Diversity of microsatellite markers in the populations of Picea asperata originating from the mountains of China

Plant Science 168 (2005) 707–714 www.elsevier.com/locate/plantsci

Diversity of microsatellite markers in the populations of Picea asperata originating from the mountains of China Yuhua Wanga,b, Jianxun Luoc, Ximei Xuea,b, Helena Korpelainend, Chunyang Lia,* a

Chengdu Institute of Biology, The Chinese Academy of Sciences, P.O. Box 416, Chengdu 610041, PR China b Graduate School of the Chinese Academy of Sciences, Beijing 100039, PR China c Sichuan Academy of Forestry, Chengdu 610081, PR China d Department of Applied Biology, P.O. Box 27, 00014 University of Helsinki, Finland Received 23 July 2004; received in revised form 29 September 2004; accepted 1 October 2004 Available online 26 October 2004

Abstract The genetic structure and diversity of natural populations of Picea asperata Mast. were investigated based on seven microsatellite loci. For a total of 300 individuals from 10 populations sampled, a moderate to high level of genetic diversity was observed at population levels with the number of alleles per locus (A) ranging from 6.86 to 11.86 (average 10.89), the proportion of polymorphic loci (P) equaling 100.0%, and the expected heterozygosity (He) ranging from 0.409 to 0.440 (average 0.426). The proportion of genetic differentiation present among populations accounted for 22.3% of the whole diversity. Such extensive inter-population differentiation detected in P. asperata could have resulted from several factors, including restricted gene flow between populations. In fact, the estimate of gene flow obtained in the study was low (Nm = 0.871). Habitat fragmentation caused by human disturbances may be a factor attributing to the low level of gene flow and high genetic differentiation among populations. Our results suggest that microsatellite markers are powerful high-resolution tools for the accurate assessment of population genetic parameters, which are greatly needed in the conservation genetics of P. asperata. # 2004 Elsevier Ireland Ltd. All rights reserved. Keywords: Picea asperata; Simple sequence repeat (SSR); Microsatellite; Genetic diversity; Genetic differentiation

1. Introduction Picea asperata Mast. is one of the most important tree species used for the production of pulp wood and timber, and it is a prime reforestation species in western China. P. asperata occurs in the alpine and canyon regions of northwestern Sichuan province and southeastern Gansu province, which are important water self-restraint regions [1]. Given the significant commercial and ecological roles of this fast-growing species, the maintenance of genetic variation is an important objective. In order to investigate population structures as well as to facilitate various programs in genetic improvement, restoration conservation and sustainable management of P. asperata, suitable molecular markers are needed to assess genetic variability. * Corresponding author. Tel.: +86 28 85221347; fax: +86 28 85222753. E-mail addresses: [email protected], [email protected] (C. Li).

However, until now, no published genetic information of P. asperata based on molecular marker techniques has been available. Due to their hyper-variability, co-dominance and highly informative nature, simple sequence repeats (SSR) or microsatellites, form a class of molecular markers, which are ideal for investigations, including studies in population and conservation genetics [2,3], the construction of genetic maps [4–6], parentship analyses [7], the assessment of genetic effects of forest management practices, and the development of strategies for conservation and sustainable management of forest genetic resource [3]. However, the development of microsatellite DNA markers in coniferous trees is quite difficult, time-consuming and costly, probably due to great proportions of highly repetitive sequences in the nuclear DNA and the large size of the conifer genomes. In the present study, we tested the usability of SSR primer pairs originally developed by Pfeiffer et al. [8] for the

0168-9452/$ – see front matter # 2004 Elsevier Ireland Ltd. All rights reserved. doi:10.1016/j.plantsci.2004.10.002

708

Y. Wang et al. / Plant Science 168 (2005) 707–714

congeneric species P. abies, and then utilized seven primer pairs to investigate the level and distribution of genetic diversity in 10 populations of P. asperata. Since previously, no studies using molecular markers to detect the genetic structure of natural populations have been reported in P. asperata, this work will provide a deep insight into its genetic diversity and population genetic structure and valuable information for further conservation and breeding programs on P. asperata.

2. Materials and methods 2.1. Plant materials Ten natural populations of P. asperata, nine from northwestern Sichuan province and one from southeastern Gansu province, were included in the study (Table 1). The sampled populations inhabit disjunctive mountainous areas with a narrow geographic range of latitude and longitude (100–1058E, 30–358N) (Fig. 1). The vertical distribution is from altitude 2450 to 3300 m. In order to keep relatively uniform sample sizes, we limited our sampling to 30 mother trees per population, each separated by a distance of 50 m. All the 300 sampled trees were in the age range of 60–80 years. 2.2. DNA extraction For each mother tree, at least 60 seeds were collected and used for DNA extractions. First, the seed coats and embryos were removed, and megagametophytes were ground in 2.5ml Eppendorf tubes, with 2 ml of extraction buffer (2% CTAB, 100 mM Tris, 1.4 M NaCl, and 20 mM EDTA, pH 8.3). Then, DNA was extracted following the procedure of Isabel et al. [9].

spruce (Picea abies K.) [8]. The SSR reactions were based on the protocol of Pfeiffer et al. [8] with some modifications. Amplification reactions were performed in a volume of 25 ml containing 1
reaction buffer (TaKaRa), 10–15 ng genomic DNA, 200 mM of each dNTP (Promega), 0.2 mM of each primer, and one unit of Taq DNA polymerase (TaKaRa). For DNA amplifications, a Perkin-Elmer Gene Amp PCR System 9700 was programmed according to the following profile: (i) 95 8C for 3 min, one cycle; (ii) 94 8C for 30 s, 57 8C (SpAG2 for 538C, SpAC1H8 for 60 8C) for 30 s, 72 8C for 45 s, 37 cycles; (iii) 72 8C for 10 min, one cycle. After amplifications were completed, 5 ml of each sample was loaded and electrophoresed on a 2% horizontal agarose gel to check for positive amplification and to determine the approximate amount of the product. Then, 3 ml of each sample was electrophoresed on an 8% polyacrylamide gel containing 1
TBE buffer. Following electrophoresis, the gel was silver-stained using the procedure of Panaud et al. [10]. In all cases, PCR reactions were performed at least twice in order to ensure that absence was a real absence and not a failed reaction. 2.4. Allele scoring The fragments amplified by microsatellite primers were scored as alleles on the basis of size in comparison with external standards using the Gel Doc 1000TM image analysis system (Biorad). A cluster of two to four discrete bands (stutter) was commonly observed. In those cases, the size of the most intensely amplified band was determined based on its migration relative to molecular weight (mw) size markers. For some markers, two or more bands amplified with equal intensity, in which case the molecular weight of the fragment nearest to the expected size for P. abies [8] was selected as the representative allele at that locus. 2.5. Data analysis

2.3. PCR amplification and electrophoresis The seven SSR markers used in the present study were previously developed for genetic studies in the Norway

The data were entered in the form of single-individual genotypes. The following parameters of genetic variation were assessed for each population: the mean number of

Table 1 Picea asperata populations surveyed, and their ecological and geographical parameters Population

Landform feature

Water system

Longitude (E)

Latitude (N)

Altitude (m)

Annual rainfall (ml)

Annual average temperature (8C)

January average temperature (8C)

July average temperature (8C)

XJ CP TB AB HS ZN LP DL BX RWG

Alpine and canyon Alpine and canyon Alpine and canyon Alpine and canyon Alpine and canyon Alpine and canyon Alpine and canyon Alpine and canyon Plateau and hilly Plateau and mountain

Minjang River Minjiang River Dadu River Dadu River Minjiang River Bailong River Minjiang River Dadu River Bailong River Minjiang River

1028270 1038370 1038080 1018270 1038190 1038320 1038380 1038400 1038130 1038270

318300 328530 348070 328330 328250 348200 328450 338280 338360 318240

3300 3100 2450 3100 2900 2800 3200 2800 2980 2850

614 730 570 712 833 564 730 553 647 730

12.0 5.7 7.0 3.3 9.0 4.3 5.7 12.7 0.7 5.7

2.2 4.3 10.5 7.9 0.9 2.9 4.3 1.7 10.5 4.3

19.9 14.5 10.7 12.5 17.5 13.7 14.5 22.2 10.7 14.5

Y. Wang et al. / Plant Science 168 (2005) 707–714

709

Fig. 1. The geographical distribution of Picea asperata in China. The locations of the natural population sampled in this study are shown. Population abbreviations are given in Table 1.

alleles per locus (A), the effective number of alleles (Ne), percentage of polymorphic loci (99% criterion) (P), expected heterozygosity (He) [11] and observed heterozygosity (Ho). Departures from the Hardy–Weinberg (H–W) equilibrium were assessed at each locus for every population, and per locus across all populations using Fstatistics of Wright [12]. The significance of the deviations was evaluated with a chi-square test following the method of Workman and Niswander [13]. To investigate linkage disequilibrium, Ohta’s two-locus analyses of population subdivision (D-statistics) for multiple populations were performed (P 2 0.05) [14,15]. The genetic structures were further investigated using Wright’s analysis of hierarchical F-statistics [12]. Gene flow (Nm) was estimated from Nm = 0.25(1  Fst)/Fst. Nei’s [11] unbiased genetic distances were calculated for all population pairs and used to construct a phylogenetic tree (UPGMA). All of the above calculations were performed using POPGENE program version 1.32 [16]. The proportion of null alleles was calculated as (He  Ho)/(1 + He) [17].

sizes at each locus was irregular except for the locus SpAC1F7 (showing unimodality) (Fig. 2). The genotypic linkage disequilibrium tests resulted in no significant values (P < 0.05), which suggest an absence of linkage disequilibrium between the loci (data not shown). All SSRs used in the study are based on dinucleotide repeats. The product sizes were found to vary widely, the differences between the longest and shortest alleles ranging from 24 to 102 bp among the seven loci. When the alleles detected at each locus were sorted in an ascending order by their size, 73% of adjacent alleles differed by one dinucleotide repeat unit. However, 5.0% of the adjacent alleles were separated by one basepair and 22.0% by more

3. Results 3.1. Allelic variation at microsatellite loci The total number of alleles per locus and the size ranges of the alleles detected in P. asperata (this study) and P. abies [8] are given in Table 2. All seven loci assayed in P. asperata possessed a high level of polymorphism, with the number of alleles per locus ranging from 13 at SpAC1F7 to 25 at SpAGG3 and SpAGD1. The distribution of observed allele

Fig. 2. The distribution of allele frequencies at the loci SpAC1F7 (A) and SpAGC2 (B).

710

Y. Wang et al. / Plant Science 168 (2005) 707–714

Table 2 Characterization of SSR loci Locus

Repeat type

Expected size (bp)

SpAC1F7 SpAG2 SpAGC2 SpAGC1 SpAGG3 SpAGD1 SpAC1H8

(AC)12 (TC)16 (TA)11(GA)20 (TC)5TT(TC)10 (GA)24 (AG)25 (GT)27

109 105 126 103 136 147 135

P. abies

P. asperata (this study)

Number of alleles

Range of sizes (bp)

Number of alleles

Range of sizes (bp)

6 10 11 8 17 22 18

100–118 96–114 86–146 79–117 110–148 124–204 100–168

13 18 21 17 25 25 23

109–133 96–135 80–157 84–129 110–182 108–210 100–192

Table 3 Allelic variability at the seven SSR loci in 10 Picea asperata populations Population

Number of alleles SpAG2

SpAGC2

SpAGC1

SpAGG3

SpAGD1

SpAC1H8

XJ CP TB AB HS ZN LP DL BX RWG

SpAC1F7 8 7 4 7 7 5 6 6 5 8

11 13 9 8 10 12 10 11 12 7

7 11 11 9 13 9 10 9 14 11

15 7 11 9 10 10 13 9 3 13

15 15 18 15 17 18 17 13 12 14

15 14 13 19 14 13 6 14 6 15

11 16 13 16 6 15 8 14 11 15

82 83 79 83 77 82 70 76 63 83

Total

13

18

21

17

25

25

23

142

than two basepairs. These phenomena were associated with the rarity of the alleles. All seven microsatellite loci were polymorphic across all 10 populations sampled. A summary of the loci and alleles for each population is provided in Table 3. A total of 142 alleles were detected, with the mean number of alleles per locus equaling 20.3. No private alleles present in a single population only were detected. Instead, 64 alleles were common to the majority of the populations (at least six populations), while the other alleles were shared among fewer populations. 3.2. Genetic variation within populations

equaling 0.440, while the lowest values existed in population TB, equaling 0.409. Although population ZN originating from the highest latitude did not possess the lowest values (0.423), most populations followed the pattern that genetic diversities within populations decreased from the south to the north. The correlation coefficients between the latitude and the expected heterozygosity was significantly negative, equaling 0.744 (P < 0.05, d.f. = 8). However, correlation values obtained between longitude and different diversity values were all nonsignificant. Table 4 Genetic variation within population based on seven SSR loci P

Ho

He

XJ CP TB AB HS ZN LP DL BX RWG

30 30 30 30 30 30 30 30 30 30

11.71 11.86 11.29 11.86 11.00 11.71 9.86 10.86 6.86 11.86

7.80 7.29 6.19 6.34 6.75 6.54 5.18 6.09 4.17 7.46

100 100 100 100 100 100 100 100 100 100

0.364 0.423 0.404 0.399 0.416 0.410 0.382 0.418 0.353 0.428

0.434 0.432 0.409 0.417 0.434 0.423 0.429 0.429 0.417 0.440

Mean

30

10.89

6.40

100

0.400

0.426

300

20.3

11.84

100

0.425

0.543

Population

Genetic diversity parameters based on allelic frequencies are shown in Table 4. In individual populations, the mean number of alleles per locus (A) varied from 6.86 to 11.86, with an average of 10.89, while the effective number of alleles per locus (Ae) varied from 4.17 to 7.80, with an average of 6.40. The observed heterozygosities (Ho) ranged from 0.353 to 0.428, with an average of 0.400. The average expected heterozygosity (He) equaled 0.426, and varied from 0.409 to 0.440. For the whole species, the Ho and He values equaled 0.425 and 0.543, respectively, with the effective number of alleles per locus (Ne) equaling 11.84. A comparison of the genetic diversity of P. asperata was performed among the 10 populations. The highest level of diversity existed in population RWG with the He values

Total

Species

N

A

Ne

N, sample size; A, mean number of alleles; P, percentage of polymorphic loci; Ne, effective number of alleles per locus [47]; Ho, observed heterozygosity; He, expected heterozygosity.

Y. Wang et al. / Plant Science 168 (2005) 707–714

711

Table 5 Relative measurements of genetic differentiation and the estimates of gene flow among populations of Picea asperata Locus

Fis

Fit

Fst

He

Ho

Nm

Proportion of null alleles

SpAC1F7 SpAG2 SpAGC2 SpAGC1 SpAGG3 SpAGD1 SpAC1H8

0.359 0.101 0.108 0.111 0.030 0.104 0.177

0.555 0.145 0.214 0.107 0.069 0.306 0.105

0.307 0.223 0.291 0.196 0.096 0.225 0.240

0.496 0.528 0.556 0.529 0.562 0.553 0.578

0.212 0.440 0.458 0.450 0.460 0.462 0.496

0.564 0.860 0.609 1.255 2.354 0.861 0.790

0.19 0.06 0.06 0.05 0.07 0.06 0.05

Mean

0.009

0.215

0.223

0.543

0.425

0.871

0.08

Fis, deficiencies of heterozygotes relative to Hardy–Weinberg expectations; Nm, gene flow estimated from Fst (Nm = 0.25(1  Fst)/Fst; Proportion of null alleles = (He  Ho)/(1 + He) [17].

Fit is the overall inbreeding coefficient of an individual relative to the whole set of populations, while Fis is the inbreeding coefficient of an individual relative to its own population. The mean Fit value of 0.215 indicates that, overall, P. asperata has a deficiency of heterozygotes. Within populations, all loci but one (SpAGG3) contained a surplus of heterozygotes (Fis ranging from 0.101 to 0.117) or a deficiency of heterozygotes (SpAC1F1, Fis = 0.359), with the average Fis value equaling 0.009, which does not significantly differ from zero (Table 5). This means that there is no regular tendency toward heterozygote excess or deficiency, which indicates the absence of inbreeding within the populations of P. asperata. In addition, the proportion of null alleles was estimated, and they were found to occur at a moderate frequency: for six loci, the estimated values varied from 0.05 to 0.07 but for locus SpAC1F7 the value equaled 0.19 (Table 5). The mean value equaled 0.08.

3.3. Population genetic structure The genetic analyses revealed high levels of differentiation among populations. The coefficient of hierarchical Fst (Table 5), estimated according to Wright, ranged from 0.096 for locus SpAGG3 to 0.307 for locus SpAC1F7, with the average value equaling 0.223. This showed that 22.3% of the total genetic diversity existed among populations. The loci SpAGC2 and SpAC1F7 possessed the highest Fst value. These values indicated the important role of the SpAGC2 and SpAC1F7 loci in inter-population differentiation. The overall gene flow (Nm) among populations was estimated to equal 0.871, which gives an estimate of the average number of migrants between all studied populations per generation. The observed value indicates that gene exchange between populations is low. 3.4. Genotypic structure and departure from Hardy–Weinberg equilibrium

3.5. Genetic relationships In individual population, tests for the departure from the Hardy-Weinberg equilibrium showed significant deviations for at least one locus in every population. Most of the loci followed the Hardy–Weinberg equilibrium in the majority of populations. However, significant departures from the Hardy–Weinberg equilibrium were observed in some cases (30.0%). The deviations were primarily due to the surplus of heterozygotes.

The genetic distances were calculated for each pair of populations to estimate the extent of their divergence (Table 6). The average genetic distance among populations equaled 0.135. The lowest genetic distance (0.087) was found between populations CP and LP, and the greatest genetic distance (0.180) was found between populations XJ and BX.

Table 6 Genetic distances and genetic identities among populations of Picea asperata Population

XJ

CP

TB

AB

HS

ZN

LP

DL

BX

RWG

XJ CP TB AB HS ZN LP DL BX RWG

**** 0.1500 0.1683 0.1692 0.1405 0.1546 0.1007 0.1366 0.1796 0.1503

0.8505 **** 0.1120 0.1284 0.1220 0.1361 0.0865 0.0907 0.1135 0.1280

0.8319 0.8881 **** 0.1672 0.1618 0.1677 0.1032 0.1192 0.1205 0.1493

0.8309 0.8715 0.8329 **** 0.1365 0.1172 0.1519 0.1255 0.1523 0.1495

0.8587 0.8781 0.8382 0.8635 **** 0.1332 0.1130 0.1331 0.1450 0.1426

0.8454 0.8639 0.8325 0.8828 0.8669 **** 0.1245 0.1106 0.1665 0.1483

0.8994 0.9135 0.8969 0.8481 0.8871 0.8755 **** 0.1027 0.1530 0.1308

0.8634 0.9094 0.8808 0.8745 0.8669 0.8895 0.8975 **** 0.1429 0.1184

0.8207 0.8867 0.8796 0.8477 0.8551 0.8335 0.8472 0.8573 **** 0.1207

0.8497 0.8720 0.8507 0.8505 0.8574 0.8517 0.8692 0.8816 0.8794 ****

Above diagonal: genetic identities; below diagonal: genetic distances.

712

Y. Wang et al. / Plant Science 168 (2005) 707–714

Fig. 3. UPGMA dendrogram of Picea asperata populations using Nei’s unbiased genetic distances [11].

The UPGMA cluster analyses based on Nei’s unbiased genetic distances was performed to further show the genetic relationships among the populations (Fig. 3). The dendrogram separated the 10 populations into three main groups. The first group consisted of the populations CP, LP, DL, TB, BX and RWG. Within the first group, the populations BX and RWG were distinct from the others. The second group consisted of the populations AB, HS and ZN. In this group, populations AB and ZN clustered together while population HS was more distant. The population XJ differed considerably from the other populations and was clustered alone as a third group.

4. Discussion In the present study, 10 populations of P. asperata were characterized using a set of seven microsatellite markers, which resulted in a large number of alleles. For all loci, the observed numbers of alleles were higher than those reported in P. abies, for which the markers had originally been developed [8]. This may be due to the varying ecogeographical environment to which the populations of P. asperata are exposed. Also, for most of the loci, the length ranges of the amplification products obtained here were wider than those detected in P. abies. A similar observation has been reported in wheat and barley by Donini et al. [18]. In addition to regular changes of allele sizes by two basepair steps, differences of more than one repeat unit were detected at six loci occasionally (22.0%). This suggests that genetic variation at the microsatellite loci investigated in this study better fits the two-phase mutation model (TMP) [19] than the stepwise mutation model (SMM) [20]. This finding is consistent with the observations reported in wheat by Huang et al. [21] and in lodgepole pine by Marshall et al. [22]. Additionally, 5.0% of the alleles differed from each other by a single basepair, which indicates that some of the size differences at the SSR loci are the results of insertion–

deletion events in the flanking regions instead of repeat number increases or decreases in the nucleotide repeat regions, as sometimes reported also in other plant taxa [23,24]. Although a great number of population genetic studies have been conducted on Picea species [8,25,26], no genetic work based on the use of molecular markers has been previously published on P. asperata. In this investigation, high levels of polymorphism were detected, the total number of alleles equaling 142. The expected heterozygosity within populations (mean He = 0.426) was lower than that detected in P. contorta (mean He = 0.73) [27] and in P. canariensis (mean He = 0.73) [26]. The amount of genetic diversity present among population was considerable (Fst = 0.223) despite relatively short geographic distances between populations included in the study, and it was clearly higher than the values detected in other conifer species, based primarily on isozymes [25,28–31]. The Fst value detected here in P. asperata is higher than the differentiation among populations observed in P. contorta (GST = 0.028), based on microsatellite markers [27] and equivalent to the observations based on cpSSR in P. canariensis (GST = 0.190) [26] and in P. pinaster (GST = 0.235) [32]. However, Bastien et al. [33] reported a very high differentiation value (GST = 0.749) for the Norway spruce (P. abies) in a study based on a mosaic minisatellite. Presumably, the high inter-population differentiation and relatively low genetic diversity detected in P. asperata may result from the differentiation of habitats, for instance, with respect to climate, temperature, annual rainfall or landform features. Also, isolation resulting in limited gene flow among populations, and human disturbances can lead to such divergence. On the other hand, the complete lack of private alleles may indicate that the populations have a relatively recent origin. The 10 natural populations of P. asperata studied here occupy fragmented habitats, the populations being confined to islands varying in size and degree of isolation within the northwestern Sichuan province and southeastern Gansu province. Despite such fragmented distributions, all populations retained quantities of microsatellite variation equivalent to those found in continuously distributed species of outcrossing coniferous trees, e.g., the mean effective number of allele per locus equaling 6.40 (range 4.17–7.80). This might mean that the effective population sizes of P. asperata have remained large enough over time to prevent the erosion of genetic diversity by genetic drift. Some evidence of a decrease in diversity from the south to the north was observed. The southern populations contained higher levels of excepted heterozygosity within populations than did the northern ones, except for the populations AB and ZN, which represent marginal populations. Vettori et al. [34] observed a similar situation when studying the geographic distribution of chloroplast variation in Italian populations of beech. In general, the population genetic structure of P. asperata seems to resemble that observed in P. strobes [3] and P. mariana [35].

Y. Wang et al. / Plant Science 168 (2005) 707–714

Marginal populations, such as AB and ZN here, could be defined as being relatively isolated from the central area of the species distribution and containing a lower level of variability. The low degree of genetic diversity present in marginal populations may be due to increased levels of selffertilization in the more extreme locations where difficulties with flowering or isolation from other populations may restrict the supply of pollen available for outcrossing. Additionally, the lower levels of genetic diversity detected in the marginal populations here may be related to the eco-zone where these populations come from. In the present study, the marginal and the central eco-zones differ in annual rainfall and several other ecological characteristics (Table 1). The central eco-zone is more likely to contain more heterogeneous microsites and environments than the marginal ecozone. It has been hypothesized that heterogeneous environments promote the maintenance of high genetic diversity due to developmental homeostasis [36]. The decline of genetic diversity detected in the marginal populations of some trees has been described by King and Ferris for Alnus glutinosa [37], by Lagerkrantz and Tyman for P. abies [38], and by Dumolin-Lapegue et al. for Quercus alba [39] and Jimenez et al. for Q. suber [40]. However, there are also studies indicating that marginal populations of some tree species do not display any loss of genetic variation [41,42]. The genetic parameters estimated for the microsatellite data indicated that there are substantial levels of genetic diversity in all populations of P. asperata. Our comparisons based on the use of microsatellites, RAPDs and allozymes in studies on P. asperata showed that the highest level of polymorphism was found for microsatellite loci, and the lowest level of polymorphism for allozyme loci (Wang et al., unpublished). Therefore, it is apparent that microsatellites are a powerful tool for population-genetic analyses in P. asperata. This result is in accordance with the common observation of high levels of variation detected in plants using microsatellite markers. Surveys on genetic variation in the natural populations of many other species have demonstrated that microsatellite loci are superior to allozymes when investigating population genetic structures [43–46]. In summary, our present genetic study revealed that: (1) there is considerable genetic diversity in the southernmost populations of P. asperata, and the diversity levels vary among populations. This is most likely due to differences in environmental conditions under which the populations occur. (2) The relatively high inter-population differentiation of P. asperata results from several factors, including restricted gene flow between populations due to isolation in of alpine and canyon areas, and human disturbances. (3) Microsatellite markers are powerful tools for monitoring genetic diversity and the genetic effects of natural disturbances and forest management practices in P. asperata. Ongoing research in P. asperata based on a wide range of markers, including also allozymes, AFLPs and RAPDs, will allow us to describe in greater detail the

713

population genetic structures of P. asperata and design specific conservation strategies for this ecologically and economically important endemic spruce species.

Acknowledgement The research was supported by the ‘‘Program of 100 Distinguished Young Scientists’’ and ‘‘Knowledge Innovation Engineering’’ of the Chinese Academy of Sciences (No. KSCX2-SW-115).

References [1] Z.Y. Wu, P.H. Raven, Flora of China, vol. 4, Science Press and Missouri Botanical Garden, Beijing, PR China and St. Louis, USA, 1994. [2] R. Streiff, T. Labbe, R. Bacilieri, Within population genetic structure in Quercus robur L. and Quercus petraea (Matt.) Liebl. assessed with isozymes and microsatellites, Mol. Ecol. 7 (1998) 317– 328. [3] O.P. Rajora, M.H. Rahman, G.P. Buchert, B.P. Dancik, Microsatellite DNA analysis of genetic effects of harvesting in old-growth eastern white pine (Pinus strobes) Ontario, Canada, Mol. Ecol. 9 (2000) 339– 348. [4] R.P.V. Brondani, C. Brondani, D. Tarchini, D. Grattapaglia, Development, characterization and mapping of microsatellite markers in Eucalyptus grandis and E. urophylla, Theor. Appl. Genet. 97 (1998) 816–827. [5] R.B. Hodgetts, M.A. Aleksiuk, A. Brown, C. Clarke, E. Macdonald, S. Nadeem, D. Khasa, Development of microsatellite markers for white spruce (Picea glauca) and related species, Theor. Appl. Genet. 102 (2001) 1252–1258. [6] M. Shepherd, M. Cross, T.L. Maguire, M.J. Dieters, C.G. Williams, R.J. Henry, Transpecific microsatellites for hard pines, Theor. Appl. Genet. 104 (2002) 819–827. [7] W.T.G. van de Ven, R.J. McNicol, Microsatellites as DNA markers in Sitka spruce, Theor. Appl. Genet. 93 (1996) 613–617. [8] A. Pfeiffer, A.M. Olivieri, M. Morgante, Identification and characterization of microsatellites in Norway spruce (Picea abies K.), Genome 40 (1997) 411–419. [9] N. Isabel, J. Beaulieu, J. Bousquet, Complete congruence between gene diversity estimates derived from genotypic data at enzyme and random amplified polymorphic DNA loci in black spruce, Proc. Natl. Acad. Sci. USA 92 (1995) 6369–6373. [10] O. Panaud, X. Chen, S.D. McCouch, Development of microsatellite markers and characterization of sample sequence lengthen polymorphism (SSLP) in rice (Oryzastiva L.), Mol. Gen. Genet. 252 (1996) 597–607. [11] M. Nei, Estimation of average heterozygosity and genetic distance from a small number of individuals, Genetics 89 (1978) 583–590. [12] S. Wright, Evolution and genetics of populations, in: Variability Within and Among Populations, vol. 4, University of Chicago Press, Chicago, 1978. [13] P.L. Workman, J.D. Niswander, Population studies on southwestern Indian tribes. II. Local genetic differentiation in the Papago, Am. J. Hum. Genet. 22 (1970) 24–49. [14] T. Ohta, Linkage disequilibrium with the island model, Genetics 101 (1982) 139–155. [15] T. Ohta, Linkage disequilibrium due to random drift in finite subdivided populations, Proc. Natl. Acad. Sci. USA 79 (1982) 1940–1944.

714

Y. Wang et al. / Plant Science 168 (2005) 707–714

[16] F.C. Yeh, R.C. Yang, T. Boyle, POPGENE, version 1.32 ed., Software Microsoft Window-Based Freeware for Population Genetic Analysis, University of Alberta, Edmonton, Alta, 1997. [17] J.F. Brookfield, A simple new method for estimating null allele frequency for heterozygote deficiency, Mol. Ecol. 5 (1996) 453–455. [18] P. Donini, P. Stephenson, G.J. Bryan, R.M.D. Koebner, The potential of microsatellites for high throughput genetic diversity assessment in wheat and barley, Genet. Res. Crop Evol. 145 (1998) 415–421. [19] A. Di Rienzo, A.C. Peterson, J.C. Garza, A.M. Valdes, M. Slatkin, N.B. Freimer, Mutational processes of simple-sequence repeat loci in human populations, Proc. Natl. Acad. Sci. USA 91 (1994) 3166–3170. [20] T. Ohta, K. Kimura, The model of mutation appropriate to estimate the number of electrophoretically detectable alleles in a genetic population, Genet. Res. 22 (1973) 201–204. [21] X.Q. Huang, A. Borner, M.S. Roder, M.W. Ganal, Assessing genetic diversity of wheat (Triticum aestivum L.) germplasm using microsatellite markers, Theor. Appl. Genet. 105 (2002) 699–707. [22] H.D. Marshall, C. Newton, K. Ritland, Chloroplast phylogeography and evolution of highly polymorphic microsatellite in lodgepole pine (Pinus contorta), Theor. Appl. Genet. 104 (2002) 367–378. [23] S. Kostia, S.L. Varvio, P. Vakkari, P. Pulkkinen, Microsatellite sequences in a conifer, Pinus sylvestris, Genome 38 (1995) 1244– 1248. [24] W. Blair Matthew, Veena Hedetale, R. McCouch Susan, Fluorescentlabeled microsatellite panels useful for detecting allelic diversity in cultivated rice (Oryza sativa L.), Theor. Appl. Genet. 105 (2002) 449– 457. [25] Y.Q. Zheng, R.A. Ennos, Genetic variability and structure of natural and domesticated populations of Caribbean pine (Pinus caribaea Morelet), Theor. Appl. Genet. 98 (1999) 765–771. [26] A. Gomez, S.C. Gonzalez-Martinez, C. Collada, J. Climent, L. Gil, Complex population genetic structure in the endemic Canary Island pine revealed using chloroplast microsatellite markers, Theor. Appl. Genet. 107 (2003) 1123–1131. [27] B.R. Thomas, S.E. Macdonald, M. Hicks, D.L. Adams, R.B. Hodgetts, Effects of reforestation methods on genetic diversity of lodgepole pine: an assessment using microsatellite and randomly amplified polymorphic DNA markers, Theor. Appl. Genet. 98 (1999) 793–801. [28] J.L. Hamrick, M.J. Godt, S.L. Sherman-Broyles, Factors influencing levels of genetic diversity in woody plant species, New For. 6 (1992) 95–124. [29] R.J. Petit, N. Bahrman, P.H. Baradat, Comparison of genetic differentiation in maritime pine (Pinus pinaster Ait.) estimated using isozyme, total protein and terpenic loci, Heredity 75 (1995) 382–389. [30] L. Salvador, R. Alı´a, D. Agu´ ndez, L. Gil, Genetic variation and migration pathways of maritime pine (Pinus pinaster Ait) in the Iberian peninsula, Theor. Appl. Genet. 100 (2000) 89–95. [31] W. Prus-Glowacki, B.R. Stephan, E. Bujas, R. Alia, A. Marciniak, Genetic differentiation of autochthonous populations of Pinus

[32]

[33]

[34]

[35]

[36] [37] [38]

[39]

[40]

[41] [42] [43]

[44]

[45]

[46]

[47]

sylvestris (Pinaceae) from the Iberian peninsula, Plant Syst. Evol. 239 (2003) 55–66. G.G. Vendramin, M. Anzidei, A. Madaghiele, G. Bucci, Distribution of genetic diversity in Pinus pinaster Ait. as revealed by chloroplast microsatellite, Theor. Appl. Genet. 97 (1998) 456–463. D. Bastien, J.M. Favre, A.M. Collignon, C. Sperisen, S. Jeandroz, Characterization of a mosaic minisatellite locus in the mitochondrial DNA of Norway spruce [Picea abies (L.) Karst.], Theor. Appl. Genet. 107 (2003) 574–580. C. Vettori, G.G. Vendramin, M. Anzidei, R. Pastorelli, D. Paffetti, R. Giannini, Geographic distribution of chloroplast variation in Italian populations of beech (Fagus sylvatica L.), Theor. Appl. Genet. 109 (2004) 1–9. O.P. Rajora, S.A. Pluhar, Genetic diversity impacts of forest fires, forest harvesting, and alternative reforestation practices in black spruce (Picea mariana), Theor. Appl. Genet. 106 (2003) 1203– 1212. I.M. Lerner, Genetic Homeostasis, Oliver and Boyd, Edinburgh, 1954. R.A. King, C. Ferris, Chloroplast DNA phylogeography of Alnus glutinosa (L.) Gaertn, Mol. Ecol. 7 (1998) 1151–1161. U. Lagerkrantz, N. Tyman, Genetic structure of Norway spruce (Picea abies K.): concordance of morphological and allozymic variation, Evolution 44 (1990) 38–53. S. Dumolin-Lapegue, B. Demesure, S. Fineschi, V. Lecorre, R.J. Petit, Phylogeographic structure of white oaks throughout the European continent, Genetics 146 (1997) 1475–1487. P. Jimenez, D. Agundez, R. Alia, L. Gil, Genetic variation in central and marginal populations of Quercus suber L., Silvae. Genet. 48 (1999) 278–284. C. Li, Isozyme variation in natural populations of Eucalyptus microtheca, Hereditas 130 (1999) 117–123. C. Li, RAPD analysis of genetic variation in Eucalyptus microtheca F. Muell. Populations, Hereditas 132 (2000) 151–156. C.R. Hughes, D.C. Queller, Detection of highly polymorphic microsatellite loci in a species with little allozyme polymorphism, Mol. Ecol. 2 (1993) 131–137. T. Lehmann, W.A. Hawley, L. Kamau, D. Fontenille, F. Simard, F.H. Collins, Genetic differentiation of Anopheles gambiae population from East and West Africa: comparison of microsatellite and allozyme loci, Heredity 77 (1996) 192–208. J.A. Sanchez, C. Clabby, D. Ramos, G. Blanco, F. Flavin, E. Vazquez, R. Powell, Protein and microsatellite single locus variability in Salmo salar L. (Atlantic salmon), Heredity 77 (1996) 423–432. G.L. Sun, O. Diaz, B. Salomon, R. von Bothmer, Genetic diversity and structure in a natural Elymus caninus population from Denmark based on microsatellite and isozyme analysis, Plant Syst. Evol. 227 (2001) 235–244. M. Kimura, J. Crow, The number of alleles that can be maintained in a finite population, Genetics 49 (1964) 725–738.