Based on the Ricker-type models, the spawning stock-recruitment (S-R) relationship of Portunus trituberculatus was analysed under the impacts of environmental factors (including red tide area (AORT), sea level height (SLH), sea surface salinity (SSS) and typhoon landing times (TYP)) in the northern East China Sea in 2001 and 2014. Besides the traditional Ricker model, two other Ricker-type S-R models were built: Ricker model with ln-linear environmental impact (Ricker-type 2) and Ricker model with ln-quadratic polynomial environmental impact (Ricker-type 3). Results showed that AORT, SLH, SSS and TYP had great influences on the recruitment of P. trituberculatus. When SSS reached 29 and 31, recruitment decreased from 20.7×10³ million to 8.3×10³ million individuals. In this case, recruitment declined, whereas AORT and TYP increased. Analysis of the S-R model showed that the Akaike information criterion (AIC) value of the traditional Ricker model was 14.619, which remarkably decreased after addition of the environmental factors. Different numbers of environmental factors were added to the Ricker model, and the best result was obtained when four factors were added to the model together. Moreover, Ricker-type 2 model, with the AIC value of −5.307, was better than Ricker-type 3 model (add above four environmental factors at the same time). The findings indicated that the mechanisms by which various environmental factors affect the S-R relationship are different.

Projection models are commonly used to evaluate the impacts of fishing. However, previously developed projection tools were not suitable for China’s fisheries as they are either overly complex and data-demanding or too simple to reflect the realistic management measures. Herein, an intermediate-complexity projection model was developed that could adequately describe fish population dynamics and account for management measures including mesh size limits, summer closure, and spatial closure. A two-patch operating model was outlined for the projection model and applied to the heavily depleted but commercially important small yellow croaker (Larimichthys polyactis) fishery in the Haizhou Bay, China, as a case study. The model was calibrated to realistically capture the fisheries dynamics with hindcasting. Three simulation scenarios featuring different fishing intensities based on status quo and maximum sustainable yield (MSY) were proposed and evaluated with projections. Stochastic projections were additionally performed to investigate the influence of uncertainty associated with recruitment strengths and the implementation of control targets. It was found that fishing at F_MSY level could effectively rebuild the depleted stock biomass, while the stock collapsed rapidly in the status quo scenario. Uncertainty in recruitment and implementation could result in variabilities in management effects; but they did not much alter the management effects of the F_MSY scenario. These results indicate that the lack of science-based control targets in fishing mortality or catch limits has hindered the achievement of sustainable fisheries in China. Overall, the presented work highlights that the developed projection model can promote the understanding of the possible consequences of fishing under uncertainty and is applicable to other fisheries in China.

DNA polymerase δ (Polδ) plays a crucial and versatile role in DNA replication and DNA repair processes. Vent shrimp Rimicaris exoculata is the primary megafaunal community living in hydrothermal vents. In this study, the Polδ from shrimp Rimicaris exoculata was cloned, expressed and characterized. The results showed that the Polδ catalytic subunit (POLD1), 852 amino acids in length, shared high homology with crayfish Procambarus clarkii and shrimp Oratosquilla oratoria. The recombinant POLD1 expressed in Escherichia coli showed that the enzyme was active in a range of 20°C to 40°C with an optimum temperature at 25°C and in a wide range of pH with an optimum at pH 6.0. The activities of POLD1 were significantly enhanced in the presence of Triton-X 100, Tween 20 and Mn²⁺. The K_m (dNTP) value of POLD1 was 4.7 μmol/L. The present study would be helpful to reveal the characterization of Polδ of deep-sea vent animals.

Dietary studies of polyps and ephyrae are important to understand the formation and magnitude of jellyfish blooms and provide important insights into the marine food web. However, the diet of polyps and ephyrae in situ is largely unknown. Here, prey species of the polyps and ephyrae of the moon jellyfish Aurelia coerulea in situ were identified using high-throughput DNA sequencing techniques. The results show that A. coerulea polyps and ephyrae consume a variety of prey items. The polyps consume both planktonic and benthic prey, including hydromedusae, copepods, ciliates, polychaetes, stauromedusae, and phytoplankton. A. coerulea ephyrae mainly feed on copepods and hydromedusae. Gelatinous zooplankton, including Rathkea octopunctata and Sarsia tubulosa, were frequently found as part of the diet of A. coerulea polyps and ephyrae. The utilization of high-throughput sequencing technique is a useful tool for studying the diet of polyps and ephyrae in the field, complementing the traditional techniques towards a better understanding of the complex role of gelatinous animals in marine ecosystems.

Philippine archipelago (PA) has strong background seismicity, but there is no systematic study of earthquake triggering in this region. There are six earthquakes (M_w > 6) occurred between 2018/12/29 and 2019/09/29 in PA, which provides an excellent opportunity to investigate the triggering relationship among these events. We calculate the static Coulomb stress changes of the first five events, and find that the local seismicity after the 2018/12/29 M_w 7.0 earthquake is mostly associated with positive Coulomb stress changes, including the 2019/05/31 M_w 6.1 event, suggesting a possible triggering relationship. However, we cannot rule out the dynamic triggering mechanism, due to increased microseismicity in both positive and negative stress change regions, and an incomplete local catalog, especially right after the first M_w 7.0 mainshock. The dynamic stresses from these M_w > 6 events are large enough (from 5 kPa to 3532 kPa) to trigger subsequent events, but a lack of seismicity and waveform evidence does not support delayed dynamic triggering among these events, even the shortest time interval is less than 24 hours. In the past 45 years, the released seismic energy shows certain peaks every 5–10 years. However, earthquakes with M_w > 6.0 were relatively infrequent between 2004 and 2018 at PA. Hence, it is possible that several regions are relatively late in their earthquake cycles, which would enhance their susceptibility of being triggered by earthquakes at nearby and regional distances.

A deep-learning-based method, called ConvLSTMP3, is developed to predict the sea surface heights (SSHs). ConvLSTMP3 is data-driven by treating the SSH prediction problem as the one of extracting the spatial-temporal features of SSHs, in which the spatial features are “learned” by convolutional operations while the temporal features are tracked by long short term memory (LSTM). Trained by a reanalysis dataset of the South China Sea (SCS), ConvLSTMP3 is applied to the SSH prediction in a region of the SCS east off Vietnam coast featured with eddied and offshore currents in summer. Experimental results show that ConvLSTMP3 achieves a good prediction skill with a mean RMSE of 0.057 m and accuracy of 93.4% averaged over a 15-d prediction period. In particular, ConvLSTMP3 shows a better performance in predicting the temporal evolution of mesoscale eddies in the region than a full-dynamics ocean model. Given the much less computation in the prediction required by ConvLSTMP3, our study suggests that the deep learning technique is very useful and effective in the SSH prediction, and could be an alternative way in the operational prediction for ocean environments in the future.

A 41-year Antarctic sea ice concentration (SIC) dataset derived from satellite passive microwave radiometers during the period of 1979–2019 has been used to analyze sea ice changes in recent decades. The trends of SIC and sea ice extent (SIE) are calculated during the periods of 1979–2019, 1979–2013, and 2014–2019. The trends show regionally dependent features. The SIC shows an increasing trend in most of the regions except the Bellingshausen Sea and Amundsen Sea (BA) during 1979–2019 and 1979–2013. The SIE trend shows a decreasing or decelerating trend in the period of 1979–2019 ((6 835±2 210) km²/a) compared with the 1979–2013 period ((18 600±2 203) km²/a). In recent years (2014–2019), the SIC and SIE have exhibited decreasing trends (–(34 567±3 521) km²/month), especially in the Weddell Sea (WS) and Ross Sea (RS) during summer and autumn. The trends are related to regionally dependent causes. The analyses show that the SIC and SIE decreased in response to the warming trend of 2 m air temperature (T_a-2m) and have exhibited a good relationship with T_a-2m in summer and autumn in recent years. The sea ice decrease in the Antarctic is mainly caused by increases in absorbed energy and southward energy transportation in recent years, such as the increase in gained solar radiation and moist static energy from the south, which demonstrate notable regional characteristics. In the WS region, the local positive feedback from the additional absorbed solar radiation, resulting in warmer air and reduced sea ice, is the main reason for the sea ice decrease in recent years. The increase in southward energy transport has also favored a decrease in sea ice. In the RS region, the increase in southward-transported moist static energy has contributed to the decrease in sea ice, and the increases in cloud cover and longwave radiation have prevented sea ice growth.

The seasonal structure and dynamic mechanism of oceanic surface thermal fronts (STFs) along the western Guangdong coast over the northern South China Sea shelf were analyzed using in situ observational data, remote sensing data, and numerical simulations. Both in situ and satellite observations show that the coastal thermal front exhibits substantial seasonal variability, being strongest in winter when it has the greatest extent and strongest sea surface temperature gradient. The winter coastal thermal front begins to appear in November and disappears after the following April. Although runoff water is more plentiful in summer, the front is weak in the western part of Guangdong. The frontal intensity has a significant positive correlation with the coastal wind speed, while the change of temperature gradient after September lags somewhat relative to the alongshore wind. The numerical simulation results accurately reflect the seasonal variation and annual cycle characteristics of the frontal structure in the simulated area. Based on vertical cross-section data, the different frontal lifecycles of the two sides of the Zhujiang (Pearl) River Estuary are analyzed.

Oceanic geostrophic turbulence theory predicts significant inverse kinetic energy (KE) cascades at scales larger than the energy injection wavelength. However, the characteristics of the mesoscale variabilities associated with the inverse KE cascade in the real oceans have not been clear enough up to now. To further examine this problem, we analyzed the spectral characteristics of the oceanic mesoscale motions over the scales of inverse KE cascades based on high-resolution gridded altimeter data. The applicability of the quasigeostrophic (QG) turbulence theory and the surface quasigeostrophic (SQG) turbulence theory in real oceans is further explored. The results show that the sea surface height (SSH) spectral slope is linearly related to the eddy-kinetic-energy (EKE) level with a high correlation coefficient value of 0.67. The findings also suggest that the QG turbulence theory is an appropriate dynamic framework at the edge of high-EKE regions and that the SQG theory is more suitable in tropical regions and low-EKE regions at mid-high latitudes. New anisotropic characteristics of the inverse KE cascade are also provided. These results indicate that the along-track spectrum used by previous studies cannot reveal the dynamics of the mesoscale variabilities well.

Based on Bayesian network (BN) and information flow (IF), a new machine learning-based model named IFBN is put forward to interpolate missing time series of multiple ocean variables. An improved BN structural learning algorithm with IF is designed to mine causal relationships among ocean variables to build network structure. Nondirectional inference mechanism of BN is applied to achieve the synchronous interpolation of multiple missing time series. With the IFBN, all ocean variables are placed in a causal network visually, making full use of information about related variables to fill missing data. More importantly, the synchronous interpolation of multiple variables can avoid model retraining when interpolative objects change. Interpolation experiments show that IFBN has even better interpolation accuracy, effectiveness and stability than existing methods.