Predicting root density in heterogeneous forest ecosystems is an important but challenging task. Mao et al. hypothesize that root density at a given point is due to the presence of roots from surrounding trees forming a polygon, and develop a simple and semi-mechanistic model based on easy-to-measure characteristics, simple forest inventory protocols and three sub-models. By comparing with root density data measured in two uneven-aged mountain forest ecosystems, they find that the model achieves a highly satisfactory compromise between the number of aerial input parameters and the fit to the observed data. In contrast to the majority of published roots models that function at the level of the individual, the model focuses on the spatial distribution of root density at the tree cluster scale.
This study reveals a new model for predicting root density which focuses on the spatial distribution of root density at the tree cluster scale.