Drought's legacy: multiyear hydraulic deterioration underlies widespread aspen forest die‐off and portends increased future risk
Bibliographic record
Abstract
Forest mortality constitutes a major uncertainty in projections of climate impacts on terrestrial ecosystems and carbon-cycle feedbacks. Recent drought-induced, widespread forest die-offs highlight that climate change could accelerate forest mortality with its diverse and potentially severe consequences for the global carbon cycle, ecosystem services, and biodiversity. How trees die during drought over multiple years remains largely unknown and precludes mechanistic modeling and prediction of forest die-off with climate change. Here, we examine the physiological basis of a recent multiyear widespread die-off of trembling aspen (Populus tremuloides) across much of western North America. Using observations from both native trees while they are dying and a rainfall exclusion experiment on mature trees, we measure hydraulic performance over multiple seasons and years and assess pathways of accumulated hydraulic damage. We test whether accumulated hydraulic damage can predict the probability of tree survival over 2 years. We find that hydraulic damage persisted and increased in dying trees over multiple years and exhibited few signs of repair. This accumulated hydraulic deterioration is largely mediated by increased vulnerability to cavitation, a process known as cavitation fatigue. Furthermore, this hydraulic damage predicts the probability of interyear stem mortality. Contrary to the expectation that surviving trees have weathered severe drought, the hydraulic deterioration demonstrated here reveals that surviving regions of these forests are actually more vulnerable to future droughts due to accumulated xylem damage. As the most widespread tree species in North America, increasing vulnerability to drought in these forests has important ramifications for ecosystem stability, biodiversity, and ecosystem carbon balance. Our results provide a foundation for incorporating accumulated drought impacts into climate-vegetation models. Finally, our findings highlight the critical role of drought stress accumulation and repair of stress-induced damage for avoiding plant mortality, presenting a dynamic and contingent framework for drought impacts on forest ecosystems.
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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 | 0.001 |
| 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 | 0.000 |
| 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".