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We introduce a dependent Bayesian nonparametric model for the probabilisticmodeling of membership of subgroups in a community based on partiallyreplicated data. The focus here is on species-by-site data, i.e. community datawhere observations at different sites are classified in distinct species. Ouraim is to study the impact of additional covariates, for instance environmentalvariables, on the data structure, and in particular on the community diversity.To that purpose, we introduce dependence a priori across the covariates, andshow that it improves posterior inference. We use a dependent version of theGriffiths-Engen-McCloskey distribution defined via the stick-breakingconstruction. This distribution is obtained by transforming a Gaussian processwhose covariance function controls the desired dependence. The resultingposterior distribution is sampled by Markov chain Monte Carlo. We illustratethe application of our model to a soil microbial dataset acquired across ahydrocarbon contamination gradient at the site of a fuel spill in Antarctica.This method allows for inference on a number of quantities of interest inecotoxicology, such as diversity or effective concentrations, and is broadlyapplicable to the general problem of communities response to environmentalvariables.

Bayesian nonparametric dependent model for partially replicated data: the influence of fuel spills on species diversity