Dynamic governance of transboundary fisheries through deep learning and high-frequency vessel tracking data for climate-resilient management: an application to the East China Sea

Authors

• Qin Lin, College of Management, Ningbo University of Finance & Economics, Ningbo 315175, China;
• Yaqing Shu, Faculty of Maritime and Transportation, Ningbo University, Ningbo, China;
• Manel Grifoll, Barcelona Innovation in Transport (BIT), Department of Civil and Environmental Engineering, Universitat Politecnica de Catalunya (UPC-Barcelona Tech), Barcelona, Spain;
• Hongxiang Feng, Faculty of Maritime and Transportation, Ningbo University, Ningbo, ChinaFollow

Abstract

Transboundary fisheries governance is increasingly challenged by jurisdictional conflicts, regulatory asymmetries, and climate-induced stock shifts in fish stocks, especially in contested regions like the East China Sea (ECS). This study capitalizes on China’s high-frequency BeiDou Navigation Satellite System (BDS) data (June 2021–May 2022) to pioneer a CNN-BiLSTM deep learning model to dynamically identify fishing hotspots and evaluate policy effectiveness. By integrating vessel trajectories, environmental variables, and regulatory events, we examine spatiotemporal patterns of fishing effort relative to seasonal bans, extreme weather, and quota systems. Key findings reveal: (1) post-moratorium (September–December) concentration of fishing intensity, peaking in November; (2) significant weather-driven suppression of spring fishing activity; (3) persistent trawler hotspots in designated closed zones, highlighting areas in need of heightened regulatory scrutiny; (4) measurable impacts of output-controlled quotas on effort distribution;  (5) Behavioral indicators derived from BeiDou data support the evaluation of governance effectiveness, including quota-based management; and (6) Chinese fishing vessels from Zhejiang Province exhibit a generally high level of compliance within the Sino-Japanese and Sino-Korean Provisional Measures Zones (PMZs). This research underscores the value of BeiDou data in enabling real-time, adaptive governance. We propose dynamic zoning and climate-responsive quotas to strengthen ecosystem-based management in transboundary fisheries, calling for integrated satellite monitoring and cross-jurisdictional coordination to enhance climate resilience and sustainable exploitation.

Abstract Image

Recommended Citation

Lin, Qin; Shu, Yaqing; Grifoll, Manel; and Feng, Hongxiang (2026) "Dynamic governance of transboundary fisheries through deep learning and high-frequency vessel tracking data for climate-resilient management: an application to the East China Sea," Journal of Marine Science and Technology–Taiwan: Vol. 34: Iss. 2, Article 3.
DOI: 10.51400/2709-6998.2818
Available at: https://jmstt.ntou.edu.tw/journal/vol34/iss2/3