Revealing legacy effects of extreme droughts on tree growth of oaks across the Northern Hemisphere
Bibliographic record
Abstract
Forests are undergoing increasing risks of drought-induced tree mortality. Species replacement patterns following mortality may have a significant impact on the global carbon cycle. Among major hardwoods, deciduous oaks (Quercus spp.) are increasingly reported as replacing dying conifers across the Northern Hemisphere. Yet, our knowledge on the growth responses of these oaks to drought is incomplete, especially regarding post-drought legacy effects. The objectives of this study were to determine the occurrence, duration, and magnitude of legacy effects of extreme droughts and how that vary across species, sites, and drought characteristics. The legacy effects were quantified by the deviation of observed from expected radial growth indices in the period 1940–2016. We used stand-level chronologies from 458 sites and 21 oak species primarily from Europe, north-eastern America, and eastern Asia. We found that legacy effects of droughts could last from 1 to 5 years after the drought and were more prolonged in dry sites. Negative legacy effects (i.e., lower growth than expected) were more prevalent after repetitive droughts in dry sites. The effect of repetitive drought was stronger in Mediterranean oaks especially of Quercus faginea. Species-specific analyses revealed that Q. petraea and Q. macrocarpa from dry sites were more negatively affected by the droughts while growth of several oak species from mesic sites increased during post-drought years. Sites showing positive correlations to winter temperature showed little to no growth depression after drought, whereas sites with a positive correlation to previous summer water balance showed decreased growth. This may indicate that although winter warming favors tree growth during droughts, previous-year summer precipitation may predispose oak trees to current-year extreme droughts. Our results revealed a massive role of repetitive droughts in determining legacy effects and highlighted how growth sensitivity to climate, drought seasonality and species-specific traits drive the legacy effects in deciduous oak species.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".