Cultural eutrophication has triggered lake regime shifts and cyanobacterial blooms, causing losses of key ecosystem services (e.g., water supply) and necessitating costly remediation. While nutrient enrichment generally is a primary driver, insufficient insight into biotic-hydrological stressor interactions may result in an overreliance on nutrient reduction as the sole intervention. Focusing on China's subtropical eutrophic Lake Dianchi, we applied an innovative coupled hydrodynamic-ecological model to identify key drivers of ecosystem degradation. This approach uniquely involved simulating multiple restoration strategies across eight degradation scenarios, 28 climate change scenarios, and over 100 regulatory scenarios, offering a comprehensive analysis of potential solutions. Our results identify the synergistic effects of grass carp introduction and elevated water levels as key drivers of the observed clear-to-turbid regime shift in the 1970s. Subsequently, fluctuations in nutrient loading became dominant drivers of eutrophication and cyanobacterial blooms. We further show that restoring a clear-water state with submerged macrophytes would optimally require a multidimensional strategy: 63% nutrient loading reduction (76% under simulated climate change conditions), 1.5 m water level lowering, and ∼80% benthivorous fish removal over three consecutive summers. Our study demonstrates that combining biotic, hydrological, and nutrient management outperforms single-factor manipulations, underscoring the importance of resilience-building through integrated, multidimensional interventions.

Harmful cyanobacterial blooms (CyanoHABs) pose a significant threat to global water quality. Although eutrophication and climate change are recognized as key drivers of CyanoHABs proliferation, their synergistic effects remain elusive, hindering effective mitigation strategies. Here, we present a causal inference framework that leverages state-space reconstruction and empirical dynamic modeling to unravel the complex, nonlinear interactions governing CyanoHABs dynamics. Focusing on Microcystis blooms dynamics in Dianchi Lake (China), our approach uniquely integrates causal inference with time-series embedding, reconstructing the ecosystem’s hidden dynamics in a higher-dimensional geometric space. This foundation enables us to rigorously quantify causal drivers—such as nutrient loading and temperature—while overcoming the limitations of traditional correlation-based analyses. Our causal network analysis reveals distinct nonlinear responses of chlorophyll-a (Chl-a) concentration and Microcystis density to different nutrient drivers. Specifically, we found that in-lake total phosphorus (TP) exerts a stronger causal influence on overall algal dynamics than total nitrogen (TN). In contrast, external nutrient loading shows greater influence over Microcystis density compared to in-lake nutrients. Through scenario simulations, we further demonstrate that rising air temperatures amplify Chl-a concentration and Microcystis biomass through increased water temperatures, whereas precipitation-induced nutrient changes preferentially stimulate Chl-a production over Microcystis growth. Notably, we identified contrasting seasonal response patterns, with Chl-a exhibiting greater sensitivity to dry-season conditions while Microcystis density responded more strongly to wet-season drivers. By bridging mechanistic understanding and predictive modeling, our work offers a transformative tool for forecasting and managing CyanoHABs in changing climates.

Biodiversity monitoring is critical for conservation and management. However, efficient species monitoring is often hindered by the complexities of ecological dynamics and the constraints of conventional techniques. This study presents an automated observation system by integrating dome camera with cascade neural networks (CNNs) to map the dynamics of waterbird communities across a semi-enclosed wetland in Dianchi Lake, southwestern China, a well-known important bird habitat. The trained model achieved performance with a Top-1 accuracy of 96.83 %, a Top-5 accuracy of 99.55 %, an F1 score of 93.54 %, a recall rate of 93.38 % and precision of 93.44 %, demonstrating its reliability for precise and well-balanced classification performance. Automatic and manual monitoring performed simultaneously showed highly significant correlations for community abundance (R2 = 0.89, n = 68, p < 0.0001), underscoring the value of this system as a tool for waterbird communty monitoring. Analysis of the monitoring results showed significant differences in species richness (n = 595, p < 0.0001) and community abundance (n = 595, p < 0.05) between morning and afternoon sessions, suggesting the need to consider ecological community changes across different time scales when conducting biodiversity surveys. In 2023, the system identified 17 species of birds in 5 orders, 6 families. By analyzing the data obtained from this system, the community composition, diversity changes, the arrival and departure times of waterbirds were revealed. Enabling high-frequency, continuous, and long-term monitoring at a lower cost, this system provides a reliable, alternative tool for developing species conservation and habitat management strategies.

Toxic Microcystis blooms often occur during summer and autumn and disappear in the winter. However, increasing observational evidence has suggested prolonged Microcystis blooms, extending into winter worldwide. The underlying mechanisms, including those associated with global warming and eutrophication, remain unclear. In this study, we examined the integrated effects of eutrophication and temperatures on the survival, regrowth, and buoyancy of Microcystis colonies in environments where temperatures were low (4, 8, 12, and 8 ± 4 °C, representing the overwintering phase) and increased to a constant 25 °C (representing the recovery phase). Our results uncovered that eutrophication exacerbates the stimulating impact of warm winter temperatures on the survival and growth of overwintering Microcystis. Low nutrients (TN:1.21 mg L−1, TP:0.064 mg L−1) suppress the survival and growth of Microcystis during overwintering and recovery phases, even when the winter temperature increases to 12 °C. In contrast, Microcystis gradually grew at 4 °C under a high nutrient treatment (TN:14.63 mg L−1, TP:0.987 mg L−1). Chlorophyll fluorescence suggested such responses were likely caused by elevated nutrients, which significantly improved Microcystis' photoprotection, limiting photodamage under low temperature and light conditions. Furthermore, nutrient enrichment enabled buoyancy recovery, allowing Microcystis to float upward during the recovery phase. These findings suggest a reduced bottleneck threshold of temperature triggering winter Microcystis blooms at higher nutrient levels, underscoring the importance of nutrient control in managing cyanobacterial blooms and lake ecosystems under climatically dynamic conditions.

Cyanobacterial harmful algal blooms (cyanoHABs) are becoming increasingly common in aquatic ecosystems worldwide. However, their heterogeneous distributions make it difficult to accurately estimate the total algae biomass and forecast the occurrence of surface cyanoHABs by using traditional monitoring methods. Although various optical instruments and remote sensing methods have been employed to monitor the dynamics of cyanoHABs at the water surface (i.e., bloom area, chlorophyll a), there is no effective in-situ methodology to monitor the dynamic change of cell density and integrated biovolume of algae throughout the water column. In this study, we propose a quantitative protocol for simultaneously measurements of multiple indicators (i.e., biovolume concentration, size distribution, cell density, and column-integrated biovolume) of cyanoHABs in water bodies by using the laser in-situ scattering and transmissometry (LISST) instrument. The accuracy of measurements of the biovolume and colony size of algae was evaluated and exceeded 95 % when the water bloom was dominated by cyanobacteria. Furthermore, the cell density of cyanobacteria was well estimated based on total biovolume and mean cell volume measured by the instrument. Therefore, this methodology has the potential to be used for broader applications, not only to monitor the spatial and temporal distribution of algal biovolume concentration but also monitor the vertical distribution of cell density, biomass and their relationship with size distribution patterns. This provides new technical means for the monitoring and analysis of algae migration and early warning of the formation of cyanoHABs in lakes and reservoirs.

Increasing occurrences of Microcystis surface scum have been observed in the context of global climate change and the increase in anthropogenic pollution, causing deteriorating water quality in aquatic ecosystems. Previous studies on scum formation mainly focus on the buoyancy-driven floating process of larger Microcystis colonies, neglecting other potential mechanisms. To study the non-buoyancy-driven rapid flotation of Microcystis, we here investigate the floating processes of two strains of single-cell species (Microcystis aeruginosa and Microcystis wesenbergii), which are typically buoyant, under light conditions (150 μmol photons s−1 m−2). Our results showed that M. wesenbergii exhibited fast upward migration and formed surface scum within 4 hours, while M. aeruginosa did not form visible scum throughout the experiments. To further explore the underlying mechanism of these processes, we compared the dissolved oxygen (DO), extracellular polymeric substance (EPS) content, and colony size of Microcystis in different treatments. We found supersaturated DO and the formation of micro-bubbles (50–200 µm in diameter) in M. wesenbergii treatments. M. aeruginosa produces bubbles in small quantities and small sizes. Additionally, M. wesenbergii produced more EPS and tended to aggregate into larger colonies. M. wesenbergii had much more derived-soluble extracellular proteins and polysaccharides compared to M. aeruginosa. At the same time, M. wesenbergii contains abundant functional groups, which was beneficial to the formation of agglomerates. The surface scum observed in M. wesenbergii is likely due to micro-bubbles attaching to the surface of cell aggregates or becoming trapped within the colony. Our study reveals a species-specific mechanism for the rapid floatation of Microcystis, providing novel insights into surface scum formation as well as succession of cyanobacterial species.

Large and shallow lakes are widely influenced by multiple anthropogenic stressors,including eutrophication. Normally,diverse strategies of eutrophication management are needed to restore the lake ecosystems. Dianchi Lake,a subtropical plateau lake in Yunnan Province,SW China,has long experienced the effects of eutrophication and cyanobacterial blooms. To mitigate the eutrophication of Dianchi Lake,various efforts have been implemented since 2000,including reducing nutrient inputs,re-establishing aquatic macrophytes in lakeside zones,manipulating fishes and so on. However,little is known about the changes in its ecosystem structure and function after long-term efforts of eutrophication management in the lake. Therefore,this study on such changes was conducted by comparing the field data-based Ecopath models between 2009 and 2019,a decade marked by a rapid implementation of eutrophication management policies. Results show that both top-down and bottom-up processes have influenced the ecosystem structure and function. First,over this decade,nutrient reduction significantly reduced phytoplankton biomass by 49.4％. Nutrient recycling and path length in food web began to decrease,as indicated by the decrease of Finn's cycling index and Finn's mean path length. Secondly,fishing management strategy has greatly changed the composition of fish assemblage,which was dominated by the small zooplantivorous fishes with ecological niche overlapping with shrimps. In general,the stability of the ecosystem has been decreasing,due to the dramatic decrease in zooplankton biomass (83.67％) and hence a collapse of the microbial loop in the food web. Therefore,we strongly advocate the persistent efforts to mitigate the risk of recurrent cyanobacteria blooms in Dianchi Lake,necessitating stricter regulation of nutrient levels and implementing effective fish population management techniques.

Changes in water quality in Dianchi Lake over a long temporal scale have been closely related to climate change and the issuance of governance policies. In this study, the Mann-Kendall test and the Theil-Sen estimation were utilized to examine the average trends of water quality changes at ten monitoring sites within Dianchi Lake over the past 30 years (1989-2018) and the corresponding impacts of policies (including point-source, nonpoint-source (NPS) pollution control, ecological restoration (ER), and enforcement and supervision) and climatic conditions. Analysis of regression curves indicates that NH4+-N and total nitrogen (TN) in Dianchi Lake slightly increased, while total phosphorus (TP), biochemical oxygen demand (BOD5), permanganate index (CODMn), and Chl-a decrease when compared to baseline concentrations at the initial study period. During the medium phase of the study period, policy impacts on water quality were inconspicuous, with certain pollutants exhibiting no relationship with policy issuance, evidenced instead by water temperature-induced fluctuations in Chl-a, which inversely correlated with nutrient trends. During the 11th Five-Year Plan, pollutants including NH4+-N, TN, TP, and CODMn, which reached peak levels, subsequently underwent a marked reduction between 2009 and 2015. This pronounced decrease is likely attributable to the implementation of comprehensive NPS and ER policies during this period.

Global climate change as well as human activities have been reported to increase the frequency and severity of both salinization and harmful algal blooms (HABs) in many freshwater systems, but their co-effect on benthic invertebrates has rarely been studied. This study simultaneously examined the joint toxicity of salinity and different cyanobacterial diets on the behavior, development, select biomarkers, and partial life cycle of Chironomus pallidivittatus (Diptera). High concentrations of salts (e.g., 1 g/L Ca2+ and Mg2+) and toxic Microcystis had synergistic toxicity, inhibiting development, burrowing ability and causing high mortality of C. pallidivittatus, especially for the Mg2+ treatment, which caused around 90% death. Low Ca2+ concentration (e.g., 0.01 g/L) promoted larval burrowing ability and inhibited toxin accumulation, which increased the tolerance of Chironomus to toxic Microcystis. However, low Mg2+ concentration (e.g., 0.01 g/L) was shown to inhibit the behavior, development and increase algal toxicity to Chironomus. Toxic Microcystis resulted in microcystin (MC) accumulation, inhibited the burrowing ability of larvae, and increased the proportion of male adults (>50%). The combined toxicity level from low to high was verified by the weight of evidence and the grey TOPSIS model, which integrated five lines of evidence to increase the risk assessment accuracy and efficiency. This is the first study that provided insights into ecological risk arising from the joint effect of salinity and harmful algae on benthic organisms. We suggest that freshwater salinization and HABs should be considered together when assessing ecological threats that arise from external stress.

为弄清滇池外海蓝藻水华暴发时空变化规律及其影响因素,将滇池外海分为北、中、南3个区域,基于2002—2018年期间中分辨率成像光谱仪(MODIS)反演的水华面积,分析了上述3个区域蓝藻水华的时空变化特征.基于2007—2018年水文、气象和出入流数据,构建了外海三维水动力生态模型(AEM3D),并计算了各区域的水力滞留时间.通过冗余分析(RDA)、随机森林(RF)和斯皮尔曼相关分析方法,分析了影响以上区域蓝藻水华暴发的主要驱动因子.结果表明:2002—2018年期间,整个滇池外海区域年平均水华面积比(水华面积占该区域总面积比例)呈缓慢下降趋势,空间上由北向南依次递减,整个外海水华暴发面积最大主要发生在秋季.在外海北部区域,其东部水华较西部更为严重,而在中部和南部区域,呈现西部水华较东部更为严重的空间分布模式.通过对各影响因子的统计分析发现,风速、水温和日照时长是上述各区域中蓝藻水华暴发的主要决定性因素.水华暴发期间以西南风为主导风向,且上述区域的水华面积比随风速增加呈下降趋势.在外海各区域,水力滞留时间与水华暴发面积均呈显著正相关,空间上水力滞留时间由北向南逐渐增大,风速和风向是影响蓝藻水华空间分布规律和严重程度的主要因素.

Climate change can induce phytoplankton blooms (PBs) in eutrophic lakes worldwide, and these blooms severely threaten lake ecosystems and human health. However, it is unclear how urbanization and its interaction with climate impact PBs, which has implications for the management of lakes. Here, we used multi-source remote sensing data and integrated the Virtual-Baseline Floating macroAlgae Height (VB-FAH) index and OTSU threshold automatic segmentation algorithm to extract the area of PBs in Lake Dianchi, China, which has been subjected to frequent PBs and rapid urbanization in its vicinity. We further explored long-term (2000-2021) trends in the phenological and severity metrics of PBs and quantified the contributions from urbanization, climate change, and also nutrient levels to these trends. When comparing data from 2011-2021 to 2000-2010, we found significantly advanced initiation of PBs (28.6 days) and noticeably longer duration (51.9 days) but an insignificant trend in time of disappearance. The enhancement of algal nutrient use efficiency, likely induced by increased water temperature and reduced nutrient concentrations, presumably contributed to an earlier initiation and longer duration of PBs, while there was a negative correlation between spring wind speed and the initiation of PBs. Fortunately, we found that both the area of the PBs and the frequency of severe blooms (covering more than 19.8 km(2)) demonstrated downward trends, which could be attributed to increased wind speed and/or reduced nutrient levels. Moreover, the enhanced land surface temperature caused by urbanization altered the thermodynamic characteristics between the land and the lake, which, in turn, possibly caused an increase in local wind speed and water temperature, suggesting that urbanization can differently regulate the phenology and severity of PBs. Our findings have significant implications for the understanding of the impacts of urbanization on PB dynamics and for improving lake management practices to promote sustainable urban development under global change.

Light is an important driver of algal growth and for the formation of surface blooms. Long-term buoyancy maintenance of Microcystis colonies is crucial for their aggregation at the water surface and the following algal bloom development. However, the effect of light-mediated variations of colony morphology on the buoyancy regulation of Microcystis colonies remains unclear. In this study, growth parameters, colony morphology and floatation/sinking performance of Microcystis colonies were determined to explore how variations in colony morphology influence the buoyancy of colonies under different light conditions. We quantified colony compactness through the cell volume to colony volume ratio (VR) and found different responses of colony size and VR under different light intensities. Microcystis colonies with higher VR could stay longer at the water surface under low light conditions, which was beneficial for the long-term growth and buoyancy maintenance. However, increased colony size and decreased compactness were observed at a later growth stage under relatively higher light intensity (i.e., >108 mu mol photons m- 2 s- 1). Interestingly, we found a counterintuitive negative correlation between colony size and buoyancy of Microcystis under high light intensity. Additionally, we found that the influence of colony morphology on buoyancy was stronger at high light intensity. These results indicate that light could regulate the buoyancy via colonial morphology and that the role of colony morphology in buoyancy regulation needs to be accounted for in further studies under variable environmental conditions.

The rapid decline of freshwater biodiversity caused by overfishing has led to the implementation of a series of conservation measures, including fishing bans. However, existing studies have mostly focused on the effects of fishing bans on economically important species, while impacts on freshwater macroinvertebrates in lake eco-systems have been rarely studied. This study used a before-and-after methodology to determine the short-term effects of the "ten-year fishing ban" on the macroinvertebrates of the Dianchi Lake, the largest highland fresh-water lake in the upper Yangtze basin, between 2015 and 2022. Following the fishing ban, the overall macro -invertebrate species richness (median [interquartile]) across sites increased from 4 [2-6] to 5 [4-7]. The total density increased from 128 [80-272] to 212 [140-325] n/m2. The median biomass increased from 0.18 [0.08-0.41] to 0.51 [0.26-2.36] g/m2. In particular, the Chironomidae density in the offshore sites increased from 16.00 [0.00-32.00] to 33.30 [16.00-48.00] n/m2, and the biomass increased from 0.03 [0.00-0.09] to 0.16 [0.07-0.22] g/m2. Within the inshore sites, the aquatic insect density increased from 4 [1.33-15.33] to 56 [22.00-86.67] n/m2. The Malacostraca density increased from 34.67 [11.67-95.33] to 110 [53.33-223.33] n/ m2, and the biomass increased from 0.43 [0.11-1.00] to 1.48 [0.50-2.00] g/m2. Two endangered Margarya species were rediscovered at multiple sites compared to the pre-fishing ban period. A significant change in macroinvertebrate community structure across the lake was observed, which can be largely attributed to the fishing ban. The immediate increase in species richness, density, and biomass of most macroinvertebrate species suggests a combination of effects from both reduced exploitation pressure and lessened disturbances on lake habitats. The findings indicate that the fishing ban is beneficial for the recovery of most macroinvertebrate species in freshwater lakes.

Harmful algal blooms (HABs) have been deteriorating global water bodies, and the accurate prediction of algal dynamics using the modelling method is a challenging research area. High-frequency monitoring and deep learning technology have opened up new horizons for HAB forecasting. However, the non-stationary and stochastic process behind algal dynamics monitoring largely limits the prediction performance and the early warning of algal booms. Through an analysis of the published literature, we found that decomposition methods are widely used in time-series analysis for hydrological processes. Predictions of ecological indicators have received less attention due to their inherent fluctuations. This study explores and demonstrates the predictive enhancement for chlorophyll fluorescence data based on the coupling of three decomposition algorithms with conventional deep learning models: the convolutional neural network (CNN) and long short-term memory (LSTM). We found that the decomposition algorithms can successfully capture the time-series patterns of chlorophyll fluorescence concentrations. The results indicate that decomposition-based models can enhance the accuracy of single models in predicting chlorophyll concentrations in terms of the improvement percentages in RMSE (with increases ranging from 25.7% to 71.3%), MAE (ranging from 28.3% to 75.7%), and R2 values (increasing ranging from 14.8% to 34.8%). In addition, the comparison experiment for different decomposition methods might suggest the superiority of singular spectral analysis in hourly predictive tasks of chlorophyll fluorescence over the wavelet transform and empirical mode decomposition models. Overall, while decomposition methods come with their respective strengths and weaknesses, they are undeniably efficient in combination with deep learning models in dealing with the high-frequency monitoring of chlorophyll fluorescence data. We also suggest that model developers pay more attention to online data preprocessing and conduct comparative analyses to determine the best model combinations for forecasting algal blooms and water management.

There are many rivers flowing from complex paths into Lake Dianchi. At present, there is a lack of inflow and water quality monitoring data for some rivers, resulting in limited accuracy of statistical results regarding water volume and external loading estimations. In this study, we used DYRESM to estimate the water volume entering Waihai of Lake Dianchi from 2007 to 2019 without historical hydrological observation data. Then, we combined this information with the monthly monitoring data of water quality to calculate the annual external loading. Our results showed that: (1) DYRESM could effectively capture the extreme changes of water level at Waihai, showing its reliable applicability to Lake Dianchi. (2) The average annual inflow of rivers entering Waihai was about 6.69 × 108 m3. The fitting relationship between river inflow and precipitation was significant on annual scale (r = 0.74), with a higher inner-annual fitting coefficient between them (r = 0.98), thus suggesting that precipitation and its caused river inflows are the main water source for Waihai. (3) From 2007 to 2010, the river loadings remained at a high level. They decreased to 2445.44 t (total nitrogen, TN) and 106.53 t (total phosphorus, TP) due to a followed drought in 2011. (4) The river loading had annual variation characteristics. The contribution rates of TN and TP loading in the rainy season were 63% and 67% respectively. (5) Panlong River, Daqing River, Jinjia River, Xinbaoxiang River, Cailian River and Hai River were the main inflow rivers. Their loadings accounted for 81.3% (TN) and 80.3% (TP) of the total inputs. (6) River loadings have gradually reduced and the water quality of Waihai has continually improved. However, Pearson analysis results showed that the water quality parameters were not significantly correlated with their corresponding external loading at Waihai, indicating that there might be other factors influencing the water quality. (7) The contribution rates of internal release to the total loads of TN and TP at Waihai were estimated to be 7.6% and 8.9% respectively, suggesting that the reductions of both external and internal loading should be considered in order to significantly improve the water quality at Waihai of Lake Dianchi.

Microalgae are a promising sustainable source of biomass while large-scale harvesting of microalgal biomass is a major technological and economic challenge. In this study, a flocculation method and mechanism for the formation of compact mega flocs was developed using two oppositely charged polyelectrolytes, quaternary ammonium chitosan (N-[(2-hydroxy-3-trimethyl ammonium) propyl] chitosan, HTCC) and xanthan gum (XG). With 8 mg/L HTCC and 16 mg/L XG, Scenedesmus cells could be flocculated into mega flocs with diameters larger than 5 mm and were harvested by 500-mu m-pore-sized sieves with an efficiency over 95%. The flocs were generated within 12 s and resisted strong hydraulic shear (stirring at 960 rpm), reducing energy consumption and handling time for the subsequent separation. With HTCC or XG alone, the harvest efficiency was consistently lower than 30%. The flocculation mechanism was investigated by changing the order in which the HTCC, XG, and Scenedesmus cells were added, and by fluorescence staining, scanning electron microscopy (SEM) and Fourier transform infrared (FTIR) spectroscopy. The results showed that the mega flocs were formed by the crosslinking of HTCC and XG, based on their long-chain molecular structure and electrostatic interaction. Scenedesmus cells were enmeshed in the polymer matrix of HTCC and XG and then aggregated into large flocs. Thus, an effective, eco-friendly, and sustainable flocculation strategy for microalgae biomass was developed using the biodegradable components HTCC and XG.