ADAPTIVE LANDSCAPES IN EVOLVING POPULATIONS OF PSEUDOMONAS FLUORESCENS
Bibliographic record
Abstract
The repeatability of adaptive evolution depends on the ruggedness of the underlying adaptive landscape. We contrasted the relative ruggedness of two adaptive landscapes by measuring the variance in fitness and metabolic phenotype within and among genetically distinct strains of Pseudomonas fluorescens in two environments differing only in the carbon source provided (glucose vs. xylose). Fitness increased in all lines, plateauing in one environment but not the other. The pattern of variance in fitness among replicate lines was unique to the selection environment; it increased over the course of the experiment in xylose but not in glucose. Metabolic phenotypes displayed two results: (1) populations adapted via changes that were distinctive to their selection environment, and (2) endpoint phenotypes were less variable in glucose than in xylose. These results indicate that although the response to selection is highly repeatable at the level of fitness, the underlying genetic routes taken were different for each environment and more variable in xylose. We suggest that this reflects a more rugged adaptive landscape in xylose compared to glucose. Our study demonstrates the utility of using replicate selection lines with different evolutionary starting points to try and quantify the relative ruggedness of adaptive landscapes.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".