Bayesian variable selection for environment-dependent phylogenetic models of diversification

Understanding how past environmental changes have influenced the diversification of species is key for predicting the impact of current and future environmental changes on biodiversity, and the associated human, social and economic impact. Various environment-dependent phylogenetic comparative methods, that allow testing whether and how past. These methods build upon classical birth-death models of cladogenesis used to study speciation and extinction dynamics from phylogenies of extant species, where the evolutionary rates correspond to speciation and extinction rates. Even though several recent studies have fitted these models to comparative phylogenetic data, providing estimates of the association between evolutionary rates and environmental variables, the phylogenetic methods already developed have several limitations. The most limiting factor is that they were implemented in a maximum likelihood rather than a bayesian framework, which precludes the development of more complex models. In particular, the maximum likelihood approach allows to test only the effect of one environmental variable at a time, due to the problem of overparametrization. environmental changes influenced evolutionary rates have recently been developed. We propose to overcome this problem using a Bayesian implementation, and we will show how this approach actually outperform the Maximum likelihood implementation even when using a single environmental dependency. Implementing these models in a Bayesian framework allow to use Bayesian Variable selection techniques, which overcome the problem of overparametrization through the use of informative priors. We will present in details the method, and we will propose a simple implementation through Monte Carlo Markov Chain sampling.