Myotis Roost Use Is Influenced by Seasonal Thermal Needs
Bibliographic record
Abstract
Abstract Reproductive bats switch frequently among roosts to select the most advantageous microclimates and avoid predation or parasitism. Many bats use human-made structures, such as bat boxes and buildings, in areas where natural structures are less abundant. Artificial structures, which may be warmer and larger than natural structures, may affect bat behavior and roost use. We studied Yuma Myotis (Myotis yumanensis) and Little Brown Myotis (M. lucifugus) in artificial structures at two sites to understand how roost conditions and reproductive pressures influenced roost switching in maternity colonies in the lower mainland of British Columbia, Canada. During summer 2019, we used Passive Integrated Technology (PIT tags and scanners) to track daily roosting locations of individuals. Yuma myotis and little brown myotis used at least five roosts at each site and switched almost daily among roosts. Bats were less likely to switch from roosts that were 25–42°C and switch roosts during lactation, particularly when the young were nonvolant. Our findings suggest that reproductive female myotis that use artificial roosts seek out warm roosts to limit energy expenditure and speed up offspring development. We also found that bats boxes were not thermally stable environments and the behavior of bats reflected temperature variability. Land managers should ensure that multiple nearby roosts are available to maternity colonies, as reproductive bats require a range of temperatures and roost types during summer.
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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 | 0.001 | 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".