Ecological data is exciting because it’s messy. Messy data means scientists need to be more creative in finding ways to separate the signal and the noise. Though many biology and ecology students dislike math and statistics, my favorite part of thinking about ecological problems is finding mathematically creative ways to visualize and explore ecological data. I am particularly excited about using mathematical modeling and simulation to investigate complex, dynamic systems. I am interested in applying these tools to investigate ecological systems because ecological systems are inherently interconnected and stochastic systems that are highly dependent on a network of background factors. Modeling ecological problems allows you to formalize your logic in interesting ways and build experiments to test population-level phenomena. As with other complex systems, the process of modeling and simulation can be incredibly useful in producing prediction tools, defining prevention and control policies, and identifying high risk factors for contracting disease. Under the direction of Dr. Michael von Fricken, I am using data collected on Mongolian herders, ticks, and livestock to examine associations between tick-borne disease markers in humans and the number of livestock owned, the disease status of the animals, and the environmental factors. Through this project, I am learning how to design and write an agent-based model in NetLogo and am developing my spatial statistics skills.
Long term, I am excited about utilizing complexity modeling and simulation to explore environmental data, particularly as it relates to socioeconomic relationships to biodiversity, disease spread, and ecosystem services. I plan to attend a graduate program this fall, and hope to one day become a professor. My experience with undergraduate research, particularly this project, has enabled me to build the quantitative skills necessary to succeed in a graduate research program. This semester, I am learning to code in python, to build an agent-based model from the ground up in NetLogo, and continuing to develop skills using R to evaluate data. Each week, I spend a portion of my time building these skills through reading, practicing my coding, and taking short courses. The second largest portion of time is spent reviewing the literature on agent-based modeling, the natural history of livestock and ticks, and tick-borne disease. Finally, I am starting to formalize and code my model. As the semester continues, a larger and larger portion of my time will be spent focusing on building and testing the model. At the end of the semester, I hope to have a finalized, publishable model that can help us better understand critical human health questions.
