A new kernel density estimator for accurate home‐range and species‐range area estimation

Kernel density estimators are widely applied to area-related problems in ecology, from estimating the home range of an individual to estimating the geographic range of a species. Currently, area estimates are obtained indirectly, by first estimating the location distribution from tracking (home range) or survey (geographic range) data, and then estimating areas from that distribution. This indirect approach leads to biased area estimates and difficulty in deriving reasonable confidence intervals. We introduce a new kernel density estimator and associated confidence intervals focused specifically on area estimation that apply to both independently sampled survey data and autocorrelated tracking data. We test our methods against simulated movement data and demonstrate its use with African buffalo data. The area-corrected kernel density estimator produces much more accurate area estimates, particularly at small sample sizes, and the newly derived confidence intervals are more reliable than existing alternatives. This new method is the most efficient non-parametric home-range estimator for animal tracking data, and should also be considered when calculating non-parametric range estimates from survey data. This estimator is now the default method in the ctmm R package. This article is protected by copyright. All rights reserved.