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（home-range estimation）是动物追踪数据的重要应用场景，但其常受自相关（autocorrelation）、采样不规则性以及有效样本量偏小等问题的制约。本研究提出一种全新的最优加权方法，可校正自相关追踪数据中的时间采样偏差。该方法可修正不规则采样与数据缺失问题：对采样过密的时段施加降权，对采样过疏的时段施加升权，从而最小化家域估计的误差。本研究同时开发了计算效率优异的算法，使该方法可适配大规模数据集的处理需求。总体而言，以下三类场景下，权重优化可显著提升家域估计的精度：一是采样方案极不规则的海洋生物追踪数据；二是观测周期内采样方案发生动态调整的占空比式采样（duty cycled）数据；三是仅观测到少量家域穿越事件的场景，此时时段首尾相较于中间时段具备更强的独立性与信息价值。本研究通过模拟数据与实证案例（涵盖礁蝠鲼（reef manta ray）、蒙古原羚（Mongolian gazelle）与非洲水牛（African buffalo）），验证了最优加权方法可降低家域估计的误差并提升其空间分辨率。该方法已实现便捷封装且计算高效的软件实现，可拓展可开展精准空间利用评估的数据集范围。