Mutually inclusive mechanisms of drought-induced tree mortality
Bibliographic record
Abstract
Abstract An extreme summer drought caused unprecedented tree dieback across Central Europe in 2018, highlighting the need for a better mechanistic understanding of drought-induced tree mortality. While numerous physiological risk factors have been identified, the principal mechanisms, hydraulic failure and carbon starvation, are still debated. We studied 9,435 trees from 12 temperate species planted in a diversity experiment in 2013 to assess how hydraulic traits, carbon dynamics, pest infestation, tree height and neighbourhood competition influence individual mortality risk. We observed a reduced mortality risk for trees with wider hydraulic safety margins, while a rising sugar fraction of the non-structural carbohydrate pool and bark beetle infestation were associated with higher risk. Taller trees had a lower mortality risk. The sign and magnitude of neighbourhood effects on mortality risk depended on the species-identity of the involved trees, with most species having beneficial and some having deleterious effects on their neighbours. While severe tissue dehydration causing hydraulic failure precedes drought-induced tree mortality, we show that the probability of this event depends on a series of mutually inclusive processes including pest infestation and starch depletion for osmotic adjustment, and is modulated by the size and species identity of a tree and its neighbours.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".