A quantitative description of light-limited cyanobacterial growth using flux balance analysis.

The metabolism of phototrophic cyanobacteria is an integral part of global biogeochemical cycles, and the capability of cyanobacteria to assimilate atmospheric CO2 into organic carbon has manifold potential applications for a sustainable biotechnology. To elucidate the properties of cyanobacterial metabolism and growth, computational reconstructions of genome-scale metabolic networks play an increasingly important role. Here, we present an updated reconstruction of the metabolic network of the cyanobacterium Synechocystis sp. PCC 6803 and its quantitative evaluation using flux balance analysis (FBA). To overcome limitations of conventional FBA, and to allow for the integration of experimental analyses, we develop a novel approach to describe light absorption and light utilization within the framework of FBA. Our approach incorporates photoinhibition and a variable quantum yield into the constraint-based description of light-limited phototrophic growth. We show that the resulting model is capable of predicting quantitative properties of cyanobacterial growth, including photosynthetic oxygen evolution and the ATP/NADPH ratio required for growth and cellular maintenance. Our approach retains the computational and conceptual simplicity of FBA and is readily applicable to other phototrophic microorganisms.

Centimetre scale functional dispersal limitation of freshwater copiotrophs.

The freshwater microbiome harbours numerous copiotrophic bacteria that rapidly respond to elevated substrate concentrations. We hypothesized that their high centimetre-scale beta diversity in lake water translates into pronounced metabolic variability, and that a large fraction of microbial 'metabolic potential' originates from point sources such as fragile organic aggregates. Three experiments were conducted in pre-alpine Lake Zurich over the course of a harmful cyanobacterial bloom: Spatially explicit 9 ml 'syringe' samples were collected in situ at centimetre distances along with equally sized 'mixed' samples drawn from pre-homogenized lake water and incubated in BIOLOG EcoPlate substrate arrays. Fewer compounds promoted bacterial growth in the syringe than in the mixed samples, in particular during the pre- and late bloom periods. Community analysis of enrichments on three frequently utilized substrates revealed both pronounced heterogeneity and functional redundancy. Bacterial consortia had higher richness in mixed than in syringe samples and differed in composition. Members of the Enterobacter cloacae complex dominated the EcoPlate assemblages during the mid-bloom period irrespective of treatment or substrate. We conclude that small-scale functional dispersal limitation among free-living copiotrophs in lake water reduces local biotransformation potential, and that lacustrine blooms of harmful cyanobacteria can be environmental reservoirs for metabolically versatile potential pathogens.

The effect of light availability and light wavelength on growth, 2-MIB biosynthesis, and 2-MIB-related gene expression in Pseudanabaena foetida var. intermedia.

2-methylisoborneol (2-MIB) is an odiferous metabolite mainly produced by cyanobacteria, contributing to taste and odor problems in drinking water. The mechanisms involved in 2-MIB biosynthesis in cyanobacteria are not yet completely understood. This study investigated the effect of light availability and wavelength on growth, 2-MIB synthesis, and related gene expression in Pseudanabaena foetida var. intermedia. A significantly lower 2-MIB production was observed in P. foetida var. intermedia during the dark period of a 12-h photoperiod. Exposure to green light resulted in a significant decrease in 2-MIB production compared to white light and red light. The relative expression levels of 2-MIB-related genes in P. foetida var. intermedia were significantly lower during the dark period of a 12-h photoperiod and when cultured under green light. The expression of 2-MIB-related genes in cyanobacteria appears to be light-dependent. This study suggests that the demand for photopigment synthesis under unfavorable light conditions affects the 2-MIB synthesis in cyanobacteria.

Influences of lower pH on phytoplankton growth in alkaline lakes after water transfer: Insights from a coupled hydrodynamic-algal ecological model and experimental analysis.

Alkaline lakes with high pH and unique ecological communities often face water-level drawdown and ecological degradation problems due to climatic and hydrologic factors. Water transfer is becoming a popular method for solving these problems. However, a high pH is often considered the key to maintaining the stability of alkaliphilic algal communities, and a lower pH induced by water transfer from a neutral-pH river may threaten ecosystems in alkaline lakes. To explore the response characteristics of phytoplankton in alkaline lakes to pH changes, we conducted cultivation experiments on one species of dominant Cyanobacteria and one species of dominant Chlorophyta from alkaline lakes under different pH conditions. Subsequently, we constructed a coupled hydrodynamic and algal mathematical model considering the effect of pH to predict the dynamic changes in phytoplankton in a typical alkaline lake under water-transfer conditions. Both species are basophilic, and pH has a "low-inhibition and high-promotion" effect on their growth. A lower pH is detrimental to cyanobacterial growth and competitiveness, which may cause Cyanobacteria to lose their dominance in weakly alkaline environments with a pH < 8.5; additionally, water transfer causes a decrease in the total biomass and proportion of Cyanobacteria in Lake Chenghai, with decreases induced by pH changes accounting for 13.4% and 70.1%, respectively. The decrease in pH is the main reason for the decrease in dominance of Cyanobacteria after water transfer. These results provide a basic summary of the effects of pH changes on the algal growth in alkaline lakes and are a useful for formulating ecological water-transfer strategies for alkaline lakes.

Production of sustainable thermoplastic composites from waste nitrogen fertilizer-grown marine filamentous cyanobacterium Geitlerinema sp.

The global demand for petroleum-derived plastics continues to increase, as does pollution caused by plastic consumption and landfilling plastic waste. Recycling waste plastics by thermomechanical molding may be advantageous, but it alone cannot address the challenges associated with plastic demand and its widespread pollution. A more sustainable and cleaner approach for recycling plastic waste could be to produce thermoplastic composite blends of waste plastic and biobased alternative materials such as marine algal biomass. In this study, Geitlerinema sp., a marine cyanobacterium, was cultivated with waste nitrogen fertilizer as a nitrogen source, resulting in phycocyanin content and biomass density of 6.5% and 0.7 g/L, respectively. The minimum and maximum tensile strengths of thermoplastic blends containing Geitlerinema sp. biomass, recycled glycerol plasticizer, and waste plastic were 0.29-23.2 MPa, respectively. The tensile strength and Young's modulus of thermoplastic composites decreased as the Geitlerinema sp. biomass concentration increased. Furthermore, thermal analysis revealed that thermoplastics containing Geitlerinema sp. biomass have lower thermal onset and biomass degradation temperatures than waste polyethylene. Nevertheless, 35-50% of Geitlerinema sp. biomass could be a sustainable biobased alternative feedstock for producing thermoplastic blends, making the recycling of waste plastics more sustainable and environmentally friendly.

Growth enhancement, metabolic profile improvement, and DXR and TPS2 gene expression changes in Thymus vulgaris L. by cyanobacterial inoculation.

BACKGROUND: In recent decades cyanobacterial species have attracted research attention as potential sources of new biostimulants. In this study, the biostimulant effects of five cyanobacterial suspensions on the growth and essential oil composition of Thymus vulgaris L. were evaluated. The expression of key genes involved in the biosynthesis of thymol and carvacrol, such as DXR and TPS2, were investigated. RESULTS: A pot culture experiment revealed that cyanobacterial application significantly improved T. vulgaris L. growth indices, including plant height, dry and fresh weight, leaf and flower number, leaf area, and photosynthetic pigment content. Total phenol and flavonoid content in inoculated plants also showed a significant increase compared with the control. Anabaena torulosa ISB213 inoculation significantly increased root and shoot biomass by about 65.38% and 92.98% compared with the control, respectively. Nostoc calcicola ISB215 inoculation resulted in the highest amount of essential oil accumulation (18.08 ± 0.62) in T. vulgaris leaves, by about 72.19% compared with the control (10.5 ± 0.50%). Interestingly, the amount of limonene in the Nostoc ellipsosporum ISB217 treatment (1.67%) increased significantly compared with the control and other treatments. The highest expression rates of DXR and TPS2 genes were observed in the treatment of N. ellipsosporum ISB217, with 5.92-fold and 5.22-fold increases over the control, respectively. CONCLUSION: This research revealed the potential of the cyanobacteria that were studied as promising biostimulants to increase the production of biomass and secondary metabolites of T. vulgaris L., which could be a suitable alternative to chemical fertilizers. © 2024 Society of Chemical Industry.

Ecological patterns of phytoplankton across lake cross-section: insights into co-evolution of physicochemical conditions in Chashma Lake on Indus River.

Physicochemical properties of water influence planktonic diversity and distribution, which is essential in obtaining basic knowledge of aquatic biodiversity. Thus current study aims to investigate the spatiotemporal diversity, abundance ratio, and distribution of phytoplankton species and their association with water quality parameters of Chashma Lake, Pakistan. During the study period from 2018 to 2019, we measured 13 physicochemical parameters across three selected sampling sites (S1, S2, and S3) in Chashma Lake, revealing both spatial and temporal variability. Dissolved oxygen (DO) was higher in S3, while S1 exhibited higher alkalinity levels, carbon dioxide, phosphorus, and chloride levels. The study identified 77 phytoplankton species grouped into five taxonomic categories, with Cyanobacteria dominating (39.90%), followed by Chlorophyta (33.4%) and Bacillariophyta (24.88%). Euglenozoa and Ochrophyta were less abundant (1.3% and 0.41%, respectively). Spatial variations in phytoplankton distribution were noted, with Chlorophyta being more abundant at S2, Bacillariophyta and Cyanobacteria at S1, and Euglenozoa dominating at S3. Canonical Correspondence Analysis (CCA) revealed the influence of various physicochemical parameters on phytoplankton distribution. This comprehensive study provides valuable insights for the ecological assessment and monitoring of water bodies. It is recommended that continuous monitoring is required to capture long-term trends, further explore the specific environmental drivers impacting phytoplankton dynamics, and consider management strategies for maintaining water quality and biodiversity in Chashma Lake.

Effect of salinity on scytonemin yield in endolithic cyanobacteria from the Atacama Desert.

Cyanobacteria inhabiting extreme environments constitute a promising source for natural products with biotechnological applications. However, they have not been studied in-depth for this purpose due to the difficulties in their isolation and mass culturing. The Atacama Desert suffers one of the highest solar irradiances that limits the presence of life on its hyperarid core to endolithic microbial communities supported by cyanobacteria as primary producers. Some of these cyanobacteria are known to produce scytonemin, a UV-screening liposoluble pigment with varied biotechnological applications in cosmetics and other industries. In this work we carried out a strain selection based on growth performance among 8 endolithic cyanobacteria of the genera Chroococcidiopsis, Gloeocapsa and Gloeocapsopsis isolated from non-saline rocks of the Atacama Desert. Then we investigated the influence of NaCl exposure on scytonemin production yield. Results in the selected strain (Chroococcidiopsis sp. UAM571) showed that rising concentrations of NaCl lead to a growth decrease while triggering a remarkable increase in the scytonemin content, reaching maximum values at 20 g L-1 of NaCl over 50-fold higher scytonemin contents than those obtained without NaCl. Altogether, these findings point out to cyanobacteria from the Atacama Desert as potentially suitable candidates for pilot-scale cultivation with biotechnological purposes, particularly to obtain scytonemin.

Reservoir regulation-induced variations in water level impacts cyanobacterial bloom by the changing physiochemical conditions.

Gaining insight into the impact of reservoir regulation on algal blooms is essential for comprehending the dynamic changes and response mechanisms in the reservoir ecosystem. In this study, we conducted a comprehensive field investigation linking physiochemical parameters, and phytoplankton community to different water regimes in the Three Gorges Reservoir. Our aim was to explore the effects of reservoir regulation on the extinction of cyanobacterial blooms. The results showed that during the four regulatory events, the water levels decreased by 2.02-4.33 m, and the average water velocity increased 68 % compared to before. The average total phosphorus and total nitrogen concentrations reduced by up to 20 %, and the cyanobacterial biomass correspondingly declined dramatically, between 66.94 % and 75.17 %. As the change of water level decline increasing, there was a significant increase of algal diversity and a notable decrease of algal cell density. Additionally, a shift in the dominant phytoplankton community from Cyanobacteria to Chlorophyceae was observed. Our analysis indicated that water level fluctuations had a pronounced effect on cyanobacterial extinction, with hydrodynamic changes resulting in a reduction of cyanobacterial biomass. This research underlined the potential for employing hydrodynamic management as a viable strategy to mitigate the adverse ecological impacts of cyanobacterial blooms, providing a solution for reservoir's eco-environmental management.

Environmental drivers of phytoplankton community dynamics in an agriculturally-influenced tributary in the lower Great Lakes.

Phytoplankton community composition in tributaries differs from that in their receiving waters, due to light limitation from suspended particles and other factors such as nutrient availability and temperature. This study was designed to manipulate light levels in early, mid, and late summer to determine the combined effects of light attenuation and naturally varying nutrient availability on phytoplankton community composition in an agriculturally-influenced tributary of the lower Great Lakes. In all trials, in situ microcosm experiments show that phytoplankton abundance increased under three light attenuation treatments (60 %, 75 %, and 85 % attenuation) relative to time-zero, but higher light attenuation reduced total phytoplankton abundance relative to controls. Highest phytoplankton diversity in terms of richness and evenness occurred in September (late summer), and across all three trials was lowest under the highest light attenuation treatments (85 %). Phytoplankton community composition followed a normal seasonal shift from diatoms dominating in June (early summer), followed by cyanobacteria dominating in mid to late summer. In general, lower light levels (especially 85 % attenuation) corresponded with an increased dominance of cyanobacteria. These findings support the hypothesis that phytoplankton abundance and diversity vary with light and nutrient availability and that light attenuation promotes the shift from buoyant cyanobacteria to other taxa more tolerant of low light levels.

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.

Recent advances in algal bloom detection and prediction technology using machine learning.

Harmful algal blooms (HAB) including red tides and cyanobacteria are a significant environmental issue that can have harmful effects on aquatic ecosystems and human health. Traditional methods of detecting and managing algal blooms have been limited by their reliance on manual observation and analysis, which can be time-consuming and costly. Recent advances in machine learning (ML) technology have shown promise in improving the accuracy and efficiency of algal bloom detection and prediction. This paper provides an overview of the latest developments in using ML for algal bloom detection and prediction using various water quality parameters and environmental factors. First, we introduced ML for algal bloom prediction using regression and classification models. Then we explored image-based ML for algae detection by utilizing satellite images, surveillance cameras, and microscopic images. This study also highlights several real-world examples of successful implementation of ML for algal bloom detection and prediction. These examples show how ML can enhance the accuracy and efficiency of detecting and predicting algal blooms, contributing to the protection of aquatic ecosystems and human health. The study also outlines recent efforts to enhance the field applicability of ML models and suggests future research directions. A recent interest in explainable artificial intelligence (XAI) was discussed in an effort to understand the most influencing environmental factors on algal blooms. XAI facilitates interpretations of ML model results, thereby enhancing the models' usability for decision-making in field management and improving their overall applicability in real-world settings. We also emphasize the significance of obtaining high-quality, field-representative data to enhance the efficiency of ML applications. The effectiveness of ML models in detecting and predicting algal blooms can be improved through management strategies for data quality, such as pre-treating missing data and integrating diverse datasets into a unified database. Overall, this paper presents a comprehensive review of the latest advancements in managing algal blooms using ML technology and proposes future research directions to enhance the utilization of ML techniques.

Environmental drivers behind the exceptional increase in cyanobacterial blooms in Okavango Delta, Botswana.

The Okavango Delta region in Botswana experienced exceptionally intense landscape-wide cyanobacterial harmful algal blooms (CyanoHABs) in 2020. In this study, the drivers behind CyanoHABs were determined from thirteen independent environmental variables, including vegetation indices, climate and meteorological parameters, and landscape variables. Annual Land Use Land Cover (LULC) maps were created from 2017 to 2020, with ∼89% accuracy to compute landscape variables such as LULC change. Generalized Additive Models (GAM) and Structural Equation Models (SEM) were used to determine the most important drivers behind the CyanoHABs. Normalized Difference Chlorophyll Index (NDCI) and Green Line Height (GLH) algorithms served as proxies for chlorophyll-a (green algae) and phycocyanin (cyanobacteria) concentrations. GAM models showed that seven out of the thirteen variables explained 89.9% of the variance for GLH. The models showcased that climate variables, including monthly precipitation (8.8%) and Palmer Severity Drought Index- PDSI (3.2%), along with landscape variables such as changes in Wetlands area (7.5%), and Normalized Difference Vegetation Index (NDVI) (5.4%) were the determining drivers behind the increased cyanobacterial activity within the Delta. Both PDSI and NDVI showed negative correlations with GLH, indicating that increased drought conditions could have led to large increases in toxic CyanoHAB activity within the region. This study provides new information about environmental drivers which can help monitor and predict regions at risk of future severe CyanoHABs outbreaks in the Okavango Delta, Botswana, and other similar data-scarce and ecologically sensitive areas in Africa. Plain Language Summary: The waters of the Okavango Delta in Northern Botswana experienced an exceptional increase in toxic cyanobacterial activity in recent years. Cyanobacterial blooms have been shown to affect local communities and wildlife in the past. To determine the drivers behind this increased bloom activity, we analyzed the effects of thirteen independent environmental variables using two different statistical models. Within this research, we focused on vegetation indices, meteorological, and landscape variables, as previous studies have shown their effect on cyanobacterial activity in other parts of the world. While driver determination for cyanobacteria has been done before, the environmental conditions most important for cyanobacterial growth can be specific to the geographic setting of a study site. The statistical analysis indicated that the increases in cyanobacterial bloom activity within the region were mainly driven by persistent drier conditions. To our knowledge, this is the first study to determine the driving factors behind cyanobacterial activity in this region of the world. Our findings will help to predict and monitor areas at risk of future severe cyanobacterial blooms in the Okavango Delta and other similar African ecosystems.

Enhancing water quality prediction for fluctuating missing data scenarios: A dynamic Bayesian network-based processing system to monitor cyanobacteria proliferation.

Tackling the impact of missing data in water management is crucial to ensure the reliability of scientific research that informs decision-making processes in public health. The goal of this study is to ascertain the root causes associated with cyanobacteria proliferation under major missing data scenarios. For this purpose, a dynamic missing data management methodology is proposed using Bayesian Machine Learning for accurate surface water quality prediction of a river from Limia basin (Spain). The methodology used entails a sequence of analytical steps, starting with data pre-processing, followed by the selection of a reliable dynamic Bayesian missing value prediction system, leading finally to a supervised analysis of the behavioral patterns exhibited by cyanobacteria. For that, a total of 2,118,844 data points were used, with 205,316 (9.69 %) missing values identified. The machine learning testing showed the iterative structural expectation maximization (SEM) as the best performing algorithm, above the dynamic imputation (DI) and entropy-based dynamic imputation methods (EBDI), enhancing in some cases the accuracy of imputations by approximately 50 % in R2, RMSE, NRMSE, and logarithmic loss values. These findings can impact how data on water quality is being processed and studied, thus, opening the door for more reliable water management strategies that better inform public health decisions.

Machine learning predicts the growth of cyanobacterial genera in river systems and reveals their different environmental responses.

Cyanobacterial blooms are a common and serious problem in global freshwater environments. However, the response mechanisms of various cyanobacterial genera to multiple nutrients and pollutants, as well as the factors driving their competitive dominance, remain unclear or controversial. The relative abundance and cell density of two dominant cyanobacterial genera (i.e., Cyanobium and Microcystis) in river ecosystems along a gradient of anthropogenic disturbance were predicted by random forest with post-interpretability based on physicochemical indices. Results showed that the optimized predictions all reached strong fitting with R2 > 0.75, and conventional water quality indices played a dominant role. One-dimensional and two-dimensional partial dependence plot (PDP) revealed that the responses of Cyanobium and Microcystis to nutrients and temperature were similar, but they showed differences in preferrable nutrient utilization and response to pollutants. Further prediction and PDP for the ratio of Cyanobium and Microcystis unveiled that their distinct responses to PAHs and SPAHs were crucial drivers for their competitive dominance over each other. This study presents a new way for analyzing the response of cyanobacterial genera to multiple environmental factors and their dominance relationships by interpretable machine learning, which is suitable for the identification and interpretation of high-dimensional nonlinear ecosystems with complex interactions.

Succession of particle-attached and free-living bacterial communities in response to microalgal dynamics induced by the biological cyanocide paucibactin A.

Microalgae, including cyanobacteria and eukaryotic algae, are hotspots of primary production and play a critical role in global carbon cycling. However, these species often form blooms that poses a threat to aquatic ecosystems. Although the use of bacteria-derived cyanocides is regarded as an environmentally friendly method for controlling cyanobacterial blooms, only a few studies have examined their potential impact on ecosystems. This study is the first to explore the response of particle-attached (PA) and free-living (FL) bacteria to the dynamics of microalgal communities induced by the biological cyanocide paucibactin A. The microalgal community dynamics were divided into two distinct phases [phase I (days 0-2) and phase II (days 3-7)]. In phase I, paucibactin A caused a sudden decrease in the cyanobacterial concentration. Phase II was characterized by increased growth of eukaryotic microalgae (Scenedesmus, Pediastrum, Selenastrum, and Coelastrum). The stability of the bacterial community and the contribution of stochastic processes to community assembly were more pronounced in phase II than in phase I. The microalgal dynamics triggered by paucibactin A coincided with the succession of the PA and FL bacterial communities. The lysis of cyanobacteria in phase I favored the growth of microbial organic matter degraders in both the PA (e.g., Aeromonas and Rheinheimera) and FL (e.g., Vogesella) bacterial communities. In phase II, Lacibacter, Phycisphaeraceae, and Hydrogenophaga in the PA bacterial community and Lacibacter, Peredibacter, and Prosthecobacter in the FL bacterial community showed increased relative abundances. Overall, the FL bacterial community exhibited greater sensitivity to the two sequential processes compared with the PA bacterial community. These results highlight the need for studies evaluating the impact of biological cyanocides on aquatic ecosystems when used to control natural cyanobacterial blooms.

The use of coagulant and natural soil to control cyanobacterial blooms in a tropical reservoir.

Cyanobacterial blooms have been a growing problem in water bodies and attracted attention from researcher and water companies worldwide. Different treatment methods have been researched and applied either inside water treatment plants or directly into reservoirs. We tested a combination of coagulants, polyaluminium chloride (PAC) and iron(III) chloride (FeCl3), and ballasts, luvisol (LUV) and planosol (PLAN), known as the 'Floc and Sink' technique, to remove positively buoyant cyanobacteria from a tropical reservoir water. Response Surface Methodology (RSM) based on Central Composite Design (CCD) was used to optimize the two reaction variables - coagulant dosage (x1) and ballast dosage (x2) to remove the response variables: chlorophyll-a, turbidity, true color, and organic matter. Results showed that the combination of LUV with PAC effectively reduced the concentration of the response variables, while PLAN was ineffective in removing cyanobacteria when combined to PAC or FeCl3. Furthermore, FeCl3 presented poorer floc formation and lower removal efficiency compared to PAC. This study may contribute to the theoretical and practical knowledge of the algal biomass removal for mitigating eutrophication trough different dosages of coagulants and ballasts.

Discriminating bloom-forming cyanobacteria using lab-based hyperspectral imagery and machine learning: Validation with toxic species under environmental ranges.

Cyanobacteria are major contributors to algal blooms in inland waters, threatening ecosystem function and water uses, especially when toxin-producing strains dominate. Here, we examine 140 hyperspectral (HS) images of five representatives of the widespread, potentially toxin-producing and bloom-forming genera Microcystis, Planktothrix, Aphanizomenon, Chrysosporum and Dolichospermum, to determine the potential of utilizing visible and near-infrared (VIS/NIR) reflectance for their discrimination. Cultures were grown under various light and nutrient conditions to induce a wide range of pigment and spectral variability, mimicking variations potentially found in natural environments. Importantly, we assumed a simplified scenario where all spectral variability was derived from cyanobacteria. Throughout the cyanobacterial life cycle, multiple HS images were acquired along with extractions of chlorophyll a and phycocyanin. Images were calibrated and average spectra from the region of interest were extracted using k-means algorithm. The spectral data were pre-processed with seven methods for subsequent integration into Random Forest models, whose performances were evaluated with different metrics on the training, validation and testing sets. Successful classification rates close to 90 % were achieved using either the first or second derivative along with spectral smoothing, identifying important wavelengths in both the VIS and NIR. Microcystis and Chrysosporum were the genera achieving the highest accuracy (>95 %), followed by Planktothrix (79 %), and finally Dolichospermum and Aphanizomenon (>50 %). The potential of HS imagery to discriminate among toxic cyanobacteria is discussed in the context of advanced monitoring, aiming to enhance remote sensing capabilities and risk predictions for water bodies affected by cyanobacterial harmful algal blooms.

Phytoplankton biomass in northern lakes reveals a complex response to global change.

Global change may introduce fundamental alterations in phytoplankton biomass and community structure that can alter the productivity of northern lakes. In this study, we utilized Swedish and Finnish monitoring data from lakes that are spatially (135 lakes) and temporally (1995-2019, 110 lakes) extensive to assess how phytoplankton biomass (PB) of dominant phytoplankton groups related to changes in water temperature, pH and key nutrients [total phosphorus (TP), total nitrogen (TN), total organic carbon (TOC), iron (Fe)] along spatial (Fennoscandia) and temporal (25 years) gradients. Using a machine learning approach, we found that TP was the most important determinant of total PB and biomass of a specific species of Raphidophyceae - Gonyostomum semen - and Cyanobacteria (both typically with adverse impacts on food-webs and water quality) in spatial analyses, while Fe and pH were second in importance for G. semen and TN and pH were second and third in importance for Cyanobacteria. However, in temporal analyses, decreasing Fe and increasing pH and TOC were associated with a decrease in G. semen and an increase in Cyanobacteria. In addition, in many lakes increasing TOC seemed to have generated browning to an extent that significantly reduced PB. The identified discrepancy between the spatial and temporal results suggests that substitutions of data for space-for-time may not be adequate to characterize long-term effects of global change on phytoplankton. Further, we found that total PB exhibited contrasting temporal trends (increasing in northern- and decreasing in southern Fennoscandia), with the decline in total PB being more pronounced than the increase. Among phytoplankton, G. semen biomass showed the strongest decline, while cyanobacterial biomass showed the strongest increase over 25 years. Our findings suggest that progressing browning and changes in Fe and pH promote significant temporal changes in PB and shifts in phytoplankton community structures in northern lakes.

Cyanobacterial blooms in Lake Taihu: Temporal trends and potential drivers.

Lake Taihu, an inland lake, frequently experiences Cyanobacterial blooms that have historically posed severe threats to its aquatic ecosystem. Combining field observations and satellite remote-sensed data, factors that influence algal bloom intensity in Lake Taihu over an eight-year period, from 2016 to 2023, are examined, and changes in phytoplankton community composition, climate, water quality, economic activities, and food web dynamics are reported. Sentinel-2 MSI data analysis reveals a dramatic decrease in Cyanobacterial blooms in 2023, with a reduction in the annual maximum bloom area of 76.90 % from 2016 values. From 2016 to 2022, the ratio of Cyanobacteria to other phytoplankton ranged 82.09 %-98.29 %, but in 2023, this dropped to 60.98 %. Concurrently, Cyanobacteria density dropped to an historic low of 2.29 × 107 cells/L (16.4 % of 2021 peak values). Redundancy and random forest analyses indicated that nitrogen has a greater influence on phytoplankton than phosphorus, with temperature and permanganate index being the important parameters to affect phytoplankton community structure. We attribute the decrease in Cyanobacterial blooms in Lake Taihu in 2023 to be largely caused by shifts in phytoplankton community structure, particularly the sharp decline in Microcystis sp. density, a genus often linked to bloom formation. Meteorological conditions such as reduced rainfall and wind speed during the winter and spring of 2023 also contributed to diminishing Cyanobacterial blooms. Ongoing improvements in water quality, reduced economic activities because of pandemic restrictions, and implementation of a fishing ban since 2020 have likely further contributed to reductions in bloom frequency. These results improve our understanding of the processes that affect algal blooms in Lake Taihu, and potentially other eutrophic inland lakes.