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. However, 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. The inverse calibration was done using Bayesian inference, estimating 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 the species' light niche (i.e., light requirements) are similar for the trait-based and calibrated values for both model variants, 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 to the estimates from either model variant using inverse calibration. The parameter estimates differed between the complex and the simple model, with the estimates for the complex model being closer to the trait-based parameters. 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 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 (better fit to hold out) of the inversely calibrated models underpins the importance of informing dynamic models by real-world observations. Future research should focus on even greater environmental coverage of observations of demographic processes in unmanaged forests to verify our findings at species range limits under extreme climatic conditions.

What the paper shows and why it matters

(AI-generated)

Predicting range shifts and forest dynamics under climate change relies on process-based models, and tree regeneration matters for both range shifts and forest resilience. Regeneration looks stochastic, varies across scales and is poorly observed. The forest model studied here, ForClim, therefore sets its regeneration niche in advance from species traits (light, drought and temperature requirements) plus a default amount of recruitment. These were never checked against large-scale data, and many forest models mismatch data from unmanaged European forests for reasons that remain vague (Díaz-Yáñez et al. 2024). The paper asks whether trait-derived regeneration niches are supported by real recruitment patterns.

The study argues that fitting a forest model to inventory data (inverse calibration) can recover how tree regeneration depends on light, drought and temperature, but only in part: light requirements and the overall amount of regeneration are well constrained, whereas climatic requirements are not. It tests this with the forest gap model ForClim in a simple variant and a more complex one with competition among recruits, with recruitment records from 696 plots in strict European forest reserves (11 tree species), half for Bayesian calibration and half for testing. Calibrated light requirements mostly agreed with the trait-based values (closely in the complex variant, in ranking in the simple one). Drought tolerance gave plausible estimates only in the complex variant, and temperature requirements matched the trait-based values in neither. The calibrated amount of regeneration was lower than the default in both variants, and much lower in the complex one. On held-out plots, the calibrated complex variant predicted better than the default, and it outperformed the simple variant.

**Figure 2.** Mean posterior estimate, including the 80 % CI, of the species-specific parameters for the complex model (red) and the simple model (blue). From Käber et al. (2024), *Geoscientific Model Development*, licensed under CC BY 4.0. Click the image to enlarge.

The paper reads the agreement for light requirements as strong support for the trait-based values, the lower regeneration as a challenge to the default amount, and the climatic parameters as not constrained by the data. It concludes that light availability and regeneration intensity are the main controls on regeneration. Because temperature had little influence, it hypothesises, without testing this, that climate acts mainly later, through growth and the mortality of larger trees, so predictions of forest resilience may depend more on how dynamics within stands are represented than on species range limits. It encourages inverse calibration and asks for data from more extreme climates because few plots cover boreal or Mediterranean-type conditions.

References

Díaz‐Yáñez et al. (2024). Tree regeneration in models of forest dynamics: A key priority for further research. Ecosphere 15, e4807. doi:10.1002/ecs2.4807