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BACKGROUND: The evolution of non-target site resistance (NTSR) to herbicides leads to a significant reduction in herbicide control of agricultural weed species. Detecting NTSR in weed populations prior to herbicide treatment would provide valuable information for effective weed control. While not all NTSR mechanisms have been fully identified, enhanced metabolic resistance (EMR) is one of the better studied, conferring tolerance through increased herbicide detoxification. Confirming EMR towards specific herbicides conventionally involves detecting metabolites of the active herbicide molecule in planta, but this approach is time-consuming and requires access to well-equipped laboratories. RESULTS: In this study, we explored the potential of using molecular biomarkers to detect EMR before herbicide treatment in black-grass (Alopecurus myosuroides). We tested the reliability of selected biomarkers to predict EMR and survival after herbicide treatments in both reference and 27 field-derived black-grass populations collected from sites across the UK. The combined analysis of the constitutive expression of biomarkers and metabolism studies confirmed three proteins, namely, AmGSTF1, AmGSTU2 and AmOPR1, as differential biomarkers of EMR toward the herbicides fenoxaprop-ethyl and mesosulfuron in black-grass. CONCLUSION: Our findings demonstrate that there is potential to use molecular biomarkers to detect EMR toward specific herbicides in black-grass without reference to metabolism analysis. However, biomarker development must include testing at both transcript and protein levels in order to be reliable indicators of resistance. This work is a first step towards more robust resistance biomarker development, which could be expanded into other herbicide chemistries for on-farm testing and monitoring EMR in uncharacterised black-grass populations.

As highly adaptable plants, weeds have evolved numerous mechanisms to evade control in agroecosystems. For example, reliance on herbicides has resulted in widespread evolution of resistance in many species. Minimising weed adaptation is a major driver for integrated weed management strategies. Crop mimicry is a notable example of weed adaptation, where weed species evolve to avoid control by mimicking aspects of the crop phenotype. Visual selection by hand weeding has been documented to select for crop mimics that are difficult to distinguish from the crop at the vegetative stage. With recent advancements in weed recognition technologies, image-based weed recognition for in-crop, site-specific weed control is on the cusp of becoming widely adopted. Whilst the control methods used in site-specific weed control will be varied (e.g., spot spraying or lasers), they will share weed recognition technology. Visual selection via image-based deep learning represents a selection pressure for weeds that can evade detection by mimicking crops. This mimicry may reduce weed recognition accuracy and thus weed control efficacy over time and result in difficult to manage mimetic weed phenotypes. Therefore, it is timely to explore the potential for selection of crop mimics by image-based weed recognition algorithms.

Globally, weedy plants are a major constraint to sustainable crop production. Much of the success of weeds rests with their ability to rapidly adapt in the face of human-mediated management of agroecosystems. Alopecurus myosuroides (blackgrass) is a widespread and impactful weed affecting agriculture in Europe. Here we report a chromosome-scale genome assembly of blackgrass and use this reference genome to explore the genomic/genetic basis of non-target site herbicide resistance (NTSR). Based on our analysis of F2 seed families derived from two distinct blackgrass populations with the same NTSR phenotype, we demonstrate that the trait is polygenic and evolves from standing genetic variation. We present evidence that selection for NTSR has signatures of both parallel and non-parallel evolution. There are parallel and non-parallel changes at the transcriptional level of several stress- and defence-responsive gene families. At the genomic level, however, the genetic loci underpinning NTSR are different (non-parallel) between seed families. We speculate that variation in the number, regulation and function of stress- and defence-related gene families enable weedy species to rapidly evolve NTSR via exaptation of genes within large multi-functional gene families. These results provide novel insights into the potential for, and nature of plant adaptation in rapidly changing environments.

Alopecurus myosuroides, a weed native to the Middle East, has become one of the most important weeds in western Europe, with much of it’s importance being ascribed to herbicide resistance. As different levels of competitiveness have been found within the species, this study aimed at exploring whether or not there is variance in the competitiveness of Alopecurus myosuroides and if such a variance was found, would there be patterns indicating that the weed populations from western Europe were more competitive than those from its native range. 20 different populations which had been collected from 1953 to 2021, across western Europe and the native range were selected. These were grown in a greenhouse in both monoculture and mixture with wheat. Biomass, height, number of leaves and tiller angle was measured, while another experiment with the same populations was conducted to study differences in the identity of allelochemicals, to establish a possible link between the competitiveness of the weed and the allelochemicals it produces. It was found that there was variance across the populations in competitiveness, with the new populations from western Europe being more competitive, in terms of the measured variables, than the old native populations. There were also differences in the allelochemicals produced, but no pattern in terms of age or place of origin could be established.

BACKGROUND: Unravelling the genetic architecture of non-target-site resistance (NTSR) traits in weed populations can inform questions about the inheritance, trade-offs and fitness costs associated with these traits. Classical quantitative genetics approaches allow study of the genetic architecture of polygenic traits even where the genetic basis of adaptation remains unknown. These approaches have the potential to overcome some of the limitations of previous studies into the genetics and fitness of NTSR. RESULTS: Using a quantitative genetic analysis of 400 pedigreed Alopecurus myosuroides seed families from nine field-collected populations, we found strong heritability for resistance to the acetolactate synthase and acetyl CoA carboxylase inhibitors (h2 = 0.731 and 0.938, respectively), and evidence for shared additive genetic variance for resistance to these two different herbicide modes of action, rg = 0.34 (survival), 0.38 (biomass). We find no evidence for genetic correlations between life-history traits and herbicide resistance, indicating that resistance to these two modes of action is not associated with large fitness costs in blackgrass. We do, however, demonstrate that phenotypic variation in plant flowering characteristics is heritable, h2 = 0.213 (flower height), 0.529 (flower head number), 0.449 (time to flowering) and 0.372 (time to seed shed), demonstrating the potential for adaptation to other nonchemical management practices (e.g. mowing of flowering heads) now being adopted for blackgrass control. CONCLUSION: These results highlight that quantitative genetics can provide important insight into the inheritance and genetic architecture of NTSR, and can be used alongside emerging molecular techniques to better understand the evolutionary and fitness landscape of herbicide resistance.