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NERC / UF / OTHER

 

Supervisor

Nik Cunniffe (Plant Sciences) & Beverley Glover (Plant Sciences)

 

Brief Summary

This modelling project will focus on the ecological and evolutionary implications of an unavoidable trade-off faced by plants: attractiveness to pollinators promotes reproduction, but also potentially brings plants into contact with disease.

 

Importance of Research

Some plant diseases are transmitted by pollinators. However, pollinator service is also often required for plants to reproduce. Attractiveness to pollinators promotes reproduction, but also potentially brings the plant into contact with disease. The implications of this epidemiological-ecological dynamic are not well understood. The range of many plant pathogens is increasing, caused by climate change and increased movement via trade and travel. There is also a well-reported global decline in pollinator density. Understanding the dynamics and evolutionary implications of sexually transmitted diseases in plants has therefore never been more important.

 

Project Summary

This project aims to understand the ecological and evolutionary pressures exerted on plants by pollinator-transmitted diseases. Being attractive to pollinators promotes plant reproduction, particularly for obligate outcrossing species. However, it also increases the risk of contact with sexually transmitted diseases. How flower attractiveness might respond to these contrasting pressures when there is pollinator-transmitted disease is not understood. Doing so requires techniques and insights from plant epidemiology, pollinator behavioural ecology, plant population dynamics and population genetics. In this project we will link these diverse areas via a mathematical modelling approach.

 

What will the successful applicant do?

The student will develop a mathematical model of the interaction between plant and pollinator populations over a single season, coupling transmission of disease to pollinator service. This model will be scaled-up to run over many seasons, and to represent population genetics, including plant genes that make flowers either more or less attractive to pollinators in the model. Mathematical analysis and numerical simulation will be performed. Models will be rendered stochastic, to allow for natural variability, and by scaling-up to a metapopulation, will allow for spatial spread of disease, pollinators, and plant genotypes. Where appropriate empirical data from existing studies will be used to parameterise and test the models. The framework of adaptive dynamics offers the possibility of understanding whether the plant population-or indeed the pollinator or pathogen populations-will branch into different species, allowing long-term population trajectories over evolutionary time to be predicted.

 

Training Provided

The student would learn:

i) mapping a complex biological system to a parsimonious mathematical model

ii) modern methods for simulating epidemiological models

iii) experience of applying mathematics to a biological system

iv) evolutionary modelling

The project would be well suited to a student with a background in mathematics, engineering, physics or theoretical ecology, ideally with prior knowledge of computer programming, motivated to transition to work on biological problems. However, students with a background in wet-lab biology have enjoyed and been very successful in my laboratory in the past. Any such candidates with a strong interest in making a transition to mathematical modelling are very much encouraged to get in touch to discuss.

 

References