Canadian Journal of Fisheries and Aquatic Sciences, Ahead of Print.
Accurate forecasts of sockeye salmon (Oncorhynchus nerka) in Bristol Bay, Alaska, play an important role in management and harvesting decisions for this culturally and ecologically vital species. We used a suite of parametric and nonparametric models to assess the frontiers in forecast accuracy of Bristol Bay sockeye salmon possible given currently available data. In retrospective performance testing individual models were capable of reducing pre-season forecast error at the river system level by on average 15% relative to a benchmark model. We used an ensemble modeling approach to produce pre-season forecasts based on historical performance of individual models. This ensemble model reduced river system forecast error by 13% on average in 5 of the 7 evaluated river systems, though it increased forecast error by 39% on average in the remaining 2 systems. We found potential for modest improvements in forecast accuracy across a variety of scales. However, all tested models failed to accurately predict certain periods in the historical salmon return patterns, indicating that further forecast improvements likely depend on novel data rather than more flexible models.

中文翻译：

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使用参数和非参数模型改进红鲑鱼 (Oncorhynchus nerka) 的预测

加拿大渔业和水生科学杂志，印刷前。
对阿拉斯加布里斯托尔湾红鲑 (Oncorhynchus nerka) 的准确预测，在这一文化和生态重要物种的管理和捕捞决策中发挥着重要作用。我们使用了一套参数和非参数模型来评估布里斯托尔湾红鲑可能在当前可用数据下预测准确性的前沿。在回顾性性能测试中，相对于基准模型，单个模型能够将河流系统层面的季前预报误差平均降低 15%。我们使用集成建模方法根据单个模型的历史性能生成季前预测。该集合模型在 7 个评估的河流系统中的 5 个中平均减少了 13% 的河流系统预测误差，尽管它在其余 2 个系统中平均增加了 39% 的预测误差。我们发现在各种规模的预测准确性方面有适度提高的潜力。然而，所有测试模型都未能准确预测历史鲑鱼回归模式的某些时期，这表明进一步的预测改进可能取决于新数据，而不是更灵活的模型。