Integrated analysis has increasingly been the preferred approach for conducting stock assessments and providing the basis for management advice for fish and invertebrate stocks around the world. Many decisions are required when developing integrated stock assessments. For example, the analyst needs to decide whether the model fits the data, if the optimization was successful, if estimates are consistent retrospectively, and if the model is suitable to predict future stock responses to fishing. This study provides practical guidelines for implementing selected diagnostic tools that can assist analysts in identifying problems with model specifications and alternatives that can be explored to minimize or eliminate such problems. Emphasis is placed on reviewing the implementation and interpretation of contemporary model diagnostic tools. We first describe each diagnostic approach and its utility. We then proceed by providing a “cookbook recipe” on how to implement each of the diagnostics, together with an interpretation of the results, using two worked examples of integrated stock assessments with Stock Synthesis. Further, we provide a conceptual flow chart that lays out a generic process of model development and selection using the presented model diagnostics. Based on this, we propose the following four properties as objective criteria for evaluating the plausibility of a model: (1) model convergence, (2) fit to the data, (3) model consistency, and (4) prediction skill. It would greatly benefit the stock assessment community if the next generation of stock assessment models could include the diagnostic tests presented in this study as a set of open source tools.

综合分析越来越成为进行种群评估并为世界各地鱼类和无脊椎动物种群的管理建议提供依据的首选方法。在进行综合库存评估时，需要做出许多决定。例如，分析人员需要确定模型是否适合数据，优化是否成功，估计是否追溯一致以及模型是否适合预测未来对捕捞的反应。这项研究为实施选定的诊断工具提供了实用指南，这些诊断工具可以帮助分析人员识别模型规格问题以及可以探索以最小化或消除此类问题的替代方法。重点放在审查现代模型诊断工具的实现和解释上。我们首先描述每种诊断方法及其效用。然后，我们使用两个带有股票综合功能的综合股票评估的工作示例，提供有关如何执行每种诊断的“菜谱食谱”以及对结果的解释。此外，我们提供了一个概念流程图，其中列出了使用提出的模型诊断程序进行模型开发和选择的一般过程。基于此，我们提出以下四个属性作为评估模型合理性的客观标准：（1）模型收敛，（2）拟合数据，（3）模型一致性和（4）预测技巧。如果下一代的股票评估模型可以将本研究中介绍的诊断测试作为一组开源工具使用，则将对股票评估社区大有裨益。