Estimating the age composition of a fish population is a critical first step in all stock assessments that apply age-structured models. Often this is done through the use of an Age Length Key (ALK), which links a subsample of fish that have had their ages determined to those that have only had their lengths measured in order to obtain an estimate of the age structure of the entire sample. ALKs can suffer from data gaps and sampling artifacts and are limited in both how they can reflect spatial variability and how spatial information can be incorporated.

We propose a novel spatial ALK model that uses an approximation of a Gaussian Field and has the ability to account for physical barriers (e.g. islands, coastlines) in the study area. Our approach is compared with a previously suggested spatial ALK model as well as non-spatial approaches using both real and simulated survey data. We find that spatial ALK approaches reduce errors in stratified estimates of abundance at age over non-spatial approaches and that incorporating physical barriers can deliver more realistic results.

在采用年龄结构模型的所有种群评估中，估算鱼类种群的年龄组成是关键的第一步。通常，这是通过使用年龄长度键（ALK）来完成的，该键将确定了年龄的鱼的子样本与仅测量了长度的鱼的子样本链接在一起，以便获得整个鱼的年龄结构的估计值。样本。ALK可能会遭受数据缺口和采样伪像的困扰，并且在它们如何反映空间可变性以及如何合并空间信息方面都受到限制。

我们提出了一种新颖的空间ALK模型，该模型使用了高斯场的近似值，并且能够解决研究区域中的物理障碍（例如，岛屿，海岸线）。我们的方法与先前建议的空间ALK模型以及使用实际和模拟调查数据的非空间方法进行了比较。我们发现，与非空间方法相比，空间ALK方法可减少年龄层次的分层估计中的误差，并且引入物理障碍可以提供更实际的结果。