Persistent soil organic carbon deficits from converting primary forests to plantations and secondary forests in subtropical China
Bibliographic record
Abstract
Soil organic carbon (SOC) plays a critical role in supporting key ecosystem functions and services to humanity. Despite the widespread conversion of primary forests to plantations and secondary forests, how it affects SOC content and distribution of SOC remains uncertain. We evaluated SOC content, its components in two fractions, particulate organic carbon (a mean residence time shorter than decades) and, mineral-associated organic carbon (a mean residence time of multiple decades to centuries) and the fine root characteristics (biomass and necromass) across Chinese fir plantations and secondary forests during stand development (5–41 years old) in subtropical China, employing primary forests as a reference. We found that SOC content differed significantly between forest types and soil depths, but not with stand age within plantations or secondary forests. In topsoil (0-10cm), SOC was on average 32.8 mg g -1 in primary forests, 27.8 mg g -1 in secondary forests and 22.3 mg g -1 soil in plantations, i.e., primary forests had 47% higher and secondary forests had 25% higher SOC contents than Chinese fir plantations. SOC content in the subsoil (40-60 cm) was approximately 6 mg C g -1 and did not change significantly with forest types or stand ages. The higher topsoil SOC in primary forests and secondary forests resulted primarily from particulate organic carbon preservation caused by higher fine root biomass. The fraction of mineral-associated organic carbon to SOC increased with soil depth and was higher in plantations than that of secondary and primary forests in topsoil, which was associated with a higher amount of fine root necromass that helps form mineral-associated organic carbon. Despite 40-year stand development in plantations and secondary forests, SOC content nor its fractions changed with stand age. Our results reveal persistent SOC deficits from converting primary forests to plantations and secondary forests and highlight the urgent needs to conserve remaining primary forests and increase forest ages by minimizing forest disturbances.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".