Long‐term changes in the impacts of global warming on leaf phenology of four temperate tree species
Bibliographic record
Abstract
Abstract Contrary to the generally advanced spring leaf unfolding under global warming, the effects of the climate warming on autumn leaf senescence are highly variable with advanced, delayed, and unchanged patterns being all reported. Using one million records of leaf phenology from four dominant temperate species in Europe, we investigated the temperature sensitivities of spring leaf unfolding and autumn leaf senescence (ST, advanced or delayed days per degree Celsius). The ST of spring phenology in all of the four examined species showed an increase and decrease during 1951–1980 and 1981–2013, respectively. The decrease in the ST during 1981–2013 appears to be caused by reduced accumulation of chilling units. As with spring phenology, the ST of leaf senescence of early successional and exotic species started to decline since 1980. In contrast, for late successional species, the ST of autumn senescence showed an increase for the entire study period from 1951 to 2013. Moreover, the impacts of rising temperature associated with global warming on spring leaf unfolding were stronger than those on autumn leaf senescence. The timing of leaf senescence was positively correlated with the timing of leaf unfolding during 1951–1980. However, as climate warming continued, the differences in the responses between spring and autumn phenology gradually increased, so that the correlation was no more significant during 1981–2013. Our results further suggest that since 2000, due to the decreased temperature sensitivity of leaf unfolding the length of the growing season has not increased any more. These finding needs to be addressed in vegetation models used for assessing the effects of climate change.
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 | 0.000 | 0.000 |
| 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".