The role of social preferences in the organization of a gregarious mammal colony
Bibliographic record
Abstract
Social preferences are often demonstrated to exist in “intelligent”, long-lived species, such as cetaceans and primates, where individuals have the opportunity to form relationships with conspecifics through repeated interactions. The long lifespan of bats combined with the regular roost switching behaviour of females in some temperate species, presents opportunity for females to build and act on social relationships. However, regular roost switching also poses a challenge for teasing apart the individual influences of social and habitat preferences. To gather evidence for the influence of social preference, I tested predictions of a hypothesis that bats select daily roosts based on social preferences. Using a system of little brown myotis (Myotis lucifugus) individually marked with passive integrated transponder tags and passively monitored roost boxes in Newfoundland, Canada, I quantified the number of days bats spent in each roost, and the number of days spent sharing a roost with other marked bats to characterize patterns in co-roosting association. I found that patterns of roost use do not entirely explain the frequency of roosting association between pairs of female bats. Further, association patterns showed predictable changes following estimated parturition dates, estimated based on nighttime roost revisit behaviour, as adult females are expected to experience changes in selective pressures and mobility with the onset of the lactation period. I also found that patterns of roosting association between individual bats often persisted for more than 5 years and could even persist for up to 10 years following the first observed interaction. Using this same dataset, I also address the effect of missing information on estimates of social network structure, providing a deeper understanding of the ability of these results to inform on the structure of the underlying population. Overall, my findings add to evidence that supports that bats act on social preferences, and thus make a case for social complexity in bat groups. These findings further suggest that, perhaps due to their similarly long life spans, bats may be more similar to their large-bodied, “intelligent”, mammal counterparts than previously expected.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".