Soularue Jean-Paul, Halkett Fabien, Saubin Méline, Denni Sukanya, Demené Arthur, Dutech Cyril, Robin Cécile
Univ. Bordeaux, INRAE, BIOGECO Pessac France.
Université de Lorraine, INRAE, IAM Nancy France.
Evol Appl. 2025 Jul 9;18(7):e70123. doi: 10.1111/eva.70123. eCollection 2025 Jul.
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.
定量抗性是减轻人工管理植物生态系统中流行病的重要工具。然而，其部署可能会推动病原体生活史特征的进化变化，使得预测流行病发展具有挑战性。为了研究这些影响，我们开发了一个示范遗传模型，该模型明确捕捉了病原体种群统计学与其遗传组成之间的反馈。该模型还将宿主内增殖与宿主间传播联系起来，并基于这样的假设构建：易感宿主和抗性宿主的共存会在景观尺度上对病原体种群施加不同的选择压力。我们模拟了多年生宿主植物比例和抗性效率不同的对比景观。我们的模拟证实，以几乎完全的效率（>99.99%）部署抗性能够有效降低病原体引入所引发的流行病严重程度，并促进感染基因型向易感或抗性宿主的专业化。相反，使用部分抗性会引发有限的进化变化，常常导致病原体对易感和抗性宿主都不适应。值得注意的是，在某些条件下，与完全易感种群相比，以强（89%）或中等（60%）效率部署抗性可能导致更高的宿主死亡率。这种违反直觉的结果源于感染基因型对其宿主的不适应，这延长了受感染宿主的寿命并可能增加接种体压力。我们进一步将完整模型的模拟与简化版本的模拟进行了比较，在简化版本中，（i）受感染植物对疾病传播的贡献不依赖于它们携带的病原体负荷，（ii）植物景观不是空间明确的。这些比较突出了这些组成部分在塑造模型预测方面的重要作用。最后，我们讨论了可能导致人工管理多年生植物中定量抗性部署产生有害结果的条件。
