Seasonal Stratification and Depth Effects on Chub Mackerel Fishery Yields

Assessing how environmental and anthropogenic factors jointly influence fishery yields is crucial for targeted fishery management under climate change. By integrating Gray Relational Analysis (GRA) with Bayesian Generalized Linear Mixed Models (GLMM) to quantify how sea surface temperature (SST) and sea surface salinity (SSS) affect the yields of chub mackerel (Scomber japonicus) and Japanese Spanish Mackerel (Scomberomorus niphonius) in the Bohai Sea. Through the combination, key environmental drivers are identified and ranked using GRA, and their effects are estimated while accounting for uncertainty by Bayesian GLMM. The analyses revealed a strong association between water temperature at 50 m depth and mackerel catch yields, with interaction terms involving the engine power of fishing vessels further strengthening this correlation. By incorporating lagged catch, fishing vessel count, SST at 50 m, surface SSS, and seasonal factors into a Bayesian GLMM framework, the analysis reveals that chub mackerel yields are predominantly influenced by environmental changes, particularly through interactions between SST and fishing vessels (p < 0.1). For Japanese Spanish mackerel, the interaction between SST at 50m and fishing vessel count is significant (p<0.01). Seasonal analyses indicate that summer and winter conditions notably affect catch yields. These findings underscore the complex interplay between environmental drivers and anthropogenic factors in influencing fishery productivity under climate change. The combined GRA–Bayesian GLMM approach enhances variable prioritization and statistical inference, offering a practical framework for disentangling environmental and human influences on fishery productivity.

Climate change; Chub mackerel; Fishery yields; Japanese Spanish mackerel; SST and SSS

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