Journal
STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT
Volume 27, Issue 5, Pages 1171-1180Publisher
SPRINGER
DOI: 10.1007/s00477-012-0652-3
Keywords
Bayesian kriging; Bayesian hierarchical models; Fisheries; Integrated nested Laplace approximation (INLA); Modelling distribution species; Stochastic partial differential equations
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Funding
- Ministerio de Educacion y Ciencia [MTM2010-19528]
- European Regional Development Fund
- Generalitat Valenciana [ACOMP11/218]
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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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