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
Blue marlin(Makaira nigricans)is a common bycatch species in the global tuna longline fishery.In this study,we applied a common data-poor approach,i.e.,depletion-corrected average catch(DCAC)to assess stock status of ...Blue marlin(Makaira nigricans)is a common bycatch species in the global tuna longline fishery.In this study,we applied a common data-poor approach,i.e.,depletion-corrected average catch(DCAC)to assess stock status of the Indian Ocean blue marlin.Sustainable yield(Ysust),one reference point in this case,was estimated,and its uncertainty was integrated by using Monte Carlo simulation.The results revealed the estimate of Ysust by DCAC was lower than MSY of 11,926 t by BSP-SS and is also lower than the provisional reference point of 11,704 t by the management proposal.DCAC is reliable for blue marlin in driving precautionary management quantity based on the CPUE of Japan(1980–2015).This study also implies that DCAC could be applied to other billfish stocks and uncertainty be estimated for sustainable yield.However,data-poor methods could be adjusted with precautionary approaches.展开更多
Multispecies ecological models have been used for predicting the effects of fishing activity and evaluating the performance of management strategies. Size-spectrum models are one type of physiologically-structured eco...Multispecies ecological models have been used for predicting the effects of fishing activity and evaluating the performance of management strategies. Size-spectrum models are one type of physiologically-structured ecological model that provide a feasible approach to describing fish communities in terms of individual dietary variation and ontogenetic niche shift. Despite the potential of ecological models in improving our understanding of ecosystems, their application is usually limited for data-poor fisheries. As a first step in implementing ecosystem-based fisheries management（EBFM）, this study built a size-spectrum model for the fish community in the Haizhou Bay, China. We describe data collection procedures and model parameterization to facilitate the implementation of such size-spectrum models for future studies of data-poor ecosystems. The effects of fishing on the ecosystem were exemplified with a range of fishing effort and were monitored with a set of ecological indicators. Total community biomass, biodiversity index, W-statistic, LFI（Large fish index）, Mean W（mean body weight） and Slope（slope of community size spectra） showed a strong non-linear pattern in response to fishing pressure, and largest fishing effort did not generate the most drastic responses in certain scenarios. We emphasize the value and feasibility of developing size-spectrum models to capture ecological dynamics and suggest limitations as well as potential for model improvement. This study aims to promote a wide use of this type of model in support of EBFM.展开更多
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.
基金This study was funded by National Natural Science Foundation of China(#41676120).The catch data sets analyzed in the study were originally from fishing fleets of IOTC,compiled by IOTC secretariat and further improved by the IOTC WPB.The longline CPUE indices were developed by scientists from Japan and Taiwan,China.Special thanks go to Kindong Richard for giving suggestions and revisions.Any discussion or conclusion in this study only reflects the views of authors.
文摘Blue marlin(Makaira nigricans)is a common bycatch species in the global tuna longline fishery.In this study,we applied a common data-poor approach,i.e.,depletion-corrected average catch(DCAC)to assess stock status of the Indian Ocean blue marlin.Sustainable yield(Ysust),one reference point in this case,was estimated,and its uncertainty was integrated by using Monte Carlo simulation.The results revealed the estimate of Ysust by DCAC was lower than MSY of 11,926 t by BSP-SS and is also lower than the provisional reference point of 11,704 t by the management proposal.DCAC is reliable for blue marlin in driving precautionary management quantity based on the CPUE of Japan(1980–2015).This study also implies that DCAC could be applied to other billfish stocks and uncertainty be estimated for sustainable yield.However,data-poor methods could be adjusted with precautionary approaches.
基金The Special Fund for Agriscientific Research in the Public Interest under contract No.201303050the Fundamental Research Funds for the Central Universities under contract Nos 201022001 and 201262004
文摘Multispecies ecological models have been used for predicting the effects of fishing activity and evaluating the performance of management strategies. Size-spectrum models are one type of physiologically-structured ecological model that provide a feasible approach to describing fish communities in terms of individual dietary variation and ontogenetic niche shift. Despite the potential of ecological models in improving our understanding of ecosystems, their application is usually limited for data-poor fisheries. As a first step in implementing ecosystem-based fisheries management（EBFM）, this study built a size-spectrum model for the fish community in the Haizhou Bay, China. We describe data collection procedures and model parameterization to facilitate the implementation of such size-spectrum models for future studies of data-poor ecosystems. The effects of fishing on the ecosystem were exemplified with a range of fishing effort and were monitored with a set of ecological indicators. Total community biomass, biodiversity index, W-statistic, LFI（Large fish index）, Mean W（mean body weight） and Slope（slope of community size spectra） showed a strong non-linear pattern in response to fishing pressure, and largest fishing effort did not generate the most drastic responses in certain scenarios. We emphasize the value and feasibility of developing size-spectrum models to capture ecological dynamics and suggest limitations as well as potential for model improvement. This study aims to promote a wide use of this type of model in support of EBFM.
