Data from: Integrating soil properties into species distribution models enhances predictive accuracy for terricolous macrofungi

Aim: This study aims to (1) test whether mapped soil properties can
improve the performance of species distribution models (SDMs) for 162
terricolous macrofungi at a regional level, (2) identify relevant soil
predictors for macrofungal regional distribution, and (3) quantify the
relative importance of soil properties as compared to climate and
topography in explaining macrofungal regional distribution.  
Location: The forested area (~ 12000 km2) in Switzerland.  
Taxon: Terricolous Macrofungi.   Methods: We collected
occurrences (presence-only) for 162 species of terricolous macrofungi,
including 111 ectomycorrhizal and 51 saprotrophic species, from the
SwissFungi database. We used soil property maps, generated through digital
soil mapping at a 25 m resolution, to enhance macrofungal SDMs. For each
species, we selected two climate, two topography and two soil predictors
by an automated variable selection procedure. We built SDMs with
randomized soil properties for performance comparison. We quantified the
importance of soil properties based on permutation and variance
partitioning. Finally, we projected the SDMs for three representative
species at 25 m resolution with and without soil properties to assess the
role of soil properties in shaping their biogeographical distributions.
  Results: Soil properties significantly improved the median
performance of the SDMs across the 162 species. Ectomycorrhizal fungi
showed a significantly greater improvement than saprotrophic fungi. On
average, our models were able to explain two-thirds of the variance in
macrofungal distribution, of which 11% could be independently explained by
soil properties. Air temperature and topographic slope were identified as
additional important factors controlling macrofungal distribution. Evident
changes in geographical distribution were observed for the three
representative species after adding soil properties.   Main
Conclusions: High-resolution digital soil maps significantly improve the
predictive accuracy of macrofungal regional distribution. They should
therefore be taken into account when modelling the geographical
distribution of macrofungi.