Gene drives that skew sex ratios offer a new management tool to suppress or eradicate pest populations. Early models and empirical work suggest that these suppression drives can completely eradicate well‐mixed populations, but models that incorporate stochasticity and space (i.e. drift and recolonization events) often result in loss or failure of the drive. We developed a stochastic model to examine these processes in a simple one‐dimensional space. This simple space allows us to map the events and outcomes that emerged and examine how properties of the drive's wave of invasion affect outcomes. Our simulations, across a biologically realistic section of parameter space, suggest that drive failure might be a common outcome in spatially explicit, stochastic systems, and that properties of the drive wave appear to mediate outcomes. Surprisingly, the drives that would be considered fittest in an aspatial model were strongly associated with failure in the spatial setting. The fittest drives cause relatively fast moving, and narrow waves that have a high chance of being penetrated by wild‐types (WTs) leading to WT recolonization, leading to failure. Our results also show that high rates of dispersal reduce the chance of failure because drive waves get disproportionately wider than WT waves as dispersal rates increase. Overall, wide, slow‐moving drive waves were much less prone to failure. Our results point to the complexity inherent in using a genetic system to effect demographic outcomes and speak to a clear need for ecological and evolutionary modelling to inform the drive design process.

The relevance of flowering time variation and plasticity to climate adaptation requires a comprehensive empirical assessment. We investigated natural selection and the genetic architecture of flowering time in Arabidopsis through field experiments in Europe across multiple sites and seasons.We estimated selection for flowering time, plasticity and canalization. Loci associated with flowering time, plasticity and canalization by genome-wide association studies were tested for a geographic signature of climate adaptation.Selection favored early flowering and increased canalization, except at the northernmost site, but was rarely detected for plasticity. Genome-wide association studies revealed significant associations with flowering traits and supported a substantial polygenic inheritance. Alleles associated with late flowering, including functional FRIGIDA variants, were more common in regions experiencing high annual temperature variation. Flowering time plasticity to fall vs spring and summer environments was associated with GIGANTEA SUPPRESSOR 5, which promotes early flowering under decreasing day length and temperature.The finding that late flowering genotypes and alleles are associated with climate is evidence for past adaptation. Real-time phenotypic selection analysis, however, reveals pervasive contemporary selection for rapid flowering in agricultural settings across most of the species range. The response to this selection may involve genetic shifts in environmental cuing compared to the ancestral state.

The genome of the major agricultural weed species, annual ryegrass (Lolium rigidum) was assembled, annotated and analysed. Annual ryegrass is a major weed in grain cropping, and has the remarkable capacity to evolve resistance to herbicides with various modes of action. The chromosome-level assembly was achieved using short- and long-read sequencing in combination with Hi-C mapping. The assembly size is 2.44 Gb with N50 = 361.79 Mb across 1,764 scaffolds where the seven longest sequences correspond to the seven chromosomes. Genome completeness assessed through BUSCO returned a 99.8% score for complete (unique and duplicated) and fragmented genes using the Viridiplantae set. We found evidence for the expansion of herbicide resistance-related gene families including detoxification genes. The reference genome of L. rigidum is a critical asset for leveraging genetic information for the management of this highly problematic weed species.

Understanding how the molecular variation within species supports the evolution of functional traits is a powerful way forward in ecological research through the determination of so-called genotypeto-phenotype maps. Genomic information for a species can then be utilized to understand and eventually predict population fitness under a range of eco-evolutionary scenarios. Unfortunately, this genotypeto-phenotype map is only available for a handful of traits (Hoekstra

Gene drives that skew sex ratios offer a new management tool to suppress or eradicate pest populations. Early models and empirical work suggest that these suppression drives can completely eradicate well-mixed populations, but models that incorporate stochasticity and space (i.e., drift, and founder events) often result in loss or failure of the drive. We developed a stochastic model to examine these processes in a simple 1-dimensional space. This simple space allows us to map the events and outcomes that emerged and examine how properties of the drive’s wave of invasion affect outcomes. Our simulations, across a biologically-realistic section of parameter space, suggest that drive failure might be a common outcome in spatially explicit, stochastic systems, and that properties of the drive wave appear to mediate outcomes. Surprisingly, the drives that would be considered fittest in an aspatial model were strongly associated with failure in the spatial setting. The fittest drives cause fast moving, narrow drive waves that have a high chance of being penetrated by founder events, leading to failure. Our results also show that high rates of dispersal reduce the chance of failure because drive waves get disproportionately wider as dispersal rates increase. Overall, wide, slow-moving drive waves were much less prone to failure. Our results point to the complexity inherent in using a genetic system to effect demographic outcomes and speak to a clear need for ecological and evolutionary modelling to inform the drive design process.