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
Rising heat stress due to climate warming poses a significant threat to human health,and greenness offers a nature-based solution to mitigate heatrelated health impacts and enhance resilience.Although global greenness...Rising heat stress due to climate warming poses a significant threat to human health,and greenness offers a nature-based solution to mitigate heatrelated health impacts and enhance resilience.Although global greenness has increased,it remains unclear whether these trends align with the population’s heat mitigation needs.In this study,we integrated spatially resolved demographic data with satellite-derived greenness metric and reanalysisbased heat stress data to construct a global profile of joint exposure at 131 kmresolution from 2000 to 2022.We found that 69.3%of global populated areas and 41.3%of the global population(~2.9 billion people)were exposed to increasing heat stress but decreasing greenness(IHDG),representing the most concerning situation for heat mitigation.Urban populations were disproportionately affected,with 50.8%exposed compared to 27.1%in rural areas.Low-and middle-income countries exhibited more pronounced trends of increasing heat stress and bore the greatest burden from IHDG,accounting for 85%of total exposed populations.Moreover,there was a notable demographic shift in IHDG-exposed populations toward older groups,exacerbating the heat mitigation crisis.This study advances the understanding of the joint dynamics of heat stress and greenness and provides a profile of population exposure at a fine grid level.By highlighting the scale of IHDG conditions,our findings emphasize the urgent need to address this environmental challenge and a significant opportunity for improving greenness to mitigate increasing heat globally.The spatially detailed assessment maps offer essential data for informed decision-making.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supported by the Australian Research Council(DP210102076)the Australian National Health and Medical Research Council(NHMRC.GNT2000581)+5 种基金the Leader Fellowship(GNT2008813)of the Australian National Health and Medical Research CouncilSL by an Emerging Leader Fellowship of the Australian National Health and Medical Research Council(GNT2009866)the China Scholarship Council(grant nos.201906320051,202006010044,202006010043,and 202006380055,respectively)by Monash Faculty of Medicine,Nursing and Health Sciences(FMNHS)Early Career Postdoctoral Fellowships 2023by an NHMRC e-Asia Joint Research Program Grant(GNT2000581)a Monash Graduate Scholarship and Monash International Tuition Scholarship.
文摘Rising heat stress due to climate warming poses a significant threat to human health,and greenness offers a nature-based solution to mitigate heatrelated health impacts and enhance resilience.Although global greenness has increased,it remains unclear whether these trends align with the population’s heat mitigation needs.In this study,we integrated spatially resolved demographic data with satellite-derived greenness metric and reanalysisbased heat stress data to construct a global profile of joint exposure at 131 kmresolution from 2000 to 2022.We found that 69.3%of global populated areas and 41.3%of the global population(~2.9 billion people)were exposed to increasing heat stress but decreasing greenness(IHDG),representing the most concerning situation for heat mitigation.Urban populations were disproportionately affected,with 50.8%exposed compared to 27.1%in rural areas.Low-and middle-income countries exhibited more pronounced trends of increasing heat stress and bore the greatest burden from IHDG,accounting for 85%of total exposed populations.Moreover,there was a notable demographic shift in IHDG-exposed populations toward older groups,exacerbating the heat mitigation crisis.This study advances the understanding of the joint dynamics of heat stress and greenness and provides a profile of population exposure at a fine grid level.By highlighting the scale of IHDG conditions,our findings emphasize the urgent need to address this environmental challenge and a significant opportunity for improving greenness to mitigate increasing heat globally.The spatially detailed assessment maps offer essential data for informed decision-making.
