Throughfall alterations by degree of <i>Tillandsia usneoides</i> cover in a southeastern US <i>Quercus virginiana</i> forest
Bibliographic record
Abstract
Alterations to forest canopy structure directly affect the hydrology and biogeochemistry of wooded ecosystems. Epiphytes alter canopy structure, thereby intercepting rainwater, reducing penetration of rain to the surface (as throughfall), modifying throughfall chemistry, and changing throughfall responses to storm conditions. These processes are well established for epiphyte presence versus absence; yet, it is unknown how epiphyte–throughfall interactions change across an epiphyte cover continuum (important information for prediction of ecological changes with epiphyte establishment or decline from disturbance). To fill this gap, we monitored throughfall water and dissolved ions (Na+, NH4+, K+, Mg2+, Ca2+, Cl–, NO3–, PO43–, SO42–) beneath a common epiphyte (Tillandsia usneoides L.) across cover percentages (0%–20%, 21%–40%, 41%–60%, 61%–80%, 81%–100%) for 47 storms. Throughfall amount inversely responded to epiphyte cover while increasing salt wash-off and intracellular leaching. Greater epiphyte cover released NH4+ and decreased NO3– from throughfall. Storm conditions (high vapor pressure deficit, moderate wind speeds, and low intensity) strengthened throughfall responses as T. usneoides cover increased. Factorial MANOVA results revealed significant trends for throughfall ion enrichment or depletion via wash-off, leaching, and uptake. These data suggest that inclusion of epiphyte alterations to rainwater and solute inputs in ecosystem nutrient budgeting studies should consider the full continuum of epiphyte cover represented at that site.
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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 | 0.000 | 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) | 0.000 | 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".