Estimating utilization distributions with kernel versus local convex hull methods

Authors

  • Nathanael I. Lichti,

    Corresponding author
    1. Department of Forestry and Natural Resources, Purdue University, 715 W. State Street, West Lafayette, IN 47907, USA
    • Department of Forestry and Natural Resources, Purdue University, 715 W. State Street, West Lafayette, IN 47907, USA.
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  • Robert K. Swihart

    1. Department of Forestry and Natural Resources, Purdue University, 715 W. State Street, West Lafayette, IN 47907, USA
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  • Associate Editor: Joshua Millspaugh.

Abstract

Estimates of utilization distributions (UDs) are used in analyses of home-range area, habitat and resource selection, and social interactions. We simulated data from 12 parent UDs, representing 3 series of increasingly intense space-use patterns (clustering of points around a home site, restriction of locations to a network of nodes and corridors, and dominance of a central hole in the UD) and compared the ability of kernel density estimation (KDE) and local convex hull (LCH) construction to reconstruct known UDs from samples of 10, 50, 250, and 1,000 location points. For KDE, we considered 4 bandwidth selectors: the reference bandwidth, least-squares cross-validation (LSCV), direct plug-in (DPI), and solve-the-equation (STE). For the sample sizes and UD patterns tested here, KDE achieved significantly higher volume-of-intersection (VI) scores with known parent UDs than did LCH; KDE also provided less biased home-range area estimates under many conditions. However, LCH minimized the UD volume that occurred outside the true home range boundary (Vout). Among the KDE bandwidth estimators, relative performance depended on the type and intensity of space use patterns, sample size, and the metric used to evaluate performance. Biologists should use KDE for UD and home range estimation within a probabilistic context, unless their objective is to exclude potentially unused areas by defining the area delimited by data. © 2011 The Wildlife Society.

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