Hawkins Nichola J
NIAB, Cambridge, UK.
J Plant Dis Prot (2006). 2024;131(4):1257-1264. doi: 10.1007/s41348-024-00906-0. Epub 2024 Apr 12.
Plant pathogens are highly adaptable, and have evolved to overcome control measures including multiple classes of fungicides. More effective management requires a thorough understanding of the evolutionary drivers leading to resistance. Experimental evolution can be used to investigate evolutionary processes over a compressed timescale. For fungicide resistance, applications include predicting resistance ahead of its emergence in the field, testing potential outcomes under multiple different fungicide usage scenarios or comparing resistance management strategies. This review considers different experimental approaches to in vitro selection, and their suitability for addressing different questions relating to fungicide resistance. When aiming to predict the evolution of new variants, mutational supply is especially important. When assessing the relative fitness of different variants under fungicide selection, growth conditions such as temperature may affect the results as well as fungicide choice and dose. Other considerations include population size, transfer interval, competition between genotypes and pathogen reproductive mode. However, resistance evolution in field populations has proven to be less repeatable for some fungicide classes than others. Therefore, even with optimal experimental design, in some cases the most accurate prediction from experimental evolution may be that the exact evolutionary trajectory of resistance will be unpredictable.
植物病原体具有高度适应性,并且已经进化到能够克服包括多种杀菌剂在内的防治措施。更有效的管理需要深入了解导致抗性的进化驱动因素。实验进化可用于在压缩的时间尺度上研究进化过程。对于抗药性而言,其应用包括在田间出现抗性之前预测抗性、在多种不同的杀菌剂使用场景下测试潜在结果或比较抗性管理策略。本综述考虑了体外选择的不同实验方法,以及它们对于解决与杀菌剂抗性相关的不同问题的适用性。当旨在预测新变体的进化时,突变供应尤为重要。在杀菌剂选择下评估不同变体的相对适合度时,诸如温度等生长条件以及杀菌剂的选择和剂量可能会影响结果。其他需要考虑的因素包括种群大小、转移间隔、基因型之间的竞争以及病原体的繁殖方式。然而,事实证明,对于某些杀菌剂类别,田间种群中的抗性进化比其他类别更难重复。因此,即使采用最佳实验设计,在某些情况下,实验进化最准确的预测可能是抗性的确切进化轨迹将无法预测。