Active current projects

Pollen: Cupressaceae Alert System for Texas (Pollen CAST)

PI: Daniel Katz, co-PIs: Hosein Foroutan, Theresa Crimmins, Kai Zhu
Sponsor: National Aeronautics and Space Administration, Earth Science Division
Summary: This project develops a new system to predict airborne pollen from mountain cedar trees (Juniperus ashei), one of the main causes of seasonal allergies in Texas and neighboring states. The team uses a combination of drone surveys and satellite images to estimate how much pollen is produced and when it is released. By tracking how tree cones mature and how weather moves pollen through the air, the researchers create detailed maps and timely forecasts showing where and when high pollen levels are likely. These forecasts are shared with the public, health organizations, and medical professionals through websites and digital tools, giving millions of people information to better manage allergies and asthma. This approach also has the potential to improve pollen forecasts for other regions and plant species in the future, supporting public health overall.

Forest Futures: Geospatial data science and artificial intelligence for sustainable forestry in Michigan

PI: Kai Zhu, co-PIs: Joshua P. Newell, Dimitrios Gounaridis
Sponsor: US Department of Agriculture, McIntire-Stennis Cooperative Forestry Research Program
Summary: This innovative research will integrate ground-level forest inventories and high-resolution satellite imagery with advanced ecological modeling to capture and predict forest dynamics. The project’s core objectives include an empirical assessment of historical forest changes using various datasets; projecting future forest dynamics through mechanistic models; and developing stakeholder engagement platforms featuring an AI-driven interface for interactive insights. By addressing pivotal knowledge gaps, the team aims to inform and transform forest management strategies, utilizing the maple syrup industry as a case study while extending benefits to the broader forestry sector.

CAREER: Advancing a macrosystems framework for climate-phenology coupling through integrated research and education

PI: Kai Zhu
Sponsor: National Science Foundation, Directorate for Biological Sciences
Summary: The project presents a novel framework to examine the intricate links between climate and phenology, the study of the timing of biological events. By developing dynamic models and utilizing robust multi-scale data, including that from NEON, this research aims to unravel the complexity of climate-phenology interactions on a global scale, examine plant distributional shifts, and analyze the underexplored concept of teleconnections. Emphasis will be placed on enhancing ecological forecasting and creating reproducible workflows for data integration, while simultaneously advancing climate change and data science education through innovative teaching programs and social media outreach. The ultimate goal is to deliver societal benefits, including better pollen outbreak prediction, while training the next generation of educators in environmental science.

Life-cycle analysis synthesized with ecosystems and risk focused on sustainable forestry

PI: Benjamin P. Goldstein, co-PI: Kai Zhu
Sponsor: University of Michigan, Biosciences Initiative
Summary: The project develops a new framework, named LASER (Life-cycle Analysis Synthesized with Ecology and Risk), to better understand the impact of using forests to capture carbon dioxide and combat climate change. This method evaluates the full life cycle of carbon in forests, and how it affects the environment and local communities. It uses real-world case studies from Brazil, Canada, and the United States, aiming to improve current forest management practices and carbon offset programs. Additionally, the project will create user-friendly tools for the public and experts to see and experiment with the effects of forest-based climate strategies, helping to inform better decisions for the Earth’s future and local populations.

Selected past projects

  • MRA: Macroecology of microorganisms: Scaling fungal biodiversity from soil cores to the North American continent
    • PI: Kai Zhu, co-PI: Kabir G. Peay
    • Sponsor: National Science Foundation, Directorate for Biological Sciences
  • Bayesian modeling of multi-source phenology to predict airborne allergens
    • PI: Kai Zhu, co-PI: Kerby Shedden
    • Sponsor: University of Michigan, Michigan Institute for Data Science
  • Forecasting pollen high and low: Near-term ecological forecasting with state-of-the-art machine learning algorithm
  • DISSERTATION RESEARCH: Forest climate requirements change through species life history
    • PI: James S. Clark, co-PI: Kai Zhu
    • Sponsor: National Science Foundation, Directorate for Biological Sciences