Evolutionary Constraints: Phenology and elevational range limits in an annual plant
Bibliographic record
Abstract
All species have limits to their geographic distributions and many range limits are stable over centuries, indicating a failure to adapt to conditions beyond the range edge. This simple observation contradicts copious evidence for the rapid and near limitless response to selection in single traits across diverse species. Phenology, the timing of life history events like mating and offspring development, is likely a strong determinant of species’ ranges on gradients of season length. Species colonising shorter growing seasons (increasing elevation, latitude) require earlier reproduction and more rapid offspring development. Range margins could then occur where phenological adaptation cannot keep pace with declining season length, or where doing so results in negative population growth rates. Despite its importance, we have a poor understanding of how phenology varies and whether it is locally adaptive towards range limits. I tested how phenology contributes to range limitation in Rhinanthus minor L. (Orobanchaceae), an annual plant, with a distinct elevational distribution in the Rocky Mountains, Alberta, Canada. I quantified clines in phenology, morphology, and fitness for three generations in naturally occurring populations across elevation to the upper range limit. I then tested whether observed clines in phenology were the result of differentiation by source season length or plasticity with a replicated reciprocal transplant experiment over two generations. Next, I quantified how local adaptation and population genomic variation and differentiation support competing hypotheses of range limitation. Finally, I quantified the extent to which selection shapes phenological traits across the gradient. I found substantial natural clines in phenology that arise primarily from strong co-gradient plasticity. Minimal differentiation by source season length for phenology, and limited local adaptation, was reinforced by low genomic diversity, and genomic differentiation patterns that did not follow the gradient. Controlling for individual quality, selection analyses on natural and enhanced phenotypic variation confirmed consistent and strong selection for early reproduction regardless of elevation. Together, my results suggest that a lack of genetic variation for phenology constrains elevational range expansion in Rhinanthus minor. Combining these diverse avenues of research, I provide one of the most complete assessments yet of phenology as a range limiting trait.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".