Predicting future evolutionary outcomes and explaining past and current patterns of biodiversity are fundamental goals in evolutionary biology. Trajectories of evolving populations are determined by evolutionary mechanisms (natural selection, mutation, genetic drift, and gene flow) and the environment in which the populations are found. Our ability to predict and explain evolution are thus dependent on understanding how, and when, these mechanisms and the environment affect evolutionary outcomes. However, many nuances exist in the interactions of these mechanisms with each other. Furthermore, environments can be incredibly complex– too complex to capture fully when designing controlled experiments to test evolutionary hypotheses. It is clear that several challenges exist in composing a comprehensive synthesis of the determinants of evolution.

In this thesis, I have contributed to our understanding of evolutionary dynamics and outcomes by exploring how the above-stated factors affect inferences and predictions of evolution. I leveraged both computational and biological systems to answer several evolutionary questions. I first used simulations to estimate the effects fluctuating population size had on deterministic trajectories of adaptive alleles (Paper I). I found that declines in population size can alter the rate at which adaptive sweeps occur. As a consequence of altered rates of sweeps, our ability to infer accurate strengths of selection is decreased, even when selection is very strong. In a second experiment (Paper II), I used the seed beetle Callosobruchus maculatus to investigate (1) whether environments which imposed stronger selection would result in higher phenotypic and genomic parallelism, and (2) whether the degree of parallelism was dependent on the evolutionary history of populations. Despite expectations that adaptation to the environment which imposed stronger selection would result in higher parallelism, the opposite results were observed. However, the degree of parallelism within treatments varied considerably among populations of different evolutionary histories. In a final experiment (Paper III), I explored how environmental complexity alters the dynamics and outcomes of evolution using populations of the yeast Saccharomyces cerevisiae evolved in a full-factorial combination of several environments. I found that trade-off evolution was prevalent in complex environments, and the dynamics of evolution were dependent on the level of environmental complexity and the inclusion of specific stressors. Finally, I used the same evolved populations of yeast to ask whether the outcomes of evolution in highly complex environments could be predicted based on outcomes in populations evolved to the individual components of the complex environment (Paper IV). Across all biological levels, there existed very little predictability from evolution to the individual environmental components.

The conclusions of this thesis align with the outcomes of numerous prior investigations into the predictability of evolution– it depends on context. However, this thesis highlights the importance of often-overlooked elements such as: (1) the capacity of demography to alter predictable trajectories of selected alleles, (2) the impact of evolutionary histories on the identification of parallelism in replicated populations, and (3) the potential omission of key ecological factors essential for adequately describing evolution in nature.