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. 2018 Apr 19;1:35. doi: 10.1038/s42003-018-0041-7

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© The Author(s) 2018

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. 2 — a Clustering of microbial communities (based on sample proximity in random forest classification) according to experimental design (N = 96). The +/– indicates the presence/absence of a particular treatment. Color codes for antibiotics (red/blue), color shade codes for predation (dark/light), and line style codes for spatial structure (continuous/dashed). b A hierarchical model of species communities (HMDS) of strain occurrence (strain traits account for 11% of variation in co-occurrence, after controlling for phylogenetic similarity) is able to capture about 20–90% (median 43%) of the variation (left panel), depending on the strains (low values around 5% are due to strain occurring in all samples; N = 96). Within the explained variation, strains also differ considerably in how they respond to the experimental treatments (right panel). Most variation is seen in the response to spatial structuring and this fraction tends to be negatively associated with the total fraction of variance explained (ρ = –0.38)