A spatial statistical approach for identifying population structuring of marine fish species: European sprat as a case study

Many marine fish species are widely distributed over large areas. Failing to acknowledge that such species may be composed of distinct populations may result in overestimation of the stock's true harvest potential. To avoid overexploitation, ways to identify population structuring are therefore needed. In this study, we developed and applied a statistical approach to identify biologically relevant population boundaries for a widely distributed marine fish species, European sprat (Sprattus sprattus). Specifically, we compiled and standardized multiple trawl-survey data sets and used a range of statistical tools to assess whether the current management boundaries adequately account for potential population structuring. Our results demonstrate regional differences in spatial abundance patterns, temporal dynamics and population demographics. These findings are in line with recent genetic studies of sprat, indicating reproductive isolation between the Baltic Sea/Kattegat and a larger cluster containing the North-, Irish-, Celtic Sea, and Bay of Biscay. Since relying on routinely collected survey data, our statistical approach can be a cost-effective complement to population genetic methods for detecting population structuring. These can be used to guide spatial management efforts and ensure sustainable exploitation, especially under climate change and the expected changes in species distributions across current management borders.

Automated summary

Many marine fish species have wide distribution, but failing to acknowledge distinct populations within these species may lead to overestimation of stock's harvest potential. This study developed a statistical approach to identify population boundaries for European sprat, revealing regional differences in abundance patterns and confirming genetic studies showing reproductive isolation between different regions. The statistical approach can complement genetic methods and guide spatial management efforts for sustainable exploitation under changing climate conditions.