| Title: |
Optimization of passive acoustic bird surveys: a global assessment of BirdNET settings. |
| Authors: |
Pérez‐Granados, Cristian; Funosas, David; Morant, Jon; Marín Gómez, Oscar H.; Mendoza, Irene; Mohedano‐Munoz, Miguel A.; Santamaría, Eduardo; Bastianelli, Giulia; Márquez‐Rodríguez, Alba; Budka, Michał; Bota, Gerard; De la Peña‐Rubio, José M.; García De La Morena, Eladio; Santa‐Cruz, Manu; De la Nava, Pablo; Fernández‐Tizón, Mario; Sánchez‐Mateos, Hugo; Barrero, Adrián; Traba, Juan; Osiejuk, Tomasz S. |
| Source: |
Ibis (0019-1019); Apr2026, Vol. 168 Issue 2, p785-798, 14p |
| Subject Terms: |
BIRD vocalizations; BIOACOUSTICS; ENVIRONMENTAL monitoring; MACHINE learning; IDENTIFICATION of animals; OPTIMIZATION algorithms |
| Abstract: |
BirdNET is a popular machine learning tool for automated recognition of bird sounds. However, evidence on how to optimize its settings for accurate bird monitoring remains limited. Here, we evaluate how BirdNET settings influence model performance in identifying bird vocalizations and characterizing bird communities, using 4224 1‐min recordings from 67 recording locations worldwide. Giving equal importance to recall and precision, a low confidence score threshold (0.1–0.3) appears optimal for detecting bird vocalizations, whereas higher thresholds (around 0.5) are more suitable for characterizing bird communities. Based on our findings, we recommend increasing the Overlap parameter from its default value of 0 to 2 s, as this consistently improves BirdNET performance in detecting both bird vocalizations and species presence. The effect of the Sensitivity parameter varied across regions. However, a value of 0.5 maximizes global performance for community‐level analyses across all confidence thresholds, and a value of 1.5 generally yields better results for vocalization‐level studies, particularly at low confidence thresholds. Our findings offer practical guidance for selecting BirdNET settings in passive acoustic bird surveys, enhancing both the identification of bird vocalizations and the characterization of bird communities. [ABSTRACT FROM AUTHOR] |
| : |
Copyright of Ibis (0019-1019) is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) |
| Database: |
Complementary Index |