Is there a single best estimator? Selection of home range estimators using area-under-the-curve

Abstract

BackgroundGlobal positioning system (GPS) technology for monitoring home range and movements of wildlife has resulted in prohibitively large sample sizes of locations for traditional estimators of home range. We used area-under-the-curve to explore the fit of 8 estimators of home range to data collected with both GPS and concurrent very high frequency (VHF) technology on a terrestrial mammal, the Florida panther Puma concolor coryi, to evaluate recently developed and traditional estimators.ResultsArea-under-the-curve was the highest for Florida panthers equipped with Global Positioning System (GPS) technology compared to VHF technology. For our study animal, estimators of home range that incorporated a temporal component to estimation performed better than traditional first- and second-generation estimators.ConclusionsComparisons of fit of home range contours with locations collected would suggest that use of VHF technology is not as accurate as GPS technology to estimate size of home range for large mammals. Estimators of home range collected with GPS technology performed better than those estimated with VHF technology regardless of estimator used. Furthermore, estimators that incorporate a temporal component (third-generation estimators) appeared to be the most reliable regardless of whether kernel-based or Brownian bridge-based algorithms were used and in comparison to first- and second-generation estimators. We defined third-generation estimators of home range as any estimator that incorporates time, space, animal-specific parameters, and habitat. Such estimators would include movement-based kernel density, Brownian bridge movement models, and dynamic Brownian bridge movement models among others that have yet to be evaluated.Electronic supplementary materialThe online version of this article (doi:10.1186/s40462-015-0039-4) contains supplementary material, which is available to authorized users.

A comprehensive analysis of autocorrelation and bias in home range estimation

“…, Walter et al. ). A primary reason for this difficulty is that tracking data are often strongly autocorrelated, while most home range estimators assume statistically independent data.…”

“…, Walter et al. ). However, we argue that the role that autocorrelation plays in home range estimation has yet to be fully resolved for three key reasons.…”

“…, Walter et al. ), while others have simply shown empirical results without reference to their accuracy (e.g., De Solla et al. , Dougherty et al.…”

“…is still the most widely referenced. While advances in animal tracking technology, and its increasing use, have dramatically increased our capacity to collect data to support home range analysis (Tomkiewicz et al 2010, Kays et al 2015, translating Burt's conceptual definition into a rigorous statistical method that can be applied to these data has remained challenging (Powell 2000, Hemson et al 2005, Walter et al 2015. A primary reason for this difficulty is that tracking data are often strongly autocorrelated, while most home range estimators assume statistically independent data.…”

“…Second, there has been a historical lack of objective metrics that quantify (1) the performance of home range estimators on empirical data, where the true home range area is not known, and (2) how the interplay between study design and movement behavior affects the information content of a tracking data set. In the absence of the former, some studies have used estimates based on the full empirical data set as the "truth" (e.g., Rooney et al 1998, Girard et al 2002, B€ orger et al 2006, Walter et al 2015, Histogram depicting the amount of autocorrelation at lag 1 in the empirical tracking data for each of the 369 individuals used in the present study, in comparison to the range of values addressed by previous studies. Note how previous conclusions on the influence of autocorrelation on home range estimation were based on weakly autocorrelated data that are not representative of the majority of modern GPS data.…”