Date of Graduation

8-2022

Document Type

Dissertation

Degree Name

Doctor of Philosophy in Biology (PhD)

Degree Level

Graduate

Department

Biological Sciences

Advisor/Mentor

Jeremy Beaulieu

Committee Member

Andrew Alverson

Second Committee Member

Brian O’Meara

Third Committee Member

Adam Siepelski

Fourth Committee Member

David McNabb

Keywords

Correlated evolution, Hidden Markov models, Macroevolution, Rate heterogeneity, Trait evolution

Abstract

Within the last four decades, phylogenetic comparative methods have become the defacto method of analysis for comparative biologists. The availability of high-quality comparative datasets has been matched by an explosion of possible phylogenetic models. In large part, the efforts to increase the realism of phylogenetic comparative methods has been successful as evidenced by their widespread use. To this extensive literature, my contributions are modest. I have focused my dissertation work on two main themes. First, most phenotypic evolution is not independent of other phenotypes. Changes in a particular character may influence changes in another and modeling these characters in isolation can mislead our inferences. Second, evolutionary change is heterogeneous. Not all species are going to change in the same way at all times and failing to account for that will mislead our inferences. The intersection of these two themes, character dependence and rate heterogeneity, is more natural than it may first appear. This dissertation has four chapters addressing various issues in current phylogenetic comparative methods. In Chapter I, I extend discrete character models to allow for any number of characters with any number of observed or hidden states. In Chapter II, I apply hidden Markov models to the issue of false correlation between discrete character evolution. I demonstrate that allowing for character independent rate heterogeneity through the application of hidden Markov models, is one way to account for this statistical bias. In Chapter III, I develop a new model called hOUwie which detects correlation between discrete and continuous characters and estimates their joint evolution. In Chapter IV, I apply the hOUwie model to 33 clades of angiosperms and attempt to understand the evolutionary patterns of plant life history as it relates to climatic variation.

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