Coupling fossil records and traditional discrimination metrics to test how genetic information improves species distribution models of the European beech Fagus sylvatica

Species distribution models (SDMs) are widely used to hindcast or forecast suitable habitat conditions during climate change. Although distant populations of a given species may show local adaptations to diverging environmental conditions, traditional SDMs disregard intraspecific variation. Yet, incorporating genetic information into SDMs could improve predictions. Here we aimed at investigating whether genetically informed SDMs would outperform traditional SDMs. Using published information on the spatial genetic structure of the European Beech Fagus sylvatica L. (1753), we built lineage-specific SDMs for each phylogenetic group of the species. We then combined all lineage-specific SDMs into a single genetically informed SDM that we compared against a traditional SDM approach. We finally compared SDMs’ predictions against independent datasets of present-day distribution as well as fossil distribution data from the Mid-Holocene, using six metrics of model performance. We found that aggregating lineage-specific SDMs into a single genetically informed SDM increased model performances to identify suitable areas currently occupied by F. sylvatica . In comparison to a traditional SDM, the genetically informed SDM we built for F. sylvatica assigned higher probabilities of occurrence during the Mid-Holocene at locations where fossil records were found. Aggregating lineage-specific SDMs into a single genetically informed SDM seems to outperform the traditional SDM approach, especially so when the aim is to identify potentially suitable areas of occupancy. This could be particularly useful for the identification of cryptic refugia that remain undetected by traditional SDMs. Genetically informed SDMs have the potential to improve our understanding of species redistribution under climate change.