Volume 20 Issue 6
Dec.  2024
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YANG Shiyu, FENG Ji, LI Yanan, ZHU Jiangfeng. Stock assessment of Thunnus alalunga in Indian Ocean based on climate change factors[J]. South China Fisheries Science, 2024, 20(6): 84-94. DOI: 10.12131/20240110
Citation: YANG Shiyu, FENG Ji, LI Yanan, ZHU Jiangfeng. Stock assessment of Thunnus alalunga in Indian Ocean based on climate change factors[J]. South China Fisheries Science, 2024, 20(6): 84-94. DOI: 10.12131/20240110
shu

Stock assessment of Thunnus alalunga in Indian Ocean based on climate change factors

  • 1.

    College of Marine Living Resource Sciences and Management, Shanghai Ocean University, Shanghai 201306, China

  • 2.

    Key Laboratory of Sustainable Exploitation of Oceanic Fisheries Resources, Ministry of Education, Shanghai 201306, China

  • 3.

    Key Laboratory of Sustainable Exploitation of Oceanic Fisheries Resources, Ministry of Agriculture and Rural Affairs, Shanghai 201306, China

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  • Received Date: May 21, 2024
  • Revised Date: June 29, 2024
  • Accepted Date: August 08, 2024
  • Available Online: August 12, 2024
    Graphical Abstract
    • Abstract

  • As a highly migratory fish species, understanding the relationship between resource changes of albacore (Thunnus alalunga) and climate change is crucial for its sustainable management. Considering the impact of climate change on population dynamics, we incorporated the climate indexes, such as Indian Ocean Dipole (IOD) and Madden Julian Oscillation (MJO) index, into the Just Another Bayesian Biomass Assessment (JABBA) surplus production model for albacore in the Indian Ocean (IO-ALB). Six climate-integrated assessment models were established, each assuming a different effect of climate variability on the intrinsic growth rate (r), carrying capacity (K), or and their combined effects on population dynamics. The results show that climate effects had a significant impact on the model fitting performance, especially the climate-integrated models , which consider the influence of IOD, which had a high fitting accuracy. Comparison of the assessment results from the six models indicates a relatively better stock state when the IOD-based model was applied and an overfished condition when the MJO-based model was incorporated. The study also reveals insignificant direct effects of climate factors on r but a negative effect of MJO on K. The study highlights the importance of considering climate effects in stock assessments of albacore in the Indian Ocean and, demonstrates that by incorporating environmental indexes, the model can better reflect the population dynamics, leading to more reliable assessment results and providing a scientific basis for future assessment of oceanic fish population resources and formulation of sustainable fishing strategies.

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    • References (42)
  • [1]
    朱江峰, 戴小杰, 官文江. 印度洋长鳍金枪鱼资源评估[J]. 渔业科学进展, 2014, 35(1): 1-8. doi: 10.3969/j.issn.1000-7075.2014.01.001
    [2]
    ISSF. Status of the world fisheries for tuna: March 2024[R/OL]. [2024-04-26]. https://www.iss-foundation.org/about-issf/what-we-publish/issf-documents/issf-2024-02-status-of-the-world-fisheries-for-tuna-march-2024/.
    [3]
    安康, 官文江. 利用贝叶斯动态产量模型评估印度洋长鳍金枪鱼资源状态[J]. 中国水产科学, 2023, 30(9): 1142-1154.
    [4]
    PLAGANY É. Climate change impacts on fisheries[J]. Science, 2019, 363(6430): 930-931. doi: 10.1126/science.aaw5824
    [5]
    郑好好, 杨晓明, 朱江峰. 基于多尺度地理加权回归模型的中西太平洋围网鲣渔获率环境影响机制研究[J]. 南方水产科学, 2023, 19(5): 1-10. doi: 10.12131/20230014
    [6]
    OVERLAND J E, ALHEIT J, BAKUN A, et al. Climate controls on marine ecosystems and fish populations[J]. J Mar Syst, 2010, 79(3/4): 305-315.
    [7]
    BAEZ J C, GIMENO L, REAL R. North Atlantic Oscillation and fisheries management during global climate change[J]. Rev Fish Biol Fish, 2021, 31(2): 319-336. doi: 10.1007/s11160-021-09645-z
    [8]
    AMON D J, PALACIOS A J, DRAZEN J C, et al. Climate change to drive increasing overlap between Pacific tuna fisheries and emerging deep-sea mining industry[J]. NPJ Ocean Sustain, 2023, 2(1): 9. doi: 10.1038/s44183-023-00016-8
    [9]
    MENARD F, MARSAC F, BELLIER E, et al. Climatic oscillations and tuna catch rates in the Indian Ocean: a wavelet approach to time series analysis[J]. Fish Oceanogr, 2007, 16(1): 95-104. doi: 10.1111/j.1365-2419.2006.00415.x
    [10]
    LAN K W, EVANS K, LEE M A. Effects of climate variability on the distribution and fishing conditions of yellowfin tuna (Thunnus albacares) in the western Indian Ocean[J]. Clim Change, 2013, 119: 63-77. doi: 10.1007/s10584-012-0637-8
    [11]
    LAN K W, CHANG Y J, WU Y L. Influence of oceanographic and climatic variability on the catch rate of yellowfin tuna (Thunnus albacares) cohorts in the Indian Ocean[J]. Deep Sea Res Part II, 2020, 175: 104681. doi: 10.1016/j.dsr2.2019.104681
    [12]
    SYAMSUDDIN M L, SAITOH S I, HIRAWAKE T, et al. Effects of El Niño–Southern Oscillation events on catches of bigeye tuna (Thunnus obesus) in the eastern Indian Ocean off Java[J]. Fish Bull, 2013, 111(2): 161-174. doi: 10.7755/FB.111.2.4
    [13]
    BAZE J C, CZERWINSKI I A, RAMOS M L. Climatic oscillations effect on the yellowfin tuna (Thunnus albacares) Spanish captures in the Indian Ocean[J]. Fish Oceanogr, 2020, 29(6): 572-583. doi: 10.1111/fog.12496
    [14]
    BOOTH D J, FEARY D, KOBAYASHI D, et al. Tropical marine fishes and fisheries and climate change[M/OL]//PHILLIPS B F, PEREZ-RAMIREZ M. Climate change impacts on fisheries and aquaculture: a global analysis. Hoboken: Wiley-Blackwell, 2017, 2: 875-896[2024-04-26]. https://onlinelibrary.wiley.com/doi/abs/10.1002/9781119154051.ch26.
    [15]
    DUERI S. Impacts of climate change and ocean acidification on Indian Ocean tunas[R/OL]. Kuala Lumpur: 7th Working Party on Temperate Tuna (WPTmT): Data preparatory meeting, 2019(IOTC-2019-WPTmT07(DP)-19). [2024-04-26]. https://www.iddri.org/sites/default/files/PDF/Publications/Hors%20catalogue%20Iddri/tuna-climate%20change%20indian%20oc-eanEN.pdf.
    [16]
    SCHAEFER K M, FULLER D W, BLOCK B A. Movements, behavior, and habitat utilization of yellowfin tuna (Thunnus albacares) in the northeastern Pacific Ocean, ascertained through archival tag data[J]. Mar Biol, 2007, 152: 503-525. doi: 10.1007/s00227-007-0689-x
    [17]
    LEHODEY P, SENINA I, CALMETTES B, et al. Modelling the impact of climate change on Pacific skipjack tuna population and fisheries[J]. Clim Change, 2013, 119: 95-109. doi: 10.1007/s10584-012-0595-1
    [18]
    SUNDSTORM L F, LOHMUS M, DEVLIN R H. Selection on increased intrinsic growth rates in coho salmon, Oncorhynchus kisutch[J]. Evolution, 2005, 59(7): 1560-1569.
    [19]
    GOYERT H F, GARTON E O, POE A J. Effects of climate change and environmental variability on the carrying capacity of Alaskan seabird populations[J]. Auk, 2018, 135(4): 975-991. doi: 10.1642/AUK-18-37.1
    [20]
    杨诗玉, 冯佶, 朱江峰. 基于JABBA-Select模型对不同时间序列渔获量和渔船效应的印度洋长鳍金枪鱼资源评估[J]. 大连海洋大学学报, 2023, 38(5): 828-838.
    [21]
    HOYLE S D, CHASSOT E, FU D, et al. Collaborative study of albacore tuna CPUE from multiple Indian Ocean longline fleets in 2019[C/OL]. Kuala Lumpur: 7th Working Party on Temperate Tuna (WPTmT): Data preparatory meeting, 2019(IOTC-2019-WPTmT07(DP)-19). [2024-04-26]. https://iotc.org/sites/default/files/documents/2019/01/IOTC-2019-WPTmT07DP-19.pdf.
    [22]
    SREEKALA P P, RAO S V B, RAJEEVAN K, et al. Combined effect of MJO, ENSO and IOD on the intraseasonal variability of northeast monsoon rainfall over south peninsular India[J]. Clim Dyn, 2018, 51(9): 3865-3882.
    [23]
    LU Z H, DONG W J, LU B, et al. Early warning of the Indian Ocean Dipole using climate network analysis[J]. Proc Natl Acad Sci, 2022, 119(11): e2109089119. doi: 10.1073/pnas.2109089119
    [24]
    SASIKUMAR K, NATH D, WANG X, et al. Recent enhancement and prolonged occurrence of MJO over the Indian Ocean and their impact on Indian summer monsoon rainfall[J]. Clim Dyn, 2022, 59(9/10): 2585-2598.
    [25]
    WINKER H, CARVALHO F, KAPUR M. JABBA: just another Bayesian biomass assessment[J]. Fish Res, 2018, 204: 275-288. doi: 10.1016/j.fishres.2018.03.010
    [26]
    WANG J T, YU W, CHEN X J, et al. Stock assessment for the western winter-spring cohort of neon flying squid (Ommastrephes bartramii) using environmentally dependent surplus production models[J]. Sci Mar, 2016, 80(1): 69-78.
    [27]
    肖启华. 气候变化背景下东南太平洋智利竹荚鱼资源评估模型研究[D]. 上海: 上海海洋大学, 2020: 33.
    [28]
    GILMAN E, ALLAIN V, COLLETTE B B, et al. Effects of ocean warming on Pelagic tunas, a review[R/OL]. Gland: Explaining ocean warming: causes, scale, effects and consequences, 2016 [2024-04-19]. https://repository.si.edu/bitstream/handle/10088/30589/290.Tunas_Ocean_warming_IUCN2016.pdf?sequence=1.
    [29]
    RICE J. Stock assessment of albacore tuna (Thunnus alalunga) in the Indian Ocean using Stock Synthesis[R/OL]. Online/Virtual: 8th Working Party on Temperate Tuna (WPTmT08): Assessment Meeting, 2022 (IOTC-2022-WPTmT08-09). [2024-04-26]. https://iotc.org/sites/default/files/documents/2022/07/IOTC-2022-WPTmT08-09.pdf.
    [30]
    HILLARY R M. Surplus production analyses for Indian Ocean albacore[R/OL]. Bangkok: 2nd Session of the Working Party on Temperate Tuna, 2008(IOTC-2008-WPTe-06). [2024-04-26]. https://iotc.org/sites/default/files/documents/proceedings/2008/wptmt/IOTC-2008-WPTe-06.pdf.
    [31]
    LI B, CAO J, ZHU J F. Analyzing population dynamics of Indian Ocean albacore (Thunnus alalunga) using Bayesian state-space production model[C/OL]. Shanghai: 6th Working Party on Temperate Tunas (WPTmT06), 2016(IOTC-2016-WPTmT06-24 Rev1).[2024-04-26]. https://iotc.org/sites/default/files/documents/2016/08/IOTC-WPTmT06-24_Rev1_Population_dynamics_using_Bayesian_state-space_production_model.pdf.
    [32]
    CARVALHO F, WINKER H, COURTNEY D, et al. A cookbook for using model diagnostics in integrated stock assessments[J]. Fish Res, 2021, 240: 105959. doi: 10.1016/j.fishres.2021.105959
    [33]
    HEIDELBERGER P, WELCH P D. Simulation run length control in the presence of an initial transient[J]. Oper Res, 1983, 31(6): 1109-1144. doi: 10.1287/opre.31.6.1109
    [34]
    CHEN J H, CHEN Z H. Extended Bayesian information criteria for model selection with large model spaces[J]. Biometrika, 2008, 95(3): 759-771. doi: 10.1093/biomet/asn034
    [35]
    MONDAL S, RAY A, LEE M A, et al. Projected changes in spawning ground distribution of mature albacore tuna in the Indian Ocean under various global climate change scenarios[J]. J Mar Sci Eng, 2023, 11(8): 1565. doi: 10.3390/jmse11081565
    [36]
    NUGROHO S C, SETIAWAN R Y, SETIAWATI M D, et al. Estimation of albacore tuna potential fishing grounds in the southeastern Indian Ocean[J]. IEEE Access, 2022, 11: 1141-1147.
    [37]
    LEHODEY P, SENINA I, NICOL S, et al. Modelling the impact of climate change on South Pacific albacore tuna[J]. Deep Sea Res II, 2015, 113: 246-259. doi: 10.1016/j.dsr2.2014.10.028
    [38]
    LIU S G, ZHANG L Y, WANG R, et al. The effect of climate oscillations on skipjack tuna (Katsuwonus pelamis) in the Indian Ocean[J]. J Mar Syst, 2024, 242: 103939. doi: 10.1016/j.jmarsys.2023.103939
    [39]
    JOHNSON J E, WELCH D J. Marine fisheries management in a changing climate: a review of vulnerability and future options[J]. Rev Fish Sci, 2009, 18(1): 106-124. doi: 10.1080/10641260903434557
    [40]
    官文江, 朱江峰, 高峰. 印度洋长鳍金枪鱼资源评估的影响因素分析[J]. 中国水产科学, 2018, 25(5): 1102-1114.
    [41]
    ROMANOV E V, NIKOLIC N, DHURMEEA Z, et al. Trophic ecology of albacore tuna (Thunnus alalunga) in the western tropical Indian Ocean and adjacent waters[J]. Mar Freshw Res, 2020, 71(11): 1517-1542. doi: 10.1071/MF19332
    [42]
    杨彩莉, 杨晓明, 朱江峰. 不同类型厄尔尼诺事件中环境因子对中西太平洋金枪鱼围网鲣分布响应[J]. 南方水产科学, 2021, 17(3): 8-18. doi: 10.12131/20210014
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