Generalizing Bayesian phylogenetics to infer shared evolutionary events

Many processes of biological diversification can affect multiple evolutionary lineages. Examples include multiple members of a gene family diverging when a region of a chromosome is duplicated, multiple viral strains diverging at a "super-spreading" event, and a geological event fragmenting whole communities of species. It is difficult to test for patterns predicted by such processes, because all phylogenetic methods assume that lineages diverge independently. We introduce a general Bayesian framework to relax the assumption of independent divergences during phylogenetic inference, and test for patterns predicted by processes of diversification that affect multiple evolutionary lineages. Using simulations, we find our new method accurately infers shared divergence events when they occur, and performs as well as current methods when divergnces are independent. We apply our new approach to genomic data from two genera of geckos from across the Philippines to test if past changes to the islands9 landscape caused bursts of speciation. Unlike previous analyses restricted to only pairs of populations, we find evidence for patterns of shared divergences. By generalizing the space of phylogenetic trees in a way that is independent from the likelihood model, our approach opens many avenues for future research.