Diversity of macromycetes determined by tree species, vegetation structure, and microenvironment in tropical cloud forests in Veracruz, Mexico
Bibliographic record
Abstract
Tropical montane cloud forests (TMCFs) have high levels of plant and fungal diversity. We examined whether macromycete alpha and beta diversity were associated with woody plant diversity, forest structure, or microclimate, in four TMCFs at lower (1240–1440 m) and upper altitudes (1790–1900 m) in Veracruz, Mexico. Every 2 weeks during the growing season, macromycetes were collected from ten 10 m × 10 m permanent plots per site, and air and soil temperature and humidity were measured. In total, 2059 macromycetes (509 species) and 678 woody plants (63 species) were recorded. Macromycete diversity (Shannon Index) values of the two sites located in lower forests were higher than in upper sites. Beta diversity (Jaccard index) indicated high turnover among sites and sampling times. Macromycete richness was negatively correlated with overstorey tree richness, understorey vegetation structure, and air temperature, but was positively correlated with air humidity and soil water content and altitude. Ordinations separated lower from upper altitude forest sites. Changes in composition and abundance of macromycete species with altitude were explained by precipitation, temperature, and understorey vegetation structure, while soil water content effect changes within a growing season. Results imply that understorey vegetation structure is a more important aspect for macrofungal diversity management than for woody plant diversity.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".