Biochemical Systematics and Ecology 39 (2011) 425–433

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Biochemical Systematics and Ecology

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Genetic diversity of the traditional Chinese medicinal Ypsilandra thibetica (): Applications for conservation

Hong-Tao Li a,b, Hong Wang a, Jun-Bo Yang a,b, De-Zhu Li a,b,* a Key Laboratory of Biodiversity and Biogeography, Kunming Institute of Botany, Chinese Academy of Sciences, Kunming 650204, China b The Germplasm Bank of Wild Species in Southwest China, Kunming Institute of Botany, Chinese Academy of Sciences, Kunming, Yunnan 650204, China article info abstract

Article history: Twelve microsatellite markers were developed to determine the genetic diversity and Received 31 December 2010 genetic structure of Ypsilandra thibetica, represented by a total of 90 individuals from six Accepted 3 June 2011 natural populations. All twelve microsatellite loci were polymorphic, and the results Available online 28 June 2011 indicated that a high genetic diversity was present within populations (mean RS ¼ 4.996; mean HE ¼ 0.615), with high levels of genetic structure (mean FST ¼ 0.165; mean Keywords: FIS ¼ 0.692) among populations. This pattern is likely attributable to consanguineous Conservation mating, and this hypothesis is supported by a low relatedness coefficient. Our study Genetic diversity fl Gene flow suggested that environment factors might restrict gene ow among populations. In Microsatellite markers addition, physical distances between populations were not related to genetic distances, Ypsilandra thibetica implying that ancestral populations might have been distributed over a wider area. These results suggest that Y. thibetica should be a high priority for conservation managers. Ó 2011 Elsevier Ltd. All rights reserved.

1. Introduction

The genus Ypsilandra Franchet (Melanthiaceae) comprises six herbaceous plant species and is mainly distributed in China (Chen and Minoru, 2000; Chen et al., 2003). Species of Ypsilandra are perennial with thick , and are associated with moist hillsides, shady slopes and forested habitats. Ypsilandra thibetica Franchet is endemic to southwestern China. It is distinguished from related species by the length of its pedicels and (6–10 mm), and by the 10–18 mm long that extend beyond its tepals at anthesis (Chen and Minoru, 2000). Y. thibetica has been used in traditional Chinese medicine especially in southwestern China (Xie et al., 2009). The pharmacological effects of Y. thibetica materials include hemostatic (Jiangsu, 1977; Zhou et al., 2003) and anticancer (Li et al., 1995) properties. Traditional medical treatments involving phytotherapy have played important roles for people throughout history, and modern medicine owes some if its success to these practices. More than 80% of the world’s population use medical treatments based on plant remedies, and more than 20% of the world’s pharmaceutical medicines are derived from (Rai et al., 2000). Unfortunately, overharvesting has resulted in a severe decline of many plant species and even the extinction of some (Mills, 2006). Preliminary investigations in China indicated that Y. thibetica habitats have shrunk and population numbers have declined over the last two decades. In our survey of its habitat, no wild populations of Y. thibetica harbored more than 30 individuals, and most contained fewer than 10 individuals. Their habitats have become highly fragmentized by anthropogenic disruptions. As such, successful conservation of this species will depend upon judicious interventions based on sound genetic diversity data of these populations. In this study, twelve polymorphic microsatellite markers for Y. thibetica

* Corresponding author. Key Laboratory of Biodiversity and Biogeography, Kunming Institute of Botany, Chinese Academy of Sciences, Kunming Yunnan 650204, China. Tel.: þ86 871 5223503; fax: þ86 871 5217791. E-mail addresses: [email protected] (H.-T. Li), [email protected] (H. Wang), [email protected] (J.-B. Yang), [email protected] (D.-Z. Li).

0305-1978/$ – see front matter Ó 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.bse.2011.06.004 426 H.-T. Li et al. / Biochemical Systematics and Ecology 39 (2011) 425–433 were developed and evaluated their diversity among 6 natural populations. The research objectives were to describe the genetic diversity of Y. thibetica and to propose conservation measures for this important medicinal herb.

2. Materials and methods

2.1. Materials

More than 20 wild populations of Y. thibetica were surveyed, of which most contained fewer than 10 individuals and only six had more than 20 individuals. Fourteen to 16 individuals from each of six natural populations of Y. thibetica were sampled from locations across its range, for a total of 90 individuals (Fig.1, Table 1). A 10 m minimum distance between individuals was implemented to reduce the potential of sampling ramets from the same genetic. Healthy, clean leaves were collected from each individual, then quickly desiccated and dried in silica gel. A portion of each sample was separated and deposited as a herbarium specimen at the Herbarium of the Kunming Institute of Botany, the Chinese Academy of Sciences (KUN).

2.2. DNA extraction, microsatellite marker development

For each sample, total genomic DNA was isolated from 0.2 g silica gel-dried leaf material ground in liquid nitrogen following a modified CTAB method (Doyle and Doyle, 1987), using 4% CTAB instead of 2%, and the addition of approximately 1% polyvinyl polypyrrolidone (PVP) and 2% b-mercaptoethanol. The isolation of microsatellite loci was performed according to the FIASCO method (fast isolation by AFLP of sequences containing repeats) (Zane et al., 2002). Total genomic DNA (about 500 ng) was completely digested with MseI and then ligated to an MseI AFLP adapter. A diluted digestion-ligation mixture (1:10) was amplified with adapter-specific primers (50-GAT- GAGTCCTGAGTAAN-30). Amplified DNA fragments of sizes ranging from 200 to 800 bp were enriched for repeats by magnetic 0 bead selection with 5 -biotinylated (AC) 15,(AG)15, and (AAG) 10 probes. Subsequently, enriched fragments were amplified again with adapter-specific primers. Polymerase chain reaction (PCR) products were purified using an EZNA Gel Extraction Kit (Omega Bio-Tek). Purified DNA fragments were ligated into the pGEM-T vector (Promega), and transformed into DH5a cells. Positive clones were tested by PCR using (AC) 10/(AG) 10/(AAG) 7 and T7/Sp6 primers. In total, 320 clones with positive inserts were sequenced with an ABI PRISM 3730XL DNA sequencer. A total of 240 (75%) sequences were found to contain

Fig. 1. Distribution and sampling locations for Ypsilandra thibetica in southwestern China. H.-T. Li et al. / Biochemical Systematics and Ecology 39 (2011) 425–433 427

Table 1 Localities and sample sizes for the six Ypsilandra thibetica populations.

Provinces Populations Codes Coordinates na Chongqing Nanchuan NC 29.10N, 107.05E 15 Longsheng LS 25.78N, 110.02E 15 Guangxi Maoershan ME 25.81N, 110.66E 16 Xinning XN 26.44N, 110.84E 15 Luding LD 29.92N, 102.25E 14 Yunnan Zhaotong ZT 26.90N, 102.92E 15

a The sample sizes (n) represent number of individuals analyzed.

microsatellite repeats, and 60 of them were suitable for designing locus-specific primers, using the primer 5.0 program (Clarke and Gorley, 2001). All 60 microsatellite loci were screened for polymorphisms using genomic DNA of 30 samples of Y. thibetica from six natural populations in southwest China. The PCR reactions were performed in 15 ml of reaction containing 30–50 ng genomic DNA, 0.6 mM of each primer, 7.5 ml2 Taq PCR MasterMix (Tiangen), 0.1 U Taq Polymerase/ml, 0.5 mM dNTP each, 20 mM Tris– HCl (PH8.3), 100 mM KCl, 3 mM MgCl2). PCR amplifications were conducted under the following conditions: 97 C for 3 min; 32 cycles at 94 C for 40 s, specific annealing temperature (Table 2) for 40 s, and 72 C for 1 min; followed by a final extension step at 72 C for 7 min. PCR products were separated and visualized using QIAxcel gel electrophoresis system (QIAGEN, Irvine, USA). Genotypes of 90 individuals were determined using 12 nuclear simple sequence repeat (nSSR) markers developed in this study (Y182, Y536, Y371, Y420, Y406, Y407, Y448, YL2, Y501, YL4, Y58, Y463).

2.3. Data analysis

The numbers of observed alleles (Na), numbers of alleles per population (Nap) and number of effective alleles (Ne)were calculated for each of the 12 loci, and overall Na and Ne were calculated for each of the six populations using GenALEX 6 (Peakall and Smouse, 2006). Summary statistics for allelic richness (El Mousadik and Petit, 1996), observed heterozygosity (HO), expected heterozygosity (HE; Nei, 1987), and fixation index (FIS ¼ 1HO/HE) were calculated over all loci and populations using FSTAT version 2.9.3.2 (Goudet, 1995), GENETIX 4.0.5 (Belkhir et al., 2001) and GENEPOP version 4.0 (Raymond and Rousset, 1995). FSTAT version 2.9.3.2 was used to test departures from Hardy–Weinberg equilibrium at each locus, as well as linkage disequilibrium between loci (alleles were randomized 1000 times over all samples).

Table 2 Specific primer sequences and allelic characteristics for fifteen microsatellite loci isolated from Ypsilandra thibetica.

0 0 Locus Repeat motif Primer sequences (5 –3 )Ta(C) Expect size(bp) Allele size (bp) A HO HE GenBank Accession no.

Y182 (AG)6AA(AG)7 F:TACTTAGGTGGGGTGGGC 55 228 224–248 8 0.242 0.680 GQ856940 R:AGGAACAAAAGGTGGTGA

Y536 (AC)10 F:TTCTTAGCTGTTGTGGGATT 54 103 95–105 6 0.323 0.768 GQ856933 R:CCTTCGACACTGTTTTGC

Y371 (AAG)10 F:GCTGCTCTTGGACATCGT 57 131 104–137 7 0.216 0.793 GQ856932 R:TATCCTCGCCCTGGTCTT

Y420 (AC)24AA(AC)10 F:CTTGGTCCTGTTGGTGGC 59 255 211–287 18 0.125 0.911 GQ856931 R:ATGGTGGAGAAGAGTTTGTTGA

Y406 (AG)6 F:TGTAATTCAAGCGGGATG 54 222 214–228 5 0.083 0.687 GQ856938 R:TAGCCTTCTAAACTCAGTCG

Y407 (AG)5AA(AG)4 F:AGTGGTGAAGAGGGTGAG 59 224 214–228 8 0.148 0.819 GQ856936 R:CAAGGCATCAAAGTCAAAT

Y448 (AG)9 F:CTAGATATTCAGATTCTTGGTC 55 163 157–171 6 0.306 0.663 GQ856939 R:GATCCTCCTTATGGCTCA

YL2 (AG)25 F:GTCGTGATTCCCAAGTGC 59 117 113–121 5 0.081 0.562 GQ856935 R:TGCTATGTCTCCGTCCCT

Y501 (AG)19 F:GAAAATGTAAACGCAAGA 52 107 89–119 11 0.350 0.854 GQ856929 R:CTCAGCTTTGGTAGGATC

YL4 (AC)8 F:GGGGAAGAAATAAAGACC 54 171 137–177 9 0.353 0.723 GQ856934 R:ATTGACACCTCATGTGGG

Y58 (TC)8 F:ACGTTGTGAGGAGGGACT 54 231 227–247 7 0.177 0.796 GQ856937 R:AAGGGAGATTGAGAAGAAGAG

Y463 (AG)7 F:AGAGGTTCAGGGAAGACA 54 264 256–262 4 0.030 0.415 GQ856930 R:ATCACAAACATCAAGGGA

Ta, PCR annealing temperature; A, number of alleles revealed; HO, observed heterozygosity; HE, expected heterozygosity. 428 H.-T. Li et al. / Biochemical Systematics and Ecology 39 (2011) 425–433

Table 3 Comparison of the genetic variability found at 12 microsatellite loci in Ypsilandra thibetica populations.

Locus Na Ne HO HE HT FST RhoST Y182 12 2.792 0.256 0.656 0.695 0.071 0.210 Y536 7 3.009 0.294 0.635 0.754 0.191 0.402 Y371 8 3.513 0.343 0.722 0.790 0.098 0.155 Y420 23 7.038 0.119 0.896 0.930 0.036 0.117 Y406 7 2.614 0.093 0.596 0.692 0.176 0.239 Y407 9 3.672 0.207 0.762 0.834 0.062 0.429 Y448 6 2.046 0.202 0.536 0.626 0.170 0.344 YL2 5 1.954 0.044 0.468 0.620 0.283 0.456 Y501 15 3.077 0.231 0.418 0.773 0.225 0.336 YL4 17 3.527 0.288 0.568 0.750 0.298 0.375 Y58 9 2.744 0.177 0.629 0.814 0.286 0.490 Y463 5 2.032 0.011 0.497 0.528 0.088 0.166 Mean 10.25 3.168 0.189 0.615 0.734 0.165 0.310

Locus code; number of observed alleles (Na); number of effective alleles (Ne); observed heterozygosity (HO); expected heterozygosity (HE); overall gene diversity (HT); population differentiation estimates FST and RhoST.

FSTAT and GENEPOP were used to estimate the expected total heterozygosity (HT), the expected within-population heterozygosity (HS; Nei, 1987), and the genetic differentiation among populations (FST and RhoST; Weir and Cockerham, 1984; Slatkin, 1995; Rousset, 1996). HT represents the genetic diversity in the total population, HS represents the average gene diversity within populations, and FST and RhoST are the coefficients of genetic differentiation among populations assuming the infinite allele model and a stepwise mutation model, respectively. Significant differences between these parameters at each locus were tested by the log-likelihood (G)-based exact test (Goudet et al., 1996) using a Monte Carlo Markov chain (MCMC) method. Relatedness coefficients (r-values) among all possible pairs of individuals and populations were estimated using Relat- edness 5.0.8 (Goodnight and Queller, 2001). The calculation is based on the frequency of alleles in the population. This measure of relatedness ranges from 1toþ1, where a positive value indicates that two individuals share more alleles that are identical than expected by chance. A hierarchical analysis of molecular variance (AMOVA) was implemented in ARLEQUIN version 3.11 (Excoffier et al., 2005). Population differentiation based on pairwise FST between populations was estimated. The association between pairwise estimates of population differentiation (logFST) and the natural logarithm of the corresponding geographical distance was estimated using a Mantel test (Mantel, 1967) in ARLEQUIN. The significance levels of results were tested with 1000 permutations. The relationships among populations were analyzed using a neighbor-joining (NJ) tree based on Nei’s distance among populations from nSSR data using NTSYSPC 2.11 (Rohlf, 1998). In addition, the program STRUCTURE ver. 2.3.2 (Pritchard et al., 2000) was used to ascertain a possible cryptic genetic structure in the analyzed dataset. The program estimates, using the Markov Chain Monte Carlo method, the natural logarithm of the probability that a given genotype X is part of a given population K (ln Pr (XjK)) to infer population structure. This approach assumes Hardy–Weinberg equilibrium and attempts to find population groupings that are not in linkage or Hardy–Weinberg disequilibrium. To quantify the variation in likelihood for each number of clusters (K), a series of 10 independent runs for each value of K ranging from 1 to 10 was performed, assuming an admixture model with independent allele frequencies and using a burn-in period of 2 106 iterations and a period of data collection of 2 106 iterations. The genetic relationships among populations of all individuals sampled was used in a Principal Coordinates Analysis (PCoA) to visually represent relationships between populations and to assess the distinctiveness of populations, to determine if there were distinct genetic groupings among populations using the GenAlEx V6 program. Recent gene flow was estimated using the program BayesAss, version 1.3 (Wilson and Rannala, 2003).The genetic distance and model-based clustering methods described above estimate historical and relatively long-term gene flow among

Table 4 Genetic variability estimates from populations of Ypsilandra thibetica.

Population ID No. of samples Na Ne HO HE RS FIS Pr XN 15 58 3.479 0.227 0.664 4.910 0.659 <0.001 0.0285 ZT 15 55 3.368 0.177 0.587 4.973 0.699 <0.001 0.0354 NC 15 57 3.183 0.230 0.621 5.231 0.630 <0.001 0.0211 LS 15 55 3.158 0.155 0.627 5.031 0.753 <0.001 0.0247 LD 14 54 3.005 0.152 0.658 5.250 0.769 <0.001 0.0272 ME 16 51 2.816 0.193 0.535 4.580 0.640 <0.001 0.0524 Total/Mean 90 55 3.168 0.189 0.615 4.996 0.692 <0.001 0.0316

Number of observed alleles (Na); number of effective alleles (Ne); observed heterozygosity (HO); expected heterozygosity (HE); allelic richness (RS); fixation indexes (FIS); probability of Hardy–Weinberg equilibrium (P) and relatedness coefficient (r). H.-T. Li et al. / Biochemical Systematics and Ecology 39 (2011) 425–433 429

Table 5 Results of analysis of molecular variance (AMOVA) of nSSR data from Ypsilandra thibetica populations.

Source of variation df Sum of squares Variance component Percentage of variation P-value Among populations 5 158.065 0.82998 17.62 0.01 Within populations 84 564.574 2.84111 60.32 Within individuals 90 93.500 1.03889 22.06 Total 179 816.139 4.70998

populations, whereas the method illustrated by Wilson and Rannala (2003) estimates the amount and direction of recent (i.e. over the last few generations) gene flow. The allele frequencies and inbreeding coefficients for each population, as well as the recent migration rates between all pairs of populations, were estimated with BayesAss. These analyses were performed by identifying individuals as immigrants or as descendants of recent immigrants from the observed temporary disequilibrium of multi-locus genotypic frequencies. The parameters (migration rate, allele frequency, and inbreeding coefficient) were esti- mated numerically with an MCMC simulation by inferring the estimated posterior probability. To estimate the posterior probability distribution of the parameters, the program was run with 999, 999 burn-in periods and 3 106 total iterations. Five independent runs were conducted, and the mean values were compared among populations. For the analysis of fine-scale genetic structure, an assignment test using GENECLASS ver. 2.0 (Piry et al., 2004) was con- ducted. GENECLASS uses multi-locus genotypes to assign individuals to a population of origin. In the assignment test, the likelihood of an individual’s multi-locus genotype belonging to a candidate set of populations is computed, and the individual is assigned to the population where the likelihood of its genotype is highest. The ‘leave one out’ procedure was used to reduce the bias of assigning the current individual to its source population. A simulation of 1000 randomly generated genotypes was then applied. To detect recent bottlenecks due to reductions in effective population size, the observed gene diversity was compared to equilibrium gene diversity given the observed number of alleles (Watterson, 1978, 1986) using BOTTLENECK 1.2.02 (Piry et al., 1999). All three models, i.e. the infinite allele model (IAM; Maruyama and Fuerst, 1985), two-phased model of mutation (TPM; Di Rienzo et al., 1994) and stepwise mutation model (SMM; Cornuet and Luikart, 1996) were used for the analyses with a Sign test (Cornuet and Luikart, 1996) and a Bayesian Wilcoxon sign-rank test (Luikart et al., 1998) following the manual.

3. Results

3.1. Microsatellite marker development

All twelve microsatellite primer pairs displayed polymorphisms. The number of alleles per locus (A) was 4–18 with an average of 7.83. Values for observed (HO) and expected (HE) heterozygosities ranged from 0.030 to 0.353 and from 0.415 to 0.911, with averages of 0.203 and 0.726, respectively (Table 2).

3.2. Genetic variability and relatedness

Genetic variability for Y. thibetica populations is shown in Table 3. In total, 123 alleles for the 12 nSSR loci were detected. The number of alleles detected at each of the 12 loci ranged from 5 (YL2, Y463) to 23 (Y420), with an average number of 10.25 alleles per locus. Overall gene diversity (HT) varied from 0.528 (Y463) to 0.930 (Y420) with a mean of 0.734. Observed heterozygosity (HO) over all populations also varied among loci from 0.011 (Y463) to 0.343 (Y371) with a mean of 0.189, while expected heterozygosity (HE) over all populations ranged from 0.418 (Y501) to 0.896 (Y420) with a mean of 0.615. HE averaged over all loci was relatively high, but it varied slightly across all populations, ranging from 0.535 to 0.664 (Table 4). Observed heterozygosity (HO) over all loci was relatively low compared to HE, ranging from 0.177 to 0.230. Consequently, FIS calculated at all loci for all populations ranged from 0.630 to 0.769, with an average of 0.692, indicating highly significant deviations from Hardy–Weinberg equilibrium. No significant linkage disequilibrium between loci across all populations was

Table 6

Values for pairwise comparisons of population differentiation estimates RhoST and FST for Ypsilandra thibetica populations.

XN ZT NC LS LD ME XN 0.196 0.208 0.242 0.186 0.235 ZT 0.187 0.201 0.180 0.177 0.160 NC 0.118 0.087 0.419 0.309 0.353 LS 0.093 0.170 0.144 0.260 0.392 LD 0.125 0.194 0.141 0.178 0.239 ME 0.168 0.263 0.234 0.183 0.197

Above diagonal: RhoST values; Below diagonal: FST estimates. 430 H.-T. Li et al. / Biochemical Systematics and Ecology 39 (2011) 425–433

Fig. 2. Neighbor-joining clustering of the six Ypsilandra thibetica populations based on Nei’s distances among populations.

observed, and all loci were used for further analysis. The degree of relatedness represented by r-values ranged from 0.0211 (population NC) to 0.0524 (population ME; Table 4).

3.3. Analysis of genetic differentiation and gene flow among populations

The analysis of molecular variance (AMOVA) partitioned 17.62% of the total genetic variation among populations, 60.32% within populations and 20.06% within individuals (Table 5). According to the AMOVA, genetic differentiation was moderate across the geographic range. FST and RhoST among the six populations were 0.165 and 0.310, respectively (Table 3). Results for pairwise comparisons of FST and RhoST for all populations are presented in Table 6. Pairwise FST (0.093–0.263) and RhoST (0.160–0.419) values had narrow ranges and moderate levels. Genetic differentiation was significant (P﹤0.001) among pop- ulations at all loci. A Neighbor-Joining (NJ) dendrogram based on Nei’s distances showed no relationship between clustered populations and their geographical distribution. The NJ tree revealed that sampled populations did not correspond to geographical locality (Fig. 2). The Mantel test also found no relationship between genetic differentiation and geographic distance (r ¼ 0.441, P ¼ 0.09). The STRUCTURE analysis detected four genetic clusters (Fig. 3). The posterior probability for a given K increased slightly, even after the appropriate K was reached. Individual assignments into the four clusters were consistent in the 10 replicate runs. Populations LD and ME each belonged to a single cluster, while the remaining two clusters comprised pairs of pop- ulations (XN, LS and ZT, NC) (Fig. 3). Similar results were reflected in the PCoA plots (Fig. 4). Estimates of recent migration rates between populations (up to the second generation of migrants) are shown in Table 7. Significant recent gene flow among populations was detected, but only from population LS to XN and from XN to ME. The migration rates into population XN from population LS and into population ME from population XN were 0.2728 and 0.2731, respectively. The results of tests for mutation-drift equilibrium are shown in Table 8. Under the TPM and SMM, all individual pop- ulations showed no evidence of a recent bottleneck (P > 0.05). On the other hand, the SIGN test and the Wilcoxon test under

Fig. 3. Individual assignment by STRUCTURE analysis; there were four clusters (K). Bold lines within the squares distinguish populations. Abbreviations of each population name are shown under the bars. H.-T. Li et al. / Biochemical Systematics and Ecology 39 (2011) 425–433 431

Fig. 4. Principle coordinate analysis indicated the genetic relationship between populations.

Table 7 Mean values of the posterior distribution of the migration rate (m) for each population, estimated using the program BayesAss.

Target populations Source populations

XN ZT NC LS LD ME XN 0.6882 0.0085 0.0114 0.2728 0.0091 0.0100 ZT 0.0072 0.9702 0.0085 0.0052 0.0049 0.0040 NC 0.0041 0.0073 0.9756 0.0039 0.0044 0.0047 LS 0.0084 0.0053 0.0074 0.9688 0.0055 0.0046 LD 0.0319 0.0073 0.0088 0.0197 0.9251 0.0072 ME 0.2731 0.0081 0.0099 0.0147 0.0089 0.6853

Values on the diagonal are the proportions of individuals derived from source populations. Contributions higher than 0.10 are in bold (m > 0.1). the IAM suggested the presence of a bottleneck in three populations (ZT, NC and LS) and four populations (XN, ZT, NC and LS), respectively. No population was estimated to have experienced a bottleneck according to IAM, TPM and SMM models.

4. Discussion

4.1. Population differentiation

AMOVA revealed a high level of genetic variation partitioned within the sampled populations and moderate genetic variation among populations (Nei, 1987). Moderate estimated subdivision (FST ¼ 0.165, RhoST ¼ 0.310) was consistent with the AMOVA test showing moderate population differentiation. This study suggested that the six populations of Y. thibetica were genetically differentiated. In addition, there was no relationship between genetic and geographic distance. Indeed, in some cases, populations only a few kilometers apart were less similar than those at the extremes of the geographic range. These results suggest that ancestral population of Y. thibetica might have been distributed over a wider area, and isolated pop- ulations would have accumulated genetic differences from other populations.

4.2. Gene flow

Although there was gene flow between populations, the Bayesian assignment analysis showed recent migration only from population LS to XN and from XN to ME (Wilson and Rannala, 2003). The absence of gene flow between other population pairs can be attributed to restricted pollen flow (via pollinators) and a lack of seed-dispersal mechanisms for this species. In addition, Y. thibetica is a perennial plant, and grows in cool and moist understories or on high-elevation hillsides. This environment might restrict gene movement among populations. In the absence of gene flow, genetic diversity can be expected to decline. Indeed, the SIGN test and the Wilcoxon tests under the IAM suggested recent bottlenecks in three populations.

Table 8 Bottleneck probability estimated using the program BOTTLENECK.

Population SIGN Wilcoxon

IAM TPM SMM IAM TPM SMM XN 0.05861 0.17867 0.41822 0.00464 0.09229 0.46973 ZT 0.01233 0.09147 0.54722 0.00488 0.05371 0.63770 NC 0.01897 0.49017 0.26885 0.00342 0.17480 0.70020 LS 0.04263 0.16666 0.39566 0.03418 0.12939 0.62207 LD 0.21139 0.42178 0.12958 0.08301 0.63770 0.46484 ME 0.48618 0.57644 0.40726 0.17791 0.48845 0.07328

IAM, infinite allele model; SMM, stepwise mutation model; SIGN, sign test; Wilcoxon sign-rank test. Sign test values are P values, significant results (p < 0.05) for sign test are in bold indicating evidence for bottleneck. 432 H.-T. Li et al. / Biochemical Systematics and Ecology 39 (2011) 425–433

4.3. Genetic structure and populations history

Sampled populations of Y. thibetica had genetic diversities of HE ¼ 0.615, HO ¼ 0.189 and HT ¼ 0.734 (Nei, 1978; Raymond and Rousset, 1995). Our study suggests that the each of three sampled populations, ZT, NC and LS, might have experienced a recent weak bottleneck (Piry et al., 1999), and that the entire Y. thibetica population has been reduced in size following range contraction in southwestern China due to overexploitation. However, the sampled populations still harbor genetic diversity even though their distributions have contracted. The six populations examined still harbored moderate genetic diversity and showed a low relatedness coefficient (r ¼ 0.0316), consistent with predominant crossing among descendants within a pop- ulation and little gene flow among populations.

4.4. Conservation perspectives

This study provides information about the genetic diversity of Y. thibetica populations, including relatively high fixation indices (FIS), a very low relatedness coefficient, and evidence of weak recent bottlenecks in certain populations, suggesting that each population is restricted to its distribution area and has contracted in size and range. Because Y. thibetica is an important medicinal herb, a multi-discipline approach for its effective conservation is needed. The natural populations of Y. thibetica should be targeted for in situ conservation measures, such as setting aside natural areas in Guangxi and Sichuan, as well as for ex situ conservation measures that involve introducing germplasm from more additional natural localities, if available, to perpetuate as much genetic diversity as possible. Additionally, to release the natural populations from collection pressures, improved tissue cultures techniques can increase plant production for an agricultural utilization of this species for medicinal purposes.

Acknowledgments

This study was supported by the State Key Laboratory of Phytochemistry and Plant Resources in West China, the Germ- plasm Bank of Wild Species in Southwest China, Kunming Institute of Botany (GBOWS) (2009-LSF-GBOWS-01), and the CAS Innovation Program of Kunming Institute of Botany (No. 540806321211). The authors are extremely grateful to Dr. Z. Larson- Rabin (Kunming Institute of Botany) and Dr. J.K. Triplett (Smithsonian Institution) for improving this manuscript.

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