Application of a Bayesian method to data-poor stock assessment by using Indian Ocean albacore (Thunnus alalunga) stock assessment as an example

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• 作者：Wenjiang Guan ; Lin Tang ; Jiangfeng Zhu ; Siquan Tian…
• 刊名：Acta Oceanologica Sinica
• 出版年：2016
• 出版时间：February 2016
• 年：2016
• 卷：35
• 期：2
• 页码：117-125
• 全文大小：382 KB
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• 作者单位：Wenjiang Guan (1) (2)
  Lin Tang (1)
  Jiangfeng Zhu (1) (2)
  Siquan Tian (1) (2)
  Liuxiong Xu (1) (2)
  
  1. College of Marine Sciences, Shanghai Ocean University, Shanghai, 201306, China
  2. Key Laboratory of Sustainable Exploitation of Oceanic Fisheries Resources (Shanghai Ocean University), Ministry of Education, Shanghai, 201306, China
  
• 刊物主题：Oceanography; Climatology; Ecology; Engineering Fluid Dynamics; Marine & Freshwater Sciences; Environmental Chemistry;
• 出版者：Springer Berlin Heidelberg
• ISSN：1869-1099

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.