ROBUST STATE-SPACE MODELING OF ANIMAL MOVEMENT DATA

By Ian D. Jonsen, J. Flemming, R. Myers. 2005

843 citations recorded by the source corpus.

Subfields: Ecological Modeling, Ecology, Small Animals

View Paper: https://www.semanticscholar.org/paper/4a2ace2cd575d30bb2c3801fc6eb55ef68415973

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Citations in the corpus

Animal movements in heterogeneous landscapes: identifying profitable places and homogeneous movement bouts. (2008, methodology): “It has been used by Morales et al. (2004) and Jonsen et al. (2005, 2007) to develop original path segmentation/clustering methods.”
Assessing functional connectivity using empirical data (2008, background): “State–space models are time-series models that allow inclusion of unobserved data by accounting for errors arising from imprecise observations and from stochasticity in the process being studied (Jonsen et al. 2003, 2005).”
Flexible and practical modeling of animal telemetry data: hidden Markov models and extensions. (2012, methodology): “This was accompanied by a focus on Bayesian methods, which allow inference for models with analytically intractable likelihoods (Morales et al. 2004, Jonsen et al. 2005, McClintock et al. 2012).”
Identifying and comparing phases of movement by leatherback turtles using state-space models (2008, methodology): “The model included two behavioural modes based on a switching correlated random walk (Jonsen et al., 2005).”
momentuHMM: R package for generalized hidden Markov models of animal movement (2017, methodology): “The most widely used 2-state HMMs for animal movement include “encamped” (or “foraging”) and “exploratory” (or “transit”) states characterized by area-restricted-search-type movements and migratory-type movements, respectively (Morales et al. 2004; Jonsen et al. 2005).”
A novel method for identifying behavioural changes in animal movement data. (2009, background): “A fruitful body of research has emerged recently that addresses measurement error with the use of state-space models (Jonsen et al. 2003, 2005; Royer et al. 2005; Patterson et al. 2009 Blackwell Publishing Ltd/CNRS 2008).”
Large‐scale movements and high‐use areas of western Pacific leatherback turtles, Dermochelys coriacea (2011, methodology): “The transition equation is based on a correlated random walk model and includes a process model for each of two behavioral modes (Jonsen et al. 2005).”
Analyzing animal movements using Brownian bridges. (2007, methodology): “For example, Morales et al. (2004) used characteristics of observed paths (i.e., turning angles and movement distances) and Jonsen et al. (2005) used state–space models to identify different movement states of animals (e.g., encamped vs. exploratory).”
Biologging technologies: new tools for conservation. Introduction (2010, methodology): “…to interpret these 2 Bograd et al.: Biologging technologies: Introduction data have yielded powerful new tools for understanding animal behavior (Fauchald & Tveraa 2003, Jonsen et al. 2003, 2005, 2007, Tremblay et al. 2006, 2009, Bradshaw et al. 2007, Patterson et al. 2008, Schick et al. 2008).”
State-space models for bio-loggers: A methodological road map (2013, methodology): “, 2004); t-distributions for Argos locations (Jonsen et al., 2005); Gaussian distributions for geolocations (Sibert et al.”
WinBUGS for population ecologists: bayesian modeling using markov chain Monte Carlo methods (2009, methodology): “Further applications of WinBUGS to analyze animal movement data can be found in Morales et al. (2004) and Jonsen et al. (2005) .”
Satellite tracking of a killer whale (Orcinus orca) in the eastern Canadian Arctic documents ice avoidance and rapid, long-distance movement into the North Atlantic (2011, methodology): “…locations were observed with error, and at irregular intervals, we fit a two-state (nominally resident and traveling) ‘switching’ state-space model (SSM; described in detail in Jonsen et al. (2005) and Breed et al. (2009) to Argos locations to estimate the most probable path of the whale.”
Understanding movement data and movement processes: current and emerging directions. (2008, methodology): “…(a problem that is especially acute in many marine and aerial systems); and (4) the multiple behavioral patterns common to movement data, i.e., spatial memory, site fidelity, directed movement, etc. (Lima & Zollner 1996; Morales et al. 2004; Jonsen et al. 2005; Armsworth & Roughgarden 2005).”