1. Considering that the spatial pattern of trees is a footprint of the biological processes that drive their dynamics, increasing work has been undertaken to analyse spatial patterns and fit spatial point processes to them. When diameter is taken into account, the underlying point process is a marked point process. The question then is how to correctly model the dependence between the mark and location, and the different patterns at the different scales, to gain understanding of the underlying biological processes. 2. The data used comes from the Paracou rain forest in French Guiana (5°15' N, 52°55' W). The spatial pattern of trees in this forest exhibits regularity at small distances (c. 6 m) and clustering at larger distances (c. 30 m). The pattern is linked to diameter, with a shift from clustering to regularity as trees grow. 3. Two models of spatial pattern are used. The first one is pattern-driven in the sense that it breaks down the observed pattern into a mixture of regular, clustered and random contributions. The second one is process-based and uses a simple individual-based space-dependent model of forest dynamics as a simulation algorithm. It is obtained as the limit of this spatio-temporal model when time tends to infinity. 4. Both models are consistent with the observed pattern, with a better fit provided by the second model. Moreover this second model provides a biological interpretation of the observed pattern in terms of forest dynamics. However the first model's implementation is simpler (both for simulation and for parameter estimation). 5. Synthesis. Modelling the spatial pattern of plants using spatial point processes gives insights into the biological processes that drive their dynamics. It allows the reconstruction of their dynamics given only a snapshot of plant locations. Very few solutions exist to model complex marked spatial patterns when point location and mark are dependent. We defined and compared two point processes that successfully m