Data from: Assessing the potential information content of multicomponent visual signals: a machine learning approach

Careful investigation of the form of animal signals can offer novel insights into function. Here we deconstruct the face patterns of a tribe of primates, the guenons (Cercopithecini), and examine the information that is potentially available in the perceptual dimensions of their multicomponent displays. Using standardized colour-calibrated images of guenon faces, we measure variation in appearance both within and between species. Overall face pattern was quantified using the computer vision ‘eigenface’ technique, and eyebrow and nose-spot focal traits were described using computational image segmentation and shape analysis. Discriminant function analyses established whether these perceptual dimensions could be used to reliably classify species identity, individual identity, age and sex, and if so, identify the dimensions that carry this information. Across the 13 12 species studied we found that both overall face pattern and focal trait differences could be used to categorize species and individuals reliably, whereas, correct classification of age category and sex not possible. This pattern makes sense, as guenons often form mixed-species groups in which familiar conspecifics develop complex differentiated social relationships but where heterospecifics risk hybridization. Our approach should be broadly applicable to the investigation of visual signal function across the animal kingdom.