Local adaptation primes cold‐edge populations for range expansion but not warming‐induced range shifts
Bibliographic record
Abstract
According to theory, edge populations may be poised to expand species' ranges if they are locally adapted to extreme conditions, or ill-suited to colonise beyond-range habitat if their offspring are genetically and competitively inferior. We tested these contrasting predictions by transplanting low-, mid-, and high-elevation (edge) populations of an annual plant throughout and above its elevational distribution. Seed from poor-quality edge habitat (one of two transects) had inferior emergence, but edge seeds also had adaptive phenology (both transects). High-elevation plants flowered earlier, required less heat accumulation to mature seed, and so achieved higher lifetime fitness at and above the range edge. Experimental warming improved fitness above the range, but eliminated the advantage of local cold-edge populations, supporting recent models in which cold-adapted edge populations do not facilitate warming-induced range shifts. The highest above-range fitness was achieved by a 'super edge phenotype' from a neighbouring mountain, suggesting key adaptations exist regionally even if absent from local edge populations.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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; both teacher heads agree on what is shown here.
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".