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MoveFormer: a Transformer-based model for step-selection animal movement modellinguse asterix (*) to get italics
Ondřej Cífka, Simon Chamaillé-Jammes, Antoine LiutkusPlease use the format "First name initials family name" as in "Marie S. Curie, Niels H. D. Bohr, Albert Einstein, John R. R. Tolkien, Donna T. Strickland"
<p style="text-align: justify;">The movement of animals is a central component of their behavioural strategies. Statistical tools for movement data analysis, however, have long been limited, and in particular, unable to account for past movement information except in a very simplified way. In this work, we propose MoveFormer, a new step-based model of movement capable of learning directly from full animal trajectories. While inspired by the classical step-selection framework and previous work on the quantification of uncertainty in movement predictions, MoveFormer also builds upon recent developments in deep learning, such as the Transformer architecture, allowing it to incorporate long temporal contexts. The model predicts an animal’s next movement step given its past movement history, including not only purely positional and temporal information, but also any available environmental covariates such as land cover or temperature. We apply our model to a diverse dataset made up of over 1550 trajectories from over 100 studies, and show how it can be used to gain insights about the importance of the provided context features, including the extent of past movement history. Our software, along with the trained model weights, is released as open source.</p>
https://www.movebank.orgYou should fill this box only if you chose 'All or part of the results presented in this preprint are based on data'. URL must start with http:// or https://,, should fill this box only if you chose 'Scripts were used to obtain or analyze the results'. URL must start with http:// or https://,, should fill this box only if you chose 'Codes have been used in this study'. URL must start with http:// or https://
animal movement, habitat selection, deep learning, method, spatial memory
NonePlease indicate the methods that may require specialised expertise during the peer review process (use a comma to separate various required expertises).
Behaviour & Ethology, Habitat selection
Colin Torney;, John Fieberg;, Oded Berger-Tal;, Briana Abrahms; No need for them to be recommenders of PCIEcology. Please do not suggest reviewers for whom there might be a conflict of interest. Reviewers are not allowed to review preprints written by close colleagues (with whom they have published in the last four years, with whom they have received joint funding in the last four years, or with whom they are currently writing a manuscript, or submitting a grant proposal), or by family members, friends, or anyone for whom bias might affect the nature of the review - see the code of conduct
e.g. John Doe []
2023-03-22 16:32:14
Cédric Sueur