Growth Rate and Not Growing Season Explains the Increased Productivity of Masson Pine in Mixed Stands
Bibliographic record
Abstract
Increased tree species diversity can promote forest production by reducing intra-specific competition and promoting an efficient unitization of resources. However, questions remain on whether and how mixed stands affect the dynamics of intra–annual xylem formation in trees, especially in subtropical forests. In this study, we randomly selected 18 trees from a monoculture of 63-year-old Masson pine (Pinus massoniana) growing in pure stands and mixed them with 39-year-old Castanopsis hystrix in Pinxiang, southern China. A total of 828 microcores were collected biweekly throughout the growing season from 2022 to 2023 to monitor the intra-annual xylem formation. Cell production started in early March and ended in late December and lasted about 281 to 284 days. Xylem phenology was similar between mixed and pure stands. During both seasons, the Masson pine in mixed stands showed higher xylem production and growth rates than those in pure stands. The Masson pine in mixed stands produced 45–51 cells in 2022 (growth rate of 0.22 cells day−1) and 35–41 cells in 2023 (0.17 cells day−1). Growth rate, and not growth seasons, determined the superior xylem growth in the mixed stands. Our study shows that after 39 years of management, Masson pine and C. hystrix unevenly aged mixed stands have a significant positive mixing effect on Masson pine xylem cell production, which demonstrates that monitoring intra-annual xylem growth dynamics can be an important tool to evaluate the effect of species composition and reveal the mechanisms to promote tree growth behind the mixing effect.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".