Carbon storage in successional and plantation forest soils: a tropical analysis
Bibliographic record
Abstract
Abstract Aim To analyse global patterns in soil carbon ( C ) in tropical successional and plantation forests based on climate, forest age, former land use and soil type to determine factors driving below‐ground C storage. Location Pantropical. Methods We conducted a synthesis of 81 studies reporting soil C stocks in more than 400 reforested and tree plantation sites. We used regression models and regression tree analyses to determine the importance of multiple predictor variables on soil C stocks standardized to three common depth ranges: 0–10, 0–30 and 0–100 cm. Results Mean annual temperature ( MAT ) was the most important predictor of soil C . Forest age explained little to no variability in soil C , in contrast with above‐ground studies. Data on long‐term trends in soil C are limited, as median time since forest growth was 15 years. Soil C stocks were similar between tropical secondary forests, tree plantations and reference forests. Differences between plantation and successional forests only appeared below 10 cm on sites with MAT < 21.3 ° C . Former pastures and cultivated sites differed from each other only to depths of 30 or 100 cm. Climatic variables appeared multiple times across all layers of the regression trees, consistent with strong interactions between MAT and precipitation on soil C stocks. Main conclusions Climate explained greater variability in soil C in successional and plantation forests than former land use or forest age, despite the tropical location of all sites. Human management factors were more important for predicting soil C stocks in cooler and drier sites, while environmental variables were more important in hotter and wetter sites. The relative importance and interactions between soil type, previous land use and forest cover type differed with soil depth, highlighting the importance of comparing C across consistent depths. Climatic controls suggest sensitivity of soil C stocks in successional and plantation forests to future climate change.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".