Historical succession of phytoplankton community structure in Lake Chaohu
In eutrophic lakes, phytoplankton community structure are often mainly composed of Chlorophyta-Cyanophyta or Bacillariophyta-Chlorophyta (Liu et al., 2019; Zhou et al., 2021). In eutrophic Lake Skadar, the dominant groups of phytoplankton gradually evolved from Chlorophyta to Bacillariophyta at the long-term scale, whereas Bacillariophyta-Chlorophyta in the upper layer and epiphytic Bacillariophyta in the bottom layer dominated at the vertical spatial scale (Rakočević, 2012). A total of 277 species, 85 genera and 8 phyla were recorded in Lake Chaohu in the early 1980s (Liu and Meng, 1988), and the dominant phyla were Chlorophyta, Cyanophyta and Bacillariophyta, which this pattern was consistent with subsequent investigations in Lake Chaohu, although the species succession of each phylum varied (Deng et al., 2007; Jiang et al., 2014). In this study, the species number of Chlorophyta accounted for 49.47%, followed by Bacillariophyta (20.53%) and Cyanophyta (13.68%).
Phytoplankton density and biomass are often affected by lake type, nutrients and predators (Muzaffar and Ahmed, 2007; Qian et al., 2016; Heinle et al., 2021). In Lake Pyhaselka, phytoplankton density and biomass were affected by both zooplankton abundance and physicochemical parameters together (Viljanen et al., 2009). Nutrient enrichment or the size and community structure of zooplankton transformation significantly affected phytoplankton density and structure in Lake Dynamite and Lake Skadar (Vanni, 1987; RakočevIć, 2012). Moreover, increasing of phytophagous silver carp and brightens led to the decrease of phytoplankton biomass in Lake Warniak (Napiórkowska-Krzebietke, 2017). However, the cell density and biomass of phytoplankton in Lake Chaohu changed greatly during the past 40 years (Table 3). In 1984, the cell density of phytoplankton in Lake Chaohu was (111.1 ± 164.7) ×106 cells/L, and it was higher in the western zone of the lake than that in the eastern zone, which Microcystis and Dolichospermum were dominant species (Liu and Meng, 1988). During 2002 and 2003, the density and biomass of the lake were respectively (62.73 ± 28.81)×106 cells/L and 5.05–19.70 mg/L, which it was also higher in the western zone of the lake than that in the eastern zone (Deng et al., 2007). In this study, the phytoplankton density was (54.6 ± 74.2)×106 cells/L in 2020–2021 in Lake Chaohu, which was significantly higher than in 2011–2012 (Jiang et al., 2014), but lower than in 2002–2003 (Deng et al., 2007). Moreover, phytoplankton biomass ranged from 1.27 to 15.20 mg/L in this study, which was also lower than that in 2002–2003 (Deng et al., 2007). Therefore, it was likely that increasing fish production dropped phytoplankton density and biomass in Lake Chaohu after fishing ban of 2020. This phenomenon also occurred in Lake Donghu (Liu and Xie, 1999). Moreover, in this study, the phytoplankton density and biomass in Location I was higher than that in the other three locations, which it located in the estuary of Paihe River, Shiwuli River and Nanfei River where cyanobacterial blooms occurred severely (Jiang et al., 2014). Because of close to Hefei City, its domestic sewage, industrial and agricultural waste water were drained into these rivers, resulting in an increase in nitrogen and phosphorus concentrations in this area (Deng et al., 2007; Jiang et al., 2014; Zhong et al., 2019).
Table 3
Historial changes of phytoplankton density (×106cells/L) in Lake Chaohu
Years | Months | Cyano | Bacil | Chlor | Eugle | Crypt | Total density | Reference |
1984 | Winter | 0.26 | 0.73 | 0.06 | 0.03 | 0.002 | 1.08 | Liu and Meng (1988) |
| Spring | 0.66 | 0.012 | 0.015 | 0.003 | 0 | 0.69 | |
| Summer | 48.26 | 0.001 | 0.11 | 0.0002 | 0.003 | 48.36 | |
| Autumn | 393.79 | 0.02 | 0.39 | 0.01 | 0.16 | 394.38 | |
1987–1988 | Winter | 0.84 | 1.01 | 0.52 | 0.004 | 0.67 | 3.04 | Tu et al. (1990) |
| Spring | 3.35 | 0.10 | 0.44 | 0.05 | 2.25 | 6.18 | |
| Summer | 75.39 | 0.13 | 0.20 | 0.001 | 0.84 | 76.56 | |
| Autumn | 54.22 | 0.28 | 0.36 | 0 | 1.38 | 56.01 | |
2002–2003 | Winter | 37.00 | 7.08 | 12.71 | 0.002 | 1.00 | 57.94 | Deng et al. (2007) |
| Spring | 47.81 | 0.17 | 1.65 | 0.002 | 0.49 | 50.94 | |
| Summer | 26.24 | 1.94 | 3.49 | 0.02 | 0.45 | 32.15 | |
| Autumn | 107.70 | 0.46 | 1.41 | 0.008 | 0.31 | 109.89 | |
2011–2012 | Winter | | | | | | 1.94 | Jiang et al. (2014) |
| Spring | | | | | | 6.37 | |
| Summer | | | | | | 9.15 | |
| Autumn | | | | | | 9.12 | |
2020–2021 | Winter | 0.88 | 1.63 | 2.02 | 0.02 | 0.53 | 5.47 | This study |
| Spring | 4.27 | 0.67 | 2.94 | 0.02 | 0 | 7.92 | |
| Summer | 178.93 | 0.99 | 1.56 | 0.04 | 0.77 | 182.48 | |
| Autumn | 6.27 | 1.10 | 2.05 | 0.06 | 0.09 | 9.75 | |
Influencing Factors Of Tempo-spatial Variation Of Dominant Functional Groups Of Phytoplankton
The response of phytoplankton to environmental factors is one of the main hotspots in aquatic ecosystem (Latinopouloset al., 2020). The dynamics of phytoplankton community structure and functional groups are closely related to environmental factors (Crossetti et al., 2013; Kim et al., 2020; Ma et al., 2022). In this study, both RDA ranking and VPA analysis showed that environmental factors affected temporal and spatial variations of dominant functional groups of phytoplankton in Lake Chaohu. The representative species of functional group J were Pediastrum sp. and Desmodesmus quadricauda in spring, whose habitat types were turbid and shallow eutrophic water, resistant to low light and low temperature (Reynolds et al., 2002, 2006; Padisák et al., 2006, 2009). The dominant functional group D occurred only in Location II in spring, which it was consistent with low temperature, low light and mesotrophic environments (Reynolds et al., 2002, 2006; Padisák et al., 2006; Silva et al., 2018). High temperature, high phosphorus and light are conducive to the growth of functional group M, resulting in the outbreak of cyanobacterial blooms in summer and autumn in Lake Chaohu, which it was consistent with other investigations in eutrophic waterbodies (Yoshinaga et al., 2006; Deng et al., 2007; Cao et al., 2018). Moreover, the relative biomass of function group M in Location I was higher than the other three locations (Locations II, III and IV), and a large number of nutrients and exogenous organic substances from Shiwuli River, Nanfei River, and Paihe River were responsible for the water quality of this area (Jiang et al., 2014; Zhong et al., 2019). Usually, functional group H2 grows well under low nitrogen, low light and high phosphorus, and its biomass decreases with rising water levels (Padisák et al., 2006; Nan et al., 2020; Yang et al., 2016). In this study, the relative biomass of H2 rises in autumn and occupies a higher proportion in Location IV. Function group MP is adapted to shallow turbid mesotrophic lakes, and its functional groups include both Cyanophyta and Bacillarioplyta (Reynolds et al., 2002, 2006; Padisáket al., 2009), and its distribution was related to the change of WT. In this study, Function group MP dominated by Bacillarioplyta species in spring, and it changed to Cyanophyta in autumn with the increase of WT and nutrient concentrations. The same research results were also found in Lake Luoma, Lake East Taihu, and Daning River (Zhu et al., 2013; Tian et al., 2018; Nan et al., 2020). Moreover, the dominant functional groups M, H2, MP and J were all positively correlated with WT in this study, and RDA showed that WT (15%) had the highest interpretation rate of phytoplankton functional groups, indicating that WT was one of the key factors limiting the dynamics of phytoplankton functional groups in Lake Chaohu. Similar findings have been found in Lake Santa Lucia, Lake East Taihu and Lake Okeechobee (Silva et al., 2018; Nan et al., 2020; Ma et al., 2022). In Lake Chaohu, the decrease of TN concentration and the increase of TP concentration may lead to the dominance of functional group C, and similar results also occurred in Lake Erhai and Daning River (Zhu et al., 2013; Cao et al., 2018). Function groups Y and N were dominant in winter in Lake Chaohu, and it was related to low temperature and high turbidity, which was consistent with the results of other waterbodies (Xu et al., 2011; Nan et al., 2020). Therefore, spatial heterogeneity of environment were one of key reasons affecting temporal variations and spatial distributions of dominant functional groups of phytoplankton in Lake Chaohu.
Fish predation can influence community structure and dynamics of phytoplankton directly or indirectly through top-down effects (Attayde and Hansson, 1999). On one hand, grazing pressure by bighead and silver carp may directly affect phytoplankton abundance and biomass (Vörös et al., 1997; Roozen et al., 2007). On the other hand, it may also indirectly affect phytoplankton structure and biomass by suppressing zooplankton biomass (Schindler et al., 2001; Jeppesen et al., 2003; Shen et al., 2021). The density and species of fish in Lake Chaohu showed decreasing trend and the dominant species gradually turned into small-sized species in the past 50 years (Deng et al., 2007; Liang et al., 2022). However, with the implementation of the 10-year fishing ban in Lake Chaohu on January 2020, fish predation pressure on both zooplankton and phytoplankton should have greatly enhanced, because some small zooplankton-feeding fishes, such as Cecilia ectenes and Neosalanx tangkahkeii taihuensis, fed mainly on zooplankton, whereas omnivorous fishes such as Hypophthalmichthys molitrix and Aristichthys nobilis fed on zooplankton and phytoplankton (Mao et al., 2011; Liang et al., 2022). In this study, dominant functional group J, which dominant species were Pediastrum biradiatum, Scenedesmus sp. and Desmodesmus quadricauda in spring, was related to the lower zooplankton densities (Zhang et al., unpublished data), suggesting that the fish predation pressure indirectly contributed to the community structure and tempo-spatial dynamics of phytoplankton.