不同模态下气候变化对智利竹筴鱼补充量的影响

张畅; 李纲; 陈新军

doi:10.12284/hyxb2021130

不同模态下气候变化对智利竹筴鱼补充量的影响

doi: 10.12284/hyxb2021130

张畅^1,,
李纲¹,
陈新军^{1, 2, 3, 4, 5, ,}

1.
上海海洋大学海洋科学学院，上海 201306
2.
青岛海洋科学与技术试点国家实验室海洋渔业科学与食物产出过程功能实验室，山东青岛 266237
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（2019YFD0901404）；上海市科技创新行动计划（19DZ1207500）

详细信息

作者简介:
张畅（1994－），女，浙江省金华市人，博士研究生，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，浙江省义乌市人，从事渔业资源与渔场学等研究。E-mail：xjchen@shou.edu.cn

中图分类号: S931.4; S917
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出版历程
- 收稿日期: 2021-01-28
- 修回日期: 2021-04-29
- 网络出版日期: 2021-06-10
- 刊出日期: 2021-09-25

Impact of climate change on recruitment of Trachurus murphyi based on different regimes

Zhang Chang^1
,,
Li Gang¹,
Chen Xinjun^{1, 2, 3, 4, 5
, ,}

1.
College of Marine Sciences, Shanghai Ocean University, Shanghai 201306, China
2.
Laboratory for Marine Fisheries Science and Food Production Processes, Pilot National Laboratory for Marine Science and Technology (Qingdao), Qingdao 266237, 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)是东南太平洋重要的经济鱼类之一，其资源量受补充量影响明显，了解补充量状况对智利竹筴鱼资源可持续利用和科学管理具有重要意义。本文基于模态分析将1971−2017年间智利竹筴鱼补充量划分不同模态，运用贝叶斯模型平均法，分析海表面温度、海表面盐度、海表面高度、厄尔尼诺和太平洋年代际振荡5个环境因子在不同模态中对补充量的解释能力，并探讨模态变动对补充量预测的影响。结果表明，第1模态(1971−1980年)更多的受捕捞因素的干扰；第2模态(1981−1990年)厄尔尼诺对补充量变动的解释概率最高；第3模态(1991−2001年)解释概率最高的环境因子为太平洋年代际振荡；第4模态(2002−2015年)厄尔尼诺为解释概率最高的环境因子。对比不考虑模态变动的分析结果，两者存在明显差异，基于不同模态的分析结果对智利竹筴鱼补充量变动的解释更为合理。研究认为，智利竹筴鱼补充量变动受到多个环境因子的影响，在不同模态时期起主导作用的环境因子也不同，推测年代际太平洋年代际振荡冷暖期交替与厄尔尼诺现象可能是诱发智利竹筴鱼补充量发生模态转变的重要因素。建议在未来智利竹筴鱼资源评估与管理中，应该考虑不同的模态变化及其影响因子。
- 智利竹筴鱼 /
- 贝叶斯模型平均法 /
- 模态变动 /
- 补充量 /
- 东南太平洋
Abstract: Chilean jack mackerel (Trachurus murphyi) is one of the important economic fisheries in the Southeast Pacific Ocean. The stock abundance is inherently affected by recruitment. To find out the status of recruitment is of great significance to the sustainable yield and scientific management of resources. The recruitment of Chilean jack mackerel between 1971 and 2017 is divided into different regimes based on regime test. Then, the Bayesian model averaging is applied to the relationship between recruitment and five environmental factors (sea surface temperature, sea surface salinity, sea surface height, El Niño and Pacific decadal oscillation), and the impact of regime change in recruitment forecast is also discussed. The results show that the first regime (1971−1980) is more disturbed by fishing; in the second regime (1981−1990), El Niño has the highest probability of explaining for the change of recruitment; the environmental factor with the highest explanatory probability is Pacific Decadal Oscillation in the third regime (1991−2001); in the fourth regime (2002−2015), El Niño most likely explains the change in recruitment; comparing the analysis of regime shift and no regime shift models, there is a significant difference between the two. However, the results, with different regimes, are more reasonable to explain the change of recruitment. Recruitment abundance is affected by multiple environmental factors, but in different regimes, the dominant environmental factor changes. The regime shifts of recruitment are induced by the alternation of cold and warm periods of interdecadal Pacific decadal oscillation, and El Niño. It is suggested that different regime shift and their impact factors should be considered in the future assessment and management of Chilean jack mackerel resources.
- Trachurus murphyi /
- Bayesian model averaging /
- regime shift /
- recruitment /
- Southeast Pacific Ocean

HTML全文

图 1 智利竹筴鱼产卵场

Fig. 1 The spawning ground of Chilean jack mackerel

下载: 全尺寸图片幻灯片

图 2 1971−2017年智利竹筴鱼补充量模态检测

Fig. 2 Regime shift detection in recruitment for different values of Chilean jack mackerel during 1971 to 2017

下载: 全尺寸图片幻灯片

图 3 1971−2017年智利竹筴鱼补充量跃变指数

Fig. 3 Regime shift index values of recruitment for Chilean jack mackerel during 1971 to 2017

下载: 全尺寸图片幻灯片

图 4 1971−2015年间厄尔尼诺指数的年际变化

图中数字为El Niño指数

Fig. 4 Annual changes of El Niño index during 1971 to 2015

The number in the figure is the El Niño index

下载: 全尺寸图片幻灯片

图 5 1971−2015年间PDO指数的年际变化

图中数字为PDO指数

Fig. 5 Annual changes of PDO index during 1971 to 2015

The number in the figure is the PDO index

下载: 全尺寸图片幻灯片

图 6 基于BMA的ln(R/S)预测概率

Fig. 6 Predictive probabilities for ln(R/S) based on BMA

下载: 全尺寸图片幻灯片

表 1 不同影响因子在不同模态阶段的PIP（%）

Tab. 1 PIP (%) of different impact factors on different regimes

影响因子	不分模态阶段	第1模态	第2模态	第3模态	第4模态
亲体量	99	34	86	100	98
SSH	76	41	26	23	32
SSS	20	46	27	24	22
SST	19	40	32	31	25
PDO	16	28	34	41	24
El Niño	14	24	49	23	33

下载: 导出CSV

表 2 基于BMA的PMP最高的前5个模型的后验概率（%）

Tab. 2 Posterior distribution (%) of top five models with highest PMP of all models from BMA for no shift model

影响因子	模型1	模型2	模型3	模型4	模型5
亲体量	√	√	√	√	√
SSS			√
PDO					√
SSH	√		√	√	√
El Niño
SST				√
PMP(MCMC)	36%	9%	9%	9%	7%
PMP(Exact)	36%	9%	9%	9%	7%
注：√表示该模型中包含的因子。

下载: 导出CSV

表 3 不同方案中基于BMA的PMP最高的前5个模型的后验概率（%）

Tab. 3 Posterior distribution (%) of top five models with highest PMP of all models from BMA for different program

模型	方案a	方案b	方案c
模型1	24	19	22
模型2	12	13	11
模型3	6	9	9
模型4	6	6	6
模型5	5	6	6
累计	52	51	53

下载: 导出CSV

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