Differential Environmental Response Patterns Between Spawning and Nursery Habitats of Coilia mystus in the Yangtze Estuary - Dong Wang; Xiangyu Long; Zengguang Li; Rong Wan; Tiejun Li; Yuanming Guo; Pengbo Song (Fishes, 2026)
Estuaries support distinct spawning and nursery habitats for migratory fishes, yet the differential environmental response patterns between these two critical early life habitats remain poorly understood from a spatial non-stationarity perspective. Based on six ichthyoplankton surveys conducted during peak and late spawning seasons from 2018 to 2020 in the Yangtze Estuary, this study applied geographically weighted regression (GWR) models to quantify the spatially varying effects of sea surface temperature, sea surface salinity, chlorophyll-a, water depth and distance to coast on the distributions of Coilia mystus eggs and larvae. The results reveal clear divergence in both spatial pattern and environmental drivers between spawning and nursery habitats. Spawning grounds were persistently concentrated in the middle reaches of the South Branch, and shifted approximately 10 km upstream during the spring saltwater intrusion event in 2020. Nursery grounds, by contrast, formed a stable dual-core structure, with the northern core at the North Branch mouth consistently supporting higher larval densities than the southern core in the North and South Passages. Salinity was the primary limiting factor for spawning in spring, while temperature dominated in summer, and chlorophyll-a was never retained in optimal egg models. For larvae, chlorophyll-a emerged as a consistent key driver alongside salinity and temperature, and local regression coefficients spanned a wider range than those for eggs, indicating greater spatial heterogeneity in larval distribution–environment relationships. This study provides the first comparative analysis of spatially non-stationary environmental controls on spawning versus nursery habitats of C. mystus, and offers empirical support for stage-specific habitat conservation and fisheries management in the Yangtze Estuary.

The Study on the Biological Characteristics of Photobacterium damselae subsp. damselae Affected by the Deletion of the Iron Uptake System Genes tonB1 and tonB2 - Hua Jiang; Yufei Ji; Zhiqi Zhang; Yongxiang Yu; Chunyuan Wang; Yingeng Wang; Xiaojun Rong; Aijun Ma; Shuyi Li; Zheng Zhang (Fishes, 2026)
Photobacterium damselae subsp. damselae (PDD), a marine fish pathogen, has an unclear contribution of tonB genes to iron acquisition and pathogenicity. ΔtonB1-PDD and ΔtonB2-PDD mutants as well as their complemented strains were generated via homologous recombination from the virulent strain PDD1605. Growth was evaluated by monitoring OD600 with viable-count calibration, intracellular iron was measured using a colorimetric assay, biofilm was detected by crystal violet staining, transcription was analyzed through qRT-PCR, and pathogenicity was assessed via an 8-day intramuscular challenge in black rockfish (Sebastes schlegelii). Under iron limitation induced by 100 μM 2,2′-dipyridyl, the maximum density decreased from 4.51 × 108 CFU/mL in wild-type (WT-PDD) to 4.00 × 108 and 3.64 × 108 CFU/mL in ΔtonB1-PDD and ΔtonB2-PDD respectively, and was largely restored after complementation. The intracellular iron content declined from 3.75 × 10−4 ± 4.00 × 10−6 nmol/106 cells in WT-PDD to 3.13 × 10−4 ± 1.05 × 10−5 nmol/106 cells in ΔtonB1-PDD and 2.55 × 10−4 ± 2.00 × 10−5 nmol/106 cells in ΔtonB2-PDD. Compared with WT-PDD, biofilm formation was reduced by 22.1% in ΔtonB1-PDD and by 24.8% in ΔtonB2-PDD. Deletion of tonB2 induced mild yet statistically significant transcriptional alterations in multiple iron acquisition and virulence-related genes, while colony morphology, swarming motility, hemolysis, phospholipase activity, biochemical traits, and antimicrobial susceptibility remained unchanged. In the high-dose challenge assay, WT-PDD caused 100% mortality within 2 days, whereas ΔtonB1-PDD and ΔtonB2-PDD caused 17/20 and 18/20 deaths respectively; all infected groups reached 100% mortality by day 3. In the low-dose challenge assay, WT-PDD, ΔtonB1-PDD, and ΔtonB2-PDD caused 12/20, 12/20, and 13/20 deaths by day 2, with cumulative mortalities reaching 80%, 70%, and 80% by day 8, respectively. Kaplan–Meier analysis detected no significant differences among the survival curves at either challenge dose. These findings suggest that tonB1 and tonB2 play roles in iron acquisition, low-iron adaptation, and biofilm formation, with tonB2 deletion exerting greater effects on growth and intracellular iron accumulation under iron-limited conditions.

Numerical Investigation of Fluid Flow Past a Cylinder: Effect of Surface Roughness - Xiaoxiang Wang; Yong Ma; Lei Li; Liangwan Rong (Journal of Marine Science and Engineering, 2026)
This study employs direct numerical simulation (DNS), combined with the Cartesian cut-cell method and quadtree adaptive mesh refinement, to systematically investigate the effects of surface roughness on the flow past a cylinder. The varying surface roughness is described mainly in terms of the wavenumber β. Results show that the non-uniform roughness disrupts the symmetry of flow structures and randomizes separation, forming a heterogeneous flow with coexisting small-scale groove vortices and large-scale side vortices. At Re=100, the drag coefficient exhibits a maximum at β=30, with a corresponding 1.48-fold increase in the peak local pressure coefficient over a smooth cylinder. The lift coefficient stabilizes between 0.375 and 0.38 for β≥20. The trend of force varies across different Reynolds number ranges. Beyond a critical roughness at Re>100, the mean drag and lift amplitude become roughness-insensitive.

Risk Assessment of International Seabed Mining Implementing the Analytic Hierarchy Process - Xinyu Ma; Yejian Wang; Kehong Yang; Jinrong Li; Yan Li; Dongsheng Zhang; Rong Wang; Yinxia Fang (Journal of Marine Science and Engineering, 2025)
The international seabed area (“the Area”) harbors abundant metal mineral resources that are critical to address global metal supply–demand and sustainable development. However, exploitation of mineral resources in the Area faces complex risks spanning politics, economy, technology, science, environment, society, industry, and law. No commercial-scale deep-sea mining operations have been conducted to date. Systematic risk identification and prioritization can inform strategic planning for stakeholders. This study employs literature analysis and an 80-expert questionnaire to identify key risk factors affecting mineral exploitation in the Area. Using the Analytic Hierarchy Process (AHP), we quantitatively assess the relative importance and weightings of these risks. Our results indicate that Level 1 risk groups prioritize (1) policy and public opinion risk, (2) extended continental shelf (ECS) delineation risk, (3) high sea marine protected areas (HSMPAs) establishment risk, and (4) mining area economic value risk. The five most critical Level 2 risk factors are (i) policy changes in contractor states, (ii) ECS-mining area boundary conflicts, (iii) environmental provisions in exploitation regulations at the international seabed (ER), (iv) ER implementation delays, and (v) mineral resource uncertainty. These findings provide actionable insights for contractors, policymakers, and stakeholders to optimize decision making in deep-sea mining projects.

Internal Tide Generation and Propagation in the Sulu Sea Under the Influence of Circulation - Yuhao Rong; Yunchao Yang; Chao Wang; Heng Li; Jiahui Li; Xiaodong Huang (Journal of Marine Science and Engineering, 2025)
The Sulu Sea has active internal tides (ITs) and basin-scale circulation. This study, for the first time, employs three-dimensional simulations to investigate the effects of the Sulu Sea circulation on IT generation and propagation. Results reveal that the cyclonic circulation can enhance the semi-diurnal and diurnal IT energy conversion in the Sulu Archipelago by approximately 17% and 77%, respectively, compared to those without circulation for semi-diurnal ITs (4.36 GW) and diurnal ITs (2.76 GW). This different increase portion between semi-diurnal and diurnal ITs is attributed to different influences of circulation on the positive and negative conversion rates for semi-diurnal and diurnal ITs. Energy budget analysis indicates that circulation increases the proportion of dissipation near source regions from 88% (90%) to 94% (93%) and reduces the proportion of energy flux radiation from 12% (10%) to 6% (7%) for semi-diurnal (diurnal) ITs. The ray-tracing results indicate that the cyclonic circulation induces significant westward refraction of IT rays by modulating IT speeds in counter-current/co-current regions. Further sensitive experiments reveal that circulation-induced stratification weakens the refraction, whereas the background currents strengthen it, with the latter dominating. These findings advance our understanding of the IT behaviors in the Sulu Sea under the modulation of circulation.

Spatio-Temporal Prediction of Surface Remote Sensing Data in Equatorial Pacific Ocean Based on Multi-Element Fusion Network - Tianliang Xu; Zhiquan Zhou; Chenxu Wang; Yingchun Li; Tian Rong (Journal of Marine Science and Engineering, 2025)
A basic feature of El Niño is an abnormal increase in the surface temperature of the equatorial Pacific Ocean, which can throw ocean–atmosphere interactions out of balance, resulting in heavy rainfall and severe storms. This climate anomaly causes different levels of impacts worldwide, such as causing droughts in some regions and excessive rainfall in others. Therefore, it is important to determine the formation of El Niño by predicting the changes in the sea surface temperature (SST) in the equatorial Pacific Ocean. In this paper, we propose a multi-element fusion network model based on convolutional long short-term memory (ConvLSTM) and an attention mechanism to predict the SST and analyze the effects of different elemental inputs on the model’s prediction performance using the prediction results. The experimental results show that using the sea surface wind (SSW) and sea level anomaly (SLA) as multi-element inputs to predict the SST overcame the shortcomings of the single-element forecast model, and the prediction accuracy of the two-element fusion model was better than that of the three-element fusion model. In the two-element fusion model, using the SSW as an input predicted the SST with a lower prediction error than using the SLA as an input and had better prediction performance compared with other benchmark models. For predicting the SST in the equatorial Pacific Ocean, the monthly average root mean square error (RMSE) was mainly concentrated in the range of 0.4–0.8 °C, and the regions with a larger error dispersion were located in the spatial range of 5° S–5° N and 130° W–90° W, and the monthly average regional RMSE was mainly concentrated in the range of 0.5–1 °C. Finally, we also validated the prediction performance of the model for the SST in El Niño and La Niña years, and the prediction results of the model in La Niña years were better than those in El Niño years.

Spatial Sequential Matching Enhanced Underwater Single-Photon Lidar Imaging Algorithm - Qiguang Zhu; Yuhang Wang; Chenxu Wang; Tian Rong; Buxiao Li; Xiaotian Ying (Journal of Marine Science and Engineering, 2024)
Traditional LiDAR and air-medium-based single-photon LiDAR struggle to perform effectively in high-scattering environments. The laser beams are subject to severe medium absorption and multiple scattering phenomena in such conditions, greatly limiting the maximum operational range and imaging quality of the system. The high sensitivity and high temporal resolution of single-photon LiDAR enable high-resolution depth information acquisition under limited illumination power, making it highly suitable for operation in environments with extremely poor visibility. In this study, we focus on the data distribution characteristics of active single-photon LiDAR operating underwater, without relying on time-consuming deep learning frameworks. By leveraging the differences in time-domain distribution between noise and echo signals, as well as the hidden spatial information among echo signals from different pixels, we rapidly obtain imaging results across various distances and attenuation coefficients. We have experimentally verified that the proposed spatial sequential matching enhanced (SSME) algorithm can effectively enhance the reconstruction quality of reflection intensity maps and depth maps in strong scattering underwater environments. Through additional experiments, we demonstrated the algorithm’s reconstruction effect on different geometric shapes and the system’s resolution at different distances. This rapidly implementable reconstruction algorithm provides a convenient way for researchers to preview data during underwater single-photon LiDAR studies.

Sequential Two-Mode Fusion Underwater Single-Photon Lidar Imaging Algorithm - Tian Rong; Yuhang Wang; Qiguang Zhu; Chenxu Wang; Yanchao Zhang; Jianfeng Li; Zhiquan Zhou; Qinghua Luo (Journal of Marine Science and Engineering, 2024)
Aiming at the demand for long-range and high-resolution imaging detection of small targets such as submerged submarine markers in shallow coastal waters, research on single-photon lidar imaging technology is carried out. This paper reports the sequential two-mode fusion imaging algorithm, which has a strong information extraction capability and can reconstruct scene target depth and reflection intensity images from complex signal photon counts. The algorithm consists of four steps: data preprocessing, extremely large group value estimation, noise sieving, and total variation smoothing constraints to image the target with high quality. Simulation and test results show that the imaging performance and imaging characteristics of the method are better than the current high-performance first-photon group imaging algorithm, indicating that the method has a great advantage in sparse photon counting imaging, and the method proposed in this paper constructs a clear depth and reflectance intensity image of the target scene, even in the 50,828 Lux ambient strong light and strong interference, the 0.1 Lux low-light environment, or the underwater high-attenuation environment.

Long-Term Spatiotemporal Characteristics of Ulva prolifera Green Tide and Effects of Environmental Drivers on Its Monitoring by Satellites: A Case Study in the Yellow Sea, China, from 2008 to 2023 - Yating Zhan; Zhongfeng Qiu; Yujun Wang; Yiming Su; Yin Li; Yanmei Cui; Shuai Qu; Peng Wang; Xin Rong (Journal of Marine Science and Engineering, 2024)
Ulva prolifera (U. prolifera) green tide outbreaks have occurred in the Yellow Sea of China for many years, causing serious losses to marine ecology and the marine economy. The monitoring and tracking of U. prolifera green tide is a crucial aspect of marine ecological disaster prevention and control management. This paper aims to investigate the spatiotemporal distribution changes in U. prolifera green tide in the Yellow Sea throughout its life cycle. A survey of the Yellow Sea from 2008 to 2023 was conducted using multi-source remote sensing images. Long-term monitoring and analysis of U. prolifera revealed the evolution process of the green tide, including the early development, outbreak, decline, and extinction stages, considering time, space, and frequency of occurrence. Additionally, this study examined peak coverage change patterns over the past 16 years and analyzed the influence of environmental factors such as sea surface temperature and sea surface wind field on the development of U. prolifera. The research results serve as a valuable reference for the monitoring, early warning, and scientific prevention and control of U. prolifera green tide in the Yellow Sea, as well as other similar marine disaster areas.

Short-Term Prediction of Global Sea Surface Temperature Using Deep Learning Networks - Tianliang Xu; Zhiquan Zhou; Yingchun Li; Chenxu Wang; Ying Liu; Tian Rong (Journal of Marine Science and Engineering, 2023)
The trend of global Sea Surface Temperature (SST) has attracted widespread attention in several ocean-related fields such as global warming, marine environmental protection and marine biodiversity. Sea surface temperature is influenced by climate change; with the accumulation of data from ocean remote sensing observations year by year, many scholars have started to use deep learning methods for SST prediction. In this paper, we use a dynamic region partitioning approach to process ocean big data and design a framework applied to a global SST short-term prediction system. On the architecture of a Long Short-Term Memory (LSTM) network, two deep learning multi-region SST prediction models are proposed, which extract temporal and spatial information of SST by encoding, using feature transformation and decoding to predict future multi-step states. The models are tested using OISST data and the model performance is evaluated by different metrics. The proposed MR-EDLSTM model and MR-EDConvLSTM model obtained the best results for short-term prediction, with RMSE ranging from 0.2712 °C to 0.6487 °C and prediction accuracies ranging from 97.60% to 98.81% for ten consecutive days of prediction. The results show that the proposed MR-EDLSTM model has better prediction performance in coastal areas, while the MR-EDConvLSTM model performs better in predicting the sea area near the equator. In addition, the proposed deep learning model has a smaller RMSE compared to the forecasting system based on the ocean model, indicating that the deep learning method has certain advantages in predicting global SST.

Use of Ensemble Model for Modeling the Larval Fish Habitats of Different Ecological Guilds in the Yangtze Estuary - Rong Wan; Pengbo Song; Zengguang Li; Xiangyu Long; Dong Wang; Lu Zhai (Fishes, 2023)
Estuaries provide nursery habitats for a variety of fish species of different ecological guilds and have complicated environmental conditions. In this study, we applied ensemble species distribution models (SDMs) to four abundant and different ecological guild larval fish species (Hemiculter bleekeri (freshwater guild), Pseudolaubuca sinensis (freshwater guild), Coilia mystus (brackish water guild), and Engraulis japonicus (marine guild)) to explore their suitable habitats and the fish–habitat relationships in the Yangtze Estuary. The results showed that random forest (RF) demonstrated the best performance in all single algorithms and the surface range envelop (SRE) model was scarcely accurate. The ensemble SDMs demonstrated a superior predictive ability compared with any single algorithm, with the true skill statistic (TSS) and the area under the receiver operating characteristic curve (AUC) scores being above 0.899 and 0.641, respectively. Binary presence–absence maps showed the different spatial distribution patterns of the four species. We primarily found the freshwater species (P. sinensis and H. bleekeri) present in the South Branch (west of 122° E), whereas we found the marine species (E. japonicus) anywhere except inside the South Branch (west of 121.8° E). The area for P. sinensis (1615.93 km2) was relatively larger than that for H. bleekeri (1136.87 km2). We predicted that the brackish water species (C. mystus) would most likely be present inside the North Branch (west of 122° E), Eastern Chongming, and outside the South Branch (east of 121.8° E). Salinity, as a key environmental variable, contributed to the spatial variability. A low salinity (sea surface salinity (SSS)

Preliminary Investigation on Marine Radar Oil Spill Monitoring Method Using YOLO Model - Bo Li; Jin Xu; Xinxiang Pan; Rong Chen; Long Ma; Jianchuan Yin; Zhiqiang Liao; Lilin Chu; Zhiqiang Zhao; Jingjing Lian; Haixia Wang (Journal of Marine Science and Engineering, 2023)
Due to the recent rapid growth of ocean oil development and transportation, the offshore oil spill risk accident probability has increased unevenly. The marine oil spill poses a great threat to the development of coastal cities. Therefore, effective and reliable technologies must be used to monitor oil spills to minimize disaster losses. Based on YOLO deep learning network, an automatic oil spill detection method was proposed. The experimental data preprocessing operations include noise reduction, gray adjustment, and local contrast enhancement. Then, real and synthetically generated marine radar oil spill images were used to make slice samples for training the model in the YOLOv5 network. The detection model can identify the effective oil spill monitoring region. Finally, an adaptive threshold was applied to extract the oil slicks in the effective oil spill monitoring regions. The YOLOv5 detection model generated had the advantage of high efficiency compared with existing methods. The offshore oil spill detection method proposed can support real-time and effective data for routine patrol inspection and accident emergency response.

Larval Fish Spatiotemporal Dynamics of Different Ecological Guilds in Yangtze Estuary - Rong Wan; Pengbo Song; Zengguang Li; Xiangyu Long; Dong Wang; Lu Zhai (Journal of Marine Science and Engineering, 2023)
Estuaries, as important fish nursery habitats, usually include a variety of larval fishes of different ecological guilds and exhibit complicated changing environmental conditions. We carried out a survey to examine the spatiotemporal dynamics of different ecological guild larval fishes and their relationships with environmental factors in the springs and summers from 2018 to 2020 in the Yangtze Estuary (China). The aims of the study were to provide detailed information on the characteristics of the larval fish assemblage and to explore the spatiotemporal variation in different ecological guild species and the effects of environmental variables on assemblage structure. More than 140,000 fish larvae from 26 families and 99 species were gathered during the six cruises, with the spring being the most prolific. The assemblage was dominated by a few species and was divided into three ecological guilds. Engraulidae was the most abundant family, followed by Cyprinidae and Gobiidae. Hemiculter bleekeri (freshwater), Pseudolaubuca sinensis (freshwater), Coilia mystus (brackish water), and Engraulis japonicas (marine) were the predominant species. Seasonal variations in larval fish assemblage structure were closely influenced by temperature, and the fluctuation in salinity mainly determined the spatial distribution of the larval fish community. Freshwater flows also played an important role in shaping the larval fish assemblage structure and dynamics. The conclusions improve the understanding of the ecological dynamics of larval fish assemblages in environmentally heterogeneous areas and may be applicable to other estuary ecosystems.

Marine Radar Oil Spill Extraction Based on Texture Features and BP Neural Network - Rong Chen; Baozhu Jia; Long Ma; Jin Xu; Bo Li; Haixia Wang (Journal of Marine Science and Engineering, 2022)
Marine oil spills are one of the major threats to marine ecological safety, and the rapid identification of oil films is of great significance to the emergency response. Marine radar can provide data for marine oil spill detection; however, to date, it has not been commonly reported. Traditional marine radar oil spill research is mostly based on grayscale segmentation, and its accuracy depends entirely on the selection of the threshold. With the development of algorithm technology, marine radar oil spill extraction has gradually come to focus on artificial intelligence, and the study of oil spills based on machine learning has begun to develop. Based on X-band marine radar images collected from the Dalian 716 incident, this study used image texture features, the BP neural network classifier, and threshold segmentation for oil spill extraction. Firstly, the original image was pre-processed, to eliminate co-channel interference noise. Secondly, texture features were extracted and analyzed by the gray-level co-occurrence matrix (GLCM) and principal component analysis (PCA); then, the BP neural work was used to obtain the effective wave region. Finally, threshold segmentation was performed, to extract the marine oil slicks. The constructed BP neural network could achieve 93.75% classification accuracy, with the oil film remaining intact and the segmentation range being small; the extraction results were almost free of false positive targets, and the actual area of the oil film was calculated to be 42,629.12 m2. The method proposed in this paper can provide a reference for real-time monitoring of oil spill incidents.