Evolution is shaped by both history and constraint. Similar biological processes can sometimes produce remarkably similar outcomes, while in other cases differences in demography, ecology, genomic architecture, or chance send populations along very different trajectories. My research asks what determines that difference. I use population genomics, evolutionary simulation, and complementary biological evidence to study how evolutionary histories shape biological variation through time. My work spans humans, corals, and canids, using systems that provide different opportunities to observe, reconstruct, and ultimately predict evolutionary change.
When does evolutionary history predict what happens next?
Population histories leave lasting consequences, but those consequences are not always deterministic. Bottlenecks, admixture, migration, selection, and human intervention can shape ancestry, genetic diversity, deleterious variation, phenotype, and fitness long after the original events occurred. I use forward evolutionary simulations combined with genomic and demographic data to test when knowledge of those histories improves predictions of future change—and when later biological processes erase or overwhelm their effects. This work includes studies of demographic history and genomic variation in humans, prospective simulations of hybridization and restoration in Caribbean Acropora corals, and my current research on red wolves, where I reconstruct the consequences of historical decline, admixture, captive founding, and reintroduction while testing how future management could shape the population.
When can we reconstruct the past?
The inverse problem is equally important: how much of evolutionary history can actually be recovered from the patterns we observe today? Different histories can produce similar genomic signatures, and subsequent evolution can weaken or transform the evidence left by earlier events. A model that reproduces contemporary data therefore does not necessarily identify the process that generated them. My research uses simulation to create known evolutionary histories and then asks whether those histories can be recovered from genomic observations. By varying features such as population size, gene flow, selection, genomic architecture, temporal sampling, and life history, I aim to identify both the conditions under which evolutionary histories are recoverable and the limits of genomic inference. Ancient genomes, documented population histories, and managed populations provide especially valuable opportunities to test reconstructed trajectories against observations that were not used to infer them.
What evidence distinguishes competing explanations?
Sometimes genomic data alone cannot determine which biological process produced an observed pattern. In those cases, the important question becomes: what additional observation would distinguish the remaining explanations? My work combines genomic inference with independent sources of evidence including archaeological information, function, phenotype, historical records, and demographic observations. Rather than collecting additional data simply because they are available, I use simulation to identify where competing biological explanations make different predictions. Those differences can then guide which empirical observations are most informative.
A comparative approach
The diversity of systems in my research is intentional. Humans, corals, and canids differ dramatically in generation time, population structure, mating systems, genomic architecture, environmental variation, and the historical information available to study them. Those contrasts allow me to ask which relationships represent general features of evolutionary change and which depend on biological or historical context. Ultimately, my eventual research goal is to connect reconstruction of the past with prediction of the future: determining when evolutionary history can be recovered, when it remains informative about what happens next, and what evidence is required when genomic patterns alone cannot tell us why.
These questions currently guide several interconnected projects spanning conservation genomics, ancient DNA, evolutionary simulation, and complementary biological evidence. Rather than treating these systems as independent case studies, I use each to examine a different part of the broader problem of how evolutionary history can be reconstructed and when it remains informative about biological change.
Red wolf conservation genomics
My current work at Princeton uses genomic data, documented management history, morphology, and individual-based simulations to reconstruct how historical decline, capture, captive founding, admixture, and reintroduction shaped contemporary red wolf variation. I am particularly interested in the variation retained outside the managed population in admixed Gulf Coast canids carrying red wolf ancestry. I am also using these reconstructed histories to test how alternative management strategies could affect ancestry, genetic diversity, deleterious variation, historically present alleles, and population persistence in the future.
Demographic history and deleterious variation
I am continuing work with ancient and modern human genomes to investigate how demographic history shapes deleterious variation through time. I am interested in how the burden of potentially deleterious variants changes across populations and through time. Ancient genomes make it possible to observe changes in deleterious variation at intermediate points in population history rather than infer the entire trajectory from present-day genomes. By combining these temporal observations with demographic models, I aim to understand when historical population processes produce persistent changes in deleterious variant burden.
Complementary evidence for evolutionary inference
A third set of projects asks when genomic patterns require additional biological evidence to distinguish among competing explanations. Current work connects red wolf genomic ancestry with morphology, explores historical microbial DNA as an additional record of biological change, and develops approaches for interpreting functional and regulatory genomic variation. Across these projects, the goal is to determine which additional observations provide genuinely independent information and when incorporating them changes the biological conclusions that can be drawn from genomic data.