1. Understanding how migratory animals interact with dynamic physical environments remains a major challenge in migration biology. Interactions between migrants and wind and water currents are often poorly resolved in migration models due to both the lack of high-resolution environmental data, and a lack of understanding of how migrants respond to fine-scale structure in the physical environment.
2. Here we develop a generalizable, data-driven methodology to study the migration of animals through complex physical environments. Our approach combines validated computational fluid dynamic (CFD) modelling with animal tracking data to decompose migratory movements into two components, namely movement caused by physical forcing and movement due to active locomotion. We then use a flexible recurrent neural network model to relate local environmental conditions to locomotion behaviour of the migrating animal, allowing us to predict a migrant's force production, velocity and trajectory over time.
3. We apply this framework to a large dataset containing measured trajectories of migrating Chinook salmon through a section of river in California's Sacramento-San Joaquin Delta. We show that the model is capable of describing fish migratory movements as a function of local flow variables, and that it is possible to accurately forecast migratory movements on which the model was not trained.
4. After validating our model, we show how our framework can be used to understand how migrants respond to local-flow conditions, how migratory behaviour changes as overall conditions in the system change and how the energetic cost of migratory movements depends on environmental conditions in space and time. Our framework is flexible and can readily be applied to other species and systems.

中文翻译：

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合并计算流体动力学和机器学习以揭示动物迁徙策略

1. 了解迁徙动物如何与动态物理环境相互作用仍然是迁徙生物学的主要挑战。由于缺乏高分辨率的环境数据，以及对移民如何对物理环境中的精细结构做出反应缺乏了解，移民与风和水流之间的相互作用在移民模型中往往得不到很好的解决。
2. 在这里，我们开发了一种通用的、数据驱动的方法来研究动物在复杂物理环境中的迁移。我们的方法将经过验证的计算流体动力学 (CFD) 建模与动物跟踪数据相结合，将迁徙运动分解为两个部分，即由物理强迫引起的运动和由主动运动引起的运动。然后，我们使用灵活的循环神经网络模型将当地环境条件与迁徙动物的运动行为联系起来，使我们能够预测迁徙者的力量产生、速度和轨迹随时间的推移。
3. 我们将此框架应用于一个大型数据集，该数据集包含在加利福尼亚州萨克拉门托 - 圣华金三角洲的一段河流中迁移奇努克鲑鱼的测量轨迹。我们表明该模型能够将鱼类洄游运动描述为局部流量变量的函数，并且可以准确预测模型未训练的洄游运动。
4. 在验证了我们的模型后，我们展示了我们的框架如何用于理解移民如何对当地流动条件做出反应，迁徙行为如何随着系统整体条件的变化而变化，以及迁徙运动的能量成本如何取决于空间和环境条件。时间。我们的框架很灵活，可以很容易地应用于其他物种和系统。