The critical point is an important component in the topological structure of the ocean flow field. Feature extraction based on the critical point is of great significance to reveal the topological characteristics of the ocean flow field and carry out the topological analysis of the ocean flow field. In this paper, based on critical point theory and Sperner lemma, the improved bilinear interpolation algorithm and Sperner complete labeling method were integrated to extract the critical point features of ocean flow field data. First of all, we added sliding window to the bilinear interpolation algorithm to filter the candidate grid cells of the critical points, and use the aggregation idea to solve the ambiguity problem of grid interpolation by reducing the grid resolution. At the same time, we considered nine cases of the zero value grid, and used the iterative aggregation idea to slide filter the candidate grid cell, which solves the case that the interpolation grids are all 0. Secondly, the extraction rule of critical points of minimum method based on Sperner complete labeling was proposed, and the grid center with the smallest velocity vector module is taken as the critical point to solve the non-zero critical point extraction in the actual flow field physical scene. By combining and de duplicating the two extraction results, more comprehensive critical point extraction and classification results can be obtained. Finally, through the analysis of the experimental results of the flow field data in multiple sea areas and different depths, the effectiveness and feasibility of the integrated critical point extraction algorithm was proved.

Seagrass are a kind of monocotyledonous plants growing in marine environments. Microorganisms such as bacteria, fungi, microalgae, archaea and viruses inhabiting seagrass organs and environment play an important role in controlling growth, nutrition and health of seagrass, and maintaining material cycling in seagrass meadows. In this paper, we briefly summarized some recent progresses in studying of microorganisms in seagrass meadows, and discussed the possible directions for future research in this area. Seagrasses microbiome is significantly different from the microbial community in sediment and seawater, which is distributed in discrete and highly heterogeneous ecological niches, and the model remains consistent on a wide geographical scale. It is not controlled by the seagrasses species and sediment types, but mainly depended on the environment and the metabolism of seagrasses. Most seagrass core microbial communities are associated with the sulfur cycle. In the future, methods such as simulation experiment, the ecological model, genome, metagenome, metatranscriptome and metabolome can be used to study diversity, composition, function, colonization and diseases of microorganisms in seagrass meadows. Additionally, it is of great significance to reveal the interrelationships among microorganisms, seagrasses and the environment for the protection of threatened seagrass meadows.

For understanding the effects of iron and sulfur redox processes and overlying water hypoxia on phosphorus remobilization and liberation in intertidal sediments, the coastal zone in Xiang’an, Xiamen was selected to conduct continuous sampling and monitoring for sediments, pore water and overlying water in one year by employing the in-situ high resolution sampling techniques. Results showed that hypoxia and excessive phosphorus content were severe in the overlying water, which were below 2 mg/L and above 0.06 mg/L in most months, respectively. On the vertical profile, the distribution trend of dissolved reactive phosphorus (SRP) content was consistent with that of dissolved iron content in pore water, while the distribution trend of DGT-labile P was consistent with that of DGT-labile S in local, demonstrating that the passivation and remobilization of phosphorus are mainly controlled by the redox process of iron, and locally controlled by the redox process of sulfur. However, deficiency of sediment phosphorus limits the content of phosphorus in the deep reduction zone. In terms of quarterly changes, SRP content in pore water is only consistent with SRP content in overlying water, which is attributed to the hypoxic sedimentary environment favoring the cross-boundary exchange of SRP. However, the superposition of a variety of environmental factors affects the spatiotemporal changes of SRP and DGT-labile P. The SRP content concentration gradient in surface pore water was not significant, that is, the phosphorus release risk was not significant, but the change of environmental factors is very easy to trigger the release of endogenous phosphorus in future.

The distribution and mixing behavior of chromophoric dissolved organic matter (CDOM) in the Changjiang River Estuary and adjacent areas were analyzed in March, July and October of 2019. Through discussing the salinity, absorption spectral slope S_275-295, absorption coefficient a_CDOM(355) and Chl a, it was shown that the terrestrial input and a_CDOM(355) had the highest value in July; a_CDOM(355) was affected by terrestrial input and phytoplankton production activities in March, and the value in March was higher than that in October in the Changjiang River Estuary low salinity area. In addition, in high-salt offshore regions, a_CDOM(355) was similar in March and July, and both were lower than that in October, these distribution of CDOM was mainly influenced by phytoplankton production activities. Four fluorescent components of CDOM were identified by EEMs-PARAFAC technique: one protein-like component C1 (280/330 nm) and three humic-like components C2 (300/350 nm), C3 (260/465 nm) and C4 (320/410 nm) respectively. Four fluorescent components declined from the inside to the outside of the Changjiang River Estuary in March, July and October. Affected by terrestrial input and phytoplankton production activities, the seasonal variation of the average fluorescence intensity from large to small was July, October and March. The fluorescence components of CDOM deviated from the theoretical dilution line in these three seasons. It showed that CDOM source (terrestrial input, sediment resuspension and on-site biological activities) and sink (adsorption of particulate matter, photodegradation and bacterial degradation) were complex and variable, revealing the non-conservative mixed behavior of CDOM in the Changjiang River Estuary.

The foraging depth of yellowfin tuna (Thunnus obesus), which is primarily influenced by the vertical structure of the water temperature, has a significant effect on longline catch rates. Therefore, the generalized additive model (GAM) was applied to analyse the influence of subsurface environmental variables on the longline catch per unit of effort (CPUE) in the central and western Pacific. The results show that subsurface environmental factors have significant impacts on the spatial distribution of yellowfin tuna catches in longline fisheries. The longline CPUE for the yellowfin tuna in the tropical central and western Pacific rise rapidly after 2012. A high catch rate appears in the northern hemisphere during summer in the region near 10°S, 140°E. The upper boundary temperature and depth of the thermocline, the lower depth of the thermocline, the depth of the isotherm at 18℃, and the relative depth between △8℃ and the lower depth of thermocline greatly influence the longline fishing rate. These key environmental factors affect the tropical central and western Pacific yellowfin tuna longline catch. The CPUE increases as the temperature and the depth of the upper boundary of the thermocline increased. The strongly associated relationships between the upper boundary temperature and depth with CPUE were 27−28℃ and 70−90 m, respectively. High catch rates are observed when the lower boundary depth of the thermocline is from 250 m to 280 m. Then, as the lower boundary depth increased, the CPUE value quickly decreased. The effect of the 18℃ isotherm depth on the CPUE of longline fishing initially fluctuated and then increased. The nonlinear effects of the relative depth between △8℃ and the lower depth of the thermocline first decreased and then increased slowly. The strong associations between CPUE and the 18℃ isotherm depths and relative depth are at 230 m and 70 m, respectively. The catch rates reaches a maximum when the vertical habitat is compressed, making it consistent with hooking depth. The catch rates could be changed by adjusting the depth of hooks. The vertical habitat of tuna should be taken into account in fisheries stock assessments and fishing grounds analysis.

The Changjiang River Estuary is an important habitat for many migratory fishes, and its complex environmental conditions affect the growth and reproduction of aquatic organisms in this area. Marine environmental monitoring buoys are capable of long-term, continuous, real-time and large-scale monitoring of many environmental factors, and are an important part of the modern marine environmental automatic observation system. To establish a reasonable and effective buoy monitoring network for the Chinese sturgeon nature reserve and its adjacent waters in the Changjiang River Estuary , this study used Ordinary Kriging (OK) method to simulate the spatial distribution of various environmental factors, and then compared the effects of different strata design and sample size of Stratified Random Sampling (StRS) on the monitoring results. The results showed that: (1) the sampling accuracy of salinity was better when the stratum of StRS was 3, and water temperature, dissolved oxygen and chemical oxygen demand (COD) was better when the stratum was 2; (2) the relative estimation errors became more concentrated and tended to 0 value with the increase of sample size, and when the sample size was larger than 30, the accuracy tended to be stable gradually; (3) the sampling accuracy of salinity in autumn was different to the other three seasons, and the sampling effect of COD in winter was worst. In the future, when setting up an environmental monitoring buoy network in the Chinese sturgeons nature reserve in the Changjiang River Estuary, it is suggest to adopt three stratum stratified random sampling for salinity monitoring, and the number of stations should be more than 50. When taking various hydrological environments as the monitoring target, it is recommended to use two stratified standard, and the number of stations should be more than 30.

The prediction of habitat suitability has important guiding significance for species protection, alien and pest control. In this study, Maxent model was used to predict the suitability habitat of A. molpadioides in Qingchuan Bay, Ningde, Fujian Province. The results of the model reached an excellent level. Combined with ArcGIS software, the suitable habitat of the A. molpadioides was divided into five grades. The results showed that: 1.3% of the total study area was highly suitable, and the area was 25.6 km². Water environment, sedimentary environment and ecological community all had an impact on the distribution of the A. molpadioides. Water depth, salinity, inorganic nitrogen and active phosphate were the main factors of water environment. The cumulative contribution rate to the prediction of the suitable habitat of A. molpadioides was 39%, especially, the water depth was the most important factor that restricts the distribution of the A. molpadioides, the prediction contribution rate was 27%; sediment grain size, sulfide content and total organic carbon in surface sediment were the main factors of sedimentary environment, and the cumulative contribution rate was 40.4%. The second important factor was the volume percentage of substrate of sediment clay, and the contribution rate was about 18%. The density and biomass of zooplankton and the number of benthos all had a certain impact on the distribution of A. molpadioides, the cumulative contribution rate was 16.8%, among which the contribution rate of zooplankton density to other biological factors was the most important, about 8%. On the whole, the most ideal distribution area of A. molpadioides is the shallow water muddy bottom habitat, where the water depth is less than 5 m, the salinity is relatively low, the organic carbon content of sediment is relatively high, and the bottom material is mainly clay silt.

Most marine invertebrates undergo planktonic and benthic stages during growth. As a marine economic shellfish and macrofouling organism, Mytilus coruscus has attracted widespread attention. To explore the interaction between marine bacteria and the mussel settlement, Pseudoalteromonas marina, which has a high inducing activity for the plantigrade settlement of the M. coruscus was chosen and the flagellin was extracted by acid hydrolysis ultracentrifugation. The impact of extracted flagellin mixed with agarose solution to on plantigrade settlement was tested directly. The P. marina biofilms treated with extracted flagellin were used to investigate the inducing capacity on plantigrade settlement. Changes in biomass, bacterial density and extracellular products of the biofilms were analyzed by confocal laser scanning microscopy. The results showed that the mixed gel containing P. marina flagellin and agarose significantly promoted the settlement of plantigrades. In other treatment, the flagellin-treated biofilms also had a high inductivity of plantigrade settlement. Biomass, bacterial density, membrane thickness, extracellular β-polysaccharide, lipid and protein contents all increased. The study reveals that flagellin can directly regulate the settlement of plantigrades and control the inductivity by changing the biological characteristics of the P. marina biofilm indirectly. The study is valuable for clarifying the interaction between bacterial flagellin and plantigrade settlement of the M. coruscus.

Based on the bottom-trawl survey data collected from the Haizhou Bay in spring and autumn of 2011 and 2013−2017, the habitat suitability of Trachypenaeus curvirostris was analyzed based on environmental factors such as bottom temperature, bottom salinity and water depth. Generalized additive model (GAM) was used to determine the optimal combination of environmental factors. Boosted regression tree (BRT) was used to evaluate the weight of each environmental factor in the habitat suitability index (HSI) model. The arithmetic mean model (AMM) and geometic mean model (GMM) were used to build HSI model, and the best model was selected by cross validations. Results showed that HSI model built with depth and bottom salinity in spring had the minimum AIC value, while HSI model constructed with bottom temperature and bottom salinity in autumn had the minimum AIC value. BRT model showed that the weight of depth and bottom salinity were 76.23% and 23.77% in spring, and the weight of bottom temperature and bottom salinity were 82.56% and 17.44% in autumn. The optimal range of depth and bottom salinity for T. curvirostris in spring were within 24 m and 29.7−31.8, respectively. In autumn, the optimal range of bottom temperature and bottom salinity were 18−24℃ and 29.2−31.5, respectively. This study suggested that the optimization of environmental factors was proved to be able to improve the performance of HSI models.

Unmanned aerial vehicle (UAV) remote sensing has distinct advantages of flexible use, no cloud interference, and high spatial-temporal resolution. Aim to explore UAV’s utilization potential in marine disaster monitoring, research ship was used as the UAV landing pad, and for the first time, based on the bi-temporal orthophotos acquired by the ship-borne UAV, the drift velocity of green-tide in the Yellow Sea was estimated. In addition, the velocity result extracted from satellite images was compared, and the influences of wind and tidal currents on green-tide drift were analyzed. The results show that: (1) the red-green-blue floating algae index (RGB-FAI) can extract green-tide patches from UAV-based RGB orthophotos with a high-accuracy (kappa coefficient=0.95); (2) the green-tidal speed of three sites estimated by UAV remote sensing are 0.26−0.44 m/s, and the drift direction changed significantly throughout the day; (3) the short-term drift of green-tide is forced by the wind and tidal current. The drift direction of the green-tide is basically consistent with the tidal current of M₂, at 1°−62° to the right of wind direction. The ability to estimate green-tidal velocity accurately from the ship-borne UAV images is expected to provide technical support for the precise prediction, warning and control of green-tide disaster.