School of Fisheries Sciences, Kitasato University Iwate, JAPAN

k.koike@kitasato-u.ac.jp

　

Several species of dinoflagellates belonging to genus Dinophysis cause diarrhetic shellfish poisoning (DSP), and thus the finding of their growth- promoting factors has been long wanted. However, it is still obscure mostly because of inability for maintaining the culture in a laboratory. We have concentrated on the biological studies on Dinophysis, mainly on phototrophic Dinophysis, in the field of the Sanriku coast, Japan. We here summarize on the nutrition of Dinophysis spp. and describe future task.

　

At first, we focused on the origin of Dinophysis chloroplast. The chloroplast has same properties to that of Cryptophyceae, not Dinophyceae, for its structure and pigment composition. The relation between the chloroplast and Dinophysis cell has been considered to be long established because of the lack of other cryptophycean remnants in Dinophysis cell. From the analysis of chloroplast SSU rDNA sequences obtained from several phototrophic species of Dinophysis, we conclude that the chloroplasts are not long- established but temporally obtained from Cryptophyceae in the environment. That is, they can acquire the ability of photosynthesis by termed "kleptoplastidy" (robbery of chloroplast). Other than the phototrophy, they were revealed to perform also heterotrophy. Under a transmission electron microscopy, D. fortii and D. tripos occasionally had food vacuoles containing large amount of foreign mitochondria, indicating they sometimes prey on eukaryote. Bacterial cells were also found in process of digestion in these Dinophysis. Consequently, they are considered to perform and switch these varieties of nutrition depending on the available environment.

　

　

¹Department of Ecology & Biodiversity, The University of Hong Kong Hong Kong, CHINA

lamhyi@hkusua.hku.nk

　

²School of Science and Technology, The Open University of Hong Kong Hong Kong, CHINA

　

Nutrients, total inorganic nitrogen (TIN) and dissolved inorganic phosphates (DIP) in general, are considered the major triggering and limiting factors for harmful algal blooms. While some models on the basis of TIN:DIP ratios have been developed to interpret red tide occurrences in Hong Kong, collection of real time data by in situ measurement of nutrients is still constrained by limited research and technology development. A pilot test of incorporating an auto-nutrient analyzer with telemetry system was conducted to study the possibility of providing in situ and real time data for environmental models. A synchronizing diurnal study of phytoplankton dynamics at Crooked Island, Hong Kong was reported besides evaluating the effectiveness of the integrated analyzer-telemetry system.

　

　

¹Department of Civil Engineering, Graduate School of Engineering Kyoto University, Uji, JAPAN

ywpark@rcde.dpri.kyoto-u.ac.jp

　

²Disaster Prevention Research Institute, Kyoto University Uji, JAPAN

　

In the ecosystem problem of the coastal area, it is very important research subjects for coastal engineering that makes the monitoring system, the comprehensive enviroumental database system and the development of prediction method used the observation data for environmental condition. The observation has started since 1998 joined with several research group to reveal the sea water exchange mechanism and phytoplankton dynamics for the red tide blooms in Tanabe Bay, where west side boundary is located at the border of the Kii Channel and affected Kuroshio current.

　

Real time predicting the temporal variation of phytoplankton species was examined by the artificial neural network. It is examined the interaction between the phytoplankton species and environmental condition. The commonly used learning system is the back-propagation model, which is applied in this study.

　

We have trained the system using the observed data in 1999 and 2000, and then estimated the phytoplankton blooms using only input parameter in 2001. Input data are water temperature, salinity, dissolved oxygen, solar radiation, precipitation, air temperature and nutrient (PO₄, SiO₂, NO₂, NO₃, NH₄) and the cell concentration of diatoms and dinoflagellates in the 1ml is output variable. We changed the number of input for selecting and applying the minimum input data which is necessary for real time estimate. Estimation using only water temperature, salinity and solar radiation is not so good. However if air temperature and dissolved oxygen are included, the prediction system is improved and the fluctuation of phytoplankton species can be reproduced rather.