Landscape structure and local habitat characteristics as correlates of<i>Glaucomys sabrinus</i>and<i>Tamiasciurus hudsonicus</i>occurrence
Bibliographic record
Abstract
Understanding the effects of forest fragmentation on tree-dwelling sciurids is of particular interest given their arboreal habits and the extent of anthropogenic habitat fragmentation inflicted upon North American forest ecosystems over the past 2 centuries. In this study we investigate occurrences of northern flying squirrels (Glaucomys sabrinus) and red squirrels (Tamiasciurus hudsonicus) in forest fragments in southern Ontario, Canada, as a function of local habitat and landscape features. During the summer of 2006 we measured occurrence via livetrapping in 24 forest fragments ranging in size from 4 to 2,881 ha, each adjacent to or surrounded by active row-crop agriculture. In addition to patch area and measurements of local habitat features, we calculated 4 landscape metrics in variously sized circular landscape windows: number of patches, forest cover, mean proximity index, and distance to the nearest neighboring patches. Occurrence of G. sabrinus was positively correlated with patch area (P = 0.016) but not with other features, whereas occurrence of T. hudsonicus was positively associated with basal area of coniferous trees (P = 0.047) but not with other habitat or landscape features. Populations of T. hudsonicus did not show fragmentation effects, likely due to high vagility and high population growth potential. Northern flying squirrels were not found in patches < 29 ha in size and, as estimated from a receiver operating characteristic curve, the ideal minimum fragment size for patch occupancy was 48.25 ha. Our data support conclusions that diverse management schemes may be required to preserve relatively large contiguous tracts of forest for G. sabrinus and appropriate conifer structure for T. hudsonicus in a way that will facilitate the persistence of these 2 species in deciduous Great Lakes-St. Lawrence forest ecosystems.
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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".