Search Results (535)

Public health suffers from noxious gas emitted by massive beached Sargassum algae. Net-barrages deployed in near-shore seas can contain Sargassum, provided they efficiently resist the additional hydrodynamic pressure induced by the catch. Nowadays, the design and installation of net-barrages are empiric. Structural breaks and anchor and mooring chain drifts can arise. We provide a mechanical model to evaluate stresses and loads on a structure made of fishing nets and buoy moorings. Hydrodynamic uncertainties occur through catches, fouling and sea current amplitudes appearing in lagoons or sheltered bays. This study presents a non-linear four-node finite-element model for continuous elastic membranes undergoing large displacements and small strains. The model relies on the Lagrangian linearly elastic membrane theory, employing the non-linear Green strain tensor and a non-updated hydrodynamic loading. We study forcings fixed a priori on a netting section of barrage that is 50 m long and 1 m high with double layer, e.g., two net-faces. We consider low and moderate current velocities, 0.05 and 0.35 m∙s⁻¹, while assuming specific vertical and horizontal catch pressures. A barrage installed in the reef lagoon at Le François on Martinique Island that is observable by satellite imagery could benefit of the computed net and mooring tensions. Full article

Based on previous studies and the ecological characteristics of Portunus trituberculatus, we hypothesized that climate change could substantially reshape its suitable habitat in Zhoushan fishing ground. Under present-day climate conditions (2010–2020), P. trituberculatus exhibits a distinct seasonal distribution pattern in this region. However, its potential spatial response to future climate change, and whether suitable habitat will remain available, remains poorly understood. To address this gap, we combined species occurrence records with environmental variables from the Bio-ORACLE v3.0 database, including benthic temperature, benthic salinity, benthic current velocity, primary productivity, bathymetry, topographic slope, and topographic aspect, to develop a maximum entropy (MaxEnt) model and predict the potential distribution of suitable habitat for P. trituberculatus under present-day conditions and future SSP1-2.6 and SSP2-4.5 scenarios for 2030–2040, 2040–2050, and 2090–2100. Model performance was high across all seasons, with area under the curve values exceeding 0.80. Primary productivity and benthic temperature were the dominant environmental predictors, highlighting the joint influence of trophic conditions and thermal constraints on habitat suitability. Future projections revealed pronounced seasonal reorganization of suitable habitat rather than a uniform range shift. Spring suitable habitat expanded consistently under both scenarios, with the magnitude of expansion increasing toward the end of the century and reaching 46.9% by 2100 under SSP2-4.5, likely because warming relaxed low-temperature limitation during the early seasonal transition. In contrast, suitable habitat in autumn and winter generally contracted. Autumn losses were moderate but persistent, ranging from 5.4% to 16.4%, whereas the strongest declines occurred in winter, particularly under SSP2-4.5, where habitat reductions exceeded 30% after mid-century. These contractions were likely associated with cumulative thermal stress and related environmental changes under continued warming. Summer responses were scenario-dependent, showing weak gains or net declines under SSP1-2.6 but substantial expansion under SSP2-4.5 after mid-century, reaching up to 23.6% by 2050, suggesting that habitat suitability in this season is shaped by interactions among thermal conditions, trophic support, and habitat characteristics. Overall, these findings reveal strong seasonal asymmetry in habitat responses to climate change and provide a scientific basis for seasonally adaptive management of P. trituberculatus resources in Zhoushan fishing ground. Full article

A 56-day feeding trial was conducted to evaluate the effects of varying dietary lipid and starch levels on growth performance, biochemical components, and hepatic glycolipid metabolism in hybrid grouper. Nine isonitrogenous diets were formulated to contain three levels of lipid (6%, 10%, or 14%) and starch (14%, 21%, or 28%) using a 3 × 3 factorial design. Juvenile fish (initial body weight: 19.06 ± 0.03 g) were randomly allocated to 27 floating net cages (25 fish per cage, three replicates per diet) in an indoor seawater recirculation system and hand-fed to apparent satiation twice daily. Two-way ANOVA was conducted to check treatment effects of dietary lipid and starch levels. No interaction effect between lipid and starch on growth and feed utilization was observed across all treatments; however, significant interactions between the two were observed for condition factor (CF), and some serum biochemical indicators and some hepatic glycolipid metabolic enzyme activities. Growth rate, specific growth rate, and feed efficiency (FE) exhibited a declining trend with increasing dietary lipid levels (p < 0.05). Conversely, hepatosomatic index (HSI), viscerosomatic index (VSI), condition factor, hepatic lipid and glycogen contents, muscle lipid content, serum triglyceride and high-density lipoprotein cholesterol contents, as well as hepatic carnitine palmitoyltransferase 1 (CPT-1) and lipoprotein lipase (LPL) activities, showed an increasing trend (p < 0.05). As lipid levels increased, serum total cholesterol (TC) and total protein (TP) contents dropped to a minimum at the intermediate lipid level (10%) and then rose, regardless of starch level. Hepatic fructose-1,6-bisphosphatase (FBP) activity increased significantly when lipid level rose from 6% to 10% (p < 0.05). With increasing dietary starch levels, HSI, VSI, hepatic and muscle glycogen contents, and serum low-density lipoprotein cholesterol content increased, while FE and serum TP content decreased (p < 0.05). Hepatic CPT-1, LPL, FBP, and pyruvate kinase activities were significantly enhanced when starch levels increased from 14% to 21% or 28% (p < 0.05). Serum aspartate aminotransferase activity was significantly higher in fish fed 14% lipid compared to those fed 6% or 10% lipid. These findings indicate that there is no interaction of dietary lipid and starch on growth and feed utilization, but high dietary lipid (14%) may enhance hepatic lipid oxidation while suppressing glycolysis, thereby limiting growth and promoting hepatic lipid deposition. The results provide a practical reference for optimizing dietary lipid and starch levels in cost-effective feed formulations for hybrid grouper. Full article

Silver pomfret (Pampus argenteus, family Stromateidae) is a highly valuable marine fish species with significant commercial demand; however, its aquaculture remains undeveloped due to limited knowledge of captive breeding and seed production. To our knowledge, this is the first successful report on the induction of maturation, natural spawning, and larval rearing of silver pomfret under captive conditions in Korea. Wild broodstock (33 individuals in 2020; 250 individuals in 2021) were collected from the southern coastal waters of Korea using set nets. In the first year, water temperature management alone successfully induced gonadal maturation, as evidenced by a significant increase in the gonadosomatic index (GSI) and the presence of vitellogenic oocytes (400–500 μm) in April. In the second year, natural spawning was observed on fifteen occasions from May to September 2022, yielding a total number of 157,050 eggs. Fertilized eggs were spherical, transparent, and pelagic, with diameters ranging from 1.29 to 1.37 mm. Hatched larvae (total length: 4.85 ± 0.22 mm) exhibited poor feeding responses to rotifers and high early mortality within two weeks post-hatching, with the maximum rearing period reaching 24 days post-hatching. These findings demonstrate that water temperature management alone is sufficient to induce maturation and natural spawning of silver pomfret, and highlight the critical need for optimizing larval feeds, improving broodstock nutritional management, and conducting endocrine profiling during reproduction to establish a complete aquaculture protocol for this species. Full article

Yangtze finless porpoise (Neophocaena asiaeorientalis) is the only extant cetacean species in the Yangtze River, a national first-class protected wild animal in China, and a key indicator reflecting the health status of the Yangtze River ecosystem. Environmental changes can induce adaptive trophic responses in animal communities; thus, clarifying the dynamic interactions between fish assemblages and dietary shifts in the Yangtze finless porpoise after the fishing moratorium is critically important. In this study, fishery resources in Poyang Lake were surveyed using triple-layer composite gill nets and cage traps, with samples collected and analyzed accordingly. A total of 81 fish species belonging to 8 orders and 16 families were recorded. Following the fishing ban, the abundance of small-bodied fish species (e.g., Hemiculter bleekeri, Carassius auratus) decreased, while the proportion of piscivorous species among the top ten dominant fish increased from 40% to 60%. DNA barcoding was used to analyze gastric contents of 10 dead porpoises found post-moratorium, and the results were compared with pre-ban data. Gastric content analysis identified 11 prey taxa, and comparative analysis demonstrated that prey selectivity was closely correlated with local prey availability. Concomitant with the structural reorganization of the fish community in Poyang Lake during the early stage of the fishing ban, the Yangtze finless porpoise exhibited adaptive dietary shifts toward benthic and small-bodied fish species, suggesting a potential linkage between these ecological responses. These findings provide direct implications for formulating targeted conservation strategies for this endangered cetacean and highlight the necessity of protecting small and benthic fish populations to ensure their long-term survival. Full article

Accurate identification of fly species composition and their responses to attractants is critical for risk assessment and targeted vector control. To evaluate the efficacy of different attractants in surveillance and their species-specific trapping biases, a standardized field study was conducted from June to September 2021 across seven representative cities in China’s major zoogeographical regions: Xining, Ürümqi, Yanji, Beijing, Chongqing, Kunming, and Sanya. Cage traps baited with either fish offal or sugar–vinegar solution were deployed, supplemented by hand-net collection. A total of 134 traps were set, yielding 2132 flies belonging to 21 species. Fish offal captured 1961 flies (91.9%), significantly more than the 101 flies (4.7%) caught with sugar–vinegar solution (χ² = 1582.3, p < 0.001). Lucilia sericata was the dominant species (885 individuals, 41.51%), followed by L. cuprina (178, 8.35%), Sarcophaga portschinskyi (127, 5.96%), and Sarcophaga africa (100, 4.70%). High-risk taxa (Calliphoridae and Sarcophagidae) were almost exclusively attracted to fish offal. Our findings demonstrate that protein-based baits, such as fish offal, are substantially more effective than traditional sugar–vinegar solutions for capturing epidemiologically relevant fly species across diverse ecological zones in China. We recommend prioritizing proteinaceous attractants in national fly surveillance programs and advocate for routine species-level identification to enable risk-informed vector monitoring. Full article

Oyster reef restoration projects have been developed to provide habitat for fish and crustaceans. Some novel restoration structures employ greater complexity in attempts to better restore oyster reef habitat along degraded shorelines. The Pervious Oyster Shell Habitat (POSH) was created with greater structural complexity and strength to enhance oyster reef habitat for fish and crustaceans in energetic systems. The purpose of this study was to assess the POSH’s short-term ability to provide oyster reef habitat by measuring utilization of the POSH by fish and decapod crustaceans. Nekton abundances, diversity indices, and community similarity were compared between POSH structures, Reef Innovations’ “Oyster Ball”, and a natural oyster reef control. Artificial reef modules were sampled using 2 m² bottomless lift nets, over one year, along two energetic shorelines in northeast Florida. Fish abundances were low and variable among treatments, with no significant differences detected. Crustacean abundances were greater on the POSH than the Oyster Ball, aside from winter at one site, with significant differences detected for all but two measurements. Nekton community analyses were similar among all treatments and sites. The POSH’s design provided more interstitial space for utilization by common benthic crustaceans. Stakeholders attempting to restore degraded shorelines should consider employing the method. Full article

In disease control and precision management in aquaculture, rapid and accurate identification of common fish diseases is pivotal to mitigating economic losses and ensuring aquaculture profitability. However, fish diseases are characterized by subtle symptoms, polymorphic lesions, and high susceptibility to environmental perturbations such as water turbidity and illumination fluctuations. Existing detection models generally suffer from inadequate lightweight design, poor fine-grained lesion feature extraction, and deficient adaptability to class imbalance, failing to meet the stringent requirements of precise diagnosis in real-world aquaculture scenarios. To address these challenges, this study proposes FDR-Net: a fine-grained lesion detection model for tilapia via multi-scale feature enhancement and spatial attention fusion. Using image data of Nile tilapia (Oreochromis niloticus) covering 6 common diseases and healthy individuals (from the NTD-1 dataset), the model incorporates symmetry-aware design logic, leveraging the morphological and textural symmetry of healthy tilapia tissues to capture lesion-induced symmetry-breaking features, thereby improving fine-grained lesion detection accuracy. Through depth-width scaling coefficients, FDR-Net achieves lightweight optimization while integrating three core modules and a task-specific loss function for full-chain optimization: specifically, a Micro-lesion Feature Enhancement Module (MLFEM) is embedded in key feature layers of the backbone network to accurately extract edge and texture features of incipient fine-grained lesions via multi-scale frequency decomposition and residual fusion; subsequently, a Lightweight Multi-scale Position Attention Module (MS_PSA) and a Single-modal Intra-feature Contrastive Fusion Module (SMICFM) are collaboratively deployed—the former focusing on spatial localization of lesion features, and the latter enhancing lesion-background discriminability through channel-spatial feature recalibration and contrastive fusion; finally, a Class-Aware Weighted Hybrid Loss (CAWHL) function is combined with customized small-target anchor boxes to alleviate class imbalance and further improve localization and classification accuracy of fine-grained lesions. Empirical evaluations on the NTD-1 dataset demonstrate that compared with mainstream state-of-the-art baseline models, FDR-Net achieves a peak recognition accuracy of 90.1% with substantially enhanced mAP_50-95 performance. Retaining lightweight characteristics, it exhibits superior performance in identifying incipient fine-grained lesions and strong adaptability to simulated complex aquaculture scenarios. Collectively, this study provides an efficient technical backbone for the rapid and precise detection of tilapia fine-grained lesions, offering a potential solution for precise disease management in tilapia farming. Full article

Parasitic diseases pose significant challenges to the success of mariculture. The health management strategies used in sea-cage aquaculture are not completely effective at preventing parasites from entering through interactions with wild fish. This study monitored the health status of greater amberjack (Seriola dumerili, Risso 1810) over a two-month period following their introduction to the farm in the Middle Eastern Adriatic Sea in July 2019. Zeuxapta seriolae was detected on the gills after 722 degree days (DD), with a 28% prevalence, a mean abundance of 1.00 parasite per fish (four examined arches), and a mean intensity of 3.57 parasites per infected fish. From 985 DD, prevalence was 100%; the parasite burden continued to rise, and by the final sampling at 1535 DD, the mean abundance reached 212.68 parasites per fish (53.17 per gill arch). At that point, fish express a sudden loss of appetite; no external symptoms were observed, except for mortality. This is the first report of Z. seriolae in cultured greater amberjack in the Adriatic Sea, and the study emphasises the difficulties in applying effective biosecurity measures in floating net cages. Full article

Fish identification and classification tasks allow scientists greater data on the sustainability and diversity of fisheries. Advances in computer vision, particularly convolutional neural networks (CNNs), have enabled automated fish detection at scale; however, increased model depth may not justify the additional time and computational cost. This study examines the ResNet family of models for binary fish identification to assess whether deeper networks provide meaningful performance advantages over simpler configurations. We compare ResNet-18, ResNet-50, ResNet-101, and ResNet-152 using both non-pretrained and pretrained initializations. We introduce an Energy-Weighted Score (EWS) to enable comparison of computational resource usage using cost-based weighting. A reliable fish versus no-fish classification can be achieved with as few as 18 layers, yielding 99.975% accuracy which improved to 100% accuracy following threshold optimization. For binary fish identification, increasing ResNet depth provides increased EWS scores with little impact on accuracy returns over shallower models. Overall, models with fewer layers outperformed deeper models with more parameters, and additional depth and tuning techniques were unable to outperform simpler configurations while delivering higher EWS scores. Full article

The substitution of fish meal with soybean meal (SBM) in aquafeeds aligns with sustainable development but often leads to depressed feed intake and growth in fish. This study aimed to investigate the mitigating effect of earthworm powder (EP) on these negative impacts in rice field eels (Monopterus albus), focusing on appetite regulation, intestinal health, and gut microbiota. Three isonitrogenous (~41% crude protein) and isolipidic (~6.4% crude lipid) diets (control [CON], high-SBM [SBM], and SBM + 2.5% EP [EP]) were tested in a 56-day trial. Juveniles (initial weight 18.00 ± 0.01 g) were stocked at 40 fish per net (0.5 m × 0.5 m× 0.5 m) and fed to visual satiety once daily. The results indicated that EP improved growth performance through a dual mechanism. Firstly, it was associated with significantly increased feed intake, correlated with the upregulated expression of orexigenic genes (agrp, npy) in the brain, and associated with reduced levels of anorexigenic hormones (Cholecystokinin, Leptin). Secondly, it correlated with enhanced intestinal health, evidenced by improved morphology (villus height, goblet cells), improved digestive enzyme activity, enhanced antioxidant capacity (increased Catalase and Superoxide Dismutase activities), repaired intestinal barrier function (upregulated zo-1, cla-12), and alleviated intestinal inflammation (downregulated tnf-α, il-1β). Furthermore, EP supplementation was associated with a shift in gut microbiota, including the suppression of the potential pathogen g_Clostridium_T and promotion of the beneficial bacterium g_Lactococcus_A, alongside increased concentrations of major short-chain fatty acids (acetate, propionate, and butyrate). These correlative observations suggest that EP may help mitigate the growth-inhibiting effects of SBM in Monopterus albus, offering a potential functional strategy for high-SBM aquafeeds. Full article

Understanding temporal variation in population productivity is critical for effective assessment and management of pelagic fish stocks under a changing climate. In this study, we applied a stochastic surplus production model in continuous time (SPiCT) with time-varying parameters to evaluate the productivity dynamics of yellowfin tuna (Thunnus albacares) in the western and central Pacific Ocean and to examine the influence of environmental variability on productivity. Multiple time-varying parameterization scenarios were explored to characterize uncertainties in productivity estimates and associated biological reference points. Generalized additive models were subsequently used to quantify the relationships between environmental variables and time-varying productivity. Results indicate that productivity estimates exhibit consistent temporal patterns across alternative modeling scenarios, while their magnitude and associated uncertainty are sensitive to model structure. Among the environmental factors examined, the Pacific Decadal Oscillation (PDO) and mixed layer thickness (MLT) showed consistent and statistically significant associations with maximum net productivity. Higher PDO values and greater MLT were both positively associated with population productivity. Overall, the results highlight the importance of environmental variability in shaping time-varying productivity of yellowfin tuna and demonstrate the feasibility of incorporating key environmental indicators into a state-space model. This approach provides a complementary framework for interpreting stock dynamics and supports the development of ecosystem-based fisheries management strategies in the western and central Pacific. Full article

The enormously growing demand for seafood has resulted in the over-exploitation of marine resources, pushing certain species to the brink of extinction. Overfishing is one of the main issues in sustainable marine development. To support marine resource protection and sustainable fishing, this study proposes advanced fish classification techniques using state-of-the-art machine learning (ML). Specifically, the proposed method enables the precise identification of protected fish species, among other features. In this paper, we present a system-level optimization of the MobileNet architecture, termed M-MobileNet, designed to operate efficiently on resource-limited hardware environments. Our classifier is constructed by a refined modification of the well-known MobileNet neural network, resulting in a reduction of parameters. Furthermore, we have collected, organized, and compiled an original and comprehensive labeled dataset of 37,462 images of fish native to the Indonesian archipelago. The proposed model is trained on this dataset to classify images of captured fish and accurately identify their respective species. Furthermore, the system provides recommendations regarding the consumability of the catch. Compared to the MobileNet deep neural network structure, our model utilizes only 50% of the top-layer parameters, with approximately 42% GTX 860M utility. This configuration results in achieving up to 97% accuracy of classification. Considering the constrained computing capacity prevalent on many fishing vessels, our proposed model offers a practical solution for on-site fish classification. Moreover, synchronized implementation of the proposed model across multiple vessels can provide valuable insights into the movement and location of various fish species. Full article

In recirculating aquaculture systems, fish detection is an essential component for maintaining effective farming operations. The availability of high-quality fish datasets is limited because of the richness of fish species, and the annotation of large-scale data, which is used to train models, is often labor-intensive and time-consuming. The presence of different fish species across batches introduces further challenges for consistent detection performance. This work introduces a few-shot learning approach for fish detection, utilizing a customized dataset as novel classes and the Fish4Knowledge dataset for base classes, thereby establishing a framework that enhances adaptability in data-scarce scenarios. Within the model architecture, multi-scale feature extraction is enhanced through an attention mechanism, which is integrated as a dedicated module to strengthen representation learning, thus enhancing the model’s capability to differentiate visually similar fish species. Two distinct customized fish datasets are employed to evaluate the robustness of the proposed method. Experimental results show that the proposed model performs competitively against TFA, Meta-RCNN, and VFA. In the base-training phase, it achieves a mAP of 0.775, slightly surpassing VFA, while in the 1-shot, 5-shot, and 10-shot fine-tuning settings, it obtains mAP values of 0.152, 0.247, and 0.265, respectively. A similar trend is observed on a subset of black fish, with mAP scores of 0.169, 0.253, and 0.286 in the corresponding few-shot settings. These results indicate that the proposed approach can maintain relatively stable detection accuracy and adaptability across different fish batches, offering a practical solution for fish detection tasks in aquaculture when annotated data is scarce. To further demonstrate the efficacy and practical utility of the proposed methodology, a case study in fish farming confirms that the enhanced model achieves consistent and precise detection across diverse fish species, even when trained with limited annotated data. Full article

This pioneering study investigates bacteria isolated from marine pelagic fish, fishing vessels, and gear surfaces, focusing on the variability in biofilm formation across different substrates, media, and cultivation conditions. Bacteria from fish intestines, skin, and gills, including spoilage organisms and potential fish and human pathogens, can contaminate vessel surfaces, gear, and containers and may act as microbial reservoirs and transmission vectors. In this study, biofilm formation was evaluated at air–liquid interfaces and on submerged plastic, metal, and glass surfaces under various incubation temperatures and media. Vibrio spp. were isolated both from fishing nets and fish gills, particularly Vibrio alginolyticus, V. gigantis, and V. pelagius. Although V. harveyi was examined as a representative vibrio, it did not form a biofilm on polypropylene. Photobacterium damselae subsp. damselae, Pseudomonas fragi, P. gessardii, Psychrobacter spp., and Rothia endophytica showed a strong affinity for stainless steel. Overall adhesion regardless of media type was highest for P. gessardii, followed by P. damselae and Aeromonas veronii, which adhered strongly to steel, glass, and polypropylene; however, only P. gessardii also adhered well to polystyrene, an important finding because these are known fish and human pathogens. These results highlight species-dependent biofilm triggers and their substantial variability and provide guidance for standardized marine biofilm protocols. Full article