Nitrogen increases early‐stage and slows late‐stage decomposition across diverse grasslands
Bibliographic record
Abstract
Abstract To evaluate how increased anthropogenic nutrient inputs alter carbon cycling in grasslands, we conducted a litter decomposition study across 20 temperate grasslands on three continents within the Nutrient Network, a globally distributed nutrient enrichment experiment We determined the effects of addition of experimental nitrogen (N), phosphorus (P) and potassium plus micronutrient (K μ ) on decomposition of a common tree leaf litter in a long‐term study (maximum of 7 years; exact deployment period varied across sites). The use of higher order decomposition models allowed us to distinguish between the effects of nutrients on early‐ versus late‐stage decomposition. Across continents, the addition of N (but not other nutrients) accelerated early‐stage decomposition and slowed late‐stage decomposition, increasing the slowly decomposing fraction by 28% and the overall litter mean residence time by 58%. Synthesis . Using a novel, long‐term cross‐site experiment, we found widespread evidence that N enhances the early stages of above‐ground plant litter decomposition across diverse and widespread temperate grassland sites but slows late‐stage decomposition. These findings were corroborated by fitting the data to multiple decomposition models and have implications for N effects on soil organic matter formation. For example, following N enrichment, increased microbial processing of litter substrates early in decomposition could promote the production and transfer of low molecular weight compounds to soils and potentially enhance the stabilization of mineral‐associated organic matter. By contrast, by slowing late‐stage decomposition, N enrichment could promote particulate organic matter (POM) accumulation. Such hypotheses deserve further testing.
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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) | 0.001 | 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 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".