Flexible and practical modeling of animal telemetry data: hidden Markov models and extensions.

By Roland Langrock, Ruth King, Jason Matthiopoulos, L. Thomas, D. Fortin, J. Morales. Ecology, 2012

446 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Ecology, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/cce9b99a42ca0a6c08bfe7d80fbe874cd376fd23

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Citations in the corpus

Integrated step selection analysis: bridging the gap between resource selection and animal movement (2015, methodology): “idance behaviour (Beyer et al. 2015), and interactions with conspecifics and intraspecifics (Latombe et al. 2014; Potts et al. 2014b). In fact, many of the facets of the generic approach developed by Langrock et al. (2012) can be included in an iSSA with the MLEs obtained using standard statistical ”
moveHMM: an R package for the statistical modelling of animal movement data using hidden Markov models (2016, background): “In such a case, all individuals are assumed to share the same movement model, that is the same parameters (an approach usually referred to as ‘complete pooling’; see Langrock et al. 2012).”
A multivariate mixed hidden Markov model for blue whale behaviour and responses to sound exposure (2017, background): “…can often naturally be interpreted as proxies for the behavioural states of animals, although there is not necessarily a one-to-one correspondence between nominal HMM states and biologically meaningful behavioural states [Langrock et al. (2012, 2015), Zucchini, Raubenheimer and MacDonald (2008)].”
Sex-specific and individual preferences for hunting strategies in white sharks (2016, methodology): “Movement path structure will vary in time and space in response to the environment and as the goals of movement change (e.g. Papastamatiou et al. 2011; Langrock et al. 2012).”
momentuHMM: R package for generalized hidden Markov models of animal movement (2017, unknown): “Discrete-time hidden Markov models (HMMs) have become immensely popular for the analysis of animal telemetry data (e.g. Morales et al. 2004; Jonsen et al. 2005; Langrock et al. 2012; McClintock et al. 2012).”
Understanding movements of organisms: it's time to abandon the Lévy foraging hypothesis (2015, background): “If, however, movements are considered to consist of discrete and successive linear ‘steps’, then they can be described andmodelled as a discrete time-series process with each movement step being a change in positional state for the process (Schick et al. 2008; Langrock et al. 2012).”