Show simple item record

dc.contributor.authorFoister Sen
dc.contributor.authorDoeschl-Wilson Aen
dc.contributor.authorRoehe Ren
dc.contributor.authorArnott Gen
dc.contributor.authorBoyle Len
dc.contributor.authorTurner SPen
dc.date.accessioned2018-10-08T13:17:57Z
dc.date.available2018-10-08T13:17:57Z
dc.date.issued2018
dc.identifier.citation13:10en
dc.identifier.urihttps://doi.org/10.1371/journal.pone.0205122
dc.identifier.urihttp://hdl.handle.net/11262/11525
dc.description.abstractPost-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.en
dc.language.isoenen
dc.relation.isformatof14937en
dc.relation.ispartofPLoS ONEen
dc.rightsCopyright: © 2018 Foister et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
dc.subjectAggressionen
dc.subjectSwineen
dc.subjectNetwork analysisen
dc.subjectCentralityen
dc.subjectEigenvectorsen
dc.subjectAnimal socialityen
dc.subjectAnimal behaviouren
dc.subjectSocial networksen
dc.titleSocial network properties predict chronic aggression in commercial pig systemsen
dc.typeArticleen
dc.description.versionVersion of Record
dc.extent.pageNumberse0205122en
rioxxterms.publicationdate2018-10-04
rioxxterms.typeJournal Article/Reviewen
dcterms.dateAccepted2018-09-25
refterms.accessExceptionNAen
refterms.dateDeposit2018-10-08
refterms.depositExceptionpublishedGoldOAen
refterms.depositExceptionExplanationGoldOAen
refterms.panelUnspecifieden
refterms.technicalExceptionNAen
refterms.versionVoRen


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record