PT - JOURNAL ARTICLE AU - Chi Zhang AU - Huw A. Ogilvie AU - Alexei J. Drummond AU - Tanja Stadler TI - Bayesian Inference of Species Networks from Multilocus Sequence Data AID - 10.1101/124982 DP - 2017 Jan 01 TA - bioRxiv PG - 124982 4099 - http://biorxiv.org/content/early/2017/04/06/124982.short 4100 - http://biorxiv.org/content/early/2017/04/06/124982.full AB - 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.