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. 2021 Nov 6;1:65. doi: 10.1038/s43705-021-00072-6

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© The Author(s) 2021

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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Fig. 3 — Random Forest modeling was conducted based on different predictor variables separately, including the dissimilarities of relative abundance (RA), phylogeny (PL), functional trait (FT), richness (RICH), and community composition (COMM) of overall fungal OTUs, and the modified relative abundance (MRA) profiles of shared (or unique) core root-associated fungal OTUs at cutoffs of 0.5 and 0.9. The performance differences between models using observed and shuffled data were compared by Wilcoxon tests. One-way ANOVA with post hoc Tukey HSD test was conducted to compare the distributions of AUC values for different predictor variables. Prediction performance of different predictor variables with the same letter were not significantly different at alpha value of 0.05.