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. 2021 Dec 4;9:238. doi: 10.1186/s40168-021-01159-x

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© The Author(s) 2021

Open AccessThis 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

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Fig. 4 — Microbial differentiation across SAF locations differs by radial distance from facility entrance (A–B). (x-axes) Linear regression plot of log-ratio of ancestral state predicted sporulating and non-sporulating bacteria (y-axis) by radial distance from entrance (A). Proportion of contribution from AGP and EMP empo-3 sources (y-axis) across radius from entrance (B). Pearson correlation used for linear comparison and error bars represent the standard error of the mean. C Unweighted (C) and weighted (D) microbiome meta-analysis of JPL SAF time series compared to other built environments (JPL SAF 100 swab study, International Space Station, building materials study, abalone rearing facility, NICU hospital study).