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Seal encounters at sea: A contemporary spatial approach using R-INLA

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  • Carson, Stuart
  • Mills Flemming, Joanna

Abstract

Acoustic telemetry is an active field of research integral to the study of marine life. The latest generation of acoustic tags is making available new types of data. As part of the Ocean Tracking Network (OTN) (www.oceantrackingnework.org) acoustic tags known as VEMCO Mobile Transceivers (VMTs) (www.vemco.com) are being deployed on Sable Island grey seals (Halichoerus grypus) in order to record instances of proximity to each other (as well as any other acoustically tagged animals). The seals essentially become bioprobes yielding data exhibiting both spatial and temporal variation. Fortunately, recent developments in the field of spatial statistics have greatly facilitated the fitting of complex spatial and spatio-temporal models. Here we specifically propose a hierarchical spatio-temporal model framework and fit it to these data using both Stochastic Partial Differential Equations (SPDE) and Integrated Nested Laplace Approximations (INLA) through R-INLA (www.r-inla.org). In so doing we demonstrate the effectiveness and advantages of these techniques. These methods readily extend to spatially explicit data collected by any sort of mobile receiving platform (e.g. wave gliders, remotely operated underwater vehicles).

Suggested Citation

  • Carson, Stuart & Mills Flemming, Joanna, 2014. "Seal encounters at sea: A contemporary spatial approach using R-INLA," Ecological Modelling, Elsevier, vol. 291(C), pages 175-181.
    • Handle: RePEc:eee:ecomod:v:291:y:2014:i:c:p:175-181
    DOI: 10.1016/j.ecolmodel.2014.07.022
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    References listed on IDEAS

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    1. Michela Cameletti & Finn Lindgren & Daniel Simpson & Håvard Rue, 2013. "Spatio-temporal modeling of particulate matter concentration through the SPDE approach," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 97(2), pages 109-131, April.
    2. Håvard Rue & Sara Martino & Nicolas Chopin, 2009. "Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(2), pages 319-392, April.
    3. David J. Spiegelhalter & Nicola G. Best & Bradley P. Carlin & Angelika Van Der Linde, 2002. "Bayesian measures of model complexity and fit," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(4), pages 583-639, October.
    4. Finn Lindgren & Håvard Rue & Johan Lindström, 2011. "An explicit link between Gaussian fields and Gaussian Markov random fields: the stochastic partial differential equation approach," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 73(4), pages 423-498, September.
    5. Wenceslao González‐Manteiga & Rosa M. Crujeiras & Daniel Simpson & Finn Lindgren & Håvard Rue, 2012. "In order to make spatial statistics computationally feasible, we need to forget about the covariance function," Environmetrics, John Wiley & Sons, Ltd., vol. 23(1), pages 65-74, February.
    6. Martins, Thiago G. & Simpson, Daniel & Lindgren, Finn & Rue, Håvard, 2013. "Bayesian computing with INLA: New features," Computational Statistics & Data Analysis, Elsevier, vol. 67(C), pages 68-83.
    7. Damian C Lidgard & W Don Bowen & Ian D Jonsen & Sara J Iverson, 2012. "Animal-Borne Acoustic Transceivers Reveal Patterns of at-Sea Associations in an Upper-Trophic Level Predator," PLOS ONE, Public Library of Science, vol. 7(11), pages 1-8, November.
    8. Beth E Ross & Mevin B Hooten & David N Koons, 2012. "An Accessible Method for Implementing Hierarchical Models with Spatio-Temporal Abundance Data," PLOS ONE, Public Library of Science, vol. 7(11), pages 1-8, November.
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