| 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 |
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| 引用本文： | 方学燕,陈新军,冯永玖,陈芃.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[J].海洋学报,2017,39(2):62-71. |
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| 作者姓名： | 方学燕 陈新军 冯永玖 陈芃 |
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| 作者单位： | 1.上海海海洋大学 海洋科学学院, 上海 201306 |
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| 基金项目： | 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）。 |
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| 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。
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| 关 键 词： | 茎柔鱼 空间分布 综合环境因子 协同克里金 秘鲁外海 |
| 收稿时间： | 2016/6/26 0:00:00 |
| 修稿时间： | 2016/9/30 0:00:00 |
Study of spatial distribution for Dosidicus gigas abundance off Peru based on a comprehensive environmental factor |
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| Fang Xueyan,Chen Xinjun,Feng Yongjiu and Chen Peng.Study of spatial distribution for Dosidicus gigas abundance off Peru based on a comprehensive environmental factor[J].Acta Oceanologica Sinica (in Chinese),2017,39(2):62-71. |
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| Authors: | Fang Xueyan Chen Xinjun Feng Yongjiu and Chen Peng |
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| Affiliation: | College of Marine Sciences, Shanghai Ocean University, Shanghai 201306, China,College of Marine Sciences, Shanghai Ocean University, Shanghai 201306, China;National Engineering Research Center for Oceanic Fisheries, Shanghai Ocean University, Shanghai 201306, China;The Key Laboratory of Sustainable Exploitation of Oceanic Fisheries Resources, Ministry of Education, Shanghai Ocean University, Shanghai 201306, China;Collaborative Innovation Center for Distant-water Fisheries, Shanghai 201306, China,College of Marine Sciences, Shanghai Ocean University, Shanghai 201306, China;National Engineering Research Center for Oceanic Fisheries, Shanghai Ocean University, Shanghai 201306, China;The Key Laboratory of Sustainable Exploitation of Oceanic Fisheries Resources, Ministry of Education, Shanghai Ocean University, Shanghai 201306, China;Collaborative Innovation Center for Distant-water Fisheries, Shanghai 201306, China and College of Marine Sciences, Shanghai Ocean University, Shanghai 201306, China |
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| Abstract: | Dosidicus gigas is one of the most important oceanic fishing objects. It is useful for learning the ecological habitat and increasing the fishing efficiency to study the spatial distribution of D. gigas. We used the fishing data as main variable and sea surface temperature (SST), sea surface height (SSH), sea surface salinity (SSS) and concentration of Chlorophyll a (Chl a) as co-variables by cokriging to analyze the spatial distribution of D. gigas abundance. The fishing data was selected from June to September during 2003 to 2012 provided by the Chinese squid-jigging technology group. To solve the weight of four different environmental factors in cokriging, they were transferred to the value between 0 and 1, then combined by principle component analysis as a single comprehensive co-variable. The relative test was taken between catch per unite fishing effort (CPUE) and the co-variable. Mean error (ME), root mean square error (RMSE) and root mean standardized squared error (RMSSE) were used to assess the predicting results to examine this method. The study results showed that (1) the comprehensive environmental factors from June to September had significant correlation with CPUE, (2) The ME were 0.002 6 and 0.002 5 respectively in June and July, which indicated that the average predicted results were higher than the objected data. However ME were -0.007 8 and -0.000 2 in August and September respectively, indicting the predicting accuracy were better and the average predicted results were lower than the objected data. The precision in June was the best, and it was lower in August. The RMSSE values in June and July were less than 1, suggesting over-valuating their uncertainty. This also indicated there were bias on the predicting precision and accuracy. In a word, from the view of ME, RMSE and RMSSE, the predicted data had a certain reliability. |
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| Keywords: | Dosidicus gigas spatial distribution comprehensive environmental factor cokriging off Peru |
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