Unsupervised machine learning reveals mimicry complexes in bumble bees occur along a perceptual continuum

Müllerian mimicry theory states that frequency dependent selection should favour geographic convergence of harmful species onto a shared colour pattern. As such, mimetic patterns are commonly circumscribed into discrete mimicry complexes each containing a predominant phenotype. Outside a few examples in butterflies, the location of transition zones between mimicry complexes and the factors driving mimicry zones has rarely been examined. To infer the patterns and processes of Müllerian mimicry, we integrate large-scale data on the geographic distribution of colour patterns of social bumble bees across the contiguous United States and use these to quantify colour pattern mimicry using an innovative, unsupervised machine learning approach based on computer vision. Our data suggest that bumble bees exhibit geographically clustered, but sometimes imperfect colour patterns and that mimicry patterns gradually transition spatially, rather than exhibit discrete boundaries. Additionally, examinat...

穆氏拟态（Müllerian mimicry）理论指出，频率依赖选择（frequency dependent selection）应当会促使有害物种在地理上趋同于共享的体色图案。据此，拟态模式通常被划定为若干离散的拟态复合体（mimicry complexes），每个复合体均包含一种优势表型。除少数蝴蝶类群的案例外，学界极少对拟态复合体间的过渡带位置以及驱动拟态区形成的因素展开研究。为推断穆氏拟态的模式与过程，我们整合了横跨美国本土的社会性熊蜂体色图案地理分布的大规模数据，并基于计算机视觉（computer vision）开发了一种创新的无监督机器学习（unsupervised machine learning）方法，以此量化体色拟态程度。我们的数据显示，熊蜂的体色图案呈现地理聚集特征，但有时并不完美，且拟态模式在空间上呈逐渐过渡状态，而非存在明确的离散边界。此外，本研究还对……