B. C. Carstens

This study uses deep machine learning to analyze New World Sparrows' songs, finding that environmental and cultural factors significantly influence song evolution, comparable to genetic factors.

This study uses machine learning to analyze White-crowned Sparrow songs and finds that song divergence correlates with environmental factors and migratory status, not geographic distance.

This study evaluates how transfer learning, phylogenetic distance, and sample size affect bioacoustic data automation accuracy across ten bird species.

This study applies convolutional neural networks to phylogeographic model selection, achieving high accuracy in demographic model identification compared to approximate Bayesian computation.

The paper uses machine learning to predict intraspecific genetic diversity in amphibians by integrating traits, geography, and genetic data, finding significant phylogenetic signal and lower diversity in northern salamanders.

This paper introduces delimitR, a machine learning tool for inferring species boundaries and evaluating speciation processes using genomic data.