Earlier spring reduces potential for gene flow via reduced flowering synchrony across an elevational gradient
Bibliographic record
Abstract
Premise: One of the best-documented ecological responses to climate warming involves temporal shifts of phenological events. However, we lack an understanding of how phenological responses to climate change vary among populations of the same species. Such variability has the potential to affect flowering synchrony among populations and hence the potential for gene flow. Methods: To test if an earlier start of the growing season affects the potential for gene flow among populations, we quantified the distributions of flowering times of two spring-flowering plants (Trillium erectum and Erythronium americanum) over six years along an elevation gradient. We developed a novel model-based metric of potential gene flow between pairs of populations to quantify the potential for pollen-mediated gene flow based on flowering phenology. Results: For both species investigated, earlier onset of spring led to greater separation of peak flowering dates across the elevation gradient. For T. erectum, but not E. americanum, this was also associated with a reduction in potential gene flow. Conclusion: Our study suggests that climate change could decrease gene flow via phenological separation among populations along climatic gradients. We also provide a novel method for quantifying potential pollen-mediated gene flow using data on flowering phenology, based on a quantitative, more biologically interpretable model than other available metrics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.007 |
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".