Modelling and predicting the spatial distribution of tree root density in heterogeneous forest ecosystems

Area of Science:

• Ecology
• Forestry
• Soil Science

Background:

• Mountain ecosystems exhibit significant spatial heterogeneity, complicating accurate prediction of root density.
• Existing root distribution models often lack the spatial resolution needed for complex forest structures.

Purpose of the Study:

• To develop and validate ChaMRoots, a simple, semi-mechanistic model for predicting root interception density (RID) in three dimensions (3-D).
• To account for the influence of surrounding trees on root distribution at the tree cluster scale.

Main Methods:

• ChaMRoots integrates three sub-models: spatial heterogeneity (RID vs. tree basal area and distance), diameter spectrum (RID vs. root diameter), and vertical profile (RID vs. soil depth).
• Model fitting utilized root density data from two uneven-aged mountain forest ecosystems in the French Alps.

Main Results:

• All sub-models of ChaMRoots demonstrated good fits upon validation.
• The model successfully balances the number of input parameters with accuracy in predicting observed root density data.

Conclusions:

• ChaMRoots models root density at the tree cluster scale, offering a novel approach compared to individual-based models.
• The model's reliance on easily measurable forest characteristics makes it transferable for 3-D root distribution modeling in complex ecosystems.
• ChaMRoots can be readily integrated with individual-based forest dynamics models for enhanced ecological simulations.