Trends and Variability in Temperature Sensitivity of Lilac Flowering Phenology
Bibliographic record
Abstract
Abstract The responses of plant phenology to temperature variability have many consequences for ecological processes, agriculture, forestry, and human health. Temperature sensitivity (ST) of phenology could measure how and to what degree plant could phenologically track climate change. The long‐term trends and spatial patterns in ST have been well studied for vegetative phenology such as leaf unfolding, but trends to be expected for reproductive phenology in the future remain unknown. Here we investigate trends and factors driving the temporal variation of ST of first bloom date (FBD). Using the long‐term FBD records during 1963–2013 for common lilac (Syringa vulgaris) from 613 stations in Europe, we compared changes in ST from the beginning to the end of the study period. The Spearman partial correlations were used to assess the importance of four influencing factors. The results showed that the temporal changes in ST of FBD varied considerably among time scales. Mean ST decreased significantly by 0.92 days °C−1 from 1963–1972 to 2004–2013 (P < 0.01), but remained stable from 1963–1987 to 1989–2013. The strength of FBD and temperature relationship, the spring temperature variance, and winter chill all impact ST in an expected way at most stations. No consistent responses of ST on photoperiod were found. Our results imply that the trends and variability in ST of flowering phenology are driving by multiple factors and impacted by time scales. Continued efforts are still needed to further examine the flowering‐temperature relationship for other plant species in other climates and environments using similar methods to our study.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".