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

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.