Climate warming is threatening biodiversity by increasing temperatures beyond the optima of many ectotherms. Owing to the inherent non-linear relationship between temperature and the rate of cellular processes, such shifts towards hot temperature are predicted to impose stronger selection compared with corresponding shifts towards cold temperature. This suggests that when adaptation to warming occurs, it should be relatively rapid and predictable. Here we tested this hypothesis from the level of single-nucleotide polymorphisms to life-history traits in the beetle Callosobruchus maculatus. We conducted an evolve-and-resequence experiment on three genetic backgrounds of the beetle reared at hot or cold temperature. Indeed, we find that phenotypic evolution was faster and more repeatable at hot temperature. However, at the genomic level, adaptation to heat was less repeatable when compared across genetic backgrounds. As a result, genomic predictions of phenotypic adaptation in populations exposed to hot temperature were accurate within, but not between, backgrounds. These results seem best explained by genetic redundancy and an increased importance of epistasis during adaptation to heat, and imply that the same mechanisms that exert strong selection and increase repeatability of phenotypic evolution at hot temperature reduce repeatability at the genomic level. Thus, predictions of adaptation in key phenotypes from genomic data may become increasingly difficult as climates warm.

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.

Adaptation from standing genetic variation is an important process underlying evolution in natural populations, but we rarely get the opportunity to observe the dynamics of fitness and genomic changes in real time. Here, we used experimental evolution and Pool-Seq to track the phenotypic and genomic changes of genetically diverse asexual populations of the yeast Saccharomyces cerevisiae in four environments with different fitness costs. We found that populations rapidly and in parallel increased in fitness in stressful environments. In contrast, allele frequencies showed a range of trajectories, with some populations fixing all their ancestral variation in <30 generations and others maintaining diversity across hundreds of generations. We detected parallelism at the genomic level (involving genes, pathways, and aneuploidies) within and between environments, with idiosyncratic changes recurring in the environments with higher stress. In particular, we observed a tendency of becoming haploid-like in one environment, whereas the populations of another environment showed low overall parallelism driven by standing genetic variation despite high selective pressure. This work highlights the interplay between standing genetic variation and the influx of de novo mutations in populations adapting to a range of selective pressures with different underlying trait architectures, advancing our understanding of the constraints and drivers of adaptation. 

Genes that affect adaptive traits have been identified, but our knowledge of the genetic basis of adaptation in a more general sense (across multiple traits) remains limited. We combined population-genomic analyses of evolve-and-resequence experiments, genome-wide association mapping of performance traits, and analyses of gene expression to fill this knowledge gap and shed light on the genomics of adaptation to a marginal host (lentil) by the seed beetle Callosobruchus maculatus. Using population-genomic approaches, we detected modest parallelism in allele frequency change across replicate lines during adaptation to lentil. Mapping populations derived from each lentil-adapted line revealed a polygenic basis for two host-specific performance traits (weight and development time), which had low to modest heritabilities. We found less evidence of parallelism in genotype-phenotype associations across these lines than in allele frequency changes during the experiments. Differential gene expression caused by differences in recent evolutionary history exceeded that caused by immediate rearing host. Together, the three genomic datasets suggest that genes affecting traits other than weight and development time are likely to be the main causes of parallel evolution and that detoxification genes (especially cytochrome P450s and beta-glucosidase) could be especially important for colonization of lentil by C. maculatus.