两种GAM模型对海州湾短吻红舌鳎（<i>Cynoglossus joyneri</i>）资源分布预测效果的比较研究

孙霄; 张云雷; 刘笑笑; 程远; 纪毓鹏; 任一平; 薛莹

doi:10.3969/j.issn.0253-4193.2020.06.003

两种GAM模型对海州湾短吻红舌鳎（Cynoglossus joyneri）资源分布预测效果的比较研究

doi: 10.3969/j.issn.0253-4193.2020.06.003

孙霄^1,,
张云雷¹,
刘笑笑¹,
程远³,
纪毓鹏^{1, 4},
任一平^{1, 2, 4},
薛莹^{1, 4, ,}

1.
中国海洋大学水产学院，山东青岛 266003
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.
近海（大连）生态发展有限公司，辽宁大连 116023
4.
海州湾渔业生态系统教育部野外科学观测研究站，山东青岛 266003

基金项目: 山东省支持青岛海洋科学与技术试点国家实验室重大科技专项（2018SDKJ0501-2）；国家重点研发计划（2017YFE0104400）；国家自然科学基金项目（31772852，31802301）。

详细信息

作者简介:
孙霄（1995—），女，山东省菏泽市人，主要从事渔业生态学研究。E-mail：sunx07102002@163.com

通讯作者:
薛莹，教授，主要从事食物网营养动力学、鱼类栖息地和空间分布等领域的研究。E-mail：xueying@ouc.edu.cn

中图分类号: P714⁺.5；P931.1
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- 文章访问数: 190
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- 被引次数: 0
出版历程
- 收稿日期: 2019-08-14
- 修回日期: 2019-12-02
- 网络出版日期: 2020-11-18
- 刊出日期: 2020-06-25

Evaluation of the prediction effect of two GAMs on the distribution of Cynoglossus joyneri in the Haizhou Bay

Sun Xiao^1
,,
Zhang Yunlei¹,
Liu Xiaoxiao¹,
Cheng Yuan³,
Ji Yupeng^{1, 4},
Ren Yiping^{1, 2, 4},
Xue Ying^{1, 4
, ,}

1.
Fisheries College, Ocean University of China, Qingdao 266003, China
2.
Laboratory for Marine Fisheries Science and Food Production Processes, Pilot National Laboratory for Marine Science and Technology (Qingdao), Qingdao 266237, China
3.
Offshore (Dalian) Ecological Development Co. Ltd., Dalian 116023, China
4.
Field Observation and Research Station of Haizhou Bay Fishery Ecosystem, Ministry of Education, Qingdao 266003, China

摘要

摘要: 根据2011年及2013−2018年春、秋两季在海州湾及其邻近海域进行的底拖网调查数据，研究该海域短吻红舌鳎(Cynoglossus joyneri)的资源分布特征及其受环境因子和饵料生物的影响，并比较了两种模型(普通GAM模型和PCA-GAM模型)对其资源分布的预测效果，采用交叉验证的方法对模型的预测能力及拟合效果进行评价。结果显示：PCA-GAM模型的拟合度及预测效果均优于普通GAM模型。春、秋两季海州湾短吻红舌鳎资源丰度均呈现南高北低、近岸浅水区大于深水区的分布特征，因为海州湾南部近岸海域较高的水温利于春、秋季短吻红舌鳎产卵群体性腺发育，较低的盐度利于其鱼卵及仔鱼的生长发育，同时，近岸海域丰富的饵料资源为产卵后的亲体提供大量食物供给。分别应用两种模型预测了2018年春季和秋季短吻红舌鳎在海州湾的资源分布，结果显示，PCA-GAM模型的预测值与实际调查的结果更为吻合，预测效果要优于普通GAM模型。本研究为今后开展渔业生物空间分布的研究提供了一种新的方法。
- GAM模型 /
- 主成分分析 /
- 海州湾 /
- 黄海中部 /
- 短吻红舌鳎
Abstract: Based on the bottom trawl surveys in the Haizhou Bay and adjacent waters during spring and autumn of 2011 and 2013−2018, the performance of regular GAM and PCA-GAM was compared, and the distribution of Cynoglossus joyneri in this area was predicted. The predictive ability and fitting effect of the two GAMs were evaluated by cross-validation. The results showed that the goodness of fit and prediction effects of PCA-GAM were better than those of regular GAM. In spring and autumn, the abundance of C. joyneri in the southern waters was higher than that in the northern waters, and the abundance in the near-shore shallow waters was larger than that in the deep waters. The higher water temperature in the coastal waters of the southern Haizhou Bay was conducive to the development of gonads for the spawning groups during spring and autumn. The lower salinity was conducive to the growth and development of fish eggs and larvae. At the same time, the abundant prey resources in the coastal waters provides a large amount of food for it after spawning period. In this study, two GAMs were used to predict the resource distribution of C. joyneri in the Haizhou Bay in spring and autumn of 2018. The results showed that the predicted abundance by PCA-GAM were more consistent with the actual catches, and the performance of PCA-GAM was better than the regular GAM. This study provides a new method for studying the spatial distribution of marine organisms in the future.
- GAM /
- principal component analysis /
- Haizhou Bay /
- central Yellow Sea /
- Cynoglossus joyneri

HTML全文

图 1 海州湾及邻近海域调查区域

Fig. 1 Survey areas in Haizhou Bay and adjacent waters

下载: 全尺寸图片幻灯片

图 2 海州湾春季和秋季各影响因子与短吻红舌鳎相对资源量之间的关系（a−c. 春季；A−D. 秋季）

Fig. 2 Effects of factors on C. joyneri relative abundance in regular GAM in Haizhou Bay (a−c. spring; A−D. autumn)

下载: 全尺寸图片幻灯片

图 3 海州湾春季和秋季短吻红舌鳎相对资源丰度预测值与观测值的交叉验证

实线为交叉验证的平均效应，点线为对角线

Fig. 3 Cross validation between predicted and observed abundance of C. joyneri during spring and autumn in the Haizhou Bay

The solid line is the mean of cross validation results, the dashed line is the diagonal line

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图 4 2018年海州湾春、秋季基于普通GAM模型和PCA-GAM模型的短吻红舌鳎相对资源丰度预测值与观测值的叠加图

Fig. 4 Overlapping maps of prediction and observations of relative abundance of C. joyneri in Haizhou Bay based on regular GAM model and PCA-GAM model during spring and autumn in 2018

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表 1 海州湾春季各解释变量之间的皮尔逊相关性检验

Tab. 1 Pearson correlation test among the interpreted variables in the Haizhou Bay during spring

变量	经度	纬度	底温	底盐	水深	戴氏赤虾	葛氏长臂虾	日本鼓虾
经度	−	0.084	0.000^*	0.048^*	0.000^*	0.060	0.853	0.478
纬度	−0.175	−	0.005^*	0.060	0.000^*	0.365	0.934	0.208
底温	−0.506	−0.281	−	0.003^*	0.000^*	0.510	0.950	0.886
底盐	0.199	0.190	−0.298	−	0.007^*	0.626	0.854	0.901
水深	0.510	0.429	−0.695	0.272	−	0.070	0.435	0.155
戴氏赤虾	0.190	0.092	−0.067	0.050	0.183	−	0.000^*	0.680
葛氏长臂虾	0.019	0.008	−0.006	0.019	0.079	0.489	−	0.004^*
日本鼓虾	−0.072	−0.128	−0.015	0.013	−0.144	−0.042	0.288	−
注：未加粗数据为相关系数，加粗数据为p值，^*表示数值在0.05置信水平下显著。

下载: 导出CSV

表 2 海州湾秋季各解释变量之间的皮尔逊相关性检验

Tab. 2 Pearson correlation test among the interpreted variables during autumn in the Haizhou Bay

变量	经度	纬度	底温	底盐	水深	葛氏长臂虾	日本鼓虾
经度	−	0.214	0.164	0.195	0.000^*	0.792	0.549
纬度	−0.129	−	0.874	0.185	0.003^*	0.007^*	0.000^*
温度	−0.145	−0.017	−	0.133	0.042^*	0.531	0.592
盐度	0.135	−0.138	−0.156	−	0.110	0.666	0.870
水深	0.565	0.304	−0.210	0.166	−	0.111	0.120
葛氏长臂虾	−0.028	−0.278	−0.065	−0.045	−0.165	−	0.000^*
日本鼓虾	0.063	−0.363	−0.056	0.017	−0.162	0.648	−
注：未加粗数据为相关系数，加粗数据为p值，^*表示数值在0.05置信水平下显著。

下载: 导出CSV

表 3 海州湾春季和秋季各解释变量之间多重共线性的VIF检验

Tab. 3 Multi-collinearity VIF test between explanatory variables during spring and autumn in the Haizhou Bay

变量	经度	纬度	底温	底盐	水深	戴氏赤虾	葛氏长臂虾	日本鼓虾
春季	2.077	1.746	2.230	1.129	2.832	1.464	1.534	1.219
秋季	1.741	1.472	1.076	1.096	1.987	−	1.858	1.762

下载: 导出CSV

表 4 海州湾春季和秋季各解释变量的主成分载荷

Tab. 4 Main component loads of various explanatory variables during spring and autumn in the Haizhou Bay

变量	春季			秋季
变量	PC1	PC2	PC3	PC1	PC2	PC3
经度	0.655	0.014	−0.642	−0.250	0.766	0.145
纬度	0.432	−0.166	0.815	−0.623	−0.248	0.434
底温	−0.834	0.135	0.138	0.090	−0.499	0.004
底盐	0.485	−0.074	0.058	−0.081	0.450	−0.758
水深	0.890	−0.099	0.063	−0.602	0.623	0.273
戴氏赤虾	0.328	0.687	0.167	−	−	−
葛氏长臂虾	0.152	0.874	0.133	0.780	0.363	0.212
日本鼓虾	−0.123	0.480	−0.196	0.765	0.276	0.320

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表 5 海州湾春季和秋季两种GAM模型拟合结果及各解释变量的重要性

Tab. 5 The fitting results of two GAMs and the importance of each explanatory variable

季节	模型	因子	累计偏差解释率/%	贡献率/%	AIC
春季	普通GAM	+s(P.gravieri)	45.039	45.039	166.936
		+s(Lon)	55.194	10.155	165.133
		+s(Lat)	63.256	8.062	163.604
春季	PCA-GAM	+s(PC2)	37.287	37.287	173.255
秋季	普通GAM	+s(Lat)	35.034	35.034	186.233
		+s(P.gravieri)	46.190	11.156	184.821
		+s(Lon)	62.585	16.395	174.697
		+s(SBS)	77.755	15.170	156.598
秋季	PCA-GAM	+s(PC1)	39.252	39.252	182.904
		+s(PC2)	51.837	12.585	179.265
		+s(PC3)	64.286	12.449	172.370

下载: 导出CSV

表 6 海州湾春季和秋季两种GAM模型交叉验证结果

Tab. 6 Cross-validation results of two GAM models during spring and autumn in the Haizhou Bay

季节	模型	斜率	截距	决定系数R²	均方根误差
春季	普通GAM	0.722	0.617	0.451	2.401
春季	PCA-GAM	0.900	0.116	0.521	2.214
秋季	普通GAM	0.794	0.662	0.549	3.171
秋季	PCA-GAM	0.908	0.360	0.634	2.216

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