Researchers from the UK and Japan have used modern artificial intelligence to validate what is considered evolutionary biology’s oldest mathematical model – that of Müllerian mimicry.
It was, they say, the first fully automated, objective method to successfully measure overall visual similarity – and opens up potential new research directions.
The theory of Müllerian mimicry holds that species mimic each other for mutual benefit. It was first proposed by German naturalist Fritz Müller in 1878, less than two decades after Charles Darwin published On the Origin of Species.
Testing it using conventional methods is necessarily limiting, so the team from the University of Cambridge, the University of Essex and the Natural History Museum, from the UK, and the Tokyo Institute of Technology used a machine learning algorithm to test whether butterfly species can co-evolve similar wing patterns for mutual benefit.
“We can now apply AI in new fields to make discoveries which simply weren’t possible before,” says Cambridge’s Jennifer Hoyal Cuthill, the study’s lead author.
“We wanted to test Müller’s theory in the real world. Did these species converge on each other’s wing patterns and if so, how much? We haven’t been able to test mimicry across this evolutionary system before because of the difficulty in quantifying how similar two butterflies are.”
Their chosen subjects – Heliconius butterflies – are considered a classic example of Müllerian mimicry. There are more than 30 recognisable pattern types within the two species they focused on, with each pattern type containing a pair of mimic subspecies.
Using 2400 photographs from the Natural History Museum, they trained their algorithm – appropriately, if not surprisingly, known as ButterflyNet – to quantify variations between different subspecies, from subtle differences in the size, shape, number, position and colour of wing pattern features, to broad differences in major pattern groups.
“We found that these butterfly species borrow from each other, which validates Müller’s hypothesis of mutual co-evolution,” Hoyal Cuthill says. “In fact, the convergence is so strong that mimics from different species are more similar than members of the same species.”
The researchers also found that Müllerian mimicry can generate entirely new patterns by combining features from different lineages.
“Intuitively, you would expect that there would be fewer wing patterns where species are mimicking each other, but we see exactly the opposite, which has been an evolutionary mystery,” said Hoyal Cuthill.
“Our analysis has shown that mutual co-evolution can actually increase the diversity of patterns that we see, explaining how evolutionary convergence can create new pattern feature combinations and add to biological diversity.
“By harnessing AI, we discovered a new mechanism by which mimicry can produce evolutionary novelty.
“Counterintuitively, mimicry itself can generate new patterns through the exchange of features between species which mimic each other.”
The findings are published in the journal Science Advances.
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