My research focuses on understanding the processes that generate and maintain biodiversity in plants, with a focus on how species-specific phenotypic traits interact with the environment to influence the evolutionary processes driving spatial patterns of genetic variation. Specifically, I use bioinformatic and machine learning approaches to integrate genomic, environmental, morphological, geospatial, and digitized image data from field-collected and herbarium specimens. I computationally integrate these diverse data types to improve our understanding of the evolutionary processes that create and maintain biodiversity.
My long-term research goals include:
1) developing an analytical framework to integrate genomic, phenomic, and environmental data to explore the drivers of species diversification.
2) investigating the genomic mechanisms that result in key evolutionary innovations such as tropical-to-temperate transitions and the development of fleshy fruit from dry-fruit lineages.
3) developing Prunus (the cherry genus) as a model clade for testing trait-based phylogeographic hypotheses to improve our understanding of the morphological characters that drive evolutionary processes such as populations adapting to novel environments.
Connecting different data types inside museum specimens
Understanding the genome-phenome connection represents a central goal of evolutionary biology. However, untangling the interaction between genotype, environment, and phenotype remains challenging, especially in non-model systems.
Three complimentary data types can be extracted from a single herbarium specimen—genomic data from leaf tissue, phenotypic data quantified using computer vision algorithms, and georeferenced environmental data. Simultaneously analyzing these three data sources can provide fundamental insights about the genome-phenome-environment relationship.
I have studied the above research questions in several empirical angiosperm systems, including coastal dune grasses in the southern U.S., mangroves in Florida and throughout the Caribbean, and sedge species in the Rocky Mountains. Currently, my focus is on the genus Prunus (Rosaceae), the angiosperm genus of ~300 species containing species such as cherries, peaches, plums, almonds, as well as a lot of understudied diversity. My research combines phylogenomic data extracted from field-collected samples and herbarium specimens, and morphological data gleaned from digitized herbarium specimens via a machine learning approach. I am working to use phylogenomic data to resolve phylogenetic relationships within Prunus, and deep learning algorithms to identify characters hypothesized to be associated with ancient hybridization and/or allopolyploidy events, and to investigate morphological characters associated with tropical-to-temperate biogeographic transitions.