Ocean variations associated with fishing conditions for yellowfin tuna (Thunnus albacares) in the equatorial Atlantic Ocean

Lan, Kuo‐Wei; Lee, Ming-An; Lu, Hang; Shieh, W.-C.; Wan, Lin; Kao, Szu-Chia

doi:10.1093/icesjms/fsr045

Cited by 24 publications

(25 citation statements)

References 23 publications

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“…These results suggest that the areas with a higher SST, an SSHA of approximately 10.0-20 cm, and Chl-a concentration of approximately 0.05-0.25 mg/m 3 yield higher catch rates of yellowfin tuna. The SST is a vital variable for more comprehensively understanding the interactions between ocean and atmosphere related to fish behaviors [14,17,28]. Typically, average SSTs are higher in the western Pacific than in the eastern Pacific, and annual maximum temperatures of approximately 30 • C characterize the western Pacific warm pool.…”

Section: Discussionmentioning

confidence: 99%

“…The sea surface height can be used to infer oceanic features, such as current dynamics and fronts [16], and the mesoscale oceanic dynamical processes including eddies, convergences and upwelling can be reflected in sea surface height anomaly (SSHA). Pelagic tuna species aggregate around the fronts and eddies where their preferred prey gathers [9,17].…”

Section: Introductionmentioning

confidence: 99%

“…In the TPO, historical catch information and logbook data for Taiwanese longline fishery has been available since 1964. Previous studies have investigated the stock assessment and fishery oceanography for high trophic level predators, including bigeye tuna, yellowfin tuna, albacore, and billfish, and mostly used Taiwanese longline fishery data with 5 • grid spatial resolution [15,17,20,21]. However, averaging the environmental data (i.e., SST, Chl-a, and SSH) over a 5 • block would lose detail information for the small scale environmental variabilities, such as the fluctuation of eddy current and continuous change of SSTs.…”

Section: Introductionmentioning

confidence: 99%

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Using Remote-Sensing Environmental and Fishery Data to Map Potential Yellowfin Tuna Habitats in the Tropical Pacific Ocean

et al. 2017

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Changes in marine environments affect fishery resources at different spatial and temporal scales in marine ecosystems. Predictions from species distribution models are available to parameterize the environmental characteristics that influence the biology, range, and habitats of the species of interest. This study used generalized additive models (GAMs) fitted to two spatiotemporal fishery data sources, namely 1 • spatial grid and observer record longline fishery data from 2006 to 2010, to investigate the relationship between catch rates of yellowfin tuna and oceanographic conditions by using multispectral satellite images and to develop a habitat preference model. The results revealed that the cumulative deviances obtained using the selected GAMs were 33.6% and 16.5% in the 1 • spatial grid and observer record data, respectively. The environmental factors in the study were significant in the selected GAMs, and sea surface temperature explained the highest deviance. The results suggest that areas with a higher sea surface temperature, a sea surface height anomaly of approximately −10.0 to 20 cm, and a chlorophyll-a concentration of approximately 0.05-0.25 mg/m 3 yield higher catch rates of yellowfin tuna. The 1 • spatial grid data had higher cumulative deviances, and the predicted relative catch rates also exhibited a high correlation with observed catch rates. However, the maps of observer record data showed the high-quality spatial resolutions of the predicted relative catch rates in the close-view maps. Thus, these results suggest that models of catch rates of the 1 • spatial grid data that incorporate relevant environmental variables can be used to infer possible responses in the distribution of highly migratory species, and the observer record data can be used to detect subtle changes in the target fishing grounds.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Using Remote-Sensing Environmental and Fishery Data to Map Potential Yellowfin Tuna Habitats in the Tropical Pacific Ocean

et al. 2017

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“…Variations in population abundances and distributions of pelagic tuna species have been observed to be clearly linked to large-scale climate phenomena such as the El Niño Southern Oscillation (ENSO) in the Pacific Ocean (Lehodey et al 1997;Sugimoto et al 2001;Torres-Orozco et al 2006) and the North Atlantic Oscillation in the Atlantic Ocean (Santiago 1997;Lan et al 2011).…”

Section: Introductionmentioning

confidence: 99%

Effects of climate variability on the distribution and fishing conditions of yellowfin tuna (Thunnus albacares) in the western Indian Ocean

2012

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Variations in the abundance and distribution of pelagic tuna populations have been associated with large-scale climate indices such as the Southern Oscillation Index in the Pacific Ocean and the North Atlantic Oscillation in the Atlantic Ocean. Similarly to the Pacific and Atlantic, variability in the distribution and catch rates of tuna species have also been observed in association with the Indian Ocean Dipole (IOD), a basin-scale pattern of sea surface and subsurface temperatures that affect climate in the Indian Ocean. The environmental processes associated with the IOD that drive variability in tuna populations, however, are largely unexplored. To better understand these processes, we investigated longline catch rates of yellowfin tuna and their distributions in the western Indian Ocean in relation to IOD events, sea surface water temperatures (SST) and estimates of net primary productivity (NPP). Catch per unit effort (CPUE) was observed to be negatively correlated to the IOD with a periodicity centred around 4 years. During positive IOD events, SSTs were relatively higher, NPP was lower, CPUE decreased and catch distributions were restricted to the northern and western margins of the western Indian Ocean. During negative IOD events, lower SSTs and higher NPP were associated with increasing CPUE, particularly in the Arabian Sea and seas surrounding Madagascar, and catches expanded into central regions of the western Indian Ocean. These findings provide preliminary insights into some of the key environmental features driving the distribution of yellowfin tuna in the western Indian Ocean and associated variability in fisheries catches.

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“…This methods had been used in the tuna preference in several literatures [8,12,13]. According the methods developed by Levitus [14], the seasons are divided as follow: January to March as boreal winter, April to June as boreal spring, July to September as boreal summer and October to December as boreal autumn.…”

Section: Discussionmentioning

confidence: 99%

Relationship between temporal–spatial distribution of fishing grounds of bigeye tuna (Thunnus obesus) and thermocline characteristics in the Atlantic Ocean

Yang

et al. 2015

Acta Ecologica Sinica

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Ocean variations associated with fishing conditions for yellowfin tuna (Thunnus albacares) in the equatorial Atlantic Ocean

Cited by 24 publications

References 23 publications

Using Remote-Sensing Environmental and Fishery Data to Map Potential Yellowfin Tuna Habitats in the Tropical Pacific Ocean

Using Remote-Sensing Environmental and Fishery Data to Map Potential Yellowfin Tuna Habitats in the Tropical Pacific Ocean

Effects of climate variability on the distribution and fishing conditions of yellowfin tuna (Thunnus albacares) in the western Indian Ocean

Relationship between temporal–spatial distribution of fishing grounds of bigeye tuna (Thunnus obesus) and thermocline characteristics in the Atlantic Ocean

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