Prevailing 16S rRNA gene-amplicon methods for characterizing the bacterial microbiome are economical, but result in coarse taxonomic classifications, are subject to primer and 16S copy number biases, and do not allow for direct estimation of microbiome functional potential. While deep shotgun metagenomic sequencing can overcome many of these limitations, it is prohibitively expensive for large sample sets. We evaluated the ability of shallow shotgun metagenomic sequencing to characterize taxonomic and functional patterns in the fecal microbiome of a model population of feral horses (Sable Island, Canada). Since 2007, this unmanaged population has been the subject of an individual-based, long-term ecological study. Using deep shotgun metagenomic sequencing, we determined the sequencing depth required to accurately characterize the horse microbiome. In comparing conventional versus high-throughput shotgun metagenomic library preparation techniques, we validate the use of more cost-effective lab methods. Finally, we characterize similarities between 16S amplicon and shallow shotgun characterization of the microbiome, and demonstrate that the latter recapitulates biological patterns first described in a published amplicon dataset. Unlike amplicon data, we demonstrate how shallow shotgun metagenomic data also provided useful insights about microbiome functional potential which support previously hypothesized diet effects in this study system.

Studies of microbiome variation in the wild often emphasize host physiology and diet as proximate selective pressures acting on host-associated microbiota. In contrast, microbial dispersal is more rarely considered, and when it is, spatially autocorrelated environmental variables are sometimes overlooked. Using amplicon sequencing, we characterized the bacterial microbiome of adult female (n = 86) Sable Island horses (Nova Scotia, Canada) as part of a detailed, individual-based study of the ecology and evolution of this unmanaged free-living population. Using data on sampling date, horse location, age, parental status, and local exposure to habitat variables, we contrasted the ability of spatiotemporal, physiological, and environmental factors to explain microbiome diversity among Sable Island horses. We extended inferences made from these analyses with both phylogeny-informed and phylogeny-independent null modeling approaches to identify deviations from stochastic expectations. Phylogeny-informed diversity measures were more often correlated with local habitat composition, although null modeling results did not support differential selection acting on the microbiome as the mechanism for these correlative patterns. Conversely, phylogeny-independent diversity measures were best explained by spatial terms, with evidence for spatial- and host social-structured bacterial dispersal limitation. Parental status was important but correlated with measures of β-dispersion rather than β-diversity (mares without foals had lower alpha diversity and more variable microbiomes than mares with foals). Our results suggest that inter-host microbiome variation in this population is driven more strongly by bacterial dispersal limitation and ecological drift than by differential selective pressures, highlighting the need to consider alternative ecological processes in the study of microbiomes.