Diel and Annual Patterns of Vocal Activity of Three Neotropical Wetland Birds Revealed via BirdNET

Abstract

Compared with traditional field techniques, automated and noninvasive bird monitoring techniques, such as passive acoustic monitoring, offer significant advantages. However, the extensive data collected through passive acoustic monitoring can be challenging to analyze and may require the use of machine learning algorithms for efficient processing. BirdNET is a user-friendly and ready-to-use machine learning tool that can recognize more than 6500 wildlife species, including several tropical species. However, the performance of BirdNET in tropical ecosystems has rarely been assessed. Here, we evaluate the effectiveness of BirdNET for monitoring the vocal activity of three Neotropical wetland species from recordings collected over a year in the Brazilian Pantanal: Green Ibis (Mesembrinibis cayennensis), Limpkin (Aramus guarauna), and Sunbittern (Eurypyga helias). BirdNET was able to detect the presence of the three species in 82–92% of the recordings with known presence. Similarly, BirdNET’s ability to correctly identify vocalizations was consistently greater than 77% (range 77–98%), confirming its effectiveness for monitoring these three tropical bird species. The peak vocal activity for the three species occurred during crepuscular periods, at the end of the rainy season, and during the receding season, a period when the risk of nest damage from flood pulses is low and food availability is high owing to the large presence of small water bodies. The use of machine learning algorithms such as BirdNET may improve bird monitoring in tropical areas but also facilitate research that improves our knowledge of birds’ natural history, which remains unknown for many tropical species.