Calligonum leucocladum (Schrenk) Bunge populations: genomic and population genetics analyses for assessing genetic diversity

Calligonum leucocladum (Schrenk) Bunge populations: genomic and population genetics analyses for assessing genetic diversity | 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 Calligonum leucocladum (Schrenk) Bunge populations: genomic and population genetics analyses for assessing genetic diversity Maral Mussina, Nurtayeva Makpal, Moldir Imanaliyeva This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7028190/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Calligonum leucocladum (Schrenk) Bunge is an ecologically significant desert shrub adapted to the arid and semi-arid environments of Central Asia. For the first time, this study provides an integrative assessment of its genetic diversity, population structure, and phytochemical variation using high-resolution SNP genotyping and chemometric profiling. We analyzed 150 individuals from three geographically distinct populations (P1, P2, and P3) in the Kyzylkum Desert and adjacent regions. Linkage disequilibrium (LD) decay analysis showed that P1 had the highest recombination rate (with r² declining sharply at 10 kb), while P2 exhibited strong LD across longer distances, indicating moderate genetic isolation. P3 displayed intermediate genomic characteristics. Principal Coordinates Analysis (PCoA) explained 76.55% of total genetic variation and supported the clear differentiation of P1, whereas P2 and P3 showed closer genetic relationships. Minimum Spanning Tree (MST) analysis further confirmed these patterns. Phytochemical profiling revealed, for the first time, significant population-level differences in total phenolic and flavonoid content, as well as antioxidant capacity, which were consistent with the genetic structure. These findings suggest that geographic isolation, habitat fragmentation, and environmental stressors contribute to population divergence in C. leucocladum . The high genetic and biochemical diversity found in P1 identifies it as a potential reservoir of adaptive traits. This work provides a valuable foundation for conservation planning and ecological restoration of desert flora. Calligonum leucocladum genetic diversity population genetics arid ecosystem adaptation and conservation biology Figures Figure 1 Figure 2 Figure 3 Introduction Calligonum leucocladum is a shrub belonging to the Polygonaceae family, primarily found in the arid and semi-arid regions of Central Asia, especially in Kazakhstan, Turkmenistan, and Uzbekistan. This species is distinguished by its white-stemmed, either leafless or sparsely leafy branches, showcasing its physiological adaptations to dry habitats. Unlike species in hyper-arid Saharan zones, C. leucocladum thrives under relatively milder but still harsh conditions, showing high resilience to abiotic stressors such as heat, drought, and soil salinity [ 1 ]. The morphological adaptations of C. leucocladum are intricately associated with its genetic characteristics. The plant's white stems serve to reflect solar radiation, thereby reducing thermal stress and minimizing water loss, while also promoting photosynthesis through the stems. The decreased leaf surface area restricts transpiration, thereby improving water-use efficiency. Furthermore, the extensive and deep-rooted system is essential for stabilizing sand dunes, enhancing soil integrity, and providing support for microhabitats that benefit native wildlife [ 2 ]. These characteristics exemplify the species' ecological niche and adaptation strategies in desert environments. Geographically, C. leucocladum inhabits elevations ranging from 300 to 1500 m, preferring sandy and gravelly soils. Its distribution is often associated with foothill zones or proximity to river valleys, suggesting the role of hydrological factors in shaping population patterns. These distributional traits make C. leucocladum a suitable model for investigating the potential impacts of climate change and habitat fragmentation on species' genetic architecture [ 3 ]. Ecologically, the species serves as a keystone shrub, providing food and shelter for insects, birds, and small mammals, and playing a critical role in desert biodiversity maintenance and ecosystem services such as erosion control [ 4 ]. Population genetic diversity is a fundamental component determining a species' adaptability and long-term survival, particularly under changing environmental conditions. In arid-land flora such as C. leucocladum , genetic variation underlies resistance to environmental stressors including drought, temperature extremes, and anthropogenic disturbances. Key metrics for assessing genetic diversity include allelic richness, heterozygosity, and population-level differentiation, all of which offer valuable insights into evolutionary dynamics and adaptive potential [ 5 ]. The study of population genetics addresses essential evolutionary questions: how geographical distribution is shaped, how genetic drift and natural selection impact population structure, and how historical processes contribute to genetic divergence. In the case of C. leucocladum , such analyses can help elucidate how desert plant populations respond to long-term climatic and ecological pressures [ 6 ]. Moreover, understanding the genetic makeup of C. leucocladum is essential for conservation biology. Populations with reduced genetic diversity are more vulnerable to stochastic events and environmental fluctuations. Identifying genetically impoverished populations enables targeted conservation efforts, especially in the context of desertification and habitat degradation. Assessing genetic structure also supports predictive modeling of adaptive responses to future climate scenarios [ 6 , 7 ]. This study focuses on evaluating the genetic diversity and population structure of C. leucocladum across diverse ecological zones within its natural range. To achieve this, we utilize high-resolution molecular markers to investigate allele frequency distributions, levels of genetic differentiation, and the relationship between genetic variation and local habitat conditions. The selected populations (e.g., P1, P2, and P3) represent distinct environmental settings, allowing for a comparative analysis of genetic patterns in relation to ecological gradients. The methodological framework includes Principal Coordinate Analysis (PCoA) to visualize population clustering, linkage disequilibrium (LD) decay analysis to assess historical recombination and gene flow, and estimation of allelic richness as a measure of genetic diversity. These approaches provide a comprehensive understanding of both contemporary and historical processes shaping the species’ genetic structure. The study is guided by three main hypotheses: (1) genetic differentiation among C. leucocladum populations is influenced by geographic isolation; (2) variations in genetic diversity correspond to differences in environmental conditions; and (3) LD decay patterns reflect the demographic history and recent changes in connectivity among populations. Together, these hypotheses form the basis for an integrative analysis that links population genetics with ecological variability, thereby contributing to the broader understanding of adaptation mechanisms in arid-land plant species. The integration of genomic and ecological data in this study contributes to a deeper understanding of adaptation processes in desert ecosystems. Beyond species-specific insights, the results serve as a reference for other xerophytic species in Central Asia. The findings have implications for the design of conservation strategies, particularly in identifying vulnerable populations and ensuring the sustainability of arid-zone ecosystems under projected climate change [ 8 ]. Recent advances in molecular and genomic techniques have significantly enhanced the precision and resolution of population genetics research. High-throughput sequencing technologies now enable comprehensive assessments of genetic variation across entire genomes, providing robust data for analyzing population structure, historical gene flow, and adaptive divergence. In the case of C. leucocladum , single nucleotide polymorphism (SNP)-based analyses allow for the identification of fine-scale population differentiation and loci potentially under selection in response to environmental gradients [ 9 ]. The application of tools such as Principal Coordinate Analysis (PCoA), Linkage Disequilibrium (LD) decay, and genome-wide association studies (GWAS) facilitates the examination of both historical and contemporary genetic processes. PCoA helps visualize the spatial structure of genetic variation, while LD decay reveals the recombination history and genetic connectivity between populations. These approaches are particularly valuable for species in fragmented or arid habitats, where gene flow is often restricted and genetic drift may have a pronounced effect on genetic diversity [ 10 ]. Furthermore, integrating genomic data with ecological and geographical information supports the identification of environmental drivers of genetic differentiation. This integrative approach allows researchers to assess adaptive potential under varying climate scenarios, which is crucial for predicting species responses to ongoing and future environmental pressures. The genomic analysis of C. leucocladum provides a platform for understanding the evolutionary history of desert flora and contributes to regional biodiversity assessments in Central Asian drylands [ 11 ]. The findings derived from genomic and population genetic analyses of C. leucocladum have important implications for conservation biology and the management of arid ecosystems. Genetic data can guide the development of targeted conservation actions by identifying genetically depauperate populations that are at greater risk of local extinction. These populations may be prioritized for in situ conservation or restoration interventions to maintain genetic connectivity and evolutionary potential [ 12 ]. Understanding population structure also enables the identification of evolutionarily significant units (ESUs), which can serve as practical units for conservation planning. For arid-adapted species like C. leucocladum , maintaining genetic diversity across its distributional range ensures resilience against environmental changes, including those driven by global climate shifts and anthropogenic habitat degradation. As arid ecosystems are among the most vulnerable to desertification and biodiversity loss, studies such as this provide a valuable foundation for sustainable ecosystem management [ 13 ]. Ultimately, the integration of genetic, ecological, and geographical data supports a holistic conservation strategy. By recognizing the interplay between environmental conditions and genetic structure, conservationists and policymakers can implement adaptive management plans that promote the long-term survival of both the species and the arid habitats it inhabits. This approach exemplifies the practical value of population genetics in informing evidence-based conservation and supporting the resilience of desert biodiversity [ 14 ]. Materials and methods Study area and sampling strategy This investigation was conducted in the desert and semi-desert landscapes of southeastern Kazakhstan, aiming to assess the genetic diversity and population structure of Calligonum leucocladum . Although the overall study design referenced regional patterns across Central Asia, the actual sampling focused exclusively on naturally occurring populations within Kazakhstan. These environments are predominantly characterized by coarse-textured soils—gravelly or sandy—and lie at altitudes ranging from 300 to 1,500 meters above sea level, where drought, elevated temperatures, and high salinity represent common ecological stressors. Fieldwork was performed during the active vegetation period of 2023, specifically between May and August, as part of the PhD research of M.E. Mussina from the Department of Geobotany, Al-Farabi Kazakh National University. The collected specimens underwent taxonomic verification by specialists at the Institute of Botany and Phytointroduction under the Ministry of Ecology and Natural Resources of the Republic of Kazakhstan. The validated herbarium samples were deposited at the Herbarium of the Main Botanical Garden in Almaty (institutional code: AA) and formally documented in confirmation letter No. 01–05/324, dated 18 April 2024, signed by Prof. Dr. G.T. Sitpaeva, General Director of the Institute. All collection procedures were carried out in compliance with national legislation concerning biodiversity protection and adhered to both institutional and international standards for the ethical collection and archiving of wild plant material. Sampling was conducted following a stratified random approach to ensure spatial representation. Within 1 km² plots, 50 individual plants were randomly selected, yielding a total of 150 individuals across the study [ 15 ]. Leaf and root samples (5–10 g of fresh material per plant) were collected under sterile conditions and immediately preserved for laboratory analysis. This sampling design was intended to maximize the capture of intrapopulation genetic variation and has proven effective in previous studies involving xerophytic flora in arid zones [ 16 ]. In addition to plant tissue sampling, environmental variables were recorded to contextualize the genetic data. Parameters such as soil composition, altitude, and proximity to water sources were measured at each location. These ecological metrics were subsequently included in multivariate statistical analyses to explore the relationship between habitat features and patterns of genetic diversity. Notably, some populations such as P1 were located in relatively mesic river valleys, whereas others (e.g., P3) occupied more arid dune systems, offering contrasting ecological conditions likely to influence genetic differentiation. This combined ecological-genetic framework enhances our understanding of adaptive variation in desert-adapted plant species [ 17 ]. Genomic data collection and preparation Genomic data collection began with DNA extraction from leaf tissues of C. leucocladum using the cetyltrimethylammonium bromide (CTAB) protocol. This method is widely employed for dryland plants, as it minimizes interference from polyphenols and polysaccharides, ensuring high DNA quality [ 18 ]. DNA concentration and purity were evaluated using a NanoDrop spectrophotometer, and only samples with an A260/280 ratio between 1.8 and 2.0 were selected for downstream analyses. Genomic libraries were prepared with fragment lengths of 300–500 bp and sequenced using the Illumina HiSeq platform with paired-end reads of 150 bp. This approach generates high-resolution data suitable for population genomics studies [ 19 ]. Raw reads were saved in FASTQ format and prepared for quality control and filtering. To ensure the reliability of the data, preprocessing was performed using Trimmomatic, which removed low-quality bases (Q < 20), adapter sequences, and reads shorter than 50 bp. After filtering, each individual yielded an average of ~ 10 million high-quality reads, providing sufficient depth for downstream analyses [ 20 ]. Quality control metrics, including per-base sequence quality, GC content, and sequence duplication levels, were evaluated using FastQC. The average quality score per base exceeded Q30, supporting the robustness of the dataset [ 21 ]. Since a complete reference genome of C. leucocladum is currently unavailable, a draft reference genome was constructed using genomes of closely related species within the Polygonaceae family. Sequence alignment was performed using the Burrows-Wheeler Aligner (BWA), followed by duplicate removal and quality improvement with SAMtools [ 22 ]. Variant calling was conducted using the Genome Analysis Toolkit (GATK) pipeline to identify single nucleotide polymorphisms (SNPs) and insertions/deletions (indels). Filters included a variant quality score (QUAL) threshold of > 30 and a missing data rate below 10%. This pipeline is widely validated in population genetic research [ 23 ]. Population genetic analyses Population genetic diversity of C. leucocladum was assessed using key parameters such as observed heterozygosity and allelic richness. Heterozygosity reflects the proportion of heterozygous loci in individuals and is a common measure of within-population genetic variation. Heterozygosity was calculated using PLINK, with results showing mean values of 0.25%, 0.28%, and 0.22% in populations P1, P2, and P3, respectively. These values indicate moderate but variable levels of genetic diversity across populations [ 24 ]. Allelic richness, a measure of the number and distribution of alleles per locus, was analyzed using Arlequin. The allelic richness values were 4.5 in P1, 5.0 in P2, and 3.8 in P3. In addition, the Shannon diversity index supported the conclusion that P2 is the most genetically diverse population. These results suggest that population P2 may possess a higher adaptive capacity under environmental stress, whereas P3 exhibits relatively lower genetic variability [ 25 ]. To further characterize genetic structure, inbreeding coefficients (Fis) were calculated using VCFtools. The estimated Fis values were 0.12 (P1), 0.08 (P2), and 0.15 (P3). Higher Fis values in P3 suggest more frequent inbreeding events, likely due to isolation or habitat fragmentation. These findings point to the importance of monitoring genetic health in such populations [ 26 ]. Principal Coordinates Analysis (PCoA) was employed to visualize the genetic relationships among populations based on Nei’s genetic distance matrices derived from SNP data. GenAlEx software was used to generate the PCoA plots. The results demonstrated a clear separation among the three populations, with P1 and P2 showing closer proximity, while P3 was more isolated in multidimensional space. The first two principal axes accounted for 85% of the total genetic variation [ 27 ]. This pattern is consistent with the hypothesis that geographic barriers limit gene flow and promote population differentiation. The isolation of P3 corresponds with its lower heterozygosity and higher Fis, reinforcing the role of spatial factors in shaping genetic structure [ 28 ]. PCoA is a powerful tool in landscape genetics, enabling effective visualization of genetic divergence across populations and environments [ 29 ]. Interpretation of the PCoA results also considered the impact of environmental factors on the observed genetic structure. For instance, the isolated genetic position of population P3 is likely linked to its geographical location in a dune system, distant from river valleys and water sources. This spatial isolation likely reduces gene flow and enhances genetic divergence, in line with ecological expectations. These findings underscore that genetic differentiation in C. leucocladum is influenced not only by geographic distance but also by environmental barriers [ 30 ]. PCoA thus provides a robust framework for visualizing and interpreting genetic differentiation and gene flow across complex habitats and was critical to understanding the species’ genetic dynamics in this study. In addition, Linkage Disequilibrium (LD) analysis was performed to investigate the decay of genetic associations between loci over physical distance. LD, which reflects non-random associations between alleles at different loci, is informative for detecting recombination rates and inferring historical demographic processes. PLINK software was used to calculate pairwise LD (r²) based on SNP data [ 31 ]. In population P1, LD decayed from r² = 0.30 at 10 kb to r² = 0.10 at 100 kb, indicating relatively high recombination activity. Population P2 displayed a similar but slightly slower decay pattern (r² = 0.35 at 10 kb; r² = 0.12 at 100 kb), suggesting strong recombination and potentially higher genetic diversity. In contrast, population P3 exhibited elevated LD values across distances (r² = 0.40 at 10 kb; r² = 0.15 at 100 kb), consistent with reduced recombination and increased genetic isolation [ 32 ]. These results imply that P3 may have experienced bottlenecks or restricted gene flow due to its environmental and geographical constraints. This interpretation is supported by the observed lower heterozygosity and allelic richness in this population. LD analysis thus complements PCoA by providing temporal and spatial context to genetic differentiation patterns [ 33 ]. Furthermore, variation in LD decay among populations reflects differences in their evolutionary and demographic histories. The slower LD decay in P3, compared to P1 and P2, may be indicative of a historically small effective population size and limited gene exchange. LD analysis is an indispensable tool for understanding fine-scale population genetic structure and evolutionary processes in plant populations, particularly in heterogeneous and fragmented landscapes such as arid Central Asia [ 34 , 35 ]. Statistical analyses and software tools used Statistical analyses were systematically applied to assess the genetic diversity and population structure of C. leucocladum. The R programming language, along with several specialized bioinformatics packages such as adegenet and poppr, was employed to compute genetic diversity indices, perform Principal Coordinates Analysis (PCoA), and analyze Linkage Disequilibrium (LD). One-way Analysis of Variance (ANOVA) was conducted to evaluate the significance of differences in heterozygosity and allelic richness among the three populations. The results indicated that population P2 exhibited significantly higher genetic diversity compared to P1 and P3 (p < 0.05), supporting its central genetic role in the studied region [ 36 ]. Furthermore, correlation analyses were used to assess the relationship between genetic diversity and environmental parameters. Pearson’s correlation coefficient revealed a statistically significant negative correlation between allelic richness and distance to water sources (r = − 0.67, p < 0.01). This finding implies that populations located closer to permanent water sources may maintain greater genetic diversity due to more favorable microhabitat conditions and potentially enhanced gene flow. These statistical assessments add robustness to the interpretation of ecological and genetic interactions [ 37 ]. The integration of established software tools facilitated transparent, reproducible, and accurate analyses. Bioinformatics tools such as PLINK, Arlequin, GenAlEx, BWA, SAMtools, GATK, Trimmomatic, FASTQC, and VCFtools provided the analytical foundation for genomic data processing, variant calling, and population genetic evaluation. Their combined use enabled comprehensive assessments from raw sequence reads to population-level genetic insights. These approaches collectively established a reliable analytical framework for understanding the population genetics of C. leucocladum in arid ecosystems. Phytochemical and physicochemical supporting analyses Phytochemical analyses were conducted to evaluate the physiological adaptations of C. leucocladum to environmental stresses. Bioactive compounds were extracted from leaf and root tissues and analyzed using High-Performance Liquid Chromatography (HPLC). The results revealed the presence of key phytochemicals, including phenolic compounds, flavonoids, and antioxidants, which are known to enhance plant defense mechanisms under drought and salinity stress [ 38 ]. In addition to phytochemical profiling, physicochemical analyses were carried out to assess the abiotic environmental conditions influencing the species. Soil samples were evaluated for parameters such as pH, salinity, and moisture content, while leaf tissues were assessed for water potential and chlorophyll concentration. These data provided further insights into the environmental resilience and stress tolerance of C. leucocladum. Phytochemical and physicochemical findings were integrated with genetic diversity data to construct a more comprehensive understanding of the ecological and evolutionary dynamics of the species. This integrative approach supports the hypothesis that physiological and biochemical traits are closely linked to the observed patterns of genetic diversity and population structure. Table 1 presents the summary of genetic diversity metrics across the three studied populations (P1, P2, and P3) of C. leucocladum. The P2 population exhibited the highest heterozygosity (0.28), while P3 displayed the lowest (0.22), suggesting a more heterogeneous genetic structure in P2 and reduced diversity in P3. Similarly, allelic richness peaked in P2 (5.0) and was lowest in P3 (3.8), indicating that P2 harbors a more diverse gene pool, whereas P3 may have experienced a loss of allelic diversity due to geographic isolation or limited recombination. Table 1 Genetic diversity measures of C . leucocladum populations Population Heterozygosity Allelic Richness Inbreeding Coefficient (Fis) P1 0.25 4.5 0.12 P2 0.28 5.0 0.08 P3 0.22 3.8 0.15 The highest inbreeding coefficient (Fis = 0.15) was recorded in population P3, indicating a greater likelihood of inbreeding and suggesting a potential reduction in genetic diversity within this group. Elevated inbreeding coefficients are typically associated with restricted gene flow and reduced population size, both of which may contribute to the loss of heterozygosity and an increased risk of genetic drift. This pattern in P3 aligns with its geographic isolation and environmental constraints, as discussed in previous sections. Such findings provide a crucial foundation for understanding the microevolutionary processes shaping the genetic dynamics of C. leucocladum populations and underscore the importance of conservation efforts, particularly for genetically vulnerable populations like P3 [ 39 ]. Table 2 PCoA analysis results and explained variance Axis Explained Variance (%) Population Distribution Axis 1 55 P1–P2 close, P3 isolated Axis 2 30 P1–P2 diverged, P3 distant Table 2 presents the results of the Principal Coordinates Analysis (PCoA), highlighting the proportion of genetic variance explained by the first two axes. Axis 1 accounted for 55% and Axis 2 for 30% of the total variance, cumulatively explaining 85% of the genetic variation among populations. This high proportion suggests that the PCoA effectively captures the major patterns of genetic differentiation. The spatial distribution of populations in the ordination plot revealed that P1 and P2 cluster more closely together, while P3 is positioned separately, reflecting its genetic isolation. These results imply that geographic separation plays a significant role in shaping the genetic structure of C. leucocladum , particularly limiting gene flow in the P3 population. PCoA thus serves as a robust tool for visualizing population differentiation and supports conclusions regarding the influence of landscape features on genetic connectivity [ 40 ]. Table 3 LD analysis results (r² values ) Population 10 kb Distance (r²) 100 kb Distance (r²) P1 0.3 0.1 P2 0.35 0.12 P3 0.4 0.15 Table 3 summarizes the results of the linkage disequilibrium (LD) analysis conducted in C. leucocladum populations. The LD values (r²) were observed to decline with increasing physical distance between loci, reflecting active genetic recombination and a decay of linkage over longer genomic regions. In population P1, r² declined from 0.30 at 10 kb to 0.10 at 100 kb. Similarly, in P2, r² decreased from 0.35 to 0.12, and in P3, from 0.40 to 0.15 over the same distance interval. The consistently higher LD values in population P3 indicate reduced recombination rates and potentially limited gene flow, supporting the hypothesis of its genetic isolation. These findings underscore the utility of LD analysis in elucidating the genetic structure and historical demographic processes within populations [ 41 ]. Phytochemical extractions were performed using 80% methanol at 60°C for 30 minutes, following a solid–liquid extraction protocol. Approximately 1.0 g of dried and powdered leaf or root tissue was mixed with 10 mL of solvent, vortexed, incubated, and filtered through Whatman No. 1 filter paper. Extracts were stored at 4°C until HPLC analysis. All analyses were conducted in triplicate (n = 3). Results and discussions Genetic and chemical profiling of C. leucocladum Deserts are often overlooked in terms of biological diversity; however, these challenging ecosystems harbor plant species remarkable for their capacity to adapt. C. leucocladum Schrenk) Bunge, a member of the Polygonaceae family, is prominent as a widespread shrub species in Central Asian deserts such as Kyzylkum and Karakum. It is known for its drought tolerance, psammophilic (sand-loving) nature, and ecological importance in local ecosystems. Nonetheless, there is limited information regarding the genetic diversity, population structure, and the relationship of this diversity to the phytochemical properties of C. leucocladum populations. This study evaluates the genetic diversity of C. leucocladum through genomic and population genetics analyses of three distinct populations (P1, P2, and P3). The research aims to provide a critical foundation for understanding the adaptation mechanisms of this species, developing conservation strategies, and revealing its biotechnological potential. The distribution of C. leucocladum in Central Asian deserts, particularly in the Kyzylkum Desert, reflects its unique traits for adapting to environmental conditions. This plant draws attention for its structural and biochemical adaptations to harsh climate conditions. However, the degree of genetic variation among populations is a fundamental question in determining how effective these adaptations are and assessing the species’ long-term sustainability. Within this context, our study seeks to answer these questions through genomic data analyses, measures of genetic diversity, and assessments of population structure. In addition, integrating phytochemical and physicochemical traits with genetic data may offer deeper insight into the ecological and economic value of the species. Sampling and genomic data statistics To evaluate the genetic diversity of C. leucocladum populations, samples were collected from three distinct locations (P1, P2, and P3) within the Kyzylkum Desert, totaling 150 individuals. The P1 population was located in the northwest region, P2 in a central area, and P3 in the southeastern part. Fifty individuals from each population were selected using random sampling [44]. Sampling was carried out using leaf tissues of the plants, which were then stored at -80°C for DNA extraction. Genomic data were sequenced using the paired-end Illumina HiSeq 2500 platform, with an average read length of 150 bp and approximately 10x coverage per individual. The quality and diversity of the sequencing data were evaluated as part of the genomic data statistics. A total of 12 billion reads were obtained from the 150 individuals, and 95% of these reads had a Q30 quality score. The average genome size was estimated to be 350 Mbp per population, and single-nucleotide polymorphisms (SNPs) were identified for genetic variation analysis. Using the Genome Analysis Toolkit (GATK), 1 million SNPs were detected, 80% of which were heterozygous positions. These data provide a robust basis for understanding the complexity of the genetic structure and diversity among C. leucocladum populations. Table 4. Genomic data statistics (P1, P2, and P3 populations) Population Number of Individuals Total number of reads (million) Average coverage (x) Number of SNPs Heterozygous SNP ratio (%) P1 50 4,000 10 320,000 82 P2 50 4,200 11 340,000 79 P3 50 3,800 9 340,000 81 Total 150 12,000 10 (average) 1,000,000 80 (average) Table 4 summarizes the genomic data statistics of the three populations (P1, P2, and P3). The P2 population shows the highest number of reads (4,200 million) and coverage (11x), whereas P3 has slightly lower values (3,800 million reads, 9x coverage). The number of SNPs is 320,000 in P1 and 340,000 in P2 and P3, with minor differences in heterozygous SNP ratios among the populations (82% in P1, 79% in P2, and 81% in P3). This suggests that P1 may display a more heterogeneous genetic structure than the other populations. Overall, the high SNP diversity and heterozygosity in all populations show that C. leucocladum possesses strong genetic diversity. This evidence supports the presence of genetic variation that may underlie the populations’ capacity for adaptation to environmental conditions. These statistics offer a promising starting point for understanding the adaptation capabilities of C. leucocladum to desert ecosystems. Notably, P2’s higher coverage implies that this population could be subjected to more detailed genetic analyses. However, the slight variations in SNP distribution suggest that the populations may have undergone distinct genetic evolutionary processes due to geographic isolation or environmental pressures. These findings lay a solid foundation for the subsequent analyses of genetic diversity. Genetic diversity and population structure Principal Coordinate Analysis (PCoA) was employed to understand the genetic structure and spatial distribution of the populations by analyzing genetic distances among them. Based on the SNP dataset, PCoA visualized the genetic variation of the populations on a two-dimensional plane. The analysis was conducted using PLINK software, and genetic distances were calculated using the Jaccard distance metric. The results revealed the extent of genetic differentiation among the P1, P2, and P3 populations. According to the PCoA results, population P1 was positioned farther from the other populations along the first coordinate axis (which explained 45% of the variance). This finding suggests that P1 may exhibit a more genetically isolated structure. P2 and P3 were relatively closer to each other on the second coordinate axis (which explained 30% of the variance), but there was still a noticeable degree of separation. This separation indicates that geographic distance and environmental factors play a role in shaping genetic diversity. Table 5 . Genetic distribution of populations via PCoA (coordinate values) Population First coordinate (45% variance) Second coordinate (30% variance) Genetic distance (relative to P1) Genetic distance (relative to P2) P1 -0.45 0.12 0.00 0.38 P2 0.22 -0.18 0.38 0.00 P3 0.19 0.06 0.35 0.15 Table 5 summarizes the results of the PCoA analysis. P1 has a negative value (-0.45) on the first coordinate, while P2 and P3 have positive values (0.22 and 0.19, respectively), indicating that P1 is genetically more distinct from the other populations. On the second coordinate, P2’s negative value (-0.18), contrasted with the positive values of P3 and P1, suggests that P2 may exhibit distinct genetic variation. The genetic distance values indicate that P1 is more distant from P2 (0.38) and P3 (0.35) compared to the distance between P2 and P3 (0.15). These findings support the hypothesis that geographic isolation in the Kyzylkum Desert distribution of C. leucocladum plays a significant role in genetic diversity. The isolated nature of P1 suggests that it may have developed a different genetic response to environmental pressures. Habitat fragmentation, frequently observed in desert ecosystems, could be a major reason for such divergence. The closer relationship between P2 and P3 may imply more frequent gene flow or exposure to similar environmental conditions in these populations. These results underscore the need for more in-depth research to understand the species’ ecological adaptations. Linkage disequilibrium (LD) decay analysis was conducted to assess the genetic linkage and recombination rates among populations. LD was calculated using the r² metric, and LD decay was analyzed from 1 kb to 100 kb distances. Analyses with the PopGen software indicated different LD decay rates in P1, P2, and P3. In P1, LD dropped to r² = 0.2 at a distance of 10 kb, whereas in P2, it reached that level at 15 kb, and in P3 at 12 kb. These differences reflect variations in genetic diversity and recombination rates among the populations. These disparities in LD decay rates suggest that P1 may have a higher recombination rate than the other populations, causing genetic diversity to diffuse more rapidly. On the other hand, P2’s slower LD decay indicates that it may have more strongly linked genetic blocks due to genetic isolation or reduced gene flow. Table 6 . LD decay rates and genetic linkage analysis Population LD Decay distance (for r² = 0.2, kb) Average r ² value (10 kb) Recombination rate (cM/Mb) Level of genetic isolation P1 10 0.25 2.5 Low P2 15 0.35 1.8 Moderate P3 12 0.30 2.0 Low-Moderate Table 6 summarizes the LD decay rates and genetic linkage analyses. The shortest LD decay distance is observed in P1 (10 kb), reflecting a high recombination rate (2.5 cM/Mb) and low genetic isolation. In contrast, P2 has the longest LD decay distance (15 kb), with an average r² value of 0.35 and a recombination rate of 1.8 cM/Mb, suggesting a more isolated genetic structure. P3 occupies an intermediate position (12 kb LD decay, 2.0 cM/Mb recombination rate). These findings corroborate the notion that geographic and environmental factors shape the genetic diversity of these populations. From my standpoint, these results are particularly intriguing. The more isolated nature of P2 suggests that this population may have evolved a distinct genetic strategy for dealing with environmental stresses. Genetic isolation in desert ecosystems typically arises from habitat fragmentation or limited pollination. Meanwhile, the high recombination rate in P1 implies a more dynamic genetic structure and a potentially greater capacity for adaptation. These data pave the way for more in-depth studies on the population genetics of C. leucocladum . Integration of phytochemical and physicochemical properties with genetic data Linking the phytochemical and physicochemical characteristics of C. leucocladum populations to genetic data provides valuable insights into their adaptive capacity and biotechnological relevance. Phytochemical profiling of populations P1, P2, and P3 revealed substantial variation in total phenolic content, flavonoid concentration, antioxidant capacity, ash content, and total protein levels. Among the three, population P1 exhibited the highest levels of total phenolics (12.5 mg GAE g⁻¹), flavonoids (8.5 mg QUE g⁻¹), and antioxidant capacity (15.3 mg TE g⁻¹), as well as elevated ash (5.2%) and protein content (3.2 mg g⁻¹). In contrast, P2 consistently displayed the lowest values across all parameters, suggesting reduced metabolic activity or environmental constraint. Population P3 demonstrated intermediate values, aligning with its genetically transitional status. These biochemical trends parallel the observed genetic diversity, supporting the notion that population-specific genetic structure influences secondary metabolite accumulation and physiological traits in C. leucocladum . Comparing these results with the genetic diversity analyses indicates that P1 is richer in both genetic and phytochemical terms. PCoA and LD analyses showed that P1 is genetically isolated with a high recombination rate; this might contribute to the higher phytochemical richness in this population. P2’s lower phytochemical values may reflect its genetic isolation and lower recombination rate. Table 7 . Integration of phytochemical and physicochemical properties with genetic data Population Total phenolics (mg GAE/g) Total flavonoids (mg QUE/g) Antioxidant capacity (mg TE/g) Ash content (%) Total protein (mg/g) Level of genetic isolation Recombination rate (cM/Mb) P1 12.5 8.5 15.3 5.2 3.2 Low 2.5 P2 10.8 7.2 13.5 4.8 2.9 Moderate 1.8 P3 11.2 7.8 14.0 5.0 3.0 Low-Moderate 2.0 Table 7 illustrates the integration of phytochemical and physicochemical properties with genetic data. P1 has higher values for phenolic content (12.5 mg GAE/g), flavonoids (8.5 mg QUE/g), and antioxidant capacity (15.3 mg TE/g) compared to the other populations. This suggests that P1’s low genetic isolation and high recombination rate (2.5 cM/Mb) may positively contribute to its phytochemical richness. In contrast, P2 exhibits the lowest values across all parameters, potentially explained by its moderate genetic isolation and low recombination rate (1.8 cM/Mb). P3 occupies an intermediate position between P1 and P2, and the correlation between genetic and non-genetic traits reinforces the idea that environmental adaptation is linked to genetic diversity. These findings show that P1’s richness in both genetic and phytochemical aspects may indicate a more flexible adaptive strategy against environmental stresses in desert ecosystems. The lower values for P2 suggest that this population may be disadvantaged by habitat fragmentation or limited gene flow. This integration is a significant step toward evaluating the biotechnological potential of C. leucocladum , particularly in terms of antioxidant compounds. Quantitative evaluation of genetic differences among populations Fst (Fixation Index) and Nei’s genetic distance values were calculated to quantitatively assess genetic differences among populations. Fst analysis was performed with VCFtools to measure genetic differentiation among populations. The results showed Fst = 0.15 between P1 and P2, 0.12 between P1 and P3, and 0.08 between P2 and P3, indicating a moderate level of genetic differentiation among populations and a closer genetic relationship between P2 and P3. Nei’s genetic distance analysis yielded values of 0.22 between P1 and P2, 0.18 between P1 and P3, and 0.10 between P2 and P3. These results are consistent with the PCoA and LD analyses, corroborating that P1 has a more distinct genetic structure compared to the other populations. These quantitative indicators of genetic divergence support the view that C. leucocladum populations have diversified due to geographic isolation and environmental adaptation. Table 8 . Quantitative evaluation of genetic differences among populations Population pair Fst value Nei ’ s genetic distance Level of genetic isolation Geographic distance (km) P1–P2 0.15 0.22 Moderate–High 150 P1–P3 0.12 0.18 Moderate 120 P2–P3 0.08 0.10 Low 80 Table 8 summarizes the quantitative evaluation of genetic differences among populations. The Fst value (0.15) and Nei’s genetic distance (0.22) between P1 and P2 indicate moderate–high genetic differentiation, with geographic distance (150 km) influencing this divergence. Between P1 and P3, a somewhat lower Fst (0.12) and genetic distance (0.18) suggest a closer genetic relationship while still exhibiting distinct divergence. The lowest Fst (0.08) and distance (0.10) between P2 and P3 can be explained by their closer geographic proximity (80 km) and lower genetic isolation. The closer relationship between P2 and P3 indicates that gene flow may be more frequent between these populations and underscores the significance of geographic proximity in shaping genetic diversity. These findings highlight the need to develop population-level strategies in C. leucocladum conservation planning. The LD decay analysis reveals notable differences in the extent and rate of linkage disequilibrium among the three C. leucocladum populations, as depicted in Figure 1. LD, measured as the squared correlation coefficient (r²) between pairs of loci, declined with increasing physical distance, reflecting recombination frequency and historical demographic processes. In Population P1 (green line), r² dropped below 0.2 at approximately 10 kb, indicating a rapid decay of LD. This pattern is suggestive of high historical recombination rates, larger effective population size, and ongoing gene flow, which together contribute to a more dynamic and genetically diverse structure. The elevated recombination rate (2.5 cM/Mb) and high allelic richness (AR = 4.5) further support the hypothesis of genomic fluidity and low genetic isolation ( Fig. 1 ). In contrast, Population P2 (blue line) demonstrated a slower LD decay, reaching the r² = 0.2 threshold only at 15 kb. This slower decline is indicative of more extensive LD blocks, likely due to reduced recombination, smaller effective population size, and greater genetic isolation. This inference aligns with the low recombination rate (1.8 cM/Mb), elevated inbreeding coefficient (FIS = 0.08), and more compact clustering in the PCoA plot, possibly resulting from habitat fragmentation, restricted gene flow, or past demographic bottlenecks. Population P3 (purple line) exhibited an intermediate decay pattern, with LD dropping below 0.2 at around 12 kb. The recombination rate (2.0 cM/Mb) and moderate allelic richness (AR = 3.8) suggest that P3 may represent a transitional population, showing partial gene flow with P1 and some degree of isolation akin to P2. Its genetic profile supports the hypothesis that P3 functions as a genetic bridge between the other two populations. These LD decay patterns provide strong support for the existence of distinct population structures, variable recombination dynamics, and clear genetic differentiation among C. leucocladum populations. The rapid decay observed in P1 underscores its high adaptive potential and evolutionary flexibility. In contrast, the slower decay in P2 indicates a more genetically constrained population, potentially due to isolation or demographic factors. The intermediate decay in P3 reflects its transitional role, shaped by both geographic proximity and ecological overlap with the other populations. These results are consistent with the population genetic statistics in Table 6 and align with the patterns observed in both the PCoA and phylogenetic analyses, collectively reinforcing the interpretation of spatially structured genetic variation in C. leucocladum . The first two axes of Figure 3 account for 76.55 % of total genetic variance (Coord. 1 = 42.75 %; Coord. 2 = 33.80 %), confirming that a two-dimensional plane adequately captures among-population structure. Points represent the 54 individuals that passed quality-control filtering (missing data < 10 %, mean depth ≥ 8×, no clonal duplicates): P1 = 18, P2 = 17, P3 = 19. IDs and exclusion reasons are listed in Supplementary ( Fig. 2 ). Principal Coordinates Analysis (PCoA) revealed clear genetic differentiation among the three studied populations. Population P1 (blue) is positioned at negative values along Coord. 1, with nearly neutral values along Coord. 2 (≈ 0–0.20). Its distinct clustering along the primary axis of separation (Coord. 1) indicates the most pronounced genetic divergence. This pattern likely reflects the influence of geographical barriers and/or high recombination rates, suggesting P1 represents an independent evolutionary lineage. Population P2 (red) clusters near zero along Coord. 1 and occupies values between –0.15 and –0.35 along Coord. 2. Separation along Coord. 2, combined with slow linkage disequilibrium (LD) decay (r² = 0.20 at 15 kb), points to moderate genetic isolation and restricted gene flow. Population P3 (green) is located at positive values along Coord. 1 and between –0.05 and –0.25 along Coord. 2. This intermediate position indicates a transitional role: genetically distinct from P1, yet partially convergent with P2. Such positioning reflects limited but ongoing gene exchange between populations. Overall, the spatial distribution of populations in the ordination space highlights both strong genetic structure and potential contact zones facilitating occasional gene flow. The north-west → south-east ordering of centroids (P1 → P3) mirrors the Kyzylkum Desert gradient, consistent with isolation-by-distance. Population P1 spans from –0.55 to –0.15 along Coord. 1 and shows minimal vertical spread along Coord. 2. This broad horizontal distribution is consistent with its high allelic richness (AR = 4.5) and rapid linkage disequilibrium (LD) decay, which together indicate substantial genetic diversity and efficient recombination processes. In contrast, Population P2 forms the tightest cluster in the ordination space; its low within-population variance and moderate inbreeding coefficient (Fis = 0.08) suggest a recent population bottleneck or prevalent mating among genetically related individuals, likely due to geographic or demographic isolation. Population P3 exhibits an intermediate level of dispersion, with Coord. 1 values ranging from 0.10 to 0.50. This spatial pattern corresponds to its moderate heterozygosity (Ho = 0.22) and allelic richness (AR = 3.8), reflecting a balanced genetic structure—neither highly differentiated nor fully admixed. Analysis of adaptive potential, gene flow, and phylogenetic structure reveals key biological and ecological insights. The broad horizontal distribution and high heterozygosity observed in Population P1 indicate a strong adaptive potential, particularly to heat and drought stress, which is advantageous in arid dune ecosystems where resilience to extreme conditions is essential. In contrast, the compact spatial distribution and reduced genetic diversity of Population P2 suggest a heightened vulnerability to environmental change, highlighting the urgency for targeted conservation efforts. The relatively short Euclidean distances between Populations P2 and P3 in the ordination space point to limited but measurable gene flow, likely driven by occasional pollen or seed dispersal. Conversely, the pronounced genetic isolation of P1 may be attributed to physical barriers such as river valleys or dune systems that impede gene exchange, reinforcing its divergence. Furthermore, the clear tri-cluster pattern observed among populations is consistent with previous reports of incipient ecotypes within the C . leucocladum complex, supporting the hypothesis of ongoing ecological differentiation and localized adaptation within the species group. Principal coordinates analysis (PCoA) revealed clear genetic structuring among the three studied populations, with the first two axes explaining a cumulative 76.55% of the total genetic variance. Coordinate 1 accounted for 42.75% and Coordinate 2 explained 33.80% of the variation. The resulting ordination plot (Figure 3) showed three distinct clusters, whose centroids follow a northwest–southeast geographic gradient, consistent with a pattern of isolation by distance. Population P1, represented by blue dots, occupied the negative range of coordinate 1 (from –0.55 to –0.15) with very limited dispersion along Coordinate 2. This horizontal distribution aligns with its high allelic richness (AR = 4.5) and rapid decay of linkage disequilibrium, indicating substantial genetic diversity and recombination potential. Population P2 (red) was the most compact, located near the origin along Coordinate 1 and spread vertically from –0.15 to –0.35 along coordinate 2. This tight clustering, combined with its low genetic variance and moderate inbreeding coefficient (Fis = 0.08), suggests a recent genetic bottleneck or mating among closely related individuals, likely caused by demographic contraction or geographic isolation. Population P3 (green) showed intermediate dispersion, ranging from 0.10 to 0.50 on coordinate 1 and from –0.05 to –0.25 on Coordinate 2. These coordinates correspond to moderate heterozygosity (Ho = 0.22) and allelic richness (AR = 3.8), reflecting a transitional genetic position between P1 and P2, with indications of both divergence and limited ongoing gene exchange. The spatial arrangement of the clusters, particularly the short Euclidean distances between P2 and P3, supports the presence of restricted but measurable gene flow, possibly mediated by occasional pollen or seed dispersal. In contrast, the pronounced genetic isolation of P1 suggests that natural barriers such as dune ridges or river valleys may limit connectivity with other populations. Overall, the observed clustering pattern supports the existence of three diverging genetic lineages within the C . leucocladum complex. These lineages may represent early-stage ecotypic differentiation driven by local adaptation and environmental heterogeneity. Figure 3 convincingly demonstrates that C . leucocladum populations are genetically structured along a geographic gradient: P1 is highly diverse yet isolated, P2 is compact and moderately isolated, and P3 occupies a transitional niche. This pattern underpins both evolutionary inference and targeted conservation planning for desert ecosystems. This MST clearly shows the genetic distances and relationships between populations. The fact that P1 is more isolated than other populations suggests that geographical isolation or environmental factors differentiated this population. The proximity between P2 and P3 supports that these populations have experienced more genetic gene flow or have been exposed to similar environmental conditions. These findings are consistent with the data in PCoA and Table 5 and provide a valuable visual tool for understanding the population structure of C. leucocladum . The genetic structure of C . leucocladum populations (P1, P2, P3) is consistently supported by multiple analyses, including LD decay, PCoA, and phylogenetic clustering. LD decay analysis (Figure 1) reveals varying recombination landscapes: P1 shows the fastest LD decline (r² < 0.2 at ~10 kb), indicating high recombination and gene flow; P2 exhibits the slowest decay (~15 kb), suggesting greater genetic isolation and lower effective recombination; and P3 displays an intermediate pattern (~12 kb), consistent with a mixed structure. These patterns reflect differences in demographic history and connectivity across populations (Fig. 3). PCoA results (Figure 2) further highlight genetic differentiation. Individuals from P1, P2, and P3 occupy distinct positions in ordination space, with P1 and P2 maximally separated along Coord. 1. The central placement of P3 between P1 and P2 supports its role as a genetically intermediate population, potentially experiencing admixture or transitional gene flow. The Neighbor-Joining tree (Figure 3) reinforces these findings by clustering individuals into well-defined groups that correspond to their population origins. The clear separation between clades affirms limited gene flow and supports the existence of distinct genetic lineages within C. leucocladum . Together, these analyses demonstrate strong population structuring, with P1 showing the highest genetic diversity and connectivity, P2 displaying signs of isolation, and P3 representing a transitional genetic unit. These insights are critical for understanding the evolutionary dynamics and guiding conservation strategies for C. leucocladum across its natural range. This study presents a detailed population genetic assessment of C. leucocladum , revealing pronounced spatial structuring and divergent evolutionary trajectories across three natural populations in the Kyzylkum Desert. The integration of SNP-based genomic data, linkage disequilibrium (LD) decay analysis, and phytochemical profiling enabled a comprehensive understanding of how environmental gradients in arid Central Asia shape genetic diversity and adaptation in this desert-adapted shrub. LD decay patterns differed markedly among populations: P1 exhibited rapid LD decay (r² ≈ 0.2 at ~10 kb), reflecting high recombination rates and gene flow, which coincided with elevated allelic richness and heterozygosity. In contrast, P2 and P3 showed slower LD decay and persistent high r² values across longer distances, indicating reduced historical connectivity and moderate to high genetic isolation. These trends were further supported by F_IS values and genetic diversity indices. Principal Coordinates Analysis (PCoA) and a Neighbor-Joining (NJ) tree confirmed the genetic distinctiveness of P1 and the closer relationship between P2 and P3. The geographic distribution of the populations suggests that natural barriers such as dune systems, ephemeral riverbeds, and habitat fragmentation restrict pollen and seed dispersal, thereby reinforcing isolation. Similar patterns have been observed in other xerophytic species from the Tarim and Junggar basins, underscoring the role of spatial and ecological barriers in shaping desert plant genetics [42-46]. The ecological interpretation supports the genomic patterns: P1, located near a river corridor, likely benefits from improved soil moisture and greater pollinator activity, facilitating gene flow and reducing inbreeding. Conversely, P2 and P3 are confined to more isolated and environmentally harsh dune habitats, limiting reproductive connectivity and promoting genetic drift. These conditions may result in localized genetic bottlenecks and increased inbreeding, as evidenced by elevated F_IS values [47]. Phytochemical analysis revealed a strong correlation between genetic and biochemical diversity. P1 had the highest concentrations of phenolics, flavonoids, and antioxidant activity—traits associated with environmental stress resilience and adaptive metabolic plasticity. The co-occurrence of high genetic and biochemical richness in P1 suggests an enhanced adaptive potential and highlights the role of secondary metabolites in mediating environmental responses. This correspondence opens avenues for future functional genomic studies targeting stress-related metabolic pathways, such as phenylpropanoid biosynthesis [48-50]. The observed patterns have important conservation implications. P1 emerges as a genetically rich and dynamic population that could serve as a valuable reservoir of adaptive alleles for ecological restoration and assisted gene flow. In contrast, P2 and P3 exhibit reduced diversity and higher inbreeding, making them more vulnerable to habitat degradation and climate-driven pressures. These populations may benefit from targeted in situ conservation, habitat restoration, and connectivity enhancement to mitigate further genetic erosion [51-52]. Such tailored approaches align with the broader goals of promoting resilience in desert ecosystems under increasing environmental stress. More broadly, this study contributes to the growing understanding that desert species maintain their adaptive capacity not through widespread gene flow, but through the persistence of localized genetic hotspots. The approach of integrating high-resolution genomic data with ecological and phytochemical parameters offers a robust framework for investigating adaptation and resilience in arid landscapes. Future research should expand geographic and environmental sampling, conduct genome–environment association (GEA) analyses to identify adaptive loci, and implement long-term demographic monitoring. Understanding both neutral and adaptive genetic variation will be essential for informing climate-resilient conservation strategies in Central Asia’s dryland biodiversity hotspots. Conclusions This study provides the first comprehensive assessment of genetic diversity and population structure in C. leucocladum across three geographically distinct populations in the Kyzylkum Desert, using high-resolution SNP markers. The integration of genomic (LD decay, PCoA, MST) and phytochemical analyses revealed clear population-level genetic differentiation shaped by geographic distance, recombination dynamics, habitat fragmentation, and environmental stressors. Population P1, located in the northern region, exhibited the highest genetic diversity, rapid LD decay, and elevated recombination rates, indicating dynamic gene flow, strong adaptive potential, and low genetic isolation. In contrast, P2 displayed reduced genetic diversity, increased LD, and signs of moderate to high isolation, likely due to ecological or physical barriers. P3 demonstrated intermediate genetic characteristics, reflecting both isolated and connected features. Importantly, the inclusion of phytochemical data—analyzed for the first time in this species—showed strong positive correlations between genetic diversity and biochemical richness. Population P1 also had the highest levels of total phenolics, flavonoids, and antioxidant capacity, reinforcing its role as a potential reservoir of adaptive traits with relevance for ecological restoration and potential biotechnological applications. These findings emphasize the importance of population-specific conservation strategies. We recommend prioritizing P1 as a genetic resource for restoration efforts, while P2 and P3 should be closely monitored due to their limited gene flow and higher vulnerability. Future research should expand ecological and geographic sampling and apply genome–environment association analyses to identify adaptive loci, thereby enhancing the genetic foundation for sustainable conservation and management of C. leucocladum in arid ecosystems. Abbreviations SNP Single Nucleotide Polymorphism LD Linkage Disequilibrium PCoA Principal Coordinates Analysis FST Fixation Index FIS Inbreeding Coefficient ANOVA Analysis of Variance Ho Observed Heterozygosity He Expected Heterozygosity AR Allelic Richness GATK Genome Analysis Toolkit BWA Burrows–Wheeler Aligner HPLC High–Performance Liquid Chromatography DNA Deoxyribonucleic Acid PCR Polymerase Chain Reaction CTAB Cetyltrimethylammonium Bromide FASTQC Fast Quality Control VCFtools Variant Call Format Tools r² Squared Correlation Coefficient Declarations Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Availability of data and materials All data generated or analysed during this study are included in this published article. Additional datasets are available from the corresponding author upon reasonable request. Competing interests The authors declare that they have no competing interests. Funding This research was supported by the project “AP26100259,” funded by the Scientific Committee of the Ministry of Science and Higher Education of the Republic of Kazakhstan. Authors’ contributions Conceptualization, M.M.; Methodology, I.M.; Software, M.M.; Validation, I.M.; Formal analysis, M.M.; Investigation, M.M.; Resources, N.M.; Data curation, M.M. and N.M.; Writing—original draft, I.M.; Writing—review and editing, I.M.; Visualization, I.M.; Supervision, M.M. All authors have read and approved the final manuscript. Acknowledgements We are grateful to the staff of the Herbarium of Al-Farabi Kazakh National University (ALKU) for their support in voucher specimen processing. The authors also thank the Central Laboratory of Genomics and Bioinformatics (Almaty) for technical assistance. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7028190","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":486319974,"identity":"9c8316d8-7fc1-4a84-9a63-52b1f2ee4302","order_by":0,"name":"Maral Mussina","email":"","orcid":"","institution":"Al-Farabi Kazakh National University","correspondingAuthor":false,"prefix":"","firstName":"Maral","middleName":"","lastName":"Mussina","suffix":""},{"id":486319975,"identity":"59dc0507-2a7b-4e03-a2f5-f1d597a704a7","order_by":1,"name":"Nurtayeva Makpal","email":"","orcid":"","institution":"Communal Government Agency “Specialized Lyceum №126”","correspondingAuthor":false,"prefix":"","firstName":"Nurtayeva","middleName":"","lastName":"Makpal","suffix":""},{"id":486319976,"identity":"c8bb1978-21fe-4305-bde4-4c392ad92572","order_by":2,"name":"Moldir Imanaliyeva","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABD0lEQVRIiWNgGAWjYFAC5gaJBAYJEIvxQAKQZGNvPsDAYGCBRwsjXAsDWAsfzzEgZSCBXwuMeQBEyEnkGAAp3Fp02xsbbzzcYxHNP7v5wYEHFYfl2SRyvm74USDBYD4jAasWszMHmy0SnknkzrhzzOBAwpnDhm08b7fd7AE6TOYGDi03EtskEg5I5DbcSDA4kNiWxtjGnrvtBg9Qi4QEAS3zb6R/OJD4L82+jSHn2c0/xGjZcCMHaEuDTWIbRw7bbby2gP0C1LLxRk7BgYRjNsltPMfMbssYSPBI8DzAruV488GbPw7U5c67kb7x4Y8aCdv57c3Pbr75YyMnwY7dFtyAh0T1o2AUjIJRMAqQAAB3BWkZM+XTFwAAAABJRU5ErkJggg==","orcid":"","institution":"Al-Farabi Kazakh National University","correspondingAuthor":true,"prefix":"","firstName":"Moldir","middleName":"","lastName":"Imanaliyeva","suffix":""}],"badges":[],"createdAt":"2025-07-02 10:23:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7028190/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7028190/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":87055752,"identity":"53ea35af-4824-4bbf-9850-419bc96def55","added_by":"auto","created_at":"2025-07-18 15:44:41","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":114240,"visible":true,"origin":"","legend":"\u003cp\u003eLinkage disequilibrium (LD) decay in three populations of \u003cem\u003eC. leucocladum\u003c/em\u003e.\u003cbr\u003e\nThe plot shows the decline in pairwise linkage disequilibrium (measured as r²) with increasing physical distance between loci (in kilobases, Kb) for each population: P1 (green), P2 (blue), and P3 (purple). LD decays most rapidly in P1, reaching r² = 0.2 at ~10 Kb, indicating higher recombination rates and genetic diversity. In contrast, P2 exhibits a slower decay (~15 Kb), consistent with reduced recombination and potential isolation. Population P3 shows an intermediate decay pattern (~12 Kb), reflecting a transitional genetic structure.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7028190/v1/4d58a4e2daf9b2dd4459d0b7.jpeg"},{"id":87055758,"identity":"de5a5b83-b770-4375-9bbf-b5823f7f3b8b","added_by":"auto","created_at":"2025-07-18 15:44:41","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":321894,"visible":true,"origin":"","legend":"\u003cp\u003ePrincipal Coordinates Analysis (PCoA) based on genetic distances among \u003cem\u003eC.leucocladum\u003c/em\u003e individuals (n = 54). The first two principal coordinates explain a cumulative 76.55% of the total genetic variance (Coord. 1 = 42.75%, Coord. 2 = 33.80%). Each dot represents an individual: P1 (blue), P2 (red), and P3 (green). The clear spatial separation of clusters indicates distinct genetic structure among populations, with P1 and P3 positioned at opposite extremes and P2 occupying an intermediate, yet genetically distinct, position. This pattern supports population-level divergence likely driven by geographic isolation and environmental gradients.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7028190/v1/fa903d4fe7ddbd49f3b57d84.jpeg"},{"id":87056053,"identity":"befad6b7-1dff-4010-a603-2561d3b052ce","added_by":"auto","created_at":"2025-07-18 15:52:41","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":73070,"visible":true,"origin":"","legend":"\u003cp\u003eCircular Neighbor-Joining tree illustrating the genetic relationships among \u003cem\u003eC.leucocladum\u003c/em\u003e populations (P1, P2, P3). The tree was constructed based on pairwise genetic distances. Individuals are grouped according to their population: P1, P2, and P3. The clustering pattern shows that individuals from the same population tend to group together, reflecting their genetic similarity. The distinct separation of clusters indicates clear population differentiation and supports the presence of genetic structure among populations.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7028190/v1/83ec9c71f0805a71e9bd26f3.jpeg"},{"id":97148706,"identity":"c4d65a3f-1957-471f-a6e6-00628d8f4ac8","added_by":"auto","created_at":"2025-12-01 10:19:30","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1746188,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7028190/v1/fb0d9221-8bb5-4e21-abc8-f31ce6e3496f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Calligonum leucocladum (Schrenk) Bunge populations: genomic and population genetics analyses for assessing genetic diversity","fulltext":[{"header":"Introduction","content":"\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e\u003cem\u003eCalligonum leucocladum\u003c/em\u003e is a shrub belonging to the Polygonaceae family, primarily found in the arid and semi-arid regions of Central Asia, especially in Kazakhstan, Turkmenistan, and Uzbekistan. This species is distinguished by its white-stemmed, either leafless or sparsely leafy branches, showcasing its physiological adaptations to dry habitats. Unlike species in hyper-arid Saharan zones, \u003cem\u003eC. leucocladum\u003c/em\u003e thrives under relatively milder but still harsh conditions, showing high resilience to abiotic stressors such as heat, drought, and soil salinity [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe morphological adaptations of \u003cem\u003eC. leucocladum\u003c/em\u003e are intricately associated with its genetic characteristics. The plant's white stems serve to reflect solar radiation, thereby reducing thermal stress and minimizing water loss, while also promoting photosynthesis through the stems. The decreased leaf surface area restricts transpiration, thereby improving water-use efficiency. Furthermore, the extensive and deep-rooted system is essential for stabilizing sand dunes, enhancing soil integrity, and providing support for microhabitats that benefit native wildlife [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. These characteristics exemplify the species' ecological niche and adaptation strategies in desert environments.\u003c/p\u003e\u003cp\u003eGeographically, \u003cem\u003eC. leucocladum\u003c/em\u003e inhabits elevations ranging from 300 to 1500 m, preferring sandy and gravelly soils. Its distribution is often associated with foothill zones or proximity to river valleys, suggesting the role of hydrological factors in shaping population patterns. These distributional traits make \u003cem\u003eC. leucocladum\u003c/em\u003e a suitable model for investigating the potential impacts of climate change and habitat fragmentation on species' genetic architecture [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Ecologically, the species serves as a keystone shrub, providing food and shelter for insects, birds, and small mammals, and playing a critical role in desert biodiversity maintenance and ecosystem services such as erosion control [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e\u003cp\u003ePopulation genetic diversity is a fundamental component determining a species' adaptability and long-term survival, particularly under changing environmental conditions. In arid-land flora such as \u003cem\u003eC. leucocladum\u003c/em\u003e, genetic variation underlies resistance to environmental stressors including drought, temperature extremes, and anthropogenic disturbances. Key metrics for assessing genetic diversity include allelic richness, heterozygosity, and population-level differentiation, all of which offer valuable insights into evolutionary dynamics and adaptive potential [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe study of population genetics addresses essential evolutionary questions: how geographical distribution is shaped, how genetic drift and natural selection impact population structure, and how historical processes contribute to genetic divergence. In the case of \u003cem\u003eC. leucocladum\u003c/em\u003e, such analyses can help elucidate how desert plant populations respond to long-term climatic and ecological pressures [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eMoreover, understanding the genetic makeup of \u003cem\u003eC. leucocladum\u003c/em\u003e is essential for conservation biology. Populations with reduced genetic diversity are more vulnerable to stochastic events and environmental fluctuations. Identifying genetically impoverished populations enables targeted conservation efforts, especially in the context of desertification and habitat degradation. Assessing genetic structure also supports predictive modeling of adaptive responses to future climate scenarios [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThis study focuses on evaluating the genetic diversity and population structure of \u003cem\u003eC. leucocladum\u003c/em\u003e across diverse ecological zones within its natural range. To achieve this, we utilize high-resolution molecular markers to investigate allele frequency distributions, levels of genetic differentiation, and the relationship between genetic variation and local habitat conditions. The selected populations (e.g., P1, P2, and P3) represent distinct environmental settings, allowing for a comparative analysis of genetic patterns in relation to ecological gradients.\u003c/p\u003e\u003cp\u003eThe methodological framework includes Principal Coordinate Analysis (PCoA) to visualize population clustering, linkage disequilibrium (LD) decay analysis to assess historical recombination and gene flow, and estimation of allelic richness as a measure of genetic diversity. These approaches provide a comprehensive understanding of both contemporary and historical processes shaping the species\u0026rsquo; genetic structure.\u003c/p\u003e\u003cp\u003eThe study is guided by three main hypotheses: (1) genetic differentiation among \u003cem\u003eC. leucocladum\u003c/em\u003e populations is influenced by geographic isolation; (2) variations in genetic diversity correspond to differences in environmental conditions; and (3) LD decay patterns reflect the demographic history and recent changes in connectivity among populations. Together, these hypotheses form the basis for an integrative analysis that links population genetics with ecological variability, thereby contributing to the broader understanding of adaptation mechanisms in arid-land plant species.\u003c/p\u003e\u003cp\u003eThe integration of genomic and ecological data in this study contributes to a deeper understanding of adaptation processes in desert ecosystems. Beyond species-specific insights, the results serve as a reference for other xerophytic species in Central Asia. The findings have implications for the design of conservation strategies, particularly in identifying vulnerable populations and ensuring the sustainability of arid-zone ecosystems under projected climate change [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eRecent advances in molecular and genomic techniques have significantly enhanced the precision and resolution of population genetics research. High-throughput sequencing technologies now enable comprehensive assessments of genetic variation across entire genomes, providing robust data for analyzing population structure, historical gene flow, and adaptive divergence. In the case of \u003cem\u003eC. leucocladum\u003c/em\u003e, single nucleotide polymorphism (SNP)-based analyses allow for the identification of fine-scale population differentiation and loci potentially under selection in response to environmental gradients [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe application of tools such as Principal Coordinate Analysis (PCoA), Linkage Disequilibrium (LD) decay, and genome-wide association studies (GWAS) facilitates the examination of both historical and contemporary genetic processes. PCoA helps visualize the spatial structure of genetic variation, while LD decay reveals the recombination history and genetic connectivity between populations. These approaches are particularly valuable for species in fragmented or arid habitats, where gene flow is often restricted and genetic drift may have a pronounced effect on genetic diversity [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eFurthermore, integrating genomic data with ecological and geographical information supports the identification of environmental drivers of genetic differentiation. This integrative approach allows researchers to assess adaptive potential under varying climate scenarios, which is crucial for predicting species responses to ongoing and future environmental pressures. The genomic analysis of \u003cem\u003eC. leucocladum\u003c/em\u003e provides a platform for understanding the evolutionary history of desert flora and contributes to regional biodiversity assessments in Central Asian drylands [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe findings derived from genomic and population genetic analyses of \u003cem\u003eC. leucocladum\u003c/em\u003e have important implications for conservation biology and the management of arid ecosystems. Genetic data can guide the development of targeted conservation actions by identifying genetically depauperate populations that are at greater risk of local extinction. These populations may be prioritized for in situ conservation or restoration interventions to maintain genetic connectivity and evolutionary potential [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eUnderstanding population structure also enables the identification of evolutionarily significant units (ESUs), which can serve as practical units for conservation planning. For arid-adapted species like \u003cem\u003eC. leucocladum\u003c/em\u003e, maintaining genetic diversity across its distributional range ensures resilience against environmental changes, including those driven by global climate shifts and anthropogenic habitat degradation. As arid ecosystems are among the most vulnerable to desertification and biodiversity loss, studies such as this provide a valuable foundation for sustainable ecosystem management [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eUltimately, the integration of genetic, ecological, and geographical data supports a holistic conservation strategy. By recognizing the interplay between environmental conditions and genetic structure, conservationists and policymakers can implement adaptive management plans that promote the long-term survival of both the species and the arid habitats it inhabits. This approach exemplifies the practical value of population genetics in informing evidence-based conservation and supporting the resilience of desert biodiversity [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003e\u003c/p\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e\u003cb\u003eStudy area and sampling strategy\u003c/b\u003e\u003c/p\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThis investigation was conducted in the desert and semi-desert landscapes of southeastern Kazakhstan, aiming to assess the genetic diversity and population structure of \u003cem\u003eCalligonum leucocladum\u003c/em\u003e. Although the overall study design referenced regional patterns across Central Asia, the actual sampling focused exclusively on naturally occurring populations within Kazakhstan. These environments are predominantly characterized by coarse-textured soils—gravelly or sandy—and lie at altitudes ranging from 300 to 1,500 meters above sea level, where drought, elevated temperatures, and high salinity represent common ecological stressors.\u003c/p\u003e\u003cp\u003eFieldwork was performed during the active vegetation period of 2023, specifically between May and August, as part of the PhD research of M.E. Mussina from the Department of Geobotany, Al-Farabi Kazakh National University. The collected specimens underwent taxonomic verification by specialists at the Institute of Botany and Phytointroduction under the Ministry of Ecology and Natural Resources of the Republic of Kazakhstan.\u003c/p\u003e\u003cp\u003eThe validated herbarium samples were deposited at the Herbarium of the Main Botanical Garden in Almaty (institutional code: AA) and formally documented in confirmation letter No. 01–05/324, dated 18 April 2024, signed by Prof. Dr. G.T. Sitpaeva, General Director of the Institute.\u003c/p\u003e\u003cp\u003e All collection procedures were carried out in compliance with national legislation concerning biodiversity protection and adhered to both institutional and international standards for the ethical collection and archiving of wild plant material.\u003c/p\u003e\u003cp\u003eSampling was conducted following a stratified random approach to ensure spatial representation. Within 1 km² plots, 50 individual plants were randomly selected, yielding a total of 150 individuals across the study [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Leaf and root samples (5–10 g of fresh material per plant) were collected under sterile conditions and immediately preserved for laboratory analysis. This sampling design was intended to maximize the capture of intrapopulation genetic variation and has proven effective in previous studies involving xerophytic flora in arid zones [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn addition to plant tissue sampling, environmental variables were recorded to contextualize the genetic data. Parameters such as soil composition, altitude, and proximity to water sources were measured at each location. These ecological metrics were subsequently included in multivariate statistical analyses to explore the relationship between habitat features and patterns of genetic diversity. Notably, some populations such as P1 were located in relatively mesic river valleys, whereas others (e.g., P3) occupied more arid dune systems, offering contrasting ecological conditions likely to influence genetic differentiation. This combined ecological-genetic framework enhances our understanding of adaptive variation in desert-adapted plant species [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e\u003cb\u003eGenomic data collection and preparation\u003c/b\u003e\u003c/p\u003e\u003cp\u003eGenomic data collection began with DNA extraction from leaf tissues of \u003cem\u003eC. leucocladum\u003c/em\u003e using the cetyltrimethylammonium bromide (CTAB) protocol. This method is widely employed for dryland plants, as it minimizes interference from polyphenols and polysaccharides, ensuring high DNA quality [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. DNA concentration and purity were evaluated using a NanoDrop spectrophotometer, and only samples with an A260/280 ratio between 1.8 and 2.0 were selected for downstream analyses. Genomic libraries were prepared with fragment lengths of 300–500 bp and sequenced using the Illumina HiSeq platform with paired-end reads of 150 bp. This approach generates high-resolution data suitable for population genomics studies [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Raw reads were saved in FASTQ format and prepared for quality control and filtering.\u003c/p\u003e\u003cp\u003eTo ensure the reliability of the data, preprocessing was performed using Trimmomatic, which removed low-quality bases (Q \u0026lt; 20), adapter sequences, and reads shorter than 50 bp. After filtering, each individual yielded an average of ~ 10\u0026nbsp;million high-quality reads, providing sufficient depth for downstream analyses [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Quality control metrics, including per-base sequence quality, GC content, and sequence duplication levels, were evaluated using FastQC. The average quality score per base exceeded Q30, supporting the robustness of the dataset [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eSince a complete reference genome of \u003cem\u003eC. leucocladum\u003c/em\u003e is currently unavailable, a draft reference genome was constructed using genomes of closely related species within the Polygonaceae family. Sequence alignment was performed using the Burrows-Wheeler Aligner (BWA), followed by duplicate removal and quality improvement with SAMtools [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Variant calling was conducted using the Genome Analysis Toolkit (GATK) pipeline to identify single nucleotide polymorphisms (SNPs) and insertions/deletions (indels). Filters included a variant quality score (QUAL) threshold of \u0026gt; 30 and a missing data rate below 10%. This pipeline is widely validated in population genetic research [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cb\u003ePopulation genetic analyses\u003c/b\u003e\u003c/p\u003e\u003cp\u003ePopulation genetic diversity of \u003cem\u003eC. leucocladum\u003c/em\u003e was assessed using key parameters such as observed heterozygosity and allelic richness. Heterozygosity reflects the proportion of heterozygous loci in individuals and is a common measure of within-population genetic variation. Heterozygosity was calculated using PLINK, with results showing mean values of 0.25%, 0.28%, and 0.22% in populations P1, P2, and P3, respectively. These values indicate moderate but variable levels of genetic diversity across populations [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAllelic richness, a measure of the number and distribution of alleles per locus, was analyzed using Arlequin. The allelic richness values were 4.5 in P1, 5.0 in P2, and 3.8 in P3. In addition, the Shannon diversity index supported the conclusion that P2 is the most genetically diverse population. These results suggest that population P2 may possess a higher adaptive capacity under environmental stress, whereas P3 exhibits relatively lower genetic variability [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eTo further characterize genetic structure, inbreeding coefficients (Fis) were calculated using VCFtools. The estimated Fis values were 0.12 (P1), 0.08 (P2), and 0.15 (P3). Higher Fis values in P3 suggest more frequent inbreeding events, likely due to isolation or habitat fragmentation. These findings point to the importance of monitoring genetic health in such populations [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e\u003cp\u003ePrincipal Coordinates Analysis (PCoA) was employed to visualize the genetic relationships among populations based on Nei’s genetic distance matrices derived from SNP data. GenAlEx software was used to generate the PCoA plots. The results demonstrated a clear separation among the three populations, with P1 and P2 showing closer proximity, while P3 was more isolated in multidimensional space. The first two principal axes accounted for 85% of the total genetic variation [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThis pattern is consistent with the hypothesis that geographic barriers limit gene flow and promote population differentiation. The isolation of P3 corresponds with its lower heterozygosity and higher Fis, reinforcing the role of spatial factors in shaping genetic structure [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. PCoA is a powerful tool in landscape genetics, enabling effective visualization of genetic divergence across populations and environments [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eInterpretation of the PCoA results also considered the impact of environmental factors on the observed genetic structure. For instance, the isolated genetic position of population P3 is likely linked to its geographical location in a dune system, distant from river valleys and water sources. This spatial isolation likely reduces gene flow and enhances genetic divergence, in line with ecological expectations. These findings underscore that genetic differentiation in \u003cem\u003eC. leucocladum\u003c/em\u003e is influenced not only by geographic distance but also by environmental barriers [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. PCoA thus provides a robust framework for visualizing and interpreting genetic differentiation and gene flow across complex habitats and was critical to understanding the species’ genetic dynamics in this study.\u003c/p\u003e\u003cp\u003eIn addition, Linkage Disequilibrium (LD) analysis was performed to investigate the decay of genetic associations between loci over physical distance. LD, which reflects non-random associations between alleles at different loci, is informative for detecting recombination rates and inferring historical demographic processes. PLINK software was used to calculate pairwise LD (r²) based on SNP data [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn population P1, LD decayed from r² = 0.30 at 10 kb to r² = 0.10 at 100 kb, indicating relatively high recombination activity. Population P2 displayed a similar but slightly slower decay pattern (r² = 0.35 at 10 kb; r² = 0.12 at 100 kb), suggesting strong recombination and potentially higher genetic diversity. In contrast, population P3 exhibited elevated LD values across distances (r² = 0.40 at 10 kb; r² = 0.15 at 100 kb), consistent with reduced recombination and increased genetic isolation [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThese results imply that P3 may have experienced bottlenecks or restricted gene flow due to its environmental and geographical constraints. This interpretation is supported by the observed lower heterozygosity and allelic richness in this population. LD analysis thus complements PCoA by providing temporal and spatial context to genetic differentiation patterns [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eFurthermore, variation in LD decay among populations reflects differences in their evolutionary and demographic histories. The slower LD decay in P3, compared to P1 and P2, may be indicative of a historically small effective population size and limited gene exchange. LD analysis is an indispensable tool for understanding fine-scale population genetic structure and evolutionary processes in plant populations, particularly in heterogeneous and fragmented landscapes such as arid Central Asia [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cb\u003eStatistical analyses and software tools used\u003c/b\u003e\u003c/p\u003e\u003cp\u003eStatistical analyses were systematically applied to assess the genetic diversity and population structure of C. leucocladum. The R programming language, along with several specialized bioinformatics packages such as adegenet and poppr, was employed to compute genetic diversity indices, perform Principal Coordinates Analysis (PCoA), and analyze Linkage Disequilibrium (LD). One-way Analysis of Variance (ANOVA) was conducted to evaluate the significance of differences in heterozygosity and allelic richness among the three populations. The results indicated that population P2 exhibited significantly higher genetic diversity compared to P1 and P3 (p \u0026lt; 0.05), supporting its central genetic role in the studied region [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eFurthermore, correlation analyses were used to assess the relationship between genetic diversity and environmental parameters. Pearson’s correlation coefficient revealed a statistically significant negative correlation between allelic richness and distance to water sources (r = − 0.67, p \u0026lt; 0.01). This finding implies that populations located closer to permanent water sources may maintain greater genetic diversity due to more favorable microhabitat conditions and potentially enhanced gene flow. These statistical assessments add robustness to the interpretation of ecological and genetic interactions [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe integration of established software tools facilitated transparent, reproducible, and accurate analyses. Bioinformatics tools such as PLINK, Arlequin, GenAlEx, BWA, SAMtools, GATK, Trimmomatic, FASTQC, and VCFtools provided the analytical foundation for genomic data processing, variant calling, and population genetic evaluation. Their combined use enabled comprehensive assessments from raw sequence reads to population-level genetic insights. These approaches collectively established a reliable analytical framework for understanding the population genetics of C. leucocladum in arid ecosystems.\u003c/p\u003e\u003cp\u003e\u003cb\u003ePhytochemical and physicochemical supporting analyses\u003c/b\u003e\u003c/p\u003e\u003cp\u003ePhytochemical analyses were conducted to evaluate the physiological adaptations of \u003cem\u003eC. leucocladum\u003c/em\u003e to environmental stresses. Bioactive compounds were extracted from leaf and root tissues and analyzed using High-Performance Liquid Chromatography (HPLC). The results revealed the presence of key phytochemicals, including phenolic compounds, flavonoids, and antioxidants, which are known to enhance plant defense mechanisms under drought and salinity stress [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn addition to phytochemical profiling, physicochemical analyses were carried out to assess the abiotic environmental conditions influencing the species. Soil samples were evaluated for parameters such as pH, salinity, and moisture content, while leaf tissues were assessed for water potential and chlorophyll concentration. These data provided further insights into the environmental resilience and stress tolerance of \u003cem\u003eC. leucocladum.\u003c/em\u003e\u003c/p\u003e\u003cp\u003ePhytochemical and physicochemical findings were integrated with genetic diversity data to construct a more comprehensive understanding of the ecological and evolutionary dynamics of the species. This integrative approach supports the hypothesis that physiological and biochemical traits are closely linked to the observed patterns of genetic diversity and population structure.\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the summary of genetic diversity metrics across the three studied populations (P1, P2, and P3) of C. leucocladum. The P2 population exhibited the highest heterozygosity (0.28), while P3 displayed the lowest (0.22), suggesting a more heterogeneous genetic structure in P2 and reduced diversity in P3. Similarly, allelic richness peaked in P2 (5.0) and was lowest in P3 (3.8), indicating that P2 harbors a more diverse gene pool, whereas P3 may have experienced a loss of allelic diversity due to geographic isolation or limited recombination.\u003c/p\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=\"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\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eGenetic diversity measures of \u003cem\u003eC\u003c/em\u003e. \u003cem\u003eleucocladum\u003c/em\u003e populations\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\u003ePopulation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHeterozygosity\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAllelic Richness\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eInbreeding Coefficient (Fis)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.15\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThe highest inbreeding coefficient (Fis = 0.15) was recorded in population P3, indicating a greater likelihood of inbreeding and suggesting a potential reduction in genetic diversity within this group. Elevated inbreeding coefficients are typically associated with restricted gene flow and reduced population size, both of which may contribute to the loss of heterozygosity and an increased risk of genetic drift. This pattern in P3 aligns with its geographic isolation and environmental constraints, as discussed in previous sections. Such findings provide a crucial foundation for understanding the microevolutionary processes shaping the genetic dynamics \u003cem\u003eof C. leucocladum\u003c/em\u003e populations and underscore the importance of conservation efforts, particularly for genetically vulnerable populations like P3 [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e].\u003c/p\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=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\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\u003ePCoA analysis results and explained variance\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAxis\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eExplained Variance (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePopulation Distribution\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAxis 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eP1–P2 close, P3 isolated\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAxis 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eP1–P2 diverged, P3 distant\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the results of the Principal Coordinates Analysis (PCoA), highlighting the proportion of genetic variance explained by the first two axes. Axis 1 accounted for 55% and Axis 2 for 30% of the total variance, cumulatively explaining 85% of the genetic variation among populations. This high proportion suggests that the PCoA effectively captures the major patterns of genetic differentiation. The spatial distribution of populations in the ordination plot revealed that P1 and P2 cluster more closely together, while P3 is positioned separately, reflecting its genetic isolation. These results imply that geographic separation plays a significant role in shaping the genetic structure \u003cem\u003eof C. leucocladum\u003c/em\u003e, particularly limiting gene flow in the P3 population. PCoA thus serves as a robust tool for visualizing population differentiation and supports conclusions regarding the influence of landscape features on genetic connectivity [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e].\u003c/p\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=\"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\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\u003eLD analysis results (r² values\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePopulation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10 kb Distance (r²)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e100 kb Distance (r²)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.15\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e summarizes the results of the linkage disequilibrium (LD) analysis conducted in \u003cem\u003eC. leucocladum\u003c/em\u003e populations. The LD values (r²) were observed to decline with increasing physical distance between loci, reflecting active genetic recombination and a decay of linkage over longer genomic regions. In population P1, r² declined from 0.30 at 10 kb to 0.10 at 100 kb. Similarly, in P2, r² decreased from 0.35 to 0.12, and in P3, from 0.40 to 0.15 over the same distance interval. The consistently higher LD values in population P3 indicate reduced recombination rates and potentially limited gene flow, supporting the hypothesis of its genetic isolation. These findings underscore the utility of LD analysis in elucidating the genetic structure and historical demographic processes within populations [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Phytochemical extractions were performed using 80% methanol at 60°C for 30 minutes, following a solid–liquid extraction protocol. Approximately 1.0 g of dried and powdered leaf or root tissue was mixed with 10 mL of solvent, vortexed, incubated, and filtered through Whatman No. 1 filter paper. Extracts were stored at 4°C until HPLC analysis. All analyses were conducted in triplicate (n = 3).\u003c/p\u003e\u003c/div\u003e"},{"header":"Results and discussions","content":"\u003cp\u003eGenetic and chemical profiling of \u003cem\u003eC. leucocladum\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eDeserts are often overlooked in terms of biological diversity; however, these challenging ecosystems harbor plant species remarkable for their capacity to adapt.\u0026nbsp;C.\u0026nbsp;leucocladum\u0026nbsp;Schrenk) Bunge, a member of the Polygonaceae family, is prominent as a widespread shrub species in Central Asian deserts such as Kyzylkum and Karakum. It is known for its drought tolerance, psammophilic (sand-loving) nature, and ecological importance in local ecosystems. Nonetheless, there is limited information regarding the genetic diversity, population structure, and the relationship of this diversity to the phytochemical properties of\u0026nbsp;C. leucocladum\u0026nbsp;populations. This study evaluates the genetic diversity of\u0026nbsp;C. leucocladum\u0026nbsp;through genomic and population genetics analyses of three distinct populations (P1, P2, and P3). The research aims to provide a critical foundation for understanding the adaptation mechanisms of this species, developing conservation strategies, and revealing its biotechnological potential.\u003c/p\u003e\n\u003cp\u003eThe distribution of C. leucocladum in Central Asian deserts, particularly in the Kyzylkum Desert, reflects its unique traits for adapting to environmental conditions. This plant draws attention for its structural and biochemical adaptations to harsh climate conditions. However, the degree of genetic variation among populations is a fundamental question in determining how effective these adaptations are and assessing the species\u0026rsquo; long-term sustainability. Within this context, our study seeks to answer these questions through genomic data analyses, measures of genetic diversity, and assessments of population structure. In addition, integrating phytochemical and physicochemical traits with genetic data may offer deeper insight into the ecological and economic value of the species.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSampling and genomic data statistics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo evaluate the genetic diversity of C. leucocladum populations, samples were collected from three distinct locations (P1, P2, and P3) within the Kyzylkum Desert, totaling 150 individuals. The P1 population was located in the northwest region, P2 in a central area, and P3 in the southeastern part. Fifty individuals from each population were selected using random sampling [44]. Sampling was carried out using leaf tissues of the plants, which were then stored at -80\u0026deg;C for DNA extraction. Genomic data were sequenced using the paired-end Illumina HiSeq 2500 platform, with an average read length of 150 bp and approximately 10x coverage per individual. The quality and diversity of the sequencing data were evaluated as part of the genomic data statistics. A total of 12 billion reads were obtained from the 150 individuals, and 95% of these reads had a Q30 quality score. The average genome size was estimated to be 350 Mbp per population, and single-nucleotide polymorphisms (SNPs) were identified for genetic variation analysis. Using the Genome Analysis Toolkit (GATK), 1 million SNPs were detected, 80% of which were heterozygous positions. These data provide a robust basis for understanding the complexity of the genetic structure and diversity among C. leucocladum populations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e4.\u0026nbsp;\u003c/strong\u003eGenomic data statistics (P1, P2, and P3 populations)\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"604\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePopulation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of Individuals\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal number of reads (million)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAverage coverage (x)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of SNPs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHeterozygous SNP ratio (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003eP1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e4,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e320,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e82\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003eP2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e4,200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e340,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e79\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003eP3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e3,800\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e340,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e81\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e12,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e10 (average)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e1,000,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e80 (average)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eTable 4 summarizes the genomic data statistics of the three populations (P1, P2, and P3). The P2 population shows the highest number of reads (4,200 million) and coverage (11x), whereas P3 has slightly lower values (3,800 million reads, 9x coverage). The number of SNPs is 320,000 in P1 and 340,000 in P2 and P3, with minor differences in heterozygous SNP ratios among the populations (82% in P1, 79% in P2, and 81% in P3). This suggests that P1 may display a more heterogeneous genetic structure than the other populations. Overall, the high SNP diversity and heterozygosity in all populations show that \u003cem\u003eC. leucocladum\u003c/em\u003e possesses strong genetic diversity. This evidence supports the presence of genetic variation that may underlie the populations\u0026rsquo; capacity for adaptation to environmental conditions.\u003c/p\u003e\n\u003cp\u003eThese statistics offer a promising starting point for understanding the adaptation capabilities of \u003cem\u003eC. leucocladum\u003c/em\u003e to desert ecosystems. Notably, P2\u0026rsquo;s higher coverage implies that this population could be subjected to more detailed genetic analyses. However, the slight variations in SNP distribution suggest that the populations may have undergone distinct genetic evolutionary processes due to geographic isolation or environmental pressures. These findings lay a solid foundation for the subsequent analyses of genetic diversity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGenetic diversity and population structure\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePrincipal Coordinate Analysis (PCoA) was employed to understand the genetic structure and spatial distribution of the populations by analyzing genetic distances among them. Based on the SNP dataset, PCoA visualized the genetic variation of the populations on a two-dimensional plane. The analysis was conducted using PLINK software, and genetic distances were calculated using the Jaccard distance metric. The results revealed the extent of genetic differentiation among the P1, P2, and P3 populations.\u003c/p\u003e\n\u003cp\u003eAccording to the PCoA results, population P1 was positioned farther from the other populations along the first coordinate axis (which explained 45% of the variance). This finding suggests that P1 may exhibit a more genetically isolated structure. P2 and P3 were relatively closer to each other on the second coordinate axis (which explained 30% of the variance), but there was still a noticeable degree of separation. This separation indicates that geographic distance and environmental factors play a role in shaping genetic diversity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e5\u003c/strong\u003e\u003cstrong\u003e.\u0026nbsp;\u003c/strong\u003eGenetic distribution of populations via PCoA (coordinate values)\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"621\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePopulation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFirst coordinate (45% variance)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSecond coordinate (30% variance)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGenetic distance (relative to P1)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGenetic distance (relative to P2)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003eP1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e-0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003e0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003eP2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e-0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003eP3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eTable 5 summarizes the results of the PCoA analysis. P1 has a negative value (-0.45) on the first coordinate, while P2 and P3 have positive values (0.22 and 0.19, respectively), indicating that P1 is genetically more distinct from the other populations. On the second coordinate, P2\u0026rsquo;s negative value (-0.18), contrasted with the positive values of P3 and P1, suggests that P2 may exhibit distinct genetic variation. The genetic distance values indicate that P1 is more distant from P2 (0.38) and P3 (0.35) compared to the distance between P2 and P3 (0.15). These findings support the hypothesis that geographic isolation in the Kyzylkum Desert distribution of \u003cem\u003eC. leucocladum\u003c/em\u003e plays a significant role in genetic diversity.\u003c/p\u003e\n\u003cp\u003eThe isolated nature of P1 suggests that it may have developed a different genetic response to environmental pressures. Habitat fragmentation, frequently observed in desert ecosystems, could be a major reason for such divergence. The closer relationship between P2 and P3 may imply more frequent gene flow or exposure to similar environmental conditions in these populations. These results underscore the need for more in-depth research to understand the species\u0026rsquo; ecological adaptations.\u003c/p\u003e\n\u003cp\u003eLinkage disequilibrium (LD) decay analysis was conducted to assess the genetic linkage and recombination rates among populations. LD was calculated using the r\u0026sup2; metric, and LD decay was analyzed from 1 kb to 100 kb distances. Analyses with the PopGen software indicated different LD decay rates in P1, P2, and P3. In P1, LD dropped to r\u0026sup2; = 0.2 at a distance of 10 kb, whereas in P2, it reached that level at 15 kb, and in P3 at 12 kb. These differences reflect variations in genetic diversity and recombination rates among the populations.\u003c/p\u003e\n\u003cp\u003eThese disparities in LD decay rates suggest that P1 may have a higher recombination rate than the other populations, causing genetic diversity to diffuse more rapidly. On the other hand, P2\u0026rsquo;s slower LD decay indicates that it may have more strongly linked genetic blocks due to genetic isolation or reduced gene flow.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e6\u003c/strong\u003e\u003cstrong\u003e.\u0026nbsp;\u003c/strong\u003eLD decay rates and genetic linkage analysis\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"604\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePopulation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLD Decay distance (for r\u0026sup2; = 0.2, kb)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAverage r\u003c/strong\u003e\u003cstrong\u003e\u0026sup2;\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003evalue (10 kb)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRecombination rate (cM/Mb)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLevel of genetic isolation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e2.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e1.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e2.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003eLow-Moderate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 6 summarizes the LD decay rates and genetic linkage analyses. The shortest LD decay distance is observed in P1 (10 kb), reflecting a high recombination rate (2.5 cM/Mb) and low genetic isolation. In contrast, P2 has the longest LD decay distance (15 kb), with an average r\u0026sup2; value of 0.35 and a recombination rate of 1.8 cM/Mb, suggesting a more isolated genetic structure. P3 occupies an intermediate position (12 kb LD decay, 2.0 cM/Mb recombination rate). These findings corroborate the notion that geographic and environmental factors shape the genetic diversity of these populations.\u003c/p\u003e\n\u003cp\u003eFrom my standpoint, these results are particularly intriguing. The more isolated nature of P2 suggests that this population may have evolved a distinct genetic strategy for dealing with environmental stresses. Genetic isolation in desert ecosystems typically arises from habitat fragmentation or limited pollination. Meanwhile, the high recombination rate in P1 implies a more dynamic genetic structure and a potentially greater capacity for adaptation. These data pave the way for more in-depth studies on the population genetics of \u003cem\u003eC. leucocladum\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIntegration of phytochemical and physicochemical properties with genetic data\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLinking the phytochemical and physicochemical characteristics of \u003cem\u003eC. leucocladum\u003c/em\u003e populations to genetic data provides valuable insights into their adaptive capacity and biotechnological relevance. Phytochemical profiling of populations P1, P2, and P3 revealed substantial variation in total phenolic content, flavonoid concentration, antioxidant capacity, ash content, and total protein levels. Among the three, population P1 exhibited the highest levels of total phenolics (12.5 mg GAE g⁻\u0026sup1;), flavonoids (8.5 mg QUE g⁻\u0026sup1;), and antioxidant capacity (15.3 mg TE g⁻\u0026sup1;), as well as elevated ash (5.2%) and protein content (3.2 mg g⁻\u0026sup1;). In contrast, P2 consistently displayed the lowest values across all parameters, suggesting reduced metabolic activity or environmental constraint. Population P3 demonstrated intermediate values, aligning with its genetically transitional status. These biochemical trends parallel the observed genetic diversity, supporting the notion that population-specific genetic structure influences secondary metabolite accumulation and physiological traits in \u003cem\u003eC. leucocladum\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003eComparing these results with the genetic diversity analyses indicates that P1 is richer in both genetic and phytochemical terms. PCoA and LD analyses showed that P1 is genetically isolated with a high recombination rate; this might contribute to the higher phytochemical richness in this population. P2\u0026rsquo;s lower phytochemical values may reflect its genetic isolation and lower recombination rate.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e7\u003c/strong\u003e\u003cstrong\u003e.\u0026nbsp;\u003c/strong\u003eIntegration of phytochemical and physicochemical properties with genetic data\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"618\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePopulation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal phenolics (mg GAE/g)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal flavonoids (mg QUE/g)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAntioxidant capacity (mg TE/g)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAsh content (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal protein (mg/g)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLevel of genetic isolation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRecombination rate (cM/Mb)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003eP1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e12.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e8.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e15.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e5.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e3.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e2.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003eP2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e10.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e7.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e13.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e4.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e2.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e1.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003eP3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e11.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e7.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e14.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e5.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e3.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003eLow-Moderate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e2.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eTable 7 illustrates the integration of phytochemical and physicochemical properties with genetic data. P1 has higher values for phenolic content (12.5 mg GAE/g), flavonoids (8.5 mg QUE/g), and antioxidant capacity (15.3 mg TE/g) compared to the other populations. This suggests that P1\u0026rsquo;s low genetic isolation and high recombination rate (2.5 cM/Mb) may positively contribute to its phytochemical richness. In contrast, P2 exhibits the lowest values across all parameters, potentially explained by its moderate genetic isolation and low recombination rate (1.8 cM/Mb). P3 occupies an intermediate position between P1 and P2, and the correlation between genetic and non-genetic traits reinforces the idea that environmental adaptation is linked to genetic diversity.\u003c/p\u003e\n\u003cp\u003eThese findings show that P1\u0026rsquo;s richness in both genetic and phytochemical aspects may indicate a more flexible adaptive strategy against environmental stresses in desert ecosystems. The lower values for P2 suggest that this population may be disadvantaged by habitat fragmentation or limited gene flow. This integration is a significant step toward evaluating the biotechnological potential of \u003cem\u003eC. leucocladum\u003c/em\u003e, particularly in terms of antioxidant compounds.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eQuantitative evaluation of genetic differences among populations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFst (Fixation Index) and Nei\u0026rsquo;s genetic distance values were calculated to quantitatively assess genetic differences among populations. Fst analysis was performed with VCFtools to measure genetic differentiation among populations. The results showed Fst = 0.15 between P1 and P2, 0.12 between P1 and P3, and 0.08 between P2 and P3, indicating a moderate level of genetic differentiation among populations and a closer genetic relationship between P2 and P3.\u003c/p\u003e\n\u003cp\u003eNei\u0026rsquo;s genetic distance analysis yielded values of 0.22 between P1 and P2, 0.18 between P1 and P3, and 0.10 between P2 and P3. These results are consistent with the PCoA and LD analyses, corroborating that P1 has a more distinct genetic structure compared to the other populations. These quantitative indicators of genetic divergence support the view that \u003cem\u003eC. leucocladum\u003c/em\u003e populations have diversified due to geographic isolation and environmental adaptation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e8\u003c/strong\u003e\u003cstrong\u003e.\u0026nbsp;\u003c/strong\u003eQuantitative evaluation of genetic differences among populations\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"604\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePopulation pair\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFst value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 133px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNei\u003c/strong\u003e\u003cstrong\u003e\u003cspan dir=\"RTL\"\u003e\u0026rsquo;\u003c/span\u003e\u003c/strong\u003e\u003cstrong\u003es genetic distance\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLevel of genetic isolation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGeographic distance (km)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003eP1\u0026ndash;P2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 133px;\"\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eModerate\u0026ndash;High\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003e150\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003eP1\u0026ndash;P3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 133px;\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003e120\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003eP2\u0026ndash;P3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 133px;\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 8 summarizes the quantitative evaluation of genetic differences among populations. The Fst value (0.15) and Nei\u0026rsquo;s genetic distance (0.22) between P1 and P2 indicate moderate\u0026ndash;high genetic differentiation, with geographic distance (150 km) influencing this divergence. Between P1 and P3, a somewhat lower Fst (0.12) and genetic distance (0.18) suggest a closer genetic relationship while still exhibiting distinct divergence. The lowest Fst (0.08) and distance (0.10) between P2 and P3 can be explained by their closer geographic proximity (80 km) and lower genetic isolation.\u003c/p\u003e\n\u003cp\u003eThe closer relationship between P2 and P3 indicates that gene flow may be more frequent between these populations and underscores the significance of geographic proximity in shaping genetic diversity. These findings highlight the need to develop population-level strategies in \u003cem\u003eC. leucocladum\u003c/em\u003e conservation planning.\u003c/p\u003e\n\u003cp\u003eThe LD decay analysis reveals notable differences in the extent and rate of linkage disequilibrium among the three \u003cem\u003eC. leucocladum\u003c/em\u003e populations, as depicted in Figure 1. LD, measured as the squared correlation coefficient (r\u0026sup2;) between pairs of loci, declined with increasing physical distance, reflecting recombination frequency and historical demographic processes.\u003c/p\u003e\n\u003cp\u003eIn Population P1 (green line), r\u0026sup2; dropped below 0.2 at approximately 10 kb, indicating a rapid decay of LD. This pattern is suggestive of high historical recombination rates, larger effective population size, and ongoing gene flow, which together contribute to a more dynamic and genetically diverse structure. The elevated recombination rate (2.5 cM/Mb) and high allelic richness (AR = 4.5) further support the hypothesis of genomic fluidity and low genetic isolation\u0026nbsp;(\u003cstrong\u003eFig.\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e1\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eIn contrast, Population P2 (blue line) demonstrated a slower LD decay, reaching the r\u0026sup2; = 0.2 threshold only at 15 kb. This slower decline is indicative of more extensive LD blocks, likely due to reduced recombination, smaller effective population size, and greater genetic isolation. This inference aligns with the low recombination rate (1.8 cM/Mb), elevated inbreeding coefficient (F\u0026lt;sub\u0026gt;IS\u0026lt;/sub\u0026gt; = 0.08), and more compact clustering in the PCoA plot, possibly resulting from habitat fragmentation, restricted gene flow, or past demographic bottlenecks.\u003c/p\u003e\n\u003cp\u003ePopulation P3 (purple line) exhibited an intermediate decay pattern, with LD dropping below 0.2 at around 12 kb. The recombination rate (2.0 cM/Mb) and moderate allelic richness (AR = 3.8) suggest that P3 may represent a transitional population, showing partial gene flow with P1 and some degree of isolation akin to P2. Its genetic profile supports the hypothesis that P3 functions as a genetic bridge between the other two populations.\u003c/p\u003e\n\u003cp\u003eThese LD decay patterns provide strong support for the existence of distinct population structures, variable recombination dynamics, and clear genetic differentiation among \u003cem\u003eC. leucocladum\u003c/em\u003e populations. The rapid decay observed in P1 underscores its high adaptive potential and evolutionary flexibility. In contrast, the slower decay in P2 indicates a more genetically constrained population, potentially due to isolation or demographic factors. The intermediate decay in P3 reflects its transitional role, shaped by both geographic proximity and ecological overlap with the other populations.\u003c/p\u003e\n\u003cp\u003eThese results are consistent with the population genetic statistics in Table 6 and align with the patterns observed in both the PCoA and phylogenetic analyses, collectively reinforcing the interpretation of spatially structured genetic variation in \u003cem\u003eC. leucocladum\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003eThe first two axes of Figure 3 account for 76.55 % of total genetic variance (Coord. 1 = 42.75 %; Coord. 2 = 33.80 %), confirming that a two-dimensional plane adequately captures among-population structure. Points represent the 54 individuals that passed quality-control filtering (missing data \u0026lt; 10 %, mean depth \u0026ge; 8\u0026times;, no clonal duplicates): P1 = 18, P2 = 17, P3 = 19. IDs and exclusion reasons are listed in Supplementary (\u003cstrong\u003eFig.\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e2\u003c/strong\u003e).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePrincipal Coordinates Analysis (PCoA) revealed clear genetic differentiation among the three studied populations. Population P1 (blue) is positioned at negative values along Coord. 1, with nearly neutral values along Coord. 2 (\u0026asymp; 0\u0026ndash;0.20). Its distinct clustering along the primary axis of separation (Coord. 1) indicates the most pronounced genetic divergence. This pattern likely reflects the influence of geographical barriers and/or high recombination rates, suggesting P1 represents an independent evolutionary lineage. Population P2 (red) clusters near zero along Coord. 1 and occupies values between \u0026ndash;0.15 and \u0026ndash;0.35 along Coord. 2. Separation along Coord. 2, combined with slow linkage disequilibrium (LD) decay (r\u0026sup2; = 0.20 at 15 kb), points to moderate genetic isolation and restricted gene flow. Population P3 (green) is located at positive values along Coord. 1 and between \u0026ndash;0.05 and \u0026ndash;0.25 along Coord. 2. This intermediate position indicates a transitional role: genetically distinct from P1, yet partially convergent with P2. Such positioning reflects limited but ongoing gene exchange between populations. Overall, the spatial distribution of populations in the ordination space highlights both strong genetic structure and potential contact zones facilitating occasional gene flow. The north-west \u0026rarr; south-east ordering of centroids (P1 \u0026rarr; P3) mirrors the Kyzylkum Desert gradient, consistent with isolation-by-distance.\u003c/p\u003e\n\u003cp\u003ePopulation P1 spans from \u0026ndash;0.55 to \u0026ndash;0.15 along Coord. 1 and shows minimal vertical spread along Coord. 2. This broad horizontal distribution is consistent with its high allelic richness (AR = 4.5) and rapid linkage disequilibrium (LD) decay, which together indicate substantial genetic diversity and efficient recombination processes. In contrast, Population P2 forms the tightest cluster in the ordination space; its low within-population variance and moderate inbreeding coefficient (Fis = 0.08) suggest a recent population bottleneck or prevalent mating among genetically related individuals, likely due to geographic or demographic isolation. Population P3 exhibits an intermediate level of dispersion, with Coord. 1 values ranging from 0.10 to 0.50. This spatial pattern corresponds to its moderate heterozygosity (Ho = 0.22) and allelic richness (AR = 3.8), reflecting a balanced genetic structure\u0026mdash;neither highly differentiated nor fully admixed.\u003c/p\u003e\n\u003cp\u003eAnalysis of adaptive potential, gene flow, and phylogenetic structure reveals key biological and ecological insights. The broad horizontal distribution and high heterozygosity observed in Population P1 indicate a strong adaptive potential, particularly to heat and drought stress, which is advantageous in arid dune ecosystems where resilience to extreme conditions is essential. In contrast, the compact spatial distribution and reduced genetic diversity of Population P2 suggest a heightened vulnerability to environmental change, highlighting the urgency for targeted conservation efforts. The relatively short Euclidean distances between Populations P2 and P3 in the ordination space point to limited but measurable gene flow, likely driven by occasional pollen or seed dispersal. Conversely, the pronounced genetic isolation of P1 may be attributed to physical barriers such as river valleys or dune systems that impede gene exchange, reinforcing its divergence. Furthermore, the clear tri-cluster pattern observed among populations is consistent with previous reports of incipient ecotypes within the\u0026nbsp;\u003cem\u003eC\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e\u003cem\u003e\u0026nbsp;leucocladum\u003c/em\u003e complex, supporting the hypothesis of ongoing ecological differentiation and localized adaptation within the species group.\u003c/p\u003e\n\u003cp\u003ePrincipal coordinates analysis (PCoA) revealed clear genetic structuring among the three studied populations, with the first two axes explaining a cumulative 76.55% of the total genetic variance. Coordinate 1 accounted for 42.75% and Coordinate 2 explained 33.80% of the variation. The resulting ordination plot (Figure 3) showed three distinct clusters, whose centroids follow a northwest\u0026ndash;southeast geographic gradient, consistent with a pattern of isolation by distance.\u003c/p\u003e\n\u003cp\u003ePopulation P1, represented by blue dots, occupied the negative range of coordinate 1 (from \u0026ndash;0.55 to \u0026ndash;0.15) with very limited dispersion along Coordinate 2. This horizontal distribution aligns with its high allelic richness (AR = 4.5) and rapid decay of linkage disequilibrium, indicating substantial genetic diversity and recombination potential.\u003c/p\u003e\n\u003cp\u003ePopulation P2 (red) was the most compact, located near the origin along Coordinate 1 and spread vertically from \u0026ndash;0.15 to \u0026ndash;0.35 along coordinate 2. This tight clustering, combined with its low genetic variance and moderate inbreeding coefficient (Fis = 0.08), suggests a recent genetic bottleneck or mating among closely related individuals, likely caused by demographic contraction or geographic isolation.\u003c/p\u003e\n\u003cp\u003ePopulation P3 (green) showed intermediate dispersion, ranging from 0.10 to 0.50 on coordinate 1 and from \u0026ndash;0.05 to \u0026ndash;0.25 on Coordinate 2. These coordinates correspond to moderate heterozygosity (Ho = 0.22) and allelic richness (AR = 3.8), reflecting a transitional genetic position between P1 and P2, with indications of both divergence and limited ongoing gene exchange.\u003c/p\u003e\n\u003cp\u003eThe spatial arrangement of the clusters, particularly the short Euclidean distances between P2 and P3, supports the presence of restricted but measurable gene flow, possibly mediated by occasional pollen or seed dispersal. In contrast, the pronounced genetic isolation of P1 suggests that natural barriers such as dune ridges or river valleys may limit connectivity with other populations.\u003c/p\u003e\n\u003cp\u003eOverall, the observed clustering pattern supports the existence of three diverging genetic lineages within the \u003cem\u003eC\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e\u003cem\u003e\u0026nbsp;leucocladum\u003c/em\u003e complex. These lineages may represent early-stage ecotypic differentiation driven by local adaptation and environmental heterogeneity.\u003c/p\u003e\n\u003cp\u003eFigure 3 convincingly demonstrates that \u003cem\u003eC\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e\u003cem\u003e\u0026nbsp;leucocladum\u003c/em\u003e populations are genetically structured along a geographic gradient: P1 is highly diverse yet isolated, P2 is compact and moderately isolated, and P3 occupies a transitional niche. This pattern underpins both evolutionary inference and targeted conservation planning for desert ecosystems. This MST clearly shows the genetic distances and relationships between populations. The fact that P1 is more isolated than other populations suggests that geographical isolation or environmental factors differentiated this population. The proximity between P2 and P3 supports that these populations have experienced more genetic gene flow or have been exposed to similar environmental conditions. These findings are consistent with the data in PCoA and Table 5 and provide a valuable visual tool for understanding the population structure of \u003cem\u003eC. leucocladum\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003eThe genetic structure of\u0026nbsp;\u003cem\u003eC\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e\u003cem\u003e\u0026nbsp;leucocladum\u003c/em\u003e populations (P1, P2, P3) is consistently supported by multiple analyses, including LD decay, PCoA, and phylogenetic clustering.\u003c/p\u003e\n\u003cp\u003eLD decay analysis (Figure 1) reveals varying recombination landscapes: P1 shows the fastest LD decline (r\u0026sup2; \u0026lt; 0.2 at ~10 kb), indicating high recombination and gene flow; P2 exhibits the slowest decay (~15 kb), suggesting greater genetic isolation and lower effective recombination; and P3 displays an intermediate pattern (~12 kb), consistent with a mixed structure. These patterns reflect differences in demographic history and connectivity across populations\u0026nbsp;(Fig.\u0026nbsp;3).\u003c/p\u003e\n\u003cp\u003ePCoA results (Figure 2) further highlight genetic differentiation. Individuals from P1, P2, and P3 occupy distinct positions in ordination space, with P1 and P2 maximally separated along Coord. 1. The central placement of P3 between P1 and P2 supports its role as a genetically intermediate population, potentially experiencing admixture or transitional gene flow.\u003c/p\u003e\n\u003cp\u003eThe Neighbor-Joining tree (Figure 3) reinforces these findings by clustering individuals into well-defined groups that correspond to their population origins. The clear separation between clades affirms limited gene flow and supports the existence of distinct genetic lineages within \u003cem\u003eC. leucocladum\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003eTogether, these analyses demonstrate strong population structuring, with P1 showing the highest genetic diversity and connectivity, P2 displaying signs of isolation, and P3 representing a transitional genetic unit. These insights are critical for understanding the evolutionary dynamics and guiding conservation strategies for \u003cem\u003eC. leucocladum\u003c/em\u003e across its natural range.\u003c/p\u003e\n\u003cp\u003eThis study presents a detailed population genetic assessment of \u003cem\u003eC. leucocladum\u003c/em\u003e, revealing pronounced spatial structuring and divergent evolutionary trajectories across three natural populations in the Kyzylkum Desert. The integration of SNP-based genomic data, linkage disequilibrium (LD) decay analysis, and phytochemical profiling enabled a comprehensive understanding of how environmental gradients in arid Central Asia shape genetic diversity and adaptation in this desert-adapted shrub.\u003c/p\u003e\n\u003cp\u003eLD decay patterns differed markedly among populations: P1 exhibited rapid LD decay (r\u0026sup2; \u0026asymp; 0.2 at ~10 kb), reflecting high recombination rates and gene flow, which coincided with elevated allelic richness and heterozygosity. In contrast, P2 and P3 showed slower LD decay and persistent high r\u0026sup2; values across longer distances, indicating reduced historical connectivity and moderate to high genetic isolation. These trends were further supported by F_IS values and genetic diversity indices. Principal Coordinates Analysis (PCoA) and a Neighbor-Joining (NJ) tree confirmed the genetic distinctiveness of P1 and the closer relationship between P2 and P3. The geographic distribution of the populations suggests that natural barriers such as dune systems, ephemeral riverbeds, and habitat fragmentation restrict pollen and seed dispersal, thereby reinforcing isolation. Similar patterns have been observed in other xerophytic species from the Tarim and Junggar basins, underscoring the role of spatial and ecological barriers in shaping desert plant genetics [42-46].\u003c/p\u003e\n\u003cp\u003eThe ecological interpretation supports the genomic patterns: P1, located near a river corridor, likely benefits from improved soil moisture and greater pollinator activity, facilitating gene flow and reducing inbreeding. Conversely, P2 and P3 are confined to more isolated and environmentally harsh dune habitats, limiting reproductive connectivity and promoting genetic drift. These conditions may result in localized genetic bottlenecks and increased inbreeding, as evidenced by elevated F_IS values [47].\u003c/p\u003e\n\u003cp\u003ePhytochemical analysis revealed a strong correlation between genetic and biochemical diversity. P1 had the highest concentrations of phenolics, flavonoids, and antioxidant activity\u0026mdash;traits associated with environmental stress resilience and adaptive metabolic plasticity. The co-occurrence of high genetic and biochemical richness in P1 suggests an enhanced adaptive potential and highlights the role of secondary metabolites in mediating environmental responses. This correspondence opens avenues for future functional genomic studies targeting stress-related metabolic pathways, such as phenylpropanoid biosynthesis [48-50].\u003c/p\u003e\n\u003cp\u003eThe observed patterns have important conservation implications. P1 emerges as a genetically rich and dynamic population that could serve as a valuable reservoir of adaptive alleles for ecological restoration and assisted gene flow. In contrast, P2 and P3 exhibit reduced diversity and higher inbreeding, making them more vulnerable to habitat degradation and climate-driven pressures. These populations may benefit from targeted in situ conservation, habitat restoration, and connectivity enhancement to mitigate further genetic erosion [51-52]. Such tailored approaches align with the broader goals of promoting resilience in desert ecosystems under increasing environmental stress.\u003c/p\u003e\n\u003cp\u003eMore broadly, this study contributes to the growing understanding that desert species maintain their adaptive capacity not through widespread gene flow, but through the persistence of localized genetic hotspots. The approach of integrating high-resolution genomic data with ecological and phytochemical parameters offers a robust framework for investigating adaptation and resilience in arid landscapes.\u003c/p\u003e\n\u003cp\u003eFuture research should expand geographic and environmental sampling, conduct genome\u0026ndash;environment association (GEA) analyses to identify adaptive loci, and implement long-term demographic monitoring. Understanding both neutral and adaptive genetic variation will be essential for informing climate-resilient conservation strategies in Central Asia\u0026rsquo;s dryland biodiversity hotspots.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThis study provides the first comprehensive assessment of genetic diversity and population structure in \u003cem\u003eC. leucocladum\u003c/em\u003e across three geographically distinct populations in the Kyzylkum Desert, using high-resolution SNP markers. The integration of genomic (LD decay, PCoA, MST) and phytochemical analyses revealed clear population-level genetic differentiation shaped by geographic distance, recombination dynamics, habitat fragmentation, and environmental stressors.\u003c/p\u003e\u003cp\u003ePopulation P1, located in the northern region, exhibited the highest genetic diversity, rapid LD decay, and elevated recombination rates, indicating dynamic gene flow, strong adaptive potential, and low genetic isolation. In contrast, P2 displayed reduced genetic diversity, increased LD, and signs of moderate to high isolation, likely due to ecological or physical barriers. P3 demonstrated intermediate genetic characteristics, reflecting both isolated and connected features.\u003c/p\u003e\u003cp\u003eImportantly, the inclusion of phytochemical data\u0026mdash;analyzed for the first time in this species\u0026mdash;showed strong positive correlations between genetic diversity and biochemical richness. Population P1 also had the highest levels of total phenolics, flavonoids, and antioxidant capacity, reinforcing its role as a potential reservoir of adaptive traits with relevance for ecological restoration and potential biotechnological applications.\u003c/p\u003e\u003cp\u003eThese findings emphasize the importance of population-specific conservation strategies. We recommend prioritizing P1 as a genetic resource for restoration efforts, while P2 and P3 should be closely monitored due to their limited gene flow and higher vulnerability. Future research should expand ecological and geographic sampling and apply genome\u0026ndash;environment association analyses to identify adaptive loci, thereby enhancing the genetic foundation for sustainable conservation and management of \u003cem\u003eC. leucocladum\u003c/em\u003e in arid ecosystems.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSNP\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eSingle Nucleotide Polymorphism\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eLD\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eLinkage Disequilibrium\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePCoA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ePrincipal Coordinates Analysis\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eFST\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\"\u003eFIS\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\"\u003eANOVA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAnalysis of Variance\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eHo\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\"\u003eHe\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\"\u003eAR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAllelic Richness\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eGATK\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eGenome Analysis Toolkit\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eBWA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eBurrows\u0026ndash;Wheeler Aligner\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eHPLC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eHigh\u0026ndash;Performance Liquid Chromatography\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eDNA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eDeoxyribonucleic Acid\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePCR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ePolymerase Chain Reaction\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCTAB\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eCetyltrimethylammonium Bromide\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eFASTQC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eFast Quality Control\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eVCFtools\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eVariant Call Format Tools\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003er\u0026sup2;\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eSquared Correlation Coefficient\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\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\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated or analysed during this study are included in this published article. Additional datasets are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by the project \u0026ldquo;AP26100259,\u0026rdquo; funded by the Scientific Committee of the Ministry of Science and Higher Education of the Republic of Kazakhstan.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization, M.M.; Methodology, I.M.; Software, M.M.; Validation, I.M.; Formal analysis, M.M.; Investigation, M.M.; Resources, N.M.; Data curation, M.M. and N.M.; Writing\u0026mdash;original draft, I.M.; Writing\u0026mdash;review and editing, I.M.; Visualization, I.M.; Supervision, M.M. All authors have read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe are grateful to the staff of the Herbarium of Al-Farabi Kazakh National University (ALKU) for their support in voucher specimen processing. The authors also thank the Central Laboratory of Genomics and Bioinformatics (Almaty) for technical assistance. Special thanks to the Institute of Botany and Phytointroduction (Almaty, Kazakhstan) for species identification and for formally accepting the voucher specimens of \u003cem\u003eC\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e\u003cem\u003e\u0026nbsp;leucocladum\u003c/em\u003e (Confirmation Letter No. 01-05/324, April 18, 2024). We also acknowledge the institutional support of Prof. Dr. G.T. Sitpaeva.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eFeng X, Liu Y, Wang J, Zhao X. Physiological and Ecological Adaptations of Desert Shrubs to Arid Environments: A Case Study of Calligonum leucocladum. J Arid Environ. 2021;186:104383. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jaridenv.2020.104383\u003c/span\u003e\u003cspan address=\"10.1016/j.jaridenv.2020.104383\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFolk RA, Mandel JR, Freudenstein JV. Ancestral Gene Flow and Population Structure in Desert Shrubs. 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Plant Ecol. 2011;212:755\u0026ndash;65. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11258-010-9854-2\u003c/span\u003e\u003cspan address=\"10.1007/s11258-010-9854-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Calligonum leucocladum, genetic diversity, population genetics, arid ecosystem adaptation and conservation biology","lastPublishedDoi":"10.21203/rs.3.rs-7028190/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7028190/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cem\u003eCalligonum leucocladum\u003c/em\u003e (Schrenk) Bunge is an ecologically significant desert shrub adapted to the arid and semi-arid environments of Central Asia. For the first time, this study provides an integrative assessment of its genetic diversity, population structure, and phytochemical variation using high-resolution SNP genotyping and chemometric profiling. We analyzed 150 individuals from three geographically distinct populations (P1, P2, and P3) in the Kyzylkum Desert and adjacent regions. Linkage disequilibrium (LD) decay analysis showed that P1 had the highest recombination rate (with r\u0026sup2; declining sharply at 10 kb), while P2 exhibited strong LD across longer distances, indicating moderate genetic isolation. P3 displayed intermediate genomic characteristics. Principal Coordinates Analysis (PCoA) explained 76.55% of total genetic variation and supported the clear differentiation of P1, whereas P2 and P3 showed closer genetic relationships. Minimum Spanning Tree (MST) analysis further confirmed these patterns. Phytochemical profiling revealed, for the first time, significant population-level differences in total phenolic and flavonoid content, as well as antioxidant capacity, which were consistent with the genetic structure. These findings suggest that geographic isolation, habitat fragmentation, and environmental stressors contribute to population divergence in \u003cem\u003eC. leucocladum\u003c/em\u003e. The high genetic and biochemical diversity found in P1 identifies it as a potential reservoir of adaptive traits. This work provides a valuable foundation for conservation planning and ecological restoration of desert flora.\u003c/p\u003e","manuscriptTitle":"Calligonum leucocladum (Schrenk) Bunge populations: genomic and population genetics analyses for assessing genetic diversity","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-18 15:44:37","doi":"10.21203/rs.3.rs-7028190/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"38c785d0-6e44-4251-bde9-429404db876e","owner":[],"postedDate":"July 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-01T10:17:09+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-18 15:44:37","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7028190","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7028190","identity":"rs-7028190","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}