Merging computational fluid dynamics and machine learning to reveal animal migration strategies
Merging computational fluid dynamics and machine learning to reveal animal migration strategies
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DOI：
10.1111/2041-210x.13604
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发表时间：
2021-04
影响因子：
6.6
通讯作者：
Simone Olivetti;M. Gil;V. Sridharan;Andrew M. Hein;E. Shepard
中科院分区：
文献类型：
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作者：
Simone Olivetti;M. Gil;V. Sridharan;Andrew M. Hein;E. Shepard
了解迁徙动物如何与动态的物理环境相互作用仍然是迁徙生物学的一个重大挑战。由于缺乏高分辨率的环境数据，以及缺乏对移民如何对物理环境中的细尺度结构做出反应的理解，移民与风和水流之间的相互作用在移民模型中往往解决得很差。在这里，我们开发了一种可推广的，数据驱动的方法来研究动物在复杂的物理环境中的迁移。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，将迁移运动分解为两个部分，即物理强迫引起的运动和主动运动引起的运动。然后，我们使用一个灵活的递归神经网络模型，将当地的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动物随着时间的推移产生的力、速度和轨迹。我们将这个框架应用到一个大型数据集，其中包含测量的轨迹迁移奇努克鲑鱼通过一段河流在加州的萨克拉门托-圣华金三角洲。我们表明，该模型是能够描述鱼类洄游运动作为当地流量变量的函数，它是可以准确地预测洄游运动的模型没有训练。在验证了我们的模型之后，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，移民行为如何随着系统中整体条件的变化而变化，以及移民运动的能量成本如何取决于空间和时间的环境条件。我们的框架是灵活的，可以很容易地应用到其他物种和系统。
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. 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. 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. 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.