Use of habitat-contamination spatial correlation to determine when to perform a spatially explicit ecological risk assessment

Anthropogenic contamination is typically distributed heterogeneously through space. This spatial structure can have different effects on the cumulative doses of wildlife exposed to contamination within the environment. These effects are accentuated when individual organisms pursue different movement strategies, and movement strategies can be affected by how individual organisms and species value habitat. Habitat quality is often neglected when ecological risk assessments are performed, despite evidence that inclusion of a quantitative habitat measure can have a significant effect on the overall exposure estimate. We couple an exposure model with habitat data to examine the interactions between habitat preferences, the spatial distribution of contamination, and the resulting impact on dose estimates. Dose distributions are constructed for pronghorn (Antilocapra americana) exposed to fluoride when foraging on desert sagebrush. The results show the magnitude of the difference between simulated doses when foraging concentrations are positively or negatively correlated with different spatial distributions of habitat preferences. Mean estimated exposures obtained from non-spatial versus spatial methods can vary by a factor greater than two, and variation within the movement model, due to different habitat preferences, can vary by an order of magnitude. Such differences in calculated exposures can change a remediation decision from no-action to remediation, or vice-versa, and impact the remedial design when cleanup is required. In addition, information concerning which endpoint species are more or less likely to be exposed to chemical contamination in a given spatial setting can be used by stakeholders in the endpoint selection process. Results presented here are generally applicable to other situations where terrestrial wildlife is exposed to chemical contaminants. These simple model results demonstrate that examining the strength of the spatial correlation between habitat preference and contaminant data can be quickly used to determine when the implementation of a spatially explicit ecological risk assessment is useful.