Location: University of Exeter, Penryn Campus, Penryn, Cornwall TR10 9FE
This project is one of a number that are in competition for funding from the NERC Great Western Four+ Doctoral Training Partnership (GW4+ DTP). The GW4+ DTP consists of the Great Western Four alliance of the University of Bath, University of Bristol, Cardiff University and the University of Exeter plus five Research Organisation partners: British Antarctic Survey, British Geological Survey, Centre for Ecology and Hydrology, the Natural History Museum and Plymouth Marine Laboratory. The partnership aims to provide a broad training in earth and environmental sciences, designed to train tomorrow’s leaders in earth and environmental science. For further details about the programme please see http://nercgw4plus.ac.uk/
Despite a large body of theory describing how genetic variation and selection shape evolutionary trajectories, theoretical predictions are often at odds with what we observe in the real world. Being able to understand the source(s) of this discrepancy would significantly advance our understanding of the evolutionary process and provide a much-needed understanding of the ability of population to persist in a world changing at unprecedented rates. Although considerable effort has gone into incorporating the complexities that are typical of wild populations into our models of evolutionary change, this crucially assumes that evolution is in essence predictable. However, there is a potentially important role for stochastic processes (i.e. chance) in shaping all steps of the evolutionary process. For example, non-relatives may be genetically more similar than relatives due to Mendelian sampling, two otherwise identical individuals may differ in reproductive success because one got lucky and the other not, and random genetic drift can be responsible for large genetic changes from one year to the next. However, as of yet we lack a comprehensive understanding of the importance of stochastic versus deterministic (but unknown) processes in shaping the evolutionary dynamics of populations.
Project Aims and Methods
To quantify the importance of stochasticity in shaping the evolutionary dynamics of wild populations, this project capitalises on over a decade worth of individual-based long-term data for a small and isolated population of snow voles (Chionomys nivalis) in the Swiss alps. Since 2006, all individuals have been caught, individually marked, weighed and measured. DNA samples allow for the assignment of offspring to their parents, resulting in a well-resolved multigenerational pedigree. Recently this population has provided a rare example of adaptive evolution-in-action, revealing an adaptive decline in body mass in response to a change in snow fall patterns.
In this project, we will generate high-density whole-genome sequence data for individuals from different time periods, and of varying degrees of relatedness. By combining these with extensive morphological, life-history and pedigree data, as well as individual based simulations, we can quantify the relationship between pedigree and genomic relatedness, an individual’s long-term genetic contribution to the gene pool, and the role of selection versus drift in shaping allele frequency changes. Together, this will provide a unique insight into the genomics of adaptation in a wild vertebrate and the evolutionary process in general.
This project capitalises on a uniquely rich and powerful dataset that allows for answering a wide range questions, and the student is encouraged to shape the project according to their interests.
The student will receive training in, among others, the statistical analysis of large datasets, individual-based simulation and the analysis of NGS data. This will be provided by the (co-) supervisors and their group members. The student is furthermore encouraged to attend relevant postgraduate courses and to present their results at national and international conferences. Furthermore, there is the possibility to gain experience with small mammal trapping and handling at the field site in Switzerland.
Bonnet, T. & Postma, E. 2017. Successful by Chance? The Power of Mixed Models and Neutral Simulations for the Detection of Individual Fixed Heterogeneity in Fitness Components. The American Naturalist 187: 60–74.
Bonnet, T., Wandeler, P., Camenisch, G. & Postma, E. 2017. Bigger is fitter? Quantitative genetic decomposition of selection reveals an adaptive evolutionary decline of body mass in a wild rodent population. PLoS Biology 15: e1002592.
Chen, N., Juric, I., Cosgrove, E.J., Bowman, R., Fitzpatrick, J.W., Schoech, S.J., et al. 2018. Allele frequency dynamics in a pedigreed natural population. bioRxiv, doi: 10.1101/388710.
Gienapp, P., Fior, S., Guillaume, F., Lasky, J.R., Sork, V.L. & Csilléry, K. 2017. Genomic Quantitative Genetics to Study Evolution in the Wild. Trends in Ecology & Evolution 32: 897–908.
Nietlisbach, P., Keller, L.F., Camenisch, G., Guillaume, F., Arcese, P., Reid, J.M., et al. 2017. Pedigree-based inbreeding coefficient explains more variation in fitness than heterozygosity at 160 microsatellites in a wild bird population. Proceedings of the Royal Society B 284: 20162763