Autocorrelation‐informed home range estimation: A review and practical guide
Autocorrelation‐informed home range estimation: A review and practical guide
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DOI：
10.1111/2041-210x.13786
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发表时间：
2021-12
影响因子：
6.6
通讯作者：
Inês Silva;C. Fleming;M. Noonan;Jesse Alston;Cody Folta;W. Fagan;J. Calabrese
中科院分区：
文献类型：
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作者：
Inês Silva;C. Fleming;M. Noonan;Jesse Alston;Cody Folta;W. Fagan;J. Calabrese
现代跟踪设备允许以比甚高频无线电遥测更高的采样率收集大量动物跟踪数据。活动范围估计是这些跟踪数据集的关键输出，但动物运动的固有特性可能导致传统统计方法低估或高估活动范围区域。自相关核密度估计 (AKDE) 系列估计器旨在提高统计效率，同时明确处理现代运动数据的复杂性：自相关、小样本量以及缺失或不规则采样的数据。尽管这些估计器中的每一个都已在单独的技术论文中进行了描述，但在这里我们回顾了这些估计器的工作原理，并提供了关于如何组合它们以同时减少多个偏差的用户友好指南。我们使用实证案例研究和模拟来描述这些估计器所提供的改进的幅度及其对家庭范围面积估计的影响，并比较它们的计算成本。最后，我们为研究人员提供了选择替代估计器和 R 脚本的指南，以促进 AKDE 家庭范围估计的应用和解释。
Modern tracking devices allow for the collection of high‐volume animal tracking data at improved sampling rates over very‐high‐frequency radiotelemetry. Home range estimation is a key output from these tracking datasets, but the inherent properties of animal movement can lead traditional statistical methods to under‐ or overestimate home range areas. The autocorrelated kernel density estimation (AKDE) family of estimators was designed to be statistically efficient while explicitly dealing with the complexities of modern movement data: autocorrelation, small sample sizes and missing or irregularly sampled data. Although each of these estimators has been described in separate technical papers, here we review how these estimators work and provide a user‐friendly guide on how they may be combined to reduce multiple biases simultaneously. We describe the magnitude of the improvements offered by these estimators and their impact on home range area estimates, using both empirical case studies and simulations, contrasting their computational costs. Finally, we provide guidelines for researchers to choose among alternative estimators and an R script to facilitate the application and interpretation of AKDE home range estimates.