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    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/153681


    Title: Hybrid Intelligence for Marine Biodiversity: Integrating Citizen Science with AI for Enhanced Intertidal Conservation Efforts at Cape Santiago, Taiwan.
    Authors: 陳楊文
    Chen, Vincent Y.;Lu, Day-Jye;Han, Yu-San
    Contributors: 地政系
    Keywords: marine protect areas;rocky intertidal ecosystem;hybrid intelligence;citizen science;artificial intelligence
    Date: 2024-01
    Issue Date: 2024-09-10 13:20:35 (UTC+8)
    Abstract: Marine biodiversity underpins the formation of marine protected areas (MPAs), necessitating detailed surveys to account for the dynamic temporal and spatial distribution of species influenced by tidal patterns and microhabitats. The reef rock intertidal zones adjacent to urban centers, such as Taiwan’s Cape Santiago, exhibit significant biodiversity, yet they are increasingly threatened by tourism-related activities. This study introduces an artificial intelligence (AI)-empowered citizen science (CS) approach within the local community to address these challenges. By integrating CS with AI, we establish a hybrid intelligence (HI) system that conducts in situ biological surveys and educational programs focused on reef ecological conservation. This initiative not only facilitates the collective gathering and AI-assisted analysis of critical data but also uses machine-learning outputs to gauge data quality, thus informing subsequent data collection and refinement strategies. The resulting collectivity and iterative enhancement foster a mutual and continuous HI learning environment. Our HI model proves instrumental in fostering community engagement and public involvement in CS endeavors, cultivating the skills necessary for documenting rocky intertidal biodiversity shifts. These efforts are pivotal for informing the design and governance of future MPAs, ensuring their efficacy and sustainability in marine conservation.
    Relation: Sustainability, Vol.16, No.1, 454
    Data Type: article
    DOI 連結: https://doi.org/10.3390/su16010454
    DOI: 10.3390/su16010454
    Appears in Collections:[地政學系] 期刊論文

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