Lack of relationship between forest edge proximity and nest predator activity in an eastern Canadian boreal forest
Bibliographic record
Abstract
Nest predation risk often increases near forest edges in agricultural landscapes, but this pattern has rarely been found in forested landscapes. Whether this lack of relationship is general remains unclear, especially because no assessment of statistical power has been published. To (1) assess whether and how far nest predation risk is associated with forest edges and (2) avoid confounding effects of the surrounding landscape, we measured nest predator activity by placing baits at five distances (0, 30, 60, 90, and 120 m) from sharp, rectilinear forest edges that run along extensive tracts of forest. No association was found between distance to forest edges and bait discovery rates (P = 0.7). The lack of edge effect was unlikely to be caused by a lack of statistical power (1 - β > 0.8). However, bait discovery rates were significantly heterogeneous throughout the study area, and ground baits were taken at a greater rate than arboreal baits. Mice, voles, and red squirrels (Tamiasciurus hudsonicus (Erxleben)), all nest predators, were the main users of bait. Red squirrel occurrence, as estimated by playbacks, was higher in black spruce (Picea mariana (Mill.) BSP) than in balsam fir (Abies balsamea (L.) Mill.) stands but was not associated with a high bait predation rate. Our results strengthen support to the hypothesis that nests near open areas in managed boreal forests are not more at risk than forest-interior nests.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| 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".