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• Microcystis aeruginosa concentrated arsenic (As) in As-rich water. • Algae aggravated As enrichment in silver carp tissues by digestion and skin contact. • As was mainly accumulated in the intestine and liver of silver carp. • Microcystis aeruginosa eaten by silver carp was mostly excreted in feces. • As had accumulation and food chain magnification in water-algal-silver carp system. Silver carp mediated biological control techniques are often advocated for controlling cyanobacteria blooms in eutrophic water, which are often enriched with arsenic (As). However, the transfer and fate of As during the biological control of cyanobacteria blooms by silver carp in As-rich eutrophic water remain unclear. Based on the simulated ecosystem experiment, the accumulation of As in silver carp and the transfer and fate of As in the water–algae–silver carp system during Microcystis aeruginosa blooms controlled by silver carp were investigated. Microcystis aeruginosa showed high tolerance to As(V). The accumulation of As in different tissues of silver carp was different, as follows: intestine > liver > gill > skin > muscle. After silver carp ingested As-rich Microcystis aeruginosa , As accumulation in the intestine, liver, gill, and skin of silver carp was enhanced under the action of digestion and skin contact. Compared with the system without algal, As accumulation in the intestine, liver, gill, and skin of silver carp increased by 1.1, 3.3, 3.3, and 9.6 times, respectively, after incubation for 30 days in the system with Microcystis aeruginosa , while the accumulation of As in the muscle was only slightly increased by 0.56 mg/kg. This work revealed the transfer and fate of As during algal control by silver carp, elucidated the accumulation mechanism of As in water-algae-silver carp system, enriched our understanding of As bioaccumulation and transformation in As-rich eutrophication water, and provided a scientific basis for assessing and predicting As migration and enrichment in water-algae-silver carp system. [Display omitted] [ABSTRACT FROM AUTHOR]

The conservation of hydrological resources involves continuously monitoring their contamination. A multiagent system composed of autonomous surface vehicles is proposed herein to efficiently monitor the water quality. To achieve a safe control of the fleet, the fleet policy should be able to act based on measurements and fleet state. It is proposed to use local Gaussian processes and deep reinforcement learning to jointly obtain effective monitoring policies. Local Gaussian processes, unlike classical global Gaussian processes, can accurately model the information in a dissimilar spatial correlation which captures more accurately the water quality information. A deep convolutional policy is proposed, that bases the decisions on the observation on the mean and variance of this model, by means of an information gain reward. Using a double deep Q‐learning algorithm, agents are trained to minimize the estimation error in a safe manner thanks to a Consensus‐based heuristic. Simulation results indicate an improvement of up to 24% in terms of the mean absolute error with the proposed models. Also, training results with 1–3 agents indicate that our proposed approach returns 20% and 24% smaller average estimation errors for, respectively, monitoring water quality variables and monitoring algae blooms, as compared to state‐of‐the‐art approaches. [ABSTRACT FROM AUTHOR]

Cyanobacterial harmful algal blooms release toxins and form thick blanket layers on the water surface causing widespread problems, including serious threats to human health, water ecosystem, economics, and recreation. To identify the potential drivers for the bloom, there is a need for extensive observations of the water sources with bloom occurrences. However, the traditional methods for monitoring water sources, such as collection of point ground samples, have proven limited due to spatial and temporal variability of water resources, and the cost associated with collecting samples that accurately represent this variability. These limitations can be addressed through the use of high-frequency satellite data. In this study, we explored the use of Random Forest (RF), which is one of the widely used machine learning architectures, to evaluate the performance of Sentinel-3 OLCI (Ocean and Land Color Imager) images in predicting bloom proxies in the western region of Lake Erie. The sixteen available bands of Sentinel-3 images were used as the predictor variables, while four proxies of the cyanobacterial masses, including Chlorophyll-a, Microcystin, Phycocyanin, and Secchi-depth, were considered as response variables in the RF models, with one RF model per proxy. Each of the proxies comes with a unique set of traits that can help with bloom detection. Among four RF models, the model for Chlorophyll-a performed the best with R2 = 0.55 and RMSE = 20.84 µg/L, while R2 performance for the rest of the other proxies was less than 0.5. This is because Chlorophyll-a is the most dominant and optically active pigment in water, while Phycocyanin, which is a strong indicator of harmful bloom, is present in low concentrations. Additionally, Microcystin, responsible for bloom toxicity, has limited spectral sensitivity, and Secchi-depth could be influenced by various factors besides blooms, such as colored dissolved organic and inorganic matter. On further examining the relationship between the proxies, Microcystin and Secchi-depth were significantly correlated with Chlorophyll-a, which enhances the usefulness of Chlorophyll-a in accurately identifying the presence of algal blooms. [ABSTRACT FROM AUTHOR]

Oil spillages on a sea's or an ocean's surface are a threat to marine and coastal ecosystems. They are mainly caused by ship accidents, illegal discharge of oil from ships during cleaning and oil seepage from natural reservoirs. Synthetic-Aperture Radar (SAR) has proved to be a useful tool for analyzing oil spills, because it operates in all-day, all-weather conditions. An oil spill can typically be seen as a dark stretch in SAR images and can often be detected through visual inspection. The major challenge is to differentiate oil spills from look-alikes, i.e., low-wind areas, algae blooms and grease ice, etc., that have a dark signature similar to that of an oil spill. It has been noted over time that oil spill events in Pakistan's territorial waters often remain undetected until the oil reaches the coastal regions or it is located by concerned authorities during patrolling. A formal remote sensing-based operational framework for oil spills detection in Pakistan's Exclusive Economic Zone (EEZ) in the Arabian Sea is urgently needed. In this paper, we report the use of an encoder–decoder-based convolutional neural network trained on an annotated dataset comprising selected oil spill events verified by the European Maritime Safety Agency (EMSA). The dataset encompasses multiple classes, viz., sea surface, oil spill, look-alikes, ships and land. We processed Sentinel-1 acquisitions over the EEZ from January 2017 to December 2023, and we thereby prepared a repository of SAR images for the aforementioned duration. This repository contained images that had been vetted by SAR experts, to trace and confirm oil spills. We tested the repository using the trained model, and, to our surprise, we detected 92 previously unreported oil spill events within those seven years. In 2020, our model detected 26 oil spills in the EEZ, which corresponds to the highest number of spills detected in a single year; whereas in 2023, our model detected 10 oil spill events. In terms of the total surface area covered by the spills, the worst year was 2021, with a cumulative 395 sq. km covered in oil or an oil-like substance. On the whole, these are alarming figures. [ABSTRACT FROM AUTHOR]

Cyanobacterial harmful algal blooms (CyanoHABs) in the Great Lakes pose risks to residential drinking water use, fisheries, and recreation. Active mitigation of these risks requires rapid detection of CyanoHABs and quantification of the toxins they produce. Here, we present a method of using a long‐range autonomous underwater vehicle (LRAUV) equipped with a 3rd‐generation Environmental Sample Processor (3G‐ESP) to search for and adaptively sample areas of high chlorophyll potentially representative of CyanoHAB biomass. In August 2021, this method was used in western Lake Erie. The experiment highlighted the effectiveness of the LRAUV autonomous search‐and‐sample methodology, and demonstrated how an interdisciplinary team located in different states virtually coordinated LRAUV operations and directed sampling activities via Internet connectivity using shared, web‐based situational awareness tools. The advancements made provide a foundation for future work to increase LRAUV autonomy and adaptiveness for CyanoHAB studies and monitoring in both freshwater and marine settings. [ABSTRACT FROM AUTHOR]

Harmful algal blooms caused by toxin‐producing species of the diatom genus Pseudo‐nitzschia have been linked to anomalously warm ocean conditions in the Northern California Current System. This study compares summertime concentrations of Pseudo‐nitzschia spp. and the toxin they produce, domoic acid, during a marine heatwave year (2019) and a climatologically neutral year (2021). An Imaging FlowCytobot was installed on a fishery survey vessel alongside environmental sensors to continuously sample phytoplankton and oceanographic parameters. This was paired with targeted manual sample collections for nutrients, chlorophyll, and domoic acid. Accumulations of Pseudo‐nitzschia spp. were associated with upwelling zones and established hotspot regions: the Juan de Fuca Eddy, Heceta Bank, and Trinidad Head. Overall, however, Pseudo‐nitzschia spp. and domoic acid concentrations were low during both summers and appear to have been limited by nitrate. Nutrient availability may therefore modulate the response of Pseudo‐nitzschia spp. to warm anomalies. Comparison of these results with 2015, another marine heatwave year but one that produced record concentrations of Pseudo‐nitzschia spp. and domoic acid, suggests that the timing of marine heatwave conditions in the nearshore relative to seasonal upwelling plays a key role in determining whether a Pseudo‐nitzschia spp. harmful algal bloom will occur. [ABSTRACT FROM AUTHOR]

Timely and accurate observations of harmful algal blooms dynamics help to coordinate coastal protection and reduce the damage in advance. To date, predicting changes in the spatial distribution of algal blooms has been challenging due to the lack of suitable tools. The paper proposes that the development and disappearance of algal bloom can be monitored by satellite remote sensing in a large area from the diurnal variation of chlorophyll a. In this paper, 32 pairs of observed data in 2011–2020 showed that it was most appropriate to outline the areas where the diurnal variation (the standard deviation calculated from the daily chlorophyll a) in chlorophyll a was more than 2.2 mg/m3 . Among them, 30 pairs of data showed that the high chlorophyll a diurnal variation could predict the growth of the algal bloom in the next days. In these events, the median area difference between the two spatial distributions was -0.08%. When there was a high diurnal variation in chlorophyll a in the area adjacent to where algal bloom was occurred, a new algal bloom region was likely to spread in subsequent days. Continuous multiday time series showed that the diurnal variation in chlorophyll a can reflect the algal bloom’s overall growth condition. [ABSTRACT FROM AUTHOR]

The application of machine learning (ML) techniques for understanding and predicting organic matter (OM) and harmful algal blooms (HABs) in freshwater systems has increased significantly with the availability of abundant data and advanced monitoring technologies. However, there is a lack of comprehensive reviews concentrating on practical applications and delving into the potential risks associated with misrepresentation or inflation in constructing ML models. This review aims to bridge these gaps by providing a comprehensive overview of various aspects of ML applications in the context of OM and HABs in freshwater systems. It covers practical ML applications for rapid assessment, early warning, and driver analysis, highlighting the diverse range of techniques employed in these areas. Furthermore, it discusses the challenges and considerations associated with data handling, including using in situ and remote sensing data and the importance of appropriate data-splitting techniques to avoid data leakage. To ensure unbiased and reproducible results, this review offers recommendations for model improvement, such as utilizing explainable ML techniques to gain insights into model behavior and avoiding overreliance on a single ML algorithm. It also emphasizes the significance of deploying ML models through user-friendly interfaces, enabling non-experts in ML to effectively utilize these models in real-world water environments. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Humans have always lived with and around algae. At times, algae enable life, and at others, render life difficult. This article examines two sites suffering from atypical—and potentially harmful—algae blooms: Lake Erie in Ohio (USA) and the Riviera Maya (Mexico). Referring to ethnographic fieldwork, as well as to newspaper articles, policy papers, and online fora, we demonstrate how the narratives around algae have changed over time and shed light on how changes in these narratives opened the discussion of wetland repair and coastal integrity. We argue that conceptualizing algae as the "unwanted" unifies people, brings them together, and makes the treatment of lake eutrophication and coastal protection actionable. [ABSTRACT FROM AUTHOR]

Global climate change intensifies the water cycle and makes freshest waters become fresher and vice‐versa. But how this change impacts phytoplankton in coastal, particularly harmful algal blooms (HABs), remains poorly understood. Here, we monitored a coastal bay for a decade and found a significant correlation between salinity decline and the increase of Karenia mikimotoi blooms. To examine the physiological linkage between salinity decreases and K. mikimotoi blooms, we compare chemical, physiological and multi‐omic profiles of this species in laboratory cultures under high (33) and low (25) salinities. Under low salinity, photosynthetic efficiency and capacity as well as growth rate and cellular protein content were significantly higher than that under high salinity. More strikingly, the omics data show that low salinity activated the glyoxylate shunt to bypass the decarboxylation reaction in the tricarboxylic acid cycle, hence redirecting carbon from CO2 release to biosynthesis. Furthermore, the enhanced glyoxylate cycle could promote hydrogen peroxide metabolism, consistent with the detected decrease in reactive oxygen species. These findings suggest that salinity declines can reprogram metabolism to enhance cell proliferation, thus promoting bloom formation in HAB species like K. mikimotoi, which has important ecological implications for future climate‐driven salinity declines in the coastal ocean with respect to HAB outbreaks. [ABSTRACT FROM AUTHOR]

The toxin‐producing dinoflagellate Alexandrium pseudogonyaulax has become increasingly abundant in northern European waters, replacing other Alexandrium species. A. pseudogonyaulax produces goniodomins and lytic substances, which can be cytotoxic toward other organisms, including fish, but we still know little about the environmental conditions influencing its growth and toxicity. Here, we investigated the impacts of different nitrogen sources and light intensities, common bottom‐up drivers of bloom formation, on the growth and toxin content of three A. pseudogonyaulax strains isolated from the Danish Limfjord. While the growth rates were significantly influenced by nitrogen source and light intensity, the intracellular toxin contents only showed strong differences between the exponential and stationary growth phases. Moreover, the photophysiological response of A. pseudogonyaulax showed little variation across varying light intensities, while light‐harvesting pigments were significantly more abundant under low light conditions. This study additionally highlights considerable physiological variability between strains, emphasizing the importance of conducting laboratory experiments with several algal strains. A high physiological plasticity toward changing abiotic parameters points to a long‐term establishment of A. pseudogonyaulax in northern European waters. [ABSTRACT FROM AUTHOR]

Plankton blooms are complex nonlinear phenomena whose occurrence can be described by the two-timescale (fast-slow) phytoplankton-zooplankton model introduced by Truscott and Brindley (Bulletin of Mathematical Biology 56(5):981–998, 1994). In their work, they observed that a sufficiently fast rise of the water temperature causes a critical transition from a low phytoplankton concentration to a single outburst: a so-called plankton bloom. However, the dynamical mechanism responsible for the observed transition has not been identified to the present day. Using techniques from geometric singular perturbation theory, we uncover the formerly overlooked rate-sensitive quasithreshold which is given by special trajectories called canards. The transition from low to high concentrations occurs when this rate-sensitive quasithreshold moves past the current state of the plankton system at some narrow critical range of warming rates. In this way, we identify rate-induced tipping as the underlying dynamical mechanism of largely unpredictable plankton blooms such as red tides, or more general, harmful algal blooms. Our findings explain the previously reported transitions to a single plankton bloom, and allow us to predict a new type of transition to a sequence of blooms for higher rates of warming. This could provide a possible mechanism of the observed increased frequency of harmful algal blooms. [ABSTRACT FROM AUTHOR]

There has been a limited number of studies on the effects of harmful algal blooms (HABs) on natural rocky intertidal ecosystems. From mid-September to early November 2021, an unprecedented HAB caused by Karenia selliformis hit the Pacific coast of southeast Hokkaido, Japan, for the first time, causing massive mortalities among marine organisms. To clarify the immediate impacts of the HAB on the abundance of 10 rocky intertidal species in four functional groups (macroalgae, sessile invertebrates, molluscan grazers, and molluscan carnivores), we focused on two questions: (1) How did the HAB affect the abundance of each species differently at the regional scale? and (2) How did the impacts of the HAB on the abundance of each functional groups vary spatially, and was the spatial variation of the HAB impacts related to the spatial distribution of the cell density of HAB species? To study these issues, we compared census data for 17 years before the HAB and within one month after it for five shores on the southeast coast of Hokkaido. The results showed that two macroalgae species and all three molluscan grazer species declined significantly after the HAB. Moreover, the decrease in molluscan grazers was significantly correlated with cell density. These results suggest that the impacts of the HAB in southeast Hokkaido on the abundance of rocky intertidal organisms are highly variable depending on species and locality, presumably because of differences in species-specific tolerances to HAB toxins and the spatial variation in the density of the HAB organisms. [ABSTRACT FROM AUTHOR]

Cylindrospermopsis raciborskii- dominated harmful algae blooms have been reported globally in recent years. However, our understanding of the ecology of C. raciborskii in natural conditions is still poor. In this study, we collected the water samples from a C. raciborskii -blooming lake, Yilong Lake, in Yunnan province, China, and used both culture-dependent and culture-independent approaches to investigate their microbial communities and the interactions between C. raciborskii and the other bacteria. The composition and diversity of microbial communities were revealed with 16S rRNA gene high-throughput sequencing data analysis. Microbial co-occurrences analysis suggests C. raciborskii may have complex associations with other bacteria. Based on co-inoculation tests, we obtained 14 strains of bacterial strains from the water samples that exhibited either algicidal or promoting effects on a strain of C. raciborskii. Two bacterial isolates exhibited a consistent performance between co-occurrence analysis and experimental results. Effects of these bacteria-algae interspecies interactions on the bloom event are discussed. All these results may provide new insights into the C. raciborskii -dominated blooms and how its interspecies relationships with other bacteria may influence the bloom events in eutrophic waters throughout the world. [ABSTRACT FROM AUTHOR]

Here a nonlinear mathematical model for an aquatic ecosystem containing both non-toxic and toxin-producing algal blooms is formulated and analyzed. The presence of harmful algae impacts the aquatic population, subsequently affecting the human population that relies on zooplankton (fish, crabs, etc.). It also deteriorates the water quality, and it is advisable not to use water from lakes containing harmful algae. Here, our focus is to understand the dynamics of aquatic ecosystems when we have both types of algae, one that is non-toxic and acts as food to zooplankton, and another that is toxin-producing algae that causes death to zooplankton. The predation of zooplankton on algae influences the growth of the algae population. First, we consider a deterministic model, and later, this model is extended to stochastic model to check whether it has any impact on the dynamics of the populations under consideration. Various equilibria of the proposed model are derived, and the local stability of these equilibria is thoroughly examined. The existence of Hopf-bifurcation is also demonstrated for a suitable set of parameters. The results of deterministic and stochastic models are compared using numerical simulation. Upon examining our results and simulation data, it is evident that modifying specific key parameters, such as the growth rate of non-toxic algae due to nutrient intake and the parameter associated with the predation of non-toxic algae by zooplankton, has a substantial impact on algal bloom dynamics. Minor adjustments to these parameters can result in an augmented presence of non-toxic algae and a simultaneous decrease in toxin-producing algae, aligning with the primary objectives set by policymakers. [ABSTRACT FROM AUTHOR]

Harmful blooms of the toxic dinoflagellate Ostreopsis cf. ovata have been a recurrent phenomenon along the Mediterranean coasts in the last decades. Since first recorded, there has been a widespread belief that the extension of this dinoflagellate, well-known in tropical areas, to higher latitudes was due to global warming and the general rise of seawater temperature. Blooms of O. cf. ovata along the Conero Riviera (northern Adriatic Sea) occur between the end of the summer and the beginning of the autumn since 2006. The Ostreopsis cf. ovata abundances collected from its first record to today were analysed to better define the interannual trend of this phenomenon and its possible linking to certain climate change predictors. A significant increasing trend in the magnitude of Ostreopsis phenomenon was observed up to year 2012, then a stabilization at relatively low values was observed. This trend does not follow the incessant increase in water temperature observed during the last three decades in the Adriatic Sea, but rather recalls patterns seen in invasive species, although the provenience of O. cf. ovata in the Mediterranean Sea is still unresolved. Even if the Ostreopsis bloom in this area seems to slightly lessen in the last decade, Ostreopsis abundances still reach values of up to 103 cells cm−2 which could be harmful to human health. [ABSTRACT FROM AUTHOR]

Green algae blooms of the genus Ulva are occurring globally and are primarily attributed to anthropogenic factors. At Los Tubos beach in Algarrobo Bay along the central Chilean coast, there have been blooms of these algae that persist almost year-round over the past 20 years, leading to environmental, economic, and social issues that affect the local government and communities. The objective of this study was to characterize the species that form these green tides based on a combination of ecological, morpho-anatomical, and molecular information. For this purpose, seasonal surveys of beached algal fronds were conducted between 2021 and 2022. Subsequently, the sampled algae were analyzed morphologically and phylogenetically using the molecular markers ITS1 and tufA, allowing for the identification of at least five taxa. Of these five taxa, three (U. stenophylloides, U. uncialis, U. australis) have laminar, foliose, and distromatic morphology, while the other two (U. compressa, U. aragoensis) have tubular, filamentous, and monostromatic fronds. Intertidal surveys showed that U. stenophylloides showed the highest relative coverage throughout the seasons and all intertidal levels, followed by U. uncialis. Therefore, we can establish that the green tides on the coast of Algarrobo in Chile are multispecific, with differences in relative abundance during different seasons and across the intertidal zone, opening opportunities for diverse future studies, ranging from ecology to algal biotechnology. [ABSTRACT FROM AUTHOR]

River algal blooms have become a global environmental problem due to their large impact range and environmental hazards. However, the complex mechanisms underlying these blooms make prediction and prevention challenging. Here, we employed empirical dynamic modeling (EDM) and machine learning to reveal the causes and predict diatom blooms from 2003 to 2017 in the Han River of China. The diatom cell density ranged from 0.1 to 5.1 × 107 cells L−1, whereas algal blooms often lasted for 10 days with density exceeding 107 cells L−1. The EDM results elucidated that, under consistent high nutrient concentrations, algal blooms were primarily regulated by eight environmental factors: water temperature in the Han River; water levels, flow velocities, and streamflow discharges in the Han River and the Yangtze River; and water level variation in the Han River. The poor performance (coefficient of determination R2 < 0) of the multiple linear regression, EDM, and random forest models indicated the challenge of predicting algae density. Therefore, we used machine learning classification models to predict algal blooms occurrences. With the resampling techniques to account for imbalanced data, machine learning models achieved perfect classification prediction (Kappa value = 1) of the 13 algae‐bloom events with a 10‐day lead time during a 15‐year period, providing an important reference for preemptive warning of riverine algal blooms. Plain Language Summary: River algal blooms have become a global environmental problem due to their large impact range and environmental hazards. The Han River is one of the most frequent areas of river algal blooms in China, but there is no clear conclusion on the causes of algal blooms formation. In this study, we combined two methods to reveal the causes and predict the occurrence of algal blooms in the Han River. Our results show that the prediction of algal blooms in the Han River is feasible. These blooms are primarily affected by the water temperature and seven other hydrological variables in the Han and Yangtze River. This study provides a starting point for the preemptive warning of riverine algal blooms. Key Points: Empirical dynamic modeling detects causality for river algal bloomsMachine learning accurately predicts the occurrence of algal blooms in the Han RiverResampling techniques improve robustness and accuracy of machine learning [ABSTRACT FROM AUTHOR]

As Arctic sea ice and its overlying snow cover thin, more light penetrates into the ice and upper ocean, shifting the phenology of algal growth within the bottom of sea ice, with cascading impacts on higher trophic levels of the Arctic marine ecosystem. While field data or autonomous observatories provide direct measurements of the coupled sea ice‐algal system, they are limited in space and time. Satellite observations of key sea ice variables that control the amount of light penetrating through sea ice offer the possibility to map the under‐ice light field across the entire Arctic basin. This study provides the first satellite‐based estimates of potential sea ice‐associated algal bloom onset dates since the launch of CryoSat‐2 and explores how a changing snowpack may have shifted bloom onset timings over the last four decades. Plain Language Summary: Light plays a key role in sustaining life in the ocean. In the polar regions, sea ice and its overlying snow cover limit the amount of light reaching the upper ocean. Ice algae mainly live at the bottom centimeters of the ice and are important primary producers, using CO2 and inorganic nutrients to release O2 and create organic matter that is available to other trophic levels of the marine ecosystem. Every year rapid growth of bottom‐ice algae, known as the bloom onset, is triggered by the return of the sun and the increase in light intensities above that needed for photosynthesis to occur. Studies have shown that ice algae are shade adapted and can bloom under very low light conditions, yet the impact of recent changes in the snow‐ice cover that allow more light to penetrate earlier in the spring season remain uncertain. This study takes satellite data to map how ice algal bloom onset may be changing over time. Key Points: New processing methods to satellite data allow for daily under‐ice light mappingSatellite approaches allow for scaling up to the pan‐Arctic scalePhenology of ice‐algal bloom onset is strongly tied to snow cover variability [ABSTRACT FROM AUTHOR]

Algal blooms are increasing worldwide, driven by elevated nutrient inputs. However, it is still unknown how tropical benthic algae will respond to heatwaves, which are expected to be more frequent under global warming. In the present study, a multifactorial experiment was carried out to investigate the potential synergistic effects of increased ammonium inputs (25 μM, control at 2.5 μM) and a heatwave (31°C, control at 25°C) on the growth and physiology (e.g., ammonium uptake, nutrient assimilation, photosynthetic performance, and pigment concentrations) of two bloom‐forming algal species, Cladophoropsis sp. and Laurencia sp. Both algae positively responded to elevated ammonium concentrations with higher growth and chlorophyll a and lutein concentrations. Increased temperature was generally a less important driver, interacting with elevated ammonium by decreasing the algaes' %N content and N:P ratios. Interestingly, this stress response was not captured by the photosynthetic yield (Fv/Fm) nor by the carbon assimilation (%C), which increased for both algae at higher temperatures. The negative effects of higher temperature were, however, buffered by nutrient inputs, showing an antagonistic response in the combined treatment for the concentration of VAZ (violaxanthin, antheraxanthin, zeaxanthin) and thalli growth. Ammonium uptake was initially higher for Cladophoropsis sp. and increased for Laurencia sp. over experimental time, showing an acclimation capacity even in a short time interval. This experiment shows that both algae benefited from increased ammonium pulses and were able to overcome the otherwise detrimental stress of increasingly emerging temperature anomalies, which provide them a strong competitive advantage and might support their further expansions in tropical marine systems. [ABSTRACT FROM AUTHOR]

Phytoplankton plays an essential role in the biogeochemical cycle because it is at the top of the food chain and is a source of oxygen. Eutrophication causes coastal areas to deteriorate as industrialization accelerates, leading to harmful algal blooms (HABs), severely affecting human and ecological health. The frequency and extent of HAB events potentially may increase due to climate change. HAB outbreaks have led to substantial losses for major coastal economies globally, and therefore have emerged as a critical research focus in environmental sciences. However, the lack of an overview of diverse factors influencing HABs complicates the cause identification and the effective countermeasure development for HAB occurrence, thereby impeding the formulation of targeted strategies for prediction and mitigation. Therefore, this review summarizes the influential factors affecting HABs in coastal areas, including water quality factors (nutrients, salinity, stratification, and biological factors) and climatological factors (temperature, pH and pCO2 , and irradiance and light). Recent work with several harmful algae species suggested that warmer temperatures combined with nutrient variation, stronger stratification, and ocean acidification may increase the growth of some toxic dinoflagellate species. Although the effects of factors vary for different species and locations, the intensification of anthropogenic activities and climate change likely will increase the frequency, outbreak scale, and severity of most coastal HABs. Because predicting HABs is crucial for understanding the factors and synergy affecting their growth and minimizing losses for decision makers and stakeholders, we reviewed models for predicting HABs, including process-based models, traditional statistical-empirical models, and data-driven machine learning models. Predicting HABs becomes more challenging as the spatial distribution of harmful algae is influenced by future climate patterns. This review paper presents a comprehensive overview of the various factors impacting HABs in coastal areas, serving as a valuable resource for decision makers and researchers to design targeted research and mitigation strategies. [ABSTRACT FROM AUTHOR]

One of the main threats to freshwater resources is pollution from anthropogenic activities such as rapid urbanization and excessive agricultural nutrient runoff. Remote sensing technologies have been effectively used in monitoring and mapping rapid changes in the marine environment and assessing the overall health of freshwater ecosystems. The main goal of this study is to comparatively evaluate the performance of index-based and classification-based approaches in mapping dense floating algal blooms observed in Lake Burdur using Sentinel-2 imagery. For index-based mapping, algae-specific indices, namely the Floating Algae Index (FAI), Adjusted Floating Algae Index, Surface Algal Blooms Index (SABI), and Algal Blooms Detection Index (ABDI), were used. At the same time, pixel- and object-based Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and Long Short-Term Memory Network (LSTM) were utilized for classification-based algal mapping. For this purpose, seven Sentinel-2 images, selected through time series analysis performed on the Google Earth Engine platform, were used as the primary dataset in the application. The results show that high-density floating algae formations can be detected over 99% by both indices and classification-based approaches, whereas pixel-based classification is more successful in mapping low-density algal blooms. When two-class thematic maps representing water and floating algae classes were considered, the maps produced by index-based FAI using an appropriate threshold value and the classification-based RF algorithm reached an overall accuracy of over 99%. The highest algae density in the lake was observed on July 13, 2021, and was determined to be effective in ~ 45 km2 of the lake's surface. [ABSTRACT FROM AUTHOR]

Recreational and occupational contact with freshwater harmful algal blooms (HABs) pose human health and economic risks worldwide. Individual U.S. states control monitoring, reporting, and mitigation of recreational exposure to HABs. We surveyed states to catalog responses to HAB problems. We used this data to develop a state‐specific HAB response index (HABRI) and compared it to HAB risks derived from empirical nation‐wide data and per capita state environmental management and public health spending. States varied in regulations, reporting, monitoring, communication, and mitigation. The HABRI was not correlated with empirically based risk. Several states had no limits on toxin exposure or limits that were higher than recommended by the USEPA or World Health Organization. Other states did not provide public signage or notification when HABs were occurring and recreation could be hazardous. Increased federal involvement, communication among states, and state and federal legislation could minimize this variation and positively influence responses. We identify best practices for addressing HABs in our study that could provide guidance to authorities in any part of the world while developing new programs or enhancing existing efforts. [ABSTRACT FROM AUTHOR]

Key Takeaways: Flow imaging microscopy (FIM) combines a flow cytometer's fluidic architecture with a microscope's spatial resolution to allow morphometric analysis of objects in suspensions such as water. FIM's real‐time analysis and identification of potential cyanotoxin producers help utilities understand population dynamics to maintain healthier source waters and reduce response times when a bloom is indicated. Case studies show FIM's advantages and limitations in harmful algal bloom monitoring programs for reservoir management and water treatment. [ABSTRACT FROM AUTHOR]

Harmful algal blooms (HABs) in marine environments have significant adverse effects on public health, aquaculture and recreational activities. Surges of certain phytoplanktonic toxin-producing microalgae (mostly dinoflagellates or diatoms species) can induce Amnesic, Diarrhetic or Paralytic Shellfish Poisoning (ASP, DSP and PSP). Among HAB species, the genus Dinophysis leads to DSP in human consumers; this being the most recurrent problem in the Iberian Peninsula with the biggest economic impact on clam production and harvesting. While complete elimination of HABs is not feasible, timely implementation of appropriate measures can prevent their negative consequences. This is critical for aquaculture. Research on D. acuminata (dominant Dinophysis species in the North Atlantic) has been focused on ecophysiology and population dynamics, although with few modelling attempts. Weekly monitoring along the Portuguese coast since 2006 has revealed that D. acuminata thrives under spring/summer photosynthetically active radiation (PAR) coupled with water temperatures below 20°C, which typically coincide with the local upwelling regime. In order to advance this knowledge numerically, we developed a demographic model linking D. acuminata growth rate to PAR and sea surface temperature (SST). The 13-year (1-Jan-2006 to 31-Dec-2018) time-series of observations was closely fit by model forecasts. However, the model demonstrated limitations in issuing timely warnings of harmful proliferation of D. acuminata, failing to do so in 50% of cases, and issuing incorrect warnings in 5% of the cases. Furthermore, improving the odds of emitting timely warnings always worsened the odds of emitting false warnings, and vice-versa. To simultaneously improve both aspects, the modelling results clearly indicated the need of implementing both census/ projection intervals smaller than 7 days and a laboratory detection limit below 20 cell/L. The time resolution of the census and of the model proved to be the most limiting factor that must be addressed in order to improve numerical forecasting of HABs. [ABSTRACT FROM AUTHOR]

Measurement of planktonic chlorophyll‐a—a proxy for algal biomass—in rivers may represent local production or algae transported from upstream, confounding understanding of algal bloom development in flowing waters. We modeled 3 years of chlorophyll‐a transport through a 394‐km portion of the Illinois River and found that although algal biomass is longitudinally widespread, most net production occurs at river control points in the upper reaches (up to 3.7 Mg chlorophyll‐a y−1 km−1). Up to 69% of the algal biomass in the upper river was a result of within‐reach production, with the remainder recruited from headwaters and tributaries. High chlorophyll‐a measured farther downstream was largely because of transport from source‐area control points, with substantial net losses of algal biomass occurring in the lower river. Modeling the often‐overlooked river transport component is necessary to characterize where, when, and why planktonic algae grow and predict how far and fast they move downstream. Plain Language Summary: Planktonic algae in rivers may accumulate during periods of high productivity stimulated by favorable light, temperature, nutrient, and flow conditions, which can disrupt ecological processes and affect human uses including recreation and drinking water supply. Planktonic algae observed in rivers may occur because of local growth or transport from upstream source areas. Therefore, considering both local and upstream conditions may improve early warnings of potentially harmful blooms. Along a 394‐km stretch of the Illinois River, we found that most of the algae grew in the upper reaches and was then transported to downstream reaches, contributing to potential downstream harms such as excessive turbidity, organic carbon, biological oxygen demand, and algal toxins. We demonstrate how the often‐overlooked river transport component can be quantified to better identify where, when, and why algae grow in river networks. Key Points: Planktonic algal biomass is pervasive in the Illinois River, yet production is favored at certain locations and timesMost planktonic algal biomass was produced in upper‐reach control points that supplied downstream areasTransport analysis using local and upstream data improves understanding of river algal blooms [ABSTRACT FROM AUTHOR]

BACKGROUND: Paralytic shellfish poisoning caused by human consumption of shellfish fed on toxic algae is a public health hazard. It is essential to implement shellfish monitoring programs to minimize the possibility of shellfish contaminated by paralytic shellfish toxins (PST) reaching the marketplace. RESULTS: This paper proposes a rapid detection method for PST in mussels using near‐infrared spectroscopy (NIRS) technology. Spectral data in the wavelength range of 950–1700 nm for PST‐contaminated and non‐contaminated mussel samples were used to build the detection model. Near‐Bayesian support vector machines (NBSVM) with unequal misclassification costs (u‐NBSVM) were applied to solve a classification problem arising from the fact that the quantity of non‐contaminated mussels was far less than that of PST‐contaminated mussels in practice. The u‐NBSVM model performed adequately on imbalanced datasets by combining unequal misclassification costs and decision boundary shifts. The detection performance of the u‐NBSVM did not decline as the number of PST samples decreased due to adjustments to the misclassification costs. When the number of PST samples was 20, the G‐mean and accuracy reached 0.9898 and 0.9944, respectively. CONCLUSION: Compared with the traditional support vector machines (SVMs) and the NBSVM, the u‐NBSVM model achieved better detection performance. The results of this study indicate that NIRS technology combined with the u‐NBSVM model can be used for rapid and non‐destructive PST detection in mussels. © 2023 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

The melting of Antarctic glaciers has become a significant issue as a result of global climate change. Algae on the Antarctic ice/snow is an important part of terrestrial photosynthetic organisms. Monitoring and tracking these algal blooms is crucial for understanding the melting of glaciers in the region. Due to the climatic and natural conditions of the region, traveling to and arranging logistics for monitoring and observing snow algae in the Antarctic continent becomes extremely challenging. To overcome these challenges, a novel algorithm has been developed and designed to automatically detect and analyze green algae (Chlorella sp.) from satellite images. Leveraging the vast and free available data from the Sentinel-2 satellite, the algorithm utilizes its high spectral resolution capabilities, capturing invaluable information from various spectral bands. The algorithm was formulated based on the image obtained on February 28, 2017, where green algae formations were intensively seen in the Ryder Bay. The algorithm was developed based on rule-based detection of algae, with the usage of reflection values from the areas where ground truth was established on this date. The developed algorithm was coded and tested using Python version 3.9. The accuracy analysis of the algorithm was conducted using overall accuracy (OA), F1 score, and Kappa statistical test. As a result of the analysis, the OA, F1 score, and Kappa statistic values were calculated as %91, %88.82-%95.27, and 0.901, respectively. [ABSTRACT FROM AUTHOR]

The Arctic Ocean is currently undergoing significant transformations due to climate change, leading to profound changes in its microbial planktonic communities. These communities consist of a wide range of organisms, including photoautotrophic prokaryotes and eukaryotes, as well as heterotrophic, phagotrophic, and mixotrophic protistan species. Here, for simplicity, we refer to these single-celled species as phytoplankton. Within this diversity, potentially toxic and/or harmful algal species (hereafter abbreviated as "HA") are of particular concern. These organisms have the potential to spread into Arctic waters, posing threats to both human and ecosystem health. Despite their importance, the spatial and temporal distribution of phytoplankton communities, including HA species, in the North American Arctic, remains poorly understood. To address this gap, we compiled and synthesized the largest possible body of data from different databases, individual published and unpublished datasets, and partitioned it into nine regions based on the Large Marine Ecosystem classification. Our dataset contains 385 800 individual georeferenced data points and 18 268 unique sampling events, revealing greater diversity than previously thought, with 1445 unique taxa. Heterokontophyta (which notably included diatoms) and Dinoflagellata were the most dominant phyla. Our results indicate distinct spatial patterns of diversity, with the highest diversity observed in Atlantic-influenced regions of the North American Arctic. For most of the HA species recorded in our database, no evidence was found for an increase in the northernmost latitude where HA species are observed over the years, meaning that there is no substantial spread of HA species into the North American part of the Arctic. Our study challenges the traditional view of the Arctic as being unsuitable for toxin-producing and harmful algae and highlights the importance of extensive and long-term sampling efforts to understand the region's biodiversity. Overall, our findings provide new insights into the spatial patterns and biodiversity of phytoplankton and other protists in the North American Arctic and have implications for understanding the ecological functioning and response of this region to ongoing climate change. [ABSTRACT FROM AUTHOR]

Mesoscale eddies play an important role in driving the dynamics of the Arctic Ocean. Understanding their behavior is crucial for comprehending the ongoing changes in the region. In this study, by using a novel decade‐long simulation at 1 km resolution with the unstructured‐mesh Finite volumE Sea ice‐Ocean Model, we evaluate the spatial and temporal variability of eddy kinetic energy in the Eurasian Basin. We find that monthly, annual, and interannual variability of EKE near the surface is predominantly influenced by changes in sea ice cover, while the eddy activity at deeper depth, being shielded from the surface by ocean stratification, is more strongly influenced by local baroclinic energy conversion. Moreover, our research demonstrates that eddies in the Eurasian Basin can transport ocean heat from the Atlantic Water layer toward sea ice and cause local basal melting in the order of about 20 cm per month even in wintertime. Our study suggests that eddy activity in the Arctic Ocean will strengthen along with future sea ice decline, and that the impact of ocean heat of the Atlantic Water layer on sea ice retreat may become prominent. Plain Language Summary: Mesoscale eddies are swirling currents that are generally considered the "weather" of the ocean. They can be found everywhere in the ocean and are crucial for understanding many processes such as the transport of heat and salt, the structure of ocean stratification, but also the distribution of nutrients that influence the biological system from algae blooms to micro and macro fauna. Distinct from major eddies in lower latitudes, most eddies in the Arctic Ocean are only about 10 km in diameter. This makes it incredibly hard to simulate them in numerical models because a very high spatial resolution is needed to adequately represent them. In this study we use a simulation with 1 km horizontal resolution to analyze the role of eddies in the Eurasian Basin of the Arctic Ocean and to understand what drives their variability. We found that the dynamics differ greatly between the surface and deeper water layers, with the surface being influenced strongly by sea ice cover, while the deeper layer is shielded from this effect by the stratification. We have also shown that eddies can pump warm water from the Atlantic Water layer up to the mixed layer, thus melting sea ice even in winter. Key Points: Mesoscale eddies in the Eurasian Basin can bring ocean heat from the Atlantic Water layer to sea ice and cause basal melting even in winterVariability of surface eddy kinetic energy is mainly controlled by sea ice coverEddy activity at depth is mainly controlled by baroclinic instability and ocean stratification [ABSTRACT FROM AUTHOR]

Excessive algae growth can lead to negative consequences for ecosystem function, economic opportunity, and human and animal health. Due to the cost‐effectiveness and temporal availability of satellite imagery, remote sensing has become a powerful tool for water quality monitoring. The use of remotely sensed products to monitor water quality related to algae and cyanobacteria productivity during a bloom event may help inform management strategies for inland waters. To evaluate the ability of satellite imagery to monitor algae pigments and dissolved oxygen conditions in a small inland lake, chlorophyll‐a, phycocyanin, and dissolved oxygen concentrations are measured using a YSI EXO2 sonde during Sentinel‐2 and Sentinel‐3 overpasses from 2019 to 2022 on Lake Mendota, WI. Machine learning methods are implemented with existing algorithms to model chlorophyll‐a, phycocyanin, and Pc:Chla. A novel machine learning‐based dissolved oxygen modeling approach is developed using algae pigment concentrations as predictors. Best model results based on Sentinel‐2 (Sentinel‐3) imagery achieved R2 scores of 0.47 (0.42) for chlorophyll‐a, 0.69 (0.22) for phycocyanin, and 0.70 (0.41) for Pc:Chla. Dissolved oxygen models achieved an R2 of 0.68 (0.36) when applied to Sentinel‐2 (Sentinel‐3) imagery, and Pc:Chla is found to be the most important predictive feature. Random forest models are better suited to water quality estimations in this system given built in methods for feature selection and a relatively small data set. Use of these approaches for estimation of Pc:Chla and dissolved oxygen can increase the water quality information extracted from satellite imagery and improve characterization of algae conditions among inland waters. Plain Language Summary: Agricultural runoff and wastewater discharge has fueled nutrient pollution in Lake Mendota over the last century. As a result, algae blooms have become a common summertime occurrence on Lake Mendota. Algae blooms are often made up of different algae species. Green algae are typically harmless, but cyanobacteria (blue‐green algae) can produce a range of toxins harmful to human and animal health. The ability to discriminate between cyanobacteria and green algae during a bloom may be useful for lake managers and public health officials in making decisions about closing waterfront areas and communicating with the public. In recent years, satellite imagery has become a powerful tool for monitoring water quality. In this study, we build models that use imagery from two satellites to estimate the abundance of cyanobacteria versus green algae in Lake Mendota. We also find that our algae estimates can be used to model dissolved oxygen, an important water quality indicator that cannot be directly measured from satellite imagery. The methods presented for satellite‐based monitoring of algae pigments, the Pc:Chla ratio, and dissolved oxygen has the potential to increase the water quality information extracted from satellite imagery, better characterize algae blooms, and inform management strategies for Lake Mendota. Key Points: Chlorophyll‐a and phycocyanin are sampled from 2019 to 2022 on Lake Mendota, WISentinel‐2 and Sentinel‐3 are used to model chlorophyll‐a, phycocyanin, and Pc:ChlaA model based in situ data allows for satellite‐based estimates of dissolved oxygen [ABSTRACT FROM AUTHOR]

Hazardous impacts caused by harmful algal blooms (HABs) have facilitated the development of novel effective approaches for controlling the frequent HABs. Recently, bacteria with algicidal effects, as a promising alternative tool for eliminating the HABs-forming species have received wide concerns. In the present study, a marine bacterial strain 6A1 with potent algicidal activity was successfully isolated from a bloom of Akashiwo sanguinea, andthe characteristics related to its algicidal action were systematically investigated. Sequencing results of 16S rDNA suggested that this bacterium belongs to the genus Arenibacter in the family Flavobacteriaceae, while the phylogenetic analysis revealed that Arenibacter sp. strain 6A1 was the only algicidal bacterium in Arenibacter to date, according to the best of our knowledge. Arenibacter sp. strain 6A1 had an extraordinarily high algicidal performance when it was added to A. sanguinea culture with a volume fraction of 1.5% for 2 h. The algicidal performance of strain 6A1 was concentration-dependent and time-dependent. The best algicidal efficacy of strain 6A1 was seen during the stationary phase, while the resistance of A. sanguinea to strain 6A1 declined as the growth cycle progressed. Subsequently, the indirect algicidal mode of Arenibacter sp. strain 6A1 was verified and the algicidal compounds produced by strain 6A1 were proved to be heat-resistant. Further research showed that strain 6A1 caused a severe damage in photosynthesis, oxidative stress, and also damage in three HABs-forming algae to varying degrees. The greatest algicidal performance was found on unarmored dinoflagellate A. sanguinea, followed by raphidophyte Chattonella antiqua and armored dinoflagellate Alexandrium tamarense. The Arenibacter sp. strain 6A1 exhibited selective algicidal activity on a wide range of algal species, primarily targeting Pyrrophyta and Ochrophyta. The proposed bacteria (strain 6A1) can be a powerful and eco-friendly tool for emergency treatment of HABs caused by certain algal species in coastal water bodies. [ABSTRACT FROM AUTHOR]

Phytoplankton and other protists in the 3 µmto 50 µmsize fraction are grazed on by zooplankton and form the base of Arctic marine food webs essential for local indigenous communities. Anthropogenic climate change is increasing stratification over much of the Arctic Ocean and surrounding seas, but the influence of stratification on protist communities in more coastal regions along Eastern Hudson Bay, Hudson Strait and Ungava Bay is little known. We used 18S rRNA and rDNA amplicon sequencing during two consecutive summers (2017 and 2018) and detailed water column properties to compare the 3 µm to 50 µm protist communities under contrasting stratification regimes in the Eastern Hudson Bay Complex. We found that the surface mixed layer in Eastern Hudson Bay, which is under the influence of river runoff, was strongly stratified and dominated by mixotrophic and bacterivorous taxa, mostly the dinoflagellates Heterocapsa rotundata and Gymnodiniales spp., and a more diatom-dominated community at the Subsurface Chlorophyll Maximum (SCM), which persisted in deeper colder and more saline water. The massive sequencing effort retrieved seven putative toxic algae from the upper warmer waters of eastern Hudson Bay. These included Pseudo-nitzschia spp. and potentially harmful dinoflagellates, most notably Alexandrium sp. The persistent weaker stratification conditions in Hudson Strait and Ungava Bay in summer favored a different diatom community, dominated by Chaetoceros spp. and Thalassiosira spp., and small photosynthetic flagellates including Phaeocystis pouchetii and Micromonas polaris. As freshwater input increases and stratification intensifies in the Arctic, our findings suggest the summer dinoflagellate-based community seen in coastal Hudson Bay may also be favored in other regions receiving increased river runoff. These conditions could also favor harmful algal events. The Hudson Strait and Ungava Bay protist communities were found nearer the surface and consisted of diverse species able to profit from ongoing nutrient input due to tidal mixing. These results suggest greater resilience in this and other tidally influenced coastal Arctic Bays lacking larger rivers inputs. [ABSTRACT FROM AUTHOR]

Organic-rich shale rocks from the Paleocene–Eocene Palana Formation in western Rajasthan, India, were systematically investigated based on inorganic and organic geochemistry combined with microscopic examinations to evaluate the sedimentary paleoenvironmental conditions and volcanic activity and their impact on the high organic carbon accumulation. The Palana shales are categorized by high organic matter (OM) and sulfur contents, with total values up to 36.23 wt.% and 2.24 wt.%, respectively. The richness of phytoplankton algae (i.e., telalginite and lamalginite) together with redox-sensitive trace elements further suggests a marine setting and anoxic environmental conditions during the Paleocene–Eocene. The significant low oxygen conditions may contribute to enhancing the preservation of organic matter during deposition. The mineralogical and inorganic geochemical indicators demonstrate that the Palana organic-rich shale facies was accumulated in a warm and humid climate with moderate salinity stratification conditions in the water columns, thereby contributing to the high bioproductivity of the phytoplankton algae blooms within the photic zone. The presence of significant contents of zeolite derived from volcanic material together with silica minerals such as apophyllite and tridymite in most of the Palana organic-rich shales indicates a volcanic origin and supports hydrothermal activities during the Paleocene–Eocene period. These volcanic activities in this case are considered the influx of large masses of nutrients into the photic zone due to the ash accumulation, as indicated by the presence of the zeolites in the Palana shales. Therefore, the high bio-productivity associated with effective OM preservation led to the organic carbon accumulation in the Palana Formation during the Paleocene–Eocene. [ABSTRACT FROM AUTHOR]

Cyanobacterial harmful algal blooms (cyanoHABs) can harm people, animals, and affect consumptive and recreational use of inland waters. Monitoring cyanoHABs is often limited. However, chlorophyll‐a (chl‐a) is a common water quality metric and has been shown to have a relationship with cyanobacteria. The World Health Organization (WHO) recently updated their previous 1999 cyanoHAB guidance values (GVs) to be more practical by basing the GVs on chl‐a concentration rather than cyanobacterial counts. This creates an opportunity for widespread cyanoHAB monitoring based on chl‐a proxies, with satellite remote sensing (SRS) being a potentially powerful tool. We used Sentinel‐2 (S2) and Sentinel‐3 (S3) to map chl‐a and cyanobacteria, respectively, classified chl‐a values according to WHO GVs, and then compared them to cyanotoxin advisories issued by the California Department of Water Resources (DWR) at San Luis Reservoir, key infrastructure in California's water system. We found reasonably high rates of total agreement between advisories by DWR and SRS, however rates of agreement varied for S2 based on algorithm. Total agreement was 83% for S3, and 52%–79% for S2. False positive and false negative rates for S3 were 12% and 23%, respectively. S2 had 12%–80% false positive rate and 0%–38% false negative rate, depending on algorithm. Using SRS‐based chl‐a GVs as an early indicator for possible exposure advisories and as a trigger for in situ sampling may be effective to improve public health warnings. Implementing SRS for cyanoHAB monitoring could fill temporal data gaps and provide greater spatial information not available from in situ measurements alone. Plain Language Summary: Lakes often have algal blooms that create a water quality concern, especially when they contain cyanobacteria, which can be toxic to both humans and animals. These harmful algal blooms are of great concern in areas with limited water supply in states such as California. While it is often difficult and costly to collect and monitor toxin concentrations, monitoring concentrations of chlorophyll‐a (chl‐a) –a measure of how much algae are present—is relatively common and can even be accomplished using satellite remote sensing. There have been multiple studies that have found a relationship between toxins produced by cyanobacteria and chl‐a. The World Health Organization (WHO) has recently released (2021) an updated release of their previous 1999 guidance values for toxin monitoring based on chl‐a concentration. With satellite data, we were able to measure chl‐a concentration in a major reservoir in California, and then classify the chl‐a measurements into the WHO's guidance values for toxins. We compared the satellite‐based guidance values to the public advisory levels currently set by the California Department of Water Resources. Our results indicate that SRS of chl‐a is a reasonable substitute for cyanobacteria toxin advisories, and our framework can be applied to similar cyanobacteria dominated lakes. Key Points: The World Health Organization (WHO) updated cyanobacteria harmful algal blooms (cyanoHABs) guidelines for chlorophyll‐a (chl‐a) as a proxyWith satellite remote sensing (SRS), we estimated and classified chl‐a to compare cyanotoxins advisories used by CaliforniaThis study provides a framework for evaluating public health utility of SRS for enhancing cyanotoxin monitoring globally [ABSTRACT FROM AUTHOR]

Toxic cyanobacterial blooms (CBs) are becoming more frequent globally, posing a threat to freshwater ecosystems. While making long‐range forecasts is overly challenging, predicting imminent CBs is possible from precise monitoring data of the underlying covariates. It is, however, infeasibly costly to conduct precise monitoring on a large scale, leaving most lakes unmonitored or only partially monitored. The challenge is hence to build a predictive model that can use the incomplete, partially‐monitored data to make near‐future CB predictions. By using 30 years of monitoring data for 78 water bodies in Alberta, Canada, combined with data of watershed characteristics (including natural land cover and anthropogenic land use) and meteorological conditions, we train a Bayesian network that predicts future 2‐week CB with an area under the curve (AUC) of 0.83. The only monitoring data that the model needs to reach this level of accuracy are whether the cell count and Secchi depth are low, medium, or high, which can be estimated by advanced high‐resolution imaging technology or trained local citizens. The model is robust against missing values as in the absence of any single covariate, it performs with an AUC of at least 0.78. While taking a major step toward reduced‐cost, less data‐intensive CB forecasting, our results identify those key covariates that are worth the monitoring investment for highly accurate predictions. Key Points: Toxic algae blooms pose threats globally, longing for prompt predictionOur work uses less costly, incomplete data sets to make near‐future bloom predictionsWe predict blooms with area under the curve 0.83 using partial data of regional and local information [ABSTRACT FROM AUTHOR]

The East China Sea (ECS) has experienced severe harmful algal blooms (HABs) that have deleterious ecological effects on marine organisms. Recent studies indicated that deploying of a second geostationary ocean color imager (GOCI-II) can significantly improve ocean monitoring. This study systematically assessed GOCI-II and its ability to detect HABs and distinguish between dinoflagellates and diatoms in the ECS. First, the remote-sensing reflectance ( R rs λ , λ represents the wavelength) obtained from GOCI-II was compared to the local measurement data. Compared to the bands at 412 and 443 nm, the bands at 490, 510, and 620 nm exhibited excellent consistency, which is important for HAB detection. Second, four different methods were employed to extract bloom areas in the ECS: red tide index (RI), spectral shape (SS), red band line height ratio (LHR), and algal bloom ratio ( R AB ). The SS (510) algorithm was the most applicable for detecting blooms from GOCI-II imagery. Finally, the classification capability of GOCI-II for dinoflagellates and diatoms was evaluated using three existing algorithms: the bloom index (BI), combined Prorocentrumdonghaiens index (PDI) and diatom index (DI), and the spectral slope ( R _ s l o p e ). The BI algorithm yielded more satisfactory results than the other algorithms. [ABSTRACT FROM AUTHOR]

Summary: Marine phytoplankton can interchange trace metals in various biochemical functions, particularly under metal‐limiting conditions. Here, we investigate the stimulating and toxicity effect of chromium (Cr) on a marine Chlorophyceae Osetreococcus tauri under Fe‐replete and Fe‐deficient conditions.We determined the growth, photosynthesis, and proteome expressions of Osetreococcus tauri cultured under different Cr and Fe concentrations.In Fe‐replete conditions, the presence of Cr(VI) stimulated significantly the growth rate and the maximum yield of photochemistry of photosystem II (Fv/Fm) of the phytoplankton, while the functional absorption cross‐section of photosystem II (σPSII) did not change. Minor additions of Cr(VI) partially rescued phytoplankton growth under Fe‐limited conditions. Proteomic analysis of this alga grown in Fe‐replete normal and Fe‐replete with Cr addition media (10 μM Cr) showed that the presence of Cr significantly decreased the expression of phosphate‐transporting proteins and photosynthetic proteins, while increasing the expression of proteins related to carbon assimilation.Cr can stimulate the growth and photosynthesis of O. tauri, but the effects are dependent on both the Cr(VI) concentration and the availability of Fe. The proteomic results further suggest that Cr(VI) addition might significantly increase starch production and carbon fixation. [ABSTRACT FROM AUTHOR]

Pseudo-nitzschia spp. blooms are a recurrent problem in many coastal areas globally, imposing some significant threats to the health of humans, ecosystems and the economy. Monitoring programmes have been established, where feasible, to mitigate the impacts caused by Pseudo-nitzschia spp. and other harmful algae blooms. The detection of such blooms from satellite data could really provide timely information on emerging risks but the development of taxa-specific algorithms from available multispectral data is still challenged by coupled optical properties with other taxa and water constituents, availability of ground data and generalisation capabilities of algorithms. Here, we developed a new set of algorithms (PNOI) for the detection and monitoring of Pseudo-nitzschia spp. blooms over the Galician coast (NW Iberian Peninsula) from Sentinel-3 OLCI reflectances using a support vector machine (SVM). Our algorithm was trained and tested with reflectance data from 260 OLCI images and 4607 Pseudo-nitzschia spp. match up data points, of which 2171 were of high quality. The performance of the no bloom/bloom model in the independent test set was robust, showing values of 0.80, 0.72 and 0.79 for the area under the curve (AUC), sensitivity and specificity, respectively. Similar results were obtained by our below detection limit/presence model. We also present different model thresholds based on optimisation of true skill statistic (TSS) and F1-score. PNOI outperforms linear models, while its relationship with in situ chlorophyll-a concentrations is weak, demonstrating a poor correlation with the phytoplankton abundance. We showcase the importance of the PNOI algorithm and OLCI sensor for monitoring the bloom evolution between the weekly ground sampling and during periods of ground data absence, such as due to COVID-19. [ABSTRACT FROM AUTHOR]

Spring algal blooms in mid–high-latitude lakes are facing serious challenges such as earlier outbreaks, longer duration, and increasing frequency under the dual pressure of climate warming and human activities, which threaten the health of freshwater ecosystems and water security. At present, the freeze-thaw processes is the key to distinguishing spring algal blooms in mid- to high-latitude lakes from low-latitude lakes. Based on the visualization and an analysis of the literature in the WOS database during 2007–2023, we clarified the driving mechanism of the freeze-thaw process (freeze-thaw, freeze-up, and thawing) on spring algal bloom in lakes by describing the evolution of the freeze-thaw processes on the nutrient migration and transformation, water temperature, lake transparency and dissolved oxygen, and physiological characteristics of algae between shallow lakes and deep lakes. We found that the complex phosphorus transformation process during the frozen period can better explain the spring-algal-bloom phenomenon compared to nitrogen. The dominant species of lake algae also undergo transformation during the freeze-thaw process. On this basis, the response mechanism of spring algal blooms in lakes to future climate change has been sorted out. The general framework of "principles analysis, model construction, simulation and prediction, assessment and management" and the prevention strategy for dealing with spring algal bloom in lakes have been proposed, for which we would like to provide scientific support and reference for the comprehensive prevention and control of spring algal bloom in lakes under the freezing and thawing processes. [ABSTRACT FROM AUTHOR]

In the framework of benthic harmful algal bloom monitoring, the most common sampling strategy is based on the collection of macroalgae, and the abundance of epiphytic microalgae are mainly expressed as cells g−1 macroalgal fresh weight (fw). However, this methodology has some inherent problems, due to (i) the thallus-specific weights that markedly differ among algal species, (ii) the thallus architecture, and (iii) the production of allelopathic compounds that affects the epiphyte abundances among macroalgae, irrespective of the available colonizable surface. This study proposes a method to compare the abundances of Ostreopsis cf. ovata cells on different substrata, using a conversion factor that converts the abundances expressed as cells g−1 fw (or dry weight) to cells cm−2. Expressing abundances in terms of cells cm−2, the abundances can be compared (i) among different macroalgal species and (ii) between macroalgae and other substrata (such as rocks, pebbles, or shellfish shells). We also propose to normalize abundances when different macroalgae are sampled throughout the bloom period, considering the different epiphyte loads of different macroalgal species regardless of the available surface area. [ABSTRACT FROM AUTHOR]

Harmful algae and cyanobacteria blooms are increasing in frequency and intensity in freshwater systems due to anthropogenic impacts such as nutrient loading in watersheds and engineered alterations of natural waterways. There are multiple physical factors that affect the conditions in a freshwater system that contribute to optimal habitats for harmful algae and toxin-producing cyanobacteria. A growing body of research shows that climate change stressors also are impacting water-body conditions that favor harmful algae and cyanobacteria species over other phytoplankton. The overgrowth of these organisms, or a "bloom," increases the opportunity for exposure to toxins by humans, companion animals, livestock, and wildlife. As waters warm and precipitation patterns change over time, exposure to these blooms is projected to increase. Hence, it is important that states and tribes develop monitoring and reporting strategies as well as align governmental policies to protect their citizens and ecosystems within their jurisdiction. Currently, the policies and approaches taken to monitor and report on harmful algae and cyanobacteria blooms vary widely among states, and it is undetermined if any tribes have specific policies on harmful algae blooms. This paper synthesizes research on algal blooms in inland freshwater systems of the United States. This review examines how climate change contributes to trends in bloom frequency or severity and outlines approaches that states and tribes may use to monitor, report, and respond to harmful algae and cyanobacteria blooms. Significance Statement: Inland bodies of freshwater supply drinking water for humans and animals, water for irrigating crops, habitats for aquatic species, places of cultural significance for Indigenous peoples, and other important functions. Many of these bodies of water have been polluted with runoff from industry, including agriculture, and already support harmful algal blooms during warm conditions. Hot extremes associated with climate change are expected to increase the occurrence and duration of harmful algal blooms, and in some places, initiate blooms where none have been recorded previously. These toxic blooms are harmful to people, companion animals, livestock, and wildlife. It is important to review the interconnections among biological, climate, and water systems to monitor blooms and alert the public about their toxin production. [ABSTRACT FROM AUTHOR]

Blooms of microalgal red tides and macroalgae (e.g., green and golden tides caused by Ulva and Sargassum) have caused widespread problems around China in recent years, but there is uncertainty around what triggers these blooms and how they interact. Here, we use 30 years of monitoring data to help answer these questions, focusing on the four main species of microalgae Prorocentrum donghaiense, Karenia mikimotoi, Noctiluca scintillans, and Skeletonema costatum) associated with red tides in the region. The frequency of red tides increased from 1991 to 2003 and then decreased until 2020, with S. costatum red tides exhibiting the highest rate of decrease. Green tides started to occur around China in 1999 and the frequency of green tides has since been on the increase. Golden tides were first reported to occur around China in 2012. The frequency of macroalgal blooms has a negative linear relationship with the frequency and coverage of red tides around China, and a positive correlation with total nitrogen and phosphorus loads as well as with atmospheric CO2 and sea surface temperature (SST). Increased outbreaks of macroalgal blooms are very likely due to worsening levels of eutrophication, combined with rising CO2 and SST, which contribute to the reduced frequency of red tides. The increasing grazing rate of microzooplankton also results in the decline in areas affected by red tides. This study shows a clear shift of algal blooms from microalgae to macroalgae around China over the past 30 years driven by the combination of eutrophication, climate change, and grazing stress, indicating a fundamental change in coastal systems in the region. [ABSTRACT FROM AUTHOR]

The coastal waters of Zhejiang Province in China experience a high incidence of harmful algal blooms (HABs). It is therefore important to carry out hazard zoning to prevent and mitigate these HAB disasters. According to the historical record data of HAB disasters from 1933 to 2021, the frequency, duration, area and algal toxicity of HAB occurrences can be used as evaluation indicators. A method for hazard zoning of HAB disasters and a method for verifying the rationality of the zoning results are proposed. The hazard zoning results of HAB disasters in the coastal waters of Zhejiang from 1933 to 2021 (H1933-2021) show that very-high hazard degree areas of coastal waters of Zhejiang Province were found in Shengshan waters, the eastern waters of Zhujiajian and Nanji Islands' waters. Due to the influence of marine environmental factors, the spatial distribution of hazard degree of HAB disasters is dynamic, so it is recommended that the hazard zoning of HAB disasters be carried out once every five years. The rationality of the zoning results from 2017 to 2021 is verified, showing that the hazard zoning method of HAB disasters proposed in this study has a good rationality and credibility. This study utilized a qualitative approach to visually identify the spatial distribution of hazard degree zoning of HAB disasters in the Zhejiang Province coastal waters over the last 88 years. This has provided useful information to help prevent and mitigate HAB disasters and guide mariculture and coastal tourism planning. [ABSTRACT FROM AUTHOR]

Algal bloom in eutrophication water, which can cause various risks, are closely related to phosphorus concentration of water environment, especially endogenous phosphorus. In this study, the relationship between algal bloom and phosphorus in water environment was investigated by collecting water and sediment in three different time in 2019 in Zhushan Bay, Taihu Lake. The result showed that algal bloom occurred during 27 June to 28 September in 2019. A large amount of phosphorus in overlying water was absorbed at the initial stage of algal bloom and sediment played as "source" to supplement AAP for bloom forming, which was mainly NAIP. Large phosphorus released at the decay period of algal bloom was largely OP and adsorbed by sediment, which played as "sink" of phosphorus. Moreover, the main phosphorus form of TP in the sediments of Zhushan Bay was IP, in which main component was NAIP. The main component of AAP was NAIP that phosphorus combined with iron, aluminum, and other metals. Furthermore, phosphate-solubilizing microorganisms in the sediments of Zhushan Bay were mainly Alcaligenes, which could release phosphorus in sediment into overlying water to supplement phosphorus source required for algae bloom outbreak. The results of this study showed that endogenous phosphorus played an important role in the outbreak of algal blooms, and the control of endogenous phosphorus should be the key step of algal bloom control. [ABSTRACT FROM AUTHOR]

Cyanobacteria harmful algal blooms (cHABs) are increasingly becoming an emerging threat to aquatic life, ecotourism, and certain real estate investments. Their spontaneous yet sporadic occurrence has made mitigation measures a cumbersome task; moreover, current trends regarding anthropogenic activities, especially in agriculture and industry portend further undesirable events. Apart from the aesthetic degeneration they create in their respective habitats, they are equally capable of secreting toxins, which altogether present grave environmental and medical consequences. In this paper, we gave an update on factors that influence cHABs, cyanotoxin exposure routes, and environmental public health implications, especially impacts on fish, pets, and livestock. We discussed social economic impacts, risk assessment, and management problems for cHABs and, thereafter, assessed the extant management approaches including prevention, control, and mitigation of the proliferation of cyanobacterial blooms. In light of this, we suggest that more intensified research should be directed to the standardization of procedures for cyanotoxin analysis. Also, the provision of standardized reference material for the quantification of cyanotoxins is vital for routine monitoring as well as the development of strong in situ sensors capable of quantifying and detecting HABs cells and toxins in waterbodies to prevent the adverse impacts of cHABs. Also, more investigations into the natural and environmentally friendly approach to cyanobacteria management and the necessary and appropriate deployment of artificial intelligence are required. Finally, we wish to redirect the focus of public health authorities to protecting drinking water supply sources, agriculture products, and food sources from cyanotoxins contamination as well as to implement proper monitoring and treatment procedures to protect citizens from this potential health threat. [ABSTRACT FROM AUTHOR]