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ASSESSING THE IMPACTS OF ANTHROPOGENIC STRESSORS ON MACROINVERTEBRATE INDICATORS IN OHIO
Citation:
Majumder, S., F A. Fulk, AND S M. Cormier. ASSESSING THE IMPACTS OF ANTHROPOGENIC STRESSORS ON MACROINVERTEBRATE INDICATORS IN OHIO. Presented at Society of Environmental Toxicology and Chemistry, Baltimore, MD, November 11-15, 2001.
Impact/Purpose:
The goal of this research is to develop methods and indicators that are useful for evaluating the condition of aquatic communities, for assessing the restoration of aquatic communities in response to mitigation and best management practices, and for determining the exposure of aquatic communities to different classes of stressors (i.e., pesticides, sedimentation, habitat alteration).
Description:
In the past few years, there has been increasing interest in using biological community data to provide information about specific anthropogenic factors impacting streams. Previous studies have used statistical approaches that are variants of classical and modern multiple regression with subsets of data on environmental and anthropogenic influence. This study attempts to gather data to answer questions about specific levels of stressors in different areas that can cause significant impairment. A series of generalized linear models have been developed to predict and classify macroinvertebrate indicator scores across the various ecoregions in Ohio. This differs from other regression techniques in that it predicts the probability of a location having a specific score, rather than levels of decreasing macroinvertebrate indicator scores with increasing stream impairment. Initial models confirm the existence of a few major stressors within each ecoregion that predict a decline in macroinvertebrate scores. Urbanization and levels of Total Kjeldahl Nitrogen were found to be the strongest predictors of decline in macroinvertebrate scores. The model fits are good in the Western Allegheny Plains ecoregion and less so in the Huron/Erie Lake Plains ecoregion. Further work is planned to incorporate environmental factors such as stream order, slopes and soil types to improve the fit of the models and to use them in developing threshold levels for stressors. Such models can then be used to identify locations for protection and where maximum benefits can be obtained from resources.