Long-term nitrogen addition has a positive legacy effect on soil respiration in subtropical Moso bamboo forests
Bibliographic record
Abstract
• Soil respiration exhibits clear temporal dynamics in the context of long-term N input. • N input legacy increased autotrophic respiration but reduced heterotrophic respiration. • Microbial traits and fine root biomass played a key role in changing soil respiration. Soil respiration (Rs), a critical component of the global carbon (C) cycle, is sensitive to changes in nitrogen (N) deposition. However, the temporal dynamics of the effects of long-term (≥ five years) N addition and its cessation on Rs in forests remain uncertain. We conducted a continuous field experiment, which included three years of N cessation after seven years of N addition at different rates (0, 30, 60, and 90 kg N∙ha −1 ∙yr −1 ), in a subtropical Moso bamboo forest to explore the response of Rs and its components, determine the influence of biotic and abiotic factors to long-term N addition, and identify any legacy effects. We found a two-phase pattern of Rs, with a significant increase in the first two years across three N addition rates and a constant significant increase in the last five years across low and medium N addition; however, Rs did not change under high N addition. The nitrogen addition legacy effects significantly increased Rs and autotrophic respiration but reduced heterotrophic respiration, which could persist for at least three years. The mechanism underlying the temporal variation in Rs and its components was related to the increase in fine root biomass and changes in soil microbial biomass and bacteria to fungi ratio. These findings have advanced our understanding of soil CO 2 dynamics in subtropical forests under N deposition. Moreover, they reveal that the legacy effects of long-term N addition should be incorporated into global C cycle modeling to reflect the persistent effects of N deposition on forest ecosystem C budgets.
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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) | 0.000 | 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 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".