Fishery-dependent data revealing the spatio-temporal dynamics of under-monitored fisheries

Abstract

Given that most of the world's fisheries are not properly monitored due to survey programs with limited spatio-temporal coverage, fishery-dependent data provide an opportunity to understand under-monitored species. However, due to the typically poor quality of fishery-dependent data resulting from spatially unbalanced sampling by fishermen, spatio-temporal interactions should be considered during modeling. In this study, we use fishery-dependent catch and effort data collected from fishing log of commercial vessels from 2012 to 2021 to fit a vector autoregressive spatio-temporal (VAST) model to analyze population dynamics of largehead hairtail (Trichiurus lepturus) in the Yellow and Bohai Seas. We considered both fixed and random factors including temporal variations, spatio-temporal interactions, vessel-level variations, and gear types. Our results suggested that the abundance index fluctuated inter-annually and showed a remarkable recovery trend since the large-scale summer moratorium extension in China seas in 2017. Population distributions also showed substantial shifts, with an eastward range expansion especially in the Yellow Sea after 2017. Our study highlights the opportunity of adopting fishery-dependent data to inform the spatial and temporal dynamics of under-monitored fishery species through a proper modeling framework, which may provide a basis for scientific management of data-limited fisheries.