Hostname: page-component-78c5997874-xbtfd Total loading time: 0 Render date: 2024-11-14T17:27:38.021Z Has data issue: false hasContentIssue false
  • English
  • Français

Implementation of Self-Organizing Maps (SOM) to analyses of environmental parameters and phytoplankton biomass in a macrotidal estuary and artificial lake

Published online by Cambridge University Press:  13 August 2012

Roksana Jahan ,
Hyu Chang Choi ,
Young Seuk Park ,
Young Cheol Park ,
Ji Ho Seo  and
Joong Ki Choi
Roksana Jahan
Affiliation:
Department of Oceanography, INHA University, Nam-gu, Incheon 402-751, Korea
Hyu Chang Choi
Affiliation:
Korea Hydro & Nuclear Power Co., Ltd, Gangnam-gu, Seoul 135-791, Korea
Young Seuk Park
Affiliation:
Department of Biology, Kyung Hee University, Dongdaemun-gu, Seoul 130-701, Korea
Young Cheol Park
Affiliation:
Ecocean Co., Ltd, E Dong, 101 BL-3L, 661-16, Gojan-dong, Namdong-gu, Incheon 405-819, Korea
Ji Ho Seo
Affiliation:
Department of Oceanography, INHA University, Nam-gu, Incheon 402-751, Korea
Joong Ki Choi*
Affiliation:
Department of Oceanography, INHA University, Nam-gu, Incheon 402-751, Korea
*
Correspondence should be addressed to: Joong Ki Choi, 5N 230, Plankton Laboratory, Department of Oceanography, INHA University, 253, Yonghyun-Dong, Nam-gu, Incheon 402-751, Korea email: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

Self-Organizing Maps (SOM) have been used for patterning and visualizing ten environmental parameters and phytoplankton biomass in a mactrotidal (>10 m) Gyeonggi Bay and artificial Shihwa Lake during 1986–2004. SOM segregated study areas into four groups and ten subgroups. Two strikingly alternative states are frequently observed: the first is a diverse non-eutrophic state designated by three groups (SOM 1–3), and the second is a eutrophic state (SOM 4: Shihwa Lake and Upper Gyeonggi Bay; summer season) characterized by enhanced nutrients (3 mg l−1 dissolved inorganic nitrogen, 0.1 mg l−1 PO4) that act as a signal and response to that signal as algal blooms (24 µg chlorophyll-a l−1). Bloom potential in response to nitrification is affiliated with high temperature (r = 0.26), low salinity (r = −0.40) and suspended solids (r = –0.27). Moreover, strong stratification in the Shihwa Lake has accelerated harmful algal blooms and hypoxia. The non-eutrophic states (SOM 1–3) are characterized by macro-tidal estuaries exhibiting a tolerance to pollution with nitrogen-containing nutrients and retarding any tendency toward stratification. SOM 1 (winter) is more distinct from SOM 4 due to higher suspended solids (>50 mg l−1) caused by resuspension that induces light limitation and low chlorophyll-a (<5 µg l−1). In addition, eutrophication-induced shifts in phytoplankton communities are noticed during all the seasons in Gyeonggi Bay. Overall, SOM showed high performance for visualization and abstraction of ecological data and could serve as an efficient ecological map that can specify blooming regions and provide a comprehensive view on the eutrophication process in a macrotidal estuary.

Keywords

Self-Organizing MapsGyeonggi BayShihwa Lakesummer bloomseutrophication
Type
Research Article
Information
Journal of the Marine Biological Association of the United Kingdom , Volume 93 , Issue 1 , February 2013 , pp. 1 - 12
Copyright
Copyright © Marine Biological Association of the United Kingdom 2012

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Alhoniemi, E., Himberg, J., Parhankangas, J. and Vesanto, J. (2000) SOM toolbox. http://www.cis.hut.fi/projects/somtoolbox (accessed 13 March 2012).Google Scholar
Anderson, T.R. (2005) Plankton functional type modeling: running before we can walk? Journal of Plankton Research 27, 1073–1081.CrossRefGoogle Scholar
Ainsworth, E.J. (1999) Visualization of ocean color and temperature from multispectral imagery captured by the Japanese ADEOS satellite. Journal of Visualization 2, 195–204.CrossRefGoogle Scholar
Ainsworth, E.J. and Jones, S.E. (1999) Radiance spectra classification from the ocean color and temperature scanner on ADEOS. IEEE Transactions on Geoscience and Remote Sensing 37, 1645–1659.CrossRefGoogle Scholar
Bowden, G.J., Dandy, G.C. and Maier, H.R. (2005a) Input determination for neural network models in water resources applications. Part 1. Background and methodology. Journal of Hydrology 301, 75–92.CrossRefGoogle Scholar
Bowden, G.J., Maier, H.R. and Dandy, G.C. (2005b) Input determination for natural network models in water resources applications. Part 2. Case study: forecasting salinity in a river. Journal of Hydrology 301, 93–107.CrossRefGoogle Scholar
Breitburg, D.L. (1990) Nearshore hypoxia in the Chesapeake Bay: patterns and relationships among physical factors. Estuarine, Coastal and Shelf Science 30, 593–609.CrossRefGoogle Scholar
Caputo, L., Naselli-Flores, L., Ordonez, J. and Armengol, J. (2008) Phytoplankton distribution along trophic gradients within and among reservoirs in Catalonia (Spain). Freshwater Biology 53, 2543–2556.CrossRefGoogle Scholar
Choi, J.K., Lee, E.H., Noh, J.H. and Huh, S.H. (1997) The study on the phytoplankton bloom and primary productivity in Lake Shihwa and adjacent coastal areas. The Sea, Journal of the Korean Society of Oceanography 2, 78–86.Google Scholar
Choi, J.K. and Shim, J.H. (1986a) The ecological study of phytoplankton in Kyeonggi Bay, Yellow Sea I. environmental characteristics. Journal of the Korean Society of Oceanography 21, 56–71.Google Scholar
Choi, J.K. and Shim, J.H. (1986b) The ecological study of phytoplankton in Kyeonggi Bay, Yellow Sea. II. Light intensity, transparency, suspended substances. Journal of the Korean Society of Oceanography 21, 101–109.Google Scholar
Choi, J.K. and Shim, J.H. (1986c) The ecological study of phytoplankton in Kyeonggi Bay, Yellow Sea. III. Phytoplankton composition, standing crops, tychopelagic plankton. Journal of the Korean Society of Oceanography 21, 156–170.Google Scholar
Chon, T.S. (2011) Self-Organizing Maps applied to ecological sciences. Ecological Informatics 6, 50–61.CrossRefGoogle Scholar
Cloern, J.E. (2001) Our evolving conceptual model of the coastal eutrophication problem. Marine Ecology Progress Series 210, 223–253.CrossRefGoogle Scholar
Cloern, J.E. and Jassby, A.D. (2008) Complex seasonal patterns of primary producers at the land–sea interface. Ecological Letters 11, 1294–1303.CrossRefGoogle ScholarPubMed
Cushing, D.H. (1989) A difference in structure between ecosystems in strongly stratified waters and in those that are only weakly stratified. Journal of Plankton Research 11, 1–13.CrossRefGoogle Scholar
Flynn, K.J. (2003) Modelling multi-nutrient interactions in phytoplankton; balancing simplicity and realism. Progress in Oceanography 56, 249–279.CrossRefGoogle Scholar
Flynn, K.J., Raven, J.A., Rees, T.A.V., Finkel, Z., Quigg, A. and Beardall, J. (2010) Is the growth rate hypothesis applicable to microalgae? Journal of Phycology 46, 1–12.CrossRefGoogle Scholar
Franks, P.J.S. (2009) Planktonic ecosystem models: perplexing parameterizations and a failure to fail. Journal of Plankton Research 31, 1299–1306.CrossRefGoogle Scholar
Glibert, P.M., Anderson, D.M., Gentien, P., Graneli, E. and Sellner, K.G. (2005) The global, complex phenomena of harmful algal blooms. Oceanography 18, 136–147.CrossRefGoogle Scholar
Gregg, W.W., Casey, N.W. and McClain, C.R. (2005) Recent trends in global ocean chlorophyll. Geophysical Research Letters 32, L03606. doi: 10.1029/2004GL021808.CrossRefGoogle Scholar
Grimes, C.B. and Finucane, J.H. (1991) Spatial distribution and abundances of larval and juvenile fish, chlorophyll and macrozooplankton around the Mississippi River discharge plume, and the role of the plume in fish recruitment. Marine Ecology Progress Series 75, 109–119.CrossRefGoogle Scholar
Hager, S.W. and Schemel, L.E. (1996) Dissolved inorganic nitrogen, phosphorus, and silicon in South San Francisco Bay. I. Major factors affecting distributions. In Hollibaugh, J.T. (eds) San Francisco Bay: the ecosystem. San Francisco, CA: Association for the Advancement of Science, pp. 1986–2216.Google Scholar
HAN (Harmful Algae News) (2011) Special issue: the 14th international conference on harmful algae. Hersonissos, Crete: UNESCO. www.ioc-unesco.org/hab (accessed 13 March 2012).CrossRefGoogle Scholar
Han, M.W. and Park, Y.C. (1999) The development of anoxia in the artificial Lake Shihwa, Korea, as a consequence of intertidal reclamation. Marine Pollution Bulletin 38, 1194–1199.CrossRefGoogle Scholar
Harding, L.W. Jr (1994) Long-term trends in the distribution of phytoplankton in Chesapeake Bay, 1950–2001: long-term changes in relation to nutrient loading and river flow. Estuaries 27, 634–658.Google Scholar
Hardman-Mountford, N.J., Richardson, A.J., Boyer, D.C., Kreiner, A. and Boyer, H.J. (2003) Relating sardine recruitment in the Northern Benguela to satellite-derived sea surface height using a neural network pattern recognition approach. Progress in Oceanography 59, 241–255.CrossRefGoogle Scholar
Heil, C.A., Glibert, P.M. and Fan, C. (2005) Prorocentrum minimum (Pavillard) Schiller: a review of a harmful algal bloom species of growing worldwide importance. Harmful Algae 4, 449–470.CrossRefGoogle Scholar
Kaski, S., Kangas, J. and Kohonen, T. (1998) Bibliography of self-organizing map (SOM) papers: 1981–1997. Neural Computer Survey 1, 102–350.Google Scholar
Kim, T., Park, Y., Lee, H. and Kim, D. (2004) The environmental impacts of seasonal variation on characteristics of geochemical parameters in Lake Shihwa, Korea. Journal of Environmental Science 13, 1089–1102.Google Scholar
Kiviluoto, K. (1996) Topology preservation in self-organizing maps. In Proceedings of the International Conference on Neural Networks (ICNN'96), volume 1, Piscataway, New Jersey, USA, June 1996. IEEE Neural Networks Council, pp. 294–299.CrossRefGoogle Scholar
KMA (Korea Meterological Administration) (2010) Available at http://web.kma.go.kr (accessed 13 March 2012).Google Scholar
Kohonen, T. (1982) Self-organizing information of topologically correct features maps. Biological Cybernetics 43, 59–69.CrossRefGoogle Scholar
Kohonen, T. (2001) Self-Organizing Map. Springer Series in Information Sciences. 3rd edition. Berlin: Springer-Verlag.Google Scholar
Latif, B.A. and Mercier, G. (2010) Self-Organizing Maps for processing of data with missing values and outliers: application to remote sensing images. In Matsopouls, G.K. (ed.) Self-Organizing Maps. InTech, pp. 189–210.Google Scholar
Livingston, R.J. (2001) Eutrophication processes in coastal systems. Boca Raton, FL: CRC Press.Google Scholar
Longhurst, A. (1995) Seasonal cycles of pelagic production and consumption. Progress in Oceanography 36, 77–167.CrossRefGoogle Scholar
Lucus, L.V. and Cloern, J.E. (2002) Effects of tidal swallowing and deepening on phytoplankton production dynamics: a modeling study. Estuaries 25, 497–507.CrossRefGoogle Scholar
The Mathworks (2001) The Mathworks Inc., 2001. MATLAB Version 6.1, Massachusetts.Google Scholar
Mee, L. (1992) The Black Sea in crisis: a need for concerted international action. Ambio 21, 278–286.Google Scholar
MOMAF (Ministry of Maritime Affairs and Fisheries) (2006) PEMSEA parallel site for integrated coastal management in Republic of Korea: Shihwa Lake. Seoul, Korea: MOMAF.Google Scholar
Monbet, Y. (1992) Control of phytoplankton biomass in estuaries: a comparative analysis of microtidal and macrotidal estuaries. Estuaries 15, 563–571.CrossRefGoogle Scholar
NFRDI (National Fisheries Research and Development Institute) (2008) Available at http://portal.nfrdi.re.kr/ (accessed 13 March 2012).Google Scholar
Oh, S., Kim, M.K., Yi, S.M. and Zoh, K.D. (2010) Distributions of total mercury and methylmercury in surface sediments and fishes in Lake Shihwa, Korea. Science of the Total Environment 408, 1059–1068.CrossRefGoogle ScholarPubMed
Oja, M., Kaski, S. and Kohonen, T. (2002) Bibliography of self-organizing map (SOM) papers: 1998–2001 addendum. Neural Computer Survey 3, 1–156.Google Scholar
Park, G.S. and Park, S.Y. (2000) Long-term trends and temporal heterogeneity of water quality in tidally mixed estuarine waters. Marine Pollution Bulletin 40, 1201–1209.CrossRefGoogle Scholar
Park, Y.C., Kim, S.J. and Han, M.W. (2000) Nutrients loading and its impact on the coastal environment: Kyeonggi Bay, Korea. The Yellow Sea 6, 73–76.Google Scholar
Park, J.K., Kim, E.S., Cho, S.P., Kim, K.T. and Park, Y.C. (2003a) Annual variation of water quality in the Shihwa Lake. Ocean and Polar Research 25, 459–468.CrossRefGoogle Scholar
Park, Y.S., Cereghino, R., Compin, A. and Lek, S. (2003b) Applications of artificial neural networks for patterning and predicting aquatic insect species richness waters. Ecological Modelling 160, 265–280.CrossRefGoogle Scholar
Park, Y.S., Chon, T.S., Kwak, I.S. and Lek, S. (2004) Hierarchical community classification and assessment of aquatic ecosystem using artificial neural networks. Science of the Total Environment 327, 105–122.CrossRefGoogle ScholarPubMed
Park, Y.S., Song, M.Y., Park, Y.C., Oh, K.H., Cho, E. and Chon, T.S. (2007) Community patterns of benthic macroinvertebrates collected on the national scale in Korea. Ecological Modelling 203, 26–33.CrossRefGoogle Scholar
Parsons, T.R., Maita, Y. and Lalli, C.M. (1984) A manual of chemical and biological methods for seawater analysis. Oxford: Pergamon Press.Google Scholar
Passarge, J., Hol, S., Escher, M. and Huisman, J. (2006) Competition for nutrients and light: stable coexistence, alternative stable states, or competitive exclusion? Ecological Monographs 76, 57–72.CrossRefGoogle Scholar
Philippart, C.J.M., Cadee, G.C., van Raaphorst, W. and Riegman, R. (2000) Long-term phytoplankton–nutrient interactions in a shallow coastal sea: algal community structure, nutrient budgets, and denitrification potential. Limnology and Oceanography 45, 131–144.CrossRefGoogle Scholar
Postma, H. (1967) Sediment transport and sedimentation in the estuarine environment. In Lauff, G.H. (ed.) Estuaries. Washington, DC: American Association for the Advancement of Science Publications, pp. 158–179.Google Scholar
Rabalais, N.N., Turner, R.E., Diaz, R.J. and Justic, D. (2009) Global change and eutrophication of coastal waters. ICES Journal of Marine Science 66, 1528–1537.CrossRefGoogle Scholar
Rabouille, C., Conley, D.J., Dai, M.H., Cai, W.-J., Chen, C.T.A., Lansard, B., Green, R., Yin, K., Harrison, P.J., Dagg, M. and Mckee, B. (2008) Comparison of hypoxia among four river dominated ocean margins: the Changjiang (Yangtze), Mississippi, Pearl and Rhone Rivers. Continental Shelf Research 28, 1527–1537.CrossRefGoogle Scholar
Radach, G.., Berg, J. and Hagmeier, E. (1990) Long-term changes of the annual cycles of meterological, hydrographic, nutrient and phytoplankton time series at Helgoland and at V ELBE 1 in the German Bight. Continental Shelf Research 10, 305–328.CrossRefGoogle Scholar
Richardson, A.J., Pfaff, M.C., Field, J.G., Silulwane, N.F. and Shillington, F.A. (2002) Identifying characteristic chlorophyll-a profiles in the coastal domain using an artificial neural network. Journal of Plankton Research 24, 1289–1303.CrossRefGoogle Scholar
Richardson, A.J., Risien, C. and Shillington, F.A. (2003) Using self-organizing maps to identify patterns in satellite imagery. Progress in Oceanography 59, 223–239.CrossRefGoogle Scholar
Risien, C.M., Reason, C.J.C., Shillington, F.A. and Chelton, D.B. (2004) Variability in satellite winds over the Benguela upwelling system during 1999–2000. Journal of Geophysics Research 109, C03010. doi:10.1029/2003JC001880.CrossRefGoogle Scholar
Ryn, J.S., Choi, J.W., Kang, S.G.., Koh, C.H. and Huh, S.H. (1997) Temporal and spatial changes in the species composition and abundances of benthic polychaetes after the construction of Shihwa Dyke. The Sea, Journal of the Korean Society of Oceanography 2, 101–109.Google Scholar
Shanmugam, P., Ahn, Y.H. and Ram, P.S. (2008) SeaWiFS sensing of hazardous algal blooms and their underlying mechanisms in shelf-slope waters of the Northwest Pacific during summer. Remote Sensing of Environment 112, 3248–3270.CrossRefGoogle Scholar
Sharp, J.H. (1994) What not to do about nutrients in the Delaware Estuary. In Dyer, K.R. and Orth, R.J. (eds) Changes in fluxes in estuaries: implications from science to management. Fredensborg, Denmark: Olsen and Olsen, pp. 423–428.Google Scholar
Silulwane, N.F., Richardson, A.J., Shillington, F.A. and Mitchell-Innes, B.A. (2001) Identification and classification of vertical chlorophyll patterns in the Benguela upwelling system and Angola–Benguela Front using an artificial neural network. South African Journal of Marine Science 23, 37–51.CrossRefGoogle Scholar
Smayda, T.J. (1997) Harmful phytoplankton blooms: their ecophysiology and general relevance. Limnology and Oceanography 42, 1137–1153.CrossRefGoogle Scholar
Smayda, T.J. (2008) Complexity in the eutrophication–harmful algal bloom relationship, with comment on the importance of grazing. Harmful Algae 8, 140–151.CrossRefGoogle Scholar
Song, M.Y., Hwang, H.J., Kwak, I.S., Ji, C.W., Oh, Y.N., Youn, B.J. and Chon, T.S. (2007) Self-organizing mapping of benthic macroinvertebrate communities implemented to community assessment and water quality evaluation. In Ecological Modelling: Special issue on ecological informatics: biologically-inspired machine learning. 4th conference of the International Society for Ecological Informatics, Volume 203, pp. 18–25.CrossRefGoogle Scholar
Sorokin, Y.I. (1983) The Black Sea. In Ketchum, B.H. (ed.) Ecosystems of the world: estuaries and enclosed seas. New York: Elsevier, pp. 253–292.Google Scholar
Su, S., Zhi, J., Lou, L., Huang, F., Chen, X. and Wu, J. (2011) Spatio-temporal patterns and source apportionment of pollution in Qiantang River (China) using neural-based modeling and multivariate statistical techniques. Physics and Chemistry of the Earth, Parts A/B/C 39, 379–386.CrossRefGoogle Scholar
Totterdell, I.J., Armstrong, R.A., Drange, H., Parslow, J.S., Powell, T.M. and Taylor, A.H. (1993) Trophic resolution. In Evans, G.T. and Fasham, M.J.R. (eds) Towards a model of ocean biogeochemical processes. Berlin: Springer-Verlag, pp. 71–92.Google Scholar
Tuncer, G.., Karakas, T., Balkas, T.I., Gokcay, C.F., Aygnn, S., Yurteri, C. and Tuncel, G. (1998) Land-based sources of pollution along the Black Sea coast of Turkey: concentrations and annual loads to the Black Sea. Marine Pollution Bulletin 36, 409–423.CrossRefGoogle Scholar
Ultsch, A. and Morchen, F. (2006) U-Maps: topographic visualization techniques for projections of high dimensional data. In Proceedings of the 30th annual conference of the German Classification Society, pp. 1–7.Google Scholar
Ultsch, A. and Roske, F. (2002) Self-organizing feature maps predicting sea levels. Information Sciences 144, 91–125.CrossRefGoogle Scholar
Vesanto, J., Himberg, J., Alhoniemi, E. and Parhankangas, J. (1999) Self-organizing map in Matlab: the SOM toolbox. In Proceedings of the MATLAB digital signal processing conference, Espoo, Finland, pp. 35–40.Google Scholar
Xu, J., Yin, K., Liu, H., Lee, J.H.W., Anderson, D.M., Ho, A.Y.T. and Harrison, P.J. (2010) A comparison of eutrophication impacts in two harbours in Hong Kong with different hydrodynamics. Journal of Marine Systems 83, 276–286.CrossRefGoogle Scholar
Yang, E.J., Choi, J.K. and Hyun, J.H. (2008) Seasonal variation in the community and size structure of nano and microzooplankton in Gyeonggi Bay, Yellow Sea. Estuarine, Coastal and Shelf Science 77, 320–330.CrossRefGoogle Scholar
Yoo, H., Yamashita, N., Taniyasu, S., Lee, K.T., Jones, P.D., Newsted, J.L., Khim, J.S. and Giesy, J.P. (2009) Perfluoroalkyl acids in marine organisms from Lake Shihwa, Korea. Archives of Environmental Contamination and Toxicology 57, 552–560.CrossRefGoogle ScholarPubMed