首页 >  中国水产科学 >  山东近海口虾蛄空间分布特征及其与环境因子的关系

2020, 27(12): 1515-1523. doi: 10.3724/SP.J.1118.2020.2020091

山东近海口虾蛄空间分布特征及其与环境因子的关系

1. 中国海洋大学水产学院, 山东 青岛 266003;

2. 海州湾渔业生态系统教育部野外观测研究站, 山东 青岛 266003;

3. 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

收稿日期:2020-04-06
修回日期:2020-05-27

基金项目:   国家自然科学基金项目(31802301);国家重点研发计划项目(2018YFD0900906). 

关键词: 口虾蛄 , 广义线性模型 , 广义可加模型 , 多层前馈神经网络模型 , 模型比较 , 环境因子效应 , 山东近海

Relationship between Oratosquilla oratoria spatial distribution and environmental factors in coastal Shangdong

1. College of Fisheries, Ocean University of China, Qingdao 266003, China;

2. Field Observation and Research Station of Haizhou Bay Fishery Ecosystem, Ministry of Education, Qingdao 266003, China;

3. Laboratory for Marine Fisheries Science and Food Production Processes, Pilot National Laboratory for Marine Science and Technology(Qingdao), Qingdao 266237, China

Received Date:2020-04-06
Accepted Date:2020-05-27

Keywords: Oratosquilla oratoria , generalized linear model , generalized additive model , back propagation neural network model (BP-NNM) , model comparison , environmental factors effect , coastal Shangdong

近年来口虾蛄(Oratosquilla oratoria)为代表的甲壳类生物在海洋生态系统中的数量和经济地位有着显著的增长,但其空间分布规律及与环境因子的关系尚不明确。为了解山东近海海域口虾蛄的栖息分布规律及其主要环境因子的影响,本研究根据2016-2017年在山东近海进行的4个航次渔业资源和环境调查,采用广义线性模型、广义可加模型以及多层前馈神经网络模型等方法,比较分析了口虾蛄的空间分布特征及环境因子的关系。结果表明,口虾蛄的资源密度在季节间存在明显差异,夏季密度最高,春、秋季次之,冬季最低;近岸资源密度高于远岸,且由南到北呈逐渐增加的趋势。模型分析表明,纬度、海水底层温度和底层盐度对口虾蛄的资源密度分布有显著影响。3种模型中,广义可加模型的拟合效果最好,多层前馈神经网络模型预测准确性最好。本研究通过不同模型结果的比较,揭示了影响山东近海口虾蛄的空间分布特征与关键环境因子的关系,旨在为口虾蛄的合理开发和利用提供理论支撑。

Recently, Oratosquilla oratoria (Crustacea) abundance and economic status have significantly increased in marine ecosystems, but the relationship between spatial distribution and environment factors is still unclear. To understand O. oratoria habitat distribution and the influence of the main environmental factors, the present study was carried out in 2016-2017 in the coastal waters of Shandong. Four surveys of the fishery resources and environmental factors were carried out, and the results were analyzed comparatively using generalized linear, generalized additive, and back propagation neural network models to determine the relationships between O. oratoria spatial distribution and environmental factors. The results indicated significant seasonal differences in O. oratoria density, with the highest density in summer, followed by spring and autumn, and the lowest in winter. O. oratoria density in the near shore was higher than that in the far shore, and it gradually increased from south to north. The model analysis showed that latitude, bottom-layer temperature, and bottom-layer salinity significantly affected O. oratoria density distribution. Among the three models, the generalized additive model was the best fit, and the back propagation neural network model had the best prediction accuracy. By comparing the results of the different models, this study revealed O. oratoria spatial distribution characteristics and key environmental factors affecting distribution, and provided theoretical support for rational O. oratoria fisheries development and utilization.

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山东近海口虾蛄空间分布特征及其与环境因子的关系