Ecological Dynamics of Flatfish Populations

Flatfish populations exhibit complex ecological dynamics driven by life-history strategies, environmental variability and anthropogenic pressures. From their asymmetric body plan and benthic habit to species-specific spawning migrations, flatfish such as flounders and soles occupy critical nursery habitats in estuaries before moving offshore to spawn. Temperature, salinity and depth gradients interact with local processes—nutrient supply, habitat connectivity and predation—to shape recruitment, growth and mortality at different life stages. Fishing mortality and climate-induced changes in water temperature can synchronise or destabilise subpopulation trends, while spatial refugia and cryptic offshore biomass may buffer stocks against overexploitation. Advances in habitat modelling and demographic analysis have begun to resolve how decadal shifts and environmental drivers influence distribution, abundance and stock productivity globally. Understanding these interactions is essential for defining biological reference points, protecting critical habitats and designing spatially explicit management measures that sustain both yield and ecosystem function.

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Ecological Dynamics of Flatfish Populations publication trend

The graph below shows the total number of articles in ecological dynamics of flatfish populations across all publications each year (not limited to Nature Index journals).

Technical terms

Ontogeny: The developmental sequence of morphological and physiological changes from egg through larval, juvenile and adult life stages.

Stock–recruitment relationship: The functional link between the spawning population biomass and the number of progeny entering the fishery or population at a defined recruitment stage.

Spatial refuge: Areas, often offshore or deep habitats, where adult biomass remains less exposed to fishing and other disturbances, supporting stock persistence.

Settlement: The process by which pelagic larval flatfish transition to benthic juvenile habitats in estuaries or coastal nurseries.

Boosted regression tree model: A machine-learning technique that combines multiple decision trees to predict species distribution and abundance by capturing nonlinear relationships among predictors.

References

1. Two‐Stage Boosted Regression Tree Model to Characterize Southern Flounder Distribution in Texas Estuaries at Varying Population Sizes. Marine and Coastal Fisheries (2016).
2. Externally driven changes in the abundance of summer and winter flounder. ICES Journal of Marine Science (2014).
3. Factors Influencing Daily Growth in Young‐of‐the‐Year Winter Flounder along an Urban Gradient Revealed Using Hierarchical Linear Models. Marine and Coastal Fisheries (2015).
4. The Potential for Cryptic Population Structure to Sustain a Heavily Exploited Marine Flatfish Stock. Marine and Coastal Fisheries (2018).