Int. J. Mol. Sci. 2013, 14(7), 14860-14871; doi:10.3390/ijms140714860

Article
Genetic Diversity of the Critically Endangered Thuja sutchuenensis Revealed by ISSR Markers and the Implications for Conservation
Jianfeng Liu , Shengqing Shi , Ermei Chang , Wenjuan Yang and Zeping Jiang *
State Key Lab of Tree Genetics and Breeding, Research Institute of Forestry, Chinese Academy of Forestry, Beijing 100091, China; E-Mails: liujf@caf.ac.cn (J.L.); shi.shengqing@hotmail.com (S.S.); changcem@gmail.com (E.C.); yangwenjuan31@126.com (W.Y.)
*
Author to whom correspondence should be addressed; E-Mail: jiangzp@caf.ac.cn; Tel.: +86-10-6288-9607; Fax: +86-10-6287-2015.
Received: 28 May 2013; in revised form: 2 July 2013 / Accepted: 2 July 2013 /
Published: 16 July 2013

Abstract

: Thuja sutchuenensis Franch. is a critically endangered plant endemic to the North-East Chongqing, China. Genetic variation was studied to assess the distribution of genetic diversity within and among seven populations from the single remnant locations, using inter-simple sequence repeat (ISSR) markers. A total of 15 primers generated 310 well defined bands, with an average of 20.7 bands per primer. The seven populations revealed a relatively high level of genetic diversity in the species. The percentage of polymorphic bands, Nei’s gene diversity and Shannon’s information index at the population and species level were 76.1%, 0.155, 0.252 and 100%, 0.165, 0.295, respectively. A low level of genetic differentiation among populations (GST = 0.102), in line with the results of Analyses of Molecular Variance (AMOVA), and a high level of gene flow (Nm = 4.407) were observed. Both the Unweighted Pair Group Method with Arithmatic Mean (UPGMA) cluster analysis and Principal Coordinates Analysis (PCoA) supported the grouping of all seven populations into two groups. In addition, Mantel test revealed no significant correlation between genetic and geographical distances (r = 0.329, p = 0.100). The low genetic differentiation among populations implies that the conservation efforts should aim to preserve all the extant populations of this endangered species.
Keywords:
Thuja sutchuenensis; genetic variation; ISSR markers; conservation implications

1. Introduction

Thuja sutchuenensis Franch., an evergreen coniferous species of genus Thuja in the cypress family Cupressaceae, is a critically endangered species endemic to mountain area in North-East Chongqing Municipality, China [1]. It was first described in 1899 from specimens collected by the French botanist Paul Guillaume Farges in 1892 and 1900, but was not seen again thereafter, despite many searches, for almost 100 years and was presumed to be extinct due to over-cutting for its valuable scented wood. A small number of specimens were rediscovered in 1999, growing on almost inaccessible steep ridges close to (or at the same site) where Farges had first found it [2]. The area of its occurrence has now been designated two Special Protection Area in order to protect the species. However, limited recruitment in remnant populations of the species was observed according to our continuous field investigation over 10 years.

Genetic variation within and among natural populations is crucial for the long-term survival of a species. Especially for rare or endangered species, an accurate estimate of the genetic variation among or within its populations could be helpful to address its endangered status or mechanism [36], and provide fundamental information in designing conservation programs [7]. Among various molecular tools for genetic analysis, inter-simple sequence repeats (ISSR), which involve PCR amplifications of DNA using a primer composed of a microsatellite sequence anchored at 3′ or 5′ end by 2–4 arbitrary nucleotides, are a powerful tool for investigating genetic variation within species [810]. Because of the higher annealing temperature and longer sequence of ISSR primers, they can yield more reliable and reproducible bands than Random Amplified Polymorphic DNA (RAPD) [1113], and the cost of the analyses is relatively lower comparing to other markers such as Restriction Fragment Length Polymorphism (RFLP), Simple Sequence Repeat (SSR) and Amplified Fragment Length Polymorphism (AFLP) [14,15]. In addition, it does not require previous genomic sequence information, which makes ISSR technically simpler than many other marker systems [16]. Therefore, ISSR has been widely used for population genetic analysis of various plant species, including many rare or endemic plants, Neopicrorhiza scrophulariiflora [17], Swertia tetraptera [18], Michelia coriacea [19], etc.

In the present study, we used ISSR markers to investigate the genetic composition of natural T. sutchuenensis populations, which restrictedly distributed in North-East Chongqing, China, with the following aims: (1) to evaluate the genetic diversity at the population and species levels in the T. sutchuenensis; (2) to assess the distribution of the genetic variation within and among populations and to construct a dendrogram demonstrating the genetic relationships among them; and (3) to use the genetic information as a tool for assessing the current conservation management plan for this endangered species and for designing conservation strategies.

2. Results

2.1. Genetic Diversity

Fifteen selected ISSR primers were used to amplify all 139 DNA samples from seven natural populations of T. sutchuenensis (Table 1, Figure 1), and yielded 310 bright and discernible products in the range 130–950 bp (Table 2). At the population level, the percentage of polymorphic bands (P) ranged from 59.7% (A) to 91.3% (C) (Table 3) with an average of 76.1%, while with 100.0% at species level. The mean observed number of alleles (Na) ranged from 1.597 to 1.913, while the mean effective number of alleles (Ne) varied from 1.180 to 1.237. Nei’s genetic diversity (H) varied from 0.130 to 0.178, with an average of 0.155, and Shannon’s information indices (I) ranged from 0.180 to 0.291, with an average of 0.252. At species level, H and I were 0.165 and 0.295, respectively.

2.2. Genetic Differentiation

The total genetic diversity (HT) of the species and genetic diversity within populations (HS) were 0.167 and 0.150, respectively (Table 3). The proportion of genetic variation contributed by the differences among populations (Gst) was 0.102, thus leaving 89.8% of the total genetic variation harboured within the populations. It was consistent with the results of AMOVA, which detected highest genetic variation within population (87.9%), whereas the variance among populations was only 12.1% (Table 4). This was further confirmed by the abundant gene flow among populations (Nm = 4.407).

2.3. Cluster Analysis

The Nei’s genetic distances between pairs of populations were calculated based on the 310 analyzed bands. The values ranged from 0.0064 between population E and F, to 0.0250 between population A and G (Table 5). Furthermore, the dendrogram showed that the individuals and populations were partly mixedly clustered, although being geographically more distant (Figure 2). Consistent to these results, the Mantel test revealed that there was no significant correlation between geographic and genetic distance (r = 0.329, p = 0.100).

2.4. Principal Coordinate Analysis

Principal coordinate analysis (PCoA) was performed to provide spatial representation of the relative genetic distances among individuals and to determine the consistency of differentiation among populations defined by the cluster analysis. The first two principal components explained 26.17% and 20.09% of total variation, respectively, and the 63.52% was explained by the first three components (Figure 3). In an agreement with the cluster analysis, individuals from each population formed a separate plot and could be clearly distinguished from those of other populations. The first principal coordinate separated individuals of population A, E and F from the individuals of other four populations. The second principal coordinate separated most individuals of population B, C and D from those of G, which indicated less population-specific identity for population G.

3. Discussion

3.1. High Genetic Diversity

The ISSR analysis conducted for the T. sutchuenensis populations located in North-East Chongqing, China, revealed the presence of an appreciable level of genetic diversity, which was more than that of its relatives Calocedrus macrolepis [20], Taiwania cryptomerioides [21], Cupressus gigantea [22] and C. chengiana [23]. This is an expected result for a woody, moderately long-lived, outcrossing and wind-pollinated species [24]. In general, species with small geographic ranges tend to maintain less genetic diversity than geographically widespread species. However, exceptions are not common [19,25,26]. Genetic diversity within populations is influenced by historical factors (e.g., founder effects, bottlenecks, extended time periods with low numbers of individuals and gene flow rate), and thus present-day population sizes may not be a reliable indication of genetic diversity, though population sizes could make positive effects on within population genetic variation [27]. High genetic diversity within small populations can be observed if population size reduction has occurred very recently, especially where that reduction has presented within a generation or two for the species concerned. In such cases the surviving individuals are effectively samples from the larger population that existed before [19,25,28]. It seems to be consistent with witness accounts of local people and records in the present study. During the past decades, the more accessible individuals have been over-exploited for use in home building and for making various household products, which resulting in the rapid reduction of its population size.

3.2. Low Genetic Divergence

The overall degree of genetic differentiation, as estimated by GST, was 0.102, slightly higher than the mean value (GST = 0.073) recorded for 121 woody species examined using allozyme markers [24]. Low level of population differentiation for gymnosperms has also been confirmed by Nybom and Bartish [29] based on an overview of RAPD analysis of plant species, but exceptions were also existed [23,30,31]. Geographic isolation is one major factor influencing genetic differentiation by limiting the amount of gene flow via both pollen and seeds [32]. In theory, gene flow of more than four migrants per generation is sufficient to prevent genetic differentiation between populations due to drift alone [33]. From this standpoint, the level of gene flow estimated (Nm = 4.407) in the present study was enough to rule out the possibility that some differentiation among the populations of T. sutchuenensis can be due to isolation. This was consistent with the lack of correlation between genetic and geographic distances (r = 0.329, p = 0.100), which suggested no significant geographic restriction to gene flow among the populations. The lack of significant correlation between genetic distance and geographical distance also meant that genetic drift played an important role in population differentiation; and when a population was isolated from other populations, the genetic drift became the important factor influencing the genetic structure and increases genetic variation among populations [34]. The results of UPGMA and PCoA also showed no clear geographic trends among natural populations of T. sutchuenensis. Therefore, genetic drift may be an explanation for the genetic differentiation among populations.

3.3. Implication for Conservation

Thorough understanding of the extent and patterns of genetic diversity in T. sutchuenensis is essential for its conservation and exploitation. It is important to understand where efforts should be effectively manage and conserve populations of this species. In the case of T. sutchuenensis, AMOVA results shows that each of the seven examined populations currently maintains a relatively high level of within-population genetic diversity. Therefore, the endangered status of this species currently reflects a small number of extant individuals and very poor natural regeneration. Most of the population sizes in the present study are lower than the standard minimum effective population size estimated for plants [35], so maintenance of genetic diversity is not ensured. Conservation measures should therefore focus on establishing large numbers of seedlings, both in and ex situ, involving many different individuals as parents, to preserve as much as possible of the existing genetic diversity in subsequent generations. Given the current lack of recruitment, establishing seedlings in situ will require management and cooperation from local communities. So, in situ efforts to conserve remaining habitats need to be combined with ex situ research on seed propagation, with a view to establishing a new generation of plants both in cultivation and the wild. In addition, although two local natural protection areas have been established for this species, there still have some other populations outside the protected areas, i.e., population E in the present study. Therefore, a field survey for “new” populations near the distribution range, as well as putting them into protected areas, should also be needed for the species’ protection.

4. Experimental Section

4.1. Study Species and Population Sampling

T. sutchuenensis were found only on bare limestone faces on ridges or steep mountains at North-East Chongqing, China, with different population size. According to the field survey information, seven wild populations of T. sutchuenensis with a total of 139 individuals were sampled throughout its distribution range (Table 1; Figure 1). For large populations (n > 100), 30 adult individuals were selected with distance of >10 m; while for small populations (n < 30), all available adult individuals were sampled. For each sampled individual, fresh leaves (~10 g) were collected and dried in silica gel and stored at −20 °C until subsequent DNA extraction.

4.2. DNA Extraction

Total DNA was extracted from silica gel-dried needles of each plant (0.1 g) following the CTAB method [36], as ground to a fine powder in liquid nitrogen and extracted with CTAB extraction buffer [2% (w/v) CTAB, 20 mM EDTA (Ethylenediamine Tetraacetic Acid), 1.4 mM NaCl, 100 mM Tris-HCl (pH = 8.0), 40 mM b-mercaptoethanol]. The mixture was incubated at 65 °C for 30 min followed by two extractions with chloroform: isoamyl alcohol (24:1). The pellet obtained was washed with 75% ethanol, finally dissolved in TE (Tris-EDTA) buffer [1 mM EDTA (pH = 8.0), 10 mM Tris-HCl (pH = 8.0)]. Quality and concentration of total DNA were verified by spectrophotometry at 260 and 280 nm. DNA samples were stored at −20 °C for ISSR amplification.

4.3. ISSR Amplification

A subset of 15 primers (Table 2) chosen from 100 primers (UBC primer set No. 9, Biotechnology Laboratory, University of British Columbia), which yielded bright and discernible bands in two random samples of each population, were finally used for the analysis of all 139 samples. ISSR amplifications were performed in a volume of 25 μL containing 0.4 ng total DNA, 1× PCR buffer, 1.5 mM MgCl2, 200 μM dNTPs, 1 μM primer and 0.6 U Taq DNA polymerase (Promega, Madison WI, USA). Amplification reaction was performed in a Gene Amp PCR System 9600 (Perkin Elmer, Foster, CA, USA). The cycle program included an initial 5 min denaturation at 94 °C, followed by 45 cycles of 30 s at 94 °C, 45 s at 50 °C (52 °C for the primer 895#) and 2 min at 72 °C, and 10 min final extension at 72 °C. PCR Products were electrophoresed in 1.6% agarose gel in 0.5× TBE at 100 V for 2 h, stained with ethidium bromide (0.5 μg/mL). The negative control was run by replacing template DNA with ddH2O. Gels with amplification fragments were visualized and photographed with DG-III (Beijing Dingguo Co. Ltd., Beijing, China). DL1000 bp ladder (TaKaRa Biotechnology, Dalian, China) was used as DNA molecular weight.

4.4. Data Analysis

Only distinct, reproducible, well-resolved fragments were scored as present (1) or absent (0) for each ISSR reaction. The data obtained were combined in a single matrix and genetic coefficients were firstly generated using the program POPGENE v1.32 to describe genetic variation at intra-and inter-population level: the percentage of polymorphic bands (P, %), Nei’s gene diversity (H), Shannon’s information index (I), the observed number of alleles (Na) and the effective number of alleles (Ne) [37,38]. The genetic structure was further investigated using Nei’s gene diversity statistics, including the total genetic diversity (HT), genetic diversity within populations (HS), and the relative magnitude of genetic differentiation among populations (GST = (HTHS)/HT) [37]. An estimate of gene flow among populations (Nm) was computed by the formula of Nm = (1 − GST)/2GST [39]. The AMOVA (analysis of molecular variance) was performed to describe variance components and their significance levels for variation among individuals within and among the populations, using the program GenAlEx v6.5 [40].

Genetic divergence between populations was investigated using Nei’s unbiased genetic distances and genetic identities [41]. Nei’s unbiased genetic distances were used to construct dendrograms using UPGMA (unweighted pairgroup arithmetic mean-method) [41] in POPGENE v1.32. A principal co-ordinate analysis (PCoA) in GenAlEx 6.5 was employed to examine further the genetic relationships among detected populations on the basis of the same ISSR data. Furthermore, the relationship between geographic and genetic distances was performed with the Mantel test [42].

5. Conclusions

In summary, our results indicated the genetic diversity of T. sutchuenensis was appreciable both at the population and species level. Low genetic differentiation was found among populations, which may be attributed to limited geographic distance and abundant gene flow. Both UPGMA cluster analysis and PCA supported the grouping of all seven populations into two groups. Based on these findings, strategies are proposed for the conservation of the species.

Acknowledgments

The authors are grateful to Zhengyu Liu, Mingbo Ren, Shiqiang Zhang and Jianxiu Wang for their assistance in filed observation and sample collection. We also sincerely thank Fagen Li for his enthusiastic assistance in data analysis. Thanks also go to two anonymous reviewers for valuable suggestions and comments. This study was supported by the special fund of the Research Institute of Forestry, Chinese Academy of Forestry (grant RIF2010-15) and the 948 program of the State Forestry Administration of China (grant 2013-4-47).

Conflict of Interest

The authors declare no conflict of interest.

References

  1. Xiang, Q.; Fajon, A.; Li, Z.; Fu, L.; Liu, Z. Thuja Sutchuenensis. In IUCN Red List of Threatened Species, Version 2012.2; The International Union for Conservation of Nature (IUCN): Cambridge, UK, 2012.
  2. Xiang, Q.; Fajon, A.; Li, Z.; Fu, L.; Liu, Z. Thuja sutchuenensis: A rediscovered species of the Cupressaceae. Bot. J. Linn. Soc 2002, 139, 305–310.
  3. Ge, X.J.; Yu, Y.; Zhao, N.X.; Chen, H.S.; Qi, W.Q. Genetic variation in the endangered Inner Mongolia endemic shrub Tetraena mongolica Maxim. (Zygophyllaceae). Biol. Conserv 2003, 111, 427–434.
  4. Zhang, X.P.; Li, X.H.; Qiu, Y.X. Genetic diversity of the endangered species Kirengeshoma palmata (Saxifragaceae) in China. Biochem. Syst. Ecol 2006, 34, 38–47.
  5. Zheng, D.J.; Xie, L.S.; Zhu, J.H.; Zhang, Z.L. Low genetic diversity and local adaptive divergence of Dracaena cambodiana (Liliaceae) populations associated with historical population bottlenecks and natural selection: An endangered long-lived tree endemic to Hainan Island, China. Plant Biol 2012, 14, 828–838.
  6. Ding, G.; Zhang, D.; Yu, Y.; Zhao, L.; Zhang, B. Analysis of genetic variability and population structure of the endemic medicinal Limonium sinense using molecular markers. Gene 2013, 520, 189–193.
  7. Frankham, R.; Briscoe, D.A.; Ballou, J.D. Introduction to Conservation Genetics; Cambridge University Press: Cambridge, UK, 2002.
  8. Culley, T.M.; Wolfe, A.D. Population genetic structure of the cleistogamous plant species Viola pubescens Aiton (Violaceae), as indicated by allozyme and ISSR molecular markers. Heredity 2001, 86, 545–556.
  9. Vicente, M.J.; Segura, F.; Aguado, M.; Migliaro, D.; Franco, J.A.; Martínez-Sánchez, J.J. Genetic diversity of Astragalus nitidiflorus, a critically endangered endemic of SE Spain, and implications for its conservation. Biochem. Syst. Ecol 2011, 39, 175–182.
  10. Rubio-Moraga, A.; Candel-Perez, D.; Lucas-Borja, M.E.; Tiscar, P.A.; Viñegla, B.; Linares, J.C.; Gómez-Gómez, L.; Ahrazem, O. Genetic diversity of Pinus nigra Arn. populations in southern Spain and northern Morocco revealed by inter-simple sequence repeat profiles. Int. J. Mol. Sci 2012, 13, 5645–5658.
  11. Nagaoka, T.; Ogihara, Y. Applicability of inter-simple sequence repeat polymorphisms in wheat for use as DNA markers in comparison to RFLP and RAPD markers. Theor. Appl. Genet 1997, 94, 597–602.
  12. Qian, W.; Ge, S.; Hong, D.Y. Genetic variation within and among populations of a wild rice Oryza granulata from China detected by RAPD and ISSR markers. Theor. Appl. Genet 2001, 102, 440–449.
  13. Wu, C.J.; Cheng, Z.Q.; Huang, X.Q.; Yin, S.-H.; Cao, K.M.; Sun, C.-R. Genetic diversity among and within populations of Oryza granulata from Yunnan of China revealed by RAPD and ISSR markers: Implications for conservation of the endangered species. Plant Sci 2004, 167, 35–42.
  14. Wang, C.; Zhang, H.; Qian, Z.Q.; Zhao, G.F. Genetic differentiation in endangered Gynostemma pentaphyllum (Thunb.) Makino based on ISSR polymorphism and its implications for conservation. Biochem. Syst. Ecol 2008, 36, 699–705.
  15. Yang, W.; de Oliveira, A.C.; Godwin, I.; Schertz, K.; Bennetzen, J.L. Comparison of DNA marker technologies in characterizing plant genome diversity: Variability in Chinese sorghums. Crop Sci 1996, 36, 1669–1676.
  16. Bornet, B.; Branchard, M. Nonanchored inter simple sequence repeat (ISSR) markers: Reproducible and specific tools for genome fingerprinting. Plant Mol. Biol. Report 2001, 19, 209–215.
  17. Liu, X.L.; Qian, Z.G.; Liu, F.H.; Yang, Y.W.; Pu, C.X. Genetic diversity within and among populations of Neopicrorhiza scrophulariiflora (Scrophulariaceae) in China, an endangered medicinal plant. Biochem. Syst. Ecol 2011, 39, 297–301.
  18. Yang, L.; Zhou, G.; Chen, G. Genetic diversity and population structure of Swertia tetraptera (Gentianaceae), an endemic species of Qinghai-Tibetan Plateau. Biochem. Syst. Ecol 2011, 39, 302–308.
  19. Zhao, X.; Ma, Y.; Sun, W.; Wen, X.; Milne, R. High genetic diversity and low differentiation of Michelia coriacea (Magnoliaceae), a critically endangered endemic in southeast Yunnan, China. Int. J. Mol. Sci 2012, 13, 4396–4411.
  20. Wang, D.L.; Li, Z.C.; Hao, G.; Chiang, T.Y.; Ge, X.J. Genetic diversity of Calocedrus macrolepis (Cupressaceae) in southwestern China. Biochem. Syst. Ecol 2004, 32, 797–807.
  21. Li, Z.C.; Wang, X.L.; Ge, X.J. Genetic diversity of the relict plant Taiwania cryptomerioides Hayata (Cupressaceae) in mainland China. Silvae Genet 2008, 57, 242–249.
  22. Xia, T.; Meng, L.; Mao, K.; Tian, B.; Miehe, G.; Liu, J. Genetic variation in the Qinghai-Tibetan plateau endemic and endangered conifer Cupressus gigantea, detected using RAPD and ISSR markers. Silvae Genet 2008, 57, 85–92.
  23. Hao, B.; Li, W.; Linchun, M.; Li, Y.; Rui, Z.; Mingxia, T.; Weikai, B. A study of conservation genetics in Cupressus chengiana, an endangered endemic of China, using ISSR markers. Biochem. Genet 2006, 44, 29–43.
  24. Hamrick, J.L.; Godt, M.; Sherman-Broyles, S. Factors influencing levels of genetic diversity in woody plant species. N. For 1992, 6, 95–124.
  25. Luan, S.; Chiang, T.Y.; Gong, X. High genetic diversity vs. low genetic differentiation in Nouelia insignis (Asteraceae), a narrowly distributed and endemic species in China, revealed by ISSR fingerprinting. Ann. Bot 2006, 98, 583–589.
  26. Wang, X.M.; Hou, X.Q.; Zhang, Y.Q.; Yang, R.; Feng, S.F.; Li, Y.; Ren, Y. Genetic diversity of the endemic and medicinally important plant Rheum officinale as revealed by inter-simpe sequence repeat (ISSR) markers. Int. J. Mol. Sci 2012, 13, 3900–3915.
  27. Leimu, R.; Mutikainen, P.I.A.; Koricheva, J.; Fischer, M. How general are positive relationships between plant population size, fitness and genetic variation? J. Ecol 2006, 94, 942–952.
  28. Qin, Q.; Zhang, C.; Milne, R. Population genetics and breeding system of Tupistra pingbianensis (Liliaceae), a naturally rare plant endemic to SW China. J. Syst. Evol 2010, 48, 47–57.
  29. Hilde, N.; Bartish, I.V. Effects of life history traits and sampling strategies on genetic diversity estimates obtained with RAPD markers in plants. Perspect. Plant Ecol. Evol. Syst 2000, 3, 93–114.
  30. Boratyński, A.; Lewandowski, A.; Boratyńska, K.; Montserrat, J.M.; Romo, A. High level of genetic differentiation of Juniperus phoenicea (Cupressaceae) in the Mediterranean region: Geographic implications. Plant Syst. Evol 2009, 277, 163–172.
  31. Li, F.; Xia, N. Population structure and genetic diversity of an endangered species, Glyptostrobus pensilis (Cupressaceae). Bot. Bull. Acad. Sin 2005, 46, 155–162.
  32. Pfeifer, M.; Jetschke, G. Influence of geographical isolation on genetic diversity of Himantoglossum hircinum (Orchidaceae). Folia Geobot 2006, 41, 3–20.
  33. Slatkin, M. Gene flow and the geographic structure of natural populations. Science 1987, 236, 787–792.
  34. Fischer, M.; Husi, R.; Prati, D.; Peintinger, M.; van Kleunen, M.; Schmid, B. RAPD variation among and within small and large populations of the rare clonal plant Ranunculus reptans (Ranunculaceae). Am. J. Bot 2000, 87, 1128–1137.
  35. Franklin, I.R. Evolutionary Change in Small Populations. In Conservation biology: An Evolutionary-Ecological Perspective; Soule, M., Wilcox, B., Eds.; Sinauer Associates: Sunderland, MA, USA, 1980; pp. 135–149.
  36. Doyle, J.J.; Doyle, J.L. A rapid DNA isolation procedure for small quantities of fresh leaf tissue. Phytochem. Bull 1987, 19, 11–15.
  37. Nei, M. Analysis of gene diversity in subdivided populations. Proc. Natl. Acad. Sci. USA 1973, 70, 3321–3323.
  38. Yeh, F.C.; Yang, R.; Boyle, T.; Ye, Z.; Mao, J.X. Popgene Version 1.32: The User Friendly Software for Population Genetic Analysis. In Molecular Biology and Biotechnology Centre; University of Alberta: Edmonton, AB, Canada, 1999.
  39. McDermott, J.M.; McDonald, B.A. Gene flow in plant pathosystems. Annu. Rev. Phytopathol 1993, 31, 353–373.
  40. Peakall, R.; Smouse, P.E. GenAlEx 6.5: Genetic analysis in excel. Population genetic software for teaching and research—An update. Bioinformatics 2012, 28, 2537–2539.
  41. Nei, M. Estimation of average heterozygosity and genetic distance from a small number of individuals. Genetics 1978, 89, 583–590.
  42. Mantel, N. The detection of disease clustering and a generalized regression approach. Cancer Res 1967, 27, 209–220.
Ijms 14 14860f1 200
Figure 1. Geographic distribution of seven populations of T. sutchuenensis sampled for inter-simple sequence repeats (ISSR) analysis.

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Figure 1. Geographic distribution of seven populations of T. sutchuenensis sampled for inter-simple sequence repeats (ISSR) analysis.
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Ijms 14 14860f2a 200Ijms 14 14860f2b 200
Figure 2. Dendrogram for (a) all individuals and (b) populations of T. sutchuenensis using UPGMA.

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Figure 2. Dendrogram for (a) all individuals and (b) populations of T. sutchuenensis using UPGMA.
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Figure 3. A two-dimensional plot of the Principal Coordinate Analysis (PCoA) of ISSR data showing the clustering of populations of T. sutchuenensis. The first and second principal coordinates account for 26.16% and 20.09% of total variation, respectively.

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Figure 3. A two-dimensional plot of the Principal Coordinate Analysis (PCoA) of ISSR data showing the clustering of populations of T. sutchuenensis. The first and second principal coordinates account for 26.16% and 20.09% of total variation, respectively.
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Table 1. Locations of the sampled T. sutchuenensis populations and the sampled number (N) at each site (Pop = population).

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Table 1. Locations of the sampled T. sutchuenensis populations and the sampled number (N) at each site (Pop = population).
PopNLocalityLatitude (° N)Longitude (° E)Altitude (m)
A8Erchongyan, Kaixian county31.608108.7122290
B24Anjiacao, Kaixian county31.616108.7542150
C30Wangjiayan, Kaixian county31.609108.6721840
D30Yangcun, Kaixian county31.636108.8111770
E9Jigongliang, Kaixian county31.597108.8251270
F13Geteng, Chengkou county31.651108.6751615
G25Longmen, Chengkou county31.715108.7621005
Table 2. Sequences of 15 primers successfully used in the ISSR analysis.

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Table 2. Sequences of 15 primers successfully used in the ISSR analysis.
PrimerSequence (5′–3′)PrimerSequence (5′–3′)
UBC807AGA GAG AGA GAG AGA GTUBC841GAG AGA GAG AGA GAG AYC
UBC808AGA GAG AGA GAG AGA GCUBC842GAG AGA GAG AGA GAG AYG
UBC809AGA GAG AGA GAG AGA GGUBC848CAC ACA CAC ACA CAC AR*G
UBC810GAG AGA GAG AGA GAG ATUBC878GGA TGG ATG GAT GGA T
UBC811GAG AGA GAG AGA GAG ACUBC880GGA GAG GAG AGG AGA
UBC820GTG TGT GTG TGT GTG TCUBC886VDV CTC TCT CTC TCT CT
UBC826ACA CAC ACA CAC ACA CCUBC895AGA GTT GGT AGC TCT TGA TC
UBC836AGA GAG AGA GAG AGA GY*A

*Y: C/T; R: A/G.

Table 3. Genetic diversity of T. sutchuenensis populations.

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Table 3. Genetic diversity of T. sutchuenensis populations.
ABCDEFGMeanTotal
Na1.5971.7391.9131.8811.6481.6901.8611.7612.000
Ne1.2141.1801.2261.1931.2151.2371.2201.2121.220
P59.773.991.388.164.869.086.176.1100.0
H0.1520.1300.1610.1420.1570.1630.1780.1550.165
I0.2320.1800.2720.2470.2450.2560.2910.2520.295
HT0.167
Hs0.150
Gst0.102
Nm4.407

Na, observed number of alleles; Ne, effective number of alleles; P, percentage of polymorphic loci; H, Nei’s gene diversity; I, Shannon’s information indices; HT, total genetic diversity; Hs, genetic diversity within populations; Gst, the relative magnitude of genetic differentiation among populations; Nm, gene flow among populations.

Table 4. Analyses of molecular variance (AMOVA) for T. sutchuenensis by ISSR.

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Table 4. Analyses of molecular variance (AMOVA) for T. sutchuenensis by ISSR.
Source of variationd.f.*Sum of squaresVariance componentPercentage of variancep value
Among population6783.9934.93812.092<0.01
Within population1324738.59735.89887.908<0.01

*d.f. = degree of freedom.

Table 5. Nei’s genetic distances and geographical distances among populations of T. sutchuenensis.

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Table 5. Nei’s genetic distances and geographical distances among populations of T. sutchuenensis.
PopABCDEFG
A----0.02180.01880.02100.01600.01210.0250
B4.12----0.01340.01380.02200.01860.0167
C3.787.84----0.01200.01860.01830.0130
D9.875.7713.49----0.02120.02010.0163
E10.797.0414.554.48----0.00640.0202
F5.928.434.7212.9715.41----0.0215
G12.7610.9314.549.8914.3310.85----

Above diagonal: genetic distances; below diagonal: geographical distances (km).

Int. J. Mol. Sci. EISSN 1422-0067 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert