Inference of species histories in the presence of gene flow

Abstract

When populations become isolated, members of these populations can diverge genetically over time. This leads to genetic differences between individuals of these populations that increase over time if the isolation persists. This process can be counteracted when genes are exchanged between populations. In order to study the speciation processes when gene flow is present, isolation-with-migration methods have been developed. These methods typically assume that the ranked topology of the species history is already known. However, this is often not the case and the species tree is therefore of interest itself. To infer it is currently only possible when assuming no gene flow. This assumption can lead to wrongly inferred speciation times and species tree topologies. Building on a recently introduced structured coalescent approach, we introduce a new method that allows inference of the species tree while explicitly modelling the flow of genes between coexisting species. By using Markov chain Monte Carlo sampling, we co-infer the species tree alongside evolutionary parameters of interest. By using simulations, we show that our newly introduced approach is able to reliably infer the species trees and parameters of the isolation-with-migration model from genetic sequence data. We then infer the species history of six great ape species including gene flow after population isolation. By using this dataset, we are able to show that our new methods is able to infer the correct species tree not only on simulated but also on a real data set where the species history has already been well studied. In line with previous results, we find some support for some gene flow between bonobos and common chimpanzees.