DOI: 10.5061/dryad.vx0k6djz3
title: Data from: Spatiotemporal variation in the gut microbiomes of co-occurring wild rodent species

description: Mammalian gut microbiomes differ within and among individual hosts. Hosts
 that occupy a range of environmental conditions may exhibit greater
 spatiotemporal variation in their microbiome than those constrained as
 specialists to narrower subsets of resources or habitats. This can occur
 because widespread host species encounter a variety of ecological
 conditions that act to diversify their gut microbiomes and/or because
 generalized host species tend to form large populations that promote
 sharing and maintenance of diverse microbes. We studied spatiotemporal
 variation in the gut microbiomes of three co-occurring rodent species
 across an environmental gradient in a Kenyan savanna. We hypothesized: (i)
 the taxonomic, phylogenetic, and predicted functional composition of gut
 microbiomes differ significantly among host species, (ii) microbiome
 richness increases with population size for all host species, and (iii)
 host species exhibit different rates of seasonal change in their gut
 microbiomes, reflecting different sensitivities to environmental change.
 We evaluated changes in gut microbiome according to species identity,
 site, and host population density using three years of
 capture-mark-recapture data and 351 microbiome samples. Host species
 differed significantly in microbiome composition, though those with the
 more specialized diets and higher demographic sensitivities showed only
 slightly greater microbiome variability than those of a widespread dietary
 generalist. Total microbiome richness in populations of all species
 increased significantly with population size, but only one of the more
 specialized species also exhibited greater within-individual microbiome
 richness with population size. Across co-occurring rodent species with
 diverse diets and life histories, host population growth in response to
 rainfall was associated both with strong increases in population-level
 microbiome richness (sampling effects) and turnover in the relative
 abundance of bacterial taxa (environmental effects), but there was not
 consistent change in intra-individual richness (individual variation).
 Together, our results show that maintenance of large host populations
 contributes to the maintenance of gut microbiome diversity in wild
 mammals.
description: # Spatiotemporal variation in the gut microbiomes of co-occurring wild
 rodent species \####Short title: Spatiotemporal gut microbiome variation
 Authors: Bianca R. P. Brown1,2,3,a, Leo M. Khasoha3,4, Peter Lokeny3,
 Rhiannon P. Jakopak3,4, Courtney G. Reed1,2,3, Marissa Dyck5, Alois
 Wambua4, Seth D. Newsome6, Todd M. Palmer3,7, Robert M. Pringle3,8, Jacob
 R. Goheen3,4, Tyler R. Kartzinel1,2,3,a Affiliations: 1Department of
 Ecology, Evolution, and Organismal Biology, Brown University, Providence,
 RI 02912, USA 2Institute at Brown for Environment and Society, Brown
 University, Providence, RI 02912, USA 3Mpala Research Centre, Laikipia,
 Kenya 4Department of Zoology and Physiology, University of Wyoming 82071
 WY, US 5Biological Sciences Department, Athens, OH, 45701, USA 6Department
 of Biology, University of New Mexico, Albuquerque, NM, 87131-000 7Center
 of African Studies, University of Florida, Gainesville, FL 32611, USA
 8Department of Ecology & Evolutionary Biology, Princeton University,
 Princeton, NJ 08544, USA --- ## Metadata Spatiotemporal variation in the
 gut microbiomes of co-occurring wild rodent species Table S1. Samples
 included in this study. For each 351 samples we analyzed the microbiome we
 reported Sample ID, Latin name for host species, sample date, sample Site
 (North, Central, South), treatment, sample plot (1, 2, 3), body size
 measurements for the subset of animals that were measured successfully,
 initial sequencing depth prior to processing, and the remaining sequencing
 depth after contamination was removed. Table S2. Population estimates
 based on RMark analysis. The table consists of species Latin names,
 information on the collection site (Site, treatment, plot), population
 size estimates, bout, occupancy, and the coefficient of variation for each
 species by bout. Table S3. *Aethomys hindei* spatiotemporal differential
 abundance analysis Songbird output. Log ratio of comparison between
 spatiotemporal categories using South High as reference. The table shows
 the ASVs (feature ID), Intercept, and the different categories (Central
 High, Central Low, North High, North Low, and South Low). Negative values
 represent ASVs enriched in South High and positive values represent ASVs
 enriched in respective comparison categories. The top 10 taxa are chosen
 for downstream analysis (see Table S6). Table S4. *Gerbilliscus robustus*
 spatiotemporal differential abundance analysis Songbird output. Log ratio
 of comparison between spatiotemporal categories using South High as
 reference. The table shows the ASVs (feature ID), Intercept, and the
 different categories (Central High, Central Low, North High, North Low,
 and South Low). Negative values represent ASVs enriched in South high and
 positive values represent ASVs enriched in respective comparison
 categories. The top 10 taxa are chosen for downstream analysis (see Table
 S6). Table S5. *Saccostomus mearnsi* spatiotemporal differential abundance
 analysis Songbird output. Log ratio of comparison between spatiotemporal
 categories using South High as reference. The table shows the ASVs
 (feature ID), Intercept, and the different categories (Central High,
 Central Low, North High, North Low, and South Low). Negative values
 represent ASVs enriched in South High and positive values represent ASVs
 enriched in respective comparison categories. The top 10 taxa are chosen
 for downstream analysis (see Table S6). Table S6. Top 10 ASVs with the
 highest log-ratios for comparisons between spatiotemporal categories for
 each host species (see Tables S3-S6 for all ASVs). Table consists of ASVs,
 Sample ID, Relative Abundance of ASV, Spatiotemporal Category, Site, Bout,
 Block, Treatment, Host species, and taxonomic description to the lowest
 level. Table S7. Functional Pathway predictions from PICRUSt2. Pathway and
 respective abundance of pathway is also shown. The MetaCyc Pathway
 description is also provided with level three categorical description.
 Table S8. Predicted function differential abundance analysis between
 *Aethomys hindei* and *Gerbilliscus robustus*. Log ratio of comparison
 between *A. hindei* and *G. robustus*. The table shows the ASVs (feature
 ID), Intercept, and *G. robustus* column which represents the log-ratios.
 Negative values represent ASVs enriched in *A. hindei* and positive values
 represent ASVs enriched in *G. robustus*. Table S11 shows the top 10 find
 predicted functions. Table S9. Predicted function differential abundance
 analysis between *Saccostomus mearnsi* and *Gerbilliscus robustus* . Log
 ratio of comparison between *S. mearnsi* and *G. robustus*. The table
 shows the ASVs (feature ID), Intercept, and *S. mearnsi* column which
 represents the log-ratios. Negative values represent ASVs enriched in *G.
 robustus*, and positive values represent ASVs enriched in *S. mearnsi* the
 top 10 taxa are chosen for downstream analysis. Table S11 shows the top 10
 find predicted functions. Table S10. Predictive function differential
 abundance analysis between *Aethomys hindei* and *Saccostomus mearnsi*.
 Log ratio of comparison between *A. hindei* and *S. mearnsi*. The table
 shows the ASVs (feature ID), Intercept, and *S. mearnsi* column which
 represents the log-ratios. Negative values represent ASVs enriched in *A.
 hindei* and positive values represent ASVs enriched in *S. mearnsi*. Table
 S11 shows the top 10 find predicted functions. Table S11. Top 10 predicted
 functional pathways with the highest log-ratios for comparisons between
 host species (see Tables S8-S10 for all pathways). Table consists of
 Pathway, Log ratio (Abundance), Pathway Description (MetaCyc Description),
 Group Comparison. Table S12. ASVs differential abundance analysis between
 control and exclosure plots for *Gerbilliscus robustus* samples in the
 north. The table shows the ASVs (feature ID), Intercept, and Log ratio
 which consists of the log-ratio values. Negative values represent ASVs
 enriched in control plots and positive values represent ASVs enriched in
 respective exclosure plots. Table S13. ASVs differential abundance
 analysis between control and exclosure plots for *Gerbilliscus robustus*
 samples in the central plots. The table shows the ASVs (feature ID),
 Intercept, and Log Ratio which consists of the log-ratio values. Negative
 values represent ASVs enriched in control plots and positive values
 represent ASVs enriched in respective exclosure plots. Table S14. ASVs
 differential abundance analysis between control and exclosure plots for
 *Gerbilliscus robustus* samples in the south plots. The table shows the
 ASVs (feature ID), Intercept, and Log ratio which consists of the
 log-ratio values. Negative values represent ASVs enriched in control plots
 and positive values represent ASVs enriched in respective exclosure plots.
 Table S15. Pairwise perMANOVAs testing for significant differences in
 microbiome composition between species for each microbiome community
 metric (Bray-Curtis, Unweighted UniFrac, weighted UniFrac). Pairwise
 comparisons differences range from 1 to 3% differences. The species with
 more generalist functional traits (*Gerbilliscus robustus*) microbiome was
 more distinct than the more specialist species (*Aethomys hindei* and
 *Saccostomus mearnsi*) for all metric except the weighted UniFrac. See
 corresponding PCoA plots in Fig. 2. Table includes summary statistics
 (pseudo-F, R2 and P-values) and abbreviations for sum of squares (SS),
 mean of squares (MS), degrees of freedom (Df). Table S16. Pairwise
 perMANOVAs testing for significant differences in predicted microbiome
 functions based on PICRUSt2 using Bray-Curtis dissimilarity. The species
 with more generalist functional traits (*Gerbilliscus robustus*)
 microbiome was more distinct than the more specialist species (*Aethomys
 hindei* and *Saccostomus mearnsi*) for all metric except weighted UniFrac.
 Corresponding PCoA plots and overall perMANOVA results are reported in
 Figure 2. The table reports statistics (pseudo-F, R2 and P-values) and
 abbreviations for sum of squares (SS), mean of squares (MS), degrees of
 freedom (df). List of abbreviations used in tables: AEHI: *Aethomys
 hindei* GERO: *Gerbilliscus robustus* SAME: *Saccostomus mearnsi* LMH:
 Large mammalian herbivore exclusion plots CON: Control (open) plots