Life in a northern town: rural villages in the boreal forest are islands of habitat for an endangered bat
Bibliographic record
Abstract
Abstract Urban development is detrimental to many wildlife species; however, endangered little brown bats (Myotis lucifugus) may be attracted to human settlements, making them a synurbic species. Buildings likely provide high‐quality roosting habitat, which may be a limiting factor in the boreal forest where trees are typically small and potentially unsuitable for hosting large maternity colonies. In the boreal forest, there are relatively few urban developments in a matrix of wilderness and apparently suboptimal natural roosting habitat; thus, we hypothesized that isolated rural villages were islands of summer habitat for little brown bats that may be important for their conservation and recovery. To test this hypothesis, we investigated the relationship between little brown bat activity, foraging rates, and proximity to rural villages. We expected bat activity and foraging rates to increase with proximity to villages, as bats should optimally forage near their roosts to minimize flight costs. We used ultrasonic detectors to passively monitor bat activity near three rural villages in Yukon, Canada, and characterized bat habitat with forest measurements and remotely sensed data. Bat activity increased with proximity to village centers, but foraging activity did not, suggesting that human settlements in the boreal forest were important as roosting rather than foraging habitat. Bat activity was higher near water bodies and areas with relatively high densities of linear features (e.g., roads and transmission lines), perhaps because prey were most abundant near water features and along forest edges. The island phenomenon we observed (i.e., higher bat activity near villages) has also been documented in larger human settlements at lower latitudes, where urban areas provided better roosting habitat than surrounding agricultural matrices. Given that little brown bats were concentrated near rural villages, small human settlements should be a focus of conservation efforts in the boreal forest—particularly the identification and protection of buildings used as maternity colonies. Our study advances knowledge of little brown bat habitat requirements in the boreal forest and identifies habitats that may be important for their recovery.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".