Sociality helps mitigate anthropogenic risks: evidence from elk crossing a major highway
Bibliographic record
Abstract
Phenotypic variation among individuals scales up when they associate with others, creating variation within and among groups that can shape group-level outcomes such as when and where groups move. While sociality is thought to be a fitness rewarding behavior, empirical evidence supporting how it influences individual behavior and the resulting fitness consequences (e.g., risk experienced) remains limited, especially in the context of human-modified landscapes. Here, we use empirical observations to test whether sociality helps animals cross busy roads. Our data came from free-ranging elk in a population where > 75% of the adults were tracked, and in which group size and composition were highly variable. We combined field observations with GPS collar data to quantify four social phenotypes of individuals and groups: dominance (initiation of successful agonistic interactions), social connectedness (number and connectedness of social associates), social familiarity (frequency of association with group members in the past), and social stability (time since fusion with group members). We then investigated how these four social phenotypes influenced an individual's probability of crossing a major highway, and tested if particular social phenotypes made better road crossing decisions (i.e., crossed at lower traffic volume). We found that who is in a group shapes the behavior of group members around anthropogenic risks. Individuals in groups that were more dominant, more connected, and to a lesser extent more familiar had a lower probability of crossing the highway. Individuals that had spent more time with group members had a higher probability of crossing the highway. Importantly, our results suggest that sociality plays a role in safe movements around anthropogenic risks. Individuals in highly connected and familiar groups were less likely to cross the highway at high traffic volume. Our work provides empirical evidence that sociality influences the movements of group-living individuals through anthropogenic disturbances, and helps individuals mitigate the risks associated with such disturbances. Developing a comprehensive understanding of animal sociality in human-modified landscapes is especially important as social behaviors are simultaneously threatened by human disturbances, which could be particularly detrimental for group-living species if those same behaviors help individuals mitigate risks.
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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 | 0.001 | 0.003 |
| 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 | 0.001 | 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".