A global network to study the impact of climatic stress on tree architecture and phenology, as a contribution to forest decline
Bibliographic record
Abstract
We present a study at international scale examining the influence of climate change and extreme events on tree architectural development and leaf area, and its potential contribution to forest decline. 15 conifers from Mediterranean Europe, southwestern USA and Canada were studied along altitudinal and climate gradients. We developed a model to simulate the climate - leaf area relationship based on branch length growth, architectural parameters (branching rate, polycyclism rate and sexuality), and leaf number and size. In both Europe and America, repeated droughts and heat waves considerably reduced branch vigor for all species at all sites, leading to low polycyclism and branching rates, short needles and small number of needles per shoot, and thus to a significant leaf area deficit. Climate variability and extreme climate events have both short term and long lasting impacts on leaf area through their control of tree architectural development. The slow recovery of these coniferous trees from branch deficits, hold back tree potential leaf area for many years after a prolonged or severe stress. The positive effect of repeated good years on branching rates may help trees overcoming usual climate variability, but make them more vulnerable to following extreme events. The long lasting reduction of the potential leaf area induced by the branching deficit has physiological effects that influence tree susceptibility to mortality, and may contribute to delayed forest decline and dieback.
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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 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".