Search Results (226)

The use of eco-organic ingredients as a source of protein in aquaculture diets needs important attention due to the growing demand for organic seafood products. The present study evaluated the effects of fish meal substitution by different organic ingredients on the growth, body composition, retention efficiency, enzyme activity, and nutrient digestibility of white shrimp Penaeus vannamei. The four dietary formulations tested were formulated with organic ingredients and the fish meal was replaced by the following organic protein meals: Iberian pig viscera meal (PIG), trout by-product meal (TRO), insect meal (FLY), and organic vegetable meal (WHT), in addition to a control diet (CON) that included 15% fish meal. A growth trial was carried out for 83 days, raising 1 g shrimp to commercial size (20 g). Shrimp were stocked at 167 shrimp/m³ (15 individuals per 90 L tank). The results showed that the growth obtained by shrimp fed with TRO (19.27 g) and PIG (19.35 g) were similar in weight gain to the control diet (20.76 g), while FLY (16.04 g) and WHT (16.73 g) meals resulted in a significant lower final weight. The FLY diet showed significantly lower protein digestibility (68.89%) compared to the CON, PIG, TRO, and WHT diets, and significantly higher trypsin activity (0.17 mU/g) compared to shrimp fed with the PIG, TRO, and WHT diets. Shrimp fed with WHT have a significantly lower body weight percentage of protein (19.69%) than shrimp fed with the WHT and TRO diets, and some significant differences in dietary aminoacidic levels affecting amino acid body composition. These results indicate that Iberian pig viscera and trout by-product meal can successfully replace fish meal in Pacific white shrimp aquaculture. Full article

In recent decades, fishery stock enhancement has been increasingly utilized as a restoration tool to mitigate population declines and enhance the resilience of marine fisheries. Nevertheless, persistent enhancement efforts risk eroding the evolutionary potential of wild populations via genetic homogenization and maladaptive gene flow. Using long-term monitoring data (2017–2023), we quantified the effects of large-scale Penaeus penicillatus stock enhancement (~10⁸ juveniles/yr) on wild population dynamics and genetic integrity in the Beibu Gulf ecosystem. Temporal genetic changes were assessed using eight highly polymorphic microsatellite loci, comparing founder (2017) and enhanced (2024) populations to quantify stocking impacts. Insignificantly lower expected heterozygosity was observed in the stocked population (He = 0.60, 2024) relative to natural populations (He = 0.62–0.66; p > 0.1), indicating genetic dilution effects from enhancement activities. No significant erosion of genetic diversity was detected post-enhancement, suggesting current stocking practices maintain short-term population genetic integrity. Despite conserved heterozygosity, pairwise Fst analysis detected significant genetic shifts between temporal cohorts (pre-enhancement—2017 vs. post-enhancement—2024; Fst = 0.25, p < 0.05), demonstrating stocking-induced population restructuring. Genetic connectivity analysis revealed that while the enhanced Beihai population (A-BH) maintained predominant self-recruitment (>90%), it experienced substantial stocking-derived gene flow (17% SW → A-BH). The post-stocking period showed both reduced genetic exchange with adjacent populations and increased asymmetric dispersal from A-BH (e.g., 5% to YJ), indicating that hatchery releases simultaneously enhanced population isolation while altering regional genetic structure. Our findings revealed the paradoxical dual effects of stock enhancement and allelic diversity while disrupting natural genetic architecture. This underscores the need for evolutionary-impact assessments in marine resource management. Full article

Coastal blue carbon ecosystems (traditional types such as mangroves, salt marshes, and seagrass meadows; emerging types such as tidal flats and mariculture) play pivotal roles in capturing and storing atmospheric carbon dioxide. Reliable assessment of the spatial and temporal variation and the carbon storage potential holds immense promise for mitigating climate change. Although previous field surveys and regional assessments have improved the understanding of individual habitats, most studies remain site-specific and short-term; comprehensive, multi-decadal assessments that integrate all major coastal blue carbon systems at the national scale are still scarce for China. In this study, we integrated 30 m Landsat imagery (1992–2022), processed on Google Earth Engine with a random forest classifier; province-specific, literature-derived carbon density data with quantified uncertainty (mean ± standard deviation); and the InVEST model to track coastal China’s mangroves, salt marshes, tidal flats, and mariculture to quantify their associated carbon stocks. Then the GeoDetector was applied to distinguish the natural and anthropogenic drivers of carbon stock change. Results showed rapid and divergent land use change over the past three decades, with mariculture expanded by 44%, becoming the dominant blue carbon land use; whereas tidal flats declined by 39%, mangroves and salt marshes exhibited fluctuating upward trends. National blue carbon stock rose markedly from 74 Mt C in 1992 to 194 Mt C in 2022, with Liaoning, Shandong, and Fujian holding the largest provincial stock; Jiangsu and Guangdong showed higher increasing trends. The Normalized Difference Vegetation Index (NDVI) was the primary driver of spatial variability in carbon stock change (q = 0.63), followed by precipitation and temperature. Synergistic interactions were also detected, e.g., NDVI and precipitation, enhancing the effects beyond those of single factors, which indicates that a wetter climate may boost NDVI’s carbon sequestration. These findings highlight the urgency of strengthening ecological red lines, scaling climate-smart restoration of mangroves and salt marshes, and promoting low-impact mariculture. Our workflow and driver diagnostics provide a transferable template for blue carbon monitoring and evidence-based coastal management frameworks. Full article

Gray snapper (Lutjanus griseus; Family: Lutjanidae) local habitat preferences have been assessed, but the biotic and abiotic factors influencing age and growth rates in Mosquito Lagoon, Florida, have not been quantified. To address this knowledge gap, the goal of this study was to estimate mean age and growth rate of gray snapper, and use generalized linear mixed models to investigate if prey and/or other environmental factors (e.g., abiotic/biotic conditions, time, location, or habitat restoration status) impact size at both the lagoon- and habitat-specific scales. Age data were extracted via otolith microstructural analyses, and incorporated with size into a lagoon-scale linear growth model. Based on microstructural analyses, mean age of gray snapper at the lagoon scale was 175 ± 66 days (range = 56–350 days). The results indicate the most common life stage of gray snapper in Mosquito Lagoon is juveniles, with living shoreline habitats having a greater proportion of relatively young juveniles (111 ± 36 days) and oyster reef habitats having a greater proportion of relatively older juveniles (198 ± 58 days). The estimated growth rate was 0.43 mm/day. Body mass and body length were correlated positively with habitat quality and lagged salinity levels. Hence future studies should strive to characterize benthic habitat characteristics, and investigate biotic and abiotic factors that potentially influence gray snapper growth. Collectively, this study increases our understanding of environmental drivers affecting juvenile gray snapper development and shows that the restoration of benthic habitats can produce conditions conducive to gray snapper growth. The age-, size-, and habitat-specific growth rates of juveniles from this study can be incorporated into stock assessments, and thereby be used to refine and develop more effective ecosystem-based management strategies for gray snapper fisheries. Full article

A two-factorial experiment was conducted to evaluate the potential for improving the palatability and utilization of tuna by-product meal (TBM) as a replacement for fish meal (FM) via supplementing the diet of juvenile greater amberjack (Seriola dumerili) with powdered salmon roe liquid (PSRL). FM protein in the control diet (C) was partially replaced with TBM at levels of 25% (T25R0) and 40% (T40R0). PSRL was then added at 5% and 10% to both the T25R0 and T40R0 diets, resulting in the formulations T25R5, T25R10, T40R5, and T40R10. After a 6-week rearing period, during which groups of 30 juveniles (mean initial weight of approximately 1.7 g) were stocked in 500 L tanks, there were no significant differences in the final mean weight, weight gain, or specific growth rate among fish fed diets C, T25R0, and T25R5. However, the other experimental diets showed significantly lower values compared to the C diet (p < 0.05). PSRL supplementation did not significantly enhance either palatability or feeding rate in TBM-based diets (p > 0.05). Two-way ANOVA indicated that only FM replacement with TBM had a significant effect on the growth parameters mentioned above (p < 0.05). A significantly lower whole-body crude lipid content was observed in all test diets compared to that in the C diet, resulting in significantly lower lipid retention efficiency in all test groups except T25R5 and T25R10 (p < 0.05). These results suggest that 25% of FM protein can be replaced with TBM protein in the diet of juvenile greater amberjack, but PSRL does not effectively address the palatability issues associated with TBM-based diets. Full article

Globalization and the spread of technological innovations have made world markets and economies increasingly unified and conditioned by international trade, not only for sales markets but above all for the supply of raw materials necessary for the functioning of the production complex of each country. Alongside oil and gold, the main commodities traded include industrial metals, such as aluminum and copper, mineral products such as gas, electrical and electronic components, agricultural products, and precious metals. The conflict between Russia and Ukraine tested the unification of markets, given that these are countries with notable raw materials and are strongly dedicated to exports. This suggests that commodity prices were able to influence the stock markets, especially in the countries most closely linked to the two belligerents in terms of import-export. Given the importance of industrial metals in this period of energy transition, the aim of our study is to analyze whether Industrial Metals volatility affects G7 stock markets. To this end, the BEKK-GARCH model is used. The sample period spans from 3 January 2018 to 17 September 2024. The results show that lagged shocks and volatility significantly and positively influence the current conditional volatility of commodity and stock returns during all periods. In fact, past shocks inversely influence the current volatility of stock indices in periods when external events disrupt financial markets. The results show a non-linear and positive impact of commodity volatility on the implied volatility of the stock markets. The findings suggest that the war significantly affected stock prices and exacerbated volatility, so investors should diversify their portfolios to maximize returns and reduce risk differently in times of crisis, and a lack of diversification of raw materials is a risky factor for investors. Full article

Silurus lanzhouensis is a large-sized commercial fish species endemic to the mid-upper reaches of the Yellow River, serving as a “Card of Aquatic Biological Species in the Yellow River”. However, due to factors such as overfishing and habitat changes, it has been listed as an endangered species. In order to protect and restore its wild resources, we conducted a detailed study for the first time from 2022 to 2024 on the age composition, growth characteristics, mortality rate, and current status of resource exploitation of the S. lanzhouensis population in the upper Yellow River. A total of 489 individuals underwent measurements for their total length (L) and body weight (W), with age determination conducted using lapillus otoliths. The collected samples exhibited a spectrum of total lengths spanning from 2.5 to 63.7 cm, body weights ranging from 0.11 to 1974.15 g, and ages ranging from 1 to 6 years. Further analysis of the length–weight relationships unveiled an allometric growth index (b) of 2.9562 for the overall samples, indicating an isometric growth pattern. Additionally, insights into the growth characteristics of S. lanzhouensis were provided by the von Bertalanffy growth function, revealing an asymptotic total length (L_∞) of 119.30 cm and a growth coefficient (K) of 0.1278 yr⁻¹. The growth characteristic index (φ) totaled 3.2598, suggesting a moderate growth rate in comparison to other Silurus species. The total mortality rate (Z) recorded for the population of S. lanzhouensis was found to be 0.5296 yr⁻¹. Through the application of three distinct methodologies on exhaustive samples, the average natural mortality rate (M) was calculated to be 0.3105 yr⁻¹. Consequently, the fishing mortality rate (F) for the entire sample set was determined to be 0.2191 yr⁻¹, leading to an exploitation rate (E) of 0.4137. Based on the survey findings, it is evident that the population of S. lanzhouensis has not been subjected to over-exploitation, attributable to ongoing stock enhancement. These results also provide foundational data for the protection and restoration of S. lanzhouensis in the upper Yellow River. Full article

Film-forming solutions were prepared using Tara gum (TG), with glycerol (GL) as a plasticizer and orange peel powder (OP) as a filler. A TG stock solution (10 g/L) was initially prepared to facilitate homogenization, from which appropriate dilutions were made to obtain final concentrations of 0.6%, 0.8%, and 1.0% (w/v). GL (30% and 50%) and OP (0%, 20%, and 50%) were incorporated based on the dry weight of TG, meaning their amounts were calculated relative to TG content to ensure consistent formulation ratios. Rheological parameters, including the flow behavior index, consistency coefficient, storage modulus (G′), and loss modulus (G″), were characterized via steady shear and oscillatory rheometry. Mechanical properties, such as the Young’s modulus, tensile strength, and elongation at break, were also evaluated. A strong positive correlation (R² = 0.840) was observed between G′ and the Young’s modulus, indicating that solutions with higher internal network strength yield films with greater stiffness. The synergistic interaction between TG and OP was critical: TG primarily enhanced stiffness and mechanical reinforcement, whereas OP improved structural cohesion and stability. GL functioned as a plasticizer, increasing film flexibility while reducing stiffness. These interactions led to a reduction in film solubility by up to 62.43%, particularly in formulations without orange peel powder. In contrast, mechanical strength increased by up to 50.21% in films containing orange peel powder, as those without it exhibited significantly lower tensile strength. Flexibility, expressed as elongation at break, was enhanced by up to 78.86% in formulations with higher glycerol content. Barrier properties were also improved, demonstrated by decreased water vapor permeability and increased hydrophobicity, attributed to the TG–OP synergy. A regression model (R² = 0.928) substantiated the contributions of TG to stiffness, OP to matrix reinforcement, and GL to flexibility modulation. This study underscores the pivotal role of rheological behavior in defining film performance and presents a novel analytical framework applicable to the design of sustainable, high-performance biopolymeric materials. Full article

The stochastic correlation for Brownian motions is the integrand in the formula of their quadratic covariation. The estimation of this stochastic process becomes available from the temporally localized correlation of latent price driving Brownian motions in stochastic volatility models for asset prices. By analyzing this process for Apple and Microsoft stock prices traded minute-wise, we give statistical evidence for the roughness of its paths. Moment scaling indicates fractal behavior, and both fractal dimensions (approx. 1.95) and Hurst exponent estimates (around 0.05) point to rough paths. We model this rough stochastic correlation by a suitably transformed fractional Ornstein–Uhlenbeck process and simulate artificial stock prices, which allows computing tail dependence and the Herding Behavior Index (HIX) as functions in time. The computed HIX is hardly variable in time (e.g., standard deviation of 0.003–0.006); on the contrary, tail dependence fluctuates more heavily (e.g., standard deviation approx. 0.04). This results in a higher correlation risk, i.e., more frequent sudden coincident appearance of extreme prices than a steady HIX value indicates. Full article

This study aimed to evaluate the dynamics of total carbon (TC), total nitrogen (TN), total phosphorus (TP), and some indicators of environmental sustainability of Nile tilapia reared in a biofloc technology (BFT) system. Nile tilapia fingerlings were cultured in three BFT units of production (4.2 m³ each) at a stocking density of 395 fish/m³. After 70 days, the survival rate was 98.05%, with a final average weight of 20.43 g, and apparent feed conversion of 1.05. Nutrient inputs were from supply water, initial fish biomass, and feed; outputs were measured from the final fish biomass and effluent. TC, TN, and TP concentrations in the water increased linearly over time (p < 0.001) by 1.54, 1.66, and 0.44 mg/L, respectively. Feed contributed over 88% of nutrient inputs, while fish final biomass accounted for over 50% of output. Nutrient retention in fish final biomass was 29.74% (TC), 45.38% (TN), and 46.34% (TP). The system had low eutrophication potential, estimated at 57.39 kg TC, 20.02 kg TN, and 5.70 kg TP per ton of fish. Water use was minimal (0.0074 m³/ton), but energy demand was high (114.59 MJ/kg). The closed system reduces biodiversity risks by preventing fish escape. In conclusion, BFT supports high fish productivity with efficient nutrient use, minimal water use, and limited environmental impact, contributing to environmentally sustainable aquaculture. Full article

Forecasting financial time series is challenging due to their intrinsic nonlinearity, high volatility, and complex dependencies across temporal scales. This study introduces MSGformer, a novel hybrid architecture that integrates multi-scale graph neural networks (MSGNet) with Transformer encoders to capture both local temporal fluctuations and long-term global trends in high-frequency financial data. The MSGNet module constructs multi-scale representations using adaptive graph convolutions and intra-sequence attention, while the Transformer component enhances long-range dependency modeling via multi-head self-attention. We evaluate MSGformer on minute-level stock index data from the Chinese A-share market, including CSI 300, SSE 50, CSI 500, and SSE Composite indices. Extensive experiments demonstrate that MSGformer significantly outperforms state-of-the-art baselines (e.g., Transformer, PatchTST, Autoformer) in terms of MAE, RMSE, MAPE, and R². The results confirm that the proposed hybrid model achieves superior prediction accuracy, robustness, and generalization across various forecasting horizons, providing an effective solution for real-world financial decision-making and risk assessment. Full article

Forests play a pivotal role in the global carbon cycle, making accurate estimation of forest carbon stocks essential for climate change mitigation efforts. However, the diverse methods available for assessing forest carbon yield varying results and have different limitations. This study provides a comprehensive review of current methods for estimating forest carbon stocks, including field-based measurements, remote sensing techniques, and integrated approaches. We systematically collected and analyzed recent studies (2010–2025) on forest carbon estimation across various ecosystems. Our review indicates that field-based methods, such as forest inventories and allometric equations, offer high accuracy at local scales but are labor-intensive. Remote sensing methods (e.g., LiDAR and satellite imagery) enable large-scale carbon assessment with moderate accuracy and efficiency. Integrated approaches that combine ground measurements with remote sensing data can improve accuracy while expanding spatial coverage. We discuss the strengths and weaknesses of each method category in terms of accuracy, cost, and scalability. Based on the synthesis of findings, we recommend a balanced approach that leverages both ground and remote sensing techniques for reliable forest carbon monitoring. This review also identifies knowledge gaps and suggests directions for future research to enhance the precision and applicability of forest carbon estimation methods. Full article

Monitoring changes in fishery resources, such as the density and growth of fish, following large-scale fish stocking in a reservoir is important. In this study, BioSonics DT-X (201 kHz) was used to assess the seasonal changes in the density, size distribution, and biomass of fish in Duihekou Reservoir, Zhejiang province, China, in 2020. The fish density was significantly lower in spring (3.33 ind./1000 m³) than in summer (75.24 ind./1000 m³), autumn (56.22 ind./1000 m³), and winter (20.37 ind./1000 m³) (p < 0.01). No significant difference in fish density was apparent between summer and autumn. Additionally, the average target strength (TS) values in spring (−41.05 dB) were significantly higher than in summer (−44.66 dB) and autumn (−45.55 dB) (p < 0.01), but significantly higher in winter (−38.12 dB) than in the other seasons (p < 0.01); no significant difference was observed between the summer and autumn values (p > 0.01). The fish biomass in winter (14.3 g/m³) was 13 times that in spring (1.1 g/m³). These results indicate that large-scale fish stocking can effectively adapt to reservoir habitats to achieve growth. The catch composition revealed silver carp (Hypophthalmichthys molitrix) and bighead carp (Aristichthys nobilis) to be dominant species, mostly comprising young individuals. Stock enhancement plays a critical role in reshaping the fishery population structure in a reservoir. These findings enhance our understanding of fishery resource changes in reservoirs after non-classical biomanipulation and demonstrate how hydroacoustic techniques can be successfully used to quantify the density and size distribution of fish for more effective fishery management. Full article

Forest growing stock volume (GSV) is a fundamental indicator for assessing the status of forest resources. It reflects forest carbon storage levels and serves as a key metric for evaluating the carbon sequestration capacity of forest ecosystems, thereby playing a crucial role in supporting national “dual-carbon” objectives. Traditional allometric models typically estimate GSV using tree species, diameter at breast height (DBH), and canopy height. However, at larger spatial scales, these models often neglect stand density, resulting in substantial estimation errors in regions characterized by significant density variability. To enhance the accuracy of large-scale GSV estimation, this study incorporates high-resolution, spatially continuous forest structural parameters—including dominant tree species, stand density, canopy height, and DBH—extracted through the synergistic utilization of active (e.g., Sentinel-1 SAR, ICESat-2 photon data) and passive (e.g., Landsat-8 OLI, Sentinel-2 MSI) multi-source remote sensing data. Within an allometric modeling framework, stand density is introduced as an additional explanatory variable. Subsequently, GSV is modeled in a stratified manner according to tree species across distinct ecological zones within Kunming City. The results indicate that: (1) the total estimated GSV of Kunming City in 2020, based on remote sensing imagery and second-class forest inventory data collected in the same year, was 1.01 × 10⁸ m³, which closely aligns with contemporaneous statistical records. The model yielded an R² of 0.727, an RMSE of 537.566 m³, and a MAE of 239.767 m³, indicating a high level of overall accuracy when validated against official ground-based inventory plots organized by provincial and municipal forestry authorities; (2) the incorporation of the dynamic stand density parameter significantly improved model performance, which elevated R² from 0.565 to 0.727 and significantly reduced RMSE. This result confirms that stand density is a critical explanatory factor; and (3) GSV exhibited pronounced spatial heterogeneity across both tree species and administrative regions, underscoring the spatial structural variability of forests within the study area. Full article

Accurate stock price prediction requires the integration of heterogeneous data streams, yet conventional techniques struggle to simultaneously leverage fine-grained micro-stock features and broader macroeconomic indicators. To address this gap, we propose MambaLLM, a novel framework that fuses macro-index and micro-stock inputs through the synergistic use of state-space models (SSMs) and large language models (LLMs). Our two-branch architecture comprises (i) Micro-Stock Encoder, a Mamba-based temporal encoder for processing granular stock-level data (prices, volumes, and technical indicators), and (ii) Macro-Index Analyzer, an LLM module—employing DeepSeek R1 7B distillation—capable of interpreting market-level index trends (e.g., S&P 500) to produce textual summaries. These summaries are then distilled into compact embeddings via FinBERT. By merging these multi-scale representations through a concatenation mechanism and subsequently refining them with multi-layer perceptrons (MLPs), MambaLLM dynamically captures both asset-specific price behavior and systemic market fluctuations. Extensive experiments on six major U.S. stocks (AAPL, AMZN, MSFT, TSLA, GOOGL, and META) reveal that MambaLLM delivers up to a 28.50% reduction in RMSE compared with suboptimal models, surpassing traditional recurrent neural networks and MAMBA-based baselines under volatile market conditions. This marked performance gain highlights the framework’s unique ability to merge structured financial time series with semantically rich macroeconomic narratives. Altogether, our findings underscore the scalability and adaptability of MambaLLM, offering a powerful, next-generation tool for financial forecasting and risk management. Full article