Mapping the next forest generation – the potential of national forest inventory data for identifying regeneration gaps

- In face of global change and increasing forest disturbances, forest regeneration is crucial for ensuring future generations of trees and resilient forest ecosystems. However, spatially explicit information on the current availability and climate suitability of seedlings and saplings remains scarce. - We assessed the potential to predict species-specific forest regeneration densities at high spatial resolution (1 ha) by calibrating generalized additive models (GAMs) using regeneration data from the German National Forest Inventory (NFI) and 44 environmental predictors. Regional regeneration gaps were then identified based on three indicators: low total density (<1,000 ha-1), low species richness (≤2 species) and a high proportion (≥75%) of regeneration at high future cultivation risk. - For 22 tree species, we obtained regeneration density models that performed well in spatially blocked cross-validation. We were therefore able to generate regeneration density and indicator maps for a major part of the tree species. - The indicator maps revealed considerable regeneration gaps. 13.4% of Germany’s forest area has low regeneration density, 47.1% has low species richness, and 25.2% of the Bavarian forest area lacks climate-adapted regeneration. - Our study demonstrates the potential of NFI regeneration data and its applicability for monitoring forest regeneration over large spatial scales. The regeneration indicator maps show that silvicultural interventions should prioritise increasing tree species richness and the proportion of species adapted to climate change. However, as regeneration gaps vary from region to region, management and policy must be adapted accordingly to ensure future forest resilience. - Synthesis and applications: Our study provides the first nationwide, high-resolution assessment of forest regeneration, offering a valuable baseline for monitoring forest development. The regeneration density and indicator maps enable forest managers and policymakers to identify regeneration deficits, prioritise adaptive management interventions, and contribute to the development of climate-resilient forests. DOI https://doi.org/10.32942/X2GS8X Subjects Forest Biology, Forest Management, Forest Sciences, Other Forestry and Forest Sciences, Plant Sciences

forest regeneration, species distribution models SDMs, generalized additive models GAMs, sapling density, species richness, climate-adapted species, cultivation risk Dates Published: 2025-05-31 09:33 Last Updated: 2026-03-30 07:34 Older Versions License CC-BY Attribution-NonCommercial 4.0 International Additional Metadata Conflict of interest statement: None Data and Code Availability Statement: All code supporting the findings of this study is openly available on Zenodo (https://doi.org/10.5281/zenodo.15552196) and GitHub (https://github.com/LeonieCG/GermanRegenerationMaps2012). The data used to calibrate our species-specific regeneration models was compiled from the German national forest inventory as well as several metadata sources, originally collected by various other institutions. As far as we were permitted, we have republished the data on Zenodo (https://doi.org/10.5281/zenodo.15550864) and provided the code to run the models with the reduced set of environmental variables. Language: English