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.

掌握种群的空间分布特征，是开展任何物种种群管理规划的核心前提。当利用追踪数据描述种群分布时，学界常会默认：个体的上报定位即可划定目标种群所利用区域的空间范围。

本研究首先检视了用于验证该假设的现有方法，明确其存在的局限与注意事项，并提出一种全新方法，可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）。本研究表明，通过结合利用分布（utilisation distribution）的概率特性与目标物种对栖息地的异质性利用模式，即可完成此类评估。本研究的相关方法已整合至R包SDLfilter中。

本研究通过概念模型阐释并对比了现有方法与新方法的核心流程，并利用一组包含69只佩戴高精度Fastloc-GPS标签的平背海龟（Natator depressus）追踪记录的大型卫星追踪数据集，验证了本方法的应用效果。

本方法可广泛适用于多类动物类群、种群及生活史阶段的分布模式稳健估计所需样本量的事后验证。