Effects of rainfall patterns on toxic cyanobacterial blooms in a changing climate: between simplistic scenarios and complex dynamics

Influence of climate extremes on long-term changes in cyanobacterial blooms in a eutrophic and shallow lake

Climate change and human activities have crucial effects on the variations in phytoplankton blooms in lakes worldwide. A record-breaking heatwave and drought event was reported in the middle and lower reaches of the Yangtze River during the summer of 2022, but only little is known about how cyanobacterial blooms in lakes respond to such climate extremes. Here, we utilized MODIS images to generate the area, occurrence, and initial blooming date (IBD) of cyanobacterial blooms in Lake Chaohu from 2000 to 2022. We found that the area and occurrence of cyanobacterial blooms were largely reduced. At the same time, the IBD was delayed in 2022 compared with the previous 20 years. The annual occurrence and mean area of cyanobacterial blooms in 2022 were 17 % and 23.1 km2, respectively, which were the lowest reported levels since the 21st century. The IBD in 2022 was four months late compared with the IBD in 2020. The high wind speed in spring delayed the spring blooms in 2022. The record-breaking heatwaves and drought from June to August reduced the blooms by influencing the growth of cyanobacteria and reducing the flow of nutrients from the watershed into the lake. This study highlights the compound impact of heatwave and drought climate events on reducing cyanobacterial blooms in a long-term period, enhancing additional understanding of the changes in phytoplankton blooms in lakes.

Evaluation of best management practices for mitigating harmful algal blooms risk in an agricultural lake basin using a watershed model integrated with Bayesian Network approach

Lake eutrophication caused by nitrogen and phosphorus has led to frequent harmful algal blooms (HABs), especially under the unknown challenges of climate change, which have seriously damaged human life and property. In this study, a coupled SWAT-Bayesian Network (SWAT-BN) model framework was constructed to elucidate the mechanisms between non-point source nitrogen pollution in agricultural lake watersheds and algal activities. A typical agricultural shallow lake basin, the Taihu Basin (TB), China, was chosen in this study, aiming to investigate the effectiveness of best management practices (BMPs) in controlling HABs risks in TB. By modeling total nitrogen concentration of Taihu Lake from 2007 to 2022 with four BMPs (filter strips, grassed waterway, fertilizer application reduction and no-till agriculture), the results indicated that fertilizer application reduction proved to be the most effective BMP with 0.130 of Harmful Algal Blooms Probability Reduction (HABs-PR) when reducing 40% of fertilizer, followed by filter strips with 0.01 of HABs-PR when 4815ha of filter strips were conducted, while grassed waterway and no-till agriculture showed no significant effect on preventing HABs. Furthermore, the combined practice between 40% fertilizer application reduction and 4815ha filter strips construction showed synergistic effects with HABs-PR increasing to 0.171. Precipitation and temperature data were distorted to model scenarios of extreme events. As a result, the combined approach outperformed any single BMP in terms of robustness under extreme climates. This research provides a watershed-level perspective on HABs risks mitigation and highlights the strategies to address HABs under the influence of climate change.

Cyanopeptides occurrence and diversity in a Brazilian tropical reservoir: Exploring relationships with water quality

Microcystins (MCs) are a class of toxic secondary metabolites produced by some cyanobacteria strains that endanger aquatic and terrestrial organisms in various freshwater systems. Although patterns in MC occurrence are being recognized, divergences in the global data still hamper our ability to predict the toxicity of cyanobacterial blooms. This study aimed (i) to determine the dynamics of MCs and other cyanopeptides in a tropical reservoir, (ii) to investigate the correlation between peptides and potential cyanotoxin producers (iii) identifying the possible abiotic factors that influence the peptides. We analyzed, monthly, eight MC variants (MC-RR, -LA, -LF, -LR, -LW, -YR, [D-Asp3]-RR and [D-Asp3]-LR) and other peptides in 47 water samples collected monthly, all season long, from two sampling sites in a tropical eutrophic freshwater reservoir, in southeastern Brazil. The cyanopeptides were assessed by liquid chromatography-tandem mass spectrometry (LC-MS/MS). The biomass of potential cyanobacterial producers and water quality variables were measured. MCs were detected in both sampling sites year-round; the total MC concentration varied from 0.21 to 4.04 μg L-1, and three MC variants were identified and quantified (MC-RR, [D-Asp3]-RR, -LR). Additionally, we identified 28 compounds belonging to three other cyanopeptide classes: aeruginosin, microginin, and cyanopeptolin. As potential MC producers, Microcystis spp. and Dolichospermum circinalis were dominant during the study, representing up to 75% of the total phytoplankton. Correlational and redundancy analysis suggested positive effects of dissolved oxygen, nitrate, and total phosphorus on MC and microginins concentration, while water temperature appeared to favor aeruginosins. A comparison between our results and historical data showed a reduction in total phosphorus and cyanobacteria, suggesting increased water quality in the reservoir. However, the current MC concentrations indicate a rise in cyanobacterial toxicity over the last eight years. Moreover, our study underscores the pressing need to explore cyanopeptides other than MCs in tropical aquatic systems.

A framework for identifying factors controlling cyanobacterium Microcystis flos-aquae blooms by coupled CCM-ECCM Bayesian networks

Cyanobacterial blooms in freshwater sources are a global concern, and gaining insight into their causes is crucial for effective resource management and control. In this study, we present a novel computational framework for the causal analysis of cyanobacterial harmful algal blooms (cyanoHABs) in Lake Kinneret. Our framework integrates Convergent Cross Mapping (CCM) and Extended CCM (ECCM) causal networks with Bayesian Network (BN) models. The constructed CCM-ECCM causal networks and BN models unveil significant interactions among factors influencing cyanoHAB formation. These interactions have been validated by domain experts and supported by evidence from peer-reviewed publications. Our findings suggest that Microcystis flos-aquae levels are influenced not only by community structure but also by ammonium, phosphate, oxygen, and temperature levels in the weeks preceding bloom occurrences. We demonstrated a non-parametric computational framework for causal analysis of a multivariate ecosystem. Our framework offers a more comprehensive understanding of the underlying mechanisms driving M. flos-aquae blooms in Lake Kinneret. It captures complex interactions and provides an explainable prediction model. By considering causal relationships, temporal dynamics, and joint probabilities of environmental factors, the proposed framework enhances our understanding of cyanoHABs in Lake Kinneret.

Multivariate extremes in lakes

Extreme within-lake conditions have the potential to exert detrimental effects on lakes. Here we use satellite observations to investigate how the occurrence of multiple types of extremes, notably algal blooms, lake heatwaves, and low lake levels, have varied in 2724 lakes since the 1980s. Our study, which focuses on bloom-affected lakes, suggests that 75% of studied lakes have experienced a concurrent increase in at least two of the extremes considered (27% defined as having a notable increase), with 25% experiencing an increase in frequency of all three extremes (5% had a notable increase). The greatest increases in the frequency of these extremes were found in regions that have experienced increases in agricultural fertilizer use, lake warming, and a decline in water availability. As extremes in lakes become more common, understanding their impacts must be a primary focus of future studies and they must be carefully considered in future risk assessments.

What makes a cyanobacterial bloom disappear? A review of the abiotic and biotic cyanobacterial bloom loss factors

Cyanobacterial blooms present substantial challenges to managers and threaten ecological and public health. Although the majority of cyanobacterial bloom research and management focuses on factors that control bloom initiation, duration, toxicity, and geographical extent, relatively little research focuses on the role of loss processes in blooms and how these processes are regulated. Here, we define a loss process in terms of population dynamics as any process that removes cells from a population, thereby decelerating or reducing the development and extent of blooms. We review abiotic (e.g., hydraulic flushing and oxidative stress/UV light) and biotic factors (e.g., allelopathic compounds, infections, grazing, and resting cells/programmed cell death) known to govern bloom loss. We found that the dominant loss processes depend on several system specific factors including cyanobacterial genera-specific traits, in situ physicochemical conditions, and the microbial, phytoplankton, and consumer community composition. We also address loss processes in the context of bloom management and discuss perspectives and challenges in predicting how a changing climate may directly and indirectly affect loss processes on blooms. A deeper understanding of bloom loss processes and their underlying mechanisms may help to mitigate the negative consequences of cyanobacterial blooms and improve current management strategies.

Hysteresis analysis reveals how phytoplankton assemblage shifts with the nutrient dynamics during and between precipitation patterns

The escalation of global eutrophication has significantly increased due to the impact of climate change, particularly the increased frequency of extreme rainfall events. Predicting and managing eutrophication requires understanding the consequences of precipitation events on algal dynamics. Here, we assessed the influence of precipitation events throughout the year on nutrient and phytoplankton dynamics in a drinking water reservoir from January 2020 to January 2022. Four distinct precipitation patterns, namely early spring flood rain (THX), Plum rain (MY), Typhoon rain (TF), and Dry season (DS), were identified based on rainfall intensity, duration time, and cumulative rainfall. The study findings indicate that rainfall is the primary driver of algal dynamics by altering nutrient levels and TN:TP ratios during wet seasons, while water temperature becomes more critical during the Dry season. Combining precipitation characteristics with the lag periods between algal proliferation and rainfall occurrence is essential for accurately assessing the impact of rainfall on algal blooms. The highest algae proliferation occurred approximately 20 and 30 days after the peak rainfall during the MY and DS periods, respectively. This was influenced by the intensity and cumulative precipitation. The reservoir exhibited two distinct TN/TP ratio stages, with average values of 52 and 19, respectively. These stages were determined by various forms of nitrogen and phosphorus in rainfall-driven inflows and were associated with shifts from Bacillariophyta-dominated to Cyanophyta-dominated blooms during the MY and DS seasons. Our findings underscore the interconnected effects of nutrients, temperature, and hydrological conditions driven by diverse rainfall patterns in shaping algal dynamics. This study provides valuable insights into forecasting algal bloom risks in the context of climate change and developing sustainable strategies for lake or reservoir restoration.

Integrating impacts of climate change on aquatic environments in inter-basin water regulation: Establishing a critical threshold for best management practices

Inter-basin water diversion (IBWD) is a viable strategy to tackle water scarcity and quality degradation due to climate change and increasing water demand in headwaters regions. Nevertheless, the capacity of IBWD to mitigate the impacts of climate change on water quality has rarely been quantified, and the underlying processes are not well understood. Therefore, this study aims to elucidate how the IBWD manipulated total phosphorus (TP) loading dilution and conveying patterns under climate change and determine a critical threshold for the quantity of water entering downstream reservoirs (WIN) for operational scheduling. To resolve this issue, climate-driven hydrologic variability over a 60-year period was derived utilizing the least square fitting approach. Subsequently, six scenarios evaluating the response of in-lake TP concentrations (TPL) to increased temperatures and IBWDs of 50 %, 100 %, and 150 % from the baseline water volume in 2030 and 2050 were studied by employing a calibrated hydrological-water quality model (SWAT-YRWQM). In the next stage, three datasets derived from mathematical statistics based on the observed data, the Vollenweider formula, and modeled projections were integrated to formulate best management practices. The results revealed that elevated air temperatures would lead to reduced annual catchment runoff but increased IBWD. Additionally, our study quantified the IBWD potential for mitigating water quality degradation, indicating the adverse effects of climate change on TPL would be weakened by 4.2-14.4 %. A critical threshold for WIN was also quantified at 617 million m3, maintaining WIN at or near 617 million m3 through optimized operational scheduling of IBWD could effectively restrict external inflow TP loading to lower levels. This study clearly illustrates the intricate interactive effects of climate change and IBWD on aquatic environments. The methodology elucidated in this study for determining the critical threshold of WIN could be applied in water management for analogous watershed-receiving waterbody systems.

Dynamic monitoring and analysis of chlorophyll-a concentrations in global lakes using Sentinel-2 images in Google Earth Engine

Remote estimation of Chlorophyll-a (Chl-a) has long been used to investigate the responses of aquatic ecosystems to global climate change. High-spatiotemporal-resolution Sentinel-2 satellite images make it possible to routinely monitor and trace the spatial distributions of lake Chl-a if reliable retrieval algorithms are available. In this study, Sentinel-2 images and in-situ measured data were used to develop a Chl-a retrieval algorithm based on 13 optical water types (OWTs) with a satisfying performance (R2 = 0.74, RMSE = 0.42 mg/m3, MAE = 0.33 mg/m3, and MAPE = 55.56 %). After removing the disturbance of algal blooms and other factors, the distribution of Chl-a in 3067 of the largest global lakes (≥50 km2) was mapped using the Google Earth Engine (GEE). From 2019 to 2021, the average Chl-a concentration was 16.95 ± 5.95 mg/m3 for the largest global lakes. During the COVID-19 pandemic, global lake-averaged Chl-a concentration reached its lowest value in 2020. From the perspective of spatial distribution, lakes with low Chl-a concentrations were mainly distributed in high-latitude, high-elevation, or economically underdeveloped areas. Among all the potential influencing factors, lake surface temperature had the largest contribution to Chl-a and showed a positive correlation with Chl-a in approximately 92.39 % of the lakes. Conversely, factors such as precipitation and tree cover area around the lake were negatively correlated with Chl-a concentration in nearly 61.44 % of the lakes.

A semi-analytical model to estimate Chlorophyll-a spatial-temporal patterns from Orbita Hyperspectral image in inland eutrophic waters

It can be challenging to accurately estimate the Chlorophyll-a (Chl-a) concentration in inland eutrophic lakes due to lakes' extremely complex optical properties. The Orbita Hyperspectral (OHS) satellite, with its high spatial resolution (10 m), high spectral resolution (2.5 nm), and high temporal resolution (2.5 d), has great potential for estimating the Chl-a concentration in inland eutrophic waters. However, the estimation capability and radiometric performance of OHS have received limited examination. In this study, we developed a new quasi-analytical algorithm (QAA716) for estimating Chl-a using OHS images. Based on the optical properties in Dianchi Lake, the ability of OHS to remotely estimate Chl-a was evaluated by comparing the signal-to-noise ratio (SNR) and the noise equivalent of Chl-a (NEChl-a). The main findings are as follows: (1) QAA716 achieved significantly better results than those of the other three QAA models, and the Chl-a estimation model, using QAA716, produced robust results with a mean absolute percentage difference (MAPD) of 11.54 %, which was better than existing Chl-a estimation models; (2) The FLAASH (Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes) atmospheric correction model (MAPD = 22.22 %) was more suitable for OHS image compared to the other three atmospheric correction models we tested; (3) OHS had relatively moderate SNR and NEChl-a, improving its ability to accurately detect Chl-a concentration and resulting in an average SNR of 59.47 and average NEChl-a of 72.86 μg/L; (4) The increased Chl-a concentration in Dianchi Lake was primarily related to the nutrients input, and this had a significant positive correlation with total nitrogen. These findings expand existing knowledge of the capabilities and limitations of OHS in remotely estimating Chl-a, thereby facilitating effective water quality management in eutrophic lake environments.

One-Week-Ahead Prediction of Cyanobacterial Harmful Algal Blooms in Iowa Lakes

Cyanobacterial harmful algal blooms (CyanoHABs) pose serious risks to inland water resources. Despite advancements in our understanding of associated environmental factors and modeling efforts, predicting CyanoHABs remains challenging. Leveraging an integrated water quality data collection effort in Iowa lakes, this study aimed to identify factors associated with hazardous microcystin levels and develop one-week-ahead predictive classification models. Using water samples from 38 Iowa lakes collected between 2018 and 2021, feature selection was conducted considering both linear and nonlinear properties. Subsequently, we developed three model types (Neural Network, XGBoost, and Logistic Regression) with different sampling strategies using the nine selected variables (mcyA_M, TKN, % hay/pasture, pH, mcyA_M:16S, % developed, DOC, dewpoint temperature, and ortho-P). Evaluation metrics demonstrated the strong performance of the Neural Network with oversampling (ROC-AUC 0.940, accuracy 0.861, sensitivity 0.857, specificity 0.857, LR+ 5.993, and 1/LR- 5.993), as well as the XGBoost with downsampling (ROC-AUC 0.944, accuracy 0.831, sensitivity 0.928, specificity 0.833, LR+ 5.557, and 1/LR- 11.569). This study exhibited the intricacies of modeling with limited data and class imbalances, underscoring the importance of continuous monitoring and data collection to improve predictive accuracy. Also, the methodologies employed can serve as meaningful references for researchers tackling similar challenges in diverse environments.

Dynamic response of bacterial communities to Microcystis blooms: A three-year study

Although nutrient availability is widely recognized as the driving force behind Microcystis blooms, identifying the microorganisms that play a pivotal role in their formation is a challenging task. Our understanding of the contribution of bacterial communities to the development of Microcystis blooms remains incomplete, despite the fact that the relationship between Microcystis and bacterial communities has been extensively investigated. Most studies have focused on their interaction for a single year rather than for multiple years. To determine key bacteria crucial for the formation of Microcystis blooms, we collected samples from three sites in the Daechung Reservoir (Chuso, Hoenam, and Janggye) over three years (2017, 2019, and 2020). Our results indicated that Microcystis bloom-associated bacterial communities were more conserved across stations than across years. Bacterial communities could be separated into modules corresponding to the different phases of Microcystis blooms. Dolichospermum and Aphanizomenon belonged to the same module, whereas the module of Microcystis was distinct. The microbial recurrent association network (MRAN) showed that amplicon sequence variants (ASVs) directly linked to Microcystis belonged to Pseudanabaena, Microscillaceae, Sutterellaceae, Flavobacterium, Candidatus Aquiluna, Bryobacter, and DSSD61. These ASVs were also identified as key indicators of the bloom stage, indicating that they were fundamental biological elements in the development of Microcystis blooms. Overall, our study highlights that, although bacterial communities change annually, they continue to share core ASVs that may be crucial for the formation and maintenance of Microcystis blooms.

Seasonal and interannual responses of blue-green algal taxa and chlorophyll to a monsoon climate, flow regimes, and N:P ratios in a temperate drinking-water reservoir

Blooms of blue-green algae (BGA) threaten drinking water safety and ecosystems worldwide. Understanding mechanisms and driving factors that promote BGA proliferation is crucial for effective freshwater management. This study tested the response of BGA growth to environmental variations driven by nutrients (N and P), N:P ratios, and flow regime depending on the influence of the Asian monsoon intensity and identified the critical regulatory factors in a temperate drinking-water reservoir, using weekly interval samplings collected during 2017-2022. The hydrodynamic and underwater light conditions experienced significant changes in summers due to high inflows and outflows associated with intense rainfalls, and these conditions strongly influenced the proliferation of BGA and total phytoplankton biomass (as estimated by chlorophyll-a [CHL-a]) during summer monsoons. However, the intense monsoon resulted in the post-monsoon blooms of BGA. The monsoon-induced phosphorus enrichment, facilitated through soil washing and runoff, was crucial in promoting phytoplankton blooms in early post-monsoon (September). Thus, the monomodal phytoplankton peak was evident in the system, compared to the bimodal peaks in North American and European lakes. Strong water column stability in the weak monsoon years depressed phytoplankton growth and BGA, suggesting the importance of the intensity of monsoon. The low N:P ratios and longer water residence time increased BGA abundance. The predictive model of BGA abundance accounted for the variations largely (Mallows' Cp = 0.39, adjusted R2 = 0.55, p < 0.001) by dissolved phosphorus, N:P ratios, CHL-a, and inflow volume. Overall, this study suggests that monsoon intensity was the key triggering factor regulating the interannual BGA variations and facilitated the post-monsoon blooms through increased nutrient availability.

Model evaluation of total phosphorus prediction based on model accuracy and interpretability for the surface water in the river network of the Jiangnan Plain, China

Due to climatic and hydrological changes and human activities, eutrophication and frequent outbreaks of cyanobacteria are prominent in the Jiangnan Plain basin of China. Therefore, building a suitable model to accurately predict the phosphorus concentration in surface water is of practical significance to prevent the above problems. This study built 10 models to predict the phosphorus element in the surface water of the river network in the Jiangnan Plain. The main water types in the basin include the Yangtze River, the Beijing-Hangzhou Canal, and the Gehu Lake. The 10 models in different datasets have been comprehensively evaluated by the prediction accuracy and interpretability of the model, and the calculation of the partial dependence diagram (PDP) and SHAP has proved that there is a transparent response relationship between phosphorus and different factors. The results show that the Yangtze River, Beijing-Hangzhou Canal, and Gehu Lake are suitable for random forest, linear regression, and random forest models, respectively, under the comprehensive evaluation of the prediction accuracy and interpretability of the model. Models with low prediction accuracy often show strong interpretability. In different water body types, turbidity, water temperature, and chlorophyll-a are the three factors that affect the model in predicting phosphorus.

Climate change amplifies the risk of potentially toxigenic cyanobacteria

Cyanobacterial blooms pose a significant threat to water security, with anthropogenic forcing being implicated as a key driver behind the recent upsurge and global expansion of cyanobacteria in modern times. The potential effects of land-use alterations and climate change can lead to complicated, less-predictable scenarios in cyanobacterial management, especially when forecasting cyanobacterial toxin risks. There is a growing need for further investigations into the specific stressors that stimulate cyanobacterial toxins, as well as resolving the uncertainty surrounding the historical or contemporary nature of cyanobacterial-associated risks. To address this gap, we employed a paleolimnological approach to reconstruct cyanobacterial abundance and microcystin-producing potential in temperate lakes situated along a human impact gradient. We identified breakpoints (i.e., points of abrupt change) in these time series and examined the impact of landscape and climatic properties on their occurrence. Our findings indicate that lakes subject to greater human influence exhibited an earlier onset of cyanobacterial biomass by 40 years compared to less-impacted lakes, with land-use change emerging as the dominant predictor. Moreover, microcystin-producing potential increased in both high- and low-impact lakes around the 1980s, with climate warming being the primary driver. Our findings chronicle the importance of climate change in increasing the risk of toxigenic cyanobacteria in freshwater resources.

Unbalanced impacts of nature and nurture factors on the phenology, area and intensity of algal blooms in global large lakes: MODIS observations

Under the influence of climate warming and human activities, many large lakes have experienced an increase in eutrophication and algal blooms. Although these trends have been identified using low temporal resolution (~16 days) satellites such as those of the Landsat missions, the opportunity to compare high-frequency spatiotemporal variations of algal bloom characteristics between lakes has not been explored. In the present study, we explore daily satellite observations by developing a universal, practical, and robust algorithm to identify the spatiotemporal distribution of algal bloom dynamics in large lakes (>500 km²) across the globe. Data from 161 lakes, taken from 2000 to 2020 showed an average accuracy of 79.9 %. Algal blooms were detected in 44 % of all lakes, with a higher incidence in temperate lakes (67 % of all temperate lakes), followed by tropical lakes (59 %) compared to lakes in arid climates (23 %). We found positive trends in bloom area and frequency (p < 0.05), as well as an earlier bloom time (p < 0.05). Climate factors were found to be linked to changes in annual initial bloom time (44 %); while an increase in human activities was associated to bloom duration (49 %), area (max percent: 53 %, mean percent: 45 %), and frequency (46 %). The study shows the evolution of daily algal blooms and their phenology in global large lakes for the first time. Such information enhances our understanding of algal bloom dynamics and their drivers, with important considerations to improve the management of large lake ecosystems.

Cyanobacterial bloom monitoring and assessment in Latin America

Cyanobacterial blooms have serious adverse effects on human and environmental health. In Latin America, one of the main world's freshwater reserves, information on this phenomenon remains sparse. To assess the current situation, we gathered reports of cyanobacterial blooms and associated cyanotoxins in freshwater bodies from South America and the Caribbean (Latitude 22° N to 45° S) and compiled the regulation and monitoring procedures implemented in each country. As the operational definition of what is a cyanobacterial bloom remains controversial, we also analyzed the criteria used to determine the phenomena in the region. From 2000 to 2019, blooms were reported in 295 water bodies distributed in 14 countries, including shallow and deep lakes, reservoirs, and rivers. Cyanotoxins were found in nine countries and high concentrations of microcystins were reported in all types of water bodies. Blooms were defined according to different, and sometimes arbitrary criteria including qualitative (changes in water color, scum presence), quantitative (abundance), or both. We found 13 different cell abundance thresholds defining bloom events, from 2 × 10³ to 1 × 10⁷ cells mL^-1. The use of different criteria hampers the estimation of bloom occurrence, and consequently the associated risks and economic impacts. The large differences between countries in terms of number of studies, monitoring efforts, public access to the data and regulations regarding cyanobacteria and cyanotoxins highlights the need to rethink cyanobacterial bloom monitoring, seeking common criteria. General policies leading to solid frameworks based on defined criteria are needed to improve the assessment of cyanobacterial blooms in Latin America. This review represents a starting point toward common approaches for cyanobacterial monitoring and risk assessment, needed to improve regional environmental policies.

Harmful cyanobacteria-diatom/dinoflagellate blooms and their cyanotoxins in freshwaters: A nonnegligible chronic health and ecological hazard

Human and ecological health depends on the vitality of freshwater systems, but these are increasingly threatened by cyanotoxins released from harmful algal blooms (HABs). Periodic cyanotoxin production, although undesirable, may be tolerable when there is enough time for cyanotoxins to degrade and dissipate in the environment, but the year-round presence of these toxins will be a chronic health for humans and ecosystems. The purpose of this critical review is to document the seasonal shifts of algal species and their ecophysiological acclimatation to dynamic environmental conditions. We discuss how these conditions will create successive occurrences of algal blooms and the release of cyanotoxins into freshwater. We first review the most common cyanotoxins, and evaluate the multiple ecological roles and physiological functions of these toxins for algae. Then, the annual recurring patterns HABs are considered in the context of global change, which demonstrates the capacity for algal blooms to shift from seasonal to year-round growth regimes that are driven by abiotic and biotic factors, leading to chronic loading of freshwaters with cyanotoxins. At last, we illustrate the impacts of HABs on the environment by compiling four health issues and four ecology issues emanating from their presence in the that covers atmosphere, aquatic ecosystems and terrestrial ecosystems. Our study highlights the annual patterns of algal blooms, and proposes that a "perfect storm" of events is lurking that will cause the 'seasonal toxicity' to become a full-blown, 'chronic toxicity' in the context of the deterioration of HABs, highlighting a non-negligible chronic health and ecological hazard.

Evaluating Ultrasonicator Performance for Cyanobacteria Management at Freshwater Sources

Algal blooms consisting of potentially toxic cyanobacteria are a growing source water management challenge faced by water utilities globally. Commercially available sonication devices are designed to mitigate this challenge by targeting cyanobacteria-specific cellular features and aim to inhibit cyanobacterial growth within water bodies. There is limited available literature evaluating this technology; therefore, a sonication trial was conducted in a drinking water reservoir within regional Victoria, Australia across an 18-month period using one device. The trial reservoir, referred to as Reservoir C, is the final reservoir in a local network of reservoirs managed by a regional water utility. Sonicator efficacy was evaluated through qualitative and quantitative analysis of algal and cyanobacterial trends within Reservoir C and surrounding reservoirs using field data collected across three years preceding the trial and during the 18-month duration of the trial. Qualitative assessment revealed a slight increase in eukaryotic algal growth within Reservoir C following device installation, which is likely due to local environmental factors such as rainfall-driven nutrient influx. Post-sonication quantities of cyanobacteria remained relatively consistent, which may indicate that the device was able to counteract favorable phytoplankton growth conditions. Qualitative assessments also revealed minimal prevalence variations of the dominant cyanobacterial species within the reservoir following trial initiation. Since the dominant species were potential toxin producers, there is no strong evidence that sonication altered Reservoir C’s water risk profiles during this trial. Statistical analysis of samples collected within the reservoir and from the intake pipe to the associated treatment plant supported qualitative observations and revealed a significant elevation in eukaryotic algal cell counts during bloom and non-bloom periods post-installation. Corresponding cyanobacteria biovolumes and cell counts revealed that no significant changes occurred, excluding a significant decrease in bloom season cell counts measured within the treatment plant intake pipe and a significant increase in non-bloom season biovolumes and cell counts as measured within the reservoir. One technical disruption occurred during the trial; however, this had no notable impacts on cyanobacterial prevalence. Acknowledging the limitations of the experimental conditions, data and observations from this trial indicate there is no strong evidence that sonication significantly reduced cyanobacteria occurrence within Reservoir C.

Experimental Studies on Zooplankton-Toxic Cyanobacteria Interactions: A Review

Cyanobacterial blooms have been recognized as a problem in fresh water for about 150 years. Over the past 50 years, experimental studies on the subject have gained importance considering the increasing need to control toxic cyanobacterial blooms. This article presents information on the different lines of research that have been undertaken on zooplankton-cyanobacteria interactions over the past 50 years. These include information on filtering/ingestion rates and phytoplankton preferences of small and large rotifers, cladocerans, and copepods; growth rates of zooplankton on cyanobacterial diets; feeding rates of other freshwater invertebrates on cyanobacteria; role of zooplankton in top-down biomanipulation efforts; effect of cyanotoxins on zooplankton; bioaccumulation of cyanotoxins; and physical and chemical control of cyanobacterial blooms. We also highlight measures that have led to successful lake management and improvement of water quality in selected waterbodies.

Satellite assessment of eutrophication hot spots and algal blooms in small and medium-sized productive reservoirs in Uruguay's main drinking water basin

Intensive agricultural activities favor eutrophication and harmful phytoplankton blooms due to the high export of nutrients and damming of rivers. Productive watersheds used for water purification can have multiple reservoirs with phytoplankton blooms, which constitutes a high health risk. In general, water quality monitoring does not cover small- and medium-sized reservoirs (0.25-100 ha) of productive use due to their large number and location in private properties. In this work, the in situ trophic state of fourteen reservoirs was simultaneously assessed using Sentinel-2 images in the Santa Lucía River Basin, the main drinking water basin in Uruguay. These reservoirs are hypereutrophic (0.18-5.22 mg total P L^-1) with high phytoplankton biomasses (2.8-4439 µg chlorophyll-a L^-1), mainly cyanobacteria. Based on data generated in situ and Sentinel-2 imagery, models were fitted to estimate satellite Chl-a and transparency in all the basin reservoirs (n = 486). The best fits were obtained with the green-to-red band ratio (560 and 665 nm, R² = 0.84) to estimate chlorophyll-a and reflectance at 833 nm (R² = 0.73) to determine transparency. The spatial distribution of the trophic state was explored by spatial autocorrelation and hotspot analysis, and the variation in spatial patterns could be determined prior and subsequent to a maximum cyanobacteria value in water treatment plant intakes. Therefore, reservoirs with greater potential for phytoplankton biomass export were identified. This work provides the first fitted tool for satellite monitoring of numerous reservoirs and strengthens the country's ability to respond to harmful phytoplankton blooms in its main drinking water basin.

Response of phytoplankton community composition to physicochemical and meteorological factors under different hydrological conditions in Lake Diefenbaker

Changes in hydro-meteorological conditions due to warming climate and the operation of reservoirs may support algal blooms. Lake Diefenbaker is a large reservoir on the Canadian Prairies. Annual flow volume from its major tributary, the South Saskatchewan River (SSR), varies with precipitation and temperature in the Rocky Mountains. Furthermore, plans are underway to increase water abstraction from Lake Diefenbaker for irrigation. Therefore, we used a nine-year dataset that comprised a drought year (1984), four consecutive high flow years (2011 to 2014), and four subsequent low flow years (2015 to 2018) to investigate how these changes could affect the major phytoplankton groups and cyanobacterial community. Diatoms (38.5%) were the most abundant phytoplankton, followed by cryptomonads (28.9%) under low and high flow years. Diatoms were associated with greater mixing in late spring and fall, whereas the cryptomonads were related to the high nutrients from spring flow. Cyanobacteria (79.3%) contributed the greatest to the total phytoplankton biomass under drought; we hypothesized that the high abundance of cyanobacteria during drought was associated with thermocline deepening and subsequent internal loading of nutrients. Microcystis, a potential bloom-forming and toxin-producing genus, was dominant during the drought and correlated with reduced water level, increased air temperature, and moderate wind speed. Although its biomass was low, another potential bloom-forming and toxin-producing genus, Aphanizomenon, was present in low and high flow years. Aphanizomenon was correlated with decreased SSR flow and increased particulate carbon to particulate phosphorus ratios, which may be related to their ability to cope with P limitation. These results highlight that Lake Diefenbaker and other similar reservoirs are vulnerable to an increase in potential toxic cyanobacteria species with future expectations of climate warming and water abstraction.

Nutrients and not temperature are the key drivers for cyanobacterial biomass in the Americas

Cyanobacterial blooms imperil the use of freshwater around the globe and present challenges for water management. Studies have suggested that blooms are trigged by high temperatures and nutrient concentrations. While the roles of nitrogen and phosphorus have long been debated, cyanobacterial dominance in phytoplankton has widely been associated with climate warming. However, studies at large geographical scales, covering diverse climate regions and lake depths, are still needed to clarify the drivers of cyanobacterial success. Here, we analyzed data from 464 lakes covering a 14,000 km north-south gradient in the Americas and three lake depth categories. We show that there were no clear trends in cyanobacterial biomass (as biovolume) along latitude or climate gradients, with the exception of lower biomass in polar climates. Phosphorus was the primary resource explaining cyanobacterial biomass in the Americas, while nitrogen was also significant but particularly relevant in very shallow lakes (< 3 m depth). Despite the assessed climatic gradient water temperature was only weakly related to cyanobacterial biomass, suggesting it is overemphasized in current discussions. Depth was critical for predicting cyanobacterial biomass, and shallow lakes proved more vulnerable to eutrophication. Among other variables analyzed, only pH was significantly related to cyanobacteria biomass, likely due to a biologically mediated positive feedback under high nutrient conditions. Solutions toward managing harmful cyanobacteria should thus consider lake morphometric characteristics and emphasize nutrient control, independently of temperature gradients, since local factors are more critical - and more amenable to controls - than global external forces.

Effects of physiologic activities of plankton on CO2 flux in the Three Gorges Reservoir after rainfall during algal blooms

The nutrient supply to the freshwater system may be changed by rainfall, which also encourages the cyclic succession of microorganisms. However, in a highly dynamic land-water reservoir, the microbial metabolic changes brought on by the changes of water nutrients following rainfall are not clearly documented. The study selected the Three Gorges Reservoir (TGR) backwater region during algal bloom seasons as the study area and time, and used the Biolog-EcoPlates technique to examine the heterotrophic metabolism conditions of the water before and after rain. The field monitoring assessed how biotic and abiotic variables affected CO₂ flux at the water-air interface. The tests conducted in the laboratory investigated the water-integrated metabolic process was affected by post-rainfall environmental changes. The results showed that the average flux of CO₂ at the water-air interface before rainfall was -489.17 ± 506.66 mg·(m²·d)^-1, while the average CO₂ flux reached 393.35 ± 793.49 mg·(m²·d)^-1 after rainfall. This is mostly explained by the heterotrophic metabolic variability of plankton in response to changes in the aqueous environment brought on by precipitation. These discoveries help us better understand how biological metabolisms after rain affect the CO₂ flux at the water-air interface and reservoir greenhouse gas (GHG) emission equivalents can be evaluated more accurately.

A holistic assessment of spatiotemporal variation, driving factors, and risks influencing river water quality in the northeastern Qinghai-Tibet Plateau

The Qinghai-Tibet Plateau (QTP) is the source for many of the most important rivers in Asia. It is also an essential ecological barrier in China and has the characteristic of regional water conservation. Given this importance, we analyzed the spatiotemporal distribution patterns and trends of 10 water quality parameters. These measurements were taken monthly from 67 monitoring stations in the northeastern QTP from 2015 to 2019. To evaluate water quality trends, major factors influencing water quality, and water quality risks, we used a series of analytical approaches including Mann-Kendall test, Boruta algorithm, and interval fuzzy number-based set-pair analysis (IFN-SPA). The results revealed that almost all water monitoring stations in the northeastern QTP were alkaline. From 2015 to 2019, the water temperature and dissolved oxygen of most monitoring stations were significantly reduced. Chemical oxygen demand, permanganate index, five-day biochemical oxygen demand, total phosphorus, and fluoride all showed a downward trend across this same time frame. The annual average total nitrogen (TN) concentration fluctuation did not significantly decrease across the measured time frame. Water quality index (WQI-DET) indicated bad or poor water quality in the study area; however, water quality index without TN (WQI-DET') reversed the water quality value. The difference between the two indexes suggested that TN was a significant parameter affecting river water quality in the northeastern QTP. Both Spearman correlation and Boruta algorithm show that elevation, urban land, cropland, temperature, and precipitation influence the overall water quality status in the northeastern QTP. The results showed that between 2015 and 2019, most rivers monitored had a relatively low risk of degradation in water quality. This study provides a new perspective on river water quality management, pollutant control, and risk assessment in an area like the QTP that has sensitive and fragile ecology.

Influence of cyanobacterial bloom accumulation and dissipation on underwater light attenuation in a large and shallow lake

Cyanobacterial bloom accumulation and dissipation frequently occur in Lake Taihu, a typically shallow, eutrophic lake due to wind wave disturbance. However, knowledge of the driving mechanisms of cyanobacterial blooms on underwater light attenuation is still limited. In this study, we collected a high-frequency in situ monitoring of the wind field, underwater light environment, and surface water quality to elucidate how cyanobacterial bloom accumulation and dissipation affect the variations in underwater light attenuation in the littoral zone of Lake Taihu. Results showed that cyanobacterial blooms significantly increased the diffuse attenuation coefficient of ultraviolet-B (K_d(313)), ultraviolet-A (K_d(340)), and photosynthetically active radiation (K_d(PAR)); the scattering of total suspended matter (b_bp(λ)); and the absorption of phytoplankton (a_ph(λ)) and chromophoric dissolved organic matter (CDOM, a_g(λ)) (p < 0.01). The K_d(PAR) decreased quickly during the processes of bloom dissipation, but the decrease of K_d(313) and K_d(340) lagged 0.5 day. Our results suggested that cyanobacterial blooms could increase particle matters and elevated the production of autochthonous CDOM, resulting in underwater light attenuation increase. Ultraviolet radiation (UVR) and PAR attenuation both have significant responses to cyanobacterial blooms, but the response processes were distinct due to the different changes of particle and dissolved organic matters. Our study unravels the driving mechanisms of cyanobacterial blooms on underwater light attenuation, improving lake ecosystem management and protection.

Spatial Differences in Zooplankton Community Structure between Two Fluvial Lakes in the Middle and Lower Reaches of the Yangtze River: Effects of Land Use Patterns and Physicochemical Factors

The zooplankton community composition in shallow lakes is influenced by numerous factors, such as environmental factors and the land use patterns around the lake. To investigate the interaction between the spatial differences in the zooplankton community structure, aquatic parameters, and land use patterns in the Lake Chen Yao complex (Lake Chen Yao and Lake Feng Sha), we assessed them in four seasons from October 2020 to August 2021. The results showed that the zooplankton density and biomass of Lake Chen Yao were higher than the latter. The results of Pearson correlation and RDA analysis revealed that electrical conductivity (EC), Chlorophyll a (Chl.a), dissolved oxygen (DO), and pH were the main environmental factors affecting the zooplankton community structure in the two lakes. The nutrient content of nitrogen (N) and phosphorus (P) were significantly higher in Lake Chen Yao, and there was a considerable relationship with the distribution of land use patterns around the two lakes. The land use patterns were the main reason for the difference in water quality and thus the spatial variation in the characteristics of the zooplankton communities in the two lakes.

A Promising Use of Trimethyl Chitosan for Removing Microcystis aeruginosa in Water Treatment Processes

The increase in cyanobacterial blooms linked to climate change and the eutrophication of water bodies is a global concern. The harmful cyanobacterium Microcystis aeruginosa is one of the most common bloom-forming species whose removal from fresh water and, in particular, from that used for water treatment processes, remains a crucial goal. Different biodegradable and environmentally friendly coagulants/flocculants have been assayed, with chitosan showing a very good performance. However, chitosan in its original form is of limited applicability since it is only soluble in acid solution. The objective of this work was therefore to test the coagulant/flocculant capacity of trimethylchitosan (TMC), a chitosan derivative produced from residues of the fishing industry. TMC has a constitutively net positive charge enabling it to remain in solution regardless of the pH. Results show that even at alkaline pHs, common during cyanobacterial blooms, TMC is effective in removing buoyant cyanobacteria from the water column, both in test tube and Jar-Test experiments. Cell integrity was confirmed by fluorescent stain and electron microscopy. Our findings lead us to conclude that the use of TMC to remove bloom cells early in the treatment of drinking water is both feasible and promising.

Uncovering the influence of hydrological and climate variables in chlorophyll-A concentration in tropical reservoirs with machine learning

Climate variability and change, associated with increasing water demands, can have significant implications for water availability. In the Brazilian semi-arid, eutrophication in reservoirs raises the risk of water scarcity. The reservoirs have also a high seasonal and annual variability of water level and volume, which can have important effects on chlorophyll-a concentration (Chla). Assessing the influence of climate and hydrological variability on phytoplankton growth can be important to find strategies to achieve water security in tropical regions with similar problems. This study explores the potential of machine learning models to predict Chla in reservoirs and to understand their relationship with hydrological and climate variables. The model is based mainly on satellite data, which makes the methodology useful for data-scarce regions. Tree-based ensemble methods had the best performances among six machine learning methods and one parametric model. This performance can be considered satisfactory as classical empirical relationships between Chla and phosphorus may not hold for tropical reservoirs. Water volume and the mix-layer depth are inversely related to Chla, while mean surface temperature, water level, and surface solar radiation have direct relationships with Chla. These findings provide insights on how seasonal climate prediction and reservoir operation might influence water quality in regions supplied by superficial reservoirs.

Phytoplankton community variation and ecological health assessment for impounded lakes along the eastern route of China's South-to-North Water Diversion Project

Interbasin water diversion projects have been proven to effectively alleviate water resource shortages in areas along water diversion lines, but few studies have focused on ecological health in impounded lakes compared with research on water quality and pollutants. Herein, monitoring data were collected during the nonwater diversion period (NWDP) and the water diversion period (WDP) from 2018 to 2019, and the index of biological integrity (IBI) method based on phytoplankton communities was used to evaluate the ecological health of the impounded lakes (Nansi Lake and Dongping Lake) along the eastern route of the South-to-North Water Diversion Project. The results demonstrated that water diversion improved the water quality of the impounded lakes during the WDP, especially total nitrogen and ammonia nitrogen. Meanwhile, the water diversion affected the phytoplankton community structure and diversity, and network analysis further revealed water diversion could be beneficial to the ecological health of impounded lakes. Furthermore, the P-IBI showed that the overall ecological health assessment was "good" during the WDP. Water diversion substantially improved the ecological health status and stability of the impounded lakes during the dry season. Finally, the direct correlations between the water quality parameters and the P-IBI were weak, and water quality parameters could indirectly affect the P-IBI by changing the phytoplankton community structure. These findings will enhance our understanding of the ecological health of the impounded lakes of the South-to-North Water Diversion Project. Furthermore, this study will provide a reference to support the ecosystem security of impounded lakes in other large water diversion projects.

Distribution and diversity of cyanobacteria in the Azores Archipelago: An annotated checklist

Background

Knowledge about cyanobacteria diversity in the Azores is spread over several publications, dating from 1874, with some of them not generally available to the scientific community due to their restricted access. The dispersion and sometimes inaccessibility of this information hinder a deeper analysis and a better understanding of the biodiversity of the Azores Islands and more general ecological processes in oceanic islands. Here we present the first checklist of cyanobacteria for the Azores Archipelago with updated taxonomy of all recorded taxa.

New information

This work provides a compiled and annotated checklist of all known cyanobacteria from the Azores Archipelago with morphological identification from preserved samples and cultures, based on published literature. All records of taxa known to occur in the Azores were taxonomically updated. The present checklist comprises 225 taxa distributed by six orders (Chroococcales, Nostocales, Oscillatoriales, Pleurocapsales, Spirulinales and Synechococcales). Our literature review reveals that the Azores Archipelago hosts a high diversity of cyanobacteria, despite several overlooked habitats that may present great potential regarding cyanobacteria diversity. Increasing efforts to study these neglected habitats could contribute to the knowledge of cyanobacteria taxonomy. This checklist provides the basis for future works on the taxonomy and taxa richness of cyanobacteria in the Azores and the Atlantic Islands, as also for understanding and monitoring non-indigenous and invasive species.

Satellite-Based Monitoring of the Algal Communities of Aras Dam Reservoir: Meteorological Dependence Analysis and the Footprint of COVID-19 Pandemic Lockdown on the Eutrophication Status

Aras Dam Lake is a strategic aquatic ecosystem in Iran and there are reports of toxic phytoplankton blooms in this reservoir. This study was performed to determine the effect of meteorological variables on the formation and expansion of toxic phytoplankton communities in Aras dam reservoir. The data of this project have been obtained using field studies and satellite data (MODIS and Sentinel-2). Sampling to determine the composition of phytoplankton communities in the area was carried out seasonally in two time periods from 2003 to 2014, and environmental assessments were also performed based on meteorological and satellite data over an 18-year period (2003-2020). The Chlorophyll-a content was obtained from MODIS and correlated with meteorological data. The statistical analysis showed that the highest coefficient of determination is related to the correlation of chlorophyll-a and Evaporation (R ² = 0.86). Also, the relative root mean square error is equal to 18%, 18.1% and 21.2% for the chlorophyll-a -SST, chlorophyll-a -wind and chlorophyll-a -Evaporation relations, respectively. Moreover, in a supplementary study, correlation between the chlorophyll-a content with selected meteorological variables including evaporation, wind speed and water surface temperature were investigated seasonally. The results showed that the trend of changes in chlorophyll-a content with three considered variables are parabolic functions and chlorophyll-a -Evp (R ² = 0.86, MAPE = 15.2%) model indicates better performance. The results also showed that the eutrophication rate of the reservoir during lockdown period increased in comparison with the same time at pre-pandemic period, which can be related to increase of incoming waste loads in this reservoir.

Impacts of atmospheric stilling and climate warming on cyanobacterial blooms: An individual-based modelling approach

Harmful algal blooms of the freshwater cyanobacteria genus Microcystis are a global problem and are expected to intensify with climate change. In studies of climate change impacts on Microcystis blooms, atmospheric stilling has not been considered. Stilling is expected to occur in some regions of the world with climate warming, and it will affect lake stratification regimes. We tested if stilling could affect water column Microcystis distributions using a novel individual-based model (IBM). Using the IBM coupled to a three-dimensional hydrodynamic model, we assessed responses of colonial Microcystis biomass to wind speed decrease and air temperature increase projected under a future climate. The IBM altered Microcystis colony size using relationships with turbulence from the literature, and included light, temperature, and nutrient effects on Microcystis growth using input data from a shallow urban lake. The model results show that dynamic variations in colony size are critical for accurate prediction of cyanobacterial bloom development and decay. Colony size (mean and variability) increased more than six-fold for a 20% decrease in wind speed compared with a 2 °C increase in air temperature. Our results suggest that atmospheric stilling needs to be included in projections of changes in the frequency, distribution and magnitude of blooms of buoyant, colony-forming cyanobacteria under climate change.

Physiological acclimation of Ulva prolifera to seasonal environmental factors drives green tides in the Yellow Sea

To understand how seasonal factors could drive the formation of green tide blooms and their flotation and decay, we cultured the green tide algal species Ulva prolifera at various temperatures (5, 10, 15, 20, 25, 30, and 35 °C) and light intensities (40, 80, 140, 240, and 400 μmol photons m^-2 s^-1). The results showed that the ratio of floating U. prolifera increased with increasing light and temperature, which was accompanied by morphological changes. The net photosynthetic rate and the proportion of floating U. prolifera thalli showed a nonlinear relationship. Furthermore, an enclosure experiment confirmed that the flotation of U. prolifera was influenced by light intensity via the regulation of photosynthesis of the thalli. These results suggest that seasonal factors control the photosynthesis of U. prolifera, which drives the flotation and decay of green tide algae in the Yellow Sea.

Algal bloom forecasting with time-frequency analysis: A hybrid deep learning approach

The rapid emergence of deep learning long-short-term-memory (LSTM) technique presents a promising solution to algal bloom forecasting. However, the discontinuous and non-stationary processes within algal dynamics still largely limit the functions of LSTMs. To overcome this challenge, an advanced time-frequency wavelet analysis (WA) technique was introduced to enhance the prediction accuracy of LSTMs. Herein, the novel hybrid approach (named WLSTM) successfully decreased the algal forecasting inaccuracy of classic LSTMs by 41% ± 8% in Lake Mendota (Wisconsin, USA), with powerful one-step-ahead predictions at hourly, daily, and monthly time resolutions (R² = 0.976, 0.878, and 0.814, respectively). In addition, the WLSTM outperformed the other two widely used algal forecasting approaches - deep neural network (DNN), and autoregressive-integrated-moving-average (ARIMA) model, represented by average 72% and 85% decrease in root-mean-square-error, respectively. Furthermore, the WLSTM was implemented in an experimentally fertilized lake (Lake Tuesday, Michigan) for a multi-step forecasting examination. It satisfactorily forecasted the algal fluctuations involving substantial peak and extreme values (average R² > 0.900) and presented accurate judgment outcomes to their bloom levels with high accuracy > 95% on average. This work highlighted the utility of deep learning approaches in effective early-warning for algal blooms, and demonstrated an important direction for improving the adaptability of conventional deep learning approaches to the aquatic problems.

Harmonizing science and management options to reduce risks of cyanobacteria

Management of cyanobacteria has become an increasingly complex venture. Cyanobacteria risks have amplified as society moves forward in an era of accelerated global changes. The cyanobacteria management "pendulum" has progressively shifted from prevention to mitigation, with management considerations often put forth after bloom formation. A universal system (i.e., one-size-fits-all management) fails to provide a management path forward due to the inherent complexities of each lake. A tailored management plan is needed: the right species at the right time in the right place (i.e., the three Rs). The three Rs represent a customizable management strategy that is flexible and informed by advances in scientific understanding to lower cyanobacteria-associated risks. Identifying thresholds in risk tolerance, where thresholds are defined by community collectives, is essential to frame cyanobacteria management targets and to decide on what management interventions are warranted.

Dominance of evaporation on lacustrine groundwater discharge to regulate lake nutrient state and algal blooms

As global threats to freshwater lakes, eutrophication and harmful algal blooms (HABs) are governed by various biogeochemical, climatological and anthropogenic processes. Groundwater is key to join these processes in regulating HABs, but the underlying mechanisms remain unclear. Here, we leveraged basin-wide field data of Lake Taihu (China's largest eutrophic lake) and global archives, and demonstrate the dominance of evaporation on lacustrine groundwater discharge (LGD) in shallow lakes. We extrapolated decadal LGD and the derived nutrient loadings and found that HABs promptly consume ubiquitous groundwater borne nutrients, leading lake water N: P ratios 2-3 months time lagged behind LGD N: P ratios. We conclude that evaporation dominated LGD is an unraveled but crucial regulator of nutrient states and HABs in shallow lakes, which advocates synergistical studies from both climatological and hydrogeological perspective when restoring lake ecosystems.

Short-term rainfall limits cyanobacterial bloom formation in a shallow eutrophic subtropical urban reservoir in warm season

The global increase in dominance of toxic blooms of cyanobacteria has severely impacted aquatic ecosystems and threatened human health for decades. Although it has been shown that high levels of rainfall may inhibit the growth of bloom-forming cyanobacteria, it is still unclear how cyanobacteria respond to short-term rainfall events. Based on five-year (2016-2020) high-frequency (half-week) sampling data from a shallow eutrophic urban reservoir in subtropical China, we explored the short-term effects of rainfall events on cyanobacterial biomass (CBB) by constructing generalized additive models of CBB in rainy periods during warm (April to September) and cool (December and January) months, respectively. We find evidence in support of the hypotheses that short-term rainfall events significantly reduce CBB in warm months, but the opposite response was observed in the cool months. We also highlight a difference in the factors explaining CBB decreases in warm months (precipitation, air temperature, relative humidity, dissolved oxygen and total phosphorus) compared with factors explaining the response of CBB in cool months (sunshine hours, pH and total carbon). In particular, meteorological factors (precipitation, wind speed and sunlight) might drive changes in water temperature and hydro-dynamics of the reservoir, thereby causing a rapid reduction of CBB after rainfall events in warm months. This varying response of cyanobacteria to short-term rainfall events in the shallow eutrophic subtropical reservoir may also be expected in temperate or cool lakes as climate change effects become stronger.

Effects of Weather Extremes on the Nutrient Dynamics of a Shallow Eutrophic Lake as Observed during a Three-Year Monitoring Study

The formation of algal and cyanobacterial blooms caused by the eutrophication of water bodies is a growing global concern. To examine the impact of extreme weather events on blooms, eutrophication-related parameters (e.g., water temperature, nitrate, ammonium, nitrite, and soluble reactive phosphate (SRP)) were quantitatively assessed monthly over three years (2017–2019) at Lake Seeburg (Central Germany), a shallow eutrophic lake with regular cyanobacterial blooms. In addition, SRP concentrations in sediment pore water were assessed monthly for one year (2018). The monitoring period included a three-day extremely heavy rain event in 2017 as well as a severe drought in summer 2018. No such extreme weather conditions occurred in 2019. After the heavy rain event in 2017, anoxic water containing high levels of ammonium and SRP entered the lake from flooded upstream wetlands. This external nutrient spike resulted in a heavy but short (3 weeks) and monospecific cyanobacterial bloom. A different situation occurred during the exceptionally hot and dry summer of 2018. Especially favored by high water temperatures, SRP concentrations in sediment pore waters gradually increased to extreme levels (34.4 mg/L). This resulted in a strong and sustained internal SRP delivery into the water column (69 mg/m2·d−1), which supported the longest-lasting cyanobacterial bloom (3 months) within the three-year monitoring period. Subsequent biomass decay led to oxygen-depleted conditions in the bottom waters, elevated ammonium, and, later, nitrate concentrations. Our observations demonstrate the particular effects of extreme weather events on nutrient dynamics and the phytoplankton composition in the lake. As the frequency and intensity of such events will likely increase due to climate change, their impacts need to be increasingly considered, e.g., in future remediation strategies.

Variabilities in autumn cyanobacterial responses to ecosystem external enrichments based on nutrient addition bioassay in Pengxi River, Three Gorges Reservoir, China

Nutrient availability, is a crucial anthropogenic stressor promoting freshwater eutrophication and rapid expansion of harmful algal blooms (HABs), deteriorating water quality and threatening public health worldwide. The estimation of the HABs community responses to diel changes in the nutrients while characterizing the ecosystem growth limiting factors, is key to prudent watershed management. The present study investigated the short-term variabilities in autumn cyanobacterial responses to the external nutrient inputs into the Pengxi River using the nutrient addition bioassay approach. Results reveal phytoplankton community structure dominated by the cyanobacteria: Anabaena and Aphanizomenon spp. (relative abundance = 46.20% equilibrium abundance), followed by the diatoms, out of which Lindayia bodaniica, are preponderant. Nutrient enrichment triggered strong variabilities in dominance and successions among the cyanobacterial group, with maximum dominance (76.34%) exhibited by the Aphanizomenon sp. upon NH₄ addition. Fe enrichment led to the succession of cyanobacteria, Leptolyngbya tenuis, which was below the detectable limit in the control, indicating the role of Fe in its proliferation. Studies on nutrient limitation demonstrated P/NH₄ co-limited ecosystem, with P as the primary and NH₄, a secondary limiting factor. The nitrate preference index (NO₃^-RPI = 0.991) shows a high preference for NH₄ while NO₃ constitutes the bulk of the ecosystem TN. Considering the elevated NO₃ concentration, we posit that a shift in the phytoplankton community structure from cyanobacteria to diatoms dominated ecosystem, is expected following Fe depletion and a further stretch on the current ecosystem NH₄ limitation. The study provides useful and first-ever insights for nutrient reduction in the middle Three Gorges Reservoir (TGR) before the onset of the heavy HABs during spring in the Pengxi River.

Response of algal biomass and macrophyte communities to internal or external nutrient loading

Nutrient input from internal and external sources could regulate the variability and abundance of algal and macrophytes in freshwater lakes. This study explores the response of algal and macrophyte growth in relation to internal and external nutrient loading. This study was conducted over a 12-month period in a eutrophic shallow urban lake known as Slim River Lake, which located in Perak state, Malaysia. The internal nutrient loading was calculated during five identified dry periods. Meanwhile, external nutrient loading was measured from stormwater runoff after storm events. Algal biomass was measured twice a month, while total macrophyte abundance was measured once in a month. In this lake, internal nutrient loading could contribute up to 7538.33 kg total phosphorus and 42.23 kg total nitrogen during dry periods. Meanwhile, external nutrient loading quantified from the stormwater runoff contributed up to 401,500 kg total phosphorus and 4611.67 kg total nitrogen. The highest monthly mean for algal biomass and total macrophyte abundance was recorded as 60,343.75 cells/mL and 821.50, respectively. Based on the Pearson correlation analysis, algal biomass was significantly correlated with the internal total phosphorus loading (r = 0.54, p < 0.05). In addition, algal biomass also shows an inverse relationship with the external total phosphorus loading (r =  - 0.44, p < 0.05). In contrast, total macrophyte abundance was significantly correlated with the external total phosphorus loading (r = 0.50, p < 0.05) and external total nitrogen loading (r = 0.44, p < 0.05). These results suggest that variation of nutrient sources triggers a different response by algal and macrophytes in the study lake. In implications, these findings show that a combination approach in reducing nutrients from sediment and anthropogenic sources is required for potential lake restoration.

Persistent Cyanobacteria Blooms in Artificial Water Bodies-An Effect of Environmental Conditions or the Result of Anthropogenic Change

Algal blooms are an emerging problem. The massive development of phytoplankton is driven partly by the anthropogenic eutrophication of aquatic ecosystems and the expansion of toxic cyanobacteria in planktonic communities in temperate climate zones by the continual increase in global temperature. Cyanobacterial harmful algal blooms (CyanoHABs) not only disturb the ecological balance of the ecosystem, but they also prevent the use of waterbodies by humans. This study examines the cause of an unusual, persistent bloom in a recreational, flow-through reservoir; the findings emphasize the role played by the river supplying the reservoir in the formation of its massive cyanobacterial bloom. Comprehensive ecosystem-based environmental studies were performed, including climate change investigation, hydrochemical analysis, and bio-assessment of the ecological state of the river/reservoir, together with monitoring the cyanobacteria content of phytoplankton. Our findings show that the persistent and dominant biomass of Microcystis was related to the N/P ratio, while the presence of Aphanizomenon and Dolichospermum was associated with the high-temperature end electric conductivity of water. Together with the increase in global temperature, the massive and persistent cyanobacterial bloom appears to be maintained by the inflow of biogenic compounds carried by the river and the high electric conductivity of water. Even at the beginning of the phenomenon, the reservoir water already contained cyanobacterial toxins, which excluded its recreational use for about half the year.

Spatiotemporal Distribution Pattern of Phytoplankton Community and Its Main Driving Factors in Dongting Lake, China—A Seasonal Study from 2017 to 2019

As it is the second-largest freshwater lake downstream of the Three Gorges Dam and an important international wetland for migratory birds, there have been concerns about the ecological water health of Dongting Lake for a long time. In the present study, we studied the evolutionary characteristics of water quality in Dongting Lake in three recent years. Moreover, the evolution rules and dominant groups of the phytoplankton community were explored, and the major influencing factors of phytoplankton and their distribution were assessed based on the field survey and detection data from 2017 to 2019. The results indicated that the water quality of Dongting Lake improved in recent years. The concentration of dissolved oxygen (DO) increased by 6.91%, whereas the concentrations of the five-day biochemical oxygen demand (BOD5), chemical oxygen demand (CODCr), ammonia nitrogen (NH4+–N), total phosphorus (TP), and total nitrogen (TN) decreased by 17.5%, 13.0%, 33.8%, 7.6%, and 13.3%, respectively. The mean phytoplankton density reached 4.15 × 105 cells·L−1 in September 2017, whereas it was only 1.62 × 105 cells·L−1 in December 2018. There were 15 dominant species belonging to Cyanobacteria, Chlorophyta, Bacillariophyta, Cryptophyta, and Miozoa. Moreover, Fragilaria radians (Kützing) D.M.Williams & Round and Aulacoseiragranulata (Ehrenberg) Simonsen were the dominant populations in all seasons. The Pearson and linear regression analysis also indicated that the composition and distribution of phytoplankton in Dongting Lake were mainly affected by electrical conductivity (Cond), BOD5, potassium permanganate (CODMn), and CODCr, especially in Eastern Dongting Lake. Of course, NH4+–N, TN, and TP were also the main factors affecting the density and species of the phytoplankton community, especially in Western Dongting Lake. Finally, we suggested that local government could take “The relationship between Yangtze River and Dongting Lake”, “The relationship between the seven fed rivers and Dongting Lake”, and “The relationship between human activities and Dongting Lake” as the breakthrough points to guarantee the ecological flow, water environment, and ecological quality of Dongting Lake.

A reply to "Relevant factors in the eutrophication of the Uruguay River and the Río Negro"

A recent paper by Beretta-Blanco and Carrasco-Letelier (2021) claims that agricultural eutrophication is not one of the main causes for cyanobacterial blooms in rivers and artificial reservoirs. By combining rivers of markedly different hydrological characteristics e.g., presence/absence and number of dams, river discharge and geological setting, the study speculates about the role of nutrients for modulating phytoplankton chlorophyll-a. Here, we identified serious flaws, from erratic and inaccurate data manipulation. The study did not define how erroneous original dataset values were treated, how the variables below the detection/quantification limit were numerically introduced, lack of mandatory variables for river studies such as flow and rainfall, arbitrary removal of pH > 7.5 values (which were not outliers), and finally how extreme values of other environmental variables were included. In addition, we identified conceptual and procedural mistakes such as biased construction/evaluation of model prediction capability. The study trained the model using pooled data from a short restricted lotic section of the (large) Uruguay River and from both lotic and reservoir domains of the Negro River, but then tested predictability within the (small) Cuareim River. Besides these methodological considerations, the article shows misinterpretations of the statistical correlation of cause and effect neglecting basic limnological knowledge of the ecology of harmful algal blooms (HABs) and international research on land use effects on freshwater quality. The argument that pH is a predictor variable for HABs neglects overwhelming basic paradigms of carbon fluxes and change in pH because of primary productivity. As a result, the article introduces the notion that HABs formation are not related to agricultural land use and water residence time and generate a great risk for the management of surface waterbodies. This reply also emphasizes the need for good practices of open data management, especially for public databases in view of external reproducibility.

Destratification of a Eutrophic Reservoir in South Korea Using a Novel Convectional Water Circulation System (CWCS)

Replication of natural mixing through artificial water circulation techniques, such as convectional water circulation systems (CWCS), can break thermal stratification in stagnant water and suppress algal blooms. Here, the field performance of a novel CWCS was evaluated in a eutrophic reservoir (Shingal reservoir, South Korea) for three seasons between September 2016 and September 2018. The CWCS was deployed in a corner of stagnated water in the reservoir. The changes in water temperature and dissolved oxygen (DO) concentrations at different water depths were examined at the deployment site and two control sites within the stagnant area during the 2016 and 2017 seasons. In 2018, the performance of the CWCS in replicating natural mixing was assessed by monitoring water temperature changes at different depths at the CWCS unit deployment site and a comparison site with natural water flow. The differences in water temperature and DO concentrations between the epilimnion and the hypolimnion were significantly smaller at the deployment site than at the control sites, demonstrating the effectiveness of the CWCS in vertical and horizontal mixing as well as in oxygen transfer. The findings of the field study revealed that the CWCS effectively induced natural-like mixing without disturbing the hypolimnion.

Multi-class secondary metabolites in cyanobacterial blooms from a tropical water body: Distribution patterns and real-time prediction

Cyanobacterial blooms that produce toxins occur in freshwaters worldwide and yet, the occurrence and distribution patterns of many cyanobacterial secondary metabolites particularly in tropical regions are still not fully understood. Moreover, predictive models for these metabolites by using easily accessible water quality indicators are rarely discussed. In this study, we investigated the co-occurrence and spatiotemporal trends of 18 well-known and less-studied cyanobacterial metabolites (including [D-Asp3] microcystin-LR (DM-LR), [D-Asp3] microcystin-RR (DM-RR), microcystin-HilR (MC-HilR), microcystin-HtyR (MC-HtyR), microcystin-LA (MC-LA), microcystin-LF (MC-LF), microcystin-LR (MC-LR), microcystin-LW (MC-LW), microcystin-LY (MC-LY), microcystin-RR (MC-RR) and microcystin-WR (MC-WR), Anatoxin-a (ATX-a), homoanatoxin-a (HATX-a), cylindrospermospin (CYN), nodularin (NOD), anabaenopeptin A (AptA) and anabaenopeptin B (AptB)) in a tropical freshwater lake often plagued with blooms. Random forest (RF) models were developed to predict MCs and CYN and assess the relative importance of 22 potential predictors that determined their concentrations. The results showed that 11 MCs, CYN, ATX-a, HATX-a, AptA and AptB were found at least once in the studied water body, with MC-RR and CYN being the most frequently occurring, intracellularly and extracellularly. AptA and AptB were detected for the first time in tropical freshwaters at low concentrations. The metabolite profiles were highly variable at both temporal and spatial scales, in line with spatially different phytoplankton assemblages. Notably, MCs decreased with the increase of CYN, possibly revealing interspecific competition of cyanobacteria. The rapid RF prediction models for MCs and CYN were successfully developed using 4 identified drivers (i.e., chlorophyll-a, total carbon, rainfall and ammonium for MCs prediction; and chloride, total carbon, rainfall and nitrate for CYN prediction). The established models can help to better understand the potential relationships between cyanotoxins and environmental variables as well as provide useful information for making policy decisions.

Modeling the sensitivity of cyanobacteria blooms to plausible changes in precipitation and air temperature variability

Many recent studies have attributed the observed variability of cyanobacteria blooms to meteorological drivers and have projected blooms with worsening societal and ecological impacts under future climate scenarios. Nonetheless, few studies have jointly examined their sensitivity to projected changes in both precipitation and temperature variability. Using an Integrated Assessment Model (IAM) of Lake Champlain's eutrophic Missisquoi Bay, we demonstrate a factorial design approach for evaluating the sensitivity of concentrations of chlorophyll a (chl-a), a cyanobacteria surrogate, to global climate model-informed changes in the central tendency and variability of daily precipitation and air temperature. An Analysis of Variance (ANOVA) and multivariate contour plots highlight synergistic effects of these climatic changes on exceedances of the World Health Organization's moderate 50 μg/L concentration threshold for recreational contact. Although increased precipitation produces greater riverine total phosphorus loads, warmer and drier scenarios produce the most severe blooms due to the greater mobilization and cyanobacteria uptake of legacy phosphorus under these conditions. Increases in daily precipitation variability aggravate blooms most under warmer and wetter scenarios. Greater temperature variability raises exceedances under current air temperatures but reduces them under more severe warming when water temperatures exceed optimal values for cyanobacteria growth more often. Our experiments, controlled for wind-induced changes to lake water quality, signal the importance of larger summer runoff events for curtailing bloom growth through reductions of water temperature, sunlight penetration and stratification. Finally, the importance of sequences of wet and dry periods in generating cyanobacteria blooms motivates future research on bloom responses to changes in interannual climate persistence.

Nuisance phytoplankton transport is enhanced by high flow in the main river for drinking water in Uruguay

Eutrophication, climate change, and river flow fragmentation are the main cause of nuisance algal blooms worldwide. This study evaluated the conditions that trigger the growth and occurrence of nuisance phytoplankton in the Santa Lucía River, a subtropical floodplain lotic system that supplies drinking water to 60% of the population of Uruguay. The main variables that explained phytoplankton biovolume were extracted from generalized linear models (GLM). The potential impact of nuisance organism advection on water utility was estimated by the phytoplankton biovolume transport (BV_TR, m³ day^-1), an indicator of biomass load. Santa Lucía River had a wide flow range (0.7×10⁵-1438×10⁵ m³ day^-1) and eutrophic conditions (median, TP: 0.139 mg L^-1; TN: 0.589 mg L^-1). GLMs indicated that phytoplankton biomass increased with temperature and soluble reactive phosphorus. Contrary to expectations, the presence of cyanobacteria was positively associated with periods of high flow that result in high cyanobacterial biovolume transport, with a probability of 3.35 times higher when flow increased by one standard deviation. The cyanobacterial biovolume transported (max: 9.5 m³ day^-1) suggests that biomass was subsidized by allochthonous inocula. Biovolume from other nuisance groups (diatoms, cryptophytes, and euglenophytes) was positively associated with low-flow conditions and high nutrient concentrations in the main river channel, thereby indicating that these conditions boost eukaryote blooms. The evaluation of BV_TR allows a better understanding of the dynamics of fluvial phytoplankton and can help to anticipate scenarios of nuisance species transport.

High-resolution temporal detection of cyanobacterial blooms in a deep and oligotrophic lake by high-frequency buoy data

Cyanobacterial blooms are increasing in magnitude, frequency, and duration worldwide. However, our knowledge of cyanobacterial blooms dynamics and driving mechanisms is still limited due to their high spatiotemporal variability. To determine the potential driving mechanisms of cyanobacterial blooms in oligotrophic lakes, we collected a high-frequency depth profile of chlorophyll fluorescence (ChlF) and synchronous water quality, hydrometeorological data in early spring 2016 in oligotrophic Lake Qiandaohu. The vertical distribution of ChlF exhibited two patterns, "aggregated" and "discrete", using Morisita's index, and the aggregated ChlF presented subsurface chlorophyll maxima during the thermal stratification period. The ChlF concentration was positively correlated with water temperature and negatively correlated with turbidity. Significantly linear relationships were observed between ChlF vertical structure parameters (e.g., Morisita's index, subsurface chlorophyll maxima depth and thickness) and thermal stratification parameters (e.g., mixing layer depth and relative water column stability). After rainstorm floods, the ChlF pattern suddenly change from "aggregated" to "discrete" and a ChlF concentration <1 μg/L was observed for 7-11 days with a significant increase in the mixing depth layer and turbidity. The results suggest that cyanobacterial blooms are robustly associated with thermal stratification and rainstorm floods in the deep and oligotrophic lake. Thermal stratification boosts surface phytoplankton accumulation by increasing water temperature, enhancing light availability and restricting phytoplankton vertical distribution. Rainstorm floods interrupt the accumulation by disrupting thermal stratification and decreasing the available light. Furthermore, wind speed and air temperature both regulate the phytoplankton dynamics by affecting thermal stratification. Our research quantifies the cyanobacterial bloom dynamics and their relationship between environmental factors, improving our knowledge of the driving mechanisms of cyanobacterial bloom for the protection of drinking water safety and aquatic organism health in lakes.

Toxic cyanobacteria and microcystin dynamics in a tropical reservoir: assessing the influence of environmental variables

Toxic cyanobacterial blooms (TCBs) have become a growing concern worldwide. The present study investigated the dynamic of toxic cyanobacteria and microcystin (MC) concentrations in the Tri An Reservoir (TAR), a tropical system in Vietnam, with quantitative real-time polymerase chain reaction (qPCR) and high-performance liquid chromatography (HPLC), respectively. The results of the qPCR quantification revealed that Microcystis was the dominant group and the primary MC producer in the TAR. Potentially toxigenic cyanobacteria varied from 1.2 × 10⁴ to 1.58 × 10⁷ cells/mL, and the mean proportion of toxic Microcystis to that of the total toxic cyanobacteria varied from 21 to 88%. Microcystin concentrations in raw water and sediment samples often peaked during June to October as blooms occurred and varied from 0.27 to 6.59 μg/L and from 1.79 to 544.9 ng/g in wet weight, respectively. The results of this study indicated that conditions favoring Microcystis proliferation lead to the selection of more toxic genotypes. Water temperature and light availability were not driving factor in the formation of TCBs in the TAR. However, the high loads of total nitrogen (TN), phosphate, and total phosphorus (TP) into the water via rainfall runoff in combination with a high total suspended solid (TSS) and decreased water level during the early months of the rainy seasons did lead to a shift in Microcystis blooms and higher proportions of toxic genotypes of Microcystis in the TAR. This research may provide more insight into the occurrence mechanism of TCBs in tropical waters. The strategy to control TCB problems in tropical regions should be focused on these limnological and hydrological parameters, in addition to a reduction in nitrogen and phosphorus loading.