Structured demography, stochasticity and selection in free-living populations.

Lead Research Organisation: University of Sheffield
Department Name: Animal and Plant Sciences

Abstract

Evolutionary biologists have always employed a large diversity of approaches for studying natural selection and the complex suite of species' adaptations that arise from this process. Theoretical biologists have used both simple and complex models to map out the fundamental mechanisms driving evolutionary processes and understand specific adaptations, while empirical biologists often approach this problem by carrying out careful manipulations in the laboratory or controlled field conditions. Ultimately of course, we must aim to explain the patterns and processes observed in natural populations to completely valid our theories. Evolutionary quantitative genetics (EQG) is one such approach biologists have used to meet this objective. By monitoring natural populations over long periods of time, we can build rich datasets describing the performance of individual organisms in terms of their rates of survival and reproduction (fitness), and then link these to the traits we wish to study. By employing sophisticated statistical models, it is possible to then use this data to understand how natural selection interacts with the genetics governing these traits' inheritance to predict how they should change. Although EQG has taught us much about evolutionary processes, this approach is not without problems. The assumptions of the simple model that underlies it are seldom completely met in a natural setting. For example, natural populations are often demographically structured (individuals differ as a result of processes such as ageing or growth and different generations overlap), and the environment they experience fluctuates a great deal from one year to the next. A core objective of my proposed research is to develop a methodology that can account for these complexities and improve out ability to predict and understand the traits we observe. Throughout my career I have used detailed mathematical models of natural systems to better understand how natural selection works. I have often relied on a set of mathematical tools called evolutionary game theory (often now referred to as adaptive dynamics). This approach can cope with many of issues raised above, but in doing so necessarily makes simplifying assumptions about the role of genetics. Making use of two of the world's very best long-term mammal studies (the feral Soay sheep of St Kilda, Scotland and the Yellow-bellied marmots of Gothic, Colorado), I aim to combine the best aspects of both EQG and adaptive dynamics to build a better predictive framework for studying evolution. In developing this research programme, I aim to focus much of my research on an important biological question that has interested me throughout my research career, 'How does living in an unpredictable fluctuating environment shape natural selection, and ultimately, species' traits?' We must address this question if we hope to predict how species may (or may not) respond to ongoing anthropogenic environmental change. For example, extreme weather events are predicted to become more common in the face of climate change. If my research shows that such variation is indeed important, then we will need to move beyond thinking just about changes in the average, to consider the role variation per se.
 
Description Developed new modelling techniques to investigate the short term evolutionary response of "structured populations" to selection. These tools allow us to accommodate knowledge of dynamics, among-individual variation into predictive models of their evolution.
Exploitation Route Methodological advances may be useful to other researchers in the field of population ecology and life history theory.
Sectors Environment,Other

 
Description Multiple herbicide resistance in grass weeds: from genes to AgroEcosystems
Amount £2,059,303 (GBP)
Funding ID BB/L001489/1 
Organisation Biotechnology and Biological Sciences Research Council (BBSRC) 
Sector Public
Country United Kingdom
Start 02/2014 
End 01/2018
 
Description Research Project Grant
Amount £159,000 (GBP)
Funding ID RPG-2015-049 
Organisation The Leverhulme Trust 
Sector Academic/University
Country United Kingdom
Start 03/2016 
End 04/2019