Batesian mimicry is a canonical example of evolution by natural selection, popularized by highly colorful species resembling unrelated models with astonishing precision. However, Batesian mimicry could also occur in inconspicuous species and rely on subtle resemblance. Although potentially widespread, such instances have been rarely investigated, such that the real frequency of Batesian mimicry has remained largely unknown. To fill this gap, we developed a new approach using deep learning to quantify the visual resemblance between putative mimics and models from photographs. We applied this method to Western Palearctic snakes. Potential nonvenomous mimics were revealed by an excess of resemblance to sympatric venomous snakes compared with random expectations. We found that 8% of the nonvenomous species were potential mimics, although they resembled their models imperfectly. This study is the first to quantify the frequency of Batesian mimicry in a whole community of vertebrates, and it shows that even concealed species can act as potential models. Our approach should prove useful for detecting mimicry in other communities, and more generally it highlights the benefits of deep learning for quantitative studies of phenotypic resemblance.
Keywords: Batesian mimicry; camouflage; deep neural network; imperfect mimicry.