Despite their growing socio-economic importance globally, relatively little is understood about how crustacean stocks are assessed, which has potential to compromise fishery sustainability, especially under heavy exploitation and environmental changes. To inform stock assessment model application for emergent fisheries, we evaluated model use for crustacean stocks available in the RAM Legacy Database (RAMLDB) and the evolution of model use for four case-study fisheries, emphasizing the relationship between data availability and model complexity. Differences in model use between FAO fishing regions and crustacean species sub-groups were identified. Only 60.9% of crustacean stocks in the RAMLDB identified the model used for assessment. For the remaining stocks, we collected ancillary data to fill the information gaps, amounting to 92.5% of crustacean stocks in RAMLDB. Of these, model complexity varied from count-based to environmentally explicit statistical catch-at-length methods, but tended to be data intensive, likely due to biases towards regions with more developed fishery management programmes. Furthermore, regional comparisons indicated that crustaceans are only well-assessed in a few geographical hotspots. The progression of model use over time was inconsistent between case-study fisheries, being driven by myriad factors including data availability, confidence in biological processes and ecological considerations. Our findings can be used as a resource to help inform model choice for fisheries management. Towards the goal of seeking global best practices for crustacean stock assessments, future work should address knowledge gaps in regional stock assessment model use and conduct comparative studies to evaluate stock-specific costs and benefits relating to model complexity.

中文翻译：

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全球甲壳类动物种群评估建模：协调可用数据和复杂性

尽管甲壳类动物在全球的社会经济重要性日益增加，但人们对如何评估甲壳类种群知之甚少，这有可能损害渔业的可持续性，尤其是在大量开采和环境变化的情况下。为了为新兴渔业的种群评估模型应用提供信息，我们评估了 RAM 遗留数据库 (RAMLDB) 中可用的甲壳类种群的模型使用以及四个案例研究渔业模型使用的演变，强调了数据可用性和模型复杂性之间的关系。确定了粮农组织捕捞区和甲壳类物种亚组之间模型使用的差异。RAMLDB 中只有 60.9% 的甲壳类动物种群确定了用于评估的模型。对于剩余的股票，我们收集了辅助数据以填补信息空白，共计 92 个。RAMLDB 中 5% 的甲壳类动物种群。其中，模型的复杂性从基于计数到环境明确的统计捕捞方法各不相同，但往往是数据密集型的，这可能是由于偏向于渔业管理计划更发达的地区。此外，区域比较表明，甲壳类动物仅在少数几个地理热点地区得到充分评估。案例研究渔业之间模型使用随时间的进展是不一致的，这是由包括数据可用性、对生物过程的信心和生态考虑在内的无数因素驱动的。我们的研究结果可以作为一种资源，帮助为渔业管理的模型选择提供信息。为实现寻求甲壳类动物种群评估全球最佳实践的目标，