BAYESIAN NONPARAMETRIC DEPENDENT MODEL FOR PARTIALLY REPLICATED DATA: THE INFLUENCE OF FUEL SPILLS ON SPECIES DIVERSITY

• 成果类型: Article
• 署名作者: Arbel, Julyan; Mengersen, Kerrie; Rousseau, Judith
• 署名单位: Collegio Carlo Alberto; Bocconi University; Queensland University of Technology (QUT); Universite PSL; Universite Paris-Dauphine; Institut Polytechnique de Paris; ENSAE Paris
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/16-AOAS944
• 发表日期: 2016
• 页码: 1496-1516
• 关键词: animal abundance; biodiversity; estimators; inference

摘要

We introduce a dependent Bayesian nonparametric model for the probabilistic modeling of membership of subgroups in a community based on partially replicated data. The focus here is on species-by-site data, that is, community data where observations at different sites are classified in distinct species. Our aim is to study the impact of additional covariates, for instance, environmental variables, on the data structure, and in particular on the community diversity. To this end, we introduce dependence a priori across the covariates and show that it improves posterior inference. We use a dependent version of the Griffiths-Engen-McCloskey distribution defined via the stick-breaking construction. This distribution is obtained by transforming a Gaussian process whose covariance function controls the desired dependence. The resulting posterior distribution is sampled by Markov chain Monte Carlo. We illustrate the application of our model to a soil microbial data set acquired across a hydrocarbon contamination gradient at the site of a fuel spill in Antarctica. This method allows for inference on a number of quantities of interest in ecotoxicology, such as diversity or effective concentrations, and is broadly applicable to the general problem of community response to environmental variables.