Contact patterns in wild pigs (<i>Sus scrofa</i>) from GPS tracking reveal spatial and temporal dynamics of social behaviour
Bibliographic record
Abstract
Wild pig ( Sus scrofa Linnaeus, 1758) social behaviour affects disease transmission and landscape-level population management. Recent research has incorporated analysis of social structure to better understand the risk of disease transmission in wild pigs, although the relationship between overall social structure of wild pigs remains unclear. Here, we seek to improve understanding of the spatial and temporal dynamics of wild pig social structure and contact rates to better inform management strategies. Using GPS tracking data, we measured home range overlap, and estimated contact rates of wild pigs at four study sites in the southern USA to identify pairwise social associations (i.e., contacts) based on synchronous movement. Contact rate was strongly associated with home range overlap, but exhibited substantial variation, especially at moderate levels of home range overlap. We found that female–female dyads had higher contact rates and longer duration phases of social association compared to female–male and male–male dyads. We found male–male dyads tended to experience social associations farther from their home range centers than female–female or female–male dyads. Social associations between wild pig dyads are highly dynamic in their spatial and temporal structure. Further, dyads with strong social associations still experience substantial time apart. Our findings highlight the challenges of predicting spatial and social associations in wild pig social pairs due to their dynamic social structure over space and time.
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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 | 0.001 |
| 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) | 0.001 | 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".