ABSTRACT: Passive acoustic telemetry is a powerful tool for tracking aquatic animals, yet the data derived from acoustic tags have important limitations. For example, inferences about habitat associations rely on statistical correlations, where frequent observations within a given habitat are interpreted as habitat preference. However, tagging data are not measures of movement per se, or even behavior more generally; rather, tagging data are representations of locations in space and time and can reflect limitations in the sampling technology as much as animal behavior. This interaction between sampling technology and resulting data means it is necessary to have some null expectation in order to evaluate a hypothesis predicting a habitat association. Here, we developed a null model for animal movement based on random walk simulations and examined our ability to distinguish random from intentioned movements when using passive acoustics. By comparing simulations to telemetry observations, we provide guidance for both data interpretation and future study design. We found that (1) real-world telemetry observations cannot be distinguished from random walks during initial portions of sampling and (2) researchers must account for the interaction between study duration and the ratio of organismal step size relative to detection radius when calculating site fidelity. To assist in the interpretation of passive acoustic data, we provide an analytical solution to forecast when real-world observations are reliably distinguishable from simple random walks.

中文翻译：

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使用被动声学遥测区分驻留行为与随机运动

摘要：被动声学遥测是跟踪水生动物的有力工具，但来自声学标签的数据具有重要的局限性。例如，关于栖息地关联的推断依赖于统计相关性，其中给定栖息地内的频繁观察被解释为栖息地偏好。然而，标记数据本身并不是运动的度量，甚至不是更普遍的行为；相反，标记数据是空间和时间位置的表示，可以反映采样技术和动物行为的局限性。采样技术和结果数据之间的这种相互作用意味着必须有一些零预期才能评估预测栖息地关联的假设。这里，我们开发了一个基于随机行走模拟的动物运动零模型，并检查了我们在使用被动声学时区分随机运动和有意运动的能力。通过将模拟与遥测观测进行比较，我们为数据解释和未来的研究设计提供了指导。我们发现 (1) 在采样的初始部分无法将真实世界的遥测观测与随机游走区分开来，并且 (2) 研究人员在计算站点保真度时必须考虑研究持续时间和有机体步长相对于检测半径的比率之间的相互作用. 为了帮助解释无源声学数据，我们提供了一种分析解决方案来预测何时可以可靠地区分真实世界的观察与简单的随机游走。