The role of the bandwidth matrix in influencing kernel home range estimates for snakes using VHF telemetry data

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.

rKIN: Kernel‐based method for estimating isotopic niche size and overlap

“…For example, Gitzen et al () reported that the ref estimator performed poorly relative to hpi or hlscv in all instances except when the distribution had a singular peak of activity. In contrast, Bauder et al () reported that ref and hpi provided comparable estimates, which were more robust than those produced by the hlscv. Nevertheless, few studies evaluated the consistency of size estimates with <20 data points (Bauder et al, ; Gitzen et al, ; Kie, ).…”

“…In contrast, Bauder et al () reported that ref and hpi provided comparable estimates, which were more robust than those produced by the hlscv. Nevertheless, few studies evaluated the consistency of size estimates with <20 data points (Bauder et al, ; Gitzen et al, ; Kie, ). Given our results and because of the inconsistent results in the home‐range literature, we offer the rKIN user the option of estimating niche size and overlap with all four methods and suggest that their applicability to specific datasets be evaluated.…”

“…In contrast, the plug‐in (hpi) estimator relies on an iterative process in which initial (pilot) values are arbitrarily selected and then modified based on the distribution of data until an optimal bandwidth is calculated (Gitzen et al, ). Both smoothed cross‐validation (hscv) and the least‐squares cross‐validation (hslcv) methods are based on the difference between the true and estimated distribution of the data and are calculated by minimizing the integrated square error between them, although the hscv method employs an iterative process similar to hpi (Bauder et al, ). Finally, the hbcv is based on a biased‐random‐bridge algorithm (Duong, ).…”

“…Both smoothed cross-validation (hscv) and the least-squares cross-validation (hslcv) methods are based on the difference between the true and estimated distribution of the F I G U R E 4 Isotopic niche data for least chipmunks Tamias minimus sampled in 2013 from forest (top) and sagebrush (bottom) habitats in spring (orange) and autumn (purple) in Wyoming. Size and overlap estimates were generated for 50%, 75% and 95% contour levels with MCP (a, d), standard ellipse area (b, e) and kernel utilization density (c, f) data and are calculated by minimizing the integrated square error between them, although the hscv method employs an iterative process similar to hpi (Bauder et al, 2015). Finally, the hbcv is based on a biased-random-bridge algorithm (Duong, 2018).…”

Home range and habitat use by pacas in a montane tropical forest in Bolivia

“…We chose MPC because of its known simplicity, and flexibility of shape (White andGarrot 1990, Powell 2000). Although we report fixed kernel estimates (with reference bandwidth) for comparison with other studies, we focus our discussion on MCP estimates, because of published reports of failures, or high variability of the kernel based estimations of home range, related to sample size (Hemson et al 2005, , Downs et al 2012, Signer and Balkenhol 2015, Bauder et al 2015. Furthermore, based on our field experience, we also think that kernel method over-estimated home range for our studied animals in particular.…”