基于贝叶斯状态空间产量模型的大西洋黄鳍金枪鱼资源评估

田志盼; 田思泉; 戴黎斌; 麻秋云

doi:10.12284/hyxb2021002

基于贝叶斯状态空间产量模型的大西洋黄鳍金枪鱼资源评估

doi: 10.12284/hyxb2021002

田志盼^1,,
田思泉^{1, 2, 3},
戴黎斌¹,
麻秋云^{1, 2, 3, ,}

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

基金项目: 国家重点研发计划“蓝色粮仓科技创新”项目（2019YFD0901404）；中国博士后科学基金面上项目（2019M651475）；大洋渔业资源可持续开发教育部重点实验室开放基金（2019301101）。

详细信息

作者简介:
田志盼（1996-），男，安徽省无为市人，主要研究方向为渔业资源评估。E-mail：tianzhipanwuwei@163.com

通讯作者:
麻秋云，女，讲师，研究方向为种群动力学和渔业资源评估。E-mail：qyma@shou.edu.cn

中图分类号: P714⁺.5; S931.1
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出版历程
- 收稿日期: 2019-12-11
- 修回日期: 2020-05-09
- 网络出版日期: 2020-12-24
- 刊出日期: 2021-03-02

Stock assessment for Atlantic yellowfin tuna based on Bayesian state-space production model

Tian Zhipan^1
,,
Tian Siquan^{1, 2, 3},
Dai Libin¹,
Ma Qiuyun^{1, 2, 3
, ,}

1.
College of Marine Science, 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

摘要

摘要: 黄鳍金枪鱼(Thunnus albacares)是全球远洋渔业的重要目标鱼种，要实现有效的管理，对其进行科学的资源评估是必不可少的。本文以大西洋黄鳍金枪鱼为研究对象，根据国际大西洋金枪鱼养护委员会的渔获量和单位捕捞努力量渔获量数据，使用贝叶斯状态空间模型进行资源评估，并探讨不同剩余产量函数和单位捕捞努力量渔获量数据对评估的影响。结果表明，使用美国、委内瑞拉、日本和中国台北4个船队的单位捕捞努力量渔获量数据及Fox剩余产量函数时模型拟合效果最佳。关键参数环境容纳量和内禀增长率的估计中值和95%置信区间分别为178 (140，229)×10⁴ t和0.210(0.159，0.274)；当前资源量为72.5×10⁴ t，最大可持续产量为13.7×10⁴ t时，种群既没有遭受资源型过度捕捞，也没有捕捞型过度捕捞发生。敏感性分析表明，当渔获量数据存在误报率(70%、80%、90%、110%、120%和130%)时，生物量的评估结果偏高，而捕捞死亡率的结果偏低，但种群均处于健康状态；预测分析显示，当总允许可捕量设为11×10⁴ t时，资源在2024年前仍基本保持健康状态。本研究与国际大西洋金枪鱼养护委员会现有的评估结果基本一致，且模型较稳健，可以为管理决策提供建议。根据模型结果，建议总允许可捕量为11×10⁴ t或更低，以使资源达到可持续开发水平。
- 大西洋 /
- 黄鳍金枪鱼 /
- 资源评估 /
- 剩余产量模型 /
- 敏感性分析
Abstract: Yellowfin tuna (Thunnus albacares) is an important fishing target for offshore fisheries worldwide. Stock assessment is essential for its fishery management of sustainable exploitation. According to catch and catch per unit effort (CPUE) data from the International Commission for Conservation of Atlantic Tunas (ICCAT), the Bayesian state space model was conducted to make stock assessment in an open environment (Just Another Bayesian Biomass Assessment) and to compare the effects of different surplus production forms and CPUE data on the assessment. The results showed that the model performed best with the Fox surplus production form and CPUE data of four fleets (United States, Venezuela, Japan and Chinese Taipei). The median and 95% confidence intervals for carrying capacity, intrinsic growth rate were 178 (140, 229)×10⁴ t and 0.210 (0.159, 0.274), respectively. The current stock was not overfished (B/B_MSY=1.109) and was not subject to overfishing (F/F_MSY=0.893). Sensitivity analysis revealed that when the rates of reported catch divided by the actual catch were 70%, 80%, 90%, 110%, 120%, and 130%, the current biomass assessment results were higher with lower fishing rate, but the stock was still in a healthy status. When the total allowable catch (TAC) was set at 11×10⁴ t, the stock would remain basically healthy until 2024. The results from this stock assessment is generally consistent with ICCAT's current stock assessment results, so it is recommended to set a TAC of 11×10⁴ t to keep the stock status healthy and sustainable exploitation of this important fishery.
- Atlantic Ocean /
- yellowfin tuna /
- stock assessment /
- surplus production model /
- sensitivity analysis

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图 1 大西洋黄鳍金枪鱼1950−2017年的年渔获量

Fig. 1 The annual catch of Atlantic yellowfin tuna from 1950 to 2017

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图 2 大西洋黄鳍金枪鱼JABBA模型S1−S8方案的CPUE指数趋势

黑色实线为模型预测的CPUE结果，阴影区域为其95%置信区间。彩色线段为各船队的CPUE数据

Fig. 2 Time-series of input CPUE of Atlantic yellowfin tuna and predicted CPUE of S1−S8 scenarios in JABBA

The solid black line represents the CPUE predicted by JABBA, and the shaded area is its 95% confidence interval. The colored lines are the CPUE data of each fleet

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图 3 大西洋黄鳍金枪鱼JABBA基础模型参数先验分布（深色）和后验分布（浅色）

Fig. 3 Priors (dark) and posteriors (light) of parameters of base case in JABBA for Atlantic yellowfin tuna

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图 4 1950−2017年大西洋黄鳍金枪鱼JABBA模型基础模型资源开发状态变化图

黑色点线展示了B/B_MSY和F/F_MSY在1950−2017年的变化，3个深浅不同的灰色区域分别代表2017年资源状态的置信区间，2017年资源状态落在红色、黄色和绿色象限的概率分别为26.9%、8.1%和65%

Fig. 4 Kobe phase plot showing estimated trajectories (1950−2017) of B/B_MSY and F/F_MSY of Atlantic yellowfin tuna of base case in JABBA

The black dotted line shows the interannual variation of B/B_MSY and F/F_MSY between 1950 and 2017, three different shades of gray area represent the confidence intervals of the stock status in 2017. The probabilities of the stock falling in the red, yellow and green quadrants are 26.9%, 8.1% and 65%, respectively, in 2017

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图 5 大西洋黄鳍金枪鱼JABBA基础模型1950−2017年F/F_MSY和B/B_MSY趋势

阴影区域为95%置信区间

Fig. 5 F/F_MSY and B/B_MSY of Atlantic yellowfin tuna from 1950 to 2017 of base case in JABBA

The shaded area is its 95% confidence interval

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图 6 不同TAC目标下的大西洋黄鳍金枪鱼JABBA模型基础模型B/B_MSY预测（2019−2027年）

Fig. 6 Future projection (2019−2027) of B/B_MSY of Atlantic yellowfin tuna of base case in JABBA under different TACs

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图 7 大西洋黄鳍金枪鱼JABBA基础模型B、B/B_MSY、F、F/F_MSY的回溯性分析

Refer和2016−2012年表示输入数据序列的末年分别为2017年和2016−2012年

Fig. 7 Retrospective analysis of B, B/B_MSY, F, F/F_MSY of base case in JABBA of Atlantic yellowfin tuna

Refer and 2016−2012 indicate that the last year of input data are 2017 and 2016−2012

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表 1 大西洋黄鳍金枪鱼各延绳钓船队标准化CPUE数据

Tab. 1 Standardized CPUE for each longline fleet of Atlantic yellowfin tuna

船队	缩写	时间跨度
日本	JAP	1971−2014年
乌拉圭1	URU1	1982−1991年
乌拉圭2	URU2	1992−2010年
巴西	BR	1978−2012年
委内瑞拉	VEN	1991−2014年
美国	US	1987−2014年
中国台北1	TAI1	1970−1992年
中国台北2	TAI2	1993−2014年

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表 2 大西洋黄鳍金枪鱼JABBA模型S1−S8方案设置

Tab. 2 Different scenarios (S1−S8) of Atlantic yellowfin tuna in JABBA

方案	产量函数	CPUE数据
S1	Pella-Tomlinson	JAP，URU1, URU2, BR, VEN，US，TAI1，TAI2
S2	Pella-Tomlinson	JAP，VEN，US，TAI1
S3	Pella-Tomlinson	JAP_RE，VEN，US，TAI1
S4	Fox	JAP_RE，VEN，US，TAI1
S5	Fox	VEN，US，TAI1
S6	Fox	JAP_RE，US，TAI1
S7	Fox	JAP_RE，VEN，TAI1
S8	Fox	JAP_RE，VEN，US
注：JAP_RE代表JAP去掉1976年之前的数据，即1976−2014年的数据。

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表 3 大西洋黄鳍金枪鱼JABBA模型中K和r有信息和无信息的先验分布设定以及后验分布

Tab. 3 The informative and non-informative prior and posterior distributions for K and r in the JABBA for Atlantic yellowfin tuna

情况	先验分布		后验分布
情况	K/10⁴ t	r	K/10⁴ t	r
S4（K和r均为有信息先验）	U[139.2，265.8]	U[0.14，0.34]	177(140，227)	0.211(0.162，0.273)
K为无信息先验，r为有信息先验	U[20，3000]	U[0.14，0.34]	161(114，232)	0.231(0.159，0.331)
K为有信息先验，r无信息先验	U[139.2，265.8]	U[0.01，2]	180(136，242)	0.207(0.144，0.285)
注：后验分布中，K和r的数值分别为其中值和95%置信区间。

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表 4 大西洋黄鳍金枪鱼JABBA模型S1−S8方案的拟合效果

Tab. 4 Goodness of fitting of S1−S8 scenarios in JABBA for Atlantic yellowfin tuna

拟合指标	S1	S2	S3	S4	S5	S6	S7	S8
RMSE	49.8	23.7	23.1	23.0	22.9	22.2	24.8	22.4
DIC	885.7	397.2	346.1	345.3	68.4	254.5	43.2	330.5

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表 5 大西洋黄鳍金枪鱼JABBA基础模型参数后验估计值及其95%置信区间

Tab. 5 Posterior estimates and 95% confidence intervals of parameter of base case in JABBA for Atlantic yellowfin tuna

参数	中值	2.5%	97.5%
K/10⁴ t	178	140	229
r	0.210	0.159	0.274
B₁₉₅₀/K	0.949	0.809	1.035
F_MSY	0.210	0.159	0.274
B_MSY/10⁴ t	65.4	51.3	84.3
MSY/10⁴ t	13.7	12.0	16.0
B₂₀₁₇/B_MSY	1.109	0.723	1.624
F₂₀₁₇/F_MSY	0.893	0.565	1.432

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表 6 不同TAC目标下大西洋黄鳍金枪鱼2019−2027年B>B_MSY的概率

Tab. 6 The probability that B>B_MSY of Atlantic yellowfin tuna under different TAC targets in 2019−2027

TAC/10⁴ t	2019年	2020年	2021年	2022年	2023年	2024年	2025年	2026年	2027年
8.8	0.712	0.793	0.852	0.892	0.921	0.941	0.956	0.966	0.974
9.35	0.717	0.784	0.836	0.873	0.901	0.924	0.940	0.950	0.957
9.9	0.718	0.778	0.824	0.856	0.882	0.904	0.922	0.934	0.944
10.45	0.720	0.766	0.806	0.835	0.857	0.880	0.894	0.908	0.919
11.0	0.714	0.751	0.785	0.808	0.831	0.846	0.864	0.876	0.887
12.1	0.715	0.733	0.749	0.762	0.772	0.783	0.790	0.798	0.806
13.2	0.718	0.711	0.705	0.701	0.695	0.692	0.686	0.685	0.680

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表 7 不同TAC目标下大西洋黄鳍金枪鱼2019−2027年F>F_MSY的概率

Tab. 7 The probability that F>F_MSY of Atlantic yellowfin tuna under different TAC targets in 2019−2027

TAC/10⁴ t	2019年	2020年	2021年	2022年	2023年	2024年	2025年	2026年	2027年
8.8	0.022	0.016	0.012	0.01	0.008	0.006	0.005	0.004	0.004
9.35	0.035	0.028	0.022	0.017	0.015	0.013	0.011	0.011	0.008
9.9	0.050	0.042	0.034	0.028	0.023	0.02	0.018	0.016	0.016
10.45	0.071	0.062	0.053	0.048	0.041	0.036	0.033	0.030	0.027
11.0	0.101	0.091	0.083	0.074	0.068	0.062	0.058	0.053	0.051
12.1	0.166	0.163	0.159	0.151	0.150	0.144	0.145	0.140	0.139
13.2	0.258	0.266	0.276	0.279	0.286	0.289	0.294	0.298	0.303

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表 8 不同TAC目标下大西洋黄鳍金枪鱼2019−2027年处于健康状态的概率

Tab. 8 The probability that the Atlantic yellowfin tuna is in healthy status under different TAC targets in 2019−2027

TAC/10⁴ t	2019年	2020年	2021年	2022年	2023年	2024年	2025年	2026年	2027年
8.8	0.712	0.793	0.852	0.892	0.921	0.941	0.956	0.966	0.974
9.35	0.717	0.784	0.836	0.873	0.901	0.924	0.940	0.950	0.957
9.9	0.718	0.778	0.824	0.856	0.882	0.904	0.922	0.934	0.944
10.45	0.720	0.766	0.806	0.835	0.857	0.880	0.894	0.908	0.919
11.0	0.714	0.751	0.785	0.808	0.831	0.846	0.863	0.876	0.887
12.1	0.715	0.732	0.747	0.761	0.772	0.782	0.789	0.797	0.805
13.2	0.700	0.693	0.687	0.684	0.678	0.677	0.672	0.671	0.668

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表 9 不同渔获量误报比例下大西洋黄鳍金枪鱼JABBA基础模型评估资源状态

Tab. 9 Stock status of Atlantic yellowfin tuna in different mis-reported rates of catches of base case in JABBA

报告渔获量占实际渔获量的比例/%	B₂₀₁₇/10⁴ t	B₂₀₁₇/B_MSY	F₂₀₁₇	F₂₀₁₇/F_MSY	资源健康/%
70	77.7	1.227	0.175	0.854	70.1
80	73.9	1.165	0.185	0.896	65.6
90	70.9	1.119	0.191	0.914	63.1
100	72.5	1.109	0.187	0.893	65.0
110	72.7	1.081	0.186	0.886	64.3
120	77.4	1.094	0.175	0.845	67.1
130	80.4	1.078	0.169	0.824	64.5
注：资源健康（%）指2017年资源未遭受资源型过度捕捞和捕捞型过度捕捞的概率。

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