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Genetic diversity and relationship among Camellia japonica populations in China and Japan

Lin Li 1,2,3  Ni Sui 2*  Li Ji-Yuan 3*  Hu Zhong-Yi 1

1 Ningbo City College of Vocational Technology, Ningbo 315502, PR China
2 Faculty of Life Science and Biotechnology, Ningbo University, Ningbo 315211, PR China
3 Research Institute of Subtropical Forestry, CAF, Fuyang 311400, PR China

1 Introduction

Oceanic islands are natural laboratories for studies of plant evolution (Crawford, Whitkus, and Stuessy, 1987; Adsersen, 1995; Crawford & Stuessy, 1997). One feature of the floras of oceanic islands is the high number of endemics occurring in small areas. For example, there are 570 endemics species in Canary Islands (Fransisco-Ortega, 2000). Adaptive radiation into diverse habitats and genetic drift are often considered to be important factors producing such extensive speciation (Crawford, Whitkus, and Stuessy, 1987; Fransisco-Ortega, 2000). However, island populations have a much higher risk of extinction than mainland populations (Diamond, 1984; Flesness, 1989; Case et al., 1992; Frankham, 1997). Recorded extinctions since 1600 showed that substantial proportions of extinctions in vascular plants were of island forms, even though island species represent a minority of total species (Olson, 1989). Major factors responsible for the high extinction rates of insular species include limited distribution area, habitat frangibility and small population size (Olson, 1989; Stone & Stone, 1989; Adsersen, 1991; D’Antonio & Dudley, 1995; Rieseberg & Swensen, 1996; Frankham, 1997).

Genetic diversity is the raw material for evolutionary change (Frankel & Soulé, 1981). The analysis of genetic diversity is a key element for the study of biodiversity, ecosystem functioning, and the consequences of man-made impact on natural systems such as over-exploitation, habitat loss and introduced species. Many studies suggest that island populations have lower genetic diversity than comparable mainland populations (Olson, 1989; Reid & Miller 1989; Frankham, 1997). Human activities have been an important cause of low genetic diversity on island (Olson, 1989; Reid & Miller 1989). Besides, there are other factors that can contribute to lower genetic diversity of island compared with mainland populations, namely inbreeding depression, loss of genetic variation, accumulation of mildly deleterious mutations, and genetic adaptations to island environments (flightlessness, limited ability to avoid predators and diseases) (Myers, 1979; Vitousek, 1988; Atkinson, 1989; World Conservation Monitoring Centre, 1992).

Camellia japonica (L.), a member of Theaceae, is an evergreen broad-leaved woody species, which is widely distributed in China (in Zhejiang and Shandong provinces), Japan (on Honshu, Shikoku, and Kyushu Islands) and along the southern and western coast of the Korean Peninsula (Zhang & Ren, 1998; Ueno et al., 1999; Gao et al., 2005). The plants are shrubs or small trees up to 3–10 m tall. Its flowers are bisexual and disposed in racemes. Their leaves alternate with serrate margin, and the seeds are small (length under 1 cm) (Chang, 1976; Gao et al., 2005).

Inter-simple sequence repeats (ISSR) have been extensively used to characterize genetic diversity in plants (Tsumura et al., 1996; Camacho & Liston, 2001; Barth et al., 2002; Cao et al., 2006). The technique provides the following advantages: (1) no prior information or lengthy mapping studies are required; (2) development costs are low; and (3) laboratory protocols can easily be transferred between plants (Barth et al., 2002). The sequences that ISSRs target are abundant throughout the eukaryotic genome and are rapidly evolved. Consequently, ISSR may reveal a much higher number of polymorphic fragments from every primer than RAPD (Esselman et al., 1999; Cao et al., 2006). Compared with RAPD, a series of studies have indicated that ISSR could be able to produce more reliable and reproducible bands because of the higher annealing temperature and longer sequence of ISSR primers (Nagaoka & Ogihara, 1997; Qian et al., 2001; Tsumura et al., 1996; Cao et al., 2006). Therefore, ISSR has proved to be useful in population genetic studies (Zietkiewicz et al., 1994; Barth et al., 2002; Esselman et al., 1999).

Knowing the distribution of diversity within and among populations of C. japonica is important for conservation because it provides useful guidelines for the preservation of genetic diversity within the species as a whole (Fransisco-Ortega, 2000). In this study, the main objectives were to reveal the level and partitioning of genetic diversity in C. japonica among thirteen populations using ISSR markers. It will provide the basic information for effective conservation.

2. Materials and methods

2.1 Sampling

Young leaf tissues of 390 individuals of C. japonica were collected from thirteen populations in China and Japan. The distribution of the populations studied is shown in Table 1. The young leaf tissues were stored with silica gel in zip-lock bags until DNA extraction.

Table 1  Locality of populations sampled of Camellia japonica

Population

Number

Locality

Geographical Location

Altitude/m

TH

30

Taohua Island, Zhejiang, China

29˚48΄N,122˚18΄E

12

ZJJ

30

Zhujiajian Island, Zhejiang, China

29˚25΄N,121˚42΄E

372

SCD

30

Putuo Island, Zhejiang, China

30˚00΄N,122˚24΄E

288

HJ

30

Putuo Island, Zhejiang, China

30˚00΄N,122˚23΄E

291

XS

30

Xiangshan, Zhejiang, China

29˚36΄N,121˚74΄E

203

CMY

30

Changmenyan Island, Shandong, China

36˚10΄N,120˚56΄E

36

BG*

30

Botanical Garden, Shandong, China

36˚06΄N,120˚34΄E

10

WS*

30

Wusi Square, Shandong, China

36˚11΄N,120˚53΄E

3

Kago

30

Kagoshima, Japan

31˚25΄N,130˚35΄E

144

Shiko-1

30

Shikoku Island, Japan

33˚03΄N,132˚58΄E

120

Shiko-2

30

Shikoku Island, Japan

32˚43΄N,133˚00΄E

12

Goto-1

30

Goto Island, Japan

32˚40΄N,128˚48΄E

26

Goto-2

30

Goto Island, Japan

32˚38΄N,128˚51΄E

36

Note: The individuals of BG* and WS* were immigrants from Changmenyan Island.

2.2 DNA extraction and ISSR-PCR amplification

Genomic DNA was extracted using the modified CTAB method (Doyle & Doyle, 1987). DNA was determined qualitatively and quantitatively in 1% agarose gel buffered with 0.5× TBE. Eighty primers (synthesized by Shanghai Sangon Bioengineering Technology Service Co. Ltd., Shanghai, China) from the Biotechnology Laboratory, University of British Columbia (UBC set no. 9) were initially screened for PCR amplification and 20 primers (Table 2) that produced clear and reproducible banding patterns were chosen for our final analysis. ISSR amplification was performed in a volume of 20 μL containing 40 ng genomic DNA, 2.0 μL 10×Buffer, 1.5 mmol·L−1 Mg2+, 0.2 mmol·L−1 dNTP, 0.6 μmol·L −1 primer, and l U of Taq DNA polymerase. PCR amplifications were carried out in a GeneAmp 9700 DNA Thermal Cycler (PerkineElmer, USA), with initial denaturation for 5 min at 94℃, followed by 40 cycles of denaturation for 40 s at 94℃, annealing for 45 s at respective Tm values (Table 2) of

the selected primers, and 1.5 min elongation at 72℃. Final elongation was performed for 10 min at 72℃. Amplification products were electrophoresed on a 1.5% agarose gel at 120 V for 1.5 h, stained with ethidium bromide and photographed under UV light (Fig. 1).

Table 2  Primers used for ISSR amplification

Primers

Sequence(5'-3')

Annealing temperature/℃

Number of loci recorded

Number of Polymorphism Loci

PPB/%

UBC810

(GA)8T

54.8

11

9

81.82

UBC811

(GA)8C

54.8

12

11

91.67

UBC813

(CT)8T

51.2

10

9

90

UBC815

 (CT)8G

52.9

10

9

90

UBC818

CA)8G

51.2

9

9

100

UBC824

(TC)8G

54.6

11

11

100

UBC825

(AC)8T

52.2

13

11

84.62

UBC827

(AC)8G

54.8

12

11

91.67

UBC834

(AG)8YT

53.9

7

6

85.71

UBC835

(AG)8YC

56.2

9

8

88.89

UBC836

 (AG)8YA

51.2

11

11

100

UBC841

(GA)8YC

56.2

13

10

76.92

UBC843

(CT)8RA

54.0

13

12

92.31

UBC848

(CA)8RG

54.8

10

9

90

UBC853

 (TC)8RT

51.2

10

10

100

UBC855

(AC)8 YT

56.6

8

7

87.50

UBC856

(AC)8YA

56.6

10

8

80

UBC866

(CTA)6

61.8

12

11

91.67

UBC873

(GACA)4

51.6

9

8

88.89

UBC880

(GGAGA)3

53.6

11

10

90.91

Total

 

 

211

190

90.05

Average

 

 

10.55

9.50

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Note: Y=(C, T), R=(A, G)

 ISSR profiles

 

 

 

 

 

ISSR profiles

 

 

 

 

 

 

 

 

 

Fig.1 ISSR profiles of TH (above) and ZJJ (below) population with primer UBC841
M: Molecular marker, 1-60: Sample numbers

2.3 Data analysis

Since ISSR markers are dominant, we assumed that each band represented the phenotype at a single biallellic locus (Williams et al., 1990). Amplified fragments were scored as present (1) or absent (0) to form a binary matrix. The binary data matrix was input into POPGENE version 1.32 (Yeh et al., 1997), assuming Hardy-Weinberg equilibrium. The following indices were used to quantify the amount of genetic diversity within each population examined: percentage of polymorphic bands (PPB), observed number of alleles per locus (Na), effective number of alleles per locus (Ne), expected heterozygosity (HE) (Nei, 1973), and Shannon’s information index (H) (Lewinton, 1972). Genetic diversity parameters were also calculated at the species and population level.

Genetic differentiation among populations was estimated by Nei’s gene diversity statistics (Nei, 1973) and Shannon’s information measure (Lewinton, 1972). The amount of gene flow among these populations was estimated according to the formula: Nm = (1 - Gst)/4Gst (Nei, 1973; Slatkin and Barton, 1989). Nei’s unbiased genetic identity (I) and genetic distance (D) between populations were computed using POPGENE (version 1.32) (Nei, 1972). To examine the genetic relationship among populations, a dendrogram was also constructed based on Nei’s genetic distance (D) using an unweighted paired group method of cluster analysis using arithmetic averages (UPGMA) of NTSYS-pc version 2.02c (Rohlf, 1997).

To test a correlation between genetic distances (D) and geographic distances (in km) among populations, a Mantel test was carried out using GenAlEx 6.41 software for Population Genetic Analysis (Smouse & Peak, 1986; Cao et al., 2006) (computing 999 permutations).

3 Results

3.1 Genetic diversity of C. japonica

A total of 211 bands were presented from 20 screened primers across all 390 individuals of the thirteen populations, corresponding to an average of 10.55 bands per primer. The size of the ISSR bands fragments varied from 100 bp to 2000 bp. Of these bands, 190 were polymorphic, i.e. the percentage of polymorphic bands (PPB) for this species was 90.05%. At the population level, the percentage of polymorphic bands (PPB) per population varied from 66.35% to 77.25% with an average of 71.31%. Assuming Hardye-Weinberg equilibrium, the mean expected heterozygosity (HE) was estimated to be 0.2688 within populations, and 0.3414 at the species level. The Shannon’ information indices (H) ranged from 0.3478 to 0.4319, with an average of 0.3941 at the population level and 0.5013 at the species level. Shiko-2 exhibits the greatest level of variability (PPB: 76.78%, HE: 0.2966, H: 0.4319, respectively), whereas the XS exhibits the lowest level of variability (PPB: 67.30%, HE: 0.2344, H: 0.3478, respectively), as shown in Table 3. ISSR profiles of two populations were given in Fig. 1.

Table 3 Genetic diversities for thirteen populations of C. japonica

Population

Sample sizes

Na

Ne

HE

H

PPB/%

TH

ZJJ

SCD

HJ

XS

CMY

BG

WS

Kago

Shiko-2

Shiko-1

Goto-1

Goto-2

Average

Species

30

30

30

30

30

30

30

30

30

30

30

30

30

30

390

1.6825 (0.4666)

1.6872 (0.4647)

1.6682 (0.4720)

1.6635 (0.4736)

1.6730 (0.4702)

1.7109 (0.4544)

1.7062 (0.4566)

1.7014 (0.4587)

1.7725 (0.4202)

1.7678 (0.4233)

1.7346 (0.4426)

1.7630 (0.4262)

1.7393 (0.4400)

1.7131(0.0388)

1.9005 (0.3001)

1.4206 (0.3882)

1.4417 (0.3911)

1.4304 (0.3840)

1.4540 (0.3987)

1.4079 (0.3853)

1.4896 (0.3899)

1.4862 (0.4005)

1.4698 (0.3907)

1.5223 (0.3862)

1.5338 (0.3954)

1.4997 (0.4072)

1.5176 (0.3848)

1.5204 (0.3980)

1.4765(0.0423)

1.6052 (0.3470)

0.2414 (0.2031)

0.2519 (0.2042)

0.2474 (0.2031)

0.2560 (0.2087)

0.2344 (0.2051)

0.2763 (0.2037)

0.2724 (0.2069)

0.2657 (0.2063)

0.2938 (0.1979)

0.2966 (0.2028)

0.2773 (0.2088)

0.2914 (0.1992)

0.2895 (0.2051)

0.2688(0.0211)

0.3414 (0.1682)

0.3586 (0.2852)

0.3719 (0.2873)

0.3653 (0.2882)

0.3746 (0.2949)

0.3478 (0.2889)

0.4038 (0.2871)

0.3979 (0.2902)

0.3889 (0.2909)

0.4302 (0.2749)

0.4319 (0.2812)

0.4049 (0.2897)

0.4262 (0.2779)

0.4213 (0.2861)

0.3941(0.0287)

0.5013 (0.2241)

68.25 68.72

66.82

66.35

67.30

71.09

70.62

70.14

77.25

76.78

73.46

76.30

73.93

71.31

90.05

Note: The value of standard deviation in parentheses.

3.2 Genetic structure of C. japonica

The coefficient of genetic differentiation among populations (Gst) was 0.2127, which indicated that 21.27% variation presented among populations and great majority of genetic variation (78.73%) resided among individuals. The Shannon’s information measure partitioned 21.38% of the total variation among populations, in broad agreement with the result of genetic differentiation analysis. The level of gene flow (Nm) was estimated to be 0.9255. Genetic identities among populations varied from 0.8378 to 0.9793 with an average of 0.8985 ± 0.0395. The average of genetic distance is 0.1082 ± 0.0435 (Table 4). In order to represent the relationship among populations, cluster analysis (UPGMA) was used to generate a dendrogram based on Nei’s genetic distance between the thirteen populations studied (Fig. 2). The result showed that clusters were related to the geographic distance between populations.

Dendrogram

 

 

 

 

 

 

 

 

 

 

 

Fig.2 UPGMA dendrogram for thirteen populations of C. japonica based on Nei’s genetic distance

 

Table 4 Nei's Unbiased Measures of Genetic Identity and Genetic distance
Note: Nei's genetic identity (above diagonal) and genetic distance (below diagonal)

 

TH

ZJJ

HJ

SCD

XS

CMY

BG

WS

Kago

Shiko-2

Shiko-1

Goto-1

Goto-2

TH

****

0.9640

0.9468

0.9405

0.9241

0.8770

0.8731

0.8713

0.8627

0.8570

0.8429

0.8654

0.8574

ZJJ

0.0367

****

0.9559

0.9501

0.9237

0.8868

0.8807

0.8807

0.88684

0.8624

0.8497

0.8747

0.8693

HJ

0.0547

0.0451

****

0.9793

0.9330

0.8929

0.8964

0.8913

0.8636

0.8609

0.8429

0.8787

0.8672

SCD

0.0613

0.0512

0.0210

****

0.9315

0.8970

0.9006

0.8951

0.8644

0.8617

0.8455

0.8810

0.8686

XS

0.0789

0.0794

0.0694

0.0709

****

0.8941

0.8989

0.8886

0.8634

0.8515

0.8378

0.8676

0.8545

CMY

0.1313

0.1201

0.1133

0.1087

0.1119

****

0.9721

0.9687

0.8963

0.8777

0.8703

0.8940

0.8943

BG

0.1357

0.1271

0.1093

0.1047

0.1066

0.0283

****

0.9758

0.8956

0.8744

0.8658

0.8940

0.8857

WS

0.1378

0.1270

0.1151

0.1108

0.1181

0.0318

0.0245

****

0.8921

0.8780

0.8691

0.9014

0.9001

Kago

0.1477

0.1411

0.1466

0.1457

0.1469

0.1095

0.1102

0.1142

****

0.9666

0.9577

0.9566

0.9523

Shiko-2

0.1543

0.1480

0.1498

0.1489

0.1608

0.1305

0.1342

0.1301

0.0339

****

0.9731

0.9562

0.9538

Shiko-1

0.1709

0.1629

0.1709

0.1679

0.1769

0.1389

0.1441

0.1403

0.0432

0.0272

****

0.9373

0.9397

Goto-1

0.1445

0.1339

0.1293

0.1267

0.1420

0.1121

0.1120

0.1038

0.0444

0.0448

0.0647

****

0.9766

Goto-2

0.1538

0.1401

0.1425

0.1409

0.1572

0.1117

0.1214

0.1053

0.0489

0.0473

0.0621

0.0237

****

 According to AMOVA analysis, there were highly significant (P < 0.001) genetic differences among the thirteen populations of C. japonica. Of the total genetic diversity, 22.45% was attributed to among populations and the rest (77.55%) resided among individuals. Thus, AMOVA also supports the results of Nei’s gene diversity statistics and Shannon’s information measure. Populations of C. japonica were also grouped in three geographic regions: Zhejiang, China (TH, ZJJ, SCD, HJ and XS), Shandong, China (CMY, BG and WS), and Japan (Kago, Shiko-1, Shiko-2, Goto-1 and Goto-2). Similarly, the AMOVA of three geographic regions revealed that 19.15% of the total variation could be accounted for among geographic regions, 7.68% by differentiation accounting for variation among populations within regions, and the remainder (73.17%) partitioned among individuals within populations. The AMOVA showed there was a significant (P < 0.001) partitioning of genetic differentiation among these three geographic regions (Table 5).

Table 5 Analysis of molecular variance (AMOVA) within/among C. japonica populations and within/among geographic regions

Source of variation

d.f.

SSD

MSD

Variance component

Total variance(%)

P-value

Among populations

12

2873.467

239.456

7.158

22.45

<0.001

Within populations

377

9321.833

24.726

24.726

77.55

<0.001

Among populations

within regions

10

1025.907

102.591

2.595

7.68

<0.001

Within populations

377

9321.833

24.726

24.726

73.17

<0.001

Among regions

2

1847.560

923.780

6.470

19.15

<0.001

Note: Significance tests after 99.9 permutations.

A significant correlation was found between genetic distance and geographic distance (r = 0. 8154, P <0.05) based on the Mantel test. This study shows that geographic isolation strongly influenced genetic differentiation among populations.

4 Discussions

4.1 Genetic diversity of C. japonica and the affecting factors

According to Frankham (1997), the ratio of allozyme genetic variation in island/mainland for endemic species was proportionately lower than that for nonendemic populations, which implying that insular endemic species had proportionately lower genetic variation than nonendemic specie. C. japonica is a nonendemic species, our ISSR survey of thirteen insular populations of C. japonica (BG and WS were transplanted from Changmenyan Island)revealed a high level of genetic variation at the species level, and with 90.05% of bands being polymorphic. We compared C. japonica with insular endemic species, such as Machilus thunbergii (PPB: 61.30%, at the species level) (Leng et al., 2006), Ilex integra (PPB: 57.70%, at the species level) (Leng et al., 2005), and the level of genetic diversity in C. japonica at the same level was indeed high. However, relatively low genetic diversity existed within populations where PPB values ranged from 66.35% to 77.25%, with an average of 71.31%, implying that substantial proportion of variation resided among populations. Thus C. japonica belongs to the subset of insular plants possessing high levels of genetic variability (Wendel & Parks, 1985; Chung & Kang, 1996).

C. japonica maintains higher levels of genetic variability within populations than other woody species (Wendel & Parks, 1985; Chung & Kang, 1996; Nybom, 2004). Factors contributing to the high levels of genetic diversity found in other Camellia species include long generation times, ability to regenerate by stump sprouting, predominant outcrossing included by animal vectors, and occasionally pollen dispersal by birds (Kondo et al., 1982; Hanwick & Godt, 1989; Chung & Kang, 1996; Oh et al., 1996; Ueno et al., 1996; Kunitake et al., 2004; Harue et al., 2006 ). Kondo et al. (1982) suggested that the seeds of Camellia species including C. japonica are often eaten by Microscelis amaurotis in captivity, and usually passed through the digestive tract without being killed. Higuchi (1975) observed that Parus varius owstoni in Miyake Island hoarded the seeds of C. japonica. Yomoto (1997) reported that Camellia seeds can also dispersed by Zosterops japonicais. These reports suggest that some rodents and birds contribute to the seed and pollen dispersal for C. japonica.

There are clear associations between population size and genetic variation in wildlife, both within and among species (Soulé, 1976; Hanwick & Godt, 1989; Frankham, 1996; Francisco-Ortega, 2000). Francisco-Ortega compared 22 taxa in the Canary and found that the diversities of species with large population sizes (more than 2500 individuals) were higher than those of species with small population sizes (fewer than 100 individuals). XS population exhibits the lowest level of variability (PPB: 67.30%, HE: 0.2344, H: 0.3478, respectively), corresponding to the smallest population sizes. Human activities have been the major cause of population sizes reduction in Xiangshan in the past 50 years through over-exploitation and habitat loss. Reduction in population sizes may lead to increased inbreeding depression and lowered fitness (Ellstrand & Elam, 1993; Frankham, 1998). This in turn would lower the diversity of XS population, and also lower its ability to compete with introduced species, to cope with disturbed habitats, and to adapt to natural changes in the environment (Ellstrand & Elam, 1993; Ferson & Burgman, 1995; Menges, 1998; Frankham, 1998).

4.2 Genetic structure of C. japonica and the affecting factors

Analysis of the ISSR markers using different approaches (Nei’s gene diversity statistics, Shannon’s information measure and AMOVA) demonstrated similar interpretations of the genetic structure of the populations of C. japonica. AMOVA showed that 22.45% of the total variation results from differentiation among populations. Reproductive biology is the most important factor in determining the genetic structure of plant populations (Hamrick & Godt, 1989). Typically, inbreeding species maintain relatively more of their genetic diversity among populations rather than within populations than do outcrossers (Brown, 1979). In the previous review on estimates of genetic diversity obtained with RAPD markers, Nybom and Bartish (2000) compiled mean Gst values of 0.59, 0.19 and 0.23 for selfing, mixed mating and outcrossing plants, respectively. Compared with these values, the amount and pattern of genetic variation in C. japonica is similar to outcrossing species. C. japonica is actually a predominant outcrossing species with the characteristics of self-compatibility, though its flowers are bisexual (Hanwick & Godt, 1989; Harue et al., 2006). The level of genetic differentiation is affected by a number of factors such as the species’ breeding system, genetic drift or genetic isolation of populations (Hogbin & Peakall, 1999; Chung & Kang, 1996; Frankham, 1997; Francisco-Ortega, 2000). If populations are small and isolated from one another, the genetic drift could be capable of influencing the genetic structure and increasing differentiation among populations (Barrett & Kohn, 1991; Ellstrand & Elam, 1993). Relatively low level of gene flow among populations is characteristic of C. japonica (Chung & Kang, 1996). Genetic differentiation among populations has also been reported before (Wendel & Parks, 1985; Chung & Kang, 1996; Ueno et al., 2002). Nm, the effective gene flow per generation (Nm = 0.9255), for C. japonica was lower than one successful migrant per generation, indicating limited gene flow occurs among populations. This corresponds well with the isolation of populations. In this case, the evident genetic differentiation among populations of C. japonica seems to be correlated with geographic distance among the populations. For example, there is the largest geographical distance (1120 km) between XS and Shikoku-1 and their genetic distance (0.1581) is also the largest one among all populations. As discussed in other studies (e.g. Leng et al., 2006), the presence of such a correlation suggests an important role for isolation in C. japonica, in line with the observed pronounced differentiation among populations. So genetic differentiation among populations is expected to have originated from geographic isolation, isolation would restrict gene flow and then lead to random genetic drift. Additionally, the local adaptation resulting from genotype environment interactions may also lead to genetic divergence among populations (Cao et al., 2006).

4.3 Conservation implications

The ultimate goals of conservation are to ensure sustainable survival of populations and to preserve their evolutionary potential (Cao et al., 2006). Loss of genetic diversity could be lead to a decrease in a species’ ability to survive environmental changes and demographic fluctuations both in short and in long term (Ellstrand and Elam, 1993; Milligan et al., 1994; Reisch et al., 2003). Therefore, information of the levels and distribution of genetic diversity is important for designing conservation strategies for this species (Hamrick, 1983; Hamrick and Godt, 1989; Francisco-Ortega et al., 2000; Cao et al., 2006). C. japonica is an important horticultural and economic species, distributing naturally on some islands in East Asia, due to over-exploitation after 1950s,populations and habitats of the species have decreased sharply (Zhou et al., 1994). So it became necessary to conserve the biodiversity of C. japonica. The level of population diversity and differentiation revealed here for C. japonica has clear conservation and management implications. The management for the conservation of genetic variability in C. japonica should aim to preserve the populations with low genetic diversities. This is because extinction of the populations would reduce total genetic variability considerably. For a long period, the most suitable strategy for the conservation of C. japonica is the protection of its habitat. Human utilization is the best preservation, so the successful artificial propagation of this species in future could not only guarantee its ex situ conservation and sustainable survival, but also enhance the in situ conservation. Therefore, detailed studies of the reproductive biology of this species should be carried out to yield valuable information for conservation management of C. japonica.

Acknowledgements:

We would express our great thanks to Prof. Yamaguchi Satoshi in faculty of Agriculture of Ehime University in Japan, Mr. Wang Guomin in Zhejiang Zhoushan Academy of Forestry, and Mr. Xin Zaocai in Qindao Dongdu Industry Co.Ltd., for their help in collecting samples. This work was supported by the National Key Twelfth-Five Science and Technology Program (2012BAD01B0703), International Cooperation Project of China (2011DFA30490), and Zhejiang Key Flower Breeding Program (2012C12909-6).

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