Re-description of the growth pattern of four decapod species by information theory

The growth pattern of four commercial decapod crustacean species (Arenaeus cribrarius, Ucides cordatus, Palaemon longirostris and Plesionika izumiae) was reanalyzed using published data. Six candidate growth models with asymptotic and non-asymptotic characteristics were tested. The best model was determined by information theory, according to Bayesian (BIC) and Akaike (AICc) information criteria and the weight of evidence in favour of model i (Wi). For U. cordatus, P. izumiae and the females of A. cribrarius, the best growth model corresponded to case 1 of Schnute model. In male A. cribrarius, the highest Wi was observed for the case 1 of Schnute model according to both criteria, but seasonal models were also plausible to describe growth. In P. longirostris, discrepancies were observed between criteria. The BIC supported the case 1 of Schnute in females, but the AICc did not identify a winner model (Wi > 90%); the case 1 and case 4 of Schnute displayed the highest Wi. The males that exhibited the highest Wi values were Schnute case 4 > Schnute case 3 > von Bertalanffy > Gompertz > Logistic. This study highlights the importance of considering different assumptions in growth patterns of the species and does not impose any a priori mathematical framework to available data. Abbreviations: AIC: Akaike Information Criterion. BIC: Bayesian Information Criterion Impact StatementThe multi-model approach improves the model selection based on information criteria to re-describe patterns of growth in some decapod crustaceans.The von Bertalanffy growth model is not appropriate to describe the growth pattern based on the available data.The growth patterns found were asymptotic and not asymptotic, described mainly through the Schnute model. The multi-model approach improves the model selection based on information criteria to re-describe patterns of growth in some decapod crustaceans. The von Bertalanffy growth model is not appropriate to describe the growth pattern based on the available data. The growth patterns found were asymptotic and not asymptotic, described mainly through the Schnute model.本研究利用已发表数据，对4种商业十足目甲壳动物（decapod crustacean）——团扇黄道蟹（Arenaeus cribrarius）、红树林相手蟹（Ucides cordatus）、长臂长臂虾（Palaemon longirostris）及伊豆拟对虾（Plesionika izumiae）的生长模式进行了重新分析。研究测试了6种兼具渐近与非渐近特征的候选生长模型，基于信息论方法，结合贝叶斯信息准则（BIC, Bayesian Information Criterion）、校正赤池信息准则（AICc, Akaike Information Criterion）以及模型i的证据权重（Wi）确定最优模型。 对于红树林相手蟹、伊豆拟对虾以及团扇黄道蟹的雌性个体，最优生长模型为Schnute模型（Schnute model）的案例1。团扇黄道蟹的雄性个体中，两种准则均显示Schnute模型案例1的证据权重最高，但季节生长模型同样可合理描述其生长过程。在长臂长臂虾中，不同准则的结果存在分歧：BIC支持雌性个体采用Schnute模型案例1，但AICc未识别出证据权重超过90%的最优模型，Schnute模型的案例1与案例4具有最高的证据权重。长臂长臂虾雄性个体的证据权重排序为：Schnute模型案例4 > Schnute模型案例3 > 冯·贝塔朗菲模型（von Bertalanffy） > 戈姆佩尔茨模型（Gompertz） > 逻辑斯蒂模型（Logistic）。 本研究强调了在物种生长模式分析中考虑不同假设的重要性，并未对现有数据强加任何先验数学框架。 缩写注释：AIC为赤池信息准则（Akaike Information Criterion），BIC为贝叶斯信息准则（Bayesian Information Criterion）。 影响声明 多模型方法优化了基于信息准则的模型选择流程，可用于重新阐释部分十足目甲壳动物的生长模式。基于现有数据，冯·贝塔朗菲生长模型并不适用于描述本研究涉及物种的生长模式。本研究发现的生长模式包含渐近型与非渐近型两类，主要通过Schnute模型进行描述。多模型方法优化了基于信息准则的模型选择流程，可用于重新阐释部分十足目甲壳动物的生长模式。基于现有数据，冯·贝塔朗菲生长模型并不适用于描述本研究涉及物种的生长模式。本研究发现的生长模式包含渐近型与非渐近型两类，主要通过Schnute模型进行描述。