Statistical Models for Fisheries Stock Assessment

Statistical models for fisheries stock assessment provide quantitative estimates of fish population dynamics, informing sustainable harvest limits and conservation measures worldwide. Core approaches include analytic production models, which relate catch and effort data to biomass trends, and age-structured or length-based models that integrate life-history parameters such as growth, recruitment and natural mortality. Integrated frameworks combine multiple data streams—catch records, survey indices, composition data and environmental covariates—within state-space or mixed-effects formulations to characterise both process and observation uncertainty. Bayesian implementations and likelihood-based methods permit estimation of parameter distributions and enable hindcasting or forecasting of stock status. Recent advances focus on refining variance components, improving diagnostic tools for model validation, and linking ecological processes to climate or habitat drivers. These developments enhance the realism of assessment outputs, support adaptive harvest control rules and underpin risk-based management strategies across diverse fisheries.

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Statistical Models for Fisheries Stock Assessment publication trend

The graph below shows the total number of articles in statistical models for fisheries stock assessment across all publications each year (not limited to Nature Index journals).

Technical terms

State-space model: A framework that separates biological processes (population dynamics) from observation error, allowing explicit modelling of uncertainty in each component.

Mixed-effects model: A statistical approach that includes both fixed effects (common to all data) and random effects (varying across time, space or groups) to account for non-independence and latent variation.

Process variance: The variability inherent in ecological processes such as recruitment or natural mortality, distinct from sampling error.

Sampling variance: The variability arising from the finite sampling of catches or surveys, affecting the precision of observed indices.

Prediction skill: A measure of a model’s ability to forecast unobserved data accurately, often quantified by comparing predictions to withheld observations.

Integrated stock assessment: A comprehensive modelling framework that unites multiple data types and processes within a single estimation procedure, enhancing robustness and coherence of advice.

References

1. Process and sampling variance within fisheries stock assessment models: estimability, likelihood choice, and the consequences of incorrect specification. ICES Journal of Marine Science (2023).
2. Empirical validation of integrated stock assessment models to ensuring risk equivalence: A pathway to resilient fisheries management. PLOS ONE (2024).
3. An integrated catch-at-age model for analyzing the variability in biomass of Pacific sardine (Sardinops sagax) from the Gulf of California, Mexico. Frontiers in Marine Science (2023).