Biodiversity stabilizes primary productivity through compensatory effects under warming conditions
Bibliographic record
Abstract
Abstract Aim Diversity may increase the resistance of ecosystem productivity to environmental stress, such as warming, via compensatory processes associated with adjustments of species interactions. However, experimental evidence of compensatory processes that buffer productivity declines in relation to environmental stress is limited, especially in below‐ground settings. We asked whether species richness could stabilize productivity under warming via compensatory responses in root biomass and root functional traits. Methods Using three herbaceous species, we created plant communities composed of four individuals in either monocultures or two‐ and three‐species assemblages. We grew them at three temperatures, simulating current climate conditions, moderate warming and severe warming, respectively. We built mixed‐linear mixed models to model plant productivity by species richness and warming and we also analyzed the interactive roles of species richness and warming in species interaction and root functional traits. Results We found that warming reduced both above‐ and below‐ground productivity and shifted the biodiversity–productivity relationship from negative to positive. Productivity reductions were weaker in richer species combinations. Warming ameliorated the strength of interspecific competition below‐ground in mixed‐species communities by reducing the root biomass of strong competitors, which benefitted root growth of weaker competitors. Conclusions Our results suggest warming can facilitate compensatory responses in herbaceous root productivity across species competition hierarchies. These compensatory processes by which species richness stabilizes plant community functioning emphasize the importance that plant functional diversity has in maintaining ecosystem functioning with climate change.
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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 | 0.001 |
| 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 | 0.001 | 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".