Effects of sample size on kernel home range estimates

D. Erran Seaman, Joshua J. Millspaugh, Brian J. Kernohan, Gary C. Brundige, Kenneth J. Raedeke, Robert A. Gitzen

    Research output: Contribution to journalArticlepeer-review

    1036 Scopus citations

    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.

    Original languageEnglish
    Pages (from-to)739-747
    Number of pages9
    JournalJournal of Wildlife Management
    Volume63
    Issue number2
    DOIs
    StatePublished - Apr 1999

    Keywords

    • Home range
    • Kernel estimator
    • Monte Carlo simulations
    • Nonparametric density estimator
    • Sample size
    • Utilization distribution

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