Butterfly co-mimic pairs from the species Heliconius erato (odd columns) and Heliconius melpomene (even columns).
Illustrated butterflies are sorted by greatest similarity (along rows, top left to bottom right) using machine
learning methods which enable new tests and discoveries in evolutionary theory. I Credit: J Hoyal Cuthill

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The researchers, from the University of Cambridge, the University of Essex, the Tokyo Institute of Technology and the Natural History Museum London used their machine learning algorithm to test whether butterfly species can co-evolve similar wing patterns for mutual benefit. This phenomenon, known as Müllerian mimicry, is considered evolutionary biology's oldest mathematical model and was put forward less than two decades after Darwin's theory of evolution by natural selection.

The algorithm was trained to quantify variation between different subspecies of Heliconius butterflies, from subtle differences in the size, shape, number, position and colour of wing pattern features, to broad differences in major pattern groups.

This is the first fully automated, objective method to successfully measure overall visual similarity, which by extension can be used to test how species use wing pattern evolution as a means of protection. The results are reported in the journal Science Advances.

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