive to increased conductivity.
The choice of extirpation as an endpoint results in a loss of sensitivity (as compared to
employing a 50% decline in abundance, for example). The Agency might consider incorporating
into the endpoint a safety factor, subject knowledge, or some other protocol for added protection.
On the other hand, the benchmark already approaches the background during the period of
highest conductivity in reference streams, and the method includes steps (removal of data that
could be confounding) that enhance its sensitivity compared to published approaches. The
concern about loss of abundant species speaks to the importance of a regional understanding of
impacts (e.g., what is the spatial scale of the extirpation?) and the difficulty of managing risk on
a stream-by-stream basis in a region where several thousand miles of streams are already
impaired by mining.
The approach relative to the data bins and weights seems reasonable. The nonparametric
approach and CI estimation methods are sound. As a minor point, it would be good to clarify on
Page 10 (lines 14 and 24) whether "removed" and "trimmed" are synonymous. Usually, they are
not. Also, on Page 11 (line 7), although the applied estimation of proportion [R/(N+1)] is
acceptable and commonly used, a better approximation of proportion from ranks is provided by
the Blom approximation, (R-0.375)/(N+0.25) (Looney and Gulledge 1985).
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As noted previously, rare species are not included in the SSD, nor are classes of
organisms like fish. Some method to address the influence on the benchmark of rare species or
addition of non-insect species is warranted. In this regard, freshwater mussels are a concern as
they are a unique feature of the area's biodiversity, are often listed as threatened or endangered,
and are poor volume/ionic/osmotic regulators. Focusing on one sensitive group of invertebrates
(Ephemeroptera) might limit the persuasiveness of the benchmark in risk management, and
thereby make it less defensible. Recognizing that conductivity is a surrogate for one set of
stressors (dissolved ions), it is important to include in the overall impact analysis of MTM-VF
more of the factors that contribute to the cumulative stress (e.g., risks to mussels, risks to the
broader food web from selenium), as discussed in the Panel's companion report on the aquatic
ecosystem effects of MTM-VF (see EPA-SAB-11-005).
3.7. Transferability of the Method to Other Regions
Charge Question 7. As described, the conductivity benchmark is derived using central
Appalachian field data and has been validated within Ecoregions 68, 69, and 70. Under
what conditions does the SAB believe this method would be transferable to developing a
conductivity benchmark for other regions of the United States whose streams have a
different ionic signature?
The consensus of the Panel was that the field method used to develop the conductivity
benchmark was quite general and sufficiently flexible to allow the approach (though not the
benchmark value) to be transferred to other regions with different ionic signatures, where
minimum data requirements are met. Despite the wording of Charge Question 7, the Panel
emphasizes that the conductivity benchmark of 300 |iS/cm has been validated only for portions
of Ecoregions 68, 69 and 70, and recommends that the benchmark not be applied beyond the
geographic bounds of the data set without additional validation. For application to a new region,
the Panel suggests that the following important conditions should be met:
1) High quality reference sites should be available.
The current approach requires that all genera included in calculation of a benchmark for a
region must occur at least once at a reference site (as well as be found at 30 or greater sampling
sites). In general, high quality streams have greater biodiversity than low quality streams. Thus,
availability of high quality reference sites lends itself to a longer list of genera available for the
analysis that, in turn, enables the benchmark to be based on a broader baseline of generic
extirpation data. The presence of reference sites also provides a baseline of minimally disturbed
sites for use in deriving background conductivity levels. Ideally, these reference sites should be
geographically wide-spread in order to adequately represent all portions of the study region. The
Panel notes, however, that reference sites are not an absolute requirement because some areas
may be so modified by historic human activity that no true reference exists. When reference
sites are not available, minimally disturbed locations may need to be used as surrogates for
"reference sites."
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2) Fauna found at reference sites in the region should reflect a common regional
generic pool.
Macroinvertebrate species differ significantly from one another in their degree of
pollution tolerance or intolerance. Although congeneric species can differ, differences in
sensitivity to stressors are particularly evident when comparing species from different genera or
families. On this basis, macroinvertebrates have been assigned meaningful pollution
tolerance/intolerance values using best professional judgement, based on a combination of data
from field distributions and laboratory tests (e.g., Lenat 1993). Thus, a representative sample of
genera from across the region of interest is necessary to develop a benchmark for protecting
biodiversity of streams. Failure to capture a common pool may exclude some important taxa.
3) There should be good prior knowledge and understanding of the environmental
requirements of the regional pool of genera.
Good prior knowledge lends credibility to the overall process because it can assure that
the benchmark is based on a group of genera representing a broad gradient of pollution
tolerance/intolerance across the region (e.g., reflecting differences across genera in physiology,
phylogenetic origin, trophic position in the foodweb, and life history characteristics). This
breadth in genera, in turn, assures that the benchmark will be representative and afford broad
protection for the streams in the region.
4) Background levels of conductivity should be similar across reference sites in the
region.
Similarity in background conductivity levels across the set of reference sites decreases
the possibility of misinterpretation resulting from confounding factors. The degree of variation
in conductivity among minimally disturbed sites also serves as a logical consistency check. If
some reference sites have very high conductivity, either the organisms are not responding
negatively to conductivity or the site is misclassified.
5) Relative ionic composition (ratio of ions) of the elevated conductivity should be
consistent across the region.
Specific ions contributing to conductivity (e.g., Na+, K+, Ca+ , Mg+ , Cl", HCCV, C(V ,
SO/f ) differ in their relative toxicity to macroinvertebrates in general, as well as their relative
toxicity to individual genera. Therefore, consistency in the proportion of ions in the mixture will
make it easier to defend conductivity as a surrogate. As long as the ratio of ions constituting
conductivity is consistent across the region, then the relative sensitivity of each genus to a given
level of conductivity also will be consistent across the region. If the ratio of ions varies
appreciably, then a given level of conductivity may be toxic to a particular genus in one stream
but not in another (because one stream has a higher proportion of an ion that is more toxic to the
genus in question).
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6) The potential confounding factors for the region should be understood and
addressed.
Confounding factors are variables in the test region that co-occur with conductivity.
Confounders can interfere with the ability to accurately model the relationship between level of
conductivity and occurrence of genera because confounding variables may also affect genera
occurrence. A few examples of confounding variables include temperature, pH, selenium, and
habitat quality. To be credible, the benchmark needs to be non-confounded or the confounding
factor also must be a result of mountaintop mining and valley fills. There are many ways that a
given factor can be a confounding variable, and many ways of weighting those factors.
Regardless, a process needs to be in place to vet each factor for its potential as a confounding
variable and eliminate any field data that might be confounded prior to developing the
benchmark. The process used in Appendix B of the conductivity benchmark report provides a
framework that can be applied in other regions. However, multiple analytical approaches (e.g.,
quantile regression, logistic regression, conditional probability analysis, and/or other statistical
procedures) also should be used in a weight-of-evidence approach to addressing potential
confounding factors.
7) A large field data set should be available.
One of the strengths of the benchmark development process for WV was the wealth of
available data. Specifically, the data set involved a large number of genera, which occurred
across an array of sites representing a broad gradient of conductivity levels. Thus, even after
removing genera because they were too rare or removing sites because they were confounded by
factors such as low pH, there still remained a critical mass of data to derive the benchmark.
(Note: A sensitivity analysis performed on the existing WV/ KY data set might provide insights
into the minimum sample size needed to assure an acceptable level of variance around the
benchmark.)
8) A second, independent data set should be available for the region to validate the
benchmark, but if not available, some other approach for validating the benchmark
should be used.
Validation of the benchmark is extremely important to gain widespread acceptance of its
use and to assess uncertainty in the value, and thus the potential for the benchmark to be either
overly or insufficiently protective of the environment. Ideally, validation would involve a
separate calculation of the benchmark using a second independent dataset from the region, and
comparing the second value to that derived from the primary data set. In the absence of an
independent dataset, bootstrapping or other statistical methods (e.g., jackknifing) can be used to
estimate benchmarks for comparison and to provide an estimate of certainty around the original
value. For large data sets, a subset of the data might be held aside (i.e., not used to develop the
benchmark) and used for validation. Sensitivity analysis should be used to determine the size of
this sample.
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9) The benchmark should not be extrapolated beyond the geographic bounds of the
data set unless sufficient data are available for validation.
Application of the benchmark beyond the geographic bounds of the data set would be
difficult to defend for a variety of reasons. First, there would likely be less overlap in the
taxonomic composition (at the generic level) of the macroinvertebrate community of reference
sites located beyond the bounds of the region and this would confound the selection of taxa for
the analysis. Second, it is likely that the genera in streams located beyond the geographic bounds
would be different than the mix of genera (and hence different tolerances/intolerances for
conductivity) from which the benchmark was derived. Third, reference sites outside the
geographic bounds may differ in ionic chemistry to those within the bounds of the data set (e.g.,
dissimilar levels of pH, alkalinity, and hardness), and this would exert a confounding influence
due to the effect of acclimation chemistry on the toxicity level of a given compound on a genus.
Fourth, it is likely that the dominant source of ions (and thus the ionic composition) underlying
human induced, elevated conductivity would differ in streams far outside the geographic bounds
and confound the application of the benchmark.
As noted in Section 3.1, even within an ecoregion, the latitudinal (or longitudinal) span
may be so large that taxa and geologies are vastly different between the spatial extremities of the
region. If the region for which the benchmark is being developed is too large or too
geographically fragmented in terms of key habitat/topographic features, then there may be a
taxonomic gradient at the generic level across the region (i.e., streams in one part of the region
containing genera that are unique or distinct from those in other parts). These differences in
community structure, coupled with differences in the pollution tolerance/intolerance associated
with the different genera, confound the benchmark development effort. This makes equating
extirpation of a genus with a given concentration of the stressor (in this case, conductivity, as a
surrogate for dissolved ions from MTM-VF) problematic because it may be very difficult to
distinguish between a genus being extirpated due to the contaminant of concern versus
extirpation due to an overall change in habitat (which is unsuitable for the species represented by
that genus).
3.8. Transferabilitv of the Method to Other Pollutants
Charge Question 8: The amount and quality of field data available from the states and the
federal government have substantially increased throughout the years. In addition, the
computing power available to analysts continues to increase. Given these enhancements in
data availability and quality and computing power, does the Panel feel it feasible and
advisable to apply this field-based method to other pollutants? What issues should be
considered when applying the method to other pollutants?
Water quality criteria (WQC) have been a major component of the CWA Water Quality
Standards (WQS) programs and have provided the primary pollutant targets for management of
discharges to surface waters of the United States, particularly for toxicants from point source
dischargers regulated by NPDES discharge permits. The work in this document has extended the
laboratory methodology of Stephan et al. (1985) to a field-based methodology built around
generating SSDs for conductivity for taxa in a geographic region that have sufficient data to
generate extirpation statistics (n=30 data points), that occur in reference sites, and that are not
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exotic (i.e., alien) species. The Panel concluded that the methodology can be translated to other
stressors with certain caveats, detailed below.
The SSD field methodology outlined in the EPA report provides key advantages over a
sole reliance on laboratory results. First, the Panel recommends that, where possible, the
derivation of such benchmarks should be broadly determined and include consideration of all
suitable data that can illuminate the responses of species or taxa to a stressor. Such an effort,
depending on the stressor, could include applicable standard laboratory test results (which would
demonstrate the sensitivity of some species), results from more novel controlled approaches
(e.g., mesocosm studies) and robust field-based biological and stressor data. The Panel felt that
the advantages of using field data for deriving the conductivity benchmark could apply to many
other stressors, although the specific considerations and caveats may differ from those addressed
in the Panel's report. If EPA moves forward with application of the approach to other stressors,
the SAB urges a detailed review of the scientific issues (e.g., interaction effects, speciation)
associated with the particular stressor.
As the EPA report noted, the laboratory testing approach has been successful and most
amenable to toxicants (e.g., ammonia, metals) with clear and consistent modes of effect. Some
stressors, particularly naturally occurring compounds (e.g., nutrients) and habitat-related
stressors, have proven less tractable to the standard laboratory approach used to derive
benchmarks (Stephan et al. 1985). Salinity, for example has a strong natural gradient of
occurrence (i.e., ranging from saltwater to streams with low hardness and low dissolved solids).
Expected impacts of salinity on taxa depend greatly on natural geological and soil conditions,
which are key biogeographic determinants of the distribution of species adapted to and native to
a particular salinity regime. Natural background concentrations of dissolved materials vary
geographically, as does the composition of the ions and anions that comprise the total dissolved
solids. Indeed, the EPA report emphasizes that the initial application of the conductivity
benchmark should be limited to portions of three ecoregions, "dominated by salts of SC>42 and
HCOs at circum-neutral to mildly alkaline pH," for which data have been evaluated (see Section
3.1).
Despite its promise, the Panel identified a number of caveats that needed to be considered
when applying this methodology to other stressors:
1) Natural Classifications. The Panel concluded that the methodology can be applied to
other stressors where data coverage and quality are sufficient; however, the key natural
classification features that influence and explain variation in the stressor and taxa distributions
would need to be identified. For example, natural streams can vary in their background
concentration of dissolved oxygen as a function of stream gradient, stream morphology, and
stream type. These variables are often geographically independent and variation may not be
controlled by isolating ecoregions or other geographic constructs, but may require more reach-
specific data to be applied successfully. Even so, the field-based SSD methodology should be
transferable to such streams as long as they can be accurately classified prior to derivation and
application of benchmarks.
2) Mode of Effect. The field SSD methodology was readily applicable to conductivity
because there is a relatively direct physiological mechanism and effect between the stressor (i.e.,
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conductivity, as a surrogate for concentrations of dissolved ions) and the occurrence of taxa. For
other similar stressors (e.g., dissolved oxygen, pH) a similar approach may be applicable. The
situation is more complex for stressors—in particular nutrients and physical habitat measures—
that influence the distribution of taxa indirectly. The tails of the distributions of extirpation
values may be particularly long and the species may persist at some sites where stressor levels
are suboptimal because expression of effects is moderated by other (confounding) factors. For
example, the effects of a specific total phosphorus level can be moderated by shading, habitat, or
base flow. In a stream with a total phosphorus concentration of 0.20 ppm that is a channelized
stream with an open canopy, many sensitive species would be eliminated. Conversely, in a
heavily shaded stream with a natural channel and good base flow, the same phosphorus
concentration would likely be associated with the occurrence of many sensitive species. Failure
to consider these other moderating or confounding factors could result in a benchmark that is not
protective for many species. Similarly, habitat stressors (e.g., bedded sediments, channel
modifications) can have varied effects depending on the spatial scale of impact. Widespread
aggradation of fine sediments or channel modifications can eliminate species/taxa from a
watershed. However if the sedimentation or other habitat limitations are only local, sensitive
species may routinely occur although at reduced abundance. In such cases, change points in
taxa/species abundances (e.g., Toms and Lesperance 2003) may be the more appropriate choice
for a SSD statistic than an extirpation curve.
3) Data Sufficiency. The conductivity benchmark was derived from a large data set and
the Panel concluded that a large, robust data set would be necessary for derivation of any stressor
benchmark from field data. The availability of a validation data set also was identified as
important to the use of this method for other stressors. It would be important that the data set
represent the entire expected gradient of condition including stressed and non-stressed
(reference) sites. The size of the data set needed would increase with number of stressors (i.e.,
confounding factors) that can control the distribution of species/taxa in a region. This would be
particularly important for the assessment of causation and confounding factors analyses.
4) Tiered Aquatic Life Uses. As States develop tiered aquatic life uses, a natural
consequence may be the need to develop tiered criteria for a variety of stressors. This need
would apply to multiple stressors and the implications or robustness of the field-based SSD
approach needs to be assessed. The conceptual model for the tiered use approach is provided by
the Biological Condition Gradient (BCG) model developed by US EPA (Davies and Jackson
2006). The various tiers of the BCG are based on the presence or absence of species associated
with each attribute of the BCG. Thus the derivation of stressor benchmarks for tiered uses could
be developed by dropping or adding species that comprise the species/taxa that characterize an
aquatic life or BCG tier. It would be useful to address the concept of tiered aquatic life uses and
how this methodology might apply to conductivity and other stressors.
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APPENDIX A: Charge to the Panel
UNITED STATES ENVIRONMENTAL PROTECTION AGENCY
National Center for Environmental Assessment
Office of Research and Development
June 10, 2010
MEMORANDUM
SUBJECT: Review of (1) "The Effects of Mountaintop Mines and Valley Fills on Aquatic
Ecosystems of the Central Appalachian Coalfields" and (2) "A Field-based
Aquatic Life Benchmark for Conductivity in Central Appalachian Streams"
FROM: Michael Slimak, Associate Director /signed/
National Center for Environmental Assessment
Office of Research and Development
TO: Vanessa Vu, Director
Science Advisory Board Staff Office
This memorandum provides background information and specific charge questions to the
Science Advisory Board (SAB) in its review of two reports prepared by EPA's Office of
Research and Development (ORD). These reports were developed by the National Center for
Environmental Assessment (NCEA) upon the request of EPA's Office of Water and Regions 3,
4, and 5. These reports help provide scientific information to support a set of actions EPA is
undertaking to clarify and strengthen environmental permitting requirements for Appalachian
surface coal mining operations, in coordination with other federal and state regulatory agencies.
Background
The purpose of the report entitled "The Effects of Mountaintop Mines and Valley Fills on
Aquatic Ecosystems of the Central Appalachian Coalfields," is to assess the state of the science
on the ecological impacts of Mountaintop Mining and Valley Fill (MTM-VF) operations on
streams in the Central Appalachian Coal Basin. This basin covers about 12 million acres in West
Virginia, Kentucky, Virginia, and Tennessee. The draft EPA Report reviews literature relevant
to evaluating five potential consequences of MTM-VF operations: 1) impacts on headwater
streams; 2) impacts on downstream water quality; 3) impacts on stream ecosystems; 4) the
cumulative impacts of multiple mining operations; and 5) effectiveness of mining reclamation
and mitigation. The impacts of MTM-VF operations on cultural and aesthetic resources were not
included in the review. EPA used two primary sources of information for the evaluation: (1) the
peer reviewed, published literature and (2) the federal Programmatic Environmental Impact
Statement (PEIS) on Mountaintop Mining/Valley Fills in Appalachia and its associated
appendices prepared in draft in 2003 and finalized in 2005.
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The second report entitled, "A Field-based Aquatic Life Benchmark for Conductivity in
Central Appalachian Streams," uses field data to derive an aquatic life benchmark for
conductivity. This benchmark value may be applied to waters in the Appalachian Region that
are near neutral or mildly alkaline in their pH and where dissolved ions are dominated by salts of
sulfate and bicarbonate. This benchmark is intended to protect the biological integrity of waters
in the region. It is derived by a method modeled on EPA's standard methodology for deriving
water quality criteria. In particular, the methodology was adapted for the 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 biotic communities.
This draft EPA Report provides the scientific basis for a conductivity benchmark in a specific
region rather than for the entire United States.
Both of these reports were commissioned by EPA's Office of Water (OW) and Regions
3, 4, and 5 in order to provide information that will assist OW and the Regions to further clarify
and strengthen environmental permitting requirements for Appalachian surface coal mining
projects, in coordination with federal and state regulatory agencies. Using the best available
science and applying existing legal requirements, EPA issued comprehensive guidance on April
1, 2010 that sets clear benchmarks for preventing significant and irreversible damage to
Appalachian watersheds at risk from mining activities.
Specific Charge in Reviewing the Mountaintop Mining - Valley Fill Effects Report
Charge Question 1: The Mountaintop Mining Assessment uses a conceptual model
(Figure 12 of the draft document) to formulate the problem consistent with EPA's
Ecological Risk Assessment Guidelines. Does the conceptual diagram include the key
direct and indirect ecological effects of MTM-VF? If not, please indicate the effects or
pathways that are missing or need additional elucidation.
Charge Question 2: This report relied solely on peer-reviewed, published literature and
the 2005 Final Programmatic Environmental Impact Assessment on Mountaintop
Mining/Valley Fills. Does this assessment report include the most relevant peer-
reviewed, published literature on this topic? If not, please indicate which references are
missing.
Charge Question 3: Valley fills result in the direct loss of headwater streams. Has the
review appropriately characterized the ecological effects of the loss of headwater
streams?
Charge Question 4: In addition to impacts on headwater streams, mining and valley fills
affect downstream water quality and stream biota. Does the report effectively
characterize the causal linkages between MTM-VF downstream water quality and effects
on stream biota?
Charge Question 5: The published literature is sparse regarding the cumulative
ecological impacts of filling headwater streams with mining waste (spoil). Does the
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review accurately describe the state of knowledge on cumulative ecological impacts of
MTM-VF? If not, how can it be improved?
Charge Question 6: The Surface Mining Control and Reclamation Act and its
implementing regulations set requirements for ensuring the restoration of lands disturbed
by mining through restoring topography, providing for post-mining land use, requiring
re-vegetation, and ensuring compliance with the Clean Water Act. Does the review
appropriately characterize the effectiveness of currently employed restoration methods?
Specific Charge in Reviewing the Conductivity Benchmark Report
Charge Question 1: The data sets used to derive a conductivity benchmark (described in
Section 2 of this report) were developed primarily by two central Appalachian states
(WV and KY). Please comment on the adequacy of these data and their use in developing
a conductivity benchmark.
Charge Question 2: The derivation of a benchmark value for conductivity was adapted
from EPA's methods for deriving water quality criteria. The water quality criteria
methodology relies on a lab-based procedure, whereas this report uses a field-based
approach. Has the report adapted the water quality criteria methodology to derive a water
quality advisory for conductivity using field data in a way that is clear, transparent and
reasonable?
Charge Question 3: Appendix A of the report describes the process used to establish a
causal relationship between the extirpation of invertebrate genera and levels of
conductivity. Has the report effectively made the case for a causal relationship between
species extirpation and high levels of conductivity due to surface coal mining activities?
Charge Question 4: In using field data, other variables and factors have to be accounted
for in determining causal relationships. Appendix B of the report describes the
techniques for dealing with confounding factors. Does the report effectively consider
other factors that may confound the relationship between conductivity and extirpation of
invertebrates? If not, how can the analysis be improved?
Charge Question 5: Uncertainty values were analyzed using a boot-strapped statistical
approach. Does the SAB agree with the approach used to evaluate uncertainty in the
benchmark value? If not, how can the uncertainty analysis be improved?
Charge Question 6: The field-based method results in a benchmark value that the report
authors believe is comparable to a chronic endpoint. Does the Panel agree that the
benchmark derived using this method provides for a degree of protection comparable to
the chronic endpoint of conventional ambient water quality criteria?
Charge Question 7: As described, the conductivity benchmark is derived using central
Appalachian field data and has been validated within ecoregions 68, 69, and 70. Under
what conditions does the SAB believe this method would be transferable to developing a
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conductivity benchmark for other regions of the United States whose streams have a
different ionic signature?
Charge Question 8: The amount and quality of field data available from the states and the
federal government have substantially increased throughout the years. In addition, the
computing power available to analysts continues to increase. Given these enhancements
in data availability and quality and computing power, does the Panel feel it feasible and
advisable to apply this field-based method to other pollutants? What issues should be
considered when applying the method to other pollutants?
Background Reading Materials
The following documents are accessible via the hyperlinks provided below. These
documents provide important background information from scientific, regulatory, and policy
perspectives on mountaintop mining and valley fills and are recommended reading for the SAB
Panel members.
1. Final Programmatic Environmental Impact Statement on Mountaintop Mining/Valley
Fills in Appalachia - 2005
htttp://www.epa.gov/region3/mtntop/eis2005.htm)
2. April 1, 2010 Guidance Memorandum on Appalachian Surface Coal Mining
http://www.epa.gov/owow/wetl ands/guidance/pdf/appalachian_mtntop_mining_d
etailed.pdf.
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