Animal tracking and biologging devices record large amounts of data on individual movement behaviors in natural environments. In these data, movement ecologists often view unexplained variation around the mean as “noise” when studying patterns at the population level. In the field of behavioral ecology, however, focus has shifted from population means to the biological underpinnings of variation around means. Specifically, behavioral ecologists use repeated measures of individual behavior to partition behavioral variability into intrinsic among-individual variation and reversible behavioral plasticity and to quantify: a) individual variation in behavioral types (i.e. different average behavioral expression), b) individual variation in behavioral plasticity (i.e. different responsiveness of individuals to environmental gradients), c) individual variation in behavioral predictability (i.e. different residual within-individual variability of behavior around the mean), and d) correlations among these components and correlations in suites of behaviors, called ‘behavioral syndromes’. We here suggest that partitioning behavioral variability in animal movements will further the integration of movement ecology with other fields of behavioral ecology. We provide a literature review illustrating that individual differences in movement behaviors are insightful for wildlife and conservation studies and give recommendations regarding the data required for addressing such questions. In the accompanying R tutorial we provide a guide to the statistical approaches quantifying the different aspects of among-individual variation. We use movement data from 35 African elephants and show that elephants differ in a) their average behavior for three common movement behaviors, b) the rate at which they adjusted movement over a temporal gradient, and c) their behavioral predictability (ranging from more to less predictable individuals). Finally, two of the three movement behaviors were correlated into a behavioral syndrome (d), with farther moving individuals having shorter mean residence times. Though not explicitly tested here, individual differences in movement and predictability can affect an individual’s risk to be hunted or poached and could therefore open new avenues for conservation biologists to assess population viability. We hope that this review, tutorial, and worked example will encourage movement ecologists to examine the biology of individual variation in animal movements hidden behind the population mean.

中文翻译：

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根据野外运动数据研究个体间行为差异的指南。

动物跟踪和生物记录设备会记录有关自然环境中个体运动行为的大量数据。在这些数据中，运动生态学家在研究人口水平的模式时，经常将无法解释的均值周围的变化视为“噪声”。但是，在行为生态学领域，重点已从人口均值转移到均值周围变异的生物学基础。具体而言，行为生态学家使用重复测量的个体行为将行为变异性划分为内在个体变异和可逆行为可塑性，并量化：a）行为类型的个体变异（即不同的平均行为表达），b）行为可塑性的个体变异（即个人对环境梯度的不同响应），c）行为可预测性的个体变化（即，均值周围行为的不同残留内部个体差异），以及d）这些成分之间的相关性以及一系列行为的相关性，称为“行为综合症”。我们在此建议，在动物运动中划分行为变异性将进一步促进运动生态学与行为生态学其他领域的整合。我们提供了一篇文献综述，说明运动行为的个体差异对于野生动植物和保护研究具有洞察力，并针对解决此类问题所需的数据提供了建议。在随附的R教程中，我们提供了一种统计方法指南，用于量化个体差异之间的不同方面。我们使用了来自35头非洲大象的运动数据，结果表明，大象在以下方面有所不同：a）三种常见运动行为的平均行为； b）在时间梯度上调整运动的速率； c）行为的可预测性（范围从更多到难以预测的个人）。最后，将三种运动行为中的两种与行为综合症（d）相关联，运动越远的个体的平均停留时间越短。尽管此处没有明确测试，但个体在活动和可预测性方面的差异会影响个体被猎杀或被偷猎的风险，因此可能为保护生物学家评估种群生存能力开辟新途径。我们希望这篇评论，教程，