Response of boreal songbird communities to the width of linear features created by the energy sector in Alberta, Canada
Bibliographic record
Abstract
Alberta’s boreal forest is extensively dissected by different energy sector activities, especially the creation of linear features such as seismic lines, pipelines, and transmission lines. Linear features vary substantially from one another in terms of time since disturbance, vegetation recovery, and levels of human use. Linear feature width has great potential to influence songbirds but is not currently incorporated into provincial-scale bird models used for regulatory decision making. We conducted passive acoustic bird surveys for three types of soft linear features (n = 156): seismic lines (width: 4–8 m) and pipelines and transmission lines (width: 15 to ~100 m) in upland deciduous and mixedwood forests. We assessed responses of individual bird species and changes in species richness using generalized linear models and evaluated community composition and structure with non-metric multidimensional scaling. Wider linear features (e.g., pipelines and/or transmission lines) had higher species richness compared to areas with narrow linear features, such as seismic lines. Species composition on wider linear features was dominated by early seral species and species that prefer shrubby vegetation and open habitats relative to narrow features. Species have a range of different threshold responses, such that the abundance of some species increases or decreases beyond certain threshold widths. We concluded that linear feature width is an important driver in shaping songbird communities, and highlight the importance of considering width in understanding and managing the impacts of energy sector linear features on boreal songbirds.
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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 | 0.001 | 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".