It is challenging in fisheries stock assessment to estimate cohort dynamics from length-based data for hard-to-age stocks, and existing approaches, for example, age-structured catch-at-length models (ACL) are unable to account for length-dependent processes within each cohort. Fisheries-dependent data are usually considered the default input to stock assessment models. However, with widespread recognition of the uncertainty of fisheries-dependent data and the increasing availability of high-quality survey data, a new situation emerges in some fisheries where a stock assessment model based only on survey data can provide good estimation of population dynamics. We develop an age- and length-structured statistical catch-at-length model (ALSCL) to estimate age-based dynamics from survey catch-at-length data. This approach also provides a good basis to then integrate fisheries-dependent data in the model. ALSCL can explicitly include length-dependent mortality and growth within each cohort by simultaneously tracking the three-dimensional dynamics across time, age, and length. We first use simulations of yellowtail flounder (Limanda ferruginea, Pleuronectidae) and bigeye tuna (Thunnus Obesus, Scombridae) to demonstrate that ALSCL outperforms ACL by providing more accurate estimates of age-based population dynamics when length-dependent processes are important. Next, we apply ALSCL to estimate the cohort dynamics of female yellowtail flounder on the Grand Bank off Newfoundland using survey catch-at-length, weight-at-length, and maturity-at-length data. We consider ALSCL as a hybrid between ACL and length-structured stock assessment models that keeps the advantages of both, and its ability to simultaneously track age and length dynamics is an important step toward the next-generation of fisheries stock assessment models.

中文翻译：

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难以达到年龄的渔业种群的年龄和长度结构统计渔获量模型

在渔业资源评估中，很难从基于长度的数据中估计难以达到年龄的种群的队列动态，而现有的方法，例如年龄结构的长期捕捞模型（ACL）无法解释长度-每个队列中的相关过程。渔业相关数据通常被认为是种群评估模型的默认输入。然而，随着对渔业相关数据的不确定性的广泛认识以及高质量调查数据的日益普及，一些渔业出现了一种新情况，即仅基于调查数据的种群评估模型可以很好地估计种群动态。我们开发了一个年龄和长度结构的统计长度模型 (ALSCL)，以从调查长度数据中估计基于年龄的动态。这种方法还为将渔业相关数据整合到模型中提供了良好的基础。通过同时跟踪跨时间、年龄和长度的三维动态，ALSCL 可以明确地包括每个队列中与长度相关的死亡率和增长。我们首先使用黄尾比目鱼的模拟（Limanda ferruginea , Pleuronectidae) 和大眼金枪鱼 ( Thunnus Obesus , Scombridae) 证明 ALSCL 在与长度相关的过程很重要时通过提供更准确的基于年龄的种群动态估计来优于 ACL。接下来，我们应用 ALSCL 使用调查捕获长度、长度重量和长度成熟度数据来估计纽芬兰附近大银行的雌性黄尾比目鱼的队列动态。我们认为 ALSCL 是 ACL 和长度结构种群评估模型之间的混合体，保留了两者的优势，其同时跟踪年龄和长度动态的能力是迈向下一代渔业种群评估模型的重要一步。