Neighboring edges: Interacting edge effects from linear disturbances in treed fens
Bibliographic record
Abstract
Abstract Questions Edge influence on forest biodiversity is an important environmental effect associated with habitat fragmentation, but extrapolating the influence of edges across the broader landscape has been difficult, especially for situations where multiple edges exist in close proximity. We asked whether there were differences in edge effects between two types (3 m vs 8 m width) of low‐severity linear disturbance (seismic lines) and whether there were interactions of edge effects when seismic lines occur in dense networks; that is, do multiple narrow seismic lines have a stronger or weaker edge influence than a single narrow seismic line. Location Treed peatlands in northeastern Alberta, Canada. Methods Seismic lines are created during oil and gas exploration and are responsible for dissection of boreal forests in western Canada. We sampled vascular plants along transects perpendicular to seismic lines in moderate‐rich and poor treed fens. We used the “Randomization Test of Edge Influence” (RTEI) to calculate the magnitude and distance of edge effects and then compared these between narrower (3 m) versus wider (8 m) lines and between single narrow lines versus multiple narrow lines (parallel and ~50 m apart). Results In moderate‐rich fens, we found a positive edge influence on understorey diversity from both wide and narrow seismic lines. We also found a weakening edge interaction on diversity, that is, single narrow seismic lines had a stronger edge influence on diversity than did multiple narrow seismic lines. In treed poor fens, multiple narrow seismic lines had a negative edge effect on tree density, understorey abundance, richness, and composition. In addition, we found strengthening edge interactions in treed poor fens on tree density, graminoid cover, and understorey composition. Conclusions Even narrow linear disturbances, such as seismic lines, can have significant edge effects and these are exacerbated when lines occur in dense networks.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".