Classifying movement behaviour in relation to environmental conditions using hidden Markov models.

By T. Patterson, M. Basson, M. Bravington, J. Gunn. Journal of Animal Ecology, 2009

317 citations recorded by the source corpus.

Subfields: Ecology, Modeling and Simulation, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/ee5e4003f8a40537773fb8b255cc8080d06239c9

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Citations in the corpus

Spatio-temporal foraging patterns of a giant zooplanktivore, the leatherback turtle (2010, methodology): “Similarly there are various other approaches for objectively identifying changes in behavior from tracking data including techniques such as first passage time analysis and state space models (Hamer et al., 2009; Patterson et al., 2009).”
Scale of inference: on the sensitivity of habitat models for wide‐ranging marine predators to the resolution of environmental data (2017, background): “foraging, transit) in movement-based models (Morales et al. 2004; Jonsen et al. 2005; Patterson et al. 2009; Bestley et al. 2015).”
moveHMM: an R package for the statistical modelling of animal movement data using hidden Markov models (2016, methodology): “…ecology, HMMs have successfully been used to analyse the movement of, inter alia , caribou (Franke, Caelli & Hudson 2004), fruit ﬂies (Holzmann et al. 2006), tuna (Patterson et al. 2009), panthers (van de Kerk et al. 2015), woodpeckers (McKellar et al. 2015) and white sharks (Towner et al. 2016).”
Circumpolar habitat use in the southern elephant seal: implications for foraging success and population trajectories (2016, methodology): “The behavioral classification was then derived using Hidden Markov Models (Patterson et al. 2009) using the maximum likelihood approach (Patterson et al. 2009, Sharples et al. 2012, Langrock et al. 2014).”
The trophodynamics of marine top predators: Current knowledge, recent advances and challenges (2015, methodology): “This class of modelling methods has been widely applied to fish (Patterson et al., 2009; Pedersen et al., 2011), turtles (Jonsen et al.”
Flexible and practical modeling of animal telemetry data: hidden Markov models and extensions. (2012, methodology): “Pseudo-residuals offer a convenient way for model checking in HMMs (Patterson et al. 2009; see also Appendix B).”
State-space models for bio-loggers: A methodological road map (2013, methodology): “Examples from applications in the literature include: exponential distributions for movement distance (Patterson et al., 2009); Weibull distributions for speed (Morales et al.”
Sex-specific and individual preferences for hunting strategies in white sharks (2016, methodology): “HMMs directly account for the serial dependence prevalent in electronic tagging data and allow for the decoding of latent behavioural states, or at least proxies thereof (Patterson et al. 2009).”
New Insights into Pelagic Migrations: Implications for Ecology and Conservation (2012, background): “That state affects how an animal moves, and both SSMs and HMMs fit CRW models that utilize movement properties such as turn angles, move lengths, and autocorrelation to infer latent behavioral state from telemetry data ( Jonsen et al. 2005; Morales et al. 2004; Patterson et al. 2008, 2009).”