META‐ANALYSIS OF ANIMAL MOVEMENT USING STATE‐SPACE MODELS

By Ian D. Jonsen, R. Myers, J. Flemming. 2003

305 citations recorded by the source corpus.

Subfields: Ecological Modeling, Insect Science, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/0a13d61d8d7ecdf5fc28709ee6c1118b779c313f

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In Ecological Modeling

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In Nature and Landscape Conservation

Citations in the corpus

Integrated step selection analysis: bridging the gap between resource selection and animal movement (2015, background): “election inference while treating the movement process as nuisance that must be ‘controlled for’. Here, we seek explicit inference of this process. -space models of animal movement (reviewed by State Jonsen et al. 2003 and Patterson et al. 2008) predict the future state (e.g., spatial position) of t”
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).”
Validation of geolocation estimates based on light level and sea surface temperature from electronic tags (2004, background): “…models appear to be promising frameworks for combining multiple types of location and movement information, improving location estimates, analyzing movement patterns and providing error statistics for each geolocation estimate (Sibert & Fournier 2001, Jonsen et al. 2003, Sibert et al. 2003).”
Identifying and comparing phases of movement by leatherback turtles using state-space models (2008, methodology): “Although a high number of MCMC samples were used as a burn-in to ensure convergence, the number of samples onwhich the final inferences were based was smaller (n=500) than that generated by Jonsen et al. (2007) who used 30,000 iterations, 10,000 sample burn-in and retained every 5th sample (n=4000).”
How key habitat features influence large terrestrial carnivore movements: waterholes and African lions in a semi-arid savanna of north-western Zimbabwe (2010, methodology): “…methods used have been very diverse including correlated random walk (Kareiva and Shigesada 1983), fractal analysis (With 1994; Nams 2005; e.g. Fritz et al. 2003), first passage time (Fauchald and Tveraa 2003), Lévy flights (Viswanathan et al. 1996), and state-space models (Jonsen et al. 2003).”
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).”
Identifying leatherback turtle foraging behaviour from satellite telemetry using a switching state-space model (2007, unknown): “The state-space approach has also been proposed as a powerful tool for modelling animal movement data because of its ability to deal simultaneously with potentially large measurement errors and variability in the dynamics of movement (Jonsen et al. 2003).”
Understanding movement data and movement processes: current and emerging directions. (2008, methodology): “For example, Jonsen et al. (2003) show how a hierarchical structure reduces uncertainty around specific movement parameters.”
Ecological Models and Data in R (2008, background): “It has already begun to appear in ecological journals (Jonsen et al., 2003; Morales et al., 2004; McCarthy and Parris, 2004; Okuyama and Bolker, 2005; Parris, 2006; Vesk, 2006), but statisticians continue to debate its exact meaning and appropriateness (both Spiegelhalter et al.”
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).”
Quantifying habitat use and preferences of pelagic seabirds using individual movement data: a review (2009, methodology): “Increasingly, the behavioral responses of animals to their environment are being modeled at the individual level using SSMs (Jonsen et al. 2003, Morales et al. 2004, Eckert et al. 2008), and it is hoped that SSMs will ultimately allow population-level inferences to be drawn (Patterson & Fraser 2000)”
State-space models for bio-loggers: A methodological road map (2013, unknown): “In a bio-logging context, the most common state variables used (to date) are an animal’s position (Anderson-Sprecher and Ledolter, 1991; Jonsen et al., 2003; Royer et al., 2005; Thygesen et al., 2009) and it’s behaviour, the latter is usually represented as a discrete variable with two or more nomin”
Continuous-time correlated random walk model for animal telemetry data. (2008, background): “Many movement models for analyzing telemetry data are conceptually based on locations that are collected on regular intervals (Brillinger and Stewart 1998, Jonsen et al. 2003, 2005, Forester et al. 2007).”
Analyzing animal movements using Brownian bridges. (2007, background): “However, recognizing the need for more sophisticated models to analyze these data, there has been a surge of recent work to develop state–space models for analyzing and predicting animal movements (e.g., Jonsen et al. 2003, 2005, Morales et al. 2004, Flemming et al. 2006).”
Animal ecology meets GPS-based radiotelemetry: a perfect storm of opportunities and challenges (2010, background): “…simultaneously (Rhodes et al. 2005; Johnson et al. 2008; Fieberg et al. 2010), incorporating individual stochasticity in dynamic movement models (Jonsen et al. 2003; Morales et al. 2004; Patterson et al. 2008) and upscaling from individual movement to population dynamics (Morales et al. 2010).”