Animals respond to environmental variation by changing their movement in a multifaceted way. Recent advancements in biologging increasingly allow for detailed measurements of the multifaceted nature of movement, from descriptors of animal movement trajectories (e.g., using GPS) to descriptors of body part movements (e.g., using tri-axial accelerometers). Because this multivariate richness of movement data complicates inference on the environmental influence on animal movement, studies generally use simplified movement descriptors in statistical analyses. However, doing so limits the inference on the environmental influence on movement, as this requires that the multivariate richness of movement data can be fully considered in an analysis. We propose a data-driven analytic framework, based on existing methods, to quantify the environmental influence on animal movement that can accommodate the multifaceted nature of animal movement. Instead of fitting a simplified movement descriptor to a suite of environmental variables, our proposed framework centres on predicting an environmental variable from the full set of multivariate movement data. The measure of fit of this prediction is taken to be the metric that quantifies how much of the environmental variation relates to the multivariate variation in animal movement. We demonstrate the usefulness of this framework through a case study about the influence of grass availability and time since milking on cow movements using machine learning algorithms. We show that on a one-hour timescale 37% of the variation in grass availability and 33% of time since milking influenced cow movements. Grass availability mostly influenced the cows’ neck movement during grazing, while time since milking mostly influenced the movement through the landscape and the shared variation of accelerometer and GPS data (e.g., activity patterns). Furthermore, this framework proved to be insensitive to spurious correlations between environmental variables in quantifying the influence on animal movement. Not only is our proposed framework well-suited to study the environmental influence on animal movement; we argue that it can also be applied in any field that uses multivariate biologging data, e.g., animal physiology, to study the relationships between animals and their environment.

中文翻译：

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通过生物记录推断动物的环境：量化环境对动物运动的影响


动物通过多方面改变其运动来应对环境变化。生物记录的最新进展越来越多地允许对运动的多方面性质进行详细测量，从动物运动轨迹的描述符（例如，使用GPS）到身体部位运动的描述符（例如，使用三轴加速度计）。由于运动数据的多元丰富性使得环境对动物运动影响的推断变得复杂，因此研究通常在统计分析中使用简化的运动描述符。然而，这样做限制了环境对运动影响的推断，因为这需要在分析中充分考虑运动数据的多元丰富性。我们提出了一个基于现有方法的数据驱动的分析框架，以量化环境对动物运动的影响，以适应动物运动的多方面性质。我们提出的框架不是将简化的运动描述符拟合到一组环境变量，而是集中于从全套多元运动数据中预测环境变量。该预测的拟合度被视为量化环境变化与动物运动的多元变化有多少相关的度量。我们通过使用机器学习算法了解草可用性和挤奶后时间对奶牛运动的影响的案例研究证明了该框架的有用性。我们发现，在一小时的时间尺度上，37% 的草可用性变化和 33% 的挤奶时间影响了奶牛的运动。 草的可用性主要影响放牧期间奶牛颈部的运动，而挤奶后的时间主要影响通过景观的运动以及加速度计和 GPS 数据的共享变化（例如活动模式）。此外，事实证明，在量化对动物运动的影响时，该框架对环境变量之间的虚假相关性不敏感。我们提出的框架不仅非常适合研究环境对动物运动的影响；我们认为它也可以应用于使用多元生物记录数据的任何领域，例如动物生理学，以研究动物与其环境之间的关系。