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. 2016 Dec 30;21(1):37–44. doi: 10.1080/19768354.2016.1271012

Metagenomic analysis of the marine coastal invertebrates of South Korea as assessed by Ilumina MiSeq

Hyewon Kim 1, Hyunkyong Kim 1, Hee Seung Hwang 1, Won Kim 1,CONTACT
PMCID: PMC6138350  PMID: 30460050

ABSTRACT

Metagenomic analysis was carried out for the first time on the marine coastal invertebrates of South Korea. Samples collected from coastal areas of Korea were filtered through a 63 µm mesh, then their 18S rDNA V4 regions were amplified. High-throughput sequencing of PCR amplicons using Illumina MiSeq and BLAST against the SILVA database showed that a total of 319 eukaryotic Operational Taxonomic Units were identified at the species level. Annelida, Arthropoda, Mollusca, Nematoda, and Platyhelminthes and for 92.23% of the total 103 metazoan species belonging to 101 genera, 75 families, and 10 phyla. Of these, several taxa previously unreported to exist in Korea were detected at the family level compared with the integrated database from three main Korean biodiversity DBs (MABIK, KOMBIS, and MRBR). Analysis of beta diversity of the community structure of invertebrates indicated that the composition of marine invertebrates is likely to be affected by habitat type rather than geographical distance. The present study showed that metagenomic high-throughput technology can be used to unravel species diversity and for various studies regarding marine invertebrate community structure.

KEYWORDS: High-throughput technology, 18S rDNA V4 regions, biodiversity, meta amplicon analysis

Introduction

Invertebrates are known to represent over 97% of the number of species in animal kingdom (Buchsbaum et al. 2013). They exist in every ecosystem on earth. Their astronomical numbers, ubiquity, and diversity are known to play significant roles in ecological processes. Some invertebrates (e.g. nematodes and copepods) are useful as indicators of climate change by virtue of their sensitive response to environmental changes. Moreover, it is important to consider invertebrate community structure and diversity when determining effective strategies for restoration and monitoring programs (Jansen 1997). According to reports by the CoML (Census of Marine Life), a research network of global marine life diversity and distribution, South Korean marine ecosystems have an incredibly high average of 32.3 species per 1 km2 (Costello et al. 2010). Though data indicate a high abundance of marine life in South Korea, precise information about the number of marine organisms living in Korea is lacking because of insufficient taxonomic expertise and poor infrastructure for studies on biodiversity. Additionally, traditional morphological methods are not sufficient to estimate the diversity of marine life, so many taxonomic groups and species still remain undescribed.

The Korean peninsula is situated primarily in the temperate zone; however, according to the distribution pattern of biota, the southern regions of the peninsula seem to be influenced by the subtropical climate. Several studies have indicated the occurrence of climatic regime shifts in the northern Pacific Ocean, which are likely to have profound effects on marine ecosystems (Tian et al. 2006). Therefore, climate changes as well as habitat loss and organismal invasion resulting from anthropogenic effects on the Korean peninsula should be routinely monitored in order to uncover the causes of environmental change.

Along with increased demand for high-throughput technology, metagenomic studies of microorganisms have attracted much interest in the past decade. The term ‘metagenomics’ was suggested in 1998; since then, the number of metagenomics studies using high-throughput technology has dramatically increased. Previously, metagenomic studies mainly focused on non-eukaryotic organisms (usually bacteria) and were based on 16S rDNA. Though the eukaryote-targeted approach has a relatively short history, this approach has been applied to studies of phytoplankton and metazoans as represented by recent studies on such organisms, including the following: phytoplankton (Cuvelier et al. 2010; Worden et al. 2012; Faria et al. 2014; Boopathi et al. 2015), nematodes (Porazinska et al. 2009, 2010a, 2010b), copepods (Hirai et al. 2015; Shoemaker & Moisander 2015), macroinvertebrate larvae (Carew et al. 2013), and microinvertebrates of multiple taxa (Chariton et al. 2010; Bik et al. 2012). These studies demonstrated the feasibility of using high-throughput analysis to assess marine invertebrate diversity, even at the species level.

Here, we present the first metagenomic analysis of the marine coastal invertebrates of South Korea based on the sequencing results of the 18S rDNA V4 region using Illumina MiSeq. These analyses were carried out to test ideas on the utility of high-throughput technology as a tool to unravel previously undescribed species as well as to monitor marine biodiversity and to compare community structure in different localities.

Materials and methods

Sample collection

Samples were obtained from intertidal zones at 14 sites in South Korea: DangJin (DJ), GunSan (GS), GoChang (GC), MokPo (MP), JinDo (West) (JDW), JinDo (East) (JDE), YeoSu (YS), TongYong (TY), BuSan (BS), PoHang (PH), UlJin (UJ), YangYang (YY), JeJu (JJ), and SeoGuipo (SG) (Figure 1), from 6 August to 9 2015. Three subsamples were taken from each 14 sites randomly with 3–5 kg using 3 inches by 12 inches shovel (distance between subsamples was approximately 15 m, sampling depth: 5 cm). Materials were filtered through a 63 µm mesh sieve and preserved on ice until DNA work. After sieving, the samples were extracted using the Ludox HS40 protocol (Burgess 2001).

Figure 1.

Figure 1.

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Collection sites of samples used in this study.

Dna extraction, PCR, and Illumina MiSeq

Total DNA was extracted using the Power Soil® DNA Isolation Kit (Mo Bio, Carlsbad, CA USA), following the manufacturer’s instructions. For amplification of the 18S rDNA partial V4 hypervariable region, the D512 with Illumina forward overhang adapter sequence and D978 with Illumina reverse overhang sequence primer sets were used (Zimmermann & Gemeinholzer 2011). Reactions occurred in a total volume of 20 μl consisting of 0.1 ug of environmental DNA template, 5 μl of 5X color Go Taq buffer (Promega, Madison City, USA), 1 μl of dNTP mixture (10 mM), 1 μl of each primer (10 uM), and 0.3 μl of Go Taq DNA polymerase (Promega, Madison City, USA). PCR conditions for amplification were an initial denaturation at 94°C for 3 min, followed by 35 cycles at 94°C for 45 s, annealing at 50°C for 45 s, extension at 72°C for 90 s, and final extension at 72°C for 10 min. PCR products were confirmed through visualization on a 1.5% agarose gel and purified with the QIAquick® PCR Purification Kit (QIAGEN, Hilden, Germany). Raw data from Illumina MiSeq of PCR amplicons were performed using a commercial service at Macrogen (Seoul, Korea), and is available from the MADBK (Marine Arthropod Depository Bank of Korea) database (www.madbk.org/bbs.php?table=free_board).

Data analysis

Paired-end read sequences generated from Illumina MiSeq were processed using Qiime (Quantitative Insights Into Microbial Ecology) 1.9.1v pipeline (Caporaso et al. 2010). All reads were checked and trimmed for quality (maximum number of homopolymers = 6; minimum length = 200 bp). After initial filtering, OTUs (Operational Taxonomic Units) were clustered with cutoff values of 97% similarity using Usearch (http://www.drive5.com/usearch/). Each read was searched by BLAST against the SILVA 108 database (http://arb-silva.de/, Nov 2015). In order to minimize error, singleton reads were removed. Both alpha and beta diversity were analyzed as described in the Qiime software manual. Rarefaction curves (observed species richness) were performed for comparison of OTU richness between different samples. PCoA (Principal Coordinates Analysis) plot and UPGMA (unweighted pair group method with arithmetic mean) tree were constructed by unweighted pairwise UniFrac distance matrices for beta diversity analysis. Taxon which is not informed enough to classify such as ‘unclassified eukaryota’ or ‘unclassified eukaryotic taxon name’ were summed into Unclassified eukaryotes. Sequences classified into the bacterial kingdom were excluded from subsequent diversity analysis.

Results

A total of 1,854,290 raw data reads were generated from Illumina MiSeq. 436,154 high-quality reads remained after trimming and filtering, with a range of 25,637–38,463 reads per sample. Analysis of alpha and beta diversity were measured. Alpha diversity shows species richness within the site, and beta diversity is important for understanding the difference between sites.

Alpha diversity analysis

All rarefaction curves (Figure 2) of observed species richness likely reached near-saturation. Following the view that observed species richness is usually correlated with total OTU richness, PH, with 334 OTUs, showed the highest species richness, while GC had the lowest number of OTUs, 49. The rarefaction curves also showed that the site with the second highest OTU richness was YY, with 321, followed closely by SG and UJ, both with 314, and then by JDW, TY, BS, JDE, JJ, GS, DJ, MP, YS, and GC, with 308, 297, 247, 236, 190, 178, 175, 170, 87, and 49 was estimated, respectively.

Figure 2.

Figure 2.

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Rarefaction curves showing observed species richness in samples taken from the 14 sites : Dangjin (DJ), Gunsan (GS), Gochang (CG), Mokpo (MP), Jindo (West) (JDW), Jindo (East) (JDE), Yeosu (YS), Tongyong (TY), Busan (BS), Pohang (PH), Uljin (UJ), Yangyang (YY), Jeju (JJ), and Seoguipo (SG).

Beta diversity analysis

Beta diversity of invertebrates in coastal areas of South Korea including Jeju Island, as assessed by the 18S rDNA V4 region, were analyzed with a PCoA plot and a UPGMA tree (Figure 3(A, B)). The PCoA plot was used to visualize similarities and clustering patterns in the data. Similarities and dissimilarities were determined by unweighted pairwise UniFrac distance values. The two axes of the PCoA analysis indicated 24.97% and 11.85% of the total variance of the invertebrate communities, respectively. Before analysis, study sites were divided into two large groups based on habitat characteristics: an eastern-type group (blue circle) and a western-type group (red rectangle). The habitat of the eastern-type group consists of sandy shores and sublittoral rocks with epiphytic algae, whereas that of the western-type group is mud flats except in the case of JDE, which is made up of sand without rocks and algae. The PCoA plot indicated that the 14 sites could be divided into four different groups by species composition: (1) eastern-type group (UJ, BS, YY, PH, JJ, YS), (2) major western-type (GC, DJ, GS, JDW, TY) group, (3) minor western-type group (SG, MP), and (4) JDE only (Figure 3(A)). For further comparison of community structure, we subjected the 18S rDNA sequences to analysis with a UPGMA tree. The resulting tree was consistent with the PCoA plot (Figure 3(B)).

Figure 3.

Figure 3.

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Beta diversity analysis of unweighted UniFrac distance scores. (a) PCoA plot and (b) UPGMA tree : Dangjin (DJ), Gunsan (GS), Gochang (CG), Mokpo (MP), Jindo (West) (JDW), Jindo (East) (JDE), Yeosu (YS), Tongyong (TY), Busan (BS), Pohang (PH), Uljin (UJ), Yangyang (YY), Jeju (JJ), and Seoguipo (SG).

Community structure of marine coastal eukaryotes

The abundance of eukaryotic taxonomic groups in the coastal areas of Korea was revealed in the present study. Amongst metazoans, Annelida was the most common, followed by Arthropoda, Mollusca, Nematoda, and Platyhelminthes. Amongst protists, Stramenopile was the most frequently seen phylum, followed by Ciliophora, Cerozoa, Apicomplexa, and Myzozoa. Of fungi, Ascomycota, Chytridiomycota, and Microsporidia were the common taxa.

The community structures from 14 samples taken from various localities are shown in Figure 4 (Community structures of each site: Supplementary Information 1). The major western-type group (DJ, GC, GS, JDW, and TY) had a greater percentage of metazoans (49%, 95%, 68%, 27%, and 40%, respectively) than did the eastern-type group (JJ, YS, PH, UJ, YY, and BS; 2%, 2%, 21%, 22%, 3%, and 0%, respectively). The maximum proportion of metazoans in the eastern-type group (22%, UJ) was lower than the minimum proportion of metazoans in the western-type group (27%, JDW). Conversely, the proportions of protists and algae were higher in the eastern-type group than in the western-type group. The average percentage of protists and algae in the eastern-type group were 42% and 35.17%, respectively (range, 18–80% and 5–66%, respectively), whereas the western-type group consisted on average of 18% protists and 11.2% algae (range, 1–47% and 4–30%, respectively). Relatively equal numbers of metazoans and protists were observed in JDE, which is located in the middle of the PCoA plot. The minor western-type group (SG, MP) showed considerable occupation by unclassified eukaryotes; about 74% of specimens from MP and 42% of those from SG fell into the category of ‘unclassified eukaryotes’.

Figure 4.

Figure 4.

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Proportions of eukaryotic organisms by taxon in 14 samples taken from different South Korean coastal area.

Discussion

Tool for unraveling undescribed species

The top five metazoan phyla (Nematoda, Annelida, Arthropoda, Mollusca, and Platyhelminthes) were mainly considered in the present study (Species information: Supplementary Information 2). The phylum Nematoda, consisting of about 25,000 described species, is one of the most diverse in the animal kingdom, but it is often difficult to identify nematodes accurately because of their microscopic size. Since the description of the first Draconematidae (Tenuidraconema koreense) from Korea (Rho & Kim 2004), 4 unrecorded species and 36 new species have been reported. However, taxonomic studies of nematodes in Korea have been highly focused on the family Draconematidae (85%, 34 out of 40 species). In addition, draconematids are mostly collected from areas with a water depth of 50–200 m with relatively coarse sedimentation by SCUBA divers. In the present study, six novel families were obtained from the intertidal zone (usually made up of fine mud): Chromadoridae, Anoplostomatidae, Enchelidiida, Oxystominidae, Tripyloidae, and Selachinematidae, none of which had yet been reported in Korea. The suitability of high-throughput technology for assessing soil nematode diversity has been demonstrated by previous metagenomic studies on nematodes (Porazinska et al. 2010a, 2010b). For this reason, the metagenomic approach to discovering undescribed marine nematodes seems to be quite promising.

The phylum Annelida is generally divided into two classes, Clitellata (mostly freshwater organisms) and Polychaeta (mostly marine organisms). Approximately 9000 species are known worldwide (Russell et al. 2016). The present study focused on the marine taxon, Polychaeta. Although they are present in great abundance, polychaetes are easily broken and damaged during the collection process, making them difficult to identify morphologically. Three species (Diopatra neapolitana, Ceratocephale osawai, and Sternaspis costata) as reported by Kamita and Sato (1941) are the first recorded cases of Korean polychaetes. Since then, Paik 1981 reviewed 265 species, and about 300 polychaete species have been reported in Korea so far. However, due to their similar morphologies and susceptibility to damage, the exact number of polychaete species in Korea is unclear. There is a huge discrepancy between the 300 recorded species and the 593 species that should be found according to an integration of three databases [the Marine Biodiversity Institute of Korea (MABIK) DB, the Korea Marine Biodiversity Information System (KOMBIS) DB, and the Marine BioResources Bank (MRBR) DB]. In this study, 4 clitellate and 15 polychaete families were observed. Compared with the integrated database, three families (Enchytraeidae, Propappidae, and Tubificidae) of clitellates and three families (Protodrilidae, Saccocirridae, and Potamodrilidae) of polychaetes were novel in Korea. For this reason, by accumulating well-designed high-throughput data we can gain new insight into the true number of species living in Korea.

The phylum Arthropoda is known as the largest and the most successful group in the animal kingdom. Eight families of Maxillopoda (Acartiidae, Candaciidae, Centropagidae, Clausocalanidae, Pontellidae, Ameiridae, Canthocamptidae, and Dirivultidae) and five families of Ostracoda (Cyclocyprididae, Cyprididae, Cytheridae, Leptocytheridae, and Loxoconchidae) belonging to Crustacea were detected in the present study. Of these, four species in four families (Dirivultidae, Cyclocyprididae, Cytheridae, and Loxoconchidae) are previously undescribed in Korea. All detected species are relatively small (<20 mm) crustaceans, such as calanoids, harpacticoids, siphonostomatoids, and podocopids. However, some taxa observed in the filtered samples, such as mysids and amphipods, were not detected in the metagenomic analysis. This may be caused by the primers or PCR conditions used in this study being ineffective for amplification of DNA from those species. Therefore, use of alternative primer combinations or other genetic markers or PCR optimizations might be appropriate. Mitochondrial cytochrome c oxidase subunit I (COI) is a good candidate as a marker because of its widespread use in the identification of arthropods, not only in individual DNA barcoding but also in targeted metagenetic analysis (Hajibabaei et al. 2011; Yu et al. 2012; Carew et al. 2013; Hirai et al. 2015).

The phylum Mollusca is extraordinarily varied, and more than 100,000 species have been described (Russell et al. 2016). Nine families (Laternulidae, Cardiidae, Carditidae, Tellinidae, Glauconomidae, Veneridae, Mytilidae, Ostreidae, and Pectinidae) belonging to Bivalvia and 11 families (Viviparidae, Calyptraeidae, Naticidae, Truncatellidae, Philinoglossidae, Aglajidae, Otinidae, Lymnaeidae, Placobranchidae, Haliotidae, and Turbinidae) belonging to Gastropoda were observed in this study. Of these, four families, Philinoglossidae, Otinidae, Lymnaeidae, and Placobranchidae, had not previously been reported in Korea. As in the case of the polychaetes mentioned above, the exact number of molluscan species in Korea is now unclear, because there is a discrepancy between the 1681 species listed in the catalogue of molluscan fauna published by Min (2004) and the 2213 species calculated from the integrated database. The identification of mollusc species is quite important for several reasons. First, they are indispensable for assessing marine biodiversity because of their high abundance. Second, they are frequently used as an indicator of heavy metal pollution, as their feeding behavior leads to the accumulation of heavy metals in their tissues (Wang et al. 2005). Third, recently, they have been used for metagenomic studies characterizing communities of microorganisms found on internal organs versus those found on external surfaces of marine animals (King et al. 2012; Chauhan et al. 2014).

Platyhelminthes, commonly known as flatworms, consist of about 20,000 species (Russell et al. 2016). The taxonomic study of Platyhelminthes is lacking in Korea because of difficulties in identifying them based on their relatively simple morphology. There were no common families when sequencing data from the present study were compared with the integrated database. All detected families were novel in Korea; these were Macrostomidae, Dalyelliidae, Pterastericolidae, Polycystididae, Karkinorhynchidae, Promesostomidae, Solenopharyngidae, Monocelididae, Dendrocoelidae, and Dugesiidae. These results suggest that intensive taxonomic studies on Platyhelminthes in Korea are urgently needed.

We compared species list as assessed by our sequencing data with that assessed by the integration of three databases (MABIK, KOMBIS, and MRBR) concerning four phyla (Annelida, Arthropoda, Mollusca, Platyhelminthes). The phylum Nematoda was analyzed with our personal database, since the three DBs are not updated to reflect the results of recent nematode studies. In the process of creating the integrated database, duplicated species names were removed, but this is not the case for some synonymized species. For this reason, currently, the integrated database is not perfect, so continuous updates to it are needed.

Linking community structure to habitat

Eastern-type sites and western-type sites differed in terms of physical habitat. Accordingly, small invertebrates tended to exist more in western mud flats, while protists and algae covered a large proportion of the rocky shores found in the east. Thus, community structure seems to be influenced by habitat rather than simple geographical distance. Another interesting result of our study is that although all sample sequences from the eastern-type group were clustered together, samples taken from MP and SG (the minor western-type group) were distinct from those taken from the major western-type group. It is not easy to explain this clustering pattern based on current information, since a large number of sequences of MP and SG fell into ‘unclassified eukaryotes’. One possible reason has to do with their metropolitan characteristics. MP is a city located on the southwest coast of Korea which serves as a crucial port for international shipping and commercial transportation. Anthropogenic activities in this industrial port city may result in the different clustering pattern observed. Also, physicochemical factor such as pH concentration, organic concentration, and seasonal change in plankton which could affect community structure should be considered in future studies. Of course, though the composition of community structure seemed to be largely affected by habitat, it should be also noted that the same habitat does not guarantee the same community structure.

Tool for biomonitoring

Marine invertebrates, which are widespread and have high diversity and varying sensitivities to environmental disturbances, are useful in evaluating the health of ecosystems. However, the traditional taxonomic approach based on morphological characteristics is too laborious, costly, and time-consuming for routine biomonitoring. The metagenomic approach is expected to overcome these disadvantages and also is a more environmentally friendly process because it requires only a few materials.

Certain biomass measurements of marine invertebrates such as the ratio of copepods to nematodes (Raffaelli & Mason 1981), the biomass of polychaetes, and also the inter- relatedness between these biomass measurements and organic enrichment are suggested to be useful as biomonitoring tools (Molluscs, Gray et al. 2002; Nematodes, Nilson and Rosenberg 2000). In the metagenomic approach, the number of OTUs does not exactly reflect the number of species because of certain limitations of high-throughput sequencing and analysis; however, the two are positively correlated (Porazinska et al. 2009). Metagenomic analysis of phytoplankton using pyrosequencing as a biomonitoring tool has been performed several times in Korea (Faria et al. 2014, Boopathi et al. 2015). Thus, if various pysico-chemical conditions such as the concentration of heavy metals, TOC (total organic carbon), and pH are considered together, the metagenomic approach to biomonitoring of marine invertebrates is quite promising.

Conclusion

The present metagenomic analysis of marine coastal invertebrates in Korea led to two important conclusions. First, the present study showed that high-throughput technology can be used as a tool for identifying previously undescribed species as well as monitoring marine biodiversity. This approach makes it possible to determine the priorities of taxonomic research by revealing not only novel taxonomic groups but also ones lacking in data. Moreover, high-throughput technology can help to build a more reliable reference database that will in turn enable estimation of the true number of species by accumulating sequencing data through further molecular taxonomic study. Second, community structure is more likely to be influenced by habitat rather than geographical distance. In the present study, two major habitats grouped by habitat similarity were divided into four clustered groups according to different community structures. However, similar habitats do not always have similar community structures, because of insufficient taxonomic information. Future studies on high-throughput technology should consider additional aspects: (1) more detailed sampling including taxon-targeted sampling should be conducted to clarify the variations in community structure across marine ecosystems; (2) other primer sets should be employed because the choice of primers may have a strong influence on the PCR yield; and (3) relatively long DNA fragments such as COI and LSU should be employed for cross-checking of the results obtained from the short DNA fragments used in the present study. Altogether, the metagenomic approach is a promising tool for the investigation of marine invertebrate diversity in Korea if the integrated database is continuously updated.

Funding Statement

This study was supported by a grant, Genome Analysis of Marine Organisms and Development of Functional Applications, funded by the Ministry of Oceans and Fisheries, and a grant, Development of DNA Analysis System and Database from Marine Organisms, from the Korean Coast Guard Research Center.

Acknowledgments

We would like to thank Jee Eun Park (Macrogen) and Sung Joon Song (Seoul National University) for their generous support of this work.

Disclosure statement

No potential conflict of interest was reported by the authors.

References

  1. Bik HM, Sung W, de Ley PD, Baldwin JG, Sharma J, Rocha-olivares A, Thomas WK. 2012. Metagenetic community analysis of microbial eukaryotes illuminates biogeographic patterns in deep-sea and shallow water sediments. Mol Ecol. 21:1048–1059. doi: 10.1111/j.1365-294X.2011.05297.x [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Boopathi T, Lee JB, Youn SH, Ki JS. 2015. Temporal and spatial dynamics of phytoplankton diversity in the East China Sea near Jeju Island (Korea): a pyrosequencing-based study. Biochem Syst Ecol. 63:143–152. doi: 10.1016/j.bse.2015.10.002 [DOI] [Google Scholar]
  3. Buchsbaum R, Buchsbaum M, Pearse J, Pearse V. 2013. Animals without backbones: an introduction to the invertebrates. Chicago: University of Chicago Press; 1–571. [Google Scholar]
  4. Burgess R. 2001. An improved protocol for separating meiofauna from sediments using colloidal silica sols. Mar Ecol Prog Ser. 214:161–165. doi: 10.3354/meps214161 [DOI] [Google Scholar]
  5. Carew ME, Pettigrove VJ, Metzeling L, Hoffmann AA. 2013. Environmental monitoring using next generation sequencing: rapid identification of macroinvertebrate bioindicator species. Front Zool. 10:45. doi: 10.1186/1742-9994-10-45. [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello EK, Knight R. 2010. Qiime allows analysis of high-throughput community sequencing data. Nat Methods. 7:335–336. doi: 10.1038/nmeth.f.303 [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Chariton AA, Court LN, Hartley DM, Colloff MJ, Hardy CM. 2010. Ecological assessment of estuarine sediments by pyrosequencing eukaryotic ribosomal DNA. Front Ecol Environ. 8:233–238. doi: 10.1890/090115 [DOI] [Google Scholar]
  8. Chauhan A, Wafula D, Lewis DE, Pathak A. 2014. Metagenomic assessment of the Eastern oyster-associated microbiota. Genome Announc. 2:e01083–e010114. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Costello MJ, Coll M, Danovaro R, Halpin P, Ojaveer H, Miloslavich P. 2010. A census of marine biodiversity knowledge, resources, and future challenges. PloS One. 5:e12110. doi: 10.1371/journal.pone.0012110 [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Cuvelier ML, Allen AE, Monier A, McCrow JP, Messié M, Tringe SG, Woyke, T, Welsh RM, Ishoey T, Lee JH, et al. . 2010. Targeted metagenomics and ecology of globally important uncultured eukaryotic phytoplankton. Proc Natl Acad Sci U S A. 107:14679–14684. doi: 10.1073/pnas.1001665107 [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Faria DG, Lee MD, Lee JB, Lee J, Chang M, Youn SH, Suh YS, Ki JS. 2014. Molecular diversity of phytoplankton in the East China Sea around Jeju Island (Korea), unraveled by pyrosequencing. J Oceanog. 70:11–23. doi: 10.1007/s10872-013-0208-2 [DOI] [Google Scholar]
  12. Gray JS, Wu RS, Or YY. 2002. Effects of hypoxia and organic enrichment on the coastal marine environment. Mar Ecol Prog Ser. 238:249–279. doi: 10.3354/meps238249 [DOI] [Google Scholar]
  13. Hajibabaei M, Shokralla S, Zhou X, Singer GA, Baird DJ. 2011. Environmental barcoding: a next-generation sequencing approach for biomonitoring applications using river benthos. PLoS One. 6:e17497. doi: 10.1371/journal.pone.0017497 [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Hirai J, Kuriyama M, Ichikawa T, Hidaka K, Tsuda A. 2015. A metagenetic approach for revealing community structure of marine planktonic copepods. Mol Ecol Resour. 15:68–80. doi: 10.1111/1755-0998.12294 [DOI] [PubMed] [Google Scholar]
  15. Jansen A. 1997. Terrestrial invertebrate community structure as an indicator of the success of a tropical rainforest restoration project. Restoration Ecol. 5:115–124. doi: 10.1046/j.1526-100X.1997.09714.x [DOI] [Google Scholar]
  16. Kamita T, Sato TN. 1941. Marine fauna at Jinsen Bay, Corea. J Chosen Nat Hist Soc. 8:1–3. doi: 10.1080/00222934108527182 [DOI] [Google Scholar]
  17. King GM, Judd C, Kuske CR, Smith C. 2012. Analysis of stomach and gut microbiomes of the eastern oyster (Crassostrea virginica) from coastal Louisiana, USA. PLoS One. 7:e51475. doi: 10.1371/journal.pone.0051475 [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Min DK. 2004. Mollusks in Korea. Seoul: Min Molluscan Research Institute. [Google Scholar]
  19. Nilsson HC, Rosenberg R. 2000. Succession in marine benthic habitats and fauna in response to oxygen deficiency: analysed by sediment profile-imaging and by grab samples. Mar Ecol Prog Ser. 197:139–149. doi: 10.3354/meps197139 [DOI] [Google Scholar]
  20. Porazinska DL, Giblin-Davis RM, Faller L, Farmerie W, Kanzaki N, Morris K, Powers TO, Tucker AE, Sung W, Thomas WK 2009. Evaluating high-throughput sequencing as a method for metagenomic analysis of nematode diversity. Mol Ecol Resour. 9:1439–1450. doi: 10.1111/j.1755-0998.2009.02611.x [DOI] [PubMed] [Google Scholar]
  21. Porazinska DL, Giblin-Davis RM, Esquivel A, Powers TO, Sung WAY, Thomas WK. 2010a. Ecometagenetics confirm high tropical rainforest nematode diversity. Mol Ecol. 19:5521–5530. doi: 10.1111/j.1365-294X.2010.04891.x [DOI] [PubMed] [Google Scholar]
  22. Porazinska DL, Sung WAY, Giblin-Davis RM, Thomas WK. 2010b. Reproducibility of read numbers in high-throughput sequencing analysis of nematode community composition and structure. Mol Ecol Resour. 10:666–676. doi: 10.1111/j.1755-0998.2009.02819.x [DOI] [PubMed] [Google Scholar]
  23. Raffaelli DG, Mason CF. 1981. Pollution monitoring with meiofauna, using the ratio of nematodes to copepods. Mar Pollut Bull. 12:158–163. doi: 10.1016/0025-326X(81)90227-7 [DOI] [Google Scholar]
  24. Rho HS, Kim W. 2004. Tenuidraconema koreensis, a new species of marine nematodes (Adenophorea: Desmodorida) from South Korea. Korean J Biol Sci. 8:155–163. doi: 10.1080/12265071.2004.9647748 [DOI] [Google Scholar]
  25. Russell PJ, Hertz PE, McMillan B. 2016. Biology: the dynamic science. Boston: Cengage Learning. 713. [Google Scholar]
  26. Shoemaker KM, Moisander PH. 2015. Microbial diversity associated with copepods in the North Atlantic subtropical gyre. FEMS Microbiol Ecol. 91:1–11. doi: 10.1093/femsle/fnu060 [DOI] [PubMed] [Google Scholar]
  27. Tian Y, Kidokoro H, Watanabe T. 2006. Long-term changes in the fish community structure from the Tsushima warm current region of the Japan/East Sea with an emphasis on the impacts of fishing and climate regime shift over the last four decades. Prog Oceanogr. 68:217–237. doi: 10.1016/j.pocean.2006.02.009 [DOI] [Google Scholar]
  28. Wang Y, Liang L, Shi J, Jiang G. 2005. Study on the contamination of heavy metals and their correlations in mollusks collected from coastal sites along the Chinese Bohai Sea. Environ Int. 31:1103–1113. doi: 10.1016/j.envint.2005.02.005 [DOI] [PubMed] [Google Scholar]
  29. Worden AZ, Janouskovec J, McRose D, Engman A, Welsh RM, Malfatti S, Tringe SG, Keeling PJ.. 2012. Global distribution of a wild alga revealed by targeted metagenomics. Curr Biol. 22:R675–R677. doi: 10.1016/j.cub.2012.07.054 [DOI] [PubMed] [Google Scholar]
  30. Yu DW, Ji Y, Emerson BC, Wang X, Ye C, Yang C, Ding Z. 2012. Biodiversity soup: metabarcoding of arthropods for rapid biodiversity assessment and biomonitoring. Methods Ecol Evol. 3:613–623. doi: 10.1111/j.2041-210X.2012.00198.x [DOI] [Google Scholar]
  31. Zimmermann J, Jahn R, Gemeinholzer B. 2011. Barcoding diatoms: evaluation of the V4 subregion on the 18S rRNA gene including new primers and protocols. Org Divers Evol. 11:173–192. doi: 10.1007/s13127-011-0050-6 [DOI] [Google Scholar]

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