Browse > Article
http://dx.doi.org/10.11614/KSL.2022.55.4.368

Analysis of Stomach Contents of Marine Orgnaisms in Gwangyang Bay and Yeosu Fish Market Using DNA Metabarcoding  

Gun Hee Oh (Department of Ocean Integrated Science, Chonnam National University)
Yong Jun Kim (Department of Ocean Integrated Science, Chonnam National University)
Won-Seok Kim (Department of Ocean Integrated Science, Chonnam National University)
Cheol Hong (Department of Ocean Integrated Science, Chonnam National University)
Chang Woo Ji (Fisheries Science Institute, Chonnam National University)
Ihn-Sil Kwak (Department of Ocean Integrated Science, Chonnam National University)
Publication Information
Abstract
Gut contents analysis is essential to predict the impact of organisms on food source changes due to variations of the habitat environment. Previous studies of gut content analysis have been conducted using traditional methods, such as visual observation. However, these studies are limited in analyzing food sources because of the digestive process in gut organ. DNA metabarcoding analysis is a useful method to analyze food sources by supplementing these limitations. We sampled marine fish of Pennahia argentata, Larimichthys polyactis, Crangon affinis, Loligo beka and Sepia officinalis from Gwangyang Bay and Yeosu fisheries market for analyzing gut contents by applying DNA metabarcoding analysis. 18S rRNA v9 primer was used for analyzing food source by DNA metabarcoding. Network and two-way clustering analyses characterized the relationship between organisms and food sources. As a result of comparing metabarcoding of gut contents for P. argentata between sampled from Gwangyang Bay and the fisheries market, fish and Copepoda were analyzed as common food sources. In addition, Decapoda and Copepoda were analyzed as common food sources for L. polyactis and C. affinis, respectively. Copepoda was analyzed as the primary food source for L. beka and S. officinalis. These study results demonstrated that gut contents analysis using DNA metabarcoding reflects diverse and detailed information of biological food sources in the aquatic environment. In addition, it will be possible to provide biological information in the gut to identify key food sources by applying it to the research on the food web in the ecosystem.
Keywords
gut contents; DNA metabarcoding; 18S rRNA; feeding; fish;
Citations & Related Records
Times Cited By KSCI : 5  (Citation Analysis)
연도 인용수 순위
1 Ji, C.W., D.S. Lee, D.Y. Lee, Y.S. Park and I.S. Kwak. 2021. Analysis of Food Resources of 20 Endangered Fishes in Freshwater Ecosystems of South Korea using Non-metric Multidimensional Scaling and Network Analysis. The Korean Society of Limnology 54: 130-141.
2 Jung, J.H., J.Y. Park, Y.H. Yoon, H.M. Lim and W.J. Kim. 2014. A Survey on fish habitat conditions of domestic rivers and construction of its database. Journal of Korean Society of Environmental Engineers 36: 221-230.   DOI
3 Kang, D.Y., G.C. Seong, D.G Kim, S.Y. Jin, H.Y. Soh and G.W. Baeck. 2022. Feeding Habits of Small Yellow Croaker, Larimichthys polyactis in Coastal Waters of Korea. Korean Journal of Ichthgology 34: 201-207.
4 Kim, I.S., J.Y. Park, H. Yang, H.H. Lee, Y.S. Seo and G.P. Park. 2011b. Construction of Korean freshwater fishes DB. Korea Institute of Science and Technology Information. 
5 Kim, S.T. 2017. Application of next generation sequencing (NGS) technique for the stomach content analysis of marine fish. Pukyong National University.
6 Lee, D.J., I.S. Kwak, H.W. Bang and W.C. Lee. 2010. Effects of Antibiotics, Fenbendazole and Lincomycin, in Benthic Copepod, Tigriopus japonicus s.l. Environmental Health and Toxicology 25.3: 197-205.
7 Lee, W.O., M.Y. Song and H.W. Park. 2018. Freshwater fish status and management plan for lnland fishery resources. The Korean Society of Fisheries and Aquatic Science: 86-86.
8 Levins, R. 1968. Evolution in changing environments: some theoretical explorations. Princeton University Press.
9 Na, Y.K., H.B. Jo., J.W. Park., K.H. Chang and I.S. Kwak. 2020. The Gut Content Analysis of Polypedilum scalaenum in the Large-scale Weirs of 4 Major River Ecosystems. Korean Journal of Ecology and Environment 53: 55-62.   DOI
10 Amaral-Zettler, L.A., E.A. McCliment, H.W. Ducklow and S.M. Huse. 2009. A method for studying protistan diversity using massively parallel sequencing of V9 hypervariable regions of small-subunit ribosomal RNA genes. PloS One 4: e6372.
11 Bae, M.-J. and Y.-S. Park. 2019. Evaluation of precipitation impacts on benthic macroinvertebrate communities at three different stream types. Ecological Indicators 102: 446-456.   DOI
12 Beals, E.W. 1984. Bray-Curtis ordination: an effective strategy for analysis of multivariate ecological data. Advances in Ecological Research 14: 1-55.   DOI
13 Bolyen, E., J.R. Rideout, M.R. Dillon, N.A. Bokulich, C.C. Abnet, G.A. Al-Ghalith, H. Alexander, E.J. Alm, M. Arumugam and F. Asnicar. 2019. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nature Biotechnology 37: 852-857.
14 Carreon-Martinez, L. and D.D. Heath. 2010. Revolution in food web analysis and trophic ecology: diet analysis by DNA and stable isotope analysis. Wiley Online Library.
15 Cha, S.S. and K.J. Park. 2001. Feeding Selectivity of Postlarvae of White Croaker (Argyrosomus argentatus) in Kwangyang Bay, Korea. Korean Journal of Fisheries and Aquatic Sciences 34: 27-31.
16 Csardi, G. and T. Nepusz. 2006. The igraph software package for complex network research. InterJournal, Complex Systems 1695: 1-9.
17 Clarke, L.J., R. Trebilco, A. Walters, A.M. Polanowski and B.E. Deagle. 2020. DNA-based diet analysis of mesopelagic fish from the southern Kerguelen Axis. Deep Sea Research Part II: Topical Studies in Oceanography 174.
18 Cortes, E. 1999. Standardized diet compositions and trophic levels of sharks. ICES Journal of Marine Science 56: 707-717.   DOI
19 Heo, Y.J., H.B. Jo., E.S. Jung and H.W. Kim. 2021. Application of NGS Analysis for the Food Source of Bivalve. Korean Journal of Ecology and Environment 54: 257-264.   DOI
20 Dray, S. and P. Legendre. 2008. Testing the species traits-environment relationships: the fourth-corner problem revisited. Ecology 89: 3400-3412.   DOI
21 Hobson, K.A. 1993. Trophic relationships among high Arctic seabirds: insights from tissue-dependent stable-isotope models. Marine Ecology-Progress Series 95: 7-7.   DOI
22 Hobson, K.A. and H.E. Welch. 1992. Determination of trophic relationships within a high Arctic marine food web using δ13C and δ15N analysis. Marine Ecology Progress Series: 9-18.
23 Hobson, K.A., J.F. Piatt and J. Pitocchelli. 1994. Using stable isotopes to determine seabird trophic relationships. Journal of Animal Ecology: 786-798.
24 Hong, S.Y. and C.W. OH. 1989. Ecology of sand shrimp, Crangon affinis in the Nakdong River Estuary, Korea. Korean Journal of Fisheries and Aquatic Sciences 22: 351-362.
25 Huh, S.H., H.C. Choi and J.M. Park. 2018. Feeding Relationship between Co-occurring Silver Croaker(Pennahia argentata) and Japanese Sillago (Sillago japonica) in the Nakdong River Estuary, Korea. Korean Journal of Ichthyology 30: 224-231.   DOI
26 Ji, C.W., D.S. Lee, D.Y. Lee, I.S. Kwak and Y.S. Park. 2020. Analysis of Food Resources of 45 Fish Species in Freshwater Ecosystems of South Korea (Based on Literature Data Analysis). Korean Journal of Ecology and Environment 53: 311-323.   DOI
27 Oksanen, J., F.G. Blanchet, M. Friendly, R. Kindt, P. Legendre, D. McGlinn, P.R. Minchin, R.B. O'Hara, G.L. Simpson, P. Solymos, M.H.H. Stevens, E. Szoecs and H. Wagner. 2019. vegan: Community Ecology Package. (R package version 2.5-6.).
28 Sanger, G.A. 1987. Trophic levels and trophic relationships. Seabirds: Feeding ecology and role in marine ecosystems: 229. 
29 Pauly, D. and V. Christensen. 1995. Primary production required to sustain global fisheries. Nature 374: 255-257.   DOI
30 Romanuk, T.N., A. Hayward and J.A. Hutchings. 2011. Trophic level scales positively with body size in fishes. Global Ecology and Biogeography 20: 231-240.
31 Tue, N.T., H. Hamaoka, T.D. Quy, M.T. Nhuan, A. Sogabe, N. T. Nam and K. Omori. 2014. Dual isotope study of food sources of a fish assemblage in the Red River mangrove ecosystem, Vietnam. Hydrobiologia 733: 71-83.   DOI
32 Ward Jr, J.H. 1963. Hierarchical grouping to optimize an objective function. Journal of the American Statistical Association 58: 236-244.   DOI