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Assessing the potential of BirdNET to infer European bird communities from large-scale ecoacoustic data

hal.structure.identifierEcosystèmes aquatiques et changements globaux [UR EABX]
hal.structure.identifierCentre de Recherches sur la Cognition Animale - UMR5169 [CRCA]
hal.structure.identifierÉcole nationale supérieure agronomique de Toulouse [ENSAT]
hal.structure.identifierStation d'Ecologie Théorique et Expérimentale [SETE]
dc.contributor.authorFUNOSAS, David
hal.structure.identifierDynamiques et écologie des paysages agriforestiers [DYNAFOR]
dc.contributor.authorBARBARO, Luc
hal.structure.identifierBiodiversité, Gènes & Communautés [BioGeCo]
dc.contributor.authorSCHILLÉ, Laura
hal.structure.identifierEcosystèmes aquatiques et changements globaux [UR EABX]
hal.structure.identifierCentre de Recherches sur la Cognition Animale - UMR5169 [CRCA]
hal.structure.identifierÉcole nationale supérieure agronomique de Toulouse [ENSAT]
hal.structure.identifierStation d'Ecologie Théorique et Expérimentale [SETE]
dc.contributor.authorELGER, Arnaud
hal.structure.identifierBiodiversité, Gènes & Communautés [BioGeCo]
dc.contributor.authorCASTAGNEYROL, Bastien
hal.structure.identifierStation d'Ecologie Théorique et Expérimentale [SETE]
hal.structure.identifierÉcole nationale supérieure agronomique de Toulouse [ENSAT]
hal.structure.identifierCentre de Recherches sur la Cognition Animale - UMR5169 [CRCA]
hal.structure.identifierEcosystèmes aquatiques et changements globaux [UR EABX]
dc.contributor.authorCAUCHOIX, Maxime
dc.date.issued2024-05-20
dc.identifier.issn1470-160X
dc.description.abstractEn1. Passive acoustic monitoring has become increasingly popular as a practical and cost-effective way of obtaining highly reliable acoustic data in ecological research projects. Increased ease of collecting these data means that, currently, the main bottleneck in ecoacoustic monitoring projects is often the time required for the manual analysis of passively collected recordings. In this study we evaluate the potential and current limitations of BirdNET-Analyzer v2.4, the most advanced and generic deep learning algorithm for bird recognition to date, as a tool to assess bird community composition through the automated analysis of large-scale ecoacoustic data. 2. To this end, we study 3 acoustic datasets comprising a total of 629 environmental soundscapes collected in 194 different sites spread across a 19° latitude span in Europe. We analyze these soundscapes using both BirdNET and manual listening by local expert birders, and we then compare the results obtained through the two methods to evaluate the performance of the algorithm both at the level of each single vocalization and for entire recording sequences (1, 5 or 10 min). 3. Since BirdNET provides a confidence score for each identification, minimum confidence thresholds can be used to filter out identifications with low scores, thus retaining only the most reliable ones. The volume of ecoacoustic data used in this study did not allow us to estimate species-specific minimum confidence thresholds for most taxa, so we instead used and evaluated global confidence thresholds selected for optimized results when consistently applied across all species. 4. Our analyses reveal that BirdNET identifications can be highly reliable if a sufficiently high minimum confidence threshold is used. However, the inevitable trade-off between precision and recall does not allow to obtain satisfactory results for both metrics at the same time. We found that F1-scores remain moderate (<0.5) for all datasets and confidence thresholds studied, and that acoustic datasets of extended duration seem to be currently necessary for BirdNET to provide a reliable and minimally comprehensive picture of the target bird community. We estimate, however, that the usage of species- and context-specific minimum confidence thresholds would allow to substantially improve the global performance benchmarks obtained in this study. 5. We conclude that a judicious use of AI-based identifications provided by BirdNET can represent a powerful method to assist in the assessment of bird community composition through the automated analysis of ecoacoustic data, especially when applied to acoustic datasets of extended duration.
dc.language.isoen
dc.publisherElsevier
dc.rights.urihttp://creativecommons.org/licenses/by-nc/
dc.subject.enAutomated species identification
dc.subject.enBird communities
dc.subject.enBirdNET
dc.subject.enConfidence threshold
dc.subject.enPassive Acoustic Monitoring
dc.subject.enPrecision and recall
dc.subject.enSoundscape
dc.title.enAssessing the potential of BirdNET to infer European bird communities from large-scale ecoacoustic data
dc.typeArticle de revue
dc.identifier.doi10.1016/j.ecolind.2024.112146
dc.subject.halSciences du Vivant [q-bio]
bordeaux.journalEcological Indicators
bordeaux.page112146
bordeaux.volume164
bordeaux.peerReviewedoui
hal.identifierhal-04633976
hal.version1
hal.popularnon
hal.audienceInternationale
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-04633976v1
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=Ecological%20Indicators&rft.date=2024-05-20&rft.volume=164&rft.spage=112146&rft.epage=112146&rft.eissn=1470-160X&rft.issn=1470-160X&rft.au=FUNOSAS,%20David&BARBARO,%20Luc&SCHILL%C3%89,%20Laura&ELGER,%20Arnaud&CASTAGNEYROL,%20Bastien&rft.genre=article


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