A forest structure model that determines crown layers and partitions growth and mortality rates for landscape-scale applications of tropical forests

1. We present a model to quantify tropical forest structure and explain variance in dynamic rates (growth and mortality) that is computationally simple and can be applied to landscape-scale forest inventory and, potentially, remote sensing-derived data. 2. The model is a modification of the perfect plasticity approximation (PPA) based on tree allometry, tree locations and sizes. The model quantifies crown area index (CAI) (number of crowns per unit ground area) and assigns trees to crown layers, which determines the expected number of crowns above each tree and thus its light environment. 3. The structural model, parameterized and tested for the Barro Colorado Island, Panama 50-ha forest dynamics plot using data from forest inventories and stereo aerial photographs, reproduces most canopy and understorey structural and dynamic properties. The PPA model worked as well or better than a computationally intensive, spatially explicit model. A single allometry for all trees worked equally well as functional group or species allometries. Models of growth and mortality were always improved by adding crown layers as defined by the PPAmodel. 4. The mean CAI of the 50-ha plot was 3.1 with low variance. The observed variance was lower than when tree locations were randomized, which drastically lowered the variance in tree density per plot, indicating that there are regulating forces towards a small range of crown area indices. 5. Synthesis. A number of simplifying characteristics in structure were uncovered with the PPA structural model applied to a tropical forest: species allometries were not needed despite the high species diversity in the forest; the model worked on a range of plot sizes; and the variance in CAI was surprisingly low, suggesting regulatory mechanisms. The PPA structural model can be used to develop a fully dynamic simulation model for tropical forests. The ability of the simulation model to predict temporal changes in landscape patterns of biomass, dynamic rates, and species and/or functional group composition will provide validation for the partitioning of dynamic rates by crown layers in the PPA structural model.