Effects of elevated nutrient supply on litter decomposition are robust to impacts of mammalian herbivores across diverse grasslands
Bibliographic record
Abstract
Litter decomposition is one of the largest carbon (C) fluxes in terrestrial ecosystems and links aboveground biomass to soil C pools. In grasslands, decomposition drivers have received substantial attention but the role of grassland herbivores in influencing decay rates is often ignored despite their potentially large effects on standing biomass and nutrient cycling. Recent work has demonstrated that nutrient addition increases early-stage decay and suppresses late-stage decay. Mammalian herbivores can mediate the effects of nutrient supply on biomass, suggesting herbivores may alter the effects of nutrients on decomposition, though this is largely unknown. We examined how herbivory mediates the effects of nutrient supply on long-term decomposition across 19 grassland sites of the Nutrient Network distributed experiment. At each site, a full-factorial experiment of combined nitrogen (N), phosphorus (P), and micronutrient (K) enrichment ('control' or ' + NPK') and mammalian herbivore (> ~ 50 g) exclusion ('unfenced' or 'fenced') was carried out in a randomized block design. We hypothesized that nutrient effects on litter decomposition would be strongest where herbivores caused the greatest reductions in aboveground plant biomass (i.e., at sites with more intense herbivory). After accounting for wide variation in decomposition rates across sites, we found that, within sites, elevated nutrients increased early-stage decay and suppressed late-stage decay. In contrast, neither herbivore exclusion (i.e., fencing) nor site level changes in aboveground biomass due to herbivory altered the nutrient effects on decomposition rates. Across grasslands, our results indicate that elevated nutrient supply modifies litter decomposition rates independent of herbivore impacts.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".