The Scent Gland Microbiomes of Wild Tamarins Provide New Insight Into Microbial Contributions to Olfactory Communication
Bibliographic record
Abstract
ABSTRACT The microbiome of mammalian scent glands is thought to contribute to the production of odorant compounds involved in sensory communication. Yet, the extent to which glandular microbiomes contain bacteria relevant to odor production and vary by host species, scent marking behavior, or gland morphology remains poorly understood, particularly in wild animals. We sampled microbes collected from skin swabs of suprapubic and sternal scent glands in wild Peruvian saddleback tamarins ( Leontocebus weddelli ; n = 19) and emperor tamarins ( Tamarinus imperator ; n = 20) to better understand glandular microbial communities. We aimed to: (1) profile glandular microbiomes of both species, focusing on odor‐related taxa and metabolic pathways, and (2) determine whether suprapubic glands, more often in contact with the external environment, had higher diversity and distinct composition of odor‐related taxa and pathways compared to sternal glands. We generated metagenomic reads using short‐read DNA shotgun sequencing from glandular swabs. We identified 18 odor‐associated microbial taxa in both tamarin species, mainly Staphylococcus and Corynebacterium , and 26 pathways, including pyruvate fermentation and amino acid metabolism. Suprapubic glands had lower Shannon alpha diversity relative to sternal glands, especially in L. weddelli . The glands of L. weddelli also differed in taxonomic composition, with odor‐related taxa more abundant in suprapubic glands. Our results provide evidence for the involvement of scent gland microbiomes in host communication biology. Glandular specializations differed not only between closely related tamarin species but also between gland types within the same individuals, suggesting a nuanced pattern of host–microbe coevolution that may shape interactions important for olfactory communication.
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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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".