Looking for mimicry in a snake assemblage using deep learning

Batesian mimicry, with its highly colorful species and astonishing mimic-model resemblance, is a canonical example of evolution by natural selection. 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 resemblance between putative mimics and models from photographs. We applied this method to quantify the frequency of Batesian mimicry in Western Palearctic snakes. Potential mimics were revealed by an excess of resemblance with sympatric venomous snakes compared to random expectations. We found that 8% of the non-venomous species were potential mimics, among which all were imperfect mimics. This study is the first to quantify the frequency of Batesian mimicry in a whole community of vertebrates, and shows that even concealed species can be reliably identified as potential models. Our approach should prove useful to detect mimicry in other communities, and more generally it highlights the benefits of deep learning for quantitative studies of phenotypic resemblance.