基于栖息地指数的西北太平洋日本鲭渔情预报模型构建

范秀梅; 唐峰华; 崔雪森; 杨胜龙; 朱文斌; 黄良敏

doi:10.3969/j.issn.0253-4193.2020.12.004

基于栖息地指数的西北太平洋日本鲭渔情预报模型构建

doi: 10.3969/j.issn.0253-4193.2020.12.004

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.
浙江省海洋水产研究所，浙江舟山 316021
3.
集美大学福建省海洋渔业资源与生态环境重点实验室，福建厦门 361021

基金项目: 国家重点研发计划（2019YFD0901405）；上海市自然科学基金项目（17ZR1439700）；国家自然科学基金（41606138）；中国水产科学研究院基本科研业务费项目（2018HY-ZD0103）；浙江省远洋渔业资源探捕项目（SZGXZS2019087）；福建省海洋渔业资源与生态环境重点实验室开放基金项目（fjmfre2019003）。

详细信息

作者简介:
范秀梅（1984-），女，江苏省兴化市人，助理研究员，从事渔业遥感相关工作。E-mail：fxm1fxm@163.com

通讯作者:
唐峰华，副研究员，研究方向为海洋生态与渔业遥感学。E-mail：f-h-tang@163.com

中图分类号: S931.1
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- 被引次数: 0
出版历程
- 收稿日期: 2019-12-27
- 修回日期: 2020-04-08
- 网络出版日期: 2020-12-23
- 刊出日期: 2020-12-25

Habitat suitability index for chub mackerel (Scomber japonicus) in the Northwest Pacific Ocean

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.
Marine Fisheries Research Institute of Zhejiang, Zhoushan 316021, China
3.
Fujian Provincial Key Laboratory of Marine Fishery Resources and Eco-environment, Jimei University, Xiamen 361021, China

摘要

摘要: 根据2014−2017年5−11月西北太平洋公海灯光围网日本鲭(Scomber japonicus)生产数据，结合同期的环境遥感数据，分别基于捕捞量和作业次数，构建日本鲭栖息地适宜性指数(Habitat Suitability Index，HSI)模型。选取海表水温、海面高度异常和叶绿素a浓度，采用一元指数回归拟合，建立各个环境变量的适应性指数模型，并利用线性规划方法确定各环境因子的权重，从而提高日本鲭HSI模型对渔场的预报精度。利用2018年5−11月的实际捕捞数据对模型进行预报准确率验证，在基于渔获量和作业次数构建的HSI模型中，HSI大于0.7的海域，渔获量平均占比分别为77.29%、76.79%，这表明基于不同权重环境因子的HSI模型能够较好地预测西北太平洋公海日本鲭中心渔场。
- 栖息地适宜性指数 /
- 渔场 /
- 西北太平洋 /
- 日本鲭
Abstract: Using fishery data of Scomber japonicus from Chinese light-purse seine fishing on the high seas of the Northwest Pacific Ocean from May to November during 2014 to 2017, and the environmental remote sensing data, the habitat suitability index (HSI) model of Scomber japonicus was constructed based on fish catch and nos of hauls, respectively. Applying sea surface temperature, sea level anomaly, chlorophyll a concentration, the suitability index model of each environmental variable was established by one-dimensional exponential regression fitting. The weight of each environmental factor was determined by linear programming method to improve the prediction precision of HSI model for fishing ground. The prediction accuracy of the HSI model was verified with the actual fishing data from May to November in 2018, and the results showed that in the sea areas with HSI values over 0.7, the fishing catches accounts for 77.29% for HSI model based on fish catch and 76.79% for HSI model based on nos of hauls respectively, which indicates that the HSI model based on different environmental factor weights can better predict the central fishing ground of Scomber japonicus on the high seas of the Northwest Pacific Ocean.
- habitat suitability index /
- fishing grounds /
- the Northwest Pacific Ocean /
- Scomber japonicus

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图 1 2014−2018年西北太平洋日本鲭渔获量的空间分布

Fig. 1 Distribution of fish catches of Scomber japonicus in the Northwest Pacific Ocean during 2014−2018

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图 2 西北太平洋日本鲭SST、SLA、CHL适应性指数拟合曲线

Fig. 2 The SI fitting curves of SST、SLA、CHL for Scomber japonicus in the Northwest Pacific Ocean

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图 3 各因子最适值和最适区间

Fig. 3 Most suitable value and suitable value range for each factor

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图 4 2018年6−11月的渔获量与基于作业次数的HSI模型预报结果的空间分布

Fig. 4 Spatial distribution of fish catch and HSI derived from HSI model based on nos of hauls from June to November in 2018

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表 1 2014−2018年西北太平洋日本鲭渔获数据

Tab. 1 Fish catches of Scomber japonicus in the Northwest Pacific Ocean during 2014−2018

年份	渔船数/艘	总渔获量/t	总作业次数/次
2014	19	33 943	1 460
2015	48	75 935	3 641
2016	67	96 383	5 158
2017	97	120 547	9 530
2018	61	128 586	8 818

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表 2 基于渔获量的3种环境因子SI曲线拟合参数

Tab. 2 Fitting parameters of SI curves for three environmental factors based on fish catch

环境因子	参数	5月	6月	7月	8月	9月	10月	11月
海表水温	a	10.5217	13.7032	15.5908	18.8244	17.8805	15.4039	13.1049
	b	5.6133	4.3557	4.0911	5.5231	4.8950	3.4637	5.8399
海面高度异常	a	2.4155	−0.3456	4.5216	8.7783	8.6180	11.9644	12.5578
	b	10.0246	9.3254	8.3823	10.3244	10.4226	8.0647	9.6269
叶绿素a浓度的自然对数	a	−0.2750	−0.7358	−1.1793	−1.3818	−0.8005	−0.2959	−0.7107
	b	1.1061	0.4244	0.7529	0.7999	0.6434	0.8716	0.4304
注：拟合结果均通过置信度为95%的显著性检验。

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表 3 基于作业次数的3种环境因子SI曲线拟合参数

Tab. 3 Fitting parameters of SI curves for three environmental factors based on nos of hauls

环境因子	参数	5月	6月	7月	8月	9月	10月	11月
海表水温	a	10.1418	13.3409	15.8442	18.8272	18.0433	15.3496	12.3691
	b	6.1762	4.2070	3.9831	5.5674	5.0100	3.6881	6.4226
海面高度异常	a	1.9562	1.1207	5.1430	9.5933	9.3358	12.4302	12.5191
	b	8.7420	9.0305	8.4355	9.7199	9.5060	8.1895	9.5266
叶绿素a浓度的自然对数	a	−0.5063	−0.7273	−1.2518	−1.4220	−0.8362	−0.3532	−0.7237
	b	0.8356	0.4269	0.6417	0.7820	0.7239	0.8220	0.4216
注：拟合结果均通过置信度为95%显著性的检验。

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表 4 SST、SLA、CHL的权重

Tab. 4 Weights for SST, SLA and CHL

SI来源	环境因子	权重	5月	6月	7月	8月	9月	10月	11月	平均
渔获量	海表水温	c	0.07	0.30	0.56	0.62	0.72	0.73	0.73	0.53
	海面高度异常	d	0.64	0.60	0.44	0.38	0.28	0.27	0.27	0.41
	叶绿素a浓度	e	0.29	0.10	0.00	0.00	0.00	0.00	0.00	0.06
作业次数	海表水温	c	0.00	0.38	0.81	0.74	0.95	0.61	0.47	0.56
	海面高度异常	d	0.69	0.60	0.19	0.22	0.00	0.24	0.34	0.33
	叶绿素a浓度	e	0.31	0.02	0.00	0.04	0.05	0.15	0.19	0.11

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表 5 2018年实际产量在不同HSI级别中所占比重

Tab. 5 Proportion of practical catch under different levels of HSI in 2018

HSI级别	HSI来源	实际渔获产量占比/%
HSI级别	HSI来源	5月	6月	7月	8月	9月	10月	11月	平均
0～0.3	渔获量	0.03	0.25	7.37	0.00	8.99	0.98	0.00	2.52
0.3～0.7	渔获量	15.79	12.64	40.09	6.03	16.09	50.34	0.36	20.19
0.7～1.0		84.18	87.11	52.54	93.97	74.92	48.68	99.64	77.29
0～0.3	作业次数	6.44	0.16	5.08	0.00	11.04	0.81	0.13	3.38
0.3～0.7		44.67	8.35	7.60	6.00	10.01	56.54	5.64	19.83
0.7～1.0		48.89	91.49	87.32	94.00	78.95	42.65	94.23	76.79

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