Cluster Analyses and Preferred Associations
To determine if the clustering of core units into clans was consistent over time we used SOCPROG (v.2.9: Whitehead, 2009) and split the data into two sampling periods to compare metrics by year: August 28th, 2017 – August 22nd, 2018 (sample period 1) and August 29th, 2018 – May 13th, 2019 (sample period 2). We created an association matrix for each year by calculating the simple association index of each dyad, where a value of 1 indicates the two core units were always in association and 0 indicates they were never in association. The simple association index (AI) was chosen because we were always able to positively identify all core units in association with the focal unit (Whitehead, 2008). AI was calculated as AI = N AB/ (N A + N B) or the number of times that two core units were in association during scans, divided by the total number of scans where either unit was present. Of four different clustering methods (average linkage, Ward’s weighted, complete linkage, and single linkage), the average linkage method had the highest cophenetic correlation coefficient (CCC = 0.891), so this was the clustering method that we used for hierarchical cluster analyses (see Stead & Teichroeb, 2019). We used dendrograms (Fig.2) created through the average linkage method to compare clustering into clans between sample periods. Previous work examining the graph of cumulative bifurcations from the dendrogram from sample period one showed one significant knot at an AI of 0.05 (Stead & Teichroeb, 2019), so this was the cut-off that we used to determine clan associations. We then conducted permutation tests for preferred/avoided associations using SOCPROG, and permuted association matrices 10,000 times to stabilize p-values. The network may not be static throughout the sample periods, and thus the results of this test could not reveal the variability within each sample period. Further analyses using smaller time windows was performed to adjust for this.