Effects of Sample Size on Kernel Home Range Estimates

Abstract

JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about JSTOR, please contact support@jstor.org.. Allen Press is collaborating with JSTOR to digitize, preserve and extend access to The Journal of Wildlife Management.Abstract: Kernel methods for estimating home range are being used increasingly in wildlife research, but the effect of sample size on their accuracy is not known. We used computer simulations of 10-200 points/ home range and compared accuracy of home range estimates produced by fixed and adaptive kernels with the reference (REF) and least-squares cross-validation (LSCV) methods for determining the amount of smoothing. Simulated home ranges varied from simple to complex shapes created by mixing bivariate normal distributions. We used the size of the 95% home range area and the relative mean squared error of the surface fit to assess the accuracy of the kernel home range estimates. For both measures, the bias and variance approached an asymptote at about 50 observations/home range. The fixed kernel with smoothing selected by LSCV provided the least-biased estimates of the 95% home range area. All kernel methods produced similar surface fit for most simulations, but the fixed kernel with LSCV had the lowest frequency and magnitude of very poor estimates. We reviewed 101 papers published in The Journal of Wildlife Management (JWM) between 1980 and 1997 that estimated animal home ranges. A minority of these papers used nonparametric utilization distribution (UD) estimators, and most did not adequately report sample sizes. We recommend that home range studies using kernel estimates use LSCV to determine the amount of smoothing, obtain a minimum of 30 observations per animal (but preferably -50), and report sample sizes in published results. JOURNAL OF WILDLIFE MANAGEMENT 63(2):739-747

Evaluation of satellite collar sample size requirements for mitigation of low-level military jet disturbance of the George River caribou herd

“…Kernel Home Range (KHR) using Animal Movement Analysis software (Hooge & Eichelaub, 1997) and Arcview GIS (Environmental Systems Research Institute, Red¬ lands, CA), employing the ad-hoc smoothing option. This method is a fixed-kernel range estimate, and appears to be the best method for calculating range estimates from location data (Seaman & Powell, 1996, Seaman et al, 1999, Kernohan et al, 2001. We used these estimates of area to calculate the den¬ sity of 95% of the estimated herd size (665 000).…”

Modelling ranging behaviour of female orang-utans: a case study in Tuanan, Central Kalimantan, Indonesia

“…As variance in x and y coordinates of orang-utan location data was unequal, they were automatically rescaled with a unit variance before applying the smoothing parameter selection. Least Squares Cross Validation (LSCV: Silverman 1986, Worton 1995) smoothing parameter selection is currently the recommended smoothing parameter selection in the ecological literature (Seaman et al 1999), but it has been found to have several drawbacks (Kernohan et al 2001). For example, LSCV was criticised for its high variability and its tendency to under-smooth location data (Horne & Garton 2006b).…”

“…Report sample size used for home range estimates Use fixed kernel rather than adaptive ones (Seaman et al 1999, Kernohan et al 2001 Use automated method for smoothing parameter selection and report smoothing parameter values Estimate ranges over biologically meaningful temporal scales and include temporally consistent periods (e.g. annual range)…”

Response of eastern chipmunks to single application spring prescribed fires on the Fernow Experimental Forest

“…Each individual's acceptable locations then were subject to a 5 percent outlier removal using the harmonic mean method (Dixon and Chapman 1980). We only retained data on chipmunks possessing a minimum of 28 acceptable locations for subsequent home-range analysis (Seaman et al 1999). We then used the fixed kernel method with least squares cross validation as a smoothing parameter to construct our 95 percent (home range) and 50 percent (core area) contour utilization distributions (UD) (Silverman 1986, Worton 1989.…”