Phytoplankton Community Structure and Its Response to Environmental Factors in the Cascade Hydropower Stations of the Lower Jinsha River

doi:10.21203/rs.3.rs-8172287/v1

Background

The construction of cascade hydropower stations on the lower Jinsha River has significantly altered hydrological conditions and ecological processes. Understanding how phytoplankton communities respond to these changes is essential for reservoir management and ecosystem conservation. This study examines the vertical and seasonal dynamics of phytoplankton and their relationships with key environmental factors in four cascading reservoirs.

Results

Significant seasonal stratification of water temperature, dissolved oxygen, turbidity, and total dissolved solids was observed in June but not in December. Phytoplankton community structure varied markedly with depth and season. Cyanobacterial density increased from June to December, while diatoms and green algae decreased. Mantel tests revealed that temperature, dissolved oxygen, and nitrate nitrogen were positively correlated with phytoplankton abundance and biomass in both seasons. Nutrients such as TN and TP also influenced community structure, particularly in deeper layers.

Conclusion

The findings demonstrate that thermal stratification, nutrient availability, and seasonal changes jointly regulate phytoplankton dynamics in subtropical cascade reservoirs. These insights are critical for guiding water quality management and ecological protection strategies in regulated river systems.

cascade hydropower stations

water environment

phytoplankton

With the increasing demand for global water resources management, the construction of cascade hydropower stations has drawn significant attention and research due to its impacts on river ecosystems (Xu et al., 2022; Shen et al., 2024; Optimisation of cascade reservoir operation, 2025). The cascade hydropower stations on the lower reaches of the Jinsha River, including the Wudongde, Baihetan, Xiluodu, and Xiangjiaba projects, as key water resource development initiatives, have altered the natural state of river flow and influenced the physical and chemical properties of the water body (Ijaz et al., 2022; He et al., 2020). These changes not only concern the health of aquatic ecosystems but also have profound effects on the structure and distribution of phytoplankton communities (Resende et al., 2022).

The formation and operation of reservoirs can lead to water column stratification, which, in turn, alters key environmental factors such as water temperature, dissolved oxygen, and nutrient concentrations (Tomczyk and Wiatkowski, 2021; Wang et al., 2018). These changes directly impact the growth, distribution, and community structure of phytoplankton (Liang et al., 2023; Hou et al., 2025). However, phytoplankton are not merely passive responders to environmental changes; they possess the ability to migrate vertically, adjusting their growth patterns and distribution in response to variations in the physical and chemical conditions of the water body (Dunne, 2024). Previous studies have shown that environmental factors, such as nutrient concentrations, dissolved oxygen, temperature, light intensity, and hydrological processes, play a crucial role in shaping phytoplankton communities (Water quality and habitat drive phytoplankton, 2025; Rehder, 2023; Wang et al., 2018). For instance, Cyanobacteria typically thrive in warmer water temperatures, while Bacillariophyta are better adapted to cooler temperatures (Phosphorus enrichment and carbon depletion, 2025). Additionally, different nutrient statuses of water bodies can lead to significant differences in phytoplankton community structures (Using modern coexistence theory, 2025). Furthermore, phytoplankton communities exhibit pronounced seasonal patterns, with spatiotemporal heterogeneity driven by dynamic environmental factors, which further influence community structure (Hou et al., 2024). For example, increased light, temperature, and nutrient levels in spring often lead to spring algal blooms, typically dominated by Cryptophyta and Bacillariophyta (Kavagutti et al., 2023; Lv et al., 2022). As water temperatures rise, Chlorophyta and Cyanobacteria may gradually become more dominant (Phosphorus enrichment and carbon depletion, 2025), and during summer, frequent Cyanobacteria blooms in eutrophic lakes can severely impact water quality and the ecological environment (Van de Waal et al., 2024; Foysal et al., 2024). In autumn, when water temperature decreases, the proportion of Bacillariophyta and Chlorophyta may increase again, resulting in corresponding adjustments to the structure of the phytoplankton community (Ulańczyk et al., 2021).

This study focuses on the water environments of the Wudongde, Baihetan, Xiluodu, and Xiangjiaba cascade hydropower stations, employing a comprehensive approach using Principal Coordinate Analysis (PCoA), permutation multivariate analysis of variance (PERMANOVA), Mantel tests, and ANOVA to systematically analyze the dynamic changes in water physical and chemical properties, as well as the structure and density of phytoplankton communities under different seasonal and depth conditions. The study aims to explore the underlying mechanisms linking environmental factors and phytoplankton communities, revealing the seasonal evolution patterns of reservoir ecosystems. The findings not only contribute to a deeper understanding of reservoir ecosystem functions but also provide a scientific basis for water resource management and ecological protection.

2.1 Sample Collection and Pretreatment

In this study, samples were collected from the reservoirs of four cascade hydropower stations in the lower reaches of the Jinsha River: Wudongde, Baihetan, Xiluodu, and Xiangjiaba, sampling points are shown in the Fig. 1. At Wudongde and Baihetan, sampling layers were set at depths of 0.5m, 2m, 5m, 10m, 20m, 60m, and at the bottom of the water column. At Xiluodu and Xiangjiaba, sampling layers were set at 0.5m, 5m, 10m, 20m, 60m, and the bottom. Field measurements of temperature (Temp), pH, conductivity (Cond), dissolved oxygen (DO), turbidity (Turbidity), and total dissolved solids (TDS) were made in situ using a YSI multiparameter water quality monitor (YSI Inc., USA) at each sampling point and different water depths. Water samples were collected at depths of 0.5m, 2m, 5m, and 10m using a water sampler and placed in insulated boxes with ice packs for transport to the laboratory for water chemistry analysis. The remaining water samples were immediately fixed with reagents for later analysis in the laboratory.

2.2 Sample Processing

Upon arrival at the laboratory, part of the water samples was used for chemical analysis, including suspended solids (SS), total phosphorus (TP), total nitrogen (TN), permanganate index (CODMn), dissolved total phosphorus (DTP), dissolved total nitrogen (DTN), nitrate nitrogen (NO₃-N), and chlorophyll-a (Chla) concentrations. Phytoplankton samples were left to settle for 48 hours, after which they were concentrated to 30 ml using the siphon method. The samples were then mixed and 0.1 ml was transferred to a phytoplankton counting chamber for observation, identification, and counting under an optical microscope. Phytoplankton species were identified based on the "Atlas of Common Algae in Inland Waters of China".

2.3 Data Analysis

Statistical analysis and data visualization were conducted using R software. Phytoplankton data were organized and classified by reservoir, depth, and sampling time, ensuring that the time, depth, and reservoir information for each dataset were accurate. Principal Coordinates Analysis (PCoA) and Permutational Multivariate Analysis of Variance (PERMANOVA) were used to analyze the structural characteristics of phytoplankton communities and to investigate the effects of depth and season on community structure. The Mantel test was applied to assess the relationship between community structure and environmental variables. ANOVA (Analysis of Variance) or Kruskal-Wallis tests were used to examine significant differences in phytoplankton community composition between different depths and seasons.

3.1 Temporal and Spatial Variations in Physical Properties of Water

The physical properties of water in Wudongde Reservoir exhibited significant stratification in June, with notable variations in temperature, dissolved oxygen, total dissolved solids, and conductivity. The temperature and dissolved oxygen were highest in the surface waters. Overall, water temperature decreased with increasing depth, stabilizing at depths greater than 90 meters. A prominent thermocline was observed between 25 meters and 75 meters. The vertical distribution of dissolved oxygen at depths of 0–10 meters showed distinct stratification, with the maximum concentrations found at depths of 0–3 meters. Dissolved oxygen generally decreased with increasing depth. TDS exhibited clear stratification, particularly in the deeper layers. The pH value was slightly alkaline, with a pronounced stratification within the 0–25 meter range. At depths greater than 25 meters, stratification disappeared. In the surface waters, turbidity was higher in summer than in winter. In December, no significant stratification was observed in the physical properties of the water in Wudongde Reservoir.

The changes in the physical properties of water in Baihetan Reservoir followed a similar trend to those observed in Wudongde Reservoir. In June, temperature, dissolved oxygen, pH, total dissolved solids, and conductivity all displayed clear stratification. In the shallower layers, temperature and dissolved oxygen were higher and gradually decreased with increasing depth, exhibiting distinct stratification characteristics. Conductivity and TDS showed a similar distribution pattern, with a sharp decrease at a depth of 50 meters, followed by an increase in deeper layers. In December, no significant stratification was observed in the physical properties of the water.

In Xiluodu and Xiangjiaba Reservoirs, the vertical variations in water physical properties followed a similar trend. In June, both reservoirs exhibited clear stratification in temperature, dissolved oxygen, and pH, with these properties gradually decreasing with increasing depth. However, turbidity, dissolved solids, and conductivity showed only a slight decrease in the middle layers and did not exhibit significant stratification. In December, no noticeable stratification was observed in the physical properties of the water in either reservoir.

3.2 Temporal and Spatial Variation of Water Chemistry

The temporal and spatial variations of chemical parameters (suspended solids, total phosphorus, total nitrogen, nitrate nitrogen, CODMn, soluble total phosphorus, soluble total nitrogen, and chlorophyll a) were analyzed at different reservoir sites in June and December. Suspended solids concentrations were higher in the surface layers of some reservoirs. The chemical oxygen demand by potassium permanganate (CODMn) generally decreased with depth in most reservoirs, with similar vertical distribution trends observed in both June and December. However, the CODMn concentrations at all depths in the Xiangjiaba Reservoir were consistently higher than those in the other reservoirs. The trends for TN, DTN, and NO₃-N were similar, with concentrations increasing with depth. In December, the concentrations were typically higher than those in June, suggesting a higher accumulation in deeper water layers. TP and DTP concentrations were generally higher at depths of 10–20 meters. In the Xiangjiaba Reservoir, the TP concentration was highest at a depth of 60 meters. Chlorophyll a concentration was higher in the shallow layers (0.5m and 2m) compared to the deeper layers.

3.3 Phytoplankton Community Structure Characteristics

According to Permutational Multivariate Analysis of Variance (PERMANOVA), no significant differences were found in the phytoplankton community composition between the Wudongde and Baihetan reservoirs in both June and December. In June, Bacillariophyta and Chlorophyta were the dominant groups in both reservoirs, particularly in the 0.5 to 5-meter water layers, where their relative abundance was higher. In contrast, in December, the relative abundance of cyanobacteria increased, making them the predominant phytoplankton group.

In the Wudongde Reservoir, the phytoplankton density did not change significantly from 0.5 to 10 meters in June, while in December, the highest density occurred at the 0.5-meter depth. In the Baihetan Reservoir, the highest total phytoplankton density was observed at the 5-meter depth in June, whereas in December, the highest abundance was found in the 2 to 5-meter water layers. The density in the intermediate layers was higher in December compared to June.

PCoA results revealed significant differences in phytoplankton communities at different depths. In the Xiluodu Reservoir, the vertical differences in phytoplankton community structure were significant in December (p = 0.063), with a notable increase in the proportion of cyanobacteria, especially at the 5-meter depth. The analysis for the Xiangjiaba Reservoir in June also showed significant differences (p < 0.05), with the proportion of Bacillariophyta and Chlorophyta increasing progressively with water depth. In summer (June), the total phytoplankton density across different water layers was relatively uniform, while in winter (December), the total phytoplankton density peaked at the 2-meter depth. These results indicate that seasonal variation and water depth have a significant impact on the structure and density of phytoplankton communities.

3.4 Seasonal Variations in Phytoplankton Cell Density

Principal Coordinates Analysis (PCoA) was employed to assess the similarity of phytoplankton communities across different seasons. Significant differences in the composition of surface phytoplankton communities (0.5 m depth) were observed across the four cascade reservoirs (Wudongde, p = 0.052; Baihetan, p < 0.05; Xiluodu, p < 0.01; and Xiangjiaba, p < 0.01). From June to December, the cell density of cyanobacteria showed a significant increase (WDD, BHT, XJB, p < 0.05), while the density of Bacillariophyta decreased significantly (BHT and XLD, p < 0.05). The cell density of Chlorophyta also exhibited a decreasing trend (BHT, XLD, and XJB, p < 0.05). In the upstream reservoirs of Wudongde and Baihetan, the density of Cryptophyceae was nearly zero, whereas, in the downstream reservoirs of Xiluodu and Xiangjiaba, Cryptophyceae were abundant, particularly in June.

3.5 Identification of Environmental Factors Influencing Phytoplankton Communities

Mantel tests were conducted to examine the relationships between phytoplankton community parameters and environmental factors. The results showed that different phytoplankton community parameters were influenced by different environmental factors.

In June, the abundance was strongly positively correlated with NO3-N, DO, and temperature (p < 0.001). Biomass was highly positively correlated with Temp (p < 0.001) and also positively correlated with NO3-N, DO, chlorophyll-a, turbidity, and conductivity (Cond) (p < 0.05).

In December, Abundance was significantly positively correlated with NO3-N, Chla, DO, TDS and Cond (p < 0.05). Biomass showed a highly significant positive correlation with Temp (p < 0.001) and was also significantly positively correlated with NO3-N, DO, and pH (p < 0.01).

4.1 Causes of Vertical Distribution of Physicochemical Properties in Water Bodies

Vertical stratification in water bodies, caused by seasonal changes in water temperature, is a key factor influencing the vertical differences in the physicochemical environment of deep lake (Yu et al., 2014). The concentration of dissolved oxygen is a crucial parameter in this context. Research shows that the main sources of surface water DO in rivers and lakes are oxygen dissolution from the air and photosynthesis by phytoplankton, while vertical convective diffusion is the primary source of DO in deeper water layers (Dordoni et al., 2022). The formation and maintenance of the "threelayer" vertical temperature structure in deep lakes and reservoirs during warmer seasons limits vertical exchange within the water column (Wells, 2021), making it difficult for oxygen produced by phytoplankton photosynthesis in the upper layers to penetrate the thermocline. A significant vertical stratification of dissolved oxygen is observed in reservoirs during summer, with the maximum concentration typically occurring at depths of 0–3 m (Fig. 1). Generally, as water depth increases, the concentration of DO decreases. Phytoplankton biomass directly affects the DO concentration in the water body (Wang et al., 2018). Chlorophyll-a concentrations begin to decline at a depth of 5 m, and the decomposition of dead phytoplankton by microorganisms often leads to significant oxygen consumption (Yu et al., 2024). With increasing depth, suspended solids concentrations decrease gradually, showing a clear vertical stratification, which may be attributed to deeper water layers being less influenced by windinduced mixing and surface biological activity. In deep lakes and reservoirs, the continuous release of nutrients from the decomposition of organic matter in sediments and the remains of plankton further limits the exchange of material between the upper and lower layer (Huang et al., 2022), thereby reducing the vertical diffusion and cycling of nutrients and leading to nutrient accumulation in the bottom waters. As a result, in this study, higher concentrations of TN, DTN, NO₃-N, TP, and DTP were observed in the middle or bottom layers.

4.2 Phytoplankton Community Structure in Reservoirs

Phytoplankton communities exhibit a selfregulating succession mechanism, which allows them to adapt to changing environmental conditions by adjusting species composition and relative abundance. This mechanism helps maintain community stability and adaptability (Dunne, 2024; Wei et al., 2024). In this study, the dominant phytoplankton species exhibited clear seasonal succession. In the Wudongde and Baihetan reservoirs, the summer phytoplankton community structure was dominated by Bacillariophyta and Chlorophyta, while the winter structure shifted to cyanobacteria and Bacillariophyta. In the Xiluodu and Xiangjiaba reservoirs, the summer phytoplankton community structure was a Bacillariophyta-cryptophyte-Chlorophyta type, and the winter structure was dominated by cyanobacteria and Bacillariophyta. From the perspective of cell density, vertical stratification was less pronounced in summer compared to winter, with the highest cell density occurring at depths of 2–5 m during winter, showing significant seasonal variation. At this depth, light availability supports photosynthesis, and the relatively stable temperature is conducive to phytoplankton growth (Heydari et al., 2018; Köhler, 2018). Previous studies indicate that the seasonal variation of phytoplankton populations in reservoirs is closely related to water temperature, light intensity, and nutrient conditions (Tarafdar et al., 2021; Bao et al., 2022; Wang et al., 2022; Kim et al., 2019). Bacillariophyta and Chlorophyta exhibit better adaptation to higher temperatures and light conditions, whereas cyanobacteria can dominate in low light and cooler temperatures by adjusting buoyancy and utilizing different forms of carbon for photosynthesis, allowing them to thrive in winter (Dordoni et al., 2022).

4.3 Relationship Between Phytoplankton and Environmental Factors

The relationship between phytoplankton communities and environmental factors is complex, as these factors directly or indirectly affect phytoplankton growth, distribution, succession, and community structure (Xiao et al., 2019; Paudel et al., 2016). In this study, the cell density in both summer and winter showed a significant positive correlation with NO₃-N and DO (P < 0.001), and both summer and winter biomass were strongly positively correlated with temperature (P < 0.001), and with NO₃-N and DO (P < 0.05). Water temperature is a primary control on the seasonal changes in phytoplankton (Wang et al., 2022), as it regulates the rate of photosynthetic dark reactions by influencing the activity of biological enzymes, thereby promoting biosynthesis and carbon fixation in phytoplankton, which in turn affects their distribution patterns (Effects of temperature and salinity, 2024). TN is an important indicator of the eutrophication level of lake waters and one of the essential elements for phytoplankton growth and metabolism (Kakade et al., 2021; Wojtkowska and Bojanowski, 2021; Liu et al., 2024). Reynolds pointed out that both excessively low and high nitrogen levels can impact the distribution patterns of phytoplankton communities (Reynolds, 2006). Overall, the dynamic changes in phytoplankton communities are the result of the combined effects of the water's physicochemical properties and nutrient conditions (Kutyła et al., 2025; Wen et al., 2022).

Seasonal differences in the physicochemical properties of the water body were significant. In June, the water's physicochemical properties exhibited clear stratification at different depths, especially in terms of temperature, dissolved oxygen, total dissolved solids, turbidity, and conductivity. In contrast, in December, the differences in these properties across various depths were relatively small, showing a more uniform distribution.

Regarding the chemical properties of the water, suspended solids concentrations were generally higher at the surface layers of all reservoirs. Total phosphorus and dissolved total phosphorus concentrations were relatively higher in the 10–20 m water layers. The permanganate index generally decreased with increasing depth in most reservoirs. The concentrations of total nitrogen, dissolved total nitrogen, and nitrate nitrogen were higher in June compared to December, with nitrate nitrogen concentrations gradually increasing with water depth. Chlorophyll-a concentrations were significantly higher in shallow areas than in deeper layers, particularly at the 0.5 m and 2 m depths.

Water depth had a marked effect on the structure and density of phytoplankton communities: In the Wudongde Reservoir, phytoplankton density was higher at the 0.5 m layer in December than in June, with density decreasing from shallow to deep layers. In the Xiluodu and Xiangjiaba reservoirs, the phytoplankton density was more evenly distributed across water layers in the summer, while in winter, the peak phytoplankton density occurred at the 2 m depth.

Seasonal changes significantly influenced the structure and density of phytoplankton communities: Across the four reservoirs, the density of cyanobacteria significantly increased from June to December, while the densities of Bacillariophyta and Chlorophyta significantly decreased (BHT and XLD, p < 0.05).

Environmental factors also had a significant impact on phytoplankton communities: In June, phytoplankton abundance showed a highly significant positive correlation with nitrate nitrogen (NO₃-N), dissolved oxygen (DO), and temperature (Temp). In December, abundance was significantly positively correlated with nitrate nitrogen, chlorophyll-a, dissolved oxygen, total dissolved solids, and conductivity.

Conflicts of Interest

The authors declare no conflicts of interest.

Funding

This study was supported by the National Key Research and Development Program of China (2022YFC3203404) and the National Natural Science Foundation of China (U2040211).

Data availability

The data that have been used are confidential.

Consent to Publish declaration

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Human Ethics and Consent to Participate declarations

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Author Contribution

T.L.: Conceptualization, Methodology, Investigation, Writing – Original Draft.Y.B.: Supervision, Funding Acquisition, Project Administration, Writing – Review & Editing.Y.H.: Formal Analysis, Data Curation, Visualization, Writing – Review & Editing.X.Z.: Investigation, Resources, Validation.J.T.: Investigation, Field Sampling, Data Collection.Y.D.: Software, Statistical Analysis.Y.W.: Methodology, Validation.S.L.: Investigation, Data Collection.All authors have read and approved the final version of the manuscript.

Bao, L., Chen, J., Tong, H., Qian, J. & Li, X. (2022) Phytoplankton dynamics and implications for eutrophication management in an urban river with a series of rubber dams. Journal of Environmental Management, 311, 114865. doi:10.1016/j.jenvman.2022.114865.
British Standards Institution (2020) BS 2222:2020: Technical product documentation and specification. London: British Standards Publications.
Dordoni, M., Seewald, M., Rinke, K., Schmidmeier, J. & Barth, J.A.C. (2022) Novel evaluations of sources and sinks of dissolved oxygen via stable isotopes in lentic water bodies. Science of the Total Environment, 838, 156541. doi:10.1016/j.scitotenv.2022.156541.
Foysal, M.J., Timms, V. & Neilan, B.A. (2024) Dynamics of the benthic and planktic microbiomes in a Planktothrix-dominated toxic cyanobacterial bloom in Australia. Water Research, 249, 120980. doi:10.1016/j.watres.2023.120980.
He, T., Deng, Y., Tuo, Y., Yang, Y. & Liang, N. (2020) Impact of the Dam Construction on the Downstream Thermal Conditions of the Yangtze River. International Journal of Environmental Research and Public Health, 17(8), 2973. doi:10.3390/ijerph17082973.
Henze, M., Harremoës, P., LaCour Jansen, J. & Arvin, E. (1995) Wastewater Treatment: Biological and Chemical Processes. Heidelberg: Springer.
Heydari, N., Fatemi, S.M.R., Mashinchian, A., Nadushan, R.M. & Raeisi, B. (2018) Seasonal species diversity and abundance of phytoplankton from the southwestern Caspian Sea. International Aquatic Research, 10(4), 375-390. doi:10.1007/s40071-018-0213-6.
Hou, X., Hu, X., Li, Y., et al. (2024) From disruption to adaptation: Response of phytoplankton communities in representative impounded lakes to China’s South-to-North Water Diversion Project. Water Research, 261, 122001. doi:10.1016/j.watres.2024.122001.
Hou, Y., Cheng, Y., Li, K., et al. (2025) Interannual succession of phytoplankton community in a canyon-shaped drinking water reservoir during the initial impoundment period: Taxonomic versus functional groups. Journal of Environmental Sciences, 151, 454-468. doi:10.1016/j.jes.2024.04.013.
Huang, L., Gao, Q., Fang, H., et al. (2022) Effects of bedform migration on nutrient fluxes at the sediment–water interface: a theoretical analysis. Environmental Fluid Mechanics, 22(2-3), 447-466. doi:10.1007/s10652-021-09816-3.
Ijaz, U., Zhao, X., Ayub, H., Cheng, Z., Qiu, S. & Niaz, A. (2022) Implications of evolution of circulation patterns on nutrients regime from 2008–2018, in Xiangxi Bay of the Three Gorges Reservoir, China. Water Supply, 22(9), 7197-7225. doi:10.2166/ws.2022.274.
John, P. Dunne. (2024) Fall and rise of the phytoplankton. Nature Climate Change. Available from: https://www.nature.com/articles/s41558-022-01439-w [Accessed 28th December 2024].
Kakade, A., Salama, E.S., Han, H., et al. (2021) World eutrophic pollution of lake and river: Biotreatment potential and future perspectives. Environmental Technology & Innovation, 23, 101604. doi:10.1016/j.eti.2021.101604.
Kavagutti, V.S., Bulzu, P.A., Chiriac, C.M., et al. (2023) High-resolution metagenomic reconstruction of the freshwater spring bloom. Microbiome, 11(1), 15. doi:10.1186/s40168-022-01451-4.
Kim, J.S., Seo, I.W. & Baek, D. (2019) Seasonally varying effects of environmental factors on phytoplankton abundance in the regulated rivers. Scientific Reports, 9(1), 9266. doi:10.1038/s41598-019-45621-1.
Kutyła, S., Kolada, A. & Ławniczak-Malińska, A. (2025) Hydromorphological pressure explains the status of macrophytes and phytoplankton less effectively than eutrophication but contributes to water quality deterioration. Water Research, 268, 122669. doi:10.1016/j.watres.2024.122669.
Liang, S., Zhang, F., Li, R., et al. (2023) Field investigation on the change process of microbial community structure in large-deep reservoir during the initial impoundment. Journal of Environmental Management, 338, 117827. doi:10.1016/j.jenvman.2023.117827.
Liu, X., Zhang, J., Wu, Y., et al. (2024) Intensified effect of nitrogen forms on dominant phytoplankton species succession by climate change. Water Research, 264, 122214. doi:10.1016/j.watres.2024.122214.
Lv, T., Liu, D., Zhou, P., Lin, L., Wang, Y. & Wang, Y. (2022) The coastal front modulates the timing and magnitude of spring phytoplankton bloom in the Yellow Sea. Water Research, 220, 118669. doi:10.1016/j.watres.2022.118669.
McInerney, M.J. (1999) Anaerobic metabolism and its regulation. In: Winter, J. (ed.) Biotechnology, 2nd edn. Weinheim, Germany: Wiley-VCH Verlag, pp. 455-478.
Ölmez, T., Kabdaşlı, I. & Tünay, O. (2007) The effect of the textile industry dye bath additive EDTMPA on colour removal characteristics by ozone oxidation. Water Science and Technology, 55(10), 145–153. doi:10.2166/wst.2007.320.
Optimisation of cascade reservoir operation considering environmental flows for different environmental management classes. (2025) ScienceDirect. Available from: [URL] [Accessed 3rd January 2025].
Paudel, B., Velinsky, D., Belton, T. & Pang, H. (2016) Spatial variability of estuarine environmental drivers and response by phytoplankton: A multivariate modeling approach. Ecological Informatics, 34, 1-12. doi:10.1016/j.ecoinf.2016.04.013.
Phosphorus enrichment and carbon depletion contribute to high Microcystis biomass and microcystin concentrations in Ugandan lakes. (2025) Limnology and Oceanography - X-MOL. Available from: https://www.x-mol.com/paper/1308719221801455616?adv [Accessed 2nd January 2025].
Resende, N.D.S., Santos, J.B.O.D., Josué, I.I.P., Barros, N.O. & Cardoso, S.J. (2022) Comparing Spatio-Temporal Dynamics of Functional and Taxonomic Diversity of Phytoplankton Community in Tropical Cascading Reservoirs. Frontiers in Environmental Science, 10, 903180. doi:10.3389/fenvs.2022.903180.
Reynolds, C.S. (2006) Ecology of Phytoplankton. Cambridge: Cambridge University Press.
Shen, Y., Zhang, Y., Zhou, X., et al. (2024) Environmental DNA metabarcoding revealing the distinct responses of phytoplankton and zooplankton to cascade dams along a river-way. Ecological Indicators, 166, 112545. doi:10.1016/j.ecolind.2024.112545.
Sobsey, M. & Pfaender, F. (2002) Evaluation of the H2S method for Detection of Fecal Contamination of Drinking Water. Water Sanitation and Health Programme, WHO. Report: WHO/SDE/WSH/02.08, Geneva: WHO.
Tarafdar, L., Kim, J.Y., Srichandan, S., et al. (2021) Responses of phytoplankton community structure and association to variability in environmental drivers in a tropical coastal lagoon. Science of the Total Environment, 783, 146873. doi:10.1016/j.scitotenv.2021.146873.
Tomczyk, P. & Wiatkowski, M. (2021) Impact of a small hydropower plant on water quality dynamics in a diversion and natural river channel. Journal of Environmental Quality, 50(5), 1156-1170. doi:10.1002/jeq2.20274.
Ulańczyk, R., Kliś, C., Łozowski, B., et al. (2021) Phytoplankton production in relation to simulated hydro- and thermodynamics during a hydrological wet year – Goczałkowice reservoir (Poland) case study. Ecological Indicators, 121, 106991. doi:10.1016/j.ecolind.2020.106991.
UNOOSA. (2019) UNOOSA Activates International Charter for Floods and Mudslides in South Africa. Available from: https://disasterscharter.org/web/guest/activations/-/article/flood-insouth-africa-activation-605- [Accessed 18th March 2021].
Van de Waal, D.B., Gsell, A.S., Harris, T., Paerl, H.W., de Senerpont Domis, L.N. & Huisman, J. (2024) Hot summers raise public awareness of toxic cyanobacterial bloom. Water Research, 249, 120817. doi:10.1016/j.watres.2023.120817.
Wang, F., Maberly, S.C., Wang, B. & Liang, X. (2018) Effects of dams on riverine biogeochemical cycling and ecology. Inland Waters, 8(2), 130-140. doi:10.1080/20442041.2018.1469335.
Wang, S., Flipo, N. & Romary, T. (2018) Time-dependent global sensitivity analysis of the C-RIVE biogeochemical model in contrasted hydrological and trophic contexts. Water Research, 144, 341-355. doi:10.1016/j.watres.2018.07.033.
Wang, Y., Liu, Y., Chen, X., Cui, Z., Qu, K. & Wei, Y. (2022) Exploring the key factors affecting the seasonal variation of phytoplankton in the coastal Yellow Sea. Frontiers in Marine Science, 9, 1076975. doi:10.3389/fmars.2022.1076975.
Wells, S.A. (2021) Modeling Thermal Stratification Effects in Lakes and Reservoirs. In: Devlin, A., Pan, J. & Manjur Shah, M. (eds.) Inland Waters - Dynamics and Ecology. IntechOpen. doi:10.5772/intechopen.91754.
Wen, Z., Shang, Y., Song, K., et al. (2022) Composition of dissolved organic matter (DOM) in lakes responds to the trophic state and phytoplankton community succession. Water Research, 224, 119073. doi:10.1016/j.watres.2022.119073.
Wojtkowska, M. & Bojanowski, D. (2021) Assessing trophic state of surface waters of Służewiecki Stream (Warsaw). Applied Water Science, 11(7), 118. doi:10.1007/s13201-021-01446-w.
Xiao, W., Laws, E.A., Xie, Y., et al. (2019) Responses of marine phytoplankton communities to environmental changes: New insights from a niche classification scheme. Water Research, 166, 115070. doi:10.1016/j.watres.2019.115070.
Xu, T., Chang, F., He, X., Yang, Q. & Ma, W. (2022) Influence of Cascade Hydropower Development on Water Quality in the Middle Jinsha River on the Upper Reach of the Yangtze River. Water, 14(12), 1943. doi:10.3390/w14121943.
Yu, H., Zhang, J., Yin, Z., et al. (2024) A method for quantifying the contribution of algal sources to CODMn in water bodies based on ecological chemometrics and its potential applications. Journal of Environmental Chemical Engineering, 12(2), 111943. doi:10.1016/j.jece.2024.111943.
Yu, Z., Yang, J., Amalfitano, S., Yu, X. & Liu, L. (2014) Effects of water stratification and mixing on microbial community structure in a subtropical deep reservoir. Scientific Reports, 4(1), 5821. doi:10.1038/srep05821.

Table 1. Comparison of the vertical distribution of phytoplankton structures using PERMANOVA (Wudongde June).

Pelagic phytoplankton community differences	R2	p.value
0.5m vs 5m	0.11	0.87
0.5m vs 10m	0.25	0.40
5m vs 10m	0.40	0.67

Table 2. Comparison of the vertical distribution of phytoplankton structures using PERMANOVA (Wudongde December).

Pelagic phytoplankton community differences	R2	p.value
0.5m vs 2m	0.16	0.46
0.5m vs 5m	0.14	0.59
0.5m vs 10m	0.12	0.65
2m vs 5m	0.38	0.33
2m vs 10m	0.23	1.00
5m vs 10m	0.19	0.67

Table 3. Comparison of the vertical distribution of phytoplankton structures using PERMANOVA (Baihetan June).

Pelagic phytoplankton community differences	R2	p.value
0.5m vs 5m	0.10	0.80
0.5m vs 10m	0.57	0.20
5m vs 10m	0.70	0.33

Table 4. Comparison of the vertical distribution of phytoplankton structures using PERMANOVA (Baihetan December).

Pelagic phytoplankton community differences	R2	p.value
0.5m vs 2m	0.10	0.62
0.5m vs 5m	0.13	0.59
0.5m vs 10m	0.05	0.81
2m vs 5m	0.18	0.67
2m vs 10m	0.20	1.00
5m vs 10m	0.34	0.67

Table 5. Comparison of phytoplankton structure with respect to depth using PERMANOVA (Xiluodu June).

Pelagic phytoplankton community differences	R2	p.value
0.5m vs 2m	0.01	1.00
0.5m vs 5m	0.06	0.75
0.5m vs 10m	0.11	0.46
2m vs 5m	0.06	0.90
2m vs 10m	0.10	0.80
5m vs 10m	0.02	1.00

Table 6. Comparison of phytoplankton structure with respect to depth using PERMANOVA (Xiluodu December).

Pelagic phytoplankton community differences	R2	p.value
0.5m vs 2m	0.26	0.088
0.5m vs 5m	0.01	1.000
0.5m vs 10m	0.28	0.013*
2m vs 5m	0.18	0.400
2m vs 10m	0.49	0.100
5m vs 10m	0.30	0.300

Table 7. Comparison of phytoplankton structure with respect to depth using PERMANOVA (Xiangjiaba June).

Pelagic phytoplankton community differences	R2	p.value
0.5m vs 2m	0.02	0.948
0.5m vs 5m	0.18	0.045*
0.5m vs 10m	0.29	0.005**
0.5m vs 20m	0.24	0.056
2m vs 5m	0.21	0.171
2m vs 10m	0.36	0.063
2m vs 20m	0.40	0.067
5m vs 10m	0.12	0.486
5m vs 20m	0.31	0.067
10m vs 20m	0.28	0.200

Table 8. Comparison of phytoplankton structure with respect to depth using PERMANOVA (Xiangjiaba December).

Pelagic phytoplankton community differences	R2	p.value
0.5m vs 2m	0.10	0.246
0.5m vs 5m	0.05	0.686
0.5m vs 10m	0.02	0.975
0.5m vs 20m	0.04	0.801
2m vs 5m	0.27	0.049*
2m vs 10m	0.18	0.194
2m vs 20m	0.13	0.800
5m vs 10m	0.11	0.540
5m vs 20m	0.02	1.000
10m vs 20m	0.09	0.800

No competing interests reported.

Phytoplankton Community Structure and Its Response to Environmental Factors in the Cascade Hydropower Stations of the Lower Jinsha River

Status:

Version 1

Abstract

Background

Results

Conclusion

Figures

1. Introduction

2. Materials and methods

2.1 Sample Collection and Pretreatment

2.2 Sample Processing

2.3 Data Analysis

3. Results

3.1 Temporal and Spatial Variations in Physical Properties of Water

3.2 Temporal and Spatial Variation of Water Chemistry

3.3 Phytoplankton Community Structure Characteristics

3.4 Seasonal Variations in Phytoplankton Cell Density

3.5 Identification of Environmental Factors Influencing Phytoplankton Communities

4. Discussion

4.1 Causes of Vertical Distribution of Physicochemical Properties in Water Bodies

4.2 Phytoplankton Community Structure in Reservoirs

4.3 Relationship Between Phytoplankton and Environmental Factors

5. Conclusion

Declarations

Author Contribution

References

Tables

Additional Declarations

Status:

Version 1