Interannual and seasonal variability of winter-spring cohort of neon flying squid abundance in the Northwest Pacific Ocean during 1995–2011

Abstract

The neon flying squid, Ommastrephes bartramii, is a species of economically important cephalopod in the Northwest Pacific Ocean. Its short lifespan increases the susceptibility of the distribution and abundance to the direct impact of the environmental conditions. Based on the generalized linear model (GLM) and generalized additive model (GAM), the commercial fishery data from the Chinese squid-jigging fleets during 1995 to 2011 were used to examine the interannual and seasonal variability in the abundance of O. bartramii, and to evaluate the influences of variables on the abundance (catch per unit effort, CPUE). The results from GLM suggested that year, month, latitude, sea surface temperature (SST), mixed layer depth (MLD), and the interaction term (SST×MLD) were significant factors. The optimal model based on GAM included all the six significant variables and could explain 42.43% of the variance in nominal CPUE. The importance of the six variables was ranked by decreasing magnitude: year, month, latitude, SST, MLD and SST×MLD. The squid was mainly distributed in the waters between 40˚N and 44˚N in the Northwest Pacific Ocean. The optimal ranges of SST and MLD were from 14 to 20℃ and from 10 to 30 m, respectively. The squid abundance greatly fluctuated from 1995 to 2011. The CPUE was low during 1995-2002 and high during 2003-2008. Furthermore, the squid abundance was typically high in August. The interannual and seasonal variabilities in the squid abundance were associated with the variations of marine environmental conditions and the life history characteristics of squid.

Impacts of changing spatial scales on CPUE–factor relationships of Ommastrephes bartramii in the northwest Pacific

“…where ln(CPUE + 1) is the logarithmically converted CPUE, which has widely been applied in fisheries research (Chiu, Chiu, & Chen, ; Damalas et al., ; Tian et al., ; Yu, Chen, & Yi, ; Zainuddin, Saitoh, & Saitoh, ); s (.) is a spline smoothing function, and δ is the model fitting residual.…”

Changes in overwintering ground of small yellow croaker (Larimichthys polyactis) based on MaxEnt and GARP models: A case study of the southern Yellow Sea stock

“…Most studies that examined changes in the distribution of fishery resources have estimated the centres of gravity of fish populations directly from the catch records of a few stations (Dulvy et al ., 2008; Engelhard et al ., 2014). In other studies, the relationship between the distribution of fishery resources and marine environmental factors was established using linear correlation analysis (Li et al ., 2011), geographically weighted regression (Li et al ., 2020), habitat suitability index (Yu et al ., 2019), random forest model (Nesslage et al ., 2021), generalized linear models (Yu et al ., 2016) and generalized additive models (Punya et al ., 2021) based on catch and environmental element data to predict the distribution of fishery resources directly or indirectly. However, these approaches require the comprehensive and accurate presence and actual absence of records of fishery resources, which are rarely available, especially for highly mobile marine species (Alabia et al ., 2015).…”