Spatial associations of tree species in a subtropical evergreen broad-leaved forest
Bibliographic record
Abstract
The spatial segregation hypothesis and the low-frequency hypothesis are two important proposed mechanisms that delay or prevent competitive exclusion in ecosystems. Because tree species interact with their neighbors, the importance of these potential processes can be investigated by analyzing the spatial structures of tree species. The distribution of the adults of 27 common tree species in a fully mapped 5-ha subtropical forest plot in Baishanzu, eastern China, was analyzed to investigate the community-level intra- and interspecific spatial association patterns. We first tested for the overall spatial pattern in the 5- to 40-m neighborhoods and classified first-order bivariate associations with a diametric scheme based on Ripley’s K and nearest-neighbor statistic (G-function). Then heterogeneous Poisson null models were used to distinguish second-order interactions from overall spatial associations (including first-order effects). Finally, we analyzed correlations between the existence of species interactions and some attributes of the species involved. Partial overlap and segregation increased with scale, whereas mixing decreased. Nearly 70% of the species pairs occurred less than expected at random, and only 3.4% of the species pairs were well mixed; 11.0% of all species pairs showed significant small-scale interactions, which was a greater frequency than expected by chance if species are abundant or prefer the same habitat, but less frequent than expected if species are highly aggregated. This suggests that both spatial segregation and low frequency of species facilitate species coexistence by reducing the opportunity that trees of two species encounter each other. The study also revealed that positive interactions were more prevalent than negative interactions in the forest, which indicates that positive interactions may have important effects on forest species assemblies.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 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".