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作 者:侯刚[1,2] 刘金殿[3,4] 冯波[1,2] 颜云榕[1,2] 卢伙胜[1,2]
机构地区:[1]广东海洋大学水产学院,广东湛江524025 [2]南海渔业资源监测与评估中心,广东湛江524025 [3]浙江省淡水水产研究所,浙江湖州313001 [4]中国水产科学院东海水产研究所浙江研究中心,浙江湖州313001
出 处:《应用生态学报》2014年第3期843-849,共7页Chinese Journal of Applied Ecology
基 金:国家自然科学基金项目(30771653);国家科技支撑计划项目(SQ2012BAY307301);农业部南海渔业资源专项调查项目(2008-2010)资助
摘 要:生长参数是渔业资源评估和管理策略中的关键参数,因而对目标鱼种选择合适的生长模型至关重要.本文以北部湾多齿蛇鲻为例,采用2006年12月至2009年7逐月采集的体长与年龄鉴定数据(n=2046),运用5个候选生长模型,利用最大似然法在加性误差条件下估算生长参数,并通过模型近似解释率(R2adj)、根平均方差(RMSE)、赤井信息准则(AIC)和贝叶斯信息准则(BIC)检验模型拟合度.结果表明:在当前大样本的情况下,4种统计方法在模型拟合度排序上表现一致;多模型推论检验结果表明,Generalized VBGF获得足够的模型支持,并占到AIC权重的95.9%,可以独立描述多齿蛇鲻的体长与年龄的生长关系,生长方程为:Lt=578.49[1-e-0.051(t-0.14)]0.361.Age and growth parameters are key parameters in fish stock assessment and management strategies, thus it is crucial to choose an appropriate growth model for a target species. In this study, five growth models were set to fit the length-age data of greater lizard fish Saurida tumbil ( n = 2046) collected monthly from December 2006 to July 2009 in the Beibu Gulf, South China Sea. The pa- rameters for each model were estimated using the maximum likelihood method under the assumption of the additive error structure. Adjusted coefficient of determination 2 (R^2sdj ) , root mean squared error ( RMSE), Akaike' s information criterion ( AIC), and Bayesian information criterion (BIC) were calculated for each model for fitness selection. The results indicated that the four statistical approaches were consistent in selection of the best growth model. The MMI approach indicated that the generalized VBGF was strongly verified and made up 95.9% of the AIC weight, indicating that this function fitted the length-age data of the greater lizard fish well. The growth function was L, = 578.49 [ 1 -e^-0.051 (t-0.14) ] 0. 361.
关 键 词:多齿蛇鲻 模型选择 多模型理论AIC权重 北部湾
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