Search Results (394)

Planktothrix species are among the most widespread bloom-forming cyanobacteria in freshwater ecosystems and are of particular concern because of their ability to produce cyanotoxins and form persistent harmful algal blooms (HABs). Among them, Planktothrix agardhii and Planktothrix rubescens are the most extensively studied species and are responsible for a large proportion of bloom events reported in European lakes. This review synthesizes current knowledge on the taxonomy, ecophysiology, toxin production, environmental drivers, species interactions, and management of Planktothrix blooms, with a particular focus on European freshwater ecosystems. The available evidence highlights marked ecological differences between the two dominant species. P. agardhii is primarily associated with shallow, eutrophic, and well-mixed lakes, whereas P. rubescens is typically found in deep, stratified, and relatively transparent water bodies, where it forms persistent metalimnetic populations. These contrasting ecological strategies influence bloom development, toxin dynamics, detection, and management. Nutrient availability, light climate, temperature, water column stability, and biological interactions all contribute to bloom establishment and persistence, while climate change is expected to further modify bloom frequency, duration, and geographic distribution. The review also examines current monitoring and mitigation approaches, highlighting the limitations of conventional surface-based surveys for detecting deep P. rubescens populations and emphasizing the need for integrated monitoring strategies combining depth-resolved sampling, molecular tools, and toxin analyses. Overall, understanding the ecological and physiological diversity of Planktothrix species is essential for improving risk assessment, developing effective management measures, and mitigating the impacts of cyanobacterial blooms in European freshwaters. Full article

In recent years, there has been growing interest in applying vision–language models (VLMs) to quantitative remote sensing. This study evaluates whether three commercial VLMs (GPT-4o, GPT-5.5, and Claude Sonnet 4.6) can detect and classify the severity of harmful algal blooms (HABs) from Sentinel-2 satellite imagery of western Lake Erie and compares them against classical machine learning classifiers (Random Forest (RF), Support Vector Machine (SVM), and eXtreme Gradient Boosting (XGBoost)) trained on both a three-band red, green, blue (RGB) composite representation of the imagery and a 10-band multi-spectral reflectance representation. Forty bloom events identified from the National Oceanic and Atmospheric Administration (NOAA) Harmful Algal Bloom Operational Forecast System (HAB-OFS) severity assessments were assembled into the evaluation dataset, spanning seven bloom seasons (2019–2025). For binary bloom detection, the VLMs did not match the classical RGB classifiers; their F1 scores (0.69–0.75) fell below the best RGB classifier (Random Forest, 0.76) and below a trivial always-present baseline (F1 = 0.77), and they carried false positive rates of 73–93% on bloom-absent images, against 27–40% for the RGB classifiers. The VLMs reached high recall by labeling most scenes as bloom-positive, which makes them operationally unreliable in this configuration. For severity classification, the VLMs assigned 60–70% of their predictions to the “moderate” category regardless of actual conditions and identified at most one of the two severe blooms, whereas the classical classifiers tracked the ground-truth distribution and delivered two to nearly three times the exact-match accuracy (0.44–0.59 vs. 0.20–0.225). The strongest method across all metrics was the multi-spectral SVM (F1 = 0.833, false positive rate 27%, accuracy 0.795). Switching the same SVM from RGB to multi-spectral features raised accuracy from 0.675 to 0.795, a 12-percentage-point gain that measures the spectral information carried by red-edge and shortwave infrared bands that are accessible through multi-spectral sensors but unavailable to standard VLM vision encoders. Feature-importance analysis showed that the multi-spectral classifiers ranked chlorophyll-specific indices, the Normalized Difference Chlorophyll Index (NDCI) and the Floating Algae Index (FAI), among their top predictors, the same signatures used in established operational algorithms, while the RGB classifiers relied on red-channel variability and green-dominant pixel fractions because RGB inputs cannot compute those indices. Two compounded limitations therefore constrain off-the-shelf VLMs for aquatic remote sensing: the limited spectral information available through standard RGB channels and a mismatch between the land-dominated training distributions of these models and aquatic optical conditions. Domain-specific classifiers operating on multi-spectral data remain the more suitable tools for continued development of HAB monitoring and water-quality retrieval. Full article

Gonyaulax polygramma is a bloom-forming dinoflagellate that can be difficult to identify accurately during routine harmful algal bloom (HAB) monitoring, particularly when morphologically similar Gonyaulax species occur together in field samples. To improve species discrimination and quantitative detection, we developed a species-specific TaqMan qPCR assay targeting the 28S rDNA region of G. polygramma and applied it to field samples collected during a four-year survey in Korean coastal waters. The developed primer–probe set showed high specificity without cross-reactivity with tested non-target microalgae. The assay exhibited high linearity (R² = 0.999), amplification efficiencies of 92.01–99.36%, and a detection limit of ${10 \mathrm{c}\mathrm{o}\mathrm{p}\mathrm{i}\mathrm{e}\mathrm{s} \mathrm{r}\mathrm{e}\mathrm{a}\mathrm{c}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n}}^{-1}$. Field application revealed clear regional differences in distribution patterns. G. polygramma was rarely detected in the semi-enclosed Jinhae Bay, whereas relatively high abundances were observed at offshore-influenced stations in the Tongyeong–Yeosu–Wando region during summer. These results suggest that G. polygramma occurs locally in offshore-influenced coastal waters rather than being uniformly distributed along the southern coast of Korea. The developed qPCR assay provides a reliable molecular tool for detecting G. polygramma and can complement morphology-based monitoring of Gonyaulax species in coastal HAB surveillance programs. Full article

Nutrient stoichiometry, particularly the balance of carbon (C), nitrogen (N), and phosphorus (P), plays a fundamental role in regulating freshwater ecosystem dynamics, primary production, and biogeochemical cycling. This study presents one of the first dedicated reviews to combine bibliometric mapping with ecological synthesis of C:N:P ratios in inland waters, drawing on 1004 publications indexed in the Web of Science Core Collection (2000–2025), comprising peer-reviewed articles and review articles refined by document type, language, and research area. Bibliometric mapping using VOSviewer (version 1.6.20) identified exponential growth in publications after 2010, with phosphorus dynamics and eutrophication emerging as the most-cited themes, while recent years have shown increasing attention to C:P ratios as reliable ecological indicators. Four dominant thematic clusters were identified: Nutrient Cycling and Biogeochemistry; Phytoplankton and Food Web Dynamics; Eutrophication and Water Quality; and Climate Change and Ecosystem Responses. Ecological synthesis demonstrated substantial deviations from the canonical Redfield ratio (106C:16N:1P), with pronounced stoichiometric variability across trophic states, latitudes, and ecosystem types. Case comparisons revealed high C:P ratios in Arctic and alpine lakes linked to dissolved organic carbon inputs, low N:P ratios in tropical waters that promote cyanobacterial dominance, and stable, low phosphorus concentrations in deep African lakes. These findings emphasize the significance of flexible stoichiometry in predicting ecosystem tipping points, managing harmful algal blooms (HABs), and guiding nutrient restoration strategies. By integrating bibliometric and ecological evidence, this study identifies C:P ratios as a promising candidate indicator that merits further field validation for freshwater management, while underscoring persistent research gaps in microbial stoichiometry, cross-scalar modeling, and policy uptake in the Global South. Full article

Domoic acid (DA) is a well-known seafood toxin produced by some species of marine phytoplankton in the genus Pseudo-nitzschia during harmful algal blooms (HABs). Acute toxic exposures induce overt clinical signs of neuroexcitotoxicity, such as seizures in mammals due to overstimulation of glutamate receptors in the central nervous system (CNS). Acute DA excitotoxicity via the CNS has been well-studied in both field poisoning events and laboratory exposure studies with rodent models, but little is known about the impacts of low-level DA exposures below those that cause outward signs of neurotoxicity; the impacts on other potential target organs, including the heart; or age-related sensitivities. Here, low-level DA exposures in young adult (9 mo) and old (24 mo) mice were conducted over multiple weeks. Mortality, cardiac function, frailty, and protein expression were quantified to assess age-related DA sensitivity and potential impacts on heart function. Echocardiography and proteome data confirm that chronic low-level DA exposure causes irreversible functional cardiomyopathy and protein remodeling in young adult mice that mimics natural cardiac aging. In addition, old mice exhibit higher mortality and frailty than young adult mice with the same low-level DA exposures. These results provide critical information for assessing potential health risks to humans who regularly consume seafood with low levels of DA. Full article

Ornamental fountains in the Alhambra and Generalife (Granada, Spain) constitute complex socio-ecological systems where water, stone, and biological communities interact, making them highly vulnerable to biodeterioration caused by phototrophic microorganisms such as cyanobacteria, green algae, and diatoms. Conventional chemical biocides, although widely applied, present significant drawbacks including toxicity, material degradation, ecological imbalance, and limited long-term effectiveness. In this context, this study evaluated the potential of algicidal bacteria as a sustainable alternative for controlling phototrophic growth in heritage environments. Water samples from eight ornamental fountains were analyzed using 16S ribosomal RNA (16S rRNA) gene sequencing to characterize bacterial communities and identify taxa previously reported with algicidal activity. Statistical analyses were conducted to assess relationships between microbial community structure and biofilm development. In parallel, functional screening assays using filtered fountain waters against Chlorella vulgaris were performed to evaluate intrinsic inhibitory capacity. The most active sample was selected for bacterial isolation and further validation through co-culture assays, cell density measurements, and pulse-amplitude-modulated (PAM) fluorometry. A total of 18 genera with reported algicidal capacity were detected, representing a substantial fraction of the microbiome across all samples. However, no significant association was found between these taxonomic metrics and biofilm development, highlighting a decoupling between taxonomic composition and functional activity. The most active isolate, identified as Stenotrophomonas maltophilia strain LIG25, caused a rapid decline in photosynthetic efficiency and achieved more than 98% inhibition of algal growth. These findings demonstrate that ornamental fountain microbiomes represent a reservoir of native biocontrol agents and support the development of eco-friendly strategies for cultural heritage conservation. Full article

Harmful algal blooms (HABs) are increasing in frequency in the Chesapeake Bay, posing risks to marine ecosystems, water quality, and public health. Chlorophyll-a (Chl-a) is a widely used indicator of algal biomass, and satellite observations such as Sentinel-3 Ocean and Land Color Instrument (OLCI) enable large-scale monitoring of bloom dynamics. However, cloud cover and atmospheric interference frequently introduce missing pixels in daily satellite products, reducing temporal continuity and limiting monitoring reliability. Satellite-derived chlorophyll-a (Chl-a) data exhibit substantial missingness, with daily pixel gaps ranging from approximately 52.30% to 100% (mean ≈ 88.95%). This study evaluates spatial interpolation, EOF-based, supervised machine-learning, deep-learning, and convolutional autoencoder approaches for reconstructing missing Chl-a values. Sentinel-3 OLCI Chl-a data from 2023–2024 were used for model training, while data from 2025 served as a temporally independent test set to avoid spatiotemporal leakage. To simulate cloud-induced data gaps, artificial missingness scenarios ranging from 50% to 90% were applied for the Inverse Distance Weighting (IDW) and Data Interpolating Empirical Orthogonal Functions (DINEOF) baseline approaches, while machine-learning, deep-learning, and convolutional autoencoder models were evaluated using real satellite-derived missing observations. The evaluated models include IDW, DINEOF, K-Nearest Neighbors (KNN), Random Forest (RF), Extra Trees (ET), XGBoost, a Long Short-Term Memory (LSTM) network, and a Temporal Data Interpolating Convolutional Autoencoder (Temporal DINCAE). Model performance was assessed using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), prediction bias, and the coefficient of determination (R²). Results indicate that tree-based ensemble models outperform spatial interpolation and EOF-based methods, with XGBoost achieving the best overall performance (R² ≈ 0.86; RMSE ≈ 9.61 mg m⁻³). The LSTM model achieved lower prediction errors (RMSE ≈ 5.87 mg m⁻³; MAE ≈ 2.16 mg m⁻³), highlighting the benefit of incorporating temporal dependencies, although with slightly reduced variance capture. The convolutional autoencoder-based Temporal DINCAE model achieved strong reconstruction performance (R² ≈ 0.84; RMSE ≈ 11.15 mg m⁻³). Uncertainty quantification shows that Extra Trees tends to underestimate uncertainty with narrower prediction intervals, whereas XGBoost provides better-calibrated but wider intervals. Full article

Background: Oil spills have dramatically increased, causing significant damage and pollution to marine ecosystems. The entry of petroleum hydrocarbons into the ocean may lead to the occurrence of harmful algal blooms (HABs). The amino acid changes in harmful algae after oil spills remain unclear. Methods: In order to study the effect of oil spills on the amino acid mechanism of typical causative species, the composition and relative abundance of amino acids in Heterosigma akashiwo were investigated under different water accommodated fractions (WAFs) of 180# fuel oil. Results: Random forest prediction of polycyclic aromatic hydrocarbon toxicity to microalgae identified pyrene, benzo[k]fluoranthene, and fluoranthene as significant contributors. A total of 16 species of amino acids were detected in Heterosigma akashiwo, among which alanine, proline, aspartic acid, cysteine, lysine, and histidine were the predominant ones. As the concentration of the WAF increased, alanine abundance decreased significantly, indicating that the WAF disrupted the metabolic balance of alanine, with the degree of interference being positively correlated with exposure concentration. With the increase in culture time, the abundance of cysteine increased at 1%, 3%, and 5% WAFs, whereas the cysteine increased and then decreased at 7% and 10% WAFs. The abundance of aspartic acid and lysine showed no obvious pattern with culture time under WAF stress. Significant increases in the abundance of proline and histidine were observed in the WAF treatments. Conclusions: This study investigated the impact of oil spill pressure on the amino acid content of harmful algae, providing a scientific basis for understanding the potential impact of oil spills on the occurrence of HABs. Full article

Against the backdrop of intensifying global climate change and water eutrophication, the increasing occurrence of Harmful Algal Blooms (HABs) poses a significant threat to aquatic ecosystems, human health, and socio-economic activities. The occurrence and development of HABs are complex processes governed by the interaction of physical, chemical, and biological factors. Therefore, timely and accurate monitoring is essential for early warning and scientific research. This paper comprehensively reviews recent advances in HAB monitoring technologies, with a focus on two core components: sensors and monitoring platforms. First, organized around key environmental parameters, it summarizes the principles, applications, and limitations of in situ sensors, such as multi-parameter water quality sondes, Imaging Flow Cyto-bots (IFCB), and Environmental Sample Processors (ESP), as well as laboratory-based analytical techniques such as HPLC-MS for measuring physical, chemical, and biological indicators. Second, it compares the technical characteristics of three major monitoring platforms (including field surveys, remote sensing, and autonomous systems) and discusses their potential for synergistic application. Finally, this review proposes a future framework for an integrated “Space–Air–Ground–Sea” intelligent monitoring network and explores possible pathways to address current challenges through cross-platform data fusion, sensor miniaturization, intelligentization, and artificial intelligence-driven decision support. This review aims to provide a comprehensive reference for the optimization and innovation of HAB monitoring technologies and to promote the development of the field toward greater integration, intelligence, and real-time monitoring capability. Full article

Aquaculture is the fastest-growing food production sector, supplying more than half of the world’s seafood and projected to expand further to meet rising global protein demands. Among the various pressures confronting this industry, harmful algal blooms (HABs) rank among the most alarming. Ichthyotoxic flagellates are microalgae known for producing toxins or inducing gill damage that leads to widespread fish mortality. Their increasing frequency poses a critical threat to aquaculture, emphasizing the urgent need for effective and environmentally sustainable strategies to regulate and mitigate these harmful episodes. This study investigated the responses of three ichthyotoxic flagellates renowned for impacting aquaculture operations (Prymnesium parvum, Heterosigma akashiwo, and Fibrocapsa japonica) and tested their susceptibility to two routinely applied chemical agents, hydrogen peroxide (H₂O₂) and copper sulfate (CuSO₄). Mortality, viability, and motility were assessed alongside trajectory-based metrics, including mean squared displacement (MSD) and probability density function (PDF). The results revealed species-specific sensitivities: P. parvum was highly susceptible to H₂O₂, while H. akashiwo and F. japonica were more susceptible to copper toxicity. Ichthyotoxic flagellates exhibited differential sensitivities to chemical treatments, with copper sulfate generally achieving lower EC₅₀ thresholds and greater inhibition of motility than hydrogen peroxide, except in P. parvum. The rapid attenuation of motility at sublethal concentrations highlights swimming behavior as a functional vulnerability, reinforcing the potential for behavior-based mitigation strategies that minimize chemical loading and reduce unintended impacts on cultured fish and surrounding ecosystems. Full article

Under the combined impacts of climate change and intensified human activities, harmful algal blooms (HABs) have occurred with increasing frequency in China’s coastal waters, posing growing risks to marine ecosystems and regional sustainability. Chlorophyll-a concentration (Chl-a), a key indicator of phytoplankton biomass, plays a crucial role in HAB monitoring and early warning. This study integrates satellite remote sensing data from 2000 to 2004, 2011 to 2013, and 2023 to 2024 with in situ measurements and environmental variables (e.g., dissolved oxygen) to investigate Chl-a dynamics in the East China Sea. The results indicate pronounced spatiotemporal heterogeneity across the region. Spectral features were represented using band-ratio methods and the BRG model, followed by variable selection based on the Bayesian Information Criterion (BIC) to determine the optimal band combinations for model training. Six mainstream machine learning models were evaluated, and the Backpropagation Neural Network (BP) was selected as the baseline model due to its superior performance. To further improve model robustness and global optimization capability, the Artificial Lemming Algorithm (ALA) was employed to optimize the BP network, resulting in the ALA-BP inversion model. The optimized model achieved correlation coefficients of 0.933 on the test set and 0.940 on the independent validation set, outperforming the other models. The proposed model was further applied to the 2024 algal bloom event in the East China Sea, successfully capturing the spatiotemporal variations of Chl-a. This study provides an effective retrieval framework for Chl-a in optically complex coastal waters and demonstrates its applicability in HAB monitoring. Full article

Saxitoxin (STX), produced by certain harmful algal bloom (HAB) species, bioaccumulates through the food chain and can cause paralytic toxicity in humans, potentially resulting in fatal outcomes. To date, STX detection has primarily been conducted under laboratory-controlled conditions, and the availability of a gold-standard method for the proactive monitoring and prevention of HAB-induced secondary damage remains limited. Therefore, the present study introduces an electrochemical-based biosensor that is capable of early monitoring of STX in HAB-occurred environments. The conserved region of sxtA4, a nucleic acid precursor that is essential for STX biosynthesis, is immobilized on the sensing membrane surface in a sandwich structure. In this process, target detection is recognized as an electrochemical signal by a methylene blue-labeled detection probe, and the reliability of biosensing is supplemented by an electrochemical trend that is opposite to DNA binding. The application of an alternating current electrochemical flow technique achieves more sensitive detection at attomolar levels and rapid measurement within 10 min than a conventional DNA biosensor based on hybridization. In addition, the designed biosensing structure selectively detects STX-synthesizing and non-synthesizing dinoflagellates significantly. The proposed platform can utilize the identification of STX-induced secondary damage of HAB and provide insight into a field-ready biosensor based on its characterization and detection performance. Full article

Harmful algal blooms (HABs) caused by Karenia brevis (K. brevis) present a persistent ecological and public health challenge across coastal Florida. Reliable bloom forecasting is critical for protecting public health, supporting coastal economies, and enabling timely management responses. This study develops a regionally integrated machine learning framework to predict weekly K. brevis bloom occurrence using environmental data from both the Peace and Caloosahatchee Rivers, combined with coastal bloom records from Southwest Florida and Tampa Bay to enhance the spatial and temporal continuity of the response record. A Random Forest classifier was trained on a multi-decadal dataset incorporating river discharge, nutrient concentrations (total nitrogen and total phosphorus), wind forcing, sea surface temperature, salinity, and sea surface height anomalies as a proxy for Loop Current variability. The model achieved strong predictive performance on a chronologically withheld test set, with an overall accuracy of ~90%, balanced accuracy of 87.6%, and ROC–AUC of 0.972, indicating strong discrimination between bloom and non-bloom conditions with high precision and recall for bloom events. Bloom timing and persistence were captured with strong agreement during ongoing bloom periods, while non-bloom conditions were identified with low false-positive rates. Feature-response analyses indicated that bloom probability increased most sharply under moderate discharge and nutrient conditions, with diminished sensitivity at higher extremes. Learning curve analysis demonstrated robust training performance and stable generalization, with validation accuracy plateauing near 84%, suggesting a data-limited ceiling on forecast skill. By aggregating nutrient inputs across multiple watersheds and integrating spatially aligned bloom observations, this study demonstrates the utility of multi-source machine learning frameworks for regional-scale HAB prediction. The results support the development of early warning tools and provide a reproducible foundation for evaluating how combined watershed loading and physical forcing are associated with K. brevis bloom occurrence in complex estuary systems with watershed and coastal coupling. Full article

Aquatic environments, whether freshwater, brackish, or marine, are increasingly disrupted, in terms of frequency, extent, geographic distribution, and duration, by the massive, worldwide proliferation of harmful and/or nuisance algae, the so-called Harmful algal blooms (HABs), which are a global phenomenon that poses a major threat to human and animal health and ecosystems [...] Full article

Marine mucilage events are intensifying in semi-enclosed seas under accelerating climate- and nutrient-driven pressures, yet their ecosystem-level consequences for aquaculture-linked coastal habitats remain insufficiently documented. This study provides an integrated spatiotemporal assessment of water quality, phytoplankton community structure, and biometric responses of Mytilus galloprovincialis during and after the 2025 mucilage outbreak in the Gulf of Erdek (Sea of Marmara, Türkiye). Mucilage accumulation was associated with sharp increases in turbidity, total suspended solids, and particulate organic matter, alongside declines in dissolved oxygen and pH. Phytoplankton assemblages exhibited marked seasonal restructuring: the mucilage period was characterized by the coexistence of mucilage-forming taxa, non-toxic bloomers, and multiple harmful algal bloom (HAB) groups, including DSP- and ASP-related species, whereas post-mucilage conditions were dominated by non-toxic diatoms with substantially reduced HAB representation. The dinoflagellate species representing the May period in terms of abundance were Noctiluca scintillans and Prorocentrum micans; the diatom species were Chaetoceros radiatus, Cylindrotheca closterium, Pseudo-nitzschia pseudodelicatissima, and Thalassiosira rotula; and the coccolithophore was Phaeocystis pouchetii. Mussel biometric analyses revealed biometric indices and condition values markedly below regional historical baselines during the mucilage event, alongside reduced meat yield, followed by pronounced compensatory growth during the post-mucilage period. Our findings demonstrate that mucilage acts as both a physical and biological stressor, driving short-term ecological shifts in phytoplankton diversity and imposing substantial but reversible physiological impacts on mussel stocks. These results underscore the need for continuous biodiversity monitoring frameworks that integrate mucilage dynamics, HAB occurrence, and aquaculture resilience in regions vulnerable to climate-enhanced organic aggregate formation. Full article