Morphological and ISSR molecular markers reveal genetic diversity of wild hawthorns (Crataegus songorica K. Koch.) in Xinjiang, China

Journal of Integrative Agriculture 2017, 16(11): 2482–2495 Available online at www.sciencedirect.com

ScienceDirect

RESEARCH ARTICLE

Morphological and ISSR molecular markers reveal genetic diversity of wild hawthorns (Crataegus songorica K. Koch.) in Xinjiang, China SHENG Fang1, CHEN Shu-ying2, TIAN Jia1, LI Peng1, QIN Xue1, WANG Lei3, LUO Shu-ping1, LI Jiang1 1 2 3

College of Forestry and Horticulture, Xinjiang Agricultural University, Urumqi 830052, P.R.China Academy of Forestry in Ili, Yining 835000, P.R.China Zhengzhou Fruit Research Institute, Chinese Academy of Agricultural Sciences, Zhengzhou 450009, P.R.China

Abstract The wild hawthorn species, Crataegus songorica K. Koch., is an important wild germplasm resource in Xinjiang, China that has been endangered in recent years. The genetic diversity of C. songorica K. Koch. germplasm in five populations from Daxigou, Xinjiang, China were evaluated based on phenotypic traits and ISSR molecular markers to provide basic information on resource protection, rational utilization and genetic improvement. The F-value for the phenotypic differentiation coefficient of the 33 traits measured ranged from 0.266 to 15.128, and mean value was 13.85%. The variation among populations was found to be lower than that within population. A total of 303 loci were detected within the five populations by 12 primers. Within 298 polymorphic loci, the polymorphism was 98.35%, showing a high genetic diversity in C. songorica K. Koch. The gene diversity within population, total population genetic diversity, genetic differentiation coefficient and gene flow were 0.2779, 0.3235, 0.1408, and 3.0511, respectively. Our results showed that C. songorica K. Koch. from Xinjiang has a high level of genetic diversity at both the phenotypic and molecular levels. Significant genetic differentiation existed within population and the differentiation trend showed a regional association. And in this study, in situ and ex situ conservation approaches were raised for wild hawthorn protection utilization. Keywords: phenotypic traits, ISSR marker, genetic diversity, Crataegus songorica K. Koch., germplasm resources, molecular marker

1. Introduction

Received 14 January, 2017 Accepted 18 April, 2017 SHENG Fang, E-mail: [email protected]; Correspondence LI Jiang, Tel/Fax: +86-0991-8762363, E-mail: [email protected]; TIAN Jia, E-mail: [email protected] © 2017 CAAS. Publishing services by Elsevier B.V. All rights reserved. doi: 10.1016/S2095-3119(17)61688-5

Crataegus sp., commonly known as the hawthorn, is a member of the Rosaceae family found globally in the northern hemisphere’s temperate zones. This genus is widely distributed in regions located in the latitude between 30 to 50°N, such as Asia, Europe and America. And North America has the most amount of species (Zhao and Feng 1996). A large number of wild hawthorn species have been identified in mountainou areas of Xinjiang, China, including Crataegus songorica K. Koch., Crataegus chlorocarpa Lenne. et. C. Koch., and Crataegus sanguinea Pall (Lin and Cui 2000; Liao

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2013). These wild resources not only have high nutritional value but also are used for rootstocks and breeding materials of cultivated hawthorn. For many years, wild hawthorn species were endangered and populations did not recover until human intervention. As its natural habitat became seriously destroyed, wild hawthorns were gradually declining towards the point of extinction (Yin 2006). Wild hawthorn is one of the main tree species in the Tianshan Mountain wild fruit forests in China and existed more than 50 years, additional, the wild hawthorn area here account for 30% of its total area of China (Yin 2006). Within the Tianshan Mountain range, C. songorica K. Koch. is mainly distributed in Daxigou, Xinjiang (Lin and Cui 2000; Chen 2002). The limited area in which these species grow have been severely damaged from human and livestock encroachment, which declined the number of C. songorica K. Koch. Population decline can decrease genetic diversity, and foreseeably lead to gene deficiency and degeneration of the C. songorica K. Koch. genetic stock (Yan 2004). Therefore, it is urgent to study the germplasm to protect native hawthorns and to preserve the genetic diversity in Xinjiang. Phenotypic indicators, one expression form of genetic diversity, can be used for selecting horticulturally favorable traits such as vigor and disease protection. Understanding genetic variation may help to understand species biodiversity. Importantly, these phenotypic indicators are used to identify, select, and protect germplasm. Currently, wild fruit tree germplasm studies were done on wild cherry plum (Prunus cerasifera) (Liu 2008), wild apple (Malus sieversii) (Omasheva et al.2015), wild pear (Pyrus ussuriensis) (An et al. 2014), and wild apricot (Armeniaca vulgaris) (Cao 2015). Germplasm research has employed a new molecular marker technology, inter-simple sequence repeat (ISSR) to identify species. It is robust and has excellent repeatability. Phenotypic observation and molecular markers have been widely used in studies on germplasm genetic diversity resources such as cherry plum (Zhou 2011), wild apricot (Cao 2015), and cultivated apricot (Liu 2015). Applied research on wild hawthorn was limited and simple and mainly focused on morphological characteristic description (Lin and Cui 2000; Liao 2013), fruit pigment extraction (Lv et al. 2011) and flower organ research (Liu et al. 2014, 2015). Liu et al. (2016) was the only molecular marker based study on wild

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hawthorn diversity with ISSR, which analyzed the genetic diversity relationships among different varieties of wild hawthorn till now. In the present study, 92 C. songorica K. Koch. individuals were chosen from Daxigou, Xinjiang, China. And ISSR technology and plant phenotypic traits were combined to analyze C. songorica K. Koch. genetic diversity and structure. The results would promote to protect and use wild hawthorn resources effectively.

2. Materials and methods 2.1. Materials Field investigation of C. songorica K. Koch. was carried out from April 2015 to October 2016 and sampling was performed according to the standard population biology methods (Barnosky et al. 2017). A total of 92 samples were selected from five populations of Menggumiaogou (Pop1), Yidaogou (Pop2), Miaogou (Pop3), Apeiyinggou (Pop4), and Majintanggou (Pop5) (Table 1). Samples were taken at least 50 m apart and some phenotypic traits were measured for phenotypic variation. Geographical location information was recorded and marked for the following investigation. Ten to fifteen fresh leaves of each sample were collected and dried with silica gel in sealed bags, then the dry leaves were stored at 4°C and used as the material for ISSR analysis.

2.2. Methods Phenotypic trait measurement Samples were collected during the growing season from June to July when new shoots stop growing by the methods of Falkenhagen (1978). Five intact leaves, annual branches and perennial branches from the four canopy directions of each plant were selected. Leaf length and width, petiole length and width and the branch length were measured. External character of leaves and branches were recorded and the leaf area was calculated by WinFOLIA (Regent Instruments Inc., Canada). A total of 30 fruits per tree were randomly selected, then the fruit and stone size were measured. The fruit shape index (Fruit longitudinal diameter/Fruit diameter) was calculated and the quality and external morphology were observed. Fruit and stone weight were measured using an

Table 1 Geographic information and sample number of Crataegus songorica K. Koch. in different populations Population1) Pop1 Pop2 Pop3 Pop4 Pop5 1)

Longtitude (E) 44°25´84.8´´-44°26´25.2´´ 44°25´51.5´´-44°25´94.2´´ 44°25´40.0´´-44°25´80.0´´ 44°23´45.5´´-44°23´68.8´´ 44°24´73.0´´-44°24´93.0´´

Latitude (E) 80°46´20.0´´-80°48´31.4´´ 80°45´67.0´´-80°46´56.4´´ 80°40´15.1´´-80°46´48.7´´ 80°45´16.1´´-80°46´11.9´´ 80°45´64.9´´-80°46´15.1´´

Altitude (m) 1 135-1 435 1 144-1 328 1 112-1 267 1 081-1 156 1 108-1 205

Sample number 11 30 14 20 17

Pop1-Pop5 are Menggumiaogou, Yidaogou, Miaogou, Apeiyinggou, and Majintanggou populations in Xinjiang, China, respectively.

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electronic balance, the hardness of fruit was measured by GY-1 Penetrometer (Shenzheng Graigar Technology Co., Ltd., China) and the content of fruit juice soluble solids were measured by refractometer (Shanghai Goldring Instrument Co., Ltd., China). A descriptive index was recorded based on fruit shape, fruit color, flesh color, juice content and stone shape. The observing standards refer to descriptors and data standard for hawthorn (Lü and Li 2006). Statistics Quantitative traits were assigned and the mean values of each sample were calculated by Microsoft Excel data processing software. In order to calculate the phenotypic variation among and within populations, 33 morphological characters of 92 individuals from five populations were used as feature values that were analyzed by the One-way Program in SPSS (IBM, USA). Phenotypic differentiation coefficient indicated the percentage of genetic variation among populations and reflected the phenotypic differentiation among populations. This was calculated according to the following formula: δ Vst= 2t/s (1) δ2t/s+δ2s Where, δ2t/s and δ2s are the variance component among and within populations (Van and Swings 1990). The variation coefficient (CV) was used to measure the degree of dispersion for phenotypic traits: CV=S/X (2) Where, S was the standard deviation and X was the average value. The relative range indicates the degree of extreme differences in the characters: (3) Ri´=Ri/R0 Where, Ri refers to the range within the population and R0 was the total range. Shannon-Wiener index (H´) and Simpson index (D) were calculated by the following formula: (4) H´=–∑PilnPi 2 (5) D=1–∑Pi Where, Pi was the probability of occurrence of the i th code value of a trait. ISSR marker analysis Total DNA of C. songorica K. Koch. leaves were extracted using a modified CTAB method (Lefort and Douglas 1999; Li et al. 2007) and detected on a 1% agarose gel. An ultraviolet spectrophotometer was used to determine DNA concentration, followed by DNA dilution to a final concentration of 30-50 ng μL-1. The DNA was stored at -20°C. A Taq DNA polymerase, buffer and dNTPs were purchased from Beijing Golden Biological Technology Co., Ltd. (Beijing, China). Primers were based on 100 ISSR primers provided by Columbia University, USA and synthesized by Yingjie (Shanghai) Trade Co., Ltd. (Shanghai, China). The PCR amplification was based on the ISSR hawthorn reaction system by Dai et al. (2007).

20 μL reactions using two DNA templates were selected from each hawthorn population. PCR reactions contained 2 μL 10× buffer (Mg2+), 1.6 μL 2.5 mmol L-1 dNTPs, 1.2 μL of each 10 mmol L-1 primer, 0.5 U Taq DNA polymerase, 1 μL template DNA and 14.1 μL double distilled water. PCR amplification was performed on a Biometra TProfessional Thermocycler (Beijing Hengyuan Energy Technology Co., Ltd., China). Cycling conditions were: 94°C for 3 min (one cycle), 94°C for 30 s, annealing temperature (Tm)±2°C for 1 min, 72°C for 2 min (38 cycles), and 72°C for 7 min (one cycle). The amplified products were electrophoresed on an 8% polyacrylamide gel and stained by silver nitrate. Photos were taken and clearly interpretable bandings were counted. ISSR data analysis Each polymorphism site was regarded as one allele. DNA bands that were found at sites of migration were recorded as 1. A lack of a band was written as 0. Clear bands with good repeatability were recorded, all with lengths ranging from 100 to 2 000 bp. The two metadata for all individuals formed a 0/1 matrix. The data matrix was analyzed by POPGENE 1.32 (//ftp.microsoft.com/Softlib/ MSLFILES/HPGL.EXE) that calculated the percentage of polymorphic loci (PPB), number of alleles (Na), effective number of alleles (Ne), Nei’s gene diversity index (H), Shannon diversity index (I), total population genetic diversity (Ht), population genetic diversity (Hs) and the coefficient of gene differentiation (Gst). The unweighted pair-group method with arithmetic means (UPGMA) cluster analysis among populations was performed using the NTSYS-pc 2.1 Software (Exeter, USA).

3. Results 3.1. C. songorica K. Koch. phenotypic diversity and variation analysis Qualitative traits diversity and variation analysis Qualitative traits were measured for C. songorica K. Koch. The analysis revealed that abaxial leaf surfaces as well as petioles were both rare down. All leaves have deep cracks. Other characteristics with rich diversity can be seen in Table 2. Leaf color was divided into yellowish green, light green, green, deep green and purple. Green leaves accounted for the highest proportion (56.79%). The shapes of phyllopodium were distributed in truncated, nearly round, wide wedge, wedge-shaped, wedge extension, and heartshaped. The highest proportion of these were the wide wedge and wedge-shaped found at 37.50 and 33.42%, respectively. The percentage of narrow falciform shape was the highest in the stipule (54.35%). The majority of leaf margin serrata were thin and sharp. The leaf surface was mainly flat, accounting for 74.08% of the samples. According to the statistical results, the overall characteristics of

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Table 2 Analysis of the diversity of phenotypic qualitative traits of Crataegus. songorica K. Koch. Character

1 Leaf color 5.38 Leaf base morphology 18.64 Stipule shape 6.52 Leaf margin morphology 0.92 Status of leaf surface 74.08 Fruit shape 35.87 Peel color Flesh color 1.09 Fruit flavor 41.49

2 17.39 2.93 39.13 22.88 19.51 7.83 1.39 7.39 1.06

The proportion of all levels (%)1) 3 4 5 6 7 56.79 20.43 37.50 33.42 7.50 54.35 76.20 5.65 0.76 13.83 7.96 1.35 14.78 15.48 6.43 17.83 28.43 11.48 14.04 25.30 34.00 32.22 46.81 8.51 2.13

8

2.91 20.39

Mean

CV (%)2)

2.92 3.08 2.48 2.75 1.33 3.52 5.66 3.89 2.29

26.36 38.62 25.00 16.52 46.56 69.44 28.85 25.27 50.72

Simpson Shannonindex Wiener index 0.9891 4.5160 0.9886 4.4972 0.9885 4.4878 0.9891 4.5181 0.9886 4.5016 0.9884 4.4904 0.9889 4.5087 0.9888 4.5077 0.9864 4.4878

1)

1-8 represent different grades for the corresponding trait, respectively. Leaf color: 1, yellow-green; 2, light green; 3, green; 4, dark green. Leaf base morphology: 1, truncated; 2, nearly round; 3, wide wedge; 4, wedge-shaped; 5, wedge extension. Stipule shape: 1, ear shape; 2, broad sickleshaped; 3, narrow sickleshaped. Leaf margin morphology: 1, obtusely serrate; 2, thickly serrate; 3, fine serrate. Status of leaf surface: 1, flat; 2, winding; 3, wrinkle; 4, revolute. Fruit shape: 1, nearly round; 2, oblate; 3, oval; 4, inverted oval; 5, ellipse; 6, long elliptical; 7, broadly obovate; 8, nearly square. Peel color: 1, yellowish green; 2, red-orange; 3, carmine; 4, bright red; 5, dark red; 6, bloodred; 7, purple-red; 8, purple black. Flesh color: 1, green and white to light green; 2, yellow to pale yellow; 3, orange to orange red; 4, pink to pale pink; 5, light red to crimson. Fruit flavor: 1, sweet sour taste; 2, sweet and sour fruity; 3, sour; 4, sour and astringent; 5, tasteless. 2) CV, variation coefficient. -, no data.

fruit were as followed: low fruit juice volumes, glossy peels and more fruit dots (fruit dot ≥8 cm-2), and small fruit dots (fruit dot diameter <0.3 mm). The fruit dot growth state was not obvious, the color of fruit dot was gray and white. The diversity of the qualitative fruit characters were analyzed based on fruit shape, peel color, flesh color and fruit flavor. The fruit shape was rich in diversity, ranging from nearly round to nearly square. The majority of fruit were round, accounting for 35.87%, while the oval-shaped fruit were the least abundant (1.35%). The color of the peel was mainly dark red and purple black, accounting for 28.43 and 20.39%, respectively. The percentage of the flesh color that was pink to pale pink was 34%. The fruit flavor was mainly sweet-sour and sour, accounting for 41.49 and 46.81%, respectively. The leaf and fruit traits analysis showed that fruit shape, fruit flavor and status of leaf surface accounted for 69.44, 50.72 and 46.56%, respectively, that took up a higher percentage in the coefficients of variation. The D ranged from 0.9864 to 0.9891 and the highest orders were leaf margin morphology, leaf color and peel color. The H´ ranged from 4.3872 to 4.5181 and the highest value was found in leaf margin morphology, leaf color and peel color. All traits showed an abundance of polymorphisms. Quantitative traits diversity and variation analysis A total of 24 quantitative characteristics were analyzed from branch, leaf, fruit and stone tissues of C. songorica K. Koch. (Table 3). These tissues coefficient of variation varied widely, ranging from 3.59 to 41.8%. Less CVs were found in the number (3.59%), diameter (3.75%), and longitudinal diameter of stone (4.57%), suggesting that this characteristic variation was stable in the population. Conversely, the larger CV were found in floret number of inflorescence

(28.76%), new branch length (35.73%) and annual branch length (41.8%), showing that these traits are rich in diversity and having a greater variation range. The D ranged from 0.9873 to 0.9891, while the H´ ranged from 4.3739 to 4.5208. The phenotypic diversity index was 4.4978 on average. Relatively speaking, the H and the D of the fruit and the stone were higher than those of the leaf, whose phenotypic diversity was rich. Overall, the variation degrees of the quantitative traits in the population were different for different characteristics. Diversity index of C. songorica K. Koch. populations The average H and D of the five C. songorica K. Koch. populations were 2.3165 and 0.8101, respectively, showing a high diversity. The highest phenotypic diversity was seen in Pop1 at 2.4304, while the lowest was seen in Pop4 at 2.1949. A rich genetic diversity existed among populations of C. songorica K. Koch. in Xinjiang (Table 4).

3.2. Phenotypic trait differences among populations The CV value for the phenotypic traits reflects the dispersion degree of the traits. For example, the larger the coefficient of variation is, the greater the dispersion degree of the traits is. The CV for the 33 phenotypic traits among the branches, leaves, fruits and stones of C. songorica K. Koch. had some differences among the five populations (Table 5). The average CV of the 33 phenotypic traits was 16.51% and the variation range was 3.46-47.75%. The largest degree of variation of phenotypic traits was seen in fruit flavor (CV=47.75%) and the smallest was seen in fruit longitudinal diameter (CV=3.46%). The results showed that the fruit flavor had the largest differentiation

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Table 3 Analysis of the diversity of quantitative traits and variance of phenotypic traits of Crataegus songorica K. Koch. Observation item Floret number of inflorescence Annual branch length (mm) Annual branch diameter (mm) New branch length (mm) Leaf thickness (mm) Leaf area (cm2) Leaf width (mm) Leaf length (mm) Leaf length/Leaf width Petiole length (mm) Petiole thickness (mm) Petiole length/Petiole thickness Fruit weight (g) Fruit longitudinal diameter (mm) Fruit diameter (mm) Fruit stalk length (mm) Fruit stalk thickness (mm) Stone number Fresh stone weight (g) Longitudinal diameter of stone (mm) Stone diameter (mm) Fruit set number Fruit hardness (kg cm-2) Soluble solids (%) Leaf color Leaf base morphology Leaf margin morphology Status of leaf surface Fruit shape Peel color Flesh color Fruit flavor Stipule shape

CV (%)1)

Simpson index

ShannonWiener index

F-value2)

27.86 41.80 18.57 35.73 18.45 22.55 9.97 11.25 5.73 16.64 16.52 25.96 20.00 5.40 8.30 14.33 12.94 3.59 12.02 3.75 4.57 24.72 18.28 17.07 -

0.9883 0.9873 0.9888 0.9878 0.9888 0.9886 0.9890 0.9890 0.9891 0.9888 0.9888 0.9884 0.9887 0.9891 0.9890 0.9889 0.9889 0.9891 0.9889 0.9891 0.9891 0.9884 0.9887 0.9888 -

4.4829 4.4311 4.5055 4.4569 4.5056 4.4971 4.5169 4.5155 4.5202 4.5079 4.5078 4.4892 4.4996 4.5179 4.5162 4.5091 4.5107 4.5189 4.5114 4.5208 4.5205 4.3739 4.5030 4.5089 -

14.855** 6.681** 3.037* 2.195 1.224 9.363** 9.673** 7.906** 0.266 2.461 0.635 1.755 14.394** 12.058** 15.128** 0.829 4.749** 1.252 4.114** 4.230** 2.460 0.891 5.671** 9.650** 0.627 1.865 0.754 0.953 8.326** 9.597** 5.354** 3.020* 1.294

Variance components Within Among population populations 10.6336 5.0341 1 815.1961 497.8432 0.3944 0.0504 339.8336 3 537.9271 0.0023 0.0002 11.1369 3.3656 0.2570 0.0790 0.3538 0.0968 0.0035 0.0001 18.7708 2.7248 0.0360 0.0014 36.6299 3.9797 0.0323 0.0124 0.4447 0.1446 0.8587 0.3522 2.4755 0.1369 0.0065 0.0012 0.0050 0.0003 0.0008 0.0001 0.0762 0.0092 0.0797 0.0062 0.6379 0.0188 0.1046 0.0237 6.0793 1.8191 0.0990 0.0032 0.4276 0.0408 0.0519 0.0024 0.0808 0.0024 0.7826 0.2164 0.8253 0.2522 0.3984 0.0768 1.2517 0.1480 0.3803 0.0251

Vst (%)3) 32.13 21.52 11.34 8.76 6.95 23.21 23.52 21.48 1.44 12.68 3.76 9.80 27.76 24.54 29.09 5.24 15.44 5.67 13.17 10.81 7.27 2.87 18.49 23.03 3.14 8.71 4.37 2.94 21.66 23.40 16.16 10.57 6.20

1)

CV, coefficient of variation. F-value, the statistics in the analysis of variance. and 0.01, respectively. 3) Vst, phenotypic differentiation coefficient. -, no data at the corresponding level. 2)

*

and **, correlation was significant when the confidence levels (bilateral) was 0.05

Table 4 Diversity index of Crataegus songorica K. Koch. populations

very different among different populations. The CV of

Population1) Pop1 Pop2 Pop3 Pop4 Pop5 Mean value

smallest (13.26%) in Pop4. The CV of fruit hardness was

1)

Simpson index 0.8330 0.8081 0.8028 0.7873 0.8193 0.8101

Shannon-Wiener index 2.4304 2.3020 2.2691 2.1949 2.3864 2.3165

Pop1- Pop5 are Menggumiaogou, Yidaogou, Miaogou, Apeiyinggou, and Majintanggou populations, respectively.

leaf thickness was the largest (25.11%) in Pop1 and the the largest (27.62%) in Pop3 and the smallest (5.47%) in Pop1. The results showed that the micro-environment of different populations culminated phenotypic variation differences. This also existed among the traits where the variation was very large within a population. For example, the CV of annual branch length (63.42%) was the highest and the CV of the stone (3.64%) was the lowest in Pop1.

and longitudinal diameter of stone was more stable in the population. The 33 phenotypic traits were different in the five populations and the variation of the same traits was also

The CV of fruit flavor (55.85%) was the largest and the CV of the fruit longitudinal diameter (2.22%) was the lowest in Pop4. In general, there were abundant variations for each trait in each population. Additionally, the average CV

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Table 5 A statistical table of phenotypic traits of various populations of Crataegus songorica K. Koch. Phenotypic trait Leaf thickness (mm) Annual branch length (mm) Annual branch diameter (mm) New branch length (mm) Floret number of inflorescence Leaf area (cm2) Leaf width (mm) Leaf length (mm) Leaf length/Leaf width Petiole length (mm) Petiole thickness (mm) Petiole length/Petiole thickness Fruit weight (g) Fruit longitudinal diameter (mm) Fruit diameter (mm) Fruit stalk length (mm) Fruit stalk thickness (mm) Stone number Fresh stone weight (g) Longitudinal diameter of stone (mm) Stone diameter (mm) Fruit number Fruit hardness (kg cm-2) Soluble solids (%) Leaf color Leaf base morphology Leaf margin morphology Status of leaf surface Fruit shape Peel color Flesh color Fruit flavor Stipule shape Mean value

Mean 0.26 100.13 3.39 165.04 12.00 14.84 5.10 5.30 1.04 26.35 1.15 23.81 0.90 12.41 11.16 11.09 0.62 1.98 0.25 7.44 6.24 3.29 1.78 14.27 2.93 3.09 2.76 1.32 3.65 5.55 3.84 2.28 2.53

Pop1 25.11 63.42 33.33 59.21 16.95 27.23 10.90 14.94 6.33 15.51 16.61 19.70 14.05 5.08 5.70 14.58 6.74 3.64 14.69 4.81 6.16 8.22 5.47 14.67 9.48 26.03 10.04 19.56 18.60 16.75 9.37 44.40 17.13 17.71

CV of populations (%)1) Pop2 Pop3 Pop4 18.68 17.95 13.26 35.24 32.57 30.18 13.73 16.45 13.85 36.13 33.52 20.36 25.06 8.79 23.69 15.86 11.91 20.01 6.76 5.68 9.49 8.31 6.99 10.00 4.93 4.58 5.10 16.53 13.90 14.58 14.13 18.92 18.09 24.84 30.10 22.80 14.39 26.05 12.79 4.20 6.20 2.81 5.95 11.02 4.82 6.98 17.95 17.90 9.36 9.08 17.79 3.02 4.21 3.25 11.61 7.88 9.70 3.99 3.05 2.22 4.71 4.22 3.92 21.87 15.26 39.75 10.82 27.62 13.03 14.03 17.02 12.97 6.53 7.00 17.17 18.69 12.55 28.36 8.91 9.01 6.26 24.53 9.01 22.46 22.21 26.77 22.60 10.99 17.11 13.62 11.95 20.43 12.82 50.82 40.76 55.85 23.47 17.27 31.71 15.43 15.48 16.76

Pop5 16.98 45.95 15.10 35.84 25.86 19.31 9.44 9.61 8.18 18.99 17.16 30.31 13.10 4.48 5.28 16.15 9.85 4.21 12.09 3.23 3.19 18.93 19.73 15.48 10.72 19.21 7.78 21.50 18.67 14.03 20.97 46.92 29.04 17.19

Population mean value2) CV (%) Ri 18.40 0.78 41.47 0.75 18.49 0.59 37.01 0.73 20.07 0.56 18.87 0.64 8.45 0.61 9.97 0.66 5.82 0.66 15.90 0.80 16.98 0.76 25.55 0.80 16.07 0.62 4.55 0.67 6.56 0.55 14.71 0.72 10.57 0.51 3.67 0.66 11.20 0.66 3.46 0.65 4.44 0.67 20.80 0.53 15.34 0.60 14.84 0.63 10.18 0.59 20.97 0.66 8.40 0.78 19.41 0.59 21.77 0.76 14.50 0.70 15.11 0.64 47.75 0.71 23.72 0.80 16.51

1)

CV, coefficient of variation; Pop1-Pop5 are Menggumiaogou, Yidaogou, Miaogou, Apeiyinggou, and Majintanggou populations in Xinjiang, China, respectively. 2) Ri, relative range.

of each phenotypic trait decreased as the following order:

populations so that the different traits could be compared.

branch (32.32%)>fruit (16.88%)>leaf (15.58%)>stone

The Ri of petiole length, petiole length/petiole width and

(5.69%). This indicated that the variation among these

stipude shape were the largest and the smallest Ri was

traits was relatively large and that the stone traits had the

the fruit stalk thickness. The Ri trend was not consistent

highest genetic stability.

with the CV trend, showing that the variation degree of

The CV can also indirectly reflect the richness of the

the traits in the population was not stable.

population’s phenotypic diversity. A large CV indicates that the population trait variation range is high and

3.3. Phenotypic differentiation coefficients

the phenotypic diversity is rich. On the contrary, if the variation range is small then the phenotypic diversity is

The F-value (Table 3) showed that 16 traits among the 33

poor. The CV order for different C. songorica K. Koch.

phenotypic traits of C. songorica K. Koch. were significantly

phenotypic characteristics in different populations was

different. There were very significant differences in the annu-

Pop1 (17.71%)>Pop5 (17.19%)>Pop4 (16.76%)>Pop3

al branch diameter and fruit flavor. These traits were found

(15.48%)>Pop2 (15.43%). The relative range (Ri) was

to have significant differences that accounted for 54.55% of

used to measure the degree of extreme differences among

the total, indicating that there was obvious morphological

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differentiation among different traits. Variance components and phenotypic differentiation coefficients within and among populations of the 33 phenotypic traits were calculated and used to account for the proportion of variation within population and among populations in the total variation. The results showed that the variance components within each population were greater than that among populations, and the mean variance components within and among populations were 86.41 and 13.59%, respectively. Additionally, the variance components between the annual branch length and the new branch length were the highest, indicating that the differences between the two traits were significant among populations. The phenotypic variation of each trait ranged from 1.44 to 32.13%, with the smallest being leaf shape index and the largest being floret number of inflorescence. The average phenotypic differentiation coefficient among the 33 traits was 13.85%, while the average phenotypic variation within the population was 86.15%. The phenotypic variation within the population was larger than that among the populations, indicating that the variation within population of C. songorica K. Koch. was the main source of phenotypic variation.

Pop1, Pop2, Pop3 and Pop5 showed a certain degree of variation continuity.

3.5. ISSR marker polymorphism The ISSR analysis produced 303 loci with 12 primers among 92 samples from the five populations. The loci number per primer varied from 16 to 31 with a mean of 25.25 loci, and a locus size of 100-2 000 bp. Within 298 polymorphic loci (Table 6), the polymorphism was 98.35%, showing a high genetic diversity in C. songorica K. Koch. Among populations, Nei’s genetic diversity index (H) was 0.3268 and the I was 0.4939. The percentage of polymorphic loci ranged from 58.09 to 93.07% in the population level, with an average of 84.62%. The H ranged from 0.1812 to 0.3170 with an average of 0.2779, and I was between 0.2774 and 0.4765 with an average of 0.4201. Among all populations, the genetic diversity of Pop1 was the lowest (H=0.1812, I=0.2774), while Pop5 was the highest (H=0.3170, I=0.4765). The H, I and the polymorphism loci percent reflected a consistent trend in diversity showing that there were significant differences in genetic diversity among the five populations of C. songorica

3.4. Phenotypic cluster analysis of C. songorica K. Koch. populations The 33 phenotypic traits from the five C. songorica K. Koch. populations were analyzed by UPGMA cluster analysis (Fig. 1). The five C. songorica K. Koch. populations were divided into two groups. The first group contained Pop1, Pop2, Pop3, and Pop5, while the second group contained Pop4 only. And Pop2 and Pop3 had the least differentiation among the populations. Pop4 and Pop5 had close geographical distribution, however, the phenotypic traits similarity degree was not higher in the cluster trees between them, indicating that the phenotypic differentiation of the C. songorica K. Koch. populations in this region had obvious geographical differences. The remaining populations,

Fig. 1 A cluster map of phenotypic traits of Crataegus songorica K. Koch. in five populations, constructed according to unweighted pair-group method with arithmetic means (UPGMA). Pop1, Pop2, Pop3, Pop4, and Pop5 are Menggumiaogou, Yidaogou, Miaogou, Apeiyinggou, and Majintanggou populations in Xinjiang, China, respectively.

Table 6 Genetic diversity of populations of Crataegus songorica K. Koch.1) Population2) Pop1 Pop2 Pop3 Pop4 Pop5 Mean Species level 1) 2)

Polymorphic locus number 176 283 264 277 282 256.4 298

Percentage of polymorphism loci (%) 58.09 93.40 87.13 91.42 93.07 84.62 98.35

Na

Ne

H

I

1.5809±0.4942 1.9340±0.2487 1.8713±0.3354 1.9142±0.2805 1.9307±0.2544 1.8462±0.3226 1.9835±0.1276

1.2999±0.3474 1.5138±0.3402 1.4927±0.3546 1.5024±0.3316 1.5364±0.3215 1.4690±0.3391 1.5523±0.3176

0.1812±0.1884 0.3034±0.1637 0.2889±0.1764 0.2992±0.1633 0.3170±0.1568 0.2779±0.1697 0.3268±0.1443

0.2774±0.2705 0.4588±0.2141 0.4353±0.2370 0.4527±0.2174 0.4765±0.2069 0.4201±0.2292 0.4939±0.1791

Na, number of alleles; Ne, effective number of alleles; H, Nei’s gene diversity index; I, Shannon index. Pop1-Pop5 are Menggumiaogou, Yidaogou, Miaogou, Apeiyinggou, and Majintanggou populations in Xinjiang, China, respectively.

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Table 7 Analysis of molecular variance (AMOVA) within and among populations Souce of variation Among populations Within population

df 4 91

SS 2.1766 16.6457

MS 0.5441 0.2310

Variance component 0.0398 0.2308

K. Koch. The order of populations’ genetic diversity was: Pop5>Pop2> Pop4>Pop3>Pop1.

3.6. Genetic differentiation among C. songorica K. Koch. populations

Percentage of variance component (%) 14.69 85.31

P-value <0.001

Table 8 The coefficient of gene differentiation (Gst) analysis of genetic differentiation among Crataegus songorica K. Koch. populations1) Average Standard deviation

Ht 0.3235 0.0209

Hs 0.2779 0.0148

Gst 0.1408 –

Nm 3.0511 –

1)

The population gene diversity (Hs) and the total popula-

tion gene diversity (Ht) of the five C. songorica K. Koch. populations were 0.2779 and 0.3235, respectively. The

gene differentiation coefficient (Gst) among populations

was 0.1408, showing that the genetic variation was mainly caused by variation within population. The gene differentiation coefficient was 14.08% among populations, but that was 85.92% within population of C. songorica K. Koch. The gene flow (Nm) was 3.0511 (>1), indicating that

gene flow among populations was relatively large. The

analysis of molecular variance (AMOVA) also showed that the genetic structure variation mainly existed within population (Table 7), and the genetic variation was 85.31% within population and 14.69% among populations, which was in accordance with the analysis result of genes in the populations (Table 8).

3.7. The correlation among C. songorica K. Koch. populations genetic distance, genetic identity and geographical distance The genetic distance and genetic identity of the five C. songorica K. Koch. populations were calculated by POPGENE 1.32 software. The genetic distance between Pop1 and Pop2 (0.066) and between Pop1 and Pop3 (0.0919) were significantly smaller than that of other populations, and that were corresponded to higher genetic identity, 0.9361 and 0.9122, respectively (Table 9). This indicated that the Pop1 samples had a close genetic relationship to the Pop2 and Pop3 samples. The genetic distance between Pop1 and Pop4 or between Pop1 and Pop5 was larger, which were 0.1497 and 0.1437, respectively. But their genetic identity were smaller, which were 0.8609 and 0.8661, respectively. This indicated that the Pop1 samples were more distantly related to the Pop4 and Pop5 samples. The genetic distance coefficient of variation among populations was 41.23%. The correlation coefficient between genetic distance and geographical distance was 0.651.

Ht, total population genetic diversity; Hs, population genetic diversity; Nm, gene flow. –, no data.

Table 9 Neiʼs genetic identity and genetic distance among five populations of Crataegus songorica K. Koch.1) Population2) Pop1 Pop2 Pop3 Pop4 Pop5 1)

2)

Pop1 0.0660 0.0919 0.1497 0.1437

Pop2 0.9361 0.0321 0.0605 0.0658

Pop3 0.9122 0.9684 0.0638 0.0768

Pop4 0.8609 0.9413 0.9382

Pop5 0.8661 0.9363 0.9260 0.9478

0.0536

The data in the upper triangle are genetic consistency, the data in the lower triangle are genetic distance. Pop1- Pop5 are Menggumiaogou, Yidaogou, Miaogou, Apeiyinggou, and Majintanggou populations in Xinjiang, China, respectively.

3.8. ISSR molecular marker clustering analysis The matrix of ISSR markers were analyzed by NTSYS-pc 2.1 and genetic similarity dendrogram of 92 individuals was created (Fig. 2). The genetic similarity coefficient of 92 individuals varied from 0.53 to 0.87. When the genetic similarity coefficient was 0.60, the tested materials were able to be divided into three categories. The first category contained most of the experimental samples, indicating that the genetic similarity was small among samples 41, 48, 61 and 88 compared with others. At the genetic coefficient of 0.67, the first category was subdivided into five sub-categories, where the first sub-category contains 43 samples, namely, 1-40 and 42-44, and the second sub-category contains 26 samples, namely, 45-47, 49-60, 62-68 and 78-81; the third sub-category contains eight samples, namely, 69-76; and 77 was clustered in the fourth sub-category; the fifth sub-category contains 10 samples, namely, 82-87 and 8992. The results showed that the genetic distance between the samples was small and the genetic relationships were close. Most of the germplasms clustered together according to the population distribution, with a few exceptions. The individual clusters showed that there was a geographical correlation. The populations with similar geographic envi-

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1 2 3 4 5 6 7 9 13 14 15 10 8 11 12 28 29 31 18 25 26 43 19 20 21 22 23 27 30 16 24 35 32 33 34 36 37 38 40 42 39 17 44 45 46 50 62 63 64 49 51 53 47 54 55 56 57 66 78 79 80 81 52 58 59 60 65 70 76 67 68 69 71 72 73 75 74 77 87 89 82 83 84 85 86 90 91 92 48 41 61 88

0.53

0.62

0.70 Coefficient

0.78

0.87

Fig. 2 Dendrogram among 92 samples of Crataegus songorica K. Koch. using the unweighted pair-group method with arithmetic means (UPGMA) cluster analysis. 1-92 on the right represent the sample number.

ronments showed a closer genetic relationship. This was

form another sub-category; the second group was com-

consistent with the conclusion of phenotypic variation.

prised of 37 samples, where 72, 61, and 48 were clustered into one sub-group; and 45 samples were gathered into the

3.9. Principal coordinate analysis of the ISSR molecular markers

third group. Compared to the UPGMA clustering results, there were some differences in genetic relationship among the two analytical methods. For example, samples 53 and

The plane coordinate scatter plot (Fig. 3-A) and the

62 were not clustered together in UPGMA but clustered

three-dimensional space map (Fig. 3-B) were established

together in the main principal coordinate analysis. Both

by analyzing the 92 samples using the principal coordi-

different analysis methods of genetic relatedness (UPG-

nate tool by NTSYS-pc 2.1. The first, second and third

MA clustering and principal coordinate analysis) got three

principal components explained 10.10, 6.05, and 3.77% of

separated groups, but the observed genetic variation was

the sample correlation, respectively. The C. songorica K.

not completely consistent with one each other for the in-

Koch. samples were divided into three groups, where 10

dividuals. The three-dimensional graph (Fig. 3-B) clearly

sample resources (82, 83, 85, 86, 87, 88, 89, 90, 91, 92)

represented the different levels and directions of the genetic

were found to be closely related and designated as the first

relationship found among the samples. The genetic diversity

group, in which sample 87 forms a sub-group, the others

of the 92 samples clearly showed that most of the samples

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A 0.14

–0.01

Dimension

50

7949 63

53 62

71 5446

554770 56

II

57

64 45

59

58

78 66

39

67 65 80

69

81 77

40

68

51 75

38

76 73

33 42 44

34

88

22

2 1

52 74

43

41

60

72

36 35

2521 1917 26 24 11 12 16

III

61

32 23 8 27 29 37 9 30 6 14 31 20 5 18 13 7 15 4 10 28 3

48

–0.15

87

–0.30 92

I –0.44 –0.31

91

90

82

83 84 89 85

86

–0.17

–0.02 Dimension

B

60

0.12

0.27

61 59

0.32

49

48

58

0.03

57 82 50 54 77

83 87

2 sio en Di

m

–0.11

0.14 0.14

–0.01 -0.01

–0.15 -0.15

85 89 86 84

1 68

76

92

n-

Dimension-3

0.18

55 90 91 70

88

64 65 81 52 66 46 80 79 45 78 51

63 53 47 56 62

71

17

41

2 40

67 73 7269 75

19 22 5 16 35 27 8 24 23 6 4 20 10 21 3 3031 9 137 29 44 15 39 42 32 14 33 36 18 34 28 38 26 25 37 43 11 12

74

–0.30 -0.30 –0.25 –0.31 -0.31

–0.17 -0.17

–0.02 -0.02

Dimension-1

0.12

0.27

Fig. 3 Principal coordinate analysis for 92 Crataegus songorica K. Koch. resources. A, planar graph. I, II, and III represent the first, second, and third groups, respectively. B, three-dimensional graph. 1-92 represent the sample number.

clustered together according to the distribution of their populations, indicating that there was a closer relationship between them (Fig. 3-B). The principal component analysis made the regional and genetic relationships of the cultivars more obvious, and provided important genetic information for the interpretation of the relationships among the samples.

4. Discussion 4.1. C. songorica K. Koch. genetic diversity and structure The genetic diversity of five C. songorica K. Koch. pop-

ulations exhibited rich diversity at the morphological and molecular level. The F-value for the 33 phenotypic traits measured ranged from 0.266 to 15.128 among populations and most of these traits’ differences were extremely significant. Different traits had different degrees of variation within same population. CV of the C. songorica K. Koch. phenotypic traits ranged from 3.46 to 47.75% in the population. This value is significantly higher than the CV for Acer ginnala (CV: 7.05-38.12%) (Wang et al. 2010) and Acer mono (CV: 7.98-33.41%) found in the Huoshan Mountain, Shanxi Province, China (Ji et al. 2012). Some of these differences may be due to the fact that these tree populations were located at different altitudes. A study on

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wild myrobalan plum in Xinjiang obtained a phenotypic trait CV between 2.75 and 9.13% (Liu et al. 2008). Our results showed that C. songorica K. Koch. phenotypic differences were significant, habitat adaptability was broad, and the species’ morphological plasticity was high. Using 12 ISSR primers to measure genetic polymorphisms in the 92 C. songorica K. Koch. individuals, we demonstrated that genetic diversity (percentage of polymorphism loci, 98.35%; I, 0.4939; H, 0.3268) was significantly higher than the previous results by ISSR marker studies on hawthorn, including studies by Han et al. (2009) (percentage of polymorphism loci, 92.62%) and Dai et al. (2008) (percentage of polymorphism loci, 85.45%). The genetic diversity measured here was also slightly higher than a previous wild hawthorn study from Xinjiang (percentage of polymorphism loci, 94.4%; I, 0.2331; H, 0.1507) by Liu et al. (2016). Therefore, compared to cultivated hawthorn varieties, wild hawthorn resources in Xinjiang were rich in polymorphisms. In Xinjiang, C. songorica K. Koch. is abundant than other wild hawthorn species. The geographical distribution of this species may be one of the main factors determining genetic diversity as it is known that for plant geographical distribution, the larger the distribution area, the higher level of genetic diversity is (Giri and Long 2016). The C. songorica K. Koch. samples were taken from different environments and varying ecological factors such as climate, vegetation and soil could account for the different phenotypes within population. The combination of different phenotypes and genotypes resulted in the amplification of genetic diversity among populations. Additionally, the wild hawthorn species distributed in other regions of Xinjiang are mostly artificially propagated plants, while the wild hawthorn vegetation in Daxigou are naturally present, causing increased polymorphism levels. Genetic structure refers to the distribution and differentiation of genetic diversity within and among populations. Genetic differentiation coefficient is an important parameter to understand the genetic structure of species and also an important indicator of genetic diversity (Jacquemyn et al. 2004; Zia et al. 2014). In this study, the Gst of the five populations was 0.1408, indicating that the genetic variation was mainly caused by the variation within population. Nm was one of the main factors to promote genetic differentiation among populations and the two main forms of gene flow were the seed and the spread of pollen among populations. Nm of the five populations was 3.0511, a relatively large value, and the differentiation was small among the populations. It was most likely that the higher gene flow prevented the genetic differentiation of C. songorica K. Koch. among populations. This may be related to the fact that its habitat is highly consistent and the eco-climatic environment is basically the same throughout the Daxigou

distribution regions. Within this niche, C. songorica K. Koch. is mainly distributed in 1 000-1 500 m of sunny slope. This C. songorica K. Koch. habitat is highly homogenous, suggesting that these similar habitats created a uniform natural selection pressure over the entire population and similar genotypes were likely to be preserved (Nevo 2001).

4.2. C. songorica K. Koch genetic distances and cluster analysis Genetic variation within plant population is associated with habitat characteristics and distribution patterns of the species (Ramos et al. 2016). It has also been shown that there are major discrepancies in the link between genetic distance and geographical distance. The genetic diversity and geographic differentiation of Ammopiptanthus (Leguminosae) populations saw a correlation between the genetic distance and geographical distance among populations (Ge et al. 2005), yet another study found that there was not an association between genetic and geographical distances when studying wild apricot in Xinjiang but instead found that natural selection, gene mutation and gene flow were the main factors leading to genetic variation (Yuan et al. 2007). This conclusion was consistent with the results of another more recent study on genetic diversity in Xinjiang apricot (Liu et al. 2016). Research on the genetic diversity of Drimia indica Jessop using phenotypic traits and molecular markers found that although genetic distance was related to geographical distance, it might also be affected by the ecological environment and other factors (Alluri et al. 2015). Additionally, it has also been shown that the germplasm genetic relationship was related to geographical location and environmental factors in forest savory germplasm resources (Khadivi-Khub et al. 2014). Our results showed that populations with similar ecological and geographical conditions were preferentially clustered in genetic distance clustering. The differentiation coefficient (13.58) of the 33 phenotypic traits in five wild hawthorn populations was slightly lower than the Gst (14.08) and indicated that environmental factors had less of an effect on genetic diversity. The UPGMA clustering map obtained by phenotypic traits and ISSR molecular markers showed similar clustering results. The differences between the two clustering graphs may be due to the differences existing in the environment among the populations. It is important to note that the hawthorn is a cross-pollinated plant (Li et al. 1989) and this life strategy could result in a large variation in the offspring. Moreover, hawthorn flowers have a short flowering period and are often pollinated via entomogamy, hindering the dissemination of pollen to a smaller area. The altitude where C. songorica K. Koch. naturally grows

SHENG Fang et al. Journal of Integrative Agriculture 2017, 16(11): 2482–2495

is relatively high and there is no mountain or geographical barrier between the populations, promoting gene flow among populations. This is consistent with the conclusions of another genetic diversity study in Dactylis glomerata L. (Madesis et al. 2014). Wild hawthorn populations Pop2, Pop3, and Pop5 were in close proximity to each other. The results showed that there was a close genetic relationship in populations with similar habitats or climates among the populations. The average genetic distance among C. songorica K. Koch. populations was 0.0804, presenting a significant correlation between genetic distance and geographic distance among samples (r=0.651). The genetic relationship pattern showed a correlation with the geographical position distance, where the closer the geographic locations, the smaller the genetic distances between populations.

4.3. Protection strategy Genetic diversity is the basis for the evolutionary potential of a species to adapt to environmental changes. The loss of genetic diversity greatly reduces the fitness of individuals and the adaptability of a species (Frankham et al. 2002). Human activities, such as fragmentation of wild fruit forests, habitat deterioration and loss may lead to the isolation of the large populations with high levels of genetic diversity into several smaller populations. Small populations can cause genetic drift causing loss of important genotypes and increasing the endangerment factor for a given species (Eriksson et al. 1995). Wild hawthorn is an important germplasm resource for the Tianshan Mountain wild fruit forest. Many factors make the wild hawthorn tree an important breeding resource including their rich genetic variation and their cold-resistant, drought-resistant and barren-resistant characteristics. Protection of wild hawthorn genetic resources is important for the cultivation of new varieties. Therefore, it is important to understand the extent of genetic diversity and genetic variation spatial distribution patterns to create a strategy for germplasm resource protection. C. songorica K. Koch. species and populations maintain a high level of genetic diversity, demonstrating their genetic plasticity and evolutionary abilities. Although C. songorica K. Koch. genetic diversity is high, the endangerment of the species is due to long-term overgrazing, reclamation of farmland, spread of pests and diseases in the wild fruit forests of the Tianshan Mountain. The results of this study suggest that C. songorica K. Koch. germplasm protection would benefit from in situ conservation strategies. The five C. songorica K. Koch. populations collected for this research all grow well in Daxigou, and therefore the in situ conservation should be implemented. Furthermore, higher genetic diversity was found in

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Yidaogou and Majintanggou germplasms and should be protected initially. Accelerated large-scale breeding to increase C. songorica K. Koch. germplasm resources and popular education for local herdsmen can offer a method to prevent deforestation. Proper exploitation and utilization of the germplasms can also promote in situ conservation of C. songorica K. Koch. genetic resources. Since there is a certain degree of genetic differentiation among the C. songorica K. Koch. populations, the ex situ conservation may be an effective method for protection in other populations in different areas. We suggest that samples should be collected from all populations to create a repository for wild hawthorn germplasm. Maintaining conditions for inter-population plant migration and exchanging seed and seedling tissues to preserve genetic resources as much as possible will maintain a greater genetic diversity in C. songorica K. Koch. and reduce the risk of endangered species.

5. Conclusion The results showed that C. songorica K. Koch. in Xinjiang, China had a high level of genetic diversity at both the phenotypic and molecular level. Significant genetic differentiation existed within the five populations and the differentiation trend demonstrated regional characteristics. We suggest that in situ conservation strategy can be used as an effective method to protect wild hawthorns.

Acknowedgements This work was supported by the Special Research Projects of National Forestry Industry of Public Benefit, China (201304701-1), the Key Discipline of Horticultural of Xinjiang Uygur Autonomous Region of China (2016-10758-3), and the Key Laboratory of Characteristics of Fruit Trees Center of Xinjiang Agricultural University, China.

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