Predicting animal movement with deepSSF: A deep learning step selection framework

Abstract Predictions of animal movement are vital for understanding and managing wild populations. However, the fine‐scale, complex decision‐making of animals can pose challenges for the accurate prediction of trajectories. Integrated step selection functions (iSSFs), a common tool for inferring rel...

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Bibliografiske detaljer

Principais autores:	Scott W. Forrest, Dan Pagendam, Conor Hassan, Jonathan R. Potts, Christopher Drovandi, Michael Bode, Andrew J. Hoskins
Format:	Artigo
Sprog:	Inglês
Udgivet:	Wiley 2026-02-01
Serier:	Methods in Ecology and Evolution
Fag:	conservation deep learning habitat selection movement ecology predictive ecology simulations
Online adgang:	https://doi.org/10.1111/2041-210x.70136
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