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. 2023 Feb 14;14:834. doi: 10.1038/s41467-023-36479-z

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© The Author(s) 2023

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 — a Heatmap demonstrating significant (Padj < 0.05) correlations between fungal genera (columns) abundances and bacterial genera (rows) abundances (standing communities). The color codes of the cells indicate either positive (purple) or negative (orange) correlations among the species (Padj < 0.05); n = 419 samples were used for the correlation analysis. b Interaction network demonstrating conserved correlations (interaction that is present for both standing and active communities; Padj < 0.05) between bacterial standing communities (pink nodes) and fungal (blue nodes) taxa. The size of the font indicates the key driving taxa, which are characterized as taxa with the highest number of associations with taxa from the other domain. Positive correlations are denoted in red and negative correlations are denoted in green. Data in a, b were calculated using FastSpar implementation of the FastCC algorithm (n = 999 permutations). P-value were adjusted using Benjamini–Hochberg correction. Source data are provided as a Source Data file.