Size evolution in microorganisms masks trade-offs predicted by the growth rate hypothesis
Bibliographic record
Abstract
Adaptation to local resource availability depends on responses in growth rate and nutrient acquisition. The growth rate hypothesis (GRH) suggests that growing fast should impair competitive abilities for phosphorus and nitrogen due to high demand for biosynthesis. However, in microorganisms, size influences both growth and uptake rates, which may mask trade-offs and instead generate a positive relationship between these traits (size hypothesis, SH). Here, we evolved a gradient of maximum growth rate (μmax) from a single bacterium ancestor to test the relationship amongμmax, competitive ability for nutrients and cell size, while controlling for evolutionary history. We found a strong positive correlation betweenμmaxand competitive ability for phosphorus, associated with a trade-off betweenμmaxand cell size: strains selected for highμmaxwere smaller and better competitors for phosphorus. Our results strongly support the SH, while the trade-offs expected under GRH were not apparent. Beyond plasticity, unicellular populations can respond rapidly to selection pressure through joint evolution of their size and maximum growth rate. Our study stresses that physiological links between these traits tightly shape the evolution of competitive strategies.
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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 | 0.001 | 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".