Prediction of stand carbon (C) storage and net primary production (NPP) of secondary forests in subtropical China: the effect of climate change and its contribution to carbon neutrality in 2060
Bibliographic record
Abstract
Abstract Background Forest ecosystems play an important role in carbon sequestration and climate change mitigation, as well as achieving target for carbon neutrality in 2060 proposed by the Chinese government. However, changes in carbon storage and net primary production in natural secondary forests stemming from tree growth and future climate change have not yet been investigated in subtropical areas in China with complex compositions of tree species. Here, we used data from inventory plots in four secondary forests (evergreen broad-leaved forest, deciduous and evergreen broad-leaved mixed forest, deciduous broad-leaved forest, and coniferous and broad-leaved mixed forest) at different restoration stages and run a hybrid model (TRIPLEX 1.6) to predict changes in stand carbon storage and net primary production under two future climate change scenarios (RCP4.5 and RCP8.5). Results There was a high correlation between predicted and observed values (R2 > 0.87) for average stand diameter at breast height (1.3m), stand density, carbon storage, and net primary production in the four forests, indicating that the simulations by TRIPLEX1.6 were accurate. Net primary production was highest in deciduous and evergreen broad-leaved mixed forest (3.95 t ha−1 yr−1 in 2030 and 3.81 t ha−1 yr−1 in 2060), indicating a high carbon storage capacity. Evergreen broad-leaved forest is the subtropical climax community and can store large amounts of carbon (85.22 t ha−1 in 2030 and 135.76 t ha−1 in 2060). Net primary production in coniferous and broad-leaved mixed forests increased gradually over time but decreased over time in the other three forests. Net primary production was greater in young forest compared with mature forest. The effects of climate change (RCP4.5 and RCP8.5) on carbon storage significantly differed between coniferous and broad-leaved mixed forest and the other three forest types (p < 0.001). Conclusions Stand carbon storage of evergreen broad-leaved forest was predicted to be the largest. Nevertheless, the carbon sequestration potential under future climate change was still limited in the short and medium-term. The floristic composition and tree growth of existing forests should be properly managed in order to enhance carbon sequestration for climate change mitigation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".