Effectiveness of bird species identification using Birdnet: Case study at the La Mata coastal lagoon

Monitoring avian species is fundamental to detect any negative impact from human activity. In our case study, we focus in particular on the La Mata lagoon in Torrevieja (Alicante, Spain), aiming at the monitoring of two gull species that often share the same environment: Larus Michahellis (Yellow-Legged Gull), and Ichthyaetus Audouinii (Audouin’s Gull). As of 2020, Ichtyaetus Audouinii has been included within the International Union for Conservation of Nature (IUCN) Red List, with the status of vulnerable as the global population has experienced a rapid decrease, which is expected to be approaching 40% between 2006–2030, and is projected to continue declining at a similar rate over the next three generations. Since conservation of these species requires informed, prompt and efficient decision-making, we propose constantly monitoring the ecosystem by deploying an AI-enabled IoT infrastructure to listen to bird sounds, and automatically identify bird species. To this end, we relied on the BirdNET artificial neural network for the acoustic analysis. However, using BirdNET models must be carefully planned to produce insightful data that can drive informed decisions. For this reason, we discuss our methodology and the logic behind tuning the most important parameters according to the proposed use case.