We used generalized additive models (GAM) to analyze the relationship between spatiotemporal factors and catch, and to estimate the monthly marine fishery yield of single otter trawls in Putuo district of Zhoushan, Ch...We used generalized additive models (GAM) to analyze the relationship between spatiotemporal factors and catch, and to estimate the monthly marine fishery yield of single otter trawls in Putuo district of Zhoushan, China. We used logbooks from five commercial fishing boats and data in government's monthly statistical reports. We developed two GAM models: one included temporal variables (month and hauling time) and spatial variables (longitude and latitude), and another included just two variables, month and the number of fishing boats. Our results suggest that temporal factors explained more of the variability in catch than spatial factors. Furthermore, month explained the majority of variation in catch. Change in spatial distribution of fleet had a temporal component as the boats fished within a relatively small area within the same month, but the area varied among months. The number of boats fishing in each month also explained a large proportion of the variation in catch. Engine power had no effect on catch. The pseudo-coefficients (PCf) of the two GAMs were 0.13 and 0.29 respectively, indicating the both had good fits. The model yielded estimates that were very similar to those in the governmental reports between January to September, with relative estimate errors (REE) of <18%. However, the yields in October and November were significantly underestimated, with REEs of 36% and 27%, respectively.展开更多
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)...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(E_(MA))和均方根误差(E_(RMS))均小于基于时间序列的GAM,而GLM分析的决定系数R~2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。展开更多
基金Supported by the National Natural Science Foundation for Young Scientists of China (No. 40801225)the Natural Science Foundation of Zhejiang Province (No. Y3090038)
文摘We used generalized additive models (GAM) to analyze the relationship between spatiotemporal factors and catch, and to estimate the monthly marine fishery yield of single otter trawls in Putuo district of Zhoushan, China. We used logbooks from five commercial fishing boats and data in government's monthly statistical reports. We developed two GAM models: one included temporal variables (month and hauling time) and spatial variables (longitude and latitude), and another included just two variables, month and the number of fishing boats. Our results suggest that temporal factors explained more of the variability in catch than spatial factors. Furthermore, month explained the majority of variation in catch. Change in spatial distribution of fleet had a temporal component as the boats fished within a relatively small area within the same month, but the area varied among months. The number of boats fishing in each month also explained a large proportion of the variation in catch. Engine power had no effect on catch. The pseudo-coefficients (PCf) of the two GAMs were 0.13 and 0.29 respectively, indicating the both had good fits. The model yielded estimates that were very similar to those in the governmental reports between January to September, with relative estimate errors (REE) of <18%. However, the yields in October and November were significantly underestimated, with REEs of 36% and 27%, respectively.
文摘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(E_(MA))和均方根误差(E_(RMS))均小于基于时间序列的GAM,而GLM分析的决定系数R~2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。
