Forecasting the recruitment of fish populations with skill has been a challenge in fisheries for over a century. Previous large‐scale meta‐analyses have suggested linkages between environmental or ecosystem drivers and recruitment; however, applying this information in a management setting remains underutilized. Here, we use a well‐studied database of groundfish assessments from the West Coast of the USA to ask whether environmental variables or ecosystem indicators derived from long‐term monitoring datasets offer an improvement in our ability to skilfully forecast fish recruitment. A secondary question is which types of modelling approaches (ranging from linear models to non‐parametric methods) yield the best forecast skill. Third, we examine whether simultaneous forecasting of multiple species offers an advantage over generating species‐specific forecasts. We find that for approximately one third of the 29 assessed stocks, ecosystem indicators from juvenile surveys yields the highest out of sample predictive skill compared to other covariates (including environmental variables from Regional Ocean Modeling System output) or null models. Across modelling approaches, our results suggest that simpler linear modelling approaches do as well or better than more complicated approaches (reducing out of sample Root Mean Square Error by ~40% compared to null models), and that there appears to be little benefit to performing multispecies forecasts instead of single‐species forecasts. Our results provide a general framework for generating recruitment forecasts in other species and ecosystems, as well as a benchmark for future analyses to evaluate skill. The most promising applications are likely for species that are short lived, have relatively high recruitment variability, and moderate amounts of age or length data. Forecasts using our approach may be useful in identifying covariates or mechanisms to include in operational assessments but also provide qualitative advice to managers implementing ecosystem based fisheries management.

中文翻译：

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利用生态指标改善鱼类补充的短期预测


一个多世纪以来，熟练地预测鱼类种群的补充量一直是渔业面临的挑战。之前的大规模荟萃分析表明环境或生态系统驱动因素与招聘之间存在联系；然而，在管理环境中应用这些信息仍然没有得到充分利用。在这里，我们使用来自美国西海岸的经过充分研究的底栖鱼类评估数据库来询问来自长期监测数据集的环境变量或生态系统指标是否可以提高我们巧妙地预测鱼类补充的能力。第二个问题是哪种类型的建模方法（从线性模型到非参数方法）可以产生最佳的预测技能。第三，我们研究同时预测多个物种是否比生成特定物种的预测更具优势。我们发现，对于 29 种评估种群中的大约三分之一，与其他协变量（包括区域海洋建模系统输出的环境变量）或零模型相比，来自幼年调查的生态系统指标产生了最高的样本预测技能。在建模方法中，我们的结果表明，更简单的线性建模方法比更复杂的方法效果更好，甚至更好（与零模型相比，样本均方根误差减少约 40%），并且执行似乎没有什么好处多物种预测而不是单一物种预测。我们的结果为在其他物种和生态系统中生成补充预测提供了一个总体框架，并为未来评估技能的分析提供了基准。 最有前途的应用可能是寿命短、招募变异性相对较高以及年龄或长度数据适中的物种。使用我们的方法进行的预测可能有助于确定纳入运营评估的协变量或机制，而且还可以为实施基于生态系统的渔业管理的管理者提供定性建议。