## 1 Introduction  
Tropical forests play an important role in the global carbon cycle due to their high productivity and large carbon pools [1; 2]. Forests in the tropics are also known for their high species richness with up to 300 tree species per hectare [3; 4; 5]. Species composition is a driving factor for forest succession, as well as for carbon dynamics. Simulation models are useful tools to investigate these forest dynamics at different scales [6]; for example, global vegetation models provide estimates of forest productivity and carbon budgets at large scales as a function of climate [7; 8; 9; 10; 11].  
However, including the high species richness in forest models increases enormously the effort for model parameterization and increases uncertainty in model predictions (due to the lack of empirical data for rare tropical tree species). Therefore, it is useful to classify the tree species into a few groups. Several field studies showed that species often show similar attributes (e.g., growth rates), making it possible to group tree species with similar characteristics into species groups known as plant functional types (PFTs) [12; 13; 14; 15]. Species grouping offers a powerful concept that has become relevant for integrating field data in forest models (in the form of parameter values) and comparing simulation results with measurements [16]. With a small number of PFTs, it is easier to interpret the model results, but accuracy improves with an increasing number of PFTs [17].  
Especially for dynamic global vegetation models (DGVM)--so far limited to a few PFTs--it is challenging to include a higher number of PFTs due to their global applicability [18; 19]. For example, LPJ or SEIB-DGVM normally include only one or two PFTs for tropical regions (tropical broad-leaved evergreen and deciduous trees) [11; 20] while a small number of vegetation models [21; 22] and some local individual-based forest simulation models often incorporate a higher number of species groups [23]. For local forest models, it has been shown that species grouping allows a realistic description of species dynamics in forests [24]. In particular, forest gap models, such as FORMIND, are able to analyze forest structure and productivity by including up to 20 PFTs [17; 25; 26].  
The creation of functional groups is flexible enough to include relevant trait differences and to avoid functional redundancy between species [17]. Still, it is an open question how many PFTs are appropriate to describe forest dynamics. A low number of PFTs is anyhow of interest in order to reduce the parameterization effort. In this study, we focus on the two following questions:  
1. How does the number of PFTs in forest models affect the predictions of aboveground biomass, basal area, forest productivity (GPP, NPP) and carbon sequestration (NEE)?
2. What is the influence of pioneer species on the simulation of forest productivity during the early successional phase in tropical forests?  
To address these questions, the individual-based forest gap model FORMIND was applied to a tropical forest site in the Kilimanjaro region of Africa with a single-PFT version (1 PFT), standard-PFT version (3 PFT) and a multi-PFT version (6 PFT). We hypothesize that species diversity, as considered in forest models, significantly influences productivity and carbon flux estimates of forests.