Abstracts:

QUANTIFICATION OF ALEXANDRIUM TAMARENSE BY FLOW CYTOMETRY AND IN SITU-HYBRIDISATION FOR MONITORING HARMFUL ALGAL BLOOMS

Joachim Brenner1, Gunnar Gerdts2, Christian Hummert3, Georg Donner4, Nathalie Simon5, Christian Schütt2, Bernd Luckas3, Malte Elbrächter4, Linda K. Medlin6, Hans-Dieter Görtz1

1 Universität Stuttgart, Biologisches Institut, Abteilung Zoologie, ?Pfaffenwaldring 57, 70550 Stuttgart 2 BAH/ Stiftung AWI, Meeresmikrobiologie, 27498 Helgoland 3 Universität Jena, Institut für Ernährung und Umwelt, Lebensmittelkunde, ?Dornburger Str. 25, 07743 Jena 4 BAH/ Stiftung AWI, Wattenmeerstation, Hafenstr. 43, 25992 List/Sylt 5 Station Biologique F-29682 Roscoff, Cedex, France 6 Alfred-Wegener Institut für Polar- und Meeresforschung (AWI), Am ?Handelshafen 12, 27570 Bremerhaven


During several cruises with the research vessel HEINCKE (BAH/AWI, Helgoland, Germany) we were able to detect different species of dinoflagellates - causing algal blooms - by flow cytometry. In May 1997 and May 1998 we found toxic algal blooms at the Eastcoast of Scotland and at the Orkney Islands. These blooms were caused by a saxitoxin producing species of Alexandrium tamarense. We detected these dinoflagellates in water samples by flow cytometry and in situ-hybridisation of the 18SrRNA and the 28SrRNA. The population of Alexandrium tamarense in multiparametric dot-plots can be correlated with the saxitoxin concentration measured by HPLC. We were also able to show a correlation between cell concentration measured by flow cytometry and the cell concentration determined by the microscopic method of Utermöhl. The flow cytometric data (list-mode) were also analysed automatically by a trained neural network (backpropagation) and compared with results from conventional gating, corresponding populations in 2D-dot-plots.Our measurements and results of different dinoflagellates from algal blooms clearly show that it is possible to detect specific algae species and subspecies by in situ-hybridisation with specific oligonucleotide probes and to quantify it in a flow cytometer. In combination with the automatic recognition of the dinoflagellates by a neural network this system is a further step towards automated plankton monitoring. (Supported by the German BMBF, TEPS-Project)

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