Models of Mimicry

There is no limit on nature’s talent for facsimile.


AI used to test evolution’s oldest mathematical model

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.

The researchers found that different butterfly species act both as model and as mimic, ‘borrowing’ features from each other and even generating new patterns.


Fish school by copying each other and changing directions randomly, rather than calculating and adapting to an average direction of the group:

“Everyone’s been aware of noise-induced phenomena, theoretically, but it’s quite rare to find in practice. You can only observe it when the individuals in a study can actually make decisions. For example, you wouldn’t find this type of noise-induced behaviour studying electrons or particles,” says Dr. Morris. (…)

When researchers interpret data, noise is usually an unrelated factor that obscures and distracts from the information, like glare from the sun that you would try to eliminate to get a clearer photo.

In this case, Dr. Morris explains that the random copying between pairs of fish gives rise to a different class of noise, and is actually what drives their highly coordinated behaviour. This new insight highlights the importance of noise, showing that noise may encode some important information about behavioural dynamics of fish and other animals.

“Here the signal is the noise. If you ignored the fluctuations completely, you couldn’t explain schooling at all.”

Beyond fish behaviour, the discovery has the power to reshape the understanding of collective motion in animals, and calls for a revision of how noise is treated in studies of behaviour dynamics.


Pretending often leads to becoming a reasonable facsimile of what you mimic, even if only from a distance.

— From Annihilation, by Jeff VanderMeer

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