Zhun Mao1, Laurent Saint-André2, Franck Bourrier3, Alexia Stokes4, Thomas Cordonnier3. 1. IRSTEA, UR EMGR, Centre de Grenoble, 2 Rue de la Papeterie, BP 76, 38402 Saint-Martin-d'Hères Cedex, France, Université Grenoble Alpes (UGA), 38402 Grenoble, France, maozhun04@126.com. 2. INRA, UR BEF - Biogéochimie des Ecosystèmes Forestiers, 54280 Champenoux, France, CIRAD, UMR Eco&Sols, place Viala, 34398 Montpellier Cedex 5, France. 3. IRSTEA, UR EMGR, Centre de Grenoble, 2 Rue de la Papeterie, BP 76, 38402 Saint-Martin-d'Hères Cedex, France, Université Grenoble Alpes (UGA), 38402 Grenoble, France. 4. INRA, UMR AMAP, Boulevard de la Lironde, 34398 Montpellier Cedex 5, France and.
Abstract
BACKGROUND AND AIMS: In mountain ecosystems, predicting root density in three dimensions (3-D) is highly challenging due to the spatial heterogeneity of forest communities. This study presents a simple and semi-mechanistic model, named ChaMRoots, that predicts root interception density (RID, number of roots m(-2)). ChaMRoots hypothesizes that RID at a given point is affected by the presence of roots from surrounding trees forming a polygon shape. METHODS: The model comprises three sub-models for predicting: (1) the spatial heterogeneity - RID of the finest roots in the top soil layer as a function of tree basal area at breast height, and the distance between the tree and a given point; (2) the diameter spectrum - the distribution of RID as a function of root diameter up to 50 mm thick; and (3) the vertical profile - the distribution of RID as a function of soil depth. The RID data used for fitting in the model were measured in two uneven-aged mountain forest ecosystems in the French Alps. These sites differ in tree density and species composition. KEY RESULTS: In general, the validation of each sub-model indicated that all sub-models of ChaMRoots had good fits. The model achieved a highly satisfactory compromise between the number of aerial input parameters and the fit to the observed data. CONCLUSIONS: The semi-mechanistic ChaMRoots model focuses on the spatial distribution of root density at the tree cluster scale, in contrast to the majority of published root models, which function at the level of the individual. Based on easy-to-measure characteristics, simple forest inventory protocols and three sub-models, it achieves a good compromise between the complexity of the case study area and that of the global model structure. ChaMRoots can be easily coupled with spatially explicit individual-based forest dynamics models and thus provides a highly transferable approach for modelling 3-D root spatial distribution in complex forest ecosystems.
BACKGROUND AND AIMS: In mountain ecosystems, predicting root density in three dimensions (3-D) is highly challenging due to the spatial heterogeneity of forest communities. This study presents a simple and semi-mechanistic model, named ChaMRoots, that predicts root interception density (RID, number of roots m(-2)). ChaMRoots hypothesizes that RID at a given point is affected by the presence of roots from surrounding trees forming a polygon shape. METHODS: The model comprises three sub-models for predicting: (1) the spatial heterogeneity - RID of the finest roots in the top soil layer as a function of tree basal area at breast height, and the distance between the tree and a given point; (2) the diameter spectrum - the distribution of RID as a function of root diameter up to 50 mm thick; and (3) the vertical profile - the distribution of RID as a function of soil depth. The RID data used for fitting in the model were measured in two uneven-aged mountain forest ecosystems in the French Alps. These sites differ in tree density and species composition. KEY RESULTS: In general, the validation of each sub-model indicated that all sub-models of ChaMRoots had good fits. The model achieved a highly satisfactory compromise between the number of aerial input parameters and the fit to the observed data. CONCLUSIONS: The semi-mechanistic ChaMRoots model focuses on the spatial distribution of root density at the tree cluster scale, in contrast to the majority of published root models, which function at the level of the individual. Based on easy-to-measure characteristics, simple forest inventory protocols and three sub-models, it achieves a good compromise between the complexity of the case study area and that of the global model structure. ChaMRoots can be easily coupled with spatially explicit individual-based forest dynamics models and thus provides a highly transferable approach for modelling 3-D root spatial distribution in complex forest ecosystems.
Authors: John King; Alan Gay; Roger Sylvester-Bradley; Ian Bingham; John Foulkes; Peter Gregory; David Robinson Journal: Ann Bot Date: 2003-02 Impact factor: 4.357