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 species distribution, which determine if seeds are available, complex local dynamics in forest ecosystems (e.g., competition, pathogens) exert fundamental influence on tree regeneration. Consequently, the representation of tree regeneration in dynamic forest models is a notoriously complicated process which often involves many subprocesses. The ForClim forest gap model described regeneration mainly by species traits and the ecological knowledge linking these traits to regeneration properties. However, this regeneration module was never validated with large-scale data. Here, we compare this trait-based approach with an alternative, namely an inverse calibration approach, where we estimate regeneration parameters from a large observational dataset of unmanaged European forests. The model inversion was done using Bayesian inference with a simple and complex model variant without and with competition during regeneration. In this approach, we estimate shade and drought tolerance as well as the temperature requirements for 11 common tree species along with the intensity of regeneration (i.e., the maximum regeneration rate). We find that the parameters determining species’ light niche (i.e., light requirements) are similar between the trait based and calibrated values for both model variants, but only the complex model led to plausible estimates of the drought niche. The temperature niche as defined in by traits could not be recovered from the data by either model variant using inverse calibration. The parameter estimates differed between the complex and the simple model, with the complex model performing better. In both model variants, the calibration strongly changed the parameters that determine regeneration intensity compared to the default.

We conclude that the regeneration niche of the tree species in this large European dataset can be recovered 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. Future research should focus on an even larger environmental coverage of observations of demographic processes in unmanaged forests to verify our findings at species range limits under extreme climatic conditions.