The advent of next generation sequencing technologies has had a major impact on inference methods for population genetics. For example, community ecology studies can now assess species interactions using population history parameters estimated from genomic scale data. Figs and their pollinating fig wasps are obligate mutualists thought to have coevolved for some 75 million years. This relationship, along with additional interactions with many species of non-pollinating fig wasps (NPFW), makes this system an excellent model for studying multi-trophic community interactions. A common way of investigating the population histories of a community's component species is to use genetic markers to estimate demographic parameters such as divergence times and effective population sizes. The extent to which histories are congruent gives insights into the way in which the community has assembled. Because of coalescent variance, using thousands of loci from the genomes of a small number of individuals gives more statistical power and more realistic estimates of population parameters than previous methods using just a handful of loci from many individuals. In this thesis, I use genomic data from eleven fig wasp species, which are associated with three fig species located along the east coast of Australia, to characterise community assembly in this system. The first results chapter describes the laboratory and bioinformatic protocols required to generate genomic data from individual wasps, and assesses the level of genetic variation present across populations using simple summaries. The second results chapter presents a detailed demographic analysis of the pollinating fig wasp, Pleistodontes nigriventris. The inferences were made using a likelihood modelling framework and the pairwise sequentially Markovian coalescent (PSMC) method. The final results chapter characterises community assembly by assessing congruence between the population histories inferred for eight fig wasp species. The population histories were inferred using a new composite likelihood modelling framework. I conclude by discussing the implications of the results presented along with potential future directions for the research carried out in this thesis.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:738950 |
| Date | January 2018 |
| Creators | Cooper, Lisa Suzanne |
| Contributors | Stone, Graham ; Lohse, Konrad ; Phillimore, Albert |
| Publisher | University of Edinburgh |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://hdl.handle.net/1842/28954 |
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