Legacies of climate change and extreme events on tree architecture: implications for forest decline and die-back
Bibliographic record
Abstract
This presentation deals with the influence of climate change and extreme events on tree architectural development, its relations with reproduction, phenology and productivity, its consequences on tree leaf area dynamics, and finally its potential contribution to forest decline and die-back.\nFifteen species from Europe, USA and Canada were studied in natural forest stands and in experimental plots with rainfall exclusion and irrigation. Repeated droughts and extreme heat waves reduced branch vigor for all species, leading to low polycyclism and branching rates, short leaves and a small number of leaves per shoot, and thus to a significant leaf area deficit. The slow recovery from branch deficit holds back leaf area for many years after a prolonged or severe stress, limiting photosynthesis capacity and thus tree growth and reserve build up. Reproduction was severely affected as well. Phenology shifts were observed, related with both climate warming and drought. In the Mediterranean area, some trees showed a continuous growth during hotter winters, with sometimes dramatic frost damages as shoot abortion and leaf mortality. Increasing aridity led to a significant change in the response of ring width to climate, requiring the integration of up to 5 years of climate data to better assess tree diameter increase. Isotope analyses showed that trees had to look for water deeper in the soil.\nConclusion: Climate variability and extreme climate events have both short term and long lasting impacts on leaf area through their control of tree architectural development. Long lasting reductions of the potential leaf area, induced by branch deficits, limit forest productivity and has physiological effects that influence tree susceptibility to mortality, and may contribute to 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".