title: A generalised individual-based algorithm for modelling the evolution of quantitative herbicide resistance in arable weed populations.
abstract: BACKGROUND
  Simulation models are useful tools for predicting and comparing the risk of herbicide resistance in weed populations under different management strategies. Most existing models assume a monogenic mechanism governing herbicide resistance evolution. However, growing evidence suggests that herbicide resistance is often inherited in a polygenic or quantitative fashion. Therefore, we constructed a generalised modelling framework to simulate the evolution of quantitative herbicide resistance in summer annual weeds.
  
  
  RESULTS
  Real-field management parameters based on Amaranthus tuberculatus (Moq.) Sauer (syn. rudis) control with glyphosate and mesotrione in Midwestern US maize-soybean agroecosystems demonstrated that the model can represent evolved herbicide resistance in realistic timescales. Sensitivity analyses showed that genetic and management parameters were impactful on the rate of quantitative herbicide resistance evolution, whilst biological parameters such as emergence and seed bank mortality were less important.
  
  
  CONCLUSIONS
  The simulation model provides a robust and widely applicable framework for predicting the evolution of quantitative herbicide resistance in summer annual weed populations. The sensitivity analyses identified weed characteristics that would favour herbicide resistance evolution, including high annual fecundity, large resistance phenotypic variance and pre-existing herbicide resistance. Implications for herbicide resistance management and potential use of the model are discussed. © 2016 Society of Chemical Industry.
authors.affiliation: Herbicide Bioscience, Syngenta, Bracknell, Berks, UK.
authors.forename: Chun
authors.identifier: http://orcid.org/0000-0001-5886-5785
authors.initials: C
authors.lastname: Liu
authors.affiliation: School of Life Sciences, University of Warwick, Wellesbourne, Warks, UK.
authors.forename: Melissa E
authors.initials: ME
authors.lastname: Bridges
authors.affiliation: Herbicide Bioscience, Syngenta, Bracknell, Berks, UK.
authors.forename: Shiv S
authors.initials: SS
authors.lastname: Kaundun
authors.affiliation: Herbicide Product Management, Syngenta, Greensboro, NC, USA.
authors.forename: Les
authors.initials: L
authors.lastname: Glasgow
authors.affiliation: Department of Agronomy, Iowa State University, Ames, IA, USA.
authors.forename: Micheal Dk
authors.initials: MD
authors.lastname: Owen
authors.affiliation: School of Life Sciences, University of Warwick, Wellesbourne, Warks, UK.
authors.forename: Paul
authors.initials: P
authors.lastname: Neve
mesh_terms: D000465:Algorithms; D027721:Amaranthus / Q000235:genetics; D005075:Biological Evolution; D003512:Cyclohexanones; D005998:Glycine / Q000031:analogs & derivatives*; D053209:Herbicide Resistance; D006540:Herbicides / Q000494:pharmacology; D057685:Plant Weeds / Q000235:genetics; D013025:Glycine max; D003313:Zea mays; D000097797:Glyphosate
chemical_list: D003512:Cyclohexanones; D005998:Glycine; D006540:Herbicides; D000097797:Glyphosate; C432907:mesotrione
keywords: Amaranthus tuberculatus Sauer; evolution; individual-based models; polygenic resistance; quantitative resistance; weed management