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A Dynamic Individual-Based Model for High-Resolution Ant Interactions

Author

Listed:
  • Nathan B. Wikle

    (The Pennsylvania State University)

  • Ephraim M. Hanks

    (The Pennsylvania State University)

  • David P. Hughes

    (The Pennsylvania State University)

Abstract

Ant feeding interactions (i.e., trophallaxis events) are thought to regulate the flow of nutrients and disease within a colony. Consequently, there is great interest in learning which environmental and behavioral factors drive ant trophallaxis. In this paper, we analyze ant trophallaxis behavior in a colony of 73 carpenter ants, observed at 1-s intervals over a period of 4 h. The data represent repeated observations from a dynamic contact network; however, traditional statistical analyses of network models are ill-suited for data observed at such high temporal resolution. We present a model for high-resolution longitudinal network data, where the network is assumed to be a time inhomogeneous, continuous-time Markov chain, with transition rates modeled as a function of time-varying individual and pairwise biological covariates. In particular, the high temporal resolution of the data leads to a tractable likelihood function, and likelihood-based inference procedures are utilized to explain which biological factors drive contact. Our results reveal how differences in ant social castes and individual behaviors, such as ant speed and activity levels, influence patterns of ant trophallaxis in the colony. Supplementary materials accompanying this paper appear online.

Suggested Citation

  • Nathan B. Wikle & Ephraim M. Hanks & David P. Hughes, 2019. "A Dynamic Individual-Based Model for High-Resolution Ant Interactions," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 24(4), pages 589-609, December.
    • Handle: RePEc:spr:jagbes:v:24:y:2019:i:4:d:10.1007_s13253-019-00363-5
    DOI: 10.1007/s13253-019-00363-5
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    References listed on IDEAS

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    1. Chris Groendyke & David Welch & David R. Hunter, 2011. "Bayesian Inference for Contact Networks Given Epidemic Data," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 38(3), pages 600-616, September.
    2. James C. Russell & Ephraim M. Hanks & Andreas P. Modlmeier & David P. Hughes, 2017. "Modeling Collective Animal Movement Through Interactions in Behavioral States," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 22(3), pages 313-334, September.
    3. Pavel N. Krivitsky & Mark S. Handcock, 2014. "A separable model for dynamic networks," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 76(1), pages 29-46, January.
    4. Catherine Matias & Vincent Miele, 2017. "Statistical clustering of temporal networks through a dynamic stochastic block model," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(4), pages 1119-1141, September.
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