基于综合环境因子的协同克里金法分析茎柔鱼资源丰度空间分布

发布时间：2018-05-11 05:17

本文选题：茎柔鱼 + 空间分布　；参考：《海洋学报》2017年02期

【摘要】：茎柔鱼是我国重要的远洋捕捞对象之一,研究其资源丰度空间分布问题,有助于更好地理解茎柔鱼的生态习性,并提高我国鱿钓渔船的生产效率。本文利用上海海洋大学鱿钓技术组提供的2003-2012年6-9月秘鲁外海茎柔鱼捕捞数据,结合海表面温度(SST),海表面高度(SSH),海表面盐度(SSS)和叶绿素浓度(Chl a)进行协同克里金插值预测其资源丰度的空间分布。为了解决协同克里金插值中4个环境因子的权重问题,本文将4个环境因子进行归一化处理,利用主成分分析方法将其整合为单一综合环境因子,以此作为协变量。将综合环境因子与单位捕捞努力量渔获量(CPUE)进行相关性检验后进行协同克里金插值,根据平均误差(ME),均方根误差(RMSE)和标准化均方根(RMSSE)对插值结果评价,探讨此种方法的可行性。研究结果认为:(1)主成分分析方法获得的6-9月份的综合环境因子均与CPUE具有显著相关性;(2)6-7月份ME分别为0.002 6和0.002 5,预测准确性很高,平均预测结果稍高于实际观测值;而8-9月份的ME分别为-0.007 8和-0.000 2,预测准确性较高,平均预测结果稍低于实际观测值。6月份的RMSE估值精度最高,8月份的估值精度最低。6-7月份的RMSSE值小于1,说明都高估了预测的不确定性,8-9月份的RMSSE值大于1,说明都低估了预测的不确定性,则在6-9月份中的预测精度和准确性上会有一定程度的偏差。从ME、RMSE和RMSSE三者综合来看,6-9月的预测值具有一定的可靠性。
[Abstract]:Mollusca is one of the important pelagic fishing objects in China. The study on the spatial distribution of its abundance will help to better understand the ecological habits of the squid jigging vessels and improve the production efficiency of squid jigging vessels in China. In this paper, the squid fishing data provided by squid jigging technique group of Shanghai Ocean University from June to September in Peru are used. The spatial distribution of resource abundance was predicted by using SST (sea surface temperature), sea surface height (SSH), sea surface salinity (SSS) and chlorophyll concentration (Chl 帽 a). In order to solve the weight problem of four environmental factors in cooperative Kriging interpolation, the four environmental factors are normalized and integrated into a single integrated environmental factor by principal component analysis (PCA), which is used as a covariable. The correlation between integrated environmental factors and catch per unit fishing effort (CPUE) was tested, and the results were evaluated according to the mean error, root mean square error (RMSE) and standardized root mean square error (RMSSE), and the feasibility of this method was discussed. The results showed that the synthetic environmental factors obtained by principal component analysis in June to September were significantly correlated with CPUE. The ME of June to July was 0.002 6 and 0.002 5, respectively. The prediction accuracy was very high, and the average prediction result was slightly higher than the actual observation value. The ME in August and September were -0.007 8 and -0.000 2, respectively. The average forecast result is slightly lower than the actual observational value in the month of 6. 6. The RMSE estimation accuracy is the highest. The RMSSE value in August is the lowest. The RMSSE value in June-July is less than 1, which means that the uncertainty of forecast is overestimated. The RMSSE value of August to September is greater than 1, which shows that all of them are low. Estimated the uncertainty of the prediction, There will be a certain degree of deviation in the accuracy and accuracy of the prediction in June-September. The predicted value of June to September is reliable according to the combination of RMSSE and MEMS-E.
【作者单位】：上海海海洋大学海洋科学学院;国家远洋渔业工程技术研究中心;上海海洋大学大洋渔业可持续开发教育部重点实验室;远洋渔业协同创新中心;
【基金】：海洋局公益性行业专项(20155014) 上海市科技创新行动计划(5DZ1202200) 海洋二号卫星地面应用系统项目(HY2A-HT-YWY-006)
【分类号】：S932

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