説明
[ Data paper under review - Preprint https://preprints.arphahub.com/article/90210/ ] We describe a dataset of sea turtle sightings around the coast of Taiwan and its islands. The data was collected by citizen scientists and reported to TurtleSpot Taiwan, a citizen science initiative that collects sea turtle sighting data mainly through the Facebook group (https://www.facebook.com/groups/turtlespotintw). This dataset includes 3,515 sighting data dated from March 2010, except most of the data (n=3,128; 89%) falls were between June 2017 to December 2021. A standardized format of basic sighting information was suggested to anyone who wishes to report the occurrence. We also request photos and videos for turtle species identification and to record any physical abnormality. In addition to the basic data often associated with an occurrence, TurtleSpot aims to identify each sea turtle up to the individual level using the Photo ID method. Hence, if a good quality photo of left- and right-facial scutes were available, the sighted individual can be identified and given a unique Turtle ID. In total, 762 turtle individuals were assigned a turtle ID, comprising 723 green, 38 hawksbill, and 1 olive ridley turtles. The data of TurtleSpot Taiwan has allowed some ecological observations of sea turtles in the wild, such as witnessing the recovery of some injured turtles, behaviours, intra- and inter-species interactions, and physical abnormality. These data will offer essential information that helps to understand the foraging ecology of sea turtles and assists in the development of conservation measures. The purpose for preparing the current dataset was for a few reasons: (1) a give back to our community since most of these data were their contributions, (2) for an ongoing research on sea turtle foraging ecology in Taiwan (Fong et al., in prep), and (3) publicly open the data for advancement especially in the scientific and conservation communities.
データ レコード
この オカレンス（観察データと標本) 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、3,515 レコードが含まれています。
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
注意してください、これは、古いバージョンのデータセットです。 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。:
Hoh D, Fong C (2022): Sea turtle sightings in Taiwan | 台灣海龜目擊紀錄. v1.8. TurtleSpot Taiwan 海龜點點名. Dataset/Occurrence. https://ipt.taibif.tw/resource?r=turtlespot&v=1.8
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 TurtleSpot Taiwan 海龜點點名。 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: 336b6790-062f-407f-a783-2f1d8874e6c3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TurtleSpot Taiwan 海龜點點名 が、このリソースをパブリッシュしました。
キーワード
Occurrence; Observation; Sighting data; Citizen science; Facebook group; Sea turtle; Coastal waters; Occurrence; Observation
連絡先
- メタデータ提供者 ●
- 最初のデータ採集者 ●
- 連絡先
http://orcid.org/0000-0002-7810-1034
地理的範囲
Most of the sighting data were from Taiwan, and only a few (n=35) of them were from other countries which include Indonesia, Philippines, Malaysia, Palau, Mariana Islands, Japan, Maldives, and United States.
| 座標（緯度経度） | 南 西 [-90, -180], 北 東 [90, 180] |
|---|
生物分類学的範囲
Four species of sea turtles were recorded in the dataset, including green turtle (Chelonia mydas), hawksbill (Eretmochelys imbricata), olive ridley (Lepidochelys olivacea), and kemp's ridley (Lepidochelys kempii). 97.3% and 2.4% of the sightings from Taiwan are from the green and hawksbill turtles, respectively. Occurrences that failed to assign species (n=11) were recorded as Cheloniidae.
| Family | Cheloniidae (Sea turtle) |
|---|---|
| Species | Chelonia mydas (Green sea turtle), Eretmochelys imbricata (Hawksbill turtle), Lepidochelys olivacea (Olive ridley turtle), Lepidochelys kempii (Kemp's ridley) |
時間的範囲
| 開始日 / 終了日 | 2010-03-23 / 2021-12-29 |
|---|
プロジェクトデータ
TurtleSpot Taiwan (https://turtlespottw.org/) is a community led-citizen science initiative that collects sea turtle sighting reports. It is co-founded by a group of sea turtle lovers made up of scientists, underwater photographers, and marine awareness educators in June 2017. Currently, we receive sighting reports via our Facebook group (https://www.facebook.com/groups/turtlespotintw) and our community and sighting reports are actively growing. Up to July 2022, our community is made up of at least 18,900 members and we received at least 3,500 sighting reports. We are currently developing a Photo ID database of sea turtles in Taiwan. Through the Photo ID method, we identified sea turtle individuals through their unique facial-scutes pattern and occasionally distinct characteristics of their physical appearances such as caparace or limb injury. To encourage continuous reports of the citizen scientists, we allow the sighting reporter to name the turtle if the individual is a new record in our database. The data of TurtleSpot Taiwan has allowed some ecological observations of sea turtles in the wild, such as witnessing the recovery of some injured turtles, behaviours, intra- and inter-species associations, and physical abnormality. These data will offer essential information that helps to understand the foraging ecology of sea turtles and assists in the development of conservation measures.
| タイトル | TurtleSpot Taiwan |
|---|---|
| 識別子 | https://github.com/daphnehoh/TurtleSpot |
プロジェクトに携わる要員:
- キュレーター
- キュレーター
- キュレーター
収集方法
More than half (n=2,235; 63.6%) of the data was provided by citizen scientists. The remaining data (n=1,280) was personal records contributed by two of the co-authors.
| Study Extent | We collect reports of all sea turtle sightings from any region, but our current focus is on the sighting data around the coasts of Taiwan and its islands. |
|---|---|
| Quality Control | Each sighting reported in TurtleSpot's Facebook group was first checked by the group admin for the required information pending approval. The required information includes sighting date, time, location (and dive site), depth, method, and for species and individual identification - high-quality photos and videos containing the whole body, left, and right faces of the sighted turtle. The sighting provider will be requested to provide any of the missing information unless unavailable. |
Method step description:
- Receive data: We receive sea turtle sighting reports contributed by citizen scientists via our Facebook group (https://www.facebook.com/groups/turtlespotintw).
- Approve report: Facebook post of a sighting report which followed our reporting format and passed the quality check by the group admin was approved to be visible in the Facebook group, allowing community interaction within the post.
- Record data: Sighting information contained in the report was recorded in Google Sheets as raw data. These raw data (mostly in Chinese) were translated into English.
- Determine additional information from the sighting report: We identified and recorded the additional information about the sighted individual turtle based on the photos and videos provided in each sighting report and additional notes provided by the citizen scientists. This information was mostly identified through observation on-site and through photos and videos. We observed the biological characteristics of the occurrence (sex, life stage, behaviour, associated taxa) and abnormal conditions of the turtle (e.g. fishing line entanglement, tumour and others).
- Sea turtle individual identification: If high-quality photos of the left and right faces of the sighted sea turtle were available, we use the Photo ID method to identify the turtle individual. Currently, we use two methods to perform photo identification: (1) compare the facial scute pattern manually, and (2) HotSpotter (Crall et al. 2013), an open-source software. Each sea turtle individual was assigned a unique turtle ID.
- Open data preparation: We first converted the data into Darwin Core Archive formatted occurrence data in Google Sheet using the Darwin Core Archive Assitant Add-on (Salim and Saraiva 2020) where applicable. A total of 46 Darwin Core terms were used. We then validated the occurrence data using the GBIF Data Validator (Global Biodiversity Information Facility 2017) and edited the error. Lastly, we uploaded and published the data via Taiwan Biodiversity Information Facility IPT.
書誌情報の引用
- Crall JP, Stewart CV, Berger-Wolf TY, Rubenstein DI, Sundaresan SR (2013) Hotspotter—patterned species instance recognition. 2013 IEEE workshop on applications of computer vision (WACV). IEEE. https://doi.org/10.1109/WACV.2013.6475023
- Salim JA, Saraiva AM (2020) A Google Sheet Add-on for Biodiversity Data Standardization and Sharing. Biodiversity Information Science and Standards. 4:e59228. https://doi.org/10.3897/biss.4.59228
追加のメタデータ
| 代替識別子 | 336b6790-062f-407f-a783-2f1d8874e6c3 |
|---|---|
| https://ipt.taibif.tw/resource?r=turtlespot |