Should There Be An Aquatic Life Water Quality Criterion for Conductivity? WV Mine Drainage Task Force Symposium Morgantown, WV March 29, 2011 Presented By: Robert W. Gensemer Co-authors: S. Canton, G. DeJong, C. Wolf, and C. Claytor

2 Why Conductivity?  Coal mining and valley fill (CM/VF) activities in West Virginia can be associated with increased conductivity Increased sulfate, bicarbonate  Some have suggested an adverse relationship between conductivity and benthic macroinvertebrate communities Primarily focused on “sensitive” mayflies  Thus, aquatic life benchmarks (functionally “criteria”) for conductivity are being proposed D.S. Chandler

 Conductivity is a composite variable Surrogate measure for dissolved solids (cations & anions) Ionic toxicity exists, but varies with ion composition –Composite variable cannot differentiate ionic balance differences Toxicity can be mitigated by hardness  Patterns of macroinvertebrate community composition vs. conductivity can be confounded Related to combination of abiotic and biotic factors –Abiotic: e.g., water quality, habitat, temperature –Biotic: e.g., competition, predation, colonization, biogeography 3 Conductivity Criterion – Complications Exist

 For central Appalachian streams 300 µS/cm –Sensitive species assumed to be ‘extirpated’ if exceeded Limited to streams dominated by sulfate and bicarbonate salts at circumneutral to mildly alkaline pH EPA methods for aquatic life criteria used, modified for use of field data 4 EPA’s Proposed Conductivity Benchmark

 Assumption: “sensitivity” related to field distribution Quantified as an extirpation concentration (XC) –Instead of standard LC 50 or chronic responses XC = concentration above which a genus is ‘effectively absent’ XC 95 = 95 th percentile of distribution of calculated ‘probability of occurrence’ of a genus with respect to conductivity 5 XC 95 (from EPA 2010) EPA’s Proposed Conductivity Benchmark

 Benchmark of 300 µS/cm Ranked distribution of XC 95 values 5 th percentile = 297 µS/cm (rounded to 300) Assumed to prevent extirpation of all but 5% of the most “sensitive” species 6 EPA’s Proposed Conductivity Benchmark

Primary Technical Concerns  Assumed responses to conductivity not consistent 3 types of associations noted by EPA  2 other types also present but not recognized by EPA  These are all fundamentally different responses i.e., not just varying levels of sensitivity 7

Percentage of genera with different types of stressor-response profiles with respect to conductivity and probability of capture (data from EPA 2010a). “Conflicting” Stressor- Response Profiles

 Conflicting stressor-responses result in conflicting answers: Decreasing (Ephemerella): <300 Increasing (Hemerodromia): >300 Optimum (Psephenus): >75 and <2,500 Bimodal (Diplectrona): 2,000 No response/bimodal (Tvetenia): none  How can a single benchmark value be chosen from those numbers? Stressor-Response Profiles

 Incomplete analysis of causality Correlation ≠ causality! Limited experimental evidence (few laboratory studies)  Confounding factors dismissed inappropriately Takes causality of conductivity “as a given” Important factors dismissed –Habitat, flow, substrate characteristics, etc., widely known to influence species composition 10 “Today's scientists have substituted mathematics for experiments, … and eventually build a structure which has no relation to reality” – Nikola Tesla Primary Technical Concerns

 Goal: “establish that salts are a general cause, not that they cause all impairments, nor that there are no other causes of impairment, nor that they cause the impairment at any particular site.” (emphasis added)  Epidemiological approaches used 6 characteristics of causation –Co-occurrence, preceding causation, time order, interaction, alteration, sufficiency –Weight of evidence scoring Concluded that salts (measured by conductivity) are common cause of aquatic macroinvertebrates impairment  Our conclusion: This is an incomplete analysis Weight of evidence scoring for each element relatively subjective ‒ Open to valid alternative interpretations Limited experimental evidence –Few toxicity tests –No experimental verification of extirpation in whole communities 11 EPA Approach: Causality

 Approach Used: Do confounders alter the statistical relationship between salts and macroinvertebrate assemblages? –Habitat, organic enrichment, nutrients, deposited sediment, high/low pH, Se, temp, lack of headwaters, catchment area Effect of confounders found by EPA to be “minimal and manageable” –Low pH → removed sites with pH < 6 –Influence of Se → not enough data, should be investigated  EPA’s confounding factors analysis took presumed impacts from conductivity as a given  Our conclusion: should have included rigorous, independent tests to first determine if conductivity is indeed the best (or only?) predictor of biological impairment 12 EPA Approach: Confounding Factors

EPA Approach: What about alternative explanations for community structure patterns? Habitat: 1.RBP scores not best measure of macroinvertebrate habitat quality 2.RBP scores correlated with conductivity and biological response 3.Analysis focused on relationship with Ephemeroptera (mayflies)  Excluded the rest of the benthic macroinvertebrate community Relationship to other invertebrate taxa: 1.Relationships with Ephemeroptera used to reject other stressors as potential confounders 2.Should include analyses for other invertebrates  Again, excluded the rest of the community -- Protect all invertebrates, not just mayflies! 13 Confounding Factors

 Independent analysis that considered additional information Identify key WQ and physical parameters most strongly associated with biotic responses Minimize use of composite variables (e.g., conductivity)  West Virginia Department of Environmental Protection (WVDEP) Watershed Assessment Branch Database (WABbase) – same as used by EPA Results for 3,286 sampling events –3,121 unique Station ID codes A variety of site-specific data –Regional landscape –Water quality –Aquatic habitat conditions –Macroinvertebrate community composition 14 Our Approach: ID Additional Confounders

 Principle Components Analysis (PCA) Variable reduction procedure –Identifies redundancy among numerous variables –Do variable groups “move together”? –Can 1 variable be used as a surrogate for other variables within each grouping?  All Possible Regressions (APR) Identifies 1 variable or subset of variables that explains most variation observed in biological response –Goal to identify smallest subset of variables that explains most of the variation  Chi-square Automatic Interaction Detection (CHAID) Evaluates relationships between dependent variable and independent stressor variables Selects subset of stressor variables that best predicts the dependent variable –Presents these variables in a decision tree Decision tree: –Starts with dependent variable –Progressively splits into smaller branches (nodes) based on groupings of stressor variables that best predict responses by dependent variable 15 Our Approach: Statistical Tests Used

16 Principal Component Analysis All Possible Regressions Chi-square Automatic Interaction Detection Genera-based Total Taxa Total magnesiumUndisturbed vegetationSulfate Percent finesChannel alteration SulfateTotal magnesium Embeddedness Epifaunal substrate Percent EPT Percent finesUndisturbed vegetationEpifaunal substrate Total magnesiumEpifaunal substrateFecal coliforms Total suspended solidsFecal coliformsBank vegetation ChloridepH Total manganese Independent stressors most closely associated with key dependent responses (genera-based total taxa and percent EPT): Statistical Conclusions

17  A single composite parameter, like conductivity, cannot explain the observed variation with respect to WQ and physical habitat  Rather, some combination of ionic composition, substrate, and channel features may be the most appropriate stressor variables to consider 21% variation explained in Total Taxa –Conductivity vs. Total Taxa (r 2 = 0.18) 14% variation explained in %EPT –Conductivity vs. % EPT Abundance (r 2 = 0.08) Statistical Conclusions

 Illinois sulfate criterion  <1% of WABbase samples exceeded the IL sulfate criteria Majority of exceedances occurred with hardness levels >500 mg/L  26% of these WABbase samples exceed the proposed conductivity benchmark 18 Alternative Approach: Single Ion Criteria Ion Ranges Chloride <5 mg/L Chloride 5 to <25 mg/L Chloride 25 to <500 mg/L Chloride ≥500 mg/L Hardness <100 mg/L 500 n = n = n = n = 0 Hardness 100 to <500 mg/L 500 n = 113 Eqn 1 n = 84 1 of 84 exceeded criteria Eqn 2 n = 270 2,000 n = 1 Hardness ≥500 mg/L 500 n = 10 6 of 10 exceeded criteria 2,000 n = 26 2,000 n = 15 7 of 15 exceeded criteria 2,000 n = 3 1 of 3 exceeded criteria Eqn 1: Sulfate = [ (Hardness) (Chloride)] x 0.65 Eqn 2: Sulfate = [1, (Hardness) – 1.457(Chloride)] x 0.65

Conclusions  Relationship between conductivity and changes in macroinvertebrate community structure not strong or reliable enough to derive a benchmark  EPA (2010) did not rigorously test primary hypothesis that conductivity is best predictor of changes in macroinvertebrate community structure Instead, their analysis takes it as a given that conductivity is the best predictor Confounding factors prematurely dismissed  Insufficient experimental confirmation of the proposed benchmark For similar reasons, IL, IN, and IA rejected the use of TDS or conductivity-based criteria in lieu of criteria for individual ions (sulfate or chloride) 19

Conclusions  It is inappropriate and inadvisable to adopt a conductivity benchmark at this time Many factors other than WQ are strongly related to macroinvertebrate community structure  To adopt this benchmark without additional study runs a risk of expending financial resources to reduce conductivity Little confidence that mitigating conductivity alone would provide any measureable environmental benefit 20

Acknowledgements We would like to thank: The National Mining Association 21

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