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Social network properties predict chronic aggression in commercial pig systems
Simone Foister1, Andrea Doeschl-Wilson2, Rainer Roehe1
1Animal & Veterinary Sciences, SRUC, Roslin Institute Building, Easter Bush, Midlothian, United Kingdom.
Plos One
|October 5, 2018
Summary
Network properties in pig pens predict long-term aggression, outperforming traditional measures. Early network analysis can identify pigs at risk for chronic aggression and injury, improving welfare and farm efficiency.
Area of Science:
- Animal Behavior
- Animal Welfare
- Social Network Analysis
Background:
- Post-mixing aggression in pigs is a significant issue affecting animal welfare and farm productivity.
- Existing methods using dyadic behaviors do not fully explain aggression variation.
- Indirect social interactions are crucial but often unmeasured.
Purpose of the Study:
- To investigate if pen-level network properties predict aggression-related injuries.
- To compare the predictive power of network properties versus dyadic traits.
- To explore early network indicators for chronic aggression in pigs.
Main Methods:
- Utilized Social Network Analysis (SNA) theory to construct pen-level networks from aggressive interactions.
- Recorded 78 pens for 24 hours post-mixing to capture aggressive events.
- Analyzed relationships between 24-hour network properties and injuries at 24 hours and 3 weeks post-mixing (24hr-PM and 3wk-PM) using mixed models and permutation tests.
Main Results:
- Pen-level network properties at 24hr-PM predicted long-term aggression (3wk-PM) more effectively than dyadic traits.
- Large clique formation within the first 24 hours predicted fewer 3wk-PM injuries.
- High betweenness centralization at 24hr-PM predicted increased 3wk-PM injury rates.
Conclusions:
- Network properties during the initial 24 hours post-mixing are predictive of chronic aggression.
- SNA offers a valuable tool for identifying at-risk groups of pigs.
- Early identification allows for timely intervention to mitigate aggression and improve outcomes.
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