A novel statistical workflow using pollen records and regression kriging to reconstruct the spatially and temporally explicit demographic history of tree species

Understanding the effects of past climate shifts on the demography of forest tree species is crucial to assessing their response to ongoing climate change. However, little effort has so far been made to quantify past demographic changes in a spatially and temporally explicit manner at a continental scale. We have developed a novel statistical workflow that integrates two regression kriging models to reconstruct the demographic history of tree species across Europe. Our workflow anticipates spatially the probability of species occurrence (PoO), and interpolates their relative abundances (RelAb) spatially and temporally. Climate variables can be included as covariates. Our approach can accommodate non-stationary species responses to climate, and incorporates the presence of source populations, colonization constraints, and population trends as factors influencing species RelAb. We applied this workflow to European fir species ( Abies spp.) since the Last Glacial Maximum (LGM), using fossil pollen records from 241 sites, and paleoclimate data on a 0.41-degree grid and 500-year time bins. Model performance, assessed with cross-validation, demonstrates that including climate as a covariate enhances the spatial heterogeneity. Climate has a positive effect on RelAb interpolation under millennial static spatial distribution structure conditions, while the presence of source populations plays a more important role during rapid demographic processes. Additionally, we applied our workflow to assess future regional changes in the RelAb of Abies spp. under the main future climate scenarios. Our workflow is particularly suited for temperate and boreal tree species and can be used in various downstream analyses. • Reconstructing the colonization history of forest trees at a continental scale. • Spatio-temporal regression kriging of fossil pollen records across 20k years. • Data quantity and quality allow a good quality reconstruction in the Holocene. • Climate data improves the reconstruction when the range is stable. • Presence of source population is the best predictor during range expansion.