Introduction

DNA barcoding is a standard gene fragment1 for species identification. It has been developing rapidly in recent years2, and become a useful tool for biodiversity investigation and monitoring, and molecular phylogeny and evolution3.

In 2009, the Consortium for the Barcode of Life (CBOL) Plant Working Group proposed the chloroplast gene rbcL and matK as the core barcodes of plant species, as well as intergenic sequence trnH-psbA and nuclear gene ITS as the supplement barcodes4. Since rbcL is characterized by its universality, easy amplification and comparability, this gene has been proposed as the barcode fragment5. Presently, rbcL genes have been widely used for phylogenetic analysis within family and subclass of angiosperm, and even among the different groups of the seed plants6. However, variation in rbcL sequence mainly exists at the above-species level, and variation is seldom found at the species level7,8,9,10, resulting in poor abilities in species discrimination4,11. For example, Newmaster et al. compared ~10,300 rbcL sequences (with each more than 1,000 bp) collected from GenBank by using a distance method, and found that rbcL did not recognize all plant species but distinguished plants within the same genus10.

The core barcode matK locates at the intron region in chloroplast lysine tRNA (trnK) gene, and is ~1,550 bp in length, encoding a mature enzyme that involves in type II intron splicing during RNA transcripts12. matK is a single-copy and one of the fastest evolving g“enes in protein encoding regions of the chloroplast genome12. The evolution rate of this gene is about 2–3 times higher than rbcL 13, and half lower than the ITS sequence14. Although the amplification success rate of matK is relative low7,8,10, it has been commonly used in studies of systematic and evolutionary botany15,16. For example, Lahaye et al. studied 1,667 plant samples by using matK, and obtained an amplification rate of 100%, and a species discrimination rate of over 90%17.

trnH-psbA sequence locates at intergenic (non-coding) region in chloroplast with a rapid evolution rate. There are 75-bp conserved regions at the two ends of this sequence and makes it easy to be designed as universal primers18. TrnH-psbA sequence has been successfully amplified in many plant species, and showed a high power on discrimination10. For example, Kress et al. found that the length of the amplified trnH-psbA fragments of 92% species ranged from 340 to 660 bp, and retained a unique interval sequence, making this sequence meet the criteria as a barcode19. However, insertion/deletion events often occur in this fragment in different species17,19, even in species that are genetically related20, leading to variation in fragment length, and causing difficulties in comparing species from different genera.

ITS belongs to ribosomal DNA in the nuclear genome, and is widely distributed in photosynthetic eukaryotic organisms (except ferns). A large amount of data of this fragment has been accumulated in GenBank5, and has become the most common sequence for phylogeny construction19. Components of ITS include ITS1, ITS2 and 5.8 S. Experimental evidence shows that there are large differences among the three sequences. For example, 5.8 S is the most conserved gene among the three sequences, and the discrimination power of ITS1 is higher than ITS221. The wide applications of ITS result from the following advantages: (1) ITS is highly repetitive in the nuclear genome, and has high rate of species identification7; (2) ITS can be used to solve the problem of plant phylogeny in lower taxonomic order22, helping precisely reconstruct phylogenetic relationships between plant species. Li et al. successfully got a high species discrimination resolution for ITS by studying the 6,286 samples from 1,757 seed plant species in China23; (3) ITS1 and ITS2 locates between 18S and 5.8S rDNA, and 5.8S and 26S DNA respectively. Sequences of 18S, 5.8S and 26S rDNA are highly conserved from bacteria, fungi and higher plants, enabling the design of the sequence-complemented universal primers for PCR amplification of ITS24. The use of ITS, however, was also questioned. For example, its success rate of amplification and sequencing was found relatively low (i.e. 86.20% and 71.00% respectively25). This usually results from some second-level structures in ITS26,27, which makes the sequencing quality of ITS decrease. Secondly, variation in the length of ITS is large, with most sequences longer than 1,100 bp and preserving long sequence of poly-G, poly-C and poly-A, often bringing some difficulties in sequence analysis8.

The APG system is always chosen to establish phylogenetic relationships among plant species. But the method cannot distinguish evolutionary relatedness at the species level, and thus processes low resolution of evolutionary trees28,29. DNA barcoding provides a new insight into solving this task. With this strategy, desired evolutionary relationships of different plant species within the same community can be constructed when standard DNA fragments are prepared30. For example, the optimal evolutionary relationships of tree species in Barro Colorado Island (BCI)31 and the Ailao Mountain32 have been constructed by integrating fragments rbcL, matK and trnH-psbA. Another study, however, suggested that rbcL + ITS2 fragment combination can be used as an effective way testing the phylogenetic relationships in Dinghu mountain33; and a combination of trnH-psbA + ITS fragment is favorable for identifying species in Xishuangbanna National Nature Reserve25. These cases suggest that DNA barcode combinations can be variable when building phylogenetic relationships of species deriving from different forest communities.

Tropical cloud forest is frequently covered by cloud in humid tropics areas34, which mainly distributed at the ridge of mountains with altitudes of 500–3900 m in tropical areas of America, Africa and Asia. Environmental conditions in these forests are characterized by strong wind, low temperature, frequent fog, and high levels of ultraviolet radiation compared with lower altitude forests35. Trees in cloud forests are typically more malformed and elfin, and covered in more epiphytes36. Endemic and threatened species are rich in tropical cloud forests37, and their function in capturing water condensed from clouds and fog38, all contribute to the unique ecology of such cloud forest ecosystems39. Therefore, the phylogenetic relationships of tree species in this forest community were assumed to differ from low-altitudinal tropical forests, and could not be precisely constructed with the existed APG system. In this paper, we tested the phylogenetic relationships of tree species in tropical cloud forests in Hainan Island through the analysis of DNA barcodes including ITS, matK, rbcL and trnH-psbA. We aimed that (1) ITS, matK, rbcL and trnH-psbA would be universally used as DNA barcodes for tree species in tropical cloud forests in Hainan Island, and would identify tree species; (2) phylogenetic relationships would be successfully built by using the combination of the four fragments in the tropical cloud forests.

Results

Universality of primer sequences

In the tropical cloud forest of Bawangling, samples of a total of 186 individuals and 107 tree species were collected, and 548 sequences were available for the four DNA fragments (Table 1). Among these fragments, trnH-psbA had the highest success rate of PCR amplification (83.87%), followed by rbcL (80.37%) and matK (59.63%), and the success rate of PCR amplification for ITS was the lowest (58.23%). Regarding DNA sequencing, rbcL and trnH-psbA showed the highest success rate (82.80% and 68.28%, respectively), followed by ITS (56.99%) and matK (50%).

Table 1 The success rates of PCR amplification and sequencing of the four barcode fragments in the three tropical cloud forests.
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In the tropical cloud forest of Limushan, samples of 130 individuals and 89 species were collected, and 356 sequences from the four DNA fragments were available. The success rate of PCR amplification was the highest for rbcL (69.78%), followed by trnH-psbA (68.66%) and matK (61.47%). 40.35% of samples was successfully amplified for ITS. rbcL and matK had the highest success rate of sequencing (57.36%), followed by trnH-psbA (55.81%) and ITS (39.56%).

In the tropical cloud forest of Jianfengling, samples of 255 individuals belonging to 128 species were collected, and 776 sequences of the four DNA fragments were obtained. A highest success rate of amplification was recorded for trnH-psbA (85.32%), followed by rbcL (75.62%), ITS (52.36%) and matK (50.61%). A highest rate of samples was successfully sequenced for trnH-psbA (80.00%), followed by rbcL (65.88%), ITS (46.67%), and matK (45.10%).

Success rate of species identification

When single DNA fragment was used, the highest success rate of species identification of 47.20 ± 5.76% was obtained for ITS, followed by trnH-psbA (46.16 ± 5.11%) and matK (42.88% ± 2.59%). But the rate of rbcL was the lowest (41.50% ± 2.81%) (Fig. 1). Plants belonging to Lauraceae, Fagaceae, Aquifoliaceae and Symplocaceae, however, could not be effectively identified using a single fragment.

Figure 1
figure 1

The average species identification success rate for the four barcode fragments in tropical cloud forests.

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Construction of phylogenetic trees

The phylogenetic relationships in the tropical cloud forests of Bawangling, Jianfengling, and Limushan were established by the combination of rbcL + matK + trnH-psbA (Figs 2–4), with the high average supporting values for nodes (e.g. Bawangling: 79.10% ± 17.87%; Limushan: 76.82% ± 15.69%; Jianfengling: 78.98% ± 14.50%). But the average supporting values for nodes of evolutionary trees in the three forests were relative low when using the other fragment combinations (Suplementary Figs S1–S30, Table S4). Each tree showed a “fan” shape, with closely related species clustering together, whereas distantly related species relatively scattering. Compared with the Limushan and Jianfengling, higher average supporting values were found for nodes on the phylogeny in Bawangling.

Figure 2
figure 2

The phylogenetic tree of Bawangling tropical cloud forest using fragment combination of rbcl + matk + trnH-psbA.

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Figure 3
figure 3

The phylogenetic tree of Limushan tropical cloud forest using fragment combination of rbcl + matk + trnH-psbA.

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Figure 4
figure 4

The phylogenetic tree of Jianfengling tropical cloud forest using fragment combination of rbcl + matk + trnH-psbA.

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Discussion

In the present study, the success rates of amplification and sequencing of matK fragment were 57.24 ± 4.42% and 50.82 ± 4.36%, respectively, similar to by Kress et al.31, who reported the matK had the lowest overall rate of recovery (69%). The success rates of amplification in Kress et al.7 was only 39.3% for the 96 species in 46 genera, and the correct recognition rate was 14.6%. Sass et al.8 used matK to amplify Cycas, with a success rate of only 24%. Different branch groups of the gene are hard to amplificate and sequencing primers universality is very poor6,40. The universality of primers is recognized as an important criteria for evaluating the appropriateness of DNA barcodes4,41. The low success rate of amplification and sequencing of matK fragments probably shows that it has a poor universality. This is possibly caused by in-sufficient number of primer pairs selected, and can be solved by using more and diverse primers. For example, Lu et al. included additional primers matK472F and matK1248R in the DNA barcode candidates32, and obtained a 90% of the universality of primers for subtropical forest tree species in Ailao mountain.

High success rate of amplification and sequencing (75.26% ± 3.65% and 63.84% ± 4.32%, respectively) was found for rbcL in the tropical cloud forest, which is consistent with the study in Xishuangbanna Nature Reserve25 (75.26% ± 3.65% vs. 97.60% and 63.84% ± 4.32%vs. 90.80%, respectively), indicating a promise universality for the rbcL barcode. The result is thought to be related to highly conserved and low evolutionary levels for rbcL gene. Although high success rate of amplification (79.28% ± 7.08%) can also be obtained for trnH-psbA, sequence data were generally enriched by repeated sequences. This may result from the evidence that there are mononucleotide repeats in trnH-psbA gene in some species, with continuous repeats of several to dozen bases of A or T. Moreover, the uncertainty number of nucleotide repeat sequences in trnH-psbA was prone to be caused by traditional Sanger sequencing method42.

In this study, ITS showed the lowest rate of amplification and sequencing (50.31% ± 6.64% and 45.15% ± 8.91%, respectively) in all of the fragments tested. The success rate of amplification and sequencing of ITS can be variable for tropical forest plants in different regions. For instance, only 41% of success rate of ITS was detected in 285 tree species in the Amazon tropical forests11. For the tree species from India tropical forests, however, the rates of amplification and sequencing for ITS were 74% and 62%, respectively43. The low rate of amplification and sequencing of tropical cloud forest species can be partly explained by Lauraceae and Fagaceae species, which contain large amount of secondary metabolites (such as polysaccharides and phenolic compounds) that has a negative effect on the extraction of high quality DNA. Moreover, multiple copies of fragments in ITS, as well as its incomplete concerted evolution process in Quercus species44,45 and Cinnamomum species46, may also be responsible for the rate of amplification and sequencing.

The identification success rates of rbcL and matK were 41.50% ± 2.81% and 42.88% ± 2.59% in tropical cloud forests, respectively, lower than those of trnH-psbA and ITS (Fig. 1). This may result from the fact that variation in rbcL sequence mainly exists at the above-species level7,8,9,10, as well as the relative low evolution rate of Johnson & Soltis14. Low identification success rate for both rbcL and matK have also be found by Huang et al. in the tropical rain forest in Xishuangbanna in China25, and by Tarnowski et al. in the tropical rain forest in India43. Therefore, our results suggest that both rbcL and matK be not suitable for identification of plants at the species level in tropical forests. But rbcL and matK have the advantage of high success rate of amplification and sequencing in tropical cloud forests (Fig. 1), as well as advantage of that these two fragments have been demonstrated to have high identification success rates at the genus and family level25, we thus propose that both rbcL and matK be two core barcodes identifying plant evolutionary relationships at the genus and family levels in tropical cloud forests.

Although there is a lot of debate about whether trnH-psbA fragments can be used as DNA barcodes1,47,48,49, we found that the identification rate of this fragment was 46.16% ± 5.11%. Our results are consistent with the finding of Huang et al. in the tropical rain forest in Xishuangbanna25, and show that trnH-psbA is a good candidate used as species identification in tropical cloud forest. Especially, our study shows that trnH-psbA can be used as a barcode for tree species belonging to Lauraceae and Fagaceae, when these species are seldom identified by the other three fragments. Other studies have shown that trnH-psbA can be used to identify plant species individually50, or is taken as auxiliary barcodes51. Thus our findings prove that trnH-psbA is a potential DNA barcode for forest tree species identification4,11,43.

The identification rate of ITS in the tropical cloud forest was 47.20 ± 5.76%, lower than the data of Li et al.23 (BLAST: ITS 67.20%) and Huang et al.25 (BLAST: ITS 58.10%). The relative low identification rate of ITS in tropical cloud forests may result from the low rate of amplification and sequencing of this fragment. But the highest species discrimination rate of this fragment in the tropical cloud forests probably shows that it is a core barcode of angiosperm plant. Other evidence demonstrated that ITS acted as one of the standard DNA barcodes identifying global land plants6 and green algae52. Thus, our results further prove that ITS is a plant core barcode which can effectively identify plant species.

We successfully constructed phylogenetic relationships of tree species using rbcL + matK + trnH-psbA fragment combinations in these three tropical cloud forests, which is contrary to our second hypothesis. Moreover, we found that the average supporting values of nodes on each branch were higher than 50% (Figs 2–4), indicating that we obtain highly reliable evolutionary relationships for tropical cloud forest tree species. The rbcL + matK + trnH-psbA fragments were previously used to construct phylogenetic relationships for tree species in Barro Colorado Island (BCI), Dinghu Mountains and Ailao Mountain forest31,32,53. And studies have shown that the combination of rbcL + matK + trnH-psbA is currently applicable for DNA barcoding-based phylogenetic studies on forest communities54. Our results further prove that these three fragments are of high efficiency in reconstructing phylogenetic relationships for forest plant species. Since tropical cloud forest has rich species and endemic species, the phylogenetic tree constructed by the APG online system (constrained tree) is difficult to reflect the phylogenetic relationships in the tropical cloud forests. Phylogenetic trees constructed by DNA sequences (non-constrained tree) in this study are able to clearly cluster the closely related species and separate distantly related species in tropical cloud forest (Figs 2–4).

We found a large number of Symplocaceae, Fagaceae and Lauraceae species in the three tropical cloud forests, indicating a similar species composition. These results may reflect similar origin and phylogenetic framework of tropical cloud forest species in Hainan Island. However, the three tropical cloud forests have different environmental conditions due to their different geographical locations. For example, Jianfengling (southwest Hainan Island) has the highest mean temperature, followed by Bawangling (Western Hainan Island), and Limu mountain (central Hainan Island) is the lowest. These differences may be responsible for the different direction of species evolution in the three tropical cloud forests. For example, Distylium racemosum is dominant in Bawangling35, and Camellia pitardii is dominant in Limushan, whereas Fagaceae plants such as Castanopsis faberi are more widely distributed in Jianfengling55.

In general, contrasting with the first hypothesis, we found that matK and ITS had low success rates of amplification and sequencing (Table 1), showing that these two fragments have poor universality in the tropical cloud forests. But we found high success rate of amplification and sequencing for rbcL and trnH-psbA (Table 1), indicating that these two barcodes are universal for tree species in the tropical cloud forests. Similar to other studies, both rbcL and matK had low rates of species discrimination rate (Fig. 1), but high identification success rates at the genus and family level, suggesting that these two fragments are core barcodes identifying genus- and family-level evolutionary relationships for tropical cloud forest plants. The species identification rates of trnH-psbA and ITS were relative high among the four fragments (Fig. 1), showing that they are good candidates used as species identification in tropical cloud forest. Contrary to the second hypothesis, we constructed highly reliable evolutionary relationships for tropical cloud forest tree species using a three- fragment combination (Figs 2–4; rbcL + matK + trnH-psbA), similar to the results in Barro Colorado Island (BCI), Dinghu Mountains and Ailao Mountain forest31,32,53. Our results thus prove that the three fragment combinations are of high efficiency in reconstructing phylogenetic relationships for forest plant species.

Materials and Methods

Sample collection

In 2013 and 2014, we established 12, 21 and 15 20 × 20-m plots in tropical cloud forests in Jianfengling, Bawangling and Limushan, respectively (Table 2). Fresh and intact leaves were collected from 1–2 individual trees for each species with diameter at breast height (DBH) more than 5 cm, in the wake of drying treatment by using silica gel23.

Table 2 Description of the study sites.
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DNA extraction, amplification and sequencing

Genomic DNA was extracted by using a Plant DNA Isolation Kit (Foregene, Chengdu, China), and four fragments including ITS, rbcL, matK, and trnH-psbA were selected (Suplementary Table S1). PCR reaction system was optimized and modified based on the recommended protocol4. Amplification products were sent to Huada Genomics Institute (BGI, Guangzhou, China) for DNA sequencing. Sequence editing and alignment, barcode assembly, and the construction of evolutionary trees were preformed by using BioEdit programs (http://www.softpedia.com/get/Science-CAD/BioEdit.shtm), Sequencematrix (http://www.softpedia.com/get/Science-CAD/Sequence-Matrix.shtml), and MEGA 6.0 (http://www.megasoftware.net/).

Data analysis

The success rate of PCR amplification was calculated as the proportion of the number of individuals amplified to the total number of individuals analyzed, and the successful rate of sequencing was calculated as the percentage of the number of high quality sequences to the total number of individuals31.

Species identification ability of each DNA barcodes was evaluated using the BLAST method. The work was conducted as follows: firstly, each DNA fragment of the collected species in tropical cloud forests was downloaded from NCBI database, and then a local database was established using the downloaded sequences56. Secondly, each sequence measured in this study was BLAST against the sequence in the local database, and the percentage of identical sites was calculated and was taken as the species discrimination rate of the measured sequence. If the percentage of identical sites of a sequence calculated between intraspecific individuals were higher than interspecific individuals, then the sequence was taken as the purpose one of the studied species. Finally the identification success rate of DNA barcoding was calculated as the product of sequencing success rate and species discrimination rate31.

Methods for constructing evolutionary trees mainly include discrete character methods and distance methods. Discrete character methods include minimum evolution (ME) method and maximum likelihood (ML) method, while distance methods include the unweighted pair-group method with arithmetic means (UPGMA) and neighbor joining (NJ) method. The two methods of ME and ML have long computation time and complex computation. And the UPGMA algorithm is simpler and is rarely used at present. However, the NJ method can deal with a large amount of sequence information on a personal computer, and a bootstrap test can be easily performed. This method was used to construct phylogenetic trees in the present study, because trees constructed by NJ method meet the requirements for species identification. Lahaye et al.17 demonstrated that the optimal topology was easily generated when evolutionary tree was built by using NJ method.