Growth in fishes is usually modelled by a function encapsulating a common growth mechanism across ages. However, several theoretical works suggest growth may comprise two distinct mechanistic phases arising from changes in reproductive investment, diet, or habitat. These models are termed two-state or biphasic, where acceleration in growth typically changes around some transition age. Such biphasic models have already been successfully applied in elasmobranch species, where such transitions are detectable from length-at-age data alone, but where estimation has assumed normally distributed errors, which is inappropriate for such slow-growing and long-lived fishes. Using recent advances in growth parameter estimation, we implement a biphasic growth model with asymmetric and heavy-tailed errors. We use data from six datasets, encompassing four species of elasmobranchs, to compare the performance of the von Bertalanffy and biphasic models under normal, skew-normal, and Student-t error distributions. Conditional expectation maximization estimation proves both effective and efficient in this context. Most datasets analysed here supported asymmetric and heavy-tailed errors and biphasic growth, producing parameter estimates different from previous studies.

鱼类的生长通常由封装了不同年龄的共同生长机制的函数建模。然而，一些理论工作表明，增长可能包括由生殖投资、饮食或栖息地的变化引起的两个不同的机械阶段。这些模型被称为两态或双相，其中增长的加速通常在某个过渡时期发生变化。这样的双相模型已经成功地应用于 elasmobranch 物种，其中这种转变可以仅从年龄数据中检测到，但估计假定了正态分布误差，这不适合这种生长缓慢和寿命长的鱼类。利用生长参数估计的最新进展，我们实现了具有非对称误差和重尾误差的双相增长模型。我们使用来自六个数据集的数据，t误差分布。在这种情况下，条件期望最大化估计被证明是有效的。这里分析的大多数数据集都支持不对称和重尾误差和双相增长，产生与以前研究不同的参数估计。