IMPORTANT - to apply for this project please see the information on the BBRSC website: https://bbsrcdtp.lifesci.cam.ac.uk/how-apply#iCase Please don't apply for the PhD in Plant Sciences but follow the BBSRC iCASE instructions.
Supervisor
Professor Nik Cunniffe
Overview
Fungicide resistance is a growing threat to sustainable crop production. Chemical control remains a cornerstone of plant disease management, but the evolutionary pressure it exerts on pathogen populations can lead to the rapid emergence of resistance. While substantial progress has been made in modelling resistance management for polycyclic pathogens—those with multiple infection cycles per season—very little is known about how best to manage resistance in monocyclic systems. This exciting PhD project – done in collaboration with ADAS – will begin to resolve this.
Applicants with a background in mathematics, physics, engineering, or theoretical ecology are particularly encouraged to apply. Prior experience with programming is beneficial but not essential. Candidates from experimental or biological backgrounds with a strong interest in modelling are also welcome.
Importance of Research
Fungicide resistance is an important and growing problem. Fungicides are very expensive to develop, and so stewardship of effective chemistry is paramount. Mathematical models play a key role in designing resistance management strategies, since the coupled epidemiological-evolutionary dynamics are difficult to study by other means. By generalising the current resistance management theory to the important case of monocyclic pathogens, this project addresses a key gap in our current understanding.
Project Summary
This PhD project will develop mathematical models of fungicide resistance in monocyclic plant pathogens. The work builds on earlier modelling efforts within the group (e.g., Elderfield et al., 2018; Taylor et al., 2023a,b) but shifts the focus to pathogens with very different epidemiological characteristics. The outcomes of this research will be relevant to the optimisation of current fungicide strategies and could have long-term implications for agricultural disease control.
Existing theory for resistance management is almost entirely based on diseases that cycle repeatedly during a growing season. However, many important pathogens—including Sclerotinia sclerotiorum (affecting vegetable crops) and Oculimacula yallundae/acuformis (causing eyespot in cereals)—are monocyclic, completing only one infection cycle per season. These diseases are typically managed using fungicides, yet the dynamics of resistance development in these systems remain poorly understood.
Excitingly, initial modelling work (van den Bosch et al., 2014) suggests that applying standard modelling approaches to monocyclic pathogens may produce significantly different results. Notably, assumptions underpinning current management strategies—such as the benefits of fungicide mixtures—may not hold under monocyclic dynamics. This project will explore these questions in greater depth.
What will the successful applicant do?
The student will adapt and extend existing fungicide resistance models to reflect the biology of monocyclic pathogens, incorporating within-season pathogen development and other relevant features. The work will use case study systems informed by data and expertise from our project partners at ADAS (https://adas.co.uk/), the UK’s largest independent provider of agricultural and environmental consultancy. The student will also spend six months working on-site at the ADAS research station in Boxworth (near Cambridge).
The project will be based in the Theoretical and Computational Epidemiology Group at the University of Cambridge, led by Prof. Nik Cunniffe. The student will be co-supervised by Drs. Corkley, Grimmer and van den Bosch at ADAS, who are all actively engaged in fungicide resistance research (e.g., van den Bosch et al., 2014, 2020; Grimmer et al., 2015; Corkley et al., 2025). Dr Grimmer also serves as Secretary of FRAG-UK (Fungicide Resistance Action Group UK), providing a direct link to nationallevel guidance and impact.
Training Provided
- Mathematical modelling of biological systems
- Simulation and analysis of epidemiological models
- High-performance computing (HPC)
- Data integration and model fitting
- Collaboration across academic and applied research contexts
References
- Elderfield et al. (2018). Using epidemiological principles to explain fungicide resistance management tactics: why do mixtures outperform alternations? Phytopathology, 108:803817. DOI: 10.1094/PHYTO-08-17-0277-R
- Taylor et al. (2023a). Modelling quantitative fungicide resistance and breakdown of resistant cultivars: designing integrated disease management strategies for Septoria of winter wheat. PLOS Computational Biology, 19:e1010969. DOI: 10.1371/journal.pcbi.1010969
- Taylor et al. (2023b). Optimal resistance management for mixtures of high-risk fungicides: robustness to the initial frequency of resistance and pathogen sexual reproduction. Phytopathology, 113:55–69. DOI: 10.1094/PHYTO-02-22-0050-R
- van den Bosch et al. (2014). Mixtures as a fungicide resistance management tactic. Phytopathology, 104:1264–1273. DOI: 10.1094/PHYTO-04-14-0121-RVW
- Corkley et al.(2025). Dose splitting increases selection for both target-site and non-targetsite fungicide resistance – a modelling analysis. Plant Pathology, 74:1152–1167. DOI: 10.1111/ppa.14080
- Grimmer et al. (2015). Fungicide resistance risk assessment based on traits associated with the rate of pathogen evolution. Pest Management Science, 71:207–215. DOI: 10.1002/ps.3781
- van den Bosch et al. (2020). Identifying when it is financially beneficial to increase or decrease fungicide dose as resistance develops: an evaluation from long-term field experiments. Plant Pathology, 69:631–641. DOI: 10.1111/ppa.12787