Search Results (19)

Mercury (Hg) is a global health concern due to its prevalence, persistence, and toxicity. Numerous studies have assessed Hg concentrations in seafood, but variability in reported concentrations highlights the need for continued monitoring and stricter regulations. We measured total Hg (tHg) in 148 pre-processed, packaged seafood products purchased in Raleigh, North Carolina, using thermal decomposition–gold amalgamation atomic absorption spectrophotometry. Products were grouped into three categories based on trophic ecology and physiology: (1) tuna, (2) other bony fish, and (3) shellfish and squid. Among tuna, albacore had the highest average tHg (396.4 ng/g ± 172.1), while yellowfin had the lowest (68.3 ng/g ± 64.7). Herring (54.0 ng/g ± 23.2) and crab (78.2 ng/g ± 24.1) had the highest concentrations in the other two groups. One can of albacore exceeded the FDA action level of 1 part per million (1.3 ppm or 1300 ng/g). Brand differences were significant for both albacore and light tuna, with Brand 1 consistently showing higher Hg levels. Comparisons to FDA data (1990–2012) suggest Hg concentrations in tuna have remained stable over the past two decades. This study underscores the variability of Hg concentrations across species and brands and the need for continued monitoring to protect consumers. Full article

The mercury (Hg) present in ocean fish has caused concern regarding the effects of maternal consumption on child outcomes but it is now recognized that mothers that eat more ocean fish during pregnancy have children with higher social, scholastic, and IQ scores. These findings coincide with the current understanding of the mechanism of Hg toxicity which indicates ocean fish consumption will prevent rather than cause harm. High-Hg exposures sequester selenium (Se) and inhibit Se-dependent enzymes that prevent and reverse oxidative damage in the brain and support fetal brain development. However, aside from certain types of shark and other apex marine predators, seafood contains more Se than Hg and thus counteracts instead of contributing to Hg toxicity. This study evaluates the Hg and Se present in bigeye tuna, yellowfin tuna, albacore tuna, skipjack, swordfish, striped marlin, blue marlin, spearfish, mahimahi, wahoo, monchong, escolar, mako shark, and thresher shark to establish their health benefit values (HBVs). Positive HBVs (0.3–19.6), indicating a molar excess of Se over Hg, were found in 14 of the 15 species studied. Only mako shark uniformly contained Hg in excess of Se to produce a negative HBV (−16.4), indicating its consumption should be minimized during pregnancy. Full article

Fat content is the main criterion for evaluating albacore quality. However, no reports exist on the accuracy of the tail-cutting method, a method used to assess the fat content of albacore. Here, we evaluated this method by comparing it with chemical analysis and ultrasound inspection. We measured the actual fat content in albacore using chemical analysis and compared the results with those obtained using the tail-cutting method. Significant discrepancies (99% CI, t-test) were observed in fat content among the tail-cutting samples. Using chemical analysis as the ground truth, the accuracy of tail-cutting from two different companies was 70.0% for company A and 51.9% for company B. An ultrasound inspection revealed that a higher fat content reduced the amplitude of ultrasound signals with statistical significance (99% CI, t-test). Finally, machine learning algorithms were used to enforce the ultrasound inspection. The best combination of ultrasound inspection and a machine learning algorithm achieved an 84.2% accuracy for selecting fat-rich albacore, which is better than tail-cutting (73.6%). Our findings suggested that ultrasound inspection could be a valuable and non-destructive method for estimating the fat content of albacore, achieving better accuracy than the traditional tail-cutting method. Full article

Tuna are economically important as food resources in food markets. However, because tuna is often processed into steaks or fillets, the meat can be difficult to identify through morphological features. For effective fishery management and to protect the rights of consumers, it is necessary to develop a molecular method to accurately identify the species used in tuna products. Herein, we discovered five single-nucleotide polymorphism (SNP) sites via 2b-RAD sequencing and developed five SNP-based real-time polymerase chain reaction assays for the rapid identification of five highly priced tuna species. Three species-specific TaqMan systems were designed to identify albacore tuna (Thunnus alalunga), bigeye tuna (T. obesus), and southern bluefin tuna (T. maccoyii) and two cycling systems were designed to identify yellowfin tuna (T. albacares) and Atlantic bluefin tuna (T. thynnus). The systems showed good specificity and sensitivity (sensitivity of 0.0002 ng μL⁻¹ for albacore tuna, bigeye tuna, and southern bluefin tuna and 0.002 ng μL⁻¹ for yellowfin tuna and Atlantic bluefin tuna). Both systems were able to distinguish the target species from other species in a specific, sensitive, and accurate manner. Thus, these methods can be employed for the identification of species used in tuna products, protecting consumers and producers from economic fraud. Full article

Aiming at the problems of large statistical error and the poor real-time performance of catch weight in the ocean fishing tuna industry, an algorithm based on improved YOLOv8-Pose for albacore tuna (Thunnus alalunga) fork length extraction and weight estimation is proposed, with reference to the human body’s pose estimation algorithm. Firstly, a lightweight module constructed using a heavy parameterization technique is used to replace the backbone network, and secondly, a weighted bidirectional feature pyramid network BIFPN is utilized. Finally, the upper and lower jaw and tail feature points of the albacore tuna (Thunnus alalunga) were extracted using the key point detection algorithm, and the weight of the albacore tuna (Thunnus alalunga) was estimated based on the fitted relationship between fork length and weight. The experimental results show that the improved YOLOv8-Pose algorithm reduces the number of model parameters by 13.63% and the number of floating-point operations by 14.03% compared with the baseline model without decreasing the accuracy of the target detection and key point detection and improves the model inference speed by 374%. At the same time, it reduces the drift of the key point detection, and the error of the comparison with the actual albacore tuna (Thunnus alalunga) body weight is not more than 10%. The improved key point detection algorithm has high detection accuracy and inference speed, which provides accurate yield data for pelagic fishing and is expected to solve the existing statistical problems and improve the accuracy and real-time performance of data in the fishing industry. Full article

To clarify potential trans-oceanic connectivity and variation in the natal origin of albacore tuna (Thunnus alalunga) from the southwest Indian Ocean (SWI) and the southeast Atlantic (SA), lifetime otolith elemental signatures were assessed from 46 adults sampled from Reunion Island, and 26 juveniles(group 2+) sampled from two locations along the Atlantic coast of South Africa. LA-ICP-MS analysis was used to assess the multi-elemental composition in B, Ba, Mg, P, Sr, and Zn along the otolith edge (chemical signatures of the capture area), but also near the otolith primordium (spawning origin) and in an area located at 1400–1600 µm from it (nursery origin). Two groups of distinct near-primordium multi-elemental signatures, denoting potentially discrete spawning origins (SpO), were identified using hierarchical clustering. Each of the two SpO was found to contribute to the albacore stocks from all the areas sampled, suggesting a common spawning origin in some fish from the SWI and from the SA, and complex migrations between the two oceans. Three potentially discrete primary nursery sites were identified, each contributing to SA juvenile and SWI adult capture sites differently. The timing for the trans-oceanic movements observed for each albacore capture zone and its implications for local stock management are discussed. Full article

The Tongan fisheries targeting the species of albacore (Thunnus alalunga), bigeye (Thunnus obesus), skipjack (Katsuwonus pelamis), and yellowfin tuna (Thunnus albacares), comprising the main tuna catch landed, within the EEZ of Tonga is critical to the economy of Tonga. Thus, it is crucial to study the spatiotemporal pattern of their catch and the influence of environmental and physical variables, in addition to the month and year of the catch. To this end, sets of eight generalized additive models were applied to model the distribution of these four species. Selection among competing models was carried out based on k-fold cross-validation, using RMSPE prediction error as a measure of model predictive performance. The following sets of predictors were considered; sea surface temperature, sea surface chlorophyll, bottom depth, month, and year. In addition, to assess the influence of fronts, gradients in SST and Chl-a were computed and used as predictors. Catch year was the most important variable for all, except Albacore tuna, for which month was the important variable. The third most important variable was SST for albacore and bigeye tuna, whereas bottom depth was the most important variable for skipjack and yellowfin tuna. A standardized index of CPUE indicates mostly inter-annual variation in CPUE for albacore and bigeye tuna, whereas a it indicates a general increase in CPUE for skipjack and yellowfin tuna. Hotspots of albacore tuna catches are around the northern and southern edges of the exclusive economic zone and typically during the months of June to August. The bigeye tuna hotspots were concentrated on the eastern side of the islands, in waters overlying trenches; this was most obvious during the months of January to June. Skipjack tuna hotspots were near the edges of the exclusive economic zone, although it is caught in smaller amounts to the three tuna species considered and higher catch rates were observed only after 2014. For yellowfin tuna, the highest catch rates were concentrated around the islands and descending towards the southern edge of the EEZ. As part of the initiative of this study to support national optimal resource management, this study generated standardized CPUE (indices of abundance), an important input in stock assessment, and also looked into the potential influence of environmental and physical variables on the CPUE of these valuable tuna stocks within the EEZ of Tonga. Full article

Despite the crucial role played by international and regional tuna fisheries in facilitating the successful implementation of the ecosystem approach to fisheries management, there exist disparities in viewpoints among these stakeholders, resulting in gaps between regional fisheries management and local communities. Nevertheless, the Tongan government, under the Ministry of Fisheries, is dedicated to the efficient management of its tuna resources, aiming to establish it as the preferred and optimal approach for ensuring the long-term sustainability of its tuna fisheries and the ecosystem services they provide to the community. Recognizing that an appropriate legal, policy and institutional framework is in place for sustainable management of tuna, the first part of this paper presents a review of current Tonga fisheries laws and policies for its tuna fisheries. This review reflects the implementation of an information-based management framework, namely the Tonga National Tuna Fishery Management and Development Plan. The tuna fisheries in Tonga mainly catch albacore (Thunnus alalunga), bigeye (Thunnus obesus), skipjack (Katsuwonus pelamis), and yellowfin (Thunnus albacares) tuna. These tuna species are caught within Tonga’s exclusive economic zones and play a crucial role in the country’s economy; hence, it is crucial to examine the spatio-temporal distributions of their catch in relation to their environmental conditions. In pursuit of this goal, the tasks of mapping (i) the spatio-temporal distribution of catch landed at ports and (ii) the spatio-temporal of environmental conditions were performed. The study utilizes longline catch per unit effort data spanning from 2002 to 2018 for albacore, bigeye, skipjack, and yellowfin tuna. It also incorporates data on environmental conditions, including sea surface temperature, sea surface chlorophyll, sea surface current, and sea surface salinity. Additionally, the El Nino Southern Oscillation Index is mapped in relation to catch data to examine the potential effects of climate change on the tuna catch. Results show that bigeye, skipjack, and yellowfin CPUE show a central–northernmost distribution and are primarily caught between latitudes 14° S–22° S, while albacore, shows a central–southern distribution. The highest CPUE for all species are in latitudes 15.5° S–22.5° S and longitudes 172.5° W–176.5° W. The data indicate that sea surface current velocities range from −0.03 to 0.04 ms⁻¹, sea surface salinity ranges from 34.8 to 35.6 PSU, sea surface chlorophyll concentration varies from 0.03 to 0.1 mg m⁻³, and sea surface temperature fluctuates seasonally, ranging from 18 °C to 30 °C. Mapping also reveals that times of reduced catches in Tonga coincide with periods of moderate to strong El Nino events from 2002 to 2018. Full article

The present study utilised a geometric mean model in which sea surface temperature, oxygen, and sea surface salinity were used to predict the effects of climate change on the habitats of mature albacore tuna in the Indian Ocean under multiple representative concentration pathway (RCP) scenarios. Data pertaining to the albacore tuna fishing conducted by Taiwanese longline fisheries during the October–March period in 1998–2016 were analysed. The fishery data comprised fishing location (latitude and longitude), fishing effort (number of hooks used), number of catches, fishing time (month and year), and fish weight. Nominal catch per unit effort data were standardised to mitigate the potential effects of temporal and spatial factors in causing bias and overestimation. The Habitat Suitability Index (HSI) scores of potential habitats for mature albacore in the Indian Ocean are predicted to change considerably in response to varying levels of predicted climate change. Under projected warm climate conditions (RCP 8.5), the stratification of water is predicted to cause low HSI areas to expand and potential habitats for mature albacore to shift southward by 2100. The findings derived from these mature albacore habitat forecasts can contribute to the evaluation of potential hazards and feasible adaptation measures for albacore fishery resources in the context of climate change. The distribution trends pertaining to potential habitats for mature albacore should be used with caution and can provide resource stakeholders with guidance for decision-making. Full article

The potential impacts of climate change on the distribution of tuna in Pacific Island countries’ exclusive economic zones have yet to be investigated rigorously and so their persistence and abundance in these areas remain uncertain. Here, we estimate optimal fisheries areas for four tuna species: albacore (Thunnus alalunga), bigeye (Thunnus obesus), skipjack (Katsuwonus pelamis), and yellowfin (Thunnus albacares). We consider different climate change scenarios, RCP 2.6, RCP 4.5, RCP 6.0, and RCP 8.5, within a set of tuna catch records in the exclusive economic zone of Tonga. Using environmental and CPUE datasets, species distribution modelling estimated and predicted these fisheries areas in the current and future climatic scenarios. Our projections indicate an expansion in area and a shift of productive areas to the southern part of this exclusive economic zone of Tonga. This is an indication that future climatic scenarios might be suitable for the species under study; however, changes in trophic layers, ocean currents, and ocean chemistry might alter this finding. The information provided here will be relevant in planning future national actions towards the proper management of these species. Full article

To achieve high-precision forecasting of different grades of albacore fishing grounds in the South Pacific Ocean, we used albacore fishing data and marine environmental factors data from 2009 to 2019 as data sources. An ensemble learning model (ELM) for albacore fishing grounds forecasting was constructed based on six machine learning algorithms. The overall accuracy (ACC), fishing ground forecast precision (P) and recall (R) were used as model accuracy evaluation metrics, to compare and analyze the accuracy of different machine learning algorithms for fishing grounds forecasting. We also explored the forecasting capability of the ELM for different grades of fishing grounds. A quantitative evaluation of the effects of different marine environmental factors on the forecast accuracy of albacore tuna fisheries was conducted. The results of this study showed the following: (1) The ELM achieved high accuracy forecasts of albacore fishing grounds (ACC = 86.92%), with an overall improvement of 4.39~19.48% over the machine learning models. (2) A better forecast accuracy (R₂ of 81.82–98%) for high-yield albacore fishing grounds and a poorer forecast accuracy (R₁ of 47.37–96.15%) for low-yield fishing grounds were obtained for different months based on the ELM; the high-yield fishing grounds were distributed in the sea south of 10° S. (3) A feature importance analysis based on RF found that latitude (Lat) had the greatest influence on the forecast accuracy of albacore tuna fishing grounds of different grades from February to December (0.377), and Chl-a had the greatest influence on the forecast accuracy of albacore tuna fishing grounds of different grades in January (0.295), while longitude (Lon) had the smallest effect on the forecast of different grades of fishing grounds (0.037). Full article

Tuna are economically important fish species. The automated identification of tuna species is of importance in fishery production and resource assessment in that it would facilitate the informed monitoring of tuna fishing vessels and the establishment of electronic observer systems. As morphological characteristics are important for tuna identification, this study aims to verify the performance of the automated identification of three Thunnus species through morphological characteristics based on different machine learning algorithms. Firstly, morphological outlines were visually analyzed using EFT (elliptic Fourier transform) and CNN (convolutional neural network). Then, the EFT feature data and deep feature data of the tuna outline images were extracted, and principal component analysis of the two different morphological characteristics was performed. Finally, different machine learning algorithms were used to analyze the identification performance of tuna of the same genus and different species. The experimental results showed that EFT features had the highest identification accuracy in KNN (K-nearest neighbor), with 90% for T. obesus, 90% for T. albacores, and 85% for T. alalunga. Deep features had the best identification performance in SVM (support vector machine), with 80% for T. obesus, 90% for T. albacores, and 100% for T. alalunga. Deep features were better than EFT features in identification performance. The biodiversity and intergeneric differences among tuna species can be well analyzed using these two different morphological characteristics. Machine learning algorithms open up the way for rapid near-real-time electronic observer systems in these important international fisheries. Full article

This study evaluated the vertical distribution of immature albacore tuna (Thunnus alalunga) in the Indian Ocean as a function of various environmental parameters. Albacore tuna fishing data were gathered from the logbooks of large-sized Taiwanese longline vessels. Fishery and environmental data for the period from 1998 to 2016 were collected. In addition to the surface variable, the most influential vertical temperature, dissolved oxygen (OXY), chlorophyll, and salinity layers were found at various depths (i.e., 5, 26, and 53 m for SST; 200, 244, and 147 m for OXY; 508, 628, and 411 for SSCI; and 411, 508, and 773 m for SSS) among 20 vertical layers based on Akaike criterion information value of generalized linear model. Relative to the 20 vertical layers base models, these layers had the lowest Akaike information criteria. For the correlation between the standardized and predicted catch per unit effort (CPUE), the correlation values for the generalized linear model (GLM), generalized additive model (GAM), boosted regression tree (BRT), and random forest (RF) model were 0.798, 0.832, 0.841, and 0.856, respectively. The GAM-, BRT-, and RF-derived full models were selected, whereas the GLM-derived full model was excluded because its correlation value was the lowest among the four models. From March to September, a higher immature albacore standardized CPUE was mainly observed from 30°S to 40°S. A northward shift was observed after September, and the standardized CPUE was mainly concentrated at the south coast of Madagascar from November to January. Full article

Ecological risk assessment (ERA) has been applied on assessing the relative risk of bycatch species in recent years. ERA index is calculated by productivity of species and susceptibility of fisheries on fish species. In this study, a semi-quantitative method was used to evaluate the risks of exploitation for 20 pelagic fish species by the small-scale longline fisheries in the western North Pacific Ocean. The productivity was estimated based on the ranking (high, median, and low) of seven life history parameters. The susceptibility was calculated by the multiplication of the catchability, selectivity and post-capture mortality. The ERA results indicated the risks of sharks are higher than those of tunas and billfishes, except yellowfin tuna (Thunnus albacares). The shortfin mako shark (Isurus oxyrinchus) and dusky shark (Carcharhinus obscurus) have the highest risk. Other shark species, yellowfin tuna, and sailfish (Istiophorus platypterus) have medium risk. While the striped marlin (Kajikia audax), and albacore tuna (T. alalunga) have the lowest risk. Stock assessment and rigorous management measures such as catch quota and size limit are recommended for the species in high or medium ecological risk and a consistent monitoring management scheme is suggested for those in low ecological risk. Full article

Estimated larval backward trajectories of three Tuna species, namely, Atlantic Bluefin Tuna (Thunnus thynnus, Linnaeus, 1758), Bullet Tuna (Auxis Rochei, Risso, 1801) and Albacore Tuna (Thunnus alalunga, Bonnaterre, 1788) in the central Mediterranean Sea, were used to characterize their spawning habitats, and to assess the impact of changes due to the major environmental parameters (i.e., sea surface temperature and chlorophyll-a concentration) on larval development during their advection by surface currents. We assumed that the environmental variability experienced by larvae along their paths may have influenced their development, also affecting their survival. Our results showed that the Tuna larvae underwent an accelerated growth in favorable environmental conditions, impacting on the notochord development. In addition, further updated information on spawning and larval retention habitats of Atlantic Bluefin Tuna, Bullet and Albacore Tunas in the central Mediterranean Sea were delivered. Full article