Catch per unit of eff ort(CPUE) data can display spatial autocorrelation. However, most of the CPUE standardization methods developed so far assumes independency of observations for the dependent variable, which is of...Catch per unit of eff ort(CPUE) data can display spatial autocorrelation. However, most of the CPUE standardization methods developed so far assumes independency of observations for the dependent variable, which is often invalid. In this study, we collected data of two fisheries, squid jigging fishery and mackerel trawl fishery. We used standard generalized linear model(GLM) and spatial GLMs to compare the impact of spatial autocorrelation on CPUE standardization for different fisheries. We found that spatialGLMs perform better than standard-GLM for both fisheries. The overestimation of precision of CPUE estimates was observed in both fisheries. Moran's I was used to quantify the level of autocorrelation for the two fisheries. The results show that autocorrelation in mackerel trawl fishery was much stronger than that in squid jigging fishery. According to the results of this paper, we highly recommend to account for spatial autocorrelation when using GLM to standardize CPUE data derived from commercial fisheries.展开更多
Generalized linear models （GLM） and generalized additive models （GAM） were used to standardize catch per unit fishing effort （CPUE） of Ommastrephes bartramii for Chinese squid-jigging fishery in Northwest Pacifi...Generalized linear models （GLM） and generalized additive models （GAM） were used to standardize catch per unit fishing effort （CPUE） of Ommastrephes bartramii for Chinese squid-jigging fishery in Northwest Pacific Ocean. Three groups of variables were considered in the standardization： spatial variables （longitude and latitude）, temporal variables （year and month） and environmental variables, including sea surface temperature （SST）, sea surface salinity （SSS） and sea level height （SLH）. CPUE was treated as the dependent variable and its error distribution was assumed to be log-normal in each model. The model selections of GLM and GAM were based on the finite sample-corrected Akaike information criterion （AICC） and pseudo-coefficient （Pcf） combined P-value, respectively. Both GAM and GLM analysis showed that the month was the most important variable affecting CPUE and could explain 21.3% of variability in CPUE while other variables only explained 8.66%. The interaction of spatial and temporal variables weakly influenced the CPUE. Moreover, spatio-temporal factors may be more important in influencing the CPUE of this squid than environmental variables. The standardized and nominal CPUEs were similar and had the same trends in spatio-temporal distribution, but the standardized CPUE values tended to be smaller than the nominal CPUE. The CPUE tended to have much higher monthly variation than annual variations and their values increased with month. The CPUE became higher with increasing latitude-high CPUE usually occurred in 145°E-148°E and 149°E-162°E. The CPUE was higher when SST was 14-21℃ and the SLH from -22 cm to -18 cm. In this study, GAM tended to be more suitable than GLM in analysis of CPUE.展开更多
基金Supported by the National High Technology Research and Development Program of China(863 Program)(No.2012AA092303)the Public Science and Technology Research Funds Projects of Ocean(No.20155014)+2 种基金the Shanghai Leading Academic Discipline Projectthe Funding Program for Outstanding Dissertation in Shanghai Ocean UniversitySupported by SHOU International Center for Marine Studies and Shanghai 1000 Talent Program
文摘Catch per unit of eff ort(CPUE) data can display spatial autocorrelation. However, most of the CPUE standardization methods developed so far assumes independency of observations for the dependent variable, which is often invalid. In this study, we collected data of two fisheries, squid jigging fishery and mackerel trawl fishery. We used standard generalized linear model(GLM) and spatial GLMs to compare the impact of spatial autocorrelation on CPUE standardization for different fisheries. We found that spatialGLMs perform better than standard-GLM for both fisheries. The overestimation of precision of CPUE estimates was observed in both fisheries. Moran's I was used to quantify the level of autocorrelation for the two fisheries. The results show that autocorrelation in mackerel trawl fishery was much stronger than that in squid jigging fishery. According to the results of this paper, we highly recommend to account for spatial autocorrelation when using GLM to standardize CPUE data derived from commercial fisheries.
基金Supported by the Program for New Century Excellent Talents in University (No.NCET-06-0437)the National High Technology Research and Development Program of China (863 Program) (No.2007AA092201+2 种基金2007AA092202)Shanghai Leading Academic Discipline Project (No.S30702)Doctorship Fund of Shanghai Ocean University (No.06-326)
文摘Generalized linear models （GLM） and generalized additive models （GAM） were used to standardize catch per unit fishing effort （CPUE） of Ommastrephes bartramii for Chinese squid-jigging fishery in Northwest Pacific Ocean. Three groups of variables were considered in the standardization： spatial variables （longitude and latitude）, temporal variables （year and month） and environmental variables, including sea surface temperature （SST）, sea surface salinity （SSS） and sea level height （SLH）. CPUE was treated as the dependent variable and its error distribution was assumed to be log-normal in each model. The model selections of GLM and GAM were based on the finite sample-corrected Akaike information criterion （AICC） and pseudo-coefficient （Pcf） combined P-value, respectively. Both GAM and GLM analysis showed that the month was the most important variable affecting CPUE and could explain 21.3% of variability in CPUE while other variables only explained 8.66%. The interaction of spatial and temporal variables weakly influenced the CPUE. Moreover, spatio-temporal factors may be more important in influencing the CPUE of this squid than environmental variables. The standardized and nominal CPUEs were similar and had the same trends in spatio-temporal distribution, but the standardized CPUE values tended to be smaller than the nominal CPUE. The CPUE tended to have much higher monthly variation than annual variations and their values increased with month. The CPUE became higher with increasing latitude-high CPUE usually occurred in 145°E-148°E and 149°E-162°E. The CPUE was higher when SST was 14-21℃ and the SLH from -22 cm to -18 cm. In this study, GAM tended to be more suitable than GLM in analysis of CPUE.
