Modeling the evolution of herbicide resistance in weed species with a complex life cycle. Issue 1 (23rd November 2021)

• Record Type: Journal Article
• Authors: Holmes, K. Harrison; Lindquist, John L.; Rebarber, Richard; Werle, Rodrigo; Yerka, Melinda; Tenhumberg, Brigitte
• Is Part Of: Ecological applications. Volume 32:Issue 1(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-11-23
• Subjects: bud bank -- crop rotation -- gene flow -- herbicide‐resistant crop plants -- matrix model -- polyploidy -- rhizomatous perennial weed; Ecology -- Periodicals; Environmental protection -- Periodicals; Biology, Economic -- Periodicals; 577.05
• DOI: 10.1002/eap.2473
• Languages: English
• ISSNs: 1051-0761

Abstract

Abstract: A growing number of weed species have evolved resistance to herbicides in recent years, which causes an immense financial burden to farmers. An increasingly popular method of weed control is the adoption of crops that are resistant to specific herbicides, which allows farmers to apply the herbicide during the growing season without harming the crop. If such crops are planted in the presence of closely related weed species, it is possible that resistance genes could transfer from the crop species to feral populations of the wild species via gene flow and become stably introgressed under ongoing selective pressure by the herbicide. We use a density‐dependent matrix model to evaluate the effect of planting such crops on the evolution of herbicide resistance under a range of management scenarios. Our model expands on previous simulation studies by considering weed species with a more complex life cycle (perennial, rhizomatous weed species), studying the effect of environmental variation in herbicide effectiveness, and evaluating the role of common simplifying genetic assumptions on resistance evolution. Our model predictions are qualitatively similar to previous modeling studies using species with a simpler life cycle, which is, crop rotation in combination with rotation of herbicide site of action effectively controls weed populations and slows the evolution of herbicide resistance. We find that ignoring the effect of environmental variation can lead to an over‐ or under‐prediction of the speed of resistance evolution. The effect of environmental variation in herbicide effectiveness depends on the resistance allele frequency in the weed population at the beginning of the simulation. Finally, we find that degree of dominance and ploidy level have a much larger effect on the predicted speed of resistance evolution compared to the rate of gene flow.