## 4 Discussion  
### The Influence of Species Grouping on Forest Dynamic Simulations  
In this study, we evaluated the role of species grouping for forest modeling and analyzed the impact of a reduced number of species groups on forest productivity and carbon flux estimations. Using an established forest model, we simulated the dynamics of an African tropical forest with three different model parameterization versions differing in the number of plant functional types (PFT): M\({}_{1}\) includes only one PFT and is based on the assumption that all trees are similar in their physiological attributes (mean species approach). This resulted in one species group with averaged attributes--an approach that is comparable to those made in global vegetation models (e.g. SEIB-DGVM 11, and LPJ 20). The advantage of such a mean-species approach is that it reduces parameterization effort and model runtime (c.f., Table 4). We showed that reducing the number of PFTs to only one species type has a strong influence on derived vegetation dynamics. This approach simulates only intraspecific competition between trees and hence rather corresponds to a monoculture. Moreover, we could show that the pronounced heterogeneity in forest structure prevalent in tropical forests (e.g., different height layers) could not be reproduced with one mean-species. The approach using one PFT has a reduced realism because of its constant parameter value, which does not allow for the adaptation to the environment or niches ([PERSON], 2016). However, a parameterization with one species group is sufficient to derive reasonable values for biomass, basal area, and NEE of mature forests, whereas the species grouping plays an important role in earlier successional phases. Estimates of forest productivity, on the other hand, are highly sensitive to the number of included PFTs. The absence of pioneer tree species in the species grouping (M\({}_{1}\)) leads to a decrease of gross primary production by 60% (compared to M\({}_{6}\)) and a 50% increase of carbon sinks (NEE). These findings may have a strong impact on productivity estimates at regional and continental scales ([PERSON] et al., 2016; [PERSON], 2016; [PERSON], 2016). Although our study was conducted for one study site, the pattern of our results seems to be generally valid and transferable to other locations due to the structural realism of the gap model (individual-based and process-based). In fact, our results, that young pioneer-dominated tropical forests are more productive than old-growth forests, are in close agreement with a field-inventory based study across the Amazon basin ([PERSON], 2016). A basic assumption for many models with PFT grouping is that the parameter values (e.g., for growth or mortality) always remain constant for the respective PFT--independent of the  
Figure 5: The main parameters of the FORMIND model: (**a**) mortality rate, (**b**) maximum photoproductivity and (**c**) number of seeds for two different parameterizations (M\({}_{1}\) with 1 PFT and M\({}_{6}\) with 6 PFTs). The curve of M\({}_{6}\) has been calculated for each time step by averaging the six different parameter values for each PFT, weighted by the current biomass fraction for each PFT. For M\({}_{1}\), the parameter is displayed as a dashed line. All parameter values are taken from Table 3. The early-successful phase is from 0 to 100 years, and the late successional phase starts from 200 years. species composition within the PFT. This can be a disadvantage when describing a forest by just a single shade-tolerant PFT. As in M\({}_{1}\), the representation of pioneer species is missing. An alternative approach to include functional diversity into forest models with just one PFT can be the approach of dynamic model parameters, which change their parameter value depending on the successional state of the simulated forest. For example, an early stage forest could have a higher mortality rate than a mature forest (due to a shift in species composition).