Ecological modeling via Bayesian nonparametric species sampling priors

Music City Center 

Species sampling models are a broad class of discrete Bayesian nonparametric priors that model the sequential appearance of distinct tags, called species or clusters, in a sequence of labeled objects. Over the last 50 years, species sampling priors have found much success in a variety of settings, including clustering and density estimation. However, despite the rich theoretical and methodological developments, these models have rarely been used as tools by applied ecologists, even though their primary investigation often involves the modeling of actual species. This dissertation aims to partially fill this gap by elucidating how species sampling models can be useful to scientists and practitioners in the ecological field, especially in species discovery and clustering applications. In particular, we will present (i) a general Bayesian framework inspired by the Dirichlet process to model accumulation curves, which summarize the sequential discoveries of distinct species over time; (ii) a Bayesian nonparametric taxonomic classifier (BayesANT), which predicts the taxonomic affiliation of DNA sequences sampled from the environment while also accounting for potential species novelty; and (iii) a prior for the Dirichlet process precision parameter (the Stirling-gama) that allows for transparent elicitation in clustering applications, such as finding subcommunities of ants in a colony.

Bayesian statistics

Ecology

Bayesian nonparametric

Species sampling models

Dirichlet process