Fritz Megan L
Department of Entomology University of Maryland College Park Maryland USA.
Evol Appl. 2022 Oct 9;15(10):1505-1520. doi: 10.1111/eva.13484. eCollection 2022 Oct.
Arthropods that invade agricultural ecosystems systematically evolve resistance to the control measures used against them, and this remains a significant and ongoing challenge for sustainable food production systems. Early detection of resistance evolution could prompt remedial action to slow the spread of resistance alleles in the landscape. Historical approaches used to detect emerging resistance included phenotypic monitoring of agricultural pest populations, as well as monitoring of allele frequency changes at one or a few candidate pesticide resistance genes. In this article, I discuss the successes and limitations of these traditional monitoring approaches and then consider whether whole-genome scanning could be applied to samples collected from agroecosystems over time for resistance monitoring. I examine the qualities of agroecosystems that could impact application of this approach to pesticide resistance monitoring and describe a recent retrospective analysis where genome scanning successfully detected an oligogenic response to selection by pesticides years prior to pest management failure. I conclude by considering areas of further study that will shed light on the feasibility of applying whole-genome scanning for resistance risk monitoring in agricultural pest species.
侵入农业生态系统的节肢动物会系统性地对针对它们的控制措施产生抗性,这对可持续粮食生产系统而言仍是一项重大且持续存在的挑战。抗性进化的早期检测能够促使采取补救措施,以减缓抗性等位基因在环境中的传播。过去用于检测新出现抗性的方法包括对农业害虫种群进行表型监测,以及监测一个或几个候选抗药性基因的等位基因频率变化。在本文中,我将讨论这些传统监测方法的成功之处与局限性,然后思考全基因组扫描是否可应用于随时间从农业生态系统采集的样本以进行抗性监测。我将研究可能影响这种方法应用于抗药性监测的农业生态系统特性,并描述一项近期的回顾性分析,其中基因组扫描在害虫管理失败数年之前就成功检测到了对农药选择的寡基因反应。最后,我将思考进一步研究的领域,这些研究将阐明在农业害虫物种中应用全基因组扫描进行抗性风险监测的可行性。