Inferring the tree regeneration niche from inventory data using a dynamic forest model
Abstract
The regeneration niche of trees is governed by many processes and factors that are challenging to determine. Besides a species’s geographic distribution, which determines if seeds are available, a myriad of local processes in forest ecosystems (e.g., competition and pathogens) exert influences on tree regeneration. Consequently, the representation of tree regeneration in dynamic forest models is a notoriously complicated process which often involves many subprocesses that are often data deficient. The ForClim forest gap model solved this problem by linking species traits to regeneration properties, but this regeneration module was never validated with large-scale data. Here, we compare this trait-based approach with an inverse calibration approach where we estimate regeneration parameters directly from a large dataset of unmanaged European forests, using Bayesian inference to estimate shade and drought tolerance as well as the temperature requirements for 11 common tree species along with the intensity of regeneration. We find that the parameters determining the species’ light niche are similar for the trait-based and calibrated values, but only a more complex model variant that included competition between recruits leads to plausible estimates of the drought niche; the trait-derived temperature niche did not match the estimates from either model variant using inverse calibration. We conclude that the regeneration niche of trees can be recovered from a large forestry dataset in terms of the stand-level parameters light availability and regeneration intensity, while abiotic drivers (temperature and drought) are more elusive. The higher performance of the inversely calibrated models underpins the importance of informing dynamic models by real-world observations.
What the paper shows and why it matters (AI-generated)
Comparing the ForClim model's built-in, trait-based rules for tree regeneration against parameters estimated directly from a large dataset of unmanaged European forests, the authors find good agreement on light requirements but a real divergence on drought tolerance — resolved only once the model let regenerating seedlings compete with each other. That distinction matters for climate-change forecasting specifically, since the regeneration niche is normally too hard to observe directly, yet is exactly what determines whether a forest can actually track a shifting climate as it warms.