Abstract: A novel AI-powered study explores 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 specimens, researchers found that both sexes contribute to species diversity. Males often show greater variation, supporting Darwinian theories of sexual selection, while subtle variation in females is consistent with Wallace's ideas about natural selection.
These findings expand on classical theories by showing how the two mechanisms work together to drive biodiversity.
Important facts:
- AI 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 variation in females is consistent with Wallace's theory of natural selection.
Source: University of Essex
Early AI-powered research on butterflies has probed female-studied evolution and fueled debate among evolutionary founding fathers.
University of Essex study – published in Communication biology – Explores a conflict between Victorian scientists Charles Darwin and Alfred Russel Wallace.
Darwin believed that males differed more, as females often chose mates based on males' appearance.
While Wallace believed that natural selection was the major factor in the differences between the sexes.
For more than a century, scientists have mostly studied men because their differences are more obvious, while women, with more subtle evolutionary changes, were less studied.
Using high-tech machine learning, Dr. Jennifer Hoyle Kittle examined more than 16,000 male and female bird butterflies with colleagues from the Natural History Museum and AI research institute CrossLabs, CrossCompass.
This is the first time visual differences between the sexes have been discovered in the species, which live in Southeast Asia and Australia.
Bird butterflies were chosen for this study because of their brilliant color patterns on their wings and the differences between males and females.
Dr Hoyle Kittle, from the School of Life Sciences, said: “These are exciting times, 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 extent of evolution to examine how much variation there is between different biological groups and both males and females.
“Machine learning is giving us new insights into the evolutionary processes that create and maintain biodiversity, including among historically neglected groups.”
The research looked at photographs of butterflies from Natural History Museum collections, showing a range of species characteristics, such as wing shapes, colors and patterns.
He found that while males often have more distinct shapes and patterns, both males and females contribute to overall diversity.
The research showed that the evolutionary patterns predicted by both Darwin and Wallace were found in butterflies.
Showing that both males and females contribute to diversity among species.
Males showed greater variation in appearance, which is consistent with Darwin's idea that females choose mates based on these traits.
However, deep learning also found subtle variation among females, consistent with Wallace's predictions of natural selection allowing for diversity in female phenotypes.
Dr Hoyle Kittle said: “The bird's wings have been described as one of the most beautiful butterflies in the world. This study gives us new insights into the evolution of their remarkable but threatened diversity.
“In this case study of bird-butterfly images, it is the sex that has undergone the greatest evolutionary change, including highly masculine forms, colors and patterns.
“However, in the group of bird butterflies, we found contrasting examples where female bird butterflies are more diverse in visible phenotype than males, and vice versa.
“The greater apparent diversity among male butterflies supports the true importance of sexual selection by female mate choice over male variation, as originally suggested by Darwin.
“Cases where female butterflies are markedly more diverse than males of their species support an additional, important role for naturally selected female variation in interspecies diversity, as Wallace suggests. What is it.
“Large-scale studies of evolution using machine learning provide new opportunities to address debates that have characterized evolutionary science since its inception.”
About this evolution and AI research news
the author: Ben Hall
Source: University of Essex
contact: Ben Hall – University of Essex
Image: This image is credited to Neuroscience News.
Original Research: Open access.
“Male and Female Contributions to Diversity in Bird-Butterfly Photographs” by Jennifer Hoyle-Kittle et al. Communication biology
Abstract
Male and female contributions to diversity in bird-butterfly images
Machine learning (ML) enables the test of high interspecies diversity in observable phenotype (differences) between males versus females, respectively, predictions made by Darwinian sexual selection versus Walsian natural selection, respectively.
Here, we use ML to quantify variation in a sample of 16,000 dorsal and ventral images of sexually dimorphic bird butterflies (Lepidoptera: Papilionidae).
Validation of image embedding distances, learned by a triplet-trend, deep convolutional neural network, demonstrates that ML can be used to automatically reconstruct phenotypic evolution between genetic trees. Derives measures of phylogenetic concordance from genetic species trees within a range of samples.
Quantification of sexual disparity differences (male vs. female embedding distance), reflects sexually and phylogenetically variable intergenerational disparity.
Ornithoptera High embedded male image disparity, with cases of extreme divergence in allopatry and sympatry, illustrate diversity of selective optima and rapid divergence in fitted multipack OU models.
However, Janus The triads shows the opposite patterns, including a relatively static male-embedded phenotype, and greater female than male variation—albeit within an estimated selection regime common to these females. The bird morphs and color patterns that are most phenotypically distinctive in ML matching are usually those of males.
However, either sex may contribute largely to the observed phenotypic diversity among species.