Branch mortality influences phorophyte quality for vascular epiphytes
Bibliographic record
Abstract
Trees generate resources for other guilds (e.g., lianas), including the production of supporting branches for the establishment of epiphytes. In a tropical dry forest of central Mexico, we studied whether branch mortality is associated with phorophyte quality. For a one-year period, we monitored the survival of branches with and without vascular epiphytes in tree species with high epiphyte loads (Bursera copallifera (Sessé & Moc. Ex DC.) Bullock, Bursera glabrifolia (Kunth.) Engl.) and low (Bursera fagaroides (Kunth) Engl., Conzattia multiflora (B.L. Rob.) Standl., Ipomoea pauciflora M.Martens & Galeotti, Sapium macrocarpum Müll.Arg.). The lowest (C. multiflora) and highest (I. pauciflora) branch mortalities occurred in phorophytes with low epiphyte loads, whereas branch mortality in S. macrocarpum was 60% and in all Bursera species was <25%. In B. copallifera and B. glabrifolia, the highest branch mortality was in branches with epiphytes, suggesting a negative influence of these plants, but mortality was also associated with larger/older branches. At the end of monitoring, 95% of the epiphytes of I. pauciflora were growing on dead branches. We conclude that branch mortality is low in phorophytes with high epiphyte loads; but in phorophytes with low epiphyte loads, branches can be ephemeral or long lasting. Low epiphyte abundances in phorophytes with long-lasting branches can be caused by other traits that remain to be examined (e.g., seed capture).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| 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".