Responses of herbs and shrubs to reduced root competition under canopies and in gaps: a trenching experiment in old-growth Douglas-fir forests
Bibliographic record
Abstract
We tested the effect of root trenching on vegetation in closed-canopy and gap locations in Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco) forests. Based on theory, we expected belowground competition to be intense in a region with low summer rainfall, and trench responses were expected to be greater in the high light environment of the gaps. We installed 1 m deep trenches around study plots and lined the trenches to prevent reinvasion by tree roots. Soil moisture was measured monthly during the growing season for the first 3 years after trench installation. Vegetation in these trenched plots was compared with control plots 10 years after installation of the plots. Trenched plots with no vegetation manipulation averaged 92% total understory cover, while untrenched plots averaged 47% cover. Contrary to our expectation, both vegetation and soil moisture responses to trenching were greater in areas of high tree canopy cover than in gaps. Trenched plots under closed canopies were moister than control plots throughout the growing season, while the trenching effect became apparent in the overall wetter gaps only at the end of the growing season. We conclude that understory plants at these sites were limited at least as much by belowground competition as by aboveground competition.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".