Are kernels the mustard? Data from global positioning system (GPS) collars suggests problems for kernel home- range analyses with least-squares cross-validation
作者： GRAHAM HEMSON , PAUL JOHNSON , ANDY SOUTH , ROBERT KENWARD , RUTH RIPLEY
DOI: 10.1111/J.1365-2656.2005.00944.X
关键词:
摘要: 1. Kernel-density estimation (KDE) is one of the most widely used home-range estimators in ecology. The recommended implementation uses least squares cross-validation (LSCV) to calculate smoothing factor (h) which has a considerable influence on estimate. 2. We tested performance cross-validated kernel-density (LSCV KDE) using data from global positioning system (GPS)-collared lions subsampled simulate effects hypothetical radio-tracking strategies. 3. LSCV produced variable results and 7% failure rate for fewer than 100 locations (H = 2069) 61% above points (n 1220). Patterns variation were not consistent among lions, reflecting different individual space use patterns. 4. Intensive core areas site fidelity by animals caused fail more often anticipated studies that computer-simulated data. 5. failures at large sample sizes small sizes, limits applicability KDE situations literature suggests, casts doubts over method's reliability comparability as estimator. © 2005 British Ecological Society.
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