Relative contribution of stand characteristics on carbon stocks in subtropical secondary forests in Eastern China
Bibliographic record
Abstract
Abstract. Stand structural diversity, which is characterized by species diversity, variances in tree diameter at breast height (DBH) and height, plays an important role in influencing forest carbon (C) stocks. However, the relative contribution of stand structural diversity in contrast to other stand characteristics on the variation in C stocks in subtropical forests have not been fully explored. In this study, aboveground C stock, soil organic C stock, tree species, DBH and height diversities, stand age, and stand density, and site productivity were determined across 80 subtropical forest plots in Eastern China. Using simple regression analysis, we found that DBH and height diversities, site productivity, and stand age explained 49 %, 13 %, 41 %, and 50 % of the variation in aboveground C stock, respectively, whereas species diversity and stand density did not explained any variation (i.e., < 1 %). Multiple regression analysis indicated that variation in aboveground C stock was explained to a higher degree (83 %) by the joint effects of DBH diversity, stand age, site productivity, species diversity and height diversity than by stand structural diversity (54 %), and the other three stand characteristics (79 %) alone. The structural equation modelling (SEM) showed that the effect of stand age on aboveground C stock was stronger directly (beta = 0.59) than indirectly (beta = 0.11). Stand age has also significant and strong effect on DBH (beta = 0.63) and height (beta = 0.55) diversities. Six stand characteristics did not explain any variation in soil organic C stock (i.e., < 2 %), based on both simple and multiple regressions analyses, as well as SEM analysis. Our analyses suggest that, rather than species and height diversities, DBH diversity, stand age and site productivity cumulatively contributed to variation in aboveground C stock during stand development in subtropical secondary forests in Eastern China. Therefore, improving tree DBH diversity and stand condition could be an effective approach for enhancing C storage in subtropical forests.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".