Next Article in Journal
Two Congeneric Shrubs from the Atacama Desert Show Different Physiological Strategies That Improve Water Use Efficiency under a Simulated Heat Wave
Next Article in Special Issue
Identification of the Efficacy of Ex Situ Conservation of Ammopiptanthus nanus Based on Its ETS-SSR Markers
Previous Article in Journal
Phytoremediation of Pollutants: Applicability and Future Perspective
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Plants
Volume 12
Issue 13
10.3390/plants12132463
plants-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Assessment of Genetic Diversity and Genetic Structure of Saussurea medusa (Asteraceae), a “Sky Island” Plant in the Qinghai–Tibet Plateau, Using SRAP Markers

by
Jun Wang
,
Wei Dai
,
Jie Chen
,
Kunhao Ye
,
Qianglong Lai
and
Dan Zhao
*
Crop Characteristic Resources Creation and Utilization Key Laboratory of Sichuan Province, Mianyang Academy of Agricultural Sciences, Mianyang 621022, China
*
Author to whom correspondence should be addressed.
Plants 2023, 12(13), 2463; https://doi.org/10.3390/plants12132463
Submission received: 29 May 2023 / Revised: 19 June 2023 / Accepted: 26 June 2023 / Published: 27 June 2023
(This article belongs to the Special Issue Evaluation of Genetic Diversity and Germplasm in Plants Using Molecular and DNA Markers)
Browse Figures
Versions Notes

Abstract

:
Saussurea medusa Maxim. is a typical “sky island” species and one with the highest altitude distributions among flowering plants. The present study aimed at analyzing the genetic diversity and population structure of 300 S. medusa accessions collected from 20 populations in the Qilian Mountains in the northeastern Qinghai–Tibet Plateau (QTP), using sequence-related amplified polymorphism (SRAP) markers. A total of 14 SRAP primer combinations were employed to analyze genetic diversity and population structure across all accessions. Out of 511 amplified bands, 496 (97.06%) were polymorphic. The populations in the eastern Qilian Mountains had significantly higher genetic diversity than those in the central and western groups. Population structure analysis revealed greater genetic differentiation among populations with a Gst of 0.4926. UPGMA-based clustering classified the 300 S. medusa accessions into 3 major clusters, while the Bayesian STRUCTURE analysis categorized them into 2 groups. Correlation analyses showed that the genetic affinity of the populations was based on differences in geographical distance, moisture conditions, and photothermal conditions between the habitats. This study represents the first comprehensive genetic assessment of S. medusa and provides important genetic baseline data for the conservation of the species.

1. Introduction

S. medusa, a rare monocarpic perennial herb belonging to the genus Saussurea, family Asteraceae, grows on rocky beaches, rock cracks, and gravel hillsides at a height of 3000–5600 m above sea level [1]. In China, it is mainly distributed throughout the Qinghai–Tibet Plateau (QTP) and its surrounding alpine areas, including the Qinghai, Gansu, Sichuan, and Yunnan provinces and the Tibet and Xinjiang autonomous regions [2]. In traditional Tibetan and Uygur folk medicine, S. medusa is widely used to treat rheumatoid arthritis and gynecological diseases. Modern pharmacological experiments have shown that S. medusa has anti-inflammatory, analgesic, anti-tumor, anti-oxidative, hypolipidemic, and uterine contraction stimulation effects [3,4,5,6,7]. In the past 2 decades, the focus on phytochemical research resulted in the identification of >70 chemical components in the whole plant, the main components being flavonoids, coumarins, phenylpropanoids, and lignans [8,9,10,11,12,13,14].
In addition, its populations are relatively isolated by steep valleys and high mountain ridges, showing the typical distribution model of “sky island” [15,16,17]. Limited gene flow due to habitat isolation could lead to stronger genetic differentiation among populations compared with plants found in less isolated habitats and/or lower altitudes [18]. However, populations that do not exchange genes with others decrease their genetic diversity, thus increasing their risk of extinction. S. medusa has a small range of distribution and population size, which could have produced its loss of alleles and genetic variability due to genetic drift. As one of the three main forms of biodiversity, genetic diversity provides raw materials for the evolution of species, thereby providing potential for adaptation to constantly changing environments [19]. The mating system has a significant impact on the maintenance of population genetic diversity and ultimately determines how variations are transmitted across generations [20]. S. medusa is a predominantly outcrossing plant, mainly pollinated by insects (Bombus spp., Apidae), despite a certain degree of self-pollination in the absence of pollinating agents [21]. However, long-term self-fertilization results in reduced fitness in the progeny and decreased genetic diversity in the species [22]. In addition, the genetic variation in quantitative traits can be influenced by environmental changes, although there is no evidence to suggest the universality of the reduction or increase in variation caused by environmental change [23,24]. Plant populations exhibit local adaptability to their respective habitats, as each population evolves toward its own optimum through specific genetic selection [24]. Many studies have confirmed the correlation between genetic variation and environmental factors, especially climate factors such as light regime, precipitation, and temperature [25,26,27,28]. So far, the genetic background of S. medusa, particularly in terms of genetic diversity and genetic structure, has been rarely studied owing to its small population size and sampling difficulties.
Currently, molecular genetic markers are a reliable method for the population-level genetic analysis of alpine plants. Gaudeul et al. [29] used an amplified fragment length polymorphism (AFLP) marker to measure the genetic diversity of 14 Eryngium alpinum populations (327 individuals) in the European Alps. Pluess et al. [30] used a random amplified polymorphic DNA (RAPD) marker to study the genetic diversity within and among 20 populations of Geum reptans, an outcrossing clonal plant species in the Swiss Alps. Sequence-related amplified polymorphism (SRAP) is an effective and convenient molecular marker for assessing biological genetic diversity based on PCR methods. This technology presents several advantages over other marker technologies, including its high throughput rates, high commonality, easy band separation and sequencing, and its targeting of open reading frames (ORFs) [31,32,33,34]. SRAP markers have been widely used in the genetic diversity analysis of various genera of Asteraceae, including Cynara, which is a close relative of Saussurea [35,36,37]. However, there was no record of its application to Saussurea in our literature search.
In this study, we used 14 pairs of SRAP markers to evaluate the genetic diversity and population structures of 300 S. medusa accessions collected from the Qilian Mountains in the northeastern QTP. We also analyzed the objective factors that may contribute to population differentiation, considering geographical distance and climatic factors. To our knowledge, this is the first report on the genetic diversity and population structure of S. medusa.

2. Results

2.1. Polymorphism Analysis of SRAP Amplified Products

A total of 88 pairs of SRAP primer combinations were preliminarily screened. The amplification bands of 14 pairs were clear and evenly distributed, which were used to estimate the genetic diversity of S. medusa. The primer sequences are shown in Table 1. These SRAP primer combinations amplified 511 loci (496 polymorphic loci, 97.06%) with a size range of 45–1600 bp and quantity range of 26–44 per primer. The primer combination ME8/EM9 showed the lowest polymorphism (88.46%), while the highest polymorphism (100%) was detected by the primer combinations ME1/EM4, ME3/EM1, ME3/EM3, ME4/EM3, ME4/EM4, and ME4/EM5. The range of polymorphism information content (PIC) was from 0.48 to 0.50. Examples of the PCR amplification results with various primer combinations are shown in Figure S1.

2.2. Genetic Diversity and Genetic Structure Analyses

Table 2 shows the results obtained using the POPGENE software for the genetic diversity parameters. The Na, Ne, He, and I values at the species level were 1.9706, 1.4598, 0.2757, and 0.4237, respectively. At the population level, assuming a Hardy–Weinberg equilibrium, pop SJC (Na = 1.5558, Ne = 1.3279, He = 0.1913, I = 0.2865) had the highest genetic diversity, followed by pop SNK, whereas pop LMX (Na = 1.3151, Ne = 1.1823, He = 0.1060, I = 0.1589) had the lowest genetic diversity. The mean Ht, Hs, and Gst values were 0.2752, 0.2396, and 0.4926, respectively. The Nm value was 0.5150, less than 1, indicating limited gene exchange among the populations. By AMOVA analysis, the genetic difference coefficient (PhiPT value) was found to be 0.490 (p < 0.01) and about equal to the Gst value. The results demonstrated 49% variance among populations and 51% variance within populations (Table 3).

2.3. Population Cluster Analysis

The computed genetic distance matrix of 20 populations and 300 individuals is provided in Table S1. The genetic distance of 20 populations ranged from 116.788 (pop BYBC/RSDB) to 184.004 (pop MY/NQY). The resulting distance matrix was visualized using PCoA. The results of the population cluster (Figure 1A) were highly consistent with those of individuals (Figure 1B). The PCoA for 20 populations of S. medusa showed that these populations could not be divided into distinct subgroups. However, the population aggregation had certain regional characteristics, such as 11 populations from the western Qilian Mountains and 4 populations from the east. In addition, based on the spatial representation of genetic distance, both populations and individuals in pop GRD and pop SJC were highly coincident. In contrast, pop MY was further separated from other populations.
A dendrogram constructed using the corresponding genetic similarity coefficients obtained from the UPGMA analysis was used to determine the genetic associations in and between the populations of 300 accessions. The generated UPGMA dendrogram categorized the 300 accessions into 3 main phylogenetic clades (Clades I–III; Figure 2). Clade I, represented by pop MY, was separated as a distinct outgroup, forming an independent branch. Clade II comprised 4 populations (pop GSKY, JYL, NCE, and DBSS) from the alpine area surrounding the Menyuan Basin, while Clade III included 15 populations divided into 2 sister clusters located in the central (Clade III-1: pop SJC, NQY, WRG, DDSN, and GRD) and western (Clade III-2: pop YNG, HLHQ, LMX, SNK, BYBC, RSDB, HLSO, BSS, DTYK, and GJS) Qilian Mountains.
An admixture simulation model was used to assess the clustering of the 300 accessions. Log mean probability and change in log probability (ΔK) were determined using STRUCTURE HARVESTER. A 1–20 K cluster range was evaluated; the output showed a sharp peak with the highest ΔK at K = 2 (Figure S2). Subsequently, a Bayesian bar graph was used to represent the admixture model. Each bar color with ≥0.7 probability of membership fractions (Q value) represented a subgroup in the structural analysis results at K = 2, while accessions with a Q value of <0.7 have mixed ancestry (>1 cluster). Out of 300 accessions, 78 formed Cluster I (orange color, representing 26.0% of the total number of accessions), 190 accessions formed Cluster II (blue color, 63.3%), and the other 32 accessions appeared to have descended from multiple clusters. Cluster I mainly contained accessions sourced from the east of the Qilian Mountains (pop JYL, GSKY, MY, NCE, and DBSS). Cluster II members were mostly from the central and western regions of the Qilian Mountains (pop NQY, WRG, DDSN, HLSO, SNK, BYBC, YNG, RSDB, BSS, HLHQ, DTYK, GJS, and LMX). For K = 3, of the 300 accessions, 76 appeared in Cluster I (orange color, 25.3%), 34 in Cluster II (blue color, 11.3%), 136 in Cluster III (yellow color, 45.3%), and the remaining accessions were assigned to the mixed-lineage cluster (Figure 3).

2.4. Mantel Test

Table S2 shows the ggd data of 20 populations, which ranged from 15.176 km (pop SNK/YNG) to 636.504 km (pop MY/DTYK). Table S3 provides the climatic variable data for the 20 sampling points. The Mantel test results demonstrated a significant or highly significant correlation between gd and ggd (R2 = 0.1942, p < 0.01), gd and pre (R2 = 0.2018, p < 0.01), gd and vapr (R2 = 0.3297, p < 0.01), gd and sra (R2 = 0.3849, p < 0.01), and gd and tmin (R2 = 0.0536, 0.01 < p < 0.05). However, no correlation was found between gd and win (R2 = 0.0042, p > 0.05), gd and tav (R2 = 0.0012, p > 0.05), and gd and tmax (R2 = 0.0089, p > 0.05) (Figure 4).

3. Discussion

3.1. Genetic Diversity of S. medusa

The plant population in an isolated “sky island” distribution state usually has low genetic diversity because of the limited gene flow and colonization opportunities of new sites [38]. To date, the genetic diversity and population structure of S. medusa have not been reported. In the present study, 14 pairs of SRAP primers were used to analyze the genetic diversity and structure of 300 S. medusa accessions from 20 populations. A total of 511 loci were amplified, of which 496 (97.06%) were polymorphic; using this loci information, genetic diversity indices were calculated. Indices such as Na, Ne, He, and I exhibited marked variability among sample collections and revealed genetic diversity within and among the sampled populations. The results from a species perspective showed low genetic diversity of S. medusa with an He of 0.2757, lower than the average He of 0.65 for outcrossing plant species and the He of 0.55 for short-lived perennials observed in previous genetic studies [39]. From a population-level perspective, the genetic diversity of the S. medusa populations located in the western Qilian Mountains (i.e., mountain areas around Qaidam Basin) was significantly lower than that in the central and eastern regions. In particular, pop LMX from the west had the lowest genetic diversity, while pop SJC from the east–central regions had the highest genetic diversity. The low diversity in the western populations could be linked to low gene flow with central or eastern populations due to geographic or ecological isolation by the existence of valleys in the western Qilian Mountains. Simultaneously, there are no serious geographical barriers between central and eastern native populations, and the distribution of populations was relatively concentrated. Thus, the frequent gene flow among these populations reduced the genetic divergence among populations but enhanced the genetic diversity within them.

3.2. Genetic Structure of S. medusa

Different breeding methods determine the genetic variation within a species, such as in plants that are mainly selfing or inbred, where the genetic variation mainly occurs among populations [40]. A study on the breeding system found that selfing was observed in S. medusa, although outcrossing was dominant due to the mechanism of herkogamy and dichogamy [21]. Gst represents the proportion of interpopulation variation in the total genetic variation of a species and is the most commonly used indicator to measure population genetic differentiation and structure. An increasing Gst indicates a greater degree of differentiation between species populations, and a Gst of >0.25 suggests significant genetic differentiation [41]. According to the results of Hamrick [42] and Nybom [39], species with a Gst of approximately 0.5 exhibit certain selfing, which confirms the existence of selfing in S. medusa (Gst = 0.4926). Selfing in the absence of pollinators can promote the growth of individual populations to ensure species reproduction. This growth strategy is beneficial to gain a competitive advantage in this niche, as observed in other alpine plants such as Gentiana lawrencei var. Farreri, G. straminea [43], and Pedicularis dunniana [44]. However, the low quality of seeds produced by selfing in the absence of pollinators leads to a decrease in the population growth rate of S. medusa [21]. This may explain its small population size.

3.3. Population Differentiation and Influencing Factors

To clarify the genetic relationships of the 300 accessions from 20 populations, we used 3 different clustering methods. Using the two-dimensional PCoA distance matrix to visualize the relationship between samples made it difficult to subgroup and analyze them. In contrast, cluster analysis using UPGMA and STRUCTURE showed clear separation patterns among the populations. The results, taken as a whole, showed high genetic differentiation between the regions. Individuals were clustered in 3 major subgroups, i.e., representing the single population sampled from the easternmost Qilian Mountains (pop MY), the 4 Menyuan Basin populations (pop DBSS, NCE, JYL, and GSKY), and the remaining 11 populations from the central and western Qilian Mountains. In addition, two populations, such as pop SJC and JYL, without obvious geographical isolation in the same subgroup, also had greater genetic differentiation. Conversely, populations with geographical isolation had similar genetic backgrounds, for example pop MY showed mixed lineages from populations in the central and western Qilian Mountains when K = 3. This confirmed that geographical distance was not the only explanatory factor responsible for the genetic differentiation of S. medusa populations. Genetic differentiation may also result from adaptive allele frequency generated by natural selection [45].
We collected data on climate factors in different populations, which showed significant differences in the environment of different habitats. Based on the correlation analysis results, we conclude that differences in moisture conditions such as pre and vapr (Figure 4C,D) and photothermal conditions such as sra and tmin (Figure 4E,G) may exacerbate genetic differentiation among S. medusa populations. The Qilian Mountains have a typical plateau continental climate. The hydrothermal condition is unique in this area because of the complex terrain. The annual mean temperature is very low (most areas are below 0 °C), but the annual solar radiation is extremely high, exceeding 2800 h. Annual precipitation diminishes from east to west and increases with elevation [46]. The differences in the moisture–photothermal conditions mentioned above may be an important reason for the genetic differentiation of S. medusa populations between regions. A study on Qilian juniper (Sabina przewalskii) in the semiarid eastern Qaidam Basin found that precipitation, temperature, and growing-season direct solar radiation had potential impacts on its radial growth [47]. Adequate water content is important at various stages of plant development, including the breaking of seed dormancy, seed germination, morphogenesis, and overwintering. In addition, we observed that water was also a key factor in the seed dispersal of S. medusa. Although the achenes had a dense pappus adapted to wind propagation, the seedlings were mostly distributed on either side of the water erosion line, showing that water had a certain auxiliary effect on the seed dispersal of S. medusa. During the vegetative growth stage, the leaves of S. medusa grow as a rosette. At the reproductive stage, S. medusa completes the rapid elongation of the aboveground shoot and development of floral organs in a few months (from May to September every year), during which it needs to absorb sufficient solar radiation. However, high temperatures caused by strong solar radiation can reduce pollen viability [48]. S. medusa from the western end of the Qilian Mountains is also located in the northern part of the Qaidam Basin, which has an arid/semiarid climate, receiving more solar radiation during the reproductive stage than S. medusa growing in the central and eastern Qilian Mountains. Finally, in arctic or alpine environments, the low temperature slows the development of flowers and seeds [15,16,49]. The minimum temperature exerts selective pressure because it determines whether S. medusa can survive the coldest month. This may be possible through the development of spectacular pubescence or other thermal insulation systems that have been studied by Tsukaya [15] and Yang [16]. Based on the above, the interaction between factors related to the moisture–photothermal conditions and population differentiation of S. medusa deserves further research.

3.4. Conservation of S. medusa

In the summer, flowing stones are prone to loosening and collapsing due to rainwater erosion. During the field sampling, we observed many S. medusa seeds and seedlings being washed out by flowing water or piled up under flowing rocks. The limited seed dispersal method of this species as well as the decrease in the seed settling rate caused by selfing or inbreeding greatly increases the difficulty of population expansion. Although the Qilian Mountains have the densest distribution of S. medusa, frequent human activity and overgrazing have also contributed to its population reduction. We hope that these observations together with the present study’s findings can draw the attention of the International Union for Conservation of Nature (IUCN) to this species, and then the protected areas and a core germplasm resource bank of alpine plants can be established in the future. The present study also suggests that researchers should pay attention to the genetic variation differences caused by different habitat environments when evaluating the genetic diversity of a species.

3.5. Limitations of the Study

This study has certain limitations. First, it is difficult to obtain enough S. medusa samples from all regions, covering the platform and edge areas of the QTP. We could not avoid estimation errors in the overall genetic diversity assessment. Second, due to the differences in the number and distribution uniformity of amplification sites, using a single molecular marker is often insufficient. Nowadays, genetic diversity analysis methods based on sequencing technology are used in plants, making it possible to obtain more differential site information at the whole-genome level. We look forward to more novel technologies being applied to evaluate the genetic characteristics of S. medusa in the future. Third, morphologic and geological contrasts were not taken into account when studying the influencing factors of population differentiation. Despite some of the limitations in the study, the results provide useful outcomes and knowledge that can guide government officials to develop strategies for in situ and ex situ conservation of S. medusa.

4. Materials and Methods

4.1. Sample Information

A total of 300 leaf accessions of S. medusa seedlings were collected from 20 natural populations in the Qilian Mountains from east to west. Figure 5 showed the habitat of S. medusa and morphology of two growth stages of it. Individuals in each population were randomly sampled at an interval of ≥10 m. We recorded the geographical coordinates and altitude of each sample population using GPS, provided in Table 4. The collected samples were individually placed into non-woven bags with dry silica gel and stored at room temperature. A voucher specimen was deposited in the Qinghai–Tibetan Plateau Museum of Biology, Northwest Institute of Plateau Biology, CAS (voucher specimen #: QPMB 0334412).

4.2. Genomic DNA Extraction and SRAP Amplification

The genomic DNA of each individual was manually extracted using a modified CTAB method suitable for medicinal plant [50]. The quantity and quality of the genomic DNA were evaluated using Nanodrop 2000 and 1% agarose gel electrophoresis, respectively. Among 88 pairs, 14 pairs of primer combinations with high polymorphism and good band definition were selected for SRAP amplification. The 20 μL PCR reaction system contained 1.25 U Taq DNA polymerase, 0.3 mM dNTPs, 2 mM 10× buffer (Takara, Beijing, China), 0.4 μM upstream and downstream primers (Sangon, Shanghai, China), 40 ng DNA template, and the rest was filled with ultrapure water. The procedure of SRAP-PCR amplification was based on that used on other Asteraceae species and was appropriately modified [36, 51, 52]: Initial denaturation at 94 °C for 5 min followed by 5 cycles of denaturation at 94 °C for 1 min, annealing at 35 °C for 1 min, and extension at 72 °C for 1 min. Then, for the next 35 cycles, an initial denaturation at 94 °C for 1 min, annealing at 48 °C for 1 min, and extension at 72 °C for 1 min followed by a final extension step at 72 °C for 10 min. The amplified products were stored at 4 °C.
After amplification, the PCR products were separated by 8% polyacrylamide gel electrophoresis (PAGE) followed by dying in 1× Tris-Borate-EDTA buffer (TBE) with ethidium bromide for several seconds. Then, the gel was imaged using the ChemiDoc MP Imaging System (Bio-Rad, Hercules, CA, USA).

4.3. Statistical Analysis

Amplified fragments were scored according to the presence (1) or absence (0) of homologous bands; only reproducible bands were considered. Then, band data were transformed into a binary matrix for each primer combination or population [53].
POPGENE 1.32 [54] was used to compare the amplification results of different primer combinations including the size of amplified fragments, the number of loci, and the percentage of polymorphic sites. The PIC was calculated as PIC = 1 − p2 − q2, (p was the proportion of bands; q was the proportion of no bands) [55]. Genetic indexes such as percentage of polymorphism bands (PPB), number of alleles (Na), effective number of alleles (Ne), Nei’s gene diversity (He), and Shannon’s information index (I) were calculated to estimate the genetic diversity within different populations of S. medusa. Total gene diversity (Ht), diversity within a population (Hs), and coefficient of genetic differentiation (Gst) were calculated as well. The gene flow (Nm) was calculated according to the following formula: Nm = 0.5(1 − Gst)/Gst.
The genetic distance (gd) matrix of 20 populations was calculated using the distance-based module in GenAlex 6 [56]. Then, molecular analysis of variance (AMOVA) was used to evaluate the contribution of genetic variation among and within populations. Principal coordinate analysis (PCoA) was subsequently performed based on the above results. An unweighted pair group method with arithmetic mean (UPGMA) trees based on populations and individuals was used for SHAN cluster analysis using NTSYS 2.1 [57]. STRUCTURE 2.3.4 [58] was used to analyze the original binary data to calculate the population structure. The length of the Burn-in Period and the amount of MCMC Reps after Burn-in were both set to 10,000. The initial K value was set from 1 to 20, and the number of iterations was set to 10. Then, the running results were uploaded to the online program of STRUCTURE HARVESTER (http://taylor0.biology.ucla.edu/structureHarvester/, accessed on 5 September 2021) to identify the optimal cluster number (ΔK).
In GenAlex, the population coordinate information was input to calculate the tri-matrix of geographic distance (ggd). ArcMap 10.6 (ESRI, Redlands, CA, USA) was used to obtain the average annual climate data of 20 sampling points from the WorldClim online database (https://www.worldclim.org/data/worldclim21.html, accessed on 9 February 2021) with a 2.5 arc-minute resolution. According to the needs of the calculation, the above data were sorted into seven climate factors: annual average wind speed (win), total annual precipitation (pre), annual average water vapor pressure (vapr), daily average solar radiation during the growth phase from May to September (sra), annual average temperature (tav), annual minimum temperature (tmin), and annual maximum temperature (tmax). The Mantel test revealed the correlation between genetic differentiation and geographical distance, as well as genetic differentiation and the seven climatic factors.

5. Conclusions

A low population genetic diversity is reported for the rare monocarpic perennial S. medusa in the Qilian Mountains, which can best be explained by its isolated habitats and limited gene flow. In addition, we observed genetic differentiation among populations and a clear west–east differentiation between population groups. Geographic distance and moisture–photothermal conditions may also play key roles in the population differentiation of S. medusa.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/plants12132463/s1, Figure S1: examples of PCR amplification results with various primer combinations; Figure S2: estimation of subgroups in 300 S. medusa accessions as revealed by STRUCTURE HARVESTER; Table S1: the tri-matrix of genetic distance (gd) of 20 S. medusa populations; Table S2: the tri-matrix of geographic distance (ggd/km) of 20 S. medusa populations; Table S3: the climatic variable data for the 20 sampling points.

Author Contributions

J.W. with the help of D.Z. and J.C. designed the experiment; J.W. performed the experiment; D.Z. and W.D. helped in executing the experiment; J.W., K.Y. and Q.L. performed the statistical analysis; J.W. wrote the manuscript; and D.Z., W.D. and J.C. critically reviewed and edited the manuscript. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the China Agriculture Research System of MOF and MARA (Grant No. CARS-21), Natural Science Foundation of Sichuan Province, China (Grant No. 2022NSFSC1728), and Innovation Fund Project of Mianyang Academy of Agricultural Sciences (Grant No. Cxjj89).

Data Availability Statement

Data is contained within the article and Supplementary Materials.

Acknowledgments

We would like to thank No. 51 team of Sichuan Science and Technology in the Countryside for their suggestions on sample collection. We also thank Li Wang for specimen collection, Xuefeng Lu for specimen identification, Yanping Hu for expert advice on the SRAP technique, and Yi Li for meaningful discussion about the genetic diversity of the species. Furthermore, we are grateful to the Northwest Institute of Plateau Biology, CAS for kindly providing the opportunity of conducting the experiments.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Liu, S.W. Compositae: Saussurea DC. In Flora Qinghaiica; Ho, T.-N., Liu, S.-W., Eds.; Qinghai People’s Publishing House: Xining, China, 1996; Volume 3, pp. 443–482. [Google Scholar]
  2. Shi, Z.; Raab-Straube, E. Asteraceae, Saussurea group. In Flora of China; Wu, Z.Y., Raven, P.H., Eds.; Science Press: Beijing, China; Missouri Botanical Garden Press: St. Louis, MO, USA, 2011; Volumes 20–21, pp. 42–149. [Google Scholar]
  3. Yi, T.; Zhao, Z.Z.; Yu, Z.L.; Chen, H.B. Comparison of the anti-inflammatory and anti-nociceptive effects of three medicinal plants known as “Snow Lotus” herb in traditional Uighur and Tibetan medicines. J. Ethnopharmacol. 2010, 128, 405–411. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  4. Takasaki, M.; Konoshima, T.; Komatsu, K.; Tokuda, H.; Nishino, H. Anti-tumor-promoting activity of lignans from the aerial part of Saussurea medusa. Cancer Lett. 2000, 158, 53–59. [Google Scholar] [CrossRef] [PubMed]
  5. Qiu, J.; Xue, X.F.; Chen, F.D.; Li, C.H.; Bolat, N.; Wang, X.J.; Baima, Y.Z.; Zhao, Q.; Zhao, D.X.; Ma, F.S. Quality evaluation of snow lotus (Saussurea): Quantitative chemical analysis and antioxidant activity assessment. Plant Cell Rep. 2010, 29, 1325–1337. [Google Scholar] [CrossRef] [PubMed]
  6. Bo, Y.; Yundai, C.; Min, L. Effects of cell cultures of Saussurea medusa in regulating blood lipid of hyperlipidemic rats. Heart 2013, 99, 66. [Google Scholar] [CrossRef]
  7. Fan, J.Y.; Chen, H.B.; Zhu, L.; Chen, H.L.; Zhao, Z.Z.; Yi, T. Saussurea medusa, source of the medicinal herb snow lotus: A review of its botany, phytochemistry, pharmacology and toxicology. Phytochem. Rev. 2015, 14, 353–366. [Google Scholar] [CrossRef]
  8. Duan, H.Q.; Takaishi, Y.; Momota, H.; Ohmoto, Y.; Taki, T. Immunosuppressive constituents from Saussurea medusa. Phytochemistry 2002, 59, 85–90. [Google Scholar] [CrossRef]
  9. Fan, C.Q.; Yue, J.M. Biologically active phenols from Saussurea medusa. Bioorganic Med. Chem. 2003, 11, 703–708. [Google Scholar] [CrossRef]
  10. Xie, H.H.; Wang, T.; Matsuda, H.; Morikawa, T.; Yoshikawa, M.; Tani, T. Bioactive constituents from Chinese natural medicines. XV. Inhibitory effect on aldose reductase and structures of saussureosides A and B from Saussurea medusa. Chem. Pharm. Bull. 2005, 53, 1416–1422. [Google Scholar] [CrossRef] [Green Version]
  11. Dawa, Z.; Bai, Y.; Zhou, Y.; Gesang, S.; Ding, L.S. Chemical constituents of the whole plants of Saussurea medusa. J. Nat. Med. 2009, 63, 327–330. [Google Scholar] [CrossRef] [Green Version]
  12. Wang, Y.F.; Ni, Z.Y.; Dong, M.; Cong, B.; Shi, Q.W.; Gu, Y.C.; Kiyota, H. Secondary metabolites of plants from the genus Saussurea: Chemistry and biological activity. Chem. Biodivers. 2010, 7, 2623–2659. [Google Scholar] [CrossRef]
  13. Cao, J.Y.; Wang, Z.Y.; Dong, Q.; Wang, X.J.; Yu, R.T.; Tao, Y.D. Saussurenoids A–G, seven new sesquiterpenoids from Saussurea medusa Maxim. Tetrahedron 2022, 120, 132850. [Google Scholar] [CrossRef]
  14. Wu, N.; Liu, Y.F.; Liang, X.M.; Mei, L.J.; Tao, Y.D.; Yu, R.T. Phytochemical and chemotaxonomic study on Saussurea medusa Maxim. (Compositae). Biochem. Syst. Ecol. 2020, 93, 104171. [Google Scholar] [CrossRef]
  15. Tsukaya, H.; Fujikawa, K.; Wu, S.G. Thermal insulation and accumulation of heat in the downy inflorescences of Saussurea medusa (Asteraceae) at high elevation in Yunnan, China. J. Plant Res. 2002, 115, 263–268. [Google Scholar] [CrossRef]
  16. Yang, Y.; Körner, C.; Sun, H. The ecological significance of pubescence in Saussurea medusa, a high-elevation Himalayan “woolly plant”. Arct. Antarct. Alp. Res. 2008, 40, 250–255. [Google Scholar] [CrossRef] [Green Version]
  17. Wang, Y.J.; Susanna, A.; Von Raab-Straube, E.; Milne, R.; Liu, J.Q. Island-like radiation of Saussurea (Asteraceae: Cardueae) triggered by uplifts of the Qinghai–Tibetan Plateau. Biol. J. Linn. Soc. 2009, 97, 893–903. [Google Scholar] [CrossRef] [Green Version]
  18. Ægisdóttir, H.; Kuss, P.; Stöcklin, J. Isolated populations of a rare alpine plant show high genetic diversity and considerable population differentiation. Ann. Bot. 2009, 104, 1313–1322. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  19. Aguilar, R.; Quesada, M.; Ashworth, L.; Herrerias-Diego, Y.; Lobo, J. Genetic consequences of habitat fragmentation in plant populations: Susceptible signals in plant traits and methodological approaches. Mol. Ecol. 2008, 17, 5177–5188. [Google Scholar] [CrossRef]
  20. Loveless, M.D.; Hamrick, J.L. Ecological determinants of genetic structure in plant populations. Annu. Rev. Ecol. Syst. 1984, 15, 65–95. [Google Scholar] [CrossRef]
  21. Law, W.; Salick, J.; Knight, T.M. The effects of pollen limitation on population dynamics of snow lotus (Saussurea medusa and S. laniceps, Asteraceae): Threatened Tibetan medicinal plants of the eastern Himalayas. Plant Ecol. 2010, 210, 343–357. [Google Scholar] [CrossRef]
  22. Takebayashi, N.; Morrell, P.L. Is self-fertilization an evolutionary dead end? Revisiting an old hypothesis with genetic theories and a macroevolutionary approach. Am. J. Bot. 2001, 88, 1143–1150. [Google Scholar] [CrossRef]
  23. Ary, A.H.; Juha, M. Heritable variation and evolution under favourable and unfavourable conditions. Trends Ecol. Evol. 1999, 14, 96–101. [Google Scholar]
  24. Bennington, C.; McGraw, J.B. Environmental-dependence of quantitative genetic parameters in Impatiens pallida. Evolution 1996, 50, 1083–1097. [Google Scholar] [PubMed]
  25. Savolainen, O.; Bokma, F.; Garcıía-Gil, R.; Komulainen, P.; Repo, T. Genetic variation in cessation of growth and frost hardiness and consequences for adaptation of Pinus sylvestris to climatic changes. For. Ecol. Manag. 2004, 197, 79–89. [Google Scholar] [CrossRef]
  26. Huang, W.D.; Zhao, X.Y.; Zhao, X.; Li, Y.Q.; Lian, J.; Yun, J.Y. Relationship between the genetic diversity of Artemisia halodendron and climatic factors. Acta Oecologica 2014, 55, 97–103. [Google Scholar] [CrossRef]
  27. Manel, S.; Gugerli, F.; Thuiller, W.; Alvarez, N.; Legendre, P.; Holderegger, R.; Gielly, L.; Taberlet, P.; Consortium, I. Broad-scale adaptive genetic variation in alpine plants is driven by temperature and precipitation. Mol. Ecol. 2012, 21, 3729–3738. [Google Scholar] [CrossRef] [Green Version]
  28. Wellington, C.N.; Vaillancourt, R.E.; Potts, B.M.; Worledge, D.; O’Grady, A.P. Genetic Variation in Flowering Traits of Tasmanian Leptospermum scoparium and Association with Provenance Home Site Climatic Factors. Plants 2022, 11, 1029. [Google Scholar] [CrossRef]
  29. Gaudeul, M.; Taberlet, P.; Till-Bottraud, I. Genetic diversity in an endangered alpine plant, Eryngium alpinum L.(Apiaceae), inferred from amplified fragment length polymorphism markers. Mol. Ecol. 2000, 9, 1625–1637. [Google Scholar] [CrossRef] [Green Version]
  30. Pluess, A.R.; Stöcklin, J. Population genetic diversity of the clonal plant Geum reptans (Rosaceae) in the Swiss Alps. Am. J. Bot. 2004, 91, 2013–2021. [Google Scholar] [CrossRef]
  31. Li, G.; Quiros, C.F. Sequence-related amplified polymorphism (SRAP), a new marker system based on a simple PCR reaction: Its application to mapping and gene tagging in Brassica. Theor. Appl. Genet. 2001, 103, 455–461. [Google Scholar] [CrossRef]
  32. Zheng, Y.Q.; Xu, S.J.; Liu, J.; Zhao, Y.; Liu, J.X. Genetic diversity and population structure of Chinese natural bermudagrass [Cynodon dactylon (L.) Pers.] germplasm based on SRAP markers. PLoS ONE 2017, 12, e0177508. [Google Scholar] [CrossRef] [Green Version]
  33. Jahnvi, B.; Sushil, K.; Swati, P.; Ramesh, S. Sequence-related amplified polymorphism (SRAP) markers based genetic diversity analysis of cumin genotypes. Ann. Agrar. Sci. 2017, 15, 434–438. [Google Scholar]
  34. Zagorcheva, T.; Stanev, S.; Rusanov, K.; Atanassov, I. SRAP markers for genetic diversity assessment of lavender (Lavandula angustifolia mill.) varieties and breeding lines. Biotechnol. Biotechnol. Equip. 2020, 34, 303–308. [Google Scholar] [CrossRef] [Green Version]
  35. Espósito, M.A.; Cravero, V.P.; Martin, E.; Cointry, E.L. Use of morphological, biochemical and SRAP molecular markers to differentiate varieties of Cynara cardunculus L. (Asteraceae). Rev. De La Fac. De Cienc. Agrar. Univ. Nac. De Cuyo 2011, 43, 35–45. [Google Scholar]
  36. Niloofar, M.; Mehdi, R.; Majid, T.; Mojtaba, K. Assessment of genetic diversity among and within Carthamus species using sequence-related amplified polymorphism (SRAP) markers. Plant Syst. Evol. 2013, 299, 1285–1294. [Google Scholar]
  37. Rahali, N.; Yangui, I.; Boussaid, M.; Messaoud, C. Assessment of genetic diversity and population structure of the endemic Hertia cheirifolia (L.) Kuntze based on ISSR and SRAP molecular markers. Biologia 2022, 77, 3429–3439. [Google Scholar] [CrossRef]
  38. Zhang, D.; Hao, G.Q.; Guo, X.Y.; Hu, Q.J.; Liu, J.Q. Genomic insight into “sky island” species diversification in a mountainous biodiversity hotspot. J. Syst. Evol. 2019, 57, 633–645. [Google Scholar]
  39. Nybom, H. Comparison of different nuclear DNA markers for estimating intraspecific genetic diversity in plants. Mol. Ecol. 2004, 13, 1143–1155. [Google Scholar] [CrossRef]
  40. Charlesworth, D.; Charlesworth, B. Quantitative genetics in plants: The effect of the breeding system on genetic variability. Evolution 1995, 49, 911–920. [Google Scholar] [CrossRef]
  41. Wright, S. Evolution and the Genetics of Populations, Volume 4: Variability within and among Natural Populations; University of Chicago Press: Chicago, IL, USA, 1984; Volume 4. [Google Scholar]
  42. Hamrick, J.L. Gene Flow Distribution of Genetic Variation in Plant Populations; Urbanska, K.M., Ed.; Academic Press: New York, NY, USA, 1987. [Google Scholar]
  43. Fu, P.C.; Sun, S.S.; Khan, G.; Dong, X.X.; Tan, J.Z.; Favre, A.; Zhang, F.Q.; Chen, S.L. Population subdivision and hybridization in a species complex of Gentiana in the Qinghai-Tibetan Plateau. Ann. Bot. 2020, 125, 677–690. [Google Scholar]
  44. Sun, S.G.; Guo, Y.H.; Gituru, R.W.; Huang, S.Q. Corolla wilting facilitates delayed autonomous self-pollination in Pedicularis dunniana (Orobanchaceae). Plant Syst. Evol. 2005, 251, 229–237. [Google Scholar]
  45. Jump, A.S.; Hunt, J.M.; Martínez-Izquierdo, J.; Peñuelas, J. Natural selection and climate change: Temperature-linked spatial and temporal trends in gene frequency in Fagus sylvatica. Mol. Ecol. 2006, 15, 3469–3480. [Google Scholar] [CrossRef]
  46. Deng, S.F.; Yang, T.B.; Zeng, B.; Zhu, X.F.; Xu, H.J. Vegetation cover variation in the Qilian Mountains and its response to climate change in 2000–2011. J. Mt. Sci. 2013, 10, 1050–1062. [Google Scholar] [CrossRef]
  47. Yin, Z.Y.; Li, M.M.; Zhang, Y.; Shao, X.M. Growth–climate relationships along an elevation gradient on a southeast-facing mountain slope in the semi-arid eastern Qaidam Basin, northeastern Tibetan Plateau. Trees 2016, 30, 1095–1109. [Google Scholar] [CrossRef]
  48. Patiño, S.; Grace, J. The cooling of convolvulaceous flowers in a tropical environment. Plant Cell Environ. 2002, 25, 41–51. [Google Scholar] [CrossRef] [Green Version]
  49. Yang, Y.; Sun, H. The bracts of Saussurea velutina (Asteraceae) protect inflorescences from fluctuating weather at high elevations of the Hengduan Mountains, Southwestern China. Arct. Antarct. Alp. Res. 2009, 41, 515–521. [Google Scholar]
  50. Tiwari, K.L.; Jadhav, S.K.; Gupta, S. Modified CTAB technique for isolation of DNA from some medicinal plants. Res. J. Med. Plant 2012, 6, 65–73. [Google Scholar]
  51. Wang, Y.L.; Yan, G.Q. Genetic diversity and population structure of Opisthopappus longilobus and Opisthopappus taihangensis (Asteraceae) in China determined using sequence related amplified polymorphism markers. Biochem. Syst. Ecol. 2013, 49, 115–124. [Google Scholar] [CrossRef]
  52. Al-Ajmi, A.H.; AL-Wahibi, M.S.; Mustafa, A.E.; Soliman, D.A.; Dewir, Y.H. Morphological and Molecular Assessment of Genetic Diversity of Seven Species of the Genus Artemisia L. (Asteraceae). Arab. J. Sci. Eng. 2021, 46, 5361–5371. [Google Scholar] [CrossRef]
  53. Robarts, D.W.; Wolfe, A.D. Sequence-related amplified polymorphism (SRAP) markers: A potential resource for studies in plant molecular biology1. Appl. Plant Sci. 2014, 2, 1400017. [Google Scholar] [CrossRef]
  54. Yeh, F.C.; Yang, R.C.; Boyle, T. POPGENE Version 1.32: Microsoft Windows–Based Freeware for Population Genetic Analysis, Quick User Guide; Center for International Forestry Research, University of Alberta: Edmonton, AB, Canada, 1999; pp. 1–29. [Google Scholar]
  55. Sharma, H.K.; Sarkar, M.; Choudhary, S.B.; Kumar, A.A.; Maruthi, R.T.; Mitraa, J.; Karmakar, P.G. Diversity analysis based on agro-morphological traits and microsatellite based markers in global germplasm collections of roselle (Hibiscus sabdariffa L.). Ind. Crops Prod. 2016, 89, 303–315. [Google Scholar] [CrossRef]
  56. Peakall, R.O.D.; Smouse, P.E. GENALEX 6: Genetic analysis in Excel. Population genetic software for teaching and research. Mol. Ecol. Notes 2006, 6, 288–295. [Google Scholar] [CrossRef]
  57. Rohlf, F.J. NTSYS-pc, Numerical Taxonomy and Multivariate Analysis System, Version 2.1; Applied Biostatistics Inc.: New York, NY, USA, 1998. [Google Scholar]
  58. Hubisz, M.J.; Falush, D.; Stephens, M.; Pritchard, J.K. Inferring weak population structure with the assistance of sample group information. Mol. Ecol. Resour. 2009, 9, 1322–1332. [Google Scholar] [CrossRef] [PubMed] [Green Version]
Plants 12 02463 g001 550
Figure 1. PCoA based on genetic distance was used to visualize the relationship between 20 populations (A) and between 300 individuals (B).
Figure 1. PCoA based on genetic distance was used to visualize the relationship between 20 populations (A) and between 300 individuals (B).
Plants 12 02463 g001
Plants 12 02463 g002 550
Figure 2. UPGMA dendrogram based on genetic distance values shows the relationship among 300 accessions of S. medusa. The orange block denotes Clade I; the blue block denotes Clade II; and the green block denotes Clade III. The area enclosed within the dashed lines in the topographic map represents the sampling sites in the Qilian Mountains.
Figure 2. UPGMA dendrogram based on genetic distance values shows the relationship among 300 accessions of S. medusa. The orange block denotes Clade I; the blue block denotes Clade II; and the green block denotes Clade III. The area enclosed within the dashed lines in the topographic map represents the sampling sites in the Qilian Mountains.
Plants 12 02463 g002
Plants 12 02463 g003 550
Figure 3. Population structure analysis of 300 S. medusa accessions using STRUCTURE packages (K = 2 or 3). The Q value of each color bar describes the probability of membership fractions of each cluster. When the K was 2, there were 2 single-lineage dominant clusters (orange or blue) and the remaining mixed-lineage cluster. When the K was 3, there were 3 single-lineage dominant clusters (orange, blue, or yellow) and the remaining mixed-lineage cluster.
Figure 3. Population structure analysis of 300 S. medusa accessions using STRUCTURE packages (K = 2 or 3). The Q value of each color bar describes the probability of membership fractions of each cluster. When the K was 2, there were 2 single-lineage dominant clusters (orange or blue) and the remaining mixed-lineage cluster. When the K was 3, there were 3 single-lineage dominant clusters (orange, blue, or yellow) and the remaining mixed-lineage cluster.
Plants 12 02463 g003
Plants 12 02463 g004 550
Figure 4. Mantel analysis of geographical distance, climate factor difference, and genetic distance among populations. (A) gd vs. ggd; (B) gd vs. win; (C) gd vs. pre; (D) gd vs. vapr; (E) gd vs. sra; (F) gd vs. tav; (G) gd vs. tmin; (H) gd vs. tmax.
Figure 4. Mantel analysis of geographical distance, climate factor difference, and genetic distance among populations. (A) gd vs. ggd; (B) gd vs. win; (C) gd vs. pre; (D) gd vs. vapr; (E) gd vs. sra; (F) gd vs. tav; (G) gd vs. tmin; (H) gd vs. tmax.
Plants 12 02463 g004
Plants 12 02463 g005 550
Figure 5. The habitat and plant morphology of S. medusa. (A) seedling stage; (B) reproductive stage.
Figure 5. The habitat and plant morphology of S. medusa. (A) seedling stage; (B) reproductive stage.
Plants 12 02463 g005
Table
Table 1. Results of SRAP amplification with 14 primer combinations.
Table 1. Results of SRAP amplification with 14 primer combinations.
Primer IDSequence (5’-3’)Size of Loci (bp)Number of LociNumber of Polymorphic LociPercentage of Polymorphic Loci (%)Polymorphic Information
Content
ME1/EM3TGAGTCCAAACCGGATA/45–1500272592.590.48
GACTGCGTACGAATTGAC
ME1/EM4TGAGTCCAAACCGGATA/60–100035351000.49
GACTGCGTACGAATTTGA
ME2/EM5TGAGTCCAAACCGGAGC/90–15004038950.50
GACTGCGTACGAATTAAC
ME2/EM9TGAGTCCAAACCGGAGC/45–1300353394.290.49
GACTGCGTACGAATTCGA
ME3/EM1TGAGTCCAAACCGGAAT/70–160034341000.50
GACTGCGTACGAATTAAT
ME3/EM3TGAGTCCAAACCGGAAT/110–150044441000.50
GACTGCGTACGAATTGAC
ME4/EM3TGAGTCCAAACCGGACC/65–140044441000.48
GACTGCGTACGAATTGAC
ME4/EM4TGAGTCCAAACCGGACC/60–140034341000.50
GACTGCGTACGAATTTGA
ME4/EM5TGAGTCCAAACCGGACC/65–130044441000.49
GACTGCGTACGAATTAAC
ME5/EM2TGAGTCCAAACCGGAAG/80–1500323196.880.50
GACTGCGTACGAATTTGC
ME6/EM9TGAGTCCAAACCGGTAA/80–1600383694.740.50
GACTGCGTACGAATTCGA
ME7/EM11TGAGTCCAAACCGGTCC/80–1500464597.830.50
GACTGCGTACGAATTCCA
ME8/EM7TGAGTCCAAACCGGTGC/90–1100323093.750.49
GACTGCGTACGAATTCAA
ME8/EM9TGAGTCCAAACCGGTGC/70–900262388.460.50
GACTGCGTACGAATTCGA
Table
Table 2. Genetic diversity within 20 natural populations of S. medusa
Table 2. Genetic diversity within 20 natural populations of S. medusa
Pop IDNumber of LociPPB (%)NaNeHeI
JYL22544.031.44031.25550.14780.2214
GSKY23045.011.45011.26240.15340.2302
MY26852.451.52451.26850.16230.2485
NCE26752.251.52251.28820.17120.2589
DBSS23746.381.46381.26020.15360.2317
SJC28455.581.55581.32790.19130.2865
NQY18536.201.36201.23400.13220.1949
WRG17133.461.33461.19210.11290.1698
GRD24547.951.47951.27890.16300.2444
DDSN20139.331.39331.20300.12130.1846
HLSO17834.831.34831.20710.12020.1796
SNK27453.621.53621.31550.18340.2745
BYBC21542.071.42071.22770.13390.2028
YNG16632.491.32491.19780.11450.1708
RSDB20540.121.40121.24760.14230.2116
BSS17634.441.34441.20380.11740.1754
HLHQ19237.571.37571.20010.11960.1819
DTYK16432.091.32091.19400.11300.1688
GJS19938.941.38941.22900.13300.1989
LMX16131.511.31511.18230.10600.1589
Species level51197.061.97061.45980.27570.4237
Note: PPB: percentage of polymorphism bands; Na: the number of alleles; Ne: effective number of alleles; He: Nei’s gene diversity; I: Shannon’s information index.
Table
Table 3. AMOVA analysis of 20 pops of S. medusa with SRAP data.
Table 3. AMOVA analysis of 20 pops of S. medusa with SRAP data.
SourcedfSSMSEstimated VariancePercentage (%)p
Among Pops1910,753.825565.99135.31149<0.01
Within Pops28010,282.24936.72236.72251<0.01
Total29921,036.073-72.033100-
Table
Table 4. The geographic information of 20 S. medusa populations in the Qilian Mountains.
Table 4. The geographic information of 20 S. medusa populations in the Qilian Mountains.
Pop IDLatitudeLongitudeAltitude (m)Number of IndividualsAdministrative Area
MY37.0735102.6701380515Tianzhu, Gansu Province, China
NCE37.5328101.8666403916Menyuan, Qinghai Province, China
GSKY37.6805101.4408390716Menyuan, Qinghai Province, China
DBSS37.3376101.4005389614Datong, Qinghai Province, China
JYL37.9108101.1124394216Qilian, Qinghai Province, China
SJC38.0418100.8139371016Qilian, Qinghai Province, China
WRG37.8639100.5091395816Qilian, Qinghai Province, China
DDSN38.0131100.2412413816Qilian, Qinghai Province, China
HLSO39.041598.2786433216Qilian, Qinghai Province, China
RSDB38.794598.7413415515Qilian, Qinghai Province, China
BYBC39.008998.8191444416Qilian, Qinghai Province, China
SNK38.607799.4821410616Qilian, Qinghai Province, China
YNG38.474599.4443358210Qilian, Qinghai Province, China
GRD37.6989100.4176400510Gangcha, Qinghai Province, China
NQY37.3821100.5905380015Gangcha, Qinghai Province, China
LMX37.893298.8517425416Tianjun, Qinghai Province, China
GJS37.176498.8781409516Tianjun, Qinghai Province, China
BSS37.479097.4406428815Delingha, Qinghai Province, China
HLHQ37.941697.5103461916Delingha, Qinghai Province, China
DTYK37.775395.5148416114Chaidan, Qinghai Province, China
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Wang, J.; Dai, W.; Chen, J.; Ye, K.; Lai, Q.; Zhao, D. Assessment of Genetic Diversity and Genetic Structure of Saussurea medusa (Asteraceae), a “Sky Island” Plant in the Qinghai–Tibet Plateau, Using SRAP Markers. Plants 2023, 12, 2463. https://doi.org/10.3390/plants12132463

AMA Style

Wang J, Dai W, Chen J, Ye K, Lai Q, Zhao D. Assessment of Genetic Diversity and Genetic Structure of Saussurea medusa (Asteraceae), a “Sky Island” Plant in the Qinghai–Tibet Plateau, Using SRAP Markers. Plants. 2023; 12(13):2463. https://doi.org/10.3390/plants12132463

Chicago/Turabian Style

Wang, Jun, Wei Dai, Jie Chen, Kunhao Ye, Qianglong Lai, and Dan Zhao. 2023. "Assessment of Genetic Diversity and Genetic Structure of Saussurea medusa (Asteraceae), a “Sky Island” Plant in the Qinghai–Tibet Plateau, Using SRAP Markers" Plants 12, no. 13: 2463. https://doi.org/10.3390/plants12132463

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop