Shedding light on the diversity of epiphytic mosses in some Mexican forests
Bibliographic record
Abstract
Background: Epiphytic mosses are one of the most common groups in forest environments but among the most neglected by researchers in Mexico. Questions: What is the epiphytic mosses diversity, measured in richness, turnover, and community composition in Mexican forests? Species of study: Epiphytic mosses. Study site and years: Humid mountain forest, pine-oak and tropical evergreen forests. Study period: 2015 to 2021. Methods: Through a literature review and field work we compiled data on epiphytic mosses in three forest types in Mexico. We assessed the data using alpha and beta diversity analysis, indicator species, and community composition. Additionally, we explored the influence of elevation and forest type on the observed diversity patterns. Results: We report a richness of 147 species of epiphytic mosses across three types of Mexican forests. The humid mountain forest was the best sampled forest with the highest moss species richness. Although species richness is different for the forests studied, species turnover is similar among them. We demonstrated that elevation and forest type are highly correlated with species richness of epiphytic mosses. Conclusions: The epiphytic mosses studied here collectively represent over 15 % of the moss richness of Mexico. Forest type and elevation seem to be the drivers of this widely distributed richness. Finally, we call for more in-depth studies of the forests presented here, as well as those in other latitudes including variables such as humidity and host traits, to provide a more complete picture of an overlooked Mexican flora.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".