This study focused on the quantitative evaluation of the impact of the spatio-temporal scale used in data collection and grouping on the standardization of CPUE(catch per unit effort).We used the Chinese squid-jigging...This study focused on the quantitative evaluation of the impact of the spatio-temporal scale used in data collection and grouping on the standardization of CPUE(catch per unit effort).We used the Chinese squid-jigging fishery in the northwestern Pacific Ocean as an example to evaluate 24 scenarios at different spatio-temporal scales,with a combination of four levels of temporal scale(weekly,biweekly,monthly,and bimonthly)and six levels of spatial scale(longitude×latitude:0.5°×0.5°,0.5°×1°,0.5°×2°,1°×0.5°,1°×1°,and 1°×2°).We applied generalized additive models and generalized linear models to analyze the24 scenarios for CPUE standardization,and then the differences in the standardized CPUE among these scenarios were quantified.This study shows that combinations of different spatial and temporal scales could have different impacts on the standardization of CPUE.However,at a fine temporal scale(weekly)different spatial scales yielded similar results for standardized CPUE.The choice of spatio-temporal scale used in data collection and analysis may create added uncertainty in fisheries stock assessment and management.To identify a cost-effective spatio-temporal scale for data collection,we recommend a similar study be undertaken to facilitate the design of effective monitoring programs.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
Biological data in fishery ecology have complex structures and are highly heterogeneous.Catch per unit effort(CPUE)estimated from fishery-dependent data are often used to characterize abundance indices(AI)of fish spec...Biological data in fishery ecology have complex structures and are highly heterogeneous.Catch per unit effort(CPUE)estimated from fishery-dependent data are often used to characterize abundance indices(AI)of fish species,which is critical in fish stock assessment.However,additional considerations need to be undertaken to ensure robust estimation because of the latently complicated structures in fishery-dependent data.Here,we elaborated the process of constructing multi-output artificial neural network models to standardize CPUE for heterogeneous fishing operations and applied it to the skipjack tuna(Katsuwonus pelamis)in the western and central Pacific Ocean(WCPO).Seasonal,spatial,and environmental factors were input variables,and the CPUE of four types of skipjack tuna fisheries were set as output variables.The optimal structure for multi-output neural network was evaluated by systematic comparison in 100 runs hold-out cross-validation.The results showed that the final multi-output neural network model with high accuracy can predict the spatial and temporal trends of skipjack tuna abundance.展开更多
In the western and central Pacific Ocean,upper strata waters exhibit highly dynamic oceanographic features under ENSO variability.This has been proved to be responsible for the dynamic change of both abundance and zon...In the western and central Pacific Ocean,upper strata waters exhibit highly dynamic oceanographic features under ENSO variability.This has been proved to be responsible for the dynamic change of both abundance and zonal distribution of skipjack tuna(Katsuwonus pelamis).Although causality has been suggested by researchers using physical-biological interaction models,cumulative evidence needs to be obtained and the tenability of assertion needs to be tested from an ecological habitat perspective,based on fisheries data.For purse seine fishery,the use of catch per unit effort(CPUE)as an indication of the abundance is confusing because of technical improvements over the whole exploitation history and unbalanced individual fishing characteristic of vessels.It is particularly interesting to discriminate between habitat characteristics in comparative scenarios of CPUE application.This study identified habitat traits based on a series of oceanographic factors from a global ocean reanalysis model.A comparison was conducted between two habitat models based on unprocessed purse seine CPUE and standardized CPUE considering fishing characteristics.The results suggest that standardized CPUE could model the regular zonal shift of habitat compatible with the observed fishing efforts transfer,and achieved better prediction capacity than unprocessed CPUE.Furthermore,the habitat of skipjack tuna was also characterized and linked with surface and subsurface thermal environment,ocean current,dissolved oxygen,biotic environment,and ENSO variability.The monthly-averaged habitat suitable index,derived from the optimal habitat model prediction,showed a significant linear relationship with the southern oscillation index,which suggested that El Ni?o episodes eventually provide more preferable habitat for skipjack tuna under ENSO variability.展开更多
基金Supported by Shanghai Universities First-class Disciplines Project,Discipline name:Fisheries(A),the National Natural Science Foundation of China(No.NSFC41276156)the National High Technology Research and Development Program of China(863 Program)(No.2012AA092303)+1 种基金the Shanghai Science and Technology Innovation Program(No.12231203900)CHEN Yong’s involvement was supported by the Shanghai Ocean University
文摘This study focused on the quantitative evaluation of the impact of the spatio-temporal scale used in data collection and grouping on the standardization of CPUE(catch per unit effort).We used the Chinese squid-jigging fishery in the northwestern Pacific Ocean as an example to evaluate 24 scenarios at different spatio-temporal scales,with a combination of four levels of temporal scale(weekly,biweekly,monthly,and bimonthly)and six levels of spatial scale(longitude×latitude:0.5°×0.5°,0.5°×1°,0.5°×2°,1°×0.5°,1°×1°,and 1°×2°).We applied generalized additive models and generalized linear models to analyze the24 scenarios for CPUE standardization,and then the differences in the standardized CPUE among these scenarios were quantified.This study shows that combinations of different spatial and temporal scales could have different impacts on the standardization of CPUE.However,at a fine temporal scale(weekly)different spatial scales yielded similar results for standardized CPUE.The choice of spatio-temporal scale used in data collection and analysis may create added uncertainty in fisheries stock assessment and management.To identify a cost-effective spatio-temporal scale for data collection,we recommend a similar study be undertaken to facilitate the design of effective monitoring programs.
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.
基金supported by the National Key R&D Program of China(No.2023YFD2401303).
文摘Biological data in fishery ecology have complex structures and are highly heterogeneous.Catch per unit effort(CPUE)estimated from fishery-dependent data are often used to characterize abundance indices(AI)of fish species,which is critical in fish stock assessment.However,additional considerations need to be undertaken to ensure robust estimation because of the latently complicated structures in fishery-dependent data.Here,we elaborated the process of constructing multi-output artificial neural network models to standardize CPUE for heterogeneous fishing operations and applied it to the skipjack tuna(Katsuwonus pelamis)in the western and central Pacific Ocean(WCPO).Seasonal,spatial,and environmental factors were input variables,and the CPUE of four types of skipjack tuna fisheries were set as output variables.The optimal structure for multi-output neural network was evaluated by systematic comparison in 100 runs hold-out cross-validation.The results showed that the final multi-output neural network model with high accuracy can predict the spatial and temporal trends of skipjack tuna abundance.
基金The National Key R&D Program of China under contract Nos 2020YFD0901202 and 2019YFD0901502the National Natural Science Foundation of China under contract Nos 41806110,41506151 and 31902426。
文摘In the western and central Pacific Ocean,upper strata waters exhibit highly dynamic oceanographic features under ENSO variability.This has been proved to be responsible for the dynamic change of both abundance and zonal distribution of skipjack tuna(Katsuwonus pelamis).Although causality has been suggested by researchers using physical-biological interaction models,cumulative evidence needs to be obtained and the tenability of assertion needs to be tested from an ecological habitat perspective,based on fisheries data.For purse seine fishery,the use of catch per unit effort(CPUE)as an indication of the abundance is confusing because of technical improvements over the whole exploitation history and unbalanced individual fishing characteristic of vessels.It is particularly interesting to discriminate between habitat characteristics in comparative scenarios of CPUE application.This study identified habitat traits based on a series of oceanographic factors from a global ocean reanalysis model.A comparison was conducted between two habitat models based on unprocessed purse seine CPUE and standardized CPUE considering fishing characteristics.The results suggest that standardized CPUE could model the regular zonal shift of habitat compatible with the observed fishing efforts transfer,and achieved better prediction capacity than unprocessed CPUE.Furthermore,the habitat of skipjack tuna was also characterized and linked with surface and subsurface thermal environment,ocean current,dissolved oxygen,biotic environment,and ENSO variability.The monthly-averaged habitat suitable index,derived from the optimal habitat model prediction,showed a significant linear relationship with the southern oscillation index,which suggested that El Ni?o episodes eventually provide more preferable habitat for skipjack tuna under ENSO variability.
