Observations from old forests underestimate climate change effects on tree mortality
Bibliographic record
Abstract
Understanding climate change-associated tree mortality is central to linking climate change impacts and forest structure and function. However, whether temporal increases in tree mortality are attributed to climate change or stand developmental processes remains uncertain. Furthermore, interpreting the climate change-associated tree mortality estimated from old forests for regional forests rests on an un-tested assumption that the effects of climate change are the same for young and old forests. Here we disentangle the effects of climate change and stand developmental processes on tree mortality. We show that both climate change and forest development processes influence temporal mortality increases, climate change-associated increases are significantly higher in young than old forests, and higher increases in younger forests are a result of their higher sensitivity to regional warming and drought. We anticipate our analysis to be a starting point for more comprehensive examinations of how forest ecosystems might respond to climate change. It is thought that only climate change drives temporal tree mortality increases in old forests. Here, Luo and Chen show that both forest dynamics and climate change drive temporal tree mortality increases in young and old forests, and that climate change-associated mortality increases are higher in the young forests.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".