Explore the vast range of titles available.

In this study, we collected 56 species of fishery organisms (including fish, crustaceans, cephalopods, gastropods, and bivalves) from four seasonal survey cruises at the Dachen fishery grounds. We measured the concentrations of seven heavy metals (Cd, Zn, Cu, Pb, Cr, As, and Hg) in these fisheries organisms. We determined their trophic levels using carbon and nitrogen stable isotope techniques. We analyzed the characteristics of heavy metal transfer in the food chain. The results showed significant differences in heavy metal concentrations among different species. Among all biological groups, bivalves and gastropods exhibited higher levels of heavy metal enrichment than other biological groups, while fish had the lowest levels of heavy metal enrichment. Heavy metals exhibited different patterns of nutritional transfer in the food chain. While Hg showed a biomagnification phenomenon in the food chain, it was not significant. Cd, Zn, Cu, Pb, Cr, and As exhibited a trend of biodilution with increasing nutritional levels, except for As, which showed no significant correlation with δ 15 N. Graphical Abstract

This study proposes an Automatic Identification System (AIS)-based spatiotemporal allocation methodology to estimate catch distribution and fishing effort for large purse seine fisheries in Korean waters. AIS trajectory data from July 2019 to June 2022 were analyzed to identify fishing grounds, while carrier vessel port-entry records were used to estimate daily landings. These were allocated to specific fishing segments to derive spatially explicit catch quantities. Compared with periodic surveys or voluntary reports, the AIS-based approach significantly enhanced the accuracy of fishing ground identification and the reliability of catch estimation. The results showed that fishing activity peaked between November and February, with the highest catch densities observed south of Jeju Island and in adjacent East China Sea waters. Catch declined markedly from April to June due to the mackerel closed season. These findings demonstrate the method’s potential for evaluating the effectiveness of Total Allowable Catch (TAC) regulations, supporting dynamic and adaptive management frameworks, and strengthening IUU fishing monitoring. Although the current analysis is limited to TAC-regulated species, AIS-equipped vessels, and a three-year dataset, future studies could expand the timeframe, integrate environmental data, and apply this methodology to other fisheries to improve sustainable resource management.

The fatty acid (FA) content of northern shrimp Pandalus borealis sampled on commercial shrimp fishing grounds in 2 fjords in northern Norway was analyzed to elucidate if the shrimp feed on aquaculture waste from salmon aquaculture farms. Shrimp were sampled in February and June 2021 in 1 fjord without farms (Balsfjord) and 1 fjord with farms (Kvænangen), at varying distances from the farms (1.5 to 13.3 km). A laboratory experiment conducted as part of our study showed that the FA profile of shrimp feeding on salmon feed pellets changed toward the FA profile of the pellets. The terrestrial FAs 18:2n-6 and 18:3n-3 changed the most and were chosen as suitable FA trophic markers (FATMs) for identifying feeding on aquaculture waste by shrimp in the fjords. The terrestrial FATMs were not detected at elevated levels in the lipids of the shrimp from Kvænangen, indicating that none of the sampled shrimp had been feeding on aquaculture waste. FA analysis has the potential to inform diet studies, and we found differences in the shrimp diet between and within the fjords as well as seasonal differences. Calanus finmarchicus was a more abundant prey in outer Kvænangen compared with inner Kvænangen and Balsfjord, while shrimp at the latter 2 sites had a higher content of benthic FATMs. At all sites, C. finmarchicus constituted a more important food item in winter than in summer.

Twelve commercial species exploited in the eastern Guangdong and southern Fujian waters were assessed using the Catch-Maximum Sustainable Yield (CMSY) and Bayesian Schaefer Model (BSM) methods. The carrying capacity (k), intrinsic rate of population growth (r), maximum sustainable yield (MSY), and relative biomass (Bend/k and B/BMSY) were estimated. The current stock status was defined by B/BMSY and fishing mortality (F/FMSY). The results indicate that seven stocks were overfished or below safe biological limits (B/BMSY < 0.5 or F/FMSY > 1), two stocks were in a recovery phase (0.5 < B/BMSY < 1, F/FMSY < 1), and three stocks were under sustainable fishing pressure with healthy biomass, capable of producing yields close to the MSY (B/BMSY > 1, F/FMSY < 1). The stock statuses are consistent with previous studies on the utilization of pelagic fisheries in the eastern Guangdong and southern Fujian waters and with those assessments in other waters. The results of the assessments suggest that these stocks could be expected to produce higher sustainable catches if permitted to rebuild; thus, more effective and proactive management is needed in this upwelling fishing ground.

Graphical Abstract   Highlight Research The number of shark catches in the eastern season was 618. Sharks landed at PPI Ujong Baroh in six species (Sphyma lewini, Alopias pelagicus, Carcharhinus falciformis, Loxodon macrorhinus, Carcharhinus sorrah, and Chiloscyllium punctatum). The distribution of chlorophyll-a parameters in the eastern season ranges from 0.08 to 1.23 mg/m3 with an average value of 0.17 mg/m3. The distribution of sea surface temperature parameters in the eastern season ranges from 27.65 to 30.29oC with an average value of 28.65o Based on linear regression analysis, sharks are most highly influenced by chlorophyll-a oceanographic parameters, namely Loxodon macrorhinus by 72.82%, and sharks are highest influenced by sea surface temperatures, namely Alopias pelagicus by 83.12%.     Abstract  Sharks are top-tier water predators that can maintain marine ecology balance and control the food web. As sharks are at the top of the food chain, their overfishing can disrupt the ecosystem chain. The distribution and abundance of fish in waters can be influenced by several factors of oceanographic parameters, including chlorophyll-a and sea surface temperature (SST). The use of satellite imagery for analyzing chlorophyll-a and SST parameters provides significant results in fisheries oceanographic studies. This study aimed to determine the effect of chlorophyll-a and SST parameters on shark catches. The method in this study was divided into 2 stages, namely taking shark fishing area coordinate data and downloading chlorophyll-a as well as SST satellite image data on the NASA Aqua-MODIS website. The results of the of the analysis of chlorophyll-a distribution in the eastern season ranged from 0.08 to 1.23 mg/m3 with an average value of 0.17 mg/m3, where the highest was 1.23 mg/m3 in September and the lowest was 0.08 mg/m3 in August. The SST distribution ranged from 27.65 to 30.29oC with an average of 28.65oC, the highest was 30.29oC in August and the lowest was 27.65oC in September. Based on the results of linear regression analysis, the highest shark catch was influenced by chlorophyll-a, namely Loxodon macrorhinus shark, by 72.82%, the highest shark catch type was influenced by SPL, namely Alopias pelagicus shark, by 83.12%, and the rest was influenced by other parameters.

The availability of information on fishing areas is limited for fisher in the southern Yogyakarta waters, leading to inefficient operation. To overcome this problem, there is a need to determine the existence of upwelling locations, contributing to the formation of potential fishing grounds. These locations can be predicted by analyzing big data of the aquatic environment, particularly sea surface temperature, chlorophyll-a, and mean sea level anomaly downloaded from Marine Copernicus on the website https://marine.copernicus.eu/. The current use of marine big data is suboptimal due to limited analytical methods to detect upwelling locations, showing the need for further investigations. Therefore, this research aimed to develop a method for detecting upwelling locations and predicting their occurrence as an indicator of potential fishing grounds. The results showed that the upwelling index generated for prediction was 0.56 mg m-3, 27°C, and 0.39 m, for the chlorophyll-a, sea surface temperature, and mean sea level, respectively. The analysis conducted from June to September 2020 showed upwelling occurrences in southern Yogyakarta waters were found from July to September, with the highest intensity observed in July and August. This phenomenon occurred more frequently in the coastal zone compared to offshore. Furthermore, upwelling locations were verified as potential fishing grounds, showing higher catch productivity compared to other areas without upwelling.

The Northwest Indian Ocean is a key fishing ground for China’s pelagic fisheries, with the purpleback flying squid being a significant target. This study uses commercial fishing logs of the Indian Ocean between 2015 and 2021, alongside pelagic seawater temperature and its vertical temperature difference within the 0–200 m depth range, to construct generalized additive models (GAMs) and gradient boosting tree models (GBTs). These two models are evaluated using cross-validation to assess their ability to predict the distribution of purpleback flying squid. The findings show that factors like year, latitude, longitude, and month significantly influence the distribution of purpleback flying squid, while surface water temperature, 200 m water temperature, and the 150–200 m water layer temperature difference also play a role in the GBT model. Similar factors also take effects in the GAM. Comparing the two models, both GAM and GBT align with reality in predicting purpleback flying squid resource distribution, but the precision indices of GBT model outperform those of the GAM. The predicted distribution for 2021 by GBT also has a higher overlap with the actual fishing ground than that by GAM, indicating GBT’s superior forecasting ability for the purpleback flying squid fishing ground in the Northwest Indian Ocean.

Information on small pelagic fishing areas for white-spotted spinefoot (Siganus canaliculatus) should be gathered in an effort to expand the fishing areas. This information can be collected through aerial photography technology using drones. This study aims to map the potential fishing grounds for white-spotted spinefoot in the coastal waters of Karang-karangan, Luwu Regency, South Sulawesi. The used method is a photogrammetric survey using drones and participatory mapping with a cartometric method approach. The results of the analysis show that the potential location for fishing white-spotted spinefoot in the study area is 762.08 hectares spread over 46 location points.

The traditional methods for predicting the distribution of albacore (Thunnus alalunga) fishing grounds have low performance and accuracy. Uneven sampling can result in unreasonable evaluation indicators. To address these issues, three methods, equi-frequency, K-means clustering algorithm, and 1-R split, were applied to discretize the catch per unit effort (CPUE) of albacore in the South Pacific from 2016 to 2021 and partition the fishing grounds into abundance levels. Eight machine learning models were used to predict the fishing grounds. In addition to the traditional evaluation index based on confusion matrix, top-k index was also used to evaluate the accuracy of fishery abundance predictions. The results showed that (1) When sampling is unbalanced, the reported accuracy does not fully represent the actual performance of the model in predicting the abundance of albacore in the fishing ground. F1 value can be used as the index of the model effect and stability. (2) In binary classification, the quartile stacking algorithm has the best stacking performance, with F1 0.89. (3) The top-1 prediction accuracy of three-category fishery forecasting is the highest at 0.74, and the top-1 prediction accuracy of five-category fishery forecasting is the highest at 0.54. (4) The top-k accuracy of classification of fisheries with multiple abundance using K-means is significantly better than that of equal frequency discretization (p < 0.001). The top-k evaluation index was used to predict the fishing grounds of albacore across multiple abundance levels for the first time in this study, which is significant for pioneering a new method for this application and which provides a demonstration of the development of artificial intelligence techniques for fisheries in the future.

Selecting the optimal spatiotemporal scale in fishing ground prediction models can maximize prediction accuracy. Current research on spatiotemporal scales shows that they are symmetrically distributed, which may not capture specific oceanographic features conducive to fishing ground formation. Recent studies have shown that deep learning is a promising research direction for addressing spatiotemporal scale issues. In the era of big data, deep learning outperforms traditional methods by more accurately and efficiently mining high-value, nonlinear information. In this study, taking Ommastrephes bartramii in the Northwest Pacific as an example, we used the U-Net model with sea surface temperature (SST) as the input factor and center fishing ground as the output factor. We constructed 80 different combinations of temporal scales and asymmetric spatial scales using data in 1998–2020. By comparing the results, we found that the optimal temporal scale for the deep learning fishing ground prediction model is 15 days, and the spatial scale is 0.25° × 0.25°. Larger time scales lead to higher model accuracy, and latitude has a greater impact on the model than longitude. It further enriches and refines the criteria for selecting spatiotemporal scales. This result deepens our understanding of the oceanographic characteristics of the Northwest Pacific environmental field and lays the foundation for future artificial intelligence-based fishery research. This study provides a scientific basis for the sustainable development of efficient fishery production.