Fine spatio-temporal prediction of fishing time using big data - Zhao, Yizhi; Chen, Peng; Zheng, Gang; Wang, Difeng; Yang, Jingsong; Li, Xiunan; Luo, Dan (Frontiers in Marine Science, 2024)
Overfishing, bycatch, and other anthropogenic threats may lead to the destruction of fragile habitats and substantial losses of marine life. Marine fishery resources can be protected by adjusting fishing intensity and establishing marine reserves. Currently, China adopts the closed fishing season management approach to protect traditional fishing grounds, where fine spatio-temporal prediction is essential to efficiently supervise the wide scope. Fishing vessel behaviors reflect fishers' experience as well as the information provided by fish detection radar, while the fishery resource distribution is relevant to the marine environment. In this study, we identified fishing vessel behaviors (gillnets, trawls, purse seines, and abnormal behaviors) and qualitatively assessed and predicted fishing time of different fishing vessel behaviors to search for high intensity fishing operation areas by constructing a timespace prediction model. The model was based on big data of fishing vessel automatic identification systems and the marine environment, and was verified in the East China Sea. The prediction results generally corresponded with the distribution of traditional fishery resources in the East China Sea and the fishing efforts provided by the Global Fishing Watch. This model can provide an accurate and effective refined fishing vessel operation time prediction, and benefits fishing management and fishery resources protection.