Abundance and distribution of cavity trees and the effect of topography on cavity presence in a tropical rainforest, southwestern China
Bibliographic record
Abstract
Cavity trees play a crucial role in maintaining biodiversity in forest ecosystems as they host numerous birds, mammals, and other cavity-dependent organisms. However, studies on the abundance and distribution of cavity trees in tropical forests are much less common than those in temperate forests. Also, how tree characteristics and topographic variables affect cavity presence is less clear in tropical forests. We surveyed 27 745 living trees from 386 species using ground-based observations in a tropical rainforest in southwestern China. The density of cavity trees was 86.3 trees·ha–1, which dramatically exceeded that in temperate forests. The number of cavity trees showed a left-skewed distribution with a peak at 10–20 cm diameter at breast height (DBH). The probability of cavity presence in a tree increased with DBH, although the patterns varied across species and crown positions. Moreover, cavity presence, which is influenced by topography in this tropical forest, decreased from valleys (concave terrain and low elevation) to ridges (convex terrain and high elevation). The results prove for the first time that topography is a good predictor of cavity presence, in addition to tree DBH. Our results demonstrate that the patterns determined for cavity presence in tropical forests of other regions also apply to Asian tropical forests. This study provides guidance on predicting the occurrence of cavity trees in the tropics.
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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 | 0.001 |
| 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".