Cyanobacterial blooms occur frequently in lakes due to eutrophication. Although a number of models have been proposed to forecast algal blooms, a good and applicable method is still lacking. This study explored a simp...Cyanobacterial blooms occur frequently in lakes due to eutrophication. Although a number of models have been proposed to forecast algal blooms, a good and applicable method is still lacking. This study explored a simple and effective mathematical-ecological model to evaluate the growth status and predict the population dynamics of Microcystis blooms. In this study, phytoplankton were collected and identified from 8 sampling sites in Chaohu Lake every month from July to October, 2010. The niche breadth and niche overlap of common species were calculated using standard equations, and the potential relative growth rates of Microcystis were calculated as a weighted-value of niche overlap. In July, the potential relative growth rate was 2.79 (a.u., arbitrary units) but then rapidly declined in the following months to -3.99 a.u. in September. A significant correlation (R=0.998, P<0.01) was found in the model between the net-increase in biomass of Microcystis in the field and the predicted values calculated by the niche model, we concluded that the niche model is suitable for forecasting the dynamics of Microcystis blooms. Redundancy analysis indicated that decreases in water temperature, dissolved oxygen and total dissolved phosphorus might be major factors underlying bloom decline. Based on the theory of community succession being caused by resource competition, the growth and decline of blooms can be predicted from a community structure. This may provide a basis for early warning and control of algal blooms.展开更多
基金Supported by the National Basic Research Program of China (973 Program)(No. 2008CB418002)the National Major Programs of Water Body Pollution Control and Remediation (Nos. 2009ZX07106-001, 2009ZX07104-005)the National Natural Science Foundation of China (No. 30830025)
文摘Cyanobacterial blooms occur frequently in lakes due to eutrophication. Although a number of models have been proposed to forecast algal blooms, a good and applicable method is still lacking. This study explored a simple and effective mathematical-ecological model to evaluate the growth status and predict the population dynamics of Microcystis blooms. In this study, phytoplankton were collected and identified from 8 sampling sites in Chaohu Lake every month from July to October, 2010. The niche breadth and niche overlap of common species were calculated using standard equations, and the potential relative growth rates of Microcystis were calculated as a weighted-value of niche overlap. In July, the potential relative growth rate was 2.79 (a.u., arbitrary units) but then rapidly declined in the following months to -3.99 a.u. in September. A significant correlation (R=0.998, P<0.01) was found in the model between the net-increase in biomass of Microcystis in the field and the predicted values calculated by the niche model, we concluded that the niche model is suitable for forecasting the dynamics of Microcystis blooms. Redundancy analysis indicated that decreases in water temperature, dissolved oxygen and total dissolved phosphorus might be major factors underlying bloom decline. Based on the theory of community succession being caused by resource competition, the growth and decline of blooms can be predicted from a community structure. This may provide a basis for early warning and control of algal blooms.
