What drives bacterial extinction? The role of bacteriophages
Bibliographic record
Abstract
Bacterial lineages are relatively short-lived on geological timescales, according to phylogenetic analyses, implying that bacterial extinction occurs at high rates. Since the vast majority of bacteria live in large populations in oceans and soils, many well-studied extinction mechanisms, such as demographic or environmental stochasticity, seem unlikely to drive this pattern. We outline mechanisms for the extinction of large bacterial populations, and discuss the emergence of a new virus as a possible cause of extinction. We use deterministic and stochastic models to characterise the persistence of a bacterial population, demonstrating that when resistance to a new virus does not emerge, large populations are more likely to go extinct than small populations, which contrasts with classically studied extinction mechanisms. When they go extinct, large populations also reach extinction more quickly. When phage-resistant bacteria appear, extinction is rare but its probability increases with population size in some parameter regimes. We also quantify bacterial extinction in spatially distinct subpopulations. We conclude that large bacterial populations are robust to many extinction mechanisms, and typically evolve resistance to new phages, as observed empirically. For bacterial lineages that have gone extinct, however, the failure to evolve resistance to a novel phage is a likely underlying mechanism.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".