DOI: 10.5061/dryad.9p8cz8wrv
title: Data from: Integrating soil properties into species distribution models enhances predictive accuracy for terricolous macrofungi

description: 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.
description: # 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, Predictor_MAXENT.csv and Predictor_RF.csv. ## Code **File: R_Scripts.zip** The R scripts to reproduce our results are provided. Please read the accompanying ReadME. The order and main function of the scripts are organised as follows: * Data Cleaning * Variable Selection * Model Calibration * Analyses and Visualizations ## Access information Other publicly accessible locations of the data: * Soil maps Soil data was derived from the following sources: * [https://envidat.ch/#/metadata/soil-property-maps-for-the-swiss-forest](https://envidat.ch/#/metadata/soil-property-maps-for-the-swiss-forest) * [DOI: 10.16904/envidat.484](https://www.doi.org/10.16904/envidat.484)