Tall Timber: Roost Tree Selection of Reproductive Female Silver-Haired Bats (LASIONYCTERIS NOCTIVAGANS)
Bibliographic record
Abstract
Habitat loss is the most significant contributor to the extinction of species worldwide, and yet for many species, habitat requirements remain largely unknown. Identifying habitat is important, especially because the most cost effective strategy for conservation and management is preserving habitat before it is converted or degraded rather than trying to restore it after the fact. Identifying habitat is also important from a scientific perspective because it can help to explain some of the ecological choices made by individuals when potentially conflicting priorities exist. Habitat requirements change for many species, both seasonally and throughout their life cycles, and identifying habitat during key developmental or life history periods will provide further information about priorities of these species. North American bat species show distinct differences in the habitat they use during the summer and winter, which provides us with an opportunity to understand how the selection pressures of reproduction have shaped the habitat use of these species. Silver-haired bats (Lasionycteris noctivagans) are small Vespertilionids that are solitary during their regional seasonal migration, but form small groups or maternity colonies of reproductive females on the summering ground. I captured female silver-haired bats during the reproductive season in Cypress Hills Interprovincial Park, Saskatchewan, Canada and characterized the trees that they roosted in during the day to understand why they chose the roosts that they did. These bats chose trees that were near other dead trees with cavities, presumably to reduce the cost of roost switching, a behaviour commonly undertaken by tree-roosting Vespertilionids. Bats also chose trees that were in plots with higher basal area. Roost choices did not vary over the course of the reproductive season, likely because the priorities of pregnant and lactating bats were similar. I quantified the roost characteristics chosen by silver haired bats while pregnant and lactating. The analysis suggests that the surrounding trees can also be important factors in roosting decisions. Protecting habitat critical for reproduction can be an important conservation step, but understanding why it is critical can yield even more clues for managing both natural resources and bats species.
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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 | 0.000 | 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) | 0.003 | 0.001 |
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".