A genetic analysis of group movement in an isolated population of tree-roosting bats
Bibliographic record
Abstract
(Uploaded by Plazi for the Bat Literature Project) Group fission is an important dispersal mechanism for philopatric adults. In Cypress Hills Interprovincial Park, Saskatchewan, tree-roosting big brown bats ( Eptesicus fuscus ) exhibit fission–fusion roosting behaviour. During 2004–2007, the majority of females previously resident to roosting area 1 (RA1) moved to a new roosting area (RA4). We examined how genetic relationships, inferred from data for microsatellite loci and mitochondrial DNA, influenced new roost area (RA) selection during 2006 when colony members were split between the RAs. We found that females who moved to RA4 had higher average relatedness than those that remained in RA1. We found that nearly all females belonging to matrilines with high average relatedness moved to RA4 while females from matrilines with low average relatedness were split between the two RAs. These results suggest that closely related maternal kin preferentially move to new RAs. However, daily roosting preferences within a RA are not based on genetic relationships probably because daily roosting associations between kin and non-kin are used to ensure adequate roost group size. Studying the effects of kinship on the fission and movements of groups not only enhances our understanding of social behaviour and population genetics but also informs conservation decisions.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".