Comparison of Water Sampling between Environmental DNA Metabarcoding and Conventional Microscopic Identification: A Case Study in Gwangyang Bay, South Korea
Source abstract
Abstract
Our study focuses on methodological comparison of plankton community composition in relation to ecological monitoring and assessment with data sampling. Recently, along with the advancement of monitoring techniques, metabarcoding has been widely used in the context of environmental DNA (eDNA). We examine the applicability of eDNA metabarcoding for effective monitoring and assessment of community composition, compared with conventional observation using microscopic identification in a coastal ecosystem, Gwangynag Bay in South Korea. Our analysis is based primarily on two surveys at a total of 15 study sites in early and late summer (June and September) of the year 2018. The results of our study demonstrate the similarity and dissimilarity of biological communities in composition, richness and diversity between eDNA metabarcoding and conventional microscopic identification. It is found that, overall, eDNA metabarcoding appears to provide a wider variety of species composition, while conventional microscopic identification depicts more distinct plankton communities in sites. Finally, we suggest that eDNA metabarcoding is a practically useful method and can be potentially considered as a valuable alternative for biological monitoring and diversity assessments.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Environmental DNA in Biodiversity Studies · Microbial Community Ecology and Physiology · Protist diversity and phylogeny
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Fisheries Science Institute, Chonnam National University, Yeosu 59626, Korea
Faculty of Marine Technology, Chonnam National University, Yeosu 59626, Korea
Funding
National Research Foundation of Korea
References (71)
Show source-provided references
1. Environmental DNA10.1111/j.1365-294X.2012.05542.x ↗
2. Environmental DNA—An emerging tool in conservation for monitoring past and present biodiversity10.1016/j.biocon.2014.11.019 ↗
3. The extraction and purification of microbial DNA from sediments10.1016/0167-7012(87)90025-X ↗
4. Marshall, K.C. (1986). The analysis of natural microbial populations by ribosomal RNA sequences. Advances in Microbial Ecology, Springer US.10.1007/978-1-4757-0611-6 ↗
5. Microbial Ecology and Evolution: A Ribosomal RNA Approach10.1146/annurev.mi.40.100186.002005 ↗
6. The human genome project: Past, present, and future10.1126/science.2181665 ↗
7. Environmental Genome Shotgun Sequencing of the Sargasso Sea10.1126/science.1093857 ↗
8. Next-generation monitoring of aquatic biodiversity using environmental DNA metabarcoding10.1111/mec.13428 ↗
9. From barcoding single individuals to metabarcoding biological communities: Towards an integrative approach to the study of global biodiversity10.1016/j.tree.2014.08.001 ↗
10. Next-generation sequencing technologies for environmental DNA research10.1111/j.1365-294X.2012.05538.x ↗
11. Towards next-generation biodiversity assessment using DNA metabarcoding10.1111/j.1365-294X.2012.05470.x ↗
12. Environmental DNA for wildlife biology and biodiversity monitoring10.1016/j.tree.2014.04.003 ↗
13. Monitoring the near-extinct European weather loach in Denmark based on environmental DNA from water samples10.1016/j.biocon.2014.11.023 ↗
14. The future of biotic indices in the ecogenomic era: Integrating (e)DNA metabarcoding in biological assessment of aquatic ecosystems10.1016/j.scitotenv.2018.05.002 ↗
15. From molecules to management: Adopting DNA-based methods for monitoring biological invasions in aquatic environments10.1016/j.envres.2011.02.001 ↗
16. Biomonitoring 2.0: A new paradigm in ecosystem assessment made possible by next-generation DNA sequencing10.1111/j.1365-294X.2012.05519.x ↗
17. Moving environmental DNA methods from concept to practice for monitoring aquatic macroorganisms10.1016/j.biocon.2014.11.040 ↗
18. Past, present, and future perspectives of environmental DNA (eDNA) metabarcoding: A systematic review in methods, monitoring, and applications of global eDNA
19. Monitoring endangered freshwater biodiversity using environmental DNA10.1111/j.1365-294X.2011.05418.x ↗
20. Ecosystem dynamics based on plankton functional types for global ocean biogeochemistry models10.1111/j.1365-2486.2005.1004.x ↗
21. The impact of larval and juvenile fish on zooplankton and algal dynamics10.4319/lo.1999.44.7.1655 ↗
22. Phytoplankton of the River Loire, France: A biodiversity and modelling study10.1093/plankt/fbr085 ↗
23. Wetzel, R.G., and Likens, G.E. (1991). Limnological Analysis, Springer-Verlag.10.1007/978-1-4757-4098-1 ↗
24. Metabarcoding vs. morphological identification to assess diatom diversity in environmental studies10.1111/1755-0998.12336 ↗
25. The detection of aquatic animal species using environmental DNA—A review of eDNA as a survey tool in ecology10.1111/1365-2664.12306 ↗
26. Trophic importance of benthic microalgae to macrozoobenthos in coastal bay systems in Korea: Dual stable C and N isotope analyses10.3354/meps259079 ↗
27. Tidal Influences on Biotic and Abiotic Factors in the Seomjin River Estuary and Gwangyang Bay, Korea10.1007/s12237-018-0404-9 ↗
28. (2018, July 10). Korean Statistical Information Service (KOSIS). Available online: http://kosis.kr.
29. A study on the pollution of polycyclic aromatic hydrocarbons (PAHs) in the surface sediments around Gwangyang Bay
30. Chihara, M., and Murano, M. (1997). An Illustrated Guide to Marine Plankton in Japan, Tokai University Press.
31. 18S rRNA V9 metabarcoding for diet characterization: A critical evaluation with two sympatric zooplanktivorous fish species10.1002/ece3.1986 ↗
32. Is metabarcoding suitable for estuarine plankton monitoring? A comparative study with microscopy10.1007/s00227-016-2920-0 ↗
33. Quantitative analysis of dinoflagellates and diatoms community via Miseq sequencing of actin gene and v9 region of 18S rDNA10.1038/srep34709 ↗
34. Amaral-Zettler, L.A., McCliment, E.A., Ducklow, H.W., and Huse, S.M. (2009). A Method for Studying Protistan Diversity Using Massively Parallel Sequencing of V9 Hypervariable Regions of Small-Subunit Ribosomal RNA Genes. PLoS ONE, 4.10.1371/annotation/50c43133-0df5-4b8b-8975-8cc37d4f2f26 ↗
35. Search and clustering orders of magnitude faster than BLAST10.1093/bioinformatics/btq461 ↗
36. Basic local alignment search tool10.1016/S0022-2836(05)80360-2 ↗
37. QIIME allows analysis of high-throughput community sequencing data10.1038/nmeth.f.303 ↗
38. Kohonen, T. (1997). Self-Organizing Maps, Springer.10.1007/978-3-642-97966-8 ↗
39. A comparison of self-organizing map algorithm and some conventional statistical methods for ecological community ordination10.1016/S0304-3800(01)00324-6 ↗
40. Self-Organizing Maps applied to ecological sciences10.1016/j.ecoinf.2010.11.002 ↗
41. Kim, D.-K., Jo, H., Han, I., and Kwak, I.-S. (2019). Explicit Characterization of Spatial Heterogeneity Based on Water Quality, Sediment Contamination, and Ichthyofauna in a Riverine-to-Coastal Zone. Int. J. Environ. Res. Public Health, 16.10.3390/ijerph16030409 ↗
42. Development of a mechanistic eutrophication model for wetland management: Sensitivity analysis of the interplay among phytoplankton, macrophtyes, and sediment nutrient release10.1016/j.ecoinf.2018.09.010 ↗
43. Lek, S., and Guegan, J.F. (2000). Patterning of community changes in bentic macroinvertebrates collected from urbanized streams for the short term prediction by temporal artificial neuronal networks. Artificial Neuronal Networks: Application to Ecology and Evolution, Springer.
44. Characterizing effects of landscape and morphometric factors on water quality of reservoirs using a self-organizing map10.1016/j.envsoft.2014.01.031 ↗
45. Patterning zooplankton communities in accordance with annual climatic conditions in a regulated river system (the Nakdong River, South Korea)10.1002/iroh.201111428 ↗
46. Hadjisolomou, E., Stefanidis, K., Papatheodorou, G., and Papastergiadou, E. (2018). Assessment of the Eutrophication-Related Environmental Parameters in Two Mediterranean Lakes by Integrating Statistical Techniques and Self-Organizing Maps. Int. J. Environ. Res. Public Health, 15.10.3390/ijerph15030547 ↗
47. Use of self-organizing maps (SOM) for characterization of riverine phytoplankton associations in Hungary
48. Use of unsupervised neural networks for ecoregional zoning of hydrosystems through diatom communities: Case study of Adour-Garonne watershed (France)10.1127/0003-9136/2004/0159-0409 ↗
49. Analysis of dissolved organic matter fluorescence using self-organizing maps: Mini-review and tutorial10.1039/C5AY02549D ↗
50. Implementation of a self-organizing map for investigation of impoundment impact on fish assemblages in a large, lowland river: Long-term study10.1016/j.ecolmodel.2011.12.006 ↗
51. Utilisation of non-supervised neural networks and principal component analysis to study fish assemblages10.1016/S0304-3800(01)00303-9 ↗
52. Assessment of the lake biomanipulation by introducing both piscivorous rainbow trout and herbivorous daphnids using self-organizing map analysis: A case study in Lake Shirakaba, Japan10.1016/j.ecoinf.2014.05.013 ↗
53. Patterning limnological characteristics of the Chilika lagoon (India) using a self-organizing map10.1007/s10201-008-0243-7 ↗
54. Clustering of the Self-Organizing Map10.1109/72.846731 ↗
55. Shannon, C.E., and Weaver, W. (1964). The Mathematical Theory of Communication, The University of Illinois Press.
56. Assessing ecological status with diatoms DNA metabarcoding: Scaling-up on a WFD monitoring network (Mayotte island, France)10.1016/j.ecolind.2017.06.024 ↗
57. Unreliable quantitation of species abundance based on high-throughput sequencing data of zooplankton communities10.3354/ab00629 ↗
58. Harnessing DNA to improve environmental management10.1126/science.1251156 ↗
59. Critical considerations for the application of environmental DNA methods to detect aquatic species10.1111/2041-210X.12595 ↗
60. Kembel, S.W., Wu, M., Eisen, J.A., and Green, J.L. (2012). Incorporating 16S Gene Copy Number Information Improves Estimates of Microbial Diversity and Abundance. PLoS Comp. Biol., 8.10.1371/journal.pcbi.1002743 ↗
61. Genetic and Manual Survey Methods Yield Different and Complementary Views of an Ecosystem10.3389/fmars.2016.00283 ↗
62. A morphogenetic survey on ciliate plankton from a mountain lake pinpoints the necessity of lineage-specific barcode markers in microbial ecology10.1111/1462-2920.12194 ↗
63. Gonzalez, J.M., Portillo, M.C., Belda-Ferre, P., and Mira, A. (2012). Amplification by PCR Artificially Reduces the Proportion of the Rare Biosphere in Microbial Communities. PLoS ONE, 7.10.1371/journal.pone.0029973 ↗
64. Elbrecht, V., and Leese, F. (2015). Can DNA-Based Ecosystem Assessments Quantify Species Abundance? Testing Primer Bias and Biomass—Sequence Relationships with an Innovative Metabarcoding Protocol. PLoS ONE, 10.10.7287/peerj.preprints.1023 ↗
65. A pragmatic approach to the analysis of diets of generalist predators: The use of next-generation sequencing with no blocking probes10.1111/1755-0998.12156 ↗
66. Cryptic ecological diversification of a planktonic estuarine copepod10.1111/j.1365-294X.2007.03657.x ↗
67. Lindeque, P.K., Parry, H.E., Harmer, R.A., Somerfield, P.J., and Atkinson, A. (2013). Next Generation Sequencing Reveals the Hidden Diversity of Zooplankton Assemblages. PLoS ONE, 8.10.1371/journal.pone.0081327 ↗
68. A metagenetic approach for revealing community structure of marine planktonic copepods10.1111/1755-0998.12294 ↗
69. Marine protist diversity in European coastal waters and sediments as revealed by high-throughput sequencing10.1111/1462-2920.12955 ↗
70. Bioinformatic challenges for DNA metabarcoding of plants and animals10.1111/j.1365-294X.2012.05550.x ↗
71. Effects of Temperature and Trophic State on Degradation of Environmental DNA in Lake Water10.1021/acs.est.5b05672 ↗