西北太平洋柔鱼丰度的灰色灾变预测

解明阳; 陈新军

doi:10.3969/j.issn.0253-4193.2020.04.005

西北太平洋柔鱼丰度的灰色灾变预测

doi: 10.3969/j.issn.0253-4193.2020.04.005

解明阳^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.
农业农村部大洋渔业开发重点实验室，上海 201306
3.
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: 国家自然科学基金面上项目(NSFC41876141)；上海市科技创新行动计划 (19DZ1207502)。

详细信息

作者简介:
解明阳 (1994-)，男，吉林省吉林市人，主要研究方向为渔情预报。E-mail：772966694@qq.com

通讯作者:
陈新军，教授，主要从事渔业资源渔场学、远洋鱿钓渔业等方向研究。E-mail：xjchen@shou.edu.cn

中图分类号: P714⁺.5; S931
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- 文章访问数: 113
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- PDF下载量: 11
- 被引次数: 0
出版历程
- 收稿日期: 2019-05-25
- 修回日期: 2019-06-28
- 网络出版日期: 2020-11-18
- 刊出日期: 2020-04-25

Grey catastrophe year prediction for the abundance of neon flying squid （Ommastrephes bartramii） in the Northwest Pacific

Xie Mingyang^1
,,
Chen Xinjun^{1, 2, 3, 4, 5
, ,}

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 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: 柔鱼(Ommastrephes bartramii)为短生命周期的头足类，其资源丰度极易受海洋环境变化影响，年间波动较大。根据1995−2017年西北太平洋柔鱼渔业生产统计数据，以单位捕捞努力量渔获量(CPUE)作为资源丰度指数，运用灰色灾变预测方法对上、下限灾变年份建立GM(1, 1)模型，预测未来灾变年份。结果显示，以GLM模型标准化CPUE建立的下限灾变预测模型的平均相对误差为15.32%，上限灾变预测模型的平均相对误差为8.19%，模型精度检验等级均为Ⅰ级。研究认为，下一个资源丰年(CPUE大于2.39 t/(船·a))将出现在2021年，资源歉年(CPUE小于2.13 t/(船·a))将出现在2027年；太平洋年代际涛动与El Niño-La Niña事件是驱使柔鱼丰度大幅度波动的重要因素。该预测结果可为西北太平洋鱿钓生产企业和管理部门提供参考。
- 柔鱼 /
- 资源丰度 /
- 灰色灾变预测 /
- GM（1, 1）模型 /
- 气候变化
Abstract: Neon flying squid Ommastrephes bartramii is a short-lived cephalopod species whose abundance is highly susceptible to marine environmental changes and has large interannual fluctuations. Based on the statistical data from the squid fishery in the Northwest Pacific from 1995 to 2017, catch per unit effort (CPUE) was derived as an abundance index and the grey catastrophic prediction method was used to establish the GM（1, 1）model for the upper and lower catastrophic years and to forecast the future catastrophic years. The results show that the average relative error of the lower limit catastrophic prediction model established based on GLM-model-standardized CPUE is 15.32%, the average relative error of the upper catastrophic prediction model is 8.19%, and the accuracy tests for both models attain the level I accuracy. The study forecasts that the next upper catastrophic year may occur in 2021 (CPUE≥2.39 t/(ship·a)), and the lower catastrophe year occurred in 2027 (CPUE≤2.13 t/(ship·a)). The study also suggests that the Pacific Decadal Oscillation and El Niño-La Nina events are important factors driving large fluctuations in the squid abundance. The forecast can provide a reference for squid fishing enterprises and managing departments in the Northwest Pacific.
- Ommastrephes bartramii /
- abundance /
- grey catastrophic year prediction /
- GM (1, 1) model /
- climate change

HTML全文

图 1 1995−2017年西北太平洋柔鱼资源丰度变化

Fig. 1 CPUE of Ommastrephes bartramii in the Northwest Pacific from 1995 to 2017

下载: 全尺寸图片幻灯片

图 2 1995−2017年太平洋年代际涛动（PDO）指数时间序列变化及灾变年份分布

Fig. 2 Time series of PDO and catastrophe year distribution from 1995 to 2017

下载: 全尺寸图片幻灯片

图 3 1995−2017年El Niño和La Niña事件变化及灾变年份分布

Fig. 3 El Niño and La Niña event changes and catastrophic years distribution from 1995 to 2017

下载: 全尺寸图片幻灯片

表 1 灰色系统理论模型精度检验^[18]

Tab. 1 Precision test accuracy of grey model^[18]

精度等级	Ⅰ级	Ⅱ级	Ⅲ级	Ⅳ级
方差比C	0.35	0.50	0.65	0.80
小误差概率P	0.95	0.80	0.70	0.60

下载: 导出CSV

表 2 灰色灾变GM（1, 1）模型预测值与真实值之间的相对误差

Tab. 2 Relative error between the predicted value and the true value of the GM（1, 1）model

下限灾变序列号	拟合值	相对误差 /%	上限灾变序列号	拟合值	相对误差 /%
X₅	5.08	1.67	X₁₂	10.61	11.61
X₇	7.47	6.73	X₁₃	12.61	2.99
X₈	10.98	37.25	X₁₄	14.99	7.10
X₂₁	16.14	23.16	X₁₆	17.83	11.42
X₂₂	23.72	7.80	X₂₃	21.20	7.85
平均相对误差		15.32	平均相对误差		8.19

下载: 导出CSV

表 3 灰色灾变GM（1, 1）模型的相关参数

Tab. 3 Related parameters of the GM（1, 1）model

灰色灾变模型	发展系数a	方差比C	小误差概率P	响应函数
下限灾变模型	−0.064	0.342	1.000	x_(t+1)=10.823exp(0.385t) − 8.823
上限灾变模型	0.117	0.341	1.000	x_(t+1)=56.140exp(0.173t) − 45.140

下载: 导出CSV

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