水产学报2026，Vol.50Issue(4)：66-83,18.DOI:10.11964/jfc.20251215273
基于多源数据与INLA-贝叶斯时空模型的东海小黄鱼CPUE标准化
CPUE standardization of small yellow croaker(Larimichthys polyactis)in the East China Sea using an INLA-based Bayesian spatio-temporal model and multi-source data
摘要
Abstract
Fishery-dependent and independent data each have strengths and limitations for estimating abundance indices.Com-mercial catch-per-unit-effort(CPUE)offers broad spatio-temporal coverage but suffers from gear selectivity and preferential sampling,while scientific surveys provide standardized sampling but limited coverage.Integrating these data sources is particu-larly challenging in mixed fisheries where multiple gear types with different selectivity patterns operate concurrently.Small yellow croaker(Larimichthys polyactis)in the East China Sea represents such a complex fishery,supporting important com-mercial fisheries while exhibiting strong spatio-temporal dynamics influenced by environmental conditions and gear-specific catchability.This study aimed to develop a robust CPUE standardization approach for small yellow croaker by integrating multi-gear commercial fishery data and scientific survey data within a Bayesian spatio-temporal modeling framework,evaluating alternative spatial structures and data integration strategies to obtain more reliable abundance indices for stock assessment.We analyzed 39 434 commercial fishing records from 158 vessels operating in September during 2010-2023,covering three major gear types:trawl,gillnet,and stow net,complemented by scientific survey data from 90-120 stations annually.An INLA-based Bayesian spatio-temporal generalized linear mixed model with gamma distribution and log-link was developed,incorporating year effects,gear effects,environmental covariates(depth,distance to coast,bottom temperature,bottom salinity),and their interactions.Models with independent spatial fields substantially outperformed shared spatial field models for both commercial and survey data,with the optimal model(M1)including independent spatial fields,linear environmental effects,and gear-envir-onment interactions achieving the lowest DIC(7 786)and WAIC(7 838)values.Gamma distribution provided superior predict-ive performance(R2=0.76,RMSE=616)compared to lognormal distribution(R2=0.65,RMSE=784).Gear-environment interac-tions significantly improved model fit,revealing differential environmental responses:salinity positively affected all gears but most strongly influenced trawl catch rates(effect size 0.262),while distance to coast showed negative effects on trawl(−0.259)and stow net(−0.129)but negligible effects on gillnet.Spatial random effects revealed persistent positive anomalies in the northern East China Sea(30-33°N),indicating this region as core habitat not fully explained by environmental covariates.Annual abundance indices from integrated modeling showed pronounced interannual variability,with peaks in 2015 and not-able declines during 2016-2020,followed by recovery in 2022-2023.The INLA-GLMM framework with independent spatio-temporal fields effectively disentangles gear-specific catchability,environmental effects,and true abundance variation,provid-ing a robust foundation for stock assessment and fisheries management of this important species.关键词
小黄鱼/单位捕捞努力量渔获量(CPUE)标准化/INLA-GLMM/多网具/时空异质性/东海Key words
Larimichthys polyactis/CPUE standardization/INLA-GLMM/multi-gear/spatio-temporal heterogeneity/East China Sea分类
农业科技引用本文复制引用
刘尊雷,杨林林,袁兴伟,金艳,程家骅..基于多源数据与INLA-贝叶斯时空模型的东海小黄鱼CPUE标准化[J].水产学报,2026,50(4):66-83,18.基金项目
国家重点研发计划(2024YFD2400403) （2024YFD2400403）
国家级非营利性研究机构基本研究基金(Dong2022TD01) National Key R&D Program of China(2024YFD2400403) （Dong2022TD01）
Basic Research Fund for State-Level Non-profit Research Institutes of ESCFRI,CAFS(Dong2022TD01) （Dong2022TD01）
