Island biogeography theory and the urban landscape: stopover site selection by the silver-haired bat (<i>Lasionycteris noctivagans</i>)
Bibliographic record
Abstract
Many migratory bats require forested sites for roosting and foraging along their migration path, but increased urbanization and intensive agricultural practices may reduce the availability of stopover sites. Urban forests may provide important stopover habitat, maintaining landscape connectivity in regions where the majority of natural habitat has been cleared for development. Island biogeography theory can be applied to urbanized temperate forest biomes where small urban forests represent islands separated from the larger “mainland” forest. We used acoustic monitoring during the fall migration period to investigate the use of urban forest habitat by a migratory species, the silver-haired bat (Lasionycteris noctivagans (Le Conte, 1831)). We predicted that recorded activity would have a positive relationship with forest patch area and shape and a negative relationship with isolation from other forest patches, as suggested by island biogeography theory. We observed greater activity at larger forest patches, and although relationships for shape and isolation were not statistically supported, the observed patterns were consistent with predictions. Our results demonstrate the need for more in-depth research on the habitat requirements for both migratory and resident bat species and the impact that ongoing urbanization has on local bat populations.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".