Social network properties predict chronic aggression in commercial pig systems

S Foister*, A Doeschl-Wilson, R Roehe, G Arnott, L Boyle, SP Turner

*Corresponding author for this work

Research output: Contribution to journalArticle

5 Citations (Scopus)
2 Downloads (Pure)

Abstract

Post-mixing aggression in pigs is a harmful and costly behaviour which negatively impacts both animal welfare and farm efficiency. There is vast unexplained variation in the amount of acute and chronic aggression that dyadic behaviours do not fully explain. This study hypothesised that certain pen-level network properties may improve prediction of lesion outcomes due to the incorporation of indirect social interactions that are not captured by dyadic traits. Utilising current SNA theory, we investigate whether pen-level network properties affect the number of aggression-related injuries at 24 hours and 3 weeks post-mixing (24hr-PM and 3wk-PM). Furthermore we compare the predictive value of network properties to conventional dyadic traits. A total of 78 pens were video recorded for 24hr post-mixing. Each aggressive interaction that occurred during this time period was used to construct the pen-level networks. The relationships between network properties at 24hr and the pen level injuries at 24hr-PM and 3wk-PM were analysed using mixed models and verified using permutation tests. The results revealed that network properties at 24hr could predict long term aggression (3wk-PM) better than dyadic traits. Specifically, large clique formation in the first 24hr-PM predicted fewer injuries at 3wk-PM and high betweenness centralisation at 24hr-PM predicted increased rates of injury at 3wk-PM. This study demonstrates that network properties present during the first 24hr-PM have predictive value for chronic aggression, and have potential to allow identification and intervention for at risk groups.
Original languageEnglish
Article numbere0205122
Number of pages18
JournalPLoS ONE
Volume13
Issue number10
Early online date4 Oct 2018
DOIs
Publication statusFirst published - 4 Oct 2018

Fingerprint

social networks
aggression
swine
risk groups
animal welfare
farms
prediction
testing

Keywords

  • Aggression
  • Animal behaviour
  • Animal sociality
  • Centrality
  • Eigenvectors
  • Network analysis
  • Social networks
  • Swine

Cite this

Foister, S ; Doeschl-Wilson, A ; Roehe, R ; Arnott, G ; Boyle, L ; Turner, SP. / Social network properties predict chronic aggression in commercial pig systems. In: PLoS ONE. 2018 ; Vol. 13, No. 10.
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Social network properties predict chronic aggression in commercial pig systems. / Foister, S; Doeschl-Wilson, A; Roehe, R; Arnott, G; Boyle, L; Turner, SP.

In: PLoS ONE, Vol. 13, No. 10, e0205122, 04.10.2018.

Research output: Contribution to journalArticle

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