Dispersing Siberian flying squirrels (<i>Pteromys volans</i>) locate preferred habitats in fragmented landscapes
Bibliographic record
Abstract
In fragmented landscapes, dispersal abilities determine the potential of a species to colonize temporally unoccupied habitats. For example in territorial species, even the high-quality sites will be temporally unoccupied between the death of residents and recolonization by dispersers. This has implications for conservation management, because whether or not unoccupied habitats are worthy of protecting depends on likelihood that these sites will be recolonized in the near future. We analysed the ability of dispersing Siberian flying squirrels ( Pteromys volans (L., 1758)) to locate the preferred habitats. Dispersers encountered many patches of preferred habitat and, in the end, a majority of them successfully settled in these patches. The dispersal distance increased with increasing distance between patches, but settlement in preferred patches and short-term mortality were independent of dispersal distance or distance between available patches. We conclude that dispersers were effective in locating the preferred habitat in fragmented landscapes. Indeed, large interpatch distances partly explain that dispersal distances are longer in flying squirrels than usually observed in similar-sized mammals. For conservation management, our findings encourage managers to protect temporally unoccupied, high-quality habitats in addition to occupied habitats to promote a sustainable habitat network in space and time.
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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".