Abstract 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 trees in the seedling and sapling stage remains scarce. We assessed the potential to predict species‐specific forest regeneration densities at high spatial resolution (1 ha) by calibrating generalised 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 (<1000 ha −1 ), low species richness (≤2 species) and a high proportion (≥75%) of tree species projected to be climatically unsuitable in the future. For 28 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 that represent 82.5% of the regeneration. The indicator maps revealed considerable regeneration gaps. 14.3% of Germany's forest area has low regeneration density, 30.4% has low species richness, and 15.4% 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 political guidelines 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.
Gass et al. (Sun,) studied this question.