Influence of oceanographic variability on recruitment of yellowfin tuna (Thunnus albacares) in the western and central Pacific Ocean
Abstract
Recruitment estimates for yellowfin tuna ( Thunnus albacares ) in the western and central Pacific Ocean (WCPO), derived from a stock assessment model, are highly variable seasonally, interannually, and over decadal periods. A generalized linear model (GLM) was developed that predicts the variation in yellowfin tuna recruitment in response to a range of oceanographic variables. The GLM model accounted for 54% of the variation in quarterly recruitment for the period 1980–2003, with the inclusion of seven different oceanographic variables derived from a zone within the northwestern equatorial region of the WCPO. The robustness of the recruitment model was investigated by cross-validation. The GLM was complemented by a cluster analysis approach that identified five principal oceanographic states within the northwestern zone selected by the GLM. Incorporation of the recent GLM recruitment indices in the yellowfin tuna stock assessment model is likely to improve the precision of estimates of current and projected (next 1–2 years) biomass and exploitation rates. In a broader context, the recruitment model provides a tool to investigate how yellowfin tuna recruitment might vary in response to short- and long-term variation in the oceanographic conditions of the WCPO.
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It remains unclear how food availability affects the recruitment success of bigeye tuna.
“Despite being routinely researched and assessed, it remains unclear how food availability affects the recruitment success of bigeye tuna.”
From Impacts of phytoplankton availability on bigeye tuna (Thunnus obesus) recruitment in the Indian Ocean (2024) · cites this paper as Langley et al., 2009
Only a limited amount of research has explored the influence of larval foraging success on bigeye tuna recruitment, with most information on tuna recruitment and reproduction confined to the Pacific Ocean.
“Thus, only a limited amount of research has been conducted to explore the influence of larval foraging success on the recruitment of bigeye tuna, with most information being related to tuna recruitment and reproduction confined to the Pacific Ocean (Langley et al., 2009; Zhu et al., 2010; Muhling et al., 2018; Woodworth-Jefcoats and Wren, 2020).”
From Impacts of phytoplankton availability on bigeye tuna (Thunnus obesus) recruitment in the Indian Ocean (2024) · cites this paper as Langley et al., 2009
Integrating recent GLM recruitment indices into stock assessment may enhance the precision of estimates of current and projected biomass and exploitation.
“They reported that integrating the recent GLM recruitment indices into stock assessment may enhance the precision of estimates of the current and projected (in the next 1 to 2 years) biomass and exploitation.”
From Impacts of phytoplankton availability on bigeye tuna (Thunnus obesus) recruitment in the Indian Ocean (2024) · cites this paper as Langley et al. (2009)
Langley et al. (2009) developed a generalized linear model to predict variation in yellowfin tuna recruitment across the western and central Pacific Ocean in relation to environmental variables.
“Langley et al. (2009) developed a generalized linear model (GLM) to predict the variation in yellowfin tuna (Thunnus albacares) recruitment across the western and central Pacific Ocean in relation to a multitude of environmental variables.”
From Impacts of phytoplankton availability on bigeye tuna (Thunnus obesus) recruitment in the Indian Ocean (2024) · cites this paper as Langley et al. (2009)
Cites
El Niño Southern Oscillation and tuna in the western Pacific
Lehodey, P., Bertignac, M., Hampton, J., Lewis, A., & Picaut, J. (1997) · Nature · doi:10.1038/39575 · Cited by 494 · cited 2× · see citing works
“(2003) asserted that yellowfin tuna recruitment in the WCPO is driven by the variability in the spatial extent of the warm pool, which tends to occur when the North Equatorial Countercurrent becomes more dominant than the South Equatorial Current (El Niño conditions), allowing the warm pool to extend further east (Leh…”
BackgroundTropical Atlantic variability in a coupled physical–biogeochemical ocean model
Christian, J. R., & Murtugudde, R. (2003) · Deep Sea Research Part II: Topical Studies in Oceanography · doi:10.1016/j.dsr2.2003.07.005 · Cited by 37 · see citing works
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Wang, Y., Zhang, Y., Geng, Z., & Zhu, J. (2024) · Frontiers in Marine Science · doi:10.3389/fmars.2024.1392273 · Cited by 2 · see citing works
“And the most of information IOTC-2022-WPTT24-06 about the tunas’ recruitment and reproduction is available for the Pacific Ocean (Langley et al., 2009; Zhu et al., 2010; Sun et al., 2013; Muhling et al., 2018; Woodworth-Jefcoats and Wren, 2020).”
- Authors
- Langley, A., Briand, K., Kirby, D. S., & Murtugudde, R.
- Affiliations
- Pacific Community (NC)
- Venue
- Canadian Journal of Fisheries and Aquatic Sciences
- Publisher
- Canadian Science Publishing
- Published
- 2009-09
- Volume
- 66
- Issue
- 9
- Pages
- 1462-1477
- ISSN
- 0706-652X (ISSN-L)
- 1205-7533
- OpenAlex
- W2149737828
- Cited by
- 23
- References
- 2
- Citations by year
- 2026: 3 · 2024: 3 · 2023: 1 · 2022: 2 · 2021: 3 · 2020: 2 · 2019: 1 · 2018: 1 · 2017: 1 · 2016: 1 · 2014: 1 · 2012: 1
- FWCI
- 1.06
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Canadian Journal of Fisheries and Aquatic Sciences (publishedVersion)
- Pacific Community (SPC) Oceanic Fisheries Programme (The Pacific Community (SPC)) (submittedVersion)
- Funders
- Australian Agency for International Development
- Topics
- Physical Sciences › Environmental Science › Global and Planetary Change › Marine and fisheries research
- Physical Sciences › Earth and Planetary Sciences › Oceanography › Oceanographic and Atmospheric Processes
- Physical Sciences › Environmental Science › Nature and Landscape Conservation › Fish Ecology and Management Studies
- Keywords
- yellowfin tuna, recruitment (biology), ocean variability, Western and Central Pacific Ocean, stock assessment models, generalized linear models, clustering, biomass estimation, exploitation rate, seasonal and interannual variation
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