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.

Highlights

  • Global 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

  • Mean AUC differed among several estimators and technology type (Kruskal-Wallis x2 = 573.99, df =14, P < 0.001) with the highest AUC consistently occurring for global positioning system (GPS) compared to very high frequency (VHF) technology (Figure 2)

  • Mean AUC for GPS technology was highest for Brownian Bridge Movement Model (BBMM) (mean = 0.982 ± 0.01 (SD)) and lowest for local convex hull (LOCO) (mean = 0.916 ± 0.03 (SD); Figure 2)

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Summary

Introduction

Global 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. Recent advances in global positioning system (GPS) technology for monitoring wildlife have revolutionized data collection for spatial analysis of movements, home range, and resource selection. These datasets acquired with GPS technology are more copious and locations are more precise when compared to locational data collected using very high frequency (VHF) systems. Concurrent with advances in GPS technology, alternative methods for estimation of home range have been developed to accommodate large numbers of autocorrelated relocations from GPS datasets Amongst these are first-generation methods such as kernel density estimators that have proven capable of providing home ranges using large GPS datasets (KDE; [3,4,5]), selection of the appropriate bandwidth for KDE is not always straightforward. Subsequent improvements in bandwidth selection have been developed for KDE using

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