Models to infer evolution in declining populations and across space

点击翻译按钮获取中文摘要

Evolution, demography, and genetics interact to form a complex system that determines the fate of populations. Better understanding this complex system (population biology) holds intrinsic value -- e.g., unraveling the evolutionary history of our species -- and guides practical attempts to conserve biodiversity, improve crops, and control pests and pathogens. The long-term vision of my research program is to develop deeper intuition and novel insights about population biology using mathematical models, which are essential for both complex reasoning and inference from vast amounts of data (e.g., thousands of genomes). Within this broader framework of theoretical population biology, this proposal seeks to address two fundamental questions, using a combination of mathematical modelling, computer simulation, and genomic data. 1) How does evolution affect declining populations? We are in the midst of what is perhaps a sixth mass extinction, with many populations in demographic decline. At the same time we are also barraged by pathogens and pests that we attempt to extinguish. How are these demographic declines impacted by evolution? Theory suggests that evolution will have one of two contrasting effects on declining populations: i) increasing genetic drift leading to an extinction vortex and ii) rapid adaptive evolution rescuing populations from extinction. How can we tell which, if either, is acting on natural populations? This proposal outlines two novel methods for inferring how evolution acts in declining populations, both building on recent developments in population genetics. One uses small changes in allele frequencies across the genome to jointly estimate a changing population size and relative strength of adaptation vs drift. The other reconstructs recent demography and selection from genome-wide genealogies. Both promise large advances over current summary statistic approaches. 2) How do populations move and adapt across the landscape? Present-day genetic patterns have been shaped by selection and the geographic movements of ancestors, so how do we extract information about past selection and dispersal from contemporary genomes? The newfound ability to infer gene genealogies at every site in the genome (tree-sequences) for a large number of samples opens up many new ways to approach this problem. This proposal describes four new methods that capitalize on the massive amount of information contained in tree-sequences to infer past selection and dispersal. The first derives the likely locations of genetic ancestors and uses these to deduce large scale geographic histories, such as admixture between glacial refugia. The second extends this method to detect ongoing expansions and contractions of species' ranges. The third jointly estimates selection and dispersal to describe the spatial spread of beneficial alleles. And the fourth aims to reconstruct the evolution of phenotypic clines, such as the latitudinal gradient in human height.