A geographic information systems-based, weights-of-evidence approach for diagnosing aquatic ecosystem impairment.
A Geographic Information Systems-based, watershed-level assessment using Bayesian weights of evidence (WOE) and weighted logistic regression (WLR) provides a method to determine and compare potential environmental stressors in lotic ecosystems and to create predictive models of general or species-specific biological impairment across numerous spatial scales based on limited existing sample data. The WOE/WLR technique used in the present study is a data-driven, probabilistic approach conceptualized in epidemiological research and both developed for and currently used in minerals exploration. Extrapolation of this methodology to a case-study watershed assessment of the Great and Little Miami watersheds (OH, USA) using archival data yielded baseline results consistent with previous assessments. The method additionally produced a quantitative determination of physical and chemical watershed stressor associations with biological impairment and a predicted comparative probability (i.e., favorability) of biological impairment at a spatial resolution of 0.5 km2 over the watershed study region. Habitat stressors showed the greatest spatial association with biological impairment in low-order streams (on average, 56% of total spatial association), whereas water chemistry, particularly that of wastewater effluent, was associated most strongly with biological impairment in high-order reaches (on average, 79% of total spatial association, 28% of which was attributed to effluent). Significant potential stressors varied by land-use and stream order as well as by species. This WOE/WLR method provides a highly useful "tier 1" watershed risk assessment product through the integration of various existing data sources, and it produces a clear visual communication of areas favorable for biological impairment and a quantitative ranking of candidate stressors and associated uncertainty.
Kapo
Katherine E
KE
Institute for Environmental Quality, Wright State University, 3640 Colonel Glenn Highway, Dayton, Ohio 45435, USA. kapo.2@wright.edu
Burton
G Allen
GA
D001499:Bayes Theorem; D017753:Ecosystem; D040362:Geographic Information Systems; D011336:Probability; D013045:Species Specificity