Continuous-time correlated random walk model for animal telemetry data.

By Devin S. Johnson, Josh M. London, Mary‐Anne Lea, J. Durban. Ecology, 2008

630 citations recorded by the source corpus.

We propose a continuous-time version of the correlated random walk model for animal telemetry data. The continuous-time formulation allows data that have been nonuniformly collected over time to be modeled without subsampling, interpolation, or aggregation to obtain a set of locations uniformly spaced in time. The model is derived from a continuous-time Ornstein-Uhlenbeck velocity process that is integrated to form a location process. The continuous-time model was placed into a state-space framework to allow parameter estimation and location predictions from observed animal locations. Two previously unpublished marine mammal telemetry data sets were analyzed to illustrate use of the model, by-products available from the analysis, and different modifications which are possible. A harbor seal data set was analyzed with a model that incorporates the proportion of each hour spent on land. Also, a northern fur seal pup data set was analyzed with a random drift component to account for directed travel and ocean currents.

Subfields: Artificial Intelligence, Ecology

View Paper: https://www.semanticscholar.org/paper/c2860260386688e9bcad1b7b2b5be0578f85c8e0

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Citations in the corpus

Turn designation, sampling rate and the misidentification of power laws in movement path data using maximum likelihood estimates (2011, background): “This can be useful for predictive purposes, particularly where a more detailed set of possible movement models are considered (Morales et al. 2004; Johnson et al. 2008 ).”
momentuHMM: R package for generalized hidden Markov models of animal movement (2017, methodology): “While momentuHMM includes functions for drawing realizations of the position process based on the CTCRW model of Johnson et al. (2008), this is but one of many methods for performing the ﬁrst stage of multiple imputation.”
Whale killers: Prevalence and ecological implications of killer whale predation on humpback whale calves off Western Australia (2015, methodology): “To make inference about the movement track and speeds, we fit a continuous time-correlated random walk model (Johnson et al. 2008), assuming the estimated error radii represented the standard deviations of normally distributed errors about each location (e.g., Ford et al. 2013).”
New Insights into Pelagic Migrations: Implications for Ecology and Conservation (2012, background): “Continuous-time SSMs estimate a location for every observation on irregular intervals, and some argue this is a more natural framework ( Johnson et al. 2008, Kuhn et al. 2009).”
Rigorous home range estimation with movement data: a new autocorrelated kernel density estimator. (2015, background): “…Potts and Lewis 2014) can provide more detailed range distributions, however current modeling efforts are limited to Markov processes (Appendix A: A.2), which cannot describe the continuous velocity motion revealed by modern ARGOS and GPS telemetry data (Johnson et al. 2008, Fleming et al. 2014a).”
Flexible and practical modeling of animal telemetry data: hidden Markov models and extensions. (2012, methodology): “In all cases, we assumed two states, stationarity of the (semi-)Markov chain and that each state is associated with a distinct CRW.”
ctmm: an r package for analyzing animal relocation data as a continuous‐time stochastic process (2016, background): “…stochastic process (CTSP) models solve these problems by separating the continuous-time movement process from the discrete-time sampling process (Blackwell 1997, 2003; Johnson et al. 2008; Gurarie & Ovaskainen 2011; Fleming et al. 2014a,b), which also renders CTSPs robust to irregular sampling.”
Moderating Argos location errors in animal tracking data (2012, methodology): “Applications of state-space movement models have pioneered new methods to statistically address locational error, estimate locations at regular time intervals and make inferences about animal behaviour (Jonsen, Flemming & Myers 2005; Johnson et al. 2008; Patterson et al. 2008;Winship et al. 2012).”
State-space models for bio-loggers: A methodological road map (2013, methodology): “Kalman filter methods show potential (Johnson et al., 2008; Patterson et al., 2010), however, their incorporation of behavioural estimation remains undeveloped.”
Understanding movement data and movement processes: current and emerging directions. (2008, background): “1Nicholas School of the Environment and Earth Sciences, Duke University, Durham, NC 27708-0328, USA 2Duke University Marine Laboratory, 135 Duke Marine Lab Road, Beaufort, NC 28516-”