Detecting viability selection in natural populations

Abstract

To maximize fitness, an organism faces a trade-off in balancing the limited energy and resources that it can acquire towards survival and reproduction. I took an interdisciplinary approach to explore various types of trade-offs using two well-studied and complimentary systems of wild populations. First, I discuss trade-offs in habitat use that differ between Soay sheep mothers and their offspring within the St Kilda archipelago. In an unpredictable environment, habitat quality within an individual’s home range is assumed to be paramount as overwinter mortality is high. Using a longitudinal dataset, I found that mothers only incur a small fitness benefit from grazing on nutrient-rich grass. Furthermore, on high quality habitats, offspring have higher parasite loads and subsequently, lower overwinter survival probabilities. Next, I explore trade-offs between the sexes as sexual antagonism is widespread throughout the genome, preventing both sexes from reaching their sex-specific fitness optima. Previous research discovered a “twin peaks” pattern; loci with intermediate sex-biased gene expression experience the highest amount of sexual conflict. Using genomics and transcriptomics in a species with a high effective population size, I found no consistent signal of sexual antagonism in a wild population of D. melanogaster, nor could I recreate the “twin peaks” pattern. Finally, as little is known about the demographic history of D. melanogaster, I investigated the origin of a local Scottish population of D. melanogaster collected from a kitchen, using the extensive dest.bio database, which contains samples from all continents. I found that the kitchen flies have consistently high gene flow with other UK samples and that there are also patterns of introgression with other Western European countries. Ultimately, this thesis uses a variety of techniques (behavioural data, genomics and transcriptomics) to tackle a series of trade-offs in different systems and highlights the complicated nature of studying evolutionary biology.