Supervisors and Institutions
Phylogenetic data are fundamental for understanding evolution. Building and analysing trees from genotypic and phenotypic data is necessary to reconstruct evolutionary relationships, diversifications, rates, and dynamics. Molecular data have had a renaissance with respect to development of ‘big data’ approaches, and a plethora of analytical tools. Morphological data are also essential, especially because of their role in analysis of fossils thus providing deep time-perspectives. They are, however, relatively neglected. In order for morphology to enter the 21st century and address big evolutionary questions, it also needs a modern big data approach. This PhD will directly test morphological data, morphological methods, and morphological inferences by asking 1) Are trees inferred from morphological data accurate and reproducible? 2) How important is the inference method to the inferred tree topology? 3) Are hypotheses about evolutionary dynamics (e.g. “early burst”) supported by meta-analysis of multiple datasets rather than individual case studies (i.e. what general evolutionary patterns can be inferred from big morphological data?). This is necessary not only because of the historic difficulties in reproducing published phylogenetic results from the given data, but also the disputes over inference methods from morphology (in particularly parsimony versus Bayesian inference), as well as to address major evolutionary questions. With ambiguity existing over the reproducibility of morphological trees and doubt over the accuracy of the historically dominant inference methods, we face an important (but potentially embarrassing) question: how much do we actually know about morphological evolution?
This interdisciplinary project will study both fossil and extant organisms across the tree of life. It will provide new insights into both the patterns and the mechanisms of macroevolution. It represents a unique opportunity for applicants interested in both evolution in deep time and data handling techniques, and will allow the student to achieve training in a wide range of analytical techniques, including phylogenetics, reproducibility, software engineering, and data analysis in R; these skills will lend themselves to multiple future career paths.