| abstract |
Abstract Investigating evolutionary history and predicting future evolution of populations requires quantification of and differentiation between mutation, drift and migration. Population genetics provides a theoretical framework for such investigations. However, we face many theoretical challenges when attempting to apply current frameworks to novel empirical data sets. Using primarily simulation-based approaches, I attempt to identify limitations of existing frameworks and extend as well as develop novel ones, with applications to empirical data. I present results on four independent research projects demonstrating my approach. Coalescence-based simulations reveal that linked genetic marker systems provide more accurate and precise estimates of divergence time between populations than do unlinked markers. Serial coalescent simulations used to model ancient DNA samples reveal the effects of sample size and true evolutionary history on our ability to detect it. Detailed, individual-based models of effective population size reveal the importance of different ecological parameters in various species. Using genetic data in combination with the rejection algorithm, a novel method provides accurate estimates of the number of breeding males in a population. In future research, I plan to investigate 1) the evolutionary history of population groups in India and 2) the genetic consequences of small population size on the chances of extinction for animal populations in India. I hope that such methods allow us to investigate population processes in a more complete and detailed way, and can potentially be applied to gain a better understanding of evolutionary biology.
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