不同时空尺度下近海日本鲭栖息地模型比较
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摘要
根据2003—2011年7—9月近海日本鲭生产数据,结合海洋遥感获取的海表温度(SST)和海面高度数据(SSH),利用作业网次与SST和SSH的关系建立适应性指数(SI)模型,将时间和空间尺度分别划分为3个级别,建立9个不同时空尺度下的栖息地适应性指数(HSI)模型,分析比较得出不同时空尺度下近海日本鲭栖息地适应性指数最优模型,并对最优模型进行验证。结果显示,时间尺度为月,空间尺度为1°×1°是建立近海日本鲭栖息地适应性指数模型的最适时空尺度,当HSI大于0.6时,作业网次比重为75.42%,当HSI小于0.4时,作业网次比重为12.93%。利用2012年7—9月生产数据对最优模型进行验证,结果显示,当HSI大于0.6时,作业网次比重为60.89%,当HSI小于0.4时,作业网次比重为13.88%。研究表明,在建立鱼类栖息地适应性指数模型时,需要考虑海洋环境因子的时空尺度,以便更好地预测中心渔场。
Marine fishery fishing operations are carried out in different spatial and temporal scales, and the distribution of fish may vary with the spatial-temporal scale. Spatial and temporal scales play a vital role in the study of marine fishery habitat, but there were few researches on the comparison of habitat suitability index models in different spatial-temporal scales. In this study, based on the statistical data of Scomber japonicus from July to September in 2003—2011, combined with the data of sea surface temperature(SST) and sea surface height(SSH)obtained by ocean remote sensing, using arithmetic mean model(AM) and the relationship between SST and SSH to build habitat suitability index model(HSI). The study divided the sea surface temperature(SST) and sea surface height(SSH) into different spatial and temporal scales. Spatial scales included 0.25°×0.25°, 0.5°×0.5° and 1°×1°,temporal scales included weekly, ten days and monthly. A total of 9 HSI models were constructed in different spatial-temporal scales. The results indicated that spatial scale of 1°×1° and temporal scale of monthly were the most optimum spatial-temporal scales. When HSI value was greater than 0.6, the percentage of fishing effort was75.42%, and when HSI value was less than 0.4, the percentage of fishing effort is 12.93%. According to the optimum spatial-temporal scales, using the data from July to September in 2012 to verify the optimum model,when HSI value was greater than 0.6, the percentage of fishing effort was 60.89%, and when HSI value was less than 0.4, the percentage of fishing effort is 13.88%. In summary, the spatial and temporal scales of fishing and marine environment factors should be considered during the construction of fishery habitat suitability index model.
Marine fishery fishing operations are carried out in different spatial and temporal scales, and the distribution of fish may vary with the spatial-temporal scale. Spatial and temporal scales play a vital role in the study of marine fishery habitat, but there were few researches on the comparison of habitat suitability index models in different spatial-temporal scales. In this study, based on the statistical data of Scomber japonicus from July to September in 2003—2011, combined with the data of sea surface temperature(SST) and sea surface height(SSH)obtained by ocean remote sensing, using arithmetic mean model(AM) and the relationship between SST and SSH to build habitat suitability index model(HSI). The study divided the sea surface temperature(SST) and sea surface height(SSH) into different spatial and temporal scales. Spatial scales included 0.25°×0.25°, 0.5°×0.5° and 1°×1°,temporal scales included weekly, ten days and monthly. A total of 9 HSI models were constructed in different spatial-temporal scales. The results indicated that spatial scale of 1°×1° and temporal scale of monthly were the most optimum spatial-temporal scales. When HSI value was greater than 0.6, the percentage of fishing effort was75.42%, and when HSI value was less than 0.4, the percentage of fishing effort is 12.93%. According to the optimum spatial-temporal scales, using the data from July to September in 2012 to verify the optimum model,when HSI value was greater than 0.6, the percentage of fishing effort was 60.89%, and when HSI value was less than 0.4, the percentage of fishing effort is 13.88%. In summary, the spatial and temporal scales of fishing and marine environment factors should be considered during the construction of fishery habitat suitability index model.
引文
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[10]胡贯宇,陈新军,汪金涛.基于不同权重的栖息地指数模型预报阿根廷滑柔鱼中心渔场[J].海洋学报,2015,37(8):88-95.Hu G Y,Chen X J,Wang J T.Forecasting fishing ground of Illex argentinus based on different weight habitat suitability index in the southwestern Atlantic[J].Acta Oceanologica Sinica,2015,37(8):88-95(in Chinese).
[11]Andrade H A,Garcia C A E.Skipjack tuna fishery in relation to sea surface temperature off the southern Brazilian coast[J].Fisheries Oceanography,1999,8(4):245-254.
[12]Mohri M,Nishida T.Seasonal changes in bigeye tuna fishing areas in relation to the oceanographic parameters in the Indian Ocean[J].Journal of National Fisheries University,1999,47(2):43-54.
[13]王震,陈新军,雷林.东太平洋长鳍金枪鱼栖息地指数模型的比较[J].广东海洋大学学报,2017,37(1):58-64.Wang Z,Chen X J,Lei L.Comparison of Thunnus alalunga in the eastern pacific based on habitat suitability index Model[J].Journal of Guangdong Ocean University,2017,37(1):58-64(in Chinese).
[14]Legendre P,Fortin M J.Spatial pattern and ecological analysis[J].Vegetatio,1989,80(2):107-138.
[15]Legendre P,Thrush S F,Cummings V J,et al.Spatial structure of bivalves in a sandflat::scale and generating processes[J].Journal of Experimental Marine Biology and Ecology,1997,216(1-2):99-128.
[16]汪金涛,高峰,雷林,等.阿根廷滑柔鱼渔场预报模型最适时空尺度和环境因子分析[J].中国水产科学,2015,22(5):1007-1014.Wang J T,Gao F,Lei L,et al.Impacts of temporal and spatial scale as well as environmental data on fishery forecasting models for Illex argentinus in the southwest Atlantic[J].Journal of Fishery Sciences of China,2015,22 (5):1007-1014(in Chinese).
[2]官文江.基于海洋遥感的东、黄海鲐鱼渔场与资源研究[D].上海:华东师范大学,2008.Guan W J.Remote-sensing-based assessment of Chub mackerel(Scomber japonicus)fishing ground and stock dynamics in the East China Sea and Yellow Sea[D].Shanghai:East China Normal University,2008(in Chinese).
[3]李纲,陈新军.夏季东海渔场鲐鱼产量与海洋环境因子的关系[J].海洋学研究,2009,27(1):1-8.Li G,Chen X J.Study on the relationship between catch of mackerel and environmental factors in the East China Sea in summer[J].Journal of Marine Sciences,2009,27(1):1-8(in Chinese).
[4]龚彩霞,陈新军,高峰,等.栖息地适宜性指数在渔业科学中的应用进展[J].上海海洋大学学报,2011,20(2):260-269.Gong C X,Chen X J,Gao F,et al.Review on habitat suability index in fishery science[J].Journal of Shanghai Ocean University,2011,20(2):260-269(in Chinese).
[5]蒋瑞,陈新军,雷林,等.秋冬季智利竹?鱼栖息地指数模型比较[J].水产学报,2017,41(2):240-249.Jiang R,Chen X J,Lei L,et al.A comparative study on habitat suitability index of Trachurus murphyi during autumn and winter[J].Journal of Fisheries of China,2017,41(2):240-249(in Chinese).
[6]Vayghan A H,Poorbagher H,Shahraiyni H T,et al.Suitability indices and habitat suitability index model of Caspian kutum(Rutilus frisii kutum)in the southern Caspian Sea[J].Aquatic Ecology,2013,47(4):441-451.
[7]Lee P F,Chen I C,Tzeng W N.Spatial and temporal distribution patterns of bigeye Tuna(Thunnus obesus)in the Indian Ocean[J].Zoological Studies,2005,44(2):260-270.
[8]高峰,陈新军,官文江,等.基于提升回归树的东、黄海鲐鱼渔场预报[J].海洋学报,2015,37(10):39-48.Gao F,Chen X J,Guan W J,et al.Fishing ground forecasting of chub mackerel in the yellow sea and East China sea using boosted regression trees[J].Acta Oceanologica Sinica,2015,37(10):39-48(in Chinese).
[9]陈新军,刘必林,田思泉,等.利用基于表温因子的栖息地模型预测西北太平洋柔鱼(Ommastrephes bartramii)渔场[J].海洋与湖沼,2009,40(6):707-713.Chen X J,Liu B L,Tian S Q,et al.Forecasting the fishing ground of Ommastrephes bartramii with SST-based habitat suitability modelling in northwestern Pacific[J].Oceanologia et Limnologia Sinica,2009,40(6):707-713(in Chinese).
[10]胡贯宇,陈新军,汪金涛.基于不同权重的栖息地指数模型预报阿根廷滑柔鱼中心渔场[J].海洋学报,2015,37(8):88-95.Hu G Y,Chen X J,Wang J T.Forecasting fishing ground of Illex argentinus based on different weight habitat suitability index in the southwestern Atlantic[J].Acta Oceanologica Sinica,2015,37(8):88-95(in Chinese).
[11]Andrade H A,Garcia C A E.Skipjack tuna fishery in relation to sea surface temperature off the southern Brazilian coast[J].Fisheries Oceanography,1999,8(4):245-254.
[12]Mohri M,Nishida T.Seasonal changes in bigeye tuna fishing areas in relation to the oceanographic parameters in the Indian Ocean[J].Journal of National Fisheries University,1999,47(2):43-54.
[13]王震,陈新军,雷林.东太平洋长鳍金枪鱼栖息地指数模型的比较[J].广东海洋大学学报,2017,37(1):58-64.Wang Z,Chen X J,Lei L.Comparison of Thunnus alalunga in the eastern pacific based on habitat suitability index Model[J].Journal of Guangdong Ocean University,2017,37(1):58-64(in Chinese).
[14]Legendre P,Fortin M J.Spatial pattern and ecological analysis[J].Vegetatio,1989,80(2):107-138.
[15]Legendre P,Thrush S F,Cummings V J,et al.Spatial structure of bivalves in a sandflat::scale and generating processes[J].Journal of Experimental Marine Biology and Ecology,1997,216(1-2):99-128.
[16]汪金涛,高峰,雷林,等.阿根廷滑柔鱼渔场预报模型最适时空尺度和环境因子分析[J].中国水产科学,2015,22(5):1007-1014.Wang J T,Gao F,Lei L,et al.Impacts of temporal and spatial scale as well as environmental data on fishery forecasting models for Illex argentinus in the southwest Atlantic[J].Journal of Fishery Sciences of China,2015,22 (5):1007-1014(in Chinese).
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