Aquaculture and Fisheries Stock Assessment

Stock assessment is the science of estimating the abundance and productivity of fish populations to guide sustainable exploitation. It blends life-history information, fishery-dependent data (catches and effort) and fishery-independent surveys (trawl, acoustic, plankton) in mathematical models to project population trends and set harvest limits. Core approaches range from surplus-production models to age- or length-structured frameworks, many now formulated as state-space or mixed-effects models to separate biological variability from observation error. Integrated assessments unite multiple data streams—catch-at-age, survey indices and environmental covariates—within a single likelihood or Bayesian framework, providing probabilistic estimates of spawning stock biomass, fishing mortality and recruitment. Advances include joint estimation of process and sampling variance, empirical validation of model forecasts, and inclusion of climate signals. In aquaculture, stock reassessment supports broodstock management and optimises nursery and ongrower performance. Globally, robust assessments underpin quota decisions, certification schemes and ecosystem-based management, ensuring food security and equitable use while adapting to changing ocean conditions.

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Aquaculture and Fisheries Stock Assessment publication trend

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

Technical terms

State-space model: A hierarchical framework that explicitly separates true biological processes (population dynamics) from observation error, enabling direct modelling of uncertainty in both components.

Mixed-effects model: A statistical approach incorporating fixed effects (common parameters) and random effects (yearly or area-specific deviations) to accommodate correlated data and latent variability.

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

Sampling variance: Variability arising from imperfect and finite sampling of catches or surveys, affecting precision of observed indices.

Integrated stock assessment: A modelling framework that unites multiple data types—catch-at-age, survey indices, environmental covariates—within a single likelihood or Bayesian estimation to improve coherence and robustness of management 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).