Microsatellite markers with polymorphic advantages are widely used in the exploration and utilization of marine fishery resources. In this study, 16 polymorphic microsatellite markers were used to evaluate the diversity and population structure of Setipinna tenuifilis, a nearshore fish of economic and ecological value in the western Pacific Ocean and Indian Ocean. The genetic diversity of S. tenuifilis showed a high level [mean N _a (number of alleles) is 23.25, mean H _o (observed heterozygosity) is 0.639, mean R _a (allelic richness) is 11.625, and the polymorphic information content (PIC) is 0.844] similar to other Clupeiformes fish species. The nine wild S. tenuifilis populations showed significant differentiation (F _ST ranging from 0.00384 to 0.19346) and were generally divided into southern and northern populations based on genetic structure, except for the Zhoushan population, which exhibited genetic mixture. Our results provide fundamental but significant genetic insights for the management and conservation of S. tenuifilis fishery resources.

Significant wave period is an important parameter in coastal and offshore engineering design. Traditional spectral wave models do not directly calculate this parameter, which means that it needs to be estimated from the spectral periods using empirical formulas. The wave energy period is one of the wave periods directly output by many wave models and is often used in studies of wave energy. This study investigated the relationship between significant wave period and wave energy period using wave data measured at three stations in the coastal waters of China. The observations recorded at these stations in the South China Sea, the East China Sea, and the Bohai Sea covered a wide range of surface wave conditions. Analysis indicated that the ratio of significant wave period to wave energy period is closely related to the Goda peakedness parameter of the wave spectra. Therefore, we proposed an empirical formula in which significant wave period is a function of wave energy period and the Goda peakedness parameter. Evaluation results showed that the performance of this formula is substantially better than that of fitting formulas that use constant coefficients.

Marine heatwave (MHW) events refer to periods of significantly elevated sea surface temperatures (SST), persisting from days to months, with significant impacts on marine ecosystems, including increased mortality among marine life and coral bleaching. Forecasting MHW events are crucial to mitigate their harmful effects. This study presents a two-step forecasting process: short-term SST prediction followed by MHW event detection based on the forecasted SST. Firstly, we developed the “SST-MHW-DL” model using the ConvLSTM architecture, which incorporates an attention mechanism to enhance both SST forecasting and MHW event detection. The model utilizes SST data from the preceding 60 d to forecast SST and detect MHW events for the subsequent 15 d. Verification results for SST forecasting demonstrate a root mean square error (RMSE) of 0.64℃, a mean absolute percentage error (MAPE) of 2.05%, and a coefficient of determination (R²) of 0.85, indicating the model’s ability to accurately predict future temperatures by leveraging historical sea temperature information. For MHW event detection using forecasted SST, the evaluation metrics of “accuracy”, “precision”, and “recall” achieved values of 0.77, 0.73, and 0.43, respectively, demonstrating the model’s capability to capture the occurrence of MHW events accurately. Furthermore, the attention-enhanced mechanism reveals that recent SST variations within the past 10 days have the most significant impact on forecasting accuracy, while variations in deep-sea regions and along the Taiwan Strait significantly contribute to the model’s efficacy in capturing spatial characteristics. Additionally, the proposed model and temporal mechanism were applied to detect MHWs in the Atlantic Ocean. By inputting 30 d of SST data, the model predicted SST with an RMSE of 1.02℃ and an R² of 0.94. The accuracy, precision, and recall for MHW detection were 0.79, 0.78, and 0.62, respectively, further demonstrating the model’s robustness and usability.

We introduce a new method, the piecewise Reynolds mean (PREM), for decomposing the flow velocity into the mean-flow and eddy-flow parts in the time domain for subsequent calculation of the mean flow kinetic energy (MKE) and eddy kinetic energy (EKE). Compared with conventional methods like the Reynolds mean and running mean (RUM), PREM has the advantage of exact balance between the MKE and EKE, without the additional residual kinetic energy (RKE), while retaining time-dependent mean-flow. It is mathematically simple and computationally lightweight, depending on a pre-defined separation scale for the mean-flow and eddies. Based on satellite observations and the separation scale of 1 year, we compare PREM with RUM, as well as another newly proposed method, the eddy detection and extraction (EDEX). The latter is based on objective identification of mesoscale eddies and eddy anomaly extraction algorithms, and is therefore only suitable for mesoscale eddy energetics, but independent of separation scales. It is shown that compared with RUM, PREM gives larger mean EKE and stronger interannual variability. In strong-current and eddy-rich regions, the two methods differ the most (max: Kuroshio Extension, root-mean-sqaure-difference = 60.3 J/m³); but in areas with weak current and eddy, the difference accounts for the largest fraction of total EKE (max: south of the Aleutian Islands, 208%). EKE estimated by the two methods is out of phase (min correlation coefficient = 0.38). The mean EKE and standard deviation from the EDEX method resemble the PREM with 1-year separation scale, but is generally smaller in magnitude.

Aquaculture, as the fastest-growing food production sector in the world, is becoming an increasingly nonnegligible source of greenhouse gas emissions. Despite this, there has been limited research on nitrous oxide (N₂O) emission from marine aquaculture in China, where more marine aquaculture occurs than anywhere else, globally. We estimated N₂O emissions (E) from marine mariculture of 10 fish and 6 crustacean species in China from 2003 to 2022 using production data from the China Fishery Statistical Yearbook (2004–2023), and data for feed conversion rates and types from the literature. From 2003, marine aquaculture production, the annual N₂O emissions (E_A), and the annual N₂O emissions per unit of aquaculture area (EI_A) trend upward. The E_A of fish culture was lower than that of crustaceans, but the EI_A of fish culture was generally higher. Sea bass (0.308 Tg/a, in terms of N) and white shrimp (0.945 Tg/a, in terms of N) had the highest average E_A among fish and crustacean cultures, respectively. The highest average E_A from fish and crustacean were both Guangdong Province (fish: 0.248 Tg, crustacean: 0.547 Tg), and the highest sea area were both the South China Sea (fish: 0.316 Tg, crustacean: 1.082 Tg); the highest average EI_A for fish and crustacean were Tianjin City [35.40 t/(hm²·a)] and Guangxi Zhuang Autonomous Region [19.83 t/(hm²·a)], respectively, and the highest sea areas were both the South China Sea (fish: 0.316 Tg, crustacean: 1.082 Tg). These analyses provide baseline data for a greenhouse gas emissions inventory for China, based on an interpretation of them, we provide recommendations for reducing N₂O emissions in marine fish and crustacean culture.

By establishing a distribution and environmental factor database of 21 typical harmful dinoflagellates in global waters, the MaxEnt model was used to predict shifts in the habitat of harmful dinoflagellates in Chinese waters under global climate change. The results revealed that offshore distance was the most important predictive factor and that surface seawater temperature (SST), primary productivity, and nitrate concentration were the key ecological factors influencing the distribution of harmful dinoflagellates. Under the low greenhouse gas emission scenario defined by the Intergovernmental Panel on Climate Change (IPCC), by approximately 2050, 17 of the 21 harmful dinoflagellate species in high-suitability areas (HSA) will migrate northward, six species will migrate eastward, and six species will expand their HSA. By 2100, approximately 18 of the 21 harmful dinoflagellate species in HSA will have migrated northward, seven species will have migrated eastward, and four species will have expanded their HSA. Notably, the HSA content of highly toxic Alexandrium minutum is expected to increase by 13.4% and 9.4% by 2050 and 2100, respectively. Under the high greenhouse gas emissions, there will be 17 species migrating northward, 6 species migrating eastward, and 4 species increasing in their size in HSA by 2050; moreover, there will be 16 species migrating northward, 2 migrating eastward, and 4 species according to their size of HSA by 2100. Specifically, the HSA of A. minutum is predicted to increase by 7.0% and 25.9% by 2050 and 2100, respectively. Notably, A. ostenfeldii, which is currently seldom present in the China seas, is predicted to exhibit an HSA in most coastal areas of the Yellow Sea, the Bohai Sea, the Hangzhou Bay, the Zhejiang Coast, and the Beibu Gulf of the South China Sea. Conversely, the HSA of Noctiluca scintillans, a typical red-tide species, will be reduced by 7%–90%. The northward migration of Karenia mikimotoi exceeded 100 km and 300 km under low and high greenhouse gas emission scenarios, respectively. These changes underscore the significant impact of climate change on the distribution and habitat suitability of harmful dinoflagellates, thus indicating a potential shift in their ecological dynamics and consequent effects on marine ecosystems.

Phytoplankton blooms are complex environmental phenomena driven by multiple factors. Understanding their relationships with meteorological factors and climate oscillations is essential for advancing data-driven and hybrid statistical-dynamical models. However, these relationships have rarely been investigated across different temporal scales. This study employs wavelet transform coherence and multiple wavelet coherence to examine the multiscale and multivariate relationships between phytoplankton blooms, meteorological factors, and climate oscillations in eight large marine ecosystems of the western North Pacific. The results reveal that all phytoplankton blooms in the studied ecosystems exhibit significant annual oscillations, while seasonal climate patterns demonstrate either unimodal or bimodal distributions. A comparison of the wavelet transform coherence and multiple wavelet coherence results indicates that meteorological factors primarily drive short-period variations in phytoplankton blooms, whereas climate oscillations exert more influence on long-term changes. The explanation of phytoplankton blooms increases as the driver factors increase, but there are also some decreasing due to the collinearity between different factors. The sea-air temperature difference emerges as the most significant driving factor, with its mechanisms varying across marine ecosystems: one type influences mixed-layer depth, while the other arises from interspecific differences in temperature sensitivity. Furthermore, the results underscore the importance of integrating non-dominant large-scale circulation indices with predominant meteorological factors for a more comprehensive understanding.

We investigated dissolved iodine species in seawater from the northern South China Sea Shelf. Iodide concentrations were determined by cathodic stripping square wave voltammetry, and iodate was measured by spectrophotometry. Dissolved organic iodine (DOI) was measured with reference to reduced iodide. R-TDI (R-X or rationalized-X is the concentration of X normalized to a salinity of 35, TDI represents total dissolved iodine) was in the range of 0.43–0.46 µmol/L, showing a relatively conservative behavior, while iodate, iodide, and DOI showed non-conservative behaviors. Distribution characteristics in the surface waters showed R-iodate values in the 0.28–0.32 µmol/L range and an offshore>inshore trend, while R-iodide was in the 0.11–0.19 µmol/L range and R-DOI in the 0–0.07 µmol/L range, reflecting an inshore>offshore trend for both. The vertical distribution showed the highest R-iodide concentrations in the surface waters and decreased values with depth, reaching less than 0.01 µmol/L at depths>200 m. R-iodate increased with depth with a measured peak value of 0.43 µmol/L. Seawater with high iodate/iodide ratio (up to 2.9) was found in the central upwelling region and gradually decreased to 2.0 far from this center. The relationship between R-iodide and R-iodate among all samples followed the 1:1 relationship with a slope slightly less than 1, indicating that the conversion between iodate and iodide species could not account for the observed changes. This finding also suggests that DOI may be an important participant in the mass balance. A box model was applied to calculate the input and output of iodine species, and the result showed that approximately 8% of iodate (1.50 × 10⁸ mol/a) imported to the shelf sea was reduced. Concomitantly, the amount of iodide and DOI produced in the shelf amounted to 1.07 × 10⁸ mol/a, roughly 14% higher than the input iodide.

The proposed study focuses on the reported oil spill detection and assessments of oil impacts on marine ecosystems. Five selected oil spills, including those in East China Sea, Balikpapan Bay, Red Sea, Mauritius coast, and Colombo coast were detected using the Sentinel-1 satellite dataset. Sentinel-2/Landsat 8, and Sentinel-5 Precursor (S-5P) satellite datasets were utilized to observe the impacts of oil spills on vegetation cover and air quality respectively. Synthetic aperture radar-based oil spill detection techniques are effective in monitoring oil pollution. Impacts of oil spills on vegetation are monitored via different vegetation indices. The East China Sea spill moved around 190 km from the source point. The area of vegetation cover impacted by the Balikpapan Bay oil spill was 118 km². Near real-time data of different toxic gases from S-5P were analyzed for Sri Lanka and the Red Sea using the Google Earth Engine. It is concluded that wind speed was between the range of 3 m/s to 9 m/s that is favorable for the oil spill detection, and it is also observed that wind direction had impacts on oil spill movement as well. Vegetation Indices provide highly reliable results for the four events but the Red Sea oil spill findings were not satisfactory due to low vegetation cover in this area.

As our understanding of ecology deepens and modeling techniques advance, species distribution models have grown increasingly sophisticated, enhancing both their fitting and predictive capabilities. However, the dependability of predictive accuracy remains a critical issue, as the precision of these predictions largely hinges on the quality of the base data. We developed models using both field survey and remote sensing data from 2016 to 2020 to evaluate the impact of different data sources on the accuracy of predictions for Scomber japonicus distributions. Our research findings indicate that the variability of water temperature and salinity data from field suvery is significantly greater than that from remote sensing data. Within the same season, we found that the relationship between the abundance of S. japonicus and environmental factors varied significantly depending on the data source. Models using field survey data were able to more accurately reflect the complex relationships between resource distribution and environmental factors. Additionally, in terms of model predictive performance, models based on field survey data demonstrated greater accuracy in predicting the abundance of S. japonicus compared to those based on remote sensing data, allowing for more accurate mastery of their spatial distribution characteristics. This study highlights the significant impact of data sources on the accuracy of species distribution models and offers valuable insights for fisheries resources management.