Spatial distribution of the Boreal Owl and Northern Saw-whet Owl in the Boreal region of Alberta, Canada
Bibliographic record
Abstract
Understanding what factors influence the occurrence and distribution across the landscape is necessary for species conservation and management. Distribution data for many owl species are inadequate because of their nocturnal behavior and cryptic nature. We examined the role of climate, land cover, and human disturbance in shaping spatial distribution of the Boreal Owl (Aegolius funereus) and Northern Saw-whet Owl (Aegolius acadicus) in northern Alberta. Using autonomous recording units, we conducted passive acoustic surveys to detect owls of both species throughout Alberta's boreal forest. We compiled data on environmental variables at each sample site corresponding to a local scale and at landscape scale. A boosted regression tree analysis identified average minimum winter temperature as the most important predictor of Boreal Owl distribution. Boreal Owls were more likely to be present in cool environments with cold winters, and a low percentage of grassland cover at the landscape scale. Cropland cover at the local scale was the most influential factor in the final distribution model for the Northern Saw-whet Owl, and they were more likely to be present in areas where cropland was interspersed with deciduous-dominated forests. Furthermore, these areas generally had cool summer temperatures and received less precipitation as snow. Linear features at the landscape scale negatively influenced distribution of Boreal Owls, but edges created by linear features at local scale positively influenced Northern Saw-whet Owl distribution. Our study provides new information about habitat use that can be applied in management and conservation of these two poorly studied species of owls.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".