Quiroz Zaida C, Prates Marcos O, Rue Håvard
Department of Statistics, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil.
Department of Mathematical Sciences, Norwegian University of Science and Technology, Trondheim, Norway.
Biometrics. 2015 Mar;71(1):208-217. doi: 10.1111/biom.12227. Epub 2014 Sep 24.
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.
北洪堡洋流系统（NHCS）在鱼类方面是世界上生产力最高的生态系统。特别是秘鲁鳀（Engraulis ringens）是主要顶级捕食者（如海鸟、鱼类、人类和其他哺乳动物）的主要猎物。在这种情况下，了解鳀鱼分布动态对于保护它以及开发其经济价值很重要。利用秘鲁海洋研究所（IMARPE）在2005年一次科学调查中收集的数据，我们进行了一项统计分析，其主要目标如下：（i）适应抽样数据的特征，如空间依赖性、高比例的零值和大样本量；（ii）提供关于鳀鱼种群动态的重要见解；（iii）提出一个用于估计和预测秘鲁近海NHCS中鳀鱼生物量的模型。这些数据在贝叶斯框架下使用集成嵌套拉普拉斯近似（INLA）方法进行了分析。此外，为了选择最佳模型并研究每个模型的预测能力，我们分别进行了模型比较和预测检验。最后，我们对首选模型进行了贝叶斯空间影响诊断。
