Tree spacing and area of competitive influence do not scale with tree size in an African rain forest
Bibliographic record
Abstract
Abstract Questions: Is the area of influence of individual trees determined by tree size? Does competition, inferred from spatial pattern between neighbouring trees, affect adult tree spacing patterns in an tropical forest? At what size‐class or stage is competition between neighbours most likely to affect adult tree spacing patterns? Location: Kibale National Park, western Uganda. Methods: Relationships between focal tree size and nearest neighbour distance, size, density, and species in a 4‐ha permanent plot, using point pattern analyses. Results: We found non‐random patterns of distribution of nearest tree neighbours (stems > 10 cm DBH). Independent of identity, tree density was highest and neighbours were regularly spaced within 3–5m of an individual. Tree densities were lower and relatively constant at distances >5m and neighbours were typically randomly spaced. In general, conspecific patterns conformed to the latter trends. Thus, individual area of influence was small (within a radius of 3–5 m). Rarer species were more clumped than common species. Weak competitive thinning occurred among more densely packed small trees (<20 cm DBH), and rapidly disappeared with increasing tree size and distance from an individual. The clumping and density of individuals was not significantly affected by tree size. Conclusions: Negative effects of competition among trees are weak, occur within the crown radius of most individuals, and are independent of adult tree size and identity. The density of neighbouring trees (aggregation) did not decline with increasing focal tree size at either the conspecific or the community level and tree diameter (tree size) was not a good estimator of the implied competitive influence of a tree. Mechanisms operating at the recruitment stage may be important determinants of adult tree community diversity and spacing patterns.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| 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 teacher head, 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".