Here’s to the Losers: Evolvable Residents Accelerate the Evolution of High-Fitness Invaders
Bibliographic record
Abstract
Recent work has shown that evolvability plays a key role in determining the long-term population dynamics of asexual clones. However, simple considerations suggest that the evolvability of a focal lineage of bacteria should also be influenced by the evolvability of its competitors. First, evolvable competitors should accelerate evolution by impeding the fixation of the focal lineage through a clonal interference-like mechanism. Second, evolvable competitors should increase the strength of selection by rapidly degrading the environment, increasing selection for adaptive mutations. Here we tested these ideas by allowing a high-fitness clone of the bacterium Pseudomonas aeruginosa to invade populations of two low-fitness resident clones that differ in their evolvability. Both competition from mutations in the resident lineage and environmental degradation lead to faster adaptation in the invader through fixing single mutations with a greater fitness advantage. The results suggest that competition from mutations in both the successful invader and the unsuccessful resident shapes the adaptive trajectory of the invader through both direct competition and indirect environmental effects. Therefore, to predict evolutionary outcomes, it will be necessary to consider the evolvability of all members of the community and the effects of adaptation on the quality of the environment. This is particularly relevant to mixed microbial communities where lineages differ in their adaptive potential, a common feature of chronic infections.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".