Use of NetFACS to describe the repertoire of Barbary macaques' facial behaviour

Facial signals are important social communication tools in many species, including primates. Alongside signals such as vocalisations, gestures, and body postures, they allow individuals to navigate their social world by helping anticipate future behaviour in interactional settings. Thorough analyses of the complexity of coordinated movements of facial muscles, reflected by the quantity and quality of their relationships, are necessary to apprehend the face as a communication system, and ultimately investigate the evolution of communication. Macaques communicate extensively through facial expressions, nevertheless, their facial movements are often classified in broad categories and not systematically described in a standardized way. This subjective clustering prevents researchers from exploring the subtleties of the morphology of facial displays, which are often graded and merge into one another. The Macaque Facial Action Coding System (MaqFACS) is an anatomically based objective tool used to describe facial behaviour. However, FACS datasets have features that make traditional statistical models unsuitable for reliable analyses, especially defining and quantifying complexity. Standardized methods of network science are one way to overcome these issues. NetFACS is a statistical package combining FACS and network analysis, where the face is conceptualised as a network of interconnected Action Units ( AU: the smallest unit of facial communication). AUs are represented as nodes, their combinations as edges and these connections can be weighted to indicate the strength and directionality of the link, all visualized in graphs. We FACScoded 600 videos of naturally occurring interactions in 43 semi-free ranging Barbary macaques (Macaca sylvanus) in Trentham monkey forest, United-Kingdom. We used MaqFACS to report variability, diversity and subtlety to provide a fine-grained repertoire of Barbary macaques’ facial behaviour. We used NetFACS to define and quantify the communicative complexity of the signals. This approach allows us to move away from studying facial displays as stereotyped expressions and report their morphological variability and complexity.