Biodiversity explains maximum variation in productivity under experimental warming, nitrogen addition, and grazing in mountain grasslands
Bibliographic record
Abstract
Anthropogenic global warming, nitrogen addition, and overgrazing alter plant communities and threaten plant biodiversity, potentially impacting community productivity, especially in sensitive mountain grassland ecosystems. However, it still remains unknown whether the relationship between plant biodiversity and community productivity varies across different anthropogenic influences, and especially how changes in multiple biodiversity facets drive these impacts on productivity. Here, we measured different facets of biodiversity including functional and phylogenetic richness and evenness in mountain grasslands along an environmental gradient of elevation in Yulong Mountain, Yunnan, China. We combined biodiversity metrics in a series of linear mixed-effect models to determine the most parsimonious predictors for productivity, which was estimated by aboveground biomass in community. We examined how biodiversity-productivity relationships were affected by experimental warming, nitrogen addition, and livestock-grazing. Species richness, phylogenetic diversity, and single functional traits (leaf nitrogen content, mg/g) represented the most parsimonious combination in these scenarios, supporting a consensus that single-biodiversity metrics alone cannot fully explain ecosystem function. The biodiversity-productivity relationships were positive and strong, but the effects of treatment on biodiversity-productivity relationship were negligible. Our findings indicate that the strong biodiversity-productivity relationships are consistent in various anthropogenic drivers of environmental change.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| 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 teacher head, 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".