Ecophysiology of phyto- and bacterioplankton growth in the Prydz Bay area during the austral summer 1987: I. Modelling phytoplankton growth
Lancelot, C.; Billen, G.; Mathot, S. (1988). Ecophysiology of phyto- and bacterioplankton growth in the Prydz Bay area during the austral summer 1987: I. Modelling phytoplankton growth, in: Proceedings of the Belgian National Colloquium on Antarctic Research (Brussels, October 20, 1987). pp. 115-132
In: (1988). Proceedings of the Belgian National Colloquium on Antarctic Research (Brussels, October 20, 1987). Science Policy Office: Brussel. 280 pp., meer
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| Beschikbaar in | Auteurs |
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Documenttype: Congresbijdrage
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| Trefwoorden |
Aquatic communities > Plankton > Nannoplankton
Biology > Physiology > Ecophysiology
Growth
Modelling
Phytoplankton
PSE, Antarctica, MacRobertson Land:, Prydz Bay [Marine Regions]
Marien/Kust |
| Auteurs | | Top |
- Lancelot, C., meer
- Billen, G., meer
- Mathot, S.
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| Abstract |
A model of phytoplankton growth based on the knowledge of phytoplankton physiology and vertical mixing of surface waters was applied in the Prydz Bay during end-summer 1987. Physiological parameters of the model were experimentally estimated by means of kinetics of phytoplanktonic activities, measurements combining radiotracer technology and classical biochemical methods. Their control by temperature was outlined. It was found that phytoplankton cells of the Prydz Bay were able to grow at their maximal rate between + 2°C and 12°C. An exponential dependence on temperature was however observed in the range -1.8° + 2°C. Comparison between field data and predictions of the model run for the different growing conditions encountered by the cells during end-summer in the Prydz Bay indicated that growth and physiological death of phytoplankton were well balanced resulting in no net. increase of biomass at this end-summer period. Finally additional runs of the model under the extreme growing conditions of Antarctic phytoplankton, has shown that the major trends of variations in phytoplankton biomsses could be predicted by the model. |
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