Neighboring edges: interacting edge effects of linear disturbances on vegetation in treed fens
Bibliographic record
Abstract
The influence of edges on forest biodiversity is an important environmental effect associated with habitat fragmentation, as edge effects can further reduce the remaining ‘interior’ habitat. However, extrapolating the influence of edges across the broader landscape has been difficult, especially regarding how to treat multiple edges in close proximity, where edge effects might interact. In this study, we examined the interaction of edge effects for multiple edges from a dense network of narrow (3-8 m wide) and low-severity linear disturbances called ‘seismic lines’. Seismic lines are created during oil and gas exploration and are responsible for severe dissection of boreal forests in western Canada. Specifically, our objectives were to: (1) to compare the edge influence of “wide” (~8 m) and “narrow” (~3 m) seismic lines; and (2) to determine whether edges in close proximity show interaction of edge influences, i.e. do multiple narrow seismic lines have a stronger or weaker edge influence than a single narrow seismic line. We sampled vegetation along transects perpendicular to seismic lines in treed moderate-rich and poor fens. We used randomization tests of edge influence to calculate the magnitude and distance of edge effects. In moderate-rich fens, we found a positive edge influence on understory diversity from both wide and narrow seismic lines. We also found a weakening edge interaction on diversity, i.e., single narrow seismic lines had a stronger edge influence on diversity than multiple narrow seismic lines. In treed poor fens, multiple narrow seismic lines had a negative edge effect on tree density, understory abundance, richness, and composition. In addition, we found strengthening edge interactions in treed poor fens on tree density, graminoid cover, and understory composition. Our results show how assessing the edge influence of multiple disturbances can provide a better understanding of the cumulative effects of habitat fragmentation.
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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 | 0.002 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".