Usually, topographic and climatic factors are used to predict plant distribution because they are known to explain plant presence or absence. Soil properties have been widely shown to influence plant growth and distributions. However, edaphic factors are rarely taken into account as predictors of plant species and community distribution models in an edaphically heterogeneous landscape. Or, when it happens, interpolation techniques are used to project soil properties in space. In an heterogeneous landscape, such as in the Alps regions, where soil properties change abruptly as a function of environmental conditions over short distances, interpolation techniques require a huge quantities of samples to be efficient, which is costly and time consuming, and bring more errors than predictive approach for an equivalent number of samples.
In this study we will use predictive approach to reduce the number of soil samples needed and increase the quality of the prediction. In a second step, we will integrate the predicted soil proprieties as predictors into plant SDMs. The two main question we want to address are the following:
1. Can variation in edaphic factors be modelled over large and complex areas using predictive modelling techniques?
2. Does the addition of predicted edaphic factors improve the predictive power of plant species distribution models?
Soil; edaphic factors; predictive modelling techniques; SDMs; plant species