Stand density and species richness affect carbon storage and net primary productivity in early and late successional temperate forests differently
Bibliographic record
Abstract
Abstract How stand density and species richness affect carbon (C) storage and net primary productivity (NPP) changes with forest succession is poorly understood. We quantified the C storage of trees and the aboveground NPP in an early successional secondary birch forest (birch forest) and a late successional mixed broadleaf‐Korean pine ( Pinus koraiensis ) forest (mixed forest) in northeastern China. We found that: 1) tree C storage in the mixed forest (120.3 Mg C ha −1 ) was significantly higher than that in the birch forest (78.5 Mg C ha −1 ), whereas the aboveground NPP was not different between the two forest types; and 2) only stand density had a positive linear relationship with tree C storage and aboveground NPP in the birch forest. In the mixed forest, both tree C storage and aboveground NPP were significantly affected by the combination of the stand density and species richness. The tree C storage to stand density and species richness relationships were hump‐shaped. The aboveground NPP increased with increasing stand density, but its relationship to species richness was hump‐shaped. We conclude that the effect of stand density and species richness on tree C storage and aboveground NPP was influenced by forest stand succession, and such effects should be considered in studying stand density‐ and species richness‐ ecosystem function (e.g., C storage and NPP) relationships in temperate forest ecosystems.
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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 | 0.000 |
| 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".