Cyanobacterial Harmful Algal Blooms Research Articles

Assessment of in-situ monitoring and tracking the vertical migration of cyanobacterial blooms using LISST-HAB

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

Monitoring of toxic cyanobacterial blooms in Lalla Takerkoust reservoir by satellite imagery and microcystin transfer to surrounding farms

Cyanobacterial harmful algal blooms (CyanoHABs) threaten public health and freshwater ecosystems worldwide. In this study, our main goal was to explore the dynamics of cyanobacterial blooms and how microcystins (MCs) move from the Lalla Takerkoust reservoir to the nearby farms. We used Landsat imagery, molecular analysis, collecting and analyzing physicochemical data, and assessing toxins using HPLC. Our investigation identified two cyanobacterial species responsible for the blooms: Microcystis sp. and Synechococcus sp. Our Microcystis strain produced three MC variants (MC-RR, MC-YR, and MC-LR), with MC-RR exhibiting the highest concentrations in dissolved and intracellular toxins. In contrast, our Synechococcus strain did not produce any detectable toxins. To validate our Normalized Difference Vegetation Index (NDVI) results, we utilized limnological data, including algal cell counts, and quantified MCs in freeze-dried Microcystis bloom samples collected from the reservoir. Our study revealed patterns and trends in cyanobacterial proliferation in the reservoir over 30 years and presented a historical map of the area of cyanobacterial infestation using the NDVI method. The study found that MC-LR accumulates near the water surface due to the buoyancy of Microcystis. The maximum concentration of MC-LR in the reservoir water was 160 µgL−1. In contrast, 4 km downstream of the reservoir, the concentration decreased by a factor of 5.39 to 29.63 µgL−1, indicating a decrease in MC-LR concentration with increasing distance from the bloom source. Similarly, the MC-YR concentration decreased by a factor of 2.98 for the same distance. Interestingly, the MC distribution varied with depth, with MC-LR dominating at the water surface and MC-YR at the reservoir outlet at a water depth of 10 m. Our findings highlight the impact of nutrient concentrations, environmental factors, and transfer processes on bloom dynamics and MC distribution. We emphasize the need for effective management strategies to minimize toxin transfer and ensure public health and safety.

Microbial Community Response to Granular Peroxide-Based Algaecide Treatment of a Cyanobacterial Harmful Algal Bloom in Lake Okeechobee, Florida (USA)

Cyanobacterial harmful algal blooms (cyanoHABs) occur in fresh water globally. These can degrade water quality and produce toxins, resulting in ecological and economic damages. Thus, short-term management methods (i.e., algaecides) are necessary to rapidly mitigate the negative impacts of cyanoHABs. In this study, we assess the efficacy of a hydrogen peroxide-based algaecide (PAK® 27) on a Microcystis dominated bloom which occurred within the Pahokee Marina on Lake Okeechobee, Florida, USA. We observed a significant reduction in chlorophyll a (96.81%), phycocyanin (93.17%), and Microcystis cell counts (99.92%), and a substantial reduction in microcystins (86.7%) 48 h after treatment (HAT). Additionally, there was a significant shift in bacterial community structure 48 HAT, which coincided with an increase in the relative abundance of photosynthetic protists. These results indicate that hydrogen peroxide-based algaecides are an effective treatment method for cyanoHAB control and highlight their effects on non-target microorganisms (i.e., bacteria and protists).

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.

The synchronicity of bloom-forming cyanobacteria transcription patterns and hydrogen peroxide dynamics

Hydrogen peroxide is a reactive oxygen species (ROS) naturally occurring at low levels in aquatic environments and production varies widely across different ecosystems. Oxygenic photosynthesis generates hydrogen peroxide as a byproduct, of which some portion can be released to ambient water. However, few studies have examined hydrogen peroxide dynamics in relation to cyanobacterial harmful algal blooms (cHABs). A year-long investigation of algal succession and hydrogen peroxide dynamics was conducted at the Caloosahatchee River, Florida, USA. We aimed to identify potential biological mechanisms responsible for elevated hydrogen peroxide production during cHAB events through the exploration of the freshwater microbial metatranscriptome. Hydrogen peroxide concentrations were elevated from February to September of 2021 when cyanobacteria were active and abundant. We observed one Microcystis cHAB event in spring and one in winter. Both had distinct nutrient uptake and cyanotoxin gene expression patterns. While meaningful levels of microcystin were only detected during periods of elevated hydrogen peroxide, cyanopeptolin was by far the most expressed cyanotoxin during the spring bloom when hydrogen peroxide was at its yearly maxima. Gene expressions of five microbial enzymes (Rubisco, superoxide dismutase, cytochrome b559, pyruvate oxidase, and NADH dehydrogenase) positively correlated to hydrogen peroxide concentrations. Additionally, there was higher nitrogen-fixing gene (nifDKH) expression by filamentous cyanobacteria after the spring bloom but no secondary bloom formation occurred. Overall, elevated environmental hydrogen peroxide concentrations were linked to cyanobacterial dominance and greater expression of specific enzymes in the photosynthesis of cyanobacteria. This implicates cyanobacterial photosynthesis and growth results in increased hydrogen peroxide generation as reflected in measured environmental concentrations.

Potential gap in understanding cyanoHABs: Light-dependent morphological variations in colonial cyanobacterium Microcystis

Colony formation is a crucial characteristic of Microcystis, a cyanobacterium known for causing cyanobacterial harmful algal blooms (cyanoHABs). It has been observed that as Microcystis colonies grow larger, they often become less densely packed, which correlates with a decrease in light penetration. The objective of this study was to investigate the effects of light limitation on the morphological variations in Microcystis, particularly in relation to the crowded cellular environment. The results indicated that when there was sufficient light (transmittance = 100 %) to support a growth rate of 0.11±0.01 day−1, a significant increase in colony size was found, from 466±15 μm to 1030±111 μm. However, under light limitation (transmittance = 50 % – 1 %) where the growth rate was lower than 0, there was no significant improvement in colony size. Microcystis in the light limitation groups exhibited a loose cell arrangement and even the presence of holes or pores within the colony, confirming the negative correlation between colony size and cell arrangement. This pattern is driven by regional differences in growth within the colony, as internal cells have a significantly lower frequency of division compared to peripheral cells, due to intra-colony self-shading (ICSS). The research demonstrates that Microcystis can adjust its cell arrangement to avoid excessive self-shading, which has implications for predicting and controlling cyanoHABs. These findings also contribute to the understanding of cyanobacterial variations and can potentially inform future research on the diverse phycosphere.

Evaluation of Carbonized Corncobs for Removal of Microcystins and Nodularin-R from Water

Microcystins (MCs) and nodularins (NODs) are cyanotoxins that can be found in water bodies during cyanobacterial harmful algal blooms (cyanoHABs). Consumption of water contaminated with cyanotoxins leads to health risks for humans and animals. Herein, corncob-based biochar and activated carbon (AC) were initially investigated for the sorption of six common MC congeners (MC-RR, MC-YR, MC-LR, MC-LA, MC-LW, and MC-LF) and nodularin-R (NOD-R) from spiked water. Biochar was prepared by refluxing commercial corncob with HCl and heating it to 250, 300, or 350 °C. AC was prepared by chemical activation of corncob with H3PO4 at 500 °C under a nitrogen atmosphere. Low-temperature nitrogen adsorption measurements confirmed that H3PO4-AC has a higher specific surface area (≈1100 m2/g) and total pore volume (≈0.75 cm3/g) than biochar and commercial AC. H3PO4-AC showed the maximum efficacy, among all corncob-based sorbents, to remove MCs and NOD-R from water as confirmed by experiments that involved sample analyses by ultrahigh-pressure liquid chromatography-mass spectrometry (UHPLC-MS). The effect of natural organic matter (NOM) on the adsorption of MCs was checked by incubating sorbents with Lake Erie water collected during cyanoHABs from 2020 to 2022. The total concentration (extracellular and intracellular) of studied MC congeners ranged from 1.37 µg/L to 438.51 µg/L and 50 mg of H3PO4-AC completely removed them from 3 mL of lake water. The effect of water pH on cyanotoxin adsorption was studied at pH values of 5.5, 7.0, and 8.5 at both a lower (10 μg/L each) and a higher (50 μg/L each) toxin concentration. Removal was influenced by solution pH at both concentrations when using biochar, while only at higher toxin concentration when using H3PO4-AC. At higher MC and NOD-R concentrations, competitive adsorption was prominent, and overall, the adsorption increased at acidic pH (5.5). The study results suggest that processed corncobs can remove a significant amount of MCs and NOD-R from water, and the measured sorption capacity of H3PO4-AC was ~20 mg of MC-LR and NOD-R per g of this sorbent.

Satellite Remote Sensing: A Tool to Support Harmful Algal Bloom Monitoring and Recreational Health Advisories in a California Reservoir.

Cyanobacterial harmful algal blooms (cyanoHABs) can harm people, animals, and affect consumptive and recreational use of inland waters. Monitoring cyanoHABs is often limited. However, chlorophyll-a (chl-a) is a common water quality metric and has been shown to have a relationship with cyanobacteria. The World Health Organization (WHO) recently updated their previous 1999 cyanoHAB guidance values (GVs) to be more practical by basing the GVs on chl-a concentration rather than cyanobacterial counts. This creates an opportunity for widespread cyanoHAB monitoring based on chl-a proxies, with satellite remote sensing (SRS) being a potentially powerful tool. We used Sentinel-2 (S2) and Sentinel-3 (S3) to map chl-a and cyanobacteria, respectively, classified chl-a values according to WHO GVs, and then compared them to cyanotoxin advisories issued by the California Department of Water Resources (DWR) at San Luis Reservoir, key infrastructure in California's water system. We found reasonably high rates of total agreement between advisories by DWR and SRS, however rates of agreement varied for S2 based on algorithm. Total agreement was 83% for S3, and 52%-79% for S2. False positive and false negative rates for S3 were 12% and 23%, respectively. S2 had 12%-80% false positive rate and 0%-38% false negative rate, depending on algorithm. Using SRS-based chl-a GVs as an early indicator for possible exposure advisories and as a trigger for in situ sampling may be effective to improve public health warnings. Implementing SRS for cyanoHAB monitoring could fill temporal data gaps and provide greater spatial information not available from in situ measurements alone.

Comparative genomic analysis of Microcystis strain diversity using conserved marker genes

Microcystis-dominated cyanobacterial harmful algal blooms (cyanoHABs) have a global impact on freshwater environments, affecting both wildlife and human health. Microcystis diversity and function in field samples and laboratory cultures can be determined by sequencing whole genomes of cultured isolates or natural populations, but these methods remain computationally and financially expensive. Amplicon sequencing of marker genes is a lower cost and higher throughput alternative to characterize strain composition and diversity in mixed samples. However, the selection of appropriate marker gene region(s) and primers requires prior understanding of the relationship between single gene genotype, whole genome content, and phenotype. To identify phylogenetic markers of Microcystis strain diversity, we compared phylogenetic trees built from each of 2,351 individual core genes to an established phylogeny and assessed the ability of these core genes to predict whole genome content and bioactive compound genotypes. We identified single-copy core genes better able to resolve Microcystis phylogenies than previously identified marker genes. We developed primers suitable for current Illumina-based amplicon sequencing with near-complete coverage of available Microcystis genomes and demonstrate that they outperform existing options for assessing Microcystis strain composition. Results showed that genetic markers can be used to infer Microcystis gene content and phenotypes such as potential production of bioactive compounds , although marker performance varies by bioactive compound gene and sequence similarity. Finally, we demonstrate that these markers can be used to characterize the Microcystis strain composition of laboratory or field samples like those collected for surveillance and modeling of Microcystis-dominated cyanobacterial harmful algal blooms.

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.

Controls on in-stream nitrogen loss in western Lake Erie tributaries

Management efforts to reduce cyanobacterial harmful algal blooms (cHABs) in the Great Lakes have focused on decreasing tributary inputs of phosphorus (P). Recent research has indicated that reduction of both P and nitrogen (N) can lessen cHABs severity. Microbially mediated N cycling in streambed sediment may reduce N riverine loads, yet little is known about in-stream N processing rates in the Maumee River Basin, a major source of nutrients to Lake Erie. During summer of 2019 and 2021, we sampled streambed sediment to measure potential nitrification and denitrification rates using the acetylene block method at 78 sites throughout the Maumee River network. We used structural equation models to identify indirect and direct drivers of denitrification. Precipitation was greater in 2019, resulting in a 67 % increase in mean discharge, 41 % of farm fields to be fallow, and a 50 % reduction in fertilizer use. During summer field surveys, median stream-water nitrate concentrations were not different between 2019 and 2021. Median denitrification rates were 13.3 mg N/m2/h and 31.2 mg N/m2/h, respectively, indicating high potential to remove N. Nitrate concentrations and nitrification rates were strong direct drivers of denitrification, especially in 2019 when coupled nitrification–denitrification sustained denitrification. Nitrate concentrations varied with land use. Notably, nitrate concentrations increased with the area of fallow land, which may indicate the presence of a legacy N source. These findings indicate that promoting streambed denitrification could reduce N loads to Lake Erie, but legacy N currently stored in the system may mask N reduction efforts.

Identification of Serine-Containing Microcystins by UHPLC-MS/MS Using Thiol and Sulfoxide Derivatizations and Detection of Novel Neutral Losses.

Microcystins (MCs) are hepatotoxic cyclic heptapeptides produced by cyanobacteria, and their structural diversity has led to the discovery of more than 300 congeners to date. However, with known amino acid combinations, many more MC congeners are theoretically possible, suggesting many remain unidentified. Herein, two novel serine (Ser)-containing MCs were putatively identified in a Lake Erie cyanobacterial harmful algal bloom (cyanoHAB), using high-resolution UHPLC-MS as well as thiol and sulfoxide derivatization procedures. These MCs contain an α,β-unsaturated carbonyl on methyl dehydroalanine (Mdha) residue that undergoes Michael addition to produce a thiol-derivatized MC. Derivatization reactions using various thiolation reagents were followed by MS/MS, and two Python codes were used for data analysis and structural elucidation of MCs. Two novel MCs containing Ser at position 1 (i.e., next to Mdha) were putatively identified as [Ser1]MC-RR and [Ser1]MC-YR. Using thiol- and sulfoxide-modified [Ser1]MCs, identifications were confirmed by the observation of specific neutral losses of the oxidized thiols or sulfoxides in CID-MS/MS spectra in both positive and negative electrospray ionization (ESI) modes. These novel neutral losses are unique for MCs with Mdha and an adjacent Ser residue. Data suggest that a gas-phase reaction occurs between oxygen from adjacent Ser residue and sulfur of the Mdha-bonded thiol or sulfoxide, which leads to the formation and detection of stable cyclic MC ions in MS/MS spectra at m/z values corresponding to the loss of oxidized thiols or oxidized sulfoxides from Ser1-containing MCs.

Aerosolized Cyanobacterial Harmful Algal Bloom Toxins: Microcystin Congeners Quantified in the Atmosphere.

Cyanobacterial harmful algal blooms (cHABs) have the potential to adversely affect public health through the production of toxins such as microcystins, which consist of numerous molecularly distinct congeners. Microcystins have been observed in the atmosphere after emission from freshwater lakes, but little is known about the health effects of inhaling microcystins and the factors contributing to microcystin aerosolization. This study quantified total microcystin concentrations in water and aerosol samples collected around Grand Lake St. Marys (GLSM), Ohio. Microcystin concentrations in water samples collected on the same day ranged from 13 to 23 μg/L, dominated by the d-Asp3-MC-RR congener. In particulate matter <2.5 μm (PM2.5), microcystin concentrations up to 156 pg/m3 were detected; the microcystins were composed primarily of d-Asp3-MC-RR, with additional congeners (d-Asp3-MC-HtyR and d-Asp3-MC-LR) observed in a sample collected prior to a storm event. The PM size fraction containing the highest aerosolized MC concentration ranged from 0.44 to 2.5 μm. Analysis of total bacteria by qPCR targeting 16S rDNA revealed concentrations up to 9.4 × 104 gc/m3 in aerosol samples (≤3 μm), while a marker specific to cyanobacteria was not detected in any aerosol samples. Concentrations of aerosolized microcystins varied even when concentrations in water were relatively constant, demonstrating the importance of meteorological conditions (wind speed and direction) and aerosol generation mechanism(s) (wave breaking, spillway, and aeration systems) when evaluating inhalation exposure to microcystins and subsequent impacts on human health.

Managing a cyanobacteria harmful algae bloom “hotspot” in the Sacramento – San Joaquin Delta, California

Cyanobacterial harmful algal blooms (CHABs) have become a persistent seasonal problem in the upper San Francisco Estuary, California also known as the Sacramento-San Joaquin Delta (Delta). The Delta is comprised of a complex network of open water bodies, channels, and sloughs. The terminus of the Stockton Channel is an area identified as a CHAB “hotspot.” As CHABs increase in severity, there is an urgent need to better understand CHAB drivers to identify and implement mitigation measures that can be used in an estuarine complex like the Delta. We investigated water quality conditions and nutrient dynamics in the Stockton Channel by measuring nutrients in the water column, sediments, and pore waters. In situ nutrient addition bioassay experiments were used to assess the effects of nutrient enrichment on total algal/cyanobacterial growth and pigment concentrations. In both June and September, relative to unamended controls, total chlorophyll and cyanobacterial pigment concentrations were unaffected by nutrient additions; hence, the study area showed signs of classical hypereutrophication, with ambient nitrogen and phosphorus present in excess of algal growth requirements. A cyanobacterial bloom, dominated by Microcystis spp. was present throughout the study area but was most severe and persistent at the shallowest site at the channel terminus. At this site, Microcystis spp. created water quality conditions that allowed for a prolonged bloom from June through September. While targeted nutrient reductions are recommended for long term mitigation, on a shorter timescale, our findings suggest that physical/mechanical controls are the more promising alternative approaches to reduce the severity of CHABs in the terminus of the Stockton Channel.

Toward a Predictive Understanding of Cyanobacterial Harmful Algal Blooms through AI Integration of Physical, Chemical, and Biological Data.

Freshwater cyanobacterial harmful algal blooms (cyanoHABs) are a worldwide problem resulting in substantial economic losses, due to harm to drinking water supplies, commercial fishing, wildlife, property values, recreation, and tourism. Moreover, toxins produced from some cyanoHABs threaten human and animal health. Climate warming can affect the distribution of cyanoHABs, where rising temperatures facilitate more intense blooms and a greater distribution of cyanoHABs in inland freshwater. Nutrient runoff from adjacent watersheds is also a major driver of cyanoHAB formation. While some of the physicochemical factors behind cyanoHAB dynamics are known, there are still major gaps in our understanding of the conditions that trigger and sustain cyanoHABs over time. In this perspective, we suggest that sufficient data sets, as well as machine learning (ML) and artificial intelligence (AI) tools, are available to build a comprehensive model of cyanoHAB dynamics based on integrated environmental/climate, nutrient/water chemistry, and cyanoHAB microbiome and 'omics data to identify key factors contributing to HAB formation, intensity, and toxicity. By taking a holistic approach to the analysis of all available data, including the rapidly growing number of biological data sets, we can provide the foundational knowledge needed to address the increasing threat of cyanoHABs to the security of our water resources.

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

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

Ecological niches of bloom-forming cyanobacteria in brackish Baltic Sea coastal waters

Cyanobacterial blooms occur regularly during summer and fall in various freshwater and brackish environments worldwide causing harm to aquatic flora, fauna, and water quality in natural ecosystems as well as in aquaculture. They also interfere with industrial fisheries, tourism, and human health. Meanwhile, up to date limited knowledge exists about these detrimental phenomena in brackish waters, and patterns of their expansion across the freshwater-to-marine continuum remain unclear. In this study, we analyzed long-term (1972–2016) dynamics of cyanobacterial harmful algal blooms (cyanoHABs) in the brackish southern Baltic Sea coastal waters, refined the cyanoHAB criteria, and for the first time measured ecological niches of the dominant bloom-forming cyanobacteria. The ecological niches of selected cyanoHAB species were determined considering a set of abiotic parameters as niche dimensions: water temperature, salinity, and concentrations of basic nutrients (PO43−, SiO44−, dissolved inorganic nitrogen – DIN, total phosphorus – TP, total nitrogen – TN, and TN/TP ratio), with special emphasis on salinity. We hypothesized that in highly variable brackish environments, the cyanobacterial species with broader ecological niches occur and bloom more often compared to those with narrower niches. For testing this hypothesis, we evaluated the effects of ecological niche width on the occurrence of the dominant cyanoHAB species and frequency of their blooms. The results revealed a pronounced statistically significant (p < 0.05) positive correlation between salinity-niche width and frequency of cyanoHABs. Moreover, the occurrence of cyanoHAB species with wider DIN and PO43− concentration-niches was substantially higher than that of the species with narrower nutrient-concentration niches. The effect of temperature-niche width on the occurrence of cyanoHAB species, on the contrary, was negligible and statistically insignificant. The latter result concurs with the fact that most of these cyanobacteria are highly thermophilic: on average, they bloom at temperatures above 21 °C, and their realized temperature niches in the southern Baltic coastal waters are relatively narrow.

Constructing a Consistent and Continuous Cyanobacteria Bloom Monitoring Product from Multi-Mission Ocean Color Instruments

Satellite-based monitoring of cyanobacterial harmful algal blooms (CyanoHABs) heavily utilizes historical Envisat-MERIS and current Sentinel-OLCI observations due to the availability of the 620 nm and 709 nm bands. The permanent loss of communication with Envisat in April 2012 created an observational gap from 2012 until the operationalization of OLCI in 2016. Although MODIS-Terra has been used to bridge the gap from 2012 to 2015, differences in band architecture and the absence of the 709 nm band have complicated generating a consistent and continuous CyanoHAB monitoring product. Moreover, several Terra bands often saturate during extreme high-concentration CyanoHAB events. This study trained a fully connected deep network (CyanNet) to model MERIS-Cyanobacteria Index (CI)—a key satellite algorithm for detecting and quantifying cyanobacteria. The network was trained with Rayleigh-corrected surface reflectance at 12 Terra bands from 2002–2008, 2010–2012, and 2017–2021 and validated with data from 2009 and 2016 in Lake Okeechobee. Model performance was satisfactory, with a ~17% median difference in Lake Okeechobee annual bloom magnitude. The median difference was ~36% with 10-day Chlorophyll-a time series data, with differences often due to variations in data availability, clouds or glint. Without further regional training, the same network performed well in Lake Apopka, Lake George, and western Lake Erie. Validation success, especially in Lake Erie, shows the generalizability of CyanNet and transferability to other geographic regions.

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.

Historic and recent trends of cyanobacterial harmful algal blooms and environmental conditions in Clear Lake, California: A 70-year perspective

Clear Lake is a large, natural lake in northern California, USA, with many beneficial uses but also substantive environmental issues. The lake has a long history of water quality problems including mercury contamination, pesticide usage, invasive species, and high rates of primary production. In recent years, an increase in cyanobacterial harmful algal blooms (cyanoHABs) has been documented in the lake, adding to the environmental issues faced by aquatic species present in the lake and the local community. Extensive observations of various physical, chemical, and biological parameters in Clear Lake began in the mid-1900s. The most pertinent of these data sets and findings have been reviewed and analyzed with the intent of improving our understanding of the causes and drivers of cyanoHABs, toxin production, and identifying data gaps. Several parameters including average annual water temperature have remained relatively constant over the past 70 years, although the seasonally averaged water temperatures have shifted in a manner that may now favor cyanobacterial dominance. Clear Lake has also witnessed recent changes in several environmental variables such as total phosphorus concentrations that might contribute to blooms. An analysis of lake conditions prior to and following the enactment of a total maximum daily load (TMDL) for phosphorus in 2007 indicates little measurable influence on total phosphorus concentrations in Clear Lake. The present trajectory of lake chemistry suggests that additional research and management efforts will be needed to address the recurrence of cyanoHABs in the future. Future lake management strategies should include consideration of the role of internal nutrient loads to lessen cyanoHABs. Furthermore, a better understanding of cyanobacterial community interactions and top-down effects on bloom formation within the lake can help guide future cyanoHAB management strategies.