摘要
柔鱼(Ommastrephes bartramii)是北太平洋重要的经济头足类。短生命周期的特征使其资源丰度主要取决于补充量,早期生活史阶段的海洋环境将直接影响补充量大小。利用2004−2015年我国鱿钓船队在西北太平洋的生产统计数据,以及产卵场和索饵场海表水温,将产卵场和索饵场等分成不同数量的海区,运用相关性分析和随机森林模型,筛选出产卵期各月产卵场最适海表水温范围占总面积的比值(Ps)和索饵期各月索饵场最适海表水温范围占总面积的比值(Pf)与单位捕捞努力量渔获量(CPUE)显著相关的海区,将Ps值或Pf值作为神经网络模型的输入变量,分别构建基于产卵场、索饵场的资源丰度预报模型,分析模型的优劣与预报准确度。结果表明:产卵场划分方案为5°×5°,索饵场为2.5°(经)×4°(纬)较为合适。随机森林筛选出的海区与相关性分析筛选出的适宜海区范围大致吻合,且随机森林能够识别与CPUE相关的潜在海域。模型的预报结果表明,其预报准确度均达到90%以上,随机森林筛选出的海区的最适海表水温范围占该海区的比值作为神经网络模型输入因子构建的模型优于相关性分析,预报准确度更高。基于产卵场海域构建的模型相对于索饵场,模型精确度更高、更稳定。
Neon flying squid Ommastrephes bartramii is a cephalopod species of economically importance distributed in the North Pacific Ocean.Because of short lifespan,their abundances mainly depend on the recruitments and the marine environment during their early life stages will directly affect the recruitments.Using fishery data collected by Chinese squid-jigging fleets in the Northwest Pacific Ocean from 2004 to 2015 and the sea surface temperature(SST)of spawning and feeding grounds,which were divided into different numbers of subareas,correlation analysis and random forest model were used to screen out the subareas that CPUE has significant relationships with Ps(the proportion of favorable-SST in spawning grounds)of spawning grounds and Pf(the proportion of favorable-SST in feeding grounds)of feeding grounds during spawning and feeding periods.The Ps and Pf were used as input variables of the neural network model to forecast recruitments based on spawning ground and feeding ground,respectively,and the advantages and disadvantages of the model and forecast accuracy were analyzed.The results show that the schemes of dividing spawning ground into 5°×5°and feeding field into 2.5°(longitude)×4°(latitude)were optimal.The ranges of subareas selected by random forests are largely consistent with those selected by correlation analysis,both random forests and correlation analysis can identify potential subareas associated with CPUE,and both have forecasting accuracy of>90%.However,the subareas selected by the random forest are better and the forecast accuracy is higher than those selected by correlation analysis.In addition,the model based on the spawning ground is more accurate and stable than that based on feeding ground.
作者
魏广恩
陈新军
Wei Guang’en;Chen Xinjun(College of Marine Sciences,Shanghai Ocean University,Shanghai 201306,China;Key Laboratory for Sustainable Exploitation of Oceanic Fisheries Resources,Ministry of Education,Shanghai 201306,China;National Engineering Research Center for Oceanic Fisheries,Shanghai 201306,China)
出处
《海洋学报》
CAS
CSCD
北大核心
2020年第12期14-25,共12页
基金
国家重点研发计划(2019YFD0901404)
国家自然科学基金面上项目(NSFC41876141)
上海市科技创新行动计划(10DZ1207500)。
关键词
西北太平洋
柔鱼
资源量
随机森林
神经网络
Northwast Pacific Ocean
Ommastrephes bartramii
abundance
random forests
neural network
