bioRxivAnimal Behavior & Cognition
Social network dynamics and gut microbiota composition during alpha male challenges in Colobus vellerosus
S. Samartino, D. Christie, A. Penna, P. Sicotte, N. Ting, E. Wikberg
Abstract
The gut microbiota of group-living animals is strongly influenced by their social interactions, but it is unclear how it responds to social instability. We investigated whether social instability associated with the immigration of new males and challenges to the alpha male position could explain differences in the gut microbiota in adult female Colobus vellerosus at Boabeng-Fiema, Ghana. During May-August 2007 and May 2008-May 2009, we collected: 1) 53 fecal samples from adult females in 8 social groups for v4 16S rRNA sequencing to determine gut microbiota composition; and 2) demographic and behavioral data ad libitum to determine male immigration, challenges to the alpha male position, and infant births and deaths. We estimated Sorensen and Bray-Curtis beta diversity indices (i.e., between-sample microbiome variation), and they were predicted by year, alpha male stability, group identity, age, and individual identity. We then created 1-m proximity networks using detailed behavioral data via focal follows of 19 adult females in 3 of these groups. Yearly 1-m proximity ties predicted adult female beta-diversity in the two socially stable groups. An alpha male takeover in the third group was associated with infant mortality and temporal variation in proximity networks. Beta-diversity among adult females was predicted by similarity in infant loss status and short-term (rather than yearly) 1-m proximity ties. Although the mechanism driving this association needs to be further investigated in future studies, our findings indicate that alpha male takeovers and social stability are associated with gut microbiota variation and highlight the importance of taking demographic and social network dynamics into account.
- organism: non-human primate
- assay: metagenomics
- research focus: ecology & biodiversity
- approach: statistical model
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