Genomic insights into adaptive divergence and genetic vulnerability to climate change of the medicinal plant Isodon rubescens (Hemsl.) H. Hara in central China

Genomic insights into adaptive divergence and genetic vulnerability to climate change of the medicinal plant Isodon rubescens (Hemsl.) H. Hara in central China | 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 Research Article Genomic insights into adaptive divergence and genetic vulnerability to climate change of the medicinal plant Isodon rubescens (Hemsl.) H. Hara in central China Saibin Fan, Yang Lu, Caipeng Yue, Shixin Zhu, Jinyong Huang, Yuhui Wang, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8136960/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 01 May, 2026 Read the published version in BMC Plant Biology → Version 1 posted 10 You are reading this latest preprint version Abstract Background Global climate change is rapidly impacting biodiversity and threatening the sustainable use of medicinal plant species by reducing their availability and increasing harvest uncertainty. Understanding the adaptive genetic variation and genetic vulnerability of medicinal plants under climate change is crucial for effective germplasm management, cultivation, and breeding efforts. In this study, we assessed the genetic differentiation, local adaptation, and genomic vulnerability of the medicinal plant Isodon rubescens (Hemsl.) H. Hara, with the goals of elucidating the impacts of geographic and environmental factors on its genetic structure and identifying at-risk populations for informed conservation and breeding under climate change. Results We applied restriction site-associated DNA sequencing (RAD-seq) to 17 populations of I. rubescens spanning its central and peripheral ranges, including the Taihang and Qinling-Funiu Mountains. The analysis revealed two distinct genetic groups: one in the Taihang Mountains and the other in the Qinling-Funiu Mountains. Significant patterns of isolation by distance (IBD), environment (IBE), and resistance (IBR) were detected, alongside high niche differentiation. We identified 456 candidate adaptive SNPs, some linked to genes involved in stress responses and biosynthesis. Precipitation was a key environmental driver of local adaptation. Populations in the northern Taihang Mountains and southern Funiu Mountains showed higher genomic vulnerability, indicating a greater risk of maladaptation. Conclusion Our findings demonstrate that geographic isolation and environmental factors, particularly precipitation, are key drivers of genetic differentiation and local adaptation in I. rubescens . The identified genomic vulnerability pinpoints specific populations at high risk under climate change. These insights provide a crucial genetic basis for formulating targeted conservation strategies and developing climate-resilient breeding programs for this medicinal species. Isodon rubescens landscape genomics genetic differentiation local adaptation genomic vulnerability Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Background Medicinal plants are essential for the livelihoods and well-being of a significant portion of the global population [ 1 – 3 ]. The ongoing global climate change is impacting the productivity and quality of medicinal plants worldwide. Substantial evidence demonstrates the effects of rapid climate change on the distribution, growth patterns, phenology, synthesis of secondary metabolites, and abundance of medicinal plant species [ 4 – 6 ]. For sessile plant species, local adaptation represents a crucial strategy to cope with rapid climate change [ 7 ]. Medicinal plants frequently inhabit heterogeneous environments and adapt locally through adaptive evolution, a phenomenon more prevalent in widely distributed species [ 8 , 9 ]. Adaptive divergence influences gene expression, physiology, and metabolism in medicinal plant species, facilitating their adaptation to environmental changes [ 10 ]. Consequently, given their economic and cultural significance, understanding the adaptive divergence and vulnerability of medicinal plants to climate change is imperative for their sustainable utilization and effective management. In addition, evaluating the ability of medicinal plants to adapt to future climatic conditions and identifying their environment-adaptive genes not only protects critical germplasm resources and clarifies the underlying adaptation mechanisms, but also facilitates the breeding of climate-resilient cultivars [ 11 ]. By incorporating these adaptive genes into high-quality varieties, such efforts can effectively boost productivity, ensuring a stable supply to meet the increasingly pressing demand for medicinal plants [ 12 , 13 ]. Ecological niche models (ENMs) have been extensively utilized to predict the impact of climate change on the distribution and vulnerability of medicinal plant species [ 2 , 5 , 14 – 16 ]. However, ENMs do not account for evolutionary processes and genetic adaptations of species and populations to climate change [ 17 , 18 ]. The recent development of landscape genomics provides a valuable tool for investigating genetic adaptation across landscapes, incorporating intra-specific diversity and local adaptation when projecting species responses to climate change [ 19 , 20 ]. Genotype-environment association (GEA) methods integrate genomic and environmental data to identify genetic loci associated with climate adaptation [ 21 , 22 ]. Redundancy analysis (RDA) can detect the influence of geographical and environmental factors in shaping adaptive divergence [ 22 , 23 ]. Additionally, gradient forest (GF) models can assess the genomic vulnerability (or genomic offset) of species and populations to future climate change, measured by the genetic change required to track future climate shifts [ 17 , 24 ]. In recent years, the landscape genomics approach has been successfully applied to various species, including forest trees [ 25 – 28 ], crops [ 29 , 30 ], primates [ 31 ], birds [ 32 , 33 ], fish [ 34 ], and amphibians [ 35 ]. However, landscape genomics studies of medicinal plants remain limited, with only a few reported cases, such as those of Forsythia suspensa (Thunb.) Vahl [ 9 ] and Tetrastigma hemsleyanum Diels & Gilg [ 36 ]. The landscape-genomics approach offers a novel framework for guiding the management and conservation of wild medicinal-plant germplasm and for defining provenance strategies that will underpin future cultivation and breeding under a changing climate. Within medicinal-plant researches, identifying adaptive genes can enhances our comprehension of the genetic underpinnings governing environmental adaptation, and furnishes invaluable genomic resources for the development of climate-smart cultivars. Isodon rubescens (Hemsl.) H. Hara, also known as Dong Ling Cao, is a valuable traditional Chinese medicinal plant belonging to the genus Isodon in the Lamiaceae family [ 37 , 38 ]. This subshrub or perennial herb typically thrives on stony slopes, in thickets, forests, and along roadsides. Wild populations of I. rubescens are widely distributed across several Chinese provinces, including Henan, Shaanxi, Shanxi, Hebei, and Hubei, with a central distribution area encompassing the Taihang Mountains and the Qinling Mountains [ 37 ]. The aerial portions of I. rubescens have been utilized in traditional Chinese medicine to treat sore throat, gastrointestinal diseases, inflammation, cancer, and other diseases [ 39 ]. Notably, it has been employed as a folk remedy for esophageal cancer by local communities in Henan province, central China [ 40 ]. To date, over 300 substances have been isolated and identified from I. rubescens , including diterpenoids, triterpenoids, phenols, alkaloids, volatile oils, and other compounds [ 41 ]. Previous researches have demonstrated that I. rubescens produces a diverse range of biologically active diterpenoids [ 39 , 42 ]. Oridonin, a significant diterpenoid extracted from I. rubescens , exhibits potential antitumor effects on various human cancer cells, including ovarian, breast, leukemia, colorectal, liver, laryngeal, and prostate cancers [ 43 , 44 ]. The I. rubescens industry, encompassing traditional Chinese medicine, syrups, and tea beverages, generates an annual revenue of approximately 100 million USD in China [ 45 ]. Currently, the southern part of the Taihang Mountains in Jiyuan, Henan Province serves as the primary production area for I. rubescens . Due to the superior quality and high yield of I. rubescens in and around the Jiyuan region, "Jiyuan Donglingcao" has been designated as a National Geographical Indication Protected Product [ 41 , 43 ]. However, the wild resource of I. rubescens within the Taihang Mountains has experienced significant population declines due to long-term unsustainable harvesting [ 46 ]. In recent years, artificial cultivation of I. rubescens has been progressively developed to meet the growing market demand [ 47 ]. Consequently, genetic assessment of the wild germplasm resources for I. rubescens is crucial for effective conservation and successful breeding of this medicinal plant in the region. Given its extensive medicinal properties, the phytochemical composition and pharmacological activity of I. rubescens have been thoroughly investigated [ 39 , 41 , 42 ]. However, limited research has focused on the identification of original materials and quality control of I. rubescens products. Accurate identification and systematic analysis of I. rubescens germplasm resources will optimize material selection in the I. rubescens processing industry and facilitate future breeding and improvement efforts. Recent studies have utilized chloroplast genome sequencing data to explore phylogenetic relationships among Isodon species and develop molecular markers for identifying I. rubescens and related species [ 48 , 49 ]. Furthermore, previous research has demonstrated significant variations in morphological traits and chemical constituents (e.g., diterpenoids) of I. rubescens from different geographical regions [ 38 , 43 , 44 , 50 – 52 ], potentially affecting the quality stability of I. rubescens medicinal products. Consequently, selection and cultivation of I. rubescens germplasm resources with high bioactive component yields (such as oridonin, ponicidin) are necessary. For instance, Harris et al [ 43 ] identified I. rubescens from Henan province as the best source of oridonin. Population genetics studies using molecular markers such as random amplified polymorphic DNA (RAPD), inter-simple sequence repeat (ISSR), and microsatellites have revealed population differentiation in I. rubescens between geographical regions [ 43 , 53 ]. However, the drivers of population divergence in I. rubescens and its response to future climate change remain largely unknown. Genomic assessment of adaptive divergence and genetic vulnerability to climate change is crucial for effective introduction, conservation, and utilization of medicinal plants. The chromosome-level genome assembly of I. rubescens provides a valuable resource for further studies on population genomics and adaptive evolution of this species [ 45 ]. To ensure quality control and sustainable supply in the future, landscape genomics research on population structure, local adaptation, and genomic vulnerability of I. rubescens is essential to provide rapid and scientific guidelines for germplasm management, utilization, and the development of climate-resilient breeding strategies under ongoing climate change. This study employed a landscape genomics approach to investigate the impact of climate change on I. rubescens populations in central China. Restriction site-associated DNA sequencing (RAD-seq) was conducted on 130 individuals from 17 populations across the species' central distribution areas. Utilizing single-nucleotide polymorphisms (SNPs) derived from RAD-seq data, the research aimed to test the following hypotheses: (a) genetic differentiation exists at the genomic level between I. rubescens populations in the Taihang Mountains and the Qinling-Funiu Mountains; (b) both geography and environmental factors drive the divergence of I. rubescens populations; and (c) some populations could not adapt to future climates, showing high genomic vulnerability. The findings of this study could offer valuable insights for germplasm resource management, future cultivation practices, and breeding efforts aimed at enhancing the adaptive capacity of cultivars for this important medicinal plant in the context of climate change. Furthermore, this research offers a methodological framework applicable to the study and management of other medicinal plants in a changing environment. Material and methods Sample collection, DNA extraction, and RAD sequencing We collected 17 populations of I. rubescens across the central distribution area in China, spanning the Qinling, Funiu, and Taihang Mountains (Fig. 1 A). A total of 130 individuals were sampled, with 5 to 8 individuals per population (Table 1 ). In each population, the sampled individuals were spaced at least 10 m apart. Fresh leaves were collected and preserved in silica gel. Genomic DNA was extracted using a modified cetyltrimethylammonium bromide (CTAB) method [ 54 ]. The RAD library preparation was carried out following the protocol established by Peterson et al. [ 55 ]. The process involved digesting genomic DNA with restriction enzymes Eco RI and Mse I. Subsequently, the digested and ligated DNA was pooled, purified, and amplified via PCR. DNA fragments with sizes ranging from 350 bp to 500 bp were selected. Sequencing of the library was performed using the Illumina NovaSeq platform with paired-end 150 bp reads at JieRui BioScience Co. Ltd., Guangzhou, China. Data Processing Data for each sample were processed using the process_radtags in STACKS v 2.41 [ 56 ]. All reads were trimmed to a uniform length of 135 bp to eliminate low-quality nucleotides. The filtered reads were aligned to the reference genome of I. rubescens [ 45 ] using BWA v0.7.17-4 [ 57 ], allowing a maximum of five mismatches. For each sample, paired-end read alignments were converted to binary format and sorted by SAMtools v1.10-3 [ 58 ]. The gstacks within STACKS was employed to identify loci with the default settings. SNPs were filtered using the populations within STACKS based on the following criteria: (a) removal of SNPs that deviated from Hardy-Weinberg equilibrium; (b) exclusion of loci with heterozygosity greater than 0.5 to avoid potential homologs; (c) discarding loci with a minor allele frequency (MAF) below 0.01; (d) retention of only the first SNP locus per read ( write_single_snp ); and (e) keeping only loci present in at least 16 populations and in at least 70% of individuals within each population. Lastly, VCFtools v 0.1.16 [ 59 ] were utilized to exclude loci with missing data in over 15% of individuals and to retain only biallelic SNPs. We used the R package CMplot v4.5.1 [ 60 ] to examine the distribution of the SNPs on chromosomes of I. rubescens . Population structure and genetic diversity ADMIXTURE v1.3.0 [ 61 ] was used to evaluate the maximum likelihood ancestry of each individual based on all SNPs. The number of ancestral clusters ( K ) was set to range from 1 to 10, with 10-fold cross-validation performed. Principal component analysis (PCA) was conducted using PLINK v1.9 [ 62 ], and the results were visualized with the R package ggplot2 v3.4.4 [ 63 ]. Additionally, a neighbor-joining (NJ) tree was constructed with PHYLIP v3.698 [ 64 ]. Genome-wide genetic diversity parameters were calculated using the populations within STACKS. These parameters included percentage of polymorphic loci ( PPL ), the number of private alleles ( PA ), nucleotide diversity ( π ), observed heterozygosity ( H O ), expected heterozygosity ( H E ), and Wright’s inbreeding coefficient ( F IS ). Pairwise population differentiation ( F ST ) values were determined using the R package hierfstat v0.5-11 [ 65 ]. Additionally, we used the lm function in R to investigate the relationships between π , H O , H E , F IS , and geographical coordinates (latitude and longitude). The R package ggpubr v0.6.0 was utilized to compare genetic diversity parameters among different groups. Analysis of molecular variance (AMOVA) was performed using ARLEQUIN v3.5 [ 66 ] to quantify genomic variance among groups and populations, and the significance tests were based on 10,000 permutations. TreeMix v1.13 [ 67 ] was employed to infer gene flow between populations based on allele frequency data, simulating 0–20 migration events. The optimal number of migration events ( m ) was estimated using the R package OptM v0.1.6 [ 68 ]. Estimated effective migration surface We employed estimated effective migration surfaces (EEMS) as described by Petkova et al. [ 69 ] to visualize the spatial distribution of gene flow. This approach utilizes a stepping-stone model, as proposed by Kimura and Weiss [ 70 ], to pinpoint geographic areas where the decline in genetic similarity does not align with the predictions of isolation by distance (IBD), thereby revealing barriers or pathways for gene flow. The parameters were configured as follows: deme size at 800, Markov chain Monte Carlo (MCMC) run length at 20 million iterations, burn-in period at 10 million iterations, and thinning interval at 9999. The accuracy of the EEMS models was assessed by examining the linear correlation between observed and fitted dissimilarities both within and across demes. The final visualizations of migratory surfaces were generated using rEEMSplot ( https://github.com/dipetkov/eems ). Ecological niche modelling A total of 64 effective occurrence records for I. rubescens were collected from the Chinese Virtual Herbarium database (CVH, https://www.cvh.ac.cn/ ), the Global Biodiversity Information Facility (GBIF, https://www.gbif.org/ ) and our own field surveys (Table S1 ). Nineteen bioclimatic variables (Bio1–Bio19) from 1970 to 2000 and elevation data were obtained from the WorldClim database ( https://www.worldclim.org ), while slope and aspect were extracted from elevation data [ 71 ]. Soil variables were acquired from the National Tibetan Plateau Data Center (TPDC, https://www.tpdc.ac.cn/ ) (Table S2). All environmental variables featured a spatial resolution of 2.5 arc minutes. The R package raster [ 72 ] was used to extract environmental data for each distribution point. Eight environmental variables with Pearson’s correlation coefficients < 0.8 were retained: Bio4 (temperature seasonality), Bio13 (precipitation of wettest month), Bio14 (precipitation of driest month), Bio16 (precipitation of wettest quarter), elevation, slope, T_OC (topsoil organic carbon), and T_PH (topsoil pH). MAXENT v3.4.3 [ 73 ] was employed to predict the current potential distributions of the two groups (Taihang, Qinling-Funiu; see Results) of I. rubescens . Model accuracy was evaluated using the area under the receiver operating characteristic curve (AUC). According to the methods of Li et al. [ 74 ] and Feng et al. [ 75 ], niche overlap and identity tests were conducted using the R package ENMtools [ 76 ] to discern niche differences among genetic groups. These tests involved calculating Schoener’s D [ 77 ] and Hellinger’s I , with values for both metrics ranging from 0 (no overlap) to 1 (complete overlap). Detection of candidate SNPs under selection Three approaches were employed to detect candidate SNPs under selection: two GEA methods—redundancy analyses (RDA) [ 23 ] and latent factor mixed model (LFMM) [ 78 ]—and a F ST -based method, PCAdapt [ 79 ]. Prior to conducting GEA analysis, GF analysis was conducted using the R package gradientForest [ 80 ] to select appropriate environmental variables. According to ranked importance and pairwise correlation coefficients (|r| < 0.8), six environmental variables were retained for GEA analyses (Fig. S1 ): Bio3 (isothermality), Bio7 (temperature annual range), Bio12 (annual precipitation), Bio13 (precipitation of wettest month), Bio15 (precipitation seasonality), and Bio16 (precipitation of wettest quarter). Subsequently, multivariate RDA was conducted using the R package vegan , employing a standard deviation cutoff of 3 to define outliers [ 81 ]. Latent factor mixed model (LFMM) was then performed using the R package lfmm [ 78 ]. Based on population structure results (ADMIXTURE and PCA), K = 2 was considered the optimal number of latent factors. SNPs with adjusted p -values less than 0.001 were considered to strongly support associations between allele frequencies and environmental variables. A false discovery rate (FDR) of 0.05 was adopted. For PCAdapt, outliers were identified with respect to population structure, with two principal components ( K = 2) selected. Outlier SNPs were identified at a FDR of 0.05 by the R package pcadapt [ 82 ]. The overlap between SNPs identified by the three approaches was visualized using a Venn diagram. Moreover, the distribution of these loci on chromosomes was generated by ggplot2 . Candidate loci were annotated through the following procedure. First, the DNA sequences of the candidate loci were translated in all six reading frames. Subsequently, each translated peptide sequence was subjected to a BLASTx search against the NCBI non-redundant protein database, with a maximum E-value cutoff set at 10⁻⁵. Only hits meeting two criteria were retained: the alignment coverage of the query sequence was at least 50%, and the sequence identity was ≥ 40%. Finally, for each query, the highest-scoring hit was analyzed to extract the gene name, product description, and taxonomic lineage, which were then integrated into the corresponding locus records as annotations. Environmental and geographic contribution to spatial genetic variation To elucidate the influence of geography and environment on the spatial genetic differentiation of I. rubescens , we analyzed IBD, isolation by environment (IBE), isolation by resistance (IBR), and their correlations. Genetic distances [ F ST/ (1 − F ST )] were computed using the R package hierfstat . Geographical distances between sites were determined based on population coordinates (latitude and longitude) using the R package geosphere [ 83 ]. Six climatic variables (Bio3, Bio7, Bio12, Bio13, Bio15, Bio16) were utilized to estimate environmental distances between populations. IBD and IBE analyses were conducted using Mantel tests with 999 permutations in the R package vegan [ 81 ]. The resistance distance matrix was computed using topoLCP in the R package topoDistance v1.0.2 [ 84 ], based on an elevation raster layer and the latitude and longitude information of the sites. A habitat suitability model was employed to parameterize the resistance surface, assuming higher suitability corresponds to lower resistance. Redundancy analyses (RDA) were employed to evaluate the relative contributions of geography and environment to population genetic differentiation using the vegan package. The analysis utilized a matrix of allele frequencies for each population as the dependent variable and two independent matrices: one comprising environmental variables (Bio3, Bio7, Bio12, Bio13, Bio15, Bio16) and another containing geographical variables (latitude and longitude). A series of full and partial RDAs were conducted for various SNP sets (all SNPs, RDA SNPs, LFMM SNPs, PCAdapt SNPs, and GEA SNPs) using the vegan R package, with 999 permutations. Prediction of genomic offset To forecast the genomic offset under future climatic conditions, we conducted GF analyses utilizing all SNPs and GEA SNPs [ 80 ]. The genomic offset was computed as a measure of the Euclidean distance between the genomic compositions under contemporary and projected future climates, with higher values suggesting increased population vulnerability in the future [ 19 ]. We assessed the discrepancy between current and predicted genomic compositions under future climate projections for the period 2061–2080, considering two shared socioeconomic pathways (SSPs): scenario SSP126 and scenario SSP585, representing low and high emission scenarios, respectively. Future environmental data for the 19 bioclimatic variables were obtained from the WorldClim CMIP6 dataset of the BCC-CSM2-MR model [ 85 ]. Six environmental variables (see Section 2.6) were incorporated into the GF models. To enhance the analysis's representativeness and mitigate bias resulting from under-sampling of allelic diversity [ 30 ], the GF analysis was confined to the distribution areas of I. rubescens in the Taihang, Qinling, and Funiu Mountains. Each GF model was evaluated using 500 regression trees per SNP. The genomic offset was visualized geographically for all SNPs and GEA SNPs. We compared four distinct genomic offset patterns of I. rubescens in the study area. Box plots were generated using ggplot2 . Results Population structure, genetic diversity and gene flow analyses RAD sequencing conducted on 130 individuals of I. rubescens yielded a total of 794,399,728 paired-end reads, with 738,103,422 (92.9%) reads passing initial quality filtration. The average data size per sample was 0.85 Gb, with a mean read depth of 14.65× (Table S3). Following rigorous quality control measures, 4674 high-quality SNPs that were relatively evenly distributed across the 12 chromosomes of I. rubescens (Fig. S2) were retained for subsequent analyses. Utilizing 4674 SNPs, ADMIXTURE analysis identified K = 2 as the optimal number of clusters among the 17 populations of I. rubescens (Fig. 1 A, B; Fig. S3). Six populations in the northeast were grouped into the Taihang Mountains cluster, while eleven populations in the southwest formed the Qinling-Funiu Mountains cluster. At K = 3, the Qinling-Funiu cluster further subdivided into Qinling and Funiu groups, aligning with geographic zones (Fig. 1 B, Fig. S4A). PCA corroborated these findings. All samples segregated into the Taihang and Qinling-Funiu groups along PC1, with further differentiation into Qinling and Funiu groups along PC2 (Fig. 1 C, Fig. S4B). Moreover, the NJ tree exhibited a highly consistent genetic structure pattern, supporting the division of all samples into two distinct genetic groups (Fig. 1 D). Table 1 Genetic diversity indices for 17 Isodon rubescens populations sampled in this study. Population Location Longitude (°E) Latitude (°N) Elevation (m) Sample size ( N ) PPL (%) PA π H O H E F IS Taihang group CYC Chaoyangcun, Henan 113.71 36.16 1021 8 40.65 0 0.125 0.028 0.116 0.279 JLG Jiuligou, Henan 112.42 35.20 467 7 41.27 0 0.132 0.025 0.122 0.299 LTX Longtanxia, Henan 112.00 34.96 461 8 41.16 0 0.129 0.027 0.120 0.292 YTS Yuntaishan, Henan 113.34 35.48 433 8 45.04 0 0.134 0.025 0.126 0.325 WXS Wanxianshan, Henan 113.60 35.74 1191 8 42.79 0 0.131 0.028 0.123 0.300 ZWD Zhouwangdian, Henan 114.01 35.71 579 8 43.11 1 0.129 0.028 0.120 0.304 Average 42.34 0.16 0.130 0.027 0.121 0.300 Qinling-Funiu group BTM Baotianman, Henan 111.95 33.35 1204 8 36.22 3 0.120 0.025 0.112 0.260 HSG Hongshigu, Henan 111.08 34.02 818 8 40.76 1 0.130 0.024 0.120 0.300 LCM Longchiman, Henan 111.99 33.71 1541 8 40.76 1 0.128 0.025 0.119 0.294 LJL Laojieling, Henan 111.77 33.66 1769 8 36.03 13 0.120 0.027 0.112 0.255 LYW Longyuwan, Henan 111.78 33.70 1277 7 37.40 0 0.124 0.025 0.115 0.269 XQL Xiaoqinling, Henan 110.52 34.45 900 8 39.41 0 0.119 0.025 0.111 0.277 YSZ Yaoshan, Henan 112.38 33.79 401 8 40.82 0 0.118 0.025 0.111 0.285 JNP Jinniuping, Shaanxi 108.35 33.93 874 8 35.99 0 0.107 0.024 0.100 0.250 NBL Niubeiliang, Shaanxi 108.99 33.84 1118 7 33.03 1 0.107 0.023 0.099 0.231 TBS Taibaishan, Shaanxi 107.89 34.04 1080 8 35.86 1 0.111 0.023 0.104 0.255 XHZ Xihezhan, Shaanxi 107.76 33.62 1232 5 20.00 0 0.081 0.023 0.072 0.135 Average 36.03 1.82 0.115 0.024 0.107 0.256 Total (Average) 38.25 1.23 0.120 0.025 0.112 0.271 PPL percentage of polymorphic loci, PA the number of private alleles, π nucleotide diversity, H O observed heterozygosity, H E expected heterozygosity, F IS Wright’s inbreeding coefficient The percentage of polymorphic loci varied from 20.00% (XHZ) to 45.04% (YTS), with the Taihang group exhibiting a higher average value (42.34%) compared to the Qinling-Funiu group (36.03%) (Table 1 ). Most populations contained few private alleles, ranging from 0 to 3, with the exception of the LJL population (13 private alleles). The Taihang group demonstrated significantly higher genetic diversity ( π = 0.130, H O = 0.027, H E = 0.121) than the Qinling-Funiu group ( π = 0.115, H O = 0.024, H E = 0.107) (Table 1 , Fig. 2 I-K). The inbreeding coefficient ( F IS ) ranged from 0.135 (XHZ) to 0.325 (YTS), with average values of 0.300 and 0.256 for the Taihang and Qinling-Funiu groups, respectively (Table 1 ). Nucleotide diversity ( π ), observed heterozygosity ( H O ), expected heterozygosity ( H E ), and inbreeding coefficient ( F IS ) all exhibited significant positive correlations with latitude and longitude (Fig. 2 A-H). The analysis of molecular variance revealed that the majority of genetic variation occurred within populations (77.83%; Table 2 ). The genetic differentiation ( F ST ) value of the Qinling-Funiu group (0.19) exceeded that of the Taihang group (0.06; Table 2 ). Genetic differentiation between groups ( F CT = 0.09) was minimal, while a moderate level of genetic differentiation was observed among populations within groups ( F SC = 0.13; Table 2 ). Pairwise F ST values ranged from 0.05 to 0.12, with higher values noted between the populations of the Qinling-Funiu group (Fig. 2 M). Table 2 Analysis results of molecular variance (AMOVA) at different levels of Isodon rubescens . Source of variation d.f. Sum of squares Variance components Percentage of variation (%) Fixation indices All populations Among groups 1 1423.859 17.77625 8.98 F CT = 0.09 *** Among populations within groups 15 5298.266 26.09317 13.18 F SC = 0.13 *** Within populations 113 17406.829 154.04273 77.83 F ST = 0.22 *** Taihang group Among populations 5 1226.629 10.1972 5.8 F ST = 0.06 *** Within populations 41 6784.839 165.48389 94.2 Qinling-Funiu group Among populations 10 4071.637 34.45762 18.93 F ST = 0.19 *** Within populations 72 10621.989 147.52763 81.07 Note: Significant level, *** p < 0.001. TreeMix analysis detected eleven migration events among the 17 I. rubescens populations (Fig. 1 E, Fig. S5). These migration events predominantly occurred within the Qinling-Funiu group. Additionally, a strong signal of gene flow was observed between the LTX and JLG populations within the Taihang group. Gene flow between the Taihang and Qinling-Funiu groups was limited, with only one migration event identified between the XHZ and ZWD populations. The EEMS analysis revealed several potential barriers to gene flow, primarily aligning with population divisions (Fig. 1 F, orange areas). A significant barrier was identified at the intersection of the Taihang and Qinling-Funiu Mountains, limiting connectivity between northeastern and southwestern populations. Further west, another substantial barrier restricted migration between the Funiu and Qinling groups. Within each geographical zone, minor barriers were also detected. Regions exhibiting higher-than-expected gene flow generally occurred at lower elevations (Fig. 1 F, blue areas). These barriers and corridors were corroborated by high posterior probabilities (> 0.90) of the migration parameters (Fig. S6). Ecological niche differences Ecological niche modelling was conducted for the two groups of I. rubescens to predict their current potential distributions, and the predicted distribution aligned closely with their actual distributions. The AUC values of all Maxent models were 0.998 for the Taihang group and 0.996 for the Qinling-Funiu group, indicating high predictive performance for all models (Fig. S7). The model revealed a small overlap between Taihang and Qinling-Funiu (Fig. 3 A). The observed values of Schoener's D and Hellinger's I were below the null distributions for the comparison between Taihang and Qinling-Funiu, indicating niche differentiation between these two groups (Fig. 3 B). Niche identity tests performed for the three groups (Taihang, Qinling and Funiu) showed Schoener's D and Hellinger's I values below the critical values for null distributions concerning group pairs, indicating significant niche differentiation between these pairs (Fig. S8). The lowest niche overlap was observed between Taihang and Qinling ( D = 0.08, I = 0.22), while the greatest overlap was between Qinling and Funiu ( D = 0.38, I = 0.67). Candidate SNPs detection and annotation The methods RDA, LFMM, and PCAdapt identified 152, 109, and 277 candidate SNPs under selection, respectively (Fig. 4 A, Figs. S9 and S10; Tables S4, S5 and S6). A total of 456 SNPs, distributed across all 12 chromosomes of I. rubescens (Fig. S11), were identified by at least one of the three methods, with six SNPs identified by all three approaches. For RDA, we identified 58, 16, 16, 14, 44, and 9 SNPs associated with the environmental variables Bio3, Bio7, Bio12, Bio13, Bio15, and Bio16, respectively (Table S4). For LFMM, we detected 52, 34, 20, 40, 14, and 20 SNPs associated with the environmental variables Bio3, Bio7, Bio12, Bio13, Bio15, and Bio16, respectively (Table S5, Fig. S9). In the Taihang group, 43 GEA SNPs were detected by both RDA and LFMM, with 40 of them being group-specific (Fig. 4 B). In the Qinling-Funiu group, 33 GEA SNPs were identified by both RDA and LFMM, and 32 of them were unique to the group (Fig. 4 B). Only one GEA SNP was detected by both GEA approaches in the two groups. The study identified 456 candidate SNPs under selection, of which 88 were successfully annotated, and 45 had functional descriptions (Table S7 and A.8). These genes are implicated in regulating growth, development, and diverse biosynthetic pathways, including 'pollen development (DPD1)', 'responding to biological and abiotic stresses (SRK2E)', and 'alkaloid synthesis (P450 CYP72A219)'. Notably, 27 of the 45 genes demonstrated associations with at least one climatic variable (Table S8). Environmental and spatial associations with genetic variation The present study identified significant patterns of IBD ( r = 0.5323, p = 0.001), IBE ( r = 0.4556, p = 0.001), and IBR ( r = 0.5870, p = 0.001) (Fig. 5 A–C). Additionally, strong autocorrelation was observed between geographical, environmental, and resistance distances ( r = 0.5332–0.9802, p = 0.001; Fig. 5 D–F), with the correlation between geographical distance and resistance distance exhibiting the strongest relationship. The RDA revealed that environmental factors accounted for 14.6%–20.6% of the total variation across different SNP sets, while geographical factors explained a slightly lower proportion, ranging from 13.2% to 16.5% (Table 3 ). After controlling for geography, environmental variables accounted for 5.5% and 8.4% of genetic variation in PCAdapt SNPs and GEA SNPs, respectively, with non-significant results in the other three data sets. Conversely, when controlling for environmental factors, geographical variables explained 9.5%–13.4% of the variation. The combined influence of environmental and geographical factors accounted for 12.1%–18.7% of the variation across different SNP sets, leaving 81.3%–87.9% of the variation unexplained (Table 3 ). Furthermore, utilizing RDA, we identified key environmental variables that explain genetic variation in different I. rubescens groups, using both all SNPs and GEA SNPs. The RDA results were consistent across both datasets (Fig. 6 , Fig. S12). In the Qinling-Funiu group, precipitation-related variables (Bio12, Bio16) were the primary contributors to genetic variation. Conversely, in the Taihang group, Bio7, Bio15, and latitude were the most significant factors explaining genetic variation. Table 3 Summary of genetic variation associated with environment (env.), geography (geog.), and their combined effects based on redundancy analysis (RDA) in Isodon rubescens . All SNPs (4674 SNPs) RDA SNPs (152 SNPs) LFMM SNPs (109 SNPs) PCAdapt SNPs (277 SNPs) GEA SNPs (73 SNPs) Combined fractions F ~ env. 0.146 *** 0.147 ** 0.178 ** 0.168 *** 0.206 *** F ~ geo. 0.132 *** 0.138 *** 0.152 *** 0.147 *** 0.165 *** Individual fractions F ~ env.|geo. 0.037 ns 0.070 ns 0.105 ns 0.055 * 0.084 * F ~ geo.|env. 0.108 *** 0.095 *** 0.096 ** 0.119 *** 0.134 *** Total explained 0.130 ** 0.121 ns 0.179 ns 0.145 ** 0.187 *** Total unexplained 0.870 0.879 0.821 0.855 0.813 Note: Data represent adjusted R 2 values, and asterisks indicate statistical significance ( * p < 0.05, ** p < 0.01, *** p < 0.001, ns nonsignificant); Total explained, total adjusted R 2 of individual fractions; Abbreviations: F, dependent matrix of minor allele frequencies; RDA tests are of the form: F ~ independent matrices | covariate matrices; env., six retained environmental variables; geo., geography (longitude + latitude). Genomic offset under future climates The GF models revealed distinct genomic turnover across the geographical range of I. rubescens (Fig. S13). Precipitation seasonality (Bio15) emerged as the most influential bioclimatic variable, followed by Bio12, Bio7, and Bio3 (Fig. S14A, Fig. S15A). The GEA SNPs GF model indicated a sharp change in allele frequencies occurring between 650 and 700 mm of annual precipitation (Bio12) (Fig. S15B). This turnover was also observed for precipitation seasonality (Bio15) between 70 and 75 mm (Fig. S15B). Utilizing different future climate scenarios (SSP126 and SSP585 of 2061–2080), GF analyses were employed to calculate the genomic offset of the 17 populations of I. rubescens based on all SNPs and GEA SNPs (Fig. 7 ). The results demonstrated that more regions exhibited high genomic offset under the high-emission SSP585 compared to the low-emission scenario (SSP126). Additionally, the estimated genomic offset was higher when using GEA SNPs than when using all SNPs. In the current study areas, the northern populations of the Taihang group (such as CYC, WXS, and ZWD) and the southern marginal population of the Qinling-Funiu group (BTM) displayed relatively higher levels of genomic offsets (Fig. 7 , Fig. S16). Discussion Population structure and genetic diversity of I. rubescens Understanding the genetic background of medicinal plants is crucial for the further development and application of medicinal resources. Our population structure analysis revealed two distinct genetic groups across the studied populations of I. rubescens : the Taihang Mountains group and the Qinling-Funiu Mountains group. The latter can be further subdivided into the Qinling Mountains group and the Funiu Mountains group ( K = 3), corresponding to geographic zones. This finding aligns with previous genetic studies utilizing RAPD, ISSR, chloroplast genome, and microsatellite markers [ 43 , 52 , 53 ]. Similar patterns of population structure have been observed in other species in this region, including Forsythia suspensa [ 86 ] and Pinus bungeana Zucc. ex Endl. [ 87 ]. The population structure within I. rubescens may be attributed to geographical isolation, niche differences, and divergent selection. We identified significant IBD and IBR patterns among the sampled populations of I. rubescens , indicating the presence of barriers to gene flow between populations. The EEMS analysis revealed a strong barrier between the Taihang group and the Qinling-Funiu group, coupled with complex topography, which reduces connectivity between northeastern and southwestern populations and impedes gene exchange between groups. TreeMix results indicated scarce gene flow between the Taihang and Qinling-Funiu groups. Additionally, significant niche differentiation between groups reflected the heterogeneous habitats of I. rubescens , further corroborated by the IBE results. Most GEA SNPs detected by both RDA and LFMM were unique to each group, suggesting that divergent selection and local adaptation may have contributed to the genetic structure of I. rubescens . Furthermore, RDA results demonstrated that precipitation-related variables (Bio12, Bio16) contributed most to the genetic variation in the Qinling-Funiu group, while Bio7, Bio15, and latitude explained most of the genetic variation in the Taihang group. These findings suggest that environmental factors may have influenced the current genetic structure of I. rubescens . In this study, the genetic diversity level ( π , H O , H E ) of the Taihang group exceeded that of the Qinling-Funiu group, indicating a gradual decrease in genetic diversity from east to west. Two potential explanations exist for this phenomenon. Firstly, it may result from the east-west stepwise colonization of I. rubescens , a pattern observed in other plant species (e.g., Pinus densata Mast, Actinidia eriantha Bentham) [ 88 , 89 ]. During range expansion, founder effect and genetic drift can reduce genetic variation [ 90 ]. Secondly, the Taihang group of I. rubescens may represent more recent colonization from east Qinling (i.e., Funiu Mountains). The Taihang group likely experienced rapid growth, while the Qinling-Funiu group underwent contraction due to tectonic changes and climate fluctuations in its demographic history. This aligns with the "out of Qinling" migration hypothesis, suggesting northward expansion along the north-south orientation of the Taihang Mountains [ 87 ]. Low private alleles, weaker differentiation ( F ST = 0.11), and high inbreeding coefficient in the Taihang group support this view. The unidirectional gene flow from west to east (LTX to JLG, XHZ to ZWD) further corroborates this hypothesis. Some admixed individuals were also identified in populations between the genetic groups. However, we caution that this study is based on limited sampling size, and more comprehensive population genomic investigations encompassing all populations may provide a more detailed evolutionary history and colonization route of I. rubescens . Local adaptation of I. rubescens populations Medicinal plants with extensive distributions frequently inhabit highly heterogeneous environments and adapt locally to diverse conditions [ 8 – 10 ]. The Mantel test revealed significant IBE in I. rubescens . Redundancy analysis demonstrated that both environmental and geographic variables significantly influenced genomic variation, with environmental factors more likely to explain the observed adaptive genetic variation in I. rubescens . Additionally, the GF models indicated clear genomic turnover across the geographical range of I. rubescens . Collectively, these findings suggest that local adaptation may have contributed to the spatial distribution of genetic variation within I. rubescens . Recent planting experiments have identified differences in morphological traits and chemical constituents among various I. rubescens provenances, providing further evidence of local adaptation [ 52 ]. Similar signatures of local adaptation and their effects on genetic divergence have been reported in other plant species from this region [ 9 , 86 , 87 , 91 ]. Temperature and precipitation patterns are crucial environmental factors influencing the distribution, survival, growth, phenology, and productivity of most medicinal plants [ 2 , 4 , 8 , 36 ]. RDA and GF analyses indicated that precipitation-related factors play a significant role in shaping adaptive variation patterns for I. rubescens . This finding aligns with previous research demonstrating precipitation's impact on adaptive genetic variation in Cotinus coggygria Scop. [ 91 ], Forsythia suspensa [ 86 ], and Pinus bungeana [ 92 ] within the region. Precipitation seasonality (Bio15) and annual precipitation (Bio12) emerged as the primary environmental factors explaining genomic variation in I. rubescens . This observation may be consistent with the species' ecological habitats, such as its ability to "tolerate drought" [ 46 ]. Ecological niche models revealed substantial environmental differentiation between the two genetic groups (Taihang, Qinling-Funiu) of this species. The Qinling Mountains serve as a natural demarcation line separating the dry temperate climates of northern China from the moist subtropical climates of southern China [ 93 ]. The Qinling-Funiu Mountains receive relatively higher precipitation (annual precipitation: 500 mm-1300 mm) compared to the Taihang Mountains (annual precipitation: 320 mm-685 mm) [ 94 , 95 ]. Previous studies have shown higher oridonin yields in I. rubescens populations from the Taihang Mountains in Henan province [ 43 , 52 ], potentially due to increased environmental stress (e.g., drought) in this area. The GF model also demonstrated a steep turnover in allele frequencies occurring around 650 mm and 700 mm of annual precipitation (Bio12) in the adaptive SNP model. As water stress is an active driver of adaptation, genetic variation between the two groups of I. rubescens likely reflects local environmental adaptation. Considering ongoing climate change, precipitation is expected to be a key driver for the adaptation of I. rubescens in the future. This study identified 27 genes associated with environmental variables. Twelve genes were associated with both temperature and precipitation variables, indicating potential polygenic adaptation in I. rubescens , consistent with findings in other plant species such as Actinidia eriantha [ 89 ], Populus koreana Rehder [ 96 ], Tetrastigma hemsleyanum [ 36 ], and Quercus acutissima Carruth [ 28 ]. Further research is required to elucidate this phenomenon. The successful annotation of adaptive sites on coding genes may play a crucial role in chemical defense and gene regulation. For instance, the LOC131021042 ( WRKY48 ) gene, associated with Bio7 and Bio15 (Table S8), is a member of WRKY transcription factors and may play a vital role in plant growth and environmental stress response [ 97 ]. The LOC131004713 ( SRK2E ) gene is involved in the abscisic acid (ABA) signaling pathway, which is essential for controlling seed development and dormancy [ 98 ]. Genes related to stress resistance may be significant for I. rubescens in stressed environments. The LOC125190681 ( CYP72A219 ) may be a candidate gene involved in indole alkaloid biosynthesis in Catharanthus roseus (L.) G. Don and crocin biosynthesis in saffron [ 99 – 101 ]. The LOC130985631 ( GsSRK ) gene plays a critical role in plant response to salt stress [ 102 ]. Other genes significantly associated with bioclimatic variables included LOC131008999 ( DPD1 ) and LOC113725547 (indole-3-acetaldehyde oxidase), which are involved in leaf senescence and auxin synthesis, respectively [ 103 , 104 ]. Additionally, some SNPs were annotated into gene families known to confer tolerance to environmental stresses in model species. These findings from limited genome sampling represent an important initial step in understanding the adaptive genetic variation in I. rubescens . Further research, based on whole genome sequencing and common garden experiments, is necessary to identify more precise functions of genes under selective pressure and enhance our understanding of the genetic basis of climate adaptation in I. rubescens . While exploring the relationship between adaptive genetic variation and variability of active ingredient content (e.g., oridonin production) among different populations of I. rubescens [ 43 ] would be intriguing, it is beyond the scope of this study. Future work combining chemical and genetic analyses would be required to address this aspect [ 10 , 105 ]. Genomic vulnerability under future climate change Comprehending the adaptive capacity and susceptibility of medicinal plants to future climate change is crucial for their sustainable utilization and effective conservation [ 20 , 36 ]. In the study areas, the northern populations of the Taihang Mountains (such as CYC, WXS and ZWD) and the southern marginal population (BTM) of the Funiu Mountains exhibited relatively higher levels of genomic vulnerability, suggesting that these populations may face a greater risk of maladaptation under future climatic conditions. Furthermore, the level of genomic offset was higher at GEA SNPs than all SNPs, indicating that adaptive loci appear more sensitive to climate change. Although the overall genetic offset of the study region was lower under the SSP585 scenario compared to SSP126, populations already at risk of high offset, particularly those in the northern Taihang Mountains, exhibited a dramatic increase in genetic vulnerability under this harsher climate trajectory. This pattern highlights that extreme climate scenarios disproportionately magnify the risk of maladaptation in populations that are inherently susceptible to environmental changes. Notably, anthropogenic activities (e.g., continuous harvesting, increased tourism) are more prevalent in the Taihang Mountains. The complex topography of mountains and geographical isolation may restrict the ability of I. rubescens populations to migrate to suitable habitats, potentially leading to local population decline or extinction. In the context of climate change, these vulnerable populations should be prioritized for sustainable management and conservation efforts. However, genomic offset assumes that current genotype-environment relationships arise solely from local adaptation and will remain optimal, thereby overlooking gene flow, phenotypic plasticity, and other biotic interactions such as canopy layer dynamics and light competition with trees that can constrain the distribution of a subshrub like I. rubescens [ 106 , 107 ]. Consequently, its projections should be interpreted cautiously until validated through reciprocal transplants, common garden experiments, and refined models that explicitly incorporate these ecological complexities [ 108 , 109 ]. Implications for the utilization and conservation of I. rubescens germplasm This study provides novel insights into the population genomic structure, local adaptation, and genomic vulnerability of I. rubescens populations using RAD-seq data. Based on these findings, we propose some management strategies to facilitate effective utilization and scientific conservation of I. rubescens germplasm resources. According to the results of this study, two distinct management units and seed zones should be established for I. rubescens in the Taihang Mountains and the Qinling-Funiu Mountains areas, based on spatial genomic variation. Each genetic group should be managed and protected independently. First, in-situ conservation measures should be implemented separately for the two genetic groups of I. rubescens . Special emphasis should be placed on the Taihang Mountains, as this region faces severe human-induced disturbances that threaten the species’ survival. Second, ex-situ conservation efforts should be carried out by collecting representative populations from both the Taihang Mountains and the Qinling–Funiu region to establish living germplasm repositories and seed banks. Furthermore, given the pronounced local adaptation exhibited by these two genetic groups, artificial cultivation or introduction initiatives must ensure precise matching of seed sources to planting sites, while strictly avoiding practices that could lead to outbreeding depression [ 110 ]. Populations of the Taihang group exhibiting high genetic diversity could serve as valuable breeding materials. The LJL population in the Funiu Mountains, possessing the highest number of private alleles, may represent unique genetic resources. Priority should be given to preserving these wild populations by protecting the genetic variation harbored within these populations, and we can preserve the species’ evolutionary potential and strengthen its capacity to adapt to emerging challenges such as climate change, pest pressures, and habitat degradation [ 111 ]. In addition, selective breeding of I. rubescens can restore damaged populations and develop new cultivars with higher yields, better quality, or stronger resistance, thereby reducing dependence on wild resources [ 112 ]. Genomic offset offers a practical tool for non-model and protected species—where experimentation is impractical or impossible—to pinpoint threatened populations, select sources for assisted gene flow or recolonization, and anticipate future suitable habitats [ 113 ]. Genomic vulnerability forecasts indicated that Taihang Mountains populations (CYC, WXS, ZWD) and the Funiu Mountain population (BTM) faced the greatest risk under projected climates and therefore warrant immediate, targeted management. In contrast, most populations showed moderate to low genomic offset and could be prioritized as source material for breeding climate-resilient cultivars of I. rubescens . Taihang Mountains populations, however, harbored unique allelic combinations and exhibited exceptionally high offset, making them a priority for ex-situ seed and living collections that capture this irreplaceable genetic diversity before climatic conditions deteriorate further. To reduce maladaptation risk across the species’ range, we recommend integrating provenance trials with landscape genomic analyses to delineate optimal seed-transfer zones and to identify beneficial alleles for assisted gene flow [ 7 ]. Specifically, adaptive variants from lower-risk provenances can be introduced into the high-vulnerability Taihang and Funiu populations, as demonstrated for other taxa [ 114 ]. Such reciprocal transplant and common-garden experiments will simultaneously validate genomic projections and provide the empirical basis for breeding programs that combine high medicinal yield with climatic tolerance, securing both the species and its economic value under rapid environmental change. The methods and results presented here aim to guide germplasm utilization and conservation practices, potentially accelerating the sustainable development of the I. rubescens -related industry. This study also provides a practicable and effective reference for predicting climate change responses in other medicinal plants. Conclusion This study elucidates significant genetic divergence and adaptive potential in the medicinal plant I. rubescens in central China. Population structure analyses revealed two distinct genetic groups corresponding to the Taihang Mountains and the Qinling-Funiu Mountains, with further subdivision within the latter. The Taihang group exhibited higher genetic diversity but greater inbreeding. Geographic isolation (IBD) and environmental heterogeneity (IBE/IBR), particularly driven by precipitation variables, were key drivers of population differentiation, supported by significant niche divergence between groups. We identified 456 putatively adaptive SNPs, some linked to stress response and biosynthesis genes (e.g., SRK2E, CYP72A219), highlighting genomic signatures of local adaptation. Critically, genomic offset projections under future climate scenarios (SSP126, SSP585) indicate heightened vulnerability for northern Taihang and southern marginal Qinling-Funiu populations (e.g., CYC, WXS, ZWD, BTM), suggesting greater risk of maladaptation. These findings provide crucial insights into the genetic basis of local adaptation and identify populations at greatest conservation risk, informing strategies for germplasm resource management and the development of climate-resilient breeding programs for I. rubescens . Declarations Acknowledgements We thank local staff at the Funiu Mountain National Nature Reserve and Shaanxi Foping National Nature Reserve for their assistance during the fieldwork. We wish also to thank Mingmei Zhou, Xiaoman Dai, Shuai Li, Hao Dong, Mengyun Qin, Ruichang Tian, and Sai Jia for help during sampling activities. This work was supported by the Supercomputing Center in Zhengzhou University (Zhengzhou). Authors’ contributions Ningning Zhang contributed to conceptualization, methodology, project administration, funding acquisition, and reviewed and edited the manuscript. Saibin Fan performed data curation, formal analysis, investigation, visualization, and wrote the original draft. Yang Lu contributed to conceptualization, methodology, investigation, resources, project administration, funding acquisition, and wrote and reviewed the manuscript. Caipeng Yue provided resources and supervision. Shixin Zhu supervised the research and reviewed the manuscript. Jinyong Huang contributed to project administration and resources. Yuhui Wang participated in methodology and visualization. Yong Lai performed formal analysis and software development. All authors reviewed and approved the final version of the manuscript. Funding This work was supported by the National Natural Science Foundation of China [grant numbers 31800551, 81903747] and the Science and Technology Research Project of Henan Province [grant Number 252102110244]. Data availability The raw sequence data reported in this paper have been deposited in the Genome Sequence Archive (Chen et al., 2021) in National Genomics Data Center (CNCB-NGDC Members and Partners, 2025), China National Center for Bioinformation/Beijing Institute of Genomics, Chinese Academy of Sciences (GSA: CRA029154) that are publicly accessible at https://ngdc.cncb.ac.cn/gsa/browse/CRA029154. Ethics approval and consent to participate The authors confirm that permission for the collection of plant materials from all mountainous field sites was obtained from the relevant local forestry and natural resources authorities. The field studies complied with relevant institutional, national, and international guidelines and legislation. 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09:57:18","extension":"html","order_by":19,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":336289,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8136960/v1/5986fbb7f33c3ae2805204a4.html"},{"id":97234034,"identity":"89f7d130-eca0-4a8d-9a18-c789316668da","added_by":"auto","created_at":"2025-12-02 09:57:17","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":508788,"visible":true,"origin":"","legend":"\u003cp\u003eGeographic distribution, population genetic structure, and gene flow of \u003cem\u003eIsodon rubescens\u003c/em\u003e. (A) Sampling locations of populations, with colors distinguishing genetic groups. (B) Ancestry assignments for 130 individuals across 17 populations of \u003cem\u003eI. rubescens \u003c/em\u003eat \u003cem\u003eK \u003c/em\u003e= 2 and \u003cem\u003eK \u003c/em\u003e= 3, inferred by ADMIXTURE. Each bar corresponds to an individual, with colors indicating different ancestries. (C) PCA of \u003cem\u003eI. rubescens\u003c/em\u003e. (D) Neighbor-joining (NJ) tree of the 130 individuals. (E) Gene flow between the 17 \u003cem\u003eI. rubescens \u003c/em\u003epopulations detected by TreeMix. Each branch represents a population, with lines indicating gene flow and arrows denoting direction. Migration events are color-coded according to their weight. (F) Estimated effective migration surface (\u003cem\u003em\u003c/em\u003e) as inferred by EEMS in Taihang (blue dots) and Qinling-Funiu (red dots). Blue and orange contours indicate areas of high migration (dispersal corridors) and low migration (dispersal barriers), respectively.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8136960/v1/a8e5bce3efc238dcd585c1d1.png"},{"id":97250248,"identity":"d119a916-900d-446c-b48a-2264108e07c4","added_by":"auto","created_at":"2025-12-02 13:14:10","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":333413,"visible":true,"origin":"","legend":"\u003cp\u003e(A-D) Relationships between absolute longitude (°E) and nucleotide diversity (\u003cem\u003eπ\u003c/em\u003e), observed heterozygosity (\u003cem\u003eH\u003c/em\u003e\u003csub\u003eO\u003c/sub\u003e), expected heterozygosity (\u003cem\u003eH\u003c/em\u003e\u003csub\u003eE\u003c/sub\u003e), Wright's inbreeding coefficient (\u003cem\u003eF\u003c/em\u003e\u003csub\u003eIS\u003c/sub\u003e). (E-H) Relationships between absolute latitude (°N) and nucleotide diversity (\u003cem\u003eπ\u003c/em\u003e), observed heterozygosity (\u003cem\u003eH\u003c/em\u003e\u003csub\u003eO\u003c/sub\u003e), expected heterozygosity (\u003cem\u003eH\u003c/em\u003e\u003csub\u003eE\u003c/sub\u003e), Wright's inbreeding coefficient (\u003cem\u003eF\u003c/em\u003e\u003csub\u003eIS\u003c/sub\u003e). (I-L) Comparison of genetic diversity parameters among distinct genetic groups. Statistical significance was calculated using the two-tailed Wilcoxon test. (M) Heatmap depicting pairwise \u003cem\u003eF\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e values between 17 populations.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8136960/v1/2897e4cdffbae296de41fd6b.png"},{"id":97234036,"identity":"c706e5ee-d12b-4b9a-813b-9ceb1e85e134","added_by":"auto","created_at":"2025-12-02 09:57:17","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":185679,"visible":true,"origin":"","legend":"\u003cp\u003eEcological niche models for the Taihang and Qinling-Funiu groups of \u003cem\u003eIsodon rubescens\u003c/em\u003e, and niche differences between distinct groups. (A) Current potential distributions of the two groups predicted by Maxent. (B) Niche identity tests comparing the Taihang and Qinling-Funiu groups. The red bars represent the null distributions of Schoener's \u003cem\u003eD\u003c/em\u003e, while the blue bars indicate Hellinger's distance (\u003cem\u003eI\u003c/em\u003e). The arrows denote the values of \u003cem\u003eD \u003c/em\u003eand \u003cem\u003eI \u003c/em\u003ein Maxent runs.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8136960/v1/e89d7b5691ad0a041a1479b7.png"},{"id":97250816,"identity":"925b43a9-f6d7-4515-80bb-884990f16b21","added_by":"auto","created_at":"2025-12-02 13:15:21","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":319766,"visible":true,"origin":"","legend":"\u003cp\u003e(A) Venn diagram depicting the intersections among outlier SNPs identified through RDA, LFMM, and PCAdapt analyses. (B) Venn diagrams illustrating the overlaps between GEA outlier SNPs detected in the Taihang and Qinling-Funiu groups using RDA and LFMM methods. The quantity of SNPs is presented in each section, with the total number of SNPs for each group using each method indicated in parentheses.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8136960/v1/13f435d9e3c2d3fbed677e84.png"},{"id":97234037,"identity":"444450f3-a627-4975-b048-7acbc55bec5b","added_by":"auto","created_at":"2025-12-02 09:57:17","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1026759,"visible":true,"origin":"","legend":"\u003cp\u003eIsolation by geography, environment and resistance in \u003cem\u003eIsodon rubescens\u003c/em\u003e. Mantel test for genetic distance \u003cem\u003eF\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e/(1−\u003cem\u003eF\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e) versus geographical distance (A), environmental distance (B), and resistance distance (C), and correlations between geographical, environmental, and resistance distances (D-F). The gray shading surrounding each regression line indicates the 95% confidence interval.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-8136960/v1/4d61e687266490f1bfd3e5c2.png"},{"id":97250810,"identity":"aedd5c74-db74-433b-bdfc-afe49df07bdd","added_by":"auto","created_at":"2025-12-02 13:15:17","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":374788,"visible":true,"origin":"","legend":"\u003cp\u003eRDA partitioning of genetic differentiation sources among \u003cem\u003eIsodon rubescens \u003c/em\u003epopulations, considering environment and geography using GEA SNPs. The plot illustrates the first two RDA axes, depicting populations as colored circles, environmental and geographical variables as black vectors, and GEA SNPs as black circles.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-8136960/v1/d995a63affd818b9c1577fcb.png"},{"id":97251271,"identity":"d3bb899d-84d1-44b3-a9d8-7d2ea5ec58ae","added_by":"auto","created_at":"2025-12-02 13:16:39","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":312571,"visible":true,"origin":"","legend":"\u003cp\u003ePrediction of genomic offset to future climate change based on six environmental variables for all SNPs (A, B) and GEA SNPs (C, D). (A) and (C) depict the scenario SSP126 2061–2080; (B) and (D) illustrate the scenario SSP585 2061–2080. The color scale indicates genomic offset. Blue and red dots represent sampling sites of the Taihang and Qinling-Funiu groups, respectively. (E, F, G, H) Comparison among four modes of genomic offset. The gray circles denote outliers. Statistical significance was assessed using the two-tailed Wilcoxon test.\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-8136960/v1/95fcee64843e2be27b095220.png"},{"id":108437735,"identity":"d6b035dc-d88f-48a1-a483-897af0327949","added_by":"auto","created_at":"2026-05-04 16:03:00","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3875996,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8136960/v1/5313bff2-90a9-4cfc-a240-d7f886e251f7.pdf"},{"id":97234062,"identity":"1495d25a-fd2a-4dfc-8bc2-a00b87a7553f","added_by":"auto","created_at":"2025-12-02 09:57:21","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":27958442,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-8136960/v1/81df80b68583edeb521fe50b.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Genomic insights into adaptive divergence and genetic vulnerability to climate change of the medicinal plant Isodon rubescens (Hemsl.) H. Hara in central China","fulltext":[{"header":"Background","content":"\u003cp\u003eMedicinal plants are essential for the livelihoods and well-being of a significant portion of the global population [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The ongoing global climate change is impacting the productivity and quality of medicinal plants worldwide. Substantial evidence demonstrates the effects of rapid climate change on the distribution, growth patterns, phenology, synthesis of secondary metabolites, and abundance of medicinal plant species [\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. For sessile plant species, local adaptation represents a crucial strategy to cope with rapid climate change [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Medicinal plants frequently inhabit heterogeneous environments and adapt locally through adaptive evolution, a phenomenon more prevalent in widely distributed species [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Adaptive divergence influences gene expression, physiology, and metabolism in medicinal plant species, facilitating their adaptation to environmental changes [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Consequently, given their economic and cultural significance, understanding the adaptive divergence and vulnerability of medicinal plants to climate change is imperative for their sustainable utilization and effective management. In addition, evaluating the ability of medicinal plants to adapt to future climatic conditions and identifying their environment-adaptive genes not only protects critical germplasm resources and clarifies the underlying adaptation mechanisms, but also facilitates the breeding of climate-resilient cultivars [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. By incorporating these adaptive genes into high-quality varieties, such efforts can effectively boost productivity, ensuring a stable supply to meet the increasingly pressing demand for medicinal plants [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eEcological niche models (ENMs) have been extensively utilized to predict the impact of climate change on the distribution and vulnerability of medicinal plant species [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. However, ENMs do not account for evolutionary processes and genetic adaptations of species and populations to climate change [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. The recent development of landscape genomics provides a valuable tool for investigating genetic adaptation across landscapes, incorporating intra-specific diversity and local adaptation when projecting species responses to climate change [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Genotype-environment association (GEA) methods integrate genomic and environmental data to identify genetic loci associated with climate adaptation [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Redundancy analysis (RDA) can detect the influence of geographical and environmental factors in shaping adaptive divergence [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Additionally, gradient forest (GF) models can assess the genomic vulnerability (or genomic offset) of species and populations to future climate change, measured by the genetic change required to track future climate shifts [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. In recent years, the landscape genomics approach has been successfully applied to various species, including forest trees [\u003cspan additionalcitationids=\"CR26 CR27\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], crops [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], primates [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], birds [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], fish [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], and amphibians [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. However, landscape genomics studies of medicinal plants remain limited, with only a few reported cases, such as those of \u003cem\u003eForsythia suspensa\u003c/em\u003e (Thunb.) Vahl [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] and \u003cem\u003eTetrastigma hemsleyanum\u003c/em\u003e Diels \u0026amp; Gilg [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. The landscape-genomics approach offers a novel framework for guiding the management and conservation of wild medicinal-plant germplasm and for defining provenance strategies that will underpin future cultivation and breeding under a changing climate. Within medicinal-plant researches, identifying adaptive genes can enhances our comprehension of the genetic underpinnings governing environmental adaptation, and furnishes invaluable genomic resources for the development of climate-smart cultivars.\u003c/p\u003e\u003cp\u003e\u003cem\u003eIsodon rubescens\u003c/em\u003e (Hemsl.) H. Hara, also known as Dong Ling Cao, is a valuable traditional Chinese medicinal plant belonging to the genus \u003cem\u003eIsodon\u003c/em\u003e in the Lamiaceae family [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. This subshrub or perennial herb typically thrives on stony slopes, in thickets, forests, and along roadsides. Wild populations of \u003cem\u003eI. rubescens\u003c/em\u003e are widely distributed across several Chinese provinces, including Henan, Shaanxi, Shanxi, Hebei, and Hubei, with a central distribution area encompassing the Taihang Mountains and the Qinling Mountains [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. The aerial portions of \u003cem\u003eI. rubescens\u003c/em\u003e have been utilized in traditional Chinese medicine to treat sore throat, gastrointestinal diseases, inflammation, cancer, and other diseases [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Notably, it has been employed as a folk remedy for esophageal cancer by local communities in Henan province, central China [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. To date, over 300 substances have been isolated and identified from \u003cem\u003eI. rubescens\u003c/em\u003e, including diterpenoids, triterpenoids, phenols, alkaloids, volatile oils, and other compounds [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Previous researches have demonstrated that \u003cem\u003eI. rubescens\u003c/em\u003e produces a diverse range of biologically active diterpenoids [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Oridonin, a significant diterpenoid extracted from \u003cem\u003eI. rubescens\u003c/em\u003e, exhibits potential antitumor effects on various human cancer cells, including ovarian, breast, leukemia, colorectal, liver, laryngeal, and prostate cancers [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. The \u003cem\u003eI. rubescens\u003c/em\u003e industry, encompassing traditional Chinese medicine, syrups, and tea beverages, generates an annual revenue of approximately 100\u0026nbsp;million USD in China [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Currently, the southern part of the Taihang Mountains in Jiyuan, Henan Province serves as the primary production area for \u003cem\u003eI. rubescens\u003c/em\u003e. Due to the superior quality and high yield of \u003cem\u003eI. rubescens\u003c/em\u003e in and around the Jiyuan region, \"Jiyuan Donglingcao\" has been designated as a National Geographical Indication Protected Product [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. However, the wild resource of \u003cem\u003eI. rubescens\u003c/em\u003e within the Taihang Mountains has experienced significant population declines due to long-term unsustainable harvesting [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. In recent years, artificial cultivation of \u003cem\u003eI. rubescens\u003c/em\u003e has been progressively developed to meet the growing market demand [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Consequently, genetic assessment of the wild germplasm resources for \u003cem\u003eI. rubescens\u003c/em\u003e is crucial for effective conservation and successful breeding of this medicinal plant in the region.\u003c/p\u003e\u003cp\u003eGiven its extensive medicinal properties, the phytochemical composition and pharmacological activity of \u003cem\u003eI. rubescens\u003c/em\u003e have been thoroughly investigated [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. However, limited research has focused on the identification of original materials and quality control of \u003cem\u003eI. rubescens\u003c/em\u003e products. Accurate identification and systematic analysis of \u003cem\u003eI. rubescens\u003c/em\u003e germplasm resources will optimize material selection in the \u003cem\u003eI. rubescens\u003c/em\u003e processing industry and facilitate future breeding and improvement efforts. Recent studies have utilized chloroplast genome sequencing data to explore phylogenetic relationships among \u003cem\u003eIsodon\u003c/em\u003e species and develop molecular markers for identifying \u003cem\u003eI. rubescens\u003c/em\u003e and related species [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Furthermore, previous research has demonstrated significant variations in morphological traits and chemical constituents (e.g., diterpenoids) of \u003cem\u003eI. rubescens\u003c/em\u003e from different geographical regions [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan additionalcitationids=\"CR51\" citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e], potentially affecting the quality stability of \u003cem\u003eI. rubescens\u003c/em\u003e medicinal products. Consequently, selection and cultivation of \u003cem\u003eI. rubescens\u003c/em\u003e germplasm resources with high bioactive component yields (such as oridonin, ponicidin) are necessary. For instance, Harris et al [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e] identified \u003cem\u003eI. rubescens\u003c/em\u003e from Henan province as the best source of oridonin. Population genetics studies using molecular markers such as random amplified polymorphic DNA (RAPD), inter-simple sequence repeat (ISSR), and microsatellites have revealed population differentiation in \u003cem\u003eI. rubescens\u003c/em\u003e between geographical regions [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. However, the drivers of population divergence in \u003cem\u003eI. rubescens\u003c/em\u003e and its response to future climate change remain largely unknown. Genomic assessment of adaptive divergence and genetic vulnerability to climate change is crucial for effective introduction, conservation, and utilization of medicinal plants. The chromosome-level genome assembly of \u003cem\u003eI. rubescens\u003c/em\u003e provides a valuable resource for further studies on population genomics and adaptive evolution of this species [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. To ensure quality control and sustainable supply in the future, landscape genomics research on population structure, local adaptation, and genomic vulnerability of \u003cem\u003eI. rubescens\u003c/em\u003e is essential to provide rapid and scientific guidelines for germplasm management, utilization, and the development of climate-resilient breeding strategies under ongoing climate change.\u003c/p\u003e\u003cp\u003eThis study employed a landscape genomics approach to investigate the impact of climate change on \u003cem\u003eI. rubescens\u003c/em\u003e populations in central China. Restriction site-associated DNA sequencing (RAD-seq) was conducted on 130 individuals from 17 populations across the species' central distribution areas. Utilizing single-nucleotide polymorphisms (SNPs) derived from RAD-seq data, the research aimed to test the following hypotheses: (a) genetic differentiation exists at the genomic level between \u003cem\u003eI. rubescens\u003c/em\u003e populations in the Taihang Mountains and the Qinling-Funiu Mountains; (b) both geography and environmental factors drive the divergence of \u003cem\u003eI. rubescens\u003c/em\u003e populations; and (c) some populations could not adapt to future climates, showing high genomic vulnerability. The findings of this study could offer valuable insights for germplasm resource management, future cultivation practices, and breeding efforts aimed at enhancing the adaptive capacity of cultivars for this important medicinal plant in the context of climate change. Furthermore, this research offers a methodological framework applicable to the study and management of other medicinal plants in a changing environment.\u003c/p\u003e"},{"header":"Material and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eSample collection, DNA extraction, and RAD sequencing\u003c/h2\u003e\u003cp\u003eWe collected 17 populations of \u003cem\u003eI. rubescens\u003c/em\u003e across the central distribution area in China, spanning the Qinling, Funiu, and Taihang Mountains (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). A total of 130 individuals were sampled, with 5 to 8 individuals per population (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). In each population, the sampled individuals were spaced at least 10 m apart. Fresh leaves were collected and preserved in silica gel. Genomic DNA was extracted using a modified cetyltrimethylammonium bromide (CTAB) method [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. The RAD library preparation was carried out following the protocol established by Peterson et al. [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. The process involved digesting genomic DNA with restriction enzymes \u003cem\u003eEco\u003c/em\u003eRI and \u003cem\u003eMse\u003c/em\u003eI. Subsequently, the digested and ligated DNA was pooled, purified, and amplified via PCR. DNA fragments with sizes ranging from 350 bp to 500 bp were selected. Sequencing of the library was performed using the Illumina NovaSeq platform with paired-end 150 bp reads at JieRui BioScience Co. Ltd., Guangzhou, China.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eData Processing\u003c/h3\u003e\n\u003cp\u003eData for each sample were processed using the \u003cem\u003eprocess_radtags\u003c/em\u003e in STACKS v 2.41 [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. All reads were trimmed to a uniform length of 135 bp to eliminate low-quality nucleotides. The filtered reads were aligned to the reference genome of \u003cem\u003eI. rubescens\u003c/em\u003e [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e] using BWA v0.7.17-4 [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e], allowing a maximum of five mismatches. For each sample, paired-end read alignments were converted to binary format and sorted by SAMtools v1.10-3 [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. The \u003cem\u003egstacks\u003c/em\u003e within STACKS was employed to identify loci with the default settings. SNPs were filtered using the \u003cem\u003epopulations\u003c/em\u003e within STACKS based on the following criteria: (a) removal of SNPs that deviated from Hardy-Weinberg equilibrium; (b) exclusion of loci with heterozygosity greater than 0.5 to avoid potential homologs; (c) discarding loci with a minor allele frequency (MAF) below 0.01; (d) retention of only the first SNP locus per read (\u003cem\u003ewrite_single_snp\u003c/em\u003e); and (e) keeping only loci present in at least 16 populations and in at least 70% of individuals within each population. Lastly, VCFtools v 0.1.16 [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e] were utilized to exclude loci with missing data in over 15% of individuals and to retain only biallelic SNPs. We used the R package \u003cem\u003eCMplot\u003c/em\u003e v4.5.1 [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e] to examine the distribution of the SNPs on chromosomes of \u003cem\u003eI. rubescens\u003c/em\u003e.\u003c/p\u003e\n\u003ch3\u003ePopulation structure and genetic diversity\u003c/h3\u003e\n\u003cp\u003eADMIXTURE v1.3.0 [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e] was used to evaluate the maximum likelihood ancestry of each individual based on all SNPs. The number of ancestral clusters (\u003cem\u003eK\u003c/em\u003e) was set to range from 1 to 10, with 10-fold cross-validation performed. Principal component analysis (PCA) was conducted using PLINK v1.9 [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e], and the results were visualized with the R package \u003cem\u003eggplot2\u003c/em\u003e v3.4.4 [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]. Additionally, a neighbor-joining (NJ) tree was constructed with PHYLIP v3.698 [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]. Genome-wide genetic diversity parameters were calculated using the \u003cem\u003epopulations\u003c/em\u003e within STACKS. These parameters included percentage of polymorphic loci (\u003cem\u003ePPL\u003c/em\u003e), the number of private alleles (\u003cem\u003ePA\u003c/em\u003e), nucleotide diversity (\u003cem\u003eπ\u003c/em\u003e), observed heterozygosity (\u003cem\u003eH\u003c/em\u003e\u003csub\u003eO\u003c/sub\u003e), expected heterozygosity (\u003cem\u003eH\u003c/em\u003e\u003csub\u003eE\u003c/sub\u003e), and Wright\u0026rsquo;s inbreeding coefficient (\u003cem\u003eF\u003c/em\u003e\u003csub\u003eIS\u003c/sub\u003e). Pairwise population differentiation (\u003cem\u003eF\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e) values were determined using the R package \u003cem\u003ehierfstat\u003c/em\u003e v0.5-11 [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]. Additionally, we used the \u003cem\u003elm\u003c/em\u003e function in R to investigate the relationships between \u003cem\u003eπ\u003c/em\u003e, \u003cem\u003eH\u003c/em\u003e\u003csub\u003eO\u003c/sub\u003e, \u003cem\u003eH\u003c/em\u003e\u003csub\u003eE\u003c/sub\u003e, \u003cem\u003eF\u003c/em\u003e\u003csub\u003eIS\u003c/sub\u003e, and geographical coordinates (latitude and longitude). The R package \u003cem\u003eggpubr\u003c/em\u003e v0.6.0 was utilized to compare genetic diversity parameters among different groups. Analysis of molecular variance (AMOVA) was performed using ARLEQUIN v3.5 [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e] to quantify genomic variance among groups and populations, and the significance tests were based on 10,000 permutations. TreeMix v1.13 [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e] was employed to infer gene flow between populations based on allele frequency data, simulating 0\u0026ndash;20 migration events. The optimal number of migration events (\u003cem\u003em\u003c/em\u003e) was estimated using the R package \u003cem\u003eOptM\u003c/em\u003e v0.1.6 [\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eEstimated effective migration surface\u003c/h3\u003e\n\u003cp\u003eWe employed estimated effective migration surfaces (EEMS) as described by Petkova et al. [\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e] to visualize the spatial distribution of gene flow. This approach utilizes a stepping-stone model, as proposed by Kimura and Weiss [\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e], to pinpoint geographic areas where the decline in genetic similarity does not align with the predictions of isolation by distance (IBD), thereby revealing barriers or pathways for gene flow. The parameters were configured as follows: deme size at 800, Markov chain Monte Carlo (MCMC) run length at 20\u0026nbsp;million iterations, burn-in period at 10\u0026nbsp;million iterations, and thinning interval at 9999. The accuracy of the EEMS models was assessed by examining the linear correlation between observed and fitted dissimilarities both within and across demes. The final visualizations of migratory surfaces were generated using \u003cem\u003erEEMSplot\u003c/em\u003e (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/dipetkov/eems\u003c/span\u003e\u003cspan address=\"https://github.com/dipetkov/eems\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eEcological niche modelling\u003c/h3\u003e\n\u003cp\u003eA total of 64 effective occurrence records for \u003cem\u003eI. rubescens\u003c/em\u003e were collected from the Chinese Virtual Herbarium database (CVH, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cvh.ac.cn/\u003c/span\u003e\u003cspan address=\"https://www.cvh.ac.cn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), the Global Biodiversity Information Facility (GBIF, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.gbif.org/\u003c/span\u003e\u003cspan address=\"https://www.gbif.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and our own field surveys (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Nineteen bioclimatic variables (Bio1\u0026ndash;Bio19) from 1970 to 2000 and elevation data were obtained from the WorldClim database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.worldclim.org\u003c/span\u003e\u003cspan address=\"https://www.worldclim.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), while slope and aspect were extracted from elevation data [\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e]. Soil variables were acquired from the National Tibetan Plateau Data Center (TPDC, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.tpdc.ac.cn/\u003c/span\u003e\u003cspan address=\"https://www.tpdc.ac.cn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) (Table S2). All environmental variables featured a spatial resolution of 2.5 arc minutes. The R package \u003cem\u003eraster\u003c/em\u003e [\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e] was used to extract environmental data for each distribution point. Eight environmental variables with Pearson\u0026rsquo;s correlation coefficients\u0026thinsp;\u0026lt;\u0026thinsp;0.8 were retained: Bio4 (temperature seasonality), Bio13 (precipitation of wettest month), Bio14 (precipitation of driest month), Bio16 (precipitation of wettest quarter), elevation, slope, T_OC (topsoil organic carbon), and T_PH (topsoil pH). MAXENT v3.4.3 [\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e] was employed to predict the current potential distributions of the two groups (Taihang, Qinling-Funiu; see Results) of \u003cem\u003eI. rubescens\u003c/em\u003e. Model accuracy was evaluated using the area under the receiver operating characteristic curve (AUC). According to the methods of Li et al. [\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e] and Feng et al. [\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e], niche overlap and identity tests were conducted using the R package \u003cem\u003eENMtools\u003c/em\u003e [\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e] to discern niche differences among genetic groups. These tests involved calculating Schoener\u0026rsquo;s \u003cem\u003eD\u003c/em\u003e [\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e] and Hellinger\u0026rsquo;s \u003cem\u003eI\u003c/em\u003e, with values for both metrics ranging from 0 (no overlap) to 1 (complete overlap).\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eDetection of candidate SNPs under selection\u003c/h2\u003e\u003cp\u003eThree approaches were employed to detect candidate SNPs under selection: two GEA methods\u0026mdash;redundancy analyses (RDA) [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] and latent factor mixed model (LFMM) [\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e]\u0026mdash;and a \u003cem\u003eF\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e-based method, PCAdapt [\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e]. Prior to conducting GEA analysis, GF analysis was conducted using the R package \u003cem\u003egradientForest\u003c/em\u003e [\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e] to select appropriate environmental variables. According to ranked importance and pairwise correlation coefficients (|r| \u0026lt; 0.8), six environmental variables were retained for GEA analyses (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e): Bio3 (isothermality), Bio7 (temperature annual range), Bio12 (annual precipitation), Bio13 (precipitation of wettest month), Bio15 (precipitation seasonality), and Bio16 (precipitation of wettest quarter). Subsequently, multivariate RDA was conducted using the R package \u003cem\u003evegan\u003c/em\u003e, employing a standard deviation cutoff of 3 to define outliers [\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e]. Latent factor mixed model (LFMM) was then performed using the R package \u003cem\u003elfmm\u003c/em\u003e [\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e]. Based on population structure results (ADMIXTURE and PCA), \u003cem\u003eK\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2 was considered the optimal number of latent factors. SNPs with adjusted \u003cem\u003ep\u003c/em\u003e-values less than 0.001 were considered to strongly support associations between allele frequencies and environmental variables. A false discovery rate (FDR) of 0.05 was adopted. For PCAdapt, outliers were identified with respect to population structure, with two principal components (\u003cem\u003eK\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2) selected. Outlier SNPs were identified at a FDR of 0.05 by the R package \u003cem\u003epcadapt\u003c/em\u003e [\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e]. The overlap between SNPs identified by the three approaches was visualized using a Venn diagram. Moreover, the distribution of these loci on chromosomes was generated by \u003cem\u003eggplot2\u003c/em\u003e. Candidate loci were annotated through the following procedure. First, the DNA sequences of the candidate loci were translated in all six reading frames. Subsequently, each translated peptide sequence was subjected to a BLASTx search against the NCBI non-redundant protein database, with a maximum E-value cutoff set at 10⁻⁵. Only hits meeting two criteria were retained: the alignment coverage of the query sequence was at least 50%, and the sequence identity was \u0026ge;\u0026thinsp;40%. Finally, for each query, the highest-scoring hit was analyzed to extract the gene name, product description, and taxonomic lineage, which were then integrated into the corresponding locus records as annotations.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eEnvironmental and geographic contribution to spatial genetic variation\u003c/h3\u003e\n\u003cp\u003eTo elucidate the influence of geography and environment on the spatial genetic differentiation of \u003cem\u003eI. rubescens\u003c/em\u003e, we analyzed IBD, isolation by environment (IBE), isolation by resistance (IBR), and their correlations. Genetic distances [\u003cem\u003eF\u003c/em\u003e\u003csub\u003eST/\u003c/sub\u003e(1\u0026thinsp;\u0026minus;\u0026thinsp;\u003cem\u003eF\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e)] were computed using the R package \u003cem\u003ehierfstat\u003c/em\u003e. Geographical distances between sites were determined based on population coordinates (latitude and longitude) using the R package \u003cem\u003egeosphere\u003c/em\u003e [\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e]. Six climatic variables (Bio3, Bio7, Bio12, Bio13, Bio15, Bio16) were utilized to estimate environmental distances between populations. IBD and IBE analyses were conducted using Mantel tests with 999 permutations in the R package \u003cem\u003evegan\u003c/em\u003e [\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e]. The resistance distance matrix was computed using topoLCP in the R package \u003cem\u003etopoDistance\u003c/em\u003e v1.0.2 [\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e], based on an elevation raster layer and the latitude and longitude information of the sites. A habitat suitability model was employed to parameterize the resistance surface, assuming higher suitability corresponds to lower resistance. Redundancy analyses (RDA) were employed to evaluate the relative contributions of geography and environment to population genetic differentiation using the \u003cem\u003evegan\u003c/em\u003e package. The analysis utilized a matrix of allele frequencies for each population as the dependent variable and two independent matrices: one comprising environmental variables (Bio3, Bio7, Bio12, Bio13, Bio15, Bio16) and another containing geographical variables (latitude and longitude). A series of full and partial RDAs were conducted for various SNP sets (all SNPs, RDA SNPs, LFMM SNPs, PCAdapt SNPs, and GEA SNPs) using the \u003cem\u003evegan\u003c/em\u003e R package, with 999 permutations.\u003c/p\u003e\n\u003ch3\u003ePrediction of genomic offset\u003c/h3\u003e\n\u003cp\u003eTo forecast the genomic offset under future climatic conditions, we conducted GF analyses utilizing all SNPs and GEA SNPs [\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e]. The genomic offset was computed as a measure of the Euclidean distance between the genomic compositions under contemporary and projected future climates, with higher values suggesting increased population vulnerability in the future [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. We assessed the discrepancy between current and predicted genomic compositions under future climate projections for the period 2061\u0026ndash;2080, considering two shared socioeconomic pathways (SSPs): scenario SSP126 and scenario SSP585, representing low and high emission scenarios, respectively. Future environmental data for the 19 bioclimatic variables were obtained from the WorldClim CMIP6 dataset of the BCC-CSM2-MR model [\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e]. Six environmental variables (see Section 2.6) were incorporated into the GF models. To enhance the analysis's representativeness and mitigate bias resulting from under-sampling of allelic diversity [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], the GF analysis was confined to the distribution areas of \u003cem\u003eI. rubescens\u003c/em\u003e in the Taihang, Qinling, and Funiu Mountains. Each GF model was evaluated using 500 regression trees per SNP. The genomic offset was visualized geographically for all SNPs and GEA SNPs. We compared four distinct genomic offset patterns of \u003cem\u003eI. rubescens\u003c/em\u003e in the study area. Box plots were generated using \u003cem\u003eggplot2\u003c/em\u003e.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003ePopulation structure, genetic diversity and gene flow analyses\u003c/h2\u003e\u003cp\u003eRAD sequencing conducted on 130 individuals of \u003cem\u003eI. rubescens\u003c/em\u003e yielded a total of 794,399,728 paired-end reads, with 738,103,422 (92.9%) reads passing initial quality filtration. The average data size per sample was 0.85 Gb, with a mean read depth of 14.65\u0026times; (Table S3). Following rigorous quality control measures, 4674 high-quality SNPs that were relatively evenly distributed across the 12 chromosomes of \u003cem\u003eI. rubescens\u003c/em\u003e (Fig. S2) were retained for subsequent analyses.\u003c/p\u003e\u003cp\u003eUtilizing 4674 SNPs, ADMIXTURE analysis identified \u003cem\u003eK\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2 as the optimal number of clusters among the 17 populations of \u003cem\u003eI. rubescens\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA, B; Fig. S3). Six populations in the northeast were grouped into the Taihang Mountains cluster, while eleven populations in the southwest formed the Qinling-Funiu Mountains cluster. At \u003cem\u003eK\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3, the Qinling-Funiu cluster further subdivided into Qinling and Funiu groups, aligning with geographic zones (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB, Fig. S4A). PCA corroborated these findings. All samples segregated into the Taihang and Qinling-Funiu groups along PC1, with further differentiation into Qinling and Funiu groups along PC2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC, Fig. S4B). Moreover, the NJ tree exhibited a highly consistent genetic structure pattern, supporting the division of all samples into two distinct genetic groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eGenetic diversity indices for 17 \u003cem\u003eIsodon rubescens\u003c/em\u003e populations sampled in this study.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"12\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePopulation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLocation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLongitude (\u0026deg;E)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLatitude (\u0026deg;N)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eElevation (m)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSample size (\u003cem\u003eN\u003c/em\u003e)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003ePPL\u003c/em\u003e (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cem\u003ePA\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cem\u003eπ\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cem\u003eH\u003c/em\u003e\u003csub\u003eO\u003c/sub\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u003cem\u003eH\u003c/em\u003e\u003csub\u003eE\u003c/sub\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c12\"\u003e\u003cp\u003e\u003cem\u003eF\u003c/em\u003e\u003csub\u003eIS\u003c/sub\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"12\" nameend=\"c12\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTaihang group\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCYC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eChaoyangcun, Henan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e113.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" 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align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.292\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYTS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYuntaishan, Henan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e113.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e35.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e433\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e45.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.134\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.025\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.126\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.325\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWXS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWanxianshan, Henan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e113.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e35.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1191\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e42.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.131\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.028\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.123\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.300\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eZWD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eZhouwangdian, Henan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e114.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e35.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e579\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e43.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.129\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.028\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.120\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.304\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAverage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e42.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.130\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.027\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.121\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.300\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"12\" nameend=\"c12\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eQinling-Funiu group\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBTM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBaotianman, Henan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e111.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e33.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1204\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e36.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.120\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.025\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.112\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.260\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHSG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHongshigu, Henan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e111.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e34.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e818\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e40.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.130\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.120\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.300\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLCM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLongchiman, Henan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e111.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e33.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1541\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e40.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.128\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.025\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.119\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.294\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLJL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLaojieling, Henan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e111.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e33.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1769\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e36.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.120\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.027\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.112\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.255\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLYW\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLongyuwan, Henan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e111.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e33.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1277\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e37.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.124\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.025\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.115\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.269\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eXQL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eXiaoqinling, Henan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e110.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e34.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e900\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e39.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.119\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.025\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.111\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.277\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYSZ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYaoshan, Henan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e112.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e33.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e401\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e40.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.118\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.025\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.111\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.285\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJNP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eJinniuping, Shaanxi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e108.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e33.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e874\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e35.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.107\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.250\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNBL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNiubeiliang, Shaanxi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e108.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e33.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1118\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e33.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.107\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.099\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.231\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTBS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTaibaishan, Shaanxi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e107.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e34.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1080\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e35.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.111\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.104\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.255\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eXHZ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eXihezhan, Shaanxi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e107.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e33.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1232\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e20.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.081\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.072\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.135\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAverage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e36.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.115\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.107\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.256\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal (Average)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e38.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.120\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.025\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.112\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.271\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003ePPL\u003c/em\u003e percentage of polymorphic loci, \u003cem\u003ePA\u003c/em\u003e the number of private alleles, \u003cem\u003eπ\u003c/em\u003e nucleotide diversity, \u003cem\u003eH\u003c/em\u003e\u003csub\u003eO\u003c/sub\u003e observed heterozygosity, \u003cem\u003eH\u003c/em\u003e\u003csub\u003eE\u003c/sub\u003e expected heterozygosity, \u003cem\u003eF\u003c/em\u003e\u003csub\u003eIS\u003c/sub\u003e Wright\u0026rsquo;s inbreeding coefficient\u003c/p\u003e\u003cp\u003eThe percentage of polymorphic loci varied from 20.00% (XHZ) to 45.04% (YTS), with the Taihang group exhibiting a higher average value (42.34%) compared to the Qinling-Funiu group (36.03%) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Most populations contained few private alleles, ranging from 0 to 3, with the exception of the LJL population (13 private alleles). The Taihang group demonstrated significantly higher genetic diversity (\u003cem\u003eπ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.130, \u003cem\u003eH\u003c/em\u003e\u003csub\u003eO\u003c/sub\u003e = 0.027, \u003cem\u003eH\u003c/em\u003e\u003csub\u003eE\u003c/sub\u003e = 0.121) than the Qinling-Funiu group (\u003cem\u003eπ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.115, \u003cem\u003eH\u003c/em\u003e\u003csub\u003eO\u003c/sub\u003e = 0.024, \u003cem\u003eH\u003c/em\u003e\u003csub\u003eE\u003c/sub\u003e = 0.107) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eI-K). The inbreeding coefficient (\u003cem\u003eF\u003c/em\u003e\u003csub\u003eIS\u003c/sub\u003e) ranged from 0.135 (XHZ) to 0.325 (YTS), with average values of 0.300 and 0.256 for the Taihang and Qinling-Funiu groups, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Nucleotide diversity (\u003cem\u003eπ\u003c/em\u003e), observed heterozygosity (\u003cem\u003eH\u003c/em\u003e\u003csub\u003eO\u003c/sub\u003e), expected heterozygosity (\u003cem\u003eH\u003c/em\u003e\u003csub\u003eE\u003c/sub\u003e), and inbreeding coefficient (\u003cem\u003eF\u003c/em\u003e\u003csub\u003eIS\u003c/sub\u003e) all exhibited significant positive correlations with latitude and longitude (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA-H).\u003c/p\u003e\u003cp\u003eThe analysis of molecular variance revealed that the majority of genetic variation occurred within populations (77.83%; Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The genetic differentiation (\u003cem\u003eF\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e) value of the Qinling-Funiu group (0.19) exceeded that of the Taihang group (0.06; Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Genetic differentiation between groups (\u003cem\u003eF\u003c/em\u003e\u003csub\u003eCT\u003c/sub\u003e = 0.09) was minimal, while a moderate level of genetic differentiation was observed among populations within groups (\u003cem\u003eF\u003c/em\u003e\u003csub\u003eSC\u003c/sub\u003e = 0.13; Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Pairwise \u003cem\u003eF\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e values ranged from 0.05 to 0.12, with higher values noted between the populations of the Qinling-Funiu group (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eM).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAnalysis results of molecular variance (AMOVA) at different levels of \u003cem\u003eIsodon rubescens\u003c/em\u003e.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSource of variation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ed.f.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSum of squares\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eVariance components\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePercentage of\u003c/p\u003e\u003cp\u003evariation (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eFixation indices\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAll populations\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAmong groups\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1423.859\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e17.77625\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e8.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003eF\u003c/em\u003e\u003csub\u003eCT\u003c/sub\u003e = 0.09\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAmong populations within groups\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5298.266\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e26.09317\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e13.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003eF\u003c/em\u003e\u003csub\u003eSC\u003c/sub\u003e = 0.13\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWithin populations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e113\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e17406.829\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e154.04273\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e77.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003eF\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e = 0.22\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTaihang group\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAmong populations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1226.629\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e10.1972\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e5.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003eF\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e = 0.06\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWithin populations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6784.839\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e165.48389\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e94.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eQinling-Funiu group\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAmong populations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4071.637\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e34.45762\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e18.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003eF\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e = 0.19\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWithin populations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10621.989\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e147.52763\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e81.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eNote: Significant level, \u003csup\u003e***\u003c/sup\u003e\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTreeMix analysis detected eleven migration events among the 17 \u003cem\u003eI. rubescens\u003c/em\u003e populations (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE, Fig. S5). These migration events predominantly occurred within the Qinling-Funiu group. Additionally, a strong signal of gene flow was observed between the LTX and JLG populations within the Taihang group. Gene flow between the Taihang and Qinling-Funiu groups was limited, with only one migration event identified between the XHZ and ZWD populations.\u003c/p\u003e\u003cp\u003eThe EEMS analysis revealed several potential barriers to gene flow, primarily aligning with population divisions (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eF, orange areas). A significant barrier was identified at the intersection of the Taihang and Qinling-Funiu Mountains, limiting connectivity between northeastern and southwestern populations. Further west, another substantial barrier restricted migration between the Funiu and Qinling groups. Within each geographical zone, minor barriers were also detected. Regions exhibiting higher-than-expected gene flow generally occurred at lower elevations (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eF, blue areas). These barriers and corridors were corroborated by high posterior probabilities (\u0026gt;\u0026thinsp;0.90) of the migration parameters (Fig. S6).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eEcological niche differences\u003c/h2\u003e\u003cp\u003eEcological niche modelling was conducted for the two groups of \u003cem\u003eI. rubescens\u003c/em\u003e to predict their current potential distributions, and the predicted distribution aligned closely with their actual distributions. The AUC values of all Maxent models were 0.998 for the Taihang group and 0.996 for the Qinling-Funiu group, indicating high predictive performance for all models (Fig. S7). The model revealed a small overlap between Taihang and Qinling-Funiu (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). The observed values of Schoener's \u003cem\u003eD\u003c/em\u003e and Hellinger's \u003cem\u003eI\u003c/em\u003e were below the null distributions for the comparison between Taihang and Qinling-Funiu, indicating niche differentiation between these two groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). Niche identity tests performed for the three groups (Taihang, Qinling and Funiu) showed Schoener's \u003cem\u003eD\u003c/em\u003e and Hellinger's \u003cem\u003eI\u003c/em\u003e values below the critical values for null distributions concerning group pairs, indicating significant niche differentiation between these pairs (Fig. S8). The lowest niche overlap was observed between Taihang and Qinling (\u003cem\u003eD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.08, \u003cem\u003eI\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.22), while the greatest overlap was between Qinling and Funiu (\u003cem\u003eD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.38, \u003cem\u003eI\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.67).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eCandidate SNPs detection and annotation\u003c/h2\u003e\u003cp\u003eThe methods RDA, LFMM, and PCAdapt identified 152, 109, and 277 candidate SNPs under selection, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA, Figs. S9 and S10; Tables S4, S5 and S6). A total of 456 SNPs, distributed across all 12 chromosomes of \u003cem\u003eI. rubescens\u003c/em\u003e (Fig. S11), were identified by at least one of the three methods, with six SNPs identified by all three approaches. For RDA, we identified 58, 16, 16, 14, 44, and 9 SNPs associated with the environmental variables Bio3, Bio7, Bio12, Bio13, Bio15, and Bio16, respectively (Table S4). For LFMM, we detected 52, 34, 20, 40, 14, and 20 SNPs associated with the environmental variables Bio3, Bio7, Bio12, Bio13, Bio15, and Bio16, respectively (Table S5, Fig. S9). In the Taihang group, 43 GEA SNPs were detected by both RDA and LFMM, with 40 of them being group-specific (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). In the Qinling-Funiu group, 33 GEA SNPs were identified by both RDA and LFMM, and 32 of them were unique to the group (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). Only one GEA SNP was detected by both GEA approaches in the two groups. The study identified 456 candidate SNPs under selection, of which 88 were successfully annotated, and 45 had functional descriptions (Table S7 and A.8). These genes are implicated in regulating growth, development, and diverse biosynthetic pathways, including 'pollen development (DPD1)', 'responding to biological and abiotic stresses (SRK2E)', and 'alkaloid synthesis (P450 CYP72A219)'. Notably, 27 of the 45 genes demonstrated associations with at least one climatic variable (Table S8).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eEnvironmental and spatial associations with genetic variation\u003c/h2\u003e\u003cp\u003eThe present study identified significant patterns of IBD (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.5323, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001), IBE (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.4556, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001), and IBR (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.5870, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA\u0026ndash;C). Additionally, strong autocorrelation was observed between geographical, environmental, and resistance distances (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.5332\u0026ndash;0.9802, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001; Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD\u0026ndash;F), with the correlation between geographical distance and resistance distance exhibiting the strongest relationship. The RDA revealed that environmental factors accounted for 14.6%\u0026ndash;20.6% of the total variation across different SNP sets, while geographical factors explained a slightly lower proportion, ranging from 13.2% to 16.5% (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). After controlling for geography, environmental variables accounted for 5.5% and 8.4% of genetic variation in PCAdapt SNPs and GEA SNPs, respectively, with non-significant results in the other three data sets. Conversely, when controlling for environmental factors, geographical variables explained 9.5%\u0026ndash;13.4% of the variation. The combined influence of environmental and geographical factors accounted for 12.1%\u0026ndash;18.7% of the variation across different SNP sets, leaving 81.3%\u0026ndash;87.9% of the variation unexplained (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Furthermore, utilizing RDA, we identified key environmental variables that explain genetic variation in different \u003cem\u003eI. rubescens\u003c/em\u003e groups, using both all SNPs and GEA SNPs. The RDA results were consistent across both datasets (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, Fig. S12). In the Qinling-Funiu group, precipitation-related variables (Bio12, Bio16) were the primary contributors to genetic variation. Conversely, in the Taihang group, Bio7, Bio15, and latitude were the most significant factors explaining genetic variation.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSummary of genetic variation associated with environment (env.), geography (geog.), and their combined effects based on redundancy analysis (RDA) in \u003cem\u003eIsodon rubescens\u003c/em\u003e.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAll SNPs\u003c/p\u003e\u003cp\u003e(4674 SNPs)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRDA SNPs\u003c/p\u003e\u003cp\u003e(152 SNPs)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLFMM SNPs\u003c/p\u003e\u003cp\u003e(109 SNPs)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePCAdapt SNPs\u003c/p\u003e\u003cp\u003e(277 SNPs)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eGEA SNPs\u003c/p\u003e\u003cp\u003e(73 SNPs)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\u003cp\u003eCombined fractions\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eF\u0026thinsp;~\u0026thinsp;env.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.146\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.147\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.178\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.168\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.206\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eF\u0026thinsp;~\u0026thinsp;geo.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.132\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.138\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.152\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.147\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.165\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\u003cp\u003eIndividual fractions\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eF\u0026thinsp;~\u0026thinsp;env.|geo.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.037\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.070\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.105\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.055\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.084\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eF\u0026thinsp;~\u0026thinsp;geo.|env.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.108\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.095\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.096\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.119\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.134\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal explained\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.130\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.121\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.179\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.145\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.187\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal unexplained\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.870\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.879\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.821\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.855\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.813\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eNote: Data represent adjusted \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e values, and asterisks indicate statistical significance (\u003csup\u003e*\u003c/sup\u003e\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, \u003csup\u003e**\u003c/sup\u003e\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01, \u003csup\u003e***\u003c/sup\u003e\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, \u003csup\u003ens\u003c/sup\u003e nonsignificant); Total explained, total adjusted \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e of individual fractions; Abbreviations: F, dependent matrix of minor allele frequencies; RDA tests are of the form: F\u0026thinsp;~\u0026thinsp;independent matrices | covariate matrices; env., six retained environmental variables; geo., geography (longitude\u0026thinsp;+\u0026thinsp;latitude).\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eGenomic offset under future climates\u003c/h2\u003e\u003cp\u003eThe GF models revealed distinct genomic turnover across the geographical range of \u003cem\u003eI. rubescens\u003c/em\u003e (Fig. S13). Precipitation seasonality (Bio15) emerged as the most influential bioclimatic variable, followed by Bio12, Bio7, and Bio3 (Fig. S14A, Fig. S15A). The GEA SNPs GF model indicated a sharp change in allele frequencies occurring between 650 and 700 mm of annual precipitation (Bio12) (Fig. S15B). This turnover was also observed for precipitation seasonality (Bio15) between 70 and 75 mm (Fig. S15B). Utilizing different future climate scenarios (SSP126 and SSP585 of 2061\u0026ndash;2080), GF analyses were employed to calculate the genomic offset of the 17 populations of \u003cem\u003eI. rubescens\u003c/em\u003e based on all SNPs and GEA SNPs (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). The results demonstrated that more regions exhibited high genomic offset under the high-emission SSP585 compared to the low-emission scenario (SSP126). Additionally, the estimated genomic offset was higher when using GEA SNPs than when using all SNPs. In the current study areas, the northern populations of the Taihang group (such as CYC, WXS, and ZWD) and the southern marginal population of the Qinling-Funiu group (BTM) displayed relatively higher levels of genomic offsets (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, Fig. S16).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003e\u003cb\u003ePopulation structure and genetic diversity of\u003c/b\u003e \u003cb\u003eI. rubescens\u003c/b\u003e\u003c/p\u003e\u003cp\u003eUnderstanding the genetic background of medicinal plants is crucial for the further development and application of medicinal resources. Our population structure analysis revealed two distinct genetic groups across the studied populations of \u003cem\u003eI. rubescens\u003c/em\u003e: the Taihang Mountains group and the Qinling-Funiu Mountains group. The latter can be further subdivided into the Qinling Mountains group and the Funiu Mountains group (\u003cem\u003eK\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3), corresponding to geographic zones. This finding aligns with previous genetic studies utilizing RAPD, ISSR, chloroplast genome, and microsatellite markers [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. Similar patterns of population structure have been observed in other species in this region, including \u003cem\u003eForsythia suspensa\u003c/em\u003e [\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e] and \u003cem\u003ePinus bungeana\u003c/em\u003e Zucc. ex Endl. [\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e]. The population structure within \u003cem\u003eI. rubescens\u003c/em\u003e may be attributed to geographical isolation, niche differences, and divergent selection. We identified significant IBD and IBR patterns among the sampled populations of \u003cem\u003eI. rubescens\u003c/em\u003e, indicating the presence of barriers to gene flow between populations. The EEMS analysis revealed a strong barrier between the Taihang group and the Qinling-Funiu group, coupled with complex topography, which reduces connectivity between northeastern and southwestern populations and impedes gene exchange between groups. TreeMix results indicated scarce gene flow between the Taihang and Qinling-Funiu groups. Additionally, significant niche differentiation between groups reflected the heterogeneous habitats of \u003cem\u003eI. rubescens\u003c/em\u003e, further corroborated by the IBE results. Most GEA SNPs detected by both RDA and LFMM were unique to each group, suggesting that divergent selection and local adaptation may have contributed to the genetic structure of \u003cem\u003eI. rubescens\u003c/em\u003e. Furthermore, RDA results demonstrated that precipitation-related variables (Bio12, Bio16) contributed most to the genetic variation in the Qinling-Funiu group, while Bio7, Bio15, and latitude explained most of the genetic variation in the Taihang group. These findings suggest that environmental factors may have influenced the current genetic structure of \u003cem\u003eI. rubescens\u003c/em\u003e.\u003c/p\u003e\u003cp\u003eIn this study, the genetic diversity level (\u003cem\u003eπ\u003c/em\u003e, \u003cem\u003eH\u003c/em\u003e\u003csub\u003eO\u003c/sub\u003e, \u003cem\u003eH\u003c/em\u003e\u003csub\u003eE\u003c/sub\u003e) of the Taihang group exceeded that of the Qinling-Funiu group, indicating a gradual decrease in genetic diversity from east to west. Two potential explanations exist for this phenomenon. Firstly, it may result from the east-west stepwise colonization of \u003cem\u003eI. rubescens\u003c/em\u003e, a pattern observed in other plant species (e.g., \u003cem\u003ePinus densata\u003c/em\u003e Mast, \u003cem\u003eActinidia eriantha\u003c/em\u003e Bentham) [\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e, \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e]. During range expansion, founder effect and genetic drift can reduce genetic variation [\u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e]. Secondly, the Taihang group of \u003cem\u003eI. rubescens\u003c/em\u003e may represent more recent colonization from east Qinling (i.e., Funiu Mountains). The Taihang group likely experienced rapid growth, while the Qinling-Funiu group underwent contraction due to tectonic changes and climate fluctuations in its demographic history. This aligns with the \"out of Qinling\" migration hypothesis, suggesting northward expansion along the north-south orientation of the Taihang Mountains [\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e]. Low private alleles, weaker differentiation (\u003cem\u003eF\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e = 0.11), and high inbreeding coefficient in the Taihang group support this view. The unidirectional gene flow from west to east (LTX to JLG, XHZ to ZWD) further corroborates this hypothesis. Some admixed individuals were also identified in populations between the genetic groups. However, we caution that this study is based on limited sampling size, and more comprehensive population genomic investigations encompassing all populations may provide a more detailed evolutionary history and colonization route of \u003cem\u003eI. rubescens\u003c/em\u003e.\u003c/p\u003e\u003cp\u003e\u003cb\u003eLocal adaptation of\u003c/b\u003e \u003cb\u003eI. rubescens\u003c/b\u003e \u003cb\u003epopulations\u003c/b\u003e\u003c/p\u003e\u003cp\u003eMedicinal plants with extensive distributions frequently inhabit highly heterogeneous environments and adapt locally to diverse conditions [\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The Mantel test revealed significant IBE in \u003cem\u003eI. rubescens\u003c/em\u003e. Redundancy analysis demonstrated that both environmental and geographic variables significantly influenced genomic variation, with environmental factors more likely to explain the observed adaptive genetic variation in \u003cem\u003eI. rubescens\u003c/em\u003e. Additionally, the GF models indicated clear genomic turnover across the geographical range of \u003cem\u003eI. rubescens\u003c/em\u003e. Collectively, these findings suggest that local adaptation may have contributed to the spatial distribution of genetic variation within \u003cem\u003eI. rubescens\u003c/em\u003e. Recent planting experiments have identified differences in morphological traits and chemical constituents among various \u003cem\u003eI. rubescens\u003c/em\u003e provenances, providing further evidence of local adaptation [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. Similar signatures of local adaptation and their effects on genetic divergence have been reported in other plant species from this region [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e, \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e, \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eTemperature and precipitation patterns are crucial environmental factors influencing the distribution, survival, growth, phenology, and productivity of most medicinal plants [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. RDA and GF analyses indicated that precipitation-related factors play a significant role in shaping adaptive variation patterns for \u003cem\u003eI. rubescens\u003c/em\u003e. This finding aligns with previous research demonstrating precipitation's impact on adaptive genetic variation in \u003cem\u003eCotinus coggygria\u003c/em\u003e Scop. [\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e], \u003cem\u003eForsythia suspensa\u003c/em\u003e [\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e], and \u003cem\u003ePinus bungeana\u003c/em\u003e [\u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e92\u003c/span\u003e] within the region. Precipitation seasonality (Bio15) and annual precipitation (Bio12) emerged as the primary environmental factors explaining genomic variation in \u003cem\u003eI. rubescens\u003c/em\u003e. This observation may be consistent with the species' ecological habitats, such as its ability to \"tolerate drought\" [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Ecological niche models revealed substantial environmental differentiation between the two genetic groups (Taihang, Qinling-Funiu) of this species. The Qinling Mountains serve as a natural demarcation line separating the dry temperate climates of northern China from the moist subtropical climates of southern China [\u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e]. The Qinling-Funiu Mountains receive relatively higher precipitation (annual precipitation: 500 mm-1300 mm) compared to the Taihang Mountains (annual precipitation: 320 mm-685 mm) [\u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e94\u003c/span\u003e, \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e95\u003c/span\u003e]. Previous studies have shown higher oridonin yields in \u003cem\u003eI. rubescens\u003c/em\u003e populations from the Taihang Mountains in Henan province [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e], potentially due to increased environmental stress (e.g., drought) in this area. The GF model also demonstrated a steep turnover in allele frequencies occurring around 650 mm and 700 mm of annual precipitation (Bio12) in the adaptive SNP model. As water stress is an active driver of adaptation, genetic variation between the two groups of \u003cem\u003eI. rubescens\u003c/em\u003e likely reflects local environmental adaptation. Considering ongoing climate change, precipitation is expected to be a key driver for the adaptation of \u003cem\u003eI. rubescens\u003c/em\u003e in the future.\u003c/p\u003e\u003cp\u003eThis study identified 27 genes associated with environmental variables. Twelve genes were associated with both temperature and precipitation variables, indicating potential polygenic adaptation in \u003cem\u003eI. rubescens\u003c/em\u003e, consistent with findings in other plant species such as \u003cem\u003eActinidia eriantha\u003c/em\u003e [\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e], \u003cem\u003ePopulus koreana\u003c/em\u003e Rehder [\u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e96\u003c/span\u003e], \u003cem\u003eTetrastigma hemsleyanum\u003c/em\u003e [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], and \u003cem\u003eQuercus acutissima\u003c/em\u003e Carruth [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Further research is required to elucidate this phenomenon. The successful annotation of adaptive sites on coding genes may play a crucial role in chemical defense and gene regulation. For instance, the LOC131021042 (\u003cem\u003eWRKY48\u003c/em\u003e) gene, associated with Bio7 and Bio15 (Table S8), is a member of WRKY transcription factors and may play a vital role in plant growth and environmental stress response [\u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e97\u003c/span\u003e]. The LOC131004713 (\u003cem\u003eSRK2E\u003c/em\u003e) gene is involved in the abscisic acid (ABA) signaling pathway, which is essential for controlling seed development and dormancy [\u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e98\u003c/span\u003e]. Genes related to stress resistance may be significant for \u003cem\u003eI. rubescens\u003c/em\u003e in stressed environments. The LOC125190681 (\u003cem\u003eCYP72A219\u003c/em\u003e) may be a candidate gene involved in indole alkaloid biosynthesis in \u003cem\u003eCatharanthus roseus\u003c/em\u003e (L.) G. Don and crocin biosynthesis in saffron [\u003cspan additionalcitationids=\"CR100\" citationid=\"CR99\" class=\"CitationRef\"\u003e99\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e101\u003c/span\u003e]. The LOC130985631 (\u003cem\u003eGsSRK\u003c/em\u003e) gene plays a critical role in plant response to salt stress [\u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e102\u003c/span\u003e]. Other genes significantly associated with bioclimatic variables included LOC131008999 (\u003cem\u003eDPD1\u003c/em\u003e) and LOC113725547 (indole-3-acetaldehyde oxidase), which are involved in leaf senescence and auxin synthesis, respectively [\u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e103\u003c/span\u003e, \u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e104\u003c/span\u003e]. Additionally, some SNPs were annotated into gene families known to confer tolerance to environmental stresses in model species. These findings from limited genome sampling represent an important initial step in understanding the adaptive genetic variation in \u003cem\u003eI. rubescens\u003c/em\u003e. Further research, based on whole genome sequencing and common garden experiments, is necessary to identify more precise functions of genes under selective pressure and enhance our understanding of the genetic basis of climate adaptation in \u003cem\u003eI. rubescens\u003c/em\u003e. While exploring the relationship between adaptive genetic variation and variability of active ingredient content (e.g., oridonin production) among different populations of \u003cem\u003eI. rubescens\u003c/em\u003e [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e] would be intriguing, it is beyond the scope of this study. Future work combining chemical and genetic analyses would be required to address this aspect [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e105\u003c/span\u003e].\u003c/p\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003eGenomic vulnerability under future climate change\u003c/h2\u003e\u003cp\u003eComprehending the adaptive capacity and susceptibility of medicinal plants to future climate change is crucial for their sustainable utilization and effective conservation [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. In the study areas, the northern populations of the Taihang Mountains (such as CYC, WXS and ZWD) and the southern marginal population (BTM) of the Funiu Mountains exhibited relatively higher levels of genomic vulnerability, suggesting that these populations may face a greater risk of maladaptation under future climatic conditions. Furthermore, the level of genomic offset was higher at GEA SNPs than all SNPs, indicating that adaptive loci appear more sensitive to climate change. Although the overall genetic offset of the study region was lower under the SSP585 scenario compared to SSP126, populations already at risk of high offset, particularly those in the northern Taihang Mountains, exhibited a dramatic increase in genetic vulnerability under this harsher climate trajectory. This pattern highlights that extreme climate scenarios disproportionately magnify the risk of maladaptation in populations that are inherently susceptible to environmental changes. Notably, anthropogenic activities (e.g., continuous harvesting, increased tourism) are more prevalent in the Taihang Mountains. The complex topography of mountains and geographical isolation may restrict the ability of \u003cem\u003eI. rubescens\u003c/em\u003e populations to migrate to suitable habitats, potentially leading to local population decline or extinction. In the context of climate change, these vulnerable populations should be prioritized for sustainable management and conservation efforts. However, genomic offset assumes that current genotype-environment relationships arise solely from local adaptation and will remain optimal, thereby overlooking gene flow, phenotypic plasticity, and other biotic interactions such as canopy layer dynamics and light competition with trees that can constrain the distribution of a subshrub like \u003cem\u003eI. rubescens\u003c/em\u003e [\u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e106\u003c/span\u003e, \u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e107\u003c/span\u003e]. Consequently, its projections should be interpreted cautiously until validated through reciprocal transplants, common garden experiments, and refined models that explicitly incorporate these ecological complexities [\u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e108\u003c/span\u003e, \u003cspan citationid=\"CR109\" class=\"CitationRef\"\u003e109\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cb\u003eImplications for the utilization and conservation of\u003c/b\u003e \u003cb\u003eI. rubescens\u003c/b\u003e \u003cb\u003egermplasm\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis study provides novel insights into the population genomic structure, local adaptation, and genomic vulnerability of \u003cem\u003eI. rubescens\u003c/em\u003e populations using RAD-seq data. Based on these findings, we propose some management strategies to facilitate effective utilization and scientific conservation of \u003cem\u003eI. rubescens\u003c/em\u003e germplasm resources. According to the results of this study, two distinct management units and seed zones should be established for \u003cem\u003eI. rubescens\u003c/em\u003e in the Taihang Mountains and the Qinling-Funiu Mountains areas, based on spatial genomic variation. Each genetic group should be managed and protected independently. First, in-situ conservation measures should be implemented separately for the two genetic groups of \u003cem\u003eI. rubescens\u003c/em\u003e. Special emphasis should be placed on the Taihang Mountains, as this region faces severe human-induced disturbances that threaten the species\u0026rsquo; survival. Second, ex-situ conservation efforts should be carried out by collecting representative populations from both the Taihang Mountains and the Qinling\u0026ndash;Funiu region to establish living germplasm repositories and seed banks. Furthermore, given the pronounced local adaptation exhibited by these two genetic groups, artificial cultivation or introduction initiatives must ensure precise matching of seed sources to planting sites, while strictly avoiding practices that could lead to outbreeding depression [\u003cspan citationid=\"CR110\" class=\"CitationRef\"\u003e110\u003c/span\u003e]. Populations of the Taihang group exhibiting high genetic diversity could serve as valuable breeding materials. The LJL population in the Funiu Mountains, possessing the highest number of private alleles, may represent unique genetic resources. Priority should be given to preserving these wild populations by protecting the genetic variation harbored within these populations, and we can preserve the species\u0026rsquo; evolutionary potential and strengthen its capacity to adapt to emerging challenges such as climate change, pest pressures, and habitat degradation [\u003cspan citationid=\"CR111\" class=\"CitationRef\"\u003e111\u003c/span\u003e]. In addition, selective breeding of \u003cem\u003eI. rubescens\u003c/em\u003e can restore damaged populations and develop new cultivars with higher yields, better quality, or stronger resistance, thereby reducing dependence on wild resources [\u003cspan citationid=\"CR112\" class=\"CitationRef\"\u003e112\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eGenomic offset offers a practical tool for non-model and protected species\u0026mdash;where experimentation is impractical or impossible\u0026mdash;to pinpoint threatened populations, select sources for assisted gene flow or recolonization, and anticipate future suitable habitats [\u003cspan citationid=\"CR113\" class=\"CitationRef\"\u003e113\u003c/span\u003e]. Genomic vulnerability forecasts indicated that Taihang Mountains populations (CYC, WXS, ZWD) and the Funiu Mountain population (BTM) faced the greatest risk under projected climates and therefore warrant immediate, targeted management. In contrast, most populations showed moderate to low genomic offset and could be prioritized as source material for breeding climate-resilient cultivars of \u003cem\u003eI. rubescens\u003c/em\u003e. Taihang Mountains populations, however, harbored unique allelic combinations and exhibited exceptionally high offset, making them a priority for ex-situ seed and living collections that capture this irreplaceable genetic diversity before climatic conditions deteriorate further. To reduce maladaptation risk across the species\u0026rsquo; range, we recommend integrating provenance trials with landscape genomic analyses to delineate optimal seed-transfer zones and to identify beneficial alleles for assisted gene flow [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Specifically, adaptive variants from lower-risk provenances can be introduced into the high-vulnerability Taihang and Funiu populations, as demonstrated for other taxa [\u003cspan citationid=\"CR114\" class=\"CitationRef\"\u003e114\u003c/span\u003e]. Such reciprocal transplant and common-garden experiments will simultaneously validate genomic projections and provide the empirical basis for breeding programs that combine high medicinal yield with climatic tolerance, securing both the species and its economic value under rapid environmental change. The methods and results presented here aim to guide germplasm utilization and conservation practices, potentially accelerating the sustainable development of the \u003cem\u003eI. rubescens\u003c/em\u003e-related industry. This study also provides a practicable and effective reference for predicting climate change responses in other medicinal plants.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study elucidates significant genetic divergence and adaptive potential in the medicinal plant \u003cem\u003eI. rubescens\u003c/em\u003e in central China. Population structure analyses revealed two distinct genetic groups corresponding to the Taihang Mountains and the Qinling-Funiu Mountains, with further subdivision within the latter. The Taihang group exhibited higher genetic diversity but greater inbreeding. Geographic isolation (IBD) and environmental heterogeneity (IBE/IBR), particularly driven by precipitation variables, were key drivers of population differentiation, supported by significant niche divergence between groups. We identified 456 putatively adaptive SNPs, some linked to stress response and biosynthesis genes (e.g., SRK2E, CYP72A219), highlighting genomic signatures of local adaptation. Critically, genomic offset projections under future climate scenarios (SSP126, SSP585) indicate heightened vulnerability for northern Taihang and southern marginal Qinling-Funiu populations (e.g., CYC, WXS, ZWD, BTM), suggesting greater risk of maladaptation. These findings provide crucial insights into the genetic basis of local adaptation and identify populations at greatest conservation risk, informing strategies for germplasm resource management and the development of climate-resilient breeding programs for \u003cem\u003eI. rubescens\u003c/em\u003e.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank local staff at the Funiu Mountain National Nature Reserve and Shaanxi Foping National Nature Reserve for their assistance during the fieldwork. We wish also to thank Mingmei Zhou, Xiaoman Dai, Shuai Li, Hao Dong, Mengyun Qin, Ruichang Tian, and Sai Jia for help during sampling activities. This work was supported by the Supercomputing Center in Zhengzhou University (Zhengzhou).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNingning Zhang contributed to conceptualization, methodology, project administration, funding acquisition, and reviewed and edited the manuscript. Saibin Fan performed data curation, formal analysis, investigation, visualization, and wrote the original draft. Yang Lu contributed to conceptualization, methodology, investigation, resources, project administration, funding acquisition, and wrote and reviewed the manuscript. Caipeng Yue provided resources and supervision. Shixin Zhu supervised the research and reviewed the manuscript. Jinyong Huang contributed to project administration and resources. Yuhui Wang participated in methodology and visualization. Yong Lai performed formal analysis and software development. All authors reviewed and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National Natural Science Foundation of China [grant numbers 31800551, 81903747] and the Science and Technology Research Project of Henan Province [grant Number 252102110244].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe raw sequence data reported in this paper have been deposited in the Genome Sequence Archive (Chen et al., 2021) in National Genomics Data Center (CNCB-NGDC Members and Partners, 2025), China National Center for Bioinformation/Beijing Institute of Genomics, Chinese Academy of Sciences (GSA: CRA029154) that are publicly accessible at https://ngdc.cncb.ac.cn/gsa/browse/CRA029154.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors confirm that permission for the collection of plant materials from all mountainous field sites was obtained from the relevant local forestry and natural resources authorities. The field studies complied with relevant institutional, national, and international guidelines and legislation. The voucher specimens (voucher numbers: LY2019063003, LY2020061434, LY2020071509, LY2023051709, LY2023052101, LY2023062601, LY2023062701, LY2023062706) have been deposited in the Herbarium of Zhengzhou University and were identified by Yang Lu from Zhengzhou University.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSmith-Hall C, Larsen HO, Pouliot M. People, plants and health: a conceptual framework for assessing changes in medicinal plant consumption. 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Nucleic Acids Res. 2025;53:D30\u0026ndash;44. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/nar/gkae978\u003c/span\u003e\u003cspan address=\"10.1093/nar/gkae978\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"bmc-plant-biology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pbio","sideBox":"Learn more about [BMC Plant Biology](http://bmcplantbiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pbio/default.aspx","title":"BMC Plant Biology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Isodon rubescens, landscape genomics, genetic differentiation, local adaptation, genomic vulnerability","lastPublishedDoi":"10.21203/rs.3.rs-8136960/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8136960/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eGlobal climate change is rapidly impacting biodiversity and threatening the sustainable use of medicinal plant species by reducing their availability and increasing harvest uncertainty. Understanding the adaptive genetic variation and genetic vulnerability of medicinal plants under climate change is crucial for effective germplasm management, cultivation, and breeding efforts. In this study, we assessed the genetic differentiation, local adaptation, and genomic vulnerability of the medicinal plant \u003cem\u003eIsodon rubescens\u003c/em\u003e (Hemsl.) H. Hara, with the goals of elucidating the impacts of geographic and environmental factors on its genetic structure and identifying at-risk populations for informed conservation and breeding under climate change.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eWe applied restriction site-associated DNA sequencing (RAD-seq) to 17 populations of \u003cem\u003eI. rubescens\u003c/em\u003e spanning its central and peripheral ranges, including the Taihang and Qinling-Funiu Mountains. The analysis revealed two distinct genetic groups: one in the Taihang Mountains and the other in the Qinling-Funiu Mountains. Significant patterns of isolation by distance (IBD), environment (IBE), and resistance (IBR) were detected, alongside high niche differentiation. We identified 456 candidate adaptive SNPs, some linked to genes involved in stress responses and biosynthesis. Precipitation was a key environmental driver of local adaptation. Populations in the northern Taihang Mountains and southern Funiu Mountains showed higher genomic vulnerability, indicating a greater risk of maladaptation.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eOur findings demonstrate that geographic isolation and environmental factors, particularly precipitation, are key drivers of genetic differentiation and local adaptation in \u003cem\u003eI. rubescens\u003c/em\u003e. The identified genomic vulnerability pinpoints specific populations at high risk under climate change. These insights provide a crucial genetic basis for formulating targeted conservation strategies and developing climate-resilient breeding programs for this medicinal species.\u003c/p\u003e","manuscriptTitle":"Genomic insights into adaptive divergence and genetic vulnerability to climate change of the medicinal plant Isodon rubescens (Hemsl.) H. Hara in central China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-02 09:57:12","doi":"10.21203/rs.3.rs-8136960/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-01-28T08:24:17+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-23T08:47:34+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-17T10:35:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"96951439923869354164554520966198213563","date":"2025-12-02T08:23:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"311471597739210010923403054281005472679","date":"2025-12-01T05:57:59+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-01T02:26:43+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-01T02:07:00+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-11-27T15:10:10+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-27T09:31:58+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Plant Biology","date":"2025-11-27T09:00:04+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-plant-biology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pbio","sideBox":"Learn more about [BMC Plant Biology](http://bmcplantbiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pbio/default.aspx","title":"BMC Plant Biology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1863a9dd-d457-48b9-bcf4-165b0adaeba5","owner":[],"postedDate":"December 2nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-05-04T16:01:37+00:00","versionOfRecord":{"articleIdentity":"rs-8136960","link":"https://doi.org/10.1186/s12870-026-08880-5","journal":{"identity":"bmc-plant-biology","isVorOnly":false,"title":"BMC Plant Biology"},"publishedOn":"2026-05-01 15:57:45","publishedOnDateReadable":"May 1st, 2026"},"versionCreatedAt":"2025-12-02 09:57:12","video":"","vorDoi":"10.1186/s12870-026-08880-5","vorDoiUrl":"https://doi.org/10.1186/s12870-026-08880-5","workflowStages":[]},"version":"v1","identity":"rs-8136960","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8136960","identity":"rs-8136960","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}