Genetic species identification and population structure of Halophila(Hydrocharitaceae) from the Western Pacific to the Eastern Indian Ocean
© Nguyen et al.; licensee BioMed Central Ltd. 2014
Received: 23 October 2013
Accepted: 25 April 2014
Published: 30 April 2014
The Indo-Pacific region has the largest number of seagrass species worldwide and this region is considered as the origin of the Hydrocharitaceae. Halophila ovalis and its closely-related species belonging to the Hydrocharitaceae are well-known as a complex taxonomic challenge mainly due to their high morphological plasticity. The relationship of genetic differentiation and geographic barriers of H. ovalis radiation was not much studied in this region. Are there misidentifications between H. ovalis and its closely related species? Does any taxonomic uncertainty among different populations of H. ovalis persist? Is there any genetic differentiation among populations in the Western Pacific and the Eastern Indian Ocean, which are separated by the Thai-Malay peninsula? Genetic markers can be used to characterize and identify individuals or species and will be used to answer these questions.
Phylogenetic analyses of the nuclear ribosomal internal transcribed spacer region based on materials collected from 17 populations in the Western Pacific and the Eastern Indian Ocean showed that some specimens identified as H. ovalis belonged to the H. major clade, also supported by morphological data. Evolutionary divergence between the two clades is between 0.033 and 0.038, much higher than the evolutionary divergence among H. ovalis populations. Eight haplotypes were found; none of the haplotypes from the Western Pacific is found in India and vice versa. Analysis of genetic diversity based on microsatellite analysis revealed that the genetic diversity in the Western Pacific is higher than in the Eastern Indian Ocean. The unrooted neighbor-joining tree among 14 populations from the Western Pacific and the Eastern Indian Ocean showed six groups. The Mantel test results revealed a significant correlation between genetic and geographic distances among populations. Results from band-based and allele frequency-based approaches from Amplified Fragment Length Polymorphism showed that all samples collected from both sides of the Thai-Malay peninsula were clustered into two clades: Gulf of Thailand and Andaman Sea.
Our study documented the new records of H. major for Malaysia and Myanmar. The study also revealed that the Thai-Malay peninsula is a geographic barrier between H. ovalis populations in the Western Pacific and the Eastern Indian Ocean.
KeywordsEastern Indian Ocean Evolution Genetic distance Halophila ovalis Western Pacific Ocean
The Indo-Pacific Ocean – the origin of seagrass - has the largest number of seagrass species worldwide with huge meadows of mixed species stands, but the taxonomy of Halophila members is still unclear and genetic variation has not been much investigated so far . In comparison to other seagrass species in the meadows, Halophila ovalis (R. Br.) Hooker is the dominant species and very commonly found in the region. Recently, some new records of Halophila members such as Halophila major (Zoll.) Miquel, were documented in Southeast Asian countries including Indonesia, Viet Nam and Thailand [2, 3]. Additionally, H. sulawesii J. Kuo was found and described for the first time in Indonesia . Traditional classification of H. ovalis and closely related species based on leaf morphological data is very challenging, and species misidentification among Halophila members is reported in various studies [1, 5, 6]. Genetic markers are considered as helpful tools to resolve boundaries between species as well as the genetic variation among populations within species [6–8].
The Indo-Pacific Ocean also shows a high diversity of landscapes, habitats as well as several existing geographic barriers. Geographic isolation refers to a situation where a species, or a population of a species, becomes separated by a physical barrier, allowing each group to diverge along separate evolutionary paths . The effect of geographic isolation is that the two populations are subjected to different selection pressures, since the conditions in the two areas are different . Thus different alleles will be selected and genetic differences will gradually accumulate between the populations. In general, halophytes such as mangroves, marine algae, and seagrass grow in the coastal zone, which is connective between land and sea [1, 11]. Currents along the coast or ocean currents play an important role for the migration of species from one coastal area to another . Recently, there were several studies published on mangroves [12, 13] and animals [14, 15] from this region revealing the genetic variation isolated by barriers.
Among the members of Halophila, H. ovalis is widespread in the Indo-Pacific Ocean. In the Pacific, it occurs from southern Japan throughout Southeast Asia, many islands of the western Pacific, and through all but the southern coast of Australia, as well as Lord Howe and Norfolk Islands, and as far east as Tonga and Samoa. In the Indian Ocean, H. ovalis is found from southwestern Australia to East Africa and the Red Sea, including Madagascar, with the exception of islands or coastlines with no records. Recently, H. ovalis has been also discovered in the Atlantic Ocean on the Island of Antigua . The plant is diminutive and lacks strongly lignified tissue, making it flexible, but vulnerable to physical disturbances . Halophila ovalis grows on a variety of substrates and is often the first to colonize newly available sediments [5, 17]. The species can grow at a range of temperatures and is distributed from tropical to warm-temperate waters [16, 18]. This species has a wide depth distribution as well, with individuals growing from the intertidal up to a depth of 30 m . Like other seagrass species, H. ovalis reproduces vegetatively by branching of rhizomes and the formation of new shoots, and sexually through seeds . Due to high variation of leaf morphology and adaptation, Den Hartog  emphasized the need for detailed studies of this species to better understand the link between morphological variability and environmental parameters.
Leaf morphology is used as the main key to identify and name Halophila species [11, 20]. However, traits of leaf morphology are overlapping among members of this genus . Recently, genetic markers of plastid sequences have been used to reveal the genetic relationships among the members of the Halophila genus [2, 21]. However, the species boundaries could not be fully resolved. Using phylogenetic analyses of the nuclear ribosomal internal transcribed spacer (ITS1-5.8S-ITS2) region showed that some specimens identified as H. ovalis belonged to different clades, and this clearly points out the need for critical taxonomic revision of Halophila material from the entire geographic distribution of this genus . This nuclear sequence was also used to identify the genetic relation of H. ovalis and closely related species namely H. major, Halophila nipponica J. Kuo, Halophila minor (Zoll.) den Hartog and Halophila hawaiana Doty and B. C. Stone [6, 7, 22, 23].
There are several techniques including isozyme analyses [24, 25], Random Amplified Polymorphic DNA (RAPD) [26–29], Amplified Fragment Length polymorphism (AFLP) [30–33] and microsatellites [34, 35] to access genetic variation among and between seagrass populations. The major advantage of the AFLP technique is the large number of polymorphisms that the method generates compared with other markers. However, the methodology of AFLP experiments and post-run data analysis are complex and time consuming compared with other markers [36, 37]. Microsatellites are simple sequence repeats (SSRs) with advantages like locus-specificity, co-dominance, high degree of polymorphism, and it is also possible to work with partially degraded DNA . So far there is only little information of DNA fingerprinting techniques applied for H. ovalis.
It is hypothesized that (i) taxonomic uncertainty among different populations of H. ovalis persists and (ii) geographic distance, differentiation of habitats or the geographic barrier of the Indo-Malay peninsula may affect the genetic variation of H. ovalis from the Western Pacific to the Eastern Indian Ocean. The aims of this study are (i) to identify Halophila species collected in Hong Kong, Thailand, Malaysia and India based on the molecular marker (ITS1-5.8S-ITS2) and (ii) to search for the genetic structure of H. ovalis from the Western Pacific to the Eastern Indian Ocean based on microsatellite and AFLP approaches.
Species identification based on the nuclear ITS sequences and morphology
Locations/abbreviations, regions, coordinates, sample size and taxa used in this study
Kind of analysis
X, Hap. 2
X, Hap. 3
X, Hap. 4
X, Hap. 5
X, Hap. 6
X, Hap. 7
X, Hap. 8
X, Hap. 9
X, Hap. 10
X, Hap. 11
X, Hap. 12
X, Hap. 13
X, Hap. 14-15
X, Hap. 16
X, Hap. 17-18
X, Hap. 19
Comparisons of leaf morphology characteristics of H. major collected in this study and published data from H. ovalis
Lamina width (mm)
12 – 15
12 – 15
Lamina length (mm)
18 – 22
18 – 22
15 – 25
No. of cross veins
10 – 25
18 – 20
18 – 20
14 - 17
Space between intramaginal vein (mm)
0.1 – 0.3
0.25 – 0.3
0.25 – 0.3
Half lamina width: distance between intramarginal veins and lamina margin ration
1:21 – 22
1:21 – 22
Genetic diversity and population structure of H. ovalisfrom the Western Pacific to the Eastern Indian Ocean
Comparison of genetic diversity among H. ovalis populations
Observed heterozygosity (Ho)
Expected heterozygosity (He)
Allelic richness (A)
Pairwise comparison of population differentiation among H. ovalis populations
AMOVA (Analysis of Molecular Variance)  results for SSR variation at 14 collection sites of H. ovalis
Source of variation
Sum of squares
Variance of components
Percentage of variation
p < 0.01*
Among populations within groups
p < 0.01*
p < 0.01*
For the Eastern Indian Ocean, a very high genetic distance between the two regions VII and IX, ranking from 0.731 to 1.296 was observed (Table 4). For the Andaman Sea, the genetic distance between two populations, TH-sa and TH-tr, was determined as zero and non-significant (p = 0.53). In contrast, the genetic distance between IN-ma and IN-ka was very high and significantly different (1.280, p < 0.001). The results of AMOVA for SSRs variation of H. ovalis populations in the two regions VII and IX (see Table 1 for abbreviations) indicated that the percentage of variations among groups, among populations within groups and within populations were 20.85, 28.74 and 50.41%, respectively. Significant difference was just found among groups and among populations within groups (p < 0.01) and there were no significant differences within populations (p = 0.5). Moreover, the results of AMOVA for SSRs variation of H. ovalis populations in both oceanic systems (Western Pacific vs Eastern Indian) showed significant differences among groups (p < 0.01), among populations within groups (p < 0.01), and within populations (p < 0.01). The overall genetic variation from the Halophila populations in the Eastern Indian Ocean was 0.485 calculated from FSTAT.
For the AFLP analysis, the genetic similarities (Dice index) among the 24 individual H. ovalis samples were estimated based on the number of common fragments ranged from 0.560 to 0.928. It also showed that the similarity values of the populations within the Andaman Sea (TH-tr and TH-sa) and within the Gulf of Thailand (TH-kn) were 0.565 to 0.928 and 0.624 to 0.822, respectively. The similarity values between the populations of TH-tr and TH-sa were higher than between the population of TH-tr and TH-kn (0.634 to 0.820 vs 0.582 to 0.731).
AMOVA (Analysis of Molecular Variance)  results for AFLP variation at three collection sites of H. ovalis
Source of variation
Sum of squares
Variance of components
Percentage of variation
p < 0.01*
Among populations within groups
p < 0.01*
p = 0.3
The present study is the first report of genetic diversity, as well as genetic differences, within and among populations of H. ovalis collected from the Western Pacific Ocean to the Eastern Indian Ocean using nuclear sequence comparison (ITS) and two DNA fingerprinting approaches: AFLP and SSRs. Conformation of new records for H. major in Malaysia and Myanmar and detection of high levels of polymorphism underlined impressively that genetic markers are powerful tools for species identification and assessing genetic diversity in seagrass.
New records of Halophila majorfor Malaysia and Myanmar
Variation of leaf morphology has been detected within several species of the Halophila genus, namely H. ovalis [50, 51], H. hawaiana , and H. nipponica . Short et al. [1, 52] argued that the taxonomy of H. major was unclear, because of overlapping leaf characteristics between H. ovalis and H. major. Molecular markers, especially ITS, were shown to be a valuable tool in resolving genetic relationships among the species of Halophila. For instance, Halophila euphlebia Makino was once treated as synonym for H. ovalis [11, 53]; then, this species was transferred to H. major . Results of Uchimura et al.  and Shimada et al.  supported the conclusion of Kuo et al.  that H. major and H. ovalis are distinct species based on ITS analysis and morphological data. Recently, Short et al.  suggested that species in general should be accepted as a new species only if a complete published taxonomic description existed, documenting unique sexual reproductive characters and significant genetic differences. There are three and six species of Halophila currently reported in Myanmar and Malaysia, respectively, [17, 54] not including H. major. Halophila major has been found in the recent years along the coastlines of Southeast Asian countries including Indonesia, Thailand, Viet Nam and Japan [3, 23]. As it was demonstrated recently that Halophila members could not be fully resolved among closely related species such as H. ovalis, H. major and H. ovata Gaudich based on concatenated sequences of the two plastid markers rbcL and matK [2, 21]. In contrast, the phylogenetic analysis of the nuclear ITS sequence indicated that H. ovalis, H. major and H. minor are distinct species [3, 7, 23]. Hence, the use of the ITS marker to classify the entire set of samples collected for this study is the best choice based on the current knowledge. In this study, cluster analysis, direct comparison of nucleotide differences and evolutionary divergence between the two clades H. ovalis and H. major revealed that the materials collected in Mabul Island (MY-mb) and Gusungan Island (MY-gs), both in Malaysia, and one population (MM-gy) in Myanmar differ significantly from the H. ovalis clade. Moreover, four methods of constructing phylogenic trees also indicated that materials collected in Mabul Island and Gusungan Island (both in Malaysia) and Myanmar are H. major. Only the indications of the molecular methods initiated a detailed microscopic analysis of the leaf samples. The leaf morphology based on the ratio of the distance between the intra-marginal vein and the lamina margin confirmed the ITS analysis. Hence, morphological and nuclear sequence (ITS) analysis indicated that the materials collected in Mabul Island and Gusungan Island (both in Malaysia) and one population in Myanmar are actually H. major. In the field this kind of analysis is usually not possible, but our results suggest that careful analysis of seagrass samples need to be conducted before classifying them as H. ovalis.
The Indo-Pacific region has the largest number of seagrass species worldwide and this region was considered as the origin of the Hydrocharitaceae family [52, 55]. Malaysia not only shows the highest number of Halophila species, summing up to seven species , this study, but also the highest diversity of H. ovalis haplotypes: there are four haplotypes found in six populations in Malaysia. In contrast, Nguyen et al.  found only one haplotype in four populations in Viet Nam. This finding is congruent with the hypothesis of Malaysia being the center of origin of the seagrasses.
Genetic and geographic distance of H. ovalisbased on SSRs
The genetic diversity indices showed relatively high values from 0.298 to 0.306. Compared to results reported from other studies on seagrass species including Z. marina (0.504 to 0.601) , (0.310 to 0.460) , Zostera noltii Hornemann (0.442 – 0.630) , Posidonia oceanica (L.) Delile (0.191 to 0.363)  and Cymodocea nodosa (U.) Ascherson (0.286 to 0.564) , (0.383 to 0.647)  using SSRs markers showed that the genetic diversity of H. ovalis is lower. Unfortunately, there are no studies on the genetic diversity of H. ovalis based on SSRs markers for direct comparison so far. In other AFLP approaches of seagrass species the genetic diversity such as Thalassia testudinum Banks ex König (H = 0.35, ) is slightly higher or much lower than in our results, such as for Z. marina (H = 0.007 to 0.072, ). Hence, the genetic diversity of seagrass varied indeed from species to species, geographic distribution, and different DNA fingerprinting approaches.
The present distribution of the genetic structure within species is influenced by evolutionary history . In this study, genetic and AMOVA analyses indicated significant genetic differences among populations in the Western Pacific Ocean (FST = 0.483), among populations in the Eastern Indian Ocean (FST = 0.485), and larger significant differences among 14 populations surveyed in the Western Pacific Ocean and the Eastern Indian Ocean (FST = 0.679). All above results indicated that great genetic differentiation among populations was detected and/or gene flow among populations is very low. For the Celebes Sea, pair wise genetic differentiation among populations showed genetic differentiation although the geographic distance among populations is about 30 to 40 km. However, genetic differentiation between MY-mg and MY-bd is very low (0.048) in contrast to other populations in the Celebes Sea. This could be explained by the diversity of the habitat such as substratum, currents, and time exposure to air during low tide etc., and those factors may affect the genetic differentiation. Japar et al.  stated that there are remarkable variations of H. ovalis, which grows in different substratum and depth. Significant genetic differences were also found in Z. marina between the Wadden Sea and the Baltic Sea where geographic distance among populations is within areas of 10 to 50 km . Leaf morphology (small form) of H. ovalis collected in Tiga Island showed great differences in comparison to the other populations in the Celebes Sea (Prof. Japar, Malaysia, personal observation).
For the South China Sea, there are very great genetic differentiations among populations in the northern part of the South China Sea (HK-tc) and the remaining populations in the western, eastern and southern part of the South China Sea. Perhaps high latitude (or lower average temperature) in the northern part of the South China Sea may lead to the genetic differentiation. Both populations collected in Viet Nam also showed significant differences, although the geographic distance between two populations is less than 100 km. In fact, there are great differences between the environmental conditions from two populations, in the lagoon and in the open sea. It could be explained by the differentiation of salinity, with high salinity (open sea) and low salinity (lagoon). The genetic difference between H. ovalis populations in the open sea and the lagoon were also found in India based on AFLP data . For the population of the Gulf of Thailand, the results indicated low genetic differentiation between populations in Thailand and the western part of the South China Sea. Perhaps there was no geographic barrier found between the western part of the South China Sea and the Gulf of Thailand. A study of Morton and Blackmore  shows surface currents between the Gulf of Thailand and the western part of the South China Sea, that frequently occur in both summer and winter season.
The genetic differentiation between populations in the Andaman Sea and in the Bay of Bengal is significantly different. This could be explained by a very long geographic distance (more than 2,000 km) between the Bay of Bengal and the Andaman Sea. However, there is no significant genetic differentiation between populations of TH-tr and TH-sa in the Andaman Sea. Perhaps short geographic distances and the same habitat are the main causes that led to the high similarity between the two populations. Results from AFLP analysis also indicated that the genetic distance between populations from TH-tr and TH-sa within the Andaman Sea is much lower than between populations from the Andaman Sea and the Gulf of Thailand. Moreover, surface currents in the winter (from TH-tr to TH-sa) and in the summer (TH-sa to TH-tr)  support species dispersal between TH-tr and TH-sa. In contrast, genetic differentiation between populations from IN-ka and IN-ma was also high. It could be explained by the geographic distance as well as habitat differences (lagoon vs open sea).
The result from the unrooted neighbor-joining tree based on Slatkin’s genetic distance showed the identified six main clusters corresponding to populations from different regions. Based on the genetic distance, the population in MY-jo seems to be in between the Western Pacific and the Eastern Indian Ocean, which corresponds to the geographic distribution of H. ovalis populations in the study. However, one of the most striking results is the unexpected result in the case of the HK-tc population. It showed no simple relationship between genetic differentiation and distance between pairs of population. The HK-tc population was genetically closer to the population in the Eastern Indian Ocean than to populations in the Western Pacific Ocean. At present we are unable to explain this puzzling result.
Role of the Thai–Malay Peninsula as a geographic barrier to H. ovalispopulations in Thailand based on AFLP analysis
Among a total of 231 bands, 208 (90.05%) were polymorphic bands. This contrasts with a level of variability of 30% using AFLP in land plant species, such as rice . In a recent study by Nguyen et al.  it was shown that the 17.5% of polymorphic bands are presented in the H. ovalis – H. ovata complex. High level of polymorphic bands has previously been reported in Thalassia testudinum Banks ex König  and Zostera marina Linnaeus  using AFLP. The percentage of polymorphic bands varies from species to species, geographic distribution, and primer combinations. For the band-based approach performed in this study, the similarity index showed comparable values to the similarity index of H. ovalis populations found in India . Comparison between clustering analysis (UPGMA) (Figure 4) and PCoA (Additional file 2) showed that the pattern of clustering the taxa was similar with both analyses: The individuals collected in the Gulf of Thailand clustered as single clade, whereas individuals collected in the Andaman Sea grouped together. AMOVA results (Table 6) also indicate this variation between two groups. In this study, pair wise genetic differentiation (FST) and genetic distance (Additional file 3) among populations support the hypothesis that H. ovalis in the Gulf of Thailand and the Andaman Sea are genetically different. The results from AFLP analysis are also in agreement with the results of ITS analysis when different haplotypes in the Gulf of Thailand and the Andaman Sea were classified. In addition, the previous studies on marine animals [12, 13] and mangroves [14, 15] also indicated that the Thai-Malay peninsula is an effective geographic barrier for populations of different organisms in the Gulf of Thailand and the Andaman Sea.
Based on ITS, AFLP and SSRs analysis of genetic variation of H. ovalis, results indicated that the genetic markers are powerful tools to assess the genetic differentiation on the broad sample collection sites. However, the sample size was still low and in the case of TH-kn that may affect the standard error of the diversity in the population of the species as discussed by Singh et al. . According to all our results, missing haplotypes were made visible in the haplotype network, hence we recommend the collection of more samples from populations in the Philippines, somewhere between the two mainlands of Malaysia (Peninsular Malaysia and East Malaysia), and somewhere between the Andaman Sea and the Bay of Bengal (Myanmar and Nicobar Islands) to be included in future studies.
Our study documented the new records of H. major for Malaysia and Myanmar. The study also revealed that the Thai-Malay peninsula is a geographic barrier of H. ovalis populations in the Western Pacific and the Eastern Indian Ocean. Characteristics of habitat are also an ecological barrier to the evolution of H. ovalis in the smaller scale area.
Sample collection, DNA extraction and morphological analysis
Samplings of Halophila species were carried out at the Pacific Ocean and the Indian Ocean. Samples were collected from 17 populations belonging to eight regions depending on the geographic distribution. Regions were determined by long geographic distance (more than 1,000 km in this study) or geographic barrier. Region I (northern part of South China Sea): 1-Hong Kong (HK-tc). Region II (western part of South China Sea): 2-Van Phong (VN-vp), 3-Thuy Trieu (VN-tt). Region III (eastern part of South China Sea): 4-Sarawak (MY-sr). Region IV (Celebes Sea): 5-Tiga Island (MY-tg), 6-Mabul Island (MY-mb), 7-Gusungan Island (MY-gs), 8-Sibangat Island (MY-sb), 9-Bodgaya Island (MY-bd), 10-Maiga Island (MY-mg). Region V (Gulf of Thailand): 11-Kanom (TH-kn). Region VI (southern part of South China Sea): 12-Johore (MY-jo). All above six regions belong to the Pacific Ocean. Region VII (eastern part of Andaman Sea): 13-Satun (TH-sa), 14-Trang (TH-tr). Region VIII (northern part of Andaman Sea): 15-Myanmar (MM-gy). Region IX (Bay of Bengal): 16-Marakanam (IN-ma), 17-Kanyakumari (IN-ka). Details of each sampling site are presented in Figure 1 and Table 1. At each sampling point, plants containing root, rhizome and leaf were selected, and washed with seawater in the field to remove the epiphytes and debris attached to the plants. Each plant sample was placed in a single plastic bag and kept on ice. Plant material was transferred to the laboratory at the same day. In the laboratory, materials were re-washed with de-ionized water to remove seawater. One plant was divided into two parts, one part was pressed as a herbarium voucher specimen and the remaining part was desiccated in silica gel  for later DNA extraction. Parts with a length of 10 to 12 cm in a developmentally comparable state from five to ten different plants were haphazardly collected across the beds with a distance of 10 to 15 m among individuals. Materials desiccated in silica gel were brought to the Institute of Botany, Leibniz University Hannover, Germany, for further analysis. Eight to ten young leaves of each individual were homogenized by a bead mill (22 Hz, 2 min), and 100 mg of the fine powdered plant material was used for DNA extraction. DNA extraction was carried out using the Plant Nucleospin II Kit (Macherey & Nagel, Düren, Germany) following manufacture’s instruction with slight modifications according to Lucas et al. . DNA quality was checked on agarose gels stained with ethidium bromide and the concentration was measured by a microplate reader with micro-volume plates (Synergy Mx Multi-Mode, BioTek, Germany).
For the morphological analysis, ten adult leaves collected from ten different individuals from each location were used for the analysis. The five most important and differentiating parameters of leaf morphology including lamina width, lamina length, number of paired cross veins, space between intra-marginal veins and the ratio of the distance between intra-marginal vein (r) and lamina margin (R) were measured under the microscope Olympus SZ (Olympus, Tokyo, Japan). Photographs were taken using a U-TV1X-2 digital camera (Olympus) connected to a computer. The test for equal variances of each data set of leaf morphology among groups was checked by Levene’s test for homoscedasticity. Levene’s test, one-way analysis of variance (ANOVA), Tukey test was carried out by Minitab software (State College, PA, USA). Specimens were identified using the keys of Kuo et al. .
ITS amplification procedure and sequencing
Sequence of primers/adaptors used for ITS, AFLP and SSRs
Sequence of primers used for ITS
Name of primer
Ann. temp. (°C)
Length of PCR product (bp)
Sequence of adaptors and primers used for AFLP
5′-GACTGCGTACCAATTCA-3′ (EcoRI + A)
5′-GATGAGTCCTGAGTAAA-3′ (MseI + A)
EcoRI + ACA/MseI + ATC (set1)
EcoRI + ACC/MseI + ATC (set2)
EcoRI + ACA/MseI + ACA (set3)
EcoRI + ACC/MseI + ACA (set4)
Sequence of primers used for SSRs
One hundred individuals (data given from Table 1) collected from 14 populations in the Pacific and the Indian Ocean were used for the analysis. Details of sample size, names of locations and coordinates are presented in Table 1. Among 10 primer pairs suggested by Xu et al. , we used five primer pairs resulting in highly polymorphic bands (HO5, HO8, HO36, HO48 and HO51) (Table 7) for PCR. Thirty ng of template DNA was used in each 15 μl PCR including 1x Williams buffer, 0.2 mM dNTPs, 1 U Taq polymerase (MBI Fermentas), and 1 pmol primer each. The PCR was performed in a PTC 200 thermocycler (Biozym-Diagnostik GmbH under the following conditions: initial denaturation for 5 min at 94°C followed by 25 cycles of denaturation for 30 s at 94°C, primer annealing for 30 s at 52 to 59°C and extension for 35 s at 72°C, and terminated by a final hold at 10°C. To each sample, 200 μl of dye (98% formamide, 10 mM EDTA, 0.05% pararosaniline) was added. Reactions were heated up to 72°C for 5 min before loading onto 6% AFLP gels (Sequagel XR, National Diagnostics, Hull, England). For running an AFLP gel on the 4300 DNA Analyzer (LI-COR, Biosciences, Germany) manufacture’s instruction were followed. Base pair lengths obtained from visual analysis was resolved with previously published allele lengths  and sequencing was performed when necessary.
Samples were collected from three populations from the Andaman Sea and the Gulf of Thailand. Initially, 10 to 15 individuals per population were collected in Thailand for AFLP analysis. Unfortunately, DNA extracted from some plant samples was degraded. Degradation may have been caused by the humid and hot climate during the collection period in Thailand. Meudt et al.  indicated that use of degraded DNA could result in poor quality profiles with low reproducibility in AFLP analysis. Hence, only the samples retrieving high quality DNA were subjected for further experiments. According to Pruett and Winker , a sample size of 20 to 30 individuals is recommendable for genetic population studies. However, five to six samples are sufficient to obtain a standard error equal to 10% of the diversity in the population of the species . In this study, there are four and twenty samples included from the Gulf of Thailand and the Andaman Sea, respectively, showing high quality of DNA.
Details of sample size, name of locations and coordinates are presented in Table 1. The AFLP procedure was carried out as reported by Vos et al.  with few modifications. In brief, genomic DNA (250 ng) was digested with two restriction enzymes in a total volume of 25 μl including 5 U EcoRI, 3 U MseI, 1x Restriction Ligation (RL) buffer (10 mM Tris/HCl, 10 mM MgAc, 50 mM KAc, 5 mM DTT, pH 7.5) for overnight at 37°C. Adapters were prepared in a total volume of 5 μl including 50 pmol of MseI adapters, 5 pmol of EcoRI adapters, 0.5 mM ATP and 1.2 U of T4 DNA ligase, and 1x RL buffer. The mix of digested DNA and adapters were incubated at 37°C for 3.5 h and then used as a template for PCR. The pre-selective PCR contained 5 μl of template, 1 U of Taq polymerase (MBI Fermentas, St. Leon-Rot, Germany), 0.25 mM of each of the four dNTPs, 1x Williams buffer (10 mM Tris/HCl pH 8.3, 50 mM KCl, 2 mM MgCl2, 0.001% gelatine) and 50 ng of EcoRI and MseI primers with one selective nucleotide (A) in a total volume of 50 μl. The PCR program consisted of twenty cycles of 30 s at 94°C, 30 s at 60°C and 1 min at 72°C, followed by 10 min at 72°C. An aliquot of the reaction mix was diluted 1:20 with 1x TE Buffer (10 mM Tris/HCl pH 7.5, 1 mM EDTA). The selective PCR contained 2.5 μl of the diluted (1:20) product of the pre-selective PCR, 2 mM dNTPs, and 5 U Taq polymerase in a total volume of 10 μl. Four primer pairs, EcoRI + ACA/MseI + ATC, EcoRI + ACC/MseI + ATC, EcoRI + ACA/MseI + ACA and EcoRI + ACC/MseI + ACA, (Eurofins MWG Operon, Ebersberg, Germany) were used for the selective amplification. The first amplification cycle was carried out for 30 s at 94°C, 30 s at 65°C and 1 min at 72°C. In each of the following 11 cycles, the annealing temperature was reduced by 0.7°C. The last 24 cycles were carried out at an annealing temperature of 56°C, and the final extension step was carried out at 72°C for 10 min. To each sample, 50 μl of dye (see above) was added. Running conditions and instruments were the same as for SSRs.
The obtained ITS sequences and known sequence of Halophila decipiens Ostenfeld (KC175913) and H. minor (AF366405; AF366406) were aligned by CLUSTAL X  and the alignment was further modified by eye. Gaps were considered as missing data. Identical sequences within each species were excluded from the alignment. Additional in-group sequences were obtained from GenBank (Table 1), and included in the alignment. The program jModelTest 0.1.1  was used to find the model of sequence evolution that fitted best with the data set. Phylogenetic analyses were performed using ML, NJ  with the model Tamura 3-parameter, MP  in MEGA5.2 , and BA (Metropolis-coupled Markov chain Monte Carlo method) performed in MrBayes v.3.2 . Halophila decipiens was used as out-group, because it is closer to its ancestor than the Halophila ovalis complex . In the analyses, trees were tested by the bootstrapping method with 1,000 replications. All phylogenetic trees achieved from analysis were analyzed and exactly constructed by the "tree of trees" approach . Moreover, a network of relationships among haplotypes was constructed as well as a cladogram that showed the nested structure of the haplotypes. This analysis was conducted in software TCS version 1.21 . Only populations determined as H. ovalis based on ITS analysis were used for AFLP and SSRs analysis.
For the AFLP analysis, only polymorphic fragments were scored as binary data (1, band present; 0, band absent). The binary scores were manually compared with the pictures to re-confirm presence or absence of bands. A presence/absence binomial matrix of 30 individuals and 201 polymorphic loci was used as basis for the analysis. In this study, the analysis with two approaches including band-based approach (for individual level) and allele frequency-based approach (for population level)  was carried out. In the individual level, the similarity among 30 individuals was calculated by the Dice coefficient . A cluster analysis was performed using unweighted pair group method with arithmetic mean (UPGMA) based on the Dice index . Bootstrap values (based on 1,000 re-samplings) were used to estimate the reliability of the clustering pattern. This analysis was carried out in FreeTree software . The dendrogram was edited and displayed by MEGA5.2 . Principal Coordinates Analysis (PCoA) of the correlation matrix was used to further investigate relationships between individuals using NTSYSpc version 2.20 . At the population level, the allelic diversity at each locus was calculated as h = 1 - ∑pi2, where pi is the frequency of the ith allele . Allelic diversity within each population was the mean allelic diversities among the 114 loci. Nei’s GST  was used as a value of genetic differentiation. GST was calculated using the formula GST = (HT- HS)/HT , where HT represents the total gene diversity and HS represents the gene diversity within populations. Those values and the dendrograms (UPGMA) were assessed by POPGENE 3.2  and MEGA5.2 . In addition, pairwise genetic distances were calculated and used in AMOVA (Analysis of Molecular Variance, ). The analyses were conducted with the Arlequin version 3.5 .
For the SSRs, genetic diversity was measured for each site using the indices described by Williams and Orth . These indices include: expected heterozygosity under Hardy-Weinberg equilibrium (He) = (Σ expected frequency of heterozygotes at each locus)/(total number of loci); observed heterozygosity (Ho) = (Σ frequency of heterozygotes at each locus)/(number of individuals); and allele richness (A) = (Σ number of alleles at each locus)/(total number of loci). All those parameters were assessed by Microsatellite Toolkit for Excel  and FSTAT version 22.214.171.124 . Deviation from Hardy-Weinberg proportion was tested using a Markov-chain algorithm developed by Guo and Thompson  and implemented in the Genepop’007 . Linkage disequilibrium among all pairs of loci for each population and all populations in the Western Pacific and the Indian Ocean was also tested by Genepop’007 . For the population structure, Wright’s F-statistics (FST) was calculated. FST measures the degree of inbreeding in the subpopulation relative to the total population, and is commonly used to estimate population differentiation. The software FSTAT version 126.96.36.199  was also used for calculation. Significant differences among groups (FST), among populations within groups (FSC) and within population (FCT) were test by AMOVA (Analysis of Molecular Variance). This analysis was carried out by Arlequin 3.5 . Pairwise distances were calculated from allele frequency data using the Slatkin’s distance  in Arlequin 3.5 . The unrooted neighbor joining tree was constructed using neighbor joining with bootstrap resampling (1,000 replications) in package Phylip version 3.5  and a consensus tree was created using FigTree version 1.3.1 . The tree was edited and displayed in MEGA5.2 . Geographic distances (km) among populations were determined from NOAA digital map (Figure 1). The genetic-geographic distance matrix was statistically tested for correlation using the Mantel test . This test was carried out by Genepop’007 .
Availability of supporting data
The data sets supporting the results of this article are available in the TreeBASE repository, http://purl.org/phylo/treebase/phylows/study/TB2:S15597.
This work was financially supported by the Leibniz University Hannover, Hannover, Germany; the Ministry of Education and Training, Viet Nam; the Prince of Songkla University, the Higher Education Research Promotion and National Research University Project of Thailand; the Office of The Higher Education Commission, Universiti Putra Malaysia and Science Fund 04-01-04-SF1171, Ministry of Science, Technology and Innovation (MOSTI), Malaysia. We are deeply indebted to Prof. Dr. T. Debener and Dr. M. Linde for giving technical and scientific advice on AFLP and SSRs analysis and for the possibility to use their equipment. We would like to thank Mr. Felix Hirschmann, Leibniz University Hannover, Germany, for correcting the English language and for collecting samples together with the esteemed colleagues at the Centre of Advanced Study in Marine Biology, Faculty of Marine Sciences, Annamalai University, India. Thanks to Prof. Put O Ang, Jr for sample collection in Hong Kong. We acknowledge support by Deutsche Forschungsgemeinschaft and Open Access Publishing Fund of Leibniz Universität Hannover.
- Short FT, Polidoro B, Livingstone SR, Carpenter KE, Bandeira S, Bujang JS, Calumpong HP, Carruthers TJB, Coles RG, Dennison WC, Erftemeijer PLA, Fortes MD, Freeman AS, Jagtap TG, Kamal AHM, Kendrick GA, Kenworthy WJ, LaNafie YA, Nasution IM, Orth RJ, Prathep A, Sanciangco JC, Tussenbroek BV, Vergara SG, Waycott M, Zieman JC: Extinction risk assessment of the world’s seagrass species. Biol Cons. 2011, 144: 1961-1971.View ArticleGoogle Scholar
- Nguyen XV, Bujang JS, Papenbrock J: Variability of leaf morphology and marker genes of members of the Halophila complex collected in Viet Nam. Aquat Bot. 2013, 110: 6-15.View ArticleGoogle Scholar
- Uchimura M, Fay EJ, Shimada S, Arai S, Inoue T, Nakamura Y: A reassessment of Halophila species (Hydrocharitaceae) diversity with special reference to Japanese representatives. Bot Mar. 2008, 51: 258-268.View ArticleGoogle Scholar
- Kuo JJ: New monoecious seagrass of Halophila sulawesii (Hydrocharitaceae) from Indonesia. Aquat Bot. 2007, 87: 171-175. 10.1016/j.aquabot.2007.04.006.View ArticleGoogle Scholar
- Kuo JJ, Kanomoto Z, Iizumi H, Mukai H: Seagrasses of the genus Halophila Thours (Hydrocharitaceae) from Japan. Acta Phytotax Geobot. 2006, 57: 129-154.Google Scholar
- Shimada S, Watanabe M, Ichihara K, Uchimura U: Morphological variations of seagrass species, Halophila nipponica (Hydrocharitaceae, Alismatales). Coast Mar Sci. 2012, 35: 85-90.Google Scholar
- Waycott M, Freshwater D, York R, Calladine R, Kenworthy W: Evolutionary trends in the seagrass genus Halophila (Thouars): Insights from molecular phylogeny. Bull Mar Sci. 2002, 71: 1299-1308.Google Scholar
- Uchimura M, Faye EJ, Shimada S, Arai S, Inoue T, Nakamura Y: A re-evaluation of the taxonomic status of Halophila euphlebia Makino (Hydrocharitaceae) based on morphological features and ITS sequence data. Bot Mar. 2006, 49: 111-121.View ArticleGoogle Scholar
- Braillet C, Charmantier A, Archaux F, Dos Santos A, Perret P, Lambrechts MM: Two blue tit Parus caeruleus populations from Corsica differ in social dominance. Avian Biol. 2002, 33: 446-450. 10.1034/j.1600-048X.2002.02956.x.View ArticleGoogle Scholar
- Roy D, Kelly DW, Fransen CHJM, Heath DD, Haffner GD: Evidence of small-scale vicariance in Caridina lanceolata (Decapoda: Atyidae) from the Malili Lakes, Sulawesi. Evol Ecol Res. 2006, 8: 1087-1099.Google Scholar
- Den Hartog C: The sea-grasses of the world. 1970, North-Holland: Amsterdam Publishing Company, Amsterdam/LondonGoogle Scholar
- Liao PC, Chiang YC, Huang S, Wang JC: Gene flow of Ceriops tagal (Rhizophoraceae) across the Kra Isthmus in the Thai Malay Peninsula. Bot Stud. 2009, 50: 193-204.Google Scholar
- Su GH, Huang YL, Tan FX, Ni XW, Tang T, Shi SH: Genetic variation in Lumnitzera racemosa, a mangrove species from the Indo-West Pacific. Aquat Bot. 2009, 84: 341-346.View ArticleGoogle Scholar
- Khamnamtong B, Klinbunga S, Menasveta P: Genetic diversity and geographic differentiation of the Giant Tiger shrimp (Penaeus monodon) in Thailand analyzed by mitochondrial COI sequences. Biochem Genet. 2009, 47: 42-55. 10.1007/s10528-008-9205-3.PubMedView ArticleGoogle Scholar
- Zhang JB, Cai ZP, Huang LM: Population genetic structure of Crimson snapper Lutjanus erythropterus in East Asia, revealed by analysis of the mitochondrial control region. Mar Sci. 2006, 63: 693-704.Google Scholar
- Short FT, Moore GE, Peyton KA: Halophila ovalis in the tropical Atlantic Ocean. Aquat Bot. 2010, 93: 141-146. 10.1016/j.aquabot.2010.05.001.View ArticleGoogle Scholar
- Japar SB, Muta HZ, Mohd FI, Khairul AMS, Arshad A: Growth performance of Malaysia’s spoongrass, Halophila ovalis (R. Br.) Hooker f. under different substrate, salinity and light regime. Coast Mar Sci. 2010, 34: 103-107.Google Scholar
- McMillan C: Morphological diversity under controlled conditions for the Halophila ovalis-H. minor complex and the Halodule uninervis complex from Shark Bay, Western Australia. Aquat Bot. 1983, 17: 29-42. 10.1016/0304-3770(83)90016-5.View ArticleGoogle Scholar
- Coles RG, Lee-Long WJ, McKenzie LJ, Roelofs AJ, De’ath G: Stratification of seagrasses in the GBR world heritage area, N.E. Australia, and implications for management. Biol Mar Medit. 2007, 7: 345-348.Google Scholar
- Den Hartog C, Kuo J: Taxonomy and biogeography of seagrasses. Seagrasses: Biology, ecology and conservation. Edited by: Larkum AWD, Orth RJ, Duarte CM. 2006, Dordrecht, The Netherlands: Springer, 1-23.Google Scholar
- Lucas C, Thangaradjou T, Papenbrock J: Development of a DNA barcoding system for seagrasses: successful but not simple. PLoS ONE. 2012, 7: e29987-10.1371/journal.pone.0029987.PubMedPubMed CentralView ArticleGoogle Scholar
- McDermid KJ, Gregoritza MC, Reeves JW, Freshwater DW: Morphological and genetic variation in the endemic sea grass Halophila hawaiiana (Hydrocharitaceae) in the Hawaiian Archipelago. Pac Sci. 2003, 57: 199-209. 10.1353/psc.2003.0017.View ArticleGoogle Scholar
- Nguyen XV, Holzmeyer L, Papenbrock J: New record of the seagrass species Halophila major (Zoll.) Miquel in Viet Nam: evidence from leaf morphology and ITS analysis. Bot Mar. 2013, 56: 313-321.Google Scholar
- Capiomont A, Sandmeier M, Caye G, Meinesz A: Enzyme polymorphism in Posidonia oceanica, a seagrass endemic to the Mediterranean. Aquat Bot. 1996, 54: 265-277. 10.1016/0304-3770(96)01029-7.View ArticleGoogle Scholar
- Reusch TBH: Fitness-consequences of geitonogamousselfing in a clonal marine angiosperm (Zostera marina). Evol Biol. 2001, 14: 129-138. 10.1046/j.1420-9101.2001.00257.x.View ArticleGoogle Scholar
- Angel J: Genetic diversity of Halodule wrightii using Random Amplified Polymorphic DNA. Bot Mar. 2002, 74: 165-174.Google Scholar
- Jover MA, del Casstillo-Agudo L, Garcia-Carrascosa M, Segura J: Random amplified polymorphic DNA assessment of diversity in western Mediterranean populations of the seagrass Posidonia oceanica. Am J Bot. 2003, 90: 364-369. 10.3732/ajb.90.3.364.PubMedView ArticleGoogle Scholar
- Kirsten JH, Dawes CJ, Cochrane BJ: Randomly amplified polymorphism detection (RAPD) reveals high genetic diversity in Thalassia testudinum banks ex Konig (Turtlegrass). Aquat Bot. 1998, 61: 269-287. 10.1016/S0304-3770(98)00070-9.View ArticleGoogle Scholar
- Procaccini G, Acunto S, Famà P, Maltagliati F: Structural, morphological and genetic variability in Halophila stipulacea (Hydrocharitaceae) populations in the western Mediterranean. Mar Biol. 1999, 135: 181-189. 10.1007/s002270050615.View ArticleGoogle Scholar
- Jewett-Smith J, McMillan C, Kenworthy WJ, Bird K: Flowering and genetic banding patterns of Halophila johnsonii and conspecifics. Aquat Bot. 1997, 59: 323-331. 10.1016/S0304-3770(97)00064-8.View ArticleGoogle Scholar
- Travis SE, Sheridan P: Genetic structure of natural and restored shoalgrass Halodule wrightii populations in the NW Gulf of Mexico. Mar Ecol Prog Ser. 2006, 322: 117-127.View ArticleGoogle Scholar
- Waycott M, Barnes PAG: AFLP diversity within and between populations of the Caribbean seagrass Thalassia testudinum (Hydrocharitaceae). Mar Biol. 2001, 139: 1021-1028. 10.1007/s002270100670.View ArticleGoogle Scholar
- Nguyen XV, Thangaradjou T, Papenbrock J: Genetic variation among Halophila ovalis (Hydrocharitaceae) and closely related seagrass species from the coast of Tamil Nadu, India – an AFLP fingerprint approach. Syst Biodiver. 2013, 11: 467-476. 10.1080/14772000.2013.838317.View ArticleGoogle Scholar
- Reusch TBH: Microsatellites reveal high population connectivity in Eelgrass (Zostera marina) in two contrasting coastal areas. Limnol Oceanogr. 2002, 47: 78-85. 10.4319/lo.2002.47.1.0078.View ArticleGoogle Scholar
- Reynolds LK, McGlathery KJ, Waycott M: Genetic diversity enhances restoration success by augmenting ecosystem services. PLoS ONE. 2012, 7: e38397-10.1371/journal.pone.0038397.PubMedPubMed CentralView ArticleGoogle Scholar
- Krauss SL: Complete exclusion of nonsires in an analysis of paternity in a natural plant population using amplified fragment length polymorphism (AFLP). Mol Ecol. 1999, 8: 217-226. 10.1046/j.1365-294X.1999.00552.x.View ArticleGoogle Scholar
- Law JR, Donini P, Koebner RMD, Jones CR, Cooke RJ: DNA profiling and plant variety registration III: the statistical assessment of distinctness in wheat using amplified fragment length polymorphisms. Euphytica. 1998, 10: 335-342.View ArticleGoogle Scholar
- Kimberly AS, Toonen RJ: Microsatellites for ecologists: a practical guide to using and evaluating microsatellite markers. Ecol Lett. 2006, 9: 615-629. 10.1111/j.1461-0248.2006.00889.x.View ArticleGoogle Scholar
- Park SDE: The excel microsatellite toolkit: Trypanotolerance in West African cattle and the population genetic effects of selection (Ph.D. thesis). 2001, University of Dublin, Available at http://animalgenomics.ucd.ie/sdepark/ms-toolkit,Google Scholar
- Goudet J: FSTAT, a program to estimate and test gene diversities and fixation indices (version 2.9.3). 2001, Updated from Goudet (1995). Available from http://www2.unil.ch/popgen/softwares/fstat.htm,Google Scholar
- Slatkin M: A measure of population subdivision based on microsatellite allele frequencies. Genetics. 1995, 139: 457-462.PubMedPubMed CentralGoogle Scholar
- Excoffier L, Lischer HEL: Arlequin suite ver. 3.5: a new series of programs to perform population genetics analyses under Linux and Windows. Mol Ecol Resour. 2010, 10: 564-567. 10.1111/j.1755-0998.2010.02847.x.PubMedView ArticleGoogle Scholar
- Excoffier L, Smousee PE, Quanttro JM: Analysis of molecular variance inferred from metric distances among DNA haplotypes: application to human mitochondrial DNA restriction data. Genetics. 1992, 131: 479-491.PubMedPubMed CentralGoogle Scholar
- Felsenstein J: PHYLIP - phylogeny inference package (Version 3.2). Cladistics. 1989, 5: 164-166.Google Scholar
- Morariu VI, Srinivasan BV, Raykar VC, Duraiswami R, Davis LS: Automatic online tuning for fast Gaussian summation. Advances in Neural Information Processing Systems (NIPS). 2008, http://tree.bio.ed.ac.uk/software/figtree/,Google Scholar
- Tamura K, Peterson D, Peterson N, Stecher G, Nei M, Kumar S: MEGA5: molecular evolutionary genetics analysis using maximum likelihood, evolutionary distance, and maximum parsimony methods. Mol Bio Evol. 2011, 28: 2731-2739. 10.1093/molbev/msr121.View ArticleGoogle Scholar
- Rousset F: Genepop’007: a complete re-implementation of the genepop software for Windows and Linux. Mol Ecol Resour. 2008, 8: 103-106. 10.1111/j.1471-8286.2007.01931.x.PubMedView ArticleGoogle Scholar
- Bonin A, Ehrich D, Manel S: Statistical analysis of amplified fragment length polymorphism data: a toolbook for molecular ecologists and evolutionists. Mol Ecol. 2007, 16: 3737-3758. 10.1111/j.1365-294X.2007.03435.x.PubMedView ArticleGoogle Scholar
- Nei M: Analysis of gene diversity in subdivided populations. Proc Natl Acad Sci USA. 1973, 70: 3321-3323. 10.1073/pnas.70.12.3321.PubMedPubMed CentralView ArticleGoogle Scholar
- Annaletchumy L, Japar S, Muta HH, Arshad A: Morphology of Halophila ovalis (R.Br.) Hook. f. from Peninsular and East Malaysia. Pertanika J Trop Agric Sci. 2005, 28: 1-12.Google Scholar
- Hedge S, Smith N, Unsworth RKF: Report to the Marine and Tropical Sciences Research Facility. Temporal and spatial morphological variability of the seagrasses Halophila ovalis and Halodule uninervis throughout the Great Barrier Reef region: Preliminary analysis. 2009, Cairns: Reef and Rainforest Research Centre LimitedGoogle Scholar
- Short F, Carruthers T, Dennison W, Waycott M: Global seagrass distribution and diversity: a bioregional model. Exp Mar Biol Ecol. 2007, 350: 3-20. 10.1016/j.jembe.2007.06.012.View ArticleGoogle Scholar
- Miki S: On the sea-grasses in Japan (II). Cymodocea and marine Hydrocharitaceae. Bot Mag Tokyo. 1934, 48: 131-141.View ArticleGoogle Scholar
- Soe-Htun U, San-Tha-Htun U, Mu-Mu-Aye D, Ni-Ni-Win D, Lei-Lei-Win D, Ohno M: Notes on seagrasses along Myanmar Coastal Regions. Bull Mar Sci Fish, Kochi Uni. 2001, 21: 13-22.Google Scholar
- Chen LY, Chen JM, Gituru RW, Wang QF: Generic phylogeny, historical biogeography and character evolution of the cosmopolitan aquatic plant family Hydrocharitaceae. BMC Evol Biol. 2012, 12: 30-10.1186/1471-2148-12-30.PubMedPubMed CentralView ArticleGoogle Scholar
- Wyllie-Echeverria S, Talbot SL, Rearick JR: Genetic structure and diversity of Zostera marina (eelgrass) in the San Juan Archipelago, Washington, USA. Estuar Coast Shelf Sci. 2010, 33: 811-827. 10.1007/s12237-009-9243-z.View ArticleGoogle Scholar
- Diekmann OE, Coyer JA, Ferreira J, Olsen JL, Stam WT, Pearson GA, Serrão EA: Population genetics of Zostera noltii along the west Iberian coast: consequences of small population size, habitat discontinuity and near-shore currents. Mar Ecol Prog Ser. 2005, 290: 89-96.View ArticleGoogle Scholar
- Procaccini G, Orsini L, Ruggiero MV, Scardi M: Spatial patterns of genetic diversity in Posidonia oceanica, an endemic Mediterranean seagrass. Mol Ecol. 2001, 10: 1413-1421. 10.1046/j.1365-294X.2001.01290.x.PubMedView ArticleGoogle Scholar
- Alberto F, Arnaud-Haond S, Duarte CM, Serrão EA: Genetic diversity of a clonal angiosperm near its range limit: the case of Cymodocea nodosa at the Canary Islands. Mar Ecol Prog Ser. 2006, 309: 117-129.View ArticleGoogle Scholar
- Alberto F, Massa S, Manent P, Diaz-Almela E, Arnaud-Haond S, Duarte CM, Serrão EA: Genetic differentiation and secondary contact zone in the seagrass Cymodocea nodosa across the Mediterranean–Atlantic transition region. Biogeosciences. 2008, 35: 1279-1294.Google Scholar
- Li Y, Song N, Li WT, Gao TX: Population genetics of Zostera marina Linnaeus (Zosteraceae) based on AFLP analysis. Biochem Sys Ecol. 2012, 44: 216-223.View ArticleGoogle Scholar
- Hamrick JL, Godt MJW: Conservation genetics of endemic plant species. Conservation Genetics. Edited by: Avise JC, Hamrick JL. 1996, New York: Chapman and Hall, 281-304.View ArticleGoogle Scholar
- Morton B, Blackmore G: South China Sea. Mar Pollut Bull. 2001, 42: 1236-1263. 10.1016/S0025-326X(01)00240-5.PubMedView ArticleGoogle Scholar
- Mackill DJ, Zhang Z, Redona ED: Level of polymorphism and genetic mapping of AFLP markers in rice. Genome. 1996, 39: 969-977. 10.1139/g96-121.PubMedView ArticleGoogle Scholar
- Singh M, Chabane K, Valkoun J, Blake T: Optimum sample size for estimating gene diversity in wild wheat using AFLP markers. Genet Resour Crop Evol. 2006, 53: 23-33. 10.1007/s10722-004-0597-6.View ArticleGoogle Scholar
- Chase MW, Hills HH: Silica gel: an ideal material for field preservation of leaf samples for DNA studies. Taxon. 1991, 40: 215-220. 10.2307/1222975.View ArticleGoogle Scholar
- Stanford AM, Harden R, Parks CR: Phylogeny and biogeography of Juglans (Juglandaceae) based on matK and ITS sequence data. Am J Bot. 2000, 87: 872-882. 10.2307/2656895.PubMedView ArticleGoogle Scholar
- White TJ, Bruns T, Lee S, Taylor JW: Amplification and direct sequencing of fungal ribosomal RNA genes for phylogenetics. PCR Protocols: A Guide to Methods and Applications. Edited by: Innes MA, Delfand DH, Sninsky JJ, White TJ. 1990, New York: Academic Press, 315-322.Google Scholar
- Vos P, Hogers R, Bleeker M, Reijans M, Miranda H, Frijters A, Pot J, Peleman J, Kuiper M, Zabeau M: AFLP: a new technique for DNA fingerprinting. Nucleic Acids Res. 1995, 23: 4407-4414. 10.1093/nar/23.21.4407.PubMedPubMed CentralView ArticleGoogle Scholar
- Xu NN, Yu S, Zhang FG, Tsang PKE, Chen XY: Microsatellite primers for Halophila ovalis and cross-amplification in H. minor (Hydrocharitaceae). Am J Bot. 2010, 97: e56-e57. 10.3732/ajb.1000111.PubMedView ArticleGoogle Scholar
- Meudt MH, Clarke CA: Almost forgotten or latest practice? AFLP applications, analyses and advances. Trends Plant Sci. 2007, 12: 106-117. 10.1016/j.tplants.2007.02.001.PubMedView ArticleGoogle Scholar
- Pruett LC, Winker K: The effects of sample size on population genetic diversity estimates in song sparrows Melospiz amelodia. Avian Biol. 2008, 39: 252-256. 10.1111/j.0908-8857.2008.04094.x.View ArticleGoogle Scholar
- Posada D: jModel test: phylogenetic model averaging. Mol Biol Evol. 2008, 25: 1253-1256. 10.1093/molbev/msn083.PubMedView ArticleGoogle Scholar
- Saitou N, Nei M: The neighbor-joining method: a new method for reconstructing phylogenetic trees. Mol Biol Evol. 1987, 4: 406-425.PubMedGoogle Scholar
- Felsenstein J: Phylogenies from restriction sites: a maximum-likelihood approach. Evolution. 1992, 46: 159-173. 10.2307/2409811.View ArticleGoogle Scholar
- Ronquist F, Huelsenbeck J, Teslenko M: MRBAYES 3.2: Bayesian phylogenetic inference under mixed models. Bioinformatics. 2011, 19: 1572-1574.View ArticleGoogle Scholar
- Nye TMW: Trees of trees: an approach to comparing multiple alternative phylogenies. Syst Biol. 2008, 57: 785-794. 10.1080/10635150802424072.PubMedView ArticleGoogle Scholar
- Clement M, Posada D, Crandall KA: TCS: a computer program to estimate gene genealogies. Mol Ecol. 2000, 9: 1657-1659. 10.1046/j.1365-294x.2000.01020.x.PubMedView ArticleGoogle Scholar
- Dice LR: Measures of the amount of ecologic association between species. Ecology. 1945, 26: 297-302. 10.2307/1932409.View ArticleGoogle Scholar
- Hampl V, Pavlicek A, Flegr J: Construction and bootstrap analysis of DNA fingerprinting based phylogenetic trees with the freeware program FreeTree: application to Trichomonad parasites. Inter J Syst Evol Microbiol. 2001, 51: 731-735. 10.1099/00207713-51-3-731.View ArticleGoogle Scholar
- Rohlf FJ: NTSYS-pc: Numerical Taxonomy and Multivariate Analysis System version 2.2 Exeter Software. 2005, Setauket New York, Available at http://www.exetersoftware.com/cat/ntsyspc/ntsyspc.html,Google Scholar
- Nei M, Chesser RK: Estimation of fixation indices and gene diversities. Anna Hum Genet. 1983, 47: 253-259. 10.1111/j.1469-1809.1983.tb00993.x.View ArticleGoogle Scholar
- Yeh FC, Yang R, Boyle TJ, Ye Z: Popgene 32, Microsoft Ware Windows based Freeware for Population Genetic Analysis Version 1.32. 2000, University of Alberta, Edmonton, Alberta, Canada: Molecular Biology and Biotechnology Centre, Available at http://www.ualberta.ca/~fyeh/popgene_download.html,Google Scholar
- Williams SL, Orth RJ: Genetic diversity and structure of natural and transplanted eelgrass populations in the Chesapeake and Chincoteague Bays. Estuaries. 1998, 21: 118-128. 10.2307/1352551.View ArticleGoogle Scholar
- Guo SW, Thompson EA: Performing the exact test of Hardy-Weinberg proportion for multiple alleles. Biometrics. 1992, 48: 361-372. 10.2307/2532296.PubMedView ArticleGoogle Scholar
- Mantel N: The detection of disease clustering and a generalized regression approach. Cancer Res. 1967, 27: 209-220.PubMedGoogle Scholar
- Nei M: Estimation of average heterozygosity and genetic distance from a small number of individuals. Genetics. 1978, 89: 583-590.PubMedPubMed CentralGoogle Scholar
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