Experimental trials of the northern flying squirrel (Glaucomys sabrinus) traversing managed rainforest landscapes: perceptual range and fine-scale movements
Bibliographic record
Abstract
Successful dispersal in many species may be a function of the distance at which animals can perceive a particular landscape feature (i.e., perceptual range), as well as energetic costs associated with traversing the distance towards that feature. We used a model, relating perceptual range to body size of mammals, to predict the perceptual range of the northern flying squirrel ( Glaucomys sabrinus (Shaw, 1801)) in fragmented forests of Southeast Alaska. We hypothesized that the perceptual range of flying squirrels would be 325.5–356.5 m in clearcuts and 159.7–174.9 m in second-growth stands. The distance advantage in clearcuts may, however, be lost if the cost of transport in that habitat is higher. Our results suggest that as heuristically predicted by the model, the perceptual range of flying squirrels was greater in clearcut habitats than in second-growth stands. Nonetheless, for both habitats the actual perceptual range was significantly shorter than predicted by the model. We found that precipitation, and associated cloud cover and illumination, and wind speed, which affect olfaction capabilities, influenced orientation success. Although squirrels more often oriented towards the forest edge in clearcuts, they paused more often during their movements, which may lead to higher costs of dispersing through this habitat. The application of the mass-based model to nonagricultural landscapes should be done with caution, and variables such as wind and illumination be measured concurrently. Our data illustrate that dispersing squirrels likely will not venture into managed habitats because logging creates clearcuts larger than the perceptual range of these mammals.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".