Are kernels the mustard? Data from global positioning system (GPS) collars suggests problems for kernel home-range analyses with least-squares cross-validation

被引：241

作者：

Hemson, G

Johnson, P

South, A

Kenward, R

Ripley, R

Macdonald, D

机构：

[1] Wildlife Conservat Res Unit, Tubney OX13 5QL, Oxon, England

[2] Univ Oxford, Dept Zool, EGI, Anatrack Ltd, Oxford OX1 3PS, England

[3] Winfrith Technol Ctr, Ctr Ecol & Hydrol, Dorchester DT2 8ZD, England

[4] Univ Oxford, Dept Stat, Oxford OX1 3TG, England

来源：

JOURNAL OF ANIMAL ECOLOGY | 2005年 / 74卷 / 03期

关键词：

algorithm; Panthera leo; subsampling; systematic error; variability;

D O I：

10.1111/j.1365-2656.2005.00944.x

中图分类号：

Q14 [生态学（生物生态学）];

学科分类号：

071012 ; 0713 ;

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 the smoothing factor (h) which has a considerable influence on the home-range estimate. 2. We tested the performance of least squares cross-validated kernel-density estimation (LSCV KDE) using data from global positioning system (GPS)-collared lions subsampled to simulate the effects of hypothetical radio-tracking strategies. 3. LSCV produced variable results and a 7% failure rate for fewer than 100 locations (n = 2069) and a 61% failure rate above 100 points (n = 1220). Patterns of failure and variation were not consistent among lions, reflecting different individual space use patterns. 4. Intensive use of core areas and site fidelity by animals caused LSCV to fail more often than anticipated from studies that used computer-simulated data. 5. LSCV failures at large sample sizes and variation at small sample sizes, limits the applicability of LSCV KDE to fewer situations than the literature suggests, and casts doubts over the method's reliability and comparability as a home-range estimator.

引用

页码：455 / 463

页数：9

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