Environmental Monitoring and Assessment

, Volume 185, Issue 11, pp 9299–9320 | Cite as

Application of a benthic observed/expected-type model for assessing Central Appalachian streams influenced by regional stressors in West Virginia and Kentucky



Stream bioassessments rely on taxonomic composition at sites compared with natural, reference conditions. We developed and tested an observed/expected (O/E) predictive model of taxonomic completeness and an index of compositional dissimilarity (BC index) for Central Appalachian streams using combined macroinvertebrate datasets from riffle habitats in West Virginia (WV) and Kentucky (KY). A total of 102 reference sites were used to calibrate the O/E model, which was then applied to assess over 1,200 sites sampled over a 10-year period. Using an all subsets discriminant function analysis (DFA) procedure, we tested combinations of 14 predictor variables that produced DF and O/E models of varying performance. We selected the most precise model using a probability of capture at >0.5 (O/E 0.5, SD = 0.159); this model was constructed with only three simple predictor variables—Julian day, latitude, and whether a site was in ecoregion 69a. We evaluated O/E and BC indices between reference and test sites and compared their response to regional stressors, including coal mining, residential development, and acid deposition. The Central Appalachian O/E and BC indices both showed excellent discriminatory power and were significantly correlated to a variety of regional stressors; in some instances, the BC index was slightly more sensitive and responsive than the O/E 0.5 model. These indices can be used to supplement existing bioassessment tools crucial to detecting and diagnosing stream impacts in the Central Appalachian region of WV and KY.


Predictive model O/E Macroinvertebrates Bioassessment Mining Acid deposition Residential development 



This research was conducted jointly by EPA’s Environmental Assessment and Innovation Division in Region III and EPA’s National Exposure Research Laboratory in the Office of Research and Development. The US Environmental Protection Agency through its Office of Research and Development partially funded and collaborated in the research described here under contract number EP-D-06-096 to Dynamac Corporation. We thank Randy Pomponio and John Forren (Region III, Philadelphia, PA), Joe Flotemersch and Brad Autrey (ORD, Cincinnati, OH), and Justicia Rhodus (Dynamac Corporation) for programmatic support. We also thank John Van Sickle and Karen Blocksom (EPA, ORD, Corvallis, OR) for statistical and software programming advice and reviews. Datasets were supplied by Jeff Bailey and Mike Whitman (WVDEP) and John Brumley and Mark Vogel (KYDEP). GIS support was provided by Ellen D’Amico and Elisabeth Hagenbuch (Dynamac Corporation). Earlier versions of the manuscript were improved by reviews from Maggie Passmore (Region III, Wheeling, WV), John Forren and Stefania Shamet (Region III, Philadelphia, PA), Matt Klasen (EPA Headquarters, Washington, DC), and editing and formatting of the manuscript was performed by Justicia Rhodus (Dynamac Corporation). We also thank two anonymous reviewers who greatly improved the final manuscript. Although this research was supported by EPA, the views and opinions expressed in this article are those of the authors and do not represent the official views or positions of the EPA or the US government.


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Copyright information

© Springer Science+Business Media Dordrecht (outside the USA) 2013

Authors and Affiliations

  1. 1.Office of Monitoring and AssessmentU.S. Environmental Protection Agency, Region IIIWheelingUSA
  2. 2.Office of Research and DevelopmentDynamac Corporation, c/o US Environmental Protection AgencyCincinnatiUSA

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