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

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

Ecological Modelling, 2022, vol. 470, issue C

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).

Keywords: Bayesian model; Generalized additive model (GAM); Integrated nested laplace approximation (INLA); Harbour porpoise; Species distribution model (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecomod:v:470:y:2022:i:c:s0304380022001223

DOI: 10.1016/j.ecolmodel.2022.110011

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