Data from: Interannual variation in season length is linked to strong co-gradient plasticity of phenology in a montane annual plant
Bibliographic record
Abstract
Species are commonly distributed along latitudinal and elevational gradients of growing season length to which they might respond via phenotypic plasticity and / or adaptive genetic differentiation. However, the relative contribution of these processes and whether plasticity, if it occurs, facilitates expansion along season length gradients remains unclear but are important for predicting species fates during anthropogenic change. We quantified phenological trait variation in the montane annual Rhinanthus minor for three generations at 12 sites across 900 m of elevation in the Canadian Rocky Mountains and conducted a reciprocal transplant experiment for two generations among nine sites. We compared clines and interannual variation of phenological traits between natural and transplanted individuals. Season length declined by ~ 37% along our elevational gradient and, as expected, plants emerged, reached first flower and made their first seed in ~ 41% fewer growing degree days under shorter growing seasons. Although reciprocal transplants revealed modest genetic differentiation across elevation, trait clines were primarily due to striking co-gradient plasticity that paralleled genetic differentiation. Co-gradient plasticity likely evolved in response to considerable interannual variation in season length across our elevational transect, and should prepare R. minor to make adaptive changes to phenology in response to ongoing climate change predicted for montane environments.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.036 | 0.034 |
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".