基于GAM模型研究水温垂直结构对热带中西太平洋黄鳍金枪鱼渔获率的影响

杨胜龙; 范秀梅; 吴祖立; 伍玉梅; 戴阳

doi:10.12284/hyxb2021040

基于GAM模型研究水温垂直结构对热带中西太平洋黄鳍金枪鱼渔获率的影响

doi: 10.12284/hyxb2021040

杨胜龙^{1, 2,},
范秀梅¹,
吴祖立¹,
伍玉梅¹,
戴阳^1, ,

1.
中国水产科学研究院东海水产研究所农业农村部远洋与极地渔业创新重点实验室，上海 200090
2.
中国水产科学研究院渔业资源与遥感信息技术重点开放实验室，上海 200090

基金项目: 国家重点研发计划（2019YFD0901405）；国家自然科学基金（41606138）；中央级公益性科研院所基本科研业务费（2019T09）

详细信息

作者简介:
杨胜龙（1982-），男，江西省九江市人，主要从事渔场次表层环境和金枪鱼渔场变动研究。E-mail：ysl6782195@126.com

通讯作者:
戴阳，副研究员。E-mail：daiyangbox@163.com

中图分类号: S931
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出版历程
- 收稿日期: 2020-05-06
- 修回日期: 2020-09-04
- 网络出版日期: 2021-06-18
- 刊出日期: 2021-04-01

Analysis of the influence of the vertical structure of water temperature on the catch rate of yellowfin tuna in the tropical central and western Pacific based on the GAM model

Yang Shenglong^{1, 2
,},
Fan Xiumei¹,
Wu Zuli¹,
Wu Yumei¹,
Dai Yang^{1
, ,}

1.
Key Laboratory of Oceanic and Polar Fisheries, Ministry of Agriculture and Rural Affairs, East China Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Shanghai 200090, China
2.
Key and Open Laboratory of Remote Sensing Information Technology in Fishing Resource, East China Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Shanghai 200090, China

摘要

摘要: 黄鳍金枪鱼索饵水层影响延绳钓捕捞效率，而黄鳍金枪鱼索饵水层分布受水温垂直结构的影响，因此本文采用GAM模型分析次表层环境变量对延绳钓黄鳍金枪鱼渔获率的影响，评估黄鳍金枪鱼垂直水层分布对中西太平洋黄鳍金枪鱼延绳钓单位捕捞努力量渔获量(Catch Per Unite Effort, CPUE)的作用。模型结果表明，环境因子对热带中西太平洋延绳钓黄鳍金枪鱼渔获率空间分布影响明显。黄鳍金枪鱼延绳钓CPUE在2012年之后快速增多，高渔获率月份出现在北半球夏季，空间上在10°S，140°E附近区域。温跃层上界温度和深度、温跃层下界深度、18℃等温线深度、△8℃等温线深度及其和温跃层下界深度的深度差对延绳钓渔获率影响较大，是影响热带中西太平洋黄鳍金枪鱼延绳钓渔获率的关键环境因子。随着温跃层上界温度和深度值变大，延绳钓CPUE逐渐递增，对延绳钓CPUE影响密切的温度和深度分别为27～28℃和70～90 m。温跃层下界深度对延绳钓CPUE影响在250～280 m时最大；之后随着下界深度的变大，CPUE快速下降。18℃等温线深度对延绳钓CPUE影响呈现先震荡后递增的趋势，影响密切的区域在230 m深度上下。△8℃等温线深度与温跃层下界深度的差值对热带中西太平洋黄鳍金枪鱼延绳钓CPUE影响呈现先快速递减后缓慢增加的趋势，在深度差为70 m上下时影响最密切。研究结果揭示，在黄鳍金枪鱼活动水层受限或栖息水层和延绳钓作业深度相吻合时，延绳钓渔获率最高。依据黄鳍金枪鱼垂直活动水层调整延绳钓投钩，可以提高渔获率。因此，采用延绳钓CPUE进行渔场和资源评估时要考虑金枪鱼适宜垂直活动空间。
- 黄鳍金枪鱼 /
- 次表层环境 /
- 水温垂直结构 /
- 单位捕捞努力量渔获量
Abstract: The foraging depth of yellowfin tuna (Thunnus obesus), which is primarily influenced by the vertical structure of the water temperature, has a significant effect on longline catch rates. Therefore, the generalized additive model (GAM) was applied to analyse the influence of subsurface environmental variables on the longline catch per unit of effort (CPUE) in the central and western Pacific. The results show that subsurface environmental factors have significant impacts on the spatial distribution of yellowfin tuna catches in longline fisheries. The longline CPUE for the yellowfin tuna in the tropical central and western Pacific rise rapidly after 2012. A high catch rate appears in the northern hemisphere during summer in the region near 10°S, 140°E. The upper boundary temperature and depth of the thermocline, the lower depth of the thermocline, the depth of the isotherm at 18℃, and the relative depth between △8℃ and the lower depth of thermocline greatly influence the longline fishing rate. These key environmental factors affect the tropical central and western Pacific yellowfin tuna longline catch. The CPUE increases as the temperature and the depth of the upper boundary of the thermocline increased. The strongly associated relationships between the upper boundary temperature and depth with CPUE were 27−28℃ and 70−90 m, respectively. High catch rates are observed when the lower boundary depth of the thermocline is from 250 m to 280 m. Then, as the lower boundary depth increased, the CPUE value quickly decreased. The effect of the 18℃ isotherm depth on the CPUE of longline fishing initially fluctuated and then increased. The nonlinear effects of the relative depth between △8℃ and the lower depth of the thermocline first decreased and then increased slowly. The strong associations between CPUE and the 18℃ isotherm depths and relative depth are at 230 m and 70 m, respectively. The catch rates reaches a maximum when the vertical habitat is compressed, making it consistent with hooking depth. The catch rates could be changed by adjusting the depth of hooks. The vertical habitat of tuna should be taken into account in fisheries stock assessments and fishing grounds analysis.
- Thunnus obesus /
- subsurface environmental /
- vertical structure of the water temperature /
- CPUE

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图 1 2007−2017年中西太平洋延绳钓黄鳍金枪鱼CPUE分布

Fig. 1 CPUE distribution of longline yellowfin tuna in western and central Pacific from 2007 to 2017

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图 2 时空变量对中西太平洋黄鳍金枪鱼延绳钓CPUE的影响

Fig. 2 The effects of spatial-temporal predictors on CPUE of western and central Pacific

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图 3 次表层环境变量对中西太平洋黄鳍金枪鱼延绳钓CPUE的影响

Fig. 3 The effects of subsurface environmental variables on CPUE of yellow tuna in the western and central Pacific

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表 1 中西太平洋黄鳍金枪鱼时空变量GAM模型F-检验值

Tab. 1 F-test value of the spatio-temporal GAM model of yellowfin tuna in western and central Pacific

变量	自由度	F	p
年	2.79	191.09	<2×10⁻¹⁶
月	2.89	47.13	<2×10⁻¹⁶
纬度	3	2 613.34	<2×10⁻¹⁶
经度	3	1606.35	<2×10⁻¹⁶

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表 2 中西太平洋黄鳍金枪鱼时空变量GAM模型统计参数

Tab. 2 Statistical characteristics of the spatio-temporal GAM model of yellowfin tuna in western and central Pacific

公式	AIC值	偏差解释率/%	决定系数
$ {{\rm{ln}} ({\rm{CPUE}} + 1) = s({\rm{year}})}$	28 039.2	2	0.02
$ { {\rm{ln} }({\rm{CPUE}}+1)=s({\rm{year}})+s({\rm{month}})}$	27 879.19	3.16	0.031
$ {\begin{aligned}&{\rm{ln}}({\rm{CPUE}}+1)=s({\rm{year}})+\\&s({\rm{month}})+{{s}}({\rm{lon}})\end{aligned}}$	24 090.88	26.3	0.263
$ {\begin{aligned}&{\rm{ln}}({\rm{CPUE}}+1)=s({\rm{year}})+\\&s({\rm{month}})+{{s}}({\rm{lon}})+s({\rm{lat}})\end{aligned}}$	19 965.13	45.3	0.452

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表 3 次表层环境变量GAM模型统计参数

Tab. 3 Statistical characteristics of the spatio-temporal GAM model for subsurface environmental variables

变量	自由度	F	p
上界温度	1.55	458.19	<10⁻⁶
上界深度	2.33	80.83	<10⁻⁶
下界温度	2.97	39.95	<10⁻⁶
下界深度	3	199.81	<10⁻⁶
厚度	2.9	23.47	<10⁻⁶
强度	2.86	10.55	<10⁻⁶
12℃深度	2.97	29.69	<10⁻⁶
18℃深度	2.96	95.09	<10⁻⁶
深度差	2.98	106.09	<10⁻⁶

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表 4 次表层环境变量GAM模型检验值

Tab. 4 GAM model test value of subsurface environmental variables

公式	AIC值	偏差解释率/%	决定系数
$ {{\rm{ln}} ({\rm{CPUE}} + 1) = s({\rm{upwd}})}$	25 918.51	15.9	0159
$ {{\rm{ln}} ({\rm{CPUE}} + 1) = s({\rm{upwd}}) + s({\rm{upsd}})}$	25 508.2	18.4	0.183
$ {{\rm{ln}} ({\rm{CPUE}} + 1) = s({\rm{upwd}}) + s({\rm{upsd}}) + s({\rm{downwd}})}$	25 266.73	19.8	0.197
$ {{\rm{ln}} ({\rm{CPUE}} + 1) = s({\rm{upwd}}) + s({\rm{upsd}}) + s({\rm{downsd}}) + s({\rm{downwd}})}$	24 769.75	22.6	0.226
$ {\begin{array}{l} {\rm{ln}} ({\rm{CPUE}} + 1) = s({\rm{upwd}}) + s({\rm{upsd}}) + s(hd) + s({\rm{downsd}}) + s({\rm{downwd}}) \end{array} }$	24 666.62	23.2	0.232
$ {\begin{array}{l} {\rm{ln}} ({\rm{CPUE}} + 1) = s({\rm{upwd}}) + s({\rm{upsd}}) + s({\rm{hd}}) + s({\rm{downsd}}) + s({\rm{downwd}}) + s({\rm{intensity}}) \end{array} }$	24 622.31	23.5	0.234
$ {\begin{array}{l} {\rm{ln}} ({\rm{CPUE}} + 1) = s({\rm{upwd}}) + s({\rm{upsd}}) + s({\rm{hd}}) + s({\rm{downsd}}) + s({\rm{downwd}}) + s({\rm{intensity}}) + s({\rm{D}}12) \end{array} }$	24 409.33	24.7	0.246
$ {\begin{array}{l} {\rm{ln} } ({\rm{CPUE} } + 1) = s({\rm{upwd} }) + s({\rm{upsd} }) + s({\rm{hd} }) + s({\rm{downsd} })+ s({\rm{downwd} }) + s({\rm{intensity} }) + s({\rm{D} }12) + s({\rm{D}}18) \end{array} }$	24 066.41	26.6	0.265
$ {\begin{array}{l} {\rm{ln} } ({\rm{CPUE} } + 1) = s({\rm{upwd} }) + s({\rm{upsd} }) + s({\rm{hd} }) + s({\rm{downsd} }) + s({\rm{downwd} }) + s({\rm{intensity} }) + s({\rm{D} }12) + s({\rm{D} }18) + s({{sdc} }) \end{array} }$	23 784.19	28.1	0.279

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