A more detailed description of the dataset in this data paper: https://doi.org/10.3897/BDJ.10.e90196
We describe a dataset of sea turtle sightings around the coast of Taiwan and its islands. This data collection was initiated by TurtleSpot Taiwan, a citizen-science project that collects sea turtle sighting data. This dataset includes 3,515 sighting data dated from March 2010, except most of the data (n = 3,128; 89%) were collected between June 2017 to December 2021. Sightings were reported by citizen scientists to the Facebook Group of TurtleSpot Taiwan (https://www.facebook.com/groups/turtlespotintw) by providing occurrence information. We also requested photos and videos for species identification and to record any physical abnormality of the turtle, if observable. In addition to recording data often associated with an occurrence, TurtleSpot aims to identify each sea turtle up to the individual level using the Photo Identification (Photo ID) method. Hence, if photos of left facial scutes were available, the sighted individual can be identified and given a unique turtle ID. In total, 762 individuals were assigned a turtle ID, comprising 723 Greens (Chelonia mydas), 38 Hawksbills (Eretmochelys imbricata) and one Olive Ridley (Lepidochelys olivacea) turtle. It is hoped that the data may assist in future ecological studies and the development of conservation measures.
This dataset is currently the largest public dataset of sea turtle sighting records in Taiwan. Post-publication of this dataset to the GBIF platform demonstrated that the number of Green sea turtle Chelonia mydas records in Taiwan is one of the largest in the world (last accessed date: 15-10-2022).
The dataset contains data of two major categories: data associated with the occurrence and data related to the biological characteristics of the sighted turtle individual. The former category consists of information during the sighting event such as date, time, location, geographical coordinates, observation method and species. The latter category characterised the observed turtle individual using our controlled vocabulary during the sighting, including data such as living status, life stage, sex, physical abnormality and associated organism. The data allowed future research studies, such as biogeography, sea turtle foraging ecology that includes habitat use, sex ratio, abnormalities encountered and intra- and interspecies interaction. The data may also potentially guide any policy-making process through the assessment of species conservation status and diversity in the area of occurrences.
The data in this occurrence resource has been published as a Darwin Core Archive (DwC-A), which is a standardized format for sharing biodiversity data as a set of one or more data tables. The core data table contains 3,515 records.
This IPT archives the data and thus serves as the data repository. The data and resource metadata are available for download in the downloads section. The versions table lists other versions of the resource that have been made publicly available and allows tracking changes made to the resource over time.
The table below shows only published versions of the resource that are publicly accessible.
How to cite
Researchers should cite this work as follows:
Hoh D, Fong C (2022): Sea turtle sightings in Taiwan. v1.9. TurtleSpot Taiwan. Dataset/Occurrence. https://ipt.taibif.tw/resource?r=turtlespot&v=1.9
Researchers should respect the following rights statement:
The publisher and rights holder of this work is TurtleSpot Taiwan 海龜點點名. This work is licensed under a Creative Commons Attribution (CC-BY) 4.0 License.
This resource has been registered with GBIF, and assigned the following GBIF UUID: 336b6790-062f-407f-a783-2f1d8874e6c3. TurtleSpot Taiwan 海龜點點名 publishes this resource, and is itself registered in GBIF as a data publisher endorsed by Taiwan Biodiversity Information Facility.
Occurrence; Observation; Sighting data; Citizen science; Facebook group; Sea turtle; Coastal waters; Photo identification; Occurrence; Observation
Most of the sighting data were from Taiwan and its islands and only a few (n = 35) were from other countries which include Indonesia, Philippines, Malaysia, Palau, the Mariana Islands, Japan, Maldives and United States.
|Bounding Coordinates||South West [-90, -180], North East [90, 180]|
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). Most of the sea turtle sightings in the dataset were of Green and Hawksbill turtles (97.3% and 2.4%). 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)|
|Start Date / End Date||2010-03-23 / 2021-12-29|
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 Nov 2022, our community is made up of at least 19,800 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-scute pattern and occasionally distinct characteristics of their physical appearances such as carapace 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.
The personnel involved in the project:
See the step description below for more detail.
|Study Extent||We collect sighting reports of all sea turtle sightings from any region, which is included in the dataset. But our current research focus is on the sighting data around the coasts of Taiwan and its islands.|
Method step description:
- Data collection: Citizens who encountered sea turtles reported their sightings to us via our Facebook Group. Reporters post a regular post to the Group following our reporting format to contribute sighting information including sighting location, date, time, depth, observation method, photographs of the whole body and left- and right faces of the turtle individual.
- Quality control of sighting report received: Each sighting reported to the Group was first checked by the group administration prior to approval. The group administration checked if the post followed the reporting format mentioned above and the sighting provider will be requested to provide any of the missing information unless unavailable. Once the submitted post passed the quality check, the post will be approved by the group administration to be visible in the Facebook Group.
- Data transcription: Sighting information contained in the post/report was transcribed into Google Sheets as raw data.
- Determine additional information from the sighting report: We recorded additional information about the occurrence through the sighting reporter’s notes of onsite observation and our identification through the provided photos and videos. Additional information included the biological characteristics of the sighted individual turtle (sex, life stage, behaviour, associated taxa) and physical abnormality of the turtle (e.g. fishing line entanglement, tumour and others).
- Sea turtle individual identification: If clear photos of the left face of the sighted turtle were provided in the report, we use the Photo Identification (Photo ID) method to identify the turtle individual. Currently, we use two methods to perform Photo ID: (1) compare the facial scute pattern manually and (2) HotSpotter (Crall et al. 2013, Dunbar et al. 2021), open-source software for pattern recognition in wildlife research. Each sea turtle individual was assigned a unique turtle ID. The turtle ID was assigned as follows: Country code, site code, species code and sequence number. For example, in TW01G0082, “TW”, “01”, “G” and “0082” stands for Taiwan, island or county label, green turtle and unique number for the individual, respectively.
- Open data preparation: The language used in most of the recorded data is Traditional Chinese. Nevertheless, valuable information including sighting location, method, common name and life stages which allowed future data use was translated into English. We converted the occurrence data into Darwin Core Archive standard in Google Sheets, an online spreadsheet tool, using the Darwin Core Archive Assitant Add-on (Salim and Saraiva 2020). Refer to the Data resources section for a detailed description of each column. We then validated the occurrence dataset using the Data Validator developed by GBIF (Global Biodiversity Information Facility 2017). Lastly, we uploaded, stored and published the dataset using The Integrated Publishing Toolkit (IPT) of GBIF installed under the Taiwan Biodiversity Information Facility. The data is then opened on the IPT and GBIF for the public to access.
- 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
- Dunbar S, Anger E, Parham J, Kingen C, Wright M, Hayes C, Safi S, Holmberg J, Salinas L, Baumbach D (2021) HotSpotter: Using a computer-driven photo-id application to identify sea turtles. Journal of Experimental Marine Biology and Ecology 535 https://doi.org/10.1016/j.jembe.2020.151490