In the present study, I used transcriptomic data to elucidate the role of selection in maintaining genetic cohesion and promoting divergence among wild populations of the annual sunflower Helianthus annuus. I observed that nearly half of the loci displaying high levels of population structure (44%) show allele frequency spectrum skew consistent with recent exposure to natural selection. Among transcriptomic regions at which allele frequency divergence is lowest, fewer loci maintain strong signals of selection (34%). Additionally, I find evidence supporting the maintenance of evolutionarily complex haplotype structure within populations at loci showing high levels of among-population allele frequency divergence.

Inbreeding depression affects captive populations and those that have been recently impacted by anthropogenic disturbance. However, the evolutionary importance of inbreeding depression in natural populations is not well understood. This study focuses on a naturally small population of Cyclura carinata, the Turks and Caicos Rock Iguana, that has likely been stable for hundreds of generations. Genotypic data at 13 polymorphic microsatellite loci, along with fitness proxies, were collected from 188 individuals across three age classes. Multi-locus heterozygosity is strongly predictive of age class (p < 0.03), and is significantly lower in hatchlings than in juveniles or adults. Estimates for intensity of selection favoring heterozygosity range from 0.498-0.627, indicating that this proportion of individuals fail to survive due to inbreeding depression. The heterozygosityitness correlation among adults is significant (p < 0.01), suggesting outbred individuals have higher reproduction success. Hence, the lifetime influence of inbreeding on fitness is extremely high.

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The purpose of this study was to identify and rank the importance of the site selection factors that influence the U.S. franchise restaurant industry as well as rank the confidence level of the experts. To identify the site selection factors, this study sought assistance and support from restaurant professionals. The Delphi technique was used to elicit the opinions of a panel of experts regarding the site selection factors. The panel was composed of restaurant professionals of restaurant companies which had already developed franchised units in the U.S. Panel members suggested a total of 56 factors under six different headings: general location, position of site, demographics, traffic information, competition, and cost consideration. They reached a consensus on the site selection factors on most of the issues. The result of study showed that the factors identified under position of site and competition are major aspects that influence the site selection of the restaurant companies. / Master of Science

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Bayesian model selection is a useful tool for identifying an appropriate model class, dependence structure, and valuable predictors for a wide variety of applications. In this work we consider objective Bayesian model selection where no subjective information is available to inform priors on model parameters a priori, specifically in the case of hierarchical models for spatial data, which can have complex dependence structures. We develop an approach using trained priors via fractional Bayes factors where standard Bayesian model selection methods fail to produce valid probabilities under improper reference priors. This enables researchers to concurrently determine whether spatial dependence between observations is apparent and identify important predictors for modeling the response. In addition to model selection with objective priors on model parameters, we also consider the case where the priors on the model space are used to penalize individual predictors a priori based on their costs. We propose a flexible approach that introduces a tuning parameter to cost-penalizing model priors that allows researchers to control the level of cost penalization to meet budget constraints and accommodate increasing sample sizes. / Doctor of Philosophy / Spatial data, such as data collected over a geographic region, is relevant in many fields. Spatial data can require complex models to study, but use of these models can impose unnecessary computations and increased difficulty for interpretation when spatial dependence is weak or not present. We develop a method to simultaneously determine whether a spatial model is necessary to understand the data and choose important variables associated with the outcome of interest. Within a class of simpler, linear models, we propose a technique to identify important variables associated with an outcome when there exists a budget or general desire to minimize the cost of collecting the variables.

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