A comparison study on structure-function relationship of polysaccharides obtained from sea buckthorn berries using different methods:antioxidant and bile acid-binding capacity

In this study,the structural characters,antioxidant activities and bile acid-binding ability of sea buckthorn polysaccharides(HRPs)obtained by the commonly used hot water(HRP-W),pressurized hot water(HRP-H),ultrasonic(HRP-U),acid(HRP-C)and alkali(HRP-A)assisted extraction methods were investigated.The results demonstrated that extraction methods had significant effects on extraction yield,monosaccharide composition,molecular weight,particle size,triple-helical structure,and surface morphology of HRPs except for the major linkage bands.Thermogravimetric analysis showed that HRP-U with filamentous reticular microstructure exhibited better thermal stability.The HRP-A with the lowest molecular weight and highest arabinose content possessed the best antioxidant activities.Moreover,the rheological analysis indicated that HRPs with higher galacturonic acid content and molecular weight showed higher viscosity and stronger crosslinking network(HRP-C,HRP-W and HRP-U),which exhibited stronger bile acid binding capacity.The present findings provide scientific evidence in the preparation technology of sea buckthorn polysaccharides with good antioxidant and bile acid binding capacity which are related to the structure affected by the extraction methods.

Influence of Irregular Coastlines on a Tornadic Mesovortex in the Pearl River Delta during the Monsoon Season. Part Ⅰ:Pre-storm Environment and Storm Evolution

The Pearl River Delta(PRD),a tornado hotspot,forms a distinct trumpet-shaped coastline that concaves toward the South China Sea.During the summer monsoon season,low-level southwesterlies over the PRD’s sea surface tend to be turned toward the west coast,constituting a convergent wind field along with the landward-side southwesterlies,which influences regional convective weather.This two-part study explores the roles of this unique land–sea contrast of the trumpet-shaped coastline in the formation of a tornadic mesovortex within monsoonal flows in this region.Part I primarily presents observational analyses of pre-storm environments and storm evolutions.The rotating storm developed in a lowshear environment(not ideal for a supercell)under the interactions of three air masses under the influence of the land–sea contrast,monsoon,and storm cold outflows.This intersection zone(or“triple point”)is typically characterized by local enhancements of ambient vertical vorticity and convergence.Based on a rapid-scan X-band phased-array radar,finger-like echoes were recognized shortly after the gust front intruded on the triple point.Developed over the triple point,they rapidly wrapped up with a well-defined low-level mesovortex.It is thus presumed that the triple point may have played roles in the mesovortex genesis,which will be demonstrated in Part II with multiple sensitivity numerical simulations.The findings also suggest that when storms pass over the boundary intersection zone in the PRD,the expected possibility of a rotating storm occurring is relatively high,even in a low-shear environment.Improved knowledge of such environments provides additional guidance to assess the regional tornado risk.

Assessments of Data-Driven Deep Learning Models on One-Month Predictions of Pan-Arctic Sea Ice Thickness

In recent years,deep learning methods have gradually been applied to prediction tasks related to Arctic sea ice concentration,but relatively little research has been conducted for larger spatial and temporal scales,mainly due to the limited time coverage of observations and reanalysis data.Meanwhile,deep learning predictions of sea ice thickness(SIT)have yet to receive ample attention.In this study,two data-driven deep learning(DL)models are built based on the ConvLSTM and fully convolutional U-net(FC-Unet)algorithms and trained using CMIP6 historical simulations for transfer learning and fine-tuned using reanalysis/observations.These models enable monthly predictions of Arctic SIT without considering the complex physical processes involved.Through comprehensive assessments of prediction skills by season and region,the results suggest that using a broader set of CMIP6 data for transfer learning,as well as incorporating multiple climate variables as predictors,contribute to better prediction results,although both DL models can effectively predict the spatiotemporal features of SIT anomalies.Regarding the predicted SIT anomalies of the FC-Unet model,the spatial correlations with reanalysis reach an average level of 89%over all months,while the temporal anomaly correlation coefficients are close to unity in most cases.The models also demonstrate robust performances in predicting SIT and SIE during extreme events.The effectiveness and reliability of the proposed deep transfer learning models in predicting Arctic SIT can facilitate more accurate pan-Arctic predictions,aiding climate change research and real-time business applications.

Arctic Sea Ice Variations in the First Half of the 20th Century:A New Reconstruction Based on Hydrometeorological Data

The shrinking Arctic sea-ice area(SIA) in recent decades is a striking manifestation of the ongoing climate change.Variations of the Arctic sea ice have been continuously observed by satellites since 1979, relatively well monitored since the 1950s, but are highly uncertain in the earlier period due to a lack of observations. Several reconstructions of the historical gridded sea-ice concentration(SIC) data were recently presented based on synthesized regional sea-ice observations or by applying a hybrid model–empirical approach. Here, we present an SIC reconstruction for the period1901–2019 based on established co-variability between SIC and surface air temperature, sea surface temperature, and sea level pressure patterns. The reconstructed sea-ice data for March and September are compared to the frequently used Had ISST1.1 and SIBT1850 datasets. Our reconstruction shows a large decrease in SIA from the 1920 to 1940 concurrent with the Early 20th Century Warming event in the Arctic. Such a negative SIA anomaly is absent in Had ISST1.1 data. The amplitude of the SIA anomaly reaches about 0.8 mln km^(2) in March and 1.5 mln km^(2) in September. The anomaly is about three times stronger than that in the SIBT1850 dataset. The larger decrease in SIA in September is largely due to the stronger SIC reduction in the western sector of the Arctic Ocean in the 70°–80°N latitudinal zone. Our reconstruction provides gridded monthly data that can be used as boundary conditions for atmospheric reanalyses and model experiments to study the Arctic climate for the first half of the 20th century.

Physical and mechanical properties and microstructures of submarine soils in the Yellow Sea

In recent years,the exploration of seabed has been intensified,but the submarine soils of silt and sand in the Yellow Sea area have not been well investigated so far.In this study,the physical and mechanical properties of silt and sand from the Yellow Sea were measured using a direct shear apparatus and their microstructures were observed using a scanning electron microscope.The test results suggest that the shear strength of silt and sand increases linearly with the increase of normal stress.Based on the direct shear test,the scanning electron microscope was used to observe the section surface of sand.It is observed that the section surface becomes rough,with many“V”‐shaped cracks.Many particles appear on the surface of the silt structure and tend to be disintegrated.The X‐ray diffraction experiment reveals that the sand and silt have different compositions.The shear strength of sand is slightly greater than that of silt under high stress,which is related to the shape of soil particles and the mineral composition.These results can be a reference for further study of other soils in the Yellow Sea;meanwhile,they can serve as soil parameters for the stability and durability analyses of offshore infrastructure construction.

Projecting Wintertime Newly Formed Arctic Sea Ice through Weighting CMIP6 Model Performance and Independence

Precipitous Arctic sea-ice decline and the corresponding increase in Arctic open-water areas in summer months give more space for sea-ice growth in the subsequent cold seasons. Compared to the decline of the entire Arctic multiyear sea ice,changes in newly formed sea ice indicate more thermodynamic and dynamic information on Arctic atmosphere–ocean–ice interaction and northern mid–high latitude atmospheric teleconnections. Here, we use a large multimodel ensemble from phase 6 of the Coupled Model Intercomparison Project(CMIP6) to investigate future changes in wintertime newly formed Arctic sea ice. The commonly used model-democracy approach that gives equal weight to each model essentially assumes that all models are independent and equally plausible, which contradicts with the fact that there are large interdependencies in the ensemble and discrepancies in models' performances in reproducing observations. Therefore, instead of using the arithmetic mean of well-performing models or all available models for projections like in previous studies, we employ a newly developed model weighting scheme that weights all models in the ensemble with consideration of their performance and independence to provide more reliable projections. Model democracy leads to evident bias and large intermodel spread in CMIP6 projections of newly formed Arctic sea ice. However, we show that both the bias and the intermodel spread can be effectively reduced by the weighting scheme. Projections from the weighted models indicate that wintertime newly formed Arctic sea ice is likely to increase dramatically until the middle of this century regardless of the emissions scenario.Thereafter, it may decrease(or remain stable) if the Arctic warming crosses a threshold(or is extensively constrained).

Ecological problems and ecological restoration zoning of the Aral Sea

The Aral Sea was the fourth largest lake in the world but it has shrunk dramatically as a result of irrational human activities, triggering the "Aral Sea ecological crisis". The ecological problems of the Aral Sea have attracted widespread attention, and the alleviation of the Aral Sea ecological crisis has reached a consensus among the five Central Asian countries(Kazakhstan, Uzbekistan, Tajikistan, Kyrgyzstan, and Turkmenistan). In the past decades, many ecological management measures have been implemented for the ecological restoration of the Aral Sea. However, due to the lack of regional planning and zoning, the results are not ideal. In this study, we mapped the ecological zoning of the Aral Sea from the perspective of ecological restoration based on soil type, soil salinity, surface water, groundwater table, Normalized Difference Vegetation Index(NDVI), land cover, and aerosol optical depth(AOD) data. Soil salinization and salt dust are the most prominent ecological problems in the Aral Sea. We divided the Aral Sea into 7 first-level ecological restoration subregions(North Aral Sea catchment area in the downstream of the Syr Darya River(Subregion Ⅰ);artificial flood overflow area in the downstream of the Aral Sea(Subregion Ⅱ);physical/chemical remediation area of the salt dust source area in the eastern part of the South Aral Sea(Subregion Ⅲ);physical/chemical remediation area of severe salinization in the central part of the South Aral Sea(Subregion Ⅳ);existing water surface and potential restoration area of the South Aral Sea(Subregion Ⅴ);Aral Sea vegetation natural recovery area(Subregion Ⅵ);and vegetation planting area with slight salinization in the South Aral Sea(Subregion Ⅶ)) and 14 second-level ecological restoration subregions according to the ecological zoning principles. Implementable measures are proposed for each ecological restoration subregion. For Subregion Ⅰ and Subregion Ⅱ with lower elevations, artificial flooding should be carried out to restore the surface of the Aral Sea. Subregion Ⅲ and Subregion Ⅳ have severe salinization, making it difficult for vegetation to grow. In these subregions, it is recommended to cover and pave the areas with green biomatrix coverings and environmentally sustainable bonding materials. In Subregion Ⅴ located in the central and western parts of the South Aral Sea, surface water recharge should be increased to ensure that this subregion can maintain normal water levels. In Subregion Ⅵ and Subregion Ⅶ where natural conditions are suitable for vegetation growth, measures such as afforestation and buffer zones should be implemented to protect vegetation. This study could provide a reference basis for future comprehensive ecological management and restoration of the Aral Sea.

Deep-large faults controlling on the distribution of the venting gas hydrate system in the middle of the Qiongdongnan Basin, South China Sea

Many locations with concentrated hydrates at vents have confirmed the presence of abundant thermogenic gas in the middle of the Qiongdongnan Basin(QDNB).However,the impact of deep structures on gasbearing fluids migration and gas hydrates distribution in tectonically inactive regions is still unclear.In this study,the authors apply high-resolution 3D seismic and logging while drilling(LWD)data from the middle of the QDNB to investigate the influence of deep-large faults on gas chimneys and preferred gasescape pipes.The findings reveal the following:(1)Two significant deep-large faults,F1 and F2,developed on the edge of the Songnan Low Uplift,control the dominant migration of thermogenic hydrocarbons and determine the initial locations of gas chimneys.(2)The formation of gas chimneys is likely related to fault activation and reactivation.Gas chimney 1 is primarily arises from convergent fluid migration resulting from the intersection of the two faults,while the gas chimney 2 benefits from a steeper fault plane and shorter migration distance of fault F2.(3)Most gas-escape pipes are situated near the apex of the two faults.Their reactivations facilitate free gas flow into the GHSZ and contribute to the formation of fracture‐filling hydrates.

Relative Impacts of Sea Ice Loss and Atmospheric Internal Variability on the Winter Arctic to East Asian Surface Air Temperature Based on Large-Ensemble Simulations with NorESM2

To quantify the relative contributions of Arctic sea ice and unforced atmospheric internal variability to the “warm Arctic, cold East Asia”(WACE) teleconnection, this study analyses three sets of large-ensemble simulations carried out by the Norwegian Earth System Model with a coupled atmosphere–land surface model, forced by seasonal sea ice conditions from preindustrial, present-day, and future periods. Each ensemble member within the same set uses the same forcing but with small perturbations to the atmospheric initial state. Hence, the difference between the present-day(or future) ensemble mean and the preindustrial ensemble mean provides the ice-loss-induced response, while the difference of the individual members within the present-day(or future) set is the effect of atmospheric internal variability. Results indicate that both present-day and future sea ice loss can force a negative phase of the Arctic Oscillation with a WACE pattern in winter. The magnitude of ice-induced Arctic warming is over four(ten) times larger than the ice-induced East Asian cooling in the present-day(future) experiment;the latter having a magnitude that is about 30% of the observed cooling. Sea ice loss contributes about 60%(80%) to the Arctic winter warming in the present-day(future) experiment. Atmospheric internal variability can also induce a WACE pattern with comparable magnitudes between the Arctic and East Asia. Ice-lossinduced East Asian cooling can easily be masked by atmospheric internal variability effects because random atmospheric internal variability may induce a larger magnitude warming. The observed WACE pattern occurs as a result of both Arctic sea ice loss and atmospheric internal variability, with the former dominating Arctic warming and the latter dominating East Asian cooling.

Spatiotemporal variation and freeze-thaw asymmetry of Arctic sea ice in multiple dimensions during 1979 to 2020

Arctic sea ice is broadly regarded as an indicator and amplifier of global climate change.The rapid changes in Arctic sea ice have been widely concerned.However,the spatiotemporal changes in the horizontal and vertical dimensions of Arctic sea ice and its asymmetry during the melt and freeze seasons are rarely quantified simultaneously based on multiple sources of the same long time series.In this study,the spatiotemporal variation and freeze-thaw asymmetry of Arctic sea ice were investigated from both the horizontal and vertical dimensions during 1979–2020 based on remote sensing and assimilation data.The results indicated that Arctic sea ice was declining at a remarkably high rate of–5.4×10^(4) km^(2)/a in sea ice area(SIA)and–2.2 cm/a in sea ice thickness(SIT)during 1979 to 2020,and the reduction of SIA and SIT was the largest in summer and the smallest in winter.Spatially,compared with other sub-regions,SIA showed a sharper declining trend in the Barents Sea,Kara Sea,and East Siberian Sea,while SIT presented a larger downward trend in the northern Canadian Archipelago,northern Greenland,and the East Siberian Sea.Regarding to the seasonal trend of sea ice on sub-region scale,the reduction rate of SIA exhibited an apparent spatial heterogeneity among seasons,especially in summer and winter,i.e.,the sub-regions linked to the open ocean exhibited a higher decline rate in winter;however,the other sub-regions blocked by the coastlines presented a greater decline rate in summer.For SIT,the sub-regions such as the Beaufort Sea,East Siberian Sea,Chukchi Sea,Central Arctic,and Canadian Archipelago always showed a higher downward rate in all seasons.Furthermore,a striking freeze-thaw asymmetry of Arctic sea ice was also detected.Comparing sea ice changes in different dimensions,sea ice over most regions in the Arctic showed an early retreat and rapid advance in the horizontal dimension but late melting and gradual freezing in the vertical dimension.The amount of sea ice melting and freezing was disequilibrium in the Arctic during the considered period,and the rate of sea ice melting was 0.3×10^(4) km^(2)/a and 0.01 cm/a higher than that of freezing in the horizontal and vertical dimensions,respectively.Moreover,there were notable shifts in the melting and freezing of Arctic sea ice in 1997/2003 and 2000/2004,respectively,in the horizontal/vertical dimension.

Spatial patterns of zooplankton abundance,biovolume,and size structure in response to environmental variables:a case study in the Yellow Sea and East China Sea

The Yellow Sea(YS)and East China Sea(ECS)are highly dynamic marginal seas of the northwestern Pacific Ocean.To gain an in-depth understanding of zooplankton community structure,zooplankton abundance,biovolume,and size structure in summer 2017 in the YS and ECS were assessed using ZooScan imaging analysis.Zooplankton abundance and biovolume ranged 2.94–1187.14 inds./m^(3)and 3.13–3438.51 mm^(3)/m^(3),respectively.Based on the biovolume data of the categorized size classes of 26 identified taxonomic groups,the zooplankton community was classified into five groups,and each group was coupled with distinctive oceanographic features.Under the influence of the Yellow Sea Cold Water Mass,the Yellow Sea offshore group featured the lowest bottom temperature(10.84±3.42℃)and the most abundant Calanoids(mainly in the 2–3 mm size class).In the Yellow Sea inshore group,Hydrozoans showed the largest biovolume and dominated in the 3–4-mm and>5-mm size classes.The East China Sea offshore group,which was affected by the Kuroshio Branch Current,featured high temperature and salinity,and the lowest bottom dissolved oxygen(2.58±0.5 mg/L).The lowest values of zooplankton abundance and biovolume in the East China Sea offshore group might be attributed to the bottom dissolved oxygen contents.The East China Sea inshore group,which was mainly influenced by the Zhejiang-Fujian Coastal Current and Changjiang Diluted Water,was characterized by high chlorophyll a and the largest biovolume of carnivorous Siphonophores(280.82±303.37 mm^(3)/m^(3)).The Changjiang River estuary offshore group showed the most abundant Cyclopoids,which might be associated with the less turbid water mass in this region.Seawater temperature was considered the most important factor in shaping the size compositions of Calanoids in different groups.

Long-Chain Alkenones in the South Yellow Sea Sediments and Their Indicative Significance for Haptophytes Species

Long-chain alkenones(LCAs)have been widely used as important biomarkers in palaeoceanographic studies.However,the commonly used LCAs proxies are mainly based on C_(37) alkenones,and it is still lack of the studies about the distribution and in-dications of LCAs with different chain lengths other than C_(37) alkenones.Here,the composition and distribution of LCAs were ana-lyzed in surface sediments from the southern Yellow Sea(SYS)and a sedimentary core(A02-C)from the central Yellow Sea(YS)mud area.The results showed that C_(37),C_(38) and C_(39) alkenones were the major LCAs in surface sediments of the SYS,and the relative contents of C_(38:2)Et,C_(37:2)Me,C_(37:3)Me,C_(38:2)Me,C_(38:3)Et,C_(38:3)Me,C_(39:2)Et and C_(39:3)Et were 18.3%-59.8%,22.6%-41.2%,7.4%-23.0%,6.6%-15.4%,3.8%-13.3%,3.6%-8.7%,2.8%-6.0% and 0.7%-3.0%,respectively.Then the relationships of U_(38Me)^(K)-U_(38Et)^(K) and U_(37)^(K')-U_(38Et)^(K) indicate that LCAs are mainly derived from Emiliania huxleyi(E.huxleyi).High ratios of total C_(37) alkenones to total C_(38) alkenones(K_(37)/K_(38))(1-1.2)were found in the central SYS,corresponding to the relatively high abundance of E.huxleyi;while low ratios of K_(37)/K_(38)(0.7-0.9)were observed at nearshore area of the SYS where Gephyrocapsa oceanica(G.oceanica)has rela-tively high abundance.The spatial distribution of K_(37)/K_(38) ratio is also consistent with that of coccolithophores nannofossil in the sediments.In addition,K_(37)/K_(38) ratio in core A02-C varied from 0.7 to 1.1 with a gradual decreasing trend during the past 5.5 kyr.This suggests that the relative abundance of E.huxleyi decreased gradually,caused by the changes in the Yellow Sea Warm Current(YSWC)and the East Asian Winter Monsoon(EAWM)during this period.

Wintertime Arctic Sea-Ice Decline Related to Multi-Year La Niña Events

Arctic sea ice has undergone a significant decline in the Barents-Kara Sea(BKS)since the late 1990s.Previous studies have shown that the decrease in sea ice caused by increased poleward moisture transport is modulated by tropical sea temperature changes(mainly referring to La Niña events).The occurrence of multi-year La Niña(MYLA)events has increased significantly in recent decades,and their impact on Arctic sea ice needs to be further explored.In this study,we investigate the relationship between sea-ice variation and different atmospheric diagnostics during MYLA and other La Niña(OTLA)years.The decline in BKS sea ice during MYLA winters is significantly stronger than that during OTLA years.This is because MYLA events tend to be accompanied by a warm Arctic-cold continent pattern with a barotropic high pressure blocked over the Urals region.Consequently,more frequent northward atmospheric rivers intrude into the BKS,intensifying longwave radiation downward to the underlying surface and melting the BKS sea ice.However,in the early winter of OTLA years,a negative North Atlantic Oscillation presents in the high latitudes of the Northern Hemisphere,which obstructs the atmospheric rivers to the south of Iceland.We infer that such a different response of BKS sea-ice decline to different La Niña events is related to stratospheric processes.Considering the rapid climate changes in the past,more frequent MYLA events may account for the substantial Arctic sea-ice loss in recent decades.

Contribution of Surface Waves to Sea Surface Temperatures in the Arctic Ocean

The aim of our study was to examine the contribution of surface waves from WAVEWATCH-III(WW3)to the variation in sea surface temperature(SST)in the Arctic Ocean.The simulated significant wave height(SWH)were validated against the products from Haiyang-2B(HY-2B)in 2021,obtaining a root mean squared error(RMSE)of 0.45 with a correlation of 0.96 and scatter index of 0.18.The wave-induced effects,i.e.,wave breaking and mixing induced by nonbearing waves resulting in changes in radiation stress and Stokes drift,were calculated from WW3,ERA-5 wind,SST,and salinity data from the National Centers for Environmental Prediction and were taken as forcing fields in the Stony Brook Parallel Ocean Model.The results showed that an RMSE of 0.81℃ with wave-induced effects was less than the RMSE of 1.11℃ achieved without the wave term compared with the simulated SST with the measurements from Argos.Considering the four wave effects and sea ice freezing,the SST in the Arctic Ocean decreased by up to 1℃ in winter.Regression analysis revealed that the SWH was linear in SST(values without subtraction of waves)in summer and autumn,but this behavior was not observed in spring or winter due to the presence of sea ice.The interannual variation also presented a negative relationship between the difference in SST and SWH.

Acoustic Velocity-Based Inversion of the Physical Properties of Sea Ice in the Central Arctic Region

Studying the Arctic sea ice contributes to a comprehensive understanding of the climate system in polar regions and offers valuable insights into the interplay between polar climate change and the global climate and environment.One of the key research aspects is the investigation of the temperature,salinity,and density parameters of sea ice to obtain essential insights.During the 11th Chinese National Arctic Research Expedition,acoustic velocity was measured on an ice core at a short-term ice station,however,temperature,salinity,and density were not measured.In the present work,we utilized a genetic algorithm to invert these obtained acoustic velocity data to sea ice temperature,salinity,and density parameters on the basis of the relationship between acoustic velocity and the physical properties of Arctic summer sea ice.We validated the effectiveness of this inversion procedure by comparing its findings with those of other researchers.The results indicate that within the normalized depth range of 0.43-0.94,the ranges for temperature,salinity,and density are -0.48--0.29℃,1.63-3.35,and 793.1-904.1 kg m^(-3),respectively.

Contributors to tidal duration asymmetry with varied coastline configurations on western shelf of Yellow Sea

Coastal management in China is confronted with an urgent choice between natural restoration and maintenance of existing seawalls and reclaimed land for economic development.A key criterion for making this decision is the resilience to coastal flooding,which depends on the ability to predict tidal level.Tidal duration asymmetry(TDA)is a key parameter in determination of the arrival and duration of flood tides.This study selected the western inner shelf of the Yellow Sea(WYS)as the study area and investigated the responses of TDA to different shoreline configurations and relative sea level rise.The responses of TDA to shoreline reconstruction yielded spatial variability locally and remotely.In the nearshore area,the responses of TDA to the complex ocean environment mainly originated from the combined functions of reflection,bottom friction,and advection,which controlled the energy transfer from M2 or S2 constituents to their overtides or compound tides.The sensitivity of TDA to coastline typologies was not limited to coastal waters but could stretch over the entire inner shelf.The vulnerability of tidal responses was due to the displacement of the M2 amphidrome of the Kelvin wave on the WYS,which in turn changed tidal energy fluxes over the regime.The relative sea level rise could intensify the feedback of TDA to seawalls and land reclamation.

Sea Cucumber Fishery in Aurora, Philippines

This study was conducted to provide a preliminary assessment of the sea cucumber fishery in Aurora, Philippines. This was conducted from February to June 2013. All sea cucumber gatherers and traders, obtained through snowball sampling, in each identified sea cucumber fishery area in six coastal municipalities (Baler, Dipaculao, Dinalungan, Casiguran, Dilasag and Dingalan) were interviewed using a structured interview schedule and focused group discussions (FGD). Results revealed that gathering sea cucumbers were accomplished by handpicking, skin diving or compressor diving. A total of 15 commercial species were regularly gathered. Out of this, three species are of very high economic value (≥Php 4000/kg) (Holothuria nobilis, Holothuria scabra, and Actinopyga lecanora). The average number of kilograms harvested per hour per trip ranges from 0.21 (Holothuria fuscopunctata) to 2.71 (Holothuria scabra), while the average size of commonly gathered sea cucumber varies among species with Holothuria lecanora showed an average length (29.26 cm) greater than recorded 24 cm. Processing mainly involves washing, slicing, boiling, drying and packing. Processed sea cucumbers or trepan were sold to middlemen or direct buyers who usually offered higher prices (36.36% up to 69.20%) than the former. Problems encountered by sea cucumber gatherers and traders include a decline in total catch, a shift in area of collection from intertidal zones to deeper waters, an increase in the number of hours spent in finding and gathering sea cucumbers, the use of compressors in gathering, the decline of larger species and gathering of smaller sizes in huge amount, the absence of conservation program for sea cucumber, and the lenient implementation of government policies with regards to coastal resources management.

An ensemble learning method to retrieve sea ice roughness from Sentinel-1 SAR images

Sea ice surface roughness(SIR)affects the energy transfer between the atmosphere and the ocean,and it is also an important indicator for sea ice characteristics.To obtain a small-scale SIR with high spatial resolution,a novel method is proposed to retrieve SIR from Sentinel-1 synthetic aperture radar(SAR)images,utilizing an ensemble learning method.Firstly,the two-dimensional continuous wavelet transform is applied to obtain the spatial information of sea ice,including the scale and direction of ice patterns.Secondly,a model is developed using the Adaboost Regression model to establish a relationship among SIR,radar backscatter and the spatial information of sea ice.The proposed method is validated by using the SIR retrieved from SAR images and comparing it to the measurements obtained by the Airborne Topographic Mapper(ATM)in the summer Beaufort Sea.The determination of coefficient,mean absolute error,root-mean-square error and mean absolute percentage error of the testing data are 0.91,1.71 cm,2.82 cm,and 36.37%,respectively,which are reasonable.Moreover,K-fold cross-validation and learning curves are analyzed,which also demonstrate the method’s applicability in retrieving SIR from SAR images.

Morphological,phylogenetic and metabolite profile of Prorocentrum clipeus,a newly recorded epiphytic dinoflagellate in the northern Yellow Sea

More than 30 species of benthic Prorocentrum have been identified,some of which produce okadaic acid(OA)and its derivatives,dinophysistoxins(DTXs),which cause diarrhetic shellfish poisoning(DSP).Increasing numbers of benthic Prorocentrum species have been reported in tropical and subtropical waters of China.In contrast,only a few benthic Prorocentrum species have been reported in temperate waters.In this study,morphological descriptions obtained using light microscopy,scanning electron microscopy and molecular characterization of one Prorocentrum clipeus strain isolated from the Yellow Sea are presented.Prorocentrum clipeus cells were nearly circular in shape,with a collar,ridge,and one protrusion.The periflagellar area was wide U-shaped,with two curved projections on platelet 1a.Nine periflagellar platelets of different sizes were observed.The morphology closely fits that of the species isolated from other locations.Phylogenetic analysis based on the molecular sequences of the small subunit(SSU)rDNA,internal transcribed spacer(ITS),and large subunit(LSU)rDNA was performed.A comprehensive metabolomic analysis incorporating target,suspect and non-target screenings was first applied to investigate the intracellular and extracellular metabolite profiles of the current isolate of P.clipeus.According to the results of the target and suspect screenings,179 metabolites or toxins produced by DSP-related algal species,including OA,dinophysistoxin-1(DTX1),dinophysistoxin-2(DTX2)and pectenotoxin-2(PTX2),were not detected.Non-target screening involving feature-based molecular networking(FBMN)provided a global view of major metabolites produced by the P.clipeus DF128 strain and revealed 23 clusters belonging to at least 13 compound classes,with organometallic compounds,lipids and lipid-like molecules,phenylpropanoids and polyketides,and benzenoids as major types.To date,this is the first record of the characterization of P.clipeus in samples from Chinese waters.Our results support the wide distribution of epibenthic Prorocentrum species.

Deep Learning Shows Promise for Seasonal Prediction of Antarctic Sea Ice in a Rapid Decline Scenario

The rapidly changing Antarctic sea ice has garnered significant interest. To enhance the prediction skill for sea ice and respond to the Sea Ice Prediction Network-South's latest call, this study presents the reforecast results of Antarctic sea-ice area and extent from December to June of the coming year with a Convolutional Long Short-Term Memory(Conv LSTM)Network. The reforecast experiments demonstrate that Conv LSTM captures the interannual and interseasonal variability of Antarctic sea ice successfully, and performs better than the European Centre for Medium-Range Weather Forecasts. Based on this, we present the prediction from December 2023 to June 2024, indicating that the Antarctic sea ice will remain at lows, but may not create a new record low. This research highlights the promising application of deep learning in Antarctic sea-ice prediction.