Data from: Unsupervised machine learning reveals mimicry complexes in bumble bees occur along a perceptual continuum
Ezray, Briana; Wham, Drew; Hill, Carrie; Hines, Heather (2019), Data from: Unsupervised machine learning reveals mimicry complexes in bumble bees occur along a perceptual continuum, Dryad, Dataset, https://doi.org/10.5061/dryad.sd7cd06
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.
National Science Foundation, Award: NSF CAREER Grant DEB1453473