A Spatially Explicit Model to Predict Radiocesium Body Burdens in White-Tailed Deer on the Department of Energy's Savannah River Site

Abstract

A spatially explicit exposure model was developed to interpolate and predict radiocesium (137Cs) body burdens found in white-tailed deer (Odocoileus virginianus) on the U.S. Department of Energy's Savannah River Site (SRS) in west-central South Carolina. Since 1965 hunting has been allowed from permanent stands as a mechanism to manage the herd and all animals have been monitored in the field for gross beta/gamma activity levels providing a long-term spatially explicit dataset. Until now no study has attempted to model deer 137Cs at the hunt-stand level, but rather at the compartment level (1,000 - 5,200 ha). The models described here use the relative locations of the hunted stands to predict 137Cs exposure distributions between the years of 1984 - 2005 and takes into consideration the number of deer harvested and their body burdens. Kriging was used as the first deterministic method that created an interpolation surface using a best linear unbiased estimator. This geostatistical approach was used based on its ability to use variance components of neighbors at multiple distances using mean 137 Cs body burdens as the weighted variable. Krigint enabled an increased confidence of the relationship between 137Cs body-burdens, their spatial association with each stand, and enabled the differentiation between sources of 137Cs to deer on the SRS. A series of regression models were then used to investigate if the bioavailability of 137Cs and the number of deer harvested were a function of habitat composition, landscape structure, and clay content on the SRS. The interpolation surfaces, coupled with the regression analyses, provided a comprehensive assessment of deer 137Cs body burdens and habitat structure on the SRS, which then may be used in human and ecological risk assessments.

Date of Award	2010
Original language	American English
Awarding Institution	Eastern Illinois University
Supervisor	Karen Gaines (Supervisor)