Detecting social (in)stability in primates from their temporal co-presence network

TL;DR

This study analyzes the long-term social stability of Guinea baboons using dynamic co-presence networks derived from cognitive testing data, revealing patterns of stability and change aligned with social balance theory.

Contribution

It introduces a novel approach combining dynamic social network analysis with a null model to detect social (in)stability in primates over three years.

Findings

Networks are structurally balanced and tend to preserve social balance.

Similarity measures effectively detect periods of social stability and instability.

Large fluctuations in triads can limit the applicability of social balance theory.

Abstract

The stability of social relationships is important to animals living in groups, and social network analysis provides a powerful tool to help characterize and understand their (in)stability and the consequences at the group level. However, the use of dynamic social networks is still limited in this context as it requires long-term social data and new analytical tools. Here, we study the dynamic evolution of a group of 29 Guinea baboons using a dataset of automatically collected cognitive tests comprising more than 16M records collected over 3 years. We first built a monthly aggregated temporal network describing the baboon's co-presence in the cognitive testing booths. We then used a null model, considering the heterogeneity in the baboons' activity, to define both positive (association) and negative (avoidance) monthly networks. We tested social balance theory by combining these positive and negative social networks. The results showed that the networks were structurally balanced and that newly created edges also tended to preserve social balance. We investigated several network metrics to gain insights into the individual level and group level social networks long-term temporal evolution. A measure of similarity between successive monthly networks was able to pinpoint periods of stability and instability and to show how some baboons' ego-networks remained stable while others changed radically. Our study confirms the prediction of social balance theory but also shows that large fluctuations in the numbers of triads may limit its applicability to study the dynamic evolution of animal social networks. In contrast, the use of the similarity measure proved to be very versatile and sensitive in detecting relationships' (in)stabilities at different levels. The changes we identified can be linked, at least in some cases, to females changing primary male, as observed in the wild.