Functional leaf traits of vascular epiphytes: vertical trends within the forest, intra‐ and interspecific trait variability, and taxonomic signals
Bibliographic record
Abstract
Summary Analysing functional traits along environmental gradients can improve our understanding of the mechanisms structuring plant communities. Within forests, vertical gradients in light intensity, temperature and humidity are often pronounced. Vascular epiphytes are particularly suitable for studying the influence of these vertical gradients on functional traits because they lack contact with the soil and thus individual plants are entirely exposed to different environmental conditions, from the dark and humid understorey to the sunny and dry outer canopy. In this study, we analysed multiple aspects of the trait‐based ecology of vascular epiphytes: shifts in trait values with height above ground (as a proxy for vertical environmental gradients) at community and species level, the importance of intra‐ vs. interspecific trait variability, and trait differences among taxonomic groups. We assessed ten leaf traits for 1151 individuals belonging to 83 epiphyte species of all major taxonomic groups co‐occurring in a Panamanian lowland forest. Community mean trait values of many leaf traits were strongly correlated with height and particularly specific leaf area and chlorophyll concentration showed nonlinear, negative trends. Intraspecific trait variability was pronounced and accounted for one‐third of total observed trait variance. Intraspecific trait adjustments along the vertical gradient were common and seventy per cent of all species showed significant trait–height relationships. In addition, intraspecific trait variability was positively correlated with the vertical range occupied by species. We observed significant trait differences between major taxonomic groups (orchids, ferns, aroids, bromeliads). In ferns, for instance, leaf dry matter content was almost twofold higher than in the other taxonomic groups. This indicates that some leaf traits are taxonomically conserved. Our study demonstrates that vertical environmental gradients strongly influence functional traits of vascular epiphytes. In order to understand community composition along such gradients, it is central to study several aspects of trait‐based ecology, including both community and intraspecific trends of multiple traits.
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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 | 0.001 |
| 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".