Despite the routine nature of estimating overlapping space use in ecological research, to date no formal inferential framework for home range overlap has been available to ecologists. Part of this issue is due to the inherent difficulty of comparing the estimated home ranges that underpin overlap across individuals, studies, sites, species, and times. Because overlap is calculated conditionally on a pair of home range estimates, biases in these estimates will propagate into biases in overlap estimates. Further compounding the issue of comparability in home range estimators is the historical lack of confidence intervals on overlap estimates. This means that it is not currently possible to determine if a set of overlap values are statistically different from one another. As a solution, we develop the first rigorous inferential framework for home range overlap. Our framework is based on the AKDE family of home range estimators, which correct for biases due to autocorrelation, small effective sample size, and irregular sampling in time. Collectively, these advances allow AKDE estimates to validly be compared even when sampling strategies differ. We then couple the AKDE estimates with a novel bias-corrected Bhattacharyya Coefficient (BC) to quantify overlap. Finally, we propagate uncertainty in the AKDE estimates through to overlap, and thus are able to put confidence intervals on the BC point estimate.
Recommended citation: Kevin Winner, Michael J. Noonan, Chris H. Fleming, Kirk Olson, Thomas Mueller, Dan Sheldon, and Justin M. Calabrese. Statistical inference for home range overlap. Methods in Ecology and Evolution, 2018 http://kwinner.github.io/files/Winneretal2018.pdf