The influence of linear feature width on small mammal communities in northeastern Alberta
Bibliographic record
Abstract
Anthropogenic linear features, such as seismic lines, pipelines, and powerlines, are widespread in the boreal forests of western Canada. Although these corridors are widely recognized as drivers of habitat loss and fragmentation, the extent of their impact is likely depending on their width. Understanding how linear feature width influences small mammal communities is important for informing more effective management and restoration efforts. We deployed remote cameras at 354 sites across a gradient of soft (vegetated) linear feature widths (3–150 m) and paired each site with forest interior control sites. We recorded twelve small mammal species. Community-level analyses showed that forest-affiliated species were associated with narrower features and forest interiors whereas open habitat species showed the opposite pattern. Species-specific models indicated that four of nine analyzed species showed significant negative responses to increasing width. No species showed a significant positive response to width. Contrary to expectations, wider features were not more likely to be occupied by early-successional species but showed reduced overall small mammal use. Our findings indicate that wide linear features may act as ecological barriers for small mammals and suggest that minimizing linear feature width and/or promoting revegetation could mitigate impacts on small mammals in the boreal forest.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".