THE DYNAMICS OF DIVERSIFICATION IN EVOLVING PSEUDOMONAS POPULATIONS
Bibliographic record
Abstract
Determining the mechanisms that promote the evolution of diversity is a central problem in evolutionary biology. Previous studies have demonstrated that diversification occurs in complex environments and that genotypes specialized on alternative resources can be maintained over short time scales. Here, we describe a selection experiment that has tracked the dynamics of adaptive diversification within selection lines of the asexual bacteria Pseudomonas fluorescens over about 900 generations. We cultured experimental populations from the same two isogenic ancestral strains in simple, single-substrate environments or in complex, four-substrate environments. Following selection we assayed the growth of genotypes from each population on each substrate individually. We estimated mutational heritability, Vm/VE, as 1 x 10(-3) per generation in simple environments and 3 x 10(-3) per generation in complex environments. These values are roughly consistent with estimates reported in other systems. Populations selected in complex environments evolved into genetically diverse communities. Genotypes exhibited greater metabolic differentiation from other genotypes in their own population than to genotypes evolving in other populations, presumably as a result of resource competition. In populations selected in simple environments, little genetic diversity evolved, and genotypes shared very similar phenotypes. Our findings suggest that ecological opportunity provided by environmental complexity plays a major role in the evolution and maintenance of diversity.
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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 | 0.001 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".