Summary: A new artificial intelligence study examines the evolutionary differences between male and female bird butterflies, shedding new light on the historic debate between Charles Darwin and Alfred Russel Wallace.
Using machine learning to analyze more than 16,000 butterfly samples, the researchers found that both sexes contribute to species diversity. Males often show more variation, supporting Darwin’s theories of sexual selection, while the subtle variation in females is consistent with Wallace’s ideas about natural selection.
These findings extend classical theories by showing how both mechanisms work together to drive biodiversity.
Key facts:
- Artificial intelligence analyzed more than 16,000 male and female bird butterflies for evolutionary patterns.
- Males showed more variation, supporting Darwin’s theory of sexual selection.
- Subtle variations in females are consistent with Wallace’s theory of natural selection.
Source: University of Essex
Pioneering artificial intelligence-based butterfly research has explored the understudied evolution of females and contributed to the debate among the founders of evolution.
A study by the University of Essex – published in Communication biology – examines the dispute between Victorian scientists Charles Darwin and Alfred Russel Wallace.
Darwin thought that males had more variation because females often chose mates based on male appearance.
While Wallace thought natural selection between the sexes was the biggest factor in the difference.
For more than a century, scientists have mostly studied men because their differences are more obvious, while women, with more subtle evolutionary changes, have been studied less.
Using high-tech machine learning, Dr Jennifer Hoyal Cuthill examined more than 16,000 male and female avian butterflies with collaborators from the Natural History Museum and AI research institute Cross Labs, Cross Compass.
It is the first time that visual gender differences have been explored in a species that lives in Southeast Asia and Australasia.
Birdwing butterflies were chosen for this study because of their spectacular wing color patterns and differences between males and females.
Dr Hoyal Cuthill, from the School of Life Sciences, said: “This is an exciting time when machine learning is enabling new, large-scale tests of long-standing questions in evolutionary science.
“For the first time, we are able to measure the visible extents of evolution to test how much variation is present in different biological groups and between males and females.”
“Machine learning is giving us new information about the evolutionary processes that create and maintain biodiversity, including historically neglected groups.”
The study looked at photographs of butterflies from the Natural History Museum’s collections that show a range of characteristics, such as wing shapes, colors and patterns, across several species.
It found that while males often have more distinct shapes and patterns, both males and females contribute to the overall diversity.
Research has shown that evolutionary patterns predicted by both Darwin and Wallace have been found in butterflies.
It shows that both males and females contribute to the diversity between species.
Males showed more variation in appearance, consistent with Darwin’s idea that females choose mates based on these traits.
However, deep learning has also found subtle variations in women that match Wallace’s predictions of natural selection allowing for diversity in female phenotypes.
Dr Hoyal Cuthill said: “The bird’s wings have been described as some of the most beautiful butterflies in the world. This study gives us new insight into the evolution of their remarkable but threatened diversity.
“In this case study of birdwing butterfly photographs, sex appears to have caused the greatest evolutionary change, including extreme male shapes, colors and patterns.
“However, within the butterfly group we found contrasting examples where female butterflies are more diverse in visible phenotype than males and vice versa.
“The highly visible diversity among male butterflies supports the true importance of sexual selection from female selection for male variation, as originally proposed by Darwin.
“Instances where female butterflies are visibly more diverse than males of their species support another important role for naturally selected female variation in interspecific diversity as suggested by Wallace.
“Large-scale studies of evolution using machine learning offer new opportunities to resolve debates that have been outstanding since the inception of evolutionary science.”
About this evolution and news in AI research
Author: Ben Hall
Source: University of Essex
Contact: Ben Hall – University of Essex
Picture: Image is credited to Neuroscience News
Original Research: Open access.
“Male and Female Contributions to Diversity Among Birdwing Butterfly Images” by Jennifer Hoyal Cuthill et al. Communication biology
Abstract
Male and female contributions to diversity among birdwing butterfly images
Machine learning (ML) newly enables tests for higher interspecies diversity in visible phenotype (disparity) between males versus females, predictions made from Darwinian sexual selection versus Wallacean natural selection, respectively.
Here, we use ML to quantify variation in a sample of >16,000 dorsal and ventral photographs of sexually dimorphic bird butterflies (Lepidoptera: Papilionidae).
Validation of image embedding distances, obtained using a triplet-trained deep convolutional neural network, shows that ML can be used for automated reconstruction of phenotypic evolution achieving a degree of phylogenetic congruence to genetic species trees to the extent sampled among the genetic trees themselves.
Quantification of the difference in sex disparities (male versus female insertion distance) shows sexually and phylogenetically variable interspecific differences.
Ornithoptera illustrate high male image disparity, diversification of the selective optimum in fitted multipeak OU models, and accelerated divergence with cases of extreme divergence in allopatry and sympatry.
However, the genus Troides shows inverted patterns, including a relatively static male embedded phenotype, and higher female than male disparities—albeit within the derived selective regime common to these females. Bird wing shapes and color patterns that are phenotypically most striking in ML similarity are generally those of the male.
However, each sex may contribute significantly to the observed phenotypic diversity between species.