Quantifying contributions of social situations to population
structure
To quantify the relative contribution of each social situation to the
position of individuals in the population’s social structure (the
aggregate network), we calculated the Spearman’s correlation (ρ )
for each centrality measure between each social situation and the
aggregate network. For example, we correlated the degree of individuals
in the co-flight network with the degree of individuals in the aggregate
network, the strength in the co-flight network with strength in the
aggregate etc. These comparisons resulted in 9 correlations (3 indices x
3 social situations). To further determine the contribution of each
social situation to the population’s social structure we asked whether
the observed correlation coefficients differed from those expected by
chance by comparing observed ρ values to those extracted from reference
models. We created 10,000 reference (randomized) networks using node
permutations (Hobson et al. 2021). In each iteration, the node
IDs within each of the three social situations were permuted without
replacement and the three centrality measures were calculated for each
situation. By permuting only node IDs we maintained the observed network
structure while breaking the relationship between social positions
within and across situations. For each iteration the reference aggregate
network was created by combining the permuted networks of the three
social situations. The three centrality measures were then calculated
for the aggregate network. We then computed the Spearman’s correlation
for each centrality measure between the social situations and the
aggregate network for each of the permutation iterations. We determined
statistical significance by computing a p-value as the proportion of
iterations in which the observed Spearman’s correlation coefficient (ρ)
was larger or smaller than 95% of the ρ coefficients in the permutated
data.
Analysis was conducted in R version 3.4 (R Core Team 2013). Network
analysis was conducted using the ‘igraph’ R package (Csardi and Nepusz
2006) and Muxviz (De Domenico et al. 2015). Data is provided as
part of the supplementary material and the analysis code is available on
GitHub
(https://github.com/NitikaIISc/VulturesMovementAnalysis_manuscript1).