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. 2021 Feb 10;3:18. doi: 10.1186/s42523-021-00081-9

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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/.

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Fig. 4 — Ruminal resistome profiles of cows with different feed intake and cows with different milking traits. Spearman’s rank correlations between Shannon index (a) and Chao 1 richness (b) of ARGs and dry matter intake. c Biplot of the redundancy analysis showed relationships between ARGs and dry matter intake. The top 10 most abundant ARGs were used in this analysis. HDMI, cows with the highest dry matter intake (n = 10); LDMI, cows with the lowest dry matter intake (n = 10). d Heatmap of the abundances of the top 20 ARGs in each sample. The abundances (CPM, counts per million) of ARGs were log10- transformed. The cows were clustered and coloured by different groups (green, high intake; red, low intake). The animals used in the above analysis were all selected from study 1. Spearman’s rank correlations between Shannon index (e) and Chao 1 richness (f) of ARGs and milk protein yield. The animals used were selected from study 2. R² = correlation coefficient. ARGs: antimicrobial resistance genes