Harnessing landscape genomics to evaluate genomic vulnerability and future climate resilience in an East Asia perennial

Harnessing landscape genomics to evaluate genomic vulnerability and future climate resilience in an East Asia perennial | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL Molecular Ecology This is a preprint and has not been peer reviewed. Data may be preliminary. 10 March 2025 V1 Latest version Share on Harnessing landscape genomics to evaluate genomic vulnerability and future climate resilience in an East Asia perennial Authors : Li Feng 0000-0002-8252-9463 [email protected] , Cong-Ying Wang , Li-Pan Zhou , Yihan Wang , Jing Wang 0000-0002-3793-3264 , Zheng-Yuan Wang , Tao Zhou , and Xu-Mei Wang 0000-0002-1468-6635 Authors Info & Affiliations https://doi.org/10.22541/au.174160448.87582077/v1 Published Molecular Ecology Version of record Peer review timeline 347 views 184 downloads Contents Abstract Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract In this era of rapid climate change, understanding the adaptive potential of organisms is imperative for buffering biodiversity loss. Genomic forecasting provides invaluable insights into population vulnerability and adaptive potential under diverse climatic conditions, thereby facilitating management interventions and bolstering shaping species-specific germplasm conservation strategies. We integrated population genomics and landscape genomics approaches, leveraging single-nucleotide polymorphisms obtained through whole-genome resequencing of 201 individuals across 43 Rheum palmatum complex populations, to pinpoint adaptive variation and its significance in the context of future climates, delineate seed zones, and established guidelines for ex situ germplasm conservation. The species complex exhibited strong signatures of local adaptation and differential genomic vulnerabilities across its distribution range, with eastern lineage populations facing significant maladaptation risks under future climate scenarios. Using diverse datasets of putatively adaptive loci and climate change scenarios, we delineated three distinct seed zones within the species' range, estimated varying sample sizes per zone to capture most adaptive diversity, and predicted shifts in seed zone centroids ranging from 48.3 km to 359.3 km from historical distributions to mitigate climate change impacts. Overall, our findings provide a genome-wide perspective on climate adaptation and valuable insights into germplasm conservation strategies aimed at enhancing population resilience in future climates, serving as a blueprint for restoration plans of other vulnerable species. not-yet-known not-yet-known not-yet-known unknown Harnessing landscape genomics to evaluate genomic vulnerability and future climate resilience in an East Asia perennial Running title : The genomic vulnerability of Rheum species Li Feng 1, 5 , Cong-Ying Wang 1, 5 , Li-Pan Zhou 2, 5 , Yi-Han Wang 3 , Jing Wang 4 , Zheng-Yuan Wang 1 , Tao Zhou 1 , Xu-Mei Wang 1, * 1 School of Pharmacy, Xi’an Jiaotong University, Xi’an, China. 2 Department of Pharmacy, Ninth Hospital of Xi’an, Xi’an, China. 3 College of Life Sciences, Henan Agriculture University, Zhengzhou, China. 4 Key Laboratory for Bio-Resources and Eco-Environment, College of Life Sciences, Sichuan University, Chengdu, China. 5 These authors contributed equally: Li Feng, Cong-Ying Wang, Li-Pan Zhou. * Corresponding author : Xu-Mei Wang (Email: [email protected] ) Authors’ ORCIDs: Li Feng: https://orcid.org/0000-0002-8252-9463 Yi-Han Wang: https://orcid.org/0000-0002-8219-2901 Jing Wang: https://orcid.org/0000-0002-3793-3264 Tao Zhou: https://orcid.org/0000-0002-8869-7599 Xu-Mei Wang: https://orcid.org/0000-0002-1468-6635 Total word counts: 7603 , comprising 212 words in the Abstract, comprising 1099 words in the Introduction, 2758 words in the Materials and Methods, 1927 words in the Results, and 1607 words in the Discussion section. Number of figures: 6 (all in color). Number of tables: 1 Number of supporting information files: 26 (Tables S1-S8; Figs. S1-S18) Abstract In this era of rapid climate change, understanding the adaptive potential of organisms is imperative for buffering biodiversity loss. Genomic forecasting provides invaluable insights into population vulnerability and adaptive potential under diverse climatic conditions, thereby facilitating management interventions and bolstering shaping species-specific germplasm conservation strategies. We integrated population genomics and landscape genomics approaches, leveraging single-nucleotide polymorphisms obtained through whole-genome resequencing of 201 individuals across 43 Rheum palmatum complex populations, to pinpoint adaptive variation and its significance in the context of future climates, delineate seed zones, and established guidelines for ex situ germplasm conservation. The species complex exhibited strong signatures of local adaptation and differential genomic vulnerabilities across its distribution range, with eastern lineage populations facing significant maladaptation risks under future climate scenarios. Using diverse datasets of putatively adaptive loci and climate change scenarios, we delineated three distinct seed zones within the species’ range, estimated varying sample sizes per zone to capture most adaptive diversity, and predicted shifts in seed zone centroids ranging from 48.3 km to 359.3 km from historical distributions to mitigate climate change impacts. Overall, our findings provide a genome-wide perspective on climate adaptation and valuable insights into germplasm conservation strategies aimed at enhancing population resilience in future climates, serving as a blueprint for restoration plans of other vulnerable species. Keywords: climate change; genomic vulnerability; germplasm conservation; local adaptation; Rheum palmatum complex; seed zone. In this era of rapid climate change, a profound understanding of organisms’ adaptive potential is paramount for advancing proactive conservation efforts and mitigating biodiversity loss (Hoffmann & Sgrò, 2011; Hoffmann, et al. 2021). Numerous studies have indicated that many species are unlikely to adapt to climatic change by shifting their geographical distribution; instead, their adaptation relies on phenotypic plasticity and evolutionary adaptation (Scheffers, et al. 2016; Aitken, et al. 2024). As a result, climate adaptation strategies have become crucial for preserving and managing wild populations, to ensure an appropriate and swift response to the climatic alterations (Forester, et al. 2022). These strategies must prioritize the identification or augmentation of species’ or populations’ evolutionary potential and adaptive capabilities, grounded in established or hypothesized pathway(s) and mechanism(s) through which climate impacts the focal taxa (Bay, et al. 2018; Exposito-Alonso, et al. 2019). Among the diverse methodologies available, genomic forecasting has emerged as a particularly cost-effective technique for predicting future disruptions to locally adaptive gene-environment associations (Capblancq, et al. 2020). This approach offers invaluable insights into population vulnerability and adaptive potential across various environments, enabling informed targeted management interventions, the enhancement of population resilience, and the development of species-specific germplasm conservation strategies (Capblancq, et al. 2020; Bernatchez, et al. 2024). Genomic forecasting, which commonly employs genomic offset (alternatively termed genetic offset (Fitzpatrick & Keller, 2015) or genomic vulnerability (Bay, et al. 2018)), has found applications in various taxa (Gougherty, et al. 2021; Layton, et al. 2021; Chen, et al. 2023). This method involves mapping the current spatial distribution of adaptive alleles onto existing environmental conditions, and subsequently computing the disparity between the present and future climate-associated genomic composition as an indicator of climate change maladaptation (Fitzpatrick & Keller, 2015). Nevertheless, solely relying on genomic forecasting is insufficient for guiding adaptive management interventions and enhancing the resilience of species possessing significant ecological and economic value. Identifying the genomic targets of climate-related selection stands as a pivotal step in comprehending local adaptation within these species, and in formulating strategies for preserving pertinent genomic diversity. One approach to achieving this aim is through the lens of seed zones, where a species range is partitioned into the minimal number of regions that effectively delineate climate and ecoregions (Sandercock, et al. 2024). Seed zones, which accommodate local adaptation and population variation, are extensively employed for germplasm management and conservation, with the primary goal of preventing the utilization of maladaptive seed sources in order to bolster population resilience in future (Hufford & Mazer, 2003; Yu, et al. 2022). Traditionally, seed zones have partitioned the entire species range into distinct regions, with the allocation of seeds confined within each designated zone, following the “local is best” paradigm (Broadhurst, et al. 2008). This presumption is based on the belief that existing populations are well-acclimated to their local conditions. Nevertheless, as climate change progresses, the concept of a static “local climate” is becoming challenged, leading to a mismatch between the climatic conditions that local populations have historically adapted to and the future climatic scenarios they are destined to encounter (Aitken, et al. 2008). Climate change has induced swift alterations in preexisting habitat conditions, thus presenting novel challenges in aligning appropriate planting environments with suitable seed sources (Harrison, 2021). Consequently, the development of climate-driven seed transfer models becomes imperative, facilitating the implementation of genetically informed management strategies, such as assisted gene flow (Aitken & Whitlock, 2013). Despite the implementation of climate-based seed transfer practices, there remains a necessity to classify populations according to their comparable adaptive diversity, thereby facilitating streamlined management and deployment procedures. As genomic and environmental data rapidly accumulate and become easily accessible, landscape genomic methods are arising as a viable alternative for geographically delineating seed zones, vital for managing adaptive variation and breeding (Yu, et al. 2022; Sandercock, et al. 2024). Based on this information provided by landscape genomics, we are able to assess the necessary sampling intensity to capture the existing adaptive diversity within the designated seed zones, constituting a logical step towards enhancing population resilience in the face of future climate changes. The Rheum palmatum complex, comprising R. officinale , R. palmatum , and R. tanguticum , predominantly occurs at mid- to high-altitudes ( ca . 1,000-5,100 m above sea level) within the mountainous areas of western China (Bao & Grabovskaya-Borodina, 2003). These regions are distinguished by their exceptional biodiversity and endemism but are also highly vulnerable to the impacts of climate change (Myers, et al. 2000; Rahbek, et al. 2019). Rhubarb derived from the dried roots and rhizomes of these species and revered as the ”king of herbs”, constitutes an important Traditional Chinese Medicine (TCM) that has been utilized for over 2,000 years as a purgative (Xiang, et al. 2020). Previous studies have uncovered that these herbs are likely to constitute a single species, with intraspecific lineages exhibiting a phylogeographical west-east split near/across the Sichuan Basin (Wang, et al. 2018; Feng, et al. 2020). These lineages often display confinement to ecologically and topologically heterogeneous habitats, thus suggesting potential signals of climate-driven selection. Therefore, this species complex provides an exemplary system for determining the intraspecific variability in genotype-climate associations and its resultant implications for climate change-induced vulnerability. To date, the majority of genomic vulnerability research has concentrated on trees (Gougherty, et al. 2021; Sang, et al. 2022; Sandercock, et al. 2024) and vertebrates (Bay, et al. 2018; Brauer, et al. 2023; Chen, et al. 2023). Although a growing number of studies have delved into the genomic vulnerability of herbs (Exposito-Alonso, et al. 2019; Zhao, et al. 2023; Zhang, et al. 2024), a substantial knowledge deficit remains concerning the response of herbs to projected climate changes, especially given their occupancy of up to 40% of terrestrial areas and the considerable variability in their response to climatic stressors (Craine, et al. 2013; Petermann and Buzhdygan 2021). Here, we describe the genomic basis of local adaptation within the species complex and present sampling recommendations aimed at bolstering germplasm conservation initiatives. By harnessing whole-genome resequencing data from 201 accessions of the species complex, we seek to: i) pinpoint genetic loci that exhibit significant associations with climatic variables; ii) quantitatively assess and geographically map the vulnerable populations in the context of future climatic shifts; iii) synthesize how these climate-linked loci collectively vary across landscapes, thereby enabling the delineation of seed zones critical for germplasm preservation; and iv) formulate sampling guidelines targeted at capturing adaptive diversity in wild populations. Our findings provide a comprehensive, genome-wide lens on climate adaptation within the R. palmatum complex, offering valuable insights into the progress of ex situ germplasm conservation efforts aimed at enhancing population resilience in the face of future climates. Materials and Methods Sampling, sequencing and SNP calling In line with the previous recommendations for landscape genomic studies (Bragg, et al. 2015; Aguirre-Liguori, et al. 2023), we collected 213 accessions of the R. palmatum complex from 43 natural populations throughout its distribution range for whole-genome resequencing. Genomic DNA was extracted from fresh leaves or roots and subsequently sequenced on the DNBSEQ-T7 platform, targeting a minimum coverage depth of ≥ 15× per individual. Following the manufacturer’s protocols, DNA libraries were crafted and subjected to 150 base pair (bp) paired-end sequencing. Raw sequence data underwent rigorous quality control using Trimmomatic v0.39 (Bolger, et al. 2014), eliminating adapter sequences and low-quality bases (Phred score < 30). The high-quality reads were then mapped to the R. palmatum genome (Zhang, et al. 2024) using BWA v0.7.17 (Li and Durbin 2009) with default parameters. The resulting SAM files were transformed into sorted BAM files and indexed utilizing SAMtools v0.1.19 (Li, et al. 2009). PCR duplicates were marked with Picard (https://github.com/broadinstitute/picard), and candidate variants were identified on a per-chromosome basis employing the HaplotypeCaller from GATK v4.2.6.1 (McKenna, et al. 2010). These individual GVCF files were amalgamated into a single GVCF per sample using GatherVcfs, which were further processed by GenotypeGVCFs to yield a raw variant call file (VCF). Quality filtration of this VCF was performed with the VariantFiltration module within GATK, applying stringent criteria (QD 60.0 || MQRankSum < -12.5 || ReadPosRankSum 3.0 || MQ < 40.0). Lastly, BCFtools (Danecek, et al. 2011) was employed to remove INDELs and multiallelic SNPs under default settings, leading to a refined dataset encompassing 14,299,652 SNPs across the 213 accessions. Next, strict criteria for soft filtration (Marees, et al. 2018) were implemented to further curtail the quantity of low-quality genotype calls via PLINK v1.9 (Purcell, et al. 2007). The parameters employed for SNP filtering in PLINK encompassed a minor allele frequency threshold of 0.05 (–maf 0.05), a maximum permissible per-SNP missingness of 2% (–geno 0.02), and a maximum allowed per-sample missingness of 2% (–mind 0.02). This filtering process resulted in the identification of 1,432,892 high-quality SNPs from 201 samples. Additionally, SNPs exhibiting high correlation were eliminated through LD-based SNP pruning in PLINK, utilizing the parameter “–indep-pairwise 50 10 0.2” (this removed any SNP having a correlation coefficient ( r 2 ) exceeding 0.2 with another SNP within sliding windows containing 50 SNPs, with a step advancement of 10 SNPs across the genome). The resultant SNP dataset, comprising 378,620 SNPs from 201 accessions, was subsequently utilized for clustering analyses. not-yet-known not-yet-known not-yet-known unknown Genetic diversity, linkage disequilibrium and population structure analyses We employed the R package hierfstat (Goudet, 2005) to ascertain population differentiation (F ST) (Weir & Cockerham, 1984) amongst various populations, and further assessed diversity indices, specifically observed heterozygosity (H O) and genetic diversity (H S) within these populations. Additionally, we utilized PLINK to determine nucleotide diversity (π) within our focal species’ populations. Tajima’s D statistics were also calculated for the all populations of our focal species utilizing VCFtools v0.1.15 (Danecek, et al. 2011) in 100-kbp non-overlapping windows. To further evaluate and compare the patterns of linkage disequilibrium among distinct population groups, PopLDdecay v.3.40 (Zhang, et al. 2018) was employed to calculate the squared correlation coefficient (r 2) between SNP pairs with a MAF > 0.05 within a 100-kbp window, subsequently averaging these values across the entire genome. In our study, three methodologies were utilized to ascertain the genetic structure of the focal species. Initially, a principal component analysis (PCA) was executed in R v4.2.1 (Team, 2013) to assess the genetic structure, preceded by the acquisition of eigenvalues from 378,620 SNPs via PLINK. Secondly, a discriminant analysis of principal components (DAPC) was conducted to determine the number of discrete clusters, facilitated by the R package adegenet (Jombart & Ahmed, 2011). Lastly, STRUCTURE v2.3.4 (Pritchard, et al. 2000) was employed to investigate the population structure across all individuals, wherein the predefined number of genetic clusters K, representing population ancestries, ranged from 1 to 10 for both the entire population and those belonging to western lineages (see results). The optimal K was deduced through likelihood plots (Pritchard, et al. 2000) and the ∆K method (Evanno, et al. 2005), implemented in the web-based software, STRUCTURE HARVESTER (Earl & vonHoldt, 2012). The programs CLUMPP v1.1.2 (Jakobsson & Rosenberg, 2007) and DISTRUCT v1.1 (Rosenberg, 2004) were subsequently utilized to consolidate the individual STRUCTURE outcomes. not-yet-known not-yet-known not-yet-known unknown Estimation of demographic history Given the recent divergence of western and eastern lineage populations of our focal species (Wang, et al. 2018; Feng, et al. 2020), we estimated the demographic histories of the three genetic clusters separately utilizing the stairway plot v2 (Liu & Fu, 2015) and SMC++ v1.15.4 (Terhorst, et al. 2017). Assuming a mutation rate of 7.0×10-9 (per site per generation) and adhering to a generation time of 5 years (Feng, et al. 2020), the scaled outcomes were subsequently transformed into years and effective population size (Ne ). Median estimates and confidence intervals for the Ne in the stairway plot were calculated using the built-in bootstrap function with 200 subsets of the input data. To infer the demographic history with SMC++, we converted a VCF file containing high-quality SNPs to the SMC format using the vcf2smc command. Subsequently, the estimate command was executed for each group using default parameters. Determining climate-associated SNPs and their impact on genetic variation In our study, we employed two distinct methods to pinpoint SNPs that exhibit strong associations with BIOCLIM variables across the genomic regions of our focal species. To detect genotype-environment association (GEA) using the latent factor mixed model (LFMM) implemented within the R package LEA (Frichot, et al. 2013; Frichot & Francois, 2015), we executed five independent Markov chain Monte Carlo runs, incorporating a latent factor of K = 3 to account for population structure. The P -values obtained from all five runs were subsequently aggregated and adjusted for multiple testing using a false discovery rate correction, with a threshold of P < 0.01. Due to the fact that LFMM fails to account for the correlation structure among environmental variables, we adhered to the developers’ guidelines and condensed the six climate variables (see below) into synthetic variables based on principal component axes. We contend that this decision is justified, given that climate variables, whether utilized in the analysis or excluded, exhibit correlations with one another within each specific context, thereby complicating our endeavor to causally ascribe associations to any particular variable. Redundancy analysis (RDA, Capblancq & Forester, 2021) has emerged as one of the most effective multivariate GEA approaches, demonstrating low false-positive rates across various demographic scenarios (Forester, et al. 2016; 2018). After considering the ranked importance of the 19 BIOCLIM variables, which were assessed using gradient forest (GF) analyses in the R package gradientForest (Ellis, et al. 2012), along with the correlations among these variables, we selected six BIOCLIM variables (specifically, BIO2, BIO3, BIO4, BIO10, BIO12, and BIO15) with pairwise correlation coefficients | r | < 0.8 for the RDA, implemented in the R package vegan (Oksanen, et al. 2019). SNPs were regarded as putatively adaptive loci if their loading values exceeded 2.7 standard deviations from the mean loading values. We designated the outlier loci that were identified by both approaches as core adaptation loci, whereas those detected by either method were classified as pan-adaptation loci. Following this classification, we employed BEDTools v2.31.0 (Quinlan & Hall, 2010) to annotate both core adaptation and pan-adaptation loci onto their corresponding genes. Subsequently, we performed a gene ontology (GO) enrichment analysis on the annotated genes using the R package clusterProfile (Wu, et al. 2021), which enables identifying the significantly enriched biological pathways associated with these genes. In addition, we performed Mantel and partial Mantel tests to ascertain the influence of isolation-by-distance (IBD) and isolation-by-environment (IBE) on shaping the spatial genetic variation of both adaptive and neutral loci. The significance of associations between F ST ( F ST /1 − F ST ) and geographic as well as environmental distances (after accounting for the effect of geographic distance) was determined through 999 permutations using the R package vegan . Furthermore, we conducted partial RDA (pRDA) to estimate the relative contributions of geography, neutral population structure, and environment in driving genetic variation across the R. palmatum complex populations. To construct one full model and three partial models, we utilized three datasets: (i) six selected BIOCLIM variables (‘clim’); (ii) loadings from the first three principal components of a genetic PCA based on neutral loci (‘struct’); and (iii) population coordinates, including latitude and longitude (’geog’). These models were built using the R package vegan . Population allele frequencies served as the response variable in all four models, and the significance of the explanatory variables was evaluated using 999 permutations with the ‘anova.cca’ function in the vegan package. Genomic offset estimation For each sampling location, we obtained the 19 BIOCLIM variables corresponding to the current (1970-2000) and future (2050 and 2090) timeframes at a 2.5-min resolution from WorldClim v2.1 (Fick and Hijmans 2017). To accommodate uncertainties inherent in climate modeling, we forecasted genomic offset for the species complex using four distinct climate models: BCC-CSM2-MR, ACCESS-CM2, MIROC6, and MPI-ESM1-2-HR, along with two common socioeconomic pathways (SSPs), namely SSP126 and SSP585. we integrated the four future climate models across the same time scales and SSPs via the R package raster (Hijmans, et al. 2015). To assess the genomic offset of our focal species in response to the anticipated climate changes, we employed two distinct methodologies. Firstly, we utilized a nonparametric, machine-learning GF model to evaluate the genomic offset across the species range of R. palmatum complex. This was achieved through the utilization of the R package gradientForest , following the methodologies outlined by Fitzpatrick and Keller (2015). For each climate model considered, we constructed a GF model to estimate the genomic offset under varying climatic conditions, incorporating six unrelated environmental variables. The GF model predicts genomic offset as the divergence in climatically adaptive SNPs between current and future climates within the same location, and elevated values of genomic offset serve as indicators of heightened risk of maladaptation under forecasted climatic conditions (Fitzpatrick & Keller, 2015). When constructing GF model, we utilized 500 regression trees per SNP, set a variable correlation threshold of 0.5, and maintained the default settings for all other parameters. In addition, after considering recent studies that focus on evaluating the efficiencies of genomic offset in predicting maladaptation to future climates (Aguirre-Liguori, et al. 2023; Lind, et al. 2024), we calculated and contrasted these offsets between two distinct types of datasets (i.e., the datasets of core adaptation loci and pan-adaptation loci) at both the species and lineage levels. In addition, as a complementary approach to GF, we investigated the influence of adaptive variation across diverse landscapes adopting the methodology of Capblancq et al (2020). Initially, we conducted a secondary RDA utilizing outliers previously identified through LFMM and RDA methods. These outliers constitute an adaptively enriched genetic space (Steane, et al. 2014), and performing a secondary RDA on these specific loci enables the identification of environmental variables most strongly correlated with putatively adaptive variation. Subsequently, we constructed composite indices using scores from the six uncorrelated environmental variables along the first two RDA axes, aiming to predict the adaptive score of individuals within their environment. The GEAs employed here can be extrapolated to future climates, allowing for predictions of potential shifts in adaptive optima triggered by climatic changes. We replicated the procedure used for current climatic conditions to forecast the optimal adaptive index based on future climate scenarios (specifically, 2050 and 2090) at two distinct SSPs. The Euclidean distance between the adaptive indices under present and future climates for each pixel offers an estimation of the genomic offset (Capblancq & Forester, 2021). We aggregated the genomic offsets across both RDA axes and visualized the results with the aid of R packages raster and ggplot2 (Wickham, 2011). Akin to the genomic offset evaluated using the GF model, the genetic offset was computed among datasets containing two types of outliers, both at the species and lineage levels. Finally, in order to evaluate the genetic load within the R. palmatum complex population, genetic variants were annotated as either synonymous or non-synonymous. Understanding genetic load is important to assess its impact on the health and viability of endangered populations, because it affects the extinction risk and recovery potential of populations (Bertorelle, et al. 2022; Dussex, et al. 2023). For the non-synonymous variants, SIFT scores were computed utilizing the SIFT 4G software (Vaser, et al. 2016), with UniRef100 serving as the protein database. Subsequently, these variants were categorized into four distinct groups: loss of function (LOF), deleterious (with a SIFT score < 0.05), tolerated (with a SIFT score ≥ 0.05), or synonymous. Furthermore, the determination of the derived versus ancestral allelic state at each SNP position was carried out by employing est-sfs (Keightley and Jackson 2018), with Rheum nobile (Feng, et al. 2023) serving as the outgroup. The genetic load was approximated by calculating the ratio of the number of derived mutations (comprising LOF and deleterious mutations) to the number of synonymous variants. In addition, we employed the ‘stat_cor’ function in the ggpubr package (Kassambara, 2018) to assess the correlation between the genomic offsets under two future scenarios (SSP126 and SSP585) across two distinct timescales (2050 and 2090) and genetic diversity across all populations, as well as the association between genomic offset and proxies of genetic load across all populations. not-yet-known not-yet-known not-yet-known unknown Delineating seed zones and determining their changes under future climates In the context of multivariate adaptive genomic variation, seed zones represent areas of relative homogeneity, serving as a basis for the ex situ germplasm conservation and guiding the breeding of climate-matched restoration populations (Sandercock, et al. 2024). Given that climate served as a primary determinant of genomic variation within our focal species, we postulated the feasibility of delineating seed zones, whose boundaries would reflect precipitation and temperature gradients within the potential range of the species complex. The principal components, representing the continuous genomic variation predicted by GF, can be clustered into varying numbers of classes to delineate seed zones, thereby enabling ex situ germplasm conservation that facilitates climate-matched population resilience (Yu, et al. 2022; Sandercock, et al. 2024). Following the method of Yu et al . (2022), we employed the partitioning around medoids (k-medoids) algorithm along with the R package factoextra (Kassambara & Mundt, 2017) to assess the variation within clusters for various cluster numbers. This assessment was based on GF-predicted continuous genomic variation utilizing two distinct outlier datasets. We investigated cluster numbers ranging from two to ten, determining the optimal count by identifying the elbow point­­–the juncture where adding an additional cluster results in significantly less reduction of within-cluster variation compared to previous additions. The chosen number of clusters was then utilized to categorize the spatial distribution of GF-predicted continuous genomic variation into GF-based seed zones across the ranges of our focal species. To anticipate the shifts in defined seed zones from the present to the future climates, we followed the approach of Sandercock et al. (2024). Utilizing the ’knn.dist’ function from the R package FNN (Beygelzimer, et al. 2013) with k = 1 (representing the number of nearest neighbors to search), we matched each present-day location (pixel) with its closest future analog based on the minimal genomic offset. This approach facilitates effective prediction of the minimal migration path required to mitigate the climate maladaptation. For each seed zone, we calculated the centroids separately for pixels representing the current climate conditions and those uniquely aligned with future scenarios. Subsequently, we quantified the migratory distances between these present and future centroids, thereby deriving an estimation of the average minimum migration distance required for each seed zone. Evaluating the sampling numbers for ex situ germplasm conservation To assess the requisite sampling sizes for each seed zone, aimed at recapitulating genomic variation at adaptive loci and guiding germplasm conservation strategies, we employed linear regression analysis to compare allele frequencies of putatively adaptive loci in the entire population with those from bootstrap samples of varying sizes (Sandercock, et al. 2024). The Python script facilitating this process is accessible at: https://github.com/alex-sandercock/Capturing_genomic_diversity. Employing the R² values derived from these regressions as a proxy for the percentage of diversity captured in each iteration, we iteratively augmented the sample size until the R² achieved or surpassed a predefined threshold (e.g., 90%). This procedure was replicated 100 times, and the recommended sampling numbers for each designated seed zone were averaged across all iterations. The outcome represents an estimation of the mean sampling number required for each seed zone to achieve the desired coefficient of determination between the sample’s adaptive genomic composition and that of the full dataset. not-yet-known not-yet-known not-yet-known unknown Results Genetic differentiation and population demography Our comprehensive whole-genome resequencing effort has yielded approximately 14.30 million single nucleotide polymorphisms (SNPs) that successfully passed the initial quality filtering, derived from 213 individuals spanning the natural range of the species complex (Fig. 1; Table S1). Notably, an average of approximately 94.7% of the clean reads were successfully aligned to the reference genome (2.8 Gb) of R. palmatum (Zhang, et al. 2024), achieving a mean depth of 18.9× and a coverage of 94.3% (Table S2). After applying rigorous and stringent thresholds for SNP calling, we identified approximately 1.43 million high-quality SNPs from 201 samples of our focal species. Our findings revealed a high genetic differentiation ( F ST ) across all populations, with an average F ST value of 0.22 (Table S3). Furthermore, we observed variable levels of genetic diversity among these populations, exhibiting a mean observed heterozygosity ( H O ) of 0.19 and a mean heterozygosity ( H S ) of 0.13 within populations (Table S1). Bayesian clustering utilizing 378,620 SNPs extracted from an LD-pruned dataset comprising 1.43 million SNPs identified a distinct separation between western and eastern populations emerged, with an optimal k-value of two (Figs. 1a, b; S1a, b). Furthermore, the western cluster, primarily occupying the Hengduan mountains and adjacent areas, could be further subdivided into two distinct lineages (Figs. 1b; S1c, d). It is worth mentioning that some individuals, particularly those belonging to western sub-lineages, exhibit evident admixture between sub-lineages. Both PCA and DAPC consistently supported these findings (Fig. S1e, f). Furthermore, our cluster analyses, which were based on pan-adaptation or core adaptation loci, yielded results that were comparable to those obtained through the utilization of 378,620 SNPs (Fig. S2). Additionally, based on putatively neutral SNPs, we detected significant signals of IBD within the species complex (Fig. 1c). Collectively, these diverse lines of evidence strongly suggest that our focal species is likely to have originated from distinct refugia. Subsequently, we utilized both the stairway plot and SMC++ to independently assess the changes in Ne for each of the three genetic lineages throughout their respective evolutionary trajectories (Fig. S3a, b). Generally, the Ne trajectories inferred from the both methods provided evidence of a population decline across the three clusters, which was further substantiated by Tajima’s D statistics indicating positive D values for all populations (Table S1). Ultimately, an analysis of linkage disequilibrium (LD) decay across the genome, taking into account physical distance, unveiled comparable trends among the three clusters. Specifically, the r² value decreased to below 0.1 at mean physical distances of approximately 5 kb and 1 kb for the eastern and western populations, respectively (Fig. S3c). Collectively, these lines of evidence point to a potential recent divergence among the lineages within the species complex. Genomic signatures of local adaptation We integrated genomic and climatic datasets to detect signatures of divergent selection by employing GEA methodologies. Initially, we utilized GF to pinpoint the climatic variables exhibiting the strongest association with genetic variation within the species complex. Among the 19 BIOCLIM variables tested (Table S4), we selected the top six uncorrelated variables: mean diurnal range (BIO2), isothermality (BIO3), temperature seasonality (BIO4), mean temperature of the warmest quarter (BIO10), annual precipitation (BIO12), and precipitation seasonality (BIO15). These were retained as climate candidates based on their ranked importance and the correlations observed among the 19 climatic variables (Fig. S4), ensuring the avoidance of multicollinearity issues in subsequent GEA analyses. To identify SNPs associated with the six climatic variables, we employed both the LFMM and RDA. Through these analyses, we aimed to determine the genomic signatures underlying local adaptation in our focal species. The comprehensive set of 16,709 putatively adaptive SNPs, identified through a combination of LFMM and RDA analyses, were collectively regarded as pan-adaptation loci (Fig. S5a). Among the 10,988 significant adaptive SNPs identified via LFMM, 1,198 SNPs exhibited extreme loadings (with a standard deviation > 2.7) along one or multiple RDA axes, qualifying them as core adaptation loci. These two distinct categories of putatively adaptive loci are broadly distributed across the genome (Fig. S5b). Functional enrichment analysis focusing on the genes associated with these loci revealed a significant enrichment in gene ontology (GO) categories related to biological processes and molecular function such as transcriptional regulation, DNA repair and response to cold (Table S5), which are crucial for understanding the plant’s persistence under anticipated climate changes (Wellenreuther & Bernatchez, 2018). To further assess the validity of the SNPs identified in this study, we employed Mantel and partial Mantel tests to explore patterns of IBD and IBE for both potentially adaptive and neutral SNPs, respectively (Figs. 1c, d; S6). Our findings revealed significant IBD patterns for both adaptive and neutral SNPs, albeit with stronger patterns observed for adaptive SNPs compared to neutral ones. Nonetheless, akin to IBD, both SNP types exhibited significant IBE patterns in partial Mantel tests, indicating that the genetic variation of adaptive SNPs was predominantly influenced by geography and environment. To disentangle the relative contributions of climate, geography, and population structure in explaining both adaptive and neutral genetic variation, we conducted pRDA focused on pan-adaptation and core adaptation loci, respectively. The pan-adaptation loci analysis showed that, climate, geography, and neutral structure accounted for 78.3% of the variance in allele frequencies among localities of our focal species (Table S6). Specifically, the exclusive contribution of climate effects was notable, explaining 9.8% of the total genetic variation (representing 12.5% of the variation explicable by the full model). The pure effect of neutral genetic structure was also significant, accounting for 16.2% of the total genetic variance (20.6% of the explicable variation), while geography contributed significantly to 2.3% (3.0% of the explicable variation). The results from pRDA centered on core adaptation loci mirrored those observation for pan-adaptation loci (Tables S7). Interestingly, we discovered that a substantial portion of the explained genetic variance was entangled across various predictor groups, indicating a high level of collinearity among climate, geography, and neutral genetic structure. Spatial distribution of adaptive genomic diversity and genomic offset Using the aggregate community-level turnover functions derived from putatively adaptive SNPs integrated within the GF framework and adaptively enriched RDA, we constructed a visual representation of the genotype-climate turnover surface spanning the potential ranges of the species complex. This visualization was achieved by utilizing the first two principal components extracted from the outputs of both the GF model and RDA. Our findings from the GF analysis indicated that the spatial turnover patterns were comparable between pan-adaptation and core adaptation loci, highlighting distinct genomic composition among populations within the species complex’s distribution range (Fig. 2). Furthermore, the adaptively enriched RDA conducted on the pan-adaptation and core adaptation loci delineated two principal gradients of climatic adaptation within our focal species (Figs. S7-S8). The first axis of variation, denoted as RDA1, juxtaposed localities exhibiting a marked increase in isothermality (BIO3) – predominantly observed in the western regions of the species complex’s distribution range – against those experiencing greater temperature seasonality (BIO4), primarily situated in the central and eastern portions of its range. The second axis, RDA2, exhibited a stronger correlation with annual precipitation (BIO12) in low-elevation habitats, whereas it was associated with increased precipitation seasonality (BIO15) and a higher diurnal temperature range (BIO2) in western mountainous regions. These findings suggest distinct differences in genotype-climate associations between the western and eastern lineages, likely reflecting local adaptation to diverse climatic conditions. Extrapolations of genomic offset for the two SSPs in years 2050 and 2090, conducted through GF and RDA, unveiled analogous spatial patterns (Figs. 3; S9). These patterns highlighted elevated risks of maladaptation in the central-eastern and southeastern portions of the species’ range, with diminished risks in the western regions. Notably, the estimations of genomic offset for SSP585 in 2090 using core adaptation loci via GF and RDA suggested that populations residing in certain southern sections of the western regions might maladapt to future climate changes (Fig. S9). Moreover, projections of genomic offset utilizing both pan-adaptation and core adaptation loci across diverse SSPs and timeframes consistently demonstrated that populations within the eastern lineage exhibited higher offset values than those within the western lineage, with the exception of genomic offset estimations using core adaptation loci via RDA for SSP585 in 2090 (Figs. 4; S10). Additionally, we conducted lineage-level predictions of variable importance using two datasets of putatively adaptive loci based on GF (Fig. S11), and then estimated maladaptation regions, considering the potential for unrealistic estimations due to substantial genetic structure. The genomic offset estimations based on GF at lineage levels uncovered consistent trends in estimating genomic offsets at the species level, and further revealed some fine-scale maladaptation zones arose within western regions under projected climate changes. (Figs. 3a-d, 5; S9a-d). However, genomic offset estimations at lineage levels using RDA were inconsistent with the results obtained at the species level (Figs. 3e-h; S9e-h and S12). Further exploration is imperative to elucidate the impact of substantial genetic structure on the projection of maladaptation regions when employing RDA. Nonetheless, our results indicate that distinct GEAs between the western and eastern lineages within the distribution range of our focal species are potentially facilitating estimations of fine-scale maladaptation regions compared to those at the species level in response to impending climatic perturbations. We ultimately investigated the correlations between genomic offset, genetic diversity, and genetic load, aiming to ascertain whether populations exhibiting a higher genetic offset also bear an augmented burden of deleterious mutations or diminished genetic diversity. The results disclosed no discernible patterns of association between the anticipated genomic offsets and genetic loads, a finding that remained consistent even when considering LOF variants presumed to exert substantial deleterious effects (Figs. S13-S16). Additionally, our results also elucidated that, in the majority of instances, populations exhibiting high genomic offsets typically possessed low genetic diversity, lending support to the conclusion that genetic diversity is proximate to adaptive potential and crucial for sustaining species’ adaptation (Scott, et al. 2020; Exposito-Alonso, et al. 2022). Whether this relationship holds true across other species remains to be rigorously evaluated in future studies within a meticulously designed framework. Seed zone delineation, range shift prediction and sampling recommendations We determined that three zones were optimal for both datasets of putatively adaptive loci (Fig. S17). A longitudinal boundary situated at approximately 103°E and a latitudinal boundary positioned at roughly 28°N delineated the species’ range into three distinct seed zones: SZ1 and SZ3, both located in the western region, and SZ2, situated in the eastern region (Fig. 6a). SZ1 was heavily influenced by BIO03, whereas SZ2 exhibited a strong influence from BIO04 (Fig. 6b). To mitigate the effects of climate change, the geographic centroids of these seed zones required shifts ranging from approximately 48.3 km to 359.3 km from their current ranges, as estimated by genomic offsets derived from pan-adaptation loci under low and high climate change scenarios (Fig. 6c-f). Additionally, the SZ2 centroid was predicted to necessitate the greatest average shift among all scenarios, followed by SZ3 and SZ1. The outcomes of climate-driven seed zone shift prediction using core adaptation loci echoed those points for pan-adaptation loci (Fig. S18). We have ultimately determined the sample sizes for each seed zone to capture the majority of adaptive diversity existing in environments for the purpose of germplasm preservation. To comprehensively capture the adaptive genomic diversity, our findings revealed that the greatest sampling demand was observed in SZ2, with 30.60 herbs required for 90% coverage of pan-adaptation loci, 58.70 herbs for 95%, and 280.80 herbs for 99% (Table 1); similarly, for core adaptation loci, 16.10, 29.20, and 122.90 herbs were needed for 90%, 95%, and 99% coverage, respectively (Table S8). Notably, despite the populations within SZ1 and SZ3 were identical, the datasets delineated distinct sampling requirements for each. Discussion The goals of this study were to dissect the genomic basis underlying local adaptation within the R. palmatum complex, assess the genomic vulnerability of its populations in anticipated climatic conditions, define seed zones for effective germplasm preservation, and develop a quantitative framework to inform sampling strategies. To achieve these aims, we used SNPs arising from the whole-genome resequencing of 43 R. palmatum complex populations to identify signatures of environmental adaptation and to model the distribution of this adaptive variation across the landscape. Our comprehensive analyses revealed a polygenic basis for climate adaptation, which can be neatly summarized by the three distinct seed zones, from which we recommend collecting a relatively small number of individuals to effectively capture 95% of the wide adaptive diversity exhibited by our focal species. We emphasize the profound value of combining population genomic and landscape genomic approaches in predicting and monitoring species’ and populations’ responses to global climate change. Collinearity between geography, neutral genetic structure, and climatic gradients Characterizing GEAs remains a focal point of intense methodological and empirical advancement and there is a growing recognition of the relative contributions of climatic selection, geographic distance, and population structure to genetic differentiation within a given species (Savolainen, et al. 2013; Rellstab, et al. 2015; Lasky, et al. 2022). However, a significant challenge lies in the fact that these factors are often intertwined in natural landscapes (Wang, et al. 2013; Forester, et al. 2018). In our study focusing on the R. palmatum complex, we observed significant patterns of IBD and IBE based on both putatively neutral and adaptive SNPs. Furthermore, the results obtained through pRDA based on pan-adaptation and core adaptation loci revealed that a substantial portion of the explained variance in allele frequencies was shared among the neutral population structure, climate and geography. This likely reflects the post-glacial recolonization of our focal species, which created collinearity among its geographic distribution, genetic structure, and climatic gradients. This collinearity poses a challenge for genomic scans aiming to disentangle genetic variation arising from neutral and selective processes. Although it is common to conduct genome scans using proxies of neutral population structure to mitigate the risk of false positives (de Villemereuil, et al. 2014; Frichot, et al. 2015), such methods may obscure signals of climatic selection and hinder the identification of genes actually involved in climate adaptation (Anderson, et al. 2011; Savolainen, et al. 2013). In this study, we employed two GEA methods: LFMM, which treats population structure as a latent factor, and RDA without structure correction. We then utilized the pan-adaptation loci derived from the combined results of these two methods and the core adaptation loci identified through overlapping analysis to separately estimate genomic vulnerability, delineate seed zones, and evaluate germplasm conservation strategies. Although the dataset of core adaptation loci is thought to bolster confidence in frequency of true positives, it tends to skew detection towards regions undergoing pronounced selective sweeps (François, et al. 2016; Forester, et al. 2018). Conversely, the pan-adaptation loci, despite encompassing much more loci bearing weak selective signatures and those with small effects, may elevate the incidence of frequency of false positives. Nevertheless, a recent study has revealed that in scenarios where environmental gradients exhibit strong covariance with recolonization routes, we must acquiesce to a heightened rate of false positives if our aim is to pinpoint the genomic regions implicated in adapting to these gradients (Capblancq, et al. 2023). Signals of local adaptation and genomic vulnerability under anticipated climates Local adaptation typically involves a polygenic foundation governed by numerous genetic loci (Savolainen, et al. 2013; Barghi, et al. 2020). Under a polygenic adaptation model, natural selection operates to alter the allele frequencies of multiple causal loci (Barghi, et al. 2020; Fagny & Austerlitz, 2021). Our findings suggest that a certain degree of multilocus selection—specifically, selection targeting numerous loci with small effects in response to environmental gradients—has influenced the adaptive capacity of populations across both western and eastern lineages. Within our focal species, adaptation manifests along two primary axes that distinguish populations in the western and eastern regions of its range. While the precise adaptive traits and their underlying genetic bases remain elusive in the R. palmatum complex, our discovery of climate-associated SNPs linked to a diverse array of genes and functional categories hints at the significant role of polygenic adaptation in the species’ evolutionary trajectory. Importantly, the putatively adaptive SNPs identified in this study await validation with phenotypic data, and their molecular functions remain unknown. Furthermore, considering that the genomic diversity of our target species is significantly lower compared to other perennial herbs such as alfalfa (Zhang, et al. 2024) and lettuce (Wei, et al. 2021), epigenetic variations, including DNA methylation, can emerge rapidly in species with limited genetic diversity as a response to environmental stimuli and facilitate swift adaptation to novel environments (Huang, et al. 2017; Aagaard, et al. 2022). Future research endeavors, harnessing advanced analytical techniques from functional genomics and incorporating epigenomic variations into genomic offset estimations, hold the potential to enhance prediction accuracy. Such research can offer a more mechanistic insight into climate (mal)adaptation and provide direct evidence elucidating the functional roles of these outlier loci. Exploring and understanding the evolutionary forces and genomic architecture that underlie contemporary climate adaptation offer a pivotal foundation for predicting species’ responses to ongoing climatic shifts (Aguirre-Liguori, et al. 2021; Bernatchez, et al. 2024). In light of the swift pace of climate change, adaptation necessarily hinges on preexisting genetic variation, such as standing genetic variation, both within and across populations (Aitken, et al. 2008; Barrett & Schluter, 2008; Bomblies & Peichel, 2022). Our species-level estimations of genomic offsets, derived from pan-adaptation and core adaptation loci under various climatic scenarios, generally revealed that populations belonging to the eastern lineage were likely to encounter more pronounced maladaptation. Conversely, populations occupying the western portion of the range were projected to experience a more tempered maladaptation. Populations devoid of preexisting adaptive variability may remain unable to acclimatize in the forthcoming decades without the influx of adaptive variation from external populations. To alleviate local maladaptation, assisted gene flow from populations exhibiting lower genomic offsets within the same lineage emerges as a potential strategy. This is particularly pertinent given the limited gene flow between western and eastern lineages and the potential risk of outbreeding depression resulting from the admixture of individuals from distinct evolutionary lineages (Hufford & Mazer, 2003; Mijangos, et al. 2015; Grummer, et al. 2022). The feasibility of this resilience strategy is further supported by the inconsistent genomic offsets observed in species with high genetic structure, as evident in both our study and other recent investigations (Lind, et al. 2024; Lind & Lotterhos, 2024). Therefore, future assessments of genomic offset should be conducted at the lineage level for species with high genetic structure, if the populations and individuals per population across different lineages meet the criteria of sampling density (Aguirre-Liguori, et al. 2023). Although certain studies have emphasized the need for caution when utilizing genomic offset estimates to inform management decisions, due to inherent concerns such as the presence of local adaptation (Lind & Lotterhos, 2024), fixed GEAs across varying climates (Capblancq, et al. 2020; Rellstab, 2021), disregard for phenotypic plasticity (Foden, et al. 2019) and genetic load (Brady, et al. 2019), which can collectively affect the reliability of genomic offset; nevertheless, for immediate assisted gene flow efforts, offset models may offer the highest degree of precision and relevance. Effectiveness of seed zone delineation and germplasm conservation An effective delineation of seed zones must accurately reflect patterns of local adaptation among populations of the targeted species, ensuring that germplasms sourced from each zone can be strategically deployed within environmentally similar ranges to optimize adaptation and productivity, while maintaining acceptable risk levels (Li, et al. 2017). Historically, characterizing the nature and extent of local adaptation, as well as the subsequent delineation of seed zones, has typically relied on reciprocal common garden experiments (Bower, et al. 2014; Bucharova, et al. 2017). However, given the impossibility of acquiring wild seeds representative of all, or even most, historically occupied climates by the species, we have sought to utilize correlations between multivariate genotypes and their respective climate spaces as a proxy for adaptation. In this study, by leveraging adaptive genomic and climate data specific to our focal species, we elucidated that the range of the R. palmatum complex could be most parsimoniously partitioned into three distinct seed zones, which best represent the species’ adaptive boundaries. Notably, the longitudinal boundaries of these three seed zones roughly correspond to the phylogeographical divide between western and eastern lineages (Wang, et al. 2018; Feng, et al. 2020), which have traditionally relied on genetic variation to delineate management units (Funk, et al. 2012; García-Dorado & Caballero, 2021). This concordance suggests a parallelism between population structure and adaptation, particularly when axes of recolonization align with those of climate differentiation (Sandercock, et al. 2024). We also estimated the sampled individuals for each seed zone based on the adaptive genomic diversity derived from pan-adaptation and core adaptation loci, respectively. The differing estimations between these two datasets highlight the need for differentiated sampling strategies. The divergent sampling strategies imply that, when formulating germplasm preservation strategies, relying on sampling recommendations based on pan-adaptation loci is preferable, as it enables the preservation of maximal adaptive genetic diversity, thereby enhancing future population resilience, albeit with higher false-positive rates compared to core adaptation loci. However, given the possibility that some adaptive genomic diversities may remain undetected even with the utilization of pan-adaptation loci in our investigation, it would be wise to adopt a cautious approach and conduct extensive sampling within the three seed zones when formulating a restoration plan for our focal species. Acknowledgements We extend our heartfelt gratitude to Prof. Fang Du from Beijing Forestry University, Prof. Nian Wang from Shandong Agricultural University, and Dr. Shan-Shan Zhu from Ningbo University for their invaluable suggestions and comments, which have significantly enhanced the initial manuscript. This research was funded by the National Natural Science Foundation of China (32271550 to L.F., 32371911 to Y.W., and 32370408 to X.W.) and the Natural Science Basic Research Program of Shaanxi Province (22JHZ005 to X.W.). not-yet-known not-yet-known not-yet-known unknown Author contributions L.F. conceived and supervised the study. T.Z., Z.W., and X.W. performed the sample collections. L.F., C.W., L.Z., and Y.W. conducted all bioinformatics analyses. L.F. and C.W. wrote the manuscript, with contributions from J.W. and X.W. All authors approved the final manuscript. Competing interests The authors declare no competing interests. Data availability The whole-genome resequencing data for 213 individuals belonging to the Rheum palmatum complex have been deposited in the National Genomics Data Center (https://ngdc.cncb.ac.cn) under the accession number PRJCA033457. The VCF dataset, alongside the location records of 43 R. palmatum complex populations and the climatic data utilized for the identification of putatively adaptive loci and the estimation of genomic offset, are accessible at https://doi.org/10.6084/m9.figshare.28060457.v1. All scripts used in this study are available at https://github.com/Feng-Li-hub/Landscape-genomes-of-Rheum-palmatum-complex. References Aagaard, A., Liu, S., Tregenza, T., Braad, L.M., Schramm, A., Verhoeven, K.J.F., Bechsgaard, J., & Bilde, T. (2022). Adapting to climate with limited genetic diversity: Nucleotide, DNA methylation and microbiome variation among populations of the social spider Stegodyphus dumicola . Molecular Ecology , 31, 5765-5783. 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Fig. S3 Demographic histories of the three lineages within the Rheum palmatum complex. Fig. S4 Spearman’s correlation coefficient (two-sided test) among 19 environmental variables, alongside the ranked importance of these variables determined through gradient forest analysis. Fig. S5 Venn diagram of the putatively adaptive SNPs identified within the Rheum palmatum complex and their distributions across the whole-genome of R. palmatum . Fig. S6 Patterns of genetic differentiation within the Rheum palmatum complex. Fig. S7 The adaptive landscape of Rheum palmatum complex retrieved from gradient forest analyses. Fig. S8 The Adaptive landscape and adaptive genetic turnover of Rheum palmatum complex retrieved from redundancy analysis. Fig. S9 Predicted genomic offsets across the range of Rheum palmatum complex in response to future climates, derived from core adaptation loci using gradient forest and redundancy analysis. Fig. S10 Predicted genomic offsets of the western and eastern lineage populations derived from species-level estimations, utilizing both pan-adaption loci and core adaptation loci via redundancy analysis. Fig. S11 Consistency in environmental accuracy and weighted importance rankings outputted by trained gradient forest models utilizing pan-adaptation loci and core adaptation loci at species and lineage levels. Fig. S12 Predicted genomic offsets at the lineage level based on pan-adaptation lociand core adaptation loci using redundancy analysis. Fig. S13 Relationships between genetic diversity or various proxies of genetic load (y-axis) and genomic offset (x-axis) via gradient forest based on pan-adaptation loci. Fig. S14 Relationships between genetic diversity or various proxies of genetic load (y-axis) and genomic offset (x-axis) via redundancy analysis based on pan-adaptation loci. Fig. S15 Relationships between genetic diversity or various proxies of genetic load (y-axis) and genomic offset (x-axis) via gradient forest based on core adaptation loci. Fig. S16 Relationships between genetic diversity or various proxies of genetic load (y-axis) and genomic offset (x-axis) via redundancy analysis based on core adaptation loci. Fig. S17 Intra-cluster variation (black solid line) and its reduction (blue bars) with increasing number of clusters. Fig. S18 Predicted seed zones based on core adaptation loci and their range shifts to minimize genomic offset under climate change. Supplementary tables Table S1 . Geographical sampling information and summary statistics of genetic diversity and Tajima’s D values across populations in this study. Table S2 . Summary statistics of whole genome resequencing data for samples used in this study. Table S3 . Population structure (pairwise F ST ) among the 43 populations of the Rheum palmatum complex. Table S4 . Environmental variables used in this study derived from WorldClim. Table S5 . List of the genes associated with the putatively adaptive loci identified by genomic scans, including gene names, a description of the associated protein functions. Table S6 . The influence of climate, geography and neutral genetic (excluding the pan-adaptation loci) structure on genetic variation of the Rheum palmatum complex decomposed with partial redundancy analysis (pRDA) using pan-adaptation loci. Table S7 . The influence of climate, geography and neutral genetic (excluding the pan-adaptation loci) structure on genetic variation of the Rheum palmatum complex decomposed with partial redundancy analysis (pRDA) using core adaptation loci. Table S8 . Sampling estimates for capturing adaptive diversity across wild seed zones of the Rheum palmatum complex based on core adaptation loci. Figures and figure legends Fig. 1 Population structure and genetic differentiation within the Rheum palmatum complex. (a) Geographical distribution of the 43 populations, with color-coded circles representing specific lineages identified by STRUCTURE. (b) Population assignment of 201 individuals based on STRUCTURE. Panels (c) and (d) represent the isolation-by-distance analysis (two-sided Mantel test) and isolation-by-environment analysis (two-sided partial Mantel test) for putative neutral loci, respectively, with shadows indicating the 95% confidence interval. not-yet-known not-yet-known not-yet-known unknown Fig. 2 Predicted spatial variation in population-level genetic composition derived from gradient forest analysis for (a) pan-adaptation loci and (b) core adaptation loci. Regions shaded in similar colors are anticipated to host populations exhibiting comparable genetic composition. The biplot in each subfigure illustrates the influence of climate variables on the predicted genetic turnover, with labeled vectors highlighting the impact of the six top climate variables. Fig. 3 Predicted genomic offsets across the range of Rheum palmatum complex in response to future climates, derived from pan-adaptation loci using gradient forest (panels a-d) and redundancy analysis (panels e-h). Panels (a) and (e) represent the scenario of shared socioeconomic pathways SSP126 in 2050, while (b) and (f) depict SSP126 in 2090. Similarly, (c) and (g) illustrate SSP 585 in 2050, and (d) and (h) show SSP585 in 2090. The size of the blue circles in each panel corresponds to the magnitude of the genomic offset under the respective climate scenarios. Fig. 4 Predicted genomic offsets of the western and eastern lineage populations derived from species-level estimations, utilizing both pan-adaption loci (panels a and b) and core adaptation loci (panels c and d) via gradient forest. On the x-axis, the numerals 5026 and 9026 denote the scenarios corresponding to the shared socioeconomic pathways SSP126 in 2050 and 2090, respectively, while 5085 and 9085 represent the SSP585 scenarios in 2050 and 2090, respectively. Fig. 5 Predicted genomic offsets at the lineage level based on pan-adaptation loci (panels a-h) and core adaptation loci (panels i-p) using gradient forest. Panels (a) and (i) depict the offsets for western lineage populations under the scenarios corresponding to the shared socioeconomic pathways SSP126 in 2050, while (b) and (j) represent the same for 2090. Panels (c) and (k) exhibit offsets for SSP585 in 2050, and (d) and (l) for 2090. For eastern lineage populations, panels (e) and (m) display offsets under SSP126 in 2050, (f) and (n) for 2090, (g) and (o) for SSP585 in 2050, and finally, (h) and (p) for 2090. Fig. 6 Predicted seed zones based on pan-adaptation loci and their range shifts to minimize genomic offset under climate change. Panel (a) represents predicted three seed zones for the Rheum palmatum complex using gradient forest (GF). The biplot in subfigure illustrates the GF-predicted genomic variation used for seed zone delineation. Panels (b-e) represent the predicted range adjustments for each seed zone to minimize genomic disparities derived from pan-adaptation loci under various climate scenarios. Solid-colored numerals denote the current centroids of the seed zones, whereas numerals on a white background indicate the anticipated centroid shifts in alignment with future climate projections. Panels (b) and (c) correspond to the SSP126 socioeconomic pathway scenarios in 2050 and 2090, respectively. Panels (d) and (e) reflect the SSP585 scenarios in 2050 and 2090, respectively. The colored pixels represent the predicted relocation points for each seed zone, where genomic disparities are minimized based on future climate forecasts (red = Seed Zone 1, yellow = Seed Zone 2, green = Seed Zone 3). The inset table clarifies the distance in kilometers between the present and predicted centroids for each seed zone. Table 1 . Sampling estimates for capturing adaptive diversity across wild seed zones of the Rheum palmatum complex based on pan-adaptation loci. 1 8 90 8.80 (7.90, 9.70) 95 16.70 (15.46, 17.94) 99 74.60 (68.00, 81.20) 2 29 90 30.60 (28.22, 32.98) 95 58.70 (53.14, 64.26) 99 280.80 (257.21, 304.39) 3 6 90 5.55 (5.21, 5.89) 95 9.75 (9.22, 10.28) 99 35.30 (33.05, 37.55) Information & Authors Information Version history V1 Version 1 10 March 2025 Peer review timeline Published Molecular Ecology Version of Record 3 Oct 2025 Published Copyright This work is licensed under a Non Exclusive No Reuse License. Collection Molecular Ecology Keywords climate change genomic vulnerability germplasm conservation local adaptation rheum palmatum complex seed zone Authors Affiliations Li Feng 0000-0002-8252-9463 [email protected] Xi'an Jiaotong University View all articles by this author Cong-Ying Wang Xi'an Jiaotong University View all articles by this author Li-Pan Zhou Ninth Hospital of Xi'an View all articles by this author Yihan Wang Henan Agricultural University View all articles by this author Jing Wang 0000-0002-3793-3264 Sichuan University View all articles by this author Zheng-Yuan Wang Xi'an Jiaotong University View all articles by this author Tao Zhou Xi'an Jiaotong University View all articles by this author Xu-Mei Wang 0000-0002-1468-6635 Xi'an Jiaotong University View all articles by this author Metrics & Citations Metrics Article Usage 347 views 184 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Li Feng, Cong-Ying Wang, Li-Pan Zhou, et al. 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