Optimal Resistance Management for Mixtures of High-Risk Fungicides: Robustness to the Initial Frequency of Resistance and Pathogen Sexual Reproduction

Abstract

There is a strong consensus that selection for fungicide resistant pathogen strains can be most effectively limited by using applications of mixtures of fungicides designed to balance disease control against selection. However, how to do this in practice is not entirely characterised. Previous work indicates optimal mixtures of pairs of fungicides which are both at a high risk of resistance can be constructed using pairs of doses which select equally for both single resistant strains in the first year of application. What has not been addressed thus far is the important real-world case in which the initial levels of resistance to each fungicide differ, for example because the chemicals have been available for different lengths of time. We show how recommendations based on equal selection in the first year can be sub-optimal in this case. We introduce a simple alternative approach, based on equalising the frequencies of single resistant strains in the year that achieving acceptable levels of control is predicted to become impossible. We show that this strategy is robust to changes in parameters controlling pathogen epidemiology and fungicide efficacy. We develop our recommendation using a pre-existing, parameterised model of Zymoseptoria tritici (the pathogen causing Septoria leaf blotch on wheat), which exemplifies the range of plant pathogens which predominantly spread clonally, but for which sexual reproduction forms an important component of the life cycle. We show that pathogen sexual reproduction can influence the rate at which fungicide resistance develops, but does not qualitatively affect our optimal resistance management recommendation.

Modelling quantitative fungicide resistance and breakdown of resistant cultivars: designing integrated disease management strategies for Septoria of winter wheat

“…However, particularly when compared to the vast majority of fungicide resistance models ( Hobbelen et al, 2011a, 2013 ; van den Berg et al, 2013 ; Elderfield et al, 2018 ; Taylor and Cunniffe, 2022 ) in which environmental stochasticity is not considered at all, we contend this is a sensible treatment. We also follow many past modelling studies in using a fixed value for inoculum ( Hobbelen et al, 2011a, 2013 ; Elderfield et al, 2018 ; Taylor and Cunniffe, 2022 ) but inoculum will vary year on year, and further the type of inoculum (ascospores vs pycnidiospores) can have complex effects on the latent period and the dynamics over multiple seasons ( Suffert and Thompson, 2018 ). Finally spatial effects are ignored, despite promising early studies showing the potential of fungicide application patterns based on spatial risk ( Liu et al, 2017 ).…”

“…The model could be extended to address other disease management questions. Many of these future lines are obvious extensions now we have a fitted model of quantitative resistance in hand, given the long history and diverse applications of past models of mongenic resistance ( Hobbelen et al, 2011a, 2013 ; Elderfield et al, 2018 ; Taylor and Cunniffe, 2022 ). We have considered full-dose applications of fungicide here; future work could include an analysis of dose choice and optimise for number of applications and dose in each application.…”

“…We also assume that the infection rate is fixed within each season -a more complex model could include temporal variation in infection rate within a single growing season, whereas we only change the infection rate value between different growing seasons. However, particularly when compared to the vast majority of fungicide resistance models (Hobbelen et al, 2011a(Hobbelen et al, , 2013van den Berg et al, 2013;Elderfield et al, 2018;Taylor and Cunniffe, 2022) in which environmental stochasticity is not considered at all, we contend this is a sensible treatment. We also follow many past modelling studies in using a fixed value for inoculum (Hobbelen et al, 2011a(Hobbelen et al, , 2013Elderfield et al, 2018; Taylor and Cunniffe, 2022) but inoculum will vary year on year, and further the type of inoculum (ascospores vs pycnidiospores) can have complex effects on the latent period and the dynamics over multiple seasons (Suffert and Thompson, 2018).…”

“…given the long history and diverse applications of past models of mongenic resistance (Hobbelen et al, 2011a(Hobbelen et al, , 2013Elderfield et al, 2018;Taylor and Cunniffe, 2022). We have considered full-dose applications of fungicide here; future work could include an analysis of dose choice and optimise for number of applications and dose in each application.…”

Coupling machine learning and epidemiological modelling to characterise optimal fungicide doses when fungicide resistance is partial or quantitative

“…Fungicide dose choice can be important in determining how quickly resistance develops (van den Bosch et al, 2011). Although the existing literature suggests that higher doses contribute to increased selection (van den Bosch et al, 2011(van den Bosch et al, , 2014Hobbelen et al, 2013;Elderfield et al, 2018;Taylor and Cunniffe, 2022b), many studies on optimal dosage neglect partial resistance, and to the best of our knowledge no modelling study considers the case of quantitative resistance. We show that although higher doses can often lead to increased selection for resistance in these scenarios, the control offered by higher doses may still outperform that offered by lower doses, both in the case of partial qualitative resistance (Figures 1, 2) and in quantitative resistance (Figure 3, 4).…”