Search Results (1,020)

Marine heatwaves (MHWs) pose a serious threat to the marine ecosystems and fishery resources in the East China Sea (ECS). Based on National Oceanic and Atmospheric Administration Optimum Interpolation Sea Surface Temperature High Resolution version 2 data, this study investigated the regional divergence in long-term trends of MHWs in the ECS from 1982 to 2023. The principal findings were as follows. Concerning MHWs, the coastal waters of China from northern Jiangsu coast to northeast of Taiwan Island experienced a relatively high annual average frequency, the longest duration, largest number of total days, strongest intensity, and the most pronounced seasonal signals. Additionally, the areas along the Kuroshio path showed significant levels of frequency, duration, and total days, but with comparatively weak intensity. In the empirical orthogonal function (EOF) analysis, EOF1 of the total days and cumulative intensity exhibited notable variation along the path of the Kuroshio and its offshoots, and in Chinese coastal areas. EOF2 showed significantly more conspicuous variation in areas extending from the Yangtze River Estuary to the northern Jiangsu coast. Furthermore, the MHW indices generally showed a positive trend in the ECS from 1982 to 2023. Importantly, the regions with high annual average MHW indices were also characterized by a significantly positive increasing trend. Moderate (79.10%) and strong (19.94%) events were most prevalent, whereas severe (0.82%) and extreme (0.14%) events occurred infrequently. The enhanced solar radiation and the reduced latent heat loss were the main contributing factors of MHWs in the ECS. These findings provide valuable insights into the ecological environment and resources of the ECS as a marine pastoral area. Full article

Coastal communities and marine ecosystems face increasing risks due to changing ocean conditions, yet effective wave monitoring remains limited in many low-resource regions. This study investigates the use of seismic data to predict significant wave height (SWH), offering a low-cost and scalable solution to support coastal conservation and safety. We developed a baseline machine learning (ML) model and improved it using a longest-stretch algorithm for seismic data selection and station-specific hyperparameter tuning. Models were trained and tested on consumer-grade hardware to ensure accessibility and availability. Applied to the Sicily–Malta region, the enhanced models achieved up to a 0.133 increase in R² and a 0.026 m reduction in mean absolute error compared to existing baselines. These results demonstrate that seismic signals, typically collected for geophysical purposes, can be repurposed to support ocean monitoring using accessible artificial intelligence (AI) tools. The approach may be integrated into conservation planning efforts such as early warning systems and ecosystem monitoring frameworks. Future work may focus on improving robustness in data-sparse areas through augmentation techniques and exploring broader applications of this method in marine and coastal sustainability contexts. Full article

The Blue Schools initiative integrates the ocean into classroom learning through project-based approaches, cultivating environmental awareness and a deeper sense of responsibility toward marine ecosystems and human–ocean interactions. Although the European Blue School initiative has grown steadily since its launch in 2020, its uneven uptake raises important questions about the territorial factors that influence certification. This study examines the spatial determinants of Blue School certification in Mallorca, Spain, where a bottom-up pilot initiative successfully certified 100 schools. Using Maximum Entropy (MaxEnt) modeling, we estimated the spatial probability of certification based on 16 geospatial variables, including proximity to Blue Economy actors, hydrological networks, transport accessibility, and socio-economic indicators. The model achieved strong predictive performance (AUC = 0.84) and revealed that features such as freshwater ecosystems, traditional economic structures, and sustainable public transport play a greater role in school engagement than coastal proximity alone. The resulting suitability map identifies over 30 high-potential, non-certified schools, offering actionable insights for targeted outreach and educational policy. This research highlights the potential of presence-only modeling to guide the strategic expansion of Blue Schools networks. Full article

Marine pollution incidents pose significant threats to marine ecosystems and coastal communities across Pacific Island nations, necessitating advanced predictive capabilities for effective environmental management. This study analyzes 8133 marine pollution incidents from 2001–2014 across 25 Pacific Island nations to develop predictive models for pollution type classification, hotspot identification, and seasonal pattern forecasting. Our analysis reveals Papua New Guinea as the dominant pollution hotspot, experiencing 51.9% of all regional incidents, with plastic waste dumping comprising 78.8% of pollution events and exhibiting pronounced seasonal peaks during June (coinciding with critical fish breeding periods). Machine learning classification achieved 99.1% accuracy in predicting pollution types, with material composition emerging as the strongest predictor, followed by seasonal timing and geographic location. Temporal analysis identified distinct seasonal dependencies, with June representing peak pollution activity (755 average incidents), coinciding with vulnerable marine ecological periods. The predictive framework successfully distinguishes between persistent geographic hotspots and episodic pollution events, enabling targeted conservation interventions during high-risk periods. These findings demonstrate that pollution type and location are highly predictable from environmental and temporal variables, providing marine conservationists with tools to anticipate when and where pollution will most likely impact fish populations and ecosystem health. The study establishes the first comprehensive baseline for Pacific Island marine pollution patterns and validates machine learning approaches for proactive pollution monitoring, offering scalable solutions for protecting ocean ecosystems and supporting evidence-based policy formulation across the region. Full article

Phytoplankton serves as the primary producer in estuarine ecosystems, with its community structure and dynamics being directly influenced by the concentration and ratio of nitrogen (N) and phosphorus (P) nutrients. This study utilized raw water from the Yangtze Estuary to establish a series of ocean microcosm systems, setting up gradients of dissolved inorganic nitrogen (DIN) and reactive phosphate (SRP) concentrations to explore the reaction of phytoplankton communities over 30 days. The results indicated that total phytoplankton abundance significantly increased under prolonged exposure to high concentrations of DIN and SRP. However, the community diversity indices exhibited a declining tendency, indicating a simplification and increased instability of the community structure. Diatoms and dinoflagellates, the predominant phytoplankton taxa, differed in their response to DIN and SRP. Diatom abundance rose at elevated DIN concentrations and initially increased and then decreased at high SRP concentrations, while dinoflagellate abundance diminished at high DIN concentrations and persisted in increasing at elevated SRP concentrations. An ecological threshold is the critical point at which the structure or function of an ecosystem undergoes significant changes when subjected to external disturbances or internal changes. The Threshold Indicator Taxa Analysis (TITAN) was employed to identify indicator species within the microcosm systems, revealing that the ecological response thresholds of phytoplankton communities to DIN and SRP were 0.50 mg/L and 0.030 mg/L, respectively. This study quantitatively analyzed the environmental exposure concentrations of DIN and SRP at the community level and calculated the ecological response thresholds, providing fundamental data and a scientific basis for nitrogen and phosphorus management in estuaries. Full article

Sea surface salinity (SSS) observations play a crucial role in the study of ocean circulation, climate variability, and marine ecosystems. However, current satellite SSS products suffer from systematic biases due to factors such as radio frequency interference (RFI) and land contamination, resulting in fundamental limitations to their application for SSS monitoring. To address this issue, we propose a physics-informed neural network (PINN) approach that directly integrates radiative transfer physical processes into the neural network architecture for SMAP L2 SSS bias correction. This method ensures oceanographically consistent corrections by embedding physical constraints into the forward propagation model. The results demonstrate that PINN achieved a root mean square error (RMSE) of 0.249 PSU, representing a 5.3% to 8.5% relative performance improvement compared to conventional methods—GBRT, ANN, and XGBoost. Further temporal stability analysis reveals that PINN exhibits significantly reduced RMSE variations over multi-year periods, demonstrating exceptional long-term correction stability. Meanwhile, this method achieves more uniform bias improvement in contaminated nearshore regions, showing distinct advantages over the inconsistent correction patterns of conventional methods. This study establishes a physics-constrained machine learning framework for satellite SSS data correction by integrating oceanographic domain knowledge, providing a novel technical pathway for reliable enhancement of Earth observation data. Full article

Although giant viruses have introduced new perspectives on the definition and evolution of viruses and are increasingly recognized for their significant biological roles within marine ecosystems, systematic evaluations of development trends and scientific contributions in this research field remain limited. This study conducted a bibliometric analysis of the global academic literature on marine giant viruses (MGVs), focusing on nucleocytoplasmic large DNA viruses (NCLDVs), from 1996 to 2024. Using the Web of Science Core Collection, 1544 publications related to giant viruses were identified. After filtering using marine-related keywords and manual review, 300 studies specifically addressing marine giant viruses were selected for the final analysis. This study comprehensively examined the structural characteristics and evolutionary trends in this field by analyzing annual publication productivity, citation patterns, contributions by countries and institutions, author collaboration networks, and keyword co-occurrence patterns. The results show that research on MGVs has steadily increased since the mid-2000s, with a notable surge after 2018 driven by advancements in metagenomics, next-generation sequencing technologies, and global ocean exploration initiatives. The United States and France have taken leading positions in terms of research productivity and impact, with key institutions such as the CNRS (Centre National de la Recherche Scientifique) and Aix-Marseille Université playing central roles. A multipolar network of international collaborations between countries and institutions has been formed. Research topics have evolved from an early focus on virus classification and genome analysis to more diverse themes, including interactions with marine microbiota, viral ecological functions, infection dynamics, virophage research, and metagenome-based ecosystem-level studies. This study provides an overview of the chronological and structural evolution of the marine giant virus research field by systematically presenting key research themes and collaborative networks. The results provide a valuable foundation for determining future academic directions and planning strategic research initiatives. Furthermore, it is expected to facilitate interdisciplinary research in marine biology, environmental science, systems biology, and artificial intelligence-based functional predictions. Full article

Background: Abiogenesis is hypothesized to have occurred in the aquatic environments of the early Earth approximately 3.8–4.0 billion years ago, in oceans containing high concentrations of ions (Na⁺ ≈ 470 mmol/L, Cl⁻ ≈ 545 mmol/L, Mg²⁺ ≈ 51–53 mmol/L, Ca²⁺ ≈ 10 mmol/L, K⁺ ≈ 10 mmol/L, SO₄²⁻ ≈ 28–54 mmol/L, HCO₃⁻ ≈ 2.3 mmol/L). Primitive membranes evolved ion-regulatory mechanisms to sustain electrochemical gradients, enabling metabolic activity. Objectives: This review compares the composition of amniotic fluid (AF) to seawater, framing AF as a “biological ocean” for the fetus, and evaluates the impact of micro- and nanoplastics (MNPs) on this protected milieu. Methods: We synthesized data from published studies on concentrations of and ions and other important substances in AF during pregnancy and compared them with marine values. Reports of MNPs detected in placenta, AF, and human organs were systematically reviewed. Results: AF exhibits high ionic similarity to seawater, although the absolute concentrations of ions are lower, reflecting evolutionary conservation. Recent analytical studies identified MNPs in samples of human placenta (4–10 particles per 1 g of tissue), meconium (median 3–5 particles per g), and AF (detectable in >60% of tested samples). Co-exposure to heavy metals, persistent organic pollutants, and endocrine disruptors were reported in 20–40% of maternal–fetal samples. Conclusions: The analogy between oceans and AF underscores a conserved evolutionary continuum. However, the infiltration of MNPs into intrauterine environments is a novel toxicological challenge with potential implications for neurodevelopment, immune programming, and epigenetic regulation. Within the One Health framework, protecting AF from anthropogenic contaminants is as critical as safeguarding marine ecosystems. Full article

Plastic pollution is an increasing global concern, with estuaries being especially vulnerable as transition zones between freshwater and marine systems. These ecosystems often accumulate large amounts of waste, affecting wildlife and water quality. This study focuses on analysing the circulation patterns of the Ave Estuary, a small, shallow system on Portugal’s north-western coast, and their influence on litter transport and distribution. This site was selected for installing an aquatic litter removal technology under the EU-funded MAELSTROM project. A 2DH hydrodynamic model using Delft3D FM, coupled with the Wflow hydrological model, was implemented and validated. Various scenarios were simulated to assess estuarine dynamics and pinpoint zones prone to litter accumulation and flood risk. The results show that tidal action and river discharge mainly drive the estuary’s behaviour. Under low discharge, floating litter should be mostly transported toward the ocean, while high discharge conditions should result in litter movement at all depths due to stronger currents. High water levels and flooding occur mainly upstream and in specific low-lying areas near the mouth. Low-velocity zones, which can favour litter accumulation, were found around the main channel and on the western margin near the estuary’s mouth, even during high flows. These findings highlight persistent accumulation zones, even under extreme event conditions. Full article

Salinity extremes (SEs) play a crucial role in marine ecosystems, ocean circulation, and climate variability. Understanding their distribution and drivers is essential for predicting changes in ocean salinity under climate change, particularly in dynamic regions such as the Hawaiian Islands, where mesoscale eddies significantly modulate water mass properties. This study investigates the three-dimensional characteristics of SEs and their responses to mesoscale eddies using mooring observations and sea surface salinity data. We find that high salinity extremes (HSEs) generally occur more frequently than low salinity extremes (LSEs) in the study region, though LSEs exhibit greater duration and intensity. Mesoscale eddies modulate SEs significantly—anticyclonic eddies (AEs) enhance LSEs, whereas cyclonic eddies (CEs) promote HSEs in the upper layer. This relationship reverses in the deeper layer, with AEs favoring HSEs and CEs enhancing LSEs. These opposing effects are driven by a vertical displacement of the subsurface salinity maximum layer, where CEs lift high-salinity subsurface water to the upper ocean via upwelling, creating HSEs in the upper layer and LSEs in the deeper layer, while AEs subduct high-salinity water downward, reducing upper-layer salinity (LSEs) but increasing deeper-layer salinity (HSEs) via downwelling. Spatially, CEs exhibit a single-core high-salinity anomaly, displaced westward by 0.3 times of the eddy radius from the eddy center, with HSEs peaking in frequency and intensity near the core. In contrast, AEs display a dipole salinity anomaly (low northwest/high southeast), aligning with LSE frequency distribution, while HSEs show an inverse pattern. Mooring data further reveal that AE-LSE co-occurrence is highest within 1.2 times of the eddy radius, whereas CE-HSE probability declines with eddy intensity. Notably, AE-HSE and CE-LSE probabilities, though initially weaker, surpass AE-LSE and CE-HSE at certain depths, underlining the complexity of depth-dependent eddy modulation. These findings may advance understanding of ocean salinity dynamics and provide insights into how mesoscale processes modulate extreme events, with implications for marine biogeochemistry and climate modeling. Full article

Atlantic salmon aquaculture has become an important seafood producer, contributing to the human diet. The natural productivity of Atlantic salmon populations is not sufficient to meet even a fraction of current aquaculture production, and it has not been able to do so in the past. Alternative process technologies are needed to maintain aquaculture production at current levels while mitigating the environmental impact along the coasts. Future aquaculture development must align with the UN Sustainable Development Goals. This study describes an aquaculture process vessel operating in the open sea and powered largely by renewable energy. Production conditions are fully adapted to the biology of salmon, improving production reliability, reducing coastal environmental impacts, and enabling more sustainable production. This study specifies the biological and technological aspects, provides evidence of the technical and economic feasibility, and justifies the relocation of salmon aquaculture to a large oceanic ecosystem, the North Atlantic Ocean. Full article

This study examines the upper-ocean response to Typhoon Khanun, which traversed the northern South China Sea in October 2017, by integrating multi-satellite observations with numerical simulations from the Regional Ocean Modeling System (ROMS). For the ROMS simulations, an Arakawa C-grid was adopted with a 4-km horizontal resolution and 40 vertical terrain-following $\sigma$-layers, covering the domain of 105° E to 119° E and 15° N to 23° N. Typhoons significantly influence ocean dynamics, altering sea surface temperature (SST), sea surface salinity (SSS), and ocean currents, thereby modulating air–sea exchange processes and marine ecosystem dynamics. High-resolution satellite datasets, including GHRSSST for SST, SMAP for SSS, GPM IMERG for precipitation, and GLORYS12 for sea surface height, were combined with ROMS simulations configured at a 4-km horizontal resolution with 40 vertical layers to analyze ocean changes from 11 to 18 October 2017. The results show that Typhoon Khanun induced substantial SST cooling, with ROMS simulations indicating a maximum decrease of 1.94 °C and satellite data confirming up to 1.5 °C, primarily on the right side of the storm track due to wind-driven upwelling and vertical mixing. SSS exhibited a complex response: nearshore regions, such as the Beibu Gulf, experienced freshening of up to 0.1 psu driven by intense rainfall, while the right side of the storm track showed a salinity increase of 0.6 psu due to upwelling of saltier deep water. Ocean currents intensified significantly, reaching speeds of 0.5–1 m/s near coastal areas, with pronounced vertical mixing in the upper 70 m driven by Ekman pumping and wave-current interactions. By effectively capturing typhoon-induced oceanic responses, the integration of satellite data and the ROMS model enhances understanding of typhoon–ocean interaction mechanisms, providing a scientific basis for risk assessment and disaster management in typhoon-prone regions. Future research should focus on refining model parameterizations and advancing data assimilation techniques to improve predictions of typhoon–ocean interactions, providing valuable insights for disaster preparedness and environmental management in typhoon-prone regions. Full article

The Western and Central Pacific Ocean (WCPO), the key global purse seine fishing ground for skipjack tuna (Katsuwonus pelamis), sees frequent ENSO events. These events drastically alter marine ecosystems and fishery resource patterns, complicating fisheries management—given skipjack tuna’s high mobility and sensitivity to marine environmental changes. To address this, the study proposes an improved spatial prediction framework that incorporates the MGWR model to capture environmental changes. The spatial regression results generated by the MGWR model are incorporated as the mean-field input for the BME model. Additionally, the interannual standard deviation of skipjack tuna resources is fed into the BME model as a measure of spatial uncertainty. The results indicate that the mean field and uncertainty field exhibit a strong correlation, with an R² of 0.54, an RMSE of 583.32, an MAE of 377.22, and an ME of 334.77. Compared to the single prediction models BME and MGWR, the MGWR-BME integrated framework has improved R² by 12%, 30%, and 13% in the 2021–2023 predictions, respectively. Additionally, its prediction performance for distinguishing El Niño, La Niña, and normal years has significantly improved, with R² increasing from 0.6 to 0.67 in 2021, from 0.34 to 0.62 in 2022, and from 0.30 to 0.40 in 2023. According to the evaluation results based on Kernel Density Estimation (KDE) curves, the model performs well in fitting low values but shows weaker performance in fitting high values. By applying this approach, we have clarified the multiscale driving mechanisms through which marine environmental heterogeneity affects the distribution of skipjack tuna under ENSO conditions. This insight enables fishery managers to more accurately predict the dynamic changes in skipjack tuna fishing grounds under different climatic scenarios, thereby providing a reliable scientific basis for formulating rational fishing quotas, optimizing fishing operation layouts, and implementing targeted conservation measures—ultimately contributing to the balanced development of fishery resource utilization and ecological protection. Full article

To understand the impacts of climate change on local marine ecosystems, assessing ocean climate velocity in regional seas is essential. This study investigated changes in sea surface temperature (SST) and associated shifts in isotherm location and ocean climate velocity in the East Sea of Korea from 2000 to 2024, utilizing satellite-derived SST data. The results revealed a significant acceleration in the ocean climate velocity of SST, reaching 66.99 km/decade over the past 25 years. The velocity significantly increased during Phase 4, indicating rapid changes with potential ecosystem impacts. The 18 °C SST zone expanded by more than twofold from the early 2000s to the early 2020s. The annual average SST exhibited a steady, consistent decadal increase. These trends are associated with the northward shift of isotherms, which significantly influences the SST distribution patterns, particularly in the 16–18 °C range. Given the accelerating ocean climate velocity, urgent attention is needed to mitigate climate change impacts, particularly in vulnerable regions such as the East Sea. This study enhances the understanding of SST dynamics and underscores the importance of proactive conservation and management in climate-affected marine ecosystems. Full article

The northern South China Sea has abundant frontal systems near coastal and island regions, which play crucial roles in regional ocean dynamics and ecosystem. While previous studies have established preliminary understanding of their spatial distribution, seasonal variability, and dynamic characteristics, the atmospheric response to these frontal systems remains poorly understood. This study integrates observations from a moored buoy deployed on the continental shelf of the South China Sea with satellite remote sensing data to analyze oceanic and atmospheric variations during frontal passage. The results reveal that the ocean front can not only induce pronounced oceanic changes characterized by significant cooling, saltiness, and surface current acceleration, but also exert substantial influence on the overlying atmosphere, with consistent decreasing trends in air temperature, humidity, and atmospheric pressure, all of which rapidly recovered following frontal retreat. Notably, when the front directly traversed the buoy location, diurnal temperature cycles were markedly suppressed, while turbulent heat flux and downfront wind-stress curl reached peak magnitudes. These findings demonstrate that ocean fronts and associated sea surface temperature gradients can trigger intense air–sea exchange processes at the ocean–atmosphere interface. Full article