杨诗玉, 冯佶, 李亚楠, 朱江峰. 基于气候变化因子的印度洋长鳍金枪鱼资源评估[J]. 南方水产科学. DOI: 10.12131/20240110
引用本文: 杨诗玉, 冯佶, 李亚楠, 朱江峰. 基于气候变化因子的印度洋长鳍金枪鱼资源评估[J]. 南方水产科学. DOI: 10.12131/20240110
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. 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. DOI: 10.12131/20240110

基于气候变化因子的印度洋长鳍金枪鱼资源评估

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

  • 摘要: 长鳍金枪鱼 (Thunnus alalunga) 作为高度洄游性鱼类,了解其资源量变动与气候变化间的关系对其可持续管理至关重要。考虑气候变化对种群动态的影响,在印度洋长鳍金枪鱼 (IO-ALB) 资源评估模型JABBA (Just Another Bayesian Biomass Assessment) 中引入气候变化因子,即印度洋偶极子 (Indian Ocean Dipole, IOD) 和马登-朱利安振荡 (Madden Julian Oscillation, MJO) 指数,在分别假设气候变化影响种群的内禀增长率 (r)、环境容纳量 (K) 和同时影响rK的前提下,构建了6种考虑气候变化的资源评估模型。结果显示,气候变化因子对评估模型的拟合精度具有显著影响,特别是考虑IOD影响的气候融合模型,拟合精度较高。比较6种模型评估的结果发现,基于IOD因子的模型,其结果显示资源状况较好;考虑MJO因子的模型,其结果显示资源状况处于资源型过度捕捞。此外,研究发现气候因子对印度洋长鳍金枪鱼r的直接影响有限,但环境因子MJO对K具有负面影响。该研究强调了在IO-ALB资源评估中考虑气候变化因子的重要性,验证了纳入环境因子的模型可以更准确地反映种群动态,从而产生更可靠的评估结果,为未来大洋性鱼类种群资源评估和可持续捕捞策略的制定提供科学依据。

     

    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 considering 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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