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Multiplexing, the simultaneous sequencing of multiple barcoded DNA samples on a single flow cell, has made Oxford Nanopore sequencing cost-effective for small genomes. However, it depends on the ability to sort the resulting sequencing reads by barcode, and current demultiplexing tools fail to classify many reads. Here we present Deepbinner, a tool for Oxford Nanopore demultiplexing that uses a deep neural network to classify reads based on the raw electrical read signal. This 'signal-space' approach allows for greater accuracy than existing 'base-space' tools (Albacore and Porechop) for which signals must first be converted to DNA base calls, itself a complex problem that can introduce noise into the barcode sequence. To assess Deepbinner and existing tools, we performed multiplex sequencing on 12 amplicons chosen for their distinguishability. This allowed us to establish a ground truth classification for each read based on internal sequence alone. Deepbinner had the lowest rate of unclassified reads (7.8%) and the highest demultiplexing precision (98.5% of classified reads were correctly assigned). It can be used alone (to maximise the number of classified reads) or in conjunction with other demultiplexers (to maximise precision and minimise false positive classifications). We also found cross-sample chimeric reads (0.3%) and evidence of barcode switching (0.3%) in our dataset, which likely arise during library preparation and may be detrimental for quantitative studies that use multiplexing. Deepbinner is open source (GPLv3) and available at https://github.com/rrwick/Deepbinner.

Growth models describe the change in length or weight as a function of age. Growth curves in tunas can take different forms from relatively simple von Bertalanffy growth curves (Atlantic bluefin, albacore tunas) to more complex two- or three-stanza growth curves (yellowfin, bigeye, skipjack, southern bluefin tunas). We reviewed the growth of the principal market tunas (albacore, bigeye, skipjack, yellowfin and the three bluefin tuna species) in all oceans to ascertain the different growth rates among tuna species and their implications for population productivity and resilience. Tunas are among the fastest-growing of all fishes. Compared to other species, tunas exhibit rapid growth (i.e., relatively high K) and achieve large body sizes (i.e., high L∞). A comparison of their growth functions reveals that tunas have evolved different growth strategies. Tunas attain asymptotic sizes (L∞), ranging from 75 cm FL (skipjack tuna) to 400 cm FL (Atlantic bluefin tuna), and reach L∞ at different rates (K), varying from 0.95 year−1 (skipjack tuna) to 0.05 year−1 (Atlantic bluefin tuna). Skipjack tuna (followed by yellowfin tuna) is considered the “fastest growing” species of all tunas. Growth characteristics have important implications for population dynamics and fisheries management outcomes since tunas, and other fish species, with faster growth rates generally support higher estimates of Maximum Sustainable Yield (MSY) than species with slower growth rates.

Delay-difference models are intermediate between simple surplus-production models and complicated age-structured models. Such intermediate models are more efficient and require less data than age-structured models. In this study, a delay-difference model was applied to fit catch and catch per unit effort (CPUE) data (1975–2011) of the southern Atlantic albacore ( Thunnus alalunga ) stock. The proposed delay-difference model captures annual fluctuations in predicted CPUE data better than Fox model. In a Monte Carlo simulation, white noises (CVs) were superimposed on the observed CPUE data at four levels. Relative estimate error was then calculated to compare the estimated results with the true values of parameters α and β in Ricker stock-recruitment model and the catchability coefficient q. a is more sensitive to CV than β and q . We also calculated an 80% percentile confidence interval of the maximum sustainable yield (MSY, 21756 t to 23408 t; median 22490 t) with the delay-difference model. The yield of the southern Atlantic albacore stock in 2011 was 24122 t, and the estimated ratios of catch against MSY for the past seven years were approximately 1.0. We suggest that care should be taken to protect the albacore fishery in the southern Atlantic Ocean. The proposed delay-difference model provides a good fit to the data of southern Atlantic albacore stock and may be a useful choice for the assessment of regional albacore stock.

Aim: We examined potential environmental drivers of broad-scale spatial patterns in the trophic structure of marine ecosystems as represented by nitroge-n stable isotopes in globally distributed marine predators. Additionally, we assessed the effects of spatial scale on the predictive capabilities of environmental variables. Location: Global oceans. Time period: 2000 to 2015. Major taxa studied: Tunas: Thunnus albacares, Thunnus obesus, Thunnus alalunga. Methods: We undertook a global compilation and meta-analysis of the bulk nitrogen stable isotope ratios (δ15N values) of three tuna species (n = 4,281). After adjusting for regional variations in baseline δ15N values using a global ocean biogeochemistry model, generalized additive mixed models were employed to infer global-scale oceanographic controls of trophic structure, using cosmopolitan tuna species as a model. Results: For the three tuna species, variation in trophic position estimated using bulk δ15N values was largely explained by geographical location and the corresponding oxygen minimum layer depth. Tuna trophic positions declined in areas with reduced oxygen at depth. Food-chain length, as captured by maximum trophic position, was longer in areas of the western Pacific Ocean and shorter in the northern Atlantic and eastern Pacific Oceans. Trophic adaptability of the tuna predators, as indicated by intraspecific variability, was highest in the western and central Pacific Ocean and lowest in the northern Atlantic Ocean. Our analysis demonstrated that while tunas share similar functional trophic roles, deeper-foraging tuna species had higher trophic positions globally. The predictive capacity of environmental variables decreased at finer (regional) spatial scales. Main conclusions: Our work suggests that habitat compression resulting from the predicted global expansion of oxygen minimum zones with ocean warming will impact the trophic structure of marine food webs and the corresponding foraging habits of marine predators. Spatial scale analyses highlighted the importance of representing differences in regional ecological dynamics in global-scale trophic and ecosystem models.

Indonesia is one of the largest tuna producers in the world, which contributes 16% to world tuna production. The dominant tuna species catched in Indonesia are Albacore Tuna ( Thunnus alalunga ), Madidihang/Yellowfin Tuna ( T. albacares ), Big Eye Tuna ( T. obesus ) dan Southern Bluefin Tuna ( T. maccoyii ). The tuna fisheries have contributed significant jobs or livelihood to the coastal communities. Profit and revenue sharing is a common remuneration system found on tuna fisheries though out Indonesia. However, these fishers are vulnerable given their economic and welfare conditions and of usually limited options of others livelihood. Small fishers have limited access to livelihoods, access to finance and access to skills or fishing technology. Therefore, the inclusion of socio-economic performance or indicators into the tuna fisheries management is crucial in Indonesia.

Accurate forecasts of species distributions in response to changing climate is essential for proactive management and conservation decision‐making. However, species distribution models (SDMs) often have limited capacity to produce robust forecasts under novel environmental conditions, partly due to limitations in model training data. Model‐based approaches that leverage diverse types of data have advanced over the last decade, yet their forecasting skill, especially during episodic climatic events, remains uncertain. Here, we develop a suite of SDMs for a commercially important fishery species, albacore tuna Thunnus alalunga, to evaluate forecast skill under marine heatwave conditions. We compare models that use different methods to leverage data sources (data‐pooling versus joint‐likelihood) and to address spatial dependence (environmental and spatial effects versus environmental‐only) to assess their relative performance in predicting species distributions under novel environmental conditions. Our results indicate model performance declined across all model types as environmental novelty increased as expected. However, joint‐likelihood approaches were more resilient to novel conditions, demonstrating greater predictive skill and ecological realism than traditional SDMs. These results suggest that ecological forecasts under novel environmental conditions are more skillful with a model framework that accounts for unmeasured spatial and temporal variability and uses model‐based data integration to explicitly leverage diverse data types. As access to diverse data sources continues to increase, maximizing their utility will be key for delivering accurate forecasts of species distributions and advancing proactive, climate‐ready management and conservation strategies.

Stable isotopes of carbon and nitrogen were used to test the hypothesis that stomach content analysis has systematically overlooked the consumption of gelatinous zooplankton by pelagic mesopredators and apex predators. The results strongly supported a major role of gelatinous plankton in the diet of bluefin tuna (Thunnus thynnus), little tunny (Euthynnus alletteratus), spearfish (Tetrapturus belone) and swordfish (Xiphias gladius). Loggerhead sea turtles (Caretta caretta) in the oceanic stage and ocean sunfish (Mola mola) also primarily relied on gelatinous zooplankton. In contrast, stable isotope ratios ruled out any relevant consumption of gelatinous plankton by bluefish (Pomatomus saltatrix), blue shark (Prionace glauca), leerfish (Lichia amia), bonito (Sarda sarda), striped dolphin (Stenella caerueloalba) and loggerhead sea turtles (Caretta caretta) in the neritic stage, all of which primarily relied on fish and squid. Fin whales (Balaenoptera physalus) were confirmed as crustacean consumers. The ratios of stable isotopes in albacore (Thunnus alalunga), amberjack (Seriola dumerili), blue butterfish (Stromaeus fiatola), bullet tuna (Auxis rochei), dolphinfish (Coryphaena hyppurus), horse mackerel (Trachurus trachurus), mackerel (Scomber scombrus) and pompano (Trachinotus ovatus) were consistent with mixed diets revealed by stomach content analysis, including nekton and crustaceans, but the consumption of gelatinous plankton could not be ruled out completely. In conclusion, the jellyvorous guild in the Mediterranean integrates two specialists (ocean sunfish and loggerhead sea turtles in the oceanic stage) and several opportunists (bluefin tuna, little tunny, spearfish, swordfish and, perhaps, blue butterfish), most of them with shrinking populations due to overfishing.

Illumina and nanopore sequencing technologies are powerful tools that can be used to determine the bacterial composition of complex microbial communities. In this study, we compared nasal microbiota results at genus level using both Illumina and nanopore 16S rRNA gene sequencing. We also monitored the progression of nanopore sequencing in the accurate identification of species, using pure, single species cultures, and evaluated the performance of the nanopore EPI2ME 16S data analysis pipeline. Fifty-nine nasal swabs were sequenced using Illumina MiSeq and Oxford Nanopore 16S rRNA gene sequencing technologies. In addition, five pure cultures of relevant bacterial species were sequenced with the nanopore sequencing technology. The Illumina MiSeq sequence data were processed using bioinformatics modules present in the Mothur software package. Albacore and Guppy base calling, a workflow in nanopore EPI2ME (Oxford Nanopore Technologies—ONT, Oxford, UK) and an in-house developed bioinformatics script were used to analyze the nanopore data. At genus level, similar bacterial diversity profiles were found, and five main and established genera were identified by both platforms. However, probably due to mismatching of the nanopore sequence primers, the nanopore sequencing platform identified Corynebacterium in much lower abundance compared to Illumina sequencing. Further, when using default settings in the EPI2ME workflow, almost all sequence reads that seem to belong to the bacterial genus Dolosigranulum and a considerable part to the genus Haemophilus were only identified at family level. Nanopore sequencing of single species cultures demonstrated at least 88% accurate identification of the species at genus and species level for 4/5 strains tested, including improvements in accurate sequence read identification when the basecaller Guppy and Albacore, and when flowcell versions R9.4 (Oxford Nanopore Technologies—ONT, Oxford, UK) and R9.2 (Oxford Nanopore Technologies—ONT, Oxford, UK) were compared. In conclusion, the current study shows that the nanopore sequencing platform is comparable with the Illumina platform in detection bacterial genera of the nasal microbiota, but the nanopore platform does have problems in detecting bacteria within the genus Corynebacterium. Although advances are being made, thorough validation of the nanopore platform is still recommendable.

Juvenile albacore tuna ( Thunnus alalunga ) undertake long migrations across the North Pacific that they fuel by feeding in the California Current System (CCS) during the summer. The CCS is a highly dynamic system, which can lead to differences in foraging success that may be indicated by differences in body condition. Assessed through stomach content analysis, albacore diet composition by mean proportional weight showed high interannual variability including some years characterized by large increases in consumption of anchovy (2011 = 81%, 2017, 2022) and sardine ( Sardinops sagax , 2022). We used a generalized additive model (GAM) to investigate the impact of albacore length, month of collection, diet composition, and environmental conditions on albacore muscle energy content. Two proxies for muscle energy content, the carbon to nitrogen ratio (C:N) and ash free dry weight (AFDW), were positively but weakly correlated. Albacore C:N increased with month, body size, and upwelling in the first half of the year, and decreased with increasing sea surface temperature. The mean energy density of prey and chlorophyll- a concentration were not important predictors of muscle energy content, indicating that albacore may be resilient to changes in prey composition and productivity. Sea surface temperature was the most important predictor of muscle energy content, which suggests that marine heat waves and projected future warming in the CCS may have detrimental effects on albacore body condition or the value of the CCS as a foraging habitat.

Albacore is one of the most important commercially harvested species in the world’s oceans. Despite a long history of scientific research, there is no global review or synthesis of knowledge about the species across all oceanic regions. We analysed 613 published studies that report on albacore and summarize the current state of knowledge on biology, stock structure, fisheries and management. To describe the trends in albacore fisheries, we examined the catch and effort databases of Regional Fisheries Management Organisations. The stocks of albacore are generally largest in the Pacific Ocean and smallest in the Mediterranean Sea. The biology of Atlantic and Pacific Ocean stocks are well documented, while the Indian Ocean and the Mediterranean Sea stocks are more data deficient. These two latter areas should be considered as priorities for future research and data collection in order to better understand the state of global stocks of albacore tuna. Improved information would also assist with delineating stock boundaries needed for sustainable management of this species.