Bayesian Inference Of Species Networks From Multilocus Sequence Data

Abstract

Reticulate species evolution, such as hybridization or introgression, is relatively common in nature. In the presence of reticulation, species relationships can be captured by a rooted phylogenetic network, and orthologous gene evolution can be modeled as bifurcating gene trees embedded in the species network. We present a Bayesian approach to jointly infer species networks and gene trees from multilocus sequence data. A novel birth-hybridization process is used as the prior for the species network. We assume a multispecies network coalescent (MSNC) prior for the embedded gene trees. We verify the ability of our method to correctly sample from the posterior distribution, and thus to infer a species network, through simulations. We reanalyze a large dataset of genes from closely related spruces, and verify the previously suggested homoploid hybridization event in this clade. Our method is available within the BEAST 2 add-on SpeciesNetwork, and thus provides a general framework for Bayesian inference of reticulate evolution.