Utilization distribution estimation using weighted kernel density estimators

Ecologists and wildlife biologists have long recognized the importance of random sampling but have largely used haphazard (i.e., nonrandom) designs for collecting location data for home-range and habitat-use studies. Using simulated movement paths, I illustrate the importance of random sampling in obtaining unbiased estimates of space use in home-range and habitat-use studies. Stratified random sampling will typically be more time efficient and easier to implement than simple random sampling. Therefore, I propose 2 weighted kernel density estimators (WKDEs) for use with stratified designs. Simulations indicate that these weighted estimators perform considerably better than traditional kernel density estimators when observations are sampled nonuniformly in time. Lastly, I illustrate the use of WKDEs to analyze data for a female northern white-tailed deer (Odocoileus virginianus) collected using Global Positioning Systems with seasonally varying intensity levels. By correcting for nonuniform sampling intensities, these estimators may provide a more accurate description of space use over the fixed study period.