不同时间尺度下智利竹筴鱼资源量与环境要素的关系

张畅; 陈新军

doi:10.12284/hyxb2025004

不同时间尺度下智利竹筴鱼资源量与环境要素的关系

doi: 10.12284/hyxb2025004

张畅^1,,
陈新军^{2, 3, 4, 5, ,}

1.
浙江海洋大学，浙江舟山 316022
2.
上海海洋大学海洋生物资源与管理学院，上海 201306
3.
上海海洋大学大洋渔业资源可持续开发教育部重点实验室，上海 201306
4.
上海海洋大学国家远洋渔业工程技术研究中心，上海 201306
5.
上海海洋大学农业农村部大洋渔业开发重点实验室，上海 201306

基金项目: 国家重点研发计划（2023YFD2401302）资助。

详细信息

作者简介:
张畅（1994—）女，讲师，主要研究方向为渔业资源。E-mail：itschang@foxmail.com

通讯作者:
陈新军（1967—），男，教授，主要研究方向为渔业资源、渔业生物学。E-mail：xjchen@shou.edu.cn

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- 文章访问数: 62
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- 被引次数: 0
出版历程
- 收稿日期: 2024-07-03
- 修回日期: 2024-11-29
- 网络出版日期: 2024-12-14

The relationship between Trachurus murphyi and the environment at different time scales

Zhang Chang^1
,,
Chen Xinjun^{2, 3, 4, 5
, ,}

1.
School of Fishery, Zhejiang Ocean University, Zhoushan 316022, China
2.
College of Marine Living Resource Sciences and Management, Shanghai Ocean University, Shanghai 201306, China
3.
Key Laboratory of Sustainable Exploitation of Oceanic Fisheries Resources, Ministry of Education, Shanghai Ocean University, Shanghai 201306, China
4.
National Engineering Research Center for Oceanic Fisheries, Shanghai Ocean University, Shanghai 201306, China
5.
Key Laboratory of Oceanic Fisheries Exploitation, Ministry of Agriculture and Rural Affairs, Shanghai 201306, China

摘要

摘要: 智利竹筴鱼（Trachurus murphyi）资源量受环境影响明显，而环境本身也会随时间的变化而变化，存在短期的季节性变动和长期的模态变动。为更好地探究环境与渔业资源之间的关系，本文基于1970−2017年智利竹筴鱼资源量与环境气候数据，利用积分回归分析月间变化趋势，利用模态分析和全子集回归分析，从年间尺度分析了环境气候与智利竹筴鱼资源变动之间的联系。月间分析结果表明：海表面温度对资源量的影响随季节变化最为明显，尤其在产卵和越冬季节。其次为太平洋年代际涛动，海表面盐度和厄尔尼诺指数的影响在不同月份变动幅度较小，海表面高度产生的影响几乎不随月份改变。年间分析结果表明：在长时间尺度上，智利竹筴鱼资源变动存在4个模态变化时期，每个模态中占据主导地位的影响因子组合存在区别，尤其是随着近些年全球气候变化的加剧，渔业资源可能受到了更多种环境要素的影响，导致环境对渔业的影响模式发生了明显改变。
- 智利竹筴鱼 /
- 积分回归 /
- 全子集回归 /
- 模态变化 /
- 环境
Abstract: The Trachurus murphyi is affected by the environment, and the environment itself changes with time, with short-term seasonal changes and long-term regime shifts. Based on the data on jack mackerel stock and the environment from 1970 to 2017, this paper analyzes the relationship between the environment and jack mackerel stock from month to year using integral regression, modal analysis, and full subset regression analysis. The month-on-month analysis results show that the influence of Sea Surface Temperature (SST) on stock biomass changes most obviously with time. Chilean jack mackerel is more dependent on SST in spawning and overwintering seasons; Followed by the Pacific Decadal Oscillation (PDO), The effects of Sea Surface Salinity (SSS) and Oceanic Nino Index (ONI) vary less in different months; The impact of Sea Surface Height (SSH) hardly changes from month to month. The annual analysis revealed four distinct regime shifts in Chilean mackerel resources over a long-time scale, with each regime characterized by unique dominant factor combinations. Notably, with the escalation of global climate change in recent years, a broader array of environmental factors has potentially influenced fishery resources, leading to substantial modifications in the environmental impact mode on fisheries.
- Chilean Jack mackerel /
- integral regression /
- full subset regression /
- regime shift /
- environment

HTML全文

图 1 1970−2017年各环境要素影响系数的逐月变化

Fig. 1 Monthly changes in the impact coefficients of the environmental factors from 1970 to 2017

下载: 全尺寸图片幻灯片

图 2 1970−2017年智利竹筴鱼资源量模态检测

Fig. 2 Regime shift detection for Chilean jack mackerel biomass from 1971 to 2017

下载: 全尺寸图片幻灯片

图 3 1970−2017年智利竹筴鱼资源量跃变指数

Fig. 3 Regime shift index values of Chilean jack mackerel biomass from 1971 to 2017

下载: 全尺寸图片幻灯片

表 1 1970−2017年智利竹筴鱼资源量与环境因子的全子集分析

Tab. 1 All-subsets regression of Chilean jack mackerel resources and environmental factors from 1970 to 2017

非模态组合	包含的环境数据
组合1	SSH	SST
组合2	SSH	SST	SSS
组合3	SSH	SST	PDO	SSS
组合4	SSH	SST	ONI	PDO	SSS
组合5	SSH

下载: 导出CSV

表 2 第一、二、三、四模态下资源量与环境因子的全子集分析

Tab. 2 Full subset analysis of Chilean jack mackerel resources and environmental factors in the first, second, third, and fourth regime

模态		包含的环境数据
第一模态	组合1	SSH		PDO	ONI
	组合2	SSH		PDO	ONI	SSS
	组合3			PDO	ONI
	组合4	SSH	SST	PDO	ONI	SSS
	组合5				ONI
第二模态	组合1		SST		ONI	SSS
	组合2	SSH	SST
	组合3	SSH
	组合4	SSH	SST		ONI	SSS
	组合5	SSH	SST	PDO	ONI	SSS
第三模态	组合1	SSH		PDO	ONI
	组合2	SSH		PDO	ONI	SSS
	组合3	SSH	SST	PDO	ONI	SSS
	组合4	SSH
	组合5	SSH			ONI
第四模态	组合1			PDO	ONI
	组合2				ONI
	组合3		SST	PDO	ONI
	组合4		SST	PDO	ONI	SSS
	组合5	SSH	SST	PDO	ONI	SSS

下载: 导出CSV

表 3 各模态中的最佳模型及AIC值

Tab. 3 The best model and AIC value in each regime

	回归模型	AIC值
不区分模态	Ln (bio) = 0.3 × SSH −0.6 × SST + 14.9	54.2
第一模态	Ln (bio) = 0.03 × SSH + 0.04 × PDO + 0.07 × ONI + 7.1	−34.3
第二模态	Ln (bio) = −7.4 × SSS −0.4 × SST + 0.07 × ONI + 252.8	−20.8
第三模态	Ln (bio) = 0.06 × SSH + 0.1 × PDO −0.2 × ONI + 6.7	−3.7
第四模态	Ln (bio) = −0.1 × PDO + 0.2 × ONI + 6.2	6.2

下载: 导出CSV

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