Effects of Elevation, Stand Density, and Inter-Tree Competition on Tree Sizes, Vulnerability, and Health of Planted <i>Zelkova serrata</i> and <i>Quercus glauca</i> in Reforestation
Bibliographic record
Abstract
Taiwan has a long history of reforestation due to land degradation. However, there is a lack of understanding of how tree species grow on reclaimed lands. This study looked at tree sizes, vulnerability, and health of economically important Zelkova serrata and Quercus glauca trees planted on reclaimed agricultural lands. Thirteen former agricultural sites with trees of six to seven years old were sampled along the elevation from 107 to 2514 m above sea level. Results showed that increasing inter-tree competition reduced tree sizes and health and increased vulnerability to damage, primarily wind, for both tree species. For example, a 1 m2 ha−1 increase in inter-tree competition was associated with a 5.68 cm decrease in tree diameter, a 3.21 m decrease in tree height, a 59.31% decrease in tree health for Z. serrata. Responses of Z. serrata to inter-tree competition were generally stronger than those of Q. glauca. Elevation generally reduced tree sizes of both species and reduced health of only Z. serrata trees. Stand density has minimal effects on the tree attributes of both species. Our study suggests that Z. serrata responds strongly to inter-tree competition leading to stratification of stand structures, which agrees with past studies showing Z. serrata developing different growth strategies. Q. glauca could resist inter-tree competition so that suppressed trees could compete with its neighbors. This supports past observations that Q. glauca could persist under suppression. Our findings of the elevation caution planting both tree species outside their native habitat ranges, which was not shown before.
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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 | 0.000 | 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) | 0.001 | 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".