Page 88 - 《水产学报》2026年第04期

4 期 水 产 学 报 50 卷

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

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LIU Zunlei , YANG Linlin , YUAN Xingwei , JIN Yan , CHENG Jiahua
(Key Laboratory of East China Sea Fishery Resources Exploitation, Ministry of Agriculture and Rural Affairs,
East China Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Shanghai 200090, China)

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-
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ive performance (R =0.76, RMSE=616) compared to lognormal distribution (R =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.
Key words: Larimichthys polyactis; CPUE standardization; INLA-GLMM; multi-gear; spatio-temporal heterogeneity; East
China Sea
Corresponding author: JIN Yan. E-mail: jenniferyanjin@163.com
Funding projects: National Key R & D Program of China (2024YFD2400403); Basic Research Fund for State-Level Non-
profit Research Institutes of ESCFRI, CAFS (Dong2022TD01)

https://www.china-fishery.cn 中国水产学会主办 sponsored by China Society of Fisheries
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