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. 2022 May 23;4:33. doi: 10.1186/s42523-022-00182-z

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© The Author(s) 2022

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/.

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Fig. 2 — Microbial (A, C, E) and metabolomic (B, D, F) multivariate analysis of nenue gut subsections. Non metric multidimensional scaling plots of the weighted unifrac distances among microbial communities (A) and bray Curtis distances among metabolomic samples (B) from the nenue gut. Samples are colored by gut subsection. Arrows track the transition of microbiota/metabolomes in an individual fish, starting in the stomach and ending in the hindgut. Box and whisker plots depict multivariate dispersion of microbial communities (unifrac distance to centroid) (C) and metabolomic samples (bray curtis distance to centroid) (D). Samples are color coded by gut subsection. Dispersion in both microbial communities and metabolomic samples responded significantly to gut subsection, but only metabolomic samples showed significant post hoc pairwise differences (α = .05, Tukey Post Hoc test), which are indicated by letter significance indicators. Clustering heatmaps depict pairwise R² values between samples from PERMANOVAs for (E) microbiota and (F) metabolome datasets