The role of epiphytes in rainfall interception by forests in the Pacific Northwest. II. Field measurements at the branch and canopy scale
Bibliographic record
Abstract
To determine how epiphytes affect the canopy hydrology of old-growth Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco) forests, we measured rainfall interception by individual branches and an entire stand from March 2003 to May 2004. Epiphyte-laden branches at heights of 3.1, 24.8 and 46.5 m remained partially saturated for most of the wet season and required more than 30 mm of rainfall to become saturated. We used the mean, minimum, and individual storm methods to estimate canopy water storage capacity. Canopy water storage capacity averaged 3.15.0 mm, but these are probably underestimates of the maximum canopy water storage capacity, because the canopy was partially saturated prior to most storm events and the saturation of the canopy was delayed by preferential flow through the epiphyte-laden branches. Contrary to expectation, the water stored on epiphyte-laden branches after exposure to natural rainfall increased with rainfall intensity because the rough three-dimensional structure of the lichen and bryophyte mats limits water loss from raindrop splash and impedes the drainage of water from the branch. We conclude that epiphytic lichens and bryophytes increase canopy water storage capacity, prolong the time required for the canopy to saturate and dry, and alter the transfer of water through the canopy.
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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".