Monitoring bloom-forming Aphanizomenon using environmental DNA metabarcoding: Method development, validation, and field application.

Harmful algal blooms (HABs) have emerged as a critical global environmental and ecological concern. Timely and accurate monitoring of the prevalent bloom-forming genera is crucial for HAB management. Conventional microscope-based methods are time-consuming, labor-intensive, and specialized expertise-dependent, often making them impractical for large-scale surveillance. Molecular methods, such as metabarcoding, provide efficient technical solutions; however, the lack of competent PCR primers and further field validation present obstacles to their wide use. Here, we successfully developed Aphanizomenon-specific primers and validated the application of environmental DNA (eDNA) metabarcoding for field-based monitoring of Aphanizomenon in 37 sites across lentic and lotic freshwater ecosystems in Beijing. The sensitivity and specificity tests of newly developed primers demonstrated high performance - comprehensive recovery of biodiversity in Aphanizomenon communities and high ratios (>95%) of Aphanizomenon sequences in datasets. We observed significant correlations between the sequence abundance derived from eDNA metabarcoding and the total cell density determined through microscopic identification across all the sampling sites, both in the spring (r = 0.8086, p < 0.0001) and summer (r = 0.7902, p < 0.0001), thus validating the utility of eDNA metabarcoding based on the newly developed primers for monitoring in the field. Further, we identified key environmental variables that were primary drivers responsible for the spatiotemporal distribution of Aphanizomenon abundance. These variables included temperature, total nitrogen, and dissolved oxygen in lentic ecosystems, and total phosphorus in lotic ecosystems. The method developed and validated here offers an accurate, efficient, and high-throughput tool for the monitoring of Aphanizomenon blooms in freshwater ecosystems.

Spatiotemporal dynamics of dinoflagellate communities in the Taiwan Strait and their correlations with micro-eukaryotic and bacterial communities.

Dinoflagellate blooms have negative adverse effects on marine ecosystems. However, our knowledge about the spatiotemporal distribution of dinoflagellate communities and their correlations with micro-eukaryotic and bacterial communities is still rare. Here, the sediment micro-eukaryotic and bacterial communities were explored in the Taiwan Strait (TWS) by 16S and 18S rRNA gene high-throughput sequencing. We found that the dinoflagellates were the most abundant algal group in TWS, and their relative abundance was higher in spring and autumn than in summer. Moreover, the species richness and community composition of dinoflagellates showed strong seasonal patterns. NO3-N and NH4-N had the strongest correlations with the spatiotemporal dynamics of community composition of dinoflagellates. The dinoflagellates had a significantly wider niche breadth than other algal groups for NH4-N, NO3-N and NO2-N, and therefore potentially contributed to a wider distribution range and high abundance in TWS. In addition, the dinoflagellates had stronger impacts on microeukaryotes than on bacteria for both community composition and species richness. However, the dinoflagellates showed close coexistence with bacteria but loose coexistence with microeukaryotes in spring co-occurrence networks. This close coexistence suggests the potentially strong synergy effects between dinoflagellates and bacteria in spring dinoflagellate blooms in TWS. Overall, this study revealed the distribution mechanisms of dinoflagellates in TWS based on niche breadth and also unveiled the different effects of dinoflagellates on micro-eukaryotic and bacterial communities.

Explainable artificial intelligence for the interpretation of ensemble learning performance in algal bloom estimation.

Chlorophyll-a (Chl-a) concentrations, a key indicator of algal blooms, were estimated using the XGBoost machine learning model with 23 variables, including water quality and meteorological factors. The model performance was evaluated using three indices: root mean square error (RMSE), RMSE-observation standard deviation ratio (RSR), and Nash-Sutcliffe efficiency. Nine datasets were created by averaging 1 hour data to cover time frequencies ranging from 1 hour to 1 month. The dataset with relatively high observation frequencies (1-24 h) maintained stability, with an RSR ranging between 0.61 and 0.65. However, the model's performance declined significantly for datasets with weekly and monthly intervals. The Shapley value (SHAP) analysis, an explainable artificial intelligence method, was further applied to provide a quantitative understanding of how environmental factors in the watershed impact the model's performance and is also utilized to enhance the practical applicability of the model in the field. The number of input variables for model construction increased sequentially from 1 to 23, starting from the variable with the highest SHAP value to that with the lowest. The model's performance plateaued after considering five or more variables, demonstrating that stable performance could be achieved using only a small number of variables, including relatively easily measured data collected by real-time sensors, such as pH, dissolved oxygen, and turbidity. This result highlights the practicality of employing machine learning models and real-time sensor-based measurements for effective on-site water quality management. PRACTITIONER POINTS: XAI quantifies the effects of environmental factors on algal bloom prediction models The effects of input variable frequency and seasonality were analyzed using XAI XAI analysis on key variables ensures cost-effective model development.

Spatiotemporal insights of phytoplankton dynamics in a northern, rural-urban lake using a 3D water quality model.

Lake St. Charles, located north of Quebec City, Canada, is a shallow fluvial lake with two distinct basins bridging rural and urban landscapes. Mainly used as a source of drinking water for 300,000 residents, the lake has faced a steady degradation in water quality due to urbanization and the discharge of domestic wastewater. This study introduces a 3D hydrodynamics and water quality model using the Environmental Fluid Dynamics Code to enhance our understanding of algal bloom dynamics in Lake St. Charles. More specifically, we ran simulations for eight years (i.e., a three-year period for calibration, 2015 to 2017; and a five-year period for validation, 2018 to 2022) to reproduce the complex circulation patterns and dynamics of water quality within the system. The simulation results for chlorophyll-a demonstrate seasonal fluctuations in phytoplankton biomass, closely aligning with in situ observations and achieving Relative Root Mean Square Error (RRMSE) values below 50%. (i) In spring, runoff from snowmelt brought phosphorus into the lake, triggering primary production. Diatom growth was initially predominant in the shallow southern basin, then spread to the deeper northern basin due to favorable environmental conditions, including flow- and wind-induced currents, warmer water temperatures and nutrient availability. (ii) In summer, warm water temperatures stimulated biological activity, leading to the growth of cyanobacteria at the expense of diatoms, as well as a drop in phosphorus. (iii) The cyanobacteria persisted into the fall but began to decline in mid-November. (iv) Winter conditions, including the presence of an ice cover, limited the input of phosphorus and minimized phytoplankton production, but diatoms were observed in low concentrations near Des Hurons River inflow. Overall, during the open-water period, the lake-maintained chlorophyll-a concentrations indicative of mesotrophic conditions, with occasional periods when the biomass increased above the eutrophic threshold. Temperature, nutrient levels, and the fluvial dynamics of the lake are the primary factors influencing phytoplankton formation and distribution in lake St. Charles.

Nanobubble-enabled foam fractionation to remove algogenic odorous micropollutants.

Due to climate change and environmental pollution, natural lakes and reservoir water suffer increasingly serious algal blooms and associated water quality problems due to the presence of algal or algogenic organic matter (AOM) such as algal odour and toxins. Effective removal of these micropollutants, especially in the event of algal blooms, is critical to aesthetic values of water bodies, drinking water security and human health. The study investigated the removal efficiency of two common odorous compounds, trans-1,10-dimethyl-trans-9-decalol (geosmin) and 2-Methylisoborneol (2-MIB), using foam fractionation enabled by air nanobubbles with addition of two common cationic and anionic surfactants, sodium dodecyl sulfate (SDS) and cetyltrimethylammonium bromide (CTAB), to enhance foaming ability and stability. The results showed that the cationic surfactant (i.e., CTAB), a low pH, and high ionic strength significantly promoted the removal of geosmin and 2-MIB. For example, the removal tests using the synthetic water determined that the conditions of pH = 7, [CTAB] = 20 mg·L-1 and IS = 10 mM as NaCl resulted in both the highest geosmin removal rate of 91.81% and highest 2-MIB removal rate of 85.0%. The removal of two odorous compounds in real lake water was evaluated, which yielded removal rates of 83.2% for geosmin and 48.1% for 2-MIB, highlighting the minor inhibition from water matrixes on the removal performances. Compared to microbubbles, nanobubbles enabled greater surface areas of foam and higher removal efficiencies. The study provided new insights into the use of foam fractionation with air nanobubbles to enhance the removal of odorous compounds from impaired water and mitigate the negative environmental and health impacts of harmful algal blooms (HABs).

Epidemiologic and clinical features of cyanobacteria harmful algal bloom exposures reported to the National Poison Data System, United States, 2010-2022: a descriptive analysis.

BACKGROUND: Harmful algal bloom occurrences have been increasingly reported globally and over time. Exposure to the variety of toxins and co-contaminants that may be present in harmful algal blooms can cause illness and even death. Poison control data is a valuable public health information source that has been used to characterize many types of toxin exposures, including harmful algal blooms. Prior studies have been limited by location and time, and knowledge gaps remain regarding cyanobacteria harmful algal bloom (cyanoHAB) exposure circumstances, and the breadth and severity of associated clinical effect. METHODS: The objective of this study was to characterize epidemiologic and clinical features of cyanoHAB exposure cases reported to 55 US poison control centers and available in the National Poison Data System (NPDS). We identified 4260 NPDS cyanoHAB exposure cases reported from 2010 to 2022, including symptomatic exposure cases with and without clinical effects related to the exposure and asymptomatic exposure cases. We assessed demographics; exposure routes, locations, chronicity; clinical effects; and medical outcomes. We calculated case rates annually and 13-year case rates by US geographic division. RESULTS: Over half of cyanoHAB exposure cases were children < 20 years old (n = 2175). Most cyanoHABs exposures occurred in a "public area" (n = 2902, 68.1%); most were acute (≤ 8 h) (n = 3824, 89.8%). Dermal and ingestion routes and gastrointestinal effects predominated. 2% (n = 102) of cases experienced a moderate or major medical outcome; no deaths were reported. National rates increased from 0.4 cases/1 million (1 M) person-years in 2010 to 1.4 cases/1 M person-years in 2022. The Mountain division had the highest 13-year rate (7.8 cases/1 M person-years). CONCLUSIONS: CyanoHAB exposure case rates increased 2010-2022, despite a decrease in all-cause exposure cases during the same period. NPDS data provide valuable public health information for characterization of cyanoHAB exposures, an emerging public health challenge.

Biogenic ROS mediated degradation mechanism of marine toxin domoic acid.

Domoic acid (DA) is a compound generated as a secondary metabolite during harmful algal blooms, has historically received attention as the potent neurotoxicity in marine environment. However, the aerobic degradation mechanism of DA and the DA-degrader remain largely unknown. Here, we revealed the mechanism of aerobic degradation of DA by a ubiquitous marine Pseudoalteromonas sp., and more importantly, we confirmed that the degradation of DA is mediated by biogenic reactive oxygen species (ROS), rather than direct enzyme-mediated as traditionally conceived. Results indicated that DA degradation was caused by biogenic O2- and OH, where DA underwent reactions of decarboxylation, hydroxylation, and oxidation to yield the detoxification terminal product. Besides, whole genome sequencing and RT-qPCR analysis revealed that the genes conferring to encoding leucine dehydrogenase (ldh) and Na+-translocated NADH-quinone oxidoreductase (nqrA, nqrF) are responsible for biogenic ROS production. Finally, we found through comparative proteomic analysis that biogenic ROS mediated the DA degradation may be prevalent in the environment. Overall, this work not only reveals aerobic biotransformation mechanism of DA, but also identifies a novel mechanism of DA degradation, which provides new perspective into the environmental fate of DA and the artificial bioremediation of DA.

Determining ecological interactions of key dinoflagellate species using an intensive metabarcoding approach in a semi-closed coastal ecosystem of South Korea.

Marine phytoplankton communities are pivotal in biogeochemical cycles and impact global climate change. However, the dynamics of the dinoflagellate community, its co-occurrence relationship with other eukaryotic plankton communities, and environmental factors remain poorly understood. In this study, we aimed to analyze the temporal changes in the eukaryotic plankton community using a 18S rDNA metabarcoding approach. We performed intensive monitoring for 439 days at intervals of three days during the period from November 2018 to June 2020 (n = 260) in Jangmok Bay Time-series Monitoring Site in South Korea. Among the 16,224 amplicon sequence variants (ASVs) obtained, dinoflagellates were the most abundant in the plankton community (38 % of total relative abundance). The dinoflagellate community was divided into 21 groups via cluster analysis, which showed an annually similar distribution of low-temperature periods. Additionally, we selected 11 taxa that had an occurrence mean exceeding 1 % of the total dinoflagellate abundance, accounting for 93 % of the total dinoflagellate community: namely Heterocapsa rotundata, Gymnodinium sp., Akashiwo sanguinea, Amoebophrya sp., Euduboscquella sp., Spiniferites ramosus, Dissodinium pseudolunula, Sinophysis sp., Karlodinium veneficum, and Katodinium glaucum. The key dinoflagellate species were well represented at temporally variable levels over an entire year. Heterocapsa rotundata was not significantly affected by water temperature, whereas its dynamics were largely influenced by strong predation pressure, competition, and/or the supplementation of food sources. The growth of A. sanguinea was associated with dissolved inorganic phosphorus concentrations, while Euduboscquella sp. showed a significant relationship with D. pseudolunula and K. glaucum, largely representing a positive association that implies possible parasitic mechanisms. This study demonstrated interactions between key dinoflagellate species and the environment, as well as parasites, predators, competitors, and feeders.

An effective algaecide for the targeted destruction of Karenia brevis.

We address the targeted destruction of Karenia brevis using the algaecide calcium peroxide, in tandem with the flocculation and sinking of the species. The specific aspect of the approach is the incorporation of the algaecide within the floc to rapidly kill K. brevis, thus minimizing escape of cells from the floc and reentry to the water column. CaO2 gradually produces H2O2, which diffuses through cell membranes and induces oxidative stress, leading to cell death via excessive reactive oxygen species (ROS) formation. The effect of varying doses of calcium peroxide on K. brevis cells was measured with pulse amplitude modulated fluorometry and indicated that doses as low as 30 mg/L when integrated into flocs are effective in suppressing photosynthesis. Cell viability assays also indicate that such low levels are sufficient to cause cell death in a 3-6 hour time period. Thus, the proposed technology involving the incorporation of calcium peroxide in a cationic flocculating agent (polyaluminum chloride, PAC) leads to an inexpensive and scalable technology to mitigate harmful algal blooms of K. brevis.

The diel disconnect between cell growth and division in Aureococcus is interrupted by giant virus infection.

Viruses of eukaryotic algae have become an important research focus due to their role(s) in nutrient cycling and top-down control of algal blooms. Omics-based studies have identified a boon of genomic and transcriptional potential among the Nucleocytoviricota, a phylum of large dsDNA viruses which have been shown to infect algal and non-algal eukaryotes. However, little is still understood regarding the infection cycle of these viruses, particularly in how they take over a metabolically active host and convert it into a virocell state. Of particular interest are the roles light and the diel cycle in virocell development. Yet despite such a large proportion of Nucleocytoviricota infecting phototrophs, little work has been done to tie infection dynamics to the presence, and absence, of light. Here, we examine the role of the diel cycle on the physiological and transcriptional state of the pelagophyte Aureococcus anophagefferens while undergoing infection by Kratosvirus quantuckense strain AaV. Our observations demonstrate how infection by the virus interrupts the diel growth and division of this cell strain, and that infection further complicates the system by enhancing export of cell biomass.

Physiological and transcriptomic response of dinoflagellate Gymnodinium catenatum to nitrate deficiency.

The paralytic shellfish toxin producing dinoflagellate Gymnodinium catenatum is a globally distributed species and often forms massive blooms. However, the physiological and molecular responses of G. catenatum to nitrate starvation have not been thoroughly investigated. Our results showed that multiple forms of N could be utilized by G. catenatum under nitrate-deficient conditions. Nitrate deficiency adversely affected the growth, cellular Chlorophyll a (Chl a) content, and toxin production of G. catenatum. Transcriptomic analysis revealed significant down-regulation of gene expressions involved in the light reaction of photosynthesis, while genes related to fatty acids synthesis and antioxidation were significantly upregulated in the N-depleted cultures. Our results suggested that excess carbon was channeled into lipid synthesis for energy storage, and antioxidant reactions were upregulated to eliminate toxic peroxides caused by nitrate limitation. These findings highlight the adaptative strategy of G. catenatum in low-nitrate environments, which are crucial factors driving its bloom formation.

Elevated temperature as the dominant stressor on the harmful algal bloom-causing dinoflagellate Prorocentrum obtusidens in a future ocean scenario.

Marine dinoflagellates are increasingly affected by ongoing global climate changes. While understanding of their physiological and molecular responses to individual stressors anticipated in the future ocean has improved, their responses to multiple concurrent stressors remain poorly understood. Here, we investigated the individual and combined effects of elevated temperature (26 °C relative to 22 °C), increased pCO2 (1000 µatm relative to 400 µatm), and high nitrogen: phosphorus ratio (180:1 relative to 40:1) on a harmful algal bloom-causing dinoflagellate Prorocentrum obtusidens under short-term (28 days) exposure. Elevated temperature was the most dominant stressor affecting P. obtusidens at physiological and transcriptomic levels. It significantly increased cell growth rate and maximum photosynthetic efficiency (Fv/Fm), but reduced chlorophyll a, particulate organic carbon, particulate organic nitrogen, and particulate organic phosphorus. Elevated temperature also interacted with other stressors to produce synergistic positive effects on cell growth and Fv/Fm. Transcriptomic analysis indicated that elevated temperature promoted energy production by enhancing glycolysis, tricarboxylic acid cycle, and nitrogen and carbon assimilation, which supported rapid cell growth but reduced material storage. Increased pCO2 enhanced the expression of genes involved in ionic acid-base regulation and oxidative stress resistance, whereas a high N:P ratio inhibited photosynthesis, compromising cell viability, although the effect was alleviated by elevated temperature. The combined effect of these multiple stressors resulted in increased energy metabolism and up-regulation of material-synthesis pathways compared to the effect caused by elevated temperature alone. Our results underscore ocean warming as the predominant stressor for dinoflagellates and highlight the complex, synergistic effects of multi-stressors on dinoflagellates.

Towards sustainable coastal management: aerial imagery and deep learning for high-resolution Sargassum mapping.

The massive arrival of pelagic Sargassum on the coasts of several countries of the Atlantic Ocean began in 2011 and to date continues to generate social and environmental challenges for the region. Therefore, knowing the distribution and quantity of Sargassum in the ocean, coasts, and beaches is necessary to understand the phenomenon and develop protocols for its management, use, and final disposal. In this context, the present study proposes a methodology to calculate the area Sargassum occupies on beaches in square meters, based on the semantic segmentation of aerial images using the pix2pix architecture. For training and testing the algorithm, a unique dataset was built from scratch, consisting of 15,268 aerial images segmented into three classes. The images correspond to beaches in the cities of Mahahual and Puerto Morelos, located in Quintana Roo, Mexico. To analyze the results the fß-score metric was used. The results for the Sargassum class indicate that there is a balance between false positives and false negatives, with a slight bias towards false negatives, which means that the algorithm tends to underestimate the Sargassum pixels in the images. To know the confidence intervals within which the algorithm performs better, the results of the f0.5-score metric were resampled by bootstrapping considering all classes and considering only the Sargassum class. From the above, we found that the algorithm offers better performance when segmenting Sargassum images on the sand. From the results, maps showing the Sargassum coverage area along the beach were designed to complement the previous ones and provide insight into the field of study.

Time of day of infection shapes development of a eukaryotic algal-Nucleocytoviricota virocell.

Aureococcus anophagefferens forms a model host-virus system with the "giant virus" Kratosvirus quantuckense. Studies to define its ribocell (uninfected) and virocell (virus-infected) forms are needed as these states co-occur during algal blooms. Previously, a link between light-derived energy, virus particle production, and virocell formation was noted. We explored how the time of day (morning, midday, or late day) of virus-host contact shaped virocell ontogeny. In parallel, we explored the dependence on light-derived energy in this mixotrophic plankter by inhibiting photosystem II, testing the role of heterotrophic energy in infection dynamics. Using flow cytometry and photochemical assessments, we examined the physiology of infected cells and controls, and estimated virus particle production. We observed differences between ribocell and virocell response to treatments, including reductions in virus particle production during reduced light duration) and PSII inhibition (i.e. "forced heterotrophy"). This work demonstrates the importance of light in shaping the fate of infected cells and provides insight into factors that constrain in situ blooms. Most significantly, we show that time of the solar day when a virus and host come into contact influences viral particle production, and therefore bloom dynamics; a factor that needs to be considered in bloom modeling work.

Molecular forecasting of domoic acid during a pervasive toxic diatom bloom.

In 2015, the largest recorded harmful algal bloom (HAB) occurred in the Northeast Pacific, causing nearly 100 million dollars in damages to fisheries and killing many protected marine mammals. Dominated by the toxic diatom Pseudo-nitzschia australis, this bloom produced high levels of the neurotoxin domoic acid (DA). Through molecular and transcriptional characterization of 52 near-weekly phytoplankton net-tow samples collected at a bloom hotspot in Monterey Bay, California, we identified active transcription of known DA biosynthesis (dab) genes from the three identified toxigenic species, including P. australis as the primary origin of toxicity. Elevated expression of silicon transporters (sit1) during the bloom supports the previously hypothesized role of dissolved silica (Si) exhaustion in contributing to bloom physiology and toxicity. We find that coexpression of the dabA and sit1 genes serves as a robust predictor of DA one week in advance, potentially enabling the forecasting of DA-producing HABs. We additionally present evidence that low levels of iron could have colimited the diatom population along with low Si. Iron limitation represents an overlooked driver of both toxin production and ecological success of the low-iron-adapted Pseudo-nitzschia genus during the 2015 bloom, and increasing pervasiveness of iron limitation may fuel the escalating magnitude and frequency of toxic Pseudo-nitzschia blooms globally. Our results advance understanding of bloom physiology underlying toxin production, bloom prediction, and the impact of global change on toxic blooms.

Enhancing early warning: A DNA biosensor with polyaniline/graphene nanocomposite for label-free voltammetric detection of saxitoxin-producing harmful algae.

Yearly reports of detrimental effects resulting from harmful algal blooms (HAB) are still received in Malaysia and other countries, particularly concerning fish mortality and seafood contamination, both of which bear consequences for the fisheries industry. The underlying reason is the absence of a dependable early warning system. Hence, this research aims to develop a single DNA biosensor that can detect a group of HAB species known for producing saxitoxin (SXT), which is commonly found in Malaysian waters. The screen-printed carbon electrode (SPCE)-based DNA biosensor was fabricated by covalent grafting of the 3' aminated DNA probe of the sxtA4 conserved domain in SXT-producing dinoflagellates on the reverse-phase polymerized polyaniline/graphene (PGN) nanocomposite electrode via carbodiimide linkage. The introduction of a carboxyphenyl layer to the PGN nanotransducing element was essential to augment the carboxylic groups on the graphene (RGO), facilitating attachment with the aminated DNA. The synergistic effect of the asynthesized nanocomposite of PANI and RGO, tremendously enhanced the electron transfer rate of the ferri/ferrocyanide redox probe at the SPCE transducer surface, allowing for the label-free bioanalytical assay of complementary DNA targets. The developed DNA biosensor featuring the capacity to detect a broad range of Alexandrium minutum (A. minutum) cell concentrations, ranging from 10 to 10,000,000 cells L-1. The quantification of A. minutum cells from pure algal culture by the electrochemical DNA biosensor has been well-validated with traditional microscopic techniques. Furthermore, Alexandrium tamiyavanichii, another toxigenic HAB species, exhibited a similar electrochemical characteristic signal to those observed with A. minutum, whilst the biosensor yielded appreciably distinctive results when subjected to a non-toxigenic microalgae species as a negative control, i.e. Isochrysis galbana. A compendium DNA biosensor design and electrochemical detection strategy at laboratory scale serves as a precursor to the potential development of portable device for on-site detection, thus expanding the utility and scope of biosensor technology.

Tracking Extensive Portfolio of Cyanotoxins in Five-Year Lake Survey and Identifying Indicator Metabolites of Cyanobacterial Taxa.

Cyanobacterial blooms require monitoring, as they pose a threat to ecosystems and human health, especially by the release of toxins. Along with widely reported microcystins, cyanobacteria coproduce other bioactive metabolites; however, information about their dynamics in surface waters is sparse. We investigated dynamics across full bloom successions throughout a five-year lake monitoring campaign (Greifensee, Switzerland) spanning 150 sampling dates. We conducted extensive suspect screening of cyanobacterial metabolites using the database CyanoMetDB. Across all 850 samples, 35 metabolites regularly co-occurred. Microcystins were present in 70% of samples, with [d-Asp3,(E)-Dhb7]MC-RR reaching concentrations of 70 ng/L. Anabaenopeptins, meanwhile, were detected in 95% of all samples with concentrations of Oscillamide Y up to 100-fold higher than microcystins. Based on LC-MS response and frequency, we identified indicator metabolites exclusively produced by one of three cyanobacteria isolated from the lake, these being [d-Asp3,(E)-Dhb7]MC-RR from Planktothrix sp. G2020, Microginin 761B from Microcystis sp. G2011, and Ferintoic acid B from Microcystis sp. G2020. These indicators showed distinct temporal trends and peaking seasons that reflect the variance in either the abundance of the producing cyanobacteria or their toxin production dynamics. Our approach demonstrates that selecting high LC-MS response and frequent and species-specific indicator metabolites can be advantageous for cyanobacterial monitoring.

A newly identified algicidal bacterium of Pseudomonas fragi YB2: Algicidal compounds and effects.

Harmful algal bloom (HAB) is an unresolved existing problem worldwide. Here, we reported a novel algicidal bacterium, Pseudomonas fragi YB2, capable of lysing multiple algal species. To Chlorella vulgaris, YB2 exhibited a maximum algicidal rate of 95.02 % at 120 h. The uniqueness of YB2 lies in its ability to self-produce three algicidal compounds: 2-methyl-1, 3-cyclohexanedione (2-MECHD), N-phenyl-2-naphthylamine, and cyclo (Pro-Leu). The algicidal properties of 2-MECHD have not been previously reported. YB2 significantly affected the chloroplast and mitochondrion, thus decreasing in chlorophyll a by 4.74 times for 120 h and succinate dehydrogenase activity by 103 times for 36 h. These physiological damages disrupted reactive oxygen species and Ca2+ homeostasis at the cellular level, increasing cytosolic superoxide dismutase (23 %), catalase (35 %), and Ca2+ influx. Additionally, the disruption of Ca2+ homeostasis rarely reported in algicidal bacteria-algae interaction was observed using the non-invasive micro-test technology. We proposed a putative algicidal mechanism based on the algicidal outcomes and physiological algicidal effects and explored the potential of YB2 through an algicidal simulation test. Overall, this study is the first to report the algicidal bacterium P. fragi and identify a novel algicidal compound, 2-MECHD, providing new insights and a potent microbial resource for the biocontrol of HAB.

Smartphone as an alternative to measure chlorophyll-a concentration in small waterbodies.

Monitoring chlorophyll-a concentrations (Chl-a, µg·L-1) in aquatic ecosystems has attracted much attention due to its direct link to harmful algal blooms. However, there has been a lack of a cost-effective method for measuring Chl-a in small waterbodies. Inspired by the increase of smartphone photography, a Smartphone-based convolutional neural networks (CNN) framework (SCCA) was developed to estimate Chl-a in Aquatic ecosystem. To evaluate the performance of SCCA, 238 paired records (a smartphone image with a 12-color background and a measured Chl-a value) were collected from diverse aquatic ecosystems (e.g., rivers, lakes and ponds) across China in 2023. Our performance-evaluation results revealed a NS and R2 value of 0.90 and 0.94 in Chl-a estimation, demonstrating a satisfactory (NS = 0.84, R2 = 0.86) model fit in lower Chl-a (<30 µg L-1) conditions. SCCA had involved a realtime-update method with hyperparameter optimization technology. In comparison with the existing methods of measuring Chl-a, SCCA provides a useful screening tool for cost-effective measurement of Chl-a and has the potential for being an algal bloom screening means in small waterbodies, using Huajin River as a case study, especially under limited resources for water measurement. Overall, we highlight that the SCCA can be potentially integrated into a smartphone application in the future to diverse waterbodies in environmental management.

An unusual winter bloom of dinoflagellates with notable damage to kelp cultivation around Shandong peninsula, China.

In the coastal waters around Shandong peninsula, an unprecedented winter bloom of dinoflagellates Gonyaulax polygramma and Akashiwo sanguinea occurred in 2021 from late November to early December. The bloom affected a wide area of coastal waters extending from west to east along the northern Shandong peninsula and had a devastating blow to the kelp cultivation industry. Based on the remote-sensing data, the initiation of the bloom was traced back to the region adjacent to the mouth of the Yellow River in Laizhou Bay, where enhanced freshwater discharge from the Yellow River was recorded from September to November. It's proposed that the increased precipitation in the Yellow River basin associated with northward extension of the precipitation band in China could be an important reason for this winter bloom. This unusual winter bloom around Shandong peninsula highlights the potential risks of harmful algal blooms and their impacts on coastal ecosystems under the background of climate change.