Ocean and Polar Research

Korea Institute of Ocean Science & Technology (한국해양과학기술원)
권호 표지
DOI QR코드

DOI QR Code

Temporal Dynamics and Patterning of Meiofauna Community by Self-Organizing Artificial Neural Networks

  • Lee, Won-Cheol (Department of Life Science, Hanyang University) ;
  • Kang, Sung-Ho (Korea Polar Research Institute(KOPRI), KORDI) ;
  • Montagna Paul A. (Marine Science Institute, The University of Texas at Austin) ;
  • Kwak Inn-Sil (Department of Life Science, Hanyang University)
  • Published : 2003.09.30
Download PDF
⟨ Previous Next ⟩

Abstract

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 $2634ind./10cm^2$ during the study periods, which is about as high as that found in temperate regions. Nematodes were most abundant representing $2399ind./10cm^2$. Mean abundance of harpacticoids, including copepodite and nauplius was $131ind./10cm^2$ by kinorhynchs $(26ind./10cm^2)$. The overall abundance of other identified organisms was $31ind./10cm^2$ Other organisms consisted of a total of 11 taxa including Ostracoda $(6ind./10cm^2)$, Polycheata $(7ind./10cm^2)$, Oligochaeta $(8ind./10cm^2)$, and Bivalvia $(6ind./10cm^2)$. Additionally, protozoan Foraminifera occurred at the study area with a mean abundance of $263ind./10cm^2$. 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.

Keywords

References

  1. 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.
  2. 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. https://doi.org/10.1016/0198-0149(88)90127-6
  3. Ansari, Z.A. and A.H. Parulekar. 1993. Environmental stability and seasonality of a harpacticoid copepod community. Mar. Biol., 115, 279-286. https://doi.org/10.1007/BF00346345
  4. 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.
  5. 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. https://doi.org/10.1111/j.1365-2427.1986.tb00949.x
  6. 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.
  7. 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.
  8. 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. https://doi.org/10.1016/S0304-3800(01)00305-2
  9. 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. https://doi.org/10.1016/S0304-3800(01)00305-2
  10. 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.
  11. 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. https://doi.org/10.1016/S0304-3800(00)00312-4
  12. 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. https://doi.org/10.1016/0304-3800(95)00148-4
  13. Coull, B.C. 1985. Long-term variability of esturine meiobenthos: an 11 year study. Mar. Ecol. Prog. Ser., 24, 205-218. https://doi.org/10.3354/meps024205
  14. 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. https://doi.org/10.3354/meps024219
  15. 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. https://doi.org/10.1080/00288330.1981.9515933
  16. 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. https://doi.org/10.13031/2013.28168
  17. Findley, S.E.G. 1981. Small-scale spatial distribution of meiofauna on a mud- and sandflat. Est. Coast Shelf Sci., 12, 471-484. https://doi.org/10.1016/S0302-3524(81)80006-0
  18. 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.
  19. 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. https://doi.org/10.1016/0278-4343(94)E0036-L
  20. Hecht-Nielsen, R. 1990. Neurocomputing. Addison-Wesley, New York. 433 p.
  21. 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.
  22. 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. https://doi.org/10.1016/0022-0981(84)90223-5
  23. Higgins, R.P. and H. Thiel. 1988. Introduction to the study of meiofauna. Smithsonian Institution Press, Washington D.C. 488 p.
  24. 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.
  25. 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.
  26. 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. https://doi.org/10.1016/0077-7579(86)90004-9
  27. Kohonen, T. 1989. Self-organization and Associative Memory. Springer-Verlag, Berlin. 312 p.
  28. KORDI. 1997. Wintering report of the 9th Korea Antarctic Research Program at King Sejong Station (Dec.-1995 Dec. 1996). 490 p. (In Korean)
  29. 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.
  30. Legendre, P. and L. Legendre. 1983. Numerical Ecology. Elsevier, Netherlands. 419 p.
  31. Legendre, P. and L. Legendre. 1987. Developments in numerical ecology. Springer-Verlag, Berlin. 585 p.
  32. 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. https://doi.org/10.1016/0304-3800(95)00142-5
  33. Levine, E.R., D.S. Kimes, and V.G. Sigillito. 1996. Classifying soil structure using neural networks. Ecol. Model., 92, 101-108. https://doi.org/10.1016/0304-3800(95)00199-9
  34. Lippmann, R.P. 1987. An introduction to computing with neural nets. IEEE Acoustics, Speech Signal Process Mag. 4-22.
  35. Ludwig, J.A. and J.F. Reynolds. 1988. Statistical Ecology: a Primer of Methods and Computing. John Wiley and Sons, New York, 337 p.
  36. 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. https://doi.org/10.1016/0022-0981(94)00138-4
  37. 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. https://doi.org/10.1016/S0272-7714(05)80053-8
  38. Palmer, M.A. 1988. Epibenthic predators and marine meiofauna: separating predation, disturbance, and hydrodynamic effects. Ecology, 69, 1251-1260. https://doi.org/10.2307/1941280
  39. 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.
  40. 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. https://doi.org/10.1007/BF00443943
  41. 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. https://doi.org/10.1093/jee/84.6.1710
  42. 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. https://doi.org/10.1016/S0304-3800(96)00049-X
  43. Sherman, K.M. and B.C. Coull. 1980. The response of meiofauna to sediment disturbance. J. Exp. Mar. Biol. Ecol., 45, 59-71.
  44. 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. https://doi.org/10.1139/f79-073
  45. 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. https://doi.org/10.1016/0022-0981(87)90167-5
  46. 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.
  47. Szymelfenig, M., S. Kwasniewski, and J.M. Westawski. 1995. Intertidal zone of Svalbard. 2. Meiobenthos density and occurrence. Polar Biol., 15, 137-141. https://doi.org/10.1007/BF00241052
  48. Tan, S.S. and F.E. Smeins. 1996. Predicting grassland community changes with an artificial neural network model. Ecol. Model., 84, 91-97. https://doi.org/10.1016/0304-3800(94)00131-6
  49. 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. https://doi.org/10.1016/0198-0149(89)90058-7
  50. 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. https://doi.org/10.1016/0304-3800(95)00018-6
  51. Zurada, J.M. 1992. Introduction to Artificial Neural Systems. West Pub. Co., New York. 683 p.

Cited by

  1. Spatial Characteristics of Meiobenthic Community of Kongfjorden Sediment in the Svalbard Island, the Arctic Sea vol.27, pp.3, 2005, https://doi.org/10.4217/OPR.2005.27.3.299
  2. The Community Structure of Meiofauna in Marian Cove, King George Island, Antarctica vol.33, pp.3, 2011, https://doi.org/10.4217/OPR.2011.33.3.265