Analysis of BirdNET Configuration and Performance Applied to the Acoustic Monitoring of a Restored Quarry

In the global context of biodiversity loss, increased demand for natural resources, and major efforts to restore ecosystems altered by human activities, the widespread use of passive acoustic monitoring (PAM) and acoustic recording devices allows for the collection of enormous amounts of data for monitoring the health of ecosystems. BirdNET Analyzer is a freely accessible machine learning tool that has had a great impact on the scientific community due to its apparent ease of use for identifying animals by sound. However, the literature shows some gaps regarding the influence of certain BirdNET configuration parameters on the results of its predictions. This study applies PAM and uses BirdNET in a real acoustic monitoring project and analyzes the potential impact of the configuration parameters Overlap and Sensitivity on the results of the bird inventory of a wetland created on the site of a former limestone quarry in Spain. Our results guide other researchers in the optimal combination of configuration parameters at the community level. Higher Sensitivity configuration values provided the optimal solution for minimizing the loss of species in the bird inventory. On the other hand, we identified that Recall is the best indicator to identify all combinations of BirdNET configuration parameters that cause the lowest species loss, in line with the goal of this monitoring program.