The bioacoustic soundscape of a pandemic: Continuous annual monitoring using a deep learning system in Agmon Hula Lake Park

Continuous bioacoustic monitoring is an emerging opportunity as well as a challenge, allowing detection of cryptic species' activity while producing high computational demands. In this paper, we present an automated framework that allows the monitoring of a large number of bird species by their vocalizations over extended periods. The framework relies on the BirdNET-Analyzer deep learning model. We applied the framework to >80 species; 20 species with the highest recall scores were selected for further analysis. We used the framework to analyze acoustic signals recorded continuously for over two years using autonomous recorders at various locations in Agmon Hula Lake Park, Israel. During this period there was an acute outbreak of avian influenza in the area. We analyzed differences in acoustic occupancy for various species between two consecutive years (November 2020 to October 2022). We examined between-year population trends for 17 species, both migratory and resident, and found a significant decline in vocal activity between the two years for 10 species. We assume that this decline is related to the avian influenza outbreak, suggesting that the impact of the pandemic may be more widespread and affected a greater number of local species than was previously realized. This further highlights the power and effectiveness of bioacoustic monitoring in detecting cryptic but dramatic dynamics.