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| Format: | Artículo Open Access |
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Wiley
2025
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| Online Access: | https://onlinelibrary.wiley.com/doi/10.1111/ibi.70013 |
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Table of Contents:
- Optimization of passive acoustic bird surveys: a global assessment of BirdNET settings Cristian Pérez‐Granados David Funosas Jon Morant Oscar H. Marín Gómez Irene Mendoza Miguel A. Mohedano‐Munoz Eduardo Santamaría Giulia Bastianelli Alba Márquez‐Rodríguez Michał Budka Gerard Bota José M. De la Peña‐Rubio Eladio García De La Morena Manu Santa‐Cruz Pablo De la Nava Mario Fernández‐Tizón Hugo Sánchez‐Mateos Adrián Barrero Juan Traba Tomasz S. Osiejuk Patrick J. Hart Amanda K. Navine Andrés F. Montoya Muñoz Carlos B. De Araújo Gabriel L. M. Rosa Ingrid M. D. Torres Ana L. C. Catalano Cassio Rachid Simões Diego Llusia Manuel B. Morales Pablo Acebes Juan A. Medina Nicholas Brown Christos Astaras Ilias Karmiris Elizabeth Navarrete Maxime Cauchoix Luc Barbaro Dominik Arend Sandra Müeller Fernando González‐García Alberto González‐Romero Christos Mammides Michaelangelo Pontikis Giordano Jacuzzi Julian D. Olden Sara P. Bombaci Gabriel Marcacci Alain Jacot Juan P. Zurano Elena Gangenova Diego Varela Facundo Di Sallo Gustavo A. Zurita Andrey Atemasov Junior A. Tremblay Anja Hutschenreiter Alan Monroy‐Ojeda Mauricio Díaz‐Vallejo Sergio Chaparro‐Herrera Robert A. Briers Renata Sousa‐Lima Thiago Pinheiro Wigna C. Da Silva Alice Calvente Anamaria Dal Molin Alexandre Antonelli Svetlana Gogoleva Igor Palko Hiếu V. Trong Marina H. L. Duarte Natalia Dos Santos Saturnino Samuel R. Silva Ana Rainho Paula Lopes Karl‐L. Schuchmann Marinêz I. Marques Ana S. De Oliverira Tissiani Nick A. Littlewood Mao‐Ning Tuanmu Yi‐Ru Cheng Hsuan Chao Sebastian Kepfer‐Rojas Andrea L. Aguilera Lluís Brotons Mariano J. Feldman Louis Imbeau Pooja Panwar Aaron S. Weed Anant Dehwal Alfredo Attisano Jörn Theuerkauf Dorgival D. Oliveira‐Júnior Cicero S. Lima‐Santos Carlos Salustio‐Gomes Raiane V. Paz Mauro Pichorim Eben Goodale Esther Sebastián‐González Ibis 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. 10.1111/ibi.70013 http://creativecommons.org/licenses/by-nc-nd/4.0/