Group-level differences in social network structure remain repeatable after accounting for environmental drivers

Abstract

Individuals show consistent between-individual behavioural variation when they interact with conspecifics or heterospecifics. Such patterns might underlie emergent group-specific behavioural patterns and between-group behavioural differences. However, little is known about (i) how social and non-social drivers (external drivers) shape group-level social structures and (ii) whether animal groups show consistent between-group differences in social structure after accounting for external drivers. We used automated tracking to quantify daily social interactions and association networks in 12 colonies of zebra finches ( Taeniopygia guttata ). We quantified the effects of five external drivers (group size, group composition, ecological factors, physical environments and methodological differences) on daily interaction and association networks and tested whether colonies expressed consistent differences in day-to-day network structure after controlling for these drivers. Overall, we found that external drivers contribute significantly to network structure. However, even after accounting for the contribution of external drivers, there remained significant support for consistent between-group differences in both interaction (repeatability R : up to 0.493) and association (repeatability R : up to 0.736) network structures. Our study demonstrates how group-level differences in social behaviour can be partitioned into different drivers of variation, with consistent contributions from both social and non-social factors.

Modelling animal social networks: New solutions and future directions

“…the number of observations) and some biological (e.g. weather conditions), with consequences for within‐ or between‐population contrasts (Ogino, Maldonado‐Chaparro, et al., 2023). These have long been dealt with by using permutation tests—randomisation procedures that swap elements in the data—to generate a distribution of expected values against which a network‐based metric can be compared and a p ‐value calculated.…”

“…(2023) largely conform to the expectations of the data from the perspective of the model. Yet, confounding factors in animal social networks are likely to universally violate these expectations, even when individuals are studied in controlled conditions and extremely well sampled (Ogino, Maldonado‐Chaparro, et al., 2023). As noted by the authors, STRAND (and the similar package BISON, Hart et al., 2023) have only been implemented and tested with minimal functionality.…”

“…Tracking groups of animals using markers (Alarcón‐Nieto et al., 2018), deep learning (Ferreira et al., 2020; Schofield et al., 2023) and GPS (He et al., 2023) can provide an almost continuous record of the social behaviour of animals. From these data, we can generate replicated—daily (Ogino, Maldonado‐Chaparro, et al., 2023), weekly (Aplin et al., 2015) or monthly (Ogino, Strauss, et al., 2023)—snapshots of networks. Future work should look at analytical workflows that can detect and model consistent confounds (e.g.…”