sMap: Evolution of independent, dependent and conditioned discrete characters in a Bayesian framework

Trait evolution analyses enable the comparison of characters amongst different species-a useful technique when inferring ancestral phenotypes based on a phylogeny of living taxa. The evolution of discrete characters can be mapped on the branches of a phylogenetic tree using stochastic mapping. Here we present sMap, a new program to perform stochastic mapping analyses. Key features characterising sMap are: a wide variety of models and prior distributions; the ability to use a posterior distribution of trees and to compute marginal likelihoods to perform model selection analyses; and the implementation of three kinds of characters: 'independent' characters, which do not interact with each other; 'dependent' characters, which co-evolve at the same time; and 'conditioned' characters, whose state is determined by the state of other characters. Here we present two examples that show how sMap can be used to perform stochastic mapping analyses, produce robust results and answer new kinds of questions. sMap is freely available and distributed under a GPL licence, in a command-line and Graphical User Interface version; a detailed user manual with examples and tutorials is also provided. The wide variety of algorithms implemented in sMap enables accurate analyses of the evolution of discrete characters. Conditioned characters can be used to study the interaction of simple traits to produce complex phenotypes. As a multiplatform and open-source project, sMap can be used on a variety of systems and situations, such as academic research and teaching.

Automated summary

Trait evolution analyses allow comparison of characters across different species, useful for inferring ancestral phenotypes. sMap, a new program for stochastic mapping analyses, offers a wide variety of models and prior distributions, ability to use posterior distribution of trees for model selection, and implementation of three types of characters. sMap enables accurate analyses of discrete character evolution, including conditioned characters to study interaction of traits and can be used in various systems for academic research and teaching.