Home range estimation is routine practice in ecological research. While advances in animal tracking technology have increased our capacity to collect data to support home range analysis, these same advances have also resulted in increasingly autocorrelated data. Consequently, the question of which home range estimator to use on modern, highly autocorrelated tracking data remains open. This question is particularly relevant given that most estimators assume independently sampled data. Here, we provide a comprehensive evaluation of the effects of autocorrelation on home range estimation. We base our study on an extensive data set of GPS locations from 369 individuals representing 27 species distributed across five continents. We first assemble a broad array of home range estimators, including Kernel Density Estimation (KDE) with four bandwidth optimizers (Gaussian reference function, autocorrelated‐Gaussian reference function [AKDE], Silverman's rule of thumb, and least squares cross‐validation), Minimum Convex Polygon, and Local Convex Hull methods. Notably, all of these estimators except AKDE assume independent and identically distributed (IID) data. We then employ half‐sample cross‐validation to objectively quantify estimator performance, and the recently introduced effective sample size for home range area estimation ( ${\widehat{\mathrm{N}}}_{\mathrm{area}}$) to quantify the information content of each data set. We found that AKDE 95% area estimates were larger than conventional IID‐based estimates by a mean factor of 2. The median number of cross‐validated locations included in the hold‐out sets by AKDE 95% (or 50%) estimates was 95.3% (or 50.1%), confirming the larger AKDE ranges were appropriately selective at the specified quantile. Conversely, conventional estimates exhibited negative bias that increased with decreasing ${\widehat{\mathrm{N}}}_{\mathrm{area}}$. To contextualize our empirical results, we performed a detailed simulation study to tease apart how sampling frequency, sampling duration, and the focal animal's movement conspire to affect range estimates. Paralleling our empirical results, the simulation study demonstrated that AKDE was generally more accurate than conventional methods, particularly for small ${\widehat{\mathrm{N}}}_{\mathrm{area}}$. While 72% of the 369 empirical data sets had >1,000 total observations, only 4% had an ${\widehat{\mathrm{N}}}_{\mathrm{area}}$ >1,000, where 30% had an ${\widehat{\mathrm{N}}}_{\mathrm{area}}$ <30. In this frequently encountered scenario of small ${\widehat{\mathrm{N}}}_{\mathrm{area}}$, AKDE was the only estimator capable of producing an accurate home range estimate on autocorrelated data.

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家庭距离估计中自相关和偏差的综合分析

家庭范围估计是生态研究中的常规做法。尽管动物跟踪技术的进步提高了我们收集数据以支持家庭范围分析的能力，但这些进步也导致了越来越多的自相关数据。因此，在现代的，高度自相关的跟踪数据上使用哪个家庭距离估计器的问题仍然悬而未决。鉴于大多数估算器都假设独立采样的数据，所以这个问题特别相关。在这里，我们提供了自相关对房屋范围估计的影响的综合评估。我们的研究基于来自369个个体的GPS位置的广泛数据集，这些个体代表了分布在五大洲的27个物种。我们首先组装各种各样的家庭范围估计器，包括具有四个带宽优化器（高斯参考函数，自相关高斯参考函数[AKDE]，Silverman经验法则和最小二乘交叉验证）的内核密度估计（KDE），最小凸多边形和局部凸壳方法。值得注意的是，除AKDE以外，所有这些估计量均假设独立且均匀分布（IID）的数据。然后，我们使用半样本交叉验证来客观地量化估算器的性能，并采用最近引入的有效样本量进行家庭区域面积估算（除AKDE以外，所有这些估算器均假设独立且均匀分布（IID）的数据。然后，我们使用半样本交叉验证来客观地量化估算器的性能，并采用最近引入的有效样本量进行家庭区域面积估算（除AKDE以外，所有这些估算器均假设独立且均匀分布（IID）的数据。然后，我们使用半样本交叉验证来客观地量化估算器的性能，并采用最近引入的有效样本量进行家庭区域面积估算（${\widehat{\mathrm{ñ}}}_{\mathrm{区域}}$）以量化每个数据集的信息内容。我们发现AKDE 95％的面积估计值比传统的基于IID的估计值大2倍。AKDE95％（或50％）的估计值包含在保留集中的交叉验证位置的中位数为95.3。 ％（或50.1％），确认较大的AKDE范围在指定的分位数上具有适当的选择性。相反，常规估计值显示出负偏见，负偏见随着减少而增加${\widehat{\mathrm{ñ}}}_{\mathrm{区域}}$。为了结合我们的经验结果，我们进行了详细的模拟研究，以弄清楚采样频率，采样持续时间以及焦点动物的运动如何共同影响距离估计。与我们的经验结果平行，模拟研究表明，AKDE通常比常规方法更准确，特别是对于小型方法而言。${\widehat{\mathrm{ñ}}}_{\mathrm{区域}}$。尽管369个经验数据集中有72％的观测值总数超过1,000，但只有4％的观测值${\widehat{\mathrm{ñ}}}_{\mathrm{区域}}$ > 1,000，其中30％的 ${\widehat{\mathrm{ñ}}}_{\mathrm{区域}}$<30。在这种经常遇到的小情况下${\widehat{\mathrm{ñ}}}_{\mathrm{区域}}$，AKDE是唯一能够对自相关数据产生准确的原始范围估计值的估计器。