Detecting density dependence from spatial patterns in a heterogeneous subtropical forest of central China
Bibliographic record
Abstract
Accumulating evidence suggests that density dependence, whether at early or late life stages, is an important mechanism regulating plant population structure. However, the opposing effects of habitat heterogeneity and species-level variation might have confounded the prevalence of density dependence in natural forests. These compatible ideas were rarely considered simultaneously. In this study, we applied a spatial statistical technique to examine (i) the prevalence of density dependence at late life stages after controlling for habitat heterogeneity and (ii) the relationships between species traits and the strength of density dependence in a newly established, 25 ha subtropical mountain forest plot in central China. Of the 88 (75%) tree species analyzed, 66 were found to exhibit density dependence predominantly at very close distances among neighbors in the species-rich subtropical forest. In addition, the strength of density dependence was associated with species traits. Our findings identified strong density dependence among trees that had greater stature and were rarer. We concluded that density dependence was a prevalent mechanism for regulating the population structure of most tree species and both habitat heterogeneity and species-level variation played crucial roles in shaping the strength of density dependence 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 | 0.001 |
| 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) | 0.000 | 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".