The generalized linear model （GLM） and generalized additive model （GAM） were applied to the standardization of catch per unit effort （CPUE） for Chilean jack mackerel from Chinese factory trawl fishing fleets in ...The generalized linear model （GLM） and generalized additive model （GAM） were applied to the standardization of catch per unit effort （CPUE） for Chilean jack mackerel from Chinese factory trawl fishing fleets in the Southeast Pacific Ocean from 2001 to 2010 by removing the operational, environmental, spatial and temporal impacts. A total of 9 factors were selected to build the GLM and GAM, i.e., Year, Month, Vessel, La Nifia and E1 Nifio events （ELE）, Latitude, Longitude, Sea surface temperature （SST）, SST anomaly （SSTA）, Nino3.4 index and an interaction term between Longitude and Latitude. The first 5 factors were significant components in the GLM, which in combination explained 27.34% of the total variance in nominal CPUE. In the stepwise GAM, all factors explained 30.78% of the total variance, with Month, Year and Vessel as the main factors influencing CPUE. The higher CPUE occurred during the period April to July at a SST range of 12-15℃ and a SSTA range of 0.2-1.0℃. The CPUE was significantly higher in normal years compared with that in La Nifia and E1 Nifio years. The abundance of Chilean jack mackerel declined during 2001 and 2010, with an increase in 2007. This work provided the relative abundance index of Chilean jack mackerel for stock as- sessment by standardizing catch and effort data of Chinese trawl fisheries and examined the influence of temporal, spatial, environ- mental and fisheries operational factors on Chilean jack mackerel CPUE.展开更多
基金co-funded by the National High Technology Research and Development program of China(No.2012AA092301)the Agriculture Science Technology Achievement Transformation Fund(No.2010C00001)the Project of Fishery Exploration in High Seas of the Ministry of Agriculture of China(2010–2011)
文摘The generalized linear model （GLM） and generalized additive model （GAM） were applied to the standardization of catch per unit effort （CPUE） for Chilean jack mackerel from Chinese factory trawl fishing fleets in the Southeast Pacific Ocean from 2001 to 2010 by removing the operational, environmental, spatial and temporal impacts. A total of 9 factors were selected to build the GLM and GAM, i.e., Year, Month, Vessel, La Nifia and E1 Nifio events （ELE）, Latitude, Longitude, Sea surface temperature （SST）, SST anomaly （SSTA）, Nino3.4 index and an interaction term between Longitude and Latitude. The first 5 factors were significant components in the GLM, which in combination explained 27.34% of the total variance in nominal CPUE. In the stepwise GAM, all factors explained 30.78% of the total variance, with Month, Year and Vessel as the main factors influencing CPUE. The higher CPUE occurred during the period April to July at a SST range of 12-15℃ and a SSTA range of 0.2-1.0℃. The CPUE was significantly higher in normal years compared with that in La Nifia and E1 Nifio years. The abundance of Chilean jack mackerel declined during 2001 and 2010, with an increase in 2007. This work provided the relative abundance index of Chilean jack mackerel for stock as- sessment by standardizing catch and effort data of Chinese trawl fisheries and examined the influence of temporal, spatial, environ- mental and fisheries operational factors on Chilean jack mackerel CPUE.
