The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY (한국해양학회지:바다)

The Korean Society of Oceanography (한국해양학회)
권호 표지
DOI QR코드

DOI QR Code

A Brief Introduction to Marine Ecosystem Modeling

해양 생태모델링 고찰

  • Kim, Hae-Cheol (I. M. Systems Group at Environmental Modeling Center, NCEP/NWS/NOAA) ;
  • Cho, Yang-Ki (School of Earth and Environment Sciences/Research Institute of Oceanography, Seoul National University)
  • Received : 2013.01.04
  • Accepted : 2013.02.05
  • Published : 2013.02.28
Download PDF
⟨ Previous Next ⟩

Abstract

Ecosystem models are mathematical representations of underlying mechanistic relationships among ecological components and processes. Ecosystem modeling is a useful tool to visualize inherent complexities of ecological relationships among components and the characteristic variability in ecological systems, and to quantitatively predict effects of modification of systems due to human activities and/or climate change. A number of interdisciplinary programs in recent 20 to 30 years motivated oceanographic communities to explore and employ systematic and holistic approaches, and as an outcome of these efforts, synthesis and modeling became a popular and important way of integrating lessons learned from many on-going projects. This is a brief review that includes: background information of ecosystem dynamics model; what needs to be considered in building a model framework; biologically-physically coupled processes; end-to-end modeling efforts; and parameterization and related issues.

생태모델은 생태계 구성 요소간의 관계를 수치적으로 표현하여 생태계 내에 존재하는 다양한 요인들의 시간에 따른 내재적 변동과 외부 조건의 변화에 따른 반응을 예측하는데 유용한 도구다. 해양 생태모델은 학제간 공동 연구결과를 토대로, 보다 체계적이고 종합적인 접근 방법을 통해, 최근 수 십 년 동안 많은 발전을 이룩하였다. 이 글은 해양 생태모델의 이론적 배경을 살피고, 모델 수립 시 고려해야 할 사항 및 최근 동향에 대하여 소개한다.

Keywords

References

  1. Blower, S. and J. Roughgarden, 1987. Population dynamics and parasitic castration: a mathematical model, American Naturalist, 129: 730-754.
  2. DeCoursey, D.G. 1992. Developing models with more detail: do more algorithms give more truth? Weed Technology, 6: 709-715.
  3. Denman, K.L., 1993. Scale-determining biological-physical interactions in oceanic food webs, In: Aquatic Ecology, Pattern and Process, 377-402 pp.
  4. Denman, K.L. and M.A. Pena, 1999. A coupled 1-D biological/physical model of the northeast subarctic Pacific Ocean with iron limitation. Deep-Sea Research II, 46: 2877−2908. https://doi.org/10.1016/S0967-0645(99)00087-9
  5. Doney, S.C., D.M. Glover and R.G. Najjar, 1996. A new coupled, one-dimensional biological-physical model for the upper ocean: Applications to the JGOFS Bermuda Atlantic Time-series Study (BATS) site. Deep-Sea Research II, 43: 591−624. https://doi.org/10.1016/0967-0645(95)00104-2
  6. Doney, S.C., J.A. Kleypasa, J.L. Sarmiento and P.G. Falkowski, 2002. The US JGOFS Synthesis and Modeling Project-An introduction. Deep-Sea Research II, 49: 1−20. https://doi.org/10.1016/S0967-0637(01)00043-7
  7. Dugdale, R.C., 1967. Nutrient limitation in the sea: dynamics, identification, and significance. Limnology and Oceanography, 12: 685−695. https://doi.org/10.4319/lo.1967.12.4.0685
  8. Dugdale, R.C., 1976. Modeling. In: The Sea, 6: 789−806.
  9. Eigenheer, A., W. Kuhn and G. Radach, 1996. On the sensitivity of ecosystem box model simulations on mixed-layer depth estimates, Deep-Sea Research I, 43: 1011−1027. https://doi.org/10.1016/0967-0637(96)00046-5
  10. Eppley, R.W., 1972. Temperature and phytoplankton growth in the sea. Fisheries Bulletin, 70: 1063−1085.
  11. Evans, G.T. and J.S. Parslow, 1985. A model of annual plankton cycles. Biological Oceanography, 3: 327−347.
  12. Fasham, M.J.R., H.W. Ducklow and S.M. Mckelvie, 1990. A nitrogen based model of plankton dynamics in the oceanic mixed layer, Journal of Marine Research, 48: 591−639.
  13. Fasham, M.J.R., J. L. Sarmiento, R. D. Slater, H.W. Ducklow and R. Williams, 1993. A seasonal three-dimensional ecosystem model of nitrogen cycling in the North Atlantic euphotic zone: A comparison of the model results with observation from Bermuda Station "S" and OWS "India", Global Biogeochemical Cycles, 7: 417−450.
  14. Fasham, M.J.R. and G.T. Evans, 1995. The use of optimization techniques to model marine ecosystem dynamics at the JGOFS station at $17^{\circ}N$ $20^{\circ}W$, Philosophical Transactions of the Royal Society of London, 348: 203−209. https://doi.org/10.1098/rstb.1995.0062
  15. Fennel, W., 2008. Towards bridging biogeochemical and fish-production models, Journal of Marine Systems, 71: 171−194.
  16. Franks, P.J.S., J.S. Wroblewski and G.R. Flierl, 1986a. Behavior of a simple plankton model with food-level acclimation by herbivores, Marine Biology, 91: 121−129.
  17. Franks, P.J.S., J.S. Wroblewski and G.R. Flierl, 1986b. Prediction of phytoplankton growth in response to the frictional decay of a warm-core ring, Journal of Geophysical Research, 91: 7603−7610.
  18. Franks, P.J.S., 1995. Coupled physical-biological models in oceanography, Review of Geophysics Supplement, July: 1177−1187.
  19. Franks, P.J.S., 2009. Planktonic ecosystem models: perplexing parameterizations and a failure to fail, Journal of Plankton Research, 31: 1299−1306.
  20. Freedman, G.I., 1980. Single-species growth, In: Deterministic Mathematical Models in Population Ecology, 3-19 pp.
  21. Freedman, G.I., 1980. Chapters 3, 4 and 5, In: Deterministic Mathematical Models in Population Ecology, 33-109 pp.
  22. Friedrichs, M.M., R.R. Hood, J.D. and Wiggert, 2006. Ecosystem model complexity versus physical forcing: Quantification of their relative impact with assimilated Arabian Sea data, Deep-Sea Research II, 53: 576−600.
  23. Fulton, E.A., A.D.M. Smith and C.R. Johnson,2003. Effect of complexity on marine ecosystem models, Marine Ecology Progress Series, 253: 1−16.
  24. Horne, J.K. and D.C. Schneider, 1994. Analysis of scale-dependent processes with dimensionless ratios, Oikos, 70: 201−211.
  25. Hurtt, G.C. and R.A. Armstrong, 1999. A pelagic ecosystem model calibrated with BATS and OWSI data, Deep-Sea Research II, 46: 27-61. https://doi.org/10.1016/S0967-0637(98)00055-7
  26. Ito, S, M.J. Kishi, K. Kurita, Y. Oozeki, Y. Yamanaka, B.A. Megrey and F.E. Werner 2004. A fish bioenergetics model application to Pacific saury coupled with a lower trophic ecosystem model, Fisheries Oceanography, 13(Suppl 1): 111-124.
  27. Ivelev, V.S., 1955. Experimental ecology of the feeding of fishes, Pischerpromizdat. Moscow. 302pp (translated from the Russian by D. Scott (1969), New Haven, Yale University Press).
  28. Large, W. G., J. C. McWilliams and S. C. Doney, 1994: Oceanic vertical mixing: a review and a model with a nonlocal boundary layer parameterization. Review Geophysics, 32: 363−403. https://doi.org/10.1029/94RG01872
  29. Lawson, L.M., E.E. Hofmann and Y.H. Spitz, 1996. Time series sampling and data assimilation in a simple marine ecosystem model, Deep Sea Research II, 43: 625−651.
  30. Lotka, A.J., 1925. Elements of Physical Biology, Williams and Wilkins.
  31. Martin, J.H. and S.E. Fitzwater, 1988. Iron deficiency limits phytoplankton growth in the north-east Pacific subarctic, Nature 331: 341. doi:10.1038/331341a0.
  32. Matear, R.J., 1995. Parameter optimization and analysis of ecosystem models using simulated annealing: A case study at Station P, Journal of Marine Research, 53: 571−607.
  33. Malthus, T.R., 1798. An Essay on the Principle of Population. Johnson, London.
  34. Megrey, B.A., K.A. Rose, R. A. Klumb, D.E. Hay, F.E.Werner, D.L. Eslinger and S.L. Smith 2007. A bioenergetics-based population dynamics model of Pacific herring (Clupea harengus pallasi) coupled to a lower trophic level nutrient-phytoplankton-zooplankton model: description, calibration, and sensitivity analysis, Ecological Modelling, 202: 144-164. https://doi.org/10.1016/j.ecolmodel.2006.08.020
  35. Michaelis, L. and M. Menten, 1913. Die Kinetik der Invertinwirkung, Biochem. Z., 49: 333−369.
  36. Mills, E.L., 1989. Disciplined thinking in biological oceanography: phytoplankton dynamics, physical oceanography and Riley's "synthetic method", In: Biological Oceanography, An Early History, 1870−1960, pp. 282-309.
  37. O'Brien, J.J. and J.S. Wroblewski, 1971. On advection in phytoplankton models, Journal of Theoretical Biology, 38: 197−202.
  38. Prunet, P., J.F. Minster, V. Echevin and I. Dadou, 1996a. Assimilation of surface data in a one-dimensional physical biogeochemical model of the surface ocean: 1. Adjusting a simple trophic model to chlorophyll, temperature, nitrate, and $pCO_{2}$ data, Global Biogeochemical Cycles, 10: 139−158. https://doi.org/10.1029/95GB03435
  39. Prunet, P., J.F. Minster, D. Ruiz-Pino and I. Dadou, 1996b. Assimilation of surface data in a one-dimensional physical biogeochemical model of the surface ocean: 1. Method and preliminary results, Global Biogeochemical Cycles, 10: 111-138. https://doi.org/10.1029/95GB03436
  40. Riley, G.A., 1946. Factors controlling phytoplankton populations on Georges Bank, Journal of Marine Research, 6: 54−73.
  41. Rudstam, L.G., 1988. Exploring the dynamics of herring consumption in the Baltic: applications of an energetic model of fish growth, Kieler Meeresforschung Sonderheft 6: 321-322.
  42. Spitz, Y.H., J.R. Moisan, M.R. Abott and J.G. Richman, 1998. Data assimilation and a pelagic ecosystem model: Parameterization using time series observations, Journal of Marine System, 16: 51−68.
  43. Steele, J.H. and M.M. Mullin, 1976. Zooplankton dynamics, In: The Sea, 6: 857−890.
  44. Steele, J.H. and B.W. Frost, 1977. The structure of plankton communities, Philosophical Transactions of the Royal Society of London, 270: 485−534.
  45. Vallino, J.J., 2000. Improving marine ecosystem models: Use of data assimilation and mesocosm experiments, Journal of Marine Research, 58: 117−164.
  46. Verhulst, P.-F., 1838. Notice sur la loi que la population poursuit dans son accroissement. Correspondance mathématique et physique 10: 113-121. Retrieved 09/08/2009.
  47. Volterra, V., 1926. Variazioni e fluttuazioni del numero d'individui in specie animali conviventi, Mem. Acad. Lincei Roma, 2: 31-113.
  48. Walsh, J.J., 1972. Implications of a systems approach to oceanography. Science, 176: 969−975. https://doi.org/10.1126/science.176.4038.969
  49. Wroblewski, J.S., 1977. A model of phytoplankton plume formation during variable Oregon upwelling. Journal of Marine Research, 35: 357−394.
  50. Wroblewski, J.S. and J.J. O'Brien, 1981. On modeling the turbulent transport of passive biological variables in aquatic ecosystems, Ecological Modelling, 12: 29−44. https://doi.org/10.1016/0304-3800(81)90023-5
  51. Wroblewski, J.S., 1983. The role of modeling in biological oceanography. Ocean Science and Engineering, 8: 245−285.

Cited by

  1. Technical Reviews on Ecosystem Modeling Approach and its Applicability in Ecosystem-Based Coastal Management in Saemangeum Offshore and Geum River Estuary vol.18, pp.3, 2015, https://doi.org/10.7846/JKOSMEE.2015.18.3.233
  2. DEVELOPMENT OF SIGHTSEENG TRAVEL BEHAVIOR ANALYSIS METHOD USING SNS BIG DATA vol.75, pp.5, 2013, https://doi.org/10.2208/jscejipm.75.i_129