Interactions between social groups of colobus monkeys ( <i>Colobus vellerosus</i> ) explain similarities in their gut microbiomes
Bibliographic record
Abstract
Abstract The gut microbiome is structured by social groups in a variety of host taxa. Whether this pattern is driven by relatedness, similar diets, or shared social environments is under debate because few studies have had access to the data necessary to disentangle these factors in wild populations. We investigated whether diet, relatedness, or the 1-meter proximity network best explains differences in the gut microbiome among 45 female colobus monkeys in 8 social groups residing at Boabeng-Fiema, Ghana. We combined demographic and behavioural data collected May-August 2007 and October 2008-April 2009 with 16S rRNA sequencing of faecal samples collected during the latter part of each observation period. Social group identity explained a large percentage of the variation in gut microbiome beta-diversity. When comparing the predictive power of dietary dissimilarity, relatedness, and connectedness in the 1-meter proximity network, the models with social connectedness received the strongest support, even in our analyses that excluded within-group dyads. This novel finding indicates that microbes may be transmitted during intergroup encounters, which could occur either indirectly via shared environments or directly via social contact. Lastly, some of the gut microbial taxa that appear to be transmitted via 1-meter proximity are associated with digestion of plant material, but further research is needed to investigate whether this type of gut microbe transmission yields health benefits, which could provide an incentive for the formation and maintenance of social bonds within and between social groups.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".