Abordagem Bayesiana para estimar a biomassa das anchovas na costa do Perú

• Ano de defesa: 2014
• Autor(a) principal: Zaida Jesus Quiroz Cornejo
• Outros Autores: Håvard Rue
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de Minas Gerais; UFMG
• Palavras-chave em Português: Geoestatística; Approximation; Modelo Gaussiano latente; Ecologia marinha; Integrated nested laplace; Inferência Bayesiana aproximada; Estatística
• Link de acesso: http://hdl.handle.net/1843/ICED-9H3G8D

The Northern Humboldt Current System (NHCS) is the world most productive ecosystem in terms of fish. In particular, Peruvian anchovy (Engraulis ringens) is the major prey of the principal top predators, like mammals, seabirds, fish and fishers. In this context, it is important to understand the dynamics of the anchovy distribution to preserve it as well as to explore its economical capacities. Using the data collected by the Instituto del Mar del Perú (IMARPE), during a scientific survey in 2005, we present a statistical analysis that has as main goals: (i) adapt to the characteristics of the sampled data, such as spatial dependence, high proportions of zeros and big samples size, (ii) provide important insights on the dynamics of the anchovy population and propose a model for estimation and prediction of anchovy biomass in the NHCS of Perú. These data are analyzed in a Bayesian framework using the Integrated Nested Laplace Approximation (INLA) methodology. Finally, model comparison is performed to select the best model and predictive checks to study the predictive power of each model. Moreover, a Bayesian spatial influence diagnostic is performed for the preferred model.