TY - JOUR T1 - An epi-evolutionary model to predict spore-producing pathogens adaptation to quantitative resistance in heterogeneous environments JF - bioRxiv DO - 10.1101/423467 SP - 423467 AU - Ramsès Djidjou-Demasse AU - Jean-Baptiste Burie AU - Arnaud Ducrot AU - Sébastien Lion AU - Quentin Richard AU - Frédéric Fabre Y1 - 2020/01/01 UR - http://biorxiv.org/content/early/2020/09/16/423467.abstract N2 - In contrast to the many theoretical studies on the adaptation of plant pathogens to qualitative resistances, few studies have investigated how quantitative resistance selects for increased pathogen aggressiveness. We formulate an integro-differential model with nonlocal effects of mutations to describe the evolutionary epidemiology dynamics of spore-producing pathogens in heterogeneous agricultural environments sharing a well-mixed pool of spores. Parasites reproduce clonally and each strain is characterized by pathogenicity traits corresponding to the epidemic process: (i) infection efficiency and (ii) sporulation curve (including the latent period, the total spore production and the shape of the sporulation curve). We first derive a general expression of the basic reproduction number for fungal pathogens in heterogeneous host environments. Next, by characterizing evolutionary attractors, we investigate how the choice of quantitative resistances altering pathogenicity traits impacts the evolutionary dynamics of the pathogen population both at equilibrium and during transient epidemiological dynamics. We show that evolutionary attractors of the model coincide with local maxima of the only for traits involved in the sporulation curve. Quantitative resistance impacting the sporulation curve will always select a monomorphic population while dimorphism can occur with resistance altering infection efficiency. We also highlight how the shape of the relationship between the latent period and the total number of spores produced during the infectious period, impacts resistance durability and how to take advantage of evolutionary dynamics to increase the durability of quantitative resistance. Our analyses can guide experimentations by providing testable hypotheses and help plant breeders to design breeding programs.Competing Interest StatementThe authors have declared no competing interest. ER -