| 黄海渔业资源密度空间插值方法的比较研究 |
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| 引用本文： | 陈云龙,单秀娟,金显仕,杨涛,戴芳群,杨顶田.黄海渔业资源密度空间插值方法的比较研究[J].海洋学报(英文版),2016,35(12):65-72. |
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| 作者姓名： | 陈云龙 单秀娟 金显仕 杨涛 戴芳群 杨顶田 |
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| 作者单位： | 中国科学院海洋研究所, 青岛 266071, 中国;农业部海洋渔业资源可持续利用重点开放实验室, 中国水产科学研究院黄海水产研究所, 青岛 266071, 中国;山东省渔业资源与生态环境重点实验室, 中国水产科学研究院黄海水产研究所, 青岛 266071, 中国;中国科学院大学, 北京 100049, 中国,青岛海洋科学与技术国家实验室, 海洋渔业科学与食物产出过程功能实验室, 青岛 266235, 中国;农业部海洋渔业资源可持续利用重点开放实验室, 中国水产科学研究院黄海水产研究所, 青岛 266071, 中国;山东省渔业资源与生态环境重点实验室, 中国水产科学研究院黄海水产研究所, 青岛 266071, 中国,青岛海洋科学与技术国家实验室, 海洋渔业科学与食物产出过程功能实验室, 青岛 266235, 中国;农业部海洋渔业资源可持续利用重点开放实验室, 中国水产科学研究院黄海水产研究所, 青岛 266071, 中国;山东省渔业资源与生态环境重点实验室, 中国水产科学研究院黄海水产研究所, 青岛 266071, 中国,农业部海洋渔业资源可持续利用重点开放实验室, 中国水产科学研究院黄海水产研究所, 青岛 266071, 中国;山东省渔业资源与生态环境重点实验室, 中国水产科学研究院黄海水产研究所, 青岛 266071, 中国,农业部海洋渔业资源可持续利用重点开放实验室, 中国水产科学研究院黄海水产研究所, 青岛 266071, 中国;山东省渔业资源与生态环境重点实验室, 中国水产科学研究院黄海水产研究所, 青岛 266071, 中国,中国科学院南海海洋研究所, 广州 510301, 中国 |
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| 摘 要： | 空间插值是渔业生态学研究，特别是海洋生态系统模型构建中的常用工具。为找到适合黄海渔业资源密度的插值方法，本文对四种常用的空间插值方法进行了比较研究。四种插值方法分别为反距离加权（Inverse distance weighted，IDW）、全局多项式（global polynomial interpolation，GPI）、局部多项式（local polynomial interpolation，LPI）和普通克里格（ordinary kriging，OK）。采用交叉验证分析（cross-validation diagnostic）确定不同插值方法的准确度。同时，结合渔业资源密度空间分布的可视化表达，比较不同插值方法结果的优劣。结果表明，原始的生物学数据具有典型的非正态分布特征，对数转化方法能让其显著地接近或符合正态分布。四个调查阶段中，指数模型在2014年8月和10月为最佳半方差函数，2015年1月和5月数据具有明显的纯块金效应。配对样本T检验表明，预测数据和实测数据之间没有显著差异（P>0.05）。交叉验证结果表明，OK在2014年10月插值效果最好，IDW在剩余的三个航次表现最佳，GPI和LPI表现效果不佳。OK在空间分布的可视化表达方面效果最好，既不像IDW一样有较多的“牛眼”效应，也不像GPI和LPI一样具有过多的平滑效果。然而，纯块金效应的存在有时会影响OK的应用。综上所述，我们推荐操作简单且处理快速的IDW作为黄海渔业资源密度常规的插值方法。
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| 关 键 词： | 空间插值方法 渔业资源密度 黄海 |
| 收稿时间： | 4/3/2016 12:00:00 AM |
| 修稿时间： | 2016/8/19 0:00:00 |
A comparative study of spatial interpolation methods for determining fishery resources density in the Yellow Sea |
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| CHEN Yunlong,SHAN Xiujuan,JIN Xianshi,YANG Tao,DAI Fangqun and YANG Dingtian.A comparative study of spatial interpolation methods for determining fishery resources density in the Yellow Sea[J].Acta Oceanologica Sinica,2016,35(12):65-72. |
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| Authors: | CHEN Yunlong SHAN Xiujuan JIN Xianshi YANG Tao DAI Fangqun and YANG Dingtian |
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| Affiliation: | Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266071, China;Key Laboratory of Sustainable Development of Marine Fisheries of Ministry of Agriculture, Yellow Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Qingdao 266071, China;Shandong Provincial Key Laboratory of Fishery Resources and Ecological Environment, Yellow Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Qingdao 266071, China;University of Chinese Academy of Sciences, Beijing 100049, China,Function Laboratory for Marine Fisheries Science and Food Production Processes, Qingdao National Laboratory for Marine Science and Technology, Qingdao 266100, China;Key Laboratory of Sustainable Development of Marine Fisheries of Ministry of Agriculture, Yellow Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Qingdao 266071, China;Shandong Provincial Key Laboratory of Fishery Resources and Ecological Environment, Yellow Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Qingdao 266071, China,Function Laboratory for Marine Fisheries Science and Food Production Processes, Qingdao National Laboratory for Marine Science and Technology, Qingdao 266100, China;Key Laboratory of Sustainable Development of Marine Fisheries of Ministry of Agriculture, Yellow Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Qingdao 266071, China;Shandong Provincial Key Laboratory of Fishery Resources and Ecological Environment, Yellow Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Qingdao 266071, China,Key Laboratory of Sustainable Development of Marine Fisheries of Ministry of Agriculture, Yellow Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Qingdao 266071, China;Shandong Provincial Key Laboratory of Fishery Resources and Ecological Environment, Yellow Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Qingdao 266071, China,Key Laboratory of Sustainable Development of Marine Fisheries of Ministry of Agriculture, Yellow Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Qingdao 266071, China;Shandong Provincial Key Laboratory of Fishery Resources and Ecological Environment, Yellow Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Qingdao 266071, China and State Key Laboratory of Oceanography in the Tropics, South China Sea Institute of Oceanology, Chinese Academy of Sciences, Guangzhou 510301, China |
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| Abstract: | Spatial interpolation is a common tool used in the study of fishery ecology, especially for the construction of ecosystem models. To develop an appropriate interpolation method of determining fishery resources density in the Yellow Sea, we tested four frequently used methods, including inverse distance weighted interpolation (IDW), global polynomial interpolation (GPI), local polynomial interpolation (LPI) and ordinary kriging (OK). A cross-validation diagnostic was used to analyze the efficacy of interpolation, and a visual examination was conducted to evaluate the spatial performance of the different methods. The results showed that the original data were not normally distributed. A log transformation was then used to make the data fit a normal distribution. During four survey periods, an exponential model was shown to be the best semivariogram model in August and October 2014, while data from January and May 2015 exhibited the pure nugget effect. Using a paired-samples t test, no significant differences (P>0.05) between predicted and observed data were found in all four of the interpolation methods during the four survey periods. Results of the cross-validation diagnostic demonstrated that OK performed the best in August 2014, while IDW performed better during the other three survey periods. The GPI and LPI methods had relatively poor interpolation results compared to IDW and OK. With respect to the spatial distribution, OK was balanced and was not as disconnected as IDW nor as overly smooth as GPI and LPI, although OK still produced a few "bull''s-eye" patterns in some areas. However, the degree of autocorrelation sometimes limits the application of OK. Thus, OK is highly recommended if data are spatially autocorrelated. With respect to feasibility and accuracy, we recommend IDW to be used as a routine interpolation method. IDW is more accurate than GPI and LPI and has a combination of desirable properties, such as easy accessibility and rapid processing. |
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| Keywords: | spatial interpolation methods fishery resources density Yellow Sea |
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