Maowei Sea Research Articles

The increasing influence of oyster farming on sedimentary organic matter in a semi-closed subtropical bay

Oyster farming activities play a pivotal role in the biogeochemical cycles of coastal marine ecosystems, particularly in terms of sedimentary carbon cycling. To gain deep insights into the influence of expanding oyster culture on the sedimentary carbon cycle, surface sediments were collected from the Maowei Sea, which is the largest oyster farming bay in south China, based on six filed surveys between July 2010 and December 2022. The sediment samples were analyzed for total organic carbon (TOC), total nitrogen (TN), stable carbon and nitrogen isotopes (δ13C and δ15N) to evaluate the inter-annual variations in the source contribution to sedimentary organic matter (SOM). The results revealed that the average contents of sedimentary TOC and TN were 0.67 ± 0.41 % and 0.06 ± 0.03 %, respectively. Fluctuations in the C/N molar ratios ranged from 5.8 to 23.6, with an average of 12.6 ± 2.9, indicating a significant terrestrial input contribution to SOM in the study area. Furthermore, the integration of stable isotope analysis and Bayesian mixing model demonstrated a gradual increase in the mean proportion of shellfish biodeposition to SOM, from 12.0 ± 5.6 % in July 2010 to 21.1 ± 7.3 % in December 2022, consistent with the progressive expansion of oyster aquaculture along this coastal area, thereby emphasizing the substantial influence of oyster farming on SOM composition. With the anticipated expansion of oyster farming scale and production in the future, shellfish biodeposition is expected to assume a more important role in shaping SOM dynamics and sedimentary organic carbon cycling in coastal waters. Overall, this study provided an important perspective for better assessing the impact of expanding mariculture scale on coastal biogeochemical cycles, thereby making valuable contributions to future policy formulation concerning mariculture and ecological conservation.

RGO/MWCNT-COOH-Modified Electrodes for the Detection of Trace Cd(II) and Zn(II) in Coastal Seawater

Cadmium (Cd) and zinc (Zn) in seawater enter the human body through the food chain. Combined toxicity tests indicated that high concentrations of Cd(II) and low concentrations of Zn(II) had a synergistic effect on humans. Thus, there is an urgent need to prepare a sensor for rapid and simultaneous detection of Cd(II) and Zn(II) in seawater. Herein, a reduced graphene oxide/carboxylated multi-walled carbon nanotube (rGO/MWCNT-COOH)-modified glassy carbon electrode was prepared in the experiments using the dropping method. The synthesis of various materials achieved the purpose of expanding the surface area, and scanning electron microscopy was used to observe the structure of the composite membrane. Moreover, the large number of functional groups on the surface of the composite membrane can also increase the adsorption of ions. For the determination of trace cadmium (II) and zinc (II) in seawater, the method used was differential pulse voltammetry (DPV). The results show that the peak current, which was obtained in the range of 5–400 μg/L for Cd(II) and Zn(II), has a linear relationship with concentration, corresponding to the detection limits of 0.8 μg/L for Cd(II) and 0.98 μg/L for Zn(II). The modified electrode was used to determine the Cd(II) and Zn(II) content in the coastal seawater of the Maowei Sea, and the recovery rate was between 95.8 and 98.2% for Cd(II) and 96.7~99.4% for Zn(II), which provided a novel approach of detection to define trace Cd(II) and Zn(II) in seawater.

Metabarcoding analysis of potential HABs in the Maowei Sea, China: A baseline information for the “Pinglu Canal Waterway” mega-waterway project

Maowei Sea is one of the cleanest sea areas in China, with relatively little anthropogenic activity. The Maowei Sea is rich in biodiversity and is the largest natural ground for the Hong Kong oyster Crassostrea hongkongensis seed collection. In recent years, the Chinese government has launched a mega project called “Pinglu Canal Waterway”, which will connect the main river system to the Maowei Sea. This project is expected to alter the physical and chemical properties of the Maowei Sea and affect the structure of the phytoplankton community. This study was conducted to screen potential harmful algal bloom species (HABs) in Maowei Sea using metabarcoding technique. In general, nine potential HABs were identified, including six toxin producers (Chattonella spp., Heterosigma akashiwo, Heterocapsa rotundata, Heterocapsa pygmaea, Karenia mikimotoi and Karlodinium veneficum) and three non-toxin producers (Gymnodinium impudicum, Skeletonema costatum and Ulva prolifera). The occurrence and relative abundance of these potential HABs increases with increasing water salinity. The findings of this study not only provide information on the occurrence of potential HABs in Maowei Sea, but also provide valuable information that contributes to the development of aquaculture and fishery management plans to be implemented in Maowei Sea.

Effects of intensive oyster farming on nitrogen speciation in surface sediments from a typical subtropical mariculture bay

The spatial-temporal distributions of various nitrogen (N) species in surface sediments were examined in a typical subtropical mariculture bay (Maowei Sea) in the northern Beibu Gulf to assess the impact of intensive oyster culture activities on sedimentary N speciation. The results indicated that the mean contents of total nitrogen (TN), extractable (labile) nitrogen (LN) and residual nitrogen (RN) in the surface sediments were 33.3 ± 15.5 μmol g−1, 13.8 ± 1.3 μmol g−1 and 19.5 ± 15.0 μmol g−1, respectively, which lacked significant seasonal variability (P > 0.05). Four forms of LN, namely ion extractable form (IEF-N), weak acid extractable form (WAEF-N), strong alkali extractable form (SAEF-N) and strong oxidant extractable form (SOEF-N) were identified based on sequential extraction. SOEF-N was the dominant form of LN, accounting for 67.8 ± 2.5 % and 63.7 ± 5.9 % in summer and winter, respectively. Spatially, the contents of sedimentary TN, LN, RN, WAEF-N and SOEF-N in intensive mariculture areas (IMA) were significantly higher than those in non-intensive mariculture areas (NIMA) during summer (P < 0.05). Stable nitrogen isotope (δ15N) mixing model revealed that shellfish biodeposition was the predominant source of sedimentary TN in IMA with a contribution of 67.8 ± 23.0 %, approximately 5.4 times that of NIMA (12.6 ± 3.3 %). Significant positive correlations (P < 0.05) were observed between most forms of N species (WAEF-N, SOEF-N, LN and RN) and shellfish-biodeposited N in the surface sediments during summer, indicating that intensive oyster farming greatly enhanced sedimentary TN accumulation.

Coupling UAV Hyperspectral and LiDAR Data for Mangrove Classification Using XGBoost in China’s Pinglu Canal Estuary

The fine classification of mangroves plays a crucial role in enhancing our understanding of their structural and functional aspects which has significant implications for biodiversity conservation, carbon sequestration, water quality enhancement, and sustainable development. Accurate classification aids in effective mangrove management, protection, and preservation of coastal ecosystems. Previous studies predominantly relied on passive optical remote sensing images as data sources for mangrove classification, often overlooking the intricate vertical structural complexities of mangrove species. In this study, we address this limitation by incorporating unmanned aerial vehicle-LiDAR (UAV-LiDAR) point cloud 3D data with UAV hyperspectral imagery to perform multivariate classification of mangrove species. Five distinct variable scenarios were employed: band characteristics (S1), vegetation index (S2), texture measures (S3), fused hyperspectral characteristics (S4), and a canopy height model (CHM) combined with UAV hyperspectral characteristics and LiDAR point cloud data (S5). To execute this classification task, an extreme gradient boosting (XGBoost) machine learning algorithm was employed. Our investigation focused on the estuary of the Pinglu Canal, situated within the Maowei Sea of the Beibu Gulf in China. By comparing the classification outcomes of the five variable scenarios, we assessed the unique contributions of each variable to the accurate classification of mangrove species. The findings underscore several key points: (1) The fusion of multiple features in the image scenario led to a higher overall accuracy (OA) compared to models that employed individual features. Specifically, scenario S4 achieved an OA of 88.48% and scenario S5 exhibited an even more impressive OA of 96.78%. These figures surpassed those of the individual feature models where the results were S1 (83.35%), S2 (83.55%), and S3 (71.28%). (2) Combining UAV hyperspectral and LiDAR-derived CHM data yielded improved accuracy in mangrove species classification. This fusion ultimately resulted in an OA of 96.78% and kappa coefficient of 95.96%. (3) Notably, the incorporation of data from individual bands and vegetation indices into texture measures can enhance the accuracy of mangrove species classification. The approach employed in this study—a combination of the XGBoost algorithm and the integration of UAV hyperspectral and CHM features from LiDAR point cloud data—proved to be highly effective and exhibited strong performance in classifying mangrove species. These findings lay a robust foundation for future research efforts focused on mangrove ecosystem services and ecological restoration of mangrove forests.

Coupling isotopic signatures and partial extraction method to examine lead pollution in mangrove sediments

Elevated lead (Pb) has been widely observed in mangrove sediments due to human activities, yet understanding the sources of Pb in these sediments and the factors influencing Pb accumulation is challenging. Here, we combined Pb isotopes with partial extraction methods to study Pb contamination levels in mangrove sediments from the eastern and western parts of the Maowei Sea, China. Our results showed that the Pb in the leachate and residual fraction was mainly from anthropogenic and natural sources, respectively. The use of 204Pb isotope analysis can reveal some overlooked differences between anthropogenic and natural sources. Calculation by Bayesian mixing model showed no significant difference in the total anthropogenic contribution between the two sites, but the relative contribution of each end member differed. The contribution of Pb/Zn ores was much higher in the eastern sites (30.9 ± 5.1%) than in the west (18.4 ± 5.5%), while that of agricultural activities was much lower in the east (5.2 ± 3.1%) than in the west (13.5 ± 4.6%). The elevated anthropogenic Pb accumulation in mangrove sediments was ascribed to organic matter. This study provides more data on Pb isotopic composition and new insights into Pb biogeochemistry in the mangrove environment.

Distinguishing the main components of submarine groundwater and estimating the corresponding fluxes based on radium tracing method—taking the Maowei Sea for example

Distinguishing the main components of submarine groundwater and estimating the corresponding fluxes based on radium tracing method—taking the Maowei Sea for example

Quantifying scattering characteristics of mangrove species from Optuna-based optimal machine learning classification using multi-scale feature selection and SAR image time series

Mangroves play a significant role in carbon sequestration and storage. Mapping mangrove species and monitoring their conditions have been a crucial issue for achieving sustainable development goals. Currently combing multidimensional optical and SAR images with machine learning have become an important approach for mangrove species classification, but there are still some challenges in feature selection and hyperparameter optimizations. In this study, we proposed a novel classification framework by combing multi-scale variable selection algorithm (MUVR) with state-of-the-art machine learning hyperparameter optimization method (Optuna) for mapping mangrove species in the Beilun Estuary and Maowei Sea nature reserves using optical and dual-polarization SAR images, and further quantified the scattering characteristics of mangrove species using SAR image time series. We found that: (1) The MUVR algorithm could determine the optimal scale features for different scenarios and mangrove species, and improve the classification performance of machine learning with an overall accuracy (OA) improvement of 12.85%; (2) The Optuna-based optimal CatBoost outperforms LightGBM and NGBoost algorithms in mapping mangrove species, which achieved the highest OA (93.18%). This study demonstrated that LightGBM was suitable for identifying Aegiceras corniculatum, while the CatBoost algorithm was suitable for discriminating Avicennia marina, Bruguiera gymnorrhiza, Cyperus malaccensis, Kandelia candel and Sonneratia apetala; (3) SAR images and its derivatives improved identification ability of mangrove species, and collaboration of multispectral images and SAR-derived features produced the better classification; (4) From 2018 to 2020, the backscattering coefficients of mangrove species in VV and VH polarization focused on 0.053–0.327 and 0.015–0.062, respectively. The coherence coefficients of mangroves displayed a seasonal change trend with the large variations in summer and small variations in winter. The range of Entropy and Alpha of mangrove species was from 0.65 to 0.88 and 17–33, which indicated that the main scattering mechanism of mangroves was moderate random surface scattering.

Atmospheric radioactive nuclide deposition on the coast of the Maowei Sea, northern Beibu Gulf, China

The natural radioisotopes 7Be, 210Pb, and 210Po, with different half-lives, are all particle-reactive and serve as natural tracers to study sources and transportation of sediments, sedimentation rates, and sediment chronology. Atmospheric deposition of these radioisotopes is the premise and foundation of their tracing application. The Maowei Sea is a semi-closed bay along the Beibu Gulf, which is an important gulf in the northwest of the South China Sea, but the atmospheric deposition of the abovementioned radioisotopes has not been systematically reported along the coast. In this research paper, the atmospheric depositional fluxes of 7Be, 210Pb, and 210Po were observed over a period from June 2018 to December 2021 on the coast of the Maowei Sea. The annual atmospheric depositional fluxes (Bq m-2 yr-1) of 7Be, 210Pb, and 210Po on the coast of the Maowei Sea were 496.80, 201.72, and 58.08, respectively. The distributions for 7Be and 210Pb depositional fluxes during a whole year (years 2019 and 2021) followed a bimodal pattern, with one peak from February to April and another peak from August to October, while the distribution for 210Po depositional flux showed only one peak during the year of 2019 and another during 2021. The deposition flux and activity of 7Be showed a strong positive correlation with the deposition flux and activity of 210Pb, respectively; the deposition flux and activity of 210Po also showed positive correlations with the deposition fluxes and activities of 210Pb and 7Be, respectively, indicating a similarly scavenging behavior from the atmosphere. A Pearson correlation matrix was used to illustrate the factors influencing the atmospheric depositions and found that precipitation, air quality index (AQI), and PM (both PM2.5 and PM10) were the major factors that influenced the deposition of these three radionuclides. Precipitation had significant positive correlations with the deposition fluxes of all three radionuclides, indicating that, for these radionuclides, rainfall was the main scavenging way from the atmosphere. The observations for specific single rainfall events and their air mass backward trajectory analyses showed that the air masses movement during the rainfall may be another important factor that impacted the depositional fluxes for 7Be, 210Pb, and 210Po.

High-frequency mangrove degradation events during the Holocene climatic optimum in the Maowei Sea of tropical China

Mangrove forests are ecologically and economically significant ecosystems in the (sub)tropics, and it is important to reconstruct their historical changes to understand the future of mangrove forests in the context of global warming. Although the Holocene climatic optimum (HCO) is regarded as an analogue of future climatic change, high-resolution studies of mangrove ecosystem changes during this period are rare. This study presents a high-resolution sedimentary record for the HCO (7800–6900 cal. yr BP) in the Maowei Sea, northern Beibu Gulf, China. Contributions of mangrove-derived organic matter in the sediments were quantified using an end-member mixing model and a Monte Carlo simulation. The model reveals there were high-frequency degradation events (11 times) of the mangrove forests during 7800–6900 cal. yr BP. Palaeo-environmental records (e.g., sea-level, stalagmite δ18O values, and sediment Ti contents) from near the study area demonstrate that an intensified Asian winter monsoon (AWM), rather than sea-level changes, was the key factor responsible for the high-frequency mangrove degradation events during the HCO in this region. The results indicate that the effects of the intensified winter monsoon on tropical ecosystems may be greater than previously thought, particularly in the context of anthropogenic warming scenarios.

Mangrove Biodiversity Assessment Using UAV Lidar and Hyperspectral Data in China’s Pinglu Canal Estuary

Mangrove forests are a valuable resource for biological and species diversity, and play a critical role in maintaining biodiversity. However, traditional plant biodiversity survey methods, which rely on labor-intensive field surveys, are not suitable for large-scale continuous spatial observations. To overcome this challenge, we propose an innovative framework for mangrove biodiversity assessment and zoning management based on drone low-altitude remote sensing, integrating data such as vertical structure features and spectral diversity features extracted from on-site measurements, airborne LiDAR, and hyperspectral data. This study focuses on the Maowei Sea mangrove community, located in the estuary of China’s first Pinglu Canal since the founding of the People’s Republic of China. Using the proposed framework, we construct an evaluation index for mangrove biodiversity at the levels of species diversity, ecosystem diversity, and landscape diversity, achieving a quantitative calculation of mangrove biodiversity and an evaluation of spatial distribution patterns. The results show that the biodiversity index of mangroves ranges from 0 to 0.63, with an average value of 0.29, and high-biodiversity areas are primarily concentrated in the southwest of the study area, while low-value areas are mainly located in the north. We also select the elevation and offshore distance of mangrove growth for the spatial zoning of biodiversity. The core area of biodiversity occupies the smallest area, at 2.32%, and is mainly distributed in areas with an elevation of 1.43–1.59 m and an offshore distance of 150.08–204.28 m. Buffer zones and experimental zones account for a significant proportion, with values of 35.99% and 61.69%, respectively. Compared to traditional methods for monitoring mangrove biodiversity, such as community field-sample surveys, the proposed method using unmanned-aerial-vehicle LiDAR and hyperspectral coupling technology to assess mangrove biodiversity and establish a zoning management framework is more conducive to formulating mangrove biodiversity conservation strategies. The study provides a feasible solution for the large-scale biodiversity mapping of mangroves in the Maowei Sea at the estuary of the Pinglu Canal.

Spatio-Temporal Changes of Mangrove-Covered Tidal Flats over 35 Years Using Satellite Remote Sensing Imageries: A Case Study of Beibu Gulf, China

Tidal flats provide ecosystem services to billions of people worldwide; however, their changing status is largely unknown. Several challenges in the fine extraction of tidal flats using remote sensing techniques, including tide-level and water-edge line changes, exist at present, especially regarding the spatial and temporal distribution of mangroves. This study proposed a tidal flats extraction method using a combination of threshold segmentation and tidal-level correction, considering the influence of mangrove changes. We extracted the spatial distribution of tidal flats in Beibu Gulf, Southwest China, from 1987 to 2021 using time-series Landsat and Sentinel-2 images, and further analyzed the dynamic variation characteristics of the total tidal flats, each coastal segment, and the range of erosion and silting. To quantitatively investigate the interaction between tidal flats and mangroves, this study established a regression model based on multi-temporal tidal flats and mangrove data. The results indicated that the overall accuracy of the tidal flat extraction results was 93.9%, and the kappa coefficient was 0.82. The total area of tidal flats in Beibu Gulf decreased by 130 km2 from 1987 to 2021, with an average annual change of −3.7 km2/a. In addition, a negative correlation between the tidal flat change area and mangrove change area in Shankou, Maowei Sea, and Pearl Bay was observed, with correlation coefficients of −0.28, −0.30 and −0.64, respectively. These results demonstrate that the distribution of tidal flats provides a good environment and expansion space for the rapid growth of mangroves. These results can provide references for tidal flats’ resource conservation, ecological health assessment, and vegetation changes in coastal wetlands in China and other countries in Southeast Asia.

Assessment of the impact of wetland changes on carbon storage in coastal urban agglomerations from 1990 to 2035 in support of SDG15.1

The quantitative assessment and spatial representation of wetland carbon storage, which play a critical role in the global carbon cycle and human production, can provide useful data and knowledge for decision-making in achieving sustainable development goals (SDGs). Currently, human activities and climate change impacts pose a challenge for the assessment of wetland carbon storage in coastal urban clusters. We proposed a “past-present-future” long time series refined wetland carbon storage assessment model using Guangxi Beibu Gulf (GBG) and Guangdong, Hong Kong, Macao and the Greater Bay Area (GBA) as the study area. The CLUE-S and InVEST models were coupled to conduct a comparative analysis of the spatial and temporal changes in wetland carbon storage and the spatial identification of damages from 1990 to 2035 and finally explore the sensitivity of wetland changes to carbon storage and quantitatively assess the SDG15.1 target. The results showed that (1) both urban clusters are characterized by many reservoirs/farming ponds, large river areas and few lakes. 1990–2035 rivers, shallow waters and mudflats have a decreasing trend to be distributed in the middle of their respective regions, mangroves are on an increasing trend, GBG is mainly distributed in the Maowei Sea and GBA is mainly distributed in Shenzhen Bay. (2) Wetland carbon storage of the two urban clusters show an overall fluctuating downward trend, with rivers, lakes and beaches all showing a downward trend. The multiyear average carbon storage of the GBG are 3.2 times higher than those of the GBA. In ecological protection scenario (EPS) policy planning, it is reasonable to help wetland carbon sequestration in coastal urban clusters. (3) The trend of wetland change from 1990 to 2020 was positive for carbon storage. The rate of recovery of wetland carbon stocks is lower in GBA than in GBG under the natural increase scenario (NIS) and the ecological protection scenario (EPS). The economic development scenario (EDS) contributes least to the realisation of SDG15.1 for the coastal urban agglomeration. The ecological protection scenario (EPS) contributes the most to the realisation of SDG15.1 for the coastal urban agglomeration.

Paenibacillus mangrovi sp. nov., a novel endophytic bacterium isolated from bark of Kandelia candel.

A endospore-forming bacterium, designated strain KQZ6P-2T, was isolated from surface-sterilized bark of the mangrove plant Kandelia candel, collected from Maowei Sea Mangrove Nature Reserve in Guangxi Zhuang Autonomous Region, China. Strain KQZ6P-2T was able to grow at NaCl concentrations in the range of 0-3 % (w/v) with optimum growth at 0-1 % (w/v) NaCl. Growth occurred at 20-42 °C (optimal growth at 30-37 °C) and pH 5.5-6.5 (optimal growth at pH 6.5). The 16S rRNA gene sequence similarity between strain KQZ6P-2T and its closest phylogenetic neighbour Paenibacillus chibensis JCM 9905T was 98.2 %. Phylogenetic analyses using 16S rRNA gene sequences showed that strain KQZ6P-2T formed a distinct lineage with Paenibacillus chibensis JCM 9905T. The draft genome of strain KQZ6P-2T was 5 937 633 bp in size and its DNA G+C content was 47.2mol%. Comparative genome analysis revealed that the average nucleotide identity, digital DNA-DNA hybridization and average amino acid identity values among strain KQZ6P-2T and its related species were below the cut-off levels of 95, 70 and 95.5%, respec-tively. The cell-wall peptidoglycan of strain KQZ6P-2T contained meso-diaminopimelic acid as the diagnostic diamino acid. Major cellular fatty acids were anteiso-C15:0 and C16:0. The polar lipids comprised diphosphatidylglycerol, phosphatidylglycerol, phosphatidylethanolamine, two unidentified aminophospholipids, four unidentified phospholipids, an unidentified aminolipid and five unidentified lipids. Based on phylogenetic, phenotypic and chemotaxonomic data, strain KQZ6P-2T represents a novel species of the genus Paenibacillus, for which the name Paenibacillus mangrovi sp. nov. is proposed. The type strain is KQZ6P-2T (=MCCC 1K07172T =JCM 34931T).

Distribution Characteristics of Dissolved Oxygen in Spring in the Northern Coastal Beibu Gulf, a Typical Subtropical Bay

Dissolved oxygen (DO) is an important parameter for evaluating the seawater quality of the oce-anic environment, but the distribution of DO and its possible mechanisms of marine environ-mental regulation are less studied. Here, the spatial distribution of DO and the influence of main environmental factors on DO were explored using in-situ observation data collected in the northern coastal waters of the Beibu Gulf in April 2021, based on cluster analysis, partial correla-tion analysis, and multiple linear regression. The results showed that DO in surface water of the study area gradually increased from nearshore to offshore, with the nearshore’s DO being nearly saturated and the offshore supersaturated. Tieshan Port and the Maowei Sea had the lowest degree of DO saturation. The partial correlation analysis between DO concentration and environmental factors revealed that the distribution of DO concentration was mainly influenced by temperature and salinity as well as other biochemical factors (i.e., chlorophyll-a, turbidity, and pH). Significant differences existed between the dominant factors in various parts of the northern Beibu Gulf. The western river (Beilun River, Fangcheng River, Maoling River, Qinjiang River) estuaries and offshore areas were dominated by photosynthesis, while the eastern river (Fengfeng River, Nanliu River) estuaries were dominated by oxidation-decomposition. In addition, DO was also affected by the mangrove ecosystem, aquaculture, and coastal industry.

Isolation, Structure Elucidation, and First Total Synthesis of Quinomycins K and L, Two New Octadepsipeptides from the Maowei Sea Mangrove-Derived Streptomyces sp. B475.

Mangrove actinomycetia have been proven to be one of the promising sources for discovering novel bioactive natural products. Quinomycins K (1) and L (2), two rare quinomycin-type octadepsipeptides without intra-peptide disulfide or thioacetal bridges, were investigated from the Maowei Sea mangrove-derived Streptomyces sp. B475. Their chemical structures, including the absolute configurations of their amino acids, were elucidated by a combination of NMR and tandem MS analysis, electronic circular dichroism (ECD) calculation, advanced Marfey's method, and further unequivocally confirmed by the first total synthesis. The two compounds displayed no potent antibacterial activity against 37 bacterial pathogens and had no significant cytotoxic activity against H460 lung cancer cells.

Two-Stream Translating LSTM Network for Mangroves Mapping Using Sentinel-2 Multivariate Time Series

Monitoring mangroves is critical to protect the coastal ecosystems, and deep learning has gained great popularity in mapping mangroves using remote sensing. However, mangroves are usually submerged by cyclical tide since they are grown in land-sea interface places, resulting in some drawbacks of existing mangroves mapping models. For one side, the correlations between vegetation index (VI) and water index (WI) time series of mangroves are not fully considered. For another side, existing models rarely explored the local differences between mangroves and other land covers. Considering the above two aspects, we propose a novel two-stream translating long short-term memory network (TSTLN) for mangroves mapping. Firstly, we construct multivariate time series (MTS) by compositing VI and WI based on Sentinel-2 time series data. Secondly, we build a two-stream architecture and design a siamese translating (ST) module in both streams. In global stream, MTS are embedded into ST module directly to get global features. Whereas in local stream, a depthwise convolutional self-attention (DCA) module is conceived to capture local information firstly, and then local features are further learnt by ST module. Finally, a fully connected layer and softmax are used to classify the representations extracted from the two streams. Experiments conducted over Maowei Sea, Dongzhai Port, and Quanzhou Bay in 2019 demonstrate that: 1) TSTLN outperforms other methods, with improved OA of 0.49%-3.89%, 1.35%-6.85%, and 0.73%-3.65% in the three areas, respectively; 2) Two-stream architecture, ST module, and DCA module all contribute to the good performance of TSTLN; 3) TSTLN maintains higher accuracy with few parameters and less running time compared to other counterparts.

ZnO-CNT/Nano-Au modified electrodes for the detection of trace Hg(II) in coastal seawater

Mercury (Hg) in seawater enters the body through the food chain, causing damage to organs and the nervous system. Thus, there is an urgent need to explore a rapid and convenient sensor for the detection and monitoring of Hg(II) in seawater. Herein, a ZnO-CNTs/Nano-Au modified glassy carbon electrode was prepared by the dropping method. The structure of the composite membrane is mainly observed by scanning electron microscopy (SEM), and the results show that the composite has a larger specific surface area. Moreover, the composite can increase the ion adsorption of the surface electrode and enhance the conductivity. Differential pulse voltammetry (DPV) was applied to determine trace amounts of Hg(II) in seawater. The optimized conditions were as follows: accumulation potential, accumulation time, pH value, film thickness and concentration. Under the optimal experimental conditions, the linear relationship between the values of the oxidation peak current and concentration was kept in the range of 1.49 ∼ 5.97 μM, with a linear correlation coefficient R2 = 0.991 and a detection limit of Hg(II) of 0.0118 μM. The proposed method was applied to the analysis of coastal water of the Maowei Sea, giving values of recovery in the range of 94.2%∼98.4%. The ZnO-CNTs/Nano-Au-modified electrode has high sensitivity, convenient operation and good practical application value.

Effects of environmental factors on mycoplankton diversity and trophic modes in coastal surface water

Mycoplankton play a key role in aquatic microbial food webs and nutrient cycling. However, the environmental factors that affect their composition and trophic modes in coastal water remain unclear. In this study, we used fungal metabarcoding to characterize seasonal mycoplanktonic communities in the surface water of the Maowei Sea. Dothideomycetes and Sordariomycetes were the dominant classes in the Maowei Sea. Random forest modeling analyses suggested that Ochroconis, Rhodotorula, Perenniporia and Derxomyces were the best seasonal bioindicators of environmental changes. Spearman’s correlation analysis showed that TOC (total organic carbon) is the main factor affecting mycoplanktonic bioindicators. Through FUNGuild analysis, we classified mycoplankton in the Maowei Sea into eight trophic modes and found that saprotrophs were the most abundant. Random forest analysis and Spearman’s correlation indicated that the mycoplankton trophic modes could reflect environmental changes in the Maowei Sea and were mainly influenced by dissolved inorganic phosphorus (DIP), dissolved oxygen (DO), and total organic carbon (TOC). Mycoplanktonic alpha and beta diversities significantly varied in different seasons (p < 0.05). Spearman rank’s test, Mantel test, and partial Mantel test indicated that TOC was the key environmental factor that affected the mycoplanktonic alpha and beta diversities. Variation partition analysis revealed that mycoplankton community structure was affected more due to nutrient variability than water quality (18 % vs 7 %). Overall, this study enhanced our understanding of the key controlling environmental factors affecting mycoplanktonic diversities and trophic modes in the coastal environment.

Applying a fish expert system for ranking the biological effects of polycyclic aromatic hydrocarbons on the rockfish Sebastiscus marmoratus in the Maowei Sea, China

Applying a fish expert system for ranking the biological effects of polycyclic aromatic hydrocarbons on the rockfish Sebastiscus marmoratus in the Maowei Sea, China