IST Austria Thesis

This thesis concerns itself with the interactions of evolutionary and ecological forces and the consequences on genetic diversity and the ultimate survival of populations. It is important to understand what signals processes leave on the genome and what we can infer from such data, which is usually abundant but noisy. Furthermore, understanding how and when populations adapt or go extinct is important for practical purposes, such as the genetic management of populations, as well as for theoretical questions, since local adaptation can be the first step toward speciation. In Chapter 2, we introduce the method of maximum entropy to approximate the demographic changes of a population in a simple setting, namely the logistic growth model with immigration. We show that this method is not only a powerful tool in physics but can be gainfully applied in an ecological framework. We investigate how well it approximates the real behavior of the system, and find that is does so, even in unexpected situations. Finally, we illustrate how it can model changing environments. In Chapter 3, we analyze the co-evolution of allele frequencies and population sizes in an infinite island model. We give conditions under which polygenic adaptation to a rare habitat is possible. The model we use is based on the diffusion approximation, considers eco-evolutionary feedback mechanisms (hard selection), and treats both drift and environmental fluctuations explicitly. We also look at limiting scenarios, for which we derive analytical expressions. In Chapter 4, we present a coalescent based simulation tool to obtain patterns of diversity in a spatially explicit subdivided population, in which the demographic history of each subpopulation can be specified. We compare the results to existing predictions, and explore the relative importance of time and space under a variety of spatial arrangements and demographic histories, such as expansion and extinction. In the last chapter, we give a brief outlook to further research

Polygenic local adaptation in metapopulations: A stochastic eco‐evolutionary model

This paper analyses the conditions for local adaptation in a metapopulation with infinitely many islands under a model of hard selection, where population size depends on local fitness. Each island belongs to one of two distinct ecological niches or habitats. Fitness is influenced by an additive trait which is under habitat‐dependent directional selection. Our analysis is based on the diffusion approximation and accounts for both genetic drift and demographic stochasticity. By neglecting linkage disequilibria, it yields the joint distribution of allele frequencies and population size on each island. We find that under hard selection, the conditions for local adaptation in a rare habitat are more restrictive for more polygenic traits: even moderate migration load per locus at very many loci is sufficient for population sizes to decline. This further reduces the efficacy of selection at individual loci due to increased drift and because smaller populations are more prone to swamping due to migration, causing a positive feedback between increasing maladaptation and declining population sizes. Our analysis also highlights the importance of demographic stochasticity, which exacerbates the decline in numbers of maladapted populations, leading to population collapse in the rare habitat at significantly lower migration than predicted by deterministic arguments

Using gridCoal to assess whether standard population genetic theory holds in the presence of spatio-temporal heterogeneity in population size

Spatially explicit population genetic models have long been developed, yet have rarely been used to test hypotheses about the spatial distribution of genetic diversity or the genetic divergence between populations. Here, we use spatially explicit coalescence simulations to explore the properties of the island and the two-dimensional stepping stone models under a wide range of scenarios with spatio-temporal variation in deme size. We avoid the simulation of genetic data, using the fact that under the studied models, summary statistics of genetic diversity and divergence can be approximated from coalescence times. We perform the simulations using gridCoal, a flexible spatial wrapper for the software msprime (Kelleher et al., 2016, Theoretical Population Biology, 95, 13) developed herein. In gridCoal, deme sizes can change arbitrarily across space and time, as well as migration rates between individual demes. We identify different factors that can cause a deviation from theoretical expectations, such as the simulation time in comparison to the effective deme size and the spatio-temporal autocorrelation across the grid. Our results highlight that F-ST, a measure of the strength of population structure, principally depends on recent demography, which makes it robust to temporal variation in deme size. In contrast, the amount of genetic diversity is dependent on the distant past when N-e is large, therefore longer run times are needed to estimate N-e than F-ST. Finally, we illustrate the use of gridCoal on a real-world example, the range expansion of silver fir (Abies alba Mill.) since the last glacial maximum, using different degrees of spatio-temporal variation in deme size.ISSN:1755-098XISSN:1471-8278ISSN:1755-0998ISSN:1471-828

Supplementary code for: Polygenic local adaptation in metapopulations: A stochastic eco-evolutionary model

This paper analyzes the conditions for local adaptation in a metapopulation with infinitely many islands under a model of hard selection, where population size depends on local fitness. Each island belongs to one of two distinct ecological niches or habitats. Fitness is influenced by an additive trait which is under habitat-dependent directional selection. Our analysis is based on the diffusion approximation and accounts for both genetic drift and demographic stochasticity. By neglecting linkage disequilibria, it yields the joint distribution of allele frequencies and population size on each island. We find that under hard selection, the conditions for local adaptation in a rare habitat are more restrictive for more polygenic traits: even moderate migration load per locus at very many loci is sufficient for population sizes to decline. This further reduces the efficacy of selection at individual loci due to increased drift and because smaller populations are more prone to swamping due to migration, causing a positive feedback between increasing maladaptation and declining population sizes. Our analysis also highlights the importance of demographic stochasticity, which exacerbates the decline in numbers of maladapted populations, leading to population collapse in the rare habitat at significantly lower migration than predicted by deterministic arguments

Dynamic maximum entropy provides accurate approximation of structured population dynamics

Realistic models of biological processes typically involve interacting components on multiple scales, driven by changing environment and inherent stochasticity. Such models are often analytically and numerically intractable. We revisit a dynamic maximum entropy method that combines a static maximum entropy with a quasi-stationary approximation. This allows us to reduce stochastic non-equilibrium dynamics expressed by the Fokker-Planck equation to a simpler low-dimensional deterministic dynamics, without the need to track microscopic details. Although the method has been previously applied to a few (rather complicated) applications in population genetics, our main goal here is to explain and to better understand how the method works. We demonstrate the usefulness of the method for two widely studied stochastic problems, highlighting its accuracy in capturing important macroscopic quantities even in rapidly changing non-stationary conditions. For the Ornstein-Uhlenbeck process, the method recovers the exact dynamics whilst for a stochastic island model with migration from other habitats, the approximation retains high macroscopic accuracy under a wide range of scenarios in a dynamic environment