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.

Original languageEnglish (US)
Article number20212176
JournalProceedings of the Royal Society B: Biological Sciences
Issue number1967
StatePublished - 2022


  • alarm behaviour
  • behaviour tracking
  • information flow networks
  • social insects
  • supervised machine learning

ASJC Scopus subject areas

  • General Immunology and Microbiology
  • General Environmental Science
  • General Biochemistry, Genetics and Molecular Biology
  • General Agricultural and Biological Sciences


Dive into the research topics of 'Decoding alarm signal propagation of seed-harvester ants using automated movement tracking and supervised machine learning'. Together they form a unique fingerprint.

Cite this