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

Nathan B. Wikle (), Ephraim M. Hanks and David P. Hughes
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Nathan B. Wikle: The Pennsylvania State University
Ephraim M. Hanks: The Pennsylvania State University
David P. Hughes: The Pennsylvania State University

Journal of Agricultural, Biological and Environmental Statistics, 2019, vol. 24, issue 4, No 3, 589-609

Abstract: 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.

Keywords: Animal contact network; Ant trophallaxis; Camponotus pennsylvanicus; Longitudinal network data; Markov process (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1007/s13253-019-00363-5

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