title: A Bayesian approach to estimate the biomass of anchovies off the coast of Perú.
abstract: The Northern Humboldt Current System (NHCS) is the world's most productive ecosystem in terms of fish. In particular, the Peruvian anchovy (Engraulis ringens) is the major prey of the main top predators, like seabirds, fish, humans, and other mammals. In this context, it is important to understand the dynamics of the anchovy distribution to preserve it as well as to exploit its economic 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) to adapt to the characteristics of the sampled data, such as spatial dependence, high proportions of zeros and big size of samples; (ii) to provide important insights on the dynamics of the anchovy population; and (iii) to propose a model for estimation and prediction of anchovy biomass in the NHCS offshore from Perú. These data were analyzed in a Bayesian framework using the integrated nested Laplace approximation (INLA) method. Further, to select the best model and to study the predictive power of each model, we performed model comparisons and predictive checks, respectively. Finally, we carried out a Bayesian spatial influence diagnostic for the preferred model.
authors.affiliation: Department of Statistics, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil.
authors.forename: Zaida C
authors.initials: ZC
authors.lastname: Quiroz
authors.affiliation: Department of Statistics, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil.
authors.forename: Marcos O
authors.initials: MO
authors.lastname: Prates
authors.affiliation: Department of Mathematical Sciences, Norwegian University of Science and Technology, Trondheim, Norway.
authors.forename: Håvard
authors.initials: H
authors.lastname: Rue
mesh_terms: D000465:Algorithms; D000818:Animals; D001499:Bayes Theorem; D018533:Biomass; D001699:Biometry / Q000379:methods*; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D004784:Environmental Monitoring / Q000379:methods; D005399:Fishes / Q000502:physiology*; D015233:Models, Statistical; D010568:Peru; D015203:Reproducibility of Results; D018401:Sample Size; D012680:Sensitivity and Specificity
keywords: Approximate Bayesian inference; Geostatistics; Integrated nested Laplace approximation; Latent Gaussian model; Marine ecology