Network structure and the optimization of proximity‐based association criteria

Animal social network analysis (SNA) often uses proximity data obtained from automated tracking of individuals. Identifying associations based on proximity requires deciding on quantitative criteria such as the maximum distance or the longest time interval between visits of different individuals to still consider them associated. These quantitative criteria are not easily chosen based on a priori biological arguments alone. Here we propose a procedure for optimizing proximity‐based association criteria in SNA, whereby different spatial and temporal criteria are screened to determine which combination detects more network structure. If we assume that biologically relevant associations among individuals are non‐random, and that proximity data are mostly influenced by those associations, then it is logical to select criteria that minimise random associations and show the underlying network structure more clearly. We first used simulations to evaluate which of four simple descriptors of network structure remain unbiased (i.e. do not change directionally) when reducing the number of observations, since unbiased descriptors are necessary for comparing the structure of networks using different association criteria. Then, using two of those descriptors (coefficient of variation of the strength of associations and network entropy) and empirical proximity data from automated tracking of common waxbills Estrilda astrild in a mesocosm environment, we found that the structure‐based optimization procedure selected the most biologically relevant combination of spatial and temporal proximity criteria, in the sense that those criteria were also the best at distinguishing between previously known social subgroups of individuals. These results indicate that, provided that the assumptions for structure‐based optimization are met, this procedure can find the most biologically relevant association criteria. Thus, under the condition that proximity data are shaped by non‐random social associations, and if using adequate descriptors of network structure, structure‐based optimization may be a useful tool for SNA, particularly when a priori biological arguments are insufficient to inform the choice of proximity‐based association criteria.