The emergence and spread of crop pathogens resistant to chemical control threatens yields and food security. Mathematical models can help identify strategies which slow the evolution or spread of resistance while maintaining effective disease control. Building in part on BBSRC-funded research from our group (e.g., Elderfield et al., 2018, Phytopathology; Taylor et al., 2023, Phytopathology; Taylor et al., 2023, PLOS Computational Biology), we now have a well-developed theoretical framework for resistance management. However, existing models focus primarily on polycyclic diseases, in which multiple cycles of infection and transmission occur within a growing season.

For monocyclic diseases, which typically involve a single major cycle of infection per growing season, the processes determining fungicide resistance may be fundamentally different. In particular, the development of the pathogen within infected plants, and how this interacts with fungicide exposure and disease transmission between seasons, may influence the strength of selection for resistance and the effectiveness of different management strategies. Yet, we lack a clear understanding of how these processes should be represented in resistance-management models, or whether strategies developed for polycyclic diseases remain effective in monocyclic systems.

This project will develop mathematical models of fungicide resistance for monocyclic plant pathogens, focusing on two important diseases of oilseed rape, stem rot (caused by Sclerotinia sclerotiorum) and phoma stem canker (caused by Leptosphaeria maculans and Leptosphaeria biglobosa). Models will be informed by datasets and biological insights provided by our industrial partner, ADAS. We will investigate how alternative representations of pathogen development within infected plants affect predictions of resistance evolution and the relative performance of different resistance-management strategies. This will establish whether existing theoretical principles can be generalised to monocyclic diseases or whether their biology requires fundamentally different approaches to fungicide resistance management.

Would suit someone with a maths background looking to make a transition to plant disease epidemiology, or a biologist with some quantitative training.

More information on work within the QPH Group in the Department of Plant Sciences at the University of Cambridge is at https://www.plantsci.cam.ac.uk/research/groups/quantitative-plant-health