Search Results (1,204)

The coastal waters of the northern Shandong Peninsula have abundant fishery resources, which serve as a critical transitional fishing ground for economic fish migrating into the Bohai Sea for spawning and departing for overwintering habitats. However, anthropogenic pressures such as garbage dumping have led to severe degradation of local fishery resources and concomitant adverse effects on zooplankton communities. To assess these impacts, we analyzed the spatiotemporal distribution, community structure, dominant species, and diversity indices of zooplankton based on sampling data collected in spring from 2015 to 2018 in this region. A total of 24 zooplankton species and 11 larval classes were identified, with the highest species richness observed in 2016. Calanus sinicus and Centropages abdominalis were the primary dominant species, with C. sinicus consistently predominant across all four years. Notably, the dominant species exhibited marked annual variability. The abundance and biomass of zooplankton in the surveyed area exhibited significant annual variations, both showing a trend of first decreasing and then increasing. Peak abundance occurred in 2015 (594.36 ind/m³), while the lowest was recorded in 2017 (118.73 ind/m³). Spatially, abundance and biomass were heterogeneous, with coastal waters exhibiting higher concentrations than offshore areas. The overall low level of community diversity and its significant annual variations indicated that the zooplankton community structure in the surveyed sea area was unstable and showed a trend of degenerative succession. The community structure of zooplankton and larger-bodied dominant species showed stronger correlations with phytoplankton dynamics, whereas smaller-bodied species were more influenced by water temperature. Full article

Coastal ecosystems are crucial for territorial development but they face increasing pressure from population growth and climate change. These factors threaten ecosystems, communities, and tourism infrastructure. It is essential to assess vulnerability to achieve adaptation and indices are widely used for this purpose due to their simplicity. However, inconsistencies persist in definitions, methodologies, dimensions, and variable selection. This systematic review of 43 second-generation studies analyzes the evolution of conceptual approaches, identifies the most common indicators, and examines index methodologies. The results reveal that, although the IPCC has updated its definition of vulnerability, many publications still use previous conceptual frameworks. While temperature is relevant to tourism, most studies focus on increasing sea level and its effects. In some cases, social and economic dimensions are treated jointly whereas in other studies they are considered separately. Variable selection remains case-specific and a robust, standardized framework is still lacking, especially for social aspects. Despite the undoubted importance of tourism, specific research on this sector is scarce. This review underscores the need for standardized indices tailored to coastal tourism management under climate change. Future research directions are also proposed. Full article

The main objective of the study is to evaluate the recovery potential of the collapsed semi-anadromous pikeperch population (Sander lucioperca L.) in the Azov Sea during 2021–2030. We use a Ricker-based age-structured model that accounts for the effects of salinity and temperature on reproduction. In earlier work, the model predicted and explained the pikeperch stock collapse as the consequence of salinity and temperature exceeding the species’ tolerance limits. To assess the probability of stock recovery, we conducted a long-term retrospective validation and ran Monte Carlo projections under alternative climate scenarios with supplemental management actions. The results confirm that the dynamics of the pikeperch population in the Azov Sea are essentially environment-driven and negatively impacted by the large positive anomalies in both water temperature and salinity. Simulations suggest that either a substantial and persistent artificial restocking of juvenile recruits, or mostly unlikely scenarios of simultaneous reduction in salinity and temperature combined with additional restocking can provide conditions for the stock restoration within the decade considered. Based on these projections, we recommend a suite of urgent restoration measures to create the conditions required for future stock recovery. Full article

Evaporation Duct Height (EDH) is a crucial parameter in evaporation duct modeling, as it directly influences the strength of the waveguide trapping effect and significantly impacts the over-the-horizon detection performance of maritime radars. To address the limitations of low prediction accuracy and limited interpretability in existing deep learning models under complex marine meteorological conditions, this study proposes a surrogate model, BLA-EDH, designed to emulate the output of the Naval Postgraduate School (NPS) model for real-time EDH estimation. Experimental results demonstrate that BLA-EDH can effectively replace the traditional NPS model for real-time EDH prediction, achieving higher accuracy than Multilayer Perceptron (MLP) and Long Short-Term Memory (LSTM) models. Random Forest analysis identifies relative humidity (0.2966), wind speed (0.2786), and 2-m air temperature (0.2409) as the most influential environmental variables, with importance scores exceeding those of other factors. Validation using the parabolic equation shows that BLA-EDH attains excellent fitting performance, with coefficients of determination reaching 0.9999 and 0.9997 in the vertical and horizontal dimensions, respectively. This research provides a robust foundation for modeling radio wave propagation in the Yellow Sea and Bohai Sea regions and offers valuable insights for the development of marine communication and radar detection systems. Full article

Global warming has been altering the East Asian climate at an unprecedented rate since the 20th century. In order to evaluate the changes in the East Asian winter climate (EAWC) and support policy-making for climate mitigation and adaptation strategies, this paper utilizes the multimodel ensemble from the Couple Model Intercomparison Project 6 and a temperature threshold method to investigate the EAWC changes during the period 1979–2100. The results show that the EAWC has been undergoing widespread and robust changes in response to global warming. The winter length in East Asia has shortened and will continue shortening owing to later onsets and earlier withdrawals, leading to a drastic contraction in length from 100 days in 1979 to 43 days (27 days) in 2100 under SSP2-4.5 (SSP5-8.5). While most regions of the East Asian continent are projected to become warmer in winter, the Japan and marginal seas of northeastern Asia will face the risks from colder winters with more frequent extreme cold events, accompanied by less precipitation. Meanwhile, the Tibetan Plateau is very likely to have colder winters in the future, though its surface snow amounts will significantly decline. Greenhouse gas (GHG) emissions are found to be responsible for the EAWC changes. GHG traps heat inside the Earth’s atmosphere and notably increases the air temperature; moreover, its force modulates large-scale atmospheric circulation, facilitating an enhanced and northward-positioned Aleutian low together with a weakened Siberian high, East Asian trough, and East Asian jet stream. These two effects work together, resulting in a contracted winter with robust and uneven regional changes in the EAWC. This finding highlights the urgency of curbing GHG emissions and improving forecasts of the EAWC, which are crucial for mitigating their major ecological and social impacts. Full article

High-resolution spatiotemporal prediction information on fishing vessel activities is essential for formulating and effectively implementing fisheries policies that ensure the sustainability of marine resources and fishing practices. This study focused on the tuna longline fishery in the Western and Central Pacific Ocean (130° E–150° W, 20° S–20° N) and constructed a CLA U-Net deep learning model to predict fishing effort (FE) distribution based on 2017–2023 FE records and environmental variables. Two modeling schemes were designed: Scheme 1 incorporated both historical FE and environmental data, while Scheme 2 used only environmental variables. The model predicts not only the binary outcome (presence or absence of fishing effort) but also the magnitude of FE. Results show that in short-term predictions, Scheme 1 achieved F1 scores of 0.654 at the 0.5°-1-day scale and 0.763 at the 1°-1-day scale, indicating substantial improvement from including historical FE data. In medium-term predictions, Scheme 1 and Scheme 2 reached maximum F1 scores of 0.77 and 0.72, respectively, at the optimal spatiotemporal scale of 1°-30 days. The analysis also quantified the relative importance of environmental variables, with sea surface temperature (SST) and chlorophyll-a (Chl-a) identified as the most influential. These findings provide methodological insights for spatiotemporal prediction of fishing effort and support the refinement of fisheries management and sustainability strategies. Full article

The recently developed localized particle filter (LPF) is extended to a fully coupled general circulation model (CGCM), specifically the Community Earth System Model (CESM), to assess its efficacy in assimilating multisource ocean observations, including satellite sea surface temperature (SST) and in situ temperature and salinity (TS) profiles. The LPF introduces localization in the weighting and resampling steps to avoid the filter degeneracy problem, thereby enhancing its performance in assimilating nonlinear systems. Data assimilation experiments using real ocean observations reveal that the LPF has notable advantages in improving the quality of subsurface and deep ocean temperature and salinity, particularly below 200 m. The results are evaluated against objective analysis data, confirming the potential applicability of the LPF in operational settings. Furthermore, a comparative analysis with the ensemble adjustment Kalman filter (EAKF) elucidates the merits and limitations of the LPF, and further underscores the pronounced advantage of LPF in the deep ocean. However, when TS profiles are already assimilated, supplementing the LPF with additional SST data produces adverse effects, a behavior markedly different from that of the EAKF. This discrepancy signals the need for refined data pre-processing strategies within the LPF in real operational applications. Full article

Amid intensifying global climate change, coastal cities are facing increased heat stress. The sea breeze plays a crucial role in mitigating the urban heat island effect and improving outdoor thermal comfort, warranting detailed investigation of its spatiotemporal impacts. This research, conducted in Sendai, Japan, combines the Weather Research and Forecasting (WRF) model with the Rayman thermal comfort model to assess the spatiotemporal evolution of the Physiological Equivalent Temperature (PET) on typical sea breeze days, exploring heat stress patterns. The findings indicate significant PET reductions in the area due to sea breeze influence, although high heat stress persists in urban centers. The coastal zone (0–4 km) experiences the longest period of low heat stress, whereas the inland zone (20–26 km) suffers from poor thermal comfort. Heat stress intensifies in the northwestern inland regions, while improvement progresses from the coast inland. Vegetated areas reach low heat stress states earlier than built-up areas; both coastal and urban zones quickly revert to “no heat stress” conditions. The results demonstrate that the cooling effect of sea breezes decreases with distance, its efficacy hindered by urban environments, whereas vegetated lands prolong comfort inland. These insights are crucial for planning thermal environments in coastal cities. Full article

Climate change threatens coastal biodiversity, necessitating proactive conservation for endangered species like the mangrove Acanthus ebracteatus. This study integrated the MaxEnt and OPGD models to simulate its potential suitable habitats under current and three future SSP scenarios (SSP126, SSP245, and SSP585). Based on the MaxEnt model, sea surface salinity (SSS_range), sea surface temperature (SST_max), soil texture (T_silt, T_sand), and annual precipitation (Bio12) were identified as the dominant factors influencing its distribution, with SSS_range emerging as the key constraint. Furthermore, interaction analysis using the OPGD model revealed significant synergistic effects, particularly between salinity and soil properties (q > 0.8), underscoring the importance of multi-factor interactions in ecological niche modeling. Under the three SSP scenarios, the suitable habitat is projected to expand northeastward, accompanied by a poleward shift in the distribution centroid, driven predominantly by warming temperatures and altered rainfall patterns. KDE analysis revealed that existing protected areas do not fully cover regions with high habitat suitability. We propose a stratified conservation strategy that enhances in situ protection in core zones, initiates assisted restoration in potential habitats, and promotes experimental outplanting in future climatically suitable areas. This study provides scientific insights for the conservation and management of Acanthus ebracteatus under global climate change. 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

This study presents a comprehensive assessment of long-term nutrient dynamics in the northern South China Sea (NSCS), a region that hosts the world’s largest marine ranching cluster and serves as a cornerstone of China’s “Blue Granary” initiative. By integrating multi-sensor satellite remote sensing data (Landsat and Sentinel-2, 2002–2024) with in situ observations, we developed robust retrieval algorithms for total nitrogen (TN) and total phosphorus (TP), achieving high accuracy (TN: R² = 0.82, RMSE = 0.09 mg/L; TP: R² = 0.94, RMSE = 0.0071 mg/L; n = 63). Results showed that TP concentrations increased significantly faster than TN, leading to a decline in the TN:TP ratio (NP) from 19.2 to 13.2 since 2013. This shift indicates a transition from phosphorus (P) limitation to nitrogen (N) limitation, driven by warming sea surface temperatures (SST) (about 1.16 °C increase) and increased anthropogenic phosphorus inputs (about 27.84% increase). The satellite-based framework offers a scalable, cost-effective solution for monitoring aquaculture water quality. When integrated with artificial intelligence (AI) technologies, these near-real-time nutrient anomaly data can support early warning of harmful algal blooms (HABs), offering key insights for ecosystem-based management and climate adaptation. Overall, our findings highlight the utility of remote sensing in advancing sustainable marine resource governance amid environmental change. Full article

In this paper, we develop a deep learning neural network characterized by feature fusion and physical guidance (denoted as FFPG-net) for reconstructing subsurface sea temperature (T) and salinity (S) from sea surface data. Designed with the idea of feature fusion, FFPG-net combines the deep learning algorithms of residual and channel attention with the physical constraints of vertical modes of T/S profiles decomposed by empirical orthogonal functions (EOFs). The results from a series of single point experiments show that FFPG-net outperforms the CNN or CNN-PG (without physical guidance or feature fusion) in the reconstruction of subsurface T/S in a region of the South China Sea (SCS), with monthly mean RMSEs of 0.31 °C (0.35 °C) and 0.06 psu (0.07 psu) for the reconstructed T/S profiles in winter (summer), averaged over the water depth of 1200 m and the study area. In addition, the performance of the FFPG-net can be improved significantly by incorporating full surface currents or geostrophic currents derived from SSH into the input variables for training the neural network. The preliminary application of FFPG-net in the SCS using satellite-derived sea surface observations indicates that FFPG-net is reliable and feasible for reconstructing subsurface ocean thermal fields in real situations. Our study highlights the advantages and necessity of combining deep learning algorithms with physical constraints in reconstructing subsurface T/S profiles. It provides an effective tool for reconstructing the subsurface global ocean from remote-sensing sea surface observations in the future. Full article

High viscosity of alginate means a relatively low substrate concentration, which limits the efficiency of hydrolysis, resulting in one of the main challenges for the large-scale production of alginate oligosaccharides (AOS). In this study, a pilot-scale degradation product (PSDP) of the recombinant enzyme 102C300C-Vgb was produced for the first time at a substrate concentration of up to 20% sodium alginate. The optimal conditions for SA digestion were: enzyme dosage of 25 U/g, enzymatic temperature of 45 °C, enzymatic pH of 7.0, and enzymatic time of 24 h. Under these conditions, the yield of enzymatic hydrolysis was consistently in the range of 69% to 70%. The average molecular weight (Mw) of PSDP was 1496.36 Da, mainly containing oligosaccharides with degrees of polymerization ranging from 2 to 4. The low-Mw PSDP was subsequently applied in the diet of sea cucumber Stichopus japonicus. The results showed that the body wall weight of S. japonicus increased significantly after 40 days of feeding with a 0.09% PSDP-supplemented diet. Furthermore, PSDP-supplemented diet significantly increased the thickness of the serosal and submucosal layers and the width folds of mucosa of the sea cucumber gut. The abundance of pathogenic bacteria was reduced effectively, and that of beneficial bacteria increased significantly after being fed with PSDP. The results demonstrated that PSDP can serve as a digestive health enhancer for sea cucumbers, promoting their healthy growth. Full article

This study examined the spatial distribution and environmental determinants of hairtail (Trichiurus lepturus) catch volumes in the southern coastal waters of South Korea, employing a Spatial Durbin Model (SDM) based on grid-level data collected from 2020 to 2022. Key explanatory variables included chlorophyll-a concentration, dissolved oxygen, salinity, sea surface temperature, and fishing effort. Spatial autocorrelation was confirmed through Moran’s I test, justifying the application of a spatial econometric framework. Among the environmental factors, salinity exhibited the strongest positive direct effect on catch volumes, whereas dissolved oxygen consistently showed a negative effect. Chlorophyll-a concentration exhibited significant positive effects both within local grids and in neighboring areas. Sea surface temperature also had a modest but significant direct effect on catch volumes. Additionally, higher fishing effort was associated with increased catch volumes, emphasizing the spatial impact of human activities on fishery resources. These findings reveal that hairtail tend to aggregate in high-salinity, low-oxygen environments and respond to seasonal oceanographic variations. Overall, the results highlight the value of spatial econometric models in fisheries research by revealing how environmental and anthropogenic factors influence fish catch through both direct and indirect effects. The spatial framework offers deeper insight into the mechanisms driving hairtail distribution, particularly in ecologically complex regions like the Jeju Strait. Full article

Increased Atlantic water transport and river discharge are more pronounced effects of global warming at high latitudes. Both phenomena may lead to changes in the species composition of small-celled algae populations in marine ecosystems, as well as to the emergence of new species. This study investigated the spatial distribution of picocyanobacterial (PC) abundance and the phylogenetic diversity of PC Synechococcus in the Kara Sea. PC abundance varied from 2 to 88 cells mL⁻¹ and increased with warming temperatures and decreasing salinity caused by river water influence. The contribution of Synechococcus to the total picophytoplankton biomass was low (<16%). The Synechococcus community was characterized at deep taxonomic level using amplicon sequencing targeting the petB gene. Diversity was low, revealing only Synechococcus subcluster 5.1 polar lineages I and IV, and euryhaline subcluster 5.2. Synechococcus subcluster 5.1.I represented on average 97% of the total reads assigned to cyanobacteria. For the first time, the presence of estuarine Synechococcus subcluster 5.2 was documented as far north as 82° N. Modified Atlantic water was the main source of cyanobacteria in the Kara Sea, followed by river discharge. Our study contributes to the understanding of PC sources in the Kara Sea and allows for the further monitoring of PC distribution and evolution. Full article