Cited by 106Marine and fisheries researchClimate variability and modelsFish Ecology and Management Studies

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

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Lan, Kuo-Wei, Evans, Karen, Lee, Ming-An·2013·Climatic Change

Abstract

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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  • GAMs have been previously used to study habitat preferences of many tuna species using fishery data, larval survey data, and electronic tagging data.

    “GAMs have been previously used to study habitat preferences of many tuna species using fishery data (Lan et al., 2013;Maury et al., 2001;Mugo et al., 2010;Sagarminaga and Arrizabalaga 2010), larval survey data (Rooker et al., 2013) as well as electronic tagging data (Hazen et al., 2013).”

    From Global habitat preferences of commercially valuable tuna (2015) · cites this paper as Lan et al., 2013

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Authors
Lan, K.-W., Evans, K., & Lee, M.-A.
Affiliations
  • National Taiwan Ocean University (TW)
  • CSIRO Oceans and Atmosphere (AU)
Corresponding author
Lee, Ming-An
Venue
Climatic Change
Published
2012-12-01
Volume
119
Issue
1
Pages
63-77
ISSN
  • 0165-0009 (ISSN-L)
  • 1573-1480
OpenAlex
W1978319249
Cited by
106
References
4
Citations by year
2026: 11 · 2025: 10 · 2024: 11 · 2023: 12 · 2022: 8 · 2021: 9 · 2020: 10 · 2019: 8 · 2018: 9 · 2017: 6 · 2016: 4 · 2015: 2 · 2014: 5 · 2012: 1
FWCI
2.13
Type
journal article
Language
en
Open access
Locations
  • Climatic Change (publishedVersion, cc-by)
  • RePEc: Research Papers in Economics (submittedVersion)
Funders
  • Asia-Pacific Network for Global Change Research
  • National Science Council
Topics
  • Physical Sciences › Environmental Science › Global and Planetary Change › Marine and fisheries research
  • Physical Sciences › Environmental Science › Global and Planetary Change › Climate variability and models
  • Physical Sciences › Environmental Science › Nature and Landscape Conservation › Fish Ecology and Management Studies
Keywords
yellowfin tuna, Indian Ocean Dipole, climate variability, tuna distribution, catch per unit effort, sea surface temperature, net primary productivity, longline fisheries, Western Indian Ocean, Arabian Sea, Madagascar

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