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RAPD diversity of Stipa grandis populations and its relationship with some ecological factors
ACTA ECOLOGICA SINICA Volume 26, Issue 5, May 2006 Online English edition of the Chinese language journal Cite this article as: Acta Ecologica Sinica, 2006, 26(5), 1312−1319.
RESEARCH PAPER
RAPD diversity of Stipa grandis populations and its relationship with some ecological factors Zhao Nianxi, Gao Yubao*, Wang Jinlong, Ren Anzhi, Xu Hua College of Life Sciences, Nankai University, Tianjin 300071, China
Abstract: The genetic diversity of Stipa grandis P.Smirn and its relationship with the climatic variables were studied using the RAPD technique for 90 genes from five natural populations sampled in the Xilingol steppe, China. Sixteen oligonucleotides screened from 100 random primers were used to amplify 310 trackable RAPD loci, which were all polymorphic. By analyzing the RAPD data using POPGENE software, different geographic S. grandis populations were studied, which indicated a high level of genetic diversity, and the maximum variation was observed within the populations with a 28% variation observed among the populations. Using Pearson correlation analysis, significant (P < 0.05) or highly significant (P < 0.01) relationships were found between gene diversity indexes and temperature factors (≥10℃ cumulative temperature in a year, annual mean temperature and mean temperature in January). Mantel’s tests showed that there was no significant correlation between Nei’s unbiased genetic distance and the geographic distance of S. grandis populations (r = 0.184，P = 0.261). However, there were significant or highly significant correlations between Nei’s genetic distance and the several climatic divergences in pairwise S. grandis populations. All results indicated that natural selection resulting from variations in water and temperature was responsible for the adaptive eco-geographical differentiation indicated by the RAPD markers of different S. grandis populations, and that immigration and gene drift did not play an important role in affecting the differentiation of S. grandis populations. Key Words:
Stipa grandis P.Smirn; dominant species; RAPD; genetic diversity; climatic factor; natural selection
In recent years, conservation biologists have paid increasing attention to the genetic diversity and the genetic differentiation of species as awareness of the importance of species diversity and protection grows. Several dominant/constructive － species have been studied in addition to rare species[1 5]. Such information as the genetic variation of dominant/constructive species will be useful not only for the utilization and protection of these important species, but also for providing basic data for the evaluation of the ecosystem’s genetic health[2]. Stipa grandis P. Smirn, a typical Dahuli-Mongolia species in the center of the Asian grassland region, belongs to Sect. Leiostipa Dum of genus Stipa, Gramineae. The steppe, dominated by S. grandis, is a tussock steppe in Mid-Asian grasslands of the Eurasian Steppe zone. It is the most stable and representative formation of steppes in Inner Mongolia, China, and is indicative of the division of steppe vegetation zones[6]. But there lack studies on the genetic diversity and the genetic structure of the S. grandis populations[7,8], and the analysis for correlating the genetic diversity of S. grandis with ecological
factors needs to be more comprehensive. In the present study, relationships of five natural S. grandis populations from Xilingol steppe, China were analyzed using RAPD markers. The relationship between the genetic diversity/population genetic structure and eco-geographic factors was then analyzed. The major objectives of the study were to determine one or more ecological factors that were responsible for the genetic diversity of S. grandis, and to determine the dominant factor in determining the genetic differentiation of S. grandis. Such information would play a useful role in explaining the mechanism of genetic differentiation of S. grandis. It could provide scientific evidence to assist in the conservation of this important germplasm resource.
1
Materials and methods
1.1 Study sites The study sites were located in the Xilingol typical steppe of Inner Mongolia, China, the focal point of the S. grandis
Received date: 2005-10-27; Accepted date: 2006-02-05 *Corresponding author. E-mail: [email protected] Copyright © 2006, Ecological Society of China. Published by Elsevier BV. All rights reserved.
ZHAO Nianxi et al. / Acta Ecologica Sinica, 2006, 26(5): 1312–1319
Table 1 Geographic and climatic data (means of 30 years between 1965 and 1995) for five populations of Stipa grandis
Population namea
Geographic
Altitude
location
(m)
o
S. grandis Plot Gasong Mountain West Xilinhot East Xilinhot Bayanwula
43.54 N 116.55oE 43.5oN 116.82oE 43.93oN 15.74 oE 44.14oN 116.36oE 44.64 oN 117.72 oE
1170 1300
Soil type
Dark chestnutsoil Dark chestnut soil
Annual pre-
≥10℃cumulative
cipitation
temperature in a
(mm)
year (℃)
340
2136
2
400
1776
－0.8
Annual mean
Aridity
temperature (℃)
indexb
Mean temperature in January (℃)
Mean temperature in July (℃)
1.01
－22.3
18.8
0.7
－27
17.8
1152
Chestnut soil
290
2496
1.8
1.38
－19.8
20.8
1121
Chestnut soil
300
2400
1.4
1.28
－19.1
20.7
340
2256
1.5
1.06
－19.4
19.5
1152
Dark chestnut soil
a: S. grandis Plot was set up in the S. grandis Plot of the Inner Mongolia Grassland Ecosystem Research Station of Chinese Academy of Sciences; Gasong Mountain was set up in Gasong Mountain in the vicinity of the Inner Mongolia Grassland Ecosystem Research Station of Chinese Academy of Sciences; West Xilinhot was set up 35 km west of Xilinhot City; East Xilinhot was set up 27 km east of Xilinhot City; Bayanwula was set up 12 km northeast of Bayanwula Town of Xiwu Banner in Inner Mongolia, China. b: Aridity index = 0.16×≥10℃cumulative temperature in a year / Annual precipitation
community. The region’s climate is temperate, semi-arid, and continental, and varies substantially from season to season. It is mild and rather moist during the summer, which is affected by the southeastern oceanic monsoon, and cold and dry during the winter, which is dominated by the Inner Mongolia highpressure air mass. The seasonality of precipitation is typical of North China/Northeast China summer rainfall. Rainfall occurs mainly from July to September, accounting for 70% of total annual precipitation. The landform in the region mainly takes the form of plateaus, while the corresponding zonal soil type is dark chestnut soil or chestnut soil. Five study sites with relatively little disturbance were established in typical stands of the S. grandis community. Fresh leaves were collected from S. grandis individuals for DNA extraction. The population names, geographic locations and climatic characteristics of the study sites are summarized in Table 1. The geographical location and altitude were recorded using a global positioning system (GPS). The climatic data, including annual precipitation, cumulative temperature (≥10℃), mean temperature in January, and mean temperature in July, were obtained from the meteorological stations of corresponding administrative divisions in Xilingol, China. 1.2 Study materials and methods 1.2.1 Study materials Fresh leaves were collected from S. grandis individuals in an area of 50 m × 50 m at each study site at an interval of at least 1 m to avoid collecting ramets from the same gene. These were stored with silica gel in zip-lock bags. A total of 20 samples were collected from each study site and stored in the laboratory for later use[9]. 1.2.2 Study methods
Genomic DNA was extracted from the silica-dried leaves of S. grandis using a modification of the protocol of the SDS method[10]. After being digested by RNase (without DNase), OD values (absorbency) were recorded at 260 nm and 280 nm using a UV-VIS spectrophotometer (TU-1800), and the level of purity was determined by the O.D.260/O.D.280 value (the ratio of a high level of purity is 1.8)[11]. Purity and molecular weight were also determined by electrophoresis in 0.7% aga－ rose gel. DNA was diluted to 30 ng·µl 1 at the final stage. The protocol for RAPD amplification described by Williams[12] was optimized for use on an S. grandis template DNA. The PCR reaction volume was 25 µl, and the reaction was run in a Programmable Thermal Controller-100 (MJ Research, Waltham MA, USA) with the following temperature profiles: preliminary denaturation of DNA at 94℃ for 4 min; followed by 40 cycles at 94℃ for 1 min, 36℃ for 1 min and 72 ℃ for 2 min; a final step for 10 min at 72℃, followed by 4℃ soaking until recovery. After the gradient screening of Mg2+ concentration, template concentration, dNTPs concentration, and primer concentration, the optimal reaction system contained 1 － µL template DNA (about 30 ng), 2 µl MgCl2 (25 mmol·L 1), 2+ 2.5μl 10×reaction buffer (without Mg ), 0.5 µl dNTPs (10 － － mmol·L 1), 1µL primer (5 pmol·μl 1), 0.2 µl Taq DNA poly－1 merase (5U·µl ), and 17.8µl sterile double-distilled water. The random primers were 10-bp oligonucleotides from Operon Technologies. The amplification products were separated by electrophoresis in a 1.5% agarose gel containing ethidium bromide in a 1×TBE (Tris-Borate-EDTA) buffer and were observed and photographed under UV illumination. The primers, which exhibited amplified, separated, clear, and reproducible bands, were used for further experiments. Each
ZHAO Nianxi et al. / Acta Ecologica Sinica, 2006, 26(5): 1312–1319
amplification was performed at least twice in order to confirm the results obtained. The amplification results were analyzed using the Bio IMAGING SYSTEM (SynGENE). Molecular marker SD005 (Beijing Dingguo Biotechnology Development Center, China) was used as a size marker. The RAPD bands with different relative migratory distances were regarded as different bands, whereas the bands with identical relative migratory distances were regarded as the same RAPD band. The clearly amplified DNA fragments with a size of 400–2000 bp were denoted as “1” if they were present, or “0” if they were absent. The entire data were compiled in a computer for statistical analysis. 1.2.3 Data analysis 1.2.3.1 Estimation of population genetic diversity indexes and divergence indexes Based on the binary matrix obtained in the study, genetic diversity indexes, including the percentage of polymorphic bands (PPB), the observed number of alleles (na), the expected number of alleles (ne), Nei’s gene diversity (h), and Shannon’s information index of diversity (H), were calculated in the dominant diploid of the POPGENE 32 program[13]. Using the population mean diversity indexes and the total diversity indexes analyzed with POPGENE, Nei’s genetic differentiation index (GST = (hT – hs)/hT) and Shannon’s genetic differentiation index [(HT − Hs)/HT] were calculated. Here, hs and Hs were the population mean of Nei’s and Shannon’s diversity indexes, respectively; hT and HT were the total of Nei’s and Shannon’s diversity indexes, respectively. 1.2.3.2 Construction of dendrogram A dendrogram was generated by UPGMA using NTSYS-pc software[14] based on Nei’s unbiased genetic distance coefficient matrix calculated with POPGENE software. 1.2.3.3 Correlation analysis The relationships between the genetic diversity indexes and the climatic factors were estimated using the Pearson correlation with SPSS 11.0 software. Meanwhile, the relationships between the Nei’s unbiased genetic distance matrix measured using POPGENE and the geographic distance matrix, and those between Nei’s unbiased genetic distance matrix and the climatic divergences were assessed using Mantel’s test[15]. In this case, the geographic distance between two populations was defined as the linear distance between the sites, and the climatic divergence was estimated as the Euclidean distance between the pairwise populations calculated by following the data standardization of the climatic variables[1].
2
Results
2.1 Genetic diversity and genetic structure of S. grandis populations After screening hundreds of 10-base oligonucleotide primers (Operon Technologies, USA), 16 primers that displayed intense and reproducible bands were selected for further PCR
amplification of 90 individuals (18 individuals per population) from five S. grandis populations (Table 2). All these primers generated 310 amplified bands in total and these were polymorphic (the percentage of polymorphism was 100%) (Table 3). The amplified bands of different populations varied between 239 and 275, and their lengths ranged from 400 to 2000 bp (Fig.1). The amplified bands per primer varied between 16 and 26 with an average of 19.4. Table 2 Numbers and sequences of the 16 primers Primer
Sequence 5’ to 3’
Primer
Sequence 5’ to 3’
A01
CAGGCCCTTC
N08
ACCTCAGCTC
A02
TGCCGAGCTG
N09
TGCCGGCTTG
A04
AATCGGGCTG
N20
GGTGCTCCGT
A08
GTGACGTAGG
Q01
GGGACGATGG
I03
CAGAAGCCCA
Q05
CCGCGTCTTG
I07
CAGCGACAAG
Q09
GGCTAACCGA
N02
ACCAGGGGCA
Q12
AGTAGGGCAC
N04
GACCGACCCA
Q18
AGGCTGGGTG
Fig.1 RAPD electrophoretogram of Operon I07 for S. grandis individuals of different geographic populations A: West Xilinhot; B: East Xilinhot; C: Bayanwula
A summary of the genetic diversity of S. grandis populations based on the RAPD data with POPGENE 32 software is given in Table 3. Both Nei’s gene diversity index and Shannon’s information diversity index showed a descending order as: Gasong Mountain > S. grandis Plot > West Xilinhot > East Xilinhot > Bayanwula, i.e., the genetic diversity of southern S. grandis populations was higher than that of the northern populations of the same latitude. There was a slight discrepancy in the results estimated using other diversity indexes. However, among the five populations, the Gasong Mountain population displayed the greatest genetic diversity estimated using any genetic diversity index (Table 3). Based on the genetic diversity indexes estimated by POP
ZHAO Nianxi et al. / Acta Ecologica Sinica, 2006, 26(5): 1312–1319
Table 3 Patterns of genetic diversity for S. grandis populations Population name
Sample size
S. grandis Plot
18
Gasong Mountain
18
1.8355
1.4902
West Xilinhot
18
1.7484
1.3697
East Xilinhot
18
1.7161
1.3730
Bayanwula
18
1.7258
Mean Total
90
Polymorphic loci
PPB (％)
0.3452
241
77.74
0.2847
0.4261
259
83.55
0.2224
0.3411
232
74.84
0.2209
0.3363
222
71.61
1.3275
0.2007
0.312
225
72.58
1.7606
1.3859
0.2305
0.3521
235.8
2
1.5265
0.3207
0.4894
310
na 1.7774
ne
h
1.3692
0.2236
H
76.06 100
Note of Abbreviation: na = observed number of alleles; ne = effective number of alleles; h = Nei's gene diversity; H = Shannon's information diversity index; PPB = percentage of polymorphic bands
GENE 32 in Table 3, Nei’s genetic differentiation coefficient (GST = (hT − hs)/hT) was 28.14% and Shannon’s differentiation coefficient ((HT − Hs)/HT) was 28.05%, both suggesting that a variation of about 28% existed among the populations, and the maximum variation was observed within the populations. 2.2 Genetic relationships among populations and construction of dendrogram Nei’s unbiased genetic distances varied from 0.1096 to 0.1937 between the five S. grandis populations estimated using POPGENE 32. The nearest distance (0.1096) was between West Xilinhot and East Xilinhot (Table 4). A UPGMA dendrogram based on Nei’s genetic distance matrix showed that the five populations were clustered into two groups, i.e., West Xilinhot and East Xilinhot in one group, and S. grandis Plot Table 4 Nei's unbiased genetic distances between pairwise S. grandis populations Population
S. grandis
name
Plot
S. grandis Plot
Gasong
West
East
Mountain Xilinhot Xilinhot
and Gasong Mountain together with Bayanwula in the other group (Fig. 2). 2.3 Correlation analysis The relationships between the genetic diversity indexes (Table 3) and the habitat climatic variables (Table 1) were determined using Pearson correlation analysis. The results of the correlation analysis showed that there were significant (P < 0.05) or highly significant (P < 0.01) negative correlations between each of the six genetic diversity indexes and the mean temperature in January, while there were significant (P < 0.05) negative correlations between some of the indexes and the cumulative temperature (≥10℃) or annual mean temperature, and positive but insignificant (P > 0.05) correlations between the genetic diversity indexes and the annual precipitation, suggesting that temperature variables played an important role in affecting the genetic diversity of S. grandis populations (Table 5). A matrix of geographic distances and that of climatic vari-
Bayanwula
****
Gasong Mountain
0.1290
****
West Xilinhot
0.1613
0.1937
****
East Xilinhot
0.1563
0.1573
0.1096
****
Bayanwula
0.1328
0.1592
0.1490
0.1504
****
Fig.2 Dendrogram generated by UPGMA based on Nei’s unbiased genetic distances between S. grandis populations
Table 5 Pearson correlation analysis for the relationships between genetic diversity parameters within populations of S. grandis and climatic factors ne
h
H
0.826
na
0.714
0.734
0.749
0.826
Cumulative temperature (≥10℃)
–0.879*
–0.784
–0.802
–0.816
–0.879*
Annual mean temperature
–0.749
–0.908*
–0.902*
–0.894*
–0.749
Aridity index
–0.817
–0.691
–0.711
–0.727
–0.817
Mean temperature in January
–0.983**
–0.913*
–0.931*
–0.944*
–0.983**
Mean temperature in July
–0.850
–0.647
–0.675
–0.698
–0.85
Annual precipitation
PPB
Note of Abbreviation: na = observed number of alleles; ne = effective number of alleles; h = Nei's gene diversity; H = Shannon's information diversity index; PPB = percentage of polymorphic bands; *, **: significant at 0.05 and 0.01 levels, respectively
ZHAO Nianxi et al. / Acta Ecologica Sinica, 2006, 26(5): 1312–1319
able divergence were calculated (geographic distance matrix and divergence matrix of the six climatic variables between pairwise populations are shown in Table 6, but the matrix of single climatic variables is not shown). The relationships between Nei’s unbiased genetic distance matrix and geographic distance matrix (Table 6), and between Nei’s unbiased genetic distance matrix and the divergence matrixes of the climatic variables were estimated using Mantel’s tests. No significant relationship was found between Nei’s unbiased genetic distance matrix and the geographic distance matrix, implying that the geographic distance between the pairwise populations was not the main factor affecting the genetic differentiation of S. grandis populations. In contrast, significant relationships were found between Nei’s unbiased genetic distance matrix and the total divergence matrix of the six climatic variables, indicating that the climatic divergence dominated by water and temperature played important roles in affecting the differentiation of S. grandis populations. From the results of Mantel’s tests between Nei’s genetic distance matrix and the divergence matrixes of a single climatic variable, it was found that annual precipitation, cumulative temperature (≥10℃), aridity index, and mean temperature in July were among the six climatic variables having a major impact on the population differentia-
tion of S. grandis populations, suggesting that several factors or a combination of these factors were responsible for the differentiation of S. grandis populations (Table 7).
3
Discussion
The objective of this study was to analyze the RAPD diversity of S. grandis populations, and to detect the relationships between the genetic diversity within populations (or the population genetic structure) and the ecological variables. The RAPD amplification results and POPGENE analysis indicated that 310 bands were marked for all individuals of the five populations, and these were all polymorphic. The percentage of polymorphic bands ranged from 71.65% to 83.55% within different populations; the total Nei’s gene diversity index (h) was 0.3207 and the total Shannon’s information diversity index (H) was 0.4894. These values were higher than the results of Zhang et al. who studied the same species, S. grandis [7]. The discrepancy might have resulted from the difference in the study areas and the plots selected in these two studies. In this study, the value of Nei’s gene diversity index (h) was 0.2305, close to the mean h value 0.257 (0.174–0.328) of nine out-crossing plants summarized by Schoen & Brown[16], wh-
Table 6 Geographic distances (km) (below the diagonal) and climatic divergence (six factors in total) (above the diagonal) between the locations of different S. grandis populations Population name
S. grandis plot
Gasong Mountain
West Xilinhot
East Xilinhot
Bayanwula
–
3.699
2.828
2.493
1.217
Gasong Mountain
22.0
–
5.932
5.452
4.227
West Xilinhot
98.5
77.7
–
0.7
2.157
East Xilinhot
54.7
79.4
67.7
–
1.646
121.9
176.6
144.6
153.0
S. grandis Plot
Bayanwula
–
Table 7 Mantel’s tests between the matrix of Nei’s unbiased genetic distances among different S grandis populations and the matrix of geographic distances, and between the matrix of Nei’s unbiased genetic distances and several matrixes of climatic divergence coefficients between the locations of different S. grandis populations (3000 permutations) Matrix of divergence of geographic locations and climatic factors Variable
Nei’s unbiased genetic distance matrix of different S. grandis populations Correlation coefficient (r)
Level of significance (P)
Geographic distance
0.184
0.261
Annual precipitation
0.759
0.022*
≥10℃ cumulative temperature
0.762
0.009**
Annual mean temperature
0.328
0.152
Aridity index
0.816
0.007**
Mean temperature in January
0.527
0.052
Mean temperature in July
0.895
0.010*
Six climatic factors stated above
0.731
0.007**
*, **: Significant at 0.05 and 0.01 levels, respectively
ZHAO Nianxi et al. / Acta Ecologica Sinica, 2006, 26(5): 1312–1319
ich was consistent with the general characteristics of S. grandis, in which wind-pollinated and out-crossing processes were dominant in the mating system. The UPGMA dendrogram, based on Nei’s unbiased genetic distance calculated with POPGENE, indicated that genetic differentiation was inconsistent with the geographic distance between pairwise S. grandis populations. Moreover, Mantel’s test showed no significant relationship between the genetic distances and the geographic distances (P = 0.261). Since S. grandis is a wind-pollinated and out-crossing plant, local seed/pollen dispersal is one of the main factors affecting population genetic differentiation[17]. In some studies on genetic differentiation of wind-pollinated plants, many scholars concluded that genetic distances must be positively correlated to the geographical distances that limited the pollen and seed dispersals, whereas the other correlations were not significant[17,18], i.e., genetic distances should be significantly correlated with geographic distances. However, there were many other scholars who obtained similar results to those in this study that genetic distances between populations were not significantly correlated with the geographic distances[4,19]. They believed that genetic drift or natural selection rather than migration or gene flow was the main factors affecting the population genetic differentiation[20]. Although the plots selected in this study were all located in the typical steppe of Xilingol, China, the environmental variables differed among these plots. The results in Table 5 indicated that there were significant or highly significant relationships between the genetic diversity indexes of S. grandis populations and certain temperature variables. Zhao et al.[8] showed that the percentage of unique loci of the S. grandis populations was highly significantly correlated with the aridity and the cumulative temperature above 10℃ at their habitats when genetic differentiation was analyzed for bulk DNA using RAPD. These results indicated that the temperature or water parameter or a combination of several environmental factors were responsible for the genetic differentiation of S. grandis populations, providing strong experimental evidence for the mechanism of S. grandis population differentiation. Similar results were found in other plants. In a study of 9 Aegiceras corniculatum populations by means of allozyme analysis[21], the total N content of the soil and the annual average temperature were significantly correlated with alleles’ diversity indexes, suggesting that environmental factors imposed selective pressure on A. corniculatum alleles and that the change of alleles was related to environmental variables. In another study on 11 Triticum dicoccoides populations distributed in Israel and Turkey using RAPD analysis[20], there were significant relationships between RAPD polymorphism and altitude, temperature, and available water at their habitats, which indicated that natural selection resulted in adaptive RAPD eco-geographic differentiation.
In this study, it could be conjectured that RAPD polymorphism of S. grandis populations was related to adaptation and was under natural selection. At least two lines of evidence were available. (1) There were significant or highly significant relationships between the genetic diversity of S. grandis populations and certain climatic factors, suggesting that the RAPD polymorphism of S. grandis populations was not randomly distributed and was greatly affected by the climatic factors at their habitats. (2) The genetic differentiation of S. grandis populations was not significantly correlated with the geographic distances, but was significantly or highly significantly correlated with the divergence of climatic variables. According to the published reports, genetic differentiation studies among populations were mainly focused on the following four aspects. (1) Genetic differentiation resulted from gene flow among populations, i.e., seed/pollen dispersals. (2) Genetic differentiation was affected by local natural selection and population differentiation, and discontinuous habitat heterogeneity was viewed as one of the natural selection factors[22]. (3) Genetic differentiation among populations was due to gene mutation. However, gene mutation was not generally considered as the main factor that affected the population differentiation because of low incidence rate. (4) Genetic differentiation among populations was induced by a genetic drift. In this study, the genetic differentiation of S. grandis populations was significantly correlated with the divergence of climatic factors but not with geographic distances, indicating that the selection of climatic factors rather than the gene flow resulted in the genetic differentiation of S. grandis populations. In addition, this result eliminated the possibility that genetic differentiation of S. grandis populations was induced by genetic drift because if genetic differentiation of S. grandis populations were a result of genetic drift, genetic distances should not have been significantly correlated with the divergence of climatic variables[22]. Similar results about the effect of ecological factors on plant genetic diversity of populations and on the genetic differentiation of populations were reported early on other plants. For example, Nevo et al.[23], Fahima et al.[20], and Li et al.[24] pointed out that genetic differentiation of T. dicoccoides populations was mainly induced by ecological factors when they used allozyme, RAPD and microsatellite techniques successively. Using RAPD and microsatellite molecular markers in their studies of Hordeum spontaneum, Nevo et al.[25] and Turpeinen et al.[26] found that a high genetic diversity of populations was associated with environmental stress. All these results suggested that natural selection was the main factor affecting the population genetic differentiation.
Acknowledgements This work was supported by the National Basic Research Program of China (G 200018601).
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[14] Rohlf F J. NTSYS-pc, numerical taxonomy and multivariate analysis system. Version 1.80, New York: Exeter Software, 1994. [15] Mantel N A. The detection of disease clustering and a generalized regression approach. Cancer Research, 1967, 27: 209 – 220. [16] Schoen D J, Brown A H D. Intraspecific variation in population gene diversity and effective population size correlates with the mating system in plants. Proceedings of the National Academy of Sciences, USA, 1991, 88: 4494–4497. [17] Wen C S, Hsiao J Y. Genetic differentiation of Lilium longiflorum Thunb. var. Scabrum masam. (Liliaceae) in Taiwan using Random Amplified Polymorphic DNA and morphological characters. Botanical Bulletin of Academia Sinica, 1999, 40: 65–71. [18] Falconer D S, Mackay T F C. Introduction to quantitative genetics. 4th ed. Essex, England: Longman Group Ltd, 1996. [19] Jordano P, Godoy J A. RAPD variation and population genetic structure in Prunus mahaleb (Rosaceae), an animal-dispersed tree. Molecular Ecology, 2000, 9: 1293–1305. [20] Fahima T, Sun G L, Beharav A, et al. RAPD polymorphism of wild emmer wheat populations, Triticum dicoccoides, in Israel. Theoretical and Applied Genetics, 1999, 98: 434–447. [21] Li Z B, Lin P. Relationships between environmental factors and genetic variation of Aegiceras corniculatum populations. Acta Ecologica Sinica, 2004, 22(11): 1912–1916. [22] Volis S, Yakubov B, Shulgina I, et al. Tests for adaptive RAPD variation in population genetic structure of wild barley, Hordeum spontaneum Koch. Biological Journal of Linnean Society, 2001, 74: 289–303. [23] Nevo E, Noy-Meir I, Beiles A, et al. Natural selection of allozyme polymorphism: micro-geographical spatial and temporal ecological differentiation in wild emmer wheat. Israel Journal of Botany, 1991, 40: 419–450. [24] Li Y C, Röder M S, Fahima T, et al. Natural selection causing microsatellite divergence in wild emmer wheat at the ecological variable microsite at Ammiad, Israel. Theoretical and Applied Genetics, 2000, 100: 985–999. [25] Nevo E, Baum B, Beiles A, et al. Ecological correlates of RAPD DNA diversity of wild barley, Hordeum spontaneum, in the fertile crescent. Genetic Resources and Crop Evolution, 1998, 45: 151–159. [26] Turpeinen T, Tenhola T, Manninen O, et al. Microsatellite diversity associated with ecological factors in Hordeum spontaneum populations in Israel. Molecular Ecology, 2001, 10: 1577– 1582.
RESEARCH PAPER
RAPD diversity of Stipa grandis populations and its relationship with some ecological factors Zhao Nianxi, Gao Yubao*, Wang Jinlong, Ren Anzhi, Xu Hua College of Life Sciences, Nankai University, Tianjin 300071, China
Abstract: The genetic diversity of Stipa grandis P.Smirn and its relationship with the climatic variables were studied using the RAPD technique for 90 genes from five natural populations sampled in the Xilingol steppe, China. Sixteen oligonucleotides screened from 100 random primers were used to amplify 310 trackable RAPD loci, which were all polymorphic. By analyzing the RAPD data using POPGENE software, different geographic S. grandis populations were studied, which indicated a high level of genetic diversity, and the maximum variation was observed within the populations with a 28% variation observed among the populations. Using Pearson correlation analysis, significant (P < 0.05) or highly significant (P < 0.01) relationships were found between gene diversity indexes and temperature factors (≥10℃ cumulative temperature in a year, annual mean temperature and mean temperature in January). Mantel’s tests showed that there was no significant correlation between Nei’s unbiased genetic distance and the geographic distance of S. grandis populations (r = 0.184，P = 0.261). However, there were significant or highly significant correlations between Nei’s genetic distance and the several climatic divergences in pairwise S. grandis populations. All results indicated that natural selection resulting from variations in water and temperature was responsible for the adaptive eco-geographical differentiation indicated by the RAPD markers of different S. grandis populations, and that immigration and gene drift did not play an important role in affecting the differentiation of S. grandis populations. Key Words:
Stipa grandis P.Smirn; dominant species; RAPD; genetic diversity; climatic factor; natural selection
In recent years, conservation biologists have paid increasing attention to the genetic diversity and the genetic differentiation of species as awareness of the importance of species diversity and protection grows. Several dominant/constructive － species have been studied in addition to rare species[1 5]. Such information as the genetic variation of dominant/constructive species will be useful not only for the utilization and protection of these important species, but also for providing basic data for the evaluation of the ecosystem’s genetic health[2]. Stipa grandis P. Smirn, a typical Dahuli-Mongolia species in the center of the Asian grassland region, belongs to Sect. Leiostipa Dum of genus Stipa, Gramineae. The steppe, dominated by S. grandis, is a tussock steppe in Mid-Asian grasslands of the Eurasian Steppe zone. It is the most stable and representative formation of steppes in Inner Mongolia, China, and is indicative of the division of steppe vegetation zones[6]. But there lack studies on the genetic diversity and the genetic structure of the S. grandis populations[7,8], and the analysis for correlating the genetic diversity of S. grandis with ecological
factors needs to be more comprehensive. In the present study, relationships of five natural S. grandis populations from Xilingol steppe, China were analyzed using RAPD markers. The relationship between the genetic diversity/population genetic structure and eco-geographic factors was then analyzed. The major objectives of the study were to determine one or more ecological factors that were responsible for the genetic diversity of S. grandis, and to determine the dominant factor in determining the genetic differentiation of S. grandis. Such information would play a useful role in explaining the mechanism of genetic differentiation of S. grandis. It could provide scientific evidence to assist in the conservation of this important germplasm resource.
1
Materials and methods
1.1 Study sites The study sites were located in the Xilingol typical steppe of Inner Mongolia, China, the focal point of the S. grandis
Received date: 2005-10-27; Accepted date: 2006-02-05 *Corresponding author. E-mail: [email protected] Copyright © 2006, Ecological Society of China. Published by Elsevier BV. All rights reserved.
ZHAO Nianxi et al. / Acta Ecologica Sinica, 2006, 26(5): 1312–1319
Table 1 Geographic and climatic data (means of 30 years between 1965 and 1995) for five populations of Stipa grandis
Population namea
Geographic
Altitude
location
(m)
o
S. grandis Plot Gasong Mountain West Xilinhot East Xilinhot Bayanwula
43.54 N 116.55oE 43.5oN 116.82oE 43.93oN 15.74 oE 44.14oN 116.36oE 44.64 oN 117.72 oE
1170 1300
Soil type
Dark chestnutsoil Dark chestnut soil
Annual pre-
≥10℃cumulative
cipitation
temperature in a
(mm)
year (℃)
340
2136
2
400
1776
－0.8
Annual mean
Aridity
temperature (℃)
indexb
Mean temperature in January (℃)
Mean temperature in July (℃)
1.01
－22.3
18.8
0.7
－27
17.8
1152
Chestnut soil
290
2496
1.8
1.38
－19.8
20.8
1121
Chestnut soil
300
2400
1.4
1.28
－19.1
20.7
340
2256
1.5
1.06
－19.4
19.5
1152
Dark chestnut soil
a: S. grandis Plot was set up in the S. grandis Plot of the Inner Mongolia Grassland Ecosystem Research Station of Chinese Academy of Sciences; Gasong Mountain was set up in Gasong Mountain in the vicinity of the Inner Mongolia Grassland Ecosystem Research Station of Chinese Academy of Sciences; West Xilinhot was set up 35 km west of Xilinhot City; East Xilinhot was set up 27 km east of Xilinhot City; Bayanwula was set up 12 km northeast of Bayanwula Town of Xiwu Banner in Inner Mongolia, China. b: Aridity index = 0.16×≥10℃cumulative temperature in a year / Annual precipitation
community. The region’s climate is temperate, semi-arid, and continental, and varies substantially from season to season. It is mild and rather moist during the summer, which is affected by the southeastern oceanic monsoon, and cold and dry during the winter, which is dominated by the Inner Mongolia highpressure air mass. The seasonality of precipitation is typical of North China/Northeast China summer rainfall. Rainfall occurs mainly from July to September, accounting for 70% of total annual precipitation. The landform in the region mainly takes the form of plateaus, while the corresponding zonal soil type is dark chestnut soil or chestnut soil. Five study sites with relatively little disturbance were established in typical stands of the S. grandis community. Fresh leaves were collected from S. grandis individuals for DNA extraction. The population names, geographic locations and climatic characteristics of the study sites are summarized in Table 1. The geographical location and altitude were recorded using a global positioning system (GPS). The climatic data, including annual precipitation, cumulative temperature (≥10℃), mean temperature in January, and mean temperature in July, were obtained from the meteorological stations of corresponding administrative divisions in Xilingol, China. 1.2 Study materials and methods 1.2.1 Study materials Fresh leaves were collected from S. grandis individuals in an area of 50 m × 50 m at each study site at an interval of at least 1 m to avoid collecting ramets from the same gene. These were stored with silica gel in zip-lock bags. A total of 20 samples were collected from each study site and stored in the laboratory for later use[9]. 1.2.2 Study methods
Genomic DNA was extracted from the silica-dried leaves of S. grandis using a modification of the protocol of the SDS method[10]. After being digested by RNase (without DNase), OD values (absorbency) were recorded at 260 nm and 280 nm using a UV-VIS spectrophotometer (TU-1800), and the level of purity was determined by the O.D.260/O.D.280 value (the ratio of a high level of purity is 1.8)[11]. Purity and molecular weight were also determined by electrophoresis in 0.7% aga－ rose gel. DNA was diluted to 30 ng·µl 1 at the final stage. The protocol for RAPD amplification described by Williams[12] was optimized for use on an S. grandis template DNA. The PCR reaction volume was 25 µl, and the reaction was run in a Programmable Thermal Controller-100 (MJ Research, Waltham MA, USA) with the following temperature profiles: preliminary denaturation of DNA at 94℃ for 4 min; followed by 40 cycles at 94℃ for 1 min, 36℃ for 1 min and 72 ℃ for 2 min; a final step for 10 min at 72℃, followed by 4℃ soaking until recovery. After the gradient screening of Mg2+ concentration, template concentration, dNTPs concentration, and primer concentration, the optimal reaction system contained 1 － µL template DNA (about 30 ng), 2 µl MgCl2 (25 mmol·L 1), 2+ 2.5μl 10×reaction buffer (without Mg ), 0.5 µl dNTPs (10 － － mmol·L 1), 1µL primer (5 pmol·μl 1), 0.2 µl Taq DNA poly－1 merase (5U·µl ), and 17.8µl sterile double-distilled water. The random primers were 10-bp oligonucleotides from Operon Technologies. The amplification products were separated by electrophoresis in a 1.5% agarose gel containing ethidium bromide in a 1×TBE (Tris-Borate-EDTA) buffer and were observed and photographed under UV illumination. The primers, which exhibited amplified, separated, clear, and reproducible bands, were used for further experiments. Each
ZHAO Nianxi et al. / Acta Ecologica Sinica, 2006, 26(5): 1312–1319
amplification was performed at least twice in order to confirm the results obtained. The amplification results were analyzed using the Bio IMAGING SYSTEM (SynGENE). Molecular marker SD005 (Beijing Dingguo Biotechnology Development Center, China) was used as a size marker. The RAPD bands with different relative migratory distances were regarded as different bands, whereas the bands with identical relative migratory distances were regarded as the same RAPD band. The clearly amplified DNA fragments with a size of 400–2000 bp were denoted as “1” if they were present, or “0” if they were absent. The entire data were compiled in a computer for statistical analysis. 1.2.3 Data analysis 1.2.3.1 Estimation of population genetic diversity indexes and divergence indexes Based on the binary matrix obtained in the study, genetic diversity indexes, including the percentage of polymorphic bands (PPB), the observed number of alleles (na), the expected number of alleles (ne), Nei’s gene diversity (h), and Shannon’s information index of diversity (H), were calculated in the dominant diploid of the POPGENE 32 program[13]. Using the population mean diversity indexes and the total diversity indexes analyzed with POPGENE, Nei’s genetic differentiation index (GST = (hT – hs)/hT) and Shannon’s genetic differentiation index [(HT − Hs)/HT] were calculated. Here, hs and Hs were the population mean of Nei’s and Shannon’s diversity indexes, respectively; hT and HT were the total of Nei’s and Shannon’s diversity indexes, respectively. 1.2.3.2 Construction of dendrogram A dendrogram was generated by UPGMA using NTSYS-pc software[14] based on Nei’s unbiased genetic distance coefficient matrix calculated with POPGENE software. 1.2.3.3 Correlation analysis The relationships between the genetic diversity indexes and the climatic factors were estimated using the Pearson correlation with SPSS 11.0 software. Meanwhile, the relationships between the Nei’s unbiased genetic distance matrix measured using POPGENE and the geographic distance matrix, and those between Nei’s unbiased genetic distance matrix and the climatic divergences were assessed using Mantel’s test[15]. In this case, the geographic distance between two populations was defined as the linear distance between the sites, and the climatic divergence was estimated as the Euclidean distance between the pairwise populations calculated by following the data standardization of the climatic variables[1].
2
Results
2.1 Genetic diversity and genetic structure of S. grandis populations After screening hundreds of 10-base oligonucleotide primers (Operon Technologies, USA), 16 primers that displayed intense and reproducible bands were selected for further PCR
amplification of 90 individuals (18 individuals per population) from five S. grandis populations (Table 2). All these primers generated 310 amplified bands in total and these were polymorphic (the percentage of polymorphism was 100%) (Table 3). The amplified bands of different populations varied between 239 and 275, and their lengths ranged from 400 to 2000 bp (Fig.1). The amplified bands per primer varied between 16 and 26 with an average of 19.4. Table 2 Numbers and sequences of the 16 primers Primer
Sequence 5’ to 3’
Primer
Sequence 5’ to 3’
A01
CAGGCCCTTC
N08
ACCTCAGCTC
A02
TGCCGAGCTG
N09
TGCCGGCTTG
A04
AATCGGGCTG
N20
GGTGCTCCGT
A08
GTGACGTAGG
Q01
GGGACGATGG
I03
CAGAAGCCCA
Q05
CCGCGTCTTG
I07
CAGCGACAAG
Q09
GGCTAACCGA
N02
ACCAGGGGCA
Q12
AGTAGGGCAC
N04
GACCGACCCA
Q18
AGGCTGGGTG
Fig.1 RAPD electrophoretogram of Operon I07 for S. grandis individuals of different geographic populations A: West Xilinhot; B: East Xilinhot; C: Bayanwula
A summary of the genetic diversity of S. grandis populations based on the RAPD data with POPGENE 32 software is given in Table 3. Both Nei’s gene diversity index and Shannon’s information diversity index showed a descending order as: Gasong Mountain > S. grandis Plot > West Xilinhot > East Xilinhot > Bayanwula, i.e., the genetic diversity of southern S. grandis populations was higher than that of the northern populations of the same latitude. There was a slight discrepancy in the results estimated using other diversity indexes. However, among the five populations, the Gasong Mountain population displayed the greatest genetic diversity estimated using any genetic diversity index (Table 3). Based on the genetic diversity indexes estimated by POP
ZHAO Nianxi et al. / Acta Ecologica Sinica, 2006, 26(5): 1312–1319
Table 3 Patterns of genetic diversity for S. grandis populations Population name
Sample size
S. grandis Plot
18
Gasong Mountain
18
1.8355
1.4902
West Xilinhot
18
1.7484
1.3697
East Xilinhot
18
1.7161
1.3730
Bayanwula
18
1.7258
Mean Total
90
Polymorphic loci
PPB (％)
0.3452
241
77.74
0.2847
0.4261
259
83.55
0.2224
0.3411
232
74.84
0.2209
0.3363
222
71.61
1.3275
0.2007
0.312
225
72.58
1.7606
1.3859
0.2305
0.3521
235.8
2
1.5265
0.3207
0.4894
310
na 1.7774
ne
h
1.3692
0.2236
H
76.06 100
Note of Abbreviation: na = observed number of alleles; ne = effective number of alleles; h = Nei's gene diversity; H = Shannon's information diversity index; PPB = percentage of polymorphic bands
GENE 32 in Table 3, Nei’s genetic differentiation coefficient (GST = (hT − hs)/hT) was 28.14% and Shannon’s differentiation coefficient ((HT − Hs)/HT) was 28.05%, both suggesting that a variation of about 28% existed among the populations, and the maximum variation was observed within the populations. 2.2 Genetic relationships among populations and construction of dendrogram Nei’s unbiased genetic distances varied from 0.1096 to 0.1937 between the five S. grandis populations estimated using POPGENE 32. The nearest distance (0.1096) was between West Xilinhot and East Xilinhot (Table 4). A UPGMA dendrogram based on Nei’s genetic distance matrix showed that the five populations were clustered into two groups, i.e., West Xilinhot and East Xilinhot in one group, and S. grandis Plot Table 4 Nei's unbiased genetic distances between pairwise S. grandis populations Population
S. grandis
name
Plot
S. grandis Plot
Gasong
West
East
Mountain Xilinhot Xilinhot
and Gasong Mountain together with Bayanwula in the other group (Fig. 2). 2.3 Correlation analysis The relationships between the genetic diversity indexes (Table 3) and the habitat climatic variables (Table 1) were determined using Pearson correlation analysis. The results of the correlation analysis showed that there were significant (P < 0.05) or highly significant (P < 0.01) negative correlations between each of the six genetic diversity indexes and the mean temperature in January, while there were significant (P < 0.05) negative correlations between some of the indexes and the cumulative temperature (≥10℃) or annual mean temperature, and positive but insignificant (P > 0.05) correlations between the genetic diversity indexes and the annual precipitation, suggesting that temperature variables played an important role in affecting the genetic diversity of S. grandis populations (Table 5). A matrix of geographic distances and that of climatic vari-
Bayanwula
****
Gasong Mountain
0.1290
****
West Xilinhot
0.1613
0.1937
****
East Xilinhot
0.1563
0.1573
0.1096
****
Bayanwula
0.1328
0.1592
0.1490
0.1504
****
Fig.2 Dendrogram generated by UPGMA based on Nei’s unbiased genetic distances between S. grandis populations
Table 5 Pearson correlation analysis for the relationships between genetic diversity parameters within populations of S. grandis and climatic factors ne
h
H
0.826
na
0.714
0.734
0.749
0.826
Cumulative temperature (≥10℃)
–0.879*
–0.784
–0.802
–0.816
–0.879*
Annual mean temperature
–0.749
–0.908*
–0.902*
–0.894*
–0.749
Aridity index
–0.817
–0.691
–0.711
–0.727
–0.817
Mean temperature in January
–0.983**
–0.913*
–0.931*
–0.944*
–0.983**
Mean temperature in July
–0.850
–0.647
–0.675
–0.698
–0.85
Annual precipitation
PPB
Note of Abbreviation: na = observed number of alleles; ne = effective number of alleles; h = Nei's gene diversity; H = Shannon's information diversity index; PPB = percentage of polymorphic bands; *, **: significant at 0.05 and 0.01 levels, respectively
ZHAO Nianxi et al. / Acta Ecologica Sinica, 2006, 26(5): 1312–1319
able divergence were calculated (geographic distance matrix and divergence matrix of the six climatic variables between pairwise populations are shown in Table 6, but the matrix of single climatic variables is not shown). The relationships between Nei’s unbiased genetic distance matrix and geographic distance matrix (Table 6), and between Nei’s unbiased genetic distance matrix and the divergence matrixes of the climatic variables were estimated using Mantel’s tests. No significant relationship was found between Nei’s unbiased genetic distance matrix and the geographic distance matrix, implying that the geographic distance between the pairwise populations was not the main factor affecting the genetic differentiation of S. grandis populations. In contrast, significant relationships were found between Nei’s unbiased genetic distance matrix and the total divergence matrix of the six climatic variables, indicating that the climatic divergence dominated by water and temperature played important roles in affecting the differentiation of S. grandis populations. From the results of Mantel’s tests between Nei’s genetic distance matrix and the divergence matrixes of a single climatic variable, it was found that annual precipitation, cumulative temperature (≥10℃), aridity index, and mean temperature in July were among the six climatic variables having a major impact on the population differentia-
tion of S. grandis populations, suggesting that several factors or a combination of these factors were responsible for the differentiation of S. grandis populations (Table 7).
3
Discussion
The objective of this study was to analyze the RAPD diversity of S. grandis populations, and to detect the relationships between the genetic diversity within populations (or the population genetic structure) and the ecological variables. The RAPD amplification results and POPGENE analysis indicated that 310 bands were marked for all individuals of the five populations, and these were all polymorphic. The percentage of polymorphic bands ranged from 71.65% to 83.55% within different populations; the total Nei’s gene diversity index (h) was 0.3207 and the total Shannon’s information diversity index (H) was 0.4894. These values were higher than the results of Zhang et al. who studied the same species, S. grandis [7]. The discrepancy might have resulted from the difference in the study areas and the plots selected in these two studies. In this study, the value of Nei’s gene diversity index (h) was 0.2305, close to the mean h value 0.257 (0.174–0.328) of nine out-crossing plants summarized by Schoen & Brown[16], wh-
Table 6 Geographic distances (km) (below the diagonal) and climatic divergence (six factors in total) (above the diagonal) between the locations of different S. grandis populations Population name
S. grandis plot
Gasong Mountain
West Xilinhot
East Xilinhot
Bayanwula
–
3.699
2.828
2.493
1.217
Gasong Mountain
22.0
–
5.932
5.452
4.227
West Xilinhot
98.5
77.7
–
0.7
2.157
East Xilinhot
54.7
79.4
67.7
–
1.646
121.9
176.6
144.6
153.0
S. grandis Plot
Bayanwula
–
Table 7 Mantel’s tests between the matrix of Nei’s unbiased genetic distances among different S grandis populations and the matrix of geographic distances, and between the matrix of Nei’s unbiased genetic distances and several matrixes of climatic divergence coefficients between the locations of different S. grandis populations (3000 permutations) Matrix of divergence of geographic locations and climatic factors Variable
Nei’s unbiased genetic distance matrix of different S. grandis populations Correlation coefficient (r)
Level of significance (P)
Geographic distance
0.184
0.261
Annual precipitation
0.759
0.022*
≥10℃ cumulative temperature
0.762
0.009**
Annual mean temperature
0.328
0.152
Aridity index
0.816
0.007**
Mean temperature in January
0.527
0.052
Mean temperature in July
0.895
0.010*
Six climatic factors stated above
0.731
0.007**
*, **: Significant at 0.05 and 0.01 levels, respectively
ZHAO Nianxi et al. / Acta Ecologica Sinica, 2006, 26(5): 1312–1319
ich was consistent with the general characteristics of S. grandis, in which wind-pollinated and out-crossing processes were dominant in the mating system. The UPGMA dendrogram, based on Nei’s unbiased genetic distance calculated with POPGENE, indicated that genetic differentiation was inconsistent with the geographic distance between pairwise S. grandis populations. Moreover, Mantel’s test showed no significant relationship between the genetic distances and the geographic distances (P = 0.261). Since S. grandis is a wind-pollinated and out-crossing plant, local seed/pollen dispersal is one of the main factors affecting population genetic differentiation[17]. In some studies on genetic differentiation of wind-pollinated plants, many scholars concluded that genetic distances must be positively correlated to the geographical distances that limited the pollen and seed dispersals, whereas the other correlations were not significant[17,18], i.e., genetic distances should be significantly correlated with geographic distances. However, there were many other scholars who obtained similar results to those in this study that genetic distances between populations were not significantly correlated with the geographic distances[4,19]. They believed that genetic drift or natural selection rather than migration or gene flow was the main factors affecting the population genetic differentiation[20]. Although the plots selected in this study were all located in the typical steppe of Xilingol, China, the environmental variables differed among these plots. The results in Table 5 indicated that there were significant or highly significant relationships between the genetic diversity indexes of S. grandis populations and certain temperature variables. Zhao et al.[8] showed that the percentage of unique loci of the S. grandis populations was highly significantly correlated with the aridity and the cumulative temperature above 10℃ at their habitats when genetic differentiation was analyzed for bulk DNA using RAPD. These results indicated that the temperature or water parameter or a combination of several environmental factors were responsible for the genetic differentiation of S. grandis populations, providing strong experimental evidence for the mechanism of S. grandis population differentiation. Similar results were found in other plants. In a study of 9 Aegiceras corniculatum populations by means of allozyme analysis[21], the total N content of the soil and the annual average temperature were significantly correlated with alleles’ diversity indexes, suggesting that environmental factors imposed selective pressure on A. corniculatum alleles and that the change of alleles was related to environmental variables. In another study on 11 Triticum dicoccoides populations distributed in Israel and Turkey using RAPD analysis[20], there were significant relationships between RAPD polymorphism and altitude, temperature, and available water at their habitats, which indicated that natural selection resulted in adaptive RAPD eco-geographic differentiation.
In this study, it could be conjectured that RAPD polymorphism of S. grandis populations was related to adaptation and was under natural selection. At least two lines of evidence were available. (1) There were significant or highly significant relationships between the genetic diversity of S. grandis populations and certain climatic factors, suggesting that the RAPD polymorphism of S. grandis populations was not randomly distributed and was greatly affected by the climatic factors at their habitats. (2) The genetic differentiation of S. grandis populations was not significantly correlated with the geographic distances, but was significantly or highly significantly correlated with the divergence of climatic variables. According to the published reports, genetic differentiation studies among populations were mainly focused on the following four aspects. (1) Genetic differentiation resulted from gene flow among populations, i.e., seed/pollen dispersals. (2) Genetic differentiation was affected by local natural selection and population differentiation, and discontinuous habitat heterogeneity was viewed as one of the natural selection factors[22]. (3) Genetic differentiation among populations was due to gene mutation. However, gene mutation was not generally considered as the main factor that affected the population differentiation because of low incidence rate. (4) Genetic differentiation among populations was induced by a genetic drift. In this study, the genetic differentiation of S. grandis populations was significantly correlated with the divergence of climatic factors but not with geographic distances, indicating that the selection of climatic factors rather than the gene flow resulted in the genetic differentiation of S. grandis populations. In addition, this result eliminated the possibility that genetic differentiation of S. grandis populations was induced by genetic drift because if genetic differentiation of S. grandis populations were a result of genetic drift, genetic distances should not have been significantly correlated with the divergence of climatic variables[22]. Similar results about the effect of ecological factors on plant genetic diversity of populations and on the genetic differentiation of populations were reported early on other plants. For example, Nevo et al.[23], Fahima et al.[20], and Li et al.[24] pointed out that genetic differentiation of T. dicoccoides populations was mainly induced by ecological factors when they used allozyme, RAPD and microsatellite techniques successively. Using RAPD and microsatellite molecular markers in their studies of Hordeum spontaneum, Nevo et al.[25] and Turpeinen et al.[26] found that a high genetic diversity of populations was associated with environmental stress. All these results suggested that natural selection was the main factor affecting the population genetic differentiation.
Acknowledgements This work was supported by the National Basic Research Program of China (G 200018601).
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