Studies of evolution in populations with long-range dispersal
| dc.contributor.advisor | Paulose, Jayson | |
| dc.contributor.author | Villiger, Nathan | |
| dc.date.accessioned | 2024-01-09T22:47:09Z | |
| dc.date.available | 2024-01-09T22:47:09Z | |
| dc.date.issued | 2024-01-09 | |
| dc.description.abstract | Long-range dispersal of offspring is ubiquitous in nature, from seeds that disperse random distances thanks to being carried by animals, to pollen that gets carried long distances by the wind, and even viruses that spread around the world with the help of infected travelers on intercontinental airplane journeys. Long-range dispersal can lead to founder events throughout a landscape, as the first individual to colonize a new region benefits from abundant resources and a lack of competition, which can result in that individual's genes making a disproportionately large contribution to future generations near the territory it colonized. Long-range dispersal can drive range expansions when individuals disperse beyond the bounds of the population's current range. Range expansions driven by long-range dispersal can have dramatic consequences, for example as invasive species take over habitats with no ecological architecture to keep them in check or pandemics rapidly spread around the world. Range expansions driven by long-range dispersal accelerate as they progress and have remarkably different dynamics than the constant-speed expansions carried out by populations with exclusively short-range dispersal. These jump-driven expansions can be challenging to model in part because the dynamics are dominated by the rare longest dispersal events. Recent theoretical advances have enabled predictions about such quantities as population growth rates and the evolution of neutral diversity during range expansions driven by power law dispersal kernels. However, these theories rely on various simplifying assumptions which are not always met by natural populations, and their applicability to more complex but realistic population dynamics remains an open question. Another open question is how to connect theoretical results with real-world biological populations. This dissertation addresses these open questions by developing methods of simulating range expansions with more realistic population dynamics and extracting dispersal parameters from genomic data. In Chapter II, we use simulations to explore the consequences of departing from assumptions of the simplified models that led to the aforementioned predictions about population growth and the evolution of neutral diversity. We show that qualitative trends are preserved but reveal quantitative signals of the more realistic local dynamics. In Chapter III, we use simulations to investigate what determines the fate of fitness-affecting mutations that appear during range expansions driven by long-range dispersal, a situation for which there is no existing theory. We find that mutation outcomes are independent of the fitness effect they confer across a wide range of effect sizes. In Chapter IV, we show that convolutional neural networks can learn dispersal parameters from genomic samples taken from individuals in populations with long-range dispersal, bringing the growing body of theoretical work in this field closer to samples that could be taken from actual biological populations. This dissertation contains previously published and unpublished coauthored material. | en_US |
| dc.identifier.uri | https://hdl.handle.net/1794/29167 | |
| dc.language.iso | en_US | |
| dc.publisher | University of Oregon | |
| dc.rights | All Rights Reserved. | |
| dc.title | Studies of evolution in populations with long-range dispersal | |
| dc.type | Electronic Thesis or Dissertation | |
| thesis.degree.discipline | Department of Physics | |
| thesis.degree.grantor | University of Oregon | |
| thesis.degree.level | doctoral | |
| thesis.degree.name | Ph.D. |
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