Using vessel surveillance data to estimate spatiotemporal patterns in the short mackerel purse-seine fishery: Implications for time–area closure management in Thai waters

Abstract

Mapping fishing effort and fish abundance across management areas is required to adjust and enforce time–area management measures. Seasonal closures are imposed in Thai waters to mitigate the overexploitation of short mackerel (Rastrelliger brachysoma), one of Southeast Asia’s most economically important pelagic species. This study applied machine learning to fishing vessel surveillance data to estimate spatiotemporal distributions of fishing effort and fish abundance in the Gulf of Thailand (GOT) and the Andaman Sea (ADS) to provide reliable and near real-time information to support seasonal closures. Data collected between 2018 and 2020 from Thai purse seiners targeting short mackerel included VMS data, fishing trip records, and landing information. Spatiotemporal patterns in fishing effort and catch per unit effort (CPUE) were then visualized in relation to the closures in different statistical management zones. The results show that fishing effort in the ADS increased in 2020 from 2018 and 2019, and peaked in 2019 in the GOT. No significant spatiotemporal trend in the distribution of CPUE was identified over the 3 years. Notably, the results reveal spatiotemporal adjustments to fishing effort and CPUE following the implementation of seasonal closures. This study is the first to present a practical approach to using vessel surveillance data to examine seasonal patterns in fishing effort and estimated fish distribution in Thai waters. The findings improve our understanding of fishing effort in response to area closures. The information generated here supports the time–area management of an intensively exploited migratory fish species, given timely and accurate information.