Unpicking the demographic causes and consequences of individual differences.

Supervisors: Dr Dylan Childs, Professor Jon Slate and Dr Dan Nussey (Edinburgh)

Demographically informed models are widely employed to investigate how vital rate variation over an organism’s life cycle influences population-level phenomena. For example, numerous mathematical approaches have been developed to explore how changes in abundance result from the interplay between environmental factors and age- or stage-structured processes. More recently, methods have been developed to examine how among-individual variation in traits such as body mass or individual “quality” modulate population-level processes. However, despite significant advances in bio-demographic theory, these powerful methods are seldom employed outside the boundaries of traditional population biology.

This multidisciplinary PhD project will use demographic methods to investigate the genetic and physiological basis of demographic differences, and in turn, explore how demographic processes influence the dynamics of genetic and physiological variation. The project will use data from the long-term study of Soay sheep on St Kilda, Scotland. Arguably the best long-term ecological dataset in the world, the Soay project data set contains abundant demographic, genomic, and physiological data arising from 30 years of intensive data collection.

This in an open-ended project, with considerable scope for the student to shape their own research agenda. Two potential research strands of interest to the supervisory team are:

Demographically informed genomic prediction: Genomic prediction is currently being used to examine genetic responses to natural selection in the Soay sheep. A significant challenge for this approach in natural populations is the stochastic nature of individual life history trajectories and fitness: the realised performance of an individual is largely a consequence of chance demographic events and the variable environmental conditions they experience. However, a demographic model can be used to derive the expected fitness of individuals under standardised environmental conditions. We aim to use this approach to disaggregate demographic and environmental stochastic effects, so that we may improve the performance of genomic prediction.

Physiological drivers of demographic variation: Bio-demographic methods are increasingly being used to investigate how variation in fitness-related traits impacts vital rates and population dynamics. However, the vast majority analyses only investigate easily measured morphological and reproductive timing traits. The interplay between demography and physiology is largely unstudied. The Soay project has accumulated an extensive longitudinal dataset of ageing (telomeres) and immune function (antibodies) biomarkers. We will use a subset of these data to explore how among individual variation in physiological status drives variation in demographic rates and population dynamics.

The PhD will suit a motivated student excited by the opportunity to work across traditional discipline boundaries, using state-of-the-art population modelling and statistical techniques. Candidates with interests in Bayesian statistics are particularly encouraged to apply. The project is funded by the Leverhulme Centre for Advanced Biological Modelling (CABM) doctoral training programme. The CABM DTP supports students with a mathematics, statistics, physical sciences, or engineering background who wish to make the transition to a research career in mathematical biology. Potential candidates are encouraged to email Dr Dylan Childs ([Email Address Removed]) with informal enquiries.