Habitat Selection and Space Use of Little Brown Bats During Fall Swarm
Bibliographic record
Abstract
In Canada, the Species at Risk Act requires critical habitat, or habitat needed for the survival and recovery of a species, to be identified and protected for all listed species. Little brown bats (Myotis lucifugus) are listed as endangered by the IUCN and in Canada because of population declines from the fungal disease white-nose syndrome (WNS). Currently, for little brown bats, hibernacula are defined as critical habitat in the species’ Recovery Plan, and numerous hibernacula therefore benefit from protection. Although not yet classified as critical habitat, data also exist on maternity roosts of little brown bats. However, little is known about fall habitat requirements when bats swarm at entrances of hibernacula, mate, and fatten for hibernation. Understanding whether bats roost and feed close to swarm sites, identifying these habitats, and linking behaviour with habitat use could enable management actions that help bats accumulate larger fat reserves and survive the winter with WNS. The overall objective of my thesis is to understand movements, habitat preferences, and behaviours of little brown bats during this fall period. To do so, I captured 40 little brown bats near a hibernaculum and swarming site in Manitoba Canada, quantified the activity and sociability components of personality using a y-maze test, and tracked them to roosts and foraging locations for up to 30 days using radio-tags. Many bats (n = 21) were never detected again, and, presumably left the study area. Those that remained roosted exclusively in natural structures, including snags, live trees, and caves. Nighttime home range size varied widely (0.63 km2-37.02 km2, n = 8 bats), with the furthest foraging location fix being ~15 km away from the hibernaculum. I found links between individual personality traits and space use, with bats that were more sociable being less likely to select for rangeland and forest-edge habitats. These results confirm the relationship between individual personality and space use, suggesting that populations might require a range of habitat types to cater to the different tendencies of individual animals. My findings also provide support for the management and protection of large forested areas around hibernacula, as many bats use this habitat during the critical pre-hibernation period.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".