Enhancing species diversity through mixed plantations of native tree species mytilaria laosensis and michelia macclurei in southern China
Bibliographic record
Abstract
• Mixed forests show higher mixed–monoculture similarity than monocultures. • Most diversity indices in mixed forests exceed the average values of monocultures. • Regeneration diversity in plantations declines exponentially with ground diameter. • Planting patterns and species alter plantation species composition and diversity. Mixed-species planting promotes forest growth and enhances ecological functions. However, it remains unclear whether mixed forests are more conducive to fostering species diversity than monocultures over long time scales. To address this, we established fixed plots of 140 m × 40 m, 70 m × 50 m, and 70 m × 40 m in plantations of Mytilaria laosensis (Lecomte) and Michelia macclurei (Dandy) in southern Guangxi, China. We analyzed species composition and diversity characteristics using the Jaccard similarity index, principal component analysis and four traditional diversity indices. The results indicated: 1) The species composition similarity between mixed forest and monocultures was higher than that observed between monocultures. 2) As plot area increased, the species richness (SR) and Shannon diversity index (Hʹ) of stands and regenerations followed a power-law distribution, while species abundance increased linearly and evenness (E H ) gradually decreased. 3) Regeneration was sparse in the M. laosensis stand but denser in the other two forests. 4) The majority of regenerations had a ground diameter (GD) smaller than 5 cm. Both SR and Hʹ decreased rapidly at first and then more slowly as GD increased, approaching 0, while the relationship between E H and GD was weak. Three out of four species diversity indices of the mixed forest were higher than those of the M. laosensis stand and the mean values of the two monocultures. These results suggest that planting patterns significantly alter the species composition and diversity of plantations, while emphasizing the importance of tree species selection in promoting species diversity.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".