Evidence map›Paper›PMID 40128653›Full record

ArticleBMC ecology and evolution2025

Crowdsourcing conservation: unveiling Taiwan's sea turtle foraging grounds, emerging threats, and residency with broad societal engagement.

Chia-Ling Fong, Daphne Z Hoh, Huai Su, Peng-Yu Chen, Chia-Chen Tsai, Kelly W H Tseng, Hao-Chih Huang, Jun-Yi Wu, Yoko Nozawa, Benny K K Chan

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Article in BMC ecology and evolution, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

10 authors.

Chia-Ling FongBiodiversity Program, Taiwan International Graduate Program Program, Academia Sinica, Taipei, Taiwan.
Daphne Z HohTurtleSpot Taiwan, Pingtung, Taiwan.
Huai SuTurtleSpot Taiwan, Pingtung, Taiwan.
Peng-Yu ChenTurtleSpot Taiwan, Pingtung, Taiwan.
Chia-Chen TsaiTurtleSpot Taiwan, Pingtung, Taiwan.
Kelly W H TsengIndependent Researcher, Taipei, Taiwan.
Hao-Chih HuangIndependent Researcher, Taipei, Taiwan.
Jun-Yi WuSchool of Forestry and Resource Conservation, National Taiwan University, Taipei, Taiwan.
Yoko NozawaBiodiversity Research Center, Academia Sinica, Taipei, Taiwan.
Benny K K ChanBiodiversity Research Center, Academia Sinica, Taipei, Taiwan. chankk@gate.sinica.edu.tw.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDetermining sea turtle foraging grounds, emerging threats, and population status are essential for conservation management. Crowdsourced science is a recently recognized approach that enables internet-based data collection, providing important contributions to scientific goals while also benefiting society and public education. This study is based on the published dataset from TurtleSpot Taiwan (2017-2022) with the aim to leverage crowdsourced data to determine sea turtle foraging grounds, emerging threats, demography, and residency patterns in Taiwan.

resultsWe identified three green turtle (Chelonia mydas) foraging grounds in Taiwan (Liuqiu Island, Kenting, and Green Island), defined as sites with > 100 sightings and > 50 individuals. Among all sites, Liuqiu Island contributed 77% of the total sightings, suggesting this island is a hotspot. Emerging threats to foraging aggregations of sea turtles in Taiwan were evident from the reported sightings, with ~ 10% of the total sightings involving turtles with fishing line entanglement, ingested debris, missing flippers, or injuries. Most of these sightings occurred in Liuqiu Island, indicating a significant level of human-turtle disturbance. Residency patterns identified from sighting data showed that 43.4% of individuals stayed in the same area for one or more years, with adult-sized turtle residency greater than that of immature turtles.

conclusionsTaiwan supports healthy foraging grounds for green turtles, where adults often stay for more than one year and with dynamic populations of younger individuals. However, despite a certain number of foraging green turtles observed in Liuqiu Island, many of these turtles displayed injuries. This high population density combined with increased injury frequency suggests that a comprehensive management plan for turtle foraging grounds is urgently needed, including measures to reduce boat speeds in hotspot areas and strict regulations on coastal human activity.

Indexed as

Conservation of Natural ResourcesCrowdsourcingTurtlesAnimalsHumansTaiwanChelonia mydasCitizen scienceDemographyEretmochelys imbricataForaging habitatPhoto-identification

Identifiers

PMID40128653
PMCPMC11931839

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LicenceCC BY-NC-ND
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.