Leaf isotopes reveal tree diversity effects on the functional responses to the pan‐European 2018 summer drought
Bibliographic record
Abstract
Summary Recent droughts have strongly impacted forest ecosystems and are projected to increase in frequency, intensity, and duration in the future together with continued warming. While evidence suggests that tree diversity can regulate drought impacts in natural forests, few studies examine whether mixed tree plantations are more resistant to the impacts of severe droughts. Using natural variations in leaf carbon (C) and nitrogen (N) isotopic ratios, that is δ13C and δ15N, as proxies for drought response, we analyzed the effects of tree species richness on the functional responses of tree plantations to the pan‐European 2018 summer drought in seven European tree diversity experiments. We found that leaf δ13C decreased with increasing tree species richness, indicating less drought stress. This effect was not related to drought intensity, nor desiccation tolerance of the tree species. Leaf δ15N increased with drought intensity, indicating a shift toward more open N cycling as water availability diminishes. Additionally, drought intensity was observed to alter the influence of tree species richness on leaf δ15N from weakly negative under low drought intensity to weakly positive under high drought intensity. Overall, our findings suggest that dual leaf isotope analysis helps understand the interaction between drought, nutrients, and species richness.
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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 | 0.000 |
| 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".