DOI: 10.5061/dryad.v6wwpzgws
title: Social groups constrain the spatiotemporal dynamics of wild sifaka gut microbiomes

description: Primates acquire gut microbiota from conspecifics through direct social
 contact and shared environmental exposures. Host behavior is a prominent
 force in structuring gut microbial communities, yet the extent to which
 group or individual-level forces shape the long-term dynamics of gut
 microbiota is poorly understood. We investigated the effects of three
 aspects of host sociality (social groupings, dyadic interactions, and
 individual dispersal between groups) on gut microbiome composition and
 plasticity in 58 wild Verreaux’s sifaka (Propithecus verreauxi) from six
 social groups. Over the course of three dry seasons in a five-year period,
 the six social groups maintained distinct gut microbial signatures, with
 the taxonomic composition of individual communities changing in tandem
 among co-residing group members. Samples collected from group members
 during each season were more similar than samples collected from single
 individuals across different years. In addition, new immigrants and
 individuals with less stable social ties exhibited elevated rates of
 microbiome turnover across seasons. Our results suggest that permanent
 social groupings shape the changing composition of commensal and
 mutualistic gut microbial communities and thus may be important drivers of
 health and resilience in wild primate populations.
description: In this study, we utilize behavioral, genetic, and demographic
 data obtained as part of The Sifaka Research Project, a continuous
 long-term field study of wild lemurs (Verreaux’s sifaka,
 <i>Propithecus verreauxi</i>) in western Madagascar, to
 consider the impact of dynamic contact patterns on the gut microbiome at
 multiple scales, including between social groups (i.e., at the
 population-level), among individuals within groups, and within individual
 hosts. To measure short- and long-term gut microbial plasticity, we
 assayed sifaka gut microbial communities during three dry seasons over a
 five-year period. We collected fecal samples from six social groups on a
 weekly basis during one-to-two-month periods in 2012, 2015, and 2016, with
 a total of 65 unique individuals (58 of known identity) sampled across the
 entire study period.  <em>Fecal sample processing
 and 16S rRNA gene sequencing</em><b> </b>
 We preserved fecal samples in RNAlater® (ThermoFisher, Waltham,
 MA, USA) and stored them at ambient temperature until their arrival at The
 University of Texas at Austin, where they were frozen at -80°C until
 further analysis. We extracted DNA from the majority of 2012 fecal samples
 (N = 125) using a phenol chloroform bead-beating procedure and from the
 remaining samples (N = 220) using the DNeasy Powersoil Kit (Qiagen,
 Germantown, MD, USA). We amplified the V4 region of the bacterial 16S
 ribosomal RNA gene with primers 515F and 806R. The resulting amplicons
 were pooled and paired end (2 x 150) sequenced on the Illumina MiSeq
 platform at Argonne National Laboratory (Lemont, IL, USA). 16S rRNA
 sequence data are deposited in the NCBI Sequence Read Archive (BioProject
 PRJNA756780). We demultiplexed raw Illumina sequence
 reads with QIIME 1 and processed demultiplexed reads with DADA2 1.8 in R,
 following the authors’ published workflow
 (https://benjjneb.github.io/dada2/tutorial_1_8.html). DADA2 implements a
 quality-aware model of Illumina amplicon errors to infer exact biological
 sequences (i.e., amplicon sequence variants, ASVs). We assigned taxonomic
 classifications to ASVs based on their best match in the Silva reference
 database v138. After taxonomic assignment, we removed 19 putative
 contaminants (decontam R package), eukaryotic, chloroplast, and
 mitochondrial ASVs (N = 54), and singletons (N = 46). We discarded 23
 samples with fewer than 3,000 sequence reads because rarefaction curves
 plateaued at this sequencing depth. Lastly, we omitted six samples
 collected from unmarked individuals in Groups III and VI in
 2012.  Prior to estimating pairwise dissimilarities
 among gut microbial communities, we retained ASVs that appeared more than
 30 times in the dataset and that were detected in at least two samples,
 totaling 603 unique sequences. We applied a variance stabilizing
 transformation (VST), based on a negative binomial mixture model of
 microbiome count data, to estimate sample-specific normalization factors
 and rescale ASV counts (estimateSizeFactors function with
 type=“poscounts”, DESeq2 package).
description: The README.md file contains information on R scripts and inputs
 to reproduce the main results and figures in the associated manuscript
 referenced above. The data dictionary file
 (perofsky_2021_mol_ecol_data_dictionary.xlsx) defines variables in each
 dataset ("Column"), allowable values for variables
 ("Value"), whether variables contain sample or host information
 ("Category"), and variable definitions
 ("Explanation").