When
Location
Harvill 107
Seminar Format
Available in-person and via Zoom webinar. Contact the department to subscribe to the email list (zoom link provided in announcement).
Abstract
Rapidly changing environmental conditions have exposed woody vegetation to unusual drought and caused increased mortality in many ecosystems across the globe. It remains challenging for ecosystem models to predict plant responses to drought. My research aims to identify and characterize key mechanisms necessary to predict plant function and mortality risk across the landscape. This seminar will address how I’ve used a combination of geospatial observations and process-based modeling to investigate the influence of groundwater in mediating forest mortality, sensitivity to climate change, and informing management practices.
Bio
Dr. Xiaonan Tai is an Assistant Professor in the Department of Biology at New Jersey Institute of Technology. Before NJIT, she was a Postdoctoral Fellow at U of Utah after receiving her PhD from U at Buffalo. Xiaonan’s research integrates plant hydraulics with groundwater hydrology to understand the two-way interactions between vegetation and hydrology and how they might influence ecosystem functions in the context of novel environmental conditions.
Tai combines organism-level plant physiology with landscape-scale hydrological processes to understand the two-way interactions between vegetation and hydrology and how they might influence ecosystem function and resource supply in the context of novel environmental conditions. Her research program uses process-based modeling and empirical approaches to combine in situ and remote sensing observations in order to answer questions related to:
- What are the mechanisms underlying ecosystem response to anticipated warming and drought?
- How do biotic diversity and abiotic heterogeneity influence ecosystem resilience and resource sustainability to changing climate?
- How to increase ecosystem resilience through effective management strategies?
Xiaonan Tai Email | Website | Google Scholar