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Drivers of adaptation and rapid radiation in wild tobaccos

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Project Description

Nicotiana section Suaveolentes represents the majority of taxa within the genus of tobaccos, most of which are found across Australia, and with most diversity in the central arid regions of Australia. Over the past decade collaborative research has helped to collect and verify populations from across the range of most taxa within the group. This has enabled us to produce a new and robust phylogenetic framework for section Suaveolentes and to gain a better understanding of the recent and rapid radiation of species within this group. What has become apparent is that within major clades, there are ongoing radiations that are extremely recent (<100,000 years old), and which represent taxa with more subtle morphological differences and ecologies. Using a high-throughput DNA dataset (RADseq) you will focus on reconstructing the phylogenetic history of several species complexes using SNP data, and help to delimit the species. Phylogenomic and population genomic analyses will be used to reciprocally illuminate morphological comparisons. This may also provide evidence for the recognition of new taxa that have been hiding within an understudied group of plants, as well as the definition of more widespread but poorly understood taxa. Furthermore, this project will compare different clades and groups of sister species, looking at key adaptive traits. This will investigate both key morphology and ecology, including ecological niche models and the role of environment in driving speciation.

Research themes
Project Specific Training

The student will be trained in technical methods as well as general scientific writing, communication, presentations and proposal writing by the supervisory team as a whole. Field botany, plant collection and molecular lab analyses will be conducted by the lead supervisor; bioinformatic and computational analyses will be trained by both the lead and secondary supervisor, with support from both colleges in the University of London (including HPC support). 

Potential Career Trajectory

This project will equip a student for a variety of careers both within and beyond academia. Computational (i.e., bioinformatic) skills will enable students to progress to roles within data science and analytics, in both industry and other sectors. Training in collecting and analysing plants will equip students for curatorial and collections-based roles in museums and herbaria (e.g., RBB, Kew, RBGE, NHM). Laboratory skills can be widely applied to labs within academia, industry and other sectors. 

Project supervisor/s
Steven Dodsworth
Natural Sciences
Birkbeck
steven.dodsworth@bbk.ac.uk
Andrew Leitch
Biological and Behavioural Sciences
QMUL
a.r.leitch@qmul.ac.uk
Supervision balance
70:30