Host phylogeny and functional traits differentiate gut microbiomes in a diverse natural community of small mammals

Differences in the bacteria inhabiting mammalian gut microbiomes tend to reflect the phylogenetic relatedness of their hosts, a pattern dubbed phylosymbiosis. Although most research on this pattern has compared the gut microbiomes of host species across biomes, understanding the evolutionary and ecological processes that generate phylosymbiosis requires comparisons across phylogenetic scales and under similar ecological conditions. We analyzed the gut microbiomes of 14 sympatric small-mammal species in a semi-arid African savanna, hypothesizing that there would be a strong phylosymbiosis pattern associated with the different body sizes and diets of the mammalian lineages present. Consistent with phylosymbiosis, microbiome dissimilarity increased with phylogenetic distance among hosts, ranging from congeneric sets of mice and hares that did not differ significantly in microbiome composition to species from different taxonomic orders that had almost no gut bacteria in common. While phylosymbiosis was detected among just the 11 species of rodents, it was substantially weaker than comparisons involving all 14 species together. In contrast, microbiome diversity and composition were generally more strongly correlated with body size, dietary breadth, and dietary overlap in comparisons restricted to rodents than in those including all lineages. The starkest divides in microbiome composition thus reflected the broad evolutionary divergence of hosts, regardless of body size or dietary composition, while subtler microbiome differences reflected variation in ecologically important traits between closely related hosts. Strong phylosymbiotic patterns arose deep in the phylogeny, and ecological filters that promote functional differentiation of cooccurring host species may disrupt or obscure this pattern near the tips.

Methods
Microbiome data: before rarefying

This dataset was created by running the DADA2 sequence-variant algorithm (dadaFs/Rs) on dereplicated paired-end sequences with their assigned error rates. After running the DADA2 algorithm, we merged error-free forward and reverse sequence reads into ASVs and removed chimeras. 

ASV_table.csv

Microbiome data: Contamination Removed

We screened potential contaminants by comparing ASVs from the mock community sequenced with our samples against the sequences from the mock reference community provided by the manufacturer. We identified 9 ASVs that did not match the mock reference community for removal from all samples as putative contaminants. File ASV_table_contamination_removed represents the remaining ASVs after the removal of all contaminants.

ASV_table_contamination_removed.csv

Microbiome data: Rarefied

All our microbiome diversity and composition comparisons were performed on the rarefied table. We rarefied data representing 7,720,016 sequences across 126 samples to the lowest read depth (N = 7,017 reads per sample). This final dataset contained 9,887 ASVs, including singletons that were retained after rarefying. 

ASV_table_rarefied.csv