Ardeola, Год журнала: 2025, Номер 72(2)
Опубликована: Апрель 17, 2025
Язык: Английский
Journal of Ornithology, Год журнала: 2024, Номер 165(3), С. 777 - 782
Опубликована: Фев. 14, 2024
Язык: Английский
Процитировано
36
Ecological Indicators, Год журнала: 2024, Номер 164, С. 112146 - 112146
Опубликована: Май 20, 2024
Passive acoustic monitoring has become increasingly popular as a practical and cost-effective way of obtaining highly reliable data in ecological research projects. Increased ease collecting these means that, currently, the main bottleneck ecoacoustic projects is often time required for manual analysis passively collected recordings. In this study we evaluate potential current limitations BirdNET-Analyzer v2.4, most advanced generic deep learning algorithm bird recognition to date, tool assess community composition through automated large-scale data. To end, 3 datasets comprising total 629 environmental soundscapes 194 different sites spread across 19° latitude span Europe. We analyze using both BirdNET listening by local expert birders, then compare results obtained two methods performance at level each single vocalization entire recording sequences (1, 5 or 10 min). Since provides confidence score identification, minimum thresholds can be used filter out identifications with low scores, thus retaining only ones. The volume did not allow us estimate species-specific taxa, so instead evaluated global selected optimized when consistently applied all species. Our analyses reveal that if sufficiently high threshold used. However, inevitable trade-off between precision recall does obtain satisfactory metrics same time. found F1-scores remain moderate (<0.5) studied, extended duration seem currently necessary provide minimally comprehensive picture target community. estimate, however, usage species- context-specific would substantially improve benchmarks study. conclude judicious use AI-based provided represent powerful method assist assessment data, especially duration.
Язык: Английский
Процитировано
18
Sensors, Год журнала: 2023, Номер 23(16), С. 7176 - 7176
Опубликована: Авг. 15, 2023
The efficient analyses of sound recordings obtained through passive acoustic monitoring (PAM) might be challenging owing to the vast amount data collected using such technique. development species-specific recognizers (e.g., deep learning) may alleviate time required for but are often difficult create. Here, we evaluate effectiveness BirdNET, a new machine learning tool freely available automated recognition and processing, correctly identifying detecting two cryptic forest bird species. BirdNET precision was high both Coal Tit (Peripatus ater) Short-toed Treecreeper (Certhia brachydactyla), with mean values 92.6% 87.8%, respectively. Using default values, successfully detected in 90.5% 98.4% annotated recordings, We also tested impact variable confidence scores on performance estimated optimal score each Vocal activity patterns species, PAM reached their peak during first hours after sunrise. hope that our study encourage researchers managers utilize this user-friendly ready-to-use software, thus contributing advancements sensing environmental monitoring.
Язык: Английский
Процитировано
24
Remote Sensing in Ecology and Conservation, Год журнала: 2024, Номер 10(4), С. 517 - 530
Опубликована: Фев. 25, 2024
Abstract Passive acoustic monitoring (PAM) has gained increasing popularity to study behaviour, habitat preferences, distribution and community assembly of birds other animals. Automated species classification algorithms like ‘BirdNET’ are capable detecting classifying avian vocalizations within extensive audio data, covering entire assemblages. PAM reveals substantial potential for biodiversity that informs evidence‐based conservation. Nevertheless, fully realizing this remains challenging, especially due the issue false‐positive detections. Here, we introduce an optimized thresholding framework, which incorporates contextual information extracted from time‐series automated detections (i.e. covariates on quality quantity species' measured at varying time intervals) improve differentiation true false positives. We verified a sample BirdNET per modelled species‐specific thresholds using conditional inference trees. These were designed minimize while maximizing preservation positives in dataset. tested framework large dataset (5760 h 60 sites) recorded over breeding season. Our results revealed considerable interspecific variability precision (percentage positives) raw data. approach achieved high (≥0.9) 70% 61 detected species, solely relying confidence scores only 31% species. Conservative universal (not species‐specific) reached 48% outperformed previous approaches enhanced comparability bird analyses. By incorporating detections, was substantially improved. may enhance straightforward application research, landscape planning
Язык: Английский
Процитировано
12
Biological Control, Год журнала: 2025, Номер unknown, С. 105702 - 105702
Опубликована: Янв. 1, 2025
Язык: Английский
Процитировано
1
Ardeola, Год журнала: 2023, Номер 70(2)
Опубликована: Май 15, 2023
Las vocalizaciones de las aves, como cualquier otra señal acústica, se atenúan con la distancia y, por lo tanto, estructura aves degrada progresivamente. Tal degradación puede tener un impacto en capacidad programas automatizados reconocimiento señales a hora detectar e identificar correctamente aves. BirdNET es reconocedor automatizado cantos pájaros reciente creación y comúnmente empleado investigadores el público. Sin embargo, pocos estudios han evaluado su rendimiento nuestro conocimiento actual sobre cómo variar función o entre especies muy limitado. Aquí, mi objetivo era evaluar si habilidad para tres variaba según distancia, tipo grabadora empleada especies, utilizando una grabación reproducida 10 150 m. La los varió general, disminuyó pero no dos tipos grabadores testados. tasa detección BirdNET, definida porcentaje detectadas identificadas software, fue del 59,9% (499 840 reproducidas). Se identificó manera correcta significativa mayor número cuando emitieron 50 m más cerca (tasa media 92,2%), comparación emitidas esa 34,9%). también significativamente alta chingolo saltamontes reinita encapuchada, vireo gris. El clasificaciones erróneas distancias siguió patrón lineal. Ese estudio proporciona información valiosa que contribuir mejorar futuros muestreos expandir uso censar comunidades usando monitoreo acústico pasivo.—Pérez-Granados, C. (2023). Un primer análisis variables: experimento playback. Ardeola, 70: 221-233.
Процитировано
18
Biological Invasions, Год журнала: 2024, Номер 26(4), С. 1269 - 1279
Опубликована: Янв. 25, 2024
Abstract Biological invasions pose significant threats to biodiversity and ecosystem functioning. Removal of introduced species is most successful when detected early. We evaluate the effectiveness passive acoustics combined with automated recognition in detecting invasive American bullfrog ( Lithobates catesbeianus ). applied this technique two real-world monitoring programs aimed at determining optimal time day for Europe, which we recorded Belgium Italy; evaluating BirdNET (a free user-friendly recognizer) analyzing a large dataset collected Spain. was highly effective automatically presence, detection rate (compared visual inspection sonograms) 89.5% using default settings (85 95 recordings known presence), 95.8% user-specific (91 detected). The system showed remarkable precision, correctly identifying 99.7% (612 out 614) verified predictions, only one mislabelled recording (predicted be present it absent). species’ vocal activity Italy higher during night compared crepuscular periods. Recording analyses output verification Spain carried 3.8% time, resulted significantly reduced effort inspection. Our study highlights remotely surveying bullfrog, making potential tool informing management decisions, particularly early arrival new areas.
Язык: Английский
Процитировано
9
Proceedings of the National Academy of Sciences, Год журнала: 2024, Номер 121(33)
Опубликована: Авг. 6, 2024
Tracking biodiversity and its dynamics at scale is essential if we are to solve global environmental challenges. Detecting animal vocalizations in passively recorded audio data offers an automatable, inexpensive, taxonomically broad way monitor biodiversity. However, the labor expertise required label new fine-tune algorithms for each deployment a major barrier. In this study, applied pretrained bird vocalization detection model, BirdNET, 152,376 h of comprising datasets from Norway, Taiwan, Costa Rica, Brazil. We manually listened subset detections species dataset, calibrated classification thresholds, found precisions over 90% 109 136 species. While some were reliably detected across multiple datasets, performance others was dataset specific. By filtering out unreliable detections, could extract community-level insight into diel (Brazil) seasonal (Taiwan) temporal scales, as well landscape (Costa Rica) national (Norway) spatial scales. Our findings demonstrate that, with relatively fast but local calibration, single model can deliver multifaceted community species-level highly diverse datasets; unlocking which acoustic monitoring immediate impact.
Язык: Английский
Процитировано
9
Canadian Journal of Zoology, Год журнала: 2023, Номер 101(12), С. 1031 - 1051
Опубликована: Июнь 23, 2023
Processing methods that maximize species richness from acoustic recordings obtained regional monitoring programs can increase detections of uncommon, rare, and cryptic provide key information on status distribution. Using data bird in Yukon, Canada, we (1) compared the number detected (species richness) cost associated with four processing ( Listening, Visual Scanning, Recognizer, Recognizer Validation) (2) combined Listening Validation to all at ecoregion scale. We used comprehensive Scanning detect recordings. processed ∼1% using 56% community 71.5 h human effort. (multispecies recognizer BirdNET) 89% ∼22% effort required for (56 257 h, respectively). As an application our approach, process five northern ecoregions found a 23%–63% little additional Combining large passive sets.
Язык: Английский
Процитировано
14
Biological Conservation, Год журнала: 2024, Номер 296, С. 110722 - 110722
Опубликована: Июль 19, 2024
Hedgerows are a semi-natural habitat that supports farmland biodiversity by providing food, shelter, and connectivity. Hedgerow planting goals have been set across many countries in Europe agri-environment schemes (AES) play key role reaching these targets. Passive acoustic monitoring using automated vocalisation identification (automated PAM), offers valuable opportunity to assess changes following AES implementation simple, community-level metrics, such as vocal activity of birds bats. To evaluate whether could be used indicate the effectiveness hedgerow future result-based or hybrid schemes, we surveyed twenty-four hedgerows England classified into chrono-sequence three age categories (New, Young, Old). We recorded 4466 h over course 30 days measured bird bat BirdNET for Kaleidoscope Vocal all birds, bats were modelled with predictors hedgerow, habitat, weather conditions occurring from maturity. show an increase Young Old compared New ones highlight elements surrounding landscape should considered when evaluating on communities. found high precision low species-level observations, argue may novel link payment component PAM results, incentivising effective management farmers landowners.
Язык: Английский
Процитировано
6