NERC E4 Dissecting the causes of reproductive mode variation in the plant Mimulus guttatus

Interested individuals must follow the "how to apply" link on the Geosciences E4 Doctoral Training Partnership web page: http://www.ed.ac.uk/e4-dtp/how-to-apply

Summary
This project will analyse genome data from UK populations of Mimulus guttatus (yellow monkeyflower) to determine how the local ecology and geography affects its reproductive mode.

Project background
Perennial plants exhibit a wide variety of reproductive modes, from exclusively sexual reproduction through seeds, to relying almost entirely on asexual propagation. Various theories exist to explain the evolutionary and ecological advantages of these reproductive modes, but testing theories can be hampered by a lack of empirical data. The facultative sexual plant Mimulus guttatus (yellow monkeyflower) was introduced into the UK in the 1800s from Western North America, and is currently widespread throughout the British Isles. Previous work has showed that introduced populations exhibit large variation in reproductive modes, ranging from highly clonal to highly sexual. Hence it is an exciting model system for investigating the evolution of different reproductive modes, and what ecological and evolutionary factors affect them.

Research questions
By investigating genetic diversity across populations and linking it to the local reproductive mode across environments, we can ask: (1) Are different modes (sex or asex) associated with specific ecogeographic regions? (2) To what extent is reproduction affected by local ecology, compared to large environmental factors such as climate? (3) How does reproductive mode (sex, asex) and mating–system (selfing, outcrossing) interact? (4) Are these reproductive modes related to how M. guttatus was introduced into the UK? (5) How does the reproductive mode influence genetic diversity?

Methodology
The project will involve the analysis of new genomic data from previously–collected samples to quantify the extent of self-fertilisation and clonal reproduction in natural populations in the UK. This data can then be compared with geographical, ecological, and climatic data to determine if these factors correlate with the local reproductive mode. Genetic data from UK samples will then be compared with those from the United States, to determine their population history and investigate if different degrees of asexual and sexual reproduction arose at specific times in the past. These data will allow us to investigate how the evolutionary history differs between populations exhibiting different reproductive modes. There will also be scope to develop theoretical models to further investigate the consequences of reproductive mode on genetic variation, or to collect more samples for analysis. The project will provide the student with cutting-edge genomics and bioinformatics skills, which are essential for modern biological research, and put them at the forefront of developing an exciting new model for evolutionary genetics study.

Year 1: Training in bioinformatics and genome sequence analysis; statistical inference; theory on the effect of reproduction on genome evolution.

Year 2: Analysis of populations from the UK; quantification of reproductive modes from genome data; determining if it correlates with local ecology and environment.

Year 3: Comparison of data from UK populations with those from the US; quantifying invasion history; timing of reproductive mode shifts if applicable.

Training
A comprehensive training programme will be provided comprising both specialist scientific training and generic transferable and professional skills. Advance training in bioinformatics, genome sequence analyses and evolutionary inference will be given. Further training in statistical analyses, population genetics, and computational programming can also be arranged if desired. There will also be scope for students to interact with local plant conservation societies, and become involved with the management and direction of the project.

Requirements
Students should have at least an upper 2:1 degree from a relevant biological discipline. Training will be provided but experience with bioinformatic work, computational analyses and/or botanical knowledge will be an advantage.