Mapping the Future of Germany’s Forests: Modelling Potential Natural Vegetation under Climate Change Scenarios

 

Climate change poses a challenge for European forestry, requiring the selection of tree species adapted to future conditions. Analyzing this for a large country like Germany requires considering diverse regional environmental conditions in climate, soil, and management history. A promising approach is to utilize simulation models to derive potential natural vegetation (PNV) under climate change, which can help to identify robust candidate species for regions.

 

We employed the process-based forest landscape model iLand to investigate: (i) the impact of climate change on PNV species composition and carbon stocks  across regions in Germany, and (ii) regional adaptation deficits by comparing future PNV composition with current forest composition (derived from national inventory data). We defined 12 representative ecoregions via cluster analysis of climate, soil, and vegetation data. For each, we created generic landscapes (20-30k ha) reflecting regional environmental gradients. We used these landscapes to simulate PNV with iLand under historical and nine climate change scenarios. Changes in equilibrium species composition and attainable carbon stocks were calculated relative to historical climate simulations. Finally, we created high-resolution maps of future PNV in Germany by mapping the stands of our simulated landscapes to country scale. 

 

Our landscapes cover 95% of Germany’s forested climate and soil space (defined by the ratio of forest pixels, after removing outliers). Simulations identified regions particularly vulnerable to climate change, as well as those with the greatest mismatch between expected PNV and current forests. To account for regional differences in species suitability is crucial for developing climate change adaptation policies at the national level within Germany.