Nutrient enrichment increases invertebrate herbivory and pathogen damage in grasslands
Bibliographic record
Abstract
Abstract Plant damage by invertebrate herbivores and pathogens influences the dynamics of grassland ecosystems, but anthropogenic changes in nitrogen and phosphorus availability can modify these relationships. Using a globally distributed experiment, we describe leaf damage on 153 plant taxa from 27 grasslands worldwide, under ambient conditions and with experimentally elevated nitrogen and phosphorus. Invertebrate damage significantly increased with nitrogen addition, especially in grasses and non‐leguminous forbs. Pathogen damage increased with nitrogen in grasses and legumes but not forbs. Effects of phosphorus were generally weaker. Damage was higher in grasslands with more precipitation, but climatic conditions did not change effects of nutrients on leaf damage. On average, invertebrate damage was relatively higher on legumes and pathogen damage was relatively higher on grasses. Community‐weighted mean damage reflected these functional group patterns, with no effects of N on community‐weighted pathogen damage (due to opposing responses of grasses and forbs) but stronger effects of N on community‐weighted invertebrate damage (due to consistent responses of grasses and forbs). Synthesis . As human‐induced inputs of nitrogen and phosphorus continue to increase, understanding their impacts on invertebrate and pathogen damage becomes increasingly important. Our results demonstrate that eutrophication frequently increases plant damage and that damage increases with precipitation across a wide array of grasslands. Invertebrate and pathogen damage in grasslands is likely to increase in the future, with potential consequences for plant, invertebrate and pathogen communities, as well as the transfer of energy and nutrients across trophic levels.
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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".