Hybrid Intelligence for Marine Biodiversity: Integrating Citizen Science with AI for Enhanced Intertidal Conservation Efforts at Cape Santiago, Taiwan

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.

An Integrated Framework with ADD-LSTM and DeepLabCut for Dolphin Behavior Classification

“…Marine sustainability refers to the responsible and balanced use of marine resources and ecosystems to ensure their long-term health and resilience [1]. Marine sustainability and the well-being of dolphins are interconnected because dolphins are marine mammals that depend on healthy and sustainable marine ecosystems for their survival.…”

“…Additionally, we conducted a comparative analysis of the root mean square error (RMSE) values and confidence scores of the model for different body parts. The RMSE formula is presented in Equation (1). A comprehensive performance evaluation and analysis of these results are provided and summarized in Table 3.…”