Long‐term Nitrogen Addition Decreases Organic Matter Decomposition and Increases Forest Soil Carbon
Bibliographic record
Abstract
Core Ideas N additions alter soil microbial community composition and reduce forest soil microbial biomass and enzyme activity. Litter decomposition and soil organic matter degradation was slowed by N additions. Reduced decomposition increases soil C, but long‐term effects on forest productivity are unknown. Eastern North American forests receive anthropogenically elevated nitrogen (N) deposition that alters soil processes and forest productivity. We examined N deposition effects on soil carbon (C) and N in temperate, N‐rich forest plots fertilized annually (100 kg N ha −1 y −1 ) since 1993. After nearly two decades, soil C in O, A, and upper 50 cm of B horizons of N‐addition plots was 17% greater (14.2 ± 0.7 kg C m −2 ) than control plots. Aboveground tree biomass growth and litterfall were not affected by fertilization. Fine root mass (0–1 mm) was 34% greater in N‐addition plots, but did not explain soil C increases. Rather, reduced decomposition of litter and soil organic matter drove C increases in N‐addition plots. Decomposition rates of black cherry, sugar maple, and mixed leaf litter were 43, 67, and 36%, greater, respectively, in control than N‐addition plots. Light fraction organic matter was greater in N‐addition plots than in control plots, due to either enhanced root production or decreased decomposition of soil organic matter. Soil respiration was reduced, and microbial biomass in O, A, and upper‐B horizons was lower in N‐addition plots than controls. The soil microbial community composition was also altered dramatically with N additions. Recalcitrant organic matter enzyme activity (peroxidase) was reduced in the O‐horizon by N addition. Available Ca, Mg, and K were reduced in O and A horizons by N fertilization. These results suggest that chronic elevated atmospheric N inputs can increase forest soil C storage by decreasing decomposition, however the long‐term stability of this additional C sequestration is unknown.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".