The rates of starch depletion and hydraulic failure both play a role in drought-induced seedling mortality
Bibliographic record
Abstract
Abstract Key message The elapsed times to deplete starch concentrations and to reach a null hydraulic safety margin were related to tree seedling mortality under experimental drought. Starch concentration showed an accelerated decline across all species during the early stages of dehydration, while the concentrations of soluble sugars and total nonstructural carbohydrates remained stable. Concomitant carbohydrate depletion and hydraulic failure drive seedling mortality under drought. Context Current upsurges of drought events are provoking impacts on tree physiology, resulting in forest mortality. Hydraulic dysfunction and nonstructural carbohydrate (NSC) depletion have been posited as the main mechanisms leading to plant mortality under drought. Aims This study explores the dynamics of the two mortality-inducing processes during drought stress using an experimental approach with 12 evergreen tree species. Methods Seedlings were subjected to drought until 100% mortality was observed. Midday (ΨMD) and predawn (ΨPD) water potentials, xylem pressure leading to a 50% loss of hydraulic conductivity (Ψ50), along with NSC concentrations in different organs (leaves, stems, and roots) were measured regularly during drought. Results Total NSC concentrations and soluble sugar pools did not decline during drought. However, starch pools showed strong reductions early during drought stress as ΨPD decreased, and the time leading to starch depletion emerged as a strong mortality predictor. Ψ50 alone did not provide an accurate estimate of mortality, while the elapsed time to reach a null hydraulic safety margin (ΨMD—Ψ50 = 0) was related to seedling mortality. Conclusion Adopting a dynamic approach by estimating the times to consume both starch reserves and hydraulic safety margins is highly relevant to improve predictions of tree mortality under the current context of increasing global drought.
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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 | 0.000 | 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".