Site occupancy and cavity use by the northern flying squirrel in the boreal forest
Bibliographic record
Abstract
Abstract Tree cavities are used as shelter and breeding nests by numerous avian and mammalian species. In cold environments, tree cavities are often proposed as the best winter nest choice because of the superior protection they offer from precipitation, wind, cold temperatures, and predators. As such, they represent a critical resource, which has the potential to limit population size of non‐excavating species. We assessed factors affecting site occupancy in the boreal forest by northern flying squirrels, a secondary user of tree cavities, and to identify which nest type is preferred during the colder days of the autumn–winter period. We trapped flying squirrels twice in 59 aspen‐dominated stands in the autumn period using low‐ (1.5 m above ground‐level) and high‐mounted (4 m) traps to determine site occupancy. A total of 85 individuals were captured on 2,880 trap‐nights. During the winter period, we radio‐tracked 26 individuals to 87 diurnal nests in 220 locations. None of the habitat variables considered (cavity availability, woody debris, and lateral cover) explained site occupancy. Detectability decreased with precipitation, and was lower using high traps than low traps. Both females and males used tree cavities (26%), external nests (39%), and ground nests (35%). In cold weather, females preferred ground nests, whereas males preferred external nests. Our results do not support the hypothesis that tree cavities represent a limiting factor to northern flying squirrels in cold environments. Instead, this species seems to be a generalist and is opportunistic, using a variety of nest types. Nevertheless, practices ensuring the persistence of large diameter live cavity trees, providing better insulative properties, are likely to increase the relative use of tree cavities as nest sites by northern flying squirrels. © 2011 The Wildlife Society.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".