基于环境因子的东南太平洋茎柔鱼资源补充量预报模型研究

发布时间：2018-01-15 21:19

本文关键词：基于环境因子的东南太平洋茎柔鱼资源补充量预报模型研究　出处：《海洋与湖沼》2014年06期 　论文类型：期刊论文

【摘要】：东南太平洋茎柔鱼(Dosidicus gigas)是短生命周期种类,其资源量极易受到海洋环境变化的影响。根据2003—2012年我国鱿钓船在东南太平洋的生产统计数据,以及茎柔鱼栖息地的海表温度(SST)、海面高度(SSH)、叶绿素a浓度(chl a)数据,利用相关性分析法分析茎柔鱼资源丰度和补充量(以单位捕捞努力量渔获量为指标,t/d)与栖息海域20°S—20°N、110°W—70°W的SST、SSH、chl a浓度的相关性,获取相关系数大的关键海区位置,同时加入茎柔鱼产卵场、索饵场最适表层水温范围占总面积的比例(分别用PS、PF表示)两个参数,建立三种基于主要环境因子的误差反向传播(EBP)神经网络资源补充量预报模型,进行了比较。结果表明:茎柔鱼资源丰度与SST、SSH、chl a浓度的相关系数最大值海域为7月份的Point1(13°N,102°W)海区、9月份的Point3(11°N,102°W)海区和3月份的Point5(8°S,107°W)海区;资源补充量与SST、SSH、chl a浓度的相关系数最大值海域为6月份的Point2(8°N,103.5°W)海区、2月份的Point4(12°N,97.5°W)海区和10月份的Point6(10°S,93.5°W)海区。EBP神经网络预报模型结果认为:基于产卵环境关键影响因子的方案2(以Point2的SST、Point4的SSH、Point6的chl a浓度、PS作为模型输入因子)和基于全部环境关键影响因子的方案3(以Point1与Point2的SST、Point3与Point4的SSH、Point5与Point6的chl a浓度、PS、PF作为模型输入因子)的两种神经网络预报模型均方误差较小,其准确率可达90%左右。
[Abstract]:Dosidicus gigas) is a short-life cycle species in the southeastern Pacific Ocean. According to the statistical data of squid jigging ship production in the southeastern Pacific Ocean from 2003 to 2012 and the sea surface temperature (SST) of roe habitat. The correlation analysis method was used to analyze the abundance and supplementation of the resources of Mollusca chinensis (the catch per fishing effort was taken as the index) with the data of sea surface height (SSH) and chlorophyll a concentration (Chl a). The correlation between t / d) and the concentration of SSTS SSHnchl a of 20 掳S-20 掳Nao 110 掳W-70 掳W in the perched sea area, and the location of the key sea area with high correlation coefficient was obtained. At the same time, the proportion of the optimum surface water temperature range to the total area (expressed by PSN PF) is two parameters. In this paper, three kinds of error back-propagation (EBP) neural network resource supplementary forecasting models based on main environmental factors were established and compared. The results showed that the abundance of the resource of Mollusca and SST-SSH was higher than that of SST-SSH. The maximum correlation coefficient of chl a concentration is Point3(11 掳N in the Point1(13 掳N ~ (102 掳W) sea area in July. 102 掳W) sea area and Point5(8 掳Schion 107 掳W) sea area on March; The maximum correlation coefficient between the resource supplementation and the concentration of SSTS Hnchl a is in the Point2(8 掳NV 103.5 掳W area in June. The Point4(12 掳Nian 97.5 掳W) sea area in February and Point6(10 掳S in October. The prediction model of EBP neural network in the sea area of 93.5 掳W shows that the scheme 2 is based on the key factors affecting the oviposition environment (SSH of Point2 SST-Point4). The chl a concentration of Point6 was used as the input factor of the model and the scheme 3 based on all the critical environmental impact factors (Point1 and Point2 SST). The concentration of chl a in Point3 and Point4 was PS. The mean square error of the two neural network prediction models with PF as the input factor of the model is small, and the accuracy can reach about 90%.
【作者单位】：上海海洋大学海洋科学学院;远洋渔业协同创新中心;大洋渔业资源可持续开发省部共建教育部重点实验室;国家远洋渔业工程技术研究中心;
【基金】：国家高技术研究发展计划(863项目),2012AA092303号国家发改委产业化专项项目,2159999号上海市科技创新行动计划项目,12231203900号国家科技支撑计划项目,2013BAD13B01号
【分类号】：S932
【正文快照】： 茎柔鱼(Dosidicus gigas)是一种大洋性头足类,广泛分布于东太平洋海域(王尧耕等,2005)。目前世界上多个国家和地区已对茎柔鱼资源进行了开发和利用,年产量超过80万吨(Nigmatullin et al,2001;Waluda et al,2004)。资源量预报属于中长期渔情预报的一种,对资源补充量进行精确的

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