Delay-difference models are intermediate between simple surplus-production models and complicated age-structured models. Such intermediate models are more efficient and require less data than age-structured models. In...Delay-difference models are intermediate between simple surplus-production models and complicated age-structured models. Such intermediate models are more efficient and require less data than age-structured models. In this study, a delay-differ- ence model was applied to fit catch and catch per unit effort （CPUE） data （1975-2011） of the southern Atlantic albacore （Thunnus alalunga） stock. The proposed delay-difference model captures annual fluctuations in predicted CPUE data better than Fox model. In a Monte Carlo simulation, white noises （CVs） were superimposed on the observed CPUE data at four levels. Relative estimate error was then calculated to compare the estimated results with the true values of parameters a and fl in Ricker stock-recruitment model and the catchability coefficient q. a is more sensitive to CV than fl and q. We also calculated an 80% percentile confidence interval of the maximum sustainable yield （MSY, 21756 t to 23408 t; median 22490 t） with the delay-difference model. The yield of the southern Atlantic albacore stock in 2011 was 24122t, and the estimated ratios of catch against MSY for the past seven years were approxi- mately 1.0. We suggest that care should be taken to protect the albacore fishery in the southern Atlantic Ocean. The proposed de- lay-difference model provides a good fit to the data of southern Atlantic albacore stock and may be a useful choice for the assessment of regional albacore stock.展开更多
It is widely recognized that assessments of the status of data-poor fish stocks are challenging and that Bayesian analysis is one of the methods which can be used to improve the reliability of stock assessments in dat...It is widely recognized that assessments of the status of data-poor fish stocks are challenging and that Bayesian analysis is one of the methods which can be used to improve the reliability of stock assessments in data-poor situations through borrowing strength from prior information deduced from species with good-quality data or other known information. Because there is considerable uncertainty remaining in the stock assessment of albacore tuna（Thunnus alalunga） in the Indian Ocean due to the limited and low-quality data, we investigate the advantages of a Bayesian method in data-poor stock assessment by using Indian Ocean albacore stock assessment as an example. Eight Bayesian biomass dynamics models with different prior assumptions and catch data series were developed to assess the stock. The results show（1） the rationality of choice of catch data series and assumption of parameters could be enhanced by analyzing the posterior distribution of the parameters;（2） the reliability of the stock assessment could be improved by using demographic methods to construct a prior for the intrinsic rate of increase（r）. Because we can make use of more information to improve the rationality of parameter estimation and the reliability of the stock assessment compared with traditional statistical methods by incorporating any available knowledge into the informative priors and analyzing the posterior distribution based on Bayesian framework in data-poor situations, we suggest that the Bayesian method should be an alternative method to be applied in data-poor species stock assessment, such as Indian Ocean albacore.展开更多
Over the years there has been growing interest regarding the effects of climatic variations on marine biodiversity. The exclusive economic zones of South Pacific Islands and territories are home to major international...Over the years there has been growing interest regarding the effects of climatic variations on marine biodiversity. The exclusive economic zones of South Pacific Islands and territories are home to major international exploitable stocks of albacore tuna (Thunnus alalunga);however the impact of climatic variations on these stocks is not fully understood. This study was aimed at determining the climatic variables which have impact on the time series stock fluctuation pattern of albacore tuna stock in the Eastern and Western South Pacific Ocean which was divided into three zones. The relationship of the climatic variables for the global mean land and ocean temperature index (LOTI), the Pacific warm pool index (PWI) and the Pacific decadal oscillation (PDO) was investigated against the albacore tuna catch per unit effort (CPUE) time series in Zone 1, Zone 2 and Zone 3 of the South Pacific Ocean from 1957 to 2008. From the results it was observed that LOTI, PWI and PDO at different lag periods exhibited significant correlation with albacore tuna CPUE for all three areas. LOTI, PWI and PDO were used as independent variables to develop suitable stock reproduction models for the trajectory of albacore tuna CPUE in Zone 1, Zone 2 and Zone 3. Model selection was based on Akaike Information Criterion (AIC), R2 values and significant parameter estimates at p < 0.05. The final models for albacore tuna CPUE in all three zones incorporated all three independent variables of LOTI, PWI and PDO. From the findings it can be said that the climatic conditions of LOTI, PWI and PDO play significant roles in structuring the stock dynamics of the albacore tuna in the Eastern and Western South Pacific Ocean. It is imperative to take these factors into account when making management decisions for albacore tuna in these areas.展开更多
A continuous time delay-difference model(CTDDM) has been established that considers continuous time delays of biological processes.The southern Atlantic albacore(Thunnus alalunga) stock is the one of the commercially ...A continuous time delay-difference model(CTDDM) has been established that considers continuous time delays of biological processes.The southern Atlantic albacore(Thunnus alalunga) stock is the one of the commercially important tuna population in the marine world.The age structured production model(ASPM) and the surplus production model(SPM) have already been used to assess the albacore stock.However,the ASPM requires detailed biological information and the SPM lacks the biological realism.In this study,we focus on applying a CTDDM to the southern Atlantic albacore(T.alalunga) species,which provides an alternative method to assess this fishery.It is the first time that CTDDM has been provided for assessing the Atlantic albacore(T.alalunga) fishery.CTDDM obtained the 80%confidence interval of MSY(maximum sustainable yield) of(21 510 t,23 118 t).The catch in 2011(24 100 t) is higher than the MSY values and the relative fishing mortality ratio(F_(2011)/F_(MSY)) is higher than 1.0.The results of CTDDM were analyzed to verify the proposed methodology and provide reference information for the sustainable management of the southern Atlantic albacore stock.The CTDDM treats the recruitment,the growth,and the mortality rates as all varying continuously over time and fills gaps between ASPM and SPM in this stock assessment.展开更多
Objective:To evaluate the anti-inflamattory activity of Thunnus alalunga by both in vitro and in vivo methods.Methods:Anti-inflammatory activity of the chloroform water extract of Thunnus alalunga was done by both in ...Objective:To evaluate the anti-inflamattory activity of Thunnus alalunga by both in vitro and in vivo methods.Methods:Anti-inflammatory activity of the chloroform water extract of Thunnus alalunga was done by both in vitro and in vivo methods.In vitro method was done by human red blood cells membrane stabilization method(HRBC).In vivo evaluation was estimated on Wister albino rats.Acute toxicity studies were done on the extract and no toxicity was reported.Results: The percentage protection exhibited by 300 mg/mL concentration was more when compared to the other ones.The 400 mg/mL concentration showed potent activity on comparison with the standard during in vivo evaluation.Conclusions:In both means of estimation the extract of Thunnus alalunga was found to possess significant anti-inflammatory activity.展开更多
Various population structures or spatial heterogeneities in population distribution have been an important source of model misspecification and have had an impact on estimation performance in fisheries stock assessmen...Various population structures or spatial heterogeneities in population distribution have been an important source of model misspecification and have had an impact on estimation performance in fisheries stock assessment.In this study,we simulated the Indian Ocean albacore spatial heterogeneity in age-structure using Stock Synthesis according to the stage-dependent migration rate and region-dependent fishing mortality rate and generated the stock assessment data.Based on these data,we investigated the performances of different spatial configurations,selectivity curves and selections of CPUE(catch per unit effort)indices of the assessment models which were used to account for spatial heterogeneity.The results showed:(1)although the spatially explicit configurations,which exactly matched the operating model,provided unbiased and accurate estimates of relative spawning biomass,relative fishing mortality rate and maximum sustainable yield in all simulation scenarios,their performance may be very poor if there were mismatches between them and the operating model due to gaps in knowledge and data;(2)for spatially explicit assessment configuration,the correct boundary was required,but for non-spatially explicit assessment configuration,it seemed more important for analysts to partition the area to properly reflect the transition in field data and to effectively account for the impacts of ignoring the spatial structure by using the additional spatially referenced parameters;(3)although the areas-as-fleets methods and flexible time-varying selectivity curves could be used as better alternative approaches to account for spatial structure,these configurations could not completely eliminate the impacts of model misspecification and the quality of estimates of different quantities from the same assessment model may be inconsistent or the performance of the same assessment configuration may fluctuate significantly between simulation scenarios;(4)although the worst estimates could generally be avoided by using multiple CPUE indices,there were no best solutions to select or regenerate the CPUE indices to account for the impacts of the ignored spatial structure to obviously improve the quality of stock assessment.Compared with the results of assessment model configurations which are used to account for the spatial structure by different modelers,the performances of the configurations are always casespecific except for spatially explicit configurations which exactly match the operating model.In this sense,our study will not only provide some insights into the current Indian Ocean albacore stock assessment but also enrich existing knowledge regarding the performance of assessment configurations to account for spatial structure.展开更多
基金supported by the Fundamental Research Funds for the Central Universities of China (Grant No. 201022001)
文摘Delay-difference models are intermediate between simple surplus-production models and complicated age-structured models. Such intermediate models are more efficient and require less data than age-structured models. In this study, a delay-differ- ence model was applied to fit catch and catch per unit effort （CPUE） data （1975-2011） of the southern Atlantic albacore （Thunnus alalunga） stock. The proposed delay-difference model captures annual fluctuations in predicted CPUE data better than Fox model. In a Monte Carlo simulation, white noises （CVs） were superimposed on the observed CPUE data at four levels. Relative estimate error was then calculated to compare the estimated results with the true values of parameters a and fl in Ricker stock-recruitment model and the catchability coefficient q. a is more sensitive to CV than fl and q. We also calculated an 80% percentile confidence interval of the maximum sustainable yield （MSY, 21756 t to 23408 t; median 22490 t） with the delay-difference model. The yield of the southern Atlantic albacore stock in 2011 was 24122t, and the estimated ratios of catch against MSY for the past seven years were approxi- mately 1.0. We suggest that care should be taken to protect the albacore fishery in the southern Atlantic Ocean. The proposed de- lay-difference model provides a good fit to the data of southern Atlantic albacore stock and may be a useful choice for the assessment of regional albacore stock.
基金The Innovation Program of Shanghai Municipal Education Commission under contract No.14ZZ147the Opening Project of Key Laboratory of Sustainable Exploitation of Oceanic Fisheries Resources(Shanghai Ocean University),Ministry of Education under contract No.A1-0209-15-0503-1
文摘It is widely recognized that assessments of the status of data-poor fish stocks are challenging and that Bayesian analysis is one of the methods which can be used to improve the reliability of stock assessments in data-poor situations through borrowing strength from prior information deduced from species with good-quality data or other known information. Because there is considerable uncertainty remaining in the stock assessment of albacore tuna（Thunnus alalunga） in the Indian Ocean due to the limited and low-quality data, we investigate the advantages of a Bayesian method in data-poor stock assessment by using Indian Ocean albacore stock assessment as an example. Eight Bayesian biomass dynamics models with different prior assumptions and catch data series were developed to assess the stock. The results show（1） the rationality of choice of catch data series and assumption of parameters could be enhanced by analyzing the posterior distribution of the parameters;（2） the reliability of the stock assessment could be improved by using demographic methods to construct a prior for the intrinsic rate of increase（r）. Because we can make use of more information to improve the rationality of parameter estimation and the reliability of the stock assessment compared with traditional statistical methods by incorporating any available knowledge into the informative priors and analyzing the posterior distribution based on Bayesian framework in data-poor situations, we suggest that the Bayesian method should be an alternative method to be applied in data-poor species stock assessment, such as Indian Ocean albacore.
文摘Over the years there has been growing interest regarding the effects of climatic variations on marine biodiversity. The exclusive economic zones of South Pacific Islands and territories are home to major international exploitable stocks of albacore tuna (Thunnus alalunga);however the impact of climatic variations on these stocks is not fully understood. This study was aimed at determining the climatic variables which have impact on the time series stock fluctuation pattern of albacore tuna stock in the Eastern and Western South Pacific Ocean which was divided into three zones. The relationship of the climatic variables for the global mean land and ocean temperature index (LOTI), the Pacific warm pool index (PWI) and the Pacific decadal oscillation (PDO) was investigated against the albacore tuna catch per unit effort (CPUE) time series in Zone 1, Zone 2 and Zone 3 of the South Pacific Ocean from 1957 to 2008. From the results it was observed that LOTI, PWI and PDO at different lag periods exhibited significant correlation with albacore tuna CPUE for all three areas. LOTI, PWI and PDO were used as independent variables to develop suitable stock reproduction models for the trajectory of albacore tuna CPUE in Zone 1, Zone 2 and Zone 3. Model selection was based on Akaike Information Criterion (AIC), R2 values and significant parameter estimates at p < 0.05. The final models for albacore tuna CPUE in all three zones incorporated all three independent variables of LOTI, PWI and PDO. From the findings it can be said that the climatic conditions of LOTI, PWI and PDO play significant roles in structuring the stock dynamics of the albacore tuna in the Eastern and Western South Pacific Ocean. It is imperative to take these factors into account when making management decisions for albacore tuna in these areas.
基金Supported by the Special Fund of Chinese Central Government for Basic Scientific Research Operations in Commonweal Research Institutes(No.201022001)
文摘A continuous time delay-difference model(CTDDM) has been established that considers continuous time delays of biological processes.The southern Atlantic albacore(Thunnus alalunga) stock is the one of the commercially important tuna population in the marine world.The age structured production model(ASPM) and the surplus production model(SPM) have already been used to assess the albacore stock.However,the ASPM requires detailed biological information and the SPM lacks the biological realism.In this study,we focus on applying a CTDDM to the southern Atlantic albacore(T.alalunga) species,which provides an alternative method to assess this fishery.It is the first time that CTDDM has been provided for assessing the Atlantic albacore(T.alalunga) fishery.CTDDM obtained the 80%confidence interval of MSY(maximum sustainable yield) of(21 510 t,23 118 t).The catch in 2011(24 100 t) is higher than the MSY values and the relative fishing mortality ratio(F_(2011)/F_(MSY)) is higher than 1.0.The results of CTDDM were analyzed to verify the proposed methodology and provide reference information for the sustainable management of the southern Atlantic albacore stock.The CTDDM treats the recruitment,the growth,and the mortality rates as all varying continuously over time and fills gaps between ASPM and SPM in this stock assessment.
文摘Objective:To evaluate the anti-inflamattory activity of Thunnus alalunga by both in vitro and in vivo methods.Methods:Anti-inflammatory activity of the chloroform water extract of Thunnus alalunga was done by both in vitro and in vivo methods.In vitro method was done by human red blood cells membrane stabilization method(HRBC).In vivo evaluation was estimated on Wister albino rats.Acute toxicity studies were done on the extract and no toxicity was reported.Results: The percentage protection exhibited by 300 mg/mL concentration was more when compared to the other ones.The 400 mg/mL concentration showed potent activity on comparison with the standard during in vivo evaluation.Conclusions:In both means of estimation the extract of Thunnus alalunga was found to possess significant anti-inflammatory activity.
基金The National Key Research and Development Program of China under contract No.2016YFC1400903the NSFC Zhejiang Joint Fund for the Integration of Industrialization and Informatization under contract No.U1609202
文摘Various population structures or spatial heterogeneities in population distribution have been an important source of model misspecification and have had an impact on estimation performance in fisheries stock assessment.In this study,we simulated the Indian Ocean albacore spatial heterogeneity in age-structure using Stock Synthesis according to the stage-dependent migration rate and region-dependent fishing mortality rate and generated the stock assessment data.Based on these data,we investigated the performances of different spatial configurations,selectivity curves and selections of CPUE(catch per unit effort)indices of the assessment models which were used to account for spatial heterogeneity.The results showed:(1)although the spatially explicit configurations,which exactly matched the operating model,provided unbiased and accurate estimates of relative spawning biomass,relative fishing mortality rate and maximum sustainable yield in all simulation scenarios,their performance may be very poor if there were mismatches between them and the operating model due to gaps in knowledge and data;(2)for spatially explicit assessment configuration,the correct boundary was required,but for non-spatially explicit assessment configuration,it seemed more important for analysts to partition the area to properly reflect the transition in field data and to effectively account for the impacts of ignoring the spatial structure by using the additional spatially referenced parameters;(3)although the areas-as-fleets methods and flexible time-varying selectivity curves could be used as better alternative approaches to account for spatial structure,these configurations could not completely eliminate the impacts of model misspecification and the quality of estimates of different quantities from the same assessment model may be inconsistent or the performance of the same assessment configuration may fluctuate significantly between simulation scenarios;(4)although the worst estimates could generally be avoided by using multiple CPUE indices,there were no best solutions to select or regenerate the CPUE indices to account for the impacts of the ignored spatial structure to obviously improve the quality of stock assessment.Compared with the results of assessment model configurations which are used to account for the spatial structure by different modelers,the performances of the configurations are always casespecific except for spatially explicit configurations which exactly match the operating model.In this sense,our study will not only provide some insights into the current Indian Ocean albacore stock assessment but also enrich existing knowledge regarding the performance of assessment configurations to account for spatial structure.
