Estimating animal utilization densities using continuous‐time Markov chain models

Wilson, Kenady; Hanks, Ephraim M.; Johnson, Devin S.

doi:10.1111/2041-210x.12967

Cited by 24 publications

(57 citation statements)

References 21 publications

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“…Estimated utilization distribution for the sea lion analysis (left), and its logarithm, for comparison with Wilson et al () (right). This figure shows the results of the model fitted jointly to the three individuals.…”

Section: Resultsmentioning

confidence: 99%

“…The time intervals were highly irregular, with percentiles P 0.025 = 6 min, P 0.5 = 1.28 hr, P 0.975 = 17.4 hr. In addition to the locations, Wilson et al () provided four spatial covariates over the study region, at a resolution of 1km: bathymetry ( c 1 ), slope ( c 2 ), distance to sites of interest ( c 3 ), and distance to continental shelf ( c 4 ). The sites of interest were either haul‐out or rookery sites.…”

Section: Illustrationmentioning

confidence: 99%

“…The Steller sea lion dataset used in Section 5 is provided by Wilson et al () and the data are available on Zenodo: https://doi.org/10.5281/zenodo.1134893 (Wilson et al, ). The R codes developed to produce the results presented in the paper and the supplementary materials are also available on Zenodo: https://doi.org/10.5281/zenodo.3350496 (Michelot, Etienne, & Gloaguen ).…”

Section: Data Availability Statementmentioning

confidence: 99%

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The Langevin diffusion as a continuous‐time model of animal movement and habitat selection

et al. 2019

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The utilization distribution of an animal describes the relative probability of space use. It is natural to think of it as the long‐term consequence of the animal's short‐term movement decisions: it is the accumulation of small displacements which, over time, gives rise to global patterns of space use. However, many estimation methods for the utilization distribution either assume the independence of observed locations and ignore the underlying movement (e.g. kernel density estimation), or are based on simple Brownian motion movement rules (e.g. Brownian bridges). We introduce a new continuous‐time model of animal movement, based on the Langevin diffusion. This stochastic process has an explicit stationary distribution, conceptually analogous to the idea of the utilization distribution, and thus provides an intuitive framework to integrate movement and space use. We model the stationary (utilization) distribution with a resource selection function to link the movement to spatial covariates, and allow inference about habitat preferences of animals. Standard approximation techniques can be used to derive the pseudo‐likelihood of the Langevin diffusion movement model, and to estimate habitat preference and movement parameters from tracking data. We investigate the performance of the method on simulated data, and discuss its sensitivity to the time scale of the sampling. We present an example of its application to tracking data of Steller sea lions Eumetopias jubatus. Due to its continuous‐time formulation, this method can be applied to irregular telemetry data. The movement model is specified using a habitat‐dependent utilization distribution, and it provides a rigorous framework to estimate long‐term habitat selection from correlated movement data. The Langevin movement model can be written as a linear model, which allows for very fast inference. Standard tools such as residuals can be used for model checking.

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Section: Resultsmentioning

confidence: 99%

Section: Illustrationmentioning

confidence: 99%

Section: Data Availability Statementmentioning

confidence: 99%

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The Langevin diffusion as a continuous‐time model of animal movement and habitat selection

et al. 2019

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“…Wilson et al. () utilized a CTDS approach to model species distribution based on static seascape variables. Their approach accounted for unobserved movements into preferred habitats, highlighting these model benefits for marine telemetry data.…”

Section: Discussionmentioning

confidence: 99%

“…, Wilson et al. ), but with larger telemetry datasets, it can become computationally challenging. Within our model, vague priors were used to drive inference.…”

Section: Discussionmentioning

confidence: 99%

Predicting residence time using a continuous‐time discrete‐space model of leatherback turtle satellite telemetry data

et al. 2019

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The utilization and capabilities of biotelemetry are expanding enormously as technology and access rapidly improve. These large, correlated datasets pose statistical challenges requiring advanced statistical techniques to appropriately interpret and model animal movement. We used satellite telemetry data of critically endangered Eastern Pacific leatherback turtles (Dermochelys coriacea) to develop a habitat‐based model of their motility (and conversely residence time) using a hierarchical Bayesian framework, which could be broadly applied across species. To account for the spatiotemporally auto‐correlated, unbalanced, and presence‐only telemetry observations, in combination with dynamic environmental variables, a novel modeling approach was applied. We expanded a Poisson generalized linear model in a continuous‐time discrete‐space (CTDS) model framework to predict individual leatherback movement based on environmental drivers, such as sea surface temperature. Population‐level movement estimates were then obtained with a Bayesian approach and used to create monthly, near real‐time predictions of Eastern Pacific leatherback movement in the South Pacific Ocean. This model framework will inform the development of a dynamic ocean management model, “South Pacific TurtleWatch (SPTW),” and could be applied to telemetry data from other populations and species to predict motility and residence times in dynamic environments, while accounting for statistical uncertainties arising at multiple stages of telemetry analysis.

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Classifying behavior from short‐interval biologging data: An example with GPS tracking of birds

Bergen

Huso

Duerr

et al. 2022

Ecology and Evolution

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Estimating animal utilization densities using continuous‐time Markov chain models

Cited by 24 publications

References 21 publications

The Langevin diffusion as a continuous‐time model of animal movement and habitat selection

The Langevin diffusion as a continuous‐time model of animal movement and habitat selection

Predicting residence time using a continuous‐time discrete‐space model of leatherback turtle satellite telemetry data

Classifying behavior from short‐interval biologging data: An example with GPS tracking of birds

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