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dc.contributor.authorStanley, Christina
dc.contributor.authorvon Hardenberg, Achaz
dc.contributor.authorPanaccio, Matteo
dc.contributor.authorFerrari, Caterina
dc.contributor.authorBassano, Bruno
dc.date.accessioned2021-03-24T10:51:43Z
dc.date.available2021-03-24T10:51:43Z
dc.identifierhttps://chesterrep.openrepository.com/bitstream/handle/10034/624389/PanaccioEtAl_ETH-20-0212.R2_preprint.pdf?sequence=1
dc.identifier.citationPanaccio, M., Ferrari, C., Bassano, B., Stanley, C.R. & von Hardenberg, A. (2021). Social network analysis of small social groups: Application of a hurdle GLMM approach in the Alpine marmot (Marmota marmota). Ethology (epub ahead of print). https://doi.org/10.1111/eth.13151en_US
dc.identifier.issn0179-1613
dc.identifier.doi10.1111/eth.13151
dc.identifier.urihttp://hdl.handle.net/10034/624389
dc.descriptionThis is the peer reviewed version of the following article: Panaccio, M., Ferrari, C., Bassano, B., Stanley, C.R. & von Hardenberg, A. (2021). Social Network Analysis of small social groups: application of a hurdle GLMM approach in the Alpine marmot (Marmota marmota). Ethology, which has been published in final form at [https://doi.org/10.1111/eth.13151]. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archivingen_US
dc.description.abstractSocial Network Analysis (SNA) has recently emerged as a fundamental tool to study animal behavior. While many studies have analyzed the relationship between environmental factors and behavior across large, complex animal populations, few have focused on species living in small groups due to limitations of the statistical methods currently employed. Some of the difficulties are often in comparing social structure across different sized groups and accounting for zero-inflation generated by analyzing small social units. Here we use a case study to highlight how Generalized Linear Mixed Models (GLMMs) and hurdle models can overcome the issues inherent to study of social network metrics of groups that are small and variable in size. We applied this approach to study aggressive behavior in the Alpine marmot (Marmota marmota) using an eight-year long dataset of behavioral interactions across 17 small family groups (7.4 ± 3.3 individuals). We analyzed the effect of individual and group-level factors on aggression, including predictors frequently inferred in species with larger groups, as the closely related yellow-bellied marmot (Marmota flaviventris). Our approach included the use of hurdle GLMMs to analyze the zero-inflated metrics that are typical of aggressive networks of small social groups. Additionally, our results confirmed previously reported effects of dominance and social status on aggression levels, thus supporting the efficacy of our approach. We found differences between males and females in terms of levels of aggression and on the roles occupied by each in agonistic networks that were not predicted in a socially monogamous species. Finally, we provide some perspectives on social network analysis as applied to small social groups to inform subsequent studies.en_US
dc.publisherWileyen_US
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en_US
dc.subjectsocial network analysisen_US
dc.subjectsocialityen_US
dc.subjectaggressionen_US
dc.subjectGLMMsen_US
dc.subjectzero-inflated measuresen_US
dc.titleSocial Network Analysis of small social groups: application of a hurdle GLMM approach in the Alpine marmot (Marmota marmota)en_US
dc.typeArticleen_US
dc.identifier.eissn1439-0310en_US
dc.contributor.departmentUniversity of Chester; University of Turin; Alpine Wildlife Research Centre, Gran Paradiso National Parken_US
dc.identifier.journalEthologyen_US
or.grant.openaccessYesen_US
rioxxterms.funderunfundeden_US
rioxxterms.identifier.projectunfundeden_US
rioxxterms.versionAMen_US
rioxxterms.licenseref.startdate2022-03-24
dcterms.dateAccepted2021-03-14
rioxxterms.publicationdate2021-03-24
dc.date.deposited2021-03-24en_US
dc.indentifier.issn0179-1613en_US


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Attribution 4.0 International
Except where otherwise noted, this item's license is described as Attribution 4.0 International