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Data from: Covariation of diet and gut microbiome in African megafauna

Citation

Kartzinel, Tyler R. et al. (2020), Data from: Covariation of diet and gut microbiome in African megafauna, Dryad, Dataset, https://doi.org/10.5061/dryad.c119gm5

Abstract

A major challenge in biology is to understand how phylogeny, diet, and environment shape the mammalian gut microbiome. Yet most studies of non-human microbiomes have relied on relatively coarse dietary categorizations and have focused either on individual wild populations or on captive animals that are sheltered from environmental pressures, which may obscure the effects of dietary and environmental variation on microbiome composition in diverse natural communities. We analyzed plant and bacterial DNA in fecal samples from an assemblage of 33 sympatric large-herbivore species (27 native, 6 domesticated) in a semi-arid East African savanna, which enabled high-resolution assessment of seasonal variation in both diet and microbiome composition. Phylogenetic relatedness strongly predicted microbiome composition (r = 0.91) and was weakly but significantly correlated with diet composition (r = 0.20). Dietary diversity did not significantly predict microbiome diversity across species or within any species except kudu; however, diet composition was significantly correlated with microbiome composition both across and within most species. We found a spectrum of seasonal sensitivity at the diet-microbiome nexus: seasonal changes in diet composition explained 25% of seasonal variation in microbiome composition across species. Species’ positions on (and deviations from) this spectrum were not obviously driven by phylogeny, body size, digestive strategy, or diet composition; however, domesticated species tended to exhibit greater diet-microbiome turnover than wildlife. Our results reveal marked differences in the influence of environment on the degree of diet-microbiome covariation in free-ranging African megafauna, and this variation is not well explained by canonical predictors of nutritional ecology.

Usage Notes

Dietary Illumina data

Dietary DNA metabarcode data. These trnL-P6 metabarcode data are de-multiplexed according to sample of origin and provided in fastq format. These files represent samples that correspond to sample metadata in Table S1 of the manuscript.

20190606_P6Data.tar.gz.txt

Microbiome Illumina data

Microbiome data files. These paired-end 16S rRNA data are de-multiplexed according to sample of origin and provided in fastq format. These files represent samples that correspond to sample metadata in Table S1 of the manuscript. The six directories correspond to six libraries sequenced at the MSU core facility. Note that sample LM0035 was dropped from further analysis because it originated from either one of two Lepus spp.

20190606_16SData.tar.gz.txt

Dietary DNA metabarcoding data table: prior to rarefying

The analysis presented in the paper is based on a rarefied data set in which sequence read counts were equalized across samples. This table presents the total count of dietary DNA sequences within each sample, prior to rarefying. Because rare sequence reads were dropped when rarefying, this data table contains more plant taxa than does Table S2. The rows represent samples that can be matched to metadata in Table S1; columns are labeled with the exact DNA sequence of the plant taxon.

unrarefied_trnL_P6.csv

Microbiome data: prior to rarefying

The analysis presented in the paper is based on a rarefied data set in which sequence read counts were equalized across samples. This table presents the total count of microbiome 16S sequences within each sample, prior to rarefying. Because rare sequence reads were dropped when rarefying, this data table contains more bacterial taxa than does Table S3. The rows represent samples that can be matched to metadata in Table S1; columns are labeled with the exact DNA sequence of the 16S sequence variant.

unrarefied_16S.csv

Funding

National Science Foundation, Award: DEB-1355122

National Science Foundation, Award: DEB-1457697

National Science Foundation, Award: IOS-1656527