An epi-evolutionary model for predicting the adaptation of spore-producing pathogens to quantitative resistance in heterogeneous environments.

Frédéric Fabre,Jean-Baptiste Burie,Ramsès Djidjou-Demasse,Quentin Richard,Sébastien Lion,Arnaud Ducrot

doi:10.1111/eva.13328

Abstract

We have modeled the evolutionary epidemiology of spore‐producing plant pathogens in heterogeneous environments sown with several cultivars carrying quantitative resistances. The model explicitly tracks the infection‐age structure and genetic composition of the pathogen population. Each strain is characterized by pathogenicity traits determining its infection efficiency and a time‐varying sporulation curve taking into account lesion aging. We first derived a general expression of the basic reproduction number R0 for fungal pathogens in heterogeneous environments. We show that the evolutionary attractors of the model coincide with local maxima of R0 only if the infection efficiency is the same on all host types. We then studied the contribution of three basic resistance characteristics (the pathogenicity trait targeted, resistance effectiveness, and adaptation cost), in interaction with the deployment strategy (proportion of fields sown with a resistant cultivar), to (i) pathogen diversification at equilibrium and (ii) the shaping of transient dynamics from evolutionary and epidemiological perspectives. We show that quantitative resistance affecting only the sporulation curve will always lead to a monomorphic population, whereas dimorphism (i.e., pathogen diversification) can occur if resistance alters infection efficiency, notably with high adaptation costs and proportions of the resistant cultivar. Accordingly, the choice of the quantitative resistance genes operated by plant breeders is a driver of pathogen diversification. From an evolutionary perspective, the time to emergence of the evolutionary attractor best adapted to the resistant cultivar tends to be shorter when resistance affects infection efficiency than when it affects sporulation. Conversely, from an epidemiological perspective, epidemiological control is always greater when the resistance affects infection efficiency. This highlights the difficulty of defining deployment strategies for quantitative resistance simultaneously maximizing epidemiological and evolutionary outcomes.

Highlights

Resistance to parasites, that is, the capacity of a host to decrease its parasite development (Raberg et al, 2009), is a widespread defense mechanism in plants
How can we model the joint epidemiological and evolutionary dynamics of the host–pathogen interaction? we first formulate a general model for the dynamics of spore-producing plant pathogens in structured populations, which we apply to the study of specific scenarios
This work follows a current trend toward the combined modeling of epidemiological and evolutionary dynamics in host–parasite interactions

Summary

| INTRODUCTION

Resistance to parasites, that is, the capacity of a host to decrease its parasite development (Raberg et al, 2009), is a widespread defense mechanism in plants. Whatever the basic resistance characteristics (pathogenicity trait targeted, resistance effectiveness, and cost of adaptation), the relative proportion of the evolutionary attractor is already ≥5% in the pool of spores in the air at the initial time point for a wide range of deployment strategies, mostly when the R cultivar is not dominant in the environment (Figure 4, lines 1 and 2, level A0). This configuration is obtained for small ranges of intermediate proportions of the R cultivar at planting combined (i) with an adaptation cost and resistance effectiveness ≥0.9, or (ii) with the highest adaptation cost considered Cadp = 0.99 for all resistance effectiveness values tested (Figure 4, line 3, level "2R") In this configuration, the pathogen population remains for a relatively long time around the initially dominant phenotype and shifts by mutation to the evolutionary attractor after crossing a fitness minimum. For both scenarios, epidemiological control is not correlated with time to emergence in the production situations explored (compare Figures 4 and 5)

| DISCUSSION

Findings

| Notes on model assumptions