Impact of animal socioecology on gut microbial communities : insights from wild meerkats in the Kalahari
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Wiley
Abstract
1. The social organisation of animals likely shapes the composition, diversity and stability of microbiomes, giving rise to the concept of the 'social microbiome'-microbial communities shared within and across social units, or 'islands', ranging from individuals to entire ecosystems. Understanding the connections and their underlying drivers is crucial for revealing how socioecology influences microbiomes and associated health outcomes. However, empirical assessments are still limited, and the relative influence of social organisation compared to intrinsic (biological) and extrinsic (environmental) factors in shaping microbiomes is particularly unclear.
2. Here, we used a long-term, individual-based study of Kalahari meerkats (Suricata suricatta) to test predictions from the social microbiome concept. We assessed the relative influence of social factors, biological traits and environmental variables on gut microbial communities, while also accounting for the effects of microbial phylogenetic relatedness and within-host associations or co-occurrence independent of phylogeny.
3. Meerkat microbiomes exhibited highly 'nested' and weakly 'modular' structures: individuals with lower diversity hosted amplicon sequence variants (ASVs) that were subsets of the overall community, though some bacterial taxa clustered distinctly among hosts. Microbiomes were more similar within social groups than between them.
4. Group membership strongly influenced the co-occurrence of many beneficial ASVs, as well as a few potentially harmful ones. This effect was stronger than that of kinship, though closer relatives shared more similar microbiomes within some groups. While a range of social, biological and environmental factors influenced bacterial abundance, group membership, individual age and sampling time since sunrise had the most significant impact. ASV-ASV co-occurrence within hosts, independent of phylogeny, also played a major role. In contrast, individual-level social traits (e.g. dominance, immigration), other environmental (e.g. sampling temperature, rainfall, hours since foraging), demographic (sex) and health-related factors (body condition, disease status) had weaker effects on bacterial abundance.
5. We show that gut microbiomes are shaped by a combination of factors, highlighting the importance of separating the effects of social organisation from individual social traits, biological factors, environmental influences and microbe-microbe interactions. By identifying drivers of both beneficial and detrimental bacterial co-occurrence, we provide a foundation for assessing how the social microbiome affects animal health and fitness.
Description
DATA AVAILABILITY STATEMENT : The sequences used in the study for bacterial phylogenetic reconstruction are all available at NCBI BioProject PRJNA764180. All data used for this project and R code to replicate our analyses have been made available publicly via figshare: https://doi.org/10.6084/m9.figshare.28496426.
SUPPORTING INFORMATION : FIGURE S1. Possible, non-mutually exclusive patterns of bipartite network associations between bacterial amplicon sequence variants (ASVs) and meerkats (adapted from Strona & Veech, 2015). FIGURE S2. Temperature plots showing the ‘incidence’ of ASV in meerkats. FIGURE S3. Comparisons of model-fit parameters (mean CSR2 values from bacterial ASVs) from across four JSDMs, that is, across a full model that included all covariates, and the three covariate-specific models. TABLE S1. Meerkat groups investigated during three different study periods. TABLE S2. Final list of 119 bacterial ASVs (phylum, family & genus) analysed in the study. TABLE S3. Multivariate MR-QAP to examine the effects of group membership (‘same’ vs. ‘different’ groups) and kinship (Wang's relatedness coefficient) on the degree of Jaccard pairwise β-diversity similarity in bacterial community composition (ASV abundance and diversity). Period of study was included as a control variable. TABLE S4. Univariate MR-QAPs to examine within-group effects of kinship (Wang's relatedness coefficient) on the degree of Jaccard pairwise β-diversity similarity in bacterial community composition (ASV abundance and diversity). TABLE S5. Parameter estimates from a generalised linear mixed model (GLMM) used to evaluate the fit of CSR2 values (outcome variables) of four joint-species distribution models (JSDMs). TABLE S6. Parameter estimates from GLMMs used to compare the mean effect sizes of covariates, on the relative abundance of bacterial ASVs.
SUPPORTING INFORMATION : FIGURE S1. Possible, non-mutually exclusive patterns of bipartite network associations between bacterial amplicon sequence variants (ASVs) and meerkats (adapted from Strona & Veech, 2015). FIGURE S2. Temperature plots showing the ‘incidence’ of ASV in meerkats. FIGURE S3. Comparisons of model-fit parameters (mean CSR2 values from bacterial ASVs) from across four JSDMs, that is, across a full model that included all covariates, and the three covariate-specific models. TABLE S1. Meerkat groups investigated during three different study periods. TABLE S2. Final list of 119 bacterial ASVs (phylum, family & genus) analysed in the study. TABLE S3. Multivariate MR-QAP to examine the effects of group membership (‘same’ vs. ‘different’ groups) and kinship (Wang's relatedness coefficient) on the degree of Jaccard pairwise β-diversity similarity in bacterial community composition (ASV abundance and diversity). Period of study was included as a control variable. TABLE S4. Univariate MR-QAPs to examine within-group effects of kinship (Wang's relatedness coefficient) on the degree of Jaccard pairwise β-diversity similarity in bacterial community composition (ASV abundance and diversity). TABLE S5. Parameter estimates from a generalised linear mixed model (GLMM) used to evaluate the fit of CSR2 values (outcome variables) of four joint-species distribution models (JSDMs). TABLE S6. Parameter estimates from GLMMs used to compare the mean effect sizes of covariates, on the relative abundance of bacterial ASVs.
Keywords
Animal socioecology, Gut microbial communities, Joint‐species‐distribution model (JSDM), Meerkats (Suricata suricatta), Microbial co‐occurrence networks, Social microbiome, Animals, Herpestidae, Gastrointestinal Microbiome,, Male, Female, Bacteria, Social Behavior, Phylogeny
Sustainable Development Goals
SDG-15: Life on land
Citation
Balasubramaniam, K., Mueller-Klein, N., Vink, T., Clutton-Brock, T.H., Manser, M. B. & Sommer, S. (2025). Impact of animal socioecology on gut microbial communities: Insights from wild meerkats in the Kalahari. Journal of Animal Ecology, 94, 2687–2703. https://doi.org/10.1111/1365-2656.70168.
