The relative influence of landscape features on maternity roost and hibernaculum selection in temperate bats
Bibliographic record
Abstract
Many animal life-history stages center around residences (nests, roosts, etc.) where the availability of resources within an optimal range can affect fitness. Understanding factors influencing residence selection is fundamental for efficient management or recovery plans. Many bat species use permanent roosts during different periods of the year, and while most conservation plans aim to protect these roosts, the availability of suitable habitat near the roosts (e.g., foraging habitat) is also critical to consider. We evaluated the importance of landscape features at multiple scales surrounding seasonal bat roosts in two regions (north and south) of Québec (Canada), using data from participatory science and government databases. In the human-altered environment of south Québec, bats selected maternity roosts with high anthropogenic cover and water edge density at the 150 m and 2 km scales, respectively. Conversely, roost selection in north Québec, a forested area, could not be explained by any landscape features. In winter, fewer bats used hibernacula located in heavily human-modified landscapes—opposite to the trend observed with maternity roost selection. Our study demonstrates how considering landscape features at the appropriate temporal and spatial scales can promote more efficient conservation for bats.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".