Fieberg John
Minnesota Department of Natural Resources, Biometrics Unit, 5463-C W. Broadway, Forest Lake 55025, USA.
Ecology. 2007 Apr;88(4):1059-66. doi: 10.1890/06-0930.
Two oft-cited drawbacks of kernel density estimators (KDEs) of home range are their sensitivity to the choice of smoothing parameter(s) and their need for independent data. Several simulation studies have been conducted to compare the performance of objective, data-based methods of choosing optimal smoothing parameters in the context of home range and utilization distribution (UD) estimation. Lost in this discussion of choice of smoothing parameters is the general role of smoothing in data analysis, namely, that smoothing serves to increase precision at the cost of increased bias. A primary goal of this paper is to illustrate this bias-variance trade-off by applying KDEs to sampled locations from simulated movement paths. These simulations will also be used to explore the role of autocorrelation in estimating UDs. Autocorrelation can be reduced (1) by increasing study duration (for a fixed sample size) or (2) by decreasing the sampling rate. While the first option will often be reasonable, for a fixed study duration higher sampling rates should always result in improved estimates of space use. Further, KDEs with typical data-based methods of choosing smoothing parameters should provide competitive estimates of space use for fixed study periods unless autocorrelation substantially alters the optimal level of smoothing.
家域核密度估计器（KDEs）有两个常被提及的缺点，即它们对平滑参数选择的敏感性以及对独立数据的需求。已经进行了多项模拟研究，以比较在估计家域和利用分布（UD）的背景下，基于数据的客观方法选择最优平滑参数的性能。在关于平滑参数选择的讨论中，人们忽略了平滑在数据分析中的一般作用，也就是说，平滑是以增加偏差为代价来提高精度。本文的一个主要目标是通过将KDEs应用于模拟移动路径的采样位置来说明这种偏差 - 方差权衡。这些模拟还将用于探讨自相关在估计UD中的作用。自相关可以通过以下方式降低：（1）增加研究持续时间（对于固定样本量）或（2）降低采样率。虽然第一种选择通常是合理的，但对于固定的研究持续时间，较高的采样率应该总是能带来对空间利用的更好估计。此外，除非自相关显著改变最优平滑水平，否则使用基于数据的典型方法选择平滑参数的KDEs应该能在固定研究期内提供具有竞争力的空间利用估计。
