Lin-lin CAI , Guang-wei ZHU* Meng-yuan ZHU Hai XU Bo-qiang QIN

1. State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology,Chinese Academy of Sciences, Nanjing 210008, P. R. China

2. Wuxi Taihu Lake Management Company Limited, Wuxi 214063, P. R. China

1 Introduction

Freshwater lakes undertake an important ecological function for human beings in terms of their ecological and societal values, including water supply, sewage disposal, fisheries,flood protection, and recreation (Moss et al. 1996; Guo 2007). The algal bloom is one of the most common problems reducing the ecological value of freshwater bodies (Khan and Ansari 2005; Moss et al. 1996; James et al. 2009). Widespread dominance of Microcystis spp. in various lakes of the world has been explained by temperature, underwater light (turbidity,suspended solids (SS), or Secchi depth (SD)), nutrients, buoyancy regulation, and zooplankton grazing or fish grazing (Reynolds et al. 2001; Schindler 2006; Guo 2007).

Generally, the amplification of algal blooms has long been recognized as the result of increasingly imported nutrients, mainly nitrogen and phosphorus (Paerl and Fulton 2006;James et al. 2009). Water temperature also directly affects the growth of algae (Stella et al.2007). For example, Paerl and Huisman (2008)showed that rising temperature favors harmful algal blooms. Reynolds (2006)found that cyanobacteria grows better in high-temperature(often above 25°C)environment than other phytoplankton species such as diatoms and green algae, and cyanobacterial blooms may increase water temperature conversely through intensifying the absorption of light.

Taihu Lake, the third largest freshwater lake in China, has been heavily studied for its eutrophication and algal blooms (Qin and Zhu 2006; Qin et al. 2007; Guo 2007; Qin 2008,2009; Yang et al. 2008; Xu et al. 2010). Changes of the water quality of Taihu Lake during the last 30 years, including increases of the frequency and magnitude of algal blooms, have been blamed on more nutrients from rivers and farming (Gao et al. 2004; Qin et al. 2007). Besides higher nutrients contents, the extremely warm weather combined with local wind conditions favored the expansion of blooms (Qin et al. 2010). Duan et al. (2009)found a correlation between higher spring temperature and earlier occurrences and more serious blooms in Taihu Lake. However, more detailed and quantitative relationship between water temperature and algal blooms in the lake is still unknown.

Accumulated temperature and effective accumulated temperature are common indicators used in the field of growth of crops and insects (Bunting 1976; Ma et al. 2008). The tight correlation between the recruitment of phytoplankton and effective accumulated temperature has been reported both in laboratory and field studies on Taihu Lake (Cao et al. 2008; Liu et al.2011). But few researchers have quantitatively determined the relationship between algal blooms and both temperature and nutrients. Therefore, the objectives of this study were: (1)to use the monthly and daily monitoring data to evaluate annual changes of and links between chlorophyll-a (Chl-a), nutrients, and the main physical environmental parameters during algal bloom seasons from 1999 to 2007, and (2)to obtain the relative importance of temperature and nutrients on the intensity of Microcystis blooms in Taihu Lake.

2 Methods

2.1 Study site

Taihu Lake is located in the Yangtze River Delta between 30°56′N and 31°32′N and between 119°54′E and 120°36′E (Fig. 1)with a mean depth of 1.9 m, a volume of 4.4 km3, an area of 2 338 km2, and a catchment area of 36 500 km2(Li et al. 1994; Qin et al. 2010). It is located in a subtropical monsoon zone with prevailing southeast wind in summer and northwest wind in winter. There are in total more than 200 brooks, canals, and rivers connected to the lake, and the annual water input is about 7.6 km3(Qin et al. 2007). There are several large cities, such as Shanghai, Suzhou, and Wuxi, around the lake, and about 40 million people live in the watershed. Taihu Lake played an important role in water supply and flood protection,with fisheries, shipping, and tourism being additionally important economic functions. It is also a repository for sewage from urban areas and nearby agricultural and industrial segments due to rapidly growing local economy (Duan et al. 2009; Qin et al. 2010).

Fig. 1 Location of Taihu Lake and distribution of sampling sites in Meiliang Bay and Central Lake

During the 1960s, the water quality of Taihu Lake was quite good and there were no reports about algal blooms in the open region of the lake (Sun and Huang 1993). Since the mid-1980s, with the rapid growth of the local economy and nutrient input to the lake, blooms of Microcystis spp. have been occurring every summer in Meiliang Bay in the northern part of Taihu Lake (Shi and Zai 1994; Qin et al. 2007). Monthly monitoring data of water quality and the phytoplankton community of Taihu Lake since 1991 shows highly different temporal and spatial distributions of water quality and blooms (Qin et al. 2007; Zhang et al. 2007; Duan et al. 2009).

Meiliang Bay, one of the main drinking water sources for Wuxi City, is one of the most eutrophic bays in the northern part of Taihu Lake. The surface area of the bay is about 124 km2.There are two main rivers, the Liangxi River and the Zhihugang River, connected to the bay.Meiliang Bay, with high nutrient concentrations, is relatively isolated and has more serious algal blooms, while Central Lake is further away from the inflowing rivers and has stronger wind disturbance, so algal blooms are not so serious.

2.2 Data collection

All the water quality and water temperature data of Taihu Lake were collected from the Taihu Laboratory for Lake Ecosystem Research (TLLER). TLLER launched monthly water quality and phytoplankton monitoring and daily water temperature recording in 1991. The data at three sites in Meiliang Bay (B1, B2, and B3 in Fig. 1)and two sites in Central Lake (C1 and C2 in Fig. 1)have been included to represent the surface water variables during algal bloom seasons. Physical parameters such as SD and pH were measured in the field. SS and chemical variables such as the concentrations of total nitrogen (TN), dissolved total nitrogen (DTN),ammonium nitrogen ( NH-N), nitrate nitrogen (NO-N), total phosphorus (TP), dissolved total phosphorus (DTP), soluble reactive phosphorus (SRP), and Chl-a were determined in the laboratory according to the Standard of Lake Eutrophication Survey (Jin and Tu 1990).

About 300 mL of lake water was filtered with a glass membrane (GF/C)(Whatman, U.K.)to determine DTN and DTP. After digestion with alkaline potassium persulphate, TN and DTN were analyzed spectrophotometrically at a wavelength of 210 nm. TP and DTP were analyzed at 700 nm with the molybdenum blue method (Murphy and Riley 1962). NH-N, NO-N,and SRP were analyzed with a Skalar SAN++flow injection analyzer (Skalar Co., Netherlands).Chl-a was determined spectrophotometrically at wavelengths of 665 nm and 750 nm after the thawed GF/C filters were extracted from 90% hot ethanol (Lorenzen 1967; Chen et al. 2006),and the concentration of Chl-a, which was an indicator of phytoplankton biomass, was calculated based on the difference of absorbance values (Lorenzen 1967).

Water temperature at 0.5 m below the surface was measured three times every day (at 8:00, 14:00, and 20:00)at the coastal area of Taihu Lake 100 m away from the shore near TLLER (site A in Fig. 1). The mean value of the three water temperature values was used as the daily water temperature of Taihu Lake. Since the 1990s, Cyanophyta have become the predominant species, especially during the bloom season. Generally, cyanobacteria grow rapidly when the water temperature is between 18°C and 20°C (Karlsson and Brunberga 2004;Cao et al. 2008), which is consistent with the result that Microcystis dominated in Meiliang Bay, in Taihu Lake, during the warmest periods of a year when the water temperature was in the range of 18.2°C to 32.5 °C (Chen et al. 2003). We defined the effective accumulated water temperature (EAWT)as the sum of daily water temperature minus 17°C, which was calculated only when water temperature was over 17°C. Extreme climate change, such as extremely hot springs or cold springs, combined with nutrients, affects the timing and magnitude of events during the spring succession of phytoplankton, and this effect is posterior to the change of water temperature (Stella et al. 2007; Paerl and Huisman 2008; Duan et al. 2009). Therefore,we selected the earlier period of EAWT (from March to June)to study its influence on algal blooms (from May to August).

2.3 Statistical methods

Multivariate data analysis was performed using SAS version 9.1 for Windows. Long-term developments of Chl-a and nutrients were calculated using the monthly averages (from May to August)of the three/two sampling sites in Meiliang Bay/Central Lake. The sum of the positive values of the daily water temperature from March to June minus 17°C is EAWT. The annual spatial and temporal difference was compared using the one-way analysis of variance(ANOVA). A t-test was performed to detect the overall significant differences between Meiliang Bay and Central Lake. Multivariate statistical analysis was used to calculate the correlation coefficients between Chl-a and nutrients and between Chl-a and EAWT.

3 Results

3.1 Spatial and temporal variation of Chl-a

The concentrations of Chl-a varied between 2.62 μg/L and 150.66 μg/L during the bloom seasons of 1999 to 2007 (with a maximum value occurring in Meiliang Bay in June 2007 and a minimum value occurring in Central Lake in August 1999). The concentration of Chl-a was significantly higher in Meiliang Bay than in Central Lake (P ＜ 0.01, where P is the Pearson significance level). The Chl-a concentrations in both Meiliang Bay and Central Lake increased during the nine years, and increased relatively more in Meiliang Bay than in Central Lake(Fig. 2). The Chl-a concentration in Meiliang Bay reached a peak value in the bloom season of 2007, with a mean value of 51.13 μg/L. This was due to the outbreak of a massive bloom near a water intake in Gonghu Bay (Qin et al. 2010). It is of interest to note that during the algal bloom seasons of 2005 to 2007, the Chl-a concentrations in Central Lake were higher than those in Meiliang Bay in 1999, and algal blooms have been expanding from Meiliang Bay to Central Lake since 2005 (Zhu 2009).

Fig. 2 Annual mean values of Chl-a concentration in Meiliang Bay and Central Lake

3.2 Annual trends of nutrients, SS, and SD

Fig. 3 Concentrations of nutrients and SS, and SD in Meiliang Bay and Central Lake from 1999 to 2007

The TN and TP concentrations increased a lot in the two zones, and increased more in Meiliang Bay than in Central Lake (Figs. 3(a)and (b)). The TN concentration ranged from 0.76 mg/L to 8.34 mg/L (with a maximum value occurring in Meiliang Bay in June 2007 and a minimum value occurring in Central Lake in August 2000). The TP concentration ranged from 0.01 mg/L to 0.42 mg/L (with a maximum value occurring in Meiliang Bay in June 2007 and a minimum value occurring in Central Lake in July 1999). For TN, there were two significant drops in 2000 and 2005, while the TP concentration had a more smooth increasing trend than the TN concentration. TN and TP had similar spatial and temporal trends with Chl-a in general,and both of them had significantly higher values in Meiliang Bay than in Central Lake (P ＜0.01)(Table 1).

Table 1 Values of parameters (mean ± standard deviation)in Meiliang Bay and Central Lake during algal bloom seasons

The TP concentration in Meiliang Bay increased significantly from 2005 to 2007, while it did not increase so much in Central Lake as it did in Meiliang Bay from 1999 to 2004(Fig. 3(b)). The trends of DTP were similar to TP. The ratios of DTP/TP in Meiliang Bay and Central Lake were similar (Fig. 3(i)). The concentration of SRP in Meiliang Bay was significantly higher than in Central Lake (P ＜ 0.01)(Table 1).

There was no significant differences in SS and SD between Meiliang Bay and Central Lake (Table 1 and Fig. 3). SS increased year by year, while SD decreased year by year. Both of them gave a stong signal that the lake became more and more turbid during the nine years.

3.3 Annual variation of EAWT

EAWT showed an increasing trend during the initial algal bloom seasons (Fig. 4). It increased from 335.2°C in 1999 to 551.6°C in 2000, then decreased year by year until 2002,and increased again to 655.4°C in 2007. The concentrations of Chl-a and TP have similar trends with EAWT in Meiliang Bay, but the peak value of EAWT in 2000 is much larger than Chl-a and TP (Fig. 4). In Central Lake, the changes of Chl-a, TN, TP, and DTP trends lagged about one year behind EAWT. No similar trends were found between EAWT and other nutrient concentrations, including NO-N, NH-N, DTN, and SRP.

Fig. 4 Changes of EAWT of Taihu Lake during 1999 to 2007

The water temperature collected from 0.5 m below the surface at site A is shown in Table 2, both the annual average value and annual maximum value showed peaks in 2000 and 2007, and from 2003 they both showed an increasing trend.

Table 2 Annual water temperature at 0.5 m below water surface in Taihu Lake

3.4 Factors influencing Chl-a

The results of stepwise multiple linear regression analysis showed that EAWT and TP were the main influencing factors of Chl-a, and eventually affected the growth of phytoplankton in both Meiliang Bay and Central Lake (Fig. 5). In Meiliang Bay, EAWT, the DTP concentration, and the TP concentration could explain 99.2% of the variation of Chl-a concentration (Fig. 5(a)):

where ρ means the concentration of parameters, andΘ(E AWT)is the value of EAWT.

Therefore, phosphorus is crucial for phytoplankton growth in the bay. One reason is that river input is the main supplier of nitrogen during the bloom season, and Meiliang Bay connects to two major rivers, which means that the phytoplankton growth is not limited by nitrogen, but by phosphorus.

In Central Lake, EAWT, the TP concentration, and the NH-N concentration explained 98.7% of the variation of Chl-a concentration (Fig. 5(b)):

The relationships between Chl-a and nutrient concentrations indicate that EAWT, TP,and DTP are important factors affecting the growth of phytoplankton in Meiliang Bay, and EAWT, TP, and NH-N are major drivers to the changes of phytoplankton in Central Lake.Anyway, higher EAWT will lead to more serious algal blooms.

Fig. 5 Comparison between observed and predicted Chl-a concentrations in Meiliang Bay and Central Lake

4 Discussion

The climate factor and nutrients controlled the risk of algal blooms in Taihu Lake. In different periods there are different characteristics. EAWT is a crucial parameter reflecting the climate factor at the early stage of Microcystis blooms. Based on the statistic analysis of the relationship between phytoplankton biomass and EAWT/nutrients, it could be concluded that both EAWT and nutrients could strongly influence the development of algal blooms.

4.1 Influence of EAWT on phytoplankton biomass

Lots of studies have shown that water temperature often plays an important role in the growth of phytoplankton (Paerl and Fulton 2006; Stella et al. 2007; Duan et al. 2009). Liu et al.(2011), using the 11-year records of environmental data (from 1992 to 2002)and phytoplankton species, found that high water temperature was the principal force driving Microcystis blooms. As daily water temperature fluctuated frequently with weather conditions,and the growth of phytoplankton normally related to long-term climate conditions instead of short-term weather conditions, we replaced water temperature with EAWT to study the relationship between water temperature and bloom intensity.

EAWT showed an increasing trend in general in initial algal bloom seasons from 1999 to 2007, and at the same time Chl-a had a similar trend in Meiliang Bay. This may imply that the annual variation of phytoplankton was affected by EAWT. In the view of a whole year, the higher the value of EAWT is, the stronger the algal bloom is.

The relationships between Chl-a and EAWT in Meiliang Bay and in Central Lake are presented in Table 3 and Table 4, respectively. Both in Meiliang Bay and in Central Lake,Chl-a was significantly correlated with EAWT. Besides EAWT, Chl-a was also significantly correlated with TP, TN, NH-N, SRP, and SS in Meiliang Bay, while it was only correlated with TP and SS in Central Lake. The relationship between Chl-a and SS should be interpreted by the contribution of algal blooms to SS.

Table 3 Kendall rank correlation coefficients of parameters in Meiliang Bay

Table 4 Kendall rank correlation coefficients of parameters in Central Lake

The variability of EAWT can be considered a major climate factor contributing to the changes of Chl-a. The effects of EAWT on Chl-a have been demonstrated both in Meiliang Bay and Central Lake, where EAWT was positively correlated with Chl-a. The relationship between Chl-a and EAWT in Central Lake, where less nutrients are available (Table 1 and Fig. 3), was not as significant as that in Meiliang Bay. This may be due to there being more factors influencing phytoplankton growth in Central Lake than in Meiliang Bay. For example,nutrients also limited the phytoplankton biomass and the bloom intensity in Taihu Lake (Xu et al. 2010). It must be pointed out that the SRP concentration was often lower than 0.005 mg/L in Central Lake, and the TN/TP ratio was higher than 25 (Fig. 3), which showed that phosphorus could be a limitation factor for growth of phytoplankton (Schindler 1977). Wind disturbance was also much stronger in Central Lake than in Meiliang Bay. Stronger wind leads to higher turbidity, which more strictly limits the biomass accumulation in Central Lake than in Meiliang Bay of Taihu Lake (James et al. 2009), and ultimately reduced the relative weight of EAWT. James et al. (2009)found that the prevailing south wind from May to August in Taihu Lake could make the algae float from Central Lake to Meiliang Bay, which contributed to the concentration of Chl-a in Meiliang Bay, and redistribute the spatial pattern of the phytoplankton biomass. This also weakens the importance of water temperature to phytoplankton growth.

There was a significant increase of EAWT in 2000, while the increase of algae was not so great (Fig. 2 and Fig. 4). This can be caused by the graze of zooplankton. Sherr et al. (1992)noted the importance of micro-grazers as dominant consumers of phytoplankton in eutrophic oceans. Higher EAWT during spring bloom periods may improve the grazing rate of herbivore of ciliate communities (Aberle et al. 2007). Combined with the grazing impacts on phytoplankton, the increase of algal biomass was less than that of EAWT. In Central Lake, the change of Chl-a lagged about one year behind EAWT, which may have been caused by the lower nutrient contents, and the influence of EAWT on algae did not occur until enough nutrients were supplied to phytoplankton (Xu et al. 2010). The intensity of wind disturbance also contributed to the bloom intensity (James et al. 2009; Qin et al. 2006).

4.2 Influence of nutrients supply on bloom intensity

Previous studies have shown that supplies of nutrients are important factors in the growth of phytoplankton, and in turn, the growth of phytoplankton also affects the change of particular nutrients. There were high correlation coefficients between phytoplankton biomass and nutrient concentrations in Taihu Lake (Wang et al. 2007; Xu et al. 2010; Liu et al. 2011).In this study, the concentrations of nutrients and Chl-a showed similar spatial and temporal distribution patterns. Both TN and TP concentrations had a clear decreasing trend from Meiliang Bay to Central Lake (Table 1 and Fig. 3). The decreasing trend of nutrient concentrations from Meiliang Bay to Central Lake may relate to external nutrient loadings and the accumulation of phytoplankton biomass in the northern part driven by wind (Wang and Liu 2005). The average values of monthly data from May to August of NH-N (r = 0.61, P＜ 0.05, and n = 9), TN (r = 0.72, P ＜ 0.01, and n = 9), SRP (r = 0.57, P ＜ 0.05, and n = 9), and TP (r = 0.89, P ＜ 0.01, and n = 9)showed significant positive linear correlations with Chl-a in Meiliang Bay (Table 3), while in Central Lake the correlations of Chl-a with nutrient variables were generally not significant except TP (r = 0.69, P ＜ 0.05, and n = 9)(Table 4).

With the development of agriculture around Meiliang Bay, more nitrogen and phosphorus fertilizers were applied to the field (Gao et al. 2004; Qin et al. 2007)and, subsequently, more and more nutrients were discharged into Meiliang Bay. The significant correlation between Chl-a and nutrients both in Meiliang Bay and in Central Lake implied that nutrients still strongly contributed to the biomass of phytoplankton, mainly Microcystis spp. in bloom seasons in Taihu Lake. This was also proved by the results from Xu et al. (2010)and Paerl et al. (2011).

5 Conclusions

Based on the long-term in situ ecological observatory data in Taihu Lake, it was revealed that both nutrients and EAWT were strongly correlated with the phytoplankton biomass in both Meiliang Bay and Central Lake. EAWT, TP, and DTP were responsible for 99.2% of the variation of Chl-a in Meiliang Bay, and EAWT, TP, and NH-N explained 98.7% of the variation of Chl-a in Central Lake. This suggested that the variability of EAWT among different years caused by climate change could be a considerable climate factor of determination for the intensity of algal blooms in Taihu Lake. EAWT from March to June is a relatively more stable and predictable parameter than air temperature, so it could be useful information for the prediction of future algal blooms.

It was found that nutrient concentrations, especially the TP and NH-N concentrations,also significantly controlled the intensity of blooms, and partially determined the risk of harmful bloom events in Taihu Lake and similar lakes. It is hard to control the change of climate for human beings. What we can do against algal blooms is to cut down the nutrient input step by step. On the other hand, both the climate and nutrient factors affects the risk of harmful algal blooms in Taihu Lake. It is important to consider the two factors during the analysis of future patterns of blooms.

Acknowledgements

We thank all the data contributors of the Taihu Laboratory for Lake Ecosystem Research.

Aberle, N., Lengfellner, K., and Sommer, U. 2007. Spring bloom succession, grazing impact and herbivore selectivity of ciliate communities in response to winter warming. Oecologia, 150(4), 668-681. [doi:10.1007/s00442-006-0540-y]

Bunting, E. S. 1976. Accumulated temperature and maize development in England. The Journal of Agricultural Science, 87(3), 577-583. [doi:10.1017/S0021859600033207]

Cao, H. S., Tao, Y., Kong, F. X., and Yang, Z. 2008. Relationship between temperature and cyanobacterial recruitment from sediments in laboratory and field studies. Journal of Freshwater Ecology, 23(3),405-412. [doi:10.1080/02705060.2008.9664217]

Chen, Y. W., Chen, K. N., and Hu, Y. H. 2006. Discussion on possible error for phytoplankton chlorophyll-a concentration analysis using hot-ethanol extraction method. Journal of Lake Sciences, 18(5), 550-552.(in Chinese).

Chen, Y. W., Qin, B., Teubner, K., and Dokulil, M. K. 2003. Long-term dynamics of phytoplankton assemblages: Microcystis-domination in Lake Taihu, a large shallow lake in China. Journal of Plankton Research, 25(1), 445-453. [doi:10.1093/plankt/25.4.445]

Duan, H. T., Ma, R. H., Xu, X. F., Kong, F. X., Zhang, S. X., Kong, W. J., Hao, J. Y., and Shang, L. L. 2009.Two decade reconstruction of algal blooms in China’s Lake Taihu. Environmental Science and Technology, 43(10), 3522-3528. [doi:10.1021/es8031852]

Gao, C., Zhu, J. G., Zhu, J. Y., Gao, X., Dou, Y. J., and Hosen, Y. 2004. Nitrogen export from an agriculture watershed in the Taihu Lake area, China. Environmental Geochemistry and Health, 26(2-3), 199-207.[doi:10.1023/B:EGAH.0000039582.68882.7f]

Guo, L. 2007. Doing battle with the green monster of Taihu Lake. Science, 317(5842), 1166. [doi:10.1126/science.317.5842.1166]

James, R. T., Havens, K., Zhu, G. W., and Qin, B. Q. 2009. Comparative analysis of nutrients, chlorophyll and transparency in two large shallow lakes (Lake Taihu, P. R. China and Lake Okeechobee, USA).Hydrobiologia, 627(1), 211-231. [doi:10.1007/s10750-009-9729-5]

Jin, X. C., and Tu, Q. Y. 1990. Standard of Lake Eutrophication Survey. 2nd ed. Beijing: Chinese Environmental Science Press. (in Chinese)

Karlsson, E. I., and Brunberga, K. 2004. The importance of shallow sediments in the recruitment of Anabaena and Aphanizomenon (cyanophyceae). Journal of Phycology, 40(5), 831-836. [doi:10.1111/j.1529-8817.2004.04070.x]

Khan, F. A., and Ansari, A. A. 2005. Eutrophication, an ecological vision. The Botanical Review, 71(4),449-482. [doi:10.1663/0006-8101(2005)071[0449:EAEV]2.0.CO;2]

Li, W., Yang, Q., and Liu, G. 1994. Algal bloom in Lake Taihu and its control. Sund, H., Yu, X., Stabel, H.,Yuan, K., Geller, W., and She, F., eds., Environmental Protection and Lake Ecosystem, 243-261. Beijing:China Science and Technology Press.

Liu, X., Lu, X. H., and Chen, Y. W. 2011. The effects of temperature and nutrient ratios on Microcystis blooms in Lake Taihu, China: An 11-year investigation. Harmful Algae, 10(3), 337-343. [doi:10.1016/j.hal.2010.12.002]

Lorenzen, C. J. 1967. Determination of chlorophyll-a and phaeopigments: Spectrophotometric equations.Limnology and Oceanography, 12(1), 343-346. [doi:10.4319/lo.1967.12.2.0343]

Ma, L. Q., Gao, S. J., Wen, J. B., Zong, S. X., and Xu, Z. C. 2008. Effective accumulated temperature and developmental threshold temperature for Semanotus bifasciatus (Motschulsky)in Beijing. Forestry Studies in China, 10(2), 125-129. [doi:10.1007/s11632-008-0021-y]

Moss, B., Phillips, G., and Madgwick, J. 1996. A Guide to the Restoration of Nutrient-enriched Shallow Lakes.Norwich: The Broads Authority.

Murphy, J. R., and Riley, J. P. 1962. A modified single solution method for the determination of phosphorus in natural waters. Analytica Chimica Acta, 27(1), 31-36. [doi:10.1016/S0003-2670(00)88444-5]

Paerl, H. W., and Fulton, R. S. 2006. Ecology of harmful cyanobacteria. Granéli, E., and Turner, J. T., eds.,Ecology of Harmful Marine Algae, 95-107. Berlin: Springer-Verlag.

Paerl, H. W., and Huisman, J. 2008. Blooms like it hot. Science, 320(5872), 57-58. [doi:10.1126/science.1155398]

Paerl, H. W., Xu, H., McCarthy, J. M., Zhu, G. W., Qin, B. Q., Li, Y. P., and Gardner, S. W. 2011. Controlling harmful cyanobacterial blooms in a hypereutrophic lake (Lake Taihu, China): The need for a dual nutrient(N & P)management strategy. Water Research, 45(5), 1573-1583. [doi:10.1016/j.watres.2010.09.019]

Qin, B. Q., and Zhu, G. W. 2006. The nutrient forms, cycling and exchange flux in the sediment and overlying water system in lakes from the middle and lower reaches of Yangtze River. Sciences in China, Ser. D:Earth Sciences, 49(s1), 1-13. [doi:10.1007/s11430-006-8101-0]

Qin, B. Q., Zhu, G. W., Zhang, L., Luo, L. C., and Gao, G. 2006. Estimation of internal nutrient release in large shallow Lake Taihu, China. Science in China, Ser. D: Earth Sciences, 49(s1), 38-50. [doi:10.1007/s11430-006-8104-x]

Qin, B. Q., Xu, P. Z., Wu, Q. L., Luo, L. C., and Zhang, Y. L. 2007. Environment issues of Lake Taihu, China.Hydrobiologia, 581, 3-14. [doi:10.1007/978-1-4020-6158-5_2]

Qin, B. Q. 2008. Lake Taihu, China – Dynamics and Environmental Change. Springer.

Qin, B. Q. 2009. Lake eutrophication: Control countermeasures and recycling exploitation. Ecological Engineering, 35(11), 1569-1573. [doi:10.1016/j.ecoleng.2009.04.003]

Qin, B. Q., Zhu, G. W., Gao, G., Zhang, Y. L., Li, W., Paerl, H. W., and Carmichael, W. W. 2010. A drinking water crisis in Lake Taihu, China: Linkage to climatic variability and lake management. Environmental Management, 45(1), 105-112. [doi:10.1007/s00267-009-9393-6]

Reynolds, C. S., Irish, A. E., and Eliott, J. A. 2001. The ecological basis for simulating phytoplankton responses to environmental change. Ecology Modeling, 140(3), 271-291. [doi:10.1016/S0304-3800(01)00330-1]

Reynolds, C. S. 2006. Ecology of Phytoplankton. Cambridge: Cambridge University Press.

Schindler, D. W. 1977. Evolution of phosphorus limitation in lakes. Science, 195(4275), 260-262.

Schindler, D. W. 2006. Recent advances in the understanding and management of eutrophication. Limnology and Oceanography, 51(1), 356-363.

Sherr, B. F., Sherr, E. B., and McDaniel, J. 1992. Effect of protistan grazing on the frequency of dividing cells in bacterioplankton assemblages. Applied and Environmental Microbiology, 58(8), 2381-2385.

Shi, J., and Zai, S. 1994. Eutrophication of Lake Taihu and its control. Sund, H., Yu, X., Stabel, H., Yuan, K.,Geller, W., and She, F., eds., Environmental Protection and Lake Ecosystem, 207-215. Beijing: China Science and Technology Press.

Stella, A. B., Sebastian, D., Herwig, S., Gabriele, T., Miriam, R., Angelika, W., Achim, W., Christoph, G. J.,and Maren, S. 2007. Water temperature and mixing depth affect timing and magnitude of events during spring succession of the plankton. Oecologia, 150(4), 643-654. [doi:10.1007/s00442-006-0550-9]

Sun, S., and Huang, Y. 1993. Lake Taihu. Beijing: Chinese Marine Press. (in Chinese)

Wang, X. J., and Liu, R. M. 2005. Spatial analysis and eutrophication assessment for chlorophyll a in Taihu Lake. Environmental Monitoring and Assessment, 101(1-3), 167-174. [doi:10.1007/s10661-005-9154-9]

Wang, X. J., Lu, Y. L., He, G. Z., Han, J. Y., and Wang, T. Y. 2007. Multivariate analysis of interactions between phytoplankton biomass and environmental variables in Taihu Lake, China. Environmental Monitoring and Assessment, 133(1-3), 243-253. [doi:10.1007/s10661-006-9577-y]

Xu, H., Paerl, W. H., Qin, B. Q., Zhu, G. W., and Gao, G. 2010. Nitrogen and phosphorus inputs control phytoplankton growth in eutrophic Lake Taihu, China. Limnology and Oceanography, 55(1), 420-432.[doi:10.4319/lo.2010.55.1.0420]

Yang, M., Yu, J. W., Li, Z. L., Guo, Z. H., Burch, M., and Lin, T. F. 2008. Taihu Lake not to blame for Wuxi’s woes. Science, 319(5860),158. [doi:10.1126/science.319.5860.158a]

Zhang, Y. L., Qin, B. Q., and Liu, M. L. 2007. Temporal-spatial variation of chlorophyll a and primary production in Meiliang Bay, Lake Taihu, China from 1995 to 2003. Journal of Plankton Research, 29(8),707-719. [doi:10.1093/plankt/fbm049]

Zhu, G., Wang, F., Zhang, Y., Gao, G., and Qin, B. 2008. Hypoxia and its environmental influences in large,shallow, and eutrophic Lake Taihu, China. Verhandlungen des Internationalen Verein Limnologie, 30(3),361-365.

Zhu, G. W. 2009. Spatial-temporal distribution pattern of water quality in Lake Taihu and its relation with cyanobacterial blooms. Resources and Environment in the Yangtze Basin, 18(5), 439-445. (in Chinese)