Social network properties predict chronic aggression in commercial pig systems.
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-l...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2018-01-01
|
| Series: | PLoS ONE |
| Online Access: | http://europepmc.org/articles/PMC6171926?pdf=render |
| _version_ | 1828443066724253696 |
|---|---|
| author | Simone Foister Andrea Doeschl-Wilson Rainer Roehe Gareth Arnott Laura Boyle Simon Turner |
| author_facet | Simone Foister Andrea Doeschl-Wilson Rainer Roehe Gareth Arnott Laura Boyle Simon Turner |
| author_sort | Simone Foister |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-10T21:23:41Z |
| format | Article |
| id | doaj.art-39df990908cd4c8aba2daf30ab7f430f |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-10T21:23:41Z |
| publishDate | 2018-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-39df990908cd4c8aba2daf30ab7f430f2022-12-22T01:33:03ZengPublic Library of Science (PLoS)PLoS ONE1932-62032018-01-011310e020512210.1371/journal.pone.0205122Social network properties predict chronic aggression in commercial pig systems.Simone FoisterAndrea Doeschl-WilsonRainer RoeheGareth ArnottLaura BoyleSimon TurnerPost-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.http://europepmc.org/articles/PMC6171926?pdf=render |
| spellingShingle | Simone Foister Andrea Doeschl-Wilson Rainer Roehe Gareth Arnott Laura Boyle Simon Turner Social network properties predict chronic aggression in commercial pig systems. PLoS ONE |
| title | Social network properties predict chronic aggression in commercial pig systems. |
| title_full | Social network properties predict chronic aggression in commercial pig systems. |
| title_fullStr | Social network properties predict chronic aggression in commercial pig systems. |
| title_full_unstemmed | Social network properties predict chronic aggression in commercial pig systems. |
| title_short | Social network properties predict chronic aggression in commercial pig systems. |
| title_sort | social network properties predict chronic aggression in commercial pig systems |
| url | http://europepmc.org/articles/PMC6171926?pdf=render |
| work_keys_str_mv | AT simonefoister socialnetworkpropertiespredictchronicaggressionincommercialpigsystems AT andreadoeschlwilson socialnetworkpropertiespredictchronicaggressionincommercialpigsystems AT rainerroehe socialnetworkpropertiespredictchronicaggressionincommercialpigsystems AT garetharnott socialnetworkpropertiespredictchronicaggressionincommercialpigsystems AT lauraboyle socialnetworkpropertiespredictchronicaggressionincommercialpigsystems AT simonturner socialnetworkpropertiespredictchronicaggressionincommercialpigsystems |