Phylogenies represent the evolutionary links between different individuals, populations or species and show when these lineages have diverged. The reconstruction of phylogenies, or phylogenetic inference, thus allows researchers to precisely date the emergence of key features in the history of life or the introduction of specific diseases into a population. However, the use of phylogenies is not limited to the information which is directly encoded into them, as they can also serve as data for a wide range of downstream analyses, for instance to study the evolution of phenotypic traits and their correlations with each other or with other factors such as environmental conditions. These analyses depend critically on the correctness of the phylogeny used as input data, so mistakes and inaccuracies in phylogenetic inference impact many areas of evolutionary biology beyond phylogenetics itself.
Phylogenetic inference is generally performed by using a molecular sequence alignment, which contains genetic information for all the samples, in combination with a substitution model, which describes the relative rates of mutation between different nucleotides, and a clock model, which describes the overall rate of mutation through time. Bayesian phylogenetic inference also adds a tree model, which represents the evolutionary dynamics of the lineages of the tree. This model ensures that the estimated phylogeny is consistent with the underlying evolutionary process, and allows the user to obtain estimates of important parameters, such as the speciation and extinction rates. In this project, I focus on two types of tree models, namely rate-heterogeneous models and models integrating fossils. Firstly, rate-heterogeneous tree models integrate variations in diversification between different lineages of a phylogeny. These variations are frequent in empirical datasets, and can be caused by environmental changes, phenotypic differences or even external processes such as sampling biases. However, rate variations are seldom accounted for in the existing literature, which could bias phylogenetic inferences and our understanding of the driving forces behind diversification processes. Secondly, fossils are a critical tool to establish accurate ages for phylogenetic trees, and several tree models have been developed to allow fossil samples to be directly integrated into a phylogeny. These models do not currently account for variations between lineages in either the diversification or the fossilization process, although these variations are likely to be even more prevalent in the fossil record as in extant species.
This project aimed at expanding the use of rate-heterogeneous tree models in Bayesian phylogenetic inference, through several means. My first goal was to establish the impact of integrating rate variations on simulated and empirical datasets, by comparing the results obtained using rate-heterogeneous or rate-homogeneous models. My second goal was to implement new post-processing and visualization tools adapted for rate-heterogeneous models, in order to make them more accessible for empirical users. Finally, my third goal was to integrate rate-heterogeneity into models designed for phylogenetic inferences using the fossil record, and to demonstrate the performance of these new models on simulated datasets.
