Algal Blooms Research Articles

Study of biological quality of lake waters based on phycocyanin using tree‐based methodologies

AbstractThe provision of drinking water, agricultural, and industrial applications by reservoirs has made lake exploration and monitoring unavoidable. The features of the ecosystem, particularly physical and chemical elements, influence the evaluation of the quality of water resources. Lakes undergo extensive qualitative changes due to their vast amount of water. In general, these bodies of water represent geological conditions as well as water contamination produced by natural and human activities. In the present research, the prediction of the amount of phycocyanin (fPC) in the water of Lake Michigan has been implemented employing four tree‐based machine learning techniques based on seasonality factors. Phycocyanin has significant effects on quality parameters such as turbidity, chlorophyll concentration, algal bloom, and dissolved oxygen in water by affecting the photosynthesis process of algae. Therefore, in this study, the prediction of the amount of phycocyanin dissolved in the lake water using the mentioned variables, along with the temperature of the water, specific conductance, and pH, has been able to interpret the quality of the water and the occurrence of phenomena such as algal blooms. The results of the models in predicting fPCs equal to 0.44 and 0.55 μg/L were consistent with the natural conditions of the lake, and it seems that ensemble tree–based models, along with the biological index of fPC, formed the right combination of input and output parameters in modeling and obtained the lowest prediction error (root‐mean‐square error [RMSE] boosted trees = 0.0140 and RMSE random forests = 0.0141 μg/L).

Tracking a large‐scale and highly toxic Arctic algal bloom: Rapid detection and risk communication

AbstractIn recent years, blooms of the neurotoxic dinoflagellate Alexandrium catenella have been documented in Pacific Arctic waters, and the paralytic shellfish toxins (PSTs) that this species produces have been detected throughout the food web. These observations have raised significant concerns about the role that harmful algal blooms (HABs) will play in a rapidly changing Arctic. During a research cruise in summer 2022, a massive bloom of A. catenella was detected in real time as it was advected through the Bering Strait region. The bloom was exceptional in both spatial scale and density, extending > 600 km latitudinally, reaching concentrations > 174,000 cells L−1, and producing high‐potency PST congeners. Throughout the event, coastal stakeholders in the region were engaged and a multi‐faceted community response was mobilized. This unprecedented bloom highlighted the urgent need for response capabilities to ensure safe utilization of critical marine resources in a region that has little experience with HABs.

Phaeocystis globosa and diatom blooms promote distinct bacterial communities and associations in a coastal ecosystem.

Phytoplankton and bacteria form the foundation of marine food webs. While most studies on phytoplankton bloom influence on bacteria dynamics focus on diatom-dominated blooms due to their global ecological significance, it is unclear if similar patterns extend to other species that compete with diatoms like Phaeocystis spp. This study aimed to contribute to the understanding of associations between phytoplankton and bacteria in a temperate ecosystem. For this, we studied the dynamics of phytoplankton and bacteria, combining 16S metabarcoding, microscopy, and flow cytometry over 4 years (282 samples). Phytoplankton and bacterial communities were studied throughout the year, particularly during contrasting phytoplankton blooms dominated by the Haptophyte Phaeocystis globosa or diatoms. We applied extended local similarity analysis (eLSA) to construct networks during blooming and non-blooming periods. Overall, the importance of seasonal and species-specific interactions between phytoplankton and bacteria is highlighted. In winter, mixed diatom communities were interconnected with bacteria, indicating a synergistic degradation of diverse phytoplankton-derived substrates. In spring, despite the intensity variations of P. globosa blooms, the composition of bacterial communities remained consistent over several years, suggesting establishing a stable-state environment for bacterial communities. Specific associations between monospecific diatom blooms and bacteria were evidenced in summer.

Evaluating impacts of climate and management on reservoir water quality using environmental fluid dynamics code

Climate change and human interference, notably nutrient input, affect the water quality. Nitrogen (N) and phosphorus (P) are pivotal in managing eutrophication. This study investigated the effects of water dynamics and chemical constituents on water quality in Hongfeng Lake, a typical weakly stratified reservoir suffering from algae blooms in Southwest China, using the Environmental Fluid Dynamics Code. Leveraging climate, hydrological, and water quality data, we constructed, calibrated, and validated the temperature-hydrodynamics-water quality-sediment model. Various scenarios were analyzed, including wind speed, air temperature, solar radiation, rainfall, water discharge, N and P external input, and internal release. The findings revealed that no rain and warming increased trophic state index (TSI) and chlorophyll-a (Chl-a) concentration, and no solar radiation initially elevated nitrate concentration, followed by an increase in ammonium concentration. Besides, no solar radiation and changes in rainfall significantly increased total phosphate concentration. The management scenarios of N and P reduction, halving tributary, and mainstream flow scenarios improved water quality and reduced eutrophication. The wind speed under the N and P reduced scenarios showed that a doubling in wind led to increased concentrations of the particulate organic matter, Chl-a, and dissolved oxygen, alongside decreased ammonium and nitrate, while TSI exhibited minimal change. However, 5- and 10-times wind speed scenarios amplified TSI in shallow water, potentially due to a substantial rise in internal nutrient release. The degradation trend observed in drinking water quality amid climate change (warming and flooding) raises concerns regarding health-related risks. These simulations provided the quantified influence of climate change and environmental management strategies on water quality in the weakly stratified reservoir, notably highlighting the looming threat of exacerbated eutrophication due to warming, necessitating more stringent N and P reduction measures compared to current practices.

Effective remediation of agricultural drainage at three influent strengths by bioaugmented constructed wetlands filled with mixture of iron‑carbon and organic solid substrates: Performance and mechanisms

Agricultural drainage containing a large quantity of nutrients can cause quality deterioration and algal blooming of receiving water bodies, thus needs to be effectively remediated. In this study, iron‑carbon (FeC) composite-filled constructed wetlands (Fe-C-CWs) were employed to treat farmland drainage at three pollution levels, and organic solid substrates (walnut shells) and phosphate-accumulating denitrifying bacteria (Pseudomonas sp. DWP1) were supplemented to enhance the treatment performance. The results showed that the Fe-C-CWs exhibited notably superior removal efficiency for total nitrogen (TN, 52.0–58.2 %), total phosphorus (TP, 67.8–70.2 %) and chemical oxygen demand (COD, 56.7–70.4 %) than the control systems filled solely with gravel (28.5–32.5 % for TN, 33.2–40.5 % for TP and 30.2–55.0 % for COD) at all influent strengths, through driving autotrophic denitrification, Fe-based dephosphorization, and organic degradation processes. The addition of organic substrates and functional bacteria markedly enhanced pollutant removal in the Fe-C-CWs. Furthermore, use of FeC and organic substrates and denitrifier inoculation decreased CO2 and CH4 emissions from the CWs, and reduced global warming potential of the CWs at low influent strength. Pollutant removal efficiencies in the CWs were only marginally impacted by the increasing influent loads except for NO3−-N, and pollutant removal mass was largely increased with the increase of influent strengths. The microbial community in the FeC composite-filled CWs exhibited distinct distribution patterns compared to the gravel-filled CWs regardless of the influent strengths, with obviously higher proportions of dominant genera Trichococcus, Geobacter and Ferritrophicum. Keystone taxa associated with pollutant removal in the Fe-C-filled CWs were identified to be Pseudomonas, Geobacter, Ferritrophicum, Denitratisoma and Sediminibacterium. The developed augmented Fe-C-filled CWs show great promises for remediating agricultural drainage with varied pollutant loads.

Are Harmful Algal Blooms Increasing in the Great Lakes?

This study used satellite remote sensing to investigate trends in harmful algal blooms (HABs) over the last 21 years, focusing on four regions within the Laurentian Great Lakes: western Lake Erie, Green Bay, Saginaw Bay, and western Lake Superior. HABs in the water column were identified from remote sensing-derived chlorophyll concentrations, and surface HAB scums were classified based on the Normalized Difference Vegetation Index (NDVI) band ratio index. Using imagery from the Moderate Resolution Imaging Spectroradiometer sensor on the Aqua satellite (MODIS-Aqua) from 2002 to 2022, we generated daily estimates of the HAB and surface scum extents for each region, which were then averaged to generate mean annual extents. We observed a significant decline in the Saginaw Bay mean annual HAB extents over the 21-year study period. Otherwise, no significant changes were observed over this period in any region for either the HAB or surface scum mean annual extents, thus suggesting that HABs are not increasing in the Great Lakes. Despite the lack of increasing trends, the blooms are still recurring annually and causing a negative impact on the nearby communities; thus, we believe that it is crucial to continue studying Great Lakes HABs to monitor the impact of current and future abatement strategies.

Classification machine learning to detect de facto reuse and cyanobacteria at a drinking water intake

Harmful algal blooms (HABs) or higher levels of de facto water reuse (DFR) can increase the levels of certain contaminants at drinking water intakes. Therefore, the goal of this study was to use multi-class supervised machine learning (SML) classification with data collected from six online instruments measuring fourteen total water quality parameters to detect cyanobacteria (corresponding to approximately 950 cells/mL, 2900 cells/mL, and 8600 cells/mL) or DFR (0.5, 1 and 2 % of wastewater effluent) events in the raw water entering an intake. Among 56 screened models from the caret package in R, four (mda, LogitBoost, bagFDAGCV, and xgbTree) were selected for optimization. mda had the greatest testing set accuracy, 98.09 %, after optimization with 7 false alerts. Some of the most important water parameters for the different models were phycocyanin-like fluorescence, UVA254, and pH. SML could detect algae blending events (estimated <9000 cells/mL) due in part to the phycocyanin-like fluorescence sensor. UVA254 helped identify higher concentrations of DFR. These results show that multi-class SML classification could be used at drinking water intakes in conjunction with online instrumentation to detect and differentiate HABs and DFR events. This could be used to create alert systems for the water utilities at the intake, rather than the finished water, so any adjustment to the treatment process could be implemented.

Investigating the molecular mechanisms of Pseudalteromonas sp. LD-B1's algicidal effects on the harmful alga Heterosigma akashiwo

Heterosigma akashiwo is a harmful algal bloom species that causes significant detrimental effects on marine ecosystems worldwide. The algicidal bacterium Pseudalteromonas sp. LD-B1 has demonstrated potential effectiveness in mitigating these blooms. However, the molecular mechanisms underlying LD-B1’s inhibitory effects on H. akashiwo remain poorly understood. In this study, we employed the comprehensive methodology, including morphological observation, assessment of photosynthetic efficiency (Fv/Fm), and transcriptomic analysis, to investigate the response of H. akashiwo to LD-B1. Exposure to LD-B1 resulted in a rapid decline of H. akashiwo's Fv/Fm ratio, with cells transitioning to a rounded shape within 2 hours, subsequently undergoing structural collapse and cytoplasmic leakage. Transcriptomic data revealed sustained downregulation of photosynthetic genes, indicating impaired functionality of the photosynthetic system. Additionally, genes related to the respiratory electron transfer chain and antioxidant defenses were consistently downregulated, suggesting prolonged oxidative stress beyond the cellular antioxidative capacity. Notably, upregulation of autophagy-related genes was observed, indicating autophagic responses in the algal cells. This study elucidates the molecular basis of LD-B1's algicidal effects on H. akashiwo, advancing our understanding of algicidal mechanisms and contributing to the development of effective strategies for controlling harmful algal blooms.

The impact of flooding from the Minjiang River on the succession of harmful algal blooms (HABs) caused by diatoms in China's offshore waters

This study examines diatom assemblages in the Matsu Archipelago, an area influenced by Minjiang River runoff. It focuses on harmful algal blooms (HABs) that occurred between August 2021 and July 2022. Utilizing 18S rRNA metabarcoding and microscopic analysis, we observed a significant diatom bloom during early summer runoff, peaking at 5 × 105 cells L−1. The research reveals dynamic community changes during the runoff season, with dominant genera including Pseudo-nitzschia, Chaetoceros, and Skeletonema. Skeletonema cell density correlated with NO3 levels, Chaetoceros had a slight PO4 affinity, and Pseudo-nitzschia showed a negative correlation with Skeletonema. Pseudo-nitzschia, which prefers high light and pH conditions, had notably high concentrations in the flood season and in the autumn. In both, it was dominated by potential toxin-producing species - P. multistriata and P. pungens during the flooding, and P. cuspidate in the autumn. These findings highlight the intricate relationship between diatom dynamics and environmental factors, providing essential insights for managing HABs, especially Pseudo-nitzschia species, amidst environmental changes.

Effect of fear with saturated fear cost and harvesting on aquatic food chain model (plankton–fish model) in the presence of nanoparticles

Studying the interplay of phytoplankton–zooplankton–fish (PP–ZP–F) in an aquatic system is crucial for better understanding of nutrient cycling, assessing ecosystem health, predicting and mitigating harmful algal blooms, and managing fisheries in the water bodies. In order to investigate the effectiveness of nanoparticles (NPs), fear, and harvesting, this paper focuses on exploring the dynamics of a food chain model among PP–ZP–F species. We consider the fear of fish on zooplankton species (which reduces the reproduction rate of ZPs) with saturated fear cost in the presence of nanoparticles (NPs) and harvesting in fish. The system dynamics are studied from the viewpoint of proving positivity, boundedness, and uniqueness, followed by analysing the existence and local stability of biologically feasible equilibria. Conditions for the global stability of the interior equilibrium point are also found. Furthermore, we established the transversality conditions for the occurrence of Hopf, transcritical, and saddle–node bifurcations. To validate our theoretical results, we made numerous phase portraits, time-series graphs, tables showing the extinction of species, and bifurcation diagrams. It is numerically observed that increasing the contact rate of NPs with PPs makes the system stable from chaos, and further increase of contact rate may lead to extinction. Chaos at a low contact rate can also be managed by increasing the fear level, and the chaotic behaviour at a low fear level can again be controlled by enhancing the harvesting of fish species. Over-exploitation may result in the extinction of fish, whereas fear may promote coexistence, stability, and long-term survival of the species. Increased saturated fear cost can make the system chaotic from stable dynamics. Therefore, the theoretical as well as numerical findings of our paper may be of great interest in estimating the behaviour of aquatic systems biologically and practically.

Linking global terrestrial and ocean biogeochemistry with process-based, coupled freshwater algae–nutrient–solid dynamics in LM3-FANSY v1.0

Abstract. Estimating global river solids, nitrogen (N), and phosphorus (P), in both quantity and composition, is necessary for understanding the development and persistence of many harmful algal blooms, hypoxic events, and other water quality issues in inland and coastal waters. This requires a comprehensive freshwater model that can resolve intertwined algae, solid, and nutrient dynamics, yet previous global watershed models have limited mechanistic resolution of instream biogeochemical processes. Here we develop the global, spatially explicit, and process-based Freshwater Algae, Nutrient, and Solid cycling and Yields (FANSY) model and incorporate it within the Land Model (LM3). The resulting model, LM3-FANSY v1.0, is intended as a baseline for eventual linking of global terrestrial and ocean biogeochemistry in next-generation Earth system models to project global changes that may challenge empirical approaches. LM3-FANSY explicitly resolves interactions between algae, N, P, and solid dynamics in rivers and lakes at 1° spatial and 30 min temporal resolution. Simulated suspended solids (SS), N, and P in multiple forms (particulate or dissolved, organic or inorganic) agree well with measurement-based yield (kg km−2 yr−1), load (kt yr−1), and concentration (mg L−1) estimates across a globally distributed set of large rivers, with an accuracy comparable to other global nutrient and SS models. Furthermore, simulated global river loads of SS, N, and P in different forms to the coastal ocean are consistent with published ranges, though regional biases are apparent. River N loads are estimated to contain approximately equal contributions by dissolved inorganic N (41 %) and dissolved organic N (39 %), with a lesser contribution by particulate organic N (20 %). For river P load estimates, particulate P, which includes both organic and sorbed inorganic forms, is the most abundant form (64 %), followed by dissolved inorganic and organic P (25 % and 11 %). Time series analysis of river solid and nutrient loads in large US rivers for the period ∼ 1963–2000 demonstrates that simulated SS and N loads in different N forms covary with variations of measurement-based loads. LM3-FANSY, however, has less capability to capture interannual variability of P loads, likely due to the lack of terrestrial P dynamics in LM3. Analyses of the model results and sensitivity to components, parameters, and inputs suggest that fluxes from terrestrial litter and soils, wastewater, and weathering are the most critical inputs to the fidelity of simulated river nutrient loads for observation-based estimates. Sensitivity analyses further demonstrate a critical role of algal dynamics in controlling the ratios of inorganic and organic nutrient forms in freshwaters. While the simulations are able to capture significant cross-watershed contrasts at a global scale, disagreement for individual rivers can be substantial. This limitation is shared by other global river models and could be ameliorated through further refinements in nutrient sources, freshwater model dynamics, and observations. Current targets for future LM3-FANSY development include the additions of terrestrial P dynamics, freshwater carbon, alkalinity, enhanced sediment dynamics, and anthropogenic hydraulic controls.

Evaluating Solid Phase Adsorption Toxin Tracking (SPATT) for passive monitoring of per- and polyfluoroalkyl substances (PFAS) with Ion Mobility Spectrometry-Mass Spectrometry (IMS-MS)

Detection and monitoring of per- and polyfluoroalkyl substances (PFAS) in aquatic environments has become an increasingly higher priority of regulatory agencies as public concern for human intake of these chemicals continues to grow. While many methods utilize active sampling strategies (“grab samples”) for precise PFAS quantitation, here we evaluate the efficacy of low-cost passive sampling devices (Solid Phase Adsorption Toxin Tracking, or SPATTs) for spatial and temporal PFAS assessment of aquatic systems. For this study, passive samplers were initially deployed in North Carolina along the Cape Fear River during the summer and fall of 2016 and 2017. These were originally intended for the detection of microcystins and monitoring potentially harmful algal blooms, though this period also coincided with occurrences of PFAS discharge from a local fluorochemical manufacturer into the river. Additional samplers were then deployed in 2022 to evaluate changes in PFAS fingerprint and abundances. Assessment of PFAS showed legacy compounds were observed across almost all sampling sites over all 3 years (PFHxS, PFOS, PFHxA, etc.), while emerging replacement PFAS (e.g., Nafion byproducts) were predominantly localized downstream from the manufacturer. Furthermore, samplers deployed downstream from the manufacturer in 2022 noted sharp decreases in observed signal for replacement PFAS in comparison to samplers deployed in 2016 and 2017, indicating mitigation and remediation efforts in the area were able to reduce localized fluorochemical contamination.

Dynamics of Noctiluca scintillans blooms: A 20-year study in Jangmok Bay, Korea

This 20-year study (2001–2020) conducted in Jangmok Bay, Korea, assessed the intricate relationships between environmental factors and Noctiluca scintillans blooms. Granger causality tests and PCA analysis were used to assess the impact of sea surface temperature (SST), salinity, dissolved oxygen (DO) concentration, wind patterns, rainfall, and chlorophyll-a (Chl-a) concentration on bloom dynamics. The results revealed significant, albeit delayed, influences of these variables on bloom occurrence, with SST exhibiting a notable 2-month lag and salinity a 1-month lag in their impact. Additionally, the analysis highlighted the significant roles of phosphate, ammonium, and silicate, which influenced N. scintillans blooms with lags of 1 to 3 months. The PCA demonstrates how SST and wind speed during spring and summer, along with wind direction and salinity in winter, significantly impact N. scintillans blooms. We noted not only an increase in large-scale N. scintillans blooms but also a cyclical pattern of occurrence every 3 years. These findings underscore the synergistic effects of environmental factors, highlighting the complex interplay between SST, salinity, DO concentration, and weather conditions to influence bloom patterns. This research enhances our understanding of harmful algal blooms (HABs), emphasizing the importance of a comprehensive approach that considers multiple interconnected environmental variables for predicting and managing N. scintillans blooms.

Nutrient sources, phytoplankton blooms, and hypoxia along the Chinese coast in the East China Sea: Insight from summer 2014

Phytoplankton blooms are common along the Chinese coast in the East China Sea, driven by various nutrient sources including river discharge, bottom water regeneration, and Kuroshio subsurface water intrusion. A notable 2014 summer bloom off the Zhejiang coast, exhibiting a Chl a concentration of 20.1 μg L−1, was significantly influenced by Changjiang River discharge, and high nutrient concentrations are often observed in the region's surface water. During blooms, primary production peaks at 1686.3 mg C m−3 d−1, indicating substantial CO2 absorption, with surface water fCO2 declining to 299.5 μatm, closely linked to plankton activities. Hypoxia often coincides with these frequent bloom occurrences, implicating marine-derived organic matter decomposition as a pivotal factor. Elevated particulate organic carbon concentrations further support this assumption, alongside increased nutrient levels, fCO2, and low pH in hypoxic waters. These findings underscore the intricate interplay between phytoplankton, nutrient cycling, and hypoxia formation, essential for effective coastal ecosystem management.

Scatalysis: Exploring the mechanism of photocatalytic algal removal

Photocatalytic algal removal is an environmentally friendly and low-cost algal bloom control technology. In this work, carbon nitride nanosheets based on morphology control were synthesized, and a ternary Z-scheme photocatalyst was designed through heterojunction engineering for photocatalytic removal of harmful algae and their organic matter, and the removal rate of chlorophyll a was close to 100 % in 300 min. Three-dimensional fluorescence spectrograms and cellular SEM maps revealed the removal of algae and their organic matter, and the mechanism of Ag/AgCl/ZIF-8/SCN photocatalytic algal removal was explored in conjunction with the changes in membrane permeability and various physiological functions. Mechanistic studies showed that excess O2·− played a crucial role in cell inactivation and organic matter degradation. Oxidative stress caused by O2·− promoted the accumulation of H2O2 in cells and accelerates cell death. This study provides new ideas and support for the application of harmful algal bloom control and the mechanism of algal cell inactivation in real water bodies.

Integration of CaO2/Fe2+ and ultrafiltration acts as multiple-workfunctions pretreatment for seawater desalination during harmful algal blooms

This study employed calcium peroxide/ferrous iron (CaO2/Fe2+) as an innovative pretreatment strategy for seawater reverse osmosis (SWRO) operation during harmful algae blooms (HABs). CaO2/Fe2+ showed improved coagulation performance compared to conventional coagulation (Fe3+ coagulant). The dynamic dominance of foulants between micro-particles and algal organic matters (AOMs), due to the different dosages of CaO2/Fe2+, was found to be crucial in reducing ultrafiltration (UF) fouling. As a result, the optimal dosage of 0.05 mM/0.1 mM of CaO2/Fe2+ achieved a higher reduction of 71.6 % of modified fouling index (MFI) compared to conventional coagulation (38.1 % via 0.1 mM Fe3+). Specifically, CaO2/Fe2+ demonstrated a greater capacity to remove micro-particles across size ranges of 1–5 μm and various AOMs (i.e., biopolymer and humics) in algae-laden seawater compared to conventional coagulation. This resulted in reduced flux decline and less accumulation of algal cells, proteinaceous substances and humics on membrane surfaces, which were validated through Optical-Photothermal Infrared Spectromicroscopy (O-PTIR) with sub-micron resolution. Additionally, CaO2/Fe2+ reduced dissolved Fe3+ in UF permeate, and generated combined chlorine simultaneously with disinfection effects on marine algae (evidenced by O-PTIR and Raman spectroscopy). Thus, it would reduce fouling of subsequent SWRO membranes. Overall, CaO2/Fe2+ presents a multiple-workfunctions pretreatment method for HAB mitigation in seawater desalination plants.

Mechanism of rapid settling algal flocs formation in a novel fluidized bed flocculator

Coagulation-sedimentation has been widely used for the emergency removal of harmful algal blooms (HABs). The technical challenge of how to construct special structured flocs in a targeted manner to achieve ideal settling performance remains unsolved, especially for algae with gas vesicles, such as Microcystis. In this study, a novel fluidized bed flocculator (FBF) was proposed to construct nearly spherical and compact algal flocs with its multi-stage velocity gradient flow field characteristics, thus enhancing the continuous sedimentation performance of Microcystis flos-aquae flocs, compared with a conventional mechanical stirring flocculator (MSF). The results revealed that the harvesting efficiency in the FBF reached 93 % after 30 s of settling at a dose of 30 mg L−1 FeCl3 and 1 mg L−1 PAM, whereas the efficiency in the MSF was only 47 %. Although the flocs generated in the FBF and MSF were similar in size, the former exhibited a settling velocity of 22.4 m h−1, which was 2.7 times higher than that of the latter. This difference can be attributed to the significantly higher fractal dimension of the flocs in the FBF (1.80) compared to the MSF (1.62). The settling velocity of the flocs was determined by both the floc size and fractal dimension, and flocs with large, compact and spherical shape structure yielded faster settling performance than small, loose, and multi-branched floc. The velocity, turbulent kinetic energy, and turbulent kinetic energy dissipation rate in the FBF decreased uniformly and gradually along the direction of floc formation, resulting in the formation of compact and spherical flocs and consequently rapid settling performance. This study may provide a novel solution for the rapid sedimentation of microalgae and enhance our understanding of the flow field characteristics required for an effective flocculator.

Navigating aquaculture losses: Tackling fish‐killing phytoflagellates in a changing global landscape

AbstractAquaculture has emerged as a pivotal alternative for seafood production, addressing the challenges posed by depleting wild fish stocks. However, the industry faces substantial challenges, notably the presence of harmful algal blooms (HABs), which can cause serious economic consequences. The proliferation of major species of phytoflagellates leading to massive fish mortality is on the rise, possibly attributed to increasing global temperatures and eutrophication. This underscores the critical need for robust detection, prevention, and mitigation strategies to safeguard the aquaculture industry. This contribution offers valuable insights into comprehending and addressing the challenges posed by HABs in the fish aquaculture sector in an era of global change.

Monitoring Harmful Algal Blooms and Water Quality Using Sentinel-3 OLCI Satellite Imagery with Machine Learning

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.

CO2‐induced seawater acidification impairs the stinging cells of a jellyfish

AbstractCO2‐induced seawater acidification has been shown to modify predator–prey interactions in many marine taxa. Scyphozoans play an important role in the trophic dynamics of marine ecosystems during their blooms in coastal waters; however, the impacts of seawater acidification on the predation behavior of these animals are poorly understood. Here, we aimed to examine the impact of a decrease in seawater pH on the feeding behavior and growth of ephyrae (juvenile medusae) of the scyphozoan Aurelia coerulea. Combining bulk and single‐cell RNA sequencing approaches, we assessed transcriptomic changes of ephyrae under a laboratory‐based pH 7.6 condition. We found that the feeding rates and growth of ephyrae were significantly inhibited by a decrease in seawater pH. Furthermore, transcriptome analysis showed that a decline in pH significantly reduced the expression of genes related to toxins and nematocyst structure in ephyrae. These findings were further confirmed by single‐cell transcriptomic analyses and revealed that low pH impaired the toxin activity and energy metabolism of stinging cells. The pH recovery experiment indicated that moving ephyrae from seawater with pH 7.6 into seawater with pH 8.1 greatly restored their feeding, growth, and toxin‐related and nematocyst structure–related gene expression. However, exposure to pH 7.6 for 23 d could not recover the decrease in the feeding and growth of ephyrae. Together, these findings indicate that CO2‐induced acidification compromised the stinging cells of A. coerulea ephyrae, with concomitant negative consequences on predation and growth that are likely to alter predator–prey interactions, with consequent effects on community structure and ecosystem.