Journal article
A Dynamic Individual-Based Model for High-Resolution Ant Interactions
Journal of agricultural, biological, and environmental statistics, Vol.24(4), pp.589-609
12/01/2019
DOI: 10.1007/s13253-019-00363-5
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.
Details
- Title: Subtitle
- A Dynamic Individual-Based Model for High-Resolution Ant Interactions
- Creators
- Nathan B. Wikle - Pennsylvania State UniversityEphraim M. Hanks - Pennsylvania State UniversityDavid P. Hughes - Pennsylvania State University
- Resource Type
- Journal article
- Publication Details
- Journal of agricultural, biological, and environmental statistics, Vol.24(4), pp.589-609
- Publisher
- Springer Nature
- DOI
- 10.1007/s13253-019-00363-5
- ISSN
- 1085-7117
- eISSN
- 1537-2693
- Number of pages
- 21
- Grant note
- 1414296 / NSF EEID GM 116927-01 / NIH; United States Department of Health & Human Services; National Institutes of Health (NIH) - USA
- Language
- English
- Date published
- 12/01/2019
- Academic Unit
- Statistics and Actuarial Science
- Record Identifier
- 9984446448402771
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