A machine learning technique for deriving the optimal mesh size of a gizzard shad (Konosirus punctatus) gillnet

Abstract

This study aims to prevent the bycatch of young gizzard shads, a non-target fish species, and to derive mesh sizes appropriate for economic fishery management. Test fishing operations (11 times) were conducted by manufacturing gillnet fishing gear with different mesh sizes (50.5, 55.1, 60.6, and 67.3 mm) in the coastal waters of the southern Gyeongsang Province. Selectivity curve estimation and machine learning models were developed to design an appropriate mesh using data measured at sea. Model 1 was constructed using mesh variables to classify score groups. In contrast, Model 2 was constructed using mature, bycatch, and CPUE variables to categorize score groups. As a result of this study, the total length with a 0.5 gillnet selection ratio was estimated to be 179.3, 195.6, 215.1, and 238.9 mm for the 50.5, 55.1, 60.6, and 67.3 mm mesh sizes, respectively. In Model 1, a mesh size of 57.85 mm or less was determined as the most appropriate mesh size for biological and economic measurements. Considering biological and economic aspects, shad gillnets should have a mesh size in the 50.5-55.1 mm range.