Testing the accuracy of evolutionary inferences from morphological phylogeny

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.

Funding 

At Manchester we offer a range of scholarships, studentships and awards at university, faculty and department level, to support both UK and overseas postgraduate researchers. 

For more information, visit our funding page or search our funding database for specific scholarships, studentships and awards you may be eligible for. 

Before you apply 

We strongly recommend that you contact the lead supervisor for this project before you apply. 

How to apply 

To be considered for this project you’ll need to complete a formal application through our online application portal. 

When applying, you’ll need to specify the full name of this project, the name of your supervisor, how you’re planning on funding your research, details of your previous study, and names and contact details of two referees. 

Your application will not be processed without all of the required documents submitted at the time of application, and we cannot accept responsibility for late or missed deadlines. Incomplete applications will not be considered.  

If you have any questions about making an application, please contact our admissions team by emailing [Email Address Removed]. 

Equality, diversity and inclusion 

Equality, diversity and inclusion is fundamental to the success of The University of Manchester and is at the heart of all of our activities. We know that diversity strengthens our research community, leading to enhanced research creativity, productivity and quality, and societal and economic impact.

We actively encourage applicants from diverse career paths and backgrounds and from all sections of the community, regardless of age, disability, ethnicity, gender, gender expression, sexual orientation and transgender status. 

We also support applications from those returning from a career break or other roles. We consider offering flexible study arrangements (including part-time: 50%, 60% or 80%, depending on the project/funder).