Measuring site fidelity and spatial segregation within animal societies

Thomas O Richardson*, Luca Giuggioli, Nigel R. Franks, Ana B. Sendova-Franks

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

13 Citations (Scopus)
320 Downloads (Pure)


Animals often display a marked tendency to return to previously visited locations that contain important resources, such as water, food, or developing brood that must be provisioned. A considerable body of work has demonstrated that this tendency is strongly expressed in ants, which exhibit fidelity to particular sites both inside and outside the nest. However, thus far many studies of this phenomena have taken the approach of reducing an animal's trajectory to a summary statistic, such as the area it covers. Using both simulations of biased random walks, and empirical trajectories from individual rock ants, Temnothorax albipennis, we demonstrate that this reductive approach suffers from an unacceptably high rate of false negatives. To overcome this, we describe a site-centric approach which, in combination with a spatially-explicit null model, allows the identification of the important sites towards which individuals exhibit statistically significant biases. Using the ant trajectories, we illustrate how the site-centric approach can be combined with social network analysis tools to detect groups of individuals whose members display similar space-use patterns. We also address the mechanistic origin of individual site fidelity; by examining the sequence of visits to each site, we detect a statistical signature associated with a self-attracting walk - a non-Markovian movement model that has been suggested as a possible mechanism for generating individual site fidelity.

Original languageEnglish
Pages (from-to)965-975
Number of pages11
JournalMethods in Ecology and Evolution
Issue number8
Early online date20 Mar 2017
Publication statusPublished - Aug 2017


  • Temnothorax albipennis
  • Animal movement
  • Ant
  • Non-Markov
  • Random walk
  • Social insect
  • Social network


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