自然死亡和亲体补充关系对黄鳍金枪鱼资源评估的影响

崔明远; 麻秋云; 田思泉; 林龙山; 李渊

doi:10.12284/hyxb2023044

自然死亡和亲体补充关系对黄鳍金枪鱼资源评估的影响

doi: 10.12284/hyxb2023044

崔明远^1,,
麻秋云^{1, 2, 3, 4, ,},
田思泉^{1, 2, 3, 4},
林龙山⁵,
李渊⁵

1.
上海海洋大学海洋科学学院，上海 201306
2.
国家远洋渔业工程技术研究中心，上海 201306
3.
大洋渔业资源可持续开发教育部重点实验室，上海 201306
4.
农业农村部大洋渔业可持续利用重点实验站，上海 201306
5.
自然资源部第三海洋研究所，福建厦门 361005

基金项目: 国家自然科学基金（32202934）；全球变化与海气相互作用（二期）专项（GASI-01-EIND-YD01/02spr/aut）；国家重点研发计划（2019YFD0901404）。

详细信息

作者简介:
崔明远（1994-），男，山东省潍坊市人，博士研究生，主要从事渔业资源评估研究。E-mail：470235020@qq.com

通讯作者:
麻秋云（1987-），女，讲师，主要从事种群动力学和渔业资源评估研究。E-mail：qyma@shou.edu.cn

中图分类号: S932.4
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出版历程
- 收稿日期: 2022-08-17
- 修回日期: 2022-10-11
- 网络出版日期: 2022-10-21
- 刊出日期: 2023-02-01

Influence of natural mortality and stock-recruitment relationship on yellowfin tuna (Thunnus albacares) stock assessment

Cui Mingyuan^1
,,
Ma Qiuyun^{1, 2, 3, 4
, ,},
Tian Siquan^{1, 2, 3, 4},
Lin Longshan⁵,
Li Yuan⁵

1.
College of Marine Sciences, Shanghai Ocean University, Shanghai 201306, China
2.
National Engineering Research Center for Oceanic Fisheries, Shanghai 201306, China
3.
Key Laboratory of Sustainable Exploitation of Oceanic Fisheries Resources, Ministry of Education, Shanghai 201306, China
4.
Key Laboratory of Sustainable Use of Oceanic Fisheries Resources, Ministry of Agriculture and Rural Affairs, Shanghai 201306, China
5.
Third Institute of Oceanography, Ministry of Natural Resources, Xiamen 361005, China

摘要

摘要: 黄鳍金枪鱼（Thunnus albacares）是全球经济和生态价值最重要的鱼类之一，其资源养护和管理受到各方的高度关注。本文依据年龄结构产量模型研究了印度洋黄鳍金枪鱼的资源状态，着重探讨了其生活史特征的不确定性对资源评估结果的影响。研究结果显示，1960−1985年间印度洋黄鳍金枪鱼资源量保持相对稳定，之后开始逐渐下降，相应的捕捞死亡系数也在2010年之后迅速增加，目前其种群可能存在过度捕捞（F₂₀₂₀/F_MSY>1，SSB₂₀₂₀/SSB_MSY<1）。印度洋黄鳍金枪鱼的资源评估结果对自然死亡系数（M）和亲体−补充量关系陡度参数（h）的改变较为敏感。当h增大时，SSB_MSY和初始SSB（即SSB₀）的变化较大，分别减少了约25.53万t和34.04万t；F₂₀₂₀/F_MSY减小了1.15。当M增大时，F₂₀₂₀/F_MSY、SSB_MSY、SSB₀均减小。综上所述，今后应重视印度洋黄鳍金枪鱼资源的开发程度，重视其资源养护管理，同时充分了解黄鳍金枪鱼的生活史特征，提高自然死亡系数和陡度参数估算的准确性，以期为印度洋黄鳍金枪鱼的资源评估和渔业管理提供更准确的信息，实现该渔业的长期可持续发展。
- 远洋渔业 /
- 种群动力学 /
- 敏感性分析 /
- 自然死亡 /
- 陡度
Abstract: Yellowfin tuna (Thunnus albacares) is one of the most important fishes with great global economic and ecological value, and its conservation and management have received much concerns. The stock status of yellowfin tuna in the Indian Ocean based on the age-structured assessment program model is evaluated in this study, focusing on the uncertainties of its life history characteristics on the stock assessment results. The results show that the resources of yellowfin tuna in the Indian Ocean remained relatively stable from 1960 to 1985 and then declined gradually, while the fishing mortality coefficient F increased rapidly after 2010. This stock in 2020 may be overfished, since the estimated F₂₀₂₀ was greater than F_MSY (F that could attain maximum sustainable yield MSY), while spawning stock biomass, SSB₂₀₂₀ was less than SSB_MSY. Sensitivity analysis was also conducted to evaluate the uncertainties of stock assessment. Two important life history characteristics, natural mortality M and steepness of spawning-stock relationship h, were analyzed for their influence on the estimates of F, SSB and biological reference points. When h was set to 0.7, 0.8, and 0.9, SSB_MSY and SSB₀ (the unfished SSB) reduced by about 255 300 t and 340 400 t; and F₂₀₂₀/F_MSY gradually decreased (from 2.88 to 2.21 and 1.73). When the M was set to M₁ (0.963, 0.663, 0.548, 0.493, 0.463, 0.446) and M₂ (1.068, 0.735, 0.608, 0.547, 0.514, 0.495) respectively, the larger M₂ leads to lower SSB and F₂₀₂₀/F_MSY. In summary, the conservation and management of Indian Ocean yellowfin tuna should be tightened in the future to achieve long-term sustainable development of this fishery. The life history characteristics of yellowfin tuna should be fully understood, especially M and h estimation should be improved, to provide more accurate information for stock assessment and fisheries management for Indian Ocean yellowfin tuna.
- pelagic fishery /
- fish population dynamics /
- sensitivity analysis /
- natural mortality /
- steepness

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图 1 印度洋黄鳍金枪鱼渔获量及年龄组成

Fig. 1 Catch and age composition of yellowfin tuna in the Indian Ocean

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图 2 印度洋黄鳍金枪鱼丰度指数观测值与预测值（基准模型）

Fig. 2 Observed and predicted indices of yellowfin tuna in the Indian Ocean (S-base case)

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图 3 印度洋黄鳍金枪鱼年渔获量的观测值和预测值（基准模型）

Fig. 3 Observed and predicted catch of yellowfin tuna in the Indian Ocean (S-base case)

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图 4 基准模型下印度洋黄鳍金枪鱼捕捞死亡系数（F_MSY）和产卵亲体生物量（SSB）估计值

Fig. 4 Estimated fishing mortality (F_MSY) and spawning stock biomass (SSB) of yellowfin tuna in the Indian Ocean in base case model

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图 5 印度洋黄鳍金枪鱼资源数量的年龄结构（基准模型）

Fig. 5 Estimates of stock numbers by age of yellowfin tuna in the Indian Ocean (S-base case)

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图 6 不同陡度下印度洋黄鳍金枪鱼捕捞死亡系数（F_MSY）和产卵亲体生物量（SSB）估计值

横线与曲线的交点为F_MSY和SSB_MSY

Fig. 6 Estimated fishing mortality (F_MSY) and spawning stock biomass (SSB) of yellowfin tuna in the Indian Ocean in different steepness

The intersections of straight lines and curves are F_MSY and SSB_MSY

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图 7 不同自然死亡系数下印度洋黄鳍金枪鱼捕捞死亡系数（F_MSY）和产卵亲体生物量（SSB）估计值

横线与曲线的交点为F_MSY和SSB_MSY

Fig. 7 Estimated fishing mortality (F_MAY) and spawning stock biomass (SSB) of yellowfin tuna in the Indian Ocean in different natural mortality

The intersections of straight lines and curves are F_MSY and SSB_MSY

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图 8 不同模型下印度洋黄鳍金枪鱼各个年龄对应的资源数量

Fig. 8 Estimates of stock numbers by age of yellowfin tuna in the Indian Ocean in different scenarios

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表 1 各个模型的目标函数值及其主要组成

Tab. 1 The objective function of each model and its main components

模型	目标函数值	渔获量成分	丰度指数	渔获年龄组成成分	补充量偏差
S1	4 048	696	81	2998	273
S-base case	4 044	696	86	2993	269
S2	4 042	696	92	2988	266
S3	4 129	753	107	2994	275

下载: 导出CSV

表 2 各个模型的生物学参考点及相关参数

Tab. 2 Biological reference points and related parameters from each assessment model

生物参考点	模型
生物参考点	S1	S-base case	S2	S3
F₂₀₂₀	0.98	0.93	0.90	0.96
SSB₂₀₂₀/t	558 920.7	575 049.9	589 316.9	602 132.8
SSB₀/t	3 046 767	2 849 765	2 706 407	2 469 225
F_MSY	0.34	0.42	0.52	0.52
F₂₀₂₀/F_MSY	2.88	2.21	1.73	1.85
MSY/t	327 559	341 191	354 961	334 197
C₂₀₂₀/MSY	1.32	1.27	1.22	1.30
SSB_MSY/t	991 582	854 369	736 282	745 318
SSB₂₀₂₀/SSB_MSY	0.56	0.67	0.80	0.81
注：F₂₀₂₀为2020年捕捞死亡系数，SSB₂₀₂₀为2020年产卵亲体生物量，SSB₀为未开发时的产卵亲体生物量，F_MSY为最大可持续产量对应的捕捞死亡系数，MSY为最大可持续产量，SSB_MSY为最大可持续产量对应的产卵亲体生物量，C₂₀₂₀为2020年渔获量。

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表 3 印度洋黄鳍金枪鱼ASAP模型中的性成熟度、不同模型中设置的自然死亡系数和陡度参数

Tab. 3 Maturity, natural mortality and steepness parameter set in the ASAP model of yellowfin tuna in the Indian Ocean

年龄	S1		S-base case		S2		S3		性成熟度^[30]
年龄	M₁	h₁	M₁	h₂	M₁	h₃	M₂	h₂	性成熟度^[30]
1	0.963	0.7	0.963	0.8	0.963	0.9	1.068	0.8	0
2	0.663	0.7	0.663	0.8	0.663	0.9	0.735	0.8	0.3
3	0.548	0.7	0.548	0.8	0.548	0.9	0.608	0.8	1
4	0.493	0.7	0.493	0.8	0.493	0.9	0.547	0.8	1
5	0.463	0.7	0.463	0.8	0.463	0.9	0.514	0.8	1
6+	0.446	0.7	0.446	0.8	0.446	0.9	0.495	0.8	1

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表 4 各个模型均方根误差

Tab. 4 Root mean square error for each model

模型	渔获量成分	丰度指数成分	补充量偏差成分
S1	0.62	1.37	1.58
S-base case	0.63	1.39	1.58
S2	0.63	1.41	1.57
S3	0.90	1.47	1.54

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