文章摘要
武胜男,陈新军.基于GLM和GAM的日本鲭太平洋群体补充量与产卵场影响因子关系分析[J].水产学报,2020,44(1):61~70
基于GLM和GAM的日本鲭太平洋群体补充量与产卵场影响因子关系分析
Relationship between the recruitment of the Pacific-cohort of chub mackerel (Scomber japonicus) and the influence factors on the spawning ground based on GLM and GAM
投稿时间:2018-08-25  修订日期:2019-04-22
DOI:10.11964/jfc.20180811426
中文关键词: 日本鲭  GLM模型  GAM模型  环境因子  亲体量
英文关键词: Scomber japonicus  GLM model  GAM model  marine environmental factors  spawning stock biomass
基金项目:国家自然科学基金(NSFC41876141)
作者单位E-mail
武胜男 上海海洋大学海洋科学学院, 上海 201306
上海海洋大学, 农业部大洋渔业开发重点实验室, 上海 201306 
 
陈新军 上海海洋大学海洋科学学院, 上海 201306
上海海洋大学, 农业部大洋渔业开发重点实验室, 上海 201306
上海海洋大学, 国家远洋渔业工程技术研究中心, 上海 201306
上海海洋大学, 大洋渔业资源可持续开发省部共建教育部重点实验室, 上海 201306
上海海洋大学, 农业部大洋渔业资源环境科学观测实验站, 上海 201306 
xjchen@shou.edu.cn 
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中文摘要:
      依据日本渔业机构提供的1980—2016年日本鲭太平洋群体资源丰度(补充量和亲体量)数据,对补充量的自然对数进行正态性检验,通过正态性检验的时间为1980—1999年,再结合产卵场海洋环境数据,利用广义线性模型(GLM)和广义加性模型(GAM)对1980—1999年日本鲭太平洋群体产卵场的海表面高度(sea surface height,SSH)、海表面盐度(sea surface salinity,SSS)、海表面温度(sea surface temperature,SST)、亲体量[ln(spawning stock biomass),ln(SSB)]与补充量之间的关系进行研究。GLM模型结果显示,考虑因子的综合效应,影响程度依次为ln(SSB)×年、ln(SSB)、SSS×年、SSS对补充量的影响最显著;考虑单因子对补充量的影响,影响程度依次为产卵场SST、SSH、年份、ln(SSB)和SSS。GAM模型研究表明,基于赤池信息准则,包含年份、产卵场SST和SSH的GAM模型为最优模型,模型中各因子的影响程度由大到小依次为年份、产卵场SST、产卵场SSH;考虑单因子对补充量的影响,GAM模型中影响程度依次为年份、产卵场SSS、ln(SSB)、产卵场SST和SSH,补充量的适宜SSH范围为62~65 cm,适宜SSS范围为34.72~34.74和34.78~34.83,适宜SST范围为20.2~20.6 ℃。当ln(SSB)>6.0时,补充量处于较高水平。
英文摘要:
      Chub mackerel (Scomber japonicus) is an important pelagic fish in the Northwest Pacific Ocean. It is necessary for us to find the relationship between the abundance and influence factors, which are beneficial to exploit and utilize this resource. In this study, based on the recruitment data and the spawning stock biomass (SSB) data of the Pacific-cohort of S.japonicus during 1980—2016 obtained from Japan fisheries institution, the normality test of natural logarithm of recruitment was finished and the time period that passed the normality test was during 1980—1999, with the environmental data of spawning ground, we analyzed the relationship between the sea surface height (SSH), sea surface salinity (SSS), sea surface temperature (SST) and the natural logarithm of SSB [ln(SSB)] and the recruitment during 1980—1999 with generalized linear model (GLM) and generalized additive model (GAM). The GLM results revealed the order of importance of variables ranked by decreasing magnitude was ln(SSB)×Year, ln(SSB), SSS×Year and SSS, which were significant (P<0.05) and considered the combined effects of factors. Considering the single factor in GLM models affecting the recruitment, order of the importance of variables ranked by decreasing magnitude was SST, SSS, Year, ln(SSB) and SSS. The GAM results indicated that the model which contained Year, SST and SSH was the optimal model based on Akaike’s Information Criterion (AIC), the importance ranked by decreasing magnitude was Year, SST and SSH. However, considering the single factor in GAM models affecting the recruitment, the importance of variables ranked by decreasing magnitude was Year, SSS, ln(SSB), SST and SSH. The suitable range of SSH was 62—65 cm, the suitable range of SSS was 34.72—34.74 and 34.78—34.83 and the suitable range of SST was 20.2—20.6 ℃. When ln(SSB)>6.0,the recruitment was at a high level, based on GAM analysis.
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