The Digitaria genomes reveal local adaption and herbicide resistance mediated by introgression

The Digitaria genomes reveal local adaption and herbicide resistance mediated by introgression | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article The Digitaria genomes reveal local adaption and herbicide resistance mediated by introgression Longjiang Fan, yujie Huang, Jian Li, Shiyu Zhang, Zhefu Li, Xingxiang Gao, and 13 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7329239/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 12 Feb, 2026 Read the published version in Nature Communications → Version 1 posted You are reading this latest preprint version Abstract Crabgrasses ( Digitaria spp.), the most problematic upland weeds, are well-documented for their broad-spectrum herbicide resistance and ecological adaptability. The genetic mechanism of these adaptive traits remains largely unexplored. We assembled three telomere-to-telomere (T2T) reference genomes of the globally invasive species Digitaria sanguinalis , along with its diploid and tetraploid progenitors. In addition, we re-sequenced 579 accessions from sympatric populations sampled over the past decade, coupled with nicosulfuron (an ALS inhibitor herbicide) resistance phenotyping. Genomic analysis of D. sanguinalis revealed adaptation driven by polyploidization. Extensive sampling across Digitaria species uncovered widespread introgression among sympatric lineages. Notably, introgression contributed to enhanced cold tolerance, likely facilitating adaptation to northern environments. Dose-response assays revealed a recent surge in nicosulfuron resistance in D. sanguinalis , which cannot be explained by target-site mutations alone. Genome-wide association study identified 40 SNPs significantly associated with non-target-site resistance (NTSR). We further identified a recently introgressed region from sympatric D. ciliaris associated with NTSR. Moreover, herbicide-resistant populations exhibited a higher number of introgressed genomic blocks compared to susceptible ones. These findings reveal adaptive introgression from relatives as a key source of variation, promoting rapid adaptation under selection pressure. Biological sciences/Plant sciences/Plant genetics Biological sciences/Genetics/Population genetics Digitaria complex weed genome sympatric introgression rapid adaptation allopolyploids herbicide resistance non-target-site resistance Figures Figure 1 Figure 2 Figure 3 Figure 4 Highlights The telomere-to-telomere (T2T) reference genomes of the most important upland weed, Digitaria sanguinalis (2n = 6× = 54), along with its tetraploid, diploid progenitors and other polyploid species, provide a genomic lens for exploring polyploidization-driven adaptive evolution. Whole-genome re-sequencing of 579 accessions collected over ten years, combined with herbicide resistance phenotyping, provides insights into population-scale adaptive dynamics. A genome-wide association study (GWAS) based on large-scale herbicide dose-response assays identifies candidate genes associated with non-target-site resistance (NTSR) in D. sanguinalis populations. Introgression from sympatric relatives has contributed to ecological adaptation in D. sanguinalis and enabled the rapid evolution of NTSR. Introduction Weeds are ecologically resilient organisms that thrive in agricultural ecosystems, exhibiting broad adaptability and robust population dynamics (Mahaut et al., 2020 ; Sharma et al., 2021 ; Stewart, 2017 ). Digitaria sanguinalis (L.) Scop., commonly known as large crabgrass, is one of the world’s worst weeds, occurring from tropical to temperate regions and proliferating in both cultivated and no-tillage farming systems (Burton et al., 2025 ; Galeano et al., 2016 ; Holm et al., 1977 ; Ito et al., 1986 ). At high densities, this species can cause yield losses above 90% and is frequently infested in soybean, maize, and sorghum fields (Oreja et al., 2020, 2012). The genus Digitaria comprises over 220 species, whose identification is complicated by extensive phenotypic convergence (Boonsuk et al., 2016 ; Gould, 1963 ; Kok et al., 1989 ; Sharma and Sharma, 1979 ; Touafchia et al., 2023 ). This taxonomic ambiguity has led to inconsistent assessments of species distributions, ultimately hindering the development and implementation of precise weed management strategies. Polyploidy is common across the plant kingdom and is particularly prevalent among weedy species, where it often contributes to enhanced adaptive plasticity (Jiao et al., 2011 ; Soltis et al., 2015 ). In allopolyploids, the coordinated regulation of homoeologous gene expression and the dynamic reshaping of epigenomic landscapes within a single nucleus introduce substantial complexity, referred as “genomic shock” (McDaniel, 2024 ; Shan et al., 2024 ; Shimizu, 2022 ). In grasses, multiple polyploid lineages have acquired novel stress resilience through this mechanism (Menardo et al., 2016 ; Takahagi et al., 2018 ; Yang et al., 2014 ). A well-known example is hexaploidy wheat ( Triticum aestivum ), which exhibits increased fitness by combining root sodium retention, mediated by HKT1.5 from the diploid Aegilops tauschii , with a higher germination rate inherited from tetraploid emmer wheat (Yang et al., 2014 ). In addition, patterns of gene retention and loss during polyploidization reflect selection pressures driving adaptive evolution (Cheng et al., 2018 ). Notably, disease-resistance genes experienced significant loss in barnyardgrass ( Echinochloa crus-galli ), in contrast to their expansion in wheat, suggesting a trade-off between growth and defense under diverse environmental conditions during polyploidization (Ye et al., 2020 ). D. sanguinalis is a hexaploidy (2n = 6× = 54) (Morin et al., 2015 ), capable of both self-and cross-pollination. Therefore, understanding the evolutionary dynamics associated with polyploidization is crucial for elucidating its genetic variability and adaptive plasticity (Chao et al., 2013 ; Meimberg et al., 2009 ; Soltis et al., 2015 ). The escalating use of agrochemicals has consequently driven the evolution of herbicide resistance in populations of 273 weed species worldwide (Délye, 2013 ; Heap, 2025 ; Huang et al., 2017a ; Powles and Yu, 2010a ). Two primary mechanisms underlie the resistance are target-site resistance (TSR), resulting from mutations or amplification of herbicide target genes, and non-target-site resistance (NTSR), conferred largely through enhanced metabolic detoxification or sequestration pathways involving such major detoxification enzyme families as cytochrome P450s, Glutathione transferases (GSTs), Aldo/keto reductases (AKRs) and ATP-binding cassette transporters (ABC transporters) (Beckie et al., 2019 ; Kreiner et al., 2018a ; Pan et al., 2022 , 2021 ; Powles and Yu, 2010b ; Yuan et al., 2007a ). For D. sanguinalis , resistance has been documented to acetolactate synthase (ALS), photosystem II, Acetyl CoA carboxylase (ACCase), and 5-enolpyruvylshikimate-3-phosphate synthase (EPSPS) inhibitor herbicides (Guan et al., 2024 ; Laforest et al., 2017 ; Wang et al., 2023 ; Yanniccari et al., 2022 ). The ALS-inhibitor herbicide nicosulfuron has been widely used for large crabgrass control since its registration in China in the 1990s (Wang et al., 2023 ). In recent years, however, many populations of large crabgrass have evolved resistance to nicosulfuron through both TSR and NTSR mechanisms (J. Li et al., 2017 ; Mei et al., 2017 ; Zhao et al., 2023 ). This escalating resistance evolution is a growing concern, posing a significant threat to the effectiveness and sustainability of current chemical weed management strategies. However, the molecular and evolutionary basis of NTSR remain unclear due to the lack of genome resources. Hybridization is common in nature (Baack and Rieseberg, 2007 ; Payseur and Rieseberg, 2016 ). Interspecific hybridization followed by introgression is recognized as a powerful evolutionary force across diverse taxa, particularly under rapidly changing environmental conditions where standing genetic variation and de novo mutations are insufficient (Kersten et al., 2023 ; Lyu et al., 2024 ; North et al., 2024 ). While natural interspecific hybridization has been documented in Digitaria , the functional implications of such hybridization events remain poorly understood (Carnahan and Hill, 1961 ; Yasue, 1957 , 1956 ). In European aspen ( Populus tremula ), adaptive introgression has been revealed facilitating adaptation to high latitudes (Rendón-Anaya et al., 2021 ). Another notable example is the Gulf killifish ( Fundulus grandis ), which acquired pollution tolerance through recent introgression of aryl hydrocarbon receptor (AHR) loci from F. heteroclitus (Oziolor et al., 2019 ; Reid et al., 2016 ). Similarly, adaptive introgression may serve as a key mechanism underpinning the wide geographic distribution and rapid evolutionary response of large crabgrass to increasing herbicide selection pressure. In this study, to investigate the evolutionary trajectory and adaptive mechanisms of Digitaria species under environmental change, we assembled T2T genomes of hexaploidy D. sanguinalis and its tetraploid and diploid progenitors. In parallel, we conducted large-scale genomic analyses of 579 Digitaria accessions and herbicide dose-response assays on 196 accessions. Our results revealed that adaptive introgression from closely related species contributed to ecological adaptation in D. sanguinalis . It also mediated the introgression of NTSR-associated genotypes, potentially accelerating the recent evolution of herbicide resistance. Furthermore, GWAS identified loci associated with herbicide resistance. Together, these findings advance our understanding of weed adaptive evolution, informing precision weed management strategies. Results Genomic landscape of Digitaria reference genomes Genome assembly A representative D. sanguinalis accession (#YJ2023) was collected from agricultural fields in Shandong Province, China and its genome was sequenced, as well as its tetraploid progenitor, D. milanjiana (accession #DZ2) and diploid progenitor, D. radicosa (accession #YZGJ2) (Supplementary Note 1) . Cytological analysis confirmed its hexaploidy status (2n = 6× = 54) (Supplementary Fig. 1) , and k -mer analysis ( k = 21, peak depth = 34) of short-read data estimated a genome size of 1.24 Gb (Supplementary Fig. 2) , consistent with flow cytometry results (1.35 pg/1C) of D. sanguinalis (Supplementary Fig. 3) . The estimated heterozygosity was 0.05%. For de novo assembly of D. sanguinalis , we employed complementary long-read technologies: 81× coverage PacBio HiFi reads (N50 = 15.9 kb) and 88× coverage Nanopore ultra-long reads (N50 = 100.2 kb). A chromosome-level assembly was generated using Hi-C data (79× coverage) to scaffold the initial contigs. In total, 419 contigs were anchored into 27 scaffolds, yielding a final assembly size of 1.35 Gb. Due to complexity of the hexaploidy genomes, particularly the high collinearity among homologous chromosomes, phasing posed a major challenge. To delineate subgenomes, we identified subgenome-specific k -mers and clustered homeolog-differentiating scaffolds, enabling consistent partitioning into three distinct subgenomes (Supplementary Fig. 4) . The three subgenomes were designated as C, D, and E, based on the markedly low mapping rate (19.72%) and genome coverage (17.53%) with D. exilis , supporting its distinct genomic origin (Abrouk et al., 2020 ). Following error correction and scaffold ordering, a chromosome-level high-quality assembly was generated with subgenome sizes of 453.1 Mb (CH), 419.9 Mb (DH), and 474.8 Mb (EH), respectively, where “H” denotes hexaploidy origin. The assembly contains only two unresolved gaps (Supplementary Fig. 5) . Assessment with BUSCO (v5.6.1, Poales lineage dataset) showed 99.3% completeness of conserved genes (Manni et al., 2021 ). Genome annotation was performed using a combination of homology-based, transcript-based, and ab initio prediction approaches, and 114,996 gene models were identified after filtering 709.61 Mb (51.8%) of repetitive sequences. Conserved centromeric regions containing tandem repeats (detailed below) were identified on all 27 chromosomes. Telomeric arrays (TTTAGGGₙ) were resolved at 50 of 54 chromosomal, comprising 23 fully terminal chromosomes (telomeres at both ends) and 4 partially terminal chromosomes (single telomere detected) (Supplemental Table 1) . rDNA sequences were identified on seven chromosomes (Supplemental Table 2) . By nation-wide sampling in China, we identified the diploid (2n = 2× = 18) D. radicosa , and tetraploid (2n = 4× = 36) D. milanjiana , as the progenitors of D. sanguinalis (details provided in the next section) (Supplementary Note 2; Fig. 1 a ) . In the same way, two high-quality genomes for D. radicosa (489.39 Mb) and tetraploid D. milanjiana (909 Mb, with subgenome sizes of 453.1 Mb (DT) and 474.8 Mb (ET)) were also generated in this study (details see Methods ). To assess the assembly quality of the three Digitaria reference genomes, we first mapped Illumina paired-end reads to respective assemblies. The results showed that a high percentage (99.51%, 99.48%, and 99.58% in the diploid, tetraploid, and hexaploidy Digitaria genomes, respectively) of sequencing reads could be successfully mapped, with properly paired (Supplemental Table 3) . RNA-seq reads showed normal alignment ratios to their respective genomes (94.12% for D. radicosa , 89.81% for D. milanjiana and 93.32% for D. sanguinalis ) (Supplemental Table 4) . The genome assembly index LAI scores were 14.41, 17.30 and 15.62 for the three genomes, respectively, comparable to those of Arabidopsis (TAIR10) and Vitis vinifera (Jaillon et al., 2007 ; Lamesch et al., 2012 ). We also estimated base-level accuracy and completeness of these assemblies and high assembly consensus quality values, 58.70 (99.74%), 54.51 (99.74%) and 50.08 (99.60%), were achieved for the three genomes, respectively (Supplementary Table 5) . For continuity, we detected potential assembly gaps with low-confidence read supports using CRAQ, and high scores were also gained for the three assemblies (Li et al., 2023 ) (Supplementary Tables 5 and 6) . Taken together, these results suggest that the three assembled Digitaria genomes are of high quality in terms of continuity, completeness, and accuracy. Whole-genome alignments revealed extensive synteny between D. sanguinalis and its diploid/tetraploid progenitors, with conserved macro-collinearity across 95.0% of the genome ( Fig. 1 b; Supplementary Fig. 5) . The results also provide independent validation of our chromosome-scale assembly quality. Speciation of Digitaria genus We calculated the synonymous substitutions per synonymous site ( K s ) for orthologous gene pairs among the Digitaria species with other grass species to estimate their divergence times ( Fig. 1 c ) . The K s peak for orthologs between D. radicosa and O. sativa was 0.57, corresponding to an estimated divergence time of approximately 45 million years ago (mya) ( Fig. 1 c ) , while Digitaria diverged from Eragrostis approximately ~ 35 mya and subsequently from Setaria around ~ 17 mya. This phylogenetic branching timeline is consistent with previous estimates (Huang et al., 2022 ; Zhang et al., 2022 ) (Supplementary Fig. 6) . The two lineages with diagnostic spikelet arrangement phenotypes, D. exilis and D. sanguinalis , diverged at 12 mya. A large-scale amplification of Gypsy-type transposable elements was observed in the D. sanguinalis genome compared to D. exilis (Supplementary Fig. 7a) . Further analysis of subgenome differentiation in D. sanguinalis resolved three ancestral lineages: the C subgenome diverged around 6.9 mya, followed by the bifurcation of the D and E subgenomes at approximately 6.4 mya. Using insertion polymorphisms of long terminal repeat retrotransposons, calibrated against synonymous mutation rates, we estimated that the tetraploidization event leading to D. milanjiana occurred around 0.9 mya, while hexaploidization in D. sanguinalis followed at approximately 0.4 mya. Interesting, the DH and EH subgenomes in D. sanguinalis exhibit similar patterns to those in D. milanjiana (Supplementary Fig. 8) . In addition, we reconstructed a maximum-likelihood (ML) phylogeny based on a concatenated matrix of 2,030 single-copy orthologs among the 14 (sub)genomes and a coalescent-based phylogenetic analysis was also performed integrating individual gene trees ( Fig. 1 c; Supplementary Fig. 9) . These divergence estimates were concordant with our K s -based molecular dating analyses, with robust statistical support at all key nodes. Contraction and expansion of gene family Using ​domain-based gene family quantification across 18 genomes, we identified significant ​lineage-specific contraction in biotic stress-responsive gene families ( Fig. 1 d ) . Consistent with evolutionary patterns observed in other weeds (Wu et al., 2022b ), Digitaria exhibits pronounced contraction of ​NB-ARC domain-containing genes​ (median 252 ± 48.7 copies vs . 447 ± 94.0 in crops; two-tailed t -tests, p = 0.002) and ​D-mannose-binding lectin genes​ (61 ± 9.1 vs . 116 ± 22.8; two-tailed t -tests, p = 0.001). The decay in defense-related genes likely reflects ecological trade-offs that favor growth in ruderal habitats, where biotic stress responses may be reduced in importance (Brown and Rant, 2013 ; Wu et al., 2022b ). Interestingly, comparative analyses revealed a significant expansion of UDP-glucuronosyl/glucosyl transferase (UDP/GT) genes in Digitaria (median ± SD: 162 ± 28.5 copies) relative to Echinochloa species (64 ± 13.1; two-tailed t -tests, p = 1.63e-5). Furthermore, gene families such as GAI-RGA-SCR (GRAS, 67.0 ± 5.6 vs . 43.5 ± 7.3; two-tailed t -tests, p = 0.008) also exhibited expansion (Supplementary Data 1) . These expansions may have contributed to adaptation to herbicide selection in modern weed management systems. Notably, UDP/GTs were significantly enriched on Chr3 in Digitaria (fisher’s test, p < 0.01) (Supplementary Fig. 10) , which also harbors the highest transposon density across the genome (Supplementary Fig. 7b) . This co-localization suggests that the abundance of transposable elements may have contributed to the dynamic expansion of UDP/GTs, potentially facilitating rapid local adaptation. Flood-adapted genes, Echinochloa ​​ displayed marked expansion of ​anaerobic-response Apetala2 (AP2) genes​ (182.0 ± 12.3 copies vs . 159.0 ± 9.9 in D. sanguinalis ; two-tailed t -tests, p = 0.008), reflecting adaptation to prolonged submergence (Supplementary Data 1) . Compared with ancestors’ genomes, ​ D. sanguinalis exhibited a lineage-specific amplification of far-red impaired response 1 (FAR1) DNA-binding genes​ (avg. 71.3 copies vs. 8.5 copies in D. exilis , 59.5 copies in D. milanjiana and 54 copies in D. radicosa ) likely associated with its mat-forming growth habit and the shaded microhabitats it occupies ( Fig. 1 . d) . Concurrently, D. exilis showed distinctive expansion in Heat shock protein 70 (Hsp70, 107 copies), consistent with its adaptation to extreme thermal environments in west African provenances ( Fig. 1 . d) . Population structure and demography of Digitaria species Genome-based species classification To investigate the genetic diversity and population structure of Digitaria , we re-sequenced the genomes of 579 Digitaria accessions collected over the past decade from a wide range of habitats across China ( Fig. 2 a; Supplementary Table 7) . Based on reads mapping rates and genome coverage to the D. sanguinalis reference genome ( Fig. 2 b; Supplementary Table 7) , combined with morphological traits and genome size estimations (flow cytometry and k -mer analysis) (Supplementary Fig. 11; Supplementary Table 7) , the 579 accessions were classified into two major clades defined by distinct spikelet arrangements. The first clade, the ternate-type group, includes D. exilis , D. ischaemum , and D. violascens , species with primarily European distributions. The second clade, the binate-type group, comprises D. ciliaris , D. bicornis , and D. sanguinalis , which collectively represent the most globally invasive agricultural weeds (Areces-Berazain, 2022 ). On average, D. bicornis showed a 76.82% mapping rate with average coverage of 65.44%, 83.89%, and 85.12% across the CH, DH, and EH, respectively (Supplementary Fig. 12) . D. ciliaris achieved a 94.63% mapping rate, with CH, DH, and EH coverage of 95.36%, 84.20%, and 85.94%, respectively (Supplementary Fig. 12) . D. sanguinalis accessions showed the highest mapping rates, averaging 97.46%, with CH, DH, and EH coverage of 98.11%, 98.04%, and 97.03%. Particularly, the D. milanjiana and D. radicosa had differential subgenome coverage patterns ( Fig. 2 a ) . For example, D. radicosa exhibited a mapping rate of 95.05%, with subgenome compartment coverage of CH: 95.63%, DH: 2.07%, and EH: 1.80%, while D. milanjiana showed a mapping rate of 93.17%, with CH, DH, and EH coverage of 7.96%, 86.57%, and 87.75%, respectively (Supplementary Table 7) . This asymmetric subgenome coverage provides genomic evidence supporting their ancestral contributions (i.e. tetraploid and diploid progenitor) to the allohexaploid genome of D. sanguinalis . In contrast, ternate-type Digitaria species consistently exhibited low mapping rates (~ 18.2%) and subgenome coverage (~ 13.6%), consistent with K s -based molecular divergence estimates ( Fig. 1 c; Fig. 2 a ) , indicating a more distant evolutionary relationship with the binate-type clade. Geographic distribution and population structure Our nationwide sampling effort covered 25 provinces across China, spanning from Hainan (18.3°N) in the tropical south to Heilongjiang (47.3°N) in the subarctic north ( Fig. 2 b ) . D. sanguinalis was the dominant species (78.9%) in the sampling population, with notable prevalence in major agricultural regions such as Henan, Shandong, and Shanxi (Supplementary Table 7) . In contrast, the majority (68.8%) of D. violascens accessions were collected from northeastern provinces, Heilongjiang and Jilin. Regional differences in diversity were evident. Southern coastal provinces exhibited higher diversity, with Hainan showing the highest Simpson diversity index and Shanno index, 0.60 and 0.99, respectively, followed by Guangxi and Guangdong. In contrast, lower diversity levels were observed in Anhui and Jiangsu, with Simpson index values of 0.07 and 0.11, respectively (Supplementary Table 8) . We called single nucleotide polymorphisms (SNPs) across the CH, DH, and EH subgenomes, yielding 10.76 million SNPs and 23.76 SNPs/kb for CH, 9.07 million and 21.60 SNPs/kb for DH, and 7.45 million and 15.68 SNPs/kb for EH. These SNPs were used for population structure and phylogenetic analyses of binate-type group (Ternate-type accessions were excluded from subsequent analyses due to low mapping rate) ( Fig. 2 d; Supplementary Figs. 13 and 14) . Within the retained binate-type specimens, each species formed a monophyletic clade in maximum likelihood phylogenies ( Fig. 2 d ) . ​Meanwhile, we identified phylogenetically intermediate outliers ​that​ displayed three convergent signatures of potential hybridization: firstly, they occupied intermediate branch positions between established species clades; secondly, principal component analysis (PCA) positioned them centrally between primary species groupings (e.g. between D. ciliaris and D. sanguinalias ); thirdly, ancestry composition analysis revealed admixed genomic profiles ( Fig. 2 d; Supplementary Figs. 13 and 14) . These patterns collectively support the presence of reticulate evolution within the Digitaria complex. Within D. sanguinalis , four distinct varieties and three hybrid populations were identified, each showing marked biogeographic specialization across China ( Fig. 2 b, d; Supplementary Fig. 15) . The phylogenetically basal var. glabra predominates in northeastern regions and is characterized by the narrowest leaf blades, most compact tiller angles, and the heaviest grain weight among all varieties ( Fig. 2 d, f ) . In contrast, var. parvispicula is dominant in southern provinces, defined by elongated, slender leaves and the lightest grain weight ( Fig. 2 d, f; Supplementary Figs. 16 and 17) . In the middle-lower Yellow River basin, the remaining two varieties exhibit north-south partitioning across the Yellow river ( Fig. 2 b; Supplementary Fig. 15) . Accessions north of the divide share morphological similarities with their southern counterparts; however, var. pubescens , localized on the northern slopes, is distinct in producing heavier seeds and possessing golden bristles on the lemma surface ( Fig. 2 b, e; Supplementary Figs. 16 and 17) . Notably, field surveys revealed that admixed populations consistently inhabit transitional ecotones along the contact zones between discrete varietal ranges ( Fig. 2 b; Supplementary Fig. 15) , further supporting their role as dynamic genetic intermediates within the species complex. Demography history of D. sanguinalis By comparing the unfolded joint site frequency spectrum (SFS) of unlinked SNPs, polarized using known ancestral alleles, we inferred that archaic introgression from D. ciliaris into D. sanguinalis started at ~ 431,653 year before present (yr BP, 95% confidence interval (CI) 350,044 − 2,420,993 year BP), following the speciation of D. sanguinalis , which itself was estimated to have occurred ~ 1.072 mya (95% CI 712,489-2,921,965 year BP) ( Fig. 2 f ) . Var. glabra ​ diverged from the ancestral D. sanguinalis lineage at ​~53,926 year BP (95% CI: 37,101-1,423,489 year BP). This was followed by the separation of the common ancestor of var. sanguinalis and var. parvispicula from var. pubescens at approximately 46,719 year BP (95% CI: 18,199-1,156,488 year BP). During this diversification process, introgression was detected between the ancestral var. pubescens and the common ancestor of the other two varieties. The best-fitting demographic model also predicted ongoing, bidirectional introgression between D. ciliaris and multiple D. sanguinalis varieties since the emergence of var. parvispicula around 39,561 year BP (95% CI: 2,919 − 438,092 year BP) ( Fig. 2 f; Supplementary Fig. 18; Supplementary Tables 9 and 10) . Given the weed’s prevalence in upland agricultural systems, these patterns likely reflect human-mediated dispersal. Agricultural practices, especially mechanized harvesting and commercial seed exchange, may have accelerated the nationwide spread of D. sanguinalis and facilitated genetic exchange among historically isolated subspecies. To examine more recent demographic trends over the past 10,000 years, both folded and unfolded SFS were analyzed and a sharp population bottleneck was detected in var. glabra around 30,000 years ago (Supplementary Fig. 19) . For D. ciliaris , a severe and continuous decline in the effective population size began approximately 1,000 years ago, following an earlier bottleneck. In contrast, after recovering from a bottleneck around 5,000 years ago, the population sizes of var. sanguinalis and var. parvispicula have remained relatively stable over the past 2,000 years. Overall, nucleotide diversity (π) displayed an asymmetric distribution across the Digitaria genomes, with the D subgenome in both D. ciliaris and D. sanguinalis exhibiting the lowest levels of diversity (Supplementary Fig. 20) . Except in var. pubescens , the E subgenome showed higher diversity than the C subgenome, suggesting that var. pubescens underwent distinct genomic alterations leading to elevated diversity. Moreover, the divergence between the D and E subgenomes was more pronounced in D. sanguinalis than in other Digitaria species (Supplementary Fig. 9) , implying that D. sanguinalis experienced stronger post-origin introgression than other species, with a directional bias favoring the CH and EH genomic compartments. Population structure restructuration in ten years As mentioned above, we sampled in Huang-Huai-Hai region over the past decade. Longitudinal genomic surveillance of D. sanguinalis across the Huang-Huai-Hai agroecosystems reveals accelerating restructuring of its population genetic structure (Supplementary Fig. 21a; Supplementary Table 11) . Contemporary populations, encompassing all four recognized varieties (var. glabra , var. sanguinalis , var. parvispicula , and var. pubescens ), exhibit markedly higher admixture diversity compared to historical collections (Supplementary Fig. 21b) . While core geographic clusters have retained notable spatial structure, temporal analyses show genetic homogenization over time. For example, in GR7 population, the proportion of the k 4 ancestral component declined sharply from 89.47% in 2013 to 31.95% in 2023. Concurrently, the admixture index increased from 0.29 to 0.55, reflecting a substantial rise in genomic intermixing (Supplementary Fig. 22; Supplementary Table 11) . Sympatric introgression driving local environmental adaptation Introgression revealed by chloroplast genomes Given the substantial number of hybrids identified through nuclear genomic data, we further constructed a ML phylogeny using SNPs from chloroplast genomes to infer the maternal origins of Digitaria topologies ( Fig. 3 a ) .​ Species-level divergence patterns in the tree were congruent with those inferred from the nuclear SNPs, notably with D. violascens and D. ciliaris forming a monophyletic clade ( Fig. 2 a; Fig. 3 a ) . Interestingly, a subset of D. sanguinalis accessions ( n = 9) were scattered within the clade containing D. ischaemum and D. violascens , suggesting persistent chloroplast introgression between lineages that diverged more than 10 mya ( Fig. 3 a, b ) . Although D. bicornis forms a monophyletic group in the nuclear phylogeny, its chloroplast genomes clustered within Clade 4, which is dominated by D. sanguinalis accessions (95.68%) ( Fig. 2 d; Fig. 3 a, b ) . This implies a D. sanguinalis origin of the D. bicornis chloroplast genome, likely resulting from historical chloroplast capture through hybridization or horizontal transfer. The chloroplast phylogeny further supports the hypothesis of at least two distinct maternal donors in the origin of D. sanguinalis . One lineage corresponds to the monophyletic Clade 4, while the other branches as a sister group to the D. ciliaris monophyly, collectively forming Clade 3, a topology that is discordant with the nuclear genome ( Fig. 3 a, b ) . Both diploid and tetraploid ancestors of D. sanguinalis are positioned within Clade 3, with D. radicosa nested inside the D. ciliaris subclade ( Fig. 3 a ) . This placement aligns with the high subgenome C coverage observed in D. ciliaris (95.36%), suggesting shared ancestry of the C subgenome between D. ciliaris and D. sanguinalis ( Fig. 3 a; Supplementary Fig. 12) . Overall, the chloroplast phylogenies point to extensive interspecific introgression within the genus Digitaria , potentially facilitated by historical hybridization events, especially considering the rarity of natural grafting in grasses. Introgression between sympatric Digitaria accessions Sympatric populations were selected for topological hypothesis testing (Supplementary Fig. 23a, d) . These analyses revealed pervasive introgression, evidenced by consistently significant D -statistics ( D > 0.11; Z > 3 across 7 populations; Supplementary Table 12 ). The strongest introgression signal was detected in the SX population ( D = 0.16), whereas JN exhibited the weakest evidence of introgression ( D = 0.11) (Supplementary Table 12) . This pattern was further supported by sliding-window analysis, revealing that SX harbored the greatest PIG across the genome ( Fig. 3 c; Supplementary Table 13) . In contrast, eastern populations, including DZ, JN and LF, displayed fewer introgressed regions, indicating minimal impact of introgression in these lineages (PIG in Fig. 3 c). To assess the adaptive significance of sympatric introgression, we conducted matrix regression analyses (Mantel/partial Mantel tests) on pairwise shared introgressed genomic regions (PSIG), defined as the Jaccard similarity index of introgressed haplotypes between ‘ D. sanguinalis - D. ciliaris ’ population pairs (Fu et al., 2022 ). These tests aimed to quantify the relative contributions of geographic distance or environmental divergence to observed introgression patterns (Supplementary Fig. 23c; Supplementary Data 3) . Both Mantel tests revealed significant correlations between ​PSIG similarity​ and geographic distance (Pearson's r = -0.70, p = 0.02), as well as environmental divergence (Pearson's r = -0.64, p = 0.01) (Supplementary Table 14) . However, ​partial Mantel tests​ did not identify significant independent contributions from either factor, likely due to high collinearity between geographic and environmental distances (Pearson's r = 0.61, p = 1.48e-5) (Supplementary Fig. 23d; Supplementary Table 14 ). This result held true regardless of whether environmental variation was summarized as a composite distance matrix or decomposed into individual eigenvectors of environmental variables (Supplementary Fig. 23d; Supplementary Table 14 ). Additionally, we observed that introgression tends to reduce genetic differentiation (Supplementary Figs. 24a, c) , suggesting that similar environments facilitate repeated introgression at homologous genomic regions between species. To investigate the functional relevance of introgressed genomic regions, we performed Pfam domain enrichment analyses across populations ( Fig. 3 d ) . The number of introgressed genes varied from 2,181 to 3,472 among populations, with significant enrichment for stress-related domains such as Myb, FBD, AP2, and D-mannose lectin ( Fig. 3 d ) . Notably, the enrichment profiles were largely population-specific: the number of enriched domains ranged from 13 in SX to 25 in LF, with only 14 domains shared across more than three populations (Supplementary Fig. 24d) , suggesting localized introgression preferences possibly shaped by distinct environmental pressures. Among the enriched domains, several detoxification-related families, including cytochrome P450s, UDP/GTs, and ABC transporters, were recurrently detected across multiple populations ( Fig. 3 d; Supplementary Table 15) , indicating potential adaptive roles in metabolic stress responses. Importantly, in the SX population, which is the lowest-latitude population among all sampled groups, introgressed genes were specifically enriched in the Hsp20 protein family, molecular chaperones known to mediate heat shock responses ( Fig. 3 d; Supplementary Fig. 23a) , further supporting the role of environment-driven selection in shaping introgression patterns. Candidate genes of local adaptation across environmental gradient To identify genetic variants associated with environmental adaptation, we conducted genotype-environment association (GEA) analyses using the latent factor mixed model (LFMM), which accounts for background population structure while testing for associations between genotypes and environmental variables ( Fig. 3 g; Supplementary Fig. 25) (Frichot et al., 2013 ). The analysis included 19 environmental variables, comprising 10 temperature-related and 9 precipitation-related factors (Supplementary Data 2) . In total, we identified 9,437 SNPs significantly associated with one or more environmental variables, corresponding to 1,831 genes (Supplementary Fig. 25; Supplementary Table 16) . These environment-associated variants were broadly distributed across the genome, without significant clustering in specific genomic regions, suggesting that environmental adaptation in Digitaria is governed by polygenic architectures rather than localized genomic islands of selection (Supplementary Fig. 25) . Several genes previously implicated in climate adaptation were identified in our analysis as harboring variants significantly associated with environmental variables (Supplementary Fig. 25; Supplementary Table 16). For example, the gene DsRZ2 , which is homologs to OsRZ2 , exhibited strong associations with the minimum temperature of the coldest month (Bio6) ( Fig. 3 g ) . This gene encodes a protein containing both a zinc knuckle domain (PF00098) and an RNA recognition motif (PF00076), and is known to play a critical role in plant protection against cold and freezing stress (Kim et al., 2010 ; Xu et al., 2013 ). Expression analysis confirmed ubiquitous expression of DsRZ2 across all examined tissues, with RPKM values ranging from 4.40 to 7.24. We identified three major DsRZ2 haplotypes, exhibiting distinct geographic distributions (Supplementary Table 17) . Hap1 was fixed (> 90%) in accessions locating northeast that experience coldest winter temperatures − 7.65°C, whereas Hap3 dominated (87% frequency) in southern populations subjected to -4.77°C ( Fig. 3 h; Supplementary Fig. 26) . Haplotype frequencies also showed strong correlation with environmental gradients across accessions ( Fig. 3 i ) . Furthermore, extended haplotype homozygosity (EHH) analysis at the DsRZ2 locus didn’t exhibit significant differences between haplotypes carrying the T or the C allele at the focal SNP ( Supplementary Fig. 27 ; standardized |iHS| score = 0.72). Given the recurrent introgression observed in Digitaria , we tested whether local adaptation in D. sanguinalis may have been facilitated by introgression. In ABBA-BABA statistics, significantly elevated f d values were detected at the DsRZ2 locus in sympatric populations (Fig, 3f; Supplementary Fig. 24b) . It means that, in these D. sanguinalis accessions, genetic variation within an environmentally associated region on Chr4 more closely resembled sympatric D. ciliaris haplotypes than those of allopatric D. sanguinalis populations, supporting a hybrid origin for this ~ 500 kb genomic segment. This introgressed segment​ contains a functionally coordinated cluster of six stress-adaptive loci, including RZ2 and four genes previously characterized in rice ( OsRALyase , OsBIHD1 , OsUBC26 , BK-PP2A ) ( Fig. 3 e, f ) . The presence of these genes in a introgressed block suggests that introgression from D. ciliaris acted as a beneficial reservoir of adaptive alleles, enhancing the environmental resilience of D. sanguinalis in sympatric regions. Temporal escalation and geographic spread of ALS-inhibitor resistance Temporal and geographic distribution of nicosulfuron resistance To quantify the temporal dynamics of ALS-inhibitor resistance in Digitaria , we performed nicosulfuron single-dose bioassays on accessions collected from the Huang-Huai-Hai agroecosystems in 2013, 2015, and 2023, respectively (Supplementary Table 18; Supplementary Data 4) . As expected, resistance levels among surveyed populations exhibited a marked increase over a decade time. Survival rates following herbicide treatment rose from 36.5% in 2013 to 74.2% in 2023 (Supplementary Table 18) , while the mean herbicide control efficacy concurrently declined from 86.0–70.7%. To further characterize resistance variation, we conducted dose-response assays on 196 representative accessions, determining GR₅₀ (herbicide dose causing 50% plant growth reduction) and GR₉₀ values (Supplementary Table 19) . The GR₅₀ estimates varied by more than 2000-fold, ranging from 0.083 g a.i. ha − 1 (observed in accession #15 − 9 from Anhui) to 168 g a.i. ha − 1 (accession #W-21 from Shandong), reflecting substantial inter-population differences in resistance levels. Despite being predominantly collected from ecologically comparable environments, different Digitaria species displayed substantial variation in herbicide resistance ( Fig. 4 a; Supplementary Table 19) .​ Accessions of D. bicornis , primarily sourced from the Hainan island, exhibited the lowest GR 50 values, indicating high susceptibility to nicosulfuron ( Fig. 4 a ) . ​In contrast, D. ischaemum , D. ciliaris , and D. sanguinalis consistently showed higher GR 50 values ( Fig. 4 a; Supplementary Table 19) . ​Notably, the median GR₉₀ value of three weeds reached 84 g a.i. ha − 1 , with 100 g a.i. ha − 1 , 80 g a.i. ha − 1 , and 84 g a.i. ha − 1 of D. ischaemum , D. ciliaris , and D. sanguinalis accessions, respectively, above the recommended field application dose (60 g a.i. ha − 1 ). These variations in GR 50 /GR 90 values in surveyed have already rendered nicosulfuron weed control ineffective. ​To assess the temporal dynamics of resistance development, D. sanguinalis accessions collected from the lower-middle Yellow River region (32°-40°N, 108°-120°E) were used in analysis. Both GR₅₀ (upper-tailed t -tests, p = 4.26e-2) and GR₉₀ (upper-tailed t -tests, p = 4.16e-3) values showed positive correlations with collection years, indicating a progressive increase in resistance over time ( Fig. 4 b ) . This trend highlights the rapid adaptation of D. sanguinalis populations to escalating herbicide selection pressure within agroecosystems. Importantly, accessions exhibiting resistance (GR₉₀ >60 g a.i. ha − 1 ) were found to be geographically widespread across multiple provinces (Supplementary Fig. 28) . This widespread distribution supports the hypothesis that resistance has evolved independently in multiple locations through parallel evolution, rather than through the expansion of a single resistant lineage. ​ Target and non-target site variation Whole-genome sequencing of the Digitaria accessions ( n = 554) identified 26 mutations in the target-site ALS gene, and 25 of the 26 mutations were previously not documented in plants or bacteria (Supplementary Fig. 29a; Supplementary Tables 20 and 21) . Hence it is not sure if these mutations are related to nicosulfuron resistance. Notably, the CH subgenome bore the highest mutational load. Most of these target-site mutations were found in a heterozygous state within individual accessions, in contrast to the predominantly homozygous resistance alleles commonly observed in globally collected Echinochloa populations​ (Supplementary Table 20) (Wu et al., 2022b ).​​ ​Furthermore, these variants remained at low-frequency alleles within the population (Supplementary Fig. 29a; Supplementary Tables 20 and 21) .​​ No shared mutation sites were detected among the three D. sanguinalis ( DsALS) copies, and no accessions harbored all three copies in either homozygous or heterozygous variant states (Supplementary Table 21) .​​ The copy number of DsALS remained stable across the population, with read depths at the three loci following a normal distribution (avg. depth: 0.96 for DsALS-C , shapiro and normality test, p > 0.01; 0.95 for DsALS-D , shapiro and normality test, p > 0.01 and 0.92 for DsALS-E , shapiro and normality test, p > 0.01). As a result, Digitaria , especially of hexaploidy, faces evolutionary constraints in achieving rapid resistance through classical TSR mechanisms, even under strong herbicide selection. ​Beside TSR, NTSR has emerged as an important mechanism underpinning herbicide resistance in weed populations.​​ Gene duplication-mediated dosage effects represent a mechanism for rapid adaptation to intense selection pressures, which illustrated in Alopecurus myosuroides (blackgrass), and bamboo rats (Cai et al., 2023 ; Klure et al., 2025 ).​​ ​​To determine whether NTSR in D. sanguinalis involves expansions, we assessed copy number variation for six key resistance-related gene families (cytochrome P450s, GSTs, UDP/GTs, AKRs, NB-ARC and ABC transporters). We observed that the copy numbers of these gene families predominantly clustered around 1, yet 6 to 73 accessions exhibited multicopy genes (> 3; Supplementary Fig. 29b ). Notably, five D. sanguinalis accessions harbored over 8 copies of P450 family genes (Chr18.2080) (Supplementary Table 22) . However, correlation analysis between resistance levels and NTSR gene copy numbers revealed no association with nicosulfuron resistance. (Supplementary Fig. 29c; Supplementary Table 22) . These findings argue against a model of metabolic resistance mediated by gene amplification, and instead suggest alternative NTSR regulation, such as transcriptional reprogramming, may underlie the observed resistance phenotypes in D. sanguinalis . Transcriptome analysis We performed time-course RNA sequencing on leaf tissue of the resistant (R; accession #21 − 17) versus the susceptible (S; accession #15 − 2) accessions treated with nicosulfuron (Supplementary Note 3; Supplementary Table 23) . In the R accession, 152 NTSR-related genes exhibited induced upregulation, including AKR1 , UGT706D1 , CYP81A6 , and ABCG43 , which are involved in reactive oxygen species (ROS) scavenging and stress response processes (Guo et al., 2024 ; ODA et al., 2011 ; Pan et al., 2022 ; Peng et al., 2017 ). Notably, we identified a short-chain dehydrogenase/reductase (SDR), DsSOH1 , a widely conserved enzyme family implicated in detoxification and abiotic stress responses (Chatterjee et al., 2025 ; Du et al., 2022 ; Loubet et al., 2023 ; Nakazono et al., 2000 ). It exhibited nicosulfuron-inducible upregulation specifically in the R but not the S accession (Supplementary Fig. 30) . In addition, a total of 149 genes exhibited constitutive differential expression, comprising 2 genes ( CYP75B3 and CYP92C21 ) that have homologs in rice involved in biotic stress responses (Chen et al., 2022 ; Li et al., 2021 ). Adaptive introgression enables evolutionary escape from herbicide To identify genomic loci underpinning NTSR, we conducted a genome-wide association study (GWAS) using 141 accessions based on their GR₅₀ values under nicosulfuron treatment (Kang et al., 2010b ). A total of 40 resistance-associated SNPs (rSNPs) was identified, corresponding to 19 unique genes ( Fig. 4 d ) . Among these, three GWAS-prioritized genes correspond to homologs of known herbicide resistance determinants in Poaceae (Chen et al., 2025 ; Goldberg-Cavalleri et al., 2023 ; Pan et al., 2021 , 2022 ), including an ortholog of the ABCC8 transporter, previously functionally validated in barnyardgrass as mediating glyphosate resistance via vacuolar sequestration (Pan et al., 2021 ). Notably, rSNPs were located near DsSOH1 and DsCYP81A6 , both of which showed herbicide-induced differential expression following the nicosulfuron treatment ( Fig. 4 d; Supplementary Fig. 30; Supplementary Note 3) . Herbicide resistance levels were quantitatively assessed using the Resistance Index (RI). Based on the RI values, we categorized individuals into four distinct resistance groups: Susceptible (S), Low Resistance (LR), Moderate Resistance (MR), and High Resistance (HR) (Supplementary Table 19) . To investigate the origin of herbicide resistance, we employed the f d statistic, a metric optimized for detecting local ancestry and admixture within genomic windows. A prominent f d peak was detected specifically within the DsSOH1 locus in HR populations, whereas the MR and LR groups exhibited progressively weaker admixture signals ( Fig. 4 e ) . Notably, the DsSOH1 variant was not fixed in HR accessions (allele frequency ≈ 0.4), collectively suggesting recent adaptive introgression through introgression ( Fig. 4 e ) . In parallel, elevated genome-wide F ST values revealed considerable population subdivision within D. sanguinalis (Jakobsson et al., 2013 ; Martin et al., 2015a ) (Fig. 4 e). Remarkably, the regions with the highest F ST differentiation between resistant and susceptible populations coincided precisely to the f d peak interval, strongly implicating introgression from D. ciliaris as the driving force behind resistance-associated divergence in D. sanguinalis ( Fig. 4 e; Fig. 4 d ) . Although the EHH analysis revealed a notable difference between the A and C alleles, the |iHS| value (1.39, within the 90th percentile of the genome-wide distribution) did not reach the conventional threshold for detecting recent strong positive selection (Sabeti et al., 2002 ) (Supplementary Fig. 31a) . To clarify the ancestry of the introgressed genomic region, we employed Bayesian Phylogenetics and Phasing (BPP v4.0) (Flouri et al., 2020 ) under an explicit introgression model. Parameter estimates supported a topology where the DsSOH1 haplotypes in resistant D. sanguinalis originated from recent introgression with D. ciliaris ( φ = 0.3, 2.5% HPD = 0.24, 97.5% HPD = 0.36), while alternative topologies were statistically rejected (Supplementary Table 24) . Moreover, ​speciation between D. sanguinalis and D. ciliaris substantially predates the inferred introgression event​ (divergence τ = 0.002010 vs . introgression divergence τ = 0.000056), providing further confirmation that resistance alleles arose via admixture. Aligning the geographically widespread distribution of resistant D. sanguinalis accessions, maximum-likelihood phylogenies constructed from introgressed SNPs consistently grouped resistant accessions within clades of sympatric D. ciliaris ( Fig. 4 h ) . Specifically, HR haplotypes from ​Hebei and ​Anhui formed sister clades with locally collected D. ciliaris accessions, with short branch lengths ( Fig. 4 i ) . This recurrent phylogenetic pattern suggests that NTSR evolved through parallel, spatially restricted, and recent introgression events between neighboring Digitaria populations. Analysis of introgression patterns across different resistance population revealed a gradient in the distribution of genomic windows derived from D. ciliaris , with PIG positively correlating with resistance level (LR: 18.93 Mb; MR: 27.64 Mb; HR: 30.86 Mb). Leveraging ​RFmix v2.0​ for local ancestry inference at rSNPs ( n = 131,240), we quantified the ​introgression dosage, defined as the proportion of D. ciliaris ancestry, across all individuals (Supplementary Fig. 31c) . ​​Longitudinal analysis of rSNPs further revealed a temporal increase in resistance allele frequency (RAF) over the past decade (Supplementary Fig. 31d) . ​​The cumulative genomic burden of introgressed haplotypes supports a polygenic architecture underlying NTSR, wherein increasing D. ciliaris ancestry dosage may contribute to enhanced herbicide tolerance. Discussion Accurate taxonomic delineation of Digitaria species forms a foundation for basic research and precision weed management in these species (Kok et al., 1989 ; Sharma and Sharma, 1979 ). However, classification within the genus remains challenging due to morphological convergence among over 220 Digitaria species (Georgia and Georgia, 1916 ; Henrard, 1950 ; Vietmeyer et al., 1996 ). To address these complexities, we employed an integrative approach that combined phenotypic traits, such as genome size, 1000-grain weight, and aspect ratio, with genomic indicators, including whole-genome mapping rates and coverage depth profiles ( Fig. 2 a, d, e ) . This multidimensional strategy enabled precise species discrimination across the genus and revealed ecotypic differentiation within D. sanguinalis , where geographically distinct populations exhibited adaptations to local environmental conditions. For example, D. sanguinalis var. parvispicula exhibits ​significantly expanded leaf area indices, facilitating accelerated carbon acquisition under ​high-temperature/high-precipitation regimes. Together, we provided a robust taxonomic framework for the genus Digitaria , resolving long-standing classification ambiguities and informing targeted herbicide strategies for managing economically important weed species. Evolution of herbicide resistance in populations of numerous weed species underscores the urgent need to understand the genetic basis of resistance and their underlying evolutionary dynamics, in order to inform effective management strategies (Heap, 2025 ; Luo and Liu, 2025 ). Target site mutation is often the popular resistance mechanism (Beckie et al., 2019 ; Délye et al., 2013 ; Powles and Yu, 2010b ). However, our analysis of 81 D. sanguinalis resistant populations revealed ​no known resistance mutations​ or copy number variation associated with herbicide resistance phenotypes (Supplementary Fig. 29a; Supplementary Tables 20 and 21) , consistent with previous findings in Chinese resistant Digitaria populations (Guan et al., 2024 ; Mei et al., 2017 ; Wang et al., 2023 ). This observation presents a paradox, given the long-standing hypothesis that polyploid genomes are expected to favor TSR evolution due to the presence of multiple gene copies. Nevertheless, in polyploid species, the effect of target site mutations can be masked or diluted by co-existing wild-type alleles, and hence its contribution to resistance may be influenced by several factors, including ploidy level, the number of mutant alleles, the expression level of the homolog harboring the mutation, and the dominance of the mutation itself, as discussed in Yu et al (Yu et al., 2013 ). For example, a transcriptomic study by Hereward et al. (Hereward et al., 2018 ) on glyphosate resistance in polyploid Conyza bonariensis revealed that the glyphosate target EPSPS 106 mutation was present in both resistant and susceptible lines. However, the mutated allele was expressed at lower levels than the wild-type copies, and thus no contribution to glyphosate resistance. Therefore, despite the general ecological success and invasiveness of polyploids, our findings suggest that a large number of crabgrass populations do not exhibit an elevated propensity for evolving target-site mutations (Rosche et al., 2017 ; Stevens et al., 2020 ). Among other factors (see below), the dilution effect by multiple WT alleles may weaken the mutant alleles and hence un-favors the allele fixation in the population. NTSR poses a potentially greater threat to agricultural systems than TSR, due to its inherently polygenic nature and unpredictable evolutionary trajectories (Baucom, 2019 ; Kreiner et al., 2018b ; Yuan et al., 2007b ). In our study, we identified 40 NTSR candidate loci that were distant from ALS genes ( Fig. 4 d ) . Instead, these loci were near cytochrome P450, GST, AKR and UDP/GT genes, key families functionally associated with detoxification. Whereas NTSR often acts in combination with TSR to modulate resistance phenotypes, as observed in many other herbicide resistant weedy species, it appears to be the predominant mechanism in D. sanguinalis , likely due to the lack of TSR mechanisms (Supplementary Table 20) (Kreiner et al., 2021 ). This pattern mirrors findings in blackgrass, where mesosulfuron-methyl and clodinafop resistance is likewise more dominated by NTSR pathways (Délye et al., 2010 ; Goldberg-Cavalleri et al., 2023 ; Kersten et al., 2023 ). In our study, significantly associated mutants in GWAS were present at low frequencies within resistant accessions (average allele frequency = 0.063). This spatial heterogeneity in resistance architecture likely reflects mechanistic diversification shaped by localized agricultural selection regimes. Divergent cropping patterns and herbicide application histories/strategies, particularly the increasing reliance on complex chemical mixtures in modern farming, have promoted context-dependent adaptive specialization (Huang et al., 2017b ; Liu et al., 2021 ). For instance, sequential treatments involving pre-emergence soil-applied herbicides (e.g., pyroxasulfone or oxadiazon) followed by post-emergence applications of nicosulfuron or mesotrione potentially reduce the effectiveness of single target-site mutations. Instead, they intensify selection pressure for polygenic NTSR architectures (Comont et al., 2020 ). Notably, the observed low-frequency mutant may act as compensatory adaptations that mitigate fitness penalties associated with primary resistance mechanisms (Rutland et al., 2021 ). The functional relevance of these variants will be further evaluated in future studies by integrating key phenotypic covariates, such as relative growth rate. In conclusion, our findings illustrate a compelling case in which NTSR emerges as the prevalent survival strategy under the selection pressures imposed by multi-herbicide treatments. Although introgression is increasingly recognized as a powerful driver of adaptive evolution, instances of beneficial interspecific introgression remain relatively rare, primarily due to anticipated genomic incompatibilities (Fu et al., 2022 ; Hedrick, 2013 ). Our study uncovers an exception in Digitaria , particularly between D. ciliaris and D. sanguinalis , where recurrent post-divergence introgression appears to have facilitated environmental adaptation ( Figs. 2 ) . Specifically, haplotypes introgressed from D. ciliaris into D. sanguinalis populations are associated with enhanced cold tolerance, a key advantage given their overlapping distributions across climatically heterogeneous regions. The integration may not be driven solely by the fitness advantages of foreign fragments (Harrison and Larson, 2014 ; Mallet, 2005 ). The geographic proximity and environmental similarity between the two species likely facilitated such introgression. Furthermore, the ancestral introgression between D. ciliaris and D. sanguinalis following their divergence, may have contributed to their similar genomic background (Supplementary Fig. 11) , thereby reducing the risk of deleterious effects typically associated with interspecific hybridization (Johnson, 2010 ; Maheshwari and Barbash, 2011 ). Together, these conditions reduce the likelihood of epistatic incompatibilities commonly associated with interspecific introgression and illustrated that introgression accelerated by phylogenetic proximity and ecological convergence. While introgression events in weeds have garnered increasing documentation in recent years, this study provides the first empirical evidence of ​adaptive introgression at herbicide resistance loci (Ribeiro et al., 2025 ; Wedger et al., 2024 ; Wu et al., 2022b ). Our analyses detected introgression signals flanking mutations identified in GWAS, and revealed a correlation between the proportion of introgressed regions and resistance level (Supplementary Fig. 31) . This pattern implicates weedy relatives as critical reservoirs for herbicide adaptation, paralleling documented cases of ​crop-to-weed resistance transmission​ via pollen-mediated introgression (L. Li et al., 2017 ; Li et al., 2024 ; Zhu et al., 2024 ). The rapid evolution of herbicide resistance in weed populations illustrates how extreme anthropogenic selection can override the fitness costs typically associated with introgressed alleles (Baucom, 2019 ; Kreiner et al., 2022 , 2021 ). Severe anthropogenic selection may offset the relative fitness costs of foreign alleles, promoting the retention, or even fixation, of introgressed haplotypes that contributed to resistance (Kersten et al., 2023 ; Kreiner et al., 2022 ). In waterhemp ( Amaranthus tuberculatus ), introgression is considered to have shaped the landscape-scale distribution of herbicide resistance (Kreiner et al., 2022 ; Meimberg et al., 2009 ). However, no similar introgression signals associated with herbicide resistance were identified in hexaploidy barnyardgrass ( Echinochloa crus-galli ) populations (Wu et al., 2022b ). Instead, population structure analysis reveals distinct subspecies differentiation in barnyardgrass, which likely reflects a long history of self-fertilization and limited interspecific genetic exchange. Indeed, crabgrass and waterhemp exhibit sympatric distribution with closely related species, providing opportunities for rapid dissemination of herbicide-resistant genotypes through introgression (Supplementary Fig. 12) (Kreiner et al., 2019 ). To our knowledge, most weed species have sympatric closely related species, including crops (e.g., weedy rice and cultivated rice) or other weedy species (such as Amaranthus and Setaria ). Introgression likely plays a significant role in the adaptation of these weed complexes to agricultural environments. However, limited genomic research on weedy species has hindered our understanding of this model for rapid adaptation. In summary, this study presents genome assemblies for diploid, tetraploid, and hexaploidy Digitaria species, establishing essential genomic resources for global accession profiling, herbicide-resistance crop breeding, weed adaptive evolution studies and development of next-generation nucleotide herbicides. Most importantly, we demonstrate that introgression constitutes a significant source of adaptation related variation, offering critical insights that can inform the development of more integrated and sustainable weed management strategies. Method Genome sequencing and assembly Voucher specimens of the three sequenced species were deposited in the Herbarium of Zhejiang University (HZU), with accession numbers HZU60147516 ( D. sanguinalis , #YJ2023), HZU60147511 ( D. milanjiana , #DZ2), and HZU60147514 ( D. radicosa , #YZGJ2). For D. sanguinalis , total genomic DNA was extracted from young leaves using the cetyltrimethylammonium bromide (CTAB) method. High-molecular-weight DNA was prepared via the nuclei method (M. Zhang et al., 2012 ) for Nanopore ultralong sequencing and library construction, followed by sequencing on a PromethION platform. Approximately 116.98 Gb of ultralong reads (N50 > 100 kb) were assembled de novo with NextDenovo v2.5.2 using default parameters (Hu et al., 2024b ). In parallel, single-molecule real-time (SMRT) sequencing libraries were constructed according to the Pacific Biosciences protocols and sequenced on the PacBio Sequel II system using the circular consensus sequencing (CCS) approach. HiFi reads and ultralong reads were co-assembled with Hifiasm v0.19.8-r603, and the contiguity of initial assembly was improved combining initial NextDenovo assembly using Quickmerge (Chakraborty et al., 2016 ; Cheng et al., 2021 ), with validation by long-read mapping depth. The assembly were further polished by PacBio HiFi data and Illumina data using NextPolish2 (v0.2.1) with recommended parameters (Hu et al., 2024a ). Hi-C data (2 × 150 bp paired-end reads) were processed with YaHS v1.1 to generate a chromosome-scale contact map (Supplementary Fig. 4a) , which was manually curated in Juicebox v1.11 (Durand et al., 2016 ; Zhang et al., 2021 ; Zhou et al., 2023 ). Subgenome phasing was conducted using a k -mer based approach implemented in SubPhaser (Jia et al., 2022 ). For D. milanjiana , genomic DNA was also extracted from young leaves using the CTAB method. Illumina paired-end libraries were constructed according to the manufacturer’s protocol (Illumina, USA). PacBio long reads were generated, error-corrected, and assembled into contigs using Hifiasm. The high-contiguity D. sanguinalis assembly was used as a reference to anchor and order fragments, and the contigs were further scaffolded into pseudochromosomes against the D and E subgenomes of D. sanguinalis using Ragtag v2.1.0 (Alonge et al., 2022 ). For D. radicosa (#YZGJ2), DNA extraction, library construction, and assembly followed the procedures described for D. milanjiana . Genome annotation Repetitive elements in the three genomes were annotated following previously described methods (Huang et al., 2024 ). Briefly, repeat families were identified de novo and initially classified using RepeatModeler v1.0.10 (Tarailo-Graovac and Chen, 2009 ), and genome-wide repeat annotation was subsequently performed with RepeatMasker v4.0.7 (Tarailo-Graovac and Chen, 2009 ). Protein-coding genes were predicted using an integrative strategy that combined ab initio prediction, homology-based inference, and transcriptome-supported annotation. Ab initio predictions were generated with Fgenesh and AUGUSTUS v3.2.2, whereas homology-based evidence was obtained using GMAP (Salamov and Solovyev, 2000 ; Stanke et al., 2006 ; Wu and Watanabe, 2005 ). All evidence types were merged using EVidenceModeler v1.1.1 (Haas et al., 2008 ; Stanke et al., 2006 ), resulting in a non-redundant consensus gene set. Gene models were retained only if supported by homologous proteins evidence, transcript alignments, or by at least two independent ab initio predictions. Low-confidence models, defined as those encoding peptides < 50 amino acids or showing significant similarity to repetitive elements in Repbase (E-value 30%, and coverage > 25%), were filtered out to improve annotation accuracy. Functional annotation of the predicted protein-coding genes was conducted using InterProScan v5.24-63.0 for Digitaria spp., Echinochloa spp., O. sativa , S.italica and P. hallii (Abrouk et al., 2020 ; Goff et al., 2002 ; Lovell et al., 2018 ; Zdobnov and Apweiler, 2001 ; G. Zhang et al., 2012 ). Homologs previously cloned in rice and maize were annotated via BLAST searches, retaining hits with E-value 50%. Repetitive elements annotation Centromeric satellite repeats were predicted using the Tandem Repeat Annotation and Structural Hierarchy (TRASH) pipeline (Wlodzimierz et al., 2023 ) in two iterative rounds. In the first round, genomic sequences were partitioned into 1-kb windows, and local k -mer frequencies were calculated to detect repeat-enriched regions under default parameters. The most abundant repeat templates were clustered and extracted using CD-HIT (Li and Godzik, 2006 ). In the second round, TRASH was executed with the parameters “--simpleplot --frep 10 --N.max.div 5 --par 5 --seqt,” with the extracted templates provided through the “--seqt” option. Windows were scored based on the proportion of repeated k -mers, and regions exceeding the threshold were classified as repeat-rich. Tandem repeats within these windows were further characterized by the distances between identical k -mers, enabling the identification of consensus repeat units, including CEN113 , CEN159 , and CEN178 , hereafter collectively referred to as cenSat. Ribosomal DNA (rDNA) regions were annotated using BLAST searches against maize rDNA references, including 5S rDNA (DQ351339), 5.8S rDNA (AF019817), and the intergenic spacer (AF013103). Telomeric repeats were identified by scanning chromosome termini for high-copy-number tandem arrays of the canonical monomer “TTTAGGG”. Genome quality assessment Assembly quality for each genome was evaluated in terms of completeness, correctness, and continuity. For completeness, NGS short reads and PacBio HiFi long reads were mapped to their respective assemblies using BWA mem (0.7.17-r1188) (Li and Durbin, 2010 ) and Minimap2 (v2.03) (Li, 2021 ) under default parameters, respectively. Mapping statistics were summarized with Sambamba Flagstat (v 1.0.1) (Tarasov et al., 2015 ). Assembly completeness was further assessed using the LTR Assembly Index (LAI; Ou et al., 2018 ) and BUSCO v5.5.0 with poales_odb 10 database (Manni et al., 2021 ). Additionally, k -mer based completeness was estimated with Merqury v1.3 (Rhie et al., 2020 ). For this, short reads were split into 21-mers using Meryl v1.4, and rare k -mers were removed (meryl gt 1) prior to generating a k -mer reference library. For correctness, base-level accuracy was evaluated by estimating quality values (QV) with Merqury, based on the same k -mer library. For continuity, potential assembly gaps and structural inconsistencies were identified using CRAQ (Li et al., 2023 ). Long and short reads were aligned against assemblies, and AQI scores were computed to detect local and large-scale assembly errors by examining clipped alignments. Phylogenetic analysis Homoeologous exchanges (HEs) in polyploids can bias phylogenetic inference; thus, candidate HE regions in the D. sanguinalis genome were identified and excluded prior to phylogenetic analyses. Approximately 99× short reads from D. radicosa and 50× from D. milanjiana were mapped to the D. sanguinalis reference genome using Bowtie2 under default settings (Langmead and Salzberg, 2012a ). Mapping depths were calculated in 100-kb sliding windows across the genome. For the D and E subgenomes, windows with coverage exceeding 20× and showing higher mapping depth for D. radicosa than D. milanjiana were defined as candidate HE windows; the same criteria were applied to the C subgenome. Adjacent candidate HE windows were merged, yielding 4 HE regions, and genes within these regions were excluded from subsequent phylogenetic reconstruction. Phylogenetic relationships among Digitaria and related species were inferred using three approaches. First, genetic divergence was estimated based on synonymous substitution ( K s ) values, and pairwise K s peak distributions were used to infer relative divergence times. Second, a concatenated alignment of 2,030 single-copy orthologs, identified by OrthoFinder (Emms and Kelly, 2015 ) and free of HE-associated genes, was used to construct a maximum-likelihood tree, thereby minimizing potential HE-related artifacts. Amino acid sequences were aligned with MAFFT v7.310 (Rozewicki et al., 2019 ) and trimmed with TrimAl under default parameters (Capella-Gutiérrez et al., 2009 ). The ML tree was generated in IQ-TREE v1.6.12 (Minh et al., 2020 ) with 1,000 bootstrap replicates and the best-fit model (GAMMA + JTT + F4) selected by ModelFinder. Third, a total of 2,030 single gene trees were inferred individually using RAxML under the best-fitting amino acid substitution models, and a coalescent-based species tree was subsequently estimated with ASTRAL v5.7.8 (Stamatakis, 2014 ; Yin et al., 2019 ). Multi-year collection and phenotyping of Digitaria accessions A total of 579 Digitaria accessions were collected from 24 maize-producing provinces in China between 2013 and 2023 (Supplementary Table 7) . Seed collected prior to 2023 were preserved at the Plant Protection Research Institute, Shandong Academy of Agricultural Sciences, with the majority of accessions originating from the middle-lower Yellow River basin (Supplementary Fig. 27) . In 2023, 496 accessions were grown under uniform field conditions in fields at the Jiyang Research Station of the Shandong Academy of Agricultural Sciences (Jinan), with five representative individuals planted per accession for phenotypic assessment. The geographic distribution of collected accessions was visualized using R v4.3.1. Based on detailed collection-site metadata, 272 accessions were further categorized into three ecological habitat types: natural habitats ( n = 40; riverbanks or wilderness), agricultural habitats ( n = 221; active croplands), and disturbed habitats ( n = 11; parks or roadsides). Resequencing and variant calling Genomic DNA was extracted from fresh leaves following standard CTAB-based protocols. Paired-end resequencing libraries were constructed and sequenced on the DNBSEQ T7 platform. Raw reads were quality-filtered using fastp v0.24.2 (Chen, 2023 ), and high-quality paired-end reads were mapped to the updated D. sanguinalis reference genome (#YJ2023) using Bowtie2 with default parameters (Langmead and Salzberg, 2012b ). Whole-genome variant detection and filtering were performed using an integrated pipeline (Ye et al., 2019 ). To minimize false positives due to high sequence similarity between subgenomes, resequencing reads of YJ2023 were realigned to its reference, and variants detected in this process were excluded from the final dataset. These variants called in YJ2023 were removed from the final variant dataset. These variants were further filtered with the minor allele frequency (MAF) greater than 0.01 and missing rate less than 30%. Functional annotation of all high-confidence variants was performed using SnpEff v3.652 (Cingolani et al., 2012 ). Population structure and genetic diversity Species identity of each accession was first assessed by calculating read mapping rates to the YJ2023 reference genome and estimating genome coverage using sambamba v1.5 and bamdst (He et al., 2021 ; Tarasov et al., 2015 ). Population structure was inferred with FastStructure (Raj et al., 2014 ) using whole-subgenome SNPs and synonymous SNPs from subgenomes C, D, and E, with k values ranging from 2 to 9. Phylogenetic relationships were reconstructed with FastTreeMP (Price et al., 2010 ) based on SNPs from 499 accessions (excluding D. violascens and D. ischaemum due to low genome coverage), using 51 D. ciliaris accessions as the outgroup; branch support was assessed with 1,000 bootstrap replicates. Trees were visualized with iTOL v7 ( http://itol.embl.de ) (Letunic and Bork, 2024 ). Principal component analysis (PCA) was conducted in PLINK v1.90b6.20 (Chang et al., 2015 ) using a linkage-disequilibrium pruned SNP set (10 SNPs per 50-kb sliding window, r ² < 0.5). Nucleotide diversity (π) was estimated with VCFtools v0.1.17 (Danecek et al., 2011 ) in non-overlapping 20-kb, 50-kb and 100-kb windows, and genome-wide or subgenome-level diversity was calculated as the mean π across all windows within each population. Demographic history inference based on site frequency spectrum (SFS) To reconstruct the demographic history of Digitaria spp. and assess the role of introgression during species and lineage divergence, we performed composite maximum-likelihood (ML) inference based on the site frequency spectrum (SFS). Joint folded two-dimensional SFSs (2D-SFSs) were generated from four-fold synonymous SNPs using easySFS.py ( https://github.com/isaacovercast/easySFS ). Population groupings were defined according to the FastStructure results ( k = 4). Eight demographic scenarios were designed to test the occurrence and duration of introgression between lineages and species (Supplementary Fig. 17) , with divergence time priors determined from fossil calibrations in TimeTree 5 (Kumar et al., 2022 ). Likelihood estimation for each scenario was conducted using fastsimcoal2 (Marchi et al., 2024 ), with 100,000 coalescent simulations per likelihood estimation (-n 100,000) and 40 expectation-conditional maximization (ECM) cycles (-L 40). Model selection was performed using the Akaike information criterion (AIC), calculated as AIC = \(\:\text{2}\text{k}\text{-2}\text{ln}\text{(L)}\) (MaxEstLhood), where k represents the number of estimated parameters and MaxEstLhood is the ML function value for each model. To avoid convergence to local optima, each analysis was repeated at least twice, and the best-supported model based on AIC was rerun 100 times to obtain refined parameter estimates. After that, 100 independent DNA polymorphism datasets were simulated as joint SFSs conditional on estimated demographic parameters. ML analysis was then applied to each joint SFS over 40 ECM cycles to obtain confidence intervals (CIs) for final estimates. Changes in effective population size through time were further inferred using Stairway Plot v2.0 (Liu and Fu, 2020 ), based on both folded and unfolded SFSs. SFSs for each population group were constructed and folded from the same SNP dataset used for fastsimcoal2 analyses. Phylogeny of chloroplast genomes The reference chloroplast genome of YJ2023 was assembled de novo using GetOrganelle (v1.7.7.1) (Jin et al., 2020 ). For other accessions, chloroplast-derived reads were extracted from clean paired-end resequencing data with GetOrganelle and mapped to the YJ2023 chloroplast reference genome. Variants were called using the GATK pipeline, resulting in 4,742 high-quality chloroplast SNPs after filtering for a minor allele frequency (MAF) > 0.05 and a missing rate < 0.2 across 579 Digitaria individuals. A maximum-likelihood (ML) tree was inferred using IQtree with 1500 bootstrap replications (Minh et al., 2020 ), and the tree was rooted with Setaria italica as the outgroup. Detection of sympatric introgression between D. ciliaris and D. sanguinalis Sympatric population pairs were defined based on pairwise geographic distances calculated with the “great_circle” method in the geopy package, using a 150-km threshold. Populations with at least two D. ciliaris and five D. sanguinalis accessions were retained for downstream analyses. Introgression between sympatric populations was assessed using D -statistics (Durand et al., 2011 ; Green et al., 2010 ), and the extent of introgressed genomic regions was quantified with the modified f d statistic (Martin et al., 2015b ). The phylogenetic topology (((P1, P2), P3), O) was applied, with D. bicornis as the outgroup (O), sympatric D. sanguinalis as P2, sympatric D. ciliaris as P3, and allopatric D. sanguinalis accessions as P1. Under the null hypothesis, ABBA and BABA site patterns are expected to occur at equal frequencies due to incomplete lineage sorting, whereas an excess of ABBA sites indicates introgression between P2 and P3. To minimize sample size bias, eight individuals were randomly selected for P1 and P2, and three for P3 and the outgroup. Introgression was evaluated in two steps. First, D -statistics were calculated at whole-genome and chromosome levels. To account for linkage disequilibrium, significance was assessed using a block jackknife approach (Durand et al., 2011 ), dividing the genome into 1-Mb blocks and sequentially removing one block at a time to estimate the mean and variance of D . Second, genomic regions affected by introgression were identified using non-overlapping windows of 10, 50, and 100 kb. Both D and f d statistics were computed with “ABBABABAwindows.py” and F ST values for the same windows were calculated with ‘popgenWindows.py’ (Martin et al., 2015c ). Windows containing fewer than 200, 100, or 20 SNPs (for 100-, 50-, and 10-kb windows, respectively) were excluded. According to the filtering criteria used by Zhou et al. (Zhou et al., 2020 ), f d values outside the 0–1 range were set to zero. The overall introgression level was quantified as the Proportion of Introgression across the Genome (PIG; Zhou et al., 2020 ). Regions with f d >0.4 were defined as significantly introgressed, and genes within these regions were subjected to functional enrichment analysis. Environmental factors and correlation with introgression To investigate the relationship between introgression and environmental variation, 91 environmental variables were retrieved from the WorldClim database ( http://www.worldclim.org/ ) (Supplementary Data 2 and 3) . These included four primary categories, temperature, precipitation, wind speed, and solar radiation, and 19 additional bioclimatic variables. Pairwise environmental distance matrices for the four primary categories were calculated as Euclidean distances using the R package ‘ecodist’ (Goslee and Urban, 2007 ), while a separate environmental distance matrix was generated from the 19 bioclimatic variables. Longitude and latitude were also used as two complex environmental factors that reflect humidity and circadian rhythm, respectively. Geographic distances among populations were computed using the “great_circle” method in the ‘geopy’ package, and statistical significance in subsequent analyses was assessed with 10,000 permutations. The proportion of shared introgressed genomic regions across the genome (PSIG; Fu et al., 2022 ) was used to quantify shared introgression among sympatric populations. Mantel and partial Mantel tests were performed in ‘ecodist’ to evaluate correlations between PSIG and pairwise geographic distances, as well as environmental distance matrices, thereby assessing the relative contributions of genetic drift and environment-driven selection to introgressed allele distribution. Identification of environment-associated genetic variants Variants with a minor allele frequency (MAF) > 0.01 and a missing genotype rate (GENO) > 0.2 were retained, resulting in a dataset of 3,981,523 SNPs for downstream analyses. Associations between allele frequencies and 19 environmental variables were first assessed using a univariate latent factor mixed model (LFMM) implemented in the R package LEA v3.14.0 (Gain and François, 2021 ). Latent factors were decided based on the ancestry clusters inferred from FastStructure, consistent with the demographic history analysis. For each environmental variable, five independent runs were performed, with 5,000 iterations as burn-in followed by 10,000 sampling iterations. Median p values from the five runs were adjusted for multiple testing using a 5% false discovery rate (FDR) and Bonferroni correction threshold to identify significant associations. Single-dose bioassays on Digitaria A single-dose herbicide bioassay was performed on Digitaria accessions ( n = 53 in 2013, n = 56 in 2015, n = 52 in 2023) at the 2–3 leaf stage. Plants were grown in 9-cm-diameter pots filled with sterilized potting soil and maintained under controlled environmental conditions (30/25°C day/night, 16-h photoperiod) until herbicide application. Commercial formulations of nicosulfuron (field-recommended dose: 60 g a.i. ha − 1 ) was applied using an enclosed cabinet sprayer calibrated to deliver 450 L ha − 1 at a pressure of 0.4 MPa (ASS-5, Information Technology Research Center, Beijing, China). Untreated control (0 g a.i. ha − 1 ) was also included for comparison. Each treatment, including the control, was replicated four times with one pot per replicate. Survival rate (%) was calculated as: $$\:\left({\text{N}}_{\text{s}}\text{÷}{\text{N}}_{\text{T}}\right)\text{×100%}$$ N s is the number of surviving plants, and N T is the total number of plants per pot. Plants were classified as surviving if they exhibited active regrowth or retained green tissue. Aboveground biomass from each pot was harvested, oven-dried at 70°C for 72 hours, and weighed. Control efficacy (%) was assessed as the percentage of biomass reduction relative to the untreated control: $$\:\left(1-{\text{B}}_{t}\text{÷}{\text{B}}_{c}\right)\text{×100%}$$ (1 − Treated biomass / Mean control biomass) × 100%. B t is the biomass in the treatment, and B c is the biomass in the control. Whole-Plant Resistance Bioassay Seeds of 196 Digitaria accessions were collected, air-dried, and stored at 4°C prior to use. Seeds were sown in moist loam soil in 9-cm-diameter pots, covered with 1 cm of soil, and grown under greenhouse conditions (30/25°C day/night, 16-h photoperiod) with sub-irrigation. Herbicide resistance to nicosulfuron was evaluated using whole-plant bioassays. At the three-leaf stage, ten seedlings were foliar treated with the commercial formulation of nicosulfuron at seven doses, 0, 30, 60, 120, 240, 480 and 960 g a.i. ha − 1 (field-recommended dose: 60 g a.i. ha − 1 ). Applications were performed using an enclosed cabinet sprayer (ASS-5, Information Technology Research Center, Beijing, China) calibrated to deliver 450 L ha − 1 at 0.4 MPa. Plant survival and aboveground dry biomass were assessed 21 days after treatment. Each treatment included four biological replicates pots (10 seedlings per pot) and was independently repeated. Dose-response for biomass reduction (expressed as a percentage of untreated controls) were analyzed by nonlinear regression in SigmaPlot v12.5 (Systat Software Inc.), fitting a four-parameter log-logistic model: where Y is the response (% of control), X is the herbicide dose, C and D are the lower and upper asymptotes, GR 50 is the herbicide dose causing plant growth reduction by 50%, and b is the slope of the curve. A mixed-model ANOVA was performed to assess differences in percentage control and biomass reduction across treatments. Resistance indices (R/S ratios) were calculated as GR 50 _R / GR 50 _S. The resistance levels of different accessions are classified into four groups based on the resistance index: S (Susceptible, RI 2 and RI 4 and RI 10). Herbicide resistance analyses ALS genes were identified in the D. sanguinalis genome using BLASTP. The predicted ALS protein sequences were aligned with orthologous sequences from O. sativa and A. thaliana using MAFFT (Rozewicki et al., 2019 ), and the known resistance-associated sites were annotated across three DsALS copies. A comprehensive catalog of causative mutations was then established for all accessions (Supplementary Table 21) . Genealogical relationships among haplotypes were reconstructed using HapNetworkView based on variant profiles across the three ALS subgenome copies (Chi et al., 2025 ). Copy number variation (CNV) of NTSR-related gene families was inferred from NGS data based on normalized read depth. NTSR-related genes were first annotated in the reference genome using InterProScan (E-value < 1e-10). Sequencing read depth for each annotated locus was calculated, and total gene family abundance was estimated as the sum of average depths across all loci, using custom scripts (see Code availability ). Copy number was estimated by normalizing the read depth of each gene family against the mean sequencing depth of 2,030 single-copy genes. Accessions with an overall sequencing depth < 5 were excluded to minimize bias in CNV estimation. Plant material and nicosulfuron treatment for RNA-seq The most nicosulfuron-resistant (#21 − 17, GR 50 = 122.53 g a.i. ha − 1 ) and susceptible (#15 − 2, GR 50 = 0.59 g a.i. ha − 1 ) D. sanguinalis accessions were selected for RNA-seq analysis. Plants were grown in 9-cm-diameter pots filled with moist loam soil, covered with a 1-cm soil layer, and maintained under greenhouse conditions (30/25°C day/night, 16-h photoperiod) with sub-irrigation at the Jiyang Research Station of the Shandong Academy of Agricultural Sciences, Jinan. At the three-leaf stage, seedlings were treated with 2 g a.i. ha − 1 nicosulfuron according to the GR 50 value distribution of susceptible populations in dose-response assays, to assess herbicide response. Each treatment included three biological replicates, with three individual plants pooled to constitute one replicate. Leaf tissues were collected at three time points: 0 h (untreated control), 6 h (early response), and 24 h (late response) post-treatment. Samples were immediately frozen in liquid nitrogen and stored at -80°C until RNA extraction and sequencing. RNA sequencing and transcriptome analysis Total RNA was extracted from 18 samples, comprising three time points (0 h, 6 h, and 24 h) for each accession after nicosulfuron treatment, with three biological replicates per time point. Messenger RNA was purified using poly-T oligo-attached magnetic beads, and sequencing libraries were constructed following the DNBSEQ standard protocol. Libraries were pooled according to effective concentration and target data yield. The 5′ ends of libraries were phosphorylated and circularized, followed by rolling circle amplification to generate DNA nanoballs, which were subsequently loaded onto a flow cell for sequencing on the DNBSEQ-T7 platform. After clipping adaptor sequences and removing low-quality reads, the clean reads were mapped to YJ2023 using Hisat2 (v2.1.0) (Kim et al., 2019 ) and gene expression were quantified by StringTie v2.2.1 (Kovaka et al., 2019 ) with default parameters. Differentially expressed genes were identified with the pyDESeq2 package v0.5.2 (Muzellec et al., 2023 ), with significance defined as |log2(fold change)| ≥1 and a false discovery rate (FDR)-adjusted p -value < 0.05. GO enrichment analyses were performed using the clusterProfiler package (Wu et al., 2021 ). Local ancestry inference Local ancestry was inferred using Loter (Dias-Alves et al., 2018 ), which reconstructs chromosomal ancestry in admixed individuals based on haplotype information from reference populations. To detect potential introgression from D. ciliaris within resistance-associated genomic regions, D. ciliaris ( n = 46) and D. sanguinalis ( n = 40) accessions were designated as parental reference populations. Phased haplotype data were used as input, and local ancestry along the chromosomes of D. sanguinalis was estimated under default parameters. GWAS of nicosulfuron resistance Genome-wide association analyses (GWAS) were performed across D. sanguinalis accessions ( n = 141) based on GR 50 values of each accession using EMMAX (Kang et al., 2010a ), with SNPs filtered for a minor allele frequency (MAF) > 0.05 and a missing genotype rate < 0.1. A pairwise genetic distance matrix, derived from simple matching coefficients of SNPs, was incorporated to model the variance-covariance structure of random effects in the linear mixed model. To correct for multiple hypothesis testing, an FDR threshold of 5% was applied, as the Bonferroni correction was deemed overly stringent for this dataset. Manhattan plots were generated using the R package ‘qqman’ (Turner, 2018 ) for visualization of association signals. To investigate selection pressures on adaptive variants associated with herbicide resistance and climatic adaptation, extended haplotype homozygosity (EHH) was evaluated for strongly associated loci, and the integrated haplotype score (iHS) was calculated for common variants using hapbin v1.3.0 (Maclean et al., 2015 ). Declarations Data availability Three Digitaria genome assemblies and annotations generated in this study are available at National Genomics Data Center (NGDC) database (https://bigd.big.ac.cn) under the following accession numbers: GWHGGEX00000000, GWHGGEY00000000 and GWHGGEZ00000000. The HiFi sequencing, Hi-C, and RNA-Seq data for genome assembly and annotation in this study have been deposited in NGDC (project accession: PRJCA044252). The raw data of all re-sequenced accessions are available at National Center for Biotechnology Information (NCBI) database (https:/www.ncbi.nlm.nih.gov) under the project accession number PRJNA1296823. Besides Digitaria genomes, other monocot genomes used in this study were retrieved as described in previous study (Wu et al., 2022a). The resequencing data of green foxtail were downloaded from the European Nucleotide Archive (ENA) (https://www.ebi.ac.uk/ena/browser/home). Code availability The custom scripts used in this study have been deposited in the GitHub repository [https://github.com/Ne0tea/DigitariaPop]. Acknowledgements This work was supported by National Key Research and Development Program (2023YFD1400502). We would like to express our gratitude to Yu Fang, Fanjing Yang, Yutong Liu, Jiaxin Li and Tao Li for their dedicated efforts in sample collection. Author contribution L.F., L.B. and M.L. conceived the study. Y.H. contributed to genomic analyses, pipeline development, and interpretation of evolutionary patterns. J.L. contributed to D. sanguinalis accessions phenotyping and herbicide resistance bioassay. S.Z., K.Y., Z.L., X.G, and R.Z. collected Digitaria accessions, processed genome annotation and conducted cytological analysis on Digitaria species. S.W. and Z.Li performed quality control and initial data filtering. L.X. conducted tandem repeat annotation. L.F performed GWAS analysis of resistance. Y.F. provided insights on phylogenetic modeling and evolutionary inference. B.K.S. and A.M. assisted in the validation of functional genetic variants. Q.Y., F.L. and L.B. supervised the experimental design and contributed to population-level analysis. L.F., L.B., M.L. and D.W. jointly supervised the study. Y.H. wrote the initial manuscript with input from all co-authors. All authors discussed the results and approved the final version of the manuscript. Competing interests All authors declare no competing interests. References Abrouk, M., Ahmed, H.I., Cubry, P., Šimoníková, D., Cauet, S., Pailles, Y., Bettgenhaeuser, J., Gapa, L., Scarcelli, N., Couderc, M., Zekraoui, L., Kathiresan, N., Čížková, J., Hřibová, E., Doležel, J., Arribat, S., Bergès, H., Wieringa, J.J., Gueye, M., Kane, N.A., Leclerc, C., Causse, S., Vancoppenolle, S., Billot, C., Wicker, T., Vigouroux, Y., Barnaud, A., Krattinger, S.G., 2020. Fonio millet genome unlocks African orphan crop diversity for agriculture in a changing climate. Nat Commun 11, 4488. Alonge, M., Lebeigle, L., Kirsche, M., Jenike, K., Ou, S., Aganezov, S., Wang, X., Lippman, Z.B., Schatz, M.C., Soyk, S., 2022. Automated assembly scaffolding using RagTag elevates a new tomato system for high-throughput genome editing. Genome Biology 23, 258. Areces-Berazain, F., 2022. Digitaria sanguinalis (large crabgrass). CABI Compendium CABI Compendium, 18916. Baack, E.J., Rieseberg, L.H., 2007. A genomic view of introgression and hybrid speciation. Curr Opin Genet Dev 17, 513–518. Baucom, R.S., 2019. Evolutionary and ecological insights from herbicide-resistant weeds: what have we learned about plant adaptation, and what is left to uncover? New Phytologist 223, 68–82. Beckie, H.J., Busi, R., Bagavathiannan, M.V., Martin, S.L., 2019. Herbicide resistance gene flow in weeds: Under-estimated and under-appreciated. Agriculture, Ecosystems & Environment 283, 106566. Boonsuk, B., Chantaranothai, P., Hodkinson, T.R., 2016. A taxonomic revision of the genus Digitaria (Panicoideae: Poaceae) in mainland Southeast Asia. Phytotaxa 246, 248. Brown, J.K.M., Rant, J.C., 2013. Fitness costs and trade-offs of disease resistance and their consequences for breeding arable crops. Plant Pathology 62, 83–95. Burton, G.P., Ceci, P., MacKinnon, L., Masters, L.E., Ryan, P., Turnbull, C.G.N., Ulian, T., Vorontsova, M.S., 2025. Phylogenetics, evolution and biogeography of four Digitaria food crop lineages across West Africa, India, and Europe. bioRxiv 2025.03.27.645812. Cai, L., Comont, D., MacGregor, D., Lowe, C., Beffa, R., Neve, P., Saski, C., 2023. The blackgrass genome reveals patterns of non-parallel evolution of polygenic herbicide resistance. New Phytologist 237, 1891–1907. Capella-Gutiérrez, S., Silla-Martínez, J.M., Gabaldón, T., 2009. trimAl: a tool for automated alignment trimming in large-scale phylogenetic analyses. Bioinformatics 25, 1972–1973. Carnahan, H.L., Hill, H.D., 1961. Cytology and genetics of forage grasses. Bot. Rev 27, 1–162. Chakraborty, M., Baldwin-Brown, J.G., Long, A.D., Emerson, J.J., 2016. Contiguous and accurate de novo assembly of metazoan genomes with modest long read coverage. Nucleic Acids Research 44, e147. Chang, C.C., Chow, C.C., Tellier, L.C., Vattikuti, S., Purcell, S.M., Lee, J.J., 2015. Second-generation PLINK: rising to the challenge of larger and richer datasets. GigaScience 4, s13742-015-0047–8. Chao, D.-Y., Dilkes, B., Luo, H., Douglas, A., Yakubova, E., Lahner, B., Salt, D.E., 2013. Polyploids Exhibit Higher Potassium Uptake and Salinity Tolerance in Arabidopsis . Science. Chatterjee, Y., Tomar, S., Mishra, M., Pareek, A., Singla-Pareek, S.L., 2025. OsLdh7 Overexpression in Rice Confers Submergence Tolerance by Regulating Key Metabolic Pathways: Anaerobic Glycolysis, Ethanolic Fermentation and Amino Acid Metabolism. Plant, Cell & Environment 48, 2804–2820. Chen, S., 2023. Ultrafast one-pass FASTQ data preprocessing, quality control, and deduplication using fastp. iMeta 2, e107. Chen, S., Sun, B., Shi, Z., Miao, X., Li, H., 2022. Identification of the rice genes and metabolites involved in dual resistance against brown planthopper and rice blast fungus. Plant, Cell & Environment 45, 1914–1929. Chen, W., Bai, D., Liao, Y., Yu, Q., Bai, L., Pan, L., 2025. PfGSTF2 endows resistance to quizalofop-p-ethyl in Polypogon fugax by GSH conjugation. Plant Biotechnology Journal 23, 216–231. Cheng, F., Wu, J., Cai, X., Liang, J., Freeling, M., Wang, X., 2018. Gene retention, fractionation and subgenome differences in polyploid plants. Nature Plants 4, 258–268. Cheng, H., Concepcion, G.T., Feng, X., Zhang, H., Li, H., 2021. Haplotype-resolved de novo assembly using phased assembly graphs with hifiasm. Nat Methods 18, 170–175. Chi, L., Dong, Y., Wang, R., Yang, S., Wu, L., Xue, Y., Chen, H., 2025. HapNetworkView: a tool for haplotype network exploration and visualization. BMC Genomics 26, 52. Cingolani, P., Platts, A., Wang, L.L., Coon, M., Nguyen, T., Wang, L., Land, S.J., Lu, X., Ruden, D.M., 2012. A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w1118; iso-2; iso-3. Fly (Austin) 6, 80–92. Comont, D., Lowe, C., Hull, R., Crook, L., Hicks, H.L., Onkokesung, N., Beffa, R., Childs, D.Z., Edwards, R., Freckleton, R.P., Neve, P., 2020. Evolution of generalist resistance to herbicide mixtures reveals a trade-off in resistance management. Nat Commun 11, 3086. Danecek, P., Auton, A., Abecasis, G., Albers, C.A., Banks, E., DePristo, M.A., Handsaker, R.E., Lunter, G., Marth, G.T., Sherry, S.T., McVean, G., Durbin, R., 1000 Genomes Project Analysis Group, 2011. The variant call format and VCFtools. Bioinformatics 27, 2156–2158. Délye, C., 2013. Unravelling the genetic bases of non-target-site-based resistance (NTSR) to herbicides: a major challenge for weed science in the forthcoming decade. Pest Management Science 69, 176–187. Délye, C., Jasieniuk, M., Corre, V.L., 2013. Deciphering the evolution of herbicide resistance in weeds. Trends in Genetics 29, 649–658. Délye, C., Michel, S., Bérard, A., Chauvel, B., Brunel, D., Guillemin, J.-P., Dessaint, F., Le Corre, V., 2010. Geographical variation in resistance to acetyl-coenzyme A carboxylase-inhibiting herbicides across the range of the arable weed Alopecurus myosuroides (black-grass). New Phytol 186, 1005–1017. Dias-Alves, T., Mairal, J., Blum, M.G.B., 2018. Loter: A Software Package to Infer Local Ancestry for a Wide Range of Species. Molecular Biology and Evolution 35, 2318–2326. Du, H.-M., Liu, C., Jin, X.-W., Du, C.-F., Yu, Y., Luo, S., He, W.-Z., Zhang, S.-Z., 2022. Overexpression of the Aldehyde Dehydrogenase Gene ZmALDH Confers Aluminum Tolerance in Arabidopsis thaliana. Int J Mol Sci 23, 477. Durand, E.Y., Patterson, N., Reich, D., Slatkin, M., 2011. Testing for Ancient Admixture between Closely Related Populations. Molecular Biology and Evolution 28, 2239–2252. Durand, N.C., Shamim, M.S., Machol, I., Rao, S.S.P., Huntley, M.H., Lander, E.S., Aiden, E.L., 2016. Juicer provides a one-click system for analyzing loop-resolution Hi-C experiments. Cell Systems 3, 95–98. Emms, D.M., Kelly, S., 2015. OrthoFinder: solving fundamental biases in whole genome comparisons dramatically improves orthogroup inference accuracy. Genome Biology 16. Flouri, T., Jiao, X., Rannala, B., Yang, Z., 2020. A Bayesian Implementation of the Multispecies Coalescent Model with Introgression for Phylogenomic Analysis. Molecular Biology and Evolution 37, 1211–1223. Frichot, E., Schoville, S.D., Bouchard, G., François, O., 2013. Testing for Associations between Loci and Environmental Gradients Using Latent Factor Mixed Models. Molecular Biology and Evolution 30, 1687–1699. Fu, R., Zhu, Y., Liu, Y., Feng, Y., Lu, R.-S., Li, Y., Li, P., Kremer, A., Lascoux, M., Chen, J., 2022. Genome-wide analyses of introgression between two sympatric Asian oak species. Nat Ecol Evol 6, 924–935. Gain, C., François, O., 2021. LEA 3: Factor models in population genetics and ecological genomics with R. Molecular Ecology Resources 21, 2738–2748. Galeano, E., Barroso, A.A.M., Vasconcelos, T.S., López-Rubio, A., Albrecht, A.J.P., Victoria Filho, R., Carrer, H., 2016. EPSPS variability, gene expression, and enzymatic activity in glyphosate-resistant biotypes of Digitaria insularis . Genet. Mol. Res. 15. Georgia, A.E., Georgia, A.E., 1916. A manual of weeds, with descriptions of all the most pernicious and troublesome plants in the United States and Canada, their habits of growth and distribution, with methods of control. The Macmillan Company, New York. Goff, S.A., Ricke, D., Lan, T.-H., Presting, G., Wang, R., Dunn, M., Glazebrook, J., Sessions, A., Oeller, P., Varma, H., Hadley, D., Hutchison, D., Martin, C., Katagiri, F., Lange, B.M., Moughamer, T., Xia, Y., Budworth, P., Zhong, J., Miguel, T., Paszkowski, U., Zhang, S., Colbert, M., Sun, W., Chen, L., Cooper, B., Park, S., Wood, T.C., Mao, L., Quail, P., Wing, R., Dean, R., Yu, Y., Zharkikh, A., Shen, R., Sahasrabudhe, S., Thomas, A., Cannings, R., Gutin, A., Pruss, D., Reid, J., Tavtigian, S., Mitchell, J., Eldredge, G., Scholl, T., Miller, R.M., Bhatnagar, S., Adey, N., Rubano, T., Tusneem, N., Robinson, R., Feldhaus, J., Macalma, T., Oliphant, A., Briggs, S., 2002. A Draft Sequence of the Rice Genome ( Oryza sativa L. ssp. japonica ). Science. Goldberg-Cavalleri, A., Onkokesung, N., Franco-Ortega, S., Edwards, R., 2023. ABC transporters linked to multiple herbicide resistance in blackgrass ( Alopecurus myosuroides ). Front. Plant Sci. 14. Goslee, S.C., Urban, D.L., 2007. The ecodist Package for Dissimilarity-based Analysis of Ecological Data. Journal of Statistical Software 22, 1–19. Gould, F.W., 1963. Cytotaxonomy of Digitaria Sanguinalis and D. Adscendens. Brittonia 15, 241. Green, R.E., Krause, J., Briggs, A.W., Maricic, T., Stenzel, U., Kircher, M., Patterson, N., Li, H., Zhai, W., Fritz, M.H.-Y., Hansen, N.F., Durand, E.Y., Malaspinas, A.-S., Jensen, J.D., Marques-Bonet, T., Alkan, C., Prüfer, K., Meyer, M., Burbano, H.A., Good, J.M., Schultz, R., Aximu-Petri, A., Butthof, A., Höber, B., Höffner, B., Siegemund, M., Weihmann, A., Nusbaum, C., Lander, E.S., Russ, C., Novod, N., Affourtit, J., Egholm, M., Verna, C., Rudan, P., Brajkovic, D., Kucan, Ž., Gušic, I., Doronichev, V.B., Golovanova, L.V., Lalueza-Fox, C., de la Rasilla, M., Fortea, J., Rosas, A., Schmitz, R.W., Johnson, P.L.F., Eichler, E.E., Falush, D., Birney, E., Mullikin, J.C., Slatkin, M., Nielsen, R., Kelso, J., Lachmann, M., Reich, D., Pääbo, S., 2010. A Draft Sequence of the Neandertal Genome. Science 328, 710–722. Guan, Y., Liu, L., Zou, Y., Yang, C., Ji, M., 2024. Involvement of P450s in the metabolic resistance of Digitaria sanguinalis (L.) Scop. To ALS-inhibiting herbicides. Pesticide Biochemistry and Physiology 204, 106038. Guo, R., Zhang, Q., Chen, C.Z., Sun, J.Y., Tu, C.Y., He, M.X., Shen, R.F., Huang, J., Zhu, X.F., 2024. A novel aldo-keto reductase gene, OsAKR1, from rice confers higher tolerance to cadmium stress in rice by an in vivo reactive aldehyde detoxification. Journal of Hazardous Materials 470, 134212. Haas, B.J., Salzberg, S.L., Zhu, W., Pertea, M., Allen, J.E., Orvis, J., White, O., Buell, C.R., Wortman, J.R., 2008. Automated eukaryotic gene structure annotation using EVidenceModeler and the Program to Assemble Spliced Alignments. Genome Biology 9, R7. Harrison, R.G., Larson, E.L., 2014. Hybridization, Introgression, and the Nature of Species Boundaries. Journal of Heredity 105, 795–809. He, X., Chen, S., Li, R., Han, X., He, Z., Yuan, D., Zhang, S., Duan, X., Niu, B., 2021. Comprehensive fundamental somatic variant calling and quality management strategies for human cancer genomes. Briefings in Bioinformatics 22, bbaa083. Heap, I., 2025. The International Herbicide-Resistant Weed Database. Hedrick, P.W., 2013. Adaptive introgression in animals: examples and comparison to new mutation and standing variation as sources of adaptive variation. Molecular Ecology 22, 4606–4618. Henrard, J.T., 1950. Monograph of the Genus Digitaria . Agronomy Journal 42, 631–631. Hereward, J.P., Werth, J.A., Thornby, D.F., Keenan, M., Chauhan, B.S., Walter, G.H., 2018. Gene expression in response to glyphosate treatment in fleabane (Conyza bonariensis) - glyphosate death response and candidate resistance genes. Pest Manag Sci 74, 2346–2355. Holm, L.G., Plucknett, D.L., Pancho, J.V., Herberger, J.P., 1977. The world’s worst weeds. University Press., Honolulu, Hawaii. Hu, J., Wang, Z., Liang, F., Liu, S.-L., Ye, K., Wang, D.-P., 2024a. NextPolish2: A Repeat-aware Polishing Tool for Genomes Assembled Using HiFi Long Reads. Genomics, Proteomics & Bioinformatics 22, qzad009. Hu, J., Wang, Z., Sun, Z., Hu, B., Ayoola, A.O., Liang, F., Li, J., Sandoval, J.R., Cooper, D.N., Ye, K., Ruan, J., Xiao, C.-L., Wang, D., Wu, D.-D., Wang, S., 2024b. NextDenovo: an efficient error correction and accurate assembly tool for noisy long reads. Genome Biology 25, 107. Huang, J., Wang, S., Xiao, Z., 2017a. Rising Herbicide Use and Its Driving Forces in China. Eur J Dev Res 29, 614–627. Huang, J., Wang, S., Xiao, Z., 2017b. Rising Herbicide Use and Its Driving Forces in China. Eur J Dev Res 29, 614–627. Huang, W., Zhang, L., Columbus, J.T., Hu, Y., Zhao, Y., Tang, L., Guo, Z., Chen, W., McKain, M., Bartlett, M., Huang, C.-H., Li, D.-Z., Ge, S., Ma, H., 2022. A well-supported nuclear phylogeny of Poaceae and implications for the evolution of C4 photosynthesis. Molecular Plant 15, 755–777. Huang, Y., Guo, L., Xie, L., Shang, N., Wu, D., Ye, C., Rudell, E.C., Okada, K., Zhu, Q.-H., Song, B.-K., Cai, D., Junior, A.M., Bai, L., Fan, L., 2024. A reference genome of Commelinales provides insights into the commelinids evolution and global spread of water hyacinth (Pontederia crassipes). GigaScience 13, giae006. Ito, M., Kamada, Y., Ueki, K., 1986. Determination of Allelopathic Potential of Young and Mature Digitaria adscendens on Crop Growth. Journal of Weed Science and Technology 31, 116–123. Jaillon, O., Aury, J.-M., Noel, B., Policriti, A., Clepet, C., Casagrande, A., Choisne, N., Aubourg, S., Vitulo, N., Jubin, C., Vezzi, A., Legeai, F., Hugueney, P., Dasilva, C., Horner, D., Mica, E., Jublot, D., Poulain, J., Bruyère, C., Billault, A., Segurens, B., Gouyvenoux, M., Ugarte, E., Cattonaro, F., Anthouard, V., Vico, V., Del Fabbro, C., Alaux, M., Di Gaspero, G., Dumas, V., Felice, N., Paillard, S., Juman, I., Moroldo, M., Scalabrin, S., Canaguier, A., Le Clainche, I., Malacrida, G., Durand, E., Pesole, G., Laucou, V., Chatelet, P., Merdinoglu, D., Delledonne, M., Pezzotti, M., Lecharny, A., Scarpelli, C., Artiguenave, F., Pè, M.E., Valle, G., Morgante, M., Caboche, M., Adam-Blondon, A.-F., Weissenbach, J., Quétier, F., Wincker, P., The French–Italian Public Consortium for Grapevine Genome Characterization, 2007. The grapevine genome sequence suggests ancestral hexaploidization in major angiosperm phyla. Nature 449, 463–467. Jakobsson, M., Edge, M.D., Rosenberg, N.A., 2013. The Relationship Between FST and the Frequency of the Most Frequent Allele. Genetics 193, 515–528. Jia, K.-H., Wang, Z.-X., Wang, L., Li, G.-Y., Zhang, W., Wang, X.-L., Xu, F.-J., Jiao, S.-Q., Zhou, S.-S., Liu, H., Ma, Y., Bi, G., Zhao, W., El-Kassaby, Y.A., Porth, I., Li, G., Zhang, R.-G., Mao, J.-F., 2022. SubPhaser: a robust allopolyploid subgenome phasing method based on subgenome-specific k-mers. New Phytologist 235, 801–809. Jiao, Y., Wickett, N.J., Ayyampalayam, S., Chanderbali, A.S., Landherr, L., Ralph, P.E., Tomsho, L.P., Hu, Y., Liang, H., Soltis, P.S., Soltis, D.E., Clifton, S.W., Schlarbaum, S.E., Schuster, S.C., Ma, H., Leebens-Mack, J., dePamphilis, C.W., 2011. Ancestral polyploidy in seed plants and angiosperms. Nature 473, 97–100. Jin, J.-J., Yu, W.-B., Yang, J.-B., Song, Y., dePamphilis, C.W., Yi, T.-S., Li, D.-Z., 2020. GetOrganelle: a fast and versatile toolkit for accurate de novo assembly of organelle genomes. Genome Biology 21, 241. Johnson, N.A., 2010. Hybrid incompatibility genes: remnants of a genomic battlefield? Trends in Genetics 26, 317–325. Kang, H.M., Sul, J.H., Service, S.K., Zaitlen, N.A., Kong, S., Freimer, N.B., Sabatti, C., Eskin, E., 2010a. Variance component model to account for sample structure in genome-wide association studies. Nat Genet 42, 348–354. Kang, H.M., Sul, J.H., Service, S.K., Zaitlen, N.A., Kong, S.-Y., Freimer, N.B., Sabatti, C., Eskin, E., 2010b. Variance component model to account for sample structure in genome-wide association studies. Nat Genet 42, 348–354. Kersten, S., Chang, J., Huber, C.D., Voichek, Y., Lanz, C., Hagmaier, T., Lang, P., Lutz, U., Hirschberg, I., Lerchl, J., Porri, A., Van de Peer, Y., Schmid, K., Weigel, D., Rabanal, F.A., 2023. Standing genetic variation fuels rapid evolution of herbicide resistance in blackgrass. Proceedings of the National Academy of Sciences 120, e2206808120. Kim, D., Paggi, J.M., Park, C., Bennett, C., Salzberg, S.L., 2019. Graph-based genome alignment and genotyping with HISAT2 and HISAT-genotype. Nat Biotechnol 37, 907–915. Kim, J.Y., Kim, W.Y., Kwak, K.J., Oh, S.H., Han, Y.S., Kang, H., 2010. Zinc finger-containing glycine-rich RNA-binding protein in Oryza sativa has an RNA chaperone activity under cold stress conditions. Plant Cell Environ 33, 759–768. Klure, D.M., Greenhalgh, R., Orr, T.J., Shapiro, M.D., Dearing, M.D., 2025. Parallel gene expansions drive rapid dietary adaptation in herbivorous woodrats. Science 387, 156–162. Kok, P.D.F., Robbertse, P.J., van Wyk, A.E., 1989. Systematic study of Digitaria section Digitaria (Poaceae) in southern Africa. South African Journal of Botany 55, 141–153. Kovaka, S., Zimin, A.V., Pertea, G.M., Razaghi, R., Salzberg, S.L., Pertea, M., 2019. Transcriptome assembly from long-read RNA-seq alignments with StringTie2. Genome Biology 20, 278. Kreiner, J.M., Giacomini, D.A., Bemm, F., Waithaka, B., Regalado, J., Lanz, C., Hildebrandt, J., Sikkema, P.H., Tranel, P.J., Weigel, D., Stinchcombe, J.R., Wright, S.I., 2019. Multiple modes of convergent adaptation in the spread of glyphosate-resistant Amaranthus tuberculatus. Proceedings of the National Academy of Sciences 116, 21076–21084. Kreiner, J.M., Sandler, G., Stern, A.J., Tranel, P.J., Weigel, D., Stinchcombe, J.R., Wright, S.I., 2022. Repeated origins, widespread gene flow, and allelic interactions of target-site herbicide resistance mutations. eLife 11, e70242. Kreiner, J.M., Stinchcombe, J.R., Wright, S.I., 2018a. Population Genomics of Herbicide Resistance: Adaptation via Evolutionary Rescue. Annual Review of Plant Biology 69, 611–635. Kreiner, J.M., Stinchcombe, J.R., Wright, S.I., 2018b. Population Genomics of Herbicide Resistance: Adaptation via Evolutionary Rescue. Annual Review of Plant Biology 69, 611–635. Kreiner, J.M., Tranel, P.J., Weigel, D., Stinchcombe, J.R., Wright, S.I., 2021. The genetic architecture and population genomic signatures of glyphosate resistance in Amaranthus tuberculatus . Molecular Ecology 30, 5373–5389. Kumar, S., Suleski, M., Craig, J.M., Kasprowicz, A.E., Sanderford, M., Li, M., Stecher, G., Hedges, S.B., 2022. TimeTree 5: An Expanded Resource for Species Divergence Times. Mol Biol Evol 39, msac174. Laforest, M., Soufiane, B., Simard, M.-J., Obeid, K., Page, E., Nurse, R.E., 2017. Acetyl-CoA carboxylase overexpression in herbicide-resistant large crabgrass ( Digitaria sanguinalis ). Pest Management Science 73, 2227–2235. Lamesch, P., Berardini, T.Z., Li, D., Swarbreck, D., Wilks, C., Sasidharan, R., Muller, R., Dreher, K., Alexander, D.L., Garcia-Hernandez, M., Karthikeyan, A.S., Lee, C.H., Nelson, W.D., Ploetz, L., Singh, S., Wensel, A., Huala, E., 2012. The Arabidopsis Information Resource (TAIR): improved gene annotation and new tools. Nucleic Acids Research 40, D1202–D1210. Langmead, B., Salzberg, S.L., 2012a. Fast gapped-read alignment with Bowtie 2. Nat Methods 9, 357–359. Langmead, B., Salzberg, S.L., 2012b. Fast gapped-read alignment with Bowtie 2. Nat Methods 9, 357–359. Letunic, I., Bork, P., 2024. Interactive Tree of Life (iTOL) v6: recent updates to the phylogenetic tree display and annotation tool. Nucleic Acids Research 52, W78–W82. Li, H., 2021. New strategies to improve minimap2 alignment accuracy. Bioinformatics 37, 4572–4574. Li, H., Durbin, R., 2010. Fast and accurate long-read alignment with Burrows-Wheeler transform. Bioinformatics 26, 589–595. Li, J., Li, M., Gao, X., Fang, F., 2017. A novel amino acid substitution Trp574Arg in acetolactate synthase (ALS) confers broad resistance to ALS-inhibiting herbicides in crabgrass. Pest Management Science 73, 2538–2543. Li, K., Xu, P., Wang, J., Yi, X., Jiao, Y., 2023. Identification of errors in draft genome assemblies at single-nucleotide resolution for quality assessment and improvement. Nat Commun 14, 6556. Li, L., Li, Y., Jia, Y., Caicedo, A.L., Olsen, K.M., 2017. Signatures of adaptation in the weedy rice genome. Nat Genet 49, 811–814. Li, L.-F., Pusadee, T., Wedger, M.J., Li, Y.-L., Li, M.-R., Lau, Y.-L., Yap, S.-J., Jamjod, S., Rerkasem, B., Hao, Y., Song, B.-K., Olsen, K.M., 2024. Porous borders at the wild-crop interface promote weed adaptation in Southeast Asia. Nat Commun 15, 1182. Li, W., Godzik, A., 2006. Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics 22, 1658–1659. Li, W., Wang, L., Zhou, F., Li, C., Ma, W., Chen, H., Wang, G., Pickett, J.A., Zhou, J.-J., Lin, Y., 2021. Overexpression of the homoterpene synthase gene, OsCYP92C21, increases emissions of volatiles mediating tritrophic interactions in rice. Plant, Cell & Environment 44, 948–963. Liu, X., Fu, Y.-X., 2020. Stairway Plot 2: demographic history inference with folded SNP frequency spectra. Genome Biology 21, 280. Liu, X., Merchant, A., Xiang, S., Zong, T., Zhou, X., Bai, L., 2021. Managing herbicide resistance in China. Weed Science 69, 4–17. Loubet, I., Meyer, L., Michel, S., Pernin, F., Carrère, S., Barrès, B., Le Corre, V., Délye, C., 2023. A high diversity of non-target site resistance mechanisms to acetolactate-synthase (ALS) inhibiting herbicides has evolved within and among field populations of common ragweed ( Ambrosia artemisiifolia L.). BMC Plant Biology 23, 510. Lovell, J.T., Jenkins, J., Lowry, D.B., Mamidi, S., Sreedasyam, A., Weng, X., Barry, K., Bonnette, J., Campitelli, B., Daum, C., Gordon, S.P., Gould, B.A., Khasanova, A., Lipzen, A., MacQueen, A., Palacio-Mejía, J.D., Plott, C., Shakirov, E.V., Shu, S., Yoshinaga, Y., Zane, M., Kudrna, D., Talag, J.D., Rokhsar, D., Grimwood, J., Schmutz, J., Juenger, T.E., 2018. The genomic landscape of molecular responses to natural drought stress in Panicum hallii. Nat Commun 9, 5213. Luo, Q., Liu, Y.-G., 2025. Breeding herbicide-resistant rice using CRISPR-Cas gene editing and other technologies. Plant Communications 6, 101172. Lyu, Y., Wang, F., Cheng, H., Han, Jing, Dang, R., Xia, X., Wang, H., Zhong, J., Lenstra, J.A., Zhang, H., Han, Jianlin, MacHugh, D.E., Medugorac, I., Upadhyay, M., Leonard, A.S., Ding, H., Yang, X., Wang, M.-S., Quji, S., Zhuzha, B., Quzhen, P., Wangmu, S., Cangjue, N., Wa, D., Ma, W., Liu, J., Zhang, J., Huang, B., Qi, X., Li, F., Huang, Y., Ma, Y., Wang, Y., Gao, Y., Lu, W., Lei, C., Chen, N., 2024. Recent selection and introgression facilitated high-altitude adaptation in cattle. Science Bulletin 69, 3415–3424. Maclean, C.A., Chue Hong, N.P., Prendergast, J.G.D., 2015. hapbin: An Efficient Program for Performing Haplotype-Based Scans for Positive Selection in Large Genomic Datasets. Molecular Biology and Evolution 32, 3027–3029. Mahaut, L., Cheptou, P.-O., Fried, G., Munoz, F., Storkey, J., Vasseur, F., Violle, C., Bretagnolle, F., 2020. Weeds: Against the Rules? Trends in Plant Science 25, 1107–1116. Maheshwari, S., Barbash, D.A., 2011. The Genetics of Hybrid Incompatibilities. Annual Review of Genetics 45, 331–355. Mallet, J., 2005. Hybridization as an invasion of the genome. Trends in Ecology & Evolution 20, 229–237. Manni, M., Berkeley, M.R., Seppey, M., Simão, F.A., Zdobnov, E.M., 2021. BUSCO Update: Novel and Streamlined Workflows along with Broader and Deeper Phylogenetic Coverage for Scoring of Eukaryotic, Prokaryotic, and Viral Genomes. Molecular Biology and Evolution 38, 4647–4654. Marchi, N., Kapopoulou, A., Excoffier, L., 2024. Demogenomic inference from spatially and temporally heterogeneous samples. Molecular Ecology Resources 24, e13877. Martin, S.H., Davey, J.W., Jiggins, C.D., 2015a. Evaluating the Use of ABBA–BABA Statistics to Locate Introgressed Loci. Molecular Biology and Evolution 32, 244–257. Martin, S.H., Davey, J.W., Jiggins, C.D., 2015b. Evaluating the Use of ABBA–BABA Statistics to Locate Introgressed Loci. Molecular Biology and Evolution 32, 244–257. Martin, S.H., Davey, J.W., Jiggins, C.D., 2015c. Evaluating the Use of ABBA–BABA Statistics to Locate Introgressed Loci. Molecular Biology and Evolution 32, 244–257. McDaniel, S.F., 2024. Local adaptation, recombination, and the fate of neopolyploids. New Phytologist 244, 32–38. Mei, Y., Si, C., Liu, M., Qiu, L., Zheng, M., 2017. Investigation of resistance levels and mechanisms to nicosulfuron conferred by non-target-site mechanisms in large crabgrass ( Digitaria sanguinalis L.) from China. Pesticide Biochemistry and Physiology 141, 84–89. Meimberg, H., Rice, K.J., Milan, N.F., Njoku, C.C., McKay, J.K., 2009. Multiple origins promote the ecological amplitude of allopolyploid Aegilops (Poaceae). American Journal of Botany 96, 1262–1273. Menardo, F., Praz, C.R., Wyder, S., Ben-David, R., Bourras, S., Matsumae, H., McNally, K.E., Parlange, F., Riba, A., Roffler, S., Schaefer, L.K., Shimizu, K.K., Valenti, L., Zbinden, H., Wicker, T., Keller, B., 2016. Hybridization of powdery mildew strains gives rise to pathogens on novel agricultural crop species. Nat Genet 48, 201–205. Minh, B.Q., Schmidt, H.A., Chernomor, O., Schrempf, D., Woodhams, M.D., von Haeseler, A., Lanfear, R., 2020. IQ-TREE 2: New models and efficient methods for phylogenetic inference in the genomic era. Molecular Biology and Evolution 37, 1530–1534. Morin, N., Brouillet, L., Levin, G., 2015. Flora of North America North of Mexico. Rodriguésia 66, 973–981. Muzellec, B., Teleńczuk, M., Cabeli, V., Andreux, M., 2023. PyDESeq2: a python package for bulk RNA-seq differential expression analysis. Bioinformatics 39, btad547. Nakazono, M., Tsuji, H., Li, Y., Saisho, D., Arimura, S., Tsutsumi, N., Hirai, A., 2000. Expression of a Gene Encoding Mitochondrial Aldehyde Dehydrogenase in Rice Increases under Submerged Conditions. Plant Physiol 124, 587–598. North, H.L., Fu, Z., Metz, R., Stull, M.A., Johnson, C.D., Shirley, X., Crumley, K., Reisig, D., Kerns, D.L., Gilligan, T., Walsh, T., Jiggins, C.D., Sword, G.A., 2024. Rapid Adaptation and Interspecific Introgression in the North American Crop Pest Helicoverpa zea. Molecular Biology and Evolution 41, msae129. ODA, K., OTANI, M., URAGUCHI, S., AKIHIRO, T., FUJIWARA, T., 2011. Rice ABCG43 Is Cd Inducible and Confers Cd Tolerance on Yeast. Biosci. Biotechnol. Biochem. 75, 1211–1213. Ou, S., Chen, J., Jiang, N., 2018. Assessing genome assembly quality using the LTR Assembly Index (LAI). Nucleic Acids Res 46, e126. Oziolor, E.M., Reid, N.M., Yair, S., Lee, K.M., Guberman VerPloeg, S., Bruns, P.C., Shaw, J.R., Whitehead, A., Matson, C.W., 2019. Adaptive introgression enables evolutionary rescue from extreme environmental pollution. Science 364, 455–457. Pan, L., Guo, Q., Wang, J., Shi, L., Yang, X., Zhou, Y., Yu, Q., Bai, L., 2022. CYP81A68 confers metabolic resistance to ALS and ACCase-inhibiting herbicides and its epigenetic regulation in Echinochloa crus-galli . Journal of Hazardous Materials 428, 128225. Pan, L., Yu, Q., Wang, J., Han, H., Mao, L., Nyporko, A., Maguza, A., Fan, L., Bai, L., Powles, S., 2021. An ABCC-type transporter endowing glyphosate resistance in plants. Proceedings of the National Academy of Sciences 118, e2100136118. Payseur, B.A., Rieseberg, L.H., 2016. A genomic perspective on hybridization and speciation. Mol Ecol 25, 2337–2360. Peng, M., Shahzad, R., Gul, A., Subthain, H., Shen, S., Lei, L., Zheng, Z., Zhou, J., Lu, D., Wang, S., Nishawy, E., Liu, X., Tohge, T., Fernie, A.R., Luo, J., 2017. Differentially evolved glucosyltransferases determine natural variation of rice flavone accumulation and UV-tolerance. Nat Commun 8, 1975. Powles, S.B., Yu, Q., 2010a. Evolution in Action: Plants Resistant to Herbicides. Annual Review of Plant Biology 61, 317–347. Powles, S.B., Yu, Q., 2010b. Evolution in Action: Plants Resistant to Herbicides. Annu. Rev. Plant Biol. 61, 317–347. Price, M.N., Dehal, P.S., Arkin, A.P., 2010. FastTree 2 – Approximately Maximum-Likelihood Trees for Large Alignments. PLOS ONE 5, e9490. Raj, A., Stephens, M., Pritchard, J.K., 2014. fastSTRUCTURE: Variational Inference of Population Structure in Large SNP Data Sets. Genetics 197, 573–589. Reid, N.M., Proestou, D.A., Clark, B.W., Warren, W.C., Colbourne, J.K., Shaw, J.R., Karchner, S.I., Hahn, M.E., Nacci, D., Oleksiak, M.F., Crawford, D.L., Whitehead, A., 2016. The genomic landscape of rapid repeated evolutionary adaptation to toxic pollution in wild fish. Science 354, 1305–1308. Rendón-Anaya, M., Wilson, J., Sveinsson, S., Fedorkov, A., Cottrell, J., Bailey, M.E.S., Ruņǵis, D., Lexer, C., Jansson, S., Robinson, K.M., Street, N.R., Ingvarsson, P.K., 2021. Adaptive Introgression Facilitates Adaptation to High Latitudes in European Aspen ( Populus tremula L.). Molecular Biology and Evolution 38, 5034–5050. Rhie, A., Walenz, B.P., Koren, S., Phillippy, A.M., 2020. Merqury: reference-free quality, completeness, and phasing assessment for genome assemblies. Genome Biology 21, 245. Ribeiro, V.H.V., Gallagher, J., Mallory-Smith, C., Barroso, J., Brunharo, C.A.C.G., 2025. Multiple Origins or Widespread Gene Flow in Agricultural Fields? Regional Population Genomics of Herbicide Resistance in Bromus tectorum . Molecular Ecology 34, e17791. Rosche, C., Hensen, I., Mráz, P., Durka, W., Hartmann, M., Lachmuth, S., 2017. Invasion success in polyploids: the role of inbreeding in the contrasting colonization abilities of diploid versus tetraploid populations of Centaurea stoebe s.l. Journal of Ecology 105, 425–435. Rozewicki, J., Li, S., Amada, K.M., Standley, D.M., Katoh, K., 2019. MAFFT-DASH: integrated protein sequence and structural alignment. Nucleic Acids Research 47, W5–W10. Rutland, C.A., Hall, N.D., McElroy, J.S., 2021. The Impact of Polyploidization on the Evolution of Weed Species: Historical Understanding and Current Limitations. Front. Agron. 3. Sabeti, P.C., Reich, D.E., Higgins, J.M., Levine, H.Z.P., Richter, D.J., Schaffner, S.F., Gabriel, S.B., Platko, J.V., Patterson, N.J., McDonald, G.J., Ackerman, H.C., Campbell, S.J., Altshuler, D., Cooper, R., Kwiatkowski, D., Ward, R., Lander, E.S., 2002. Detecting recent positive selection in the human genome from haplotype structure. Nature 419, 832–837. Salamov, A.A., Solovyev, V.V., 2000. Ab initio gene finding in Drosophila genomic DNA. Genome Res. 10, 516–522. Shan, S., Gitzendanner, M.A., Boatwright, J.L., Spoelhof, J.P., Ethridge, C.L., Ji, L., Liu, X., Soltis, P.S., Schmitz, R.J., Soltis, D.E., 2024. Genome-wide DNA methylation dynamics following recent polyploidy in the allotetraploid Tragopogon miscellus (Asteraceae). New Phytologist 242, 1363–1376. Sharma, G., Barney, J.N., Westwood, J.H., Haak, D.C., 2021. Into the weeds: new insights in plant stress. Trends in Plant Science 26, 1050–1060. Sharma, M.L., Sharma, K., 1979. Cytological studies in the north Indian grasses. cytologia 44, 861–872. Shimizu, K.K., 2022. Robustness and the generalist niche of polyploid species: Genome shock or gradual evolution? Current Opinion in Plant Biology 69, 102292. Soltis, P.S., Marchant, D.B., Van De Peer, Y., Soltis, D.E., 2015. Polyploidy and genome evolution in plants. Current Opinion in Genetics & Development 35, 119–125. Stamatakis, A., 2014. RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics 30, 1312–1313. Stanke, M., Keller, O., Gunduz, I., Hayes, A., Waack, S., Morgenstern, B., 2006. AUGUSTUS: ab initio prediction of alternative transcripts. Nucleic Acids Research 34, W435–W439. Stevens, A.V., Nicotra, A.B., Godfree, R.C., Guja, L.K., 2020. Polyploidy affects the seed, dormancy and seedling characteristics of a perennial grass, conferring an advantage in stressful climates. Plant Biol (Stuttg) 22, 500–513. Stewart, C.N., 2017. Becoming weeds. Nat Genet 49, 654–655. Takahagi, K., Inoue, K., Shimizu, M., Uehara-Yamaguchi, Y., Onda, Y., Mochida, K., 2018. Homoeolog-specific activation of genes for heat acclimation in the allopolyploid grass Brachypodium hybridum . GigaScience 7, giy020. Tarailo-Graovac, M., Chen, N., 2009. Using RepeatMasker to identify repetitive elements in genomic sequences. Current Protocols in Bioinformatics 25, 4.10.1-4.10.14. Tarasov, A., Vilella, A.J., Cuppen, E., Nijman, I.J., Prins, P., 2015. Sambamba: fast processing of NGS alignment formats. Bioinformatics 31, 2032–2034. Touafchia, S., Maurin, O., Boonsuk, B., Hodkinson, T.R., Chantaranothai, P., Rakotomalala, N., Randrianarimanana, F., Randriamampianina, J.A., Roy, S., MacKinnon, L., Rakotoarinivo, M., Besnard, G., Haevermans, T., Vorontsova, M.S., 2023. Evolutionary history, traits, and weediness in Digitaria (Poaceae: Panicoideae). Botanical Journal of the Linnean Society 203, 1–19. Turner, S.D., 2018. qqman: an R package for visualizing GWAS results using Q-Q and manhattan plots. Journal of Open Source Software 3, 731. Vietmeyer, N., Borlaugh, N., Axtell, J., Burton, J., Harlan, J., Rachie, K., 1996. Lost Crops of Africa: Volume I: Grains. National Academies Press, Washington, D.C. Wang, R., Sun, Y., Lan, Y., Wei, S., Huang, H., Li, X., Huang, Z., 2023. ALS gene overexpression and enhanced metabolism conferring Digitaria sanguinalis resistance to nicosulfuron in China. Front. Plant Sci. 14. Wedger, M.J., Xiao, E., Butts, T.R., Chlapecka, J.L., Webster, L.C., Olsen, K.M., 2024. Recent Crop-To-Weed Adaptive Introgression Has Reshaped the Genomic Composition and Geographical Structure of US Weedy Rice ( Oryza spp.). Mol Ecol e17604. Wlodzimierz, P., Rabanal, F.A., Burns, R., Naish, M., Primetis, E., Scott, A., Mandáková, T., Gorringe, N., Tock, A.J., Holland, D., Fritschi, K., Habring, A., Lanz, C., Patel, C., Schlegel, T., Collenberg, M., Mielke, M., Nordborg, M., Roux, F., Shirsekar, G., Alonso-Blanco, C., Lysak, M.A., Novikova, P.Y., Bousios, A., Weigel, D., Henderson, I.R., 2023. Cycles of satellite and transposon evolution in Arabidopsis centromeres. Nature 618, 557–565. Wu, D., Jiang, B., Ye, C.-Y., Timko, M.P., Fan, L., 2022a. Horizontal transfer and evolution of the biosynthetic gene cluster for benzoxazinoids in plants. Plant Communications, Special Issue on Rice Functional Genomics 3, 100320. Wu, D., Shen, E., Jiang, B., Feng, Y., Tang, W., Lao, S., Jia, L., Lin, H.-Y., Xie, L., Weng, X., Dong, C., Qian, Q., Lin, F., Xu, H., Lu, H., Cutti, L., Chen, H., Deng, S., Guo, L., Chuah, T.-S., Song, B.-K., Scarabel, L., Qiu, J., Zhu, Q.-H., Yu, Q., Timko, M.P., Yamaguchi, H., Merotto, A., Qiu, Y., Olsen, K.M., Fan, L., Ye, C.-Y., 2022b. Genomic insights into the evolution of Echinochloa species as weed and orphan crop. Nat Commun 13, 689. Wu, T., Hu, E., Xu, S., Chen, M., Guo, P., Dai, Z., Feng, T., Zhou, L., Tang, W., Zhan, L., Fu, X., Liu, S., Bo, X., Yu, G., 2021. clusterProfiler 4.0: A universal enrichment tool for interpreting omics data. Innovation (Camb) 2, 100141. Wu, T.D., Watanabe, C.K., 2005. GMAP: a genomic mapping and alignment program for mRNA and EST sequences. Bioinformatics 21, 1859–1875. Xu, T., Han, J.H., Kang, H., 2013. Structural features important for the RNA chaperone activity of zinc finger-containing glycine-rich RNA-binding proteins from wheat ( Triticum avestivum ) and rice ( Oryza sativa ). Phytochemistry 94, 28–35. Yang, C., Zhao, L., Zhang, H., Yang, Z., Wang, H., Wen, S., Zhang, C., Rustgi, S., von Wettstein, D., Liu, B., 2014. Evolution of physiological responses to salt stress in hexaploid wheat. Proceedings of the National Academy of Sciences 111, 11882–11887. Yanniccari, M., Vázquez-García, J.G., Gigón, R., Palma-Bautista, C., Vila-Aiub, M., De Prado, R., 2022. A novel EPSPS Pro-106-His mutation confers the first case of glyphosate resistance in Digitaria sanguinalis . Pest Management Science 78, 3135–3143. Yasue, T., 1956. Studies on hybrids in Digitaria. : I. Technique of hybridization. Japanese Journal of Breeding 6, 197–201. Yasue, T., 1957. Studies on hybrids in Digitaria II. The F1 plants, Digitaria ciliaris HERS. x D. ischaemum MUHL. Japanese Journal of Breeding 6, 265–271. Ye, C.-Y., Tang, W., Wu, D., Jia, L., Qiu, J., Chen, M., Mao, L., Lin, F., Xu, H., Yu, X., Lu, Y., Wang, Y., Olsen, K.M., Timko, M.P., Fan, L., 2019. Genomic evidence of human selection on Vavilovian mimicry. Nat Ecol Evol 3, 1474–1482. Ye, C.-Y., Wu, D., Mao, L., Jia, L., Qiu, J., Lao, S., Chen, M., Jiang, B., Tang, W., Peng, Q., Pan, L., Wang, L., Feng, X., Guo, L., Zhang, C., Kellogg, E.A., Olsen, K.M., Bai, L., Fan, L., 2020. The genomes of the allohexaploid Echinochloa crus-galli and its progenitors provide insights into polyploidization-driven adaptation. Molecular Plant 13, 1298–1310. Yin, J., Zhang, C., Mirarab, S., 2019. ASTRAL-MP: scaling ASTRAL to very large datasets using randomization and parallelization. Bioinformatics 35, 3961–3969. Yu, Q., Ahmad-Hamdani, M.S., Han, H., Christoffers, M.J., Powles, S.B., 2013. Herbicide resistance-endowing ACCase gene mutations in hexaploid wild oat (Avena fatua): insights into resistance evolution in a hexaploid species. Heredity 110, 220–231. Yuan, J.S., Tranel, P.J., Stewart, C.N., 2007a. Non-target-site herbicide resistance: a family business. Trends in Plant Science 12, 6–13. Yuan, J.S., Tranel, P.J., Stewart, C.N., 2007b. Non-target-site herbicide resistance: a family business. Trends in Plant Science 12, 6–13. Zdobnov, E.M., Apweiler, R., 2001. InterProScan – an integration platform for the signature-recognition methods in InterPro. Bioinformatics 17, 847–848. Zhang, G., Liu, X., Quan, Z., Cheng, S., Xu, X., Pan, S., Xie, M., Zeng, P., Yue, Z., Wang, W., Tao, Y., Bian, C., Han, C., Xia, Q., Peng, X., Cao, R., Yang, X., Zhan, D., Hu, J., Zhang, Y., Li, Henan, Li, Hua, Li, N., Wang, Junyi, Wang, C., Wang, R., Guo, T., Cai, Y., Liu, C., Xiang, H., Shi, Q., Huang, P., Chen, Q., Li, Y., Wang, Jun, Zhao, Z., Wang, Jian, 2012. Genome sequence of foxtail millet ( Setaria italica ) provides insights into grass evolution and biofuel potential. Nat Biotechnol 30, 549–554. Zhang, H., Song, L., Wang, X., Cheng, H., Wang, C., Meyer, C.A., Liu, T., Tang, M., Aluru, S., Yue, F., Liu, X.S., Li, H., 2021. Fast alignment and preprocessing of chromatin profiles with Chromap. Nat Commun 12, 6566. Zhang, L., Zhu, X., Zhao, Y., Guo, J., Zhang, T., Huang, W., Huang, J., Hu, Y., Huang, C.-H., Ma, H., 2022. Phylotranscriptomics Resolves the Phylogeny of Pooideae and Uncovers Factors for Their Adaptive Evolution. Mol Biol Evol 39. Zhang, M., Zhang, Y., Scheuring, C.F., Wu, C.-C., Dong, J.J., Zhang, H.-B., 2012. Preparation of megabase-sized DNA from a variety of organisms using the nuclei method for advanced genomics research. Nat Protoc 7, 467–478. Zhao, B., Xu, X., Li, B., Qi, Z., Huang, J., Hu, A., Wang, G., Liu, X., 2023. Target-site mutation and enhanced metabolism endow resistance to nicosulfuron in a Digitaria sanguinalis population. Pesticide Biochemistry and Physiology 194, 105488. Zhou, C., McCarthy, S.A., Durbin, R., 2023. YaHS: yet another Hi-C scaffolding tool. Bioinformatics 39, btac808. Zhou, Y., Zhao, X., Li, Y., Xu, J., Bi, A., Kang, L., Xu, D., Chen, H., Wang, Ying, Wang, Yuan-ge, Liu, S., Jiao, C., Lu, H., Wang, J., Yin, C., Jiao, Y., Lu, F., 2020. Triticum population sequencing provides insights into wheat adaptation. Nat Genet 52, 1412–1422. Zhu, M., Yong, K., Xu, K., Cong, J., Zhou, Xiaofang, Liu, K., Wang, X., Fan, L., Olsen, K.M., Huang, X., Zhou, Xiaoyi, Qiu, J., 2024. Landrace introgression contributed to the recent feralization of weedy rice in East China. Plant Communications 5, 101066. Additional Declarations There is NO Competing Interest. 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species: \u003cem\u003eD. radicosa\u003c/em\u003e (#YZGJ2), \u003cem\u003eD. milanjiana\u003c/em\u003e (#DZ2), and \u003cem\u003eD. sanguinalis\u003c/em\u003e (#YJ2023), shown from left to right. \u003cstrong\u003eb,\u003c/strong\u003e Overview of the \u003cem\u003eD. sanguinalis\u003c/em\u003e reference genome architecture. Track i represents the chromosomes. Tracks ii-iv depict the density of annotated genes (ii), transposable elements (iii), and GC content (iv), respectively. Track v illustrates syntenic relationships. \u003cstrong\u003ec,\u003c/strong\u003e Phylogeny of the \u003cem\u003eDigitaria\u003c/em\u003e genus constructed from single-copy orthologs using ASTRAL. Gray nodes denote speciation events outside \u003cem\u003eDigitaria\u003c/em\u003e, while red and blue nodes indicate polyploidization and intraspecific speciation within the genus, respectively. Subgenome labels (e.g., A, B, C) correspond to inferred ancestral contributions in polyploids. \u003cstrong\u003ed,\u003c/strong\u003eDistribution of stress-related gene families across Poaceae genomes. Abundance of gene family relative copy number is normalized (\u003cem\u003eZ\u003c/em\u003e-score) and displayed as a gradient heatmap. Right color bars differentiate subfamilies. Gene families associated with abiotic and biotic stress responses are marked in blue and red, respectively. Families previously implicated in non-target-site herbicide resistance are highlighted in bold green. FAR1, Far-red impaired response 1 DNA-binding domain; Hsp70, Heat shock protein 70 family; P450, Cytochrome P450 family; ABC transporter, ATP-binding cassette (ABC) transporter; GRAS, GAI-RGA- and -SCR; AP2, Apetala2; NAM C-terminal, No Apical Meristem Protein C-terminal domain; UDP/GT, UDP-glucoronosyl and UDP-glucosyl transferase; D-mannose binding lectin, D-mannose binding lectin domain; NB-ARC, NB-ARC domain.\u003c/p\u003e","description":"","filename":"fig1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7329239/v1/ca3e37cb7a7a83b22005e229.jpg"},{"id":91245839,"identity":"571759b0-5953-4ce8-8189-2a7c5d0e891b","added_by":"auto","created_at":"2025-09-13 13:58:29","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":684565,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePopulation structure and demographic history of \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eDigitaria\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e species.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ea,\u003c/strong\u003e Species identity was determined by mapping rates and genome coverage of resequencing reads aligned to the \u003cem\u003eD. sanguinalis\u003c/em\u003e reference genome (#YJ2023). \u003cstrong\u003eb,\u003c/strong\u003e Geographic distribution of \u003cem\u003eDigitaria\u003c/em\u003eaccessions used in this study. Different species are denoted by distinct symbols, while \u003cem\u003eD. sanguinalis\u003c/em\u003e varieties are indicated by colored circles. The approximate primary distribution areas of subspecies are shaded in corresponding colors. \u003cstrong\u003ec,\u003c/strong\u003e An unrooted maximum-likelihood phylogenetic tree was constructed for \u003cem\u003eD. sanguinalis\u003c/em\u003e, \u003cem\u003eD. ciliaris\u003c/em\u003e, and \u003cem\u003eD. bicornis\u003c/em\u003e, with clades distinguished by color. \u003cstrong\u003ed,\u003c/strong\u003e A rooted maximum-likelihood tree depicting population structure among \u003cem\u003eD. sanguinalis\u003c/em\u003eaccessions, with \u003cem\u003eD. ciliaris\u003c/em\u003e serving as the outgroup, based on genome-wide SNPs. \u003cstrong\u003ee, \u003c/strong\u003ePhenotypic variation in seed morphology (aspect ratio and 1000-seed weight) across \u003cem\u003eDigitaria\u003c/em\u003especies (sample sizes: \u003cem\u003eD. ischaemum\u003c/em\u003e [\u003cem\u003en\u003c/em\u003e= 9], \u003cem\u003eD. violascens\u003c/em\u003e [\u003cem\u003en\u003c/em\u003e = 16], \u003cem\u003eD. bicornis\u003c/em\u003e [\u003cem\u003en\u003c/em\u003e = 5], \u003cem\u003eD. ciliaris\u003c/em\u003e[\u003cem\u003en\u003c/em\u003e = 51], \u003cem\u003eD. sanguinalis\u003c/em\u003e [\u003cem\u003en\u003c/em\u003e = 485]) and \u003cem\u003eD. sanguinalis\u003c/em\u003evarieties (\u003cem\u003epubescens\u003c/em\u003e [\u003cem\u003en\u003c/em\u003e = 111], \u003cem\u003esanguinalis\u003c/em\u003e [\u003cem\u003en\u003c/em\u003e = 105], \u003cem\u003eparvispicula\u003c/em\u003e[\u003cem\u003en\u003c/em\u003e = 78], \u003cem\u003eglabra\u003c/em\u003e [\u003cem\u003en\u003c/em\u003e = 35]). Dot plots indicate median (central node) with whiskers extending to the 25th and 75th percentiles. \u003cem\u003eP\u003c/em\u003e-values were computed using two-tailed \u003cem\u003et\u003c/em\u003e-tests. *, p \u0026lt; 0.05; **, p \u0026lt; 0.01; ***, p \u0026lt; 0.001. \u003cstrong\u003ef,\u003c/strong\u003eDemographic inference of \u003cem\u003eD. sanguinalis\u003c/em\u003e varieties. Time points T1-T4 denote estimated divergence events. \u003cem\u003eNₑ\u003c/em\u003e, effective population size; Kya, thousand years ago.\u003c/p\u003e","description":"","filename":"fig2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7329239/v1/5f5c8f14f285e9d071f308ef.jpg"},{"id":91246630,"identity":"7a558744-75d7-4777-b8ba-02be942f8910","added_by":"auto","created_at":"2025-09-13 14:14:29","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":706813,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSympatric introgression from \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eD. ciliaris\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e to \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eD. sanguinalis\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e contributed to cold adaptation.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ea,\u003c/strong\u003e A maximum-likelihood phylogeny based on single nucleotide polymorphisms (SNPs) in the chloroplast genomes of \u003cem\u003eDigitaria\u003c/em\u003e accessions (\u003cem\u003en\u003c/em\u003e = 579). \u003cem\u003eD. sanguinalis \u003c/em\u003eand its progenitors (YJ2023, DZ2, and YZGJ2) are marked by asterisks. \u003cstrong\u003eb,\u003c/strong\u003e Bar plots below the phylogeny summarize species and varietal composition within major clades. \u003cstrong\u003ec,\u003c/strong\u003e Distribution of introgression signals between sympatric \u003cem\u003eD. ciliaris\u003c/em\u003e and \u003cem\u003eD. sanguinalis\u003c/em\u003e population pairs. Red dots represent mean genome-wide proportions of introgression (PIG, right Y-axis). The violin plots are based on 8,517, 9,002, 12,958, 14,703, 9,673, 9,640 and 11,143 sliding windows with introgression signal for populations LF to ZJ, respectively, as identified by ABBA-BABA tests. Boxplot elements indicate median (center line), interquartile range (box), and 1.5 × IQR (whiskers).\u003cstrong\u003e d,\u003c/strong\u003e Pfam domain enrichment within introgressed regions across sympatric populations. Detoxification-related domains are highlighted in green, while heat-shock-related domains are shown in red. ABC, ABC transporter; Myb-like, Myb-like DNA-binding domain; UDP/GTs, UDP-glucoronosyl and UDP-glucosyl transferase; P450, Cytochrome P450; C2, C2 domain; AP2, AP2 domain; Hsp20, Hsp20/alpha crystallin family; D-mannose, D-mannose binding lectin. \u003cstrong\u003ee,\u003c/strong\u003e Candidate stress-responsive genes identified within the 54.2-54.6 Mb region of Chr4. \u003cstrong\u003ef,\u003c/strong\u003e ABBA-BABA-derived \u003cem\u003ef\u003c/em\u003e\u003csub\u003e\u003cem\u003ed\u003c/em\u003e\u003c/sub\u003e values across Chr4. The test design assumes \u003cem\u003eD. bicornis\u003c/em\u003e (O) as the outgroup, with \u003cem\u003eD. sanguinalis\u003c/em\u003e populations arranged as P1 (allopatric), P2 (sympatric), and P3 (\u003cem\u003eD. ciliaris\u003c/em\u003e, sympatric): (((P1, P2), P3), O). \u003cstrong\u003eg,\u003c/strong\u003e Manhattan plot showing SNP associations with minimum temperature of the coldest month (Bio6), inferred using LFMM. Dashed horizontal lines denote significance thresholds (blue represents the FDR correction, adjusted \u003cem\u003ep\u003c/em\u003e = 0.05; red represents the Bonferroni correction, adjusted \u003cem\u003ep\u003c/em\u003e = 0.05). Homologs previously validated in \u003cem\u003eOryza sativa\u003c/em\u003e are annotated at their genomic positions. \u003cstrong\u003eh, \u003c/strong\u003eGeographic distribution of candidate adaptive haplotypes associated with Bio6 in \u003cem\u003eD. sanguinalis\u003c/em\u003e. Colors reflect variation in climatic variables across the sampled range.\u003cstrong\u003e i,\u003c/strong\u003e Frequency of candidate haplotypes in \u003cem\u003eD. ciliaris\u003c/em\u003e and \u003cem\u003eD. sanguinalis\u003c/em\u003e. Accessions with missing genotypes in the focal region were excluded.\u003c/p\u003e","description":"","filename":"fig3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7329239/v1/92a6ddc6fc975ba9ce4aed22.jpg"},{"id":91245840,"identity":"b879f2a0-021f-4955-a1e1-0f054f3677c2","added_by":"auto","created_at":"2025-09-13 13:58:29","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":390807,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eNicosulfuron resistance across \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eDigiatria\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e species and its genomic basis in resistant populations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ea,\u003c/strong\u003e Screening of \u003cem\u003eDigitaria\u003c/em\u003e species for nicosulfuron resistance. Circles indicate mean GR₅₀ values (g a.i. ha\u003csup\u003e-1\u003c/sup\u003e) per population, with error bars representing interquartile ranges. \u003cstrong\u003eb,\u003c/strong\u003e Temporal trend of nicosulfuron resistance in \u003cem\u003eD. sanguinalis\u003c/em\u003e populations sampled from 2013 to 2023. Scatter plots display GR₅₀ (left) and GR₉₀ (right) values, with linear regression fits (solid lines, upper-tailed \u003cem\u003et\u003c/em\u003e-tests) and 95% confidence intervals (green shading). \u003cstrong\u003ec,\u003c/strong\u003e Genome-wide association study (GWAS) results showing SNPs significantly associated with resistance across all chromosomes. Dashed horizontal lines denote significance thresholds (yellow represents the FDR correction, adjusted \u003cem\u003ep\u003c/em\u003e = 0.05; red represents the Bonferroni correction, adjusted \u003cem\u003ep\u003c/em\u003e = 0.05). SNPs located near UDP-glycosyltransferase or cytochrome P450 gene families are highlighted in green. Previously validated homologs from \u003cem\u003eOryza sativa\u003c/em\u003e are annotated in yellow at their genomic coordinates. \u003cstrong\u003ed,\u003c/strong\u003e GR₅₀ values of accessions with distinct genotypes at SNP Chr20: 34,032,644 (CC: \u003cem\u003en\u003c/em\u003e = 126; CA: \u003cem\u003en\u003c/em\u003e = 3; AA:\u003cem\u003e n\u003c/em\u003e = 6). Statistical significance was assessed by two-tailed \u003cem\u003et\u003c/em\u003e-tests (***, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001; ****, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.0001). \u003cstrong\u003ee,\u003c/strong\u003e Allele frequency distribution of the SNP at Chr20: 34,032,644 among populations with different resistance levels. \u003cstrong\u003ef,\u003c/strong\u003e Zoomed-in view of GWAS signals within Chr20: 34.0-34.1 Mb. The region contains several candidate genes including \u003cem\u003eDsSOH1\u003c/em\u003e. Bottom panel shows ABBA-BABA derived \u003cem\u003ef\u003c/em\u003e\u003csub\u003e\u003cem\u003ed\u003c/em\u003e\u003c/sub\u003e introgression scores between R and S populations across Chr20: 33.7-34.2 Mb. Gray rectangles represent 99% bootstrap quantiles, drak green points are windows with \u003cem\u003eZ\u003c/em\u003e-score \u0026gt; 4 and yellow points are windows with one-tailed \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001 based on randomization tests. \u003cstrong\u003eg,\u003c/strong\u003e Mean time to coalescence of 100k surrounding the associated SNP (Chr20: 34,032,644). Gray shading represents the divergence events among \u003cem\u003eD. bicornis\u003c/em\u003e, \u003cem\u003eD. ciliaris\u003c/em\u003e and \u003cem\u003eD. sanguinalis\u003c/em\u003e. \u003cstrong\u003eh,\u003c/strong\u003e An unrooted maximum-likelihood phylogeny inferred from 5,260 SNPs within the 100 kb region around the candidate locus (Chr20: 34,032,644).\u003c/p\u003e","description":"","filename":"fig4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7329239/v1/57f998ecf49f17e70410cfcc.jpg"},{"id":105264330,"identity":"5e1ad993-3fd0-420a-8c52-ead875e60409","added_by":"auto","created_at":"2026-03-24 07:12:02","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5769810,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7329239/v1/1c0615ce-b222-46df-beb3-4d73b5371e81.pdf"},{"id":91245931,"identity":"88acc7c9-9fc5-4081-b3a3-a6d097be6925","added_by":"auto","created_at":"2025-09-13 14:06:29","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":58382,"visible":true,"origin":"","legend":"Supplementary Notes","description":"","filename":"DigitariaSupplementaryNote.docx","url":"https://assets-eu.researchsquare.com/files/rs-7329239/v1/baae35180d8de92aad91cf97.docx"},{"id":91245932,"identity":"04972b1d-9c1e-4937-a085-0b675ad44ef2","added_by":"auto","created_at":"2025-09-13 14:06:29","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":643405,"visible":true,"origin":"","legend":"Supplementary data","description":"","filename":"DigitariaSupplementarydata.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7329239/v1/805ff10b65c1761137e04073.xlsx"},{"id":91245933,"identity":"6326ebb1-3802-45bb-aa06-3ca2afb74cbb","added_by":"auto","created_at":"2025-09-13 14:06:29","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":816096,"visible":true,"origin":"","legend":"Supplementary Tables","description":"","filename":"DigitariaSupplementarytablesv3.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7329239/v1/5d24904b92b8f7cba99433a7.xlsx"},{"id":91245855,"identity":"df27d0cc-5f8d-49a9-9d7e-9b4bedf1bd6c","added_by":"auto","created_at":"2025-09-13 13:58:29","extension":"pdf","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":11271664,"visible":true,"origin":"","legend":"Supplementary Figs","description":"","filename":"CombinedSFigs.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7329239/v1/d490b25e1c10e35ee9f16e35.pdf"},{"id":91245846,"identity":"b8be72d1-27eb-435d-b248-6ee1c68e65c7","added_by":"auto","created_at":"2025-09-13 13:58:29","extension":"pdf","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":1425385,"visible":true,"origin":"","legend":"Reporting Summary","description":"","filename":"RS.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7329239/v1/bf6fab451abaaec4ff0192c5.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"The \u003ci\u003eDigitaria\u003c/i\u003e genomes reveal local adaption and herbicide resistance mediated by introgression","fulltext":[{"header":"Highlights","content":"\u003cul\u003e\n \u003cli\u003eThe telomere-to-telomere (T2T) reference genomes of the most important upland weed, \u003cem\u003eDigitaria sanguinalis\u0026nbsp;\u003c/em\u003e(2n\u0026thinsp;=\u0026thinsp;6\u0026times;\u0026thinsp;=\u0026thinsp;54), along with its tetraploid, diploid progenitors and other polyploid species, provide a genomic lens for exploring polyploidization-driven adaptive evolution.\u003c/li\u003e\n \u003cli\u003eWhole-genome re-sequencing of 579 accessions collected over ten years, combined with herbicide resistance phenotyping, provides insights into population-scale adaptive dynamics.\u003c/li\u003e\n \u003cli\u003eA genome-wide association study (GWAS) based on large-scale herbicide dose-response assays identifies candidate genes associated with non-target-site resistance (NTSR) in \u003cem\u003eD. sanguinalis\u0026nbsp;\u003c/em\u003epopulations.\u003c/li\u003e\n \u003cli\u003eIntrogression from sympatric relatives has\u0026nbsp;contributed to\u0026nbsp;ecological adaptation in \u003cem\u003eD. sanguinalis\u003c/em\u003e and enabled the rapid evolution of NTSR.\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"Introduction","content":"\u003cp\u003eWeeds are ecologically resilient organisms that thrive in agricultural ecosystems, exhibiting broad adaptability and robust population dynamics (Mahaut et al., \u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Sharma et al., \u003cspan citationid=\"CR140\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Stewart, \u003cspan citationid=\"CR147\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). \u003cem\u003eDigitaria sanguinalis\u003c/em\u003e (L.) Scop., commonly known as large crabgrass, is one of the world\u0026rsquo;s worst weeds, occurring from tropical to temperate regions and proliferating in both cultivated and no-tillage farming systems (Burton et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Galeano et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Holm et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e1977\u003c/span\u003e; Ito et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e1986\u003c/span\u003e). At high densities, this species can cause yield losses above 90% and is frequently infested in soybean, maize, and sorghum fields (Oreja et al., 2020, 2012). The genus \u003cem\u003eDigitaria\u003c/em\u003e comprises over 220 species, whose identification is complicated by extensive phenotypic convergence (Boonsuk et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Gould, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e1963\u003c/span\u003e; Kok et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e1989\u003c/span\u003e; Sharma and Sharma, \u003cspan citationid=\"CR141\" class=\"CitationRef\"\u003e1979\u003c/span\u003e; Touafchia et al., \u003cspan citationid=\"CR151\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This taxonomic ambiguity has led to inconsistent assessments of species distributions, ultimately hindering the development and implementation of precise weed management strategies.\u003c/p\u003e\u003cp\u003ePolyploidy is common across the plant kingdom and is particularly prevalent among weedy species, where it often contributes to enhanced adaptive plasticity (Jiao et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Soltis et al., \u003cspan citationid=\"CR143\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). In allopolyploids, the coordinated regulation of homoeologous gene expression and the dynamic reshaping of epigenomic landscapes within a single nucleus introduce substantial complexity, referred as \u0026ldquo;genomic shock\u0026rdquo; (McDaniel, \u003cspan citationid=\"CR110\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Shan et al., \u003cspan citationid=\"CR139\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Shimizu, \u003cspan citationid=\"CR142\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In grasses, multiple polyploid lineages have acquired novel stress resilience through this mechanism (Menardo et al., \u003cspan citationid=\"CR113\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Takahagi et al., \u003cspan citationid=\"CR148\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Yang et al., \u003cspan citationid=\"CR162\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). A well-known example is hexaploidy wheat (\u003cem\u003eTriticum aestivum\u003c/em\u003e), which exhibits increased fitness by combining root sodium retention, mediated by \u003cem\u003eHKT1.5\u003c/em\u003e from the diploid \u003cem\u003eAegilops tauschii\u003c/em\u003e, with a higher germination rate inherited from tetraploid emmer wheat (Yang et al., \u003cspan citationid=\"CR162\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). In addition, patterns of gene retention and loss during polyploidization reflect selection pressures driving adaptive evolution (Cheng et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Notably, disease-resistance genes experienced significant loss in barnyardgrass (\u003cem\u003eEchinochloa crus-galli\u003c/em\u003e), in contrast to their expansion in wheat, suggesting a trade-off between growth and defense under diverse environmental conditions during polyploidization (Ye et al., \u003cspan citationid=\"CR167\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). \u003cem\u003eD. sanguinalis\u003c/em\u003e is a hexaploidy (2n\u0026thinsp;=\u0026thinsp;6\u0026times; = 54) (Morin et al., \u003cspan citationid=\"CR115\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), capable of both self-and cross-pollination. Therefore, understanding the evolutionary dynamics associated with polyploidization is crucial for elucidating its genetic variability and adaptive plasticity (Chao et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Meimberg et al., \u003cspan citationid=\"CR112\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Soltis et al., \u003cspan citationid=\"CR143\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe escalating use of agrochemicals has consequently driven the evolution of herbicide resistance in populations of 273 weed species worldwide (D\u0026eacute;lye, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Heap, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Huang et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2017a\u003c/span\u003e; Powles and Yu, \u003cspan citationid=\"CR126\" class=\"CitationRef\"\u003e2010a\u003c/span\u003e). Two primary mechanisms underlie the resistance are target-site resistance (TSR), resulting from mutations or amplification of herbicide target genes, and non-target-site resistance (NTSR), conferred largely through enhanced metabolic detoxification or sequestration pathways involving such major detoxification enzyme families as cytochrome P450s, Glutathione transferases (GSTs), Aldo/keto reductases (AKRs) and ATP-binding cassette transporters (ABC transporters) (Beckie et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Kreiner et al., \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2018a\u003c/span\u003e; Pan et al., \u003cspan citationid=\"CR122\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, \u003cspan citationid=\"CR123\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Powles and Yu, \u003cspan citationid=\"CR127\" class=\"CitationRef\"\u003e2010b\u003c/span\u003e; Yuan et al., \u003cspan citationid=\"CR170\" class=\"CitationRef\"\u003e2007a\u003c/span\u003e). For \u003cem\u003eD. sanguinalis\u003c/em\u003e, resistance has been documented to acetolactate synthase (ALS), photosystem II, Acetyl CoA carboxylase (ACCase), and 5-enolpyruvylshikimate-3-phosphate synthase (EPSPS) inhibitor herbicides (Guan et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Laforest et al., \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR154\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Yanniccari et al., \u003cspan citationid=\"CR163\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The ALS-inhibitor herbicide nicosulfuron has been widely used for large crabgrass control since its registration in China in the 1990s (Wang et al., \u003cspan citationid=\"CR154\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In recent years, however, many populations of large crabgrass have evolved resistance to nicosulfuron through both TSR and NTSR mechanisms (J. Li et al., \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Mei et al., \u003cspan citationid=\"CR111\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Zhao et al., \u003cspan citationid=\"CR177\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This escalating resistance evolution is a growing concern, posing a significant threat to the effectiveness and sustainability of current chemical weed management strategies. However, the molecular and evolutionary basis of NTSR remain unclear due to the lack of genome resources.\u003c/p\u003e\u003cp\u003eHybridization is common in nature (Baack and Rieseberg, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Payseur and Rieseberg, \u003cspan citationid=\"CR124\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Interspecific hybridization followed by introgression is recognized as a powerful evolutionary force across diverse taxa, particularly under rapidly changing environmental conditions where standing genetic variation and \u003cem\u003ede novo\u003c/em\u003e mutations are insufficient (Kersten et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Lyu et al., \u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; North et al., \u003cspan citationid=\"CR118\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). While natural interspecific hybridization has been documented in \u003cem\u003eDigitaria\u003c/em\u003e, the functional implications of such hybridization events remain poorly understood (Carnahan and Hill, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1961\u003c/span\u003e; Yasue, \u003cspan citationid=\"CR165\" class=\"CitationRef\"\u003e1957\u003c/span\u003e, \u003cspan citationid=\"CR164\" class=\"CitationRef\"\u003e1956\u003c/span\u003e). In European aspen (\u003cem\u003ePopulus tremula\u003c/em\u003e), adaptive introgression has been revealed facilitating adaptation to high latitudes (Rend\u0026oacute;n-Anaya et al., \u003cspan citationid=\"CR131\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Another notable example is the Gulf killifish (\u003cem\u003eFundulus grandis\u003c/em\u003e), which acquired pollution tolerance through recent introgression of aryl hydrocarbon receptor (AHR) loci from \u003cem\u003eF. heteroclitus\u003c/em\u003e (Oziolor et al., \u003cspan citationid=\"CR121\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Reid et al., \u003cspan citationid=\"CR130\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Similarly, adaptive introgression may serve as a key mechanism underpinning the wide geographic distribution and rapid evolutionary response of large crabgrass to increasing herbicide selection pressure.\u003c/p\u003e\u003cp\u003eIn this study, to investigate the evolutionary trajectory and adaptive mechanisms of \u003cem\u003eDigitaria\u003c/em\u003e species under environmental change, we assembled T2T genomes of hexaploidy \u003cem\u003eD. sanguinalis\u003c/em\u003e and its tetraploid and diploid progenitors. In parallel, we conducted large-scale genomic analyses of 579 \u003cem\u003eDigitaria\u003c/em\u003e accessions and herbicide dose-response assays on 196 accessions. Our results revealed that adaptive introgression from closely related species contributed to ecological adaptation in \u003cem\u003eD. sanguinalis\u003c/em\u003e. It also mediated the introgression of NTSR-associated genotypes, potentially accelerating the recent evolution of herbicide resistance. Furthermore, GWAS identified loci associated with herbicide resistance. Together, these findings advance our understanding of weed adaptive evolution, informing precision weed management strategies.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cb\u003eGenomic landscape of\u003c/b\u003e \u003cb\u003eDigitaria\u003c/b\u003e \u003cb\u003ereference genomes\u003c/b\u003e\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eGenome assembly\u003c/h2\u003e\u003cp\u003eA representative \u003cem\u003eD. sanguinalis\u003c/em\u003e accession (#YJ2023) was collected from agricultural fields in Shandong Province, China and its genome was sequenced, as well as its tetraploid progenitor, \u003cem\u003eD. milanjiana\u003c/em\u003e (accession #DZ2) and diploid progenitor, \u003cem\u003eD. radicosa\u003c/em\u003e (accession #YZGJ2) \u003cb\u003e(Supplementary Note 1)\u003c/b\u003e. Cytological analysis confirmed its hexaploidy status (2n\u0026thinsp;=\u0026thinsp;6\u0026times; = 54) \u003cb\u003e(Supplementary Fig.\u0026nbsp;1)\u003c/b\u003e, and \u003cem\u003ek\u003c/em\u003e-mer analysis (\u003cem\u003ek\u003c/em\u003e\u0026thinsp;=\u0026thinsp;21, peak depth\u0026thinsp;=\u0026thinsp;34) of short-read data estimated a genome size of 1.24 Gb \u003cb\u003e(Supplementary Fig.\u0026nbsp;2)\u003c/b\u003e, consistent with flow cytometry results (1.35 pg/1C) of \u003cem\u003eD. sanguinalis\u003c/em\u003e \u003cb\u003e(Supplementary Fig.\u0026nbsp;3)\u003c/b\u003e. The estimated heterozygosity was 0.05%.\u003c/p\u003e\u003cp\u003eFor \u003cem\u003ede novo\u003c/em\u003e assembly of \u003cem\u003eD. sanguinalis\u003c/em\u003e, we employed complementary long-read technologies: 81\u0026times; coverage PacBio HiFi reads (N50\u0026thinsp;=\u0026thinsp;15.9 kb) and 88\u0026times; coverage Nanopore ultra-long reads (N50\u0026thinsp;=\u0026thinsp;100.2 kb). A chromosome-level assembly was generated using Hi-C data (79\u0026times; coverage) to scaffold the initial contigs. In total, 419 contigs were anchored into 27 scaffolds, yielding a final assembly size of 1.35 Gb. Due to complexity of the hexaploidy genomes, particularly the high collinearity among homologous chromosomes, phasing posed a major challenge. To delineate subgenomes, we identified subgenome-specific \u003cem\u003ek\u003c/em\u003e-mers and clustered homeolog-differentiating scaffolds, enabling consistent partitioning into three distinct subgenomes \u003cb\u003e(Supplementary Fig.\u0026nbsp;4)\u003c/b\u003e. The three subgenomes were designated as C, D, and E, based on the markedly low mapping rate (19.72%) and genome coverage (17.53%) with \u003cem\u003eD. exilis\u003c/em\u003e, supporting its distinct genomic origin (Abrouk et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Following error correction and scaffold ordering, a chromosome-level high-quality assembly was generated with subgenome sizes of 453.1 Mb (CH), 419.9 Mb (DH), and 474.8 Mb (EH), respectively, where \u0026ldquo;H\u0026rdquo; denotes hexaploidy origin. The assembly contains only two unresolved gaps \u003cb\u003e(Supplementary Fig.\u0026nbsp;5)\u003c/b\u003e. Assessment with BUSCO (v5.6.1, Poales lineage dataset) showed 99.3% completeness of conserved genes (Manni et al., \u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Genome annotation was performed using a combination of homology-based, transcript-based, and \u003cem\u003eab initio\u003c/em\u003e prediction approaches, and 114,996 gene models were identified after filtering 709.61 Mb (51.8%) of repetitive sequences. Conserved centromeric regions containing tandem repeats (detailed below) were identified on all 27 chromosomes. Telomeric arrays (TTTAGGGₙ) were resolved at 50 of 54 chromosomal, comprising 23 fully terminal chromosomes (telomeres at both ends) and 4 partially terminal chromosomes (single telomere detected) \u003cb\u003e(Supplemental Table\u0026nbsp;1)\u003c/b\u003e. rDNA sequences were identified on seven chromosomes \u003cb\u003e(Supplemental Table\u0026nbsp;2)\u003c/b\u003e. By nation-wide sampling in China, we identified the diploid (2n\u0026thinsp;=\u0026thinsp;2\u0026times; = 18) \u003cem\u003eD. radicosa\u003c/em\u003e, and tetraploid (2n\u0026thinsp;=\u0026thinsp;4\u0026times; = 36) \u003cem\u003eD. milanjiana\u003c/em\u003e, as the progenitors of \u003cem\u003eD. sanguinalis\u003c/em\u003e (details provided in the next section) \u003cb\u003e(Supplementary Note 2;\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea\u003cb\u003e)\u003c/b\u003e. In the same way, two high-quality genomes for \u003cem\u003eD. radicosa\u003c/em\u003e (489.39 Mb) and tetraploid \u003cem\u003eD. milanjiana\u003c/em\u003e (909 Mb, with subgenome sizes of 453.1 Mb (DT) and 474.8 Mb (ET)) were also generated in this study (details see \u003cb\u003eMethods\u003c/b\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTo assess the assembly quality of the three \u003cem\u003eDigitaria\u003c/em\u003e reference genomes, we first mapped Illumina paired-end reads to respective assemblies. The results showed that a high percentage (99.51%, 99.48%, and 99.58% in the diploid, tetraploid, and hexaploidy \u003cem\u003eDigitaria\u003c/em\u003e genomes, respectively) of sequencing reads could be successfully mapped, with properly paired \u003cb\u003e(Supplemental Table\u0026nbsp;3)\u003c/b\u003e. RNA-seq reads showed normal alignment ratios to their respective genomes (94.12% for \u003cem\u003eD. radicosa\u003c/em\u003e, 89.81% for \u003cem\u003eD. milanjiana\u003c/em\u003e and 93.32% for \u003cem\u003eD. sanguinalis\u003c/em\u003e) \u003cb\u003e(Supplemental Table\u0026nbsp;4)\u003c/b\u003e. The genome assembly index LAI scores were 14.41, 17.30 and 15.62 for the three genomes, respectively, comparable to those of \u003cem\u003eArabidopsis\u003c/em\u003e (TAIR10) and \u003cem\u003eVitis vinifera\u003c/em\u003e (Jaillon et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Lamesch et al., \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). We also estimated base-level accuracy and completeness of these assemblies and high assembly consensus quality values, 58.70 (99.74%), 54.51 (99.74%) and 50.08 (99.60%), were achieved for the three genomes, respectively \u003cb\u003e(Supplementary Table\u0026nbsp;5)\u003c/b\u003e. For continuity, we detected potential assembly gaps with low-confidence read supports using CRAQ, and high scores were also gained for the three assemblies (Li et al., \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) \u003cb\u003e(Supplementary Tables\u0026nbsp;5 and 6)\u003c/b\u003e. Taken together, these results suggest that the three assembled \u003cem\u003eDigitaria\u003c/em\u003e genomes are of high quality in terms of continuity, completeness, and accuracy.\u003c/p\u003e\u003cp\u003eWhole-genome alignments revealed extensive synteny between \u003cem\u003eD. sanguinalis\u003c/em\u003e and its diploid/tetraploid progenitors, with conserved macro-collinearity across 95.0% of the genome \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb; \u003cb\u003eSupplementary Fig.\u0026nbsp;5)\u003c/b\u003e. The results also provide independent validation of our chromosome-scale assembly quality.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eSpeciation of Digitaria genus\u003c/h3\u003e\n\u003cp\u003eWe calculated the synonymous substitutions per synonymous site (\u003cem\u003eK\u003c/em\u003e\u003csub\u003e\u003cem\u003es\u003c/em\u003e\u003c/sub\u003e) for orthologous gene pairs among the \u003cem\u003eDigitaria\u003c/em\u003e species with other grass species to estimate their divergence times \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec\u003cb\u003e)\u003c/b\u003e. The \u003cem\u003eK\u003c/em\u003e\u003csub\u003e\u003cem\u003es\u003c/em\u003e\u003c/sub\u003e peak for orthologs between \u003cem\u003eD. radicosa\u003c/em\u003e and \u003cem\u003eO. sativa\u003c/em\u003e was 0.57, corresponding to an estimated divergence time of approximately 45\u0026nbsp;million years ago (mya) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec\u003cb\u003e)\u003c/b\u003e, while \u003cem\u003eDigitaria\u003c/em\u003e diverged from \u003cem\u003eEragrostis\u003c/em\u003e approximately\u0026thinsp;~\u0026thinsp;35 mya and subsequently from \u003cem\u003eSetaria\u003c/em\u003e around ~\u0026thinsp;17 mya. This phylogenetic branching timeline is consistent with previous estimates (Huang et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR175\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) \u003cb\u003e(Supplementary Fig.\u0026nbsp;6)\u003c/b\u003e. The two lineages with diagnostic spikelet arrangement phenotypes, \u003cem\u003eD. exilis\u003c/em\u003e and \u003cem\u003eD. sanguinalis\u003c/em\u003e, diverged at 12 mya. A large-scale amplification of Gypsy-type transposable elements was observed in the \u003cem\u003eD. sanguinalis\u003c/em\u003e genome compared to \u003cem\u003eD. exilis\u003c/em\u003e \u003cb\u003e(Supplementary Fig.\u0026nbsp;7a)\u003c/b\u003e. Further analysis of subgenome differentiation in \u003cem\u003eD. sanguinalis\u003c/em\u003e resolved three ancestral lineages: the C subgenome diverged around 6.9 mya, followed by the bifurcation of the D and E subgenomes at approximately 6.4 mya. Using insertion polymorphisms of long terminal repeat retrotransposons, calibrated against synonymous mutation rates, we estimated that the tetraploidization event leading to \u003cem\u003eD. milanjiana\u003c/em\u003e occurred around 0.9 mya, while hexaploidization in \u003cem\u003eD. sanguinalis\u003c/em\u003e followed at approximately 0.4 mya. Interesting, the DH and EH subgenomes in \u003cem\u003eD. sanguinalis\u003c/em\u003e exhibit similar patterns to those in \u003cem\u003eD. milanjiana\u003c/em\u003e \u003cb\u003e(Supplementary Fig.\u0026nbsp;8)\u003c/b\u003e. In addition, we reconstructed a maximum-likelihood (ML) phylogeny based on a concatenated matrix of 2,030 single-copy orthologs among the 14 (sub)genomes and a coalescent-based phylogenetic analysis was also performed integrating individual gene trees \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec; \u003cb\u003eSupplementary Fig.\u0026nbsp;9)\u003c/b\u003e. These divergence estimates were concordant with our \u003cem\u003eK\u003c/em\u003e\u003csub\u003e\u003cem\u003es\u003c/em\u003e\u003c/sub\u003e-based molecular dating analyses, with robust statistical support at all key nodes.\u003c/p\u003e\n\u003ch3\u003eContraction and expansion of gene family\u003c/h3\u003e\n\u003cp\u003eUsing ​domain-based gene family quantification across 18 genomes, we identified significant ​lineage-specific contraction in biotic stress-responsive gene families \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed\u003cb\u003e)\u003c/b\u003e. Consistent with evolutionary patterns observed in other weeds (Wu et al., \u003cspan citationid=\"CR158\" class=\"CitationRef\"\u003e2022b\u003c/span\u003e), \u003cem\u003eDigitaria\u003c/em\u003e exhibits pronounced contraction of ​NB-ARC domain-containing genes​ (median 252\u0026thinsp;\u0026plusmn;\u0026thinsp;48.7 copies \u003cem\u003evs\u003c/em\u003e. 447\u0026thinsp;\u0026plusmn;\u0026thinsp;94.0 in crops; two-tailed \u003cem\u003et\u003c/em\u003e-tests, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002) and ​D-mannose-binding lectin genes​ (61\u0026thinsp;\u0026plusmn;\u0026thinsp;9.1 \u003cem\u003evs\u003c/em\u003e. 116\u0026thinsp;\u0026plusmn;\u0026thinsp;22.8; two-tailed \u003cem\u003et\u003c/em\u003e-tests, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001). The decay in defense-related genes likely reflects ecological trade-offs that favor growth in ruderal habitats, where biotic stress responses may be reduced in importance (Brown and Rant, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Wu et al., \u003cspan citationid=\"CR158\" class=\"CitationRef\"\u003e2022b\u003c/span\u003e). Interestingly, comparative analyses revealed a significant expansion of UDP-glucuronosyl/glucosyl transferase (UDP/GT) genes in \u003cem\u003eDigitaria\u003c/em\u003e (median\u0026thinsp;\u0026plusmn;\u0026thinsp;SD: 162\u0026thinsp;\u0026plusmn;\u0026thinsp;28.5 copies) relative to \u003cem\u003eEchinochloa\u003c/em\u003e species (64\u0026thinsp;\u0026plusmn;\u0026thinsp;13.1; two-tailed \u003cem\u003et\u003c/em\u003e-tests, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.63e-5). Furthermore, gene families such as GAI-RGA-SCR (GRAS, 67.0\u0026thinsp;\u0026plusmn;\u0026thinsp;5.6 \u003cem\u003evs\u003c/em\u003e. 43.5\u0026thinsp;\u0026plusmn;\u0026thinsp;7.3; two-tailed \u003cem\u003et\u003c/em\u003e-tests, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.008) also exhibited expansion \u003cb\u003e(Supplementary Data 1)\u003c/b\u003e. These expansions may have contributed to adaptation to herbicide selection in modern weed management systems. Notably, UDP/GTs were significantly enriched on Chr3 in \u003cem\u003eDigitaria\u003c/em\u003e (fisher\u0026rsquo;s test, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) \u003cb\u003e(Supplementary Fig.\u0026nbsp;10)\u003c/b\u003e, which also harbors the highest transposon density across the genome \u003cb\u003e(Supplementary Fig.\u0026nbsp;7b)\u003c/b\u003e. This co-localization suggests that the abundance of transposable elements may have contributed to the dynamic expansion of UDP/GTs, potentially facilitating rapid local adaptation. Flood-adapted genes, \u003cem\u003eEchinochloa\u003c/em\u003e​​ displayed marked expansion of ​anaerobic-response Apetala2 (AP2) genes​ (182.0\u0026thinsp;\u0026plusmn;\u0026thinsp;12.3 copies \u003cem\u003evs\u003c/em\u003e. 159.0\u0026thinsp;\u0026plusmn;\u0026thinsp;9.9 in \u003cem\u003eD. sanguinalis\u003c/em\u003e; two-tailed \u003cem\u003et\u003c/em\u003e-tests, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.008), reflecting adaptation to prolonged submergence \u003cb\u003e(Supplementary Data 1)\u003c/b\u003e. Compared with ancestors\u0026rsquo; genomes, ​\u003cem\u003eD. sanguinalis\u003c/em\u003e exhibited a lineage-specific amplification of far-red impaired response 1 (FAR1) DNA-binding genes​ (avg. 71.3 copies \u003cem\u003evs.\u003c/em\u003e 8.5 copies in \u003cem\u003eD. exilis\u003c/em\u003e, 59.5 copies in \u003cem\u003eD. milanjiana\u003c/em\u003e and 54 copies in \u003cem\u003eD. radicosa\u003c/em\u003e) likely associated with its mat-forming growth habit and the shaded microhabitats it occupies \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. \u003cb\u003ed)\u003c/b\u003e. Concurrently, \u003cem\u003eD. exilis\u003c/em\u003e showed distinctive expansion in Heat shock protein 70 (Hsp70, 107 copies), consistent with its adaptation to extreme thermal environments in west African provenances \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. \u003cb\u003ed)\u003c/b\u003e.\u003c/p\u003e\u003cp\u003e\u003cb\u003ePopulation structure and demography of\u003c/b\u003e \u003cb\u003eDigitaria\u003c/b\u003e \u003cb\u003especies\u003c/b\u003e\u003c/p\u003e\n\u003ch3\u003eGenome-based species classification\u003c/h3\u003e\n\u003cp\u003eTo investigate the genetic diversity and population structure of \u003cem\u003eDigitaria\u003c/em\u003e, we re-sequenced the genomes of 579 \u003cem\u003eDigitaria\u003c/em\u003e accessions collected over the past decade from a wide range of habitats across China \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea; \u003cb\u003eSupplementary Table\u0026nbsp;7)\u003c/b\u003e. Based on reads mapping rates and genome coverage to the \u003cem\u003eD. sanguinalis\u003c/em\u003e reference genome \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb; \u003cb\u003eSupplementary Table\u0026nbsp;7)\u003c/b\u003e, combined with morphological traits and genome size estimations (flow cytometry and \u003cem\u003ek\u003c/em\u003e-mer analysis) \u003cb\u003e(Supplementary Fig.\u0026nbsp;11; Supplementary Table\u0026nbsp;7)\u003c/b\u003e, the 579 accessions were classified into two major clades defined by distinct spikelet arrangements. The first clade, the ternate-type group, includes \u003cem\u003eD. exilis\u003c/em\u003e, \u003cem\u003eD. ischaemum\u003c/em\u003e, and \u003cem\u003eD. violascens\u003c/em\u003e, species with primarily European distributions. The second clade, the binate-type group, comprises \u003cem\u003eD. ciliaris\u003c/em\u003e, \u003cem\u003eD. bicornis\u003c/em\u003e, and \u003cem\u003eD. sanguinalis\u003c/em\u003e, which collectively represent the most globally invasive agricultural weeds (Areces-Berazain, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). On average, \u003cem\u003eD. bicornis\u003c/em\u003e showed a 76.82% mapping rate with average coverage of 65.44%, 83.89%, and 85.12% across the CH, DH, and EH, respectively \u003cb\u003e(Supplementary Fig.\u0026nbsp;12)\u003c/b\u003e. \u003cem\u003eD. ciliaris\u003c/em\u003e achieved a 94.63% mapping rate, with CH, DH, and EH coverage of 95.36%, 84.20%, and 85.94%, respectively \u003cb\u003e(Supplementary Fig.\u0026nbsp;12)\u003c/b\u003e. \u003cem\u003eD. sanguinalis\u003c/em\u003e accessions showed the highest mapping rates, averaging 97.46%, with CH, DH, and EH coverage of 98.11%, 98.04%, and 97.03%. Particularly, the \u003cem\u003eD. milanjiana\u003c/em\u003e and \u003cem\u003eD. radicosa\u003c/em\u003e had differential subgenome coverage patterns \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea\u003cb\u003e)\u003c/b\u003e. For example, \u003cem\u003eD. radicosa\u003c/em\u003e exhibited a mapping rate of 95.05%, with subgenome compartment coverage of CH: 95.63%, DH: 2.07%, and EH: 1.80%, while \u003cem\u003eD. milanjiana\u003c/em\u003e showed a mapping rate of 93.17%, with CH, DH, and EH coverage of 7.96%, 86.57%, and 87.75%, respectively \u003cb\u003e(Supplementary Table\u0026nbsp;7)\u003c/b\u003e. This asymmetric subgenome coverage provides genomic evidence supporting their ancestral contributions (i.e. tetraploid and diploid progenitor) to the allohexaploid genome of \u003cem\u003eD. sanguinalis\u003c/em\u003e. In contrast, ternate-type \u003cem\u003eDigitaria\u003c/em\u003e species consistently exhibited low mapping rates (~\u0026thinsp;18.2%) and subgenome coverage (~\u0026thinsp;13.6%), consistent with \u003cem\u003eK\u003c/em\u003e\u003csub\u003e\u003cem\u003es\u003c/em\u003e\u003c/sub\u003e-based molecular divergence estimates \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea\u003cb\u003e)\u003c/b\u003e, indicating a more distant evolutionary relationship with the binate-type clade.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003eGeographic distribution and population structure\u003c/h3\u003e\n\u003cp\u003eOur nationwide sampling effort covered 25 provinces across China, spanning from Hainan (18.3\u0026deg;N) in the tropical south to Heilongjiang (47.3\u0026deg;N) in the subarctic north \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb\u003cb\u003e)\u003c/b\u003e. \u003cem\u003eD. sanguinalis\u003c/em\u003e was the dominant species (78.9%) in the sampling population, with notable prevalence in major agricultural regions such as Henan, Shandong, and Shanxi \u003cb\u003e(Supplementary Table\u0026nbsp;7)\u003c/b\u003e. In contrast, the majority (68.8%) of \u003cem\u003eD. violascens\u003c/em\u003e accessions were collected from northeastern provinces, Heilongjiang and Jilin. Regional differences in diversity were evident. Southern coastal provinces exhibited higher diversity, with Hainan showing the highest Simpson diversity index and Shanno index, 0.60 and 0.99, respectively, followed by Guangxi and Guangdong. In contrast, lower diversity levels were observed in Anhui and Jiangsu, with Simpson index values of 0.07 and 0.11, respectively \u003cb\u003e(Supplementary Table\u0026nbsp;8)\u003c/b\u003e.\u003c/p\u003e\u003cp\u003eWe called single nucleotide polymorphisms (SNPs) across the CH, DH, and EH subgenomes, yielding 10.76\u0026nbsp;million SNPs and 23.76 SNPs/kb for CH, 9.07\u0026nbsp;million and 21.60 SNPs/kb for DH, and 7.45\u0026nbsp;million and 15.68 SNPs/kb for EH. These SNPs were used for population structure and phylogenetic analyses of binate-type group (Ternate-type accessions were excluded from subsequent analyses due to low mapping rate) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed; \u003cb\u003eSupplementary Figs.\u0026nbsp;13 and 14)\u003c/b\u003e. Within the retained binate-type specimens, each species formed a monophyletic clade in maximum likelihood phylogenies \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed\u003cb\u003e)\u003c/b\u003e. ​Meanwhile, we identified phylogenetically intermediate outliers ​that​ displayed three convergent signatures of potential hybridization: firstly, they occupied intermediate branch positions between established species clades; secondly, principal component analysis (PCA) positioned them centrally between primary species groupings (e.g. between \u003cem\u003eD. ciliaris\u003c/em\u003e and \u003cem\u003eD. sanguinalias\u003c/em\u003e); thirdly, ancestry composition analysis revealed admixed genomic profiles \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed; \u003cb\u003eSupplementary Figs.\u0026nbsp;13 and 14)\u003c/b\u003e. These patterns collectively support the presence of reticulate evolution within the \u003cem\u003eDigitaria\u003c/em\u003e complex. Within \u003cem\u003eD. sanguinalis\u003c/em\u003e, four distinct varieties and three hybrid populations were identified, each showing marked biogeographic specialization across China \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb, d; \u003cb\u003eSupplementary Fig.\u0026nbsp;15)\u003c/b\u003e. The phylogenetically basal var. \u003cem\u003eglabra\u003c/em\u003e predominates in northeastern regions and is characterized by the narrowest leaf blades, most compact tiller angles, and the heaviest grain weight among all varieties \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed, f\u003cb\u003e)\u003c/b\u003e. In contrast, var. \u003cem\u003eparvispicula\u003c/em\u003e is dominant in southern provinces, defined by elongated, slender leaves and the lightest grain weight \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed, f; \u003cb\u003eSupplementary Figs.\u0026nbsp;16 and 17)\u003c/b\u003e. In the middle-lower Yellow River basin, the remaining two varieties exhibit north-south partitioning across the Yellow river \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb; \u003cb\u003eSupplementary Fig.\u0026nbsp;15)\u003c/b\u003e. Accessions north of the divide share morphological similarities with their southern counterparts; however, var. \u003cem\u003epubescens\u003c/em\u003e, localized on the northern slopes, is distinct in producing heavier seeds and possessing golden bristles on the lemma surface \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb, e; \u003cb\u003eSupplementary Figs.\u0026nbsp;16 and 17)\u003c/b\u003e. Notably, field surveys revealed that admixed populations consistently inhabit transitional ecotones along the contact zones between discrete varietal ranges \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb; \u003cb\u003eSupplementary Fig.\u0026nbsp;15)\u003c/b\u003e, further supporting their role as dynamic genetic intermediates within the species complex.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eDemography history of D. sanguinalis\u003c/h2\u003e\u003cp\u003eBy comparing the unfolded joint site frequency spectrum (SFS) of unlinked SNPs, polarized using known ancestral alleles, we inferred that archaic introgression from \u003cem\u003eD. ciliaris\u003c/em\u003e into \u003cem\u003eD. sanguinalis\u003c/em\u003e started at ~\u0026thinsp;431,653 year before present (yr BP, 95% confidence interval (CI) 350,044\u0026thinsp;\u0026minus;\u0026thinsp;2,420,993\u0026nbsp;year BP), following the speciation of \u003cem\u003eD. sanguinalis\u003c/em\u003e, which itself was estimated to have occurred\u0026thinsp;~\u0026thinsp;1.072 mya (95% CI 712,489-2,921,965\u0026nbsp;year BP) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ef\u003cb\u003e)\u003c/b\u003e. Var. \u003cem\u003eglabra\u003c/em\u003e​ diverged from the ancestral \u003cem\u003eD. sanguinalis\u003c/em\u003e lineage at ​~53,926\u0026nbsp;year BP (95% CI: 37,101-1,423,489\u0026nbsp;year BP). This was followed by the separation of the common ancestor of var. \u003cem\u003esanguinalis\u003c/em\u003e and var. \u003cem\u003eparvispicula\u003c/em\u003e from var. \u003cem\u003epubescens\u003c/em\u003e at approximately 46,719\u0026nbsp;year BP (95% CI: 18,199-1,156,488\u0026nbsp;year BP). During this diversification process, introgression was detected between the ancestral var. \u003cem\u003epubescens\u003c/em\u003e and the common ancestor of the other two varieties. The best-fitting demographic model also predicted ongoing, bidirectional introgression between \u003cem\u003eD. ciliaris\u003c/em\u003e and multiple \u003cem\u003eD. sanguinalis\u003c/em\u003e varieties since the emergence of var. \u003cem\u003eparvispicula\u003c/em\u003e around 39,561\u0026nbsp;year BP (95% CI: 2,919\u0026thinsp;\u0026minus;\u0026thinsp;438,092\u0026nbsp;year BP) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ef; \u003cb\u003eSupplementary Fig.\u0026nbsp;18; Supplementary Tables\u0026nbsp;9 and 10)\u003c/b\u003e. Given the weed\u0026rsquo;s prevalence in upland agricultural systems, these patterns likely reflect human-mediated dispersal. Agricultural practices, especially mechanized harvesting and commercial seed exchange, may have accelerated the nationwide spread of \u003cem\u003eD. sanguinalis\u003c/em\u003e and facilitated genetic exchange among historically isolated subspecies. To examine more recent demographic trends over the past 10,000 years, both folded and unfolded SFS were analyzed and a sharp population bottleneck was detected in var. \u003cem\u003eglabra\u003c/em\u003e around 30,000 years ago \u003cb\u003e(Supplementary Fig.\u0026nbsp;19)\u003c/b\u003e. For \u003cem\u003eD. ciliaris\u003c/em\u003e, a severe and continuous decline in the effective population size began approximately 1,000 years ago, following an earlier bottleneck. In contrast, after recovering from a bottleneck around 5,000 years ago, the population sizes of var. \u003cem\u003esanguinalis\u003c/em\u003e and var. \u003cem\u003eparvispicula\u003c/em\u003e have remained relatively stable over the past 2,000 years.\u003c/p\u003e\u003cp\u003eOverall, nucleotide diversity (π) displayed an asymmetric distribution across the \u003cem\u003eDigitaria\u003c/em\u003e genomes, with the D subgenome in both \u003cem\u003eD. ciliaris\u003c/em\u003e and \u003cem\u003eD. sanguinalis\u003c/em\u003e exhibiting the lowest levels of diversity \u003cb\u003e(Supplementary Fig.\u0026nbsp;20)\u003c/b\u003e. Except in var. \u003cem\u003epubescens\u003c/em\u003e, the E subgenome showed higher diversity than the C subgenome, suggesting that var. \u003cem\u003epubescens\u003c/em\u003e underwent distinct genomic alterations leading to elevated diversity. Moreover, the divergence between the D and E subgenomes was more pronounced in \u003cem\u003eD. sanguinalis\u003c/em\u003e than in other \u003cem\u003eDigitaria\u003c/em\u003e species \u003cb\u003e(Supplementary Fig.\u0026nbsp;9)\u003c/b\u003e, implying that \u003cem\u003eD. sanguinalis\u003c/em\u003e experienced stronger post-origin introgression than other species, with a directional bias favoring the CH and EH genomic compartments.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003ePopulation structure restructuration in ten years\u003c/h3\u003e\n\u003cp\u003eAs mentioned above, we sampled in Huang-Huai-Hai region over the past decade. Longitudinal genomic surveillance of \u003cem\u003eD. sanguinalis\u003c/em\u003e across the Huang-Huai-Hai agroecosystems reveals accelerating restructuring of its population genetic structure \u003cb\u003e(Supplementary Fig.\u0026nbsp;21a; Supplementary Table\u0026nbsp;11)\u003c/b\u003e. Contemporary populations, encompassing all four recognized varieties (var. \u003cem\u003eglabra\u003c/em\u003e, var. \u003cem\u003esanguinalis\u003c/em\u003e, var. \u003cem\u003eparvispicula\u003c/em\u003e, and var. \u003cem\u003epubescens\u003c/em\u003e), exhibit markedly higher admixture diversity compared to historical collections \u003cb\u003e(Supplementary Fig.\u0026nbsp;21b)\u003c/b\u003e. While core geographic clusters have retained notable spatial structure, temporal analyses show genetic homogenization over time. For example, in GR7 population, the proportion of the \u003cem\u003ek\u003c/em\u003e4 ancestral component declined sharply from 89.47% in 2013 to 31.95% in 2023. Concurrently, the admixture index increased from 0.29 to 0.55, reflecting a substantial rise in genomic intermixing \u003cb\u003e(Supplementary Fig.\u0026nbsp;22; Supplementary Table\u0026nbsp;11)\u003c/b\u003e.\u003c/p\u003e\n\u003ch3\u003eSympatric introgression driving local environmental adaptation\u003c/h3\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eIntrogression revealed by chloroplast genomes\u003c/h2\u003e\u003cp\u003eGiven the substantial number of hybrids identified through nuclear genomic data, we further constructed a ML phylogeny using SNPs from chloroplast genomes to infer the maternal origins of \u003cem\u003eDigitaria\u003c/em\u003e topologies \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea\u003cb\u003e)\u003c/b\u003e.​ Species-level divergence patterns in the tree were congruent with those inferred from the nuclear SNPs, notably with \u003cem\u003eD. violascens\u003c/em\u003e and \u003cem\u003eD. ciliaris\u003c/em\u003e forming a monophyletic clade \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea\u003cb\u003e)\u003c/b\u003e. Interestingly, a subset of \u003cem\u003eD. sanguinalis\u003c/em\u003e accessions (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;9) were scattered within the clade containing \u003cem\u003eD. ischaemum\u003c/em\u003e and \u003cem\u003eD. violascens\u003c/em\u003e, suggesting persistent chloroplast introgression between lineages that diverged more than 10 mya \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea, b\u003cb\u003e)\u003c/b\u003e. Although \u003cem\u003eD. bicornis\u003c/em\u003e forms a monophyletic group in the nuclear phylogeny, its chloroplast genomes clustered within Clade 4, which is dominated by \u003cem\u003eD. sanguinalis\u003c/em\u003e accessions (95.68%) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea, b\u003cb\u003e)\u003c/b\u003e. This implies a \u003cem\u003eD. sanguinalis\u003c/em\u003e origin of the \u003cem\u003eD. bicornis\u003c/em\u003e chloroplast genome, likely resulting from historical chloroplast capture through hybridization or horizontal transfer. The chloroplast phylogeny further supports the hypothesis of at least two distinct maternal donors in the origin of \u003cem\u003eD. sanguinalis\u003c/em\u003e. One lineage corresponds to the monophyletic Clade 4, while the other branches as a sister group to the \u003cem\u003eD. ciliaris\u003c/em\u003e monophyly, collectively forming Clade 3, a topology that is discordant with the nuclear genome \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea, b\u003cb\u003e)\u003c/b\u003e. Both diploid and tetraploid ancestors of \u003cem\u003eD. sanguinalis\u003c/em\u003e are positioned within Clade 3, with \u003cem\u003eD. radicosa\u003c/em\u003e nested inside the \u003cem\u003eD. ciliaris\u003c/em\u003e subclade \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea\u003cb\u003e)\u003c/b\u003e. This placement aligns with the high subgenome C coverage observed in \u003cem\u003eD. ciliaris\u003c/em\u003e (95.36%), suggesting shared ancestry of the C subgenome between \u003cem\u003eD. ciliaris\u003c/em\u003e and \u003cem\u003eD. sanguinalis\u003c/em\u003e \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea; \u003cb\u003eSupplementary Fig.\u0026nbsp;12)\u003c/b\u003e. Overall, the chloroplast phylogenies point to extensive interspecific introgression within the genus \u003cem\u003eDigitaria\u003c/em\u003e, potentially facilitated by historical hybridization events, especially considering the rarity of natural grafting in grasses.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eIntrogression between sympatric Digitaria accessions\u003c/h2\u003e\u003cp\u003eSympatric populations were selected for topological hypothesis testing \u003cb\u003e(Supplementary Fig.\u0026nbsp;23a, d)\u003c/b\u003e. These analyses revealed pervasive introgression, evidenced by consistently significant \u003cem\u003eD\u003c/em\u003e-statistics (\u003cem\u003eD\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.11; \u003cem\u003eZ\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;3 across 7 populations; \u003cb\u003eSupplementary Table\u0026nbsp;12\u003c/b\u003e). The strongest introgression signal was detected in the SX population (\u003cem\u003eD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.16), whereas JN exhibited the weakest evidence of introgression (\u003cem\u003eD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.11) \u003cb\u003e(Supplementary Table\u0026nbsp;12)\u003c/b\u003e. This pattern was further supported by sliding-window analysis, revealing that SX harbored the greatest PIG across the genome \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec; \u003cb\u003eSupplementary Table\u0026nbsp;13)\u003c/b\u003e. In contrast, eastern populations, including DZ, JN and LF, displayed fewer introgressed regions, indicating minimal impact of introgression in these lineages (PIG in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec).\u003c/p\u003e\u003cp\u003eTo assess the adaptive significance of sympatric introgression, we conducted matrix regression analyses (Mantel/partial Mantel tests) on pairwise shared introgressed genomic regions (PSIG), defined as the Jaccard similarity index of introgressed haplotypes between \u0026lsquo;\u003cem\u003eD. sanguinalis\u003c/em\u003e - \u003cem\u003eD. ciliaris\u003c/em\u003e\u0026rsquo; population pairs (Fu et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). These tests aimed to quantify the relative contributions of geographic distance or environmental divergence to observed introgression patterns \u003cb\u003e(Supplementary Fig.\u0026nbsp;23c; Supplementary Data 3)\u003c/b\u003e. Both Mantel tests revealed significant correlations between ​PSIG similarity​ and geographic distance (Pearson's \u003cem\u003er\u003c/em\u003e = -0.70, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.02), as well as environmental divergence (Pearson's \u003cem\u003er\u003c/em\u003e = -0.64, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.01) \u003cb\u003e(Supplementary Table\u0026nbsp;14)\u003c/b\u003e. However, ​partial Mantel tests​ did not identify significant independent contributions from either factor, likely due to high collinearity between geographic and environmental distances (Pearson's \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.61, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.48e-5) \u003cb\u003e(Supplementary Fig.\u0026nbsp;23d; Supplementary Table\u0026nbsp;14\u003c/b\u003e). This result held true regardless of whether environmental variation was summarized as a composite distance matrix or decomposed into individual eigenvectors of environmental variables \u003cb\u003e(Supplementary Fig.\u0026nbsp;23d; Supplementary Table\u0026nbsp;14\u003c/b\u003e). Additionally, we observed that introgression tends to reduce genetic differentiation \u003cb\u003e(Supplementary Figs.\u0026nbsp;24a, c)\u003c/b\u003e, suggesting that similar environments facilitate repeated introgression at homologous genomic regions between species.\u003c/p\u003e\u003cp\u003eTo investigate the functional relevance of introgressed genomic regions, we performed Pfam domain enrichment analyses across populations \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed\u003cb\u003e)\u003c/b\u003e. The number of introgressed genes varied from 2,181 to 3,472 among populations, with significant enrichment for stress-related domains such as Myb, FBD, AP2, and D-mannose lectin \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed\u003cb\u003e)\u003c/b\u003e. Notably, the enrichment profiles were largely population-specific: the number of enriched domains ranged from 13 in SX to 25 in LF, with only 14 domains shared across more than three populations \u003cb\u003e(Supplementary Fig.\u0026nbsp;24d)\u003c/b\u003e, suggesting localized introgression preferences possibly shaped by distinct environmental pressures. Among the enriched domains, several detoxification-related families, including cytochrome P450s, UDP/GTs, and ABC transporters, were recurrently detected across multiple populations \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed; \u003cb\u003eSupplementary Table\u0026nbsp;15)\u003c/b\u003e, indicating potential adaptive roles in metabolic stress responses. Importantly, in the SX population, which is the lowest-latitude population among all sampled groups, introgressed genes were specifically enriched in the Hsp20 protein family, molecular chaperones known to mediate heat shock responses \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed; \u003cb\u003eSupplementary Fig.\u0026nbsp;23a)\u003c/b\u003e, further supporting the role of environment-driven selection in shaping introgression patterns.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eCandidate genes of local adaptation across environmental gradient\u003c/h2\u003e\u003cp\u003eTo identify genetic variants associated with environmental adaptation, we conducted genotype-environment association (GEA) analyses using the latent factor mixed model (LFMM), which accounts for background population structure while testing for associations between genotypes and environmental variables \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eg; \u003cb\u003eSupplementary Fig.\u0026nbsp;25)\u003c/b\u003e (Frichot et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). The analysis included 19 environmental variables, comprising 10 temperature-related and 9 precipitation-related factors \u003cb\u003e(Supplementary Data 2)\u003c/b\u003e. In total, we identified 9,437 SNPs significantly associated with one or more environmental variables, corresponding to 1,831 genes \u003cb\u003e(Supplementary Fig.\u0026nbsp;25; Supplementary Table\u0026nbsp;16)\u003c/b\u003e. These environment-associated variants were broadly distributed across the genome, without significant clustering in specific genomic regions, suggesting that environmental adaptation in \u003cem\u003eDigitaria\u003c/em\u003e is governed by polygenic architectures rather than localized genomic islands of selection \u003cb\u003e(Supplementary Fig.\u0026nbsp;25)\u003c/b\u003e.\u003c/p\u003e\u003cp\u003eSeveral genes previously implicated in climate adaptation were identified in our analysis as harboring variants significantly associated with environmental variables \u003cb\u003e(Supplementary Fig.\u0026nbsp;25; Supplementary Table\u0026nbsp;16).\u003c/b\u003e For example, the gene \u003cem\u003eDsRZ2\u003c/em\u003e, which is homologs to \u003cem\u003eOsRZ2\u003c/em\u003e, exhibited strong associations with the minimum temperature of the coldest month (Bio6) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eg\u003cb\u003e)\u003c/b\u003e. This gene encodes a protein containing both a zinc knuckle domain (PF00098) and an RNA recognition motif (PF00076), and is known to play a critical role in plant protection against cold and freezing stress (Kim et al., \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Xu et al., \u003cspan citationid=\"CR161\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Expression analysis confirmed ubiquitous expression of \u003cem\u003eDsRZ2\u003c/em\u003e across all examined tissues, with RPKM values ranging from 4.40 to 7.24. We identified three major \u003cem\u003eDsRZ2\u003c/em\u003e haplotypes, exhibiting distinct geographic distributions \u003cb\u003e(Supplementary Table\u0026nbsp;17)\u003c/b\u003e. Hap1 was fixed (\u0026gt;\u0026thinsp;90%) in accessions locating northeast that experience coldest winter temperatures \u0026minus;\u0026thinsp;7.65\u0026deg;C, whereas Hap3 dominated (87% frequency) in southern populations subjected to -4.77\u0026deg;C \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eh; \u003cb\u003eSupplementary Fig.\u0026nbsp;26)\u003c/b\u003e. Haplotype frequencies also showed strong correlation with environmental gradients across accessions \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ei\u003cb\u003e)\u003c/b\u003e. Furthermore, extended haplotype homozygosity (EHH) analysis at the \u003cem\u003eDsRZ2\u003c/em\u003e locus didn\u0026rsquo;t exhibit significant differences between haplotypes carrying the T or the C allele at the focal SNP (\u003cb\u003eSupplementary Fig.\u0026nbsp;27\u003c/b\u003e; standardized |iHS| score\u0026thinsp;=\u0026thinsp;0.72).\u003c/p\u003e\u003cp\u003eGiven the recurrent introgression observed in \u003cem\u003eDigitaria\u003c/em\u003e, we tested whether local adaptation in \u003cem\u003eD. sanguinalis\u003c/em\u003e may have been facilitated by introgression. In ABBA-BABA statistics, significantly elevated \u003cem\u003ef\u003c/em\u003e\u003csub\u003ed\u003c/sub\u003e values were detected at the \u003cem\u003eDsRZ2\u003c/em\u003e locus in sympatric populations \u003cb\u003e(Fig, 3f; Supplementary Fig.\u0026nbsp;24b)\u003c/b\u003e. It means that, in these \u003cem\u003eD. sanguinalis\u003c/em\u003e accessions, genetic variation within an environmentally associated region on Chr4 more closely resembled sympatric \u003cem\u003eD. ciliaris\u003c/em\u003e haplotypes than those of allopatric \u003cem\u003eD. sanguinalis\u003c/em\u003e populations, supporting a hybrid origin for this\u0026thinsp;~\u0026thinsp;500 kb genomic segment. This introgressed segment​ contains a functionally coordinated cluster of six stress-adaptive loci, including \u003cem\u003eRZ2\u003c/em\u003e and four genes previously characterized in rice (\u003cem\u003eOsRALyase\u003c/em\u003e, \u003cem\u003eOsBIHD1\u003c/em\u003e, \u003cem\u003eOsUBC26\u003c/em\u003e, \u003cem\u003eBK-PP2A\u003c/em\u003e) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ee, f\u003cb\u003e)\u003c/b\u003e. The presence of these genes in a introgressed block suggests that introgression from \u003cem\u003eD. ciliaris\u003c/em\u003e acted as a beneficial reservoir of adaptive alleles, enhancing the environmental resilience of \u003cem\u003eD. sanguinalis\u003c/em\u003e in sympatric regions.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eTemporal escalation and geographic spread of ALS-inhibitor resistance\u003c/h2\u003e\u003cdiv id=\"Sec15\" class=\"Section3\"\u003e\u003ch2\u003eTemporal and geographic distribution of nicosulfuron resistance\u003c/h2\u003e\u003cp\u003eTo quantify the temporal dynamics of ALS-inhibitor resistance in \u003cem\u003eDigitaria\u003c/em\u003e, we performed nicosulfuron single-dose bioassays on accessions collected from the Huang-Huai-Hai agroecosystems in 2013, 2015, and 2023, respectively \u003cb\u003e(Supplementary Table\u0026nbsp;18; Supplementary Data 4)\u003c/b\u003e. As expected, resistance levels among surveyed populations exhibited a marked increase over a decade time. Survival rates following herbicide treatment rose from 36.5% in 2013 to 74.2% in 2023 \u003cb\u003e(Supplementary Table\u0026nbsp;18)\u003c/b\u003e, while the mean herbicide control efficacy concurrently declined from 86.0\u0026ndash;70.7%. To further characterize resistance variation, we conducted dose-response assays on 196 representative accessions, determining GR₅₀ (herbicide dose causing 50% plant growth reduction) and GR₉₀ values \u003cb\u003e(Supplementary Table\u0026nbsp;19)\u003c/b\u003e. The GR₅₀ estimates varied by more than 2000-fold, ranging from 0.083 g a.i. ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (observed in accession #15\u0026thinsp;\u0026minus;\u0026thinsp;9 from Anhui) to 168 g a.i. ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (accession #W-21 from Shandong), reflecting substantial inter-population differences in resistance levels.\u003c/p\u003e\u003cp\u003eDespite being predominantly collected from ecologically comparable environments, different \u003cem\u003eDigitaria\u003c/em\u003e species displayed substantial variation in herbicide resistance \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea; \u003cb\u003eSupplementary Table\u0026nbsp;19)\u003c/b\u003e.​ Accessions of \u003cem\u003eD. bicornis\u003c/em\u003e, primarily sourced from the Hainan island, exhibited the lowest GR\u003csub\u003e50\u003c/sub\u003e values, indicating high susceptibility to nicosulfuron \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea\u003cb\u003e)\u003c/b\u003e. ​In contrast, \u003cem\u003eD. ischaemum\u003c/em\u003e, \u003cem\u003eD. ciliaris\u003c/em\u003e, and \u003cem\u003eD. sanguinalis\u003c/em\u003e consistently showed higher GR\u003csub\u003e50\u003c/sub\u003e values \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea; \u003cb\u003eSupplementary Table\u0026nbsp;19)\u003c/b\u003e. ​Notably, the median GR₉₀ value of three weeds reached 84 g a.i. ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, with 100 g a.i. ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, 80 g a.i. ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, and 84 g a.i. ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e of \u003cem\u003eD. ischaemum\u003c/em\u003e, \u003cem\u003eD. ciliaris\u003c/em\u003e, and \u003cem\u003eD. sanguinalis\u003c/em\u003e accessions, respectively, above the recommended field application dose (60 g a.i. ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e). These variations in GR\u003csub\u003e50\u003c/sub\u003e/GR\u003csub\u003e90\u003c/sub\u003e values in surveyed have already rendered nicosulfuron weed control ineffective. ​To assess the temporal dynamics of resistance development, \u003cem\u003eD. sanguinalis\u003c/em\u003e accessions collected from the lower-middle Yellow River region (32\u0026deg;-40\u0026deg;N, 108\u0026deg;-120\u0026deg;E) were used in analysis. Both GR₅₀ (upper-tailed \u003cem\u003et\u003c/em\u003e-tests, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4.26e-2) and GR₉₀ (upper-tailed \u003cem\u003et\u003c/em\u003e-tests, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4.16e-3) values showed positive correlations with collection years, indicating a progressive increase in resistance over time \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb\u003cb\u003e)\u003c/b\u003e. This trend highlights the rapid adaptation of \u003cem\u003eD. sanguinalis\u003c/em\u003e populations to escalating herbicide selection pressure within agroecosystems. Importantly, accessions exhibiting resistance (GR₉₀ \u0026gt;60 g a.i. ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) were found to be geographically widespread across multiple provinces \u003cb\u003e(Supplementary Fig.\u0026nbsp;28)\u003c/b\u003e. This widespread distribution supports the hypothesis that resistance has evolved independently in multiple locations through parallel evolution, rather than through the expansion of a single resistant lineage. ​\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eTarget and non-target site variation\u003c/h2\u003e\u003cp\u003eWhole-genome sequencing of the \u003cem\u003eDigitaria\u003c/em\u003e accessions (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;554) identified 26 mutations in the target-site ALS gene, and 25 of the 26 mutations were previously not documented in plants or bacteria \u003cb\u003e(Supplementary Fig.\u0026nbsp;29a; Supplementary Tables\u0026nbsp;20 and 21)\u003c/b\u003e. Hence it is not sure if these mutations are related to nicosulfuron resistance. Notably, the CH subgenome bore the highest mutational load. Most of these target-site mutations were found in a heterozygous state within individual accessions, in contrast to the predominantly homozygous resistance alleles commonly observed in globally collected \u003cem\u003eEchinochloa\u003c/em\u003e populations​ \u003cb\u003e(Supplementary Table\u0026nbsp;20)\u003c/b\u003e (Wu et al., \u003cspan citationid=\"CR158\" class=\"CitationRef\"\u003e2022b\u003c/span\u003e).​​ ​Furthermore, these variants remained at low-frequency alleles within the population \u003cb\u003e(Supplementary Fig.\u0026nbsp;29a; Supplementary Tables\u0026nbsp;20 and 21)\u003c/b\u003e.​​ No shared mutation sites were detected among the three \u003cem\u003eD. sanguinalis\u003c/em\u003e (\u003cem\u003eDsALS)\u003c/em\u003e copies, and no accessions harbored all three copies in either homozygous or heterozygous variant states \u003cb\u003e(Supplementary Table\u0026nbsp;21)\u003c/b\u003e.​​ The copy number of \u003cem\u003eDsALS\u003c/em\u003e remained stable across the population, with read depths at the three loci following a normal distribution (avg. depth: 0.96 for \u003cem\u003eDsALS-C\u003c/em\u003e, shapiro and normality test, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.01; 0.95 for \u003cem\u003eDsALS-D\u003c/em\u003e, shapiro and normality test, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.01 and 0.92 for \u003cem\u003eDsALS-E\u003c/em\u003e, shapiro and normality test, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.01). As a result, \u003cem\u003eDigitaria\u003c/em\u003e, especially of hexaploidy, faces evolutionary constraints in achieving rapid resistance through classical TSR mechanisms, even under strong herbicide selection.\u003c/p\u003e\u003cp\u003e​Beside TSR, NTSR has emerged as an important mechanism underpinning herbicide resistance in weed populations.​​ Gene duplication-mediated dosage effects represent a mechanism for rapid adaptation to intense selection pressures, which illustrated in \u003cem\u003eAlopecurus myosuroides\u003c/em\u003e (blackgrass), and bamboo rats (Cai et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Klure et al., \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).​​ ​​To determine whether NTSR in \u003cem\u003eD. sanguinalis\u003c/em\u003e involves expansions, we assessed copy number variation for six key resistance-related gene families (cytochrome P450s, GSTs, UDP/GTs, AKRs, NB-ARC and ABC transporters). We observed that the copy numbers of these gene families predominantly clustered around 1, yet 6 to 73 accessions exhibited multicopy genes (\u0026gt;\u0026thinsp;3; \u003cb\u003eSupplementary Fig.\u0026nbsp;29b\u003c/b\u003e). Notably, five \u003cem\u003eD. sanguinalis\u003c/em\u003e accessions harbored over 8 copies of P450 family genes (Chr18.2080) \u003cb\u003e(Supplementary Table\u0026nbsp;22)\u003c/b\u003e. However, correlation analysis between resistance levels and NTSR gene copy numbers revealed no association with nicosulfuron resistance. \u003cb\u003e(Supplementary Fig.\u0026nbsp;29c; Supplementary Table\u0026nbsp;22)\u003c/b\u003e. These findings argue against a model of metabolic resistance mediated by gene amplification, and instead suggest alternative NTSR regulation, such as transcriptional reprogramming, may underlie the observed resistance phenotypes in \u003cem\u003eD. sanguinalis\u003c/em\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eTranscriptome analysis\u003c/h2\u003e\u003cp\u003eWe performed time-course RNA sequencing on leaf tissue of the resistant (R; accession #21\u0026thinsp;\u0026minus;\u0026thinsp;17) versus the susceptible (S; accession #15\u0026thinsp;\u0026minus;\u0026thinsp;2) accessions treated with nicosulfuron \u003cb\u003e(Supplementary Note 3; Supplementary Table\u0026nbsp;23)\u003c/b\u003e. In the R accession, 152 NTSR-related genes exhibited induced upregulation, including \u003cem\u003eAKR1\u003c/em\u003e, \u003cem\u003eUGT706D1\u003c/em\u003e, \u003cem\u003eCYP81A6\u003c/em\u003e, and \u003cem\u003eABCG43\u003c/em\u003e, which are involved in reactive oxygen species (ROS) scavenging and stress response processes (Guo et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; ODA et al., \u003cspan citationid=\"CR119\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Pan et al., \u003cspan citationid=\"CR122\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Peng et al., \u003cspan citationid=\"CR125\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Notably, we identified a short-chain dehydrogenase/reductase (SDR), \u003cem\u003eDsSOH1\u003c/em\u003e, a widely conserved enzyme family implicated in detoxification and abiotic stress responses (Chatterjee et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Du et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Loubet et al., \u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Nakazono et al., \u003cspan citationid=\"CR117\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). It exhibited nicosulfuron-inducible upregulation specifically in the R but not the S accession \u003cb\u003e(Supplementary Fig.\u0026nbsp;30)\u003c/b\u003e. In addition, a total of 149 genes exhibited constitutive differential expression, comprising 2 genes (\u003cem\u003eCYP75B3\u003c/em\u003e and \u003cem\u003eCYP92C21\u003c/em\u003e) that have homologs in rice involved in biotic stress responses (Chen et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003eAdaptive introgression enables evolutionary escape from herbicide\u003c/h2\u003e\u003cp\u003eTo identify genomic loci underpinning NTSR, we conducted a genome-wide association study (GWAS) using 141 accessions based on their GR₅₀ values under nicosulfuron treatment (Kang et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2010b\u003c/span\u003e). A total of 40 resistance-associated SNPs (rSNPs) was identified, corresponding to 19 unique genes \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed\u003cb\u003e)\u003c/b\u003e. Among these, three GWAS-prioritized genes correspond to homologs of known herbicide resistance determinants in Poaceae (Chen et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Goldberg-Cavalleri et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Pan et al., \u003cspan citationid=\"CR123\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, \u003cspan citationid=\"CR122\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), including an ortholog of the \u003cem\u003eABCC8\u003c/em\u003e transporter, previously functionally validated in barnyardgrass as mediating glyphosate resistance via vacuolar sequestration (Pan et al., \u003cspan citationid=\"CR123\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Notably, rSNPs were located near \u003cem\u003eDsSOH1\u003c/em\u003e and \u003cem\u003eDsCYP81A6\u003c/em\u003e, both of which showed herbicide-induced differential expression following the nicosulfuron treatment \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed; \u003cb\u003eSupplementary Fig.\u0026nbsp;30; Supplementary Note 3)\u003c/b\u003e.\u003c/p\u003e\u003cp\u003eHerbicide resistance levels were quantitatively assessed using the Resistance Index (RI). Based on the RI values, we categorized individuals into four distinct resistance groups: Susceptible (S), Low Resistance (LR), Moderate Resistance (MR), and High Resistance (HR) \u003cb\u003e(Supplementary Table\u0026nbsp;19)\u003c/b\u003e. To investigate the origin of herbicide resistance, we employed the \u003cem\u003ef\u003c/em\u003e\u003csub\u003ed\u003c/sub\u003e statistic, a metric optimized for detecting local ancestry and admixture within genomic windows. A prominent \u003cem\u003ef\u003c/em\u003e\u003csub\u003ed\u003c/sub\u003e peak was detected specifically within the \u003cem\u003eDsSOH1\u003c/em\u003e locus in HR populations, whereas the MR and LR groups exhibited progressively weaker admixture signals \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ee\u003cb\u003e)\u003c/b\u003e. Notably, the \u003cem\u003eDsSOH1\u003c/em\u003e variant was not fixed in HR accessions (allele frequency\u0026thinsp;\u0026asymp;\u0026thinsp;0.4), collectively suggesting recent adaptive introgression through introgression \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ee\u003cb\u003e)\u003c/b\u003e. In parallel, elevated genome-wide \u003cem\u003eF\u003c/em\u003e\u003csub\u003e\u003cem\u003eST\u003c/em\u003e\u003c/sub\u003e values revealed considerable population subdivision within \u003cem\u003eD. sanguinalis\u003c/em\u003e (Jakobsson et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Martin et al., \u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e2015a\u003c/span\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ee). Remarkably, the regions with the highest \u003cem\u003eF\u003c/em\u003e\u003csub\u003e\u003cem\u003eST\u003c/em\u003e\u003c/sub\u003e differentiation between resistant and susceptible populations coincided precisely to the \u003cem\u003ef\u003c/em\u003e\u003csub\u003ed\u003c/sub\u003e peak interval, strongly implicating introgression from \u003cem\u003eD. ciliaris\u003c/em\u003e as the driving force behind resistance-associated divergence in \u003cem\u003eD. sanguinalis\u003c/em\u003e \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ee; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed\u003cb\u003e)\u003c/b\u003e. Although the EHH analysis revealed a notable difference between the A and C alleles, the |iHS| value (1.39, within the 90th percentile of the genome-wide distribution) did not reach the conventional threshold for detecting recent strong positive selection (Sabeti et al., \u003cspan citationid=\"CR137\" class=\"CitationRef\"\u003e2002\u003c/span\u003e) \u003cb\u003e(Supplementary Fig.\u0026nbsp;31a)\u003c/b\u003e.\u003c/p\u003e\u003cp\u003eTo clarify the ancestry of the introgressed genomic region, we employed Bayesian Phylogenetics and Phasing (BPP v4.0) (Flouri et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) under an explicit introgression model. Parameter estimates supported a topology where the \u003cem\u003eDsSOH1\u003c/em\u003e haplotypes in resistant \u003cem\u003eD. sanguinalis\u003c/em\u003e originated from recent introgression with \u003cem\u003eD. ciliaris\u003c/em\u003e (\u003cem\u003eφ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.3, 2.5% HPD\u0026thinsp;=\u0026thinsp;0.24, 97.5% HPD\u0026thinsp;=\u0026thinsp;0.36), while alternative topologies were statistically rejected \u003cb\u003e(Supplementary Table\u0026nbsp;24)\u003c/b\u003e. Moreover, ​speciation between \u003cem\u003eD. sanguinalis\u003c/em\u003e and \u003cem\u003eD. ciliaris\u003c/em\u003e substantially predates the inferred introgression event​ (divergence \u003cem\u003eτ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002010 \u003cem\u003evs\u003c/em\u003e. introgression divergence \u003cem\u003eτ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.000056), providing further confirmation that resistance alleles arose via admixture. Aligning the geographically widespread distribution of resistant \u003cem\u003eD. sanguinalis\u003c/em\u003e accessions, maximum-likelihood phylogenies constructed from introgressed SNPs consistently grouped resistant accessions within clades of sympatric \u003cem\u003eD. ciliaris\u003c/em\u003e \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eh\u003cb\u003e)\u003c/b\u003e. Specifically, HR haplotypes from ​Hebei and ​Anhui formed sister clades with locally collected \u003cem\u003eD. ciliaris\u003c/em\u003e accessions, with short branch lengths \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ei\u003cb\u003e)\u003c/b\u003e. This recurrent phylogenetic pattern suggests that NTSR evolved through parallel, spatially restricted, and recent introgression events between neighboring \u003cem\u003eDigitaria\u003c/em\u003e populations.\u003c/p\u003e\u003cp\u003eAnalysis of introgression patterns across different resistance population revealed a gradient in the distribution of genomic windows derived from \u003cem\u003eD. ciliaris\u003c/em\u003e, with PIG positively correlating with resistance level (LR: 18.93 Mb; MR: 27.64 Mb; HR: 30.86 Mb). Leveraging ​RFmix v2.0​ for local ancestry inference at rSNPs (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;131,240), we quantified the ​introgression dosage, defined as the proportion of \u003cem\u003eD. ciliaris\u003c/em\u003e ancestry, across all individuals \u003cb\u003e(Supplementary Fig.\u0026nbsp;31c)\u003c/b\u003e. ​​Longitudinal analysis of rSNPs further revealed a temporal increase in resistance allele frequency (RAF) over the past decade \u003cb\u003e(Supplementary Fig.\u0026nbsp;31d)\u003c/b\u003e. ​​The cumulative genomic burden of introgressed haplotypes supports a polygenic architecture underlying NTSR, wherein increasing \u003cem\u003eD. ciliaris\u003c/em\u003e ancestry dosage may contribute to enhanced herbicide tolerance.\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eAccurate taxonomic delineation of \u003cem\u003eDigitaria\u003c/em\u003e species forms a foundation for basic research and precision weed management in these species (Kok et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e1989\u003c/span\u003e; Sharma and Sharma, \u003cspan citationid=\"CR141\" class=\"CitationRef\"\u003e1979\u003c/span\u003e). However, classification within the genus remains challenging due to morphological convergence among over 220 \u003cem\u003eDigitaria\u003c/em\u003e species (Georgia and Georgia, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e1916\u003c/span\u003e; Henrard, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e1950\u003c/span\u003e; Vietmeyer et al., \u003cspan citationid=\"CR153\" class=\"CitationRef\"\u003e1996\u003c/span\u003e). To address these complexities, we employed an integrative approach that combined phenotypic traits, such as genome size, 1000-grain weight, and aspect ratio, with genomic indicators, including whole-genome mapping rates and coverage depth profiles \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea, d, e\u003cb\u003e)\u003c/b\u003e. This multidimensional strategy enabled precise species discrimination across the genus and revealed ecotypic differentiation within \u003cem\u003eD. sanguinalis\u003c/em\u003e, where geographically distinct populations exhibited adaptations to local environmental conditions. For example, \u003cem\u003eD. sanguinalis\u003c/em\u003e var. \u003cem\u003eparvispicula\u003c/em\u003e exhibits ​significantly expanded leaf area indices, facilitating accelerated carbon acquisition under ​high-temperature/high-precipitation regimes. Together, we provided a robust taxonomic framework for the genus \u003cem\u003eDigitaria\u003c/em\u003e, resolving long-standing classification ambiguities and informing targeted herbicide strategies for managing economically important weed species.\u003c/p\u003e\u003cp\u003eEvolution of herbicide resistance in populations of numerous weed species underscores the urgent need to understand the genetic basis of resistance and their underlying evolutionary dynamics, in order to inform effective management strategies (Heap, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Luo and Liu, \u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Target site mutation is often the popular resistance mechanism (Beckie et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; D\u0026eacute;lye et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Powles and Yu, \u003cspan citationid=\"CR127\" class=\"CitationRef\"\u003e2010b\u003c/span\u003e). However, our analysis of 81 \u003cem\u003eD. sanguinalis\u003c/em\u003e resistant populations revealed ​no known resistance mutations​ or copy number variation associated with herbicide resistance phenotypes \u003cb\u003e(Supplementary Fig.\u0026nbsp;29a; Supplementary Tables\u0026nbsp;20 and 21)\u003c/b\u003e, consistent with previous findings in Chinese resistant \u003cem\u003eDigitaria\u003c/em\u003e populations (Guan et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Mei et al., \u003cspan citationid=\"CR111\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR154\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This observation presents a paradox, given the long-standing hypothesis that polyploid genomes are expected to favor TSR evolution due to the presence of multiple gene copies. Nevertheless, in polyploid species, the effect of target site mutations can be masked or diluted by co-existing wild-type alleles, and hence its contribution to resistance may be influenced by several factors, including ploidy level, the number of mutant alleles, the expression level of the homolog harboring the mutation, and the dominance of the mutation itself, as discussed in Yu et al (Yu et al., \u003cspan citationid=\"CR169\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). For example, a transcriptomic study by Hereward et al. (Hereward et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) on glyphosate resistance in polyploid \u003cem\u003eConyza bonariensis\u003c/em\u003e revealed that the glyphosate target EPSPS 106 mutation was present in both resistant and susceptible lines. However, the mutated allele was expressed at lower levels than the wild-type copies, and thus no contribution to glyphosate resistance.\u003c/p\u003e\u003cp\u003eTherefore, despite the general ecological success and invasiveness of polyploids, our findings suggest that a large number of crabgrass populations do not exhibit an elevated propensity for evolving target-site mutations (Rosche et al., \u003cspan citationid=\"CR134\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Stevens et al., \u003cspan citationid=\"CR146\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Among other factors (see below), the dilution effect by multiple WT alleles may weaken the mutant alleles and hence un-favors the allele fixation in the population.\u003c/p\u003e\u003cp\u003eNTSR poses a potentially greater threat to agricultural systems than TSR, due to its inherently polygenic nature and unpredictable evolutionary trajectories (Baucom, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Kreiner et al., \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2018b\u003c/span\u003e; Yuan et al., \u003cspan citationid=\"CR171\" class=\"CitationRef\"\u003e2007b\u003c/span\u003e). In our study, we identified 40 NTSR candidate loci that were distant from ALS genes \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed\u003cb\u003e)\u003c/b\u003e. Instead, these loci were near cytochrome P450, GST, AKR and UDP/GT genes, key families functionally associated with detoxification. Whereas NTSR often acts in combination with TSR to modulate resistance phenotypes, as observed in many other herbicide resistant weedy species, it appears to be the predominant mechanism in \u003cem\u003eD. sanguinalis\u003c/em\u003e, likely due to the lack of TSR mechanisms \u003cb\u003e(Supplementary Table\u0026nbsp;20)\u003c/b\u003e (Kreiner et al., \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This pattern mirrors findings in blackgrass, where mesosulfuron-methyl and clodinafop resistance is likewise more dominated by NTSR pathways (D\u0026eacute;lye et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Goldberg-Cavalleri et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Kersten et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In our study, significantly associated mutants in GWAS were present at low frequencies within resistant accessions (average allele frequency\u0026thinsp;=\u0026thinsp;0.063). This spatial heterogeneity in resistance architecture likely reflects mechanistic diversification shaped by localized agricultural selection regimes. Divergent cropping patterns and herbicide application histories/strategies, particularly the increasing reliance on complex chemical mixtures in modern farming, have promoted context-dependent adaptive specialization (Huang et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2017b\u003c/span\u003e; Liu et al., \u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). For instance, sequential treatments involving pre-emergence soil-applied herbicides (e.g., pyroxasulfone or oxadiazon) followed by post-emergence applications of nicosulfuron or mesotrione potentially reduce the effectiveness of single target-site mutations. Instead, they intensify selection pressure for polygenic NTSR architectures (Comont et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Notably, the observed low-frequency mutant may act as compensatory adaptations that mitigate fitness penalties associated with primary resistance mechanisms (Rutland et al., \u003cspan citationid=\"CR136\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The functional relevance of these variants will be further evaluated in future studies by integrating key phenotypic covariates, such as relative growth rate. In conclusion, our findings illustrate a compelling case in which NTSR emerges as the prevalent survival strategy under the selection pressures imposed by multi-herbicide treatments.\u003c/p\u003e\u003cp\u003eAlthough introgression is increasingly recognized as a powerful driver of adaptive evolution, instances of beneficial interspecific introgression remain relatively rare, primarily due to anticipated genomic incompatibilities (Fu et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Hedrick, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Our study uncovers an exception in \u003cem\u003eDigitaria\u003c/em\u003e, particularly between \u003cem\u003eD. ciliaris\u003c/em\u003e and \u003cem\u003eD. sanguinalis\u003c/em\u003e, where recurrent post-divergence introgression appears to have facilitated environmental adaptation \u003cb\u003e(\u003c/b\u003eFigs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. Specifically, haplotypes introgressed from \u003cem\u003eD. ciliaris\u003c/em\u003e into \u003cem\u003eD. sanguinalis\u003c/em\u003e populations are associated with enhanced cold tolerance, a key advantage given their overlapping distributions across climatically heterogeneous regions. The integration may not be driven solely by the fitness advantages of foreign fragments (Harrison and Larson, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Mallet, \u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). The geographic proximity and environmental similarity between the two species likely facilitated such introgression. Furthermore, the ancestral introgression between \u003cem\u003eD. ciliaris\u003c/em\u003e and \u003cem\u003eD. sanguinalis\u003c/em\u003e following their divergence, may have contributed to their similar genomic background \u003cb\u003e(Supplementary Fig.\u0026nbsp;11)\u003c/b\u003e, thereby reducing the risk of deleterious effects typically associated with interspecific hybridization (Johnson, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Maheshwari and Barbash, \u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Together, these conditions reduce the likelihood of epistatic incompatibilities commonly associated with interspecific introgression and illustrated that introgression accelerated by phylogenetic proximity and ecological convergence.\u003c/p\u003e\u003cp\u003eWhile introgression events in weeds have garnered increasing documentation in recent years, this study provides the first empirical evidence of ​adaptive introgression at herbicide resistance loci (Ribeiro et al., \u003cspan citationid=\"CR133\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Wedger et al., \u003cspan citationid=\"CR155\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Wu et al., \u003cspan citationid=\"CR158\" class=\"CitationRef\"\u003e2022b\u003c/span\u003e). Our analyses detected introgression signals flanking mutations identified in GWAS, and revealed a correlation between the proportion of introgressed regions and resistance level \u003cb\u003e(Supplementary Fig.\u0026nbsp;31)\u003c/b\u003e. This pattern implicates weedy relatives as critical reservoirs for herbicide adaptation, paralleling documented cases of ​crop-to-weed resistance transmission​ via pollen-mediated introgression (L. Li et al., \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Zhu et al., \u003cspan citationid=\"CR180\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The rapid evolution of herbicide resistance in weed populations illustrates how extreme anthropogenic selection can override the fitness costs typically associated with introgressed alleles (Baucom, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Kreiner et al., \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Severe anthropogenic selection may offset the relative fitness costs of foreign alleles, promoting the retention, or even fixation, of introgressed haplotypes that contributed to resistance (Kersten et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Kreiner et al., \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In waterhemp (\u003cem\u003eAmaranthus tuberculatus\u003c/em\u003e), introgression is considered to have shaped the landscape-scale distribution of herbicide resistance (Kreiner et al., \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Meimberg et al., \u003cspan citationid=\"CR112\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). However, no similar introgression signals associated with herbicide resistance were identified in hexaploidy barnyardgrass (\u003cem\u003eEchinochloa crus-galli\u003c/em\u003e) populations (Wu et al., \u003cspan citationid=\"CR158\" class=\"CitationRef\"\u003e2022b\u003c/span\u003e). Instead, population structure analysis reveals distinct subspecies differentiation in barnyardgrass, which likely reflects a long history of self-fertilization and limited interspecific genetic exchange. Indeed, crabgrass and waterhemp exhibit sympatric distribution with closely related species, providing opportunities for rapid dissemination of herbicide-resistant genotypes through introgression \u003cb\u003e(Supplementary Fig.\u0026nbsp;12)\u003c/b\u003e (Kreiner et al., \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). To our knowledge, most weed species have sympatric closely related species, including crops (e.g., weedy rice and cultivated rice) or other weedy species (such as \u003cem\u003eAmaranthus\u003c/em\u003e and \u003cem\u003eSetaria\u003c/em\u003e). Introgression likely plays a significant role in the adaptation of these weed complexes to agricultural environments. However, limited genomic research on weedy species has hindered our understanding of this model for rapid adaptation.\u003c/p\u003e\u003cp\u003eIn summary, this study presents genome assemblies for diploid, tetraploid, and hexaploidy \u003cem\u003eDigitaria\u003c/em\u003e species, establishing essential genomic resources for global accession profiling, herbicide-resistance crop breeding, weed adaptive evolution studies and development of next-generation nucleotide herbicides. Most importantly, we demonstrate that introgression constitutes a significant source of adaptation related variation, offering critical insights that can inform the development of more integrated and sustainable weed management strategies.\u003c/p\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003eMethod\u003c/h2\u003e\u003cdiv id=\"Sec21\" class=\"Section3\"\u003e\u003ch2\u003eGenome sequencing and assembly\u003c/h2\u003e\u003cp\u003eVoucher specimens of the three sequenced species were deposited in the Herbarium of Zhejiang University (HZU), with accession numbers HZU60147516 (\u003cem\u003eD. sanguinalis\u003c/em\u003e, #YJ2023), HZU60147511 (\u003cem\u003eD. milanjiana\u003c/em\u003e, #DZ2), and HZU60147514 (\u003cem\u003eD. radicosa\u003c/em\u003e, #YZGJ2).\u003c/p\u003e\u003cp\u003eFor \u003cem\u003eD. sanguinalis\u003c/em\u003e, total genomic DNA was extracted from young leaves using the cetyltrimethylammonium bromide (CTAB) method. High-molecular-weight DNA was prepared via the nuclei method (M. Zhang et al., \u003cspan citationid=\"CR173\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) for Nanopore ultralong sequencing and library construction, followed by sequencing on a PromethION platform. Approximately 116.98 Gb of ultralong reads (N50\u0026thinsp;\u0026gt;\u0026thinsp;100 kb) were assembled \u003cem\u003ede novo\u003c/em\u003e with NextDenovo v2.5.2 using default parameters (Hu et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2024b\u003c/span\u003e). In parallel, single-molecule real-time (SMRT) sequencing libraries were constructed according to the Pacific Biosciences protocols and sequenced on the PacBio Sequel II system using the circular consensus sequencing (CCS) approach. HiFi reads and ultralong reads were co-assembled with Hifiasm v0.19.8-r603, and the contiguity of initial assembly was improved combining initial NextDenovo assembly using Quickmerge (Chakraborty et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Cheng et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), with validation by long-read mapping depth. The assembly were further polished by PacBio HiFi data and Illumina data using NextPolish2 (v0.2.1) with recommended parameters (Hu et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2024a\u003c/span\u003e). Hi-C data (2 \u0026times; 150 bp paired-end reads) were processed with YaHS v1.1 to generate a chromosome-scale contact map \u003cb\u003e(Supplementary Fig.\u0026nbsp;4a)\u003c/b\u003e, which was manually curated in Juicebox v1.11 (Durand et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR174\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Zhou et al., \u003cspan citationid=\"CR178\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Subgenome phasing was conducted using a \u003cem\u003ek\u003c/em\u003e-mer based approach implemented in SubPhaser (Jia et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eFor \u003cem\u003eD. milanjiana\u003c/em\u003e, genomic DNA was also extracted from young leaves using the CTAB method. Illumina paired-end libraries were constructed according to the manufacturer\u0026rsquo;s protocol (Illumina, USA). PacBio long reads were generated, error-corrected, and assembled into contigs using Hifiasm. The high-contiguity \u003cem\u003eD. sanguinalis\u003c/em\u003e assembly was used as a reference to anchor and order fragments, and the contigs were further scaffolded into pseudochromosomes against the D and E subgenomes of \u003cem\u003eD. sanguinalis\u003c/em\u003e using Ragtag v2.1.0 (Alonge et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eFor \u003cem\u003eD. radicosa\u003c/em\u003e (#YZGJ2), DNA extraction, library construction, and assembly followed the procedures described for \u003cem\u003eD. milanjiana\u003c/em\u003e.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003eGenome annotation\u003c/h2\u003e\u003cp\u003eRepetitive elements in the three genomes were annotated following previously described methods (Huang et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Briefly, repeat families were identified \u003cem\u003ede novo\u003c/em\u003e and initially classified using RepeatModeler v1.0.10 (Tarailo-Graovac and Chen, \u003cspan citationid=\"CR149\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), and genome-wide repeat annotation was subsequently performed with RepeatMasker v4.0.7 (Tarailo-Graovac and Chen, \u003cspan citationid=\"CR149\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eProtein-coding genes were predicted using an integrative strategy that combined \u003cem\u003eab initio\u003c/em\u003e prediction, homology-based inference, and transcriptome-supported annotation. \u003cem\u003eAb initio\u003c/em\u003e predictions were generated with Fgenesh and AUGUSTUS v3.2.2, whereas homology-based evidence was obtained using GMAP (Salamov and Solovyev, \u003cspan citationid=\"CR138\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Stanke et al., \u003cspan citationid=\"CR145\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Wu and Watanabe, \u003cspan citationid=\"CR160\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). All evidence types were merged using EVidenceModeler v1.1.1 (Haas et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Stanke et al., \u003cspan citationid=\"CR145\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), resulting in a non-redundant consensus gene set. Gene models were retained only if supported by homologous proteins evidence, transcript alignments, or by at least two independent \u003cem\u003eab initio\u003c/em\u003e predictions. Low-confidence models, defined as those encoding peptides\u0026thinsp;\u0026lt;\u0026thinsp;50 amino acids or showing significant similarity to repetitive elements in Repbase (E-value\u0026thinsp;\u0026lt;\u0026thinsp;1e-5, identity\u0026thinsp;\u0026gt;\u0026thinsp;30%, and coverage\u0026thinsp;\u0026gt;\u0026thinsp;25%), were filtered out to improve annotation accuracy.\u003c/p\u003e\u003cp\u003eFunctional annotation of the predicted protein-coding genes was conducted using InterProScan v5.24-63.0 for \u003cem\u003eDigitaria\u003c/em\u003e spp., \u003cem\u003eEchinochloa\u003c/em\u003e spp., \u003cem\u003eO. sativa\u003c/em\u003e, \u003cem\u003eS.italica\u003c/em\u003e and \u003cem\u003eP. hallii\u003c/em\u003e (Abrouk et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Goff et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Lovell et al., \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Zdobnov and Apweiler, \u003cspan citationid=\"CR172\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; G. Zhang et al., \u003cspan citationid=\"CR173\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Homologs previously cloned in rice and maize were annotated via BLAST searches, retaining hits with E-value\u0026thinsp;\u0026lt;\u0026thinsp;1e-5 and identity\u0026thinsp;\u0026gt;\u0026thinsp;50%.\u003c/p\u003e\u003cdiv id=\"Sec23\" class=\"Section3\"\u003e\u003ch2\u003eRepetitive elements annotation\u003c/h2\u003e\u003cp\u003eCentromeric satellite repeats were predicted using the Tandem Repeat Annotation and Structural Hierarchy (TRASH) pipeline (Wlodzimierz et al., \u003cspan citationid=\"CR156\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) in two iterative rounds. In the first round, genomic sequences were partitioned into 1-kb windows, and local \u003cem\u003ek\u003c/em\u003e-mer frequencies were calculated to detect repeat-enriched regions under default parameters. The most abundant repeat templates were clustered and extracted using CD-HIT (Li and Godzik, \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). In the second round, TRASH was executed with the parameters \u0026ldquo;--simpleplot --frep 10 --N.max.div 5 --par 5 --seqt,\u0026rdquo; with the extracted templates provided through the \u0026ldquo;--seqt\u0026rdquo; option. Windows were scored based on the proportion of repeated \u003cem\u003ek\u003c/em\u003e-mers, and regions exceeding the threshold were classified as repeat-rich. Tandem repeats within these windows were further characterized by the distances between identical \u003cem\u003ek\u003c/em\u003e-mers, enabling the identification of consensus repeat units, including \u003cem\u003eCEN113\u003c/em\u003e, \u003cem\u003eCEN159\u003c/em\u003e, and \u003cem\u003eCEN178\u003c/em\u003e, hereafter collectively referred to as cenSat.\u003c/p\u003e\u003cp\u003eRibosomal DNA (rDNA) regions were annotated using BLAST searches against maize rDNA references, including 5S rDNA (DQ351339), 5.8S rDNA (AF019817), and the intergenic spacer (AF013103). Telomeric repeats were identified by scanning chromosome termini for high-copy-number tandem arrays of the canonical monomer \u0026ldquo;TTTAGGG\u0026rdquo;.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e\u003ch2\u003eGenome quality assessment\u003c/h2\u003e\u003cp\u003eAssembly quality for each genome was evaluated in terms of completeness, correctness, and continuity. For completeness, NGS short reads and PacBio HiFi long reads were mapped to their respective assemblies using BWA mem (0.7.17-r1188) (Li and Durbin, \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) and Minimap2 (v2.03) (Li, \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) under default parameters, respectively. Mapping statistics were summarized with Sambamba Flagstat (v 1.0.1) (Tarasov et al., \u003cspan citationid=\"CR150\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Assembly completeness was further assessed using the LTR Assembly Index (LAI; Ou et al., \u003cspan citationid=\"CR120\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) and BUSCO v5.5.0 with poales_odb 10 database (Manni et al., \u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Additionally, \u003cem\u003ek\u003c/em\u003e-mer based completeness was estimated with Merqury v1.3 (Rhie et al., \u003cspan citationid=\"CR132\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). For this, short reads were split into 21-mers using Meryl v1.4, and rare \u003cem\u003ek\u003c/em\u003e-mers were removed (meryl gt 1) prior to generating a \u003cem\u003ek\u003c/em\u003e-mer reference library. For correctness, base-level accuracy was evaluated by estimating quality values (QV) with Merqury, based on the same \u003cem\u003ek\u003c/em\u003e-mer library. For continuity, potential assembly gaps and structural inconsistencies were identified using CRAQ (Li et al., \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Long and short reads were aligned against assemblies, and AQI scores were computed to detect local and large-scale assembly errors by examining clipped alignments.\u003c/p\u003e\u003cdiv id=\"Sec25\" class=\"Section3\"\u003e\u003ch2\u003ePhylogenetic analysis\u003c/h2\u003e\u003cp\u003eHomoeologous exchanges (HEs) in polyploids can bias phylogenetic inference; thus, candidate HE regions in the \u003cem\u003eD. sanguinalis\u003c/em\u003e genome were identified and excluded prior to phylogenetic analyses. Approximately 99\u0026times; short reads from \u003cem\u003eD. radicosa\u003c/em\u003e and 50\u0026times; from \u003cem\u003eD. milanjiana\u003c/em\u003e were mapped to the \u003cem\u003eD. sanguinalis\u003c/em\u003e reference genome using Bowtie2 under default settings (Langmead and Salzberg, \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2012a\u003c/span\u003e). Mapping depths were calculated in 100-kb sliding windows across the genome. For the D and E subgenomes, windows with coverage exceeding 20\u0026times; and showing higher mapping depth for \u003cem\u003eD. radicosa\u003c/em\u003e than \u003cem\u003eD. milanjiana\u003c/em\u003e were defined as candidate HE windows; the same criteria were applied to the C subgenome. Adjacent candidate HE windows were merged, yielding 4 HE regions, and genes within these regions were excluded from subsequent phylogenetic reconstruction.\u003c/p\u003e\u003cp\u003ePhylogenetic relationships among \u003cem\u003eDigitaria\u003c/em\u003e and related species were inferred using three approaches. First, genetic divergence was estimated based on synonymous substitution (\u003cem\u003eK\u003c/em\u003e\u003csub\u003e\u003cem\u003es\u003c/em\u003e\u003c/sub\u003e) values, and pairwise \u003cem\u003eK\u003c/em\u003e\u003csub\u003e\u003cem\u003es\u003c/em\u003e\u003c/sub\u003e peak distributions were used to infer relative divergence times. Second, a concatenated alignment of 2,030 single-copy orthologs, identified by OrthoFinder (Emms and Kelly, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) and free of HE-associated genes, was used to construct a maximum-likelihood tree, thereby minimizing potential HE-related artifacts. Amino acid sequences were aligned with MAFFT v7.310 (Rozewicki et al., \u003cspan citationid=\"CR135\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) and trimmed with TrimAl under default parameters (Capella-Guti\u0026eacute;rrez et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). The ML tree was generated in IQ-TREE v1.6.12 (Minh et al., \u003cspan citationid=\"CR114\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) with 1,000 bootstrap replicates and the best-fit model (GAMMA\u0026thinsp;+\u0026thinsp;JTT\u0026thinsp;+\u0026thinsp;F4) selected by ModelFinder. Third, a total of 2,030 single gene trees were inferred individually using RAxML under the best-fitting amino acid substitution models, and a coalescent-based species tree was subsequently estimated with ASTRAL v5.7.8 (Stamatakis, \u003cspan citationid=\"CR144\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Yin et al., \u003cspan citationid=\"CR168\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cb\u003eMulti-year collection and phenotyping of\u003c/b\u003e \u003cb\u003eDigitaria\u003c/b\u003e \u003cb\u003eaccessions\u003c/b\u003e\u003c/p\u003e\u003cp\u003eA total of 579 \u003cem\u003eDigitaria\u003c/em\u003e accessions were collected from 24 maize-producing provinces in China between 2013 and 2023 \u003cb\u003e(Supplementary Table\u0026nbsp;7)\u003c/b\u003e. Seed collected prior to 2023 were preserved at the Plant Protection Research Institute, Shandong Academy of Agricultural Sciences, with the majority of accessions originating from the middle-lower Yellow River basin \u003cb\u003e(Supplementary Fig.\u0026nbsp;27)\u003c/b\u003e. In 2023, 496 accessions were grown under uniform field conditions in fields at the Jiyang Research Station of the Shandong Academy of Agricultural Sciences (Jinan), with five representative individuals planted per accession for phenotypic assessment. The geographic distribution of collected accessions was visualized using R v4.3.1. Based on detailed collection-site metadata, 272 accessions were further categorized into three ecological habitat types: natural habitats (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;40; riverbanks or wilderness), agricultural habitats (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;221; active croplands), and disturbed habitats (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;11; parks or roadsides).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec26\" class=\"Section3\"\u003e\u003ch2\u003eResequencing and variant calling\u003c/h2\u003e\u003cp\u003eGenomic DNA was extracted from fresh leaves following standard CTAB-based protocols. Paired-end resequencing libraries were constructed and sequenced on the DNBSEQ T7 platform. Raw reads were quality-filtered using fastp v0.24.2 (Chen, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), and high-quality paired-end reads were mapped to the updated \u003cem\u003eD. sanguinalis\u003c/em\u003e reference genome (#YJ2023) using Bowtie2 with default parameters (Langmead and Salzberg, \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2012b\u003c/span\u003e). Whole-genome variant detection and filtering were performed using an integrated pipeline (Ye et al., \u003cspan citationid=\"CR166\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). To minimize false positives due to high sequence similarity between subgenomes, resequencing reads of YJ2023 were realigned to its reference, and variants detected in this process were excluded from the final dataset. These variants called in YJ2023 were removed from the final variant dataset. These variants were further filtered with the minor allele frequency (MAF) greater than 0.01 and missing rate less than 30%. Functional annotation of all high-confidence variants was performed using SnpEff v3.652 (Cingolani et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec27\" class=\"Section3\"\u003e\u003ch2\u003ePopulation structure and genetic diversity\u003c/h2\u003e\u003cp\u003eSpecies identity of each accession was first assessed by calculating read mapping rates to the YJ2023 reference genome and estimating genome coverage using sambamba v1.5 and bamdst (He et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Tarasov et al., \u003cspan citationid=\"CR150\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Population structure was inferred with FastStructure (Raj et al., \u003cspan citationid=\"CR129\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) using whole-subgenome SNPs and synonymous SNPs from subgenomes C, D, and E, with \u003cem\u003ek\u003c/em\u003e values ranging from 2 to 9. Phylogenetic relationships were reconstructed with FastTreeMP (Price et al., \u003cspan citationid=\"CR128\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) based on SNPs from 499 accessions (excluding \u003cem\u003eD. violascens\u003c/em\u003e and \u003cem\u003eD. ischaemum\u003c/em\u003e due to low genome coverage), using 51 \u003cem\u003eD. ciliaris\u003c/em\u003e accessions as the outgroup; branch support was assessed with 1,000 bootstrap replicates. Trees were visualized with iTOL v7 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://itol.embl.de\u003c/span\u003e\u003cspan address=\"http://itol.embl.de\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) (Letunic and Bork, \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Principal component analysis (PCA) was conducted in PLINK v1.90b6.20 (Chang et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) using a linkage-disequilibrium pruned SNP set (10 SNPs per 50-kb sliding window, \u003cem\u003er\u003c/em\u003e\u0026sup2; \u0026lt; 0.5). Nucleotide diversity (π) was estimated with VCFtools v0.1.17 (Danecek et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) in non-overlapping 20-kb, 50-kb and 100-kb windows, and genome-wide or subgenome-level diversity was calculated as the mean π across all windows within each population.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec28\" class=\"Section2\"\u003e\u003ch2\u003eDemographic history inference based on site frequency spectrum (SFS)\u003c/h2\u003e\u003cp\u003eTo reconstruct the demographic history of \u003cem\u003eDigitaria\u003c/em\u003e spp. and assess the role of introgression during species and lineage divergence, we performed composite maximum-likelihood (ML) inference based on the site frequency spectrum (SFS). Joint folded two-dimensional SFSs (2D-SFSs) were generated from four-fold synonymous SNPs using easySFS.py (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/isaacovercast/easySFS\u003c/span\u003e\u003cspan address=\"https://github.com/isaacovercast/easySFS\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Population groupings were defined according to the FastStructure results (\u003cem\u003ek\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4). Eight demographic scenarios were designed to test the occurrence and duration of introgression between lineages and species \u003cb\u003e(Supplementary Fig.\u0026nbsp;17)\u003c/b\u003e, with divergence time priors determined from fossil calibrations in TimeTree 5 (Kumar et al., \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Likelihood estimation for each scenario was conducted using fastsimcoal2 (Marchi et al., \u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), with 100,000 coalescent simulations per likelihood estimation (-n 100,000) and 40 expectation-conditional maximization (ECM) cycles (-L 40). Model selection was performed using the Akaike information criterion (AIC), calculated as AIC = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{2}\\text{k}\\text{-2}\\text{ln}\\text{(L)}\\)\u003c/span\u003e\u003c/span\u003e (MaxEstLhood), where \u003cem\u003ek\u003c/em\u003e represents the number of estimated parameters and MaxEstLhood is the ML function value for each model. To avoid convergence to local optima, each analysis was repeated at least twice, and the best-supported model based on AIC was rerun 100 times to obtain refined parameter estimates. After that, 100 independent DNA polymorphism datasets were simulated as joint SFSs conditional on estimated demographic parameters. ML analysis was then applied to each joint SFS over 40 ECM cycles to obtain confidence intervals (CIs) for final estimates.\u003c/p\u003e\u003cp\u003eChanges in effective population size through time were further inferred using Stairway Plot v2.0 (Liu and Fu, \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), based on both folded and unfolded SFSs. SFSs for each population group were constructed and folded from the same SNP dataset used for fastsimcoal2 analyses.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec29\" class=\"Section2\"\u003e\u003ch2\u003ePhylogeny of chloroplast genomes\u003c/h2\u003e\u003cp\u003eThe reference chloroplast genome of YJ2023 was assembled \u003cem\u003ede novo\u003c/em\u003e using GetOrganelle (v1.7.7.1) (Jin et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). For other accessions, chloroplast-derived reads were extracted from clean paired-end resequencing data with GetOrganelle and mapped to the YJ2023 chloroplast reference genome. Variants were called using the GATK pipeline, resulting in 4,742 high-quality chloroplast SNPs after filtering for a minor allele frequency (MAF)\u0026thinsp;\u0026gt;\u0026thinsp;0.05 and a missing rate\u0026thinsp;\u0026lt;\u0026thinsp;0.2 across 579 \u003cem\u003eDigitaria\u003c/em\u003e individuals. A maximum-likelihood (ML) tree was inferred using IQtree with 1500 bootstrap replications (Minh et al., \u003cspan citationid=\"CR114\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), and the tree was rooted with \u003cem\u003eSetaria italica\u003c/em\u003e as the outgroup.\u003c/p\u003e\u003cp\u003e\u003cb\u003eDetection of sympatric introgression between\u003c/b\u003e \u003cb\u003eD. ciliaris\u003c/b\u003e \u003cb\u003eand\u003c/b\u003e \u003cb\u003eD. sanguinalis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eSympatric population pairs were defined based on pairwise geographic distances calculated with the \u0026ldquo;great_circle\u0026rdquo; method in the geopy package, using a 150-km threshold. Populations with at least two \u003cem\u003eD. ciliaris\u003c/em\u003e and five \u003cem\u003eD. sanguinalis\u003c/em\u003e accessions were retained for downstream analyses. Introgression between sympatric populations was assessed using \u003cem\u003eD\u003c/em\u003e-statistics (Durand et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Green et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), and the extent of introgressed genomic regions was quantified with the modified \u003cem\u003ef\u003c/em\u003e\u003csub\u003e\u003cem\u003ed\u003c/em\u003e\u003c/sub\u003e statistic (Martin et al., \u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e2015b\u003c/span\u003e). The phylogenetic topology (((P1, P2), P3), O) was applied, with \u003cem\u003eD. bicornis\u003c/em\u003e as the outgroup (O), sympatric \u003cem\u003eD. sanguinalis\u003c/em\u003e as P2, sympatric \u003cem\u003eD. ciliaris\u003c/em\u003e as P3, and allopatric \u003cem\u003eD. sanguinalis\u003c/em\u003e accessions as P1. Under the null hypothesis, ABBA and BABA site patterns are expected to occur at equal frequencies due to incomplete lineage sorting, whereas an excess of ABBA sites indicates introgression between P2 and P3. To minimize sample size bias, eight individuals were randomly selected for P1 and P2, and three for P3 and the outgroup.\u003c/p\u003e\u003cp\u003eIntrogression was evaluated in two steps. First, \u003cem\u003eD\u003c/em\u003e-statistics were calculated at whole-genome and chromosome levels. To account for linkage disequilibrium, significance was assessed using a block jackknife approach (Durand et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), dividing the genome into 1-Mb blocks and sequentially removing one block at a time to estimate the mean and variance of \u003cem\u003eD\u003c/em\u003e. Second, genomic regions affected by introgression were identified using non-overlapping windows of 10, 50, and 100 kb. Both \u003cem\u003eD\u003c/em\u003e and \u003cem\u003ef\u003c/em\u003e\u003csub\u003e\u003cem\u003ed\u003c/em\u003e\u003c/sub\u003e statistics were computed with \u0026ldquo;ABBABABAwindows.py\u0026rdquo; and \u003cem\u003eF\u003c/em\u003e\u003csub\u003e\u003cem\u003eST\u003c/em\u003e\u003c/sub\u003e values for the same windows were calculated with \u0026lsquo;popgenWindows.py\u0026rsquo; (Martin et al., \u003cspan citationid=\"CR109\" class=\"CitationRef\"\u003e2015c\u003c/span\u003e). Windows containing fewer than 200, 100, or 20 SNPs (for 100-, 50-, and 10-kb windows, respectively) were excluded. According to the filtering criteria used by Zhou et al. (Zhou et al., \u003cspan citationid=\"CR179\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), \u003cem\u003ef\u003c/em\u003e\u003csub\u003ed\u003c/sub\u003e values outside the 0\u0026ndash;1 range were set to zero. The overall introgression level was quantified as the Proportion of Introgression across the Genome (PIG; Zhou et al., \u003cspan citationid=\"CR179\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Regions with \u003cem\u003ef\u003c/em\u003e\u003csub\u003ed\u003c/sub\u003e \u0026gt;0.4 were defined as significantly introgressed, and genes within these regions were subjected to functional enrichment analysis.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eEnvironmental factors and correlation with introgression\u003c/h3\u003e\n\u003cp\u003eTo investigate the relationship between introgression and environmental variation, 91 environmental variables were retrieved from the WorldClim database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.worldclim.org/\u003c/span\u003e\u003cspan address=\"http://www.worldclim.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) \u003cb\u003e(Supplementary Data 2 and 3)\u003c/b\u003e. These included four primary categories, temperature, precipitation, wind speed, and solar radiation, and 19 additional bioclimatic variables. Pairwise environmental distance matrices for the four primary categories were calculated as Euclidean distances using the R package \u0026lsquo;ecodist\u0026rsquo; (Goslee and Urban, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), while a separate environmental distance matrix was generated from the 19 bioclimatic variables. Longitude and latitude were also used as two complex environmental factors that reflect humidity and circadian rhythm, respectively. Geographic distances among populations were computed using the \u0026ldquo;great_circle\u0026rdquo; method in the \u0026lsquo;geopy\u0026rsquo; package, and statistical significance in subsequent analyses was assessed with 10,000 permutations.\u003c/p\u003e\u003cp\u003eThe proportion of shared introgressed genomic regions across the genome (PSIG; Fu et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) was used to quantify shared introgression among sympatric populations. Mantel and partial Mantel tests were performed in \u0026lsquo;ecodist\u0026rsquo; to evaluate correlations between PSIG and pairwise geographic distances, as well as environmental distance matrices, thereby assessing the relative contributions of genetic drift and environment-driven selection to introgressed allele distribution.\u003c/p\u003e\u003cdiv id=\"Sec31\" class=\"Section2\"\u003e\u003ch2\u003eIdentification of environment-associated genetic variants\u003c/h2\u003e\u003cp\u003eVariants with a minor allele frequency (MAF)\u0026thinsp;\u0026gt;\u0026thinsp;0.01 and a missing genotype rate (GENO)\u0026thinsp;\u0026gt;\u0026thinsp;0.2 were retained, resulting in a dataset of 3,981,523 SNPs for downstream analyses. Associations between allele frequencies and 19 environmental variables were first assessed using a univariate latent factor mixed model (LFMM) implemented in the R package LEA v3.14.0 (Gain and Fran\u0026ccedil;ois, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Latent factors were decided based on the ancestry clusters inferred from FastStructure, consistent with the demographic history analysis. For each environmental variable, five independent runs were performed, with 5,000 iterations as burn-in followed by 10,000 sampling iterations. Median \u003cem\u003ep\u003c/em\u003e values from the five runs were adjusted for multiple testing using a 5% false discovery rate (FDR) and Bonferroni correction threshold to identify significant associations.\u003c/p\u003e\u003cp\u003e\u003cb\u003eSingle-dose bioassays on\u003c/b\u003e \u003cb\u003eDigitaria\u003c/b\u003e\u003c/p\u003e\u003cp\u003eA single-dose herbicide bioassay was performed on \u003cem\u003eDigitaria\u003c/em\u003e accessions (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;53 in 2013, \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;56 in 2015, \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;52 in 2023) at the 2\u0026ndash;3 leaf stage. Plants were grown in 9-cm-diameter pots filled with sterilized potting soil and maintained under controlled environmental conditions (30/25\u0026deg;C day/night, 16-h photoperiod) until herbicide application. Commercial formulations of nicosulfuron (field-recommended dose: 60 g a.i. ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) was applied using an enclosed cabinet sprayer calibrated to deliver 450 L ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e at a pressure of 0.4 MPa (ASS-5, Information Technology Research Center, Beijing, China). Untreated control (0 g a.i. ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) was also included for comparison. Each treatment, including the control, was replicated four times with one pot per replicate.\u003c/p\u003e\u003cp\u003eSurvival rate (%) was calculated as:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:\\left({\\text{N}}_{\\text{s}}\\text{\u0026divide;}{\\text{N}}_{\\text{T}}\\right)\\text{\u0026times;100%}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003csub\u003e\u003cem\u003es\u003c/em\u003e\u003c/sub\u003e is the number of surviving plants, and \u003cem\u003eN\u003c/em\u003e\u003csub\u003e\u003cem\u003eT\u003c/em\u003e\u003c/sub\u003e is the total number of plants per pot. Plants were classified as \u003cem\u003esurviving\u003c/em\u003e if they exhibited active regrowth or retained green tissue.\u003c/p\u003e\u003cp\u003eAboveground biomass from each pot was harvested, oven-dried at 70\u0026deg;C for 72 hours, and weighed. Control efficacy (%) was assessed as the percentage of biomass reduction relative to the untreated control:\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:\\left(1-{\\text{B}}_{t}\\text{\u0026divide;}{\\text{B}}_{c}\\right)\\text{\u0026times;100%}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003e(1\u0026thinsp;\u0026minus;\u0026thinsp;Treated biomass / Mean control biomass) \u0026times; 100%.\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eB\u003c/em\u003e\u003csub\u003e\u003cem\u003et\u003c/em\u003e\u003c/sub\u003e is the biomass in the treatment, and \u003cem\u003eB\u003c/em\u003e\u003csub\u003e\u003cem\u003ec\u003c/em\u003e\u003c/sub\u003e is the biomass in the control.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec32\" class=\"Section2\"\u003e\u003ch2\u003eWhole-Plant Resistance Bioassay\u003c/h2\u003e\u003cp\u003eSeeds of 196 \u003cem\u003eDigitaria\u003c/em\u003e accessions were collected, air-dried, and stored at 4\u0026deg;C prior to use. Seeds were sown in moist loam soil in 9-cm-diameter pots, covered with 1 cm of soil, and grown under greenhouse conditions (30/25\u0026deg;C day/night, 16-h photoperiod) with sub-irrigation.\u003c/p\u003e\u003cp\u003eHerbicide resistance to nicosulfuron was evaluated using whole-plant bioassays. At the three-leaf stage, ten seedlings were foliar treated with the commercial formulation of nicosulfuron at seven doses, 0, 30, 60, 120, 240, 480 and 960 g a.i. ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (field-recommended dose: 60 g a.i. ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e). Applications were performed using an enclosed cabinet sprayer (ASS-5, Information Technology Research Center, Beijing, China) calibrated to deliver 450 L ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e at 0.4 MPa. Plant survival and aboveground dry biomass were assessed 21 days after treatment. Each treatment included four biological replicates pots (10 seedlings per pot) and was independently repeated.\u003c/p\u003e\u003cp\u003eDose-response for biomass reduction (expressed as a percentage of untreated controls) were analyzed by nonlinear regression in SigmaPlot v12.5 (Systat Software Inc.), fitting a four-parameter log-logistic model:\u003c/p\u003e\u003cp\u003e\u003cimg src=\"https://myfiles.space/user_files/83062_751fab6dfaef2446/83062_custom_files/img1757771582.png\" style=\"width: 202px; height: 29.2201px;\" width=\"202\" height=\"29.2201\"\u003e\u003c/p\u003e\u003cp\u003ewhere Y is the response (% of control), X is the herbicide dose, C and D are the lower and upper asymptotes, GR\u003csub\u003e50\u003c/sub\u003e is the herbicide dose causing plant growth reduction by 50%, and b is the slope of the curve. A mixed-model ANOVA was performed to assess differences in percentage control and biomass reduction across treatments. Resistance indices (R/S ratios) were calculated as GR\u003csub\u003e50\u003c/sub\u003e_R / GR\u003csub\u003e50\u003c/sub\u003e_S. The resistance levels of different accessions are classified into four groups based on the resistance index: S (Susceptible, RI\u0026thinsp;\u0026lt;\u0026thinsp;2), LR (Low Resistant, RI\u0026thinsp;\u0026gt;\u0026thinsp;2 and RI\u0026thinsp;\u0026lt;\u0026thinsp;4), MR (Moderate Resistant, RI\u0026thinsp;\u0026gt;\u0026thinsp;4 and RI\u0026thinsp;\u0026lt;\u0026thinsp;10), and HR (High Resistant, RI\u0026thinsp;\u0026gt;\u0026thinsp;10).\u003c/p\u003e\u003cdiv id=\"Sec33\" class=\"Section3\"\u003e\u003ch2\u003eHerbicide resistance analyses\u003c/h2\u003e\u003cp\u003eALS genes were identified in the \u003cem\u003eD. sanguinalis\u003c/em\u003e genome using BLASTP. The predicted ALS protein sequences were aligned with orthologous sequences from \u003cem\u003eO. sativa\u003c/em\u003e and \u003cem\u003eA. thaliana\u003c/em\u003e using MAFFT (Rozewicki et al., \u003cspan citationid=\"CR135\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), and the known resistance-associated sites were annotated across three \u003cem\u003eDsALS\u003c/em\u003e copies. A comprehensive catalog of causative mutations was then established for all accessions \u003cb\u003e(Supplementary Table\u0026nbsp;21)\u003c/b\u003e. Genealogical relationships among haplotypes were reconstructed using HapNetworkView based on variant profiles across the three ALS subgenome copies (Chi et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eCopy number variation (CNV) of NTSR-related gene families was inferred from NGS data based on normalized read depth. NTSR-related genes were first annotated in the reference genome using InterProScan (E-value\u0026thinsp;\u0026lt;\u0026thinsp;1e-10). Sequencing read depth for each annotated locus was calculated, and total gene family abundance was estimated as the sum of average depths across all loci, using custom scripts (see \u003cb\u003eCode availability\u003c/b\u003e). Copy number was estimated by normalizing the read depth of each gene family against the mean sequencing depth of 2,030 single-copy genes. Accessions with an overall sequencing depth\u0026thinsp;\u0026lt;\u0026thinsp;5 were excluded to minimize bias in CNV estimation.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec34\" class=\"Section3\"\u003e\u003ch2\u003ePlant material and nicosulfuron treatment for RNA-seq\u003c/h2\u003e\u003cp\u003eThe most nicosulfuron-resistant (#21\u0026thinsp;\u0026minus;\u0026thinsp;17, GR\u003csub\u003e50\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;122.53 g a.i. ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) and susceptible (#15\u0026thinsp;\u0026minus;\u0026thinsp;2, GR\u003csub\u003e50\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0.59 g a.i. ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) \u003cem\u003eD. sanguinalis\u003c/em\u003e accessions were selected for RNA-seq analysis. Plants were grown in 9-cm-diameter pots filled with moist loam soil, covered with a 1-cm soil layer, and maintained under greenhouse conditions (30/25\u0026deg;C day/night, 16-h photoperiod) with sub-irrigation at the Jiyang Research Station of the Shandong Academy of Agricultural Sciences, Jinan.\u003c/p\u003e\u003cp\u003eAt the three-leaf stage, seedlings were treated with 2 g a.i. ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e nicosulfuron according to the GR\u003csub\u003e50\u003c/sub\u003e value distribution of susceptible populations in dose-response assays, to assess herbicide response. Each treatment included three biological replicates, with three individual plants pooled to constitute one replicate. Leaf tissues were collected at three time points: 0 h (untreated control), 6 h (early response), and 24 h (late response) post-treatment. Samples were immediately frozen in liquid nitrogen and stored at -80\u0026deg;C until RNA extraction and sequencing.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\n\u003ch3\u003eRNA sequencing and transcriptome analysis\u003c/h3\u003e\n\u003cp\u003eTotal RNA was extracted from 18 samples, comprising three time points (0 h, 6 h, and 24 h) for each accession after nicosulfuron treatment, with three biological replicates per time point. Messenger RNA was purified using poly-T oligo-attached magnetic beads, and sequencing libraries were constructed following the DNBSEQ standard protocol. Libraries were pooled according to effective concentration and target data yield. The 5\u0026prime; ends of libraries were phosphorylated and circularized, followed by rolling circle amplification to generate DNA nanoballs, which were subsequently loaded onto a flow cell for sequencing on the DNBSEQ-T7 platform.\u003c/p\u003e\u003cp\u003eAfter clipping adaptor sequences and removing low-quality reads, the clean reads were mapped to YJ2023 using Hisat2 (v2.1.0) (Kim et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) and gene expression were quantified by StringTie v2.2.1 (Kovaka et al., \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) with default parameters. Differentially expressed genes were identified with the pyDESeq2 package v0.5.2 (Muzellec et al., \u003cspan citationid=\"CR116\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), with significance defined as |log2(fold change)| \u0026ge;1 and a false discovery rate (FDR)-adjusted \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05. GO enrichment analyses were performed using the clusterProfiler package (Wu et al., \u003cspan citationid=\"CR159\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eLocal ancestry inference\u003c/h3\u003e\n\u003cp\u003eLocal ancestry was inferred using Loter (Dias-Alves et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), which reconstructs chromosomal ancestry in admixed individuals based on haplotype information from reference populations. To detect potential introgression from \u003cem\u003eD. ciliaris\u003c/em\u003e within resistance-associated genomic regions, \u003cem\u003eD. ciliaris\u003c/em\u003e (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;46) and \u003cem\u003eD. sanguinalis\u003c/em\u003e (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;40) accessions were designated as parental reference populations. Phased haplotype data were used as input, and local ancestry along the chromosomes of \u003cem\u003eD. sanguinalis\u003c/em\u003e was estimated under default parameters.\u003c/p\u003e\u003cdiv id=\"Sec37\" class=\"Section2\"\u003e\u003ch2\u003eGWAS of nicosulfuron resistance\u003c/h2\u003e\u003cp\u003eGenome-wide association analyses (GWAS) were performed across \u003cem\u003eD. sanguinalis\u003c/em\u003e accessions (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;141) based on GR\u003csub\u003e50\u003c/sub\u003e values of each accession using EMMAX (Kang et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2010a\u003c/span\u003e), with SNPs filtered for a minor allele frequency (MAF)\u0026thinsp;\u0026gt;\u0026thinsp;0.05 and a missing genotype rate\u0026thinsp;\u0026lt;\u0026thinsp;0.1. A pairwise genetic distance matrix, derived from simple matching coefficients of SNPs, was incorporated to model the variance-covariance structure of random effects in the linear mixed model. To correct for multiple hypothesis testing, an FDR threshold of 5% was applied, as the Bonferroni correction was deemed overly stringent for this dataset. Manhattan plots were generated using the R package \u0026lsquo;qqman\u0026rsquo; (Turner, \u003cspan citationid=\"CR152\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) for visualization of association signals.\u003c/p\u003e\u003cp\u003eTo investigate selection pressures on adaptive variants associated with herbicide resistance and climatic adaptation, extended haplotype homozygosity (EHH) was evaluated for strongly associated loci, and the integrated haplotype score (iHS) was calculated for common variants using hapbin v1.3.0 (Maclean et al., \u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eData availability\u003c/p\u003e\n\u003cp\u003eThree \u003cem\u003eDigitaria\u003c/em\u003e genome assemblies and annotations generated in this study are available at National Genomics Data Center (NGDC) database (https://bigd.big.ac.cn) under the following accession numbers: GWHGGEX00000000, GWHGGEY00000000 and GWHGGEZ00000000. The HiFi sequencing, Hi-C, and RNA-Seq data for genome assembly and annotation in this study have been deposited in NGDC (project accession: PRJCA044252). The raw data of all re-sequenced accessions are available at National Center for Biotechnology Information (NCBI) database (https:/www.ncbi.nlm.nih.gov) under the project accession number PRJNA1296823. Besides \u003cem\u003eDigitaria\u003c/em\u003e genomes, other monocot genomes used in this study were retrieved as described in previous study\u0026nbsp;(Wu et al., 2022a). The resequencing data of green foxtail were downloaded from the European Nucleotide Archive (ENA) (https://www.ebi.ac.uk/ena/browser/home).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCode availability\u003c/p\u003e\n\u003cp\u003eThe custom scripts used in this study have been deposited in the GitHub repository [https://github.com/Ne0tea/DigitariaPop].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAcknowledgements\u003c/p\u003e\n\u003cp\u003eThis work was supported by National Key Research and Development Program (2023YFD1400502). We would like to express our gratitude to Yu Fang, Fanjing Yang, Yutong Liu, Jiaxin Li and Tao Li for their dedicated efforts in sample collection.\u003c/p\u003e\n\u003cp\u003eAuthor contribution\u003c/p\u003e\n\u003cp\u003eL.F., L.B. and M.L. conceived the study. Y.H. contributed to genomic analyses, pipeline development, and interpretation of evolutionary patterns. J.L. contributed to \u003cem\u003eD. sanguinalis\u003c/em\u003e accessions phenotyping and herbicide resistance bioassay. S.Z., K.Y., Z.L., X.G, and R.Z. collected \u003cem\u003eDigitaria\u003c/em\u003e accessions, processed genome annotation and conducted cytological analysis on \u003cem\u003eDigitaria\u003c/em\u003e species. S.W. and Z.Li performed quality control and initial data filtering. L.X. conducted tandem repeat annotation. L.F performed GWAS analysis of resistance. Y.F. provided insights on phylogenetic modeling and evolutionary inference. B.K.S. and A.M. assisted in the validation of functional genetic variants. Q.Y., F.L. and L.B. supervised the experimental design and contributed to population-level analysis. L.F., L.B., M.L. and D.W. jointly supervised the study. Y.H. wrote the initial manuscript with input from all co-authors. All authors discussed the results and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eAll authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbrouk, M., Ahmed, H.I., Cubry, P., \u0026Scaron;imon\u0026iacute;kov\u0026aacute;, D., Cauet, S., Pailles, Y., Bettgenhaeuser, J., Gapa, L., Scarcelli, N., Couderc, M., Zekraoui, L., Kathiresan, N., Č\u0026iacute;žkov\u0026aacute;, J., Hřibov\u0026aacute;, E., Doležel, J., Arribat, S., Berg\u0026egrave;s, H., Wieringa, J.J., Gueye, M., Kane, N.A., Leclerc, C., Causse, S., Vancoppenolle, S., Billot, C., Wicker, T., Vigouroux, Y., Barnaud, A., Krattinger, S.G., 2020. Fonio millet genome unlocks African orphan crop diversity for agriculture in a changing climate. Nat Commun 11, 4488.\u003c/li\u003e\n\u003cli\u003eAlonge, M., Lebeigle, L., Kirsche, M., Jenike, K., Ou, S., Aganezov, S., Wang, X., Lippman, Z.B., Schatz, M.C., Soyk, S., 2022. Automated assembly scaffolding using RagTag elevates a new tomato system for high-throughput genome editing. Genome Biology 23, 258.\u003c/li\u003e\n\u003cli\u003eAreces-Berazain, F., 2022. \u003cem\u003eDigitaria sanguinalis\u003c/em\u003e (large crabgrass). CABI Compendium CABI Compendium, 18916.\u003c/li\u003e\n\u003cli\u003eBaack, E.J., Rieseberg, L.H., 2007. A genomic view of introgression and hybrid speciation. Curr Opin Genet Dev 17, 513\u0026ndash;518.\u003c/li\u003e\n\u003cli\u003eBaucom, R.S., 2019. Evolutionary and ecological insights from herbicide-resistant weeds: what have we learned about plant adaptation, and what is left to uncover? New Phytologist 223, 68\u0026ndash;82.\u003c/li\u003e\n\u003cli\u003eBeckie, H.J., Busi, R., Bagavathiannan, M.V., Martin, S.L., 2019. Herbicide resistance gene flow in weeds: Under-estimated and under-appreciated. Agriculture, Ecosystems \u0026amp; Environment 283, 106566.\u003c/li\u003e\n\u003cli\u003eBoonsuk, B., Chantaranothai, P., Hodkinson, T.R., 2016. A taxonomic revision of the genus \u003cem\u003eDigitaria \u003c/em\u003e(Panicoideae: Poaceae) in mainland Southeast Asia. Phytotaxa 246, 248.\u003c/li\u003e\n\u003cli\u003eBrown, J.K.M., Rant, J.C., 2013. Fitness costs and trade-offs of disease resistance and their consequences for breeding arable crops. Plant Pathology 62, 83\u0026ndash;95.\u003c/li\u003e\n\u003cli\u003eBurton, G.P., Ceci, P., MacKinnon, L., Masters, L.E., Ryan, P., Turnbull, C.G.N., Ulian, T., Vorontsova, M.S., 2025. Phylogenetics, evolution and biogeography of four \u003cem\u003eDigitaria\u003c/em\u003e food crop lineages across West Africa, India, and Europe. bioRxiv 2025.03.27.645812.\u003c/li\u003e\n\u003cli\u003eCai, L., Comont, D., MacGregor, D., Lowe, C., Beffa, R., Neve, P., Saski, C., 2023. The blackgrass genome reveals patterns of non-parallel evolution of polygenic herbicide resistance. New Phytologist 237, 1891\u0026ndash;1907.\u003c/li\u003e\n\u003cli\u003eCapella-Guti\u0026eacute;rrez, S., Silla-Mart\u0026iacute;nez, J.M., Gabald\u0026oacute;n, T., 2009. trimAl: a tool for automated alignment trimming in large-scale phylogenetic analyses. Bioinformatics 25, 1972\u0026ndash;1973.\u003c/li\u003e\n\u003cli\u003eCarnahan, H.L., Hill, H.D., 1961. Cytology and genetics of forage grasses. Bot. Rev 27, 1\u0026ndash;162.\u003c/li\u003e\n\u003cli\u003eChakraborty, M., Baldwin-Brown, J.G., Long, A.D., Emerson, J.J., 2016. Contiguous and accurate de novo assembly of metazoan genomes with modest long read coverage. Nucleic Acids Research 44, e147.\u003c/li\u003e\n\u003cli\u003eChang, C.C., Chow, C.C., Tellier, L.C., Vattikuti, S., Purcell, S.M., Lee, J.J., 2015. Second-generation PLINK: rising to the challenge of larger and richer datasets. GigaScience 4, s13742-015-0047\u0026ndash;8.\u003c/li\u003e\n\u003cli\u003eChao, D.-Y., Dilkes, B., Luo, H., Douglas, A., Yakubova, E., Lahner, B., Salt, D.E., 2013. Polyploids Exhibit Higher Potassium Uptake and Salinity Tolerance in \u003cem\u003eArabidopsis\u003c/em\u003e. Science.\u003c/li\u003e\n\u003cli\u003eChatterjee, Y., Tomar, S., Mishra, M., Pareek, A., Singla-Pareek, S.L., 2025. \u003cem\u003eOsLdh7\u003c/em\u003e Overexpression in Rice Confers Submergence Tolerance by Regulating Key Metabolic Pathways: Anaerobic Glycolysis, Ethanolic Fermentation and Amino Acid Metabolism. Plant, Cell \u0026amp; Environment 48, 2804\u0026ndash;2820.\u003c/li\u003e\n\u003cli\u003eChen, S., 2023. Ultrafast one-pass FASTQ data preprocessing, quality control, and deduplication using fastp. iMeta 2, e107.\u003c/li\u003e\n\u003cli\u003eChen, S., Sun, B., Shi, Z., Miao, X., Li, H., 2022. Identification of the rice genes and metabolites involved in dual resistance against brown planthopper and rice blast fungus. Plant, Cell \u0026amp; Environment 45, 1914\u0026ndash;1929.\u003c/li\u003e\n\u003cli\u003eChen, W., Bai, D., Liao, Y., Yu, Q., Bai, L., Pan, L., 2025. \u003cem\u003ePfGSTF2\u003c/em\u003e endows resistance to quizalofop-p-ethyl in Polypogon fugax by GSH conjugation. Plant Biotechnology Journal 23, 216\u0026ndash;231.\u003c/li\u003e\n\u003cli\u003eCheng, F., Wu, J., Cai, X., Liang, J., Freeling, M., Wang, X., 2018. Gene retention, fractionation and subgenome differences in polyploid plants. Nature Plants 4, 258\u0026ndash;268.\u003c/li\u003e\n\u003cli\u003eCheng, H., Concepcion, G.T., Feng, X., Zhang, H., Li, H., 2021. Haplotype-resolved \u003cem\u003ede novo\u003c/em\u003e assembly using phased assembly graphs with hifiasm. Nat Methods 18, 170\u0026ndash;175.\u003c/li\u003e\n\u003cli\u003eChi, L., Dong, Y., Wang, R., Yang, S., Wu, L., Xue, Y., Chen, H., 2025. HapNetworkView: a tool for haplotype network exploration and visualization. BMC Genomics 26, 52.\u003c/li\u003e\n\u003cli\u003eCingolani, P., Platts, A., Wang, L.L., Coon, M., Nguyen, T., Wang, L., Land, S.J., Lu, X., Ruden, D.M., 2012. A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w1118; iso-2; iso-3. Fly (Austin) 6, 80\u0026ndash;92.\u003c/li\u003e\n\u003cli\u003eComont, D., Lowe, C., Hull, R., Crook, L., Hicks, H.L., Onkokesung, N., Beffa, R., Childs, D.Z., Edwards, R., Freckleton, R.P., Neve, P., 2020. Evolution of generalist resistance to herbicide mixtures reveals a trade-off in resistance management. Nat Commun 11, 3086.\u003c/li\u003e\n\u003cli\u003eDanecek, P., Auton, A., Abecasis, G., Albers, C.A., Banks, E., DePristo, M.A., Handsaker, R.E., Lunter, G., Marth, G.T., Sherry, S.T., McVean, G., Durbin, R., 1000 Genomes Project Analysis Group, 2011. The variant call format and VCFtools. Bioinformatics 27, 2156\u0026ndash;2158.\u003c/li\u003e\n\u003cli\u003eD\u0026eacute;lye, C., 2013. Unravelling the genetic bases of non-target-site-based resistance (NTSR) to herbicides: a major challenge for weed science in the forthcoming decade. Pest Management Science 69, 176\u0026ndash;187.\u003c/li\u003e\n\u003cli\u003eD\u0026eacute;lye, C., Jasieniuk, M., Corre, V.L., 2013. Deciphering the evolution of herbicide resistance in weeds. Trends in Genetics 29, 649\u0026ndash;658.\u003c/li\u003e\n\u003cli\u003eD\u0026eacute;lye, C., Michel, S., B\u0026eacute;rard, A., Chauvel, B., Brunel, D., Guillemin, J.-P., Dessaint, F., Le Corre, V., 2010. Geographical variation in resistance to acetyl-coenzyme A carboxylase-inhibiting herbicides across the range of the arable weed \u003cem\u003eAlopecurus myosuroides\u003c/em\u003e (black-grass). New Phytol 186, 1005\u0026ndash;1017.\u003c/li\u003e\n\u003cli\u003eDias-Alves, T., Mairal, J., Blum, M.G.B., 2018. Loter: A Software Package to Infer Local Ancestry for a Wide Range of Species. Molecular Biology and Evolution 35, 2318\u0026ndash;2326.\u003c/li\u003e\n\u003cli\u003eDu, H.-M., Liu, C., Jin, X.-W., Du, C.-F., Yu, Y., Luo, S., He, W.-Z., Zhang, S.-Z., 2022. Overexpression of the Aldehyde Dehydrogenase Gene ZmALDH Confers Aluminum Tolerance in Arabidopsis thaliana. Int J Mol Sci 23, 477.\u003c/li\u003e\n\u003cli\u003eDurand, E.Y., Patterson, N., Reich, D., Slatkin, M., 2011. Testing for Ancient Admixture between Closely Related Populations. Molecular Biology and Evolution 28, 2239\u0026ndash;2252.\u003c/li\u003e\n\u003cli\u003eDurand, N.C., Shamim, M.S., Machol, I., Rao, S.S.P., Huntley, M.H., Lander, E.S., Aiden, E.L., 2016. Juicer provides a one-click system for analyzing loop-resolution Hi-C experiments. Cell Systems 3, 95\u0026ndash;98.\u003c/li\u003e\n\u003cli\u003eEmms, D.M., Kelly, S., 2015. OrthoFinder: solving fundamental biases in whole genome comparisons dramatically improves orthogroup inference accuracy. Genome Biology 16.\u003c/li\u003e\n\u003cli\u003eFlouri, T., Jiao, X., Rannala, B., Yang, Z., 2020. A Bayesian Implementation of the Multispecies Coalescent Model with Introgression for Phylogenomic Analysis. Molecular Biology and Evolution 37, 1211\u0026ndash;1223.\u003c/li\u003e\n\u003cli\u003eFrichot, E., Schoville, S.D., Bouchard, G., Fran\u0026ccedil;ois, O., 2013. Testing for Associations between Loci and Environmental Gradients Using Latent Factor Mixed Models. Molecular Biology and Evolution 30, 1687\u0026ndash;1699.\u003c/li\u003e\n\u003cli\u003eFu, R., Zhu, Y., Liu, Y., Feng, Y., Lu, R.-S., Li, Y., Li, P., Kremer, A., Lascoux, M., Chen, J., 2022. Genome-wide analyses of introgression between two sympatric Asian oak species. Nat Ecol Evol 6, 924\u0026ndash;935.\u003c/li\u003e\n\u003cli\u003eGain, C., Fran\u0026ccedil;ois, O., 2021. LEA 3: Factor models in population genetics and ecological genomics with R. Molecular Ecology Resources 21, 2738\u0026ndash;2748.\u003c/li\u003e\n\u003cli\u003eGaleano, E., Barroso, A.A.M., Vasconcelos, T.S., L\u0026oacute;pez-Rubio, A., Albrecht, A.J.P., Victoria Filho, R., Carrer, H., 2016. \u003cem\u003eEPSPS\u003c/em\u003e variability, gene expression, and enzymatic activity in glyphosate-resistant biotypes of \u003cem\u003eDigitaria insularis\u003c/em\u003e. Genet. Mol. Res. 15.\u003c/li\u003e\n\u003cli\u003eGeorgia, A.E., Georgia, A.E., 1916. A manual of weeds, with descriptions of all the most pernicious and troublesome plants in the United States and Canada, their habits of growth and distribution, with methods of control. The Macmillan Company, New York.\u003c/li\u003e\n\u003cli\u003eGoff, S.A., Ricke, D., Lan, T.-H., Presting, G., Wang, R., Dunn, M., Glazebrook, J., Sessions, A., Oeller, P., Varma, H., Hadley, D., Hutchison, D., Martin, C., Katagiri, F., Lange, B.M., Moughamer, T., Xia, Y., Budworth, P., Zhong, J., Miguel, T., Paszkowski, U., Zhang, S., Colbert, M., Sun, W., Chen, L., Cooper, B., Park, S., Wood, T.C., Mao, L., Quail, P., Wing, R., Dean, R., Yu, Y., Zharkikh, A., Shen, R., Sahasrabudhe, S., Thomas, A., Cannings, R., Gutin, A., Pruss, D., Reid, J., Tavtigian, S., Mitchell, J., Eldredge, G., Scholl, T., Miller, R.M., Bhatnagar, S., Adey, N., Rubano, T., Tusneem, N., Robinson, R., Feldhaus, J., Macalma, T., Oliphant, A., Briggs, S., 2002. A Draft Sequence of the Rice Genome (\u003cem\u003eOryza sativa\u003c/em\u003e L. ssp. \u003cem\u003ejaponica\u003c/em\u003e). Science.\u003c/li\u003e\n\u003cli\u003eGoldberg-Cavalleri, A., Onkokesung, N., Franco-Ortega, S., Edwards, R., 2023. ABC transporters linked to multiple herbicide resistance in blackgrass (\u003cem\u003eAlopecurus myosuroides\u003c/em\u003e). Front. Plant Sci. 14.\u003c/li\u003e\n\u003cli\u003eGoslee, S.C., Urban, D.L., 2007. The ecodist Package for Dissimilarity-based Analysis of Ecological Data. Journal of Statistical Software 22, 1\u0026ndash;19.\u003c/li\u003e\n\u003cli\u003eGould, F.W., 1963. Cytotaxonomy of Digitaria Sanguinalis and D. Adscendens. Brittonia 15, 241.\u003c/li\u003e\n\u003cli\u003eGreen, R.E., Krause, J., Briggs, A.W., Maricic, T., Stenzel, U., Kircher, M., Patterson, N., Li, H., Zhai, W., Fritz, M.H.-Y., Hansen, N.F., Durand, E.Y., Malaspinas, A.-S., Jensen, J.D., Marques-Bonet, T., Alkan, C., Pr\u0026uuml;fer, K., Meyer, M., Burbano, H.A., Good, J.M., Schultz, R., Aximu-Petri, A., Butthof, A., H\u0026ouml;ber, B., H\u0026ouml;ffner, B., Siegemund, M., Weihmann, A., Nusbaum, C., Lander, E.S., Russ, C., Novod, N., Affourtit, J., Egholm, M., Verna, C., Rudan, P., Brajkovic, D., Kucan, Ž., Gu\u0026scaron;ic, I., Doronichev, V.B., Golovanova, L.V., Lalueza-Fox, C., de la Rasilla, M., Fortea, J., Rosas, A., Schmitz, R.W., Johnson, P.L.F., Eichler, E.E., Falush, D., Birney, E., Mullikin, J.C., Slatkin, M., Nielsen, R., Kelso, J., Lachmann, M., Reich, D., P\u0026auml;\u0026auml;bo, S., 2010. A Draft Sequence of the Neandertal Genome. Science 328, 710\u0026ndash;722.\u003c/li\u003e\n\u003cli\u003eGuan, Y., Liu, L., Zou, Y., Yang, C., Ji, M., 2024. Involvement of P450s in the metabolic resistance of \u003cem\u003eDigitaria sanguinalis\u003c/em\u003e (L.) Scop. To ALS-inhibiting herbicides. Pesticide Biochemistry and Physiology 204, 106038.\u003c/li\u003e\n\u003cli\u003eGuo, R., Zhang, Q., Chen, C.Z., Sun, J.Y., Tu, C.Y., He, M.X., Shen, R.F., Huang, J., Zhu, X.F., 2024. A novel aldo-keto reductase gene, OsAKR1, from rice confers higher tolerance to cadmium stress in rice by an in vivo reactive aldehyde detoxification. Journal of Hazardous Materials 470, 134212.\u003c/li\u003e\n\u003cli\u003eHaas, B.J., Salzberg, S.L., Zhu, W., Pertea, M., Allen, J.E., Orvis, J., White, O., Buell, C.R., Wortman, J.R., 2008. Automated eukaryotic gene structure annotation using EVidenceModeler and the Program to Assemble Spliced Alignments. Genome Biology 9, R7.\u003c/li\u003e\n\u003cli\u003eHarrison, R.G., Larson, E.L., 2014. Hybridization, Introgression, and the Nature of Species Boundaries. Journal of Heredity 105, 795\u0026ndash;809.\u003c/li\u003e\n\u003cli\u003eHe, X., Chen, S., Li, R., Han, X., He, Z., Yuan, D., Zhang, S., Duan, X., Niu, B., 2021. Comprehensive fundamental somatic variant calling and quality management strategies for human cancer genomes. Briefings in Bioinformatics 22, bbaa083.\u003c/li\u003e\n\u003cli\u003eHeap, I., 2025. The International Herbicide-Resistant Weed Database.\u003c/li\u003e\n\u003cli\u003eHedrick, P.W., 2013. Adaptive introgression in animals: examples and comparison to new mutation and standing variation as sources of adaptive variation. Molecular Ecology 22, 4606\u0026ndash;4618.\u003c/li\u003e\n\u003cli\u003eHenrard, J.T., 1950. Monograph of the Genus \u003cem\u003eDigitaria\u003c/em\u003e. Agronomy Journal 42, 631\u0026ndash;631.\u003c/li\u003e\n\u003cli\u003eHereward, J.P., Werth, J.A., Thornby, D.F., Keenan, M., Chauhan, B.S., Walter, G.H., 2018. Gene expression in response to glyphosate treatment in fleabane (Conyza bonariensis) - glyphosate death response and candidate resistance genes. Pest Manag Sci 74, 2346\u0026ndash;2355.\u003c/li\u003e\n\u003cli\u003eHolm, L.G., Plucknett, D.L., Pancho, J.V., Herberger, J.P., 1977. The world\u0026rsquo;s worst weeds. University Press., Honolulu, Hawaii.\u003c/li\u003e\n\u003cli\u003eHu, J., Wang, Z., Liang, F., Liu, S.-L., Ye, K., Wang, D.-P., 2024a. NextPolish2: A Repeat-aware Polishing Tool for Genomes Assembled Using HiFi Long Reads. Genomics, Proteomics \u0026amp; Bioinformatics 22, qzad009.\u003c/li\u003e\n\u003cli\u003eHu, J., Wang, Z., Sun, Z., Hu, B., Ayoola, A.O., Liang, F., Li, J., Sandoval, J.R., Cooper, D.N., Ye, K., Ruan, J., Xiao, C.-L., Wang, D., Wu, D.-D., Wang, S., 2024b. NextDenovo: an efficient error correction and accurate assembly tool for noisy long reads. Genome Biology 25, 107.\u003c/li\u003e\n\u003cli\u003eHuang, J., Wang, S., Xiao, Z., 2017a. Rising Herbicide Use and Its Driving Forces in China. Eur J Dev Res 29, 614\u0026ndash;627.\u003c/li\u003e\n\u003cli\u003eHuang, J., Wang, S., Xiao, Z., 2017b. Rising Herbicide Use and Its Driving Forces in China. Eur J Dev Res 29, 614\u0026ndash;627.\u003c/li\u003e\n\u003cli\u003eHuang, W., Zhang, L., Columbus, J.T., Hu, Y., Zhao, Y., Tang, L., Guo, Z., Chen, W., McKain, M., Bartlett, M., Huang, C.-H., Li, D.-Z., Ge, S., Ma, H., 2022. A well-supported nuclear phylogeny of Poaceae and implications for the evolution of C4 photosynthesis. Molecular Plant 15, 755\u0026ndash;777.\u003c/li\u003e\n\u003cli\u003eHuang, Y., Guo, L., Xie, L., Shang, N., Wu, D., Ye, C., Rudell, E.C., Okada, K., Zhu, Q.-H., Song, B.-K., Cai, D., Junior, A.M., Bai, L., Fan, L., 2024. A reference genome of Commelinales provides insights into the commelinids evolution and global spread of water hyacinth (Pontederia crassipes). GigaScience 13, giae006.\u003c/li\u003e\n\u003cli\u003eIto, M., Kamada, Y., Ueki, K., 1986. Determination of Allelopathic Potential of Young and Mature \u003cem\u003eDigitaria adscendens\u003c/em\u003e on Crop Growth. Journal of Weed Science and Technology 31, 116\u0026ndash;123.\u003c/li\u003e\n\u003cli\u003eJaillon, O., Aury, J.-M., Noel, B., Policriti, A., Clepet, C., Casagrande, A., Choisne, N., Aubourg, S., Vitulo, N., Jubin, C., Vezzi, A., Legeai, F., Hugueney, P., Dasilva, C., Horner, D., Mica, E., Jublot, D., Poulain, J., Bruy\u0026egrave;re, C., Billault, A., Segurens, B., Gouyvenoux, M., Ugarte, E., Cattonaro, F., Anthouard, V., Vico, V., Del Fabbro, C., Alaux, M., Di Gaspero, G., Dumas, V., Felice, N., Paillard, S., Juman, I., Moroldo, M., Scalabrin, S., Canaguier, A., Le Clainche, I., Malacrida, G., Durand, E., Pesole, G., Laucou, V., Chatelet, P., Merdinoglu, D., Delledonne, M., Pezzotti, M., Lecharny, A., Scarpelli, C., Artiguenave, F., P\u0026egrave;, M.E., Valle, G., Morgante, M., Caboche, M., Adam-Blondon, A.-F., Weissenbach, J., Qu\u0026eacute;tier, F., Wincker, P., The French\u0026ndash;Italian Public Consortium for Grapevine Genome Characterization, 2007. The grapevine genome sequence suggests ancestral hexaploidization in major angiosperm phyla. Nature 449, 463\u0026ndash;467.\u003c/li\u003e\n\u003cli\u003eJakobsson, M., Edge, M.D., Rosenberg, N.A., 2013. The Relationship Between FST and the Frequency of the Most Frequent Allele. Genetics 193, 515\u0026ndash;528.\u003c/li\u003e\n\u003cli\u003eJia, K.-H., Wang, Z.-X., Wang, L., Li, G.-Y., Zhang, W., Wang, X.-L., Xu, F.-J., Jiao, S.-Q., Zhou, S.-S., Liu, H., Ma, Y., Bi, G., Zhao, W., El-Kassaby, Y.A., Porth, I., Li, G., Zhang, R.-G., Mao, J.-F., 2022. SubPhaser: a robust allopolyploid subgenome phasing method based on subgenome-specific k-mers. New Phytologist 235, 801\u0026ndash;809.\u003c/li\u003e\n\u003cli\u003eJiao, Y., Wickett, N.J., Ayyampalayam, S., Chanderbali, A.S., Landherr, L., Ralph, P.E., Tomsho, L.P., Hu, Y., Liang, H., Soltis, P.S., Soltis, D.E., Clifton, S.W., Schlarbaum, S.E., Schuster, S.C., Ma, H., Leebens-Mack, J., dePamphilis, C.W., 2011. Ancestral polyploidy in seed plants and angiosperms. Nature 473, 97\u0026ndash;100.\u003c/li\u003e\n\u003cli\u003eJin, J.-J., Yu, W.-B., Yang, J.-B., Song, Y., dePamphilis, C.W., Yi, T.-S., Li, D.-Z., 2020. GetOrganelle: a fast and versatile toolkit for accurate de novo assembly of organelle genomes. Genome Biology 21, 241.\u003c/li\u003e\n\u003cli\u003eJohnson, N.A., 2010. Hybrid incompatibility genes: remnants of a genomic battlefield? Trends in Genetics 26, 317\u0026ndash;325.\u003c/li\u003e\n\u003cli\u003eKang, H.M., Sul, J.H., Service, S.K., Zaitlen, N.A., Kong, S., Freimer, N.B., Sabatti, C., Eskin, E., 2010a. Variance component model to account for sample structure in genome-wide association studies. Nat Genet 42, 348\u0026ndash;354.\u003c/li\u003e\n\u003cli\u003eKang, H.M., Sul, J.H., Service, S.K., Zaitlen, N.A., Kong, S.-Y., Freimer, N.B., Sabatti, C., Eskin, E., 2010b. Variance component model to account for sample structure in genome-wide association studies. Nat Genet 42, 348\u0026ndash;354.\u003c/li\u003e\n\u003cli\u003eKersten, S., Chang, J., Huber, C.D., Voichek, Y., Lanz, C., Hagmaier, T., Lang, P., Lutz, U., Hirschberg, I., Lerchl, J., Porri, A., Van de Peer, Y., Schmid, K., Weigel, D., Rabanal, F.A., 2023. Standing genetic variation fuels rapid evolution of herbicide resistance in blackgrass. Proceedings of the National Academy of Sciences 120, e2206808120.\u003c/li\u003e\n\u003cli\u003eKim, D., Paggi, J.M., Park, C., Bennett, C., Salzberg, S.L., 2019. Graph-based genome alignment and genotyping with HISAT2 and HISAT-genotype. Nat Biotechnol 37, 907\u0026ndash;915.\u003c/li\u003e\n\u003cli\u003eKim, J.Y., Kim, W.Y., Kwak, K.J., Oh, S.H., Han, Y.S., Kang, H., 2010. Zinc finger-containing glycine-rich RNA-binding protein in \u003cem\u003eOryza sativa\u003c/em\u003e has an RNA chaperone activity under cold stress conditions. Plant Cell Environ 33, 759\u0026ndash;768.\u003c/li\u003e\n\u003cli\u003eKlure, D.M., Greenhalgh, R., Orr, T.J., Shapiro, M.D., Dearing, M.D., 2025. Parallel gene expansions drive rapid dietary adaptation in herbivorous woodrats. Science 387, 156\u0026ndash;162.\u003c/li\u003e\n\u003cli\u003eKok, P.D.F., Robbertse, P.J., van Wyk, A.E., 1989. Systematic study of Digitaria section Digitaria (Poaceae) in southern Africa. South African Journal of Botany 55, 141\u0026ndash;153.\u003c/li\u003e\n\u003cli\u003eKovaka, S., Zimin, A.V., Pertea, G.M., Razaghi, R., Salzberg, S.L., Pertea, M., 2019. Transcriptome assembly from long-read RNA-seq alignments with StringTie2. Genome Biology 20, 278.\u003c/li\u003e\n\u003cli\u003eKreiner, J.M., Giacomini, D.A., Bemm, F., Waithaka, B., Regalado, J., Lanz, C., Hildebrandt, J., Sikkema, P.H., Tranel, P.J., Weigel, D., Stinchcombe, J.R., Wright, S.I., 2019. Multiple modes of convergent adaptation in the spread of glyphosate-resistant Amaranthus tuberculatus. Proceedings of the National Academy of Sciences 116, 21076\u0026ndash;21084.\u003c/li\u003e\n\u003cli\u003eKreiner, J.M., Sandler, G., Stern, A.J., Tranel, P.J., Weigel, D., Stinchcombe, J.R., Wright, S.I., 2022. Repeated origins, widespread gene flow, and allelic interactions of target-site herbicide resistance mutations. eLife 11, e70242.\u003c/li\u003e\n\u003cli\u003eKreiner, J.M., Stinchcombe, J.R., Wright, S.I., 2018a. Population Genomics of Herbicide Resistance: Adaptation via Evolutionary Rescue. Annual Review of Plant Biology 69, 611\u0026ndash;635.\u003c/li\u003e\n\u003cli\u003eKreiner, J.M., Stinchcombe, J.R., Wright, S.I., 2018b. Population Genomics of Herbicide Resistance: Adaptation via Evolutionary Rescue. Annual Review of Plant Biology 69, 611\u0026ndash;635.\u003c/li\u003e\n\u003cli\u003eKreiner, J.M., Tranel, P.J., Weigel, D., Stinchcombe, J.R., Wright, S.I., 2021. The genetic architecture and population genomic signatures of glyphosate resistance in \u003cem\u003eAmaranthus tuberculatus\u003c/em\u003e. Molecular Ecology 30, 5373\u0026ndash;5389.\u003c/li\u003e\n\u003cli\u003eKumar, S., Suleski, M., Craig, J.M., Kasprowicz, A.E., Sanderford, M., Li, M., Stecher, G., Hedges, S.B., 2022. TimeTree 5: An Expanded Resource for Species Divergence Times. Mol Biol Evol 39, msac174.\u003c/li\u003e\n\u003cli\u003eLaforest, M., Soufiane, B., Simard, M.-J., Obeid, K., Page, E., Nurse, R.E., 2017. Acetyl-CoA carboxylase overexpression in herbicide-resistant large crabgrass (\u003cem\u003eDigitaria sanguinalis\u003c/em\u003e). Pest Management Science 73, 2227\u0026ndash;2235.\u003c/li\u003e\n\u003cli\u003eLamesch, P., Berardini, T.Z., Li, D., Swarbreck, D., Wilks, C., Sasidharan, R., Muller, R., Dreher, K., Alexander, D.L., Garcia-Hernandez, M., Karthikeyan, A.S., Lee, C.H., Nelson, W.D., Ploetz, L., Singh, S., Wensel, A., Huala, E., 2012. The Arabidopsis Information Resource (TAIR): improved gene annotation and new tools. Nucleic Acids Research 40, D1202\u0026ndash;D1210.\u003c/li\u003e\n\u003cli\u003eLangmead, B., Salzberg, S.L., 2012a. Fast gapped-read alignment with Bowtie 2. Nat Methods 9, 357\u0026ndash;359.\u003c/li\u003e\n\u003cli\u003eLangmead, B., Salzberg, S.L., 2012b. Fast gapped-read alignment with Bowtie 2. Nat Methods 9, 357\u0026ndash;359.\u003c/li\u003e\n\u003cli\u003eLetunic, I., Bork, P., 2024. Interactive Tree of Life (iTOL) v6: recent updates to the phylogenetic tree display and annotation tool. Nucleic Acids Research 52, W78\u0026ndash;W82.\u003c/li\u003e\n\u003cli\u003eLi, H., 2021. New strategies to improve minimap2 alignment accuracy. Bioinformatics 37, 4572\u0026ndash;4574.\u003c/li\u003e\n\u003cli\u003eLi, H., Durbin, R., 2010. Fast and accurate long-read alignment with Burrows-Wheeler transform. Bioinformatics 26, 589\u0026ndash;595.\u003c/li\u003e\n\u003cli\u003eLi, J., Li, M., Gao, X., Fang, F., 2017. A novel amino acid substitution Trp574Arg in acetolactate synthase (ALS) confers broad resistance to ALS-inhibiting herbicides in crabgrass. Pest Management Science 73, 2538\u0026ndash;2543.\u003c/li\u003e\n\u003cli\u003eLi, K., Xu, P., Wang, J., Yi, X., Jiao, Y., 2023. Identification of errors in draft genome assemblies at single-nucleotide resolution for quality assessment and improvement. Nat Commun 14, 6556.\u003c/li\u003e\n\u003cli\u003eLi, L., Li, Y., Jia, Y., Caicedo, A.L., Olsen, K.M., 2017. Signatures of adaptation in the weedy rice genome. Nat Genet 49, 811\u0026ndash;814.\u003c/li\u003e\n\u003cli\u003eLi, L.-F., Pusadee, T., Wedger, M.J., Li, Y.-L., Li, M.-R., Lau, Y.-L., Yap, S.-J., Jamjod, S., Rerkasem, B., Hao, Y., Song, B.-K., Olsen, K.M., 2024. Porous borders at the wild-crop interface promote weed adaptation in Southeast Asia. Nat Commun 15, 1182.\u003c/li\u003e\n\u003cli\u003eLi, W., Godzik, A., 2006. Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics 22, 1658\u0026ndash;1659.\u003c/li\u003e\n\u003cli\u003eLi, W., Wang, L., Zhou, F., Li, C., Ma, W., Chen, H., Wang, G., Pickett, J.A., Zhou, J.-J., Lin, Y., 2021. Overexpression of the homoterpene synthase gene, OsCYP92C21, increases emissions of volatiles mediating tritrophic interactions in rice. Plant, Cell \u0026amp; Environment 44, 948\u0026ndash;963.\u003c/li\u003e\n\u003cli\u003eLiu, X., Fu, Y.-X., 2020. Stairway Plot 2: demographic history inference with folded SNP frequency spectra. Genome Biology 21, 280.\u003c/li\u003e\n\u003cli\u003eLiu, X., Merchant, A., Xiang, S., Zong, T., Zhou, X., Bai, L., 2021. Managing herbicide resistance in China. Weed Science 69, 4\u0026ndash;17.\u003c/li\u003e\n\u003cli\u003eLoubet, I., Meyer, L., Michel, S., Pernin, F., Carr\u0026egrave;re, S., Barr\u0026egrave;s, B., Le Corre, V., D\u0026eacute;lye, C., 2023. A high diversity of non-target site resistance mechanisms to acetolactate-synthase (ALS) inhibiting herbicides has evolved within and among field populations of common ragweed (\u003cem\u003eAmbrosia artemisiifolia\u003c/em\u003e L.). BMC Plant Biology 23, 510.\u003c/li\u003e\n\u003cli\u003eLovell, J.T., Jenkins, J., Lowry, D.B., Mamidi, S., Sreedasyam, A., Weng, X., Barry, K., Bonnette, J., Campitelli, B., Daum, C., Gordon, S.P., Gould, B.A., Khasanova, A., Lipzen, A., MacQueen, A., Palacio-Mej\u0026iacute;a, J.D., Plott, C., Shakirov, E.V., Shu, S., Yoshinaga, Y., Zane, M., Kudrna, D., Talag, J.D., Rokhsar, D., Grimwood, J., Schmutz, J., Juenger, T.E., 2018. The genomic landscape of molecular responses to natural drought stress in \u0026lt;i\u0026gt;Panicum hallii\u0026lt;\\i\u0026gt;. Nat Commun 9, 5213.\u003c/li\u003e\n\u003cli\u003eLuo, Q., Liu, Y.-G., 2025. Breeding herbicide-resistant rice using CRISPR-Cas gene editing and other technologies. Plant Communications 6, 101172.\u003c/li\u003e\n\u003cli\u003eLyu, Y., Wang, F., Cheng, H., Han, Jing, Dang, R., Xia, X., Wang, H., Zhong, J., Lenstra, J.A., Zhang, H., Han, Jianlin, MacHugh, D.E., Medugorac, I., Upadhyay, M., Leonard, A.S., Ding, H., Yang, X., Wang, M.-S., Quji, S., Zhuzha, B., Quzhen, P., Wangmu, S., Cangjue, N., Wa, D., Ma, W., Liu, J., Zhang, J., Huang, B., Qi, X., Li, F., Huang, Y., Ma, Y., Wang, Y., Gao, Y., Lu, W., Lei, C., Chen, N., 2024. Recent selection and introgression facilitated high-altitude adaptation in cattle. Science Bulletin 69, 3415\u0026ndash;3424.\u003c/li\u003e\n\u003cli\u003eMaclean, C.A., Chue Hong, N.P., Prendergast, J.G.D., 2015. hapbin: An Efficient Program for Performing Haplotype-Based Scans for Positive Selection in Large Genomic Datasets. Molecular Biology and Evolution 32, 3027\u0026ndash;3029.\u003c/li\u003e\n\u003cli\u003eMahaut, L., Cheptou, P.-O., Fried, G., Munoz, F., Storkey, J., Vasseur, F., Violle, C., Bretagnolle, F., 2020. Weeds: Against the Rules? Trends in Plant Science 25, 1107\u0026ndash;1116.\u003c/li\u003e\n\u003cli\u003eMaheshwari, S., Barbash, D.A., 2011. The Genetics of Hybrid Incompatibilities. Annual Review of Genetics 45, 331\u0026ndash;355.\u003c/li\u003e\n\u003cli\u003eMallet, J., 2005. Hybridization as an invasion of the genome. Trends in Ecology \u0026amp; Evolution 20, 229\u0026ndash;237.\u003c/li\u003e\n\u003cli\u003eManni, M., Berkeley, M.R., Seppey, M., Sim\u0026atilde;o, F.A., Zdobnov, E.M., 2021. BUSCO Update: Novel and Streamlined Workflows along with Broader and Deeper Phylogenetic Coverage for Scoring of Eukaryotic, Prokaryotic, and Viral Genomes. Molecular Biology and Evolution 38, 4647\u0026ndash;4654.\u003c/li\u003e\n\u003cli\u003eMarchi, N., Kapopoulou, A., Excoffier, L., 2024. Demogenomic inference from spatially and temporally heterogeneous samples. Molecular Ecology Resources 24, e13877.\u003c/li\u003e\n\u003cli\u003eMartin, S.H., Davey, J.W., Jiggins, C.D., 2015a. Evaluating the Use of ABBA\u0026ndash;BABA Statistics to Locate Introgressed Loci. Molecular Biology and Evolution 32, 244\u0026ndash;257.\u003c/li\u003e\n\u003cli\u003eMartin, S.H., Davey, J.W., Jiggins, C.D., 2015b. Evaluating the Use of ABBA\u0026ndash;BABA Statistics to Locate Introgressed Loci. Molecular Biology and Evolution 32, 244\u0026ndash;257.\u003c/li\u003e\n\u003cli\u003eMartin, S.H., Davey, J.W., Jiggins, C.D., 2015c. Evaluating the Use of ABBA\u0026ndash;BABA Statistics to Locate Introgressed Loci. Molecular Biology and Evolution 32, 244\u0026ndash;257.\u003c/li\u003e\n\u003cli\u003eMcDaniel, S.F., 2024. Local adaptation, recombination, and the fate of neopolyploids. New Phytologist 244, 32\u0026ndash;38.\u003c/li\u003e\n\u003cli\u003eMei, Y., Si, C., Liu, M., Qiu, L., Zheng, M., 2017. Investigation of resistance levels and mechanisms to nicosulfuron conferred by non-target-site mechanisms in large crabgrass (\u003cem\u003eDigitaria sanguinalis\u003c/em\u003e L.) from China. Pesticide Biochemistry and Physiology 141, 84\u0026ndash;89.\u003c/li\u003e\n\u003cli\u003eMeimberg, H., Rice, K.J., Milan, N.F., Njoku, C.C., McKay, J.K., 2009. Multiple origins promote the ecological amplitude of allopolyploid \u003cem\u003eAegilops\u003c/em\u003e (Poaceae). American Journal of Botany 96, 1262\u0026ndash;1273.\u003c/li\u003e\n\u003cli\u003eMenardo, F., Praz, C.R., Wyder, S., Ben-David, R., Bourras, S., Matsumae, H., McNally, K.E., Parlange, F., Riba, A., Roffler, S., Schaefer, L.K., Shimizu, K.K., Valenti, L., Zbinden, H., Wicker, T., Keller, B., 2016. Hybridization of powdery mildew strains gives rise to pathogens on novel agricultural crop species. Nat Genet 48, 201\u0026ndash;205.\u003c/li\u003e\n\u003cli\u003eMinh, B.Q., Schmidt, H.A., Chernomor, O., Schrempf, D., Woodhams, M.D., von Haeseler, A., Lanfear, R., 2020. IQ-TREE 2: New models and efficient methods for phylogenetic inference in the genomic era. Molecular Biology and Evolution 37, 1530\u0026ndash;1534.\u003c/li\u003e\n\u003cli\u003eMorin, N., Brouillet, L., Levin, G., 2015. Flora of North America North of Mexico. Rodrigu\u0026eacute;sia 66, 973\u0026ndash;981.\u003c/li\u003e\n\u003cli\u003eMuzellec, B., Teleńczuk, M., Cabeli, V., Andreux, M., 2023. PyDESeq2: a python package for bulk RNA-seq differential expression analysis. Bioinformatics 39, btad547.\u003c/li\u003e\n\u003cli\u003eNakazono, M., Tsuji, H., Li, Y., Saisho, D., Arimura, S., Tsutsumi, N., Hirai, A., 2000. Expression of a Gene Encoding Mitochondrial Aldehyde Dehydrogenase in Rice Increases under Submerged Conditions. Plant Physiol 124, 587\u0026ndash;598.\u003c/li\u003e\n\u003cli\u003eNorth, H.L., Fu, Z., Metz, R., Stull, M.A., Johnson, C.D., Shirley, X., Crumley, K., Reisig, D., Kerns, D.L., Gilligan, T., Walsh, T., Jiggins, C.D., Sword, G.A., 2024. Rapid Adaptation and Interspecific Introgression in the North American Crop Pest Helicoverpa zea. Molecular Biology and Evolution 41, msae129.\u003c/li\u003e\n\u003cli\u003eODA, K., OTANI, M., URAGUCHI, S., AKIHIRO, T., FUJIWARA, T., 2011. Rice ABCG43 Is Cd Inducible and Confers Cd Tolerance on Yeast. Biosci. Biotechnol. Biochem. 75, 1211\u0026ndash;1213.\u003c/li\u003e\n\u003cli\u003eOu, S., Chen, J., Jiang, N., 2018. Assessing genome assembly quality using the LTR Assembly Index (LAI). Nucleic Acids Res 46, e126.\u003c/li\u003e\n\u003cli\u003eOziolor, E.M., Reid, N.M., Yair, S., Lee, K.M., Guberman VerPloeg, S., Bruns, P.C., Shaw, J.R., Whitehead, A., Matson, C.W., 2019. Adaptive introgression enables evolutionary rescue from extreme environmental pollution. Science 364, 455\u0026ndash;457.\u003c/li\u003e\n\u003cli\u003ePan, L., Guo, Q., Wang, J., Shi, L., Yang, X., Zhou, Y., Yu, Q., Bai, L., 2022. \u003cem\u003eCYP81A68\u003c/em\u003e confers metabolic resistance to ALS and ACCase-inhibiting herbicides and its epigenetic regulation in \u003cem\u003eEchinochloa crus-galli\u003c/em\u003e. Journal of Hazardous Materials 428, 128225.\u003c/li\u003e\n\u003cli\u003ePan, L., Yu, Q., Wang, J., Han, H., Mao, L., Nyporko, A., Maguza, A., Fan, L., Bai, L., Powles, S., 2021. An ABCC-type transporter endowing glyphosate resistance in plants. Proceedings of the National Academy of Sciences 118, e2100136118.\u003c/li\u003e\n\u003cli\u003ePayseur, B.A., Rieseberg, L.H., 2016. A genomic perspective on hybridization and speciation. Mol Ecol 25, 2337\u0026ndash;2360.\u003c/li\u003e\n\u003cli\u003ePeng, M., Shahzad, R., Gul, A., Subthain, H., Shen, S., Lei, L., Zheng, Z., Zhou, J., Lu, D., Wang, S., Nishawy, E., Liu, X., Tohge, T., Fernie, A.R., Luo, J., 2017. Differentially evolved glucosyltransferases determine natural variation of rice flavone accumulation and UV-tolerance. Nat Commun 8, 1975.\u003c/li\u003e\n\u003cli\u003ePowles, S.B., Yu, Q., 2010a. Evolution in Action: Plants Resistant to Herbicides. Annual Review of Plant Biology 61, 317\u0026ndash;347.\u003c/li\u003e\n\u003cli\u003ePowles, S.B., Yu, Q., 2010b. Evolution in Action: Plants Resistant to Herbicides. Annu. Rev. Plant Biol. 61, 317\u0026ndash;347.\u003c/li\u003e\n\u003cli\u003ePrice, M.N., Dehal, P.S., Arkin, A.P., 2010. FastTree 2 \u0026ndash; Approximately Maximum-Likelihood Trees for Large Alignments. PLOS ONE 5, e9490.\u003c/li\u003e\n\u003cli\u003eRaj, A., Stephens, M., Pritchard, J.K., 2014. fastSTRUCTURE: Variational Inference of Population Structure in Large SNP Data Sets. Genetics 197, 573\u0026ndash;589.\u003c/li\u003e\n\u003cli\u003eReid, N.M., Proestou, D.A., Clark, B.W., Warren, W.C., Colbourne, J.K., Shaw, J.R., Karchner, S.I., Hahn, M.E., Nacci, D., Oleksiak, M.F., Crawford, D.L., Whitehead, A., 2016. The genomic landscape of rapid repeated evolutionary adaptation to toxic pollution in wild fish. Science 354, 1305\u0026ndash;1308.\u003c/li\u003e\n\u003cli\u003eRend\u0026oacute;n-Anaya, M., Wilson, J., Sveinsson, S., Fedorkov, A., Cottrell, J., Bailey, M.E.S., Ruņǵis, D., Lexer, C., Jansson, S., Robinson, K.M., Street, N.R., Ingvarsson, P.K., 2021. Adaptive Introgression Facilitates Adaptation to High Latitudes in European Aspen (\u003cem\u003ePopulus tremula\u003c/em\u003e L.). Molecular Biology and Evolution 38, 5034\u0026ndash;5050.\u003c/li\u003e\n\u003cli\u003eRhie, A., Walenz, B.P., Koren, S., Phillippy, A.M., 2020. Merqury: reference-free quality, completeness, and phasing assessment for genome assemblies. Genome Biology 21, 245.\u003c/li\u003e\n\u003cli\u003eRibeiro, V.H.V., Gallagher, J., Mallory-Smith, C., Barroso, J., Brunharo, C.A.C.G., 2025. Multiple Origins or Widespread Gene Flow in Agricultural Fields? Regional Population Genomics of Herbicide Resistance in \u003cem\u003eBromus tectorum\u003c/em\u003e. Molecular Ecology 34, e17791.\u003c/li\u003e\n\u003cli\u003eRosche, C., Hensen, I., Mr\u0026aacute;z, P., Durka, W., Hartmann, M., Lachmuth, S., 2017. Invasion success in polyploids: the role of inbreeding in the contrasting colonization abilities of diploid versus tetraploid populations of \u003cem\u003eCentaurea stoebe\u003c/em\u003e s.l. Journal of Ecology 105, 425\u0026ndash;435.\u003c/li\u003e\n\u003cli\u003eRozewicki, J., Li, S., Amada, K.M., Standley, D.M., Katoh, K., 2019. MAFFT-DASH: integrated protein sequence and structural alignment. Nucleic Acids Research 47, W5\u0026ndash;W10.\u003c/li\u003e\n\u003cli\u003eRutland, C.A., Hall, N.D., McElroy, J.S., 2021. The Impact of Polyploidization on the Evolution of Weed Species: Historical Understanding and Current Limitations. Front. Agron. 3.\u003c/li\u003e\n\u003cli\u003eSabeti, P.C., Reich, D.E., Higgins, J.M., Levine, H.Z.P., Richter, D.J., Schaffner, S.F., Gabriel, S.B., Platko, J.V., Patterson, N.J., McDonald, G.J., Ackerman, H.C., Campbell, S.J., Altshuler, D., Cooper, R., Kwiatkowski, D., Ward, R., Lander, E.S., 2002. Detecting recent positive selection in the human genome from haplotype structure. Nature 419, 832\u0026ndash;837.\u003c/li\u003e\n\u003cli\u003eSalamov, A.A., Solovyev, V.V., 2000. \u003cem\u003eAb initio\u003c/em\u003e gene finding in \u003cem\u003eDrosophila\u003c/em\u003e genomic DNA. Genome Res. 10, 516\u0026ndash;522.\u003c/li\u003e\n\u003cli\u003eShan, S., Gitzendanner, M.A., Boatwright, J.L., Spoelhof, J.P., Ethridge, C.L., Ji, L., Liu, X., Soltis, P.S., Schmitz, R.J., Soltis, D.E., 2024. Genome-wide DNA methylation dynamics following recent polyploidy in the allotetraploid \u003cem\u003eTragopogon miscellus\u003c/em\u003e (Asteraceae). New Phytologist 242, 1363\u0026ndash;1376.\u003c/li\u003e\n\u003cli\u003eSharma, G., Barney, J.N., Westwood, J.H., Haak, D.C., 2021. Into the weeds: new insights in plant stress. Trends in Plant Science 26, 1050\u0026ndash;1060.\u003c/li\u003e\n\u003cli\u003eSharma, M.L., Sharma, K., 1979. Cytological studies in the north Indian grasses. cytologia 44, 861\u0026ndash;872.\u003c/li\u003e\n\u003cli\u003eShimizu, K.K., 2022. Robustness and the generalist niche of polyploid species: Genome shock or gradual evolution? Current Opinion in Plant Biology 69, 102292.\u003c/li\u003e\n\u003cli\u003eSoltis, P.S., Marchant, D.B., Van De Peer, Y., Soltis, D.E., 2015. Polyploidy and genome evolution in plants. Current Opinion in Genetics \u0026amp; Development 35, 119\u0026ndash;125.\u003c/li\u003e\n\u003cli\u003eStamatakis, A., 2014. RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics 30, 1312\u0026ndash;1313.\u003c/li\u003e\n\u003cli\u003eStanke, M., Keller, O., Gunduz, I., Hayes, A., Waack, S., Morgenstern, B., 2006. AUGUSTUS: \u003cem\u003eab initio\u003c/em\u003e prediction of alternative transcripts. Nucleic Acids Research 34, W435\u0026ndash;W439.\u003c/li\u003e\n\u003cli\u003eStevens, A.V., Nicotra, A.B., Godfree, R.C., Guja, L.K., 2020. Polyploidy affects the seed, dormancy and seedling characteristics of a perennial grass, conferring an advantage in stressful climates. Plant Biol (Stuttg) 22, 500\u0026ndash;513.\u003c/li\u003e\n\u003cli\u003eStewart, C.N., 2017. Becoming weeds. Nat Genet 49, 654\u0026ndash;655.\u003c/li\u003e\n\u003cli\u003eTakahagi, K., Inoue, K., Shimizu, M., Uehara-Yamaguchi, Y., Onda, Y., Mochida, K., 2018. Homoeolog-specific activation of genes for heat acclimation in the allopolyploid grass \u003cem\u003eBrachypodium hybridum\u003c/em\u003e. GigaScience 7, giy020.\u003c/li\u003e\n\u003cli\u003eTarailo-Graovac, M., Chen, N., 2009. Using RepeatMasker to identify repetitive elements in genomic sequences. Current Protocols in Bioinformatics 25, 4.10.1-4.10.14.\u003c/li\u003e\n\u003cli\u003eTarasov, A., Vilella, A.J., Cuppen, E., Nijman, I.J., Prins, P., 2015. Sambamba: fast processing of NGS alignment formats. Bioinformatics 31, 2032\u0026ndash;2034.\u003c/li\u003e\n\u003cli\u003eTouafchia, S., Maurin, O., Boonsuk, B., Hodkinson, T.R., Chantaranothai, P., Rakotomalala, N., Randrianarimanana, F., Randriamampianina, J.A., Roy, S., MacKinnon, L., Rakotoarinivo, M., Besnard, G., Haevermans, T., Vorontsova, M.S., 2023. Evolutionary history, traits, and weediness in Digitaria (Poaceae: Panicoideae). Botanical Journal of the Linnean Society 203, 1\u0026ndash;19.\u003c/li\u003e\n\u003cli\u003eTurner, S.D., 2018. qqman: an R package for visualizing GWAS results using Q-Q and manhattan plots. Journal of Open Source Software 3, 731.\u003c/li\u003e\n\u003cli\u003eVietmeyer, N., Borlaugh, N., Axtell, J., Burton, J., Harlan, J., Rachie, K., 1996. Lost Crops of Africa: Volume I: Grains. National Academies Press, Washington, D.C.\u003c/li\u003e\n\u003cli\u003eWang, R., Sun, Y., Lan, Y., Wei, S., Huang, H., Li, X., Huang, Z., 2023. ALS gene overexpression and enhanced metabolism conferring \u003cem\u003eDigitaria sanguinalis\u003c/em\u003e resistance to nicosulfuron in China. Front. Plant Sci. 14.\u003c/li\u003e\n\u003cli\u003eWedger, M.J., Xiao, E., Butts, T.R., Chlapecka, J.L., Webster, L.C., Olsen, K.M., 2024. Recent Crop-To-Weed Adaptive Introgression Has Reshaped the Genomic Composition and Geographical Structure of US Weedy Rice (\u003cem\u003eOryza\u003c/em\u003e spp.). Mol Ecol e17604.\u003c/li\u003e\n\u003cli\u003eWlodzimierz, P., Rabanal, F.A., Burns, R., Naish, M., Primetis, E., Scott, A., Mand\u0026aacute;kov\u0026aacute;, T., Gorringe, N., Tock, A.J., Holland, D., Fritschi, K., Habring, A., Lanz, C., Patel, C., Schlegel, T., Collenberg, M., Mielke, M., Nordborg, M., Roux, F., Shirsekar, G., Alonso-Blanco, C., Lysak, M.A., Novikova, P.Y., Bousios, A., Weigel, D., Henderson, I.R., 2023. Cycles of satellite and transposon evolution in \u003cem\u003eArabidopsis\u003c/em\u003e centromeres. Nature 618, 557\u0026ndash;565.\u003c/li\u003e\n\u003cli\u003eWu, D., Jiang, B., Ye, C.-Y., Timko, M.P., Fan, L., 2022a. Horizontal transfer and evolution of the biosynthetic gene cluster for benzoxazinoids in plants. Plant Communications, Special Issue on Rice Functional Genomics 3, 100320.\u003c/li\u003e\n\u003cli\u003eWu, D., Shen, E., Jiang, B., Feng, Y., Tang, W., Lao, S., Jia, L., Lin, H.-Y., Xie, L., Weng, X., Dong, C., Qian, Q., Lin, F., Xu, H., Lu, H., Cutti, L., Chen, H., Deng, S., Guo, L., Chuah, T.-S., Song, B.-K., Scarabel, L., Qiu, J., Zhu, Q.-H., Yu, Q., Timko, M.P., Yamaguchi, H., Merotto, A., Qiu, Y., Olsen, K.M., Fan, L., Ye, C.-Y., 2022b. Genomic insights into the evolution of \u003cem\u003eEchinochloa\u003c/em\u003e species as weed and orphan crop. Nat Commun 13, 689.\u003c/li\u003e\n\u003cli\u003eWu, T., Hu, E., Xu, S., Chen, M., Guo, P., Dai, Z., Feng, T., Zhou, L., Tang, W., Zhan, L., Fu, X., Liu, S., Bo, X., Yu, G., 2021. clusterProfiler 4.0: A universal enrichment tool for interpreting omics data. Innovation (Camb) 2, 100141.\u003c/li\u003e\n\u003cli\u003eWu, T.D., Watanabe, C.K., 2005. GMAP: a genomic mapping and alignment program for mRNA and EST sequences. Bioinformatics 21, 1859\u0026ndash;1875.\u003c/li\u003e\n\u003cli\u003eXu, T., Han, J.H., Kang, H., 2013. Structural features important for the RNA chaperone activity of zinc finger-containing glycine-rich RNA-binding proteins from wheat (\u003cem\u003eTriticum avestivum\u003c/em\u003e) and rice (\u003cem\u003eOryza sativa\u003c/em\u003e). Phytochemistry 94, 28\u0026ndash;35.\u003c/li\u003e\n\u003cli\u003eYang, C., Zhao, L., Zhang, H., Yang, Z., Wang, H., Wen, S., Zhang, C., Rustgi, S., von Wettstein, D., Liu, B., 2014. Evolution of physiological responses to salt stress in hexaploid wheat. Proceedings of the National Academy of Sciences 111, 11882\u0026ndash;11887.\u003c/li\u003e\n\u003cli\u003eYanniccari, M., V\u0026aacute;zquez-Garc\u0026iacute;a, J.G., Gig\u0026oacute;n, R., Palma-Bautista, C., Vila-Aiub, M., De Prado, R., 2022. A novel \u003cem\u003eEPSPS\u003c/em\u003e Pro-106-His mutation confers the first case of glyphosate resistance in \u003cem\u003eDigitaria sanguinalis\u003c/em\u003e. Pest Management Science 78, 3135\u0026ndash;3143.\u003c/li\u003e\n\u003cli\u003eYasue, T., 1956. Studies on hybrids in Digitaria. : I. Technique of hybridization. Japanese Journal of Breeding 6, 197\u0026ndash;201.\u003c/li\u003e\n\u003cli\u003eYasue, T., 1957. Studies on hybrids in Digitaria II. The F1 plants, Digitaria ciliaris HERS. x D. ischaemum MUHL. Japanese Journal of Breeding 6, 265\u0026ndash;271.\u003c/li\u003e\n\u003cli\u003eYe, C.-Y., Tang, W., Wu, D., Jia, L., Qiu, J., Chen, M., Mao, L., Lin, F., Xu, H., Yu, X., Lu, Y., Wang, Y., Olsen, K.M., Timko, M.P., Fan, L., 2019. Genomic evidence of human selection on Vavilovian mimicry. Nat Ecol Evol 3, 1474\u0026ndash;1482.\u003c/li\u003e\n\u003cli\u003eYe, C.-Y., Wu, D., Mao, L., Jia, L., Qiu, J., Lao, S., Chen, M., Jiang, B., Tang, W., Peng, Q., Pan, L., Wang, L., Feng, X., Guo, L., Zhang, C., Kellogg, E.A., Olsen, K.M., Bai, L., Fan, L., 2020. The genomes of the allohexaploid \u003cem\u003eEchinochloa crus-galli\u003c/em\u003e and its progenitors provide insights into polyploidization-driven adaptation. Molecular Plant 13, 1298\u0026ndash;1310.\u003c/li\u003e\n\u003cli\u003eYin, J., Zhang, C., Mirarab, S., 2019. ASTRAL-MP: scaling ASTRAL to very large datasets using randomization and parallelization. Bioinformatics 35, 3961\u0026ndash;3969.\u003c/li\u003e\n\u003cli\u003eYu, Q., Ahmad-Hamdani, M.S., Han, H., Christoffers, M.J., Powles, S.B., 2013. Herbicide resistance-endowing ACCase gene mutations in hexaploid wild oat (Avena fatua): insights into resistance evolution in a hexaploid species. Heredity 110, 220\u0026ndash;231.\u003c/li\u003e\n\u003cli\u003eYuan, J.S., Tranel, P.J., Stewart, C.N., 2007a. Non-target-site herbicide resistance: a family business. Trends in Plant Science 12, 6\u0026ndash;13.\u003c/li\u003e\n\u003cli\u003eYuan, J.S., Tranel, P.J., Stewart, C.N., 2007b. Non-target-site herbicide resistance: a family business. Trends in Plant Science 12, 6\u0026ndash;13.\u003c/li\u003e\n\u003cli\u003eZdobnov, E.M., Apweiler, R., 2001. InterProScan \u0026ndash; an integration platform for the signature-recognition methods in InterPro. Bioinformatics 17, 847\u0026ndash;848.\u003c/li\u003e\n\u003cli\u003eZhang, G., Liu, X., Quan, Z., Cheng, S., Xu, X., Pan, S., Xie, M., Zeng, P., Yue, Z., Wang, W., Tao, Y., Bian, C., Han, C., Xia, Q., Peng, X., Cao, R., Yang, X., Zhan, D., Hu, J., Zhang, Y., Li, Henan, Li, Hua, Li, N., Wang, Junyi, Wang, C., Wang, R., Guo, T., Cai, Y., Liu, C., Xiang, H., Shi, Q., Huang, P., Chen, Q., Li, Y., Wang, Jun, Zhao, Z., Wang, Jian, 2012. Genome sequence of foxtail millet (\u003cem\u003eSetaria italica\u003c/em\u003e) provides insights into grass evolution and biofuel potential. Nat Biotechnol 30, 549\u0026ndash;554.\u003c/li\u003e\n\u003cli\u003eZhang, H., Song, L., Wang, X., Cheng, H., Wang, C., Meyer, C.A., Liu, T., Tang, M., Aluru, S., Yue, F., Liu, X.S., Li, H., 2021. Fast alignment and preprocessing of chromatin profiles with Chromap. Nat Commun 12, 6566.\u003c/li\u003e\n\u003cli\u003eZhang, L., Zhu, X., Zhao, Y., Guo, J., Zhang, T., Huang, W., Huang, J., Hu, Y., Huang, C.-H., Ma, H., 2022. Phylotranscriptomics Resolves the Phylogeny of Pooideae and Uncovers Factors for Their Adaptive Evolution. Mol Biol Evol 39.\u003c/li\u003e\n\u003cli\u003eZhang, M., Zhang, Y., Scheuring, C.F., Wu, C.-C., Dong, J.J., Zhang, H.-B., 2012. Preparation of megabase-sized DNA from a variety of organisms using the nuclei method for advanced genomics research. Nat Protoc 7, 467\u0026ndash;478.\u003c/li\u003e\n\u003cli\u003eZhao, B., Xu, X., Li, B., Qi, Z., Huang, J., Hu, A., Wang, G., Liu, X., 2023. Target-site mutation and enhanced metabolism endow resistance to nicosulfuron in a \u003cem\u003eDigitaria sanguinalis\u003c/em\u003e population. Pesticide Biochemistry and Physiology 194, 105488.\u003c/li\u003e\n\u003cli\u003eZhou, C., McCarthy, S.A., Durbin, R., 2023. YaHS: yet another Hi-C scaffolding tool. Bioinformatics 39, btac808.\u003c/li\u003e\n\u003cli\u003eZhou, Y., Zhao, X., Li, Y., Xu, J., Bi, A., Kang, L., Xu, D., Chen, H., Wang, Ying, Wang, Yuan-ge, Liu, S., Jiao, C., Lu, H., Wang, J., Yin, C., Jiao, Y., Lu, F., 2020. Triticum population sequencing provides insights into wheat adaptation. Nat Genet 52, 1412\u0026ndash;1422.\u003c/li\u003e\n\u003cli\u003eZhu, M., Yong, K., Xu, K., Cong, J., Zhou, Xiaofang, Liu, K., Wang, X., Fan, L., Olsen, K.M., Huang, X., Zhou, Xiaoyi, Qiu, J., 2024. Landrace introgression contributed to the recent feralization of weedy rice in East China. Plant Communications 5, 101066.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Digitaria complex, weed genome, sympatric introgression, rapid adaptation, allopolyploids, herbicide resistance, non-target-site resistance","lastPublishedDoi":"10.21203/rs.3.rs-7329239/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7329239/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCrabgrasses (\u003cem\u003eDigitaria\u003c/em\u003e spp.), the most problematic upland weeds, are well-documented for their broad-spectrum herbicide resistance and ecological adaptability. The genetic mechanism of these adaptive traits remains largely unexplored. We assembled three telomere-to-telomere (T2T) reference genomes of the globally invasive species \u003cem\u003eDigitaria sanguinalis\u003c/em\u003e, along with its diploid and tetraploid progenitors. In addition, we re-sequenced 579 accessions from sympatric populations sampled over the past decade, coupled with nicosulfuron (an ALS inhibitor herbicide) resistance phenotyping. Genomic analysis of \u003cem\u003eD. sanguinalis\u003c/em\u003e revealed adaptation driven by polyploidization. Extensive sampling across \u003cem\u003eDigitaria\u003c/em\u003e species uncovered widespread introgression among sympatric lineages. Notably, introgression contributed to enhanced cold tolerance, likely facilitating adaptation to northern environments. Dose-response assays revealed a recent surge in nicosulfuron resistance in \u003cem\u003eD. sanguinalis\u003c/em\u003e, which cannot be explained by target-site mutations alone. Genome-wide association study identified 40 SNPs significantly associated with non-target-site resistance (NTSR). We further identified a recently introgressed region from sympatric \u003cem\u003eD. ciliaris\u003c/em\u003e associated with NTSR. Moreover, herbicide-resistant populations exhibited a higher number of introgressed genomic blocks compared to susceptible ones. These findings reveal adaptive introgression from relatives as a key source of variation, promoting rapid adaptation under selection pressure.\u003c/p\u003e","manuscriptTitle":"The Digitaria genomes reveal local adaption and herbicide resistance mediated by introgression","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-13 13:58:24","doi":"10.21203/rs.3.rs-7329239/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-communications","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"NCOMMS","sideBox":"Learn more about [Nature Communications](http://www.nature.com/ncomms/)","snPcode":"","submissionUrl":"https://mts-ncomms.nature.com/","title":"Nature Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature Communications","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"1601e86e-e540-4705-a581-ba864da15721","owner":[],"postedDate":"September 13th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":54592232,"name":"Biological sciences/Plant sciences/Plant genetics"},{"id":54592233,"name":"Biological sciences/Genetics/Population genetics"}],"tags":[],"updatedAt":"2026-03-24T07:08:34+00:00","versionOfRecord":{"articleIdentity":"rs-7329239","link":"https://doi.org/10.1038/s41467-026-69076-x","journal":{"identity":"nature-communications","isVorOnly":false,"title":"Nature Communications"},"publishedOn":"2026-02-12 05:00:00","publishedOnDateReadable":"February 12th, 2026"},"versionCreatedAt":"2025-09-13 13:58:24","video":"","vorDoi":"10.1038/s41467-026-69076-x","vorDoiUrl":"https://doi.org/10.1038/s41467-026-69076-x","workflowStages":[]},"version":"v1","identity":"rs-7329239","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7329239","identity":"rs-7329239","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}