Bayesian Modeling of C3 & C4 Grass Distributions
Research Overview
This project uses Bayesian statistical modeling to analyze the distribution of C3 and C4 grasses across the western United States. Using National Park Service inventory and monitoring vegetation data, we aim to understand what drives species distribution patterns today and how climate change may impact them in the future.
Beginning My PhD at Northern Arizona University
In Fall 2024, I began my PhD in Informatics and Computing with an emphasis on ecology at Northern Arizona University.
- Northern Arizona is a hub for ecological research, with diverse landscapes and opportunities to study ecoinformatics.
- The Ecoinformatics program is the perfect combination of my interests in ecology and data science.
- I joined Dr. Kiona Ogle’s Ecological Synthesis Lab, where we use quantitative methods to answer ecological questions.
Working with National Park Service Vegetation Data
This year, I started a project using National Park Service (NPS) inventory and monitoring data to study grass species distribution.
- We are working with six different NPS Inventory & Monitoring networks.
- The goal is to model the probability of C3 and C4 grass presence across a large geographic range from Texas to Montana.
Bayesian Modeling to Study Climate Drivers
We are using a Bayesian statistical model to analyze:
- Which climate factors influence grass distributions today
- What environmental conditions favor C3 vs. C4 grasses
- How these distributions may shift with climate change
This model allows us to quantify uncertainty and improve predictions of how grass species may respond to future climate scenarios.
Looking Ahead
I am really enjoying my PhD experience in Arizona and excited to continue:
- Expanding my Bayesian modeling skills
- Collaborating with NPS and the ecological research community
- Applying data science techniques to real-world conservation challenges
Project Images
Key Takeaways
- Started a PhD at NAU focusing on ecoinformatics
- Working with National Park Service vegetation monitoring data
- Developing a Bayesian model to study C3 and C4 grass distributions
- Investigating climate drivers of species distribution patterns
- Exploring how grass distributions may change with climate change