Foraging habitat drives the distribution of an endangered bat in an urbanizing boreal landscape
Bibliographic record
Abstract
Abstract The boreal forest is the largest intact forest in the world, and a refuge for species experiencing range retractions as a consequence of climate and landscape change. Yet, large tracts of the boreal forest are threatened by the cumulative impacts of climate change, natural resource extraction, agriculture, and urbanization, perhaps warranting a shift in focus from biodiversity conservation in intact wilderness to that in anthropologically modified landscapes. We investigated landscape features that influence the distribution of the endangered little brown bat (Myotis lucifugus) in an urbanizing boreal landscape at two spatial scales. We hypothesized that little brown bat activity would be influenced by proximity to available building roosts, because roosts are a potential limiting factor for boreal bats. Secondarily, we predicted that bats would use potential foraging habitat, such as waterbodies, and would avoid young, cluttered forests at the landscape scale. We conducted acoustic surveys of bat activity at 210 sites distributed across the study area in Yukon, Canada, within 1‐km grid cells. We tested a priori hypotheses with a set of candidate regression models, accounting for spatial autocorrelation. Our hypothesis about the relative importance of anthropogenic roosts was not supported. Little brown bats were equally active in urban areas (high building density) and rural areas (low building density), perhaps because roosts were adequately available throughout the region. Instead, habitat use was driven by the distribution of potential foraging habitat, particularly waterbodies, which are important sources of aerial insect prey. Little brown bats also avoided young (≤100‐yr‐old) forest at the landscape scale (including areas regenerating after fire), which may have been poor foraging or roosting habitat, and used areas with a smaller agricultural and industrial footprint. Our results suggest that waterbodies and mature forest are important little brown bat habitats that should be protected from urban encroachment. Proactive conservation of important habitat for species at risk is still possible throughout much of the boreal forest, where human densities are comparatively low and the urban footprint is currently small.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".