Community assembly in a tropical cloud forest related to specific leaf area and maximum species height
Bibliographic record
Abstract
Abstract Question We tested whether co‐existing tree species in tropical dwarf forests were deterministically assembled along gradients of air temperature, relative humidity and light availability, according to two important functional traits, specific leaf area (SLA) and maximum species height (Hmax). Location Tropical montane cloud forest, Bawangling Nature Reserve, Hainan Island, south China. Methods Null model analyses were used in conjunction with trait and species composition data collected to test our hypotheses at four plot sizes (25 m2, 100 m2, 400 m2 and 900 m2), addressing whether the consistent importance of variation in SLA and Hmax extends to these unique forests, as well as theoretical predictions concerning how patterns change with plot size. Results Low SLA species were significantly over‐represented within forest communities for the two largest plot sizes, and taller‐growing tree species were over‐represented across all four plot sizes. Plot‐level analyses indicated that low SLA species were associated with lower temperatures. Conclusions Our results show that tropical dwarf forests are deterministically assembled with respect to these two traits, and are consistent with other studies indicating that SLA responds to abiotic filters. Co‐existing tree species were not significantly divergent for these two traits, indicating that variation in these two traits among trees does not contribute to niche differences (i.e. limiting similarity) and therefore co‐existence within forest plots. Finally, our study demonstrates that patterns of community assembly change with plot size; however, trait convergence did not increase with plot size as previously predicted.
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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 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".