Molecular EcologyGenomicsPeer-reviewed journal article
Climate-driven niche tracking and genomic resilience shape future distribution of a widespread agricultural weed
Abstract
Understanding how agriculturally important species respond to environmental change is critical for maintaining productivity, mitigating agroecosystem threats, and sustaining resilience. While crops have traditionally been the focus in agroecosystems, agricultural weeds are integral components that often face even stronger selective pressures, making them powerful models for investigating ecological and evolutionary responses to climatic and human-mediated challenges. Insights from how weeds adapt rapidly under these pressures can inform strategies to improve agricultural outcomes, since both pests and crops evolve under the same multivariate selective pressures. Here, we integrate two centuries of distribution records with whole-genome sequencing from natural populations of the most damaging weed in Europe - Alopecurus myosuroides (blackgrass) - to examine its ecological and evolutionary responses in agroecosystems. Blackgrass largely maintained its historical climatic niche, expanding its range primarily by tracking environments analogous to those it historically occupied. Genome-wide analyses revealed a polygenic basis of environmental responses, with most loci linked to single environmental variables and a subset showing limited environmental pleiotropy, indicating modular adaptation to the complex selective pressures of managed agricultural landscapes. Coupling these genomic-environment relationships with projected climate change and genomic offset analyses indicated that most blackgrass populations will remain well aligned with future conditions. Our findings show that ecological niche tracking and polygenic adaptation allow agricultural weeds like blackgrass to persist under rapid environmental change, offering insights relevant not only for weed management but also for designing resilient cropping systems under future climates.
- assay: whole-genome sequencing
- research focus: gene regulatory network inference
- research focus: variant effect prediction
- approach: statistical model
- approach: simulation
- Citations
- 0
- FWCI
- 0.00
- Field percentile
- top 31%
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