Assessing social structure: a data-driven approach to define associations between individuals

Our interpretation of animal social structures is inherently dependent on our ability to define association criteria that are biologically meaningful. However, association thresholds are often based upon generalized preconceptions of a species’ social behaviour, and the impact of using these arbitrary definitions has been largely overlooked. In this study we suggest a probability-based method for defining association thresholds using lagged identification rates on photographic records of identifiable individuals. This technique uses a simple model of emigration/immigration from photographable clusters to identify the time-dependent lag value between identifications of two individuals that corresponds to approximately 75% probability of being in close spatial proximity and likely associating. This lag value is then used as the threshold to define associations for social analyses. We applied the technique to a dataset of northern resident killer whales (Orcinus orca) in the Northeast Pacific and tested its performance against two arbitrary thresholds. The probabilistic association maximized the variation in association strengths at different levels of the social structure, in line with known social patterns in this population. Furthermore, variability in inferred social structure metrics generated by different association criteria highlighted the consequential effect of choosing arbitrary thresholds. Data-driven association thresholds are a promising approach to study populations without the need to subjectively define associations in the field, especially in societies with prominent fission–fusion dynamics. This method is applicable to any dataset of sequential identifications where it can be assumed that associated individuals will tend to be identified in close proximity.