Genetic diversity of endangered Manglietia patungensis assessed by inter simple sequence repeat and sequence-related amplified polymorphism markers

Biochemical Systematics and Ecology 57 (2014) 231e237

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Genetic diversity of endangered Manglietia patungensis assessed by inter simple sequence repeat and sequence-related amplified polymorphism markers Li Xiao a, 1, Xueping Li b, 1, Liyuan Chen c, Yubing Wang a, Xiaoling Li a, Faju Chen a, * a b c

Biotechnology Research Center, China Three Gorges University, Yichang, Hubei 443002, PR China College of Forestry, Henan University of Science and Technology, Luoyang 471003, PR China Key Laboratory for Silviculture and Conservation of the Ministry of Education, Beijing Forestry University, Beijing 100083, PR China

a r t i c l e i n f o

a b s t r a c t

Article history: Received 15 May 2014 Accepted 23 August 2014 Available online

Manglietia patungensis Hu is an endangered plant native to China. Knowledge of its genetic diversity and structure would aid its conservation. This study assessed nine natural populations of M. patungensis using two methods: inter simple sequence repeat (ISSR) and sequence-related amplified polymorphism (SRAP) markers. Using 10 ISSR primer pairs, 334 bands were generated, and 10 SRAP primer pairs generated 276 bands. The percent of polymorphic bands (91.32% and 93.48%), Nei's genetic diversity (0.3448 and 0.3323), and Shannon's information index (0.5075 and 0.4935) revealed a high level of genetic diversity at the species level. Total heterozygosity was 0.3439 by ISSR and 0.3281 by SRAP. The mean heterozygosity was 0.2323 by ISSR and 0.2521 by SRAP. The coefficient of genetic differentiation among natural populations was 0.3245 by ISSR and 0.2316 by SRAP. These data indicated higher levels of genetic diversity of M. patungensis within, rather than among, populations. Estimates of gene flow among natural populations were 1.0411 and 1.0589, which implied a certain amount of gene exchange among populations. A Mantel test revealed no significant correlation between genetic and geographic distance. ISSR and SRAP markers are both effective for genetic diversity research in M. Patungensis. Based on these results, conservation of M. patungensis should be performed both in situ and ex situ. © 2014 Published by Elsevier Ltd.

Keywords: Manglietia patungensis Genetic diversity Genetic differentiation ISSR SRAP conservation strategy

1. Introduction The Magnoliaceae are important plants for phylogenetic and evolutionary research in angiosperms because of their relatively primitive status. In China, there are 11 genera and more than 130 species of Magnoliaceae (Liu et al., 1997). Thirtynine of those species are listed in the “China Plant Red Data Book” as rare and endangered plants deserving national protection (Fu and Jin, 1992). One such species, Manglietia patungensis Hu., grows in evergreen broadleaves forestry at altitudes ranging from 700 to 1000 m, and is distributed in Badong and Lichuan, as well as the Shennongjia forestry of Hubei Province, Sangzhi and the Xiaoxi Natural Reserve area of Hunan Province, and the Guanshan Natural Reserve area in Jiangxi Province.

* Corresponding author. Tel./fax: þ86 (0)7176397188. E-mail address: [email protected] (F. Chen). 1 Equally contributed to this work. http://dx.doi.org/10.1016/j.bse.2014.08.017 0305-1978/© 2014 Published by Elsevier Ltd.

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Fig. 1. Locations of M. patungensis populations.

It has a restricted distribution because of small populations and slow reproduction. Previous research on M. patungensis focused on its propagation (Huang and Li, 2002; Huang et al., 1998), biocoenosis structure (Li et al., 2006; Ge et al., 2009), distribution (Li et al., 2004) and allozymes (He et al., 2005). As the next step toward the conservation of this species, the present study examined the genetic diversity and population structure using inter simple sequence repeat (ISSR) and sequence-related amplified polymorphism (SRAP) molecular markers, which are widely used to study endangered plants (Agostini et al., 2010; Yu et al., 2011; Trindade et al., 2012; Ferreira et al., 2013; Manners et al., 2013; Harish et al., 2014). 2. Materials and methods 2.1. Plant material According to the distribution range and geographic locations of M. patungensis, five populations were chosen: Badong (BD), Lichuan (LC), Sangzhi (SZ), Guanshan (GS) and Hunan Xiaoxi (HN). The largest population, Hunan Xiaoxi, was divided into five subpopulations along elevation and distance, HN1, HN2, HN3, HN4 and HN5 (Fig. 1). Fresh unfolding leaves were randomly picked from mature trees (each tree was more than 50 m apart), labeled with the population name, and stored at 70  C. Information concerning the samples is provided in Table 1. 2.2. Methods 2.2.1. DNA extraction and measurement An improved cetyl trimethylammonium bromide protocol was used to extract the genomic DNA (Wan et al., 2008), which was detected by 1.0% agarose gel electrophoreses, purified and quantified by UV spectroscopy. 2.2.2. ISSR and SRAP analyses ISSR primers were designed following the standards provided by the University of British Columbia (UBC801-UBC900) and synthesized by Sangon Biotech Co. Ltd. (Shanghai, China). The optimized ISSR-PCR reaction system for Manglietia patungensis

Table 1 Information of samples. Name

Location

Longitude

Latitude

Attitude(m)

Sample size

BD LC SZ GS HN1 HN2 HN3 HN4 HN5

Badong, Hubei Lichuan, Hubei Sangzhi, Hunan Guanshan, Jiangxi Xiaoxi Xiannvchi, Hunan Xiaoxi Badongmulian waterfall, Hunan Xiaoxi Xiaohexi, Hunan Above of Xiaoxi Badongmulian waterfall, Hunan Xiaoxi Paohuxi, Hunan

110 200 17.6200 108 560 51.5100 110 100 34.3100 114 240 20.5200 110 150 44.5300 110 150 44.5300 110 150 44.5300 110 150 44.5300 110 150 44.5300

30 500 19.9600 30 170 31.7800 29 240 16.2600 27 470 47.6600 28 48'8.8900 28 480 8.8900 28 480 8.8900 28 480 8.8900 28 480 8.8900

415 1230 722 406 545 538 515 550 540

3 20 12 24 27 35 15 21 11

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was: 1  buffer, 2.0 mmol/L Mg2þ, 0.4 mmol/L dNTPs, 0.4 mmol/L primers, 0.5 U of Taq polymerase and 40 ng template DNA (total volume 20 mL). The reaction program was: 94  C for 5 min; 35 cycles of 94  C for 45 s, 59  C for 1 min and 72  C for 1 min; and then 72  C holding for 7 min. The reactions were stored at 4  C. Ten primer pairs were screened for their ability to produce clear and polymorphic bands (Table 2). One hundred pairs of SRAP primers (Em1~Em10  Me1~Me10) were purchased from Sangon Biotech and included in a PCR amplification with random sample DNA, from which we chose 10 pair primers with more polymorphic loci (Table 2). The SRAP-PCR reaction system was a 20-mL volume that contained 1  buffer, 2.0 mmol/L Mg2þ, dNTPs 0.25 mmol/L, 0.45 mmol/L forward and reverse primers, 0.5 U of Taq and 40 ng of template DNA. The SRAP-PCR program was: 94  C for 1 min; 10 cycles of 94  C for 1 min, 33  C for 1 min and 72  C for 1 min; followed by 30 cycles of 94  C for 1 min and 55  C for 1 min and 72  C for 1 min; followed by holding at 72  C for 5 min. 2.2.3. Detection of ISSR and SRAP amplification results Pre-detection was performed using 1.5% agarose gel electrophoresis and formal detection used 3% denaturing polyacrylamide gel for ISSR and a 6% gel for SRAP. 2.2.4. Statistics and analyses On the polyacrylamide gels, the bands were scored as either 1 or 0 according to their presence or absence at the same position referring to Marker ladders, from top to bottom, from which we obtained the data matrix for ISSR and SRAP. POPGENE version 1.32 (Yeh et al., 1997) analyzed these data to provide the total and polymorphic bands number, the percentage of polymorphic bands (PPB), the effective allele number (Ae), Nei's genetic diversity (He) (Nei, 1973), Shannon's information index (I) (Lewontin, 1972), Nei's genetic distance (Nei, 1972), total gene diversity (Ht), gene diversity within populations (Hs) and the coefficient of genetic differentiation (Gst), assuming HardyeWeinberg equilibrium. Gene flow (Nm) between populations was calculated from the Фst values using (Nm ¼ (1-Фst)/4Фst) (Wright, 1951). A Mantel test was performed by TFPGA version 1.3 (Miller, 1997) to analyze the population genetic distance and geographic distance. Further, Bayesian model-based clustering was applied using STRUCTURE version 2.3.4 (Pritchard et al., 2000; Hubisz et al., 2009) to infer the number of genetic clusters (K) without prior information of their origin. Ten independent runs for K ¼ 1 to 10 with an admixture model were performed using 100,000 interactions after a burn-in period of 10,000 runs. After three repeats, the optimal K was determined by examination of the DK statistic (Evanno et al., 2005), using Structure Harvester (Earl and vonHoldt, 2012). 3. Results 3.1. Polymorphic bands With 10 ISSR primer pairs and 168 samples from nine populations of Manglietia patungensis, 334 bands were scored in total, with an average of 33.4 bands per primer. Among these bands, 199 were polymorphic, with an average of 19.9 bands per primer and mean percentage of polymorphic band of 60% (Table 3). With 10 SRAP primer pairs and 168 samples, 276 bands were scored, with an average of 27.6 bands per pair. Among these bands, 157 were polymorphic, with an average of 15.7 polymorphic bands per pair and mean percentage of polymorphic band of 58.5% (Table 3). The results of ISSR and SRAP demonstrated abundant genetic diversity in this species. 3.2. Genetic diversities The genetic diversity analyses of M. Patungensis, by ISSR, gave the following results: 91.32% for the percentage of polymorphic bands (PPB), 1.9132 for the number of alleles, 1.6082 for Ae, 0.3448 for He, and 0.5075 for I, all of which indicated a high level of genetic diversity in M. patungensis. ISSR and SRAP analyses showed the same trend for these measures (Table 4). Table 2 Sequences of ISSR and SRAP primers used in the analysis. ISSR primers

Sequence (50 e30 )

SRAP primers

Forward primer (50 e30 )

Reverse primer (50 e30 )

UBC804 UBC807 UBC808 UBC811 UBC815 UBC834 UBC835 UBC836 UBC840 UBC842

(TA)8A (AG)8 T (AG)8C (GA)8C (CT)8G (AG)8YT (AG)8YC (AG)8YA (GA)8YT (GA)8YG

M9-E1 M10-E1 M1-E3 M4-E4 M7-E4 M4-E5 M1-E7 M6-E7 M9-E7 M8-E8

TGAGTCCAAACCGGATG TGAGTCCAAACCGGCTT TGAGTCCAAACCGGATA TGAGTCCAAACCGGACA TGAGTCCAAACCGGTAA TGAGTCCAAACCGGACA TGAGTCCAAACCGGATA TGAGTCCAAACCGGGCT TGAGTCCAAACCGGATG TGAGTCCAAACCGGTGC

GACTGCGTACGAATTATT GACTGCGTACGAATTATT GACTGCGTACGAATTGAC GACTGCGTACGAATTTGA GACTGCGTACGAATTTGA GACTGCGTACGAATTAAC GACTGCGTACGAATTATG GACTGCGTACGAATTATG GACTGCGTACGAATTATG GACTGCGTACGAATTCTG

ISSR, inter simple sequence repeat; SRAP sequence-related amplified polymorphism; Y ¼ (C, T).

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Table 3 Total bands and polymorphic bands resulting from SRAP and ISSR. ISSR

SRAP

Primer

Total

P

PPB(%)

Primer

Total

P

PPB(%)

UBC804 UBC807 UBC808 UBC811 UBC815 UBC834 UBC835 UBC836 UBC840 UBC842 Sum Mean

29 45 40 34 27 27 40 41 32 19 334 33.4

16 25 25 21 18 21 23 23 16 11 199 19.9

55.2 55.6 62.5 61.8 66.7 77.8 57.6 56.1 50.0 57.9

M9-E1 M10-E1 M1-E3 M4-E4 M7-E4 M4-E5 M1-E7 M6-E7 M9-E7 M8-E8 Sum Mean

36 25 21 30 14 22 30 35 34 29 276 27.6

21 9 11 20 10 16 13 20 19 18 157 15.7

58.3 36.0 52.4 66.7 71.4 72.7 43.3 57.1 69.6 57.9

60.0

58.5

P, number of polymorphic bands.

The genetic diversity of individual populations varied as follows: PPB from 22.16% to 77.25%, He from 0.0935 to 0.2858 and I from 0.1350 to 0.4219. The trend of He matched that of I. The trend in the genetic diversity indexes of each population was: HN1 (Ae ¼ 1.4991, He ¼ 0.2858, I ¼ 0.4219) >HN2 (Ae ¼ 1.4816, He ¼ 0.2763, I ¼ 0.4100) >LC (Ae ¼ 1.4803, He ¼ 0.2756, I ¼ 0.4078) >HN4 (Ae ¼ 1.4577, He ¼ 0.2631, I ¼ 0.3902) >HN3 (Ae ¼ 1.4384, He ¼ 0.2523, I ¼ 0.3757) >GS (Ae ¼ 1.4358, He ¼ 0.2517, I ¼ 0.3752) >SZ (Ae ¼ 1.3958, He ¼ 0.2246, I ¼ 0.3302) >HN5 (Ae ¼ 1.2923, He ¼ 0.1679, I ¼ 0.2491) >BD (Ae ¼ 1.1703, He ¼ 0.0935, I ¼ 0.1350). These data imply that among nine populations of M. Patungensis, the genetic diversities of HN1, HN2, LC, HN4, HN3, GS, SZ were higher than that of BD, HN5. 3.3. Genetic distance among populations Using POPGENE 1.32, the ISSR data (Table 5) showed that between every two populations, He varied from 0.0492 to 0.3669, while the genetic identity was 0.6929e0.9520. The closest genetic distance was 0.0492 between HN3 and HN4; the farthest was 0.3669 between BD and HN5. Thus, the highest genetic identity exists between HN3 and HN4, and the lowest between BD and HN5. The SRAP data (Table 6), as processed by POPGENE 1.32, showed that the genetic distance varied from 0.0367 to 0.2173, while the genetic identity was from 0.8047 to 0.9640. The closest genetic distance was between HN1 and HN2 (0.0367) and the furthest was between BD and HN5 (0.2173). Thus, the highest genetic identity is between HN1 and HN2 and the lowest between BD and HN5. 3.4. Genetic structure POPGENE1.32 was used to calculate the genetic differentiation between populations. For the ISSR data, the total Ht was 0.3439; the Hs was 0.2323; the Gst was 0.3245; and the genetic variance was 32.45% among populations and 67.55% within populations, which demonstrated that genetic differentiation exists within the populations. The Nm among populations was 1.0411, which indicated a high level of genetic exchange within, rather than among, natural populations of M. patungensis. Bayesian model-based cluster analysis showed a clear peak value of the DK statistic (DK ¼ 154.27 by ISSR, 195.95 by SRAP) Table 4 Genetic diversity of M. patungensis populations revealed by ISSR and SRAP. POP ID

Sample

BD LC SZ GS HN1 HN2 HN3 HN4 HN5 Within Among

3 20 12 24 27 35 15 21 11 19 168

ISSR

SRAP

PPB (%)

Ae

He

I

PPB (%)

Ae

He

I

22.16 75.15 58.98 73.05 76.95 77.25 70.96 73.05 46.71 64.0 91.32

1.1703 1.4803 1.3958 1.4358 1.4991 1.4816 1.4384 1.4577 1.2923 1.4057 1.6082

0.0935 0.2756 0.2246 0.2517 0.2858 0.2763 0.2523 0.2631 0.1679 0.2323 0.3448

0.1350 0.4078 0.3302 0.3752 0.4219 0.4100 0.3757 0.3902 0.2491 0.3439 0.5075

32.97 81.16 64.49 81.52 82.25 85.87 70.65 77.17 59.06 70.57 93.48

1.2591 1.4846 1.4252 1.4688 1.5413 1.5070 1.4439 1.4631 1.3676 1.4400 1.5790

0.1411 0.2833 0.2246 0.2723 0.3051 0.2911 0.2529 0.2690 0.2129 0.2503 0.3323

0.2031 0.4239 0.3354 0.4078 0.4488 0.4341 0.3745 0.4017 0.3170 0.3718 0.4935

POP ID, population ID; PPB, percentage of polymorphic bands; Ae, effective allele number; He, Nei's genetic distance; I, Shannon's information index.

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Table 5 Nei's genetic identity (above diagonal) and genetic distance (below diagonal) by ISSR analysis. POP ID

BD

LC

SZ

GS

HN1

HN2

HN3

HN4

HN5

BD LC SZ GS HN1 HN2 HN3 HN4 HN5

e 0.0974 0.1719 0.2267 0.2481 0.2366 0.2530 0.2520 0.3669

0.9072 e 0.0831 0.1202 0.1397 0.1349 0.1267 0.1449 0.2451

0.8421 0.9202 e 0.1309 0.1592 0.1515 0.1811 0.1776 0.2619

0.7972 0.8867 0.8773 e 0.1170 0.1343 0.1698 0.1633 0.2446

0.7802 0.8696 0.8529 0.8896 e 0.0535 0.1470 0.1409 0.2137

0.7893 0.8738 0.8594 0.8743 0.9479 e 0.1040 0.0982 0.1738

0.7764 0.8810 0.8343 0.8439 0.8633 0.9013 e 0.0492 0.1595

0.7772 0.8651 0.8373 0.8493 0.8685 0.9065 0.9520 e 0.1037

0.6929 0.7826 0.7696 0.7830 0.8076 0.8367 0.8526 0.9015 e

Symbols are the same as in Table 1.

at K ¼ 3. Using the ISSR and SRAP data, samples of BD, LC, SZ and GS clustered into one group, and HN1, HN2 and HN3, HN4, HN5 of HN into another two groups. Populations of BD, LC, SZ and GS with different geographic characters (showed in Table 1) are in a cluster, while HN1, HN2, HN3, HN4, and HN5, five subpopulations of HN are in two different clusters. Geographic distances of samples were measured on Google Earth, and tested using a Mantel test among five natural populations of M. patungensis. The results of ISSR (r ¼ 0.1933, p ¼ 0.3780) and SRAP (r ¼ 0.1112, p ¼ 0.4310) markers revealed a non-significant positive correlation between genetic and geographic distance. 4. Discussion 4.1. Genetic diversity of M. patungensis Based on our results, M. Patungensis has a high genetic diversity at the species level (PPB ¼ 91.32% by ISSR and 93.48% by SRAP). He et al. (2005) detected eight enzyme systems of M. patungensis and found 18 enzyme loci, which was evidence of high genetic diversity. Widespread species often have high levels of genetic diversity (Zuo et al., 2008; J. Wang et al., 2010; L. Li et al., 2012; Chen et al., 2013), and endangered species often have low levels of genetic diversity (Wang et al., 1996; Xiao et al., 2004; Li et al., 2005). However, some studies have demonstrated that some rare, native species still maintain high genetic diversity; for example, the endangered tree Cercidiphyllum japonicum (PPB ¼ 69.59%, He ¼ 0.2310, I ¼ 0.3514) (Wang et al., 2010), Davidia involucrata (PPB ¼ 96.44%, He ¼ 0.3429, I ¼ 0.5107) (Li et al., 2012), Tuberaria major (PPB ¼ 97.7%, He ¼ 0.197, I ¼ 0.324) (Helena et al., 2012) and Camellia japonica (PPB ¼ 90.1%, He ¼ 0.3414, I ¼ 0.5013) (Lin et al., 2012). The factors maintaining high levels of genetic diversity of endangered plants are mainly the breeding system, distribution and ecological characteristics. The most important is the breeding system. Hamrick et al. (1992) found that plants that reproduced by outbreeding, spreading seeds by wind or animals, maintained a high level of genetic diversity. Plants in the Manglietia genus have dichogamous flowers and often outbreed by pollination by beetles. Animals such as squirrels and birds disperse their seeds, which could be one reason why the level of genetic diversity within species and populations is high. Another reason could be related to its evolutionary history. As a broad evergreen tree of the Magnoliaceae, a basal group of angiosperm plants, it spread widely during the Cretaceous and Tertiary periods (Liu et al., 1995). The surviving trees from the Quaternary Glacier would preserve the high level of genetic diversity to the present time. 4.2. Genetic structure of populations The Gst of the ISSR and SRAP markers were 0.3245 and 0.2316, indicating that for ISSR 32.45% of the genetic variance occurred among populations and 67.55% was within populations; for SRAP 23.16% occurred among populations and 76.84% was within populations, which implied that the genetic diversity of M. patungensis mainly occurs within, rather than among,

Table 6 Nei's genetic identity (above diagonal) and genetic distance (below diagonal) by SRAP analysis. POP ID

BD

LC

SZ

GS

HN1

HN2

HN3

HN4

HN5

BD LC SZ GS HN1 HN2 HN3 HN4 HN5

e 0.0606 0.1883 0.1243 0.1242 0.1499 0.1136 0.1547 0.2173

0.9412 e 0.0851 0.0747 0.0600 0.0822 0.0903 0.0933 0.1613

0.8283 0.9184 e 0.0628 0.1026 0.1288 0.1455 0.1169 0.1684

0.8831 0.9280 0.9391 e 0.0625 0.0967 0.0909 0.0924 0.1343

0.8832 0.9417 0.9025 0.9394 e 0.0367 0.0756 0.0771 0.1332

0.8608 0.9211 0.8791 0.9078 0.9640 e 0.0831 0.0813 0.1458

0.8927 0.9136 0.8646 0.9131 0.9272 0.9203 e 0.0498 0.1292

0.8567 0.9109 0.8896 0.9117 0.9258 0.9219 0.9514 e 0.0685

0.8047 0.8510 0.8450 0.8743 0.8753 0.8643 0.8788 0.9338 e

Symbols are the same as in Table 1.

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populations. Nm between populations was 1.0411 for ISSR and 1.0589 for SRAP, suggesting a high frequency of gene exchange among populations. The factors that affect the genetic variance of a population are likely to be evolutionary history, mutation, recombination, genetic drifting, breeding system, gene flow and natural selection (Zhang et al., 2012; X.P. Li et al., 2012; Wang et al., 2014). Breeding system and gene flow are the main explanations for the genetic diversity of M. patungenesis. Breeding systems play the most fundamental role in the evolution of species, which result in various genotypes after natural selection. Different breeding methods can produce different ratios of heterozygous and homozygous genotypes. This results in a distribution of genetic variability within and among populations and affects the genetic structures (Wang et al., 2011; Coppi et al., 2014). Hamrick and Godt (1989) found that 21% of species pollinated by insects kept their genetic diversity among populations, which agrees with our analysis of M. patungensis. Additionally, Hamrick (1989) found that if Nm > 1, then gene flow neutralizes the variance in genes caused by genetic drift. Otherwise, if Nm < 1, genetic drift becomes the main cause of genetic variance in a population. In this study, the gene flow of M. patungensis was Nm ¼ 1.0411. This result demonstrated that gene flow happens frequently among populations, which suggests a low level of genetic variation among populations. Both gene flow and genetic drift have affected the population structure of M. patungensis; however, most of the impact is within populations. Other studies, such as amplified fragment length polymorphism analyses of Magnolia wufengensis (Nm ¼ 2.1055, Gst ¼ 0.1919) (He et al., 2007), and ISSR analyses of endemic endangered species such as Reaumuria trigyna (Nm ¼ 4.1055, Gst ¼ 0.1071) (Zhang and Wang, 2008), also revealed the genetic diversity mainly exists within populations, despite high gene flow among populations. Based on our analyses, there was no significant difference between the results of the two molecular markers, which suggests that both ISSR and SRAP markers are effective for genetic diversity research on M. patungensis. 4.3. Conservation strategy of M. patungensis In recent decades, biodiversity conservation in China has been taken seriously. However, further policy revisions and publications are required to promote and encourage further conservation (Ma et al., 2013). The maintenance of genetic variation is one of the major objectives in conserving endangered and threatened species. Like Michelia coriacea (Zhao et al., 2012), there is a high level of within-population genetic diversity of M. Patungensis. Conservation measures should focus initially on the restoration of its small and few natural populations. Limits to deforestation and cooperation with local nurseries should be encouraged. Concerning its high genetic diversity, it is worth noting that subpopulations of HN can be divided into two groups, which indicates we should protect as many individuals of M. Patungensis as possible without too many selections when carrying out in-situ and ex-situ protection. Considering its disjunct distributions, ex-situ protection is important. In the case of M. Patungensis, there is another problem: its low natural regeneration of dehydrated seeds (Chen et al., 2007). Thus, for long-term protection of this species, further research on its regeneration and the collection and growth of seedlings should be encouraged. In-situ protection measures should be initiated at first with subsequent equal attention paid to ex-situ protection. Acknowledgments This work was supported by National Nature Science Foundation of China (30670202) and Science Foundation of the National Forestry Bureau (2008-3). References Agostini, G., Echeverrigaray, S., Souza-Chies, T.T., 2010. Genetic diversity of the endangered Brazilian endemic herb Cunila menthoides Benth. (Lamiaceae) and its implications for conservation. Biochem. Syst. Ecol. 38, 1111e1115. Chen, F.J., Liang, H.W., Wang, X., He, Z.Q., Li, F.L., 2007. Seed dormancy and germination characteristics of Manglietia patungensis, an endangered plant endemic to China. Biodivers. Sci. 15 (5), 492e499. Chen, S.X., Zhou, J., Chen, Q., Chang, Y.X., Du, J.N., Meng, H.W., 2013. Analysis of the genetic diversity of garlic (Allium sativum L.) germplasm by SRAP. Biochem. Syst. Ecol. 50, 139e146. Coppi, A., Cecchi, L., Mengoni, A., Pustahija, F., Tomovi
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