Quantitative Resistance Deployment Can Strengthen Epidemics in Perennial Plants by Selecting Maladapted Pathogen Strains.

Quantitative resistances are essential tools for mitigating epidemics in managed plant ecosystems. However, their deployment can drive evolutionary changes in pathogen life-history traits, making predictions of epidemic development challenging. To investigate these effects, we developed a demo-genetic model that explicitly captures feedbacks between the pathogen's population demography and its genetic composition. The model also links within-host multiplication and between-host transmission, and is built on the assumption that the coexistence of susceptible and resistant hosts imposes divergent selection pressures on the pathogen population at the landscape scale. We simulated contrasting landscapes of perennial host plants with varying proportions of resistant plants and resistance efficiencies. Our simulations confirmed that deploying resistances with nearly complete efficiency (> 99.99%) effectively reduces the severity of epidemics caused by pathogen introduction and promotes the specialization of infectious genotypes to either susceptible or resistant hosts. Conversely, the use of partial resistances induces limited evolutionary changes, often resulting in pathogen maladaptation to both susceptible and resistant hosts. Notably, deploying resistances with strong (89%) or moderate (60%) efficiencies can, under certain conditions, lead to higher host mortality compared to entirely susceptible populations. This counterintuitive outcome arises from the maladaptation of infectious genotypes to their hosts, which prolongs the lifespan of infected hosts and can increase inoculum pressure. We further compared simulations of the full model with those of simplified versions in which (i) the contribution of infected plants to disease transmission did not depend on the pathogen load they carried, (ii) plant landscapes were not spatially explicit. These comparisons highlighted the essential role of these components in shaping model predictions. Finally, we discuss the conditions that may lead to detrimental outcomes of quantitative resistance deployments in managed perennial plants.