Today we’re joined by Bryan Carstens, a professor in the Department of Evolution, Ecology, and Organismal Biology & Head of the Tetrapod Division in the Museum of Biological Diversity at The Ohio State University.
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In our conversation with Bryan, who comes from a traditional biology background, we cover a ton of ground, including a foundational layer of understanding for the vast known unknowns in species and biodiversity, and how he came to apply machine learning to his lab’s research.
We explore a few of his lab’s projects, including applying ML to genetic data to understand the geographic and environmental structure of DNA, what factors keep machine learning from being used more frequently used in biology, and what’s next for his group.
Connect with Bryan!
- Process‐based species delimitation leads to identification of more biologically relevant species
- Demographic model selection using random forests and the site frequency spectrum
- Phylogeographic model selection using convolutional neural networks
- Identifying cryptic diversity with predictive phylogeography
- Testing for the presence of cryptic diversity in tail-dropper slugs (Prophysaon) using molecular data
- Predicting amphibian intraspecific diversity with machine learning: Challenges and prospects for integrating traits, geography, and genetic data
- Geographical range size and latitude predict population genetic structure in a global survey | Biology Letters
- A global analysis of bats using automated comparative phylogeography uncovers a surprising impact of Pleistocene glaciation
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“More On That Later” by Lee Rosevere licensed under CC By 4.0