Rapid evolution of cold tolerance in stickleback
Bibliographic record
Abstract
Climate change is predicted to lead to increased average temperatures and greater intensity and frequency of high and low temperature extremes, but the evolutionary consequences for biological communities are not well understood. Studies of adaptive evolution of temperature tolerance have typically involved correlative analyses of natural populations or artificial selection experiments in the laboratory. Field experiments are required to provide estimates of the timing and strength of natural selection, enhance understanding of the genetics of adaptation and yield insights into the mechanisms driving evolutionary change. Here, we report the experimental evolution of cold tolerance in natural populations of threespine stickleback fish (Gasterosteus aculeatus). We show that freshwater sticklebacks are able to tolerate lower minimum temperatures than marine sticklebacks and that this difference is heritable. We transplanted marine sticklebacks to freshwater ponds and measured the rate of evolution after three generations in this environment. Cold tolerance evolved at a rate of 0.63 haldanes to a value 2.5°C lower than that of the ancestral population, matching values found in wild freshwater populations. Our results suggest that cold tolerance is under strong selection and that marine sticklebacks carry sufficient genetic variation to adapt to changes in temperature over remarkably short time scales.
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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 | 0.000 |
| 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) | 0.001 | 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".