Intraspecific Variation in Roost-site Selection by Little Brown Bats ( Myotis lucifugus )
Bibliographic record
Abstract
Although many species of bats select roost sites in large trees that are in open areas, intraspecific variation in roost-site selection may exist. We collected data on the roosting behaviour of little brown bats in the Cypress Hills, Saskatchewan, to determine the extent of intraspecific variation in roost-site selection. In addition, we examined the thermal microclimate of the tree-roosts selected by bats, to determine if roost-microsite variation can explain why certain cavities are selected over others. We found little brown bats roosting in trees as well as buildings. With the exception of a male who roosted in a spruce ( Picea glauca ) stump, tree-roosts selected by male and female little brown bats were all in trembling aspen ( Populus tremuloides ) trees. We found variation in roost-site fidelity and differential use of torpor by male bats. Temperatures within conifer snag cavities differed from aspen cavities during the day, and mirrored ambient temperature, which tended to be warmer than aspen cavities. We propose that bats select cavities in aspens because they are susceptible to heart rot. Aspen trees with heart rot provide cavities with an intact sapwood shell that protects bats against harsh ambient conditions as well as predators, and provides a unique thermal microclimate. Our results suggest that the origin of a roost site may be unimportant to a bat, provided certain other requirements are met.
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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 | 0.000 |
| 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".