Relevant Thesis-Based Degree Programs
Graduate Student Supervision
Doctoral Student Supervision
Dissertations completed in 2010 or later are listed below. Please note that there is a 6-12 month delay to add the latest dissertations.
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.
Urban Norway rats (Rattus norvegicus) carry a number of pathogens transmissible to people, and the prevalence of these pathogens can vary across fine spatial scales. While pathogen prevalence is an important determinant of human health risk, the transmission of these pathogens to people is closely linked to how rats and humans interact in cities. In this thesis, I investigated how interactions between urban rats, their environment, and people could influence human health risks. To do this, I explored whether rat movement could explain heterogeneous patterns of pathogen prevalence. First, in Chapter 2, I synthesized the published literature and found that rat movement is largely restricted by resource availability and landscape barriers such as roadways. Then, in Chapters 3 – 5, I combined ecological and genomics-based approaches to describe rat movement in Vancouver’s Downtown Eastside, an area where pathogen clustering has been previously documented. In Chapter 3, I demonstrated that movement estimates derived from capture-mark-recapture methods are prone to bias due to smaller individuals more frequently re-entering traps than larger individuals. Given issues of unequal trappability, in Chapter 4, I evaluated the utility of using Global Positioning System tags to track urban rats and found that these tools are currently ineffective due to tag loss and signal obstruction. In Chapter 5, I used rat genetics to identify related individuals and the distances between them. I demonstrated that 99% of highly related rat pairs (i.e., parent-offspring and full-sibling pairs) were trapped in the same city block, revealing infrequent dispersal among blocks, which aligned with patterns of pathogen clustering in this population. Finally, in Chapter 6, I interviewed residents of this neighbourhood about their experiences living with rats and illustrated that frequent and close contact with rats negatively impacted the mental health of residents. Overall, my research suggests that minimal movement of rats may lead to a clustering of rat-associated pathogens. Further, my work reveals that even in the absence of disease, interactions with rats may negatively impact the mental health of those living with them. Together, this information can be used to more effectively manage rat-associated health risks in cities.
Evolution is driven by four major processes that create, maintain, or eliminate genetic diversity within and among populations: mutation, gene flow, genetic drift, and natural selection. My thesis examines the role of demographic history and its interactions with each of these processes in impacting the evolution of populations. Demographic history can cause various states of non-equilibria in populations creating the potential to mis-inform important evolutionary inferences. Such inferences may be key for making conservation decisions in applied biology. Chapter 2 investigates methods for estimating effective population sizes under the assumption-violating scenario of migration among populations. Effective population size is proportional to the amount of genetic drift a population experiences, yet gene flow can affect measures of drift and thus estimates of population size. Using simulated data to understand the impact of migration on estimation accuracy, I find that two existing estimation methods function best. I next present two studies on species range expansions and the roles of migration, mutation, selection, and drift on expansion dynamics. Range expansion is a common demographic history in many species and can lead to non-equilibrium genetic scenarios. The first of these studies shows the interaction of deleterious mutation accumulation and local adaptation to environmental gradients during range expansions (Chapter 3). The interplay of expansion load, mutation load, and migration load lead to different levels of local adaptation in expanding populations. Chapter 4 examines the ability of species to expand over patches of environmental optima under different genetic architecture regimes. Expansion is enhanced by certain genetic architectures, and each of these interacts with the size of patches on the landscape as well as how strongly selection varies across patches. My final study assesses the reproducibility of analyses using the common stochastic algorithm structure (Chapter 5). This research finds 30% failure of reproducibility for results from structure using published datasets and elucidates the reasons for failure of reproducibility. In sum, my thesis contributes to our understanding of how gene flow, population size, heterogeneous selection, and mutation interact to impact the genetics of populations and thus the fate of evolving biodiversity.
No abstract available.
No abstract available.
Master's Student Supervision
Theses completed in 2010 or later are listed below. Please note that there is a 6-12 month delay to add the latest theses.
Environments are not stable, and the way they change may influence the evolutionary dynamics. Consequently, taxa may adapt to how change happens and not just to the effect of change. Here we suggest that trait covariance can change depending on how the fitness landscape fluctuates over time. So far, there is evidence for three adaptive causes for evolution in trait covariance: phenotypic plasticity, correlational selection, and antagonistic pleiotropy. This thesis shows how the synchrony of the change in selective pressures on different traits, even without a causal selective connection, can lead to covariance. We use an individual-based birth-death model to run experiments and compare the effects of correlated change in selective pressures over different traits with uncorrelated change. Our results agree with our hypothesis and show how correlated environmental change can lead to positive covariance.