State-space models of individual animal movement.

By T. Patterson, L. Thomas, C. Wilcox, Otso Ovaskainen, Jason Matthiopoulos. Trends in Ecology & Evolution, 2008

939 citations recorded by the source corpus.

Detailed observation of the movement of individual animals offers the potential to understand spatial population processes as the ultimate consequence of individual behaviour, physiological constraints and fine-scale environmental influences. However, movement data from individuals are intrinsically stochastic and often subject to severe observation error. Linking such complex data to dynamical models of movement is a major challenge for animal ecology. Here, we review a statistical approach, state-space modelling, which involves changing how we analyse movement data and draw inferences about the behaviours that shape it. The statistical robustness and predictive ability of state-space models make them the most promising avenue towards a new type of movement ecology that fuses insights from the study of animal behaviour, biogeography and spatial population dynamics.

Subfields: Ecology, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/4df1242323b210ebf7c94c4ffd3720d8b4d305fe

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Citations in the corpus

Integrated step selection analysis: bridging the gap between resource selection and animal movement (2015, methodology): “State-space models of animal movement (reviewed by Jonsen, Myers & Flemming 2003; and Patterson et al. 2008) predict the future state (e.g. spatial position) of the animal given its current state (where an ‘observation model’ provides the probability of observing these states), environmental…”
Towards understanding wild boar Sus scrofa movement: a synthetic movement ecology approach (2014, background): “Such interactions can be incorporated into individual-based models simulating movement in a range of physiological and behavioural states, as well as environmental conditions (Patterson et al. 2008).”
Cost distance modelling of landscape connectivity and gap‐crossing ability using radio‐tracking data (2010, background): “Recent developments such as state-space modelling allow the identification of the factors driving animal movements, even when the data are measured inaccurately or when organisms switch between multiple behavioural states (Patterson et al. 2008; review in Schick et al. 2008).”
A movement ecology paradigm for unifying organismal movement research (2008, background): “The enhanced computational capacity has also fueled the development of new analytical tools, including Lagrangian simulations of wind-dispersed seeds (21), and statespace models of individual animal movement (22) that represent a quantum leap beyond those used a decade ago (7).”
Distinguishing technology from biology: a critical review of the use of GPS telemetry data in ecology (2010, background): “…sophisticated tools that allow us to statistically distinguish between different movement modes in a GPS movement dataset (Morales et al. 2004; Nathan et al. 2008; Patterson et al. 2008; Schick et al. 2008), we only have a nascent idea of what animals were really doing from a behavioural viewpoint.”
Revisiting the ontogenetic shift paradigm: The case of juvenile green turtles in the SW Atlantic (2012, methodology): “To accomplish this, we satellite tracked juveniles recovered from entanglement on the coast of Argentina and used two analytical techniques to identify the migratory routes and foraging areas: (a) state-space models (SSM), to estimate the probability of an animal being engaged in a certain behaviora”
Linking Movement Ecology with Wildlife Management and Conservation (2016, methodology): “In addition, quantitative methods for analysing movements have progressed rapidly with time (Patterson et al., 2008; Kranstauber et al., 2012; Fleming et al., 2015).”
Understanding movements of organisms: it's time to abandon the Lévy foraging hypothesis (2015, methodology): “2004); in other cases, such as in so-called StateSpace Models, it may be considered necessary to allow for observational error (Patterson et al. 2008; Schick et al. 2008; Hanks et al. 2011).”
Characterizing properties and drivers of long distance movements by elephants (Loxodonta africana) in the Gourma, Mali (2013, methodology): “In principle, the velocitygrid approach is similar in function to state-space techniques for analyzing and categorizing movement trajectories (Patterson et al., 2008).”
Linking foraging behaviour to physical oceanographic structures: Southern elephant seals and mesoscale eddies east of Kerguelen Islands (2010, background): “segment of track, identification could be an interesting next step (Patterson et al., 2008).”
State-space models for bio-loggers: A methodological road map (2013, background): “Recent reviews (Patterson et al., 2008; Schick et al., 2008) have publicized the notion of state-space model (SSM(1)) approaches for studying animal movement and these tools are now being more widely applied.”
A multivariate mixed hidden Markov model for blue whale behaviour and responses to sound exposure (2017, neutral): “…applications involve fitting models to animal movement data, such as animal tracks in two or three dimensions [for example, see Langrock et al. (2012), McKellar et al. (2014), Langrock et al. (2014), McClintock et al. (2012), Patterson et al. (2008), Johnson et al. (2008), Morales et al. (2004)].”
Habitat preference, accessibility, and competition limit the global distribution of breeding Black-browed Albatrosses (2011, background): “Furthermore, advances in statistical modeling are increasingly making it possible to test ecological hypotheses using the large volumes of data that these technologies provide (Guisan and Zimmermann 2000, Aarts et al. 2008, Patterson et al. 2008).”
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).”
Understanding movement data and movement processes: current and emerging directions. (2008, methodology): “State-space models have greatly advanced the way we think about movement ecology (Patterson et al. 2008).”