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Development of a DNA Barcoding System for Seagrasses: Successful but Not Simple

Development of a DNA Barcoding System for Seagrasses: Successful but Not Simple

  • Christina Lucas, 
  • Thirunavakkarasu Thangaradjou, 
  • Jutta Papenbrock


Seagrasses, a unique group of submerged flowering plants, profoundly influence the physical, chemical and biological environments of coastal waters through their high primary productivity and nutrient recycling ability. They provide habitat for aquatic life, alter water flow, stabilize the ground and mitigate the impact of nutrient pollution. at the coast region. Although on a global scale seagrasses represent less than 0.1% of the angiosperm taxa, the taxonomical ambiguity in delineating seagrass species is high. Thus, the taxonomy of several genera is unsolved. While seagrasses are capable of performing both, sexual and asexual reproduction, vegetative reproduction is common and sexual progenies are always short lived and epimeral in nature. This makes species differentiation often difficult, especially for non-taxonomists since the flower as a distinct morphological trait is missing. Our goal is to develop a DNA barcoding system assisting also non-taxonomists to identify regional seagrass species. The results will be corroborated by publicly available sequence data. The main focus is on the 14 described seagrass species of India, supplemented with seagrasses from temperate regions. According to the recommendations of the Consortium for the Barcoding of Life (CBOL) rbcL and matK were used in this study. After optimization of the DNA extraction method from preserved seagrass material, the respective sequences were amplified from all species analyzed. Tree- and character-based approaches demonstrate that the rbcL sequence fragment is capable of resolving up to family and genus level. Only matK sequences were reliable in resolving species and partially the ecotype level. Additionally, a plastidic gene spacer was included in the analysis to confirm the identification level. Although the analysis of these three loci solved several nodes, a few complexes remained unsolved, even when constructing a combined tree for all three loci. Our approaches contribute to the understanding of the morphological plasticity of seagrasses versus genetic differentiation.


Seagrasses are higher plants capable to complete their life cycle under submerged conditions in the marine environment [1]. They evolved independently at least three times between 75 and 17 million years ago [2][4]; hence, seagrasses form a paraphyletic group including four core families (Cymodoceaceae, Hydrocharitaceae, Posidoniaceae, and Zosteraceae). These marine plants cover large geographic ranges worldwide [5], surviving most diverse environmental conditions. They have fundamental roles in the ecology of coastal areas, e.g. as breeding and nursery ground for a variety of marine organisms, coastal stabilizers or coast protectors next to coral reefs and mangroves. Decline in seagrass species and cover were observed throughout the world and the recent estimates indicate that these resources are gradually disappearing at the rate of 110 km2 yr−1 since 1980 [6]. Main factors for the loss of seagrasses are eutrophication and high turbidity due to natural and human influences. Furthermore seagrasses contain highly valuable secondary compounds such as phenolic acids used for traditional medicine and biotechnological purposes (e.g. rosmarinic acid as antioxidant or zosteric acid as an effective antifouling agent).

Due to the similar lifestyle, morphology of seagrasses is reduced and shares a number of similarities e.g. strap-like leaves in case of Posidoniaceae, Cymododoceaceae and Zosteraceae. In addition, seagrasses predominantly propagate by vegetative growth in units, so called ramets. Sexual reproduction occurs rarely due to irregular and seldom flowering [7]. Another phenomenon is the ‘trait’ of morphological plasticity within the same species, hypothesized to enable survival in different ecological niches [8]. All these factors make correct assignment of species based alone on conventional identification keys difficult or even impossible. For these reasons, closely related species e.g. Halophila ssp. [9] still form a taxonomically unresolved complex without correct assignment of species and subspecies. Even molecular phylogenies using matK, rbcL, and trnK and ITS spacer regions yielded in different results resolving the genus complexes. Moreover, only Australian, Southeast Asian and Mediterranean species are included in the molecular databases. At present, no molecular data exist to elucidate Indian seagrass communities.

The accelerating decrease of seagrass meadows in India and other parts of the world, as well as lack of traditional conservation approaches (e.g. cultivation in botanical gardens) were the main reasons to invest in a seagrass barcoding system.

DNA barcoding is defined as: Methods for identifying species by using short orthologous DNA sequences, known as “DNA barcodes”, that have been proposed and initiated to facilitate biodiversity studies, identify juveniles, associate sexes, and enhance forensic analyses [10], [11]. The criteria for the development of reliable barcode data was defined by the Consortium for the Barcoding of Life (CBOL) as follows: Candidate loci should be suitable for a wide range of taxa, show a high variation between species, but should be conserved within species, so that the intra-specific variation will be insignificant [12]. Ideal barcodes should be routinely retrievable with a single primer pair, be amenable to bidirectional sequencing with little requirement for manual editing of sequence traces and be short enough to ease PCR amplification. Alignment and analysis of the resulting sequences ought to be straightforward, allowing a fast identification without profound prior knowledge about bioinformatics.

Short standard regions that enable cost-effective species identification are preferable, such as cytochrome C oxidase (COI) for most animal species [10], [13]. Within 2 h the express barcode system can be successfully recognize and identify certain animal species [14], coming close to the goal of fast and cheap identification. However, so far there is no generally accepted DNA barcode standard for the plant kingdom although a number of recommendations by the CBOL plant working group [12] and others [15][20] exists. The performance of different loci combinations remains insufficient among different plant families [18], [21], [22]. Some researchers solved it by designing family specific primers and came closer to accepted phylogeny using this approach [23].

Sequences in the plant chondriom evolve too slowly; therefore they cannot be used for discrimination. The nuclear genes contain introns and frequent recombination is observed. Prior knowledge about copy number and linkage groups is needed to perform a neutral analysis [24]. Therefore genes from the plastid genome are the most promising candidate genes for plant DNA barcoding. The plastid genome is uniparentally inherited, non-recombining, and a structurally stable genome [11]. Plastid regions with raw sequence differences ≥2% were categorized as the most variable segments, and therefore the most promising of the plastid genome for DNA barcoding when normalized for length of 300 to 800 bp. Current results indicate that at least two plastid genes, better a multi-locus code, are necessary to specify a reliable plant DNA barcode, one as a “robust and basic” gene like rbcL and one a more “variable” like an internal spacer (e.g. rps16, trnH-psbA) or matK. Resulting barcodes of the “robust” gene rbcL could be identified using BLAST and resolve to genus and family level, while the “variable” part of the code is for species verification. So far, most promising combinations are rbcL/matK [12] and rbcL/psbA-trnH [11]. Single-locus barcodes for plants are not advisable, although proposed for matK [16], [25] but clearly lacking the feature of universal amplification. In this approach, we followed the CBOL recommendations and expanded the two-locus system by incorporation of the trnH-psbA spacer as a third locus. This spacer region tends to be more variable and might enhance discrimination power of the two-locus system.

Besides identification of suitable DNA barcoding sequences, reliable searching algorithm and specialized barcode databases are currently missing [26]. So far, barcoding is relying on methods used in phylogeny although goals are different: (1) fast identification of an operating taxonomic unit, (2) screening of diversity hot spots (3) and no compulsory resolution of relationships across complete families including hybrids and introgression.

It is well known that tools for tree-based analysis methods have several disadvantages concerning their application on DNA barcoding [27]. Therefore, a character-based method such as the Character Attribute Organization System (CAOS, reviewed in [28]) is a promising approach to combine all kinds of data sets goaling at a comprehensive barcoding platform. Additional to these methods, verification of the species using BLAST algorithm based on the data deposited on NCBI was tried. Up to date the CBOL database is only supporting search with matK and rbcL as barcodes, based on a small dataset which was not supporting seagrasses or related plant families.

Seagrasses comprise a limited number of species. Several genera are monospecific or show small species representation and their genetic plasticity is limited. Moreover hydrophilic pollination limits the gene flow across the water bodies [29]. On the contrary, recent comparisons of ITS sequences from Chinese and Australian material of Enhalus, a monospecific genus, show that they are quite distinct. This warrants the possibility of the existence of additional new species [6]. Though different markers were used for different species (rbcL and matK of the Hydrocharitaceae [30], nuclear (18SrRNA, ITS) and other cpDNA (trnL) loci in Thalassia, nuclear (ITS) and cpDNA (rbcL, trnL) in Halodule [5], chloroplast trnL intron and rbcL of several seagrasses [31], nuclear (ITS) and cpDNA (rbcL, trnL) loci of Posidonia, nuclear ITS of Halophila [32], ITS (ITS-1 and ITS-2 regions including the 5.8S rDNA gene), trnK introns and rbcL of Zostera [33], rbcL and matK of Zostera [4], ITS1, 5.8S rDNA and ITS2 of Halophila [34], and ITS1, 5.8S rDNA and ITS2 of Halophila [35]) there is no generally agreed consensus on conserved molecular region useful for seagrass taxonomy.

The International Union for Conservation of Nature provides a criterion giving equal importance to conserve both: genetic diversity and ecosystem diversity. Spielman et al. (2004) [36] emphasized that most species are not driven to extinction before genetic factors impact them and thus ecological, demographic and genetic processes must be considered together. Therefore a clear understanding of genetic diversity of the seagrass species becomes important by considering the global seagrass decline. But Jones et al. (2008) [37] alerted that the genetic similarity was highest within each of the seagrass beds indicating a low degree of gene flow from one population to another at different sites and suggested that future restoration and conservation projects should use only local eco-sourced materials rather than using populations of different regions.

Despite several studies on molecular taxonomy of seagrasses, there is no seagrass sequence in the CBOL database. Therefore, a barcoding system for seagrasses has to be developed based on the experiences with related species. Hence the present study was carried out with the aim to develop a simple but efficient DNA barcoding system for seagrasses, especially for sampling in the tropics. The primer universality for seagrasses was tested. The number of loci for species discrimination and the percentage that can be discriminated was determined by analysing the combination of different loci.

Materials and Methods

2. 1 Plant Material

Plant material from tropical seagrasses [Cymodocea rotundata Ehrenb. & Hempr. ex Asch., Cymodocea serrulata (R.Br.) Asch. & Magnus, Enhalus acoroides (L. f.) Royle, Halodule pinifolia (Miki) den Hartog, Halodule uninervis (Forsk.) Asch., Halodule wrightii Asch., Halophila beccarii Asch., Halophila decipiens Ostenf., Halophila ovalis (R.Br.) Hook. f., Halophila ovata Gaud., Halophila ovalis subsp. ramamurthiana, Halophila stipulacea (Forsk.) Asch., Syringodium isoetifolium (Asch.) Dandy, Thalassia hemprichii (Ehrenb.) Asch.] were collected in the Palk Bay, Tamil Nadu, and in the Chilika Lagoon, Orissa, India while plant material of temperate seagrasses was collected in three different locations on the North sea island Sylt: Zostera marina L. (three populations), Zostera noltii L. (two populations). The longitude and latitude coordinates were determined by using google maps location ( (Table S1).

2.2 DNA extraction

Several methods including shade-drying, immersion in high NaCl/cetyltrimethylammonium bromide (CTAB) buffer or freezing in liquid nitrogen were applied to preserve the plant material for DNA extraction. In general, the plants were dug out and cleaned in seawater to remove debris and epiphytes. Clean seagrass leaf samples were directly submerged in CTAB buffer [38]. Others were dried overnight at a dry and dark place. The material was grinded to a fine powder at 22 Hz for 2 min using a bead mill (Retsch, Haan, Germany). Then the material was stored at −80°C until analysis. The DNA was extracted from 5 to 10 mg plant material by using the Nucleo Spin Plant Kit II (Machery & Nagel, Düren, Germany) with slight modifications. The first step, incubation time in CTAB buffer, was prolonged to 30 min. The concentration was determined spectrophotometrically (Synergy MX, BioTek Instruments, Bad Friedrichshall, Germany) and analyzed for its intactness by agarose gel electrophoresis.

2.3 PCR conditions

Amplification of rbcL and matK sequences by PCR was performed using conditions suggested by the Plant working group of the CBOL [12]. The following primer pairs were designed: P609 5′-GTAAAATCAAGTCCACCRCG-3′ and P610 5′-ATGTCACCACAAACAGAGACTAAAGC-3′ [17] for a rbcL fragment of 599 bp for all species; P607 5′-CGTACAGTACTTTTGTGTTTACGAG-3′ and P608 5′-ACCCAGTCCATCTGGAAATCTTGGTTC-3′ for a matK fragment of 889 bp for C. rotundata, C. serulata, H. ovata, Z. marina sp., Z. noltii sp. (Ki-Joong Kim, Korea, unpublished), and P646 5′-TAATTTACGATCAATTCATTC-3′ and P647 5′-GTTCTAGCACAAGAAAGTCG-3′ [39] for a matK of 945 bp for E. acoroides, H. becarrii, H. decipiens, H. ovalis, H. pinifolia, H. uninervis, S. isoetifolium, T. hemprichii (Palk Bay) and H. pinifolia, H. uninervis, H. beccarii, H. ovalis (Chilika Lagoon).

The PCR analysis was extended using the following primer pairs for all species: P672 5′-GCGTGGCCAAGYGGTAAGGC-3′ (trnQ(UUG)) and P673 5′-GTTGCTTTYTACCACATCGTTT-3′ (rpS16x1) for the amplification of 850 and 1,400 bp [20], P674 5′-CCTTATCATTTAGAGGAAGGAG-3′ (ITS5a) [40], [11] and P675 5′-TCCTCCGCTTATTGATATGC-3′ (ITS4) [41] for ITS of 900 bp and 1,800 bp, P676 5′-GTTATGCATGAACGTAATGCTC-3′ (psbA3′f) [17], [42] and P677 5′-CGCGCATGGTGGATTCACAATCC-3′ (trnHf) [43] for the trnH-psbA spacer of 296 to 415 bp.

The following PCR conditions using Dream Taq polymerase (MBI Fermentas, St. Leon-Rot, Germany) with P609/P610 were applied: 1× Dream Taq Green buffer, 0.2 mM dNTPs, 1% BSA, 1% PVP, 2 mM MgCl2, 1 U Taq polymerase, 10 to 30 ng template DNA, 1 pmol primer each in a total volume of 25 µl. The PCR was performed in a PTC 200 thermocycler (BiozymDiagnostik GmbH, Hess. Oldendorf, Germany) with a heated lid under the following conditions: an initial denaturation (95°C, 5 min) followed by 30 cycles with a denaturation of 94°C for 30 s, an annealing of 56°C for 35 s, an extension of 70°C for 60 s, and a final extension of 72°C for 8 min. PVP was added to bind and precipitate the large amounts of phenolic acids in DNA preparations of seagrasses.

With P607/P608 following conditions were applied: 1.5× Dream Taq Green buffer, 0.2 mM dNTPs, 1% BSA, 2% DMSO, 1% PVP, 3 mM MgCl2, 1 U Taq polymerase, 20 to 50 ng template DNA, 1.5 pmol primer each in a total volume of 25 µl. The PCR was performed under the following conditions: After a pre-cycling of five cycles (initial denaturation, 95°C, 5 min) followed with a denaturation of 95°C for 60 s, an annealing of 49°C for 80 s, an extension of 70°C for 120 s the full cycling of 30 cycles with a denaturation of 95°C for 30 s, an annealing of 56°C for 45 s, an extension of 70°C for 80 s, and a final extension of 70°C for 8 min.

With P646/P647 following conditions were applied: 1× Dream Taq Green buffer, 0.2 mM dNTPs, 1% BSA, 4% DMSO, 1 U Taq polymerase, 20 to50 ng template DNA, 1 pmol primer each in a total volume of 25 µl. The PCR was performed under the following conditions: an initial denaturation (95°C, 3 min) followed by 30 cycles with a denaturation of 95°C for 30 s, an annealing of 50°C for 40 s, an extension of 72°C for 40 s, and a final extension of 72°C for 5 min. For the other primer pairs, cycling conditions where adapted as already published [11], [20].

All PCR reactions were repeated three times independently to reduce errors in the final consensus sequence to a minimum. The PCR fragments were eluted (GenElute, Sigma Aldrich, Taufkirchen) and ligated into the pGEM-T vector (Promega, Mannheim, Germany). Colonies were analyzed by colony PCR and the clones by restriction analysis. Sequencing was done by Eurofins MWG Operon (Ebersberg, Germany) and GATC (Konstanz, Germany) from both directions resulting in a six times coverage for all sequences analyzed.

2.4 Bioinformatic analysis

2.4.1 Assembly of obtained sequences.

The obtained raw sequence data was analyzed using Clone Manager 9 (Sci-Ed, Cary, NC, USA). The sequence files obtained were assembled and analysed by choosing “Simple”, a method for optimized small sequence sets. Parameters were left at default [Expected coverage = 6x, Overlap score = 50]. The sequence assembly was manually edited to obtain a consensus sequence. The consensus sequence was subsequently analyzed using BLAST [nucleotide blast; database: Others nr; BLAST algorithm: Megablast; Algorithm parameters: default] to verify the gene fragment and/or taxon. After verification, the sequence was examined for the appropriate forward and reverse primer sequences. Sequences flanked by the specific primers were maintained while the contaminating vector sequence was discarded. The resulting sequences were subsequently used for phylogenetic analysis.

2.4.2 Analysis and sequence alignment.

For sequence alignment and creation of the NEXUS file, Bioedit 7.0.9 [44] was used. The sequence alignment was carried out with the implemented CLUSTAL X using default parameters. The alignment was further optimized manually and exported as NEXUS file format to identify an evolutionary model by jModel Test 0.1.1 [45], for phylogenetic analysis using MrBayes [46] and for the CAOS-workbench [47]. All sites were included into the analysis, especially in the case of trnH-psbA.

2.4.3 Character-based analysis.

The search for diagnostic characters was performed with the web-based CAOS Workbench (; [47]). For this, the aligned sequences and the tree from ML-analysis were loaded in the workbench. To search for diagnostic characters, CAOS-Analyzer and CAOS-Barcoder were used. For the seagrass dataset only the matK and rbcL marker were chosen, as the alignment of the plastid spacer trnH-psbA caused several problems. These two sets were analyzed independently. The software does not provide a continuous analysis so far.

2.4.4 Tree-based analysis.

For comparative analysis MEGA 5.0 was used, supporting phylogenetic tree reconstruction methods such as Neighbour-Joining (NJ), Maximum Likelihood (ML) and Maximum Parsimony (MP) [48]. The distribution of sites in the dataset was computed also by using MEGA 5.0. Combination matrices of different loci were obtained using SequenceMatrix 1.6.5 [49]. For phylogenetics, the optimal fitting evolutionary model was determined by jModel Test 0.1.1. This is a commonly used program, carrying out different calculations to identify the best fitting model dependent on sequence length, sample size, and purpose of analysis.

The NEXUS file alignment was loaded into the software and likelihood scores for 24 substitution models were computed [Number of substitution schemes: 3, Base frequencies: +F, Rate variation: +G +I nCat = 4, Base tree = ML optimized]. After computation of the likelihood scores by the implemented PHYML algorithm [50], different statistical calculations were carried out: Akaike Information Criterion [parameters: default, small-sample-size correction (AICc)], Bayesian Information Criterion [parameters: default] and Decision Theory [parameters: default] test. Results were compared and the consensus optimal model was implemented in the phylogenetic software with likelihood computations suggested by PHYML. The appropriate and supported model was then used for ML and Bayesian Analysis. The following model parameters were used for NJ and MP analysis: rbcL, rbcL/matK, trnH-psbA/rbcL and matK/trnH-psbA/rbcL GTR+G and for matK, trnH-psbA, and matK/trnH-psbA GTR+I+G.

For each analysis in MrBayes the following parameters were used: mcmcp ngen = 1,000,000, nruns = 2, nchains = 4, temp = 0.100, printfreq = 1,000, samplefreq = 100, diagnfreq = 100, stop rule = yes, stopval = 0.01, burninfrac = 0.33, nswaps = 3. No heating was applied based on preliminary results (data not shown). A stop rule was programmed, stopping the analysis when standard deviation of split frequencies was below 0.01. When more than one locus was used, the matrix was set up as portioned one, allowing an unlinked analysis. According to this setup analysis was stopped when 33% of the samples were deleted.

For ML the same model was used, considering a bootstrap value of 1,000 and Nearest-Neighbour-Interchange. The analysis of MP and NJ was performed using p-distance as model and pairwise deletion for gap treatment, also considering a bootstrap value of 1,000. The resulting consensus trees were analyzed for branch support and topology congruence. Mean support and standard deviation for each method and each locus was determined for comparison. For the combined tree, trees of single- and double-loci analysis were combined with the help of TreeJuxtaposer [51], generating a consensus topology of all methods and loci-combinations.

2.4.5 Distance analysis.

For the distance analysis, Species Identifier was used in a modified way. Since sampling strategy did not support species identification on a statistical reliable basis, genus level was used as minimal taxonomical unit. Following this approach the intra- and inter genus distances were determined. Mean overall, intra- and inter species distances were calculated as well as the distribution of variance among the different loci.

2.4.6 Inverted repeat (IR) analysis.

Analysis of the IR regions of the trnH-psbA spacer, was performed as recently reported by Whitlock et al. (2010) [52], using EMBOSS Software package [53]. The EINVERTED algorithm was used with a modified threshold of 40 to analyze the short spacer fragment properly.


After optimization of the DNA extraction method from seagrasses, amplicons for almost all species and primer pairs could be produced in the PCR reactions. Especially for dried herbarium samples the extracted DNA was often degraded and the amplification of large fragments difficult. The recommended primer pairs for the amplification of rbcL [12] worked well in case of rbcL after PCR optimization. For the matK locus, another primer pair as recommended by Ford et al. (2009) [39] lead to successful and reproducible amplification although the primers deviate only slightly. In trials with the CBOL recommended pair reverse-reverse primer 840 bp-fragments were observed in some seagrass species. The best working combination was primer pair 608 and 647 at a wide range of annealing temperatures. The primer universality is summarized in Table S2. For the larger fragments - matK and rbcL - sequence identity was verified by bidirectional sequencing, with less than 0.01% error rates. For the short trnH-psbA spacer region bidirectional sequencing was always accurate. Consequently, trnH-psbA was only sequenced in one direction for the rest of the analysis.

Since BLAST can be used as a rapid identification tool [10], its performance was tested on the seagrass sequences. Over 80% of the taxa used in this study were deposited on NCBI taxonomy. In 62% of all taxa the rbcL sequence could be used for direct comparison, but less for matK and trnH-psbA (37% each). Sequence overlap was generally close to 99%. Only in the case of H. uninervis the support was lowered to 86% (Table S1, S3). The BLAST identification level was analyzed for all species, taking the first five BLAST hits into account. Based on this, nearly all species could be identified to genus level. For these with a corresponding sequence often species level was reached.

The complete sequence sets of rbcL and matK were analyzed first by tree-based methods focussing on resolution and topology. Exemplary trees obtained by NJ analysis (1,000 Bootstrap replicates) of both sequences were constructed and compared using TreeJuxtaposer [51] (Figure S1). The species resolution and topology analysis underlines the need of a two-locus barcode, since species identification was not reliable using a single-locus system. The lines and taxa indicated in red (Figure S1) reveal different topologies among the trees indicating that both sequence sets reflect different relationships. The resolution is not satisfying, in particular for the Halophila complex and the Halodule species. The other algorithms, ML, MP, and Bayesian analysis, used for tree generation resulted in similar conclusions. The DNA barcoding would be of highest benefit to distinguish between the Halophila species because of their morphological similarity.

Testing different combinations of two-loci barcodes revealed higher resolution as well as decreased statistical errors when comparing the different algorithms (Figure 1 and 2). The resolution obtained by distance-based methods like NJ and MP was always higher compared to phylogeny model-based algorithms like Bayesian analysis and ML. In the beginning of the project species resolution obtained by a two-locus barcode was poor, resulting in the need of a third locus. However, during the project the number of sampled taxa was increased and higher number of samples also improved results obtained by the two-locus barcode. Moreover matK amplification was also tested on herbarium samples, ranging from 100- to five-year-old material, without any satisfying result. Since shortness and high variability (e.g. trnH-psbA, Figure S2) are crucial parameters a spacer region was chosen for further analysis. Based on literature [13], [54] it was decided to test three spacer fragments from the plastid and the nuclear genome: trnH-psbA, rps16-trnQ and ITS. All three loci showed 100% universality for seagrass (Table S2).

Figure 1. Average branch support analysis across different loci and phylogenetic methods.

Data of single- and combined-loci was analyzed using Bayesian analysis (Bayes), Maximum Likelihood (ML), Neighbor Joining (NJ) and Maximum Parsimony (MP). The average branch support was calculated for well resolved clades (above 50%), resulting from all methods used. Error bars indicate standard error of support values. The average support varied significantly using different methods for single locus analysis, but decreased with the combination of loci.

Figure 2. Higher content of parsimony informative characters (PIC) is not related to high species resolution.

The percentage of PIC was plotted across the average species resolution calculated over all methods used for single or combined datasets. Error bars show standard error of species resolution among different methods used. trnH-psbA spacer performed poorly compared to matK and rbcL in single locus analysis, while rbcL/matK nearly reach the same resolution percentage as the three-loci combination.

Gel-based analysis of trnH-psbA amplicons showed an optimal result concerning length and its variability. Furthermore this spacer was successfully included in several hot spot barcoding projects [55], [56]. ITS was less variable focused on length, and rps16-trnQ resulted in sizes inapplicable for barcoding purposes.

Since the number of parsimony informative characters (PIC) in a sequence set is one of the basic parameters, the whole dataset and combinations of loci were analyzed for PICs (Figure S2). As expected from other barcoding studies, rbcL showed the highest content of conserved sites, the trnH-psbA spacer the lowest and the opposite in the case of variable characters. The percentage of singleton sites was highest in rbcL which could be useful in a character-based approach. Combination of the loci revealed an average content of about 24% PIC. The most promising combination, focused on PIC content is trnH-psbA/matK with 28.4% PIC, while matK/rbcL and rbcL/trnH-psbA did poorly with only 22% PIC (Figure S2).

We expected that a high percentage of PIC and high singleton content would be coupled with a high species discrimination power. Therefore, the percentage of PIC was compared over the species resolution for the different loci and combinations (Figure 2). The all-loci combination and rbcL/matK were out-performing the single combinations and all other combinations. Although trnH-psbA showed the highest variability (>30%), it resulted in lowest discrimination of seagrass species (<60%). Therefore, we analyzed the “barcoding gap” for all loci used. In particular for seagrass species which show a high phenotypic plasticity the continuous discrimination power between species and genus is a parameter of high importance. The frequency of uncorrected intra- and inter-generic p-distance was analyzed to visualize the “barcoding gap” (Figure 3). The data clearly indicated that trnH-psbA is out-performing with an average inter-generic distance of more than 20%. The intra-generic distance was observed less than 6%, in most cases (47%) even less than 0.5% distance.

Figure 3. Observed frequency of uncorrected intra- and intergeneric p-distance.

The frequency of the uncorrected p-distance was analyzed to visualize the “barcoding gap”. Frequency was logged on a scale from 0 to 20% distance, indicating that trnH-psbA is out performing with an average inter-specific p-distance of more than 20%. Inter-generic p-distance shown in darker color and intra-generic p-distance lighter color, blue for trnH-psbA spacer, violet for matK and green for rbcL, respectively.

An optimal locus should show a continuous distribution [57]. rbcL as single locus is closest to this optimum, matK was useful but performed better in combination with rbcL. The intra-specific distance could not be determined due to the sampling strategy. Therefore the intra-generic distance was used, pointing out that the p-distance performance of trnH-psbA is not appropriate for inclusion in a robust barcode system for seagrass.

The alignment of the spacer at the 3′ end was almost impossible whereas the 5′ prime end was usable for further analysis. The species resolution did not differ when excluding the 3′ end in the alignment-based analysis of the dataset. Since it is known that this spacer often contains an inverted repeat (IR) region [52] analysis was performed (Figure S3) with EINVERTED [50]. Interestingly, Halodule sp. and Halophila sp. shared with only little exchange the IR sequence with Cymodocea, Thalassia and Enhalus as well. Only Zostera possessed a differently located IR, supporting divergent evolution. Conservation is visible in a new IR close to the 3′ end which was also partially found in Syringodium.

Since there is no common barcoding and visualization software available, several tree- and character-based approaches were used. The data of single- and combined-loci were analyzed using Bayesian analysis, ML, NJ, and MP. The average branch support was calculated for well resolved clades (above 50%), resulting from all used methods. The average support varied significantly using different methods for single-locus analysis, and increased with the combination of loci.

The combination of rbcL/matK resulted in 100% average branch support, without any ambiguity in MP algorithm. On average the third locus increased the branch support by about 5% (Figure 1). The inclusion of trnH-psbA in rbcL increased the species resolving power of rbcL less than 2% in comparison to rbcL alone. By combination of trnH-psbA with matK the species resolving power of matK decreased from 82% to 79%. Here it is evident that trnH-psbA when combined with rbcL or matK lowered the average branch support irrespective of algorithms used for analysis. Although trnH-psbA consists of a higher number of PIC (Figure S2) than any other gene tested, its power in species discrimination is low (>60%). The two-locus combination of matK/rbcL provided an average species resolution of 83%, similar to the three-locus combination of rbcL/trnH-psbA/matK with an increased efficiency of 86%. All algorithms showed a good average branch support for the combination of all three loci (Figure 1). As shown in Figure S1, single-locus analysis resulted in different topologies for the obtained trees and resolution as well as support, was low compared to a two-locus combination. The exemplary trees obtained from NJ analysis, indicated the low resolution ability of rbcL, while the topology of matK is not reflecting the accepted phylogenetic view e.g. for Syringodium. The impact of the matK sequence data can also be tracked by looking at the two-locus matK/rbcL combined tree (Figure 4), especially for Syringodium and Halodule sp. By taking a detailed look, the matK/rbcL tree support for all genera is close to 100%. The more complex genera, Halodule and Halophila are only partly resolved (H. beccarii, H. stipulacea) and subpopulations are not distinguishable.

Figure 4. Meta tree of all taxa using matK/rbcL.

Combined tree of matK/rbcL loci, branch support values are given in percent. Support values highlighted by the method used: Maximum Likelihood (black), Maximum Parsimony (violet), Neighbor Joining (green) and Bayesian Analysis (turquoise). Taxa that differed in topology along the different methods are shaded in violet.

Targeting at a the maximum of informative value in the dataset, it was analyzed for diagnostic characters. Both genes, matK/rbcL, were analyzed separately to investigate their usability for character-based barcoding. Furthermore the obtained results were examined for coincidence with the tree-based methods. For rbcL the CAOS analysis underlined the results of the tree-based methods (compare Figure S1). For the family and genus level CAs (characteristic attributes) could be found, ranging from 3/599 CAs for Cymodocea to 36/599 CAs for Halodule (Table S4).

In the Halophila complex, only for H. beccarii and H. stipulacea CAs could be found. The remaining Halophila species did not show any difference in their sequence, consequently no CAs were found. The same was true for Halodule were no CAs between H. pinifolia, H. uninervis and H. wrightii have been detected. Genetic unique characters were only found for the unknown Halodule species in a surprisingly high amount (25/599). In case of the remaining species diagnostic CAs were found as presented in Table S4.

CAs for the families and genera were detected by looking at matK,. For most species characteristic attributes could be found which underlines its variability.For the Halophila complex, still two blocks remain, bearing the same sequences: (1) H. ovata, H. ovalis* and H. sp. B, (2) H. ovalis and H. decipiens. For H. beccarii (18/845) sites could be found whereas only (3/845) CAs in H. stipulacea were observed. This approach also identified H. sp. A as H. beccarii. Interestingly, for the subspecies of H. ovalis (5/845) CAs were observed. In case of the Halodule complex, complete similarity was only detected for H. uninervis and H. pinifolia from Palk Bay, whereas Halodule species from the Chilika lagoon appear to have unique attributes (Table S5).

The final combined tree resolved three distinct clades of seagrasses: Hydrocharitaceae, Zosteraceae and Cymodoceaceae (Figure 5). The missing family Posidoniaceae is arranged in order to the Cymodoceaceae family in pre-analysis trees (data not shown). The support values of the different nodes are taken from the full-dataset analysis. The results highlight that Bayesian and ML analysis performed mostly similar and less critical than NJ and MP.

Figure 5. Final consensus tree of all barcode loci and methods.

Combined tree of matK/rbcL/trnH-psbA loci, branch support values are given in percent. Support values highlighted by the method used: Maximum Likelihood (black), Maximum Parsimony (violet), Neighbor Joining (green) and Bayesian Analysis (turquoise).

The described small genera like Syringodium, Enhalus and Thalassia are well supported and the implied species could be clearly resolved and identified (support ranging from 79 to 100%). The resolution of the complex genera does not improve in comparison to the combination of rbcL/matK (Figure 4). A stringent dichotomic tree could not be achieved with the tree-based methods.

For the Halophila genus, two unidentified species from the Chilika Lagoon were incorporated into the analysis. The identification based on minor morphological characters is challenging since single leaf-paired Halophila species show a high phenotypic plasticity. While H. beccarii and H. stipulacea form distinct clades, the other Halophila species remain as a complex using matK/rbcL (Figure 4). Here one unidentified species could be assigned as H. beccarii with strong support. Congruent to the two-locus combination, also in the three-locus combination three distinct clades can be found in the inner Halophila complex: (1) H. ovata grouped with H. ovalis from the Chilika lagoon and the other unknown species; (2) Halophila ovalis subsp. ramamurthiana and (3) H. decipiens and H. ovalis from Palk Bay. These three groups are congruent with the findings of the character-based identification approach using matK (see Table S5). Consequently, the likelihood of wrong morphological-based assignment can be taken into account.


Recent research indicates seagrasses as a potential alternate source for isolation and extraction of secondary metabolites of high value in medicine and biotechnological applications [58], [59]. Our research aim is to identify and exploit the secondary compounds from seagrasses. For this purpose seagrasses need to be cultured or the valuable compounds need to be synthesized chemically. Based on discussion with Indian scientists, literature studies and on-site visits of Indian seagrass beds, the need for a genetic classification of the investigated species became obvious. At times, separation of different seagrass species becomes challenging, even for a seagrass taxonomist. However, there was the need for a fast, reliable, and cost-efficient system for recognition and identification of seagrasses also by non-experts. In addition there were a number of questions by local ecologists concerning the composition of the seagrass mats including some unassigned species which had not been found before at the specific sites [60]. These kinds of questions can be answered using a technically simple DNA barcoding system.

To establish a DNA barcoding system for seagrasses the recommendations by Hollingsworth et al. [12] for plant species in general were followed using a two-locus approach with specific primers for the amplification of rbcL and matK. To the best of our knowledge there are no seagrass barcode data available in the CBOL database at present.

Seagrasses expand their meadows by vegetative propagation through rhizomes. Over time individuals can therefore spread over wide areas. Collection of homogeneous seagrass samples is hampered because different genotypes grow in direct vicinity forming vast networks. This was observed for well accessible seagrass locations (e.g. Z. marina [31]) populations. Such networks are often multi-specific, comprised of up to 12 species in complex seagrass beds [61] and/or contain different individuals of the same plant species. In addition a voucher specimen should be directly collected and dried to conserve this individual plant also for future references. However, as several people were involved in the collection process, specimen collection of voucher plant individuals could not be ensured. The access to some of the sampling sites was difficult and divers were not always able to follow well-known collection rules. Focus during collection goaled more on finding and identification, than on correct sampling. For the identification of genotypes and their role in the ecosystem the sampling strategy has to be improved and probably one has to concentrate on a small number of species with facile access.

Seagrasses contain a number of different phenolic compounds which make them attractive from a biotechnological point of view, but difficult to handle for genetic analysis from dried material. Several DNA extraction methods have been tested and only a few lead to a DNA quality which could be used for PCR amplifications. Also the PCR conditions have been optimized and several additives were tested to obtain reproducible amplification results. Still the amplification of larger fragments, such as the matK fragment of more than 800 bp caused difficulties.

The recommended two-locus DNA barcode consisting of matK and rbcL [12] was suggested to be the best compromise in comparison to all other loci tested. The results (Figure 4) indicate that two plastid genes are not sufficient to fully resolve seagrasses at the species level. For most projects, a discrimination power of 70% is sufficient, but for higher resolution of complex species, the use of additional loci was supposed [54].

For this project the discrimination power needed to be at a high level so that subspecies level differences can be resolved. This is important for some seagrasses genera where several subspecies and even ecotypes are encountered. Therefore the more variable plastid spacer was included into the analysis. This three-locus combination was successfully used in barcoding tropic plants at diversity hot spots [55], [56]. The trnH-psbA spacer, on average about 450 bp, is varying from 296 to 1120 bp based on available data, is one of the most variable plastid regions in angiosperms and is easily amplified across a broad range of land plants [13], [17]. For seagrass species the universality of matK and rbcL primers is high. After optimization, PCR, cloning and sequencing was straight-forward for all three barcodes used. As expected combined loci analyses dramatically improved the average branch support at different combinations compared to the single-locus analysis. This underlines that single-locus analysis of closely related species does not result in a robust system as already proposed for matK [25]. For the two-locus combinations, rbcL/matK performed best, while the combinations with trnH-psbA where significantly lower in support than in single locus performance. A high value of PICs is not connected with a high discrimination power, at least for sea grass species (Figure 3). The advantage of a three-locus combination is the reproducibility using different algorithms for analysis. Setting the focus on simple and cost effective identification [54] the two-locus combination matK/rbcL is adequate as was also demonstrated by the character-based approach. Furthermore the data processing of trnH-psbA is not straightforward [52], decreasing the chance for plant barcoding to catch up with fast systems already used for the animal kingdom [62].

Vijayan and Tsou (2010) [13] suggested that until more useful barcodes are identified, the presently proposed DNA barcodes (rbcL/matK) can be used to initiate barcoding of all land and water plants, with some family exceptions [18], [21], [22]. This tendency is supported by in our analysis using BLAST as simple and fast identification tool (Table S3). The matK and rbcL sequences which are already contained in the database provide a good basis for identification at least to the genus level with a database providing comparable data for nearly 70% of the considered seagrass taxa. Since not all species are deposited in publicly available databases, especially questionable subspecies, the combined tree construction is based on the three-locus barcodes. This approach ensures the identification and classification of unknown species (e.g. from the Chilika Lagoon) and Halophila subsp. by a trustable average branch support for the different tree constructions methods used. From the consensus tree all genera are separated into clear clades, especially genera with single species (e.g. Enhalus or Thalassia). For the Zosteraceae, the distance-based methods like MP and NJ could also distinguish subpopulations (e.g. of Zostera marina). Still, uncertainties persist with the more complex genera like Halodule and Halophila. In case of H. uninervis the populations of the Chilika lagoon and Palk Bay were distantly out-grouped, while H. pinifolia from both areas is grouped together. This is reflecting the confusion and ambiguous status of the taxonomy between these two species [63], although Ito & Tanaka (2011) [64] obtained a clear taxonomy for Asian Halodule populations. H. wrightii appears to be genetically unique [63], [65], although it is morphologically distinguished only through the form of the leaf tip [1], like H. pinifolia and H. uninervis.

The character-based barcoding presents a method for unambiguous identification based on analysis of unique sequence sites. This approach is not restricted to DNA-based barcoding applications, nearly every kind of data can be used. For the seagrass dataset no new findings could be elucidated with this approach, but conjectural relations could be verified. The role of rbcL as a basic, conservative marker fragment could be underlined as CAs where only found on genus level. The percentage of species identification was similarly low compared to tree-based methods. MatK provided much more CAs, archiving the same percentage of species identification as with a tree-based approach. The character-based barcoding indicates additionally the misidentification of H. decipiens at Palk Bay and H. ovalis at Chilika Lagoon. This misidentification can be considered with high likelihood as it is in congruence with the tree-based approach. Furthermore, the Halodule species found at the Lagoon can be verified as genetically different to species found in the open sea. Clear advantage of the character-based system is its correctness, but nevertheless profound bioinformatic knowledge is needed, as the software is based on a compulsory dichotomic tree and a very accurate alignment. Here the identification using BLAST is still the simplest approach.

It is important to understand genetic differences and population structure to sustain current level genetic diversity within the Halodule species. Based on this point the plants collected at Chilika Lagoon could represent a species or subspecies of Halodule as it was out-grouped separately from all three Halodule species of India. One cannot refuse the probable genetic mix-up among the species. Our data indicate this possibility especially in case of H. pinifolia and H. uninervis. Furthermore, the hit number for rbcL was ambiguous for H. uninervis as well as H. pinifolia while performing the BLAST identification approach. This indicates that more detailed genetic studies among the different populations of Halodule in India are needed, similar to the approach of Ito & Tanaka (2011) [64] for Asian Halodule populations.

In case of the Halophila genus the position of H. decipiens is not correct. In previous studies H. decipiens was found on the base of the Halophila clade [32], [34], indicating a potential genetic mix-up between indo-pacific species, possibly by cross breeding. This is supported by its worldwide distribution, where no sequence differences occur between samples from the Caribbean and from around the Indo-Pacific [63]. H. ovalis and H. ovata could be differentiated only with leaf lamina and number of leaf veins, but confusions persist in identification of juveniles. Comparison to previous sequencing results could be only tested using rbcL, showing a high overlap (Table S3).

In case of the simple thallus-like Halophila species, the rbcL sequence characters are as similar as their morphological characters and do not provide an informative content for barcoding (see Table S4 and S5). In particular, misidentifications in the Halophila genus could be elucidated with the presented barcoding approach, though this could not be achieved for Halodule. DNA barcoding is wrongly addressed to this taxonomically problematic genus. The presented barcoding system verifies that these two genera are challenging for identification. So for establishing a correct seagrass barcoding database, close ups like microsatellites should be used, as recently published for H. beccarii [66] rather than misleading, environmentally influenced morphological characters.

This could uncover whether a genetic mix-up occurred or phenotypic plasticity is an underestimated trait for seagrasses [8], helping them to survive in different niches like deep water, deepwater with light limitation, intertidal areas, shallow turbid waters, eutrophic waters etc. where they are capable of adapting to overcome the local conditions. Only more detailed loci may solve the complex taxonomy and cleanup questionable assignments for species which share nearly all morphological identities like H. ovata, H. gaudichaudii and H. okinawensis [34]. To summarize the results of the different trees, it is obvious that a single locus is not able to resolve well-described species properly (rbcL, trnH-psbA) or the marker is not robust using only a few specimens (matK). Hence, one gets a result not fitting to the well-known phylogeny of Alismatales.

A rapid system for identification close to species level is provided by using matK and rbcL. The spacer region trnH-psbA cannot be recommended for barcoding purposes at least for the ecological group of seagrasses. The fact of a clearly existing “barcoding gap” makes it not suitable for barcoding seagrass, not providing more information than e.g. rbcL or matK. Including the structure of internal spacers could be a promising approach for phylogenetic analysis. ITS or phyC could serve as additional supplemental loci and were used successfully in several other phylogenetic analyses [33], [34], [63]. One should be aware that barcoding is not a replacement for phylogenetic analysis and maybe the remaining complexes of Halodule and Halophila are a more taxonomic and systematic problem and it is wrongly addressed by a simple method like DNA barcoding. The complex formations of Halodule and Halophila need to be more carefully analyzed as they show more phenotypic plasticity and are known to be problematic concerning species discrimination [6], [34], [64].

In summary, recognition of seagrasses by DNA barcoding is possible and feasible. Two unknown species from the Chilika Lagoon could successfully be identified (H. beccarii and H. ovata) and were verified on the congruence of tree- and character-based analysis. Misidentifications can be unambiguously defined as the bottleneck for establishing a solid database for DNA-based identification of seagrass. Furthermore these misidentifications, e.g. H. ovalis from the Chilika Lagoon or H. decipiens from Palk Bay, could be revealed by the developed system. The known complex of Halodule remained, unfortunately, unresolved. This indicates, as discussed above, the demand for a more detailed study including worldwide populations and the choice of a high resolution marker system (e.g. microsatellites) [66]. Our recommendation is to use the matK/rbcL combination as a cost effective and straight forward method. The attained identification level is good enough for ecological survey and conservation purposes, as meadows constantly decrease in area and species diversity.

Supporting Information

Figure S1.

Comparison of rbcL and matK tree using Neighbor Joining (NJ) method. Trees resulting from NJ analysis (1,000 Bootstrap replicates) of rbcL and matK dataset compared using TreeJuxtaposer [48]. Red lines and taxons in red indicate different topology, letters in green topology in the compared tree. Collection sites other than Palk Bay are marked as follows: *Tonnenlegerbay, **Puan Klent, ***Ellenbogen and ° Chilika Lagoon. H. ovalis subsp. ramamurthiana is abbreviated as H. ovalis subsp.



Figure S2.

Dataset composition. The composition of the single- and combined-loci was analyzed for the percentage of conserved, variable, singleton and parsimony informative characters (PIC). rbcL shows the highest content of conserved sites, psbA spacer the lowest and opposite in the case of diverged characters. Combination of loci revealed in an average content of ∼24% PIC, psbA+matK with 28.4% PIC, and matK+rbcL and rbcL+psbA 22% PIC.



Figure S3.

Inverted repeats (IR) in the trnH-psbA spacer and its conservation in seagrasses. Parts incorporating the recognized IR sequence from the trnH-psbA alignment, IRs are shaded in grey color. A) Conserved IR in Zostera and Syringodium, close to the 3′ prime end. B) Conserved IR in Halodule and Halophila, showing a non-conserved part in Zostera but nearly full conservation in Syringodium.



Table S1.

Deposited accession numbers and collection places with GPS data for all observed specimens, as well as downloaded accession numbers for comparison (see Table S3).



Table S2.

Primer universality for each locus used. Performance of all six primer combinations used on the 24 species in the dataset and five additional herbarium species.



Table S3.

Identification level using BLAST and overlap percentage with the already deposited sequences on NCBI database. Sequences of species already available on NCBI are labeled in grey color, for those overlap was estimated in percent. Identification level is coded as following: O order, F family, G genus and S species level. Origins indicated as follows * Chilika, ** Ellenbogen, *** Puan Klent.



Table S4.

Diagnosis of Characteristic Attributes (CAs) for the rbcL fragment. Diagnostic characters for each genus (number of included species) or species are listed with position and respective nucleotide. SNP analysis was carried out using CAOS software.



Table S5.

Diagnosis of Characteristic Attributes (CAs) for the matK fragment. Diagnostic characters for the identified species are listed with position and respective nucleotide. SNP analysis was carried out using CAOS software. Species marked with an asterisk (*) originate from the Chilika Lagoon. Sequences with complete similarity are marked as included (incl.).




We would like to thank Dr. A. Pattnaik, Chilika Development Authority, Bubaneshwar, Orissa, India, for his hospitality and constant encouragement. We obtained herbarium material of seagrasses from Dr. Klaus Pistrick, Institute of plant genetics and crop plants, Gatersleben, Germany. Members of the Waddensee station, Sylt, belonging to the Alfred-Wegener Institute, are gratefully acknowledged for help in the field. Tjard Bergmann, University of Veterinary Medicine Hannover, helped to use the CAOS approach in a meaningful way. We are grateful to Sascha Offermann, Hannover, who corrected the English language.

The sequence data were deposited at GenBank. The accession numbers are summarized (Table S1).

Author Contributions

Conceived and designed the experiments: JP CL. Performed the experiments: CL JP. Analyzed the data: CL JP TT. Contributed reagents/materials/analysis tools: JP TT. Wrote the paper: JP CL TT.


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