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Individual attributes and self-organizational processes affect dominance network structure in pukeko

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  • Cody J. Dey
  • James S. Quinn

Abstract

Dominance relationships are an important type of social relationship that can influence group dynamics and individual fitness. However, most studies on dominance have been restricted to investigating the orderliness of dominance hierarchies and how individual traits influence dominance rank. Here, we used a social network approach to investigate the patterns and quality of dominance interactions in the pukeko, a cooperatively breeding bird that lives in stable, mixed-sex social groups. By using a combination of modern statistical techniques, including one of the first applications of exponential random graph models in behavioral ecology, we show that pukeko dominance networks emerge from both the attributes of individuals, as well as from endogenous, self-organization of dominance relationships (i.e., structural dependence). Pukeko dominance networks were influenced by sexual differences in dominance interactions, sexual homophily, characteristics of status signals, and a tendency to form transitive triad motifs. These factors have differential effects on submissive and aggressive behaviors but ultimately lead to the formation of orderly and highly asymmetrical dominance hierarchies that are temporally stable. This study demonstrates the utility of multivariate statistical tools for network analysis of animal societies and provides a rich understanding of the factors that influence dominance interactions in this interesting species.

Suggested Citation

  • Cody J. Dey & James S. Quinn, 2014. "Individual attributes and self-organizational processes affect dominance network structure in pukeko," Behavioral Ecology, International Society for Behavioral Ecology, vol. 25(6), pages 1402-1408.
  • Handle: RePEc:oup:beheco:v:25:y:2014:i:6:p:1402-1408.
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    File URL: http://hdl.handle.net/10.1093/beheco/aru138
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    References listed on IDEAS

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    1. Hunter, David R. & Handcock, Mark S. & Butts, Carter T. & Goodreau, Steven M. & Morris, Martina, 2008. "ergm: A Package to Fit, Simulate and Diagnose Exponential-Family Models for Networks," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 24(i03).
    2. Handcock, Mark S. & Hunter, David R. & Butts, Carter T. & Goodreau, Steven M. & Morris, Martina, 2008. "statnet: Software Tools for the Representation, Visualization, Analysis and Simulation of Network Data," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 24(i01).
    3. David B. McDonald & Daizaburo Shizuka, 2013. "Comparative transitive and temporal orderliness in dominance networks," Behavioral Ecology, International Society for Behavioral Ecology, vol. 24(2), pages 511-520.
    4. Lee Alan Dugatkin & Ryan L. Earley, 2003. "Group fusion: the impact of winner, loser, and bystander effects on hierarchy formation in large groups," Behavioral Ecology, International Society for Behavioral Ecology, vol. 14(3), pages 367-373, May.
    5. Stanley Wasserman & Philippa Pattison, 1996. "Logit models and logistic regressions for social networks: I. An introduction to Markov graphs andp," Psychometrika, Springer;The Psychometric Society, vol. 61(3), pages 401-425, September.
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    Cited by:

    1. Elizabeth A Hobson & Simon DeDeo, 2015. "Social Feedback and the Emergence of Rank in Animal Society," PLOS Computational Biology, Public Library of Science, vol. 11(9), pages 1-20, September.
    2. Anna Favati & Hanne Løvlie & Olof Leimar, 2017. "Individual aggression, but not winner–loser effects, predicts social rank in male domestic fowl," Behavioral Ecology, International Society for Behavioral Ecology, vol. 28(3), pages 874-882.
    3. Changwei Yuan & Jinrui Zhu & Shuai Zhang & Jiannan Zhao & Shibo Zhu, 2024. "Analysis of the Spatial Correlation Network and Driving Mechanism of China’s Transportation Carbon Emission Intensity," Sustainability, MDPI, vol. 16(7), pages 1-23, April.
    4. Gong, Yuanyuan & Sun, Hui & Wang, Zhiwei & Ding, Chenxin, 2023. "Spatial correlation network pattern and evolution mechanism of natural gas consumption in China—Complex network-based ERGM model," Energy, Elsevier, vol. 285(C).

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