Changing ecosystem conditions present a challenge for the monitoring and management of living marine resources, where decisions often require lead-times of weeks to months. Consistent improvement in the skill of regional ocean models to predict physical ocean states at seasonal time scales provides opportunities to forecast biological responses to changing ecosystem conditions that impact fishery management practices. In this study, we used 8-month lead-time predictions of temperature at 250 m depth from the J-SCOPE regional ocean model, along with stationary habitat conditions (e.g., distance to shelf break), to forecast Pacific hake (Merluccius productus) distribution in the northern California Current Ecosystem (CCE). Using retrospective skill assessments, we found strong agreement between hake distribution forecasts and historical observations. The top performing models [based on out-of-sample skill assessments using the area-under-the-curve (AUC) skill metric] were a generalized additive model (GAM) that included shelf-break distance (i.e., distance to the 200 m isobath) (AUC = 0.813) and a boosted regression tree (BRT) that included temperature at 250 m depth and shelf-break distance (AUC = 0.830). An ensemble forecast of the top performing GAM and BRT models only improved out-of-sample forecast skill slightly (AUC = 0.838) due to strongly correlated forecast errors between models (r = 0.88). Collectively, our results demonstrate that seasonal lead-time ocean predictions have predictive skill for important ecological processes in the northern CCE and can be used to provide early detection of impending distribution shifts of ecologically and economically important marine species.

中文翻译：

---

太平洋无须鳕分布的环境驱动的季节性预测

不断变化的生态系统条件对海洋生物资源的监测和管理提出了挑战，其中的决策通常需要数周到数月的提前期。区域海洋模型在季节性时间尺度上预测物理海洋状态的技能的持续改进为预测对影响渔业管理实践的不断变化的生态系统条件的生物反应提供了机会。在这项研究中，我们使用了 J-SCOPE 区域海洋模型对 250 m 深度温度的 8 个月提前期预测，以及静止栖息地条件（例如，到架子断裂的距离），来预测太平洋无须鳕（Merluccius productus）北加州洋流生态系统 (CCE) 的分布。使用回顾性技能评估，我们发现鳕鱼分布预测与历史观察之间存在很强的一致性。表现最好的模型 [基于使用曲线下面积 (AUC) 技能指标的样本外技能评估] 是广义加性模型 (GAM)，其中包括货架断裂距离（即到 200 m 等深线) (AUC = 0.813) 和包含 250 m 深度温度和货架断裂距离 (AUC = 0.830) 的增强回归树 (BRT)。由于模型之间的预测误差强相关（r = 0.88），对表现最佳的 GAM 和 BRT 模型的集合预测仅略微提高了样本外预测技能（AUC = 0.838）。总的来说，我们的结果表明，季节性提前期海洋预测对 CCE 北部的重要生态过程具有预测能力，可用于提供对生态和经济上重要的海洋物种即将发生的分布变化的早期检测。