State-space models for bio-loggers: A methodological road map

By Ian D. Jonsen, M. Basson, S. Bestley, M. Bravington, T. Patterson, M. W. Pedersen, Russell J. Thomson, Uffe Høgsbro Thygesen, Simon Wotherspoon. 2013

258 citations recorded by the source corpus.

Ecologists have an unprecedented array of bio-logging technologies available to conduct in situ studies of horizontal and vertical movement patterns of marine animals. These tracking data provide key information about foraging, migratory, and other behaviours that can be linked with bio-physical datasets to understand physiological and ecological influences on habitat selection. In most cases, however, the behavioural context is not directly observable and therefore, must be inferred. Animal movement data are complex in structure, entailing a need for stochastic analysis methods. The recent development of state-space modelling approaches for animal movement data provides statistical rigor for inferring hidden behavioural states, relating these states to bio-physical data, and ultimately for predicting the potential impacts of climate change. Despite the widespread utility, and current popularity, of state-space models for analysis of animal tracking data, these tools are not simple and require considerable care in their use. Here we develop a methodological ‘‘road map’’ for ecologists by reviewing currently available state-space implementations. We discuss appropriate use of state-space methods for location and/or behavioural state estimation from different tracking data types. Finally, we outline key areas where the methodology is advancing, and where it needs further development.

Subfields: Agronomy and Crop Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/1dd11cbc2e7e2c57853aff0ec1caf350aad7bd0b

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In Global and Planetary Change

Citations in the corpus

Research Priorities to Support Effective Manta and Devil Ray Conservation (2018, background): “Recent improvements to geolocation and subsequent state space models are quantifying and reducing the levels of uncertainty associated with lightbased geolocation (Patterson et al., 2008; Jonsen et al., 2013; Gredzens et al., 2014; Braun et al., 2018).”
Circumpolar habitat use in the southern elephant seal: implications for foraging success and population trajectories (2016, methodology): “State- space models (Jonsen et al. 2013) were used (1) to minimize positional errors (2) to estimate location points along movement paths at regular time steps, and (3) to infer whether the movements fell into one of two stochastic movement modes; an area- restricted search (ARS) mode characterized…”
moveHMM: an R package for the statistical modelling of animal movement data using hidden Markov models (2016, background): “The package bsam (Jonsen et al. 2013) does include multistate models, but focuses on the situation where location error is signiﬁcant.”