Unlocking the Forgotten Dimension of Biodiversity: A Scalable Genetic Diversity Index for Multi-Species Analysis

Abstract Geographic patterns of community-level genetic diversity provide key information about evolutionary processes and are increasingly relevant for conservation planning under rapid environmental change. Yet, continental-scale, multi-taxon assessments of genetic diversity remain rare, largely due to difficulties in integrating heterogeneous datasets across species and markers. Here, we develop a scalable framework to quantify and map nucleotide diversity (π) across co-occurring taxa using georeferenced sequence data. We compiled and harmonized genetic metadata for European vascular plants and calculated site-level π for individual species. By spatially aggregating values across taxa, we derived the Genetic Diversity Index (GDI), designed to capture shared geographic structure in intraspecific variation while minimizing species-specific noise. We evaluated robustness using simulations and rarefaction procedures to assess sensitivity to sampling intensity and taxonomic composition. Using ∼630,000 sequences from 1,860 species, GDI identified consistent hotspots of genetic diversity in the Anatolian Peninsula, Southern Iberia, and the Eastern Alps. These regions coincide with areas widely recognized as long-term refugia and biogeographic transition zones. GDI showed weak relationships with species richness and phylogenetic diversity, indicating that it captures a distinct dimension of biodiversity linked to demographic persistence and evolutionary history. In addition, GDI increased significantly with late-Quaternary climate stability, supporting the expectation that regions experiencing reduced climatic fluctuation tend to retain higher levels of genetic variation. Our results demonstrate that publicly available sequence data can be synthesized into reproducible, large-scale estimates of community-level diversity. The GDI provides a practical tool for integrating genetic information into biodiversity assessments and for identifying regions where evolutionary potential may be concentrated. This framework opens new opportunities for incorporating genomic resources into conservation prioritization and macroecological research. Competing Interest Statement The authors have declared no competing interest. Footnotes Marvin Klümpen: maklu114{at}uni-duesseldorf.de, Laura E. Rose: laura.rose{at}hhu.de In this version, we added multiple tests for spatial autocorrelation, evolutionary signals of habitat heterogeneity and habitat suitability. We further included more sequence data and accessions to the overall analysis.