The role of epiphytes in rainfall interception by forests in the Pacific Northwest. I. Laboratory measurements of water storage
Bibliographic record
Abstract
Old-growth Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco) forests frequently contain large populations of epiphytic lichens and bryophytes. To determine the effect these epiphytes have on canopy hydrology we measured the maximum water fraction (f(x)max; maximum mass of internal and external water stored by an epiphyte divided by its tissue dry mass) of common lichens, bryophytes, and dead branches in the laboratory and the water storage and interception efficiency (pi) (the rainfall stored on a branch divided by the rainfall intercepted by the branch) of whole epiphyte-laden branches under a rainfall simulator at three intensities (11.3, 16.1, and 39.8 mm·h1). The f(x)max values for epiphytic fruticose lichens, foliose lichens, and bryophytes were 2.2 ± 0.4, 3.4 ± 0.6, and 10.0 ± 0.5, respectively. The water stored by an epiphyte-laden branch during and after exposure to simulated rainfall could be predicted if the biomass of epiphytic lichens and bryophytes on the branch was known (R2 = 0.8, p value < 0.0001). For all three rainfall intensities, the branches required >6 mm of rainfall to saturate. Values of pi averaged between 0.5 and 0.7 after 2 mm of rainfall and did not differ among the three intensities (all p values > 0.05). We conclude that epiphytes increase the canopy water storage of a typical old growth Douglas-fir forest by >1.3 mm.
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) | 0.000 | 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".