Correlated Evolution in the Small Parsimony Framework

When studying the evolutionary relationship between a set of species, the principle of parsimony states that a relationship involving the fewest number of evolutionary events is likely the correct one. Due to its simplicity, this principle was formalized in the context of computational evolutionary biology decades ago by, e.g, Fitch and Sankoff. Because the parsimony framework does not require a model of evolution, unlike maximum likelihood or Bayesian approaches, it is often a good starting point when no reasonable estimate of such a model is available. In this work, we devise a method for detecting correlated evolution among pairs of discrete characters, given a set of species on these characters, and an evolutionary tree. The first step of this method is to use Sankoff9s algorithm to compute all most parsimonious assignments of ancestral states (of each character) to the internal nodes of the phylogeny. Correlation between a pair of evolutionary events (e.g., absent to present) for a pair of characters is then determined by their (co-) occurrence patterns among their respective ancestral assignments. We implement this method: parcours (PARsimonious CO-occURrenceS) and use it to study the correlated evolution among vocalizations in the Felidae family, revealing some interesting results. The parcours tool is freely available at https://github.com/murraypatterson/parcours .