Carbon and nitrogen dynamics in tropical ecosystems following fire
Bibliographic record
Abstract
Abstract Aim Tropical ecosystems have grown increasingly prone to fire over the last century. However, no consensus has yet emerged regarding the effects of fire disturbances on tropical biogeochemical cycles. Location Tropics. Time period 1960–2018. Major taxa studied Tropical ecosystems: Above‐ and below‐ground carbon (C) and nitrogen (N) dynamics. Methods We analysed the impacts of fire on C and N dynamics in tropical ecosystems through a meta‐analysis of 1,420 observations from 87 studies. Results Fire reduced both above‐ and below‐ground C and N pools, with greater reductions above‐ than below‐ground. Fire decreased soil total carbon (TC), total nitrogen (TN) and nitrate nitrogen ( ) and increased ammonium nitrogen ( ) in surface mineral soil layers but did not affect those in deep layers. Fire decreased TC and TN in savanna but did not affect those in tropical dry and moist forests. Fire did not affect and in savanna because of non‐significant responses of N mineralization rate (N min ) to fire. Conversely, fire increased and decreased in tropical dry forest, but did not affect and increased in tropical moist forest owing to thermal decomposition of soil organic N and increased soil nitrification, respectively. Moreover, declined and increased initially and then decreased with time after fire. Above‐ and below‐ground response variables to prescribed fire were mediated largely by fire frequency and experimental duration, respectively. Main conclusions Our results suggest a high vulnerability of the above‐ground C and N pools to fire, whereas the biogeochemical cycles below‐ground are of high complexity. Fire effects on below‐ground C and N pools, which are highly uncertain and vegetation specific, should be investigated further.
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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) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".