Novel informed modelling approaches to investigate the evolution and management of herbicide resistance in Alopecurus myosuroides

大穗看麦娘（Alopecurus myosuroides）是一种竞争性很强的禾本科杂草，对除草剂的抗性很容易发生进化。目前，在英国和法国，进化的除草剂抗性的程度最大，其中对乙酰辅酶A羧化酶（ACCase）抑制和乙酰乳酸合酶（ALS）抑制除草剂的抗性的记录案例是常见和普遍的。最近，该物种正迅速向北和向东扩展其分布范围，在德国和其他国家，有关耐药性的报告也在增加。与此同时，目前欧盟农药登记的变化正在减少可用于黑草控制的除草剂作用模式的数量，这使得对剩余作用模式的抗性可能在未来成为一个更大的问题。除草剂耐药性的流行和风险的增加必须推动农用化学品公司、农民和顾问提供更可持续的除草剂使用战略，并更多地采用综合杂草管理。人口遗传模型可以结合联合收割机知识的人口学和生活史的黑草与我们目前的理解除草剂抗性的遗传基础，研究各种管理措施对除草剂抗性的进化和传播的影响。这些模型以前已经由其他杂草物种的学术主管开发。该项目将通过使用拜耳作物科学自2008年以来一直在整理的广泛的黑草抗性案例数据库，建立并加强这些方法。对于每一个被送到拜耳的疑似抗性黑草种群，都进行了一系列广泛的实验室和温室测试，以确定抗性的程度及其遗传和机制基础。这提供了一个独特的数据集，记录了整个欧洲不同耐药机制的程度和分布，以及各种耐药点突变的频率和表型后果（耐药谱）的信息。该数据集更引人注目，因为它包括每个位置的田间管理历史，从该位置对黑草进行采样，从而提供了将抗性的程度和机制与过去的管理联系起来的机会。以前的除草剂抗性进化模型的一个主要局限性是缺乏各种抗性机制和突变的相对频率的数据，据设想，通过将这些数据结合到一个建模格式中，可以实现抗性管理的重大进展。所开发的模型将能够通过多种耐药机制同时模拟耐药性的演变，并且机制和突变的相对频率及其耐药性概况的知识将用于探索管理策略以降低耐药性演变的风险。在第一种情况下，将在各个领域模拟抗性的演变。然而，该项目的长期目标是通过模拟田间网络中的黑草种群来考虑景观尺度上的抗性进化，该网络具有对比管理和田间之间的基因流。除了建模，学生还将对英国的黑草种群进行年度随机调查。将在沃里克大学的温室试验中确定这些种群的抗性程度。将进行进一步的实验，以检查这些群体中的抗性的适应性后果，这些信息将用于帮助开发人口遗传模型。每年，学生将在法兰克福的拜耳作物科学综合杂草管理和除草剂抗性诊断实验室度过2-3个月的时间。在此期间，他们将进行一系列分子生理学测定，以确定英国收集的种群中存在的抗性机制。

The highly competitive grass weed, Alopecurus myosuroides (black-grass) is prone to the evolution of resistance to herbicides. The extent of evolved herbicide resistance is currently greatest in the UK and France where documented cases of resistance to acetyl co-enzyme A carboxylase (ACCase) -inhibiting and acetolactate synthase (ALS) -inhibiting herbicides are common and widespread. More recently, the species is rapidly expanding its range northwards and eastwards and reports of resistance are increasing in Germany and other countries. At the same time, current changes to EU pesticide registration are reducing the number of herbicide modes of action available for black-grass control, making it probable that resistance to remaining modes of action will be an even greater issue in future. These increases in the prevalence and risk of herbicide resistance must drive agrichemical companies, farmers and advisers towards the provision of more sustainable herbicide use strategies and greater adoption of integrated weed management. Demo-genetic models can combine knowledge of the demography and life cycle of black-grass with our current understanding of the genetic basis of herbicide resistance to examine the influence of various management practices on the evolution and spread of herbicide resistance. These models have been developed previously by the academic supervisor for other weed species. This project will build upon and enhance these approaches by using the extensive database of black-grass resistance cases that Bayer CropScience has been collating since 2008. For each suspected resistant black-grass population that is sent to Bayer, an extensive suite of laboratory and glasshouse tests are performed to determine the extent of resistance, as well its genetic and mechanistic basis. This provides a unique dataset documenting the extent and distribution of different resistance mechanisms across Europe as well as information on the frequency and phenotypic consequences (resistance profile) of various resistance-endowing point mutations. This dataset is even more compelling as it includes a field management history for each location from which black-grass is sampled, providing the opportunity to relate the extent and mechanism of resistance to past management. A major limitation of previous models of herbicide resistance evolution has been the lack of data on the relative frequency of various resistance mechanisms and mutations and it is envisaged that major advances in resistance management can be achieved by combining this data into a modelling format. The developed model will be able to simultaneously simulate evolution of resistance via multiple resistance mechanisms and knowledge of the relative frequencies of mechanisms and mutations together with their resistance profile will be used to explore management strategies to reduce risks of resistance evolution. In the first instance, evolution of resistance will be simulated in individual fields. However, a longer term aim of the project will be to consider evolution of resistance on a landscape scale by simulating black-grass populations in a network of fields with contrasting management and with gene flow between the fields. In addition to modelling, the student will also conduct an annual random survey of black-grass populations from the UK. The extent of resistance in these populations will be determined in glasshouse assays at the University of Warwick. Further experiments will be conducted to examine the fitness consequences of resistance in these populations and this information will be used to help develop the demo-genetic model. During each year, the student will spend a 2-3 month period at the Bayer Crop Science Integrated Weed Management and Herbicide Resistance diagnostics laboratory in Frankfurt. During this time they will perform a suite of molecular physiological assays to determine the resistance mechanisms present in the UK collected populations.