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Understanding the ecology of plankton

Published online by Cambridge University Press:  13 July 2009

John Woods
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Abstract

Plankton are microscopic organisms living in the sea. They are responsible for more than half of the biological production of our planet. They also influence global climate, fisheries and pollution. Understanding the ever-changing distribution of plankton in the ocean is one of the grand challenges of science. The scope of the problem has been well documented in the 20th century, and new research tools should greatly accelerate progress in the 21st century. A particularly promising tool is mathematical simulation of the ecosystem, in which the demographics of plankton populations are derived from the growth and behaviour of individual organisms, each responding to its immediate environment. These use new computer codes, based on the Lagrangian Ensemble method, which are at the leading edge of complexity simulation. When integrated on massively parallel computers they can simulate the individual histories of millions of plankters for several decades. These ‘virtual plankton ecosystems’ help us to understand the complex processes responsible for the ever-changing patterns of planktonic life in the ocean.

Type
Europe and Asia: Two contrasting views
Information
European Review , Volume 7 , Issue 3 , July 1999 , pp. 371 - 384
Copyright
Copyright © Academia Europaea 1999

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References

1.van Leeuwenhoek, A. (1674) Phil. Trans. Roy. Soc. 12(133), 821–831.Google Scholar
2.Hensen, V. (1911) Das Leben in Ozean nach Zählung seiner Bewohner. Übersicht und Resultate der quantitativen Untersuchungen. In: Ergibnesse der Plankton-Expedition der Humboldt-Stiftung V.0., 1–406.Google Scholar
3.Mills, E. L. (1989) Biological Oceanography: An Early History, 1870–1960 (Ithaca and London: Cornell University Press) 378 pp.Google Scholar
4.Pinker, S. (1998) How the Mind Works (London: Allen Lane, Penguin Press) 660 pp.Google Scholar
5.Hutchinson, G. E. (1961) The paradox of plankton. American Naturalist 95, 137–146.CrossRefGoogle Scholar
6.Kraus, E. B. and Bussinger, I. A. (1994) Atmosphere–Ocean Interaction (Oxford: Oxford University Press) 362 pp.Google Scholar
7.Krey, J. and Babenerd, B. (1985) Plankton Atlas of the Indian Ocean Expedition (Kiel: Institut für Meereskunde) 70 pp.Google Scholar
8.Weiner, J. (1994) The Beak of the Finch, Evolution in Real Time (London: Jonathan Cape) 332 pp.Google Scholar
9.May, R. (1976) Simple mathematical models with very complicated dynamics. Nature 261, 459–467.CrossRefGoogle ScholarPubMed
10.Pahl-Wostl, C. (1995) The Dynamic Nature of Ecosystems: Chaos and Order Entwined (Chichester: Wiley) 267 pp.Google Scholar
11.Richardson, L. F. (1922) Weather Prediction by Numerical Process (Cambridge University Press) 236 pp.Google Scholar
12.Casti, J. (1997) Would be Worlds: How Simulation is Changing the Frontiers of Science (New York: Wiley) 242 pp.Google Scholar
13.Hastings, A. (1997) Population biology – concepts and models (Springer–Verlag, New York) 220 pp.CrossRefGoogle Scholar
14.Richardson, L. F. (1919) An apology for the use of mathematics. In: Richardson, L. F. (Ed.) Mathematical Psychology of War (Collected Works, 1993, 2, 63–100) (Cambridge University Press) 67–70.Google Scholar
15.Waldrop, M. M. (1992) Complexity (New York: Simon & Shuster) 380 pp.Google Scholar
16.Kauffman, S. A. (1993) The Origins of Order (Oxford: Oxford University Press) 709 pp.CrossRefGoogle Scholar
17.Evans, G. T. and Fashom, M. J. R. (1993) “Towards a model of biogeochemical processes” (Berlin: Springer–Verlag) 350 pp.CrossRefGoogle Scholar
18.DeAngelis, D. L. and Gross, L. J. (1992) Individual-based Models and Approaches in Ecology: Populations, Communities and Ecosystems (New York: Chapman & Hall) 525 pp.CrossRefGoogle Scholar
19.Lomnicki, A. (1988) Population Ecology of Individuals (Princeton University Press) 223 pp.Google ScholarPubMed
20.Woods, J. and Barkmann, W. (1994) Simulating plankton ecosystems by the Lagrangian Ensemble method. Philosophical Transactions of the Royal Society B 343, 27–31.Google Scholar
21.Eddington, A. (1935) New Pathways in Science (London: MacMillan).Google Scholar
22.Milton, J. (1674) Paradise Lost (London: Simmons) (Quotation from Book 2, line 891).Google Scholar
23.Lythgoe, K., Brice, A. and Woods, J. D. (1998) The Lifespan Program for Computing Plankton Demography Virtual Plankton Ecology, Technical Report No.3 (London: Imperial College).Google Scholar