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Step selection techniques uncover the environmental predictors of space use patterns in flocks of Amazonian birds

机译：步骤选择技术揭示了亚马逊鸟类群中空间使用方式的环境预测因素

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AbstractUnderstanding the behavioral decisions behind animal movement and space use patterns is a key challenge for behavioral ecology. Tools to quantify these patterns from movement and animal–habitat interactions are vital for transforming ecology into a predictive science. This is particularly important in environments undergoing rapid anthropogenic changes, such as the Amazon rainforest, where animals face novel landscapes. Insectivorous bird flocks are key elements of avian biodiversity in the Amazonian ecosystem. Therefore, disentangling and quantifying the drivers behind their movement and space use patterns is of great importance for Amazonian conservation. We use a step selection function (SSF) approach to uncover environmental drivers behind movement choices. This is used to construct a mechanistic model, from which we derive predicted utilization distributions (home ranges) of flocks. We show that movement decisions are significantly influenced by canopy height and topography, but depletion and renewal of resources do not appear to affect movement significantly. We quantify the magnitude of these effects and demonstrate that they are helpful for understanding various heterogeneous aspects of space use. We compare our results to recent analytic derivations of space use, demonstrating that the analytic approximation is only accurate when assuming that there is no persistence in the animals' movement. Our model can be translated into other environments or hypothetical scenarios, such as those given by proposed future anthropogenic actions, to make predictions of spatial patterns in bird flocks. Furthermore, our approach is quite general, so could potentially be used to understand the drivers of movement and spatial patterns for a wide variety of animal communities.
机译：摘要理解动物运动和空间利用方式背后的行为决定是行为生态学面临的主要挑战。从运动和动物-栖息地相互作用量化这些模式的工具对于将生态学转变为预测科学至关重要。这在人为快速变化的环境中尤其重要，例如亚马逊雨林，那里的动物面对着新颖的风景。食虫鸟类是亚马逊河生态系统中鸟类生物多样性的关键要素。因此，解开并量化其运动和空间使用方式背后的驱动因素对于亚马逊保护至关重要。我们使用步进选择功能（SSF）方法来发现运动选择背后的环境驱动因素。这被用来构建一个机械模型，从中我们可以得出鸡群的预测利用率分布（家庭范围）。我们表明，运动决策受树冠高度和地形的影响很大，但是资源的耗竭和更新似乎不会显着影响运动。我们量化了这些影响的大小，并证明它们有助于理解空间使用的各个方面。我们将我们的结果与最近的空间利用分析推论进行了比较，证明只有在假设动物运动没有持久性的情况下，分析逼近才是准确的。我们的模型可以转换为其他环境或假设情景，例如拟议的未来人为行动所提供的情景，以预测禽群的空间格局。此外，我们的方法相当笼统，因此有可能被用于了解各种动物群落的运动和空间格局的驱动因素。

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