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Evolution | Herpetology | Conservation | Genomics

Lisa N. Barrow, Ph.D. (she/her)

Division of Amphibians & Reptiles, Museum of Southwestern Biology
Department of Biology, University of New Mexico
Assistant Professor & Curator

  We are part of the University of New Mexico Biology Department and the Division of Amphibians & Reptiles at the Museum of Southwestern Biology.


  Our research combines fieldwork, natural history collections, ecological data, genomic sequencing, and computational analyses spanning phylogenetics and population genetics to investigate how species respond to global change.

R. Barrow

Recent Publications

Amador LA#, Arroyo-Torres I, Barrow LN.  2024.  Machine learning and phylogenetic models identify predictors of genetic variation in Neotropical amphibians. Journal of Biogeography. doi: 10.1111/jbi.14795

  In this paper led by postdoc Dr. Luis Amador, we compiled 4,052 mitochondrial DNA sequences from 256 amphibian species, georeferencing sequences from 176 species that were not linked to occurrence data. We identified predictors of nucleotide diversity, isolation-by-distance (IBD), and isolation-by-environment (IBE) used machine learning (Random Forests) and phylogenetic models. We also identified biogeographic units that harbor high genetic diversity across many species.


Larkin IE, Myers, EA, Carstens BC, Barrow LN.  2023.  Predictors of genomic diversity in North American squamates. Journal of Heredity 114:131–142.

  We analyzed new and existing mtDNA sequences and genome-wide nuclear data (genotyping-by-sequencing; GBS) for 30 North American squamate species sampled in the Southeastern and Southwestern United States. Species range size was positively correlated with both mtDNA and GBS diversity. We also showed that GBS diversity estimates varied widely among individuals within some species indicating that sampling across geographic space will be important for comparative population genomic studies.

Barrow LN, Fonseca EM‡, Thompson CEP‡, Carstens BC.  2021.  Predicting amphibian intraspecific diversity with machine learning: Challenges and prospects for integrating traits, geography, and genetic data.  Molecular Ecology Resources.  doi: 10.1111/1755-0998.13303

  In this Special Issue on Machine Learning in Molecular Ecology, we gathered repurposed data for Nearctic amphibians to investigate predictors of genetic diversity using Random Forests. We found that life history traits were not important predictors of genetic diversity for this dataset, but that salamander species at more northern latitudes have lower genetic diversity. We highlight several challenges for aggregating data to understand global biodiversity patterns, such as the majority (>75%) of open-access genetic sequences without direct links to geographic coordinates.

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