Summary:

This is the last and final research component of my dissertation and I am excited to start working on it shortly! 

West Nile Virus (WNV) was introduced to the U.S. in 1999, reached the West Coast by 2003. Birds are the primary hosts, and their migratory routes are thought to have contributed to its spread. WNV cases are highest in Central Valley counties, where climate change is expected to drive more extreme weather events, including prolonged droughts (e.g., 2010–2017) and atmospheric rivers (e.g., 2023–2024). The region’s landscape has been heavily modified by agriculture and urbanization, yet how these changes influence bird movement and therefore WNV transmission remains unclear. My research will integrate data from eBird, NOAA, USGS, and the PRISM Climate Modeling Group to model bird movement and predict how extreme weather alters disease dynamics.

Questions:

1. Where on the landscape does local bird movement occur in the Central Valley during the late spring and early summer, before WNV infections are detected in humans?

2. How will precipitation under climate change impact bird movement within this region?

3. Will bird movement within landscapes of urban, rural, and native change in response?

I will use Boosted Regression Trees, to evaluate and predict the interactions of climate change driven weather and human land use on bird movement and WNV exposure. Boosted Regression Trees are a form of machine learning that can be used in this case for handling the different types of data needing to be incorporated into the analyses whilst accommodating missing data.The ability to handle interactions among variables is important to my work, where I expect a significant interaction between precipitation events and land use cover on bird movement and WNV infection. I will then use agent-based models to simulate bird movement between the landscape cover types during periods of extreme drought and extreme precipitation to see if it confirms the prediction results from the Boosted Regression Trees.