Animal behaviour on the move: the use of auxiliary information and semi-supervision to improve behavioural inferences from Hidden Markov Models applied to GPS tracking datasets

Animal behaviour on the move: the use of auxiliary information and semi-supervision to improve behavioural inferences from Hidden Markov Models applied to GPS tracking datasets

Abstract Background State-space models, such as Hidden Markov Models (HMMs), are increasingly used to classify animal tracks into behavioural states. Typically, step length and turning angles of successive locations are used to infer where and when an animal is resting, foraging, or travelling. Howe...

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Bibliographic Details
Main Authors: Sarah Saldanha, Sam L. Cox, Teresa Militão, Jacob González-Solís
Format: Article
Language:English
Published: BMC 2023-07-01
Series:Movement Ecology
Subjects:
Semi-supervision
HMM
Behavioural classification
Opportunistic foraging
Tropical oceans
Behavioural modes
Online Access:https://doi.org/10.1186/s40462-023-00401-5

Internet

https://doi.org/10.1186/s40462-023-00401-5

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