Susan Frances Bailey

I use a combination of microbial experimental evolution, bioinformatics, and mathematical/ statistical modeling approaches to investigate processes that drive evolutionary adaptation and diversification. Currently, my research has two main themes: 
1) The drivers of repeated evolution: Repeated evolutionary changes arising in independently evolving populations, (known as parallel or convergent evolution), are often taken to be evidence of strong selection. However, theoretical models suggest that heterogeneity in mutation rate across the genome can play an equally important role. The relative contribution of mutation and selection for driving patterns of parallel evolution in real populations is still unclear. We explore potential for parallelism by tracking replicated evolution experiments in different types of lab environments and developing novel statistical approaches to quantify the importance of mutation versus selection.
2) Evolution in heterogeneous environments: The world is heterogeneous and these complexities have important implications for the dynamics of evolution. We examine the effects of spatial structure and other environmental complexities on evolution, using both theoretical models and evolution experiments in the lab. The aim is to better identify and understand the processes that play important roles in populations evolving in the complex natural world and, ultimately, to predict evolutionary dynamics.