Data from: Spatiotemporal variation in the gut microbiomes of co-occurring wild rodent species
Data files
Mar 19, 2024 version files 59.66 MB
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README.md
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TableS1.txt
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tableS2.txt
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TableS3.tsv
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Abstract
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.
README: 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