Causal models as a scientific framework for next-generation ecosystem and climate-linked stock assessments

Rapid changes in marine ecosystems highlight the need to account for time-varying productivity in stock assessment models used to support fisheries management. Common approaches incorporate annual variation or regress processes like recruitment, natural mortality, or growth on environmental covariates. While the latter represents a step towards biological realism, it often fails accounting for interactions among covariates and may yield biased inferences when key drivers are correlated or unmeasured. We introduce a novel framework, Structural Causal Enhanced Stock Assessment Modelling (SCEAM), that integrates a Dynamic Structural Equation Model (DSEM) into a state-space stock assessment. SCEAM encompasses and extends the full range of existing time-varying approaches within a single framework, enabling direct comparison among them. We applied SCEAM to walleye pollock in the Gulf of Alaska to improve recruitment forecasting. When we compared three causal models of increasing complexity to recruitment modelled as random deviations around a mean, a first order autoregressive process, or regressed on a single covariate, we found that a causal model with intermediate complexity best balanced fit, parsimony, and predictive skill. This configuration reduced unexplained variance of recruitment by 69% and improved one-year-ahead forecasts. Key predictors included juvenile body condition and juvenile and larval catch rates. Our study represents the first application of a structural causal model embedded within a fisheries population model. SCEAM offers a unified, hypothesis-driven approach to integrating multiple non-independent covariates. We therefore propose that SCEAM can serve as a general scientific and statistical framework for building next-generation ecosystem- and climate-linked fisheries stock assessment models.