Influence of phylogeny and abiotic factors varies across early and late reproductive phenology of Himalayan<i>Rhododendrons</i>
Bibliographic record
Abstract
Abstract Understanding the influence of evolutionary history on species‐specific phenological events of high‐altitude plants and their sensitivity to the abiotic factors has gained importance mainly in the context of climate change. However, the majority of phenology studies across altitudinal gradient are carried out on flowering, whereas other reproductive phenology events are seldom considered. We tested the role of abiotic factors and evolutionary history on the reproductive phenology traits of high‐altitudeRhododendroncommunity which comprised of 10 locally aggregated species in Kyongnosla Alpine Sanctuary, Sikkim Himalaya. The study was carried out from 2013 to 2015 across an altitude gradient of 3400–4230 m a.s.l. We generated dated phylogenetic hypotheses to test for phylogenetic signal in reproductive phenology events, and its durations across 10Rhododendronspecies and also among groups of species distributed at every 100 m altitude. Comparative phylogenetic methods were used to explore the relationship between phenology traits and abiotic variables such as daylength and temperature. The early phenology events such as budding, flowering, and initial fruiting, which occurred during the favorable month of the year, exhibited strong phylogenetic signal and were mainly associated with daylength and temperature. In contrast, the later events such as immature fruiting, mature fruiting, and fruit dehiscence, which occurred during the later months of the year, showed a weak phylogenetic signal and were mostly associated with daylength. With the increase in altitude, we found a decreasing trend of phylogenetic signal for the early phenology events and later events did not show a significant trend. Our results suggest that only early events are constrained by evolutionary history; thus, the closely related species share the similar timing of the early phenology events. Also, the role of shared evolutionary history in phenological trait sensitivity to the abiotic factors reduces from early to the late phenology events. This approach can be extended to other representative plant families of the Himalayan region to better understand the response of reproductive traits to abiotic factors in deep evolutionary time.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".