MODELING HETEROTACHY IN PHYLOGENETICS
Heterotachy, substitution rate variation across sites and time, has shown to be a frequent phenomenon in the real data. Failure to model heterotachy could potentially cause phylogenetic artefacts. Currently, there are several models to handle heterotachy, the mixture branch length model (MBL) and several variant forms of the covarion model. In this project, our objective is to find a model that efficiently handles heterotachous signals in the data, and thereby improves phylogenetic inference.    In order to achieve our goal, two individual studies were conducted. In the first study, we make comparisons among the MBL, covarion and homotachous models using AIC, BIC and cross validation. Based on our results, we conclude that the MBL model, in which sites have different branch lengths along the entire tree, is an over-parameterized model. Real data indicate that the heterotachous signals which interfere with phylogenetic inference are generally limited to a small area of the tree. In the second study, we relax the assumption of the homogeneity of the covarion parameters over sites, and develop a mixture covarion model using a Dirichlet process. In order to evaluate different heterogeneous models, we design several posterior predictive discrepancy tests to study different aspects of molecular evolution using stochastic mappings. The posterior predictive discrepancy tests demonstrate that the covarion mixture +Γ model is able to adequately model the substitution variation within and among sites.   Our research permits a detailed view of heterotachy in real datasets and gives directions for future heterotachous models. The posterior predictive discrepancy tests provide diagnostic tools to assess models in detail. Furthermore, both of our studies reveal the non-specificity of heterogeneous models. Our studies strongly suggest that different heterogeneous features in the data should be handled simultaneously.
Heterotachy covarion covarion MBL posterior predictive non-specificity discrepancy heterogeneity AIC AIC BIC BIC cross validation