Spatial modelling of soil water holding capacity improves models of plant distributions in mountain landscapes

Aims The aims of this study were: 1) to test a new methodology to overcome the issue of the predictive capacity of soil water availability in geographic space due to measurement scarcity, 2) to model and generalize soil water availability spatially to a whole region, and 3) to test its predictive capacity in plant SDMs. Methods First, we modelled the measured Soil Water Holding Capacity (SWHC at different pFs) of 24 soils in a focal research area, using a weighted ensemble of small bivariate models (ESM). We then used these models to predict 256 locations of a larger region and used the differences in these pF predictions to calculate three different indices of soil water availability for plants (SWAP. These SWAP variables were added one by one to a set of conventional topo-climatic predictors to model 104 plant species distributions. Results We showed that adding SWAP to the SDMs could improve our ability to predict plant species distributions, and more specifically, pF1.8–pF4.2 became the third most important predictor across all plant models. Conclusions Soil water availability can contribute a significant increase in the predictive power of plant distribution models, by identifying important additional abiotic information to describe plant ecological niches.