Social network dynamics and gut microbiota composition during alpha male challenges in <i>Colobus vellerosus</i>
Bibliographic record
Abstract
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 Sørensen 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.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".