T
Théo Michelot
Researcher at University of St Andrews
Publications - 34
Citations - 960
Théo Michelot is an academic researcher from University of St Andrews. The author has contributed to research in topics: Hidden Markov model & Selection (genetic algorithm). The author has an hindex of 11, co-authored 30 publications receiving 683 citations. Previous affiliations of Théo Michelot include Institut national des sciences appliquées de Rouen & University of Sheffield.
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moveHMM: an R package for the statistical modelling of animal movement data using hidden Markov models
TL;DR: The R package moveHMM allows ecologists to process GPS tracking data into series of step lengths and turning angles, and to fit an HMM to these data, allowing, in particular, for the incorporation of environmental covariates.
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momentuHMM: R package for generalized hidden Markov models of animal movement
TL;DR: Recently, momentuHMM as mentioned in this paper has been proposed as an open-source R package for modeling animal behavior from telemetry data using discrete-time hidden Markov models (HMM).
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momentuHMM: R package for generalized hidden Markov models of animal movement
TL;DR: Recently, momentuHMM as discussed by the authors has been proposed for inferring latent animal behaviors from telemetry data using discrete-time hidden Markov models (HMMs) and user-specified probability distributions for an unlimited number of data streams.
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Understanding the ontogeny of foraging behaviour: insights from combining marine predator bio-logging with satellite-derived oceanography in hidden Markov models.
TL;DR: It is demonstrated that adult gannets are more proficient foragers than immatures, supporting the hypothesis that foraging specializations are learned during individual exploratory behaviour in early life.
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Estimation and simulation of foraging trips in land-based marine predators
Théo Michelot,Roland Langrock,Sophie Bestley,Sophie Bestley,Ian D. Jonsen,Theoni Photopoulou,Theoni Photopoulou,Toby A. Patterson +7 more
TL;DR: An approach based on hidden Markov models, which splits foraging trips into segments labeled as "outbound", "search", "forage", and "inbound", is described, which is able to develop realistic simulations from the fitted model.