| 牛明香,王俊,吴强,孙坚强.基于遥感的黄海中南部越冬鳀资源密度分布与环境因子的关系研究.渔业科学进展,2020,41(1):11-20 |
| 基于遥感的黄海中南部越冬鳀资源密度分布与环境因子的关系研究 |
| The relationship of stock density distribution of wintering anchovy (Engraulis japonicus) and environmental factors based on remote sensing in central and southern Yellow Sea |
| 投稿时间：2018-11-16 修订日期：2018-11-26 |
| DOI： |
| 中文关键词: 时空分布 环境因子 广义可加模型 海洋遥感 鳀 黄海 |
| 英文关键词: Spatio-temporal distribution Environmental factors Generalized additive model Marine remote sensing Engraulis japonicus Yellow Sea |
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| 中文摘要: |
| 基于遥感和GIS(Geographic information system)技术，利用2000~2015年的底拖网调查数据和海表温度、叶绿素a浓度以及海水温度梯度等遥感数据，在定性分析黄海中南部越冬鳀(Engraulis japonicus)资源密度分布与环境因子关系的基础上，利用时空和环境因子构建GAM (Generalized additive model)模型进行定量分析。结果显示，时空因子(年、下网时间、经度和纬度)和环境因子对越冬鳀资源密度的总偏差解释率为44.76%，其中，时空因子对其的影响均显著，以空间因子影响最大，对总偏差的解释率为35.4%；环境因子中，水深、海表温度和温度梯度对其影响显著，而叶绿素a浓度影响不显著；越冬鳀分布的最适海表温度、叶绿素a浓度和海表温度梯度范围分别为7~11℃、1.2~2.3 mg/m3和0.7~2.5℃。研究结果对环境变动下的渔业管理具有重要意义。 |
| 英文摘要: |
| Marine environmental factors affect the survival, growth, and reproduction of fish, etc., which play an important role in controlling fish population distribution. In addition, variation of marine environmental factors influences the spatial distribution and aggregation of fish. Based on remote sensing (RS) technology and geographic information system (GIS), the relationship between the spatial distribution of wintering anchovy (Engraulis japonicas) and environmental factors are analyzed, and then generalized additive models (GAMs) were established to quantitatively investigate the effects of spatio-temporal and environmental factors on stock density, using data collected by bottom trawl surveys and RS in central and southern Yellow Sea during 2000~2015. The results showed that wintering anchovy was concentrated in certain ranges of sea surface temperature (SST), chlorophyll a (Chl-a) concentration, as well as temperature gradient (TGR). The final model accounted for 44.76% of the variance in anchovy stock density. The spatio-temporal factors (year, hour, longitude, latitude) all had significant effects (P<0.05) on stock density, and spatial factors had the greatest impacts, accounting for 35.4% of the variance. Environmental factors such as water depth, SST, and TGR all had significant impacts on stock density (P<0.05). However, Chl-a concentration did not have a significant effect on anchovy stock density. The distribution of Chl-a concentration represents certain hydrodynamic characteristics; therefore, Chl-a concentration was included in the final model. Wintering anchovy was most abundant where the SST was between 7℃ and 11℃. The effect of SST on stock density was positive for temperatures lower than 9.5℃, and then there was a negative effect at warmer temperatures. Stock density was high where Chl-a concentration was between 1.2 and 2.3 mg/m3 and where TGR was from about 0.7℃ to 2.5℃; however, there were slight changes between the abundant ranges. The results of this study have important implications for fisheries management under marine environment dynamic scenarios in the Yellow Sea. |
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