Drought legacies are dependent on water table depth, wood anatomy and drought timing across the eastern US
Bibliographic record
Abstract
Severe droughts can impart long-lasting legacies on forest ecosystems through lagged effects that hinder tree recovery and suppress whole-forest carbon uptake. However, the local climatic and edaphic factors that interact to affect drought legacies in temperate forests remain unknown. Here, we pair a dataset of 143 tree ring chronologies across the mesic forests of the eastern US with historical climate and local soil properties. We found legacy effects to be widespread, the magnitude of which increased markedly in diffuse porous species, sites with deep water tables, and in response to late-season droughts (August-September). Using an ensemble of downscaled climate projections, we additionally show that our sites are projected to drastically increase in water deficit and drought frequency by the end of the century, potentially increasing the size of legacy effects by up to 65% and acting as a significant process shaping forest composition, carbon uptake and mortality.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".