Spatial distribution of tree species in a species-rich subtropical mountain forest in central China
Bibliographic record
Abstract
Inferring the processes underlying the spatial distribution patterns of tree species is fundamental for understanding species coexistence. Here, we examined spatial distribution patterns of woody plants by using the univariate pair correlation function to quantify spatial patterns of species in a fullly mapped 25 ha subtropical plot in China. We analyzed the relationships between the species attributes and spatial distribution patterns of 137 tree species with at least one individual per hectare. The results showed that aggregated distributions were the dominant pattern for species in the Badagongshan subtropical forests, and that the percentage of significantly aggregated species decreased with increasing spatial scales. Rare species were more aggregated than intermediate and abundant species, but they were more easily influenced by habitat heterogeneity. Also, there was significantly negative relationship between species abundance and species aggregation intensity. The aggregation intensity showed negative relationships to species mean diameter at breast height (DBH) and maximum DBH, i.e., species became more regularly spaced as species stature increased. Species functional traits (e.g., growth form and phenological guild) also had obvious effects on the spatial patterns of species. However, spatial patterns of tree species were not related to the dispersal mode. Our results partially conformed to the prediction that species’ attributes influenced species’ spatial patterns following similar laws, even after controlling for the effects of habitat heterogeneity. Consequently, species attributes (species abundance, mean DBH, maximal DBH, growth form, phenological guild, etc.) and habitat heterogeneity may primarily contribute to spatial patterns and species coexistence in natural forests.
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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 | 0.001 |
| 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) | 0.000 | 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".