LAUSR

Acoustic recordings have emerged as a promising tool to monitor nocturnal bird migration, as it can uniquely provide species‐level detection of migratory movements under the darkness of the night sky. This study explores the use of acoustics to quantify nocturnal bird migration across Europe, a region where research on the topic remains relatively sparse. We examine three migration intensity measures derived from acoustic recordings, that is, nocturnal flight call rates, nocturnal flight passage rates and species diversity, in the French Pyrenees in 2021 and 2022. To assess the effectiveness of these acoustic measurements, we compare them with migratory traffic rates estimated by a dedicated bird radar at three taxonomic levels: all birds, passerines and thrushes. We also test if weather conditions influence these relationships and whether combining acoustic data from multiple simultaneous sites improve the predictive performance. Nocturnal flight passage rates, that is, the number of estimated passing birds independent of call abundance, outperformed predictions using species diversity or nocturnal flight call rates. The predictive accuracy of the acoustics data increased with taxonomic detail: predicting thrush migration using acoustics was far more accurate (R2 = 63%) than for passerines (R2 = 29%) or birds in general (R2 = 27%). Prediction using simultaneous acoustics measurements from several sites strongly reduced the uncertainty of the quantification. We did not find any evidence that weather conditions affected the predictive performance of the acoustics data. Accurate, automated monitoring of migratory flows is crucial as many bird species face steep population declines. Acoustic monitoring offers valuable species‐specific insights, making it a powerful tool for nocturnal bird migration studies. This study advances the integration of acoustic methods into bird monitoring by testing their benefits and limitations and provides recommendations and guidelines to enhance the effectiveness of future studies using acoustic data.