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Red tide events are increasingly impacting economies, public health, ecosystems, and aquaculture worldwide, with China experiencing particularly severe effects. This study investigates the changes of red tide events in the Northern South China Sea (NSCS) from 1998 to 2018, focusing on frequency, composition, and environmental drivers. A total of 278 red tide events were reported, with peak in 2003 with 19 events, followed by 14 events in 2004, 9 in 2005, and 7 in 2023. The results indicate that Daya Bay (DYB) is a hotspot for red tide occurrences, with 166 events predominantly caused by Phaeocystis globosa and Noctiluca scintillans . Factors such as thermal discharge and nutrient-rich effluents contribute to the high frequency of red tides in DYB. Other significant locations include Zhanjiang Port (ZJP) with 48 events, Qinzhou Bay (QZB) with 31 events, and Qianjiang Bay (QJB) with 21 events. In contrast, Hongsha Port (HSP) recorded the fewest events, with only 12. The study identified a total of 33 algal species, revealing changes in species composition over the study period. Shifts in dominant species are linked to changing environmental conditions, including rising seawater temperatures and increased nutrient availability. A potential correlation between red tide events and the Nino 3.4 index suggests that global climate patterns may influence red tide occurrences in the NSCS. Additionally, anthropogenic activities, such as industrial wastewater discharge, contribute to the prevalence of red tide events. The study highlights the complex interactions driving red tide blooms in the NSCS and underscores the need for effective management strategies to mitigate their impacts. Highlights 278 red tide events occurred from 1998 to 2018, with a peak in 2003. Daya Bay was the major hotspot. High red tide frequency in Daya Bay is linked to thermal discharge, nutrient-rich effluents, and shifts in algal species due to changing environmental conditions. Potential correlation with the Nino 3.4 index suggests global climate patterns influence red tides, with anthropogenic activities also playing a role.

Red tide causes significant damage to marine resources such as aquaculture and fisheries in coastal regions. Such red tide events occur globally, across latitudes and ocean ecoregions. Satellite observations can be an effective tool for tracking and investigating red tides and have great potential for informing strategies to minimize their impacts on coastal fisheries. However, previous satellite-based red tide detection algorithms have been mostly conducted over short time scales and within relatively small areas, and have shown significant differences from actual field data, highlighting a need for new, more accurate algorithms to be developed. In this study, we present the newly developed normalized red tide index (NRTI). The NRTI uses Geostationary Ocean Color Imager (GOCI) data to detect red tides by observing in situ spectral characteristics of red tides and sea water using spectroradiometer in the coastal region of Korean Peninsula during severe red tide events. The bimodality of peaks in spectral reflectance with respect to wavelengths has become the basis for developing NRTI, by multiplying the heights of both spectral peaks. Based on the high correlation between the NRTI and the red tide density, we propose an estimation formulation to calculate the red tide density using GOCI data. The formulation and methodology of NRTI and density estimation in this study is anticipated to be applicable to other ocean color satellite data and other regions around the world, thereby increasing capacity to quantify and track red tides at large spatial scales and in real time.

Harmful algal blooms (HABs) are intensifying globally, with well-documented ecological and health consequences. Yet their socioeconomic dimensions remain largely unexplored. Here, we quantify how red tides, a specific type of HABs, disrupt urban mobility and local commerce in coastal regions. Leveraging high-frequency mobile device data, we construct a tract-by-week panel for Florida’s coastal counties (2019–2023) and apply fixed-effects models to estimate the effects of algae cell concentrations on visits and time spent at social infrastructure places. A one-million-cells-per-liter increase reduces time spent by 0.7%–1.1% and visits by 0.8%–0.9%, with stronger impacts during summer and at high algae cell concentration thresholds. These behavioral shifts translate into economic losses of US$0.9–1.2 million for dining and grocery activities in 2023 alone. Our findings reveal that red tides impose substantial social and economic costs, underscoring the need for adaptation strategies in response to rising environmental hazards to protect vulnerable coastal communities.

The recent rise of red tide harmful algal blooms has induced ecosystem degradation, economic losses, and aquaculture damage, yet little is known on prevention and mitigation of red tides. Actual control methods involve physical, chemical, and biological processes, with varying success. Here, we review physical, chemical, and biological control methods applicable to red tide species in marine and estuarine water bodies. We discuss mechanisms of algal blooms outbreak and their applications to prevent outbreaks.

Existing red tide detection methods have mainly been developed for ocean color satellite data with low spatial resolution and high spectral resolution. Higher spatial resolution satellite images are required for red tides with fine scale and scattered distribution. However, red tide detection methods for ocean color satellite data cannot be directly applied to medium–high spatial resolution satellite data owing to the shortage of red tide responsive bands. Therefore, a new red tide detection method for medium–high spatial resolution satellite data is required. This study proposes the red tide detection U−Net (RDU−Net) model by considering the HY−1D Coastal Zone Imager (HY−1D CZI) as an example. RDU−Net employs the channel attention model to derive the inter−channel relationship of red tide information in order to reduce the influence of the marine environment on red tide detection. Moreover, the boundary and binary cross entropy (BBCE) loss function, which incorporates the boundary loss, is used to obtain clear and accurate red tide boundaries. In addition, a multi−feature dataset including the HY−1D CZI radiance and Normalized Difference Vegetation Index (NDVI) is employed to enhance the spectral difference between red tides and seawater and thus improve the accuracy of red tide detection. Experimental results show that RDU−Net can detect red tides accurately without a precedent threshold. Precision and Recall of 87.47% and 86.62%, respectively, are achieved, while the F1−score and Kappa are 0.87. Compared with the existing method, the F1−score is improved by 0.07–0.21. Furthermore, the proposed method can detect red tides accurately even under interference from clouds and fog, and it shows good performance in the case of red tide edges and scattered distribution areas. Moreover, it shows good applicability and can be successfully applied to other satellite data with high spatial resolution and large bandwidth, such as GF−1 Wide Field of View 2 (WFV2) images.

Globally, climate change impacts on marine ecosystems are evident in physical, chemical, and biological processes, and are generally more extensive in faster warming regions. China makes the largest contribution of any country to global fisheries production and has experienced severe declines in marine health and biodiversity, and so the current and potential impacts of marine climate change are a large concern for both fisheries and biodiversity. China also has marine regions warming in the top 10% globally, necessitating a thorough understanding of how marine systems are changing so that appropriate corresponding countermeasures can be identified and prioritized. Here, we review and collate what is currently understood about documented and projected responses of marine systems to climate change in Chinese coasts and oceans, from physical, biological, and ecological perspectives, through to impacts on key ecosystems. Our results show extensive change attributed to climate change throughout Chinese marine systems, including red tide bloom events that have been recorded an order of magnitude more frequently in recent decades. Ocean acidification has led to the increased mortality of marine calcifying organisms through effects on the biomineralization process and physiological functions. Moreover, many species have been documented undergoing extensive changes in geographic distribution, with potential implications for species interactions and trophic food webs, as well as important habitats like coral reefs, seagrass, and mangroves. Some constructive laws and actions have been introduced in response to these climate-driven changes, such as actions to reduce pollution and increase artificial propagation and replanting of habitat species, however, addressing the impacts of marine climate change remains a considerable and escalating challenge.

Algal blooms are a pervasive problem for Florida, and successful management decisions must rely on the best available science. In 2019, Florida Sea Grant convened a forum of harmful algal bloom (HAB) scientists for the first Harmful Algal Bloom State of the Science Symposium. The goals of the two-day forum were to develop consensus statements identifying the current state of the science regarding what we know and what we think we know, data gaps and areas of uncertainty, and research priorities, with a focus on Karenia brevis red tides and Microcystis aeruginosa cyanobacterial blooms. In 2023, Florida Sea Grant convened a second symposium at the request of the state. This symposium focused specifically on cyanobacteria and assessed progress made over the four-year period between symposia. Consensus statements summarizing what we’ve learned, new research priorities, and best practices for cyanobacterial HAB research and management efforts were developed. The symposia consensus reports are used to inform Florida’s Harmful Algal Bloom and Blue-Green Algae Task Forces by aligning and prioritizing the management and research needs of the agencies and scientific institutions and to facilitate cohesive public outreach.

Ichthyotoxic red tide is a problem that the world is facing and needs to solve. The use of antialgal compounds from marine macroalgae to suppress ichthyotoxic red tide is considered a promising biological control method. Antialgal substances were screened and isolated from Bangia fusco-purpurea, Gelidium amansii, Gloiopeltis furcate, Hizikia fusifarme, Laminaria japonica, Palmaria palmata, and Sargassum sp. to obtain new materials for the development of algaecides against ichthyotoxic red tide microalgae using bioactivity-guided isolation methods. The fractions of seven macroalgae exhibited selective inhibitory activities against Amphidinium carterae and Karenia mikimotoi, of which the ethyl acetate fractions had the strongest and broadest antialgal activities for the two tested red tide microalgae. Their inhibitory effects on A. carterae and K. mikimotoi were even stronger than that of potassium dichromate, such as ethyl acetate fractions of B. purpurea, H. fusifarme, and Sargassum sp. Thin-layer chromatography and ultraviolet spectroscopy were further carried out to screen the ethyl acetate fraction of Sargassum sp. Finally, a new glycolipid derivative, 2-O-eicosanoyl-3-O-(6-amino-6-deoxy)-β-D-glucopyranosyl-glycerol, was isolated and identified from Sargassum sp., and it was isolated for the first time from marine macroalgae. The significant antialgal effects of 2-O-eicosanoyl-3-O-(6-amino-6-deoxy)-β-D-glucopyranosyl-glycerol on A. carterae and K. mikimotoi were determined.

Breaking‐wave induced bioluminescence is a critical component of the biogeochemical process in the ocean. Understanding bioluminescence is important for monitoring red tides caused by bioluminescent microorganisms. In this study, we present the first numerical effort to quantify bioluminescent light intensity based on high‐fidelity direct numerical simulations of breaking waves and a quantitative bioluminescent model. The dynamics of breaking waves are extensively validated through comparison with existing studies. We find that the time‐averaged and Lagrangian‐averaged shear stress saturates as surface tension effects decrease and wave steepness increases. The spatial distribution of light intensity correlates with the wave crest overturning and air bubbles generated in plunging breakers. Furthermore, we observe that the maximum light intensity asymptotically approaches the emission of single cells, suggesting the potential for cost‐effective prediction models in future studies. Plain Language Summary Marine microorganisms, such as dinoflagellates, flash when stimulated by mechanical forces caused by breaking waves. Understanding this phenomenon, also known as the ‘blue tears’ of ocean, is helpful for predicting ‘red tides’, a hazardous algal blooms caused by dinoflagellates. We use computer simulations to determine how much light is emitted when breaking waves stimulates bioluminescence. Our analysis show that there is an upper limit for the level of the mechanical force in breaking waves. We also find that the maximum bioluminescence light intensity is similar to that emitted by a single cell. Key Points A numerical framework is developed to quantify bioluminescence stimulated by ocean surface breaking waves The time‐averaged and Lagrangian‐averaged shear stress saturates as surface tension effects decrease and wave steepness increases Maximum bioluminescent light intensity asymptotically approaches single cell emission at the time of flashing

Kim, S.M.; Shin, J.; Baek, S., and Ryu, J.-H., 2019. U-Net convolutional neural network model for deep red tide learning using GOCI. In: Jung, H.-S.; Lee, S.; Ryu, J.-H., and Cui, T. (eds.), Advances in Remote Sensing and Geoscience Information Systems of Coastal Environments. Journal of Coastal Research, Special Issue No. 90, pp. 302-309. Coconut Creek (Florida), ISSN 0749-0208. GOCI launched in 2010 is a geostationary satellite image sensor that monitors ocean color. It captures 8-band spectral satellite images of northeast Asian regions hourly, eight times a day. The spatial resolution of GOCI is about 500 m. GOCI is capable of monitoring a large ocean area for sensing various events such as red tide occurrences, tidal movement changes and ocean disasters. In this study, we propose a deep convolutional neural network model, U-Net, for automatic pixel-based detection of red tide occurrence from the spectral images captured by GOCI. We construct two training datasets with GOCI images and the corresponding red-tide index maps (RI maps) accumulated through 2011 to 2018. The RI maps indicate where red tides occurred and what kind of red tide species were there. U-Net consists of five U-shaped encoder and decoder layers to extract spectral features relating to red-tide species from GOCI images. We compared the performances of U-Nets trained from two datasets (i) consisting of only four spectral bands and (ii) consisting of all six spectral bands. The RI maps predicted by the trained U-Nets showed considerably matching spatial occurrence tendencies of three red tide species to the ground truths for validation images. The mean target accuracy with the four-band dataset was 13 % lower than that with the six-band dataset. The trained U-Net for pixel-wise red tide detection would be able to effectively inspect red tide occurrences in the huge area of water surrounding the Korean peninsula.