TLDR:
Researchers have used AI to analyze genetic diversity in two species, revealing that the sibilator frog’s genetics were shaped by past population changes, while the granular toad’s genetics were influenced by current landscape factors. This study is the first to use AI to consider both historical and landscape influences on genetic diversity simultaneously.
- Researchers used AI to analyze genetic diversity in two amphibian species.
- AI revealed that historical population changes and current landscape factors shaped genetic diversity in different ways for each species.
Using AI to scrutinize and validate theories on animal evolution
By harnessing the power of machine learning, researchers have constructed a framework for analyzing what factors most significantly contribute to a species’ genetic diversity. The study, recently published in the journal Molecular Phylogenetics and Evolution, suggests that the genetic variation of the Brazilian sibilator frog and the granular toad were shaped by different processes.
The research demonstrated that the sibilator frog’s genetic variation was primarily influenced by population demographic events in response to habitat changes over the past 100,000 years. In contrast, genetic diversity in the granular toad was mostly shaped by contemporary landscape factors, with more isolated toads showing greater genetic variation.
Previous investigations had explored these factors separately, but this study is the first to use artificial intelligence to simultaneously consider both historical and landscape influences on genetic diversity. Researchers were able to simulate processes happening ecologically and during evolutionary events to compare findings to actual data collected from these frogs.
Researchers believe that this AI-powered approach will pave the way for more advanced machine learning frameworks to be applied to unique investigations of other species in the future.