Decoding alarm signal propagation of seed-harvester ants using automated movement tracking and supervised machine learning
Abstract
Alarm signal propagation through ant colonies provides an empirically tractable context for analysing information flow through a natural system, with useful insights for network dynamics in other social animals. Here, we develop a methodological approach to track alarm spread within a group of harvester ants, Pogonomyrmex californicus . We initially alarmed three ants and tracked subsequent signal transmission through the colony. Because there was no actual standing threat, the false alarm allowed us to assess amplification and adaptive damping of the collective alarm response. We trained a random forest regression model to quantify alarm behaviour of individual workers from multiple movement features. Our approach translates subjective categorical alarm scores into a reliable, continuous variable. We combined these assessments with automatically tracked proximity data to construct an alarm propagation network. This method enables analyses of spatio-temporal patterns in alarm signal propagation in a group of ants and provides an opportunity to integrate individual and collective alarm response. Using this system, alarm propagation can be manipulated and assessed to ask and answer a wide range of questions related to information and misinformation flow in social networks.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Jan 26, 2022
- Source ID
- 10.1098/rspb.2021.2176
Entities
People
- Asma Azizi
- Jennifer H. Fewell
- Lucas P. Saldyt
- Michael Lin
- Theodore P Pavlic
- Xiaohui Guo
- Yun Kang
Organizations
- Arizona State University
- Kennesaw State University
- National Science Foundation