The C:N:P Stoichiometry of Planted and Natural Larix principis-rupprechtii Stands along Altitudinal Gradients on the Loess Plateau, China
Bibliographic record
Abstract
Carbon:nitrogen:phosphorus (C:N:P) stoichiometry plays a critical role in nutrient cycling, biodiversity, and ecosystem functionality. However, our understanding of the responses of C:N:P stoichiometry to elevation and forest management remains elusive. Here we sampled 18 Larix principis-rupprechtii sites along altitudinal gradients (1700-2300 m) on Guandishan Mountain in the Loess Plateau, China. We determined the leaf, litter, and soil C N P contents and C:N:P stoichiometric ratios, as well as nutrient resorption efficiency (NuRE), and diameter at breast height (DBH) increments in both planted and natural stands, and then tested the impacts of elevation and stand origin on these parameters’ management. We found different C:N:P stoichiometry between natural and planted forests. The results revealed that: soil C, N, and N:P ratios, litter C:P and N:P ratios, leaf C:N and N:P ratios increased significantly; however, soil C:N ratios, litter P, leaf N and P, nitrogen resorption efficiency (NRE), and DBH increments decreased significantly with elevation in the planted forests. Soil C,N and N:P ratios, litter C, as well as C:N and C:P ratios increased significantly with elevation in natural forests. The soil N, P and N:P ratios, litter C:P and N:P ratios, leaf C, C:P and N:P ratios, nitrogen resorption efficiency (NRE), phosphorus resorption efficiency (PRE), and DBH increments were, on average, higher in the planted, rather than natural forests. Our results indicated that there was an enhancing P-limitation in both the planted and natural forests, and the plantations were more restricted by P. Moreover, compared to natural forests, plantations converged toward a higher conservative N- and P-use strategy by enhancing resorption efficiencies of internal nutrient cycling and a higher annual growth rate.
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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 | 0.000 |
| 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) | 0.000 | 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".