Integrating population genetics and species distribution models to predict red seabream distribution under climate change

In the context of global climate change, accurately predicting changes in marine fish distributions is crucial for the utilization, protection, and management of fisheries resources. Species distribution models (SDMs) have been widely used in terrestrial ecosystems and have played a crucial role in marine ecosystems by linking species occurrence, abundance, or life traits to unobserved spatiotemporal environmental effects and habitat characteristics. However, unlike terrestrial ecosystems, marine ecosystems exhibit more extensive exchanges among biological populations, making it difficult to classify marine fish into distinct populations or subspecies. Therefore, it is necessary to conduct population genetic studies and confirm the existence of population divisions before predicting their distributions using SDMs. In this study, the results based on mtDNA sequences revealed weak genetic divergence among red seabream ( Pagrus major ) populations in the northwest Pacific Ocean, suggesting the appropriateness of species-level SDMs. We constructed the ensemble model based on eight modelling algorithms; the evaluation scores revealed that it performed well. According to the SDMs, temperature was the primary environmental factor influencing the distribution of red seabream, followed by depth and offshore distance. The suitable habitats for red seabream were located mainly in the waters south of the Yangtze River mouth. Predictions for future habitat suitability and changes in P. major revealed that by the end of the 21st century, under both the SSP2–4.5 and SSP5–8.5 scenarios, a slight decline in overall suitable habitats was projected, accompanied by a substantial expansion of general habitats, showing a clear trend towards northeast migration. Our study highlights the usefulness of population genetics and SDMs in marine fishery resources, and the results have important implications for guiding future utilization, protection, and management of red seabream. • Use SDMS to predict climate change impacts on the range of red seabream based on species/population genetics. • Weak genetic divergence suggests species-level SDMs are more applicable than population-level ones. • Constructing a weighted ensemble model based on eight SDM algorithms to further improve prediction accuracy. • Temperature, depth, and offshore distance are key to red seabream's warm-temperate coastal benthic distribution patterns. • By 2100, red seabream habitats will shift under both high and low carbon emission scenarios.