The impact of sampling scale: A comparison of methods for estimating external contaminant exposure in free-ranging...

The impacts of contaminants on wildlife are dose dependent, and thus being able to track or predict exposure following contamination events is important for monitoring ecosystem health. However, the ability to track exposure in free-ranging wildlife is often severely limited. Consequently, researchers have predominantly relied on simple methods for estimating contaminant exposures in wildlife with little regard for spatial contaminant heterogeneity or an animal's use of diverse habitats. We evaluated the influence sampling scale (i.e., how finely contaminant distribution and organism's spatial use of the landscape is mapped) has on (1) realism and (2) conservativeness of exposure estimates. To do this, we monitored the actual exposure of wild boar (Sus scrofa) in Fukushima, Japan to radioactive contamination using GPS-coupled contaminant monitors placed on individual animals. We compared empirical exposures to estimates generated by combining varying amounts of information about an individual boar's location and/or movement, with the distribution of contamination on the landscape. We found that the most realistic exposure estimates were produced when finer-scale contaminant distribution surveys (e.g., airborne surveys) were combined with more accurate estimates of an individual's space use (e.g., home ranges or core areas). Importantly, estimates of exposure based on single point surveys at a trap site (a simple method commonly used in the literature), did not correlate with actual exposure rates, suggesting dose-effects studies using this method may result in spurious conclusions. These results suggest that researchers seeking realistic estimates of exposure, such as in dose-effect studies, should ensure they have adequately accounted for fine-scale contaminant distribution patterns and areas of higher use by study organisms. However, conservative estimates of exposure (i.e., intentionally over-predicting exposure as is done in initial tiers of ecological risk analyses) were not as scale sensitive and could be achieved with a single known location and coarse contaminant distribution maps.