Measurement of major elements, trace elements and rare earth elements (REY) for sediment Core GC11 collected in the central Indian Ocean Basin were performed. Based on the analysis of interelemental correlation, REY enrichment and post-Archean Australian Shale (PAAS) normalization style, the factors impacting the REY enrichment are discussed. The study suggests that: the total amount of REY varies from 400.64×10⁻⁶ to 742.74×10⁻⁶, with an average of 658.41×10⁻⁶, which is slightly lower than that of Core GC02, however, is equivalent to that of the zeolite-bearing rich deep-sea deposit in the Core DSDP213 . The sediment exhibits distinct negative abnormal δCe and lower (La/Yb)_N为0.42, with obvious positive correlation between REY and P₂O₅. The average value of CaO/P₂O₅ ratio is 2.3, demonstrating that the bio-apatite may be the main host mineral of REY. Although there is positive correlation between REY and Fe and Mn, hydroxyl hydrate of Fe and Mn have low impact to the enrichment of REY for the distinct negative abnormal δCe. The mixture with the terregeneous materials maybe the main cause inducing the differences of REY enrichment degree, δCe and PAAS normalization style.

Growth parameters of fish are commonly assumed homogeneous in traditional fish stock assessment. However, increasing studies in recent years have shown that the growth of marine fish is characterized by spatial heterogeneity. To evaluate the spatial heterogeneity of growth traits of the fishes in the Haizhou Bay and its adjacent waters, this study analyzed the spatial distribution of 4 fish species and estimated their von Bertalanffy growth function parameters using otter trawl data collected from 2013 to 2018. We fitted the growth equations for Pholis fangi, Syngnatus acus, Larimichthys polyactis and Thryssa kammalensis using Electronic Length Frequency Analysis method in combination with the Bootstrap and compared the differences in growth parameters between deep- and shallow-water regions. The results show that growth parameters of the fish species exhibit certain levels of spatial heterogeneity, in particular Syngnatus acus and Larimichthys polyactis show substantial spatial heterogeneity. These differences may be attributed to the variations in spatial physical and chemical conditions, community structure and migration of the species.

Water temperature is one of the important factors affecting the growth, reproduction and supplement of the winter fishes of Todarodes in the Pacific. According to the fishery data of the Pacific winter Todarodes from 2004 to 2015 and its habitat environment data, including the January spawning ground (28°–35°N, 125°–130°E) and the September feeding ground (31°–38°N, 128°–132°E) sea surface temperature (SST), a surplus production model based on SST factor was established to analyze the effect of SST on the resources of the winter migrating fish population of the North Pacific Todarodes. By verifying the indicators of the model of the surplus production model, this study find that the model has higher prediction accuracy. The results show that the resources and catches of the winter fishes of Todarodes pacificus are mainly affected by the SST of the feeding ground, but the spawning ground SST has no significant effect on its catch in the current year. Therefore, it is recommended to strengthen the research on whether the SST factor of the spawning ground may affect the catch and resources of the next year, and the fishery management department should also determine its maximum sustainable yield according to the state of the marine environment every year, and the management plan should be adjusted in real time.

According to the data collected from bottom-trawl surveys in Haizhou Bay and adjacent waters during spring and fall in 2011 and 2013–2016, generalized additive model (GAM) based on the Tweedie distribution was used to examine the relationship between the distribution of small yellow croaker (Larimichthys polyactis) and environment factors in different seasons and ontogenetic stages. Combined with the bottom sea water temperature (BSWT), bottom sea water salinity (BSWS), water depth, sediment types, and abundance of prey (Crangon affinis, Leptochela gracilis, Engraulis japonicus, Thrissa kammalensis), the conditional index k and Variance Inflation Factor (VIF) were used to measure the degree of multicollinearity between these factors. The results of multicollinearity indicated that all factors can be used as explanatory variables. The results of Tweedie-GAM show that the factors affecting the distribution of small yellow croaker and their deviance explained varied greatly with seasons (spring and fall) and ontogenetic stages (juvenile and adult). For example, the factors affecting the spring-juvenile small yellow croaker distribution were BSWT, BSWS, water depth and the abundance of C. affinis, which depth had the largest deviance explained (16.09%). In addition, the factors affecting the spring-adultl yellow croaker distribution were BSWT, BSWS, water depth and the abundance of C. affinis and E.japonicus, which BSWS had the largest deviance explained (13.56%). The distribution of small yellow croaker in different seasons and ontogenetic stages was found to be closely related to its ecological habits, environmental factors and distribution of prey in Haizhou Bay and adjacent waters.

In April, July, October 2012, and January, April 2013, marine nematode dominant genera and feeding types were sampled for diversity analysis from five mangrove wetlands（Zhangjiang Estuary in Yunxiao Country, Jiulongjiang Estuary in Longhai City, Fenglin in Xiamen City, Luoyangjiang Estuary in Quanzhou City, Wanwu in Ningde City）in Fujian Province. It was found that the nematode dominant genera with dominance of percentages≥5% were Sabatieria, Ptycholaimellus, Parasphaerolaimus, Terschellingia, Daptonema, Viscosia and Dichromadora, which had dominance by 19.82%, 7.88%, 7.45%, 7.26%, 6.79 %, 6.00%, 5.25%, respectively. Sabatieria was the most predominant genus in the mangroves wetlands of Zhangjiang Estuary, Jiulongjiang Estuary and Luoyangjiang Estuary, with dominances are 27.87%, 28.37%, and 23.40%, respectively. Terschellingia was the most dominant genus in Fenglin mangrove wetlands, with a dominance of 18.10%. Daptonema was the most dominant genus in Wanwu mangrove wetlands, and dominance was 22.86%. The seasonal variations in abundance of their feeding types were as follows, in Zhangjiang Estuary wetlands, 1B>2A>2B>1A. In Jiulongjiang Estuary wetlands, 1B>2B>2A>1A. In Fenglin wetlands, 1B>2A>2B>1A. In Luoyangjiang Estuary wetlands, 2B>1B>2A>1A. In Wanwu wetlands, 2A>1B>2B>1A. Therefore, type 1B marine nematode showed to be the dominant in Zhangjiang Estuary, Jiulongjiang Estuary and Fenglin, type 2A was dominant in Wanwu wetlands, type 2B was dominant in Luoyangjiang Estuary wetlands, type 1A are the lowest in five regions.

Emiliania huxleyi, the numerically dominant coccolithophore in the modern oceans and its specific lytic virus EhV exert a critical impact upon the oceanic carbon, sulfur cycle and global climate, thus serving as a key host-pathogen model system. Despite their impact on biogeochemical cycling, the transcriptional dynamics of these important oceanic events is still poorly understood. To understand the host-virus interaction in E. huxleyi-EhV system, the transcriptome of E. huxleyi BOF92 involved in virus infection was investigated by using Illumina HiSeq 2 000 high-throughput sequencing technology. Two cDNA libraries, generated 6 h and 45 h after viral infection (Exp) were compared with two libraries from the corresponding times uninfected cultures (Con). A total of 32 909 unigenes with an average length of 1 153 bp were generated. Totally 2 617 and 5 229 differentially expressed genes (DEGs) associated with viral infection were identified in 6 hpi and 45 hpi, respectively, among which 465 genes were the common DEGs in the two time points. Ten DEGs were random selected for quantitative RT-PCR (qRT-PCR) analysis, and the results confirmed that the transcriptome analysis was reliable. Furthermore, the DEGs were subject to GO and KEGG enrichment analysis. The results showed that most of the DEGs were involved in oxidation-reduction reactions, glutathione metabolism, lipid metabolism, carbohydrate metabolism and signal transduction. Some of the reactive oxygen species (ROS) scavenging genes were screened out, in which 9 genes were up-regulated and 11 genes were down-regulated. These results suggested that ROS signaling molecules might play a central role during host-virus interaction.

To better understand the potential of carbon fixation in China Sea, it is necessary to conduct the researches on the mechanism of carbon fixed and stored by benthic seaweeds, the important primary productivities in marine ecosystems. In this paper, some dominant seaweeds from intertidal zones in Dalian were measured on the daily fixed and respired carbon, and daily release of organic carbon. Furthermore, seasonal variation of carbon fixed and stored by intertidal seaweeds from three seaweed beds in Dalian were elucidated, combined with the measurements of the biomass. The results showed that green algae had the strongest capacity in carbon fixation, followed by brown algae and red algae. The carbon fixed and stored, and organic carbon released by intertidal seaweeds in Dalian were higher in December-May, and lower in June-November. The annual total carbon fixed, and organic carbon released by intertidal seaweeds of each seaweed bed were 1.72×10⁵ g/a and 2.1×10⁴ g/a respectively. The amount of monthly fixed carbon was 1.7 times that of stored carbon.

The technical bottlenecks of threshold determination and key parameter calibration in carrying capacity of resources and environment urgently need to be broken through, and then establish a standardized quantitative evaluation method. In the present study, several key parameters such as primary productivity, phytoplankton organic carbon content and trophic level were obtained through investigation and experimental analysis. The nutrition dynamic model and the Tait coastal energy flow model were used to estimate the total quantities of marine biological resources. Then, the threshold of carrying capacity of marine biological resources was calculated based on the “resource-consumption” model. For example, according to the survey results in 2016, the annual average primary productivity of sea area under Rizhao jurisdiction was 428.22 mg/(m²·d) and the annual production of phytoplankton was 9.19×10⁶ t. Meanwhile, the average trophic levels of fishes, shrimps and crabs, and cephalopods were 3.85, 3.92 and 3.90, respectively. The annual production of fishery resources (fish, shrimp and crab, and cephalopod) in the sea area was 38.9 thousand tons calculated by the “nutritional dynamic model”. In addition, the shellfish resources in shallow sea within 10 m depth contour was 55 thousand tons calculated by the Tait coastal energy flow model. Thus, according to the annual per capita intake of aquatic products of 21 kg, the total carrying capacity of marine biological resources in Rizhao coastal waters was 1.928 6×10⁶ people. Meanwhile, according to the annual per capita protein intake of 30 kg, the total carrying capacity of marine biological resources in Rizhao area was calculated to be 1.687×10⁵ people. Taken together, this paper describes a quantitative assessment technique with wide applicability for carrying capacity of marine biological resources. This will contribute to substantial utilization of the marine biological resources and establishment of the monitoring and early warning of resources and environment carrying capacity in a way of overall planning of land and sea.

Based on the concentrations and spatial distributions of heavy metals in 404 surface sediments from the Bohai Sea, we evaluated the marine sediment environmental quality indexes, such as heavy metal potential ecological risk, geoaccumulation index. Moreover, the factors that impact on the concentrations of heavy metals in surface sediments were also analyzed. The results showed that the mean concentrations of all eight heavy metals were highest in the Bohai Bay, and were lowest in the Bohai Strait. The potential ecological risks of Cd and Hg elements were high and moderate-high, respectively. The assessment of total potential ecological risk revealed Bohai Sea belongs to moderate potential ecological risk. Only few stations of Cr and Ni exceed the upper limit of toxic threshold, and the others are less than the lower limit of toxic threshold. This implies the occurrence probability of toxic pollution is low. The concentrations and spatial distributions of heavy metals were mainly dominated by grain size effect of surface sediments from the Bohai Sea. However, Cd element which was rich in the Jinzhou Bay and Fuzhou Bay of Liaodong Bay was mainly contributed by human activities. Hg element, supplied by Yellow River possible impacted by human activities, was rich in the Yellow River Delta and Laizhou Bay.

Based on the investigation of dissolved organic matter (DOM) in the Yellow Sea from August to September 2017, the spatial distributions of dissolved organic carbon (DOC) and chromophoric dissolved organic matter (CDOM) were studied in the Yellow Sea. In surface seawater, CDOM was higher in the nearshore water, which might be caused by the relatively significant influence by the river input, while colorless DOC concentration in the north Yellow Sea Cold Water (YSCW) area was enhanced by aquaculture. The DOC concentration decreased along depth in the water column, but an inverse trend for CDOM, especially in the YSCW area. Terrestrial DOC input and primary production were mainly responsible for the surface DOC enhancement, while photobleaching induced the decrease of CDOM. Additionally, photobleaching also decreased the molecular weight and aromaticity of CDOM in surface water. In the YSCW, O₂ was slightly unsaturated, with the saturation degree of 80%–93%. In this area, strong stratification prevented the vertical diffusion of O₂ to bottom water. Additionally, stratification could also restrain the vertical mixing of DOC and CDOM, which was mainly responsible for the large difference of DOC and CDOM between surface and bottom waters.