Sea urchin (Loxechinus albus) is one of the most important benthic resource in Chile. Due to their large-scale spatial metapopulation structure, sea urchin subpopulations are interconnected by larval dispersion, so the recovery of local abundance depends on the distance and hydrodynamic characteristics of their spatial domain. Currently, this resource is evaluated with classical stock assessment models, using standardized catch per unit effort (an index of relative abundance) as a key piece of information to determine catch quotas and achieve sustainability. However, these estimates assume hyperstability for the total population, ignoring spatial dependence among fishing sites, which is a fundamental concept for populations structured as metapopulation. We develop a Bayesian catch standardization model with explicit spatial dependence to better address the structure of this population. The proposed model performs statistically better compared to a model without spatial dependence, based on leave-one-out cross-validation, and predictive distributions also show that parameter estimation is consistent with the data. We argue that incorporating spatial structure improves the estimated relative abundance index in a population structured as a metapopulation. Our improved index of abundance will lead to better assessments and management advice, thus improving the sustainability of the stock.

海胆（Loxechinus黄鳝）是智利最重要的底栖资源之一。由于海胆的大规模空间种群结构，它们通过幼虫分散而相互联系，因此局部丰度的恢复取决于其空间域的距离和水动力特征。当前，该资源已通过经典的种群评估模型进行了评估，使用标准的每单位工作量捕获量（相对丰度指数）作为确定捕获量配额和实现可持续性的关键信息。但是，这些估计值假设总人口具有高度稳定性，而忽略了捕鱼地点之间的空间依赖性，而这是构成人口结构的人口的基本概念。我们开发了具有明确空间依赖性的贝叶斯渔获标准化模型，以更好地解决该种群的结构。与基于空间的模型相比，基于留一法式交叉验证，该模型的统计性能要好于无空间依赖的模型，并且预测性分布还表明参数估计与数据一致。我们认为，合并空间结构可改善以人口结构构成的人口中的估计相对丰度指数。我们提高的丰度指数将带来更好的评估和管理建议，从而提高存量的可持续性。预测分布也表明参数估计与数据一致。我们认为，合并空间结构可改善以人口结构构成的人口中的估计相对丰度指数。我们提高的丰度指数将带来更好的评估和管理建议，从而提高存量的可持续性。预测分布也表明参数估计与数据一致。我们认为，合并空间结构可改善以人口结构构成的人口中的估计相对丰度指数。我们提高的丰度指数将带来更好的评估和管理建议，从而提高存量的可持续性。