Mutually inclusive mechanisms of drought‐induced tree mortality
Bibliographic record
Abstract
Unprecedented tree dieback across Central Europe caused by recent global change-type drought events highlights the need for a better mechanistic understanding of drought-induced tree mortality. Although numerous physiological risk factors have been identified, the importance of two principal mechanisms, hydraulic failure and carbon starvation, is still debated. It further remains largely unresolved how the local neighborhood composition affects individual mortality risk. We studied 9435 young trees of 12 temperate species planted in a diversity experiment in 2013 to assess how hydraulic traits, carbon dynamics, pest infestation, tree height and neighborhood competition influence individual mortality risk. Following the most extreme global change-type drought since record in 2018, one third of these trees died. Across species, hydraulic safety margins (HSMs) were negatively and a shift towards a higher sugar fraction in the non-structural carbohydrate (NSC) pool positively associated with mortality risk. Moreover, trees infested by bark beetles had a higher mortality risk, and taller trees a lower mortality risk. Most neighborhood interactions were beneficial, although neighborhood effects were highly species-specific. Species that suffered more from drought, especially Larix spp. and Betula spp., tended to increase the survival probability of their neighbors and vice versa. While severe tissue dehydration marks the final stage of drought-induced tree mortality, we show that hydraulic failure is interrelated with a series of other, mutually inclusive processes. These include shifts in NSC pools driven by osmotic adjustment and/or starch depletion as well as pest infestation and are modulated by the size and species identity of a tree and its neighbors. A more holistic view that accounts for multiple causes of drought-induced tree mortality is required to improve predictions of trends in global forest dynamics and to identify mutually beneficial species combinations.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".