Smaller Time-Window Comparisons
We used the R package netTS (Bonnell & Vilette, 2019), that makes use of time-aggregated windows to analyse temporal changes in social networks. This program allows the user to alter the window size, dependent on the types of questions being asked. A benefit of using a shifting-window is the ability to see patterns and variation that may not be visible in larger window size comparisons. To test the effects of our ecological and social variables on core unit social networks, we created 20 time-aggregated networks using a 31-day window size and 31-day window shift that spanned the full dataset (August 28th, 2017 to May 13th, 2019). Within each time-window, a new network was created. To justify our choice of window size, the netTS package tutorials provide guidance on choosing a lower time scale for window-size using a bootstrap technique (Fig. S1). This technique measures the similarity between the observed network in a window and networks created using bootstrap samples from within the window. The idea here is that the similarity between bootstrapped networks and the observed network provides information about the similarity of the observed to the full network (i.e., if perfect sampling was possible), equivalent to the traditional bootstrapping approach (Efron, 1992). To choose a window size above the minimum threshold, we estimated the variability in network density, as window sizes too large will reach asymptote (i.e., density of 1) and window sizes too small will have densities close to 0, resulting in lower variation (Caceres, Berger-Wolf, & Grossman, 2011). The optimal window size, in terms of maximizing variability in edge density, for our data was 31 days. We thus used the netTS package with a window size of 31 days to calculate and analyze social network metrics to determine the connectedness of core units over time. At the node (core-unit) level, we calculated degree (i.e., the number of core units associated with) and strength (i.e., sum of all edge weights for a given node, indicating the total association rate for a given core unit). At the network level, we calculated edge density (i.e., the ratio of the number of edges and the number of possible edges), clustering coefficient (i.e., number of core units associated with that also associated with one another), and cosine similarity (see below). At the dyad level, we calculated dyad association measures (AI).