Data from: 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, examination of colour pattern transition zones of three comimicking, polymorphic species, where active selection is driving phenotype frequencies, revealed their transition zones to differ in location within a broad region of poor mimicry. Potential factors influencing mimicry transition zone dynamics are discussed.缪氏拟态（Müllerian mimicry）理论指出，频率依赖选择（frequency dependent selection）应会促使有害物种在地理上趋同于共享的体色图案。据此，拟态图案通常被划分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（predominant phenotype）。除少数蝴蝶类群中的已知案例外，拟态复合体间的过渡区位置以及驱动拟态区形成的相关因素，目前鲜有系统性研究。为解析缪氏拟态的演化模式与背后过程，本研究整合了横跨美国本土的社会性熊蜂（bumble bees）体色图案地理分布的大规模数据集，并基于计算机视觉（computer vision）开发了创新性无监督机器学习（unsupervised machine learning）方法，以此量化体色图案的拟态特征。研究结果显示，熊蜂的体色图案呈现地理聚集特征，但部分类群的图案并不完美；且拟态图案在空间上呈渐进式过渡，而非存在清晰的离散边界。此外，针对三种受活跃选择调控表型频率变化的共拟态多态物种（polymorphic species）的体色图案过渡区进行的分析表明，其过渡区在拟态不佳区域的广阔范围内，位置存在显著差异。本研究最后对可能影响拟态过渡区动态变化的潜在驱动因素展开了讨论。