Search Results (183)

As an important component of waters constituent that affects ocean color and the underwater ecological environment, the accurate assessment of Chromophoric Dissolved Organic Matter (CDOM) is crucial for observing the continuous changes in the marine ecosystem. However, remote sensing estimation of CDOM remains challenging for both coastal and oceanic waters due to its weak optical signals and complex optical conditions. Therefore, the development of efficient, practical, and robust models for estimating the CDOM absorption coefficient in both coastal and oceanic waters remains an active research focus. This study presents a novel algorithm (denoted as DQAAG) that incorporates ultraviolet bands into the inversion model. The design leverages the distinct spectral absorption characteristics of phytoplankton versus detrital particles in the ultraviolet (UV) region, enabling improved discrimination of water color parameters. Furthermore, the algorithm replaces empirical formulas commonly used in semi-analytical approaches with an artificial intelligence model (deep learning) to achieve enhanced inversion accuracy. Using IOCCG hyperspectral simulation data and NOMAD dataset to evaluates Shanmugam (2011) (S2011), Aurin et al. (2018) (A2018), Zhu et al. (2011) (QAA-CDOM), DQAAG, the results indicate that the a_g(443) derived from the DQAAG exhibit good agreement with the validation data, with root mean square deviation (RMSD) < 0.3 m⁻¹, mean absolute relative difference (MARD) < 0.30, mean bias (bias) < 0.028 m⁻¹, coefficient of determination (R²) > 0.78. The DQAAG algorithm was applied to SeaWiFS remote sensing data, and validation was performed through match-up analysis with the NOMAD dataset. The results show the RMSD = 0.14 m⁻¹, MARD = 0.39, and R² = 0.62. Through a sensitivity analysis of the algorithm, the study reveals that R_rs(670) and R_rs(380) exhibit more significant characteristics. These results demonstrate that UV bands play a crucial role in enhancing the retrieval accuracy of ocean color parameters. In addition, DQAAG, which integrates semi-analytical algorithms with artificial intelligence, presents an encouraging approach for processing ocean color imagery to retrieve a_g(443). Full article

Deep-sea hydrothermal vent fauna is often regarded as highly endemic, although exceptions have been reported. We examined genetic connectivity across broad spatial scales within the alvinocaridid genus Rimicaris, which has undergone substantial adaptive radiation worldwide. We analyzed six Rimicaris species using three genetic markers, cytochrome c oxidase subunit I (COI), 16S ribosomal rRNA gene (16S), and histone h3 (H3), and complete mitogenomes, employing newly generated sequences combined with publicly available sequence data. A genetic tree and haplotype networks were constructed, and divergence analyses were performed. Three clades of paired Rimicaris species were identified, each made up of taxa from different oceanic regions but showing relatively low COI divergence (0.35–1.90%). In Clade I, Rimicaris chacei and Rimicaris hybisae are morphologically similar and exhibit bidirectional gene flow, implying a dispersal route between the Mid-Atlantic Ridge (MAR) and the Mid-Cayman Spreading Center (MCSC). In Clade II, Rimicaris exoculata and Rimicaris kairei are morphologically, genetically, and ecologically distinct, reflecting restricted connectivity between the MAR and the Carlsberg Ridge (CR)–Central Indian Ridge (CIR). In Clade III, Rimicaris variabilis and Rimicaris cf. variabilis differ in nutritional strategies, showing a unidirectional dispersal route from the CIR to the southwestern Pacific (SWP), but morphological data to distinguish them are currently lacking. Some Rimicaris lineages maintain connectivity across distinct oceanic regions while others still form unique regional populations. This finding highlights the need for conservation strategies that incorporate both global-scale connectivity and regional endemism, rather than treating individual vent ecosystems as a single homogeneous management unit. Full article

Accurate monitoring of chlorophyll-a (Chl-a) is essential for managing coastal aquaculture, as Chl-a indicates phytoplankton biomass and water quality. This study developed a hybrid deep learning model integrating convolutional neural networks (CNN), bidirectional long short-term memory (BiLSTM), and an attention mechanism (Attention) to classify Chl-a using hourly, water quality datasets collected from the GOT001 station in Si Racha Bay, Eastern Gulf of Thailand (2020–2024). A random forest (RF) identified sea surface temperature (SEATEMP), dew point temperature (DEWPOINT), and turbidity (TURB) as the most influential variables, accounting for over 90% of the accuracy. Chl-a concentrations were categorized into ecological groups (low, medium, and high) using quantile-based binning and K-means clustering to support operational classification. Model performance comparison showed that the CNN–BiLSTM model achieved the highest classification accuracy (81.3%), outperforming the CNN–LSTM model (59.7%). However, the addition of the Attention did not enhance predictive performance, likely due to the limited number of key predictive variables and their already high explanatory power. This study highlights the potential of CNN–BiLSTM as a near-real-time classification tool for Chl-a levels in highly variable coastal ecosystems, supporting aquaculture management, early warning of algal blooms or red tides, and water quality risk assessment in the Gulf of Thailand and comparable coastal regions. Full article

The dynamics of chemical components in sediment porewater are crucial for marine ecological research, resource assessment, and environmental monitoring. A scientific sampling strategy is key to obtaining high-quality porewater. This study aims to explore the effects of circular sampling hole size and layout on sampling effectiveness to optimize the sampling strategy. First, this study analyzed the flow field from time and spatial flow. Then, a simulation model was built using COMSOL Multiphysics 6.2 to simulate changes in the flow field, Darcy velocity, and effective sampling depth under different conditions. The results showed that the sampling holes finished sampling earlier due to being close to the open boundary; small sample hole sizes could suppress this time lag but reduce efficiency, and the effective sampling range increased exponentially with volume. When R = 5 mm, D = 150 mm, and V = 10 mL, interference between adjacent layers was effectively avoided, balancing timeliness and sample representativeness. Laboratory experiments and sea trials validated the effectiveness of the sampling strategy. This study provides theoretical and practical guidance for deep-sea porewater sampling technology, supporting marine scientific research. Full article

Marine plastic pollution represents a critical ecological challenge, exerting long-lasting impacts on ecosystems, biodiversity, and human well-being. This study introduces the DEEP-PLAST project, an integrated AI-based framework designed for the detection and trajectory prediction of floating marine plastic waste using open-access Sentinel-2 satellite imagery and environmental models of ocean currents and wind. The DEEP-PLAST methodology integrates object detection (YOLOv5 on UAV data), semantic segmentation (U-Net/U-Net++ on Sentinel-2), and drift simulation using Copernicus and NOAA datasets. U-Net++ achieved the best performance (F1 = 0.84, false positive rate 5.2%), outperforming other models. Detected debris locations were linked to Lagrangian drift models to identify accumulation zones in the Black Sea, supporting targeted cleanup efforts. While promising, drift validation remains qualitative due to limited ground truth, to be addressed in future work with in situ and NGO data. This approach supports EU Mission Ocean, the Marine Strategy Framework Directive, and UN SDGs, demonstrating the potential of AI and remote sensing for marine protection. Future efforts will expand datasets, apply the platform to other seas, and launch a web tool for NGOs and policymakers. Full article

Ice krill (Euphausia crystallorophias) play a key role in the Antarctic coastal ecosystem, yet its spatial growth variability remains poorly understood. This study examined 5298 krill individuals from 52 stations across the Amundsen Sea, transitional waters, and the Ross Sea, collected between 2020 and 2024. Length–weight relationships (LWR) were constructed to derive the condition factor a and the allometric growth exponent b, followed by regional comparisons and environmental response analyses using boxplots, redundancy analysis (RDA), and generalized additive models (GAM). Boxplots revealed that a was significantly higher in the Amundsen Sea and transitional zone than in the Ross Sea, while b was highest and most variable in the Amundsen Sea. RDA indicated that a was primarily associated with depth, latitude, mean temperature, and mean salinity, whereas b was influenced by sea surface temperature, chlorophyll-a, sea ice concentration, and longitude. GAM further showed nonlinear responses of a to mean temperature, mean salinity, and depth, with peaks near −0.5 °C, 34.2 PSU, and 3500 m, respectively. These results suggest that krill in deep, cold, and less-productive transitional zone allocate more energy to body condition (high value a), while those in warmer, moderately productive regions like the Amundsen Sea invest more in structural growth (high value b). This study provides new insights into the environmentally driven growth strategies of ice krill and contributes to understanding its ecological adaptability under changing climatic and oceanographic conditions. Full article

Shallow gas drilling in polar seas poses severe geological hazards, particularly unexpected eruptions that threaten platform safety and the marine environment. Accurate prediction of shallow gas occurrence and eruption risk is therefore essential for safe deep-water operations. However, previous studies seldom considered the coupled effects of gas pressure and pore-structure evolution on acoustic wave velocity, leading to deviations in hazard assessment. In this study, laboratory experiments and numerical simulations were conducted to clarify these mechanisms. Results revealed a non-monotonic relationship between porosity and P-wave velocity in shallow gas-bearing sediments: P-wave velocity decreases with increasing porosity at low porosity levels but increases beyond a critical threshold. This is attributed to changes in particle interactions and cementation that enhance the shear modulus. The inflection porosity for shallow gas (78%) highlights the diagnostic role of pore-structure evolution in predicting shallow gas distribution. A mathematical correlation between P-wave velocity and formation pressure was further established, and MP-PIC simulations showed that higher pressure coefficients significantly accelerate eruption rates, with a 0.1 increase in the pressure coefficient raising the instantaneous eruption velocity by 5.27 m³/min. Based on these findings, a quantitative evaluation method was developed to assess shallow gas hazard risk, providing engineering guidance for site selection and real-time risk prediction, and contributing to safer offshore drilling and ecological protection in polar environments. Full article

Multi-object tracking (MOT) is a critical task in computer vision, with widespread applications in intelligent surveillance, behavior analysis, autonomous navigation, and marine ecological monitoring. In particular, accurate tracking of underwater fish plays a significant role in scientific fishery management, biodiversity assessment, and behavioral analysis of marine species. However, MOT remains particularly challenging due to low visibility, frequent occlusions, and the highly non-linear, burst-like motion of fish. To address these challenges, this paper proposes an improved tracking framework that integrates Interacting Multiple Model Kalman Filtering (IMM-KF) into DeepSORT, forming a self-adaptive multi-object tracking algorithm tailored for underwater fish tracking. First, a lightweight YOLOv8n (You Only Look Once v8 nano) detector is employed for target localization, chosen for its balance between detection accuracy and real-time efficiency in resource-constrained underwater scenarios. The tracking stage incorporates two complementary motion models—Constant Velocity (CV) for regular cruising and Constant Acceleration (CA) for rapid burst swimming. The IMM mechanism dynamically evaluates the posterior probability of each model given the observations, adaptively selecting and fusing predictions to maintain both responsiveness and stability. The proposed method is evaluated on a real-world underwater fish dataset collected from the East China Sea, comprising 19 species of marine fish annotated in YOLO format. Experimental results show that the IMM-DeepSORT framework outperforms the original DeepSORT in terms of MOTA, MOTP, and IDF1. In particular, it significantly reduces false matches and improves tracking continuity, demonstrating the method’s effectiveness and reliability in complex underwater multi-target tracking scenarios. Full article

Toxin-antitoxin (TA) systems, ubiquitous in bacterial and archaeal genomes, play pivotal roles in responding to environmental stresses, forming biofilms, defending against phages, and influencing pathogen virulence. The marine environment harbors Earth’s most diverse and abundant microbial communities, where microorganisms have evolved unique genetic adaptations and specialized metabolic processes to thrive amid distinct environmental challenges. Research on the presence and function of TA systems in marine bacteria lags significantly behind that in model bacteria and pathogens. Here, we explored the diversity of the TA system in marine bacteria, including species from the Global Ocean Microbiome Catalogue (GOMC) and the Mariana Trench Environment and Ecology Research (MEER) databases. Our findings revealed that types I to VII (featuring protein toxins) of eight types of TA systems are prevalent in these microorganisms, with unidentified TA combinations diverging from previously characterized systems. Interestingly, some toxins or antitoxins lack canonical counterparts, indicating evolutionary divergence. Additionally, previously uncharacterized potential TA systems have been identified in extremophilic bacteria from the deep-sea Mariana Trench. These results highlight the adaptive importance of marine TA systems, which are likely operating through unconventional mechanisms. Full article

Understanding the feeding mechanisms and interspecific coexistence of sharks is crucial for effective conservation. This study conducted stable isotope analysis on muscle and liver samples from 449 individuals of eight common bycatch shark species collected via bottom trawling in the northern South China Sea (NSCS). Results revealed significant differences in δ¹³C and δ¹⁵N values among species and tissue types. Scoliodon laticaudus exhibited the highest trophic position (TP_muscle = 4.60 ± 0.33; TP_liver = 4.53 ± 0.29), while Apristurus platyrhynchus had the lowest (TP_muscle = 2.97 ± 0.44; TP_liver = 2.75 ± 0.53). Muscle and liver isotopic signals were consistent, but δ¹³C differences indicated distinct carbon sources, with Carcharhinus sorrah linked to deep-sea organic matter and S. laticaudus to coastal inputs. Significant correlations between δ¹³C/δ¹⁵N and body length in A. platyrhynchus and Cephaloscyllium fasciatum suggest ontogenetic shifts in diet and habitat toward deeper waters. Trophic niche analysis using corrected standard ellipse area (SEAc) showed Halaelurus burgeri with the widest trophic niche (SEAc > 1.7‰²), reflecting a broad diet, while C. fasciatum had the narrowest (SEAc < 0.3‰²), indicating specialized feeding. Additionally, H. burgeri and C. sarawakensis exhibited significant niche differentiation, reducing interspecific competition, whereas C. fasciatum and Squalus megalops showed high niche overlap, suggesting intense resource competition. The narrower liver niche of C. sarawakensis may reflect recent habitat constriction due to bottom trawling. This study elucidates the feeding ecology and habitat resource utilization of NSCS sharks, providing a scientific basis for effective conservation strategies for shark populations in the region. Full article

Macellicephaloides Uschakov, 1955 (Annelida: Polynoidae) is a genus of deep-sea polychaetes characterized by a specialized pharynx bearing two pairs of jaws (with the dorsal pair fused) and three pairs of lateral papillae, the middle pair of which is greatly elongated, and remarkable adaptability to diverse deep-sea habitats. Most species in this genus inhabit abyssal depths (>7200 m), with high diversity in western Pacific trenches, while a few occur in relatively shallow habitats such as deep-sea seamounts and hydrothermal vents. This paper presents a new species, Macellicephaloides lingshuiensis sp. nov., found in deep-sea cold seeps in the South China Sea, representing the shallowest distribution record for the genus to date and the first record from cold seep environments. The classification and phylogeny of Macellicephaloides and related genera have long been the subject of debate. A previous study suggested that Macellicephaloides is nested within the Macellicephala clade, but our analyses—based on 13 mitochondrial protein-coding genes, 12S, 16S, 18S, 28S rRNA, and ITS1-ITS2 sequences—tentatively indicate that these two genera form independent evolutionary clades. Additionally, our phylogeny indicates a close evolutionary relationship between deep-sea Macellicephaloides and cave-dwelling polynoids (e.g., Gesiella), highlighting ecological connections between deep-sea and cave habitats. These conclusions are supported by morphological comparisons and genetic distance analyses. Although the subfamily Macellicephalinae is recovered as a monophyletic group, intergeneric phylogenetic relationships within it remain unresolved, highlighting the need for additional data from more species and genera. We amend the generic diagnosis of Macellicephaloides and provide an identification key to all valid species in the genus. This study clarifies the taxonomy and phylogeny of Macellicephaloides and related taxa, emphasizing the importance of continued sampling in understudied deep-sea habitats to enhance our understanding of their biodiversity. Full article

Marine heatwaves (MHWs) have been shown to exert a substantial influence on marine ecosystems and associated industries. Consequently, the development of accurate prediction models is imperative for mitigating ecological risks. This study concentrates on the East China Sea, employing sea surface temperature (SST) data from the OISST v2.1 dataset, which spans from 1982 to 2024, for the purpose of examining the spatial and temporal characteristics of six significant MHW indicators. The results reveal a clear annual increase in all six indicators. This study employed the deep learning-based SegRNN_ST model to forecast future MHW trends. It integrated a spatiotemporal attention mechanism and was optimized using mean absolute error (MAE) and mean squared error (MSE) as loss functions. And the coefficient of determination (R²) was utilized as a performance metric for predicting MHWs in the East China Sea. The findings indicate that the improved SegRNN_ST model increased the R² by 8% compared to the original model, with MSE and MAE showing reductions of 20% and 15%, respectively. This study demonstrates fewer errors than SegRNN and enhanced accuracy in predicting MHWs. This study introduces a new method for predicting and providing early warnings of MHWs, improving the accuracy of forecasts for extreme marine weather events. Full article

Fungi of the genus Parengyodontium (Ascomycota, Hypocreales, Cordycipitaceae) are emerging as promising sources of secondary metabolites with significant biotechnological potential. While traditionally understudied, species such as Parengyodontium album, Parengyodontium torokii and Parengyodontium americanum have been isolated from diverse and sometimes extreme environments—including deep-sea sediments, mangroves, and NASA clean rooms—suggesting remarkable ecological adaptability. This review presents a comprehensive synthesis of current knowledge on the chemical diversity, biological activities, and potential industrial applications of secondary metabolites produced by fungi belonging to the genus. A wide variety of compounds have been identified, including polyketides (e.g., engyodontiumones, alternaphenol B2), terpenoids (e.g., cytochalasin K), alkaloids, and torrubielline derivatives. These metabolites exhibit cytotoxic, antibacterial, and antifouling properties, with promising anticancer and antimicrobial activities. In addition, recent evidence points to the genus’s role in bioremediation, particularly through the degradation of polyethylene by P. album. Despite the advances highlighted here, challenges remain in scaling production, elucidating biosynthetic pathways, and confirming in vivo efficacy. This review underscores the value of integrating chemical, genomic, and metabolomic approaches to fully unlock the biotechnological potential of Parengyodontium species. Additionally, we broaden the perspective by comparing trends in secondary metabolites among Cordycipitaceae, highlighting lifestyle-related chemical compounds that serve as a reference for the Parengyodontium profile. Full article

Deep-sea polymetallic nodule provinces harbor rich benthic microbial communities that underpin biogeochemical cycles and sustain abyssal ecosystem functions. Recent studies have begun to map their abundance, diversity and community structure, emphasizing the role of environmental gradients and spatial heterogeneity. Yet the spatiotemporal dynamics and assembly mechanisms of these microbes remain largely unresolved. Mining-induced sediment plumes further complicate the picture: they modify microbial biomass, activity and composition, but the trajectories of community succession and the functional consequences of disturbance are still unclear. Thresholds used to gauge plume impacts also differ markedly among studies, hampering consistent risk assessments. In summary, a stark contrast exists between the limited in situ observational data, the widely varying impact thresholds reported across studies, and the pressing need for unified standards in environmental impact assessments for deep-sea mining. It recommends future work that integrates multi-omics, time-series in situ monitoring, cross-regional comparisons and standardized evaluation frameworks to refine microbial indicators and ecological thresholds for deep-sea mining impact assessments. Full article

It is widely recognized that benthic sediment plumes generated by deep-sea mining may pose significant potential risks to ecosystems, yet their dispersion behavior remains difficult to predict with accuracy. In this study, we combined laboratory experiments with three-dimensional numerical simulations using the Environmental Fluid Dynamics Code (EFDC) to investigate the dispersion of sediment plumes composed of particles of different sizes. Laboratory experiments were conducted with deep-sea clay samples from the western Pacific under varying conditions for plume dispersion. Experimental data were used to capture horizontal diffusion and vertical entrainment through a Gaussian plume model, and the results served for parameter calibration in large-scale plume simulations. The results show that ambient current velocity and discharge height are the primary factors regulating plume dispersion distance, particularly for fine particles, while discharge rate and sediment concentration mainly control plume duration and the extent of dispersion in the horizontal direction. Although the duration of a single-source release is short, continuous mining activities may sustain broad dispersion and result in thicker sediment deposits, thereby intensifying ecological risks. This study provides the first comprehensive numerical assessment of deep-sea mining plumes across a range of particle sizes with clay from the western Pacific. The findings establish a mechanistic framework for predicting plume behavior under different operational scenarios and contribute to defining threshold values for discharge-induced plumes based on scientific evidence. By integrating experimental, theoretical, and numerical approaches, this work offers quantitative thresholds that can inform environmentally responsible strategies for deep-sea resource exploitation. Full article