Territorial behavior as a route of social microbial transmission in an asocial mammal
Bibliographic record
Abstract
ABSTRACT Microbial transmission is a major benefit of sociality, facilitated by affiliative behaviors such as grooming and communal nesting in group-living animals. The spread of microbial symbionts through these pathways, and their incorporation into host microbiomes, can enhance host health and fitness by contributing to pathogen protection and metabolic flexibility. Are pathways that facilitate microbial transfer across hosts also present in animals that do not form social groups because territoriality limits social interactions and prevents group formation? Here, we addressed this question by combining longitudinal sampling of individual gut microbial communities, demographic data, and dynamic behavioral and spatial measures of territoriality from a non-social, highly territorial small mammal: wild North American red squirrels ( Tamiasciurus hudsonicus). As squirrel densities increased, individual gut microbial communities became richer and more phylogenetically diverse, while among-individual differences in composition decreased. This pattern was characterized primarily by increases in obligately anaerobic and non-sporulating taxa with little to no tolerance for oxygen-rich environments, suggesting social rather than environmental routes of transmission. Moreover, territorial intrusions—in which conspecifics were found on within an individual’s territorial space—increased gut microbial diversity among individuals defending larger territorial spaces. Using an intrusion-based social network analysis, we found that that pairs with stronger social association (via intrusions) exhibited higher gut microbial similarity. Taken together, our findings provide some of the first evidence for social microbial transmission in a non-social species, and suggest that increased density and territorial behavior can diversify and homogenize host gut microbial communities despite social isolation.
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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 | 0.001 | 0.001 |
| 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".