Natural Selection on Phenology Across an Elevational Gradient in Seasonality
Bibliographic record
Abstract
Many populations are locally adapted to their environment, but across an environmental gradient, trade-offs among phenotypic traits may result in a failure to adapt. Species exist across a seasonal gradient, which can create selection for phenology, the timing of key life history events. Flowering phenology is important because flowering late exposes the plants to abiotic risks, like frost damage, whereas flowering early can lower fecundity if there is a trade-off between time to and size at flowering. This trade-off should result in different patterns of selection across a seasonal gradient, with stabilizing selection favouring intermediate flowering in long growing seasons, and selection favouring early flowering in short growing seasons. While this theory is well established, few studies have measured selection on flowering phenology across a seasonal gradient in natural populations. In 2015 and 2016, I measured selection on the time to and size at flowering from 12 populations (24 site × year combinations) of the annual herb Rhinanthus minor (yellow rattle), across an elevational gradient of 900m, in the Rocky Mountains of Alberta, Canada. For each site × year I quantified the growing season length measured using cumulative growing degree days (CGDD). I predicted that 1) CGDD would vary linearly with elevation, 2) There would be selection favouring early flowering in sites with low CGDD, 3) There would be stabilizing selection for intermediate flowering in sites with moderate and high CGDD, 4) There would be a trade-off between time to and size at flowering. Contrary to my predictions I found that 1) CGDD varied quadratically with elevation, as low and mid elevational sites had similar CGDD, 2) Selection favoured early flowering plants across the gradient in season length, 3) There was no stabilizing selection for flowering time, 4) There was no evidence of a trade-off between time to and size at flowering. There was no evidence for a trade-off, therefore earlier flowering plants were able to flower both early and at a larger size, resulting in higher fitness. My results challenge commonly held life history assumptions and demonstrate the importance of foundational research in natural systems.
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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 | 0.001 |
| 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 | 0.000 | 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".