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Title: Modelling fungicide resistance
Author: Hall, R. J.
Awarding Body: University of Cambridge
Current Institution: University of Cambridge
Date of Award: 2004
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Fungicide resistance, whereby a mutation conferring reduced sensitivity to chemical control arises and spreads through a fungal population, severely inhibits the successful control of crop disease. Mathematical models play a vital role in assessing the risk of invasion of fungicide-resistant pathogens, and in the design of effective resistance management strategies. In this thesis, I investigate the factors affecting the invasion of resistance in heterogeneous crop environments. I develop a simple, nonlinear model for fungicide resistance which, improving on existing work, incorporates the dynamics of the host crop and quantities how the amount, decay and timing of a fungicide dose affect selection for resistance. The model structure is similar to those used to describe antibiotic resistance, and hence much of the analysis presented here applies more generally to drug and pesticide resistance. I identify a threshold for the invasion of resistance in terms of two key parameters, both of which are amenable to estimation in the field. These are the fitness of the resistant strain relative to the wild-type, and treatment efficacy (which summarises how control inhibits pathogen survival and reproduction). Using a discrete, stochastic formulation of the model, I demonstrate that this threshold is robust to the effects of demographic stochasticity, and estimate the probabilities of resistance pre-existing or emerging during treatment. In the final section of the thesis, I extend the simple model to examine the dynamics of multiple pathogen strains, the effects of seasonal disturbance to the host (through planting and harvesting) on persistence of the resistant pathogen, and how the scale of pathogen dispersal affects the spatial propagation of resistance.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available