Fig. 4. Effects on the soil fungal community of different global change factors applied singly and using different numbers of factors (2, 5, 8, 10 interacting factors).

For each biodiversity property (each row), single factor effects were estimated (1st column) and then used to predict multi-factor effects based on certain assumptions (2nd column). As with soil functions (Fig. 3), the effect directions were consistent along the number of factors for all properties as predicted using random forest machine learning (3rd column). The model predictability is shown in the 4th column (dark blue). Adding factor identity (dark yellow) or single factor effect size information (dark green) to the model improved predictability only for community composition, indicating that factor interactions exist (4th column). Fungal diversity is represented by ASV richness (A-D), community composition (E-H) and community dispersion (I-L). Community composition is represented by the 1st axis of an unconstrained multivariate ordination (NMDS) of the Bray-Curtis sample pairwise dissimilarities.