Spatial and temporal assessment of the initial pattern of phytoplankton population in a newly built coastal reservoir

Abstract

For decades, the main threat to the water security of a metropolis, such as the city of Shanghai, has been the rapidly growing demand for water and at the same time, the decrease in water quality, including eutrophication. Therefore Shanghai shifted the preferred freshwater source to the Yangtze Estuary and constructed the Qingcaosha Reservoir, which is subject to less eutrophic water from the Yangtze River. To assess the population of phytoplankton for the first time in the newly built reservoir, this study improved an integrated method to assess the phytoplankton pattern in large-water-area reservoirs and lakes, using partial triadic analysis and Geographic Information Systems. Monthly sampling and monitoring from 10 stations in the reservoir from July 2010 to December 2011 were conducted. The study examined the common pattern of the phytoplankton population structure and determined the differences in the specific composition of the phytoplankton community during the transition period of the reservoir. The results suggest that in all but three sampling stations in the upper parts of Qingcaosha Reservoir, there was a strong common compromise in 2011. The two most important periods occurred from late summer to autumn and from winter to early spring. The former was characterized by the dominance of cyanobacteria, whereas the latter was characterized by the dominance of both chlorophyta and diatoms. Cyanobacteria (Microcystis spp. as the main genus) were the monopolistic dominant species in the summer after reservoir operation. The statistical analysis also indicated the necessity for regular monitoring to focus on the stations in the lower parts of the reservoir and on several limited species.

Keywords

phytoplankton dynamics / Partial Triadic Analysis / Geographic Information Systems / management / Qingcaosha Reservoir / Shanghai

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Acknowledgments

This work was funded and supported by the National Natural Science Foundation of China (Grant No. 71073055) and the Science and Technology Commission of Shanghai Municipality (09DZ120010A). Lin Ma and Xiaoyu Zheng are acknowledged for their help with monitoring samples, and Haiying Zhang, Meng Wu, Sheng Xie, and Yan Jin are acknowledged for their help with the sampling. We are indebted to Xin Tong for guidance regarding R software. We are also grateful to Professor Yongjie Gu for her work in the field survey and her support in the identification of the phytoplankton.

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2015 Higher Education Press and Springer-Verlag Berlin Heidelberg