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Article

Seasonal Dynamics of Phytoplankton Communities and Bloom Risk Assessment in Baiyangdian Lake During the 2025 Critical Growing Season

by
Yao Li
1,
Shaowei Bian
1,*,
Fanqing Kong
1,
Yanfeng Huang
1,
Jianwu He
1,
Yunfei Zhang
1,
Wenhui Shi
1,
Zhe Wang
2 and
Wengeng Cao
3,4
1
Center of Eco-Environmental Monitoring and Scientific Research, Administration of Ecology and Environment of Haihe River Basin and Beihai Sea Area, Ministry of Ecology and Environment of the People’s Republic of China, Tianjin 300170, China
2
Hydrology Bureau of Haihe River Water Conservancy Commission, Tianjin 300170, China
3
The Institute of Hydrogeology and Environmental Geology, Shijiazhuang 050061, China
4
Key Laboratory of Groundwater Contamination and Remediation, Hebei Province & China Geological Survey, Shijiazhuang 050061, China
*
Author to whom correspondence should be addressed.
Water 2026, 18(10), 1172; https://doi.org/10.3390/w18101172
Submission received: 8 April 2026 / Revised: 30 April 2026 / Accepted: 8 May 2026 / Published: 13 May 2026
(This article belongs to the Special Issue Biological and Ecological Protection in the Freshwater Ecosystems)
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Abstract

Phytoplankton are the primary producers in freshwater lake ecosystems and play a fundamental role in maintaining the structure and function of lacustrine food webs. Baiyangdian Lake, located at the core of Xiong’an New Area, is vital for regional aquatic ecological security. However, systematic data on phytoplankton community dynamics throughout the phytoplankton critical growing season are scarce. In this study, we conducted a monthly investigation of phytoplankton communities in Baiyangdian Lake from April to October 2025, analyzing community composition, abundance, and diversity patterns. A total of 152 phytoplankton taxa across 8 major algal groups were identified, with Chlorophyta, Bacillariophyta, and Cyanobacteria being the dominant groups. Phytoplankton abundance exhibited distinct seasonal variation, peaking in August and reaching its lowest in October. The Shannon–Wiener diversity index (H′) and Pielou evenness index (J′) were generally at favorable levels, indicating a relatively stable community structure. The mean phytoplankton density across all sampling sites during the growing season was 8.70 × 106 cells/L, categorizing the lake as having “no obvious bloom” according to standard bloom severity classifications. The overall trophic state of Baiyangdian Lake during the study period was mesotrophic. These findings provide fundamental baseline data and scientific support for the management of algal bloom risks and the long-term conservation of the lake’s aquatic ecosystem.

1. Introduction

Phytoplankton are sensitive to environmental changes and are widely used as bioindicators of aquatic ecosystem health [1,2,3]. In temperate freshwater lakes, they exhibit clear seasonal succession patterns driven by variations in temperature, light, and hydrological conditions, resulting in shifts in species composition and abundance [4,5,6]. Systematic studies of these seasonal dynamics are essential for understanding long-term ecological trends and provide a solid theoretical framework for the assessment and management of aquatic environments [7,8].
Baiyangdian Lake is the largest freshwater wetland lake in North China, playing a critical role in regional climate regulation, water conservation, and biodiversity maintenance [9,10]. Baiyangdian was formed through long-term sedimentation and hydrological evolution in the alluvial plain. In recent decades, it has been widely studied due to increasing environmental pressures, such as eutrophication and water quality degradation, making it a representative system for ecological and limnological research in northern China [11,12]. However, most previous studies have focused primarily on either standalone assessments of eutrophication or short-term observations of phytoplankton community dynamics, with few simultaneously integrating nutrient background conditions, high-frequency phytoplankton monitoring throughout the entire growing season, and algal bloom events to conduct a comprehensive coupled correlation characteristics analysis [13,14,15,16]. As a result, it remains challenging to elucidate the interrelationships and underlying driving mechanisms among these three components [17,18,19,20].
In this study, monthly field surveys were conducted to investigate phytoplankton communities in Baiyangdian Lake throughout the growing season (April to October 2025). The primary objective was to systematically characterize phytoplankton community composition, structure, abundance, and diversity, while also examining their seasonal dynamics and succession patterns. Furthermore, this study explores the relationships among eutrophication status, continuous phytoplankton dynamics, and algal bloom events. Key environmental factors influencing these interactions are identified, providing new insights into their underlying mechanisms. By linking phytoplankton dynamics with nutrient conditions and bloom occurrence, this study provides a scientific basis for improving early warning of algal blooms and understanding ecological risks in shallow lake systems. These findings are expected to support water quality management, eutrophication control, and decision-making for ecological restoration in Baiyangdian Lake and similar shallow lakes.

2. Materials and Methods

2.1. Overview of the Research Area

Lake Baiyangdian (38°44′–38°59′ N, 115°45′–116°06′ E) covers an area of approximately 366 km2 and has a mean annual water storage capacity of about 1.32 billion m3. It is located in the North China Plain and is widely recognized as the largest freshwater lake system in this region [21,22]. The lake represents a typical shallow lake system, consisting of numerous interconnected sub-lakes, channels, and water bodies, with relatively weak hydrological connectivity (Figure 1).
Eight sampling sites were established across Lake Baiyangdian with moderate spatial separation, ensuring representative coverage of the entire lake. Access between sampling sites required navigation through narrow channels lined with emergent reeds, a typical characteristic of shallow lakes and lacustrine wetlands in northern China [23]. The region features a warm temperate monsoon-influenced semi-humid and semi-arid climate, characterized by uneven seasonal distribution and substantial interannual variability. Potential evapotranspiration far exceeds annual precipitation in this area [24].

2.2. Sampling Methods

The study area had an average water depth of approximately 3 m. The sampling period was initiated in April based on long-term monitoring data from Baiyangdian Lake, which indicate that biological activity becomes more stable and detectable from this time. From April to October 2025, eight phytoplankton sampling sites were established in the lake (Figure 1). The quantitative collection of phytoplankton samples was conducted as follows: Quantitative water samples (1000 mL) were collected using an acrylic water sampler (Beijing Pulite Instrument Co., Ltd., Beijing, China). Qualitative sampling was conducted using a 25# plankton net (Beijing Pulite Instrument Co., Ltd., Beijing, China; note: net specifications were as follows: length 50 cm, inner ring diameter 20 cm, and 200-mesh nylon netting with a pore size of 0.064 mm). Vertical tows were performed from the water surface down to a depth of 0.5 m. The net was towed slowly in a horizontal figure-eight pattern at a speed of 20–30 cm/s for 1–3 min. After retrieval, qualitative samples were collected from the cod-end container of the net and transferred into sample bottles. Phytoplankton samples were preserved immediately after collection using 1% Lugol’s iodine solution, and all samples were transported to the laboratory within 6 h, concentrated by sedimentation for 48 h, and processed and analyzed within 72 h of collection (maximum 1 week) to minimize potential cell degradation. For relevant details, refer to HJ 1296-2023 [25].
Phytoplankton density (cells/L) was determined using a light microscope (Zeiss Axio Imager 2, Carl Zeiss Microscopy Deutschland GmbH, Oberkochen, Germany). Samples preserved with Lugol’s iodine solution were concentrated by sedimentation, and cell abundance was determined in a 0.1 mL counting chamber under a microscope following standard methods [25]. Biomass was not directly measured, and phytoplankton abundance was represented by cell density. Taxonomic identification was conducted with reference to the relevant literature: Hu Hongjun & Wei Yinxin [26], Hu Hongjun [27], and www.algaebase.org. Due to the inherent limitations of light microscopy, including morphological similarity among closely related species and fixation-induced artifacts, some phytoplankton taxa were identified to the genus level, which is consistent with common practice in routine phytoplankton analyses.

2.3. Environmental Data Collection

Comprehensive trophic level index (TLI(Σ)) data for the study area were obtained from the relevant environmental protection authorities. The TLI(Σ) was calculated according to the Technical Regulation for Lake (Reservoir) Eutrophication Assessment and Classification (China National Environmental Monitoring Centre, 2001) [28]. The detailed calculation procedure is described in Section 2.4. Water quality data were collected in temporal and spatial alignment with the sampling of biological data to ensure comparability between datasets.

2.4. Data Analysis Methods

Sampling site distribution and study area maps were generated using ArcGIS Desktop 10.8.2 (Esri Inc., Redlands, CA, USA). The river data used in this study were obtained from the 1:4,000,000 SHP file of the National Fundamental Geographic Information System.
PRIMER 6 software was used for preliminary data collation and statistical analysis of biodiversity metrics, including the calculation of diversity indices such as the Shannon–Wiener Index (H′), Pielou’s evenness index (J′), Margalef’s richness index (d), and dominance values. Data visualization was conducted using Origin 2025b and Microsoft Excel.
The formula for TLI(Σ) is as follows:
T L I ( ∑ ) = ∑ j = 1 m W j T L I ( j )
  • TLI(∑)—comprehensive trophic level index;
  • TLI(j)—trophic level index of the j-th individual parameter;
  • m—number of parameters; m = 5 in this study;
  • Wj—relative weight of the trophic level index for the j-th parameter.
Pearson correlation analysis was conducted to identify key environmental factors influencing biological diversity (i.e., to examine linear relationships between each environmental factor and each biodiversity index). The analysis was performed using Python 3.9.4 [29], with the “pearson” function from Scipy 1.10.1 used to calculate correlation coefficients and corresponding two-tailed t-values for significance testing. Before the statistical analysis, the normality of the environmental factor data was tested using the Shapiro–Wilk test (implemented in Scipy 1.10.1), and the homogeneity of variances was verified with Levene’s test (implemented in the Statsmodels package, version 0.14.0). To avoid multicollinearity among environmental variables, for variable pairs with a correlation coefficient |r| ≥ 0.7, the variable with stronger ecological relevance to the biological community was retained. Multicollinearity was further evaluated using variance inflation factor (VIF) analysis, which was performed in Python 3.9.2 with the statsmodels 0.14.0 package; all VIF values were <3, confirming no significant multicollinearity. The significance level for all statistical tests was set at α = 0.05.

3. Results

3.1. Composition and Seasonal Dynamics of Phytoplankton

A total of 152 phytoplankton taxa were identified. The phytoplankton community comprised eight major algal groups (Figure 2). Chlorophyta (35%) and Bacillariophyta (27%) were the dominant groups, collectively accounting for over 60% of the total phytoplankton abundance. Cyanobacteria followed as the third most abundant component (18%), while Euglenophyta (10%), Dinophyta (5%), and Cryptophyta (3%) were minor components. Chrysophyceae and Xanthophyceae were the least abundant groups, each contributing only 1% to the total community. The dominant taxa were Pseudanabaena sp. (dominance = 0.22) and Cyclotella sp. (dominance = 0.04) (Figure 3).
Pronounced seasonal shifts in phytoplankton community composition were observed. In spring, Bacillariophyta and Chlorophyta were the dominant groups, with dominant species including Dinobryon divergens O.E.Imhof, Cryptomonas erosa Ehrenberg, and Ulnaria acus (Kützing) Aboal (dominance > 0.02). In summer, Chlorophyta and Cyanobacteria became dominant, with Pseudanabaena sp. and Cyclotella sp. as the main genera (dominance > 0.02). In autumn, Bacillariophyta and Cyanobacteria were the dominant groups, with Pseudanabaena sp., Cyclotella sp., and Cryptomonas ovata Ehrenberg as dominant taxa (dominance > 0.02).

3.2. Temporal Variation in Phytoplankton Abundance and Bloom Severity

Temporal variations in phytoplankton taxon richness and algal density are presented in Figure 4. Taxon richness (indicated by the blue line) remained relatively stable from April to June, ranging from 59 to 62 taxa, before rising to a peak of 74 taxa in August, followed by a marked decline to 40 taxa in October. Algal density (expressed in ×106 cells/L), including the mean, maximum, and minimum values across sampling sites, exhibited a pronounced seasonal peak in August, coinciding with the peak in taxon richness. Based on our data, the algal peak observed in August was primarily associated with the dominance of Pseudanabaena sp., Aphanizomenon sp., Cyclotella sp., and Merismopedia convoluta Brébisson ex Kützing. Cell densities were consistently low in spring (April–May) and autumn (September–October). Specifically, across the entire study period, phytoplankton cell density in spring ranged from 1.3 × 106 to 9.1 × 106 cells/L, with a mean of 4.0 × 106 cells/L; in summer, it varied between 1.1 × 106 and 5.0 × 107 cells/L, with a mean of 1.5 × 107 cells/L; in autumn, the density ranged from 5.2 × 105 to 1.4 × 107 cells/L, with a mean of 4.3 × 106 cells/L. This pattern is consistent with the typical seasonal dynamics of phytoplankton in northern freshwater lakes.
In accordance with the algal bloom severity classification criteria (based on algal density) defined in Section 6.3.1 of the Technical Specification for Remote Sensing and Ground Monitoring and Evaluation of Algal Blooms (HJ 1098-2020) [30], 62.5% of all monitoring sites were classified as “no obvious bloom” (Table 1), while the remaining 37.5% were assigned to the “slight bloom” category. Total phytoplankton abundance exhibited significant seasonal variability, with a maximum of 4.97 × 107 cells/L recorded in summer and a minimum of 5.2 × 105 cells/L observed in autumn. The mean algal density across all sampling sites during the growing season was 8.70 × 106 cells/L, indicating that the lake was generally in a “no obvious bloom” state. Seasonally, 6.3% of sampling sites were classified as “no bloom” and 93.7% as “no obvious bloom” in spring. In summer, 4.2% of sites were categorized as “no bloom”, 50% as “no obvious bloom”, and 45.8% as “slight bloom”. In autumn, 43.7% of sites were classified as “no bloom”, 50% as “no obvious bloom”, and 6.3% as “slight bloom”. Consistent with this classification result, field surveys identified no visible algal accumulations (e.g., ribbon-like, strip-like, or patch-like scums) at any of the eight monitoring sites, and no suspended algal particles were detected in the water column.
It should be noted that bloom classification in this study was based on total phytoplankton density rather than specific taxonomic groups. However, Cyanobacteria dominated the phytoplankton community during slight bloom periods, with Pseudanabaena sp. identified as the predominant genus.

3.3. The Diversity Index of Phytoplankton

Temporal variations in phytoplankton diversity indices are illustrated in Figure 5. The H′ value ranged from 1.8 to 2.6 and remained relatively stable during the study period, peaking in August and declining to its minimum in October. The d value fluctuated between 0.3 and 0.7, with the highest value recorded in August. The J′ value fluctuated without a clear seasonal pattern. Notably, the relatively narrow ranges of H′ and d indicate limited temporal variability, suggesting that the phytoplankton community structure was generally stable over time. The moderate values of H′ further imply a moderately diverse community, with no abrupt shifts in community composition. Although J′ exhibited some variability, its consistently moderate-to-high values indicate that the phytoplankton community was relatively evenly distributed across most sampling periods. Additionally, the spatial distribution trends of H′ and J′ were similar.
Collectively, these patterns suggest that the phytoplankton community maintained relatively stable diversity and evenness during spring and summer, with a slight decline observed in autumn, particularly in October. This decline may reflect seasonal environmental changes that influence community structure.

3.4. Eutrophication Degree Assessment

The TLI(Σ) of the studied water column was calculated to be 41.18, based on five core indicators including chlorophyll a (Chla), total phosphorus (TP), total nitrogen (TN), Secchi depth (SD), and permanganate index (CODMn). According to the classification standard of lake trophic status in China, the water column was in a mesotrophic state as a whole (Table 2 and Table 3).
Marked discrepancies were observed in the trophic status evaluated by individual indicator-specific TLI(j). The single-factor TLI(j) of TN was 66.85, falling into the range of moderate eutrophication, which was the only indicator exceeding the eutrophication threshold (TLI(j) > 50) among all parameters. In contrast, the single-factor TLI(j) of TP was 25.04, belonging to the oligotrophic level, with an extremely low measured concentration of 0.01 mg/L. The TLI(j) values of Chla (36.97), SD (44.51), and CODMn (35.46) all fell within the mesotrophic range, which corresponded to low phytoplankton biomass, high water transparency, and mild organic pollution in the water column.
The relative contribution rate of each indicator to the comprehensive TLI(Σ) was ranked as follows: TN (29.07%) > Chla (23.89%) > SD (19.81%) > CODMn (15.78%) > TP (11.41%). TN was identified as the dominant contributing factor to the comprehensive trophic status of the water column. Meanwhile, the molar ratio of TN to TP in the water column was calculated to be 458, which was far higher than the Redfield ratio (16:1) for phytoplankton growth, indicating that TP was the main limiting nutrient factor for phytoplankton growth in the studied water column.

3.5. Relationship Between Phytoplankton Community and Environmental Factors

Pearson correlation analysis was conducted to quantify the linear relationships between environmental factors and phytoplankton community indices (Figure 6). The results of the Shapiro–Wilk test and Levene’s test confirmed that all datasets satisfied the assumptions for parametric analysis (p > 0.05); therefore, no data transformation (e.g., log10(x + 1)) was applied in the Pearson correlation analysis.
As illustrated in Figure 6, most environmental variables exhibited clear directional relationships with phytoplankton community characteristics. Nutrient-related factors, particularly TN and TP, showed predominantly positive correlations with biological indices (p < 0.05), indicating that nutrient levels within an appropriate range promote phytoplankton growth and influence community structure. In contrast, other factors displayed weaker or variable correlations, suggesting more complex or indirect effects.
In particular, TP was significantly positively correlated with algal density (r = 0.83, p < 0.05), suggesting that elevated phosphorus concentrations were associated with increased phytoplankton biomass. Similarly, TN showed a significant positive correlation with the community J′ (r = 0.82, p < 0.05), indicating that higher nitrogen availability may contribute to a more even community distribution. Conversely, CODMn exhibited a strong negative correlation with J′ (r = −0.80), implying that increased organic pollution may adversely affect community evenness.
These results collectively suggest that moderate concentrations of nutrients, especially nitrogen and phosphorus, tend to enhance phytoplankton abundance and community organization, whereas organic pollution may negatively influence community stability.

4. Discussion

This study revealed clear seasonal succession in the phytoplankton community of Baiyangdian Wetland, with distinct taxonomic dominance patterns among spring, summer, and autumn. Seasonal shifts in dominant phytoplankton taxa are well established as effective bioindicators of the trophic status of the water column [31], and the community succession pattern observed in this study was generally consistent with the mesotrophic state identified through our comprehensive physicochemical assessment of the water column. Bacillariophyta exhibited the highest relative abundance and was the dominant group in both spring and autumn, whereas Chlorophyta became the most abundant group in summer. Notably, Pseudanabaena sp. (Cyanobacteria) was identified as a core and consistently dominant taxon throughout the three surveyed seasons, and the relative abundance of total Cyanobacteria was statistically significantly higher in summer and autumn than in spring (p < 0.05). It should be noted that the use of Lugol’s iodine for preservation may affect the integrity of certain fragile or motile phytoplankton taxa (e.g., some flagellates), potentially leading to their underestimation. Therefore, this methodological limitation may partially affect the comprehensive assessment of the seasonal dynamics and ecological functions of these taxa. The seasonal succession of dominant phytoplankton taxa observed here was primarily driven by the synergistic shifts of key environmental factors among seasons [32,33,34]. Due to differences in sampling design, analytical methods, and monitoring periods among previous studies, direct quantitative comparisons across years or lakes should be interpreted with caution. Nevertheless, previous studies on Baiyangdian Lake and established knowledge of shallow lake ecosystems suggest that the observed phytoplankton dynamics exhibit patterns commonly associated with shallow lakes under eutrophication pressure while also reflecting local environmental conditions and hydrological characteristics [12,13,15,16,17].
Water temperature, light availability, and nutrient concentrations are widely recognized as core regulators shaping phytoplankton community dynamics, as these factors directly determine the growth rates and competitive hierarchy of different algal taxa [35,36]. In summer, the gradual rise in water temperature was accompanied by concurrent increases in light intensity and photoperiod, a combination that created favorable conditions for the rapid proliferation of eurythermal, high-light-adapted taxa such as Chlorophyta and non-bloom-forming filamentous Cyanobacteria (e.g., Pseudanabaena sp.). The dominance of Chlorophyta in summer suggests a moderate and reversible increase in nutrient availability in the water column during the warm season, as green algae possess high nutrient uptake capacities and exhibit rapid growth responses to elevated nitrogen and phosphorus levels; however, this seasonal fluctuation in nutrient levels did not exceed the eutrophication threshold, and the water body maintained a mesotrophic state throughout the study period, and no algal bloom outbreak occurred. In contrast, Bacillariophyta typically have higher growth efficiencies under low temperatures and moderate light conditions, a trait that explains their absolute dominance in the cooler spring and autumn periods. Consistent with previous findings, Bacillariophyta, Dinophyta, and Cryptophyta are usually the dominant taxa in mesotrophic lakes, while bloom-forming Chlorophyta and Cyanobacteria tend to gain absolute dominance in eutrophic water bodies [37,38]. However, this pattern shows significant taxon-specificity; non-bloom-forming cyanobacterial taxa can maintain stable, core dominance in mesotrophic water bodies with moderate nutrient levels [39,40,41], and their nutritional ecological niches are significantly differentiated from those of bloom-forming Cyanobacteria [42,43].
Notably, the marked stoichiometric imbalance between TN and TP concentrations is the core driver of the stable dominance of non-bloom-forming Pseudanabaena sp. without concurrent cyanobacterial bloom formation or transition to a eutrophic state. This finding further confirms that cyanobacterial dominance is not equivalent to eutrophication [44,45]. On the one hand, the elevated TN concentration provided a sufficient nitrogen source for the growth of filamentous Pseudanabaena sp., which has a low nutrient threshold for growth and high tolerance to elevated nitrogen concentrations [15]; on the other hand, the extremely low TP concentration (0.01 mg/L) imposed a strong phosphorus limitation on phytoplankton growth, which effectively inhibited the explosive proliferation of bloom-forming Cyanobacteria (e.g., Microcystis sp.), thus maintaining the overall mesotrophic status of the water body [46]. Consistent with this regulatory mechanism of nitrogen-phosphorus stoichiometry, Qin [17] found that the low P loss of shallow lakes via sedimentation and high N loss via denitrification led to a low N:P ratio and N and P co-limitation, which, together with our findings, demonstrated the significance of simultaneous nitrogen and phosphorus reduction for eutrophication control in shallow lake ecosystems. Furthermore, the significant positive correlation between TN and J′ is also ecologically interpretable. When the nitrogen level is moderate and not excessively enriched, different algal species have distinct strategies for nitrogen utilization, which facilitate the coexistence of multiple algal species and thus increase the community J′ [47,48].
Algal blooms are widely documented to arise from the dynamic synergistic interactions between eutrophication and phytoplankton community dynamics [49,50]. Phytoplankton seasonal dynamics directly drive algal bloom risk in shallow wetlands; consistent with this, no formal bloom events occurred in our study area during monitoring, with peak algal biomass only meeting the “slight bloom” threshold per relevant technical specification. Despite this low bloom risk, sustained monitoring and disaster prevention efforts remain imperative to strengthen early warning and emergency response capabilities for potential algal bloom events. Nutrient control has long been recognized as the core strategy for mitigating large-scale algal blooms [51], while hydrological connectivity and wind-driven currents have been shown to regulate the spatial distribution of water quality parameters and algal bloom dynamics [17,52]. Accordingly, targeted management measures, including water level elevation, sediment nutrient reduction, and ecological fish stocking, have been demonstrated to effectively suppress excessive algal growth [53]. Collectively, the findings of this study provide a robust scientific foundation for the long-term maintenance of water quality stability, prevention and control of algal bloom risks, and protection of aquatic ecosystems in the mesotrophic Baiyangdian Wetland.

5. Conclusions

This study analyzed the taxon richness, community structure, and biodiversity of phytoplankton in Baiyangdian Lake. During the April–October 2025 growing season, the phytoplankton community was primarily dominated by Chlorophyta, Bacillariophyta, and Cyanobacteria, with peak abundance occurring in summer, indicating seasonal regulation of growth and distribution. Diversity indices (H′ and J′) suggested a relatively stable community structure. The mean phytoplankton density across all sites during the growing season was 8.70 × 106 cells/L, corresponding to the “no obvious bloom” category. The water body was overall in a mesotrophic state, with TN identified as the primary driver of trophic status, whereas TP was the main limiting nutrient for phytoplankton growth. These findings emphasize that controlling nutrient inputs and regulating phytoplankton community structure are crucial strategies for mitigating algal blooms and maintaining ecosystem stability.
Despite providing a baseline for ecological assessment and sustainable management, this single-year study is insufficient to capture interannual variability and long-term successional dynamics. Future research should integrate multi-year, high-frequency monitoring with laboratory experiments and numerical modeling to clarify phytoplankton–environment interactions. Such approaches will improve early-warning capabilities, support adaptive management, and promote the long-term conservation of Baiyangdian Lake.

Author Contributions

Conceptualization, Y.L. and S.B.; methodology, Y.L., Y.H. and J.H.; software, Y.L.; validation, Z.W. and W.C.; formal analysis, Y.L.; investigation, Y.L., Y.H. and J.H.; resources, Y.Z. and W.S.; data curation, Y.L. and S.B.; writing—original draft preparation, Y.L.; writing—review and editing, F.K. and S.B.; visualization, Y.Z. and W.S.; supervision, S.B.; project administration, F.K. and S.B. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the National Key Research and Development Program of China (2024YFD1702000).

Data Availability Statement

The data presented in this study are available upon request from the corresponding author. Due to the fact that the data are currently confidential as required by national regulations, relevant information will be shared publicly once declassified.

Acknowledgments

We thank Hengzhi Lyu for his assistance with statistical analysis.

Conflicts of Interest

The authors declare no conflicts of interest. The funders played no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

Abbreviations

Chla: chlorophyll a; CODMn: permanganate index; SD: Secchi depth; TN: total nitrogen; TP: total phosphorus; TLI: trophic level index; H′: Shannon–Wiener Index; J′: Pielou’s evenness index; d: Margalef’s richness index.

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Figure 1. Map of the research area.
Figure 1. Map of the research area.
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Figure 2. Composition and relative abundance of phytoplankton groups.
Figure 2. Composition and relative abundance of phytoplankton groups.
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Figure 3. Representative dominant phytoplankton taxa observed in the study area.
Figure 3. Representative dominant phytoplankton taxa observed in the study area.
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Figure 4. Temporal variations in phytoplankton taxon richness and algal density (April–October).
Figure 4. Temporal variations in phytoplankton taxon richness and algal density (April–October).
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Figure 5. Temporal variations in phytoplankton diversity indices (April–October).
Figure 5. Temporal variations in phytoplankton diversity indices (April–October).
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Figure 6. Correlation heatmap of phytoplankton community and water quality factors. Statistical significance: * p < 0.05 and ≥0.01. No marking indicates no statistical significance (p ≥ 0.05). Note: No multiple comparison correction was performed for the correlation analysis results.
Figure 6. Correlation heatmap of phytoplankton community and water quality factors. Statistical significance: * p < 0.05 and ≥0.01. No marking indicates no statistical significance (p ≥ 0.05). Note: No multiple comparison correction was performed for the correlation analysis results.
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Table 1. Classification criteria for algal bloom severity based on algal density.
Table 1. Classification criteria for algal bloom severity based on algal density.
Bloom Severity LevelAlgal Density D (Cells/L)Bloom CategoryReference Phenomena
I0 ≤ D < 2.0 × 106No bloomNo algal accumulation on the water surface; algal particles are barely detectable in the water.
II2.0 × 106 ≤ D < 1.0 × 107No obvious bloomScattered algal accumulation on the water surface; or a small number of algal particles can be distinguished in the water.
III1.0 × 107 ≤ D < 5.0 × 107Slight bloomAlgal accumulation on the water surface in the form of ribbons, strips, or patches; or suspended algal particles are visible in the water.
IV5.0 × 107 ≤ D < 1.0 × 108Moderate bloomAlgal accumulation on the water surface, floating continuously and covering part of the monitored water column; or suspended algae are clearly visible in the water.
VD ≥ 1.0 × 108Severe bloomAlgal accumulation on the water surface, floating continuously and covering most of the monitored water column; or suspended algae are clearly visible in the water.
Table 2. Classification standard of trophic level based on TLI(Σ).
Table 2. Classification standard of trophic level based on TLI(Σ).
Comprehensive Trophic IndexTrophic Level
TLI(Σ) < 30Oligotrophic
30 ≤ TLI(Σ) ≤ 50Mesotrophic
TLI(Σ) > 50Eutrophic
50 < TLI(Σ) ≤ 60Light Eutrophic
60 < TLI(Σ) ≤ 70Moderate Eutrophic
TLI(Σ) > 70Heavy Eutrophic
Table 3. Evaluation of trophic status and eutrophication characteristics in Baiyangdian Lake.
Table 3. Evaluation of trophic status and eutrophication characteristics in Baiyangdian Lake.
Chla (mg/m3)TP (mg/L)TN (mg/L)SD (m)CODMn (mg/L)Total
concentration3.010.012.071.413.64/
TLI(j)36.9725.0466.8544.5135.46/
Wj·TLI(j)9.844.7011.978.166.5041.18
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Li, Y.; Bian, S.; Kong, F.; Huang, Y.; He, J.; Zhang, Y.; Shi, W.; Wang, Z.; Cao, W. Seasonal Dynamics of Phytoplankton Communities and Bloom Risk Assessment in Baiyangdian Lake During the 2025 Critical Growing Season. Water 2026, 18, 1172. https://doi.org/10.3390/w18101172

AMA Style

Li Y, Bian S, Kong F, Huang Y, He J, Zhang Y, Shi W, Wang Z, Cao W. Seasonal Dynamics of Phytoplankton Communities and Bloom Risk Assessment in Baiyangdian Lake During the 2025 Critical Growing Season. Water. 2026; 18(10):1172. https://doi.org/10.3390/w18101172

Chicago/Turabian Style

Li, Yao, Shaowei Bian, Fanqing Kong, Yanfeng Huang, Jianwu He, Yunfei Zhang, Wenhui Shi, Zhe Wang, and Wengeng Cao. 2026. "Seasonal Dynamics of Phytoplankton Communities and Bloom Risk Assessment in Baiyangdian Lake During the 2025 Critical Growing Season" Water 18, no. 10: 1172. https://doi.org/10.3390/w18101172

APA Style

Li, Y., Bian, S., Kong, F., Huang, Y., He, J., Zhang, Y., Shi, W., Wang, Z., & Cao, W. (2026). Seasonal Dynamics of Phytoplankton Communities and Bloom Risk Assessment in Baiyangdian Lake During the 2025 Critical Growing Season. Water, 18(10), 1172. https://doi.org/10.3390/w18101172

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