High Genetic Diversity but Limited Gene Flow Among Remnant and Fragmented Natural Populations of Liriodendron Chinense Sarg

Biochemical Systematics and Ecology 54 (2014) 230–236

Contents lists available at ScienceDirect

Biochemical Systematics and Ecology

journal homepage: www.elsevier.com/locate/biochemsyseco

High genetic diversity but limited gene ﬂow among remnant and fragmented natural populations of Liriodendron chinense Sarg

Kangqin Li, Long Chen, Yuanheng Feng, Junxiu Yao, Bo Li, Meng Xu, Huogen Li*

Key Laboratory of Forest Genetics & Gene Engineering of the Ministry of Education, Nanjing Forestry University, Longpan Road 159#, Nanjing 210037, Jiangsu, China article info abstract

Article history: As a relic species, Liriodendron chinense is now recognized as an endangered species. To better Received 29 October 2013 understand the genetic structure and differentiation among remnant populations of L. chi- Accepted 25 January 2014 nense, we determined the genotypes of 14 simple sequence repeats (SSRs) loci across 318 Available online individuals from 12 natural populations and 750 seedlings from five offspring populations. We found that L. chinense maintained high genetic diversity (He ¼ 0.7385) within populations Keywords: but moderate genetic differentiation (Fst ¼ 0.1956) and low gene flow (Nm ¼ 1.0283) between Liriodendron chinense populations. The genetic diversity was slightly lower for offspring populations than for their Genetic structure Relic species corresponding natural populations. Moreover, using a two-phased model of mutation (TPM), fi SSR we demonstrated that signi cant bottlenecks had occurred in six populations. A Mantel test revealed a statistically significant correlation between the geographic distances and genetic distances between populations (r ¼ 0.5011, P ¼ 0.002). Hence, we presume that geographical isolation and habitat fragmentation might contribute jointly to current population structure of L. chinense. We suggest that populations from southern Yunnan can be regarded as a variety of L. chinense, given their large deviation from other populations. Our findings may be of value for the conservation and use of L. chinense. Ó 2014 Elsevier Ltd. All rights reserved.

1. Introduction

As members of the class of “basal angiosperms” defined by plant phylogeny, plants belonging to the order Magnoliales have been keys to understanding the evolutionary history of flowering plants (Craene et al., 2003). The only two extant members of Liriodendron are L. chinense Sarg. and L. tulipifera Linn. L. chinense is native to southern China and northern Vietnam, existing in evergreen or deciduous broadleaved mixed forests at elevations from 700 m to 1900 m (Hao et al., 1995). Our understanding about the factors that contributed to the endangered status of L. chinense has improved considerably in recent decades. These factors include small and isolated populations, a low percentage of seed setting, a low seed germination rate, and anthropogenic disturbances (Fang et al., 1994; Hao et al., 1995; He and Hao, 1999; Huang et al., 1998). However, the paucity of knowledge about the genetic structure and level of differentiation within natural populations of L. chinense may greatly limit our capacities to assess accurately the extent to which genetic diversity is preserved, and to develop scientific strategies to conserve the germplasm of L. chinense.

* Corresponding author. Fax: þ86 25 85427760. E-mail addresses: [email protected], [email protected] (H. Li). http://dx.doi.org/10.1016/j.bse.2014.01.019 0305-1978/Ó 2014 Elsevier Ltd. All rights reserved. K. Li et al. / Biochemical Systematics and Ecology 54 (2014) 230–236 231

In this paper, we used microsatellite markers to quantify the genetic variation, genetic structure, and genetic differentiation within natural mature populations of L. chinense. We also compared the level of genetic diversity between natural mature populations of L. chinense and their corresponding offspring populations. Our hope in designing this study was to provide useful information to develop reasonable strategies for the conservation, management, and optimal exploitation of the genetic resources of L. chinense.

2. Materials and methods

2.1. Plant populations and plant sampling

Twelve populations that covered most of the natural distribution area of L. chinense were selected as sample populations (Table 1). The geographical location and elevation of each population was recorded using a Global Positioning System (GPS). The spatial distance among sampled individuals was predetermined to be no less than 50 m to reduce the probability of finding related samples. The sample size for each population ranged from 27 to 36 individuals, except for three small populations (populations 7, 8, and 10) for which the population size was limited or it was too difficult to collect plant samples. In total, 318 individuals were sampled. For each individual, fresh leaves or winter buds were dried with silica gel and stored at room temperature. In five of the twelve populations (population codes 1, 2, 3, 4, and 5; Table 1), seeds were collected from individuals that were sampled. Seeds from the five populations were sowed on seedbeds around the middle of February. In late April, the seeds germi- nated, and 800–1500 seedlings were obtained for each population. In early June of the same year, 150 seedlings were randomly sampled from each offspring population to collect fresh leaves. Leaf samples were collected from a total of 750 seedlings. All of these leaf samples were stored at 70 C prior to DNA extraction.

2.2. DNA extraction and EST-SSR genotyping

Genomic DNA was extracted using an improved method involving the use of cetyltrimethyl ammonium bromide (Zhang et al., 2004). Concentrations of genomic DNA were estimated after electrophoresis through 1% agarose gels. Fourteen pairs of high polymorphic EST-SSR markers that were screened from 176 nuclear microsatellite markers (Xu et al., 2010,1)were applied to genotype all of the plant samples mentioned above. The polymerase chain reaction (PCR) ampliﬁcation was carried out in a 10 ml reaction mixture that contained 20 ng of DNA, 1 mlof10 Taq polymerase buffer, 0.2 mmol/L of MgCl2, 0.2 mmol/L of dNTPs, 0.25 mmol/L of each primer, and 0.25 U of Taq polymerase. PCR cycling conditions involved one cycle of 94 C for 4 min; 15 touch-down cycles of 94 C for 15 s, 60 C for 15 s (with a temperature decrease of 0.7 C for each cycle), 72 C for 30 s; 15 cycles of 94 C for 15 s, 49.5 C for 15 s, 72 C for 30 s; and one cycle of 72 C for 20 min. The ampliﬁed products were detected by separation on 8% SDS-PAGE, and the bands were visualized by silver staining.

2.3. Data analysis

2.3.1. Genetic diversity and differentiation within populations POPGENE 1.32 (Yeh and Boyle, 1997) was used to estimate several parameters that describe population genetics. These included the average number of alleles (A), effective number of alleles (Ne), observed heterozygosity (Ho), expected heterozygosity (He), Shannon’s Information index (I), and Nei gene diversity index (Nei). The proportion of diversity among populations was evaluated by F-statistic values (Fst), and the gene ﬂow (Nm) among populations was estimated using the equation Nm ¼ (1 – Fst)/4Fst. Then, MICRO-CHECKER was used to test the null alleles for each polymorphic locus (Van Oosterhout, 2004).

2.3.2. Genetic relationships between populations A cluster dendrogram (unweighted pair-group method with arithmetic means, UPGMA), which was based on the average genetic distances estimated from POPGEN 1.32, was constructed to evaluate the genetic relationships between populations using NTSYS-pc 2.10e (Rohlf, 2000). A Mantel test was used to evaluate the correlation between matrices of geographic distance and genetic distance among population using TFPGA (Miller, 1997). A Bayesian clustering algorithm was applied to assign the accessions among 12 populations using the software STRUCTURE version 2.3 (Pritchard et al., 2000). To determine the most likely number of populations, the assumed population number (K) was ascertained to range from 2 to 20. (Pritchard et al., 2000). The simulation was replicated independently ten times, following 10,000 burn-in periods and 10,000 iterations for each value of K. The optimum number of clusters was inferred by determining the maximum value of Ln P(D) (SD) (Falush et al., 2003).

2.3.3. Bottleneck detection To evaluate whether the natural populations had undergone bottlenecks in history, Bottleneck version 1.2.02 software (Piry et al., 1999) was used with a two-phased mutation model (TPM), and the Wilcoxon sign-rank test was employed to determine statistical signiﬁcance.

1 Reference contains information about SSR markers, such as oligo sequence, motif repeat number, etc. 232 K. Li et al. / Biochemical Systematics and Ecology 54 (2014) 230–236

Table 1 Information about the twelve populations of L. chinense.

Population Site Date of Latitude (N)& Elevation Sample Voucher numbers & code tissue Longitude (E) (m) size depositarya collection 1 Longwangshan Nature Reserve, Anji County, Zhejiang Province 2007 11925.960E 935–1000 32 00081457,PE 3024.250N 2 Qingliangfeng Nature Reserve, Jixi County, Anhui Province 2007 11849.940E 750–1190 32 00081529,PE 3007.390N 3 Danshan Nature Reserve, Xuyong County, Sichuan Province 2007 10529.210E 1100–1410 29 0028959,CDBI 2811.800N 4 Laoshanjie, Shangzhong, Liping County, Guizhou Province 2006 10840.320E 900–1100 34 20010020014, 2630.110N NJFU 5 Panxing, Songtao County, Guizhou Province 2006 10919.180E 700–900 33 00875730,PE 2809.208N 6 Jiulongshan, Suichang County, Zhejiang Province 2007 11849.620E 880–1410 36 00081540,PE 2824.660N 7 Mount Nature Reserve, Wuyishan City, Fujian Province 2007 11748.250E 1680–1790 6 00081629,PE 2752.870N 8 Jiugongshan, Xianning City, Hubei Province 2007 11418.270E 900–1000 12 20010020089, 2949.590N NJFU 9 Xishui Nature Reserve, Xishui County, Guizhou Province 2006 10553.290E 1100–1250 34 0028958, CDBI 2814.520N 10 Fenshuiling Nature Reserve, Jinping County, Yunnan Province 2007 10313.530E 1260–1470 13 40579,KUN 2246.620N 11 Xiajinchang, Malipo County, Yunnan Province 2007 10427.900E 1420–1480 27 00081648,PE 2317.720N 12 Maoershan Nature Reserve, Ziyuan County, Guangxi Province 2011 11023.840E 1100–1200 30 0014029, IBSC 2552.4210N

a PE, Herbarium code of Institute of Botany, the Chinese Academy of Sciences (CAS); CDBI, Herbarium code of Chengdu Institute of Biology, CAS; NJFU, Herbarium code of Nanjing Forestry University; KUN, Herbarium code of Kunming Institute of Botany, CAS; IBSC, Herbarium code of South China Instituteof Botany, CAS.

3. Results

3.1. The overall genetic diversity across 12 natural populations of L. chinense

The genetic diversity based on 14 SSR loci was evaluated in 318 individuals from 12 natural populations of L. chinense.A total of 94 alleles were detected over 14 SSR loci, with an average number of alleles (A) of 6.71 and a range from 3 to 14 for each locus. The effective number of alleles (Ne) varied from 2.16 to 8.46 per locus, with an average number of 4.22. The expected heterozygosity (He) ranged from 0.53 to 0.89 per locus, with an average value of 0.74, indicating that the remnant natural populations of L. chinense maintain a high level of genetic diversity. Results of MICRO-CHECKER identiﬁed potential null alleles at seven loci (LT26, LT49, LT62, LT80, LT91, LT102, LT151), however, no evidence for scoring error or large allele dropout was detected (Van Oosterhout et al., 2004). So, we retained these loci for genotyping of all populations.

3.2. The genetic diversity within natural populations of L. chinense

The genetic variations over 14 SSR loci within 12 natural populations were also evaluated. The effective number of alleles (Ne) varied from 1.98 (population 10) to 3.74 (population 5). Nei’s expected heterozygosity index (Nei) ranged from 0.41 (population 10) to 0.70 (population 5). The highest level of Shannon’s Information index (I) was 1.41 (population 5), and the lowest value was 0.71 (population 10) (Table 2). Overall, population 5 had the highest genetic diversity, whereas population 10 had the lowest.

3.3. Population genetic structure among natural populations of L. chinense

A moderate level of genetic differentiation (Fst ¼ 0.1956) was observed among the 12 natural populations. And, Pairwise Fst values between populations were detected. The lowest value of Fst (0.0523) was between population 4 and population 5, and the highest Fst value (0.2176) was found between population 3 and population 11. Two populations (10, 11) hold the highest pairwise Fst values when compared with other populations. A signiﬁcant correlation was found existing between Fst values and genetic distance (r ¼ 0.4410, P ¼ 0.004).

3.4. Gene ﬂow

A low level of gene ﬂow (Nm ¼ 1.0283) was observed among the 12 populations. A signiﬁcant negative correlation was found existing between Nm and the genetic distance among population pairs (r ¼0.5002, P ¼ 0.001). K. Li et al. / Biochemical Systematics and Ecology 54 (2014) 230–236 233

Table 2 Genetic diversity of natural populations and their offspring populations in L. chinense, as revealed by analysis of SSR markers.

Population code ANe IHo He FNei Natural population 1 5.7 3.51 1.35 0.57 0.68 0.15 0.67 2 5.79 3.47 1.38 0.52 0.70 0.25 0.69 3 4.43 2.35 0.97 0.37 0.52 0.30 0.51 4 5.21 3.59 1.31 0.60 0.67 0.11 0.66 5 5.50 3.74 1.41 0.54 0.71 0.24 0.70 6 4.93 3.08 1.21 0.46 0.64 0.30 0.64 7 4.07 3.13 1.21 0.57 0.72 0.21 0.66 8 3.57 2.82 1.07 0.57 0.63 0.09 0.60 9 4.14 2.60 1.01 0.40 0.55 0.27 0.54 10 2.93 1.98 0.71 0.31 0.43 0.28 0.41 11 2.86 2.03 0.74 0.30 0.45 0.32 0.44 12 4.29 2.96 1.15 0.48 0.64 0.24 0.63 Eastern 6.57 4.12 1.52 0.52 0.73 0.29 0.73 Western 6.50 3.94 1.45 0.45 0.71 0.38 0.71 Offspring population AJ 4.71 2.61 1.05 0.39 0.57 0.32 0.57 JX 5.07 3.01 1.18 0.49 0.63 0.23 0.63 XY 3.21 2.040 0.72 0.25 0.41 0.39 0.41 LP 4.93 3.00 1.19 0.54 0.62 0.14 0.62 ST 5.21 2.81 1.17 0.48 0.61 0.20 0.60

A, average number of alleles; Ne, Effective number of alleles; I, Shannon’s Information index; Ho, observed heterozygosity; He, average expected heterozygosity; F, Fixation index; Nei, Nei’s expected heterozygosity.

3.5. Genetic relationships among populations

To elucidate the genetic relationships between the studied populations, the average genetic distances was used to generate a UPGMA tree (Fig. 1). Twelve populations were grouped into two distinct clusters. Two populations (10, 11) from southern Yunnan formed a cluster, and the remaining ten populations formed another cluster, which comprised three subgroups. The first subgroup included three populations, two of which (populations 1 and 2) were from the east, and one of which (population 12) was from the west. The second subgroup comprised four western populations (populations 3, 4, 5, and 9), and the third subgroup consisted of three eastern populations (populations 6, 7, and 8). This grouping enabled two in- ferences to be drawn easily. First, there is a significant deviation between populations from southern Yunnan and populations from other districts. Secondly, there is not a clear borderline between eastern populations and western populations, although the genetic relationship among adjacent populations was closer than that among remote populations. A Mantel test, which was implemented to test the correlation between genetic distance and geographic distance, revealed that the correlation was statistically significant (r ¼ 0.5011, P ¼ 0.002). This suggests that the proposed isolation by distance in L. chinense might be authentic. Next, the STRUCTURE software package, which uses Bayesian analysis, was applied to investigate the structure of natural populations in L. chinense. The optimum K value of 9 indicates the number of clusters. All 318 individuals were assigned to the nine newly formed clusters (Table 3). Twelve populations were entirely grouped into these nine clusters. In addition, dispersion over long distance was found within the population pair 1 and 4, the population pair 6 and 4, the population pair 6 and 5, the population pair 2 and 5, and the population pair 2 and 4 (Table 1).

3.6. Bottleneck detection

Among twelve natural populations, six populations (populations 4, 5, 7, 8, 11, and 12) were inferred to undergo recent bottleneck events (Table 4), indicating that these six populations were probably derived from the bottleneck events.

3.7. Comparison of genetic diversity between natural populations and their corresponding offspring populations

In the five offspring populations examined, a total of 87 alleles were observed, ranging from 3 to 12 alleles per locus. The mean values of Ne and He across fourteen SSR loci were 3.51 (range,1.66–5.16) and 0.68 (range, 0.40–0.81), respectively. Across the five offspring populations, values of Ne ranged from 2.04 (population 3) to 3.01 (population 2). Values of Nei ranged from 0.41 in population 3 to 0.63 in population 2. The offspring population from population 4 maintained the highest I (1.19) (Table 2). The genetic differentiation coefficient (Fst) among five offspring populations was 0.1722, which was significantly more than that of 5 natural populations (0.1254). Overall, the genetic diversity of offspring populations was a little lower than that of corresponding natural populations. This indicates that L. chinense may have self-maintaining mechanism to control its level of genetic diversity. 234 K. Li et al. / Biochemical Systematics and Ecology 54 (2014) 230–236

Fig. 1. Dendrogram showing levels of relatedness of twelve natural populations of L. chinense.

4. Discussion

4.1. Why does L. chinense maintain a high level of genetic diversity?

The genetic diversity of L. chinense estimated in this study (He ¼ 0.7385) is high in comparison with most of woody plants (Kikuchi and Isagi, 2002; Dayanandan et al., 1998; Hornero et al., 2001; Amarasinghe and Carlson, 2002), which is an un- expected result so far as its endangered status. The lowest value of Nei (0.4253) in the 12 natural populations of L. chinense is obviously higher than that in perennial woody plants (Nei ¼ 0.18) and outcrossing woody species (Nei ¼ 0.21) (Hamrick and Godt, 1989). Furthermore, the genetic diversity in offspring populations was only slightly less than that of current natural populations. All these ﬁndings above indicate that L. chinense relies on its own capacity to maintain genetic diversity, as reported for other epibiotic species (Hattemer, 1995). This might be attributed to the high level of outcrossing in L. chinense (Feng et al., 2010), given that outcrossing augments the level of heterozygosity in offspring populations.

4.2. The moderate genetic differentiation and low gene ﬂow between populations of L. chinense

High levels of genetic differentiation may result from small population sizes and low levels of gene flow caused by habitat fragmentation (Young et al., 1996). The effects that result from fragmentation and small population sizes can be assessed by observing patterns of change in the genetic structures of populations. Given that 19.56% genetic differentiation among populations exceeds the lower threshold value of 10% in outbreeding species, L. chinense appears to maintain a moderate level of genetic variation between populations. Moreover, the significant correlation between Fst and genetic distance among populations indicated that long-term spatial isolation hinders the movement of pollinators and the dispersal of seeds among populations and promotes population divergence. Gene flow is an important force that might counteract the effect of natural selection, an evolutionary force that results in the differentiation of populations (Hamrick and Godt, 1989). A low level of gene

Table 3 The number of individuals in each of twelve natural populations assigned to the nine clusters respectively.

Pop. Cluster

C1 C2 C3 C4 C5 C6 C7 C8 C9 1 29 3 2 21 29 3 326 4 12 31 51 2813 6232 1 1 75 1 812 91 33 10 13 11 27 12 30 Total 19 34 30 40 34 34 60 35 32 K. Li et al. / Biochemical Systematics and Ecology 54 (2014) 230–236 235

Table 4 The detection of bottleneck effect for six natural populations in L. chinense.

Population P value Population P value 1 0.12195 7 0.00201** 2 0.08386 8 0.00085** 3 0.83020 9 0.06360 4 0.01074* 10 0.28467 5 0.00085** 11 0.00061** 6 0.08386 12 0.00031**

*Signiﬁcant at the 0.05 level. **Signiﬁcant at the 0.01 level.

flow increases the level of genetic variation within populations and promotes genetic differentiation between populations (Epperson and Allard, 1989). Generally, perennial woody plants maintain a steady level of gene flow by virtue of their abilities to produce massive amounts of pollen and seed (Vranckx et al., 2012). The low level of gene flow (Nm ¼ 1.0283) in the 12 natural populations of L. chinense studied here probably results from small population sizes and fragmentation (Semaan and Dodd, 2008). The significant negative correlation between Nm and genetic distance implied that smaller geographic distances led to increased rates of gene exchange, such as the relatively short geographic distances existed between population pairs (populations 1 and 2, populations 6 and 7, and populations 3 and 9), and high level of gene flow in them respectively.

4.3. Populations in Southern Yunnan of L. chinense could be regarded as a variety

The two populations (populations 10 and 11) from southern Yunnan constitute one cluster, the remaining ten populations make up another cluster (Fig. 1). This classification is consistent with apparent deviations in phenotype and phenology between these two clusters. For example, whereas L. chinense plants in most districts have a deciduous habit, those in southern Yunnan are almost exclusively evergreen. This difference in phenology may derive from different climatic categories, with a tropical climate in southern Yunnan and subtropical climate in other places. Given the large differentiation between populations in southern Yunnan and those in other areas, not only in phenotype and phenology but also in genetics, we suggest that populations within southern Yunnan (probably as well as populations in northern Vietnam) should be classified as a variety of L. chinense. Why are these populations in southern Yunnan so different from populations in other places? This may be attributed to the Yunnan-Kweichow Plateau, which stretches 800 km east–west at elevations from 1000 m to 2000 m. Two populations (populations 10 and 11) in southern Yunnan (as well as populations in northern Vietnam) are situated at the south edge of Yunnan-Kweichow Plateau, whereas another ten populations are located in the north or to the east of Yunnan-Kweichow Plateau. Gene flow between southern Yunnan populations and other populations had been blocked for millions of years by the Yunnan-Kweichow Plateau. Furthermore, the different climate types led to different mutation and selection pressures within the distinct populations and further contributed to the substantial divergence between these two groups.

Acknowledgments

This study was ﬁnancially supported by grants from the National Natural Science Foundation of China (31170621, 30972391), Natural Science Foundation of Jiangsu Provincial College (10KJA220017), the National Science and Technology Support Program of China (2012BAD01B05), the Doctorate Fellowship Foundation of Nanjing Forestry University; and the Program Development of Jiangsu Higher Education Institutions (PAPD). We are grateful to Xiaofei Zhang, Hongyin Li, Jian Yang, Liming Bian, Xiaoyang Wang, Longqiang Wang, Bei Wang, Yanghui Fang, Xiaofeng Yuan, and Jing Ye for volunteering to assist with plant sample collection, and we also thank Qunfeng Luo, Wenting Pan, Li Qi, and Ying Yang for their assistance in lab work.

References

Amarasinghe, V., Carlson, J.E., 2002. The development of microsatellite DNA markers for genetic analysis in Douglas-ﬁr. Can. J. For. Res. 32, 1904–1915. Craene, L.P.R.D., Soltis, P.S., Soltis, D.E., 2003. Evolution of ﬂoral structures in basal angiosperms. Int. J. Plant Sci. 164, 329–363. Dayanandan, S., Rajora, O.P., Bawa, K.S., 1998. Isolation and characterization of microsatellites in trembling aspen (Populus tremuloides). Theor. Appl. Genet. 96, 950–956. Epperson, B.K., Allard, R.W., 1989. Spatial auto-correlation analysis of the distribution of genotypes within populations of lodgepole pine. Genetics 121, 369–377. Falush, D., Stephens, M., Pritchard, J.K., 2003. Inference of population structure using multilocus genotype data: linked loci and correlated allele frequencies. Genetics 164, 1567–1587. Fang, Y.M., You, L.X., Fan, R.W., 1994. Fecundity of natural and cultivated populations of Liriodendron Chinense (Hemsl.) Sarg. J. Plant Resour. Environ. 3 (3), 9– 13 (in Chinese with English abstract). Feng, Y.H., Li, H.G., Yang, J., Wang, X.Y., Wang, B., Yao, J.X., 2010. Comparison of reproduction success of Liriodendron chinense Sarg. and L. tulipifera Linn. J. Trop. Subtrop. Bot 18, 9–14 (in Chinese with English abstract). Hamrick, J.L., Godt, M.J.W., 1989. Allozyme diversity in plant species. In: Brown, A.H.D., Clegg, M.T., Kahler, A.L., Weir, B.S. (Eds.), Plant Population Genetics, Breeding and Genetic Resources. Sinauer, Sunderland, Massachusetts, USA, pp. 43–63. 236 K. Li et al. / Biochemical Systematics and Ecology 54 (2014) 230–236

Hao, R.M., He, S.A., Tang, S.J., Wu, S.P., 1995. Geographical distribution of Liriodendron chinense in China and its signiﬁcance. J. Plant Resour. Environ. 4 (1), 1– 6 (in Chinese with English abstract). Hattemer, H.H., 1995. Concepts and requirements in the conservation of forest genetic resources. For. Genet. 2, 125–134. He, S.A., Hao, R.M., 1999. Study on the natural population dynamics and the endangering habitat of Liriodendron Chinense in China. Acta Phytoecol. Sin. 23, 87–95 (in Chinese with English abstract). Hornero, J., Gallego, F.J., Martínez, I., Toribio, M., 2001. Testing the conservation of Quercus spp. microsatellites in cork oak, Q. suber L. Silvae Genet. 50, 162–167. Huang, S.Q., Guo, Y.H., Chen, J.K., 1998. Pollination rates and pollen tube growth in a vulnerable plant, Liriodendron chinense (Hemsl.) Sarg. (Magnoliaceae). Acta Phytotax. Sin. 36, 310–316 (in Chinese with English abstract). Kikuchi, S., Isagi, Y., 2002. Microsatellite genetic variation in small and isolated populations of Magnolia sieboldii ssp. japonica. Heredity 88, 313–321. Miller, M.P., 1997. Tools for Population Genetic Analyses (TFPGA) 1.3 a WindowsÒ Program for the Analysis of Allozyme and Molecular Population Genetic Data. Department of Biological Sciences, Northern Arizona State University. Piry, S., Luikart, G., Cornuet, J.M., 1999. BOTTLENECK: a computer program for detecting recent reductions in the effective population size using allele frequency data. J. Hered. 90, 502–503. Pritchard, J.K., Stephens, M., Donnelly, P., 2000. Inference of population structure using multilocus genotype data. Genetics 155, 945–959. Rohlf, F.J., 2000. NTSYS-pc. Numerical Taxonomy and Multivariate Analysis System, Version 2.1. Exeter Software, Setauket, NY. Semaan, M.T., Dodd, R.S., 2008. Genetic variability and structure of the remnant natural populations of Cedrus libani (Pinaceae) of Lebanon. Tree Genet. Genomes 4, 757–766. Van Oosterhout, C., Hutchinson, W.F., Wills, D.P.M., Shipley, P., 2004. MICRO-CHECKER: software for identifying and correcting genotyping errors in microsatellite data. Mol. Ecol. Notes 4, 535–538. Vranckx, G., Jacquemyn, H., Muys, B., Honnay, O., 2012. Meta-analysis of susceptibility of woody plants to loss of genetic diversity through habitat fragmentation. Conserv. Biol. 26, 228–237. Xu, M., Sun, Y.G., Li, H.G., 2010. EST-SSRs development and paternity analysis for Liriodendron spp. New. 40, 361–382. Yeh, F.C., Boyle, T.J.B., 1997. Population genetic analysis of codominant and dominant markers and quantitative traits. Belg. J. Bot. 129, 157–159. Young, A., Boyle, T., Brown, T., 1996. The population genetic consequences of habitat fragmentation for plants. Trends Ecol. Evol. 11, 413–418. Zhang, B., Zhang, L., Zhuge, Q., Wang, M.X., Huang, M.R., 2004. A rapid and simple method of total DNA extraction from tree. J. Nanjing For. Univ. 28 (1), 13– 16 (in Chinese with English abstract).