Prime real estate : how urban landscape variables influence bat presence in Vancouver, Canada
Bibliographic record
Abstract
Understanding how wildlife use urban landscapes is increasingly important as cities grow and impact the world’s biodiversity. Bats are a critical part of urban ecosystems, but little is known about which bat species live in cities, how urban variables affect their presence, and at which spatial scales. To answer these questions, we acoustically sampled Vancouver and Richmond, Canada, in 2021 via mobile bicycle transects. We found a diversity of bats (10 species) in the city, including rare and endangered species, which responded to the urban landscape at all tested spatial scales. Using Bayesian models, we found that bats were overall attracted to greenness, parks, and tall vegetation. But they were negatively affected by light pollution, intensive urban land use, and increasing distance from freshwater, which we suggest might be abiotic filters on bat presence. While all bats responded similarly to the aforementioned variables, we found nuances between low- and high-frequency functional groups that suggested spatial partitioning to avoid competition. To boost bat abundance, cities might improve or create parks and freshwater sources to increase roosting and foraging opportunities, and introduce traffic and light-pollution mitigation strategies to reduce sources of mortality and disturbance.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".