Nutrients cause grassland biomass to outpace herbivory
Bibliographic record
Abstract
Human activities are transforming grassland biomass via changing climate, elemental nutrients, and herbivory. Theory predicts that food-limited herbivores will consume any additional biomass stimulated by nutrient inputs ('consumer-controlled'). Alternatively, nutrient supply is predicted to increase biomass where herbivores alter community composition or are limited by factors other than food ('resource-controlled'). Using an experiment replicated in 58 grasslands spanning six continents, we show that nutrient addition and vertebrate herbivore exclusion each caused sustained increases in aboveground live biomass over a decade, but consumer control was weak. However, at sites with high vertebrate grazing intensity or domestic livestock, herbivores consumed the additional fertilization-induced biomass, supporting the consumer-controlled prediction. Herbivores most effectively reduced the additional live biomass at sites with low precipitation or high ambient soil nitrogen. Overall, these experimental results suggest that grassland biomass will outstrip wild herbivore control as human activities increase elemental nutrient supply, with widespread consequences for grazing and fire risk.
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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 | 0.000 |
| 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".