Premium
Spatial patterns in benthic biodiversity of Chesapeake Bay, USA (1984–1999): Association with water quality and sediment toxicity
Author(s) -
Preston Benjamin L.
Publication year - 2002
Publication title -
environmental toxicology and chemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.1
H-Index - 171
eISSN - 1552-8618
pISSN - 0730-7268
DOI - 10.1002/etc.5620210122
Subject(s) - benthic zone , environmental science , estuary , stressor , water quality , sediment , ecology , pollution , biodiversity , oceanography , biology , geology , paleontology , neuroscience
Non–point‐source pollution is an increasing source of stress to aquatic, estuarine, and marine ecosystems. Such pollution may be of unknown etiology, distributed over extensive spatial scales, and comprised of multiple stressors. Current stressor‐based paradigms for ecological risk assessment (ERA) may be insufficient to characterize risk from multiple stressors at regional spatial scales, necessitating the use of effects‐based approaches. Historical data (1984–1999) for benthic macroinvertebrate biodiversity in Chesapeake Bay, USA, were incorporated into a geographic information system (GIS) and spatial analysis tools were used to model zones within the bay predicted to be of low or high anthropogenic impact. Data for benthic water quality and sediment toxicant concentrations from each of these zones were subsequently analyzed and compared to identify associations between benthic biodiversity and potential stressors. A number of stressors were significantly associated with high‐impact zones, including increased nitrogen and phosphorus concentrations, low dissolved oxygen, heavy metals, pesticides, polycyclic aromatic hydrocarbons, and polychlorinated biphenyls. The spatial autocorrelation among multiple stressors suggests that traditional stressor‐based approaches to ERA may result in the a priori exclusion of ecologically relevant stressors. Considering the effects of individual stressors rather than net effects of multiple stressors may result in underestimation of risk. The GISs are a useful tool for integrating multiple data sets in support of comprehensive regional ERA.