The robustness of thermal performance curves limits adaptation in growth rate of wild bacterial strains
Bibliographic record
Abstract
Thermal adaptation of organisms is a property emerging from the complex interplay of biophysical constraints and selective forces. The shape of thermal performance curves has been well investigated but we lack knowledge of how they may evolve. Two extreme cases can be expected: i) under the hypothesis of local adaptation, species should shift their thermal performance curves and have an optimum at the temperature at which they evolve, or ii) under the hypothesis of thermodynamical constraints, universal biophysical rules dictate a fixed performance curve with an optimum at warm temperatures. We perform an evolutionary experiment to test these two hypotheses on the thermal response of bacteria growth rate, expecting a strong evolutionary response of the thermal performance curve. We use four wild bacterial strains and allow them to evolve at ten different temperatures (ranging from 8.5 to 40°C) to subsequently measure their growth rate at these ten temperatures. We investigate the difference in growth rate between evolved lines and their ancestors. We observe signs of adaptation, as growth rates of evolved and ancestral strains exhibit small but significant differences. Our analysis shows however that the shape of the thermal performance curves does not systematically vary between evolved and ancestral strains, and none of the evolved lines have an optimal growth rate at the evolution temperature. One strain grows significantly faster than its ancestor at the temperature of evolution, but we find that for other strains, evolution leads to faster as well as slower growth rates. These differences are repeated between evolutionary replicates, suggesting they are selected. Our study demonstrates that adaptation does not always overcome thermodynamical constraints on growth rates, and helps to better understand how microbes will respond to temperature changes.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".