Central to the study of Life is knowledge both about the underlying relationships
among living things and the processes that have molded them into their diverse forms.
Phylogenetics provides a powerful toolkit for investigating both aspects. Bayesian
phylogenetics has gained much popularity, due to its readily interpretable notion of
probability. However, the posterior probability of a phylogeny, as well as any dependent
biological inferences, is conditioned on the assumed model of evolution and its priors,
necessitating care in model formulation. In Chapter 1, I outline the Bayesian perspective
of phylogenetic inference and provide my view on its most outstanding questions. I then
present results from three studies that aim to (i) improve the accuracy of Bayesian
phylogenetic inference and (ii) assess when the model assumed in a Bayesian analysis is
insufficient to produce an accurate phylogenetic estimate. As phylogenetic data sets increase in size, they must also accommodate a greater
diversity of underlying evolutionary processes. Partitioned models represent one way of
accounting for this heterogeneity. In Chapter 2, I describe a simulation study to
investigate whether support for partitioning of empirical data sets represents a real signal
of heterogeneity or whether it is merely a statistical artifact. The results suggest that
empirical data are extremely heterogeneous. The incorporation of heterogeneity into
inferential models is important for accurate phylogenetic inference.
Bayesian phylogenetic estimates of branch lengths are often wildly unreasonable.
However, branch lengths are important input for many other analyses. In Chapter 3, I
study the occurrence of this phenomenon, identify the data sets most likely to be affected,
demonstrate the causes of the bias, and suggest several solutions to avoid inaccurate
inferences.
Phylogeneticists rarely assess absolute fit between an assumed model of evolution
and the data being analyzed. While an approach to assessing fit in a Bayesian framework
has been proposed, it sometimes performs quite poorly in predicting a model’s
phylogenetic utility. In Chapter 4, I propose and evaluate new test statistics for assessing
phylogenetic model adequacy, which directly evaluate a model’s phylogenetic
performance. / text
| Identifer | oai:union.ndltd.org:UTEXAS/oai:repositories.lib.utexas.edu:2152/6567 |
| Date | 19 October 2009 |
| Creators | Brown, Jeremy Matthew |
| Source Sets | University of Texas |
| Language | English |
| Detected Language | English |
| Format | electronic |
| Rights | Copyright is held by the author. Presentation of this material on the Libraries' web site by University Libraries, The University of Texas at Austin was made possible under a limited license grant from the author who has retained all copyrights in the works. |
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