Aquaculture International最新文献

This study assessed the impact of different start-feeding regimes on lumpfish (Cyclopterus lumpus) larvae until 35 days post-hatch. Diets included enriched Artemia nauplii, cryopreserved cirriped nauplii (Semibalanus balanoides), cultivated copepodites (Acartia tonsa), formulated feed, and combinations thereof. Enriched Artemia yielded the best outcomes—fast early growth, with the largest larvae, most normal ossification, and the highest survival at the end of the experiment. Cryopreserved cirripeds from the start led to poor initial growth, but growth improved during later stages, and larval size and normal ossification were significantly better in the end than for larvae fed copepodites and formulated feed from the start. Copepodites and formulated feed resulted in poor nutritional status (starvation) from the start and with smaller size and frequent bone anomalies at the end. The largest live prey (Artemia, cirripeds) contained high levels of dietary PUFA n-3 fatty acids and supported the most normal development. They were also easiest to capture, and prey size and easy capture seemed important for the substrate-dwelling early start-feeding lumpfish larvae. Liver vacuolization served as a reliable diet-quality biomarker. Microbial patterns varied significantly across treatments, indicating active regulation of the larval microbiome by feed and water interactions. Our recommendation based on the results is the start-feeding of lumpfish larvae with enriched Artemia nauplii and optional co-feeding with cirripeds.

The accurate classification of fish feeding intensity serves as a fundamental prerequisite and key to realizing precision feeding in aquaculture. Aiming at the issue that current methods for quantifying fish school feeding behavior are susceptible to external conditions and exhibit low reliability, this study proposed a fish feeding intensity classification model based on an optical flow algorithm and improved deep residual shrinkage network. First, the Gunnar Farneback dense optical flow algorithm was used to quantify the movement state of fish during feeding accurately; subsequently, the Squeeze-and-Excitation Networks (SENet) attention mechanism was added to the network to focus the model on key features and enhance its important feature learning ability; finally, the soft threshold function was applied to reinforce effective information and suppress redundancy, thus achieving accurate fish feeding intensity classification. To assess the effectiveness of the proposed model, its performance was analyzed under different activation functions and model depths, and it was compared with classical Convolutional Neural Network (CNN) architectures such as Residual Networks (ResNet), Visual Geometry Group (VGG), ShuffleNet, and Inception. The experimental results indicate that the proposed model achieves a final evaluation accuracy, average precision, average recall, and F1-score of 97.53%, 97.53%, 97.46%, and 97.49%, respectively, all of which outperform the corresponding metrics of the aforementioned classical CNNs. Additionally, the model also has a significant advantage in terms of parameter quantity. In conclusion, the model proposed in this study can accurately evaluate fish feeding intensity in the complex environment of a Recirculating Aquaculture System (RAS), balancing performance and parameter efficiency well and laying a solid foundation for its subsequent practical use in production.

Synthetic pesticides such as deltamethrin (DLM) pose severe risks to aquatic organisms, causing oxidative damage, immune dysregulation, and growth impairment in farmed fish. In this study, we investigated the protective role of Lactobacillus plantarum SB8, focusing on the bioactive metabolites present in its cell-free supernatant (CFS). In vitro, assays demonstrated that SB8 metabolites exerted strong antibacterial activity against Aeromonas hydrophila and Pseudomonas fluorescens. In vivo experiments with Nile tilapia (Oreochromis niloticus) revealed that dietary supplementation with SB8 markedly attenuated DLM-induced hematotoxicity, restoring leukocyte counts and protein profiles. Moreover, SB8 supplementation normalized antioxidant enzyme activities (SOD, CAT, GPx), reduced lipid peroxidation (MDA levels), and downregulated pro-inflammatory cytokines (IL-1β, IL-6) in liver tissue. GC–MS analysis of SB8 L. plantarum metabolites identified components such as lactic acid, acetic acid, phenolic derivatives, exopolysaccharides, peptides, and bacteriocins, which support antioxidant, antimicrobial, and immunomodulatory activities. These findings highlight that bioactive compounds secreted by L. plantarum SB8 can counteract pesticide-induced oxidative stress and inflammation, providing a promising molecular strategy for mitigating chemical toxicity in aquaculture species.

Bacteriocins, the antimicrobial peptides produced by bacteria, have emerged as a highly effective replacement for traditional antibiotics in aquaculture. Being eco-friendly and of natural origin, these peptides, especially those derived from marine sources, are advantageous for controlling aquatic pathogens in aquaculture systems due to their broad-spectrum antimicrobial properties. They are produced by certain bacterial strains, and possess unique mechanisms of action, allowing them to selectively target pathogenic bacteria, with minimal impact on the beneficial microbiota. An added advantage of these antimicrobial peptides is their marine source, which enables them to adapt to diverse environmental pressures without hindering their properties. Bacteriocins offer themselves as a potent alternative to antibiotics, particularly in managing multidrug-resistant pathogens. Advancements in their production, including in vitro and genetic engineering methods, have expanded their scope of applicability. Additionally, nanoformulation techniques are discussed that can potentially enhance bacteriocin stability, bioavailability, and delivery, further boosting their efficacy. This comprehensive review presents an overview of the types of bacteriocins, and their mode of action against drug-resistant pathogens, highlighting their potential as alternatives in pathogen control for sustainable aquaculture practices.

Graphical abstract

The detection and classification of juvenile abalone is a key link in the abalone breeding industry, and juvenile abalone are small, densely distributed, and prone to complex occlusion, which makes the efficiency of manual measurement of biomass low and the error large. Based on the framework of a deep learning network, this paper proposes a method of dense juvenile abalone segmentation with an improved coordinate attention mechanism, and realizes the high-efficiency non-contact segmentation detection of dense juvenile abalone in complex situations. The improved shape feature attention mechanism designed by the algorithm model makes the network pay more attention to the details of juvenile abalone by emphasizing or suppressing the importance of features. At the same time, the algorithm model also constructs a Transformer module embedded with a double-layer Gaussian prior encoder, which improves the accuracy of juvenile abalone feature prediction by replacing the feed forward network with a Gaussian prior. Finally, the segmentation data set of juvenile abalone (JASD) is established, and the effectiveness of the proposed algorithm model is verified by experiments.

Aquaculture models influence the physicochemical properties of edaphic and aquatic environments as well as microbial community structures and functions. Accurately understanding the interactions behind this phenomenon remains challenging. This study compared three models that have been continuously cultivated for 5 years: the rice–crayfish integrated farming (RC), intensive culture pond (IPC), and rice monoculture (RM), through a comprehensive analysis of environmental parameters, microbial community composition (bacteria and fungi), and ecological network structures. RC effectively reduced aquatic ammonia nitrogen and total phosphorus levels while simultaneously enhancing soil fertility parameters and reducing soil heavy metal accumulation. The approach markedly increased the richness of aquatic microbial communities, and altered the microbial community compositions, including both bacteria and fungi. In RC, both water and soil bacterial networks exhibited greater complexity, with a higher average degree and shorter average path distance. Correlation analysis identified total phosphorus as a key factor influencing aquatic community structures and core species. Besides total phosphorus, β-glucosidase and nitrate nitrogen were key factors for soil bacterial dynamics, with ammonium nitrogen being critical for soil fungi. The prediction of bacterial functions revealed that upregulated functional genes were mainly involved in the response processes of environmental signals, ABC transport proteins, and energy metabolism. RC improved environmental sustainability and stability of microbial communities, associated with community compositions variation, more complicated ecological networks and upregulated functional genes. Overall, the rice–crayfish farming improved the resilience of aquaculture ecosystems, providing a new approach to alleviating the environmental pressure caused by aquaculture activities.

Dissolved oxygen (DO) represents a critical water quality parameter in aquaculture environments, where its accurate prediction and effective control play a determining role in ensuring the health and productivity of cultured organisms. Conventional prediction methods struggle to capture the nonlinear dynamic characteristics of DO variations, while traditional control strategies rely on complex physical models with limited adaptability. This study proposes a hybrid model combining long short-term memory (LSTM) networks with Kolmogorov–Arnold networks (KAN), optimized by a quantum-inspired electric eel foraging algorithm (QEFA), for DO prediction and control systems. The proposed approach leverages probability representation and evolution mechanisms from quantum computing, enhancing global search and local refinement capabilities through classical computational strategies that simulate quantum superposition, entanglement, and interference effects. This study employed IoT-based continuous monitoring at 5-min intervals from June 23 to July 31, 2022. After quality control, 10,934 valid data points were obtained, measuring key parameters including dissolved oxygen, water temperature, pH, ambient temperature, humidity, and atmospheric CO₂ concentration. The experimental findings reveal that the QEFA-LSTM-KAN model exhibits superior performance relative to current methodologies when evaluated using various assessment criteria. The model demonstrates exceptional accuracy with a root mean square error (RMSE) value of 0.0244, a mean absolute error (MAE) measuring 0.0197, and a mean absolute percentage error (MAPE) recorded at 0.3054%. Building upon this model, a deep learning model predictive control (NNMPC) framework was developed, which delivers precise dissolved oxygen (DO) control capabilities. This system shows a notable improvement in performance, with the integral time-weighted absolute error (ITAE) decreasing by 5.17% when benchmarked against traditional model predictive control (MPC) approaches. This research provides a novel technological pathway for intelligent water quality management in aquaculture, with significant implications for improving farming efficiency and promoting sustainable aquaculture practices.

Cannibalism poses a major challenge in the commercial aquaculture of Scylla paramamosain, particularly during the crablet phase, primarily due to inadequate feed availability. Artificial feeds are generally less attractive, making live feeds preferable. This study aims to evaluate the preying capacity of crablets and the feeding ability of different sizes of mud crabs against amphipods. The experiment began with assessing the mass culture of Grandidierella megnae, followed by proximate analysis to evaluate its nutritional composition. Two additional experiments examined amphipods’ viability as live prey. The first assessed consumption rates of 4-day-old crabs, while the second evaluated prey consumption across different developmental stages. Results indicate that the indoor mass culture of amphipods supports population growth from 1.2 to 78.67 ± 19.86 g over a 60-day rearing period, providing adult-stage amphipods as live feed. Additionally, amphipods exhibit satisfactory nutritional content conducive to mud crab growth. Crablets’ consumption rates increased significantly (P < 0.05) with amphipod density, peaking at 30.3 individuals per crab. However, larger mud crabs exhibited reduced predation ability. Juveniles (~ 24 g body weight) consumed 50% of the available amphipods during exposure. Findings suggest amphipods are most effective as live feed during crablet and early juvenile stages. This study presents an initial success of amphipods feeding on mud crab juveniles. Furthermore, the first described mass culture technique for G. megnae may serve as a foundation for commercial amphipod cultivation, facilitating their use as natural live feed and supplementary ingredients for various fish and crustaceans.

This study investigated the interactive effects of dietary fat levels and selenium–vitamin E (Se + VE) supplementation on the growth performance, antioxidant status, immune response, and disease resistance of Oreochromis niloticus. The fish were fed diets containing 5%, 10%, or 15% lipids with or without Se + VE supplementation over a 60-day experimental period. Growth metrics, including final weight, weight gain, average daily gain, and feed efficiency, were significantly improved in the group receiving 10% fat supplemented with Se + VE, whereas high-fat (15%) diets without supplementation resulted in impaired performance (P < 0.01). Se + VE significantly improved antioxidant enzyme activities (SOD, CAT) and reduced oxidative damage, as indicated by lower malondialdehyde (MDA) levels, particularly under high-fat dietary conditions (P < 0.0001, with a significant interaction). Proinflammatory cytokines (IL-1β and TNF-α) were elevated by increased fat inclusion, whereas Se + VE mitigated this inflammatory response and elevated anti-inflammatory IL-10 levels. Lipid profile analysis revealed that Se + VE reduced total cholesterol and LDL concentrations while preserving HDL, especially at relatively high fat levels (interaction P < 0.01). The levels of liver enzymes (ALT, AST, and ALP) increased with increasing amounts of dietary fat but were significantly attenuated by antioxidant supplementation. Histopathological assessments of the heart, kidney, and liver confirmed progressive tissue degeneration with increasing fat levels, which was significantly ameliorated by Se + VE. Following challenge with Streptococcus agalactiae, Kaplan–Meier survival analysis revealed significantly improved survival rates in the Se + VE-supplemented groups, with the greatest protection observed in the 5% and 10% fat diet groups. The clinical and pathological scores corroborated these findings, revealing reduced lesion severity in the supplemented groups. Collectively, these results demonstrate that dietary supplementation with Se and VE effectively counters the adverse effects of high-fat diets, improving metabolic balance, tissue integrity, and disease resistance in O. niloticus.