Forecasting nocturnal bird migration for dynamic aeroconservation: the value of short-term dataset

Placing wind turbines within large migration flyways, such as the North Sea basin, can contribute to the decline of vulnerable migratory bird populations by increasing mortality through collisions. Curtailment of wind turbines limited to short periods with intense migration can minimize these negative impacts, and near-term bird migration forecasts can inform such decisions. Although near-term forecasts are usually created with long-term datasets, the pace of environmental alteration due to wind energy calls for urgent development of conservation measures that rely on existing data, even when it does not have long temporal coverage. Here, we use five years of tracking bird radar data collected off the western Dutch coast, weather, and phenological variables to develop seasonal near-term forecasts of low-altitude nocturnal bird migration over the southern North Sea. Overall, the models explained 71% of the variance and correctly predicted migration intensity above or below a threshold for intense hourly migration in more than 80% of hours in both seasons. However, the percentage of correctly predicted intense migration hours (top 5% of hours with the most intense migration) was low, likely due to the short-term dataset and their rare occurrence. We, therefore, advise careful consideration of a curtailment threshold to achieve optimal results. Synthesis and applications: Near-term forecasts of migration fluxes evaluated against measurements can be used to define curtailment thresholds for offshore wind energy. We show that to minimize collision risk for 50% of migrants, if predicted correctly, curtailments should be applied during 18 hours in spring and 26 in autumn in the focal year of model assessments, resulting in an estimated annual wind energy loss of 0.12%. Drawing from the Dutch curtailment framework, which pioneered the 'international first' offshore curtailment, we argue that using forecasts developed from limited temporal datasets alongside expert insight and data-driven policies can expedite conservation efforts in a rapidly changing world. This approach is particularly valuable in light of increasing interannual variability in weather conditions.

在大型候鸟迁徙通道（如北海盆地）内布设风力涡轮机，可能会因碰撞导致鸟类死亡，进而加剧易危候鸟种群的衰退。将风力涡轮机关停限制在候鸟大规模迁徙的短时段内，可最大限度减轻此类负面影响，而短期候鸟迁徙预报可为这类决策提供依据。尽管短期预报通常依托长期数据集构建，但风电开发带来的环境变化速率，亟需我们研发基于现有数据的保护措施——即便这些数据的时间覆盖时长有限。本研究依托荷兰西海岸外五年的鸟类雷达追踪数据、气象数据及物候变量，构建了北海南部低空夜间候鸟迁徙的季节性短期预报模型。整体而言，模型可解释71%的方差，且在两个迁徙季中，超80%的时段都能准确预测每小时迁徙强度是否达到大规模迁徙阈值。不过，模型对大规模迁徙时段（即迁徙强度最高的前5%的时段）的准确预测占比较低，这可能是因为该数据集时长有限，且大规模迁徙时段本身较为罕见。因此，我们建议谨慎设定关停阈值，以达成最优的保护与发电平衡效果。综合与应用部分：通过实测数据验证的迁徙通量短期预报，可用于确定海上风电的风力涡轮机关停阈值。研究表明，若预报准确，为将50%候鸟的碰撞风险降至最低，在模型评估的基准年份中，春季需关停18小时、秋季需关停26小时，由此带来的年度风电发电量损失预计为0.12%。借鉴首创“全球首例”海上风电关停机制的荷兰关停框架，我们认为，依托有限时长数据集构建的预报模型，结合专家研判与数据驱动的政策，可在快速变化的全球环境中加快保护工作的推进。鉴于近年来气象条件的年际变率不断加剧，该方法的应用价值尤为凸显。