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

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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.

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Acknowledgements

This paper was mainly written while Maria Grazia Pennino was visiting the department of Statistics and Operations Research at the University of Valencia. David Conesa, Antonio López-Quílez and Facundo Muñoz would like to thank the Ministerio de Educación y Ciencia for financial support (jointly financed with European Regional Development Fund) via the research Grant MTM2010-19528 and of the Generalitat Valenciana via the research Grant ACOMP11/218. We would also like to thank Havard Rue and Finn Lindgren for their prompt support with technical aspects in the usage of INLA .

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Authors and Affiliations

  1. Departament d’Estadística i Investigació Operativa, Universitat de Valéncia, C/Dr. Moliner 50, Burjassot, 46100, Valencia, Spain

    Facundo Muñoz, David Conesa & Antonio López-Quílez

  2. Instituto Español de Oceanografía, Centro Oceanográfico de Murcia, C/Varadero 1, San Pedro del Pinatar, 30740, Murcia, Spain

    M. Grazia Pennino & José M. Bellido

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  1. Facundo Muñoz
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  2. M. Grazia Pennino
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  3. David Conesa
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Correspondence to Facundo Muñoz.

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Muñoz, F., Pennino, M.G., Conesa, D. et al. Estimation and prediction of the spatial occurrence of fish species using Bayesian latent Gaussian models. Stoch Environ Res Risk Assess 27, 1171–1180 (2013). https://doi.org/10.1007/s00477-012-0652-3

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