The Potential Vertical Distribution of Bigeye Tuna (Thunnus obesus) and Its Influence on the Spatial Distribution of CPUEs in the Tropical Atlantic Ocean

Yang, Shenglong; Song, Liming; Zhang, Yu; Fan, Wei; Zhang, Bianbian; Yang, Dongfang; Zhang, Heng; Zhang, Shengmao; Wu, Yumei

doi:10.1007/s11802-020-4264-0

Cited by 6 publications

(4 citation statements)

References 35 publications

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“…This vertical movement of yellowfin tuna is primarily influenced and constrained by subsurface seawater temperature. Therefore, the impact of subsurface seawater temperature on the resource situation of yellowfin tuna is significant [37,69]. At the same time, many countries target adult yellowfin tuna for fishing [70], employing deep longline fishing methods [13,40,68].…”

Section: Spatial Association Of Yellowfin Tuna Catch With Dominant En...mentioning

confidence: 99%

“…In conducting research related to yellowfin tuna, addressing multicollinearity among marine environmental factors is crucial prior to model construction. Existing studies predominantly employ the variance inflation factor (VIF) method to eliminate collinear factors [20,69]. However, these eliminated environmental factors may still significantly influence yellowfin tuna catch.…”

Section: Application Of Gwpca-mgwr Model In Yellowfin Tuna Catch Studymentioning

confidence: 99%

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The Use of the GWPCA-MGWR Model for Studying Spatial Relationships between Environmental Variables and Longline Catches of Yellowfin Tunas

Li,

Yang,

Wang

et al. 2024

JMSE

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The yellowfin tuna represents a significant fishery resource in the Pacific Ocean. Its resource endowment status and spatial variation mechanisms are intricately influenced by marine environments, particularly under varying climate events. Consequently, investigating the spatial variation patterns of dominant environmental factors under diverse climate conditions, and understanding the response of yellowfin tuna catch volume based on the spatial heterogeneity among these environmental factors, presents a formidable challenge. This paper utilizes comprehensive 5°×5° yellowfin tuna longline fishing data and environmental data, including seawater temperature and salinity, published by the Western and Central Pacific Fisheries Commission (WCPFC) and the Inter-American Tropical Tuna Commission (IATTC) for the period 2000–2021 in the Pacific Ocean. In conjunction with the Niño index, a multiscale geographically weighted regression model based on geographically weighted principal component analysis (GWPCA-MGWR) and spatial association between zones (SABZ) is employed for this study. The results indicate the following: (1) The spatial distribution of dominant environmental factors affecting the catch of Pacific yellowfin tuna is primarily divided into two types: seawater temperature dominates in the western Pacific Ocean, while salinity dominates in the eastern Pacific Ocean. When El Niño occurs, the area with seawater temperature as the dominant environmental factor in the western Pacific Ocean further extends eastward, and the water layers where the dominant environmental factors are located develop to deeper depths; when La Niña occurs, there is a clear westward expansion in the area with seawater salinity as the dominant factor in the eastern Pacific Ocean. This change in the spatial distribution pattern of dominant factors is closely related to the movement of the position of the warm pool and cold tongue under ENSO events. (2) The areas with a higher catch of Pacific yellowfin tuna are spatially associated with the dominant environmental factor of mid-deep seawater temperature (105–155 m temperature) to a greater extent than other factors, the highest correlation exceeds 70%, and remain relatively stable under different ENSO events. The formation of this spatial association pattern is related to the vertical movement of yellowfin tuna as affected by subsurface seawater temperature. (3) The GWPCA-MGWR model can fully capture the differences in environmental variability among subregions in the Pacific Ocean under different climatic backgrounds, intuitively reflect the changing areas and influencing boundaries from a macro perspective, and has a relatively accurate prediction on the trend of yellowfin tuna catch in the Pacific Ocean.

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Section: Spatial Association Of Yellowfin Tuna Catch With Dominant En...mentioning

confidence: 99%

Section: Application Of Gwpca-mgwr Model In Yellowfin Tuna Catch Studymentioning

confidence: 99%

The Use of the GWPCA-MGWR Model for Studying Spatial Relationships between Environmental Variables and Longline Catches of Yellowfin Tunas

Li,

Yang,

Wang

et al. 2024

JMSE

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“…For example, it remains at a depth of approximately 50 m during the night and could dive to depths of 500 m at sunrise (Abascal et al, 2018; Brill et al, 2005; Matsumoto et al, 2013). Several studies have reported that the spatial variation in trophic positions of bigeye tuna was related to the depth of the thermocline, vertical temperature, vertical salinity, and vertical dissolved oxygen concentration (Houssard et al, 2017; Yang et al, 2020; Cai et al, 2020; Zhang, Liao et al 2021). However, impacts of vertical environmental factors on the distribution of the bigeye tuna in the Atlantic Ocean at different spatial scales are still not clear, which adds uncertainty in choosing appropriate models to forecast fishery grounds.…”

Section: Introductionmentioning

confidence: 99%

Comparison of machine learning models within different spatial resolutions for predicting the bigeye tuna fishing grounds in tropical waters of the Atlantic Ocean

Song

Zhang

et al. 2023

Fisheries Oceanography

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To understand the effects of the machine learning models and the spatial resolutions on the prediction accuracy of bigeye tuna (Thunnus obesus) fishing grounds, logbook data of 13 Chinese longliners operating in the high seas of the Atlantic Ocean from 2016 to 2019 were collected. The environmental factors were selected based on the correlation analysis of calculation of catch per unit effort (CPUE) and the marine vertical environmental factors. Five machine learning models: random forest, gradient‐boosting decision tree, K‐nearest neighbor, logistic regression and stacking ensemble learning (STK) within four spatial resolutions of .5° × .5°, 1° × 1°, 2° × 2° and 5° × 5° grids were constructed and compared. Results showed that (1) the prediction performance of STK model was the best, with the highest scores of the four evaluation indexes, accuracy (Acc), precision (P), recall (R), and F1‐score (F1), and the highest correct prediction rate for predicting “high CPUE fishing ground”; (2) models within the spatial resolution of 1° × 1° grids predicted the better results compared with .5° × .5°, 2° × 2° and 5° × 5° grids; (3) the vertical environmental factors selected based on the correlation analysis could be used as reliable predictors in the models. Results suggested that using STK within 1° × 1° grids could improve the generalization performance and prediction accuracy for predicting the bigeye tuna fishing grounds in the Atlantic Ocean.

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“…Erauskin-Extramiana et al, 2020; 2 Chang et al, 2013; 3 Hoey, 1983; 4 Podestáet al, 1993; 5 Govoni and Hare, 2001; 6 Govoni et al, 2003; 7 Monllor-Hurtado et al, 2017; 8 Dueri et al, 2014; 9 Erauskin-Extramiana et al, 2019; 10 Muhling et al, 2015; 11 Reglero et al, 2014; 12 Luckhurst and Arocha, 2016; 13 Dell'Apa et al, 2018; 14 Graham et al, 1989; 15 Brill, 1994; 16 Arrizabalaga et al, 2015; 17 Wexler et al, 2011; 18 Miyashita et al, 1999; 19 Orbesen et al, 2019; 20 Teo et al, 2007; 21 Muhling et al, 2011; 22 Liu et al, 2012; 23 Prince and Goodyear, 2006; 24 Stramma et al, 2012; 25 Simms et al, 2010; 26 Prince et al, 2010; 27 Trenkel et al, 2014; 28 Bernal et al, 2012; 29 Musyl et al, 2011; 30 Carlson and Gulak, 2012; 31 Howey-Jordan et al, 2013; 32 Tolotti et al, 2015a; 33 Bangley et al, 2020; 34 Vedor et al, 2021; 35 Schlaff et al, 2014, 36 Carlson and Parson, 2001; 37 Gallagher et al, 2014a; 38 Gallagher et al, 2014c; 39 Campana et al, 2016; 40 Crear et al, 2020; 41 Vaudo et al, 2016; 42 Dickson and Graham, 2004; 43 Skomal and Mandelman, 2012; 44 Dapp et al, 2016; 45 Campana and Joyce, 2004. YFT, yellowfin tuna; SKT, skipjack tuna; ALT, albacore tuna; BET, bigeye tuna; BLM, Atlantic blue marlin; SAF, Atlantic sailfish; OWS, oceanic whitetip shark; DUS, dusky shark; BLS, blue shark; SBS, sandbar shark; SMS, shortfin mako shark; PBS, porbeagle shark.5 However,Brill et al (2005) recommended avoiding a classification into "tropical" and "temperate" tunas because these terms usually refer to sea surface conditions, despite the fact that these species do not live solely at the sea surface.6 Between 300-500 m in the Atlantic(Matsumoto et al, 2005), and between 100-400 m in the tropical Atlantic(Yang et al, 2020).Dell'Apa et al 10.3389/fmars.2023.1206911 Frontiers in Marine Science frontiersin.org…”

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confidence: 99%

Effects of climate change and variability on large pelagic fish in the Northwest Atlantic Ocean: implications for improving climate resilient management for pelagic longline fisheries

Dell’Apa,

Boenish,

Fujita

et al. 2023

Front. Mar. Sci.

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Climate change influences marine environmental conditions and is projected to increase future environmental variability. In the North Atlantic, such changes will affect the behavior and spatiotemporal distributions of large pelagic fish species (i.e., tunas, billfishes, and sharks). Generally, studies on these species have focused on specific climate-induced changes in abiotic factors separately (e.g., water temperature) and on the projection of shifts in species abundance and distribution based on these changes. In this review, we consider the latest research on spatiotemporal effects of climate-induced environmental changes to HMS’ life history, ecology, physiology, distribution, and habitat selection, and describe how the complex interplay between climate-induced changes in biotic and abiotic factors, including fishing, drives changes in species productivity and distribution in the Northwest Atlantic. This information is used to provide a baseline for investigating implications for management of pelagic longline fisheries and to identify knowledge gaps in this region. Warmer, less oxygenated waters may result in higher post-release mortality in bycatch species. Changes in climate variability will likely continue to alter the dynamics of oceanographic processes regulating species behavior and distribution, as well as fishery dynamics, creating challenges for fishery management. Stock assessments need to account for climate-induced changes in species abundance through the integration of species-specific responses to climate variability. Climate-induced changes will likely result in misalignment between current spatial and temporal management measures and the spatiotemporal distribution of these species. Finally, changes in species interactions with fisheries will require focused research to develop best practices for adaptive fisheries management and species recovery.

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The Potential Vertical Distribution of Bigeye Tuna (Thunnus obesus) and Its Influence on the Spatial Distribution of CPUEs in the Tropical Atlantic Ocean

Cited by 6 publications

References 35 publications

The Use of the GWPCA-MGWR Model for Studying Spatial Relationships between Environmental Variables and Longline Catches of Yellowfin Tunas

The Use of the GWPCA-MGWR Model for Studying Spatial Relationships between Environmental Variables and Longline Catches of Yellowfin Tunas

Comparison of machine learning models within different spatial resolutions for predicting the bigeye tuna fishing grounds in tropical waters of the Atlantic Ocean

Effects of climate change and variability on large pelagic fish in the Northwest Atlantic Ocean: implications for improving climate resilient management for pelagic longline fisheries

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