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HP8 Field Biological Monitoring of Ecosystem Impairment
() Inferring causes of biological impairment in Appalachian streams (2): empirical model development to identify multiple stressors.
Zheng, L.1, Bailey, J.4, Boschen, C.2, Burton, J.2, Gerritsen, J.1, Lowman, B.4, Ludwig, J.3, Wilkes, S.3, Wirts, J.4, 1 Tetra Tech Inc., Owings Mills, MD, USA4 West Virginia Department of Environmental Protection, Charleston, WV, USA2 Tetra Tech Inc., Fairfax, VA, USA3 Tetra Tech Inc., Charleston, WV, USA
ABSTRACT- Human activities such as mining, logging, agriculture and residential development have caused biological degradation to streams of West Virginia, USA. To help identify nonpoint sources of pollution and diagnose biological stressors, thousands of biological and chemical samples were collected by the West Virginia Department of Environmental Protection throughout West Virginia streams and analyzed over the past 5 years. Because of the large sample size of the dataset, data partitioning was implemented to examine the macroinvertebrate community response to single stressors. A "dirty reference" approach was used to define groups of sites affected by a single stressor. Four "dirty" reference groups were identified consisting of sites with high measured levels of a single stressor category: dissolved metals (Al and Fe); excessive sedimentation; high nutrients and organic enrichment; increased ionic strength (using sulfate concentration as a surrogate). In addition, a "clean reference" group of sites (WVDEP reference sites) with low levels of all stressors was identified. Nonparametric multi-dimensional scaling (NMDS) was used to examine the separation of the "dirty" reference groups from each other and from the clean group. The results indicated that the centroids of the "dirty" reference groups were significantly different from the clean reference group and also different from each other. The Bray-Curtis similarity index was used to measure the similarities of test sites with each of the reference sets, and multiple stressors were ranked according to the similarities to each reference set. The relative similarity and the variation explained by each model were taken into account in the final ranking of the predicted stressors for each impaired site. These model predictions will be incorporated into a strength of evidence analysis for final stressor determinations.
Key words: bioassessment, stressor identification, streams, TMDL
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