Research
PhD dissertation
Dissertation Overview
My dissertation explored the intersection of complex traits, methodological approaches in macroevolution, and the evolution of bird migration. Three key questions:
- How effectively do our current methods identify and analyze "complex" biological traits when simplified into discrete states?
- To what extent can migration in birds be adequately represented by discrete states?
- What can we gain by pushing the limits of discrete state analysis in the context of complex traits?
Quantifying Migratory Behavior with eBird
The study of avian migration presents a distinctive challenge due to the many ways a bird can be considered a "migrant," most of which are difficult to measure directly. Developing effective methods to measure and model these axes is essential for understanding the evolution of migration over long time scales.
Using Hidden State Markov Models
The first part of my dissertation focuses on the efficacy of Structured Hidden Markov Models (SHMMs) in identifying threshold traits — discretized traits with underlying one-dimensional continuous variation. This allows us to test whether a discrete trait is best described as a one-dimensional threshold trait, even when only discrete data are available.