Fisheries Abundance Estimation and Management

Estimating the abundance of fish populations underpins sustainable harvest and ecosystem conservation worldwide. Traditional approaches have relied heavily on fishery‐dependent indicators such as catch‐per‐unit‐effort (CPUE) and on fishery‐independent survey data derived from trawls, acoustic records or visual surveys. Such data are standardised through statistical models to produce abundance indices that inform quotas, spatial management and rebuilding plans. Recent advances in spatio-temporal modelling, machine learning and joint species distribution frameworks have enhanced our capacity to account for sampling bias, environmental covariates and interactions among species. By integrating multiple surveys, remote sensing and environmental predictors, modern assessments can disentangle genuine changes in biomass from shifts in distribution or fishing effort. These developments support adaptive management under climate change, reduce uncertainty in quota setting and promote the design of spatial measures such as marine protected areas. The global nature of fisheries and their socio-economic importance demand robust, transparent estimators and management strategies that balance exploitation with conservation of marine biodiversity.

Research from Nature Portfolio

Recent studies have applied dynamic species distribution modelling to mixed fisheries, revealing coherent spatial assemblages and their interannual variability. By reducing species‐habitat dimensions, this work demonstrates how spatial targeting could decouple exploitation of high-quota and low-quota species, informing landing‐obligation policies. Such models highlight consistent community structure in regions like the Celtic Sea, define axes of maximal separation and pinpoint where spatial management can minimise bycatch. This approach has become a blueprint for integrating spatio-temporal predictions into quota allocation and mixed-fishery regulation.

Fisheries Abundance Estimation and Management publication trend

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

Technical terms

Catch-per-unit-effort (CPUE): A measure of fishery yield per unit of fishing effort, used as an index of relative abundance.

Abundance index: A standardised estimator of population size derived from survey or fishery data.

Species distribution model (SDM): A statistical framework that relates species occurrence or abundance to environmental and spatial covariates to predict distributions.

Spatio-temporal model: A model that jointly accounts for spatial and temporal variability in ecological data.

Delta-generalised linear mixed model (delta-GLMM): A two-part model combining a presence/absence component and a positive-catch component to standardise CPUE and predict abundance.

References

1. Predicting unseen chub mackerel densities through spatiotemporal machine learning: Indications of potential hyperdepletion in catch-per-unit-effort due to fishing ground contraction. Ecological Informatics (2025).
2. Untangling multi-species fisheries data with species distribution models. Reviews in Fish Biology and Fisheries (2024).
3. Geostatistical delta-generalized linear mixed models improve precision for estimated abundance indices for West Coast groundfishes. ICES Journal of Marine Science (2015).
4. Spatial separation of catches in highly mixed fisheries. Scientific Reports (2018).
5. Impacts of fisheries-dependent spatial sampling patterns on catch-per-unit-effort standardization: A simulation study and fishery application. Fisheries Research (2022).
6. Understanding transboundary stocks’ availability by combining multiple fisheries-independent surveys and oceanographic conditions in spatiotemporal models. ICES Journal of Marine Science (2022).