Cyanobacterial Blooms Research Articles

Photochemical regulation of microcystin synthesis and release in cyanobacteria Microcystis aeruginosa by triplet state dissolved organic matter.

The increasing frequency of cyanobacterial blooms, particularly those induced by Microcystis aeruginosa (M. aeruginosa), poses severe economic, ecological and health challenges due to the production of microcystins (MCs). Environmental parameters such as light and nutrient availability influence MCs production, while the role of dissolved organic matter (DOM) photochemical processes in regulating these remains unclear. This study investigates the effects of light-induced triplet dissolved organic matter (3DOM⁎), derived from various DOM origins, on the photosynthesis, MC synthesis and release by M. aeruginosa. Using DOM photochemical experiments and spectroscopic analysis for humic acid, fulvic acid and extracellular organic matter, the regulatory mechanisms underlying gene expression related to MCs production (mcyD and mcyH) using real-time quantitative PCR was elucidated. Our findings indicate that 3DOM⁎ induces an oxidative stress in M. aeruginosa, reducing photosynthetic efficiency and modulating gene expression, thereby regulating MCs synthesis and release. Moreover, the optical properties of DOM from various sources exhibited distinct differential impacts on M. aeruginosa, highlighting the complex influence of DOM photochemistry on aquatic ecosystems. This research offers novel insights into the effects of DOM photochemical processes on MCs regulation and proposes strategies for managing cyanobacterial blooms and mitigating MCs contamination, contributing to significantly improved water quality management.

Conformation and binding of 12 Microcystin (MC) congeners to PPP1 using molecular dynamics simulations: A potential approach in support of an improved MC risk assessment.

Microcystins (MCs) occur frequently during cyanobacterial blooms worldwide, representing a group of currently about 300 known MC congeners, which are structurally highly similar. Human exposure to MCs via contaminated water, food or dietary supplements can lead to severe intoxications with ensuing high morbidity and in some cases mortality. Currently, one MC congener (MC-LR) is almost exclusively considered for risk assessment (RA) by the WHO. Many MC congeners co-occur during bloom events, of which MC-LR is not the most toxic. Indeed, MC congeners differ dramatically in their inherent toxicity, consequently raising question about the reliability of the WHO RA and the derived guidance values. Molecular dynamics (MD) simulation can aid in understanding differences in toxicity, as experimental validation for all known MC congeners is not feasible. Therefore, we present MD simulations of a total of twelve MC congeners, of which eight MC congeners were simulated for the first time. We show that depending on their structure and toxicity class, MCs adapt to different backbone conformations. These backbone conformations are specific to certain MC congeners and can change or shift to other conformations upon binding to PPP1, affecting the stability of the binding. Analysis of the interactions with PPP1 demonstrated that there are frequently occurring patterns for individual MC congeners, and that published PPP interactions could be reproduced. In addition, common but also unique patterns were found for individual MC congeners, suggesting differences in binding behaviour. The MD simulations presented here therefore enhance our understanding of MC congener-specific differences and demonstrated that congener-specific investigations are prerequisite for allowing characterisation of yet untested or even unknown MC congeners, thereby allowing for a novel potential approach in support of an improved RA of microcystins in humans.

Bioaerosol Characterization with Vibrational Spectroscopy: Overcoming Fluorescence with Photothermal Infrared (PTIR) Spectroscopy.

Aerosols containing biological material (i.e., bioaerosols) impact public health by transporting toxins, allergens, and diseases and impact the climate by nucleating ice crystals and cloud droplets. Single particle characterization of primary biological aerosol particles (PBAPs) is essential, as individual particle physicochemical properties determine their impacts. Vibrational spectroscopies, such as infrared (IR) or Raman spectroscopy, provide detailed information about the biological components within atmospheric aerosols but these techniques have traditionally been limited due to the diffraction limit of IR radiation (particles >10 μm) and fluorescence of bioaerosol components overwhelming the Raman signal. Herein, we use photothermal infrared spectroscopy (PTIR) to overcome these limitations and characterize individual PBAPs down to 0.18 μm. Both optical-PTIR (O-PTIR) and atomic force microscopy-PTIR (AFM-PTIR) were used to characterize bioaerosol particles generated from a cyanobacterial harmful algal bloom (cHAB) dominated by Planktothrix agardhii. PTIR spectra contained modes consistent with traditional Fourier transform infrared (FTIR) spectra for biological species, including amide I (1630-1700 cm-1) and amide II (1530-1560 cm-1). The fractions of particles containing biological materials were greater in supermicron particles (1.8-3.2 μm) than in submicron particles (0.18-0.32 and 0.56-1.0 μm) for aerosolized cHAB water. These results demonstrate the potential of both O-PTIR and AFM-PTIR for studying a range of bioaerosols with vibrational spectroscopy.

Proliferative and viability effects of two cyanophages on freshwater bloom-forming species Microcystis aeruginosa and Raphidiopsis raciborskii vary between strains

Viruses that infect cyanobacteria are an integral part of aquatic food webs, influencing nutrient cycling and ecosystem health. However, the significance of virus host range, replication efficiency, and host compatibility on cyanobacterial dynamics, growth, and toxicity remains poorly understood. In this study, we examined the effects of cyanophage additions on the dynamics and activity of optimal, sub-optimal, and non-permissive cyanobacterial hosts in cultures of Microcystis aeruginosa and Raphidiopsis raciborskii. Our findings reveal that cross-infectivity can substantially reduce the proliferative success of the cyanophage under conditions of high-density of sub-optimal hosts which suggests phage dispersal limitation as a result of shared infections, in turn impairing their top-down control over the host community. Furthermore, we found that cyanophage addition triggers host strain-specific responses in photosynthetic performance, population size and toxin production, even among non-permissive hosts. These non-lytic effects suggest indirect impacts on co-existing cyanobacteria, increasing the overall complexity and variance in many ecologically relevant cyanobacterial traits. The high variability in responses observed with a limited subset of cyanophage-cyanobacteria combinations not only highlights the intricate role of viral infections in microbial ecosystems but also underscores the significant challenges in predicting the composition, toxicity, and dynamics of cyanobacterial blooms.

Monitoring phycocyanin in global inland waters by remote sensing: Progress and future developments.

Cyanobacterial blooms are increasingly becoming major threats to global inland aquatic ecosystems. Phycocyanin (PC), a pigment unique to cyanobacteria, can provide important reference for the study of cyanobacterial blooms warning. New satellite technology and cloud computing platforms have greatly improved research on PC, with the average number of studies examining it having increased from 5 per year before 2018 to 17 per year thereafter. Many empirical, semi-empirical, semi-analytical, quasi-analytical algorithm (QAA) and machine learning (ML) algorithms have been developed based on unique absorption characteristics of PC at approximately 620 nm. However, most models have been developed for individual lakes or clusters of them in specific regions, and their applicability at greater spatial scales requires evaluation. A review of optical mechanisms, principles and advantages and disadvantages of different model types, performance advantages and disadvantages of mainstream sensors in PC remote sensing inversion, and an evaluation of global lacustrine PC datasets is needed. We examine 230 articles from the Web of Science citation database between 1900 and 2024, summarize 57 of them that deal with construction of PC inversion models, and compile a list of 6526 PC sampling sites worldwide. This review proposed the key to achieving global lacustrine PC remote sensing inversion and spatiotemporal evolution analysis is to fully use existing multi-source remote sensing big data platforms, and a deep combination of ML and optical mechanisms, to classify the object lakes in advance based on lake optical characteristics, eutrophication level, water depth, climate type, altitude, population density within the watershed. Additionally, integrating data from multi-source satellite sensors, ground-based observations, and unmanned aerial vehicles, will enable future development of global lacustrine PC remote estimation, and contribute to achieving United Nations Sustainable Development Goals inland water goals.

Direct Evidence of Microbial Sunscreen Production by Scum-Forming Cyanobacteria in the Baltic Sea.

Mycosporine-like amino acids are water-soluble secondary metabolites that protect photosynthetic microorganisms from ultraviolet radiation. Here, we present direct evidence for the production of these compounds in surface scums of cyanobacteria along the Baltic Sea coast. We collected 59 environmental samples from the southern coast of Finland during the summers of 2021 and 2022 and analysed them using high-resolution liquid chromatography-mass spectrometry. Our results revealed the presence of microbial sunscreens in nearly all surface scum samples. Mycosporine-like amino acids are synthesised through the coordinated action of four biosynthetic enzymes encoded in a compact biosynthetic pathway. Bioinformatics analysis of the mysB biosynthetic gene from a surface scum indicated that the cyanobacteria responsible for production belonged to the Anabaena/Dolichospermum/Aphanizomenon species complex. We mapped the distribution of biosynthetic enzymes onto a phylogenomic tree, utilising 120 bacterial single-copy conserved genes from 101 draft or complete genomes within the species complex. This analysis showed that 48% of identified species possess the ability to produce these compounds, with biosynthetic pathways being most common in Dolichospermum and Aphanizomenon strains. We detected the production of porphyra-334 and shinorine, two widely reported family members, in Dolichospermum strains isolated from the Gulf of Finland's surface layer. The estimated content of porphyra-334 in Dolichospermum sp. UHCC 0684 was 7.4 mg per gram dry weight. Our results suggest that bloom-forming cyanobacteria could be a potential source of these compounds for cosmetic and biotechnological applications and may play a significant role in cyanobacterial bloom formation.

Chronic exposure to low-dose MC-LR induces ileal inflammation in mice through the PI3K/AKT/mTOR pathway

ABSTRACT The global phenomenon of cyanobacterial bloom pollution is spreading globally due to climate change and eutrophication. It is well established that harmful cyanobacteria produce a wide range of toxins including microcystin-LR (MC-LR), a cyclic heptapeptide toxin known to damage various organs. The intestinal tract is the main site of MC-LR absorption and one of the targets susceptible to toxicity. Currently, studies on the enterotoxic effects of MC-LR predominantly focused on the colorectum, with limited investigations addressing the impact of microcystins on the small intestine. Therefore, the aim of our study was to examine the impact of chronic 9-month exposure of mice to low-dose 120 μg/L MC-LR in drinking water on ileal inflammation and potential mechanisms underlying these effects. Our findings showed that in mice chronically administered with low-dose MC-LR disorganized intestinal epithelial cells, lymphocytic infiltration and disturbed crypt arrangement were detected. The results of qPCR and Western blot demonstrated that, in comparison to control, the mRNA expression levels of pro-inflammatory factors IL-6, IL-17, IL-18, and IFN-γ were markedly elevated in the ileal tissue of mice treated with MC-LR, associated with significant increases in protein expression levels of p-PI3K, p-AKT, and p-mTOR. Taken together, evidence indicates that MC-LR induces ileal inflammation and histopathological damage involved activation of the PI3K/AKT/mTOR signaling pathway.

Driving Factors and Variability of Cyanobacterial Blooms in Qionghai Lake, Yunnan–Guizhou Plateau, China

Driving Factors and Variability of Cyanobacterial Blooms in Qionghai Lake, Yunnan–Guizhou Plateau, China

Record-setting cyanobacterial bloom in the largest freshwater lake in northern China caused by joint effects of hydrological variations and nutrient enrichment.

Cyanobacterial blooms represent a significant environmental issue posing widespread threats to global aquatic ecological health. Climate and nutrient enrichment were the most studied factors modulating cyanobacterial blooms in eutrophic lakes. However, in many floodplain lakes, the importance of hydrological variation in driving and predicting cyanobacterial blooms is often overlooked and largely underestimated, which has hampered the effectiveness of lake management. Here, we use a process-based lake ecosystem model (GOTM-WET) to evaluate the potential drivers of the record-setting cyanobacterial bloom during summer 2022 (>70% of lake area) in the largest shallow lake in Northern China (Hulun Lake). The model was calibrated based on a comprehensive field dataset including both remote sensing (surface water temperature) and in-lake observations (water quality). We performed a scenario analysis with various combinations of nutrient loading, hydrological variations and climate change. Our modeling results unravel that the profound water level rise through 2021 to 2022 serves as the main trigger of the severe cyanobacterial bloom in summer 2022, whereas climate factors demonstrate marginal impacts. Our model predicted a decrease of 60.5% in the annual average of cyanobacterial biomass when water level remained stable. In addition, both water level change and nutrient concentration explains 72.5% of the variance in long-term maximum area of cyanobacterial blooms. Our model further reveals that the water level rise drives the cyanobacterial bloom via bringring in excessive nutrient to lake water column from the lake basin. Thus, our results suggest that continuous increase in water level across two years could serve as an early warning signal to cyanobacterial blooms in the consecutive summer. Our findings may be applicable to similar temperate shallow lakes in floodplain areas, especially for those located in agricultural and pasture regions with abundant nutrient legacy, thus may provide a new indicator for cyanobacterial blooms prediction.

Metagenomics reveals spatial variation in cyanobacterial composition, function, and biosynthetic potential in the Winam Gulf, Lake Victoria, Kenya.

The Winam Gulf in the Kenyan region of Lake Victoria experiences prolific, year-round cyanobacterial harmful algal blooms (cyanoHABs) which pose threats to human, livestock, and ecosystem health. To our knowledge, there is limited molecular research on the gulf's cyanoHABs, and thus, the strategies employed for survival and proliferation by toxigenic cyanobacteria in this region remain largely unexplored. Here, we used metagenomics to analyze the Winam Gulf's cyanobacterial composition, function, and biosynthetic potential. Dolichospermum was the dominant bloom-forming cyanobacterium, co-occurring with Microcystis at most sites. Microcystis and Planktothrix were more abundant in shallow and turbid sites. Metagenome-assembled genomes (MAGs) of Dolichospermum harbored nitrogen fixation genes, suggesting diazotrophy as a potential mechanism supporting the proliferation of Dolichospermum in the nitrogen-limited gulf. Over 300 biosynthetic gene clusters (BGCs) putatively encoding the synthesis of toxins and other secondary metabolites were identified across the gulf, even at sites where there were no visible cyanoHAB events. Almost all BGCs identified had no known synthesis product, indicating a diverse and novel biosynthetic repertoire capable of synthesizing harmful or potentially therapeutic metabolites. Microcystis MAGs contained mcy genes encoding the synthesis of hepatotoxic microcystins which are a concern for drinking water safety. These findings illustrate the spatial variation of bloom-forming cyanobacteria in the Winam Gulf and their available strategies to dominate different ecological niches. This study underscores the need for further use of genomic techniques to elucidate the dynamics and mitigate the potentially harmful effects of cyanoHABs and their associated toxins on human, environmental, and economic health.

Spatiotemporal Variation Assessment and Improved Prediction Of Cyanobacteria Blooms in Lakes Using Improved Machine Learning Model Based on Multivariate Data.

Cyanobacterial blooms in shallow lakes pose a significant threat to aquatic ecosystems and public health worldwide, highlighting the urgent need for advanced predictive methodologies. As impounded lakes along the Eastern Route of the South-to-North Water Diversion Project, Lakes Hongze and Luoma play a key role in water resource management, making the prediction of cyanobacterial blooms in these lakes particularly important. To address this, satellite remote sensing data were utilized to analyze the spatiotemporal dynamics of cyanobacterial blooms in these lakes. Subsequently, a precise machine learning model, integrating the Projection Pursuit Model and Random Forest (PP-RF) algorithms, was developed to predict the extent of cyanobacterial blooms, considering a range of influencing factors, including physical, chemical, climatic, and hydrologic variables. The findings indicated pronounced seasonal fluctuations in cyanobacterial blooms, with higher levels in summer than in other seasons. Key determinants for cyanobacterial blooms prediction included solar radiation, temperature and total nitrogen for Lake Hongze, while for Lake Luoma, significant predictors were identified as temperature, water temperature, and solar radiation. Compared with traditional data preprocessing methods, PP-RF model has advantages in addressing multicollinearity. This study provides a feasible method for predicting cyanobacterial blooms in impounded lakes within inter-basin water transfer projects. By inputting region-specific data, this model could be applied broadly, contributing to against the adverse effects of cyanobacterial blooms and provide scientific guidance for the protection and management of aquatic ecosystems.

Biotic factors shape the structure and dynamics of denitrifying communities within cyanobacterial aggregates.

Eutrophication caused by human activities has severely impacted freshwater ecosystems, leading to harmful cyanobacterial blooms that threaten water quality and ecosystem stability. During blooms, denitrification is a key process for nitrogen removal, which can occur both in the sediment and in the waterbody mediated by cyanobacterial aggregate (CA)-associated microorganisms. In this study, the structure, dynamics and assembly mechanisms of CA-associated nirK-, nirS-, and nosZ-encoding denitrifying communities were investigated in the eutrophic Lake Taihu across the bloom season. External environmental factors showed limited influence on denitrifying community structure, which were more strongly shaped by biotical factors. Network analysis revealed both monofunctional and multifunctional modules, with a small subset of linker OTUs playing a critical role in maintaining these multifunctional modules by mediating interactions among different functional communities. Biotic regulation was further demonstrated by coupling patterns among denitrifiers associated with specific microbial lineages, as well as niche partitioning driven by cyanobacterial composition. While the assembly of the total phycospheric communities was governed by stochastic processes, the denitrifying communities were predominantly shaped by homogeneous selection. These findings indicate biological processes, particularly synergistic relationships and autotroph-heterotroph interactions, were key drivers of the structure and dynamics of denitrifying communities within CAs. Moreover, the key taxa identified in this study may provide potential targets for regulating nitrogen cycling in the management of cyanobacterial blooms.

Modelling harmful algal blooms in a mono- and a polydominant eutrophic lake under temperature and nutrient changes.

Cyanobacterial blooms, driven by nutrient loading and temperature, pose significant ecological and economic challenges. This study employs a combined data-driven and trait-based modelling approach to predict changes in cyanobacterial communities in a mono- and a polydominant shallow temperate lakes under varying temperature and nutrient scenarios. Results of the AQUATOX simulation model for two aquatic systems suggest that a 2 °C temperature increase, consistent with Intergovernmental Panel on Climate Change's predictions, may influence cyanobacteria species composition and dominance, with trends indicating a possible shift favouring Nostocales over Oscillatoriales and Chroococcales. Temperature increases by 4 °C clearly promoted the dominance of Nostocales. Nutrient dynamics appear to influence community structure. In a nutrient-rich monodominant lake, temperature was the primary driver, while in a nutrient-limited polydominant lake, phosphorus availability influenced cyanobacteria species dominance. Combined warming and phosphorus alterations significantly affected cyanobacteria bloom intensity and duration, particularly enhancing Nostocales growth. The study highlights the complexity of cyanobacterial responses to climate change, emphasizing the need for more analysis and comprehensive models to predict harmful algal blooms (HABs) in freshwater ecosystems. While the findings suggest that temperature and nutrient availability may be critical drivers of cyanobacterial dominance, additional research across a broader range of systems is necessary.

Unexpected cyanobacterial communities in highly heterogeneous toxic blooms from a Mediterranean protected area.

The negative effects associated with cyanobacterial blooms are of particular concern in protected ecosystems, as these areas are ecologically significant and attract a high number of visitors. This study aims to explore the cyanobacterial communities and associated toxicity in three reservoirs located within a Mediterranean National Park with a compromised situation at basin-level. Our results demonstrate the occurrence of dense toxic blooms containing microcystins (reaching values close to 280 μg L-1) and low levels of anatoxin-a and saxitoxins (up to 0.02 μg L-1). Comprehensive metabarcoding analyses based on cyanobacterial 16S rRNA and cyanotoxin-biosynthesis genes (mcyE, anaF and sxtA) unveiled three highly heterogeneous communities, despite the spatial proximity of reservoirs. Additionally, our results suggested the influence of water conductivity on the blooms composition. Among the diverse bloom-forming taxa found, Microcystis sp. and Planktothrix sp. were revealed as the microcystins-producer candidates, and Cuspidothrix issatschenkoi and Dolichospermum/Aphanizomenom sp. as the potential producers of anatoxin-a and saxitoxins, respectively. A polyphasic characterization confirmed the first report of the tropical-related species Planktothrix spiroides in Europe, showing elevated level of dominance. As a whole, we present the scenario of an ecologically important protected area facing significant challenges in the proper management of cyanobacterial blooms.

Inhibition mechanism of Microcystis aeruginosa in coculture of Lemna and Azolla: Insights from non-targeted Metabonomics.

Microcystis aeruginosa, a harmful alga in cyanobacterial blooms, damages aquatic ecosystems. Species diversity may control the blooms by increasing ecosystem stability and resource utilization. The growth and photosynthetic systems of M. aeruginosa were investigated using the water from monocultures of Lemna aequinoctialis and Azolla imbricata group, as well as their mixtures. The highest rate of inhibition (84%) of M. aeruginosa was observed in the water excretions from the mixture of the two species across the three experimental groups. Greater disruption of cell membranes and a more significant decrease in the maximum electron transfer rate and photochemical quantum yield of M. aeruginosa were observed under mixed conditions compared to the monoculture, indicating the increased disruption of their photosynthetic systems in the mixed group. Liquid chromatography-mass spectrometry identified 479 and 431 differential metabolites in the mixed group compared to monocultures of L. aequinoctialis group and A. imbricata, respectively. Dihydrocapsaicin and 13-hydroxy-9-methoxy-10-oxo-11-octadecenoic acid, previously known to participate in oxidative stress and induce the secretion of benzoic acid to disrupt the cell membrane, were found to be abundant in the mixed group compared to the monoculture groups of L. aequinoctialis and A. imbricata. Our results showed that a mixture of L. aequinoctialis and A. imbricata is a potential novel antialgal agent to inhibit harmful algae.

Intra-colony light regulates morphology diversity of colonial cyanobacteria

Morphological diversity widely exists in colonial cyanobacteria, which poses a challenge to monitor and predict population dynamics. Light-induced adaptation plays a key role in the underlying mechanisms. Here, to demonstrate how morphological parameters adapt to physiological features (such as pigments and gas vesicles) under light limitation, a model of intra-colony light regimes (ICLR) was developed based on the colonies of Microcystis Kützing. that caused cyanobacterial harmful blooms worldwide. Cell pigments and gas vesicles positively regulated cellular light attenuation coefficient (D(λ)cell), and both accounted for the D(λ)cell variation of 68.2 % and 30.1 %, respectively. According to the ICLR model, D(λ)cell negatively correlated to colony thickness, and a positive relationship existed between colony thickness and cell-to-cell distance (an indicator of colony compactness). Therefore, increasing cell-to-cell distance is an effective strategy when the D(λ)cell is high or colonies are light-limiting, facilitating the formation of loose, large colonies. This pattern was also observed in Microcystis populations in eutrophic Lake Taihu (China) and cyanobacterial Nostoc sphaeroides Kützing, showing cell-to-cell distance positively related to colony thickness or colony depth. This suggests that the predicted morphological adaptation patterns based on the ICLR model are general in colony-forming cyanobacteria. These findings provide new insights into the morphology diversity of colonial cyanobacteria toward changing environments.

Cyanobacteria hot spot detection integrating remote sensing data with convolutional and Kolmogorov-Arnold networks.

Prompt and accurate monitoring of cyanobacterial blooms is essential for public health management and understanding aquatic ecosystem dynamics. Remote sensing, in particular satellite observations, presents a good alternative for continuous monitoring. This study employs multispectral images from the Sentinel-2 constellation alongside ERA5-Land to enable broad-scale data acquisition. A simple deep convolutional neural network (CNN) architecture was proposed to analyze cyanobacteria (CB) concentration dynamics in Pigeon Lake, Canada, over five years. The model achieved an R2 value of 0.81 and an RMSE score of 0.03 for the training set and 0.15 for the testing set, demonstrating high predictive accuracy. Using the Local Getis-Ord statistic, we identified and analyzed trends in hot and cold spots under the null hypothesis that such spots are randomly distributed, observing changes in their distribution and the median CB concentration in hot spots over time. Additionally, a Kolmogorov-Arnold Network (KAN) and dense neural networks (NN) with a single hidden layer were trained to classify sections of the lake shoreline into hot and no hot spots using the Dynamic World dataset within a 500m radius of the lake. The KAN achieved a recall metric of 0.83 for detecting hot spots.

Direct detection of phycocyanin in sediments by hyperspectral imaging

Cyanobacteria are ubiquitous aquatic organisms with a remarkable evolutionary history reaching as far as 1.9 Ga. They play a vital role in ecosystems yet also raise concerns due to their association with harmful algal blooms. Understanding the historical patterns and drivers behind these blooms is crucial for effective ecosystem management. Lake-sediment cores are valuable natural environmental archives, recording the histories of such blooms. Among others, phycocyanin, a pigment specific to cyanobacteria, emerges as a promising biomarker for reconstructing past cyanobacterial bloom events. However, due to the physicochemical properties of phycocyanin, there is no validated method available to extract and measure this pigment from complex sediment matrix. This study explores the applicability of hyperspectral imaging (HSI), a non-destructive technique, as a novel approach for high resolution in-situ detection and quantification of phycocyanin in lake sediments. Our experiments show that phycocyanin can be detected by HSI with an absorption trough at 620 nm (relative absorption band depth, RABD620). We established a semi-quantitative calibration of the spectral index RABD620 by conducting spiking experiments with phycocyanin standard (known phycocyanin mass) on organic-rich and mineral-rich sediments of varying water contents. We also assessed potential interference from chlorophyll a, another photosynthetic pigment, ensuring the reliability of hyperspectral phycocyanin measurements. Our findings demonstrate a significant correlation (R2 ranging from 0.37 to 0.997) between the RABD620 index and associated phycocyanin amounts in organic-rich and minerogenic sediments. This indicates the potential of the spectral index to directly measure in-situ biomarker concentrations on split sediment cores. Although confounding factors such as water and chlorophyll a content can influence the spectral signal, this method offers a rapid and non-destructive approach for studying historical cyanobacterial blooms in sedimentary records. This opens promising grounds for various applications, including ecosystem-health assessment and environmental change monitoring.

ANALYSIS OF THE IMPACT OF GLOBAL CLIMATE CHANGE TRENDS ON THE BLOOMING OF THE DNIPRO RIVER IN THE DNIPRO-DONBAS CANAL AREA

The hydrodynamic properties of a reservoir can affect the potential for cyanobacterial blooming. Slow-flowing or stagnant waters are more conducive to blooming stability. Therefore, water with a faster flow, more intensive mixing, or higher rotation speed is less likely to develop cyanobacterial blooming. The Kremenchutske and Kamianske reservoirs have an essential impact on the blooming of the Dnieper River in the Dnieper-Donbas Canal area. Their temperature regime in the warm season favors the development of zooplankton and phytoplankton. Aquatic vegetation is most common in shallow water. Water blooming is observed in summer, and this process covers up to 70% of the area of ​​reservoirs, especially in the southern part and bays, deteriorating water quality. Higher frequency and intensity of precipitation, accompanied by longer periods of drought, can create contribute greater nutrient mobility. Longer periods of high temperatures also contribute to this process, at that there is no mixing of water layers. Cyanobacteria can quickly utilize nutrients that enter water bodies due to rainfall. Strong winds can also affect the cyanobacteria population, pushing cyanobacterial cells and colonies towards the banks, where they accumulate.These reservoirs are located in the temperate continental climate zone and belong to water bodies that warm up well. That is due to their width, which makes wind mixing possible in the middle and lower parts of the reservoirs, as a result of which the temperature is distributed evenly and horizontally. To confirm and supplement the results of field studies, statistical processing of water quality indicators of the Dnipro River in the area of ​​the Dnipro-Donbas Canal was carried out. Trends were determined by regression analysis of time series of water quality indicators. The distribution was checked for normality using the Kolmogorov-Smirnov and Shapiro-Wilk tests. Correlation analysis was performed using the Pearson parametric method and the Spearman nonparametric method. The fluctation period was determined using the spectral Fourier transform method. The comprehensive analysis made it possible to establish the factors that are the main cause of water blooming in the studied area, which makes it possible to control the necessary water treatment processes.

Metabarcoding of Bacterial Communities Impacting the Dynamics of Cyanobacterial Blooms in Freshwater Reservoirs, South India

Metabarcoding of Bacterial Communities Impacting the Dynamics of Cyanobacterial Blooms in Freshwater Reservoirs, South India