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Temporal Dynamics and Patterning of Meiofauna Community by Self-Organizing Artificial Neural Networks
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  • Journal title : Ocean and Polar Research
  • Volume 25, Issue 3,  2003, pp.237-247
  • Publisher : Korea Institute of Ocean Science & Technology
  • DOI : 10.4217/OPR.2003.25.3.237
 Title & Authors
Temporal Dynamics and Patterning of Meiofauna Community by Self-Organizing Artificial Neural Networks
Lee, Won-Cheol; Kang, Sung-Ho; Montagna Paul A.; Kwak Inn-Sil;
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The temporal dynamics of the meiofauna community in Marian Cove, King George Island were observed from January 22 to October 29 1996. Generally, 14 taxa of metazoan meiofauna were found. Nematodes were dominant comprising 90.12% of the community, harpacticoid 6.55%, and Kinorhynchs 1.54%. Meiofauna abundance increased monthly from January to May 1996, while varying in abundance after August 1996. Overall mean abundance of metazoan meiofauna was during the study periods, which is about as high as that found in temperate regions. Nematodes were most abundant representing . Mean abundance of harpacticoids, including copepodite and nauplius was by kinorhynchs . The overall abundance of other identified organisms was Other organisms consisted of a total of 11 taxa including Ostracoda , Polycheata , Oligochaeta , and Bivalvia . Additionally, protozoan Foraminifera occurred at the study area with a mean abundance of . Foraminiferans were second in dominance to nematodes. The dominant taxa such as nematodes, harpacticoids, kinorhynchs and the other tua were trained and extensively scattered in the map through the Kohonen network. The temporal pattern of the community composition was most affected by the abundance dynamics of kinorhynchs and harpacticoids. The neural network model also allowed for simulation of data that was missing during two months of inclement weather. The lowest meiofauna abundance was found in August 1996 during winter. The seasonal changes were likely caused by temperature and salinity changes as a result of meltwater runoff, and the physical impact by passing icebergs.
meiofauna;Antarctica;artificial neural network;temporal dynamics;
 Cited by
남극 King George Islands, Marian Cove의 중형저서생물 군집 구조에 관한 연구,방현우;강성호;이원철;

환경생물, 2005. vol.23. 2, pp.191-199
2002년 하계 북극 바렌츠해 연안지역의 중형저서생물 군집 구조에 관한 연구,이강현;정경호;강성호;이원철;

환경생물, 2005. vol.23. 3, pp.257-268
북극해 스발바드 군도 Kongsfjorden 퇴적물에 서식하는 중형저서동물 군집의 공간 특성,김동성;신재철;강성호;정호성;

Ocean and Polar Research, 2005. vol.27. 3, pp.299-309 crossref(new window)
남극 킹조지섬 마리안소만의 중형저서동물 군집구조,홍정호;김기춘;이승한;백진욱;이동주;이원철;

Ocean and Polar Research, 2011. vol.33. 3, pp.265-280 crossref(new window)
Spatial Characteristics of Meiobenthic Community of Kongfjorden Sediment in the Svalbard Island, the Arctic Sea, Ocean and Polar Research, 2005, 27, 3, 299  crossref(new windwow)
The Community Structure of Meiofauna in Marian Cove, King George Island, Antarctica, Ocean and Polar Research, 2011, 33, 3, 265  crossref(new windwow)
Ahn, I.Y. and Y.C. Kang. 1991. Preliminary study on the macrobenthic community of Maxwell Bay, South Shetland Islands, Antarctica. Korea J. Polar Resear., 2(2), 61-72.

Alongi, D.M. and M. Pichon. 1988. Bathyal meiobenthos of the western Coral Sea: distribution and abundance in relation to microbial standing stocks and environmental factors. Deep-Sea Res., 35, 491-503. crossref(new window)

Ansari, Z.A. and A.H. Parulekar. 1993. Environmental stability and seasonality of a harpacticoid copepod community. Mar. Biol., 115, 279-286. crossref(new window)

Bouvy, M. and J. Soyer. 1989. Benthic Seasonality in an intertidal Mud Flat at Kerguelen Islands (Austral Ocean). The Relationships between Meiofaunal Abundance and Their Potential Microbial Food. Polar Biol., 10, 19-27.

Bunn, S.E., D.H. Edward, and N.R. Loneragan. 1986. Spatial and temporal variation in the macroinvertebrate fauna of streams of the northern jarrah forest, Wester Australia: community structure. Freshwater Biol., 16, 67-91. crossref(new window)

Choe, B.L., J.R. Lee, I.Y. Ahn, and H. Chung. 1994. Preliminary study of malacofauna of maxwell Bay, South Shetland Islands, Antarctica. Kor. J. Polar Res., 5, 15-28.

Chon, T.-S., I.S. Kwak, and Y.S. Park. 2000a. Pattern recognition of long-term ecological data in community changes by using artificial neural networks: benthic macroinvertebrates and chironomids in a polluted stream. Kor. J. Ecol., 23, 89-100.

Chon, T.-S., I.S. Kwak, and Y.S. Park. 2001a. Patterning and short-term predictions of benthic macroinvertebrate community dynamics by using a recurrent artificial neural network. Ecol. Model., 146, 181-193. crossref(new window)

Chon, T.-S., I.S. Kwak, Y.S. Park, T.H. Kim, and Y. Kim. 2001b. Patterning and short-term predictions of benthic macroinvertebtate community dynamics by using a recurrent artificial neural network. Ecol. Model., 146, 181-193. crossref(new window)

Chon, T.-S., Y.S. Park, and E.Y. Cha. 2000b. Patterning of community changes in benthic macroinvertebrates collected from urbanized streams for the short time prediction by temporal artificial neural networks. In: Artificial Neural Networks in Ecology and Evolution, eds. by S. Lek and J.F. Guegan. Springer-Verlag, Berlin.

Chon, T.-S., Y.S. Park, and J.H. Park. 2000c. Determining temporal pattern of community dynamics by using unsupervised learning algorithms. Ecol. Model., 132, 151-166. crossref(new window)

Chon, T.-S., Y.S. Park, K.H. Moon, and E.Y. Cha. 1996. Patternizing communities by using an artificial neural network. Ecol. Model., 90, 69-78. crossref(new window)

Coull, B.C. 1985. Long-term variability of esturine meiobenthos: an 11 year study. Mar. Ecol. Prog. Ser., 24, 205-218. crossref(new window)

Coull, B.C. and B.W. Dudley. 1985. Dynamics of meiobenthic copepod populations: a long-term study (1973-1983). Mar. Ecol. Prog. Ser., 24, 219-229. crossref(new window)

Coull, B.C. and J.B.J. Wells. 1981. Density of mud dwelling meiobenthos from three sites in the Wellington region. New Zealand J. Mar. Freshwat. Res., 15, 411-415. crossref(new window)

Elizondo, D.A., R.W. McClendon, and G. Hoongenboom. 1994. Neural network models for predicting flowering and physiological maturity of soybean. Trans. ASAE., 37, 981-988. crossref(new window)

Findley, S.E.G. 1981. Small-scale spatial distribution of meiofauna on a mud- and sandflat. Est. Coast Shelf Sci., 12, 471-484. crossref(new window)

Guellec, C. and P. Bodin. 1992. Meiobenthos of the Bay of Saint-Brieuc (North Brittany, France). I: Qualitative distribution in subtidal and intertidal zones. Oceanol. Acta., 15, 166-671.

Guidi-Guilvard, L.D. and R. Buscail. 1995. Seasonal survey of metazoan meiofauna and surface sediment organics in a non-tidal turbulent sublittoral prodelta (Northwestern Mediterranean). Cont Shelf Res., 15, 633-653. crossref(new window)

Hecht-Nielsen, R. 1990. Neurocomputing. Addison-Wesley, New York. 433 p.

Herman, R.L. and H.U. Dahms. 1992. Meiofauna communities along a depth transact off Halley Bay (Weddell Sea-Antarctica). Polar Biol., 12, 313-320.

Hicks, G.R.F. 1984. Spatio-temporal dynamics of a meiobenthic copepod and the impact of predation-disturbance. J. Exp. Mar. Biol. Ecol., 81, 47-72. crossref(new window)

Higgins, R.P. and H. Thiel. 1988. Introduction to the study of meiofauna. Smithsonian Institution Press, Washington D.C. 488 p.

Hong, S.M., B.K. Park, H.I. Yoon, Y. Kim, and J.K. Oh. 1991. Deposital environment in and paleoglacial setting around Marian Cove, King George Island, Antarctica. Kor. J. Polar Res., 2, 73-85.

Huntingford, C. and P.M. Cox. 1996. Use of statistical and neural network techniques to detect how stomatal conductance responds to changes in the local environment. Ecol. Model., 97, 217-246.

Huys, R., R.L. Herman, and C. Heip. 1986. Seasonal Fluctuations in vertical distribution and breeding activity of a subtidal harpacticoid community in the Southern Bight, North Sea. Netherlands J. Sea Res., 20, 375-383. crossref(new window)

Kohonen, T. 1989. Self-organization and Associative Memory. Springer-Verlag, Berlin. 312 p.

KORDI. 1997. Wintering report of the 9th Korea Antarctic Research Program at King Sejong Station (Dec.-1995 Dec. 1996). 490 p. (In Korean)

Kwak, Inn-Sil, Guanchun Liu, Tae-Soo Chon, and Young-Seuk Park. 2000. Community patterning of benthic macroinvertebrates in streams of South Korea by utilizing an artificial neural network. Kor. J. Limnol., 33, 230-243.

Legendre, P. and L. Legendre. 1983. Numerical Ecology. Elsevier, Netherlands. 419 p.

Legendre, P. and L. Legendre. 1987. Developments in numerical ecology. Springer-Verlag, Berlin. 585 p.

Lek, S., M. Delacoste, P. Baran, I. Dimopoulos, J. Lauga, and S. Aulagnier. 1996. Application of neural networks to modelling nonlinear relationships in ecology. Ecol. Model., 90, 39-52. crossref(new window)

Levine, E.R., D.S. Kimes, and V.G. Sigillito. 1996. Classifying soil structure using neural networks. Ecol. Model., 92, 101-108. crossref(new window)

Lippmann, R.P. 1987. An introduction to computing with neural nets. IEEE Acoustics, Speech Signal Process Mag. 4-22.

Ludwig, J.A. and J.F. Reynolds. 1988. Statistical Ecology: a Primer of Methods and Computing. John Wiley and Sons, New York, 337 p.

Montagna, P.A., G.F. Blanchard, and A. Dinet. 1995. Effect of production and biomass of intertidal microphytobenthos on meiofaunal grazing rates. J. Exp. Mar. Biol. Ecol., 185, 149-165. crossref(new window)

Palacin, C., J.M. Gili, and D. Martin. 1992. Evidence for coincidence of meiofauna spatial heterogeneity with eutrophication processes in a shallow-water Mediterranean Bay. Est. Coast Shelf Sci., 35, 1-16. crossref(new window)

Palmer, M.A. 1988. Epibenthic predators and marine meiofauna: separating predation, disturbance, and hydrodynamic effects. Ecology, 69, 1251-1260. crossref(new window)

Pfannkuiche, O. 1985. The deep-sea meiofauna of the Porcupine seabight and abyssal plain (NE Atlantic): population structure, distribution, standing stocks. Oceanol. Acta., 8, 343-353.

Pfannkuiche, O. and H. Thiel. 1987. Meiobenthic Stocks and Benthic Activity on the NE-Svalbard Shelf and in the Nansen Basin. Polar. Biol., 7, 253-266. crossref(new window)

Quinn, M.A., S.E. Halbert, and L. Williams III. 1991. Spatial and temporal changes in aphid (Homoptera: Aphididae) species assemblages collected with suction traps in Idaho. J. Econ. Entomol., 84, 1710-1716. crossref(new window)

Recknagel, F., M. French, P. Harkonen, and K.-I. Yabunaka. 1997. Artificial neural network approach for modelling and prediction of algal blooms. Ecol. Model., 96, 11-28. crossref(new window)

Sherman, K.M. and B.C. Coull. 1980. The response of meiofauna to sediment disturbance. J. Exp. Mar. Biol. Ecol., 45, 59-71.

Sibert, J.R. 1979. Detritus and juvenile salmon production in Nanaimo Estuary: II. Meiofauna available as food for juvenile chum salmon (Oncorhynchus keta). J. Fish Res. Board Can., 36, 497-503. crossref(new window)

Smith, L.D. and B.C. Coull. 1987. Juvenile spot (Pisces) and grass shrimp predation on meiobenthos in muddy and sandy substrata. J. Exp. Mar. Biol. Ecol., 105, 123-136. crossref(new window)

Stankovski, V., M. Debeljak, I. Bratko, and M. Adamic. 1998. Modelling the population dynamics of red deer (Cervus elaphus L.) with regard to forest development. Ecol. Model., 108, 143-153.

Szymelfenig, M., S. Kwasniewski, and J.M. Westawski. 1995. Intertidal zone of Svalbard. 2. Meiobenthos density and occurrence. Polar Biol., 15, 137-141. crossref(new window)

Tan, S.S. and F.E. Smeins. 1996. Predicting grassland community changes with an artificial neural network model. Ecol. Model., 84, 91-97. crossref(new window)

Tietjen, J.H., J.W. Deming, G.T. Rowe, S. Macko, and R.J. Wilke. 1989. Meiobenthos of the Hatteras abyssal plain and Puerto Rico trench: abundance, biomass and associations with bacteria and particulate fluxes. Deep-Sea Res., 36, 1567-1577. crossref(new window)

Tuma, A., H.-D. Haasis, and O. Rentz. 1996. A comparison of fuzzy expert systems, neural networks and neuro-fuzzy approaches controlling energy and materials flows. Ecol. Mode., 85, 93-98. crossref(new window)

Zurada, J.M. 1992. Introduction to Artificial Neural Systems. West Pub. Co., New York. 683 p.