A global assessment of BirdNET performance: Differences among continents, biomes, and species
Recent advances in machine learning have accelerated automated species detection across diverse ecological domains, enabling large-scale, non-invasive monitoring of biodiversity. In ornithological research, the combination of passive acoustic monitoring (PAM) and rapidly-developing novel identificat...
| Autores: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
|---|---|
| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2026 |
| País: | España |
| Institución: | Consejo Superior de Investigaciones Científicas (CSIC) |
| Repositorio: | DIGITAL.CSIC. Repositorio Institucional del CSIC |
| OAI Identifier: | oai:digital.csic.es:10261/414382 |
| Acceso en línea: | http://hdl.handle.net/10261/414382 https://api.elsevier.com/content/abstract/scopus_id/105025359154 |
| Access Level: | acceso abierto |
| Palabra clave: | Passive acoustic monitoring Automated detection Bird communities BirdNET Confidence threshold Deep learning |
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oai:digital.csic.es:10261/414382 |
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| dc.title.none.fl_str_mv |
A global assessment of BirdNET performance: Differences among continents, biomes, and species |
| title |
A global assessment of BirdNET performance: Differences among continents, biomes, and species |
| spellingShingle |
A global assessment of BirdNET performance: Differences among continents, biomes, and species Funosas, David Passive acoustic monitoring Automated detection Bird communities BirdNET Confidence threshold Deep learning |
| title_short |
A global assessment of BirdNET performance: Differences among continents, biomes, and species |
| title_full |
A global assessment of BirdNET performance: Differences among continents, biomes, and species |
| title_fullStr |
A global assessment of BirdNET performance: Differences among continents, biomes, and species |
| title_full_unstemmed |
A global assessment of BirdNET performance: Differences among continents, biomes, and species |
| title_sort |
A global assessment of BirdNET performance: Differences among continents, biomes, and species |
| dc.creator.none.fl_str_mv |
Funosas, David Sebastián-González, Esther Morant, Jon Marín Gómez, Oscar H. Mendoza, Irene Mohedano Muñoz, Miguel A. Santamaría García, Eduardo Bastianelli, Giulia Márquez Rodríguez, Alba Budka, Michał Bota, Gerard Alonso-Moya, Cristina D. Peña-Rubio, José M. de la García de la Morena, Eladio L. Santa-Cruz, Manu Nava, Pablo de la Fernández-Tizón, Mario Sánchez-Mateos, Hugo Barrero, Adrián Traba, Juan Osiejuk, Tomasz S. Hart, Patrick J. Navine, Amanda K. Montoya Muñoz, Andrés F. Araújo, Carlos B. de Rosa, Gabriel L. M. Torres, Ingrid M. D. Catalano, Ana L. C. Simões, Cassio Rachid Llusia, Diego Morales, Manuel B. Acebes, Pablo Medina, Juan A. Brown, Nicholas Astaras, Christos Karmiris, Ilias Navarrete, Elizabeth Cauchoix, Maxime Barbaro, Luc Arend, Dominik Müeller, Sandra González-García, Fernando González-Romero, Alberto Mammides, Christos Pontikis, Michaelangelo Jacuzzi, Giordano Olden, Julian D. Bombaci, Sara P. Marcacci, Gabriel Jacot, Alain Zurano, Juan P. Gangenova, Elena Varela, Diego Di Sallo, Facundo Zurita, Gustavo A. Atemasov, Andrey Tremblay, Junior A. Lamarre, Vincent Hutschenreiter, Anja Monroy-Ojeda, Alan Díaz-Vallejo, Mauricio Chaparro-Herrera, Sergio Briers, Robert A. Sousa-Lima, Renata Pinheiro, Thiago Da Silva, Wigna C. Calvente, Alice Paz, Raiane V. Salustio-Gomes, Carlos Oliveira-Júnior, Dorgival D. Lima-Santos, Cicero S. Pichorim, Mauro Molin, Anamaria Dal Antonelli, Alexandre Gogoleva, Svetlana Palko, Igor Trong, Hiếu V. Duarte, Marina H. L. Dos Santos Saturnino, Natalia Silva, Samuel R. Rainho, Ana Lopes, Paula Schuchmann, Karl L. Marques, Marinêz I. De Oliverira Tissiani, Ana S. Littlewood, Nick A. Tuanmu, Mao Ning Kepfer-Rojas, Sebastian Aguilera, Andrea L. Brotons, Lluís Feldman, Mariano J. Imbeau, Louis Panwar, Pooja Weed, Aaron S. Dehwal, Anant Attisano, Alfredo Theuerkauf, Jörn Goodale, Eben Darras, Kevin F. A. Pérez-Granados, Cristian |
| author |
Funosas, David |
| author_facet |
Funosas, David Sebastián-González, Esther Morant, Jon Marín Gómez, Oscar H. Mendoza, Irene Mohedano Muñoz, Miguel A. Santamaría García, Eduardo Bastianelli, Giulia Márquez Rodríguez, Alba Budka, Michał Bota, Gerard Alonso-Moya, Cristina D. Peña-Rubio, José M. de la García de la Morena, Eladio L. Santa-Cruz, Manu Nava, Pablo de la Fernández-Tizón, Mario Sánchez-Mateos, Hugo Barrero, Adrián Traba, Juan Osiejuk, Tomasz S. Hart, Patrick J. Navine, Amanda K. Montoya Muñoz, Andrés F. Araújo, Carlos B. de Rosa, Gabriel L. M. Torres, Ingrid M. D. Catalano, Ana L. C. Simões, Cassio Rachid Llusia, Diego Morales, Manuel B. Acebes, Pablo Medina, Juan A. Brown, Nicholas Astaras, Christos Karmiris, Ilias Navarrete, Elizabeth Cauchoix, Maxime Barbaro, Luc Arend, Dominik Müeller, Sandra González-García, Fernando González-Romero, Alberto Mammides, Christos Pontikis, Michaelangelo Jacuzzi, Giordano Olden, Julian D. Bombaci, Sara P. Marcacci, Gabriel Jacot, Alain Zurano, Juan P. Gangenova, Elena Varela, Diego Di Sallo, Facundo Zurita, Gustavo A. Atemasov, Andrey Tremblay, Junior A. Lamarre, Vincent Hutschenreiter, Anja Monroy-Ojeda, Alan Díaz-Vallejo, Mauricio Chaparro-Herrera, Sergio Briers, Robert A. Sousa-Lima, Renata Pinheiro, Thiago Da Silva, Wigna C. Calvente, Alice Paz, Raiane V. Salustio-Gomes, Carlos Oliveira-Júnior, Dorgival D. Lima-Santos, Cicero S. Pichorim, Mauro Molin, Anamaria Dal Antonelli, Alexandre Gogoleva, Svetlana Palko, Igor Trong, Hiếu V. Duarte, Marina H. L. Dos Santos Saturnino, Natalia Silva, Samuel R. Rainho, Ana Lopes, Paula Schuchmann, Karl L. Marques, Marinêz I. De Oliverira Tissiani, Ana S. Littlewood, Nick A. Tuanmu, Mao Ning Kepfer-Rojas, Sebastian Aguilera, Andrea L. Brotons, Lluís Feldman, Mariano J. Imbeau, Louis Panwar, Pooja Weed, Aaron S. Dehwal, Anant Attisano, Alfredo Theuerkauf, Jörn Goodale, Eben Darras, Kevin F. A. Pérez-Granados, Cristian |
| author_role |
author |
| author2 |
Sebastián-González, Esther Morant, Jon Marín Gómez, Oscar H. Mendoza, Irene Mohedano Muñoz, Miguel A. Santamaría García, Eduardo Bastianelli, Giulia Márquez Rodríguez, Alba Budka, Michał Bota, Gerard Alonso-Moya, Cristina D. Peña-Rubio, José M. de la García de la Morena, Eladio L. Santa-Cruz, Manu Nava, Pablo de la Fernández-Tizón, Mario Sánchez-Mateos, Hugo Barrero, Adrián Traba, Juan Osiejuk, Tomasz S. Hart, Patrick J. Navine, Amanda K. Montoya Muñoz, Andrés F. Araújo, Carlos B. de Rosa, Gabriel L. M. Torres, Ingrid M. D. Catalano, Ana L. C. Simões, Cassio Rachid Llusia, Diego Morales, Manuel B. Acebes, Pablo Medina, Juan A. Brown, Nicholas Astaras, Christos Karmiris, Ilias Navarrete, Elizabeth Cauchoix, Maxime Barbaro, Luc Arend, Dominik Müeller, Sandra González-García, Fernando González-Romero, Alberto Mammides, Christos Pontikis, Michaelangelo Jacuzzi, Giordano Olden, Julian D. Bombaci, Sara P. Marcacci, Gabriel Jacot, Alain Zurano, Juan P. Gangenova, Elena Varela, Diego Di Sallo, Facundo Zurita, Gustavo A. Atemasov, Andrey Tremblay, Junior A. Lamarre, Vincent Hutschenreiter, Anja Monroy-Ojeda, Alan Díaz-Vallejo, Mauricio Chaparro-Herrera, Sergio Briers, Robert A. Sousa-Lima, Renata Pinheiro, Thiago Da Silva, Wigna C. Calvente, Alice Paz, Raiane V. Salustio-Gomes, Carlos Oliveira-Júnior, Dorgival D. Lima-Santos, Cicero S. Pichorim, Mauro Molin, Anamaria Dal Antonelli, Alexandre Gogoleva, Svetlana Palko, Igor Trong, Hiếu V. Duarte, Marina H. L. Dos Santos Saturnino, Natalia Silva, Samuel R. Rainho, Ana Lopes, Paula Schuchmann, Karl L. Marques, Marinêz I. De Oliverira Tissiani, Ana S. Littlewood, Nick A. Tuanmu, Mao Ning Kepfer-Rojas, Sebastian Aguilera, Andrea L. Brotons, Lluís Feldman, Mariano J. Imbeau, Louis Panwar, Pooja Weed, Aaron S. Dehwal, Anant Attisano, Alfredo Theuerkauf, Jörn Goodale, Eben Darras, Kevin F. A. Pérez-Granados, Cristian |
| author2_role |
author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author |
| dc.contributor.none.fl_str_mv |
Generalitat de Catalunya Ministerio de Ciencia, Innovación y Universidades (España) Agencia Estatal de Investigación (España) European Commission Sebastián-González, Esther [0000-0001-7229-1845] Mendoza, Irene [0000-0002-3288-6366] Bastianelli, Giulia [0000-0002-0139-3067] Llusia, Diego [0000-0001-5432-2716] Morales, Manuel B. [0000-0001-8534-7895] Brotons, Lluís [0000-0002-4826-4457] Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72] |
| dc.subject.none.fl_str_mv |
Passive acoustic monitoring Automated detection Bird communities BirdNET Confidence threshold Deep learning |
| topic |
Passive acoustic monitoring Automated detection Bird communities BirdNET Confidence threshold Deep learning |
| description |
Recent advances in machine learning have accelerated automated species detection across diverse ecological domains, enabling large-scale, non-invasive monitoring of biodiversity. In ornithological research, the combination of passive acoustic monitoring (PAM) and rapidly-developing novel identification tools such as BirdNET—a deep learning–based sound recognition algorithm—offers new opportunities for surveying vocally active bird communities. Here, we present the first worldwide evaluation of BirdNET using 4224 one-minute recordings from 67 sites across all continents annotated by local experts. More specifically, we assessed the capacity of BirdNET to accurately identify individual vocalizations and characterize bird communities based on the automated analysis of passively collected soundscapes. We further analyzed how its performance varies across continents, biomes, species, and minimum confidence thresholds. The proportion of correct BirdNET predictions (precision) was generally high and consistent across continents (range: 0.57–0.71) and biomes (range: 0.55–0.76). In contrast, the proportion of vocalizations successfully detected (recall) was generally lower and more heterogeneous across continents (range: 0.24–0.52) and biomes (range: 0.34–0.72), reflecting differences in species coverage and local ecological context. BirdNET predictive power, as measured by the Precision-Recall Area Under the Curve (PR AUC; higher values indicating better performance), was highest in North America, Oceania, and Europe (range: 0.16–0.23), moderate in Central/South America (0.13), and lowest in Africa and Asia (range: 0.03–0.04). Species-specific analyses revealed substantial heterogeneity in detection accuracy, with optimal confidence thresholds varying widely by species and analytical goal. Our results establish a global reference point for BirdNET reliability and highlight where algorithmic refinement and expanded acoustic sampling are most needed. |
| publishDate |
2026 |
| dc.date.none.fl_str_mv |
2026 2026 2026 |
| dc.type.none.fl_str_mv |
info:eu-repo/semantics/article http://purl.org/coar/resource_type/c_6501 Publisher's version info:eu-repo/semantics/publishedVersion |
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article |
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publishedVersion |
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http://hdl.handle.net/10261/414382 https://api.elsevier.com/content/abstract/scopus_id/105025359154 |
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http://hdl.handle.net/10261/414382 https://api.elsevier.com/content/abstract/scopus_id/105025359154 |
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Inglés |
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Inglés |
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A global assessment of BirdNET performance: Differences among continents, biomes, and speciesFunosas, DavidSebastián-González, EstherMorant, JonMarín Gómez, Oscar H.Mendoza, IreneMohedano Muñoz, Miguel A.Santamaría García, EduardoBastianelli, GiuliaMárquez Rodríguez, AlbaBudka, MichałBota, GerardAlonso-Moya, Cristina D.Peña-Rubio, José M. de laGarcía de la Morena, Eladio L.Santa-Cruz, ManuNava, Pablo de laFernández-Tizón, MarioSánchez-Mateos, HugoBarrero, AdriánTraba, JuanOsiejuk, Tomasz S.Hart, Patrick J.Navine, Amanda K.Montoya Muñoz, Andrés F.Araújo, Carlos B. deRosa, Gabriel L. M.Torres, Ingrid M. D.Catalano, Ana L. C.Simões, Cassio RachidLlusia, DiegoMorales, Manuel B.Acebes, PabloMedina, Juan A.Brown, NicholasAstaras, ChristosKarmiris, IliasNavarrete, ElizabethCauchoix, MaximeBarbaro, LucArend, DominikMüeller, SandraGonzález-García, FernandoGonzález-Romero, AlbertoMammides, ChristosPontikis, MichaelangeloJacuzzi, GiordanoOlden, Julian D.Bombaci, Sara P.Marcacci, GabrielJacot, AlainZurano, Juan P.Gangenova, ElenaVarela, DiegoDi Sallo, FacundoZurita, Gustavo A.Atemasov, AndreyTremblay, Junior A.Lamarre, VincentHutschenreiter, AnjaMonroy-Ojeda, AlanDíaz-Vallejo, MauricioChaparro-Herrera, SergioBriers, Robert A.Sousa-Lima, RenataPinheiro, ThiagoDa Silva, Wigna C.Calvente, AlicePaz, Raiane V.Salustio-Gomes, CarlosOliveira-Júnior, Dorgival D.Lima-Santos, Cicero S.Pichorim, MauroMolin, Anamaria DalAntonelli, AlexandreGogoleva, SvetlanaPalko, IgorTrong, Hiếu V.Duarte, Marina H. L.Dos Santos Saturnino, NataliaSilva, Samuel R.Rainho, AnaLopes, PaulaSchuchmann, Karl L.Marques, Marinêz I.De Oliverira Tissiani, Ana S.Littlewood, Nick A.Tuanmu, Mao NingKepfer-Rojas, SebastianAguilera, Andrea L.Brotons, LluísFeldman, Mariano J.Imbeau, LouisPanwar, PoojaWeed, Aaron S.Dehwal, AnantAttisano, AlfredoTheuerkauf, JörnGoodale, EbenDarras, Kevin F. A.Pérez-Granados, CristianPassive acoustic monitoringAutomated detectionBird communitiesBirdNETConfidence thresholdDeep learningRecent advances in machine learning have accelerated automated species detection across diverse ecological domains, enabling large-scale, non-invasive monitoring of biodiversity. In ornithological research, the combination of passive acoustic monitoring (PAM) and rapidly-developing novel identification tools such as BirdNET—a deep learning–based sound recognition algorithm—offers new opportunities for surveying vocally active bird communities. Here, we present the first worldwide evaluation of BirdNET using 4224 one-minute recordings from 67 sites across all continents annotated by local experts. More specifically, we assessed the capacity of BirdNET to accurately identify individual vocalizations and characterize bird communities based on the automated analysis of passively collected soundscapes. We further analyzed how its performance varies across continents, biomes, species, and minimum confidence thresholds. The proportion of correct BirdNET predictions (precision) was generally high and consistent across continents (range: 0.57–0.71) and biomes (range: 0.55–0.76). In contrast, the proportion of vocalizations successfully detected (recall) was generally lower and more heterogeneous across continents (range: 0.24–0.52) and biomes (range: 0.34–0.72), reflecting differences in species coverage and local ecological context. BirdNET predictive power, as measured by the Precision-Recall Area Under the Curve (PR AUC; higher values indicating better performance), was highest in North America, Oceania, and Europe (range: 0.16–0.23), moderate in Central/South America (0.13), and lowest in Africa and Asia (range: 0.03–0.04). Species-specific analyses revealed substantial heterogeneity in detection accuracy, with optimal confidence thresholds varying widely by species and analytical goal. Our results establish a global reference point for BirdNET reliability and highlight where algorithmic refinement and expanded acoustic sampling are most needed.DF was funded by the University of Toulouse; CP-G was funded by Departament de Recerca i Universitats de la Generalitat de Catalunya through the 2021-SGR 00302 project; and ES-G was funded by the Spanish Ministry of Science, Innovation and Universities (MCIN/AEI/10.13039/501100011033), NextGenerationEU/PRTR, ESF Investing in your future (TED2021-130890B-C21 and RYC2019-027216-I), and HORIZONMSCA-2021-SE-0 (action number: 101086387).Peer reviewedElsevierGeneralitat de CatalunyaMinisterio de Ciencia, Innovación y Universidades (España)Agencia Estatal de Investigación (España)European CommissionSebastián-González, Esther [0000-0001-7229-1845]Mendoza, Irene [0000-0002-3288-6366]Bastianelli, Giulia [0000-0002-0139-3067]Llusia, Diego [0000-0001-5432-2716]Morales, Manuel B. [0000-0001-8534-7895]Brotons, Lluís [0000-0002-4826-4457]Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]202620262026info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Publisher's versioninfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://hdl.handle.net/10261/414382https://api.elsevier.com/content/abstract/scopus_id/105025359154reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Inglés#PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE#info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/TED2021-130890B-C21info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RYC2019-027216-Iinfo:eu-rpoe/grantAgreement/EC/HE/101086387The underlying dataset has been published as supplementary material of the article in the publisher platform at DOI https://doi.org/10.1016/j.ecolind.2025.114550https://doi.org/10.1016/j.ecolind.2025.114550Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/4143822026-05-22T06:33:51Z |
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15,812429 |