The role of relatedness, age, and origin in shaping social networks for two bison ( <i>Bison bison</i> ) herds in north-central Montana
Bibliographic record
Abstract
Species with fission–fusion social organization, where groups break apart and merge over time, show variable subgroup stability. Plains bison ( Bison bison bison (Linnaeus, 1758)), a keystone species in North American grasslands, exhibit fission–fusion dynamics. However, it is unclear whether subgroups are stable over time nor whether they are composed of related individuals. We used fine-scale behavioral observations and movement data from GPS ear tags to construct social networks for two plains bison herds over multiple years at American Prairie in north-central Montana. These herds are semi-free roaming and graze year-round in 32.4 and 111.6 km 2 fenced pastures. While the bison in our study did exhibit fission–fusion behavior, we did not observe stable subgroups in time-aggregated social networks constructed over single growing seasons (eigenvector modularity ranged from −0.008 to 0.027). We used Mantel tests to assess the relationships between association strength and relatedness, age, and place of origin. We found that only first-order relatives were more likely to associate, and shared age and place of origin had no significant impact. The observed lack of stable subgroups challenges prevailing assumptions and highlights the need for future research into the mechanisms of fission–fusion dynamics in plains bison and other managed social 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 | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".