The BirdCLEF challenge-as part of the 2019 LifeCLEF Lab [7]-offers a large-scale proving ground for system-oriented evaluation of bird species identification based on audio recordings. The challenge uses data collected through Xeno-canto, the worldwide community of bird sound recordists. This ensures that BirdCLEF is close to the conditions of real-world application, in particular with regard to the number of species in the training set (659). In 2019, the challenge was focused on the difficult task of recognizing all birds vocalizing in omni-directional soundscape recordings. Therefore, the dataset of the previous year was extended with more than 350 hours of manually annotated soundscapes that were recorded using 30 field recorders in Ithaca (NY, USA). This paper describes the methodology of the conducted evaluation as well as the synthesis of the main results and lessons learned.