LoCoH: Nonparameteric Kernel Methods for Constructing Home Ranges and Utilization Distributions
LoCoH: Nonparameteric Kernel Methods for Constructing Home Ranges and Utilization Distributions
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DOI：
10.1371/journal.pone.0000207
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发表时间：
2007-02-14
期刊：
影响因子：
3.7
通讯作者：
Wilmers, Christopher C.
中科院分区：
文献类型：
--
作者：
Getz, Wayne M.;Fortmann-Roe, Scott;Wilmers, Christopher C.
参数核方法目前占主导地位的文献建设的动物家域（HR）和利用分布（UD）。这些方法经常无法捕捉到许多自然系统中常见的硬边界。最近，局部凸船体（LoCoH）非参数核方法，它推广了最小凸多边形（MCP）方法，被证明是比参数核方法更适合用于构建HR和UD，因为它能够识别硬边界（例如，例如，在一个实施例中，河流，悬崖边缘）和收敛到真实的分布作为样本大小的增加。在这里，我们以两种方式扩展LoCoH：“固定影响范围”或r-LoCoH（从每个参考点的固定半径r内的所有点构造的核），以及“自适应影响范围”或a-LoCoH（由半径a内的所有点构造的核使得半径内的所有点到参考点的距离总和为小于或等于a的值），并将它们与原始的“固定点数”或k-LoCoH（所有内核都是从根点的k-1个最近邻居构建的）进行比较。我们还比较了这些非参数LoCoH参数内核的方法，使用制造的数据和数据收集的GPS项圈在克鲁格国家公园，南非的非洲布法罗。我们的研究结果表明，LoCoH方法在估计动物使用的区域方面上级参数核方法，不包括未使用的区域（孔），并且通常在构建受硬边界和不规则结构影响的动物运动所产生的UD和HR方面优于参数核方法。例如，在一个实施例中，岩石露头）。我们还证明了a-LoCoH通常上级k-和r-LoCoH（所有三种方法的软件均可在http://locoh.cnr.berkeley.edu上获得）。
Parametric kernel methods currently dominate the literature regarding the construction of animal home ranges (HRs) and utilization distributions (UDs). These methods frequently fail to capture the kinds of hard boundaries common to many natural systems. Recently a local convex hull (LoCoH) nonparametric kernel method, which generalizes the minimum convex polygon (MCP) method, was shown to be more appropriate than parametric kernel methods for constructing HRs and UDs, because of its ability to identify hard boundaries (e. g., rivers, cliff edges) and convergence to the true distribution as sample size increases. Here we extend the LoCoH in two ways: "fixed sphere-of-influence,'' or r-LoCoH (kernels constructed from all points within a fixed radius r of each reference point), and an "adaptive sphere-of-influence,'' or a-LoCoH (kernels constructed from all points within a radius a such that the distances of all points within the radius to the reference point sum to a value less than or equal to a), and compare them to the original "fixed-number-of-points,'' or k-LoCoH (all kernels constructed from k-1 nearest neighbors of root points). We also compare these nonparametric LoCoH to parametric kernel methods using manufactured data and data collected from GPS collars on African buffalo in the Kruger National Park, South Africa. Our results demonstrate that LoCoH methods are superior to parametric kernel methods in estimating areas used by animals, excluding unused areas (holes) and, generally, in constructing UDs and HRs arising from the movement of animals influenced by hard boundaries and irregular structures (e. g., rocky outcrops). We also demonstrate that a-LoCoH is generally superior to k- and r-LoCoH (with software for all three methods available at http://locoh.cnr.berkeley.edu).