The data package contains five datasets:
Spatial data of the plant species (presence(1)/absence(0)). The order of files (species names) follow the alphabetical order, as shown in Figure 1 of the manuscript.
AMF spatial data of the plant species (presence(1)/absence(0)). The order of files (species names) follow the alphabetical order, as shown in Figure 1 of the manuscript.
R script that includes three null models (fixed species presences, environmentally constrained and equiprobable null model). The script uses the data files of plants and AMF spatial data submitted here. It will calculate the different co-occurrence metrics (C-score, fij), asymmetry and nestedness of the different null matrices.
Spatial data of abiotic factors: pH and organic matter (OM).
R script that calculates mutual information on the null matrices and the significant species co-occurrences. Input files necessary are the null matrices ("Pnullmatrices.RData" and "anullmatrices.RData") produced on the null model R script "null_models_plant_am_co_occurrence.R".
The data set used is spatially explicit and based on presence/absence of plant and AMF species. We test the signiﬁcance of our observed pat-terns by using null models. Here we study a null model that incorporates the spatial autocorrelation of species patterns and we compare it with two non-spatial null models based on complete spatial randomness and environmental ﬁltering, respectively. We use spatial overlap (i.e. species co-occurrence) to test the potential for associations between plant and AMF species and we develop novel metrics to estimate it.
- spatial co-occurrence