Data from: Positive effects of tree species diversity on productivity switch to negative after severe drought mortality in a temperate forest experiment
Bibliographic record
Abstract
Synthesis of a large body of evidence from field experiments suggests more diverse plant communities are both more productive as well as more resistant to the effects of climatic extremes like drought. However, this view is strongly based on data from grasslands due to limited empirical evidence from tree diversity experiments. Here we report on the relationship between tree diversity and productivity over ten years in a field experiment established in 2005 that was then affected by the 2018 megadrought in central Europe. Across a number of years, tree species diversity and productivity were significantly positively related, however, the slope switched to negative in the year of the drought. Net diversity effects increased through time, with complementarity making greater continuations to the net diversity effect than selection effects. Complementarity was clearly positive (95 % credible interval) in three and five species mixtures before the drought (2012-2016) but was found to decrease in the year of the drought. Selection effects were clearly positive in 2016, and remained positive in 2018, the drought year in two, three, and five species mixtures. Survival of the Norway spruce (Picea abies) plummeted during drought and a negative relationship between species diversity and spruce survival was found. Our findings suggest that tree diversity per se may not buffer communities against the impacts of extreme drought and that tree species composition and the drought tolerance of tree species (i.e., species identity) will be important determinants of community productivity as the prevalence of drought increases.
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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 | 0.002 | 0.002 |
| 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) | 0.002 | 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".