Evaluating long-term contaminant effects on wildlife populations depends on spatial information about habitat quality, heterogeneity in contaminant exposure, and sensitivities and distributions of species integrated into a systems modeling approach. Rarely is this information readily available, making it difficult to determine the applicability of realistic models to quantify population-level risks. To evaluate the trade-offs between data demands and increased specificity of spatially explicit models for population-level risk assessments, we developed a model for a standard toxicity test species, the sheepshead minnow (Cyprinodon variegatus), exposed to oil contamination following the Deepwater Horizon oil spill and compared the output with various levels of model complexity to a standard risk quotient approach. The model uses habitat and fish occupancy data collected over five sampling periods throughout 2008-2010 in Pensacola and Choctawhatchee Bays, Florida, USA, to predict species distribution, field-collected and publically available data on oil distribution and concentration, and chronic toxicity data from laboratory assays applied to a matrix population model. The habitat suitability model established distribution of fish within Barataria Bay, Louisiana, USA, and the population model projected the dynamics of the species in the study area over a 5-yr period (October 2009-September 2014). Vital rates were modified according to estimated contaminant concentrations to simulate oil exposure effects. To evaluate the differences in levels of model complexity, simulations varied from temporally and spatially explicit, including seasonal variation and location-specific oiling, to simple interpretations of a risk quotient derived for the study area. The results of this study indicate that species distribution, as well as spatially and temporally variable contaminant concentrations, can provide a more ecologically relevant evaluation of species recovery from catastrophic environmental impacts but might not be cost-effective or efficient for rapid assessment needs.
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Evaluating long-term contaminant effects on wildlife populations depends on spatial information about habitat quality, heterogeneity in contaminant exposure, and sensitivities and distributions of species integrated into a systems modeling approach. Rarely is this information readily available, making it difficult to determine the applicability of realistic models to quantify population-level risks. To evaluate the trade-offs between data demands and increased specificity of spatially explicit m...
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