基于地理加权回归的渤海沙氏下鱵鱼仔稚鱼栖息地指数

doi:10.13287/j.1001-9332.201801.032

应用生态学报 ›› 2018, Vol. 29 ›› Issue (1): 293-299.doi: 10.13287/j.1001-9332.201801.032

基于地理加权回归的渤海沙氏下鱵鱼仔稚鱼栖息地指数

赵杨¹, 张学庆^1*, 卞晓东²

¹中国海洋大学环境科学与工程学院, 海洋环境与生态教育部重点实验室, 山东青岛 266100;
²中国水产科学研究院黄海水产研究所, 山东青岛 266071

收稿日期:2017-07-07 出版日期:2018-01-18 发布日期:2018-01-18
通讯作者: * E-mail: zxq@ouc.edu.cn
作者简介:赵杨,男,1992年生,硕士研究生. 主要从事海洋环境预测研究. E-mail: 18306485900@163.com
基金资助:
本文由国家重点基础研究发展计划项目(2015CB453301)和国家自然科学基金项目(41506168)资助

Habitat suitability index of larval Japanese Halfbeak (Hyporhamphus sajori) in Bohai Sea based on geographically weighted regression.

ZHAO Yang¹, ZHANG Xue-qing^1*, BIAN Xiao-dong²

¹Ministry of Education Key Laboratory of Marine Environment and Ecology, College of Environmental Science and Engineering, Ocean University of China, Qingdao 266100, Shandong, China;
²Yellow Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Qingdao 266071, Shandong, China

Received:2017-07-07 Online:2018-01-18 Published:2018-01-18
Contact: * E-mail: zxq@ouc.edu.cn
Supported by:
The work was supported by the National Basic Research Program of China (2015CB453301) and the National Natural Science Foundation of China (41506168 ).

摘要/Abstract

摘要： 为研究渤海鱼类资源早期补充过程,本文将地理加权回归法(GWR)引入栖息地指数(HSI)模型,选取海表温度、海表盐度、水深和叶绿素a浓度4个环境因子建立基于GWR的渤海沙氏下鱵鱼仔稚鱼的HSI_GWR模型.模拟发现: 在2015年8月渤海的HSI_GWR模型中,海表温度和叶绿素a浓度为全局变量,两者的回归系数分别为-0.027和0.006,对HSI影响较小.海表盐度和水深为局地变量,两者回归系数绝对值的平均值分别为0.075和0.129,对HSI的影响较大.其中,海表盐度在渤海中部与HSI呈负相关,负相关系数最大,为-0.3,在三湾呈微弱正相关,相关系数最大值为0.1;水深在整个渤海均与HSI呈负相关,且在三湾的负相关程度明显大于渤海中部,三湾的负相关系数最大,为-0.16.该HSI_GWR模型的泊松相关系数为0.705,拟合效果较好,可为今后的鱼类栖息地环境研究提供一种新的方法.

Abstract: To investigate the early supplementary processes of fishre sources in the Bohai Sea, the geographically weighted regression (GWR) was introduced to the habitat suitability index (HSI) model. The Bohai Sea larval Japanese Halfbeak HSI_GWR model was established with four environmental variables, including sea surface temperature (SST), sea surface salinity (SSS), water depth (DEP), and chlorophyll a concentration (Chl a). Results of the simulation showed that the four variables had different performances in August 2015. SST and Chl a were global variables, and had little impacts on HSI, with the regression coefficients of -0.027 and 0.006, respectively. SSS and DEP were local variables, and had larger impacts on HSI, while the average values of absolute values of their regression coefficients were 0.075 and 0.129, respectively. In the central Bohai Sea, SSS showed a negative correlation with HSI, and the most negative correlation coefficient was -0.3. In contrast, SSS was correlated positively but weakly with HSI in the three bays of Bohai Sea, and the largest correlation coefficient was 0.1. In particular, DEP and HSI were negatively correlated in the entire Bohai Sea, while they were more negatively correlated in the three bays of Bohai than in the central Bohai Sea, and the most negative correlation coefficient was -0.16 in the three bays. The Poisson regression coefficient of the HSI_GWR model was 0.705, consistent with field measurements. Therefore, it could provide a new method for the research on fish habitats in the future.

赵杨, 张学庆, 卞晓东. 基于地理加权回归的渤海沙氏下鱵鱼仔稚鱼栖息地指数[J]. 应用生态学报, 2018, 29(1): 293-299.

ZHAO Yang, ZHANG Xue-qing, BIAN Xiao-dong. Habitat suitability index of larval Japanese Halfbeak (Hyporhamphus sajori) in Bohai Sea based on geographically weighted regression.[J]. Chinese Journal of Applied Ecology, 2018, 29(1): 293-299.

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基于地理加权回归的渤海沙氏下鱵鱼仔稚鱼栖息地指数

Habitat suitability index of larval Japanese Halfbeak (Hyporhamphus sajori) in Bohai Sea based on geographically weighted regression.

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