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.

中文翻译：

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使用深度学习在蛇形集合体中寻找拟态。

贝叶斯模仿是自然选择进化的典范实例，它被高度多彩的物种所普及，这些物种类似于具有惊人精确度的无关模型。但是，Batesian模仿也可能出现在不显眼的物种中，并且依赖于细微的相似性。尽管可能广泛分布，但这种情况很少被调查，因此贝塞斯模仿的真实频率在很大程度上仍然未知。为了填补这一空白，我们开发了一种使用深度学习的新方法来量化假定的模拟物与照片模型之间的视觉相似度。我们将此方法应用于西部古北蛇。与随机期望相比，与同伴有毒蛇的过度相似揭示了潜在的非有毒模拟物。我们发现8％的非有毒物种是潜在的模仿物，尽管它们与模型不尽相同。这项研究是第一个量化整个脊椎动物群落中贝茨拟态模仿频率的研究，它表明，即使是隐性物种也可以充当潜在模型。我们的方法应该被证明对检测其他社区的模仿很有用，并且更广泛地强调了深度学习对于表型相似性定量研究的好处。