Drought-induced decoupling between carbon uptake and tree growth impacts forest carbon turnover time
Bibliographic record
Abstract
The ability of forests to withstand, and recover from, acute drought stress is a critical uncertainty regarding the impacts of climate change on the terrestrial carbon (C) cycle, but it is unclear how drought responses scale from individual trees to whole forests. Here, we assembled a dataset of tree-ring chronologies co-located within the footprint of eddy covariance towers across North America and Europe, with the aim of quantifying the sensitivity of tree radial growth versus gross primary productivity (GPP) during and following drought. We found that drought induced a large decoupling across C cycle processes, whereby GPP was relatively resistant to water stress despite large reductions in tree-ring widths. This decoupling also occurred in the year following drought (i.e., a ‘drought legacy effect’), and was similar in magnitude in response to both summer and winter droughts. By modeling whole-forest C turnover time, we show that a radial growth-GPP decoupling has important ramifications for the forest C cycle, especially if the C not used to support radial growth is instead allocated towards pools with short residence times. Our results demonstrate that quantifications of drought impacts that rely solely on C uptake are missing this fundamental pathway through which drought alters the forest C cycle and the resulting feedbacks to the climate system.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".