説明
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.
データ レコード
この sampling event リソース内のデータは、1 つまたは複数のデータ テーブルとして生物多様性データを共有するための標準化された形式であるダーウィン コア アーカイブ (DwC-A) として公開されています。 コア データ テーブルには、711,353 レコードが含まれています。
拡張データ テーブルは2 件存在しています。拡張レコードは、コアのレコードについての追加情報を提供するものです。 各拡張データ テーブル内のレコード数を以下に示します。
この IPT はデータをアーカイブし、データ リポジトリとして機能します。データとリソースのメタデータは、 ダウンロード セクションからダウンロードできます。 バージョン テーブルから公開可能な他のバージョンを閲覧でき、リソースに加えられた変更を知ることができます。
バージョン
次の表は、公にアクセス可能な公開バージョンのリソースのみ表示しています。
引用方法
研究者はこの研究内容を以下のように引用する必要があります。:
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
権利
研究者は権利に関する下記ステートメントを尊重する必要があります。:
パブリッシャーとライセンス保持者権利者は Taiwan Biodiversity Research Institute。 This work is licensed under a Creative Commons Attribution (CC-BY 4.0) License.
GBIF登録
このリソースをはGBIF と登録されており GBIF UUID: ac9b5a09-a1b4-416c-9538-ca85078a7210が割り当てられています。 Taiwan Biodiversity Information Facility によって承認されたデータ パブリッシャーとして GBIF に登録されているTaiwan Biodiversity Research Institute が、このリソースをパブリッシュしました。
キーワード
Samplingevent; Observation
連絡先
- メタデータ提供者 ●
- 最初のデータ採集者 ●
- 連絡先
https://orcid.org/0000-0003-3619-0646
- 最初のデータ採集者
- データ提供者 ●
- 最初のデータ採集者
地理的範囲
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.
| 座標（緯度経度） | 南 西 [23.275, 120.843], 北 東 [23.276, 120.844] |
|---|
生物分類学的範囲
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).
| Species | Otus spilocephalus (Mountain Scops-Owl), Taenioptynx brodiei (Collared Owlet), Otus lettia (Collared Scops-Owl), Ninox japonica (Northern Boobook) |
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時間的範囲
| 開始日 / 終了日 | 2023-01-01 / 2023-12-31 |
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プロジェクトデータ
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。
| タイトル | SILIC - Sound Identification and Labeling Intelligence for Creatures |
|---|---|
| 識別子 | SILIC |
プロジェクトに携わる要員:
収集方法
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.
| Study Extent | 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. |
|---|---|
| Quality Control | 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. |
Method step description:
- 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.
書誌情報の引用
- 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
追加のメタデータ
| 代替識別子 | ac9b5a09-a1b4-416c-9538-ca85078a7210 |
|---|---|
| https://ipt.taibif.tw/resource?r=owls_ysnp_msc02_silic_exp32 |