Merging computational fluid dynamics and machine learning to reveal animal migration strategies

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 a high-resolution environmental data, and a lack of understanding of how migrants respond to fine scale structure in the physical environment.

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) modeling with animal tracking data to decompose migratory movements into two components: 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 behavior of the migrating animal, allowing us to predict a migrant's force production, velocity and trajectory over time.

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探明迁徙动物与动态物理环境的交互机制，仍是迁徙生物学领域的重大挑战。当前迁徙模型中，迁徙动物与风、水流的交互作用往往难以得到精准解析，这一方面源于高分辨率环境数据的匮乏，另一方面则是因为我们对迁徙动物如何响应物理环境的精细尺度结构缺乏认知。

本研究开发了一种可泛化的数据驱动研究方法，用于探究动物在复杂物理环境中的迁徙行为。该方法将经过验证的计算流体动力学（Computational Fluid Dynamic, CFD）模型与动物追踪数据相结合，将迁徙运动分解为两个组成部分：由物理动力驱动的位移，以及由主动运动产生的位移。随后，我们采用灵活的循环神经网络模型，将局地环境条件与迁徙动物的运动行为建立关联，进而能够随时间推移预测迁徙动物的力输出、运动速度与运动轨迹。

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