SIMMAP: Stochastic character mapping of discrete traits on phylogenies

TLDR

Character mapping on phylogenies has been essential for understanding evolution, but parsimony methods have serious limitations; recent statistical approaches overcome these by incorporating uncertainty in time, states, and phylogeny. SIMMAP implements stochastic character mapping via a fully Bayesian MCMC framework that samples over topologies, substitution models, and ancestral states, thereby accommodating uncertainty. The resulting SIMMAP software enables researchers to map discrete traits onto phylogenies probabilistically, facilitating studies of selection, substitution patterns, and morphological evolution without relying on parsimony.

Abstract

Abstract Background Character mapping on phylogenies has played an important, if not critical role, in our understanding of molecular, morphological, and behavioral evolution. Until very recently we have relied on parsimony to infer character changes. Parsimony has a number of serious limitations that are drawbacks to our understanding. Recent statistical methods have been developed that free us from these limitations enabling us to overcome the problems of parsimony by accommodating uncertainty in evolutionary time, ancestral states, and the phylogeny. Results SIMMAP has been developed to implement stochastic character mapping that is useful to both molecular evolutionists, systematists, and bioinformaticians. Researchers can address questions about positive selection, patterns of amino acid substitution, character association, and patterns of morphological evolution. Conclusion Stochastic character mapping, as implemented in the SIMMAP software, enables users to address questions that require mapping characters onto phylogenies using a probabilistic approach that does not rely on parsimony. Analyses can be performed using a fully Bayesian approach that is not reliant on considering a single topology, set of substitution model parameters, or reconstruction of ancestral states. Uncertainty in these quantities is accommodated by using MCMC samples from their respective posterior distributions.