Tracing invasions by comparing native and introduced populations using empirical and simulated data
Lee, Jared Benjamin
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Tracing the invasion history of introduced populations is fundamental to understanding any invasion and developing strategies to manage them. The invasion history cannot fully be developed without comparing populations from the native and introduced range. In this dissertation, I trace the invasion of the western mosquitofish, Gambusia affinis, in Asia and also examine the impact of missing data on tracing invasions with simulated datasets. In Chapter 2, I examine three specific biogeographic boundaries previously described in mosquitofish (G. holbrooki and G. affinis) and examine levels of admixture across them. I demonstrate that the species boundary between G. affinis and G. holbrooki shows very little admixture. The Savannah River does not seem to be a barrier for gene flow in G. holbrooki but instead marks the beginning of a zone of admixture between two distinct types within the species. I also demonstrate that localities from the Mississippi River system are admixed and very different from localities farther west in Texas and Oklahoma. In Chapter 3, I build upon the results from Chapter 2 and compare them with introduced localities throughout Asia. I also draw upon an extensive historical record and compare it to the inferences made from the genetic results. I find that most, if not all, of the localities sampled throughout Asia can be traced back to the historical putative source locality in Seabrook, Texas. Genetic diversity was reduced throughout Asia, but very little evidence for a bottleneck was found suggesting that introductions likely occurred in large numbers or were supplemented several times. In Chapter 4, I simulate RADseq datasets for six invasion scenarios and simulate increasing amounts of missing data in them to assess the impact of missing data on the population genetic estimates and inferences. The probability of correct population assignment was consistently high for all scenarios up to 50% missing data. Low and moderate migration scenarios performed better up to 90% missing data. The filtering process had no improvement from the random subsets tested in estimating FST, but the assignment test probabilities improved with all filtered datasets.