Changes in plant lignin components and microbial necromass matter with subtropical forest restoration
Bibliographic record
Abstract
Soil organic carbon (SOC) represents the largest carbon reservoir in terrestrial ecosystems. Therefore, understanding how to enhance SOC is crucial for the global carbon cycle and atmospheric CO2 removal. While there has been reported on SOC accumulation following forest restoration efforts, changes in plant lignin and microbial necromass across soil fractions and depths remain unclear. To address this gap, we investigated the SOC-standardized concentrations of lignin phenols and amino sugars in bulk soil and soil fractions (particulate organic matter (POM) and mineral-associated organic matter (MAOM)) during subtropical forest restoration, as tracers for change in plant lignin components and microbial necromass respectively. Our findings indicate that forest restoration does not affect SOC concentrations, but does result in changes in plant lignin and microbial necromass. Specifically, in the subsoil, lignin phenols and amino sugars concentrations in bulk soil and soil fractions rise significantly with restoration time, with lignin phenols and amino sugars concentrations in bulk soil rising by 29.6% and 53.0%, respectively. This suggests that as forests recover, lignin and microbial necromass have a higher contribution to SOC accumulation in the subsoil, while the contribution of low-molecular plant-derived organic matter decreases. The relative change rate of lignin phenols (relative to SOC) is greater than that of amino sugars in POM in the topsoil, while in the subsoil, the relative change rate of amino sugars in MAOM is greater than that of lignin phenols. These results indicate that lignin in POM declines more rapidly than amino sugars during restoration, whereas in MAOM, amino sugars accumulate faster than lignin. Overall, these findings provide important insights into the regulation of SOC accumulation during forest restoration by the combination of plant lignin and microbial necromass in various stabilization pathways.
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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) | 0.001 | 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".