Stock identification of neon flying squid (Ommastrephes bartramii) in the North Pacific Ocean on the basis of beak and statolith morphology

Abstract

Cephalopods are becoming increasingly important in global fisheries as a result of increased landings and are playing an important ecological role in the trophic dynamics of marine ecosystems. Ommastrephes bartramii is a pelagic cephalopod species with two widely distributed spawning stocks in the North Pacific Ocean. It is also a major fishing target for the Chinese squid jigging fleets. Successful separation of these two spawning stocks is critical to fisheries management, but tends to be challenging because of their similar morphology. In this study we attempted to identify the stocks based on discriminant analyses of 9 morphological variables of statolith and 12 variables of beaks measured for O. bartramii samples in the North Pacific. A significant difference was revealed in the standardized beak and statolith variables between sexes in the northeast (NE) stock (P < 0.05). The northwest (NW) stock showed significant differences between sexes for all variables (P < 0.05) except for upper wing length (P > 0.05), whereas the NW stock showed no significant difference in either sex for the statolith variables (P > 0.05). The same sex also revealed different patterns with different hard structures between the two stocks. In t-tests females showed significant differences between stocks in statolith morphology (P < 0.05) and beak morphology (P < 0.05); males also showed this difference between cohorts in statolith variables (P < 0.05) except dorsal dome length and wing length (P > 0.05), but showed no difference between cohorts (P > 0.05) in beak morphometric variables. With the combination of two standardized hard parts, correct classification of stepwise discriminant analysis (SDA) was raised by nearly 20% compared with using only one structure, although overlaps of the NW stock were still found in the scatter-plots. It is concluded that adding more appropriate hard structure variables will effectively increase the success of separating geographic stocks by the SDA method.