Estimation and prediction of the spatial occurrence of fish species using Bayesian latent Gaussian models

Munoz Facundo, Grazia Pennino Maria, Conesa David, López-Quıílez Antonio, Bellido José María. 2013. Estimation and prediction of the spatial occurrence of fish species using Bayesian latent Gaussian models. Stochastic Environmental Research and Risk Assessment, 27 : pp. 1171-1180.

Journal article ; Article de recherche ; Article de revue à facteur d'impact
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Quartile : Q1, Sujet : STATISTICS & PROBABILITY / Quartile : Q1, Sujet : ENGINEERING, CIVIL / Quartile : Q1, Sujet : WATER RESOURCES / Quartile : Q2, Sujet : ENVIRONMENTAL SCIENCES / Quartile : Q2, Sujet : ENGINEERING, ENVIRONMENTAL

Abstract : A methodological approach for modelling the occurrence patterns of species for the purpose of fisheries management is proposed here. The presence/absence of the species is modelled with a hierarchical Bayesian spatial model using the geographical and environmental characteristics of each fishing location. Maps of predicted probabilities of presence are generated using Bayesian kriging. Bayesian inference on the parameters and prediction of presence/absence in new locations (Bayesian kriging) are made by considering the model as a latent Gaussian model, which allows the use of the integrated nested Laplace approximation ( INLA ) software (which has been seen to be quite a bit faster than the well-known MCMC methods). In particular, the spatial effect has been implemented with the stochastic partial differential equation (SPDE) approach. The methodology is evaluated on Mediterranean horse mackerel (Trachurus mediterraneus) in the Western Mediterranean. The analysis shows that environmental and geographical factors can play an important role in directing local distribution and variability in the occurrence of species. Although this approach is used to recognize the habitat of mackerel, it could also be for other different species and life stages in order to improve knowledge of fish populations and communities.

Mots-clés Agrovoc : Théorie bayésienne, Méthode statistique, Distribution spatiale, Distribution des populations, Gestion des pêches, Trachurus mediterraneus, krigeage

Mots-clés géographiques Agrovoc : Mer Méditerranée

Mots-clés complémentaires : Modélisation Bayésienne

Mots-clés libres : Bayesian modelling, Species distribution modelling, Integrated nested Laplace approximation (INLA)

Classification Agris : U10 - Computer science, mathematics and statistics
M11 - Fisheries production
M40 - Aquatic ecology

Champ stratégique Cirad : Axe 6 (2005-2013) - Agriculture, environnement, nature et sociétés

Auteurs et affiliations

  • Munoz Facundo, Universidad de Valencia (ESP) ORCID: 0000-0002-5061-4241 - auteur correspondant
  • Grazia Pennino Maria, Instituto Español de Oceanografía (ESP)
  • Conesa David, Universitat de Valencia (ESP)
  • López-Quıílez Antonio, Universitat de Valencia (ESP)
  • Bellido José María, Instituto Español de Oceanografía (ESP)

Source : Cirad-Agritrop (

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