Is the future blue-green? A review of the current model predictions of how climate change could affect pelagic freshwater cyanobacteria

Climate change will amplify the impacts of harmful microorganisms in aquatic ecosystems

More than 70% of the human population lives within five kilometres of a natural water feature. These aquatic ecosystems are heavily used for resource provision and recreation, and represent the interface between human populations and aquatic microbiomes, which can sometimes negatively impact human health. Diverse species of endemic aquatic microorganisms, including toxic microalgae and pathogenic bacteria, can be harmful to humans. Aquatic ecosystems are also subject to intrusions of allochthonous pathogenic microorganisms through pollution and runoff. Notably, environmental processes that amplify the abundance and impact of harmful aquatic microorganisms are occurring with increasing frequency owing to climate change. For instance, increases in water temperature stimulate outbreaks of pathogenic and toxic species, whereas more intense precipitation events escalate microbial contamination from stormwater discharge. In this Perspective we discuss the influence of aquatic microbiomes on the health and economies of human populations and examine how climate change is increasing these impacts.

Projected response of algal blooms in global lakes to future climatic and land use changes: Machine learning approaches

The eutrophication of lakes and the subsequent algal blooms have become significant environmental issues of global concern in recent years. With ongoing global warming and intensifying human activities, water quality trends in lakes worldwide varied significantly, and the trend of algal blooms in the next few decades is unclear. However, there is a lack of comprehensive quantitative research on the future projection of lake algal blooms globally due to the scarcity of long-term algal blooms observational data and the complex nonlinear relationships between algal blooms and their driving factors. We aimed to develop a global projection model to evaluate the future trend in algal bloom occurrences in large lakes under various socio-economic development scenarios. We focused our research on 161 natural lakes worldwide, each exceeding 500 km2. The results indicated that the Random Forest model performed best (Overall Accuracy: 0.9697, Kappa: 0.8721) among various machine learning models which were applied in this study. The predicted results showed that, by the end of this century, the number of lakes experiencing algal blooms and the intensity of these blooms will worsen under higher forcing scenarios (SSP370 and SSP585) (p < 0.05). In different regions, lakes with increasing algal blooms are mainly distributed in Africa, Asia, and North America, while lakes with decreasing occurrence are primarily found in Europe. Additionally, underdeveloped regions, such as Africa, exhibit greater sensitivity to different SSP scenarios due to high variability in population and economic growth. This study revealed the spatiotemporal distribution of algal blooms in global lakes from 2020 to 2100 and suggested that the intensifying algal blooms due to global warming and human activities may offset the effort of controlling the water quality.

Environmental flows in a future climate: Balancing hydropower production and ecosystem rehabilitation in the Ume River system, Sweden

Hydropower is central to renewable electricity systems, but degrades ecosystems, calling for environmental flow schemes to enhance the ecological status of river systems. Environmental flow assessments need to account for climate change, since climate-driven changes in runoff affect both hydropower operation and riverine ecosystems. Here, we quantify expected changes in hydropower production and environmental benefits of introducing environmental flows in a large regulated river system in northern Sweden in a future climate. Compared with the hydrology of 1981-2010, runoff is projected to increase with climatic conditions projected for 2040, leading to a 2.2 % increase in hydropower production with present rules for turbine and reservoir operation. Implementing environmental flows will result in lower hydropower production losses in with the 2040 climate than at present: Introducing restrictions against zero flow events, discharge to technical fishways and bypassed reaches throughout the year (with seasonal flow variation), as well as having more natural water-level variation in all run-of-river impoundments, would reduce annual hydropower production with 3.5 % with present conditions, and by 3.3 % in 2040. At the same time, the net effect of higher runoff and introducing environmental flows means that the annual hydropower production in the 2040 climate would be only 0.8 % lower compared to 1981-2010. In all scenarios, reservoir filling degree in 2040 was projected to increase compared to scenarios for 1981-2010, and flow requirements were met for both environmental flows and hydropower production over an 83-year scenario-based time series. This study demonstrates the feasibility of introducing environmental flow actions in Sweden, and other regions where increases in runoff are projected, with sustained hydropower production, having large benefits for riverine biodiversity and enhancing resilience of riverine ecosystems to climate change. For this to be successful, collaboration among stakeholders in riverine management is needed.

The shifts in microbial interactions and gene expression caused by temperature and nutrient loading influence Raphidiopsis raciborskii blooms

Climate change and the trophic status of water bodies are important factors in global occurrence of cyanobacterial blooms. The aim of this study was to explore the cyanobacteria‒bacterial interactions that occur during Raphidiopsis raciborskii (R. raciborskii) blooms by conducting microcosm simulation experiments at different temperatures (20 °C and 30 °C) and with different phosphorus concentrations (0.01 mg/L and 1 mg/L) using an ecological model of microbial behavior and by analyzing microbial self-regulatory strategies using weighted gene coexpression network analysis (WGCNA). Three-way ANOVA revealed significant effects of temperature and phosphorus on the growth of R. raciborskii (P < 0.001). The results of a metagenomics-based analysis of bacterioplankton revealed that the synergistic effects of both climate and trophic changes increased the ability of R. raciborskii to compete with other cyanobacteria for dominance in the cyanobacterial community. The antagonistic effects of climate and nutrient changes favored the occurrence of R. raciborskii blooms, especially in eutrophic waters at approximately 20 °C. The species diversity and richness indices differed between the eutrophication treatment group at 20 °C and the other treatment groups. The symbiotic bacterioplankton network revealed the complexity and stability of the symbiotic bacterioplankton network during blooms and identified the roles of key species in the network. The study also revealed a complex pattern of interactions between cyanobacteria and non-cyanobacteria dominated by altruism, as well as the effects of different behavioral patterns on R. raciborskii bloom occurrence. Furthermore, this study revealed self-regulatory strategies that are used by microbes in response to the dual pressures of temperature and nutrient loading. These results provide important insights into the adaptation of microbial communities in freshwater ecosystems to environmental change and provide useful theoretical support for aquatic environmental management and ecological restoration efforts.

Accumulation of microcystin toxin in irrigation water and alfalfa (Medicago sativa) forage plant, and assessing the potential risk to animal health

Microcystin (MC) toxin produced by cyanobacteria has become a significant concern for societies worldwide. The risk of MC in drinking water has been assessed to human health. Nonetheless, its risk to animal health has not been thoroughly evaluated. This study investigated MCs in irrigation water and alfalfa plant from nearby farmlands. Both irrigation water and alfalfa shoots contained greater MC concentrations (1.8-17.4 μg L-1 and 0.053-0.128 μg g-1) during summer than winter (2.4 μg L-1 and 0.017 μg g-1). These MC concentrations showed a correlation with the predominance of cyanobacteria in the sites, triggering the potential risk of these microorganisms in irrigation waters. Accordingly, there would be a high risk (risk quotient, RQ > 1) during summer and a moderate risk (0.1 Collapse

Key Words

• Animal feed
• Cyanotoxins
• Freshwaters
• Livestock
• cyanobacteria

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MESH Headings

• Microcystins/metabolism
• Microcystins/analysis
• Medicago sativa/metabolism
• Animals
• Agricultural Irrigation
• Risk Assessment
• Environmental Monitoring
• Seasons
• Cyanobacteria/metabolism
• Water Pollutants, Chemical/metabolism
• Water Pollutants, Chemical/analysis
• Cattle
• Sheep

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Grants Collapse

Affiliation(s)

Zakaria A Mohamed Department of Botany and Microbiology, Faculty of Science, Sohag University, Sohag, 82524, Egypt.
Yasser Mostafa King Khalid University, College of Science, Department of Biology, Abha, P.O. Box 9004, Saudi Arabia
Saad Alamri King Khalid University, College of Science, Department of Biology, Abha, P.O. Box 9004, Saudi Arabia
Mohamed Hashem Assiut University, Faculty of Science, Botany and Microbiology Department, Assiut 71516, Egypt

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Efficient Broad-Spectrum Cyanophage Function Module Mining

Cyanobacterial harmful algal blooms (CyanoHABs) cause health and environmental effects worldwide. Cyanophage is a virus that exclusively infects cyanobacteria. Using cyanophages to control blooms is the latest biological control method. However, little research on the genomics of cyanophages and the presence of numerous proteins with unidentified functions in cyanophage genomes pose challenges for their practical application and comprehensive investigation. We selected the broad-spectrum and efficient cyanophage YongM for our study. On the one hand, through rational analysis, we analyze essential genes, establish the minimal cyanophage genome and single essential gene modules, and examine the impact of essential modules on growth. Additionally, we conducted ultraviolet mutagenesis on YongM to generate more efficient cyanophages' critical modules through random mutagenesis. Then, we sequenced and analyzed the functionality of the mutational gene modules. These findings highlight several gene modules that contribute to a deeper understanding of the functional components within cyanophage genomes.

Distribution of toxigenic cyanobacteria in Alpine lakes and rivers as revealed by molecular screening

The increasing frequency of cyanobacteria blooms in waterbodies caused by ecosystem eutrophication could endanger human health. This risk can be mitigated by effective monitoring incorporating molecular methods. To date, most molecular studies on toxigenic cyanobacteria have been limited to microcystins (MCs), disregarding other cyanotoxins, to freshwater planktic habitats while ignoring benthic habitats, and to limited geographic areas (usually one or a few specific waterbodies). In this study, we used PCR-based methods including PCR product sequencing and chemical-analytical methods (LC-MS/MS) to screen many plankton (n = 123) and biofilm samples (n = 113) originating from 29 Alpine lakes and 18 rivers for their cyanotoxin production potential. Both mcyE (indicating MC synthesis) and anaC (indicating anatoxin (ATX) synthesis) gene fragments were able to qualitatively predict MC or ATX occurrence. The abundance of mcyE gene fragments was significantly related to MC concentrations in plankton samples (R2 = 0.61). mcyE gene fragments indicative of MC synthesis were most abundant in planktic samples (65 %) and were assigned to the genera Planktothrix and Microcystis. However, mcyE rarely occurred in biofilms of lakes and rivers, i.e., 4 % and 5 %, respectively, and were assigned to Microcystis, Planktothrix, and Nostoc. In contrast, anaC gene fragments occurred frequently in planktic samples (14 % assigned to Tychonema, Phormidium (Microcoleus), and Oscillatoria), but also in biofilms of lakes (49 %) and rivers (18 %) and were assigned to the genera Phormidium, Oscillatoria, and Nostocales. The cyrJ gene fragment indicating cylindrospermopsin synthesis occurred only once in plankton (assigned to Dolichospermum), while saxitoxin synthesis potential was not detected. For plankton samples, monomictic and less eutrophic conditions were positively related to mcyE/MC occurrence frequency, while oligomictic conditions were related to anaC/ATX frequency. The anaC/ATX frequency in biofilm was related to the lake habitats generally showing higher biodiversity as revealed from metabarcoding in a parallel study.

Presence of the neurotoxin β-N-methylamino-L-alanine in irrigation water and accumulation in cereal grains with human exposure risk

The present study demonstrates the presence of the neurotoxin β-N-methylamino-L-alanine and its cyanobacterial producers in irrigation water and grains of some cereal plants from farmlands irrigated with Nile River water in Egypt. BMAA detected by LC-MS/MS in phytoplankton samples was found at higher concentrations of free form (0.84-11.4 μg L-1) than of protein-bound form (0.16-1.6 μg L-1), in association with the dominance of cyanobacteria in irrigation water canals. Dominant cyanobacterial species isolated from these irrigation waters including Aphanocapsa planctonica, Chroococcus minutus, Dolichospermum lemmermanni, Nostoc commune, and Oscillatoria tenuis were found to produce different concentrations of free (4.8-71.1 µg g-1 dry weight) and protein-bound (0.1-11.4 µg g-1 dry weight) BMAA. In the meantime, BMAA was also detected in a protein-bound form only in grains of corn (3.87-4.51 µg g-1 fresh weight) and sorghum (5.1-7.1 µg g-1 fresh weight) plants, but not in wheat grains. The amounts of BMAA accumulated in these grains correlated with BMAA concentrations detected in relevant irrigation water canals. The presence of BMAA in cereal grains would constitute a risk to human and animal health upon consumption of contaminated grains. The study, therefore, suggests continuous monitoring of BMAA and other cyanotoxins in irrigation waters and edible plants to protect the public against exposure to such potent toxins.

Intensification of harmful cyanobacterial blooms in a eutrophic, temperate lake caused by nitrogen, temperature, and CO2

Warmer temperatures can significantly increase the intensity of cyanobacterial harmful algal blooms (CHABs) in eutrophic freshwater ecosystems. However, few studies have examined the effects of CO2 enrichment in tandem with elevated temperature and/or nutrients on cyanobacterial taxa in freshwater ecosystems. Here, we observed changes in the biomass of cyanobacteria, nutrients, pH, and carbonate chemistry over a two-year period in a shallow, eutrophic freshwater lake and performed experiments to examine the effects and co-effects of CO2, temperature, and nutrient enrichment on cyanobacterial and N2-fixing (diazotrophic) communities assessed via high throughput sequencing of the 16S rRNA and nifH genes, respectively. During both years, there were significant CHABs (50-500 μg cyanobacterial chlorophyll-a L-1) and lake CO2 levels were undersaturated (≤300 μatm pCO2). NH4+ significantly increased the net growth rates of cyanobacteria as well as the biomass of the diazotrophic cyanobacterial order Nostocales under elevated and ambient CO2 conditions. In a fall experiment, the N2 fixation rates of Nostocales were significantly higher when populations were enriched with CO2 and P, relative to CO2-enriched populations that were not amended with P. During a summer experiment, N2 fixation rates increased significantly under N and CO2 - enriched conditions relative to N-enriched and ambient CO2 conditions. Nostocales dominated the diazotrophic communities of both experiments, achieving the highest relative abundance under CO2-enriched conditions when N was added in the first experiment and when CO2 and temperature were elevated in the second experiment, when N2 fixation rates also increased significantly. Collectively, this study indicates that N promotes cyanobacterial blooms including those formed by Dolichospermum and that the biomass and N2 fixation rates of diazotrophic cyanobacterial taxa may benefit from enhanced CO2 levels in eutrophic lakes.

Colonial Microcystis' biomass affects its shift to diatom aggregates under aeration mixing

The effect of hydrodynamic mixing on controlling Microcystis blooms or changing the algal community to diatom dominance has been widely studied; however, the effects of colonial Microcystis biomass on the development of the algal community are poorly known. Here, in order to study the changes in Microcystis blooms under continuous aeration mixing, an experiment was carried out in a greenhouse with factors of varying biomass of Microcystis and inorganic nitrogen and phosphorus enrichment in summer. There were three chlorophyll a (Chl-a) levels in six treatments: low Chl-a level of 68.4 μg L-1 (treatments L, L-E), medium Chl-a level of 468.7 μg L-1 (treatments M, M-E), and high Chl-a level of 924.1 μg L-1 (treatments H, H-E). Treatments L-E, M-E and H-E were enriched with the same inorganic nitrogen and phosphorus nutrients. During the experiment of 30 days, the concentration of Microcystis and Chl-a decreased, and diatom Nitzschia palea cells appeared in all the treatments, which became dominant in treatments M, M-E, H and H-E, with the highest biomass of 9.41 ± 1.96 mg L-1 Nitzschia in treatment H-E on day 30. The rank order of the biomass of Nitzschia from low to high was (L = L-E) < (M = M-E) < H < H-E (P < 0.05). In addition, Nitzschia cells were aggregates attached to Microcystis colonies in all the treatments. The results showed that the initial biomass of colonial Microcystis affected the algal shift from Microcystis dominance to Nitzschia dominance. However, the enriched inorganic nitrogen and phosphorus was beneficial for the Nitzschia increase in the high biomass treatment alone. The shift from Microcystis dominance to diatom dominance under continuous aeration mixing may be caused by low light conditions as well as the nutrients released from Microcystis decay. Moreover, the aerobic condition caused by aeration mixing maintained the colonial mucilaginous sheath to support the growth of Nitzschia cells in aggregation. This study found for the first time that Microcystis blooms could shift to diatom Nitzschia dominance in aggregates. It provided a method to control and manipulate Microcystis blooms to diatom dominance through continuous aeration mixing to proper biomass of Microcystis colonies. The shift to diatoms dominance would provide more high quality food organisms for aquaculture and be beneficial to the material cycling and energy flowing in food web dynamics.

Forecasting freshwater cyanobacterial harmful algal blooms for Sentinel-3 satellite resolved U.S. lakes and reservoirs

This forecasting approach may be useful for water managers and associated public health managers to predict near-term future high-risk cyanobacterial harmful algal blooms (cyanoHAB) occurrence. Freshwater cyanoHABs may grow to excessive concentrations and cause human, animal, and environmental health concerns in lakes and reservoirs. Knowledge of the timing and location of cyanoHAB events is important for water quality management of recreational and drinking water systems. No quantitative tool exists to forecast cyanoHABs across broad geographic scales and at regular intervals. Publicly available satellite monitoring has proven effective in detecting cyanobacteria biomass near-real time within the United States. Weekly cyanobacteria abundance was quantified from the Ocean and Land Colour Instrument (OLCI) onboard the Sentinel-3 satellite as the response variable. An Integrated Nested Laplace Approximation (INLA) hierarchical Bayesian spatiotemporal model was applied to forecast World Health Organization (WHO) recreation Alert Level 1 exceedance >12 μg L-1 chlorophyll-a with cyanobacteria dominance for 2192 satellite resolved lakes in the United States across nine climate zones. The INLA model was compared against support vector classifier and random forest machine learning models; and Dense Neural Network, Long Short-Term Memory (LSTM), Recurrent Neural Network (RNN), and Gneural Network (GNU) neural network models. Predictors were limited to data sources relevant to cyanobacterial growth, readily available on a weekly basis, and at the national scale for operational forecasting. Relevant predictors included water surface temperature, precipitation, and lake geomorphology. Overall, the INLA model outperformed the machine learning and neural network models with prediction accuracy of 90% with 88% sensitivity, 91% specificity, and 49% precision as demonstrated by training the model with data from 2017 through 2020 and independently assessing predictions with data from the 2021 calendar year. The probability of true positive responses was greater than false positive responses and the probability of true negative responses was less than false negative responses. This indicated the model correctly assigned lower probabilities of events when they didn't exceed the WHO Alert Level 1 threshold and assigned higher probabilities when events did exceed the threshold. The INLA model was robust to missing data and unbalanced sampling between waterbodies.

Artificial intelligence to explain the variables that favor the cyanobacteria steady-state in tropical ecosystems: A Bayeasian network approach

The steady-state is a situation of little variability of species dominance and total biomass over time. Maintenance of cyanobacteria are often observed in tropical and eutrophic ecosystems and can cause imbalances in aquatic ecosystem. Bayeasian networks allow the construction of simpls models that summarizes a large amount of variables and can predict the probability of occurrence of a given event. Studies considering Bayeasian networks built from environmental data to predict the occurrence of steady-state in aquatic ecosystems are scarce. This study aims to propose a Bayeasian network model to assess the occurrence, composition and duration of cyanobacteria steady-state in a tropical and eutrophic ecosystem. It was hypothesized long lasting steady-state events, composed by filamentous cyanobacteria species and directly influenced by eutrophication and drought. Our model showed steady-state lasting between 3 and 17 weeks with the monodominance or co-dominance of filamentous species, mainly Raphidiopsis raciborskii and Planktothrix agardhii. These evens occurred frequently under drought and high turbidity, however higher nutrients concentrations did not increase the probability steady-state occurrence or longer duration. The proposed model appears as a tool to assess the effects of future warming on steady-state occurrence and it can be a useful to more traditional process-based models for reservoirs.

Controls of thermal response of temperate lakes to atmospheric warming

Atmospheric warming heats lakes, but the causes of variation among basins are poorly understood. Here, multi-decadal profiles of water temperatures, trophic state, and local climate from 345 temperate lakes are combined with data on lake geomorphology and watershed characteristics to identify controls of the relative rates of temperature change in water (WT) and air (AT) during summer. We show that differences in local climate (AT, wind speed, humidity, irradiance), land cover (forest, urban, agriculture), geomorphology (elevation, area/depth ratio), and water transparency explain >30% of the difference in rate of lake heating compared to that of the atmosphere. Importantly, the rate of lake heating slows as air warms (P < 0.001). Clear, cold, and deep lakes, especially at high elevation and in undisturbed catchments, are particularly responsive to changes in atmospheric temperature. We suggest that rates of surface water warming may decline relative to the atmosphere in a warmer future, particularly in sites already experiencing terrestrial development or eutrophication.

Changes of phytoplankton and water environment in a highly urbanized subtropical lake during the past ten years

Phytoplankton and water quality changes in highly urbanized lakes affect the surrounding water safety. However, due to the complexity and variability of natural changes and human disturbances, it is difficult for multi-year research with yearly sampling frequency to cover accurate changes of phytoplankton and water environment or provide constructive suggestions for managers. Based on monthly monitoring data spanning 2011-2020 in a highly urbanized subtropical lake (Hongze Lake, China), Mann-Kendall test, ANOVA analysis and variation partitioning analysis were used to assess the changes of phytoplankton and water environment, and detect dynamic responses of phytoplankton to environmental changes. Rising water temperature during winter and spring, the decrease in nitrate, and the increase in water flow and turbidity were the main environmental characteristics from 2011 to 2020. The average and maximum abundance of Chlorophyta, Bacillariophyta, and Cryptophyta significantly declined, while changes in Cyanobacteria were characterized by an increase of N₂-fixing filamentous cyanobacteria and a decrease of non-filamentous cyanobacteria. The rising water temperature during spring may promote the early growth of N₂-fixing filamentous cyanobacteria. The decrease in nitrate mainly resulted in the decrease of Chlorophyta and non-filamentous cyanobacteria, and the increase of N₂-fixing filamentous cyanobacteria during summer and autumn. The increase of turbidity and water flow inhibited the growth of Chlorophyta, Bacillariophyta, Cryptophyta, and non-filamentous cyanobacteria, but created favourable conditions for the growth of N₂-fixing filamentous cyanobacteria. In summer and autumn, managers should focus on the proliferation of N₂-fixing filamentous cyanobacteria when precipitation increase, nitrogen nutrients decrease, and non-filamentous cyanobacteria risk under opposite conditions. These findings greatly improved our understanding of the dynamic response of phytoplankton communities to natural changes and anthropogenic disturbances in the urbanized subtropical lakes, and can be used to develop lake management strategies.

Uncertainty in projections of future lake thermal dynamics is differentially driven by lake and global climate models

Freshwater ecosystems provide vital services, yet are facing increasing risks from global change. In particular, lake thermal dynamics have been altered around the world as a result of climate change, necessitating a predictive understanding of how climate will continue to alter lakes in the future as well as the associated uncertainty in these predictions. Numerous sources of uncertainty affect projections of future lake conditions but few are quantified, limiting the use of lake modeling projections as management tools. To quantify and evaluate the effects of two potentially important sources of uncertainty, lake model selection uncertainty and climate model selection uncertainty, we developed ensemble projections of lake thermal dynamics for a dimictic lake in New Hampshire, USA (Lake Sunapee). Our ensemble projections used four different climate models as inputs to five vertical one-dimensional (1-D) hydrodynamic lake models under three different climate change scenarios to simulate thermal metrics from 2006 to 2099. We found that almost all the lake thermal metrics modeled (surface water temperature, bottom water temperature, Schmidt stability, stratification duration, and ice cover, but not thermocline depth) are projected to change over the next century. Importantly, we found that the dominant source of uncertainty varied among the thermal metrics, as thermal metrics associated with the surface waters (surface water temperature, total ice duration) were driven primarily by climate model selection uncertainty, while metrics associated with deeper depths (bottom water temperature, stratification duration) were dominated by lake model selection uncertainty. Consequently, our results indicate that researchers generating projections of lake bottom water metrics should prioritize including multiple lake models for best capturing projection uncertainty, while those focusing on lake surface metrics should prioritize including multiple climate models. Overall, our ensemble modeling study reveals important information on how climate change will affect lake thermal properties, and also provides some of the first analyses on how climate model selection uncertainty and lake model selection uncertainty interact to affect projections of future lake dynamics.

Comparative assessment of algaecide performance on freshwater phytoplankton: Understanding differential sensitivities to frame cyanobacteria management

Cyanobacterial bloom represent a growing threat to global water security. With fast proliferation, they raise great concern due to potential health and socioeconomic concerns. Algaecides are commonly employed as a mitigative measure to suppress and manage cyanobacteria. However, recent research on algaecides has a limited phycological focus, concentrated predominately on cyanobacteria and chlorophytes. Without considering phycological diversity, generalizations crafted from these algaecide comparisons present a biased perpective. To limit the collateral impacts of algaecide interventions on phytoplankton communities it is critical to understand differential phycological sensitivities for establishing optimal dosage and tolerance thresholds. This research attempts to fill this knowledge gap and provide effective guidelines to frame cyanobacterial management. We investigate the effect of two common algaecides, copper sulfate (CuSO₄) and hydrogen peroxide (H₂O₂), on four major phycological divisions (chlorophytes, cyanobacteria, diatoms, and mixotrophs). All phycological divisions exhibited greater sensitivity to copper sulfate, except chlorophytes. Mixotrophs and cyanobacteria displayed the highest sensitivity to both algaecides with the highest to lowest sensitivity being observed as follows: mixotrophs, cyanobacteria, diatoms, and chlorophytes. Our results suggest that H₂O₂ represents a comparable alternative to CuSO₄ for cyanobacterial control. However, some eukaryotic divisions such as mixotrophs and diatoms mirrored cyanobacteria sensitivity, challenging the assumption that H₂O₂ is a selective cyanocide. Our findings suggest that optimizing algaecide treatments to suppress cyanobacteria while minimizing potential adverse effects on other phycological members is unattainable. An apparent trade-off between effective cyanobacterial management and conserving non-targeted phycological divisions is expected and should be a prime consideration of lake management.

Phytoplankton group identification with chemotaxonomic biomarkers: In combination they do better

Chemotaxonomic biomarkers are needed to monitor and evaluate the nutritional quality of phytoplankton communities. The biomolecules produced by different phytoplankton species do not always follow genetic phylogeny. Therefore, we analyzed fatty acids, sterols, and carotenoids from 57 freshwater phytoplankton strains to evaluate the usability of these biomolecules as chemotaxonomic biomarkers. We found 29 fatty acids, 34 sterols, and 26 carotenoids in our samples. The strains were grouped into cryptomonads, cyanobacteria, diatoms, dinoflagellates, golden algae, green algae, and raphidophytes, and the phytoplankton group explained 61%, 54%, and 89% of the variability of fatty acids, sterols, and carotenoids, respectively. Fatty acid and carotenoid profiles distinguished most phytoplankton groups, but not flawlessly. For example, fatty acids could not distinguish golden algae and cryptomonads, whereas carotenoids did not separate diatoms and golden algae. The sterol composition was heterogeneous but seemed to be useful for distinguishing different genera within a phytoplankton group. The chemotaxonomy biomarkers yielded optimal genetic phylogeny when the fatty acids, sterols, and carotenoids were used together in multivariate statistical analysis. Our results suggest that the accuracy of phytoplankton composition modeling could be enhanced by combining these three biomolecule groups.

Identifying lakes at risk of toxic cyanobacterial blooms using satellite imagery and field surveys across the United States

Harmful algal blooms caused by cyanobacteria are a threat to global water resources and human health. Satellite remote sensing has vastly expanded spatial and temporal data on lake cyanobacteria, yet there is still acute need for tools that identify which waterbodies are at-risk for toxic cyanobacterial blooms. Algal toxins cannot be directly detected through imagery but monitoring toxins associated with cyanobacterial blooms is critical for assessing risk to the environment, animals, and people. The objective of this study is to address this need by developing an approach relating satellite imagery on cyanobacteria with field surveys to model the risk of toxic blooms among lakes. The Medium Resolution Imaging Spectrometer (MERIS) and United States (US) National Lakes Assessments are leveraged to model the probability among lakes of exceeding lower and higher demonstration thresholds for microcystin toxin, cyanobacteria, and chlorophyll a. By leveraging the large spatial variation among lakes using two national-scale data sources, rather than focusing on temporal variability, this approach avoids many of the previous challenges in relating satellite imagery to cyanotoxins. For every satellite-derived lake-level Cyanobacteria Index (CI_cyano) increase of 0.01 CI_cyano/km2, the odds of exceeding six bloom thresholds increased by 23-54 %. When the models were applied to the 2192 satellite monitored lakes in the US, the number of lakes identified with ≥75 % probability of exceeding the thresholds included as many as 335 lakes for the lower thresholds and 70 lakes for the higher thresholds, respectively. For microcystin, the models identified 162 and 70 lakes with ≥75 % probability of exceeding the lower (0.2 μg/L) and higher (1.0 μg/L) thresholds, respectively. This approach represents a critical advancement in using satellite imagery and field data to identify lakes at risk for developing toxic cyanobacteria blooms. Such models can help translate satellite data to aid water quality monitoring and management.

Effects of temporal abiotic drivers on the dynamics of an allometric trophic network model

Current ecological research and ecosystem management call for improved understanding of the abiotic drivers of community dynamics, including temperature effects on species interactions and biomass accumulation. Allometric trophic network (ATN) models, which simulate material (carbon) transfer in trophic networks from producers to consumers based on mass‐specific metabolic rates, provide an attractive framework to study consumer–resource interactions from organisms to ecosystems. However, the developed ATN models rarely consider temporal changes in some key abiotic drivers that affect, for example, consumer metabolism and producer growth. Here, we evaluate how temporal changes in carrying capacity and light‐dependent growth rate of producers and in temperature‐dependent mass‐specific metabolic rate of consumers affect ATN model dynamics, namely seasonal biomass accumulation, productivity, and standing stock biomass of different trophic guilds, including age‐structured fish communities. Our simulations of the pelagic Lake Constance food web indicated marked effects of temporally changing abiotic parameters on seasonal biomass accumulation of different guild groups, particularly among the lowest trophic levels (primary producers and invertebrates). While the adjustment of average irradiance had minor effect, increasing metabolic rate associated with 1–2°C temperature increase led to a marked decline of larval (0‐year age) fish biomass, but to a substantial biomass increase of 2‐ and 3‐year‐old fish that were not predated by ≥4‐year‐old top predator fish, European perch (Perca fluviatilis). However, when averaged across the 100 simulation years, the inclusion of seasonality in abiotic drivers caused only minor changes in standing stock biomasses and productivity of different trophic guilds. Our results demonstrate the potential of introducing seasonality in and adjusting the average values of abiotic ATN model parameters to simulate temporal fluctuations in food‐web dynamics, which is an important step in ATN model development aiming to, for example, assess potential future community‐level responses to ongoing environmental changes.

Analyzing eutrophication and harmful algal bloom dynamics in a deep Mediterranean hypereutrophic reservoir

Excessive point and non-point nutrient loadings accompanied with elevated temperatures have increased the prevalence of harmful algal bloom (HAB). HABs pose significant environmental and public health concerns, particularly for inland freshwater systems. In this study, the eutrophication and HAB dynamics in the Qaraoun Reservoir, a hypereutrophic deep monomictic reservoir suffering from poor water quality, were assessed. The reservoir was mostly phosphorus limited, and large algal particulates dominated light attenuation in the water column. During bloom events, surface chlorophyll-a concentrations increased up to 961.3 µg/L, while surface concentrations of ammonia and ortho-phosphate were rapidly depleted; surface dissolved oxygen reached supersaturation levels and surface pH levels were up to 3 units higher than those measured in the hypolimnion. Meanwhile, measured Microcystin-LR toxin concentrations in the reservoir exceeded the World Health Organization 1 μg/L provisional guideline 45% of the times. Yet, the results showed that most of the toxins were intra-cellular, suggesting that they decayed rapidly when released into the reservoir. Results from a random forests ensemble model indicated that tracking the changes in surface dissolved oxygen levels, ammonium, ortho-phosphate, and pH can be an effective program towards predicting the reservoir's trophic state and algae blooms.

Toxicity and Starvation Induce Major Trophic Isotope Variation in Daphnia Individuals: A Diet Switch Experiment Using Eight Phytoplankton Species of Differing Nutritional Quality

Stable isotope values can express resource usage by organisms, but their precise interpretation is predicated using a controlled experiment-based validation process. Here, we develop a stable isotope tracking approach towards exploring resource shifts in a key primary consumer species Daphnia magna. We used a diet switch experiment and model fitting to quantify the stable carbon (δ¹³C) and nitrogen (δ¹⁵N) isotope turnover rates and discrimination factors for eight dietary sources of the plankton species that differ in their cellular organization (unicellular or filamentous), pigment and nutrient compositions (sterols and polyunsaturated fatty acids), and secondary metabolite production rates. We also conduct a starvation experiment. We evaluate nine tissue turnover models using Akaike's information criterion and estimate the repetitive trophic discrimination factors. Using the parameter estimates, we calculate the hourly stable isotope turnover rates. We report an exceedingly faster turnover value following dietary switching (72 to 96 h) and a measurable variation in trophic discrimination factors. The results show that toxic stress and the dietary quantity and quality induce trophic isotope variation in Daphnia individuals. This study provides insight into the physiological processes that underpin stable isotope patterns. We explicitly test multiple alternative dietary sources and fasting and discuss the parameters that are fundamental for field- and laboratory-based stable isotope studies.

The biomass of bloom-forming colonial Microcystis affects its response to aeration disturbance

The algal succession in Microcystis blooms of varying biomass under continuous aeration was studied in a greenhouse. There were four treatments (control, Low, Medium, and High) with initial chlorophyll a (Chl-a) of 32.5, 346.8, 1413.7, and 14,250.0 μg L^-1, respectively. During the experiment, Cyanophyta biomass was the lowest in the Medium treatment (P < 0.05), while its Chlorophyta biomass was the highest (P < 0.05). Both Chlorophyta and Bacillariophyta biomass were the lowest in the High treatment (P < 0.05). Bacillariophyta biomass, particularly the diatom Nitzschia palea was the highest in the Low treatment (P < 0.05), and Nitzschia palea cells were attached to the Microcystis colonies. Thus, the algal shift in Microcystis blooms under aeration disturbance depends on its initial biomass, and it shift to green algae or/and diatom dominance in the control, Low, Medium treatments. Diatom cells, particularly N. palea, grew in an attached form on Microcystis colonies in treatment Low, in which the colonies provided media for the adherence. The mechanism of the algal shift with different biomass must be related to the nutrient level, low light and aerobic conditions under aeration disturbance as well as the aeration itself, which destroyed the Microcystis colonies' advantage of floating on the water.

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.

The Synthesis and Assembly of a Truncated Cyanophage Genome and Its Expression in a Heterogenous Host

Cyanophages play an important role in regulating the dynamics of cyanobacteria communities in the hydrosphere, representing a promising biological control strategy for cyanobacterial blooms. Nevertheless, most cyanophages are host-specific, making it difficult to control blooming cyanobacteria via single or multiple cyanophages. In order to address the issue, we explore the interaction between cyanophages and their heterologous hosts, with the aim of revealing the principles of designing and constructing an artificial cyanophage genome towards multiple cyanobacterial hosts. In the present study, we use synthetic biological approaches to assess the impact of introducing a fragment of cyanophage genome into a heterologous cyanobacterium under a variety of environmental conditions. Based on a natural cyanophage A-4L genome (41,750 bp), a truncated cyanophage genome Syn-A-4-8 is synthesized and assembled in Saccharomyces cerevisiae. We found that a 351-15,930 bp area of the A-4L genome has a fragment that is lethal to Escherichia coli during the process of attempting to assemble the full-length A-4L genome. Syn-A-4-8 was successfully introduced into E. coli and then transferred into the model cyanobacterium Synechococcus elongatus PCC 7942 (Syn7942) via conjugation. Although no significant phenotypes of Syn7942 carrying Syn-A-4-8 (LS-02) could be observed under normal conditions, its growth exhibited a prolonged lag phase compared to that of the control strain under 290-millimolar NaCl stress. Finally, the mechanisms of altered salt tolerance in LS-02 were revealed through comparative transcriptomics, and ORF25 and ORF26 on Syn-A-4-8 turned out to be the key genes causing the phenotype. Our research represents an important attempt in designing artificial cyanophages towards multiple hosts, and offers new future insights into the control of cyanobacterial blooms.

Direct and indirect effects of climate change on distribution and community composition of macrophytes in lentic systems

Macrophytes are an important part of freshwater ecosystems and they have direct and indirect roles in keeping the water clear and providing structure and habitats for other aquatic organisms. Currently, climate change is posing a major threat to macrophyte communities by altering the many drivers that determine macrophyte abundance and composition. We synthesise current literature to examine the direct effects of climate change (i.e. changes in CO2 , temperature, and precipitation patterns) on aquatic macrophytes in lakes as well as indirect effects via invasive species and nutrient dynamics. The combined effects of climate change are likely to lead to an increased abundance and distribution of emergent and floating species, and a decreased abundance and distribution of submerged macrophytes. In small shallow lakes, these processes are likely to be faster than in deep temperate lakes; with lower light levels, water level fluctuations and increases in temperature, the systems will become dominated by algae. In general, specialized macrophyte species in high-latitude and high-altitude areas will decrease in number while more competitive invasive species are likely to outcompete native species. Given that the majority of endemic species reside in tropical lakes, climate change, together with other anthropogenic pressures, might cause the extinction of a large number of endemic species. Lakes at higher altitudes in tropical areas could therefore potentially be a hotspot for future conservation efforts for protecting endemic macrophyte species. In response to a combination of climate-change induced threats, the macrophyte community might collapse, which will change the status of lakes and may initiate a negative feedback loop that will affect entire lake ecosystems.

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.

Spatio-Temporal Monitoring of Benthic Anatoxin-a-Producing Tychonema sp. in the River Lech, Germany

Incidents with toxic benthic cyanobacteria blooms have been increasing recently. In 2019, several dogs were poisoned in the river Lech (Germany) by the benthic anatoxin-a-producing genus Tychonema. To characterize spatial and temporal distribution of potentially toxic Tychonema in this river, a systematic monitoring was carried out in 2020, focusing on the occurrence of the genus, its toxin production and habitat requirements. Tychonema and cyanobacterial community composition in benthic mats and pelagic samples were identified using a combined approach of microscopy and DNA sequencing of the 16S rRNA gene. In addition, anatoxin-a concentrations of selected samples were measured using the ELISA method. The habitat was characterized to assess the ecological requirements and growth conditions of Tychonema. Tychonema mats and anatoxin-a were detected at several sampling sites throughout the entire study period. Toxin concentrations increased with the progression of the vegetation period and with flow direction, reaching values between 0 and 220.5 µg/L. Community composition differed among pelagic and benthic samples, with life zone and substrate condition being the most important factors. The results of this study highlight the importance of monitoring and understanding the factors determining occurrence and toxin production of both pelagic and benthic cyanobacteria due to their relevance for the health of humans and aquatic ecosystems.

Simultaneous feature engineering and interpretation: Forecasting harmful algal blooms using a deep learning approach

Routine monitoring for harmful algal blooms (HABs) is generally undertaken at low temporal frequency (e.g., weekly to monthly) that is unsuitable for capturing highly dynamic variations in cyanobacteria abundance. Therefore, we developed a model incorporating reverse time attention with a decay mechanism (RETAIN-D) to forecast HABs with simultaneous improvements in temporal resolution, forecasting performance, and interpretability. The usefulness of RETAIN-D in forecasting HABs was illustrated by its application to two sites located in the lower sections of the Nakdong and Yeongsan rivers, South Korea, where HABs pose a critical water quality issue. Three variations of recurrent neural network models, i.e., long short-term memory (LSTM), gated recurrent unit (GRU), and reverse time attention (RETAIN), were adopted for comparisons of performance with RETAIN-D. Input features encompassing meteorological, hydrological, environmental, and biological factors were used to forecast cyanobacteria abundance (total cyanobacteria cell counts and cell counts of dominant cyanobacteria taxa). Incorporation of a decay mechanism into the deep learning structure in RETAIN-D allowed forecasts of HABs on a high temporal resolution (daily) without manual feature engineering, increasing the usefulness of resulting forecasts for water quality and resources management. RETAIN-D yielded a high degree of accuracy (RMSE = 0.29-1.67, R² = 0.76-0.98, MAE = 0.18-1.14, SMAPE = 9.77-87.94% for test sets; on natural log scales) across model outputs and sites, successfully capturing high variability and irregularities in the time series. RETAIN-D showed higher accuracy than RETAIN (except for comparable accuracy in forecasting Microcystis abundance at the Nakdong River site) and outperformed LSTM and GRU across all model outputs and sites. Ambient temperature had high importance in forecasting cyanobacteria abundance across all model outputs and sites, whereas the relative importance of other input features varied by the output and site. Increases in contributions with increasing irradiance, decreasing flow rates, and increasing residence time were more pronounced in summer than other seasons. Differences in the contributions of input features among different time steps (1 to 7 days prior to forecasting) were larger in the Yeongsan River site. RETAIN-D is applicable to a wide range of forecasting models that can benefit from improved temporal resolution, performance, and interpretability.

Chlorophyll and phycocyanin in-situ fluorescence in mixed cyanobacterial species assemblages: Effects of morphology, cell size and growth phase

Cyanobacteria harmful blooms can represent a major risk for public health due to potential release of toxins and other noxious compounds in the water. A continuous and high-resolution monitoring of the cyanobacteria population is required due to their rapid dynamics, which has been increasingly done using in-situ fluorescence of phycocyanin (f-PC) and chlorophyll a (f-Chl a). Appropriate in-situ fluorometers calibration is essential because f-PC and f-Chl a are affected by biotic and abiotic factors, including species composition. Measurement of f-PC and f-Chl a in mixed species assemblages during different growth phases - representative of most field conditions - has received little attention. We hypothesized that f-PC and f-Chl a of mixed assemblages of cyanobacteria may be accurately estimated if taxa composition and fluorescence characteristics are known. We also hypothesized that species with different morphologies would have different fluorescence per unit cell and biomass. We tested these hypotheses in a controlled culture experiment in which photosynthetic pigment fluorescence, chemical pigment extraction, optical density and microscopic enumeration of four common cyanobacteria species (Aphanocapsa sp, Microcystis aeruginosa, Dolichospermum circinale and Raphidiopsis raciborskii) were quantified. Both monocultures and mixed cultures were monitored from exponential to late stationary growth phases. The sum of fluorescence of individual species calculated for mixed samples was not significantly different than measured fluorescence of mixed cultures. Estimated and measured f-PC and f-Chl a of mixed cultures had higher correlations and smaller absolute median errors when estimations were based on fluorescence per biomass instead of fluorescence per cell. Largest errors were overestimations of measured fluorescence for species with different morphologies. Fluorescence per cell was significantly different among most species, while fluorescence per unit biomass was not, indicating that conversion of fluorescence to biomass reduces species-specific bias. This study presents new information on the effect of species composition on cyanobacteria fluorescence. Best practices of deployment and operation of fluorometers, and data-driven models supporting in-situ fluorometers calibration are discussed as suitable solutions to minimize taxa-specific bias in fluorescence estimates.

Mine drainage precipitates attenuate and conceal wastewater-derived phosphate pollution in stream water

Hydrous ferric-oxide (HFO) coatings on streambed sediments may attenuate dissolved phosphate (PO₄) concentrations at acidic to neutral pH conditions, limiting phosphorus (P) transport and availability in aquatic ecosystems. Mesh-covered tiles on which "natural" HFO from abandoned mine drainage (AMD) had precipitated were exposed to treated municipal wastewater (MWW) effluent or a mixture of stream water and effluent. Between 42 and 99% of the dissolved P in effluent was removed from the water to a thin coating (~2 μm) of HFO on the mesh. Geochemical equilibrium model results predicted the removal of 76 to 99% of PO₄ from the water by adsorption to the HFO, depending on the HFO quantity, initial PO₄ concentration, and pH. The measurements and model results indicated the capacity for P removal decreased as the concentration of P associated with the HFO increased. Continuing accumulation of HFO from upstream AMD sources replenish the in-stream capacity for P attenuation below the MWW discharge. This indicates AMD pollution may conceal P inputs and limit the amount of dissolved P transported to downstream ecosystems. However, HFO-rich sediments also represent a potential source of "legacy" P that could confound management practices intended to decrease nutrient and metal loadings.

Monitoring water transparency, total suspended matter and the beam attenuation coefficient in inland water using innovative ground-based proximal sensing technology

Long-term and high-frequency observations are vital to reveal water quality dynamics and responses to climate change and human activities. However, the datasets collected from traditional in situ and satellite observations may miss the rapid dynamics of water quality in the short term due to low temporal-spatial monitoring frequency and cloudy or rainy weather. To address this shortage, innovative ground-based proximal sensing (GBPS) technology was proposed to monitor water quality and identify emergencies with a wavelength range of 400-1000 nm, a spectral resolution of 1 nm and a minimal observation interval of 30 s. The GBPS was equipped with a hyperspectral imager placed 4-5 m above the water surface to minimize the impacts of the atmosphere and clouds. In this study, combined with 583 water samples obtained from four field samplings, GBPS datasets were first applied to estimate the total suspended matter (TSM), Secchi disk depth (SDD) and beam attenuation coefficient at 550 nm (C(550)) in Taihu Lake (TL), Liangxi River (LR) and Funchunjiang Reservoir (FR). The results demonstrated good performance with the TSM (R² = 0.83, RMSE = 8.35 mg/L, MAPE = 24.0%), SDD (R² = 0.88, RMSE = 0.09 m, MAPE = 14.7%), and C(550) (R² = 0.79, RMSE = 3.55 m^-1, MAPE = 35.8%). The time series of TSM and C(550) at the second-minute level showed consistent changes, but they were opposite to those of SDD. Taking TSM as an example, the datasets captured two mutations in TL with an 853.6% increase in 65 min and a rapid change from 40.3 mg/L to 256.9 mg/L and then to 51.0 mg/L in 224 min on November 1 and 3, respectively. Meanwhile, a significant decreasing trend (r = -0.83, p < 0.01) in LR from November 7 to 9 and a periodic diurnal increasing trend of TSM in FR during November 11 to 13 (0.46 ≤ R² ≤ 0.70, p < 0.01) were observed. GBPS, with the advantages of high-frequency observations and the applicability of complex weather conditions, compensates for the in situ, aircraft and satellite observation deficiencies. Therefore, GBPS allows us to capture more detailed water quality information and episodic events, which is an important part of an integrated air-space-ground monitoring system in the future.

Poor nutritional quality of primary producers and zooplankton driven by eutrophication is mitigated at upper trophic levels

Eutrophication and rising water temperature in freshwaters may increase the total production of a lake while simultaneously reducing the nutritional quality of food web components. We evaluated how cyanobacteria blooms, driven by agricultural eutrophication (in eutrophic Lake Köyliöjärvi) or global warming (in mesotrophic Lake Pyhäjärvi), influence the biomass and structure of phytoplankton, zooplankton, and fish communities. In terms of the nutritional value of food web components, we evaluated changes in the ω-3 and ω-6 polyunsaturated fatty acids (PUFA) of phytoplankton and consumers at different trophic levels. Meanwhile, the lakes did not differ in their biomasses of phytoplankton, zooplankton, and fish communities, lake trophic status greatly influenced the community structures. The eutrophic lake, with agricultural eutrophication, had cyanobacteria bloom throughout the summer months whereas cyanobacteria were abundant only occasionally in the mesotrophic lake, mainly in early summer. Phytoplankton community differences at genus level resulted in higher arachidonic acid, eicosapentaenoic acid (EPA), and docosahexaenoic acid (DHA) content of seston in the mesotrophic than in the eutrophic lake. This was also reflected in the EPA and DHA content of herbivorous zooplankton (Daphnia and Bosmina) despite more efficient trophic retention of these biomolecules in a eutrophic lake than in the mesotrophic lake zooplankton. Planktivorous juvenile fish (perch and roach) in a eutrophic lake overcame the lower availability of DHA in their prey by more efficient trophic retention and biosynthesis from the precursors. However, the most efficient trophic retention of DHA was found with benthivorous perch which prey contained only a low amount of DHA. Long-term cyanobacterial blooming decreased the nutritional quality of piscivorous perch; however, the difference was much less than previously anticipated. Our result shows that long-term cyanobacteria blooming impacts the structure of plankton and fish communities and lowers the nutritional quality of seston and zooplankton, which, however, is mitigated at upper trophic levels.

Phytoplankton and cyanobacteria abundances in mid-21st century lakes depend strongly on future land use and climate projections

Land use and climate change are anticipated to affect phytoplankton of lakes worldwide. The effects will depend on the magnitude of projected land use and climate changes and lake sensitivity to these factors. We used random forests fit with long-term (1971-2016) phytoplankton and cyanobacteria abundance time series, climate observations (1971-2016), and upstream catchment land use (global Clumondo models for the year 2000) data from 14 European and 15 North American lakes basins. We projected future phytoplankton and cyanobacteria abundance in the 29 focal lake basins and 1567 lakes across focal regions based on three land use (sustainability, middle of the road, and regional rivalry) and two climate (RCP 2.6 and 8.5) scenarios to mid-21st century. On average, lakes are expected to have higher phytoplankton and cyanobacteria due to increases in both urban land use and temperature, and decreases in forest habitat. However, the relative importance of land use and climate effects varied substantially among regions and lakes. Accounting for land use and climate changes in a combined way based on extensive data allowed us to identify urbanization as the major driver of phytoplankton development in lakes located in urban areas, and climate as major driver in lakes located in remote areas where past and future land use changes were minimal. For approximately one-third of the studied lakes, both drivers were relatively important. The results of this large scale study suggest the best approaches for mitigating the effects of human activity on lake phytoplankton and cyanobacteria will depend strongly on lake sensitivity to long-term change and the magnitude of projected land use and climate changes at a given location. Our quantitative analyses suggest local management measures should focus on retaining nutrients in urban landscapes to prevent nutrient pollution from exacerbating ongoing changes to lake ecosystems from climate change.

Plant-cyanobacteria interactions: Beneficial and harmful effects of cyanobacterial bioactive compounds on soil-plant systems and subsequent risk to animal and human health

Plant-cyanobacteria interactions occur in different ways and at many different levels, both beneficial and harmful. Plant-cyanobacteria interactions, as a beneficial symbiosis, have long been demonstrated in rice-growing areas (Poaceae) where the most efficient nitrogen-fixing cyanobacteria are present in paddies. Moreover, cyanobacteria may in turn produce and/or secrete numerous bioactive compounds that have plant growth-promoting abilities or that may make the plant more resistant to abiotic or biotic stress. In recent years, there has been a growing worldwide interest in the use of cyanobacterial biomass as biofertilizers to replace chemical fertilizers, in part to overcome increasing organic-farming demands. However, the potential presence of harmful cyanotoxins has delayed the use of such cyanobacterial biomass, which can be found in large quantities in freshwater ecosystems around the world. In this review, we describe the existing evidence for the positive benefit of plant-cyanobacteria interactions and discuss the use of cyanobacterial biomass as biofertilizers and its growing worldwide interest. Although mass cyanobacterial blooms and scums are a current and emerging threat to the degradation of ecosystems and to animal and human health, they may serve as a source of numerous bioactive compounds with multiple positive effects that could be of use as an alternative to chemical fertilizers in the context of sustainable development.

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.

Characterizing Trophic State in Tropical/Subtropical Reservoirs: Deviations among Indexes in the Lower Latitudes

Trophic state indexes (TSI) guide management strategies regarding eutrophication control worldwide. Such indexes usually consider chlorophyll-a (Chl-a), total phosphorus (TP), and Secchi disk depth (SDD) as independent variables for estimating aquatic productivity and the degree of impairment. TSIs for each of these components are frequently averaged to produce a single TSI value associated with a trophic state classification (e.g., oligotrophic, mesotrophic, or eutrophic). The potential divergence among equations and classification systems originally developed for temperate lakes or tropical/subtropical reservoirs might be particularly relevant in the tropics, where there is a lack of data and the use of equations originally developed for temperate systems may be inappropriate. We calculated two widely used TSIs for temperate lakes (TSI_temp) or tropical reservoirs (TSI_trop) and explored the deviations among TSI components in Brazilian reservoirs. When applied to our tropical/subtropical reservoirs, the TSI_temp provided a conservative approach, with lower limits anticipating increasing trophic state classification. TSI components for Chl-a and SDD significantly deviated for both sets of equations, and these discrepancies were related to turbidity, water temperature, and cyanobacterial biomass. For TSI_temp, but not for TSI_trop, TSI values in relation to Chl-a and TP were also significantly different. All such deviations have important management implications especially when Chl-a, TP, and SDD are averaged in a single TSI, representing loss of information and less useful trophic state classifications. Our results demonstrate that tropical water bodies may respond to drivers of eutrophication differently than temperate systems, highlighting the need for more data to better inform management of these understudied ecosystems. As managers collect data from more tropical water bodies, regional models may offer even better understanding of factors influencing trophic state.

Phytoplankton communities in temporary ponds under different climate scenarios

Temporary water bodies, especially vernal pools, are the most sensitive aquatic environments to climate change yet the least studied. Their functioning largely depends on their phytoplankton community structure. This study aimed to determine how temperature and photoperiod length (by simulating inundation in different parts of the year under five climate scenarios) affect the succession and structure of phytoplankton communities soon after inundation. Photoperiod was the most important factor affecting phytoplankton species richness, total abundance and the abundance of taxonomic groups in the course of succession. A long photoperiod (16 h) and a moderate temperature (16 °C) in vernal pool microcosms (late spring inundation after a warm snowless winter) were the most favourable conditions for phytoplankton growth (especially for the main taxonomic groups: chlorophytes and cryptophytes) and species richness. With short photoperiods (inundation in winter) and low temperatures, the communities transformed towards diatoms, euglenoids and cyanobacteria. In line with our predictions, a high temperature (25 °C) favoured a decline in phytoplankton species diversity. Our study shows that climate change will result in seasonal shifts in species abundance or even in their disappearance and, finally, in potential strong changes in the biodiversity and food webs of aquatic ecosystems in the future.

Quantile regression illuminates the successes and shortcomings of long-term eutrophication remediation efforts in an urban river system

Despite massive financial investment in mitigation, eutrophication remains a major water quality problem and management priority. Eutrophication science-well established for lakes-is not as well developed for rivers, and scientific understanding of how rivers respond to eutrophication management is far more limited. Long-term data are required to evaluate progress, but such datasets are relatively rare for rivers. We analyzed 23 years of water quality data for the Charles River, a major urban river system in the northeastern U.S.A., to examine nutrient and phytoplankton biomass (chl-a) responses to decades of phosphorus (P) management. Using the more novel and robust approach of quantile regression, we identified statistically and ecologically significant declines in both total phosphorus (TP) and chl-a over time, but only for middle percentiles. Statistically high concentrations of TP and chl-a persist-the segments of the data of greatest concern to managers and the public-and yet this critical result is concealed by statistical tests often employed in eutrophication studies that only evaluate mean changes. TP, temperature, precipitation, and river segment jointly explain the most chl-a variation observed at the decadal scale. Spatial variation is also considerable: despite a significant decline in TP, the impounded lower river exhibits no long-term trend in chl-a and continues to experience annual blooms of harmful cyanobacteria-a lagging response comparable to that of a recovering eutrophic lake. Despite long-term successes in reducing P, chl-a, and cyanobacteria in the Charles River system, we did not detect any significant, long-term change in the attainment of statutory compliance, illustrating the protracted and complex nature of the river's response. Our analysis demonstrates the need for high-frequency, long-term water quality data to evaluate the progress of eutrophication management in urban rivers, and the utility of quantile regression for detecting critical trends in the occurrence of statistically low-frequency but ecologically high-impact events, including blooms of harmful cyanobacteria.

Development of a sub-seasonal cyanobacteria prediction model by leveraging local and global scale predictors

In recent decades, cultural eutrophication of coastal waters and inland lakes around the world has contributed to a rapid expansion of potentially toxic cyanobacteria, threatening aquatic and human systems. For many locations, a complex array of physical, chemical, and biological variables leads to significant inter-annual variability of cyanobacteria biomass, modulated by local and large-scale climate phenomena. Currently, however, minimal information regarding expected summertime cyanobacteria biomass conditions is available prior to the season, limiting proactive management and preparedness strategies for lake and beach safety. To address this, sub-seasonal (two-month) cyanobacteria biomass prediction models are developed, drawing on pre-season predictors including stream discharge, phosphorus loads, a floating algae index, and large-scale sea-surface temperature regions, with an application to Lake Mendota in Wisconsin. A two-phase statistical modeling approach is adopted to reflect identified asymmetric relationships between predictors (drivers of inter-annual variability) and cyanobacteria biomass levels. The model illustrates promising performance overall, with particular skill in predicting above normal cyanobacteria biomass conditions which are of primary importance to lake and beach managers.

Responses of abundant and rare bacterioplankton to temporal change in a subtropical urban reservoir

Investigation of bacterial community dynamics across different time scales is important for understanding how environmental conditions drive community change over time. Bacterioplankton from the surface waters of a subtropical urban reservoir in southeast China were analyzed through high-frequency sampling over 13 months to compare patterns and ecological processes between short (0‒8 weeks), medium (9‒24 weeks) and long (25‒53 weeks) time intervals. We classified the bacterial community into different subcommunities: abundant taxa (AT); conditionally rare taxa (CRT); rare taxa (RT). CRT contributed > 65% of the alpha-diversity, and temporal change of beta-diversities was more pronounced for AT and CRT than RT. The bacterial community exhibited a directional change in the short- and medium-time intervals and a convergent dynamic during the long-time interval due to a seasonal cycle. Cyanobacteria exhibited a strong succession pattern than other phyla. CRT accounted for > 76% of the network nodes in three stations. The bacteria-environment relationship and deterministic processes were stronger for large sample size at station G (n = 116) than small sample size at stations C (n = 12) and L (n = 22). These findings suggest that a high-frequency sampling approach can provide a better understanding on the time scales at which bacterioplankton can change fast between being abundant or rare, thus providing the facts about environmental factors driving microbial community dynamics. Patterns and processes in alpha- and beta-diversities and community assembly of bacterioplankton differ among different time intervals (short-, medium- and long-time intervals) and different subcommunities (abundant, conditionally rare and rare taxa) in a subtropical urban reservoir, demonstrating the importance of temporal scale and high-frequency sampling in microbial community ecology.

Picophytoplankton identification by flow cytometry and high-throughput sequencing in a clean reservoir

Understanding picophytoplankton variations that play important roles in the material circulation and energy flow are critical to assessing overall status of waterbody, especially for clean reservoirs which remain a relatively stable community structure and high species diversity due to lower nitrogen and phosphorus nutrients. However, their response to key environmental factors and tightly acting microbial remains poorly understood. Traditional quantification methods are limited, such as chlorophyll-a, turbidity and microscope. There are still many defects with present molecular analysis. In this study, a flow cytometric analysis and high-throughput sequencing combination methodology was developed and tested on clean water from a reservoir, by a monthly dynamic for a vegetative period April-September in 2019 to improve the accuracy of dynamic monitoring for the picophytoplankton system. More species of Pico-Cyanobacteria and Pico-Eukaryotes were discovered. The increased percentage of pigment compounds from 8.2% to 76.3% proves the effective reduce of heterotrophic disturbing and enrichment of target populations. Picophytoplankton that was previously neglected due to their low relative abundance has once again entered the scope of our eyes. Phytoplankton were divided into three categories. The first one was the highly abundant and frequently present taxa, the second one was the low-abundance but highly-transient population, and the third one was the low abundance and stable group. Synechococcus, Emiliania, Tetraselmis and Thalassiosira were dominant picophytoplankton and displayed obvious temporal and spatial distribution characteristics. Pico-PE rich Cyanobacteria and Nano-Eukaryotes with high transience abnormally increased in summer. Temperature, ammonia-N, nitrate-N, turbidity and total nitrogen were most influencing factors, while some picophytoplankton with special physiological structure showed distinct competitive advantages in the microbial community. As for the off-flavor compounds, the concentration of 2-methylisoborneol and geosmin were high even 66.7% and 20.8% of the samples exceeded their olfactory threshold. Chrysochromuina, Planktothrix and Microcystis might be the potential producers.

Climate Change-Enhanced Cyanobacteria Domination in Lake Kinneret: A Retrospective Overview

The objective of this study is re-evaluation of the long-term record of limnological parameters in Lake Kinneret (1970–2018) and its drainage basin (1940–2018) aimed at an indication of the possible impact of climate change on water quality in Lake Kinneret. The methodological approach is based on indication of significant changes, of temperature increase, decline in rainfall, causing a reduction in river discharges, and lake water inflows. These climatological changes were accompanied by a reduction in nitrogen and a slight increase in phosphorus in the lake Epilimnion. The outcome was Epilimnetic Nitrogen deficiency and Phosphorus sufficiency, which enhanced domination replacement of Peridinium spp. by Cyanobacterial. We concluded sequel suggested climate change affected water quality deterioration in Lake Kinneret.

Effects of hydrological and climatic variables on cyanobacterial blooms in four large shallow lakes fed by the Yangtze River

Shallow lakes, one of the most widespread water bodies in the world, are easily shifted to a new trophic state due to external interferences. Shifting hydrologic conditions and climate change can cause cyanobacterial harmful algal blooms (CyanoHABs) in shallow lakes, which pose serious threats to ecological integrity and human health. This study analyzed the effects of hydrologic and meteorological variables on cyanobacterial blooms in Yangtze-connected lakes (Lake Dongting and Poyang) and isolated lakes (Lake Chao and Tai). The results show that (i) chlorophyll-a (Chl-a) concentration tends to decrease exponentially with increasing relative lake level fluctuations (RLLF) and precipitation, but to increase linearly with increasing wind speed and air temperature; (ii) Chl-a concentrations in lakes were significantly higher when RLLF < 100, precipitation < 2.6 mm, wind speed > 2.6 m s^-1, or air temperature > 17.8 °C; (iii) the Chl-a concentration of Yangtze-isolated lakes was more significantly affected by water level amplitude, precipitation, wind speed and air temperature than the Yangtze-connected lakes; (iv) the RLLF and the ratio of wind speed to mean water depth could be innovative coupling factors to examine variation characteristics of Chl-a in shallow lakes with greater correlation than single factors.

Reservoir trophic state confounds flow-ecology relationships in regulated streams

Cultural eutrophication and river regulation have been identified as two of the most pressing threats to global freshwater biodiversity. However, we know little about their combined ecological effects, raising questions over biomonitoring practices that typically consider such stressors in isolation. To address this inconsistency, we examined a decade-long dataset of macroinvertebrate samples and environmental data collected downstream of three reservoirs spanning a broad gradient of trophic states, from mesotrophic to hypereutrophic. We analysed the responses of routine macroinvertebrate biomonitoring indices and community composition to antecedent flow, temperature and water quality, including parameters associated with eutrophication (total phosphorous, nitrate, nitrite, ammonia, chlorophyll a). Multi-model comparisons of linear regressor combinations, variation partitioning and distance-based redundancy analyses all revealed shifts in the relative significance of flow and water quality predictors across the trophic state gradient. At the mesotrophic site biomonitoring indices and community composition were most sensitive to seasonal flow variability- particularly high-flow conditions- whereas in the hypereutrophic system stronger associations with nutrient concentrations emerged, notably nitrite and nitrate. Patterns at the eutrophic site were broadly intermediate between these, with significant biotic responses to antecedent flows mediated by water quality. Based on these results we suggest that nutrient regimes should be regarded as an integral component of environmental flows science. We therefore call on practitioners to look beyond the stressor-specific indices widely used to assess ecological status in rivers to consider the interactive effects of flow and water quality.

A Forecasting Method for Harmful Algal Bloom(HAB)-Prone Regions Allowing Preemptive Countermeasures Based only on Acoustic Doppler Current Profiler Measurements in a Large River

Harmful algal blooms (HABs) have been recognized as a serious problem for aquatic ecosystems and a threat to drinking water systems. The proposed method aimed to develop a practical and rapid countermeasure, enabling preemptive responses to massive algal blooms, through which prior to the algal bloom season we can identify HAB-prone regions based on estimations of where harmful algae initiates and develops significantly. The HAB-prone regions were derived from temperature, depth, flow velocity, and sediment concentration data based only on acoustic Doppler current profilers (ADCPs) without relying further on supplementary data collection, such as the water quality. For HAB-prone regions, we employed hot-spot analysis using K-means clustering and the Getis-Ord G*, in conjunction with the spatial autocorrelation of Moran’s I and the local index of spatial association (LISA). The validation of the derived HAB-prone regions was conducted for ADCP measurements located at the downstream of Nam and Nakdong River confluence, South Korea, which preceded three months of algal bloom season monitored by unmanned aerial vehicles (UAVs). The visual inspection demonstrated that the comparison resulted in an acceptable range of agreement and consistency between the predicted HAB-prone regions and actual UAV-based observations of actual algal blooms.

Toxic compounds produced by cyanobacteria belonging to several species of the order Nostocales: A review

Cyanobacteria are well recognised as producers of a wide range of natural compounds that are in turn recognised as toxins that have potential and useful applications in the future as pharmaceutical agents. The order Nostocales, which is largely overlooked in this regard, has become increasingly recognised as a source of toxin producers including Anabaena, Nostoc, Hapalosiphon, Fischerella, Anabaenopsis, Aphanizomenon, Gloeotrichia, Cylindrospermopsis, Scytonema, Raphidiopsis, Cuspidothrix, Nodularia, Stigonema, Calothrix, Cylindrospermum and Desmonostoc species. The toxin compounds (i.e., microcystins, nodularin, anatoxins, ambiguines, fischerindoles and welwitindolinones) and metabolites are about to have a destructive effect on both inland and aquatic environment aspects. The present review gives an overview of the various toxins that are extracted by the order Nostocales. The current research suggests that these compounds that are produced by cyanobacterial species have promising future considerations as potentially harmful algae and as promising leads for drug discovery.

Spatiotemporal Variability in Phytoplankton Bloom Phenology in Eastern Canadian Lakes Related to Physiographic, Morphologic, and Climatic Drivers

Phytoplankton bloom monitoring in freshwaters is a challenging task, particularly when biomass is dominated by buoyant cyanobacterial communities that present complex spatiotemporal patterns. Increases in bloom frequency or intensity and their earlier onset in spring were shown to be linked to multiple anthropogenic disturbances, including climate change. The aim of the present study was to describe the phenology of phytoplankton blooms and its potential link with morphological, physiographic, anthropogenic, and climatic characteristics of the lakes and their watershed. The spatiotemporal dynamics of near-surface blooms were studied on 580 lakes in southern Quebec (Eastern Canada) over a 17-year period by analyzing chlorophyll-a concentrations gathered from MODIS (Moderate Resolution Imaging Spectroradiometer) satellite images. Results show a significant increase by 23% in bloom frequency across all studied lakes between 2000 and 2016. The first blooms of the year appeared increasingly early over this period but only by 3 days (median date changing from 6 June to 3 June). Results also indicate that high biomass values are often reached, but the problem is seldom extended to the entire lake surface. The canonical correlation analysis between phenological variables and environmental variables shows that higher frequency and intensity of phytoplankton blooms and earlier onset date occurred for smaller watersheds and higher degree-days, lake surface area, and proportion of urban zones. This study provides a regional picture of lake trophic state over a wide variety of lacustrine environments in Quebec, a detailed phenology allowing to go beyond local biomass assessments, and the first steps on the development of an approach exploiting regional trends for local pattern assessments.

A systematic literature review of forecasting and predictive models for cyanobacteria blooms in freshwater lakes

Cyanobacteria harmful blooms (CyanoHABs) in lakes and reservoirs represent a major risk for water authorities globally due to their toxicity and economic impacts. Anticipating bloom occurrence and understanding the main drivers of CyanoHABs are needed to optimize water resources management. An extensive review of the application of CyanoHABs forecasting and predictive models was performed, and a summary of the current state of knowledge, limitations and research opportunities on this topic is provided through analysis of case studies. Two modelling approaches were used to achieve CyanoHABs anticipation; process-based (PB) and data-driven (DD) models. The objective of the model was a determining factor for the choice of modelling approach. PB models were more frequently used to predict future scenarios whereas DD models were employed for short-term forecasts. Each modelling approach presented multiple variations that may be applied for more specific, targeted purposes. Most models reviewed were site-specific. The monitoring methodologies, including data frequency, uncertainty and precision, were identified as a major limitation to improve model performance. A lack of standardization of both model output and performance metrics was observed. CyanoHAB modelling is an interdisciplinary topic and communication between disciplines should be improved to facilitate model comparisons. These shortcomings can hinder the adoption of modelling tools by practitioners. We suggest that water managers should focus on generalising models for lakes with similar characteristics and where possible use high frequency monitoring for model development and validation.