Descripción
This dataset summarizes species-specific nocturnal vocal activity of four owl species—Mountain Scops-Owl (Otus spilocephalus), Collared Owlet (Taenioptynx brodiei), Collared Scops-Owl (Otus lettia), and Northern Boobook (Ninox japonica)—recorded via passive acoustic monitoring (PAM) from January to December 2023 at Meishan, Yushan National Park, Taiwan. Using an AI-based sound recognition model (SILIC ver. exp32), 705,512 vocalizations were detected and classified from 1,946 hours of nighttime (1600-0800) audio recordings. The dataset provides fine-scale temporal, environmental, lunar and acoustic metadata for each occurrence, supporting future ecological studies, species distribution modeling, and long-term acoustic monitoring of nocturnal forest birds.
Registros
Los datos en este recurso de evento de muestreo han sido publicados como Archivo Darwin Core(DwC-A), el cual es un formato estándar para compartir datos de biodiversidad como un conjunto de una o más tablas de datos. La tabla de datos del core contiene 711.353 registros.
también existen 2 tablas de datos de extensiones. Un registro en una extensión provee información adicional sobre un registro en el core. El número de registros en cada tabla de datos de la extensión se ilustra a continuación.
Este IPT archiva los datos y, por lo tanto, sirve como repositorio de datos. Los datos y los metadatos del recurso están disponibles para su descarga en la sección descargas. La tabla versiones enumera otras versiones del recurso que se han puesto a disposición del público y permite seguir los cambios realizados en el recurso a lo largo del tiempo.
Versiones
La siguiente tabla muestra sólo las versiones publicadas del recurso que son de acceso público.
¿Cómo referenciar?
Los usuarios deben citar este trabajo de la siguiente manera:
Wu S, Ko J C, Tsai W (2025). Automatic acoustic detections of four owl species using SILIC in Meishan, Yushan National Park, Taiwan. Version 1.3. Taiwan Biodiversity Research Institute. Samplingevent dataset. https://ipt.taibif.tw/resource?r=owls_ysnp_msc02_silic_exp32&v=1.3
Derechos
Los usuarios deben respetar los siguientes derechos de uso:
El publicador y propietario de los derechos de este trabajo es Taiwan Biodiversity Research Institute. Esta obra está bajo una licencia Creative Commons de Atribución/Reconocimiento (CC-BY 4.0).
Registro GBIF
Este recurso ha sido registrado en GBIF con el siguiente UUID: ac9b5a09-a1b4-416c-9538-ca85078a7210. Taiwan Biodiversity Research Institute publica este recurso y está registrado en GBIF como un publicador de datos avalado por Taiwan Biodiversity Information Facility.
Palabras clave
Samplingevent; Observation
Contactos
- Proveedor De Los Metadatos ●
- Originador ●
- Punto De Contacto
https://orcid.org/0000-0003-3619-0646
- Originador
- Proveedor De Contenido ●
- Originador
Cobertura geográfica
Acoustic data were collected using a PAM station located in a sub-montane evergreen broad-leaved forest at Meishan, Yushan National Park, Taiwan (23°16'32"N 120°50'38"E; 1,264 m a.s.l.). This station is part of a long-term soundscape monitoring network established along the Southern Cross-Island Highway, jointly operated by the Taiwan Biodiversity Research Institute and the Yushan National Park Headquarters.
| Coordenadas límite | Latitud Mínima Longitud Mínima [23,275, 120,843], Latitud Máxima Longitud Máxima [23,276, 120,844] |
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Cobertura taxonómica
We focused on four commonly occurring owl species in Taiwan’s montane forests: Mountain Scops-Owl (MSO, Otus spilocephalus), Collared Owlet (CO, Taenioptynx brodiei), Collared Scops-Owl (CSO, O. lettia), and Northern Boobook (NB, Ninox japonica).
| Especie | Otus spilocephalus (Mountain Scops-Owl), Taenioptynx brodiei (Collared Owlet), Otus lettia (Collared Scops-Owl), Ninox japonica (Northern Boobook) |
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Cobertura temporal
| Fecha Inicial / Fecha Final | 2023-01-01 / 2023-12-31 |
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Datos del proyecto
The aim of Sound Identification and Labeling Intelligence for Creatures (SILIC) is to integrate online animal sound databases, PAM databases and an object detection-based model, for extracting information on the sounds of multiple species from complex soundscape recordings. SILIC計畫目的為自動化擷取聲景資料中的動物訊息，以作為生態研究及棲地經營管理之用。
| Título | SILIC - Sound Identification and Labeling Intelligence for Creatures |
|---|---|
| Identificador | SILIC |
Personas asociadas al proyecto:
Métodos de muestreo
Acoustic data were collected using an autonomous recording unit (Song Meter Mini; Wildlife Acoustics, Inc.) programmed to record at 44.1 kHz sampling rate and 16-bit resolution. The recorder was installed at a height of approximately 1.5 meters above the forest floor. The device was set to a duty cycle of 1-minute recording followed by a 2-minute pause, yielding 20 minutes of recordings per hour over a 16-hour nightly window. This schedule was maintained continuously for the entire year. Target species included four sympatric owls: Otus spilocephalus, Otus lettia, Taenioptynx brodiei, and Ninox japonica. Automated detection of vocalizations was conducted using the SILIC model (version exp32), which was trained to identify species- and sound type–specific vocalizations from spectrogram features.
| Área de Estudio | The study was conducted at the Meishan soundscape monitoring station located in a sub-montane evergreen broadleaved forest at 1,264 m elevation within Yushan National Park, Taiwan (23°16′32″N, 120°50′38″E). Passive acoustic data were collected year-round in 2023, spanning 365 consecutive days from 1 January to 31 December. The recording effort covered the nightly period from 16:00 to 08:00, resulting in a total of 1,946 hours of audio data. This station is part of a long-term ecological monitoring initiative along the Southern Cross-Island Highway jointly operated by the Taiwan Biodiversity Research Institute and the Yushan National Park Headquarters. |
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| Control de Calidad | A confidence threshold of 0.6 was applied to all automated detection outputs to retain only high-confidence vocalizations. To evaluate model precision, 100 randomly selected detections per species were manually validated by an expert, resulting in overall classification accuracy exceeding 94% for all species. Lunar variables—including moon phase, moon altitude, and night-distance (temporal distance from the nearest solar transition)—were calculated using the Python ephem library, based on the geographic coordinates and timestamps of each detection. Meteorological variables (cloud cover, wind speed, air temperature, atmospheric pressure, and precipitation) were obtained from the ERA5 reanalysis dataset and spatially interpolated to the recorder’s location. All detection timestamps were converted to local time (UTC+8), and derived hourly covariates were cross-checked for temporal alignment, missing values, and consistency with modeled nocturnal periods. |
Descripción de la metodología paso a paso:
- Deployment: The autonomous recorder was deployed in Meishan, Yushan National Park, and operated continuously from 1 January to 31 December 2023.
- Recording Schedule: The recorder followed a 1-minute on / 2-minute off cycle from 16:00 to 08:00 daily.
- Data Extraction: Audio recordings were processed using the SILIC model (ver. exp32), a convolutional neural network-based sound recognition tool.
- Species Identification: Species-specific call types for each owl species were predefined in the SILIC model (version exp32).
- Detection Filtering: Detections with a confidence score below 0.6 were discarded.
- Manual Validation: 100 detections per species were manually checked to evaluate model precision.
- Data Aggregation: Detections were aggregated into hourly vocal activity rate (VAR) measures.
- Environmental Covariates: Hourly weather and lunar data were matched to each detection timestamp using astronomical and reanalysis datasets.
- Data Structuring: The final dataset was formatted using the Event Core with Occurrence and MeasurementOrFact extensions, following Darwin Core standards.
Referencias bibliográficas
- Wu, S.-H., Chang, H.-W., Lin, R.-S., & Tuanmu, M.-N. (2022). SILIC: A cross database framework for automatically extracting robust biodiversity information from soundscape recordings based on object detection and a tiny training dataset. Ecological Informatics, 68, 101534. https://doi.org/10.1016/j.ecoinf.2021.101534
Metadatos adicionales
| Identificadores alternativos | ac9b5a09-a1b4-416c-9538-ca85078a7210 |
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| https://ipt.taibif.tw/resource?r=owls_ysnp_msc02_silic_exp32 |