Models are inherent in population genetics, but many times are used without a basic understanding of their shortcomings and assumptions. These often overlooked properties are precisely what I am interested in examining. What are the assumptions of our existing models, and what happens when these assumptions are violated? How accurate are the models even without these violations? And most importantly, how can models be improved to give us more accurate results? I want to test models and analyses and improve upon them, using both simulation and empirical approaches.
Research at The Ohio State University
I started working as a Research Assistant in the Carstens Lab in November 2017, working on posterior predictive checks. The main idea is to compare simulated datasets produced under a specific model of evolution to your empirical dataset in order to see if the proposed model is a good fit for your data. Differences between the simulated and empirical datasets could mean that your data violate model assumptions. For instance, posterior predictive checks have been implemented in the R package P2C2M, testing whether the Multispecies Coalescent Model implemented in *BEAST is a good fit for a given empirical dataset. As in the case of Myotis bats, poor fit may indicate the presence of gene flow, or some other model violation. We are currently working to extend the P2C2M package to the programs Migrate-n and SNAPP.
I started my PhD in the Carstens Lab in August 2018. My project focuses on genome sequencing, phylogeography, and adaptation in the North American water vole (Microtus richardsoni). Like many other species in the Pacific Northwest, M. richardsoni exhibits a disjunct distribution, present in the Cascades and the Rockies, but not the Columbia Basin in between. M. richardsoni provides an excellent system to study the origin of this distribution, whether it can be explained by vicariance or recent dispersal since the Last Glacial Maximum. In order to explore the phylogeographic history, I will be sequencing whole nuclear genomes for samples across the species range. The genomes will subsequently be used to explore local adaptation, as the species has spread to multiple states, likely encountering and adapting to different habitats.
Research at The Morton Arboretum
At The Morton Arboretum, my time was divided between the Tree Conservation Biologist, Dr. Sean Hoban, and the Forest Ecologist, Dr. Christy Rollinson.
Range-wide Genetics Study of the Shinnery Oak, Quercus havardii
The first major project in the Hoban lab centers around a small, drought-tolerant oak tree (some may argue shrub), Quercus havardii. We aim to understand the genetic diversity, population connectivity and clonality of this hearty little tree. So far, our efforts have involved sampling trips to the Southwestern US to collect leaf and acorn samples, and soon we will start extracting DNA for analysis with microsatellites. In addition to using genetic techniques, the project will include an examination of leaf morphology and the distribution of acorns to other US greenhouses and gardens. In the future, we would also like to examine hybridization, which havardii appears to do readily, and possibly genetic analysis with high-throughput sequencing techniques.
Research at the College of Charleston
My master's thesis focused on maximizing statistical power for detecting population structure. First, I conducted simulations to compare the utility of microsatellites, SNPs, and mitochondrial sequences for detecting differences among populations across a variety of population, marker, and sampling parameters. For these simulations, I used the SPOTG connectivity program, which can be found at Sean Hoban's site, here. Additionally, I used a hybridization sequence capture technique (check out the Naylor lab page here) to analyze the population structure of both the blacktip reef shark (Carcharhinus melanopterus), which exhibits very high structure, and the shortfin mako shark (Isurus oxyrinchus), which displays little to no structure. To tie the simulation and empirical portions of my project together, I conducted statistical resampling of empirical microsatellite, mitochondrial DNA, and sequence capture data. Resampling analyses provided further insight into the effects of study design on genetic differentiation and the genetic characteristics of the previously mentioned shark species. The results of this study should be useful not only for knowledge of these shark species, but also for future population genetics study designs to maximize power and minimize cost.
2018 - Present
PhD Evolution, Ecology, and Organismal Biology The Ohio State University
Python and R
2013 - 2016
M.S. Marine Biology
College of Charleston
Genome Assembly and Analysis
2009 - 2013
B.S. Biology and B.S. Marine Sciences
University of Georgia