title: Developing a foundation for eco-epidemiological assessment of aquatic ecological status over large geographic regions utilizing existing data resources and models.
abstract: Eco-epidemiological studies utilizing existing monitoring program data provide a cost-effective means to bridge the gap between the ecological status and chemical status of watersheds and to develop hypotheses of stressor attribution that can influence the design of higher-tier assessments and subsequent management. The present study describes the process of combining existing data and models to develop a robust starting point for eco-epidemiological analyses of watersheds over large geographic scales. Data resources from multiple federal and local agencies representing a range of biological, chemical, physical, toxicological, and other landscape factors across the state of Ohio, USA (2000-2007), were integrated with the National Hydrography Dataset Plus hydrologic model (US Environmental Protection Agency and US Geological Survey). A variety of variable reduction, selection, and optimization strategies were applied to develop eco-epidemiological data sets for fish and macroinvertebrate communities. The relative importance of landscape variables was compared across spatial scales (local catchment, watershed, near-stream) using conditional inference forests to determine the scales most relevant to variation in biological community condition. Conditional inference forest analysis applied to a holistic set of environmental variables yielded stressor-response hypotheses at the statewide and eco-regional levels. The analysis confirmed the dominant influence of state-level stressors such as physical habitat condition, while highlighting differences in predictive strength of other stressors based on ecoregional and land-use characteristics. This exercise lays the groundwork for subsequent work designed to move closer to causal inference.
authors.affiliation: Waterborne Environmental, Inc., Leesburg, Virginia, USA.
authors.forename: Katherine E
authors.initials: KE
authors.lastname: Kapo
authors.forename: Christopher M
authors.initials: CM
authors.lastname: Holmes
authors.forename: Scott D
authors.initials: SD
authors.lastname: Dyer
authors.forename: Dick
authors.initials: D
authors.lastname: de Zwart
authors.forename: Leo
authors.initials: L
authors.lastname: Posthuma
mesh_terms: D000818:Animals; D004463:Ecology; D017753:Ecosystem; D004784:Environmental Monitoring / Q000379:methods*; D005399:Fishes / Q000378:metabolism; D008962:Models, Theoretical; D009820:Ohio; D045483:Rivers / Q000737:chemistry; D012987:Soil / Q000737:chemistry; D014481:United States; D014484:United States Environmental Protection Agency; D014874:Water Pollutants, Chemical / Q000032:analysis*
chemical_list: D012987:Soil; D014874:Water Pollutants, Chemical
keywords: Eco-epidemiology; Geographic information system; Mixture; Stressor