The impact of Quaternary climate change on the historical population dynamics of Psammochloa villosa, a typical desert herb from northwest China

The impact of Quaternary climate change on the historical population dynamics of Psammochloa villosa, a typical desert herb from northwest 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 The impact of Quaternary climate change on the historical population dynamics of Psammochloa villosa, a typical desert herb from northwest China Jia Rui Jin, Yu Ping Liu, Xu Su, Ting Lv, Ying Hui Zheng, Qian Yang, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6516707/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Climatic oscillations and geographic barriers since the Quaternary have profoundly influenced plant distribution patterns, driving substantial species differentiation through range fragmentation. Investigating the interplay between genetic structure divergence and population dynamics under climatic forcing remains a central challenge in evolutionary biology. Psammochloa villosa (Poaceae), a dominant perennial grass endemic to the Inner Mongolian Plateau's desert steppe, serves critical functions in grassland restoration and livestock forage provision. Notably, its wind-pollination strategy and distant hybridization capacity establish this species as a model system for studying panmictic population equilibrium. This study employs a dual-marker approach, combining 10 low-copy nuclear gene loci (LCNG) with 13 SSR markers, to elucidate the population genetic architecture across 43 natural populations ( n = 210 individuals) spanning the distribution range of P. villosa . The results showed that the populations of P. villosa were distinctly divided into two major branches, Group 1 and Group 2, separated by the Yinshan Mountains, with Group 2 exhibiting higher genetic diversity ( H d = 0.990, π = 0.00145). The populations showed significant diversity and phylogeographic structure, with the majority of genetic variation originating from differences between populations ( F ST (LCNG) = 0.766; F ST (SSR) = 0.577). The Mantel test revealed a positive correlation between genetic distance and geographic distance. Subsequent correlation analysis of genetic differentiation (measured by F ST ) with climatic factors demonstrated an overall positive association, although only elevation showed a statistically significant correlation with F ST values. Demographic history analyses revealed that both Group 1 and Group 2 exhibited smaller effective population sizes compared with N A . Phylogenetic divergence analysis estimated that P. villosa and Achnatherum splendens diverged approximately 3.26 Ma, whereas the differentiation between Group 1 and Group 2 initiated around 0.38 Ma. Notably, bidirectional yet asymmetric gene flow was detected between the two groups. Geographical barrier analysis identified significant genetic discontinuities corresponding to major mountain ranges, including the Yinshan, Helan, Ordos Plateau, and Yabulai mountains. These findings collectively suggest that Quaternary uplift of the Tibetan Plateau, coupled with the divergent monsoon influences east and west of the Yinshan Mountains and progressive habitat fragmentation, have driven the observed genetic differentiation in P. villosa populations through environmental adaptation. Poaceae Psammochloa villosa genetic diversity genetic differentiation historical demography gene flow Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 1 Introduction Plant evolution is intricately linked to climatic and environmental dynamics, where these transformative forces not only drive species differentiation but also catalyze significant shifts in biogeographical distribution patterns [ 1 ] . The “Quaternary glaciation,” which began approximately 2.58 Ma, is a critical phase in the Earth’s environmental evolution, marked by multiple expansions and contractions of glaciers. The climate experienced dramatic fluctuations and frequent alternations between cold and warm periods. During this time, some plants were forced to adapt and evolve, driving changes in plant geographical distribution patterns and the evolution of ecosystems [ 2 ] . Over the past 30 years, many scholars have conducted biogeographical studies on various plant species worldwide, hypothesizing about the refugia of the Quaternary glaciation and the post-glacial expansion routes [ 3 – 4 ] . The molecular clock hypothesis suggests that the degree of difference between homologous genes of any two species should be approximately constant with their divergence time. In other words, the longer the time since the ancestral species began to diverge, the greater the molecular information difference between species [ 5 ] . Therefore, researchers often use population genetics methods, employing statistical tools to study changes in gene frequencies, gene flow, genetic drift, and other data within populations. Combining these tools with biogeographical methods, they explore how species respond to environmental changes, how they evolve through gene flow and isolation, and how their distribution patterns and historical evolution are shaped by geographical space [ 6 – 8 ] . In recent years, with the rapid development of molecular biology, DNA molecular markers have been extensively applied in biogeographical studies. Compared with morphological, cellular, and biochemical markers, molecular markers have the advantages of being numerous, highly polymorphic, predominantly "neutral" and co-dominant, and often expressed in DNA form, making them a relatively ideal new genetic marker. In eukaryotic research, plastid genes such as those from mitochondria and chloroplasts are widely used due to their easy acquisition, structural similarity, and sequence conservatism [ 9 ] . Ma et al. [ 10 ] studied the phylogenetic relationships of Xanthopappus subacaulis using the complete chloroplast genome. They found that X. subacaulis forms an independent and well-supported clade within the Asteraceae family. Chen et al. [ 11 ] studied the mitochondrial genome characteristics of Elymus magellanicus and reconstructed the phylogenetic relationships of Poaceae. The results indicated that E. magellanicus is closely related to Hordeum vulgare , Aegilops speltoides , Triticum aestivum , and Triticum aestivum . Park et al. [ 12 ] elucidated key features of the organelle structure, gene content, and DNA transfer dynamics based on the high-quality chloroplast and mitochondrial genome assemblies and the draft nuclear genome of Cicuta virosa . Their findings on gene loss, DNA transfer to the nucleus, and structural diversity among related species provide insights into the evolution of organelle genomes in the Apiaceae family. However, chloroplast-based molecular markers also have limitations. Chloroplast genes are inherited in a strictly maternal manner, carry limited evolutionary information, and are difficult to use for revealing deep phylogenetic relationships within large population groups [ 13 ] . Compared with organelle genes, nuclear genes have more complex evolutionary patterns, including gene duplication and gene loss. Currently, commonly used DNA molecular markers include restriction fragment length polymorphisms (RFLP), random amplified polymorphic DNA (RAPD), amplified fragment length polymorphisms (AFLP), simple sequence repeats (SSR), and single-nucleotide polymorphisms (SNP), among others [ 14 ] . Low-copy nuclear genes (LCNG) with fewer copies in a given organism's genome, inherited from both parents and with abundant information sites, are less affected by recombination and are capable of preserving parental alleles over time. In addition, the evolutionary rate in intron regions is relatively fast, making them widely used to explore the phylogenetic relationships of plant hybrids and allopolyploid groups, population geographic distribution patterns, origins and evolution, and population historical dynamics [ 15 – 16 ] . SSR molecular markers are DNA sequences consisting of repeat units of approximately one to six nucleotides, offering advantages such as rich polymorphism, good repeatability, strong stability, co-dominance, simplicity in operation, and broad distribution [ 17 ] . They are among the most widely used molecular markers. Currently, SSR molecular markers are widely applied in plant genetic diversity detection, kinship analysis, and gene mapping, among other areas. A study on the genetic diversity and population history dynamics of the species Camellia azalea based on SSR molecular markers found that C. azalea has moderate to high levels of genetic diversity. However, its current ecological niche is very narrow, and it is predominantly distributed along stream banks. Recent human activities have altered the gene flow of this species, which may have contributed to the strengthening of genetic structure in new individuals [ 18 ] . Using population transcriptomes, complete chloroplast genomes, genotyping sequencing, and microsatellite markers, Liu et al. [ 19 ] found that geographic, geological, and environmental factors are key contributors to shaping the cryptic intraspecific divergence and population dynamics of the cold-tolerant perennial herb Notopterygium oviforme . Psammochloa villosa is the type species of the genus Psammochloa , belonging to the family Poaceae and the subfamily Stipeae, 2 n = 2 x = 46 [ 20 ] . P. villosa is a typical clonal plant distributed mainly in the sandy areas of the Inner Mongolia Plateau and its neighboring regions in China, including the Badain Jaran Desert, the Ulan Buh Desert, the Mu Us Sandy Land, the Hexi Corridor in Gansu, northern and central Ningxia, and the sandy regions of northern Shaanxi. It has well-developed rhizomes and a growth cycle that lasts more than 2 years. Like other early-growth and drought-tolerant plants, P. villosa demonstrates remarkable drought tolerance and adaptability to aeolian stresses, particularly sand burial and wind erosion. As a pioneer psammophyte, this species exhibits characteristic early-successional traits including efficient sand stabilization through rapid vertical accretion, extensive lateral spread via rhizomatous growth, and significant potential for initiating vegetative recovery in mobile dune ecosystems [ 21 ] . Furthermore, its inflorescences are relatively large and elongated, with many spikelets and large seeds, which have significant economic value [ 22 ] . On the one hand, it improves ground vegetation cover in highly heterogeneous degraded ecosystems; on the other hand, it provides abundant forage for livestock, thus reducing grazing areas and slowing down desertification caused by human activities [ 23 ] . Furthermore, P. villosa exhibits wind pollination and outcrossing characteristics, making it an ideal population for random mating in an ideal state [ 24 ] . Currently, research on P. villosa focuses primarily on morphological traits [ 25 ] , ecological adaptability [ 26 ] , genetic diversity [ 27 ] , and germplasm resources [ 22 ] . Through the comparative observation of the roots, stems, leaves, and flowers of P. villosa , it was found that its clonal characteristics enable it to have a stronger survival ability in arid and barren environments. In addition, the pore density in the leaf epidermis was found to be positively correlated with the degree of environmental drought [ 25 ] . Li et al. [ 28 ] analyzed the genetic variation of seven P. villosa populations using inter-simple sequence repeat (ISSR) molecular markers, concluding that the ratio of genetic variation between populations to variation within populations was greater than 1.00. Liu et al. [ 29 ] analyzed transcriptomic data under different stress conditions, indicating that the PvTIP41 gene is the most stable internal reference gene in leaves under drought and salt stress, as well as in stems under salt stress, providing important references for future genomic research on the plant's stress resistance mechanisms. Although Lv et al. [ 30 ] used AFLP molecular markers and spatial distribution models to predict the population formation history and distribution dynamics of P. villosa , AFLP is uniparental in inheritance and unable to distinguish between homozygous and heterozygous states, and the use of a single type of molecular genetic marker may lead to uneven coverage of polymorphisms. Therefore, this study focuses on P. villosa , a unique desert herbaceous plant from the Inner Mongolia Plateau and its neighboring areas. Based on extensive field surveys and sampling, the study employs a combination of LCNG and SSR markers to examine the genetic variation distribution patterns of P. villosa populations and to determine their spatial genetic structure within populations, between populations, and across the entire geographical distribution range, comprehensively elucidating its modern distribution pattern and phylogenetic evolution history. The study aims to reveal how herbaceous plants in the arid desert areas of northwest China respond to Quaternary climate changes, providing a theoretical basis for the conservation and utilization of P. villosa germplasm resources. 2 Materials and Methods 2.1 Plant materials We collected experimental accessions of 101 natural populations of Inner Mongolia and removed the redundant accessions. Finally, we obtained a diverse collection of 43 populations and 210 individuals of P. villosa . These accessions covered the main natural geographical distribution area of P. villosa in China (Fig. 1 ). Information about the accessions, including the collected location, habitat status, longitude, latitude, altitude, and number of individuals, is provided in Additional file 1: Table S1 . We placed healthy leaves in silica gel to dry rapidly and used these for total DNA extraction. Young plants were frozen at − 80℃ after processing in liquid nitrogen for transcriptome sequencing. 2.2 Transcriptome sequencing and data analyses Total RNA was extracted separately from each tissue using the TaKaRa MiniBEST Plant RNA Extraction Kit. The sequencing platform PacBio was used for full-length transcriptome sequencing of mixed samples of roots, stems, and leaves of P. villosa . Raw sequencing data were processed, including read filtering, error correction, and quality control. We obtained unigenes by performing clustering and redundancy removal of assembled sequences using Cdhit software. Subsequently, using the transcriptome database of Achnatherum splendens as a reference (GenBank: SRP068186), we retrieved the homologous genes of P. villosa transcripts by OrthoMCL software. When the retrieved gene showed homology with A. splendens but not with its own sequence, it was considered a successful selection of LCNG in this species [ 31 ] . We designed primers and carried out experimental validation for 72 genes randomly selected from the screened LCNG of this species on the basis of primer software [ 32 ] . Meanwhile, we used MISA software to search, locate, and identify simple repetitive sequences in the obtained transcriptome data. From the identified SSR loci, we designed primers and screened out 13 pairs of SSR primers with high polymorphism, good repeatability, and strong representativeness. Finally, we conducted population genetics experiments on the screened 10 pairs of LCNG and 13 pairs of SSR primers using the DNA of 43 populations and 210 individuals as templates (Tables 1 and 2 ). Table 1 Characteristics of ten low-copy nuclear gene primers developed in P. villosa Locus Primer name Primer sequences (5'-3') LCR (bp) T a (℃) 14031 Locus1 F: TTCAGTGGCTATCCTGTTCC R: CTGCACGTTAAACTACTCACAAG 716 57.1 14004 Locus2 F: AGGTGAGGTGGGATTTGAGC R: AAGTGGGCGGATGGTTTC 736 61.5 13593 Locus3 F: CCAGCTTAGTATGGCAGAGG R: CCACGCTTTGTTGATGTTTC 772 58.8 14111 Locus4 F: CCTAACGGAAACGAGTGCC R: CCTCATCAGTTCCCGCAGAC 1217 61.6 14075 Locus5 F: AGTTGCCAGTGGGTTTG R: CGAGCACATGGAGGTATCTT 996 55.7 13604 Locus6 F: TCGCATTCCTCTTTCTCACCAAC R: CTATTCGACTTCCTCAGCTCCCT 735 60.7 14192 Locus7 F: TGTCGGTAACCTGGATAGAGC R: TGATTAACTCCCTGACTGCTTC 807 59.1 14928 Locus8 F: GAGCGGTTTGTCCCTGAT R: GTCCAGCTCTAGTGCCTGTT 918 58.4 14224 Locus9 F: GCACTCAGTCACGGCAAAC R: AGCAACAGCCAGCTTCTCAC 884 60.9 14612 Locus10 F: GATGCAGTGGCAGTGTTTG R: CTCTTACCGTCAGGCTCGT 887 57.1 Note: AFL, Aligned full length; LCR, Length of coding region; T a , Optimal annealing temperature. Table 2 13 pairs of SSR primer information in P. villosa Locus Primer name Primer sequences (5'-3') Motif Allele size (bp) T m (℃) Pvj1 Locus1 F: AAGAGCATTCCTTGCAGCA R: ATGACTCCGGCATGTGGTT (TC) 10 342–378 60.8 PvH4 Locus2 F: ATCCTTTGAGCATCCACCCCC R: GCTTGCTGAGGAGTCTCTGCGTCT (TCC) 5 202–341 61.0 Pv14 Locus3 F: GATCCTGGGTTTGGAAGTG R: CTTATAGTACTCTTCAAGCTCCTT (AGA) 6 247–283 54.6 Pv15 Locus4 F: AGCAGATTGGCAGAGAACTCAGG R: CTGCGCCTTCCTCCATCAT (GTG) 7 309–342 63.8 Pv21 Locus5 F: CCTCTACCGCCTCTTCGTCTCC R: GCGAACCAGAAGAGGTAGTACTC (CGC) 6 266–320 63.7 Pv33 Locus6 F: GCACCACGCCGGCAAGAAA R: CCGTGGACGACTTGCCGAAGA (AGG) 6 249–273 61.1 Pv39 Locus7 F: TCTGAGGTACAATATCCGAAGC R: ACTTGCGATTGAAGCGTGT (AGC) 7 306–324 59.4 Pv44 Locus8 F: AAGTAAGCCCAGAGCAGGAG R: AATCAGCCAATCATCAATCTCA (GGA) 6 230–239 57.4 Pv49 Locus9 F: GCCTTCGGGTGCGTGAGC R: CCTCGTCTTCCCCACGGTGA (GCG) 5 293–311 64.4 Pv50 Locus10 F: CGGCTGGACGAGGCGAAGGT R: CAGGAGGAGGGTGTTGGACGTGAC (GCG) 6 284–296 64.0 Pv51 Locus11 F: CGCCGCGTTCCCGCTGTA R: CCGTCCCCGCATTGCTGC (CGG) 6 216–258 64.0 Pv8 Locus12 F: TGGCAGTGGATGTGGAGCTGAT R: CACGGATGGATGGCACTGCTAC (AGC) 6 306–345 65.4 Pv23 Locus13 F: GACTAACCTCCTGGGCACT R: TGCCGCTGCATATGCCGAAG (CGG) 6 234–252 63.7 2.3 Population genetic structure To infer the population genetic structure of P. villosa , this study employed three methods to analyze the geographical distribution patterns of SSR and low-copy nuclear gene data. (1) After converting the "01" matrix into a genetic distance matrix using GenAIEx v.6.5 software, we constructed UPGMA clustering and principal coordinate analysis (PCA) according to R scripts. (2) We analyzed the spatial analysis of molecular variation among different geographical groups of P. villosa by SAMOVA v.1.0 software [ 33 ] and deduced the optimal population combination from geographical and genetic information. The process set a range of K values from 2 to 10, with each K value undergoing 100 heuristic searches. The optimal population combination was determined by selecting the K value that maximized genetic differentiation between groups ( F CT ), minimized genetic differentiation within groups ( F SC ), or achieved stable F CT . (3) We tested the different genetic clusters among species using Structure v.2.3 software [ 34 ] . The range of genetic clusters was set to 1 ≤ K ≤ 10. The likelihood values for each cluster ( K ) were estimated during the operation, and it was assumed that there was correlation in allele frequencies between different clusters. Subsequently, the most likely number of genetic clusters ( K ) for the population was estimated according to the statistical method of Pritchard et al. [ 35 ] and the calculation method of Δ K [ 36 ] . 2.4 Analysis of genetic diversity and genetic variation Diversity parameters are important for measuring the level of population genetic diversity [ 37 ] . To estimate the genetic diversity level of P. villosa , this study calculated various genetic diversity parameters for SSR and low-copy nuclear gene data with GenAIEx v.6.5 [ 38 ] and DnaSP v.5.1 software, including the number of individuals ( N ), percentage of polymorphic loci (PPL), polymorphic information content (PIC), observed number of alleles ( N a ), effective number of alleles ( N e ), Shannon's information index ( I ), observed heterozygosity ( H o ), expected heterozygosity ( H e ), haplotype diversity ( H d ), nucleotide diversity ( π ), average number of nucleotide differences ( K ), Nei's genetic diversity ( h ), total population diversity ( H t ), genetic diversity within populations ( H s ), inbreeding coefficient ( F IS ), population differentiation ( F ST ), genetic differentiation between populations ( G ST ), and gene flow ( N m ). The classic measure of genetic variation is Wright’s fixation index ( F -statistic, F ST ), a measure of detecting the degree of genetic differences at the level of species populations [ 39 ] . The theory suggests the following guidelines for the interpretation of F ST values: 0 ~ 0.05, little genetic variation; 0.05 ~ 0.15, moderate genetic differentiation; 0.15 ~ 0.25, great genetic differentiation; and above 0.25, very great genetic differentiation [ 40 ] . This study estimated the F ST value using the molecular variance method in Arlequin v.3.5 software [ 41 ] . Two different grouping populations of P. villosa were subjected to analysis of molecular variance (AMOVA) with SSR and LCNG data. All populations were considered as one group to calculate the genetic variation between populations and within populations, and to estimate the levels of genetic variation between populations, within populations, among groups, and between groups based on the spatial AMOVA (SAMOVA) grouping results. Significance levels were tested through 1000 nonparametric permutations. In addition, we estimated the genetic differentiation coefficients G ST and N ST of populations in the SAMOVA grouping and the overall level of low-copy nuclear gene data by the PERMUT software. When N ST > G ST ( P < 0.05), it indicated the presence of significant phylogeographic structure among populations. 2.5 Correlation analysis, mismatch distribution, and neutrality tests To assess whether the population's genetic structure was affected by spatial distance or other environmental factors, this study conducted a Mantel test using R scripts that examined the correlation between the genetic distance and the geographic distance. Moreover, we investigated the correlation between genetic diversity, genetic differentiation, and several ecological factors. We conducted mismatch distribution analysis on the population of P. villosa , aiming to explore the historical dynamics of this species using Arlequin v.3.5 software. Mismatch distribution analysis is a method used to assess the fit between the expected value curve and the observed value curve based on the distribution of nucleotide differences among different haplotypes [ 42 ] . If the mismatch distribution curve is unimodal, it indicates that the species has recently experienced population expansion [ 43 – 44 ] , whereas if the curve is multimodal, it suggests that the species has been in a phase of dynamic equilibrium or slow contraction for population size during a certain time period [ 43 ] . In addition, to determine whether the genetic locus conforms to the neutral evolution model. This study performed neutrality tests on the P. villosa population using DnaSP v.5.1 software. This theory assumes that the population maintains a mutation–drift equilibrium during the long-term evolution, and all nucleotides mutated with equal probability. A Tajima's D or Fu's Fs significantly greater than 0 indicates that the population has recently experienced a bottleneck effect or balancing selection. Conversely, a Tajima's D or Fu's Fs significantly less than 0 indicates that the population has undergone directional selection or an expansion event [ 45 ] . 2.6 Demographic history estimation To further investigate the historical dynamics of P. villosa , we simulated the population historical dynamics of 10 pairs of LCNG and 13 pairs of SSR primers based on an approximate Bayesian computation algorithm. This analysis aimed to determine the population divergence events, time, and changes in population size [ 46 ] . According to the population genetic structure of P. villosa , we inferred the migration process of this species. Each scenario was executed 10 6 times, and the best scenario model depends on the posterior distribution probabilities. Moreover, we inferred the goodness of fit for the best scenario through the difference between the observed value and the actual value of principal component analysis (PCA) in DIYABC v.2.0.4 software. The prior distribution parameters for the population history dynamics are presented in Table S2. Furthermore, we simulated the effective population size, effective migration rate, and divergence time of the current and ancestral population for P. villosa using the isolation-with-migration model in the IMa software. Convergence of the runs was assessed by the effective sample sizes (ESS) (≥ 200) using Tracer v.1.7 [ 47 ] and the peak graph conforming to a normal distribution. As there was a lack of fossil records of Stipeae and Psammochloa , estimates of the timescale of divergence were based on an evolutionary rate of µ = 6.1 × 10 − 9 substitutions per site per year in Poaceae plants [ 48 ] , and the generation time was set at 5 years [ 49 ] . 2.7 Estimating divergence time We selected the best nucleotide substitution model by jModelTest software [ 50 ] , which included 11 fundamental nucleotide substitution models. Then, we estimated the divergence time of the population for P. villosa through BEAST software [ 51 ] . Due to the lack of fossil records or the rate of gene fragment evolution of Psammochloa , we performed the molecular clock calibration using the divergence time between Aegilops tauschii and Triticum dicoccoides (2.97 Ma, 2.61 ~ 5.27 Ma) [ 52 ] , as well as the divergence time between Aegilops tauschii and Brachypodium distachyon (32.6 Ma, 27.8 ~ 37.0 Ma) [ 53 ] . The running parameters were set as follows. A relaxed molecular clock model was selected, and the Markov Chain Monte Carlo (MCMC) was run for 10 8 generations, with sampling every 1000 generations. The burn-in was set to 10%. Subsequently, we checked the posterior distribution probability and ensured that the ESS value was greater than 200 by Tracer v.1.6 software. If the ESS value was lower than 200, it was necessary to increase the number of iterations to continue the operation. Meanwhile, we displayed and edited the divergence time tree using Figtree v.1.4.3 software. 2.8 Population genetic barriers and gene flow Population genetic barriers are caused by external environmental factors such as gene flow, genetic drift, and natural selection. Therefore, to infer the impact of population evolutionary history on population genetic structure, we detected genetic barriers among the population of P. villosa with the "Monmonier's maximum difference algorithm" in Barrier v.2.2 software [ 54 ] . When individuals migrate from one population to another, they bring their genes to the new population, resulting in gene flow, which can lead to population homogenization. Consequently, we estimated genetic parameters for SSR data and low-copy nuclear gene data based on Migrate-n v.4.4.3 software [ 55 ] , including migration rates within and between all populations ( M ), effective population sizes ( θ ), and historical gene flow. We set the running parameters as follows: long-chains = 3, long-inc = 100, long-sample = 5000000, burn-in = 1000000, replicate = YES:5, heating = YES:1: {1.000000,1.500000,3.000000,1000000.000000}. 3 Results 3.1 Population genetic structure Achnatherum splendens was used as an outgroup. A dendrogram of P. villosa was constructed on the basis of low-copy nuclear gene data depicting the genetic relationships among the 41 populations (Fig. 2 a). The populations were divided into two main clusters, with a self-proclaimed differentiation rate of 56%. Group 1 included populations from the northeast of the Yinshan Mountains (P1-P12). Group 2 included populations from the Helan Mountains (P13-14、P16-19 and P24-28), the northeastern margin of the Qinghai-Tibet Plateau (P21-23), the Southern Yinshan mountain range (P29-43). Furthermore, the cluster results based on SSR data showed distinct genetic differentiation among populations from the northeast and southwest of the Yinshan Mountains (Fig. 2 b). Because of the low self-proclaimed differentiation rate, the genetic structure for P. villosa was assessed using SAMOVA based on low-copy nuclear gene data. The grouping is optimal when the value of F CT reaches the maximum. The results indicated that this value was the largest (0.50155) with K = 10 (Table 3 ). However, there was a single population forming a group when K was greater than 5, resulting in the inability to detect an effective phylogeographic group. Meanwhile, due to the geographical proximity and similar habitat of P1 and P3, the best grouping was K = 2, for which the 41 populations were divided into two phylogeographic groups (Table 3 ). This result was completely consistent with the phylogenetic tree. Table 3 The optimal numbers of population groups ( K ) inferred by SAMOVA algorithm based on low-copy nuclear gene datasets K Population grouping F CT F ST F SC P K = 2 (1, 2, 3, 4, 6, 7, 8, 9, 11, 12, 13, 14) (15, 16, 18, 19, 20, 21, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45) 0.407 0.822 0.700 0.000 K = 3 (1, 4, 6, 7, 8, 11, 12, 13, 14) (2, 3, 9) (15, 16, 18, 19, 20, 21, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45) 0.435 0.822 0.685 0.000 K = 4 (1, 4, 13) (2, 3, 9) (6, 7, 8, 11, 12, 14) (15, 16, 18, 19, 20, 21, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45) 0.460 0.823 0.671 0.000 K = 5 (1, 4, 13) (2, 3, 9) (6, 7, 8, 11, 12) (14) (15, 16, 18, 19, 20, 21, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45) 0.460 0.822 0.670 0.000 K = 6 (1, 4, 13) (2, 3, 9) (6, 7, 8, 11, 12) (14) (15, 16, 18, 19, 20, 21, 23, 24, 25, 26, 27, 28, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45) (29) 0.468 0.820 0.661 0.000 K = 7 (1, 4, 13) (2, 3, 9) (6, 7, 8, 11, 12) (14) (15, 16, 18, 20, 21, 23, 24, 25, 26, 27, 28, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45) (19) (29) 0.469 0.816 0.653 0.000 K = 8 (1, 4, 13) (2, 3, 9) (6, 7, 8, 11, 12, 14) (15, 16, 18, 28) (19, 20, 21, 23, 24, 25, 26, 27, 30, 31, 32, 34, 37, 38, 41, 44, 45) (29) (33, 36, 39, 40, 42, 43) (35) 0.483 0.794 0.602 0.000 K = 9 (1, 4, 13) (2, 3, 9) (6, 7) (8, 11, 12) (14) (15, 16, 18, 28) (19, 20, 21, 23, 24, 25, 26, 27, 30, 31, 32, 34, 35, 37, 38, 41, 44, 45) (29) (33, 36, 39, 40, 42, 43) 0.492 0.794 0.595 0.000 K = 10 (1, 4, 13) (2, 3, 9) (6, 7) (8, 11, 12) (14) (15, 16, 18, 28) (19, 20, 21, 23, 24, 25, 26, 27, 30, 31, 32, 34, 35, 37, 38, 41, 44, 45) (29) (33, 36, 39) (40, 42, 43) 0.502 0.793 0.585 0.000 The Bayesian clustering method of the STRUCTURE software was further applied to analyze the genetic structure of P. villosa. The most optimal number of population groups ( K ) was determined with the posterior probability LnP(D) and delta K (∆ K ), setting the population size K as 2–15 and 2–10. The results indicated that ∆ K was maximum when K = 2 (Fig. 3 a,b,d,e), indicating that populations of this species could be divided into two subgroups. At the same time, there was gene flow between the two genetic units, according to the grouping results of the STRUCTURE program, where red gene pools represent the Group 1 population and blue gene pools represent the Group 2 population (Fig. 3 c, f). The results of principal component analysis (PCoA) further confirmed the clustering results of STRUCTURE (Fig. 4 ). Overall, the population genetic structures of P. villosa obtained using different methods were almost identical. 3.2 Genetic diversity Haplotype diversity index is an important indicator for measuring the degree of variation within a population. These spliced sequences of 10 primer pairs detected a total of 52 polymorphic sites in 174 samples, with the number of polymorphic loci ( S ) ranging from 2 to 11, and the number of haplotypes ( H ) ranging from 4 to 23 (Table 4 ). The haplotype diversity ( H d ) ranged from 0.479 to 0.854, with an average of 0.658, in which the haplotype diversity was the highest at site 14111 and the lowest at site 14192. These results showed that differences in gene expression contributed to the variation observed in P. villosa . Meanwhile, the nucleotide diversity ( π ) ranged from 0.00062 to 0.00414, with an average of 0.00180, indicating that 10 primer pairs exhibited high polymorphism. Based on the genetic diversity analysis of low-copy nuclear genes in the P. villosa populations, the results showed that H d , π , and K respectively ranged from 0.000 to 1.000, 0.000 to 0.203, and 0.000 to 10.578 (Table S3). This indicated significant genetic diversity differences among P. villosa populations, in which populations located in the central Inner Mongolia Plateau exhibited higher genetic diversity. Simultaneously, this study also analyzed the genetic diversity of P. villosa populations based on the SAMOVA grouping results. Group 2 demonstrated a high level of genetic diversity, with H d and π measuring 0.990 and 0.00145, respectively (Table 5 ). Table 4 Statistics of genetic diversity for 10 pairs of low-copy nuclear genes of P. villosa in the present study Locus Number of polymorphic sites (S) Number of Haplotypes (H) Haplotype diversity ( H d) Nucleotide diversity (π) Average number of nucleotide differences ( K ) G + C Rm 14031 3 4 0.526 0.00083 0.591 0.476 0 14004 3 4 0.569 0.00093 0.684 0.443 0 13593 5 6 0.713 0.00188 1.448 0.462 0 14111 11 23 0.854 0.00355 5.147 0.649 6 14075 5 6 0.512 0.00062 0.617 0.422 0 13604 2 4 0.737 0.00135 0.996 0.589 1 14192 3 4 0.479 0.00111 0.895 0.464 1 14928 4 8 0.533 0.00093 0.851 0.434 1 14224 9 13 0.838 0.00414 3.660 0.493 5 14612 7 10 0.818 0.00262 2.321 0.474 1 Mean - - 0.658 0.00180 1.721 0.491 - Table 5 Genetic diversity for 41 populations of P. villosa based on low-copy nuclear genes in the present study The parameter of genetic diversity Populations of Group 1 Populations of Group 2 All populations Number of polymorphic sites 31 43 52 Number of Haplotypes 47 110 157 Haplotype diversity 0.975 0.990 0.993 Nucleotide diversity 0.00141 0.00145 0.00189 Average number of nucleotide differences 12.251 12.528 16.378 G + C 0.495 0.496 0.496 Rm 13 18 26 An analysis was conducted on the polymorphic loci of 13 pairs of SSR primers using POPGEN 1.32 software, and the results are presented in Table 6 . A total of 104 alleles were detected at the 13 microsatellite loci in the 195 samples of P. villosa , with the number of alleles per locus ( N a ) ranging from 3 to 14 (average of 8). The primer with the highest number of alleles was Pv21, and the primer with the lowest number of alleles was Pv44. The expected heterozygosity ( H e ) and observed heterozygosity ( H o ) of the microsatellite loci ranged from 0.183 to 0.576 and 0.215 to 0.964, respectively. The polymorphic information content (PIC) generated ranged from 0.317 to 0.747, with a mean value of 0.511. the Shannon information index ( I ) ranged from 0.278 to 0.946, with a mean value of 0.567. These results indicate that the 13 pairs of SSR primer marker loci are suitable for genetic research on P. villosa owing to their high polymorphism and genetic diversity. At the population level, the mean values of H e and H o were 0.359 and 0.509, respectively (Table S3). The heterozygosity of P30 and P31 was greater than 0.5, indicating that these two populations have high genetic diversity and relatively low human selection factors. In addition, the fixation index ( F ) can reflect the extent of deviation of a species. If F > 0, it indicates a higher frequency of homozygotes within the population, whereas if F < 0, it suggests a higher frequency of heterozygotes within the population. In view of the above, P16 and P18 have positive values among the 39 populations, whereas the F values for the other populations are less than 0. This indicates a higher frequency of heterozygotes within the P. villosa population (Table S3). Table 6 Summaries of genetic variation statistics for all SSR loci Primer EcoR I/Mse I N a N e F IS F ST I H o H e N m PIC j-F/j-R 10 1.692 -0.329 0.425 0.502 0.431 0.324 0.339 0.523 H4-F/H4-R 11 2.546 -0.674 0.264 0.946 0.964 0.576 0.697 0.747 44-F/44-R 3 1.362 -0.359 0.631 0.287 0.256 0.189 0.146 0.393 39-F/39-R 8 1.941 -0.497 0.484 0.622 0.595 0.397 0.266 0.737 33-F/33-R 6 1.484 -0.255 0.489 0.419 0.338 0.270 0.261 0.485 23-F/23-R 4 1.919 -0.849 0.187 0.662 0.862 0.467 1.090 0.482 15-F/15-R 9 1.509 -0.533 0.365 0.417 0.410 0.268 0.434 0.406 8-F/8-R 5 1.334 -0.176 0.721 0.278 0.215 0.183 0.097 0.594 14-F/14-R 8 2.245 -0.736 0.203 0.834 0.938 0.541 0.980 0.631 50-F/50-R 7 2.066 -0.053 0.113 0.779 0.518 0.492 1.956 0.474 51-F/51-R 8 1.432 -0.452 0.345 0.356 0.318 0.219 0.475 0.317 49-F/49-R 11 2.069 -0.046 0.202 0.789 0.492 0.471 0.989 0.530 21-F/21-R 14 1.482 0.004 0.193 0.482 0.277 0.278 1.047 0.329 Average 8 1.776 -0.381 0.356 0.567 0.509 0.359 0.675 0.511 Note: N a, observed number of alleles; N e, effective number of alleles; F IS , inbreeding coefficient; F ST , variance among populations; I , Shannon's information index; H o, observed heterozygosity; H e, expected heterozygosity; N m , gene flow. PIC, polymorphic information content. 3.3 Genetic differentiation and genetic variation Genetic variation analysis of P. villosa populations using low-copy nuclear gene data revealed that the proportion of genetic variation among populations was 76.6%, and that within populations was 23.4%, with F ST = 0.766 ( P < 0.01) (Table 7 ). These results showed that the genetic variation of this species existed primarily among populations. Meanwhile, this parameter of P. villosa population grouping revealed that 40.71% of the genetic variation was explained, and the genetic variation originated mainly from among populations accounting for 41.53% of the total genetic variation (Table 7 ). Genetic variation analysis of P. villosa populations using SSR data showed similar results. Table 7 Results of analyses of molecular variance (AMOVAs) for P. villosa based on low-copy nuclear genes and SSR data Data Grouping Source of variation df SS VC Percent variation (%) Fixation index LCNG Total populations Among populations 40 2242.692 6.384 Va 76.60 F ST = 0.766 ** Within populations 307 598.808 1.951 Vb 23.40 Total 347 2841.500 8.334 SAMOVA groups Among groups 1 697.447 4.396 Va 40.71 F CT = 0.407 ** Among populations within groups 39 1555.148 4.485 Vb 41.53 F SC = 0.700 ** Within populations 307 589.008 1.919 Vc 17.76 F ST = 0.822 ** Total 347 2841.603 10.800 SSR Total populations Among populations 38 1027.954 58 F ST = 0.577 ** Within populations 156 539.600 42 Total 194 1567.554 Note: df , Degrees of freedom; SS, Sum of squares; VC, Variance components; F CT , Variance among groups relative to total variance; F SC , Variance among populations within groups; F ST , Variance among populations; Significant level: ** P < 0.01. Similarly, the genetic differentiation coefficient among populations based on low-copy nuclear gene data ranged from 0.533 to 0.977, with an average of 0.754, indicating a high level of genetic differentiation among populations (Table S4). The PERMUT analysis results showed that the total genetic diversity ( H t ) of the population was 1.000, while the within-population genetic diversity ( H s ) was 0.721, suggesting that the genetic diversity of the population existed primarily within populations (Table 8 ). The genetic differentiation coefficient ( G ST ) among populations was 0.279, and the genetic differentiation coefficient within populations ( N ST ) was 0.533 (Table 8 ). The coefficient of genetic differentiation in G ST was significantly lower than that in N ST ( P < 0.05), which indicated that there was obvious pedigree geographical structure in the P. villosa populations. Although there were differences in the estimation of genetic differentiation among populations by different primers or different gene fragments, they all showed that there was high genetic differentiation among populations. In addition, the study of genetic differentiation based on SSR data also supported this result, with F ST = 0.356 ( P < 0.01) (Table 6 ). Table 8 Nei'S analysis of genetic diversity of P. villosa populations Item H t H s G ST N ST Mean 1.000 0.721 0.279 0.533 Standard error 0.002 0.034 0.034 0.035 Note: H t, Total gene diversity; H s, The average gene diversity within populations; G ST , Gene differentiation coefficient between populations; N ST , Gene differentiation coefficient within populations. 3.4 Factors influencing species differentiation The Mantel test for IBD was conducted to estimate the correlation between the genetic distance and geographical distance of P. villosa . Positive correlation of genetic distance with geographical distance produced a pattern of isolation by distance. Applying the Mantel test, there was a significant positive correlation between genetic and geographical distances ( r = 0.291, P = 0.010) for the entire sample (Fig. 5 a). A positive correlation was also detected for Group 1 and Group 2 between genetic and geographical distances, respectively (Fig. 5 b, c). Subsequently, correlation analysis between the genetic differentiation ( F ST ) and climate factors consistently revealed a positive correlation, showing a significant correlation only with altitude (Fig. 5 d). These findings confirmed that both geographical distance and altitude strongly influenced genetic differentiation. 3.5 Historical demography The majority of genetic loci in P. villosa exhibited nonsignificant positive values for SSD and H Rag , while those of 14075, 13604, 14192, and 14928 loci showed significantly positive values (Table 9 ). Estimation based on combined fragments showed that both parameters were positively correlated, with SSD and H Rag values of 0.123 and 0.309, respectively (Table 9 ). We tentatively speculated that the species might undergo population expansion. Notably, mismatch distribution analysis for the entire populations generated nonsignificant values of the H Rag and SSD indices. It produced bimodal curves, suggesting rapid population expansion (Fig. 6 ). Accordingly, the neutrality tests resulted in nonsignificant positive values of Tajima’s D , Fu and Li’s D * , and Fu and Li’s F * for genetic loci except 14075 loci (Table 9 ). Conversely, the parameters estimated on the basis of combined fragments all showed highly significant positive values, with values of D , D * , and F * of 2.922, 2.425, and 3.198, respectively (Table 9 ). Overall, these findings confirmed the rejection of population expansion. Table 9 The results of neutrality tests and mismatch distribution of P. villosa at ten nuclear loci Locus Neutrality test Mismatch distribution D D * F * SSD H Rag 14031 0.395 0.736 0.742 0.008 0.075 14004 0.691 0.736 0.859 0.064 0.203 13593 1.539 0.938 1.370 0.077 0.281 14111 1.660 1.729 ** 2.057 ** 0.196 0.458 14075 -0.369 0.938 0.584 0.010 * 0.046 * 13604 2.775 * 0.604 1.554 0.001 * 0.014 * 14192 1.362 0.736 1.124 0.012 * 0.044 * 14928 0.607 0.844 0.912 0.009 * 0.050 * 14224 2.966 ** 1.289 2.300 ** 0.146 0.391 14612 2.250 * 1.097 1.809 * 0.052 0.249 Combine 2.922 ** 2.425 ** 3.198 ** 0.123 0.309 Note: * P < 0.05; ** P < 0.01. Subsequently, the historical demography of P. villosa was simulated using DIYABC v.2.0.4 software. According to the population genetic structure of P. villosa , we designed three scenario models (Fig. 7 ): (1) Group 1 and Group 2 diverged at time T 0 , while Group 1 and ancestral species ( N A ) diverged at time T 1 ; (2) Group 2 and Group 1 diverged at time T 0 , while Group 2 and ancestral species ( N A ) diverged at time T 1 ; (3) the two groups were relatively independent, with a common ancestral species ( N A ), and diverged in a radiating pattern at time T 1 . By comparing the posterior distribution probabilities for three models, Scenario 2 was selected as the most accurate in model simulations with LCNG and SSR data (Table S5). Under Scenario 2, Group 2 diverged from N A approximately 8.20 Ma (95% HPD: 2.81 ~ 14.2 Ma) and 4.42 Ma (95% HPD: 0.396 ~ 13.3 Ma), respectively (Table 10 ). Moreover, the timing of divergence between Group 2 and Group 1 was 3.96 Ma (95% HPD: 1.36 ~ 8.10 Ma) and 0.109 Ma (95% HPD: 0.03 ~ 0.275 Ma), respectively (Table 10 ). As shown in Table 10 , parameter estimates obtained from the best model showed that the effective population size of N A , Group 1, and Group 2 was 2.14 × 10 6 (95% HPD: 5.53 × 10 5 ~2.93 × 10 6 ), 3.84 × 10 5 (95% HPD: 1.40 × 10 5 ~7.51 × 10 5 ), and 4.65 × 10 5 (95% HPD: 1.53 × 10 5 ~9.15 × 10 5 ) based on LCNG, respectively. The effective population size of N A , Group 1, and Group 2 was 2.01 × 10 6 (95% HPD: 4.27 × 10 5 ~2.91 × 10 6 ), 3.55 × 10 5 (95% HPD: 8.67 × 10 4 ~8.74 × 10 5 ), and 6.57 × 10 5 (95% HPD: 2.35 × 10 5 ~1.62 × 10 5 ) based on SSR data, respectively. Group 1 and Group 2 had a smaller effective population size than N A , which indicated that there had been contraction events in the history of P. villosa. This finding was confirmed through the isolation with migration (IM) model. Surprisingly, the divergence times estimated for Group 1 and Group 2 based on the IM model differed from those based on the approximate Bayesian computation (ABC) model. Group 1 and Group 2 began to differentiate about 0.115 Ma (95% HPD: 0.0984 ~ 0.475 Ma) and 0.180 Ma (95% HPD: 0.148 ~ 0.328 Ma) with LCNG and SSR data (Table 11 ), respectively. In addition, the model strongly supported bidirectional and asymmetric gene flow between the two groups, and the migration rate from Group 1 to Group 2 ( M 2→1 = 1.87 or 0.08) was lower than that in the opposite direction ( M 1→2 = 4.99 or 0.19) (Table 11 ). Therefore, we inferred that this species occurred population contraction in the history, and the divergence between Group 1 and Group 2 occurred during the Pleistocene (0.0117 ~ 2.59 Ma). Table 10 Estimates of the posterior distributions of demographic parameters revealed by Approximate Bayesian Computation (ABC) for the best Model (Scenario 2) Data Parameter N 1 N 2 N A T 0 T 1 LCNG Mean 4.03×10 5 4.90×10 5 1.99×10 6 4.22×10 6 8.30×10 6 Median 3.84×10 5 4.65×10 5 2.14×10 6 3.94×10 6 8.20×10 6 Mode 3.16×10 5 3.45×10 5 2.98×10 6 3.73×10 6 7.85×10 6 HPD95% lower 1.40×10 5 1.53×10 5 5.53×10 5 1.36×10 6 2.81×10 6 HPD95% upper 7.51×10 5 9.15×10 5 2.93×10 6 8.10×10 6 1.42×10 7 SSR Mean 4.00×10 5 7.58×10 5 1.88e×10 6 1.26×10 5 5.40×10 6 Median 3.55×10 5 6.57×10 5 2.01×10 6 1.09×10 5 4.42×10 6 Mode 2.31×10 5 5.63×10 5 2.94×10 6 6.90×10 4 1.15×10 5 HPD95% lower 8.67×10 4 2.35×10 5 4.27×10 5 3.00×10 4 3.96×10 5 HPD95% upper 8.74×10 5 1.62×10 6 2.91×10 6 2.75×10 5 1.33×10 7 N 1 = effective population size of Group 1 for P. villosa . N 2 = effective population size of Group 2 for P. villosa . N A = effective population size of ancestral population. T 0 = time since divergence between Group 1 and Group 2. T 1 = time since divergence between ancestral population and Group 2. Table 11 Demographic parameters scaled by the mutation rate from IMa multilocus analyses based on the group from SAMOVA Data Parameter θ 1 θ 2 θ A m 1 m 2 t N 1 N 2 N A T [year] 2 N 1 m 1 2 N 2 m 2 LCNG Hipt 0.0341 0.0854 1.2951 4.5375 4.4125 0.0011 2.80×10 5 7.00×10 5 1.06×10 7 1.80×10 5 0.08 0.19 HPD95% lower 0.0173 0.0425 0.357 1.4875 1.2875 0.0009 1.42×10 5 3.48×10 5 2.93×10 6 1.48×10 5 0.01 0.03 HPD95% upper 0.1526 0.3178 13.6443 23.0875 21.9625 0.0020 1.25×10 6 2.60×10 6 1.12×10 8 3.28×10 5 1.76 3.49 SSR Hipt 0.1875 0.7492 378.3522 9.995 19.990 0.0007 1.54×10 6 6.14×10 6 3.10×10 9 1.15×10 5 1.87 4.99 HPD95% lower 0.1875 0.7492 260.9756 5.575 17.290 0.0006 1.54×10 6 6.14×10 6 2.14×10 9 9.84×10 4 1.05 4.32 HPD95% upper 0.5626 0.7492 590.629 9.965 19.990 0.0029 4.61×10 6 6.14×10 6 4.84×10 9 4.75×10 5 1.87 4.99 θ 1 , N 1 = effective population size of Group 1 for P. villosa . θ 2 , N 2 = effective population size of Group 2 for P. villosa . θ A , N A = effective population size of ancestral population. t , T = time since divergence between Group 1 and Group 2. N = θ /4µG, G = 5; T = t /µ; 2 Nm = θ × m /2. 3.6 Divergence time estimation and gene flow After concatenating 10 sets of nuclear gene fragments, this study selected the best nucleotide substitution model as the GTR + I + G by AIC calculation. As shown in Fig. 8 , the topology of the maximum clade credibility (MCC) tree was consistent with that of the phylogenetic tree. Subsequent divergences within P. villosa occurred at various times throughout the Pleistocene, which was consistent with the results obtained based on IM model. The divergence time between P. villosa and Achnatherum splendens was 3.26 Ma (95% HPD: 2.30 ~ 4.63 Ma). Group 1 and Group 2 began to differentiate at about 0.38 Ma (95% HPD: 2.30 ~ 4.63 Ma). Within the 41 populations, divergence occurred at approximately 0.02–0.55 Ma. The species had significant genetic barriers in the Yinshan Mountains, Helan Mountains, Ordos Plateau and Yabulai Mountains based on SSR data by Barrier v.2.2 software (Fig. 9 ). The direction of gene flow among populations was analyzed by Migrate-N software. This study set up four possible models: (1) gene flow is bidirectional between Group 1 and Group 2; (2) gene flow is unidirectional from Group 1 to Group 2; (3) gene flow is unidirectional from Group 2 to Group 1; (4) Group 1 and Group 2 are the same population. The best model was determined by comparing the b values in ln(Prob(D|Model)), with the model with the largest b value considered to be the best. After comparison, the best model was model 1. The results showed that the gene flow from Group 1 to Group 2 was 0.432 and 0.299 from the past to the present, respectively (Table 12 ), whereas the gene flow from Group 2 to Group 1 was 0.489 and 0.397, respectively (Table 12 ). This indicated that the gene flow from Group 2 to Group 1 was larger than that accepted by this group, which further confirmed the results based on the IM model. Table 12 Historical gene flow among different geographic groups in P. villosa Data Model ln(Prob(D|Model)) θ 1 θ 2 M1→2 M2→1 LCNG Model 1 -3387.640 (1a) 0.463 0.514 0.432 0.489 -1835.250 (1b) Model 2 -3351.850 (1a) 0.580 0.471 - 0.564 -1853.990 (1b) Model 3 -3305.390 (1a) 0.541 0.593 0.519 - -1849.670 (1b) Model 4 -4194.297 (1a) 0.551 - - - -2116.930 (1b) SSR Model 1 -312291.460 (1a) 0.423 0.369 0.299 0.397 -52370.370 (1b) Model 2 -327473.070 (1a) 0.154 0.399 - 0.387 -55079.070 (1b) Model 3 -317875.900 (1a) 0.384 0.174 0.485 - -53363.530 (1b) Model 4 -470987.850 (1a) 0.395 - - - -77562.400 (1b) 4 Discussion 4.1 Genetic diversity and environmental adaptation In the relationship between plants and the environment, the environment has an ecological effect on plants, influencing and changing their morphological structure, physiological, and biochemical characteristics. At the same time, plants exhibit adaptability to the environment, adjusting to external changes through their own variations [ 34 ] . Every plant species has its unique gene pool and genetic organization, and genetic diversity determines its ability to adapt to the environment. This is also the foundation for maintaining the long-term stability of ecosystems [ 56 ] . When the genetic diversity of a species is higher or its genetic variation is richer, its ability to adapt to the environment is stronger. Meanwhile, when genetic diversity decreases, the species' ability to adapt, reproduce, and resist diseases weakens [ 57 ] . Therefore, studying the genetic diversity of species not only helps to explore their adaptability and evolutionary history, but also provides an important theoretical basis for the conservation and rational utilization of species [ 58 ] . Based on 10 LCNG and 13 SSR primer pairs, this study explored the genetic diversity of P. villosa . The results showed that the 10 selected primer pairs exhibited high polymorphism, with values for H d , π , and K ranging from 0.000 to 1.000, 0.000 to 0.203, and 0.000 to 10.578, respectively. At the population level, the analysis of polymorphic loci using 13 SSR primer pairs revealed that the values for PIC and I ranged from 0.317 to 0.747 and from 0.278 to 0.946, respectively. These findings collectively indicate significant genetic diversity among P. villosa populations. Moreover, the populations P30 and P31 located in Ningxia, China, showed relatively higher genetic diversity, consistent with previous results obtained from internal transcribed spacer (ITS) sequences and SSR molecular markers [ 59 – 60 ] . Furthermore, the genetic diversity of P. villosa showed similarities as well as differences when compared with other species in the Stipeae tribe. For example, Zhang et al. [ 61 ] studied the genetic diversity of seven populations of Stipa grandis distributed in the Inner Mongolia region using RAPD. The results showed that the genetic diversity within populations ( H s = 0.17) was higher than that between populations ( H t = 0.27). Current research suggests that the factors influencing species genetic diversity mainly include natural selection, pollination pathways, gene flow, genetic drift, and genetic mutations. The potential drivers of within-species genetic diversity include biological factors such as dispersal ability and life history, as well as environmental factors such as climate and human activities [ 62 – 63 ] . Accordingly, we infer that the genetic diversity of P. villosa might be rich because it is a long-lived perennial plant that can reproduce sexually through seeds and seedlings, and clonally under harsh environmental conditions. Moreover, P. villosa has strong reproductive and vegetation restoration abilities, and its adaptation to the growth environment also contributes to the formation of its genetic diversity. 4.2 Population genetic differentiation and genetic structure The genetic diversity and structure of a species are long-term products of evolution [ 64 ] . Population genetic structure includes genetic differentiation between populations and genetic variation within populations, reflecting the effects of mutation, recombination, genetic drift, and selection during the evolutionary process [ 65 ] Factors influencing the genetic structure of a species mainly include life habits, evolutionary history, reproductive systems, seed dispersal mechanisms, and gene flow [ 66 ] . According to Wright's related theory and recent studies [ 39 ] , when the F ST value exceeds 0.25, there is significant genetic differentiation between populations of a species. In this study, based on LCNG and SSR data, the F ST of P. villosa were estimated to be 0.754 and 0.356, which may be related to its reproductive mode. Meanwhile, the gene flow size of P. villosa was 0.097, which is larger than that of self-pollinating plants ( M m = 0.065), but smaller than that of cross-pollinating plants ( M m = 5.38) [ 67 ] . Therefore, we hypothesize that sexual reproduction in P. villosa may occur primarily through self-fertilization, consistent with previous research findings. AMOVA results showed that the majority of the genetic variation in P. villosa populations is derived from among-population variation. Hensen et al. [ 68 ] using RAPD studied the genetic variation of Stipa capillata and found that genetic variation also comes primarily from among-population variation. Meanwhile, Lv et al.'s [ 30 ] AFLP-based study suggested that genetic variation in P. villosa originates primarily within populations. The clonal propagation characteristics of this species lend greater credibility to subsequent analyses employing LCNG and SSR molecular markers. Previous studies have shown that species of the Stipa genus reproduce primarily asexually. When there is a barrier to gene flow between populations, the proportion of genetic variation between populations significantly increases, while the level of genetic variation within populations decreases [ 69 ] . The results of this study also indicate that G ST < N ST ( P < 0.05), suggesting a significant correlation between the genetic variation of P. villosa populations and their geographic origin. Mantel tests further revealed a significant correlation between genetic distance and spatial distance ( r = 0.291, P = 0.01), implying that spatial distance may be one of the important factors influencing genetic differentiation in P. villosa populations. At the same time, correlation analysis between the genetic diversity, F ST values, and environmental factors indicated that altitude is also a major influencing factor in the genetic differentiation of P. villosa . However, there is uncertainty regarding the genetic structure of species populations and their geographic locations [ 70 ] . That is, populations distributed in different geographic locations may not exhibit genetic differentiation or genetic variation, while populations located in the same geographic area may also have distant phylogenetic relationships. Therefore, this study employed various analytical methods, including unweighted pair group method with arithmetic mean (UPGMA), SAMOVA, PCA, and Structure, to investigate the genetic structure of P. villosa populations. The results showed that the P. villosa populations were divided into two main groups, Group 1 and Group 2, with the Yinshan Mountains acting as a boundary. Genetic introgression was observed between the groups. This is because the Yinshan Mountains have a unique geographical location, with significant climate differences on either side due to varying altitude and mountain orientation, leading to distinct geographical differences in plant species distribution and vegetation. In addition, this study found that P. villosa populations that were geographically closer did not always cluster together, and individuals within the same population were not always grouped, especially in populations P13, P36, P42, and P43. We believe this may be due to the fact that these populations are distributed mainly in the Helan Mountains region. The Helan Mountains are situated in a complex and unique tectonic setting, with intense and frequent tectonic activity since the Paleozoic, exhibiting a "multicycle" characteristic [ 71 ] . This acts as a transitional zone between sandy land and grassland vegetation. Therefore, P. villosa populations in this region may have introduced new genes from other populations, while populations outside the Helan Mountains may have dispersed genes favorable for the local habitat. In summary, we believe that the limited gene flow and unique reproductive systems among P. villosa populations have led to a high level of genetic differentiation and variation in the species. Spatial distance and altitude are probably important factors influencing the genetic structure of P. villosa populations [ 72 ] . 4.3 Speciation and population demographic history The Quaternary glacial climate oscillations are considered to have played a significant role in shaping the contemporary geographic distribution and population dynamics of plants [ 73 ] . Repeated climate fluctuations led to varying degrees of population contraction and expansion, with some species surviving in small climate zones known as glacial refugia [ 74 ] . Due to the uplift of the Qinghai-Xizang Plateau, the arid climate and complex terrain of the northwestern desert regions of China, along with the predominantly east–west orientation of many mountain ranges, the Yinshan, Helan, Qilian, and Tianshan mountains have formed natural glacial refugia and geographical barriers [ 75 ] . Current research generally suggests that regions with high nucleotide diversity and haplotype polymorphism may be glacial refugia [ 76 ] . The P. villosa populations around the Yinshan and Helan mountain ranges tend to have higher nucleotide diversity and haplotype polymorphism. Among these, the P43 in Dalate Banner has the highest nucleotide polymorphism ( π = 0.203) and haplotype diversity ( H d = 0.978), which indicates that the development of mountain ranges leads to spatial isolation, promoting differentiation between populations of the same species, thus influencing the species’ geographic distribution pattern. Moreover, genetic flow barrier detection between P. villosa populations reveals that genetic flow predominantly moves from Group 2 to Group 1. The genetic barriers originate mainly from the Yin Mountains, Helan Mountains, Ordos Plateau, and Yablun Mountains. We believe these areas may have been the main refugia for P. villosa during the Quaternary glaciations [ 77 ] . Neutrality tests and mismatch distribution analysis results both suggest that the population dynamics of P. villosa did not experience a rapid expansion event in its history. BEAST and ABC model predictions indicate that P. villosa may have originated in the Late Miocene, with population divergence occurring during the Pleistocene. The divergence time between Group 1 and Group 2 is approximately 3.94 Ma and 0.11 Ma, while the divergence time between Group 2 and N A is around 8.20 Ma and 4.42 Ma, which aligns with the previous divergence time (9 ~ 2.61 Ma) of species in the Stipa genus. Furthermore, the effective population sizes of Group 1 and Group 2 are both smaller than that of N A . These findings confirm that P. villosa underwent contraction events in its history, probably due to spatial isolation caused by changes in local mountains (such as the Yinshan and Helan Mountains) and deserts (such as the Mu Us Desert, Tengger Desert, and Badain Jaran Desert), as well as population range contraction and habitat fragmentation caused by climatic oscillations. This is consistent with the historical dynamic processes observed in other desert plants such as Gymnocarpos przewalskii and Helianthemum songaricum [ 78 – 80 ] . Since the Quaternary glaciation, the third uplift of the Qinghai-Xizang Plateau around 8 Ma, the ongoing global cooling and drying, and the influence of the east–west monsoon climate of the Yin Mountains, which led to desert expansion and habitat fragmentation, are important factors involved in the genetic differentiation between populations of P. villosa in response to environmental changes. 5 Conclusion This study investigated the genetic diversity, population genetic structure, and demographic history of 210 individuals from 43 natural populations of P. villosa using LCNG and SSR molecular markers. We found that the P. villosa populations were clearly divided into two groups, Group 1 and Group 2, by the Yinshan Mountains, with populations in Group 2 showing higher genetic diversity. Significant diversity differences and phylogeographic structure were observed among the populations, with most genetic variation occurring between populations. Geographic distance and elevation had a strong influence on genetic differentiation. No significant population expansion was detected in the history of P. villosa populations, with divergence occurring around 0.38 Ma between Group 1 and Group 2. There was bidirectional and asymmetric gene flow between the two groups. The Yinshan Mountains, Helan Mountains, Ordos Plateau, and Yabulai Mountains serve as natural and significant genetic barriers. The uplift of the Qinghai-Xizang Plateau and the influence of the monsoonal climate of the Yinshan Mountains since the Quaternary period have been important drivers of the genetic differentiation between P. villosa populations, as they adapted to environmental changes. Abbreviations LCNG low-copy nuclear genes SSR simple sequence repeats RFLP fragment length polymorphisms RAPD random amplified polymorphic DNA AFLP amplified fragment length polymorphisms SNP single-nucleotide polymorphisms ISSR inter-simple sequence repeat PCA principal coordinate analysis N the number of individuals PPL percentage of polymorphic loci PIC polymorphic information content N a number of alleles Ne effective number of alleles I Shannon's information index H o observed heterozygosity H e expected heterozygosity H d haplotype diversity π nucleotide diversity K average number of nucleotide differences h Nei's genetic diversity H t total gene diversity H s subpopulation gene diversity F IS inbreeding coefficient F ST Fixation Index G ST genetic differentiation coefficient N m gene flow AMOVA analysis of molecular variance SAMOVA spatial AMOVA MCMC Markov Chain Monte Carlo IM isolation with migration ABC approximate Bayesian computation. Declarations Ethics approval and consent to participate Not applicable Consent for publication Not applicable Competing Interests The authors declare no competing interests. Funding This study was financially supported by the National Natural Science Foundation of China (Grant Number: 32160297, 41761009), the Program of Science and Technology International Cooperation Project of Qinghai Province (Grant Number: 2023-HZ-810), and the Qinghai Provincial Major Science and Technology Special Project (2023-SF-A5). Author Contribution J.R. and T. L. : Writing – review, editing & original draft. J.R. : Formal analysis. Y. P. & X. S. : Conceptualization. we greatly appreciated Y. H. , Q. Y. , Y. L. , X. F. and K. Y. performing the investigation & experiment, and the anonymous reviewers for their helpful comments. Acknowledgements This work was financially supported by the National Natural Science Foundation of China (No. 32160297, 41761009) made to Yuping Liu, the Program of Science and Technology International Cooperation Project of Qinghai Province (No. 2023-HZ-810) made to Yuping Liu, and the Qinghai Provincial Major Science and Technology Special Project (2023-SF-A5) made to Yuping Liu. Data Availability Genome assembly of P. villosa, Iso-seq and Hi-C data have been submitted to the DDBJ/EMBL/Genbank databases under BioProject number PRJ70313651: genome assembly–JABCND000000000; Iso-seq data–SRR11787985 and SRR11787359; RNA-seq data–SRR11775845, SRR11775821, SRR11775823, SRR11775824, SRR11775822, SRR11775544. References Ma C, Tang YJ, Ying JF. Volcanic phosphorus spikes associated with supercontinent assembly supported the evolution of land plants. Earth Sci Rev. 2022;232. Bowen DQ. Questions of the Quaternary. Nature. 1984;311(5985):488. 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Table S5. Posterior distribution and 95% confidence interval of each DIYABC scenario. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 13 Aug, 2025 Reviewers agreed at journal 31 Jul, 2025 Reviewers invited by journal 09 Jul, 2025 Editor assigned by journal 09 May, 2025 Editor invited by journal 09 May, 2025 Submission checks completed at journal 08 May, 2025 First submitted to journal 08 May, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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legend\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6516707/v1/e2f67c5aadf4391f14a1096b.png"},{"id":86516587,"identity":"d38f4bcd-64be-4a3f-9967-b64578d667f8","added_by":"auto","created_at":"2025-07-11 14:12:07","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":394537,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6516707/v1/a3283a043f103cc97c0b503f.png"},{"id":86515640,"identity":"3144e7e4-6588-47dc-982f-537c2317a8e5","added_by":"auto","created_at":"2025-07-11 14:04:07","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":270254,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-6516707/v1/e32571e2b44d999816529bac.png"},{"id":86516906,"identity":"a49ab152-23e4-4bdf-9373-b5dd1f304b4c","added_by":"auto","created_at":"2025-07-11 14:20:07","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":254922,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-6516707/v1/9c1dd57fb32cc374dc78ece0.png"},{"id":86518011,"identity":"f12410d6-9f41-4f6d-88cf-573244cfd12f","added_by":"auto","created_at":"2025-07-11 14:28:07","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":328671,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-6516707/v1/0754232e6898d3a8a5750e2a.png"},{"id":86518613,"identity":"f206d4aa-0693-49ca-b315-27e762c7ad12","added_by":"auto","created_at":"2025-07-11 14:36:07","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":400865,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-6516707/v1/0c23bc70dc7b46dd7f2e5c4f.png"},{"id":87466939,"identity":"b64df6bd-d7e4-404d-b897-3dd93c43686f","added_by":"auto","created_at":"2025-07-24 07:37:05","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":8026791,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6516707/v1/63241cd2-e825-40e3-a6ef-f0b89514dc01.pdf"},{"id":86516903,"identity":"c103ec78-6165-4955-ba5e-3179acd8220b","added_by":"auto","created_at":"2025-07-11 14:20:07","extension":"doc","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":199680,"visible":true,"origin":"","legend":"\u003cp\u003eTable S1. Localities and habitats of samples from \u003cem\u003eP. villosa\u003c/em\u003e collected in this study.\u003c/p\u003e\n\u003cp\u003eTable S2. Prior distribution of parameters used in our ABC analysis.\u003c/p\u003e\n\u003cp\u003eTable S3. Genetic diversity of \u003cem\u003eP. villosa\u003c/em\u003e based on low-copy nuclear genes and SSR data.\u003c/p\u003e\n\u003cp\u003eTable S4. Summary statistics of genetic differentiation and gene flow for \u003cem\u003eP. villosa\u003c/em\u003e based on each locus from low-copy nuclear genes.\u003c/p\u003e\n\u003cp\u003eTable S5. Posterior distribution and 95% confidence interval of each DIYABC scenario.\u003c/p\u003e","description":"","filename":"PsaTableS1S5.doc","url":"https://assets-eu.researchsquare.com/files/rs-6516707/v1/3ab51cdacf1aaa7105cb60d9.doc"}],"financialInterests":"No competing interests reported.","formattedTitle":"The impact of Quaternary climate change on the historical population dynamics of Psammochloa villosa, a typical desert herb from northwest China","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003ePlant evolution is intricately linked to climatic and environmental dynamics, where these transformative forces not only drive species differentiation but also catalyze significant shifts in biogeographical distribution patterns\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. The \u0026ldquo;Quaternary glaciation,\u0026rdquo; which began approximately 2.58 Ma, is a critical phase in the Earth\u0026rsquo;s environmental evolution, marked by multiple expansions and contractions of glaciers. The climate experienced dramatic fluctuations and frequent alternations between cold and warm periods. During this time, some plants were forced to adapt and evolve, driving changes in plant geographical distribution patterns and the evolution of ecosystems\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. Over the past 30 years, many scholars have conducted biogeographical studies on various plant species worldwide, hypothesizing about the refugia of the Quaternary glaciation and the post-glacial expansion routes\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. The molecular clock hypothesis suggests that the degree of difference between homologous genes of any two species should be approximately constant with their divergence time. In other words, the longer the time since the ancestral species began to diverge, the greater the molecular information difference between species\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. Therefore, researchers often use population genetics methods, employing statistical tools to study changes in gene frequencies, gene flow, genetic drift, and other data within populations. Combining these tools with biogeographical methods, they explore how species respond to environmental changes, how they evolve through gene flow and isolation, and how their distribution patterns and historical evolution are shaped by geographical space\u003csup\u003e[\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eIn recent years, with the rapid development of molecular biology, DNA molecular markers have been extensively applied in biogeographical studies. Compared with morphological, cellular, and biochemical markers, molecular markers have the advantages of being numerous, highly polymorphic, predominantly \"neutral\" and co-dominant, and often expressed in DNA form, making them a relatively ideal new genetic marker. In eukaryotic research, plastid genes such as those from mitochondria and chloroplasts are widely used due to their easy acquisition, structural similarity, and sequence conservatism\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. Ma et al.\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e studied the phylogenetic relationships of \u003cem\u003eXanthopappus subacaulis\u003c/em\u003e using the complete chloroplast genome. They found that \u003cem\u003eX. subacaulis\u003c/em\u003e forms an independent and well-supported clade within the Asteraceae family. Chen et al.\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e studied the mitochondrial genome characteristics of \u003cem\u003eElymus magellanicus\u003c/em\u003e and reconstructed the phylogenetic relationships of Poaceae. The results indicated that \u003cem\u003eE. magellanicus\u003c/em\u003e is closely related to \u003cem\u003eHordeum vulgare\u003c/em\u003e, \u003cem\u003eAegilops speltoides\u003c/em\u003e, \u003cem\u003eTriticum aestivum\u003c/em\u003e, and \u003cem\u003eTriticum aestivum\u003c/em\u003e. Park et al.\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e elucidated key features of the organelle structure, gene content, and DNA transfer dynamics based on the high-quality chloroplast and mitochondrial genome assemblies and the draft nuclear genome of \u003cem\u003eCicuta virosa\u003c/em\u003e. Their findings on gene loss, DNA transfer to the nucleus, and structural diversity among related species provide insights into the evolution of organelle genomes in the Apiaceae family.\u003c/p\u003e\u003cp\u003eHowever, chloroplast-based molecular markers also have limitations. Chloroplast genes are inherited in a strictly maternal manner, carry limited evolutionary information, and are difficult to use for revealing deep phylogenetic relationships within large population groups\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. Compared with organelle genes, nuclear genes have more complex evolutionary patterns, including gene duplication and gene loss. Currently, commonly used DNA molecular markers include restriction fragment length polymorphisms (RFLP), random amplified polymorphic DNA (RAPD), amplified fragment length polymorphisms (AFLP), simple sequence repeats (SSR), and single-nucleotide polymorphisms (SNP), among others\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. Low-copy nuclear genes (LCNG) with fewer copies in a given organism's genome, inherited from both parents and with abundant information sites, are less affected by recombination and are capable of preserving parental alleles over time. In addition, the evolutionary rate in intron regions is relatively fast, making them widely used to explore the phylogenetic relationships of plant hybrids and allopolyploid groups, population geographic distribution patterns, origins and evolution, and population historical dynamics\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. SSR molecular markers are DNA sequences consisting of repeat units of approximately one to six nucleotides, offering advantages such as rich polymorphism, good repeatability, strong stability, co-dominance, simplicity in operation, and broad distribution\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. They are among the most widely used molecular markers. Currently, SSR molecular markers are widely applied in plant genetic diversity detection, kinship analysis, and gene mapping, among other areas. A study on the genetic diversity and population history dynamics of the species \u003cem\u003eCamellia azalea\u003c/em\u003e based on SSR molecular markers found that \u003cem\u003eC. azalea\u003c/em\u003e has moderate to high levels of genetic diversity. However, its current ecological niche is very narrow, and it is predominantly distributed along stream banks. Recent human activities have altered the gene flow of this species, which may have contributed to the strengthening of genetic structure in new individuals\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. Using population transcriptomes, complete chloroplast genomes, genotyping sequencing, and microsatellite markers, Liu et al.\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e found that geographic, geological, and environmental factors are key contributors to shaping the cryptic intraspecific divergence and population dynamics of the cold-tolerant perennial herb \u003cem\u003eNotopterygium oviforme\u003c/em\u003e.\u003c/p\u003e\u003cp\u003e\u003cem\u003ePsammochloa villosa\u003c/em\u003e is the type species of the genus \u003cem\u003ePsammochloa\u003c/em\u003e, belonging to the family Poaceae and the subfamily Stipeae, 2\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2\u003cem\u003ex\u003c/em\u003e\u0026thinsp;=\u0026thinsp;46\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. \u003cem\u003eP. villosa\u003c/em\u003e is a typical clonal plant distributed mainly in the sandy areas of the Inner Mongolia Plateau and its neighboring regions in China, including the Badain Jaran Desert, the Ulan Buh Desert, the Mu Us Sandy Land, the Hexi Corridor in Gansu, northern and central Ningxia, and the sandy regions of northern Shaanxi. It has well-developed rhizomes and a growth cycle that lasts more than 2 years. Like other early-growth and drought-tolerant plants, \u003cem\u003eP. villosa\u003c/em\u003e demonstrates remarkable drought tolerance and adaptability to aeolian stresses, particularly sand burial and wind erosion. As a pioneer psammophyte, this species exhibits characteristic early-successional traits including efficient sand stabilization through rapid vertical accretion, extensive lateral spread via rhizomatous growth, and significant potential for initiating vegetative recovery in mobile dune ecosystems\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. Furthermore, its inflorescences are relatively large and elongated, with many spikelets and large seeds, which have significant economic value\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. On the one hand, it improves ground vegetation cover in highly heterogeneous degraded ecosystems; on the other hand, it provides abundant forage for livestock, thus reducing grazing areas and slowing down desertification caused by human activities\u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e. Furthermore, \u003cem\u003eP. villosa\u003c/em\u003e exhibits wind pollination and outcrossing characteristics, making it an ideal population for random mating in an ideal state\u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eCurrently, research on \u003cem\u003eP. villosa\u003c/em\u003e focuses primarily on morphological traits\u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e, ecological adaptability\u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e, genetic diversity\u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e, and germplasm resources\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. Through the comparative observation of the roots, stems, leaves, and flowers of \u003cem\u003eP. villosa\u003c/em\u003e, it was found that its clonal characteristics enable it to have a stronger survival ability in arid and barren environments. In addition, the pore density in the leaf epidermis was found to be positively correlated with the degree of environmental drought\u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e. Li et al.\u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e analyzed the genetic variation of seven \u003cem\u003eP. villosa\u003c/em\u003e populations using inter-simple sequence repeat (ISSR) molecular markers, concluding that the ratio of genetic variation between populations to variation within populations was greater than 1.00. Liu et al.\u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e analyzed transcriptomic data under different stress conditions, indicating that the \u003cem\u003ePvTIP41\u003c/em\u003e gene is the most stable internal reference gene in leaves under drought and salt stress, as well as in stems under salt stress, providing important references for future genomic research on the plant's stress resistance mechanisms. Although Lv et al.\u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e used AFLP molecular markers and spatial distribution models to predict the population formation history and distribution dynamics of \u003cem\u003eP. villosa\u003c/em\u003e, AFLP is uniparental in inheritance and unable to distinguish between homozygous and heterozygous states, and the use of a single type of molecular genetic marker may lead to uneven coverage of polymorphisms.\u003c/p\u003e\u003cp\u003eTherefore, this study focuses on \u003cem\u003eP. villosa\u003c/em\u003e, a unique desert herbaceous plant from the Inner Mongolia Plateau and its neighboring areas. Based on extensive field surveys and sampling, the study employs a combination of LCNG and SSR markers to examine the genetic variation distribution patterns of \u003cem\u003eP. villosa\u003c/em\u003e populations and to determine their spatial genetic structure within populations, between populations, and across the entire geographical distribution range, comprehensively elucidating its modern distribution pattern and phylogenetic evolution history. The study aims to reveal how herbaceous plants in the arid desert areas of northwest China respond to Quaternary climate changes, providing a theoretical basis for the conservation and utilization of \u003cem\u003eP. villosa\u003c/em\u003e germplasm resources.\u003c/p\u003e"},{"header":"2 Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Plant materials\u003c/h2\u003e\u003cp\u003eWe collected experimental accessions of 101 natural populations of Inner Mongolia and removed the redundant accessions. Finally, we obtained a diverse collection of 43 populations and 210 individuals of \u003cem\u003eP. villosa\u003c/em\u003e. These accessions covered the main natural geographical distribution area of \u003cem\u003eP. villosa\u003c/em\u003e in China (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Information about the accessions, including the collected location, habitat status, longitude, latitude, altitude, and number of individuals, is provided in Additional file 1: Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e. We placed healthy leaves in silica gel to dry rapidly and used these for total DNA extraction. Young plants were frozen at − 80℃ after processing in liquid nitrogen for transcriptome sequencing.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003e2.2 Transcriptome sequencing and data analyses\u003c/h3\u003e\n\u003cp\u003eTotal RNA was extracted separately from each tissue using the TaKaRa MiniBEST Plant RNA Extraction Kit. The sequencing platform PacBio was used for full-length transcriptome sequencing of mixed samples of roots, stems, and leaves of \u003cem\u003eP. villosa\u003c/em\u003e. Raw sequencing data were processed, including read filtering, error correction, and quality control. We obtained unigenes by performing clustering and redundancy removal of assembled sequences using Cdhit software. Subsequently, using the transcriptome database of \u003cem\u003eAchnatherum splendens\u003c/em\u003e as a reference (GenBank: SRP068186), we retrieved the homologous genes of \u003cem\u003eP. villosa\u003c/em\u003e transcripts by OrthoMCL software. When the retrieved gene showed homology with \u003cem\u003eA. splendens\u003c/em\u003e but not with its own sequence, it was considered a successful selection of LCNG in this species\u003csup\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e. We designed primers and carried out experimental validation for 72 genes randomly selected from the screened LCNG of this species on the basis of primer software\u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e. Meanwhile, we used MISA software to search, locate, and identify simple repetitive sequences in the obtained transcriptome data. From the identified SSR loci, we designed primers and screened out 13 pairs of SSR primers with high polymorphism, good repeatability, and strong representativeness. Finally, we conducted population genetics experiments on the screened 10 pairs of LCNG and 13 pairs of SSR primers using the DNA of 43 populations and 210 individuals as templates (Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv class=\"gridtable\"\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=\"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\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\u003eCharacteristics of ten low-copy nuclear gene primers developed in \u003cem\u003eP. villosa\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLocus\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePrimer name\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePrimer sequences (5'-3')\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLCR (bp)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eT\u003c/em\u003e\u003csub\u003ea\u003c/sub\u003e (℃)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e14031\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLocus1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eF: TTCAGTGGCTATCCTGTTCC\u003c/p\u003e\u003cp\u003eR: CTGCACGTTAAACTACTCACAAG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e716\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e57.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e14004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLocus2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eF: AGGTGAGGTGGGATTTGAGC\u003c/p\u003e\u003cp\u003eR: AAGTGGGCGGATGGTTTC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e736\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e61.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e13593\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLocus3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eF: CCAGCTTAGTATGGCAGAGG\u003c/p\u003e\u003cp\u003eR: CCACGCTTTGTTGATGTTTC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e772\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e58.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e14111\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLocus4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eF: CCTAACGGAAACGAGTGCC\u003c/p\u003e\u003cp\u003eR: CCTCATCAGTTCCCGCAGAC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1217\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e61.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e14075\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLocus5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eF: AGTTGCCAGTGGGTTTG\u003c/p\u003e\u003cp\u003eR: CGAGCACATGGAGGTATCTT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e996\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e55.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e13604\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLocus6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eF: TCGCATTCCTCTTTCTCACCAAC\u003c/p\u003e\u003cp\u003eR: CTATTCGACTTCCTCAGCTCCCT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e735\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e60.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e14192\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLocus7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eF: TGTCGGTAACCTGGATAGAGC\u003c/p\u003e\u003cp\u003eR: TGATTAACTCCCTGACTGCTTC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e807\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e59.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e14928\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLocus8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eF: GAGCGGTTTGTCCCTGAT\u003c/p\u003e\u003cp\u003eR: GTCCAGCTCTAGTGCCTGTT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e918\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e58.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e14224\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLocus9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eF: GCACTCAGTCACGGCAAAC\u003c/p\u003e\u003cp\u003eR: AGCAACAGCCAGCTTCTCAC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e884\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e60.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e14612\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLocus10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eF: GATGCAGTGGCAGTGTTTG\u003c/p\u003e\u003cp\u003eR: CTCTTACCGTCAGGCTCGT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e887\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e57.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote: AFL, Aligned full length; LCR, Length of coding region; \u003cem\u003eT\u003c/em\u003e\u003csub\u003ea\u003c/sub\u003e, Optimal annealing temperature.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv class=\"gridtable\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\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\u003e13 pairs of SSR primer information in \u003cem\u003eP. villosa\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLocus\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePrimer name\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePrimer sequences (5'-3')\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMotif\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAllele size (bp)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003eT\u003c/em\u003e\u003csub\u003em\u003c/sub\u003e (℃)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePvj1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLocus1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eF: AAGAGCATTCCTTGCAGCA\u003c/p\u003e\u003cp\u003eR: ATGACTCCGGCATGTGGTT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(TC)\u003csub\u003e10\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e342–378\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e60.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePvH4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLocus2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eF: ATCCTTTGAGCATCCACCCCC\u003c/p\u003e\u003cp\u003eR: GCTTGCTGAGGAGTCTCTGCGTCT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(TCC)\u003csub\u003e5\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e202–341\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e61.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePv14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLocus3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eF: GATCCTGGGTTTGGAAGTG\u003c/p\u003e\u003cp\u003eR: CTTATAGTACTCTTCAAGCTCCTT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(AGA)\u003csub\u003e6\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e247–283\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e54.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePv15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLocus4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eF: AGCAGATTGGCAGAGAACTCAGG\u003c/p\u003e\u003cp\u003eR: CTGCGCCTTCCTCCATCAT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(GTG)\u003csub\u003e7\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e309–342\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e63.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePv21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLocus5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eF: CCTCTACCGCCTCTTCGTCTCC\u003c/p\u003e\u003cp\u003eR: GCGAACCAGAAGAGGTAGTACTC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(CGC)\u003csub\u003e6\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e266–320\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e63.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePv33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLocus6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eF: GCACCACGCCGGCAAGAAA\u003c/p\u003e\u003cp\u003eR: CCGTGGACGACTTGCCGAAGA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(AGG)\u003csub\u003e6\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e249–273\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e61.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePv39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLocus7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eF: TCTGAGGTACAATATCCGAAGC\u003c/p\u003e\u003cp\u003eR: ACTTGCGATTGAAGCGTGT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(AGC)\u003csub\u003e7\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e306–324\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e59.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePv44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLocus8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eF: AAGTAAGCCCAGAGCAGGAG\u003c/p\u003e\u003cp\u003eR: AATCAGCCAATCATCAATCTCA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(GGA)\u003csub\u003e6\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e230–239\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e57.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePv49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLocus9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eF: GCCTTCGGGTGCGTGAGC\u003c/p\u003e\u003cp\u003eR: CCTCGTCTTCCCCACGGTGA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(GCG)\u003csub\u003e5\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e293–311\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e64.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePv50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLocus10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eF: CGGCTGGACGAGGCGAAGGT\u003c/p\u003e\u003cp\u003eR: CAGGAGGAGGGTGTTGGACGTGAC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(GCG)\u003csub\u003e6\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e284–296\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e64.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePv51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLocus11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eF: CGCCGCGTTCCCGCTGTA\u003c/p\u003e\u003cp\u003eR: CCGTCCCCGCATTGCTGC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(CGG)\u003csub\u003e6\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e216–258\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e64.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePv8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLocus12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eF: TGGCAGTGGATGTGGAGCTGAT\u003c/p\u003e\u003cp\u003eR: CACGGATGGATGGCACTGCTAC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(AGC)\u003csub\u003e6\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e306–345\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e65.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePv23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLocus13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eF: GACTAACCTCCTGGGCACT\u003c/p\u003e\u003cp\u003eR: TGCCGCTGCATATGCCGAAG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(CGG)\u003csub\u003e6\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e234–252\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e63.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003e2.3 Population genetic structure\u003c/h3\u003e\n\u003cp\u003eTo infer the population genetic structure of \u003cem\u003eP. villosa\u003c/em\u003e, this study employed three methods to analyze the geographical distribution patterns of SSR and low-copy nuclear gene data. (1) After converting the \"01\" matrix into a genetic distance matrix using GenAIEx v.6.5 software, we constructed UPGMA clustering and principal coordinate analysis (PCA) according to R scripts. (2) We analyzed the spatial analysis of molecular variation among different geographical groups of \u003cem\u003eP. villosa\u003c/em\u003e by SAMOVA v.1.0 software\u003csup\u003e[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e and deduced the optimal population combination from geographical and genetic information. The process set a range of \u003cem\u003eK\u003c/em\u003e values from 2 to 10, with each \u003cem\u003eK\u003c/em\u003e value undergoing 100 heuristic searches. The optimal population combination was determined by selecting the \u003cem\u003eK\u003c/em\u003e value that maximized genetic differentiation between groups (\u003cem\u003eF\u003c/em\u003e\u003csub\u003eCT\u003c/sub\u003e), minimized genetic differentiation within groups (\u003cem\u003eF\u003c/em\u003e\u003csub\u003eSC\u003c/sub\u003e), or achieved stable \u003cem\u003eF\u003c/em\u003e\u003csub\u003eCT\u003c/sub\u003e. (3) We tested the different genetic clusters among species using Structure v.2.3 software\u003csup\u003e[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/sup\u003e. The range of genetic clusters was set to 1 ≤ \u003cem\u003eK\u003c/em\u003e ≤ 10. The likelihood values for each cluster (\u003cem\u003eK\u003c/em\u003e) were estimated during the operation, and it was assumed that there was correlation in allele frequencies between different clusters. Subsequently, the most likely number of genetic clusters (\u003cem\u003eK\u003c/em\u003e) for the population was estimated according to the statistical method of Pritchard et al.\u003csup\u003e[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/sup\u003e and the calculation method of Δ\u003cem\u003eK\u003c/em\u003e\u003csup\u003e[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\n\u003ch3\u003e2.4 Analysis of genetic diversity and genetic variation\u003c/h3\u003e\n\u003cp\u003eDiversity parameters are important for measuring the level of population genetic diversity\u003csup\u003e[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/sup\u003e. To estimate the genetic diversity level of \u003cem\u003eP. villosa\u003c/em\u003e, this study calculated various genetic diversity parameters for SSR and low-copy nuclear gene data with GenAIEx v.6.5\u003csup\u003e[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]\u003c/sup\u003e and DnaSP v.5.1 software, including the number of individuals (\u003cem\u003eN\u003c/em\u003e), percentage of polymorphic loci (PPL), polymorphic information content (PIC), observed number of alleles (\u003cem\u003eN\u003c/em\u003e\u003csub\u003ea\u003c/sub\u003e), effective number of alleles (\u003cem\u003eN\u003c/em\u003e\u003csub\u003ee\u003c/sub\u003e), Shannon's information index (\u003cem\u003eI\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), haplotype diversity (\u003cem\u003eH\u003c/em\u003e\u003csub\u003ed\u003c/sub\u003e), nucleotide diversity (\u003cem\u003eπ\u003c/em\u003e), average number of nucleotide differences (\u003cem\u003eK\u003c/em\u003e), Nei's genetic diversity (\u003cem\u003eh\u003c/em\u003e), total population diversity (\u003cem\u003eH\u003c/em\u003e\u003csub\u003et\u003c/sub\u003e), genetic diversity within populations (\u003cem\u003eH\u003c/em\u003e\u003csub\u003es\u003c/sub\u003e), inbreeding coefficient (\u003cem\u003eF\u003c/em\u003e\u003csub\u003eIS\u003c/sub\u003e), population differentiation (\u003cem\u003eF\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e), genetic differentiation between populations (\u003cem\u003eG\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e), and gene flow (\u003cem\u003eN\u003c/em\u003e\u003csub\u003em\u003c/sub\u003e).\u003c/p\u003e\u003cp\u003eThe classic measure of genetic variation is Wright’s fixation index (\u003cem\u003eF\u003c/em\u003e-statistic, \u003cem\u003eF\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e), a measure of detecting the degree of genetic differences at the level of species populations\u003csup\u003e[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]\u003c/sup\u003e. The theory suggests the following guidelines for the interpretation of \u003cem\u003eF\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e values: 0 ~ 0.05, little genetic variation; 0.05 ~ 0.15, moderate genetic differentiation; 0.15 ~ 0.25, great genetic differentiation; and above 0.25, very great genetic differentiation\u003csup\u003e[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]\u003c/sup\u003e. This study estimated the \u003cem\u003eF\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e value using the molecular variance method in Arlequin v.3.5 software\u003csup\u003e[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]\u003c/sup\u003e. Two different grouping populations of \u003cem\u003eP. villosa\u003c/em\u003e were subjected to analysis of molecular variance (AMOVA) with SSR and LCNG data. All populations were considered as one group to calculate the genetic variation between populations and within populations, and to estimate the levels of genetic variation between populations, within populations, among groups, and between groups based on the spatial AMOVA (SAMOVA) grouping results. Significance levels were tested through 1000 nonparametric permutations. In addition, we estimated the genetic differentiation coefficients \u003cem\u003eG\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e and \u003cem\u003eN\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e of populations in the SAMOVA grouping and the overall level of low-copy nuclear gene data by the PERMUT software. When \u003cem\u003eN\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e \u0026gt;\u003cem\u003eG\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05), it indicated the presence of significant phylogeographic structure among populations.\u003c/p\u003e\n\u003ch3\u003e2.5 Correlation analysis, mismatch distribution, and neutrality tests\u003c/h3\u003e\n\u003cp\u003eTo assess whether the population's genetic structure was affected by spatial distance or other environmental factors, this study conducted a Mantel test using R scripts that examined the correlation between the genetic distance and the geographic distance. Moreover, we investigated the correlation between genetic diversity, genetic differentiation, and several ecological factors.\u003c/p\u003e\u003cp\u003eWe conducted mismatch distribution analysis on the population of \u003cem\u003eP. villosa\u003c/em\u003e, aiming to explore the historical dynamics of this species using Arlequin v.3.5 software. Mismatch distribution analysis is a method used to assess the fit between the expected value curve and the observed value curve based on the distribution of nucleotide differences among different haplotypes\u003csup\u003e[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]\u003c/sup\u003e. If the mismatch distribution curve is unimodal, it indicates that the species has recently experienced population expansion\u003csup\u003e[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e–\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]\u003c/sup\u003e, whereas if the curve is multimodal, it suggests that the species has been in a phase of dynamic equilibrium or slow contraction for population size during a certain time period\u003csup\u003e[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eIn addition, to determine whether the genetic locus conforms to the neutral evolution model. This study performed neutrality tests on the \u003cem\u003eP. villosa\u003c/em\u003e population using DnaSP v.5.1 software. This theory assumes that the population maintains a mutation–drift equilibrium during the long-term evolution, and all nucleotides mutated with equal probability. A Tajima's \u003cem\u003eD\u003c/em\u003e or Fu's \u003cem\u003eFs\u003c/em\u003e significantly greater than 0 indicates that the population has recently experienced a bottleneck effect or balancing selection. Conversely, a Tajima's \u003cem\u003eD\u003c/em\u003e or Fu's \u003cem\u003eFs\u003c/em\u003e significantly less than 0 indicates that the population has undergone directional selection or an expansion event\u003csup\u003e[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.6 Demographic history estimation\u003c/h2\u003e\u003cp\u003eTo further investigate the historical dynamics of \u003cem\u003eP. villosa\u003c/em\u003e, we simulated the population historical dynamics of 10 pairs of LCNG and 13 pairs of SSR primers based on an approximate Bayesian computation algorithm. This analysis aimed to determine the population divergence events, time, and changes in population size\u003csup\u003e[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]\u003c/sup\u003e. According to the population genetic structure of \u003cem\u003eP. villosa\u003c/em\u003e, we inferred the migration process of this species. Each scenario was executed 10\u003csup\u003e6\u003c/sup\u003e times, and the best scenario model depends on the posterior distribution probabilities. Moreover, we inferred the goodness of fit for the best scenario through the difference between the observed value and the actual value of principal component analysis (PCA) in DIYABC v.2.0.4 software. The prior distribution parameters for the population history dynamics are presented in Table S2.\u003c/p\u003e\u003cp\u003eFurthermore, we simulated the effective population size, effective migration rate, and divergence time of the current and ancestral population for \u003cem\u003eP. villosa\u003c/em\u003e using the isolation-with-migration model in the IMa software. Convergence of the runs was assessed by the effective sample sizes (ESS) (≥ 200) using Tracer v.1.7\u003csup\u003e[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]\u003c/sup\u003e and the peak graph conforming to a normal distribution. As there was a lack of fossil records of \u003cem\u003eStipeae\u003c/em\u003e and \u003cem\u003ePsammochloa\u003c/em\u003e, estimates of the timescale of divergence were based on an evolutionary rate of \u003cem\u003eµ\u003c/em\u003e = 6.1 × 10\u003csup\u003e− 9\u003c/sup\u003e substitutions per site per year in Poaceae plants\u003csup\u003e[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]\u003c/sup\u003e, and the generation time was set at 5 years\u003csup\u003e[\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003e2.7 Estimating divergence time\u003c/h3\u003e\n\u003cp\u003eWe selected the best nucleotide substitution model by jModelTest software\u003csup\u003e[\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]\u003c/sup\u003e, which included 11 fundamental nucleotide substitution models. Then, we estimated the divergence time of the population for \u003cem\u003eP. villosa\u003c/em\u003e through BEAST software\u003csup\u003e[\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]\u003c/sup\u003e. Due to the lack of fossil records or the rate of gene fragment evolution of \u003cem\u003ePsammochloa\u003c/em\u003e, we performed the molecular clock calibration using the divergence time between \u003cem\u003eAegilops tauschii\u003c/em\u003e and \u003cem\u003eTriticum dicoccoides\u003c/em\u003e (2.97 Ma, 2.61 ~ 5.27 Ma)\u003csup\u003e[\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]\u003c/sup\u003e, as well as the divergence time between \u003cem\u003eAegilops tauschii\u003c/em\u003e and \u003cem\u003eBrachypodium distachyon\u003c/em\u003e (32.6 Ma, 27.8 ~ 37.0 Ma)\u003csup\u003e[\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]\u003c/sup\u003e. The running parameters were set as follows. A relaxed molecular clock model was selected, and the Markov Chain Monte Carlo (MCMC) was run for 10\u003csup\u003e8\u003c/sup\u003e generations, with sampling every 1000 generations. The burn-in was set to 10%. Subsequently, we checked the posterior distribution probability and ensured that the ESS value was greater than 200 by Tracer v.1.6 software. If the ESS value was lower than 200, it was necessary to increase the number of iterations to continue the operation. Meanwhile, we displayed and edited the divergence time tree using Figtree v.1.4.3 software.\u003c/p\u003e\n\u003ch3\u003e2.8 Population genetic barriers and gene flow\u003c/h3\u003e\n\u003cp\u003ePopulation genetic barriers are caused by external environmental factors such as gene flow, genetic drift, and natural selection. Therefore, to infer the impact of population evolutionary history on population genetic structure, we detected genetic barriers among the population of \u003cem\u003eP. villosa\u003c/em\u003e with the \"Monmonier's maximum difference algorithm\" in Barrier v.2.2 software\u003csup\u003e[\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eWhen individuals migrate from one population to another, they bring their genes to the new population, resulting in gene flow, which can lead to population homogenization. Consequently, we estimated genetic parameters for SSR data and low-copy nuclear gene data based on Migrate-n v.4.4.3 software\u003csup\u003e[\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]\u003c/sup\u003e, including migration rates within and between all populations (\u003cem\u003eM\u003c/em\u003e), effective population sizes (\u003cem\u003eθ\u003c/em\u003e), and historical gene flow. We set the running parameters as follows: long-chains = 3, long-inc = 100, long-sample = 5000000, burn-in = 1000000, replicate = YES:5, heating = YES:1: {1.000000,1.500000,3.000000,1000000.000000}.\u003c/p\u003e"},{"header":"3 Results","content":"\u003ch2\u003e3.1 Population genetic structure\u003c/h2\u003e\u003cp\u003e\u003cem\u003eAchnatherum splendens\u003c/em\u003e was used as an outgroup. A dendrogram of \u003cem\u003eP. villosa\u003c/em\u003e was constructed on the basis of low-copy nuclear gene data depicting the genetic relationships among the 41 populations (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). The populations were divided into two main clusters, with a self-proclaimed differentiation rate of 56%. Group 1 included populations from the northeast of the Yinshan Mountains (P1-P12). Group 2 included populations from the Helan Mountains (P13-14、P16-19 and P24-28), the northeastern margin of the Qinghai-Tibet Plateau (P21-23), the Southern Yinshan mountain range (P29-43). Furthermore, the cluster results based on SSR data showed distinct genetic differentiation among populations from the northeast and southwest of the Yinshan Mountains (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). Because of the low self-proclaimed differentiation rate, the genetic structure for \u003cem\u003eP. villosa\u003c/em\u003e was assessed using SAMOVA based on low-copy nuclear gene data.\u003c/p\u003e\u003cp\u003eThe grouping is optimal when the value of \u003cem\u003eF\u003c/em\u003e\u003csub\u003eCT\u003c/sub\u003e reaches the maximum. The results indicated that this value was the largest (0.50155) with \u003cem\u003eK\u003c/em\u003e = 10 (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). However, there was a single population forming a group when \u003cem\u003eK\u003c/em\u003e was greater than 5, resulting in the inability to detect an effective phylogeographic group. Meanwhile, due to the geographical proximity and similar habitat of P1 and P3, the best grouping was \u003cem\u003eK\u003c/em\u003e = 2, for which the 41 populations were divided into two phylogeographic groups (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). This result was completely consistent with the phylogenetic tree.\u003c/p\u003e\u003cdiv class=\"gridtable\"\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=\"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=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\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\u003eThe optimal numbers of population groups (\u003cem\u003eK\u003c/em\u003e) inferred by SAMOVA algorithm based on low-copy nuclear gene datasets\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eK\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePopulation grouping\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eF\u003c/em\u003e\u003csub\u003eCT\u003c/sub\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eF\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eF\u003c/em\u003e\u003csub\u003eSC\u003c/sub\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eK\u003c/em\u003e = 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1, 2, 3, 4, 6, 7, 8, 9, 11, 12, 13, 14) (15, 16, 18, 19, 20, 21, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.407\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.822\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.700\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eK\u003c/em\u003e = 3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1, 4, 6, 7, 8, 11, 12, 13, 14) (2, 3, 9) (15, 16, 18, 19, 20, 21, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.435\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.822\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.685\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eK\u003c/em\u003e = 4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1, 4, 13) (2, 3, 9) (6, 7, 8, 11, 12, 14) (15, 16, 18, 19, 20, 21, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.460\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.823\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.671\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eK\u003c/em\u003e = 5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1, 4, 13) (2, 3, 9) (6, 7, 8, 11, 12) (14) (15, 16, 18, 19, 20, 21, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.460\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.822\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.670\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eK\u003c/em\u003e = 6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1, 4, 13) (2, 3, 9) (6, 7, 8, 11, 12) (14) (15, 16, 18, 19, 20, 21, 23, 24, 25, 26, 27, 28, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45) (29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.468\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.820\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.661\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eK\u003c/em\u003e = 7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1, 4, 13) (2, 3, 9) (6, 7, 8, 11, 12) (14) (15, 16, 18, 20, 21, 23, 24, 25, 26, 27, 28, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45) (19) (29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.469\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.816\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.653\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eK\u003c/em\u003e = 8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1, 4, 13) (2, 3, 9) (6, 7, 8, 11, 12, 14) (15, 16, 18, 28) (19, 20, 21, 23, 24, 25, 26, 27, 30, 31, 32, 34, 37, 38, 41, 44, 45) (29) (33, 36, 39, 40, 42, 43) (35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.483\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.794\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.602\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eK\u003c/em\u003e = 9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1, 4, 13) (2, 3, 9) (6, 7) (8, 11, 12) (14) (15, 16, 18, 28) (19, 20, 21, 23, 24, 25, 26, 27, 30, 31, 32, 34, 35, 37, 38, 41, 44, 45) (29) (33, 36, 39, 40, 42, 43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.492\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.794\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.595\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eK\u003c/em\u003e = 10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1, 4, 13) (2, 3, 9) (6, 7) (8, 11, 12) (14) (15, 16, 18, 28) (19, 20, 21, 23, 24, 25, 26, 27, 30, 31, 32, 34, 35, 37, 38, 41, 44, 45) (29) (33, 36, 39) (40, 42, 43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.502\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.793\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.585\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003eThe Bayesian clustering method of the STRUCTURE software was further applied to analyze the genetic structure of \u003cem\u003eP. villosa.\u003c/em\u003e The most optimal number of population groups (\u003cem\u003eK\u003c/em\u003e) was determined with the posterior probability LnP(D) and delta \u003cem\u003eK\u003c/em\u003e (∆\u003cem\u003eK\u003c/em\u003e), setting the population size \u003cem\u003eK\u003c/em\u003e as 2–15 and 2–10. The results indicated that ∆\u003cem\u003eK\u003c/em\u003e was maximum when \u003cem\u003eK\u003c/em\u003e = 2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e3\u003c/span\u003ea,b,d,e), indicating that populations of this species could be divided into two subgroups. At the same time, there was gene flow between the two genetic units, according to the grouping results of the STRUCTURE program, where red gene pools represent the Group 1 population and blue gene pools represent the Group 2 population (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e3\u003c/span\u003ec, f). The results of principal component analysis (PCoA) further confirmed the clustering results of STRUCTURE (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Overall, the population genetic structures of \u003cem\u003eP. villosa\u003c/em\u003e obtained using different methods were almost identical.\u003c/p\u003e\u003ch2\u003e3.2 Genetic diversity\u003c/h2\u003e\u003cp\u003eHaplotype diversity index is an important indicator for measuring the degree of variation within a population. These spliced sequences of 10 primer pairs detected a total of 52 polymorphic sites in 174 samples, with the number of polymorphic loci (\u003cem\u003eS\u003c/em\u003e) ranging from 2 to 11, and the number of haplotypes (\u003cem\u003eH\u003c/em\u003e) ranging from 4 to 23 (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The haplotype diversity (\u003cem\u003eH\u003c/em\u003e\u003csub\u003ed\u003c/sub\u003e) ranged from 0.479 to 0.854, with an average of 0.658, in which the haplotype diversity was the highest at site 14111 and the lowest at site 14192. These results showed that differences in gene expression contributed to the variation observed in \u003cem\u003eP. villosa\u003c/em\u003e. Meanwhile, the nucleotide diversity (\u003cem\u003eπ\u003c/em\u003e) ranged from 0.00062 to 0.00414, with an average of 0.00180, indicating that 10 primer pairs exhibited high polymorphism. Based on the genetic diversity analysis of low-copy nuclear genes in the \u003cem\u003eP. villosa\u003c/em\u003e populations, the results showed that \u003cem\u003eH\u003c/em\u003e\u003csub\u003ed\u003c/sub\u003e, \u003cem\u003eπ\u003c/em\u003e, and \u003cem\u003eK\u003c/em\u003e respectively ranged from 0.000 to 1.000, 0.000 to 0.203, and 0.000 to 10.578 (Table S3). This indicated significant genetic diversity differences among \u003cem\u003eP. villosa\u003c/em\u003e populations, in which populations located in the central Inner Mongolia Plateau exhibited higher genetic diversity. Simultaneously, this study also analyzed the genetic diversity of \u003cem\u003eP. villosa\u003c/em\u003e populations based on the SAMOVA grouping results. Group 2 demonstrated a high level of genetic diversity, with \u003cem\u003eH\u003c/em\u003e\u003csub\u003ed\u003c/sub\u003e and \u003cem\u003eπ\u003c/em\u003e measuring 0.990 and 0.00145, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\u003cdiv class=\"gridtable\"\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=\"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=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eStatistics of genetic diversity for 10 pairs of low-copy nuclear genes of \u003cem\u003eP. villosa\u003c/em\u003e in the present study\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLocus\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNumber of polymorphic sites\u003c/p\u003e\u003cp\u003e(S)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNumber of Haplotypes\u003c/p\u003e\u003cp\u003e(H)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHaplotype diversity\u003c/p\u003e\u003cp\u003e(\u003cem\u003eH\u003c/em\u003ed)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNucleotide diversity\u003c/p\u003e\u003cp\u003e(π)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eAverage number of nucleotide differences\u003c/p\u003e\u003cp\u003e(\u003cem\u003eK\u003c/em\u003e)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eG + C\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eRm\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e14031\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.526\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.00083\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.591\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.476\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e14004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.569\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.00093\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.684\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.443\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e13593\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.713\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.00188\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.448\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.462\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e14111\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.854\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.00355\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e5.147\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.649\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e14075\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.512\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.00062\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.617\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.422\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e13604\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.737\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.00135\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.996\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.589\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e14192\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.479\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.00111\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.895\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.464\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e14928\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.533\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.00093\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.851\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.434\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e14224\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.838\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.00414\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3.660\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.493\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e14612\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.818\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.00262\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.321\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.474\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.658\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.00180\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.721\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.491\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cdiv class=\"gridtable\"\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\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eGenetic diversity for 41 populations of \u003cem\u003eP. villosa\u003c/em\u003e based on low-copy nuclear genes in the present study\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eThe parameter of genetic diversity\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePopulations of Group 1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePopulations of Group 2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAll populations\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNumber of polymorphic sites\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e52\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNumber of Haplotypes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e110\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e157\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHaplotype diversity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.975\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.990\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.993\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNucleotide diversity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.00141\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.00145\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.00189\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAverage number of nucleotide differences\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12.251\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12.528\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e16.378\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eG + C\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.495\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.496\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.496\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e26\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003eAn analysis was conducted on the polymorphic loci of 13 pairs of SSR primers using POPGEN 1.32 software, and the results are presented in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. A total of 104 alleles were detected at the 13 microsatellite loci in the 195 samples of \u003cem\u003eP. villosa\u003c/em\u003e, with the number of alleles per locus (\u003cem\u003eN\u003c/em\u003e\u003csub\u003ea\u003c/sub\u003e) ranging from 3 to 14 (average of 8). The primer with the highest number of alleles was Pv21, and the primer with the lowest number of alleles was Pv44. The expected heterozygosity (\u003cem\u003eH\u003c/em\u003e\u003csub\u003ee\u003c/sub\u003e) and observed heterozygosity (\u003cem\u003eH\u003c/em\u003e\u003csub\u003eo\u003c/sub\u003e) of the microsatellite loci ranged from 0.183 to 0.576 and 0.215 to 0.964, respectively. The polymorphic information content (PIC) generated ranged from 0.317 to 0.747, with a mean value of 0.511. the Shannon information index (\u003cem\u003eI\u003c/em\u003e) ranged from 0.278 to 0.946, with a mean value of 0.567. These results indicate that the 13 pairs of SSR primer marker loci are suitable for genetic research on \u003cem\u003eP. villosa\u003c/em\u003e owing to their high polymorphism and genetic diversity. At the population level, the mean values of \u003cem\u003eH\u003c/em\u003e\u003csub\u003ee\u003c/sub\u003e and \u003cem\u003eH\u003c/em\u003e\u003csub\u003eo\u003c/sub\u003e were 0.359 and 0.509, respectively (Table S3). The heterozygosity of P30 and P31 was greater than 0.5, indicating that these two populations have high genetic diversity and relatively low human selection factors. In addition, the fixation index (\u003cem\u003eF\u003c/em\u003e) can reflect the extent of deviation of a species. If \u003cem\u003eF\u003c/em\u003e \u0026gt; 0, it indicates a higher frequency of homozygotes within the population, whereas if \u003cem\u003eF\u003c/em\u003e \u0026lt; 0, it suggests a higher frequency of heterozygotes within the population. In view of the above, P16 and P18 have positive values among the 39 populations, whereas the \u003cem\u003eF\u003c/em\u003e values for the other populations are less than 0. This indicates a higher frequency of heterozygotes within the \u003cem\u003eP. villosa\u003c/em\u003e population (Table S3).\u003c/p\u003e\u003cdiv class=\"gridtable\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSummaries of genetic variation statistics for all SSR loci\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"10\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrimer EcoR I/Mse I\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003csub\u003ea\u003c/sub\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003csub\u003ee\u003c/sub\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eF\u003c/em\u003e\u003csub\u003eIS\u003c/sub\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eF\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003eI\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003eH\u003c/em\u003e\u003csub\u003eo\u003c/sub\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cem\u003eH\u003c/em\u003e\u003csub\u003ee\u003c/sub\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003csub\u003em\u003c/sub\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003ePIC\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ej-F/j-R\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\u003e1.692\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.329\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.425\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.502\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.431\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.324\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.339\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.523\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH4-F/H4-R\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.546\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.674\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.264\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.946\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.964\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.576\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.697\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.747\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e44-F/44-R\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.362\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.359\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.631\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.287\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.256\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.189\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.146\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.393\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e39-F/39-R\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.941\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.497\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.484\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.622\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.595\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.397\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.266\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.737\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e33-F/33-R\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.484\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.255\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.489\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.419\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.338\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.270\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.261\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.485\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e23-F/23-R\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.919\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.849\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.187\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.662\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.862\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.467\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e1.090\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.482\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e15-F/15-R\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.509\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.533\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.365\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.417\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.410\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.268\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.434\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.406\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e8-F/8-R\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\u003e1.334\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.176\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.721\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.278\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.215\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.183\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.097\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.594\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e14-F/14-R\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.245\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.736\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.203\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.834\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.938\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.541\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.980\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.631\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e50-F/50-R\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.066\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.053\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.113\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.779\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.518\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.492\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e1.956\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.474\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e51-F/51-R\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.432\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.452\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.345\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.356\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.318\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.219\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.475\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.317\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e49-F/49-R\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.069\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.046\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.202\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.789\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.492\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.471\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.989\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.530\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e21-F/21-R\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.482\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.193\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.482\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.277\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.278\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e1.047\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.329\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=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.776\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.381\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.356\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.567\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.509\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.359\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.675\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.511\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"10\"\u003eNote: \u003cem\u003eN\u003c/em\u003ea, observed number of alleles; \u003cem\u003eN\u003c/em\u003ee, effective number of alleles; \u003cem\u003eF\u003c/em\u003e\u003csub\u003eIS\u003c/sub\u003e, inbreeding coefficient; \u003cem\u003eF\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e, variance among populations; \u003cem\u003eI\u003c/em\u003e, Shannon's information index; \u003cem\u003eH\u003c/em\u003eo, observed heterozygosity; \u003cem\u003eH\u003c/em\u003ee, expected heterozygosity; \u003cem\u003eN\u003c/em\u003e\u003csub\u003em\u003c/sub\u003e, gene flow. PIC, polymorphic information content.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003ch2\u003e3.3 Genetic differentiation and genetic variation\u003c/h2\u003e\u003cp\u003eGenetic variation analysis of \u003cem\u003eP. villosa\u003c/em\u003e populations using low-copy nuclear gene data revealed that the proportion of genetic variation among populations was 76.6%, and that within populations was 23.4%, with \u003cem\u003eF\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e = 0.766 (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01) (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). These results showed that the genetic variation of this species existed primarily among populations. Meanwhile, this parameter of \u003cem\u003eP. villosa\u003c/em\u003e population grouping revealed that 40.71% of the genetic variation was explained, and the genetic variation originated mainly from among populations accounting for 41.53% of the total genetic variation (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). Genetic variation analysis of \u003cem\u003eP. villosa\u003c/em\u003e populations using SSR data showed similar results.\u003c/p\u003e\u003cdiv class=\"gridtable\"\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=\"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\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\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eResults of analyses of molecular variance (AMOVAs) for \u003cem\u003eP. villosa\u003c/em\u003e based on low-copy nuclear genes and SSR data\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eData\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGrouping\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSource of variation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003edf\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSS\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eVC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003ePercent variation (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eFixation index\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e\u003cp\u003eLCNG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eTotal populations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAmong populations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2242.692\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6.384 Va\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e76.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u003cem\u003eF\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e = 0.766\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eWithin populations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e307\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e598.808\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.951 Vb\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e23.40\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e347\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2841.500\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e8.334\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eSAMOVA groups\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAmong groups\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e697.447\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.396 Va\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e40.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cem\u003eF\u003c/em\u003e\u003csub\u003eCT\u003c/sub\u003e = 0.407\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAmong populations within groups\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1555.148\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.485 Vb\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e41.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cem\u003eF\u003c/em\u003e\u003csub\u003eSC\u003c/sub\u003e = 0.700\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eWithin populations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e307\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e589.008\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.919 Vc\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e17.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cem\u003eF\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e = 0.822\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e347\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2841.603\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e10.800\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eSSR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eTotal populations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAmong populations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1027.954\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u003cem\u003eF\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e = 0.577\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eWithin populations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e156\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e539.600\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e42\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e194\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1567.554\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"8\"\u003eNote: \u003cem\u003edf\u003c/em\u003e, Degrees of freedom; SS, Sum of squares; VC, Variance components; \u003cem\u003eF\u003c/em\u003e\u003csub\u003eCT\u003c/sub\u003e, Variance among groups relative to total variance; \u003cem\u003eF\u003c/em\u003e\u003csub\u003eSC\u003c/sub\u003e, Variance among populations within groups; \u003cem\u003eF\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e, Variance among populations; Significant level: **\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003eSimilarly, the genetic differentiation coefficient among populations based on low-copy nuclear gene data ranged from 0.533 to 0.977, with an average of 0.754, indicating a high level of genetic differentiation among populations (Table S4). The PERMUT analysis results showed that the total genetic diversity (\u003cem\u003eH\u003c/em\u003e\u003csub\u003et\u003c/sub\u003e) of the population was 1.000, while the within-population genetic diversity (\u003cem\u003eH\u003c/em\u003e\u003csub\u003es\u003c/sub\u003e) was 0.721, suggesting that the genetic diversity of the population existed primarily within populations (Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). The genetic differentiation coefficient (\u003cem\u003eG\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e) among populations was 0.279, and the genetic differentiation coefficient within populations (\u003cem\u003eN\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e) was 0.533 (Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). The coefficient of genetic differentiation in \u003cem\u003eG\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e was significantly lower than that in \u003cem\u003eN\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05), which indicated that there was obvious pedigree geographical structure in the \u003cem\u003eP. villosa\u003c/em\u003e populations. Although there were differences in the estimation of genetic differentiation among populations by different primers or different gene fragments, they all showed that there was high genetic differentiation among populations. In addition, the study of genetic differentiation based on SSR data also supported this result, with \u003cem\u003eF\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e = 0.356 (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01) (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e\u003cdiv class=\"gridtable\"\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\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eNei'S analysis of genetic diversity of \u003cem\u003eP. villosa\u003c/em\u003e populations\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eItem\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eH\u003c/em\u003et\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eH\u003c/em\u003es\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eG\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.721\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.279\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.533\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStandard error\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.034\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.034\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.035\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote: \u003cem\u003eH\u003c/em\u003et, Total gene diversity; \u003cem\u003eH\u003c/em\u003es, The average gene diversity within populations; \u003cem\u003eG\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e, Gene differentiation coefficient between populations; \u003cem\u003eN\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e, Gene differentiation coefficient within populations.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003ch2\u003e3.4 Factors influencing species differentiation\u003c/h2\u003e\u003cp\u003eThe Mantel test for IBD was conducted to estimate the correlation between the genetic distance and geographical distance of \u003cem\u003eP. villosa\u003c/em\u003e. Positive correlation of genetic distance with geographical distance produced a pattern of isolation by distance. Applying the Mantel test, there was a significant positive correlation between genetic and geographical distances (\u003cem\u003er\u003c/em\u003e = 0.291, \u003cem\u003eP\u003c/em\u003e = 0.010) for the entire sample (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e5\u003c/span\u003ea). A positive correlation was also detected for Group 1 and Group 2 between genetic and geographical distances, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e5\u003c/span\u003eb, c). Subsequently, correlation analysis between the genetic differentiation (\u003cem\u003eF\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e) and climate factors consistently revealed a positive correlation, showing a significant correlation only with altitude (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e5\u003c/span\u003ed). These findings confirmed that both geographical distance and altitude strongly influenced genetic differentiation.\u003c/p\u003e\u003ch2\u003e3.5 Historical demography\u003c/h2\u003e\u003cp\u003eThe majority of genetic loci in \u003cem\u003eP. villosa\u003c/em\u003e exhibited nonsignificant positive values for SSD and \u003cem\u003eH\u003c/em\u003e\u003csub\u003eRag\u003c/sub\u003e, while those of 14075, 13604, 14192, and 14928 loci showed significantly positive values (Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e). Estimation based on combined fragments showed that both parameters were positively correlated, with SSD and \u003cem\u003eH\u003c/em\u003e\u003csub\u003eRag\u003c/sub\u003e values of 0.123 and 0.309, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e). We tentatively speculated that the species might undergo population expansion. Notably, mismatch distribution analysis for the entire populations generated nonsignificant values of the \u003cem\u003eH\u003c/em\u003e\u003csub\u003eRag\u003c/sub\u003e and SSD indices. It produced bimodal curves, suggesting rapid population expansion (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Accordingly, the neutrality tests resulted in nonsignificant positive values of Tajima’s \u003cem\u003eD\u003c/em\u003e, Fu and Li’s \u003cem\u003eD\u003c/em\u003e\u003csup\u003e\u003cem\u003e*\u003c/em\u003e\u003c/sup\u003e, and Fu and Li’s \u003cem\u003eF\u003c/em\u003e\u003csup\u003e\u003cem\u003e*\u003c/em\u003e\u003c/sup\u003e for genetic loci except 14075 loci (Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e). Conversely, the parameters estimated on the basis of combined fragments all showed highly significant positive values, with values of \u003cem\u003eD\u003c/em\u003e, \u003cem\u003eD\u003c/em\u003e\u003csup\u003e\u003cem\u003e*\u003c/em\u003e\u003c/sup\u003e, and \u003cem\u003eF\u003c/em\u003e\u003csup\u003e\u003cem\u003e*\u003c/em\u003e\u003c/sup\u003e of 2.922, 2.425, and 3.198, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e). Overall, these findings confirmed the rejection of population expansion.\u003c/p\u003e\u003cdiv class=\"gridtable\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThe results of neutrality tests and mismatch distribution of \u003cem\u003eP. villosa\u003c/em\u003e at ten nuclear loci\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eLocus\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eNeutrality test\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003eMismatch distribution\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eD\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eD\u003c/em\u003e*\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eF\u003c/em\u003e*\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSSD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003eH\u003c/em\u003e\u003csub\u003eRag\u003c/sub\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e14031\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.395\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.736\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.742\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.008\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.075\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e14004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.691\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.736\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.859\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.064\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.203\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e13593\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.539\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.938\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.370\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.077\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.281\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e14111\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.660\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.729\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.057\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.196\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.458\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e14075\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.369\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.938\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.584\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.010\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.046\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e13604\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.775\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.604\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.554\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.014\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e14192\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.362\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.736\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.124\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.012\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.044\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e14928\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.607\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.844\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.912\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.009\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.050\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e14224\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.966\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.289\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.300\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.146\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.391\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e14612\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.250\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.097\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.809\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.052\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.249\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCombine\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.922\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.425\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.198\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.123\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.309\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eNote: *\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05; **\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eSubsequently, the historical demography of \u003cem\u003eP. villosa\u003c/em\u003e was simulated using DIYABC v.2.0.4 software. According to the population genetic structure of \u003cem\u003eP. villosa\u003c/em\u003e, we designed three scenario models (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e7\u003c/span\u003e): (1) Group 1 and Group 2 diverged at time \u003cem\u003eT\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e, while Group 1 and ancestral species (\u003cem\u003eN\u003c/em\u003e\u003csub\u003eA\u003c/sub\u003e) diverged at time \u003cem\u003eT\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e; (2) Group 2 and Group 1 diverged at time \u003cem\u003eT\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e, while Group 2 and ancestral species (\u003cem\u003eN\u003c/em\u003e\u003csub\u003eA\u003c/sub\u003e) diverged at time \u003cem\u003eT\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e; (3) the two groups were relatively independent, with a common ancestral species (\u003cem\u003eN\u003c/em\u003e\u003csub\u003eA\u003c/sub\u003e), and diverged in a radiating pattern at time \u003cem\u003eT\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e. By comparing the posterior distribution probabilities for three models, Scenario 2 was selected as the most accurate in model simulations with LCNG and SSR data (Table S5). Under Scenario 2, Group 2 diverged from \u003cem\u003eN\u003c/em\u003e\u003csub\u003eA\u003c/sub\u003e approximately 8.20 Ma (95% HPD: 2.81 ~ 14.2 Ma) and 4.42 Ma (95% HPD: 0.396 ~ 13.3 Ma), respectively (Table\u0026nbsp;\u003cspan refid=\"Tab10\" class=\"InternalRef\"\u003e10\u003c/span\u003e). Moreover, the timing of divergence between Group 2 and Group 1 was 3.96 Ma (95% HPD: 1.36 ~ 8.10 Ma) and 0.109 Ma (95% HPD: 0.03 ~ 0.275 Ma), respectively (Table\u0026nbsp;\u003cspan refid=\"Tab10\" class=\"InternalRef\"\u003e10\u003c/span\u003e). As shown in Table\u0026nbsp;\u003cspan refid=\"Tab10\" class=\"InternalRef\"\u003e10\u003c/span\u003e, parameter estimates obtained from the best model showed that the effective population size of \u003cem\u003eN\u003c/em\u003e\u003csub\u003eA\u003c/sub\u003e, Group 1, and Group 2 was 2.14 × 10\u003csup\u003e6\u003c/sup\u003e (95% HPD: 5.53 × 10\u003csup\u003e5\u003c/sup\u003e~2.93 × 10\u003csup\u003e6\u003c/sup\u003e), 3.84 × 10\u003csup\u003e5\u003c/sup\u003e (95% HPD: 1.40 × 10\u003csup\u003e5\u003c/sup\u003e~7.51 × 10\u003csup\u003e5\u003c/sup\u003e), and 4.65 × 10\u003csup\u003e5\u003c/sup\u003e (95% HPD: 1.53 × 10\u003csup\u003e5\u003c/sup\u003e~9.15 × 10\u003csup\u003e5\u003c/sup\u003e) based on LCNG, respectively. The effective population size of \u003cem\u003eN\u003c/em\u003e\u003csub\u003eA\u003c/sub\u003e, Group 1, and Group 2 was 2.01 × 10\u003csup\u003e6\u003c/sup\u003e (95% HPD: 4.27 × 10\u003csup\u003e5\u003c/sup\u003e~2.91 × 10\u003csup\u003e6\u003c/sup\u003e), 3.55 × 10\u003csup\u003e5\u003c/sup\u003e (95% HPD: 8.67 × 10\u003csup\u003e4\u003c/sup\u003e~8.74 × 10\u003csup\u003e5\u003c/sup\u003e), and 6.57 × 10\u003csup\u003e5\u003c/sup\u003e (95% HPD: 2.35 × 10\u003csup\u003e5\u003c/sup\u003e~1.62 × 10\u003csup\u003e5\u003c/sup\u003e) based on SSR data, respectively. Group 1 and Group 2 had a smaller effective population size than \u003cem\u003eN\u003c/em\u003e\u003csub\u003eA\u003c/sub\u003e, which indicated that there had been contraction events in the history of \u003cem\u003eP. villosa.\u003c/em\u003e This finding was confirmed through the isolation with migration (IM) model. Surprisingly, the divergence times estimated for Group 1 and Group 2 based on the IM model differed from those based on the approximate Bayesian computation (ABC) model. Group 1 and Group 2 began to differentiate about 0.115 Ma (95% HPD: 0.0984 ~ 0.475 Ma) and 0.180 Ma (95% HPD: 0.148 ~ 0.328 Ma) with LCNG and SSR data (Table\u0026nbsp;\u003cspan refid=\"Tab11\" class=\"InternalRef\"\u003e11\u003c/span\u003e), respectively. In addition, the model strongly supported bidirectional and asymmetric gene flow between the two groups, and the migration rate from Group 1 to Group 2 (\u003cem\u003eM\u003c/em\u003e2→1 = 1.87 or 0.08) was lower than that in the opposite direction (\u003cem\u003eM\u003c/em\u003e1→2 = 4.99 or 0.19) (Table\u0026nbsp;\u003cspan refid=\"Tab11\" class=\"InternalRef\"\u003e11\u003c/span\u003e). Therefore, we inferred that this species occurred population contraction in the history, and the divergence between Group 1 and Group 2 occurred during the Pleistocene (0.0117 ~ 2.59 Ma).\u003c/p\u003e\u003cdiv class=\"gridtable\"\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=\"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=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"×\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"×\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab10\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 10\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eEstimates of the posterior distributions of demographic parameters revealed by Approximate Bayesian Computation (ABC) for the best Model (Scenario 2)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eData\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eParameter\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003csub\u003eA\u003c/sub\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003eT\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003eT\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003eLCNG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"×\" colname=\"c3\"\u003e\u003cp\u003e4.03×10\u003csup\u003e5\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"×\" colname=\"c4\"\u003e\u003cp\u003e4.90×10\u003csup\u003e5\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.99×10\u003csup\u003e6\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"×\" colname=\"c6\"\u003e\u003cp\u003e4.22×10\u003csup\u003e6\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"×\" colname=\"c7\"\u003e\u003cp\u003e8.30×10\u003csup\u003e6\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMedian\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"×\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e3.84×10\u003c/b\u003e\u003csup\u003e\u003cb\u003e5\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"×\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e4.65×10\u003c/b\u003e\u003csup\u003e\u003cb\u003e5\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e2.14×10\u003c/b\u003e\u003csup\u003e\u003cb\u003e6\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"×\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e3.94×10\u003c/b\u003e\u003csup\u003e\u003cb\u003e6\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"×\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e8.20×10\u003c/b\u003e\u003csup\u003e\u003cb\u003e6\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMode\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"×\" colname=\"c3\"\u003e\u003cp\u003e3.16×10\u003csup\u003e5\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"×\" colname=\"c4\"\u003e\u003cp\u003e3.45×10\u003csup\u003e5\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.98×10\u003csup\u003e6\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"×\" colname=\"c6\"\u003e\u003cp\u003e3.73×10\u003csup\u003e6\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"×\" colname=\"c7\"\u003e\u003cp\u003e7.85×10\u003csup\u003e6\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHPD95% lower\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"×\" colname=\"c3\"\u003e\u003cp\u003e1.40×10\u003csup\u003e5\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"×\" colname=\"c4\"\u003e\u003cp\u003e1.53×10\u003csup\u003e5\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.53×10\u003csup\u003e5\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"×\" colname=\"c6\"\u003e\u003cp\u003e1.36×10\u003csup\u003e6\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"×\" colname=\"c7\"\u003e\u003cp\u003e2.81×10\u003csup\u003e6\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHPD95% upper\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"×\" colname=\"c3\"\u003e\u003cp\u003e7.51×10\u003csup\u003e5\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"×\" colname=\"c4\"\u003e\u003cp\u003e9.15×10\u003csup\u003e5\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.93×10\u003csup\u003e6\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"×\" colname=\"c6\"\u003e\u003cp\u003e8.10×10\u003csup\u003e6\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"×\" colname=\"c7\"\u003e\u003cp\u003e1.42×10\u003csup\u003e7\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003eSSR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"×\" colname=\"c3\"\u003e\u003cp\u003e4.00×10\u003csup\u003e5\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"×\" colname=\"c4\"\u003e\u003cp\u003e7.58×10\u003csup\u003e5\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.88e×10\u003csup\u003e6\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"×\" colname=\"c6\"\u003e\u003cp\u003e1.26×10\u003csup\u003e5\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"×\" colname=\"c7\"\u003e\u003cp\u003e5.40×10\u003csup\u003e6\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMedian\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"×\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e3.55×10\u003c/b\u003e\u003csup\u003e\u003cb\u003e5\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"×\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e6.57×10\u003c/b\u003e\u003csup\u003e\u003cb\u003e5\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e2.01×10\u003c/b\u003e\u003csup\u003e\u003cb\u003e6\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"×\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e1.09×10\u003c/b\u003e\u003csup\u003e\u003cb\u003e5\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"×\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e4.42×10\u003c/b\u003e\u003csup\u003e\u003cb\u003e6\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMode\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"×\" colname=\"c3\"\u003e\u003cp\u003e2.31×10\u003csup\u003e5\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"×\" colname=\"c4\"\u003e\u003cp\u003e5.63×10\u003csup\u003e5\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.94×10\u003csup\u003e6\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"×\" colname=\"c6\"\u003e\u003cp\u003e6.90×10\u003csup\u003e4\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"×\" colname=\"c7\"\u003e\u003cp\u003e1.15×10\u003csup\u003e5\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHPD95% lower\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"×\" colname=\"c3\"\u003e\u003cp\u003e8.67×10\u003csup\u003e4\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"×\" colname=\"c4\"\u003e\u003cp\u003e2.35×10\u003csup\u003e5\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.27×10\u003csup\u003e5\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"×\" colname=\"c6\"\u003e\u003cp\u003e3.00×10\u003csup\u003e4\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"×\" colname=\"c7\"\u003e\u003cp\u003e3.96×10\u003csup\u003e5\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHPD95% upper\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"×\" colname=\"c3\"\u003e\u003cp\u003e8.74×10\u003csup\u003e5\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"×\" colname=\"c4\"\u003e\u003cp\u003e1.62×10\u003csup\u003e6\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.91×10\u003csup\u003e6\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"×\" colname=\"c6\"\u003e\u003cp\u003e2.75×10\u003csup\u003e5\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"×\" colname=\"c7\"\u003e\u003cp\u003e1.33×10\u003csup\u003e7\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003cem\u003eN\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e = effective population size of Group 1 for \u003cem\u003eP. villosa\u003c/em\u003e.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003cem\u003eN\u003c/em\u003e\u003csub\u003e2\u003c/sub\u003e = effective population size of Group 2 for \u003cem\u003eP. villosa\u003c/em\u003e.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003cem\u003eN\u003c/em\u003e\u003csub\u003eA\u003c/sub\u003e = effective population size of ancestral population.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003cem\u003eT\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e = time since divergence between Group 1 and Group 2.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003cem\u003eT\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e = time since divergence between ancestral population and Group 2.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv class=\"gridtable\"\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=\"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=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"×\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"×\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"×\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"×\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab11\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 11\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDemographic parameters scaled by the mutation rate from IMa multilocus analyses based on the group from SAMOVA\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"14\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eData\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eParameter\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eθ\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eθ\u003c/em\u003e\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eθ\u003c/em\u003e\u003csub\u003eA\u003c/sub\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003em\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003em\u003c/em\u003e\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cem\u003et\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003csub\u003eA\u003c/sub\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c12\"\u003e\u003cp\u003e\u003cem\u003eT\u003c/em\u003e[year]\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c13\"\u003e\u003cp\u003e2\u003cem\u003eN\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e\u003cem\u003em\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c14\"\u003e\u003cp\u003e2\u003cem\u003eN\u003c/em\u003e\u003csub\u003e2\u003c/sub\u003e\u003cem\u003em\u003c/em\u003e\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eLCNG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHipt\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.0341\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.0854\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.2951\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e4.5375\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e4.4125\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.0011\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"×\" colname=\"c9\"\u003e\u003cp\u003e2.80×10\u003csup\u003e5\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"×\" colname=\"c10\"\u003e\u003cp\u003e7.00×10\u003csup\u003e5\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"×\" colname=\"c11\"\u003e\u003cp\u003e1.06×10\u003csup\u003e7\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"×\" colname=\"c12\"\u003e\u003cp\u003e1.80×10\u003csup\u003e5\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\u003cp\u003e0.19\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHPD95% lower\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.0173\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.0425\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.357\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.4875\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.2875\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.0009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"×\" colname=\"c9\"\u003e\u003cp\u003e1.42×10\u003csup\u003e5\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"×\" colname=\"c10\"\u003e\u003cp\u003e3.48×10\u003csup\u003e5\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"×\" colname=\"c11\"\u003e\u003cp\u003e2.93×10\u003csup\u003e6\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"×\" colname=\"c12\"\u003e\u003cp\u003e1.48×10\u003csup\u003e5\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHPD95% upper\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.1526\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.3178\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e13.6443\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e23.0875\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e21.9625\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.0020\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"×\" colname=\"c9\"\u003e\u003cp\u003e1.25×10\u003csup\u003e6\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"×\" colname=\"c10\"\u003e\u003cp\u003e2.60×10\u003csup\u003e6\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"×\" colname=\"c11\"\u003e\u003cp\u003e1.12×10\u003csup\u003e8\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"×\" colname=\"c12\"\u003e\u003cp\u003e3.28×10\u003csup\u003e5\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e1.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\u003cp\u003e3.49\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eSSR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHipt\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.1875\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.7492\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e378.3522\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e9.995\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e19.990\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.0007\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"×\" colname=\"c9\"\u003e\u003cp\u003e1.54×10\u003csup\u003e6\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"×\" colname=\"c10\"\u003e\u003cp\u003e6.14×10\u003csup\u003e6\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"×\" colname=\"c11\"\u003e\u003cp\u003e3.10×10\u003csup\u003e9\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"×\" colname=\"c12\"\u003e\u003cp\u003e1.15×10\u003csup\u003e5\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e1.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\u003cp\u003e4.99\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHPD95% lower\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.1875\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.7492\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e260.9756\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e5.575\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e17.290\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.0006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"×\" colname=\"c9\"\u003e\u003cp\u003e1.54×10\u003csup\u003e6\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"×\" colname=\"c10\"\u003e\u003cp\u003e6.14×10\u003csup\u003e6\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"×\" colname=\"c11\"\u003e\u003cp\u003e2.14×10\u003csup\u003e9\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"×\" colname=\"c12\"\u003e\u003cp\u003e9.84×10\u003csup\u003e4\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e1.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\u003cp\u003e4.32\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHPD95% upper\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.5626\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.7492\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e590.629\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e9.965\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e19.990\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.0029\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"×\" colname=\"c9\"\u003e\u003cp\u003e4.61×10\u003csup\u003e6\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"×\" colname=\"c10\"\u003e\u003cp\u003e6.14×10\u003csup\u003e6\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"×\" colname=\"c11\"\u003e\u003cp\u003e4.84×10\u003csup\u003e9\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"×\" colname=\"c12\"\u003e\u003cp\u003e4.75×10\u003csup\u003e5\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e1.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\u003cp\u003e4.99\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"14\"\u003e\u003cem\u003eθ\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e, \u003cem\u003eN\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e = effective population size of Group 1 for \u003cem\u003eP. villosa\u003c/em\u003e.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"14\"\u003e\u003cem\u003eθ\u003c/em\u003e\u003csub\u003e2\u003c/sub\u003e, \u003cem\u003eN\u003c/em\u003e\u003csub\u003e2\u003c/sub\u003e = effective population size of Group 2 for \u003cem\u003eP. villosa\u003c/em\u003e.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"14\"\u003e\u003cem\u003eθ\u003c/em\u003e\u003csub\u003eA\u003c/sub\u003e, \u003cem\u003eN\u003c/em\u003e\u003csub\u003eA\u003c/sub\u003e = effective population size of ancestral population.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"14\"\u003e\u003cem\u003et\u003c/em\u003e, \u003cem\u003eT\u003c/em\u003e = time since divergence between Group 1 and Group 2.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"14\"\u003e\u003cem\u003eN\u003c/em\u003e = \u003cem\u003eθ\u003c/em\u003e/4µG, G = 5; \u003cem\u003eT\u003c/em\u003e = \u003cem\u003et\u003c/em\u003e/µ; 2\u003cem\u003eNm\u003c/em\u003e = \u003cem\u003eθ\u003c/em\u003e × \u003cem\u003em\u003c/em\u003e/2.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003ch2\u003e3.6 Divergence time estimation and gene flow\u003c/h2\u003e\u003cp\u003eAfter concatenating 10 sets of nuclear gene fragments, this study selected the best nucleotide substitution model as the GTR + I + G by AIC calculation. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e8\u003c/span\u003e, the topology of the maximum clade credibility (MCC) tree was consistent with that of the phylogenetic tree. Subsequent divergences within \u003cem\u003eP. villosa\u003c/em\u003e occurred at various times throughout the Pleistocene, which was consistent with the results obtained based on IM model. The divergence time between \u003cem\u003eP. villosa\u003c/em\u003e and \u003cem\u003eAchnatherum splendens\u003c/em\u003e was 3.26 Ma (95% HPD: 2.30 ~ 4.63 Ma). Group 1 and Group 2 began to differentiate at about 0.38 Ma (95% HPD: 2.30 ~ 4.63 Ma). Within the 41 populations, divergence occurred at approximately 0.02–0.55 Ma.\u003c/p\u003e\u003cp\u003eThe species had significant genetic barriers in the Yinshan Mountains, Helan Mountains, Ordos Plateau and Yabulai Mountains based on SSR data by Barrier v.2.2 software (Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e9\u003c/span\u003e). The direction of gene flow among populations was analyzed by Migrate-N software. This study set up four possible models: (1) gene flow is bidirectional between Group 1 and Group 2; (2) gene flow is unidirectional from Group 1 to Group 2; (3) gene flow is unidirectional from Group 2 to Group 1; (4) Group 1 and Group 2 are the same population. The best model was determined by comparing the \u003cem\u003eb\u003c/em\u003e values in ln(Prob(D|Model)), with the model with the largest \u003cem\u003eb\u003c/em\u003e value considered to be the best. After comparison, the best model was model 1. The results showed that the gene flow from Group 1 to Group 2 was 0.432 and 0.299 from the past to the present, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab12\" class=\"InternalRef\"\u003e12\u003c/span\u003e), whereas the gene flow from Group 2 to Group 1 was 0.489 and 0.397, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab12\" class=\"InternalRef\"\u003e12\u003c/span\u003e). This indicated that the gene flow from Group 2 to Group 1 was larger than that accepted by this group, which further confirmed the results based on the IM model.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv class=\"gridtable\"\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=\"char\" char=\".\" 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\u003ctable float=\"Yes\" id=\"Tab12\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 12\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eHistorical gene flow among different geographic groups in \u003cem\u003eP. villosa\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eData\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eModel\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eln(Prob(D|Model))\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eθ\u003c/em\u003e1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eθ\u003c/em\u003e2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eM1→2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eM2→1\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e\u003cp\u003eLCNG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eModel 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-3387.640 (1a)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.463\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.514\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.432\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.489\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e-1835.250 (1b)\u003c/b\u003e\u003c/p\u003e\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\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eModel 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-3351.850 (1a)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.580\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.471\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.564\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-1853.990 (1b)\u003c/p\u003e\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\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eModel 3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-3305.390 (1a)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.541\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.593\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.519\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-1849.670 (1b)\u003c/p\u003e\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\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eModel 4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-4194.297 (1a)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.551\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-2116.930 (1b)\u003c/p\u003e\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\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e\u003cp\u003eSSR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eModel 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-312291.460 (1a)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.423\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.369\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.299\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.397\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e-52370.370 (1b)\u003c/b\u003e\u003c/p\u003e\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\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eModel 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-327473.070 (1a)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.154\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.399\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.387\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-55079.070 (1b)\u003c/p\u003e\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\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eModel 3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-317875.900 (1a)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.384\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.174\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.485\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-53363.530 (1b)\u003c/p\u003e\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\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eModel 4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-470987.850 (1a)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.395\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-77562.400 (1b)\u003c/p\u003e\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\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e"},{"header":"4 Discussion","content":"\u003ch2\u003e4.1 Genetic diversity and environmental adaptation\u003c/h2\u003e\u003cp\u003eIn the relationship between plants and the environment, the environment has an ecological effect on plants, influencing and changing their morphological structure, physiological, and biochemical characteristics. At the same time, plants exhibit adaptability to the environment, adjusting to external changes through their own variations\u003csup\u003e[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/sup\u003e. Every plant species has its unique gene pool and genetic organization, and genetic diversity determines its ability to adapt to the environment. This is also the foundation for maintaining the long-term stability of ecosystems\u003csup\u003e[\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]\u003c/sup\u003e. When the genetic diversity of a species is higher or its genetic variation is richer, its ability to adapt to the environment is stronger. Meanwhile, when genetic diversity decreases, the species' ability to adapt, reproduce, and resist diseases weakens\u003csup\u003e[\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]\u003c/sup\u003e. Therefore, studying the genetic diversity of species not only helps to explore their adaptability and evolutionary history, but also provides an important theoretical basis for the conservation and rational utilization of species\u003csup\u003e[\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eBased on 10 LCNG and 13 SSR primer pairs, this study explored the genetic diversity of \u003cem\u003eP. villosa\u003c/em\u003e. The results showed that the 10 selected primer pairs exhibited high polymorphism, with values for \u003cem\u003eH\u003c/em\u003e\u003csub\u003ed\u003c/sub\u003e, \u003cem\u003eπ\u003c/em\u003e, and \u003cem\u003eK\u003c/em\u003e ranging from 0.000 to 1.000, 0.000 to 0.203, and 0.000 to 10.578, respectively. At the population level, the analysis of polymorphic loci using 13 SSR primer pairs revealed that the values for PIC and \u003cem\u003eI\u003c/em\u003e ranged from 0.317 to 0.747 and from 0.278 to 0.946, respectively. These findings collectively indicate significant genetic diversity among \u003cem\u003eP. villosa\u003c/em\u003e populations. Moreover, the populations P30 and P31 located in Ningxia, China, showed relatively higher genetic diversity, consistent with previous results obtained from internal transcribed spacer (ITS) sequences and SSR molecular markers\u003csup\u003e[\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e–\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]\u003c/sup\u003e. Furthermore, the genetic diversity of \u003cem\u003eP. villosa\u003c/em\u003e showed similarities as well as differences when compared with other species in the Stipeae tribe. For example, Zhang et al.\u003csup\u003e[\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]\u003c/sup\u003e studied the genetic diversity of seven populations of \u003cem\u003eStipa grandis\u003c/em\u003e distributed in the Inner Mongolia region using RAPD. The results showed that the genetic diversity within populations (\u003cem\u003eH\u003c/em\u003e\u003csub\u003es\u003c/sub\u003e = 0.17) was higher than that between populations (\u003cem\u003eH\u003c/em\u003e\u003csub\u003et\u003c/sub\u003e = 0.27). Current research suggests that the factors influencing species genetic diversity mainly include natural selection, pollination pathways, gene flow, genetic drift, and genetic mutations. The potential drivers of within-species genetic diversity include biological factors such as dispersal ability and life history, as well as environmental factors such as climate and human activities\u003csup\u003e[\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e–\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]\u003c/sup\u003e. Accordingly, we infer that the genetic diversity of \u003cem\u003eP. villosa\u003c/em\u003e might be rich because it is a long-lived perennial plant that can reproduce sexually through seeds and seedlings, and clonally under harsh environmental conditions. Moreover, \u003cem\u003eP. villosa\u003c/em\u003e has strong reproductive and vegetation restoration abilities, and its adaptation to the growth environment also contributes to the formation of its genetic diversity.\u003c/p\u003e\u003ch2\u003e4.2 Population genetic differentiation and genetic structure\u003c/h2\u003e\u003cp\u003eThe genetic diversity and structure of a species are long-term products of evolution\u003csup\u003e[\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]\u003c/sup\u003e. Population genetic structure includes genetic differentiation between populations and genetic variation within populations, reflecting the effects of mutation, recombination, genetic drift, and selection during the evolutionary process\u003csup\u003e[\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]\u003c/sup\u003e Factors influencing the genetic structure of a species mainly include life habits, evolutionary history, reproductive systems, seed dispersal mechanisms, and gene flow\u003csup\u003e[\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]\u003c/sup\u003e. According to Wright's related theory and recent studies\u003csup\u003e[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]\u003c/sup\u003e, when the \u003cem\u003eF\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e value exceeds 0.25, there is significant genetic differentiation between populations of a species. In this study, based on LCNG and SSR data, the \u003cem\u003eF\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e of \u003cem\u003eP. villosa\u003c/em\u003e were estimated to be 0.754 and 0.356, which may be related to its reproductive mode. Meanwhile, the gene flow size of \u003cem\u003eP. villosa\u003c/em\u003e was 0.097, which is larger than that of self-pollinating plants (\u003cem\u003eM\u003c/em\u003e\u003csub\u003em\u003c/sub\u003e = 0.065), but smaller than that of cross-pollinating plants (\u003cem\u003eM\u003c/em\u003e\u003csub\u003em\u003c/sub\u003e = 5.38)\u003csup\u003e[\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e]\u003c/sup\u003e. Therefore, we hypothesize that sexual reproduction in \u003cem\u003eP. villosa\u003c/em\u003e may occur primarily through self-fertilization, consistent with previous research findings. AMOVA results showed that the majority of the genetic variation in \u003cem\u003eP. villosa\u003c/em\u003e populations is derived from among-population variation. Hensen et al.\u003csup\u003e[\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e]\u003c/sup\u003e using RAPD studied the genetic variation of \u003cem\u003eStipa capillata\u003c/em\u003e and found that genetic variation also comes primarily from among-population variation. Meanwhile, Lv et al.'s\u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e AFLP-based study suggested that genetic variation in \u003cem\u003eP. villosa\u003c/em\u003e originates primarily within populations. The clonal propagation characteristics of this species lend greater credibility to subsequent analyses employing LCNG and SSR molecular markers. Previous studies have shown that species of the \u003cem\u003eStipa\u003c/em\u003e genus reproduce primarily asexually. When there is a barrier to gene flow between populations, the proportion of genetic variation between populations significantly increases, while the level of genetic variation within populations decreases\u003csup\u003e[\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e]\u003c/sup\u003e. The results of this study also indicate that \u003cem\u003eG\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e \u0026lt; \u003cem\u003eN\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05), suggesting a significant correlation between the genetic variation of \u003cem\u003eP. villosa\u003c/em\u003e populations and their geographic origin. Mantel tests further revealed a significant correlation between genetic distance and spatial distance (\u003cem\u003er\u003c/em\u003e = 0.291, \u003cem\u003eP\u003c/em\u003e = 0.01), implying that spatial distance may be one of the important factors influencing genetic differentiation in \u003cem\u003eP. villosa\u003c/em\u003e populations. At the same time, correlation analysis between the genetic diversity, \u003cem\u003eF\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e values, and environmental factors indicated that altitude is also a major influencing factor in the genetic differentiation of \u003cem\u003eP. villosa\u003c/em\u003e. However, there is uncertainty regarding the genetic structure of species populations and their geographic locations\u003csup\u003e[\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e]\u003c/sup\u003e. That is, populations distributed in different geographic locations may not exhibit genetic differentiation or genetic variation, while populations located in the same geographic area may also have distant phylogenetic relationships. Therefore, this study employed various analytical methods, including unweighted pair group method with arithmetic mean (UPGMA), SAMOVA, PCA, and Structure, to investigate the genetic structure of \u003cem\u003eP. villosa\u003c/em\u003e populations. The results showed that the \u003cem\u003eP. villosa\u003c/em\u003e populations were divided into two main groups, Group 1 and Group 2, with the Yinshan Mountains acting as a boundary. Genetic introgression was observed between the groups. This is because the Yinshan Mountains have a unique geographical location, with significant climate differences on either side due to varying altitude and mountain orientation, leading to distinct geographical differences in plant species distribution and vegetation. In addition, this study found that \u003cem\u003eP. villosa\u003c/em\u003e populations that were geographically closer did not always cluster together, and individuals within the same population were not always grouped, especially in populations P13, P36, P42, and P43. We believe this may be due to the fact that these populations are distributed mainly in the Helan Mountains region. The Helan Mountains are situated in a complex and unique tectonic setting, with intense and frequent tectonic activity since the Paleozoic, exhibiting a \"multicycle\" characteristic\u003csup\u003e[\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e]\u003c/sup\u003e. This acts as a transitional zone between sandy land and grassland vegetation. Therefore, \u003cem\u003eP. villosa\u003c/em\u003e populations in this region may have introduced new genes from other populations, while populations outside the Helan Mountains may have dispersed genes favorable for the local habitat. In summary, we believe that the limited gene flow and unique reproductive systems among \u003cem\u003eP. villosa\u003c/em\u003e populations have led to a high level of genetic differentiation and variation in the species. Spatial distance and altitude are probably important factors influencing the genetic structure of \u003cem\u003eP. villosa\u003c/em\u003e populations\u003csup\u003e[\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003ch2\u003e4.3 Speciation and population demographic history\u003c/h2\u003e\u003cp\u003eThe Quaternary glacial climate oscillations are considered to have played a significant role in shaping the contemporary geographic distribution and population dynamics of plants\u003csup\u003e[\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e]\u003c/sup\u003e. Repeated climate fluctuations led to varying degrees of population contraction and expansion, with some species surviving in small climate zones known as glacial refugia\u003csup\u003e[\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e]\u003c/sup\u003e. Due to the uplift of the Qinghai-Xizang Plateau, the arid climate and complex terrain of the northwestern desert regions of China, along with the predominantly east–west orientation of many mountain ranges, the Yinshan, Helan, Qilian, and Tianshan mountains have formed natural glacial refugia and geographical barriers\u003csup\u003e[\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e]\u003c/sup\u003e. Current research generally suggests that regions with high nucleotide diversity and haplotype polymorphism may be glacial refugia\u003csup\u003e[\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e]\u003c/sup\u003e. The \u003cem\u003eP. villosa\u003c/em\u003e populations around the Yinshan and Helan mountain ranges tend to have higher nucleotide diversity and haplotype polymorphism. Among these, the P43 in Dalate Banner has the highest nucleotide polymorphism (\u003cem\u003eπ\u003c/em\u003e = 0.203) and haplotype diversity (\u003cem\u003eH\u003c/em\u003e\u003csub\u003ed\u003c/sub\u003e = 0.978), which indicates that the development of mountain ranges leads to spatial isolation, promoting differentiation between populations of the same species, thus influencing the species’ geographic distribution pattern. Moreover, genetic flow barrier detection between \u003cem\u003eP. villosa\u003c/em\u003e populations reveals that genetic flow predominantly moves from Group 2 to Group 1. The genetic barriers originate mainly from the Yin Mountains, Helan Mountains, Ordos Plateau, and Yablun Mountains. We believe these areas may have been the main refugia for \u003cem\u003eP. villosa\u003c/em\u003e during the Quaternary glaciations\u003csup\u003e[\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eNeutrality tests and mismatch distribution analysis results both suggest that the population dynamics of \u003cem\u003eP. villosa\u003c/em\u003e did not experience a rapid expansion event in its history. BEAST and ABC model predictions indicate that \u003cem\u003eP. villosa\u003c/em\u003e may have originated in the Late Miocene, with population divergence occurring during the Pleistocene. The divergence time between Group 1 and Group 2 is approximately 3.94 Ma and 0.11 Ma, while the divergence time between Group 2 and \u003cem\u003eN\u003c/em\u003e\u003csub\u003eA\u003c/sub\u003e is around 8.20 Ma and 4.42 Ma, which aligns with the previous divergence time (9 ~ 2.61 Ma) of species in the \u003cem\u003eStipa\u003c/em\u003e genus. Furthermore, the effective population sizes of Group 1 and Group 2 are both smaller than that of \u003cem\u003eN\u003c/em\u003e\u003csub\u003eA\u003c/sub\u003e. These findings confirm that \u003cem\u003eP. villosa\u003c/em\u003e underwent contraction events in its history, probably due to spatial isolation caused by changes in local mountains (such as the Yinshan and Helan Mountains) and deserts (such as the Mu Us Desert, Tengger Desert, and Badain Jaran Desert), as well as population range contraction and habitat fragmentation caused by climatic oscillations. This is consistent with the historical dynamic processes observed in other desert plants such as \u003cem\u003eGymnocarpos przewalskii\u003c/em\u003e and \u003cem\u003eHelianthemum songaricum\u003c/em\u003e\u003csup\u003e[\u003cspan additionalcitationids=\"CR79\" citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e–\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e]\u003c/sup\u003e. Since the Quaternary glaciation, the third uplift of the Qinghai-Xizang Plateau around 8 Ma, the ongoing global cooling and drying, and the influence of the east–west monsoon climate of the Yin Mountains, which led to desert expansion and habitat fragmentation, are important factors involved in the genetic differentiation between populations of \u003cem\u003eP. villosa\u003c/em\u003e in response to environmental changes.\u003c/p\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eThis study investigated the genetic diversity, population genetic structure, and demographic history of 210 individuals from 43 natural populations of \u003cem\u003eP. villosa\u003c/em\u003e using LCNG and SSR molecular markers. We found that the \u003cem\u003eP. villosa\u003c/em\u003e populations were clearly divided into two groups, Group 1 and Group 2, by the Yinshan Mountains, with populations in Group 2 showing higher genetic diversity. Significant diversity differences and phylogeographic structure were observed among the populations, with most genetic variation occurring between populations. Geographic distance and elevation had a strong influence on genetic differentiation. No significant population expansion was detected in the history of \u003cem\u003eP. villosa\u003c/em\u003e populations, with divergence occurring around 0.38 Ma between Group 1 and Group 2. There was bidirectional and asymmetric gene flow between the two groups. The Yinshan Mountains, Helan Mountains, Ordos Plateau, and Yabulai Mountains serve as natural and significant genetic barriers. The uplift of the Qinghai-Xizang Plateau and the influence of the monsoonal climate of the Yinshan Mountains since the Quaternary period have been important drivers of the genetic differentiation between \u003cem\u003eP. villosa\u003c/em\u003e populations, as they adapted to environmental changes.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eLCNG\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003elow-copy nuclear genes\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSSR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003esimple sequence repeats\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eRFLP\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003efragment length polymorphisms\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eRAPD\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003erandom amplified polymorphic DNA\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eAFLP\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eamplified fragment length polymorphisms\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSNP\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003esingle-nucleotide polymorphisms\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eISSR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003einter-simple sequence repeat\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePCA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eprincipal coordinate analysis\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cem\u003eN\u003c/em\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ethe number of individuals\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePPL\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003epercentage of polymorphic loci\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePIC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003epolymorphic information content\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cem\u003eN\u003c/em\u003e\u003csub\u003ea\u003c/sub\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003enumber of alleles\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eNe\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eeffective number of alleles\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cem\u003eI\u003c/em\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eShannon's information index\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cem\u003eH\u003c/em\u003e\u003csub\u003eo\u003c/sub\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eobserved heterozygosity\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cem\u003eH\u003c/em\u003e\u003csub\u003ee\u003c/sub\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eexpected heterozygosity\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cem\u003eH\u003c/em\u003e\u003csub\u003ed\u003c/sub\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ehaplotype diversity\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cem\u003eπ\u003c/em\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003enucleotide diversity\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cem\u003eK\u003c/em\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eaverage number of nucleotide differences\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cem\u003eh\u003c/em\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eNei's genetic diversity\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cem\u003eH\u003c/em\u003e\u003csub\u003et\u003c/sub\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003etotal gene diversity\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cem\u003eH\u003c/em\u003e\u003csub\u003es\u003c/sub\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003esubpopulation gene diversity\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cem\u003eF\u003c/em\u003e\u003csub\u003eIS\u003c/sub\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003einbreeding coefficient\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cem\u003eF\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eFixation Index\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cem\u003eG\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003egenetic differentiation coefficient\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cem\u003eN\u003c/em\u003em\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003egene flow\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eAMOVA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eanalysis of molecular variance\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSAMOVA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003espatial AMOVA\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eMCMC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eMarkov Chain Monte Carlo\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eIM\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eisolation with migration\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eABC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eapproximate Bayesian computation.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e\n\u003cp\u003eNot applicable\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\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThis study was financially supported by the National Natural Science Foundation of China (Grant Number: 32160297, 41761009), the Program of Science and Technology International Cooperation Project of Qinghai Province (Grant Number: 2023-HZ-810), and the Qinghai Provincial Major Science and Technology Special Project (2023-SF-A5).\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eJ.R. and T. L. : Writing \u0026ndash; review, editing \u0026amp; original draft. J.R. : Formal analysis. Y. P. \u0026amp; X. S. : Conceptualization. we greatly appreciated Y. H. , Q. Y. , Y. L. , X. F. and K. Y. performing the investigation \u0026amp; experiment, and the anonymous reviewers for their helpful comments.\u003c/p\u003e\n\u003ch2\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eThis work was financially supported by the National Natural Science Foundation of China (No. 32160297, 41761009) made to Yuping Liu, the Program of Science and Technology International Cooperation Project of Qinghai Province (No. 2023-HZ-810) made to Yuping Liu, and the Qinghai Provincial Major Science and Technology Special Project (2023-SF-A5) made to Yuping Liu.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eGenome assembly of P. villosa, Iso-seq and Hi-C data have been submitted to the DDBJ/EMBL/Genbank databases under BioProject number PRJ70313651: genome assembly\u0026ndash;JABCND000000000; Iso-seq data\u0026ndash;SRR11787985 and SRR11787359; RNA-seq data\u0026ndash;SRR11775845, SRR11775821, SRR11775823, SRR11775824, SRR11775822, SRR11775544.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMa C, Tang YJ, Ying JF. 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J Syst Evol. 2015;53(1):33\u0026ndash;46.\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":false,"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":"Poaceae, Psammochloa villosa, genetic diversity, genetic differentiation, historical demography, gene flow","lastPublishedDoi":"10.21203/rs.3.rs-6516707/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6516707/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eClimatic oscillations and geographic barriers since the Quaternary have profoundly influenced plant distribution patterns, driving substantial species differentiation through range fragmentation. Investigating the interplay between genetic structure divergence and population dynamics under climatic forcing remains a central challenge in evolutionary biology. \u003cem\u003ePsammochloa villosa\u003c/em\u003e (Poaceae), a dominant perennial grass endemic to the Inner Mongolian Plateau's desert steppe, serves critical functions in grassland restoration and livestock forage provision. Notably, its wind-pollination strategy and distant hybridization capacity establish this species as a model system for studying panmictic population equilibrium. This study employs a dual-marker approach, combining 10 low-copy nuclear gene loci (LCNG) with 13 SSR markers, to elucidate the population genetic architecture across 43 natural populations (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;210 individuals) spanning the distribution range of \u003cem\u003eP. villosa\u003c/em\u003e. The results showed that the populations of \u003cem\u003eP. villosa\u003c/em\u003e were distinctly divided into two major branches, Group 1 and Group 2, separated by the Yinshan Mountains, with Group 2 exhibiting higher genetic diversity (\u003cem\u003eH\u003c/em\u003e\u003csub\u003ed\u003c/sub\u003e = 0.990, \u003cem\u003eπ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.00145). The populations showed significant diversity and phylogeographic structure, with the majority of genetic variation originating from differences between populations (\u003cem\u003eF\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e(LCNG)\u0026thinsp;=\u0026thinsp;0.766; \u003cem\u003eF\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e(SSR)\u0026thinsp;=\u0026thinsp;0.577). The Mantel test revealed a positive correlation between genetic distance and geographic distance. Subsequent correlation analysis of genetic differentiation (measured by \u003cem\u003eF\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e) with climatic factors demonstrated an overall positive association, although only elevation showed a statistically significant correlation with \u003cem\u003eF\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e values. Demographic history analyses revealed that both Group 1 and Group 2 exhibited smaller effective population sizes compared with \u003cem\u003eN\u003c/em\u003e\u003csub\u003eA\u003c/sub\u003e. Phylogenetic divergence analysis estimated that \u003cem\u003eP. villosa\u003c/em\u003e and \u003cem\u003eAchnatherum splendens\u003c/em\u003e diverged approximately 3.26 Ma, whereas the differentiation between Group 1 and Group 2 initiated around 0.38 Ma. Notably, bidirectional yet asymmetric gene flow was detected between the two groups. Geographical barrier analysis identified significant genetic discontinuities corresponding to major mountain ranges, including the Yinshan, Helan, Ordos Plateau, and Yabulai mountains. These findings collectively suggest that Quaternary uplift of the Tibetan Plateau, coupled with the divergent monsoon influences east and west of the Yinshan Mountains and progressive habitat fragmentation, have driven the observed genetic differentiation in \u003cem\u003eP. villosa\u003c/em\u003e populations through environmental adaptation.\u003c/p\u003e","manuscriptTitle":"The impact of Quaternary climate change on the historical population dynamics of Psammochloa villosa, a typical desert herb from northwest China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-11 14:04:02","doi":"10.21203/rs.3.rs-6516707/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2025-08-13T15:32:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"313306434526903438244080937652560749321","date":"2025-07-31T05:41:07+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-09T09:19:40+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-09T06:31:15+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-05-09T05:29:56+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-05-08T14:09:42+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Plant Biology","date":"2025-05-08T14:08:34+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":"f5b8da42-b78c-4101-bbb1-a494970512dc","owner":[],"postedDate":"July 11th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-07-11T14:04:02+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-11 14:04:02","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6516707","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6516707","identity":"rs-6516707","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}