Data from: Correcting for missing and irregular data in home-range estimation

Home-range estimation is an important application of animal tracking data that is frequently complicated by autocorrelation, sampling irregularity, and small effective sample sizes. We introduce a novel, optimal weighting method that accounts for temporal sampling bias in autocorrelated tracking data. This method corrects for irregular and missing data, such that oversampled times are downweighted and undersampled times are upweighted to minimize error in the home-range estimate. We also introduce computationally efficient algorithms that make this method feasible with large datasets. Generally speaking, there are three situations where weight optimization improves the accuracy of home-range estimates: with marine data, where the sampling schedule is highly irregular, with duty cycled data, where the sampling schedule changes during the observation period, and when a small number of home-range crossings are observed, making the beginning and end times more independent and informative than the intermediate times. Using both simulated data and empirical examples including reef manta ray, Mongolian gazelle, and African buffalo, optimal weighting is shown to reduce the error and increase the spatial resolution of home-range estimates. With a conveniently packaged and computationally efficient software implementation, this method broadens the array of datasets with which accurate space-use assessments can be made.

家域估计是动物追踪数据的重要应用方向，但该任务常受自相关、采样不规则性以及有效样本量偏小等问题困扰。本研究提出一种全新的最优加权方法，可解决自相关追踪数据中的时间采样偏差问题。该方法可校正不规则采样与数据缺失问题：对采样过密的时段降低权重，对采样过疏的时段提升权重，从而最小化家域估计的误差。本研究同时提出了计算效率优异的算法，使该方法可适用于大规模数据集。总体而言，以下三类场景下，权重优化可有效提升家域估计的精度：一是采样计划极不规则的海洋追踪数据；二是观测期间采样计划发生变动的占空比采样数据；三是仅观测到少量家域穿越事件的场景——此时时段首尾的采样相较于中间时段更具独立性与信息价值。本研究通过模拟数据与礁蝠鲼（reef manta ray）、蒙古瞪羚（Mongolian gazelle）、非洲水牛（African buffalo）等实证案例验证发现，最优加权方法可降低家域估计的误差，同时提升其空间分辨率。本研究提供了便捷封装且计算高效的软件实现，使该方法可拓展至更多数据集，助力开展精准的空间利用评估工作。