基于GAM的西北太平洋日本鲭资源丰度预测模型建立

武胜男; 陈新军; 刘祝楠

doi:10.3969/j.issn.0253-4193.2019.08.004

基于GAM的西北太平洋日本鲭资源丰度预测模型建立

doi: 10.3969/j.issn.0253-4193.2019.08.004

武胜男^{1, 2,},
陈新军^{1, 2, 3, 4, 5, ,},
刘祝楠¹

1.
上海海洋大学海洋科学学院，上海 201306
2.
农业农村部大洋渔业开发重点实验室，上海 201306
3.
上海海洋大学国家远洋渔业工程技术研究中心，上海 201306
4.
大洋渔业资源可持续开发教育部重点实验室，上海 201306
5.
农业农村部大洋渔业资源环境科学观测实验站，上海 201306

基金项目: 海洋局公益性行业专项（20155014）；上海市科技创新计划（15DZ1202200）。

详细信息

作者简介:
武胜男(1994—)，女，山东省青岛市人，主要从事渔业资源研究。E-mail：374723906@qq.com

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

中图分类号: S931.4
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- 被引次数: 0
出版历程
- 收稿日期: 2018-06-04
- 修回日期: 2018-07-17
- 网络出版日期: 2021-04-21
- 刊出日期: 2019-08-25

Establishment of forecasting model of the abundance index for chub mackerel (Scomber japonicus) in the northwest Pacific Ocean based on GAM

Wu Shengnan^{1, 2
,},
Chen Xinjun^{1, 2, 3, 4, 5
, ,},
Liu Zhu'nan¹

1.
College of Marine Sciences, Shanghai Ocean University, Shanghai 201306, China
2.
Key Laboratory of Oceanic Fisheries Exploration, Ministry of Agriculture and Rural Affairs, Shanghai 201306, China
3.
National Engineering Research Center for Oceanic Fisheries, Shanghai Ocean University, Shanghai 201306, China
4.
Key Laboratory of Sustainable Exploitation of Oceanic Fisheries Resources, Ministry of Education, Shanghai 201306, China
5.
Scientific Observing and Experimental Station of Oceanic Fishery Resources, Ministry of Agriculture and Rural Affairs, Shanghai 201306, China

摘要

摘要: 日本鲭(Scomber japonicus)是西北太平洋重要的鱼类资源之一，科学预测日本鲭的资源丰度有利于其资源的合理开发和利用。本研究依据日本渔业机构提供的1987–2012年日本鲭太平洋群体的资源量数据，结合产卵场和渔场的海洋环境数据以及气候因子，使用广义加性模型对影响日本鲭太平洋群体的海洋环境和气候因子进行分析，筛选出有显著影响的因子并建立该群体的资源量预测模型。结果表明，与该群体资源量有显著关系的影响因子有：北极涛动指数、太平洋年代际振荡指数、渔场海表面高度、渔场海表面盐度和渔场海表面温度。基于赤池信息准则筛选出的4个资源量预测模型分析表明，包含北极涛动指数、渔场海表面高度和渔场海表面温度的模型有较好的预测效果，该模型的验证结果也通过了t检验(P<0.05)，可用于日本鲭太平洋群体资源量的预测。
- 日本鲭 /
- 太平洋群体 /
- 环境因子 /
- 气候因子 /
- GAM模型
Abstract: Chub mackerel (Scomber japonicus) is one of the important fishery resources in the northwest Pacific Ocean. Building a scientific forecast model of abundance index to this species is beneficial for its exploitation and utilization. In this study, based on the biomass data of the Pacific-cohort of Scomber japonicus during 1987–2012 obtained from Japan Fisheries Institution, as well as the marine environmental data and climatic data of spawning ground and fishing ground, we analyzed the relationship between the environmental and climatic factors and the biomass of this cohort. The significant factors were selected and the forecast models were established by using the generalized addictive models (GAM). The result shows the significant factors affecting the biomass of this cohort conclude the Arctic Oscillation index (AOI), Pacific Decadal Oscillation index (PDOI) and sea surface height (SSH2), sea surface salinity (SSS2) and sea surface temperature (SST2) both in the fishing ground. Result based on Akaike’s Information Criterion (AIC) suggests that the model 1 which included AOI, SSH2 and SST2 has the optimal model impacts. The model 1 passes the significant test (P<0.05) and the t test (P<0.05) is also passed based the validation result of model 1. Therefore, we suggest that this model can be used to forecast the abundance of the Pacific-cohort of Scomber japonicus.
- chub mackerel (Scomber japonicus) /
- the Pacific-cohort /
- environmental factors /
- climatic factors /
- GAM models

HTML全文

图 1 日本鲭太平洋群体分布区域图

Fig. 1 Distribution of the Pacific-cohort of chub mackerel (Scomber japonicus)

下载: 全尺寸图片幻灯片

图 2 2000–2012年日本鲭太平洋群体资源量以及4种预测模型结果

Fig. 2 The actual ln(Biomass) and the results of biomass forecasting models for the Pacific-cohort of chub mackerel (Scomber japonicus) during 2000–2012

下载: 全尺寸图片幻灯片

表 1 1987－1999年各环境因子和气候因子与日本鲭资源量的GAM检验

Tab. 1 Test of GAM between the environmental or climatic factors and the biomass during 1987–1999

影响因子	P值	R²
AOI	0.05*	0.24
NI	0.18	0.24
PDOI	2.64×10^–5*	0.76
SOI	0.15	0.27
产卵场SSH（SSH1）/cm	0.30	0.17
渔场SSH（SSH2）/cm	0.03*	0.68
产卵场SSS（SSS1）	0.09	0.16
渔场SSS（SSS2）	3.00×10^–3*	0.96
产卵场SST（SST1）/℃	0.10	0.16
渔场SST （SST2）/℃	1.00×10^–3*	0.86
黑潮潮差/m	0.60	0.06
亲潮春季平均面积/m²	0.28	0.20
注：*表示在显著性水平α=0.05（双侧）上显著相关。

下载: 导出CSV

表 2 1987－1999年各关键影响因子之间的回归分析

Tab. 2 Regression analysis of significant factors during 1987－1999

影响因子	P值	R²
AOI，PDOI	0.07	0.28
AOI，SSH2	0.49	0.04
AOI，SST2	0.14	0.19
AOI，SSS2	0.42	0.06
PDOI，SSH2	4.37×10^–3*	0.54
PDOI，SST2	7.37×10^–4*	0.66
PSO，SSS2	0.73	0.01
SSH2，SST2	0.06	0.28
SSH2，SSS2	0.49	0.04
SST2，SSS2	0.93	7.08×10^–4
注：*表示在显著性水平α=0.05（双侧）上显著相关。

下载: 导出CSV

表 3 1987－1999年基于环境因子和气候因子的资源量预测模型

Tab. 3 Forecasting models of biomass based on environmental and climatic factors during 1987–1999

模型	AIC	P值 (P<0.05)	调整后的R²
模型1	–90.892 55	2.80×10^–5*	1.000
模型2	–79.380 89	4.41×10^–3*	0.999
模型3	–47.796 14	6.22×10^–9*	0.995
模型4	–24.670 12	9.97×10^–8*	0.970
注：*表示在显著性水平α=0.05（双侧）上显著相关。

下载: 导出CSV

表 4 2000－2012年资源量的预测值和真实值之间的t检验

Tab. 4 t test between the predicted ln(Biomass) and the actual ln(Biomass) during 2000–2012

模型	t值	自由度	P值 (P<0.05)
模型1	2.97	18.33	8.08×10^–3*
模型2	4.27	23.88	2.66×10^–4*
模型3	1.63	20.90	0.12
模型4	1.01	20.73	0.33
注：*表示在显著性水平α=0.05（双侧）上显著相关。

下载: 导出CSV

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