UBC Theses and Dissertations
Evolution in heterogeneous environments Matthey-Doret, Rémi
Environmental heterogeneity is a fundamental feature of evolutionary biology. In this thesis, I investigate a few aspects relating to environmental heterogeneity. In chapter 2, I explore how background selection can affect detection of local adaptation. Background selection is a process whereby recurrent deleterious mutations cause a decrease in the effective population size and genetic diversity at linked loci. Several authors have suggested that variation in the intensity of background selection could cause variation in FST across the genome, which could confound signals of local adaptation in genome scans. We performed realistic simulations of DNA sequences to show that variation in the intensity of background selection does not cause much variation in FST and does not affect the false positive rate in FST outlier studies in populations connected by gene flow. In chapter 3, I investigate how developmental instability might emerge as a side-effect of two distinct mechanisms for adaptive plasticity: sensing an environmental signal and sensing a performance signal (a.k.a. developmental selection). Using a numerical model of a network of gene interactions, we show that, because a performance signal allows a regulatory feedback loop buffering against developmental noise, plasticity comes at a cost of developmental instability when the plastic response is mediated via an environmental signal, but not when it is mediated via a performance signal. We also show that a performance signal mechanism can evolve in a constant environment to increase developmental robustness, leading to genotypes pre-adapted for plasticity to novel environments. In chapter 4, I present SimBit, a general purpose and high performance forward-in-time population genetics simulator. Because different simulation scenarios require different simulation methods in order to achieve high performance, SimBit is able to use different representations of the individuals’ genotype allowing it to sustain a high performance in a wide diversity of scenarios. SimBit’s performance is benchmarked in comparison to SLiM, Nemo and SFS_CODE and I report that SimBit is most often the highest performing program.
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