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

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.

A continuous time delay-difference type model(CTDDM) applied to stock assessment of the southern Atlantic albacore Thunnus alalunga

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.

北太平洋长鳍金枪鱼的垂直分布及其与环境因子的关系

长鳍金枪鱼(Thunnus alalunga)是一种高度洄游的远洋鱼类,是金枪鱼渔业的主捕种类之一。探明长鳍金枪鱼的栖息水层是提高其捕捞效率的重要前提。根据2018年9—12月我国金枪鱼观察员在北太平洋公海收集的长鳍金枪鱼渔业调查数据和海洋环境数据,并使用单因素栖息地指数分析了长鳍金枪鱼的垂直分布与环境因子的关系。结果发现:垂直分布结构研究表明,长鳍金枪鱼最适水层为74.2~113.7 m。单因素栖息地指数分析表明,北太平洋海域长鳍金枪鱼最适海水表面温度(SST)为20℃;垂直方向上,最适水温、盐度、叶绿素浓度范围分别为12~14℃、34.4~34.6和0.2~0.4μg/L。研究结果可为北太平洋长鳍金枪鱼的延绳钓渔业提供基础数据。