Co-evolution and Co-speciation of Host-Gut Bacteria Systems
Bibliographic record
Abstract
Mammalian gut microbiomes profoundly influence host fitness, but the processes that drive the evolution of host-microbiome systems are poorly understood. Recent studies suggest that mammals and their individual gut symbionts can have parallel evolutionary histories, as represented by their congruent phylogenies. These "co-phylogenetic" patterns are signatures of ancient co-speciation events and illustrate the cohesiveness of the mammalian host-gut microbiome entity over evolutionary times. Theory predicts that co-speciation between mammals and their gut symbionts could result from their co-evolution. However, there is only limited evidence of such co-evolution. Here, we propose a model that explains cophylogenetic patterns without relying on co-evolution. Specifically, we suggest that individual gut bacteria are likely to diverge in patterns recapitulating host phylogeny when hosts undergo allopatric speciation, limiting inter-host bacterial dispersal and genomic recombination. We provide evidence that the model is empirically grounded and propose a series of observational and experimental approaches to test its validity.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".