Date of Award:

8-2019

Document Type:

Dissertation

Degree Name:

Doctor of Philosophy (PhD)

Department:

Biology

Committee Chair(s)

Zachariah Gompert

Committee

Zachariah Gompert

Committee

Karen Kapheim

Committee

Susannah French

Committee

Karen Mock

Committee

Matthew Forister

Abstract

Stephen Jay Gould, a great scientist and evolutionary biologists, suggested that if we could replay the tape of life, we would not have observed similar course of events because evolution is stochastic and if affected by several events. Since then, the possibility that evolution is repeatable or predictable has been debated. Studies using large-scale evolution experiments, long-term data for individual populations, and controlled experiments in nature, have demonstrated phenotypic and genetic convergence in several taxa. These studies suggest that despite some randomness, predictable evolutionary patterns can emerge on a large temporal and spatial scale. However, a few cases also exist where evolution is unpredictable and stochastic. One way to understand evolutionary predictability better can be to have quantitative estimates of predictability at different heirarchical levels (mutations, genetic, phenotypic). This can help better understand if evolution is predictable and the extent to which it is predictable. My dissertation uses Lycaeides butterflies to identify and quantify evolutionary predictability in different contexts such as on a geographic scale, temporal scale and genomic scale. I accomplished this by sequencing and annotating the genomes of these butterflies across a vast geographic range and on a temporal scale and by comparing natural and experimental populations. My results show that different mechanisms can assist evolution of organisms to adapt to novel environmental challenges, and that the evolutionary changes can be somewhat predictable. Through this work I demonstrate three main findings: first, quantitative estimates of evolutionary predictability indicate that degree of predictability is variable and is highly context-dependent. Second, we can predict evolutionary patterns on a spatial as well as temporal scale, and can predict patterns in nature by controlled laboratory experiments. Additionally, genomic changes underlying repeatability vary across the genome. Lastly, the approach of quantifying predictability can help us better understand the mechanisms which drive evolution and how organisms will evolve in response to similar environmental pressures. These results suggest that evolution can be constrained and if we actually replay the tape of life, we could see a considerably similar outcome in biodiversity compared to what Gould predicted.

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