Geophysical Institute, University of Bergen, 5007 Bergen, Norway.
Bjerknes Centre for Climate Research, 5020 Bergen, Norway.
PLoS One. 2018 Oct 24;13(10):e0206319. doi: 10.1371/journal.pone.0206319. eCollection 2018.
Predicting fish stock variations on interannual to decadal time scales is one of the major issues in fisheries science and management. Although the field of marine ecological predictions is still in its infancy, it is understood that a major source of multi-year predictability resides in the ocean. Here we show the first highly skilful long-term predictions of the commercially valuable Barents Sea cod stock. The 7-year predictions are based on the propagation of ocean temperature anomalies from the subpolar North Atlantic toward the Barents Sea, and the strong co-variability between these temperature anomalies and the cod stock. Retrospective predictions for the period 1957-2017 capture well multi-year to decadal variations in cod stock biomass, with cross-validated explained variance of over 60%. For lead times longer than one year the statistical long-term predictions show more skill than operational short-term predictions used in fisheries management and lagged persistence forecasts. Our results thus demonstrate the potential for ecosystem-based fisheries management, which could enable strategic planning on longer time scales. Future predictions show a gradual decline in the cod stock towards 2024.
预测鱼类种群在年际到十年际时间尺度上的变化是渔业科学和管理的主要问题之一。尽管海洋生态预测领域仍处于起步阶段，但人们已经认识到，多年预测的一个主要来源在于海洋。在这里，我们展示了对商业价值极高的巴伦支海鳕鱼种群的首次高度熟练的长期预测。这些 7 年的预测基于从亚极地北大西洋向巴伦支海传播的海洋温度异常，以及这些温度异常与鳕鱼种群之间的强共变关系。对 1957-2017 年期间的回溯预测很好地捕捉到了鳕鱼种群生物量的多年到十年际变化，交叉验证的解释方差超过 60%。对于一年以上的提前期，统计长期预测显示出比渔业管理中使用的业务短期预测和滞后持续预测更高的技能。因此，我们的研究结果表明了基于生态系统的渔业管理的潜力，这可以使战略规划在更长的时间范围内进行。未来的预测显示，鳕鱼种群在 2024 年之前逐渐减少。
