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. 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
movement behavior of individual migrants 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 behavior changes as overall conditions in the system change,
and how the energetic cost of migratory movements depend on environmental
conditions in space and time. Our framework is flexible and can readily be
applied to other species and systems.