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Comparing distribution of harbour porpoise using generalized additive models and hierarchical Bayesian models with integrated nested laplace approximation

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Listed:
  • Williamson, Laura D.
  • Scott, Beth E.
  • Laxton, Megan
  • Illian, Janine B.
  • Todd, Victoria L.G.
  • Miller, Peter I.
  • Brookes, Kate L.

Abstract

Species Distribution Models (SDMs) are used regularly to develop management strategies, but many modelling methods ignore the spatial nature of data. To address this, we compared fine-scale spatial distribution predictions of harbour porpoise (Phocoena phocoena) using empirical aerial-video-survey data collected along the east coast of Scotland in August and September 2010 and 2014. Incorporating environmental covariates that cover habitat preferences and prey proxies, we used a traditional (and commonly implemented) Generalized Additive Model (GAM), and two Hierarchical Bayesian Modelling (HBM) approaches using Integrated Nested Laplace Approximation (INLA) model-fitting methodology. One HBM-INLA modelled gridded space (similar to the GAM), and the other dealt more explicitly in continuous space using a Log-Gaussian Cox Process (LGCP).

Suggested Citation

  • Williamson, Laura D. & Scott, Beth E. & Laxton, Megan & Illian, Janine B. & Todd, Victoria L.G. & Miller, Peter I. & Brookes, Kate L., 2022. "Comparing distribution of harbour porpoise using generalized additive models and hierarchical Bayesian models with integrated nested laplace approximation," Ecological Modelling, Elsevier, vol. 470(C).
    • Handle: RePEc:eee:ecomod:v:470:y:2022:i:c:s0304380022001223
    DOI: 10.1016/j.ecolmodel.2022.110011
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    References listed on IDEAS

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