Distribution Of Tuna Research Articles

Spatial distribution models for the four commercial tuna in the sea of maritime continent using multi-sensor remote sensing and maximum entropy

The dynamic of marine environmental parameters affects the distribution of commercial tuna in the sea of the maritime continent. Hence, the objectives of this study are to develop spatial distribution models for the four main tuna species in the Maritime continent's sea with reasonable accuracy, identify their correlation with marine environmental parameters, and investigate areas of interaction between those tuna species. The study develops the distribution models for albacore (Thunnus alalunga), bigeye (Thunnus obesus), yellowfin (Thunnus albacares), and skipjack (Katsuwonus pelamis) tuna species, utilizing multi-sensor satellite remote sensing and maximum entropy. The results show models have good performance, focusing on environmental factors such as sea surface temperature (SST), chlorophyll-a (CHL), and sea surface height anomalies (SSHA), combined with eddy kinetic energy (EKE). Seasonal variations in potential tuna habitats are revealed, emphasizing the influence of those marine environmental conditions. From December to May, the four commercial tuna species were distributed in conditions characterized by SST of 26–31.5 °C, CHL levels of 0–3 mg/l, SSHA of −0.3 to 0.2 m, and EKE of 0–1 m2/s2, while from June to November, they experienced SST of 23–31 °C, CHL levels of 0–4 mg/l, SSHA of −0.5 to 0.3 m, and EKE of 0–1.1 m2/s2. The spatial persistence of the four tuna species emerged mainly around the south sea of Java, with skipjack being the most common species found in the sea of the maritime continent. With sufficient and evenly distributed tuna presence records, the results indicate the potential for extrapolation beyond the training data to estimate habitat suitability for the four commercial tuna distributions. The results also suggest potential competition between tuna species sharing ecological niches and highlight possible overlapping areas where different tuna species interact with the same fishing gear.

Modeling tropical tuna shifts: An inflated power logit regression approach.

We introduce a new class of zero-or-one inflated power logit (IPL) regression models, which serve as a versatile tool for analyzing bounded continuous data with observations at a boundary. These models are applied to explore the effects of climate changes on the distribution of tropical tuna within the North Atlantic Ocean. Our findings suggest that our modeling approach is adequate and capable of handling the outliers in the data. It exhibited superior performance compared to rival models in both diagnostic analysis and regarding the inference robustness. We offer a user-friendly method for fitting IPL regression models in practicalapplications.

Mesoscale activity drives the habitat suitability of yellowfin tuna in the Gulf of Mexico

Yellowfin tuna, Thunnus albacares, represents an important component of commercial and recreational fisheries in the Gulf of Mexico (GoM). We investigated the influence of environmental conditions on the spatiotemporal distribution of yellowfin tuna using fisheries’ catch data spanning 2012–2019 within Mexican waters. We implemented hierarchical Bayesian regression models with spatial and temporal random effects and fixed effects of several environmental covariates to predict habitat suitability (HS) for the species. The best model included spatial and interannual anomalies of the absolute dynamic topography of the ocean surface (ADTSA and ADTIA, respectively), bottom depth, and a seasonal cyclical random effect. High catches occurred mainly towards anticyclonic features at bottom depths > 1000 m. The spatial extent of HS was higher in years with positive ADTIA, which implies more anticyclonic activity. The highest values of HS (> 0.7) generally occurred at positive ADTSA in oceanic waters of the central and northern GoM. However, high HS values (> 0.6) were observed in the southern GoM, in waters with cyclonic activity during summer. Our results highlight the importance of mesoscale features for the spatiotemporal distribution of yellowfin tunas and could help to develop dynamic fisheries management strategies in Mexico and the U.S. for this valuable resource.

Poleward catch displacement of blackfin tuna Thunnus atlanticus in the southwestern Atlantic Ocean: Possible effect of increasing water temperatures

AbstractBlackfin tuna Thunnus atlanticus often inhabits warm waters of the western Atlantic Ocean from 40°N (Cape Cod, USA) to 22°S (Rio de Janeiro, Brazil). We recorded the first catches of the species in southern Brazil (34°S) and described its new distribution and fishing grounds. Since 2007, annual catches per trip and frequency of blackfin tuna landings increased for three oceanic fishing fleets in Southern Brazil. The spatial distribution of higher catches and the center of gravity of spatial fishing records moved poleward. Sea surface temperature anomalies for the region warmed from 1960 to 2019, and anomalies were primarily positive after 2000. Evidence suggests that the distribution of blackfin tuna moved poleward by six degrees of latitude (~660 km). The causes of this distribution extension were unclear, but ocean warming was the most likely driver.

Identifying optimal variables for machine-learning-based fish distribution modeling

Machine learning occupies a central position in the modeling of fish distribution patterns. The augmentation of explanatory variables in fish habitat through many kinds of observational methodologies necessitates the discernment of an optimal combination of these variables for fish distribution modeling. We proposed a feature selection technique, recursive feature elimination with cross-validation (RFECV), to determine optimal variables combinations for yellowfin tuna distribution in the Pacific Ocean. Four tree-based models, random forest, eXtreme Gradient Boosting, Light Gradient Boosting Machine, and categorical boosting driven by RFECV, were developed using comprehensive fisheries and biotic/abiotic data. Habitat variables including sea temperature, dissolved oxygen concentration, chlorophyll-a concentration, sea salinity, and sea surface height were identified as significant features by all models. The models were trained using the corresponding selected variables, and these trained models were employed to predict the spatiotemporal distribution of yellowfin tuna from 1995 to 2019. The results obtained could inform useful knowledge for the sustainable exploitation of yellowfin tuna in the Pacific Ocean and furnish a benchmark of feature selection for machine-learning-based distribution modeling of other pelagic species.

Satellite-derived prediction on habitat modelling of skipjack tuna (Katsuwonus pelamis) in the Makassar Strait, Indonesia

The Makassar Strait is one of the Indonesian Throughflow (ITF) branches that transports warm water masses from the Pacific Ocean to the Indian Ocean. These water masses have a significant impact on oceanographic parameters, which in turn affects the skipjack tuna distribution. Satellite-derived oceanographic factors from January 2015 to December 2020 included sea surface temperature (SST), chlorophyll-a, salinity, sea surface height (SSH), surface current, and surface wind are used to predict the potential habitat of skipjack tuna (Katsuwonus pelamis) in the Makassar Strait using the maximum entropy (MaxEnt) model. The SSH was the most important oceanographic variable affecting the skipjack tuna catch, contributing 49.7% to the model gain. An increasing skipjack tuna catch was observed within the following oceanographic variable ranges: 0.48–0.58 m of SSH, 34–35 ppt of salinity, 0.1–1.2 m/s of surface current, 29–30 °C of SST, 5–6 m/s of surface wind, and 0.1–0.5 mg/l of chlorophyll-a concentrations.

Plankton assessment across the distribution of West African hake and tuna based on eDNA metabarcoding

The richness of plankton communities determines the fish productivity in the ocean, including important resources that rely on extractive fisheries, such as hakes (genus Merluccius) and tunas (genus Thunnus). Their preys forage on zooplankton, and the latter feed on phytoplankton. Inventories of plankton communities for scientific advice to sustainable fishing are essential in this moment of climate change. Plankton is generally inventoried using conventional methodologies based on large water volumes and visual morphological analyses of samples. In this study, we have employed metabarcoding on environmental DNA (eDNA) samples extracted from small water volumes for plankton inventory from twelve distant sampling stations in the East Atlantic Ocean. Zones rich in hake and tuna prey were detected from eDNA, and multivariate multiple regression analysis was able to predict those zones from diatom-based indices and planktonic diversity based on functional groups. Salinity was negatively correlated with the proportion of diatoms in phytoplankton, highlighting expected impacts of current global change on marine plankton communities. The results emphasise the importance of the plankton richness for fish productivity and support the utility of environmental DNA as a tool to monitor plankton composition changes.

Evaluating the impacts of environmental and fishery variability on the distribution of bigeye tuna in the Pacific Ocean

Abstract Climate change-induced variabilities in the environment and fishing pressure affect the distribution and abundance of bigeye tuna in the Pacific Ocean. Understanding the causal relationships among these factors is complicated and challenging. We constructed a multi-output neural network model based on data from four types of bigeye tuna fisheries (longline and purse seine in the west-central and eastern Pacific Ocean, respectively) and marine environmental data, aiming to analyse the response of bigeye tuna to natural and anthropogenic factors from 1995 to 2019 in the Pacific Ocean. The input layer weights were used to explore the importance of environmental variable, while the output layer weights evaluated the contribution of fishing operations. These factors determined the final spatiotemporal distribution and abundance dynamics for bigeye tuna. The optimal model predicted a strong correlation between the locations of major habitats and El Niño southern Oscillation (ENSO) events, indicating that bigeye tuna abundance dynamics respond to the intensity of climate variability. During El Niño events, suitable conditions lead to an expansion of the main habitats east of 170°W, while during La Niña events, the strengthening of the westward advection leads to the contraction of major habitats west of 170°W. Furthermore, the resource abundance of bigeye tuna is predicted to be higher during moderate to weak El Niño events than during strong El Niño events. The abundances in purse seine and longline-dependent fisheries demonstrate significant different distribution patterns under different ENSO events, reflecting the unique environmental preferences at different life stages of bigeye tuna. Given the increasing frequency of climate variability and escalating fishing pressures, our findings provide beneficial insights for the sustainable development of bigeye tuna resource in the Pacific Ocean.

Real-time multi-month forecasting of skipjack tuna (Katsuwonus pelamis) habitat in the western and central Pacific Ocean for improved fishing efficiency and fisheries management

Abstract Marine fish experience shifts in their distribution due to changes in the physical and biological environments. These shifts pose challenges for fishery businesses and management in international fisheries organizations. In the western and central Pacific Ocean, changes in the spatial distribution of skipjack tuna (Katsuwonus pelamis) due to climate variability often influence fishing activities and economic benefits. This study provides an operational forecast to enhance the decision-making process for fishery managers and fishermen by informing them about fish distributions in the coming months. Monthly forecasts of skipjack tuna habitat are generated utilizing a species distribution model (boosted regression trees) in combination with real-time environmental forecasts. An alternative method (dynamic time warping) is proposed to improve monthly chlorophyll forecasts, which are crucial for enhancing skipjack tuna habitat forecasting. To assess the prediction skills of the model, a retrospective analysis was conducted, forecasting skipjack tuna distribution over 9-month periods and comparing the forecasts with observed occurrences. The results demonstrate that 67.9% of the observed skipjack tuna catches occurred within the forecasted habitats, indicating that the forecast model skillfully predicted skipjack tuna movements 9 months in advance. These monthly updated forecasts can serve as a potential tool for resource managers in developing effective management strategies and fishers in planning fishing operations sustainably and responsibly.

Utilizing Remote Sensing and GIS to Determine the Role of Temperature in Sardine and Tuna Distribution in the Gulf of Guinea

Utilizing Remote Sensing and GIS to Determine the Role of Temperature in Sardine and Tuna Distribution in the Gulf of Guinea

Acute thermal stress increased enzyme activity and muscle energy distribution of yellowfin tuna.

Heat is a powerful stressor for fish living in natural and artificial environments. Understanding the effects of heat stress on the physiological processes of fish is essential for better aquaculture and fisheries management. In this experiment, a heating rod was used to increase the temperature at 2°C/h to study the changes of energy allocation (CEA) and energy metabolity-related enzyme activities, including pepsin, trypsin, amylase, lipase, acid phosphatase, lactate dehydrogenase, alanine aminotransferase, glutamic oxalic aminotransferase and energy reserve (Ea), energy expenditure (ETS), in juvenile yellowfin tuna cells under acute temperature stress. The results showed that the Ea of juvenile yellowfin tuna muscles in response to high temperature (34°C) was significantly lower than that of the control (28°C), and it also increased ETS. At 6 h, CEA decreased slightly in the high-temperature group, but, the difference in CEA between 24 h and 0 h decreased. After heat stress for 6 h, the activities of acid phosphatase (ACP), lactate dehydrogenase (LDH), alanine aminotransferase (ALT) and glutamic oxalacetic transaminase (AST) increased, indicating that the metabolic rate was accelerated. After heat stress for 24 h, the activity of ALT decreased, indicating that with time elapsed, the activities of some protein metabolizing enzymes increased, and some decreased. In this study, digestive enzymes, trypsin and lipase increased gradually. After heat stress, Ea and Ec change significantly. Yellowfin tuna muscles use lipids in response to sharp temperature increases at high temperatures, red muscles respond to temperature changes by increasing energy in the early stages, but not nearly as much, and white muscles reduce lipids.

Effect of ocean circulation and chlorophyll-a concentration on yellowfin tuna catch rates in Sri Lankan logline fishery

Sri Lanka has access to fishery resources in the Indian Ocean due to its location. While traditional fishing methods are still used in the Sri Lankan fishery industry, modern technologies such as remote sensing and GIS are employed to determine spatiotemporal distribution of tuna fish resources in the offshore fishery industry. However, due to uncertain catch rates, Sri Lankan fishers report low catch per unit effort, leading to increased fishing duration to meet targeted catch. Potential fishing areas can be identified based on oceanographic conditions to reduce search time and improve efficiency, particularly for highly migratory species like yellowfin tuna. Therefore, this study is focused on identifying the impact of subsurface ocean currents and chlorophyll-a concentration on yellowfin tuna aggregations. The study analysed fishery and oceanographic data from January 2018 to December 2019 within a latitudinal range of 0°N to 20°N and a longitudinal range of 70°E to 90°E. The data were gridded at a resolution of 0.25 degrees and then matched for analysis. The generalized additive model (GAM) and the empirical cumulative distribution function (ECDF) were applied to identify the nature of relationships between CPUE and the oceanographic conditions. The results showed high tuna aggregations occurred in chlorophyll-a concentration between 0.3– 0.4 mg/m3 and current speed less than 0.4 m/s at a depth of 60 – 75 m of Sri Lankan longliners. Yellowfin tuna fishable aggregations were available throughout the year, although the spatial distribution of yellowfin tuna varied depending on the prevailing oceanographic conditions. Thus, searching for fishing locations based on oceanographic factors is essential for successful fishing operations. Hence, tuna harvest can be enhanced by maintaining fishing areas and the longline depth according to the oceanographic factors.

Study of the distribution of the catch of yellowfin tuna (Thunnus albacares) that was land at TPI (fish landing place) Ulee Cot, Meuraxa District, Banda Aceh City

Activities at fishing port greatly affect the process of distributing catches, especially the distribution of yellowfin tuna at TPI Ulee Cot. Distribution activities have not been optimal and have hampered the distribution flow. This is due to the slow process of handling fish and the incomplete facilities. The length of the distribution process will determine the quality of the product to be marketed. This study aims to describe the distribution activities of the catch landed at TPI, the condition of fishing facilities and relation to the distribution of yellowfin tuna. The data taken consisted of the handling of the catch, pattern, transportation, price, the fishing unit and TPI facilities distribution from interviews and questionnaires. Data analysis is carried out into data selection, data appearance, and drawing conclusions. Distribution activities start from fish transportation and fish handling from boats to fish transported into trucks to be distributed to destination area. The distribution pattern at TPI Ulee Cot only consists of one distribution pattern starting from the level of fishermen-exporting companies. The lack of cold storage facilities causes the quality of the catch to decrease because the fish are not immediately removed from the hold ship until the truck came.

Climate change to drive increasing overlap between Pacific tuna fisheries and emerging deep-sea mining industry

In ocean areas beyond national jurisdiction, various legal regimes and governance structures result in diffused responsibility and create challenges for management. Here we show those challenges are set to expand with climate change driving increasing overlap between eastern Pacific tuna fisheries and the emerging industry of deep-sea mining. Climate models suggest that tuna distributions will shift in the coming decades. Within the Clarion-Clipperton Zone of the Pacific Ocean, a region containing 1.1 million km2 of deep-sea mining exploration contracts, the total biomass for bigeye, skipjack, and yellowfin tuna species are forecasted to increase relative to today under two tested climate-change scenarios. Percentage increases are 10–11% for bigeye, 30–31% for skipjack, and 23% for yellowfin. The interactions between mining, fish populations, and climate change are complex and unknown. However, these projected increases in overlap indicate that the potential for conflict and resultant environmental and economic repercussions will be exacerbated in a climate-altered ocean. This has implications for the holistic and sustainable management of this area, with pathways suggested for closing these critical gaps.

Climate Change Potential Impacts on the Tuna Fisheries in the Exclusive Economic Zones of Tonga

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.

Seasonal Migration Zone of Skipjack Tuna (Katsuwonus pelamis) in the South Java Sea Using Multisensor Satellite Remote Sensing

Skipjack tuna (Katsuwonus pelamis) is the most dominating tuna species caught in the Indian Ocean. However, tuna fishermen in the Indian Ocean still face difficulties determining potential skipjack tuna fishing grounds due to limited geographic information. An attempt to improve information regarding potential skipjack tuna fishing grounds is through modeling the SST, SSC, SSH, and SSS oceanographic parameters with skipjack tuna distribution using GIS. The characteristics of the skipjack tuna habitat can be found through the generalized additive model (GAM) statistical analysis using data of skipjack tuna catch and oceanographic parameters from satellite imagery. The integrated GIS and GAM methods can improve the study of the habitat of skipjack tuna species. Therefore, this research is aimed at analyzing the condition and SST, SSC, SSH, and SSS variations in 2016-2021 in the South Java Sea; at predicting the correlation of SST, SSC, SSH, and SSS towards skipjack tuna catch in 2016-2021 in the South Java Sea using GAM; and at creating a map of skipjack tuna seasonal migration zones. The data included skipjack tuna catches, number of trips, and skipjack tuna fishing coordinates in 2016-2021 from PPS Cilacap and PPP Pondokdadap. The oceanographic parameter data in 2016-2021 was derived from Aqua MODIS level 3 (SST and SSC) and CMEMS (SSH and SSS) satellite imagery. The results showed that the average values of oceanographic parameters at skipjack tuna fishing ground coordinates in the South Java Sea (2016-2021) were SST (26.050-30.816°C), SSC (0.094-0.564 mg/m3), SSH (0.268-0.639 m), and SSS (33.075-34.514 psu). The best GAM modeling was the combination of four oceanographic parameter variables with an AIC value of 45357.92. Skipjack tuna in the South Java Sea migrates southeast during the west monsoon season to the first transitional season and tends to migrate northwest during the east monsoon season to the second transitional season.

Weak range-wide population structure in the blackfin tuna (Thunnus atlanticus) revealed by analysis of genome-wide SNPs

Abstract Blackfin tuna (Thunnus atlanticus) is a small tuna distributed in the western Atlantic Ocean where it is exploited by growing recreational and commercial regional fisheries. In this work, genome-wide genetic variation was analysed to investigate the occurrence of stock subdivision. A de novo assembly of the blackfin tuna genome was generated using Illumina paired-end sequencing data and applied as a reference for population genomic analysis of specimens from nine localities (average sample size per locality n = 72) spanning most of the blackfin tuna distribution range. A total of 2139 single-nucleotide polymorphisms were discovered and genotyped using the double-digest restriction associated DNA sequencing. Pairwise exact homogeneity tests were significant in 24 out of 36 population pairs and significant spatial autocorrelation of genotypes was observed for specimens collected within 2250 km of each other. However, divergence among locality samples was very low (pairwise FST range 0.0002–0.0025) and significant temporal variations were detected in localities sampled multiple times. Approaches to detect cryptic groups de novo were unsuccessful. Additional sampling is warranted to determine if multiple stocks need to be defined for management and assess temporal and spatial patterns of gene flow connecting them.

Blockchain Applications in Fisheries

Urbanization has led to increasing fish consumption, resulting in high demand for fish and fish products. Challenges in this sector, include fraudulent fish supply, overfishing, unscientific handling, quality concerns, etc. Blockchain-based systems in the seafood sector could provide traceability access to the seafood for the consumers and authorities to know if the seafood consumed/sold is legal, ethical, hygienic, economic, etc. Blockchain platforms like Ethereum promote secure digital collaboration of the actors across the supply chain eliminating the intermediaries. The literature documents a very limited number of blockchains that operate in the fisheries sector. Pacifical Atato is designed to promote and develop yellowfin tuna supply chains of Pacific island nations. Project Provenance Limited seeks to bring an end to the unsustainable fishing practices. Tuna distribution through transparent, novel chains is the goal of TraSeable solutions. Treum explored the investments to develop the supply chains of fisheries in the South Pacific. IBM’s Food Trust traces food supplies, including farmed shrimp from India, and is affiliated with retailers like Walmart, Nestle, etc. OpenSC is a World Wildlife Fund (WWF) project that ensures ethical product sourcing and uses QR (Quick Response) code scanning and RFID (Radio Frequency Technology) to track fish.

Potential Distribution of Skipjack Tuna Using MODIS Satellite Imagery in The Southern Waters of West Java - Banten

The West Java southern waters - Banten is among the skipjack tuna Katsiuwonus Pelamis distribution areas. It is well known that the skipjack tuna potential distribution is influenced by the environmental circumstances of the waters. This study was performed to establish the potential distribution of skipjack tuna in the West Java southern waters– Banten to support the potential management program of marine fishery resources. The data used consisted of skipjack tuna catch position, sea surface temperature, and chlorophyll-a concentrations from June 2019 to August 2019. Descriptive analysis and maximum entropy (maxent) model were applied to analyze skipjack tuna’s potential distribution. The results revealed that the potential area of skipjack tuna is scattered in parts of the southern waters of West Java - Banten, with a validation test of the model performance of 0.92 (excellent).

Modelling of tuna around fish aggregating devices: The importance of ocean flow and prey

Catch and distribution of tuna in the ocean are typically investigated with ocean basin-scale models. Due to their large scale, such models must greatly simplify tuna behaviour occurring at a scale below ∼100 km, despite interactions at this level potentially being important to both catch and distribution of tuna. For example, the associative behaviour of tuna with man-made floating objects, that are deployed by fishers to improve their catch rates (Fish Aggregating Devices; FADs), are usually ignored or simplified. Here we present a model that can be used to investigate the influence of tuna dynamics below the ∼100 km scale on larger scales. It is an Individual-Based Model (IBM) of a hypothetical, tuna-like species, that includes their interactions with each other, free-floating FADs and prey. In this IBM, both tuna and FADs are represented by Lagrangian particles that are advected by an ocean flow field, with tuna also exhibiting active swimming based on internal states such as stomach fullness. We apply the IBM in multiple configurations of idealized flow and prey fields, alongside differing interaction strengths between agents. When tuna swimming behaviour is influenced equally by prey and FADs, we find that the model simulations compare well with observations at the ≲100 km scale. For instance, compared to observations, tuna particles have a similar stomach fullness when associated or non-associated to a FAD, tuna colonize at similar timescales at FADs after their deployment and tuna particles exhibit similar variations in continuous residence times. However, we find large differences in emergent dynamics such as residence and catch among different flow configurations, because the flow determines the time scale at which tuna encounter FADs. These findings are discussed in the context of directing future research, and an improved interpretation of tuna catch and other data for the sustainable management of these economically important species.