2023, 38(2): 340-347. doi: 10.16535/j.cnki.dlhyxb.2022-208
关键词： 月相 , 拖网 , 单位捕捞努力量渔获量(CPUE) , 圆形统计 , 时间序列 , 交叉验证
Keywords： lunar phase , trawl , catch per unit effort (CPUE) , circular statistics , time series , cross-validation
为了解和掌握月相对渔业产量或单位捕捞努力量渔获量(catch per unit effort,CPUE)的影响规律,利用2016—2019年(每年3月2日—11月29日)马达加斯加西海岸底拖网独角新对虾(Metapenaeus monoceros)和2017—2020年(每年6月15日—10月9日)西白令海中层拖网狭鳕 (Theragra chalcogramma)的渔业生产数据,结合基于圆形统计的广义线性模型(GLM)和基于时间序列的广义加性模型(GAM)2种不同的月相量化和统计的方法,分析月相对拖网渔业CPUE的影响。结果表明:月相对独角新对虾的CPUE具有显著性影响(P<0.05), 2种方法得出的影响趋势较为一致,较高CPUE出现在上弦月;基于圆形统计的GLM显示,月相对狭鳕CPUE具有显著性影响(P<0.05),较高CPUE出现在新月期,而基于时间序列的GAM显示,月相对狭鳕CPUE的影响不显著(P＞0.05);交叉验证显示,基于圆形统计的GLM平均绝对误差(EMA)和均方根误差(ERMS)均小于基于时间序列的GAM,而GLM分析的决定系数R2则大于GAM,表明前者的拟合具有更好的准确性、稳定性和拟合优度。研究表明,当周期性循环变量(月份、月相和小时等)具有较弱的显著性时,使用基于圆形统计的GLM更能反映月相对拖网渔业CPUE的影响。
为了解和掌握月相对渔业产量或单位捕捞努力量渔获量(catch per unit effort,CPUE)的影响规律,利用2016—2019年(每年3月2日—11月29日)马达加斯加西海岸底拖网独角新对虾(Metapenaeus monoceros)和2017—2020年(每年6月15日—10月9日)西白令海中层拖网狭鳕 (Theragra chalcogramma)的渔业生产数据,结合基于圆形统计的广义线性模型(GLM)和基于时间序列的广义加性模型(GAM)2种不同的月相量化和统计的方法,分析月相对拖网渔业CPUE的影响。结果表明:月相对独角新对虾的CPUE具有显著性影响(P<0.05), 2种方法得出的影响趋势较为一致,较高CPUE出现在上弦月;基于圆形统计的GLM显示,月相对狭鳕CPUE具有显著性影响(P<0.05),较高CPUE出现在新月期,而基于时间序列的GAM显示,月相对狭鳕CPUE的影响不显著(P＞0.05);交叉验证显示,基于圆形统计的GLM平均绝对误差(EMA)和均方根误差(ERMS)均小于基于时间序列的GAM,而GLM分析的决定系数R2则大于GAM,表明前者的拟合具有更好的准确性、稳定性和拟合优度。研究表明,当周期性循环变量(月份、月相和小时等)具有较弱的显著性时,使用基于圆形统计的GLM更能反映月相对拖网渔业CPUE的影响。
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