Supervisors and Institutions
Macroevolution occurs over time spans of millions of years. Two approaches can be taken in identifying and understanding macroevolutionary patterns and processes - empirical and theoretical. For example, the mode and tempo of evolutionary change can be investigated using phylogenies of living and extinct species, and through computer simulations replicating evolution in digital organisms/species. These contrasting approaches are complementary, but each has limitations: empirical data contain biases (fossilisation, for example), whilst by necessity simulations are highly simplified. Used together, these limitations can be overcome. This project will use both empirical data and simulations to identify widespread patterns, and explore the dynamics of origination, diversification and extinction of clades in light of rates of evolution and possible drivers. Compilation of combined morphological and molecular phylogenetic datasets from across the tree of life will enable a meta-analysis to investigate the relationship between (rates of) phenotypic and genotypic evolution. By combining data from distantly-related groups we can test for general patterns in relative rates of change at the origin of species, clades and anatomical novelties, and shifts at major environmental transitions and extinctions. All these are topics that can then be studied further within an in silico system. REvoSim is a custom-written software package developed by members of the supervisory team. It is capable of simulating evolution for large populations (>1 million) of individuals over long time periods (>5 million iterations). While simplified, it incorporates key aspects of biological evolution, including breeding, spatial species-structure, and a changing environment. It will allow widespread patterns to be better understood, and the underlying mechanisms to be experimentally investigated.
This interdisciplinary project will study both fossil and extant organisms across the tree of life, with a complementary simulation approach. It has the potential to provide new insights into both the patterns and the mechanisms of macroevolution. It represents a unique opportunity for applicants interested in both computational techniques and evolution in deep time, and will allow the student to achieve training in a wide range of analytical techniques, including phylogenetics, software engineering, and data analysis in R; these skills will lend themselves to multiple future career paths.