Dr R Sansom
Dr R Garwood
Dr C Knight
No more applications being accepted
Competition Funded PhD Project (European/UK Students Only)
About the Project
Advances in the acquisition and analysis of genetic sequence data have led to an increasing emphasis and reliance on molecular data to build phylogenies and test models of evolution, yet phenotypic evidence (morphology) remains vital. Morphology is the only way to incorporate fossil data, and provides a direct link between organisms and their environment (Lee 2015). Morphological characters, however, are fundamentally different from molecular characters and lack any defined common model of evolution. This project aims to test models of character evolution using empirical and simulated morphological data and the use of these models in phylogenetics. To achieve this, empirical datasets of modern groups comprising molecular and morphological phylogenetic data will be compiled (Sansom et al 2017, Sansom and Wills 2017). Continuous and discrete morphological characters will be applied to molecular trees to test for different models of evolution (i.e. drift, directional selection etc), and the prevalence of character correlation will be assessed. Different approaches to character analysis will be applied and compared in terms of phylogenetic performance and inferred modes of evolution i.e. elimination of correlated characters, continuous characters versus discretized characters (Randle and Sansom 2016). Results from empirical analyses will be compared with simulation data generated using our bespoke software (TReEvoSim, Keating et al in review; REvoSim, Garwood et al in review) under differing parameters ranging from complete drift to strong natural selection. These combined approaches will enable characterization of modes of morphological evolution, enable probabilistic model-based phylogenetic analyses of morphological data to keep pace with those of molecular data, and ultimately bring morphology from the 19th century into the 21st.
Applicants for this project should have background in either evolutionary biology, or palaeobiology with experience with phylogenetics. Analytical and quantitative data handling skills would be an advantage. Training will be provided to enable the successful applicant to interact with a cross-disciplinary team of earth and life computational scientists.
Applications are invited from UK/EU nationals only. Applicants must have obtained, or be about to obtain, at least an upper second class honours degree (or equivalent) in a relevant subject.
This project is to be funded under the BBSRC Doctoral Training Programme. If you are interested in this project, please make direct contact with the Principal Supervisor to arrange to discuss the project further as soon as possible. You MUST also submit an online application form - full details on how to apply can be found on the BBSRC DTP website www.manchester.ac.uk/bbsrcdtpstudentships
As an equal opportunities institution we welcome applicants from all sections of the community regardless of gender, ethnicity, disability, sexual orientation and transgender status. All appointments are made on merit.
Keating J, Sansom RS, Sutton M, Knight C, Garwood R. in review. Testing phylogenetic methods with drift- and selection- based evolutionary simulations. Systematic Biology.
Lee, MSY 2015. Morphological phylogenetics in the genomic age. Current Biology https://doi.org/10.1016/j.cub.2015.07.009
Randle E, Sansom RS. 2017. Exploring phylogenetic relationships of Pteraspidiformes heterostracans (stem-gnathostomes) using continuous and discrete characters. Journal of Systematic Palaeontology. https://doi.org/10.1080/14772019.2016.1208293
Sansom RS, Wills MA. 2017. Differences between hard and soft phylogenetic data. Proceedings of the Royal Society B. http://dx.doi.org/10.1098/rspb.2017.2150
Sansom RS, Wills MA, Williams T. 2017. Dental data perform relatively poorly in reconstructing mammal phylogenies: morphological partitions evaluated with molecular benchmarks. Systematic Biology. https://doi.org/10.1093/sysbio/syw116
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