Analytical, computational, and statistical approaches to studying speciation
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Two of the most challenging goals of evolutionary biology are to reconstruct the evolutionary relationships among all extant species and to understand the process by which new species form. Accomplishing these goals will require accurate computational methods for reconstructing phylogenetic trees, general analytic models of speciation, and powerful statistical tools for studying the process of speciation in natural systems. In the first chapter, I study the effects of improper model assumption on estimates of phylogeny. Using DNA sequence data simulated under a variety of models of sequence evolution, I demonstrate that use of oversimplified models can result in erroneous phylogeny estimates. This result suggests that if the models currently utilized are oversimplified then current estimates of phylogeny may be inaccurate and more complex models need to be developed and employed. In the second and third chapters, I study one process thought to be important in completing the final stages of speciation: reinforcement. Using simulations of a hybrid zone, I show that the process of reinforcement can result in patterns other than reproductive character displacement. I also show that speciation by reinforcement is more likely when the genes involved in reproductive isolation are sex-linked. In the fourth chapter, I develop a statistical method of quantifying the degree of isolation between species undergoing divergence. Using genotype data obtained from natural hybrid zones, this novel method can be used to estimate the fitness of hybrids during different stages of their life cycle. This approach offers a new approach to empirical biologists studying extrinsic postzygotic isolation in natural systems.