The development of numerical methods for inferring social ranks has resulted in an overwhelming array of options to choose from. Previous work has established the validity of these methods through the use of simulated datasets, by determining whether a given ranking method can accurately reproduce the dominance hierarchy known to exist in the data. Here, we offer a complementary approach that assesses the reliability of calculated dominance hierarchies by asking whether the calculated rank order produced by a given method accurately predicts the outcome of a subsequent contest between two opponents. Our method uses a data-splitting “training–testing” approach, and we demonstrate its application to real-world data from wild vervet monkeys (Chlorocebus pygerythrus) collected over 3 years. We assessed the reliability of seven methods plus six analytical variants. In our study system, all 13 methods tested performed well at predicting future aggressive outcomes, despite some differences in the inferred rank order produced. When we split the dataset with a 6-month training period and a variable testing dataset, all methods predicted aggressive outcomes correctly for the subsequent 10 months. Beyond this 10-month cut-off, the reliability of predictions decreased, reflecting shifts in the demographic composition of the group. We also demonstrate how a data-splitting approach provides researchers not only with a means of determining the most reliable method for their dataset but also allows them to assess how rank reliability changes among age–sex classes in a social group, and so tailor their choice of method to the specific attributes of their study system.
Animal social networks are often used to describe dynamic social systems, where individual behaviour generates network‐level structures that subsequently influence individual‐level behaviour. This interdependence between individual behaviour and group structuring is of central concern for questions concerning the evolution and development of social systems and collective animal behaviour more generally. Various statistical methods exist for estimating network changes through time. One approach, time‐aggregated networks, takes repeated snapshots of interactions within windows of time to generate a time series of networks. However, there remain many analytical hurdles when implementing the time‐aggregated approach. To ameliorate this, we introduce an r package netTS that focuses on three analytical steps for analysing time‐aggregated networks: choosing appropriate time scale using bootstrapping, comparing patterns to relevant null models using permutation and finally building and interpreting statistical models using simulated data. We use simulated data to first highlight these steps, then use observed grooming data from a group of vervet monkeys as an applied example. Our results suggest that the use of bootstrapping and permutation can accurately extract known patterns from simulated data. Using this approach with vervet data suggests that there is consistent social structuring, differing from what would be expected due to chance, and that some individuals are contributing to this structure more than others (i.e. keystone individuals). We demonstrate that bootstrapping, permutation and simulation can aid in constructing and interpreting time‐aggregated networks. We suggest that the use of time‐aggregated networks to quantify patterns of network change can be a useful tool alongside process‐based approaches that seek mechanistic descriptions. Ultimately, by looking at both patterns and processes, dynamic networks can be used to better understand how individual behaviour generates social structures, and in turn how individual behaviour can be influenced by social structures, ultimately leading to a better understanding of the evolution of social behaviour.
The development of multi-layer network techniques is a boon for researchers who wish to understand how different interaction layers might influence each other, and how these in turn might influence group dynamics. Here, we investigate how integration between male and female grooming and aggression interaction networks influences male power trajectories in vervet monkeys Chlorocebus pygerythrus. Our previous analyses of this phenomenon (Young et al. 2017) used a mono-layer approach, and our aim here is to extend these analyses using a dynamic multilayer approach. To do so, we constructed a temporal series of male and female interaction layers. We then used a multivariate multilevel autoregression model to compare cross-lagged associations between a male’s centrality in the female grooming layer and changes in male Elo-ratings. Our results confirmed our original findings: changes in male centrality within the female grooming network were weakly but positively tied to changes in their Elo-ratings. However, the multi-layer network approach offered additional insights into this social process, identifying how changes in a male’s centrality cascade through the other network layers. This dynamic view indicates that the changes in Elo-ratings are likely to be short lived, but that male centrality within the female network had a much stronger impact throughout the multilayer network as a whole, especially on reducing inter-male aggression (i.e., aggression directed by males toward other males). We suggest that multilayer social network approaches can take advantage of increased amounts of social data that are more commonly collected these days, using a variety of methods. Such data are inherently multilevel and multilayered, and thus offer the ability to quantify more precisely the dynamics of animal social behaviours.
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