Field investigations were conducted to identify environmental variables influencing phytoplankton dynamics in Lake Poyang. The results showed that diatoms predominated in the phytoplankton community. Concentrations of nutrients were high, and levels of phytoplankton biomass and chlorophyll a were low. During the low water level period (WLP), from January to May 2013, phytoplankton biomass was low. It increased from July 2013 and peaked in September 2013 during the high WLP. From October 2013 to January 2014, phytoplankton biomass decreased again. Highest values were generally measured in the middle district and lowest in the northern district. It decreased from October 2013 to January 2014. Redundancy analysis showed that water temperature and suspended solids (SS) concentrations were the principal factors regulating the growth of phytoplankton. The variations in SS were contrary to the biomass variations at the spatial level. During the high WLP, the blocking effect of the Yangtze River led to decreased water velocity and prolonged water retention time in Lake Poyang. Due to both the SS sedimentation and increase in water temperature, phytoplankton grew rapidly. Based on these findings, the variety of phytoplankton dynamics was caused by the combined effects of the Yangtze River effect, water temperature, and SS.

INTRODUCTION

Phytoplankton is the main primary producer of water ecosystems and plays an important role in food chains (Reynolds 1984). Alterations in phytoplankton composition and distribution characteristics in water reflect a changing environment and indicate the trophic status (Reynolds et al. 1993; Chen et al. 2003; Wu et al. 2011). The dynamics of phytoplankton communities are influenced by a complex array of biotic and abiotic factors operating through direct and indirect pathways (Vanni & Temte 1990; Carrillo et al. 1995; Burford & Davis 2011). The nutrient content, structure, and dynamics have received particular attention in the world's northernmost temperate lakes. When compared to other macronutrients required by biota, phosphorus is the least abundant and commonly the first element to limit biological productivity (Wetzel 2001). However, the nutrient–chlorophyll a (Chl a) relationship is generally non-linear, and suggests that other factors, e.g., physical (water level, water flow, light) and biotic (predation, competition) also limit algal growth (Millard et al. 1996; Schernewski et al. 2005).

With regard to lotic lakes, hydrological factors such as water level, water flow, and water retention time are thought to be of greater importance to phytoplankton development (Pace et al. 1992; Lázár et al. 2012). Many studies have addressed patterns of phytoplankton variation in floodplain ecosystems, rivers, and estuaries, where the water level strongly affects the ecosystem (Zinabu 2002; Huang et al. 2004; Burford et al. 2012). High levels of phytoplankton biomass have been observed during periods of low water levels, when more light and nutrients are available in temperate lakes (Nõges et al. 2003). Tockner et al. (1999) showed that Chl a concentration was usually influenced more strongly by water flow than by nutrients in rivers and their associated waters. Decreases in flow velocity accelerate freshwater phytoplankton growth, while increases inhibit phytoplankton growth (Sabater et al. 2008; Palijan 2012). Water retention time is also a crucial factor affecting algal growth, and a longer water retention time will benefit phytoplankton (Søballe & Kimmel 1987; Emiliani 1997). Additionally, phytoplankton Chl a concentrations may vary with discharge, catchment area, water depth, or other physical factors (Kilkus et al. 1975; Søballe & Kimmel 1987).

As one of the largest floodplains in the world, the Yangtze River floodplain is characterized by numerous shallow lakes which are freely connected to the Yangtze River (Pan et al. 2009). Lake Poyang, which is the largest freshwater lake in China and connected to the Yangtze River, is characterized by complex hydrographic conditions. Nitrogen and phosphorus concentrations of Lake Poyang have increased in the last 30 years (Zhen 2010). The concentrations of nitrogen and phosphorus were 0.684 mg/L and 0.076 mg/L in 1998, but were 2 times and 0.2 times higher, respectively, in 2013. However, the Chl a content of Lake Poyang increased slowly relative to other eutrophic lakes, such as Taihu and Chaohu in the mid-lower regions of the Yangtze River.

Lake Poyang has five tributaries and connects to the Yangtze River (Pan et al. 2009). Water exchange between Lake Poyang and the five tributaries, the Yangtze River, or the upstream reservoirs is closely related and determines the unique seasonal fluctuation of inflow discharge, water level, and flow velocity of the lake (Jiang & Huang 1997; Guo et al. 2012; Zhang et al. 2014). These characteristics have made phytoplankton dynamics and environmental factors very complicated. Wu et al. (2013) found that the biomass of major algal groups (i.e., Bacillariophyta, Cryptophyta, and Chlorophyta) and the total biomass of Lake Poyang were significantly and positively correlated with the average transparency determined from seasonal data. The annual trends in phytoplankton Chl a were associated with nutrient concentrations and temperature, but few significant correlations between Chl a and the nutrient concentrations were observed in the dry and mid-dry seasons of Lake Poyang (Wu et al. 2013; Wang et al. 2015), and light (or turbidity) and water retention time is more important than nutrients for restricting phytoplankton biomass (Wu et al. 2014a, 2014b). Pan et al. (2009) showed that phytoplankton Chl a was closely related to certain environmental factors, especially water velocity (U) at lotic sites. Regression analyses in lotic regions revealed that a higher amount of variance in log10Chl a was accounted for by U0.5 (r2 = 0.34), and that U was the major factor influencing Chl a.

However, under the complicated variations in the river–lake relationship, especially the blocking effect of the Yangtze River to Lake Poyang during the high water level period (WLP), the dynamics of phytoplankton and the associated environmental variables during water level changes remain unclear. Therefore, we conducted field investigations at 17 sites of Lake Poyang during the high, normal, and low level period in 2012 and 2013, and during water level changes from July 2013 to January 2014 to illustrate the phytoplankton composition and the temporal–spatial distribution of phytoplankton in Lake Poyang. We also considered some environmental variables that are responsible for alterations in phytoplankton composition and distribution, such as suspended solids (SS), velocity, transparency, total nitrogen (TN), total phosphorus (TP), and temperature to explain the key factors influencing phytoplankton dynamics of the lakes connected to the Yangtze River.

MATERIALS AND METHODS

Study area

Lake Poyang (28 °22′–29 °45′N, 115 °47′–116 °45′E) is located in Jiangxi Province of southeast China in the downstream portion of the Yangtze River (Figure 1). The lake contains five tributaries (Xiu River, Gan River, Fu River, Xin River, Rao River) and connects in the north to the Yangtze River in Hukou (Pan et al. 2009). The lake undergoes seasonal fluctuations in level under the combined action of the five tributaries and the Yangtze River (Shankman et al. 2006; Ye et al. 2011). However, these fluctuations are inconsistent during different WLPs. Specifically, in the high WLP, annual discharge of the lake is approximately 6 × 1012 m3. However, in the low WLP, annual discharge of the lake is 2.4 times lower than during the high WLP (Wang et al. 2015). The average water level of Lake Poyang is 12.86 m; however, the water level fluctuates greatly. The highest water level of 22.50 m was observed in 1998, while the lowest water level of 5.90 m was observed in 1963 (Xingzi Hydrological Station).

Figure 1

Location of Lake Poyang, its tributaries, and the sites included in this study.

Figure 1

Location of Lake Poyang, its tributaries, and the sites included in this study.

Sample collection and analysis

Samples were collected during the high WLP (June 2012 and July 2013), the normal WLP (September 2012 and October 2013), and the low WLP (November 2012 and December 2013) at 17 sites covering the lake. In addition, we added another sampling period from July 2013 to January 2014 to obtain data during the period of dramatic water level changes using the same sites. To determine the spatial variations in phytoplankton, the study area was divided into three regions, the northern district (sites 1–5), the middle district (sites 6–11), and the southern district (sites 12–17) (Figure 1).

Phytoplankton samples were collected from the surface water (0.5 m) in cleaned 1 L plastic containers. Samples were fixed in situ with Lugol's iodine solution (1.5% v/v) and allowed to settle for 24 h, after which they were concentrated to 50 mL. Enumeration of the algae was done using a Leica microscope (DM750, Leica). Taxa were classified according to Hu & Wei (2006) and identified to the genus level. We used a 0.1 mL counting box containing 100 horizons at 10 × 40 magnification to calculate the algal cell density (SEPA 2012). This value was then converted into biomass (Bio) and the mean cell volume was calculated using appropriate geometric configurations (Hillebrand et al. 1999). Volume values were converted to biomass assuming that 1 mm3 of volume was equivalent to 1 mg of fresh-weight biomass. Samples were analyzed using Duncan's (D) test after one-way analysis of variance (one-way ANOVA) and redundancy analysis (RDA) conducted with CANOCO (version 4.5) (Liu et al. 2010; Wang et al. 2011).

Selected environmental parameters, including water temperature (T), pH, and dissolved oxygen (DO), were obtained using a Hydrolab Data Sonde 5 sensor in situ. Water samples were obtained and placed into acid-cleaned 1 L plastic containers and kept cool and shaded before being transported to the laboratory for analysis, which was conducted in 24 hours. TN was measured by the alkaline potassium persulfate digestion-UV spectrophotometric method, while ammonia nitrogen (NH3-N) was analyzed by Nessler's reagent spectrophotometry. TP was measured using the ammonium molybdate method, SS were analyzed using the weighing method (105 °C) and water transparency (Tran) was determined using a Secchi disk. All of the above methods are described in detail in the Water and Wastewater Monitoring Analysis Method of the Standard Methods (SEPA 2012). Chl a was determined by the acetone extraction-spectrophotometric method (Arar 1997).

A depth-averaged two-dimensional numerical model was applied based on high resolution lake survey data in 2010 to study the hydrodynamics of Lake Poyang. The computational domain is about 98 km × 124.25 km covering the whole lake with the mesh size 250 m × 250 m. There are four national flood storage and detention basins separated from the main lake by the embankment in the south and eastern part of Lake Poyang. The northern boundary is set up as water levels at Hukou which is the confluence of Lake Poyang and the Yangtze River. There are five tributaries flowing into the lake, which were treated as mass and momentum source term in the simulation.

RESULTS

Phytoplankton community structure and dominant species

Overall, 81 genera belonging to eight phyla were identified during 2013 (Appendix and Table 1; the Appendix is available with the online version of the paper). Chlorophyta (40 genera) were the largest group, representing 49.38% of the total number of genera, followed by Cyanophyta (17), Bacillariophyta (13), Euglenophyta (4), Pyrrophyta (2), Cryptophyta (2), Chrysophyta (2), and Xanthophyta (1). The average biomass of phytoplankton in all sampling sites during the investigation time was 0.488 mg/L. Bacillariophyta were the dominant groups (0.142 mg/L), accounting for 29.10% of the total biomass. The biomass of Cryptophyta (0.089 mg/L), Chlorophyta (0.088 mg/L), and Euglenuphyta (0.080 mg/L) was slightly lower than that of Bacillariophyta, contributing 18.24%, 18.03%, and 16.39% of the total biomass, respectively. The biomass of other phyla such as Chrysophyta and Xanthophyta was much lower than that of the phyla listed above. The order of magnitude of cell density was not the same as that of the phytoplankton biomass. Cyanophyta, Bacillariophyta, and Chlorophyta accounted for most of the cells (78.37%).

Table 1

Biomass and density of phytoplankton in Lake Poyang in 2013

Phylum Average biomass (mg/L) Percentage (%) Average density (cells/L) Percentage (%) 
Cyanophyta 0.043 8.88 3.78 × 105 38.68 
Chlorophyta 0.088 18.07 1.73 × 105 17.67 
Bacillariophyta 0.142 29.00 2.15 × 105 22.02 
Cryptophyta 0.089 18.19 5.07 × 104 5.18 
Pyrrophyta 0.036 7.30 1.98 × 104 2.02 
Euglenophyta 0.080 16.46 8.35 × 104 8.54 
Chrysophyta 0.010 2.00 5.64 × 104 5.78 
Xanthophyta 0.001 0.10 1.02 × 103 0.10 
Total 0.488 100 9.77 × 105 100 
Phylum Average biomass (mg/L) Percentage (%) Average density (cells/L) Percentage (%) 
Cyanophyta 0.043 8.88 3.78 × 105 38.68 
Chlorophyta 0.088 18.07 1.73 × 105 17.67 
Bacillariophyta 0.142 29.00 2.15 × 105 22.02 
Cryptophyta 0.089 18.19 5.07 × 104 5.18 
Pyrrophyta 0.036 7.30 1.98 × 104 2.02 
Euglenophyta 0.080 16.46 8.35 × 104 8.54 
Chrysophyta 0.010 2.00 5.64 × 104 5.78 
Xanthophyta 0.001 0.10 1.02 × 103 0.10 
Total 0.488 100 9.77 × 105 100 

Temporal–spatial distribution of phytoplankton

The biomass varied greatly in different WLPs. The average biomass was highest in the high WLP (0.562 mg/L), which was much higher than in the normal and low WLPs (0.187 mg/L, 0.102 mg/L) (Figure 2). However, phytoplankton biomass had a similar spatial distribution pattern in the three WLPs. Phytoplankton biomass was higher in the middle district (0.144 mg/L) than in the southern district (0.103 mg/L), and both were much higher than in the northern district (0.037 mg/L).

Figure 2

Phytoplankton biomass variations in Lake Poyang: (a) high WLP, (b) normal WLP, (c) low WLP (left, 2012; right, 2013).

Figure 2

Phytoplankton biomass variations in Lake Poyang: (a) high WLP, (b) normal WLP, (c) low WLP (left, 2012; right, 2013).

The phytoplankton community structure changed significantly in different WLPs. The dominant phytoplankton was Bacillariophyta in the high WLP, and Cryptophyta or Bacillariophyta in the normal and low WLP. In the mid-east part of the lake (sites 8–10) in the high WLP, when biomass was highest, the dominant phytoplankton was Bacillariophyta, but Cyanophyta comprised a greater proportion than at other sampling sites. The Cyanophyta cell density (4.03 × 105 cells/L) was higher than that of Bacillariophyta (2.80 × 105 cells/L). Anabaena, Aphanizomenon, and Pseudanabaena were the most important genera of Cyanophyta.

Yearly variations in phytoplankton biomass

Phytoplankton biomass was low from January 2013 to May 2013 and increased from July 2013 to obtain its maximum in September 2013, while it decreased from October 2013 to January 2014 (Figure 3). In the first stage (January to May), phytoplankton biomass increased as water level increased. From July to August, the biomass continue to increase but the water level began to decrease. From August 2013 to January 2014, the decrease in biomass coincided closely with decreases in water level. Bacillariophyta biomass showed little change from January 2013 to January 2014. Cryptophyta and Pyrrophyta prevailed in May. Chlorophyta prevailed in July and Bacillariophyta prevailed in August. Euglenophyta dominated in September and Bacillariophyta dominated from October 2013 to January 2014. Cyanophyta biomass changed markedly with variations during the water level changes period, with the highest value in July and August.

Figure 3

Variations in phytoplankton biomass and water level in Lake Poyang.

Figure 3

Variations in phytoplankton biomass and water level in Lake Poyang.

Environmental parameters

Water quality data for Lake Poyang from samples collected at the 17 stations are presented in Figure 4. The TN and TP concentration of Lake Poyang were high, with a mean value of 1.45 mg/L and 0.051 mg/L, and wide ranges of 0.32–14.22 mg/L and 0.005–0.499 mg/L, respectively. The highest concentration of TN and TP both occurred during the low WLP. NH3-N concentration did not vary significantly between the three WLPs and tended to decrease from the high WLP to the low WLP. The water transparency was low in Lake Poyang, with a mean value of 0.53 m and the range of 0.1–0.9 m. SS concentrations were high (52.32 mg/L), and varied greatly (9.4–153.5 mg/L), showing the opposite trend of water transparency. During the three WLPs, Chl a concentration was between 2.65 and 5.38 μg/L, and this value decreased as the water level decreased. Chl a variation was similar to the variations of phytoplankton biomass. There were no significant differences (p > 0.05) in DO and pH.

Figure 4

Environmental variations in Lake Poyang during three WLPs. Means shown with different letters (a, b, c, d, e) are significantly different (p < 0.05).

Figure 4

Environmental variations in Lake Poyang during three WLPs. Means shown with different letters (a, b, c, d, e) are significantly different (p < 0.05).

RDA (Figure 5) indicated that phytoplankton biomass was significantly positively correlated with transparency and temperature and significantly negatively correlated with TP and SS. Phytoplankton biomass was negatively correlated with velocity (Table 2). The distributions of Cyanophyta, Chlorophyta, Bacillariophyta, and Euglenophyta were mainly related to temperature, while Pyrrophyta and Chrysophyta were influenced by transparency (Figure 5; Table 2).

Table 2

Correlation coefficient of phytoplankton biomass and environmental parameters

  Chl a TN TP NH3-N pH DO SS Tran 
Bio 0.647** −0.060 −0.223* −0.162 0.246* −0.211 −0.082 −0.271* 0.262* −0.114 
Chl a  0.175 0.004 −0.055 0.084 0.000 0.044 −0.225* 0.199 −0.203 
TN   0.557** 0.428** −0.078 0.133 −0.112 0.132 −0.275* 0.098 
TP    0.703** 0.031 0.217* −0.175 0.310** −0.380* 0.106 
NH3-N     0.062 −0.045 −0.205 0.160 −0.338** −0.020 
     −0.276* −0.854** 0.044 0.161 0.094 
pH       0.332** 0.138 −0.085 −0.068 
DO        −0.066 −0.069 −0.135 
SS         −0.542** 0.432** 
Tran          0.066 
  Chl a TN TP NH3-N pH DO SS Tran 
Bio 0.647** −0.060 −0.223* −0.162 0.246* −0.211 −0.082 −0.271* 0.262* −0.114 
Chl a  0.175 0.004 −0.055 0.084 0.000 0.044 −0.225* 0.199 −0.203 
TN   0.557** 0.428** −0.078 0.133 −0.112 0.132 −0.275* 0.098 
TP    0.703** 0.031 0.217* −0.175 0.310** −0.380* 0.106 
NH3-N     0.062 −0.045 −0.205 0.160 −0.338** −0.020 
     −0.276* −0.854** 0.044 0.161 0.094 
pH       0.332** 0.138 −0.085 −0.068 
DO        −0.066 −0.069 −0.135 
SS         −0.542** 0.432** 
Tran          0.066 

*p < 0.05.

**p < 0.01.

Table 3

Abbreviations of phytoplankton species for RDA

Taxon Abbreviations Taxon Abbreviations 
Cyanophyta Cya Pyrrophyta Pyr 
Chlorophyta Chl Euglenophyta Eug 
Bacillariophyta Bac Chrysophyta Chr 
Cryptophyta Cry Xanthophyta Xan 
Taxon Abbreviations Taxon Abbreviations 
Cyanophyta Cya Pyrrophyta Pyr 
Chlorophyta Chl Euglenophyta Eug 
Bacillariophyta Bac Chrysophyta Chr 
Cryptophyta Cry Xanthophyta Xan 
Figure 5

RDA biplot of phytoplankton species and environmental variables of Lake Poyang. Species abbreviations are listed in Table 3.

Figure 5

RDA biplot of phytoplankton species and environmental variables of Lake Poyang. Species abbreviations are listed in Table 3.

DISCUSSION

Nutrients such as TN and TP were relatively high, while phytoplankton biomass and Chl a levels were relatively low and Bacillariophyta still dominated in Lake Poyang. These findings suggest that phytoplankton dynamics in Lake Poyang were affected not only by the nutrients, but also by other factors.

Phytoplankton biomasses and environmental variables

Being connected to five tributaries and the Yangtze River, all of which had large water flow, Lake Poyang exhibits strong hydrodynamic conditions and high SS concentrations. Figure 5 shows that SS and velocity may be the important factors affecting phytoplankton biomass. Increases in SS not only reduce light transmission (Carlson 1992), but also cause algae cells to sink by flocculation (Cao et al. 2015a, 2015b). Dong et al. (2011) showed that the percentage of cohesive sediment of Lake Poyang was 35.95–85.76%. These cohesive sediments have a flocculating effect on phytoplankton. Some other lakes in these areas, such as Lake Taihu and Lake Chaohu, had low SS concentrations (35.8 mg/L in Taihu Meiliang Bay, 20–50 mg/L in Lake Chaohu) (Chen et al. 2003; Jin et al. 2010). Chl a contents were much higher when TN and TP reached the same values as Lake Poyang a few years ago (Kun & Pu 2011; Wang et al. 2014). The water volume of the five tributaries into Lake Poyang is large, and water exchange between Lake Poyang and the Yangtze River is frequent, which may be important factors causing the high SS concentrations. Moreover, frequent shipping traffic and heavy sand exploitation in northern Lake Poyang have led to sediments' resuspension and are the main factors responsible for increased SS concentrations.

Dynamics of phytoplankton biomass

From January to May (the normal WLP), the phytoplankton biomass of Lake Poyang increased slowly. From July to August (the high WLP), the phytoplankton biomass increased rapidly in the static lake water. After October (the normal and low WLP), the biomass began to decline.

The water level and water volume of Lake Poyang increased gradually from January to May. During this time, phytoplankton biomass increased with an increase in the amount of light (Figure 6), which suggested that increases in radiant energy are conducive to the growth of phytoplankton. In July, water level and water volume increased to maximum values of 16.71 m and 13,000 m3/s, respectively. Phytoplankton biomass increased rapidly from July, and peaked in August. At this time, the water velocity of Lake Poyang decreased (Figure 7), the water retention time was prolonged (25.5 d) (Wu et al. 2014a) and the SS sedimentation increased. The five tributaries carried high volumes of nutrients into the lake, which, when coupled with the higher water temperature, accelerated the growth of the phytoplankton. However, the amount of radiant energy cannot explain the increase in biomass (Figure 6).

Figure 6

Variations in the amount of radiant energy in Lake Poyang in 2013.

Figure 6

Variations in the amount of radiant energy in Lake Poyang in 2013.

Figure 7

Variations in water velocities in the northern, middle, and southern districts of Lake Poyang in 2010.

Figure 7

Variations in water velocities in the northern, middle, and southern districts of Lake Poyang in 2010.

From October to November, the water level (8–12 m) and water volume (990–5,980 m3/s) decreased, and the water retention time was reduced (12.5 d); however, the water velocity, SS concentrations increased and amount of radiant energy decreased, which led to decreased phytoplankton growth. From December to the following February or March, the water level (7–9 m) and water volume (21.1–3,680 m3/s) decreased continuously, and the lake was transformed from lacustrine status to river status. This drastic water level fluctuation resulted in the lowest water retention time (2.7 d) and the highest water velocity (Figure 7). The SS concentrations were relatively high. Furthermore, water temperature and the radiant energy (Figure 6) were decreasing rapidly, which was not beneficial to phytoplankton growth (Wang et al. 2015). In addition, the opposite trend was observed on the spatial level for phytoplankton biomass with SS (Figure 8). Specifically, the biomass decreased as the SS increased. The relationship between phytoplankton biomass and SS was more pronounced on the spatial level than on the temporal level.

Figure 8

Variations in phytoplankton biomass and SS in Lake Poyang.

Figure 8

Variations in phytoplankton biomass and SS in Lake Poyang.

Dominant phytoplankton of Lake Poyang

Although TN and TP of Lake Poyang were relatively high, Bacillariophyta (mainly for Melosira, Cyclotella, and Navicula) were dominant for the entire WLP. This may have been because of the strong hydrodynamic conditions and high SS concentrations of Lake Poyang during the WLP. Simulation analysis showed that the average flow velocity of Lake Poyang ranged from 0.005 to 0.858 m/s and the percentage above 0.1 m/s was greater than 50% during the three WLPs (Figure 7). Cocquyt & Vyverman (2005) argued that the phytoplankton community is dominated by diatoms in good hydrodynamic condition or well-mixed water columns. For example, Bacillariophyta dominate in typical river systems (Gosselain et al. 1994; Ha et al. 2002). Diatoms also have the highest algae density in phytoplankton, and strong water hydrodynamic forces can reduce settlement losses of diatoms. Furthermore, the buoyancy regulation mechanism of Cyanobacteria is restricted by water mixing. Other studies have shown that water mineral particles were beneficial to diatoms. This is because the flocculation sedimentation effect of the particles was greater on Cyanobacteria than diatoms (Cao et al. 2015a, 2015b). In addition, a moderate amount of SS is helpful for phytoplankton diatoms' growth because SS are rich in silicate, which is necessary for diatom proliferation (Zhang 2006; Ding et al. 2013). Therefore, the heavy SS concentrations in Lake Poyang (52.32 mg/L) may be the primary cause of the dominance of diatoms.

Cyanobacterial risk in local area

Despite diatoms being the dominant group in Lake Poyang, Cyanobacteria accounted for a relatively high proportion in local areas of Lake Poyang during the high WLP, such as sites 8–10 in the mid-east area and sites 13 and 15 in the entrance of the five tributaries into the lake. Field investigations conducted in August 2007 and October 2012 revealed large Cyanobacterial blooms that lasted almost two months (Dai et al. 2015). RDA revealed that Cyanobacteria biomass was positively correlated with temperature (r = 0.216) and showed a negative relationship with velocity (r = −0.159) (Figure 5). Increases in water temperature and decreases in velocity can accelerate Cyanobacteria growth. In the high WLP, the blocking effect of the Yangtze River induced elevated water levels in Lake Poyang, which prolonged the water retention time. During this time, the water velocity and SS concentrations were very low (Figure 7), water temperature was high, Cyanobacteria grew fast and Cyanobacterial blooms' risk increased.

CONCLUSION

The phytoplankton community of Lake Poyang was found to be composed of 8 phyla and 81 genera. The biomass was much higher in the high WLP than in the normal WLP and the low WLP. During the high WLP, phytoplankton biomass increased rapidly; however, after the high WLP it decreased greatly. Phytoplankton biomass was generally highest in the middle district and lowest in the northern district. Nutrients in Lake Poyang were high, while phytoplankton biomass and chlorophyll a contents were low and the phytoplankton community was still dominated by Bacillariophyta, which may be the reason for the strong hydrodynamic condition and high SS concentrations. The relationship between biomass and SS mainly reflected on the spatial distribution. Phytoplankton biomass was extremely high in the high WLP, which was caused by the combined effects of the blocking effect of the Yangtze River, water temperature, and SS.

ACKNOWLEDGEMENTS

This work is jointly supported by the National Basic Research Program of China (973 Program) (2012CB417004) and the National Natural Science Foundation of China (51078341). The authors would like to thank PhD Yuwei Chen of Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences for providing data (phytoplankton biomass from January 2013 and April 2013) from Lake Poyang.

REFERENCES

REFERENCES
Arar
E. J.
1997
Method 446.0 In Vitro Determination of Chlorophylls a, b, c1 + c2 and//Visible Spectrophotometry. Revision 1.2 National Exposure Research Laboratory
.
US Environmental Protection Agency
,
Cincinatti, OH
,
USA
,
1
26
.
Burford
M. A.
Webster
I. T.
Revill
A. T.
Kenyon
R. A.
Whittle
M.
Curwen
G.
2012
Controls on phytoplankton productivity in a wet–dry tropical estuary
.
Estuarine Coastal & Shelf Science
113
,
141
151
.
Cao
J.
Gao
S.
Chu
Z.
Wang
Y.
2015a
Study on kinetics of flocculation and settlement between typical algae and suspended particulates in Poyang Lake
.
Journal of Environmental Sciences
35
,
1325
1332
.
Cao
J.
Liu
J.
Chu
Z.
Wang
Y.
2015b
The effect of suspended particulates in Poyang lake on the growth and flocculation of three kinds of algae
.
Journal of Environmental Sciences
35
,
1318
1324
.
Carlson
R. E.
1992
Expanding the trophic state concept to identify non-nutrient limited lakes and reservoirs
. In:
Proceedings of a National Conference on Enhancing the State's Lake Management Programs. Monitoring and Lake Impact Assessment
,
Chicago, IL
,
USA
, pp.
59
71
.
Carrillo
P.
Reche
I.
Sanchez-Castillo
P.
Cruz-Pizarro
L.
1995
Direct and indirect effects of grazing on the phytoplankton seasonal succession in an oligotrophic lake
.
Journal of Plankton Research
17
,
1363
1379
.
Dai
G.
Zhang
M.
Feng
M.
2015
Analysis of cyanobacteria bloom in Nanjishan Natural Reserve in Poyang Lake
.
Ecological Science
34
(
4
),
26
30
.
Ding
T.
Gao
J.
Shi
G.
Chen
F.
Wang
C.
Han
D.
Luo
X.
2013
The composition of the Yangtze River water suspended solids content and mineral and chemical and geological environment significance
.
Journal of Geology
5
,
634
660
.
Dong
Y.
Jin
F.
Huang
J.
2011
Poyang Lake sediments grain size characteristics and its tracing implication for formation and evolution processes
.
Geological Science and Technology Information
2
,
57
62
.
Hillebrand
H.
Dürselen
C.
Kirschtel
D.
Pollingher
U.
Zohary
T.
1999
Biovolume calculation for pelagic and benthic microalgae
.
Journal of Phycology
35
(
2
),
403
424
.
Hu
H.
Wei
Y.
2006
Chinese Freshwater Algae–System, Classification and Ecology
.
Sciences Press
,
Beijing
,
China
.
Jiang
J.
Huang
Q.
1997
Study of the Three Gorges Project impact on Poyang lake water level
.
Journal of Nature Research
3
,
24
29
.
Jin
X.
Yun-Mei
L. I.
Wang
Q.
Zhang
H.
Wang
Y. F.
Yin
B.
2010
Estimating of suspended matter concentration based on bio-optical model in Chaohu Lake
.
Environmental Science
31
(
12
),
2882
2889
.
Kilkus
S. P.
Laperriere
J. D.
Bachmann
R. W.
1975
Nutrients and algae in some central Iowa streams
.
Water Pollution Control Federation
47
(
7
),
1870
1879
.
Kun
L. I.
Pu
X.
2011
Temporal and spatial distribution of chlorophyll-a concentration and its relationships with TN, TP concentrations in Lake Chaohu
.
Journal of Biology
1
,
53
56
.
Lázár
A. N.
Wade
A. J.
Whitehead
P. G.
Neal
C.
2012
Reconciling observed and modeled phytoplankton dynamics in a major lowland UK river, the Thames
.
Hydrology Research
43
(
5
),
576
588
.
Millard
E. S.
Myles
D. D.
Johannsson
O. E.
Ralph
K. M.
1996
Seasonal phosphorus deficiency of Lake Ontario phytoplankton at two index stations: light versus phosphorus limitation of growth
.
Canadian Journal of Fisheries & Aquatic Sciences
53
(
5
),
1112
1124
.
Pace
M. L.
Findlay
S. E. G.
Lints
D.
1992
Zooplankton in advective environments: the Hudson River community and a comparative analysis
.
Canadian Journal of Fisheries & Aquatic Sciences
49
(
5
),
1060
1069
.
Pan
B. Z.
Wang
H. J.
Liang
X. M.
Wang
H. Z.
2009
Factors influencing chlorophyll a concentration in the Yangtze-connected lakes
.
Fresenius Environmental Bulletin
18
(
10
),
1894
1900
.
Reynolds
C. S.
Padisák
J.
Sommer
U.
1993
Intermediate disturbance in the ecology of phytoplankton and the maintenance of species diversity: a synthesis
.
Hydrobiologia
249
(
1–3
),
183
188
.
Sabater
S.
Artigas
J.
Durán
C.
Pardos
M.
Romaní
A. M.
Tornés
E.
Illa
Y.
2008
Longitudinal development of chlorophyll and phytoplankton assemblages in a regulated large river (the Ebro River)
.
Science of the Total Environment
404
(
1
),
196
206
.
Schernewski
G.
Podsetchine
V.
Huttula
T.
2005
Effects of the flow field on small scale phytoplankton patchiness
.
Hydrology Research
36
(
1
),
85
98
.
Shankman
D.
Keim
B. D.
Song
J.
2006
Flood frequency in China's Poyang Lake region: trends and teleconnections
.
International Journal of Climatology
26
(
9
),
1255
1266
.
State Environmental Protection Administration (SEPA)
2012
Water and Wastewater Monitoring Analysis Method
,
4th edn.
China Environmental Science Press
,
Beijing
,
China
.
Wang
Z.
Wang
Y.
Hu
M.
Li
Y.
2011
Succession of the phytoplankton community in response to environmental factors in north Lake Erhai during 2009–2010
.
Fresenius Environmental Bulletin
20
(
9
),
2221
2231
.
Wang
Z.
Zou
H.
Yang
G.
Zhang
H.
Zhuang
Y.
2014
Spatial-temporal characteristics of chlorophyll-a and its relationship with environmental factors in Lake Taihu
.
Journal of Lake Science
4
,
567
575
.
Wang
J.
Zhang
Y.
Yang
F.
Cao
X.
Bai
Z.
Zhu
J.
2015
Spatial and temporal variations of chlorophyll-a concentration from 2009 to 2012 in Poyang Lake, China
.
Environmental Earth Sciences
73
(
8
),
4063
4075
.
Wetzel
R. G.
2001
Limnology: Lake and River Ecosystems
,
3rd edn.
Academic Press
,
San Diego, CA
,
USA
.
Wu
Z.
Cai
Y.
Liu
X.
Xu
C. P.
Chen
Y.
Zhang
L.
2013
Temporal and spatial variability of phytoplankton in Lake Poyang: the largest freshwater lake in China
.
Journal of Great Lakes Research
39
(
3
),
476
483
.
Wu
Z.
Lai
X.
Zhang
L.
Cai
Y.
Chen
Y.
2014a
Phytoplankton chlorophyll a in Lake Poyang and its tributaries during dry, mid-dry and wet seasons: a 4-year study
.
Knowledge & Management of Aquatic Ecosystems
136
(
412
),
26
34
.
Ye
X.
Zhang
Q.
Guo
H.
Bai
L.
2011
Long-term trend analysis of effect of the Yangtze River on water level variation of Poyang Lake (1960 to 2007)
.
International Symposium on Water Resource and Environmental Protection (ISWREP)
1
,
543
545
.
Zhang
X. Z.
2006
Study on effect of sediment on diatom's growth in Chongqing section of the Three Gorges Reservoir Area
.
Master's Thesis
,
Chongqing University
,
China
.
Zhen
B. H.
2010
Effects on Water Quality Due to Construction of Poyang Lake Water Conservancy Project, and Effective Measures to Minify the Adverse Effects on Water Quality
.
Environmental Protection Department of Jiangxi Province
,
Beijing
,
China
.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Licence (CC BY 4.0), which permits copying, adaptation and redistribution, provided the original work is properly cited (http://creativecommons.org/licenses/by/4.0/).

Supplementary data