Changes in Patterns of Species Co‐occurrence across Two Tropical Cloud Forests Differing in Soil Nutrients and Air Temperature
Bibliographic record
Abstract
Abstract Patterns of co‐occurrence of species are increasingly used to examine the contribution of biotic interactions to community assembly. We assessed patterns of co‐occurrence at four scales, in two types of tropical cloud forests in Hainan Island, China (tropical montane evergreen forests, TMEF and tropical dwarf forests, TDF ) that varied significantly in soil nutrients and temperature. We tested if the patterns of co‐occurrence changed when we sorted species into classes by abundance and diameter at breast height (dbh). Co‐occurrence differed by forest type and with plot size, with significant species aggregation observed across larger plots in TDF and patterns of species segregation observed in smaller plots in TMEF . Analyses of differential abundance and dbh classes also showed that smaller plots in TMEF tend to have negative co‐occurrence patterns, but larger plots in TDF tend to show patterns of aggregation, suggesting competitive and facilitative interactions. This underscores the scale‐dependence of the processes contributing to community assembly. Furthermore, it is consistent with predictions of the stress gradient hypothesis that facilitation will be most important in biological systems subject to abiotic stress, while competition will be more important in less abiotically stressful habitats. Our results clearly demonstrate that these two types of tropical cloud forest exhibit different co‐occurrence patterns, and that these patterns are scale‐dependent, though independent of plant abundance and size class.
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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".