Optimising sample sizes for animal distribution analysis using tracking data

Knowledge of the spatial distribution of populations is fundamental to management plans for any species. When tracking data are used to describe distributions, it is sometimes assumed that the reported locations of individuals delineate the spatial extent of areas used by the target population. Here we examine existing approaches to validate this assumption, highlight caveats, and propose a new method for a more informative assessment of the number of tracked animals (i.e. sample size) necessary to identify distribution patterns. We show how this assessment can be achieved by considering the heterogeneous use of habitats by a target species using the probabilistic property of a utilisation distribution. Our methods are compiled in the r package SDLfilter. We illustrate and compare the protocols underlying existing and new methods using conceptual models and demonstrate an application of our approach using a large satellite tracking dataset of flatback turtles Natator depressus tagged with accurate Fastloc-GPS tags (n = 69). Our approach has applicability for the post hoc validation of sample sizes required for the robust estimation of distribution patterns across a wide range of taxa, populations and life-history stages of animals.

掌握物种种群的空间分布信息，是制定任意物种管理计划的核心基础。当利用追踪数据描述种群分布时，该领域研究人员有时会默认：个体的上报坐标即可划定目标种群所利用的空间范围。
本研究首先梳理现有用于验证该假设的方法，阐明其存在的局限，并提出一种新方法，用以更全面地评估识别分布模式所需的追踪动物数量（即样本量）。本研究通过结合目标物种种群生境利用的异质性特征，借助利用分布（utilisation distribution）的概率属性，阐明该评估的实现路径。本研究的相关方法已整合至R包SDLfilter中。
本研究借助概念模型，对现有方法与新方法背后的研究范式进行阐释与对比；同时利用一套大型平背海龟（*Natator depressus*）卫星追踪数据集——该数据集包含69只佩戴高精度Fastloc-GPS标签的个体——展示本方法的实际应用效果。
本方法可广泛适用于各类动物类群、种群及生活史阶段中，为稳健估计分布模式所需样本量的事后验证工作。