Male and female bats differ in their use of a large urban park
Bibliographic record
Abstract
(Uploaded by Plazi for the Bat Literature Project) Understanding how wildlife respond to ever-encroaching urbanization is of great concern. Bats are the second-most speciose mammalian order and while many appear to be urban adapted, we currently have a limited understanding of their demography and habitat use within urban environments. Using a combination of captures to obtain demographic data, radio-telemetry to examine foraging and roosting behaviour, and data on diet and prey availability, we examined how big brown bats (Eptesicus fuscus), a synurbic species, use an urban green space (High Park) in Canada's largest city centre, Toronto. We found that adult males outnumbered adult females more than two to one and that males were found throughout the park, while females were concentrated in an area with greater access to water, but lower prey availability. We also found that bats of both sexes were in poorer body condition than reported for other non-urban areas, including a site within southern Ontario. Our data suggest that High Park may not provide adequate resources for reproductive females as they were never found roosting in the park and beetles, their preferred prey, were limited. Although previous studies suggest urban green spaces may offer refuge to bats, most have not considered sex-specific responses to urbanization as they have largely been based on acoustic surveys. Our study therefore highlights the importance of considering demographic differences in response to urbanization to better inform urban management plans and green space development.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.022 | 0.003 |
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".