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 µg L-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.

The recent disappearance of a persistent Planktothrix bloom: Characterization of a regime shift in the phytoplankton of Sandusky Bay (USA).

Sandusky Bay is the drowned mouth of the Sandusky River in the southwestern portion of Lake Erie. The bay is a popular recreation location and a regional source for drinking water. Like the western basin of Lake Erie, Sandusky Bay is known for being host to summer cyanobacterial harmful algal blooms (cHABs) year after year, fueled by runoff from the predominantly agricultural watershed and internal loading of legacy nutrients (primarily phosphorus). Since at least 2003, Sandusky Bay has harbored a microcystin-producing bloom of Planktothrix agardhii, a species of filamentous cyanobacteria that thrives in low light conditions. Long-term sampling (2003-2018) of Sandusky Bay revealed regular Planktothrix-dominated blooms during the summer months, but in recent years (2019-2022), 16S rRNA gene community profiling revealed that Planktothrix has largely disappeared. From 2017-2022, microcystin decreased well below the World Health Organization (WHO) guidelines. Spring TN:TP ratios increased in years following dam removal, yet there were no statistically significant shifts in other physicochemical variables, such as water temperature and water clarity. With the exception of the high bloom of Planktothrix in 2018, there was no statistical difference in chlorophyll during all other years. Concurrent with the disappearance of Planktothrix, Cyanobium spp. have become the dominant cyanobacterial group. The appearance of other potential toxigenic genera (i.e., Aphanizomenon, Dolichospermum, Cylindrospermopsis) may motivate monitoring of new toxins of concern in Sandusky Bay. Here, we document the regime shift in the cyanobacterial community and propose evidence supporting the hypothesis that the decline in the Planktothrix bloom was linked to the removal of an upstream dam on the Sandusky River.

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.

A novel integrated approach employing Desertifilum tharense BERC-3 for efficient wastewater valorization and recycling for developing peri-urban algae farming system.

Peri-urban environments are significant reservoirs of wastewater, and releasing this untreated wastewater from these resources poses severe environmental and ecological threats. Wastewater mitigation through sustainable approaches is an emerging area of interest. Algae offers a promising strategy for carbon-neutral valorization and recycling of urban wastewater. Aiming to provide a proof-of-concept for complete valorization and recycling of urban wastewater in a peri-urban environment in a closed loop system, a newly isolated biocrust-forming cyanobacterium Desertifilum tharense BERC-3 was evaluated. Here, the highest growth and lipids productivity were achieved in urban wastewater compared to BG11 and synthetic wastewater. D. tharense BERC-3 showed 60-95% resource recovery efficiency and decreased total dissolved solids, chemical oxygen demand, biological oxygen demand, nitrate nitrogen, ammonia nitrogen and total phosphorus contents of the water by 60.37%, 81.11%, 82.75%, 87.91%, 85.13%, 85.41%, 95.87%, respectively, making it fit for agriculture as per WHO's safety limits. Soil supplementation with 2% wastewater-cultivated algae as a soil amender, along with its irrigation with post-treated wastewater, improved the nitrogen content and microbial activity of the soil by 0.3-2.0-fold and 0.5-fold, respectively. Besides, the availability of phosphorus was also improved by 1.66-fold. The complete bioprocessing pipeline offered a complete biomass utilization. This study demonstrated the first proof-of-concept of integrating resource recovery and resource recycling using cyanobacteria to develop a peri-urban algae farming system. This can lead to establishing wastewater-driven algae cultivation systems as novel enterprises for rural migrants moving to urban areas.

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.

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.

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.

Characterization of Taxonomic and Functional Dynamics Associated with Harmful Algal Bloom Formation in Recreational Water Ecosystems.

Harmful algal bloom (HAB) formation leads to the eutrophication of water ecosystems and may render recreational lakes unsuitable for human use. We evaluated the applicability and comparison of metabarcoding, metagenomics, qPCR, and ELISA-based methods for cyanobacteria/cyanotoxin detection in bloom and non-bloom sites for the Great Lakes region. DNA sequencing-based methods robustly identified differences between bloom and non-bloom samples (e.g., the relative prominence of Anabaena and Planktothrix). Shotgun sequencing strategies also identified the enrichment of metabolic genes typical of cyanobacteria in bloom samples, though toxin genes were not detected, suggesting deeper sequencing or PCR methods may be needed to detect low-abundance toxin genes. PCR and ELISA indicated microcystin levels and microcystin gene copies were significantly more abundant in bloom sites. However, not all bloom samples were positive for microcystin, possibly due to bloom development by non-toxin-producing species. Additionally, microcystin levels were significantly correlated (positively) with microcystin gene copy number but not with total cyanobacterial 16S gene copies. In summary, next-generation sequencing-based methods can identify specific taxonomic and functional targets, which can be used for absolute quantification methods (qPCR and ELISA) to augment conventional water monitoring strategies.

Kinetics and pathways of sub-lithic microbial community (hypolithon) development.

Type I hypolithons are microbial communities dominated by Cyanobacteria. They adhere to the underside of semi-translucent rocks in desert pavements, providing them with a refuge from the harsh abiotic stresses found on the desert soil surface. Despite their crucial role in soil nutrient cycling, our understanding of their growth rates and community development pathways remains limited. This study aimed to quantify the dynamics of hypolithon formation in the pavements of the Namib Desert. We established replicate arrays of sterile rock tiles with varying light transmission in two areas of the Namib Desert, each with different annual precipitation regimes. These were sampled annually over 7 years, and the samples were analysed using eDNA extraction and 16S rRNA gene amplicon sequencing. Our findings revealed that in the zone with higher precipitation, hypolithon formation became evident in semi-translucent rocks 3 years after the arrays were set up. This coincided with a Cyanobacterial 'bloom' in the adherent microbial community in the third year. In contrast, no visible hypolithon formation was observed at the array set up in the hyper-arid zone. This study provides the first quantitative evidence of the kinetics of hypolithon development in hot desert environments, suggesting that development rates are strongly influenced by precipitation regimes.

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.

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.

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.

Contaminant removal performance and lipid productivity of a cyanobacteria-bacteria consortium containing exogenous phytohormones during the treatment of antibiotic-polluted wastewater.

In this study, a cyanobacteria-bacteria consortium containing native wastewater bacteria and immobilized Synechococcus sp. was constructed. The cyanobacterial cellular responses (including growth, biomass and lipid productivity) and contaminant removal ability (for TN, TP, COD and antibiotics) in the consortium were evaluated during the advanced treatment of wastewater containing 10-50 µg/L of mixed antibiotics (amoxicillin, tetracycline, erythromycin, sulfadiazine and ciprofloxacin) with the addition of a certain phytohormone (indole-3-acetic acid, gibberellin A3 or 6-benzylaminopurine) at trace level within a period of four days. Each phytohormone promoted the growth of Synechococcus sp. and increased the tolerance of Synechococcus sp. to mixed antibiotics. Indole-3-acetic acid coupled to moderate antibiotic stress could elevate lipid productivity and lipid content of Synechococcus sp. to 33.50 mg/L/day and 43.75%, respectively. Phytohormones increased the pollutant removal performance of the cyanobacteria-bacteria consortium through the stimulation of cyanobacterial growth and the regulation of cyanobacteria-bacteria interaction, which increased the abundances of microalgae-associated bacteria including Flavobacterium, Allorhizobium-Neorhizobium-Pararhizobium-Rhizobium, Bosea, Sphingomonas and Emticicia. Up to 80.83%, 98.06%, 83.26%, 99.84%, 99.50%, 89.41%, 65.61% and 60.65% of TN, TP, COD, amoxicillin, tetracycline, erythromycin, sulfadiazine and ciprofloxacin were removed by the consortium with the addition of phytohormones. In general, indole-3-acetic acid was the optimal phytohormone for enhancing lipid production and contaminant removal performance of the cyanobacteria-bacteria consortium.

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.

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.

Characteristics of spatial and temporal variability in the distribution of biological soil crusts on the Loess Plateau, China.

Biological soil crust (biocrust) is widely distributed on the Loess Plateau and plays multiple roles in regulating ecosystem stability and multifunctionality. Few reports are available on the distribution characteristics of biocrust in this region, which limits the assessment of its ecological functions. Based on 388 sampling points in different precipitation zones on the Loess Plateau from 2009 to 2020, we analyzed the coverage, composition, and influencing factors of biocrust across different durations since land abandonment, precipitation levels, topography (slope aspect and position), and utilization of abandoned slopelands (shrubland, forest, and grassland). On this base, with the assistance of machine learning and spatial modeling methods, we generated a distribution map of biocrust and its composition at a resolution of 250 m × 250 m, and analyzed the spatial distribution of biocrust on the Loess Plateau. The results showed that the average biocrust coverage in the woodlands and grasslands was 47.3%, of which cyanobacterial crust accounted for 25.5%, moss crust 19.7%, and lichen crust 2.1%. There were significant temporal and spatial variations. Temporally, the coverage of biocrust in specific regions fluctuated with the extension of the abandoned durations and coverage of cyanobacterial crust, while moss crust showed a reverse pattern. In addition, the coverage of biocrust in the wet season was slightly higher than that in the dry season within a year. Spatially, the coverage of biocrusts on the sandy lands area on the Loess Plateau was higher and dominated by cyanobacterial crusts, while the coverage was lower in the hilly and gully area. Precipitation and utilization of abandoned land were the major factors driving biocrust coverage and composition, while slope direction and position did not show obvious effect. In addition, soil organic carbon content, pH, and texture were related to the distribution of biocrust. This study uncovered the spatial and temporal variability of biocrust distribution, which might provide important data support for the research and management of biocrust in the Loess Plateau region.

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.

Nanoplastics impair growth and nitrogen fixation of marine nitrogen-fixing cyanobacteria.

Nanoplastics pollution is a growing environmental problem worldwide. Recent research has demonstrated the toxic effects of nanoplastics on various marine organisms. However, the influences of nanoplastics on marine nitrogen-fixing cyanobacteria, a critical nitrogen source in the ocean, remained unknown. Here, we report that nanoplastics exposure significantly reduced growth, photosynthetic, and nitrogen fixation rates of Crocosphaera watsonii (a major marine nitrogen-fixing cyanobacterium). Transcriptomic analysis revealed that nanoplastics might harm C. watsonii via downregulation of photosynthetic pathways and DNA damage repair genes, while genes for respiration, cell damage, nitrogen limitation, and iron (and phosphorus) scavenging were upregulated. The number and size of starch grains and electron-dense vacuoles increased significantly after nanoplastics exposure, suggesting that C. watsonii allocated more resources to storage instead of growth under stress. We propose that nanoplastics can damage the cell (e.g., DNA, cell membrane, and membrane-bound transporters), inhibit nitrogen and carbon fixation, and hence lead to nutrient limitation and impaired growth. Our findings suggest the possibility that nanoplastics pollution could reduce the new nitrogen input and hence affect the productivity in the ocean. The impact of nanoplastics on marine nitrogen fixation and productivity should be considered when predicting the ecosystem response and biogeochemical cycling in the changing ocean.

Nitrogen-fixing bacterial communities differ between perennial agroecosystem crops.

Biocrusts, common in natural ecosystems, are specific assemblages of microorganisms at or on the soil surface with associated microorganisms extending into the top centimeter of soil. Agroecosystem biocrusts have similar rates of nitrogen (N) fixation as those in natural ecosystems, but it is unclear how agricultural management influences their composition and function. This study examined the total bacterial and diazotrophic communities of biocrusts in a citrus orchard and a vineyard that shared a similar climate and soil type but differed in management. To contrast climate and soil type, these biocrusts were also compared with those from an apple orchard. Unlike natural ecosystem biocrusts, these agroecosystem biocrusts were dominated by proteobacteria and had a lower abundance of cyanobacteria. All of the examined agroecosystem biocrust diazotroph communities were dominated by N-fixing cyanobacteria from the Nostocales order, similar to natural ecosystem cyanobacterial biocrusts. Lower irrigation and fertilizer in the vineyard compared with the citrus orchard could have contributed to biocrust microbial composition, whereas soil type and climate could have differentiated the apple orchard biocrust. Season did not influence the bacterial and diazotrophic community composition of any of these agroecosystem biocrusts. Overall, agricultural management and climatic and edaphic factors potentially influenced the community composition and function of these biocrusts.

Eco-friendly management of harmful cyanobacterial blooms in eutrophic lakes through vertical flow multi-soil-layering technology.

Eutrophication has led to the widespread occurrence of cyanobacterial blooms. Toxic cyanobacterial blooms with high concentrations of microcystins (MCs) have been identified in the Lalla Takerkoust reservoir in Morocco. The objective of this study was to evaluate the efficiency of the Multi-Soil-Layering (MSL) ecotechnology in removing natural cyanobacterial blooms from the lake. Two MSL pilots were used in rectangular glass tanks (60 × 10 × 70 cm). They consisted of permeable layers (PLs) made of pozzolan and a soil mixture layer (SML) containing local soil, ferrous metal, charcoal and sawdust. The main difference between the two systems was the type of local soil used: sandy soil for MSL1 and clayey soil for MSL2. Both MSL pilots effectively reduced cyanobacterial cell concentrations in the treated water to very low levels (0.09 and 0.001 cells/mL). MSL1 showed a gradual improvement in MC removal from 52 % to 99 %, while MSL2 started higher at 90 % but dropped to 54% before reaching 86%. Both MSL systems significantly reduced organic matter levels (97.2 % for MSL1 and 95.8 % for MSL2). Both MSLs were shown to be effective in removing cyanobacteria, MCs, and organic matter with comparable performance.