In size-based stock assessment models, the stochastic growth of individuals is expressed through a transition matrix representing the growth variability as probability of shift from one length class to another during a time period. This process is important because it describes the changes in population size structure brought about by the increase in length or weight of organisms over time. In this study, four stochastic growth matrices were developed within an integrated catch-at-length assessment model (ICLAM), and the changes in the population dynamics and quantities relevant to fishery management of Panopea globosa were analyzed. The growth increments were estimated through von Bertalanffy, Gompertz, Logistic, and Schnute models using a gamma probabilistic density function. A corrected Akaike information criterion was used to select the best performance among the four ICLAMs. The ICLAM associated with the von Bertalanffy matrix was the best for describing the catch-at-length data, providing conservative estimates on the condition of the stock; while the ICLAM for the Logistic matrix showed low performance, exhibiting the highest estimates for all the components of population dynamics, such as an increase of 75.3% in the recruitment and 58.5% in the total abundance with regard to the von Bertalanffy matrix. These differences emphasize the importance of determining a suitable stochastic growth matrix because it has serious implications in biasing stock assessments, resulting in inadequate management strategies.

在基于规模的股票评估模型中，个体的随机增长通过过渡矩阵表示，该矩阵将增长变异性表示为一段时间内从一种长度类别转换为另一种长度类别的概率。这个过程很重要，因为它描述了随着时间的推移，生物体长度或重量的增加所带来的种群规模结构的变化。在这项研究中，在一个综合的长距离捕捞评估模型（ICLAM）中，开发了四个随机生长矩阵，并且种群动态和数量的变化与Panopea globosa的渔业管理有关被分析。使用γ概率密度函数通过von Bertalanffy，Gompertz，Logistic和Schnute模型估算了增长增量。使用校正后的Akaike信息标准在四个ICLAM中选择最佳性能。与von Bertalanffy矩阵相关的ICLAM最适合描述长距离捕获数据，提供了对种群状况的保守估计。而Logistic矩阵的ICLAM表现不佳，对人口动态的所有组成部分都表现出最高的估计值，例如，与von Bertalanffy矩阵相比，招聘人数增加了75.3％，总人数增加了58.5％。