Evolution of Fitness Trade-Offs in Locally Adapted Populations of <i>Pseudomonas fluorescens</i>
Bibliographic record
Abstract
Local adaptation seems to be common in natural systems, but the genetic causes of its evolution remain poorly understood. Here we characterize the genetic causes of trade-offs generating local adaptation in populations of Pseudomonas fluorescens that had previously been evolved for specialization on three different carbon resources. We measured the fitness effects of mutations that arose during selection in that environment and in alternative environments to quantify the degree of specialization. We find that all mutations are beneficial in the environment of selection and that those arising later during adaptation are associated with increasingly antagonistic effects in alternative environments compared with those arising earlier, consistent with a multioptima version of Fisher's geometric model of adaptation. We also find that fitness of pairs of beneficial mutations are consistently less than additive in selection environments, producing a pattern of diminishing returns, but are more variable in alternative environments, being either positive or negative. Finally, we find that mutations in genes associated with loss of motility are beneficial across all environments, whereas mutations involving other functions, such as gene regulation, had more variable effects, being more environment specific. Taken together, these results provide a detailed account of the genetics of specialization and suggest that the evolution of trade-offs associated with local adaptation may often result from the antagonistic effects of beneficial mutations substituted later in adaptation.
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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".