2014
Kapo, K.E. (Waterborne Environmental, Inc); Holmes, C.M. (Waterborne Environmental, Inc); Dyer, S. (The Procter & Gamble Company); de Zwart, D. (National Institute for Public Health & the Environment (RIVM)); Posthuma, L. (National Institute for Public Health & the Environment (RIVM))
In: Environmental Toxicology & Chemistry, vol. 33, no. 7, pp. 1665-1677, 2014.
Abstract | Links | Tags: 2014, Peer-Reviewed Publication
@article{2014DevFound,
title = {Developing a Foundation for Eco-Epidemiological Assessment of Aquatic Ecological Status Over Large Geographic Regions Utilizing Existing Data Resources and Models},
author = {Kapo, K.E. (Waterborne Environmental, Inc) and Holmes, C.M. (Waterborne Environmental, Inc) and Dyer, S. (The Procter & Gamble Company) and de Zwart, D. (National Institute for Public Health & the Environment (RIVM)) and Posthuma, L. (National Institute for Public Health & the Environment (RIVM))},
url = {https://setac.onlinelibrary.wiley.com/doi/abs/10.1002/etc.2557, View Online},
doi = {https://doi.org/10.1002/etc.2557},
year = {2014},
date = {2014-03-19},
urldate = {2014-03-19},
journal = {Environmental Toxicology & Chemistry},
volume = {33},
number = {7},
pages = {1665-1677},
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. Environ Toxicol Chem 2014;33:1665–1677. © 2014 SETAC },
keywords = {2014, Peer-Reviewed Publication},
pubstate = {published},
tppubtype = {article}
}
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. Environ Toxicol Chem 2014;33:1665–1677. © 2014 SETAC