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 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.