Community‐level trait variation of epiphytic bryophytes supports trade‐off aligned with leaf‐economic spectrum in vertically stratified tropical montane cloud forest canopies
Bibliographic record
Abstract
Abstract Tropical montane cloud forests are among the most biodiverse ecosystems on earth and are vulnerable to climate change due to reliance on atmospheric moisture. Epiphytic bryophytes (i.e. mosses, liverworts and hornworts) dominate these ecosystems and drive important ecosystem processes, yet their underlying strategies of resource use and functional structure within the canopy are not well‐understood. Community‐level functional trait analyses along environmental gradients are valuable for understanding patterns of plant resource use in ecosystems. Along environmental gradients, intraspecific trait variation may obscure or drive patterns of functional structure but has often been overlooked. We examined bryophyte community functional structure among three vertically stratified zones in a Caribbean slope tropical montane cloud forest near Monteverde, Costa Rica. We tested how morphological and water‐related traits associated with bryophyte economic spectra differ among vertical zones within cloud forest trees and determined the relative importance of intraspecific variation in shaping this structure. Functional structure differed significantly among zones and is suggestive of an economic trade‐off whereby structural investment towards water holding capacity for species in the canopy comes at the cost of photosynthetic capacity, and vice versa on the trunk and base. Patterns of functional structure were mostly due to species turnover rather than intraspecific trait variation, which was supported by clear shifts in community composition among zones and by species with high fidelity to specific zones. We found 171 bryophyte species (50 mosses, 120 liverworts, 1 hornwort), including eight new species for Costa Rica and four new for Central America. Our results suggest that these extremely diverse epiphytic bryophyte communities exhibit acquisition–conservation trade‐offs in resource use like that known in vascular plants.
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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 | 0.000 | 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) | 0.001 | 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".