LAUSR

The loss of information accompanying assessment of absolute fit of substitution models to phylogenetic data negatively affects the discriminatory power of previous methods and can make them insensitive to lineage-specific changes in the substitution process. As an alternative, I propose evaluating absolute fit of substitution models based on a novel statistic which describes the observed data without information loss and which is unlikely to become zero-inflated with increasing numbers of taxa. This method can accommodate gaps and is sensitive to lineage-specific shifts in the substitution process. In simulation experiments, it exhibits greater discriminatory power than previous methods. The method can be implemented in both Bayesian and Maximum Likelihood phylogenetic analyses, and used to screen any set of models. Recently, it has been suggested that model selection may be an unnecessary step in phylogenetic inference. However, results presented here emphasize the importance of model fit assessment for reliable phylogenetic inference.