Using Social Network Analysis to Study Marine Protected Areas on Penghu Islands

Abstract

A major challenge for fisheries management lies in how to strike a balance between the livelihoods of fishermen and sustainable operation of the fishing industry under the impact of climate change. In the long term, important marine habitats need to be designated as marine protected areas and prevent destructive man-made activities through appropriate legislation and law enforcement to promote marine resource conservation and ecological protection. Furthermore, marine protected areas have been recognized as the simplest, cheapest and most effective way in which to restore fishery resources and protect marine biodiversity in the future. However, the competent authorities have been unable to achieve co-management of the environment and partnerships for many years, and continue to rely on extensive integration and collaboration between governments and non-governmental organizations or individuals due to implementation-related factors such as the island society, the multiple social relationships of fishermen or residents in fishing villages, the closely related place attachment, and general xenophobia. The National Penghu University of Science and Technology established marine protected area community patrols in 2018. The patrols not only protect marine resources but also allow community residents and fishermen to build consensus among communities and reduce class discrimination against fishermen, which is in line with the sustainable planning and management strategy in fisheries of the blue economy and with the spirit of sato-umi. Sato-umi is a coastal area where biological productivity and biodiversity has increased through human interaction. In this study, social network analysis was used to systematically analyze the process of stakeholder engagement in the co-management of conservation areas, link cross-domain recreational resources amongst fishing villages, and cultivate regional revitalization in fishing communities through the visualization of participants’ locations, interactions, and relative influence.