Drought-exposure history increases complementarity between plant species in response to a subsequent drought
Bibliographic record
Abstract
Growing threats from extreme climatic events and biodiversity loss have raised concerns about their interactive consequences for ecosystem functioning. Evidence suggests biodiversity can buffer ecosystem functioning during such climatic events. However, whether exposure to extreme climatic events will strengthen the biodiversity-dependent buffering effects for future generations remains elusive. We assess such transgenerational effects by exposing experimental grassland communities to eight recurrent summer droughts versus ambient conditions in the field. Seed offspring of 12 species are then subjected to a subsequent drought event in the glasshouse, grown individually, in monocultures or in 2-species mixtures. Comparing productivity between mixtures and monocultures, drought-selected plants show greater between-species complementarity than ambient-selected plants when recovering from the subsequent drought, causing stronger biodiversity effects on productivity and better recovery of drought-selected mixtures after the drought. These findings suggest exposure to recurrent climatic events can improve ecosystem responses to future events through transgenerational reinforcement of species complementarity.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".