Experimental Evolution of <i>Pseudomonas fluorescens</i> in Simple and Complex Environments
Bibliographic record
Abstract
In complex environments that contain several substitutable resources, lineages may become specialized to consume only one or a few of them. Here we investigate the importance of environmental complexity in determining the evolution of niche width over approximately 900 generations in a chemically defined experimental system. We propagated 120 replicate lines of the bacterium Pseudomonas fluorescens in environments of different complexity by using between one and eight carbon substrates in each environment. Genotypes from populations selected in complex environments evolved greater mean and variance in fitness than those from populations selected in simple environments. Thus, lineages were able to adapt to several substrates simultaneously without any appreciable loss of function with respect to other substrates present in the media. There was greater genetic and genotype-by-environment interaction variance for fitness within populations selected in complex environments. It is likely that genetic variance in populations grown on complex media was maintained because the identity of the fittest genotype varied among carbon substrates. Our results suggest that evolution in complex environments will result neither in narrow specialists nor in complete generalists but instead in overlapping imperfect generalists, each of which has become adapted to a certain range of substrates but not to all.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Research integrity | 0.000 | 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".