Tree age and tree species shape positive and negative interactions in a montane meadow
Bibliographic record
Abstract
Few studies have considered how interactions between woody and herbaceous species change in direction or magnitude over time or with traits of the dominant woody species. We used a chronosequence approach to explore these interactions in a montane meadow in which Pinus contorta Dougl. ex Loud. and Abies grandis (Dougl. ex. D. Don) Lindl. have established gradually over a period of >70 years. Effects of individual trees (18–73 years old) were quantified by comparing plant community structure and composition under and adjacent to each tree. Trees generally exerted negative effects on cover and richness of resident meadow species and positive effects on colonizing forest herbs. Despite the average decline of meadow species under the canopy, cover was elevated (compared with adjacent meadow) under 33% of trees — most often under younger Pinus . Cover (but not richness) of meadow species declined with tree age, but the rate and magnitude of this decline did not differ under Pinus or Abies . In contrast, the cover and richness of forest herbs increased steeply with age under Abies, but not under Pinus. Our results illustrate the potential for complex and sometimes unpredictable interactions between woody and herbaceous species. A dynamic view of these relationships is critical for understanding or predicting the consequences of woody plant establishment in grassland and other herb-dominated ecosystems.
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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 | 0.001 |
| 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 | 0.001 | 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".