Using colour pattern edge contrast statistics to predict detection speed and success in triggerfish (Rhinecanthus aculeatus)

Edge detection is important for object detection and recognition. However, we do not know whether edge statistics accurately predict the detection of prey by potential predators. This is crucial given the growing availability of image analysis software and their application across non-human visual systems. Here, we investigated whether Boundary Strength Analysis (BSA), Local Edge Intensity Analysis (LEIA) and the Gabor edge disruption ratio (GabRat) could predict the speed and success with which triggerfish (Rhinecanthus aculeatus) detected patterned circular stimuli against a noisy visual background, in both chromatic and achromatic presentations. We found various statistically significant correlations between edge statistics and detection speed depending on treatment and viewing distance; however, individual pattern statistics only explained up to 2% of the variation in detection time, and up to 6% when considering edge statistics simultaneously. We also found changes in fish response over time. While highlighting the importance of spatial acuity and relevant viewing distances in the study of visual signals, our results demonstrate the importance of considering explained variation when interpreting colour pattern statistics in behavioural experiments. We emphasize the need for statistical approaches suitable for investigating task-specific predictive relationships and ecological effects when considering animal behaviour. This is particularly important given the ever-increasing dimensionality and size of datasets in the field of visual ecology.