The role of the bandwidth matrix in influencing kernel home range estimates for snakes using VHF telemetry data
Javan M. Bauder A F , David R. Breininger B , M. Rebecca Bolt C , Michael L. Legare D , Christopher L. Jenkins E and Kevin McGarigal A
+ Author Affiliations
- Author Affiliations
A Department of Environmental Conservation, University of Massachusetts, 160 Holdsworth Way, Amherst, MA 01003, USA.
B InoMedic Health Applications, NASA Ecological Programs, Mail Code IHA-300, Kennedy Space Center, FL 32899, USA.
C InoMedic Health Applications, NASA Ecological Programs, Mail Code IHA-200, Kennedy Space Center, FL 32899, USA.
D Merritt Island National Wildlife Refuge, PO Box 6504, Titusville, FL 32782, USA.
E The Orianne Society, 100 Phoenix Road, Athens, GA 30605, USA.
F Corresponding author. Email: javanvonherp@gmail.com
Wildlife Research 42(5) 437-453 https://doi.org/10.1071/WR14233
Submitted: 18 November 2014 Accepted: 29 July 2015 Published: 18 September 2015
Abstract
Context: Despite the diversity of available home range estimators, no single method performs equally well in all circumstances. It is therefore important to understand how different estimators perform for data collected under diverse conditions. Kernel density estimation is a popular approach for home range estimation. While many studies have evaluated different kernel bandwidth selectors, few studies have compared different formulations of the bandwidth matrix using wildlife telemetry data. Additionally, few studies have compared the performance of kernel bandwidth selectors using VHF radio-telemetry data from small-bodied taxa.
Aims: In this study, we used eight different combinations of bandwidth selectors and matrices to evaluate their ability to meet several criteria that could be potentially used to select a home range estimator.
Methods: We used handheld VHF telemetry data from two species of snake displaying non-migratory and migratory movement patterns. We used subsampling to estimate each estimator’s sensitivity to sampling duration and fix rate and compared home range size, the number of disjunct volume contours and the proportion of telemetry fixes not included in those contours among estimators.
Key Results: We found marked differences among bandwidth selectors with regards to our criteria but comparatively little difference among bandwidth matrices for a given bandwidth selector. Least-squares cross-validation bandwidths exhibited near-universal convergence failure whereas likelihood cross-validation bandwidths showed high sensitivity to sampling duration and fix rate. The reference, plug-in and smoothed cross-validation bandwidths were more robust to variation in sampling intensity, with the former consistently producing the largest estimates of home range size.
Conclusions: Our study illustrates the performance of multiple kernel bandwidth estimators for estimating home ranges with datasets typical of many small-bodied taxa. The reference and plug-in bandwidths with an unconstrained bandwidth matrix generally had the best performance. However, our study concurs with earlier studies indicating that no single home range estimator performs equally well in all circumstances.
Implications: Although we did not find strong differences between bandwidth matrices, we encourage the use of unconstrained matrices because of their greater flexibility in smoothing data not parallel to the coordinate axes. We also encourage researchers to select an estimator suited to their study objectives and the life history of their study organism.
Additional keywords: bandwidth matrix, Crotalus oreganus, Drymarchon couperi, eastern indigo snake, home range, kernel bandwidth, utilisation distribution, western rattlesnake.
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