Spatial distributions of species in an old-growth temperate forest, northeastern China
Bibliographic record
Abstract
Studying spatial distributions of species can provide important insights into processes and mechanisms that maintain species richness. We used the relative neighborhood density Ω based on the average density of conspecific species in circular neighborhoods around each species to quantify spatial distributions of species with ≥10 individuals in a fully mapped 25 ha temperate plot at Changbaishan, northeastern China. Our results show that spatial aggregation is a dominant pattern of species in the Changbaishan temperate forests. However, the percentage of significantly aggregated species decreases with spatial scale, especially for rare species. Rare species are more aggregated than intermediate and common species. The aggregation intensity declines with increasing size class (diameter at breast height), i.e., species become more regularly spaced as species grow, which is consistent with the predictions of self-thinning and Janzen–Connell spacing effects. Species functional traits (canopy layer, seed dispersal ability, shade tolerant, etc) also havea significant effect on the spatial distributions of species. Our results partially conform to the prediction that better dispersal reduces aggregation. Consequently, dispersal limitation, self-thinning, Janzen–Connell spacing effects, and habitat heterogeneity may primarily contribute to spatial distributions of species in the temperate 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".