Studying nocturnal bird migration is challenging because direct visual observations are difficult during darkness. Radar has been the means of choice to study nocturnal bird migration for several decades, but provides limited taxonomic information. Here, to ascertain the feasibility of enhancing the taxonomic resolution of radar data, we combined acoustic data with vertical‐looking radar measurements to quantify thrush (Family: Turdidae) migration. Acoustic recordings, collected in Helsinki between August and October of 2021–2022, were used to identify likely nights of high and low thrush migration. Then, we built a random forest classifier that used recorded radar signals from those nights to separate all migrating passerines across the autumn migration season into thrushes and non‐thrushes. The classifier had a high overall accuracy (≈0.82), with wingbeat frequency and bird size being key for separation. The overall estimated thrush autumn migration phenology was in line with known migratory patterns and strongly correlated (Pearson correlation coefficient ≈0.65) with the phenology of the acoustic data. These results confirm how the joint application of acoustic and vertical‐looking radar data can, under certain migratory conditions and locations, be used to quantify ‘family‐level’ bird migration.

中文翻译：

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利用声学和垂直雷达之间的传感器数据融合来量化夜间画眉的迁徙


研究夜间鸟类迁徙具有挑战性，因为在黑暗中直接目视观察很困难。几十年来，雷达一直是研究夜间鸟类迁徙的首选手段，但提供的分类信息有限。在这里，为了确定提高雷达数据分类分辨率的可行性，我们将声学数据与垂直雷达测量相结合，以量化画眉鸟（科：Turdidae）的迁徙。 2021 年至 2022 年 8 月至 10 月期间在赫尔辛基收集的声学记录用于识别可能出现高画眉和低画眉迁徙的夜晚。然后，我们建立了一个随机森林分类器，使用那些夜晚记录的雷达信号将秋季迁徙季节的所有迁徙雀鸟分为画眉鸟和非画眉鸟。该分类器具有较高的整体精度（约 0.82），振翼频率和鸟类大小是分离的关键。总体估计的画眉秋季迁徙物候与已知的迁徙模式一致，并且与声学数据的物候密切相关（皮尔逊相关系数约0.65）。这些结果证实了在某些迁徙条件和地点下，如何联合应用声学和垂直雷达数据来量化“科级”鸟类迁徙。