Summary: A new AI-powered study examines the evolutionary differences between male and female pterosaurs, shedding new light on the historic debate between Charles Darwin and Alfred Russel Wallace.
Using machine learning to analyze over 16,000 butterfly specimens, the researchers found that both sexes contribute to species diversity. Males often show more variation, supporting Darwin’s theories of sexual selection, while subtle variation in females is consistent with Wallace’s ideas about natural selection.
These findings extend classical theories by showing how the two mechanisms work together to drive biodiversity.
Key facts:
- AI analyzed over 16,000 male and female butterfly birds 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
Pioneering AI-based butterfly research explores the understudied evolution of females and contributes to the debate between the founding fathers of evolution.
University of Essex study – published in Biology of Communications – explores the dispute between Victorian scientists Charles Darwin and Alfred Russel Wallace.
Darwin believed that males had more variation because females often chose mates based on male appearance.
While Wallace believed that 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 less studied.
Using high-tech machine learning, Dr. Jennifer Hoyal Cuthill studied more than 16,000 male and female Pteroptera butterflies with collaborators from the Natural History Museum and the Cross Labs artificial intelligence research institute, Cross Compass.
This is the first time that visual sex differences have been investigated among species that live in Southeast Asia and Australia.
Birdwing butterflies were chosen for this study because of their spectacular wing color patterns and the 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 limits of evolution to test how much variation is present across biological groups and among both males and females.
“Machine learning is giving us new insights into the evolutionary processes that generate and maintain biodiversity, including in historically neglected groups.”
The study looked at photographs of butterflies from the Natural History Museum’s collections that show a range of traits, 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.
The research showed that 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 more variation in appearance, which fits with Darwin’s idea that females choose mates based on these traits.
However, deep learning also detects subtle variation in women, consistent with Wallace’s predictions of natural selection allowing diversity in female phenotypes.
Dr Hoyal Cuthill said: “Birdwings have been described as among 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 pictures of bird-winged butterflies, sex appears to have driven the greatest evolutionary change, including extreme male shapes, colors and patterns.
“However, within the birdwing group we found contrasting examples where female birdwings are more diverse in visible phenotype than males and vice versa.
“The high apparent diversity among male butterflies supports the true importance of sexual selection by female mate choice on male variation, as originally suggested by Darwin.
“Instances where female butterflies are visibly more diverse than males of their species support an additional, important role for naturally selected female variation in interspecific diversity, as Wallace suggests.
“Large-scale studies of evolution using machine learning offer new opportunities to resolve debates that have been outstanding since the foundation of evolutionary science.”
For this evolution and AI research news
Author: Ben Hall
source: University of Essex
Contact: Ben Hall – University of Essex
Image: Image credit: Neuroscience News
Original Research: Free access.
“Male and Female Contributions to Diversity Among Birdwing Butterfly Images” by Jennifer Hoyal Cuthill et al. Biology of Communications
Summary
Male and female contributions to diversity among images of winged butterflies
Machine learning (ML) has recently enabled tests for higher interspecific diversity in the apparent phenotype (mismatch) between males and females, respectively, predictions made by Darwinian sexual selection versus Wallacean natural selection.
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 learned by a triplet-trained, deep convolutional neural network shows that ML can be used for automated reconstruction of phenotypic evolution, achieving measures of phylogenetic congruence to genetic species trees within a range taken for sampling among the genetic trees themselves.
Quantification of the difference in sex disparity (the male-to-female embedding distance) indicates sexually and phylogenetically variable interspecific disparity.
Ornithoptera illustrate high embedded male image disparity, diversification of selective optima in fitted OU models with multiple peaks, and accelerated divergence, with cases of extreme divergence in allopatry and sympatry.
However, gen Troids shows reversed patterns, including a relatively static male inbred phenotype and greater female than male disparity—albeit within an inferred selective regime common to these females. The bird wing shapes and color patterns that are most phenotypically distinctive in ML similarity tend to be those of males.
However, each sex can contribute significantly to the observed phenotypic diversity among species.
