Effects of conifer basal area on understory herb presence, abundance, and flowering in a second-growth Douglas-fir forest
Bibliographic record
Abstract
Although overstory trees exert competitive effects on understory plants, it is not clear how this competition affects the distribution and performance of herb species. This study seeks to clarify the relationship between understory herb performance and overstory basal area in second-growth Pseudotsuga menziesii (Mirb.) Franco stands. Data on 11 understory herb species were collected in a 100-ha watershed. Statistical models were constructed to control for the effects of slope, aspect, soil type, and distance from the central stream or peripheral ridge lines. Presence of old-growth associated and forest generalist herbs was positively associated with conifer basal area, as well as with north-facing aspects and proximity to the stream channel. Presence of release herbs, subordinate forest species that respond positively to canopy disturbance, was largely independent of measured variables. Abundance of individual species showed weak and inconsistent relationships with conifer basal area. In contrast, flowering of almost all species was negatively related to conifer basal area. Regression tree models suggested that conifer basal area may have stronger negative effects farther from the moist environments along stream channels. I conclude that patterns of presence of slow-growing forest species may be determined primarily by past events, while flowering better reflects current stand conditions.
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 | 0.001 |
| 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 | 0.000 |
| 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".