Transmission mode and dispersal traits correlate with host specificity in mammalian gut microbes
Bibliographic record
Abstract
Different host species associate with distinct gut microbes in mammals, a pattern sometimes referred to as phylosymbiosis. However, the processes shaping this host specificity are not well understood. One model proposes that barriers to microbial transmission promote specificity by limiting microbial dispersal between hosts. This model predicts that specificity levels measured across microbes is correlated to transmission mode (vertical vs. horizontal) and individual dispersal traits. Here, we leverage two large publicly available gut microbiota data sets (1490 samples from 195 host species) to test this prediction. We found that host specificity varies widely across bacteria (i.e., there are generalist and specialist bacteria) and depends on transmission mode and dispersal ability. Horizontally-like transmitted bacteria equipped with traits that facilitate switches between host (e.g., tolerance to oxygen) were found to be less specific (more generalist) than microbes without those traits, for example, vertically-like inherited bacteria that are intolerant to oxygen. Altogether, our findings are compatible with a model in which limited microbial dispersal abilities foster host specificity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".