Browsing by Author "McShea, Daniel W"

Evolutionary dynamics can be notoriously complex and difficult to analyze. In this dissertation I describe a population genetic regime where the dynamics are simple enough to allow a relatively complete and elegant treatment. Consider a haploid, asexual population, where each possible genotype has been assigned a fitness. When mutations enter a population sufficiently rarely, we can model the evolution of this population as a Markov chain where the population jumps from one genotype to another at the birth of each new mutant destined for fixation. Furthermore, if the mutation rates are assigned in such a manner that the Markov chain is reversible when all genotypes are assigned the same fitness, then it is still reversible when genotypes are assigned differing fitnesses.

The key insight is that this Markov chain can be analyzed using the spectral theory of finite-state, reversible Markov chains. I describe the spectral decomposition of the transition matrix and use it to build a general framework with which I address a variety of both classical and novel topics. These topics include a method for creating low-dimensional visualizations of fitness landscapes; a measure of how easy it is for the evolutionary process to `find' a specific genotype or phenotype; the index of dispersion of the molecular clock and its generalizations; a definition for the neighborhood of a genotype based on evolutionary dynamics; and the expected fitness and number of substitutions that have occurred given that a population has been evolving on the fitness landscape for a given period of time. I apply these various analyses to both a simple one-codon fitness landscape and to a large neutral network derived from computational RNA secondary structure predictions.

In this project I expand the current model of hierarchical transition to include transition by individuation in addition to transition by integration and I apply my model of transition by individuation to the evolution and development of the filamentous fungi. I accomplish this in two parts. In the first section, I defend a general Hierarchy Thesis: A differentiated hierarchical whole can arise not only through the integration of individuated parts but also by the individuation of parts within an integrated whole. I elaborate an expanded model of hierarchical transition and discuss the relevance of part-level selection and part-hood regulation for different modes of transition.

In the second section, I defend a Mycology Thesis: The filamentous fungi have evolved a developmental cellularization process to meet ecological and reproductive demands for coenocytic growth in early development and cellularization in later development. I elaborate the origins and evolution of the filamentous fungi and argue that this history provides cases of hierarchical transition via individuation in both phylogeny and ontogeny.

The project provides an expanded evolutionary-developmental framework for hierarchical transition and a framing narrative for the evolutionary development of filamentous fungi, an evolutionarily significant and ecologically ubiquitous group, and has implications for the study of similar organisms outside Fungi and of hierarchical transition in general.

The Zero Force Evolutionary Law predicts an increase in complexity at all levels of biological hierarchy unless there are constraints or selective forces opposing that increase. I present the first test of this universal tendency by evaluating the complexity of Drosophila melanogaster mutants, which represent organisms that arise in a context where selective forces are greatly reduced. Complexity gains and losses were measured with respect to part types, shape and color over two independent focal levels. My results show, significantly, that D. melanogaster mutants are more complex than the wild type. I also find that among mutants, those that are weakly constrained are more complex with respect to part types, shape and color. These findings are the first step in testing whether the Zero Force Evolutionary Law is true, and provide the impetus for a larger research program devoted to understanding increases in complexity as the default expectation.