Continuous time resource selection analysis for moving animals
Continuous time resource selection analysis for moving animals
复制标题
移动动物的连续时间资源选择分析
DOI：
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发表时间：
2019
影响因子：
6.6
通讯作者：
Jonathan R. Potts
中科院分区：
文献类型：
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作者：
Yi;P. Blackwell;J. Merkle;Jonathan R. Potts
资源选择分析（RSA）旨在了解空间丰度如何与环境特征协变。通过将RSA与运动相结合，步骤选择分析（SSA）有助于揭示动物迁移背后的机制，从而深入了解空间模式下的运动决策。然而，SSA通常假设在每个观察到的位置处，动物对下一个观察到的位置进行“选择”。这混淆了观察与行为机制，并没有考虑到在任何其他时间发生的决定沿着动物的path. To解决这个问题，我们引入了一个连续的时间框架资源选择。它是基于一个开关的Ornstein-Uhlenbeck（ONS）模型，参数化的贝叶斯蒙特卡罗技术。这样的模型已经成功地用于识别运动行为的开关，但迄今为止没有与资源选择相结合。我们测试我们的推理过程模拟路径，代表迁徙运动（景观质量根据季节变化）和觅食与资源枯竭和更新（变化是由于过去的位置的动物）。我们将我们的框架应用于迁移黑尾鹿（Odocoileus hemionus）的位置数据，以揭示迁移决策的驱动因素。在各种各样的模拟情况下，我们的推理程序返回参数值的可靠估计，包括动物权衡资源质量和旅行距离的程度（绝大多数情况下在95%的后验区间内）。当应用于骡鹿数据，我们的模型揭示了一些个别的参数值的变化。尽管如此，大多数个体的迁移决策都可以通过一个模型很好地描述，该模型考虑了迁移成本以及源斑块和目标斑块的植被质量瞬时变化之间的差异。我们引入了一种技术来推断动物运动背后的资源驱动决策，该技术解释了这些决策可能发生在路径沿着的任何一点，而不仅仅是在动物的位置已知时。这消除了逐步运动模型的一个公认但迄今为止很少解决的缺点。我们的工作对于理解环境特征如何驱动运动决策以及空间使用模式至关重要。
Resource selection analysis (RSA) seeks to understand how spatial abundance covaries with environmental features. By combining RSA with movement, step selection analysis (SSA) has helped uncover the mechanisms behind animal relocations, thereby giving insight into the movement decisions underlying spatial patterns. However, SSA typically assumes that at each observed location, an animal makes a ‘selection’ of the next observed location. This conflates observation with behavioural mechanism and does not account for decisions occurring at any other time along the animal’s path. To address this, we introduce a continuous time framework for resource selection. It is based on a switching Ornstein–Uhlenbeck (OU) model, parameterized by Bayesian Monte Carlo techniques. Such OU models have been used successfully to identify switches in movement behaviour, but hitherto not combined with resource selection. We test our inference procedure on simulated paths, representing both migratory movement (where landscape quality varies according to season) and foraging with depletion and renewal of resources (where the variation is due to past locations of the animals). We apply our framework to location data of migrating mule deer (Odocoileus hemionus) to shed light on the drivers of migratory decisions. In a wide variety of simulated situations, our inference procedure returns reliable estimations of the parameter values, including the extent to which animals trade‐off resource quality and travel distance (within 95% posterior intervals for the vast majority of cases). When applied to the mule deer data, our model reveals some individual variation in parameter values. Nevertheless, the migratory decisions of most individuals are well‐described by a model that accounts for the cost of moving and the difference between instantaneous change of vegetation quality at source and target patches. We have introduced a technique for inferring the resource‐driven decisions behind animal movement that accounts for the fact that these decisions may take place at any point along a path, not just when the animal’s location is known. This removes an oft‐acknowledged but hitherto little‐addressed shortcoming of stepwise movement models. Our work is of key importance in understanding how environmental features drive movement decisions and, as a consequence, space use patterns.