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 per..., , , # Data from: Integrating Soil Properties into Species Distribution Models Enhances Predictive Accuracy for Terricolous Macrofungi

[https://doi.org/10.5061/dryad.9p8cz8wrv](https://doi.org/10.5061/dryad.9p8cz8wrv)

## Data

##### File: Fungi\_Environmental\_Data.zip

**Description:** Fungi data were provided by the courtesy of the SwissFungi data and information centre ([https://swissfungi.wsl.ch](https://swissfungi.wsl.ch)). We provide individual csv files for all 162 fungi species (Fungi_Environmental_Data.zip), which contain presence and absence data as well as the corresponding environmental data. The species names are given in the filename. Please refer to main article and its online supplementary material for description of the data. Geographic coordinates were removed.

The environment variables are described in the file Predictor_Discription.xlsx. The names of the variables used in the three model algorithms (GLM, MAXENT, Random Forest) are given in the files Predictor_GLM.csv, ...,